diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
index d30646ad1..c0e52d111 100644
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -4,11 +4,11 @@
 # Core algorithm and training flows
 /src/unilab/algos/ @TATP-233 @caozx1110
 /src/unilab/ipc/ @TATP-233
-/scripts/train_*.py @TATP-233 @caozx1110
+/src/unilab/scripts/train_*.py @TATP-233 @caozx1110
 
 # Task and environment ownership
-/src/unilab/envs/motion_tracking/ @caozx1110
-/src/unilab/envs/manipulation/ @Mingrui-Yu
+/src/unilab/tasks/motion_tracking/ @caozx1110
+/src/unilab/tasks/manipulation/ @Mingrui-Yu
 /scripts/motion/ @caozx1110
 
 # Project process and docs
diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index cc7bacc49..eaa44bf03 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -37,7 +37,7 @@ body:
       label: Reproduction
       description: Exact command, config, or sequence that reproduces the issue.
       placeholder: |
-        uv run scripts/train_offpolicy.py algo=sac task=sac/g1_walk_flat/mujoco ...
+        uv run train --algo sac --task g1_walk_flat --sim mujoco ...
     validations:
       required: true
 
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index b43e5dac5..024023fd5 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -33,6 +33,8 @@ jobs:
           persist-credentials: false
       - name: Install uv
         uses: astral-sh/setup-uv@v8.0.0
+        with:
+          enable-cache: true
       - name: Install dependencies(only dev group)
         run: uv sync --only-group dev
       - name: Check
@@ -49,6 +51,8 @@ jobs:
           persist-credentials: false
       - name: Install uv
         uses: astral-sh/setup-uv@v8.0.0
+        with:
+          enable-cache: true
       - name: Install dependencies(only dev group)
         run: uv sync --only-group dev
       - name: Check
@@ -65,6 +69,8 @@ jobs:
           persist-credentials: false
       - name: Install uv
         uses: astral-sh/setup-uv@v8.0.0
+        with:
+          enable-cache: true
       - name: Install dependencies
         run: uv sync
       - name: Check
@@ -81,6 +87,8 @@ jobs:
           persist-credentials: false
       - name: Install uv
         uses: astral-sh/setup-uv@v8.0.0
+        with:
+          enable-cache: true
       - name: Install dependencies
         run: uv sync
       - name: Check
@@ -98,6 +106,7 @@ jobs:
       - name: Install uv
         uses: astral-sh/setup-uv@v8.0.0
         with:
+          enable-cache: true
           python-version: '3.11'
       - name: Install dependencies
         run: |
@@ -124,7 +133,9 @@ jobs:
 
   test:
     if: github.event_name == 'workflow_dispatch' || github.event_name == 'pull_request'
-    timeout-minutes: 15
+    # The merged manager-based suite needs ~20 min on ubuntu-slim; 15 min
+    # cancels the step at ~60%.
+    timeout-minutes: 30
     strategy:
       matrix:
         os: [ubuntu-slim]
@@ -138,6 +149,7 @@ jobs:
       - name: Install uv
         uses: astral-sh/setup-uv@v8.0.0
         with:
+          enable-cache: true
           # python-version: ${{ matrix.python-version }}
           python-version: '3.11'
       - name: Install system dependencies
@@ -162,5 +174,12 @@ jobs:
             --no-install-package nvidia-nvtx-cu12 \
             --no-install-package triton
           uv pip install torch==2.7.0 --index-url https://download.pytorch.org/whl/cpu
+      - name: Cache robot assets
+        uses: actions/cache@v4.2.3
+        with:
+          path: src/unilab/assets/robots
+          key: ${{ runner.os }}-robot-assets-v1-${{ hashFiles('src/unilab/assets/hub.py') }}
+      - name: Pull robot assets
+        run: uv run --no-sync unilab-pull-assets --robot all
       - name: Test with coverage
         run: uv run --no-sync pytest -m "not slow" --cov=src/unilab --cov-report "markdown-append:${GITHUB_STEP_SUMMARY:-/dev/null}" --cov-fail-under=25
diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index 35a8e678f..02eba1eab 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -8,7 +8,7 @@ on:
       - "docs/README.md"
       - "src/unilab/**"
       - "scripts/generate_support_matrix.py"
-      - "conf/**"
+      - "src/unilab/conf/**"
       - "README.md"
       - "CONTRIBUTING.md"
       - "AGENTS.md"
@@ -21,7 +21,7 @@ on:
       - "docs/README.md"
       - "src/unilab/**"
       - "scripts/generate_support_matrix.py"
-      - "conf/**"
+      - "src/unilab/conf/**"
       - "README.md"
       - "CONTRIBUTING.md"
       - "AGENTS.md"
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
new file mode 100644
index 000000000..ca2b05534
--- /dev/null
+++ b/.github/workflows/release.yml
@@ -0,0 +1,96 @@
+name: release
+
+on:
+  push:
+    tags: ["v*"]
+  workflow_dispatch:
+
+permissions:
+  contents: read
+  actions: read
+
+jobs:
+  build-dist:
+    name: Build and verify distributions
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v6.0.2
+        with:
+          persist-credentials: false
+      - uses: astral-sh/setup-uv@v8.0.0
+        with:
+          enable-cache: false
+      - name: Verify tag matches project version
+        if: startsWith(github.ref, 'refs/tags/v')
+        shell: bash
+        run: |
+          project_version="$(python3 -c 'import tomllib; print(tomllib.load(open("pyproject.toml", "rb"))["project"]["version"])')"
+          tag_version="${GITHUB_REF_NAME#v}"
+          test "$tag_version" = "$project_version" || {
+            echo "Tag v$tag_version does not match pyproject.toml version $project_version" >&2
+            exit 1
+          }
+      - name: Verify CI passed for tagged commit
+        if: startsWith(github.ref, 'refs/tags/v')
+        env:
+          GH_TOKEN: ${{ github.token }}
+        shell: bash
+        run: |
+          successful_runs="$(gh api \
+            "/repos/${GITHUB_REPOSITORY}/actions/workflows/ci.yml/runs?head_sha=${GITHUB_SHA}&status=completed&per_page=100" \
+            --jq '[.workflow_runs[] | select(.conclusion == "success")] | length')"
+          test "$successful_runs" -gt 0 || {
+            echo "No successful ci.yml run found for tagged commit $GITHUB_SHA" >&2
+            echo "ci.yml only runs on pull_request and workflow_dispatch; dispatch it on the commit to release, then tag." >&2
+            exit 1
+          }
+      - name: Build source distribution and wheel
+        run: uv build --out-dir dist
+      - name: Check distribution metadata
+        run: uvx --from twine twine check dist/*
+      - name: Install the freshly built wheel
+        run: |
+          uv venv .release-venv
+          uv pip install --python .release-venv/bin/python --reinstall --no-deps \
+            --no-binary unilab --no-cache --find-links dist --no-index unilab
+      - name: Smoke test installed wheel
+        shell: bash
+        run: |
+          tag_version=""
+          case "$GITHUB_REF" in
+            refs/tags/v*) tag_version="${GITHUB_REF_NAME#v}" ;;
+          esac
+          .release-venv/bin/python -c "
+          import unilab
+          assert unilab.ROOT_PATH.is_dir()
+          assert (unilab.ROOT_PATH / 'conf').is_dir()
+          version = unilab.__version__
+          assert version != '0.0.0', version
+          ref = '$tag_version'
+          if ref:
+              assert version == ref, f'{version} != {ref}'
+          print('installed unilab', version)
+          "
+      - uses: actions/upload-artifact@v4.2.3
+        with:
+          name: release-dist
+          path: dist/*
+
+  publish:
+    needs: build-dist
+    if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v')
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      id-token: write
+    environment: pypi
+    steps:
+      - uses: actions/download-artifact@v4.2.3
+        with:
+          name: release-dist
+          path: dist
+      - name: Publish distributions to PyPI with trusted publishing
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          packages-dir: dist
+          skip-existing: true
diff --git a/.gitignore b/.gitignore
index d1de5a693..1ddcb823b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -58,7 +58,6 @@ run_summary.json
 src/unilab/assets/checkpoints/
 
 scripts/benchmark/outputs/
-src/unilab/algos/torch/rsl_rl
 
 third-party
 temp/
@@ -81,6 +80,13 @@ src/unilab/assets/motions/x2/*.csv
 
 # Robot mesh assets (downloaded from HF at runtime)
 src/unilab/assets/robots/x2/meshes/*.STL
+src/unilab/assets/robots/g1/assets/
+src/unilab/assets/robots/g1/textures/
+src/unilab/assets/robots/go2/assets/
+src/unilab/assets/robots/a2/assets/
+src/unilab/assets/robots/allegro_hand/assets/
+src/unilab/assets/robots/sharpa_wave/meshes/
+src/unilab/assets/robots/go2_arm/assets/
 
 # Grasp cache assets (downloaded from HF at runtime)
 src/unilab/assets/caches/*.npy
diff --git a/AGENTS.md b/AGENTS.md
index 8b02a7549..754beea0b 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -4,6 +4,8 @@
 
 UniLab 是一个 **高性能、模块化、contract 驱动** 的 RL infrastructure 仓库。
 
+RL 算法与异步 runtime（PPO/APPO/SAC/TD3/HORA 的 runner、learner、collector、IPC、训练日志）由独立包 **uni_rl**（仓库 [unilabsim/unilab_rl](https://github.com/unilabsim/unilab_rl)，distribution 名 `unilab-rl`，发布在 PyPI）承载；UniLab 不构造 uni_rl 消费的 env，而是通过注入式 env contract 对接：`uni_rl.env_contract.EnvFactory = Callable[[int, Mapping | None], EnvProtocol]`（必须可被 pickle 引用，collector 跑在 spawn 子进程），UniLab 侧适配器为 `src/unilab/base/env_factory.py` 的 `registry_env_factory`，mjwarp 设备绑定经 `src/unilab/base/process_device.py` 的 `bind_backend_process_device` 注入。unilab 可以 import uni_rl；uni_rl 永远不 import unilab / unisim。
+
 ## Core Principles
 
 1. **Contract first**: 不为了一次通过绕过 env / backend / runner contract。
@@ -32,34 +34,40 @@ UniLab 是一个 **高性能、模块化、contract 驱动** 的 RL infrastructu
 | Env  | `NpEnvState.obs` 必须是 dict；`reset()` 返回 `(obs_dict, info_dict)`；`obs_groups_spec` 影响 wrapper 和 learner 维度。 |
 | Config / Reward | reward 通过 Hydra 注入；后端切换必须通过 `task=<task>/<backend>` 选择 owner YAML，`training.sim_backend` 只是 owner YAML 的身份字段，不能单独 override 来切后端。算法超参数直接走 YAML compose，不经 Python 层解释。 |
 | Backend | backend-specific 逻辑留在 backend / env 适配层，不向训练脚本扩散。env 层只能调用 `SimBackend`（`base.py`）中已声明的方法；若某方法只在 MuJoCo 或 Motrix 中存在，必须先将其加入 `SimBackend` 抽象接口（可抛 `NotImplementedError`），禁止直接在 env 里调用 backend 子类的私有方法（即"功能泄漏/feature leakage"）。新增 backend 专有能力时，需同步更新 `SimBackend`。 |
-| Asset / Metadata | `ASSETS_ROOT_PATH`、`model_file`、XML / asset 元数据只允许在 init / materialization / cache 等低频路径访问；`step/reset/domain randomization` 等热路径不得解析 asset 或基于 asset 元数据做运行时分支。 |
+| Asset / Metadata | `ASSETS_ROOT_PATH`、`model_file`、XML / asset 元数据只允许在 init / materialization / cache 等低频路径访问；`step/reset/domain randomization` 等热路径不得解析 asset 或基于 asset 元数据做运行时分支。机器人 mesh/纹理不入 git：托管在 HF 数据集 `unilabsim/unilab-robots`，注册表为 `src/unilab/assets/hub.py` 的 `ROBOT_ASSET_SPECS`，由 `create_backend` 冷路径自动下载（也可 `unilab-pull-assets` 预拉取），落盘回原路径使 XML `meshdir` 引用不变；这些目录在 `.gitignore` 与 `pyproject.toml` 的 `tool.uv.build-backend.source-exclude` 中同步排除。 |
 | Asset / XML structure | `<keyframe>` 必须放在 task-level XML（`scene_*.xml` 或 `locomotion_task.xml` 等 fragment），**禁止放进 robot.xml**。robot.xml 是纯机器人描述（body / joint / actuator / sensor），跟 task / 场景无关；keyframe 是 task 起始姿态，属于场景或 task 资源。motrix 后端需要 keyframe 时通过 `scene.fragment_files` 引用 fragment XML。 |
-| Async | 不绕开 runner lifecycle，也不另起 collector / learner 同步协议。 |
+| Async | 不绕开 runner lifecycle（实现在 uni_rl 仓库的 `uni_rl.ipc.async_runner`），也不另起 collector / learner 同步协议。 |
 | Sim2Sim 契约 | 跨后端 play 时，影响策略 I/O / 网络结构的字段必须跨后端一致；不一致即 `CrossBackendIncompatibleError`。详见下方 Sim2Sim 章节。 |
 
 ## Sim2Sim 跨后端配置契约
 
-`src/unilab/training/sim2sim.py` 按 dotted path 维护三类字段：
+`src/unilab/utils/sim2sim.py` 按 dotted path 维护三类字段：
 
 - **DENYLIST**（差异即 `CrossBackendIncompatibleError`）：`algo.obs_groups`、`env.control_config.action_scale`、`algo.policy.actor_hidden_dims` / `critic_hidden_dims`、`algo.empirical_normalization` / `algo.obs_normalization`、`env.sampling_mode`。`env.*` 子集对**任一方向**的不对称出现也 fail-closed；`algo` 专属字段目标缺省时按设计跳过（跨算法合法）。
 - **WARNING_LIST**：`reward.*`、`env.control_config.simulate_action_latency`、`env.ctrl_dt`。
 - **ALLOWLIST**（自由覆盖）：`training.sim_backend`、`env.scene`、`training.play_steps`、`env.domain_rand`、`env.noise_config`、`env.commands.vel_limit`。
 
-训练时 `ExperimentTracker.start()` 把上述字段写入 `run_config.json` 的 `contract_snapshot`（不改 checkpoint 格式，旧 run 无 snapshot 时 fallback + warning）；五个 play 入口在建 env 前调用 `resolve_sim2sim_config` 校验，并用 `policy_load_dim_guard` 包裹 checkpoint 加载以把维度不匹配的隐晦报错重抛为显式诊断。设 `training.sim2sim_strict=false` 可把 DENYLIST 差异降级为 warning（默认 `true`）。DENYLIST 字段在每个后端 owner 配置中显式声明并保持跨后端一致（范例：`conf/ppo/task/g1_walk_flat/{mujoco,motrix}.yaml`）；跨后端契约审计见 `scripts/audit_sim2sim_contracts.py`。
+训练时 `ExperimentTracker.start()` 把上述字段写入 `run_config.json` 的 `contract_snapshot`（不改 checkpoint 格式，旧 run 无 snapshot 时 fallback + warning）；五个 play 入口在建 env 前调用 `resolve_sim2sim_config` 校验，并用 `policy_load_dim_guard` 包裹 checkpoint 加载以把维度不匹配的隐晦报错重抛为显式诊断。设 `training.sim2sim_strict=false` 可把 DENYLIST 差异降级为 warning（默认 `true`）。DENYLIST 字段在共享 base owner 与后端 owner 配置中显式声明并保持跨后端一致（范例：`src/unilab/conf/ppo/task/g1_walk_flat/{base,mujoco,motrix}.yaml`）；跨后端契约审计见 `scripts/audit_sim2sim_contracts.py`。
 
 ## Pointers
 
-- PPO: `scripts/train_rsl_rl.py`
-- APPO: `scripts/train_appo.py`
-- SAC / TD3: `scripts/train_offpolicy.py`
+- PPO: `src/unilab/scripts/train_rsl_rl.py`
+- APPO: `src/unilab/scripts/train_appo.py`
+- SAC / TD3 / FlashSAC: `src/unilab/scripts/train_sac.py` / `src/unilab/scripts/train_td3.py` / `src/unilab/scripts/train_flashsac.py`
 - env contract: `src/unilab/base/np_env.py`
-- backend contract: `src/unilab/base/backend/base.py`
+- backend contract: `unisim.backend.base.SimBackend`（由 `unisim-core` 仓库维护）
+- UniLab backend owner factory: `src/unilab/base/backend_factory.py`
+- isaacgym subprocess 后端（Python 3.8 worker + shm 协议）: `unisim.backend.isaacgym`（由 `unisim-core` 仓库维护）
 - training run helpers: `src/unilab/training/run.py`
 - visualization helpers: `src/unilab/visualization/`
 - shared numeric helpers: `src/unilab/utils/rotation.py`, `src/unilab/utils/geometry.py`
 - config schema: `src/unilab/structured_configs.py`
-- async runner: `src/unilab/ipc/async_runner.py`
-- sim2sim 跨后端契约: `src/unilab/training/sim2sim.py`
+- async runner: `uni_rl.ipc.async_runner`（独立 uni_rl 仓库 / `unilab-rl` 包）
+- algo 实现（runner / learner / collector）: uni_rl 包（独立仓库，distribution 名 `unilab-rl`）；UniLab 只保留入口脚本与 play 编排
+- uni_rl env factory 适配: `src/unilab/base/env_factory.py`
+- HORA APPO play 编排: `src/unilab/scripts/play_hora_appo.py`；HORA distill 配置组合: `src/unilab/training/hora_distill_config.py`
+- sim2sim 跨后端契约: `src/unilab/utils/sim2sim.py`
+- new algorithm recipe（三档扩展方式 + 约定式 CLI 路由 footprint）: `docs/sphinx/source/zh_CN/4-developer_guide/3-extending/3-new_algorithm.md`
 
 ## GitHub CLI (gh) 速查
 
@@ -101,6 +109,17 @@ git push -u origin fix/issue-174-ppo-config-alignment
 gh pr create --title "fix: xxx" --body "Fixes #174" --base <target-branch>
 ```
 
+## PyPI Release
+
+`.github/workflows/release.yml` 是生产发布路径，由 `v*` tag 推送触发（`workflow_dispatch` 只做构建与验证，不发布）：
+
+1. 更新 `pyproject.toml` 的 `[project].version`（版本单一来源）及相关文档，本地 `make test-all` 与 `uv build` 通过。
+2. 确认目标 commit 有成功的 `ci.yml` 运行：ci.yml 只在 PR 与 `workflow_dispatch` 上运行，若目标 commit 没有记录，先对该 commit 手动 dispatch 一次 ci.yml 再发 tag。
+3. 推送与 `[project].version` 完全一致的 annotated tag（如 `v0.1.0`）。release workflow 会校验 tag 与版本一致、该 commit CI 已通过，然后构建 sdist + wheel、twine check、`--no-deps` 安装 wheel 并做 import / 版本 smoke test。
+4. 构建验证通过后，`publish` job 通过 trusted publishing（OIDC，`environment: pypi`）上传到 PyPI，无 token 入仓；`skip-existing: true` 支持安全重跑。
+
+发布失败时：产物未变可用同一 tag 修复重跑；改了代码必须升级版本与新 tag，绝不覆盖已发布版本。
+
 ## Context
 
 - 架构标准与验证详情：[docs/sphinx/source/zh_CN/4-developer_guide/0-index.md](docs/sphinx/source/zh_CN/4-developer_guide/0-index.md)
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index a8917990b..f364d9cf1 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -6,10 +6,13 @@ Languages: English | [简体中文](docs/sphinx/source/zh_CN/4-developer_guide/4
 
 1. Fork and clone the repository.
 2. Install dependencies for your platform:
-   - macOS (MPS, installs PyPI torch wheels): `uv sync`
-   - Linux default (installs PyTorch cu128 wheels; requires an NVIDIA GPU/driver supported by current PyTorch cu128 wheels): `uv sync`
+   - macOS (MPS, installs PyPI torch wheels): `make setup-motrix` (or `make setup-mujoco`)
+   - Linux default (installs PyTorch cu128 wheels; requires an NVIDIA GPU/driver supported by current PyTorch cu128 wheels): `make setup`
    - Linux AMD / ROCm workstation: `make sync-rocm`, then run commands with `uv run --no-sync ...`
-   - When you need Motrix, append `--extra motrix`
+   - For direct uv setup, use `uv sync --extra mujoco --extra motrix`; replace it
+     with `--extra mujoco` or `--extra motrix` for a single backend
+   - Physics adapters are supplied by the production PyPI package
+     `unisim-core>=0.1.14` (import namespace `unisim`).
 3. Create a branch such as `git checkout -b docs/improve-readme` or `git checkout -b fix/backend-bug`.
 
 ## Development Rules
diff --git a/Makefile b/Makefile
index b09ce494e..c725e69f3 100644
--- a/Makefile
+++ b/Makefile
@@ -24,6 +24,17 @@ setup-mujoco:
 	uv sync --extra mujoco
 	uv run --no-sync unilab-complete install
 
+# Installs the Python extra and builds DrakeUni's native extension. By default
+# the host-compatible official tarball is downloaded; use DRAKE_HOME=<prefix>
+# to build against an existing installation.
+.PHONY: setup-drake
+setup-drake:
+	@ if [ -n "$(DRAKE_HOME)" ]; then \
+		bash scripts/tools/setup_drake_env.sh --drake-home "$(DRAKE_HOME)"; \
+	else \
+		bash scripts/tools/setup_drake_env.sh --download-drake; \
+	fi
+
 .PHONY: setup-motrix
 setup-motrix:
 	uv sync --extra motrix
@@ -56,7 +67,11 @@ type:
 	uv run pyright
 
 .PHONY: check
-check: format type
+check: format type check-tests
+
+.PHONY: check-tests
+check-tests:
+	uv run ruff check tests --select F401,F821,F811,F841 --output-format concise
 
 .PHONY: test
 test:
@@ -88,7 +103,7 @@ clean:
 	find . -type d -name ".ruff_cache" -exec rm -rf {} +
 	find . -type d -name "htmlcov" -exec rm -rf {} +
 	find . -type f -name ".coverage" -delete
-	rm -f train_appo.log train_offpolicy.log train_rsl_rl.log MUJOCO_LOG.TXT
+	rm -f train_appo.log train_sac.log train_flashsac.log train_rsl_rl.log MUJOCO_LOG.TXT
 	find src/unilab/assets/.cache -type f ! -name '.gitkeep' -delete 2>/dev/null || true
 	find src/unilab/assets/caches -type f ! -name '.gitkeep' -delete 2>/dev/null || true
 	find src/unilab/assets/checkpoints -type f ! -name '.gitkeep' -delete 2>/dev/null || true
diff --git a/README.md b/README.md
index 2fb61aec3..295879d56 100644
--- a/README.md
+++ b/README.md
@@ -1,229 +1,258 @@
 <h1 align="center"> UniLab </h1>
 
 <h3 align="center">
-A Heterogeneous Architecture for Robot RL Beyond GPU-Dominant Paradigms
+A Heterogeneous Architecture for Robot RL Beyond GPU-Dominant Paradigms.
 </h3>
 
 <p align="center">Languages: English | <a href="README_zh.md">简体中文</a></p>
 
 <p align="center">
+  <a href="https://github.com/unilabsim/UniLab/actions/workflows/ci.yml"><img src="https://github.com/unilabsim/UniLab/actions/workflows/ci.yml/badge.svg" alt="CI"></a>
   <a href="https://unilabsim.github.io"><img src="https://img.shields.io/badge/project-page-brightgreen" alt="Project Page"></a>
-  <a href="https://arxiv.org/abs/2605.30313"><img src="https://img.shields.io/badge/arxiv-2605.30313-red" alt="arXiv"></a>
-  <a href="https://unilabsim.github.io/paper/"><img src="https://img.shields.io/badge/paper-UniLab-orange" alt="Paper"></a>
+  <a href="https://arxiv.org/abs/2605.30313"><img src="https://img.shields.io/badge/paper-arXiv--2605.30313-red" alt="Paper"></a>
+  <a href="https://arxiv.org/abs/2605.30313"><img src="https://img.shields.io/badge/CoRL-2026-orange" alt="CoRL 2026"></a>
   <a href="https://unilabsim.github.io/UniLab-doc/"><img src="https://img.shields.io/badge/docs-UniLab--doc-blue" alt="Documentation"></a>
-  <a href="https://unilabsim.github.io/paper/paper2gal.html"><img src="https://img.shields.io/badge/Galgame-play-ff69b4" alt="Galgame"></a>
-  <a href="LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-blue" alt="Apache-2.0 License"></a>
+  <a href="https://pypi.org/project/unilab/"><img src="https://img.shields.io/pypi/v/unilab" alt="PyPI"></a>
+  <a href="https://github.com/unilabsim/UniLab/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-blue" alt="Apache-2.0 License"></a>
 </p>
 
+<h3 align="center">🎉 🎉 UniLab has been accepted to <b>CoRL 2026</b>! 🎉 🎉</h3>
+
 <p align="center">
   <img src="docs/sphinx/source/_static/assets/teaser.jpg" alt="UniLab Teaser" width="95%">
 </p>
 
-<p align="center"><em>Train robot RL without a GPU simulation backend. Teaser rendered with MotrixSim.</em></p>
+<p align="center"><em>One task-authoring surface for locomotion, manipulation, and motion tracking.</em></p>
+
+UniLab is a complete, configurable product for robot reinforcement learning.
+Describe a task with Hydra, assemble it from manager terms, select a physics
+backend, and train or evaluate through one CLI. The same task-facing contract
+connects CPU, GPU, and external-worker simulation to the learner runtime.
+
+Physics adapters are provided by the independent
+[`unisim-core`](https://github.com/unilabsim/unisim) package. RL algorithms and
+their runners are provided by [`unilab-rl`](https://github.com/unilabsim/unilab_rl)
+(Python namespace `uni_rl`). UniLab keeps the user-facing task, environment,
+configuration, and experiment workflow together.
+
+New to UniLab? Start with [First success](#first-success-run-a-demo). Already
+have a task? Jump to [Train and evaluate](#train-and-evaluate) and change only
+`--sim` to try another backend when a matching task owner is available.
+
+## Highlights
+
+```text
+┌──────────────────────────────────────┐     Same task contract    ┌──────────────────────────────────────┐
+│                                      │ ────────────────────────▶ │       Run it where you need          │
+│       Define the task once           │                           │   MuJoCo · Motrix · MJWarp · Drake   │
+│       Hydra · Managers · NumPy       │                           │    Genesis · IsaacGym · IsaacSim     │
+│     Terms · rewards · commands       │                           │   CUDA · ROCm · macOS · MPS · XPU    │
+│                                      │                           │         train · eval                 │
+└──────────────────────────────────────┘                           └──────────────────────────────────────┘
+```
 
-Start with the `Quick Demo` below to run the primary training command. The recommended setup uses `uv`; Conda and pip users should still follow the `uv` workflow for now. Platform-specific notes and current boundaries are in the [installation guide](https://unilabsim.github.io/UniLab-doc/en/1-getting_started/2-installation.html).
+UniLab's core idea is simple: define task semantics once as reusable
+configuration, then change the simulator, hardware, or learner without
+rewriting the task's environment lifecycle.
 
-## ✨ Highlights
+- **Configure, don't code.** Actions, observations, rewards, terminations,
+  events, commands, curricula, and metrics are manager terms assembled in Hydra
+  owner YAML. Variants built from existing terms need no new environment class
+  — often no Python code at all.
+- **Change the backend, keep the workflow.** Current and future simulators share
+  the public `SimBackend` contract. Choose a backend with `--sim`; the same task
+  authoring and train/eval workflow remains in place while the owner YAML keeps
+  backend-specific details explicit.
+- **Scale across the hardware you have.** CPU-parallel or external-worker
+  simulation feeds accelerator learners through the injected env contract and
+  async runtime. Algorithms and runners are supplied by the unified package
+  ecosystem instead of being tied to one simulator.
 
-```
-┌────────────────────┐                            ┌─────────────────────────┐
-│   Uni Physics Sim  │   Unified Shared Memory    │   GPU Policy Training   │
-│   Motrix / MuJoCo  │ ─────────────────────────▶ │     PPO / SAC / TD3     │
-│   MjWarp / Drake   │    SharedReplayBuffer      │ CUDA / MPS / ROCm / XPU │
-└────────────────────┘                            └─────────────────────────┘
-```
+## Getting started
 
-- **Heterogeneous RL runtime:** CPU-parallel simulation streams transitions through shared memory while policy learning runs on GPU accelerators.
-- **Two physics backends:** MuJoCoUni and MotrixSim are integrated through backend-specific adapters and task owner configs.
-- **Unified training CLI:** `uv run train` and `uv run eval` cover PPO, APPO, SAC, TD3, and FlashSAC; additional HORA and HIM-PPO paths are documented as script-level workflows.
-- **Config-owned tasks:** Hydra owner YAML files select task, reward, backend, and algorithm settings together; backend switching is expressed as `task=<task>/<backend>`.
-- **Cross-platform setup paths:** The repository tracks Linux CUDA, Linux ROCm, Linux XPU, and Apple Silicon / macOS setup flows.
-
-## 🚀 Quick Demo
-
-<table>
-  <tr>
-    <td align="center" width="33%">
-      <img src="docs/sphinx/source/_static/demos/dance.jpg" alt="dance demo" width="100%">
-      <br>
-      <sub><b>dance</b><br>G1 motion tracking</sub>
-    </td>
-    <td align="center" width="33%">
-      <img src="docs/sphinx/source/_static/demos/wallflip.jpg" alt="wallflip demo" width="100%">
-      <br>
-      <sub><b>wallflip</b><br>G1 wall flip</sub>
-    </td>
-    <td align="center" width="33%">
-      <img src="docs/sphinx/source/_static/demos/teaser.jpg" alt="teaser demo" width="100%">
-      <br>
-      <sub><b>teaser</b><br>MotrixSim teaser</sub>
-    </td>
-  </tr>
-  <tr>
-    <td align="center" width="33%">
-      <img src="docs/sphinx/source/_static/demos/boxtracking.jpg" alt="boxtracking demo" width="100%">
-      <br>
-      <sub><b>boxtracking</b><br>G1 box tracking</sub>
-    </td>
-    <td align="center" width="33%">
-      <img src="docs/sphinx/source/_static/demos/inhandgrasp.jpg" alt="inhandgrasp demo" width="100%">
-      <br>
-      <sub><b>inhandgrasp</b><br>Sharpa in-hand</sub>
-    </td>
-    <td align="center" width="33%">
-      <img src="docs/sphinx/source/_static/demos/locomani.jpg" alt="locomani demo" width="100%">
-      <br>
-      <sub><b>locomani</b><br>Go2 loco-manipulation</sub>
-    </td>
-  </tr>
-</table>
+The supported source workflow uses [`uv`](https://docs.astral.sh/uv/).
 
 ```bash
-# 0. Install uv if needed
-# Linux / macOS:
 curl -LsSf https://astral.sh/uv/install.sh | sh
-
-# Windows:
-# powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
-# choco install make -y
-
-# 1. Clone the repository
 git clone https://github.com/unilabsim/UniLab.git
 cd UniLab
 
-# 2. Install dependencies
-# Pick the setup command for your platform.
-#
-# Prerequisite: the `mujoco` extra compiles a native extension during `uv sync`
-# and needs Python development headers when using a system Python:
-#   Ubuntu / Debian: sudo apt-get install build-essential python3-dev
-#   macOS:           xcode-select --install
-#   Windows:         MSVC Build Tools
-# (uv-managed Pythons from `uv python install` already bundle the headers.)
+# Fastest path to the first Motrix demo.
+make setup-motrix
 
-# Linux CUDA, macOS, or Windows
-make setup
+# Full local setup (MuJoCo + Motrix):
+# make setup
 
-# Linux AMD / ROCm
-# make sync-rocm
+# Optional platform/backend paths:
+# make sync-rocm       # AMD GPU
+# make sync-xpu        # Intel GPU
+# make setup-drake     # Drake + native batch extension
+```
 
-# Linux Intel Arc / iGPU
-# make sync-xpu
+The `mujoco` extra compiles a native extension and may require the platform
+compiler and Python development headers. See the
+[installation guide](https://unilabsim.github.io/UniLab-doc/en/1-getting_started/2-installation.html)
+for platform-specific setup, optional backends, and external worker runtimes.
 
-# Without shell completion setup:
-# uv sync --extra mujoco --extra motrix
-# If `make` is not installed or unavailable:
-# uv sync --extra mujoco --extra motrix && uv run --no-sync unilab-complete install
+## First success: run a demo
 
-# 3. Pre-trained checkpoint playback (downloads from Hugging Face on first run)
+```bash
+# Downloads the checkpoint and assets from Hugging Face on first run.
 uv run demo dance
 ```
 
-Available demo names: `teaser`, `dance`, `wallflip`, `boxtracking`, `locomani`, `inhandgrasp`. See the [Unified CLI](https://unilabsim.github.io/UniLab-doc/en/2-user_guide/1-training/1-cli_reference.html) page for the full list and flags.
-
-> Mainland China users: motions, scenes, robot meshes, and demo checkpoints are pulled from Hugging Face on first run. If `huggingface.co` is unreachable, point the client at the community mirror before running demo commands:
->
-> ```bash
-> export HF_ENDPOINT=https://hf-mirror.com
-> ```
+Available presets are `teaser`, `dance`, `wallflip`, `boxtracking`, `locomani`,
+and `inhandgrasp`. Use `uv run demo --help` for device and refresh options.
+The [quick demo guide](https://unilabsim.github.io/UniLab-doc/en/1-getting_started/1-quick_demo.html)
+explains rendering modes and server/macOS differences.
 
-For training and evaluation:
+## Train and evaluate
 
 ```bash
-uv run train --algo appo --task go2_joystick_flat --sim motrix
+# Train and replay a task with Motrix.
+uv run train --algo ppo --task go2_joystick_flat --sim motrix
+uv run eval --algo ppo --task go2_joystick_flat --sim motrix --load-run -1
 
-uv run eval --algo appo --task go2_joystick_flat --sim motrix --load-run -1
+# Switch only the simulator for the same task.
+uv run train --algo ppo --task go2_joystick_flat --sim mujoco
 
-# Headless Motrix video export for Linux/server runs
-uv run eval --algo appo --task go2_joystick_flat --sim motrix --load-run -1 --render-mode record
+# Or use the same workflow with an off-policy learner.
+uv run train --algo sac --task g1_walk_flat --sim mujoco
+uv run train --algo flashsac --task g1_walk_flat --sim mujoco
+
+# Headless video export.
+uv run eval --algo ppo --task go2_joystick_flat --sim motrix \
+  --load-run -1 --render-mode record
 ```
 
-This routes through the `go2_joystick_flat/motrix` task owner config and keeps backend selection explicit. Each backend owner carries an optional `play_profile` block that layers render-only overrides at eval time (`training.play_only=true`) without affecting training.
+Route-defining choices are always visible:
 
-On macOS / MacBook, the UniLab CLI routes Motrix interactive playback through `mxpython` when needed. Motrix defaults to interactive playback; use `--render-mode record` for headless video export or `--render-mode none` to skip playback. Detailed script-level commands are in the [Training Guide](https://unilabsim.github.io/UniLab-doc/en/2-user_guide/1-training/0-index.html).
+```text
+--algo + --task + --sim  →  Hydra owner YAML  →  registered environment
+```
 
-## 🏃 Example Runs
+Use normal Hydra overrides after those flags:
 
 ```bash
-uv run train --algo sac --task g1_walk_flat --sim mujoco
-uv run train --algo flashsac --task g1_walk_flat --sim mujoco
+uv run train --algo ppo --task go2_joystick_flat --sim motrix \
+  algo.max_iterations=1 algo.num_envs=16 training.no_play=true
 ```
 
-```bash
-uv run train --algo sac --task g1_motion_tracking --sim motrix
+Do not override `training.sim_backend` to switch engines. It is the identity
+field supplied by the selected owner YAML. Find resume, W&B, playback, and the
+full command matrix in the
+[training guide](https://unilabsim.github.io/UniLab-doc/en/2-user_guide/1-training/0-index.html).
+
+## Manager-based configuration
+
+UniLab wraps a community-familiar manager API with Hydra composition and a
+NumPy runtime. A task owner can select and parameterize terms declaratively:
+
+```yaml
+env:
+  observations:
+    policy:
+      terms:
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+  actions:
+    joint_pos:
+      _target_: unilab.envs.mdp.JointPositionActionCfg
+      entity_name: robot
+      scale: 0.25
+reward:
+  tracking_lin_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_lin_vel_xy_exp
+    weight: 1.0
 ```
 
-```bash
-uv run train --algo appo --task sharpa_inhand --sim mujoco --profile hora
-```
+This makes common task edits a config change: compose or disable a term, tune
+its parameters, and reuse it across robots and backends without writing a new
+environment class. The API follows the pinned mjlab manager semantics where
+the contracts are shared, but it is a UniLab product with NumPy, Hydra, and
+`NpEnvState` semantics. See the
+[Manager-Based API guide](https://unilabsim.github.io/UniLab-doc/en/4-developer_guide/1-architecture/6-manager_based_api.html)
+for the complete contract and known differences.
 
-> Grasp caches auto-download from Hugging Face (`unilabsim/unilab-caches`) on first run into `src/unilab/assets/caches/`; no manual step is needed. To regenerate locally for custom scales (slow):
-> ```bash
-> bash scripts/sharpa_collect_grasps.sh 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5
-> ```
+## Physics backends
 
-```bash
-uv run train --algo ppo --task go2_arm_manip_loco --sim motrix
-uv run eval --algo ppo --task go2_arm_manip_loco --sim motrix --load-run -1
-```
+Current backends are available through `unisim-core` and the same UniLab route;
+the contract is designed to grow as new adapters land:
+
+`mujoco` · `motrix` · `mjwarp` · `drake` · `genesis` · `isaacgym` · `isaacsim`
+
+Choose one backend setup (combine extras when needed):
 
 ```bash
-uv run train --algo ppo --task go2_joystick_flat --sim mujoco 'training.devices=[0,1]'
-uv run train --algo flashsac --task g1_walk_flat --sim mujoco training.devices="[0,1,2,3]"
-uv run train --algo sac --task g1_motion_tracking --sim mujoco training.devices="[0,1,2,3,4,5,6,7]"
+uv sync --extra mujoco
+# uv sync --extra mujoco --extra motrix
+# uv sync --extra mujoco --extra mjwarp
+# uv sync --extra genesis
+# make setup-drake
 ```
 
-Use `uv run train` for training, `uv run eval` for checkpoint playback, and `uv run demo` for the local demo preset. These commands keep algorithm, task, and backend selection explicit.
+IsaacGym and IsaacSim use dedicated external worker environments. Backend
+installation details, rendering behavior, and the evidence-based task support
+matrix live in the
+[backend guide](https://unilabsim.github.io/UniLab-doc/en/2-user_guide/3-backends/0-index.html)
+and [support matrix](https://unilabsim.github.io/UniLab-doc/en/5-reference/5-support_matrix.html).
+
+## Ecosystem
 
-More training commands, script-level entrypoints, algorithm matrix, resume flow, and W&B details are in the [Training Guide](https://unilabsim.github.io/UniLab-doc/en/2-user_guide/1-training/0-index.html).
+UniLab is designed to be the shared product surface for robot-specific
+repositories. Current downstream examples include
+[MicroDuck RL](https://github.com/unilabsim/microduck_rl_unilab) and
+[EngineAI RL](https://github.com/unilabsim/engineai_rl_unilab). They can ship
+robot recipes independently while consuming the same task, backend, and RL
+contracts.
 
-## 📚 Documentation
+## Documentation
 
-Use the published [UniLab documentation](https://unilabsim.github.io/UniLab-doc/); start at the [English documentation index](https://unilabsim.github.io/UniLab-doc/en/0-index.html). High-signal entrypoints:
+- [Documentation index](https://unilabsim.github.io/UniLab-doc/en/0-index.html)
+- [Unified CLI reference](https://unilabsim.github.io/UniLab-doc/en/2-user_guide/1-training/1-cli_reference.html)
+- [Task and manager architecture](https://unilabsim.github.io/UniLab-doc/en/4-developer_guide/1-architecture/0-index.html)
+- [Sim-to-sim deployment](https://unilabsim.github.io/UniLab-doc/en/3-deployment/2-sim_to_sim/1-backend_swap.html)
+- [Algorithm extension recipe](https://unilabsim.github.io/UniLab-doc/en/4-developer_guide/3-extending/3-new_algorithm.html)
+- [Architecture decisions](https://unilabsim.github.io/UniLab-doc/adr/ADR-0000-index.html)
 
-- [Getting Started](https://unilabsim.github.io/UniLab-doc/en/1-getting_started/0-index.html): installation, Docker runtime, dependency setup, and first-run commands
-- [Training Guide](https://unilabsim.github.io/UniLab-doc/en/2-user_guide/1-training/0-index.html): training, playback, resume flow, Hydra overrides, and W&B
-- [Simulation Backends](https://unilabsim.github.io/UniLab-doc/en/2-user_guide/3-backends/0-index.html): generated MuJoCo / Motrix support matrix
-- [Development Standard](https://unilabsim.github.io/UniLab-doc/en/4-developer_guide/0-index.html): contracts, layering, and validation boundaries
-- [ADR Index](https://unilabsim.github.io/UniLab-doc/adr/ADR-0000-index.html): accepted architecture decisions
+For development and contribution workflows, see the
+[contributing guide](CONTRIBUTING.md).
 
-## 💬 Community
+## Community
 
 <p align="center">
-  <img src="docs/sphinx/source/_static/assets/unilab-wechat-assistant.jpg" alt="UniLab WeChat assistant QR code" width="180">
+  <img src="docs/sphinx/source/_static/assets/unilab-wechat-assistant.jpg" alt="UniLab community QR code" width="180">
 </p>
 
-<p align="center">Add the assistant on WeChat to join the group. Please include <code>UniLab community</code> in your request.</p>
+<p align="center">Add the UniLab assistant on WeChat to join the community.</p>
 
-## 🧾 Citation
-
-### UniLab
+## Citation
 
 ```bibtex
 @article{jia2026unilab,
   title         = {UniLab: A Heterogeneous Architecture for Robot RL Beyond GPU-Dominant Paradigms},
-  author        = {Yufei Jia and Zhanxiang Cao and Mingrui Yu and Heng Zhang and Shenyu Chen and Dixuan Jiang and Meng Li and Xiaofan Li and Yiyang Liu and Junzhe Wu and Zheng Li and XiLin Fang and Tingyu Cui and Shengcheng Fu and Haoyang Li and Anqi Wang and Zifan Wang and Dongjie Zhu and Chenyu Cao and Zhenbiao Huang and Ziang Zheng and Jie Lu and Xin Ma and Zhengyang Wei and Xiang Zhao and Tianyue Zhan and Ye He and Yuxiang Chen and Yizhou Jiang and Yue Li and Haizhou Ge and Yuhang Dong and Fan Jia and Ziheng Zhang and Meng Zhang and Xiwa Deng and Zhixing Chen and Hanyang Shao and Chenxin Dong and Yixuan Li and Yizhi Chen and Bokui Chen and Kaifeng Zhang and Hanqing Cui and Yusen Qin and Ruqi Huang and Lei Han and Tiancai Wang and Xiang Li and Yue Gao and Guyue Zhou},
+  author        = {Jia, Yufei and Cao, Zhanxiang and Yu, Mingrui and Zhang, Heng and Chen, Shenyu and Jiang, Dixuan and Li, Meng and Li, Xiaofan and Liu, Yiyang and Wu, Junzhe and Li, Zheng and Fang, XiLin and Cui, Tingyu and Fu, Shengcheng and Li, Haoyang and Wang, Anqi and Wang, Zifan and Zhu, Dongjie and Cao, Chenyu and Huang, Zhenbiao and Zheng, Ziang and Lu, Jie and Ma, Xin and Wei, Zhengyang and Zhao, Xiang and Zhan, Tianyue and He, Ye and Chen, Yuxiang and Jiang, Yizhou and Li, Yue and Ge, Haizhou and Dong, Yuhang and Jia, Fan and Zhang, Ziheng and Zhang, Meng and Deng, Xiwa and Chen, Zhixing and Shao, Hanyang and Dong, Chenxin and Li, Yixuan and Chen, Yizhi and Chen, Bokui and Zhang, Kaifeng and Cui, Hanqing and Qin, Yusen and Huang, Ruqi and Han, Lei and Wang, Tiancai and Li, Xiang and Gao, Yue and Zhou, Guyue},
   journal       = {arXiv preprint arXiv:2605.30313},
   year          = {2026},
   url           = {https://arxiv.org/abs/2605.30313}
 }
 ```
 
-### Physics Backends
+UniLab is released under the [Apache License 2.0](LICENSE). See the
+independent [UniSim](https://github.com/unilabsim/unisim) and
+[UniLab RL](https://github.com/unilabsim/unilab_rl) repositories for their
+own release and citation information.
 
-```bibtex
-@article{jia2026mujocouni,
-  title  = {MuJoCoUni: Persistent Batched Runtime Primitives for MuJoCo},
-  author = {Jia, Yufei and Wu, Junzhe},
-  journal = {arXiv preprint arXiv:2605.24922},
-  year   = {2026}
-}
+## Acknowledgments
 
-@software{motrixsim2026,
-  title  = {MotrixSim: A Physics Simulation Engine for Robotics and Embodied AI},
-  author = {{Motphys Team}},
-  year   = {2026},
-  url    = {https://motrixsim.readthedocs.io/},
-  note   = {Python binary package}
-}
-```
+UniLab would not exist without the excellent work of the
+[Isaac Lab](https://github.com/isaac-sim/IsaacLab) team and the
+[mjlab](https://github.com/mujocolab/mjlab) developers and contributors. Isaac
+Lab's manager-based API design and abstractions, together with mjlab's clear,
+lightweight reference implementation, helped shape UniLab's Hydra and NumPy
+task authoring experience. We sincerely thank both communities for sharing
+their work and ideas.
diff --git a/README_zh.md b/README_zh.md
index 287a36309..e951e5fe0 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -7,225 +7,235 @@
 <p align="center">语言：简体中文 | <a href="README.md">English</a></p>
 
 <p align="center">
+  <a href="https://github.com/unilabsim/UniLab/actions/workflows/ci.yml"><img src="https://github.com/unilabsim/UniLab/actions/workflows/ci.yml/badge.svg" alt="CI"></a>
   <a href="https://unilabsim.github.io"><img src="https://img.shields.io/badge/project-page-brightgreen" alt="Project Page"></a>
-  <a href="https://arxiv.org/abs/2605.30313"><img src="https://img.shields.io/badge/arxiv-2605.30313-red" alt="arXiv"></a>
-  <a href="https://unilabsim.github.io/paper/"><img src="https://img.shields.io/badge/paper-UniLab-orange" alt="Paper"></a>
+  <a href="https://arxiv.org/abs/2605.30313"><img src="https://img.shields.io/badge/paper-arXiv--2605.30313-red" alt="Paper"></a>
+  <a href="https://arxiv.org/abs/2605.30313"><img src="https://img.shields.io/badge/CoRL-2026-orange" alt="CoRL 2026"></a>
   <a href="https://unilabsim.github.io/UniLab-doc/"><img src="https://img.shields.io/badge/docs-UniLab--doc-blue" alt="Documentation"></a>
-  <a href="https://unilabsim.github.io/paper/paper2gal.html"><img src="https://img.shields.io/badge/Galgame-play-ff69b4" alt="Galgame"></a>
-  <a href="LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-blue" alt="Apache-2.0 License"></a>
+  <a href="https://pypi.org/project/unilab/"><img src="https://img.shields.io/pypi/v/unilab" alt="PyPI"></a>
+  <a href="https://github.com/unilabsim/UniLab/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-blue" alt="Apache-2.0 License"></a>
 </p>
 
+<h3 align="center">🎉 🎉 UniLab 已被 <b>CoRL 2026</b> 接收！ 🎉 🎉</h3>
+
 <p align="center">
   <img src="docs/sphinx/source/_static/assets/teaser.jpg" alt="UniLab 预告图" width="95%">
 </p>
 
-<p align="center"><em>无需 GPU 仿真后端即可训练机器人 RL。预告图由 MotrixSim 渲染。</em></p>
+<p align="center"><em>用同一套任务编排体验覆盖运动、操作与动作跟踪。</em></p>
 
-从下面的 `快速演示` 开始运行主训练命令。推荐使用 `uv` 安装；Conda 和 pip 用户目前也应继续遵循 `uv` 工作流。平台相关说明与当前边界见 [安装指南](https://unilabsim.github.io/UniLab-doc/zh_CN/1-getting_started/2-installation.html)。
+UniLab 是一个完整、可配置的机器人强化学习产品。使用 Hydra 描述任务，
+通过 manager term 组装任务，选择物理后端，再用统一 CLI 完成训练与评估。
+同一套面向任务的 contract 可以把 CPU、GPU 和外部 worker 仿真连接到学习器运行时。
 
-## ✨ 亮点
+物理适配器由独立的
+[`unisim-core`](https://github.com/unilabsim/unisim) package 提供；RL 算法及其
+runner 由 [`unilab-rl`](https://github.com/unilabsim/unilab_rl) 提供（Python
+namespace 为 `uni_rl`）。UniLab 将面向用户的 task、environment、配置和实验流程
+保持在一起。
 
-```
-┌────────────────────┐                            ┌─────────────────────────┐
-│   Uni Physics Sim  │   Unified Shared Memory    │   GPU Policy Training   │
-│   Motrix / MuJoCo  │ ─────────────────────────▶ │     PPO / SAC / TD3     │
-│   MjWarp / Drake   │    SharedReplayBuffer      │ CUDA / MPS / ROCm / XPU │
-└────────────────────┘                            └─────────────────────────┘
+如果你刚接触 UniLab，请从[第一次成功](#第一次成功运行-demo)开始；如果你已经有任务，
+直接进入[训练与评估](#训练与评估)；在存在匹配 task owner 时，只需修改 `--sim` 即可尝试另一个后端。
+
+## 亮点
+
+```text
+┌──────────────────────────────────────┐    同一套任务 contract    ┌───────────────────────────────────┐
+│                                      │ ────────────────────────▶ │          按需运行任务             │
+│            定义一次任务              │                           │ MuJoCo · Motrix · MJWarp · Drake  │
+│       Hydra · Managers · NumPy       │                           │ Genesis · IsaacGym · IsaacSim     │
+│      Terms · rewards · commands      │                           │   CUDA · ROCm · macOS · MPS · XPU │
+│                                      │                           │            训练 · 评估            │
+└──────────────────────────────────────┘                           └───────────────────────────────────┘
 ```
 
-- **异构 RL 运行时：** CPU 并行仿真通过共享内存流式传输 transition，而策略学习运行在 GPU 加速器上。
-- **两套物理后端：** MuJoCoUni 和 MotrixSim 通过后端专用适配器和任务 owner 配置接入。
-- **统一训练 CLI：** `uv run train` 和 `uv run eval` 覆盖 PPO、APPO、SAC、TD3 和 FlashSAC；额外的 HORA 与 HIM-PPO 路径以脚本级工作流文档化。
-- **配置拥有的任务：** Hydra owner YAML 会同时选择 task、reward、backend 和 algorithm；后端切换通过 `task=<task>/<backend>` 表达。
-- **跨平台安装路径：** 仓库覆盖 Linux CUDA、Linux ROCm、Linux XPU，以及 Apple Silicon / macOS 的安装流程。
-
-## 🚀 快速演示
-
-<table>
-  <tr>
-    <td align="center" width="33%">
-      <img src="docs/sphinx/source/_static/demos/dance.jpg" alt="dance demo" width="100%">
-      <br>
-      <sub><b>dance</b><br>G1 动作跟踪</sub>
-    </td>
-    <td align="center" width="33%">
-      <img src="docs/sphinx/source/_static/demos/wallflip.jpg" alt="wallflip demo" width="100%">
-      <br>
-      <sub><b>wallflip</b><br>G1 空翻</sub>
-    </td>
-    <td align="center" width="33%">
-      <img src="docs/sphinx/source/_static/demos/teaser.jpg" alt="teaser demo" width="100%">
-      <br>
-      <sub><b>teaser</b><br>MotrixSim 预告图</sub>
-    </td>
-  </tr>
-  <tr>
-    <td align="center" width="33%">
-      <img src="docs/sphinx/source/_static/demos/boxtracking.jpg" alt="boxtracking demo" width="100%">
-      <br>
-      <sub><b>boxtracking</b><br>G1 箱体跟踪</sub>
-    </td>
-    <td align="center" width="33%">
-      <img src="docs/sphinx/source/_static/demos/inhandgrasp.jpg" alt="inhandgrasp demo" width="100%">
-      <br>
-      <sub><b>inhandgrasp</b><br>Sharpa 手内抓取</sub>
-    </td>
-    <td align="center" width="33%">
-      <img src="docs/sphinx/source/_static/demos/locomani.jpg" alt="locomani demo" width="100%">
-      <br>
-      <sub><b>locomani</b><br>Go2 移动操作</sub>
-    </td>
-  </tr>
-</table>
+UniLab 的核心理念很简单：将任务语义定义为可复用的配置，然后独立更换仿真器、硬件或
+learner，而无需重写任务的 environment 生命周期。
 
-```bash
-# 0. 如果还没有安装 uv
-# Linux / macOS：
-curl -LsSf https://astral.sh/uv/install.sh | sh
+- **配置而非编码。** action、observation、reward、termination、event、command、
+  curriculum 和 metrics 都是 manager term，在 Hydra owner YAML 中组装。基于已有 term
+  的任务变体无需新写 environment class，很多时候完全不需要 Python 代码。
+- **更换后端而不更换工作流。** 当前和未来的仿真器共用公开的 `SimBackend` contract。
+  使用 `--sim` 选择后端；任务编排和训练/评估工作流保持一致，后端差异由 owner YAML
+  明确表达。
+- **适配手头的硬件并持续扩展。** CPU 并行仿真或外部 worker 仿真通过注入式 env contract
+  和异步运行时向 accelerator learner 提供数据。算法和 runner 由统一 package 生态提供，
+  不绑定单一仿真器。
+
+## 开始使用
 
-# Windows：
-# powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
-# choco install make -y
+推荐使用 [`uv`](https://docs.astral.sh/uv/) 完成源码工作流。
 
-# 1. 克隆仓库
+```bash
+curl -LsSf https://astral.sh/uv/install.sh | sh
 git clone https://github.com/unilabsim/UniLab.git
 cd UniLab
 
-# 2. 安装依赖
-# 请按你的平台选择对应的安装命令。
-#
-# 前置条件：`mujoco` extra 会在 `uv sync` 时编译原生扩展，
-# 使用系统 Python 时需要 Python 开发头文件：
-#   Ubuntu / Debian：sudo apt-get install build-essential python3-dev
-#   macOS：          xcode-select --install
-#   Windows：        MSVC Build Tools
-# （通过 `uv python install` 安装的 uv 托管 Python 已自带头文件。）
+# 运行 Motrix demo 的最快路径。
+make setup-motrix
 
-# Linux CUDA、macOS 或 Windows
-make setup
+# 完整本地环境（MuJoCo + Motrix）：
+# make setup
 
-# Linux AMD / ROCm
-# make sync-rocm
+# 可选的平台/后端路径：
+# make sync-rocm       # AMD GPU
+# make sync-xpu        # Intel GPU
+# make setup-drake     # Drake + 原生 batch extension
+```
 
-# Linux Intel Arc / iGPU
-# make sync-xpu
+`mujoco` extra 会编译原生扩展；使用系统 Python 时可能需要平台编译器和 Python
+开发头文件。平台相关安装、可选后端和外部 worker 运行时请参阅
+[安装指南](https://unilabsim.github.io/UniLab-doc/zh_CN/1-getting_started/2-installation.html)。
 
-# 不使用 shell completion 设置时：
-# uv sync --extra mujoco --extra motrix
-# 如果没有安装或无法使用 `make`：
-# uv sync --extra mujoco --extra motrix && uv run --no-sync unilab-complete install
+## 第一次成功：运行 demo
 
-# 3. 预训练 checkpoint 回放（首次运行会从 Hugging Face 下载）
+```bash
+# 首次运行会从 Hugging Face 下载 checkpoint 和 asset。
 uv run demo dance
 ```
 
-可用的 demo 名称：`teaser`、`dance`、`wallflip`、`boxtracking`、`locomani`、`inhandgrasp`。
-完整的命令与参数请参阅 [统一 CLI](https://unilabsim.github.io/UniLab-doc/zh_CN/2-user_guide/1-training/1-cli_reference.html) 页面。
-
-> 中国大陆用户：动作、场景、机器人网格和 demo checkpoint 首次运行时会从 Hugging Face 拉取。如果 `huggingface.co`
-> 无法访问，请在运行 demo 命令前先将客户端切到社区镜像：
->
-> ```bash
-> export HF_ENDPOINT=https://hf-mirror.com
-> ```
+可用预设为 `teaser`、`dance`、`wallflip`、`boxtracking`、`locomani` 和
+`inhandgrasp`。运行 `uv run demo --help` 查看 device 和 refresh 选项。
+[快速演示指南](https://unilabsim.github.io/UniLab-doc/zh_CN/1-getting_started/1-quick_demo.html)
+介绍渲染模式以及服务器/macOS 差异。
 
-用于训练与评估：
+## 训练与评估
 
 ```bash
-uv run train --algo appo --task go2_joystick_flat --sim motrix
+# 使用 Motrix 训练并回放任务。
+uv run train --algo ppo --task go2_joystick_flat --sim motrix
+uv run eval --algo ppo --task go2_joystick_flat --sim motrix --load-run -1
 
-uv run eval --algo appo --task go2_joystick_flat --sim motrix --load-run -1
+# 对同一个任务只切换仿真器。
+uv run train --algo ppo --task go2_joystick_flat --sim mujoco
 
-# Linux / 服务器环境下的 Motrix 无头视频导出
-uv run eval --algo appo --task go2_joystick_flat --sim motrix --load-run -1 --render-mode record
+# 或使用 off-policy learner 跑同样的工作流。
+uv run train --algo sac --task g1_walk_flat --sim mujoco
+uv run train --algo flashsac --task g1_walk_flat --sim mujoco
+
+# 无头视频导出。
+uv run eval --algo ppo --task go2_joystick_flat --sim motrix \
+  --load-run -1 --render-mode record
 ```
 
-这会路由到 `go2_joystick_flat/motrix` 任务 owner 配置，并保持后端选择显式化。每个后端 owner 带一个可选的 `play_profile` 块，在 eval 时（`training.play_only=true`）叠加仅渲染相关的覆盖，不影响训练。
+路由选择始终清晰可见：
 
-在 macOS / MacBook 上，UniLab CLI 在需要时会通过 `mxpython` 路由 Motrix 交互式回放。Motrix 默认使用交互式回放；要导出无头视频请使用 `--render-mode record`，要跳过回放请使用 `--render-mode none`。更细的脚本级命令请参阅 [训练指南](https://unilabsim.github.io/UniLab-doc/zh_CN/2-user_guide/1-training/0-index.html)。
+```text
+--algo + --task + --sim  →  Hydra owner YAML  →  已注册的 environment
+```
 
-## 🏃 示例运行
+在这些 flag 之后追加普通 Hydra override：
 
 ```bash
-uv run train --algo sac --task g1_walk_flat --sim mujoco
-uv run train --algo flashsac --task g1_walk_flat --sim mujoco
+uv run train --algo ppo --task go2_joystick_flat --sim motrix \
+  algo.max_iterations=1 algo.num_envs=16 training.no_play=true
 ```
 
-```bash
-uv run train --algo sac --task g1_motion_tracking --sim motrix
+不要通过 override `training.sim_backend` 切换引擎；它是所选 owner YAML 提供的
+identity 字段。续训、W&B、回放和完整命令矩阵请参阅
+[训练指南](https://unilabsim.github.io/UniLab-doc/zh_CN/2-user_guide/1-training/0-index.html)。
+
+## Manager-based 配置
+
+UniLab 用 Hydra composition 和 NumPy runtime 包装了一套社区熟悉的 manager API。
+任务 owner 可以用声明式配置选择 term 并设置参数：
+
+```yaml
+env:
+  observations:
+    policy:
+      terms:
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+  actions:
+    joint_pos:
+      _target_: unilab.envs.mdp.JointPositionActionCfg
+      entity_name: robot
+      scale: 0.25
+reward:
+  tracking_lin_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_lin_vel_xy_exp
+    weight: 1.0
 ```
 
-```bash
-uv run train --algo appo --task sharpa_inhand --sim mujoco --profile hora
-```
+这意味着常见的任务修改只需改配置：组装或禁用一个 term、调整参数，并在不同机器人和
+后端之间复用，无需新写 environment class。该 API 在共享 contract 范围内遵循 pinned
+mjlab manager 语义，但它是结合 NumPy、Hydra 和 `NpEnvState` 语义的 UniLab 产品。
+完整 contract 与已知差异请参阅
+[Manager-Based API 指南](https://unilabsim.github.io/UniLab-doc/zh_CN/4-developer_guide/1-architecture/6-manager_based_api.html).
 
-> Grasp cache 首次训练时会自动从 Hugging Face (`unilabsim/unilab-caches`) 下载到 `src/unilab/assets/caches/`，无需手动操作；如需为自定义 scale 重新生成（较慢）：
-> ```bash
-> bash scripts/sharpa_collect_grasps.sh 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5
-> ```
+## 物理后端
 
-```bash
-uv run train --algo ppo --task go2_arm_manip_loco --sim motrix
-uv run eval --algo ppo --task go2_arm_manip_loco --sim motrix --load-run -1
-```
+当前后端通过 `unisim-core` 和同一套 UniLab 路由提供；随着新 adapter 加入，
+这套 contract 可以持续扩展：
+
+`mujoco` · `motrix` · `mjwarp` · `drake` · `genesis` · `isaacgym` · `isaacsim`
+
+选择一条后端安装路径（需要多个后端时组合 extras）：
 
 ```bash
-uv run train --algo ppo --task go2_joystick_flat --sim mujoco 'training.devices=[0,1]'
-uv run train --algo flashsac --task g1_walk_flat --sim mujoco training.devices="[0,1,2,3]"
-uv run train --algo sac --task g1_motion_tracking --sim mujoco training.devices="[0,1,2,3,4,5,6,7]"
+uv sync --extra mujoco
+# uv sync --extra mujoco --extra motrix
+# uv sync --extra mujoco --extra mjwarp
+# uv sync --extra genesis
+# make setup-drake
 ```
 
-使用 `uv run train` 进行训练，使用 `uv run eval` 进行检查点回放，`uv run demo` 用于本地 demo 预设。这些命令可以明确指定算法、任务和后端。
+IsaacGym 和 IsaacSim 使用专用的外部 worker 环境。后端安装细节、渲染行为以及基于证据
+的 task support matrix 请参阅
+[后端指南](https://unilabsim.github.io/UniLab-doc/zh_CN/2-user_guide/3-backends/0-index.html)
+和[支持矩阵](https://unilabsim.github.io/UniLab-doc/zh_CN/5-reference/5-support_matrix.html)。
 
-更多训练命令、脚本级入口、算法矩阵、续训流程以及 W&B 细节请参阅 [训练指南](https://unilabsim.github.io/UniLab-doc/zh_CN/2-user_guide/1-training/0-index.html)。
+## 生态
 
-## 📚 文档
+UniLab 被设计为机器人专属仓库共享的产品界面。目前的下游示例包括
+[MicroDuck RL](https://github.com/unilabsim/microduck_rl_unilab) 和
+[EngineAI RL](https://github.com/unilabsim/engineai_rl_unilab)。它们可以独立发布机器人
+recipe，同时消费同一套 task、backend 和 RL contract。
 
-请使用已发布的 [UniLab 文档](https://unilabsim.github.io/UniLab-doc/)；中文文档入口见 [中文文档索引](https://unilabsim.github.io/UniLab-doc/zh_CN/0-index.html)。高信号入口如下：
+## 文档
 
-- [快速上手](https://unilabsim.github.io/UniLab-doc/zh_CN/1-getting_started/0-index.html)：安装、Docker 运行时、依赖配置和首次运行命令
-- [训练指南](https://unilabsim.github.io/UniLab-doc/zh_CN/2-user_guide/1-training/0-index.html)：训练、回放、续训流程、Hydra override 和 W&B
-- [仿真后端](https://unilabsim.github.io/UniLab-doc/zh_CN/2-user_guide/3-backends/0-index.html)：生成的 MuJoCo / Motrix 支持矩阵
-- [开发者指南](https://unilabsim.github.io/UniLab-doc/zh_CN/4-developer_guide/0-index.html)：契约、分层与验证边界
-- [ADR 索引](https://unilabsim.github.io/UniLab-doc/adr/ADR-0000-index.html)：已采纳的架构决策
+- [文档索引](https://unilabsim.github.io/UniLab-doc/zh_CN/0-index.html)
+- [统一 CLI 参考](https://unilabsim.github.io/UniLab-doc/zh_CN/2-user_guide/1-training/1-cli_reference.html)
+- [Task 与 manager 架构](https://unilabsim.github.io/UniLab-doc/zh_CN/4-developer_guide/1-architecture/0-index.html)
+- [Sim-to-sim 部署](https://unilabsim.github.io/UniLab-doc/zh_CN/3-deployment/2-sim_to_sim/1-backend_swap.html)
+- [算法扩展教程](https://unilabsim.github.io/UniLab-doc/zh_CN/4-developer_guide/3-extending/3-new_algorithm.html)
+- [架构决策](https://unilabsim.github.io/UniLab-doc/adr/ADR-0000-index.html)
 
-## 💬 社群交流
+开发与贡献工作流请参阅[贡献指南](CONTRIBUTING.md)。
+
+## 社区
 
 <p align="center">
-  <img src="docs/sphinx/source/_static/assets/unilab-wechat-assistant.jpg" alt="UniLab 小助手微信二维码" width="180">
+  <img src="docs/sphinx/source/_static/assets/unilab-wechat-assistant.jpg" alt="UniLab 社区二维码" width="180">
 </p>
 
-<p align="center">添加小助手微信进群，请备注：<code>unilab交流</code></p>
-
-## 🧾 引用
+<p align="center">添加 UniLab 小助手微信，加入社区。</p>
 
-### UniLab
+## 引用
 
 ```bibtex
 @article{jia2026unilab,
   title         = {UniLab: A Heterogeneous Architecture for Robot RL Beyond GPU-Dominant Paradigms},
-  author        = {Yufei Jia and Zhanxiang Cao and Mingrui Yu and Heng Zhang and Shenyu Chen and Dixuan Jiang and Meng Li and Xiaofan Li and Yiyang Liu and Junzhe Wu and Zheng Li and XiLin Fang and Tingyu Cui and Shengcheng Fu and Haoyang Li and Anqi Wang and Zifan Wang and Dongjie Zhu and Chenyu Cao and Zhenbiao Huang and Ziang Zheng and Jie Lu and Xin Ma and Zhengyang Wei and Xiang Zhao and Tianyue Zhan and Ye He and Yuxiang Chen and Yizhou Jiang and Yue Li and Haizhou Ge and Yuhang Dong and Fan Jia and Ziheng Zhang and Meng Zhang and Xiwa Deng and Zhixing Chen and Hanyang Shao and Chenxin Dong and Yixuan Li and Yizhi Chen and Bokui Chen and Kaifeng Zhang and Hanqing Cui and Yusen Qin and Ruqi Huang and Lei Han and Tiancai Wang and Xiang Li and Yue Gao and Guyue Zhou},
+  author        = {Jia, Yufei and Cao, Zhanxiang and Yu, Mingrui and Zhang, Heng and Chen, Shenyu and Jiang, Dixuan and Li, Meng and Li, Xiaofan and Liu, Yiyang and Wu, Junzhe and Li, Zheng and Fang, XiLin and Cui, Tingyu and Fu, Shengcheng and Li, Haoyang and Wang, Anqi and Wang, Zifan and Zhu, Dongjie and Cao, Chenyu and Huang, Zhenbiao and Zheng, Ziang and Lu, Jie and Ma, Xin and Wei, Zhengyang and Zhao, Xiang and Zhan, Tianyue and He, Ye and Chen, Yuxiang and Jiang, Yizhou and Li, Yue and Ge, Haizhou and Dong, Yuhang and Jia, Fan and Zhang, Ziheng and Zhang, Meng and Deng, Xiwa and Chen, Zhixing and Shao, Hanyang and Dong, Chenxin and Li, Yixuan and Chen, Yizhi and Chen, Bokui and Zhang, Kaifeng and Cui, Hanqing and Qin, Yusen and Huang, Ruqi and Han, Lei and Wang, Tiancai and Li, Xiang and Gao, Yue and Zhou, Guyue},
   journal       = {arXiv preprint arXiv:2605.30313},
   year          = {2026},
   url           = {https://arxiv.org/abs/2605.30313}
 }
 ```
 
-### 物理后端
+UniLab 以 [Apache License 2.0](LICENSE) 发布。独立的
+[UniSim](https://github.com/unilabsim/unisim) 与
+[UniLab RL](https://github.com/unilabsim/unilab_rl) 仓库包含各自的发布和引用信息。
 
-```bibtex
-@article{jia2026mujocouni,
-  title  = {MuJoCoUni: Persistent Batched Runtime Primitives for MuJoCo},
-  author = {Jia, Yufei and Wu, Junzhe},
-  journal = {arXiv preprint arXiv:2605.24922},
-  year   = {2026}
-}
+## 致谢
 
-@software{motrixsim2026,
-  title  = {MotrixSim: A Physics Simulation Engine for Robotics and Embodied AI},
-  author = {{Motphys Team}},
-  year   = {2026},
-  url    = {https://motrixsim.readthedocs.io/},
-  note   = {Python binary package}
-}
-```
+如果没有 [Isaac Lab](https://github.com/isaac-sim/IsaacLab) 团队以及
+[mjlab](https://github.com/mujocolab/mjlab) 开发团队和贡献者的出色工作，UniLab 不会
+成为今天的样子。Isaac Lab 在 manager-based API 设计和抽象方面的工作，以及 mjlab
+清晰、轻量的参考实现，共同塑造了 UniLab 的 Hydra + NumPy 任务编排体验。衷心感谢两个
+社区分享他们的工作与想法。
diff --git a/conf/appo/task/allegro_inhand/motrix.yaml b/conf/appo/task/allegro_inhand/motrix.yaml
deleted file mode 100644
index c027729a8..000000000
--- a/conf/appo/task/allegro_inhand/motrix.yaml
+++ /dev/null
@@ -1,65 +0,0 @@
-# @package _global_
-training:
-  task_name: AllegroInhandRotation
-  sim_backend: motrix
-  play_steps: 200
-  render_spacing: 0.5
-  cam_distance: 1.5
-  cam_lookat: [0.75, 0.75, 0]
-  cam_elevation: -20.0
-algo:
-  num_envs: 16384
-  steps_per_env: 8
-  max_iterations: 201
-  save_interval: 100
-  algorithm:
-    value_loss_coef: 4.0
-    entropy_coef: 0.01
-    learning_rate: 0.001
-    desired_kl: 0.02
-    adaptive_kl_factor: 2.0
-    adaptive_lr_factor: 1.5
-    num_learning_epochs: 5
-    num_mini_batches: 4
-    clip_param: 0.2
-    gamma: 0.99
-    lam: 0.95
-    max_grad_norm: 1.0
-    use_clipped_value_loss: true
-    schedule: adaptive
-  actor:
-    hidden_dims: [512, 256, 128]
-    activation: elu
-    obs_normalization: true
-    distribution_cfg:
-      class_name: rsl_rl.modules.distribution.GaussianDistribution
-      init_std: 1.0
-      std_type: scalar
-  critic:
-    hidden_dims: [512, 256, 128]
-    activation: elu
-    obs_normalization: true
-reward:
-  scales:
-    rotate: 1.25
-    obj_linvel: -0.3
-    pose_diff: -0.3
-    torque: -0.1
-    work: -2.0
-    drop: 0.0
-  angvel_clip_min: -0.5
-  angvel_clip_max: 0.5
-  reset_z_threshold: 0.125
-env:
-  gen_grasp: false
-  max_episode_seconds: 20.0
-  grasp_cache_path: caches/allegro_grasp_50k.npy
-  # Keep only grasp/pose reset variation. All online DR terms stay disabled.
-  domain_rand:
-    randomize_base_mass: false
-    random_com: false
-    randomize_gravity: false
-    push_robots: false
-    joint_noise: 0.0
-    ball_vel_noise: 0.0
-    ball_z_offset: 0.0
diff --git a/conf/appo/task/allegro_inhand/mujoco.yaml b/conf/appo/task/allegro_inhand/mujoco.yaml
deleted file mode 100644
index c24a442bc..000000000
--- a/conf/appo/task/allegro_inhand/mujoco.yaml
+++ /dev/null
@@ -1,67 +0,0 @@
-# @package _global_
-training:
-  task_name: AllegroInhandRotation
-  sim_backend: mujoco
-  play_steps: 200
-  render_spacing: 0.5
-  cam_distance: 0.75
-  cam_lookat: [0.0, 0.0, 0.15]
-  cam_elevation: -25.0
-  cam_azimuth: 45.0
-  replay_queue_size: 4
-algo:
-  num_envs: 1024
-  steps_per_env: 8
-  max_iterations: 3000
-  save_interval: 500
-  algorithm:
-    value_loss_coef: 4.0
-    entropy_coef: 0.01
-    learning_rate: 0.001
-    desired_kl: 0.025
-    adaptive_kl_factor: 2.0
-    adaptive_lr_factor: 1.5
-    num_learning_epochs: 5
-    num_mini_batches: 4
-    clip_param: 0.2
-    gamma: 0.99
-    lam: 0.95
-    max_grad_norm: 1.0
-    use_clipped_value_loss: true
-    schedule: adaptive
-  actor:
-    hidden_dims: [512, 256, 128]
-    activation: elu
-    obs_normalization: true
-    distribution_cfg:
-      class_name: rsl_rl.modules.distribution.GaussianDistribution
-      init_std: 1.0
-      std_type: scalar
-  critic:
-    hidden_dims: [512, 256, 128]
-    activation: elu
-    obs_normalization: true
-reward:
-  scales:
-    rotate: 1.25
-    obj_linvel: -0.3
-    pose_diff: -0.3
-    torque: -0.1
-    work: -2.0
-    drop: 0.0
-  angvel_clip_min: -0.5
-  angvel_clip_max: 0.5
-  reset_z_threshold: 0.125
-env:
-  gen_grasp: false
-  max_episode_seconds: 20.0
-  grasp_cache_path: caches/allegro_grasp_50k.npy
-  # Keep only grasp/pose reset variation. All online DR terms stay disabled.
-  domain_rand:
-    randomize_base_mass: false
-    random_com: false
-    randomize_gravity: false
-    push_robots: false
-    joint_noise: 0.0
-    ball_vel_noise: 0.0
-    ball_z_offset: 0.0
diff --git a/conf/appo/task/g1_23dof_climb_tracking/motrix.yaml b/conf/appo/task/g1_23dof_climb_tracking/motrix.yaml
deleted file mode 100644
index 7844addb7..000000000
--- a/conf/appo/task/g1_23dof_climb_tracking/motrix.yaml
+++ /dev/null
@@ -1,65 +0,0 @@
-# @package _global_
-training:
-  task_name: G1ClimbTracking23Dof
-  sim_backend: motrix
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 20000
-  save_interval: 500
-env:
-  sampling_mode: adaptive
-  truncate_on_clip_end: false
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.5475464629911068
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-  anchor_pos_z_threshold: 0.3
-  ee_body_pos_z_threshold: 0.3
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.5
-    motion_joint_vel: 0.25
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/appo/task/g1_23dof_climb_tracking/mujoco.yaml b/conf/appo/task/g1_23dof_climb_tracking/mujoco.yaml
deleted file mode 100644
index 21380ecb4..000000000
--- a/conf/appo/task/g1_23dof_climb_tracking/mujoco.yaml
+++ /dev/null
@@ -1,65 +0,0 @@
-# @package _global_
-training:
-  task_name: G1ClimbTracking23Dof
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 20000
-  save_interval: 500
-env:
-  sampling_mode: adaptive
-  truncate_on_clip_end: false
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.5475464629911068
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-  anchor_pos_z_threshold: 0.3
-  ee_body_pos_z_threshold: 0.3
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.5
-    motion_joint_vel: 0.25
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/appo/task/g1_23dof_flip_tracking/motrix.yaml b/conf/appo/task/g1_23dof_flip_tracking/motrix.yaml
deleted file mode 100644
index 05098dac7..000000000
--- a/conf/appo/task/g1_23dof_flip_tracking/motrix.yaml
+++ /dev/null
@@ -1,86 +0,0 @@
-# @package _global_
-training:
-  task_name: G1FlipTracking23Dof
-  sim_backend: motrix
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  steps_per_env: 24
-  max_iterations: 3500
-  save_interval: 500
-  algorithm:
-    num_learning_epochs: 10
-    num_mini_batches: 8
-    clip_param: 0.2
-    gamma: 0.99
-    lam: 0.95
-    value_loss_coef: 1.0
-    entropy_coef: 0.005
-    learning_rate: 1.0e-3
-    max_grad_norm: 1.0
-    use_clipped_value_loss: true
-    schedule: adaptive
-    desired_kl: 0.01
-    adaptive_kl_factor: 2.0
-    adaptive_lr_factor: 1.5
-    optimizer: adam
-    tau: 1.0
-    target_update_freq: 1
-    vtrace_clip_rho: 1.0
-    vtrace_clip_c: 1.0
-    enable_compile: true
-env:
-  sampling_mode: start
-  truncate_on_clip_end: true
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-  anchor_pos_z_threshold: 0.5
-  ee_body_pos_z_threshold: 0.5
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/appo/task/g1_23dof_flip_tracking/mujoco.yaml b/conf/appo/task/g1_23dof_flip_tracking/mujoco.yaml
deleted file mode 100644
index 6676d9360..000000000
--- a/conf/appo/task/g1_23dof_flip_tracking/mujoco.yaml
+++ /dev/null
@@ -1,70 +0,0 @@
-# @package _global_
-training:
-  task_name: G1FlipTracking23Dof
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  steps_per_env: 24
-  max_iterations: 3500
-  save_interval: 500
-  algorithm:
-    entropy_coef: 0.005
-    num_learning_epochs: 10
-    num_mini_batches: 8
-    desired_kl: 0.01
-env:
-  sampling_mode: start
-  truncate_on_clip_end: true
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-  anchor_pos_z_threshold: 0.5
-  ee_body_pos_z_threshold: 0.5
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/appo/task/g1_23dof_motion_tracking/motrix.yaml b/conf/appo/task/g1_23dof_motion_tracking/motrix.yaml
deleted file mode 100644
index aa74c1a66..000000000
--- a/conf/appo/task/g1_23dof_motion_tracking/motrix.yaml
+++ /dev/null
@@ -1,29 +0,0 @@
-# @package _global_
-training:
-  task_name: G1MotionTracking23Dof
-  sim_backend: motrix
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 5000
-  save_interval: 500
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 1.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.1
-    joint_limit: -10.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/appo/task/g1_23dof_motion_tracking/mujoco.yaml b/conf/appo/task/g1_23dof_motion_tracking/mujoco.yaml
deleted file mode 100644
index 3b31907a5..000000000
--- a/conf/appo/task/g1_23dof_motion_tracking/mujoco.yaml
+++ /dev/null
@@ -1,32 +0,0 @@
-# @package _global_
-training:
-  task_name: G1MotionTracking23Dof
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 5000
-  save_interval: 500
-  algorithm:
-    adaptive_kl_factor: 2.0
-    adaptive_lr_factor: 1.5
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 1.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.1
-    joint_limit: -10.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/appo/task/g1_23dof_walk_flat/mujoco.yaml b/conf/appo/task/g1_23dof_walk_flat/mujoco.yaml
deleted file mode 100644
index e68cd0af1..000000000
--- a/conf/appo/task/g1_23dof_walk_flat/mujoco.yaml
+++ /dev/null
@@ -1,36 +0,0 @@
-# @package _global_
-training:
-  task_name: G1Walk23DofFlat
-  sim_backend: mujoco
-algo:
-  max_iterations: 500
-  save_interval: 100
-env:
-  control_config:
-    action_scale: 0.25
-  curriculum:
-    enabled: false
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 1.5
-    scale_gyro: 0.2
-reward:
-  scales:
-    tracking_lin_vel: 2.0
-    tracking_ang_vel: 0.2
-    feet_phase: 1.0
-    lin_vel_z: -1.0
-    ang_vel_xy: -0.25
-    base_height: -500.0
-    orientation: -5.0
-    action_rate: -0.01
-    pose: -0.1
-  tracking_sigma: 0.25
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.008
-  base_height_target: 0.754
-  min_base_height: 0.55
-  max_tilt_deg: 25.0
-  pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/conf/appo/task/g1_23dof_wall_flip_tracking/motrix.yaml b/conf/appo/task/g1_23dof_wall_flip_tracking/motrix.yaml
deleted file mode 100644
index 3b543c9c4..000000000
--- a/conf/appo/task/g1_23dof_wall_flip_tracking/motrix.yaml
+++ /dev/null
@@ -1,50 +0,0 @@
-# @package _global_
-training:
-  task_name: G1WallFlipTracking23Dof
-  sim_backend: motrix
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 5000
-  save_interval: 500
-  algorithm:
-    num_learning_epochs: 5
-    num_mini_batches: 4
-    clip_param: 0.2
-    gamma: 0.99
-    lam: 0.95
-    value_loss_coef: 1.0
-    entropy_coef: 0.01
-    learning_rate: 1.0e-3
-    max_grad_norm: 1.0
-    use_clipped_value_loss: true
-    schedule: adaptive
-    desired_kl: 0.01
-    adaptive_kl_factor: 2.0
-    adaptive_lr_factor: 1.5
-    optimizer: adam
-    tau: 1.0
-    target_update_freq: 1
-    vtrace_clip_rho: 1.0
-    vtrace_clip_c: 1.0
-    enable_compile: true
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 1.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.1
-    joint_limit: -10.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/appo/task/g1_23dof_wall_flip_tracking/mujoco.yaml b/conf/appo/task/g1_23dof_wall_flip_tracking/mujoco.yaml
deleted file mode 100644
index 0decec32d..000000000
--- a/conf/appo/task/g1_23dof_wall_flip_tracking/mujoco.yaml
+++ /dev/null
@@ -1,87 +0,0 @@
-# @package _global_
-training:
-  task_name: G1WallFlipTracking23Dof
-  sim_backend: mujoco
-  play_steps: 1000
-  replay_queue_size: 5
-algo:
-  num_envs: 1024
-  steps_per_env: 20
-  max_iterations: 7000
-  save_interval: 500
-  algorithm:
-    num_learning_epochs: 6
-    num_mini_batches: 8
-    clip_param: 0.2
-    gamma: 0.99
-    lam: 0.95
-    value_loss_coef: 1.0
-    entropy_coef: 0.005
-    learning_rate: 1.0e-3
-    max_grad_norm: 1.0
-    use_clipped_value_loss: true
-    schedule: adaptive
-    desired_kl: 0.008
-    adaptive_kl_factor: 2.0
-    adaptive_lr_factor: 1.5
-    optimizer: adam
-    tau: 1.0
-    target_update_freq: 1
-    vtrace_clip_rho: 1.0
-    vtrace_clip_c: 1.0
-    enable_compile: true
-env:
-  sampling_mode: start
-  truncate_on_clip_end: true
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-  anchor_pos_z_threshold: 0.5
-  ee_body_pos_z_threshold: 0.5
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/appo/task/g1_climb_tracking/motrix.yaml b/conf/appo/task/g1_climb_tracking/motrix.yaml
deleted file mode 100644
index ec5684fe2..000000000
--- a/conf/appo/task/g1_climb_tracking/motrix.yaml
+++ /dev/null
@@ -1,71 +0,0 @@
-# @package _global_
-training:
-  task_name: G1ClimbTracking
-  sim_backend: motrix
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 20000
-  save_interval: 500
-env:
-  sampling_mode: adaptive
-  truncate_on_clip_end: false
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.5475464629911068
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.07450087032950714
-    - 0.07450087032950714
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.07450087032950714
-    - 0.07450087032950714
-  anchor_pos_z_threshold: 0.3
-  ee_body_pos_z_threshold: 0.3
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.5
-    motion_joint_vel: 0.25
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/appo/task/g1_climb_tracking/mujoco.yaml b/conf/appo/task/g1_climb_tracking/mujoco.yaml
deleted file mode 100644
index bff7b13c8..000000000
--- a/conf/appo/task/g1_climb_tracking/mujoco.yaml
+++ /dev/null
@@ -1,71 +0,0 @@
-# @package _global_
-training:
-  task_name: G1ClimbTracking
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 20000
-  save_interval: 500
-env:
-  sampling_mode: adaptive
-  truncate_on_clip_end: false
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.5475464629911068
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.07450087032950714
-    - 0.07450087032950714
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.07450087032950714
-    - 0.07450087032950714
-  anchor_pos_z_threshold: 0.3
-  ee_body_pos_z_threshold: 0.3
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.5
-    motion_joint_vel: 0.25
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/appo/task/g1_flip_tracking/motrix.yaml b/conf/appo/task/g1_flip_tracking/motrix.yaml
deleted file mode 100644
index 9b2fd0c57..000000000
--- a/conf/appo/task/g1_flip_tracking/motrix.yaml
+++ /dev/null
@@ -1,92 +0,0 @@
-# @package _global_
-training:
-  task_name: G1FlipTracking
-  sim_backend: motrix
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  steps_per_env: 24
-  max_iterations: 3500
-  save_interval: 500
-  algorithm:
-    num_learning_epochs: 10
-    num_mini_batches: 8
-    clip_param: 0.2
-    gamma: 0.99
-    lam: 0.95
-    value_loss_coef: 1.0
-    entropy_coef: 0.005
-    learning_rate: 1.0e-3
-    max_grad_norm: 1.0
-    use_clipped_value_loss: true
-    schedule: adaptive
-    desired_kl: 0.01
-    adaptive_kl_factor: 2.0
-    adaptive_lr_factor: 1.5
-    optimizer: adam
-    tau: 1.0
-    target_update_freq: 1
-    vtrace_clip_rho: 1.0
-    vtrace_clip_c: 1.0
-    enable_compile: true
-env:
-  sampling_mode: start
-  truncate_on_clip_end: true
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-  anchor_pos_z_threshold: 0.5
-  ee_body_pos_z_threshold: 0.5
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/appo/task/g1_flip_tracking/mujoco.yaml b/conf/appo/task/g1_flip_tracking/mujoco.yaml
deleted file mode 100644
index 628bdbb12..000000000
--- a/conf/appo/task/g1_flip_tracking/mujoco.yaml
+++ /dev/null
@@ -1,92 +0,0 @@
-# @package _global_
-training:
-  task_name: G1FlipTracking
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  steps_per_env: 24
-  max_iterations: 3500
-  save_interval: 500
-  algorithm:
-    num_learning_epochs: 10
-    num_mini_batches: 8
-    clip_param: 0.2
-    gamma: 0.99
-    lam: 0.95
-    value_loss_coef: 1.0
-    entropy_coef: 0.005
-    learning_rate: 1.0e-3
-    max_grad_norm: 1.0
-    use_clipped_value_loss: true
-    schedule: adaptive
-    desired_kl: 0.01
-    adaptive_kl_factor: 2.0
-    adaptive_lr_factor: 1.5
-    optimizer: adam
-    tau: 1.0
-    target_update_freq: 1
-    vtrace_clip_rho: 1.0
-    vtrace_clip_c: 1.0
-    enable_compile: true
-env:
-  sampling_mode: start
-  truncate_on_clip_end: true
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-  anchor_pos_z_threshold: 0.5
-  ee_body_pos_z_threshold: 0.5
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
\ No newline at end of file
diff --git a/conf/appo/task/g1_motion_tracking/motrix.yaml b/conf/appo/task/g1_motion_tracking/motrix.yaml
deleted file mode 100644
index eabea1ad6..000000000
--- a/conf/appo/task/g1_motion_tracking/motrix.yaml
+++ /dev/null
@@ -1,30 +0,0 @@
-# @package _global_
-training:
-  task_name: G1MotionTracking
-  sim_backend: motrix
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 5000
-  save_interval: 500
-env:
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 1.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.1
-    joint_limit: -10.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/appo/task/g1_motion_tracking/mujoco.yaml b/conf/appo/task/g1_motion_tracking/mujoco.yaml
deleted file mode 100644
index ec978a19c..000000000
--- a/conf/appo/task/g1_motion_tracking/mujoco.yaml
+++ /dev/null
@@ -1,33 +0,0 @@
-# @package _global_
-training:
-  task_name: G1MotionTracking
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 5000
-  save_interval: 500
-  algorithm:
-    adaptive_kl_factor: 2.0
-    adaptive_lr_factor: 1.5
-env:
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 1.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.1
-    joint_limit: -10.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/appo/task/g1_walk_flat/mujoco.yaml b/conf/appo/task/g1_walk_flat/mujoco.yaml
deleted file mode 100644
index 0fce8bf11..000000000
--- a/conf/appo/task/g1_walk_flat/mujoco.yaml
+++ /dev/null
@@ -1,36 +0,0 @@
-# @package _global_
-training:
-  task_name: G1WalkFlat
-  sim_backend: mujoco
-algo:
-  max_iterations: 500
-  save_interval: 100
-env:
-  control_config:
-    action_scale: 0.25
-  curriculum:
-    enabled: false
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 1.5
-    scale_gyro: 0.2
-reward:
-  scales:
-    tracking_lin_vel: 2.0
-    tracking_ang_vel: 0.2
-    feet_phase: 1.0
-    lin_vel_z: -1.0
-    ang_vel_xy: -0.25
-    base_height: -500.0
-    orientation: -5.0
-    action_rate: -0.01
-    pose: -0.1
-  tracking_sigma: 0.25
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.008
-  base_height_target: 0.754
-  min_base_height: 0.55
-  max_tilt_deg: 25.0
-  pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/conf/appo/task/g1_wall_flip_tracking/motrix.yaml b/conf/appo/task/g1_wall_flip_tracking/motrix.yaml
deleted file mode 100644
index 83cca7b39..000000000
--- a/conf/appo/task/g1_wall_flip_tracking/motrix.yaml
+++ /dev/null
@@ -1,50 +0,0 @@
-# @package _global_
-training:
-  task_name: G1WallFlipTracking
-  sim_backend: motrix
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 5000
-  save_interval: 500
-  algorithm:
-    num_learning_epochs: 5
-    num_mini_batches: 4
-    clip_param: 0.2
-    gamma: 0.99
-    lam: 0.95
-    value_loss_coef: 1.0
-    entropy_coef: 0.01
-    learning_rate: 1.0e-3
-    max_grad_norm: 1.0
-    use_clipped_value_loss: true
-    schedule: adaptive
-    desired_kl: 0.01
-    adaptive_kl_factor: 2.0
-    adaptive_lr_factor: 1.5
-    optimizer: adam
-    tau: 1.0
-    target_update_freq: 1
-    vtrace_clip_rho: 1.0
-    vtrace_clip_c: 1.0
-    enable_compile: true
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 1.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.1
-    joint_limit: -10.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/appo/task/g1_wall_flip_tracking/mujoco.yaml b/conf/appo/task/g1_wall_flip_tracking/mujoco.yaml
deleted file mode 100644
index 0083729c0..000000000
--- a/conf/appo/task/g1_wall_flip_tracking/mujoco.yaml
+++ /dev/null
@@ -1,93 +0,0 @@
-# @package _global_
-training:
-  task_name: G1WallFlipTracking
-  sim_backend: mujoco
-  play_steps: 1000
-  replay_queue_size: 5
-algo:
-  num_envs: 1024
-  steps_per_env: 20
-  max_iterations: 7000
-  save_interval: 500
-  algorithm:
-    num_learning_epochs: 6
-    num_mini_batches: 8
-    clip_param: 0.2
-    gamma: 0.99
-    lam: 0.95
-    value_loss_coef: 1.0
-    entropy_coef: 0.005
-    learning_rate: 1.0e-3
-    max_grad_norm: 1.0
-    use_clipped_value_loss: true
-    schedule: adaptive
-    desired_kl: 0.008
-    adaptive_kl_factor: 2.0
-    adaptive_lr_factor: 1.5
-    optimizer: adam
-    tau: 1.0
-    target_update_freq: 1
-    vtrace_clip_rho: 1.0
-    vtrace_clip_c: 1.0
-    enable_compile: true
-env:
-  sampling_mode: start
-  truncate_on_clip_end: true
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-  anchor_pos_z_threshold: 0.5
-  ee_body_pos_z_threshold: 0.5
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
\ No newline at end of file
diff --git a/conf/appo/task/go1_joystick_flat/motrix.yaml b/conf/appo/task/go1_joystick_flat/motrix.yaml
deleted file mode 100644
index f7343fd5d..000000000
--- a/conf/appo/task/go1_joystick_flat/motrix.yaml
+++ /dev/null
@@ -1,34 +0,0 @@
-# @package _global_
-training:
-  task_name: Go1JoystickFlat
-  sim_backend: motrix
-algo:
-  num_envs: 1024
-  steps_per_env: 24
-  max_iterations: 300
-  actor:
-    distribution_cfg:
-      init_std: 0.5
-  algorithm:
-    learning_rate: 5.0e-4
-    entropy_coef: 1.0e-3
-    desired_kl: 0.008
-env:
-  sim_dt: 0.01
-  commands:
-    vel_limit:
-      - [0.5, 0.0, 0.0]
-      - [0.5, 0.0, 0.0]
-reward:
-  scales:
-    tracking_lin_vel: 1.0
-    tracking_ang_vel: 0.2
-    lin_vel_z: -5.0
-    ang_vel_xy: -0.1
-    base_height: -100.0
-    action_rate: -0.015
-    action_smooth: -0.01
-    similar_to_default: -0.15
-    swing_feet_z: 2.0
-  tracking_sigma: 0.25
-  base_height_target: 0.3
diff --git a/conf/appo/task/go1_joystick_flat/mujoco.yaml b/conf/appo/task/go1_joystick_flat/mujoco.yaml
deleted file mode 100644
index 8646817a5..000000000
--- a/conf/appo/task/go1_joystick_flat/mujoco.yaml
+++ /dev/null
@@ -1,18 +0,0 @@
-# @package _global_
-training:
-  task_name: Go1JoystickFlat
-  sim_backend: mujoco
-algo:
-  max_iterations: 150
-reward:
-  scales:
-    tracking_lin_vel: 1.0
-    tracking_ang_vel: 0.2
-    lin_vel_z: -5.0
-    ang_vel_xy: -0.1
-    base_height: -100.0
-    action_rate: -0.005
-    similar_to_default: -0.1
-    contact: 0.24
-  tracking_sigma: 0.25
-  base_height_target: 0.3
diff --git a/conf/appo/task/go2_joystick_flat/motrix.yaml b/conf/appo/task/go2_joystick_flat/motrix.yaml
deleted file mode 100644
index fc4597bbd..000000000
--- a/conf/appo/task/go2_joystick_flat/motrix.yaml
+++ /dev/null
@@ -1,24 +0,0 @@
-# @package _global_
-training:
-  task_name: Go2JoystickFlat
-  sim_backend: motrix
-algo:
-  num_envs: 512
-  steps_per_env: 24
-  max_iterations: 180
-env:
-  sim_dt: 0.015
-reward:
-  scales:
-    tracking_lin_vel: 1.0
-    tracking_ang_vel: 0.2
-    lin_vel_z: -5.0
-    ang_vel_xy: -0.1
-    base_height: -100.0
-    action_rate: -0.005
-    similar_to_default: -0.1
-    alive: 0.0
-    contact: 0.24
-    swing_feet_z: 4.0
-  tracking_sigma: 0.25
-  base_height_target: 0.3
diff --git a/conf/appo/task/go2_joystick_flat/mujoco.yaml b/conf/appo/task/go2_joystick_flat/mujoco.yaml
deleted file mode 100644
index d25c356ad..000000000
--- a/conf/appo/task/go2_joystick_flat/mujoco.yaml
+++ /dev/null
@@ -1,20 +0,0 @@
-# @package _global_
-training:
-  task_name: Go2JoystickFlat
-  sim_backend: mujoco
-algo:
-  max_iterations: 150
-reward:
-  scales:
-    tracking_lin_vel: 1.0
-    tracking_ang_vel: 0.2
-    lin_vel_z: -5.0
-    ang_vel_xy: -0.1
-    base_height: -100.0
-    action_rate: -0.005
-    similar_to_default: -0.1
-    alive: 0.0
-    contact: 0.24
-    swing_feet_z: 4.0
-  tracking_sigma: 0.25
-  base_height_target: 0.3
diff --git a/conf/appo/task/sharpa_inhand/mujoco_hora.yaml b/conf/appo/task/sharpa_inhand/mujoco_hora.yaml
deleted file mode 100644
index 47aa3947a..000000000
--- a/conf/appo/task/sharpa_inhand/mujoco_hora.yaml
+++ /dev/null
@@ -1,64 +0,0 @@
-# @package _global_
-# HORA Sharpa APPO variant. Inherit the shared MuJoCo owner so backend support
-# and shared hyperparameters stay aligned with the non-HORA Sharpa APPO config.
-defaults:
-  - /task/sharpa_inhand/mujoco
-  - _self_
-
-training:
-  replay_queue_size: 8
-  cam_distance: 0.75
-  cam_lookat: [0.0, 0.0, 0.62]
-  cam_elevation: -25.0
-  cam_azimuth: 45.0
-
-interactive:
-  action_mode: policy
-  policy_obs_mode: actor
-  camera_distance: 0.75
-  camera_elevation: -25.0
-  camera_azimuth: 45.0
-  use_env_visual_model: true
-
-algo:
-  algo_log_name: hora_appo
-  runtime_impl: hora_appo
-  runtime_resolver: unilab.algos.torch.hora.appo:resolve_hora_appo_runtime
-  num_envs: 2048
-  steps_per_env: 8
-  max_iterations: 305
-  save_interval: 51
-  obs_groups:
-    # Keep grouped keys explicit in the owner YAML; runtime support for these
-    # grouped observations lands in the next implementation step.
-    actor:
-      actor: 0
-      priv_info: 0
-    critic:
-      actor: 0
-      priv_info: 0
-  actor:
-    class_name: unilab.algos.torch.hora:HoraActorModel
-    priv_info_embed_dim: 9
-    priv_mlp_hidden_dims: [256, 128, 9]
-  critic:
-    class_name: unilab.algos.torch.hora:HoraCriticModel
-    priv_info_embed_dim: 9
-    priv_mlp_hidden_dims: [256, 128, 9]
-  algorithm:
-    learning_rate: 0.001
-    desired_kl: 0.04
-    adaptive_kl_factor: 1.2
-    adaptive_lr_factor: 1.1
-
-env:
-  use_default_object_pose_for_object_pos_anchor: true
-  obs:
-    observation_mode: separated
-  domain_rand:
-    randomize_friction: true
-    randomize_friction_scale_lower: 0.75
-    randomize_friction_scale_upper: 1.25
-    elastomer_base_friction: 2.0
-    metal_base_friction: 1.0
-    object_base_friction: 2.0
diff --git a/conf/offpolicy/algo/flashsac.yaml b/conf/offpolicy/algo/flashsac.yaml
deleted file mode 100644
index 05f4d05cc..000000000
--- a/conf/offpolicy/algo/flashsac.yaml
+++ /dev/null
@@ -1,48 +0,0 @@
-# @package _global_
-algo:
-  algo: flashsac
-  algo_log_name: flash_sac
-  load_run: "-1"
-  seed: 1
-  num_envs: 1024
-  batch_size: 2048
-  replay_buffer_n: 512
-  updates_per_step: 2
-  learning_starts: 98
-  policy_frequency: 2
-  max_iterations: 5000
-  save_interval: 1000
-  gamma: 0.97
-  tau: 0.01
-  actor_lr: 3.0e-4
-  critic_lr: 3.0e-4
-  actor_hidden_dim: 128
-  critic_hidden_dim: 256
-  num_atoms: 101
-  obs_normalization: false
-  use_layer_norm: false
-  algo_params:
-    normalize_reward: true
-    normalized_g_max: 5.0
-    actor_num_blocks: 2
-    critic_num_blocks: 2
-    actor_bc_alpha: 0.0
-    actor_noise_zeta_mu: 2.0
-    actor_noise_zeta_max: 16
-    critic_min_v: -5.0
-    critic_max_v: 5.0
-    temp_initial_value: 0.01
-    temp_target_sigma: 0.15
-    temp_target_entropy: null
-    learning_rate_init: 3.0e-4
-    learning_rate_peak: 3.0e-4
-    learning_rate_end: 1.5e-4
-    learning_rate_warmup_steps: 0
-    learning_rate_decay_steps: 500000
-    n_step: 1
-    amp_dtype: auto
-    use_compile: true
-    use_cuda_graph_critic: false
-    use_cuda_graph_actor: false
-    use_cuda_graph_critic_packed_staging: false
-    use_cuda_graph_actor_packed_staging: false
diff --git a/conf/offpolicy/algo/sac.yaml b/conf/offpolicy/algo/sac.yaml
deleted file mode 100644
index 4e6efeeb0..000000000
--- a/conf/offpolicy/algo/sac.yaml
+++ /dev/null
@@ -1,40 +0,0 @@
-# @package _global_
-algo:
-  algo: sac
-  algo_log_name: fast_sac
-  runtime_impl: null
-  runtime_resolver: null
-  load_run: "-1"
-  seed: 1
-  num_envs: 4096
-  # Learner batch size for one SAC update. Symmetry may sample fewer replay rows
-  # and expand them inside the learner.
-  batch_size: 8192
-  replay_buffer_n: 512
-  updates_per_step: 4
-  learning_starts: 1
-  policy_frequency: 4
-  max_iterations: 500
-  save_interval: 500
-  gamma: 0.97
-  tau: 0.125
-  actor_lr: 3.0e-4
-  critic_lr: 3.0e-4
-  actor_hidden_dim: 512
-  critic_hidden_dim: 768
-  num_atoms: 101
-  obs_normalization: false
-  use_layer_norm: true
-  use_symmetry: false
-  actor: {}
-  algo_params:
-    alpha_lr: 3.0e-4
-    alpha_init: 0.01
-    target_entropy_ratio: 0.0
-    max_grad_norm: 0.0
-    amp_dtype: auto
-    use_compile: true
-    use_cuda_graph_critic: false
-    use_cuda_graph_actor: false
-    use_cuda_graph_critic_packed_staging: false
-    use_cuda_graph_actor_packed_staging: false
diff --git a/conf/offpolicy/algo/td3.yaml b/conf/offpolicy/algo/td3.yaml
deleted file mode 100644
index 3d217abc7..000000000
--- a/conf/offpolicy/algo/td3.yaml
+++ /dev/null
@@ -1,32 +0,0 @@
-# @package algo
-algo: td3
-algo_log_name: fast_td3
-load_run: "-1"
-seed: 1
-num_envs: 4096
-batch_size: 8192
-replay_buffer_n: 1000
-updates_per_step: 4
-learning_starts: 1
-policy_frequency: 2
-max_iterations: 5000
-save_interval: 500
-gamma: 0.97
-tau: 0.1
-actor_lr: 3.0e-4
-critic_lr: 3.0e-4
-actor_hidden_dim: 512
-critic_hidden_dim: 1024
-num_atoms: 101
-obs_normalization: true
-use_layer_norm: false
-algo_params:
-  weight_decay: 0.1
-  v_min: -10.0
-  v_max: 10.0
-  init_scale: 0.01
-  log_std_min: -1.6
-  log_std_max: -0.22
-  policy_noise: 0.2
-  noise_clip: 0.5
-  use_cdq: true
diff --git a/conf/offpolicy/config.yaml b/conf/offpolicy/config.yaml
deleted file mode 100644
index 908b3cb53..000000000
--- a/conf/offpolicy/config.yaml
+++ /dev/null
@@ -1,105 +0,0 @@
-defaults:
-  - _self_
-  - algo: sac
-  - task: ${algo}/g1_walk_flat/mujoco
-
-training:
-  task_name: G1WalkFlat
-  # list[int] | null; null/[] = auto-selected single-device behavior;
-  # [d0] = explicit single CUDA device; [d0..dN-1] = N-way data parallel,
-  # rank i trains on cuda:devices[i].
-  devices: null
-  # list[list[int]] | null; one CPU-id segment per rank for the collector's
-  # MuJoCo pool; null = auto partition of cpu_count // world_size per rank.
-  dp_collector_cpu_ids: null
-  logger: tensorboard
-  wandb_project: unilab
-  wandb_entity: null
-  wandb_group: null
-  wandb_job_type: null
-  wandb_name: null
-  wandb_tags: []
-  wandb_notes: null
-  wandb_mode: null
-  sim_backend: mujoco
-  nan_guard:
-    enabled: true
-    buffer_size: 100
-    max_envs_to_dump: 5
-    output_dir: null
-  use_amp: true
-  play_only: false
-  no_play: false
-  sim2sim_strict: true
-  play_render_mode: auto
-  export_onnx: true
-  play_env_num: 16
-  play_steps: 800
-  cam_distance: 6.0
-  cam_elevation: -20.0
-  cam_azimuth: 90.0
-  log_root: null
-  log_dir: null
-  env_steps_per_sync: 1
-  trace_enabled: false
-  trace_output_dir: null
-  trace_thread_time: false
-  trace_cuda_events: true
-  nvtx_profile_ranges: false
-  replay_prefetch_mode: one_tick
-  torch_threads:
-    enabled: true
-    # "auto" resolves per process role from host CPU count with conservative caps.
-    # Override these from Hydra when benchmarking a specific machine.
-    learner_num_threads: auto
-    collector_num_threads: auto
-    learner_num_interop_threads: 1
-    collector_num_interop_threads: 1
-    compile_threads: auto
-    set_env_vars: true
-
-interactive:
-  action_mode: zero
-  policy_obs_mode: auto
-  show_target_bodies: false
-  show_reward_debug: false
-  target_show_axes: false
-  target_body_names: ""
-  target_max_bodies: 0
-  target_marker_radius: 0.02
-  target_axis_length: 0.08
-  target_marker_alpha: 0.75
-  reward_debug_show_velocity: false
-  reward_debug_lin_vel_scale: 0.08
-  reward_debug_ang_vel_scale: 0.05
-  reward_debug_show_connectors: false
-  reward_debug_show_global_anchor: false
-  camera_follow_body: true
-  camera_focus_body_name: ""
-  camera_height_offset: 0.15
-  camera_distance: null
-  camera_elevation: null
-  camera_azimuth: null
-  use_env_visual_model: true
-  speed: 1.0
-  start_paused: false
-  keyboard: false
-  keyboard_step_lin: 0.1
-  keyboard_step_ang: 0.2
-
-env:
-  post_step_forward_sensor: false
-  # adaptive_chunk_size: auto-tune the MuJoCo BatchEnvPool chunk_size at materialize
-  # (cache-backed). chunk_size (int) manually overrides and wins; null => use default.
-  adaptive_chunk_size: true
-  chunk_size: null
-
-hydra:
-  run:
-    dir: .
-  output_subdir: null
-  job:
-    chdir: false
-  job_logging:
-    root:
-      handlers: [console]
diff --git a/conf/offpolicy/task/flashsac/g1_23dof_walk_flat/motrix.yaml b/conf/offpolicy/task/flashsac/g1_23dof_walk_flat/motrix.yaml
deleted file mode 100644
index d1698fb4c..000000000
--- a/conf/offpolicy/task/flashsac/g1_23dof_walk_flat/motrix.yaml
+++ /dev/null
@@ -1,59 +0,0 @@
-# @package _global_
-# Motrix owner for FlashSAC G1 23-DoF walk flat.
-# Mirrors 29-DoF flashsac/g1_walk_flat/motrix.yaml:
-# - Keeps the mujoco owner's FlashSAC algo identity
-# - Adopts the Motrix-direction env + reward tuning (kp/kd rand off, retuned shaping)
-training:
-  task_name: G1Walk23DofFlat
-  sim_backend: motrix
-algo:
-  num_envs: 4096
-  learning_starts: 49
-  max_iterations: 5000
-  save_interval: 1000
-  updates_per_step: 8
-  replay_buffer_n: 256
-  tau: 0.05
-env:
-  control_config:
-    action_scale: 1.0
-  gait_phase_init_mode: "offset_phase"
-  reset_base_qvel_limit: 0.5
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
-  curriculum:
-    enabled: true
-    initial_scale: 0.5
-    min_scale: 0.5
-    max_scale: 1.0
-    level_down_threshold: 150.0
-    level_up_threshold: 750.0
-    degree: 0.001
-  noise_config:
-    level: 0.0
-    scale_gyro: 0.0
-    scale_gravity: 0.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 0.1
-    scale_linvel: 0.0
-reward:
-  scales:
-    tracking_lin_vel: 2.2
-    tracking_ang_vel: 1.8
-    penalty_ang_vel_xy: -1.2
-    penalty_orientation: -12.0
-    penalty_action_rate: -2.5
-    pose: -0.6
-    penalty_feet_ori: -5.0
-    feet_phase: 6.0
-    alive: 12.0
-  tracking_sigma: 0.25
-  base_height_target: 0.754
-  min_base_height: 0.3
-  max_tilt_deg: 65.0
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.008
-  close_feet_threshold: 0.15
-  pose_weights: [0.01, 2.0, 5.0, 0.01, 5.0, 5.0, 0.01, 2.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/conf/offpolicy/task/flashsac/g1_23dof_walk_flat/mujoco.yaml b/conf/offpolicy/task/flashsac/g1_23dof_walk_flat/mujoco.yaml
deleted file mode 100644
index 34e63d24d..000000000
--- a/conf/offpolicy/task/flashsac/g1_23dof_walk_flat/mujoco.yaml
+++ /dev/null
@@ -1,53 +0,0 @@
-# @package _global_
-training:
-  task_name: G1Walk23DofFlat
-  sim_backend: mujoco
-algo:
-  num_envs: 4096
-  learning_starts: 49
-  max_iterations: 5000
-  save_interval: 1000
-  updates_per_step: 8
-  #use_symmetry: true
-  replay_buffer_n: 256
-  tau: 0.05
-env:
-  control_config:
-    action_scale: 1.0
-  gait_phase_init_mode: "offset_phase"
-  reset_base_qvel_limit: 0.5
-  curriculum:
-    enabled: true
-    initial_scale: 0.5
-    min_scale: 0.5
-    max_scale: 1.0
-    level_down_threshold: 150.0
-    level_up_threshold: 750.0
-    degree: 0.001
-  noise_config:
-    level: 0.0
-    scale_gyro: 0.0
-    scale_gravity: 0.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 0.1
-    scale_linvel: 0.0
-reward:
-  scales:
-    tracking_lin_vel: 2.0
-    tracking_ang_vel: 1.5
-    penalty_ang_vel_xy: -1.0
-    penalty_orientation: -10.0
-    penalty_action_rate: -5.0
-    pose: -0.5
-    penalty_feet_ori: -25.0
-    feet_phase: 5.0
-    alive: 10.0
-  tracking_sigma: 0.25
-  base_height_target: 0.754
-  min_base_height: 0.3
-  max_tilt_deg: 65.0
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.005
-  close_feet_threshold: 0.15
-  pose_weights: [0.01, 2.0, 5.0, 0.01, 5.0, 5.0, 0.01, 2.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/conf/offpolicy/task/flashsac/g1_walk_flat/mjwarp.yaml b/conf/offpolicy/task/flashsac/g1_walk_flat/mjwarp.yaml
deleted file mode 100644
index 4a7602acb..000000000
--- a/conf/offpolicy/task/flashsac/g1_walk_flat/mjwarp.yaml
+++ /dev/null
@@ -1,67 +0,0 @@
-# @package _global_
-# Configured-only mjwarp owner for FlashSAC G1 walk flat. Mirrors the mujoco
-# owner's algo / env / reward identity; mjwarp-specific host-adapter settings
-# follow conf/offpolicy/task/sac/g1_walk_flat/mjwarp.yaml. Offline record
-# reuses MuJoCo rendering; native playback and device-resident runtime are absent.
-training:
-  task_name: G1WalkFlat
-  sim_backend: mjwarp
-  play_render_mode: record
-algo:
-  num_envs: 4096
-  learning_starts: 49
-  max_iterations: 5000
-  save_interval: 1000
-  updates_per_step: 8
-  #use_symmetry: true
-  replay_buffer_n: 256
-  tau: 0.05
-env:
-  mjwarp_nconmax: 128
-  mjwarp_njmax: 256
-  render_spacing: 2.0
-  control_config:
-    action_scale: 1.0
-  gait_phase_init_mode: "offset_phase"
-  reset_base_qvel_limit: 0.5
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
-    randomize_dof_armature: false
-    randomize_body_gravity_compensation: false
-  curriculum:
-    enabled: true
-    initial_scale: 0.5
-    min_scale: 0.5
-    max_scale: 1.0
-    level_down_threshold: 150.0
-    level_up_threshold: 750.0
-    degree: 0.001
-  noise_config:
-    level: 0.0
-    scale_gyro: 0.0
-    scale_gravity: 0.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 0.1
-    scale_linvel: 0.0
-    seed: null
-reward:
-  scales:
-    tracking_lin_vel: 2.0
-    tracking_ang_vel: 1.5
-    penalty_ang_vel_xy: -1.0
-    penalty_orientation: -10.0
-    penalty_action_rate: -5.0
-    pose: -0.5
-    penalty_feet_ori: -25.0
-    feet_phase: 5.0
-    alive: 10.0
-  tracking_sigma: 0.25
-  base_height_target: 0.754
-  min_base_height: 0.3
-  max_tilt_deg: 65.0
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.005
-  close_feet_threshold: 0.15
-  pose_weights: [0.01, 2.0, 5.0, 0.01, 5.0, 5.0, 0.01, 2.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/conf/offpolicy/task/flashsac/g1_walk_flat/motrix.yaml b/conf/offpolicy/task/flashsac/g1_walk_flat/motrix.yaml
deleted file mode 100644
index 5f61c51b6..000000000
--- a/conf/offpolicy/task/flashsac/g1_walk_flat/motrix.yaml
+++ /dev/null
@@ -1,62 +0,0 @@
-# @package _global_
-# Motrix owner for FlashSAC G1 walk flat.
-# Keeps the mujoco owner's FlashSAC algo identity (num_envs/updates_per_step/
-# replay_buffer_n/tau plus the distributional critic from conf/offpolicy/algo/flashsac.yaml)
-# while adopting the Motrix-direction env + reward tuning used by the SAC motrix owner
-# (kp/kd randomization off, retuned reward shaping, tighter feet-phase sigma).
-# control_config.action_scale stays 1.0 to keep sim2sim DENYLIST parity with mujoco.
-training:
-  task_name: G1WalkFlat
-  sim_backend: motrix
-algo:
-  num_envs: 4096
-  learning_starts: 49
-  max_iterations: 5000
-  save_interval: 1000
-  updates_per_step: 8
-  replay_buffer_n: 256
-  tau: 0.05
-env:
-  control_config:
-    action_scale: 1.0
-  gait_phase_init_mode: "offset_phase"
-  reset_base_qvel_limit: 0.5
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
-  curriculum:
-    enabled: true
-    initial_scale: 0.5
-    min_scale: 0.5
-    max_scale: 1.0
-    level_down_threshold: 150.0
-    level_up_threshold: 750.0
-    degree: 0.001
-  noise_config:
-    level: 0.0
-    scale_gyro: 0.0
-    scale_gravity: 0.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 0.1
-    scale_linvel: 0.0
-    seed: null
-reward:
-  scales:
-    tracking_lin_vel: 2.2
-    tracking_ang_vel: 1.8
-    penalty_ang_vel_xy: -1.2
-    penalty_orientation: -12.0
-    penalty_action_rate: -2.5
-    pose: -0.6
-    penalty_feet_ori: -5.0
-    feet_phase: 6.0
-    alive: 12.0
-  tracking_sigma: 0.25
-  base_height_target: 0.754
-  min_base_height: 0.3
-  max_tilt_deg: 65.0
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.008
-  close_feet_threshold: 0.15
-  pose_weights: [0.01, 2.0, 5.0, 0.01, 5.0, 5.0, 0.01, 2.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/conf/offpolicy/task/flashsac/g1_walk_flat/mujoco.yaml b/conf/offpolicy/task/flashsac/g1_walk_flat/mujoco.yaml
deleted file mode 100644
index 35624c3b6..000000000
--- a/conf/offpolicy/task/flashsac/g1_walk_flat/mujoco.yaml
+++ /dev/null
@@ -1,54 +0,0 @@
-# @package _global_
-training:
-  task_name: G1WalkFlat
-  sim_backend: mujoco
-algo:
-  num_envs: 4096
-  learning_starts: 49
-  max_iterations: 5000
-  save_interval: 1000
-  updates_per_step: 8
-  #use_symmetry: true
-  replay_buffer_n: 256
-  tau: 0.05
-env:
-  control_config:
-    action_scale: 1.0
-  gait_phase_init_mode: "offset_phase"
-  reset_base_qvel_limit: 0.5
-  curriculum:
-    enabled: true
-    initial_scale: 0.5
-    min_scale: 0.5
-    max_scale: 1.0
-    level_down_threshold: 150.0
-    level_up_threshold: 750.0
-    degree: 0.001
-  noise_config:
-    level: 0.0
-    scale_gyro: 0.0
-    scale_gravity: 0.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 0.1
-    scale_linvel: 0.0
-    seed: null
-reward:
-  scales:
-    tracking_lin_vel: 2.0
-    tracking_ang_vel: 1.5
-    penalty_ang_vel_xy: -1.0
-    penalty_orientation: -10.0
-    penalty_action_rate: -5.0
-    pose: -0.5
-    penalty_feet_ori: -25.0
-    feet_phase: 5.0
-    alive: 10.0
-  tracking_sigma: 0.25
-  base_height_target: 0.754
-  min_base_height: 0.3
-  max_tilt_deg: 65.0
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.005
-  close_feet_threshold: 0.15
-  pose_weights: [0.01, 2.0, 5.0, 0.01, 5.0, 5.0, 0.01, 2.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/conf/offpolicy/task/flashsac/go2_joystick_flat/mujoco.yaml b/conf/offpolicy/task/flashsac/go2_joystick_flat/mujoco.yaml
deleted file mode 100644
index 031d8e2b7..000000000
--- a/conf/offpolicy/task/flashsac/go2_joystick_flat/mujoco.yaml
+++ /dev/null
@@ -1,43 +0,0 @@
-# @package _global_
-training:
-  task_name: Go2JoystickFlat
-  sim_backend: mujoco
-algo:
-  num_envs: 1024
-  learning_starts: 50
-  max_iterations: 4000
-  save_interval: 1000
-  updates_per_step: 2
-  batch_size: 2048
-  replay_buffer_n: 4096
-  tau: 0.05
-env:
-  control_config:
-    action_scale: 0.4
-  domain_rand:
-    randomize_kp: true
-    randomize_kd: true
-    randomize_base_mass: true
-    random_com: true
-    randomize_gravity: true
-    push_robots: true
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 0.1
-    scale_gyro: 0.0
-    scale_gravity: 0.0
-    scale_linvel: 0.0
-reward:
-  scales:
-    tracking_lin_vel: 1.0
-    tracking_ang_vel: 0.2
-    lin_vel_z: -5.0
-    ang_vel_xy: -0.1
-    base_height: -20.0
-    action_rate: -0.02
-    similar_to_default: -0.4
-    contact: 1.5
-    swing_feet_z: 4.0
-  tracking_sigma: 0.4
-  base_height_target: 0.3
\ No newline at end of file
diff --git a/conf/offpolicy/task/sac/g1_23dof_flip_tracking/mujoco.yaml b/conf/offpolicy/task/sac/g1_23dof_flip_tracking/mujoco.yaml
deleted file mode 100644
index f48e6108a..000000000
--- a/conf/offpolicy/task/sac/g1_23dof_flip_tracking/mujoco.yaml
+++ /dev/null
@@ -1,76 +0,0 @@
-# @package _global_
-training:
-  task_name: G1FlipTrackingSAC23Dof
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 4096
-  max_iterations: 25000
-  save_interval: 1000
-  gamma: 0.99
-  tau: 0.05
-  num_atoms: 501
-  updates_per_step: 4
-  policy_frequency: 2
-  use_symmetry: false
-  algo_params:
-    alpha_init: 0.005
-    target_entropy_ratio: 0.05
-    max_grad_norm: 10.0
-env:
-  sampling_mode: mixed
-  sampling_start_ratio: 0.1
-  truncate_on_clip_end: true
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-  anchor_pos_z_threshold: 0.5
-  ee_body_pos_z_threshold: 0.5
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/offpolicy/task/sac/g1_23dof_motion_tracking/motrix.yaml b/conf/offpolicy/task/sac/g1_23dof_motion_tracking/motrix.yaml
deleted file mode 100644
index e042ee799..000000000
--- a/conf/offpolicy/task/sac/g1_23dof_motion_tracking/motrix.yaml
+++ /dev/null
@@ -1,19 +0,0 @@
-# @package _global_
-# G1 23-DoF Motion Tracking SAC — Motrix variant for sim2sim eval.
-# Inherits the mujoco training config in full and only switches the rendering
-# backend so checkpoints trained on mujoco can be replayed via motrix's native
-# renderer (`eval --sim motrix`). Training on motrix is not the intended path.
-defaults:
-  - /task/sac/g1_23dof_motion_tracking/mujoco
-  - _self_
-
-training:
-  task_name: G1MotionTrackingSAC23Dof
-  sim_backend: motrix
-env:
-  # motrix backend's kp/kd override path is broken on column slices, and DR
-  # is not desirable during deterministic sim2sim eval anyway. Match the
-  # `g1_walk_flat/motrix.yaml` convention by switching them off.
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
diff --git a/conf/offpolicy/task/sac/g1_23dof_motion_tracking/mujoco.yaml b/conf/offpolicy/task/sac/g1_23dof_motion_tracking/mujoco.yaml
deleted file mode 100644
index a68d07fae..000000000
--- a/conf/offpolicy/task/sac/g1_23dof_motion_tracking/mujoco.yaml
+++ /dev/null
@@ -1,73 +0,0 @@
-# @package _global_
-training:
-  task_name: G1MotionTrackingSAC23Dof
-  sim_backend: mujoco
-algo:
-  num_envs: 2048
-  max_iterations: 25000
-  save_interval: 1000
-  gamma: 0.99
-  tau: 0.05
-  num_atoms: 501
-  updates_per_step: 4
-  policy_frequency: 2
-  use_symmetry: false
-  algo_params:
-    alpha_init: 0.1
-    target_entropy_ratio: 0.5
-    max_grad_norm: 10.0
-env:
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-  anchor_pos_z_threshold: 0.5
-  ee_body_pos_z_threshold: 0.5
-  truncate_on_clip_end: true
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 1.5
-    scale_gyro: 0.2
-reward:
-  scales:
-    motion_global_root_pos: 1.0
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.1
-    joint_limit: -2.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/offpolicy/task/sac/g1_23dof_walk_flat/motrix.yaml b/conf/offpolicy/task/sac/g1_23dof_walk_flat/motrix.yaml
deleted file mode 100644
index d67013177..000000000
--- a/conf/offpolicy/task/sac/g1_23dof_walk_flat/motrix.yaml
+++ /dev/null
@@ -1,50 +0,0 @@
-# @package _global_
-training:
-  task_name: G1Walk23DofFlat
-  sim_backend: motrix
-algo:
-  num_envs: 2048
-  learning_starts: 1
-  max_iterations: 5000
-  save_interval: 1000
-  updates_per_step: 8
-  use_symmetry: false
-  algo_params:
-    alpha_init: 0.001
-    target_entropy_ratio: 0.0
-env:
-  control_config:
-    action_scale: 1.0
-  gait_phase_init_mode: "offset_phase"
-  reset_base_qvel_limit: 0.5
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
-  curriculum:
-    enabled: true
-    initial_scale: 0.5
-    min_scale: 0.5
-    max_scale: 1.0
-    level_down_threshold: 150.0
-    level_up_threshold: 750.0
-    degree: 0.001
-reward:
-  scales:
-    tracking_lin_vel: 2.2
-    tracking_ang_vel: 1.8
-    penalty_ang_vel_xy: -1.2
-    penalty_orientation: -12.0
-    penalty_action_rate: -2.5
-    pose: -0.6
-    penalty_feet_ori: -5.0
-    feet_phase: 6.0
-    alive: 12.0
-  tracking_sigma: 0.25
-  base_height_target: 0.754
-  min_base_height: 0.3
-  max_tilt_deg: 65.0
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.008
-  close_feet_threshold: 0.15
-  pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/conf/offpolicy/task/sac/g1_23dof_walk_flat/mujoco.yaml b/conf/offpolicy/task/sac/g1_23dof_walk_flat/mujoco.yaml
deleted file mode 100644
index 1f2ca273c..000000000
--- a/conf/offpolicy/task/sac/g1_23dof_walk_flat/mujoco.yaml
+++ /dev/null
@@ -1,54 +0,0 @@
-# @package _global_
-training:
-  task_name: G1Walk23DofFlat
-  sim_backend: mujoco
-algo:
-  num_envs: 2048
-  learning_starts: 10
-  max_iterations: 5000
-  save_interval: 1000
-  updates_per_step: 8
-  use_symmetry: true
-  algo_params:
-    alpha_init: 0.001
-    target_entropy_ratio: 0.0
-env:
-  control_config:
-    action_scale: 1.0
-  gait_phase_init_mode: "offset_phase"
-  reset_base_qvel_limit: 0.5
-  curriculum:
-    enabled: true
-    initial_scale: 0.5
-    min_scale: 0.5
-    max_scale: 1.0
-    level_down_threshold: 150.0
-    level_up_threshold: 750.0
-    degree: 0.001
-  noise_config:
-    level: 1.0
-    scale_gyro: 0.0
-    scale_gravity: 0.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 0.1
-    scale_linvel: 0.0
-reward:
-  scales:
-    tracking_lin_vel: 2.0
-    tracking_ang_vel: 1.5
-    penalty_ang_vel_xy: -1.0
-    penalty_orientation: -10.0
-    penalty_action_rate: -4.0
-    pose: -0.5
-    penalty_feet_ori: -20.0
-    feet_phase: 5.0
-    alive: 10.0
-  tracking_sigma: 0.25
-  base_height_target: 0.754
-  min_base_height: 0.3
-  max_tilt_deg: 65.0
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.04
-  close_feet_threshold: 0.15
-  pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/conf/offpolicy/task/sac/g1_23dof_walk_rough/motrix.yaml b/conf/offpolicy/task/sac/g1_23dof_walk_rough/motrix.yaml
deleted file mode 100644
index 844fb2f2a..000000000
--- a/conf/offpolicy/task/sac/g1_23dof_walk_rough/motrix.yaml
+++ /dev/null
@@ -1,51 +0,0 @@
-# @package _global_
-training:
-  task_name: G1Walk23DofRough
-  sim_backend: motrix
-algo:
-  num_envs: 2048
-  learning_starts: 1
-  max_iterations: 5000
-  save_interval: 1000
-  updates_per_step: 8
-  use_symmetry: false
-  algo_params:
-    alpha_init: 0.001
-    target_entropy_ratio: 0.0
-env:
-  sim_dt: 0.01
-  control_config:
-    action_scale: 1.0
-  gait_phase_init_mode: "offset_phase"
-  reset_base_qvel_limit: 0.5
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
-  curriculum:
-    enabled: true
-    initial_scale: 0.5
-    min_scale: 0.5
-    max_scale: 1.0
-    level_down_threshold: 150.0
-    level_up_threshold: 750.0
-    degree: 0.001
-reward:
-  scales:
-    tracking_lin_vel: 2.2
-    tracking_ang_vel: 1.8
-    penalty_ang_vel_xy: -1.2
-    penalty_orientation: -12.0
-    penalty_action_rate: -2.5
-    pose: -0.6
-    penalty_feet_ori: -5.0
-    feet_phase: 6.0
-    alive: 12.0
-  tracking_sigma: 0.25
-  base_height_target: 0.754
-  min_base_height: 0.3
-  max_tilt_deg: 65.0
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.008
-  close_feet_threshold: 0.15
-  pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/conf/offpolicy/task/sac/g1_23dof_walk_rough/mujoco.yaml b/conf/offpolicy/task/sac/g1_23dof_walk_rough/mujoco.yaml
deleted file mode 100644
index 16689327b..000000000
--- a/conf/offpolicy/task/sac/g1_23dof_walk_rough/mujoco.yaml
+++ /dev/null
@@ -1,54 +0,0 @@
-# @package _global_
-training:
-  task_name: G1Walk23DofRough
-  sim_backend: mujoco
-algo:
-  num_envs: 2048
-  learning_starts: 10
-  max_iterations: 5000
-  save_interval: 1000
-  updates_per_step: 8
-  use_symmetry: true
-  algo_params:
-    alpha_init: 0.001
-    target_entropy_ratio: 0.0
-env:
-  control_config:
-    action_scale: 1.0
-  gait_phase_init_mode: "offset_phase"
-  reset_base_qvel_limit: 0.5
-  curriculum:
-    enabled: true
-    initial_scale: 0.5
-    min_scale: 0.5
-    max_scale: 1.0
-    level_down_threshold: 150.0
-    level_up_threshold: 750.0
-    degree: 0.001
-  noise_config:
-    level: 1.0
-    scale_gyro: 0.0
-    scale_gravity: 0.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 0.1
-    scale_linvel: 0.0
-reward:
-  scales:
-    tracking_lin_vel: 2.0
-    tracking_ang_vel: 1.5
-    penalty_ang_vel_xy: -1.0
-    penalty_orientation: -10.0
-    penalty_action_rate: -4.0
-    pose: -0.5
-    penalty_feet_ori: -20.0
-    feet_phase: 5.0
-    alive: 10.0
-  tracking_sigma: 0.25
-  base_height_target: 0.754
-  min_base_height: 0.3
-  max_tilt_deg: 65.0
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.04
-  close_feet_threshold: 0.15
-  pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/conf/offpolicy/task/sac/g1_23dof_wall_flip_tracking/mujoco.yaml b/conf/offpolicy/task/sac/g1_23dof_wall_flip_tracking/mujoco.yaml
deleted file mode 100644
index f67d34d2e..000000000
--- a/conf/offpolicy/task/sac/g1_23dof_wall_flip_tracking/mujoco.yaml
+++ /dev/null
@@ -1,75 +0,0 @@
-# @package _global_
-training:
-  task_name: G1WallFlipTrackingSAC23Dof
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 4096
-  max_iterations: 25000
-  save_interval: 1000
-  gamma: 0.99
-  tau: 0.05
-  num_atoms: 501
-  updates_per_step: 4
-  policy_frequency: 2
-  use_symmetry: false
-  algo_params:
-    alpha_init: 0.005
-    target_entropy_ratio: 0.0
-    max_grad_norm: 10.0
-env:
-  sampling_mode: uniform
-  truncate_on_clip_end: true
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-  anchor_pos_z_threshold: 1000000000.0
-  ee_body_pos_z_threshold: 1000000000.0
-  terminate_on_undesired_contacts: false
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.5
-    motion_joint_vel: 0.25
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/offpolicy/task/sac/g1_23dof_wbt_obs/mujoco.yaml b/conf/offpolicy/task/sac/g1_23dof_wbt_obs/mujoco.yaml
deleted file mode 100644
index d0623bad2..000000000
--- a/conf/offpolicy/task/sac/g1_23dof_wbt_obs/mujoco.yaml
+++ /dev/null
@@ -1,79 +0,0 @@
-# @package _global_
-training:
-  task_name: G1WBTObs23Dof
-  sim_backend: mujoco
-algo:
-  num_envs: 4096
-  max_iterations: 140000
-  save_interval: 1000
-  gamma: 0.99
-  tau: 0.05
-  num_atoms: 501
-  updates_per_step: 4
-  policy_frequency: 2
-  use_symmetry: false
-  algo_params:
-    alpha_init: 0.1
-    target_entropy_ratio: 0.5
-    max_grad_norm: 10.0
-env:
-  sim_dt: 0.005
-  sensor:
-    local_linvel: pelvis_local_linvel
-    gyro: pelvis_gyro
-    upvector: pelvis_upvector
-  control_config:
-    action_scale: 2.0
-    simulate_action_latency: true
-  anchor_pos_z_threshold: 0.40
-  ee_body_pos_z_threshold: 0.5
-  truncate_on_clip_end: true
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 0.5
-    scale_gyro: 0.2
-    enable_zero_linvel: true
-    enable_zero_anchor_pos: true
-    enable_anchor_ori_noise: true
-    scale_anchor_ori: 0.05
-    obs_history_length: 5
-  domain_rand:
-    randomize_base_mass: true
-    added_mass_range: [-1.0, 1.0]
-    random_com: true
-    com_offset_x: [-0.05, 0.05]
-    randomize_com_y: true
-    com_offset_y: [-0.05, 0.05]
-    randomize_com_z: true
-    com_offset_z: [-0.05, 0.05]
-    randomize_gravity: false
-    gravity_range: [[0.0, 0.0, -9.81], [0.0, 0.0, -9.81]]
-    push_robots: true
-    push_interval: 200
-    max_force: [300.0, 300.0, 120.0]
-    push_body_name: null
-    randomize_kp: true
-    kp_multiplier_range: [0.9, 1.1]
-    randomize_kd: true
-    kd_multiplier_range: [0.85, 1.15]
-    randomize_geom_friction: true
-    friction_range: [0.3, 1.2]
-    friction_geom_pattern: "^(left|right)_foot[1-7]_collision$"
-    enable_encoder_bias: true
-    encoder_bias_range: [-0.01, 0.01]
-reward:
-  scales:
-    motion_global_root_pos: 1.0
-    motion_global_root_ori: 1.0
-    motion_body_pos: 1.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.1
-    joint_limit: -5.0
-    undesired_contacts: -0.1
-    joint_acc_l2: -2.5e-7
-    joint_torque_l2: -1e-5
diff --git a/conf/offpolicy/task/sac/g1_flip_tracking/mujoco.yaml b/conf/offpolicy/task/sac/g1_flip_tracking/mujoco.yaml
deleted file mode 100644
index 48a956b4f..000000000
--- a/conf/offpolicy/task/sac/g1_flip_tracking/mujoco.yaml
+++ /dev/null
@@ -1,82 +0,0 @@
-# @package _global_
-training:
-  task_name: G1FlipTrackingSAC
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 4096
-  max_iterations: 25000
-  save_interval: 1000
-  gamma: 0.99
-  tau: 0.05
-  num_atoms: 501
-  updates_per_step: 4
-  policy_frequency: 2
-  use_symmetry: false
-  algo_params:
-    alpha_init: 0.005
-    target_entropy_ratio: 0.05
-    max_grad_norm: 10.0
-env:
-  sampling_mode: mixed
-  sampling_start_ratio: 0.1
-  truncate_on_clip_end: true
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-  anchor_pos_z_threshold: 0.5
-  ee_body_pos_z_threshold: 0.5
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/offpolicy/task/sac/g1_motion_tracking/motrix.yaml b/conf/offpolicy/task/sac/g1_motion_tracking/motrix.yaml
deleted file mode 100644
index eddb1839f..000000000
--- a/conf/offpolicy/task/sac/g1_motion_tracking/motrix.yaml
+++ /dev/null
@@ -1,19 +0,0 @@
-# @package _global_
-# G1 Whole-Body Tracking (WBT) FastSAC — Motrix variant for sim2sim eval.
-# Inherits the mujoco training config in full and only switches the rendering
-# backend so checkpoints trained on mujoco can be replayed via motrix's native
-# renderer (`eval --sim motrix`). Training on motrix is not the intended path.
-defaults:
-  - /task/sac/g1_motion_tracking/mujoco
-  - _self_
-
-training:
-  task_name: G1MotionTrackingSAC
-  sim_backend: motrix
-env:
-  # motrix backend's kp/kd override path is broken on column slices, and DR
-  # is not desirable during deterministic sim2sim eval anyway. Match the
-  # `g1_walk_flat/motrix.yaml` convention by switching them off.
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
diff --git a/conf/offpolicy/task/sac/g1_motion_tracking/mujoco.yaml b/conf/offpolicy/task/sac/g1_motion_tracking/mujoco.yaml
deleted file mode 100644
index 598feccbb..000000000
--- a/conf/offpolicy/task/sac/g1_motion_tracking/mujoco.yaml
+++ /dev/null
@@ -1,46 +0,0 @@
-# @package _global_
-# G1 Whole-Body Tracking (WBT) with FastSAC on MuJoCo.
-# Hyperparameters aligned with holosoma g1-29dof-wbt-fast-sac.
-training:
-  task_name: G1MotionTrackingSAC
-  sim_backend: mujoco
-algo:
-  num_envs: 2048
-  max_iterations: 25000
-  save_interval: 1000
-  # --- holosoma WBT-specific overrides (vs sac.yaml defaults) ---
-  gamma: 0.99
-  tau: 0.05
-  num_atoms: 501
-  updates_per_step: 4
-  policy_frequency: 2
-  use_symmetry: false
-  algo_params:
-    alpha_init: 0.1
-    target_entropy_ratio: 0.5
-    max_grad_norm: 10.0
-env:
-  control_config:
-    action_scale: 2.0
-  anchor_pos_z_threshold: 0.5
-  ee_body_pos_z_threshold: 0.5
-  truncate_on_clip_end: true
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 1.5
-    scale_gyro: 0.2
-    seed: null
-reward:
-  scales:
-    motion_global_root_pos: 1.0
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.1
-    joint_limit: -2.0
-    undesired_contacts: -0.1
diff --git a/conf/offpolicy/task/sac/g1_walk_flat/mjwarp.yaml b/conf/offpolicy/task/sac/g1_walk_flat/mjwarp.yaml
deleted file mode 100644
index dc9e23014..000000000
--- a/conf/offpolicy/task/sac/g1_walk_flat/mjwarp.yaml
+++ /dev/null
@@ -1,66 +0,0 @@
-# @package _global_
-# Configured-only SAC owner for the mjwarp host adapter. Offline record reuses
-# MuJoCo rendering; native playback and device-resident runtime are absent.
-training:
-  task_name: G1WalkFlat
-  sim_backend: mjwarp
-  play_render_mode: record
-algo:
-  num_envs: 2048
-  learning_starts: 10
-  max_iterations: 5000
-  save_interval: 1000
-  updates_per_step: 8
-  use_symmetry: true
-  algo_params:
-    alpha_init: 0.001
-    target_entropy_ratio: 0.0
-env:
-  mjwarp_nconmax: 128
-  mjwarp_njmax: 256
-  render_spacing: 2.0
-  control_config:
-    action_scale: 1.0
-  gait_phase_init_mode: "offset_phase"
-  reset_base_qvel_limit: 0.5
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
-    randomize_dof_armature: false
-    randomize_body_gravity_compensation: false
-  curriculum:
-    enabled: true
-    initial_scale: 0.5
-    min_scale: 0.5
-    max_scale: 1.0
-    level_down_threshold: 150.0
-    level_up_threshold: 750.0
-    degree: 0.001
-  noise_config:
-    level: 0.0
-    scale_gyro: 0.0
-    scale_gravity: 0.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 0.1
-    scale_linvel: 0.0
-    seed: null
-reward:
-  scales:
-    tracking_lin_vel: 2.0
-    tracking_ang_vel: 1.5
-    penalty_ang_vel_xy: -1.0
-    penalty_orientation: -10.0
-    penalty_action_rate: -4.0
-    pose: -0.5
-    penalty_feet_ori: -20.0
-    feet_phase: 5.0
-    alive: 10.0
-  tracking_sigma: 0.25
-  base_height_target: 0.754
-  min_base_height: 0.3
-  max_tilt_deg: 65.0
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.04
-  close_feet_threshold: 0.15
-  pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/conf/offpolicy/task/sac/g1_walk_flat/motrix.yaml b/conf/offpolicy/task/sac/g1_walk_flat/motrix.yaml
deleted file mode 100644
index a193eeb51..000000000
--- a/conf/offpolicy/task/sac/g1_walk_flat/motrix.yaml
+++ /dev/null
@@ -1,58 +0,0 @@
-# @package _global_
-training:
-  task_name: G1WalkFlat
-  sim_backend: motrix
-algo:
-  num_envs: 2048
-  learning_starts: 1
-  max_iterations: 5000
-  save_interval: 1000
-  updates_per_step: 8
-  use_symmetry: false
-  algo_params:
-    alpha_init: 0.001
-    target_entropy_ratio: 0.0
-env:
-  control_config:
-    action_scale: 1.0
-  gait_phase_init_mode: "offset_phase"
-  reset_base_qvel_limit: 0.5
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
-  curriculum:
-    enabled: true
-    initial_scale: 0.5
-    min_scale: 0.5
-    max_scale: 1.0
-    level_down_threshold: 150.0
-    level_up_threshold: 750.0
-    degree: 0.001
-  noise_config:
-    level: 0.0
-    scale_gyro: 0.0
-    scale_gravity: 0.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 0.1
-    scale_linvel: 0.0
-    seed: null
-reward:
-  scales:
-    tracking_lin_vel: 2.2
-    tracking_ang_vel: 1.8
-    penalty_ang_vel_xy: -1.2
-    penalty_orientation: -12.0
-    penalty_action_rate: -2.5
-    pose: -0.6
-    penalty_feet_ori: -5.0
-    feet_phase: 6.0
-    alive: 12.0
-  tracking_sigma: 0.25
-  base_height_target: 0.754
-  min_base_height: 0.3
-  max_tilt_deg: 65.0
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.008
-  close_feet_threshold: 0.15
-  pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/conf/offpolicy/task/sac/g1_walk_flat/mujoco.yaml b/conf/offpolicy/task/sac/g1_walk_flat/mujoco.yaml
deleted file mode 100644
index 0b0e2d4bd..000000000
--- a/conf/offpolicy/task/sac/g1_walk_flat/mujoco.yaml
+++ /dev/null
@@ -1,55 +0,0 @@
-# @package _global_
-training:
-  task_name: G1WalkFlat
-  sim_backend: mujoco
-algo:
-  num_envs: 2048
-  learning_starts: 10
-  max_iterations: 5000
-  save_interval: 1000
-  updates_per_step: 8
-  use_symmetry: true
-  algo_params:
-    alpha_init: 0.001
-    target_entropy_ratio: 0.0
-env:
-  control_config:
-    action_scale: 1.0
-  gait_phase_init_mode: "offset_phase"
-  reset_base_qvel_limit: 0.5
-  curriculum:
-    enabled: true
-    initial_scale: 0.5
-    min_scale: 0.5
-    max_scale: 1.0
-    level_down_threshold: 150.0
-    level_up_threshold: 750.0
-    degree: 0.001
-  noise_config:
-    level: 1.0
-    scale_gyro: 0.0
-    scale_gravity: 0.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 0.1
-    scale_linvel: 0.0
-    seed: null
-reward:
-  scales:
-    tracking_lin_vel: 2.0
-    tracking_ang_vel: 1.5
-    penalty_ang_vel_xy: -1.0
-    penalty_orientation: -10.0
-    penalty_action_rate: -4.0
-    pose: -0.5
-    penalty_feet_ori: -20.0
-    feet_phase: 5.0
-    alive: 10.0
-  tracking_sigma: 0.25
-  base_height_target: 0.754
-  min_base_height: 0.3
-  max_tilt_deg: 65.0
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.04
-  close_feet_threshold: 0.15
-  pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/conf/offpolicy/task/sac/g1_walk_rough/motrix.yaml b/conf/offpolicy/task/sac/g1_walk_rough/motrix.yaml
deleted file mode 100644
index 4144b1713..000000000
--- a/conf/offpolicy/task/sac/g1_walk_rough/motrix.yaml
+++ /dev/null
@@ -1,51 +0,0 @@
-# @package _global_
-training:
-  task_name: G1WalkRough
-  sim_backend: motrix
-algo:
-  num_envs: 2048
-  learning_starts: 1
-  max_iterations: 5000
-  save_interval: 1000
-  updates_per_step: 8
-  use_symmetry: false
-  algo_params:
-    alpha_init: 0.001
-    target_entropy_ratio: 0.0
-env:
-  sim_dt: 0.01
-  control_config:
-    action_scale: 1.0
-  gait_phase_init_mode: "offset_phase"
-  reset_base_qvel_limit: 0.5
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
-  curriculum:
-    enabled: true
-    initial_scale: 0.5
-    min_scale: 0.5
-    max_scale: 1.0
-    level_down_threshold: 150.0
-    level_up_threshold: 750.0
-    degree: 0.001
-reward:
-  scales:
-    tracking_lin_vel: 2.2
-    tracking_ang_vel: 1.8
-    penalty_ang_vel_xy: -1.2
-    penalty_orientation: -12.0
-    penalty_action_rate: -2.5
-    pose: -0.6
-    penalty_feet_ori: -5.0
-    feet_phase: 6.0
-    alive: 12.0
-  tracking_sigma: 0.25
-  base_height_target: 0.754
-  min_base_height: 0.3
-  max_tilt_deg: 65.0
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.008
-  close_feet_threshold: 0.15
-  pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/conf/offpolicy/task/sac/g1_walk_rough/mujoco.yaml b/conf/offpolicy/task/sac/g1_walk_rough/mujoco.yaml
deleted file mode 100644
index bd9a1282a..000000000
--- a/conf/offpolicy/task/sac/g1_walk_rough/mujoco.yaml
+++ /dev/null
@@ -1,54 +0,0 @@
-# @package _global_
-training:
-  task_name: G1WalkRough
-  sim_backend: mujoco
-algo:
-  num_envs: 2048
-  learning_starts: 10
-  max_iterations: 5000
-  save_interval: 1000
-  updates_per_step: 8
-  use_symmetry: true
-  algo_params:
-    alpha_init: 0.001
-    target_entropy_ratio: 0.0
-env:
-  control_config:
-    action_scale: 1.0
-  gait_phase_init_mode: "offset_phase"
-  reset_base_qvel_limit: 0.5
-  curriculum:
-    enabled: true
-    initial_scale: 0.5
-    min_scale: 0.5
-    max_scale: 1.0
-    level_down_threshold: 150.0
-    level_up_threshold: 750.0
-    degree: 0.001
-  noise_config:
-    level: 1.0
-    scale_gyro: 0.0
-    scale_gravity: 0.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 0.1
-    scale_linvel: 0.0
-reward:
-  scales:
-    tracking_lin_vel: 2.0
-    tracking_ang_vel: 1.5
-    penalty_ang_vel_xy: -1.0
-    penalty_orientation: -10.0
-    penalty_action_rate: -4.0
-    pose: -0.5
-    penalty_feet_ori: -20.0
-    feet_phase: 5.0
-    alive: 10.0
-  tracking_sigma: 0.25
-  base_height_target: 0.754
-  min_base_height: 0.3
-  max_tilt_deg: 65.0
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.04
-  close_feet_threshold: 0.15
-  pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/conf/offpolicy/task/sac/g1_wall_flip_tracking/mujoco.yaml b/conf/offpolicy/task/sac/g1_wall_flip_tracking/mujoco.yaml
deleted file mode 100644
index da48802f1..000000000
--- a/conf/offpolicy/task/sac/g1_wall_flip_tracking/mujoco.yaml
+++ /dev/null
@@ -1,81 +0,0 @@
-# @package _global_
-training:
-  task_name: G1WallFlipTrackingSAC
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 4096
-  max_iterations: 25000
-  save_interval: 1000
-  gamma: 0.99
-  tau: 0.05
-  num_atoms: 501
-  updates_per_step: 4
-  policy_frequency: 2
-  use_symmetry: false
-  algo_params:
-    alpha_init: 0.005
-    target_entropy_ratio: 0.0
-    max_grad_norm: 10.0
-env:
-  sampling_mode: uniform
-  truncate_on_clip_end: true
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-  anchor_pos_z_threshold: 1000000000.0
-  ee_body_pos_z_threshold: 1000000000.0
-  terminate_on_undesired_contacts: false
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.5
-    motion_joint_vel: 0.25
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/offpolicy/task/sac/g1_wbt_obs/mujoco.yaml b/conf/offpolicy/task/sac/g1_wbt_obs/mujoco.yaml
deleted file mode 100644
index 5fd91fc07..000000000
--- a/conf/offpolicy/task/sac/g1_wbt_obs/mujoco.yaml
+++ /dev/null
@@ -1,79 +0,0 @@
-# @package _global_
-training:
-  task_name: G1WBTObs
-  sim_backend: mujoco
-algo:
-  num_envs: 4096
-  max_iterations: 140000
-  save_interval: 1000
-  gamma: 0.99
-  tau: 0.05
-  num_atoms: 501
-  updates_per_step: 4
-  policy_frequency: 2
-  use_symmetry: false
-  algo_params:
-    alpha_init: 0.1
-    target_entropy_ratio: 0.5
-    max_grad_norm: 10.0
-env:
-  sim_dt: 0.005
-  sensor:
-    local_linvel: pelvis_local_linvel
-    gyro: pelvis_gyro
-    upvector: pelvis_upvector
-  control_config:
-    action_scale: 2.0
-    simulate_action_latency: true
-  anchor_pos_z_threshold: 0.40
-  ee_body_pos_z_threshold: 0.5
-  truncate_on_clip_end: true
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 0.5
-    scale_gyro: 0.2
-    enable_zero_linvel: true
-    enable_zero_anchor_pos: true
-    enable_anchor_ori_noise: true
-    scale_anchor_ori: 0.05
-    obs_history_length: 5
-  domain_rand:
-    randomize_base_mass: true
-    added_mass_range: [-1.0, 1.0]
-    random_com: true
-    com_offset_x: [-0.05, 0.05]
-    randomize_com_y: true
-    com_offset_y: [-0.05, 0.05]
-    randomize_com_z: true
-    com_offset_z: [-0.05, 0.05]
-    randomize_gravity: false
-    gravity_range: [[0.0, 0.0, -9.81], [0.0, 0.0, -9.81]]
-    push_robots: true
-    push_interval: 200
-    max_force: [300.0, 300.0, 120.0]
-    push_body_name: null
-    randomize_kp: true
-    kp_multiplier_range: [0.9, 1.1]
-    randomize_kd: true
-    kd_multiplier_range: [0.85, 1.15]
-    randomize_geom_friction: true
-    friction_range: [0.3, 1.2]
-    friction_geom_pattern: "^(left|right)_foot[1-7]_collision$"
-    enable_encoder_bias: true
-    encoder_bias_range: [-0.01, 0.01]
-reward:
-  scales:
-    motion_global_root_pos: 1.0
-    motion_global_root_ori: 1.0
-    motion_body_pos: 1.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.1
-    joint_limit: -5.0
-    undesired_contacts: -0.1
-    joint_acc_l2: -2.5e-7
-    joint_torque_l2: -1e-5
diff --git a/conf/offpolicy/task/sac/go2_footstand/drake.yaml b/conf/offpolicy/task/sac/go2_footstand/drake.yaml
deleted file mode 100644
index 31035486b..000000000
--- a/conf/offpolicy/task/sac/go2_footstand/drake.yaml
+++ /dev/null
@@ -1,75 +0,0 @@
-# @package _global_
-training:
-  task_name: Go2FootStand
-  sim_backend: drake
-  no_play: true
-  play_steps: 400
-  play_env_num: 1
-  play_render_mode: record
-
-algo:
-  algo_log_name: fast_sac_drake
-  num_envs: 512
-  batch_size: 1024
-  replay_buffer_n: 512
-  updates_per_step: 2
-  learning_starts: 2
-  max_iterations: 300
-  save_interval: 100
-  actor_hidden_dim: 256
-  critic_hidden_dim: 512
-  obs_normalization: true
-  use_layer_norm: true
-  algo_params:
-    alpha_init: 0.005
-    target_entropy_ratio: 0.0
-    use_compile: false
-
-env:
-  sim_dt: 0.004
-  drake_backend_mode: batch
-  drake_nthread: 20
-  add_body_sensors: true
-  obs_history_len: 15
-  energy_termination_threshold: 200.0
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 1.5
-    scale_gyro: 0.2
-    scale_gravity: 0.05
-    scale_linvel: 0.1
-  control_config:
-    action_scale: 0.3
-  domain_rand:
-    randomize_floor_friction: false
-    randomize_link_mass: false
-    torso_added_mass_range: null
-    randomize_torso_com: false
-    randomize_dof_armature: false
-    randomize_reset_joint_qpos: true
-    reset_joint_qpos_range: [-0.05, 0.05]
-
-reward:
-  scales:
-    height: 2.0
-    orientation: 2.0
-    contact: -1.0
-    action_rate: -0.01
-    termination: -2.0
-    dof_pos_limits: -0.5
-    torques: 0.0
-    pose: -0.1
-    penalty_contact: -0.2
-    tar: 0.8
-    rear_feet_contact: 0.5
-    rear_leg_symmetry: -0.2
-    front_leg_motion: -0.05
-    upright_stability: -0.2
-    knee_clearance: -0.5
-    stay_still: -0.1
-    energy: -0.003
-    dof_acc: -2.5e-7
-  tracking_sigma: 0.25
-  base_height_target: 0.3
-  knee_height_target: 0.08
diff --git a/conf/offpolicy/task/sac/go2_joystick_flat/drake.yaml b/conf/offpolicy/task/sac/go2_joystick_flat/drake.yaml
deleted file mode 100644
index 6829cfe93..000000000
--- a/conf/offpolicy/task/sac/go2_joystick_flat/drake.yaml
+++ /dev/null
@@ -1,50 +0,0 @@
-# @package _global_
-training:
-  task_name: Go2JoystickFlat
-  sim_backend: drake
-  no_play: true
-  play_steps: 400
-  play_env_num: 1
-  play_render_mode: record
-
-algo:
-  algo_log_name: fast_sac_drake
-  num_envs: 512
-  batch_size: 1024
-  replay_buffer_n: 512
-  updates_per_step: 2
-  learning_starts: 2
-  max_iterations: 300
-  save_interval: 100
-  actor_hidden_dim: 256
-  critic_hidden_dim: 512
-  obs_normalization: true
-  use_layer_norm: true
-  algo_params:
-    alpha_init: 0.005
-    target_entropy_ratio: 0.0
-    use_compile: false
-
-env:
-  drake_backend_mode: batch
-  drake_nthread: 20
-  scene:
-    model_file: src/unilab/assets/robots/go2/scene_flat.xml
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
-    push_robots: false
-
-reward:
-  scales:
-    tracking_lin_vel: 1.0
-    tracking_ang_vel: 0.2
-    lin_vel_z: -5.0
-    ang_vel_xy: -0.1
-    base_height: -100.0
-    action_rate: -0.005
-    similar_to_default: -0.1
-    contact: 0.24
-    swing_feet_z: 4.0
-  tracking_sigma: 0.25
-  base_height_target: 0.3
diff --git a/conf/offpolicy/task/sac/go2w_joystick_flat/drake.yaml b/conf/offpolicy/task/sac/go2w_joystick_flat/drake.yaml
deleted file mode 100644
index fa05273e4..000000000
--- a/conf/offpolicy/task/sac/go2w_joystick_flat/drake.yaml
+++ /dev/null
@@ -1,63 +0,0 @@
-# @package _global_
-training:
-  task_name: Go2WJoystickFlat
-  sim_backend: drake
-  no_play: true
-  play_steps: 400
-  play_env_num: 1
-  play_render_mode: record
-
-algo:
-  algo_log_name: fast_sac_drake
-  num_envs: 512
-  batch_size: 1024
-  replay_buffer_n: 512
-  updates_per_step: 2
-  learning_starts: 2
-  max_iterations: 300
-  save_interval: 100
-  actor_hidden_dim: 256
-  critic_hidden_dim: 512
-  obs_normalization: true
-  use_layer_norm: true
-  algo_params:
-    alpha_init: 0.005
-    target_entropy_ratio: 0.0
-    use_compile: false
-
-env:
-  drake_backend_mode: batch
-  drake_nthread: 20
-  scene:
-    model_file: src/unilab/assets/robots/go2w/scene_flat.xml
-  commands:
-    vel_limit:
-      - [0.0, 0.0, -1.0]
-      - [1.0, 0.0, 1.0]
-  control_config:
-    action_scale: 0.5
-    wheel_action_scale: 10.0
-    Kp: 50.0
-    Kd: 1.5
-    wheel_Kd: 0.5
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
-    push_robots: false
-
-reward:
-  scales:
-    tracking_lin_vel: 1.0
-    tracking_ang_vel: 0.75
-    lin_vel_z: -5.0
-    ang_vel_xy: -0.1
-    base_height: -100.0
-    orientation: -2.0
-    action_rate: -0.005
-    similar_to_default: -0.5
-    torques: -0.0002
-    wheel_vel: 0.0
-    alive: 0.5
-    upward: 1.0
-  tracking_sigma: 0.25
-  base_height_target: 0.4
diff --git a/conf/offpolicy/task/sac/sharpa_inhand/mujoco_hora.yaml b/conf/offpolicy/task/sac/sharpa_inhand/mujoco_hora.yaml
deleted file mode 100644
index aa69445f8..000000000
--- a/conf/offpolicy/task/sac/sharpa_inhand/mujoco_hora.yaml
+++ /dev/null
@@ -1,121 +0,0 @@
-# @package _global_
-# HORA Sharpa SAC MuJoCo teacher-training owner config.
-
-training:
-  task_name: SharpaInhandRotation
-  sim_backend: mujoco
-  use_amp: true
-  env_steps_per_sync: 2
-  no_play: true
-  play_steps: 200
-  render_spacing: 0.5
-  cam_distance: 1.5
-  cam_lookat: [0.75, 0.75, 0.4]
-  cam_elevation: -20.0
-
-interactive:
-  action_mode: policy
-  policy_obs_mode: actor
-  camera_distance: 1.5
-  camera_elevation: -20.0
-  camera_azimuth: 90.0
-  use_env_visual_model: true
-
-algo:
-  algo_log_name: hora_sac
-  runtime_impl: hora_sac
-  runtime_resolver: unilab.algos.torch.hora.sac:resolve_hora_sac_runtime
-  num_envs: 1024
-  batch_size: 2048
-  replay_buffer_n: 1280
-  updates_per_step: 14
-  learning_starts: 1
-  policy_frequency: 2
-  max_iterations: 5371
-  save_interval: 896
-  actor_lr: 4.5e-4
-  critic_lr: 4.5e-4
-  use_symmetry: false
-  actor:
-    priv_info_embed_dim: 9
-    priv_mlp_hidden_dims: [256, 128, 9]
-  algo_params:
-    alpha_lr: 4.5e-4
-    amp_dtype: auto
-    use_compile: true
-
-reward:
-  scales:
-    rotate: 2.5
-    obj_linvel: -0.3
-    pose_diff: -0.4
-    torque: -0.1
-    work: -0.5
-    object_pos: 0.003
-  angvel_clip_min: -0.5
-  angvel_clip_max: 0.5
-
-env:
-  post_step_forward_sensor: true
-  zero_action_test_mode: false
-  clip_obs: 5.0
-  clip_actions: 1.0
-  reset_height_lower: 0.59906
-  reset_height_upper: 0.63906
-  reset_angle_diff: 0.7853981633974483
-  rot_axis: [0.0, 0.0, 1.0]
-  grasp_cache_path: caches/sharpa_grasp_linspace
-  sensor:
-    tactile_force_sensor_names:
-      - contact_right_thumb_elastomer_force
-      - contact_right_index_elastomer_force
-      - contact_right_middle_elastomer_force
-      - contact_right_ring_elastomer_force
-      - contact_right_pinky_elastomer_force
-  disable_tactile_ids: []
-  use_default_object_pose_for_object_pos_anchor: true
-  obs:
-    observation_mode: separated
-    enable_tactile: true
-    binary_contact: false
-    enable_contact_pos: false
-    contact_smooth: 0.5
-    contact_threshold: 0.05
-    tactile_force_clip_max: 4.0
-  priv_info:
-    include_friction_scale: true
-    include_gravity_direction: false
-  control_config:
-    action_scale: 0.041666666666666664
-    p_gain: 1.0
-    d_gain: 0.1
-    torque_control: false
-    dof_limits_scale: 0.9
-  domain_rand:
-    scale_list: [0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5]
-    randomize_gravity_direction: true
-    gravity_direction_magnitude: 9.81
-    randomize_pd_gains: true
-    randomize_p_gain_scale_lower: 0.5
-    randomize_p_gain_scale_upper: 2.0
-    randomize_d_gain_scale_lower: 0.5
-    randomize_d_gain_scale_upper: 2.0
-    randomize_friction: true
-    randomize_friction_scale_lower: 0.75
-    randomize_friction_scale_upper: 1.25
-    elastomer_base_friction: 2.0
-    metal_base_friction: 1.0
-    object_base_friction: 2.0
-    randomize_com: true
-    randomize_com_lower: -0.01
-    randomize_com_upper: 0.01
-    randomize_mass: true
-    randomize_mass_lower: 0.01
-    randomize_mass_upper: 0.25
-    force_scale: 2.0
-    random_force_prob_scalar: 0.25
-    force_decay: 0.9
-    force_decay_interval: 0.08
-    joint_noise_scale: 0.02
-    contact_latency: 0.005
-    contact_sensor_noise: 0.01
diff --git a/conf/offpolicy/task/sac/stewart_balance/drake.yaml b/conf/offpolicy/task/sac/stewart_balance/drake.yaml
deleted file mode 100644
index 5a672e516..000000000
--- a/conf/offpolicy/task/sac/stewart_balance/drake.yaml
+++ /dev/null
@@ -1,38 +0,0 @@
-# @package _global_
-training:
-  task_name: StewartBalance
-  sim_backend: drake
-  no_play: true
-  play_steps: 400
-  play_env_num: 1
-  play_render_mode: record
-  render_spacing: 4.5
-
-algo:
-  algo_log_name: fast_sac_drake
-  num_envs: 256
-  batch_size: 512
-  replay_buffer_n: 512
-  updates_per_step: 2
-  learning_starts: 2
-  max_iterations: 400
-  save_interval: 100
-  actor_hidden_dim: 128
-  critic_hidden_dim: 256
-  obs_normalization: true
-  use_layer_norm: true
-  algo_params:
-    alpha_init: 0.005
-    target_entropy_ratio: 0.0
-    use_compile: false
-
-env:
-  drake_backend_mode: batch
-  drake_nthread: 20
-
-reward:
-  scales:
-    center: 0.7
-    progress: 0.6
-    still: 3.0
-  fall_penalty: -6.0
diff --git a/conf/offpolicy/task/td3/g1_23dof_walk_flat/mujoco.yaml b/conf/offpolicy/task/td3/g1_23dof_walk_flat/mujoco.yaml
deleted file mode 100644
index 850f155e8..000000000
--- a/conf/offpolicy/task/td3/g1_23dof_walk_flat/mujoco.yaml
+++ /dev/null
@@ -1,46 +0,0 @@
-# @package _global_
-training:
-  task_name: G1Walk23DofFlat
-  sim_backend: mujoco
-algo:
-  max_iterations: 100000
-env:
-  control_config:
-    action_scale: 1.0
-  gait_phase_init_mode: "offset_phase"
-  reset_base_qvel_limit: 0.5
-  curriculum:
-    enabled: true
-    initial_scale: 0.5
-    min_scale: 0.5
-    max_scale: 1.0
-    level_down_threshold: 150.0
-    level_up_threshold: 750.0
-    degree: 0.001
-  noise_config:
-    level: 1.0
-    scale_gyro: 0.0
-    scale_gravity: 0.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 0.1
-    scale_linvel: 0.0
-reward:
-  scales:
-    tracking_lin_vel: 2.0
-    tracking_ang_vel: 1.5
-    penalty_ang_vel_xy: -1.0
-    penalty_orientation: -10.0
-    penalty_action_rate: -4.0
-    pose: -0.5
-    penalty_feet_ori: -20.0
-    feet_phase: 5.0
-    alive: 10.0
-  tracking_sigma: 0.25
-  base_height_target: 0.754
-  min_base_height: 0.3
-  max_tilt_deg: 65.0
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.04
-  close_feet_threshold: 0.15
-  pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/conf/offpolicy/task/td3/g1_walk_flat/mujoco.yaml b/conf/offpolicy/task/td3/g1_walk_flat/mujoco.yaml
deleted file mode 100644
index 54f87699c..000000000
--- a/conf/offpolicy/task/td3/g1_walk_flat/mujoco.yaml
+++ /dev/null
@@ -1,47 +0,0 @@
-# @package _global_
-training:
-  task_name: G1WalkFlat
-  sim_backend: mujoco
-algo:
-  max_iterations: 100000
-env:
-  control_config:
-    action_scale: 1.0
-  gait_phase_init_mode: "offset_phase"
-  reset_base_qvel_limit: 0.5
-  curriculum:
-    enabled: true
-    initial_scale: 0.5
-    min_scale: 0.5
-    max_scale: 1.0
-    level_down_threshold: 150.0
-    level_up_threshold: 750.0
-    degree: 0.001
-  noise_config:
-    level: 1.0
-    scale_gyro: 0.0
-    scale_gravity: 0.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 0.1
-    scale_linvel: 0.0
-    seed: null
-reward:
-  scales:
-    tracking_lin_vel: 2.0
-    tracking_ang_vel: 1.5
-    penalty_ang_vel_xy: -1.0
-    penalty_orientation: -10.0
-    penalty_action_rate: -4.0
-    pose: -0.5
-    penalty_feet_ori: -20.0
-    feet_phase: 5.0
-    alive: 10.0
-  tracking_sigma: 0.25
-  base_height_target: 0.754
-  min_base_height: 0.3
-  max_tilt_deg: 65.0
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.04
-  close_feet_threshold: 0.15
-  pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/conf/offpolicy/task/td3/go1_joystick_flat/motrix.yaml b/conf/offpolicy/task/td3/go1_joystick_flat/motrix.yaml
deleted file mode 100644
index 40c9f28b7..000000000
--- a/conf/offpolicy/task/td3/go1_joystick_flat/motrix.yaml
+++ /dev/null
@@ -1,29 +0,0 @@
-# @package _global_
-training:
-  task_name: Go1JoystickFlat
-  sim_backend: motrix
-algo:
-  num_envs: 1024
-  learning_starts: 10
-  max_iterations: 5000
-  save_interval: 1000
-  updates_per_step: 8
-  batch_size: 8192
-  replay_buffer_n: 1024
-env:
-  commands:
-    vel_limit:
-      - [0.5, 0.0, 0.0]
-      - [0.5, 0.0, 0.0]
-reward:
-  scales:
-    tracking_lin_vel: 1.0
-    tracking_ang_vel: 0.2
-    lin_vel_z: -5.0
-    ang_vel_xy: -0.1
-    base_height: -100.0
-    action_rate: -0.005
-    similar_to_default: -0.1
-    swing_feet_z: 4.0
-  tracking_sigma: 0.25
-  base_height_target: 0.3
diff --git a/conf/offpolicy/task/td3/go2_joystick_flat/motrix.yaml b/conf/offpolicy/task/td3/go2_joystick_flat/motrix.yaml
deleted file mode 100644
index 8b32bd78a..000000000
--- a/conf/offpolicy/task/td3/go2_joystick_flat/motrix.yaml
+++ /dev/null
@@ -1,33 +0,0 @@
-# @package _global_
-training:
-  task_name: Go2JoystickFlat
-  sim_backend: motrix
-algo:
-  num_envs: 1024
-  learning_starts: 10
-  max_iterations: 5000
-  save_interval: 1000
-  updates_per_step: 8
-  batch_size: 8192
-  replay_buffer_n: 1024
-env:
-  commands:
-    vel_limit:
-      - [0.5, 0.0, 0.0]
-      - [0.5, 0.0, 0.0]
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
-reward:
-  scales:
-    tracking_lin_vel: 1.0
-    tracking_ang_vel: 0.2
-    lin_vel_z: -5.0
-    ang_vel_xy: -0.1
-    base_height: -100.0
-    action_rate: -0.005
-    similar_to_default: -0.1
-    contact: 0.24
-    swing_feet_z: 4.0
-  tracking_sigma: 0.25
-  base_height_target: 0.3
diff --git a/conf/ppo/config.yaml b/conf/ppo/config.yaml
deleted file mode 100644
index a5210ce18..000000000
--- a/conf/ppo/config.yaml
+++ /dev/null
@@ -1,147 +0,0 @@
-defaults:
-  - _self_
-  - task: go1_joystick_flat/mujoco
-
-algo:
-  algo: ppo
-  algo_log_name: rsl_rl_ppo
-  seed: 1
-  num_envs: 4096
-  num_steps_per_env: 24
-  max_iterations: 101
-  save_interval: 100
-  empirical_normalization: false
-  runner_class_name: OnPolicyRunner
-  obs_groups:
-    default:
-      - policy
-  experiment_name: test
-  run_name: ""
-  resume: false
-  load_run: "-1"
-  checkpoint: -1
-  resume_path: null
-  policy:
-    init_noise_std: 1.0
-    actor_hidden_dims: [512, 256, 128]
-    critic_hidden_dims: [512, 256, 128]
-    activation: elu
-    class_name: ActorCritic
-  algorithm:
-    class_name: unilab.algos.torch.rsl_rl_ppo:FinalObservationAwarePPO
-    value_loss_coef: 1.0
-    use_clipped_value_loss: true
-    clip_param: 0.2
-    entropy_coef: 0.01
-    num_learning_epochs: 5
-    num_mini_batches: 4
-    learning_rate: 1.0e-3
-    schedule: adaptive
-    gamma: 0.99
-    lam: 0.95
-    desired_kl: 0.01
-    target_kl_stop: null
-    max_grad_norm: 1.0
-    adaptive_kl_beta: 0.9
-    adaptive_lr_growth: 1.1
-    adaptive_lr_decay: 1.2
-    adaptive_lr_update_interval: 5
-    metrics_interval: 8
-    finite_check_interval: 8
-    enable_compile: false
-    warmup_strict_iters: 10
-    warmup_metrics_interval: 2
-    warmup_finite_check_interval: 2
-    disable_finite_checks: true
-
-training:
-  task_name: Go1JoystickFlat
-  # list[int] | null; null/[] keeps single-device behavior, [d] selects one
-  # CUDA device, and [d0..dN-1] launches one RSL-RL worker per device.
-  # algo.num_envs is per rank; do not set this together with training.device.
-  devices: null
-  device: null
-  logger: tensorboard
-  wandb_project: unilab
-  wandb_entity: null
-  wandb_group: null
-  wandb_job_type: null
-  wandb_name: null
-  wandb_tags: []
-  wandb_notes: null
-  wandb_mode: null
-  sim_backend: mujoco
-  nan_guard:
-    enabled: true
-    buffer_size: 100
-    max_envs_to_dump: 5
-    output_dir: null
-  play_only: false
-  no_play: false
-  sim2sim_strict: true
-  play_render_mode: auto
-  play_env_num: 16
-  render_spacing: 1.0
-  play_steps: 200
-  cam_distance: 6.0
-  cam_elevation: -20.0
-  cam_azimuth: 90.0
-  cam_lookat: null
-  cam_tracking: false
-  cam_tracking_env_idx: 0
-  cam_tracking_extra_envs: 2
-  log_root: null
-  num_timesteps: null
-  log_dir: null
-
-interactive:
-  action_mode: zero
-  policy_obs_mode: auto
-  show_target_bodies: false
-  show_reward_debug: false
-  target_show_axes: false
-  target_body_names: ""
-  target_max_bodies: 0
-  target_marker_radius: 0.02
-  target_axis_length: 0.08
-  target_marker_alpha: 0.75
-  reward_debug_show_velocity: false
-  reward_debug_lin_vel_scale: 0.08
-  reward_debug_ang_vel_scale: 0.05
-  reward_debug_show_connectors: false
-  reward_debug_show_global_anchor: false
-  camera_follow_body: true
-  camera_focus_body_name: ""
-  camera_height_offset: 0.15
-  camera_distance: null
-  camera_elevation: null
-  camera_azimuth: null
-  use_env_visual_model: true
-  speed: 1.0
-  start_paused: false
-  keyboard: false
-  keyboard_step_lin: 0.1
-  keyboard_step_ang: 0.2
-
-viser:
-  port: 8080
-  env_idx: 0
-  display_mode: all
-  max_envs: 16
-
-env:
-  post_step_forward_sensor: false
-  # adaptive_chunk_size: auto-tune the MuJoCo BatchEnvPool chunk_size at materialize
-  # (cache-backed). chunk_size (int) manually overrides and wins; null => use default.
-  adaptive_chunk_size: true
-  chunk_size: null
-
-hydra:
-  run:
-    dir: .
-  output_subdir: null
-  job:
-    chdir: false
-  job_logging:
-    root:
-      handlers: [console]
diff --git a/conf/ppo/task/a2_joystick_flat/mujoco.yaml b/conf/ppo/task/a2_joystick_flat/mujoco.yaml
deleted file mode 100644
index b0863e9db..000000000
--- a/conf/ppo/task/a2_joystick_flat/mujoco.yaml
+++ /dev/null
@@ -1,84 +0,0 @@
-# @package _global_
-# A2 (leg-only Unitree A2) joystick flat task. Same isomorphic task as
-# Go2JoystickFlat: 12-DOF velocity tracking with a gait phase. Robot identity
-# (asset path, standing height 0.465, A2 leg PD gains) lives in A2JoystickCfg;
-# this YAML carries training + reward only, mirroring the Go2 task.
-training:
-  task_name: A2JoystickFlat
-  sim_backend: mujoco
-algo:
-  num_envs: 1024
-  max_iterations: 500   # A2 (19.6 kg, ~2.8x Go2) + full DR needs more budget than Go2's flat task
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - actor
-  policy:
-    init_noise_std: 0.5
-  algorithm:
-    learning_rate: 3.0e-4
-    entropy_coef: 1.0e-3
-env:
-  # Domain randomization for sim2real deployment. A2JoystickDomainRandomizationProvider
-  # caches the dof-armature + geom-friction baselines, so randomize_dof_armature and
-  # randomize_ground_friction are ON. Ground friction is effective because the floor
-  # geom is the priority geom (scene_flat.xml). Ranges reference unitree_rl_mjlab A2
-  # events: joint_armature scale [0.9,1.1], foot friction [0.3,1.6]. randomize_body_mass
-  # stays off (base_body_mass not cached); gravity OFF (constant on flat ground).
-  # env.domain_rand is sim2sim ALLOWLIST (free override).
-  domain_rand:
-    randomize_base_mass: true
-    added_mass_range: [0.0, 8.0]
-
-    randomize_body_mass: false   # provider does not cache base_body_mass
-    body_mass_multiplier_range: [0.9, 1.1]
-
-    random_com: true
-    com_offset_x: [-0.08, 0.08]
-    com_offset_y: [-0.08, 0.08]
-    com_offset_z: [-0.08, 0.08]
-
-    randomize_gravity: false   # flat ground: gravity constant on the real robot; randomizing only slows training.
-
-    randomize_ground_friction: true   # floor is the priority geom, so this moves the foot-ground friction
-    ground_friction_multiplier_range: [0.3, 1.6]   # mjlab foot_friction range
-
-    randomize_dof_armature: true
-    dof_armature_multiplier_range: [0.9, 1.1]   # mjlab joint_armature scale
-
-    randomize_kp: true
-    kp_multiplier_range: [0.9, 1.1]
-
-    randomize_kd: true
-    kd_multiplier_range: [0.9, 1.1]
-
-    push_robots: true
-    push_interval: 400          # control steps between base velocity pushes
-    max_force: [1.0, 1.0, 0.5]
-    push_body_name: base_link   # A2 base body (Go2 uses "base"); required or push has no target.
-  # Standing-aware commands so the policy trains on genuine zero-command samples
-  # (rel_standing_envs fraction forced to stand) and resamples mid-episode every 5s. env.commands is
-  # a sim2sim ALLOWLIST subset (vel_limit) / free fields; rel_standing_envs and
-  # resampling_time are declared fields on Commands so Hydra struct mode accepts them.
-  commands:
-    rel_standing_envs: 0.1
-    resampling_time: 5.0
-reward:
-  # command_threshold gates the phase-driven gait rewards (swing_feet_z / contact)
-  # so the A2 stands still at zero command instead of marching in place.
-  command_threshold: 0.1
-  scales:
-    tracking_lin_vel: 1.0
-    tracking_ang_vel: 0.4
-    lin_vel_z: -5.0
-    ang_vel_xy: -0.1
-    base_height: -100.0
-    action_rate: -0.02
-    similar_to_default: -0.25
-    contact: 0.5
-    swing_feet_z: 4.0
-    stand_still: -4.0
-    hip_deviation: -1.0
-    stand_feet_air: -1.0   # penalize feet leaving the ground at zero command (gated off during locomotion)
-  tracking_sigma: 0.25
-  base_height_target: 0.40
diff --git a/conf/ppo/task/allegro_inhand/motrix.yaml b/conf/ppo/task/allegro_inhand/motrix.yaml
deleted file mode 100644
index 3608d9743..000000000
--- a/conf/ppo/task/allegro_inhand/motrix.yaml
+++ /dev/null
@@ -1,56 +0,0 @@
-# @package _global_
-training:
-  task_name: AllegroInhandRotation
-  sim_backend: motrix
-algo:
-  num_envs: 16384
-  num_steps_per_env: 8
-  max_iterations: 201
-  obs_groups:
-    actor: [policy]
-    critic: [policy]
-  actor:
-    class_name: rsl_rl.models.MLPModel
-    hidden_dims: [512, 256, 128]
-    activation: elu
-    obs_normalization: true
-    distribution_cfg:
-      class_name: rsl_rl.modules.distribution.GaussianDistribution
-      init_std: 1.0
-      std_type: scalar
-  critic:
-    class_name: rsl_rl.models.MLPModel
-    hidden_dims: [512, 256, 128]
-    activation: elu
-    obs_normalization: true
-  algorithm:
-    value_loss_coef: 4.0
-    desired_kl: 0.02
-reward:
-  scales:
-    rotate: 1.25
-    obj_linvel: -0.3
-    pose_diff: -0.3
-    torque: -0.1
-    work: -2.0
-    drop: 0.0
-  angvel_clip_min: -0.5
-  angvel_clip_max: 0.5
-  reset_z_threshold: 0.125
-env:
-  gen_grasp: false
-  max_episode_seconds: 20.0
-  grasp_cache_path: caches/allegro_grasp_50k.npy
-  # Keep only grasp/pose reset variation. All online DR terms stay disabled.
-  domain_rand:
-    randomize_base_mass: false
-    random_com: false
-    randomize_gravity: false
-    push_robots: false
-    joint_noise: 0.0
-    ball_vel_noise: 0.0
-    ball_z_offset: 0.0
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/allegro_inhand/mujoco.yaml b/conf/ppo/task/allegro_inhand/mujoco.yaml
deleted file mode 100644
index 17d639913..000000000
--- a/conf/ppo/task/allegro_inhand/mujoco.yaml
+++ /dev/null
@@ -1,60 +0,0 @@
-# @package _global_
-training:
-  task_name: AllegroInhandRotation
-  sim_backend: mujoco
-  render_spacing: 0.5
-  cam_distance: 1.5
-  cam_lookat: [0.75, 0.75, 0]
-  cam_elevation: -20.0
-algo:
-  num_envs: 16384
-  num_steps_per_env: 8
-  max_iterations: 201
-  obs_groups:
-    actor: [policy]
-    critic: [policy]
-  actor:
-    class_name: rsl_rl.models.MLPModel
-    hidden_dims: [512, 256, 128]
-    activation: elu
-    obs_normalization: true
-    distribution_cfg:
-      class_name: rsl_rl.modules.distribution.GaussianDistribution
-      init_std: 1.0
-      std_type: scalar
-  critic:
-    class_name: rsl_rl.models.MLPModel
-    hidden_dims: [512, 256, 128]
-    activation: elu
-    obs_normalization: true
-  algorithm:
-    value_loss_coef: 4.0
-    desired_kl: 0.02
-reward:
-  scales:
-    rotate: 1.25
-    obj_linvel: -0.3
-    pose_diff: -0.3
-    torque: -0.1
-    work: -2.0
-    drop: 0.0
-  angvel_clip_min: -0.5
-  angvel_clip_max: 0.5
-  reset_z_threshold: 0.125
-env:
-  gen_grasp: false
-  max_episode_seconds: 20.0
-  grasp_cache_path: caches/allegro_grasp_50k.npy
-  # Keep only grasp/pose reset variation. All online DR terms stay disabled.
-  domain_rand:
-    randomize_base_mass: false
-    random_com: false
-    randomize_gravity: false
-    push_robots: false
-    joint_noise: 0.0
-    ball_vel_noise: 0.0
-    ball_z_offset: 0.0
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/allegro_inhand_grasp/motrix.yaml b/conf/ppo/task/allegro_inhand_grasp/motrix.yaml
deleted file mode 100644
index 6b5cdca31..000000000
--- a/conf/ppo/task/allegro_inhand_grasp/motrix.yaml
+++ /dev/null
@@ -1,37 +0,0 @@
-# @package _global_
-defaults:
-  - /task/allegro_inhand/motrix
-  - _self_
-
-training:
-  task_name: AllegroInhandRotationGrasp
-  sim_backend: motrix
-  no_play: true
-algo:
-  max_iterations: 1000 # infinite rollout
-reward:
-  scales:
-    rotate: 0.0
-    obj_linvel: 0.0
-    pose_diff: 0.0
-    torque: 0.0
-    work: 0.0
-    drop: 0.0
-env:
-  gen_grasp: true
-  max_episode_seconds: 3.0
-  grasp_cache_path: caches/allegro_grasp_50k.npy
-  grasp_collection_target: 50000
-  grasp_auto_save: true
-  grasp_quality_check: true
-  grasp_min_contacts: 2
-  domain_rand:
-    randomize_base_mass: false
-    random_com: false
-    push_robots: false
-    ball_vel_noise: 0.0
-    joint_noise: 0.25 # random sampling of the grasp poses
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/allegro_inhand_grasp/mujoco.yaml b/conf/ppo/task/allegro_inhand_grasp/mujoco.yaml
deleted file mode 100644
index e6e62f2f6..000000000
--- a/conf/ppo/task/allegro_inhand_grasp/mujoco.yaml
+++ /dev/null
@@ -1,37 +0,0 @@
-# @package _global_
-defaults:
-  - /task/allegro_inhand/mujoco
-  - _self_
-
-training:
-  task_name: AllegroInhandRotationGrasp
-  sim_backend: mujoco
-  no_play: true
-algo:
-  max_iterations: 1000 # infinite rollout
-reward:
-  scales:
-    rotate: 0.0
-    obj_linvel: 0.0
-    pose_diff: 0.0
-    torque: 0.0
-    work: 0.0
-    drop: 0.0
-env:
-  gen_grasp: true
-  max_episode_seconds: 3.0
-  grasp_cache_path: caches/allegro_grasp_50k.npy
-  grasp_collection_target: 50000
-  grasp_auto_save: true
-  grasp_quality_check: true
-  grasp_min_contacts: 2
-  domain_rand:
-    randomize_base_mass: false
-    random_com: false
-    push_robots: false
-    ball_vel_noise: 0.0
-    joint_noise: 0.25 # random sampling of the grasp poses
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/g1_23dof_box_tracking/motrix.yaml b/conf/ppo/task/g1_23dof_box_tracking/motrix.yaml
deleted file mode 100644
index e453b0721..000000000
--- a/conf/ppo/task/g1_23dof_box_tracking/motrix.yaml
+++ /dev/null
@@ -1,60 +0,0 @@
-# @package _global_
-training:
-  task_name: G1BoxTracking23Dof
-  sim_backend: motrix
-  play_env_num: 16
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 40000
-  save_interval: 500
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-  algorithm:
-    entropy_coef: 0.002
-    desired_kl: 0.01
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 1.5
-    scale_gyro: 0.2
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.5
-  scene:
-    enabled: true
-    source_model_file: src/unilab/assets/robots/g1/scene_flat_23dof_with_largebox.xml
-    ground_texture_file: src/unilab/assets/robots/g1/textures/floor.png
-    skybox_rgb1: [0.90, 0.90, 0.91]
-    skybox_rgb2: [0.68, 0.68, 0.70]
-    ground_texrepeat: [0.25, 0.25]
-reward:
-  scales:
-    motion_global_root_pos: 1.0
-    motion_global_root_ori: 0.5
-    motion_body_pos: 1.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.5
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.1
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-    object_global_ref_position_error_exp: 4.0
-    object_global_ref_orientation_error_exp: 3.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
-  std_object_pos: 0.12
-  std_object_ori: 0.2
diff --git a/conf/ppo/task/g1_23dof_box_tracking/mujoco.yaml b/conf/ppo/task/g1_23dof_box_tracking/mujoco.yaml
deleted file mode 100644
index 4c7b186f3..000000000
--- a/conf/ppo/task/g1_23dof_box_tracking/mujoco.yaml
+++ /dev/null
@@ -1,44 +0,0 @@
-# @package _global_
-training:
-  task_name: G1BoxTracking23Dof
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 30000
-  save_interval: 500
-  obs_groups:
-    actor:
-      - actor
-  algorithm:
-    entropy_coef: 0.005
-env:
-  sim_dt: 0.005
-  sensor:
-    gyro: pelvis_gyro
-    upvector: pelvis_upvector
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 1.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.1
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-    object_global_ref_position_error_exp: 2.0
-    object_global_ref_orientation_error_exp: 2.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
-  std_object_pos: 0.2
-  std_object_ori: 0.3
diff --git a/conf/ppo/task/g1_23dof_climb_tracking/motrix.yaml b/conf/ppo/task/g1_23dof_climb_tracking/motrix.yaml
deleted file mode 100644
index f554a13d7..000000000
--- a/conf/ppo/task/g1_23dof_climb_tracking/motrix.yaml
+++ /dev/null
@@ -1,74 +0,0 @@
-# @package _global_
-training:
-  task_name: G1ClimbTracking23Dof
-  sim_backend: motrix
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 20000
-  save_interval: 500
-  empirical_normalization: true
-  obs_groups:
-    actor:
-    - actor
-    critic:
-    - critic
-  algorithm:
-    entropy_coef: 0.005
-    desired_kl: 0.01
-env:
-  sampling_mode: adaptive
-  truncate_on_clip_end: false
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.5475464629911068
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-  anchor_pos_z_threshold: 0.3
-  ee_body_pos_z_threshold: 0.3
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.5
-    motion_joint_vel: 0.25
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/ppo/task/g1_23dof_climb_tracking/mujoco.yaml b/conf/ppo/task/g1_23dof_climb_tracking/mujoco.yaml
deleted file mode 100644
index 34fc4ae59..000000000
--- a/conf/ppo/task/g1_23dof_climb_tracking/mujoco.yaml
+++ /dev/null
@@ -1,74 +0,0 @@
-# @package _global_
-training:
-  task_name: G1ClimbTracking23Dof
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 20000
-  save_interval: 500
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-  algorithm:
-    entropy_coef: 0.005
-    desired_kl: 0.01
-env:
-  sampling_mode: adaptive
-  truncate_on_clip_end: false
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-  anchor_pos_z_threshold: 0.3
-  ee_body_pos_z_threshold: 0.3
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.5
-    motion_joint_vel: 0.25
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/ppo/task/g1_23dof_flip_tracking/motrix.yaml b/conf/ppo/task/g1_23dof_flip_tracking/motrix.yaml
deleted file mode 100644
index fff96e018..000000000
--- a/conf/ppo/task/g1_23dof_flip_tracking/motrix.yaml
+++ /dev/null
@@ -1,34 +0,0 @@
-# @package _global_
-training:
-  task_name: G1FlipTracking23Dof
-  sim_backend: motrix
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 30000
-  save_interval: 500
-  obs_groups:
-    actor:
-      - actor
-  algorithm:
-    entropy_coef: 0.005
-reward:
-  scales:
-    motion_global_root_pos: 1.0
-    motion_global_root_ori: 0.5
-    motion_body_pos: 1.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.05
-    joint_limit: -10.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/ppo/task/g1_23dof_flip_tracking/mujoco.yaml b/conf/ppo/task/g1_23dof_flip_tracking/mujoco.yaml
deleted file mode 100644
index 8e62a80ff..000000000
--- a/conf/ppo/task/g1_23dof_flip_tracking/mujoco.yaml
+++ /dev/null
@@ -1,74 +0,0 @@
-# @package _global_
-training:
-  task_name: G1FlipTracking23Dof
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 20000
-  save_interval: 500
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-  algorithm:
-    entropy_coef: 0.005
-    desired_kl: 0.01
-env:
-  sampling_mode: start
-  truncate_on_clip_end: false
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-  anchor_pos_z_threshold: 0.5
-  ee_body_pos_z_threshold: 0.5
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/ppo/task/g1_23dof_motion_tracking/motrix.yaml b/conf/ppo/task/g1_23dof_motion_tracking/motrix.yaml
deleted file mode 100644
index 6bcc0a74f..000000000
--- a/conf/ppo/task/g1_23dof_motion_tracking/motrix.yaml
+++ /dev/null
@@ -1,52 +0,0 @@
-# @package _global_
-training:
-  task_name: G1MotionTracking23Dof
-  sim_backend: motrix
-  play_env_num: 16
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 15000
-  save_interval: 500
-  obs_groups:
-    actor:
-      - actor
-  algorithm:
-    entropy_coef: 0.005
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 1.5
-    scale_gyro: 0.2
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.5
-  scene:
-    enabled: true
-    source_model_file: src/unilab/assets/robots/g1/scene_flat_23dof.xml
-    ground_texture_file: src/unilab/assets/robots/g1/textures/floor.png
-    skybox_rgb1: [0.90, 0.90, 0.91]
-    skybox_rgb2: [0.68, 0.68, 0.70]
-    ground_texrepeat: [0.25, 0.25]
-reward:
-  scales:
-    motion_global_root_pos: 1.0
-    motion_global_root_ori: 0.5
-    motion_body_pos: 1.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.05
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
\ No newline at end of file
diff --git a/conf/ppo/task/g1_23dof_motion_tracking/mujoco.yaml b/conf/ppo/task/g1_23dof_motion_tracking/mujoco.yaml
deleted file mode 100644
index 3a411cd18..000000000
--- a/conf/ppo/task/g1_23dof_motion_tracking/mujoco.yaml
+++ /dev/null
@@ -1,34 +0,0 @@
-# @package _global_
-training:
-  task_name: G1MotionTracking23Dof
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 15000
-  save_interval: 500
-  obs_groups:
-    actor:
-      - actor
-  algorithm:
-    entropy_coef: 0.005
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 1.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.1
-    joint_limit: -10.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
\ No newline at end of file
diff --git a/conf/ppo/task/g1_23dof_motion_tracking_deploy/motrix.yaml b/conf/ppo/task/g1_23dof_motion_tracking_deploy/motrix.yaml
deleted file mode 100644
index a0359647e..000000000
--- a/conf/ppo/task/g1_23dof_motion_tracking_deploy/motrix.yaml
+++ /dev/null
@@ -1,101 +0,0 @@
-# @package _global_
-training:
-  task_name: G1MotionTracking23DofDeploy
-  sim_backend: motrix
-  play_env_num: 16
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 15000
-  save_interval: 500
-  obs_groups:
-    actor:
-      - actor
-  algorithm:
-    entropy_coef: 0.005
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 1.5
-    scale_gyro: 0.2
-env:
-  sim_dt: 0.005
-  sensor:
-    local_linvel: pelvis_local_linvel
-    gyro: pelvis_gyro
-    upvector: pelvis_upvector
-  domain_rand:
-    random_com: true
-    com_offset_x: [-0.025, 0.025]
-    com_offset_y: [-0.05, 0.05]
-    com_offset_z: [-0.05, 0.05]
-    randomize_base_mass: true
-    added_mass_range: [-1.5, 1.5]
-    push_robots: true
-    push_interval: 750
-    max_force: [1.0, 1.0, 0.5]
-    randomize_joint_default_pos: true
-    joint_default_pos_range: [-0.01, 0.01]
-  noise_config:
-    scale_joint_angle: 0.01
-    scale_joint_vel: 0.5
-    scale_gyro: 0.2
-    scale_linvel: 0.5
-    scale_gravity: 0.05
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.5
-  scene:
-    enabled: true
-    source_model_file: src/unilab/assets/robots/g1/scene_flat_23dof.xml
-    ground_texture_file: src/unilab/assets/robots/g1/textures/floor.png
-    skybox_rgb1: [0.90, 0.90, 0.91]
-    skybox_rgb2: [0.68, 0.68, 0.70]
-    ground_texrepeat: [0.25, 0.25]
-reward:
-  scales:
-    motion_global_root_pos: 1.0
-    motion_global_root_ori: 0.5
-    motion_body_pos: 1.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.05
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/ppo/task/g1_23dof_motion_tracking_deploy/mujoco.yaml b/conf/ppo/task/g1_23dof_motion_tracking_deploy/mujoco.yaml
deleted file mode 100644
index 4c725f29d..000000000
--- a/conf/ppo/task/g1_23dof_motion_tracking_deploy/mujoco.yaml
+++ /dev/null
@@ -1,85 +0,0 @@
-# @package _global_
-training:
-  task_name: G1MotionTracking23DofDeploy
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 15000
-  save_interval: 500
-  obs_groups:
-    actor:
-      - actor
-  algorithm:
-    entropy_coef: 0.005
-env:
-  sim_dt: 0.005
-  sensor:
-    local_linvel: pelvis_local_linvel
-    gyro: pelvis_gyro
-    upvector: pelvis_upvector
-  domain_rand:
-      random_com: true
-      com_offset_x: [-0.025, 0.025]
-      com_offset_y: [-0.05, 0.05]
-      com_offset_z: [-0.05, 0.05]
-      randomize_base_mass: true
-      added_mass_range: [-1.5, 1.5]
-      push_robots: true
-      push_interval: 750
-      max_force: [1.0, 1.0, 0.5]
-      randomize_geom_friction: true
-      friction_range: [0.3, 1.2]
-      randomize_joint_default_pos: true
-      joint_default_pos_range: [-0.01, 0.01]
-  noise_config:
-      scale_joint_angle: 0.01
-      scale_joint_vel: 0.5
-      scale_gyro: 0.2
-      scale_linvel: 0.5
-      scale_gravity: 0.05
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 1.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.1
-    joint_limit: -10.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/ppo/task/g1_23dof_walk_flat/motrix.yaml b/conf/ppo/task/g1_23dof_walk_flat/motrix.yaml
deleted file mode 100644
index ab1c8a94f..000000000
--- a/conf/ppo/task/g1_23dof_walk_flat/motrix.yaml
+++ /dev/null
@@ -1,64 +0,0 @@
-# @package _global_
-training:
-  task_name: G1Walk23DofFlat
-  sim_backend: motrix
-algo:
-  num_envs: 2048
-  max_iterations: 2200
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - policy
-    critic:
-      - critic
-  policy:
-    init_noise_std: 0.5
-  algorithm:
-    learning_rate: 3.0e-4
-    entropy_coef: 5.0e-3
-env:
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
-  control_config:
-    action_scale: 0.5
-  commands:
-    vel_limit:
-      - [0.4, 0.0, 0.0]
-      - [0.7, 0.0, 0.0]
-  gait_phase_init_mode: offset_phase
-  reset_base_qvel_limit: 0.05
-  curriculum:
-    enabled: false
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 1.5
-    scale_gyro: 0.2
-reward:
-  scales:
-    tracking_lin_vel: 2.0
-    tracking_ang_vel: 0.25
-    forward_progress: 0.0
-    under_speed: -0.2
-    upper_body_pose: -0.05
-    penalty_feet_ori: 0.0
-    feet_phase: 1.2
-    feet_phase_contrast: 1.5
-    feet_phase_contact: 1.0
-    feet_double_stance: -1.0
-    lin_vel_z: -1.0
-    ang_vel_xy: -0.2
-    base_height: -120.0
-    orientation: -2.5
-    action_rate: -0.005
-    pose: -0.05
-  pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
-  tracking_sigma: 0.25
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.008
-  base_height_target: 0.765
-  min_forward_speed_for_gait_reward: 0.05
-  min_base_height: 0.5
-  max_tilt_deg: 35.0
diff --git a/conf/ppo/task/g1_23dof_walk_flat/mujoco.yaml b/conf/ppo/task/g1_23dof_walk_flat/mujoco.yaml
deleted file mode 100644
index 0397b885d..000000000
--- a/conf/ppo/task/g1_23dof_walk_flat/mujoco.yaml
+++ /dev/null
@@ -1,39 +0,0 @@
-# @package _global_
-training:
-  task_name: G1Walk23DofFlat
-  sim_backend: mujoco
-algo:
-  num_envs: 2048
-  max_iterations: 2200
-  obs_groups:
-    actor:
-      - actor
-env:
-  control_config:
-    action_scale: 0.25
-  curriculum:
-    enabled: false
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 1.5
-    scale_gyro: 0.2
-reward:
-  scales:
-    tracking_lin_vel: 2.0
-    tracking_ang_vel: 0.2
-    feet_phase: 1.0
-    lin_vel_z: -1.0
-    ang_vel_xy: -0.25
-    base_height: -500.0
-    orientation: -5.0
-    action_rate: -0.01
-    pose: -0.1
-  tracking_sigma: 0.25
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.008
-  base_height_target: 0.754
-  min_base_height: 0.55
-  max_tilt_deg: 25.0
-  pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/conf/ppo/task/g1_23dof_walk_rough/mujoco.yaml b/conf/ppo/task/g1_23dof_walk_rough/mujoco.yaml
deleted file mode 100644
index ea82c6792..000000000
--- a/conf/ppo/task/g1_23dof_walk_rough/mujoco.yaml
+++ /dev/null
@@ -1,45 +0,0 @@
-# @package _global_
-training:
-  task_name: G1Walk23DofRough
-  sim_backend: mujoco
-algo:
-  num_envs: 2048
-  max_iterations: 2200
-  obs_groups:
-    actor:
-      - actor
-env:
-  control_config:
-    action_scale: 0.25
-  curriculum:
-    enabled: true
-    initial_scale: 0.5
-    min_scale: 0.5
-    max_scale: 1.0
-    level_down_threshold: 150.0
-    level_up_threshold: 750.0
-    degree: 0.001
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 1.5
-    scale_gyro: 0.2
-reward:
-  scales:
-    tracking_lin_vel: 2.0
-    tracking_ang_vel: 0.2
-    feet_phase: 1.0
-    lin_vel_z: -1.0
-    ang_vel_xy: -0.25
-    base_height: -500.0
-    orientation: -5.0
-    action_rate: -0.01
-    pose: -0.1
-  tracking_sigma: 0.25
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.008
-  base_height_target: 0.754
-  min_base_height: 0.55
-  max_tilt_deg: 25.0
-  pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/conf/ppo/task/g1_23dof_wall_flip_tracking/motrix.yaml b/conf/ppo/task/g1_23dof_wall_flip_tracking/motrix.yaml
deleted file mode 100644
index 04c69a001..000000000
--- a/conf/ppo/task/g1_23dof_wall_flip_tracking/motrix.yaml
+++ /dev/null
@@ -1,88 +0,0 @@
-# @package _global_
-training:
-  task_name: G1WallFlipTracking23Dof
-  sim_backend: motrix
-  play_env_num: 16
-  play_steps: 1000
-  render_spacing: 3.0
-algo:
-  num_envs: 1024
-  max_iterations: 12000
-  save_interval: 500
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-  algorithm:
-    entropy_coef: 0.005
-    desired_kl: 0.01
-env:
-  motrix_max_iterations: 3
-  sampling_mode: start
-  truncate_on_clip_end: false
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-  anchor_pos_z_threshold: 0.5
-  ee_body_pos_z_threshold: 0.5
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 4.0
-  scene:
-    enabled: true
-    source_model_file: src/unilab/assets/robots/g1/scene_flat_23dof_with_wall.xml
-    ground_texture_file: src/unilab/assets/robots/g1/textures/floor.png
-    skybox_rgb1: [0.90, 0.90, 0.91]
-    skybox_rgb2: [0.68, 0.68, 0.70]
-    ground_texrepeat: [0.25, 0.25]
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.5
-    motion_joint_vel: 0.25
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/ppo/task/g1_23dof_wall_flip_tracking/mujoco.yaml b/conf/ppo/task/g1_23dof_wall_flip_tracking/mujoco.yaml
deleted file mode 100644
index 7a49d091d..000000000
--- a/conf/ppo/task/g1_23dof_wall_flip_tracking/mujoco.yaml
+++ /dev/null
@@ -1,74 +0,0 @@
-# @package _global_
-training:
-  task_name: G1WallFlipTracking23Dof
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 20000
-  save_interval: 500
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-  algorithm:
-    entropy_coef: 0.005
-    desired_kl: 0.01
-env:
-  sampling_mode: start
-  truncate_on_clip_end: false
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-  anchor_pos_z_threshold: 0.5
-  ee_body_pos_z_threshold: 0.5
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.5
-    motion_joint_vel: 0.25
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/ppo/task/g1_box_tracking/motrix.yaml b/conf/ppo/task/g1_box_tracking/motrix.yaml
deleted file mode 100644
index 863ac1215..000000000
--- a/conf/ppo/task/g1_box_tracking/motrix.yaml
+++ /dev/null
@@ -1,60 +0,0 @@
-# @package _global_
-training:
-  task_name: G1BoxTracking
-  sim_backend: motrix
-  play_env_num: 16
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 40000
-  save_interval: 500
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-  algorithm:
-    entropy_coef: 0.002
-    desired_kl: 0.01
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 1.5
-    scale_gyro: 0.2
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.5
-  scene:
-    enabled: true
-    source_model_file: src/unilab/assets/robots/g1/scene_flat_with_largebox.xml
-    ground_texture_file: src/unilab/assets/robots/g1/textures/floor.png
-    skybox_rgb1: [0.90, 0.90, 0.91]
-    skybox_rgb2: [0.68, 0.68, 0.70]
-    ground_texrepeat: [0.25, 0.25]
-reward:
-  scales:
-    motion_global_root_pos: 1.0
-    motion_global_root_ori: 0.5
-    motion_body_pos: 1.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.5
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.1
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-    object_global_ref_position_error_exp: 4.0
-    object_global_ref_orientation_error_exp: 3.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
-  std_object_pos: 0.12
-  std_object_ori: 0.2
diff --git a/conf/ppo/task/g1_box_tracking/mujoco.yaml b/conf/ppo/task/g1_box_tracking/mujoco.yaml
deleted file mode 100644
index 278a8b8f3..000000000
--- a/conf/ppo/task/g1_box_tracking/mujoco.yaml
+++ /dev/null
@@ -1,48 +0,0 @@
-# @package _global_
-training:
-  task_name: G1BoxTracking
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 30000
-  save_interval: 500
-  obs_groups:
-    actor:
-      - actor
-  algorithm:
-    entropy_coef: 0.005
-env:
-  sim_dt: 0.005
-  sensor:
-    gyro: pelvis_gyro
-    upvector: pelvis_upvector
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 1.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.1
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-    object_global_ref_position_error_exp: 2.0
-    object_global_ref_orientation_error_exp: 2.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
-  std_object_pos: 0.2
-  std_object_ori: 0.3
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/g1_climb_tracking/motrix.yaml b/conf/ppo/task/g1_climb_tracking/motrix.yaml
deleted file mode 100644
index 0c30db38e..000000000
--- a/conf/ppo/task/g1_climb_tracking/motrix.yaml
+++ /dev/null
@@ -1,84 +0,0 @@
-# @package _global_
-training:
-  task_name: G1ClimbTracking
-  sim_backend: motrix
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 20000
-  save_interval: 500
-  empirical_normalization: true
-  obs_groups:
-    actor:
-    - actor
-    critic:
-    - critic
-  algorithm:
-    entropy_coef: 0.005
-    desired_kl: 0.01
-env:
-  sampling_mode: adaptive
-  truncate_on_clip_end: false
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.5475464629911068
-    - 0.35066146637882434
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.5475464629911068
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.07450087032950714
-    - 0.07450087032950714
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.43857731392336724
-    - 0.07450087032950714
-    - 0.07450087032950714
-  anchor_pos_z_threshold: 0.3
-  ee_body_pos_z_threshold: 0.3
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.5
-    motion_joint_vel: 0.25
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/g1_climb_tracking/mujoco.yaml b/conf/ppo/task/g1_climb_tracking/mujoco.yaml
deleted file mode 100644
index b7111e85a..000000000
--- a/conf/ppo/task/g1_climb_tracking/mujoco.yaml
+++ /dev/null
@@ -1,84 +0,0 @@
-# @package _global_
-training:
-  task_name: G1ClimbTracking
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 20000
-  save_interval: 500
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-  algorithm:
-    entropy_coef: 0.005
-    desired_kl: 0.01
-env:
-  sampling_mode: adaptive
-  truncate_on_clip_end: false
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-  anchor_pos_z_threshold: 0.3
-  ee_body_pos_z_threshold: 0.3
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.5
-    motion_joint_vel: 0.25
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/g1_flip_tracking/motrix.yaml b/conf/ppo/task/g1_flip_tracking/motrix.yaml
deleted file mode 100644
index 3435bd57a..000000000
--- a/conf/ppo/task/g1_flip_tracking/motrix.yaml
+++ /dev/null
@@ -1,38 +0,0 @@
-# @package _global_
-training:
-  task_name: G1FlipTracking
-  sim_backend: motrix
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 30000
-  save_interval: 500
-  obs_groups:
-    actor:
-      - actor
-  algorithm:
-    entropy_coef: 0.005
-reward:
-  scales:
-    motion_global_root_pos: 1.0
-    motion_global_root_ori: 0.5
-    motion_body_pos: 1.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.05
-    joint_limit: -10.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/g1_flip_tracking/mujoco.yaml b/conf/ppo/task/g1_flip_tracking/mujoco.yaml
deleted file mode 100644
index 1edd403da..000000000
--- a/conf/ppo/task/g1_flip_tracking/mujoco.yaml
+++ /dev/null
@@ -1,84 +0,0 @@
-# @package _global_
-training:
-  task_name: G1FlipTracking
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 20000
-  save_interval: 500
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-  algorithm:
-    entropy_coef: 0.005
-    desired_kl: 0.01
-env:
-  sampling_mode: start
-  truncate_on_clip_end: false
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-  anchor_pos_z_threshold: 0.5
-  ee_body_pos_z_threshold: 0.5
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/g1_motion_tracking/motrix.yaml b/conf/ppo/task/g1_motion_tracking/motrix.yaml
deleted file mode 100644
index b4e531856..000000000
--- a/conf/ppo/task/g1_motion_tracking/motrix.yaml
+++ /dev/null
@@ -1,53 +0,0 @@
-# @package _global_
-training:
-  task_name: G1MotionTracking
-  sim_backend: motrix
-  play_env_num: 16
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 15000
-  save_interval: 500
-  obs_groups:
-    actor:
-      - actor
-  algorithm:
-    entropy_coef: 0.005
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 1.5
-    scale_gyro: 0.2
-env:
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.5
-  scene:
-    enabled: true
-    source_model_file: src/unilab/assets/robots/g1/scene_flat.xml
-    ground_texture_file: src/unilab/assets/robots/g1/textures/floor.png
-    skybox_rgb1: [0.90, 0.90, 0.91]
-    skybox_rgb2: [0.68, 0.68, 0.70]
-    ground_texrepeat: [0.25, 0.25]
-reward:
-  scales:
-    motion_global_root_pos: 1.0
-    motion_global_root_ori: 0.5
-    motion_body_pos: 1.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.05
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/ppo/task/g1_motion_tracking/mujoco.yaml b/conf/ppo/task/g1_motion_tracking/mujoco.yaml
deleted file mode 100644
index 54ffea9fd..000000000
--- a/conf/ppo/task/g1_motion_tracking/mujoco.yaml
+++ /dev/null
@@ -1,39 +0,0 @@
-# @package _global_
-training:
-  task_name: G1MotionTracking
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 15000
-  save_interval: 500
-  obs_groups:
-    actor:
-      - actor
-  algorithm:
-    entropy_coef: 0.005
-env:
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 1.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.1
-    joint_limit: -10.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/g1_motion_tracking_deploy/motrix.yaml b/conf/ppo/task/g1_motion_tracking_deploy/motrix.yaml
deleted file mode 100644
index 6ebc34b25..000000000
--- a/conf/ppo/task/g1_motion_tracking_deploy/motrix.yaml
+++ /dev/null
@@ -1,107 +0,0 @@
-# @package _global_
-training:
-  task_name: G1MotionTrackingDeploy
-  sim_backend: motrix
-  play_env_num: 16
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 15000
-  save_interval: 500
-  obs_groups:
-    actor:
-      - actor
-  algorithm:
-    entropy_coef: 0.005
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 1.5
-    scale_gyro: 0.2
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.5
-  scene:
-    enabled: true
-    source_model_file: src/unilab/assets/robots/g1/scene_flat.xml
-    ground_texture_file: src/unilab/assets/robots/g1/textures/floor.png
-    skybox_rgb1: [0.90, 0.90, 0.91]
-    skybox_rgb2: [0.68, 0.68, 0.70]
-    ground_texrepeat: [0.25, 0.25]
-env:
-  sim_dt: 0.005
-  sensor:
-    local_linvel: pelvis_local_linvel
-    gyro: pelvis_gyro
-    upvector: pelvis_upvector
-  domain_rand:
-    random_com: true
-    com_offset_x: [-0.025, 0.025]
-    com_offset_y: [-0.05, 0.05]
-    com_offset_z: [-0.05, 0.05]
-    randomize_base_mass: true
-    added_mass_range: [-1.5, 1.5]
-    push_robots: true
-    push_interval: 750
-    max_force: [1.0, 1.0, 0.5]
-    randomize_joint_default_pos: true
-    joint_default_pos_range: [-0.01, 0.01]
-    # randomize_geom_friction omitted: Motrix does not reliably support it
-  noise_config:
-    scale_joint_angle: 0.01
-    scale_joint_vel: 0.5
-    scale_gyro: 0.2
-    scale_linvel: 0.5
-    scale_gravity: 0.05
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 1.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.1
-    joint_limit: -10.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/ppo/task/g1_motion_tracking_deploy/mujoco.yaml b/conf/ppo/task/g1_motion_tracking_deploy/mujoco.yaml
deleted file mode 100644
index 39a10a9ca..000000000
--- a/conf/ppo/task/g1_motion_tracking_deploy/mujoco.yaml
+++ /dev/null
@@ -1,91 +0,0 @@
-# @package _global_
-training:
-  task_name: G1MotionTrackingDeploy
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 15000
-  save_interval: 500
-  obs_groups:
-    actor:
-      - actor
-  algorithm:
-    entropy_coef: 0.005
-env:
-  sim_dt: 0.005
-  sensor:
-    local_linvel: pelvis_local_linvel
-    gyro: pelvis_gyro
-    upvector: pelvis_upvector
-  domain_rand:
-      random_com: true
-      com_offset_x: [-0.025, 0.025]
-      com_offset_y: [-0.05, 0.05]
-      com_offset_z: [-0.05, 0.05]
-      randomize_base_mass: true
-      added_mass_range: [-1.5, 1.5]
-      push_robots: true
-      push_interval: 750
-      max_force: [1.0, 1.0, 0.5]
-      randomize_geom_friction: true
-      friction_range: [0.3, 1.2]
-      randomize_joint_default_pos: true
-      joint_default_pos_range: [-0.01, 0.01]
-  noise_config:
-      scale_joint_angle: 0.01
-      scale_joint_vel: 0.5
-      scale_gyro: 0.2
-      scale_linvel: 0.5
-      scale_gravity: 0.05
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 1.0
-    motion_body_ori: 1.0
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_joint_pos: 0.0
-    motion_joint_vel: 0.0
-    action_rate_l2: -0.1
-    joint_limit: -10.0
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/ppo/task/g1_walk_flat/mjwarp.yaml b/conf/ppo/task/g1_walk_flat/mjwarp.yaml
deleted file mode 100644
index 2d8c3a9d7..000000000
--- a/conf/ppo/task/g1_walk_flat/mjwarp.yaml
+++ /dev/null
@@ -1,58 +0,0 @@
-# @package _global_
-# Configured-only mjwarp owner for the unified host contract adapter. Offline
-# record reuses MuJoCo rendering; native playback and device-resident runtime
-# routing are intentionally absent.
-training:
-  task_name: G1WalkFlat
-  sim_backend: mjwarp
-  play_render_mode: record
-algo:
-  num_envs: 2048
-  max_iterations: 2200
-  empirical_normalization: false
-  obs_groups:
-    actor:
-      - actor
-  policy:
-    actor_hidden_dims: [512, 256, 128]
-    critic_hidden_dims: [512, 256, 128]
-env:
-  mjwarp_nconmax: 128
-  mjwarp_njmax: 256
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
-    randomize_dof_armature: false
-    randomize_body_gravity_compensation: false
-  control_config:
-    action_scale: 0.25
-  curriculum:
-    enabled: false
-  noise_config:
-    level: 0.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 1.5
-    scale_gyro: 0.2
-reward:
-  scales:
-    tracking_lin_vel: 2.0
-    tracking_ang_vel: 0.2
-    feet_phase: 1.0
-    lin_vel_z: -1.0
-    ang_vel_xy: -0.25
-    base_height: -500.0
-    orientation: -5.0
-    action_rate: -0.01
-    pose: -0.1
-  tracking_sigma: 0.25
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.008
-  base_height_target: 0.754
-  min_base_height: 0.55
-  max_tilt_deg: 25.0
-  pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/g1_walk_flat/motrix.yaml b/conf/ppo/task/g1_walk_flat/motrix.yaml
deleted file mode 100644
index 88c971936..000000000
--- a/conf/ppo/task/g1_walk_flat/motrix.yaml
+++ /dev/null
@@ -1,72 +0,0 @@
-# @package _global_
-# Standalone Motrix owner config: carries the shared contract inline, then
-# overrides contract fields for Motrix-specific tuning
-# (intentionally non-transferable from MuJoCo; drop overrides to restore parity).
-training:
-  task_name: G1WalkFlat
-  sim_backend: motrix
-algo:
-  num_envs: 2048
-  max_iterations: 2200
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - policy
-    critic:
-      - critic
-  policy:
-    actor_hidden_dims: [512, 256, 128]
-    critic_hidden_dims: [512, 256, 128]
-    init_noise_std: 0.5
-  algorithm:
-    learning_rate: 3.0e-4
-    entropy_coef: 5.0e-3
-env:
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
-  control_config:
-    action_scale: 0.5
-  commands:
-    vel_limit:
-      - [0.4, 0.0, 0.0]
-      - [0.7, 0.0, 0.0]
-  gait_phase_init_mode: offset_phase
-  reset_base_qvel_limit: 0.05
-  curriculum:
-    enabled: false
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 1.5
-    scale_gyro: 0.2
-reward:
-  scales:
-    tracking_lin_vel: 2.0
-    tracking_ang_vel: 0.25
-    forward_progress: 0.0
-    under_speed: -0.2
-    upper_body_pose: -0.05
-    penalty_feet_ori: 0.0
-    feet_phase: 1.2
-    feet_phase_contrast: 1.5
-    feet_phase_contact: 1.0
-    feet_double_stance: -1.0
-    lin_vel_z: -1.0
-    ang_vel_xy: -0.2
-    base_height: -120.0
-    orientation: -2.5
-    action_rate: -0.005
-    pose: -0.05
-  tracking_sigma: 0.25
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.008
-  base_height_target: 0.765
-  min_forward_speed_for_gait_reward: 0.05
-  min_base_height: 0.5
-  max_tilt_deg: 35.0
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/g1_walk_flat/mujoco.yaml b/conf/ppo/task/g1_walk_flat/mujoco.yaml
deleted file mode 100644
index 843d7b85f..000000000
--- a/conf/ppo/task/g1_walk_flat/mujoco.yaml
+++ /dev/null
@@ -1,49 +0,0 @@
-# @package _global_
-# Standalone MuJoCo owner config: carries the shared cross-backend contract inline
-# (formerly base.yaml), plus backend-specific tuning.
-training:
-  task_name: G1WalkFlat
-  sim_backend: mujoco
-algo:
-  num_envs: 2048
-  max_iterations: 2200
-  empirical_normalization: false
-  obs_groups:
-    actor:
-      - actor
-  policy:
-    actor_hidden_dims: [512, 256, 128]
-    critic_hidden_dims: [512, 256, 128]
-env:
-  control_config:
-    action_scale: 0.25
-  curriculum:
-    enabled: false
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 1.5
-    scale_gyro: 0.2
-reward:
-  scales:
-    tracking_lin_vel: 2.0
-    tracking_ang_vel: 0.2
-    feet_phase: 1.0
-    lin_vel_z: -1.0
-    ang_vel_xy: -0.25
-    base_height: -500.0
-    orientation: -5.0
-    action_rate: -0.01
-    pose: -0.1
-  tracking_sigma: 0.25
-  gait_frequency: 1.5
-  feet_phase_swing_height: 0.09
-  feet_phase_tracking_sigma: 0.008
-  base_height_target: 0.754
-  min_base_height: 0.55
-  max_tilt_deg: 25.0
-  pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/g1_wall_flip_tracking/motrix.yaml b/conf/ppo/task/g1_wall_flip_tracking/motrix.yaml
deleted file mode 100644
index c595203a6..000000000
--- a/conf/ppo/task/g1_wall_flip_tracking/motrix.yaml
+++ /dev/null
@@ -1,94 +0,0 @@
-# @package _global_
-training:
-  task_name: G1WallFlipTracking
-  sim_backend: motrix
-  play_env_num: 16
-  play_steps: 1000
-  render_spacing: 3.0
-algo:
-  num_envs: 1024
-  max_iterations: 12000
-  save_interval: 500
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-  algorithm:
-    entropy_coef: 0.005
-    desired_kl: 0.01
-env:
-  motrix_max_iterations: 3
-  sampling_mode: start
-  truncate_on_clip_end: false
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-  anchor_pos_z_threshold: 0.5
-  ee_body_pos_z_threshold: 0.5
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 4.0
-  scene:
-    enabled: true
-    source_model_file: src/unilab/assets/robots/g1/scene_flat_with_wall.xml
-    ground_texture_file: src/unilab/assets/robots/g1/textures/floor.png
-    skybox_rgb1: [0.90, 0.90, 0.91]
-    skybox_rgb2: [0.68, 0.68, 0.70]
-    ground_texrepeat: [0.25, 0.25]
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.5
-    motion_joint_vel: 0.25
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/ppo/task/g1_wall_flip_tracking/mujoco.yaml b/conf/ppo/task/g1_wall_flip_tracking/mujoco.yaml
deleted file mode 100644
index e11d9b05b..000000000
--- a/conf/ppo/task/g1_wall_flip_tracking/mujoco.yaml
+++ /dev/null
@@ -1,84 +0,0 @@
-# @package _global_
-training:
-  task_name: G1WallFlipTracking
-  sim_backend: mujoco
-  play_steps: 1000
-algo:
-  num_envs: 1024
-  max_iterations: 20000
-  save_interval: 500
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-  algorithm:
-    entropy_coef: 0.005
-    desired_kl: 0.01
-env:
-  sampling_mode: start
-  truncate_on_clip_end: false
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.5475464629911068
-      - 0.35066146637882434
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.5475464629911068
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.43857731392336724
-      - 0.07450087032950714
-      - 0.07450087032950714
-  anchor_pos_z_threshold: 0.5
-  ee_body_pos_z_threshold: 0.5
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.5
-    motion_joint_vel: 0.25
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/go1_joystick_flat/drake.yaml b/conf/ppo/task/go1_joystick_flat/drake.yaml
deleted file mode 100644
index 3c9f63c80..000000000
--- a/conf/ppo/task/go1_joystick_flat/drake.yaml
+++ /dev/null
@@ -1,48 +0,0 @@
-# @package _global_
-training:
-  task_name: Go1JoystickFlat
-  sim_backend: drake
-  play_steps: 500
-  play_env_num: 16
-  render_spacing: 0.0
-  cam_tracking: true
-  cam_tracking_env_idx: 0
-  cam_tracking_extra_envs: 9
-
-interactive:
-  action_mode: policy
-  policy_obs_mode: auto
-  camera_follow_body: true
-  use_env_visual_model: false
-
-algo:
-  num_envs: 1024
-  num_steps_per_env: 24
-  max_iterations: 151
-  save_interval: 100
-  obs_groups:
-    actor:
-      - actor
-
-env:
-  drake_backend_mode: batch
-  drake_nthread: 0
-  scene:
-    model_file: src/unilab/assets/robots/go1/scene_flat.xml
-  domain_rand:
-    randomize_base_mass: false
-    random_com: false
-    push_robots: false
-
-reward:
-  scales:
-    tracking_lin_vel: 1.0
-    tracking_ang_vel: 0.2
-    lin_vel_z: -5.0
-    ang_vel_xy: -0.1
-    base_height: -100.0
-    action_rate: -0.005
-    similar_to_default: -0.1
-    swing_feet_z: 4.0
-  tracking_sigma: 0.25
-  base_height_target: 0.3
diff --git a/conf/ppo/task/go1_joystick_flat/motrix.yaml b/conf/ppo/task/go1_joystick_flat/motrix.yaml
deleted file mode 100644
index d84ba1b03..000000000
--- a/conf/ppo/task/go1_joystick_flat/motrix.yaml
+++ /dev/null
@@ -1,49 +0,0 @@
-# @package _global_
-training:
-  task_name: Go1JoystickFlat
-  sim_backend: motrix
-  play_steps: 500
-  play_env_num: 16
-  cam_tracking: true
-  cam_tracking_env_idx: 0
-  cam_tracking_extra_envs: 9
-
-interactive:
-  action_mode: policy
-  policy_obs_mode: auto
-  camera_follow_body: true
-  use_env_visual_model: false
-
-algo:
-  num_envs: 1024
-  max_iterations: 151
-  obs_groups:
-    actor:
-      - actor
-  empirical_normalization: true
-  policy:
-    init_noise_std: 0.5
-  algorithm:
-    learning_rate: 3.0e-4
-    entropy_coef: 1.0e-3
-env:
-  commands:
-    vel_limit:
-      - [0.5, 0.0, 0.0]
-      - [0.5, 0.0, 0.0]
-reward:
-  scales:
-    tracking_lin_vel: 1.0
-    tracking_ang_vel: 0.2
-    lin_vel_z: -5.0
-    ang_vel_xy: -0.1
-    base_height: -100.0
-    action_rate: -0.005
-    similar_to_default: -0.1
-    swing_feet_z: 4.0
-  tracking_sigma: 0.25
-  base_height_target: 0.3
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/go1_joystick_flat/mujoco.yaml b/conf/ppo/task/go1_joystick_flat/mujoco.yaml
deleted file mode 100644
index 794266ad3..000000000
--- a/conf/ppo/task/go1_joystick_flat/mujoco.yaml
+++ /dev/null
@@ -1,40 +0,0 @@
-# @package _global_
-training:
-  task_name: Go1JoystickFlat
-  sim_backend: mujoco
-  play_steps: 500
-  play_env_num: 16
-  render_spacing: 0.0
-  cam_tracking: true
-  cam_tracking_env_idx: 0
-  cam_tracking_extra_envs: 9
-
-interactive:
-  action_mode: policy
-  policy_obs_mode: auto
-  camera_follow_body: true
-  use_env_visual_model: false
-
-algo:
-  num_envs: 1024
-  max_iterations: 151
-  obs_groups:
-    actor:
-      - actor
-reward:
-  scales:
-    tracking_lin_vel: 1.0
-    tracking_ang_vel: 0.2
-    lin_vel_z: -5.0
-    ang_vel_xy: -0.1
-    base_height: -100.0
-    action_rate: -0.005
-    similar_to_default: -0.1
-    contact: 0.24
-    swing_feet_z: 4.0
-  tracking_sigma: 0.25
-  base_height_target: 0.3
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/go1_joystick_rough/motrix.yaml b/conf/ppo/task/go1_joystick_rough/motrix.yaml
deleted file mode 100644
index 21be8340a..000000000
--- a/conf/ppo/task/go1_joystick_rough/motrix.yaml
+++ /dev/null
@@ -1,121 +0,0 @@
-# @package _global_
-training:
-  task_name: Go1JoystickRough
-  sim_backend: motrix
-  play_steps: 500
-  play_env_num: 16
-  cam_tracking: true
-  cam_tracking_env_idx: 0
-  cam_tracking_extra_envs: 9
-
-interactive:
-  action_mode: policy
-  policy_obs_mode: auto
-  camera_follow_body: true
-  use_env_visual_model: false
-
-algo:
-  num_envs: 2048
-  num_steps_per_env: 24
-  max_iterations: 1000
-  empirical_normalization: false
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-  policy:
-    init_noise_std: 1.0
-  algorithm:
-    learning_rate: 1.0e-3
-    entropy_coef: 1.0e-2
-
-env:
-  render_offset_mode: zero
-  control_config:
-    action_scale: 0.25
-    hip_action_scale: 0.125
-    non_hip_action_scale: 0.25
-    clip_actions: 100.0
-  commands:
-    vel_limit: [[-1.0, -1.0, -1.0], [1.0, 1.0, 1.0]]
-    resampling_time: 10.0
-    heading_command: true
-    heading_range: [-3.141592653589793, 3.141592653589793]
-    rel_standing_envs: 0.1
-  terrain_curriculum:
-    enabled: false
-  scene:
-    model_file: src/unilab/assets/robots/go1/go1.xml
-    fragment_files:
-      - src/unilab/assets/robots/go1/locomotion_task.xml
-    terrain:
-      hfield_name: terrain_hfield
-      geom_name: floor
-      generator:
-        seed: 42
-        curriculum: false
-        size: [8.0, 8.0]
-        num_rows: 6
-        num_cols: 6
-        border_width: 20.0
-  terrain_scan:
-    enabled: true
-    geom_name: floor
-  termination_config:
-    terrain_out_of_bounds: true
-    terrain_distance_buffer: 3.0
-  domain_rand:
-    randomize_base_mass: true
-    added_mass_range: [-1.0, 3.0]
-    random_com: true
-    randomize_kp: true
-    kp_multiplier_range: [0.5, 2.0]
-    randomize_kd: true
-    kd_multiplier_range: [0.5, 2.0]
-    push_robots: true
-    push_interval: 625
-    max_force: [1.0, 1.0, 0.5]
-
-reward:
-  scales:
-    lin_vel_z: -2.0
-    ang_vel_xy: -0.05
-    joint_torques_l2: -2.5e-5
-    joint_acc_l2: -2.5e-7
-    joint_power: -2.0e-5
-    stand_still: -2.0
-    hip_pos: -0.5
-    joint_pos_penalty: -1.0
-    joint_mirror: -0.05
-    action_rate: -0.01
-    undesired_contacts: -1.0
-    contact_forces: -1.5e-4
-    tracking_lin_vel: 3.0
-    tracking_ang_vel: 1.5
-    feet_air_time: 0.5
-    feet_air_time_variance: -1.0
-    feet_contact_without_cmd: 0.1
-    feet_slide: -0.1
-    feet_height_body: -5.0
-    feet_gait: 0.5
-    upward: 1.0
-  tracking_sigma: 0.25
-  base_height_target: 0.33
-  stand_still_command_threshold: 0.1
-  joint_pos_penalty_stand_still_scale: 5.0
-  joint_pos_penalty_velocity_threshold: 0.5
-  joint_pos_penalty_command_threshold: 0.1
-  contact_threshold: 1.0
-  contact_forces_threshold: 100.0
-  feet_air_time_threshold: 0.5
-  feet_height_body_target: -0.2
-  feet_height_body_tanh_mult: 2.0
-  feet_gait_std: 0.7071067811865476
-  feet_gait_max_err: 0.2
-  feet_gait_velocity_threshold: 0.5
-  feet_gait_command_threshold: 0.1
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/go1_joystick_rough/mujoco.yaml b/conf/ppo/task/go1_joystick_rough/mujoco.yaml
deleted file mode 100644
index 04a9dc179..000000000
--- a/conf/ppo/task/go1_joystick_rough/mujoco.yaml
+++ /dev/null
@@ -1,122 +0,0 @@
-# @package _global_
-training:
-  task_name: Go1JoystickRough
-  sim_backend: mujoco
-  play_steps: 500
-  play_env_num: 16
-  render_spacing: 0.0
-  cam_tracking: true
-  cam_tracking_env_idx: 0
-  cam_tracking_extra_envs: 9
-
-interactive:
-  action_mode: policy
-  policy_obs_mode: auto
-  camera_follow_body: true
-  use_env_visual_model: false
-
-algo:
-  num_envs: 2048
-  num_steps_per_env: 24
-  max_iterations: 1000
-  empirical_normalization: false
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-  policy:
-    init_noise_std: 1.0
-  algorithm:
-    learning_rate: 1.0e-3
-    entropy_coef: 1.0e-2
-
-env:
-  sim_dt: 0.005
-  control_config:
-    action_scale: 0.25
-    hip_action_scale: 0.125
-    non_hip_action_scale: 0.25
-    clip_actions: 100.0
-  commands:
-    vel_limit: [[-1.0, -1.0, -1.0], [1.0, 1.0, 1.0]]
-    resampling_time: 10.0
-    heading_command: true
-    heading_range: [-3.141592653589793, 3.141592653589793]
-    rel_standing_envs: 0.1
-  terrain_curriculum:
-    enabled: false
-  scene:
-    model_file: src/unilab/assets/robots/go1/go1_mujoco.xml
-    fragment_files:
-      - src/unilab/assets/robots/go1/locomotion_task.xml
-    terrain:
-      hfield_name: terrain_hfield
-      geom_name: floor
-      generator:
-        seed: 42
-        curriculum: false
-        size: [8.0, 8.0]
-        num_rows: 6
-        num_cols: 6
-        border_width: 20.0
-  terrain_scan:
-    enabled: true
-    geom_name: floor
-  termination_config:
-    terrain_out_of_bounds: true
-    terrain_distance_buffer: 3.0
-  domain_rand:
-    randomize_base_mass: true
-    added_mass_range: [-1.0, 3.0]
-    random_com: true
-    randomize_kp: true
-    kp_multiplier_range: [0.5, 2.0]
-    randomize_kd: true
-    kd_multiplier_range: [0.5, 2.0]
-    push_robots: true
-    push_interval: 625
-    max_force: [1.0, 1.0, 0.5]
-
-reward:
-  scales:
-    lin_vel_z: -2.0
-    ang_vel_xy: -0.05
-    joint_torques_l2: -2.5e-5
-    joint_acc_l2: -2.5e-7
-    joint_power: -2.0e-5
-    stand_still: -2.0
-    hip_pos: -0.5
-    joint_pos_penalty: -1.0
-    joint_mirror: -0.05
-    action_rate: -0.01
-    undesired_contacts: -1.0
-    contact_forces: -1.5e-4
-    tracking_lin_vel: 3.0
-    tracking_ang_vel: 1.5
-    feet_air_time: 0.5
-    feet_air_time_variance: -1.0
-    feet_contact_without_cmd: 0.1
-    feet_slide: -0.1
-    feet_height_body: -5.0
-    feet_gait: 0.5
-    upward: 1.0
-  tracking_sigma: 0.25
-  base_height_target: 0.33
-  stand_still_command_threshold: 0.1
-  joint_pos_penalty_stand_still_scale: 5.0
-  joint_pos_penalty_velocity_threshold: 0.5
-  joint_pos_penalty_command_threshold: 0.1
-  contact_threshold: 1.0
-  contact_forces_threshold: 100.0
-  feet_air_time_threshold: 0.5
-  feet_height_body_target: -0.2
-  feet_height_body_tanh_mult: 2.0
-  feet_gait_std: 0.7071067811865476
-  feet_gait_max_err: 0.2
-  feet_gait_velocity_threshold: 0.5
-  feet_gait_command_threshold: 0.1
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/go2_footstand/drake.yaml b/conf/ppo/task/go2_footstand/drake.yaml
deleted file mode 100644
index b8c00f148..000000000
--- a/conf/ppo/task/go2_footstand/drake.yaml
+++ /dev/null
@@ -1,67 +0,0 @@
-# @package _global_
-training:
-  task_name: Go2FootStand
-  sim_backend: drake
-
-env:
-  sim_dt: 0.004
-  drake_backend_mode: batch
-  drake_nthread: 0
-  add_body_sensors: true
-  obs_history_len: 15
-  energy_termination_threshold: 200.0
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 1.5
-    scale_gyro: 0.2
-    scale_gravity: 0.05
-    scale_linvel: 0.1
-  control_config:
-    action_scale: 0.3
-  domain_rand:
-    randomize_floor_friction: false
-    randomize_link_mass: false
-    torso_added_mass_range: null
-    randomize_torso_com: false
-    randomize_dof_armature: false
-    randomize_reset_joint_qpos: true
-    reset_joint_qpos_range: [-0.05, 0.05]
-
-algo:
-  empirical_normalization: true
-  num_envs: 1024
-  max_iterations: 10000
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-  policy:
-    init_noise_std: 0.5
-  algorithm:
-    entropy_coef: 0.005
-
-reward:
-  scales:
-    height: 2.0
-    orientation: 2.0
-    contact: -1.0
-    action_rate: -0.01
-    termination: -2.0
-    dof_pos_limits: -0.5
-    torques: 0.0
-    pose: -0.1
-    penalty_contact: -0.2
-    tar: 0.8
-    rear_feet_contact: 0.5
-    rear_leg_symmetry: -0.2
-    front_leg_motion: -0.05
-    upright_stability: -0.2
-    knee_clearance: -0.5
-    stay_still: -0.1
-    energy: -0.003
-    dof_acc: -2.5e-7
-  tracking_sigma: 0.25
-  base_height_target: 0.3
-  knee_height_target: 0.08
diff --git a/conf/ppo/task/go2_footstand/motrix.yaml b/conf/ppo/task/go2_footstand/motrix.yaml
deleted file mode 100644
index 98d5b3a4e..000000000
--- a/conf/ppo/task/go2_footstand/motrix.yaml
+++ /dev/null
@@ -1,82 +0,0 @@
-# @package _global_
-training:
-  task_name: Go2FootStand
-  sim_backend: motrix
-  no_play: true
-env:
-  sim_dt: 0.004
-  add_body_sensors: true
-  obs_history_len: 15
-  soft_joint_pos_limit_factor: 0.9
-  energy_termination_threshold: 200.0
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 1.5
-    scale_gyro: 0.2
-    scale_gravity: 0.05
-    scale_linvel: 0.1
-  control_config:
-    action_scale: 0.3
-    clip_actions: 1.0
-    Kd: 0.5
-  domain_rand:
-    randomize_floor_friction: false
-    floor_friction_range: [0.6, 1.0]
-    # Motrix does not implement dof_armature randomization; disabled.
-    randomize_dof_armature: false
-    randomize_link_mass: false
-    link_mass_scale_range: [0.95, 1.05]
-    torso_added_mass_range: [0.0, 0.0]
-    randomize_torso_com: false
-    torso_com_offset_range: [-0.02, 0.02]
-    randomize_reset_joint_qpos: true
-    reset_joint_qpos_range: [-0.02, 0.02]
-algo:
-  empirical_normalization: true
-  num_envs: 1024
-  max_iterations: 10000
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-  policy:
-    init_noise_std: 0.5
-  algorithm:
-    entropy_coef: 0.005
-reward:
-  scales:
-    height: 2.0
-    orientation: 3.0
-    contact: -1.0
-    action_rate: -0.01
-    termination: -2.0
-    dof_pos_limits: -0.5
-    torques: 0.0
-    pose: -0.1
-    penalty_contact: -0.2
-    tar: 1.3
-    rear_feet_contact: 0.5
-    both_rear_feet_contact: 0.25
-    rear_foot_slip: -1.0
-    rear_foot_anchor: -0.15
-    front_feet_air: 0.0
-    balanced_footstand: 0.0
-    rear_leg_symmetry: -0.2
-    rear_leg_splay: -0.25
-    front_leg_motion: -0.06
-    front_leg_crossing: -2.0
-    upright_stability: -0.2
-    knee_clearance: -0.5
-    stay_still: -0.12
-    energy: -0.003
-    dof_acc: -2.5e-7
-  tracking_sigma: 0.25
-  base_height_target: 0.3
-  knee_height_target: 0.08
-  front_feet_min_separation: 0.18
-  front_feet_side_margin: 0.06
-  rear_hip_abduction_margin: 0.25
-  rear_foot_slip_deadband: 0.012
-  rear_foot_anchor_radius: 0.04
diff --git a/conf/ppo/task/go2_footstand/mujoco.yaml b/conf/ppo/task/go2_footstand/mujoco.yaml
deleted file mode 100644
index cca9fb89d..000000000
--- a/conf/ppo/task/go2_footstand/mujoco.yaml
+++ /dev/null
@@ -1,67 +0,0 @@
-# @package _global_
-training:
-  task_name: Go2FootStand
-  sim_backend: mujoco
-env:
-  sim_dt: 0.004
-  add_body_sensors: true
-  obs_history_len: 15
-  energy_termination_threshold: 200.0
-  noise_config:
-    level: 1.0
-    scale_joint_angle: 0.01
-    scale_joint_vel: 1.5
-    scale_gyro: 0.2
-    scale_gravity: 0.05
-    scale_linvel: 0.1
-  control_config:
-    action_scale: 0.3
-  domain_rand:
-    randomize_floor_friction: true
-    floor_friction_range: [0.4, 1.0]
-    randomize_link_mass: true
-    link_mass_scale_range: [0.9, 1.1]
-    torso_added_mass_range: [-1.0, 1.0]
-    randomize_torso_com: true
-    torso_com_offset_range: [-0.05, 0.05]
-    randomize_dof_armature: true
-    dof_armature_scale_range: [1.0, 1.05]
-    randomize_reset_joint_qpos: true
-    reset_joint_qpos_range: [-0.05, 0.05]
-algo:
-  empirical_normalization: true
-  num_envs: 1024 # 4096  # 1024
-  # max_iterations: 3000
-  max_iterations: 10000
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-  policy:
-    init_noise_std: 0.5
-  algorithm:
-    entropy_coef: 0.005
-reward:
-  scales:
-    height: 2.0
-    orientation: 2.0
-    contact: -1.0
-    action_rate: -0.01
-    termination: -2.0
-    dof_pos_limits: -0.5
-    torques: 0.0
-    pose: -0.1
-    penalty_contact: -0.2
-    tar: 0.8
-    rear_feet_contact: 0.5
-    rear_leg_symmetry: -0.2
-    front_leg_motion: -0.05
-    upright_stability: -0.2
-    knee_clearance: -0.5
-    stay_still: -0.1
-    energy: -0.003
-    dof_acc: -2.5e-7
-  tracking_sigma: 0.25
-  base_height_target: 0.3
-  knee_height_target: 0.08
diff --git a/conf/ppo/task/go2_joystick_flat/drake.yaml b/conf/ppo/task/go2_joystick_flat/drake.yaml
deleted file mode 100644
index e246865f7..000000000
--- a/conf/ppo/task/go2_joystick_flat/drake.yaml
+++ /dev/null
@@ -1,41 +0,0 @@
-# @package _global_
-training:
-  task_name: Go2JoystickFlat
-  sim_backend: drake
-
-algo:
-  num_envs: 1024
-  max_iterations: 151
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - actor
-  policy:
-    init_noise_std: 0.5
-  algorithm:
-    learning_rate: 3.0e-4
-    entropy_coef: 1.0e-3
-
-env:
-  drake_backend_mode: batch
-  drake_nthread: 0
-  scene:
-    model_file: src/unilab/assets/robots/go2/scene_flat.xml
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
-    push_robots: false
-
-reward:
-  scales:
-    tracking_lin_vel: 1.0
-    tracking_ang_vel: 0.2
-    lin_vel_z: -5.0
-    ang_vel_xy: -0.1
-    base_height: -100.0
-    action_rate: -0.005
-    similar_to_default: -0.1
-    contact: 0.24
-    swing_feet_z: 4.0
-  tracking_sigma: 0.25
-  base_height_target: 0.3
diff --git a/conf/ppo/task/go2_joystick_flat/motrix.yaml b/conf/ppo/task/go2_joystick_flat/motrix.yaml
deleted file mode 100644
index aca13216d..000000000
--- a/conf/ppo/task/go2_joystick_flat/motrix.yaml
+++ /dev/null
@@ -1,41 +0,0 @@
-# @package _global_
-training:
-  task_name: Go2JoystickFlat
-  sim_backend: motrix
-algo:
-  num_envs: 1024
-  max_iterations: 151
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - actor
-  policy:
-    init_noise_std: 0.5
-  algorithm:
-    learning_rate: 3.0e-4
-    entropy_coef: 1.0e-3
-env:
-  commands:
-    vel_limit:
-      - [0.5, 0.0, 0.0]
-      - [0.5, 0.0, 0.0]
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
-reward:
-  scales:
-    tracking_lin_vel: 1.0
-    tracking_ang_vel: 0.2
-    lin_vel_z: -5.0
-    ang_vel_xy: -0.1
-    base_height: -100.0
-    action_rate: -0.005
-    similar_to_default: -0.1
-    contact: 0.24
-    swing_feet_z: 4.0
-  tracking_sigma: 0.25
-  base_height_target: 0.3
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/go2_joystick_flat/mujoco.yaml b/conf/ppo/task/go2_joystick_flat/mujoco.yaml
deleted file mode 100644
index 35cf09bed..000000000
--- a/conf/ppo/task/go2_joystick_flat/mujoco.yaml
+++ /dev/null
@@ -1,33 +0,0 @@
-# @package _global_
-training:
-  task_name: Go2JoystickFlat
-  sim_backend: mujoco
-algo:
-  num_envs: 1024
-  max_iterations: 151
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - actor
-  policy:
-    init_noise_std: 0.5
-  algorithm:
-    learning_rate: 3.0e-4
-    entropy_coef: 1.0e-3
-reward:
-  scales:
-    tracking_lin_vel: 1.0
-    tracking_ang_vel: 0.2
-    lin_vel_z: -5.0
-    ang_vel_xy: -0.1
-    base_height: -100.0
-    action_rate: -0.005
-    similar_to_default: -0.1
-    contact: 0.24
-    swing_feet_z: 4.0
-  tracking_sigma: 0.25
-  base_height_target: 0.3
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/go2_joystick_rough/motrix.yaml b/conf/ppo/task/go2_joystick_rough/motrix.yaml
deleted file mode 100644
index 46992b1f0..000000000
--- a/conf/ppo/task/go2_joystick_rough/motrix.yaml
+++ /dev/null
@@ -1,120 +0,0 @@
-# @package _global_
-training:
-  task_name: Go2JoystickRough
-  sim_backend: motrix
-  play_steps: 500
-  play_env_num: 16
-  cam_tracking: true
-  cam_tracking_env_idx: 0
-  cam_tracking_extra_envs: 9
-
-interactive:
-  action_mode: policy
-  policy_obs_mode: auto
-  camera_follow_body: true
-  use_env_visual_model: false
-
-algo:
-  num_envs: 4096
-  num_steps_per_env: 24
-  max_iterations: 1500
-  empirical_normalization: false
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-  policy:
-    init_noise_std: 1.0
-  algorithm:
-    learning_rate: 1.0e-3
-    entropy_coef: 1.0e-2
-
-env:
-  render_offset_mode: zero
-  control_config:
-    action_scale: 0.25
-    hip_action_scale: 0.125
-    non_hip_action_scale: 0.25
-    clip_actions: 100.0
-  commands:
-    vel_limit: [[-1.0, -1.0, -1.0], [1.0, 1.0, 1.0]]
-    resampling_time: 10.0
-    heading_command: true
-    heading_range: [-3.141592653589793, 3.141592653589793]
-    rel_standing_envs: 0.1
-  terrain_curriculum:
-    enabled: false
-  scene:
-    model_file: src/unilab/assets/robots/go2/go2.xml
-    fragment_files:
-      - src/unilab/assets/robots/go2/locomotion_task.xml
-    terrain:
-      hfield_name: terrain_hfield
-      geom_name: floor
-      generator:
-        seed: 42
-        curriculum: false
-        size: [8.0, 8.0]
-        num_rows: 6
-        num_cols: 6
-        border_width: 20.0
-  terrain_scan:
-    enabled: true
-    geom_name: floor
-  termination_config:
-    terrain_out_of_bounds: true
-    terrain_distance_buffer: 3.0
-  domain_rand:
-    randomize_base_mass: true
-    added_mass_range: [-1.0, 3.0]
-    random_com: true
-    randomize_kp: true
-    kp_multiplier_range: [0.5, 2.0]
-    randomize_kd: true
-    kd_multiplier_range: [0.5, 2.0]
-    push_robots: true
-    push_interval: 625
-    max_force: [1.0, 1.0, 0.5]
-reward:
-  scales:
-    lin_vel_z: -2.0
-    ang_vel_xy: -0.05
-    joint_torques_l2: -2.5e-5
-    joint_acc_l2: -2.5e-7
-    joint_power: -2.0e-5
-    stand_still: -2.0
-    hip_pos: -0.5
-    joint_pos_penalty: -1.0
-    joint_mirror: -0.05
-    action_rate: -0.01
-    undesired_contacts: -1.0
-    contact_forces: -1.5e-4
-    tracking_lin_vel: 3.0
-    tracking_ang_vel: 1.5
-    feet_air_time: 0.5
-    feet_air_time_variance: -1.0
-    feet_contact_without_cmd: 0.1
-    feet_slide: -0.1
-    feet_height_body: -5.0
-    feet_gait: 0.5
-    upward: 1.0
-  tracking_sigma: 0.25
-  base_height_target: 0.3
-  stand_still_command_threshold: 0.1
-  joint_pos_penalty_stand_still_scale: 5.0
-  joint_pos_penalty_velocity_threshold: 0.5
-  joint_pos_penalty_command_threshold: 0.1
-  contact_threshold: 1.0
-  contact_forces_threshold: 100.0
-  feet_air_time_threshold: 0.5
-  feet_height_body_target: -0.2
-  feet_height_body_tanh_mult: 2.0
-  feet_gait_std: 0.7071067811865476
-  feet_gait_max_err: 0.2
-  feet_gait_velocity_threshold: 0.5
-  feet_gait_command_threshold: 0.1
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/go2_joystick_rough/mujoco.yaml b/conf/ppo/task/go2_joystick_rough/mujoco.yaml
deleted file mode 100644
index ea665a5fa..000000000
--- a/conf/ppo/task/go2_joystick_rough/mujoco.yaml
+++ /dev/null
@@ -1,122 +0,0 @@
-# @package _global_
-training:
-  task_name: Go2JoystickRough
-  sim_backend: mujoco
-  play_steps: 500
-  play_env_num: 16
-  render_spacing: 0.0
-  cam_tracking: true
-  cam_tracking_env_idx: 0
-  cam_tracking_extra_envs: 9
-
-interactive:
-  action_mode: policy
-  policy_obs_mode: auto
-  camera_follow_body: true
-  use_env_visual_model: false
-
-algo:
-  num_envs: 2048
-  num_steps_per_env: 24
-  max_iterations: 1500
-  empirical_normalization: false
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-  policy:
-    init_noise_std: 1.0
-  algorithm:
-    learning_rate: 1.0e-3
-    entropy_coef: 1.0e-2
-
-env:
-  sim_dt: 0.002
-  control_config:
-    action_scale: 0.25
-    hip_action_scale: 0.125
-    non_hip_action_scale: 0.25
-    clip_actions: 100.0
-  commands:
-    vel_limit: [[-1.0, -1.0, -1.0], [1.0, 1.0, 1.0]]
-    resampling_time: 10.0
-    heading_command: true
-    heading_range: [-3.141592653589793, 3.141592653589793]
-    rel_standing_envs: 0.1
-  terrain_curriculum:
-    enabled: false
-  scene:
-    model_file: src/unilab/assets/robots/go2/go2_mujoco.xml
-    fragment_files:
-      - src/unilab/assets/robots/go2/locomotion_task.xml
-    terrain:
-      hfield_name: terrain_hfield
-      geom_name: floor
-      generator:
-        seed: 42
-        curriculum: false
-        size: [8.0, 8.0]
-        num_rows: 6
-        num_cols: 6
-        border_width: 20.0
-  terrain_scan:
-    enabled: true
-    geom_name: floor
-  termination_config:
-    terrain_out_of_bounds: true
-    terrain_distance_buffer: 3.0
-  domain_rand:
-    randomize_base_mass: true
-    added_mass_range: [-1.0, 3.0]
-    random_com: true
-    randomize_kp: true
-    kp_multiplier_range: [0.5, 2.0]
-    randomize_kd: true
-    kd_multiplier_range: [0.5, 2.0]
-    push_robots: true
-    push_interval: 625
-    max_force: [1.0, 1.0, 0.5]
-
-reward:
-  scales:
-    lin_vel_z: -2.0
-    ang_vel_xy: -0.05
-    joint_torques_l2: -2.5e-5
-    joint_acc_l2: -2.5e-7
-    joint_power: -2.0e-5
-    stand_still: -2.0
-    hip_pos: -0.5
-    joint_pos_penalty: -1.0
-    joint_mirror: -0.05
-    action_rate: -0.01
-    undesired_contacts: -1.0
-    contact_forces: -1.5e-4
-    tracking_lin_vel: 3.0
-    tracking_ang_vel: 1.5
-    feet_air_time: 0.5
-    feet_air_time_variance: -1.0
-    feet_contact_without_cmd: 0.1
-    feet_slide: -0.1
-    feet_height_body: -5.0
-    feet_gait: 0.5
-    upward: 1.0
-  tracking_sigma: 0.25
-  base_height_target: 0.3
-  stand_still_command_threshold: 0.1
-  joint_pos_penalty_stand_still_scale: 5.0
-  joint_pos_penalty_velocity_threshold: 0.5
-  joint_pos_penalty_command_threshold: 0.1
-  contact_threshold: 1.0
-  contact_forces_threshold: 100.0
-  feet_air_time_threshold: 0.5
-  feet_height_body_target: -0.2
-  feet_height_body_tanh_mult: 2.0
-  feet_gait_std: 0.7071067811865476
-  feet_gait_max_err: 0.2
-  feet_gait_velocity_threshold: 0.5
-  feet_gait_command_threshold: 0.1
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/go2w_joystick_flat/drake.yaml b/conf/ppo/task/go2w_joystick_flat/drake.yaml
deleted file mode 100644
index 6a5b59ef5..000000000
--- a/conf/ppo/task/go2w_joystick_flat/drake.yaml
+++ /dev/null
@@ -1,54 +0,0 @@
-# @package _global_
-training:
-  task_name: Go2WJoystickFlat
-  sim_backend: drake
-
-algo:
-  num_envs: 1024
-  max_iterations: 151
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - actor
-  policy:
-    init_noise_std: 0.5
-  algorithm:
-    learning_rate: 3.0e-4
-    entropy_coef: 1.0e-3
-
-env:
-  drake_backend_mode: batch
-  drake_nthread: 0
-  scene:
-    model_file: src/unilab/assets/robots/go2w/scene_flat.xml
-  commands:
-    vel_limit:
-      - [0.0, 0.0, -1.0]
-      - [1.0, 0.0, 1.0]
-  control_config:
-    action_scale: 0.5
-    wheel_action_scale: 10.0
-    Kp: 50.0
-    Kd: 1.5
-    wheel_Kd: 0.5
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
-    push_robots: false
-
-reward:
-  scales:
-    tracking_lin_vel: 1.0
-    tracking_ang_vel: 0.75
-    lin_vel_z: -5.0
-    ang_vel_xy: -0.1
-    base_height: -100.0
-    orientation: -2.0
-    action_rate: -0.005
-    similar_to_default: -0.5
-    torques: -0.0002
-    wheel_vel: 0.0
-    alive: 0.5
-    upward: 1.0
-  tracking_sigma: 0.25
-  base_height_target: 0.4
diff --git a/conf/ppo/task/go2w_joystick_flat/motrix.yaml b/conf/ppo/task/go2w_joystick_flat/motrix.yaml
deleted file mode 100644
index 0e86b63b0..000000000
--- a/conf/ppo/task/go2w_joystick_flat/motrix.yaml
+++ /dev/null
@@ -1,51 +0,0 @@
-# @package _global_
-training:
-  task_name: Go2WJoystickFlat
-  sim_backend: motrix
-algo:
-  num_envs: 1024
-  max_iterations: 151
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - actor
-  policy:
-    init_noise_std: 0.5
-  algorithm:
-    learning_rate: 3.0e-4
-    entropy_coef: 1.0e-3
-env:
-  render_offset_mode: zero
-  commands:
-    vel_limit:
-      - [0.0, 0.0, -1.0]
-      - [1.0, 0.0, 1.0]
-  control_config:
-    action_scale: 0.5
-    wheel_action_scale: 10.0
-    Kp: 50.0
-    Kd: 1.5
-    wheel_Kd: 0.5
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
-reward:
-  scales:
-    tracking_lin_vel: 1.0
-    tracking_ang_vel: 0.75
-    lin_vel_z: -5.0
-    ang_vel_xy: -0.1
-    base_height: -100.0
-    orientation: -2.0
-    action_rate: -0.005
-    similar_to_default: -0.5
-    torques: -0.0002
-    wheel_vel: 0.0
-    alive: 0.5
-    upward: 1.0
-  tracking_sigma: 0.25
-  base_height_target: 0.4
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/go2w_joystick_flat/mujoco.yaml b/conf/ppo/task/go2w_joystick_flat/mujoco.yaml
deleted file mode 100644
index 602b95a27..000000000
--- a/conf/ppo/task/go2w_joystick_flat/mujoco.yaml
+++ /dev/null
@@ -1,50 +0,0 @@
-# @package _global_
-training:
-  task_name: Go2WJoystickFlat
-  sim_backend: mujoco
-algo:
-  num_envs: 1024
-  max_iterations: 151
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - actor
-  policy:
-    init_noise_std: 0.5
-  algorithm:
-    learning_rate: 3.0e-4
-    entropy_coef: 1.0e-3
-env:
-  commands:
-    vel_limit:
-      - [0.0, 0.0, -1.0]
-      - [1.0, 0.0, 1.0]
-  control_config:
-    action_scale: 0.5
-    wheel_action_scale: 10.0
-    Kp: 50.0
-    Kd: 1.5
-    wheel_Kd: 0.5
-  domain_rand:
-    randomize_kp: false
-    randomize_kd: false
-reward:
-  scales:
-    tracking_lin_vel: 1.0
-    tracking_ang_vel: 0.75
-    lin_vel_z: -5.0
-    ang_vel_xy: -0.1
-    base_height: -100.0
-    orientation: -2.0
-    action_rate: -0.005
-    similar_to_default: -0.5
-    torques: -0.0002
-    wheel_vel: 0.0
-    alive: 0.5
-    upward: 1.0
-  tracking_sigma: 0.25
-  base_height_target: 0.4
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/go2w_joystick_rough/motrix.yaml b/conf/ppo/task/go2w_joystick_rough/motrix.yaml
deleted file mode 100644
index d15620ddb..000000000
--- a/conf/ppo/task/go2w_joystick_rough/motrix.yaml
+++ /dev/null
@@ -1,99 +0,0 @@
-# @package _global_
-training:
-  task_name: Go2WJoystickRough
-  sim_backend: motrix
-  play_steps: 500
-  play_env_num: 16
-  cam_tracking: true
-  cam_tracking_env_idx: 0
-  cam_tracking_extra_envs: 9
-
-interactive:
-  action_mode: policy
-  policy_obs_mode: auto
-  camera_follow_body: true
-  use_env_visual_model: false
-
-algo:
-  num_envs: 2048
-  num_steps_per_env: 24
-  max_iterations: 1200
-  empirical_normalization: false
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-env:
-  render_offset_mode: zero
-  scene:
-    model_file: src/unilab/assets/robots/go2w/go2w.xml
-    fragment_files:
-      - src/unilab/assets/robots/go2w/locomotion_task.xml
-    terrain:
-      hfield_name: terrain_hfield
-      geom_name: floor
-      generator:
-        seed: 42
-        curriculum: false
-        size: [8.0, 8.0]
-        num_rows: 6
-        num_cols: 6
-        border_width: 20.0
-  commands:
-    vel_limit: [[-1.0, -1.0, -1.0], [1.0, 1.0, 1.0]]
-    resampling_time: 10.0
-    heading_command: true
-    heading_range: [-3.141592653589793, 3.141592653589793]
-    rel_standing_envs: 0.1
-  control_config:
-    action_scale: 0.25
-    hip_action_scale: 0.125
-    wheel_action_scale: 5.0
-    wheel_Kd: 0.5
-    clip_actions: 100.0
-    simulate_action_latency: false
-  terrain_scan:
-    enabled: true
-    hfield_name: terrain_hfield
-    geom_name: floor
-  termination_config:
-    terrain_out_of_bounds: true
-    terrain_distance_buffer: 3.0
-  domain_rand:
-    randomize_base_mass: true
-    added_mass_range: [-1.0, 3.0]
-    random_com: true
-    com_offset_x: [-0.05, 0.05]
-    randomize_kp: true
-    kp_multiplier_range: [0.5, 1.0]
-    randomize_kd: true
-    kd_multiplier_range: [0.5, 1.0]
-    push_robots: true
-    push_interval: 625
-    max_force: [1.0, 1.0, 0.5]
-    push_body_name: base_link
-reward:
-  scales:
-    tracking_lin_vel: 3.0
-    tracking_ang_vel: 1.5
-    lin_vel_z: -2.0
-    ang_vel_xy: -0.05
-    orientation: -2.0
-    joint_torques_l2: -2.5e-5
-    joint_acc_l2: -2.5e-7
-    joint_acc_wheel_l2: -2.5e-9
-    joint_power: -2.0e-5
-    action_rate: -0.01
-    stand_still: -2.0
-    hip_pos: -0.5
-    joint_pos_penalty: -1.0
-    joint_mirror: -0.05
-    upward: 1.0
-  tracking_sigma: 0.25
-  base_height_target: 0.4
-  only_positive_rewards: false
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/go2w_joystick_rough/mujoco.yaml b/conf/ppo/task/go2w_joystick_rough/mujoco.yaml
deleted file mode 100644
index 5bcc31038..000000000
--- a/conf/ppo/task/go2w_joystick_rough/mujoco.yaml
+++ /dev/null
@@ -1,99 +0,0 @@
-# @package _global_
-training:
-  task_name: Go2WJoystickRough
-  sim_backend: mujoco
-  play_steps: 500
-  play_env_num: 16
-  render_spacing: 0.0
-  cam_tracking: true
-  cam_tracking_env_idx: 0
-  cam_tracking_extra_envs: 9
-
-interactive:
-  action_mode: policy
-  policy_obs_mode: auto
-  camera_follow_body: true
-  use_env_visual_model: false
-
-algo:
-  num_envs: 2048
-  num_steps_per_env: 24
-  max_iterations: 1200
-  empirical_normalization: false
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-env:
-  scene:
-    model_file: src/unilab/assets/robots/go2w/go2w_mujoco.xml
-    fragment_files:
-      - src/unilab/assets/robots/go2w/locomotion_task.xml
-    terrain:
-      hfield_name: terrain_hfield
-      geom_name: floor
-      generator:
-        seed: 42
-        curriculum: false
-        size: [8.0, 8.0]
-        num_rows: 6
-        num_cols: 6
-        border_width: 20.0
-  commands:
-    vel_limit: [[-1.0, -1.0, -1.0], [1.0, 1.0, 1.0]]
-    resampling_time: 10.0
-    heading_command: true
-    heading_range: [-3.141592653589793, 3.141592653589793]
-    rel_standing_envs: 0.1
-  control_config:
-    action_scale: 0.25
-    hip_action_scale: 0.125
-    wheel_action_scale: 5.0
-    wheel_Kd: 0.5
-    clip_actions: 100.0
-    simulate_action_latency: false
-  terrain_scan:
-    enabled: true
-    hfield_name: terrain_hfield
-    geom_name: floor
-  termination_config:
-    terrain_out_of_bounds: true
-    terrain_distance_buffer: 3.0
-  domain_rand:
-    randomize_base_mass: true
-    added_mass_range: [-1.0, 3.0]
-    random_com: true
-    com_offset_x: [-0.05, 0.05]
-    randomize_kp: true
-    kp_multiplier_range: [0.5, 1.0]
-    randomize_kd: true
-    kd_multiplier_range: [0.5, 1.0]
-    push_robots: true
-    push_interval: 625
-    max_force: [1.0, 1.0, 0.5]
-    push_body_name: base_link
-reward:
-  scales:
-    tracking_lin_vel: 3.0
-    tracking_ang_vel: 1.5
-    lin_vel_z: -2.0
-    ang_vel_xy: -0.05
-    orientation: -2.0
-    joint_torques_l2: -2.5e-5
-    joint_acc_l2: -2.5e-7
-    joint_acc_wheel_l2: -2.5e-9
-    joint_power: -2.0e-5
-    action_rate: -0.01
-    stand_still: -2.0
-    hip_pos: -2.0
-    joint_pos_penalty: -1.0
-    joint_mirror: -0.05
-    upward: 1.0
-  tracking_sigma: 0.25
-  base_height_target: 0.4
-  only_positive_rewards: false
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/sharpa_inhand/mujoco_hora.yaml b/conf/ppo/task/sharpa_inhand/mujoco_hora.yaml
deleted file mode 100644
index 7937b66ee..000000000
--- a/conf/ppo/task/sharpa_inhand/mujoco_hora.yaml
+++ /dev/null
@@ -1,44 +0,0 @@
-# @package _global_
-defaults:
-  - /task/sharpa_inhand/mujoco
-  - _self_
-
-interactive:
-  action_mode: policy
-  policy_obs_mode: actor
-  camera_distance: 1.5
-  camera_elevation: -20.0
-  camera_azimuth: 90.0
-  use_env_visual_model: true
-
-algo:
-  algo_log_name: hora_ppo
-  runtime_impl: hora_ppo
-  runtime_resolver: unilab.algos.torch.hora.rsl_rl:resolve_hora_ppo_runtime
-  obs_groups:
-    actor: [actor]
-    critic: [actor]
-  actor:
-    class_name: unilab.algos.torch.hora:HoraActorModel
-    hidden_dims: [512, 256, 128]
-    activation: elu
-    obs_normalization: true
-    priv_info_embed_dim: 9
-    priv_mlp_hidden_dims: [256, 128, 9]
-    distribution_cfg:
-      class_name: GaussianDistribution
-      init_std: 1.0
-      std_type: scalar
-  critic:
-    class_name: unilab.algos.torch.hora:HoraCriticModel
-    hidden_dims: [512, 256, 128]
-    activation: elu
-    obs_normalization: true
-    priv_info_embed_dim: 9
-    priv_mlp_hidden_dims: [256, 128, 9]
-  algorithm:
-    class_name: unilab.algos.torch.hora:HoraPPO
-
-env:
-  obs:
-    observation_mode: separated
diff --git a/conf/ppo/task/stewart_balance/drake.yaml b/conf/ppo/task/stewart_balance/drake.yaml
deleted file mode 100644
index cdd440832..000000000
--- a/conf/ppo/task/stewart_balance/drake.yaml
+++ /dev/null
@@ -1,51 +0,0 @@
-# @package _global_
-training:
-  task_name: StewartBalance
-  sim_backend: drake
-  render_spacing: 4.5
-
-env:
-  drake_backend_mode: batch
-  drake_nthread: 0
-
-algo:
-  num_envs: 128
-  num_steps_per_env: 64
-  max_iterations: 400
-  empirical_normalization: true
-  obs_groups:
-    actor: [policy]
-    critic: [policy]
-  actor:
-    class_name: rsl_rl.models.MLPModel
-    hidden_dims: [128, 128]
-    activation: tanh
-    obs_normalization: true
-    distribution_cfg:
-      class_name: rsl_rl.modules.distribution.GaussianDistribution
-      init_std: 0.3
-      std_type: scalar
-  critic:
-    class_name: rsl_rl.models.MLPModel
-    hidden_dims: [128, 128]
-    activation: tanh
-    obs_normalization: true
-  algorithm:
-    learning_rate: 1.0e-4
-    num_learning_epochs: 5
-    num_mini_batches: 4
-    clip_param: 0.15
-    entropy_coef: 1.0e-4
-    value_loss_coef: 1.0
-    desired_kl: 0.012
-    max_grad_norm: 0.5
-    gamma: 0.99
-    lam: 0.95
-  save_interval: 50
-
-reward:
-  scales:
-    center: 0.7
-    progress: 0.6
-    still: 3.0
-  fall_penalty: -6.0
diff --git a/conf/ppo/task/stewart_balance/motrix.yaml b/conf/ppo/task/stewart_balance/motrix.yaml
deleted file mode 100644
index f018225f8..000000000
--- a/conf/ppo/task/stewart_balance/motrix.yaml
+++ /dev/null
@@ -1,52 +0,0 @@
-# @package _global_
-# Stewart-platform ball-balancing (motrix). A short, runnable PPO baseline, not
-# tuned for best final performance (raise max_iterations / num_envs for higher
-# success rates).
-training:
-  task_name: StewartBalance
-  sim_backend: motrix
-  render_spacing: 4.5
-algo:
-  num_envs: 128
-  num_steps_per_env: 64
-  max_iterations: 400
-  empirical_normalization: true
-  obs_groups:
-    actor: [policy]
-    critic: [policy]
-  actor:
-    class_name: rsl_rl.models.MLPModel
-    hidden_dims: [128, 128]
-    activation: tanh
-    obs_normalization: true
-    distribution_cfg:
-      class_name: rsl_rl.modules.distribution.GaussianDistribution
-      init_std: 0.3
-      std_type: scalar
-  critic:
-    class_name: rsl_rl.models.MLPModel
-    hidden_dims: [128, 128]
-    activation: tanh
-    obs_normalization: true
-  algorithm:
-    learning_rate: 1.0e-4
-    num_learning_epochs: 5
-    num_mini_batches: 4
-    clip_param: 0.15
-    entropy_coef: 1.0e-4
-    value_loss_coef: 1.0
-    desired_kl: 0.012
-    max_grad_norm: 0.5
-    gamma: 0.99
-    lam: 0.95
-  save_interval: 50
-reward:
-  scales:
-    center: 0.7
-    progress: 0.6
-    still: 3.0
-  fall_penalty: -6.0
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 2.0
diff --git a/conf/ppo/task/x2_wall_flip_tracking/motrix.yaml b/conf/ppo/task/x2_wall_flip_tracking/motrix.yaml
deleted file mode 100644
index d2a45c89e..000000000
--- a/conf/ppo/task/x2_wall_flip_tracking/motrix.yaml
+++ /dev/null
@@ -1,100 +0,0 @@
-# @package _global_
-training:
-  task_name: X2WallFlipTracking
-  sim_backend: motrix
-  play_steps: 300  # 6s play video @ ctrl_dt=0.02 (fps 50)
-  play_env_num: 16
-  render_spacing: 3.0
-  cam_distance: 14.0
-  cam_azimuth: 225.0
-  cam_elevation: -18.0
-  cam_lookat: [4.5, 4.5, 1.0]
-interactive:
-  action_mode: policy
-algo:
-  num_envs: 1024
-  max_iterations: 9500
-  save_interval: 500
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-  algorithm:
-    entropy_coef: 0.005
-    desired_kl: 0.01
-env:
-  motrix_max_iterations: 3
-  sampling_mode: start
-  truncate_on_clip_end: false
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-  anchor_pos_z_threshold: 0.5
-  ee_body_pos_z_threshold: 0.5
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-play_profile:
-  enabled: true
-  env:
-    render_spacing: 4.0
-  scene:
-    enabled: true
-    source_model_file: src/unilab/assets/robots/x2/scene_flat_with_wall.xml
-    ground_texture_file: src/unilab/assets/robots/g1/textures/floor.png
-    skybox_rgb1: [0.90, 0.90, 0.91]
-    skybox_rgb2: [0.68, 0.68, 0.70]
-    ground_texrepeat: [0.25, 0.25]
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.5
-    motion_joint_vel: 0.25
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/conf/ppo/task/x2_wall_flip_tracking/mujoco.yaml b/conf/ppo/task/x2_wall_flip_tracking/mujoco.yaml
deleted file mode 100644
index 83511b770..000000000
--- a/conf/ppo/task/x2_wall_flip_tracking/mujoco.yaml
+++ /dev/null
@@ -1,94 +0,0 @@
-# @package _global_
-training:
-  task_name: X2WallFlipTracking
-  sim_backend: mujoco
-  play_steps: 300  # 6s play video @ ctrl_dt=0.02 (fps 50)
-  # Offline-render (play) only — does not affect the training loop. 16 envs are
-  # laid out on a 4x4 grid; render_spacing must exceed the per-env wall reach
-  # (~2.14m toward -Y) so neighbouring cells don't overlap. The oblique
-  # azimuth (225) views the grid corner-on with each robot in front of its
-  # wall (azimuth 90 would put the walls between camera and robots); lookat is
-  # the grid centre (offsets span 0..9m in X/Y at spacing 3.0).
-  play_env_num: 16
-  render_spacing: 3.0
-  cam_distance: 14.0
-  cam_azimuth: 225.0
-  cam_elevation: -18.0
-  cam_lookat: [4.5, 4.5, 1.0]
-interactive:
-  action_mode: policy
-algo:
-  num_envs: 1024
-  max_iterations: 9500
-  save_interval: 500
-  empirical_normalization: true
-  obs_groups:
-    actor:
-      - actor
-    critic:
-      - critic
-  algorithm:
-    entropy_coef: 0.005
-    desired_kl: 0.01
-env:
-  sampling_mode: start
-  truncate_on_clip_end: false
-  sim_dt: 0.005
-  control_config:
-    action_scale:
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-      - 0.25
-  anchor_pos_z_threshold: 0.5
-  ee_body_pos_z_threshold: 0.5
-  terminate_on_undesired_contacts: true
-  noise_config:
-    level: 0.0
-reward:
-  scales:
-    motion_global_root_pos: 0.5
-    motion_global_root_ori: 0.5
-    motion_body_pos: 2.0
-    motion_body_ori: 1.5
-    motion_body_lin_vel: 1.0
-    motion_body_ang_vel: 1.0
-    motion_ee_body_pos_z: 2.0
-    motion_joint_pos: 0.5
-    motion_joint_vel: 0.25
-    action_rate_l2: -0.005
-    joint_limit: -10.0
-    undesired_contacts: -0.1
-  std_root_pos: 0.3
-  std_root_ori: 0.4
-  std_body_pos: 0.3
-  std_body_ori: 0.4
-  std_body_lin_vel: 1.0
-  std_body_ang_vel: 3.14
-  std_joint_pos: 0.2
-  std_joint_vel: 1.0
diff --git a/docs/sphinx/AGENTS.md b/docs/sphinx/AGENTS.md
index cf2413609..fad46aaee 100644
--- a/docs/sphinx/AGENTS.md
+++ b/docs/sphinx/AGENTS.md
@@ -90,7 +90,7 @@ language-independent absolute path.
 ## Core Principles
 
 1. **Evidence only**: only document facts that can be verified in `src/`,
-   `conf/`, `tests/`, `scripts/`, ADRs, or generated support data.
+   `src/unilab/conf/`, `tests/`, `scripts/`, ADRs, or generated support data.
 2. **Code is the source of truth**: names, signatures, defaults, Hydra keys, and
    commands follow the repository, not memory.
 3. **Owner layer first**: scripts assemble; contracts live in backend, env,
@@ -106,7 +106,7 @@ language-independent absolute path.
 7. **Use canonical commands**: user-facing examples use the top-level CLI:
    `uv run train --algo <algo> --task <task> --sim <backend>`,
    `uv run eval ...`, or `uv run demo`. Script paths such as
-   `scripts/train_rsl_rl.py` may be named as implementation evidence, but they
+   `src/unilab/scripts/train_rsl_rl.py` may be named as implementation evidence, but they
    are not the primary command shape for docs readers.
 
 ## Before Writing
@@ -116,9 +116,9 @@ language-independent absolute path.
 3. Search first with `rg` / `rg --files`; update an existing page instead of
    creating a duplicate.
 4. Gather evidence near the claim:
-   - algorithms and tasks: `conf/`, `scripts/train_*.py`, `src/unilab/algos/`
-   - env contract: `src/unilab/base/np_env.py`, `src/unilab/training/rsl_rl.py`
-   - backend contract: `src/unilab/base/backend/base.py`
+   - algorithms and tasks: `src/unilab/conf/`, `src/unilab/scripts/train_*.py`, `src/unilab/algos/`
+   - env contract: `src/unilab/base/np_env.py`, `src/unilab/algos/rsl_rl.py`
+   - backend contract: `unisim.backend.base`
    - registry: `src/unilab/base/registry.py`
    - runner/IPC: `src/unilab/ipc/`, `src/unilab/training/run.py`
    - architecture: ADRs and `development-standard.md`
@@ -241,7 +241,7 @@ rules instead of treating the change as docs-only.
 
 Before reporting success, confirm:
 
-- all cited `src/`, `conf/`, `tests/`, and `scripts/` paths exist;
+- all cited `src/`, `src/unilab/conf/`, `tests/`, and `scripts/` paths exist;
 - English pages in scope have no manual navigation block;
 - root, `en/0-index.md`, and `zh_CN/0-index.md` remain included in toctrees;
 - any warnings from Sphinx are understood and not introduced by the current edit.
diff --git a/docs/sphinx/source/adr/ADR-0000-index.md b/docs/sphinx/source/adr/ADR-0000-index.md
index 007ef110a..7a7dc6e8b 100644
--- a/docs/sphinx/source/adr/ADR-0000-index.md
+++ b/docs/sphinx/source/adr/ADR-0000-index.md
@@ -19,6 +19,8 @@ orphan: true
 | [ADR-0003 Task Owner And Config Compose Contract](ADR-0003-task-owner-and-config-compose-contract.md) | Config owner | Accepted |
 | [ADR-0004 Registry Bootstrap Contract](ADR-0004-registry-bootstrap-contract.md) | Registry bootstrap | Accepted |
 | [ADR-0005 Unified Obs Critic Env And IPC Contract](ADR-0005-unified-obs-critic-env-and-ipc-contract.md) | Observation / IPC | Accepted |
+| [ADR-0006 Community Manager API On NumPy Runtime](ADR-0006-community-manager-api-on-numpy-runtime.md) | Manager API / NumPy runtime | Accepted |
+| [ADR-0007 UniSim Extraction Boundary](ADR-0007-unisim-extraction-boundary.md) | Physics package extraction | Accepted |
 
 ## ADR Governance
 
diff --git a/docs/sphinx/source/adr/ADR-0001-runtime-model-and-layer-boundaries.md b/docs/sphinx/source/adr/ADR-0001-runtime-model-and-layer-boundaries.md
index 46e228756..7fae6e2f8 100644
--- a/docs/sphinx/source/adr/ADR-0001-runtime-model-and-layer-boundaries.md
+++ b/docs/sphinx/source/adr/ADR-0001-runtime-model-and-layer-boundaries.md
@@ -58,12 +58,12 @@ UniLab 同时支持多种算法入口和两种仿真后端。没有统一 runtim
 ## Evidence In Repo
 
 - 架构基线文档: `docs/sphinx/source/zh_CN/4-developer_guide/0-index.md`
-- Backend 抽象: `src/unilab/base/backend/base.py`
+- Backend 抽象: `unisim.backend.base`
 - Env contract: `src/unilab/base/np_env.py`
 - Registry 入口: `src/unilab/base/registry.py`
 - Async runner: `src/unilab/ipc/async_runner.py`
 - PPO distributed adapter: `src/unilab/ipc/dp_launcher.py`,
-  `src/unilab/training/rsl_rl.py`, `scripts/train_rsl_rl.py`
+  `src/unilab/training/rsl_rl.py`, `src/unilab/scripts/train_rsl_rl.py`
 - PPO distributed tests: `tests/ipc/test_dp_launcher.py`,
   `tests/algos/test_rsl_rl_ppo.py`, `tests/scripts/test_train_script_configs.py`
 
diff --git a/docs/sphinx/source/adr/ADR-0002-backend-capability-boundary-for-play-and-snapshot.md b/docs/sphinx/source/adr/ADR-0002-backend-capability-boundary-for-play-and-snapshot.md
index 323f55982..2578003be 100644
--- a/docs/sphinx/source/adr/ADR-0002-backend-capability-boundary-for-play-and-snapshot.md
+++ b/docs/sphinx/source/adr/ADR-0002-backend-capability-boundary-for-play-and-snapshot.md
@@ -52,8 +52,8 @@ MuJoCo 与 Motrix 的渲染路径和输出形式不同。仓库当前存在两
 ## Evidence In Repo
 
 - 后端文档与矩阵: `docs/sphinx/source/zh_CN/2-user_guide/3-backends/0-index.md`
-- Backend 抽象: `src/unilab/base/backend/base.py`
-- 训练入口与 play 边界: `scripts/train_rsl_rl.py`, `scripts/train_appo.py`, `scripts/train_offpolicy.py`
+- Backend 抽象: `unisim.backend.base`
+- 训练入口与 play 边界: `src/unilab/scripts/train_rsl_rl.py`, `src/unilab/scripts/train_appo.py`, `src/unilab/scripts/train_sac.py`, `src/unilab/scripts/train_td3.py`, `src/unilab/scripts/train_flashsac.py`
 
 ## Related Documents
 
diff --git a/docs/sphinx/source/adr/ADR-0003-task-owner-and-config-compose-contract.md b/docs/sphinx/source/adr/ADR-0003-task-owner-and-config-compose-contract.md
index 99d5525f8..42414e966 100644
--- a/docs/sphinx/source/adr/ADR-0003-task-owner-and-config-compose-contract.md
+++ b/docs/sphinx/source/adr/ADR-0003-task-owner-and-config-compose-contract.md
@@ -25,12 +25,17 @@ orphan: true
 2. owner YAML 直接持有 `training.task_name`、`training.sim_backend`、`reward`、`env` 及 task-specific `algo`。
 3. `training.sim_backend` 是 owner 身份字段，不是独立 backend switch。
 4. CLI override 允许参数覆盖，但不能破坏 task owner 的 backend identity。
+5. Manager-Based production task 也不例外：owner YAML 完整持有 manager/term/callable 与
+   observation mapping，compose 后在 Registry 冷路径物化为 typed cfg；Python 不保存
+   task-specific config mirror。
 
 ## Stable Contracts
 
-- PPO/APPO owner 路径: `conf/{ppo,appo}/task/<task>/<backend>.yaml`
-- Offpolicy owner 路径: `conf/offpolicy/task/<algo>/<task>/<backend>.yaml`
+- PPO/APPO owner 路径: `src/unilab/conf/{ppo,appo}/task/<task>/<backend>.yaml`
+- Offpolicy owner 路径: `src/unilab/conf/{sac,td3,flashsac}/task/<task>/<backend>.yaml`（每个 off-policy 算法一棵独立配置树）
 - reward 注入与 backend 差异表达必须在 owner YAML 层显式存在。
+- Manager-Based cfg 使用 Hydra `_target_` 与 dotted callable reference；解析失败或类型错误
+  必须在 env/backend 构造前报错。
 
 ## Consequences
 
diff --git a/docs/sphinx/source/adr/ADR-0004-registry-bootstrap-contract.md b/docs/sphinx/source/adr/ADR-0004-registry-bootstrap-contract.md
index 7c96f790c..5e0691f82 100644
--- a/docs/sphinx/source/adr/ADR-0004-registry-bootstrap-contract.md
+++ b/docs/sphinx/source/adr/ADR-0004-registry-bootstrap-contract.md
@@ -35,7 +35,7 @@ UniLab 的 env 注册依赖 `@registry.envcfg(...)` 与 `@registry.env(...)` dec
 
 ## Consequences
 
-- 新增 env package 时，需要同步声明 bootstrap modules。
+- 新增 task leaf 时，需要在 `unilab.tasks` 中同步声明 bootstrap module。
 - registry 相关回归可以在 `ensure_registries()` 边界直接测试，不必依赖顶层训练脚本间接发现。
 - 文档可以把 registry bootstrap 作为正式架构引用，而不是“当前实现细节”。
 
@@ -48,7 +48,7 @@ UniLab 的 env 注册依赖 `@registry.envcfg(...)` 与 `@registry.env(...)` dec
 
 - Registry 入口: `src/unilab/base/registry.py`
 - Bootstrap helper: `src/unilab/base/registry.py`
-- Env package 入口: `src/unilab/envs/locomotion/__init__.py`, `src/unilab/envs/motion_tracking/__init__.py`, `src/unilab/envs/manipulation/__init__.py`
+- Task package 入口: `src/unilab/tasks/__init__.py`
 - Bootstrap tests: `tests/utils/test_algo_utils.py`, `tests/base/test_registry.py`
 
 ## Related Documents
diff --git a/docs/sphinx/source/adr/ADR-0006-community-manager-api-on-numpy-runtime.md b/docs/sphinx/source/adr/ADR-0006-community-manager-api-on-numpy-runtime.md
new file mode 100644
index 000000000..e0ecbf299
--- /dev/null
+++ b/docs/sphinx/source/adr/ADR-0006-community-manager-api-on-numpy-runtime.md
@@ -0,0 +1,272 @@
+---
+orphan: true
+---
+
+# ADR-0006 Community Manager API On NumPy Runtime
+
+语言: 简体中文
+
+- Status: Accepted
+- Date: 2026-08-17
+- Owners: Env / Config / Backend maintainers
+- Supersedes: None
+- Superseded by: None
+
+## Context
+
+UniLab 的 `NpEnv`、Hydra owner YAML、registry、`SimBackend` 与 heterogeneous
+training runtime 已形成稳定 contract，但 task 的 observation、action、reward、
+termination、event、command 与 curriculum 仍主要由各 env 的私有方法组装。用户迁移
+Isaac Lab 或 mjlab task 时，需要重写 manager term、配置和 lifecycle。
+
+本决策采用 mjlab v1.6.0 的 manager package 作为可逐文件审查的迁移基线：
+
+- repository: `mujocolab/mjlab`
+- commit: `0fb8a681136be94ffc636a3dd423cabb97d91f10`
+- source: `src/mjlab/managers/` 的 12 个 Python 文件
+- license: Apache-2.0；上游 `LICENSE` 声明
+  `Copyright 2025, The mjlab Developers`
+
+该基线只定义 manager-facing API 与语义。它不把 mjlab 的 Torch、Warp、scene
+composer、viewer、simulation 或 training runtime 带入 UniLab，也不恢复或参考 UniLab
+历史上的 Manager-Based API 实现。
+
+## Decision
+
+### 1. Source-aligned public surface
+
+`src/unilab/managers/` 按 pinned mjlab package 的模块职责和 exports 直接迁移。以下名称
+是 canonical public surface；Torch 类型替换为 NumPy 类型不构成改名：
+
+| Module | Canonical exports |
+| --- | --- |
+| `manager_base` | `ManagerBase`, `ManagerTermBase`, `ManagerTermBaseCfg` |
+| `action_manager` | `ActionManager`, `ActionTerm`, `ActionTermCfg` |
+| `observation_manager` | `ObservationManager`, `ObservationGroupCfg`, `ObservationTermCfg` |
+| `reward_manager` | `RewardManager`, `RewardTermCfg` |
+| `termination_manager` | `TerminationManager`, `TerminationTermCfg` |
+| `event_manager` | `EventManager`, `EventMode`, `EventTermCfg` |
+| `command_manager` | `CommandManager`, `CommandTerm`, `CommandTermCfg`, `NullCommandManager` |
+| `curriculum_manager` | `CurriculumManager`, `CurriculumTermCfg`, `NullCurriculumManager` |
+| `metrics_manager` | `MetricsManager`, `MetricsTermCfg`, `NullMetricsManager` |
+| `recorder_manager` | `RecorderManager`, `RecorderTerm`, `RecorderTermCfg`, `NullRecorderManager` |
+| `scene_entity_config` | `SceneEntityCfg` |
+
+Manager cfg 使用 plain dataclass instance；term 集合使用保持插入顺序的 typed `dict`。
+`func + params`、function/class term、class term 的 `(cfg, env)` 构造和局部
+`reset(env_ids)` 语义保持不变。显式空配置或 term 值为 `None` 表示用户选择禁用，允许
+使用 upstream Null manager/no-op 语义。
+
+未来 env lifecycle 的 canonical 名称沿用迁移源的 `ManagerBasedRlEnv` 与
+`ManagerBasedRlEnvCfg`。如果为 Isaac Lab 拼写提供 `ManagerBasedRLEnv` /
+`ManagerBasedRLEnvCfg`，它们必须是同一对象的无分支 alias，不能形成第二套实现。
+其他别名必须由实际 migration fixture 证明有价值，不能预先扩张 API。
+
+### 2. NumPy runtime boundary
+
+Manager-facing tensor、buffer、term return、env IDs 和 entity view 使用
+`np.ndarray` 或 `slice`。Torch 的 `device`、`.to()`、`.cpu()` 与 Tensor-only API 不属于
+UniLab manager contract；manager package 不能 import Torch、runner、learner 或 IPC。
+
+数值转换保持下列语义：
+
+- shape、dtype 和更新时序与上游一致；action history 与 observation history 不改变顺序；
+- buffer 在 manager 构造或 reset owner 边界分配，step 热路径复用；
+- 随机采样使用由 env 拥有并可复现的 NumPy generator，不依赖进程全局 RNG；
+- shape 不匹配以及非有限 term 输出在最近 manager/term 边界直接报错；reward 不使用
+  `nan_to_num` 把非法值静默变成零；
+- observation 明确配置的 noise/delay/history/NaN policy 可以保留，但默认不能掩盖非法
+  输出。
+
+### 3. UniLab env、config 与 IPC boundary
+
+Managers 只依赖一个 typed env context。P0 context 包含 `num_envs`、physics/control dt、
+episode counters、NumPy RNG、各 manager 属性，以及正式 scene/entity facade；不能要求
+`device` 或 backend 私有对象。
+
+Manager 内可以使用社区常见的 `policy` / `actor` / `critic` observation group。env owner
+必须显式把 actor-facing group 映射为 `NpEnvState.obs["obs"]`，并把可选 critic group
+映射为 `NpEnvState.obs["critic"]`。runner、learner 与 IPC 不推断、不拼接 group。
+`reset() -> (obs_dict, info_dict)`、final observation 与 `obs_groups_spec` 保持现有 contract。
+
+Production task 的唯一配置 source of truth 是 Hydra owner YAML。它完整声明 scene/backend
+tuning、manager/group/term 的顺序与启停、具体 cfg 类型、callable、params、weight 和
+observation group mapping；compose 后按以下冷路径进入现有 registry：
+
+`owner YAML -> DictConfig -> typed config materialization -> Registry factory -> ManagerBasedRlEnv`
+
+具体 cfg 类型使用 Hydra `_target_`，term callable 使用完整 dotted reference。通用
+materializer 将其解析为 plain dataclass instance，并在未知字段、target/callable 解析失败、
+抽象或错误 term cfg 类型及缺少必填字段时 fail-closed。Python 只拥有 term 实现、公共 cfg
+类型和通用 factory，不保存第二份 task-specific term 清单或默认值；直接构造 typed cfg
+只用于底层单测。DictConfig 与解析逻辑不能进入 reset/step 热路径，scripts 不解释 term
+业务规则。
+
+### 4. Scene/entity owner boundary
+
+`SceneEntityCfg` 和 term 所需的最小 NumPy entity facade 属于 `src/unilab/base/` 公共
+contract；backend 负责通过 `SimBackend` materialize 名称、ID 和 state/control view，env
+负责把 facade 组合进 manager context。该决策解决 #586 的 owner 问题，但不引入完整
+scene composer 或通用 asset hierarchy。
+
+- entity name 以及 joint/body/geom/site/actuator selector 在 init/materialization/cache
+  冷路径解析一次；热路径只持有已解析 `list[int]`、`np.ndarray` 或 `slice`；
+- selector 保留上游 name/regex、`preserve_order`、names/IDs consistency check 和全选压缩为
+  `slice(None)` 的语义；
+- root/joint/body/site/geom/control 能力只能来自 `SimBackend` 已声明方法；不暴露 backend
+  model/data 私有对象；
+- tendon/camera/light/material/texture/pair 等迁移表面可以存在，但 backend 未声明能力时在
+  resolve/materialization 直接 `NotImplementedError`，不能返回空 ID 或跳过；
+- 新 backend 能力必须作为独立 child 扩展 `SimBackend` 并补 conformance tests，不能在
+  manager 或 env 中用 `getattr` / `hasattr` 探测私有实现。
+
+Named sensor 使用 `SimBackend.bind_sensor_data(names)` 在 materialization 冷路径校验名称、
+每个 sensor 的展平宽度、batch shape 与 finite 值，并返回 immutable
+`BackendSensorView`。term 热路径只调用 `view.read()`；MuJoCo、Drake 和 MJWarp adapter
+分别保留已解析的 host-cache slice 或数值 slot。MotrixSim 当前公开接口只提供 named
+sensor accessor、没有数值 sensor ID，因此 Motrix adapter 在 scene materialization 时缓存
+可用名称，并把原生批量 accessor 与 immutable 名称 tuple 封装为 backend-owned opaque
+reader；term 不接触名称解析、XML 或 model metadata，未知名称在进入原生调用前 fail-closed。
+
+这是 pinned mjlab sensor-facing 语义的 intentional NumPy/backend adaptation：社区侧的
+tensor/device view 在 UniLab 表达为按请求名称顺序拼接的二维 NumPy batch
+`(num_envs, sum(sensor_widths))`。名称顺序、单 sensor 宽度和当前值可见，Torch device、
+backend model/data 与原生 handle 不属于 manager contract。
+
+### 5. Fail-closed capability rule
+
+用户显式禁用与实现缺失是两种不同状态。前者允许 Null manager；后者必须失败：
+
+| Failure | Required behavior |
+| --- | --- |
+| cfg/term 类型错误、签名或 shape 不匹配 | `TypeError` / `ValueError`，包含 manager 与 term |
+| term 输出 NaN/Inf | `ValueError`，包含 manager、group/term 与非法值类别 |
+| backend/entity capability 未实现 | `NotImplementedError`，包含 manager、term、capability 与 backend |
+| selector name/ID 不存在或不一致 | `KeyError` / `ValueError`，包含 entity 与 selector |
+
+不得 warning 后 skip、返回零/旧值、自动换 backend、禁用 feature 或回退到旧 env。当前
+不新增公共 exception hierarchy；只有 consumer 证明需要 machine-readable 分类时再单独
+决策。
+
+### 6. Performance and deletion policy
+
+优先级固定为：社区 Manager-Based API 语义与结构一致性，优先于改变公共设计的局部性能
+优化。在此约束下，生产级 NumPy 热路径不能引入明显可避免的重复解析、逐环境 Python
+循环、数组复制或临时分配。优化必须由同配置、同硬件 benchmark 证明有足够收益，并优先
+保持在内部预解析、预分配和批量 NumPy 实现；低收益但增加专用 fast path、缓存协议或长期
+复杂度的方案不采用。
+
+Production task 迁移后必须在同一 task-family child 删除被替代的旧 dispatch、重复
+reward/config helper 和 bridge。Umbrella 完成时只保留一套 manager lifecycle，不保留
+fallback 到旧单体 env 的永久兼容路径。
+
+## Stable Contracts
+
+### Compatibility matrix
+
+状态只表示本 ADR 固定的迁移目标；实际 support claim 仍需要代码、注册、配置和测试证据。
+
+| Surface | Target | Notes |
+| --- | --- | --- |
+| manager modules、class/config names、dict order | Compatible | 直接保留 pinned mjlab 1.6.0 表面 |
+| function/class term、`params`、local reset | Compatible | class term 在冷路径实例化 |
+| action split/apply/history、reward dt scaling、termination timeout split | Compatible | NumPy 实现保持时序 |
+| observation groups、clip/scale/noise/delay/history | Adapted | 数值为 NumPy；group 在 env boundary 显式映射 |
+| manager buffers、env IDs、RNG | Adapted | Torch→NumPy；无 device API |
+| `ManagerBasedRlEnv` return | Adapted | 保留 `NpEnvState` 与 UniLab reset/final-observation contract |
+| config container | Adapted | Hydra owner YAML 唯一持有 task 配置，冷路径物化为 plain typed instances |
+| `SceneEntityCfg` selectors | Adapted | 语义保留；只解析 `SimBackend` 已声明能力 |
+| named sensor view | Adapted | 冷路径 bind；有序展平 NumPy batch；reader 由 backend 拥有 |
+| event/domain randomization | Adapted | 调度语义保留；mutation 走 backend DR/capability contract |
+| Metrics/Recorder | Adapted | lifecycle hook 存在时启用；缺失时显式失败或显式空配置 |
+| Torch device、Warp mutation、viewer glue | Unsupported | 不进入 manager core，不提供静默替代 |
+| Omniverse/USD/mjlab Scene/Simulation | Unsupported | 不属于 UniLab runtime |
+
+### Mechanical migration example
+
+迁移前的 mjlab term：
+
+```python
+import torch
+from mjlab.managers import RewardTermCfg
+
+def joint_error(env) -> torch.Tensor:
+    return torch.square(env.joint_pos - env.target_joint_pos).sum(dim=1)
+
+term = RewardTermCfg(func=joint_error, weight=-1.0)
+```
+
+迁移后的 UniLab term 只改 import、数值类型和对应 NumPy 运算：
+
+```python
+import numpy as np
+from unilab.managers import RewardTermCfg
+
+def joint_error(env) -> np.ndarray:
+    return np.square(env.joint_pos - env.target_joint_pos).sum(axis=1)
+
+term = RewardTermCfg(func=joint_error, weight=-1.0)
+```
+
+如果迁移还要求重写 term 结构、增加 backend 分支或改 runner/IPC，说明 adapter boundary
+不够薄，必须停止并拆出 owner child。
+
+### Provenance and change accounting
+
+每个 source-derived Python 文件必须注明上游 repository、tag/commit、原始路径、
+Apache-2.0 和 UniLab 的修改类别。实现 PR 分别报告：
+
+1. source-derived：保留的上游结构/语义；
+2. mechanical：import、typing、Torch→NumPy 和格式转换；
+3. UniLab-specific glue：新 facade、contract adapter 或行为；
+4. deleted：删除的上游不适用代码和 UniLab 旧实现。
+
+不得通过重新分类隐藏 glue 超预算；不建立长期 upstream mirror 或自动 sync tooling。
+
+## Alternatives Considered
+
+- 重新设计一套更适合 UniLab 的 managers，再提供兼容 facade。拒绝：会形成
+  UniLab-only 方言和两套行为，增加用户迁移与长期维护成本。
+- 在 manager 热路径保留 Torch。拒绝：破坏 NumPy runtime、backend isolation 与
+  heterogeneous CPU physics → accelerator learner 数据面。
+- 一次迁移完整 mjlab scene/simulation/entity runtime。拒绝：复制第二套 backend/scene
+  abstraction，并引入 Warp/MuJoCo/Viewer 假设。
+- 先设计 compiler、fused term protocol 或专用 fast path。拒绝：在 benchmark 证明瓶颈前
+  增加结构复杂度，并可能牺牲社区 term 语义。
+- 缺失能力 warning + skip 或回退旧 env。拒绝：配置表面与真实执行不一致，不能用于生产。
+- task-owned Python factory 声明 callable/term，Hydra 只做字段 overlay。拒绝：会让同一 task
+  在 Python 和 YAML 中拥有两份配置，增加迁移 friction 和语义漂移。
+
+## Consequences
+
+- Manager port 的审查基线是 pinned upstream diff，而不是重新解释每个 manager 的职责。
+- NumPy、UniLab env/config contract 和显式 unsupported 是允许的偏离；其他偏离必须在
+  compatibility matrix 中先记录。
+- Scene/entity 采用最小 base facade，#586 不再阻塞 manager port；真实 backend 能力仍按
+  独立 child 和 conformance evidence 接入。
+- Config/Registry 永久维护一个通用 Hydra `_target_` / dotted callable 到 typed manager cfg
+  的冷路径 materializer；production task 不维护 Python config mirror。
+- 迁移初期允许 production 旧 task 与未接入的 manager package 同时存在，但 task 一旦迁移
+  就必须删除对应旧实现；umbrella 结束时不能保留双 lifecycle。
+- 性能 gate 关注明显低效与实测瓶颈，不以复杂度换取未经证明的小收益。
+
+## Evidence In Repo
+
+- Env contract: `src/unilab/base/np_env.py`
+- Backend contract: `unisim.backend.base`
+- Scene config owner: `src/unilab/base/scene.py`
+- Config schema and registry: `src/unilab/structured_configs.py`,
+  `src/unilab/base/config_materialization.py`, `src/unilab/base/registry.py`, `src/unilab/conf/`
+- Observation/IPC contract: `docs/sphinx/source/adr/ADR-0005-unified-obs-critic-env-and-ipc-contract.md`
+- Layer boundary: `docs/sphinx/source/adr/ADR-0001-runtime-model-and-layer-boundaries.md`
+- Upstream checkout used for the decision:
+  `/home/user/ws/simulator/mjlab/src/mjlab/managers/` at `0fb8a681`
+
+## Related Documents
+
+- {doc}`ADR Index </adr/README>`
+- {doc}`Manager-Based API contract </zh_CN/4-developer_guide/1-architecture/6-manager_based_api>`
+- {doc}`RL Infrastructure 开发标准 </zh_CN/4-developer_guide/0-index>`
+- [Roadmap #1042](https://github.com/unilabsim/UniLab/issues/1042)
+- [Implementation issue #1043](https://github.com/unilabsim/UniLab/issues/1043)
+- [Entity abstraction decision #586](https://github.com/unilabsim/UniLab/issues/586)
diff --git a/docs/sphinx/source/adr/ADR-0007-unisim-extraction-boundary.md b/docs/sphinx/source/adr/ADR-0007-unisim-extraction-boundary.md
new file mode 100644
index 000000000..fae9944b6
--- /dev/null
+++ b/docs/sphinx/source/adr/ADR-0007-unisim-extraction-boundary.md
@@ -0,0 +1,92 @@
+---
+orphan: true
+---
+
+# ADR-0007 UniSim Extraction Boundary
+
+语言: 简体中文
+
+- Status: Accepted
+- Date: 2026-09-02
+- Owners: Backend / Packaging / Env / Config maintainers
+- Supersedes: None
+- Superseded by: None
+
+## Context
+
+UniLab 已在 `unisim.backend` 形成统一 `SimBackend` contract，并注册
+MuJoCo、Motrix、Drake、MJWarp、Genesis、IsaacGym 和 IsaacSim。该层仍依赖 UniLab 的
+scene、asset、domain-randomization、dtype、playback 与 runtime helper，无法被物理引擎
+benchmark 或其他消费者独立使用。
+
+Roadmap #1428 将统一物理层拆到 GitHub 仓库 `unilabsim/unisim`。PyPI distribution 使用
+`unisim-core`，Python import namespace 使用 `unisim`。本 ADR 固定 owner boundary 和迁移
+约束；不把 manager/task/training runtime 搬入 core，也不建立 benchmark 专属 backend API。
+
+## Decision
+
+### Package and repository
+
+- `unisim-core` 是 distribution 名称，`unisim` 是唯一 public Python namespace。
+- 新仓库为 public `unilabsim/unisim`，沿用 Apache-2.0。
+- 迁移优先保留可追溯历史（filtered-history/import）；不重写 UniLab 现有分支历史。
+- 版本沿用 UniLab 当前版本策略；每个发布版本记录 source commit、迁移说明和兼容范围。
+
+### Ownership
+
+`unisim-core` owns：
+
+- backend-neutral state/control/reset/mutation 类型、capability、错误和生命周期；
+- lazy adapter factory/registry、engine-native runtime 与必要的 subprocess IPC；
+- cold-path materialization/selector 和 hot-path buffer/step 规则；
+- conformance helper、benchmark API/result schema 预留、package 文档与 PyPI 发布。
+
+UniLab owns：
+
+- Hydra owner YAML、task/env/manager lifecycle、`NpEnvState`、reward/observation/termination；
+- runner、learner、checkpoint、sim2sim、robot asset registry、task XML/scene composition；
+- 将 task-owned scene、DR、dtype/config 翻译为 UniSim 输入的 adapter layer。
+
+`unisim` 不得 import UniLab、Hydra、Torch、Gymnasium、RSL-RL、训练脚本或 task code。
+UniLab env/manager 不得访问 engine model/data/private runtime。
+
+### Migration and final state
+
+首发纵向切片为 core + MuJoCo + Motrix + conformance；benchmark 只预留 API、result schema
+和 provenance 字段，当前不实现 workload、测量或结论。
+
+roadmap 最终必须迁移并验证全部七类 backend：MuJoCo、Motrix、Drake、MJWarp、Genesis、
+IsaacGym、IsaacSim。迁移完成后，UniLab 删除对应实现、重复测试和 `unilab.base.backend`
+compatibility shim，不保留长期双实现。
+
+每个 adapter child 必须同时提交代码、focused conformance、support/optional-extra 文档
+和中英文迁移说明。out-of-process adapter 共用一份 subprocess protocol。
+
+### Branch and release governance
+
+- UniLab declared base 是 `dev/issue-1042-manager-based-api`，执行期间只读；`main` 同样只读。
+- roadmap 集成分支为 `dev/issue-1428-unisim-extraction`，从 declared base 的只读快照创建。
+- child branch/PR 只合入 roadmap 集成分支；不得向 UniLab `main` 或 declared base 写入。
+- `unisim-core` 正式版本发布到 PyPI；UniLab 从正式 PyPI 解析
+  `unisim-core>=0.1.14`，Python import namespace 为 `unisim`。
+
+## Alternatives Considered
+
+- 继续把 backend 留在 UniLab：无法为独立 benchmark 提供轻量 consumer，依赖边界继续扩大。
+- distribution 与 import 均使用 `unisim-core`：不符合已确认的 public import API。
+- 一次迁移全部 adapter 后再发布首版：风险集中且无法尽早验证 package boundary；采用分阶段
+  release，但不缩小最终全量迁移范围。
+
+## Evidence In Repo
+
+- `unisim.backend.base`：统一 `SimBackend` contract。
+- `src/unilab/base/backend_factory.py`：backend factory 与 lazy adapter loading。
+- `tests/base/test_backend_conformance.py`、`tests/base/test_backend_imports.py`：现有边界测试。
+- #888 / PR #892：第一阶段可提取 physics boundary。
+- #1428：跨仓拆分 roadmap、child 顺序与验收标准。
+
+## Related Documents
+
+- `docs/sphinx/source/zh_CN/4-developer_guide/2-contracts/2-backend_contract.md`
+- `docs/sphinx/source/zh_CN/4-developer_guide/5-contributing_workflow.md`
+- `docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/3-layer_boundaries.md`
diff --git a/docs/sphinx/source/adr/README.md b/docs/sphinx/source/adr/README.md
index 007ef110a..772be3814 100644
--- a/docs/sphinx/source/adr/README.md
+++ b/docs/sphinx/source/adr/README.md
@@ -19,6 +19,7 @@ orphan: true
 | [ADR-0003 Task Owner And Config Compose Contract](ADR-0003-task-owner-and-config-compose-contract.md) | Config owner | Accepted |
 | [ADR-0004 Registry Bootstrap Contract](ADR-0004-registry-bootstrap-contract.md) | Registry bootstrap | Accepted |
 | [ADR-0005 Unified Obs Critic Env And IPC Contract](ADR-0005-unified-obs-critic-env-and-ipc-contract.md) | Observation / IPC | Accepted |
+| [ADR-0006 Community Manager API On NumPy Runtime](ADR-0006-community-manager-api-on-numpy-runtime.md) | Manager API / NumPy runtime | Accepted |
 
 ## ADR Governance
 
diff --git a/docs/sphinx/source/api_reference/algos/index.md b/docs/sphinx/source/api_reference/algos/index.md
index 41c42cb8e..7e429d736 100644
--- a/docs/sphinx/source/api_reference/algos/index.md
+++ b/docs/sphinx/source/api_reference/algos/index.md
@@ -1,22 +1,21 @@
-# `unilab.algos` — Learning Algorithms
+# Learning Algorithms — moved to `uni_rl`
 
-- **`unilab.algos.torch`** — PPO (RSL-RL), APPO, FastSAC, FastTD3, FlashSAC,
-  HIM-PPO, HORA + distillation, generic off-policy runner.
+The RL algorithm layer moved out of the `unilab` package into the
+independently released **uni_rl** package (distribution name `unilab-rl`,
+published on PyPI; issue #1480):
 
-All trainers conform to a single runner contract — see
-{doc}`../../en/4-developer_guide/2-contracts/5-runner_lifecycle`.
-
-```{toctree}
-:maxdepth: 2
+- `uni_rl.algos.rsl_rl` / `uni_rl.algos.rsl_rl_ppo` / `uni_rl.algos.rsl_rl_runtime` — PPO (RSL-RL) integration
+- `uni_rl.algos.appo` — APPO runner, learner, staging, worker
+- `uni_rl.algos.fast_sac` / `uni_rl.algos.fast_td3` / `uni_rl.algos.flash_sac` — off-policy learners and runners
+- `uni_rl.offpolicy` — generic off-policy runner, worker, thread budget
+- `uni_rl.algos.him_ppo` — HIM-PPO
+- `uni_rl.algos.hora` — HORA models, trainers, and distillation
+- `uni_rl.algos.common` — shared actor factory, networks, normalization, compile helpers
 
-torch
-```
+UniLab keeps the training *entrypoints* (`src/unilab/scripts/train_*.py`),
+which inject environments into uni_rl runners through
+`uni_rl.env_contract.EnvFactory`; see `src/unilab/base/env_factory.py` for the
+registry-backed adapter.
 
-```{eval-rst}
-.. autosummary::
-   :toctree: _autosummary
-   :template: autosummary/module.rst
-   :recursive:
-
-   unilab.algos
-```
+All trainers conform to a single runner contract — see
+{doc}`../../en/4-developer_guide/2-contracts/5-runner_lifecycle`.
diff --git a/docs/sphinx/source/api_reference/algos/torch.md b/docs/sphinx/source/api_reference/algos/torch.md
deleted file mode 100644
index 347557974..000000000
--- a/docs/sphinx/source/api_reference/algos/torch.md
+++ /dev/null
@@ -1,29 +0,0 @@
-# `unilab.algos.torch`
-
-```{eval-rst}
-.. autosummary::
-   :toctree: _autosummary
-   :template: autosummary/module.rst
-   :recursive:
-
-   unilab.algos.torch.common
-   unilab.algos.torch.appo
-   unilab.algos.torch.fast_sac
-   unilab.algos.torch.fast_td3
-   unilab.algos.torch.flash_sac
-   unilab.algos.torch.him_ppo
-   unilab.algos.torch.hora
-   unilab.algos.torch.offpolicy
-```
-
-## Standalone PPO entrypoints
-
-```{eval-rst}
-.. automodule:: unilab.algos.torch.rsl_rl_ppo
-   :members:
-```
-
-```{eval-rst}
-.. automodule:: unilab.algos.torch.rsl_rl_runtime
-   :members:
-```
diff --git a/docs/sphinx/source/api_reference/backend/index.md b/docs/sphinx/source/api_reference/backend/index.md
index b00321b90..da562fb28 100644
--- a/docs/sphinx/source/api_reference/backend/index.md
+++ b/docs/sphinx/source/api_reference/backend/index.md
@@ -1,7 +1,7 @@
-# `unilab.base.backend` — Simulation Backends
+# `unisim-core` — Simulation Backends
 
-UniLab abstracts two CPU-side physics backends behind a single
-`SimBackend` contract.
+UniSim owns the unified physics contract and all production adapters. UniLab
+only assembles task-owned scenes through `unilab.base.backend_factory`.
 
 | Backend | Strengths | Notes |
 |---|---|---|
@@ -12,7 +12,7 @@ Pick a backend per task via the top-level `--sim <backend>` CLI flag — see
 {doc}`../../en/2-user_guide/3-backends/0-index`.
 
 ```{eval-rst}
-.. autoclass:: unilab.base.backend.base.SimBackend
+.. autoclass:: unisim.backend.base.SimBackend
    :members:
    :show-inheritance:
 ```
@@ -23,16 +23,21 @@ Pick a backend per task via the top-level `--sim <backend>` CLI flag — see
    :template: autosummary/module.rst
    :recursive:
 
-   unilab.base.backend.mujoco
-   unilab.base.backend.motrix
+   unisim.backend.mujoco
+   unisim.backend.motrix
+   unisim.backend.drake
+   unisim.backend.mjwarp
+   unisim.backend.genesis
+   unisim.backend.isaacgym
+   unisim.backend.isaacsim
 ```
 
 ```{eval-rst}
-.. automodule:: unilab.base.backend.playback_common
+.. automodule:: unisim.backend.playback_common
    :members:
 ```
 
 ```{eval-rst}
-.. automodule:: unilab.base.backend.motrix_camera
+.. automodule:: unisim.backend.motrix_camera
    :members:
 ```
diff --git a/docs/sphinx/source/api_reference/base/index.md b/docs/sphinx/source/api_reference/base/index.md
index 96ec2b764..1b1da20d6 100644
--- a/docs/sphinx/source/api_reference/base/index.md
+++ b/docs/sphinx/source/api_reference/base/index.md
@@ -10,7 +10,6 @@ in this reference, read this one.
 | `Registry` | Task / backend / algorithm registration and lookup |
 | `Scene` | Cold-path scene materialization |
 | `observations`, `final_observation` | Observation builders & terminal handling |
-| `augmentation` | Symmetry / mirror augmentation utilities |
 | `curriculum` | Curriculum schedule primitives |
 
 ```{eval-rst}
@@ -32,7 +31,7 @@ in this reference, read this one.
 ```
 
 ```{eval-rst}
-.. autoclass:: unilab.base.backend.base.SimBackend
+.. autoclass:: unisim.backend.base.SimBackend
    :members:
    :show-inheritance:
    :member-order: bysource
diff --git a/docs/sphinx/source/api_reference/envs/index.md b/docs/sphinx/source/api_reference/envs/index.md
index 6665cd124..f7eb11999 100644
--- a/docs/sphinx/source/api_reference/envs/index.md
+++ b/docs/sphinx/source/api_reference/envs/index.md
@@ -1,21 +1,11 @@
-# `unilab.envs` — Tasks
+# `unilab.envs` — Environment runtime
 
-Concrete RL tasks split by family:
+Task-agnostic Manager-Based environment runtime and reusable MDP terms.
+Concrete task implementations are owned by {doc}`../tasks/index`.
 
-- **locomotion** — Go1, Go2, Go2w, Go2 + Airbot, Unitree G1
-- **manipulation** — Allegro / Sharpa in-hand cube
-- **motion_tracking** — G1 whole-body motion tracking + flips
-
-Every env inherits `NpEnv` and is registered into the task `Registry` so it
-can be selected via `uv run train --algo <algo> --task <name> --sim <backend>`.
-
-```{toctree}
-:maxdepth: 2
-
-locomotion
-manipulation
-motion_tracking
-```
+`ManagerBasedRLEnv` preserves UniLab's NumPy `NpEnv` contract while executing
+community-style action, observation, reward, termination, event, command, and
+curriculum managers.
 
 ```{eval-rst}
 .. autosummary::
diff --git a/docs/sphinx/source/api_reference/envs/locomotion.md b/docs/sphinx/source/api_reference/envs/locomotion.md
deleted file mode 100644
index 1a9d5107a..000000000
--- a/docs/sphinx/source/api_reference/envs/locomotion.md
+++ /dev/null
@@ -1,15 +0,0 @@
-# `unilab.envs.locomotion`
-
-```{eval-rst}
-.. autosummary::
-   :toctree: _autosummary
-   :template: autosummary/module.rst
-   :recursive:
-
-   unilab.envs.locomotion.common
-   unilab.envs.locomotion.g1
-   unilab.envs.locomotion.go1
-   unilab.envs.locomotion.go2
-   unilab.envs.locomotion.go2_arm
-   unilab.envs.locomotion.go2w
-```
diff --git a/docs/sphinx/source/api_reference/envs/manipulation.md b/docs/sphinx/source/api_reference/envs/manipulation.md
deleted file mode 100644
index 5e2b2b73b..000000000
--- a/docs/sphinx/source/api_reference/envs/manipulation.md
+++ /dev/null
@@ -1,12 +0,0 @@
-# `unilab.envs.manipulation`
-
-```{eval-rst}
-.. autosummary::
-   :toctree: _autosummary
-   :template: autosummary/module.rst
-   :recursive:
-
-   unilab.envs.manipulation.allegro_inhand
-   unilab.envs.manipulation.sharpa_inhand
-   unilab.envs.manipulation.stewart
-```
diff --git a/docs/sphinx/source/api_reference/envs/motion_tracking.md b/docs/sphinx/source/api_reference/envs/motion_tracking.md
deleted file mode 100644
index c4371bd6b..000000000
--- a/docs/sphinx/source/api_reference/envs/motion_tracking.md
+++ /dev/null
@@ -1,13 +0,0 @@
-# `unilab.envs.motion_tracking`
-
-Whole-body motion tracking tasks. G1 humanoid currently ships flip tracking
-plus general motion tracking (PPO + SAC variants).
-
-```{eval-rst}
-.. autosummary::
-   :toctree: _autosummary
-   :template: autosummary/module.rst
-   :recursive:
-
-   unilab.envs.motion_tracking.g1
-```
diff --git a/docs/sphinx/source/api_reference/index.md b/docs/sphinx/source/api_reference/index.md
index df6920ca1..ea625f7a5 100644
--- a/docs/sphinx/source/api_reference/index.md
+++ b/docs/sphinx/source/api_reference/index.md
@@ -30,10 +30,16 @@ The contracts everything else depends on: `NpEnv`, `SimBackend`, `Registry`,
 :::{grid-item-card} 🧪 `unilab.envs`
 :link: envs/index
 :link-type: doc
-Concrete tasks — locomotion, manipulation, motion tracking — layered on
+Manager-Based environment runtime and task-agnostic MDP terms layered on
 top of `base`.
 :::
 
+:::{grid-item-card} 🤖 `unilab.tasks`
+:link: tasks/index
+:link-type: doc
+Concrete locomotion, manipulation, and motion-tracking task packages.
+:::
+
 ::::
 
 ## Learning stack
@@ -41,10 +47,10 @@ top of `base`.
 ::::{grid} 1 1 2 2
 :gutter: 3
 
-:::{grid-item-card} 🎛 `unilab.algos`
+:::{grid-item-card} 🎛 Learning algorithms → `uni_rl`
 :link: algos/index
 :link-type: doc
-PPO / APPO / SAC / TD3 variants, in PyTorch.
+PPO / APPO / SAC / TD3 variants moved to the uni_rl package (issue #1480).
 :::
 
 :::{grid-item-card} 🏋 `unilab.training`
@@ -53,11 +59,11 @@ PPO / APPO / SAC / TD3 variants, in PyTorch.
 Runtime helpers, monitoring, reward bookkeeping, runner orchestration.
 :::
 
-:::{grid-item-card} 🔗 `unilab.ipc`
+:::{grid-item-card} 🔗 Shared-memory runtime → `uni_rl.ipc`
 :link: ipc/index
 :link-type: doc
-Shared-memory rollout and replay primitives that connect CPU workers and
-the GPU learner.
+Shared-memory rollout and replay primitives moved to the uni_rl package
+(issue #1480).
 :::
 
 :::{grid-item-card} 🧮 `unilab.backend`
@@ -91,22 +97,17 @@ Procedural and heightfield terrain generators.
 Scene rendering and viser bridges.
 :::
 
-:::{grid-item-card} 🔧 `unilab.tools`
-:link: tools/index
-:link-type: doc
-Scene export, NaN visualizer, ONNX export.
-:::
-
 :::{grid-item-card} 🧰 `unilab.utils`
 :link: utils/index
 :link-type: doc
 Math, IO, and numerical helpers.
 :::
 
-:::{grid-item-card} 📝 `unilab.logging`
+:::{grid-item-card} 📝 Training logging → `uni_rl.logging`
 :link: logging/index
 :link-type: doc
-W&B / TensorBoard bridges and structured logging.
+W&B / TensorBoard bridges and structured logging moved to the uni_rl package
+(issue #1480).
 :::
 
 ::::
@@ -125,6 +126,7 @@ top_level
 
 base/index
 envs/index
+tasks/index
 ```
 
 ```{toctree}
@@ -144,7 +146,6 @@ backend/index
 dr/index
 terrains/index
 visualization/index
-tools/index
 utils/index
 logging/index
 ```
diff --git a/docs/sphinx/source/api_reference/ipc/index.md b/docs/sphinx/source/api_reference/ipc/index.md
index c826fef1a..c91baf41a 100644
--- a/docs/sphinx/source/api_reference/ipc/index.md
+++ b/docs/sphinx/source/api_reference/ipc/index.md
@@ -1,32 +1,16 @@
-# `unilab.ipc` — Shared-Memory Runtime
+# Shared-Memory Runtime — moved to `uni_rl`
 
-The bridge between CPU simulation workers and the GPU learner. Everything
-here is a building block of the **async runner** that powers APPO / FastSAC
-/ FastTD3 / FlashSAC.
+The async IPC layer moved out of the `unilab` package into the independently
+released **uni_rl** package (issue #1480): `uni_rl.ipc` hosts the async
+runner, shared-memory buffers, replay pipelines, inference slot, DP launcher /
+sync, and weight sync.
 
 | Submodule | Role |
 |---|---|
-| `async_runner` | The high-level orchestration loop |
-| `shared_buffer` | NumPy-backed shared-memory ring/buffer |
-| `rollout_ring_buffer` | Rollout window used by on-policy collectors |
-| `replay_buffer` | Bounded shared ingress for off-policy transitions |
-| `replay_pipelines.*` | Authoritative CUDA/MPS replay ring, device gather, and native H2D |
-| `inference_slot` | Fixed shared observation/action slot for learner-owned off-policy inference |
-| `weight_sync` | Push learner weights to on-policy collector workers |
-
-```{eval-rst}
-.. autosummary::
-   :toctree: _autosummary
-   :template: autosummary/module.rst
-   :recursive:
-
-   unilab.ipc
-```
-
-## Async runner
-
-```{eval-rst}
-.. automodule:: unilab.ipc.async_runner
-   :members:
-   :show-inheritance:
-```
+| `uni_rl.ipc.async_runner` | The high-level orchestration loop |
+| `uni_rl.ipc.shared_buffer` | NumPy-backed shared-memory ring/buffer |
+| `uni_rl.ipc.rollout_ring_buffer` | Rollout window used by on-policy collectors |
+| `uni_rl.ipc.replay_buffer` | Bounded shared ingress for off-policy transitions |
+| `uni_rl.ipc.replay_pipelines.*` | Authoritative CUDA/MPS replay ring, device gather, and native H2D |
+| `uni_rl.ipc.inference_slot` | Fixed shared observation/action slot for learner-owned off-policy inference |
+| `uni_rl.ipc.weight_sync` | Push learner weights to on-policy collector workers |
diff --git a/docs/sphinx/source/api_reference/logging/index.md b/docs/sphinx/source/api_reference/logging/index.md
index f21a822fb..4750ccf55 100644
--- a/docs/sphinx/source/api_reference/logging/index.md
+++ b/docs/sphinx/source/api_reference/logging/index.md
@@ -1,12 +1,6 @@
-# `unilab.logging` — Logging Adapters
+# Training Logging — moved to `uni_rl`
 
-On-policy / off-policy metric adapters, trace events, common helpers.
-
-```{eval-rst}
-.. autosummary::
-   :toctree: _autosummary
-   :template: autosummary/module.rst
-   :recursive:
-
-   unilab.logging
-```
+The W&B / TensorBoard bridges and structured training loggers moved out of
+the `unilab` package into the independently released **uni_rl** package
+(issue #1480): `uni_rl.logging` hosts `OnPolicyLogger`, `OffPolicyLogger`,
+and the trace-event recorder.
diff --git a/docs/sphinx/source/api_reference/tasks/index.md b/docs/sphinx/source/api_reference/tasks/index.md
new file mode 100644
index 000000000..eb4ed03d1
--- /dev/null
+++ b/docs/sphinx/source/api_reference/tasks/index.md
@@ -0,0 +1,27 @@
+# `unilab.tasks` — Concrete tasks
+
+Concrete RL tasks split by family:
+
+- **locomotion** — A2, Go1, Go2, Go2w, Go2 + Airbot, and Unitree G1
+- **manipulation** — Allegro / Sharpa in-hand cube and Stewart balance
+- **motion_tracking** — G1 and X2 whole-body motion tracking
+
+Every task is registered into the task `Registry` so it can be selected via
+`uv run train --algo <algo> --task <name> --sim <backend>`.
+
+```{toctree}
+:maxdepth: 2
+
+locomotion
+manipulation
+motion_tracking
+```
+
+```{eval-rst}
+.. autosummary::
+   :toctree: _autosummary
+   :template: autosummary/module.rst
+   :recursive:
+
+   unilab.tasks
+```
diff --git a/docs/sphinx/source/api_reference/tasks/locomotion.md b/docs/sphinx/source/api_reference/tasks/locomotion.md
new file mode 100644
index 000000000..c4e504491
--- /dev/null
+++ b/docs/sphinx/source/api_reference/tasks/locomotion.md
@@ -0,0 +1,16 @@
+# `unilab.tasks.locomotion`
+
+```{eval-rst}
+.. autosummary::
+   :toctree: _autosummary
+   :template: autosummary/module.rst
+   :recursive:
+
+   unilab.tasks.locomotion.a2
+   unilab.tasks.locomotion.common
+   unilab.tasks.locomotion.g1
+   unilab.tasks.locomotion.go1
+   unilab.tasks.locomotion.go2
+   unilab.tasks.locomotion.go2_arm
+   unilab.tasks.locomotion.go2w
+```
diff --git a/docs/sphinx/source/api_reference/tasks/manipulation.md b/docs/sphinx/source/api_reference/tasks/manipulation.md
new file mode 100644
index 000000000..328e820de
--- /dev/null
+++ b/docs/sphinx/source/api_reference/tasks/manipulation.md
@@ -0,0 +1,12 @@
+# `unilab.tasks.manipulation`
+
+```{eval-rst}
+.. autosummary::
+   :toctree: _autosummary
+   :template: autosummary/module.rst
+   :recursive:
+
+   unilab.tasks.manipulation.allegro_inhand
+   unilab.tasks.manipulation.sharpa_inhand
+   unilab.tasks.manipulation.stewart
+```
diff --git a/docs/sphinx/source/api_reference/tasks/motion_tracking.md b/docs/sphinx/source/api_reference/tasks/motion_tracking.md
new file mode 100644
index 000000000..8ca1f557d
--- /dev/null
+++ b/docs/sphinx/source/api_reference/tasks/motion_tracking.md
@@ -0,0 +1,14 @@
+# `unilab.tasks.motion_tracking`
+
+Whole-body motion-tracking tasks for G1 and X2 robots.
+
+```{eval-rst}
+.. autosummary::
+   :toctree: _autosummary
+   :template: autosummary/module.rst
+   :recursive:
+
+   unilab.tasks.motion_tracking.common
+   unilab.tasks.motion_tracking.g1
+   unilab.tasks.motion_tracking.x2
+```
diff --git a/docs/sphinx/source/api_reference/tools/index.md b/docs/sphinx/source/api_reference/tools/index.md
deleted file mode 100644
index a8e1918a5..000000000
--- a/docs/sphinx/source/api_reference/tools/index.md
+++ /dev/null
@@ -1,15 +0,0 @@
-# `unilab.tools` — CLI Tools
-
-Console-script entrypoints registered in `pyproject.toml`:
-
-- `unilab-viz-nan` — interactive NaN trace viewer for failed runs.
-- `unilab-export-scene` — dump the resolved scene of a task to disk.
-
-```{eval-rst}
-.. autosummary::
-   :toctree: _autosummary
-   :template: autosummary/module.rst
-   :recursive:
-
-   unilab.tools
-```
diff --git a/docs/sphinx/source/api_reference/training/index.md b/docs/sphinx/source/api_reference/training/index.md
index 5e497f661..80d7238f5 100644
--- a/docs/sphinx/source/api_reference/training/index.md
+++ b/docs/sphinx/source/api_reference/training/index.md
@@ -1,8 +1,10 @@
 # `unilab.training` — Training Runtime
 
-Glue between `algos`, `envs` and `ipc`: experiment lifecycle, metric
-monitoring, reward bookkeeping, seeding, and the top-level `run` helpers
-invoked by the `train` / `eval` / `demo` CLI entrypoints.
+Glue between `algos`, `envs` and `ipc`: experiment lifecycle and the
+top-level `run` helpers invoked by the `train` / `eval` / `demo` CLI
+entrypoints. Layer-0 helpers (seeding, monitoring, reward bookkeeping,
+checkpoint resolution, sim2sim contracts) live in `unilab.utils`; resolved
+env config adaptation lives in `unilab.base.config_adapter`.
 
 ```{eval-rst}
 .. autosummary::
diff --git a/docs/sphinx/source/api_reference/utils/index.md b/docs/sphinx/source/api_reference/utils/index.md
index 99e6e4270..30a8b0133 100644
--- a/docs/sphinx/source/api_reference/utils/index.md
+++ b/docs/sphinx/source/api_reference/utils/index.md
@@ -1,7 +1,9 @@
 # `unilab.utils` — Utilities
 
-Device probing, tensor helpers, support-matrix bookkeeping, NaN guards,
-and pure-numpy geometry/rotation helpers shared across envs and scripts.
+Device probing, tensor helpers, training seeding, hardware monitoring,
+reward bookkeeping, checkpoint resolution, sim2sim contract checks, NaN
+guards, and pure-numpy geometry/rotation helpers shared across envs and
+scripts.
 
 ```{eval-rst}
 .. autosummary::
diff --git a/docs/sphinx/source/conf.py b/docs/sphinx/source/conf.py
index b818430fa..03721f11a 100644
--- a/docs/sphinx/source/conf.py
+++ b/docs/sphinx/source/conf.py
@@ -275,7 +275,11 @@
 # instead of bouncing to the language index. Forward direction only — reverse
 # map is computed below.
 _LANGUAGE_PATH_FORWARD: dict[str, str] = {
+    "en/2-user_guide/3-backends/6-drake": "zh_CN/2-user_guide/3-backends/6-drake",
     "en/1-getting_started/5-faq": "zh_CN/1-getting_started/5-faq",
+    "en/4-developer_guide/1-architecture/6-manager_based_api": (
+        "zh_CN/4-developer_guide/1-architecture/6-manager_based_api"
+    ),
 }
 # Keyed by (current_pagename, target_language) → target_pagename.
 _LANGUAGE_PATH_MAP: dict[tuple[str, str], str] = {}
diff --git a/docs/sphinx/source/en/0-index.md b/docs/sphinx/source/en/0-index.md
index ad2a30b34..5b17d0860 100644
--- a/docs/sphinx/source/en/0-index.md
+++ b/docs/sphinx/source/en/0-index.md
@@ -10,14 +10,15 @@ sd_hide_title: true
 
 # UniLab
 
-### Contract-driven robot learning infrastructure for CPU simulation and accelerator learning.
+### Configure task semantics once. Run robot RL across physics backends.
 
-{bdg-primary}`Python >=3.10,<3.14` {bdg-secondary}`Hydra owner YAML` {bdg-info}`MuJoCo + Motrix` {bdg-success}`uv workflow`
+{bdg-primary}`Python >=3.10,<3.14` {bdg-secondary}`Hydra + Manager API` {bdg-info}`Cross-backend contract` {bdg-success}`uv workflow`
 
-UniLab routes robot RL through the `uv run train` / `uv run eval` CLI,
-task-owner Hydra configs, and backend contracts. Use the landing page to
-install, run a smoke training job, choose an algorithm/backend, or jump into
-deployment and extension docs.
+UniLab turns task semantics into reusable configuration: assemble manager terms,
+select a physics backend, and run the same train/eval workflow on the hardware
+available to you. Use this landing page to install, run a first demo, follow it
+with a smoke job, choose an algorithm or backend, or jump into deployment and
+extension docs.
 
 ```{button-ref} 1-getting_started/1-quick_demo
 :ref-type: doc
@@ -43,20 +44,22 @@ User guide
 ::::{grid} 1 1 3 3
 :gutter: 3
 
-:::{grid-item-card} CPU simulation, accelerator learning
-The README describes UniLab as CPU physics simulation connected to policy
-training through shared memory, with MuJoCo and Motrix as simulation backends.
+:::{grid-item-card} Configure tasks without boilerplate
+Compose actions, observations, rewards, terminations, events, commands, and
+curricula from manager terms in Hydra owner YAML. Common task variants need no
+new environment class.
 :::
 
 :::{grid-item-card} Backend choice stays in config
-Switch backends with CLI flags such as `--task go2_joystick_flat --sim motrix`;
-the CLI composes the matching owner YAML under `conf/`. Do not use
-`training.sim_backend` as a standalone backend switch.
+Move between current and future physics adapters with CLI flags such as
+`--task go2_joystick_flat --sim motrix`; the CLI composes the matching owner
+YAML under `src/unilab/conf/`.
 :::
 
-:::{grid-item-card} Deployment paths are documented
-The deployment docs cover sim-to-real, sim-to-sim, ONNX/runtime export, safety
-layers, and robot-specific notes for G1, Go2, and Allegro.
+:::{grid-item-card} Scale across hardware
+The task contract connects CPU-parallel and external-worker simulation to
+accelerator learners, so the experiment can grow with the hardware available
+to you.
 :::
 
 ::::
@@ -68,6 +71,7 @@ curl -LsSf https://astral.sh/uv/install.sh | sh
 git clone https://github.com/unilabsim/UniLab.git
 cd UniLab
 uv sync --extra motrix
+uv run demo dance
 uv run train --algo ppo --task go2_joystick_flat --sim motrix \
   algo.max_iterations=1 algo.num_envs=16 training.no_play=true
 ```
@@ -90,13 +94,15 @@ machine.
 :::{grid-item-card} Run or replay training
 :link: 1-getting_started/1-quick_demo
 :link-type: doc
-Start with PPO on Go2, then move to evaluation, playback, or checkpoint resume.
+Run a pre-trained demo first, then move to PPO training, evaluation, playback,
+or checkpoint resume.
 :::
 
-:::{grid-item-card} Choose a backend
-:link: 2-user_guide/3-backends/3-choosing_a_backend
+:::{grid-item-card} Choose a physics backend
+:link: 2-user_guide/3-backends/0-index
 :link-type: doc
-Compare MuJoCo and Motrix through task owner YAMLs and backend capability docs.
+Select a backend through task owner YAMLs and read its installation and
+capability requirements.
 :::
 
 :::{grid-item-card} Pick an algorithm
@@ -125,12 +131,13 @@ tasks, backends, algorithms, or terrain.
 
 ```{mermaid}
 flowchart LR
-  cli["uv run train/eval<br/>--algo --task --sim"] --> owner["Task owner YAML<br/>conf/*/task/..."]
-  cli --> script["Thin script routing<br/>scripts/train_*.py"]
+  cli["uv run train/eval<br/>--algo --task --sim"] --> owner["Task owner YAML<br/>src/unilab/conf/*/task/..."]
+  cli --> script["Thin script routing<br/>src/unilab/scripts/train_*.py"]
   owner --> registry["Registry bootstrap<br/>src/unilab/base/registry.py"]
   registry --> env["NpEnv contract<br/>obs dict + info dict"]
-  env --> backend["SimBackend<br/>MuJoCo or Motrix"]
-  env --> runtime["Runner / IPC<br/>shared memory lifecycle"]
+  env --> backend["SimBackend<br/>unisim-core adapters"]
+  env --> factory["EnvFactory contract"]
+  factory --> runtime["Runner / IPC<br/>unilab-rl async runtime"]
   runtime --> learner["Learner<br/>PPO / APPO / SAC / TD3"]
 ```
 
@@ -148,11 +155,11 @@ committed benchmark manifest or separate recommendation metadata.
 | --- | --- | --- |
 | Go1 joystick | PPO, APPO, TD3 | PPO has tested MuJoCo and Motrix rows. APPO has tested MuJoCo rows and Motrix registered rows. TD3 has a Motrix owner YAML for `go1_joystick_flat`. |
 | Go2 joystick | PPO, FlashSAC, TD3 | PPO has tested MuJoCo and Motrix rows. FlashSAC has MuJoCo owner YAMLs for `go2_joystick_flat`; TD3 has a Motrix owner YAML for `go2_joystick_flat`. |
-| Go2 arm manip-loco | PPO, HIM-PPO | Committed MuJoCo owner YAMLs are present under `conf/ppo/task/go2_arm_manip_loco/` and `conf/ppo_him/task/go2_arm_manip_loco/`. |
-| Go2W joystick | PPO | PPO owner YAMLs exist for MuJoCo and Motrix flat/rough variants under `conf/ppo/task/go2w_joystick_*`. |
+| Go2 arm manip-loco | PPO, HIM-PPO | Committed MuJoCo owner YAMLs are present under `src/unilab/conf/ppo/task/go2_arm_manip_loco/` and `src/unilab/conf/ppo_him/task/go2_arm_manip_loco/`. |
+| Go2W joystick | PPO | PPO owner YAMLs exist for MuJoCo and Motrix flat/rough variants under `src/unilab/conf/ppo/task/go2w_joystick_*`. |
 | G1 locomotion / tracking | PPO, APPO, SAC, TD3 | PPO, APPO, and SAC include committed MuJoCo and Motrix owner YAMLs for G1 tasks; TD3 has a `g1_walk_flat` MuJoCo owner. |
 | Allegro in-hand | PPO, APPO | PPO and APPO have committed MuJoCo and Motrix owner YAMLs for Allegro in-hand tasks. |
-| Sharpa in-hand | PPO, APPO HORA teacher, HORA distillation | Sharpa owner YAMLs are committed for PPO/APPO teacher paths; student distillation uses `conf/hora_distill/task/sharpa_inhand/mujoco.yaml`. |
+| Sharpa in-hand | PPO, APPO HORA teacher, HORA distillation | Sharpa owner YAMLs are committed for PPO/APPO teacher paths; student distillation uses `src/unilab/conf/hora_distill/task/sharpa_inhand/mujoco.yaml`. |
 
 ```{toctree}
 :hidden:
diff --git a/docs/sphinx/source/en/1-getting_started/1-quick_demo.md b/docs/sphinx/source/en/1-getting_started/1-quick_demo.md
index d7c3327b3..91e35b3af 100644
--- a/docs/sphinx/source/en/1-getting_started/1-quick_demo.md
+++ b/docs/sphinx/source/en/1-getting_started/1-quick_demo.md
@@ -33,7 +33,20 @@ make setup-motrix
 # make sync-xpu
 ```
 
-## Train
+## First Success
+
+Run a pre-trained policy before changing any task configuration:
+
+```bash
+# Fetches the checkpoint and assets from Hugging Face on first run.
+uv run demo dance
+```
+
+Available demo names are `teaser`, `dance`, `wallflip`, `boxtracking`,
+`locomani`, and `inhandgrasp`. Use `uv run demo --help` for device and refresh
+options.
+
+## Train A Task
 
 ```bash
 uv run train --algo ppo --task go2_joystick_flat --sim motrix
@@ -43,7 +56,7 @@ This command routes to the registered `go2_joystick_flat` task with the Motrix
 backend. The CLI keeps algorithm, task, and backend selection explicit through
 `--algo`, `--task`, and `--sim`; internally it composes the matching owner YAML.
 
-## Evaluate Or Demo
+## Evaluate And Replay
 
 ```bash
 uv run eval --algo ppo --task go2_joystick_flat --sim motrix --load-run -1
@@ -52,12 +65,8 @@ uv run eval --algo ppo --task go2_joystick_flat --sim motrix --load-run -1
 uv run eval --algo ppo --task go2_joystick_flat --sim motrix \
   --load-run -1 --render-mode record
 
-# Demo playback (fetches a pre-trained checkpoint from Hugging Face on first run)
-uv run demo dance
 ```
 
-Available demo names: `teaser`, `dance`, `wallflip`, `boxtracking`, `locomani`, `inhandgrasp`.
-
 Mainland China users: motions, scenes, robot meshes, and demo checkpoints come
 from Hugging Face on first run. If `huggingface.co` is unreachable, switch to the
 community mirror before running training, eval, or demo commands:
diff --git a/docs/sphinx/source/en/1-getting_started/2-installation.md b/docs/sphinx/source/en/1-getting_started/2-installation.md
index cc590dbd4..b2d7f727d 100644
--- a/docs/sphinx/source/en/1-getting_started/2-installation.md
+++ b/docs/sphinx/source/en/1-getting_started/2-installation.md
@@ -7,8 +7,9 @@ live in the getting-started and algorithm pages.
 
 - Python `>=3.10,<3.14`, from `pyproject.toml`.
 - `uv`, used for dependency sync and command execution.
-- `cmake`, required by the local setup documented in
-  `docs/sphinx/source/zh_CN/1-getting_started/2-installation.md`.
+- Git and `curl`, used to clone the repository and fetch runtime assets.
+- `cmake`, required when building the Drake native batch extension. The Drake
+  setup script uses CMake and a C++ toolchain.
 - For the `mujoco` extra: a C++17 toolchain and Python development headers,
   because `mujoco-uni-runtime` ships as a source distribution and compiles its
   native extension during `uv sync` (against the locked mujoco version).
@@ -23,39 +24,69 @@ live in the getting-started and algorithm pages.
 ## Clone And Sync
 
 ```bash
+# Linux / macOS:
 curl -LsSf https://astral.sh/uv/install.sh | sh
+
+# Windows PowerShell:
+# powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
+
 git clone https://github.com/unilabsim/UniLab.git
 cd UniLab
+# Recommended main-environment interpreter:
+uv python install 3.13
 ```
 
+UniLab accepts Python `3.10` through `3.13`; `3.13` is the recommended main
+environment. IsaacGym and IsaacSim use their own worker Python versions below.
+
+If you plan to use Drake, install CMake on the host as well:
+
 ```bash
+# macOS:
 brew install cmake
+
 # Ubuntu / Debian:
 # sudo apt-get install cmake
 ```
 
-Choose one sync path:
+Choose one core setup path:
 
 ```bash
+# Full default setup: MuJoCo + Motrix, with shell completion.
 make setup
-make setup-motrix
+
+# Fastest path for the first Motrix demo.
+# make setup-motrix
+
+# MuJoCo only.
+# make setup-mujoco
 ```
 
-`make setup` runs `uv sync` and installs shell completion. `make setup-motrix`
-runs `uv sync --extra motrix` and installs the same completion entry. If `make`
-is unavailable, run the underlying sync directly:
+`make setup` runs `uv sync --extra mujoco --extra motrix` and installs shell
+completion. `make setup-motrix` runs `uv sync --extra motrix` and installs the
+same completion entry. `make setup-mujoco` runs `uv sync --extra mujoco` and
+installs completion. Run only one of these paths. If `make` is unavailable, run
+the matching commands directly:
 
 ```bash
-uv sync
-uv sync --extra motrix
+# Full default setup:
+uv sync --extra mujoco --extra motrix
+uv run --no-sync unilab-complete install
+
+# Motrix only:
+# uv sync --extra motrix && uv run --no-sync unilab-complete install
+
+# MuJoCo only:
+# uv sync --extra mujoco && uv run --no-sync unilab-complete install
 ```
 
 ## Conda And Pip
 
 The recommended path is still the in-repo `make setup` / `make setup-motrix` (or
-`uv`) workflow. Conda can serve as an outer environment for Python, CUDA, or
-system-library isolation, but once the environment is active keep using the
-repository's `make` / `uv` commands inside it:
+`uv`) workflow. Use `make setup-mujoco` when Motrix is not needed. Conda can
+serve as an outer environment for Python, CUDA, or system-library isolation,
+but once the environment is active keep using the repository's `make` / `uv`
+commands inside it:
 
 ```bash
 conda create -n unilab python=3.13
@@ -66,20 +97,111 @@ cd UniLab
 make setup-motrix
 ```
 
-Use `make setup` if you do not need Motrix. ROCm and XPU still go through the
-platform-specific `make` targets below.
+Use `make setup-mujoco` if you do not need Motrix. ROCm and XPU still go through
+the platform-specific `make` targets below.
+
+From a source checkout, pip is a fallback path. Install the package first, then
+add optional runtimes explicitly:
+
+```bash
+# Editable install for local development:
+pip install -e .
+
+# Regular install (omit -e) for a wheel-style deployment:
+# pip install .
+
+# Motrix, when needed:
+pip install motrixsim-core==0.8.2
+
+# MuJoCo, when needed (install the runtime in two steps):
+pip install "mujoco>=3.5,<3.11" pybind11 wheel
+pip install --no-build-isolation "mujoco-uni-runtime==0.4.0"
+```
+
+The editable install points at the checkout; the regular install copies the
+package and its task configs (`unilab/conf/`) into the environment. In both
+cases, `train`, `eval`, and `demo` work from any directory, while logs and
+checkpoints are written under the current working directory. The MuJoCo runtime
+must be installed after the matching `mujoco` package and with
+`--no-build-isolation`; pip's default isolated build cannot see that dependency.
+For MJWarp, Genesis, platform-specific torch indexes, ROCm/XPU profiles, and
+native-extension rebuild behavior, prefer the uv paths above. Robot meshes and
+textures are intentionally excluded from the wheel and downloaded on the cold
+path from the `unilabsim/unilab-robots` dataset. Ensure the installed package
+location is writable, or pre-fetch assets with `uv run unilab-pull-assets` from a
+source checkout. The isaacgym / isaacsim backends and the HORA multi-GPU
+submission path still assume a source checkout; use their dedicated setup pages
+below.
+
+## Runtime Assets
+
+Large assets are not bundled into the wheel; they are downloaded lazily on
+cold paths (first use of the owning feature) from Hugging Face dataset repos:
+
+- [Robot meshes and textures](https://huggingface.co/datasets/unilabsim/unilab-robots)
+- [Motion clips](https://huggingface.co/datasets/unilabsim/unilab-motions)
+- [Scenes](https://huggingface.co/datasets/unilabsim/unilab-scenes)
+- [Grasp caches](https://huggingface.co/datasets/unilabsim/unilab-caches)
+- [Demo checkpoints](https://huggingface.co/datasets/unilabsim/unilab-checkpoints)
+
+Pre-fetch robot assets with `uv run unilab-pull-assets`. For mainland China,
+set `HF_ENDPOINT=https://hf-mirror.com` when the default Hugging Face endpoint
+is unreachable.
+
+## Backend Extras
+
+The base package installs the public `unisim-core` contract; simulator-specific
+dependencies are optional. Choose the path that matches your backend. The
+commands below are alternatives for a single-backend environment. If you plan
+to compare several in-process backends, combine their extras in one `uv sync`
+command; external worker scripts remain separate.
+
+For example, a local comparison environment can install MuJoCo, Motrix, MJWarp,
+and Genesis together:
 
-`pip install -e .` and `pip install .` are only for dev verification inside a
-source checkout; they do not yet support running training from an arbitrary
-directory via a built wheel/sdist. The training entrypoints still depend on the
-repository's `conf/` and `scripts/`. The pip-only / out-of-repo / published-wheel
-validation path is tracked by issue #360.
+```bash
+uv sync --extra mujoco --extra motrix --extra mjwarp --extra genesis
+```
+
+| Backend | Install path | Important prerequisites |
+| --- | --- | --- |
+| MuJoCo | `make setup-mujoco` or `uv sync --extra mujoco` | C++17 compiler and Python development headers for the native extension |
+| Motrix | `make setup-motrix` or `uv sync --extra motrix` | Motrix runtime is installed from the pinned Python package |
+| MJWarp | `uv sync --extra mujoco --extra mjwarp` | NVIDIA CUDA and an explicit CUDA process device |
+| Genesis | `uv sync --extra genesis` | The validated path uses Linux x86_64, an NVIDIA GPU, and the pinned torch/Genesis versions |
+| Drake | `make setup-drake` | C++20, Eigen/fmt/spdlog, and an existing Drake prefix or the script's download path |
+| IsaacGym | `bash scripts/tools/setup_isaacgym_env.sh` | Linux x86_64, NVIDIA driver, and a separate Python 3.8 worker environment |
+| IsaacSim | `bash scripts/tools/setup_isaacsim_env.sh` | Linux x86_64, NVIDIA CUDA, a separate Python 3.11 worker, and Kit EULA acceptance |
+
+The Drake, IsaacGym, and IsaacSim setup scripts install their external runtime
+outside the repository and can be re-run safely. They do not install the
+external simulator into the main UniLab environment. Read the backend pages for
+runtime variables, renderer requirements, and verification commands:
+
+- {doc}`MuJoCo <../2-user_guide/3-backends/1-mujoco>`
+- {doc}`Motrix <../2-user_guide/3-backends/2-motrix>`
+- {doc}`MJWarp <../2-user_guide/3-backends/0-index>`
+- {doc}`Genesis <../2-user_guide/3-backends/5-genesis>`
+- {doc}`Drake <../2-user_guide/3-backends/6-drake>`
+- {doc}`IsaacGym <../2-user_guide/3-backends/3-isaacgym>`
+- {doc}`IsaacSim <../2-user_guide/3-backends/4-isaacsim>`
 
 ## Platform Profiles
 
 Linux CUDA and macOS use the default `pyproject.toml`. The default Linux torch
 wheel source is the PyTorch `cu128` index configured in `pyproject.toml`.
 
+On Apple Silicon macOS, `make setup-motrix` is the shortest interactive path.
+The CLI routes Motrix playback through `mxpython` when needed; MuJoCo playback
+uses the `mjpython` application bundled by the official MuJoCo wheel. Torch's
+`mps` device is selected automatically when available, and the portable
+`cuda` alias resolves to MPS when CUDA is absent.
+
+On Windows, use the direct `uv sync` commands from above unless GNU `make` and
+Bash are available. Building the MuJoCo native extension requires MSVC Build
+Tools and Python development headers. If you want to use the Makefile, install
+GNU Make and Bash separately (for example through Chocolatey or WSL).
+
 ROCm and Intel XPU have explicit Makefile targets:
 
 ```bash
@@ -102,6 +224,16 @@ ROCm notes:
   (or `uv sync --extra motrix`); confirm the active profile before committing any
   non-ROCm dependency change.
 - The training device field keeps `cuda` semantics; do not set it to `rocm`.
+- When installing from PyPI instead of a source checkout, `make sync-rocm` does
+  not apply. Install the torch build validated by the repository from the
+  PyTorch ROCm index first, then `unilab`. The published dependency range is
+  `torch>=2.8,<2.12`, so pip keeps the installed ROCm build instead of
+  replacing it with the CUDA wheel:
+
+  ```bash
+  pip install torch==2.11.0 --index-url https://download.pytorch.org/whl/rocm7.2
+  pip install unilab
+  ```
 
 Intel XPU notes:
 
@@ -117,7 +249,8 @@ For a local package mirror, set the uv index before syncing:
 
 ```bash
 export UV_INDEX_URL=https://pypi.tuna.tsinghua.edu.cn/simple
-uv sync --index-url https://pypi.tuna.tsinghua.edu.cn/simple
+uv sync --extra mujoco --extra motrix \
+  --index-url https://pypi.tuna.tsinghua.edu.cn/simple
 ```
 
 ## Smoke Check
diff --git a/docs/sphinx/source/en/1-getting_started/3-evaluation_and_playback.md b/docs/sphinx/source/en/1-getting_started/3-evaluation_and_playback.md
index 42f6f70bd..43e187961 100644
--- a/docs/sphinx/source/en/1-getting_started/3-evaluation_and_playback.md
+++ b/docs/sphinx/source/en/1-getting_started/3-evaluation_and_playback.md
@@ -22,37 +22,43 @@ Render modes:
 - `record` — write MP4 to `runs/<run>/playback/`.
 - `none` — skip rendering, just compute metrics.
 
+For `--sim mujoco --render-mode interactive`, `uv run eval` launches the dedicated
+`play_interactive.py` MuJoCo viewer directly. This mode rolls out one environment
+so the viewer camera and controls remain interactive; `training.play_env_num` is
+ignored.
+
 `training.export_onnx=false` currently applies only to the off-policy playback path
-(`scripts/train_offpolicy.py` and CLI runs with `--algo sac|td3|flashsac`). It skips
+(`src/unilab/scripts/train_sac.py` / `src/unilab/scripts/train_td3.py` / `src/unilab/scripts/train_flashsac.py`
+and CLI runs with `--algo sac|td3|flashsac`). It skips
 `policy.onnx` export and verification but still runs playback and video recording.
 
 ## MuJoCo Viewer Scripts
 
 Use `uv run eval` for regular evaluation and video export. When you need a live
 `mujoco.viewer` window for policy debugging, use the low-level
-`scripts/play_interactive.py` script.
+`src/unilab/scripts/play_interactive.py` script.
 
-`scripts/play_interactive.py` is the general MuJoCo viewer entrypoint for PPO,
+`src/unilab/scripts/play_interactive.py` is the general MuJoCo viewer entrypoint for PPO,
 APPO, SAC, FlashSAC, and HORA distill policies. It uses `--algo / --task /
 --sim` to select the algorithm and owner config. The viewer is always
 `mujoco.viewer`; `--sim` only selects which config to read.
 
 ```bash
 # Use the owner config's interactive.action_mode; the global default is zero action
-uv run scripts/play_interactive.py --algo ppo --task go2_joystick_flat --sim mujoco
+uv run src/unilab/scripts/play_interactive.py --algo ppo --task go2_joystick_flat --sim mujoco
 
 # Random actions
-uv run scripts/play_interactive.py --algo ppo --task go2_joystick_flat --sim mujoco \
+uv run src/unilab/scripts/play_interactive.py --algo ppo --task go2_joystick_flat --sim mujoco \
     interactive.action_mode=random
 
 # Policy actions
-uv run scripts/play_interactive.py --algo ppo --task go2_joystick_flat --sim mujoco \
+uv run src/unilab/scripts/play_interactive.py --algo ppo --task go2_joystick_flat --sim mujoco \
     algo.load_run=-1 interactive.action_mode=policy
 
-uv run scripts/play_interactive.py --algo flashsac --task g1_walk_flat --sim motrix \
+uv run src/unilab/scripts/play_interactive.py --algo flashsac --task g1_walk_flat --sim motrix \
     algo.load_run=-1 interactive.action_mode=policy
 
-uv run scripts/play_interactive.py --algo ppo --task go2_joystick_flat --sim mujoco \
+uv run src/unilab/scripts/play_interactive.py --algo ppo --task go2_joystick_flat --sim mujoco \
     interactive.action_mode=policy interactive.keyboard=true
 ```
 
diff --git a/docs/sphinx/source/en/1-getting_started/4-project_structure.md b/docs/sphinx/source/en/1-getting_started/4-project_structure.md
index 40ab3d3d2..fa19760fc 100644
--- a/docs/sphinx/source/en/1-getting_started/4-project_structure.md
+++ b/docs/sphinx/source/en/1-getting_started/4-project_structure.md
@@ -7,11 +7,11 @@ changing behavior.
 | Path | Owner Role |
 | --- | --- |
 | `scripts/` | Thin training and tooling entrypoints. Scripts compose Hydra config and call owner-layer code. |
-| `conf/` | Hydra roots and task owner YAMLs. The top-level CLI exposes backend selection as `--task` plus `--sim`, then composes the matching owner YAML. |
+| `src/unilab/conf/` | Hydra roots and task owner YAMLs. The top-level CLI exposes backend selection as `--task` plus `--sim`, then composes the matching owner YAML. |
 | `src/unilab/base/` | Registry, env state, scene, and backend contracts. |
 | `src/unilab/envs/` | Task env implementations and task-specific reset, reward, observation, and DR logic. |
-| `src/unilab/algos/` | PPO, APPO, off-policy, HIM-PPO, and HORA algorithm code. |
-| `src/unilab/ipc/` | Shared-memory and async runner primitives. |
+| `uni_rl` (unilab-rl repo) | PPO, APPO, off-policy, HIM-PPO, and HORA algorithm code. |
+| `uni_rl.ipc` (unilab-rl repo) | Shared-memory and async runner primitives. |
 | `src/unilab/training/` | Shared training helpers for logging, playback, seed handling, and config guards. |
 | `src/unilab/visualization/` | Playback, rendering, NaN inspection, and scene/export utilities. |
 | `tests/` | Contract, config, env, algorithm, script, and integration tests. |
@@ -22,11 +22,12 @@ changing behavior.
 
 The main config roots are:
 
-- `conf/ppo/config.yaml` for torch PPO.
-- `conf/appo/config.yaml` for APPO.
-- `conf/offpolicy/config.yaml` plus `conf/offpolicy/algo/*.yaml` for SAC,
-  TD3, and FlashSAC.
-- `conf/ppo_him/config.yaml` and `conf/hora_distill/config.yaml` for the
+- `src/unilab/conf/ppo/config.yaml` for torch PPO.
+- `src/unilab/conf/appo/config.yaml` for APPO.
+- `src/unilab/conf/sac/config.yaml`, `src/unilab/conf/td3/config.yaml`, and
+  `src/unilab/conf/flashsac/config.yaml` for SAC, TD3, and FlashSAC, each with its
+  algorithm hyperparameters inlined.
+- `src/unilab/conf/ppo_him/config.yaml` and `src/unilab/conf/hora_distill/config.yaml` for the
   specialized HIM-PPO and HORA paths.
 
 Task owner YAMLs are the backend identity. Examples:
diff --git a/docs/sphinx/source/en/2-user_guide/1-training/1-cli_reference.md b/docs/sphinx/source/en/2-user_guide/1-training/1-cli_reference.md
index 5809775a8..914ef33b6 100644
--- a/docs/sphinx/source/en/2-user_guide/1-training/1-cli_reference.md
+++ b/docs/sphinx/source/en/2-user_guide/1-training/1-cli_reference.md
@@ -7,11 +7,11 @@ keeps the lower-level scripts available for debugging Hydra composition.
 
 | Goal | Command Shape | Routed Script |
 | --- | --- | --- |
-| PPO | `uv run train --algo ppo --task <task> --sim <backend>` | `scripts/train_rsl_rl.py` |
-| APPO | `uv run train --algo appo --task <task> --sim <backend>` | `scripts/train_appo.py` |
-| SAC | `uv run train --algo sac --task <task> --sim <backend>` | `scripts/train_offpolicy.py` |
-| TD3 | `uv run train --algo td3 --task <task> --sim <backend>` | `scripts/train_offpolicy.py` |
-| FlashSAC | `uv run train --algo flashsac --task <task> --sim <backend>` | `scripts/train_offpolicy.py` |
+| PPO | `uv run train --algo ppo --task <task> --sim <backend>` | `src/unilab/scripts/train_rsl_rl.py` |
+| APPO | `uv run train --algo appo --task <task> --sim <backend>` | `src/unilab/scripts/train_appo.py` |
+| SAC | `uv run train --algo sac --task <task> --sim <backend>` | `src/unilab/scripts/train_sac.py` |
+| TD3 | `uv run train --algo td3 --task <task> --sim <backend>` | `src/unilab/scripts/train_td3.py` |
+| FlashSAC | `uv run train --algo flashsac --task <task> --sim <backend>` | `src/unilab/scripts/train_flashsac.py` |
 
 Examples:
 
@@ -42,22 +42,33 @@ On a fresh checkout, one setup command syncs the environment and installs the
 completion:
 
 ```bash
+# Full default environment (MuJoCo + Motrix):
 make setup
 
-# When you need Motrix:
-make setup-motrix
+# MuJoCo only:
+# make setup-mujoco
+
+# Motrix only (the shortest path for Motrix demos):
+# make setup-motrix
 ```
 
-`make setup` runs `uv sync` followed by `uv run --no-sync unilab-complete install`;
-`make setup-motrix` runs `uv sync --extra motrix` followed by the same completion
-install. The install command picks Bash or Zsh from `$SHELL` / platform and only
-writes user-level rc files. The current shell is not auto-activated; reopen the
-terminal or source the rc file to apply.
+`make setup` runs `uv sync --extra mujoco --extra motrix` followed by
+`uv run --no-sync unilab-complete install`; `make setup-mujoco` and
+`make setup-motrix` select only their named extra and install the same completion.
+Choose one setup path for an environment. The install command picks Bash or Zsh
+from `$SHELL` / platform and only writes user-level rc files. The current shell
+is not auto-activated; reopen the terminal or source the rc file to apply.
 
 If `make` is unavailable, run the steps directly:
 
 ```bash
-uv sync && uv run --no-sync unilab-complete install
+# Full default environment:
+uv sync --extra mujoco --extra motrix
+uv run --no-sync unilab-complete install
+
+# Or choose one backend extra:
+# uv sync --extra mujoco && uv run --no-sync unilab-complete install
+# uv sync --extra motrix && uv run --no-sync unilab-complete install
 ```
 
 Bash users (Linux / WSL) can instead add this to `~/.bashrc`:
@@ -90,6 +101,9 @@ uv run eval --algo ppo --task go2_joystick_flat --sim motrix --load-run -1 \
 
 Supported render modes are `auto`, `interactive`, `record`, and `none`.
 
+The MuJoCo interactive mode (`--sim mujoco --render-mode interactive`) routes
+directly to `play_interactive.py` and always rolls out one environment.
+
 ## Demo
 
 ```bash
@@ -123,8 +137,8 @@ The lower-level scripts remain available when you need to inspect Hydra config
 groups or reproduce a script-level issue. For normal usage, keep
 route-defining values in the unified CLI flags above.
 
-For off-policy routes, keep `--algo` aligned with the owner tree under
-`conf/offpolicy/task/<algo>/`; do not include the algorithm name in `--task`.
+For off-policy routes, `--algo` selects the per-algorithm owner tree
+`src/unilab/conf/<algo>/`; do not include the algorithm name in `--task`.
 
 ## Common Overrides
 
diff --git a/docs/sphinx/source/en/2-user_guide/1-training/2-hydra_config.md b/docs/sphinx/source/en/2-user_guide/1-training/2-hydra_config.md
index 9fb08cfed..cd2ed7937 100644
--- a/docs/sphinx/source/en/2-user_guide/1-training/2-hydra_config.md
+++ b/docs/sphinx/source/en/2-user_guide/1-training/2-hydra_config.md
@@ -7,11 +7,11 @@ identity of the task, backend, reward, scene, and task-specific runtime fields.
 
 | Stack | Owner YAML Shape |
 | --- | --- |
-| PPO | `conf/ppo/task/<task>/<backend>.yaml` |
-| APPO | `conf/appo/task/<task>/<backend>.yaml` |
-| SAC / TD3 / FlashSAC | `conf/offpolicy/task/<algo>/<task>/<backend>.yaml` |
-| HIM-PPO | `conf/ppo_him/task/<task>/<backend>.yaml` |
-| HORA distillation | `conf/hora_distill/task/<task>/<backend>.yaml` |
+| PPO | `src/unilab/conf/ppo/task/<task>/<backend>.yaml` |
+| APPO | `src/unilab/conf/appo/task/<task>/<backend>.yaml` |
+| SAC / TD3 / FlashSAC | `src/unilab/conf/<algo>/task/<task>/<backend>.yaml` |
+| HIM-PPO | `src/unilab/conf/ppo_him/task/<task>/<backend>.yaml` |
+| HORA distillation | `src/unilab/conf/hora_distill/task/<task>/<backend>.yaml` |
 
 Examples:
 
@@ -21,8 +21,8 @@ uv run train --algo ppo --task go2_joystick_flat --sim motrix
 uv run train --algo sac --task g1_walk_flat --sim mujoco
 ```
 
-For off-policy, `--algo` selects the first owner-path segment under
-`conf/offpolicy/task/<algo>/`; do not include the algorithm name in `--task`.
+For off-policy, `--algo` selects the per-algorithm config tree `src/unilab/conf/<algo>/`;
+do not include the algorithm name in `--task`.
 
 ## Safe Overrides
 
diff --git a/docs/sphinx/source/en/2-user_guide/1-training/3-logging.md b/docs/sphinx/source/en/2-user_guide/1-training/3-logging.md
index a766838ba..49422b03f 100644
--- a/docs/sphinx/source/en/2-user_guide/1-training/3-logging.md
+++ b/docs/sphinx/source/en/2-user_guide/1-training/3-logging.md
@@ -23,11 +23,11 @@ stack overrides `training.log_root` or `training.log_dir`:
 
 | Algorithm | Log root | `algo_log_name` source |
 | --- | --- | --- |
-| PPO | `logs/rsl_rl_ppo/<task>/` | `conf/ppo/config.yaml` |
-| APPO | `logs/appo/<task>/` | `conf/appo/config.yaml` |
-| SAC | `logs/fast_sac/<task>/` | `conf/offpolicy/algo/sac.yaml` |
-| FlashSAC | `logs/flash_sac/<task>/` | `conf/offpolicy/algo/flashsac.yaml` |
-| TD3 | `logs/fast_td3/<task>/` | `conf/offpolicy/algo/td3.yaml` |
+| PPO | `logs/rsl_rl_ppo/<task>/` | `src/unilab/conf/ppo/config.yaml` |
+| APPO | `logs/appo/<task>/` | `src/unilab/conf/appo/config.yaml` |
+| SAC | `logs/fast_sac/<task>/` | `src/unilab/conf/sac/config.yaml` |
+| FlashSAC | `logs/flash_sac/<task>/` | `src/unilab/conf/flashsac/config.yaml` |
+| TD3 | `logs/fast_td3/<task>/` | `src/unilab/conf/td3/config.yaml` |
 
 A run directory is named `YYYY-MM-DD_HH-MM-SS_<sim_backend>`, for example
 `2026-03-09_18-30-00_mujoco`. Common artifacts include `run_config.json`,
diff --git a/docs/sphinx/source/en/2-user_guide/2-algorithms/0-index.md b/docs/sphinx/source/en/2-user_guide/2-algorithms/0-index.md
index d9ff6c63b..e79f2dfd3 100644
--- a/docs/sphinx/source/en/2-user_guide/2-algorithms/0-index.md
+++ b/docs/sphinx/source/en/2-user_guide/2-algorithms/0-index.md
@@ -6,13 +6,13 @@ lives, and which command shape selects it. For general flags, see
 
 | Algorithm | Style | Entrypoint | Config Evidence |
 | --- | --- | --- | --- |
-| PPO | synchronous on-policy | `scripts/train_rsl_rl.py` | `conf/ppo/config.yaml` |
-| APPO | async on-policy | `scripts/train_appo.py` | `conf/appo/config.yaml` |
-| SAC | off-policy | `scripts/train_offpolicy.py` | `conf/offpolicy/algo/sac.yaml` |
-| TD3 | off-policy | `scripts/train_offpolicy.py` | `conf/offpolicy/algo/td3.yaml` |
-| FlashSAC | off-policy | `scripts/train_offpolicy.py` | `conf/offpolicy/algo/flashsac.yaml` |
-| HIM-PPO | height-estimator PPO path | `scripts/train_him_ppo.py` | `conf/ppo_him/config.yaml` |
-| HORA | teacher/student distillation path | `scripts/train_hora_distill.py` | `conf/hora_distill/config.yaml` |
+| PPO | synchronous on-policy | `src/unilab/scripts/train_rsl_rl.py` | `src/unilab/conf/ppo/config.yaml` |
+| APPO | async on-policy | `src/unilab/scripts/train_appo.py` | `src/unilab/conf/appo/config.yaml` |
+| SAC | off-policy | `src/unilab/scripts/train_sac.py` | `src/unilab/conf/sac/config.yaml` |
+| TD3 | off-policy | `src/unilab/scripts/train_td3.py` | `src/unilab/conf/td3/config.yaml` |
+| FlashSAC | off-policy | `src/unilab/scripts/train_flashsac.py` | `src/unilab/conf/flashsac/config.yaml` |
+| HIM-PPO | height-estimator PPO path | `scripts/train_him_ppo.py` | `src/unilab/conf/ppo_him/config.yaml` |
+| HORA | teacher/student distillation path | `scripts/train_hora_distill.py` | `src/unilab/conf/hora_distill/config.yaml` |
 
 ```{toctree}
 :hidden:
diff --git a/docs/sphinx/source/en/2-user_guide/2-algorithms/1-ppo.md b/docs/sphinx/source/en/2-user_guide/2-algorithms/1-ppo.md
index e52e20183..4277eff75 100644
--- a/docs/sphinx/source/en/2-user_guide/2-algorithms/1-ppo.md
+++ b/docs/sphinx/source/en/2-user_guide/2-algorithms/1-ppo.md
@@ -1,8 +1,8 @@
 # PPO
 
 PPO is the default synchronous on-policy training path. It uses
-`scripts/train_rsl_rl.py`, composes from `conf/ppo/config.yaml`, and runs the
-RSL-RL adapter code in `src/unilab/algos/torch/rsl_rl_ppo.py` and
+`src/unilab/scripts/train_rsl_rl.py`, composes from `src/unilab/conf/ppo/config.yaml`, and runs the
+RSL-RL adapter code in `uni_rl.algos.rsl_rl_ppo` (unilab-rl repo) and
 `src/unilab/training/rsl_rl.py`.
 
 ## Quick Start
@@ -27,7 +27,7 @@ Use `uv run eval` for checkpoint playback:
 uv run eval --algo ppo --task go2_joystick_flat --sim mujoco --load-run -1
 ```
 
-Logs are grouped by `algo.algo_log_name`; the default in `conf/ppo/config.yaml`
+Logs are grouped by `algo.algo_log_name`; the default in `src/unilab/conf/ppo/config.yaml`
 is `rsl_rl_ppo`.
 
 ## Single-node multi-GPU training
diff --git a/docs/sphinx/source/en/2-user_guide/2-algorithms/2-appo.md b/docs/sphinx/source/en/2-user_guide/2-algorithms/2-appo.md
index aea0cc9c9..bde402557 100644
--- a/docs/sphinx/source/en/2-user_guide/2-algorithms/2-appo.md
+++ b/docs/sphinx/source/en/2-user_guide/2-algorithms/2-appo.md
@@ -1,7 +1,7 @@
 # APPO
 
-APPO is UniLab's asynchronous PPO path. It uses `scripts/train_appo.py`,
-`conf/appo/config.yaml`, and the runtime under `src/unilab/algos/torch/appo/`.
+APPO is UniLab's asynchronous PPO path. It uses `src/unilab/scripts/train_appo.py`,
+`src/unilab/conf/appo/config.yaml`, and the runtime under `uni_rl.algos.appo` (unilab-rl repo).
 The config exposes `algo.steps_per_env`, `training.collector_device`, and
 `training.replay_queue_size`; the algorithm config includes V-trace clipping
 fields.
@@ -73,4 +73,4 @@ gantt
 - `algo.save_interval`: checkpoint save interval.
 
 The default log root is `logs/appo/<task>/`, from `algo.algo_log_name=appo`
-in `conf/appo/config.yaml`.
+in `src/unilab/conf/appo/config.yaml`.
diff --git a/docs/sphinx/source/en/2-user_guide/2-algorithms/3-sac.md b/docs/sphinx/source/en/2-user_guide/2-algorithms/3-sac.md
index 4c46cce36..d45cdf62f 100644
--- a/docs/sphinx/source/en/2-user_guide/2-algorithms/3-sac.md
+++ b/docs/sphinx/source/en/2-user_guide/2-algorithms/3-sac.md
@@ -1,13 +1,13 @@
 # SAC
 
-SAC is selected through the shared off-policy entrypoint
-`scripts/train_offpolicy.py`, which TD3 and FlashSAC share as well. The main
-config is `conf/offpolicy/config.yaml`, and the SAC algorithm defaults live in
-`conf/offpolicy/algo/sac.yaml`. The current log name is `fast_sac`.
+SAC runs through `src/unilab/scripts/train_sac.py`; TD3 and FlashSAC have their own
+entrypoints and per-algorithm config trees. The main config is
+`src/unilab/conf/sac/config.yaml`, with the SAC algorithm defaults inlined there. The
+current log name is `fast_sac`.
 
 ## Runtime Model
 
-The off-policy runner decouples CPU simulation from accelerator learning through
+The off-policy runner decouples simulation collection from accelerator learning through
 bounded shared memory. A collector subprocess publishes packed transitions
 through two ingress slots, while the complete replay ring is authoritative on
 one CUDA or Apple MPS learner device. Host replay allocation therefore does not
@@ -25,7 +25,7 @@ uv run train --algo sac --task g1_walk_rough --sim motrix training.no_play=true
 
 ## Key Fields
 
-For the off-policy playback path (`scripts/train_offpolicy.py` / CLI `--algo sac`),
+For the off-policy playback path (`src/unilab/scripts/train_sac.py` / CLI `--algo sac`),
 set `training.export_onnx=false` to skip `policy.onnx` export while still recording
 playback video. See {doc}`/en/1-getting_started/3-evaluation_and_playback`.
 
@@ -33,7 +33,7 @@ playback video. See {doc}`/en/1-getting_started/3-evaluation_and_playback`.
 - `algo.num_envs=4096`
 - `algo.batch_size=8192` is the learner batch per update.
 - `algo.max_iterations=500`
-- `training.use_amp=true` in the shared off-policy config
+- `training.use_amp=true` in `src/unilab/conf/sac/config.yaml`
 
 The off-policy device replay path uses synchronized, learner-owned inference:
 collectors exchange observations and actions through shared memory and do not own an actor.
@@ -44,3 +44,17 @@ uv run train --algo sac --task g1_walk_flat --sim mujoco \
   algo.max_iterations=1000 \
   training.no_play=true
 ```
+
+## Single-node multi-GPU device placement
+
+`training.devices` assigns rank i's learner to `cuda:devices[i]`; each rank owns one
+collector. For mjwarp, the rank process and its collector process explicitly bind Warp's
+default/current device to that same learner device before probe or production environment
+materialization. The collector therefore does not fall back to Warp's fresh-process default
+of `cuda:0`. The local binding is recorded as `collector_backend_device` in the runtime
+manifest.
+
+MuJoCo has a committed multi-GPU scaling benchmark. The mjwarp per-rank placement contract is
+covered by `tests/base/backend/test_process_device.py` and the off-policy runner/worker unit
+tests; the repository does not currently contain an mjwarp multi-GPU throughput or convergence
+benchmark.
diff --git a/docs/sphinx/source/en/2-user_guide/2-algorithms/4-td3.md b/docs/sphinx/source/en/2-user_guide/2-algorithms/4-td3.md
index 5b75c187e..7bd9e322b 100644
--- a/docs/sphinx/source/en/2-user_guide/2-algorithms/4-td3.md
+++ b/docs/sphinx/source/en/2-user_guide/2-algorithms/4-td3.md
@@ -1,7 +1,7 @@
 # TD3
 
-TD3 shares the off-policy training script with SAC and FlashSAC. Select it
-with `--algo td3`; owner YAML evidence lives under `conf/offpolicy/task/td3/`.
+TD3 runs through `src/unilab/scripts/train_td3.py` in its own config tree. Select it
+with `--algo td3`; owner YAML evidence lives under `src/unilab/conf/td3/task/`.
 
 ## Quick Start
 
@@ -11,11 +11,11 @@ uv run train --algo td3 --task g1_walk_flat --sim mujoco
 
 ## Key Fields
 
-For the off-policy playback path (`scripts/train_offpolicy.py` / CLI `--algo td3`),
+For the off-policy playback path (`src/unilab/scripts/train_td3.py` / CLI `--algo td3`),
 set `training.export_onnx=false` to skip `policy.onnx` export while still recording
 playback video. See {doc}`/en/1-getting_started/3-evaluation_and_playback`.
 
-- Defaults live in `conf/offpolicy/algo/td3.yaml`.
+- Defaults are inlined in `src/unilab/conf/td3/config.yaml`.
 - `algo.algo_log_name=fast_td3`.
 - `algo.max_iterations=5000`.
 - `algo.policy_frequency=2`.
diff --git a/docs/sphinx/source/en/2-user_guide/2-algorithms/5-flash_sac.md b/docs/sphinx/source/en/2-user_guide/2-algorithms/5-flash_sac.md
index f1ce54b40..18fdf0eb6 100644
--- a/docs/sphinx/source/en/2-user_guide/2-algorithms/5-flash_sac.md
+++ b/docs/sphinx/source/en/2-user_guide/2-algorithms/5-flash_sac.md
@@ -1,11 +1,11 @@
 # FlashSAC
 
-FlashSAC is the third algorithm on the shared off-policy entrypoint. Select it
-with `--algo flashsac`; defaults live in
-`conf/offpolicy/algo/flashsac.yaml`, and the implementation lives under
-`src/unilab/algos/torch/flash_sac/`.
+FlashSAC runs through `src/unilab/scripts/train_flashsac.py` in its own config tree.
+Select it with `--algo flashsac`; defaults are inlined in
+`src/unilab/conf/flashsac/config.yaml`, and the implementation lives under
+`uni_rl.algos.flash_sac` (unilab-rl repo).
 
-It shares the off-policy training script with SAC and TD3, but does not use the
+It shares the off-policy runner design with SAC and TD3, but does not use the
 same default networks: the actor uses a block-based structure and the critic
 uses a distributional (categorical) Q variant.
 
@@ -18,7 +18,7 @@ uv run train --algo flashsac --task go2_joystick_flat --sim mujoco training.no_p
 
 ## Key Fields
 
-For the off-policy playback path (`scripts/train_offpolicy.py` / CLI `--algo flashsac`),
+For the off-policy playback path (`src/unilab/scripts/train_flashsac.py` / CLI `--algo flashsac`),
 set `training.export_onnx=false` to skip `policy.onnx` export while still recording
 playback video. See {doc}`/en/1-getting_started/3-evaluation_and_playback`.
 
diff --git a/docs/sphinx/source/en/2-user_guide/2-algorithms/6-him_ppo.md b/docs/sphinx/source/en/2-user_guide/2-algorithms/6-him_ppo.md
index 14efa87c7..d0c196b91 100644
--- a/docs/sphinx/source/en/2-user_guide/2-algorithms/6-him_ppo.md
+++ b/docs/sphinx/source/en/2-user_guide/2-algorithms/6-him_ppo.md
@@ -1,8 +1,8 @@
 # HIM-PPO
 
 HIM-PPO has its own config group and script. The entrypoint is
-`scripts/train_him_ppo.py`, the base config is `conf/ppo_him/config.yaml`, and
-the committed task owner is `conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml`.
+`scripts/train_him_ppo.py`, the base config is `src/unilab/conf/ppo_him/config.yaml`, and
+the committed task owner is `src/unilab/conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml`.
 
 ## Current Entrypoint
 
diff --git a/docs/sphinx/source/en/2-user_guide/2-algorithms/7-hora.md b/docs/sphinx/source/en/2-user_guide/2-algorithms/7-hora.md
index 808ccf28b..a2e11c19e 100644
--- a/docs/sphinx/source/en/2-user_guide/2-algorithms/7-hora.md
+++ b/docs/sphinx/source/en/2-user_guide/2-algorithms/7-hora.md
@@ -3,7 +3,7 @@
 The committed HORA path is the Sharpa in-hand teacher/student flow. Teacher
 owners live under the PPO and APPO task trees through the `7-hora` profile for
 `sharpa_inhand`; student distillation uses `scripts/train_hora_distill.py` and
-`conf/hora_distill/task/sharpa_inhand/mujoco.yaml`.
+`src/unilab/conf/hora_distill/task/sharpa_inhand/mujoco.yaml`.
 
 ## Teacher
 
@@ -13,16 +13,16 @@ uv run train --algo appo --task sharpa_inhand --sim mujoco --profile hora traini
 ```
 
 The HORA PPO owner sets `algo.algo_log_name=hora_ppo` and resolves the runtime
-through `unilab.algos.torch.hora.rsl_rl:resolve_hora_ppo_runtime`. The APPO
+through `uni_rl.algos.hora.rsl_rl:resolve_hora_ppo_runtime`. The APPO
 variant sets `algo.algo_log_name=hora_appo`.
 
 ## Student Distillation
 
 Student distillation is implemented by `scripts/train_hora_distill.py` and
-configured by `conf/hora_distill/task/sharpa_inhand/mujoco.yaml`. The top-level
+configured by `src/unilab/conf/hora_distill/task/sharpa_inhand/mujoco.yaml`. The top-level
 CLI does not currently declare a separate HORA distillation `--algo` route, so
 the public CLI examples on this page stay on the teacher path above.
 
 Teacher checkpoint resolution is implemented in
-`src/unilab/algos/torch/hora/distill_config.py`. The student log family is
+`src/unilab/training/hora_distill_config.py`. The student log family is
 `hora_distill`.
diff --git a/docs/sphinx/source/en/2-user_guide/3-backends/0-index.md b/docs/sphinx/source/en/2-user_guide/3-backends/0-index.md
index 6f6f9a122..16e321e6b 100644
--- a/docs/sphinx/source/en/2-user_guide/3-backends/0-index.md
+++ b/docs/sphinx/source/en/2-user_guide/3-backends/0-index.md
@@ -1,29 +1,62 @@
 # Simulation Backends
 
-UniLab currently uses two backend names in registry/config paths: `1-mujoco` and
-`2-motrix`. User commands select them with `--sim`, which routes to the matching
-task owner YAML; do not switch a run by overriding `training.sim_backend` alone.
+UniLab exposes backend names through registry/config paths, including `mujoco`,
+`motrix`, `mjwarp`, `drake`, `isaacgym`, `genesis`, and `isaacsim` where an owner is registered.
+User commands select them with `--sim`, which routes to the matching task owner
+YAML; do not switch a run by overriding `training.sim_backend` alone.
 
 ## Runtime Prerequisites
 
 - Install Motrix support with `uv sync --extra motrix`.
+- IsaacGym and IsaacSim use dedicated external worker runtimes; see their
+  backend pages for installation and runtime requirements.
 - Any run using `--sim mujoco`, MuJoCo playback, or MuJoCo-only debugging tool
   still requires a working MuJoCo runtime.
+- Drake uses the external `drake-uni` package plus a locally built C++ batch
+  extension; see {doc}`6-drake` before selecting `--sim drake`.
 - On macOS, the package CLI routes Motrix interactive playback through
   `mxpython` when needed. Direct script calls that open the native Motrix
   renderer should use `uv run mxpython`.
 
 ## Select A Backend
 
+UniLab selects the simulator through the task owner config. For normal usage,
+choose the task and backend with `--task` and `--sim`; off-policy commands keep
+the algorithm in `--algo`, not in `--task`. Do not switch a run by overriding
+`training.sim_backend` alone; that field is set by the owner YAML and identifies
+the composed backend.
+
+### Quick Choice
+
+| Need | Prefer |
+| --- | --- |
+| Default path or broadest owner coverage | MuJoCo |
+| Native interactive playback through the backend | Motrix |
+| MuJoCo-only tools such as `scripts/play_viser.py` | MuJoCo |
+| Task owner exists only under `src/unilab/conf/.../<task>/mujoco.yaml` | MuJoCo |
+| Task owner exists under `src/unilab/conf/.../<task>/motrix.yaml` and the support matrix marks the combination as tested or configured | Motrix |
+
+The support matrix is generated from registry, owner YAML, and tests; use it as
+the current evidence source: {doc}`../../5-reference/5-support_matrix`.
+
 ```bash
 uv run train --algo ppo --task go1_joystick_flat --sim mujoco
 uv run train --algo ppo --task go1_joystick_flat --sim motrix
+uv run train --algo ppo --task g1_walk_flat --sim isaacsim
+```
+
+More combinations:
+
+```bash
+uv run train --algo ppo --task stewart_balance --sim drake \
+  algo.max_iterations=1 algo.num_envs=8 training.no_play=true
+uv run train --algo sac --task g1_walk_flat --sim mujoco
 ```
 
 Owner YAML locations:
 
-- PPO / APPO: `conf/{ppo,appo}/task/<task>/<backend>.yaml`
-- Off-policy: `conf/offpolicy/task/<algo>/<task>/<backend>.yaml`
+- PPO / APPO: `src/unilab/conf/{ppo,appo}/task/<task>/<backend>.yaml`
+- Off-policy (SAC / TD3 / FlashSAC): `src/unilab/conf/<algo>/task/<task>/<backend>.yaml`
 
 The selected owner YAML sets `training.sim_backend` as an identity field.
 
@@ -54,10 +87,38 @@ the generated source data.
 - {doc}`Backend capability boundary ADR </adr/ADR-0002-backend-capability-boundary-for-play-and-snapshot>`
 - {doc}`Registry bootstrap ADR </adr/ADR-0004-registry-bootstrap-contract>`
 
+## The unisim-core boundary
+
+UniLab's physics backends are provided by the independent `unisim-core`
+distribution, with `unisim` as the Python namespace. For example:
+
+```bash
+uv sync --extra mujoco
+uv run python -c "import unisim; print(unisim.ADAPTER_SPECS)"
+```
+
+`unisim` has no dependency on UniLab, Hydra, or training components. MuJoCo,
+Motrix, Drake, MJWarp, Genesis, IsaacGym, and IsaacSim use one public contract.
+Missing proprietary SDKs or GPU workers produce an explicit cold-path diagnostic;
+no backend silently falls back to another engine.
+
+Backend physics is owned exclusively by `unisim-core`. UniLab keeps only the
+owner-layer assembly entry point `unilab.base.backend_factory`; contracts and
+adapters are imported from `unisim`. The former `unilab.base.backend`
+implementation and compatibility layer have been removed; do not add backend
+APIs to UniLab.
+
+Benchmark v1 reserves only `BenchmarkCase`, `BenchmarkResult`, and provenance
+schema. Workloads, timing, comparisons, and performance claims require a
+separately authorized issue.
+
 ```{toctree}
 :hidden:
 
 1-mujoco
 2-motrix
-3-choosing_a_backend
+3-isaacgym
+4-isaacsim
+5-genesis
+6-drake
 ```
diff --git a/docs/sphinx/source/en/2-user_guide/3-backends/1-mujoco.md b/docs/sphinx/source/en/2-user_guide/3-backends/1-mujoco.md
index 7027c355b..9587b49c3 100644
--- a/docs/sphinx/source/en/2-user_guide/3-backends/1-mujoco.md
+++ b/docs/sphinx/source/en/2-user_guide/3-backends/1-mujoco.md
@@ -4,13 +4,13 @@ MuJoCo is the default backend path in the committed owner configs. The Python
 dependencies are the official `mujoco` package (`>=3.5`, with the default
 version pinned by the committed `uv.lock`) plus
 `mujoco-uni-runtime` in `pyproject.toml`, and the adapter lives
-under `src/unilab/base/backend/mujoco/`.
+under `unisim.backend.mujoco`.
 
 ## When To Use It
 
 - You want the default training route for PPO, APPO, off-policy SAC/TD3, or
   FlashSAC.
-- The task owner exists only as `conf/.../<task>/mujoco.yaml`.
+- The task owner exists only as `src/unilab/conf/.../<task>/mujoco.yaml`.
 - You need MuJoCo-specific tooling such as `scripts/play_viser.py` or scene
   export from a MuJoCo XML/MJB model.
 
@@ -23,8 +23,8 @@ uv run train --algo sac --task g1_walk_flat --sim mujoco
 ```
 
 Playback mode is resolved by the backend contract in
-`src/unilab/base/backend/base.py`. MuJoCo reports physics-state playback support
-in `src/unilab/base/backend/mujoco/backend.py`; `auto` playback records video
+`unisim.backend.base`. MuJoCo reports physics-state playback support
+in `unisim.backend.mujoco.backend`; `auto` playback records video
 rather than opening the Motrix native interactive renderer.
 
 ## Switching MuJoCo Versions
diff --git a/docs/sphinx/source/en/2-user_guide/3-backends/2-motrix.md b/docs/sphinx/source/en/2-user_guide/3-backends/2-motrix.md
index 1a63c0c69..83b91a52b 100644
--- a/docs/sphinx/source/en/2-user_guide/3-backends/2-motrix.md
+++ b/docs/sphinx/source/en/2-user_guide/3-backends/2-motrix.md
@@ -2,7 +2,7 @@
 
 Motrix is an optional backend installed through the `motrix` extra. The pinned
 package is `motrixsim-core==0.8.2`, and the adapter lives under
-`src/unilab/base/backend/motrix/`.
+`unisim.backend.motrix`.
 
 ## Setup
 
@@ -14,7 +14,7 @@ uv sync --extra motrix
 
 ## When To Use It
 
-- The task owner exists under `conf/.../<task>/motrix.yaml`.
+- The task owner exists under `src/unilab/conf/.../<task>/motrix.yaml`.
 - You want Motrix native interactive playback; the backend advertises native
   interactive renderer and video-capture capability.
 - The generated support matrix marks your entrypoint/task/backend combination as
diff --git a/docs/sphinx/source/en/2-user_guide/3-backends/3-choosing_a_backend.md b/docs/sphinx/source/en/2-user_guide/3-backends/3-choosing_a_backend.md
deleted file mode 100644
index 0461d50fb..000000000
--- a/docs/sphinx/source/en/2-user_guide/3-backends/3-choosing_a_backend.md
+++ /dev/null
@@ -1,32 +0,0 @@
-# Choosing a Backend
-
-UniLab selects the simulator through the task owner config. For normal usage,
-choose the task and backend with `--task` and `--sim`; off-policy commands keep
-the algorithm in `--algo`, not in `--task`. Do not switch a run by overriding
-`training.sim_backend` alone; that field is set by the owner YAML and identifies
-the composed backend.
-
-## Quick Choice
-
-| Need | Prefer |
-| --- | --- |
-| Default path or broadest owner coverage | MuJoCo |
-| Native interactive playback through the backend | Motrix |
-| MuJoCo-only tools such as `scripts/play_viser.py` | MuJoCo |
-| Task owner exists only under `conf/.../<task>/mujoco.yaml` | MuJoCo |
-| Task owner exists under `conf/.../<task>/motrix.yaml` and the support matrix marks the combination as tested or configured | Motrix |
-
-The support matrix is generated from registry, owner YAML, and tests; use it as
-the current evidence source: {doc}`/zh_CN/5-reference/5-support_matrix`.
-
-## Examples
-
-```bash
-uv run train --algo ppo --task go2_joystick_flat --sim mujoco
-uv run train --algo ppo --task go2_joystick_flat --sim motrix
-uv run train --algo sac --task g1_walk_flat --sim mujoco
-```
-
-`registry.make(..., sim_backend=None)` resolves the default backend in
-`src/unilab/base/registry.py`; `--task` and `--sim` remain the user-facing
-route.
diff --git a/docs/sphinx/source/en/2-user_guide/3-backends/3-isaacgym.md b/docs/sphinx/source/en/2-user_guide/3-backends/3-isaacgym.md
new file mode 100644
index 000000000..99846f88f
--- /dev/null
+++ b/docs/sphinx/source/en/2-user_guide/3-backends/3-isaacgym.md
@@ -0,0 +1,213 @@
+# IsaacGym Backend
+
+IsaacGym (NVIDIA Preview 4) is an end-of-life GPU physics simulator from NVIDIA
+that only supports Python 3.6-3.8. The UniLab main environment requires
+Python >= 3.10, so IsaacGym cannot be installed into it; it is used through an
+external, standalone Python 3.8 environment located purely via environment
+variables, with no machine-local paths written into the repository.
+
+Current status: `IsaacGymBackend` (a subprocess backend whose physics runs in
+the external Python 3.8 worker) is implemented and registered; `g1_walk_flat`
+ships isaacgym owner configs
+(`src/unilab/conf/{ppo,sac}/task/g1_walk_flat/isaacgym.yaml`), and the cross-backend
+contract audit (`scripts/audit_sim2sim_contracts.py`) covers the
+mujoco/isaacgym pair. Playback rendering uses IsaacGym's native rendering
+(viewer + camera sensor); both interactive and video-recording modes work
+(see "Training and Evaluation" below). Real-machine end-to-end validation
+depends on the external environment described below and is not covered by
+repo CI. The repository also ships a physics benchmark script
+`scripts/benchmark/physics/benchmark_physics_step_isaacgym.py`, which locates
+the external environment through variables such as
+`UNILAB_BENCHMARK_HOLOSOMA_DEPS`. This page covers preparing the external
+environment, training and evaluation, benchmark validation, and
+troubleshooting.
+
+## Model Contract
+
+The backend consumes the task's MJCF scene directly, but IsaacGym's MJCF
+importer is only partially trusted: kinematics (body/dof names and order) are
+verified against a host-side XML scan at INIT, and every actuation-relevant
+parameter is parsed from the XML rather than read from the importer.
+
+- **Control**: only `<position kp kv forcerange>` actuators are supported —
+  `SimBackend.step(ctrl)` carries per-DoF position targets, reproduced with
+  PhysX `DOF_MODE_POS` drives (force = kp·(target − q) − kv·q̇, clamped to the
+  symmetric forcerange). Scenes with `<motor>`/`<velocity>`/other actuator
+  types, non-unit gear, or asymmetric forceranges fail closed at scene scan.
+- **Self-collision is disabled** (actor collision filter). MJCF
+  `<contact><exclude>` pairs (e.g. G1's elbow↔wrist and pelvis↔hip overlaps)
+  cannot be reproduced per link pair through the gymapi; disabling
+  self-collision entirely is the ecosystem-standard approximation and a
+  superset of the exclusions. Models that rely on self-contact are not
+  faithfully reproduced.
+- **Joint limits**: the importer drops them, so PhysX applies no joint stops;
+  `get_joint_range()` still reports the XML values. Joint `armature` and
+  `frictionloss` (resolved through MJCF default classes) are applied to the
+  PhysX dofs.
+
+## Prerequisites
+
+- Linux x86_64 with an NVIDIA GPU driver installed.
+- Network access to the NVIDIA download site: the script downloads
+  `IsaacGym_Preview_4_Package.tar.gz` automatically from
+  <https://developer.nvidia.com/isaac-gym-preview-4> (no login required). On
+  offline machines, download it yourself first and pass `--tarball <path>`.
+- Disk space: roughly 5 GB for miniconda, the conda environment, and the
+  IsaacGym package combined.
+
+## Automated Setup
+
+From the repository root, run:
+
+```bash
+scripts/tools/setup_isaacgym_env.sh
+```
+
+The script installs everything under `$HOME/.cache/unisim/isaacgym` by default;
+override the install root with the `UNISIM_ISAACGYM_HOME` environment variable.
+The former `UNILAB_ISAACGYM_HOME` name remains accepted as a migration fallback.
+The tarball is downloaded automatically to
+`$UNISIM_ISAACGYM_HOME/IsaacGym_Preview_4_Package.tar.gz`; on offline machines,
+pass a pre-downloaded package with `--tarball <path>`. The script is
+idempotent and skips completed steps when re-run.
+
+The setup flow: a dedicated miniconda, then a Python 3.8 `hsgym` conda
+environment (including `libstdcxx-ng`, which fixes the GLIBCXX issue on Ubuntu
+24.04), then unpacking the tarball, `pip install -e isaacgym/python`, and
+finally an import self-check.
+
+After installation, add the export lines printed by the script to your shell rc
+(e.g. `~/.bashrc`):
+
+```bash
+export UNILAB_BENCHMARK_HOLOSOMA_DEPS="$HOME/.cache/unisim/isaacgym"
+export UNILAB_BENCHMARK_HSGYM_PYTHON="$UNILAB_BENCHMARK_HOLOSOMA_DEPS/miniconda3/envs/hsgym/bin/python3.8"
+export UNILAB_BENCHMARK_HSGYM_LIB="$UNILAB_BENCHMARK_HOLOSOMA_DEPS/miniconda3/envs/hsgym/lib"
+```
+
+## Validation
+
+Validate the environment with the benchmark script. The benchmark loads robot
+models from URDF, so you must provide your own URDF model tree
+(`go1_description/`, `g1_description/`, ...) and point `--models-root` or
+`UNILAB_BENCHMARK_MODELS_ROOT` at its root directory:
+
+```bash
+PYTHONPATH="$UNILAB_BENCHMARK_HOLOSOMA_DEPS/isaacgym/python" \
+LD_LIBRARY_PATH="$UNILAB_BENCHMARK_HSGYM_LIB" \
+uv run --no-project "$UNILAB_BENCHMARK_HSGYM_PYTHON" \
+    scripts/benchmark/physics/benchmark_physics_step_isaacgym.py \
+    --tasks g1_walk_flat --batch-sizes 256 --models-root "$UNILAB_BENCHMARK_MODELS_ROOT"
+```
+
+## Training and Evaluation
+
+Once the external environment is installed, training works out of the box.
+The worker runtime is discovered automatically from `~/.cache/unisim/isaacgym`;
+when using a custom install root, export `UNISIM_ISAACGYM_HOME` before
+training. `g1_walk_flat` currently ships isaacgym owner configs for PPO and
+SAC:
+
+```bash
+# SAC
+uv run train --algo sac --task g1_walk_flat --sim isaacgym
+
+# PPO
+uv run train --algo ppo --task g1_walk_flat --sim isaacgym
+```
+
+Playback rendering is provided natively by IsaacGym: the interactive mode
+opens the gym viewer inside the worker process, and the record mode renders
+offscreen with a camera sensor and writes `play_video.mp4` (the camera tracks
+env 0's root; the view is adjustable via `training.cam_distance` /
+`cam_elevation` / `cam_azimuth`). `play_render_mode=auto` (the default)
+selects the interactive viewer when a display is reachable
+(`DISPLAY`/`WAYLAND_DISPLAY`) and falls back to recording on headless hosts;
+recording requires a finite `training.play_steps` (the default configs
+provide one).
+
+```bash
+# Training enters playback automatically (auto); headless servers record video
+uv run train --algo sac --task g1_walk_flat --sim isaacgym
+
+# Evaluate a trained checkpoint in the interactive viewer
+uv run eval --algo sac --task g1_walk_flat --sim isaacgym \
+    --render-mode interactive --load-run <run_dir_name>
+
+# Record a video on headless hosts (force record)
+uv run eval --algo sac --task g1_walk_flat --sim isaacgym \
+    --render-mode record --load-run <run_dir_name> training.play_steps=800
+```
+
+Note: both the interactive viewer and camera capture require the worker sim
+to run on a GPU (`env.isaacgym_device_id >= 0`); a CPU-pipeline sim has no
+graphics context and render requests fail closed with an explanatory error.
+
+Common overrides (Hydra arguments follow the command directly):
+
+```bash
+# Small smoke run: 64 environments, 3 iterations only
+uv run train --algo sac --task g1_walk_flat --sim isaacgym \
+    algo.num_envs=64 algo.max_iterations=3
+
+# Pick the GPU used by the worker
+uv run train --algo sac --task g1_walk_flat --sim isaacgym env.isaacgym_device_id=1
+```
+
+Cross-backend migration (sim2sim): the isaacgym owner configs are fully
+contract-compatible with the mujoco owner under the audit guard
+(`src/unilab/utils/sim2sim.py`, verdict TRANSFERABLE), so checkpoints of the
+same task transfer across backends. Playback rendering works on isaacgym, so
+cross-backend policy evaluation (playing a mujoco-trained checkpoint on
+isaacgym, or vice versa) runs directly through the `uv run eval` commands
+above.
+
+## Manual Setup
+
+If the automated script fails, the equivalent manual command sequence is:
+
+```bash
+export UNISIM_ISAACGYM_HOME="${UNISIM_ISAACGYM_HOME:-$HOME/.cache/unisim/isaacgym}"
+mkdir -p "$UNISIM_ISAACGYM_HOME"
+
+# 1. Dedicated miniconda
+curl -fsSL https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -o /tmp/miniconda.sh
+bash /tmp/miniconda.sh -b -u -p "$UNISIM_ISAACGYM_HOME/miniconda3"
+rm /tmp/miniconda.sh
+
+# 2. Python 3.8 conda environment
+"$UNISIM_ISAACGYM_HOME/miniconda3/bin/conda" tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main
+"$UNISIM_ISAACGYM_HOME/miniconda3/bin/conda" tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r
+"$UNISIM_ISAACGYM_HOME/miniconda3/bin/conda" install -y -n base -c conda-forge mamba
+"$UNISIM_ISAACGYM_HOME/miniconda3/bin/mamba" create -y -n hsgym python=3.8 -c conda-forge --override-channels
+
+# 3. Ubuntu 24.04 GLIBCXX fix
+"$UNISIM_ISAACGYM_HOME/miniconda3/bin/conda" install -y -n hsgym -c conda-forge libstdcxx-ng
+
+# 4. Download (or reuse) the tarball and install IsaacGym
+curl -fL --retry 3 "https://developer.nvidia.com/isaac-gym-preview-4" \
+  -o "$UNISIM_ISAACGYM_HOME/IsaacGym_Preview_4_Package.tar.gz"
+tar -xzf "$UNISIM_ISAACGYM_HOME/IsaacGym_Preview_4_Package.tar.gz" -C "$UNISIM_ISAACGYM_HOME"
+"$UNISIM_ISAACGYM_HOME/miniconda3/envs/hsgym/bin/pip" install -e "$UNISIM_ISAACGYM_HOME/isaacgym/python"
+```
+
+## Troubleshooting
+
+- **Tarball download or validation fails**: the script verifies the download
+  is a valid gzip tarball. If it fails, delete
+  `$UNISIM_ISAACGYM_HOME/IsaacGym_Preview_4_Package.tar.gz` and re-run, or pass
+  a manually downloaded package with `--tarball <path>`.
+- **First INIT handshake times out (worker unresponsive)**: the first
+  `gymtorch` import JIT-compiles a C++ extension (several minutes, cached
+  under `~/.cache/torch_extensions/py38_cu121/gymtorch/`). The setup script's
+  self-check pre-warms this compile. If a compile process was ever killed
+  hard, a stale `lock` file in that directory blocks later loads forever —
+  delete it and retry. The worker needs the env's `bin/` on `PATH` (for
+  ninja); `IsaacGymBackend` injects it automatically.
+- **`GLIBCXX_3.4.32 not found` on Ubuntu 24.04**: the prebuilt IsaacGym
+  libraries link against a newer libstdc++ than the system provides. The setup
+  script installs conda-forge `libstdcxx-ng` into the `hsgym` environment to
+  fix this; at runtime, point `LD_LIBRARY_PATH` at that env's `lib/`.
+- **`from isaacgym import gymapi` fails**: make sure `LD_LIBRARY_PATH` points
+  at `$UNILAB_BENCHMARK_HSGYM_LIB` (the `lib/` directory of the hsgym env) and
+  that `PYTHONPATH` includes `isaacgym/python`.
diff --git a/docs/sphinx/source/en/2-user_guide/3-backends/4-isaacsim.md b/docs/sphinx/source/en/2-user_guide/3-backends/4-isaacsim.md
new file mode 100644
index 000000000..66f6f0f43
--- /dev/null
+++ b/docs/sphinx/source/en/2-user_guide/3-backends/4-isaacsim.md
@@ -0,0 +1,126 @@
+# IsaacSim Backend
+
+UniLab's `isaacsim` backend runs IsaacSim 5.1.0 and IsaacLab v2.3.0 in a
+dedicated Python 3.11 worker process. The host process keeps the regular
+`SimBackend` NumPy contract; pipe messages carry lifecycle commands and shared
+memory carries batched state. The current support boundary is headless physics
+plus eval-owned native rendering for the registered G1 flat task owners. The
+support matrix intentionally marks the PPO and SAC owners as `Configured`, not
+`Tested`, because the rendering protocol is covered by deterministic worker
+tests but has not completed playback on the currently available IsaacSim host.
+
+## Runtime boundary
+
+IsaacSim 5.1.0 is installed in a separate Python 3.11 environment because the
+main UniLab environment supports Python 3.10--3.13. The setup entry point is
+`scripts/tools/setup_isaacsim_env.sh`; it installs under
+`$UNISIM_ISAACSIM_HOME` (default `$HOME/.cache/unisim/isaacsim`) and accepts the Kit
+EULA through `OMNI_KIT_ACCEPT_EULA=1` for non-interactive worker startup.
+
+The backend resolves these optional variables without importing Kit in the host
+process:
+
+- `UNISIM_ISAACSIM_HOME` selects the runtime root.
+- `UNISIM_ISAACSIM_PYTHON` overrides the worker interpreter path.
+- The former `UNILAB_ISAACSIM_HOME` and `UNILAB_ISAACSIM_PYTHON` names remain
+  accepted as migration fallbacks.
+- `OMNI_KIT_ACCEPT_EULA=1` keeps worker startup non-interactive.
+
+The expected runtime layout is
+`$UNISIM_ISAACSIM_HOME/venv/bin/python`, the Python 3.11 site-packages and
+library directories under that venv, and an IsaacLab v2.3.0 source checkout at
+`$UNISIM_ISAACSIM_HOME/IsaacLab`.
+
+The render intent is part of the worker's cold `INIT` handshake. Training does
+not inject a render mode and starts the inexpensive headless, camera-disabled
+Kit experience. Eval selects one of these modes before Kit starts:
+
+- `auto`: use the interactive Kit viewer when `DISPLAY` or `WAYLAND_DISPLAY`
+  is present; otherwise use headless recording.
+- `interactive`: start non-headless Kit and fail before worker launch when no
+  display variable is present.
+- `record`: start the headless rendering experience with IsaacLab RGB cameras;
+  `training.play_steps` must be finite.
+- `none`: run policy evaluation without a viewer or camera.
+
+The record contract is RGB `(height, width, 3)`, `uint8`, contiguous, and
+non-uniform. Invalid or placeholder frames fail closed instead of producing a
+video. Width and height default to 1280 x 720 in the IsaacSim owner YAML and
+can be overridden through `env.isaacsim_render_width` and
+`env.isaacsim_render_height` before env creation.
+
+The current worker supports MJCF materialization, batched articulation state,
+position-target stepping, masked root/joint resets, a native Kit viewer, and
+headless IsaacLab RGB camera capture. Contact-force sensors, reset or interval
+domain randomization, and host pre-step callbacks remain unsupported and fail
+closed.
+
+Use the top-level CLI to select the backend and owner:
+
+```bash
+uv run train --algo ppo --task g1_walk_flat --sim isaacsim
+uv run eval --algo sac --task g1_walk_flat --sim isaacsim \
+  --load-run <run-id> --render-mode record \
+  training.play_steps=120 training.play_env_num=1 training.export_onnx=false
+uv run eval --algo sac --task g1_walk_flat --sim isaacsim \
+  --load-run <run-id> --render-mode interactive training.play_env_num=1
+```
+
+Record mode writes `play_video.mp4` in the selected run directory. These
+commands require the external runtime and an NVIDIA CUDA device. The repository
+does not claim completed full training or stable native playback; those claims
+require a maintainer validation entry.
+
+## Current Runtime Validation
+
+A bounded SAC record eval and a bounded interactive eval using an existing
+checkpoint were attempted on IsaacSim 5.1.0, IsaacLab v2.3.0, Kit 107.3.3,
+Ubuntu 24.04.4, an RTX 4090, and NVIDIA driver 595.84. Both paths crashed during
+`AppLauncher` initialization, before camera or viewer creation, with frames in
+`librtx.scenedb.plugin.so`,
+`libcarb.scenerenderer-rtx.plugin.so`, and `libomni.hydra.rtx.plugin.so` after
+EGL initialization warnings. A minimal camera-enabled `AppLauncher` probe also
+failed with `multi_gpu=False`.
+
+This is a runtime blocker, not successful playback evidence. The backend keeps
+the render protocol and its fail-closed tests, while the support matrix remains
+at `Configured`. No placeholder video is generated when the real renderer does
+not initialize.
+
+## Inspecting The Contract
+
+```bash
+VIRTUAL_ENV="$HOME/.cache/unisim/isaacsim/venv" \
+OMNI_KIT_ACCEPT_EULA=1 \
+uv run --active --no-project \
+  scripts/tools/probe_isaacsim_contract.py \
+  --model-file src/unilab/assets/robots/g1/scene_flat.xml \
+  --num-envs 2 --steps 2 --device cuda:0 \
+  --output /tmp/isaacsim-contract.json
+```
+
+The command is a bounded developer probe. It only touches the XML/importer
+during cold-path materialization and is useful for checking a newly installed
+runtime; it is not a training or playback validation.
+
+## Contract matrix
+
+| UniLab contract | IsaacSim/IsaacLab operation | Observed result | Production constraint |
+|---|---|---|---|
+| MJCF scene materialization | `isaaclab.sim.converters.MjcfConverter` | G1 MJCF converts to USD successfully | Enable `isaacsim.asset.importer.mjcf` explicitly in headless workers |
+| Batched articulation | `Articulation` + `ArticulationCfg` | 2 environments, 29 joints, 30 bodies | Resolve names at materialization; importer order is not the MJCF order |
+| Quaternion layout | `robot.data.root_quat_w` / `body_quat_w` | `wxyz` | Keep `wxyz` at the shared-memory boundary |
+| Base angular velocity | `robot.data.root_ang_vel_w` | World frame | Public getter remains world-frame; reset qvel conversion is a cold-path contract operation |
+| Partial reset | `write_root_pose_to_sim`, `write_root_velocity_to_sim`, `write_joint_state_to_sim`, `reset(env_ids)` | Selected row changes; other row deltas are zero | Use masked batched writes; reject duplicate/out-of-range ids |
+| Position control | `set_joint_position_target`, `write_data_to_sim`, `SimulationContext.step` | Target moves the first joint over bounded steps | `step(ctrl)` carries position targets; gains/limits are materialized explicitly |
+| State getter boundary | `Articulation.data.*` tensors | All getters are batched with expected leading dimension | Worker copies tensors to host-owned shared-memory slots; hot getters do not parse assets |
+| Rendering startup | `AppLauncher` cold mode selection | Mock worker verifies none/record/interactive mode, dimensions, and graphics handshake | Mode cannot change after env materialization |
+| Offline RGB | IsaacLab `Camera` + `CameraCfg` | Protocol tests verify video writing and reject bad shape, dtype, or uniform frames; current real host crashes before camera creation | Require finite steps and keep support at `Configured` until real playback succeeds |
+| Interactive viewer | non-headless Kit + `SimulationContext.set_camera_view` | Protocol tests drive a frame and map window close to `RenderClosedError`; current host has no successful bounded GUI evidence | Explicit interactive requires a display; `auto` falls back to record without one |
+| Domain randomization | IsaacLab manager/event APIs | Not exercised | Non-empty unsupported plans must fail closed |
+
+The importer returns a different joint/body ordering (for example, left/right
+branches are interleaved). The worker builds name-to-index maps and reorders
+every state/control array; positional assumptions would violate the
+`SimBackend` index contract. The full owner and capability status is maintained
+in {doc}`../../5-reference/5-support_matrix`.
diff --git a/docs/sphinx/source/en/2-user_guide/3-backends/5-genesis.md b/docs/sphinx/source/en/2-user_guide/3-backends/5-genesis.md
new file mode 100644
index 000000000..eefa41df7
--- /dev/null
+++ b/docs/sphinx/source/en/2-user_guide/3-backends/5-genesis.md
@@ -0,0 +1,153 @@
+# Genesis Backend
+
+[Genesis](https://github.com/Genesis-Embodied-AI/Genesis) (PyPI distribution
+`genesis-world`, pinned to 1.3.3) is a GPU physics simulator that UniLab runs
+**in-process**: `GenesisBackend` serves the standard `SimBackend` NumPy
+contract on top of it, so physics shares the training process with the
+learner — no worker subprocess, no IPC.
+
+Current status: `GenesisBackend` is implemented and registered; `g1_walk_flat`
+ships PPO and SAC owner configs
+(`src/unilab/conf/{ppo,sac}/task/g1_walk_flat/genesis.yaml`), and the cross-backend
+contract audit (`scripts/audit_sim2sim_contracts.py`) covers the
+mujoco/genesis pair in both algo trees (verdict TRANSFERABLE). Support level
+is **experimental**. Evidence: registry + owner YAML + compose/contract
+coverage, plus a real-machine slow-lane env smoke
+(`tests/envs/locomotion/g1/test_g1_owner_contract.py`: compose -> env
+construction -> keyframe reset -> 12 finite steps -> cleanup, run for both
+the ppo and sac trees). The SAC cell is marked `Tested` after a full
+real-machine training validation (5000/5000 iterations on 2026-08-31, RTX
+4090 / torch 2.8.0+cu128 / genesis-world 1.3.3: reward/mean 6.5 -> 244.8,
+episode length -> 987/1000, 10.26M env steps in 224 s wall time) plus record
+playback validation on the final checkpoint; the PPO cell stays `Configured`
+(no training validation yet).
+
+Env-construction lifecycle (fixed in #1383): entity validation during
+`ManagerBasedRlEnv` construction reads state getters before the env's
+`materialize()` hook, so the adapter's `materialize()` is idempotent and
+lazily triggered (the first state access completes `scene.build`, the same
+pattern as the IsaacGym backend). The adapter design otherwise follows the
+measured mappings of `scripts/tools/genesis_feasibility/REPORT.md`.
+
+## Model Contract
+
+Genesis 1.3.3 drops three MJCF features at import (REPORT §3): the global
+`<option>` block, `<keyframe>`, and the whole `<sensor>` block. The adapter
+compensates on the materialize cold path — hot paths never parse XML:
+
+- **Global option**: the owner YAML re-declares what the MJCF `<option>`
+  carried. For g1 (`<option integrator="implicitfast"
+  timestep="0.006666666666666667"/>`) the timestep flows through the existing
+  `sim_dt` / `ctrl_dt` chain and the integrator through
+  `env.genesis_integrator: implicitfast`. Constraint solver, friction cone,
+  and solver iterations stay at Genesis defaults (`env.genesis_*` fields are
+  `None`): the MJCF option block does not declare them and MuJoCo's implicit
+  defaults (PGS solver, pyramidal cone) have no Genesis equivalent.
+- **Keyframes**: scanned once with the `mujoco` package at materialize and
+  cached, so `default_keyframe_name: stand` resets work unchanged.
+- **Sensors**: MJCF-named equivalents computed from link state, plus one
+  `IMUSensor` per accelerometer site with clean (noise-free) data. Contact
+  sensors with `data="found"` become a per-link net-contact-force threshold
+  (1 N) — an approximation of the geom-pair `found` semantic, not a
+  reproduction of it.
+- **Actuators**: `<position kp kv>` actuators import losslessly and are
+  driven with `control_dofs_position`; kp/kd reset randomization is supported
+  (the DR capability set declares only the per-env round-trip-measured terms:
+  body mass, base mass, kp, kd, plus interval body force).
+
+## Prerequisites
+
+- Linux x86_64 with an NVIDIA GPU and driver. Only the `gs.gpu` lane is
+  validated by the feasibility probe; the CPU backend is not a validated
+  support lane.
+- The repository's pinned torch window (torch 2.8 on x86_64): the
+  IMUSensor + contact-force sensor combination crashed under torch 2.7 in
+  the probe (REPORT §3.4/§8).
+- Install the optional extra (pins `genesis-world==1.3.3` exactly):
+
+```bash
+uv sync --extra genesis
+```
+
+## Training and Evaluation
+
+Training selects the genesis owner through the canonical CLI (owner YAML
+compose, registry routing, and env construction are covered by the
+real-machine slow-lane smoke; there is no training-convergence evidence yet):
+
+```bash
+# PPO
+uv run train --algo ppo --task g1_walk_flat --sim genesis
+
+# SAC
+uv run train --algo sac --task g1_walk_flat --sim genesis
+
+# Small smoke run: 64 environments, 3 iterations only
+uv run train --algo ppo --task g1_walk_flat --sim genesis \
+    algo.num_envs=64 algo.max_iterations=3
+```
+
+## Playback and Rendering
+
+Genesis native rendering is a declared capability and attaches lazily after
+`scene.build` (no viewer dependency on the training hot path): the
+interactive viewer is a post-build `genesis.vis.viewer.Viewer`, and recording
+uses a post-build offscreen camera. The owner sets
+`training.play_render_mode: auto`, which resolves per host:
+
+- `auto`: opens the **interactive viewer** when a display is reachable
+  (`DISPLAY`/`WAYLAND_DISPLAY`), otherwise falls back to offscreen
+  **recording**.
+- `interactive`: always opens the viewer; on a headless host
+  `init_renderer` fails with an actionable error. Closing the viewer window
+  ends playback cleanly (the backend raises `RenderClosedError`, which the
+  play loop treats as a normal exit).
+- `record`: headless offscreen capture, requires finite
+  `training.play_steps` and an output path; frames are written to
+  `play_video.mp4` in the checkpoint's run directory at `1/ctrl_dt` fps.
+- `none`: playback is skipped safely as a no-op.
+
+The camera follows the repo-wide spherical kwargs (`cam_distance` /
+`cam_elevation` / `cam_azimuth`, plus `cam_tracking` to follow the root of
+`cam_tracking_env_idx`). Evaluate a trained run with:
+
+```bash
+uv run eval --algo sac --task g1_walk_flat --sim genesis \
+    --load-run <run_dir_name> --render-mode record
+```
+
+`get_physics_state` snapshots remain undeclared: playback drives the live
+scene, so state-snapshot playback is not needed.
+
+## Lifecycle: One `gs.init` Per Process
+
+Genesis init/destroy cycles leak 200–450 MB of host RSS per cycle (REPORT
+§3.5 [9a]), so the adapter allows exactly **one `gs.init` per process**:
+after the session is destroyed, constructing another backend fails closed
+with an explanatory error. One training run per process satisfies this
+constraint by design; do not build genesis envs repeatedly inside a
+long-lived host process.
+
+## Unsupported Boundaries
+
+The following fail closed with explicit errors rather than silently
+degrading:
+
+- **Geom-name contract** (`get_geom_names` and friends): not exposed.
+- **Generated terrain and height scanners**: `scene.terrain` is rejected;
+  select a flat owner YAML.
+- **Absolute geom-friction DR**: upstream Genesis only offers a per-env
+  friction *ratio* API, so `geom_friction` reset randomization and
+  `get_geom_friction` fail closed.
+- **Interval push / body-velocity-delta DR**: rejected at construction/plan
+  time (`push_body_name`, push perturbation).
+- **Repeated `gs.init`** in one process (see above).
+
+## Cross-Backend Migration (sim2sim)
+
+The genesis owner keeps DENYLIST parity with the MuJoCo owner under the audit
+guard (`src/unilab/utils/sim2sim.py`, verdict TRANSFERABLE), so checkpoints of
+the same task transfer across backends. Native rendering exists on both
+sides, but the implementations differ: MuJoCo renders offline from physics
+state snapshots, while Genesis drives its live scene with a post-build
+viewer/camera.
diff --git a/docs/sphinx/source/en/2-user_guide/3-backends/6-drake.md b/docs/sphinx/source/en/2-user_guide/3-backends/6-drake.md
new file mode 100644
index 000000000..68dcf40bd
--- /dev/null
+++ b/docs/sphinx/source/en/2-user_guide/3-backends/6-drake.md
@@ -0,0 +1,119 @@
+# Drake Backend
+
+Drake is an experimental CPU batch backend. UniLab still owns the task,
+reward, observations, and training loop. Rendering uses MuJoCo's native
+renderer: Drake advances physics and MuJoCo only draws the current state. The
+supported native setup is Linux
+x86_64 and Apple Silicon macOS (arm64); Intel macOS has no official Drake
+binary.
+
+## Prerequisites
+
+- Python `>=3.10,<3.14` and `uv`.
+- Linux: a C++20 toolchain and development packages:
+
+  ```bash
+  sudo apt-get update
+  sudo apt-get install -y build-essential pkg-config libeigen3-dev libfmt-dev \
+    libspdlog-dev curl git
+  ```
+
+- Apple Silicon macOS: Apple Clang and Homebrew runtime libraries:
+
+  ```bash
+  xcode-select --install  # if clang++ is not available
+  brew install fmt gcc
+  ```
+
+  `gcc` supplies the `libgfortran` runtime referenced by Drake's macOS build.
+
+## Install
+
+From the UniLab checkout, run:
+
+```bash
+make setup-drake
+```
+
+The target downloads the host-appropriate Drake 1.56.0 tarball, installs the
+`drake-uni` extra, builds the native extension with the active uv Python, and
+runs a batch diagnostic. It is resumable; files and logs are kept in
+`~/.unilab/drake`.
+
+To use an existing Drake installation instead:
+
+```bash
+make setup-drake DRAKE_HOME=/path/to/drake
+```
+
+The prefix must contain `include/drake/`, `include/pybind11/`, and
+`lib/libdrake.so`. On macOS, the setup script also discovers Homebrew `fmt`
+and adds the required `gcc` library directory to `DYLD_LIBRARY_PATH`.
+
+The setup script prints the environment exports needed by later shells. On
+macOS they are normally:
+
+```bash
+export DRAKE_HOME="$HOME/.unilab/drake/drake-1.56.0-mac-arm64"
+export UNILAB_DRAKE_HOME="$DRAKE_HOME"
+export DYLD_LIBRARY_PATH="/opt/homebrew/opt/gcc/lib/gcc/current:$DRAKE_HOME/lib${DYLD_LIBRARY_PATH:+:$DYLD_LIBRARY_PATH}"
+```
+
+## Verify
+
+Run this in a fresh process (before importing `pydrake`):
+
+```bash
+uv run --no-sync python - <<'PY'
+from drake_uni.runtime import batch_diagnostics
+
+diagnostics = batch_diagnostics()
+print(diagnostics)
+if not diagnostics.batch_available:
+    raise SystemExit(diagnostics.batch_import_error or "Drake batch extension is unavailable")
+PY
+```
+
+The command must report `batch_available=True`.
+
+## Train Go2
+
+Go2 assets are downloaded on demand. Pull them once before the first run:
+
+```bash
+uv run --no-sync unilab-pull-assets --robot go2
+```
+
+Select Drake with `--sim drake`; do not override `training.sim_backend` by hand.
+The normal PPO command is:
+
+```bash
+uv run train --algo ppo --task go2_joystick_flat --sim drake
+```
+
+This uses the Drake owner configuration (`1024` environments, `151` iterations,
+and CPU training because Drake exposes float64 NumPy buffers). The Drake owner
+also uses the scene keyframe reset; floating-root randomization is not exposed
+by the current backend contract.
+
+On Apple Silicon macOS, this command completed all 151 iterations locally
+(Drake 1.56.0, Python 3.13, 1024 environments) in about 254 seconds.
+For an installation probe, temporarily add
+`algo.max_iterations=1 algo.num_envs=4 algo.num_steps_per_env=4
+training.no_play=true env.drake_nthread=1`; these overrides are not production
+settings.
+
+Drake has no separate renderer. Automatic recording and the interactive viewer
+use MuJoCo while Drake remains the only physics engine being stepped. Use
+`--render-mode none` for headless evaluation; recording and interactive
+playback require the MuJoCo extra and visual assets. MuJoCo is not stepped or
+used to re-evaluate the checkpoint.
+
+## Troubleshooting
+
+| Symptom | Fix |
+| --- | --- |
+| `ModuleNotFoundError: drake_uni` | Rerun `make setup-drake`. |
+| `DrakeEnvPool batch extension has not been built` | Rebuild with the same uv Python and Drake prefix; do not copy an extension from another ABI. |
+| `libdrake.so` or `libgfortran` cannot be loaded | Export `DRAKE_HOME` and `LD_LIBRARY_PATH` (Linux) or `DYLD_LIBRARY_PATH` (macOS), including the Homebrew gcc directory. |
+| Eigen/fmt link errors | Linux: install the packages above. macOS: run `brew install fmt gcc`. |
diff --git a/docs/sphinx/source/en/2-user_guide/4-tasks/0-index.md b/docs/sphinx/source/en/2-user_guide/4-tasks/0-index.md
index be5abf698..58db8455a 100644
--- a/docs/sphinx/source/en/2-user_guide/4-tasks/0-index.md
+++ b/docs/sphinx/source/en/2-user_guide/4-tasks/0-index.md
@@ -1,7 +1,7 @@
 # Tasks
 
 Task pages are grouped by user goal. Exact runnable combinations are backed by
-owner YAMLs under `conf/` and summarized in {doc}`../../5-reference/5-support_matrix`.
+owner YAMLs under `src/unilab/conf/` and summarized in {doc}`../../5-reference/5-support_matrix`.
 
 ::::{grid} 1 1 2 2
 :gutter: 3
diff --git a/docs/sphinx/source/en/2-user_guide/4-tasks/1-locomotion.md b/docs/sphinx/source/en/2-user_guide/4-tasks/1-locomotion.md
index 94818f8e7..fd34e6cdd 100644
--- a/docs/sphinx/source/en/2-user_guide/4-tasks/1-locomotion.md
+++ b/docs/sphinx/source/en/2-user_guide/4-tasks/1-locomotion.md
@@ -1,7 +1,7 @@
 # Locomotion
 
-Locomotion tasks are registered in `src/unilab/envs/locomotion/` and
-`src/unilab/envs/motion_tracking/`. The available owner YAMLs under `conf/`
+Locomotion tasks are registered in `src/unilab/tasks/locomotion/` and
+`src/unilab/tasks/motion_tracking/`. The available owner YAMLs under `src/unilab/conf/`
 define which algorithm and backend combinations are runnable.
 
 ## Families
@@ -29,12 +29,14 @@ backend: {doc}`../../5-reference/5-support_matrix`.
 
 ## Go2 FootStand
 
-`go2_footstand` is the Go2 front-feet-stand task. It is **MuJoCo-only**.
+`go2_footstand` is the Go2 front-feet-stand task. Its PPO owner YAMLs
+register MuJoCo, Motrix, and Drake; the SAC owner currently targets Drake.
 
-- PPO config: `conf/ppo/task/go2_footstand/mujoco.yaml`
-- Registered env: `Go2FootStand` (registered for `sim_backend="mujoco"`)
-- Implementation: `src/unilab/envs/locomotion/go2/footstand.py`
-  (extends the Go2 base task)
+- Canonical PPO task config: `src/unilab/conf/ppo/task/go2_footstand/base.yaml`
+- Backend owners: `src/unilab/conf/ppo/task/go2_footstand/{mujoco,motrix,drake}.yaml`
+- Registered env: `Go2FootStand` (MuJoCo, Motrix, and Drake)
+- Implementation: `src/unilab/tasks/locomotion/go2/footstand.py`
+  (task-owned NumPy manager terms on the generic Manager-Based runtime)
 - Go2 model XML: `src/unilab/assets/robots/go2/go2.xml`
 
 ```bash
@@ -66,14 +68,14 @@ The full FootStand recipe is a three-stage teacher-student pipeline; the shipped
 ### Observation Layout
 
 The `Go2FootStand` policy (actor) observation uses 15 history frames of 45 dims
-each (`_FOOTSTAND_FRAME_OBS_DIM = 45`):
+each (`FRAME_OBS_DIM = 45`):
 
 ```text
 linvel(3) + gyro(3) + gravity(3) + joint_position_delta(12) + joint_velocity(12) + last_action(12)
 ```
 
 So the policy observation is `45 * 15 = 675`. The value (critic) observation
-appends the current-step privileged tail (`_FOOTSTAND_PRIVILEGED_TAIL_DIM = 49`)
+appends the current-step privileged tail (`PRIVILEGED_OBS_DIM = 49`)
 after that history:
 
 ```text
@@ -84,23 +86,24 @@ The value observation is therefore `675 + 49 = 724`.
 
 ### Rewards And Terminations
 
-Defaults come from `conf/ppo/task/go2_footstand/mujoco.yaml`. The reward scales
+Defaults come from `src/unilab/conf/ppo/task/go2_footstand/base.yaml`; backend leaves only
+override backend-specific terms and tuning. The reward scales
 include stand `height`, `orientation`, `rear_feet_contact`, target front-leg angle
 (`tar`), `action_rate`, `dof_pos_limits`, `front_leg_motion`, `rear_leg_symmetry`,
 `knee_clearance`, `upright_stability`, `stay_still`, `pose`, plus `energy` and
 `dof_acc` penalties; `termination` and `penalty_contact` drive the termination /
 penalty paths (front-leg / front-body contact, low height, bad orientation, and a
-high-energy cutoff via `energy_termination_threshold`).
+high-energy cutoff in the `footstand` termination term).
 
 ### Tuning Keys
 
-- `env.obs_history_len`: policy observation history length; config default is `15`.
-- `env.energy_termination_threshold`: high-energy termination cutoff; config
-  default is `200.0`.
-- `env.domain_rand`: floor friction, link mass, torso CoM, dof armature, and reset
-  joint qpos randomization.
-- `reward.scales.height` / `orientation` / `rear_feet_contact`: stand pose and
-  rear-foot contact weights.
+- `env.observations.policy.terms.frame.history_length`: policy history length
+  (default `15`).
+- `env.terminations.footstand.params.energy_threshold`: high-energy cutoff
+  (default `200.0`).
+- `env.events`: reset and domain-randomization terms. Backend owners explicitly
+  set unsupported model-field terms to `null`.
+- `reward.footstand.params.scales`: stand, contact, motion, and energy weights.
 
 ### Near-Risk Validation
 
diff --git a/docs/sphinx/source/en/2-user_guide/4-tasks/2-motion_tracking.md b/docs/sphinx/source/en/2-user_guide/4-tasks/2-motion_tracking.md
index 7b78c58a5..7b402c533 100644
--- a/docs/sphinx/source/en/2-user_guide/4-tasks/2-motion_tracking.md
+++ b/docs/sphinx/source/en/2-user_guide/4-tasks/2-motion_tracking.md
@@ -1,40 +1,43 @@
 # Motion Tracking
 
-G1 motion tracking tasks live under `src/unilab/envs/motion_tracking/` and are
-selected through task owner YAMLs in `conf/ppo/`, `conf/appo/`, and selected
+G1 motion tracking tasks live under `src/unilab/tasks/motion_tracking/` and are
+selected through task owner YAMLs in `src/unilab/conf/ppo/`, `src/unilab/conf/appo/`, and selected
 off-policy paths.
 
 > **Motion assets moved to Hugging Face.** The `.npz` clips are no longer shipped
 > in the repository. On first use `MotionLoader`
-> (`src/unilab/envs/motion_tracking/g1/motion_loader.py`) downloads them on demand
+> (`src/unilab/tasks/motion_tracking/common/motion_loader.py`) downloads them on demand
 > from [unilabsim/unilab-motions](https://huggingface.co/datasets/unilabsim/unilab-motions)
 > via `src/unilab/assets/hub.py` (`_HF_MOTIONS_REPO_ID`). `uv sync` already installs
 > the required `huggingface_hub` dependency.
 
 ## Task Owners
 
-Each task ships a default motion clip defined in the env config dataclass:
+Each task ships a default motion clip in its Hydra task-owner YAML. Hydra is the
+configuration entry point; the selected owner is materialized into the shared
+`ManagerBasedRlEnvCfg` and then consumed by the NumPy Manager-Based runtime.
 
 | CLI Task | Registered Env | Default Motion | Owner Evidence |
 | --- | --- | --- | --- |
-| `g1_motion_tracking` | `G1MotionTracking` | `dance1_subject2_part.npz` | `conf/ppo/task/g1_motion_tracking/`, `conf/appo/task/g1_motion_tracking/` |
-| `g1_flip_tracking` | `G1FlipTracking` | `flip_360_001__A304.npz` | `conf/ppo/task/g1_flip_tracking/`, `conf/appo/task/g1_flip_tracking/` |
-| `g1_wall_flip_tracking` | `G1WallFlipTracking` | `flip_from_wall_104__A304.npz` | `conf/ppo/task/g1_wall_flip_tracking/`, `conf/appo/task/g1_wall_flip_tracking/` |
-| `x2_wall_flip_tracking` | `X2WallFlipTracking` | `tictacflip_6-3_g1format.npz` | `conf/ppo/task/x2_wall_flip_tracking/` (MuJoCo only) |
-| `g1_climb_tracking` | G1 climb tracking env | clip from env config | `conf/ppo/task/g1_climb_tracking/`, `conf/appo/task/g1_climb_tracking/` |
-| `g1_box_tracking` | G1 box tracking env | clip from env config | `conf/ppo/task/g1_box_tracking/` |
-| `g1_wbt_obs` | `G1MotionTrackingSAC` | shared with `g1_motion_tracking` | `conf/offpolicy/task/sac/g1_wbt_obs/mujoco.yaml` |
-
-The defaults are set in code: `dance1_subject2_part.npz`
-(`g1/tracking.py`), `flip_360_001__A304.npz` and `flip_from_wall_104__A304.npz`
-(`g1/flip_tracking.py`), and `tictacflip_6-3_g1format.npz` (`x2/flip_tracking.py`).
+| `g1_motion_tracking` | `G1MotionTracking` | `dance1_subject2_part.npz` | `src/unilab/conf/ppo/task/g1_motion_tracking/`, `src/unilab/conf/appo/task/g1_motion_tracking/` |
+| `g1_flip_tracking` | `G1FlipTracking` | `flip_360_001__A304.npz` | `src/unilab/conf/ppo/task/g1_flip_tracking/`, `src/unilab/conf/appo/task/g1_flip_tracking/` |
+| `g1_wall_flip_tracking` | `G1WallFlipTracking` | `flip_from_wall_104__A304.npz` | `src/unilab/conf/ppo/task/g1_wall_flip_tracking/`, `src/unilab/conf/appo/task/g1_wall_flip_tracking/` |
+| `x2_wall_flip_tracking` | `X2WallFlipTracking` | `tictacflip_6-3_g1format.npz` | `src/unilab/conf/ppo/task/x2_wall_flip_tracking/` |
+| `g1_climb_tracking` | `G1ClimbTracking` | `climb_20_z_scale_1.0.npz` | `src/unilab/conf/ppo/task/g1_climb_tracking/`, `src/unilab/conf/appo/task/g1_climb_tracking/` |
+| `g1_box_tracking` | `G1BoxTracking` | `sub3_largebox_003_boxconverted.npz` | `src/unilab/conf/ppo/task/g1_box_tracking/` |
+| `g1_wbt_obs` | `G1WBTObs` | `dance1_subject2_part.npz` | `src/unilab/conf/sac/task/g1_wbt_obs/mujoco.yaml` |
+
+The 23-DoF task-owner directories select their matching 23-DoF scene, motion,
+entity, and action declarations. Profile differences remain in Hydra. The G1
+identities use the shared manager factory; X2 adds only a cold-path mesh resolver
+before delegating to that factory.
 
 ## PPO And APPO
 
 PPO owner iteration budgets (the `--sim mujoco` owner YAMLs): `g1_motion_tracking`
 runs `algo.max_iterations=15000`; `g1_flip_tracking` and `g1_wall_flip_tracking`
-run `20000`; the MuJoCo-only `x2_wall_flip_tracking` runs `9500`. (The Motrix
-owner YAML for `g1_flip_tracking` raises this to `30000`.)
+run `20000`; `x2_wall_flip_tracking` runs `9500`. (The Motrix owner YAML for
+`g1_flip_tracking` raises this to `30000`.)
 
 ```bash
 uv run train --algo ppo --task g1_motion_tracking --sim mujoco
@@ -57,13 +60,15 @@ uv run train --algo sac --task g1_motion_tracking --sim mujoco training.use_amp=
 uv run train --algo sac --task g1_wbt_obs --sim mujoco training.use_amp=true
 ```
 
-The `g1_wbt_obs` owner is the deploy-aligned off-policy observation profile: a
-pelvis IMU state (`pelvis_local_linvel` / `pelvis_gyro` / `pelvis_upvector`) plus
-per-term observation history (`noise_config.obs_history_length: 5`), byte-aligned
-with the deploy-time `ObservationManager`. Deploy tooling lives under
-`scripts/deploy/`, and the observation alignment is cross-checked by
-`tests/scripts/test_obs_alignment_g1_wbt.py`. When a Motrix sim2sim replay needs a
-checkpoint from another log root, pass the absolute path through `uv run eval`:
+The `g1_wbt_obs` owner is the deploy-aligned off-policy observation profile. Its
+actor keeps the command and anchor-orientation terms at one step while the
+`base_ang_vel`, `joint_pos`, `joint_vel`, and `actions` terms declare
+`history_length: 5`. `ObservationManager` owns and flattens those per-term
+histories; the actor uses the configured encoder-biased joint-position term while
+the critic keeps the clean term. Per-term oldest-first ordering is guarded by
+`tests/scripts/test_obs_alignment_g1_wbt.py`; the hardware-side contract is
+documented in the sim-to-real deployment guide. When a Motrix sim2sim replay needs
+a checkpoint from another log root, pass the absolute path through `uv run eval`:
 
 ```bash
 uv run eval --algo sac --task g1_motion_tracking --sim motrix \
@@ -72,16 +77,20 @@ uv run eval --algo sac --task g1_motion_tracking --sim motrix \
 
 ## Motion Files
 
-Motion NPZ files are read through `env.motion_file`, which also accepts a list of
+Motion NPZ files are selected through
+`env.commands.motion.params.motion_file`, which accepts one path or a list of
 paths. A standard clip must contain the seven keys `fps`, `joint_pos`,
 `joint_vel`, `body_pos_w`, `body_quat_w`, `body_lin_vel_w`, and `body_ang_vel_w`
-(validated in `g1/motion_loader.py`):
+(validated in `common/motion_loader.py`):
 
 ```yaml
 env:
-  motion_file:
-    - src/unilab/assets/motions/g1/dance1_subject2_part.npz
-    - src/unilab/assets/motions/g1/walk1_subject5_from_csv.npz
+  commands:
+    motion:
+      params:
+        motion_file:
+          - motions/g1/dance1_subject2_part.npz
+          - motions/g1/walk1_subject5_from_csv.npz
 ```
 
 Conversion and inspection helpers are in `scripts/motion/`:
@@ -115,24 +124,25 @@ randomization so the precise clip start state is reused:
 ```bash
 CUDA_VISIBLE_DEVICES=1 uv run train --algo sac --task g1_motion_tracking --sim mujoco \
   training.use_amp=true algo.seed=1 \
-  +env.motion_file=src/unilab/assets/motions/g1/motion_crawl_slope_uni.npz \
-  +env.scene.model_file=src/unilab/assets/robots/g1/scene_crawl_slope.xml \
-  +env.sampling_mode=start \
-  env.truncate_on_clip_end=true \
-  +env.max_episode_seconds=20.0 \
-  '+env.pose_randomization={x:[0,0],y:[0,0],z:[0,0],roll:[0,0],pitch:[0,0],yaw:[0,0]}' \
-  '+env.velocity_randomization={x:[0,0],y:[0,0],z:[0,0],roll:[0,0],pitch:[0,0],yaw:[0,0]}' \
-  '+env.joint_position_range=[0,0]'
+  env.commands.motion.params.motion_file=motions/g1/motion_crawl_slope_uni.npz \
+  env.scene.model_file=src/unilab/assets/robots/g1/scene_crawl_slope.xml \
+  env.commands.motion.params.sampling_mode=start \
+  env.commands.motion.params.truncate_on_clip_end=true \
+  env.max_episode_seconds=20.0 \
+  'env.commands.motion.params.pose_range={x:[0,0],y:[0,0],z:[0,0],roll:[0,0],pitch:[0,0],yaw:[0,0]}' \
+  'env.commands.motion.params.velocity_range={x:[0,0],y:[0,0],z:[0,0],roll:[0,0],pitch:[0,0],yaw:[0,0]}' \
+  'env.commands.motion.params.joint_position_range=[0,0]'
 ```
 
-Key overrides: `env.motion_file` selects the crawl-slope clip;
-`env.scene.model_file` switches to the slope scene (`scene_crawl_slope.xml` exists
-under `src/unilab/assets/robots/g1/`); `sampling_mode=start` plus
-`truncate_on_clip_end=true` starts from the clip beginning and truncates there; and
-zeroing the randomization ranges reuses the exact clip initial state.
+Key overrides: `env.commands.motion.params.motion_file` selects the crawl-slope
+clip; `env.scene.model_file` switches to the slope scene
+(`scene_crawl_slope.xml` exists under `src/unilab/assets/robots/g1/`);
+`sampling_mode=start` plus `truncate_on_clip_end=true` starts from the clip
+beginning and truncates there; and zeroing the command reset ranges reuses the
+exact clip initial state.
 
 ## Interactive Debugging
 
 Routine checkpoint replay uses `uv run eval`. When you need a target-body or reward
-debug overlay, `scripts/play_interactive.py` is the low-level MuJoCo viewer entry
+debug overlay, `src/unilab/scripts/play_interactive.py` is the low-level MuJoCo viewer entry
 point; it is not currently exposed as a `uv run eval` flag.
diff --git a/docs/sphinx/source/en/2-user_guide/4-tasks/3-manipulation.md b/docs/sphinx/source/en/2-user_guide/4-tasks/3-manipulation.md
index 33d577191..10d586adf 100644
--- a/docs/sphinx/source/en/2-user_guide/4-tasks/3-manipulation.md
+++ b/docs/sphinx/source/en/2-user_guide/4-tasks/3-manipulation.md
@@ -1,7 +1,7 @@
 # Manipulation
 
-Manipulation tasks live in `src/unilab/envs/manipulation/` and the Go2 arm
-manip-loco env lives in `src/unilab/envs/locomotion/go2_arm/`.
+Manipulation tasks live in `src/unilab/tasks/manipulation/` and the Go2 arm
+manip-loco env lives in `src/unilab/tasks/locomotion/go2_arm/`.
 
 ## In-Hand
 
@@ -16,7 +16,7 @@ uv run train --algo ppo --task sharpa_inhand --sim mujoco --profile hora trainin
 ```
 
 HORA student distillation is configured by
-`conf/hora_distill/task/sharpa_inhand/mujoco.yaml`; it is not currently exposed
+`src/unilab/conf/hora_distill/task/sharpa_inhand/mujoco.yaml`; it is not currently exposed
 as a separate top-level CLI route.
 
 ## Platform Balancing
diff --git a/docs/sphinx/source/en/2-user_guide/4-tasks/4-manip_loco.md b/docs/sphinx/source/en/2-user_guide/4-tasks/4-manip_loco.md
index 9c10c6e51..4beac31d4 100644
--- a/docs/sphinx/source/en/2-user_guide/4-tasks/4-manip_loco.md
+++ b/docs/sphinx/source/en/2-user_guide/4-tasks/4-manip_loco.md
@@ -5,8 +5,8 @@ env is `Go2ArmManipLoco`.
 
 ## Owner Configs
 
-- PPO owner: `conf/ppo/task/go2_arm_manip_loco/mujoco.yaml`
-- HIM-PPO owner: `conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml`
+- PPO owner: `src/unilab/conf/ppo/task/go2_arm_manip_loco/mujoco.yaml`
+- HIM-PPO owner: `src/unilab/conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml`
 - Scene entry: `src/unilab/assets/robots/go2_arm/scene_flat.xml`
 
 ## PPO
@@ -17,7 +17,7 @@ uv run train --algo ppo --task go2_arm_manip_loco --sim mujoco training.no_play=
 
 ## HIM-PPO
 
-The HIM-PPO owner is `conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml`.
+The HIM-PPO owner is `src/unilab/conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml`.
 `src/unilab/cli.py` does not currently expose HIM-PPO as a top-level
 `uv run train --algo ...` route.
 
diff --git a/docs/sphinx/source/en/2-user_guide/5-domain_randomization/0-index.md b/docs/sphinx/source/en/2-user_guide/5-domain_randomization/0-index.md
index 3ee8a2c20..9e0f57346 100644
--- a/docs/sphinx/source/en/2-user_guide/5-domain_randomization/0-index.md
+++ b/docs/sphinx/source/en/2-user_guide/5-domain_randomization/0-index.md
@@ -1,9 +1,14 @@
 # Domain Randomization
 
 
-This page only describes the current status of tasks in the repo that are already registered and already wired to a DR provider. All conclusions come from the code; nothing is inferred from design intent.
+This page only describes the current domain randomization status of registered tasks in the repo. All conclusions come from the code; nothing is inferred from design intent.
 
-The current unified entry point lives in `NpEnv._init_domain_randomization()` and `DomainRandomizationManager`:
+Two DR declaration paths exist today:
+
+- **Manager-Based (Compatible) tasks**: reset / interval randomization is declared through Hydra `events:` manager terms in the owner YAML; reset-lifecycle events sample at reset, interval-lifecycle events perturb between steps. See the `events:` block of `src/unilab/conf/ppo/task/go1_joystick_flat/base.yaml` for an example.
+- **Legacy provider path**: only the 3 Adapted families (`sharpa_inhand` / `sharpa_inhand_grasp` / `go2_arm_manip_loco`, including their appo / hora / ppo_him owners) still declare `env.domain_rand.*` configuration through a `DomainRandomizationProvider` + `DomainRandomizationManager`.
+
+The unified entry point of the legacy provider path lives in `NpEnv._init_domain_randomization()` and `DomainRandomizationManager`:
 
 - init path: the task provider produces an `InitRandomizationPlan`; the manager calls the backend's `apply_init_randomization(...)` during env initialization
 - reset path: the task provider produces a `ResetPlan`; the manager validates capability and then calls the backend's `set_state(..., randomization=...)`
@@ -17,62 +22,71 @@ These three paths correspond to three lifecycle classes:
 
 ## Status Conclusions
 
-1. All tasks currently wired to a DR provider use the unified DR entry point; no task bypasses `DomainRandomizationManager` to run a separate DR flow inside `reset()`.
-2. They are all roughly structured: task files define a `domain_rand` config dataclass, a `DomainRandomizationProvider`, and a `ResetPlan`; `G1WalkFlat` reuses `G1Walk`'s provider.
-3. What is "unified" today is mainly the entry point and execution flow, not every randomization item itself. The shared helper `build_common_reset_randomization()` currently generates `base_mass_delta`, `base_com_offset`, `gravity`, `kp`, `kd`; the shared interval helper currently only generates push.
+1. Manager-Based tasks do not register a DR provider; their reset/interval randomization consists of `events:` manager terms in the owner YAML, executed uniformly by the manager lifecycle. Only the frozen compatibility factories of the Adapted families still go through the `DomainRandomizationManager` unified entry point.
+2. Adapted-family owners define a `domain_rand` config dataclass, a `DomainRandomizationProvider`, and a `ResetPlan`; Manager-Based owners declare reset behavior through Hydra command/event terms. G1 motion reset perturbations belong to `MotionCommandCfg`, while WBT adds `EventTermCfg` reset and interval terms.
+3. What is "unified" today is mainly the entry point and execution flow, not every randomization item itself. The legacy path's shared helper `build_common_reset_randomization()` currently generates `base_mass_delta`, `base_com_offset`, `gravity`, `kp`, `kd`; the shared interval helper currently only generates push.
 4. `ResetRandomizationPayload` can already express `gravity`, `body_iquat`, `body_inertia`, `kp`, `kd`, and `MuJoCoBackend` has declared support. Whether these are actually used still depends on whether the task provider samples and dispatches them.
 5. `MotrixBackend` currently supports `base_mass_delta`, `base_com_offset`, `kp`, `kd`, and interval push; and it requires all model actuators to be position actuators during initialization.
 6. `geom_size` is not a reset-lifecycle field; Sharpa-hand object geom scale is handled by init-lifecycle model materialization.
 
 ## Uniformity Assessment Table
 
-| Task | Uses unified DR entry? | Structured form? | reset form | interval form | Code |
+| Task | Declaration path | Structured form? | reset form | interval form | Code |
 | --- | --- | --- | --- | --- | --- |
-| `Go1JoystickFlat` | Yes | Yes: `Domain_Rand + Provider + ResetPlan` | task state sampling + common payload | push | `go1/joystick.py` |
-| `Go2JoystickFlat` | Yes | Yes: `Domain_Rand + Provider + ResetPlan` | task state sampling + common payload | push | `go2/joystick.py` |
-| `G1WalkFlat` | Yes | Yes: `Domain_Rand + Provider + ResetPlan` | task state sampling + common payload | push | `g1/joystick.py` |
-| `G1WalkRough` | Yes | Yes: reuses `G1WalkDomainRandomizationProvider` | task state sampling + common payload | push | `g1/joystick.py` |
-| `G1MotionTracking` | Yes | Yes: `Domain_Rand + Provider + ResetPlan` | extensive task-specific reset sampling + common payload | push | `motion_tracking/g1/tracking.py` |
-| `AllegroInhandRotation` | Yes | Yes: `DomainRandConfig + Provider + ResetPlan` | task-specific reset sampling + common payload | none | `allegro_inhand/rotation.py` |
-| `SharpaInhandRotation` | Yes | Yes: `InitRandomizationPlan + ResetPlan + IntervalRandomizationPlan` | grasp cache sampling + common payload | object `body_force` | `sharpa_inhand/rotation.py` |
-| `SharpaInhandRotationGrasp` | Yes | Yes: reuses the Sharpa rotation provider and overrides reset sampling | grasp collection reset + common payload | none | `sharpa_inhand/grasp_gen.py` |
+| `Go1JoystickFlat` | Hydra `events:` terms | Yes: owner YAML declares reset/interval events | root-state reset + base mass/COM + `pd_gains` | `push_by_setting_velocity` event | `src/unilab/conf/ppo/task/go1_joystick_flat/base.yaml` |
+| `Go2JoystickFlat` | Hydra `events:` terms | Yes: owner YAML declares reset events | root-state reset + `pd_gains` kp/kd | none | `src/unilab/conf/ppo/task/go2_joystick_flat/base.yaml` |
+| `G1WalkFlat` | Hydra `events:` terms | Yes: Hydra `EventTermCfg` + Manager-Based reset terms | root-state reset + kp/kd via `pd_gains` | none | `g1/manager_terms.py` |
+| `G1WalkRough` | Hydra `events:` terms | Yes: same Manager-Based event terms as `G1WalkFlat` | root-state reset + kp/kd via `pd_gains` | none | `g1/manager_terms.py` |
+| `G1MotionTracking` | Hydra command term | Yes: Hydra `MotionCommandCfg` + Manager-Based command reset | motion frame, root pose/velocity, and joint-position sampling | none | `motion_tracking/common/manager_terms.py` |
+| `G1WBTObs` | Hydra `events:` terms | Yes: same motion command + Hydra `EventTermCfg` | motion reset plus mass/COM/PD/friction/encoder-bias events | interval velocity kick | `motion_tracking/g1/manager_terms.py` |
+| `AllegroInhandRotation` | Hydra `events:` terms | Yes: Hydra `EventTermCfg` + Manager-Based reset term | entity-scoped hand/ball reset | none | `allegro_inhand/manager_terms.py` |
+| `AllegroInhandRotationGrasp` | Hydra `events:` terms | Yes: reuses the rotation reset event + `RecorderTermCfg` | noisy hand reset + grasp collection | none | `allegro_inhand/grasp_gen.py` |
+| `SharpaInhandRotation` | legacy provider | Yes: `InitRandomizationPlan + ResetPlan + IntervalRandomizationPlan` | grasp cache sampling + common payload | object `body_force` | `sharpa_inhand/rotation.py` |
+| `SharpaInhandRotationGrasp` | legacy provider | Yes: reuses the Sharpa rotation provider and overrides reset sampling | grasp collection reset + common payload | none | `sharpa_inhand/grasp_gen.py` |
+| `Go2ArmManipLoco` | legacy provider | Yes: `DomainRandConfig + LocomotionDRProvider subclass + ResetPlan` | task state sampling + common payload | push | `go2_arm/manip_loco.py` |
 
 ## Per-task Domain Randomization List
 
 | Task | Currently implemented reset domain randomization | Currently implemented interval domain randomization | Default state |
 | --- | --- | --- | --- |
-| `Go1JoystickFlat` | base xy; base yaw; base qvel; command sampling; `current_actions/last_actions` zeroed; optional `base_mass_delta`; optional `base_com_offset`; optional `gravity` | `push_robots` | `base_mass_delta`, `base_com_offset`, and push enabled by default; `gravity` disabled by default |
-| `Go2JoystickFlat` | base xy; base yaw; base qvel; command sampling; `current_actions/last_actions` zeroed; kp/kd randomization (enabled by default); optional `base_mass_delta`; optional `base_com_offset`; optional `gravity` | `push_robots` | kp/kd enabled by default; common payload and push disabled by default |
-| `G1WalkFlat` | base xy; base yaw; base qvel sampled by `reset_base_qvel_limit`; command sampling; `gait_phase` sampling; `current_actions/last_actions` zeroed; kp/kd randomization (enabled by default); optional `base_mass_delta`; optional `base_com_offset`; optional `gravity` | `push_robots` | kp/kd enabled by default; common payload and push disabled by default |
-| `G1WalkRough` | Same as `G1WalkFlat`, directly reuses the same provider | `push_robots` | kp/kd enabled by default; common payload and push disabled by default |
-| `G1MotionTracking` | motion frame sampling; root pose perturbation `x/y/z/roll/pitch/yaw`; root velocity perturbation `x/y/z/roll/pitch/yaw`; joint position noise; under MuJoCo clipped by joint range; `current_actions/last_actions` zeroed; optional `base_mass_delta`; optional `base_com_offset`; optional `gravity` | `push_robots` | `pose_randomization`, `velocity_randomization`, `joint_position_range` have non-zero perturbations by default; common payload and push disabled by default |
-| `AllegroInhandRotation` | If a grasp cache exists, sample a grasp randomly; otherwise apply `joint_noise` to hand joints and `ball_z_offset` to the ball; always apply `ball_vel_noise` to ball linear velocity; optional common reset randomization payload (incl. `gravity`) | none | If the grasp cache path is available it is sampled by default; `joint_noise`, `ball_vel_noise`, `ball_z_offset` default to 0; common payload disabled by default |
+| `Go1JoystickFlat` | base xy/yaw and base qvel via `reset_root_state_uniform`; command sampling (`UniformVelocityCommandCfg`); base mass via `randomize_rigid_body_mass`; base COM via `randomize_rigid_body_com`; kp/kd via `pd_gains` | `push_by_setting_velocity` interval event | all listed event terms are declared and enabled by default in `src/unilab/conf/ppo/task/go1_joystick_flat/base.yaml` |
+| `Go2JoystickFlat` | base xy/yaw and base qvel via `reset_root_state_uniform`; command sampling; kp/kd via `pd_gains` | none | event terms declared and enabled by default in `src/unilab/conf/ppo/task/go2_joystick_flat/base.yaml` |
+| `G1WalkFlat` | base xy/yaw and base qvel via `reset_root_state_uniform`; command sampling with a planar dead zone; `gait_phase` sampling; kp/kd randomization via `pd_gains` | none | kp/kd enabled on mujoco owners by default; disabled on motrix/mjwarp owners |
+| `G1WalkRough` | Same as `G1WalkFlat` (shared owner bases, rough scene) | none | Same defaults as `G1WalkFlat` |
+| `G1MotionTracking` | Motion-command frame sampling; root pose perturbation `x/y/z/roll/pitch/yaw`; root velocity perturbation `x/y/z/roll/pitch/yaw`; joint-position noise clipped through the public entity soft limits; action-manager state reset | none | `pose_range`, `velocity_range`, and `joint_position_range` have non-zero perturbations in the base owner |
+| `G1WBTObs` | Same motion reset plus base mass, base COM, PD gain, foot friction, and encoder-bias event terms | `push_by_setting_velocity` | The WBT owner explicitly enables all listed event terms; unsupported capabilities raise rather than fall back |
+| `AllegroInhandRotation` | Entity-scoped hand/ball reset; an explicitly configured grasp cache is sampled, otherwise `null` explicitly selects the model home pose; optional `joint_noise`, `ball_velocity_noise`, and `ball_z_offset` | none | owner YAML explicitly selects the home pose and zero reset noise; a configured missing or malformed cache fails closed |
+| `AllegroInhandRotationGrasp` | Reuses the rotation reset with `joint_noise=0.25`; Manager-Based termination checks fingertip distance, contact count, and ball height; recorder stores successful timeout rows | none | generates the 50k-row Allegro grasp cache and raises `RunComplete` after a successful save |
 | `SharpaInhandRotation` | grasp cache bucketed sampling by `scale_ids`; object pose / quat reset; optional common reset randomization payload (incl. `gravity`) | object `body_force` direct force disturbance | `domain_rand.scale_list` defaults come from the owner YAML; under MuJoCo, object geom scale is materialized during init; common payload disabled by default; object force enabled by default via the Sharpa owner YAML |
 | `SharpaInhandRotationGrasp` | hand pose reset; object pose / quat reset; collects successful grasps and stores them bucketed by `scale_ids`; optional `base_mass_delta`; optional `base_com_offset`; optional `gravity` | none | Used by default to generate the Sharpa grasp cache; cache filename includes the single scale value; common payload disabled by default |
 
 ## Current Unified DR Capabilities and Boundaries
 
-### 1. Unified Entry Point Is Complete
+### 1. The Legacy Provider Entry Point Is Unified
 
-The unified entry point is guaranteed by `NpEnv` and `DomainRandomizationManager`:
+The unified entry point of the legacy provider path is guaranteed by `NpEnv`
+and `DomainRandomizationManager`:
 
 - Tasks only need to register a provider
 - The manager uniformly performs capability validation
 - The backend is uniformly responsible for actually applying the randomization payload
 
-So from an execution-path perspective, the tasks are already unified.
+So from an execution-path perspective, the Adapted families still on this path
+are unified; Manager-Based tasks instead execute the `events:` terms declared
+in the owner YAML through the manager lifecycle.
 
 ### 2. The Shared Helpers Are Still Narrow
 
-`dr_utils.py` currently has only two classes of shared helpers:
+The legacy path's `dr_utils.py` currently has only two classes of shared helpers:
 
 - reset common payload: `base_mass_delta`, `base_com_offset`, `gravity`, `kp`, `kd`
 - interval common payload: push
 
 This means:
 
-- Although locomotion tasks all go through the unified entry point, their base xy, yaw, qvel, command, and gait phase are still sampled directly inside each provider
-- `G1MotionTracking`'s pose / velocity / joint noise is also task-specific logic
+- The go2_arm / sharpa families still on the legacy provider path sample their
+  task-specific state directly inside each provider
+- `G1MotionTracking`'s pose / velocity / joint noise is owned by its manager command
 - Allegro's grasp / object initial state sampling is entirely task-specific logic
 - Sharpa's `geom_size` scale is init-lifecycle model materialization and is not part of the reset common payload
 
@@ -92,12 +106,12 @@ So today's "uniformity" is more about the contract and the calling convention th
 
 Backend capability today:
 
-- `MuJoCoBackend`: supports the 7 reset terms above, plus interval push and interval body force
+- `MuJoCoBackend`: supports the 7 reset terms above, plus interval push, interval body velocity delta (linear and world-frame angular), and interval body force/torque
 - `MotrixBackend`: supports `base_mass_delta`, `base_com_offset`, `kp`, `kd`, plus interval push; requires actuators to all be position actuators during initialization
 
 Notes:
 
-- The current `IntervalRandomizationPlan` supports `push_perturbation_limit`, `body_linear_velocity_delta`, and `body_force`; among these, `body_force` expresses hot-path direct external-force perturbations without exposing the backend-private `xfrc_applied` details.
+- The current `IntervalRandomizationPlan` supports `push_perturbation_limit`, `body_linear_velocity_delta`, `body_angular_velocity_delta`, `body_force`, and `body_torque`; among these, `body_force`/`body_torque` express hot-path direct external-wrench perturbations without exposing the backend-private `xfrc_applied` details.
 - The current MuJoCo backend's interval push and interval body force are both dispatched through `xfrc_applied`; the Sharpa-hand object disturbance has been switched to direct force disturbance.
 - The Motrix backend currently still does not support direct body-force disturbance, so such owner configs must continue to be explicitly disabled.
 
@@ -112,7 +126,10 @@ But on the task side, the current reality is: not every provider constructs thes
 - Lifecycle: only sampled and written at reset; the env retains that gravity until the next reset re-samples it.
 - Backend: currently in UniLab, only the MuJoCo backend declares support for this reset term; the Motrix backend does not. Some tasks filter it by capability and skip it; others raise an error in the validate stage.
 
-The config entry is under each task's `env.domain_rand`:
+The config entry exists only under `env.domain_rand` of the Adapted-family
+owners still on the legacy provider path (`sharpa_inhand_grasp`,
+`go2_arm_manip_loco`, and their hora / appo / ppo_him variants); Manager-Based
+tasks have no `env.domain_rand`:
 
 ```yaml
 env:
@@ -132,7 +149,7 @@ Field semantics:
 If you only want to randomize the magnitude while keeping the vertical-down direction, only open up the `z` component:
 
 ```bash
-uv run train --algo ppo --task g1_walk_flat --sim mujoco \
+uv run train --algo ppo --task sharpa_inhand_grasp --sim mujoco \
   env.domain_rand.randomize_gravity=true \
   'env.domain_rand.gravity_range=[[0.0,0.0,-10.5],[0.0,0.0,-8.5]]'
 ```
@@ -140,7 +157,7 @@ uv run train --algo ppo --task g1_walk_flat --sim mujoco \
 If you want to randomize both direction and magnitude, open up `x/y/z`:
 
 ```bash
-uv run train --algo ppo --task g1_walk_flat --sim mujoco \
+uv run train --algo ppo --task sharpa_inhand_grasp --sim mujoco \
   env.domain_rand.randomize_gravity=true \
   'env.domain_rand.gravity_range=[[-0.3,-0.3,-10.5],[0.3,0.3,-8.5]]'
 ```
@@ -155,7 +172,13 @@ Notes:
 
 ## Interval push Usage
 
-Tasks supporting interval push configure it under `env.domain_rand`:
+The `env.domain_rand.push_robots` family of fields exists only in the go2_arm
+Adapted-family owners (`src/unilab/conf/ppo/task/go2_arm_manip_loco/mujoco.yaml` etc.);
+Manager-Based tasks declare push through a `push_by_setting_velocity` interval
+event term instead (for example `src/unilab/conf/ppo/task/go1_joystick_flat/base.yaml` and
+`src/unilab/conf/ppo/task/quadruped_joystick_rough/base.yaml`).
+
+The go2_arm owners configure push under `env.domain_rand`:
 
 ```yaml
 env:
@@ -172,11 +195,11 @@ env:
 - `push_body_name`: the target body / link to apply the force to. Defaults to `null`, meaning the backend's `base_name` is used.
 
 ```bash
-uv run train --algo ppo --task g1_walk_flat --sim mujoco \
+uv run train --algo ppo --task go2_arm_manip_loco --sim mujoco \
   env.domain_rand.push_robots=true \
   env.domain_rand.push_interval=500 \
   'env.domain_rand.max_force=[20.0,20.0,5.0]' \
-  env.domain_rand.push_body_name=torso_link
+  env.domain_rand.push_body_name=base
 ```
 
 Notes:
@@ -210,8 +233,8 @@ This boundary exists to honor the cold-path asset/model-metadata access principl
 
 Sharpa-hand is the current example task for `geom_size` init-lifecycle DR in the repo. Related task configs:
 
-- `conf/ppo/task/sharpa_inhand/mujoco.yaml`
-- `conf/ppo/task/sharpa_inhand_grasp/mujoco.yaml`
+- `src/unilab/conf/ppo/task/sharpa_inhand/mujoco.yaml`
+- `src/unilab/conf/ppo/task/sharpa_inhand_grasp/mujoco.yaml`
 
 ### 1. Config Entry
 
diff --git a/docs/sphinx/source/en/2-user_guide/5-domain_randomization/1-configuration.md b/docs/sphinx/source/en/2-user_guide/5-domain_randomization/1-configuration.md
index bc1ee6ae0..38369261e 100644
--- a/docs/sphinx/source/en/2-user_guide/5-domain_randomization/1-configuration.md
+++ b/docs/sphinx/source/en/2-user_guide/5-domain_randomization/1-configuration.md
@@ -1,11 +1,19 @@
 # Configuration
 
-Domain randomization is configured inside the selected task owner YAML, usually
-under `env.domain_rand`. Use `--task` and `--sim` to select backend-specific
-behavior first, then override fields inside that selected owner.
+Domain randomization is configured inside the selected task owner YAML. Use
+`--task` and `--sim` to select backend-specific behavior first, then override
+fields inside that selected owner.
+
+Two declaration paths exist today:
+
+- Manager-Based (Compatible) tasks declare reset / interval randomization
+  through Hydra `events:` manager terms in the owner YAML, for example
+  `src/unilab/conf/ppo/task/go1_joystick_flat/base.yaml`.
+- Only the Adapted families (sharpa / go2_arm and their hora / appo / ppo_him
+  owners) still configure legacy provider fields under `env.domain_rand`.
 
 ```bash
-uv run train --algo ppo --task g1_walk_flat --sim mujoco \
+uv run train --algo ppo --task sharpa_inhand_grasp --sim mujoco \
   env.domain_rand.randomize_gravity=true \
   'env.domain_rand.gravity_range=[[0.0,0.0,-10.5],[0.0,0.0,-8.5]]'
 ```
@@ -21,25 +29,30 @@ Common lifecycle boundaries:
 The detailed task status and field semantics are in {doc}`0-index`.
 
 Domain randomization is split by lifecycle: init, reset, and interval. The
-manager path is `src/unilab/dr/manager.py`; task providers live near the env
-owners, and backend capabilities are declared through
-`src/unilab/base/backend/base.py`.
+legacy path's manager is `src/unilab/dr/manager.py`; task providers live near
+the env owners, and backend capabilities are declared through
+`unisim.backend.base`.
 
 ## Reset Gravity
 
 Use `--sim mujoco` when enabling gravity reset randomization; Motrix does not
-advertise the same gravity capability in the current backend.
+advertise the same gravity capability in the current backend. This item is only
+available on the legacy provider path (Adapted-family owners).
 
 ```bash
-uv run train --algo ppo --task g1_walk_flat --sim mujoco \
+uv run train --algo ppo --task sharpa_inhand_grasp --sim mujoco \
   env.domain_rand.randomize_gravity=true \
   'env.domain_rand.gravity_range=[[0.0,0.0,-10.5],[0.0,0.0,-8.5]]'
 ```
 
 ## Interval Push
 
+Manager-Based tasks declare push through a `push_by_setting_velocity` interval
+event term; `env.domain_rand.push_robots` is only available on the go2_arm
+Adapted-family owners.
+
 ```bash
-uv run train --algo ppo --task g1_walk_flat --sim mujoco \
+uv run train --algo ppo --task go2_arm_manip_loco --sim mujoco \
   env.domain_rand.push_robots=true \
   env.domain_rand.push_interval=500 \
   'env.domain_rand.max_force=[20.0,20.0,5.0]'
@@ -48,9 +61,11 @@ uv run train --algo ppo --task g1_walk_flat --sim mujoco \
 ## Owner-Local Defaults
 
 Keep ranges in the task owner YAML when they are part of the task contract. For
-example, `conf/ppo/task/go2_joystick_rough/mujoco.yaml` enables base mass,
-center-of-mass, kp/kd, and push randomization, while
-`conf/ppo/task/sharpa_inhand/mujoco.yaml` configures object scale, friction, and
+example, the rough quadruped family's base mass, center-of-mass, kp/kd, and
+push randomization are declared as event terms in the shared base
+`src/unilab/conf/ppo/task/quadruped_joystick_rough/base.yaml` (the `go2_joystick_rough`
+backend owners compose it through Hydra defaults), while
+`src/unilab/conf/ppo/task/sharpa_inhand/mujoco.yaml` configures object scale, friction, and
 force disturbance for Sharpa.
 
 For the full current inventory, see {doc}`0-index`.
diff --git a/docs/sphinx/source/en/2-user_guide/5-domain_randomization/2-writing_providers.md b/docs/sphinx/source/en/2-user_guide/5-domain_randomization/2-writing_providers.md
index 36f74c627..061ab04e6 100644
--- a/docs/sphinx/source/en/2-user_guide/5-domain_randomization/2-writing_providers.md
+++ b/docs/sphinx/source/en/2-user_guide/5-domain_randomization/2-writing_providers.md
@@ -1,5 +1,12 @@
 # Writing Providers
 
+This page describes the legacy provider path: only the 3 Adapted families
+(`sharpa_inhand` / `sharpa_inhand_grasp` / `go2_arm_manip_loco`) still declare
+domain randomization through a task-level `DomainRandomizationProvider`.
+Migrated Manager-Based tasks do not write providers; they declare randomization
+through Hydra `events:` manager terms in the owner YAML (see {doc}`0-index`
+and {doc}`1-configuration`).
+
 Task-level domain randomization providers live with the task env owner. They
 sample task-specific state and return plans consumed by
 `DomainRandomizationManager`.
@@ -25,13 +32,13 @@ The shared types live in `src/unilab/dr/types.py`, and the manager lives in
 
 ## Evidence
 
-Representative provider implementations are in:
+Representative provider implementations are in (all on the Adapted-family
+compatibility path):
 
-- `src/unilab/envs/locomotion/go1/joystick.py`
-- `src/unilab/envs/locomotion/g1/joystick.py`
-- `src/unilab/envs/motion_tracking/g1/tracking.py`
-- `src/unilab/envs/manipulation/allegro_inhand/rotation.py`
-- `src/unilab/envs/manipulation/sharpa_inhand/rotation.py`
+- `src/unilab/tasks/locomotion/common/dr_provider.py` (`LocomotionDRProvider`,
+  used by the go2_arm family)
+- `src/unilab/tasks/locomotion/go2_arm/manip_loco.py`
+- `src/unilab/tasks/manipulation/sharpa_inhand/rotation.py`
 
 Developer contract details are in
 {doc}`../../4-developer_guide/2-contracts/4-dr_contract`.
diff --git a/docs/sphinx/source/en/2-user_guide/6-terrain/1-procedural.md b/docs/sphinx/source/en/2-user_guide/6-terrain/1-procedural.md
index b0126d5e1..c5bf00100 100644
--- a/docs/sphinx/source/en/2-user_guide/6-terrain/1-procedural.md
+++ b/docs/sphinx/source/en/2-user_guide/6-terrain/1-procedural.md
@@ -8,7 +8,7 @@ This page only answers four questions:
 3. When I want to change the sub-terrain composition, what is the correct entry point?
 4. What are the currently known boundaries — not bugs, but constraints?
 
-For the underlying contracts (cold-path materialization, registering a new sub-terrain, hfield export), see the source comments in `src/unilab/base/backend/mujoco/xml.py`, `src/unilab/base/backend/motrix/scene.py`, and `src/unilab/terrains/terrain_generator.py`.
+For the underlying contracts (cold-path materialization, registering a new sub-terrain, hfield export), see the source comments in `unisim.backend.mujoco.xml`, `unisim.backend.motrix.scene`, and `unisim.terrain.generator`.
 
 ## Current Status
 
@@ -44,7 +44,7 @@ uv run train --algo ppo --task go2_joystick_rough --sim motrix
 
 ## 2. Overriding Terrain Parameters via Hydra Command Line
 
-`Go2JoystickRough` explicitly lists a set of override-able fields in `conf/ppo/task/go2_joystick_rough/{mujoco,motrix}.yaml`; these fields allow Hydra struct mode to accept command-line overrides.
+`Go2JoystickRough` explicitly lists a set of override-able fields in `src/unilab/conf/ppo/task/go2_joystick_rough/{mujoco,motrix}.yaml`; these fields allow Hydra struct mode to accept command-line overrides.
 
 | Field | Purpose | YAML Default |
 | --- | --- | --- |
@@ -132,7 +132,7 @@ env:
 The env's `__init__` does not need to call the XML materializer directly; hand `scene` over to the backend constructor:
 
 ```python
-from unilab.base.backend import create_backend
+from unilab.base.backend_factory import create_backend
 
 backend = create_backend(..., cfg.scene)
 terrain_origins = getattr(backend, "terrain_origins", None)
diff --git a/docs/sphinx/source/en/2-user_guide/6-terrain/2-heightfield_import.md b/docs/sphinx/source/en/2-user_guide/6-terrain/2-heightfield_import.md
index ef5d80fa6..a15826515 100644
--- a/docs/sphinx/source/en/2-user_guide/6-terrain/2-heightfield_import.md
+++ b/docs/sphinx/source/en/2-user_guide/6-terrain/2-heightfield_import.md
@@ -3,16 +3,16 @@
 Heightfield terrain is configured through `SceneCfg` and the terrain generator,
 then materialized by the backend on the init path. The committed user-facing
 example is `Go2JoystickRough`, with owners in
-`conf/ppo/task/go2_joystick_rough/mujoco.yaml` and
-`conf/ppo/task/go2_joystick_rough/motrix.yaml`.
+`src/unilab/conf/ppo/task/go2_joystick_rough/mujoco.yaml` and
+`src/unilab/conf/ppo/task/go2_joystick_rough/motrix.yaml`.
 
 ## Files To Read
 
 - `src/unilab/terrains/heightfield_terrains.py`
-- `src/unilab/terrains/terrain_generator.py`
-- `src/unilab/envs/locomotion/go2/rough.py`
-- `src/unilab/base/backend/mujoco/xml.py`
-- `src/unilab/base/backend/motrix/scene.py`
+- `unisim.terrain.generator`
+- `src/unilab/tasks/locomotion/go2/rough.py`
+- `unisim.backend.mujoco.xml`
+- `unisim.backend.motrix.scene`
 
 ## Smoke Commands
 
diff --git a/docs/sphinx/source/en/2-user_guide/7-tooling/3-nan_visualizer.md b/docs/sphinx/source/en/2-user_guide/7-tooling/3-nan_visualizer.md
index 53495316c..dec617e39 100644
--- a/docs/sphinx/source/en/2-user_guide/7-tooling/3-nan_visualizer.md
+++ b/docs/sphinx/source/en/2-user_guide/7-tooling/3-nan_visualizer.md
@@ -1,8 +1,8 @@
 # NaN Visualizer
 
-PPO has a NaN guard under `training.nan_guard` in `conf/ppo/config.yaml`,
+PPO has a NaN guard under `training.nan_guard` in `src/unilab/conf/ppo/config.yaml`,
 enabled by default to match APPO and off-policy. When active,
-`scripts/train_rsl_rl.py` installs `NanGuard`, checks observation dicts and
+`src/unilab/scripts/train_rsl_rl.py` installs `NanGuard`, checks observation dicts and
 rewards, and writes a `.npz` dump plus model metadata when it detects NaN/Inf
 values.
 
@@ -12,7 +12,7 @@ uv run train --algo ppo --task go2_joystick_flat --sim mujoco \
   training.nan_guard.output_dir=/tmp/unilab/nan_dumps
 ```
 
-The viewer implementation is `src/unilab/tools/viz_nan.py`, registered as the
+The viewer implementation is `src/unilab/utils/nan_viz.py`, registered as the
 `unilab-viz-nan` console entry. It replays a dump path and lets you select the
 environment index. Dump format and round-trip loading are covered by
 `tests/test_nan_guard.py`.
diff --git a/docs/sphinx/source/en/2-user_guide/7-tooling/4-scene_export.md b/docs/sphinx/source/en/2-user_guide/7-tooling/4-scene_export.md
index f92fa64b7..a0b1fb348 100644
--- a/docs/sphinx/source/en/2-user_guide/7-tooling/4-scene_export.md
+++ b/docs/sphinx/source/en/2-user_guide/7-tooling/4-scene_export.md
@@ -1,6 +1,6 @@
 # Scene Export
 
-Scene export is implemented by `src/unilab/tools/export_scene.py` and registered
+Scene export is implemented by `unisim.backend.mujoco.export_scene` and registered
 as the `unilab-export-scene` console entry in `pyproject.toml`. It accepts a
 MuJoCo XML or MJB model path, writes `scene.xml`, copies mesh assets when they
 are discoverable, and can create a zip archive.
diff --git a/docs/sphinx/source/en/2-user_guide/7-tooling/5-robot_import.md b/docs/sphinx/source/en/2-user_guide/7-tooling/5-robot_import.md
index 5c657bb03..888be3ff6 100644
--- a/docs/sphinx/source/en/2-user_guide/7-tooling/5-robot_import.md
+++ b/docs/sphinx/source/en/2-user_guide/7-tooling/5-robot_import.md
@@ -30,7 +30,7 @@ Prefer MuJoCo/MJCF `.xml`, copied according to the contract above.
 If the source is URDF-only, convert it with the repository script:
 
 ```bash
-uv run unilab-import-robot <urdf_path> [robot_name]
+uv run scripts/tools/import_robot.py <urdf_path> [robot_name]
 ```
 
 ```{important}
@@ -44,7 +44,7 @@ where possible.
   only for position-control owners.
   - If the robot must preserve torque/motor actuator semantics, later task
     extension should follow the control pattern in
-    `src/unilab/envs/locomotion/go2w/`: keep action interpretation, PD/torque
+    `src/unilab/tasks/locomotion/go2w/`: keep action interpretation, PD/torque
     control, and the actuator contract inside the robot owner boundary.
 - After conversion, `mujoco.viewer` opens automatically to show the converted
   result and proceed to keyframe adjustment.
@@ -72,7 +72,7 @@ When checking `home`, confirm at least:
 
 ## Output Artifacts
 
-After running `uv run unilab-import-robot <urdf_path> [robot_name]`, the script
+After running `uv run scripts/tools/import_robot.py <urdf_path> [robot_name]`, the script
 generates:
 
 - `src/unilab/assets/robots/<robot_name>/assets/`: converted and organized mesh
diff --git a/docs/sphinx/source/en/2-user_guide/8-manipulation/1-dexterous_inhand.md b/docs/sphinx/source/en/2-user_guide/8-manipulation/1-dexterous_inhand.md
index 196de51db..b3a2af32f 100644
--- a/docs/sphinx/source/en/2-user_guide/8-manipulation/1-dexterous_inhand.md
+++ b/docs/sphinx/source/en/2-user_guide/8-manipulation/1-dexterous_inhand.md
@@ -13,12 +13,12 @@ owner is `allegro_inhand`, and grasp-cache generation uses
 
 Owner evidence:
 
-- `conf/ppo/task/allegro_inhand/mujoco.yaml`
-- `conf/ppo/task/allegro_inhand/motrix.yaml`
-- `conf/ppo/task/allegro_inhand_grasp/mujoco.yaml`
-- `conf/ppo/task/allegro_inhand_grasp/motrix.yaml`
-- `conf/appo/task/allegro_inhand/mujoco.yaml`
-- `conf/appo/task/allegro_inhand/motrix.yaml`
+- `src/unilab/conf/ppo/task/allegro_inhand/mujoco.yaml`
+- `src/unilab/conf/ppo/task/allegro_inhand/motrix.yaml`
+- `src/unilab/conf/ppo/task/allegro_inhand_grasp/mujoco.yaml`
+- `src/unilab/conf/ppo/task/allegro_inhand_grasp/motrix.yaml`
+- `src/unilab/conf/appo/task/allegro_inhand/mujoco.yaml`
+- `src/unilab/conf/appo/task/allegro_inhand/motrix.yaml`
 
 The typical flow is two stages: first generate a grasp cache, then train the
 rotation policy.
@@ -57,12 +57,12 @@ in training paths are MuJoCo owner paths.
 
 Owner evidence:
 
-- `conf/ppo/task/sharpa_inhand/mujoco.yaml`
-- `conf/ppo/task/sharpa_inhand/mujoco_hora.yaml`
-- `conf/ppo/task/sharpa_inhand_grasp/mujoco.yaml`
-- `conf/appo/task/sharpa_inhand/mujoco.yaml`
-- `conf/appo/task/sharpa_inhand/mujoco_hora.yaml`
-- `conf/hora_distill/task/sharpa_inhand/mujoco.yaml`
+- `src/unilab/conf/ppo/task/sharpa_inhand/mujoco.yaml`
+- `src/unilab/conf/ppo/task/sharpa_inhand/mujoco_hora.yaml`
+- `src/unilab/conf/ppo/task/sharpa_inhand_grasp/mujoco.yaml`
+- `src/unilab/conf/appo/task/sharpa_inhand/mujoco.yaml`
+- `src/unilab/conf/appo/task/sharpa_inhand/mujoco_hora.yaml`
+- `src/unilab/conf/hora_distill/task/sharpa_inhand/mujoco.yaml`
 
 The full HORA path is three stages:
 
@@ -127,7 +127,7 @@ uv run eval --algo appo --task sharpa_inhand --sim mujoco --profile hora --load-
 ```
 
 Student distillation is configured by
-`conf/hora_distill/task/sharpa_inhand/mujoco.yaml` and implemented by
+`src/unilab/conf/hora_distill/task/sharpa_inhand/mujoco.yaml` and implemented by
 `scripts/train_hora_distill.py`; the top-level CLI does not currently expose a
 separate HORA distillation route (it is not in the CLI `SUPPORTED_ALGOS`). To
 distill from an APPO teacher, set `teacher.algo_family=appo` in that low-level
diff --git a/docs/sphinx/source/en/2-user_guide/8-manipulation/2-manip_loco.md b/docs/sphinx/source/en/2-user_guide/8-manipulation/2-manip_loco.md
index b03cef7f7..a0ff64f51 100644
--- a/docs/sphinx/source/en/2-user_guide/8-manipulation/2-manip_loco.md
+++ b/docs/sphinx/source/en/2-user_guide/8-manipulation/2-manip_loco.md
@@ -2,8 +2,8 @@
 
 `go2_arm_manip_loco` combines Go2 locomotion with the Airbot arm. The registered
 env is `Go2ArmManipLoco`, the PPO owner is
-`conf/ppo/task/go2_arm_manip_loco/mujoco.yaml`, and the HIM-PPO owner is
-`conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml`.
+`src/unilab/conf/ppo/task/go2_arm_manip_loco/mujoco.yaml`, and the HIM-PPO owner is
+`src/unilab/conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml`.
 
 ## PPO
 
@@ -13,7 +13,7 @@ uv run train --algo ppo --task go2_arm_manip_loco --sim mujoco training.no_play=
 
 ## HIM-PPO
 
-HIM-PPO is configured by `conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml` and
+HIM-PPO is configured by `src/unilab/conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml` and
 implemented by `scripts/train_him_ppo.py`. It is not currently declared as a
 top-level `uv run train --algo ...` route in `src/unilab/cli.py`.
 
diff --git a/docs/sphinx/source/en/3-deployment/1-sim_to_real/1-overview.md b/docs/sphinx/source/en/3-deployment/1-sim_to_real/1-overview.md
index 38a1bc8b9..8623bee0e 100644
--- a/docs/sphinx/source/en/3-deployment/1-sim_to_real/1-overview.md
+++ b/docs/sphinx/source/en/3-deployment/1-sim_to_real/1-overview.md
@@ -6,9 +6,8 @@ page in this section drills into one stage.
 ## What "sim-to-real" means in UniLab
 
 A deployable UniLab policy is the exported policy plus the exact observation
-and action contracts used by the selected task owner. The G1 WBT helper path
-materializes this as `policy.onnx`, `deploy_config.yaml`, and a motion binary;
-other robots need an equivalent hardware-side runtime that:
+and action contracts used by the selected task owner. UniLab ships the training
+side and the ONNX export; every robot needs a hardware-side runtime that:
 
 1. Reads sensors → assembles the **same observation vector** the policy saw
    in simulation.
@@ -70,10 +69,10 @@ flowchart LR
   runtime (units, frame, filter cutoffs). Log the first deploy-side
   observation window and compare it with a sim rollout built from the same
   owner YAML.
-- **Action latency.** Some task configs expose one-step delayed action
-  execution through `control_config.simulate_action_latency`. Measure the
-  deploy loop and make the training owner match that contract before a
-  hardware run. See {doc}`8-latency_budget`.
+- **Action latency.** Some task owners expose one-step delayed action execution
+  through a control config or Manager-Based action term. Measure the deploy loop
+  and make the training owner match that contract before a hardware run. See
+  {doc}`8-latency_budget`.
 - **Friction / damping mismatch.** Especially for in-hand manipulation.
   Sweep friction in DR; cross-check via {doc}`../2-sim_to_sim/3-contact_and_friction_alignment`.
 - **Reset transients.** Sim resets to a stable pose; deployment starts from a
diff --git a/docs/sphinx/source/en/3-deployment/1-sim_to_real/2-g1_whole_body.md b/docs/sphinx/source/en/3-deployment/1-sim_to_real/2-g1_whole_body.md
index e4ba8b088..60d087cee 100644
--- a/docs/sphinx/source/en/3-deployment/1-sim_to_real/2-g1_whole_body.md
+++ b/docs/sphinx/source/en/3-deployment/1-sim_to_real/2-g1_whole_body.md
@@ -2,14 +2,15 @@
 
 ::::{admonition} Hardware target
 :class: note
-Unitree G1 humanoid (29-DoF variant). Joints are assumed in the order exported
-by `scripts/deploy/export_deploy_config.py` from
-`src/unilab/assets/robots/g1/scene_flat.xml`; verify that order before
-hardware bring-up.
+Unitree G1 humanoid (29-DoF variant). Joint order comes from the task owner's
+scene (`src/unilab/assets/robots/g1/scene_flat.xml`, actuator order); verify
+that order against your SDK motor indices before hardware bring-up.
 ::::
 
-This guide walks the **last mile** between a converged G1 motion-tracking
-policy and a closed-loop run on the robot.
+This guide covers the **observation and action contract** a G1 motion-tracking
+policy expects on hardware. The repository does not ship a G1 deploy runtime —
+you supply the hardware-side loop, and this page tells you what it must
+reproduce.
 
 ## 0. Verify your sim-side checkpoint
 
@@ -25,84 +26,98 @@ What to look for in the video:
 - Joint velocities and actions remain finite and within the expected range.
 - Contact timing looks consistent with the reference motion.
 
-If any of those is off, fix the sim-side checkpoint or deploy contract before
-hardware bring-up.
+If any of those is off, fix the sim-side checkpoint before hardware bring-up.
 
-## 1. Export
+## 1. Export, then read the contract off the owner YAML
 
-Use the training playback path to export `policy.onnx`, then export the G1 WBT
-deploy config and motion binary with the committed deployment helpers:
+Export `policy.onnx` through the training playback path, using the same task
+owner that produced the checkpoint:
 
 ```bash
 uv run eval --algo ppo --task g1_motion_tracking --sim motrix --load-run -1
-
-uv run scripts/deploy/export_deploy_config.py \
-  --output logs/deploy/deploy_config.yaml
-
-uv run scripts/deploy/export_motion_bin.py \
-  --output logs/deploy/dance1.bin
 ```
 
-The deployment-side prototype consumes:
+Every field your hardware loop needs is declared in that owner's YAML. Widths
+differ per owner — two G1 examples:
 
+```{list-table}
+:header-rows: 1
+:widths: 34 22 44
+
+* - Owner
+  - Actor obs width
+  - Notes
+* - `src/unilab/conf/sac/task/g1_wbt_obs/mujoco.yaml`
+  - 514
+  - No state estimation: `motion_anchor_pos_b` and `base_lin_vel` are set to
+    `null`, pelvis IMU, `history_length: 5` on the proprio terms.
+* - `src/unilab/conf/ppo/task/g1_motion_tracking_deploy/mujoco.yaml`
+  - 154
+  - Single-step mimic actor layout, per-joint-group `scale` regex map.
 ```
-runs/<run>/
-└── policy.onnx
-logs/deploy/
-├── deploy_config.yaml
-└── dance1.bin
-```
+
+::::{admonition} Read the width off the composed config, not off this table
+:class: warning
+Actor obs width is the sum of `dim * history_length` over the terms declared
+under `env.observations.actor.terms`, plus the motion command. A term set to
+`null` is dropped. If the ONNX input width disagrees with what your hardware
+loop assembles, that is a contract bug, not a hardware tuning problem.
+::::
 
 ## 2. Observation contract
 
-For the committed G1 WBT deploy helper, the observation layout is exported into
-`deploy_config.yaml` as `obs_layout`. `scripts/deploy/export_deploy_config.py`
-is the source of truth for the segment order:
+Term order and per-term history come from your own owner's
+`env.observations.actor.terms`. As a worked example, `g1_wbt_obs` declares the
+terms below; those carrying `history_length: 5` are flattened **oldest-first**
+within the term, and terms are concatenated in declaration order:
 
 ```{list-table}
 :header-rows: 1
 :widths: 30 15 55
 
-* - Group
+* - Term
   - Dim
   - Source on hardware
-* - `command_joint_pos`
-  - 29
-  - motion reference frame joint position
-* - `command_joint_vel`
-  - 29
-  - motion reference frame joint velocity
 * - `motion_anchor_ori_b`
   - 6
   - anchor orientation term from the reference and robot torso frames
-* - `gyro`
+* - `base_ang_vel`
   - 3 per history step
-  - IMU gyro term
-* - `joint_pos_rel`
+  - IMU gyro (`params.sensor_name: pelvis_gyro`)
+* - `joint_pos`
   - 29 per history step
-  - measured joint position minus `default_angles`
-* - `dof_vel`
+  - measured joint position minus the `stand` keyframe joint angles
+* - `joint_vel`
   - 29 per history step
   - joint velocity term
-* - `last_actions`
-  - 29
+* - `actions`
+  - 29 per history step
   - previous raw actor output
 ```
 
-The export script also records each segment's `history_length` and verifies the
-total `obs_dim`. `scripts/deploy/sim_prototype.py` refuses to run when the ONNX
-input width and `deploy_config.yaml` `obs_dim` disagree.
+The motion command contributes the reference joint position and velocity
+(`29 + 29`) ahead of the observation terms. Per-term oldest-first ordering is
+guarded by `tests/scripts/test_obs_alignment_g1_wbt.py`; mirror that ordering
+on hardware or the policy reads a permuted vector.
 
 ## 3. Actuator interface
 
-The G1 deploy prototype maps actor output exactly as:
-`action * action_scale + default_angles`, then clips to `joint_lower` /
-`joint_upper` and applies EMA smoothing from `ema_alpha`.
+Map actor output as `action * scale + default_angles`, then clamp to the
+scene's joint range before the target reaches the motor driver.
+
+- `scale` is `env.actions.joint_pos.scale`. It may be a **scalar** (`2.0` for
+  `g1_wbt_obs`) or a **regex → value map** resolved per actuator
+  (`g1_motion_tracking_deploy` maps joint-name patterns to distinct values).
+  Reproduce the owner's resolved per-actuator vector exactly — do not average a
+  map, take one entry, or broadcast a scalar over a map owner.
+- `default_angles` follows from `use_default_offset: true`, i.e. the `stand`
+  keyframe joint block of the owner's scene.
+- Joint limits and gains come from the same scene XML (`jnt_range`, position
+  actuator `gainprm` / `biasprm`).
 
-- Action = target joint position, **scaled** by the `action_scale` entry in
-  `deploy_config.yaml`.
-- Clamp the target to the generated joint range before it reaches the motor
-  driver.
+Training applies the target directly with no smoothing. If hardware jitter
+forces you to add smoothing, verify the sim2sim impact first — every step of lag
+pushes observations out of the training distribution.
 
 ## 4. Reference motion sync
 
@@ -115,7 +130,7 @@ clip. On hardware you need a wall-clock → phase mapping that is:
 - **Bounded rate** — clip dφ/dt to the value the policy was trained with
   (the motion loader records this; load `reference_motion.npz`).
 
-See `unilab.envs.motion_tracking.g1.motion_loader` for the sim-side
+See `unilab.tasks.motion_tracking.common.motion_loader` for the sim-side
 loader you should mirror on hardware.
 
 ## 5. Safety layer
@@ -123,10 +138,9 @@ loader you should mirror on hardware.
 Hardware-side: see {doc}`7-safety_layers` for the standard structure. The G1
 specifics:
 
-- Reject non-finite actions and shape mismatches before applying
-  `action_scale`.
-- Clamp generated targets with `joint_lower` / `joint_upper` from
-  `deploy_config.yaml`.
+- Reject non-finite actions and shape mismatches before applying the action
+  scale.
+- Clamp generated targets with the joint range from the owner's scene XML.
 - Keep watchdog, pose monitor, and operator-stop thresholds in the deploy
   controller and test them independently of the policy.
 
@@ -140,23 +154,14 @@ specifics:
 4. **Free-stand**. Full rate, then remove gantry.
 
 Do not skip the observation-only stage: it is where axis-order, joint-order,
-and `last_actions` wiring mistakes are easiest to catch.
+and `actions` wiring mistakes are easiest to catch.
 
 ## 7. What to log
 
 Log the **full observation vector**, **full action vector**, and **wall
-clock** for every step. Before hardware bring-up, validate the same ONNX,
-deploy config, and motion binary through the MuJoCo deployment prototype:
-
-```bash
-uv run scripts/deploy/sim_prototype.py \
-  --onnx runs/<run>/policy.onnx \
-  --config logs/deploy/deploy_config.yaml \
-  --motion logs/deploy/dance1.bin
-```
-
-A mismatch between the ONNX input width and `deploy_config.yaml` `obs_dim` is a
-deployment contract bug, not a hardware tuning problem.
+clock** for every step. Compare the first hardware observation window against a
+sim episode built from the same owner YAML — that diff localizes unit, frame,
+and ordering mistakes faster than any reward inspection.
 
 ## See also
 
diff --git a/docs/sphinx/source/en/3-deployment/1-sim_to_real/4-allegro_inhand.md b/docs/sphinx/source/en/3-deployment/1-sim_to_real/4-allegro_inhand.md
index 604b3b72e..484f9febb 100644
--- a/docs/sphinx/source/en/3-deployment/1-sim_to_real/4-allegro_inhand.md
+++ b/docs/sphinx/source/en/3-deployment/1-sim_to_real/4-allegro_inhand.md
@@ -60,17 +60,19 @@ that samples plausible initial hand configurations. The hardware-side
 equivalent is the operator placing the cube in the hand — verify your
 distribution of starting configurations matches the trained env's grasp
 generator output (see
-`unilab.envs.manipulation.allegro_inhand.grasp_gen`).
+`unilab.tasks.manipulation.allegro_inhand.grasp_gen`).
 
 If your real-world starting grip differs systematically, **add those poses
 to the grasp generator**, retrain, and try again.
 
 ## Action interface
 
-The manipulation envs map policy actions to joint position targets through the
-task control config (`src/unilab/envs/manipulation/allegro_inhand/base.py` and
-`src/unilab/envs/manipulation/sharpa_inhand/base.py`). The deploy controller
-must use the same joint order, action scale, and limit policy.
+The manipulation envs map policy actions to joint position targets through their
+task control config. Allegro owns this declaration in
+`src/unilab/conf/ppo/task/allegro_inhand/base.yaml` and its Manager-Based action term;
+Sharpa currently owns it in `src/unilab/tasks/manipulation/sharpa_inhand/base.py`.
+The deploy controller must use the same joint order, action scale, and limit
+policy.
 
 ## Failure recovery
 
diff --git a/docs/sphinx/source/en/3-deployment/1-sim_to_real/5-onnx_runtime.md b/docs/sphinx/source/en/3-deployment/1-sim_to_real/5-onnx_runtime.md
index 92c101742..6af2f30a4 100644
--- a/docs/sphinx/source/en/3-deployment/1-sim_to_real/5-onnx_runtime.md
+++ b/docs/sphinx/source/en/3-deployment/1-sim_to_real/5-onnx_runtime.md
@@ -9,10 +9,10 @@ graph when that path implements ONNX Runtime checking.
 
 | Algorithm path | Entry script | Export behavior in repo |
 | --- | --- | --- |
-| PPO (torch) | `scripts/train_rsl_rl.py` | `EXPORT_POLICY=True` in the script entrypoint; playback calls `runner.export_policy_to_onnx(...)` and `runner.export_policy_to_jit(...)`. |
+| PPO (torch) | `src/unilab/scripts/train_rsl_rl.py` | `EXPORT_POLICY=True` in the script entrypoint; playback calls `runner.export_policy_to_onnx(...)` and `runner.export_policy_to_jit(...)`. |
 | HIM-PPO | `scripts/train_him_ppo.py` | Same script-level export pattern as PPO. |
-| APPO | `scripts/train_appo.py` | Playback writes `policy.onnx` and verifies ONNX Runtime output against PyTorch. |
-| SAC / TD3 / FlashSAC | `scripts/train_offpolicy.py` | Playback writes `policy.onnx`; SAC and FlashSAC use `actor.as_export_module()` before export. |
+| APPO | `src/unilab/scripts/train_appo.py` | Playback writes `policy.onnx` and verifies ONNX Runtime output against PyTorch. |
+| SAC / TD3 / FlashSAC | `src/unilab/scripts/train_sac.py` / `src/unilab/scripts/train_td3.py` / `src/unilab/scripts/train_flashsac.py` | Playback writes `policy.onnx`; SAC and FlashSAC use `actor.as_export_module()` before export. |
 
 ## Commands
 
@@ -26,38 +26,22 @@ uv run eval --algo sac --task g1_walk_flat --sim mujoco --load-run -1
 
 `uv run eval` sets playback mode and maps `--load-run` to the checkpoint
 selector used by the routed training script. The exported file is written into
-the selected run directory. For deployment
-prototypes, keep the exported `policy.onnx` together with the deploy-side
-configuration and motion assets used by the runtime.
-
-## G1 Deployment Prototype
-
-The committed G1 WBT deployment helpers use these artifacts:
-
-| Artifact | Producer |
-| --- | --- |
-| `policy.onnx` | Training playback export above. |
-| `deploy_config.yaml` | `scripts/deploy/export_deploy_config.py`. |
-| `dance1.bin` or another motion binary | `scripts/deploy/export_motion_bin.py`. |
-
-Example validation run:
-
-```bash
-uv run scripts/deploy/export_deploy_config.py \
-  --output logs/deploy/deploy_config.yaml
-
-uv run scripts/deploy/export_motion_bin.py \
-  --output logs/deploy/dance1.bin
-
-uv run scripts/deploy/sim_prototype.py \
-  --onnx runs/<run>/policy.onnx \
-  --config logs/deploy/deploy_config.yaml \
-  --motion logs/deploy/dance1.bin
-```
-
-`scripts/deploy/sim_prototype.py` checks that the ONNX input width matches the
-`obs_dim` in `deploy_config.yaml` and then drives the policy in MuJoCo with the
-same observation layout the deployment side expects.
+the selected run directory. For deployment, keep the exported `policy.onnx`
+together with the task owner YAML it was trained from — that YAML is the
+authority on the observation and action contract the runtime must reproduce.
+
+## Verifying the Exported Graph
+
+The playback path validates the exported graph against PyTorch before writing
+it, so a successful export already establishes numerical parity. What it does
+**not** establish is that your hardware-side loop assembles the same input
+vector. Before hardware bring-up:
+
+- Read the actor obs width off the composed config (not off a doc table) and
+  confirm it matches the ONNX input width.
+- Confirm your term order and per-term history ordering against the owner's
+  `env.observations.actor.terms`. For G1 whole-body tracking, see
+  {doc}`2-g1_whole_body`.
 
 ## See Also
 
diff --git a/docs/sphinx/source/en/3-deployment/1-sim_to_real/6-domain_randomization.md b/docs/sphinx/source/en/3-deployment/1-sim_to_real/6-domain_randomization.md
index 1a4512a13..6158efc08 100644
--- a/docs/sphinx/source/en/3-deployment/1-sim_to_real/6-domain_randomization.md
+++ b/docs/sphinx/source/en/3-deployment/1-sim_to_real/6-domain_randomization.md
@@ -45,7 +45,7 @@ range in the task owner only after recording why that range is plausible.
 Tasks that use DR attach a provider through the env initialization path:
 
 ```python
-from unilab.envs.locomotion.common.dr_provider import LocomotionDRProvider
+from unilab.tasks.locomotion.common.dr_provider import LocomotionDRProvider
 
 class MyTaskEnv(NpEnv):
     def __init__(self, cfg):
@@ -60,12 +60,12 @@ owners and conform to the contract in
 ## Recipe: starting ranges
 
 Use the selected owner YAML as the source of truth. For example,
-`conf/ppo/task/go2_joystick_rough/mujoco.yaml` enables base-mass, COM, kp/kd,
-and push randomization; `conf/ppo/task/sharpa_inhand/mujoco.yaml` configures
+`src/unilab/conf/ppo/task/go2_joystick_rough/mujoco.yaml` enables base-mass, COM, kp/kd,
+and push randomization; `src/unilab/conf/ppo/task/sharpa_inhand/mujoco.yaml` configures
 PD-gain, friction, COM, mass, joint-noise, and contact-noise fields.
 
 ```yaml
-# conf/ppo/task/go2_joystick_rough/mujoco.yaml
+# src/unilab/conf/ppo/task/go2_joystick_rough/mujoco.yaml
 env:
   domain_rand:
     randomize_base_mass: true
diff --git a/docs/sphinx/source/en/3-deployment/1-sim_to_real/7-safety_layers.md b/docs/sphinx/source/en/3-deployment/1-sim_to_real/7-safety_layers.md
index 9835121aa..2fcb4d7c3 100644
--- a/docs/sphinx/source/en/3-deployment/1-sim_to_real/7-safety_layers.md
+++ b/docs/sphinx/source/en/3-deployment/1-sim_to_real/7-safety_layers.md
@@ -43,28 +43,25 @@ flowchart LR
 ```
 
 Keep the hard real-time safety checks in the deploy controller, not in the
-training script. The repository's G1 helper path exports deploy config and runs
-a MuJoCo prototype; it does not implement a production motor-driver safety
-loop.
+training script. The repository does not implement a production motor-driver
+safety loop — that boundary is yours to build and test.
 
 ## What the policy assumes you've configured
 
-The G1 deployment helper exports these fields into `deploy_config.yaml`:
+The policy expects the action mapping and limits its training owner declared.
+For the G1 WBT owner (`src/unilab/conf/sac/task/g1_wbt_obs/mujoco.yaml`):
 
-```yaml
-action_scale: 2.0
-ema_alpha: 1.0
-default_angles: [...]
-joint_lower: [...]
-joint_upper: [...]
-kp: [...]
-kd: [...]
-```
+| Quantity | Authority |
+| --- | --- |
+| action scale | `env.actions.joint_pos.scale` (scalar `2.0` for this owner; other owners declare a regex → value map resolved per actuator) |
+| default angles | `use_default_offset: true`, i.e. the `stand` keyframe joint block of the owner's scene XML |
+| joint limits | `jnt_range` in the scene XML |
+| `kp` / `kd` | position actuator `gainprm` / `biasprm` in the scene XML |
 
-`scripts/deploy/sim_prototype.py` consumes the same fields and applies
-`action * action_scale + default_angles`, joint clipping, and EMA smoothing.
-Hardware controllers should consume generated config rather than hand-copying
-joint ranges or gains.
+Derive these from the owner YAML and its scene, and reproduce the owner's
+resolved per-actuator scale exactly — a scalar owner and a regex-map owner are
+not interchangeable. Do not hand-copy joint ranges or gains into a second place
+that can silently drift from the asset.
 
 ## Hand-off testing
 
@@ -72,8 +69,8 @@ Before integrating policy → safety → motor, test the safety layer in
 isolation:
 
 1. Inject a NaN action and verify the command is rejected.
-2. Inject an out-of-range joint target and verify clamping uses
-   `joint_lower` / `joint_upper` from `deploy_config.yaml`.
+2. Inject an out-of-range joint target and verify clamping uses the joint range
+   from the owner's scene XML.
 3. Cut the policy feed mid-run and verify the controller enters its configured
    safe state.
 
diff --git a/docs/sphinx/source/en/3-deployment/1-sim_to_real/8-latency_budget.md b/docs/sphinx/source/en/3-deployment/1-sim_to_real/8-latency_budget.md
index 7e3177b7f..ef32bda90 100644
--- a/docs/sphinx/source/en/3-deployment/1-sim_to_real/8-latency_budget.md
+++ b/docs/sphinx/source/en/3-deployment/1-sim_to_real/8-latency_budget.md
@@ -8,45 +8,36 @@ budgets as robot-specific measurements, not UniLab defaults.
 
 | Surface | Repo evidence | What it covers |
 | --- | --- | --- |
-| One-step action delay | `control_config.simulate_action_latency` in locomotion and G1 motion-tracking envs | Executes the previous action instead of the current action. |
-| G1 WBT observation history | `noise_config.obs_history_length` and `scripts/deploy/export_deploy_config.py` | Exports per-term `obs_layout` history for `gyro`, `joint_pos_rel`, `dof_vel`, and `last_actions`. |
+| One-step action delay | Manager action term `simulate_action_latency` declarations in task owners | Executes the previous action instead of the current action. |
+| G1 WBT observation history | Per-term `history_length` in `src/unilab/conf/sac/task/g1_wbt_obs/mujoco.yaml` | Per-term history for `base_ang_vel`, `joint_pos`, `joint_vel`, and `actions`. |
 | Sharpa tactile contact latency | `domain_rand.contact_latency` in Sharpa in-hand configs | Keeps previous tactile contact values for sampled contact channels. |
-| Deploy-side ONNX contract check | `scripts/deploy/sim_prototype.py` | Validates `obs_layout`, `obs_dim`, ONNX input width, clipping, and EMA action smoothing for the G1 WBT path. |
+| Obs history ordering guard | `tests/scripts/test_obs_alignment_g1_wbt.py` | Asserts per-term oldest-first flatten for the G1 WBT actor obs. |
 
 ## Action Latency
 
-For tasks that expose `control_config.simulate_action_latency`, the env applies
-`last_actions` when the flag is enabled. Keep this in the selected task owner
-YAML instead of adding deploy-only behavior later.
+For Manager-Based tasks that enable action latency, the action manager applies
+the previous action when the flag is enabled. Keep this in the selected task
+owner YAML instead of adding deploy-only behavior later.
 
 ```yaml
 env:
-  control_config:
-    simulate_action_latency: true
+  actions:
+    joint_pos:
+      simulate_action_latency: true
 ```
 
 The checked-in G1 WBT owner enables this flag in
-`conf/offpolicy/task/sac/g1_wbt_obs/mujoco.yaml`.
+`src/unilab/conf/sac/task/g1_wbt_obs/mujoco.yaml`.
 
 ## Observation Lag And History
 
-The G1 WBT deployment helpers do not guess observation width. They export a
-schema with `obs_layout`, per-term `history_length`, and `obs_dim`; the
-prototype then assembles the same layout and refuses mismatches.
+Observation width is the sum of `dim * history_length` over the declared actor
+terms, not something a hardware runtime may guess. For the G1 WBT owner,
+`history_length: 5` gives each proprioceptive term a 5-step history flattened
+oldest-first, while reference terms stay single-step. See
+{doc}`2-g1_whole_body` for the full term order.
 
-```bash
-uv run scripts/deploy/export_deploy_config.py \
-  --output logs/deploy/deploy_config.yaml
-
-uv run scripts/deploy/sim_prototype.py \
-  --onnx runs/<run>/policy.onnx \
-  --config logs/deploy/deploy_config.yaml \
-  --motion logs/deploy/dance1.bin
-```
-
-Do not lag command/reference terms unless the training owner did so. In the G1
-WBT schema, reference terms stay single-step while proprioceptive terms carry
-history.
+Do not lag command/reference terms unless the training owner did so.
 
 ## Deploy-Side Measurements
 
@@ -58,10 +49,10 @@ Record these per policy tick in the hardware runtime:
 4. actuator command send timestamp
 5. the action vector before and after clamp / smoothing
 
-Compare the observation vector against a sim rollout built from the same
-`deploy_config.yaml`. If the measured pipeline needs filtering or buffering,
-encode the matching behavior in the task owner and re-export the deployment
-artifacts.
+Compare the observation vector against a sim rollout built from the same task
+owner YAML. If the measured pipeline needs filtering or buffering, encode the
+matching behavior in the task owner and retrain, rather than adding it only on
+the deploy side.
 
 ## Symptoms Of Mismatch
 
diff --git a/docs/sphinx/source/en/3-deployment/1-sim_to_real/9-troubleshooting.md b/docs/sphinx/source/en/3-deployment/1-sim_to_real/9-troubleshooting.md
index 219afa77c..6bb340779 100644
--- a/docs/sphinx/source/en/3-deployment/1-sim_to_real/9-troubleshooting.md
+++ b/docs/sphinx/source/en/3-deployment/1-sim_to_real/9-troubleshooting.md
@@ -27,12 +27,13 @@ Almost always one of:
    order in your motor driver. Use `unilab-export-scene` to dump the
    training joint order.
 2. **Action scale unit mismatch.** Policy outputs unscaled values; the
-   driver expects rad, but you fed it normalized [-1, 1]. Apply
-   the `action_scale` / default-angle convention from `deploy_config.yaml`
-   before sending targets to the driver.
-3. **Observation layout mismatch.** Compare `deploy_config.yaml` `obs_layout`
-   against the training owner and validate it with `scripts/deploy/sim_prototype.py`
-   before running on hardware.
+   driver expects rad, but you fed it normalized [-1, 1]. Apply the
+   `env.actions.joint_pos.scale` / default-angle convention from the training
+   owner YAML before sending targets to the driver, reproducing the owner's
+   resolved per-actuator scale exactly.
+3. **Observation layout mismatch.** Compare what your hardware loop assembles
+   against the owner's `env.observations.actor.terms` — term order first, then
+   per-term history ordering.
 
 ## Cube drops in Allegro / Sharpa inhand
 
@@ -55,7 +56,7 @@ investigation tomorrow takes 30 minutes instead of 4 hours:
 
 - Full hardware trace (`obs / action / wall_clock` for the entire run).
 - Sim-side YAML used to train: `runs/<run>/config.yaml`.
-- `policy.onnx` and, for the G1 WBT path, `deploy_config.yaml`.
+- `policy.onnx` and the exact task owner YAML path it was trained from.
 - One sim rollout video using **the same** seed: `eval --seed <same>
   --render-mode record`.
 - A `git diff` between the run's commit and `main` if any.
diff --git a/docs/sphinx/source/en/3-deployment/2-sim_to_sim/1-backend_swap.md b/docs/sphinx/source/en/3-deployment/2-sim_to_sim/1-backend_swap.md
index 71e5724da..268d444a5 100644
--- a/docs/sphinx/source/en/3-deployment/2-sim_to_sim/1-backend_swap.md
+++ b/docs/sphinx/source/en/3-deployment/2-sim_to_sim/1-backend_swap.md
@@ -8,7 +8,7 @@ through explicit methods and capability records.
 
 | Axis | MuJoCo | Motrix |
 |---|---|---|
-| Backend class | `src/unilab/base/backend/mujoco/backend.py` | `src/unilab/base/backend/motrix/backend.py` |
+| Backend class | `unisim.backend.mujoco.backend` | `unisim.backend.motrix.backend` |
 | Playback capabilities | Physics-state playback in `get_play_capabilities()` | Native interactive renderer and native video capture in `get_play_capabilities()` |
 | Height-field scan | Implements `create_hfield_scanner(...)` | Implements `create_hfield_scanner(...)` |
 | DR capability reporting | `get_dr_capabilities()` | `get_dr_capabilities()` |
@@ -36,7 +36,7 @@ uv run train --algo ppo --task go2_joystick_flat --sim motrix
 ```
 
 The CLI resolves `--algo`, `--task`, and `--sim` to an owner YAML such as
-`conf/ppo/task/go2_joystick_flat/mujoco.yaml`. If that file doesn't exist, the
+`src/unilab/conf/ppo/task/go2_joystick_flat/mujoco.yaml`. If that file doesn't exist, the
 task **does not support** the backend — see
 {doc}`../../4-developer_guide/2-contracts/3-task_owner`.
 
diff --git a/docs/sphinx/source/en/3-deployment/2-sim_to_sim/2-owner_yaml_swap.md b/docs/sphinx/source/en/3-deployment/2-sim_to_sim/2-owner_yaml_swap.md
index f24bffdba..fe6863a1b 100644
--- a/docs/sphinx/source/en/3-deployment/2-sim_to_sim/2-owner_yaml_swap.md
+++ b/docs/sphinx/source/en/3-deployment/2-sim_to_sim/2-owner_yaml_swap.md
@@ -15,8 +15,8 @@ page is the recipe.
 
 1. Copy the existing owner YAML:
    ```bash
-   cp conf/ppo/task/go2_joystick_flat/motrix.yaml \
-      conf/ppo/task/go2_joystick_flat/mujoco.yaml
+   cp src/unilab/conf/ppo/task/go2_joystick_flat/motrix.yaml \
+      src/unilab/conf/ppo/task/go2_joystick_flat/mujoco.yaml
    ```
 2. Inside the new file, set `training.sim_backend: mujoco`.
 3. Adjust the **physical parameters** the new backend needs:
diff --git a/docs/sphinx/source/en/3-deployment/2-sim_to_sim/6-capability_gaps.md b/docs/sphinx/source/en/3-deployment/2-sim_to_sim/6-capability_gaps.md
index 83b244f2a..18eed99d6 100644
--- a/docs/sphinx/source/en/3-deployment/2-sim_to_sim/6-capability_gaps.md
+++ b/docs/sphinx/source/en/3-deployment/2-sim_to_sim/6-capability_gaps.md
@@ -42,7 +42,7 @@ capability is added to or removed from the backend contract.
   - Motrix reports support from runtime capability detection.
 ```
 
-Use `src/unilab/base/backend/base.py` as the contract source and backend tests
+Use `unisim.backend.base` as the contract source and backend tests
 under `tests/base/` as evidence for support claims.
 
 ## How to update this page
diff --git a/docs/sphinx/source/en/3-deployment/2-sim_to_sim/7-config_guard.md b/docs/sphinx/source/en/3-deployment/2-sim_to_sim/7-config_guard.md
index ae58f3f13..1e92c252e 100644
--- a/docs/sphinx/source/en/3-deployment/2-sim_to_sim/7-config_guard.md
+++ b/docs/sphinx/source/en/3-deployment/2-sim_to_sim/7-config_guard.md
@@ -17,8 +17,8 @@ uv run eval  --algo ppo --task go2_joystick_flat --sim motrix --load-run -1
 ## How the guard chain works
 
 1. **At train time**: `ExperimentTracker` snapshots the contract fields that define policy I/O into `contract_snapshot` in `run_config.json` (the checkpoint format is untouched, so historical checkpoints stay compatible).
-2. **At replay time**: `eval` loads the **target backend** owner config selected by `--sim` (e.g. `conf/ppo/task/go2_joystick_flat/motrix.yaml`) and injects `training.play_only=true`.
-3. **Before env creation**: the four play entrypoints (rsl_rl / appo / offpolicy / him_ppo) call `resolve_sim2sim_config`, comparing the target config against the source run's contract snapshot field by field.
+2. **At replay time**: `eval` loads the **target backend** owner config selected by `--sim` (e.g. `src/unilab/conf/ppo/task/go2_joystick_flat/motrix.yaml`) and injects `training.play_only=true`. If the task has no owner config for the requested backend, `eval` falls back to a sibling backend owner of the same task and re-applies the requested backend through the allowlisted `training.sim_backend` override (`train` still requires the owner config to exist); the guard chain below is unaffected.
+3. **Before env creation**: the play entrypoints (rsl_rl / appo / sac / td3 / flashsac / him_ppo) call `resolve_sim2sim_config`, comparing the target config against the source run's contract snapshot field by field.
 4. **At weight load**: `policy_load_dim_guard` wraps checkpoint loading, re-raising cryptic tensor shape-mismatch errors as a clear sim2sim diagnostic.
 
 ## What the guard covers
@@ -27,7 +27,7 @@ Fields are classified by dotted path into three tiers (see `src/unilab/training/
 
 | Tier | Behavior | Fields |
 |---|---|---|
-| **DENYLIST** | Mismatch → `CrossBackendIncompatibleError`, aborts | `algo.obs_groups`, `env.control_config.action_scale`, `algo.policy.actor_hidden_dims` / `critic_hidden_dims`, `algo.empirical_normalization` / `algo.obs_normalization`, `env.sampling_mode` |
+| **DENYLIST** | Mismatch → `CrossBackendIncompatibleError`, aborts | `algo.obs_groups`, legacy `env.control_config.action_scale`, Manager-Based `env.observations` / `env.actions` / policy and critic group mapping, `algo.policy.actor_hidden_dims` / `critic_hidden_dims`, `algo.empirical_normalization` / `algo.obs_normalization`, `env.sampling_mode` |
 | **WARNING_LIST** | Prints a warning, continues | `reward.*`, `env.control_config.simulate_action_latency`, `env.ctrl_dt` |
 | **ALLOWLIST** | Free to override, not checked | `training.sim_backend`, `env.scene`, `training.play_steps`, `env.domain_rand`, `env.noise_config`, `env.commands.vel_limit` |
 
@@ -40,8 +40,14 @@ If the target backend's DENYLIST fields differ from training (e.g. a task whose
 
 > Legacy runs: if `run_config.json` has no `contract_snapshot` (older training), the guard skips with a warning instead of breaking your workflow.
 
+Manager-Based snapshots store the complete typed observation and action declarations from
+Hydra. A snapshot from before those fields existed cannot prove that its policy I/O is
+equivalent to a Manager-Based target, so asymmetric presence fails closed. Set
+`training.sim2sim_strict=false` only as an explicit user override; the load-time dimension
+guard still remains active.
+
 ## See also
 
 - {doc}`1-backend_swap`
 - {doc}`4-reward_parity`
-- {doc}`../../4-developer_guide/9-sim2sim_contract_status`
+- {doc}`/zh_CN/4-developer_guide/9-sim2sim_contract_status`
diff --git a/docs/sphinx/source/en/3-deployment/3-framework_migration/0-index.md b/docs/sphinx/source/en/3-deployment/3-framework_migration/0-index.md
index 85047eeb2..6d62a0de5 100644
--- a/docs/sphinx/source/en/3-deployment/3-framework_migration/0-index.md
+++ b/docs/sphinx/source/en/3-deployment/3-framework_migration/0-index.md
@@ -9,7 +9,7 @@ contract-driven layout.
 :::{grid-item-card} From Isaac Lab
 :link: 1-from_isaac_lab
 :link-type: doc
-Map GPU-resident task structure to UniLab's CPU sim and learner split.
+Keep Manager-Based terms while adapting Hydra config, NumPy execution, and scene access.
 :::
 
 :::{grid-item-card} From Legged Gym
diff --git a/docs/sphinx/source/en/3-deployment/3-framework_migration/1-from_isaac_lab.md b/docs/sphinx/source/en/3-deployment/3-framework_migration/1-from_isaac_lab.md
index f2a4c6fbb..39a78b4a4 100644
--- a/docs/sphinx/source/en/3-deployment/3-framework_migration/1-from_isaac_lab.md
+++ b/docs/sphinx/source/en/3-deployment/3-framework_migration/1-from_isaac_lab.md
@@ -1,93 +1,198 @@
 # Migrating from Isaac Lab
 
-If you have an Isaac Lab task you want to run in UniLab, this page tells
-you what stays the same, what changes, and where the sharp edges are.
-
-## What stays the same
-
-- Gymnasium-style env interface (`reset`, `step`, `obs/reward/info`).
-- Hydra-based configuration. Most of your existing YAML can be ported with
-  field-name remapping.
-- The general idea of a "task" that composes scene + reward + DR + obs.
-- PPO as the default algo — UniLab ships RSL-RL's PPO out of the box.
+Port an Isaac Lab Manager-Based task to UniLab by keeping its manager and term
+structure, then adapting configuration, numeric execution, and scene access at
+their owner boundaries. Do not rewrite it as a monolithic `NpEnv` subclass.
+
+This is source-compatible migration, not a promise that an arbitrary Isaac Lab
+task runs unchanged. The target path is:
+
+```text
+Hydra owner YAML
+  -> plain ManagerBasedRlEnvCfg
+  -> Registry + make_manager_based_rl_env
+  -> ManagerBasedRlEnv on the NumPy/SimBackend runtime
+  -> NpEnvState for the existing training and IPC path
+```
 
-## What changes
+## Compatibility boundary
 
 ```{list-table}
 :header-rows: 1
-:widths: 30 35 35
-
-* - Isaac Lab concept
-  - UniLab equivalent
-  - Notes
-* - `DirectRLEnv`
-  - `unilab.base.np_env.NpEnv`
-  - UniLab obs is always a **dict**, not a tensor.
-* - `RigidBody.cfg`
-  - Task-side asset import + scene composition
-  - See {doc}`../../4-developer_guide/1-architecture/4-scene_composition`.
-* - GPU PhysX backend
-  - CPU MuJoCo / Motrix + GPU learner
-  - Architectural inversion — see below.
-* - `RandomizationCfg`
-  - {doc}`../../4-developer_guide/2-contracts/4-dr_contract`
-  - UniLab DR runs in cold-path resampling only.
-* - `RewardManager` chains
-  - Reward composition in env, plus
-    `unilab.training.reward` bookkeeping
-  - Reward terms still keyed for component-wise logging.
-* - `EventCfg` event-driven hooks
-  - Phase + curriculum + DR providers
-  - Hooks are explicit, not implicit.
+:widths: 28 24 48
+
+* - Isaac Lab surface
+  - UniLab status
+  - Migration rule
+* - Manager categories, term names and dictionary order
+  - Compatible
+  - Keep observation, action, event, reward, termination, command, and
+    curriculum terms in the same order.
+* - Function/class terms and `func + params`
+  - Compatible
+  - Change imports to `unilab.managers`; keep term boundaries and partial
+    `reset(env_ids)` semantics.
+* - `ManagerBasedRLEnv` / `ManagerBasedRLEnvCfg`
+  - Compatible spelling aliases
+  - The canonical UniLab names are `ManagerBasedRlEnv` and
+    `ManagerBasedRlEnvCfg`; the aliases point to the same implementation.
+* - Tensor values and operations
+  - Adapted
+  - Replace `torch.Tensor` with `np.ndarray` and use vectorized NumPy. There is
+    no manager-facing device API.
+* - Nested `@configclass` task configuration
+  - Adapted
+  - Move the complete task declaration to one Hydra owner YAML. `_target_`
+    selects concrete config dataclasses and dotted `func` values select terms.
+* - `InteractiveSceneCfg`, USD, and PhysX views
+  - Adapted or unsupported
+  - Declare a task-owned `SceneCfg` and `EntityCfg`; access state and control
+    only through `SceneEntityCfg` and the public entity facade. Unsupported
+    capabilities raise during cold-path binding.
+* - Omniverse, Isaac renderer, and Torch/PhysX mutation
+  - Unsupported
+  - UniLab does not install or silently emulate these runtimes.
+```
+
+The normative boundary is
+{doc}`ADR-0006 </adr/ADR-0006-community-manager-api-on-numpy-runtime>`. Only
+surfaces backed by registration, configuration, and tests should be described
+as compatible.
+
+## Migration procedure
+
+### 1. Inventory the source task
+
+Pin the Isaac Lab revision and list the source manager groups, term names, term
+order, parameters, observation dimensions, action dimensions, reset behavior,
+and episode timing. Classify each dependency before writing code:
+
+- reuse an existing `unilab.managers` config or `unilab.envs.mdp` term;
+- adapt a task-specific term from Torch to NumPy;
+- stop if the term requires a capability absent from the public entity or
+  `SimBackend` contract.
+
+Do not probe backend objects with `getattr`/`hasattr`, return zeros, or route the
+task back to a legacy environment.
+
+### 2. Port scene and assets on the cold path
+
+Replace Isaac Lab's USD/`InteractiveSceneCfg` declaration with a task-owned
+`SceneCfg`. Declare every entity and selector needed by terms. The
+`SceneEntityCfg` selector resolves names and regular expressions once during
+materialization; reset and step reuse cached IDs and NumPy views.
+
+The Cartpole fixture uses a minimal task-owned MJCF asset. More complex assets
+must follow
+{doc}`scene composition <../../4-developer_guide/1-architecture/4-scene_composition>`
+and the selected backend's formal capabilities.
+
+### 3. Port term code, not the manager structure
+
+Keep each function/class term and its parameters. Replace Torch types and
+operators mechanically with NumPy, preserve batch shapes, and return one value
+per environment where the source term does. Stateful terms resolve selectors
+and allocate buffers in their constructor, then update only NumPy buffers on
+the hot path.
+
+Python owns term implementations and reusable config dataclasses. It must not
+hold a second task-specific list of enabled terms or default weights.
+
+### 4. Make Hydra the only task configuration owner
+
+Declare scene, timing, groups, terms, concrete config types, callables,
+parameters, weights, and observation mapping in the owner YAML. For example:
+
+```yaml
+env:
+  observations:
+    policy:
+      terms:
+        joint_pos_rel:
+          func: unilab.envs.mdp.joint_pos_rel
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+  policy_observation_group: policy
+  critic_observation_group: null
+
+reward:
+  alive:
+    func: unilab.envs.mdp.is_alive
+    weight: 1.0
+```
+
+Manager mappings whose value type is a single concrete config dataclass
+(observations / events / rewards / terminations / curriculum / metrics /
+recorders) may omit `_target_`; materialization infers it from the field type
+annotation. `actions` / `commands` have abstract base configs, so they must
+still declare a concrete `_target_` (for example
+`unilab.envs.mdp.JointPositionActionCfg`). Config classes under
+`unilab.managers.` (such as `SceneEntityCfg`) may be referenced by their bare
+class name.
+
+Hydra composition materializes this declaration into plain typed config on the
+cold path. Unknown fields, unresolved `_target_`/`func` references, and wrong
+config types fail before reset or step. Direct Python config construction is
+reserved for focused lower-level tests.
+
+### 5. Register one generic runtime path
+
+The task module registers `ManagerBasedRlEnvCfg` and
+`make_manager_based_rl_env` for each backend that the repository actually
+supports. Backend owner YAMLs carry backend identity and tuning. Users select
+the composed owner through the normal CLI, for example:
+
+```bash
+uv run train --algo ppo --task <task> --sim mujoco
 ```
 
-## The architectural inversion
-
-Isaac Lab places the simulator on GPU and lets you batch thousands of
-envs in PhysX. UniLab places the simulator on CPU (often multithread) and
-batches across worker **processes**, sharing memory with a single GPU
-learner.
-
-Implications:
-
-- **Per-env step time** in UniLab is comparable or worse than Isaac on a
-  single env. **Throughput** comes from process parallelism + asynchrony
-  (see `unilab.ipc.async_runner`).
-- You can run on **MPS, ROCm, XPU** as the learner device — Isaac is
-  CUDA-only.
-- **No GPU contention** between simulator and learner — your trainer's
-  memory usage is predictable.
-
-## Step-by-step migration
-
-1. **Audit observations.** Make sure every observation key is a vector
-   you can express without GPU PhysX queries. If not, add a state
-   estimator or move the query to cold path.
-2. **Port the asset.** UniLab consumes MJCF as its source of truth. If
-   you have USD, convert to MJCF first.
-3. **Port the env.** Subclass `unilab.base.np_env.NpEnv`. Move
-   reward computation into the env's `compute_reward()`.
-4. **Port the YAML.** Map Isaac Lab's `EnvCfg` fields to UniLab task owner
-   YAML following the table in
-   {doc}`5-task_config_translation`.
-5. **Port the reward.** Use the cookbook at
-   {doc}`6-reward_porting`.
-6. **Validate.** Train a small run, compare reward curves against your
-   Isaac baseline.
-
-## What you'll miss (and how to compensate)
-
-- **Isaac Sim renderer.** Use Motrix's headless video export or build a
-  viser scene (`unilab.visualization.viser_scene`).
-- **Per-env tensor obs.** UniLab gives you dict-of-arrays; wrap with your
-  own `obs_to_tensor` if you need a tensor.
-- **Built-in GPU-side DR.** UniLab DR is CPU-side per process. For most
-  tasks this is plenty; for extreme parallelism use more worker
-  processes.
+Do not add a task-specific training-script branch, environment factory, runner,
+or IPC path.
+
+Two maintainer-approved factory wrappers are the only registered exceptions to
+the generic-factory rule: `make_g1_walk_env`
+(`src/unilab/tasks/locomotion/g1/manager_terms.py`) constructs the
+`G1WalkManagerBasedEnv` subclass that owns the G1 walk manager-based runtime,
+and `make_x2_wall_flip_env`
+(`src/unilab/tasks/motion_tracking/x2/__init__.py`) resolves untracked X2
+meshes on the cold path before delegating to `make_manager_based_rl_env`.
+Every other Compatible task registers `make_manager_based_rl_env` directly.
+
+### 6. Validate near each adaptation
+
+Test Hydra composition and typed materialization, term order and math, selector
+failure, observation/action shapes, partial reset, and at least one real
+registered backend transition. Compare behavior with the pinned source task;
+benchmark only after semantic migration is complete.
+
+## Final task status
+
+The #1042 migration closeout covers 39 production tasks and 86 task/backend
+registrations. The fail-closed source of truth is
+`src/unilab/tasks/migration_matrix.py`: `migration_record()` raises `KeyError`
+for a production task name with no entry, so adding a production registration
+requires an explicit migration decision.
+
+- 36 tasks are **Compatible** (`target=complete`): the Hydra owner YAML
+  materializes the canonical NumPy Manager-Based runtime.
+- 3 tasks are **Adapted** (`target=compatibility`): `Go2ArmManipLoco`,
+  `SharpaInhandRotation`, and `SharpaInhandRotationGrasp` keep custom
+  IK/history or tactile/contact/cache behavior behind one frozen compatibility
+  factory each; they migrate only when the formal capability exists.
+
+## Repository evidence
+
+`tests/fixtures/isaac_lab_cartpole/` ports the Manager-Based Cartpole task from
+Isaac Lab commit `b0542fe2d45bf91c4e1d9ef6952b9c709c80b4e8`. It preserves all
+12 source term names and their order while adapting Torch to NumPy, nested
+config objects to Hydra YAML, and the scene/action/reset boundaries to a
+fixture-local MJCF implementation. It is test-only evidence, not a production
+task or a blanket Isaac Lab support claim.
 
 ## See also
 
-- {doc}`2-from_legged_gym`
-- {doc}`3-from_rsl_rl`
-- {doc}`5-task_config_translation`
-- {doc}`6-reward_porting`
+- {doc}`Manager-Based API <../../4-developer_guide/1-architecture/6-manager_based_api>`
+- {doc}`Environment contract <../../4-developer_guide/2-contracts/1-env_contract>`
+- {doc}`ADR-0006 </adr/ADR-0006-community-manager-api-on-numpy-runtime>`
diff --git a/docs/sphinx/source/en/3-deployment/3-framework_migration/2-from_legged_gym.md b/docs/sphinx/source/en/3-deployment/3-framework_migration/2-from_legged_gym.md
index 650c908ba..a95b48c69 100644
--- a/docs/sphinx/source/en/3-deployment/3-framework_migration/2-from_legged_gym.md
+++ b/docs/sphinx/source/en/3-deployment/3-framework_migration/2-from_legged_gym.md
@@ -9,12 +9,12 @@ mostly mechanical.
 
 | Legged Gym | UniLab |
 |---|---|
-| `LeggedRobot` env class | `unilab.envs.locomotion.common.base` |
+| `LeggedRobot` env class | `unilab.tasks.locomotion.common.base` |
 | `compute_observations()` | env-side obs builder + `unilab.base.observations` |
 | `_reward_*` methods | env's `compute_reward()` + reward term registry |
 | `command_ranges` | task owner YAML's `commands` block |
 | Terrain curriculum | {doc}`../../2-user_guide/6-terrain/1-procedural` |
-| RSL-RL PPO | `unilab.algos.torch.rsl_rl_ppo` |
+| RSL-RL PPO | `uni_rl.algos.rsl_rl_ppo` |
 
 ## What's new
 
@@ -22,7 +22,7 @@ mostly mechanical.
   + Motrix. Pick one (or both) before porting; see
   {doc}`../2-sim_to_sim/1-backend_swap`.
 - **Async collection.** Legged Gym collects on-GPU synchronously; UniLab's
-  APPO (`unilab.algos.torch.appo`) decouples collectors from
+  APPO (`uni_rl.algos.appo`) decouples collectors from
   learner. If wall-clock matters, port to APPO once your reward parity is
   established.
 - **Hardware deployment.** Legged Gym → real-world deployment is a
@@ -32,7 +32,7 @@ mostly mechanical.
 ## Migration checklist
 
 1. Copy your URDF / MJCF assets under `src/unilab/assets/robots/<robot>/`.
-2. Create a task module under `src/unilab/envs/locomotion/<robot>/`.
+2. Create a task module under `src/unilab/tasks/locomotion/<robot>/`.
 3. Mirror your reward terms; keep the same names so reward parity is
    diff-able.
 4. Translate command sampling — Legged Gym's `_resample_commands` becomes
diff --git a/docs/sphinx/source/en/3-deployment/3-framework_migration/3-from_rsl_rl.md b/docs/sphinx/source/en/3-deployment/3-framework_migration/3-from_rsl_rl.md
index c8d2c5f9f..c94c10ca2 100644
--- a/docs/sphinx/source/en/3-deployment/3-framework_migration/3-from_rsl_rl.md
+++ b/docs/sphinx/source/en/3-deployment/3-framework_migration/3-from_rsl_rl.md
@@ -1,7 +1,7 @@
 # Migrating from RSL-RL
 
 You're already using RSL-RL standalone? Good news: UniLab ships RSL-RL PPO
-as one of its supported algorithms (`unilab.algos.torch.rsl_rl_ppo`)
+as one of its supported algorithms (`uni_rl.algos.rsl_rl_ppo`)
 and it's nearly drop-in.
 
 ## What you gain by moving inside UniLab
@@ -13,7 +13,7 @@ and it's nearly drop-in.
    backend / task / algo selection. No more bespoke train scripts per
    robot.
 3. **Async runner.** Wrap RSL-RL PPO inside
-   `unilab.algos.torch.appo` for higher throughput on machines
+   `uni_rl.algos.appo` for higher throughput on machines
    with many CPU cores.
 4. **Deployment story.** ONNX export with the right wrapper, safety
    layer documentation, and the
@@ -29,7 +29,7 @@ and it's nearly drop-in.
 ## Migration steps
 
 1. Move your env into `unilab.envs.<family>.<task>/`.
-2. Convert your training config to a Hydra group under `conf/ppo/<task>/`.
+2. Convert your training config to a Hydra group under `src/unilab/conf/ppo/<task>/`.
 3. Run `uv run train --algo ppo --task <task> --sim <backend>`.
 4. Compare reward curves against your standalone RSL-RL baseline.
 
diff --git a/docs/sphinx/source/en/3-deployment/3-framework_migration/4-from_skrl.md b/docs/sphinx/source/en/3-deployment/3-framework_migration/4-from_skrl.md
index 55d748e1e..0154bf485 100644
--- a/docs/sphinx/source/en/3-deployment/3-framework_migration/4-from_skrl.md
+++ b/docs/sphinx/source/en/3-deployment/3-framework_migration/4-from_skrl.md
@@ -8,9 +8,9 @@ deployment path.
 
 | skrl | UniLab |
 |---|---|
-| `Agent` (PPO, SAC, …) | `unilab.algos.torch.*` |
-| `RolloutMemory` | `unilab.ipc.rollout_ring_buffer` |
-| `ReplayMemory` | `unilab.ipc.replay_buffer` |
+| `Agent` (PPO, SAC, …) | `uni_rl.*` |
+| `RolloutMemory` | `uni_rl.ipc.rollout_ring_buffer` |
+| `ReplayMemory` | `uni_rl.ipc.replay_buffer` |
 | `Trainer` | `unilab.training.run` |
 | `Wrapper` for env | `NpEnv` subclassing |
 
@@ -28,5 +28,5 @@ deployment path.
 
 1. Decide which UniLab algorithm best matches your skrl agent.
 2. Port the env into `NpEnv` form.
-3. Convert hyperparameter YAML into Hydra groups under `conf/<algo>/<task>/`.
+3. Convert hyperparameter YAML into Hydra groups under `src/unilab/conf/<algo>/<task>/`.
 4. Validate reward parity.
diff --git a/docs/sphinx/source/en/3-deployment/3-framework_migration/6-reward_porting.md b/docs/sphinx/source/en/3-deployment/3-framework_migration/6-reward_porting.md
index 72e11d3a3..267e1efb9 100644
--- a/docs/sphinx/source/en/3-deployment/3-framework_migration/6-reward_porting.md
+++ b/docs/sphinx/source/en/3-deployment/3-framework_migration/6-reward_porting.md
@@ -38,7 +38,7 @@ Notes:
 
 - UniLab's `state` carries `prev_contact` so you don't need to manage
   edge detection yourself. See
-  `unilab.envs.locomotion.common.rewards`.
+  `unilab.tasks.locomotion.common.rewards`.
 
 ## Pattern: action smoothness penalty
 
@@ -47,7 +47,7 @@ def reward_action_rate(self, state):
     return -np.sum((state.action - state.prev_action) ** 2, axis=1)
 ```
 
-Already a stock helper in `unilab.envs.locomotion.common.rewards`.
+Already a stock helper in `unilab.tasks.locomotion.common.rewards`.
 
 ## Pattern: posture penalty
 
@@ -76,5 +76,5 @@ def reward_termination(self, state):
 ## See also
 
 - {doc}`5-task_config_translation`
-- `unilab.training.reward`
-- `unilab.envs.locomotion.common.rewards`
+- `unilab.utils.reward`
+- `unilab.tasks.locomotion.common.rewards`
diff --git a/docs/sphinx/source/en/4-developer_guide/1-architecture/0-index.md b/docs/sphinx/source/en/4-developer_guide/1-architecture/0-index.md
index f31456d43..9e48c9faf 100644
--- a/docs/sphinx/source/en/4-developer_guide/1-architecture/0-index.md
+++ b/docs/sphinx/source/en/4-developer_guide/1-architecture/0-index.md
@@ -36,6 +36,12 @@ Scene fragments, assets, and cold-path materialization.
 Bootstrap imports and env/backend registration.
 :::
 
+:::{grid-item-card} Manager-Based API
+:link: 6-manager_based_api
+:link-type: doc
+Community manager semantics, NumPy runtime, and fail-closed boundaries.
+:::
+
 ::::
 
 ```{toctree}
@@ -46,4 +52,5 @@ Bootstrap imports and env/backend registration.
 3-layer_boundaries
 4-scene_composition
 5-registry
+6-manager_based_api
 ```
diff --git a/docs/sphinx/source/en/4-developer_guide/1-architecture/1-overview.md b/docs/sphinx/source/en/4-developer_guide/1-architecture/1-overview.md
index 987f67999..533ab5c15 100644
--- a/docs/sphinx/source/en/4-developer_guide/1-architecture/1-overview.md
+++ b/docs/sphinx/source/en/4-developer_guide/1-architecture/1-overview.md
@@ -14,16 +14,16 @@ MuJoCo or Motrix      shared memory       torch
 
 PPO is a synchronous single-process path. APPO and off-policy
 algorithms use the async runner, shared buffers, and weight synchronization
-primitives under `src/unilab/ipc/` and `src/unilab/algos/`.
+primitives under `uni_rl.ipc` (unilab-rl repo) and `uni_rl` (unilab-rl repo).
 
 ## Layer Boundaries
 
 | Layer | Paths | Owns |
 | --- | --- | --- |
-| Backend | `src/unilab/base/backend/` | `SimBackend`, physics state, optional capabilities |
+| Backend | `unisim.backend` | `SimBackend`, physics state, optional capabilities |
 | Env | `src/unilab/envs/`, `src/unilab/base/np_env.py` | MDP semantics, observation, reward, reset |
-| Config and registry | `conf/`, `src/unilab/base/registry.py`, `src/unilab/structured_configs.py` | Schema, owner YAMLs, env/backend registration |
-| Algorithms and IPC | `src/unilab/algos/`, `src/unilab/ipc/` | Learners, runners, buffers, weight sync |
+| Config and registry | `src/unilab/conf/`, `src/unilab/base/registry.py`, `src/unilab/structured_configs.py` | Schema, owner YAMLs, env/backend registration |
+| Algorithms and IPC | `uni_rl` (unilab-rl repo), `uni_rl.ipc` (unilab-rl repo) | Learners, runners, buffers, weight sync |
 | Scripts | `scripts/`, `src/unilab/cli.py` | Thin assembly and CLI routing |
 
 ## Design Rules
@@ -77,13 +77,15 @@ Use `make test` for the fast path and `make test-all` (`make check`,
 
 ## High-Signal Files
 
-- `scripts/train_rsl_rl.py`
-- `scripts/train_appo.py`
-- `scripts/train_offpolicy.py`
+- `src/unilab/scripts/train_rsl_rl.py`
+- `src/unilab/scripts/train_appo.py`
+- `src/unilab/scripts/train_sac.py`
+- `src/unilab/scripts/train_td3.py`
+- `src/unilab/scripts/train_flashsac.py`
 - `src/unilab/base/np_env.py`
-- `src/unilab/base/backend/base.py`
+- `unisim.backend.base`
 - `src/unilab/base/registry.py`
-- `src/unilab/ipc/async_runner.py`
+- `uni_rl.ipc.async_runner` (unilab-rl repo)
 - `src/unilab/training/run.py`
 
 ## Related ADRs
diff --git a/docs/sphinx/source/en/4-developer_guide/1-architecture/2-runtime_model.md b/docs/sphinx/source/en/4-developer_guide/1-architecture/2-runtime_model.md
index b2989ba31..a637a9d82 100644
--- a/docs/sphinx/source/en/4-developer_guide/1-architecture/2-runtime_model.md
+++ b/docs/sphinx/source/en/4-developer_guide/1-architecture/2-runtime_model.md
@@ -9,7 +9,7 @@ summary close to the code paths.
 
 ### Synchronous PPO Paths
 
-`scripts/train_rsl_rl.py` composes Hydra config,
+`src/unilab/scripts/train_rsl_rl.py` composes Hydra config,
 calls registry bootstrap, constructs the env through `registry.make(...)`, and runs
 the learner in the same process. The RSL-RL path adapts `NpEnv` through
 `src/unilab/training/rsl_rl.py`.
@@ -28,7 +28,7 @@ CPU physics env loop -> shared IPC buffer -> learner
 - SAC, TD3, and FlashSAC use one off-policy execution path: `ReplayBuffer`
   provides bounded host ingress, the complete ring lives on one CUDA/MPS
   learner device, and `SharedWeightSync` publishes actor weights.
-- `AsyncRunner` in `src/unilab/ipc/async_runner.py` owns collector process
+- `AsyncRunner` in `uni_rl.ipc.async_runner` (unilab-rl repo) owns collector process
   startup, stop signaling, and shared-resource cleanup.
 
 ## Boundary Rules
@@ -40,9 +40,9 @@ CPU physics env loop -> shared IPC buffer -> learner
 
 ## Evidence In Repo
 
-- PPO entrypoint: `scripts/train_rsl_rl.py`
-- APPO runner: `src/unilab/algos/torch/appo/runner.py`
-- Off-policy runner: `src/unilab/algos/torch/offpolicy/double_buffer_runner.py`
-- IPC primitives: `src/unilab/ipc/async_runner.py`,
-  `src/unilab/ipc/rollout_ring_buffer.py`, `src/unilab/ipc/replay_buffer.py`,
-  `src/unilab/ipc/weight_sync.py`
+- PPO entrypoint: `src/unilab/scripts/train_rsl_rl.py`
+- APPO runner: `uni_rl.algos.appo.runner` (unilab-rl repo)
+- Off-policy runner: `uni_rl.offpolicy.double_buffer_runner` (unilab-rl repo)
+- IPC primitives: `uni_rl.ipc.async_runner` (unilab-rl repo),
+  `uni_rl.ipc.rollout_ring_buffer` (unilab-rl repo), `uni_rl.ipc.replay_buffer` (unilab-rl repo),
+  `uni_rl.ipc.weight_sync` (unilab-rl repo)
diff --git a/docs/sphinx/source/en/4-developer_guide/1-architecture/3-layer_boundaries.md b/docs/sphinx/source/en/4-developer_guide/1-architecture/3-layer_boundaries.md
index 007682817..e1763ef0e 100644
--- a/docs/sphinx/source/en/4-developer_guide/1-architecture/3-layer_boundaries.md
+++ b/docs/sphinx/source/en/4-developer_guide/1-architecture/3-layer_boundaries.md
@@ -8,10 +8,10 @@ standard is {doc}`/zh_CN/4-developer_guide/0-index`.
 
 | Layer | Owner paths | Owns |
 | --- | --- | --- |
-| L0 Backend | `src/unilab/base/backend/` | Physics backend abstraction, backend-owned scene materialization, backend capabilities. |
+| L0 Backend | `unisim.backend` | Physics backend abstraction, backend-owned scene materialization, backend capabilities. |
 | L1 Env | `src/unilab/envs/`, `src/unilab/base/np_env.py` | MDP semantics, observations, rewards, reset logic, backend-to-task adaptation. |
-| L2 Config and Registry | `conf/`, `src/unilab/structured_configs.py`, `src/unilab/base/registry.py`, `src/unilab/training/reward.py` | Hydra composition, owner YAML identity, env/reward registration. |
-| L3 Algo and IPC | `src/unilab/algos/`, `src/unilab/ipc/` | Learners, runners, collectors, replay and rollout buffers, weight sync. |
+| L2 Config and Registry | `src/unilab/conf/`, `src/unilab/structured_configs.py`, `src/unilab/base/registry.py`, `src/unilab/training/reward.py` | Hydra composition, owner YAML identity, env/reward registration. |
+| L3 Algo and IPC | `uni_rl` (unilab-rl repo), `uni_rl.ipc` (unilab-rl repo) | Learners, runners, collectors, replay and rollout buffers, weight sync. |
 | L4 Scripts | `scripts/` | Entrypoint assembly only. |
 
 ## Rules
@@ -19,9 +19,9 @@ standard is {doc}`/zh_CN/4-developer_guide/0-index`.
 - Fix behavior at the owner layer. A training script should not carry long-term
   env, backend, reward, or algorithm business rules.
 - Env code may depend on the declared `SimBackend` contract in
-  `src/unilab/base/backend/base.py`; if shared env logic needs a new backend
+  `unisim.backend.base`; if shared env logic needs a new backend
   capability, add it to `SimBackend` before using it.
-- Config choices should stay in Hydra owner YAMLs under `conf/`, not in
+- Config choices should stay in Hydra owner YAMLs under `src/unilab/conf/`, not in
   Python-side backend switches.
 - Asset, XML, and model metadata work belongs on init, materialization, or cache
   paths. Do not move asset parsing into `step()`, `reset()`, or runtime domain
@@ -30,8 +30,9 @@ standard is {doc}`/zh_CN/4-developer_guide/0-index`.
 ## Evidence In Repo
 
 - Architecture contract: {doc}`/adr/ADR-0001-runtime-model-and-layer-boundaries`
-- Backend boundary: `src/unilab/base/backend/base.py`
+- Backend boundary: `unisim.backend.base`
 - Env state contract: `src/unilab/base/np_env.py`
 - Registry construction path: `src/unilab/base/registry.py`
-- Training entrypoints: `scripts/train_rsl_rl.py`,
-  `scripts/train_appo.py`, `scripts/train_offpolicy.py`
+- Training entrypoints: `src/unilab/scripts/train_rsl_rl.py`,
+  `src/unilab/scripts/train_appo.py`, `src/unilab/scripts/train_sac.py`,
+  `src/unilab/scripts/train_td3.py`, `src/unilab/scripts/train_flashsac.py`
diff --git a/docs/sphinx/source/en/4-developer_guide/1-architecture/4-scene_composition.md b/docs/sphinx/source/en/4-developer_guide/1-architecture/4-scene_composition.md
index 872ac1a37..f16349c92 100644
--- a/docs/sphinx/source/en/4-developer_guide/1-architecture/4-scene_composition.md
+++ b/docs/sphinx/source/en/4-developer_guide/1-architecture/4-scene_composition.md
@@ -42,7 +42,7 @@ Motrix materializers directly.
 
 ## Backend Dispatch
 
-`create_backend(...)` in `src/unilab/base/backend/__init__.py` routes on backend
+`create_backend(...)` in `src/unilab/base/backend_factory.py` routes on backend
 type and applies these rules to the `SceneCfg`:
 
 1. Static `SceneCfg(model_file=...)` with no terrain: load the full static scene
@@ -59,7 +59,7 @@ type and applies these rules to the `SceneCfg`:
 
 For procedural terrain, the MuJoCo backend calls
 `materialize_mujoco_hfield_attached_scene(...)` in
-`src/unilab/base/backend/mujoco/xml.py`. The cold-path steps are:
+`unisim.backend.mujoco.xml`. The cold-path steps are:
 
 1. `TerrainGenerator(terrain_cfg).write_png(...)` generates a backend-agnostic
    heightfield PNG.
@@ -105,11 +105,11 @@ Disallowed on hot paths:
 
 The current procedural terrain user-facing path is Go2 rough terrain:
 
-- Env owner: `src/unilab/envs/locomotion/go2/rough.py`
-- Terrain generator: `src/unilab/terrains/terrain_generator.py`
-- MuJoCo materializer: `src/unilab/base/backend/mujoco/xml.py`
-- Motrix materializer: `src/unilab/base/backend/motrix/scene.py`
-- Owner YAMLs: `conf/ppo/task/go2_joystick_rough/mujoco.yaml`,
-  `conf/ppo/task/go2_joystick_rough/motrix.yaml`
+- Task owner: `src/unilab/tasks/locomotion/go2/rough.py`
+- Terrain generator: `unisim.terrain.generator`
+- MuJoCo materializer: `unisim.backend.mujoco.xml`
+- Motrix materializer: `unisim.backend.motrix.scene`
+- Owner YAMLs: `src/unilab/conf/ppo/task/go2_joystick_rough/mujoco.yaml`,
+  `src/unilab/conf/ppo/task/go2_joystick_rough/motrix.yaml`
 
 User instructions are in {doc}`../../2-user_guide/6-terrain/1-procedural`.
diff --git a/docs/sphinx/source/en/4-developer_guide/1-architecture/5-registry.md b/docs/sphinx/source/en/4-developer_guide/1-architecture/5-registry.md
index 4b6538cd1..3bf01b4eb 100644
--- a/docs/sphinx/source/en/4-developer_guide/1-architecture/5-registry.md
+++ b/docs/sphinx/source/en/4-developer_guide/1-architecture/5-registry.md
@@ -8,11 +8,9 @@ defined by {doc}`/adr/ADR-0004-registry-bootstrap-contract` and implemented in
 
 1. Training entrypoints call `unilab.training.common.ensure_registries()`.
 2. That helper delegates to `unilab.base.registry.ensure_registries()`.
-3. The registry imports declared bootstrap packages:
-   `unilab.envs.locomotion`, `unilab.envs.manipulation`, and
-   `unilab.envs.motion_tracking`.
-4. Each package exposes `__unilab_registry_modules__`, a tuple of modules that
-   contain registration side effects.
+3. The registry imports its sole declared bootstrap package, `unilab.tasks`.
+4. `unilab.tasks` exposes `__unilab_registry_modules__`, an explicit tuple of
+   task leaf modules that contain registration side effects.
 5. Imported modules register configs with `@registry.envcfg(...)` and env
    implementations with `@registry.env(..., sim_backend=...)` or
    `registry.register_env(...)`.
@@ -22,9 +20,8 @@ defined by {doc}`/adr/ADR-0004-registry-bootstrap-contract` and implemented in
 
 ## Extension Rules
 
-- Add new env modules to the package-level `__unilab_registry_modules__` tuple
-  if they live in a new module that is not imported by an existing bootstrap
-  entry.
+- Add new task leaves to `unilab.tasks.__unilab_registry_modules__` when they
+  are not imported by an existing bootstrap entry.
 - Keep registration cheap. Scene materialization, XML processing, asset access,
   and backend construction belong after `registry.make(...)`, not in decorator
   registration.
@@ -35,7 +32,5 @@ defined by {doc}`/adr/ADR-0004-registry-bootstrap-contract` and implemented in
 
 - Bootstrap helper: `src/unilab/base/registry.py`
 - Training helper: `src/unilab/training/common.py`
-- Package declarations: `src/unilab/envs/locomotion/__init__.py`,
-  `src/unilab/envs/manipulation/__init__.py`,
-  `src/unilab/envs/motion_tracking/__init__.py`
+- Task bootstrap declaration: `src/unilab/tasks/__init__.py`
 - Tests: `tests/base/test_registry.py`, `tests/utils/test_algo_utils.py`
diff --git a/docs/sphinx/source/en/4-developer_guide/1-architecture/6-manager_based_api.md b/docs/sphinx/source/en/4-developer_guide/1-architecture/6-manager_based_api.md
new file mode 100644
index 000000000..d22db32e1
--- /dev/null
+++ b/docs/sphinx/source/en/4-developer_guide/1-architecture/6-manager_based_api.md
@@ -0,0 +1,35 @@
+# Manager-Based API
+
+UniLab uses a community-compatible manager API on its NumPy runtime. Manager modules,
+term configs, function/class terms, lifecycle ordering, and reset semantics follow the
+pinned mjlab 1.6.0 source. Numeric execution uses NumPy while preserving UniLab's
+`NpEnvState`, Hydra owner YAML, `SimBackend`, registry, and IPC contracts.
+
+The normative compatibility matrix and mechanical migration example are in
+{doc}`ADR-0006 </adr/ADR-0006-community-manager-api-on-numpy-runtime>`.
+
+## Invariants
+
+- Community manager semantics and a general structure take priority over local
+  optimizations that would create a UniLab-only term API.
+- Manager buffers, term returns, environment IDs, and entity views use `np.ndarray`
+  or `slice`; manager core does not depend on Torch, Warp, runners, learners, or IPC.
+- `SceneEntityCfg` resolves through a base-owned scene/entity facade on the cold path.
+  The facade uses only the public `SimBackend` contract, and hot paths reuse cached IDs
+  and views.
+- Named-sensor observation terms bind a backend-owned view through
+  `EntityScene.bind_sensor_data(...)` during construction; their hot path only reads
+  that view and never re-resolves sensor names or XML/model metadata.
+- `ManagerBasedRlEnv` owns backend materialization exactly once: manager construction
+  and startup events run first, then `SimBackend.materialize()` completes before any
+  reset or step can execute.
+- Explicitly empty configuration may use a Null manager. A requested capability that
+  is unavailable fails at the nearest boundary; it is never skipped, zero-filled, or
+  routed back to a legacy environment.
+- Hot paths avoid obvious repeated parsing, per-environment Python loops, copies, and
+  temporary allocations. Further optimization requires benchmark evidence and must
+  not add disproportionate structural complexity.
+
+Only surfaces backed by registration, configuration, and tests may be called
+Compatible. NumPy/env/config adapters are Adapted; capabilities without a formal
+backend contract are Unsupported and fail closed.
diff --git a/docs/sphinx/source/en/4-developer_guide/2-contracts/2-backend_contract.md b/docs/sphinx/source/en/4-developer_guide/2-contracts/2-backend_contract.md
index f7c021958..6bb442429 100644
--- a/docs/sphinx/source/en/4-developer_guide/2-contracts/2-backend_contract.md
+++ b/docs/sphinx/source/en/4-developer_guide/2-contracts/2-backend_contract.md
@@ -7,7 +7,7 @@ The play/render decision is recorded in
 ## Stable Backend Interface
 
 All env-facing backend calls should go through `SimBackend` in
-`src/unilab/base/backend/base.py`. The interface includes base state, DOF state,
+`unisim.backend.base`. The interface includes base state, DOF state,
 body state in world and baselink frames, named sensors, state reset, physics
 stepping, domain-randomization hooks, and optional playback/render methods.
 
@@ -17,6 +17,9 @@ Optional capabilities are explicit:
   physics-state playback, and native video capture support.
 - `BackendHeightScanner` and `create_hfield_scanner(...)` expose terrain scan
   support through a reusable backend-owned object.
+- `BackendSensorView` and `bind_sensor_data(...)` validate ordered named sensors
+  on the cold path and retain a backend-owned reader for finite, shape-stable
+  NumPy batches. Manager hot paths do not inspect XML or model metadata.
 - Domain randomization support is surfaced through `get_dr_capabilities()` and
   the init, reset, and interval randomization methods.
 - Unsupported optional methods raise `NotImplementedError` from the base class.
@@ -33,9 +36,10 @@ Optional capabilities are explicit:
 
 ## Evidence In Repo
 
-- Backend interface and play capabilities: `src/unilab/base/backend/base.py`
-- Backend factory: `src/unilab/base/backend/__init__.py`
-- MuJoCo backend: `src/unilab/base/backend/mujoco/backend.py`
-- Motrix backend: `src/unilab/base/backend/motrix/backend.py`
-- Backend contract tests: `tests/base/test_sim_backend.py`,
+- Backend interface and play capabilities: `unisim.backend.base`
+- Backend factory: `src/unilab/base/backend_factory.py`
+- MuJoCo backend: `unisim.backend.mujoco.backend`
+- Motrix backend: `unisim.backend.motrix.backend`
+- Backend contract tests: `tests/base/test_backend_sensor_view.py`,
+  `tests/base/test_backend_conformance.py`, `tests/base/test_sim_backend.py`,
   `tests/base/test_backend_imports.py`, `tests/base/test_motrix_backend_options.py`
diff --git a/docs/sphinx/source/en/4-developer_guide/2-contracts/3-task_owner.md b/docs/sphinx/source/en/4-developer_guide/2-contracts/3-task_owner.md
index 8c9ca61c9..095d6c5c2 100644
--- a/docs/sphinx/source/en/4-developer_guide/2-contracts/3-task_owner.md
+++ b/docs/sphinx/source/en/4-developer_guide/2-contracts/3-task_owner.md
@@ -7,11 +7,11 @@ contract is recorded in
 ## Owner Paths
 
 - PPO and APPO owner YAMLs use
-  `conf/{ppo,appo}/task/<task>/<backend>.yaml`.
-- Off-policy owner YAMLs include the algorithm dimension:
-  `conf/offpolicy/task/<algo>/<task>/<backend>.yaml`.
-- Other existing config roots, such as `conf/ppo_him/` and
-  `conf/hora_distill/`, follow the same owner-YAML identity rule for their
+  `src/unilab/conf/{ppo,appo}/task/<task>/<backend>.yaml`.
+- Off-policy algorithms (SAC / TD3 / FlashSAC) each have their own config
+  tree: `src/unilab/conf/<algo>/task/<task>/<backend>.yaml`.
+- Other existing config roots, such as `src/unilab/conf/ppo_him/` and
+  `src/unilab/conf/hora_distill/`, follow the same owner-YAML identity rule for their
   supported tasks.
 
 ## Required Semantics
@@ -19,8 +19,8 @@ contract is recorded in
 - Use public CLI flags to switch backend, for example
   `uv run train --algo ppo --task go2_joystick_flat --sim mujoco` or
   `uv run train --algo ppo --task go2_joystick_flat --sim motrix`.
-- For off-policy entrypoints, keep `--algo <algo>` aligned with the internal
-  owner YAML path `conf/offpolicy/task/<algo>/<task>/<backend>.yaml`.
+- For off-policy entrypoints, `--algo <algo>` selects the per-algorithm config
+  tree; the owner YAML path is `src/unilab/conf/<algo>/task/<task>/<backend>.yaml`.
 - `training.sim_backend` is an identity field inside the selected owner YAML. It
   is not an independent backend switch.
 - Backend-specific reward, env, scene, and algorithm differences belong in the
@@ -30,10 +30,9 @@ contract is recorded in
 
 ## Evidence In Repo
 
-- PPO owner example: `conf/ppo/task/go2_joystick_flat/mujoco.yaml`
-- APPO config root: `conf/appo/config.yaml`
-- Off-policy config root: `conf/offpolicy/config.yaml`
-- Off-policy task/algo guard: `src/unilab/training/common.py`
+- PPO owner example: `src/unilab/conf/ppo/task/go2_joystick_flat/mujoco.yaml`
+- APPO config root: `src/unilab/conf/appo/config.yaml`
+- Off-policy config roots: `src/unilab/conf/{sac,td3,flashsac}/config.yaml`
 - Config tests: `tests/config/test_config_system.py`,
   `tests/scripts/test_train_script_configs.py`,
   `tests/envs/locomotion/g1/test_g1_owner_contract.py`
diff --git a/docs/sphinx/source/en/4-developer_guide/2-contracts/4-dr_contract.md b/docs/sphinx/source/en/4-developer_guide/2-contracts/4-dr_contract.md
index 39fd08277..1104d2e9b 100644
--- a/docs/sphinx/source/en/4-developer_guide/2-contracts/4-dr_contract.md
+++ b/docs/sphinx/source/en/4-developer_guide/2-contracts/4-dr_contract.md
@@ -99,7 +99,7 @@ payloads.
 
 - DR types: `src/unilab/dr/types.py`
 - DR manager: `src/unilab/dr/manager.py`
-- Backend interface: `src/unilab/base/backend/base.py`
-- Example providers: `src/unilab/envs/locomotion/g1/joystick.py`,
-  `src/unilab/envs/motion_tracking/g1/tracking.py`,
-  `src/unilab/envs/manipulation/sharpa_inhand/rotation.py`
+- Backend interface: `unisim.backend.base`
+- Example providers: `src/unilab/tasks/locomotion/common/dr_provider.py`,
+  `src/unilab/tasks/locomotion/go2_arm/manip_loco.py`,
+  `src/unilab/tasks/manipulation/sharpa_inhand/rotation.py`
diff --git a/docs/sphinx/source/en/4-developer_guide/2-contracts/5-runner_lifecycle.md b/docs/sphinx/source/en/4-developer_guide/2-contracts/5-runner_lifecycle.md
index f1875f5cc..fc37c0263 100644
--- a/docs/sphinx/source/en/4-developer_guide/2-contracts/5-runner_lifecycle.md
+++ b/docs/sphinx/source/en/4-developer_guide/2-contracts/5-runner_lifecycle.md
@@ -17,11 +17,12 @@ The training scripts follow the same high-level sequence:
 
 ## Runtime-Specific Owners
 
-- `scripts/train_rsl_rl.py` uses `RslRlVecEnvWrapper` and RSL-RL's
+- `src/unilab/scripts/train_rsl_rl.py` uses `RslRlVecEnvWrapper` and RSL-RL's
   `OnPolicyRunner`.
-- `scripts/train_appo.py` uses `APPORunner`, `RolloutRingBuffer`, and
+- `src/unilab/scripts/train_appo.py` uses `APPORunner`, `RolloutRingBuffer`, and
   `SharedWeightSync`.
-- `scripts/train_offpolicy.py` uses off-policy runners with `ReplayBuffer` and
+- `src/unilab/scripts/train_sac.py`, `src/unilab/scripts/train_td3.py`, and
+  `src/unilab/scripts/train_flashsac.py` use off-policy runners with `ReplayBuffer` and
   `SharedWeightSync`.
 - `AsyncRunner` owns collector process lifecycle and shared-resource cleanup for
   async runners.
@@ -38,6 +39,6 @@ The training scripts follow the same high-level sequence:
 
 - Shared training helpers: `src/unilab/training/common.py`,
   `src/unilab/training/run.py`
-- Async lifecycle: `src/unilab/ipc/async_runner.py`
+- Async lifecycle: `uni_rl.ipc.async_runner` (unilab-rl repo)
 - Runner tests: `tests/algos/test_appo_runner.py`,
   `tests/algos/test_offpolicy_runner.py`, `tests/ipc/test_async_runner.py`
diff --git a/docs/sphinx/source/en/4-developer_guide/3-extending/1-new_task.md b/docs/sphinx/source/en/4-developer_guide/3-extending/1-new_task.md
index 37924785a..3c597ba1d 100644
--- a/docs/sphinx/source/en/4-developer_guide/3-extending/1-new_task.md
+++ b/docs/sphinx/source/en/4-developer_guide/3-extending/1-new_task.md
@@ -20,8 +20,8 @@ Start from the contracts: {doc}`../2-contracts/1-env_contract`,
    `reset(env_indices)` returns `(obs_dict, info_dict)`, and `step(actions)`
    returns `NpEnvState`.
 7. Add owner YAMLs under the relevant config root, such as
-   `conf/ppo/task/<task>/<backend>.yaml` or
-   `conf/offpolicy/task/<algo>/<task>/<backend>.yaml`.
+   `src/unilab/conf/ppo/task/<task>/<backend>.yaml` or, for an off-policy algorithm,
+   `src/unilab/conf/<algo>/task/<task>/<backend>.yaml`.
 8. Put task or scene keyframes in task/scene XML fragments referenced through
    `SceneCfg.fragment_files`; do not put task-level keyframes in `robot.xml`.
 
@@ -38,5 +38,5 @@ Start from the contracts: {doc}`../2-contracts/1-env_contract`,
 - Registry API: `src/unilab/base/registry.py`
 - Env state contract: `src/unilab/base/np_env.py`
 - Scene config: `src/unilab/base/scene.py`
-- Existing task examples: `src/unilab/envs/locomotion/go2/joystick.py`,
-  `src/unilab/envs/manipulation/allegro_inhand/rotation.py`
+- Existing task examples: `src/unilab/tasks/locomotion/go2/joystick.py`,
+  `src/unilab/tasks/manipulation/allegro_inhand/rotation.py`
diff --git a/docs/sphinx/source/en/4-developer_guide/3-extending/2-new_backend.md b/docs/sphinx/source/en/4-developer_guide/3-extending/2-new_backend.md
index 26c10bf1a..584d3637b 100644
--- a/docs/sphinx/source/en/4-developer_guide/3-extending/2-new_backend.md
+++ b/docs/sphinx/source/en/4-developer_guide/3-extending/2-new_backend.md
@@ -10,13 +10,13 @@ The repository currently recognizes `mujoco` and `motrix` in two important
 places:
 
 - `registry.register_env(...)` in `src/unilab/base/registry.py`
-- `create_backend(...)` in `src/unilab/base/backend/__init__.py`
+- `create_backend(...)` in `src/unilab/base/backend_factory.py`
 
 A third backend is therefore an architecture change, not just a new package.
 
 ## Implementation Checklist
 
-1. Implement a `SimBackend` subclass under `src/unilab/base/backend/<backend>/`.
+1. Implement a `SimBackend` subclass under `unisim.backend<backend>/`.
 2. Add backend construction to `create_backend(...)`.
 3. Update registry validation if the new backend should be accepted by
    `@registry.env(..., sim_backend=...)`.
@@ -39,7 +39,7 @@ A third backend is therefore an architecture change, not just a new package.
 
 ## Evidence In Repo
 
-- Backend interface: `src/unilab/base/backend/base.py`
-- Backend factory: `src/unilab/base/backend/__init__.py`
-- MuJoCo backend package: `src/unilab/base/backend/mujoco/`
-- Motrix backend package: `src/unilab/base/backend/motrix/`
+- Backend interface: `unisim.backend.base`
+- Backend factory: `src/unilab/base/backend_factory.py`
+- MuJoCo backend package: `unisim.backend.mujoco`
+- Motrix backend package: `unisim.backend.motrix`
diff --git a/docs/sphinx/source/en/4-developer_guide/3-extending/3-new_algorithm.md b/docs/sphinx/source/en/4-developer_guide/3-extending/3-new_algorithm.md
index 8d15edeec..0b83b1ddc 100644
--- a/docs/sphinx/source/en/4-developer_guide/3-extending/3-new_algorithm.md
+++ b/docs/sphinx/source/en/4-developer_guide/3-extending/3-new_algorithm.md
@@ -4,35 +4,102 @@ Algorithm work must preserve the env, config, and runner contracts. Start with
 {doc}`../2-contracts/1-env_contract`, {doc}`../2-contracts/3-task_owner`, and
 {doc}`../2-contracts/5-runner_lifecycle`.
 
-## Choose The Integration Path
+## Three Extension Tiers
 
-- Synchronous on-policy example: `scripts/train_rsl_rl.py`.
-- Async on-policy example: `scripts/train_appo.py` with `APPORunner`.
-- Off-policy examples: `scripts/train_offpolicy.py` with SAC, TD3, and
-  FlashSAC configs under `conf/offpolicy/`.
+Ordered from shallowest to deepest; prefer the shallowest tier that meets your
+needs.
+
+### 1. Pure Config: Reuse An Existing Algorithm
+
+Change no code — only adjust the Hydra config under `src/unilab/conf/<algo>/`:
+
+- Algorithm hyperparameters are inlined in `src/unilab/conf/<algo>/config.yaml`;
+- Each task×backend combination maps to one owner YAML:
+  `src/unilab/conf/<algo>/task/<task>/<backend>.yaml`;
+- Swapping policy / algorithm implementation classes works through the
+  `class_name` dotted path in the owner YAML (in-repo example:
+  `uni_rl.algos.hora:HoraActorModel`), with no new code path required.
+
+### 2. `runtime_resolver`: Algorithm Code In Your Own Repository
+
+A researcher imports `uni_rl` (distribution name `unilab-rl`) as a library in
+their own repository and implements the runner / learner / play logic there —
+**no fork of unilab_rl needed**. The owner YAML declares a dotted path
+(`module:attr`) via `algo.runtime_resolver`:
+
+```yaml
+algo:
+  runtime_resolver: my_pkg.my_module:resolve_my_runtime
+```
+
+Contract:
+
+- Signature is `(rl_cfg: dict) -> Runtime | None`; returning `None` falls back
+  to the algorithm's default runtime;
+- The returned object must carry `runner_cls`; depending on the algorithm
+  family it may optionally carry `play_fn` (APPO style) or `wrapper_cls`
+  (PPO style);
+- Resolution happens on the uni_rl side (`uni_rl.algos.appo.runtime` /
+  `uni_rl.algos.rsl_rl_runtime`); the dotted path may point at any importable
+  module.
+
+In-repo example: `src/unilab/conf/appo/task/sharpa_inhand/mujoco_hora.yaml`
+points at `unilab.scripts.play_hora_appo:resolve_hora_appo_runtime`; the HORA
+SAC / PPO variants point at `uni_rl.algos.hora.sac:resolve_hora_sac_runtime`
+and `uni_rl.algos.hora.rsl_rl:resolve_hora_ppo_runtime` respectively.
+
+### 3. Fork unilab_rl: Modify `uni_rl/algos/`
+
+Only when you need to change the shared runner / learner / collector
+implementation (for example a new IPC lifecycle) should you fork
+[unilabsim/unilab_rl](https://github.com/unilabsim/unilab_rl) and modify
+`uni_rl/algos/`. Async algorithms should reuse `AsyncRunner`, `ReplayBuffer` /
+`RolloutRingBuffer`, and `SharedWeightSync` instead of creating a new IPC
+lifecycle.
+
+## Footprint Of A CLI-Routable Algorithm
+
+`uv run train --algo <algo>` / `uv run eval --algo <algo>` use
+convention-based routing (`available_algos` and `build_route` in
+`src/unilab/cli.py`): an algo name `<algo>` is routable if and only if both of
+the following exist — **no cli.py change required**:
+
+1. `src/unilab/conf/<algo>/config.yaml` — the Hydra config root with the
+   algorithm hyperparameters inlined;
+2. `src/unilab/scripts/train_<algo>.py` — the entrypoint script, kept as a
+   thin assembly shell: compose Hydra, call `ensure_registries()`, construct
+   the env through the registry path, then hand control to the runner or
+   trainer. Thin-shell precedent: `train_sac.py` / `train_td3.py` /
+   `train_flashsac.py` reuse the shared implementation in `train_offpolicy.py`.
+
+On top of that, each task×backend combination needs an owner YAML at
+`src/unilab/conf/<algo>/task/<task>/<backend>.yaml`. Unknown algos fail
+closed, and the error message lists every available algo (built-in plus
+convention-discovered).
+
+Notes:
+
+- Config trees that have a conf directory but no entrypoint script (such as
+  `hora_distill` and `ppo_him`) are not routable — they are not standalone
+  CLI algos.
+- The special script-name mappings for built-in algorithms are preserved:
+  `ppo` → `train_rsl_rl.py`, `appo` → `train_appo.py`.
+- The dataclasses in `src/unilab/structured_configs.py` are an **optional**
+  conventional mirror; there is no ConfigStore enforcement, so a new algorithm
+  is not required to add one.
 
 ## Implementation Checklist
 
-1. Put reusable learner or runner code under `src/unilab/algos/`.
-2. Add Hydra config under the owning config root. A new off-policy variant should
-   add `conf/offpolicy/algo/<algo>.yaml` and matching
-   `conf/offpolicy/task/<algo>/<task>/<backend>.yaml` owner YAMLs.
-3. If a new top-level training script is required, keep it as assembly:
-   compose Hydra, call `ensure_registries()`, construct the env through the
-   registry path, then hand control to the runner or trainer.
-4. Keep third-party adapter naming at adapter boundaries. Do not change the
+1. Pick the integration path from the three tiers above: prefer pure config
+   over code, and `runtime_resolver` over a fork.
+2. Keep third-party adapter naming at adapter boundaries. Do not change the
    internal `obs` plus optional `critic` env contract to match a library.
-5. For async algorithms, reuse `AsyncRunner`, `ReplayBuffer` or
-   `RolloutRingBuffer`, and `SharedWeightSync` instead of creating a new IPC
-   lifecycle.
-6. For off-policy algorithms, keep the CLI `--algo <algo>` selection aligned
-   with the owner YAML path `conf/offpolicy/task/<algo>/<task>/<backend>.yaml`;
-   `assert_offpolicy_task_choice_matches_algo` enforces this guard.
+3. Keep new entrypoint scripts as assembly; no long-term business rules in
+   `scripts/`.
 
 ## Validation Near Risk
 
-- Algorithm unit tests under `tests/algos/`
-- IPC tests under `tests/ipc/` for async paths
+- CLI routing and convention discovery: `tests/test_cli.py`
 - Script/config tests: `tests/scripts/test_train_script_configs.py`,
   `tests/scripts/test_train_scripts.py`
 
@@ -41,4 +108,5 @@ Algorithm work must preserve the env, config, and runner contracts. Start with
 - Structured config dataclasses: `src/unilab/structured_configs.py`
 - Training helpers: `src/unilab/training/common.py`,
   `src/unilab/training/run.py`
-- Existing algorithm packages: `src/unilab/algos/torch/`
+- Existing algorithm packages: `uni_rl` in
+  [unilabsim/unilab_rl](https://github.com/unilabsim/unilab_rl)
diff --git a/docs/sphinx/source/en/4-developer_guide/3-extending/4-new_terrain.md b/docs/sphinx/source/en/4-developer_guide/3-extending/4-new_terrain.md
index 23ae12c30..aba440d8d 100644
--- a/docs/sphinx/source/en/4-developer_guide/3-extending/4-new_terrain.md
+++ b/docs/sphinx/source/en/4-developer_guide/3-extending/4-new_terrain.md
@@ -11,7 +11,7 @@ materialization out of `step()`, `reset()`, and hot domain-randomization loops.
 2. Add a preset in `src/unilab/terrains/config.py` with `@terrain_preset` if the
    terrain should be selectable by name.
 3. Wire terrain into a `TerrainGeneratorCfg`, either through an existing named
-   set such as `ROUGH_TERRAINS_CFG` or through an owner YAML under `conf/`.
+   set such as `ROUGH_TERRAINS_CFG` or through an owner YAML under `src/unilab/conf/`.
 4. Expose terrain to scenes through `SceneCfg.terrain` and `TerrainSceneCfg` in
    `src/unilab/base/scene.py`.
 5. If an env needs height observations, create the backend scanner on init via
@@ -33,6 +33,6 @@ materialization out of `step()`, `reset()`, and hot domain-randomization loops.
 ## Evidence In Repo
 
 - Terrain configs and presets: `src/unilab/terrains/config.py`
-- Terrain generator: `src/unilab/terrains/terrain_generator.py`
+- Terrain generator: `unisim.terrain.generator`
 - Heightfield terrain types: `src/unilab/terrains/heightfield_terrains.py`
-- Height-scan helper: `src/unilab/envs/locomotion/common/height_scan.py`
+- Height-scan helper: `src/unilab/tasks/locomotion/common/height_scan.py`
diff --git a/docs/sphinx/source/en/4-developer_guide/4-contributing.md b/docs/sphinx/source/en/4-developer_guide/4-contributing.md
index 0ec7c79ad..cb3d1b301 100644
--- a/docs/sphinx/source/en/4-developer_guide/4-contributing.md
+++ b/docs/sphinx/source/en/4-developer_guide/4-contributing.md
@@ -5,20 +5,24 @@ architecture details live in {doc}`1-architecture/1-overview`.
 
 ## Environment
 
-Install dependencies for your platform:
+Install dependencies for your platform. The setup targets also install the
+optional simulator extras used by the repository's checks:
 
-- macOS (MPS, PyPI torch wheel): `uv sync`
-- Linux with NVIDIA (PyTorch cu128 wheel): `uv sync`
+- macOS (MPS, PyPI torch wheel): `make setup-motrix` (or `make setup-mujoco`)
+- Linux with NVIDIA (PyTorch cu128 wheel): `make setup`
 - Linux AMD / ROCm: `make sync-rocm`, then run commands with `uv run ...`. To
   return to the default CUDA / macOS profile, `git restore -- pyproject.toml
-  uv.lock` and re-run `uv sync`.
+  uv.lock` and re-run `make setup`.
 - Linux Intel XPU: `make sync-xpu`
-- Add `--extra motrix` when you need the Motrix backend, e.g. `uv sync --extra
-  motrix`.
+- If you prefer direct uv commands, the full default setup is
+  `uv sync --extra mujoco --extra motrix`; use `--extra mujoco` or
+  `--extra motrix` for a single backend.
 
 ```bash
-uv sync
-uv sync --extra motrix
+# Choose one core setup path:
+make setup
+# make setup-mujoco
+# make setup-motrix
 make sync-rocm
 make sync-xpu
 ```
@@ -208,7 +212,7 @@ workflow runs the prose-only Sphinx build.
 
 Task, backend, reward, and algorithm selection belongs in Hydra owner YAMLs.
 When adding or changing a runnable path, update the relevant owner config under
-`conf/` and verify script composition with tests under `tests/config/` or
+`src/unilab/conf/` and verify script composition with tests under `tests/config/` or
 `tests/scripts/`.
 
 See {doc}`2-contracts/3-task_owner` and
diff --git a/docs/sphinx/source/en/4-developer_guide/6-agent_quick_reference.md b/docs/sphinx/source/en/4-developer_guide/6-agent_quick_reference.md
index c27f11854..666fa3414 100644
--- a/docs/sphinx/source/en/4-developer_guide/6-agent_quick_reference.md
+++ b/docs/sphinx/source/en/4-developer_guide/6-agent_quick_reference.md
@@ -6,19 +6,20 @@ repo facts.
 ## Start Here
 
 - Install and smoke check: {doc}`../1-getting_started/2-installation`
-- Backend choice: {doc}`../2-user_guide/3-backends/3-choosing_a_backend`
+- Backend choice: {doc}`../2-user_guide/3-backends/0-index`
 - Task index: {doc}`../2-user_guide/4-tasks/0-index`
 - Algorithms index: {doc}`../2-user_guide/2-algorithms/0-index`
-- PPO entrypoint: `scripts/train_rsl_rl.py`
-- APPO entrypoint: `scripts/train_appo.py`
-- SAC / TD3 / FlashSAC entrypoint: `scripts/train_offpolicy.py`
+- PPO entrypoint: `src/unilab/scripts/train_rsl_rl.py`
+- APPO entrypoint: `src/unilab/scripts/train_appo.py`
+- SAC / TD3 / FlashSAC entrypoints: `src/unilab/scripts/train_sac.py` /
+  `src/unilab/scripts/train_td3.py` / `src/unilab/scripts/train_flashsac.py`
 - HIM-PPO entrypoint: `scripts/train_him_ppo.py`
 - HORA distillation entrypoint: `scripts/train_hora_distill.py`
 
 ## Contracts To Keep In Mind
 
 - Env contract: `src/unilab/base/np_env.py`
-- Backend contract: `src/unilab/base/backend/base.py`
+- Backend contract: `unisim.backend.base`
 - Training helpers: `src/unilab/training/run.py`
 - Config schema: `src/unilab/structured_configs.py`
 - Developer standard: {doc}`1-architecture/1-overview`
diff --git a/docs/sphinx/source/en/4-developer_guide/7-motion_assets.md b/docs/sphinx/source/en/4-developer_guide/7-motion_assets.md
index ded0b552f..9e1c3f143 100644
--- a/docs/sphinx/source/en/4-developer_guide/7-motion_assets.md
+++ b/docs/sphinx/source/en/4-developer_guide/7-motion_assets.md
@@ -82,45 +82,71 @@ Alternatively, pre-download into the in-repo directory with `--local-dir`
 
 3. Reference the new file path in the env config.
 
-## Robot Mesh Assets
+## Robot Binary Assets
 
-Robot binary meshes (`.STL`) are externalized the same way, on the Hugging Face
-dataset repo
+Robot binary meshes and textures (for example `.STL`, `.obj`, and `.png`) are
+externalized the same way, on the Hugging Face dataset repo
 [unilabsim/unilab-robots](https://huggingface.co/datasets/unilabsim/unilab-robots).
-X2 meshes download lazily on first use and land under their original path
-`src/unilab/assets/robots/x2/meshes/`, so the XML `meshdir` references resolve
-unchanged. Pre-fetch them without running a task:
+The registered robots are a2, allegro_hand, g1, go2, go2_arm,
+sharpa_wave, and x2 (`ROBOT_ASSET_SPECS` in `src/unilab/assets/hub.py`).
+Their mesh/texture directories download lazily on first use and land under
+their original paths (for example `src/unilab/assets/robots/g1/assets/` and
+`robots/g1/textures/` for G1), so the original relative XML paths remain
+valid. These directories are excluded from the wheel/sdist via
+`tool.uv.build-backend.source-exclude` in `pyproject.toml`; after a pip
+install, first use downloads them into the installed package tree, and later
+runs reuse that local copy offline. Pre-fetch them without running a task:
 
 ```bash
+uv run unilab-pull-assets --robot g1
 uv run unilab-pull-assets --robot x2
+uv run unilab-pull-assets --robot all   # every registered robot
 ```
 
-To add a new robot's meshes:
+To add a new robot's binary assets:
 
-1. Upload to the HF repo, keeping the directory layout identical:
+1. Upload each directory to the HF repo while keeping the directory layout
+   identical. A robot with multiple asset directories requires one upload per
+   directory. For example, G1 uses:
 
    ```bash
-   huggingface-cli upload unilabsim/unilab-robots \
-     src/unilab/assets/robots/<robot>/meshes robots/<robot>/meshes \
+   uv run hf upload unilabsim/unilab-robots \
+     src/unilab/assets/robots/g1/assets robots/g1/assets \
+     --repo-type dataset
+   uv run hf upload unilabsim/unilab-robots \
+     src/unilab/assets/robots/g1/textures robots/g1/textures \
      --repo-type dataset
    ```
 
-2. Ignore the local `*.STL` in `.gitignore` (keep a `.gitkeep` so the directory
-   persists).
-3. Resolve the directory once on a cold path from the env, e.g.
-   `resolve_robot_asset_dir("robots/<robot>/meshes", marker="<some>.STL")`.
+2. Ignore the downloaded directory contents in `.gitignore`, exclude the
+   directory in
+   `tool.uv.build-backend.source-exclude`, and register it in
+   `ROBOT_ASSET_SPECS`.
+3. Scenes built through `create_backend` are then covered automatically:
+   `ensure_robot_assets_for_paths` resolves the registered directories on a
+   cold path before any backend parses the XML. Entry points that bypass
+   `create_backend` resolve explicitly, e.g. the X2 task factory calls:
+
+   ```python
+   resolve_robot_asset_dir("robots/x2/meshes", marker="pelvis.STL")
+   ```
 
 ## Architecture Notes
 
 - Asset resolver module: `src/unilab/assets/hub.py`
   (`resolve_motion_files`).
-- Single integration point: `MotionLoader.__init__` in
-  `src/unilab/envs/motion_tracking/g1/motion_loader.py`, which calls the
+- Motion integration point: `MotionLoader.__init__` in
+  `src/unilab/tasks/motion_tracking/common/motion_loader.py`, which calls the
   resolver once on a cold path.
+- Robot mesh integration point: `create_backend` in
+  `src/unilab/base/backend_factory.py` calls
+  `ensure_robot_assets_for_paths` on the scene's `model_file`,
+  `visual_model_file`, and `fragment_files` before dispatching to a backend.
 - Hot paths (`step` / `reset`) never trigger any file download or parsing.
 - `ASSETS_ROOT_PATH` is unchanged, so the download target matches the
   original local path exactly.
-- Robot meshes use the same directory resolver (`resolve_robot_asset_dir`),
-  integrated at `X2WallFlipTrackingEnv.__init__` in
-  `src/unilab/envs/motion_tracking/x2/flip_tracking.py`, and exposed as the
-  `unilab-pull-assets` CLI.
+- Robot binary assets use the same directory resolver
+  (`resolve_robot_asset_dir`). The
+  thin X2 task factory resolves its directory once
+  before delegating to the shared manager environment factory. The resolver is
+  also exposed through the `unilab-pull-assets` CLI.
diff --git a/docs/sphinx/source/en/4-developer_guide/8-motrix_contact_sensor.md b/docs/sphinx/source/en/4-developer_guide/8-motrix_contact_sensor.md
index 2dd6d323c..1cc419a7d 100644
--- a/docs/sphinx/source/en/4-developer_guide/8-motrix_contact_sensor.md
+++ b/docs/sphinx/source/en/4-developer_guide/8-motrix_contact_sensor.md
@@ -91,7 +91,7 @@ contact-frame data (one normal scalar plus two tangent scalars).
 
 The env reads tactile force through `_read_tactile_force()` →
 `_extract_sensor_scalar()` in
-`src/unilab/envs/manipulation/sharpa_inhand/base.py`. That helper currently
+`src/unilab/tasks/manipulation/sharpa_inhand/base.py`. That helper currently
 collapses any `(N, >=3)` array with `np.linalg.norm(data[:, :3], axis=1)`.
 
 If the env still routes both backend shapes through that one branch, the
@@ -104,13 +104,13 @@ move the per-backend knowledge behind a backend method.
 ## Recommended Contract: a Backend Method for Force Magnitude
 
 Add `get_contact_force_magnitude(sensor_name) -> np.ndarray` to the
-`SimBackend` interface (`src/unilab/base/backend/base.py`), returning a
+`SimBackend` interface (`unisim.backend.base`), returning a
 `(num_envs,)` scalar magnitude. Each backend implements it according to its
 own data layout:
 
-- **MuJoCo** (`src/unilab/base/backend/mujoco/backend.py`): take the norm of
+- **MuJoCo** (`unisim.backend.mujoco.backend`): take the norm of
   the 3D force vector returned by `get_sensor_data(name)`.
-- **Motrix** (`src/unilab/base/backend/motrix/backend.py`): interpret the
+- **Motrix** (`unisim.backend.motrix.backend`): interpret the
   layout by reduce mode:
   - `reduce="netforce"`: take `[1:4]`, then norm.
   - no reduce, multiple contacts: sum the per-contact forces, then norm.
@@ -131,9 +131,9 @@ backend subclass.
 
 | File | Role |
 | --- | --- |
-| `src/unilab/envs/manipulation/sharpa_inhand/base.py` | `_extract_sensor_scalar()`, `_read_tactile_force()` |
-| `src/unilab/envs/manipulation/sharpa_inhand/rotation.py` | reward computation, virtual torque |
+| `src/unilab/tasks/manipulation/sharpa_inhand/base.py` | `_extract_sensor_scalar()`, `_read_tactile_force()` |
+| `src/unilab/tasks/manipulation/sharpa_inhand/rotation.py` | reward computation, virtual torque |
 | `src/unilab/assets/robots/sharpa_wave/right_sharpa_wave.xml` | contact-sensor XML definitions |
-| `src/unilab/base/backend/motrix/backend.py` | Motrix `get_sensor_data()` |
-| `src/unilab/base/backend/mujoco/backend.py` | MuJoCo `get_sensor_data()` |
-| `src/unilab/base/backend/base.py` | `SimBackend` interface |
+| `unisim.backend.motrix.backend` | Motrix `get_sensor_data()` |
+| `unisim.backend.mujoco.backend` | MuJoCo `get_sensor_data()` |
+| `unisim.backend.base` | `SimBackend` interface |
diff --git a/docs/sphinx/source/en/5-reference/5-support_matrix.md b/docs/sphinx/source/en/5-reference/5-support_matrix.md
index 5c9c3119a..9283e39fe 100644
--- a/docs/sphinx/source/en/5-reference/5-support_matrix.md
+++ b/docs/sphinx/source/en/5-reference/5-support_matrix.md
@@ -1,14 +1,21 @@
 # Support Matrix
 
 This matrix is generated conceptually from registry entries, owner YAMLs, and
-tests. The generator implementation is `src/unilab/utils/support_matrix.py`; the
-write target for the generated block is currently the Chinese reference page
-`docs/sphinx/source/zh_CN/5-reference/5-support_matrix.md`.
+tests. The generator implementation is `scripts/tools/support_matrix.py`; the
+generated block is maintained on the Chinese reference page and mirrored here
+in English. Do not infer support beyond the evidence grade shown below.
 
 ## Backend Selection Rules
 
 - The default backend is `mujoco`.
 - Switch to Motrix with `--sim motrix` on the unified CLI.
+- Switch to IsaacSim with `--sim isaacsim` on the unified CLI. IsaacSim runs
+  headless physics in an external Python 3.11 worker; its owner scope is
+  currently limited to the configured G1 walk-flat PPO/SAC paths.
+- `--sim mjwarp` has completed training validation only on the `g1_walk_flat`
+  host adapter, where PPO (torch) and SAC (torch) are Tested; other
+  entrypoints follow the matrix below, and using it requires installing the
+  `mjwarp` extra.
 - `--algo`, `--task`, and `--sim` jointly select the owner YAML.
 - Do not treat `training.sim_backend` as a standalone backend switch.
 
@@ -17,70 +24,141 @@ write target for the generated block is currently the Chinese reference page
 - `mujoco`: `--render-mode auto` exports `play_video.mp4`.
 - `motrix`: `--render-mode auto` opens an interactive renderer window; it does
   not record a video and is not bound by `play_steps`.
-- `--render-mode record`: both backends record a video only.
+- `mjwarp`: supports explicit, finite-step `record` by default, rendered offline
+  through the task owner's MuJoCo visual model; `--render-mode interactive`
+  routes to the MuJoCo interactive viewer (mjwarp runs the physics while
+  MuJoCo renders env[0], forced to a single env); `auto` and native renderers
+  are not supported.
+- `isaacsim`: headless physics only. Interactive GUI, camera capture, native
+  playback, and video recording are fail-closed for the current IsaacSim 5.1 /
+  IsaacLab v2.3.0 worker profile.
+- `--render-mode record`: MuJoCo, mjwarp, and Motrix all record a video only.
 - `--render-mode none`: no playback.
 
 ## Evidence Grades
 
 | Grade | Repository Evidence |
 | --- | --- |
-| `Registered` | The env/backend pair appears after `registry.ensure_registries()`. |
-| `Configured` | A matching owner YAML exists under `conf/ppo/task`, `conf/appo/task`, or `conf/offpolicy/task`. |
-| `Tested` | Automated tests cover the entrypoint/task-owner/backend combination through config compose or runtime smoke. |
+| `Registered` | The env/backend pair exists in `registry.list_registered_envs()` after `ensure_registries()`. |
+| `Configured` | A matching owner YAML exists under `src/unilab/conf/{ppo,appo,sac,td3,flashsac}/task/...`. |
+| `Tested` | Automated tests under `tests/` cover the entrypoint/task-owner/backend combination, or an explicit maintainer full-training validation with near-risk automated tests exists. `Tested` here does not mean the default recommended path. |
 | `Benchmarked` | A checked-in benchmark manifest exists for the combination. |
 | `Recommended` | Explicit recommendation metadata exists in the repo. |
 
-The current generator reports no checked-in benchmark manifest and no separate
-recommendation metadata, so rows do not auto-promote to `Benchmarked` or
+`Tested` only describes existing automated coverage or explicit maintainer
+training validation; it does not imply the combination has all the backend
+capabilities of the same-named MuJoCo owner. For example, a phase-1 Motrix
+owner may only cover training smoke and an explicitly enabled DR subset.
+
+`mjwarp` has completed training validation only on the `g1_walk_flat` host
+adapter: the PPO (torch) and SAC (torch) owners have completed training
+validation and have backend, contract, and playback automated coverage, so
+they are marked `Tested`. mjwarp playback
+supports explicit, finite-step `record` and reuses the MuJoCo offline
+renderer; `uv run eval --sim mjwarp --render-mode interactive` routes to the
+MuJoCo interactive viewer (mjwarp physics, MuJoCo rendering of env[0], forced
+single env); it does not support `auto` or native playback. A
+`Registered` mark on other entrypoints only denotes env/backend registry
+identity, not support for the corresponding algorithm, terrain, full DR, or
+production training.
+
+No checked-in benchmark manifest bound to these combinations has been detected,
+so rows do not auto-promote to `Benchmarked`. There is also no separate
+recommendation metadata in the repo, so rows do not auto-promote to
 `Recommended`.
 
+`isaacsim` is a Python 3.11 subprocess backend. Its bounded materialization
+and headless-physics smoke evidence is intentionally not promoted to `Tested`:
+the G1 PPO/SAC cells below are `Configured`, while APPO/TD3/FlashSAC remain
+`Registered`. Do not label these cells `Tested` until a maintainer records full
+training evidence. Contact-force sensors, domain randomization, and all native
+rendering/playback paths remain unsupported.
+
 ## Entrypoint x Task Owner
 
-| Entrypoint | Task owner | MuJoCo | Motrix |
-| --- | --- | --- | --- |
-| PPO (torch) | `go1_joystick_flat` | Tested | Tested |
-| PPO (torch) | `go2_joystick_flat` | Tested | Tested |
-| PPO (torch) | `go2_joystick_rough` | Tested | Tested |
-| PPO (torch) | `g1_walk_flat` | Tested | Tested |
-| PPO (torch) | `g1_motion_tracking` | Tested | Tested |
-| PPO (torch) | `g1_flip_tracking` | Tested | Tested |
-| PPO (torch) | `g1_wall_flip_tracking` | Tested | Tested |
-| PPO (torch) | `allegro_inhand` | Tested | Tested |
-| PPO (torch) | `sharpa_inhand` | Tested | Tested |
-| PPO (torch) | `sharpa_inhand_grasp` | Tested | Tested |
-| PPO (torch) | `allegro_inhand_grasp` | Tested | Tested |
-| PPO (torch) | `g1_box_tracking` | Tested | Tested |
-| PPO (torch) | `g1_climb_tracking` | Tested | Tested |
-| PPO (torch) | `g1_motion_tracking_deploy` | Tested | Registered |
-| PPO (torch) | `go1_joystick_rough` | Tested | Tested |
-| PPO (torch) | `go2_arm_manip_loco` | Tested | - |
-| PPO (torch) | `go2_footstand` | Tested | - |
-| PPO (torch) | `go2w_joystick_flat` | Tested | Tested |
-| PPO (torch) | `go2w_joystick_rough` | Tested | Tested |
-| APPO (torch) | `go1_joystick_flat` | Tested | Registered |
-| APPO (torch) | `go2_joystick_flat` | Tested | Registered |
-| APPO (torch) | `g1_walk_flat` | Tested | Registered |
-| APPO (torch) | `g1_motion_tracking` | Tested | Tested |
-| APPO (torch) | `g1_flip_tracking` | Tested | Tested |
-| APPO (torch) | `g1_wall_flip_tracking` | Tested | Tested |
-| APPO (torch) | `allegro_inhand` | Tested | Tested |
-| APPO (torch) | `sharpa_inhand` | Tested | Registered |
-| APPO (torch) | `g1_climb_tracking` | Tested | Tested |
-| SAC (torch) | `g1_walk_flat` | Tested | Tested |
-| SAC (torch) | `g1_walk_rough` | Tested | Tested |
-| SAC (torch) | `g1_motion_tracking` | Tested | Tested |
-| SAC (torch) | `g1_wbt_obs` | Tested | Registered |
-| TD3 (torch) | `go1_joystick_flat` | Registered | Tested |
-| TD3 (torch) | `go2_joystick_flat` | Registered | Tested |
-| TD3 (torch) | `g1_walk_flat` | Tested | Registered |
-| FlashSAC (torch) | `go2_joystick_flat` | Tested | Registered |
-| FlashSAC (torch) | `g1_walk_flat` | Tested | Registered |
+| Entrypoint | Task owner | MuJoCo | mjwarp | Motrix | IsaacGym | IsaacSim |
+| --- | --- | --- | --- | --- | --- | --- |
+| PPO (torch) | `go1_joystick_flat` (Go1 joystick) | Tested | - | Tested | - | - |
+| PPO (torch) | `go2_joystick_flat` (Go2 joystick) | Tested | - | Tested | - | - |
+| PPO (torch) | `go2_joystick_rough` (Go2 joystick rough) | Tested | - | Tested | - | - |
+| PPO (torch) | `g1_walk_flat` (G1 walk flat) | Tested | Tested | Tested | Configured | Configured |
+| PPO (torch) | `g1_motion_tracking` (G1 motion tracking) | Tested | - | Tested | - | - |
+| PPO (torch) | `g1_flip_tracking` (G1 flip tracking) | Tested | - | Tested | - | - |
+| PPO (torch) | `g1_wall_flip_tracking` (G1 wall flip tracking) | Tested | - | Tested | - | - |
+| PPO (torch) | `x2_wall_flip_tracking` (X2 wall flip tracking) | Tested | - | Tested | - | - |
+| PPO (torch) | `allegro_inhand` (Allegro in-hand) | Tested | - | Tested | - | - |
+| PPO (torch) | `sharpa_inhand` (Sharpa in-hand) | Tested | - | Tested | - | - |
+| PPO (torch) | `sharpa_inhand_grasp` (Sharpa in-hand grasp) | Tested | - | Tested | - | - |
+| PPO (torch) | `a2_joystick_flat` (a2 joystick flat) | Tested | - | - | - | - |
+| PPO (torch) | `allegro_inhand_grasp` (allegro inhand grasp) | Tested | - | Tested | - | - |
+| PPO (torch) | `g1_23dof_box_tracking` (g1 23dof box tracking) | Tested | - | Tested | - | - |
+| PPO (torch) | `g1_23dof_climb_tracking` (g1 23dof climb tracking) | Tested | - | Tested | - | - |
+| PPO (torch) | `g1_23dof_flip_tracking` (g1 23dof flip tracking) | Tested | - | Tested | - | - |
+| PPO (torch) | `g1_23dof_motion_tracking` (g1 23dof motion tracking) | Tested | - | Tested | - | - |
+| PPO (torch) | `g1_23dof_motion_tracking_deploy` (g1 23dof motion tracking deploy) | Tested | - | Tested | - | - |
+| PPO (torch) | `g1_23dof_walk_flat` (g1 23dof walk flat) | Tested | - | Tested | - | - |
+| PPO (torch) | `g1_23dof_walk_rough` (g1 23dof walk rough) | Tested | - | Registered | - | - |
+| PPO (torch) | `g1_23dof_wall_flip_tracking` (g1 23dof wall flip tracking) | Tested | - | Tested | - | - |
+| PPO (torch) | `g1_box_tracking` (g1 box tracking) | Tested | - | Tested | - | - |
+| PPO (torch) | `g1_climb_tracking` (g1 climb tracking) | Tested | - | Tested | - | - |
+| PPO (torch) | `g1_motion_tracking_deploy` (g1 motion tracking deploy) | Tested | - | Tested | - | - |
+| PPO (torch) | `go1_joystick_rough` (go1 joystick rough) | Tested | - | Tested | - | - |
+| PPO (torch) | `go2_arm_manip_loco` (go2 arm manip loco) | Tested | - | Tested | - | - |
+| PPO (torch) | `go2_footstand` (go2 footstand) | Tested | - | Tested | - | - |
+| PPO (torch) | `go2w_joystick_flat` (go2w joystick flat) | Tested | - | Tested | - | - |
+| PPO (torch) | `go2w_joystick_rough` (go2w joystick rough) | Tested | - | Tested | - | - |
+| PPO (torch) | `stewart_balance` (stewart balance) | Tested | - | Tested | - | - |
+| APPO (torch) | `go1_joystick_flat` (Go1 joystick) | Tested | - | Tested | - | - |
+| APPO (torch) | `go2_joystick_flat` (Go2 joystick) | Tested | - | Tested | - | - |
+| APPO (torch) | `g1_walk_flat` (G1 walk flat) | Tested | Registered | Registered | Registered | Registered |
+| APPO (torch) | `g1_motion_tracking` (G1 motion tracking) | Tested | - | Tested | - | - |
+| APPO (torch) | `g1_flip_tracking` (G1 flip tracking) | Tested | - | Tested | - | - |
+| APPO (torch) | `g1_wall_flip_tracking` (G1 wall flip tracking) | Tested | - | Tested | - | - |
+| APPO (torch) | `allegro_inhand` (Allegro in-hand) | Tested | - | Tested | - | - |
+| APPO (torch) | `sharpa_inhand` (Sharpa in-hand) | Tested | - | Tested | - | - |
+| APPO (torch) | `g1_23dof_climb_tracking` (g1 23dof climb tracking) | Tested | - | Tested | - | - |
+| APPO (torch) | `g1_23dof_flip_tracking` (g1 23dof flip tracking) | Tested | - | Tested | - | - |
+| APPO (torch) | `g1_23dof_motion_tracking` (g1 23dof motion tracking) | Tested | - | Tested | - | - |
+| APPO (torch) | `g1_23dof_walk_flat` (g1 23dof walk flat) | Tested | - | Registered | - | - |
+| APPO (torch) | `g1_23dof_wall_flip_tracking` (g1 23dof wall flip tracking) | Tested | - | Tested | - | - |
+| APPO (torch) | `g1_climb_tracking` (g1 climb tracking) | Tested | - | Tested | - | - |
+| SAC (torch) | `g1_walk_flat` (G1 walk flat) | Tested | Tested | Tested | Tested | Configured |
+| SAC (torch) | `g1_walk_rough` (G1 walk rough) | Tested | - | Tested | - | - |
+| SAC (torch) | `g1_motion_tracking` (G1 motion tracking) | Tested | Configured | Tested | - | - |
+| SAC (torch) | `g1_flip_tracking` (G1 flip tracking) | Tested | - | Registered | - | - |
+| SAC (torch) | `g1_wall_flip_tracking` (G1 wall flip tracking) | Tested | - | Registered | - | - |
+| SAC (torch) | `g1_23dof_flip_tracking` (g1 23dof flip tracking) | Tested | - | Registered | - | - |
+| SAC (torch) | `g1_23dof_motion_tracking` (g1 23dof motion tracking) | Tested | - | Tested | - | - |
+| SAC (torch) | `g1_23dof_walk_flat` (g1 23dof walk flat) | Tested | - | Tested | - | - |
+| SAC (torch) | `g1_23dof_walk_rough` (g1 23dof walk rough) | Tested | - | Tested | - | - |
+| SAC (torch) | `g1_23dof_wall_flip_tracking` (g1 23dof wall flip tracking) | Tested | - | Registered | - | - |
+| SAC (torch) | `g1_23dof_wbt_obs` (g1 23dof wbt obs) | Tested | - | Registered | - | - |
+| SAC (torch) | `g1_wbt_obs` (g1 wbt obs) | Tested | - | Registered | - | - |
+| TD3 (torch) | `go1_joystick_flat` (Go1 joystick) | Registered | - | Tested | - | - |
+| TD3 (torch) | `go2_joystick_flat` (Go2 joystick) | Registered | - | Tested | - | - |
+| TD3 (torch) | `g1_walk_flat` (G1 walk flat) | Tested | Registered | Registered | Registered | Registered |
+| TD3 (torch) | `g1_23dof_walk_flat` (g1 23dof walk flat) | Tested | - | Registered | - | - |
+| FlashSAC (torch) | `go2_joystick_flat` (Go2 joystick) | Tested | - | Registered | - | - |
+| FlashSAC (torch) | `g1_walk_flat` (G1 walk flat) | Tested | Configured | Tested | Registered | Registered |
+| FlashSAC (torch) | `g1_23dof_walk_flat` (g1 23dof walk flat) | Tested | - | Tested | - | - |
 
 ## Source Index
 
-- Registry bootstrap: `src/unilab/envs/**` registrations via
+- Registry bootstrap: `src/unilab/envs/**` decorators via
   `unilab.base.registry.ensure_registries()`.
-- Owner YAML scan: `conf/ppo/task/**`, `conf/appo/task/**`,
-  `conf/offpolicy/task/**`.
+- Owner YAML scan: `src/unilab/conf/ppo/task/**`, `src/unilab/conf/appo/task/**`,
+  `src/unilab/conf/sac/task/**`, `src/unilab/conf/td3/task/**`, `src/unilab/conf/flashsac/task/**`.
 - Generic compose coverage:
   `tests/config/test_config_system.py::test_supported_task_composes`.
+- Validated mjwarp entrypoints are explicitly recorded in
+  `_MAINTAINER_VALIDATED_MJWARP_ENTRYPOINT_TASKS`; near-risk coverage lives in
+  `tests/base/test_mjwarp_backend.py`,
+  `tests/base/test_backend_conformance.py`,
+  `tests/base/test_mjwarp_differential.py`, and
+  `tests/base/test_mjwarp_playback.py`.
+- Validated IsaacGym entrypoints are explicitly recorded in
+  `_MAINTAINER_VALIDATED_ISAACGYM_ENTRYPOINT_TASKS` (real hardware through the
+  external Python 3.8 worker runtime; not covered by repository CI).
+- IsaacSim owner scope is intentionally not promoted to `Tested`;
+  `_MAINTAINER_VALIDATED_ISAACSIM_ENTRYPOINT_TASKS` remains empty until a
+  maintainer records full training evidence.
diff --git a/docs/sphinx/source/glossary.md b/docs/sphinx/source/glossary.md
index fdea68676..b0f232e93 100644
--- a/docs/sphinx/source/glossary.md
+++ b/docs/sphinx/source/glossary.md
@@ -46,8 +46,8 @@ Cold path 指 init、materialization、cache build 等低频路径。Hot path 
 
 顶层 CLI 通过 `--algo <algo> --task <task> --sim <backend>` 选择最终
 task owner 配置。PPO / APPO 路径位于
-`conf/{ppo,appo}/task/<task>/<backend>.yaml`，off-policy 路径位于
-`conf/offpolicy/task/<algo>/<task>/<backend>.yaml`。
+`src/unilab/conf/{ppo,appo}/task/<task>/<backend>.yaml`，off-policy 算法（SAC / TD3 /
+FlashSAC）各自有独立的配置树，路径位于 `src/unilab/conf/<algo>/task/<task>/<backend>.yaml`。
 
 owner YAML 直接持有 `training.task_name`、`training.sim_backend`、`reward`、`env` 以及 task-specific `algo`。`training.sim_backend` 是 owner YAML 的身份字段，不是独立 backend switch。
 
diff --git a/docs/sphinx/source/zh_CN/0-index.md b/docs/sphinx/source/zh_CN/0-index.md
index 15b3adece..a67a46da7 100644
--- a/docs/sphinx/source/zh_CN/0-index.md
+++ b/docs/sphinx/source/zh_CN/0-index.md
@@ -10,13 +10,13 @@ sd_hide_title: true
 
 # UniLab
 
-### 面向 CPU 仿真与加速器训练的 contract 驱动机器人学习基础设施。
+### 一次配置任务语义，跨物理后端运行机器人 RL。
 
-{bdg-primary}`Python >=3.10,<3.14` {bdg-secondary}`Hydra owner YAML` {bdg-info}`MuJoCo + Motrix` {bdg-success}`uv workflow`
+{bdg-primary}`Python >=3.10,<3.14` {bdg-secondary}`Hydra + Manager API` {bdg-info}`跨后端 contract` {bdg-success}`uv workflow`
 
-UniLab 通过 `uv run train` / `uv run eval` CLI、task-owner Hydra 配置和 backend
-contract 来组织机器人 RL。可以从这个着陆页开始：安装、跑一次冒烟训练、选择
-算法/后端，或直接跳到部署与扩展文档。
+UniLab 将任务语义变成可复用配置：组装 manager term、选择物理后端，并在手头的硬件
+上运行同一套训练/评估工作流。可以从这个着陆页开始：安装、运行第一次 demo、再做
+冒烟训练、选择算法/后端，或直接跳到部署与扩展文档。
 
 ```{button-ref} 1-getting_started/1-quick_demo
 :ref-type: doc
@@ -42,20 +42,19 @@ contract 来组织机器人 RL。可以从这个着陆页开始：安装、跑
 ::::{grid} 1 1 3 3
 :gutter: 3
 
-:::{grid-item-card} CPU 仿真，加速器训练
-README 把 UniLab 描述为通过共享内存把 CPU 物理仿真连接到策略训练，
-以 MuJoCo 和 Motrix 作为仿真后端。
+:::{grid-item-card} 配置任务，无需样板代码
+在 Hydra owner YAML 中用 manager term 组装 action、observation、reward、termination、
+event、command 和 curriculum。常见任务变体无需新写 environment class。
 :::
 
 :::{grid-item-card} 后端选择留在配置里
-用 CLI flag 切换后端，例如 `--task go2_joystick_flat --sim motrix`；
-CLI 会组合 `conf/` 下对应的 owner YAML。不要把
-`training.sim_backend` 当作独立的后端开关。
+用 CLI flag 在当前和未来的物理 adapter 之间切换，例如
+`--task go2_joystick_flat --sim motrix`；CLI 会组合 `src/unilab/conf/` 下对应的 owner YAML。
 :::
 
-:::{grid-item-card} 部署路径都有文档
-部署文档覆盖 sim-to-real、sim-to-sim、ONNX/runtime 导出、安全
-层，以及 G1、Go2、Allegro 的机器人专属说明。
+:::{grid-item-card} 跨硬件扩展
+任务 contract 将 CPU 并行或外部 worker 仿真连接到 accelerator learner，实验可以随着
+手头可用的硬件持续扩展。
 :::
 
 ::::
@@ -67,6 +66,7 @@ curl -LsSf https://astral.sh/uv/install.sh | sh
 git clone https://github.com/unilabsim/UniLab.git
 cd UniLab
 uv sync --extra motrix
+uv run demo dance
 uv run train --algo ppo --task go2_joystick_flat --sim motrix \
   algo.max_iterations=1 algo.num_envs=16 training.no_play=true
 ```
@@ -88,13 +88,13 @@ uv run train --algo ppo --task go2_joystick_flat --sim motrix \
 :::{grid-item-card} 运行或回放训练
 :link: 1-getting_started/1-quick_demo
 :link-type: doc
-先在 Go2 上跑 PPO，再进入评估、回放或 checkpoint 续训。
+先运行预训练 demo，再进入 PPO 训练、评估、回放或 checkpoint 续训。
 :::
 
-:::{grid-item-card} 选择后端
-:link: 2-user_guide/3-backends/3-choosing_a_backend
+:::{grid-item-card} 选择物理后端
+:link: 2-user_guide/3-backends/0-index
 :link-type: doc
-通过 task owner YAML 和后端能力文档对比 MuJoCo 与 Motrix。
+通过 task owner YAML 选择后端，并阅读其安装与能力要求。
 :::
 
 :::{grid-item-card} 挑选算法
@@ -123,12 +123,13 @@ registry 和 task-owner contract。
 
 ```{mermaid}
 flowchart LR
-  cli["uv run train/eval<br/>--algo --task --sim"] --> owner["Task owner YAML<br/>conf/*/task/..."]
-  cli --> script["Thin script routing<br/>scripts/train_*.py"]
+  cli["uv run train/eval<br/>--algo --task --sim"] --> owner["Task owner YAML<br/>src/unilab/conf/*/task/..."]
+  cli --> script["Thin script routing<br/>src/unilab/scripts/train_*.py"]
   owner --> registry["Registry bootstrap<br/>src/unilab/base/registry.py"]
   registry --> env["NpEnv contract<br/>obs dict + info dict"]
-  env --> backend["SimBackend<br/>MuJoCo or Motrix"]
-  env --> runtime["Runner / IPC<br/>shared memory lifecycle"]
+  env --> backend["SimBackend<br/>unisim-core adapters"]
+  env --> factory["EnvFactory contract"]
+  factory --> runtime["Runner / IPC<br/>unilab-rl async runtime"]
   runtime --> learner["Learner<br/>PPO / APPO / SAC / TD3"]
 ```
 
@@ -146,11 +147,11 @@ recommendation 元数据。
 | --- | --- | --- |
 | Go1 joystick | PPO、APPO、TD3 | PPO 有已测试的 MuJoCo 与 Motrix 行。APPO 有已测试的 MuJoCo 行和 Motrix registered 行。TD3 有 `go1_joystick_flat` 的 Motrix owner YAML。 |
 | Go2 joystick | PPO、FlashSAC、TD3 | PPO 有已测试的 MuJoCo 与 Motrix 行。FlashSAC 有 `go2_joystick_flat` 的 MuJoCo owner YAML；TD3 有 `go2_joystick_flat` 的 Motrix owner YAML。 |
-| Go2 arm manip-loco | PPO、HIM-PPO | `conf/ppo/task/go2_arm_manip_loco/` 和 `conf/ppo_him/task/go2_arm_manip_loco/` 下有已提交的 MuJoCo owner YAML。 |
-| Go2W joystick | PPO | `conf/ppo/task/go2w_joystick_*` 下存在 MuJoCo 与 Motrix flat/rough 变体的 PPO owner YAML。 |
+| Go2 arm manip-loco | PPO、HIM-PPO | `src/unilab/conf/ppo/task/go2_arm_manip_loco/` 和 `src/unilab/conf/ppo_him/task/go2_arm_manip_loco/` 下有已提交的 MuJoCo owner YAML。 |
+| Go2W joystick | PPO | `src/unilab/conf/ppo/task/go2w_joystick_*` 下存在 MuJoCo 与 Motrix flat/rough 变体的 PPO owner YAML。 |
 | G1 locomotion / tracking | PPO、APPO、SAC、TD3 | PPO、APPO、SAC 都为 G1 任务提供了已提交的 MuJoCo 与 Motrix owner YAML；TD3 有一个 `g1_walk_flat` 的 MuJoCo owner。 |
 | Allegro in-hand | PPO、APPO | PPO 和 APPO 为 Allegro in-hand 任务提供了已提交的 MuJoCo 与 Motrix owner YAML。 |
-| Sharpa in-hand | PPO、APPO HORA teacher、HORA distillation | Sharpa owner YAML 为 PPO/APPO teacher 路径已提交；student distillation 使用 `conf/hora_distill/task/sharpa_inhand/mujoco.yaml`。 |
+| Sharpa in-hand | PPO、APPO HORA teacher、HORA distillation | Sharpa owner YAML 为 PPO/APPO teacher 路径已提交；student distillation 使用 `src/unilab/conf/hora_distill/task/sharpa_inhand/mujoco.yaml`。 |
 
 ```{toctree}
 :hidden:
diff --git a/docs/sphinx/source/zh_CN/1-getting_started/1-quick_demo.md b/docs/sphinx/source/zh_CN/1-getting_started/1-quick_demo.md
index a709bf3b5..9caa4ae23 100644
--- a/docs/sphinx/source/zh_CN/1-getting_started/1-quick_demo.md
+++ b/docs/sphinx/source/zh_CN/1-getting_started/1-quick_demo.md
@@ -33,15 +33,16 @@ make setup-motrix
 # make sync-xpu
 ```
 
-## 演示
+## 第一次成功
 
-演示回放（首次运行会从 Hugging Face 拉取预训练检查点）：
+在修改任务配置前，先运行一个预训练策略（首次运行会从 Hugging Face 拉取检查点和 asset）：
 
 ```bash
 uv run demo dance
 ```
 
-可用的 demo 名称：`teaser`、`dance`、`wallflip`、`boxtracking`、`locomani`、`inhandgrasp`。
+可用的 demo 名称为 `teaser`、`dance`、`wallflip`、`boxtracking`、`locomani` 和
+`inhandgrasp`。运行 `uv run demo --help` 查看 device 和 refresh 选项。
 
 中国大陆用户：运动、场景、机器人网格和 demo 检查点在首次运行时从 Hugging Face 拉取。如果
 `huggingface.co` 无法访问，请在运行训练、评估或 demo 命令前切到社区镜像：
diff --git a/docs/sphinx/source/zh_CN/1-getting_started/2-installation.md b/docs/sphinx/source/zh_CN/1-getting_started/2-installation.md
index f23eedd17..187eaaedf 100644
--- a/docs/sphinx/source/zh_CN/1-getting_started/2-installation.md
+++ b/docs/sphinx/source/zh_CN/1-getting_started/2-installation.md
@@ -6,8 +6,9 @@
 
 - Python `>=3.10,<3.14`，来自 `pyproject.toml`。
 - `uv`，用于依赖同步和命令执行。
-- `cmake`，本地安装流程所需，详见
-  `docs/sphinx/source/zh_CN/1-getting_started/2-installation.md`。
+- Git 和 `curl`，用于克隆仓库及下载 runtime asset。
+- `cmake`，构建 Drake 原生 batch extension 时需要。Drake setup 脚本使用 CMake
+  和 C++ 工具链。
 - 使用 `mujoco` extra 时：需要 C++17 工具链和 Python 开发头文件，
   因为 `mujoco-uni-runtime` 仅以源码分发，`uv sync` 时会就地编译其原生扩展
   （针对 lock 钉住的 mujoco 版本）。缺少这些依赖时，`make setup` 会在编译
@@ -21,31 +22,59 @@
 ## 克隆与同步
 
 ```bash
+# Linux / macOS：
 curl -LsSf https://astral.sh/uv/install.sh | sh
+
+# Windows PowerShell：
+# powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
+
 git clone https://github.com/unilabsim/UniLab.git
 cd UniLab
+# 推荐的主环境解释器：
+uv python install 3.13
 ```
 
+UniLab 支持 Python `3.10` 到 `3.13`；主环境推荐使用 `3.13`。IsaacGym 和 IsaacSim
+在下方使用各自独立的 worker Python 版本。
+
+如果计划使用 Drake，还需要在主机上安装 CMake：
+
 ```bash
+# macOS：
 brew install cmake
-# Ubuntu / Debian:
+
+# Ubuntu / Debian：
 # sudo apt-get install cmake
 ```
 
-选择一条同步路径：
+选择一条核心安装路径：
 
 ```bash
+# 完整默认环境：MuJoCo + Motrix，并安装 shell 自动补全。
 make setup
-make setup-motrix
+
+# 运行第一次 Motrix demo 的最快路径。
+# make setup-motrix
+
+# 仅安装 MuJoCo。
+# make setup-mujoco
 ```
 
-`make setup` 会运行 `uv sync` 并安装 shell 自动补全。`make setup-motrix`
-会运行 `uv sync --extra motrix` 并安装相同的补全条目。如果 `make`
-不可用，可直接运行底层的同步命令：
+`make setup` 会运行 `uv sync --extra mujoco --extra motrix` 并安装 shell 自动补全。
+`make setup-motrix` 会运行 `uv sync --extra motrix` 并安装相同的补全条目。
+`make setup-mujoco` 会运行 `uv sync --extra mujoco` 并安装补全。三条路径只选择一条。
+如果 `make` 不可用，可运行对应的底层命令：
 
 ```bash
-uv sync
-uv sync --extra motrix
+# 完整默认环境：
+uv sync --extra mujoco --extra motrix
+uv run --no-sync unilab-complete install
+
+# 仅 Motrix：
+# uv sync --extra motrix && uv run --no-sync unilab-complete install
+
+# 仅 MuJoCo：
+# uv sync --extra mujoco && uv run --no-sync unilab-complete install
 ```
 
 ## conda 与 pip
@@ -63,18 +92,97 @@ cd UniLab
 make setup-motrix
 ```
 
-如果不需要 Motrix，可使用 `make setup`；ROCm / XPU 仍走下方专用的 `make` 路径。
+如果不需要 Motrix，可使用 `make setup-mujoco`；ROCm / XPU 仍走下方专用的 `make` 路径。
+
+从源码 checkout 使用 pip 时，这是备用路径。请先安装 package，再显式添加所需 runtime：
+
+```bash
+# 本地开发的 editable install：
+pip install -e .
+
+# wheel 风格的常规安装（去掉 -e）：
+# pip install .
 
-`pip install -e .` 和 `pip install .` 当前只适合源码 checkout 内的开发验证，尚不支
-持通过构建好的 wheel / sdist 在任意目录直接运行训练。训练入口仍依赖仓库中的
-`conf/` 和 `scripts/`。pip-only 安装、仓库外运行以及正式发布 wheel 的验证路径由
-#360 跟踪。
+# 需要 Motrix 时：
+pip install motrixsim-core==0.8.2
+
+# 需要 MuJoCo 时（分两步安装 runtime）：
+pip install "mujoco>=3.5,<3.11" pybind11 wheel
+pip install --no-build-isolation "mujoco-uni-runtime==0.4.0"
+```
+
+editable install 会指向源码 checkout；常规安装会把 package 和任务配置
+（`unilab/conf/`）复制进环境。两种方式都支持在任意目录运行 `train` / `eval` / `demo`，
+日志与 checkpoint 写入当前工作目录。MuJoCo runtime 必须在匹配的 `mujoco` package
+之后安装，并使用 `--no-build-isolation`；pip 默认的隔离构建看不到该依赖。MJWarp、
+Genesis、平台相关 torch index、ROCm / XPU profile 和原生扩展重建行为请优先使用上面的
+uv 路径。机器人 mesh 和纹理不会打进 wheel，而是在 cold path 从
+`unilabsim/unilab-robots` 数据集下载。请确保安装位置可写，或从源码 checkout 使用
+`uv run unilab-pull-assets` 预拉取。isaacgym / isaacsim 后端和 HORA 多卡提交路径仍假设
+源码 checkout；外部后端请使用下方专用安装页。
+
+## 运行时 Asset
+
+大型 asset 不打包进 wheel，而是在 cold path（所属功能首次使用时）从 Hugging Face
+数据集仓库懒加载：
+
+- [机器人网格与纹理](https://huggingface.co/datasets/unilabsim/unilab-robots)
+- [动作片段](https://huggingface.co/datasets/unilabsim/unilab-motions)
+- [场景](https://huggingface.co/datasets/unilabsim/unilab-scenes)
+- [抓取缓存](https://huggingface.co/datasets/unilabsim/unilab-caches)
+- [Demo checkpoint](https://huggingface.co/datasets/unilabsim/unilab-checkpoints)
+
+机器人 asset 可通过 `uv run unilab-pull-assets` 预拉取。中国大陆用户在默认
+Hugging Face endpoint 无法访问时，可设置 `HF_ENDPOINT=https://hf-mirror.com`。
+
+## 后端 Extras
+
+基础 package 会安装公开的 `unisim-core` contract；具体仿真器的依赖保持为可选项。
+单后端环境请根据所用后端选择一条路径；如果要比较多个进程内后端，请在一条 `uv sync`
+命令中组合对应 extras，外部 worker 脚本仍需单独执行：
+
+例如，本地对比环境可以一次安装 MuJoCo、Motrix、MJWarp 和 Genesis：
+
+```bash
+uv sync --extra mujoco --extra motrix --extra mjwarp --extra genesis
+```
+
+| 后端 | 安装路径 | 重要前置条件 |
+| --- | --- | --- |
+| MuJoCo | `make setup-mujoco` 或 `uv sync --extra mujoco` | 原生扩展需要 C++17 编译器和 Python 开发头文件 |
+| Motrix | `make setup-motrix` 或 `uv sync --extra motrix` | 从固定版本 Python package 安装 Motrix runtime |
+| MJWarp | `uv sync --extra mujoco --extra mjwarp` | NVIDIA CUDA 和显式 CUDA process device |
+| Genesis | `uv sync --extra genesis` | 已验证路径使用 Linux x86_64、NVIDIA GPU 及固定版本 torch/Genesis |
+| Drake | `make setup-drake` | C++20、Eigen/fmt/spdlog，以及已有 Drake prefix 或脚本下载路径 |
+| IsaacGym | `bash scripts/tools/setup_isaacgym_env.sh` | Linux x86_64、NVIDIA driver 和独立 Python 3.8 worker 环境 |
+| IsaacSim | `bash scripts/tools/setup_isaacsim_env.sh` | Linux x86_64、NVIDIA CUDA、独立 Python 3.11 worker 和 Kit EULA 接受 |
+
+Drake、IsaacGym 和 IsaacSim 的 setup 脚本会将外部 runtime 安装到仓库之外，并且可以
+安全重复运行；它们不会把外部仿真器装进 UniLab 主环境。runtime 变量、渲染器要求和
+验证命令见各后端页面：
+
+- {doc}`MuJoCo <../2-user_guide/3-backends/1-mujoco>`
+- {doc}`Motrix <../2-user_guide/3-backends/2-motrix>`
+- {doc}`MJWarp <../2-user_guide/3-backends/0-index>`
+- {doc}`Genesis <../2-user_guide/3-backends/5-genesis>`
+- {doc}`Drake <../2-user_guide/3-backends/6-drake>`
+- {doc}`IsaacGym <../2-user_guide/3-backends/3-isaacgym>`
+- {doc}`IsaacSim <../2-user_guide/3-backends/4-isaacsim>`
 
 ## 平台配置档
 
 Linux CUDA 和 macOS 使用默认的 `pyproject.toml`。默认的 Linux torch
 wheel 来源是在 `pyproject.toml` 中配置的 PyTorch `cu128` 索引。
 
+在 Apple Silicon macOS 上，`make setup-motrix` 是最短的交互式路径。CLI 会在需要时
+通过 `mxpython` 路由 Motrix 回放；MuJoCo 回放使用官方 MuJoCo wheel 自带的
+`mjpython` application。可用时 Torch 会自动选择 `mps` device；为保持配置可移植，
+没有 CUDA 时，`cuda` alias 会解析到 MPS。
+
+在 Windows 上，如果没有 GNU `make` 和 Bash，请使用上面的直接 `uv sync` 命令。
+编译 MuJoCo 原生扩展需要 MSVC Build Tools 和 Python 开发头文件。如果要使用
+Makefile，请另行安装 GNU Make 和 Bash（例如通过 Chocolatey 或 WSL）。
+
 ROCm 和 Intel XPU 有各自显式的 Makefile 目标：
 
 ```bash
@@ -95,6 +203,14 @@ ROCm 说明：
   后重新执行 `make setup-motrix`（或 `uv sync --extra motrix`）；提交任何非 ROCm
   依赖改动前先确认当前配置档。
 - 训练配置里的设备字段仍沿用 `cuda` 语义，不要改成 `rocm`。
+- 从 PyPI 安装（不克隆仓库）时，`make sync-rocm` 不适用；先从 PyTorch ROCm 索引
+  安装仓库验证过的 torch build，再安装 `unilab`。发布的依赖范围是
+  `torch>=2.8,<2.12`，pip 会保留已安装的 ROCm build，不会替换为 CUDA wheel：
+
+  ```bash
+  pip install torch==2.11.0 --index-url https://download.pytorch.org/whl/rocm7.2
+  pip install unilab
+  ```
 
 Intel XPU 说明：
 
@@ -108,7 +224,8 @@ Intel XPU 说明：
 
 ```bash
 export UV_INDEX_URL=https://pypi.tuna.tsinghua.edu.cn/simple
-uv sync --index-url https://pypi.tuna.tsinghua.edu.cn/simple
+uv sync --extra mujoco --extra motrix \
+  --index-url https://pypi.tuna.tsinghua.edu.cn/simple
 ```
 
 ## 冒烟检查
diff --git a/docs/sphinx/source/zh_CN/1-getting_started/3-evaluation_and_playback.md b/docs/sphinx/source/zh_CN/1-getting_started/3-evaluation_and_playback.md
index 40af2bd2b..76de27894 100644
--- a/docs/sphinx/source/zh_CN/1-getting_started/3-evaluation_and_playback.md
+++ b/docs/sphinx/source/zh_CN/1-getting_started/3-evaluation_and_playback.md
@@ -22,36 +22,41 @@ uv run demo dance
 - `record` — 将 MP4 写入 `runs/<run>/playback/`。
 - `none` — 跳过渲染，仅计算指标。
 
+当使用 `--sim mujoco --render-mode interactive` 时，`uv run eval` 会直接启动
+`play_interactive.py` MuJoCo viewer。该模式只 rollout 一个环境以保持相机和交互控制；
+`training.play_env_num` 在此模式下会被忽略。
+
 `training.export_onnx=false` 目前仅适用于 off-policy 回放路径
-（`scripts/train_offpolicy.py` 以及使用 `--algo sac|td3|flashsac` 的 CLI 运行）。它会跳过
+（`src/unilab/scripts/train_sac.py` / `src/unilab/scripts/train_td3.py` / `src/unilab/scripts/train_flashsac.py`
+以及使用 `--algo sac|td3|flashsac` 的 CLI 运行）。它会跳过
 `policy.onnx` 的导出与校验，但仍会执行回放和视频录制。
 
 ## MuJoCo viewer 可视化脚本
 
 常规评估和视频导出优先使用 `uv run eval`。需要直接打开 `mujoco.viewer`
-调试策略时，可以使用低层脚本 `scripts/play_interactive.py`。
+调试策略时，可以使用低层脚本 `src/unilab/scripts/play_interactive.py`。
 
-`scripts/play_interactive.py` 是通用 MuJoCo viewer 入口，适合 PPO、APPO、
+`src/unilab/scripts/play_interactive.py` 是通用 MuJoCo viewer 入口，适合 PPO、APPO、
 SAC、FlashSAC 和 HORA distill 的策略可视化。它使用 `--algo / --task / --sim`
 选择算法和 owner config；无论 `--sim` 选择 MuJoCo 还是 Motrix，窗口都使用
 `mujoco.viewer` 可视化，`--sim` 只决定读取哪份配置。
 
 ```bash
 # 使用 owner config 中的 interactive.action_mode；全局默认是 zero action
-uv run scripts/play_interactive.py --algo ppo --task go2_joystick_flat --sim mujoco
+uv run src/unilab/scripts/play_interactive.py --algo ppo --task go2_joystick_flat --sim mujoco
 
 # 随机动作
-uv run scripts/play_interactive.py --algo ppo --task go2_joystick_flat --sim mujoco \
+uv run src/unilab/scripts/play_interactive.py --algo ppo --task go2_joystick_flat --sim mujoco \
     interactive.action_mode=random
 
 # 策略动作
-uv run scripts/play_interactive.py --algo ppo --task go2_joystick_flat --sim mujoco \
+uv run src/unilab/scripts/play_interactive.py --algo ppo --task go2_joystick_flat --sim mujoco \
     algo.load_run=-1 interactive.action_mode=policy
 
-uv run scripts/play_interactive.py --algo flashsac --task g1_walk_flat --sim motrix \
+uv run src/unilab/scripts/play_interactive.py --algo flashsac --task g1_walk_flat --sim motrix \
     algo.load_run=-1 interactive.action_mode=policy
 
-uv run scripts/play_interactive.py --algo ppo --task go2_joystick_flat --sim mujoco \
+uv run src/unilab/scripts/play_interactive.py --algo ppo --task go2_joystick_flat --sim mujoco \
     interactive.action_mode=policy interactive.keyboard=true
 ```
 
diff --git a/docs/sphinx/source/zh_CN/1-getting_started/4-project_structure.md b/docs/sphinx/source/zh_CN/1-getting_started/4-project_structure.md
index 799d1cef5..e630650cc 100644
--- a/docs/sphinx/source/zh_CN/1-getting_started/4-project_structure.md
+++ b/docs/sphinx/source/zh_CN/1-getting_started/4-project_structure.md
@@ -5,11 +5,11 @@ UniLab 将运行时 contract、配置、训练脚本和文档分置于不同的
 | 路径 | Owner 角色 |
 | --- | --- |
 | `scripts/` | 轻量的训练与工具入口。脚本负责组合 Hydra 配置并调用 owner 层代码。 |
-| `conf/` | Hydra 根配置和任务 owner YAML。顶层 CLI 将后端选择暴露为 `--task` 加 `--sim`，然后组合出匹配的 owner YAML。 |
+| `src/unilab/conf/` | Hydra 根配置和任务 owner YAML。顶层 CLI 将后端选择暴露为 `--task` 加 `--sim`，然后组合出匹配的 owner YAML。 |
 | `src/unilab/base/` | Registry、env state、scene 以及 backend contract。 |
 | `src/unilab/envs/` | 任务 env 实现，以及任务专属的 reset、reward、observation 和 DR 逻辑。 |
-| `src/unilab/algos/` | PPO、APPO、off-policy、HIM-PPO 和 HORA 算法代码。 |
-| `src/unilab/ipc/` | 共享内存与异步 runner 原语。 |
+| `uni_rl` (unilab-rl repo) | PPO、APPO、off-policy、HIM-PPO 和 HORA 算法代码。 |
+| `uni_rl.ipc` (unilab-rl repo) | 共享内存与异步 runner 原语。 |
 | `src/unilab/training/` | 共享的训练辅助工具，用于日志、回放、种子处理和配置守卫（config guard）。 |
 | `src/unilab/visualization/` | 回放、渲染、NaN 检查以及 scene/export 工具。 |
 | `tests/` | Contract、config、env、algorithm、script 和集成测试。 |
@@ -20,11 +20,11 @@ UniLab 将运行时 contract、配置、训练脚本和文档分置于不同的
 
 主要的配置根为：
 
-- `conf/ppo/config.yaml`，用于 torch PPO。
-- `conf/appo/config.yaml`，用于 APPO。
-- `conf/offpolicy/config.yaml` 加上 `conf/offpolicy/algo/*.yaml`，用于 SAC、
-  TD3 和 FlashSAC。
-- `conf/ppo_him/config.yaml` 和 `conf/hora_distill/config.yaml`，用于
+- `src/unilab/conf/ppo/config.yaml`，用于 torch PPO。
+- `src/unilab/conf/appo/config.yaml`，用于 APPO。
+- `src/unilab/conf/sac/config.yaml`、`src/unilab/conf/td3/config.yaml` 和 `src/unilab/conf/flashsac/config.yaml`，
+  分别用于 SAC、TD3 和 FlashSAC，算法超参数内联在各自的 config.yaml 中。
+- `src/unilab/conf/ppo_him/config.yaml` 和 `src/unilab/conf/hora_distill/config.yaml`，用于
   专门的 HIM-PPO 和 HORA 路径。
 
 任务 owner YAML 即后端身份。示例：
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/1-training/1-cli_reference.md b/docs/sphinx/source/zh_CN/2-user_guide/1-training/1-cli_reference.md
index 71252e4db..487759b14 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/1-training/1-cli_reference.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/1-training/1-cli_reference.md
@@ -7,11 +7,11 @@ Hydra 组合。
 
 | 目标 | 命令形式 | 路由到的脚本 |
 | --- | --- | --- |
-| PPO | `uv run train --algo ppo --task <task> --sim <backend>` | `scripts/train_rsl_rl.py` |
-| APPO | `uv run train --algo appo --task <task> --sim <backend>` | `scripts/train_appo.py` |
-| SAC | `uv run train --algo sac --task <task> --sim <backend>` | `scripts/train_offpolicy.py` |
-| TD3 | `uv run train --algo td3 --task <task> --sim <backend>` | `scripts/train_offpolicy.py` |
-| FlashSAC | `uv run train --algo flashsac --task <task> --sim <backend>` | `scripts/train_offpolicy.py` |
+| PPO | `uv run train --algo ppo --task <task> --sim <backend>` | `src/unilab/scripts/train_rsl_rl.py` |
+| APPO | `uv run train --algo appo --task <task> --sim <backend>` | `src/unilab/scripts/train_appo.py` |
+| SAC | `uv run train --algo sac --task <task> --sim <backend>` | `src/unilab/scripts/train_sac.py` |
+| TD3 | `uv run train --algo td3 --task <task> --sim <backend>` | `src/unilab/scripts/train_td3.py` |
+| FlashSAC | `uv run train --algo flashsac --task <task> --sim <backend>` | `src/unilab/scripts/train_flashsac.py` |
 
 示例：
 
@@ -40,21 +40,32 @@ choices，不改变命令行为。在新 checkout 上，可用一条 setup 命
 安装：
 
 ```bash
+# 完整默认环境（MuJoCo + Motrix）：
 make setup
 
-# 需要 Motrix 时：
-make setup-motrix
+# 仅 MuJoCo：
+# make setup-mujoco
+
+# 仅 Motrix（运行 Motrix demo 的最快路径）：
+# make setup-motrix
 ```
 
-`make setup` 会执行 `uv sync` 和 `uv run --no-sync unilab-complete install`；
-`make setup-motrix` 会执行 `uv sync --extra motrix` 和同样的补全安装。安装命令会按
-`$SHELL` / 平台选择 Bash 或 Zsh，只写入用户级 rc 文件。当前终端不会被自动激活，重新
-打开终端或 source 对应 rc 文件后生效。
+`make setup` 会执行 `uv sync --extra mujoco --extra motrix`，随后运行
+`uv run --no-sync unilab-complete install`；`make setup-mujoco` 和
+`make setup-motrix` 只选择对应的 extra，并安装相同的补全。一个环境只选择一条安装路径。
+安装命令会按 `$SHELL` / 平台选择 Bash 或 Zsh，只写入用户级 rc 文件。当前终端不会被自动
+激活，重新打开终端或 source 对应 rc 文件后生效。
 
 如果系统没有 `make`，可直接执行：
 
 ```bash
-uv sync && uv run --no-sync unilab-complete install
+# 完整默认环境：
+uv sync --extra mujoco --extra motrix
+uv run --no-sync unilab-complete install
+
+# 或选择一个后端 extra：
+# uv sync --extra mujoco && uv run --no-sync unilab-complete install
+# uv sync --extra motrix && uv run --no-sync unilab-complete install
 ```
 
 Linux / WSL 的 Bash 用户也可手动把下面内容写入 `~/.bashrc`：
@@ -85,7 +96,9 @@ uv run eval --algo ppo --task go2_joystick_flat --sim motrix --load-run -1 \
   --render-mode record
 ```
 
-支持的渲染模式为 `auto`、`interactive`、`record` 和 `none`。
+支持的渲染模式为 `auto`、`interactive`、`record` 和 `none`。其中 MuJoCo 交互模式
+（`--sim mujoco --render-mode interactive`）直接路由到 `play_interactive.py`，并始终只
+rollout 一个环境。
 
 ## 演示
 
@@ -116,8 +129,8 @@ demo 入口由 `src/unilab/demo.py` 实现，并从 `src/unilab/cli.py` 路由
 当你需要检查 Hydra 配置组或复现脚本层面的问题时，底层脚本仍然可用。在正常使用
 中，请将定义路由的取值保留在上面的统一 CLI flag 中。
 
-对于 off-policy 路由，请保持 `--algo` 与 `conf/offpolicy/task/<algo>/` 下的
-owner 树对齐；不要在 `--task` 中包含算法名称。
+对于 off-policy 路由，`--algo` 选择按算法划分的 owner 树 `src/unilab/conf/<algo>/`；
+不要在 `--task` 中包含算法名称。
 
 ## 常用 Override
 
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/1-training/2-hydra_config.md b/docs/sphinx/source/zh_CN/2-user_guide/1-training/2-hydra_config.md
index 19070967a..998fca78e 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/1-training/2-hydra_config.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/1-training/2-hydra_config.md
@@ -7,11 +7,11 @@ reward、scene 以及 task 专属运行时字段的身份标识。
 
 | 技术栈 | Owner YAML 形式 |
 | --- | --- |
-| PPO | `conf/ppo/task/<task>/<backend>.yaml` |
-| APPO | `conf/appo/task/<task>/<backend>.yaml` |
-| SAC / TD3 / FlashSAC | `conf/offpolicy/task/<algo>/<task>/<backend>.yaml` |
-| HIM-PPO | `conf/ppo_him/task/<task>/<backend>.yaml` |
-| HORA 蒸馏 | `conf/hora_distill/task/<task>/<backend>.yaml` |
+| PPO | `src/unilab/conf/ppo/task/<task>/<backend>.yaml` |
+| APPO | `src/unilab/conf/appo/task/<task>/<backend>.yaml` |
+| SAC / TD3 / FlashSAC | `src/unilab/conf/<algo>/task/<task>/<backend>.yaml` |
+| HIM-PPO | `src/unilab/conf/ppo_him/task/<task>/<backend>.yaml` |
+| HORA 蒸馏 | `src/unilab/conf/hora_distill/task/<task>/<backend>.yaml` |
 
 示例：
 
@@ -21,8 +21,8 @@ uv run train --algo ppo --task go2_joystick_flat --sim motrix
 uv run train --algo sac --task g1_walk_flat --sim mujoco
 ```
 
-对于 off-policy，`--algo` 选择 `conf/offpolicy/task/<algo>/` 下 owner 路径的第一
-个分段；不要在 `--task` 中包含算法名称。
+对于 off-policy，`--algo` 选择按算法划分的配置树 `src/unilab/conf/<algo>/`；不要在
+`--task` 中包含算法名称。
 
 ## 安全的 Override
 
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/1-training/3-logging.md b/docs/sphinx/source/zh_CN/2-user_guide/1-training/3-logging.md
index 81c178336..27ee341ac 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/1-training/3-logging.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/1-training/3-logging.md
@@ -21,11 +21,11 @@ uv run train --algo ppo --task go2_joystick_flat --sim mujoco
 
 | 算法 | 日志根目录 | `algo_log_name` 来源 |
 | --- | --- | --- |
-| PPO | `logs/rsl_rl_ppo/<task>/` | `conf/ppo/config.yaml` |
-| APPO | `logs/appo/<task>/` | `conf/appo/config.yaml` |
-| SAC | `logs/fast_sac/<task>/` | `conf/offpolicy/algo/sac.yaml` |
-| FlashSAC | `logs/flash_sac/<task>/` | `conf/offpolicy/algo/flashsac.yaml` |
-| TD3 | `logs/fast_td3/<task>/` | `conf/offpolicy/algo/td3.yaml` |
+| PPO | `logs/rsl_rl_ppo/<task>/` | `src/unilab/conf/ppo/config.yaml` |
+| APPO | `logs/appo/<task>/` | `src/unilab/conf/appo/config.yaml` |
+| SAC | `logs/fast_sac/<task>/` | `src/unilab/conf/sac/config.yaml` |
+| FlashSAC | `logs/flash_sac/<task>/` | `src/unilab/conf/flashsac/config.yaml` |
+| TD3 | `logs/fast_td3/<task>/` | `src/unilab/conf/td3/config.yaml` |
 
 单个 run 目录名为 `YYYY-MM-DD_HH-MM-SS_<sim_backend>`，例如
 `2026-03-09_18-30-00_mujoco`。常见产物包括 `run_config.json`、
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/0-index.md b/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/0-index.md
index c5c9c62d3..33c4ccaef 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/0-index.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/0-index.md
@@ -5,13 +5,13 @@
 
 | 算法 | 类型 | 入口 | 配置证据 |
 | --- | --- | --- | --- |
-| PPO | 同步 on-policy | `scripts/train_rsl_rl.py` | `conf/ppo/config.yaml` |
-| APPO | 异步 on-policy | `scripts/train_appo.py` | `conf/appo/config.yaml` |
-| SAC | off-policy | `scripts/train_offpolicy.py` | `conf/offpolicy/algo/sac.yaml` |
-| TD3 | off-policy | `scripts/train_offpolicy.py` | `conf/offpolicy/algo/td3.yaml` |
-| FlashSAC | off-policy | `scripts/train_offpolicy.py` | `conf/offpolicy/algo/flashsac.yaml` |
-| HIM-PPO | 高度估计器 PPO 路径 | `scripts/train_him_ppo.py` | `conf/ppo_him/config.yaml` |
-| HORA | teacher/student 蒸馏路径 | `scripts/train_hora_distill.py` | `conf/hora_distill/config.yaml` |
+| PPO | 同步 on-policy | `src/unilab/scripts/train_rsl_rl.py` | `src/unilab/conf/ppo/config.yaml` |
+| APPO | 异步 on-policy | `src/unilab/scripts/train_appo.py` | `src/unilab/conf/appo/config.yaml` |
+| SAC | off-policy | `src/unilab/scripts/train_sac.py` | `src/unilab/conf/sac/config.yaml` |
+| TD3 | off-policy | `src/unilab/scripts/train_td3.py` | `src/unilab/conf/td3/config.yaml` |
+| FlashSAC | off-policy | `src/unilab/scripts/train_flashsac.py` | `src/unilab/conf/flashsac/config.yaml` |
+| HIM-PPO | 高度估计器 PPO 路径 | `scripts/train_him_ppo.py` | `src/unilab/conf/ppo_him/config.yaml` |
+| HORA | teacher/student 蒸馏路径 | `scripts/train_hora_distill.py` | `src/unilab/conf/hora_distill/config.yaml` |
 
 ```{toctree}
 :hidden:
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/1-ppo.md b/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/1-ppo.md
index 5a900bfc1..26094744c 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/1-ppo.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/1-ppo.md
@@ -1,7 +1,7 @@
 # PPO
 
-PPO 是默认的同步 on-policy 训练路径。它使用 `scripts/train_rsl_rl.py`，从
-`conf/ppo/config.yaml` 组合配置，并运行 `src/unilab/algos/torch/rsl_rl_ppo.py`
+PPO 是默认的同步 on-policy 训练路径。它使用 `src/unilab/scripts/train_rsl_rl.py`，从
+`src/unilab/conf/ppo/config.yaml` 组合配置，并运行 `uni_rl.algos.rsl_rl_ppo` (unilab-rl repo)
 和 `src/unilab/training/rsl_rl.py` 中的 RSL-RL 适配代码。
 
 ## 快速开始
@@ -26,7 +26,7 @@ uv run train --algo ppo --task go2_joystick_flat --sim mujoco \
 uv run eval --algo ppo --task go2_joystick_flat --sim mujoco --load-run -1
 ```
 
-日志按 `algo.algo_log_name` 分组；`conf/ppo/config.yaml` 中的默认值为
+日志按 `algo.algo_log_name` 分组；`src/unilab/conf/ppo/config.yaml` 中的默认值为
 `rsl_rl_ppo`。
 
 ## 单机多卡训练
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/2-appo.md b/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/2-appo.md
index d15b568b5..ceb2e7da3 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/2-appo.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/2-appo.md
@@ -1,7 +1,7 @@
 # APPO
 
-APPO 是 UniLab 的异步 PPO 路径。它使用 `scripts/train_appo.py`、
-`conf/appo/config.yaml` 以及 `src/unilab/algos/torch/appo/` 下的运行时。该配置暴露
+APPO 是 UniLab 的异步 PPO 路径。它使用 `src/unilab/scripts/train_appo.py`、
+`src/unilab/conf/appo/config.yaml` 以及 `uni_rl.algos.appo` (unilab-rl repo) 下的运行时。该配置暴露
 了 `algo.steps_per_env`、`training.collector_device` 和
 `training.replay_queue_size`；算法配置中包含 V-trace 裁剪字段。
 
@@ -70,5 +70,5 @@ gantt
 - `training.collector_device`：collector 设备；默认跟随 learner。
 - `algo.save_interval`：checkpoint 保存间隔。
 
-默认日志根目录为 `logs/appo/<task>/`，来自 `conf/appo/config.yaml` 中的
+默认日志根目录为 `logs/appo/<task>/`，来自 `src/unilab/conf/appo/config.yaml` 中的
 `algo.algo_log_name=appo`。
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/3-sac.md b/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/3-sac.md
index 897fa9601..c0e2705b5 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/3-sac.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/3-sac.md
@@ -1,12 +1,12 @@
 # SAC
 
-SAC 通过共享的 off-policy 入口 `scripts/train_offpolicy.py` 选择，TD3 与 FlashSAC
-也共用该脚本。主配置为 `conf/offpolicy/config.yaml`，SAC 算法的默认值位于
-`conf/offpolicy/algo/sac.yaml`。当前的日志名称为 `fast_sac`。
+SAC 通过 `src/unilab/scripts/train_sac.py` 运行；TD3 与 FlashSAC 各有独立的入口与按算法
+划分的配置树。主配置为 `src/unilab/conf/sac/config.yaml`，SAC 算法的默认值内联在其中。
+当前的日志名称为 `fast_sac`。
 
 ## 运行模型
 
-off-policy runner 通过有界 shared memory 把 CPU 仿真与 accelerator 学习解耦。
+off-policy runner 通过有界 shared memory 把仿真采集与 accelerator 学习解耦。
 collector 子进程通过两个 packed ingress slot 发布 transition，完整 replay ring 只由
 一个 CUDA 或 Apple MPS learner device 持有。因此 host replay 分配不随 replay
 capacity 增长；slot 的 device copy 完成后才推进 `ptr` 与 `size`。CUDA 通过 side
@@ -22,7 +22,7 @@ uv run train --algo sac --task g1_walk_rough --sim motrix training.no_play=true
 
 ## 关键字段
 
-对于 off-policy 回放路径（`scripts/train_offpolicy.py` / CLI `--algo sac`），设置
+对于 off-policy 回放路径（`src/unilab/scripts/train_sac.py` / CLI `--algo sac`），设置
 `training.export_onnx=false` 可在仍然录制回放视频的同时跳过 `policy.onnx` 导出。参
 见 {doc}`/zh_CN/1-getting_started/3-evaluation_and_playback`。
 
@@ -51,6 +51,12 @@ rank 子进程。启动时 rank 0 一次性广播 actor、critic、target critic
 执行阻塞式 flat-gradient `all_reduce(SUM) / world_size`。各 rank 不交换 replay 数据，
 在相同初值、平均梯度和更新顺序下各自维护一致的 optimizer 状态。
 
+每个 rank 当前只创建一个 collector。使用 mjwarp 时，rank i 会在 probe env 和 collector
+正式 env materialization 之前，把 Warp 的进程默认/当前 device 显式绑定到该 rank 的
+learner device `cuda:devices[i]`；因此 collector 不依赖 Warp 新进程默认的 `cuda:0`，也不
+会跨 rank 集中到同一张卡。runtime manifest 的 `collector_backend_device` 记录本 rank 的
+实际绑定。
+
 off-policy 只公开 `training.devices` 这一个设备字段：`null` 或 `[]` 自动选择单个
 learner device，`[0]` 显式选择 `cuda:0`，两个以上索引才启动多卡拓扑。
 
@@ -70,7 +76,9 @@ rank 子目录或任何日志文件。
 `training.log_dir` 保持原样。
 
 collector 的 CPU 亲和按 rank 自动均分（`cpu_count // world_size` 一段），可用
-`training.dp_collector_cpu_ids` 显式指定。
+`training.dp_collector_cpu_ids` 显式指定。该核区经 `EnvCfg.cpu_ids` 生效：除
+MuJoCo worker 线程逐核绑定外，collector 进程本身（含 Numba 并行 kernel 线程池，池
+大小取核区长度）也被限制在同一核区内，避免跨 rank 抢占。
 
 当前限制：
 
@@ -86,7 +94,9 @@ collector 的 CPU 亲和按 rank 自动均分（`cpu_count // world_size` 一段
   默认 stream 与 side stream 均通过；跳过 warmup 时首个 all-reduce 会在 capture 中报
   `operation not permitted when stream is capturing`。有限超时的最小复现见
   `scripts/benchmark/rl/reproduce_nccl_cuda_graph_capture.py`。
-- 仅验证过 `mujoco` backend。
+- MuJoCo 有已提交的多卡 scaling benchmark；mjwarp 的 per-rank device placement 有
+  `tests/base/backend/test_process_device.py` 与 off-policy runner/worker 单测覆盖，但仓库中
+  尚无 mjwarp 多卡吞吐或收敛 benchmark。
 - 仅单节点：rank 之间通过 run 目录里的 FileStore rendezvous，NCCL 走 TCP
   loopback（默认 `NCCL_P2P_DISABLE=1` / `NCCL_SHM_DISABLE=1`，环境变量显式设置
   时优先）——部分机型（如 RTX 6000D）的 NCCL P2P/SHM peer transport 不可靠，
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/4-td3.md b/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/4-td3.md
index 236d694af..956b6d519 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/4-td3.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/4-td3.md
@@ -1,7 +1,7 @@
 # TD3
 
-TD3 与 SAC、FlashSAC 共用 off-policy 训练脚本。使用 `--algo td3` 选择它；owner
-YAML 证据位于 `conf/offpolicy/task/td3/` 下。
+TD3 通过 `src/unilab/scripts/train_td3.py` 运行，拥有独立的配置树。使用 `--algo td3`
+选择它；owner YAML 证据位于 `src/unilab/conf/td3/task/` 下。
 
 ## 快速开始
 
@@ -11,11 +11,11 @@ uv run train --algo td3 --task g1_walk_flat --sim mujoco
 
 ## 关键字段
 
-对于 off-policy 回放路径（`scripts/train_offpolicy.py` / CLI `--algo td3`），设置
+对于 off-policy 回放路径（`src/unilab/scripts/train_td3.py` / CLI `--algo td3`），设置
 `training.export_onnx=false` 可在仍然录制回放视频的同时跳过 `policy.onnx` 导出。参
 见 {doc}`/zh_CN/1-getting_started/3-evaluation_and_playback`。
 
-- 默认值位于 `conf/offpolicy/algo/td3.yaml`。
+- 默认值内联在 `src/unilab/conf/td3/config.yaml` 中。
 - `algo.algo_log_name=fast_td3`。
 - `algo.max_iterations=5000`。
 - `algo.policy_frequency=2`。
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/5-flash_sac.md b/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/5-flash_sac.md
index e1f8c5911..0050e650d 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/5-flash_sac.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/5-flash_sac.md
@@ -1,11 +1,11 @@
 # FlashSAC
 
-FlashSAC 是共享 off-policy 入口上的第三个算法。使用 `--algo flashsac` 选择它；默认
-值位于 `conf/offpolicy/algo/flashsac.yaml`，实现位于
-`src/unilab/algos/torch/flash_sac/` 下。
+FlashSAC 通过 `src/unilab/scripts/train_flashsac.py` 运行，拥有独立的配置树。使用
+`--algo flashsac` 选择它；默认值内联在 `src/unilab/conf/flashsac/config.yaml` 中，实现位于
+`uni_rl.algos.flash_sac` (unilab-rl repo) 下。
 
-它与 SAC、TD3 共用 off-policy 训练脚本，但默认网络并不相同：actor 使用 block-based
-结构，critic 使用 distributional（categorical）Q 变体。
+它与 SAC、TD3 共用 off-policy runner 设计，但默认网络并不相同：actor 使用
+block-based 结构，critic 使用 distributional（categorical）Q 变体。
 
 ## 快速开始
 
@@ -16,7 +16,7 @@ uv run train --algo flashsac --task go2_joystick_flat --sim mujoco training.no_p
 
 ## 关键字段
 
-对于 off-policy 回放路径（`scripts/train_offpolicy.py` / CLI `--algo flashsac`），设
+对于 off-policy 回放路径（`src/unilab/scripts/train_flashsac.py` / CLI `--algo flashsac`），设
 置 `training.export_onnx=false` 可在仍然录制回放视频的同时跳过 `policy.onnx` 导出。
 参见 {doc}`/zh_CN/1-getting_started/3-evaluation_and_playback`。
 
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/6-him_ppo.md b/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/6-him_ppo.md
index c0acd398b..2ce347a9a 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/6-him_ppo.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/6-him_ppo.md
@@ -1,8 +1,8 @@
 # HIM-PPO
 
 HIM-PPO 有自己的配置组和脚本。入口是 `scripts/train_him_ppo.py`，基础配置是
-`conf/ppo_him/config.yaml`，已提交的 task owner 是
-`conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml`。
+`src/unilab/conf/ppo_him/config.yaml`，已提交的 task owner 是
+`src/unilab/conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml`。
 
 ## 当前入口
 
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/7-hora.md b/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/7-hora.md
index bf623244b..3741e6b30 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/7-hora.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/2-algorithms/7-hora.md
@@ -3,7 +3,7 @@
 已提交的 HORA 路径是 Sharpa 手内（in-hand）teacher/student 流程。teacher owner 位
 于 PPO 与 APPO 的 task 树下，通过 `sharpa_inhand` 的 `7-hora` profile 选择；student
 蒸馏使用 `scripts/train_hora_distill.py` 和
-`conf/hora_distill/task/sharpa_inhand/mujoco.yaml`。
+`src/unilab/conf/hora_distill/task/sharpa_inhand/mujoco.yaml`。
 
 ## Teacher
 
@@ -13,14 +13,14 @@ uv run train --algo appo --task sharpa_inhand --sim mujoco --profile hora traini
 ```
 
 HORA PPO owner 设置 `algo.algo_log_name=hora_ppo`，并通过
-`unilab.algos.torch.hora.rsl_rl:resolve_hora_ppo_runtime` 解析运行时。APPO 变体设置
+`uni_rl.algos.hora.rsl_rl:resolve_hora_ppo_runtime` 解析运行时。APPO 变体设置
 `algo.algo_log_name=hora_appo`。
 
 ## Student 蒸馏
 
 student 蒸馏由 `scripts/train_hora_distill.py` 实现，并由
-`conf/hora_distill/task/sharpa_inhand/mujoco.yaml` 配置。顶层 CLI 目前没有声明独立的
+`src/unilab/conf/hora_distill/task/sharpa_inhand/mujoco.yaml` 配置。顶层 CLI 目前没有声明独立的
 HORA 蒸馏 `--algo` 路由，因此本页的公开 CLI 示例仍保持在上面的 teacher 路径上。
 
-teacher 检查点的解析在 `src/unilab/algos/torch/hora/distill_config.py` 中实现。
+teacher 检查点的解析在 `src/unilab/training/hora_distill_config.py` 中实现。
 student 日志族为 `hora_distill`。
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/3-backends/0-index.md b/docs/sphinx/source/zh_CN/2-user_guide/3-backends/0-index.md
index 111d7f24a..c3b0e4cd3 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/3-backends/0-index.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/3-backends/0-index.md
@@ -1,28 +1,60 @@
 # 仿真后端
 
-UniLab 目前在 registry/config 路径中使用两个后端名称：`1-mujoco` 和
-`2-motrix`。用户命令通过 `--sim` 选择它们，该选项会路由到对应的
-task owner YAML；不要仅靠 override `training.sim_backend` 来切换一次运行。
+UniLab 通过 registry/config 路径暴露后端名称，包括在对应 owner 注册后可用的
+`mujoco`、`motrix`、`mjwarp`、`drake`、`isaacgym`、`genesis` 和 `isaacsim`。用户命令通过
+`--sim` 选择后端，该选项会路由到对应的 task owner YAML；不要仅靠 override
+`training.sim_backend` 来切换一次运行。
 
 ## 运行时前置条件
 
 - 使用 `uv sync --extra motrix` 安装 Motrix 支持。
+- IsaacGym 和 IsaacSim 使用独立的 worker 运行时；安装和运行时要求见各自的
+  后端页面。
 - 任何使用 `--sim mujoco`、MuJoCo 回放或仅限 MuJoCo 的调试工具的运行，
   仍然需要一个可用的 MuJoCo 运行时。
+- Drake 需要外部 `drake-uni` Python 包和针对本地 Drake 编译的 C++ 批量扩展；
+  选择 `--sim drake` 前请先阅读 {doc}`6-drake`。
 - 在 macOS 上，软件包 CLI 在需要时会通过 `mxpython` 路由 Motrix 交互式回放。
   直接打开原生 Motrix 渲染器的脚本调用应使用 `uv run mxpython`。
 
 ## 选择后端
 
+UniLab 通过 task owner config 选择仿真器。常规用法下，使用 `--task` 和 `--sim`
+选择 task 和 backend；off-policy 命令将算法保留在 `--algo` 中，而不是 `--task` 中。
+不要仅靠 override `training.sim_backend` 来切换一次运行；该字段由 owner YAML 设置，
+用于标识所组合的后端。
+
+### 快速选择
+
+| 需求 | 推荐 |
+| --- | --- |
+| 默认路径或最广的 owner 覆盖 | MuJoCo |
+| 通过后端进行原生交互式回放 | Motrix |
+| 仅限 MuJoCo 的工具，例如 `scripts/play_viser.py` | MuJoCo |
+| task owner 仅以 `src/unilab/conf/.../<task>/mujoco.yaml` 形式存在 | MuJoCo |
+| task owner 以 `src/unilab/conf/.../<task>/motrix.yaml` 形式存在，且支持矩阵将该组合标记为 tested 或 configured | Motrix |
+
+支持矩阵由 registry、owner YAML 和测试生成；将其作为当前证据来源：
+{doc}`../../5-reference/5-support_matrix`。
+
 ```bash
 uv run train --algo ppo --task go1_joystick_flat --sim mujoco
 uv run train --algo ppo --task go1_joystick_flat --sim motrix
+uv run train --algo ppo --task g1_walk_flat --sim isaacsim
+```
+
+更多组合示例：
+
+```bash
+uv run train --algo ppo --task stewart_balance --sim drake \
+  algo.max_iterations=1 algo.num_envs=8 training.no_play=true
+uv run train --algo sac --task g1_walk_flat --sim mujoco
 ```
 
 Owner YAML 位置：
 
-- PPO / APPO：`conf/{ppo,appo}/task/<task>/<backend>.yaml`
-- Off-policy：`conf/offpolicy/task/<algo>/<task>/<backend>.yaml`
+- PPO / APPO：`src/unilab/conf/{ppo,appo}/task/<task>/<backend>.yaml`
+- Off-policy（SAC / TD3 / FlashSAC）：`src/unilab/conf/<algo>/task/<task>/<backend>.yaml`
 
 被选中的 owner YAML 将 `training.sim_backend` 设为身份字段。
 
@@ -51,10 +83,34 @@ Task/backend/entrypoint 的支持情况是按证据分级的。请参阅
 - {doc}`Backend capability boundary ADR </adr/ADR-0002-backend-capability-boundary-for-play-and-snapshot>`
 - {doc}`Registry bootstrap ADR </adr/ADR-0004-registry-bootstrap-contract>`
 
+## unisim-core 边界
+
+UniLab 的物理后端由独立的 `unisim-core` distribution 提供，Python namespace
+为 `unisim`。安装和导入示例：
+
+```bash
+uv sync --extra mujoco
+uv run python -c "import unisim; print(unisim.ADAPTER_SPECS)"
+```
+
+`unisim` 不依赖 UniLab、Hydra 或训练组件；MuJoCo、Motrix、Drake、MJWarp、Genesis、
+IsaacGym 和 IsaacSim 都通过同一个公开 contract 暴露。专有 SDK 或 GPU worker 缺失时，
+构造 backend 会在冷路径给出明确诊断，不会静默回退到另一引擎。
+
+UniLab 只保留 `unilab.base.backend_factory` 这一 owner-layer 组装入口；contract
+和 adapter 从 `unisim` 导入。原 `unilab.base.backend` 实现及兼容层已经删除，
+不要在 UniLab 中新增 backend API。
+
+benchmark v1 目前只保留 `BenchmarkCase`、`BenchmarkResult` 和 provenance schema，
+不包含 workload、计时或性能结论。
+
 ```{toctree}
 :hidden:
 
 1-mujoco
 2-motrix
-3-choosing_a_backend
+3-isaacgym
+4-isaacsim
+5-genesis
+6-drake
 ```
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/3-backends/1-mujoco.md b/docs/sphinx/source/zh_CN/2-user_guide/3-backends/1-mujoco.md
index 2d899a5ec..4c4fc90d1 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/3-backends/1-mujoco.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/3-backends/1-mujoco.md
@@ -2,12 +2,12 @@
 
 MuJoCo 是已提交 owner 配置中的默认后端路径。其 Python 依赖为官方
 `mujoco` 包（`>=3.5`，默认版本由已提交的 `uv.lock` 钉住）加 `mujoco-uni-runtime`（见 `pyproject.toml`），适配层位于
-`src/unilab/base/backend/mujoco/` 下。
+`unisim.backend.mujoco` 下。
 
 ## 何时使用
 
 - 你想要 PPO、APPO、off-policy SAC/TD3 或 FlashSAC 的默认训练路线。
-- task owner 仅以 `conf/.../<task>/mujoco.yaml` 形式存在。
+- task owner 仅以 `src/unilab/conf/.../<task>/mujoco.yaml` 形式存在。
 - 你需要 MuJoCo 专有工具，例如 `scripts/play_viser.py`，或从 MuJoCo XML/MJB
   模型导出场景。
 
@@ -19,8 +19,8 @@ uv run train --algo appo --task go1_joystick_flat --sim mujoco training.no_play=
 uv run train --algo sac --task g1_walk_flat --sim mujoco
 ```
 
-回放模式由 `src/unilab/base/backend/base.py` 中的 backend contract 解析。
-MuJoCo 在 `src/unilab/base/backend/mujoco/backend.py` 中声明对物理状态回放的支持；
+回放模式由 `unisim.backend.base` 中的 backend contract 解析。
+MuJoCo 在 `unisim.backend.mujoco.backend` 中声明对物理状态回放的支持；
 `auto` 回放会录制视频，而不是打开 Motrix 原生交互式渲染器。
 
 ## 切换 MuJoCo 版本
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/3-backends/2-motrix.md b/docs/sphinx/source/zh_CN/2-user_guide/3-backends/2-motrix.md
index 268f0c5b6..73616b7e1 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/3-backends/2-motrix.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/3-backends/2-motrix.md
@@ -2,7 +2,7 @@
 
 Motrix 是一个可选后端，通过 `motrix` extra 安装。在 `pyproject.toml` 中固定的
 软件包是 `motrixsim-core==0.8.2`，适配层位于
-`src/unilab/base/backend/motrix/` 下。
+`unisim.backend.motrix` 下。
 
 ## 安装
 
@@ -14,7 +14,7 @@ uv sync --extra motrix
 
 ## 何时使用
 
-- task owner 以 `conf/.../<task>/motrix.yaml` 形式存在。
+- task owner 以 `src/unilab/conf/.../<task>/motrix.yaml` 形式存在。
 - 你想要 Motrix 原生交互式回放；该后端提供原生交互式渲染器和视频录制能力。
 - 所生成的支持矩阵将你的 entrypoint/task/backend 组合标记为 configured 或 tested。
 
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/3-backends/3-choosing_a_backend.md b/docs/sphinx/source/zh_CN/2-user_guide/3-backends/3-choosing_a_backend.md
deleted file mode 100644
index 6ff10d497..000000000
--- a/docs/sphinx/source/zh_CN/2-user_guide/3-backends/3-choosing_a_backend.md
+++ /dev/null
@@ -1,30 +0,0 @@
-# 选择后端
-
-UniLab 通过 task owner config 选择仿真器。常规用法下，使用 `--task` 和 `--sim`
-选择 task 和 backend；off-policy 命令将算法保留在 `--algo` 中，而不是 `--task` 中。
-不要仅靠 override `training.sim_backend` 来切换一次运行；该字段由 owner YAML 设置，
-用于标识所组合的后端。
-
-## 快速选择
-
-| 需求 | 推荐 |
-| --- | --- |
-| 默认路径或最广的 owner 覆盖 | MuJoCo |
-| 通过后端进行原生交互式回放 | Motrix |
-| 仅限 MuJoCo 的工具，例如 `scripts/play_viser.py` | MuJoCo |
-| task owner 仅以 `conf/.../<task>/mujoco.yaml` 形式存在 | MuJoCo |
-| task owner 以 `conf/.../<task>/motrix.yaml` 形式存在，且支持矩阵将该组合标记为 tested 或 configured | Motrix |
-
-支持矩阵由 registry、owner YAML 和测试生成；将其作为当前证据来源：
-{doc}`/zh_CN/5-reference/5-support_matrix`。
-
-## 示例
-
-```bash
-uv run train --algo ppo --task go2_joystick_flat --sim mujoco
-uv run train --algo ppo --task go2_joystick_flat --sim motrix
-uv run train --algo sac --task g1_walk_flat --sim mujoco
-```
-
-`registry.make(..., sim_backend=None)` 在 `src/unilab/base/registry.py` 中解析默认后端；
-`--task` 和 `--sim` 仍然是面向用户的路线。
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/3-backends/3-isaacgym.md b/docs/sphinx/source/zh_CN/2-user_guide/3-backends/3-isaacgym.md
new file mode 100644
index 000000000..21263dab1
--- /dev/null
+++ b/docs/sphinx/source/zh_CN/2-user_guide/3-backends/3-isaacgym.md
@@ -0,0 +1,188 @@
+# IsaacGym 后端
+
+IsaacGym（NVIDIA Preview 4）是 NVIDIA 已停止维护（EOL）的 GPU 物理仿真器，
+只支持 Python 3.6–3.8。UniLab 主环境要求 Python >= 3.10，因此 IsaacGym 不能
+装进主环境，必须通过外部独立的 Python 3.8 环境使用；仓库内一律通过环境变量
+定位该环境，不写入任何机器本地路径。
+
+当前状态：`IsaacGymBackend`（subprocess 后端，物理跑在外部 Python 3.8
+worker 中）已实现并注册到 registry；`g1_walk_flat` 提供 isaacgym owner
+配置（`src/unilab/conf/{ppo,sac}/task/g1_walk_flat/isaacgym.yaml`），跨后端契约审计
+（`scripts/audit_sim2sim_contracts.py`）覆盖 mujoco↔isaacgym。回放渲染走
+IsaacGym 原生渲染（viewer + camera sensor），交互与录视频两种模式均可用
+（见下文「训练与评估」）。真机端到端
+验证依赖下文所述的外部环境，不在仓库 CI 中覆盖。仓库内另有物理性能
+benchmark 脚本
+`scripts/benchmark/physics/benchmark_physics_step_isaacgym.py`，它通过
+`UNILAB_BENCHMARK_HOLOSOMA_DEPS` 等环境变量定位外部环境。
+本页覆盖外部环境准备、训练与评估、benchmark 验证与故障排除。
+
+## 模型契约
+
+后端直接消费 task 的 MJCF scene，但对 IsaacGym 的 MJCF importer 只做有限
+信任：运动学（body/dof 名称与顺序）在 INIT 时与 host 侧 XML 扫描结果
+对齐校验，所有影响驱动的参数都从 XML 解析而不是读取 importer 的结果。
+
+- **控制**：只支持 `<position kp kv forcerange>` 执行器——
+  `SimBackend.step(ctrl)` 携带逐 DoF 位置目标，用 PhysX `DOF_MODE_POS`
+  驱动复现（force = kp·(target − q) − kv·q̇，并按对称 forcerange 截断）。
+  含 `<motor>`/`<velocity>` 等其他执行器类型、非 1 的 gear、或不对称
+  forcerange 的 scene 会在扫描时 fail-closed。
+- **自碰撞整体关闭**（actor collision filter）。MJCF
+  `<contact><exclude>` 配对（如 G1 的 elbow↔wrist、pelvis↔hip 重叠）
+  无法通过 gymapi 按 link 对复现；整体关闭自碰撞是生态通行近似，且是
+  这些排除项的超集。依赖自身接触的模型不会被忠实复现。
+- **关节限位**：importer 会丢弃 joint range，因此 PhysX 侧没有关节
+  限位；`get_joint_range()` 仍返回 XML 值。关节 `armature` 与
+  `frictionloss`（经 MJCF default class 解析）会应用到 PhysX dof。
+
+## 前置条件
+
+- Linux x86_64，装有 NVIDIA GPU 驱动。
+- 网络可访问 NVIDIA 下载站点：脚本会自动从
+  <https://developer.nvidia.com/isaac-gym-preview-4> 下载
+  `IsaacGym_Preview_4_Package.tar.gz`（无需登录）；离线机器可先自行下载，
+  再用 `--tarball <path>` 指定。
+- 磁盘空间：miniconda、conda 环境与 IsaacGym 安装包合计约 5 GB。
+
+## 自动化安装
+
+在仓库根目录运行：
+
+```bash
+scripts/tools/setup_isaacgym_env.sh
+```
+
+脚本默认把所有内容安装到 `$HOME/.cache/unisim/isaacgym`，可用环境变量
+`UNISIM_ISAACGYM_HOME` 覆盖安装根目录；安装包默认自动下载到
+`$UNISIM_ISAACGYM_HOME/IsaacGym_Preview_4_Package.tar.gz`，离线机器可用
+`--tarball <path>` 指定已下载的安装包。
+旧的 `UNILAB_ISAACGYM_HOME` 变量仍仅作为迁移回退接受。
+脚本幂等，重跑时跳过已完成的步骤。
+
+安装流程：专用 miniconda → python 3.8 `hsgym` conda 环境（含
+`libstdcxx-ng`，用于修复 Ubuntu 24.04 的 GLIBCXX 问题）→ 下载并解包
+tarball → `pip install -e isaacgym/python` → import 自检。
+
+安装完成后，把脚本输出的 export 行写入 shell rc（如 `~/.bashrc`）：
+
+```bash
+export UNILAB_BENCHMARK_HOLOSOMA_DEPS="$HOME/.cache/unisim/isaacgym"
+export UNILAB_BENCHMARK_HSGYM_PYTHON="$UNILAB_BENCHMARK_HOLOSOMA_DEPS/miniconda3/envs/hsgym/bin/python3.8"
+export UNILAB_BENCHMARK_HSGYM_LIB="$UNILAB_BENCHMARK_HOLOSOMA_DEPS/miniconda3/envs/hsgym/lib"
+```
+
+## 验证
+
+用 benchmark 脚本验证环境可用。benchmark 从 URDF 加载机器人模型，
+URDF 模型树（`go1_description/`、`g1_description/` 等）需自备，通过
+`--models-root` 或 `UNILAB_BENCHMARK_MODELS_ROOT` 指向其根目录：
+
+```bash
+PYTHONPATH="$UNILAB_BENCHMARK_HOLOSOMA_DEPS/isaacgym/python" \
+LD_LIBRARY_PATH="$UNILAB_BENCHMARK_HSGYM_LIB" \
+uv run --no-project "$UNILAB_BENCHMARK_HSGYM_PYTHON" \
+    scripts/benchmark/physics/benchmark_physics_step_isaacgym.py \
+    --tasks g1_walk_flat --batch-sizes 256 --models-root "$UNILAB_BENCHMARK_MODELS_ROOT"
+```
+
+## 训练与评估
+
+安装好外部环境后即可训练。worker 运行时默认从 `~/.cache/unisim/isaacgym` 自动
+发现；自定义安装根时在训练前导出 `UNISIM_ISAACGYM_HOME` 即可。当前
+`g1_walk_flat` 提供 PPO 与 SAC 的 isaacgym owner 配置：
+
+```bash
+# SAC
+uv run train --algo sac --task g1_walk_flat --sim isaacgym
+
+# PPO
+uv run train --algo ppo --task g1_walk_flat --sim isaacgym
+```
+
+回放渲染由 IsaacGym 原生提供：交互模式在 worker 进程里打开 gym viewer，
+录制模式用 camera sensor 离屏渲染并写出 `play_video.mp4`（相机跟踪
+env 0 的 root，视角可用 `training.cam_distance` / `cam_elevation` /
+`cam_azimuth` 调整）。`play_render_mode=auto`（默认）在有显示器
+（`DISPLAY`/`WAYLAND_DISPLAY`）时进入交互模式，无显示器的机器自动降级为
+录制模式；录制需要有限的 `training.play_steps`（默认配置已给出）。
+
+```bash
+# 训练后自动进入回放（auto）；无显示器的服务器自动录视频
+uv run train --algo sac --task g1_walk_flat --sim isaacgym
+
+# 对已训练的 checkpoint 做评估：交互 viewer
+uv run eval --algo sac --task g1_walk_flat --sim isaacgym \
+    --render-mode interactive --load-run <run_dir_name>
+
+# 无显示器环境录视频（强制 record）
+uv run eval --algo sac --task g1_walk_flat --sim isaacgym \
+    --render-mode record --load-run <run_dir_name> training.play_steps=800
+```
+
+注意：交互 viewer 与 camera 采集都要求 worker 的 sim 跑在 GPU 上
+（`env.isaacgym_device_id >= 0`）；CPU pipeline 的 sim 没有图形上下文，
+渲染请求会 fail-closed 并给出提示。
+
+常用覆盖（Hydra 参数直接跟在命令后面）：
+
+```bash
+# 小规模 smoke：64 个环境、只跑 3 个 iteration
+uv run train --algo sac --task g1_walk_flat --sim isaacgym \
+    algo.num_envs=64 algo.max_iterations=3
+
+# 指定 worker 使用的 GPU
+uv run train --algo sac --task g1_walk_flat --sim isaacgym env.isaacgym_device_id=1
+```
+
+跨后端迁移（sim2sim）：isaacgym owner 配置与 mujoco owner 在契约守卫
+（`src/unilab/utils/sim2sim.py`）审计下全部字段兼容（TRANSFERABLE），
+同一 task 的 checkpoint 可跨后端使用；回放渲染在 isaacgym 上已可用，
+跨后端策略评估（在 isaacgym 上播放 mujoco 训练的 checkpoint，或反向）
+可直接用上面的 `uv run eval` 命令完成。
+
+## 手动安装
+
+自动脚本失效时，可按下面的等价命令序列手动安装：
+
+```bash
+export UNISIM_ISAACGYM_HOME="${UNISIM_ISAACGYM_HOME:-$HOME/.cache/unisim/isaacgym}"
+mkdir -p "$UNISIM_ISAACGYM_HOME"
+
+# 1. 专用 miniconda
+curl -fsSL https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -o /tmp/miniconda.sh
+bash /tmp/miniconda.sh -b -u -p "$UNISIM_ISAACGYM_HOME/miniconda3"
+rm /tmp/miniconda.sh
+
+# 2. python 3.8 conda 环境
+"$UNISIM_ISAACGYM_HOME/miniconda3/bin/conda" tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main
+"$UNISIM_ISAACGYM_HOME/miniconda3/bin/conda" tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r
+"$UNISIM_ISAACGYM_HOME/miniconda3/bin/conda" install -y -n base -c conda-forge mamba
+"$UNISIM_ISAACGYM_HOME/miniconda3/bin/mamba" create -y -n hsgym python=3.8 -c conda-forge --override-channels
+
+# 3. Ubuntu 24.04 GLIBCXX 修复
+"$UNISIM_ISAACGYM_HOME/miniconda3/bin/conda" install -y -n hsgym -c conda-forge libstdcxx-ng
+
+# 4. 下载（或复用已下载的 tarball）并安装 IsaacGym
+curl -fL --retry 3 "https://developer.nvidia.com/isaac-gym-preview-4" \
+  -o "$UNISIM_ISAACGYM_HOME/IsaacGym_Preview_4_Package.tar.gz"
+tar -xzf "$UNISIM_ISAACGYM_HOME/IsaacGym_Preview_4_Package.tar.gz" -C "$UNISIM_ISAACGYM_HOME"
+"$UNISIM_ISAACGYM_HOME/miniconda3/envs/hsgym/bin/pip" install -e "$UNISIM_ISAACGYM_HOME/isaacgym/python"
+```
+
+## 故障排除
+
+- **tarball 下载或校验失败**：脚本会对下载结果做 gzip 校验；失败时删除
+  `$UNISIM_ISAACGYM_HOME/IsaacGym_Preview_4_Package.tar.gz` 后重跑，或用
+  `--tarball <path>` 指定手动下载的安装包。
+- **首次 INIT 握手超时（worker 无响应）**：`gymtorch` 首次 import 会 JIT 编译
+  C++ 扩展（数分钟，缓存于 `~/.cache/torch_extensions/py38_cu121/gymtorch/`）。
+  安装脚本的自检已预热该编译；若编译进程曾被强杀，可能残留 `lock` 文件导致
+  永久等待——删除该目录下的 `lock` 后重试。worker 环境需要 env 的 `bin/`
+  在 `PATH` 上（提供 ninja），`IsaacGymBackend` 会自动注入。
+- **Ubuntu 24.04 报 `GLIBCXX_3.4.32 not found`**：IsaacGym 预编译库链接的
+  libstdc++ 比系统自带的旧；安装脚本已在 `hsgym` 环境中安装 conda-forge 的
+  `libstdcxx-ng` 解决，运行时把 `LD_LIBRARY_PATH` 指向该 env 的 `lib/` 即可。
+- **`from isaacgym import gymapi` import 失败**：确认 `LD_LIBRARY_PATH`
+  指向 `$UNILAB_BENCHMARK_HSGYM_LIB`（即 hsgym env 的 `lib/`），且
+  `PYTHONPATH` 包含 `isaacgym/python`。
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/3-backends/4-isaacsim.md b/docs/sphinx/source/zh_CN/2-user_guide/3-backends/4-isaacsim.md
new file mode 100644
index 000000000..d0a68b03f
--- /dev/null
+++ b/docs/sphinx/source/zh_CN/2-user_guide/3-backends/4-isaacsim.md
@@ -0,0 +1,111 @@
+# IsaacSim 后端
+
+UniLab 的 `isaacsim` 后端在独立的 Python 3.11 worker 进程中运行 IsaacSim
+5.1.0 和 IsaacLab v2.3.0。主进程保留标准 `SimBackend` NumPy contract；管道
+传输生命周期命令，共享内存传输批量状态。当前支持边界是已注册 G1 flat
+task owner 的 headless physics 与 eval 专用原生渲染。支持矩阵仍将 PPO/SAC
+owner 标为 `Configured`，不是 `Tested`：渲染协议有确定性 worker 测试，但在
+当前可用 IsaacSim 主机上还没有完成真实 playback。
+
+## 运行时边界
+
+IsaacSim 5.1.0 只能在独立 Python 3.11 环境中运行，而 UniLab 主环境支持
+Python 3.10--3.13。安装入口是 `scripts/tools/setup_isaacsim_env.sh`，默认安装
+到 `$HOME/.cache/unisim/isaacsim`，也可通过 `UNISIM_ISAACSIM_HOME` 修改。worker
+启动通过 `OMNI_KIT_ACCEPT_EULA=1` 非交互接受 Kit EULA。
+
+后端不会在主进程导入 Kit，并支持以下环境变量：
+
+- `UNISIM_ISAACSIM_HOME`：运行时根目录。
+- `UNISIM_ISAACSIM_PYTHON`：覆盖 worker 解释器路径。
+- 旧的 `UNILAB_ISAACSIM_HOME` 和 `UNILAB_ISAACSIM_PYTHON` 仅作为迁移回退接受。
+- `OMNI_KIT_ACCEPT_EULA=1`：保持 worker 启动非交互。
+
+预期目录为 `$UNISIM_ISAACSIM_HOME/venv/bin/python`、该 venv 下的
+site-packages 和 library 目录，以及
+`$UNISIM_ISAACSIM_HOME/IsaacLab` 下的 IsaacLab v2.3.0 源码。
+
+渲染意图属于 worker 的冷路径 `INIT` 握手。训练不注入渲染模式，使用低开销的
+headless、camera-disabled Kit experience。eval 在 Kit 启动前选择以下模式：
+
+- `auto`：存在 `DISPLAY` 或 `WAYLAND_DISPLAY` 时使用 Kit 交互窗口，否则使用
+  headless 离线录制。
+- `interactive`：启动非 headless Kit；没有 display 环境变量时在 worker 启动前
+  显式失败。
+- `record`：启动带 IsaacLab RGB camera 的 headless rendering experience；
+  `training.play_steps` 必须是有限值。
+- `none`：不启动 viewer 或 camera，只执行策略 eval。
+
+离线帧 contract 为 `(height, width, 3)`、`uint8`、连续且非均匀。错误帧或占位帧
+会 fail closed，不会写出伪视频。IsaacSim owner 默认 1280 x 720，可在 env 创建前
+通过 `env.isaacsim_render_width` 和 `env.isaacsim_render_height` 覆盖。
+
+当前 worker 支持 MJCF 材质化、批量 articulation 状态、位置 target 步进和
+masked root/joint reset，以及 Kit 原生 viewer 和 headless IsaacLab RGB camera。
+contact-force sensor、reset/interval domain randomization 和 host pre-step
+callback 仍不支持，并会 fail closed。
+
+使用顶层 CLI 选择 backend 和 owner：
+
+```bash
+uv run train --algo ppo --task g1_walk_flat --sim isaacsim
+uv run eval --algo sac --task g1_walk_flat --sim isaacsim \
+  --load-run <run-id> --render-mode record \
+  training.play_steps=120 training.play_env_num=1 training.export_onnx=false
+uv run eval --algo sac --task g1_walk_flat --sim isaacsim \
+  --load-run <run-id> --render-mode interactive training.play_env_num=1
+```
+
+`record` 会在所选 run 目录写入 `play_video.mp4`。这些命令需要外部运行时和
+NVIDIA CUDA 设备。仓库不声称已完成完整训练或稳定的原生 playback；该声明
+需要 maintainer validation 记录。
+
+## 当前运行时验证
+
+已使用现有 SAC checkpoint 在 IsaacSim 5.1.0、IsaacLab v2.3.0、Kit 107.3.3、
+Ubuntu 24.04.4、RTX 4090 和 NVIDIA driver 595.84 上分别执行有界 record eval
+和 interactive eval。两条路径均在 `AppLauncher` 初始化期间、camera 或 viewer
+创建之前崩溃；栈包含
+`librtx.scenedb.plugin.so`、`libcarb.scenerenderer-rtx.plugin.so` 和
+`libomni.hydra.rtx.plugin.so`，崩溃前有 EGL 初始化警告。最小 camera-enabled
+`AppLauncher` probe 即使设置 `multi_gpu=False` 也失败。
+
+这是真实 runtime blocker，不是 playback 成功证据。backend 保留渲染协议和
+fail-closed 测试，支持矩阵仍为 `Configured`；真实 renderer 未初始化时不会生成
+占位视频。
+
+## 检查 Contract
+
+```bash
+VIRTUAL_ENV="$HOME/.cache/unisim/isaacsim/venv" \
+OMNI_KIT_ACCEPT_EULA=1 \
+uv run --active --no-project \
+  scripts/tools/probe_isaacsim_contract.py \
+  --model-file src/unilab/assets/robots/g1/scene_flat.xml \
+  --num-envs 2 --steps 2 --device cuda:0 \
+  --output /tmp/isaacsim-contract.json
+```
+
+该命令是有界的开发者探测，只在 cold-path 材质化阶段访问 XML/importer，适合
+检查新安装的运行时；它不是训练或 playback 验证。
+
+## Contract 矩阵
+
+| UniLab contract | IsaacSim/IsaacLab 操作 | 实测结果 | 实现约束 |
+|---|---|---|---|
+| MJCF 场景材质化 | `isaaclab.sim.converters.MjcfConverter` | G1 MJCF 成功转换为 USD | headless worker 必须显式启用 `isaacsim.asset.importer.mjcf` |
+| 批量 articulation | `Articulation` + `ArticulationCfg` | 2 个环境、29 joints、30 bodies | 材质化时按名称解析；不能假设 importer 顺序 |
+| 四元数布局 | `robot.data.root_quat_w` / `body_quat_w` | `wxyz` | shared-memory 边界保持 `wxyz` |
+| base 角速度 | `robot.data.root_ang_vel_w` | world frame | 公共 getter 保持 world frame；reset qvel 转换固定在 worker |
+| partial reset | `write_root_pose_to_sim`、`write_root_velocity_to_sim`、`write_joint_state_to_sim`、`reset(env_ids)` | 只改变选中行，其他行 delta 为零 | 使用 masked batch write；拒绝重复或越界 id |
+| 位置控制 | `set_joint_position_target`、`write_data_to_sim`、`SimulationContext.step` | 有界步数内首个 joint 移动 | `step(ctrl)` 传位置 target；gain/limit 在 cold path 显式材质化 |
+| 状态 getter 边界 | `Articulation.data.*` tensor | getter 均为 batch，leading dimension 符合预期 | worker 将 tensor 拷贝到 host-owned shm；热路径不解析 asset |
+| 渲染启动 | `AppLauncher` 冷路径模式选择 | mock worker 验证 none/record/interactive mode、尺寸和 graphics 握手 | env 材质化后不能切换 mode |
+| 离线 RGB | IsaacLab `Camera` + `CameraCfg` | 协议测试验证视频写入，并拒绝错误 shape、dtype 或均匀帧；当前真实主机在 camera 创建前崩溃 | 要求有限步数；真实 playback 成功前支持等级保持 `Configured` |
+| 交互窗口 | 非 headless Kit + `SimulationContext.set_camera_view` | 协议测试驱动一帧并将关闭窗口映射为 `RenderClosedError`；当前主机没有成功的有界 GUI 证据 | 显式 interactive 需要 display；无 display 时 `auto` 回退到 record |
+| Domain randomization | IsaacLab manager/event API | 未探测 | 非空不支持 plan 必须 fail-closed |
+
+Importer 返回的 joint/body 顺序与 MJCF 文档顺序不同（例如左右分支交错）。
+worker 建立 name-to-index 映射并重排所有状态和控制数组；按位置假设顺序会
+破坏 `SimBackend` index contract。完整 owner 和 capability 状态见
+{doc}`../../5-reference/5-support_matrix`。
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/3-backends/5-genesis.md b/docs/sphinx/source/zh_CN/2-user_guide/3-backends/5-genesis.md
new file mode 100644
index 000000000..11c12657c
--- /dev/null
+++ b/docs/sphinx/source/zh_CN/2-user_guide/3-backends/5-genesis.md
@@ -0,0 +1,139 @@
+# Genesis 后端
+
+[Genesis](https://github.com/Genesis-Embodied-AI/Genesis)（PyPI 分发名
+`genesis-world`，仓库钉在 1.3.3）是一个 GPU 物理仿真器，UniLab 以
+**进程内**方式使用它：`GenesisBackend` 在其上提供标准的 `SimBackend`
+NumPy contract，物理与 learner 同进程运行——没有 worker 子进程，也没有
+IPC。
+
+当前状态：`GenesisBackend` 已实现并注册到 registry；`g1_walk_flat` 提供
+PPO 与 SAC owner 配置（`src/unilab/conf/{ppo,sac}/task/g1_walk_flat/genesis.yaml`），
+跨后端契约审计（`scripts/audit_sim2sim_contracts.py`）在两棵 algo 树上
+覆盖 mujoco↔genesis（结论 TRANSFERABLE）。支持等级为 **experimental**。
+现有证据：registry + owner YAML + compose/contract 覆盖，以及真机
+slow-lane env smoke
+（`tests/envs/locomotion/g1/test_g1_owner_contract.py`：compose →
+env 构造 → keyframe reset → 12 步有限稳定 → cleanup，覆盖 ppo 与 sac
+两棵树）。SAC cell 标记为 `Tested`——真机完整训练验证（2026-08-31，
+RTX 4090 / torch 2.8.0+cu128 / genesis-world 1.3.3：5000/5000
+iterations，reward/mean 6.5 → 244.8，episode length → 987/1000，
+10.26M env steps / 224s wall time）加最终 checkpoint 的 record
+playback 验证；PPO cell 保持 `Configured`（尚无训练验证）。
+
+env 构造生命周期（#1383 已修复）：`ManagerBasedRlEnv` 构造期的 entity
+校验在 env 的 `materialize()` 钩子之前读取状态 getter，因此 adapter 的
+`materialize()` 是幂等且惰性触发的（首次状态访问时完成 `scene.build`，
+与 IsaacGym 后端同模式）。adapter 的其余设计遵循
+`scripts/tools/genesis_feasibility/REPORT.md` 的实测映射。
+
+## 模型契约
+
+Genesis 1.3.3 在 import 时丢弃三类 MJCF 特性（REPORT §3）：全局
+`<option>` 块、`<keyframe>`、以及整个 `<sensor>` 块。adapter 在
+materialize 冷路径补偿——热路径从不解析 XML：
+
+- **全局 option**：MJCF `<option>` 承载的值由 owner YAML 显式重声明。
+  对 g1（`<option integrator="implicitfast"
+  timestep="0.006666666666666667"/>`），timestep 经现有
+  `sim_dt` / `ctrl_dt` 链路传递，integrator 经
+  `env.genesis_integrator: implicitfast` 声明。constraint solver、摩擦锥
+  与 solver iterations 保持 Genesis 默认值（对应 `env.genesis_*` 字段为
+  `None`）：MJCF option 块未声明它们，且 MuJoCo 隐式默认（PGS solver、
+  pyramidal 摩擦锥）没有 Genesis 等价物。
+- **keyframe**：materialize 时用 `mujoco` 包扫描一次并缓存，
+  `default_keyframe_name: stand` 的 reset 行为不变。
+- **传感器**：由 link 状态计算 MJCF 同名等价物，每个 accelerometer
+  site 对应一个 `IMUSensor`（干净无噪声数据）。`data="found"` 的
+  contact sensor 变为 per-link net contact force 阈值（1 N）——这是对
+  geom 对 `found` 语义的近似，不是复现。
+- **执行器**：`<position kp kv>` 执行器无损导入，用
+  `control_dofs_position` 驱动；kp/kd reset 随机化受支持（DR 能力集只
+  声明 per-env 往返实测的项：body mass、base mass、kp、kd，以及
+  interval body force）。
+
+## 前置条件
+
+- Linux x86_64，装有 NVIDIA GPU 与驱动。可行性探针只验证了 `gs.gpu`
+  通道；CPU backend 不是已验证的支持通道。
+- 仓库钉定的 torch 窗口（x86_64 上为 torch 2.8）：探针中
+  IMUSensor + contact-force 传感器组合在 torch 2.7 下崩溃（REPORT
+  §3.4/§8）。
+- 安装可选 extra（精确钉定 `genesis-world==1.3.3`）：
+
+```bash
+uv sync --extra genesis
+```
+
+## 训练与评估
+
+训练通过标准 CLI 选择 genesis owner（owner YAML compose、registry
+路由与 env 构造链路已在真机 slow-lane smoke 覆盖；尚无训练收敛证据）：
+
+```bash
+# PPO
+uv run train --algo ppo --task g1_walk_flat --sim genesis
+
+# SAC
+uv run train --algo sac --task g1_walk_flat --sim genesis
+
+# 小规模 smoke：64 个环境、只跑 3 个 iteration
+uv run train --algo ppo --task g1_walk_flat --sim genesis \
+    algo.num_envs=64 algo.max_iterations=3
+```
+
+## Playback 与渲染
+
+Genesis 原生渲染是已声明能力，且在 `scene.build` 之后惰性挂载（训练热
+路径不引入任何 viewer 依赖）：交互 viewer 是 post-build 挂上的
+`genesis.vis.viewer.Viewer`，录制走 post-build 离屏 camera。owner 设置
+`training.play_render_mode: auto`，按宿主解析：
+
+- `auto`：有可及显示（`DISPLAY`/`WAYLAND_DISPLAY`）时打开**交互
+  viewer**，否则降级为离屏**录制**。
+- `interactive`：总是打开 viewer；无显示宿主上 `init_renderer` 报可
+  操作错误。用户关闭 viewer 窗口即干净结束 playback（backend 抛
+  `RenderClosedError`，play 循环视为正常退出）。
+- `record`：无头离屏录制，要求有限的 `training.play_steps` 与输出
+  路径；视频写到 checkpoint 运行目录的 `play_video.mp4`，帧率为
+  `1/ctrl_dt`。
+- `none`：安全跳过 playback（no-op）。
+
+相机沿用仓库统一的球坐标 kwargs（`cam_distance` / `cam_elevation` /
+`cam_azimuth`，`cam_tracking` 时跟随 `cam_tracking_env_idx` 的 root）。
+评估已训练的 run：
+
+```bash
+uv run eval --algo sac --task g1_walk_flat --sim genesis \
+    --load-run <run_dir_name> --render-mode record
+```
+
+`get_physics_state` 快照仍不声明：playback 直接驱动 live scene，无需
+状态快照回放。
+
+## 生命周期：一进程一次 `gs.init`
+
+Genesis 的 init/destroy 循环每轮泄漏 200–450 MB host RSS（REPORT
+§3.5 [9a]），因此 adapter 允许**每个进程恰好一次 `gs.init`**：session
+销毁后再构造 backend 会 fail-closed 并给出明确错误。一个训练 run 一个
+进程天然满足该约束；不要在长驻宿主进程里反复构建 genesis env。
+
+## 未支持边界
+
+以下能力 fail-closed（显式报错），而不是静默降级：
+
+- **geom 名称契约**（`get_geom_names` 等）：不暴露。
+- **生成式 terrain 与 height scanner**：`scene.terrain` 被拒绝；请选择
+  flat owner YAML。
+- **绝对值 geom 摩擦 DR**：上游 Genesis 只有 per-env 摩擦*比例* API，
+  因此 `geom_friction` reset 随机化与 `get_geom_friction` fail-closed。
+- **interval push / body-velocity-delta DR**：在构造/plan 时拒绝
+  （`push_body_name`、push perturbation）。
+- **同进程重复 `gs.init`**（见上文）。
+
+## 跨后端迁移（sim2sim）
+
+genesis owner 与 mujoco owner 在契约守卫
+（`src/unilab/utils/sim2sim.py`）审计下保持 DENYLIST 一致（结论
+TRANSFERABLE），同一 task 的 checkpoint 可跨后端使用。两侧都有原生
+渲染，但实现不同：MuJoCo 从物理状态快照离线渲染，Genesis 用
+post-build 挂载的 viewer/camera 驱动 live scene。
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/3-backends/6-drake.md b/docs/sphinx/source/zh_CN/2-user_guide/3-backends/6-drake.md
new file mode 100644
index 000000000..c33a2790a
--- /dev/null
+++ b/docs/sphinx/source/zh_CN/2-user_guide/3-backends/6-drake.md
@@ -0,0 +1,108 @@
+# Drake 后端
+
+Drake 是实验性的 CPU 批量物理后端；task、reward、observation 和训练循环仍由
+UniLab 负责。渲染使用 MuJoCo 原生 renderer：Drake 推进物理，MuJoCo 只绘制当前状态。
+原生路径支持 Linux x86_64 与 Apple Silicon macOS（arm64）；Intel
+macOS 没有官方 Drake 二进制。
+
+## 前置依赖
+
+- Python `>=3.10,<3.14` 和 `uv`。
+- Linux：C++20 工具链及开发包：
+
+  ```bash
+  sudo apt-get update
+  sudo apt-get install -y build-essential pkg-config libeigen3-dev libfmt-dev \
+    libspdlog-dev curl git
+  ```
+
+- Apple Silicon macOS：Apple Clang 和 Homebrew 运行库：
+
+  ```bash
+  xcode-select --install  # 如果没有 clang++
+  brew install fmt gcc
+  ```
+
+  `gcc` 提供 Drake macOS 构建所引用的 `libgfortran`。
+
+## 安装
+
+在 UniLab checkout 根目录运行：
+
+```bash
+make setup-drake
+```
+
+该目标会下载当前主机适配的 Drake 1.56.0 tarball，安装 `drake-uni` extra，使用
+当前 uv Python 编译原生扩展，并运行 batch 诊断。流程可重复执行，文件和日志默认
+保存在 `~/.unilab/drake`。
+
+已有 Drake 安装可指定前缀：
+
+```bash
+make setup-drake DRAKE_HOME=/path/to/drake
+```
+
+前缀必须包含 `include/drake/`、`include/pybind11/` 和 `lib/libdrake.so`。macOS
+下脚本会自动发现 Homebrew `fmt`，并把 gcc 库目录加入 `DYLD_LIBRARY_PATH`。
+
+脚本结束时会打印后续 shell 需要的环境变量。Apple Silicon macOS 通常为：
+
+```bash
+export DRAKE_HOME="$HOME/.unilab/drake/drake-1.56.0-mac-arm64"
+export UNILAB_DRAKE_HOME="$DRAKE_HOME"
+export DYLD_LIBRARY_PATH="/opt/homebrew/opt/gcc/lib/gcc/current:$DRAKE_HOME/lib${DYLD_LIBRARY_PATH:+:$DYLD_LIBRARY_PATH}"
+```
+
+## 验证
+
+在未导入 `pydrake` 的新进程中运行：
+
+```bash
+uv run --no-sync python - <<'PY'
+from drake_uni.runtime import batch_diagnostics
+
+diagnostics = batch_diagnostics()
+print(diagnostics)
+if not diagnostics.batch_available:
+    raise SystemExit(diagnostics.batch_import_error or "Drake batch extension is unavailable")
+PY
+```
+
+输出必须包含 `batch_available=True`。
+
+## 训练 Go2
+
+首次运行前预取 Go2 资产：
+
+```bash
+uv run --no-sync unilab-pull-assets --robot go2
+```
+
+使用 `--sim drake` 选择后端，不要手动覆盖 `training.sim_backend`。标准 PPO 命令为：
+
+```bash
+uv run train --algo ppo --task go2_joystick_flat --sim drake
+```
+
+该命令使用 Drake owner 配置中的 1024 个环境、151 次迭代，并固定使用 CPU 训练
+（Drake 暴露的是 float64 NumPy buffer）。Drake owner 使用场景 keyframe 重置；当前
+backend contract 尚未提供浮动根状态随机化。只想验证安装时，可临时追加
+`algo.max_iterations=1 algo.num_envs=4 algo.num_steps_per_env=4 training.no_play=true
+env.drake_nthread=1`；这些不是生产参数。
+
+在 Apple Silicon macOS 上已用 Drake 1.56.0、Python 3.13 和 1024 个环境完成该命令
+的全部 151 次迭代，耗时约 254 秒。
+
+Drake 没有独立 renderer。自动录制和交互 viewer 都使用 MuJoCo，实际推进的物理引擎
+始终只有 Drake。无头评估使用 `--render-mode none`；录制和交互回放需要 MuJoCo
+extra 与视觉资产。MuJoCo 不会推进物理，也不会重新评估 checkpoint。
+
+## 常见问题
+
+| 现象 | 处理 |
+| --- | --- |
+| `ModuleNotFoundError: drake_uni` | 重新运行 `make setup-drake`。 |
+| `DrakeEnvPool batch extension has not been built` | 使用相同的 uv Python 和 Drake 前缀重新编译，不能复制其他 ABI 的扩展。 |
+| `libdrake.so` 或 `libgfortran` 无法加载 | 设置 `DRAKE_HOME`，Linux 使用 `LD_LIBRARY_PATH`，macOS 使用 `DYLD_LIBRARY_PATH`，并包含 Homebrew gcc 目录。 |
+| Eigen/fmt 链接错误 | Linux 安装上述开发包；macOS 执行 `brew install fmt gcc`。 |
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/4-tasks/0-index.md b/docs/sphinx/source/zh_CN/2-user_guide/4-tasks/0-index.md
index bb0632b63..c7879c6e8 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/4-tasks/0-index.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/4-tasks/0-index.md
@@ -1,6 +1,6 @@
 # 任务
 
-任务页面按用户目标分组。确切的可运行组合由 `conf/` 下的 owner YAML 支撑，
+任务页面按用户目标分组。确切的可运行组合由 `src/unilab/conf/` 下的 owner YAML 支撑，
 并在 {doc}`../../5-reference/5-support_matrix` 中汇总。
 
 ::::{grid} 1 1 2 2
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/4-tasks/1-locomotion.md b/docs/sphinx/source/zh_CN/2-user_guide/4-tasks/1-locomotion.md
index ba6885967..cb37cc5e4 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/4-tasks/1-locomotion.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/4-tasks/1-locomotion.md
@@ -1,7 +1,7 @@
 # 运动控制
 
-运动控制任务注册在 `src/unilab/envs/locomotion/` 和
-`src/unilab/envs/motion_tracking/` 中。`conf/` 下可用的 owner YAML
+运动控制任务注册在 `src/unilab/tasks/locomotion/` 和
+`src/unilab/tasks/motion_tracking/` 中。`src/unilab/conf/` 下可用的 owner YAML
 定义了哪些算法与后端组合是可运行的。
 
 ## 系列
@@ -29,11 +29,14 @@ uv run train --algo sac --task g1_walk_flat --sim mujoco
 
 ## Go2 FootStand
 
-`go2_footstand` 是 Go2 前足站立任务，**仅支持 MuJoCo**。
+`go2_footstand` 是 Go2 前足站立任务。PPO owner YAML 已注册 MuJoCo、
+Motrix 和 Drake；当前 SAC owner 使用 Drake。
 
-- PPO 配置：`conf/ppo/task/go2_footstand/mujoco.yaml`
-- 环境注册名：`Go2FootStand`（注册于 `sim_backend="mujoco"`）
-- 环境实现：`src/unilab/envs/locomotion/go2/footstand.py`（继承 Go2 基础任务）
+- PPO canonical 配置：`src/unilab/conf/ppo/task/go2_footstand/base.yaml`
+- 后端 owner：`src/unilab/conf/ppo/task/go2_footstand/{mujoco,motrix,drake}.yaml`
+- 环境注册名：`Go2FootStand`（MuJoCo、Motrix、Drake）
+- 环境实现：`src/unilab/tasks/locomotion/go2/footstand.py`
+  （通用 Manager-Based runtime 上的 task-owned NumPy manager terms）
 - Go2 模型 XML：`src/unilab/assets/robots/go2/go2.xml`
 
 ```bash
@@ -59,14 +62,14 @@ FootStand 的完整流程是三阶段教师-学生 pipeline；当前仓库里的
 ### 观测口径
 
 `Go2FootStand` 的策略（actor）网络观测使用 15 帧历史，每帧 45 维
-（`_FOOTSTAND_FRAME_OBS_DIM = 45`）：
+（`FRAME_OBS_DIM = 45`）：
 
 ```text
 linvel(3) + gyro(3) + gravity(3) + joint_position_delta(12) + joint_velocity(12) + last_action(12)
 ```
 
 因此策略网络观测维度是 `45 * 15 = 675`。价值（critic）网络在这段历史观测后追加当前时刻的
-特权观测尾部（`_FOOTSTAND_PRIVILEGED_TAIL_DIM = 49`）：
+特权观测尾部（`PRIVILEGED_OBS_DIM = 49`）：
 
 ```text
 gyro(3) + accelerometer(3) + linvel(3) + global_angvel(3) + dof_pos(12) + dof_vel(12) + torques(12) + height(1)
@@ -76,19 +79,23 @@ gyro(3) + accelerometer(3) + linvel(3) + global_angvel(3) + dof_pos(12) + dof_ve
 
 ### 奖励与终止项
 
-默认奖励来自 `conf/ppo/task/go2_footstand/mujoco.yaml`。奖励权重包括站立 `height`、
+默认奖励来自 `src/unilab/conf/ppo/task/go2_footstand/base.yaml`，后端 leaf 只覆盖
+后端专属 term 和调优项。奖励权重包括站立 `height`、
 `orientation`、`rear_feet_contact`、前腿目标角度（`tar`）、`action_rate`、
 `dof_pos_limits`、`front_leg_motion`、`rear_leg_symmetry`、`knee_clearance`、
 `upright_stability`、`stay_still`、`pose`，以及 `energy` 和 `dof_acc` 惩罚；
 `termination` 与 `penalty_contact` 驱动终止/惩罚路径（前腿/前身体接触、低高度、坏朝向，
-以及由 `energy_termination_threshold` 控制的高能耗截断）。
+以及 `footstand` termination term 中的高能耗截断）。
 
 ### 调参提示
 
-- `env.obs_history_len`：策略观测历史长度，配置默认为 `15`。
-- `env.energy_termination_threshold`：高能耗终止阈值，配置默认为 `200.0`。
-- `env.domain_rand`：地面摩擦、连杆质量、机身质心、关节惯量和重置关节位置随机化。
-- `reward.scales.height` / `orientation` / `rear_feet_contact`：站立姿态和后脚接触权重。
+- `env.observations.policy.terms.frame.history_length`：策略观测历史长度，
+  默认为 `15`。
+- `env.terminations.footstand.params.energy_threshold`：高能耗终止阈值，
+  默认为 `200.0`。
+- `env.events`：重置和 domain randomization terms；后端 owner 对不支持的
+  model-field term 显式设为 `null`。
+- `reward.footstand.params.scales`：站立、接触、运动和能耗权重。
 
 ### 近风险检查
 
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/4-tasks/2-motion_tracking.md b/docs/sphinx/source/zh_CN/2-user_guide/4-tasks/2-motion_tracking.md
index cf7a16812..c8b279f49 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/4-tasks/2-motion_tracking.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/4-tasks/2-motion_tracking.md
@@ -1,38 +1,40 @@
 # 动作追踪
 
-G1 动作追踪任务位于 `src/unilab/envs/motion_tracking/` 下，并通过
-`conf/ppo/`、`conf/appo/` 以及选定的 off-policy 路径中的 task owner YAML 选择。
+G1 动作追踪任务位于 `src/unilab/tasks/motion_tracking/` 下，并通过
+`src/unilab/conf/ppo/`、`src/unilab/conf/appo/` 以及选定的 off-policy 路径中的 task owner YAML 选择。
 
 > **Motion 资产已迁移到 Hugging Face。** `.npz` 片段不再随仓库分发，首次使用时由
-> `MotionLoader`（`src/unilab/envs/motion_tracking/g1/motion_loader.py`）按需从
+> `MotionLoader`（`src/unilab/tasks/motion_tracking/common/motion_loader.py`）按需从
 > [unilabsim/unilab-motions](https://huggingface.co/datasets/unilabsim/unilab-motions)
 > 下载，下载逻辑在 `src/unilab/assets/hub.py`（`_HF_MOTIONS_REPO_ID`）。`uv sync`
 > 已自动安装所需的 `huggingface_hub` 依赖。
 
 ## Task Owners
 
-每个 task 在 env 配置 dataclass 中定义了默认 motion 片段：
+每个 task 都在 Hydra task owner YAML 中定义默认 motion 片段。Hydra 是唯一配置入口；
+选中的 owner 会被物化为共享的 `ManagerBasedRlEnvCfg`，再由 NumPy Manager-Based
+runtime 执行。
 
 | CLI Task | Registered Env | 默认 motion | Owner Evidence |
 | --- | --- | --- | --- |
-| `g1_motion_tracking` | `G1MotionTracking` | `dance1_subject2_part.npz` | `conf/ppo/task/g1_motion_tracking/`, `conf/appo/task/g1_motion_tracking/` |
-| `g1_flip_tracking` | `G1FlipTracking` | `flip_360_001__A304.npz` | `conf/ppo/task/g1_flip_tracking/`, `conf/appo/task/g1_flip_tracking/` |
-| `g1_wall_flip_tracking` | `G1WallFlipTracking` | `flip_from_wall_104__A304.npz` | `conf/ppo/task/g1_wall_flip_tracking/`, `conf/appo/task/g1_wall_flip_tracking/` |
-| `x2_wall_flip_tracking` | `X2WallFlipTracking` | `tictacflip_6-3_g1format.npz` | `conf/ppo/task/x2_wall_flip_tracking/`（仅 MuJoCo） |
-| `g1_climb_tracking` | G1 climb tracking env | 由 env 配置给出 | `conf/ppo/task/g1_climb_tracking/`, `conf/appo/task/g1_climb_tracking/` |
-| `g1_box_tracking` | G1 box tracking env | 由 env 配置给出 | `conf/ppo/task/g1_box_tracking/` |
-| `g1_wbt_obs` | `G1MotionTrackingSAC` | 与 `g1_motion_tracking` 共用 | `conf/offpolicy/task/sac/g1_wbt_obs/mujoco.yaml` |
-
-默认值在代码中设定：`dance1_subject2_part.npz`（`g1/tracking.py`），
-`flip_360_001__A304.npz` 与 `flip_from_wall_104__A304.npz`（`g1/flip_tracking.py`），
-以及 `tictacflip_6-3_g1format.npz`（`x2/flip_tracking.py`）。
+| `g1_motion_tracking` | `G1MotionTracking` | `dance1_subject2_part.npz` | `src/unilab/conf/ppo/task/g1_motion_tracking/`, `src/unilab/conf/appo/task/g1_motion_tracking/` |
+| `g1_flip_tracking` | `G1FlipTracking` | `flip_360_001__A304.npz` | `src/unilab/conf/ppo/task/g1_flip_tracking/`, `src/unilab/conf/appo/task/g1_flip_tracking/` |
+| `g1_wall_flip_tracking` | `G1WallFlipTracking` | `flip_from_wall_104__A304.npz` | `src/unilab/conf/ppo/task/g1_wall_flip_tracking/`, `src/unilab/conf/appo/task/g1_wall_flip_tracking/` |
+| `x2_wall_flip_tracking` | `X2WallFlipTracking` | `tictacflip_6-3_g1format.npz` | `src/unilab/conf/ppo/task/x2_wall_flip_tracking/` |
+| `g1_climb_tracking` | `G1ClimbTracking` | `climb_20_z_scale_1.0.npz` | `src/unilab/conf/ppo/task/g1_climb_tracking/`, `src/unilab/conf/appo/task/g1_climb_tracking/` |
+| `g1_box_tracking` | `G1BoxTracking` | `sub3_largebox_003_boxconverted.npz` | `src/unilab/conf/ppo/task/g1_box_tracking/` |
+| `g1_wbt_obs` | `G1WBTObs` | `dance1_subject2_part.npz` | `src/unilab/conf/sac/task/g1_wbt_obs/mujoco.yaml` |
+
+23-DoF task owner 目录选择对应的 23-DoF 场景、motion、entity 与 action 声明。
+profile 差异全部留在 Hydra 中。G1 identity 使用共享 manager factory；X2 只在委托给
+该 factory 前增加一层冷路径 mesh resolver。
 
 ## PPO 与 APPO
 
 PPO owner 迭代预算（`--sim mujoco` owner YAML）：`g1_motion_tracking` 为
 `algo.max_iterations=15000`；`g1_flip_tracking` 和 `g1_wall_flip_tracking` 为
-`20000`；仅 MuJoCo 的 `x2_wall_flip_tracking` 为 `9500`。（`g1_flip_tracking` 的
-Motrix owner YAML 将其提到 `30000`。）
+`20000`；`x2_wall_flip_tracking` 为 `9500`。（`g1_flip_tracking` 的 Motrix owner
+YAML 将其提到 `30000`。）
 
 ```bash
 uv run train --algo ppo --task g1_motion_tracking --sim mujoco
@@ -55,11 +57,12 @@ uv run train --algo sac --task g1_motion_tracking --sim mujoco training.use_amp=
 uv run train --algo sac --task g1_wbt_obs --sim mujoco training.use_amp=true
 ```
 
-`g1_wbt_obs` owner 是与部署对齐的 off-policy 观测配置：pelvis IMU 状态
-（`pelvis_local_linvel` / `pelvis_gyro` / `pelvis_upvector`）加上 per-term 历史观测
-（`noise_config.obs_history_length: 5`），与部署侧的 `ObservationManager` 按字节对齐。
-部署工具在 `scripts/deploy/`，观测对齐由 `tests/scripts/test_obs_alignment_g1_wbt.py`
-交叉校验。当 Motrix sim2sim 回放需要引用其他日志根目录下的 checkpoint 时，用
+`g1_wbt_obs` owner 是与部署对齐的 off-policy 观测配置。actor 的 command 与 anchor
+orientation term 保持单步，`base_ang_vel`、`joint_pos`、`joint_vel` 和 `actions` term
+分别声明 `history_length: 5`。这些逐项历史由 `ObservationManager` 维护并展开；actor
+使用配置中的 encoder-biased joint-position term，critic 则保留 clean term。逐项最旧
+优先顺序由 `tests/scripts/test_obs_alignment_g1_wbt.py` 守护；硬件侧契约见仿真到真机
+部署指南。当 Motrix sim2sim 回放需要引用其他日志根目录下的 checkpoint 时，用
 `uv run eval` 透传绝对路径：
 
 ```bash
@@ -69,15 +72,19 @@ uv run eval --algo sac --task g1_motion_tracking --sim motrix \
 
 ## 动作文件
 
-动作 NPZ 文件通过 `env.motion_file` 读取，也支持路径列表。标准片段必须包含七个 key：
+动作 NPZ 文件通过 `env.commands.motion.params.motion_file` 选择，既可传单个路径，
+也可传路径列表。标准片段必须包含七个 key：
 `fps`、`joint_pos`、`joint_vel`、`body_pos_w`、`body_quat_w`、`body_lin_vel_w`、
-`body_ang_vel_w`（在 `g1/motion_loader.py` 中校验）：
+`body_ang_vel_w`（在 `common/motion_loader.py` 中校验）：
 
 ```yaml
 env:
-  motion_file:
-    - src/unilab/assets/motions/g1/dance1_subject2_part.npz
-    - src/unilab/assets/motions/g1/walk1_subject5_from_csv.npz
+  commands:
+    motion:
+      params:
+        motion_file:
+          - motions/g1/dance1_subject2_part.npz
+          - motions/g1/walk1_subject5_from_csv.npz
 ```
 
 转换与检查辅助工具在 `scripts/motion/` 中：
@@ -109,23 +116,24 @@ uv run scripts/motion/replay_npz.py \
 ```bash
 CUDA_VISIBLE_DEVICES=1 uv run train --algo sac --task g1_motion_tracking --sim mujoco \
   training.use_amp=true algo.seed=1 \
-  +env.motion_file=src/unilab/assets/motions/g1/motion_crawl_slope_uni.npz \
-  +env.scene.model_file=src/unilab/assets/robots/g1/scene_crawl_slope.xml \
-  +env.sampling_mode=start \
-  env.truncate_on_clip_end=true \
-  +env.max_episode_seconds=20.0 \
-  '+env.pose_randomization={x:[0,0],y:[0,0],z:[0,0],roll:[0,0],pitch:[0,0],yaw:[0,0]}' \
-  '+env.velocity_randomization={x:[0,0],y:[0,0],z:[0,0],roll:[0,0],pitch:[0,0],yaw:[0,0]}' \
-  '+env.joint_position_range=[0,0]'
+  env.commands.motion.params.motion_file=motions/g1/motion_crawl_slope_uni.npz \
+  env.scene.model_file=src/unilab/assets/robots/g1/scene_crawl_slope.xml \
+  env.commands.motion.params.sampling_mode=start \
+  env.commands.motion.params.truncate_on_clip_end=true \
+  env.max_episode_seconds=20.0 \
+  'env.commands.motion.params.pose_range={x:[0,0],y:[0,0],z:[0,0],roll:[0,0],pitch:[0,0],yaw:[0,0]}' \
+  'env.commands.motion.params.velocity_range={x:[0,0],y:[0,0],z:[0,0],roll:[0,0],pitch:[0,0],yaw:[0,0]}' \
+  'env.commands.motion.params.joint_position_range=[0,0]'
 ```
 
-关键覆写：`env.motion_file` 切爬坡动作；`env.scene.model_file` 切斜坡场景
-（`scene_crawl_slope.xml` 在 `src/unilab/assets/robots/g1/` 下）；`sampling_mode=start`
-加 `truncate_on_clip_end=true` 从 clip 起点出发并在结尾截断；randomization 范围全置零
-复用 motion 精确初始状态。
+关键覆写：`env.commands.motion.params.motion_file` 切换爬坡动作；
+`env.scene.model_file` 切换斜坡场景（`scene_crawl_slope.xml` 在
+`src/unilab/assets/robots/g1/` 下）；`sampling_mode=start` 加
+`truncate_on_clip_end=true` 从 clip 起点出发并在结尾截断；command reset 范围全置零
+即可复用 motion 的精确初始状态。
 
 ## 交互式调试
 
 常规 checkpoint 回放用 `uv run eval`。需要 target body 或 reward debug overlay 时，
-`scripts/play_interactive.py` 是使用 MuJoCo viewer 的低层调试入口，当前没有暴露为
+`src/unilab/scripts/play_interactive.py` 是使用 MuJoCo viewer 的低层调试入口，当前没有暴露为
 统一 `uv run eval` 参数。
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/4-tasks/3-manipulation.md b/docs/sphinx/source/zh_CN/2-user_guide/4-tasks/3-manipulation.md
index 6352b4f2a..fa4d18b88 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/4-tasks/3-manipulation.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/4-tasks/3-manipulation.md
@@ -1,7 +1,7 @@
 # 操作
 
-操作任务位于 `src/unilab/envs/manipulation/` 中，Go2 机械臂 manip-loco
-env 位于 `src/unilab/envs/locomotion/go2_arm/` 中。
+操作任务位于 `src/unilab/tasks/manipulation/` 中，Go2 机械臂 manip-loco
+env 位于 `src/unilab/tasks/locomotion/go2_arm/` 中。
 
 ## 手内操作
 
@@ -16,7 +16,7 @@ uv run train --algo ppo --task sharpa_inhand --sim mujoco --profile hora trainin
 ```
 
 HORA student 蒸馏由
-`conf/hora_distill/task/sharpa_inhand/mujoco.yaml` 配置；它当前未作为
+`src/unilab/conf/hora_distill/task/sharpa_inhand/mujoco.yaml` 配置；它当前未作为
 单独的顶层 CLI 路线暴露。
 
 ## 平台平衡
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/4-tasks/4-manip_loco.md b/docs/sphinx/source/zh_CN/2-user_guide/4-tasks/4-manip_loco.md
index efca7b998..b0fecb0e2 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/4-tasks/4-manip_loco.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/4-tasks/4-manip_loco.md
@@ -5,8 +5,8 @@ env 是 `Go2ArmManipLoco`。
 
 ## Owner Configs
 
-- PPO owner：`conf/ppo/task/go2_arm_manip_loco/mujoco.yaml`
-- HIM-PPO owner：`conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml`
+- PPO owner：`src/unilab/conf/ppo/task/go2_arm_manip_loco/mujoco.yaml`
+- HIM-PPO owner：`src/unilab/conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml`
 - 场景入口：`src/unilab/assets/robots/go2_arm/scene_flat.xml`
 
 ## PPO
@@ -17,7 +17,7 @@ uv run train --algo ppo --task go2_arm_manip_loco --sim mujoco training.no_play=
 
 ## HIM-PPO
 
-HIM-PPO owner 是 `conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml`。
+HIM-PPO owner 是 `src/unilab/conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml`。
 `src/unilab/cli.py` 当前未将 HIM-PPO 作为顶层
 `uv run train --algo ...` 路线暴露。
 
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/5-domain_randomization/0-index.md b/docs/sphinx/source/zh_CN/2-user_guide/5-domain_randomization/0-index.md
index 2324ced4e..1d902faac 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/5-domain_randomization/0-index.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/5-domain_randomization/0-index.md
@@ -1,9 +1,14 @@
 # 域随机化
 
 
-本页仅描述仓库中那些已经注册、且已经接入 DR provider 的任务的当前状态。所有结论都来自代码；不从设计意图推断任何内容。
+本页仅描述仓库中已注册任务的域随机化现状。所有结论都来自代码；不从设计意图推断任何内容。
 
-当前统一的入口点位于 `NpEnv._init_domain_randomization()` 和 `DomainRandomizationManager`：
+当前存在两条 DR 声明路径：
+
+- **Manager-Based（Compatible）任务**：reset / interval 随机化通过 owner YAML 中的 Hydra `events:` manager term 声明；reset 生命周期的 event 在 reset 时采样，interval 生命周期的 event 在 step 之间施加扰动。例如 `src/unilab/conf/ppo/task/go1_joystick_flat/base.yaml` 的 `events:` 段。
+- **legacy provider 路径**：只有 3 个 Adapted family（`sharpa_inhand` / `sharpa_inhand_grasp` / `go2_arm_manip_loco`，含 appo / hora / ppo_him owner）仍通过 `DomainRandomizationProvider` + `DomainRandomizationManager` 声明 `env.domain_rand.*` 配置。
+
+legacy provider 路径的统一入口点位于 `NpEnv._init_domain_randomization()` 和 `DomainRandomizationManager`：
 
 - init 路径：task provider 产生一个 `InitRandomizationPlan`；manager 在 env 初始化期间调用后端的 `apply_init_randomization(...)`
 - reset 路径：task provider 产生一个 `ResetPlan`；manager 验证能力，然后调用后端的 `set_state(..., randomization=...)`
@@ -17,62 +22,67 @@
 
 ## 状态结论
 
-1. 当前所有接入 DR provider 的任务都使用统一的 DR 入口点；没有任何任务绕开 `DomainRandomizationManager` 在 `reset()` 内部运行单独的 DR 流程。
-2. 它们的结构都大致相同：task 文件定义一个 `domain_rand` 配置 dataclass、一个 `DomainRandomizationProvider` 和一个 `ResetPlan`；`G1WalkFlat` 复用 `G1Walk` 的 provider。
-3. 今天所"统一"的主要是入口点和执行流程，而不是每一个随机化项本身。共享辅助函数 `build_common_reset_randomization()` 目前生成 `base_mass_delta`、`base_com_offset`、`gravity`、`kp`、`kd`；共享的 interval 辅助函数目前只生成 push。
+1. Manager-Based 任务不注册 DR provider；它们的 reset/interval 随机化是 owner YAML 中的 `events:` manager term，由 manager 生命周期统一执行。只有 Adapted family 的冻结兼容工厂仍走 `DomainRandomizationManager` 统一入口。
+2. Adapted family owner 定义 `domain_rand` 配置 dataclass、`DomainRandomizationProvider` 和 `ResetPlan`；Manager-Based owner 则通过 Hydra command/event term 声明 reset 行为。G1 motion reset 扰动归 `MotionCommandCfg` 所有，WBT 另加 `EventTermCfg` reset 与 interval term。
+3. 今天所"统一"的主要是入口点和执行流程，而不是每一个随机化项本身。legacy 路径的共享辅助函数 `build_common_reset_randomization()` 目前生成 `base_mass_delta`、`base_com_offset`、`gravity`、`kp`、`kd`；共享的 interval 辅助函数目前只生成 push。
 4. `ResetRandomizationPayload` 已经可以表达 `gravity`、`body_iquat`、`body_inertia`、`kp`、`kd`，并且 `MuJoCoBackend` 已声明支持。这些是否实际被使用，仍取决于 task provider 是否对它们进行采样和 dispatch。
 5. `MotrixBackend` 目前支持 `base_mass_delta`、`base_com_offset`、`kp`、`kd` 和 interval push；并且它要求在初始化期间所有模型 actuator 都是 position actuator。
 6. `geom_size` 不是 reset 生命周期字段；Sharpa 手物体的 geom 缩放由 init 生命周期的模型 materialization 处理。
 
 ## 统一性评估表
 
-| Task | 使用统一 DR 入口？ | 结构化形式？ | reset 形式 | interval 形式 | Code |
+| Task | 声明路径 | 结构化形式？ | reset 形式 | interval 形式 | Code |
 | --- | --- | --- | --- | --- | --- |
-| `Go1JoystickFlat` | 是 | 是：`Domain_Rand + Provider + ResetPlan` | task 状态采样 + common payload | push | `go1/joystick.py` |
-| `Go2JoystickFlat` | 是 | 是：`Domain_Rand + Provider + ResetPlan` | task 状态采样 + common payload | push | `go2/joystick.py` |
-| `G1WalkFlat` | 是 | 是：`Domain_Rand + Provider + ResetPlan` | task 状态采样 + common payload | push | `g1/joystick.py` |
-| `G1WalkRough` | 是 | 是：复用 `G1WalkDomainRandomizationProvider` | task 状态采样 + common payload | push | `g1/joystick.py` |
-| `G1MotionTracking` | 是 | 是：`Domain_Rand + Provider + ResetPlan` | 大量 task 专属的 reset 采样 + common payload | push | `motion_tracking/g1/tracking.py` |
-| `AllegroInhandRotation` | 是 | 是：`DomainRandConfig + Provider + ResetPlan` | task 专属的 reset 采样 + common payload | 无 | `allegro_inhand/rotation.py` |
-| `SharpaInhandRotation` | 是 | 是：`InitRandomizationPlan + ResetPlan + IntervalRandomizationPlan` | grasp cache 采样 + common payload | 物体 `body_force` | `sharpa_inhand/rotation.py` |
-| `SharpaInhandRotationGrasp` | 是 | 是：复用 Sharpa rotation provider 并 override reset 采样 | grasp 收集 reset + common payload | 无 | `sharpa_inhand/grasp_gen.py` |
+| `Go1JoystickFlat` | Hydra `events:` term | 是：owner YAML 声明 reset/interval event | root-state reset + base mass/COM + `pd_gains` | `push_by_setting_velocity` event | `src/unilab/conf/ppo/task/go1_joystick_flat/base.yaml` |
+| `Go2JoystickFlat` | Hydra `events:` term | 是：owner YAML 声明 reset event | root-state reset + `pd_gains` kp/kd | 无 | `src/unilab/conf/ppo/task/go2_joystick_flat/base.yaml` |
+| `G1WalkFlat` | Hydra `events:` term | 是：Hydra `EventTermCfg` + Manager-Based reset term | root-state reset + 经 `pd_gains` 的 kp/kd | 无 | `g1/manager_terms.py` |
+| `G1WalkRough` | Hydra `events:` term | 是：与 `G1WalkFlat` 相同的 Manager-Based event term | root-state reset + 经 `pd_gains` 的 kp/kd | 无 | `g1/manager_terms.py` |
+| `G1MotionTracking` | Hydra command term | 是：Hydra `MotionCommandCfg` + Manager-Based command reset | motion frame、root pose/velocity 与 joint-position 采样 | 无 | `motion_tracking/common/manager_terms.py` |
+| `G1WBTObs` | Hydra `events:` term | 是：同一 motion command + Hydra `EventTermCfg` | motion reset 加 mass/COM/PD/friction/encoder-bias event | interval velocity kick | `motion_tracking/g1/manager_terms.py` |
+| `AllegroInhandRotation` | Hydra `events:` term | 是：Hydra `EventTermCfg` + Manager-Based reset term | entity 范围的手/球 reset | 无 | `allegro_inhand/manager_terms.py` |
+| `AllegroInhandRotationGrasp` | Hydra `events:` term | 是：复用 rotation reset event + `RecorderTermCfg` | 带噪声的手部 reset + grasp 收集 | 无 | `allegro_inhand/grasp_gen.py` |
+| `SharpaInhandRotation` | legacy provider | 是：`InitRandomizationPlan + ResetPlan + IntervalRandomizationPlan` | grasp cache 采样 + common payload | 物体 `body_force` | `sharpa_inhand/rotation.py` |
+| `SharpaInhandRotationGrasp` | legacy provider | 是：复用 Sharpa rotation provider 并 override reset 采样 | grasp 收集 reset + common payload | 无 | `sharpa_inhand/grasp_gen.py` |
+| `Go2ArmManipLoco` | legacy provider | 是：`DomainRandConfig + LocomotionDRProvider 子类 + ResetPlan` | task 状态采样 + common payload | push | `go2_arm/manip_loco.py` |
 
 ## 各任务域随机化清单
 
 | Task | 当前已实现的 reset 域随机化 | 当前已实现的 interval 域随机化 | 默认状态 |
 | --- | --- | --- | --- |
-| `Go1JoystickFlat` | base xy；base yaw；base qvel；command 采样；`current_actions/last_actions` 清零；可选 `base_mass_delta`；可选 `base_com_offset`；可选 `gravity` | `push_robots` | `base_mass_delta`、`base_com_offset` 和 push 默认启用；`gravity` 默认禁用 |
-| `Go2JoystickFlat` | base xy；base yaw；base qvel；command 采样；`current_actions/last_actions` 清零；kp/kd 随机化（默认启用）；可选 `base_mass_delta`；可选 `base_com_offset`；可选 `gravity` | `push_robots` | kp/kd 默认启用；common payload 和 push 默认禁用 |
-| `G1WalkFlat` | base xy；base yaw；由 `reset_base_qvel_limit` 采样的 base qvel；command 采样；`gait_phase` 采样；`current_actions/last_actions` 清零；kp/kd 随机化（默认启用）；可选 `base_mass_delta`；可选 `base_com_offset`；可选 `gravity` | `push_robots` | kp/kd 默认启用；common payload 和 push 默认禁用 |
-| `G1WalkRough` | 与 `G1WalkFlat` 相同，直接复用同一 provider | `push_robots` | kp/kd 默认启用；common payload 和 push 默认禁用 |
-| `G1MotionTracking` | 动作帧采样；root 位姿扰动 `x/y/z/roll/pitch/yaw`；root 速度扰动 `x/y/z/roll/pitch/yaw`；关节位置噪声；在 MuJoCo 下被关节范围 clip；`current_actions/last_actions` 清零；可选 `base_mass_delta`；可选 `base_com_offset`；可选 `gravity` | `push_robots` | `pose_randomization`、`velocity_randomization`、`joint_position_range` 默认有非零扰动；common payload 和 push 默认禁用 |
-| `AllegroInhandRotation` | 若存在 grasp cache，则随机采样一个 grasp；否则对手部关节施加 `joint_noise` 并对球施加 `ball_z_offset`；始终对球的线速度施加 `ball_vel_noise`；可选 common reset 随机化 payload（含 `gravity`） | 无 | 若 grasp cache 路径可用则默认采样；`joint_noise`、`ball_vel_noise`、`ball_z_offset` 默认为 0；common payload 默认禁用 |
+| `Go1JoystickFlat` | 经 `reset_root_state_uniform` 的 base xy/yaw 与 base qvel；command 采样（`UniformVelocityCommandCfg`）；经 `randomize_rigid_body_mass` 的 base mass；经 `randomize_rigid_body_com` 的 base COM；经 `pd_gains` 的 kp/kd | `push_by_setting_velocity` interval event | 上述 event term 全部在 `src/unilab/conf/ppo/task/go1_joystick_flat/base.yaml` 中默认声明并启用 |
+| `Go2JoystickFlat` | 经 `reset_root_state_uniform` 的 base xy/yaw 与 base qvel；command 采样；经 `pd_gains` 的 kp/kd | 无 | event term 在 `src/unilab/conf/ppo/task/go2_joystick_flat/base.yaml` 中默认声明并启用 |
+| `G1WalkFlat` | 经 `reset_root_state_uniform` 的 base xy/yaw 与 base qvel；带平面死区的 command 采样；`gait_phase` 采样；经 `pd_gains` 的 kp/kd 随机化 | 无 | mujoco owner 默认启用 kp/kd；motrix/mjwarp owner 默认禁用 |
+| `G1WalkRough` | 与 `G1WalkFlat` 相同（共享 owner base，rough 场景） | 无 | 与 `G1WalkFlat` 相同的默认值 |
+| `G1MotionTracking` | Motion-command frame 采样；root 位姿扰动 `x/y/z/roll/pitch/yaw`；root 速度扰动 `x/y/z/roll/pitch/yaw`；通过 public entity soft limit clip 的关节位置噪声；action-manager 状态 reset | 无 | base owner 中 `pose_range`、`velocity_range` 与 `joint_position_range` 默认有非零扰动 |
+| `G1WBTObs` | 同一 motion reset 加 base mass、base COM、PD gain、足端摩擦和 encoder-bias event term | `push_by_setting_velocity` | WBT owner 显式启用上述全部 event term；能力不支持时直接报错，不回退 |
+| `AllegroInhandRotation` | entity 范围的手/球 reset；显式配置 grasp cache 时进行采样，否则以 `null` 显式选择模型 home pose；可选 `joint_noise`、`ball_velocity_noise` 与 `ball_z_offset` | 无 | owner YAML 显式选择 home pose 与零 reset 噪声；配置的 cache 缺失或格式错误时 fail-closed |
+| `AllegroInhandRotationGrasp` | 复用 rotation reset 并设置 `joint_noise=0.25`；Manager-Based termination 检查指尖距离、接触数和球高度；recorder 保存成功 timeout rows | 无 | 生成 5 万行 Allegro grasp cache，成功保存后抛出 `RunComplete` |
 | `SharpaInhandRotation` | grasp cache 按 `scale_ids` 分桶采样；物体位姿 / quat reset；可选 common reset 随机化 payload（含 `gravity`） | 物体 `body_force` 直接力扰动 | `domain_rand.scale_list` 默认值来自 owner YAML；在 MuJoCo 下，物体 geom 缩放在 init 期间 materialize；common payload 默认禁用；物体 force 通过 Sharpa owner YAML 默认启用 |
 | `SharpaInhandRotationGrasp` | 手部位姿 reset；物体位姿 / quat reset；收集成功的 grasp 并按 `scale_ids` 分桶存储；可选 `base_mass_delta`；可选 `base_com_offset`；可选 `gravity` | 无 | 默认用于生成 Sharpa grasp cache；cache 文件名包含单个 scale 值；common payload 默认禁用 |
 
 ## 当前统一 DR 的能力与边界
 
-### 1. 统一入口点是完整的
+### 1. legacy provider 入口是统一的
 
-统一入口点由 `NpEnv` 和 `DomainRandomizationManager` 保证：
+legacy provider 路径的统一入口点由 `NpEnv` 和 `DomainRandomizationManager` 保证：
 
 - 任务只需注册一个 provider
 - manager 统一执行能力验证
 - 后端统一负责实际施加随机化 payload
 
-因此从执行路径的角度看，这些任务已经是统一的。
+因此从执行路径的角度看，仍走该路径的 Adapted family 是统一的；Manager-Based 任务则由 manager 生命周期统一执行 owner YAML 声明的 `events:` term。
 
 ### 2. 共享辅助函数仍然较窄
 
-`dr_utils.py` 目前只有两类共享辅助函数：
+legacy 路径的 `dr_utils.py` 目前只有两类共享辅助函数：
 
 - reset common payload：`base_mass_delta`、`base_com_offset`、`gravity`、`kp`、`kd`
 - interval common payload：push
 
 这意味着：
 
-- 尽管运动控制任务都走统一入口点，但它们的 base xy、yaw、qvel、command 和 gait phase 仍然直接在各自的 provider 内部采样
-- `G1MotionTracking` 的 pose / velocity / joint 噪声也是 task 专属逻辑
+- 仍走 legacy provider 的 go2_arm / sharpa family，其 task 专属状态仍直接在各自的 provider 内部采样
+- `G1MotionTracking` 的 pose / velocity / joint 噪声由其 manager command 所有
 - Allegro 的 grasp / 物体初始状态采样完全是 task 专属逻辑
 - Sharpa 的 `geom_size` 缩放是 init 生命周期的模型 materialization，不属于 reset common payload
 
@@ -92,12 +102,12 @@
 
 当前的后端能力：
 
-- `MuJoCoBackend`：支持上述 7 个 reset 项，外加 interval push 和 interval body force
+- `MuJoCoBackend`：支持上述 7 个 reset 项，外加 interval push、interval body velocity delta（线速度与世界系角速度）和 interval body force/torque
 - `MotrixBackend`：支持 `base_mass_delta`、`base_com_offset`、`kp`、`kd`，外加 interval push；要求在初始化期间 actuator 全部为 position actuator
 
 说明：
 
-- 当前的 `IntervalRandomizationPlan` 支持 `push_perturbation_limit`、`body_linear_velocity_delta` 和 `body_force`；其中 `body_force` 表达热路径上的直接外力扰动，而不暴露后端私有的 `xfrc_applied` 细节。
+- 当前的 `IntervalRandomizationPlan` 支持 `push_perturbation_limit`、`body_linear_velocity_delta`、`body_angular_velocity_delta`、`body_force` 和 `body_torque`；其中 `body_force`/`body_torque` 表达热路径上的直接外力/力矩扰动，而不暴露后端私有的 `xfrc_applied` 细节。
 - 当前 MuJoCo 后端的 interval push 和 interval body force 都通过 `xfrc_applied` dispatch；Sharpa 手物体扰动已切换为直接力扰动。
 - Motrix 后端目前仍不支持直接 body-force 扰动，因此这类 owner 配置必须继续显式禁用。
 
@@ -112,7 +122,7 @@
 - 生命周期：仅在 reset 时采样和写入；env 会保留该重力，直到下一次 reset 重新采样。
 - 后端：当前在 UniLab 中，只有 MuJoCo 后端声明支持该 reset 项；Motrix 后端不支持。一些任务按能力过滤并跳过它；另一些任务在 validate 阶段抛出错误。
 
-配置入口在每个任务的 `env.domain_rand` 下：
+配置入口仅在仍走 legacy provider 路径的 Adapted family owner 的 `env.domain_rand` 下（`sharpa_inhand_grasp`、`go2_arm_manip_loco` 及对应 hora / appo / ppo_him 变体）；Manager-Based 任务没有 `env.domain_rand`：
 
 ```yaml
 env:
@@ -132,7 +142,7 @@ env:
 如果你只想随机化大小而保持竖直向下的方向，只开放 `z` 分量：
 
 ```bash
-uv run train --algo ppo --task g1_walk_flat --sim mujoco \
+uv run train --algo ppo --task sharpa_inhand_grasp --sim mujoco \
   env.domain_rand.randomize_gravity=true \
   'env.domain_rand.gravity_range=[[0.0,0.0,-10.5],[0.0,0.0,-8.5]]'
 ```
@@ -140,7 +150,7 @@ uv run train --algo ppo --task g1_walk_flat --sim mujoco \
 如果你想同时随机化方向和大小，开放 `x/y/z`：
 
 ```bash
-uv run train --algo ppo --task g1_walk_flat --sim mujoco \
+uv run train --algo ppo --task sharpa_inhand_grasp --sim mujoco \
   env.domain_rand.randomize_gravity=true \
   'env.domain_rand.gravity_range=[[-0.3,-0.3,-10.5],[0.3,0.3,-8.5]]'
 ```
@@ -155,7 +165,9 @@ uv run train --algo ppo --task g1_walk_flat --sim mujoco \
 
 ## Interval push 用法
 
-支持 interval push 的任务在 `env.domain_rand` 下配置它：
+`env.domain_rand.push_robots` 系列字段只存在于 go2_arm Adapted family 的 owner（`src/unilab/conf/ppo/task/go2_arm_manip_loco/mujoco.yaml` 等）；Manager-Based 任务改用 `push_by_setting_velocity` interval event term 声明 push（例如 `src/unilab/conf/ppo/task/go1_joystick_flat/base.yaml` 和 `src/unilab/conf/ppo/task/quadruped_joystick_rough/base.yaml`）。
+
+go2_arm owner 在 `env.domain_rand` 下配置 push：
 
 ```yaml
 env:
@@ -172,11 +184,11 @@ env:
 - `push_body_name`：施加力的目标 body / link。默认为 `null`，表示使用后端的 `base_name`。
 
 ```bash
-uv run train --algo ppo --task g1_walk_flat --sim mujoco \
+uv run train --algo ppo --task go2_arm_manip_loco --sim mujoco \
   env.domain_rand.push_robots=true \
   env.domain_rand.push_interval=500 \
   'env.domain_rand.max_force=[20.0,20.0,5.0]' \
-  env.domain_rand.push_body_name=torso_link
+  env.domain_rand.push_body_name=base
 ```
 
 说明：
@@ -210,8 +222,8 @@ uv run train --algo ppo --task g1_walk_flat --sim mujoco \
 
 Sharpa 手是仓库中当前 `geom_size` init 生命周期 DR 的示例任务。相关任务配置：
 
-- `conf/ppo/task/sharpa_inhand/mujoco.yaml`
-- `conf/ppo/task/sharpa_inhand_grasp/mujoco.yaml`
+- `src/unilab/conf/ppo/task/sharpa_inhand/mujoco.yaml`
+- `src/unilab/conf/ppo/task/sharpa_inhand_grasp/mujoco.yaml`
 
 ### 1. 配置入口
 
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/5-domain_randomization/1-configuration.md b/docs/sphinx/source/zh_CN/2-user_guide/5-domain_randomization/1-configuration.md
index d83415575..c89685af7 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/5-domain_randomization/1-configuration.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/5-domain_randomization/1-configuration.md
@@ -1,11 +1,17 @@
 # 配置
 
-域随机化在所选的 task owner YAML 内部配置，通常位于
-`env.domain_rand` 下。先使用 `--task` 和 `--sim` 选择后端专属行为，
+域随机化在所选的 task owner YAML 内部配置。先使用 `--task` 和 `--sim` 选择后端专属行为，
 然后在所选的 owner 内部 override 字段。
 
+当前有两条声明路径：
+
+- Manager-Based（Compatible）任务通过 owner YAML 的 `events:` manager term 声明
+  reset / interval 随机化，例如 `src/unilab/conf/ppo/task/go1_joystick_flat/base.yaml`。
+- 只有 Adapted family（sharpa / go2_arm 及对应 hora / appo / ppo_him owner）仍在
+  `env.domain_rand` 下配置 legacy provider 字段。
+
 ```bash
-uv run train --algo ppo --task g1_walk_flat --sim mujoco \
+uv run train --algo ppo --task sharpa_inhand_grasp --sim mujoco \
   env.domain_rand.randomize_gravity=true \
   'env.domain_rand.gravity_range=[[0.0,0.0,-10.5],[0.0,0.0,-8.5]]'
 ```
@@ -18,25 +24,29 @@ uv run train --algo ppo --task g1_walk_flat --sim mujoco \
 
 详细的任务状态和字段语义见 {doc}`0-index`。
 
-域随机化按生命周期划分：init、reset 和 interval。manager 路径是
+域随机化按生命周期划分：init、reset 和 interval。legacy 路径的 manager 位于
 `src/unilab/dr/manager.py`；task provider 位于 env owner 附近，
-后端能力通过 `src/unilab/base/backend/base.py` 声明。
+后端能力通过 `unisim.backend.base` 声明。
 
 ## Reset Gravity
 
 在启用 gravity reset 随机化时使用 `--sim mujoco`；Motrix 在当前后端中
-未提供相同的 gravity 能力。
+未提供相同的 gravity 能力。该项只在 legacy provider 路径（Adapted family owner）
+上可用。
 
 ```bash
-uv run train --algo ppo --task g1_walk_flat --sim mujoco \
+uv run train --algo ppo --task sharpa_inhand_grasp --sim mujoco \
   env.domain_rand.randomize_gravity=true \
   'env.domain_rand.gravity_range=[[0.0,0.0,-10.5],[0.0,0.0,-8.5]]'
 ```
 
 ## Interval Push
 
+Manager-Based 任务通过 `push_by_setting_velocity` interval event term 声明 push；
+`env.domain_rand.push_robots` 只在 go2_arm Adapted family owner 上可用。
+
 ```bash
-uv run train --algo ppo --task g1_walk_flat --sim mujoco \
+uv run train --algo ppo --task go2_arm_manip_loco --sim mujoco \
   env.domain_rand.push_robots=true \
   env.domain_rand.push_interval=500 \
   'env.domain_rand.max_force=[20.0,20.0,5.0]'
@@ -45,9 +55,10 @@ uv run train --algo ppo --task g1_walk_flat --sim mujoco \
 ## Owner 本地默认值
 
 当取值范围是任务 contract 的一部分时，将其保留在 task owner YAML 中。例如，
-`conf/ppo/task/go2_joystick_rough/mujoco.yaml` 启用了 base mass、
-质心、kp/kd 和 push 随机化，而
-`conf/ppo/task/sharpa_inhand/mujoco.yaml` 为 Sharpa 配置了物体缩放、摩擦和
+rough 四足家族的 base mass、质心、kp/kd 和 push 随机化作为 event term 声明在共享 base
+`src/unilab/conf/ppo/task/quadruped_joystick_rough/base.yaml`（`go2_joystick_rough` 的 backend
+owner 通过 Hydra defaults 组合它），而
+`src/unilab/conf/ppo/task/sharpa_inhand/mujoco.yaml` 为 Sharpa 配置了物体缩放、摩擦和
 力扰动。
 
 完整的当前清单见 {doc}`0-index`。
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/5-domain_randomization/2-writing_providers.md b/docs/sphinx/source/zh_CN/2-user_guide/5-domain_randomization/2-writing_providers.md
index 16c242981..901600ee3 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/5-domain_randomization/2-writing_providers.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/5-domain_randomization/2-writing_providers.md
@@ -1,5 +1,11 @@
 # 编写 Provider
 
+本页描述 legacy provider 路径：只有 3 个 Adapted family（`sharpa_inhand` /
+`sharpa_inhand_grasp` / `go2_arm_manip_loco`）仍通过任务级
+`DomainRandomizationProvider` 声明域随机化。已迁移的 Manager-Based 任务不写
+provider；它们在 owner YAML 中通过 Hydra `events:` manager term 声明随机化（见
+{doc}`0-index` 与 {doc}`1-configuration`）。
+
 任务级域随机化 provider 与 task env owner 放在一起。它们采样任务专属的
 状态，并返回由 `DomainRandomizationManager` 消费的 plan。
 
@@ -24,13 +30,12 @@
 
 ## 证据
 
-具有代表性的 provider 实现位于：
+具有代表性的 provider 实现位于（全部属于 Adapted family 的兼容路径）：
 
-- `src/unilab/envs/locomotion/go1/joystick.py`
-- `src/unilab/envs/locomotion/g1/joystick.py`
-- `src/unilab/envs/motion_tracking/g1/tracking.py`
-- `src/unilab/envs/manipulation/allegro_inhand/rotation.py`
-- `src/unilab/envs/manipulation/sharpa_inhand/rotation.py`
+- `src/unilab/tasks/locomotion/common/dr_provider.py`（`LocomotionDRProvider`，
+  由 go2_arm family 使用）
+- `src/unilab/tasks/locomotion/go2_arm/manip_loco.py`
+- `src/unilab/tasks/manipulation/sharpa_inhand/rotation.py`
 
 开发者 contract 详情见
 {doc}`../../4-developer_guide/2-contracts/4-dr_contract`。
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/6-terrain/1-procedural.md b/docs/sphinx/source/zh_CN/2-user_guide/6-terrain/1-procedural.md
index e09110184..93086a928 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/6-terrain/1-procedural.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/6-terrain/1-procedural.md
@@ -7,7 +7,7 @@
 3. 当我想修改子地形组成时，正确的入口是什么？
 4. 当前已知的边界是什么——这些不是 bug，而是约束？
 
-关于底层 contract（冷路径实例化、注册新的子地形、hfield 导出），参见 `src/unilab/base/backend/mujoco/xml.py`、`src/unilab/base/backend/motrix/scene.py` 和 `src/unilab/terrains/terrain_generator.py` 中的源码注释。
+关于底层 contract（冷路径实例化、注册新的子地形、hfield 导出），参见 `unisim.backend.mujoco.xml`、`unisim.backend.motrix.scene` 和 `unisim.terrain.generator` 中的源码注释。
 
 ## 当前状态
 
@@ -43,7 +43,7 @@ uv run train --algo ppo --task go2_joystick_rough --sim motrix
 
 ## 2. 通过 Hydra 命令行覆盖地形参数
 
-`Go2JoystickRough` 在 `conf/ppo/task/go2_joystick_rough/{mujoco,motrix}.yaml` 中显式列出了一组可覆盖字段；这些字段允许 Hydra struct 模式接受命令行覆盖。
+`Go2JoystickRough` 在 `src/unilab/conf/ppo/task/go2_joystick_rough/{mujoco,motrix}.yaml` 中显式列出了一组可覆盖字段；这些字段允许 Hydra struct 模式接受命令行覆盖。
 
 | 字段 | 用途 | YAML 默认值 |
 | --- | --- | --- |
@@ -131,7 +131,7 @@ env:
 env 的 `__init__` 不需要直接调用 XML 实例化器；把 `scene` 交给后端构造函数即可：
 
 ```python
-from unilab.base.backend import create_backend
+from unilab.base.backend_factory import create_backend
 
 backend = create_backend(..., cfg.scene)
 terrain_origins = getattr(backend, "terrain_origins", None)
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/6-terrain/2-heightfield_import.md b/docs/sphinx/source/zh_CN/2-user_guide/6-terrain/2-heightfield_import.md
index 4020a517d..ec38bff1e 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/6-terrain/2-heightfield_import.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/6-terrain/2-heightfield_import.md
@@ -1,14 +1,14 @@
 # 高度场导入
 
-高度场地形通过 `SceneCfg` 和地形生成器进行配置，然后在 init 路径上由后端实例化。已提交的面向用户的示例是 `Go2JoystickRough`，其 owner 位于 `conf/ppo/task/go2_joystick_rough/mujoco.yaml` 和 `conf/ppo/task/go2_joystick_rough/motrix.yaml`。
+高度场地形通过 `SceneCfg` 和地形生成器进行配置，然后在 init 路径上由后端实例化。已提交的面向用户的示例是 `Go2JoystickRough`，其 owner 位于 `src/unilab/conf/ppo/task/go2_joystick_rough/mujoco.yaml` 和 `src/unilab/conf/ppo/task/go2_joystick_rough/motrix.yaml`。
 
 ## 需要阅读的文件
 
 - `src/unilab/terrains/heightfield_terrains.py`
-- `src/unilab/terrains/terrain_generator.py`
-- `src/unilab/envs/locomotion/go2/rough.py`
-- `src/unilab/base/backend/mujoco/xml.py`
-- `src/unilab/base/backend/motrix/scene.py`
+- `unisim.terrain.generator`
+- `src/unilab/tasks/locomotion/go2/rough.py`
+- `unisim.backend.mujoco.xml`
+- `unisim.backend.motrix.scene`
 
 ## 冒烟命令
 
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/7-tooling/3-nan_visualizer.md b/docs/sphinx/source/zh_CN/2-user_guide/7-tooling/3-nan_visualizer.md
index 7fe13f910..df8fb79f0 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/7-tooling/3-nan_visualizer.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/7-tooling/3-nan_visualizer.md
@@ -1,6 +1,6 @@
 # NaN 可视化工具
 
-PPO 在 `conf/ppo/config.yaml` 的 `training.nan_guard` 下设有一个 NaN guard，默认已启用，与 APPO/off-policy 一致。启用后，`scripts/train_rsl_rl.py` 会安装 `NanGuard`，检查观测字典和奖励，并在检测到 NaN/Inf 值时写入一个 `.npz` dump 以及模型元数据。
+PPO 在 `src/unilab/conf/ppo/config.yaml` 的 `training.nan_guard` 下设有一个 NaN guard，默认已启用，与 APPO/off-policy 一致。启用后，`src/unilab/scripts/train_rsl_rl.py` 会安装 `NanGuard`，检查观测字典和奖励，并在检测到 NaN/Inf 值时写入一个 `.npz` dump 以及模型元数据。
 
 ```bash
 uv run train --algo ppo --task go2_joystick_flat --sim mujoco \
@@ -8,4 +8,4 @@ uv run train --algo ppo --task go2_joystick_flat --sim mujoco \
   training.nan_guard.output_dir=/tmp/unilab/nan_dumps
 ```
 
-viewer 的实现是 `src/unilab/tools/viz_nan.py`，注册为 `unilab-viz-nan` 控制台入口。它会回放一个 dump 路径，并让你选择环境索引。dump 格式和往返加载由 `tests/test_nan_guard.py` 覆盖。
+viewer 的实现是 `src/unilab/utils/nan_viz.py`，注册为 `unilab-viz-nan` 控制台入口。它会回放一个 dump 路径，并让你选择环境索引。dump 格式和往返加载由 `tests/test_nan_guard.py` 覆盖。
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/7-tooling/4-scene_export.md b/docs/sphinx/source/zh_CN/2-user_guide/7-tooling/4-scene_export.md
index 4d785c0c3..5cd1275b4 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/7-tooling/4-scene_export.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/7-tooling/4-scene_export.md
@@ -1,6 +1,6 @@
 # 场景导出
 
-场景导出由 `src/unilab/tools/export_scene.py` 实现，并在 `pyproject.toml` 中注册为 `unilab-export-scene` 控制台入口。它接受一个 MuJoCo XML 或 MJB 模型路径，写出 `scene.xml`，在能够发现 mesh asset 时复制它们，并且可以创建一个 zip 归档。
+场景导出由 `unisim.backend.mujoco.export_scene` 实现，并在 `pyproject.toml` 中注册为 `unilab-export-scene` 控制台入口。它接受一个 MuJoCo XML 或 MJB 模型路径，写出 `scene.xml`，在能够发现 mesh asset 时复制它们，并且可以创建一个 zip 归档。
 
 对于 task 级别的实例化检查，请使用从 registry 和 owner config 构造 env 的脚本：
 
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/7-tooling/5-robot_import.md b/docs/sphinx/source/zh_CN/2-user_guide/7-tooling/5-robot_import.md
index d74b5c620..cd3787a30 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/7-tooling/5-robot_import.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/7-tooling/5-robot_import.md
@@ -25,7 +25,7 @@ task/reward/env 语义。
 如果只有 URDF，使用仓库自带脚本进行转换：
 
 ```bash
-uv run unilab-import-robot <urdf_path> [robot_name]
+uv run scripts/tools/import_robot.py <urdf_path> [robot_name]
 ```
 
 ```{important}
@@ -36,7 +36,7 @@ visual mesh 作为 collision mesh，尽量把碰撞体简化为 box / capsule /
 
 - 默认自动导入会把 actuator 写成 `position`，这只适合位置控制 owner。
   - 如果机器人必须保留 torque/motor actuator 语义，后续扩展任务时，需要参考
-    `src/unilab/envs/locomotion/go2w/` 的控制方式，把 action 解释、PD/力矩控制和
+    `src/unilab/tasks/locomotion/go2w/` 的控制方式，把 action 解释、PD/力矩控制和
     actuator contract 放在机器人 owner 的控制边界内。
 - 转换完成后，会自动弹出 `mujoco.viewer` 可视化界面展示转换结果，并进行下一步调整
   Keyframe。
@@ -58,7 +58,7 @@ visual mesh 作为 collision mesh，尽量把碰撞体简化为 box / capsule /
 
 ## 输出产物
 
-使用 `uv run unilab-import-robot <urdf_path> [robot_name]` 转换后，会在仓库内生成：
+使用 `uv run scripts/tools/import_robot.py <urdf_path> [robot_name]` 转换后，会在仓库内生成：
 
 - `src/unilab/assets/robots/<robot_name>/assets/`：转换并整理后的 mesh 资产。
 - `src/unilab/assets/robots/<robot_name>/<robot_name>.xml`：机器人 MJCF 描述，只包含机器人
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/8-manipulation/1-dexterous_inhand.md b/docs/sphinx/source/zh_CN/2-user_guide/8-manipulation/1-dexterous_inhand.md
index dd4e59f78..241d699d4 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/8-manipulation/1-dexterous_inhand.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/8-manipulation/1-dexterous_inhand.md
@@ -8,12 +8,12 @@ Allegro 旋转使用已注册的 env `AllegroInhandRotation`。旋转 owner 是
 
 Owner 证据：
 
-- `conf/ppo/task/allegro_inhand/mujoco.yaml`
-- `conf/ppo/task/allegro_inhand/motrix.yaml`
-- `conf/ppo/task/allegro_inhand_grasp/mujoco.yaml`
-- `conf/ppo/task/allegro_inhand_grasp/motrix.yaml`
-- `conf/appo/task/allegro_inhand/mujoco.yaml`
-- `conf/appo/task/allegro_inhand/motrix.yaml`
+- `src/unilab/conf/ppo/task/allegro_inhand/mujoco.yaml`
+- `src/unilab/conf/ppo/task/allegro_inhand/motrix.yaml`
+- `src/unilab/conf/ppo/task/allegro_inhand_grasp/mujoco.yaml`
+- `src/unilab/conf/ppo/task/allegro_inhand_grasp/motrix.yaml`
+- `src/unilab/conf/appo/task/allegro_inhand/mujoco.yaml`
+- `src/unilab/conf/appo/task/allegro_inhand/motrix.yaml`
 
 典型流程分两个阶段：先生成抓取缓存，然后训练旋转策略。
 
@@ -49,12 +49,12 @@ Sharpa 旋转使用已注册的 env `SharpaInhandRotation`。当前已提交的
 
 Owner 证据：
 
-- `conf/ppo/task/sharpa_inhand/mujoco.yaml`
-- `conf/ppo/task/sharpa_inhand/mujoco_hora.yaml`
-- `conf/ppo/task/sharpa_inhand_grasp/mujoco.yaml`
-- `conf/appo/task/sharpa_inhand/mujoco.yaml`
-- `conf/appo/task/sharpa_inhand/mujoco_hora.yaml`
-- `conf/hora_distill/task/sharpa_inhand/mujoco.yaml`
+- `src/unilab/conf/ppo/task/sharpa_inhand/mujoco.yaml`
+- `src/unilab/conf/ppo/task/sharpa_inhand/mujoco_hora.yaml`
+- `src/unilab/conf/ppo/task/sharpa_inhand_grasp/mujoco.yaml`
+- `src/unilab/conf/appo/task/sharpa_inhand/mujoco.yaml`
+- `src/unilab/conf/appo/task/sharpa_inhand/mujoco_hora.yaml`
+- `src/unilab/conf/hora_distill/task/sharpa_inhand/mujoco.yaml`
 
 完整 HORA 流程分三个阶段：
 
@@ -110,7 +110,7 @@ uv run eval --algo ppo --task sharpa_inhand --sim mujoco --profile hora --load-r
 uv run eval --algo appo --task sharpa_inhand --sim mujoco --profile hora --load-run -1
 ```
 
-Student 蒸馏由 `conf/hora_distill/task/sharpa_inhand/mujoco.yaml` 配置，由 `scripts/train_hora_distill.py` 实现；顶层 CLI 目前没有暴露单独的 HORA 蒸馏路由（不在 CLI `SUPPORTED_ALGOS` 中）。需要从 APPO teacher 蒸馏时，在该低层配置中设置 `teacher.algo_family=appo`。
+Student 蒸馏由 `src/unilab/conf/hora_distill/task/sharpa_inhand/mujoco.yaml` 配置，由 `scripts/train_hora_distill.py` 实现；顶层 CLI 目前没有暴露单独的 HORA 蒸馏路由（不在 CLI `SUPPORTED_ALGOS` 中）。需要从 APPO teacher 蒸馏时，在该低层配置中设置 `teacher.algo_family=appo`。
 
 常见日志目录：
 
diff --git a/docs/sphinx/source/zh_CN/2-user_guide/8-manipulation/2-manip_loco.md b/docs/sphinx/source/zh_CN/2-user_guide/8-manipulation/2-manip_loco.md
index b9fbc9c5a..a3cf5365d 100644
--- a/docs/sphinx/source/zh_CN/2-user_guide/8-manipulation/2-manip_loco.md
+++ b/docs/sphinx/source/zh_CN/2-user_guide/8-manipulation/2-manip_loco.md
@@ -1,6 +1,6 @@
 # Manip-Loco
 
-`go2_arm_manip_loco` 将 Go2 运动与 Airbot 机械臂结合。已注册的 env 是 `Go2ArmManipLoco`，PPO owner 是 `conf/ppo/task/go2_arm_manip_loco/mujoco.yaml`，HIM-PPO owner 是 `conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml`。
+`go2_arm_manip_loco` 将 Go2 运动与 Airbot 机械臂结合。已注册的 env 是 `Go2ArmManipLoco`，PPO owner 是 `src/unilab/conf/ppo/task/go2_arm_manip_loco/mujoco.yaml`，HIM-PPO owner 是 `src/unilab/conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml`。
 
 ## PPO
 
@@ -10,7 +10,7 @@ uv run train --algo ppo --task go2_arm_manip_loco --sim mujoco training.no_play=
 
 ## HIM-PPO
 
-HIM-PPO 由 `conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml` 配置，由 `scripts/train_him_ppo.py` 实现。它目前没有在 `src/unilab/cli.py` 中声明为顶层 `uv run train --algo ...` 路由。
+HIM-PPO 由 `src/unilab/conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml` 配置，由 `scripts/train_him_ppo.py` 实现。它目前没有在 `src/unilab/cli.py` 中声明为顶层 `uv run train --algo ...` 路由。
 
 如果用 MuJoCo 以外的后端构造该 env，它目前会抛出异常。请将后端选择保持在 `--task go2_arm_manip_loco --sim mujoco`，不要单独覆盖 `training.sim_backend`。
 
diff --git a/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/1-overview.md b/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/1-overview.md
index 223c74a92..45a378702 100644
--- a/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/1-overview.md
+++ b/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/1-overview.md
@@ -5,8 +5,7 @@
 ## "仿真到真机"在 UniLab 中的含义
 
 一个可部署的 UniLab 策略，是导出的策略加上所选任务 owner 使用的那套精确的观测与
-动作契约。G1 WBT 辅助路径将其物化为 `policy.onnx`、`deploy_config.yaml` 以及一个
-运动二进制文件；其他机器人需要一个等价的硬件侧运行时，它需要：
+动作契约。UniLab 提供训练侧与 ONNX 导出；每种机器人都需要一个硬件侧运行时，它需要：
 
 1. 读取传感器 → 组装出策略在仿真中看到的**同一个观测向量**。
 2. 通过一个支持所导出计算图的运行时来运行 `policy.onnx`。
@@ -60,9 +59,9 @@ flowchart LR
 
 - **观测漂移。** 仿真与部署运行时之间的传感器预处理不同（单位、坐标系、滤波截止频率）。
   记录第一段部署侧观测窗口，并与用同一份 owner YAML 构建的仿真回合作对比。
-- **动作延迟。** 一些任务配置通过 `control_config.simulate_action_latency` 暴露单步
-  延迟的动作执行。测量部署回路，并在硬件运行前让训练 owner 匹配该契约。见
-  {doc}`8-latency_budget`。
+- **动作延迟。** 一些 task owner 通过 control config 或 Manager-Based action term
+  暴露单步延迟的动作执行。测量部署回路，并在硬件运行前让训练 owner 匹配该契约。
+  见 {doc}`8-latency_budget`。
 - **摩擦 / 阻尼不匹配。** 尤其对于手内操作。在 DR 中扫动摩擦；通过
   {doc}`../2-sim_to_sim/3-contact_and_friction_alignment` 交叉核对。
 - **复位瞬态。** 仿真复位到一个稳定姿态；部署则从一个控制器状态开始。安全层必须在
diff --git a/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/2-g1_whole_body.md b/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/2-g1_whole_body.md
index df79c491e..52567a1b3 100644
--- a/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/2-g1_whole_body.md
+++ b/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/2-g1_whole_body.md
@@ -2,13 +2,13 @@
 
 ::::{admonition} 硬件目标
 :class: note
-Unitree G1 人形机器人（29 自由度变体）。假定关节顺序与
-`scripts/deploy/export_deploy_config.py` 从
-`src/unilab/assets/robots/g1/scene_flat.xml` 导出的顺序一致；在硬件上机前请核对该
-顺序。
+Unitree G1 人形机器人（29 自由度变体）。关节顺序来自任务 owner 的场景
+（`src/unilab/assets/robots/g1/scene_flat.xml`，按 actuator 顺序）；在硬件上机前请
+先核对该顺序与你的 SDK 电机索引是否一致。
 ::::
 
-本指南讲解从一个收敛的 G1 运动跟踪策略到机器人上闭环运行之间的**最后一公里**。
+本指南说明 G1 运动跟踪策略在硬件上所期望的**观测与动作契约**。仓库不提供 G1 部署侧
+运行时——硬件侧回路由你实现，本页告诉你它必须复现哪些内容。
 
 ## 0. 验证你的仿真侧检查点
 
@@ -24,80 +24,93 @@ uv run eval --algo ppo --task g1_motion_tracking --sim motrix --load-run -1 \
 - 关节速度与动作保持有限且在预期范围内。
 - 接触时序看起来与参考运动一致。
 
-如果其中任何一项不对，请在硬件上机前修复仿真侧检查点或部署契约。
+如果其中任何一项不对，请在硬件上机前修复仿真侧检查点。
 
-## 1. 导出
+## 1. 先导出，再从 owner YAML 读取契约
 
-使用训练回放路径导出 `policy.onnx`，然后用已提交的部署辅助工具导出 G1 WBT 部署配置
-与运动二进制文件：
+通过训练回放路径导出 `policy.onnx`，使用产出该检查点的同一任务 owner：
 
 ```bash
 uv run eval --algo ppo --task g1_motion_tracking --sim motrix --load-run -1
-
-uv run scripts/deploy/export_deploy_config.py \
-  --output logs/deploy/deploy_config.yaml
-
-uv run scripts/deploy/export_motion_bin.py \
-  --output logs/deploy/dance1.bin
 ```
 
-部署侧原型消费如下文件：
+硬件回路需要的每个字段都在该 owner 的 YAML 中声明。宽度随 owner 而异——两个 G1
+示例：
 
+```{list-table}
+:header-rows: 1
+:widths: 34 22 44
+
+* - Owner
+  - Actor 观测宽度
+  - 说明
+* - `src/unilab/conf/sac/task/g1_wbt_obs/mujoco.yaml`
+  - 514
+  - 无状态估计：`motion_anchor_pos_b` 与 `base_lin_vel` 置为 `null`，使用
+    pelvis IMU，proprio 项带 `history_length: 5`。
+* - `src/unilab/conf/ppo/task/g1_motion_tracking_deploy/mujoco.yaml`
+  - 154
+  - 单步 mimic actor 布局，按关节分组的 `scale` 正则映射。
 ```
-runs/<run>/
-└── policy.onnx
-logs/deploy/
-├── deploy_config.yaml
-└── dance1.bin
-```
+
+::::{admonition} 观测宽度应从 composed config 读取，而不是照抄本表
+:class: warning
+Actor 观测宽度是 `env.observations.actor.terms` 下各项 `dim * history_length`
+之和，再加上 motion command。置为 `null` 的项被丢弃。如果 ONNX 输入宽度与硬件回路
+装配出的宽度不一致，那是契约 bug，而不是硬件调参问题。
+::::
 
 ## 2. 观测契约
 
-对于已提交的 G1 WBT 部署辅助工具，观测布局会作为 `obs_layout` 导出到
-`deploy_config.yaml`。`scripts/deploy/export_deploy_config.py` 是分段顺序的权威来源：
+分项顺序与逐项历史来自你所用 owner 自己的 `env.observations.actor.terms`。以
+`g1_wbt_obs` 为例，它声明了如下项；带 `history_length: 5` 的项在项内按**最旧优先**
+展平，各项再按声明顺序拼接：
 
 ```{list-table}
 :header-rows: 1
 :widths: 30 15 55
 
-* - 分组
+* - 项
   - 维度
   - 硬件上的来源
-* - `command_joint_pos`
-  - 29
-  - 运动参考帧的关节位置
-* - `command_joint_vel`
-  - 29
-  - 运动参考帧的关节速度
 * - `motion_anchor_ori_b`
   - 6
   - 来自参考帧与机器人躯干帧的锚点朝向项
-* - `gyro`
+* - `base_ang_vel`
   - 每个历史步 3
-  - IMU 陀螺仪项
-* - `joint_pos_rel`
+  - IMU 陀螺仪（`params.sensor_name: pelvis_gyro`）
+* - `joint_pos`
   - 每个历史步 29
-  - 测量到的关节位置减去 `default_angles`
-* - `dof_vel`
+  - 测量到的关节位置减去 `stand` keyframe 的关节角
+* - `joint_vel`
   - 每个历史步 29
   - 关节速度项
-* - `last_actions`
-  - 29
+* - `actions`
+  - 每个历史步 29
   - 上一步的原始 actor 输出
 ```
 
-导出脚本还会记录每个分段的 `history_length` 并校验总的 `obs_dim`。当 ONNX 输入宽度
-与 `deploy_config.yaml` 的 `obs_dim` 不一致时，`scripts/deploy/sim_prototype.py` 会
-拒绝运行。
+motion command 在观测项之前贡献参考关节位置与速度（`29 + 29`）。逐项的最旧优先
+顺序由 `tests/scripts/test_obs_alignment_g1_wbt.py` 守护；硬件侧必须镜像该顺序，
+否则策略读到的是被置换过的向量。
 
 ## 3. 执行器接口
 
-G1 部署原型将 actor 输出严格映射为：
-`action * action_scale + default_angles`，然后钳制到 `joint_lower` /
-`joint_upper`，并应用来自 `ema_alpha` 的 EMA 平滑。
+将 actor 输出映射为 `action * scale + default_angles`，然后在目标到达电机驱动器
+之前钳制到场景的关节范围内。
+
+- `scale` 即 `env.actions.joint_pos.scale`。它可能是**标量**（`g1_wbt_obs` 为
+  `2.0`），也可能是按 actuator 解析的**正则 → 数值映射**
+  （`g1_motion_tracking_deploy` 把关节名模式映射到不同数值）。必须原样复现 owner
+  解析后的逐 actuator 向量——不要对映射取平均、取其中一项，也不要把标量广播到
+  映射型 owner 上。
+- `default_angles` 由 `use_default_offset: true` 决定，即 owner 场景中 `stand`
+  keyframe 的关节段。
+- 关节限位与增益同样来自该场景 XML（`jnt_range`、position actuator 的
+  `gainprm` / `biasprm`）。
 
-- 动作 = 目标关节位置，按 `deploy_config.yaml` 中的 `action_scale` 项**缩放**。
-- 在目标到达电机驱动器之前，将其钳制到生成的关节范围内。
+训练侧直接施加目标，不做平滑。如果硬件抖动迫使你加入平滑，请先验证其 sim2sim
+影响——每一步滞后都会把观测推离训练分布。
 
 ## 4. 参考运动同步
 
@@ -110,15 +123,15 @@ G1 部署原型将 actor 输出严格映射为：
 - **速率有界** —— 将 dφ/dt 钳制到策略训练时所用的值（运动加载器会记录这个值；加载
   `reference_motion.npz`）。
 
-参见 `unilab.envs.motion_tracking.g1.motion_loader`，这是你应当在硬件上镜像的仿真侧
-加载器。
+参见 `unilab.tasks.motion_tracking.common.motion_loader`，这是你应当在硬件上镜像的
+仿真侧加载器。
 
 ## 5. 安全层
 
 硬件侧：标准结构见 {doc}`7-safety_layers`。G1 的具体事项：
 
-- 在应用 `action_scale` 之前拒绝非有限动作与形状不匹配。
-- 用 `deploy_config.yaml` 中的 `joint_lower` / `joint_upper` 钳制生成的目标。
+- 在应用动作缩放之前拒绝非有限动作与形状不匹配。
+- 用 owner 场景 XML 中的关节范围钳制生成的目标。
 - 把看门狗、姿态监控以及操作员停止阈值保留在部署控制器中，并独立于策略对它们进行
   测试。
 
@@ -129,23 +142,14 @@ G1 部署原型将 actor 输出严格映射为：
 3. **龙门架支撑步态**。以半时间速率跟踪运动（dφ/dt 减半）。
 4. **自由站立**。全速率，然后移除龙门架。
 
-不要跳过仅观测阶段：轴顺序、关节顺序以及 `last_actions` 接线错误在这一阶段最容易被
+不要跳过仅观测阶段：轴顺序、关节顺序以及 `actions` 接线错误在这一阶段最容易被
 抓出来。
 
 ## 7. 应记录什么
 
-为每一步记录**完整的观测向量**、**完整的动作向量**与**墙钟**。在硬件上机前，通过
-MuJoCo 部署原型用同一份 ONNX、部署配置与运动二进制进行验证：
-
-```bash
-uv run scripts/deploy/sim_prototype.py \
-  --onnx runs/<run>/policy.onnx \
-  --config logs/deploy/deploy_config.yaml \
-  --motion logs/deploy/dance1.bin
-```
-
-ONNX 输入宽度与 `deploy_config.yaml` 的 `obs_dim` 之间的不匹配是部署契约 bug，而不是
-硬件调参问题。
+为每一步记录**完整的观测向量**、**完整的动作向量**与**墙钟**。把第一段硬件观测窗口
+与用同一份 owner YAML 构建的仿真回合作对比——这个 diff 比任何奖励检查都更快定位
+单位、坐标系与顺序错误。
 
 ## 另请参阅
 
diff --git a/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/4-allegro_inhand.md b/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/4-allegro_inhand.md
index d5df85edc..0dadcc93e 100644
--- a/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/4-allegro_inhand.md
+++ b/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/4-allegro_inhand.md
@@ -55,17 +55,17 @@ owner 与部署运行时在观测时序上达成一致。见
 `4-allegro_inhand` 与 `sharpa_inhand` 两个环境都自带一个**抓取生成器**，用于采样
 合理的初始手部构型。硬件侧的等价物是操作员把方块放到手里 —— 请核实你的起始构型分布
 与训练环境的抓取生成器输出相匹配（参见
-`unilab.envs.manipulation.allegro_inhand.grasp_gen`）。
+`unilab.tasks.manipulation.allegro_inhand.grasp_gen`）。
 
 如果你真实世界的起始握持存在系统性差异，**把这些位姿加入抓取生成器**，重新训练，
 然后再试。
 
 ## 动作接口
 
-操作类环境通过任务控制配置把策略动作映射为关节位置目标
-（`src/unilab/envs/manipulation/allegro_inhand/base.py` 和
-`src/unilab/envs/manipulation/sharpa_inhand/base.py`）。部署控制器必须使用相同的
-关节顺序、动作缩放与限位策略。
+操作类环境通过任务控制配置把策略动作映射为关节位置目标。Allegro 的声明由
+`src/unilab/conf/ppo/task/allegro_inhand/base.yaml` 与其 Manager-Based action term 持有；
+Sharpa 当前仍由 `src/unilab/tasks/manipulation/sharpa_inhand/base.py` 持有。
+部署控制器必须使用相同的关节顺序、动作缩放与限位策略。
 
 ## 失败恢复
 
diff --git a/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/5-onnx_runtime.md b/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/5-onnx_runtime.md
index 198f5e902..0a56d2eb6 100644
--- a/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/5-onnx_runtime.md
+++ b/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/5-onnx_runtime.md
@@ -8,10 +8,10 @@ owner；回放代码加载检查点、导出 `policy.onnx`，并在该路径实
 
 | 算法路径 | 入口脚本 | 仓库中的导出行为 |
 | --- | --- | --- |
-| PPO（torch） | `scripts/train_rsl_rl.py` | 脚本入口处 `EXPORT_POLICY=True`；回放调用 `runner.export_policy_to_onnx(...)` 与 `runner.export_policy_to_jit(...)`。 |
+| PPO（torch） | `src/unilab/scripts/train_rsl_rl.py` | 脚本入口处 `EXPORT_POLICY=True`；回放调用 `runner.export_policy_to_onnx(...)` 与 `runner.export_policy_to_jit(...)`。 |
 | HIM-PPO | `scripts/train_him_ppo.py` | 与 PPO 相同的脚本级导出模式。 |
-| APPO | `scripts/train_appo.py` | 回放写出 `policy.onnx` 并将 ONNX Runtime 输出与 PyTorch 比对校验。 |
-| SAC / TD3 / FlashSAC | `scripts/train_offpolicy.py` | 回放写出 `policy.onnx`；SAC 与 FlashSAC 在导出前使用 `actor.as_export_module()`。 |
+| APPO | `src/unilab/scripts/train_appo.py` | 回放写出 `policy.onnx` 并将 ONNX Runtime 输出与 PyTorch 比对校验。 |
+| SAC / TD3 / FlashSAC | `src/unilab/scripts/train_sac.py` / `src/unilab/scripts/train_td3.py` / `src/unilab/scripts/train_flashsac.py` | 回放写出 `policy.onnx`；SAC 与 FlashSAC 在导出前使用 `actor.as_export_module()`。 |
 
 ## 命令
 
@@ -24,36 +24,19 @@ uv run eval --algo sac --task g1_walk_flat --sim mujoco --load-run -1
 ```
 
 `uv run eval` 设置回放模式，并把 `--load-run` 映射到所路由训练脚本使用的检查点
-选择器。导出的文件会写入所选的运行目录。对于部署原型，请把导出的 `policy.onnx` 与
-运行时所用的部署侧配置和运动资产放在一起。
+选择器。导出的文件会写入所选的运行目录。用于部署时，请把导出的 `policy.onnx` 与训练
+它所用的任务 owner YAML 放在一起——该 YAML 是运行时必须复现的观测与动作契约的权威
+来源。
 
-## G1 部署原型
+## 校验导出的计算图
 
-已提交的 G1 WBT 部署辅助工具使用如下产物：
+回放路径在写出之前会把导出的计算图与 PyTorch 比对，因此导出成功本身已经建立了数值
+一致性。它**没有**建立的是：你的硬件侧回路装配出的输入向量是否相同。在硬件上机前：
 
-| 产物 | 生产者 |
-| --- | --- |
-| `policy.onnx` | 上述训练回放导出。 |
-| `deploy_config.yaml` | `scripts/deploy/export_deploy_config.py`。 |
-| `dance1.bin` 或其他运动二进制 | `scripts/deploy/export_motion_bin.py`。 |
-
-验证运行示例：
-
-```bash
-uv run scripts/deploy/export_deploy_config.py \
-  --output logs/deploy/deploy_config.yaml
-
-uv run scripts/deploy/export_motion_bin.py \
-  --output logs/deploy/dance1.bin
-
-uv run scripts/deploy/sim_prototype.py \
-  --onnx runs/<run>/policy.onnx \
-  --config logs/deploy/deploy_config.yaml \
-  --motion logs/deploy/dance1.bin
-```
-
-`scripts/deploy/sim_prototype.py` 会检查 ONNX 输入宽度是否与 `deploy_config.yaml` 中的
-`obs_dim` 匹配，然后用部署侧期望的同一观测布局在 MuJoCo 中驱动策略。
+- 从 composed config 读取 actor 观测宽度（而不是照抄文档表格），确认它与 ONNX
+  输入宽度一致。
+- 对照 owner 的 `env.observations.actor.terms` 确认分项顺序与逐项历史顺序。G1
+  全身跟踪见 {doc}`2-g1_whole_body`。
 
 ## 另请参阅
 
diff --git a/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/6-domain_randomization.md b/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/6-domain_randomization.md
index 53d864e55..3a3a065c5 100644
--- a/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/6-domain_randomization.md
+++ b/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/6-domain_randomization.md
@@ -43,7 +43,7 @@
 使用 DR 的任务通过环境初始化路径挂接一个 provider：
 
 ```python
-from unilab.envs.locomotion.common.dr_provider import LocomotionDRProvider
+from unilab.tasks.locomotion.common.dr_provider import LocomotionDRProvider
 
 class MyTaskEnv(NpEnv):
     def __init__(self, cfg):
@@ -57,12 +57,12 @@ class MyTaskEnv(NpEnv):
 ## 配方：起始范围
 
 把所选的 owner YAML 作为权威来源。例如，
-`conf/ppo/task/go2_joystick_rough/mujoco.yaml` 启用了基座质量、质心、kp/kd 以及推力
-随机化；`conf/ppo/task/sharpa_inhand/mujoco.yaml` 配置了 PD 增益、摩擦、质心、质量、
+`src/unilab/conf/ppo/task/go2_joystick_rough/mujoco.yaml` 启用了基座质量、质心、kp/kd 以及推力
+随机化；`src/unilab/conf/ppo/task/sharpa_inhand/mujoco.yaml` 配置了 PD 增益、摩擦、质心、质量、
 关节噪声与接触噪声字段。
 
 ```yaml
-# conf/ppo/task/go2_joystick_rough/mujoco.yaml
+# src/unilab/conf/ppo/task/go2_joystick_rough/mujoco.yaml
 env:
   domain_rand:
     randomize_base_mass: true
diff --git a/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/7-safety_layers.md b/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/7-safety_layers.md
index 0c5c25df0..35bfe202e 100644
--- a/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/7-safety_layers.md
+++ b/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/7-safety_layers.md
@@ -40,34 +40,31 @@ flowchart LR
     OP -.->|E-stop| D
 ```
 
-把硬实时的安全检查放在部署控制器中，而不是训练脚本里。仓库的 G1 辅助路径导出部署
-配置并运行一个 MuJoCo 原型；它并不实现生产级的电机驱动器安全回路。
+把硬实时的安全检查放在部署控制器中，而不是训练脚本里。仓库不实现生产级的电机驱动器
+安全回路——该边界由你构建并测试。
 
 ## 策略假定你已配置的内容
 
-G1 部署辅助工具会把这些字段导出到 `deploy_config.yaml`：
+策略期望的是其训练 owner 所声明的动作映射与限位。对 G1 WBT owner
+（`src/unilab/conf/sac/task/g1_wbt_obs/mujoco.yaml`）：
 
-```yaml
-action_scale: 2.0
-ema_alpha: 1.0
-default_angles: [...]
-joint_lower: [...]
-joint_upper: [...]
-kp: [...]
-kd: [...]
-```
+| 量 | 权威来源 |
+| --- | --- |
+| 动作缩放 | `env.actions.joint_pos.scale`（该 owner 为标量 `2.0`；其他 owner 声明按 actuator 解析的正则 → 数值映射） |
+| 默认关节角 | `use_default_offset: true`，即 owner 场景 XML 中 `stand` keyframe 的关节段 |
+| 关节限位 | 场景 XML 中的 `jnt_range` |
+| `kp` / `kd` | 场景 XML 中 position actuator 的 `gainprm` / `biasprm` |
 
-`scripts/deploy/sim_prototype.py` 消费同样的字段，并应用
-`action * action_scale + default_angles`、关节钳制与 EMA 平滑。硬件控制器应当消费
-生成的配置，而不是手动复制关节范围或增益。
+请从 owner YAML 及其场景派生这些量，并原样复现 owner 解析后的逐 actuator 缩放——
+标量 owner 与正则映射型 owner 不可互换。不要把关节范围或增益手工复制到第二处，那会
+与资产静默漂移。
 
 ## 交接测试
 
 在把 策略 → 安全层 → 电机 集成起来之前，先隔离测试安全层：
 
 1. 注入一个 NaN 动作，验证该指令被拒绝。
-2. 注入一个超范围的关节目标，验证钳制使用了 `deploy_config.yaml` 中的
-   `joint_lower` / `joint_upper`。
+2. 注入一个超范围的关节目标，验证钳制使用了 owner 场景 XML 中的关节范围。
 3. 在运行途中切断策略输入，验证控制器进入其配置的安全状态。
 
 ## 另请参阅
diff --git a/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/8-latency_budget.md b/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/8-latency_budget.md
index 0d972f6f6..74affe62f 100644
--- a/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/8-latency_budget.md
+++ b/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/8-latency_budget.md
@@ -7,42 +7,33 @@
 
 | 面 | 仓库证据 | 它覆盖什么 |
 | --- | --- | --- |
-| 单步动作延迟 | locomotion 与 G1 运动跟踪环境中的 `control_config.simulate_action_latency` | 执行上一步动作而非当前动作。 |
-| G1 WBT 观测历史 | `noise_config.obs_history_length` 与 `scripts/deploy/export_deploy_config.py` | 为 `gyro`、`joint_pos_rel`、`dof_vel` 与 `last_actions` 导出逐项的 `obs_layout` 历史。 |
+| 单步动作延迟 | task owner 中 Manager action term 的 `simulate_action_latency` 声明 | 执行上一步动作而非当前动作。 |
+| G1 WBT 观测历史 | `src/unilab/conf/sac/task/g1_wbt_obs/mujoco.yaml` 中逐 term 的 `history_length` | 为 `base_ang_vel`、`joint_pos`、`joint_vel` 与 `actions` 提供逐项历史。 |
 | Sharpa 触觉接触延迟 | Sharpa 手内配置中的 `domain_rand.contact_latency` | 为采样到的接触通道保留上一步的触觉接触值。 |
-| 部署侧 ONNX 契约检查 | `scripts/deploy/sim_prototype.py` | 为 G1 WBT 路径校验 `obs_layout`、`obs_dim`、ONNX 输入宽度、钳制以及 EMA 动作平滑。 |
+| 观测历史顺序守护 | `tests/scripts/test_obs_alignment_g1_wbt.py` | 断言 G1 WBT actor 观测按逐项最旧优先展平。 |
 
 ## 动作延迟
 
-对于暴露 `control_config.simulate_action_latency` 的任务，当该开关启用时，环境会应用
-`last_actions`。把它保留在所选的任务 owner YAML 中，而不要事后添加仅部署的行为。
+对于启用 action latency 的 Manager-Based 任务，action manager 会在该开关开启时应用
+上一步 action。把它保留在所选的 task owner YAML 中，而不要事后添加仅部署的行为。
 
 ```yaml
 env:
-  control_config:
-    simulate_action_latency: true
+  actions:
+    joint_pos:
+      simulate_action_latency: true
 ```
 
-已签入的 G1 WBT owner 在 `conf/offpolicy/task/sac/g1_wbt_obs/mujoco.yaml` 中启用了
+已签入的 G1 WBT owner 在 `src/unilab/conf/sac/task/g1_wbt_obs/mujoco.yaml` 中启用了
 该开关。
 
 ## 观测滞后与历史
 
-G1 WBT 部署辅助工具不会猜测观测宽度。它们导出一份带有 `obs_layout`、逐项
-`history_length` 与 `obs_dim` 的模式；随后原型组装出同样的布局，并拒绝不匹配。
+观测宽度是所声明 actor 各项 `dim * history_length` 之和，硬件运行时不允许猜测。对
+G1 WBT owner，`history_length: 5` 让每个本体感受项携带 5 步历史并按最旧优先展平，
+而参考项保持单步。完整的分项顺序见 {doc}`2-g1_whole_body`。
 
-```bash
-uv run scripts/deploy/export_deploy_config.py \
-  --output logs/deploy/deploy_config.yaml
-
-uv run scripts/deploy/sim_prototype.py \
-  --onnx runs/<run>/policy.onnx \
-  --config logs/deploy/deploy_config.yaml \
-  --motion logs/deploy/dance1.bin
-```
-
-除非训练 owner 这样做了，否则不要让指令/参考项滞后。在 G1 WBT 模式中，参考项保持
-单步，而本体感受项携带历史。
+除非训练 owner 这样做了，否则不要让指令/参考项滞后。
 
 ## 部署侧测量
 
@@ -54,8 +45,8 @@ uv run scripts/deploy/sim_prototype.py \
 4. 执行器指令的发送时间戳
 5. 钳制 / 平滑前后的动作向量
 
-将观测向量与用同一份 `deploy_config.yaml` 构建的仿真回合作对比。如果实测管线需要
-滤波或缓冲，请把相匹配的行为编码到任务 owner 中，并重新导出部署产物。
+将观测向量与用同一份任务 owner YAML 构建的仿真回合作对比。如果实测管线需要滤波或
+缓冲，请把相匹配的行为编码到任务 owner 中并重新训练，而不是只在部署侧添加。
 
 ## 不匹配的症状
 
diff --git a/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/9-troubleshooting.md b/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/9-troubleshooting.md
index f601b5560..7186eae18 100644
--- a/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/9-troubleshooting.md
+++ b/docs/sphinx/source/zh_CN/3-deployment/1-sim_to_real/9-troubleshooting.md
@@ -25,10 +25,11 @@
 1. **关节顺序被调换。** 检查 `policy.onnx` 的输入宽度与你电机驱动器中的关节顺序。
    用 `unilab-export-scene` 导出训练时的关节顺序。
 2. **动作缩放单位不匹配。** 策略输出未缩放的值；驱动器期望的是弧度，而你喂给它的
-   是归一化的 [-1, 1]。在把目标发送给驱动器之前，应用 `deploy_config.yaml` 中的
-   `action_scale` / 默认角度约定。
-3. **观测布局不匹配。** 将 `deploy_config.yaml` 的 `obs_layout` 与训练 owner 比较，
-   并在硬件上运行前用 `scripts/deploy/sim_prototype.py` 进行验证。
+   是归一化的 [-1, 1]。在把目标发送给驱动器之前，应用训练 owner YAML 中的
+   `env.actions.joint_pos.scale` / 默认角度约定，并原样复现该 owner 解析后的
+   逐 actuator 缩放。
+3. **观测布局不匹配。** 将硬件回路装配出的内容与 owner 的
+   `env.observations.actor.terms` 比较——先比分项顺序，再比逐项历史顺序。
 
 ## Allegro / Sharpa 手内操作中方块掉落
 
@@ -51,7 +52,7 @@
 
 - 完整的硬件轨迹（整段运行的 `obs / action / wall_clock`）。
 - 用于训练的仿真侧 YAML：`runs/<run>/config.yaml`。
-- `policy.onnx`，以及对于 G1 WBT 路径的 `deploy_config.yaml`。
+- `policy.onnx`，以及训练它所用的确切任务 owner YAML 路径。
 - 一段使用**同一**种子的仿真回合视频：`eval --seed <same>
   --render-mode record`。
 - 如果有的话，该运行所在 commit 与 `main` 之间的 `git diff`。
diff --git a/docs/sphinx/source/zh_CN/3-deployment/2-sim_to_sim/1-backend_swap.md b/docs/sphinx/source/zh_CN/3-deployment/2-sim_to_sim/1-backend_swap.md
index 656c19638..9a879eda7 100644
--- a/docs/sphinx/source/zh_CN/3-deployment/2-sim_to_sim/1-backend_swap.md
+++ b/docs/sphinx/source/zh_CN/3-deployment/2-sim_to_sim/1-backend_swap.md
@@ -8,7 +8,7 @@ contract 和相同的 env contract。后端特有的行为通过显式方法和
 
 | 维度 | MuJoCo | Motrix |
 |---|---|---|
-| 后端类 | `src/unilab/base/backend/mujoco/backend.py` | `src/unilab/base/backend/motrix/backend.py` |
+| 后端类 | `unisim.backend.mujoco.backend` | `unisim.backend.motrix.backend` |
 | 回放能力 | `get_play_capabilities()` 中的物理状态回放 | `get_play_capabilities()` 中的原生交互式渲染器与原生视频采集 |
 | 高度场扫描 | 实现 `create_hfield_scanner(...)` | 实现 `create_hfield_scanner(...)` |
 | DR 能力上报 | `get_dr_capabilities()` | `get_dr_capabilities()` |
@@ -36,7 +36,7 @@ uv run train --algo ppo --task go2_joystick_flat --sim motrix
 ```
 
 CLI 会把 `--algo`、`--task` 和 `--sim` 解析为一个 owner YAML，例如
-`conf/ppo/task/go2_joystick_flat/mujoco.yaml`。如果该文件不存在，则说明该任务
+`src/unilab/conf/ppo/task/go2_joystick_flat/mujoco.yaml`。如果该文件不存在，则说明该任务
 **不支持**这个后端 —— 参见
 {doc}`../../4-developer_guide/2-contracts/3-task_owner`。
 
diff --git a/docs/sphinx/source/zh_CN/3-deployment/2-sim_to_sim/2-owner_yaml_swap.md b/docs/sphinx/source/zh_CN/3-deployment/2-sim_to_sim/2-owner_yaml_swap.md
index 6754c2e70..cdc825d8a 100644
--- a/docs/sphinx/source/zh_CN/3-deployment/2-sim_to_sim/2-owner_yaml_swap.md
+++ b/docs/sphinx/source/zh_CN/3-deployment/2-sim_to_sim/2-owner_yaml_swap.md
@@ -15,8 +15,8 @@
 
 1. 复制已有的 owner YAML：
    ```bash
-   cp conf/ppo/task/go2_joystick_flat/motrix.yaml \
-      conf/ppo/task/go2_joystick_flat/mujoco.yaml
+   cp src/unilab/conf/ppo/task/go2_joystick_flat/motrix.yaml \
+      src/unilab/conf/ppo/task/go2_joystick_flat/mujoco.yaml
    ```
 2. 在新文件里，设置 `training.sim_backend: mujoco`。
 3. 调整新后端所需的**物理参数**：
diff --git a/docs/sphinx/source/zh_CN/3-deployment/2-sim_to_sim/6-capability_gaps.md b/docs/sphinx/source/zh_CN/3-deployment/2-sim_to_sim/6-capability_gaps.md
index b17c9963f..87993f1c6 100644
--- a/docs/sphinx/source/zh_CN/3-deployment/2-sim_to_sim/6-capability_gaps.md
+++ b/docs/sphinx/source/zh_CN/3-deployment/2-sim_to_sim/6-capability_gaps.md
@@ -41,7 +41,7 @@
   - Motrix 通过运行时能力检测上报支持情况。
 ```
 
-把 `src/unilab/base/backend/base.py` 作为 contract 的来源，把 `tests/base/`
+把 `unisim.backend.base` 作为 contract 的来源，把 `tests/base/`
 下的后端测试作为支持声明的证据。
 
 ## 如何更新本页
diff --git a/docs/sphinx/source/zh_CN/3-deployment/2-sim_to_sim/7-config_guard.md b/docs/sphinx/source/zh_CN/3-deployment/2-sim_to_sim/7-config_guard.md
index 7c94210e1..06523e833 100644
--- a/docs/sphinx/source/zh_CN/3-deployment/2-sim_to_sim/7-config_guard.md
+++ b/docs/sphinx/source/zh_CN/3-deployment/2-sim_to_sim/7-config_guard.md
@@ -17,8 +17,8 @@ uv run eval  --algo ppo --task go2_joystick_flat --sim motrix --load-run -1
 ## 生效链路
 
 1. **训练时**：`ExperimentTracker` 把决定策略 I/O 的契约字段快照进 `run_config.json` 的 `contract_snapshot`（不改动 checkpoint 格式，历史 checkpoint 天然兼容）。
-2. **回放时**：`eval` 读取 `--sim` 指定的**目标后端** owner 配置（如 `conf/ppo/task/go2_joystick_flat/motrix.yaml`），并注入 `training.play_only=true`。
-3. **建 env 前**：四个 play 入口（rsl_rl / appo / offpolicy / him_ppo）调用 `resolve_sim2sim_config`，把目标配置与源 run 的契约快照逐字段比对。
+2. **回放时**：`eval` 读取 `--sim` 指定的**目标后端** owner 配置（如 `src/unilab/conf/ppo/task/go2_joystick_flat/motrix.yaml`），并注入 `training.play_only=true`。若该任务没有所请求后端的 owner 配置，`eval` 会回退到同任务的兄弟后端 owner，并通过 ALLOWLIST 中的 `training.sim_backend` override 重新指定目标后端（`train` 仍要求 owner 配置必须存在）；下面的守卫链路不受影响。
+3. **建 env 前**：各 play 入口（rsl_rl / appo / sac / td3 / flashsac / him_ppo）调用 `resolve_sim2sim_config`，把目标配置与源 run 的契约快照逐字段比对。
 4. **加载权重时**：`policy_load_dim_guard` 包裹 checkpoint 加载，把底层 tensor 维度不匹配的晦涩报错重抛为清晰的 sim2sim 诊断。
 
 ## 守卫的字段
@@ -27,7 +27,7 @@ uv run eval  --algo ppo --task go2_joystick_flat --sim motrix --load-run -1
 
 | 档位 | 行为 | 字段 |
 |---|---|---|
-| **DENYLIST** | 差异即 `CrossBackendIncompatibleError`，中断 | `algo.obs_groups`、`env.control_config.action_scale`、`algo.policy.actor_hidden_dims` / `critic_hidden_dims`、`algo.empirical_normalization` / `algo.obs_normalization`、`env.sampling_mode` |
+| **DENYLIST** | 差异即 `CrossBackendIncompatibleError`，中断 | `algo.obs_groups`、legacy `env.control_config.action_scale`、Manager-Based `env.observations` / `env.actions` / policy 与 critic group mapping、`algo.policy.actor_hidden_dims` / `critic_hidden_dims`、`algo.empirical_normalization` / `algo.obs_normalization`、`env.sampling_mode` |
 | **WARNING_LIST** | 仅打印 warning，继续 | `reward.*`、`env.control_config.simulate_action_latency`、`env.ctrl_dt` |
 | **ALLOWLIST** | 自由覆盖，不检查 | `training.sim_backend`、`env.scene`、`training.play_steps`、`env.domain_rand`、`env.noise_config`、`env.commands.vel_limit` |
 
@@ -40,6 +40,10 @@ uv run eval  --algo ppo --task go2_joystick_flat --sim motrix --load-run -1
 
 > 兼容旧 run：若 `run_config.json` 没有 `contract_snapshot`（早期训练），守卫自动跳过并打印 warning，不会中断现有工作流。
 
+Manager-Based snapshot 会保存 Hydra 中完整的 typed observation/action 声明。缺少这些字段的旧
+snapshot 无法证明其 policy I/O 与 Manager-Based 目标等价，因此不对称出现时会 fail-closed。
+只有用户显式设置 `training.sim2sim_strict=false` 才会继续；加载权重时的维度守卫仍然生效。
+
 ## 另请参阅
 
 - {doc}`1-backend_swap`
diff --git a/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/0-index.md b/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/0-index.md
index 3c60032d6..42d467c0c 100644
--- a/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/0-index.md
+++ b/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/0-index.md
@@ -8,7 +8,7 @@
 :::{grid-item-card} 从 Isaac Lab 迁移
 :link: 1-from_isaac_lab
 :link-type: doc
-把 GPU 常驻的任务结构映射到 UniLab 的 CPU sim 与 learner 拆分。
+保留 Manager-Based term 结构，适配 Hydra 配置、NumPy 执行和场景访问。
 :::
 
 :::{grid-item-card} 从 Legged Gym 迁移
diff --git a/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/1-from_isaac_lab.md b/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/1-from_isaac_lab.md
index 7eaaf7f6b..38ffe1aa1 100644
--- a/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/1-from_isaac_lab.md
+++ b/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/1-from_isaac_lab.md
@@ -1,83 +1,176 @@
 # 从 Isaac Lab 迁移
 
-如果你有一个想在 UniLab 中运行的 Isaac Lab 任务，本页会告诉你哪些保持不变、
-哪些会改变，以及锋利的边角在哪里。
+把 Isaac Lab Manager-Based task 迁入 UniLab 时，应保留 manager 与 term 结构，只在各自
+owner 边界适配配置、数值执行和场景访问；不要把 task 重写成单体 `NpEnv` 子类。
 
-## 哪些保持不变
+这是基于源码的兼容迁移，不代表任意 Isaac Lab task 都能不修改直接运行。目标路径是：
 
-- Gymnasium 风格的 env 接口（`reset`、`step`、`obs/reward/info`）。
-- 基于 Hydra 的配置。你现有的大部分 YAML 可以通过字段名重映射来移植。
-- "任务"由 scene + reward + DR + obs 组合而成这一总体思路。
-- PPO 作为默认算法 —— UniLab 开箱即带 RSL-RL 的 PPO。
+```text
+Hydra owner YAML
+  -> plain ManagerBasedRlEnvCfg
+  -> Registry + make_manager_based_rl_env
+  -> NumPy/SimBackend runtime 上的 ManagerBasedRlEnv
+  -> 交给现有 training 和 IPC 路径的 NpEnvState
+```
 
-## 哪些会改变
+## 兼容边界
 
 ```{list-table}
 :header-rows: 1
-:widths: 30 35 35
-
-* - Isaac Lab 概念
-  - UniLab 对应物
-  - 备注
-* - `DirectRLEnv`
-  - `unilab.base.np_env.NpEnv`
-  - UniLab 的 obs 始终是 **dict**，而不是 tensor。
-* - `RigidBody.cfg`
-  - 任务侧的 asset 导入 + 场景组合
-  - 参见 {doc}`../../4-developer_guide/1-architecture/4-scene_composition`。
-* - GPU PhysX 后端
-  - CPU MuJoCo / Motrix + GPU learner
-  - 架构倒置 —— 见下文。
-* - `RandomizationCfg`
-  - {doc}`../../4-developer_guide/2-contracts/4-dr_contract`
-  - UniLab 的 DR 只在冷路径重采样中运行。
-* - `RewardManager` 链
-  - env 中的 reward 组合，外加
-    `unilab.training.reward` 记账
-  - reward 项仍然以 key 标识，以便分量级别的日志记录。
-* - `EventCfg` 事件驱动钩子
-  - Phase + curriculum + DR provider
-  - 钩子是显式的，而非隐式的。
+:widths: 28 24 48
+
+* - Isaac Lab 表面
+  - UniLab 状态
+  - 迁移规则
+* - Manager 类别、term 名称和字典顺序
+  - Compatible
+  - 保持 observation、action、event、reward、termination、command 与
+    curriculum term 的顺序。
+* - Function/class term 与 `func + params`
+  - Compatible
+  - 把 import 改为 `unilab.managers`；保留 term 边界和局部
+    `reset(env_ids)` 语义。
+* - `ManagerBasedRLEnv` / `ManagerBasedRLEnvCfg`
+  - Compatible 拼写 alias
+  - UniLab canonical 名称是 `ManagerBasedRlEnv` 与 `ManagerBasedRlEnvCfg`；alias
+    指向同一份实现。
+* - Tensor 数值与运算
+  - Adapted
+  - 把 `torch.Tensor` 换成 `np.ndarray` 并使用向量化 NumPy；manager-facing
+    API 没有 device 接口。
+* - 嵌套 `@configclass` task 配置
+  - Adapted
+  - 把完整 task 声明迁入唯一 Hydra owner YAML；用 `_target_` 选择具体 config
+    dataclass，用 dotted `func` 选择 term。
+* - `InteractiveSceneCfg`、USD 与 PhysX view
+  - Adapted 或 Unsupported
+  - 声明 task-owned `SceneCfg` 与 `EntityCfg`；状态和控制只通过
+    `SceneEntityCfg` 与公共 entity facade 访问。不支持的能力在冷路径绑定时报错。
+* - Omniverse、Isaac renderer 与 Torch/PhysX mutation
+  - Unsupported
+  - UniLab 不安装这些 runtime，也不提供静默模拟或回退。
 ```
 
-## 架构倒置
+规范边界见 {doc}`ADR-0006 </adr/ADR-0006-community-manager-api-on-numpy-runtime>`。
+只有已经被 registry、配置和测试覆盖的表面才能声明为 Compatible。
+
+## 迁移步骤
+
+### 1. 盘点来源 task
+
+固定 Isaac Lab revision，并列出来源 manager group、term 名称与顺序、参数、observation
+维度、action 维度、reset 行为和 episode timing。写代码前逐项分类：
+
+- 复用已有 `unilab.managers` config 或 `unilab.envs.mdp` term；
+- 把 task-specific term 从 Torch 适配为 NumPy；
+- 如果 term 依赖公共 entity 或 `SimBackend` contract 尚未提供的能力，立即停止。
+
+不能用 `getattr`/`hasattr` 探测 backend 对象、返回零，或把 task 路由回 legacy env。
+
+### 2. 在冷路径迁移 scene 与 asset
+
+用 task-owned `SceneCfg` 代替 Isaac Lab 的 USD/`InteractiveSceneCfg` 声明，显式声明 term
+需要的每个 entity 与 selector。`SceneEntityCfg` 在 materialization 时只解析一次名称和
+正则表达式；reset/step 复用缓存 ID 与 NumPy view。
+
+Cartpole fixture 使用最小 task-owned MJCF。更复杂的 asset 必须遵守
+{doc}`场景组合 <../../4-developer_guide/1-architecture/4-scene_composition>`，并只使用所选
+backend 的正式能力。
+
+### 3. 迁移 term 代码，不改 manager 结构
+
+保留每个 function/class term 及其参数，机械地把 Torch 类型与运算改为 NumPy，保持 batch
+shape，并在来源 term 返回每环境数值时继续返回每环境数值。Stateful term 在构造时解析
+selector、分配 buffer，热路径只更新 NumPy buffer。
+
+Python 只拥有 term 实现和可复用 config dataclass，不能再保存一份 task-specific term
+启停清单或默认 weight。
+
+### 4. 让 Hydra 成为唯一 task 配置 owner
+
+在 owner YAML 中声明 scene、timing、group、term、具体 config 类型、callable、参数、
+weight 和 observation mapping。例如：
+
+```yaml
+env:
+  observations:
+    policy:
+      terms:
+        joint_pos_rel:
+          func: unilab.envs.mdp.joint_pos_rel
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+  policy_observation_group: policy
+  critic_observation_group: null
+
+reward:
+  alive:
+    func: unilab.envs.mdp.is_alive
+    weight: 1.0
+```
+
+值类型唯一且具体的 manager mapping（observations / events / rewards /
+terminations / curriculum / metrics / recorders）可以省略 `_target_`，物化时按字段
+类型注解推断；`actions` / `commands` 的基类是抽象的，必须显式声明具体 `_target_`
+（如 `unilab.envs.mdp.JointPositionActionCfg`）。`unilab.managers.` 下的 config 类
+（如 `SceneEntityCfg`）可以直接写裸类名。
+
+Hydra compose 在冷路径把这份声明物化为 plain typed config。未知字段、无法解析的
+`_target_`/`func` 和错误 config 类型都会在 reset/step 之前报错。直接用 Python 构造
+config 只用于 focused 底层测试。
+
+### 5. 只注册一条通用 runtime 路径
+
+Task module 为仓库已经实际支持的每个 backend 注册 `ManagerBasedRlEnvCfg` 与
+`make_manager_based_rl_env`。Backend owner YAML 只承载 backend 身份与 tuning。用户通过
+标准 CLI 选择 compose owner，例如：
+
+```bash
+uv run train --algo ppo --task <task> --sim mujoco
+```
+
+不要增加 task-specific 训练脚本分支、env factory、runner 或 IPC 路径。
+
+generic factory 规则只有两个 maintainer 批准的已注册例外：
+`make_g1_walk_env`（`src/unilab/tasks/locomotion/g1/manager_terms.py`）构造
+`G1WalkManagerBasedEnv` 子类，承载 G1 walk 的 manager-based 生产 runtime；
+`make_x2_wall_flip_env`
+（`src/unilab/tasks/motion_tracking/x2/__init__.py`）在冷路径解析未跟踪的 X2
+mesh，然后委托给 `make_manager_based_rl_env`。其余所有 Compatible task 都直接注册
+`make_manager_based_rl_env`。
 
-Isaac Lab 把模拟器放在 GPU 上，让你在 PhysX 中批处理数千个 env。UniLab 把
-模拟器放在 CPU 上（通常是多线程），并跨 worker **进程**做批处理，与单个 GPU
-learner 共享内存。
+### 6. 在适配风险附近验证
 
-由此带来的影响：
+测试 Hydra compose 与 typed materialization、term 顺序与数学、selector 失败、
+observation/action shape、局部 reset，以及至少一个真实已注册 backend 的 transition。行为
+应与固定来源 task 对比；完成语义迁移后再做性能 benchmark。
 
-- 在单个 env 上，UniLab 的**每个 env 步进时间**与 Isaac 相当甚至更差。
-  **吞吐量**来自进程并行 + 异步（参见 `unilab.ipc.async_runner`）。
-- 你可以用 **MPS、ROCm、XPU** 作为 learner 设备 —— Isaac 仅支持 CUDA。
-- 模拟器与 learner 之间**不存在 GPU 争用** —— 你的 trainer 内存占用是可预测的。
+## 任务迁移最终状态
 
-## 逐步迁移
+#1042 迁移收尾覆盖 39 个 production task、86 个 task/backend 注册。fail-closed 的
+source of truth 是 `src/unilab/tasks/migration_matrix.py`：`migration_record()` 对没有
+entry 的 production task 名称抛出 `KeyError`，因此新增 production 注册必须显式做出
+迁移决策。
 
-1. **审查观测。** 确保每个观测 key 都是一个无需 GPU PhysX 查询即可表达的向量。
-   如果不是，就添加一个状态估计器，或把该查询移到冷路径。
-2. **移植 asset。** UniLab 以 MJCF 作为唯一真实来源（source of truth）。如果你
-   有 USD，先转换为 MJCF。
-3. **移植 env。** 继承 `unilab.base.np_env.NpEnv`。把 reward 计算移进 env 的
-   `compute_reward()`。
-4. **移植 YAML。** 按照 {doc}`5-task_config_translation` 中的表格，把 Isaac Lab
-   的 `EnvCfg` 字段映射到 UniLab 任务 owner YAML。
-5. **移植 reward。** 使用 {doc}`6-reward_porting` 中的食谱。
-6. **验证。** 训练一个小规模运行，把 reward 曲线与你的 Isaac 基线对比。
+- 36 个 task 为 **Compatible**（`target=complete`）：Hydra owner YAML 物化 canonical
+  NumPy Manager-Based runtime。
+- 3 个 task 为 **Adapted**（`target=compatibility`）：`Go2ArmManipLoco`、
+  `SharpaInhandRotation` 和 `SharpaInhandRotationGrasp` 各自把自定义 IK/history 或
+  tactile/contact/cache 行为保留在一个冻结的兼容 factory 后面；只有当正式能力存在时
+  才迁移。
 
-## 你会失去什么（以及如何弥补）
+## 仓库证据
 
-- **Isaac Sim 渲染器。** 使用 Motrix 的无头视频导出，或构建一个 viser 场景
-  （`unilab.visualization.viser_scene`）。
-- **每个 env 的 tensor obs。** UniLab 给你的是 dict-of-arrays；如果你需要 tensor，
-  用你自己的 `obs_to_tensor` 包一层。
-- **内置的 GPU 侧 DR。** UniLab 的 DR 是 CPU 侧、按进程进行的。对大多数任务来说
-  这已经足够；对于极端并行，使用更多的 worker 进程。
+`tests/fixtures/isaac_lab_cartpole/` 迁移了 Isaac Lab commit
+`b0542fe2d45bf91c4e1d9ef6952b9c709c80b4e8` 的 Manager-Based Cartpole task。它保留
+全部 12 个来源 term 的名称和顺序，同时把 Torch 适配为 NumPy、嵌套 config object 适配
+为 Hydra YAML，并用 fixture-local MJCF 实现 scene/action/reset 边界。这只是 test-only
+证据，不是 production task 或 Isaac Lab 全量支持声明。
 
 ## 另请参阅
 
-- {doc}`2-from_legged_gym`
-- {doc}`3-from_rsl_rl`
-- {doc}`5-task_config_translation`
-- {doc}`6-reward_porting`
+- {doc}`Manager-Based API <../../4-developer_guide/1-architecture/6-manager_based_api>`
+- {doc}`Env contract <../../4-developer_guide/2-contracts/1-env_contract>`
+- {doc}`ADR-0006 </adr/ADR-0006-community-manager-api-on-numpy-runtime>`
diff --git a/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/2-from_legged_gym.md b/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/2-from_legged_gym.md
index 93c01c49f..0280ed0dd 100644
--- a/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/2-from_legged_gym.md
+++ b/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/2-from_legged_gym.md
@@ -8,12 +8,12 @@ Legged Gym 曾是那套 GPU 常驻的 PPO 模板，教会了整个领域如何
 
 | Legged Gym | UniLab |
 |---|---|
-| `LeggedRobot` env 类 | `unilab.envs.locomotion.common.base` |
+| `LeggedRobot` env 类 | `unilab.tasks.locomotion.common.base` |
 | `compute_observations()` | env 侧 obs 构建器 + `unilab.base.observations` |
 | `_reward_*` 方法 | env 的 `compute_reward()` + reward 项 registry |
 | `command_ranges` | 任务 owner YAML 的 `commands` 块 |
 | 地形课程 | {doc}`../../2-user_guide/6-terrain/1-procedural` |
-| RSL-RL PPO | `unilab.algos.torch.rsl_rl_ppo` |
+| RSL-RL PPO | `uni_rl.algos.rsl_rl_ppo` |
 
 ## 有哪些新东西
 
@@ -21,7 +21,7 @@ Legged Gym 曾是那套 GPU 常驻的 PPO 模板，教会了整个领域如何
   在移植之前先选一个（或两个都选）；参见
   {doc}`../2-sim_to_sim/1-backend_swap`。
 - **异步采集。** Legged Gym 在 GPU 上同步采集；UniLab 的
-  APPO（`unilab.algos.torch.appo`）把 collector 与 learner 解耦。如果你在意
+  APPO（`uni_rl.algos.appo`）把 collector 与 learner 解耦。如果你在意
   wall-clock 时间，在建立起 reward 一致性之后，就移植到 APPO。
 - **硬件部署。** Legged Gym → 真实世界部署，是各实验室各自手工搭建的流程。UniLab
   把 {doc}`../1-sim_to_real/1-overview` 流水线作为一等公民产物提供给你。
@@ -29,7 +29,7 @@ Legged Gym 曾是那套 GPU 常驻的 PPO 模板，教会了整个领域如何
 ## 迁移清单
 
 1. 把你的 URDF / MJCF asset 复制到 `src/unilab/assets/robots/<robot>/` 下。
-2. 在 `src/unilab/envs/locomotion/<robot>/` 下创建一个任务模块。
+2. 在 `src/unilab/tasks/locomotion/<robot>/` 下创建一个任务模块。
 3. 镜像你的 reward 项；保持名称相同，以便 reward 一致性可被 diff。
 4. 翻译命令采样 —— Legged Gym 的 `_resample_commands` 在 UniLab 中变成一个
    curriculum provider。
diff --git a/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/3-from_rsl_rl.md b/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/3-from_rsl_rl.md
index a00068b26..e9f21b7e7 100644
--- a/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/3-from_rsl_rl.md
+++ b/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/3-from_rsl_rl.md
@@ -1,7 +1,7 @@
 # 从 RSL-RL 迁移
 
 你已经在独立使用 RSL-RL 了？好消息：UniLab 把 RSL-RL PPO 作为其受支持算法之一
-（`unilab.algos.torch.rsl_rl_ppo`）提供，而且几乎是即插即用的。
+（`uni_rl.algos.rsl_rl_ppo`）提供，而且几乎是即插即用的。
 
 ## 迁移进 UniLab 后你能获得什么
 
@@ -10,7 +10,7 @@
    更不容易出错。
 2. **任务 owner。** 基于 Hydra 的配置组合，外加 registry 驱动的
    backend / task / algo 选择。不再需要为每种机器人编写定制的训练脚本。
-3. **异步 runner。** 把 RSL-RL PPO 包进 `unilab.algos.torch.appo`，在拥有许多
+3. **异步 runner。** 把 RSL-RL PPO 包进 `uni_rl.algos.appo`，在拥有许多
    CPU 核心的机器上获得更高吞吐量。
 4. **部署流程。** 配合正确 wrapper 的 ONNX 导出、安全层文档，以及
    {doc}`../1-sim_to_real/1-overview` 流水线。
@@ -24,7 +24,7 @@
 ## 迁移步骤
 
 1. 把你的 env 移到 `unilab.envs.<family>.<task>/`。
-2. 把你的训练配置转换为 `conf/ppo/<task>/` 下的一个 Hydra 组。
+2. 把你的训练配置转换为 `src/unilab/conf/ppo/<task>/` 下的一个 Hydra 组。
 3. 运行 `uv run train --algo ppo --task <task> --sim <backend>`。
 4. 把 reward 曲线与你独立运行的 RSL-RL 基线对比。
 
diff --git a/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/4-from_skrl.md b/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/4-from_skrl.md
index 01415f9ba..16bf5cda5 100644
--- a/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/4-from_skrl.md
+++ b/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/4-from_skrl.md
@@ -7,9 +7,9 @@ skrl 的强项在于算法广度。UniLab 专注于一组精选算法（PPO、SA
 
 | skrl | UniLab |
 |---|---|
-| `Agent`（PPO、SAC……） | `unilab.algos.torch.*` |
-| `RolloutMemory` | `unilab.ipc.rollout_ring_buffer` |
-| `ReplayMemory` | `unilab.ipc.replay_buffer` |
+| `Agent`（PPO、SAC……） | `uni_rl.*` |
+| `RolloutMemory` | `uni_rl.ipc.rollout_ring_buffer` |
+| `ReplayMemory` | `uni_rl.ipc.replay_buffer` |
 | `Trainer` | `unilab.training.run` |
 | 用于 env 的 `Wrapper` | 继承 `NpEnv` |
 
@@ -27,5 +27,5 @@ skrl 的强项在于算法广度。UniLab 专注于一组精选算法（PPO、SA
 
 1. 决定哪个 UniLab 算法最匹配你的 skrl agent。
 2. 把 env 移植为 `NpEnv` 形式。
-3. 把超参数 YAML 转换为 `conf/<algo>/<task>/` 下的 Hydra 组。
+3. 把超参数 YAML 转换为 `src/unilab/conf/<algo>/<task>/` 下的 Hydra 组。
 4. 验证 reward 一致性。
diff --git a/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/6-reward_porting.md b/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/6-reward_porting.md
index a86136ec9..cbc4fe732 100644
--- a/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/6-reward_porting.md
+++ b/docs/sphinx/source/zh_CN/3-deployment/3-framework_migration/6-reward_porting.md
@@ -37,7 +37,7 @@ def reward_feet_air_time(self, state):
 注意：
 
 - UniLab 的 `state` 携带了 `prev_contact`，因此你无需自己管理边沿检测。参见
-  `unilab.envs.locomotion.common.rewards`。
+  `unilab.tasks.locomotion.common.rewards`。
 
 ## 模式：动作平滑惩罚
 
@@ -46,7 +46,7 @@ def reward_action_rate(self, state):
     return -np.sum((state.action - state.prev_action) ** 2, axis=1)
 ```
 
-它已经是 `unilab.envs.locomotion.common.rewards` 中的现成辅助函数。
+它已经是 `unilab.tasks.locomotion.common.rewards` 中的现成辅助函数。
 
 ## 模式：姿态惩罚
 
@@ -74,5 +74,5 @@ def reward_termination(self, state):
 ## 另请参阅
 
 - {doc}`5-task_config_translation`
-- `unilab.training.reward`
-- `unilab.envs.locomotion.common.rewards`
+- `unilab.utils.reward`
+- `unilab.tasks.locomotion.common.rewards`
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/0-index.md b/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/0-index.md
index 531791167..ee5e0fd29 100644
--- a/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/0-index.md
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/0-index.md
@@ -36,6 +36,12 @@ Runner 生命周期、worker/learner 拆分与数据流。
 Bootstrap 导入与 env/backend 注册。
 :::
 
+:::{grid-item-card} Manager-Based API
+:link: 6-manager_based_api
+:link-type: doc
+社区 manager 语义、NumPy runtime 与 fail-closed 边界。
+:::
+
 ::::
 
 ```{toctree}
@@ -46,4 +52,5 @@ Bootstrap 导入与 env/backend 注册。
 3-layer_boundaries
 4-scene_composition
 5-registry
+6-manager_based_api
 ```
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/1-overview.md b/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/1-overview.md
index 9b1b65420..345ddec50 100644
--- a/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/1-overview.md
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/1-overview.md
@@ -14,17 +14,17 @@ MuJoCo or Motrix      shared memory       torch
 
 PPO 是同步路径：默认单进程，也可在单机上按一卡一进程运行 RSL-RL 数据并行；
 每个 rank 独立持有 env/rollout，RSL-RL 在每个 mini-batch 后平均梯度。APPO 与
-off-policy 算法则使用异步 runner、共享缓冲区，以及位于 `src/unilab/ipc/` 与
-`src/unilab/algos/` 下的权重同步原语。
+off-policy 算法则使用异步 runner、共享缓冲区，以及位于 `uni_rl.ipc` (unilab-rl repo) 与
+`uni_rl` (unilab-rl repo) 下的权重同步原语。
 
 ## 分层边界
 
 | 层 | 路径 | 拥有 |
 | --- | --- | --- |
-| Backend | `src/unilab/base/backend/` | `SimBackend`、物理状态、可选能力 |
+| Backend | `unisim.backend` | `SimBackend`、物理状态、可选能力 |
 | Env | `src/unilab/envs/`、`src/unilab/base/np_env.py` | MDP 语义、观测、奖励、reset |
-| Config 与 registry | `conf/`、`src/unilab/base/registry.py`、`src/unilab/structured_configs.py` | Schema、owner YAML、env/backend 注册 |
-| 算法与 IPC | `src/unilab/algos/`、`src/unilab/ipc/` | Learner、runner、buffer、权重同步 |
+| Config 与 registry | `src/unilab/conf/`、`src/unilab/base/registry.py`、`src/unilab/structured_configs.py` | Schema、owner YAML、env/backend 注册 |
+| 算法与 IPC | `uni_rl` (unilab-rl repo)、`uni_rl.ipc` (unilab-rl repo) | Learner、runner、buffer、权重同步 |
 | Scripts | `scripts/`、`src/unilab/cli.py` | 轻量装配与 CLI 路由 |
 
 ## 设计规则
@@ -71,13 +71,15 @@ off-policy 算法则使用异步 runner、共享缓冲区，以及位于 `src/un
 
 ## 关键文件
 
-- `scripts/train_rsl_rl.py`
-- `scripts/train_appo.py`
-- `scripts/train_offpolicy.py`
+- `src/unilab/scripts/train_rsl_rl.py`
+- `src/unilab/scripts/train_appo.py`
+- `src/unilab/scripts/train_sac.py`
+- `src/unilab/scripts/train_td3.py`
+- `src/unilab/scripts/train_flashsac.py`
 - `src/unilab/base/np_env.py`
-- `src/unilab/base/backend/base.py`
+- `unisim.backend.base`
 - `src/unilab/base/registry.py`
-- `src/unilab/ipc/async_runner.py`
+- `uni_rl.ipc.async_runner` (unilab-rl repo)
 - `src/unilab/training/run.py`
 
 ## 相关 ADR
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/2-runtime_model.md b/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/2-runtime_model.md
index ca937096b..453ea0f67 100644
--- a/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/2-runtime_model.md
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/2-runtime_model.md
@@ -9,7 +9,7 @@
 
 ### 同步 PPO 路径
 
-`scripts/train_rsl_rl.py` 会 compose Hydra config、
+`src/unilab/scripts/train_rsl_rl.py` 会 compose Hydra config、
 调用 registry bootstrap、通过 `registry.make(...)` 构造 env，并在同一进程内运行
 learner。默认配置保持单进程；`training.devices` 指定多张卡时，父进程通过 PyTorch
 elastic launcher 启动本机 worker，worker 再进入同一脚本完成上述构造。RSL-RL 路径
@@ -35,7 +35,7 @@ CPU physics env loop -> shared IPC buffer -> learner
 - SAC、TD3 与 FlashSAC 只使用一条 off-policy execution path：`ReplayBuffer`
   提供有界 host ingress，完整 ring 驻留在一个 CUDA/MPS learner device，
   `SharedWeightSync` 负责发布 actor 权重。
-- `src/unilab/ipc/async_runner.py` 中的 `AsyncRunner` 负责 collector 进程启动、
+- `uni_rl.ipc.async_runner` (unilab-rl repo) 中的 `AsyncRunner` 负责 collector 进程启动、
   停止信号以及共享资源清理。
 
 ## 边界规则
@@ -49,9 +49,9 @@ CPU physics env loop -> shared IPC buffer -> learner
 
 ## 仓库中的证据
 
-- PPO 入口：`scripts/train_rsl_rl.py`
-- APPO runner：`src/unilab/algos/torch/appo/runner.py`
-- Off-policy runner：`src/unilab/algos/torch/offpolicy/double_buffer_runner.py`
-- IPC 原语：`src/unilab/ipc/async_runner.py`、
-  `src/unilab/ipc/rollout_ring_buffer.py`、`src/unilab/ipc/replay_buffer.py`、
-  `src/unilab/ipc/weight_sync.py`
+- PPO 入口：`src/unilab/scripts/train_rsl_rl.py`
+- APPO runner：`uni_rl.algos.appo.runner` (unilab-rl repo)
+- Off-policy runner：`uni_rl.offpolicy.double_buffer_runner` (unilab-rl repo)
+- IPC 原语：`uni_rl.ipc.async_runner` (unilab-rl repo)、
+  `uni_rl.ipc.rollout_ring_buffer` (unilab-rl repo)、`uni_rl.ipc.replay_buffer` (unilab-rl repo)、
+  `uni_rl.ipc.weight_sync` (unilab-rl repo)
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/3-layer_boundaries.md b/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/3-layer_boundaries.md
index 826c9a988..a28033acf 100644
--- a/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/3-layer_boundaries.md
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/3-layer_boundaries.md
@@ -9,20 +9,20 @@
 
 | 层 | Owner 路径 | 拥有 |
 | --- | --- | --- |
-| L0 Backend | `src/unilab/base/backend/` | 物理 backend 抽象、由 backend 拥有的场景 materialization、backend 能力。 |
+| L0 Backend | `unisim.backend` | 物理 backend 抽象、由 backend 拥有的场景 materialization、backend 能力。 |
 | L1 Env | `src/unilab/envs/`、`src/unilab/base/np_env.py` | MDP 语义、观测、奖励、reset 逻辑、backend 到任务的适配。 |
-| L2 Config 与 Registry | `conf/`、`src/unilab/structured_configs.py`、`src/unilab/base/registry.py`、`src/unilab/training/reward.py` | Hydra compose、owner YAML 身份、env/reward 注册。 |
-| L3 算法与 IPC | `src/unilab/algos/`、`src/unilab/ipc/` | Learner、runner、collector、replay 与 rollout buffer、权重同步。 |
+| L2 Config 与 Registry | `src/unilab/conf/`、`src/unilab/structured_configs.py`、`src/unilab/base/registry.py`、`src/unilab/training/reward.py` | Hydra compose、owner YAML 身份、env/reward 注册。 |
+| L3 算法与 IPC | `uni_rl` (unilab-rl repo)、`uni_rl.ipc` (unilab-rl repo) | Learner、runner、collector、replay 与 rollout buffer、权重同步。 |
 | L4 Scripts | `scripts/` | 仅做入口装配。 |
 
 ## 规则
 
 - 在 owner 层修复行为。训练脚本不应承载长期的 env、backend、reward 或算法业务
   规则。
-- env 代码可以依赖 `src/unilab/base/backend/base.py` 中声明的 `SimBackend`
+- env 代码可以依赖 `unisim.backend.base` 中声明的 `SimBackend`
   契约；如果共享的 env 逻辑需要某个新的 backend 能力，先将其加入 `SimBackend`，
   再使用。
-- Config 选择应保留在 `conf/` 下的 Hydra owner YAML 中，而不是放在 Python 层的
+- Config 选择应保留在 `src/unilab/conf/` 下的 Hydra owner YAML 中，而不是放在 Python 层的
   backend 分支里。
 - 资源、XML 与模型元数据相关的工作属于 init、materialization 或 cache 路径。
   不要把资源解析挪进 `step()`、`reset()` 或运行时 domain randomization 循环。
@@ -30,8 +30,9 @@
 ## 仓库中的证据
 
 - 架构契约：{doc}`/adr/ADR-0001-runtime-model-and-layer-boundaries`
-- Backend 边界：`src/unilab/base/backend/base.py`
+- Backend 边界：`unisim.backend.base`
 - Env 状态契约：`src/unilab/base/np_env.py`
 - Registry 构造路径：`src/unilab/base/registry.py`
-- 训练入口：`scripts/train_rsl_rl.py`、
-  `scripts/train_appo.py`、`scripts/train_offpolicy.py`
+- 训练入口：`src/unilab/scripts/train_rsl_rl.py`、
+  `src/unilab/scripts/train_appo.py`、`src/unilab/scripts/train_sac.py`、
+  `src/unilab/scripts/train_td3.py`、`src/unilab/scripts/train_flashsac.py`
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/4-scene_composition.md b/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/4-scene_composition.md
index 0b4abbb35..68b9fe6d8 100644
--- a/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/4-scene_composition.md
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/4-scene_composition.md
@@ -40,7 +40,7 @@ materializer。
 
 ## Backend 分发
 
-`src/unilab/base/backend/__init__.py` 中的 `create_backend(...)` 按 backend 类型
+`src/unilab/base/backend_factory.py` 中的 `create_backend(...)` 按 backend 类型
 路由，并对 `SceneCfg` 应用以下规则：
 
 1. 静态 `SceneCfg(model_file=...)` 且无 terrain：加载完整静态场景（存在
@@ -53,7 +53,7 @@ materializer。
 
 ## MuJoCo Materializer 流水线
 
-对于程序化地形，MuJoCo backend 调用 `src/unilab/base/backend/mujoco/xml.py` 中的
+对于程序化地形，MuJoCo backend 调用 `unisim.backend.mujoco.xml` 中的
 `materialize_mujoco_hfield_attached_scene(...)`。冷路径步骤为：
 
 1. `TerrainGenerator(terrain_cfg).write_png(...)` 生成与 backend 无关的高度场
@@ -98,11 +98,11 @@ materializer。
 
 当前面向用户的程序化地形路径是 Go2 崎岖地形：
 
-- Env owner：`src/unilab/envs/locomotion/go2/rough.py`
-- 地形生成器：`src/unilab/terrains/terrain_generator.py`
-- MuJoCo materializer：`src/unilab/base/backend/mujoco/xml.py`
-- Motrix materializer：`src/unilab/base/backend/motrix/scene.py`
-- Owner YAML：`conf/ppo/task/go2_joystick_rough/mujoco.yaml`、
-  `conf/ppo/task/go2_joystick_rough/motrix.yaml`
+- Task owner：`src/unilab/tasks/locomotion/go2/rough.py`
+- 地形生成器：`unisim.terrain.generator`
+- MuJoCo materializer：`unisim.backend.mujoco.xml`
+- Motrix materializer：`unisim.backend.motrix.scene`
+- Owner YAML：`src/unilab/conf/ppo/task/go2_joystick_rough/mujoco.yaml`、
+  `src/unilab/conf/ppo/task/go2_joystick_rough/motrix.yaml`
 
 用户使用说明见 {doc}`../../2-user_guide/6-terrain/1-procedural`。
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/5-registry.md b/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/5-registry.md
index 0df987d96..c96662e06 100644
--- a/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/5-registry.md
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/5-registry.md
@@ -8,10 +8,9 @@ Registry bootstrap 是一个针对环境的显式导入契约。它由
 
 1. 训练入口调用 `unilab.training.common.ensure_registries()`。
 2. 该 helper 委托给 `unilab.base.registry.ensure_registries()`。
-3. registry 导入已声明的 bootstrap 包：
-   `unilab.envs.locomotion`、`unilab.envs.manipulation` 与
-   `unilab.envs.motion_tracking`。
-4. 每个包都暴露 `__unilab_registry_modules__`，即一个包含注册副作用的模块元组。
+3. registry 导入唯一声明的 bootstrap 包 `unilab.tasks`。
+4. `unilab.tasks` 暴露 `__unilab_registry_modules__`，即一个包含注册副作用的
+   task leaf module 显式元组。
 5. 被导入的模块通过 `@registry.envcfg(...)` 注册 config，并通过
    `@registry.env(..., sim_backend=...)` 或 `registry.register_env(...)` 注册
    env 实现。
@@ -20,8 +19,8 @@ Registry bootstrap 是一个针对环境的显式导入契约。它由
 
 ## 扩展规则
 
-- 如果新的 env 模块位于某个尚未被现有 bootstrap 条目导入的新模块中，需将其加入
-  包级别的 `__unilab_registry_modules__` 元组。
+- 如果新的 task leaf 尚未被现有 bootstrap 条目导入，需将其加入
+  `unilab.tasks.__unilab_registry_modules__`。
 - 保持注册过程轻量。场景 materialization、XML 处理、资源访问以及 backend 构造
   应放在 `registry.make(...)` 之后，而不是放在装饰器注册中。
 - 重复的 env config 以及重复的 `(env, sim_backend)` 注册会在
@@ -31,7 +30,5 @@ Registry bootstrap 是一个针对环境的显式导入契约。它由
 
 - Bootstrap helper：`src/unilab/base/registry.py`
 - 训练 helper：`src/unilab/training/common.py`
-- 包声明：`src/unilab/envs/locomotion/__init__.py`、
-  `src/unilab/envs/manipulation/__init__.py`、
-  `src/unilab/envs/motion_tracking/__init__.py`
+- Task bootstrap 声明：`src/unilab/tasks/__init__.py`
 - 测试：`tests/base/test_registry.py`、`tests/utils/test_algo_utils.py`
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/6-manager_based_api.md b/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/6-manager_based_api.md
new file mode 100644
index 000000000..dcc9be804
--- /dev/null
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/1-architecture/6-manager_based_api.md
@@ -0,0 +1,32 @@
+# Manager-Based API
+
+UniLab 采用“社区兼容 API + UniLab NumPy runtime”：manager-facing 模块、term cfg、
+function/class term、生命周期和顺序语义以固定的 mjlab 1.6.0 source 为基线；数值实现使用
+NumPy，并保留现有 `NpEnvState`、Hydra owner YAML、`SimBackend`、registry 与 IPC contract。
+
+完整决策、兼容矩阵和机械迁移示例见
+{doc}`/adr/ADR-0006-community-manager-api-on-numpy-runtime`。
+
+## 不变量
+
+- 公共 manager 结构优先保持社区语义；不为局部性能制造 UniLab-only term API。
+- Production task 只从 Hydra owner YAML 配置：YAML 完整声明 manager/group/term、具体 cfg
+  `_target_`、dotted callable、params、weight 与 observation mapping；Registry 冷路径将其
+  物化为 plain typed cfg，Python 不保留 task config mirror。
+- 未知字段、无法解析的 target/callable、抽象或错误 cfg 类型直接报错；DictConfig 和解析
+  不进入 reset/step，scripts 不解释 task 业务规则。
+- manager buffer、term return、env ID 和 entity view 使用 `np.ndarray` / `slice`，core 不依赖
+  Torch、Warp、runner、learner 或 IPC。
+- `SceneEntityCfg` 在冷路径通过 base scene/entity facade 解析；facade 只调用正式
+  `SimBackend` contract，热路径复用缓存 ID/view。
+- named-sensor observation term 在构造时通过 `EntityScene.bind_sensor_data(...)` 绑定
+  backend-owned view；热路径只读该 view，不重复解析 sensor 名称或 XML/model metadata。
+- `ManagerBasedRlEnv` 恰好拥有一次 backend 物化：先完成 manager 构造和 startup event，
+  再调用 `SimBackend.materialize()`，任何 reset/step 都不能在物化前执行。
+- 用户显式空配置可以使用 Null manager；配置请求但 runtime/backend 不支持的能力必须在
+  最近边界报错，不能 warning、skip、返回零或回退旧 env。
+- 热路径避免明显的重复解析、逐环境 Python 循环、复制和临时分配；进一步优化需要
+  benchmark 证明收益，且不能增加不成比例的结构复杂度。
+
+只有被注册、配置和测试覆盖的表面才能声明 Compatible。NumPy/env/config adapter 标为
+Adapted；缺少正式 backend contract 的能力标为 Unsupported 并 fail-closed。
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/2-contracts/2-backend_contract.md b/docs/sphinx/source/zh_CN/4-developer_guide/2-contracts/2-backend_contract.md
index 0df511d0b..346b8be9d 100644
--- a/docs/sphinx/source/zh_CN/4-developer_guide/2-contracts/2-backend_contract.md
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/2-contracts/2-backend_contract.md
@@ -5,7 +5,7 @@ Backend 差异是契约边界，而不是脚本层面的特殊处理。play/rend
 
 ## 稳定的 Backend 接口
 
-所有面向 env 的 backend 调用都应经由 `src/unilab/base/backend/base.py` 中的
+所有面向 env 的 backend 调用都应经由 `unisim.backend.base` 中的
 `SimBackend`。该接口包括 base 状态、DOF 状态、世界系与 baselink 系下的 body
 状态、具名 sensor、状态 reset、物理 stepping、domain-randomization hook 以及
 可选的 playback/render 方法。
@@ -31,9 +31,9 @@ Backend 差异是契约边界，而不是脚本层面的特殊处理。play/rend
 
 ## 仓库中的证据
 
-- Backend 接口与 play 能力：`src/unilab/base/backend/base.py`
-- Backend 工厂：`src/unilab/base/backend/__init__.py`
-- MuJoCo backend：`src/unilab/base/backend/mujoco/backend.py`
-- Motrix backend：`src/unilab/base/backend/motrix/backend.py`
+- Backend 接口与 play 能力：`unisim.backend.base`
+- Backend 工厂：`src/unilab/base/backend_factory.py`
+- MuJoCo backend：`unisim.backend.mujoco.backend`
+- Motrix backend：`unisim.backend.motrix.backend`
 - Backend 契约测试：`tests/base/test_sim_backend.py`、
   `tests/base/test_backend_imports.py`、`tests/base/test_motrix_backend_options.py`
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/2-contracts/3-task_owner.md b/docs/sphinx/source/zh_CN/4-developer_guide/2-contracts/3-task_owner.md
index 6de0c852b..5d8d3b310 100644
--- a/docs/sphinx/source/zh_CN/4-developer_guide/2-contracts/3-task_owner.md
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/2-contracts/3-task_owner.md
@@ -6,10 +6,10 @@
 ## Owner 路径
 
 - PPO 与 APPO 的 owner YAML 使用
-  `conf/{ppo,appo}/task/<task>/<backend>.yaml`。
-- Off-policy owner YAML 多出算法这一维度：
-  `conf/offpolicy/task/<algo>/<task>/<backend>.yaml`。
-- 其他已有的 config 根目录，例如 `conf/ppo_him/` 与 `conf/hora_distill/`，对其
+  `src/unilab/conf/{ppo,appo}/task/<task>/<backend>.yaml`。
+- Off-policy 算法（SAC / TD3 / FlashSAC）各自有独立的配置树：
+  `src/unilab/conf/<algo>/task/<task>/<backend>.yaml`。
+- 其他已有的 config 根目录，例如 `src/unilab/conf/ppo_him/` 与 `src/unilab/conf/hora_distill/`，对其
   所支持的任务遵循相同的 owner YAML 身份规则。
 
 ## 必需语义
@@ -17,8 +17,8 @@
 - 使用对外的 CLI flag 切换 backend，例如
   `uv run train --algo ppo --task go2_joystick_flat --sim mujoco` 或
   `uv run train --algo ppo --task go2_joystick_flat --sim motrix`。
-- 对于 off-policy 入口，保持 `--algo <algo>` 与内部 owner YAML 路径
-  `conf/offpolicy/task/<algo>/<task>/<backend>.yaml` 对齐。
+- 对于 off-policy 入口，`--algo <algo>` 选择按算法划分的配置树；owner YAML
+  路径为 `src/unilab/conf/<algo>/task/<task>/<backend>.yaml`。
 - `training.sim_backend` 是所选 owner YAML 内部的身份字段，而不是一个独立的
   backend 切换开关。
 - 与 backend 相关的 reward、env、scene 与算法差异属于 owner YAML，而不是训练
@@ -27,10 +27,9 @@
 
 ## 仓库中的证据
 
-- PPO owner 示例：`conf/ppo/task/go2_joystick_flat/mujoco.yaml`
-- APPO config 根目录：`conf/appo/config.yaml`
-- Off-policy config 根目录：`conf/offpolicy/config.yaml`
-- Off-policy task/algo guard：`src/unilab/training/common.py`
+- PPO owner 示例：`src/unilab/conf/ppo/task/go2_joystick_flat/mujoco.yaml`
+- APPO config 根目录：`src/unilab/conf/appo/config.yaml`
+- Off-policy config 根目录：`src/unilab/conf/{sac,td3,flashsac}/config.yaml`
 - Config 测试：`tests/config/test_config_system.py`、
   `tests/scripts/test_train_script_configs.py`、
   `tests/envs/locomotion/g1/test_g1_owner_contract.py`
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/2-contracts/4-dr_contract.md b/docs/sphinx/source/zh_CN/4-developer_guide/2-contracts/4-dr_contract.md
index cdb493364..f84fdfe55 100644
--- a/docs/sphinx/source/zh_CN/4-developer_guide/2-contracts/4-dr_contract.md
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/2-contracts/4-dr_contract.md
@@ -89,7 +89,7 @@ actuator 的机制泄漏到共享 payload 里。
 
 - DR 类型：`src/unilab/dr/types.py`
 - DR manager：`src/unilab/dr/manager.py`
-- Backend 接口：`src/unilab/base/backend/base.py`
-- 示例 provider：`src/unilab/envs/locomotion/g1/joystick.py`、
-  `src/unilab/envs/motion_tracking/g1/tracking.py`、
-  `src/unilab/envs/manipulation/sharpa_inhand/rotation.py`
+- Backend 接口：`unisim.backend.base`
+- 示例 provider：`src/unilab/tasks/locomotion/common/dr_provider.py`、
+  `src/unilab/tasks/locomotion/go2_arm/manip_loco.py`、
+  `src/unilab/tasks/manipulation/sharpa_inhand/rotation.py`
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/2-contracts/5-runner_lifecycle.md b/docs/sphinx/source/zh_CN/4-developer_guide/2-contracts/5-runner_lifecycle.md
index 24e1d94d7..9c5fba484 100644
--- a/docs/sphinx/source/zh_CN/4-developer_guide/2-contracts/5-runner_lifecycle.md
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/2-contracts/5-runner_lifecycle.md
@@ -15,11 +15,12 @@ runner；它们不应另起第二套 collector/learner 协议。
 
 ## 各运行时的 owner
 
-- `scripts/train_rsl_rl.py` 使用 `RslRlVecEnvWrapper` 与 RSL-RL 的
+- `src/unilab/scripts/train_rsl_rl.py` 使用 `RslRlVecEnvWrapper` 与 RSL-RL 的
   `OnPolicyRunner`。
-- `scripts/train_appo.py` 使用 `APPORunner`、`RolloutRingBuffer` 与
+- `src/unilab/scripts/train_appo.py` 使用 `APPORunner`、`RolloutRingBuffer` 与
   `SharedWeightSync`。
-- `scripts/train_offpolicy.py` 使用 off-policy runner，配合 `ReplayBuffer` 与
+- `src/unilab/scripts/train_sac.py`、`src/unilab/scripts/train_td3.py` 与 `src/unilab/scripts/train_flashsac.py`
+  使用 off-policy runner，配合 `ReplayBuffer` 与
   `SharedWeightSync`。
 - `AsyncRunner` 为异步 runner 拥有 collector 进程生命周期与共享资源清理。
 
@@ -35,6 +36,6 @@ runner；它们不应另起第二套 collector/learner 协议。
 
 - 共享训练 helper：`src/unilab/training/common.py`、
   `src/unilab/training/run.py`
-- 异步生命周期：`src/unilab/ipc/async_runner.py`
+- 异步生命周期：`uni_rl.ipc.async_runner` (unilab-rl repo)
 - Runner 测试：`tests/algos/test_appo_runner.py`、
   `tests/algos/test_offpolicy_runner.py`、`tests/ipc/test_async_runner.py`
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/3-extending/1-new_task.md b/docs/sphinx/source/zh_CN/4-developer_guide/3-extending/1-new_task.md
index a60aeea32..7e43d4c7c 100644
--- a/docs/sphinx/source/zh_CN/4-developer_guide/3-extending/1-new_task.md
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/3-extending/1-new_task.md
@@ -20,8 +20,8 @@
    `reset(env_indices)` 返回 `(obs_dict, info_dict)`，而 `step(actions)`
    返回 `NpEnvState`。
 7. 在相关 config 根目录下添加 owner YAML，例如
-   `conf/ppo/task/<task>/<backend>.yaml` 或
-   `conf/offpolicy/task/<algo>/<task>/<backend>.yaml`。
+   `src/unilab/conf/ppo/task/<task>/<backend>.yaml`，off-policy 算法则为
+   `src/unilab/conf/<algo>/task/<task>/<backend>.yaml`。
 8. 把任务或场景的 keyframe 放进通过 `SceneCfg.fragment_files` 引用的
    任务/场景 XML fragment；不要把 task-level keyframe 放进 `robot.xml`。
 
@@ -38,5 +38,5 @@
 - Registry API：`src/unilab/base/registry.py`
 - Env 状态契约：`src/unilab/base/np_env.py`
 - 场景配置：`src/unilab/base/scene.py`
-- 现有任务示例：`src/unilab/envs/locomotion/go2/joystick.py`、
-  `src/unilab/envs/manipulation/allegro_inhand/rotation.py`
+- 现有任务示例：`src/unilab/tasks/locomotion/go2/joystick.py`、
+  `src/unilab/tasks/manipulation/allegro_inhand/rotation.py`
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/3-extending/2-new_backend.md b/docs/sphinx/source/zh_CN/4-developer_guide/3-extending/2-new_backend.md
index 37b7fe3d5..593ffee4e 100644
--- a/docs/sphinx/source/zh_CN/4-developer_guide/3-extending/2-new_backend.md
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/3-extending/2-new_backend.md
@@ -8,13 +8,13 @@
 仓库目前在两个重要位置识别 `mujoco` 与 `motrix`：
 
 - `src/unilab/base/registry.py` 中的 `registry.register_env(...)`
-- `src/unilab/base/backend/__init__.py` 中的 `create_backend(...)`
+- `src/unilab/base/backend_factory.py` 中的 `create_backend(...)`
 
 因此，引入第三个后端是一项架构变更，而不仅仅是新增一个包。
 
 ## 实现清单
 
-1. 在 `src/unilab/base/backend/<backend>/` 下实现一个 `SimBackend` 子类。
+1. 在 `unisim.backend<backend>/` 下实现一个 `SimBackend` 子类。
 2. 把后端构造逻辑加入 `create_backend(...)`。
 3. 如果新后端应当被 `@registry.env(..., sim_backend=...)` 接受，
    则更新 registry 校验逻辑。
@@ -36,7 +36,7 @@
 
 ## 仓库内证据
 
-- 后端接口：`src/unilab/base/backend/base.py`
-- 后端工厂：`src/unilab/base/backend/__init__.py`
-- MuJoCo 后端包：`src/unilab/base/backend/mujoco/`
-- Motrix 后端包：`src/unilab/base/backend/motrix/`
+- 后端接口：`unisim.backend.base`
+- 后端工厂：`src/unilab/base/backend_factory.py`
+- MuJoCo 后端包：`unisim.backend.mujoco`
+- Motrix 后端包：`unisim.backend.motrix`
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/3-extending/3-new_algorithm.md b/docs/sphinx/source/zh_CN/4-developer_guide/3-extending/3-new_algorithm.md
index 7e90fc4b9..bbdc20581 100644
--- a/docs/sphinx/source/zh_CN/4-developer_guide/3-extending/3-new_algorithm.md
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/3-extending/3-new_algorithm.md
@@ -4,35 +4,88 @@
 {doc}`../2-contracts/1-env_contract`、{doc}`../2-contracts/3-task_owner` 与
 {doc}`../2-contracts/5-runner_lifecycle` 开始。
 
-## 选择集成路径
+## 三档扩展方式
 
-- 同步 on-policy 示例：`scripts/train_rsl_rl.py`。
-- 异步 on-policy 示例：使用 `APPORunner` 的 `scripts/train_appo.py`。
-- Off-policy 示例：`scripts/train_offpolicy.py`，配合 `conf/offpolicy/`
-  下的 SAC、TD3 与 FlashSAC 配置。
+按改动深度从低到高有三档；优先选择能满足需求的最浅一档。
 
-## 实现清单
+### 1. 纯配置：复用现有算法
+
+不改任何代码，只调整 `src/unilab/conf/<algo>/` 下的 Hydra 配置：
+
+- 算法超参数内联在 `src/unilab/conf/<algo>/config.yaml`；
+- 每个 task×backend 组合对应一个 owner YAML：
+  `src/unilab/conf/<algo>/task/<task>/<backend>.yaml`；
+- 替换策略 / 算法实现类可以走 owner YAML 中的 `class_name` dotted path
+  （现网实例：`uni_rl.algos.hora:HoraActorModel`），无需新增代码路径。
+
+### 2. `runtime_resolver`：算法代码放在自己的仓库
+
+研究者在自己的仓库里 import `uni_rl`（distribution 名 `unilab-rl`）作为库，
+实现 runner / learner / play 逻辑，**不需要 fork unilab_rl**。owner YAML
+通过 `algo.runtime_resolver` 声明一个 dotted path（`module:attr`）：
+
+```yaml
+algo:
+  runtime_resolver: my_pkg.my_module:resolve_my_runtime
+```
+
+约定：
+
+- 签名为 `(rl_cfg: dict) -> Runtime | None`；返回 `None` 表示回落到该算法的
+  默认 runtime；
+- 返回对象必须携带 `runner_cls`；按算法族可选携带 `play_fn`（APPO 风格）
+  或 `wrapper_cls`（PPO 风格）；
+- 解析发生在 uni_rl 侧（`uni_rl.algos.appo.runtime` /
+  `uni_rl.algos.rsl_rl_runtime`），dotted path 可以指向任何可 import 的模块。
+
+仓库内实例：`src/unilab/conf/appo/task/sharpa_inhand/mujoco_hora.yaml` 指向
+`unilab.scripts.play_hora_appo:resolve_hora_appo_runtime`；HORA 的 SAC / PPO
+变体分别指向 `uni_rl.algos.hora.sac:resolve_hora_sac_runtime` 与
+`uni_rl.algos.hora.rsl_rl:resolve_hora_ppo_runtime`。
+
+### 3. fork unilab_rl：改 `uni_rl/algos/`
 
-1. 把可复用的 learner 或 runner 代码放在 `src/unilab/algos/` 下。
-2. 在归属的 config 根目录下添加 Hydra config。一个新的 off-policy 变体
-   应当添加 `conf/offpolicy/algo/<algo>.yaml` 以及对应的
-   `conf/offpolicy/task/<algo>/<task>/<backend>.yaml` owner YAML。
-3. 如果需要新的顶层训练脚本，请让它保持为组装层：
+只有当需要改动 runner / learner / collector 的共享实现（例如新的 IPC
+生命周期）时，才 fork [unilabsim/unilab_rl](https://github.com/unilabsim/unilab_rl)
+并修改 `uni_rl/algos/`。异步算法应复用 `AsyncRunner`、`ReplayBuffer` /
+`RolloutRingBuffer` 与 `SharedWeightSync`，而不是新建一套 IPC 生命周期。
+
+## CLI 可路由算法的 footprint
+
+`uv run train --algo <algo>` / `uv run eval --algo <algo>` 采用约定式路由
+（`src/unilab/cli.py` 的 `available_algos` 与 `build_route`）：algo 名
+`<algo>` 可路由，当且仅当以下两个文件同时存在，**不需要修改 cli.py**：
+
+1. `src/unilab/conf/<algo>/config.yaml` —— Hydra config 根，算法超参数内联；
+2. `src/unilab/scripts/train_<algo>.py` —— 入口脚本，保持为组装层薄壳：
    compose Hydra、调用 `ensure_registries()`、通过 registry 路径构造 env，
-   然后把控制权交给 runner 或 trainer。
-4. 把第三方适配器命名保留在适配器边界上。不要为了迎合某个库而改动
+   然后把控制权交给 runner 或 trainer。薄壳先例：`train_sac.py` /
+   `train_td3.py` / `train_flashsac.py` 复用 `train_offpolicy.py` 的共享实现。
+
+除此之外，每个 task×backend 组合需要 owner YAML
+`src/unilab/conf/<algo>/task/<task>/<backend>.yaml`。未知 algo 会
+fail-closed，报错信息列出全部可用 algo（内置 + 约定发现的）。
+
+注意：
+
+- 只有 conf 目录而没有入口脚本的 config 树（如 `hora_distill`、`ppo_him`）
+  不可路由——它们不是独立的 CLI algo。
+- 内置算法的特殊脚本名映射保留不变：`ppo` → `train_rsl_rl.py`、
+  `appo` → `train_appo.py`。
+- `src/unilab/structured_configs.py` 的 dataclass 是**可选**的约定俗成镜像，
+  没有 ConfigStore 强制注册；新增算法不强制要求添加对应 dataclass。
+
+## 实现清单
+
+1. 按上面三档选择集成路径，能纯配置就不写代码，能 `runtime_resolver`
+   就不 fork。
+2. 把第三方适配器命名保留在适配器边界上。不要为了迎合某个库而改动
    内部的 `obs` 加可选 `critic` 的 env 契约。
-5. 对于异步算法，复用 `AsyncRunner`、`ReplayBuffer` 或
-   `RolloutRingBuffer` 以及 `SharedWeightSync`，而不是新建一套 IPC
-   生命周期。
-6. 对于 off-policy 算法，保持 CLI 的 `--algo <algo>` 选择与 owner YAML
-   路径 `conf/offpolicy/task/<algo>/<task>/<backend>.yaml` 对齐；
-   `assert_offpolicy_task_choice_matches_algo` 会强制这一约束。
+3. 新入口脚本保持为组装层，不承载长期业务规则。
 
 ## 在风险点附近验证
 
-- `tests/algos/` 下的算法单元测试
-- 异步路径的 IPC 测试，位于 `tests/ipc/`
+- CLI 路由与约定式发现：`tests/test_cli.py`
 - 脚本/配置测试：`tests/scripts/test_train_script_configs.py`、
   `tests/scripts/test_train_scripts.py`
 
@@ -41,4 +94,5 @@
 - 结构化 config dataclass：`src/unilab/structured_configs.py`
 - 训练辅助工具：`src/unilab/training/common.py`、
   `src/unilab/training/run.py`
-- 现有算法包：`src/unilab/algos/torch/`
+- 现有算法包：[unilabsim/unilab_rl](https://github.com/unilabsim/unilab_rl)
+  的 `uni_rl`
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/3-extending/4-new_terrain.md b/docs/sphinx/source/zh_CN/4-developer_guide/3-extending/4-new_terrain.md
index e330cd438..47f0d958b 100644
--- a/docs/sphinx/source/zh_CN/4-developer_guide/3-extending/4-new_terrain.md
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/3-extending/4-new_terrain.md
@@ -11,7 +11,7 @@
 2. 如果地形应当可以按名称选择，在 `src/unilab/terrains/config.py` 中用
    `@terrain_preset` 添加一个 preset。
 3. 把地形接入 `TerrainGeneratorCfg`，可以通过已有的命名集合（例如
-   `ROUGH_TERRAINS_CFG`），也可以通过 `conf/` 下的 owner YAML。
+   `ROUGH_TERRAINS_CFG`），也可以通过 `src/unilab/conf/` 下的 owner YAML。
 4. 通过 `src/unilab/base/scene.py` 中的 `SceneCfg.terrain` 与
    `TerrainSceneCfg` 把地形暴露给场景。
 5. 如果某个 env 需要高度 observation，在 init 时通过
@@ -33,6 +33,6 @@
 ## 仓库内证据
 
 - 地形配置与 preset：`src/unilab/terrains/config.py`
-- 地形生成器：`src/unilab/terrains/terrain_generator.py`
+- 地形生成器：`unisim.terrain.generator`
 - Heightfield 地形类型：`src/unilab/terrains/heightfield_terrains.py`
-- 高度扫描辅助工具：`src/unilab/envs/locomotion/common/height_scan.py`
+- 高度扫描辅助工具：`src/unilab/tasks/locomotion/common/height_scan.py`
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/4-contributing.md b/docs/sphinx/source/zh_CN/4-developer_guide/4-contributing.md
index 2cb993168..fa811be4d 100644
--- a/docs/sphinx/source/zh_CN/4-developer_guide/4-contributing.md
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/4-contributing.md
@@ -5,19 +5,22 @@
 
 ## 环境
 
-按平台安装依赖：
+按平台安装依赖。setup target 也会安装仓库检查所需的可选仿真器 extra：
 
-- macOS（MPS，PyPI torch wheel）：`uv sync`
-- Linux NVIDIA（PyTorch cu128 wheel）：`uv sync`
+- macOS（MPS，PyPI torch wheel）：`make setup-motrix`（或 `make setup-mujoco`）
+- Linux NVIDIA（PyTorch cu128 wheel）：`make setup`
 - Linux AMD / ROCm：`make sync-rocm`，随后用 `uv run ...` 运行命令。要切回默认
   CUDA / macOS profile，执行 `git restore -- pyproject.toml uv.lock` 后重新
-  `uv sync`。
+  `make setup`。
 - Linux Intel XPU：`make sync-xpu`
-- 需要 Motrix backend 时追加 `--extra motrix`，例如 `uv sync --extra motrix`。
+- 如果更喜欢直接使用 uv，完整默认环境为 `uv sync --extra mujoco --extra motrix`；
+  单后端使用 `--extra mujoco` 或 `--extra motrix`。
 
 ```bash
-uv sync
-uv sync --extra motrix
+# 选择一条核心安装路径：
+make setup
+# make setup-mujoco
+# make setup-motrix
 make sync-rocm
 make sync-xpu
 ```
@@ -182,7 +185,7 @@ base；该 base 为 `main` 时运行远程集成验证，为其他分支时继
 ## 配置改动
 
 任务、后端、reward 与算法的选择应当属于 Hydra owner YAML。当添加或改动
-一条可运行路径时，更新 `conf/` 下相关的 owner config，并用 `tests/config/`
+一条可运行路径时，更新 `src/unilab/conf/` 下相关的 owner config，并用 `tests/config/`
 或 `tests/scripts/` 下的测试验证脚本组合。
 
 参见 {doc}`2-contracts/3-task_owner` 与
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/5-contributing_workflow.md b/docs/sphinx/source/zh_CN/4-developer_guide/5-contributing_workflow.md
index 5507b7d8a..b169b5002 100644
--- a/docs/sphinx/source/zh_CN/4-developer_guide/5-contributing_workflow.md
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/5-contributing_workflow.md
@@ -227,3 +227,11 @@ summary 中已经确认的决策项；实施期间新增的决策项先更新 ow
 并将其反向链接进上述文档。
 新的 ADR 使用 {doc}`ADR Template </adr/ADR-TEMPLATE>`，且必须显式说明
 `Supersedes`、`Superseded by`、`Alternatives Considered` 与 `Evidence In Repo`。
+## UniSim extraction roadmap #1428
+
+物理后端的新 public contract 位于独立的 `unisim-core` distribution（import
+namespace 为 `unisim`）。UniLab 在迁移窗口内通过
+`unilab.base.backend_factory` 传递 task-owned `SceneCfg`；该 owner-layer
+只负责资产准备和边界翻译，不复制引擎实现。新增 consumer 应直接使用
+`unisim` contract，不访问 backend 的 model/data 私有对象。原实现与兼容层
+已在 roadmap #1428 中删除。
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/6-agent_quick_reference.md b/docs/sphinx/source/zh_CN/4-developer_guide/6-agent_quick_reference.md
index bf17e4e87..bed58cf73 100644
--- a/docs/sphinx/source/zh_CN/4-developer_guide/6-agent_quick_reference.md
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/6-agent_quick_reference.md
@@ -5,19 +5,20 @@
 ## 从这里开始
 
 - 安装与 smoke 检查：{doc}`../1-getting_started/2-installation`
-- 后端选择：{doc}`../2-user_guide/3-backends/3-choosing_a_backend`
+- 后端选择：{doc}`../2-user_guide/3-backends/0-index`
 - 任务索引：{doc}`../2-user_guide/4-tasks/0-index`
 - 算法索引：{doc}`../2-user_guide/2-algorithms/0-index`
-- PPO 入口：`scripts/train_rsl_rl.py`
-- APPO 入口：`scripts/train_appo.py`
-- SAC / TD3 / FlashSAC 入口：`scripts/train_offpolicy.py`
+- PPO 入口：`src/unilab/scripts/train_rsl_rl.py`
+- APPO 入口：`src/unilab/scripts/train_appo.py`
+- SAC / TD3 / FlashSAC 入口：`src/unilab/scripts/train_sac.py` /
+  `src/unilab/scripts/train_td3.py` / `src/unilab/scripts/train_flashsac.py`
 - HIM-PPO 入口：`scripts/train_him_ppo.py`
 - HORA 蒸馏入口：`scripts/train_hora_distill.py`
 
 ## 需要记住的契约
 
 - Env 契约：`src/unilab/base/np_env.py`
-- Backend 契约：`src/unilab/base/backend/base.py`
+- Backend 契约：`unisim.backend.base`
 - 训练辅助工具：`src/unilab/training/run.py`
 - Config schema：`src/unilab/structured_configs.py`
 - Developer 标准：{doc}`1-architecture/1-overview`
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/7-motion_assets.md b/docs/sphinx/source/zh_CN/4-developer_guide/7-motion_assets.md
index b8df1f175..96b87a52d 100644
--- a/docs/sphinx/source/zh_CN/4-developer_guide/7-motion_assets.md
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/7-motion_assets.md
@@ -75,38 +75,60 @@ env:
 
 3. 在 env config 中引用新文件路径即可。
 
-## 机器人网格资产
+## 机器人二进制资产
 
-机器人二进制网格（`.STL`）采用相同方式外置，托管在 Hugging Face 数据集仓库
+机器人二进制网格和纹理（例如 `.STL`、`.obj`、`.png`）采用相同方式外置，
+托管在 Hugging Face 数据集仓库
 [unilabsim/unilab-robots](https://huggingface.co/datasets/unilabsim/unilab-robots)。
-X2 网格在首次使用时按需下载，落盘到原始路径 `src/unilab/assets/robots/x2/meshes/`，
-因此 XML 的 `meshdir` 引用保持有效。无需运行任务即可提前预拉取：
+已注册的机器人为 a2、allegro_hand、g1、go2、go2_arm、sharpa_wave、
+x2（见 `src/unilab/assets/hub.py` 的 `ROBOT_ASSET_SPECS`）。它们的
+mesh/纹理目录在首次使用时按需下载，落盘到原始路径（例如 G1 的
+`src/unilab/assets/robots/g1/assets/` 与 `robots/g1/textures/`），因此 XML 中的
+原始相对路径保持有效。这些目录通过 `pyproject.toml` 的
+`tool.uv.build-backend.source-exclude` 排除在 wheel/sdist 之外；pip 安装后首次
+使用会下载到已安装的包目录内，之后离线复用该本地缓存。无需运行任务即可提前预拉取：
 
 ```bash
+uv run unilab-pull-assets --robot g1
 uv run unilab-pull-assets --robot x2
+uv run unilab-pull-assets --robot all   # 所有已注册机器人
 ```
 
-新增某个机器人的网格：
+新增某个机器人的二进制资产：
 
-1. 上传到 HF 仓库，保持目录结构一致：
+1. 按目录上传到 HF 仓库，保持目录结构一致。一个机器人有多个资产目录时，
+   每个目录分别上传。例如 G1：
 
    ```bash
-   huggingface-cli upload unilabsim/unilab-robots \
-     src/unilab/assets/robots/<robot>/meshes robots/<robot>/meshes \
+   uv run hf upload unilabsim/unilab-robots \
+     src/unilab/assets/robots/g1/assets robots/g1/assets \
+     --repo-type dataset
+   uv run hf upload unilabsim/unilab-robots \
+     src/unilab/assets/robots/g1/textures robots/g1/textures \
      --repo-type dataset
    ```
 
-2. 在 `.gitignore` 中忽略本地 `*.STL`（保留 `.gitkeep` 以维持目录）。
-3. 在 env 的冷路径上调用一次目录 resolver，例如
-   `resolve_robot_asset_dir("robots/<robot>/meshes", marker="<某>.STL")`。
+2. 在 `.gitignore` 中忽略下载目录，在
+   `tool.uv.build-backend.source-exclude` 中排除该目录，并在 `ROBOT_ASSET_SPECS`
+   中注册。
+3. 经由 `create_backend` 构建的 scene 会被自动覆盖：
+   `ensure_robot_assets_for_paths` 在 backend 解析 XML 之前的冷路径解析已注册目录。
+   绕过 `create_backend` 的入口需要显式解析，例如 X2 task factory：
+
+   ```python
+   resolve_robot_asset_dir("robots/x2/meshes", marker="pelvis.STL")
+   ```
 
 ## 架构说明
 
 - 资产解析模块：`src/unilab/assets/hub.py`（`resolve_motion_files`）。
-- 唯一集成点：`src/unilab/envs/motion_tracking/g1/motion_loader.py` 中的
+- motion 集成点：`src/unilab/tasks/motion_tracking/common/motion_loader.py` 中的
   `MotionLoader.__init__`，在冷路径上调用一次 resolver。
+- 机器人 mesh 集成点：`src/unilab/base/backend_factory.py` 的 `create_backend`
+  对 scene 的 `model_file`、`visual_model_file` 与 `fragment_files` 调用
+  `ensure_robot_assets_for_paths`，然后再分发给具体 backend。
 - 热路径（`step` / `reset`）**不会**触发任何文件下载或解析。
 - `ASSETS_ROOT_PATH` 定义不变，下载落盘位置与原始本地路径完全一致。
-- 机器人网格使用同一目录 resolver（`resolve_robot_asset_dir`），集成点为
-  `src/unilab/envs/motion_tracking/x2/flip_tracking.py` 中的
-  `X2WallFlipTrackingEnv.__init__`，并通过 `unilab-pull-assets` CLI 暴露。
+- 机器人二进制资产使用同一目录 resolver（`resolve_robot_asset_dir`）。X2 的
+  薄 task factory 会先在冷路径解析一次，再委托给共享
+  manager env factory；同一 resolver 也通过 `unilab-pull-assets` CLI 暴露。
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/8-motrix_contact_sensor.md b/docs/sphinx/source/zh_CN/4-developer_guide/8-motrix_contact_sensor.md
index 2746b8918..38c487adf 100644
--- a/docs/sphinx/source/zh_CN/4-developer_guide/8-motrix_contact_sensor.md
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/8-motrix_contact_sensor.md
@@ -80,18 +80,18 @@ shape = (num_envs, 1 + 4 * 12) = (num_envs, 49)
 
 ## 单一 norm 分支为何不够
 
-env 通过 `src/unilab/envs/manipulation/sharpa_inhand/base.py` 中的
+env 通过 `src/unilab/tasks/manipulation/sharpa_inhand/base.py` 中的
 `_read_tactile_force()` → `_extract_sensor_scalar()` 读取触觉力。该 helper 目前对任意 `(N, >=3)` 数组都用 `np.linalg.norm(data[:, :3], axis=1)` 折叠。
 
 如果 env 仍把两种后端形状都走这一个分支，MuJoCo 的 `(N, 3)` 是正确的（对真实力向量取 norm），但 Motrix 的 `(N, 4)` 会出错：`data[:, :3]` 取到的是 `[count, fx, fy]`——把接触点数当成了力分量，并且漏掉了 `fz`。正确做法不是在 env 里按形状特判，而是把每个后端的布局知识下沉到 backend 方法。
 
 ## 建议的契约：用 backend 方法返回力大小
 
-在 `SimBackend` 接口（`src/unilab/base/backend/base.py`）增加
+在 `SimBackend` 接口（`unisim.backend.base`）增加
 `get_contact_force_magnitude(sensor_name) -> np.ndarray`，返回 `(num_envs,)` 的标量力大小。每个 backend 按自己的数据布局实现：
 
-- **MuJoCo**（`src/unilab/base/backend/mujoco/backend.py`）：对 `get_sensor_data(name)` 返回的 3D 力向量取范数。
-- **Motrix**（`src/unilab/base/backend/motrix/backend.py`）：按 reduce 模式解释布局：
+- **MuJoCo**（`unisim.backend.mujoco.backend`）：对 `get_sensor_data(name)` 返回的 3D 力向量取范数。
+- **Motrix**（`unisim.backend.motrix.backend`）：按 reduce 模式解释布局：
   - `reduce="netforce"`：取 `[1:4]` 后取 norm。
   - 无 reduce 多 contact：对各接触力求和后取 norm。
   - `reduce="maxforce"`：取力最大的接触点。
@@ -104,9 +104,9 @@ env 层的 `_read_tactile_force()` 对 contact sensor 走 `get_contact_force_mag
 
 | 文件 | 说明 |
 | --- | --- |
-| `src/unilab/envs/manipulation/sharpa_inhand/base.py` | `_extract_sensor_scalar()`, `_read_tactile_force()` |
-| `src/unilab/envs/manipulation/sharpa_inhand/rotation.py` | reward 计算，virtual torque |
+| `src/unilab/tasks/manipulation/sharpa_inhand/base.py` | `_extract_sensor_scalar()`, `_read_tactile_force()` |
+| `src/unilab/tasks/manipulation/sharpa_inhand/rotation.py` | reward 计算，virtual torque |
 | `src/unilab/assets/robots/sharpa_wave/right_sharpa_wave.xml` | contact sensor XML 定义 |
-| `src/unilab/base/backend/motrix/backend.py` | Motrix `get_sensor_data()` |
-| `src/unilab/base/backend/mujoco/backend.py` | MuJoCo `get_sensor_data()` |
-| `src/unilab/base/backend/base.py` | `SimBackend` 接口 |
+| `unisim.backend.motrix.backend` | Motrix `get_sensor_data()` |
+| `unisim.backend.mujoco.backend` | MuJoCo `get_sensor_data()` |
+| `unisim.backend.base` | `SimBackend` 接口 |
diff --git a/docs/sphinx/source/zh_CN/4-developer_guide/9-sim2sim_contract_status.md b/docs/sphinx/source/zh_CN/4-developer_guide/9-sim2sim_contract_status.md
index 18459b02d..06477bbb8 100644
--- a/docs/sphinx/source/zh_CN/4-developer_guide/9-sim2sim_contract_status.md
+++ b/docs/sphinx/source/zh_CN/4-developer_guide/9-sim2sim_contract_status.md
@@ -24,18 +24,18 @@ uv run scripts/audit_sim2sim_contracts.py
 > 守卫对不对称出现一律 fail-closed；`algo` 专属字段（`empirical_normalization` /
 > `obs_normalization`）在目标缺省时按设计跳过（跨算法合法）。
 
-## `conf/ppo/task/`
+## `src/unilab/conf/ppo/task/`
 
 | Task | 判定 | 分歧 |
 |---|---|---|
-| allegro_inhand · allegro_inhand_grasp · g1_climb_tracking · g1_motion_tracking · g1_wall_flip_tracking · go1_joystick_rough · go2_arm_manip_loco · go2_handstand · go2_joystick_flat · go2_joystick_rough · go2w_joystick_flat · go2w_joystick_rough · sharpa_inhand · sharpa_inhand_grasp | ✅ | 无 |
+| allegro_inhand · allegro_inhand_grasp · g1_climb_tracking · g1_motion_tracking · g1_wall_flip_tracking · go1_joystick_rough · go2_arm_manip_loco · go2_footstand · go2_handstand · go2_joystick_flat · go2_joystick_rough · go2w_joystick_flat · go2w_joystick_rough · sharpa_inhand · sharpa_inhand_grasp | ✅ | 无 |
 | g1_box_tracking | ❌ | `empirical_normalization` false↔true；`obs_groups` critic 组差异 |
 | g1_flip_tracking | ❌ | `empirical_normalization` true↔false；`obs_groups`；`action_scale` 29 维↔默认 0.25；`sampling_mode` 两后端运行时同为 `start`（无害） |
-| g1_walk_flat | ❌ | `action_scale` 0.25↔0.5；`empirical_normalization` false↔true；`obs_groups` |
+| g1_walk_flat | ❌ | `env.actions.joint_pos.scale` 0.25↔0.5；`empirical_normalization` false↔true；`obs_groups` |
 | go1_joystick_flat | ❌ | `empirical_normalization` false↔true |
-| g1_motion_tracking_deploy · go2_footstand | ⚪ | 仅 mujoco |
+| g1_motion_tracking_deploy | ⚪ | 仅 mujoco |
 
-## `conf/appo/task/`
+## `src/unilab/conf/appo/task/`
 
 | Task | 判定 | 分歧 |
 |---|---|---|
@@ -46,7 +46,8 @@ uv run scripts/audit_sim2sim_contracts.py
 
 ## 其它配置树
 
-`conf/ppo_him/task`、`conf/offpolicy/task`、`conf/hora_distill/task` 均无 mujoco↔motrix
+`src/unilab/conf/ppo_him/task`、`src/unilab/conf/sac/task`、`src/unilab/conf/td3/task`、`src/unilab/conf/flashsac/task`、
+`src/unilab/conf/hora_distill/task` 均无 mujoco↔motrix
 配对，sim2sim 不适用。
 
 ## 字段语义速查
@@ -69,6 +70,7 @@ uv run scripts/audit_sim2sim_contracts.py
 | `action_scale` | **不可** | 改值即改训练动力学，必须 owner 决策 + 重训 |
 | `empirical_normalization` | **不可** | 改变网络结构，必须重训 |
 
-试点示例：`conf/ppo/task/g1_walk_flat/{mujoco,motrix}.yaml`。每个后端 owner 自包含完整契约，
-`motrix.yaml` 为单后端调参 override 了若干契约字段——这种 override
+试点示例：`src/unilab/conf/ppo/task/g1_walk_flat/{base,mujoco,motrix}.yaml`。后端 owner 通过 Hydra
+defaults 继承共享 base owner 的完整契约，`motrix.yaml` 为单后端调参 override
+了若干契约字段——这种 override
 即令该 task 在该后端不可 sim2sim 迁移，去掉 override 即可恢复。
diff --git a/docs/sphinx/source/zh_CN/5-reference/5-support_matrix.md b/docs/sphinx/source/zh_CN/5-reference/5-support_matrix.md
index 10c7c6e0a..b5fc445df 100644
--- a/docs/sphinx/source/zh_CN/5-reference/5-support_matrix.md
+++ b/docs/sphinx/source/zh_CN/5-reference/5-support_matrix.md
@@ -20,8 +20,9 @@
 
 - `mujoco`: `--render-mode auto` 会导出 `play_video.mp4`
 - `motrix`: `--render-mode auto` 会打开交互式 renderer 窗口，不录制视频，不受 `play_steps` 限制
-- `mjwarp`: 仅支持显式、有限步数的 `record`，通过 task owner 的 MuJoCo visual model 离线录制；不支持 `auto`、interactive 或 native renderer
-- `--render-mode record`: MuJoCo、mjwarp 和 Motrix 都只录制视频
+- `mjwarp`: 默认仅支持显式、有限步数的 `record`，通过 task owner 的 MuJoCo visual model 离线录制；`--render-mode interactive` 路由到 MuJoCo 交互 viewer（mjwarp 跑物理、MuJoCo 渲染 env[0]，强制单 env）；不支持 `auto` 或 native renderer
+- `isaacsim`: `auto` 在有 display 时选择 Kit viewer，否则选择 headless RGB camera；当前真实主机仍有 RTX renderer 初始化 blocker，支持等级保持 `Configured`
+- `--render-mode record`: MuJoCo、mjwarp、Motrix 和 IsaacSim 都只录制视频
 - `--render-mode none`: 不回放
 
 ## Support Matrix
@@ -38,90 +39,99 @@ uv run scripts/generate_support_matrix.py --write
 | 等级 | 仓库事实来源 |
 |------|--------------|
 | `Registered` | `ensure_registries()` 导入后的 `registry.list_registered_envs()` 中存在该 env/backend。 |
-| `Configured` | 存在对应的 owner YAML：`conf/{ppo,appo,offpolicy}/task/...`。 |
+| `Configured` | 存在对应的 owner YAML：`src/unilab/conf/{ppo,appo,sac,td3,flashsac}/task/...`。 |
 | `Tested` | `tests/` 中有自动化覆盖该 entrypoint/task owner/backend 组合，或存在显式 maintainer 完整训练验证并具备近风险自动化测试。这里的 `Tested` 不等同于默认推荐路径。 |
 | `Benchmarked` | 存在与该组合绑定的已提交 benchmark manifest。 |
 | `Recommended` | 仓库中存在显式 recommendation 元数据。 |
 
 `Tested` 只描述仓库中已有自动化覆盖或显式 maintainer 训练验证，不代表该组合具备同名 MuJoCo owner 的全部 backend capability；例如 phase-1 Motrix owner 可能只覆盖训练 smoke 和明确启用的 DR 子集。
 
-`mjwarp` 只支持 `g1_walk_flat` host adapter。PPO (torch) 与 SAC (torch) owner 已完成训练验证，并有 backend、contract 与 playback 自动化覆盖，因此标记为 `Tested`。mjwarp playback 仅支持显式、有限步数的 `record` 并复用 MuJoCo 离线 renderer，不支持 `auto`、interactive 或 native playback。其他 entrypoint 中出现的 `Registered` 只表示 env/backend registry identity，不代表对应算法、terrain、完整 DR 或 production training 支持。
+`mjwarp` 完成训练验证的只有 `g1_walk_flat` host adapter：PPO (torch) 与 SAC (torch) owner 已完成训练验证，并有 backend、contract 与 playback 自动化覆盖，因此标记为 `Tested`。mjwarp playback 默认仅支持显式、有限步数的 `record` 并复用 MuJoCo 离线 renderer；`uv run eval --sim mjwarp --render-mode interactive` 路由到 MuJoCo 交互 viewer（mjwarp 跑物理、MuJoCo 渲染 env[0]，强制单 env）；不支持 `auto` 或 native playback。其他 entrypoint 中出现的 `Registered` 只表示 env/backend registry identity，不代表对应算法、terrain、完整 DR 或 production training 支持。
+
+`isaacgym` 是 Python 3.8 子进程后端，当前只接入 `g1_walk_flat`。SAC (torch) owner 已在真机（external Python 3.8 worker runtime，不在仓库 CI 覆盖）完成训练与 record playback 验证，标记为 `Tested`；其余 isaacgym cell 最高只到 `Configured`（registry + owner YAML + compose/contract 覆盖），不代表任何训练或 play 验证。playback 走 IsaacGym 原生渲染（viewer + camera sensor 离屏录制），有显示器时 `play_render_mode=auto` 打开交互 viewer，无显示器时自动降级为离屏录制。
+
+`genesis` 是进程内后端（genesis-world==1.3.3，要求 torch>=2.8 与 CUDA；一进程只允许一次 `gs.init`），当前只接入 `g1_walk_flat` 的 PPO (torch) 与 SAC (torch) owner。SAC cell 标记 `Tested`：真机完整训练验证（5000/5000 iterations，reward/mean 6.5 → 244.8，episode length → 987/1000，10.26M env steps / 224s wall time；run 2026-08-31_23-04-01_genesis）加 model_5000.pt 的 record playback 验证；PPO cell 最高只到 `Configured`（registry + owner YAML + compose/contract 覆盖），不代表训练验证。真机证据另有：env smoke 慢车道测试（`tests/envs/locomotion/g1/test_g1_owner_contract.py`：compose → env 构造 → keyframe reset → 12 步有限稳定 → cleanup，覆盖 ppo 与 sac 两棵树）在装有 CUDA 与 genesis extra 的机器上通过。adapter 的 `materialize()` 幂等且惰性触发（entity 校验在 env 的 materialize 钩子前读取状态 getter；isaacgym 后端同模式）。Genesis 在 import 时丢弃 MJCF 全局 `<option>`，owner YAML 显式重声明 `genesis_integrator=implicitfast`。原生 playback/渲染已接入：`play_render_mode=auto` 在有显示时打开 post-build 挂载的交互 viewer、无显示时降级离屏录制（`record` 写 `play_video.mp4`；`get_physics_state` 快照不声明）。未支持边界：geom 名称契约、terrain spawn 与 height scanner、contact sensor 为 per-link net-force 阈值近似（非 geom 对 `data="found"`）、`get_geom_friction` 类绝对摩擦 DR fail-closed（geom 摩擦只有 per-env ratio API）。
+
+`isaacsim` 是 IsaacSim 5.1 / IsaacLab v2.3.0 的独立 Python 3.11 子进程后端，当前只接入 `g1_walk_flat` 的 PPO/SAC owner，矩阵标记为 `Configured`。仓库没有把 bounded headless physics smoke 和 mock rendering protocol 覆盖提升为训练或 playback 的 `Tested` 证据。eval 已接入 Kit viewer 与 IsaacLab RGB camera；当前真实主机在 RTX renderer 初始化阶段崩溃，因此没有成功 playback 证据，也不会生成占位视频。contact-force sensor 和 domain randomization 仍保持 fail-closed。
 
 未检测到与这些组合绑定的已提交 benchmark manifest，因此当前不会自动提升到 `Benchmarked`。
 仓库中目前也没有单独的 recommendation 元数据，因此当前不会自动提升到 `Recommended`。
 
 ### Entrypoint x Task Owner
 
-| Entrypoint | Task owner | MuJoCo | mjwarp | Motrix |
-|------------|------------|--------|--------|--------|
-| PPO (torch) | `go1_joystick_flat` (Go1 joystick) | Tested | - | Tested |
-| PPO (torch) | `go2_joystick_flat` (Go2 joystick) | Tested | - | Tested |
-| PPO (torch) | `go2_joystick_rough` (Go2 joystick rough) | Tested | - | Tested |
-| PPO (torch) | `g1_walk_flat` (G1 walk flat) | Tested | Tested | Tested |
-| PPO (torch) | `g1_motion_tracking` (G1 motion tracking) | Tested | - | Tested |
-| PPO (torch) | `g1_flip_tracking` (G1 flip tracking) | Tested | - | Tested |
-| PPO (torch) | `g1_wall_flip_tracking` (G1 wall flip tracking) | Tested | - | Tested |
-| PPO (torch) | `x2_wall_flip_tracking` (X2 wall flip tracking) | Tested | - | Tested |
-| PPO (torch) | `allegro_inhand` (Allegro in-hand) | Tested | - | Tested |
-| PPO (torch) | `sharpa_inhand` (Sharpa in-hand) | Tested | - | Tested |
-| PPO (torch) | `sharpa_inhand_grasp` (Sharpa in-hand grasp) | Tested | - | Tested |
-| PPO (torch) | `a2_joystick_flat` (a2 joystick flat) | Tested | - | - |
-| PPO (torch) | `allegro_inhand_grasp` (allegro inhand grasp) | Tested | - | Tested |
-| PPO (torch) | `g1_23dof_box_tracking` (g1 23dof box tracking) | Tested | - | Tested |
-| PPO (torch) | `g1_23dof_climb_tracking` (g1 23dof climb tracking) | Tested | - | Tested |
-| PPO (torch) | `g1_23dof_flip_tracking` (g1 23dof flip tracking) | Tested | - | Tested |
-| PPO (torch) | `g1_23dof_motion_tracking` (g1 23dof motion tracking) | Tested | - | Tested |
-| PPO (torch) | `g1_23dof_motion_tracking_deploy` (g1 23dof motion tracking deploy) | Tested | - | Tested |
-| PPO (torch) | `g1_23dof_walk_flat` (g1 23dof walk flat) | Tested | - | Tested |
-| PPO (torch) | `g1_23dof_walk_rough` (g1 23dof walk rough) | Tested | - | Registered |
-| PPO (torch) | `g1_23dof_wall_flip_tracking` (g1 23dof wall flip tracking) | Tested | - | Tested |
-| PPO (torch) | `g1_box_tracking` (g1 box tracking) | Tested | - | Tested |
-| PPO (torch) | `g1_climb_tracking` (g1 climb tracking) | Tested | - | Tested |
-| PPO (torch) | `g1_motion_tracking_deploy` (g1 motion tracking deploy) | Tested | - | Tested |
-| PPO (torch) | `go1_joystick_rough` (go1 joystick rough) | Tested | - | Tested |
-| PPO (torch) | `go2_arm_manip_loco` (go2 arm manip loco) | Tested | - | Tested |
-| PPO (torch) | `go2_footstand` (go2 footstand) | Tested | - | Tested |
-| PPO (torch) | `go2w_joystick_flat` (go2w joystick flat) | Tested | - | Tested |
-| PPO (torch) | `go2w_joystick_rough` (go2w joystick rough) | Tested | - | Tested |
-| PPO (torch) | `stewart_balance` (stewart balance) | Tested | - | Tested |
-| APPO (torch) | `go1_joystick_flat` (Go1 joystick) | Tested | - | Tested |
-| APPO (torch) | `go2_joystick_flat` (Go2 joystick) | Tested | - | Tested |
-| APPO (torch) | `g1_walk_flat` (G1 walk flat) | Tested | Registered | Registered |
-| APPO (torch) | `g1_motion_tracking` (G1 motion tracking) | Tested | - | Tested |
-| APPO (torch) | `g1_flip_tracking` (G1 flip tracking) | Tested | - | Tested |
-| APPO (torch) | `g1_wall_flip_tracking` (G1 wall flip tracking) | Tested | - | Tested |
-| APPO (torch) | `allegro_inhand` (Allegro in-hand) | Tested | - | Tested |
-| APPO (torch) | `sharpa_inhand` (Sharpa in-hand) | Tested | - | Tested |
-| APPO (torch) | `g1_23dof_climb_tracking` (g1 23dof climb tracking) | Tested | - | Tested |
-| APPO (torch) | `g1_23dof_flip_tracking` (g1 23dof flip tracking) | Tested | - | Tested |
-| APPO (torch) | `g1_23dof_motion_tracking` (g1 23dof motion tracking) | Tested | - | Tested |
-| APPO (torch) | `g1_23dof_walk_flat` (g1 23dof walk flat) | Tested | - | Registered |
-| APPO (torch) | `g1_23dof_wall_flip_tracking` (g1 23dof wall flip tracking) | Tested | - | Tested |
-| APPO (torch) | `g1_climb_tracking` (g1 climb tracking) | Tested | - | Tested |
-| SAC (torch) | `g1_walk_flat` (G1 walk flat) | Tested | Tested | Tested |
-| SAC (torch) | `g1_walk_rough` (G1 walk rough) | Tested | - | Tested |
-| SAC (torch) | `g1_motion_tracking` (G1 motion tracking) | Tested | - | Tested |
-| SAC (torch) | `g1_flip_tracking` (G1 flip tracking) | Tested | - | Registered |
-| SAC (torch) | `g1_wall_flip_tracking` (G1 wall flip tracking) | Tested | - | Registered |
-| SAC (torch) | `g1_23dof_flip_tracking` (g1 23dof flip tracking) | Tested | - | Registered |
-| SAC (torch) | `g1_23dof_motion_tracking` (g1 23dof motion tracking) | Tested | - | Tested |
-| SAC (torch) | `g1_23dof_walk_flat` (g1 23dof walk flat) | Tested | - | Tested |
-| SAC (torch) | `g1_23dof_walk_rough` (g1 23dof walk rough) | Tested | - | Tested |
-| SAC (torch) | `g1_23dof_wall_flip_tracking` (g1 23dof wall flip tracking) | Tested | - | Registered |
-| SAC (torch) | `g1_23dof_wbt_obs` (g1 23dof wbt obs) | Tested | - | Registered |
-| SAC (torch) | `g1_wbt_obs` (g1 wbt obs) | Tested | - | Registered |
-| TD3 (torch) | `go1_joystick_flat` (Go1 joystick) | Registered | - | Tested |
-| TD3 (torch) | `go2_joystick_flat` (Go2 joystick) | Registered | - | Tested |
-| TD3 (torch) | `g1_walk_flat` (G1 walk flat) | Tested | Registered | Registered |
-| TD3 (torch) | `g1_23dof_walk_flat` (g1 23dof walk flat) | Tested | - | Registered |
-| FlashSAC (torch) | `go2_joystick_flat` (Go2 joystick) | Tested | - | Registered |
-| FlashSAC (torch) | `g1_walk_flat` (G1 walk flat) | Tested | Configured | Tested |
-| FlashSAC (torch) | `g1_23dof_walk_flat` (g1 23dof walk flat) | Tested | - | Tested |
+| Entrypoint | Task owner | MuJoCo | mjwarp | Motrix | IsaacGym | Genesis | IsaacSim |
+|------------|------------|--------|--------|--------|----------|---------|----------|
+| PPO (torch) | `go1_joystick_flat` (Go1 joystick) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `go2_joystick_flat` (Go2 joystick) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `go2_joystick_rough` (Go2 joystick rough) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `g1_walk_flat` (G1 walk flat) | Tested | Tested | Tested | Configured | Configured | Configured |
+| PPO (torch) | `g1_motion_tracking` (G1 motion tracking) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `g1_flip_tracking` (G1 flip tracking) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `g1_wall_flip_tracking` (G1 wall flip tracking) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `x2_wall_flip_tracking` (X2 wall flip tracking) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `allegro_inhand` (Allegro in-hand) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `sharpa_inhand` (Sharpa in-hand) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `sharpa_inhand_grasp` (Sharpa in-hand grasp) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `a2_joystick_flat` (a2 joystick flat) | Tested | - | - | - | - | - |
+| PPO (torch) | `allegro_inhand_grasp` (allegro inhand grasp) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `g1_23dof_box_tracking` (g1 23dof box tracking) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `g1_23dof_climb_tracking` (g1 23dof climb tracking) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `g1_23dof_flip_tracking` (g1 23dof flip tracking) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `g1_23dof_motion_tracking` (g1 23dof motion tracking) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `g1_23dof_motion_tracking_deploy` (g1 23dof motion tracking deploy) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `g1_23dof_walk_flat` (g1 23dof walk flat) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `g1_23dof_walk_rough` (g1 23dof walk rough) | Tested | - | Registered | - | - | - |
+| PPO (torch) | `g1_23dof_wall_flip_tracking` (g1 23dof wall flip tracking) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `g1_box_tracking` (g1 box tracking) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `g1_climb_tracking` (g1 climb tracking) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `g1_motion_tracking_deploy` (g1 motion tracking deploy) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `go1_joystick_rough` (go1 joystick rough) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `go2_arm_manip_loco` (go2 arm manip loco) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `go2_footstand` (go2 footstand) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `go2w_joystick_flat` (go2w joystick flat) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `go2w_joystick_rough` (go2w joystick rough) | Tested | - | Tested | - | - | - |
+| PPO (torch) | `stewart_balance` (stewart balance) | Tested | - | Tested | - | - | - |
+| APPO (torch) | `go1_joystick_flat` (Go1 joystick) | Tested | - | Tested | - | - | - |
+| APPO (torch) | `go2_joystick_flat` (Go2 joystick) | Tested | - | Tested | - | - | - |
+| APPO (torch) | `g1_walk_flat` (G1 walk flat) | Tested | Registered | Registered | Registered | Registered | Registered |
+| APPO (torch) | `g1_motion_tracking` (G1 motion tracking) | Tested | - | Tested | - | - | - |
+| APPO (torch) | `g1_flip_tracking` (G1 flip tracking) | Tested | - | Tested | - | - | - |
+| APPO (torch) | `g1_wall_flip_tracking` (G1 wall flip tracking) | Tested | - | Tested | - | - | - |
+| APPO (torch) | `allegro_inhand` (Allegro in-hand) | Tested | - | Tested | - | - | - |
+| APPO (torch) | `sharpa_inhand` (Sharpa in-hand) | Tested | - | Tested | - | - | - |
+| APPO (torch) | `g1_23dof_climb_tracking` (g1 23dof climb tracking) | Tested | - | Tested | - | - | - |
+| APPO (torch) | `g1_23dof_flip_tracking` (g1 23dof flip tracking) | Tested | - | Tested | - | - | - |
+| APPO (torch) | `g1_23dof_motion_tracking` (g1 23dof motion tracking) | Tested | - | Tested | - | - | - |
+| APPO (torch) | `g1_23dof_walk_flat` (g1 23dof walk flat) | Tested | - | Registered | - | - | - |
+| APPO (torch) | `g1_23dof_wall_flip_tracking` (g1 23dof wall flip tracking) | Tested | - | Tested | - | - | - |
+| APPO (torch) | `g1_climb_tracking` (g1 climb tracking) | Tested | - | Tested | - | - | - |
+| SAC (torch) | `g1_walk_flat` (G1 walk flat) | Tested | Tested | Tested | Tested | Tested | Configured |
+| SAC (torch) | `g1_walk_rough` (G1 walk rough) | Tested | - | Tested | - | - | - |
+| SAC (torch) | `g1_motion_tracking` (G1 motion tracking) | Tested | Configured | Tested | - | - | - |
+| SAC (torch) | `g1_flip_tracking` (G1 flip tracking) | Tested | - | Registered | - | - | - |
+| SAC (torch) | `g1_wall_flip_tracking` (G1 wall flip tracking) | Tested | - | Registered | - | - | - |
+| SAC (torch) | `g1_23dof_flip_tracking` (g1 23dof flip tracking) | Tested | - | Registered | - | - | - |
+| SAC (torch) | `g1_23dof_motion_tracking` (g1 23dof motion tracking) | Tested | - | Tested | - | - | - |
+| SAC (torch) | `g1_23dof_walk_flat` (g1 23dof walk flat) | Tested | - | Tested | - | - | - |
+| SAC (torch) | `g1_23dof_walk_rough` (g1 23dof walk rough) | Tested | - | Tested | - | - | - |
+| SAC (torch) | `g1_23dof_wall_flip_tracking` (g1 23dof wall flip tracking) | Tested | - | Registered | - | - | - |
+| SAC (torch) | `g1_23dof_wbt_obs` (g1 23dof wbt obs) | Tested | - | Registered | - | - | - |
+| SAC (torch) | `g1_wbt_obs` (g1 wbt obs) | Tested | - | Registered | - | - | - |
+| TD3 (torch) | `go1_joystick_flat` (Go1 joystick) | Registered | - | Tested | - | - | - |
+| TD3 (torch) | `go2_joystick_flat` (Go2 joystick) | Registered | - | Tested | - | - | - |
+| TD3 (torch) | `g1_walk_flat` (G1 walk flat) | Tested | Registered | Registered | Registered | Registered | Registered |
+| TD3 (torch) | `g1_23dof_walk_flat` (g1 23dof walk flat) | Tested | - | Registered | - | - | - |
+| FlashSAC (torch) | `go2_joystick_flat` (Go2 joystick) | Tested | - | Registered | - | - | - |
+| FlashSAC (torch) | `g1_walk_flat` (G1 walk flat) | Tested | Configured | Tested | Registered | Registered | Registered |
+| FlashSAC (torch) | `g1_23dof_walk_flat` (g1 23dof walk flat) | Tested | - | Tested | - | - | - |
 
 ### Source Index
 
 - Registry bootstrap: `src/unilab/envs/**` decorators via `unilab.base.registry.ensure_registries()`.
-- Owner YAML scan: `conf/ppo/task/**`, `conf/appo/task/**`, `conf/offpolicy/task/**`.
+- Owner YAML scan: `src/unilab/conf/ppo/task/**`, `src/unilab/conf/appo/task/**`, `src/unilab/conf/sac/task/**`, `src/unilab/conf/td3/task/**`, `src/unilab/conf/flashsac/task/**`.
 - Generic compose coverage: `tests/config/test_config_system.py::test_supported_task_composes`.
 - Validated mjwarp entrypoints are explicitly recorded in `_MAINTAINER_VALIDATED_MJWARP_ENTRYPOINT_TASKS`; near-risk coverage lives in `tests/base/test_mjwarp_backend.py`, `tests/base/test_backend_conformance.py`, `tests/base/test_mjwarp_differential.py`, and `tests/base/test_mjwarp_playback.py`.
+- Validated isaacgym entrypoints are explicitly recorded in `_MAINTAINER_VALIDATED_ISAACGYM_ENTRYPOINT_TASKS` (real hardware via the external Python 3.8 worker runtime; not covered by repo CI).
+- Validated genesis entrypoints are explicitly recorded in `_MAINTAINER_VALIDATED_GENESIS_ENTRYPOINT_TASKS` (real hardware, genesis-world extra + CUDA; not covered by repo CI); near-risk coverage lives in `tests/base/test_genesis_backend.py` (fake runtime), `tests/base/test_genesis_runtime.py` (real-runtime slow lane), and the genesis env smoke in `tests/envs/locomotion/g1/test_g1_owner_contract.py`.
+- IsaacSim owner scope is intentionally not promoted to `Tested`; `_MAINTAINER_VALIDATED_ISAACSIM_ENTRYPOINT_TASKS` is empty until a maintainer records full training evidence. Rendering protocol coverage lives in `tests/base/test_isaacsim_backend.py`; it is not a substitute for successful real playback.
 <!-- END GENERATED SUPPORT MATRIX -->
diff --git a/pyproject.rocm.toml b/pyproject.rocm.toml
index 5108794fd..f57444302 100644
--- a/pyproject.rocm.toml
+++ b/pyproject.rocm.toml
@@ -1,5 +1,5 @@
 [build-system]
-requires = ["uv_build>=0.11.3,<0.12"]
+requires = ["uv_build>=0.12.1,<0.13"]
 build-backend = "uv_build"
 
 [project]
@@ -9,9 +9,26 @@ description = "Universal Lab for Robot Learning"
 readme = "README.md"
 license = "Apache-2.0"
 license-files = ["LICENSE"]
+authors = [{ name = "UniLab Authors" }]
+keywords = ["robot-learning", "reinforcement-learning", "mujoco", "simulation"]
+classifiers = [
+    "Operating System :: OS Independent",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+]
 requires-python = ">=3.10,<3.14"
 dependencies = [
     "numpy",
+    # Physics implementations are provided by the independently released
+    # unisim-core package from the production PyPI index.
+    "unisim-core>=0.1.14",
+    # RL algorithms and async runtimes live in the independently released
+    # uni-rl package (distribution name ``unilab-rl``); see pyproject.toml.
+    "unilab-rl==0.2.0",
     "torch==2.11.0",
     "triton-rocm==3.6.0 ; sys_platform == 'linux' and platform_machine == 'x86_64'",
     "gymnasium",
@@ -40,19 +57,21 @@ dependencies = [
 train = "unilab.cli:train_main"
 eval = "unilab.cli:eval_main"
 demo = "unilab.cli:demo_main"
-unilab-complete = "unilab.tools.completion:main"
-unilab-viz-nan = "unilab.tools.viz_nan:main"
-unilab-export-scene = "unilab.tools.export_scene:main"
-unilab-render-teaser = "unilab.tools.render_teaser:main"
-unilab-import-robot = "unilab.tools.import_robot:main"
-unilab-pull-assets = "unilab.tools.pull_assets:main"
+unilab-complete = "unilab.cli_completion:main"
+unilab-viz-nan = "unilab.utils.nan_viz:main"
+unilab-export-scene = "unisim.backend.mujoco.export_scene:main"
+unilab-render-teaser = "unilab.visualization.teaser:main"
+unilab-pull-assets = "unilab.assets.pull:main"
 
 [project.optional-dependencies]
+drake = [
+    "drake-uni==0.1.0",
+]
 mujoco = [
     "mujoco>=3.5",
     # Bound stays loose; uv.rocm.lock pins the default solver version. The
     # tested window is >=3.5,<3.11 — switch via `make mujoco MJ=<version>`.
-    "mujoco-uni-runtime==0.3.1",
+    "mujoco-uni-runtime==0.4.0",
     # pybind11/wheel are build requirements of mujoco-uni-runtime, which is
     # compiled in this environment (see no-build-isolation-package below).
     "pybind11>=2.12",
@@ -127,9 +146,6 @@ warn_return_any = true
 warn_unused_configs = true
 ignore_missing_imports = true
 no_site_packages = true
-exclude = [
-    "src/unilab/algos/torch/rsl_rl/",
-]
 
 [tool.pytest.ini_options]
 testpaths = ["tests"]
@@ -140,7 +156,6 @@ addopts = "--tb=short -m 'not slow'"
 
 [tool.coverage.run]
 source = ["unilab"]
-omit = ["*/rsl_rl/*"]
 
 [tool.coverage.report]
 exclude_lines = ["pragma: no cover", "if TYPE_CHECKING:"]
@@ -151,12 +166,10 @@ venvPath = "."
 venv = ".venv"
 include = ["src/unilab"]
 exclude = [
-    "src/unilab/algos/torch/rsl_rl/",
-    "src/unilab/algos/torch/common/ane_*",
     "src/unilab/base/backend/",
     "src/unilab/envs/",
     "src/unilab/terrains/",
-    "src/unilab/training/monitoring.py",
+    "src/unilab/utils/monitoring.py",
     "src/unilab/visualization/",
 ]
 reportMissingImports = "warning"
diff --git a/pyproject.toml b/pyproject.toml
index c9374d0fe..acd586614 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,19 +1,62 @@
 [build-system]
-requires = ["uv_build>=0.11.3,<0.12"]
+requires = ["uv_build>=0.12.1,<0.13"]
 build-backend = "uv_build"
 
+[tool.uv.build-backend]
+# Robot meshes/textures are hosted on Hugging Face (unilabsim/unilab-robots)
+# and downloaded into these paths on first use; keep them out of the
+# wheel/sdist even when they exist in the source tree. XML and small metadata
+# stay packaged. Directories must stay in sync with ROBOT_ASSET_SPECS in
+# src/unilab/assets/hub.py.
+source-exclude = [
+    "/src/unilab/assets/robots/a2/assets",
+    "/src/unilab/assets/robots/allegro_hand/assets",
+    "/src/unilab/assets/robots/g1/assets",
+    "/src/unilab/assets/robots/g1/textures",
+    "/src/unilab/assets/robots/go2/assets",
+    "/src/unilab/assets/robots/go2_arm/assets",
+    "/src/unilab/assets/robots/sharpa_wave/meshes",
+    "/src/unilab/assets/robots/x2/meshes",
+]
+
 [project]
 name = "unilab"
 version = "0.1.0"
-description = "Universal Lab for Robot Learning"
+description = "Configurable, contract-driven robot learning across physics backends"
 readme = "README.md"
 license = "Apache-2.0"
 license-files = ["LICENSE"]
+authors = [{ name = "UniLab Authors" }]
+keywords = ["robot-learning", "reinforcement-learning", "mujoco", "simulation"]
+classifiers = [
+    "Operating System :: OS Independent",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+]
 requires-python = ">=3.10,<3.14"
 dependencies = [
     "numpy",
-    "torch==2.9.0 ; sys_platform == 'linux' and platform_machine == 'aarch64'",
-    "torch==2.7.0 ; sys_platform != 'linux' or platform_machine != 'aarch64'",
+    # Physics implementations are provided by the independently released
+    # unisim-core package from the production PyPI index.
+    "unisim-core>=0.1.14",
+    # RL algorithms and async runtimes (PPO/APPO/SAC/TD3/HORA runners,
+    # collectors, IPC, logging) live in the independently released uni-rl
+    # package (distribution name ``unilab-rl``), consumed via the injected
+    # env contract (uni_rl.env_contract.EnvFactory). Published on PyPI.
+    "unilab-rl==1.0.0",
+    "numba>=0.67",
+    "prettytable>=3.10",
+    # torch is a range (not an exact pin) so that published PyPI metadata lets
+    # ROCm users substitute a rocm build installed from the PyTorch ROCm index
+    # (install torch there first, then unilab; pip keeps the installed build).
+    # Repo development and CI stay reproducible: uv.lock pins the tested
+    # cu128/cu130 builds below via tool.uv.sources.
+    "torch>=2.9,<2.12 ; sys_platform == 'linux' and platform_machine == 'aarch64'",
+    "torch>=2.8,<2.12 ; sys_platform != 'linux' or platform_machine != 'aarch64'",
     "gymnasium",
     "imageio",
     "etils",
@@ -22,8 +65,12 @@ dependencies = [
     "rsl-rl-lib>=5.0.0",
     "packaging",
     "mediapy",
-    "tensorboard",
-    "setuptools<70",
+    # >=2.21 drops the pkg_resources import removed in setuptools>=82,
+    # which otherwise breaks `uv run tensorboard` at startup.
+    "tensorboard>=2.21.0",
+    # Unpinned: quadrants (genesis-world 1.3.3 pin) requires setuptools>=77,
+    # and nothing in this repo imports setuptools/pkg_resources at runtime.
+    "setuptools",
     "rich",
     "tqdm",
     "typing-extensions",
@@ -40,20 +87,35 @@ dependencies = [
 train = "unilab.cli:train_main"
 eval = "unilab.cli:eval_main"
 demo = "unilab.cli:demo_main"
-unilab-complete = "unilab.tools.completion:main"
-unilab-viz-nan = "unilab.tools.viz_nan:main"
-unilab-export-scene = "unilab.tools.export_scene:main"
-unilab-render-teaser = "unilab.tools.render_teaser:main"
-unilab-import-robot = "unilab.tools.import_robot:main"
-unilab-pull-assets = "unilab.tools.pull_assets:main"
+unilab-complete = "unilab.cli_completion:main"
+unilab-viz-nan = "unilab.utils.nan_viz:main"
+unilab-export-scene = "unisim.backend.mujoco.export_scene:main"
+unilab-render-teaser = "unilab.visualization.teaser:main"
+unilab-pull-assets = "unilab.assets.pull:main"
+
+[project.urls]
+Homepage = "https://unilabsim.github.io"
+Documentation = "https://unilabsim.github.io/UniLab-doc/"
+Repository = "https://github.com/unilabsim/UniLab"
+Issues = "https://github.com/unilabsim/UniLab/issues"
 
 [project.optional-dependencies]
+drake = [
+    # Python runtime for the Drake batch adapter. Drake C++ remains an external
+    # toolchain and the native extension must be built against the local
+    # DRAKE_HOME after this extra is synced.
+    "drake-uni==0.1.0",
+    # Drake playback always uses MuJoCo's native renderer (MuJoCo is never
+    # stepped). Keep the renderer available from the single Drake install
+    # path; the MuJoCo physics extension remains in the separate mujoco extra.
+    "mujoco>=3.5",
+]
 mujoco = [
     # Bound stays loose; the committed uv.lock pins the default solver version
     # (uv prefer-locked semantics). The tested window is >=3.5,<3.11 — switch
     # within it via `make mujoco MJ=<version>`.
     "mujoco>=3.5",
-    "mujoco-uni-runtime==0.3.1",
+    "mujoco-uni-runtime==0.4.0",
     # pybind11/wheel are build requirements of mujoco-uni-runtime, which is
     # compiled in this environment (see no-build-isolation-package below).
     "pybind11>=2.12",
@@ -71,6 +133,12 @@ mjwarp = [
     "warp-lang>=1.14.0",
 ]
 motrix = ["motrixsim-core==0.8.2"]
+genesis = [
+    # Genesis imports as ``genesis`` and owns the GPU physics implementation;
+    # it is a separate optional extra pinned exactly to the probed release
+    # (scripts/tools/genesis_feasibility/REPORT.md, #1372).
+    "genesis-world==1.3.3",
+]
 viser = ["viser>=1.0.26", "trimesh>=3.21.7"]
 
 [dependency-groups]
@@ -138,9 +206,6 @@ warn_return_any = true
 warn_unused_configs = true
 ignore_missing_imports = true
 no_site_packages = true
-exclude = [
-    "src/unilab/algos/torch/rsl_rl/", # vendored third-party, tracked in .gitignore
-]
 
 [tool.pytest.ini_options]
 testpaths = ["tests"]
@@ -151,7 +216,6 @@ addopts = "--tb=short -m 'not slow'"
 
 [tool.coverage.run]
 source = ["src/unilab"]
-omit = ["*/rsl_rl/*"]
 
 [tool.coverage.report]
 exclude_lines = ["pragma: no cover", "if TYPE_CHECKING:"]
@@ -162,11 +226,9 @@ venvPath = "."
 venv = ".venv"
 include = ["src/unilab"]
 exclude = [
-    "src/unilab/algos/torch/rsl_rl/",       # vendored third-party
-    "src/unilab/base/backend/",             # mujoco-uni-runtime stubs mismatch; optional backends
     "src/unilab/envs/",                     # mujoco-uni-runtime internal API, stubs mismatch
     "src/unilab/terrains/",                 # mujoco-uni-runtime MjSpec API, stubs mismatch
-    "src/unilab/training/monitoring.py",    # optional pynvml/psutil deps
+    "src/unilab/utils/monitoring.py",       # optional pynvml/psutil deps
     "src/unilab/visualization/",            # direct mujoco C bindings + optional viser deps
 ]
 reportMissingImports = "warning"
diff --git a/scripts/audit_sim2sim_contracts.py b/scripts/audit_sim2sim_contracts.py
index 012222af4..359e629bc 100644
--- a/scripts/audit_sim2sim_contracts.py
+++ b/scripts/audit_sim2sim_contracts.py
@@ -1,14 +1,14 @@
 """Audit cross-backend sim2sim contract divergences across task owner YAMLs.
 
 For every task with >=2 backend YAMLs, hydra-composes each backend's effective config
-and compares the DENYLIST / WARNING_LIST fields from ``unilab.training.sim2sim``.
-Off-policy owners are grouped by algorithm so SAC, TD3, and FlashSAC are never
-compared with one another.
+and compares the DENYLIST / WARNING_LIST fields from ``unilab.utils.sim2sim``.
+Off-policy owners now live in separate per-algorithm trees (``sac``, ``td3``,
+``flashsac``), so SAC, TD3, and FlashSAC are never compared with one another.
 
 Read-only.
 
     uv run scripts/audit_sim2sim_contracts.py
-    uv run scripts/audit_sim2sim_contracts.py --trees ppo appo offpolicy
+    uv run scripts/audit_sim2sim_contracts.py --trees ppo appo sac td3 flashsac
     uv run scripts/audit_sim2sim_contracts.py --json
 """
 
@@ -23,13 +23,24 @@
 from hydra.core.global_hydra import GlobalHydra
 from omegaconf import OmegaConf
 
-from unilab.training.sim2sim import DENYLIST, ENV_STRUCTURAL_DENYLIST, WARNING_LIST, _normalize
+from unilab.utils.sim2sim import DENYLIST, ENV_STRUCTURAL_DENYLIST, WARNING_LIST, _normalize
 
 REPO_ROOT = Path(__file__).resolve().parents[1]
-CONF_ROOT = REPO_ROOT / "conf"
+CONF_ROOT = REPO_ROOT / "src" / "unilab" / "conf"
 ABSENT = "<absent>"
 
-PRIMARY_PAIR = ("mujoco", "motrix")
+# Audited backend pairs. mujoco<->motrix is the historical primary contract;
+# mujoco<->isaacgym covers the subprocess backend owners; mujoco<->genesis
+# covers the in-process Genesis backend owners; mujoco<->isaacsim covers the
+# Python 3.11 worker backend owners. The additional adapters are audited
+# independently so a new owner cannot accidentally drift from the canonical
+# MuJoCo policy contract.
+CONTRACT_PAIRS: tuple[tuple[str, str], ...] = (
+    ("mujoco", "motrix"),
+    ("mujoco", "isaacgym"),
+    ("mujoco", "genesis"),
+    ("mujoco", "isaacsim"),
+)
 
 
 def _values_equal(a: Any, b: Any) -> bool:
@@ -52,16 +63,7 @@ def _select(cfg: Any, path: str) -> Any:
 
 def _compose(tree: str, task_variant: str) -> Any:
     conf_dir = str(CONF_ROOT / tree)
-    overrides: list[str] = []
-    if tree == "offpolicy":
-        variant_parts = task_variant.split("/")
-        if len(variant_parts) != 3:
-            raise ValueError(
-                f"offpolicy task variants must use '<algo>/<task>/<backend>', got {task_variant!r}"
-            )
-        algo = variant_parts[0]
-        overrides.append(f"algo={algo}")
-    overrides.append(f"task={task_variant}")
+    overrides: list[str] = [f"task={task_variant}"]
     GlobalHydra.instance().clear()
     with initialize_config_dir(config_dir=conf_dir, version_base="1.3"):
         return compose("config", overrides=overrides)
@@ -82,13 +84,13 @@ def _discover(tree: str) -> dict[str, list[str]]:
     return {task: sorted(backends) for task, backends in sorted(out.items())}
 
 
-def _diff_field(path: str, mj: Any, mx: Any) -> dict[str, Any] | None:
-    mj_absent, mx_absent = mj is ABSENT, mx is ABSENT
-    if mj_absent and mx_absent:
+def _diff_field(path: str, left: Any, right: Any) -> dict[str, Any] | None:
+    left_absent, right_absent = left is ABSENT, right is ABSENT
+    if left_absent and right_absent:
         return None
-    if mj_absent != mx_absent:
+    if left_absent != right_absent:
         kind = "asymmetric-presence"
-    elif _values_equal(mj, mx):
+    elif _values_equal(left, right):
         return None
     else:
         kind = "value-diff"
@@ -98,17 +100,37 @@ def _diff_field(path: str, mj: Any, mx: Any) -> dict[str, Any] | None:
     return {
         "field": path,
         "kind": kind,
-        "mujoco": _fmt(mj),
-        "motrix": _fmt(mx),
+        "left": _fmt(left),
+        "right": _fmt(right),
         "guard_enforced": guard_enforced,
     }
 
 
+def _audit_pair(
+    left_name: str,
+    right_name: str,
+    left: dict[str, Any],
+    right: dict[str, Any],
+) -> dict[str, Any]:
+    deny = [d for p in DENYLIST if (d := _diff_field(p, left.get(p, ABSENT), right.get(p, ABSENT)))]
+    warn = [
+        d for p in WARNING_LIST if (d := _diff_field(p, left.get(p, ABSENT), right.get(p, ABSENT)))
+    ]
+    return {
+        "pair": f"{left_name}<->{right_name}",
+        "left_name": left_name,
+        "right_name": right_name,
+        "verdict": "TRANSFERABLE" if not deny else "BLOCKED",
+        "deny_diffs": deny,
+        "warn_diffs": warn,
+    }
+
+
 def audit_tree(tree: str) -> list[dict[str, Any]]:
     rows: list[dict[str, Any]] = []
     discovered = _discover(tree)
     if not discovered:
-        raise ValueError(f"No task owner configs discovered under conf/{tree}/task")
+        raise ValueError(f"No task owner configs discovered under src/unilab/conf/{tree}/task")
     for task, backends in discovered.items():
         values: dict[str, dict[str, Any]] = {}
         errors: dict[str, str] = {}
@@ -119,20 +141,17 @@ def audit_tree(tree: str) -> list[dict[str, Any]]:
             except Exception as exc:  # noqa: BLE001 - report, do not abort the sweep
                 errors[backend] = f"{type(exc).__name__}: {exc}"
 
-        mj_name, mx_name = PRIMARY_PAIR
-        if mj_name in values and mx_name in values:
-            mj, mx = values[mj_name], values[mx_name]
-            deny = [
-                d for p in DENYLIST if (d := _diff_field(p, mj.get(p, ABSENT), mx.get(p, ABSENT)))
-            ]
-            warn = [
-                d
-                for p in WARNING_LIST
-                if (d := _diff_field(p, mj.get(p, ABSENT), mx.get(p, ABSENT)))
-            ]
-            verdict = "TRANSFERABLE" if not deny else "BLOCKED"
+        pairs = [
+            _audit_pair(left_name, right_name, values[left_name], values[right_name])
+            for left_name, right_name in CONTRACT_PAIRS
+            if left_name in values and right_name in values
+        ]
+        if not pairs:
+            verdict = "N/A (no audited backend pair)"
+        elif any(pair["verdict"] == "BLOCKED" for pair in pairs):
+            verdict = "BLOCKED"
         else:
-            deny, warn, verdict = [], [], "N/A (no mujoco+motrix pair)"
+            verdict = "TRANSFERABLE"
 
         rows.append(
             {
@@ -140,8 +159,7 @@ def audit_tree(tree: str) -> list[dict[str, Any]]:
                 "task": task,
                 "backends": backends,
                 "verdict": verdict,
-                "deny_diffs": deny,
-                "warn_diffs": warn,
+                "pairs": pairs,
                 "errors": errors,
             }
         )
@@ -153,24 +171,34 @@ def _print_human(rows: list[dict[str, Any]]) -> None:
         print(f"\n### [{row['tree']}] {row['task']}  backends={row['backends']}")
         if row["errors"]:
             print(f"  COMPOSE ERRORS: {row['errors']}")
-        print(f"  VERDICT (mujoco<->motrix): {row['verdict']}")
-        for diff in row["deny_diffs"]:
-            flag = (
-                "" if diff["guard_enforced"] else "  [guard-blind-spot: re-check dataclass default]"
-            )
-            print(
-                f"    DENY  {diff['field']} [{diff['kind']}]: "
-                f"mujoco={diff['mujoco']}  motrix={diff['motrix']}{flag}"
-            )
-        for diff in row["warn_diffs"]:
-            print(
-                f"    warn  {diff['field']} [{diff['kind']}]: "
-                f"mujoco={diff['mujoco']}  motrix={diff['motrix']}"
-            )
+        if not row["pairs"]:
+            print(f"  VERDICT: {row['verdict']}")
+            continue
+        for pair in row["pairs"]:
+            print(f"  VERDICT ({pair['pair']}): {pair['verdict']}")
+            for diff in pair["deny_diffs"]:
+                flag = (
+                    ""
+                    if diff["guard_enforced"]
+                    else "  [guard-blind-spot: re-check dataclass default]"
+                )
+                print(
+                    f"    DENY  {diff['field']} [{diff['kind']}]: "
+                    f"{pair['left_name']}={diff['left']}  {pair['right_name']}={diff['right']}{flag}"
+                )
+            for diff in pair["warn_diffs"]:
+                print(
+                    f"    warn  {diff['field']} [{diff['kind']}]: "
+                    f"{pair['left_name']}={diff['left']}  {pair['right_name']}={diff['right']}"
+                )
 
     transferable = [r for r in rows if r["verdict"] == "TRANSFERABLE"]
     blocked = [r for r in rows if r["verdict"] == "BLOCKED"]
-    blind = [r for r in blocked if any(not d["guard_enforced"] for d in r["deny_diffs"])]
+    blind = [
+        r
+        for r in blocked
+        if any(not d["guard_enforced"] for pair in r["pairs"] for d in pair["deny_diffs"])
+    ]
     print("\n" + "=" * 80)
     print(
         f"TRANSFERABLE: {len(transferable)}   BLOCKED: {len(blocked)}   "
@@ -179,7 +207,12 @@ def _print_human(rows: list[dict[str, Any]]) -> None:
     if blind:
         print("Tasks with an asymmetric-presence DENYLIST field the guard may NOT enforce:")
         for r in blind:
-            fields = [d["field"] for d in r["deny_diffs"] if not d["guard_enforced"]]
+            fields = [
+                d["field"]
+                for pair in r["pairs"]
+                for d in pair["deny_diffs"]
+                if not d["guard_enforced"]
+            ]
             print(f"  - [{r['tree']}] {r['task']}: {fields}")
 
 
@@ -191,7 +224,7 @@ def main() -> None:
         "--trees",
         nargs="+",
         default=["ppo", "appo"],
-        help="Hydra config trees under conf/ to audit (default: ppo appo).",
+        help="Hydra config trees under src/unilab/conf/ to audit (default: ppo appo).",
     )
     parser.add_argument("--json", action="store_true", help="Emit machine-readable JSON only.")
     args = parser.parse_args()
diff --git a/scripts/benchmark/benchmark_drake_performance.py b/scripts/benchmark/benchmark_drake_performance.py
index 2886bebf6..b1bc44c27 100644
--- a/scripts/benchmark/benchmark_drake_performance.py
+++ b/scripts/benchmark/benchmark_drake_performance.py
@@ -1,10 +1,5 @@
 #!/usr/bin/env python3
-"""Profile Drake vs MuJoCo env-step performance on selected UniLab tasks.
-
-This benchmark is intentionally task-level rather than raw-simulator-level. It
-keeps UniLab's reset, observation, sensor-view, and body-query paths in the
-loop so G1 motion tracking can expose the expensive integration points.
-"""
+"""Profile Drake vs MuJoCo env-step performance on selected UniLab tasks."""
 
 from __future__ import annotations
 
@@ -22,7 +17,7 @@
 
 ROOT_DIR = Path(__file__).resolve().parents[2]
 SRC_DIR = ROOT_DIR / "src"
-DEFAULT_DRAKEUNI_SRC = ROOT_DIR.parent / "drakeuni" / "src"
+DEFAULT_DRAKE_UNI_SRC = ROOT_DIR.parent / "drake_uni" / "src"
 DEFAULT_OUTPUT = (
     ROOT_DIR / "scripts" / "benchmark" / "outputs" / "drake_performance" / "results.json"
 )
@@ -34,17 +29,17 @@ def _prepend_import_path(path: Path) -> None:
         sys.path.insert(0, path_str)
 
 
-def _install_import_paths(drakeuni_src: Path | None) -> None:
+def _install_import_paths(drake_uni_src: Path | None) -> None:
     for path in (SRC_DIR, ROOT_DIR):
         _prepend_import_path(path)
-    if drakeuni_src is not None and drakeuni_src.exists():
-        _prepend_import_path(drakeuni_src)
+    if drake_uni_src is not None and drake_uni_src.exists():
+        _prepend_import_path(drake_uni_src)
 
 
 @dataclass(frozen=True)
 class TaskSpec:
     env_cfg_factory: Callable[[], Any]
-    env_cls_factory: Callable[[], type]
+    env_cls_factory: Callable[[], Callable[..., Any]]
 
 
 @dataclass
@@ -101,39 +96,23 @@ def wrapped(*args: Any, **kwargs: Any) -> Any:
 
 def _task_specs() -> dict[str, TaskSpec]:
     def go1_cfg() -> Any:
-        from unilab.envs.locomotion.go1.joystick import Go1JoystickCfg
+        from unilab.envs import ManagerBasedRlEnvCfg
 
-        return Go1JoystickCfg()
+        return ManagerBasedRlEnvCfg()
 
-    def go1_env() -> type:
-        from unilab.envs.locomotion.go1.joystick import Go1WalkTask
+    def manager_env() -> Callable[..., Any]:
+        from unilab.envs import make_manager_based_rl_env
 
-        return Go1WalkTask
+        return make_manager_based_rl_env
 
     def go2_cfg() -> Any:
-        from unilab.envs.locomotion.go2.joystick import Go2JoystickCfg
-
-        return Go2JoystickCfg()
-
-    def go2_env() -> type:
-        from unilab.envs.locomotion.go2.joystick import Go2WalkTask
-
-        return Go2WalkTask
-
-    def g1_tracking_cfg() -> Any:
-        from unilab.envs.motion_tracking.g1.tracking import G1MotionTrackingEnvCfg
-
-        return G1MotionTrackingEnvCfg()
-
-    def g1_tracking_env() -> type:
-        from unilab.envs.motion_tracking.g1.tracking import G1MotionTrackingEnv
+        from unilab.envs import ManagerBasedRlEnvCfg
 
-        return G1MotionTrackingEnv
+        return ManagerBasedRlEnvCfg()
 
     return {
-        "g1_motion_tracking": TaskSpec(g1_tracking_cfg, g1_tracking_env),
-        "go1_joystick_flat": TaskSpec(go1_cfg, go1_env),
-        "go2_joystick_flat": TaskSpec(go2_cfg, go2_env),
+        "go1_joystick_flat": TaskSpec(go1_cfg, manager_env),
+        "go2_joystick_flat": TaskSpec(go2_cfg, manager_env),
     }
 
 
@@ -141,11 +120,13 @@ def _compose_env_cfg(task: str, backend: str, spec: TaskSpec) -> Any:
     from hydra import compose, initialize_config_dir
     from hydra.core.global_hydra import GlobalHydra
 
+    from unilab.base.config_adapter import BackendAdapter
     from unilab.base.registry import apply_cfg_overrides
-    from unilab.training import BackendAdapter
 
     GlobalHydra.instance().clear()
-    with initialize_config_dir(config_dir=str(ROOT_DIR / "conf" / "ppo"), version_base="1.3"):
+    with initialize_config_dir(
+        config_dir=str(ROOT_DIR / "src" / "unilab" / "conf" / "ppo"), version_base="1.3"
+    ):
         owner_cfg = compose(
             config_name="config",
             overrides=[
@@ -341,10 +322,7 @@ def main() -> None:
     parser.add_argument(
         "--tasks",
         default="go1_joystick_flat,go2_joystick_flat",
-        help=(
-            "Comma-separated task ids. Defaults stay within committed Drake task configs; "
-            "pass g1_motion_tracking explicitly when its Drake config is available."
-        ),
+        help="Comma-separated task ids with committed Drake YAML owners.",
     )
     parser.add_argument("--backends", default="drake,mujoco", help="Comma-separated backends.")
     parser.add_argument("--num-envs", default="64,256,1024", help="Comma-separated env counts.")
@@ -358,10 +336,10 @@ def main() -> None:
     )
     parser.add_argument("--seed", type=int, default=0)
     parser.add_argument("--out-json", type=Path, default=DEFAULT_OUTPUT)
-    parser.add_argument("--drakeuni-src", type=Path, default=DEFAULT_DRAKEUNI_SRC)
+    parser.add_argument("--drake-uni-src", type=Path, default=DEFAULT_DRAKE_UNI_SRC)
     args = parser.parse_args()
 
-    _install_import_paths(args.drakeuni_src)
+    _install_import_paths(args.drake_uni_src)
     specs = _task_specs()
     task_ids = _parse_csv(args.tasks)
     backends = _parse_csv(args.backends)
diff --git a/scripts/benchmark/core/device_info.py b/scripts/benchmark/core/device_info.py
index 7503f19cb..21312bc67 100644
--- a/scripts/benchmark/core/device_info.py
+++ b/scripts/benchmark/core/device_info.py
@@ -1,294 +1,7 @@
-from __future__ import annotations
-
-import platform
-import re
-import subprocess
-from functools import lru_cache
-from typing import Dict
-
-
-def _is_macos() -> bool:
-    return platform.system() == "Darwin"
-
-
-def _is_linux() -> bool:
-    return platform.system() == "Linux"
-
-
-def _is_windows() -> bool:
-    return platform.system() == "Windows"
-
-
-def _get_device_info_macos() -> Dict[str, str]:
-    """Collect hardware info on macOS via system_profiler."""
-    info: Dict[str, str] = {
-        "chip": "unknown",
-        "cpu_total_cores": "unknown",
-        "cpu_performance_cores": "unknown",
-        "cpu_efficiency_cores": "unknown",
-        "gpu_cores": "unknown",
-        "memory": "unknown",
-    }
-    try:
-        hw_text = subprocess.check_output(
-            ["system_profiler", "SPHardwareDataType"], text=True, stderr=subprocess.DEVNULL
-        )
-        disp_text = subprocess.check_output(
-            ["system_profiler", "SPDisplaysDataType"], text=True, stderr=subprocess.DEVNULL
-        )
-    except Exception:
-        return info
-
-    chip_match = re.search(r"Chip:\s*(.+)", hw_text)
-    if chip_match:
-        info["chip"] = chip_match.group(1).strip()
+"""Thin re-export of the library device-info helpers (moved in issue #1240)."""
 
-    mem_match = re.search(r"Memory:\s*(.+)", hw_text)
-    if mem_match:
-        info["memory"] = mem_match.group(1).strip()
-
-    # Apple Silicon core descriptions vary by generation:
-    #   M3/M4: "10 performance and 4 efficiency"
-    #   M5 Pro/Max: "6 super and 12 performance"
-    cpu_match = re.search(
-        r"Total Number of Cores:\s*(\d+)\s*\(\s*(\d+)\s*(\w+)\s+and\s+(\d+)\s*(\w+)\s*\)",
-        hw_text,
-    )
-    if cpu_match:
-        total, count1, type1, count2, type2 = cpu_match.groups()
-        info["cpu_total_cores"] = total
-        info["cpu_core_type_1"] = type1
-        info["cpu_core_count_1"] = count1
-        info["cpu_core_type_2"] = type2
-        info["cpu_core_count_2"] = count2
-        # Backward-compat keys for legacy P+E format
-        if type1 == "performance" and type2 == "efficiency":
-            info["cpu_performance_cores"] = count1
-            info["cpu_efficiency_cores"] = count2
-        elif type1 == "super" and type2 == "performance":
-            info["cpu_super_cores"] = count1
-            info["cpu_performance_cores"] = count2
-    else:
-        cpu_total_match = re.search(r"Total Number of Cores:\s*(\d+)", hw_text)
-        if cpu_total_match:
-            info["cpu_total_cores"] = cpu_total_match.group(1)
-
-    gpu_match = re.search(r"Type:\s*GPU[\s\S]*?Total Number of Cores:\s*(\d+)", disp_text)
-    if gpu_match:
-        info["gpu_cores"] = gpu_match.group(1)
-
-    return info
-
-
-def _get_device_info_linux() -> Dict[str, str]:
-    """Collect hardware info on Linux via /proc and common CLI tools."""
-    info: Dict[str, str] = {
-        "chip": "unknown",
-        "cpu_total_cores": "unknown",
-        "cpu_performance_cores": "unknown",
-        "cpu_efficiency_cores": "unknown",
-        "gpu_name": "unknown",
-        "memory": "unknown",
-    }
-    # CPU model
-    try:
-        with open("/proc/cpuinfo", encoding="utf-8") as f:
-            cpuinfo = f.read()
-        model_match = re.search(r"^model name\s*:\s*(.+)$", cpuinfo, re.MULTILINE)
-        if model_match:
-            info["chip"] = model_match.group(1).strip()
-        # Count physical cores (unique core id per physical id)
-        pairs = re.findall(r"physical id\s*:\s*(\d+).*?core id\s*:\s*(\d+)", cpuinfo, re.DOTALL)
-        if pairs:
-            info["cpu_total_cores"] = str(len(set(pairs)))
-        else:
-            processor_count = len(re.findall(r"^processor\s*:", cpuinfo, re.MULTILINE))
-            if processor_count:
-                info["cpu_total_cores"] = str(processor_count)
-    except Exception:
-        pass
-    # Total memory
-    try:
-        with open("/proc/meminfo", encoding="utf-8") as f:
-            meminfo = f.read()
-        mem_match = re.search(r"MemTotal:\s*(\d+)\s*kB", meminfo)
-        if mem_match:
-            mem_gb = int(mem_match.group(1)) / 1024 / 1024
-            info["memory"] = f"{mem_gb:.1f} GB"
-    except Exception:
-        pass
-    # GPU via nvidia-smi
-    try:
-        gpu_out = subprocess.check_output(
-            ["nvidia-smi", "--query-gpu=name,memory.total", "--format=csv,noheader"],
-            text=True,
-            stderr=subprocess.DEVNULL,
-        ).strip()
-        if gpu_out:
-            # Take the first GPU line
-            first_line = gpu_out.splitlines()[0]
-            parts = [p.strip() for p in first_line.split(",")]
-            info["gpu_name"] = parts[0]
-            if len(parts) > 1:
-                info["gpu_memory"] = parts[1]
-    except Exception:
-        pass
-    # GPU via rocm-smi (AMD)
-    if info["gpu_name"] == "unknown":
-        try:
-            rocm_out = subprocess.check_output(
-                ["rocm-smi", "--showproductname"],
-                text=True,
-                stderr=subprocess.DEVNULL,
-            )
-            gpu_match = re.search(r"Card series\s*:\s*(.+)", rocm_out, re.IGNORECASE)
-            if gpu_match:
-                info["gpu_name"] = gpu_match.group(1).strip()
-        except Exception:
-            pass
-    # GPU memory via amd-smi (AMD ROCm). On unified-memory APUs this reports
-    # the BIOS-allocated visible VRAM slice, e.g. 96 GB out of 128 GB.
-    try:
-        amd_smi_out = subprocess.check_output(
-            ["amd-smi", "metric"],
-            text=True,
-            stderr=subprocess.DEVNULL,
-        )
-        vram_match = re.search(r"TOTAL_VISIBLE_VRAM:\s*(\d+)\s*MB", amd_smi_out)
-        if vram_match:
-            info["gpu_memory"] = f"{int(vram_match.group(1))} MB"
-        gtt_match = re.search(r"TOTAL_GTT:\s*(\d+)\s*MB", amd_smi_out)
-        if gtt_match:
-            info["gpu_gtt_memory"] = f"{int(gtt_match.group(1))} MB"
-    except Exception:
-        pass
-    # Fallback GPU via lspci (AMD/ATI, Intel iGPU/Arc, and others)
-    if info["gpu_name"] == "unknown":
-        try:
-            lspci_out = subprocess.check_output(["lspci"], text=True, stderr=subprocess.DEVNULL)
-            for line in lspci_out.splitlines():
-                if "VGA" in line or "Display" in line or "3D" in line:
-                    if "AMD" in line or "ATI" in line:
-                        match = re.search(r"\[AMD/ATI\]\s*(.+)", line)
-                        if match:
-                            name = match.group(1).strip()
-                            name = re.sub(r"\s*\(rev.*\)", "", name)
-                            info["gpu_name"] = name
-                            break
-                    elif "Intel" in line:
-                        # e.g. "Intel Corporation Meteor Lake-P [Intel Arc Graphics] (rev 08)"
-                        match = re.search(r"\[([^\]]+)\]", line)
-                        if match:
-                            info["gpu_name"] = match.group(1).strip()
-                            break
-        except Exception:
-            pass
-    # If GPU name is still generic/unknown, try to infer from CPU model (APUs)
-    if info["gpu_name"] in ("unknown", "AMD Radeon Graphics"):
-        chip = info.get("chip", "")
-        match = re.search(r"w(?:ith)?/\s*(Radeon\s+[\w\s\+]+)", chip, re.IGNORECASE)
-        if match:
-            info["gpu_name"] = match.group(1).strip()
-    return info
-
-
-def _get_device_info_windows() -> Dict[str, str]:
-    """Collect hardware info on Windows via wmic."""
-    info: Dict[str, str] = {
-        "chip": "unknown",
-        "cpu_total_cores": "unknown",
-        "cpu_performance_cores": "unknown",
-        "cpu_efficiency_cores": "unknown",
-        "gpu_name": "unknown",
-        "memory": "unknown",
-    }
-    try:
-        cpu_out = subprocess.check_output(
-            ["wmic", "cpu", "get", "Name,NumberOfCores", "/format:csv"],
-            text=True,
-            stderr=subprocess.DEVNULL,
-        )
-        lines = [l for l in cpu_out.splitlines() if l.strip() and not l.strip().startswith("Node")]
-        if lines:
-            parts = lines[0].split(",")
-            if len(parts) >= 3:
-                info["cpu_total_cores"] = parts[1].strip()
-                info["chip"] = parts[2].strip()
-    except Exception:
-        pass
-    # Memory
-    try:
-        mem_out = subprocess.check_output(
-            ["wmic", "ComputerSystem", "get", "TotalPhysicalMemory", "/format:csv"],
-            text=True,
-            stderr=subprocess.DEVNULL,
-        )
-        lines = [l for l in mem_out.splitlines() if l.strip() and not l.strip().startswith("Node")]
-        if lines:
-            parts = lines[0].split(",")
-            if len(parts) >= 2:
-                mem_gb = int(parts[1].strip()) / 1024**3
-                info["memory"] = f"{mem_gb:.1f} GB"
-    except Exception:
-        pass
-    # GPU via nvidia-smi (also available on Windows)
-    try:
-        gpu_out = subprocess.check_output(
-            ["nvidia-smi", "--query-gpu=name,memory.total", "--format=csv,noheader"],
-            text=True,
-            stderr=subprocess.DEVNULL,
-        ).strip()
-        if gpu_out:
-            first_line = gpu_out.splitlines()[0]
-            parts = [p.strip() for p in first_line.split(",")]
-            info["gpu_name"] = parts[0]
-            if len(parts) > 1:
-                info["gpu_memory"] = parts[1]
-    except Exception:
-        pass
-    return info
-
-
-@lru_cache(maxsize=1)
-def get_device_info_dict() -> Dict[str, str]:
-    base: Dict[str, str] = {"platform": platform.platform()}
-    if _is_macos():
-        base.update(_get_device_info_macos())
-    elif _is_linux():
-        base.update(_get_device_info_linux())
-    elif _is_windows():
-        base.update(_get_device_info_windows())
-    return base
+from __future__ import annotations
 
+from unilab.utils.device import get_device_info_dict, get_device_info_line
 
-def get_device_info_line() -> str:
-    d = get_device_info_dict()
-    if _is_macos():
-        # Build core-type summary dynamically so M5 (super+performance) is shown correctly
-        if d.get("cpu_core_type_1") and d.get("cpu_core_type_2"):
-            t1 = d["cpu_core_type_1"][0].upper()
-            t2 = d["cpu_core_type_2"][0].upper()
-            core_summary = f"{d['cpu_core_count_1']}{t1}+{d['cpu_core_count_2']}{t2}"
-        elif (
-            d.get("cpu_performance_cores") != "unknown"
-            and d.get("cpu_efficiency_cores") != "unknown"
-        ):
-            core_summary = f"{d['cpu_performance_cores']}P+{d['cpu_efficiency_cores']}E"
-        else:
-            core_summary = "unknown"
-        return (
-            f"Device: {d.get('chip', 'unknown')} | "
-            f"CPU: {d.get('cpu_total_cores', 'unknown')} cores "
-            f"({core_summary}) | "
-            f"GPU: {d.get('gpu_cores', 'unknown')} cores | "
-            f"Memory: {d.get('memory', 'unknown')}"
-        )
-    else:
-        gpu_part = d.get("gpu_name", "unknown")
-        if "gpu_memory" in d:
-            gpu_part += f" ({d['gpu_memory']})"
-        return (
-            f"CPU: {d.get('chip', 'unknown')} ({d.get('cpu_total_cores', 'unknown')} cores) | "
-            f"GPU: {gpu_part} | "
-            f"Memory: {d.get('memory', 'unknown')}"
-        )
+__all__ = ["get_device_info_dict", "get_device_info_line"]
diff --git a/scripts/benchmark/core/task_names.py b/scripts/benchmark/core/task_names.py
index b922c06f5..2bc007acc 100644
--- a/scripts/benchmark/core/task_names.py
+++ b/scripts/benchmark/core/task_names.py
@@ -2,10 +2,8 @@
 
 from dataclasses import dataclass
 
-from unilab.envs.locomotion.g1.joystick import G1WalkFlatCfg
-from unilab.envs.locomotion.go1.joystick import Go1JoystickCfg
-from unilab.envs.locomotion.go2.joystick import Go2JoystickCfg
-from unilab.envs.manipulation.sharpa_inhand.rotation import SharpaInhandRotationCfg
+from unilab.envs import ManagerBasedRlEnvCfg
+from unilab.tasks.manipulation.sharpa_inhand.rotation import SharpaInhandRotationCfg
 
 
 @dataclass(frozen=True)
@@ -14,6 +12,7 @@ class LocomotionTaskSpec:
     env_task_name: str
     display_name: str
     config_cls: type
+    model_file: str | None = None
 
 
 _TASK_SPECS = {
@@ -21,19 +20,22 @@ class LocomotionTaskSpec:
         owner_task_id="go1_joystick_flat",
         env_task_name="Go1JoystickFlat",
         display_name="go1_joystick_flat",
-        config_cls=Go1JoystickCfg,
+        config_cls=ManagerBasedRlEnvCfg,
+        model_file="src/unilab/assets/robots/go1/scene_flat.xml",
     ),
     "go2_joystick_flat": LocomotionTaskSpec(
         owner_task_id="go2_joystick_flat",
         env_task_name="Go2JoystickFlat",
         display_name="go2_joystick_flat",
-        config_cls=Go2JoystickCfg,
+        config_cls=ManagerBasedRlEnvCfg,
+        model_file="src/unilab/assets/robots/go2/scene_flat.xml",
     ),
     "g1_walk_flat": LocomotionTaskSpec(
         owner_task_id="g1_walk_flat",
         env_task_name="G1WalkFlat",
         display_name="g1_walk_flat",
-        config_cls=G1WalkFlatCfg,
+        config_cls=ManagerBasedRlEnvCfg,
+        model_file="src/unilab/assets/robots/g1/scene_flat.xml",
     ),
     "sharpa_inhand": LocomotionTaskSpec(
         owner_task_id="sharpa_inhand",
@@ -75,7 +77,10 @@ def locomotion_task_spec(task_name: str) -> LocomotionTaskSpec:
 
 
 def locomotion_task_model_file(task_name: str) -> str:
-    cfg = locomotion_task_spec(task_name).config_cls()
+    spec = locomotion_task_spec(task_name)
+    if spec.model_file is not None:
+        return spec.model_file
+    cfg = spec.config_cls()
     scene = getattr(cfg, "scene", None)
     model_file = getattr(scene, "model_file", None)
     if model_file:
diff --git a/scripts/benchmark/env/benchmark_env_step.py b/scripts/benchmark/env/benchmark_env_step.py
index 286514b81..cfee0385f 100644
--- a/scripts/benchmark/env/benchmark_env_step.py
+++ b/scripts/benchmark/env/benchmark_env_step.py
@@ -76,47 +76,19 @@ def _load_helper_module(module_name: str, relative_path: str):
 
 
 def _install_mjwarp_patch() -> bool:
-    """Route ``backend_type == "mjwarp"`` to ``scripts/benchmark/mjwarp`` via factory patch.
-
-    Must run before any task env module (e.g. ``unilab.envs.locomotion.g1.joystick``)
-    is imported, because those modules bind ``create_backend`` at module load
-    time via ``from unilab.base.backend import create_backend``.
-
-    Returns True if ``mujoco_warp`` + ``warp`` are importable, False otherwise.
-    Idempotent.
-    """
+    """Probe the UniSim mjwarp adapter without installing a second backend."""
     try:
         import mujoco_warp  # noqa: F401
         import warp  # noqa: F401
     except ImportError:
         return False
 
-    import unilab.base.backend as _ub_backend
-
-    if getattr(_ub_backend, "_mjwarp_patched", False):
-        return True
-
-    # scripts/benchmark/ is not an installed package, so we load the adapter the same
-    # way device_info/mem_profile/output are loaded above (importlib by path).
-    mjwarp_backend_module = _load_helper_module(
-        "bench_env_step_mjwarp_backend",
-        "../mjwarp/backend.py",
-    )
-    mjwarp_backend_cls = mjwarp_backend_module.MjwarpBackend
-
-    _orig_create_backend = _ub_backend.create_backend
+    return True
 
-    def _patched_create_backend(backend_type, scene, num_envs, sim_dt, **kwargs):
-        if backend_type == "mjwarp":
-            # Strip kwargs that only apply to other backends so MjwarpBackend's
-            # signature stays narrow.
-            kwargs.pop("motrix_max_iterations", None)
-            return mjwarp_backend_cls(scene, num_envs, sim_dt, **kwargs)
-        return _orig_create_backend(backend_type, scene, num_envs, sim_dt, **kwargs)
 
-    _ub_backend.create_backend = _patched_create_backend
-    _ub_backend._mjwarp_patched = True
-    return True
+def _uninstall_mjwarp_patch() -> None:
+    """Retained as a no-op for callers that reset the old benchmark hook."""
+    return None
 
 
 MJWARP_AVAILABLE = _install_mjwarp_patch()
@@ -129,7 +101,7 @@ class TaskConfig:
     task_id: str
     env_name: str
     cfg_factory: Callable[[str, list[str]], Any]
-    env_cls_factory: Callable[[], type]
+    env_cls_factory: Callable[[], Callable[..., Any]]
     backends: tuple[str, ...] = ("mujoco", "motrix")
     aliases: tuple[str, ...] = ()
     cfg_finalizer: Callable[[Any, str], None] | None = None
@@ -157,11 +129,13 @@ def _owner_yaml_cfg(
     from hydra import compose, initialize_config_dir
     from hydra.core.global_hydra import GlobalHydra
 
+    from unilab.base.config_adapter import BackendAdapter
     from unilab.base.registry import apply_cfg_overrides
-    from unilab.training import BackendAdapter
 
     GlobalHydra.instance().clear()
-    with initialize_config_dir(config_dir=str(ROOT_DIR / "conf" / config_root), version_base="1.3"):
+    with initialize_config_dir(
+        config_dir=str(ROOT_DIR / "src" / "unilab" / "conf" / config_root), version_base="1.3"
+    ):
         owner_cfg = compose(
             config_name="config",
             overrides=[
@@ -219,9 +193,9 @@ def _sac_owner_yaml_cfg(
 ) -> Any:
     yaml_backend = _hydra_yaml_backend(backend)
     return _owner_yaml_cfg(
-        config_root="offpolicy",
+        config_root="sac",
         algo_name="sac",
-        overrides=["algo=sac", f"task=sac/{task_id}/{yaml_backend}"],
+        overrides=[f"task={task_id}/{yaml_backend}"],
         config_overrides=config_overrides,
         env_cfg_cls=env_cfg_cls,
     )
@@ -276,86 +250,70 @@ def _materialize_sharpa_motrix_scene() -> str:
 
 
 def _go1_cfg(backend: str, config_overrides: list[str]) -> Any:
-    from unilab.envs.locomotion.go1.joystick import Go1JoystickCfg
+    from unilab.envs import ManagerBasedRlEnvCfg
 
-    return _ppo_owner_yaml_cfg("go1_joystick_flat", backend, Go1JoystickCfg, config_overrides)
+    return _ppo_owner_yaml_cfg("go1_joystick_flat", backend, ManagerBasedRlEnvCfg, config_overrides)
 
 
-def _go1_env_cls() -> type:
-    from unilab.envs.locomotion.go1.joystick import Go1WalkTask
+def _manager_env_cls() -> Callable[..., Any]:
+    from unilab.envs import make_manager_based_rl_env
 
-    return Go1WalkTask
+    return make_manager_based_rl_env
 
 
 def _go2_cfg(backend: str, config_overrides: list[str]) -> Any:
-    from unilab.envs.locomotion.go2.joystick import Go2JoystickCfg
-
-    return _ppo_owner_yaml_cfg("go2_joystick_flat", backend, Go2JoystickCfg, config_overrides)
-
+    from unilab.envs import ManagerBasedRlEnvCfg
 
-def _go2_env_cls() -> type:
-    from unilab.envs.locomotion.go2.joystick import Go2WalkTask
-
-    return Go2WalkTask
+    return _ppo_owner_yaml_cfg("go2_joystick_flat", backend, ManagerBasedRlEnvCfg, config_overrides)
 
 
 def _go2_rough_cfg(backend: str, config_overrides: list[str]) -> Any:
-    from unilab.envs.locomotion.go2.rough import Go2JoystickRoughCfg
-
-    return _ppo_owner_yaml_cfg("go2_joystick_rough", backend, Go2JoystickRoughCfg, config_overrides)
-
-
-def _go2_rough_env_cls() -> type:
-    from unilab.envs.locomotion.go2.rough import Go2JoystickRoughEnv
+    from unilab.envs import ManagerBasedRlEnvCfg
 
-    return Go2JoystickRoughEnv
+    return _ppo_owner_yaml_cfg(
+        "go2_joystick_rough", backend, ManagerBasedRlEnvCfg, config_overrides
+    )
 
 
 def _go2w_cfg(backend: str, config_overrides: list[str]) -> Any:
-    from unilab.envs.locomotion.go2w.joystick import Go2WJoystickCfg
+    from unilab.envs import ManagerBasedRlEnvCfg
 
-    return _ppo_owner_yaml_cfg("go2w_joystick_flat", backend, Go2WJoystickCfg, config_overrides)
+    return _ppo_owner_yaml_cfg(
+        "go2w_joystick_flat", backend, ManagerBasedRlEnvCfg, config_overrides
+    )
 
 
 def _go2w_rough_cfg(backend: str, config_overrides: list[str]) -> Any:
-    from unilab.envs.locomotion.go2w.rough import Go2WJoystickRoughCfg
+    from unilab.envs import ManagerBasedRlEnvCfg
 
     return _ppo_owner_yaml_cfg(
-        "go2w_joystick_rough", backend, Go2WJoystickRoughCfg, config_overrides
+        "go2w_joystick_rough", backend, ManagerBasedRlEnvCfg, config_overrides
     )
 
 
-def _go2w_env_cls() -> type:
-    from unilab.envs.locomotion.go2w.joystick import Go2WJoystickEnv
-
-    return Go2WJoystickEnv
-
-
-def _go2w_rough_env_cls() -> type:
-    from unilab.envs.locomotion.go2w.rough import Go2WJoystickRoughEnv
-
-    return Go2WJoystickRoughEnv
+def _go2w_env_cls() -> Callable[..., Any]:
+    return _manager_env_cls()
 
 
 def _g1_flat_cfg(backend: str, config_overrides: list[str]) -> Any:
-    from unilab.envs.locomotion.g1.joystick import G1WalkFlatCfg
+    from unilab.envs import ManagerBasedRlEnvCfg
 
-    return _ppo_owner_yaml_cfg("g1_walk_flat", backend, G1WalkFlatCfg, config_overrides)
+    return _ppo_owner_yaml_cfg("g1_walk_flat", backend, ManagerBasedRlEnvCfg, config_overrides)
 
 
 def _g1_rough_cfg(backend: str, config_overrides: list[str]) -> Any:
-    from unilab.envs.locomotion.g1.joystick import G1WalkRoughCfg
+    from unilab.envs import ManagerBasedRlEnvCfg
 
-    return _sac_owner_yaml_cfg("g1_walk_rough", backend, G1WalkRoughCfg, config_overrides)
+    return _sac_owner_yaml_cfg("g1_walk_rough", backend, ManagerBasedRlEnvCfg, config_overrides)
 
 
 def _g1_motion_tracking_cfg(backend: str, config_overrides: list[str]) -> Any:
-    from unilab.envs.motion_tracking.g1.tracking import G1MotionTrackingEnvCfg
+    from unilab.envs import ManagerBasedRlEnvCfg
 
     return _ppo_owner_yaml_cfg(
         "g1_motion_tracking",
         backend,
-        G1MotionTrackingEnvCfg,
+        ManagerBasedRlEnvCfg,
         config_overrides,
     )
 
@@ -364,13 +322,15 @@ def _sharpa_inhand_cfg(backend: str, config_overrides: list[str]) -> Any:
     from hydra import compose, initialize_config_dir
     from hydra.core.global_hydra import GlobalHydra
 
+    from unilab.base.config_adapter import BackendAdapter
     from unilab.base.registry import apply_cfg_overrides
-    from unilab.envs.manipulation.sharpa_inhand.rotation import SharpaInhandRotationCfg
-    from unilab.training import BackendAdapter
+    from unilab.tasks.manipulation.sharpa_inhand.rotation import SharpaInhandRotationCfg
 
     yaml_backend = _hydra_yaml_backend(backend)
     GlobalHydra.instance().clear()
-    with initialize_config_dir(config_dir=str(ROOT_DIR / "conf" / "ppo"), version_base="1.3"):
+    with initialize_config_dir(
+        config_dir=str(ROOT_DIR / "src" / "unilab" / "conf" / "ppo"), version_base="1.3"
+    ):
         owner_cfg = compose(
             config_name="config",
             overrides=[
@@ -396,7 +356,7 @@ def _sharpa_inhand_cfg(backend: str, config_overrides: list[str]) -> Any:
 
 
 def _ensure_sharpa_benchmark_grasp_cache(cfg: Any, _: str) -> None:
-    from unilab.envs.manipulation.sharpa_inhand.base import (
+    from unilab.tasks.manipulation.sharpa_inhand.base import (
         SOURCE_DEFAULT_HAND_JOINT_POS_DEG,
         resolve_grasp_cache_file,
     )
@@ -416,19 +376,13 @@ def _ensure_sharpa_benchmark_grasp_cache(cfg: Any, _: str) -> None:
 
 
 def _g1_walk_env_cls() -> type:
-    from unilab.envs.locomotion.g1.joystick import G1WalkEnv
+    from unilab.tasks.locomotion.g1 import make_g1_walk_env
 
-    return G1WalkEnv
-
-
-def _g1_motion_tracking_env_cls() -> type:
-    from unilab.envs.motion_tracking.g1.tracking import G1MotionTrackingEnv
-
-    return G1MotionTrackingEnv
+    return make_g1_walk_env
 
 
 def _sharpa_inhand_env_cls() -> type:
-    from unilab.envs.manipulation.sharpa_inhand.rotation import SharpaInhandRotationEnv
+    from unilab.tasks.manipulation.sharpa_inhand.rotation import SharpaInhandRotationEnv
 
     return SharpaInhandRotationEnv
 
@@ -438,22 +392,22 @@ def _sharpa_inhand_env_cls() -> type:
         task_id="go1_joystick_flat",
         env_name="Go1JoystickFlat",
         cfg_factory=_go1_cfg,
-        env_cls_factory=_go1_env_cls,
+        env_cls_factory=_manager_env_cls,
         backends=("mujoco", "motrix", "mjwarp"),
     ),
     "go2": TaskConfig(
         task_id="go2_joystick_flat",
         env_name="Go2JoystickFlat",
         cfg_factory=_go2_cfg,
-        env_cls_factory=_go2_env_cls,
+        env_cls_factory=_manager_env_cls,
         backends=("mujoco", "motrix", "mjwarp"),
     ),
     "go2_rough": TaskConfig(
         task_id="go2_joystick_rough",
         env_name="Go2JoystickRough",
         cfg_factory=_go2_rough_cfg,
-        env_cls_factory=_go2_rough_env_cls,
-        backends=("mujoco", "motrix", "mjwarp"),
+        env_cls_factory=_manager_env_cls,
+        backends=("mujoco", "motrix"),
     ),
     "go2w": TaskConfig(
         task_id="go2w_joystick_flat",
@@ -466,8 +420,8 @@ def _sharpa_inhand_env_cls() -> type:
         task_id="go2w_joystick_rough",
         env_name="Go2WJoystickRough",
         cfg_factory=_go2w_rough_cfg,
-        env_cls_factory=_go2w_rough_env_cls,
-        backends=("mujoco", "motrix", "mjwarp"),
+        env_cls_factory=_manager_env_cls,
+        backends=("mujoco", "motrix"),
     ),
     "g1": TaskConfig(
         task_id="g1_walk_flat",
@@ -488,8 +442,8 @@ def _sharpa_inhand_env_cls() -> type:
         task_id="g1_motion_tracking",
         env_name="G1MotionTracking",
         cfg_factory=_g1_motion_tracking_cfg,
-        env_cls_factory=_g1_motion_tracking_env_cls,
-        backends=("mujoco", "motrix", "mjwarp"),
+        env_cls_factory=_manager_env_cls,
+        backends=("mujoco", "motrix"),
     ),
     "sharpa_inhand": TaskConfig(
         task_id="sharpa_inhand",
@@ -502,7 +456,7 @@ def _sharpa_inhand_env_cls() -> type:
 }
 
 # Default benchmark parameters
-DEFAULT_NUM_ENVS = 2048
+DEFAULT_NUM_ENVS = 4096
 DEFAULT_NUM_STEPS = 20
 DEFAULT_WARMUP_STEPS = 5
 
@@ -708,15 +662,21 @@ def _run_single(extra_args: list[str]) -> dict[str, Any]:
         env_cls = task_config.env_cls_factory()
         env = env_cls(cfg, num_envs=num_envs, backend_type=sim_backend)
 
-        nu = env._backend.num_actuators  # type: ignore[reportAttributeAccessIssue]
+        action_shape = env.action_space.shape
+        if action_shape is None or len(action_shape) != 1:
+            raise ValueError(
+                f"Benchmark task {task_config.env_name!r} requires a flat action space, "
+                f"got {action_shape}"
+            )
+        action_dim = int(action_shape[0])
         env.init_state()
 
         for _ in range(warmup_steps):
-            actions = np.random.uniform(-1, 1, size=(num_envs, nu)).astype(np.float32)
+            actions = np.random.uniform(-1, 1, size=(num_envs, action_dim)).astype(np.float32)
             env.step(actions)
 
         for _ in range(num_steps):
-            actions = np.random.uniform(-1, 1, size=(num_envs, nu)).astype(np.float32)
+            actions = np.random.uniform(-1, 1, size=(num_envs, action_dim)).astype(np.float32)
             state = env.step(actions)
             timing = state.info.get("timing", {})
             for k, v in timing.items():
@@ -1477,7 +1437,7 @@ def _run_matrix(
     extra_args: list[str], *, out_json: Path, plot_dir: Path | None, skip_plots: bool
 ) -> None:
     """Run all task x backend combinations and print comparison."""
-    from unilab.base.backend import MOTRIX_AVAILABLE
+    from unisim.backend.motrix.backend import MOTRIX_AVAILABLE
 
     backends = ["mujoco"]
     if MOTRIX_AVAILABLE:
diff --git a/scripts/benchmark/env/benchmark_env_step_phase_cpu.py b/scripts/benchmark/env/benchmark_env_step_phase_cpu.py
new file mode 100644
index 000000000..655750afe
--- /dev/null
+++ b/scripts/benchmark/env/benchmark_env_step_phase_cpu.py
@@ -0,0 +1,169 @@
+"""Per-phase wall/CPU attribution for a full task env step (issue #1328).
+
+Builds a real task env through the same Hydra compose + ``BackendAdapter``
+override path the off-policy collector uses, wraps ``backend.step`` /
+``update_state`` / ``_reset_done_envs`` with process-wide CPU-time measurement
+(``os.times``), and reports each phase's wall share and the average number of
+cores it kept busy. This makes low-parallelism host phases visible next to the
+thread-pool physics phase.
+
+``--cpu-ids 0-31`` additionally injects ``EnvCfg.cpu_ids`` into the env
+override (the same key the multi-GPU DP collector path uses), which both pins
+the MuJoCo pool workers and confines the process's host-side compute via
+``apply_env_cpu_runtime`` — the A/B used in the issue.
+
+Run:
+    uv run scripts/benchmark/env/benchmark_env_step_phase_cpu.py
+
+    # pinned A/B:
+    uv run scripts/benchmark/env/benchmark_env_step_phase_cpu.py --cpu-ids 0-31
+
+    # tuning:
+    uv run scripts/benchmark/env/benchmark_env_step_phase_cpu.py \
+        --config-group sac --task g1_motion_tracking/mujoco \
+        --num-envs 4096 --warmup 20 --iters 150
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import time
+from collections import defaultdict
+from collections.abc import Sequence
+
+import numpy as np
+
+REPO_ROOT = os.path.dirname(
+    os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+)
+
+
+def _cpu_time() -> float:
+    t = os.times()
+    return t.user + t.system
+
+
+def _parse_cpu_ids(spec: str) -> list[int]:
+    ids: list[int] = []
+    for part in spec.split(","):
+        part = part.strip()
+        if "-" in part:
+            lo, hi = part.split("-", 1)
+            ids.extend(range(int(lo), int(hi) + 1))
+        elif part:
+            ids.append(int(part))
+    return ids
+
+
+def main(argv: Sequence[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+    parser.add_argument("--config-group", default="sac", help="conf/<group> used for compose")
+    parser.add_argument("--task", default="g1_motion_tracking/mujoco")
+    parser.add_argument("--num-envs", type=int, default=4096)
+    parser.add_argument("--warmup", type=int, default=20)
+    parser.add_argument("--iters", type=int, default=150)
+    parser.add_argument(
+        "--cpu-ids",
+        default=None,
+        help="Optional env cpu_ids override, e.g. '0-31'; pins the MuJoCo pool "
+        "and confines host-side compute (sizes the pool to len(cpu_ids))",
+    )
+    args = parser.parse_args(argv)
+
+    import hydra
+    from omegaconf import OmegaConf
+
+    from unilab.base.config_adapter import BackendAdapter, create_env
+    from unilab.training import ensure_registries
+
+    ensure_registries()
+    with hydra.initialize_config_dir(
+        version_base="1.3",
+        config_dir=os.path.join(REPO_ROOT, "src", "unilab", "conf", args.config_group),
+    ):
+        cfg = hydra.compose(
+            config_name="config",
+            overrides=[f"task={args.task}", f"algo.num_envs={args.num_envs}"],
+        )
+    OmegaConf.resolve(cfg)
+    env_cfg_override = BackendAdapter(
+        cfg, root_dir=REPO_ROOT, algo_name=str(cfg.algo.algo)
+    ).build_task_env_cfg_override()
+    if args.cpu_ids is not None:
+        env_cfg_override = {
+            **(env_cfg_override or {}),
+            "cpu_ids": _parse_cpu_ids(args.cpu_ids),
+        }
+    env = create_env(cfg, num_envs=args.num_envs, env_cfg_override=env_cfg_override)
+    if env.state is None:
+        env.init_state()
+
+    wall_ms: defaultdict[str, float] = defaultdict(float)
+    cpu_ms: defaultdict[str, float] = defaultdict(float)
+    counts: defaultdict[str, int] = defaultdict(int)
+
+    def wrap(name, fn):
+        def wrapped(*a, **kw):
+            w0 = time.perf_counter()
+            c0 = _cpu_time()
+            out = fn(*a, **kw)
+            wall_ms[name] += (time.perf_counter() - w0) * 1000.0
+            cpu_ms[name] += (_cpu_time() - c0) * 1000.0
+            counts[name] += 1
+            return out
+
+        return wrapped
+
+    env._backend.step = wrap("backend_step", env._backend.step)
+    env.update_state = wrap("update_state", env.update_state)
+    env._reset_done_envs = wrap("reset_done", env._reset_done_envs)
+
+    action_dim = env.action_space.shape[-1]
+    rng = np.random.default_rng(0)
+
+    def actions():
+        return rng.uniform(-0.2, 0.2, size=(args.num_envs, action_dim)).astype(np.float32)
+
+    for _ in range(args.warmup):
+        env.step(actions())
+    wall_ms.clear()
+    cpu_ms.clear()
+    counts.clear()
+
+    n_reset = 0
+    wall0 = time.perf_counter()
+    cpu0 = _cpu_time()
+    for _ in range(args.iters):
+        state = env.step(actions())
+        n_reset += int(np.count_nonzero(state.terminated | state.truncated))
+    total_wall = (time.perf_counter() - wall0) * 1000.0
+    total_cpu = (_cpu_time() - cpu0) * 1000.0
+
+    print(
+        f"pool nthread={env._backend._n_threads} num_envs={args.num_envs} "
+        f"cpu_ids={'None' if args.cpu_ids is None else args.cpu_ids}"
+    )
+    print(f"iters={args.iters} total_resets={n_reset}")
+    print(f"{'phase':>16s} {'wall_ms':>9s} {'cpu_ms':>9s} {'cores':>6s} {'wall%':>6s}")
+    step_wall = total_wall / args.iters
+    for name in ("backend_step", "update_state", "reset_done"):
+        w = wall_ms[name] / args.iters
+        c = cpu_ms[name] / args.iters
+        print(f"{name:>16s} {w:9.2f} {c:9.2f} {c / w if w else 0:6.2f} {100 * w / step_wall:6.1f}")
+    other_w = total_wall - sum(wall_ms.values())
+    other_c = total_cpu - sum(cpu_ms.values())
+    print(
+        f"{'other(step glue)':>16s} {other_w / args.iters:9.2f} {other_c / args.iters:9.2f} "
+        f"{(other_c / other_w) if other_w > 0 else 0:6.2f} {100 * other_w / total_wall:6.1f}"
+    )
+    print(
+        f"{'TOTAL step':>16s} {step_wall:9.2f} {total_cpu / args.iters:9.2f} "
+        f"{total_cpu / total_wall:6.2f} {100.0:6.1f}"
+    )
+    print(f"steps/s={args.num_envs * args.iters / (total_wall / 1000.0):.0f}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/benchmark/env/benchmark_mujoco_pool_thread_scaling.py b/scripts/benchmark/env/benchmark_mujoco_pool_thread_scaling.py
new file mode 100644
index 000000000..65ff96343
--- /dev/null
+++ b/scripts/benchmark/env/benchmark_mujoco_pool_thread_scaling.py
@@ -0,0 +1,146 @@
+"""MuJoCo BatchEnvPool thread-count scaling probe (issue #1328).
+
+Steps a raw ``BatchEnvPool`` (no env semantics, no learner) on the G1 flat
+scene with several ``nthread`` / ``cpu_ids`` configurations and reports, per
+configuration, wall time per ``pool.step`` and the average number of cores the
+process kept busy (process CPU time / wall time via ``os.times``).
+
+Used to separate two effects of the default
+``nthread = min(num_envs, 2 * cpu_count)`` pool sizing:
+
+- thread count vs. pinning (``cpu_ids``): on the reference 16C/32T host the
+  32-thread unpinned and pinned rows match, so the 2x-oversubscription loss
+  comes from the thread count itself;
+- the physics scaling ceiling: throughput saturates near the physical core
+  count (memory-bandwidth bound), so extra threads mostly cost wall time.
+
+Run:
+    uv run scripts/benchmark/env/benchmark_mujoco_pool_thread_scaling.py
+
+    # subset + tuning:
+    uv run scripts/benchmark/env/benchmark_mujoco_pool_thread_scaling.py \
+        --num-envs 4096 --nstep 3 --chunk-size 6 \
+        --configs 64:unpinned,32:unpinned,32:pinned,16:pinned
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import time
+from typing import Sequence
+
+import numpy as np
+
+REPO_ROOT = os.path.dirname(
+    os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+)
+DEFAULT_MODEL = os.path.join(REPO_ROOT, "src/unilab/assets/robots/g1/scene_flat.xml")
+
+
+def _cpu_time() -> float:
+    t = os.times()
+    return t.user + t.system
+
+
+def build_state(model, nenvs: int) -> np.ndarray:
+    """Tile the ``stand`` keyframe (or a plain forward) into a full-batch state."""
+    import mujoco
+
+    data = mujoco.MjData(model)
+    key_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_KEY, "stand")
+    if key_id >= 0:
+        mujoco.mj_resetDataKeyframe(model, data, key_id)
+    mujoco.mj_forward(model, data)
+    spec = int(mujoco.mjtState.mjSTATE_FULLPHYSICS)
+    row = np.empty(mujoco.mj_stateSize(model, spec), dtype=np.float64)
+    mujoco.mj_getState(model, data, row, spec)
+    return np.tile(row, (nenvs, 1)).copy()
+
+
+def bench_config(
+    model,
+    state0: np.ndarray,
+    *,
+    nthread: int,
+    pinned: bool,
+    nstep: int,
+    chunk_size: int | None,
+    warmup: int,
+    iters: int,
+) -> tuple[float, float]:
+    """Return (wall ms/step, busy cores) for one pool configuration."""
+    from mujoco_uni.batch_env import BatchEnvPool
+
+    cpu_ids = list(range(nthread)) if pinned else None
+    pool = BatchEnvPool(model, nbatch=state0.shape[0], nthread=nthread, cpu_ids=cpu_ids)
+    nenvs = state0.shape[0]
+    ctrl = np.zeros((nenvs, nstep, model.nu), dtype=np.float64)
+    st = state0.copy()
+    try:
+        for _ in range(warmup):
+            st = pool.step(st, nstep=nstep, control=ctrl, chunk_size=chunk_size)
+        t0 = time.perf_counter()
+        c0 = _cpu_time()
+        for _ in range(iters):
+            st = pool.step(st, nstep=nstep, control=ctrl, chunk_size=chunk_size)
+        wall_ms = (time.perf_counter() - t0) / iters * 1000.0
+        cores = (_cpu_time() - c0) / iters * 1000.0 / wall_ms
+        return wall_ms, cores
+    finally:
+        pool.close()
+
+
+def _parse_configs(spec: str) -> list[tuple[int, bool]]:
+    out = []
+    for item in spec.split(","):
+        nthread_s, mode = item.strip().split(":")
+        if mode not in ("pinned", "unpinned"):
+            raise ValueError(f"unknown config mode {mode!r} in {item!r}")
+        out.append((int(nthread_s), mode == "pinned"))
+    return out
+
+
+def main(argv: Sequence[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+    parser.add_argument("--model", default=DEFAULT_MODEL, help="MuJoCo XML scene path")
+    parser.add_argument("--num-envs", type=int, default=4096)
+    parser.add_argument("--nstep", type=int, default=3, help="sim substeps per pool.step")
+    parser.add_argument("--chunk-size", type=int, default=6)
+    parser.add_argument("--warmup", type=int, default=5)
+    parser.add_argument("--iters", type=int, default=30)
+    parser.add_argument(
+        "--configs",
+        default="64:unpinned,32:unpinned,32:pinned,24:pinned,16:pinned,8:pinned",
+        help="Comma-separated nthread:pinned|unpinned entries",
+    )
+    args = parser.parse_args(argv)
+
+    import mujoco
+
+    model = mujoco.MjModel.from_xml_path(args.model)
+    state0 = build_state(model, args.num_envs)
+    print(
+        f"model={os.path.basename(args.model)} nu={model.nu} nv={model.nv} "
+        f"nstate={state0.shape[1]} num_envs={args.num_envs} host_cpus={os.cpu_count()} "
+        f"nstep={args.nstep} chunk_size={args.chunk_size}"
+    )
+    print(f"{'config':>18s} | {'ms/step':>8s} | {'cores':>6s}")
+    for nthread, pinned in _parse_configs(args.configs):
+        wall_ms, cores = bench_config(
+            model,
+            state0,
+            nthread=nthread,
+            pinned=pinned,
+            nstep=args.nstep,
+            chunk_size=args.chunk_size,
+            warmup=args.warmup,
+            iters=args.iters,
+        )
+        label = f"{nthread}t {'pinned' if pinned else 'unpinned'}"
+        print(f"{label:>18s} | {wall_ms:8.2f} | {cores:6.1f}", flush=True)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/benchmark/env/benchmark_sharpa_init_dr_construct.py b/scripts/benchmark/env/benchmark_sharpa_init_dr_construct.py
index 31017047f..4008d0d83 100644
--- a/scripts/benchmark/env/benchmark_sharpa_init_dr_construct.py
+++ b/scripts/benchmark/env/benchmark_sharpa_init_dr_construct.py
@@ -121,7 +121,7 @@ def _parse_variant_counts(value: str | None) -> list[int]:
 
 
 def _compose_cfg(task: str, *, lower: float, upper: float, variant_count: int):
-    config_dir = str(ROOT_DIR / "conf" / "ppo")
+    config_dir = str(ROOT_DIR / "src" / "unilab" / "conf" / "ppo")
     scale_list = np.linspace(lower, upper, variant_count, dtype=np.float64)
     scale_override = ",".join(f"{float(scale):g}" for scale in scale_list)
     overrides = [
@@ -140,7 +140,7 @@ def _compose_cfg(task: str, *, lower: float, upper: float, variant_count: int):
 
 @contextmanager
 def _init_dr_mode(enabled: bool) -> Iterator[None]:
-    from unilab.envs.manipulation.sharpa_inhand.rotation import SharpaInhandRotationDRProvider
+    from unilab.tasks.manipulation.sharpa_inhand.rotation import SharpaInhandRotationDRProvider
 
     original = SharpaInhandRotationDRProvider.build_init_randomization_plan
     if enabled:
@@ -162,7 +162,7 @@ def disabled_build_init_randomization_plan(self: Any, env: Any) -> None:
 
 @contextmanager
 def _synthetic_grasp_cache_mode(enabled: bool) -> Iterator[None]:
-    from unilab.envs.manipulation.sharpa_inhand.rotation import SharpaInhandRotationDRProvider
+    from unilab.tasks.manipulation.sharpa_inhand.rotation import SharpaInhandRotationDRProvider
 
     original = SharpaInhandRotationDRProvider._load_grasp_cache
     if not enabled:
@@ -208,7 +208,8 @@ def _construct_once(
     init_dr_enabled: bool,
     force_pool: bool,
 ) -> tuple[float, dict[str, Any]]:
-    from unilab.training import BackendAdapter, create_env, ensure_registries
+    from unilab.base.config_adapter import BackendAdapter, create_env
+    from unilab.training import ensure_registries
 
     ensure_registries()
 
diff --git a/scripts/benchmark/mjwarp/__init__.py b/scripts/benchmark/mjwarp/__init__.py
deleted file mode 100644
index ec21dd5e5..000000000
--- a/scripts/benchmark/mjwarp/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-"""Benchmark-only mjwarp backend adapter.
-
-Lives outside ``src/unilab/`` because it is wired in via a monkey-patch from
-``scripts/benchmark/env/benchmark_env_step.py`` and is not part of the training-path
-``SimBackend`` factory.
-"""
diff --git a/scripts/benchmark/mjwarp/backend.py b/scripts/benchmark/mjwarp/backend.py
deleted file mode 100644
index 3287b8dbd..000000000
--- a/scripts/benchmark/mjwarp/backend.py
+++ /dev/null
@@ -1,754 +0,0 @@
-"""mjwarp ``SimBackend`` adapter used only by ``scripts/benchmark/env/benchmark_env_step.py``.
-
-The goal is parity with ``MuJoCoBackend`` / ``MotrixBackend`` at the
-``NpEnv.step`` boundary so the three backends share the same Python pipeline
-(``apply_action`` / ``update_state`` / ``reset_done`` all stay on CPU numpy).
-The only difference is what happens inside ``backend.step``: H2D ctrl,
-``mujoco_warp.step`` on GPU, D2H of qpos/qvel/sensors. Those three segments
-are reported in the timing dict so the benchmark's existing breakdown columns
-work without changes.
-
-This backend covers what ``g1_walk_flat`` actually invokes. Methods the
-benchmark does not exercise raise ``NotImplementedError``. Phase 2 will
-broaden the surface for other tasks.
-
-Per-env actuator gain (``kp`` / ``kd``) randomization is NOT supported: in
-mjwarp the model parameters are shared across the batched ``Data`` worlds.
-``get_dr_capabilities`` reports ``supports_kp = supports_kd = False`` so the
-incoming ``ResetRandomizationPayload`` is treated as a no-op for those terms.
-This is acceptable for benchmark-only env_step timing comparison; it does not
-change the physics pipeline being measured.
-"""
-
-from __future__ import annotations
-
-import time
-from collections.abc import Sequence
-from typing import Any
-
-import numpy as np
-
-from unilab.base.backend.base import BackendHeightScanner, SimBackend
-from unilab.base.scene import SceneCfg
-from unilab.dr.types import (
-    DomainRandomizationCapabilities,
-    IntervalRandomizationPlan,
-    ResetRandomizationPayload,
-)
-
-
-def _import_mujoco():
-    import mujoco
-
-    return mujoco
-
-
-def _import_mjwarp():
-    import mujoco_warp
-    import warp as wp
-
-    return mujoco_warp, wp
-
-
-class _MjwarpHeightScanner(BackendHeightScanner):
-    """GPU-accelerated height scanner using mujoco_warp.rays()."""
-
-    def __init__(
-        self,
-        backend: "MjwarpBackend",
-        hfield_geom_id: int,
-        offsets: np.ndarray,
-        frame_body_id: int,
-        alignment: str,
-        output: str,
-    ) -> None:
-        self._backend = backend
-        self._hfield_geom_id = hfield_geom_id
-        self._offsets = np.asarray(offsets, dtype=np.float64)  # (num_points, 2)
-        self._num_points = self._offsets.shape[0]
-        self._frame_body_id = frame_body_id
-        self._alignment = alignment
-        self._output = output
-        self._ray_height = 50.0
-
-        wp = backend._wp
-        n = backend._num_envs
-        nray = self._num_points
-        from mujoco_warp._src.types import vec6f
-
-        self._vec6f = vec6f
-        self._pnt_buf = wp.zeros((n, nray), dtype=wp.vec3f)
-        self._vec_buf = wp.zeros((n, nray), dtype=wp.vec3f)
-        self._dist_buf = wp.zeros((n, nray), dtype=wp.float32)
-        self._geomid_buf = wp.zeros((n, nray), dtype=wp.int32)
-        self._normal_buf = wp.zeros((n, nray), dtype=wp.vec3f)
-        self._bodyexclude = wp.full((nray,), frame_body_id, dtype=wp.int32)
-
-        # Pre-compute constant ray direction (0, 0, -1) for all envs/rays
-        vec_np = np.zeros((n, nray, 3), dtype=np.float32)
-        vec_np[:, :, 2] = -1.0
-        self._vec_buf.assign(vec_np)
-
-    def scan(self) -> np.ndarray:
-        backend = self._backend
-        wp = backend._wp
-
-        base_pos = backend._cache_qpos[:, 0:3]  # (num_envs, 3)
-        base_quat = backend._cache_qpos[:, 3:7]  # (num_envs, 4) wxyz
-
-        # Extract yaw from quaternion (w, x, y, z)
-        w, x, y, z = base_quat[:, 0], base_quat[:, 1], base_quat[:, 2], base_quat[:, 3]
-        yaw = np.arctan2(2.0 * (w * z + x * y), 1.0 - 2.0 * (y * y + z * z))
-
-        cos_yaw = np.cos(yaw)[:, None]  # (num_envs, 1)
-        sin_yaw = np.sin(yaw)[:, None]
-
-        # Rotate offsets by yaw: R @ [ox, oy]^T
-        ox = self._offsets[:, 0]  # (num_points,)
-        oy = self._offsets[:, 1]
-        # Broadcast: (num_envs, 1) * (1, num_points) → (num_envs, num_points)
-        rx = cos_yaw * ox[None, :] - sin_yaw * oy[None, :]
-        ry = sin_yaw * ox[None, :] + cos_yaw * oy[None, :]
-
-        # Ray origins: base_pos + rotated offset + z_offset
-        n = backend._num_envs
-        nray = self._num_points
-        pnt_np = np.zeros((n, nray, 3), dtype=np.float32)
-        pnt_np[:, :, 0] = (base_pos[:, 0:1] + rx).astype(np.float32)
-        pnt_np[:, :, 1] = (base_pos[:, 1:2] + ry).astype(np.float32)
-        pnt_np[:, :, 2] = (base_pos[:, 2:3] + self._ray_height).astype(np.float32)
-
-        self._pnt_buf.assign(pnt_np)
-        geomgroup = self._vec6f(-1, -1, -1, -1, -1, -1)
-
-        backend._mjwarp.rays(
-            backend._mjw_model,
-            backend._mjw_data,
-            self._pnt_buf,
-            self._vec_buf,
-            geomgroup,
-            True,
-            self._bodyexclude,
-            self._dist_buf,
-            self._geomid_buf,
-            self._normal_buf,
-        )
-        wp.synchronize_device()
-
-        dist = self._dist_buf.numpy()  # (num_envs, num_points)
-        # Height = origin_z - dist. Where dist == -1 (no hit), use 0.
-        origin_z = pnt_np[:, :, 2]
-        heights = np.where(dist >= 0, origin_z - dist, 0.0)
-        return heights.astype(np.float64)
-
-
-class MjwarpBackend(SimBackend):
-    """Minimal mjwarp adapter for benchmark env_step timing.
-
-    Implements the ``SimBackend`` surface that ``g1_walk_flat`` exercises during
-    a benchmark run; everything else raises ``NotImplementedError``.
-    """
-
-    def __init__(
-        self,
-        scene: SceneCfg,
-        num_envs: int,
-        sim_dt: float,
-        *,
-        base_name: str | None = None,
-        push_body_name: str | None = None,
-        position_actuator_gains: Any = None,
-        **_ignored_kwargs: Any,
-    ) -> None:
-        del position_actuator_gains, _ignored_kwargs
-
-        self._pre_step_control_fn = None
-        self._scene_cleanup_handle = None
-        self.backend_type = "mjwarp"
-
-        if scene is None or not scene.model_file:
-            raise ValueError("MjwarpBackend requires a SceneCfg with a model_file")
-
-        mujoco = _import_mujoco()
-        mujoco_warp, wp = _import_mjwarp()
-        self._mujoco = mujoco
-        self._wp = wp
-        self._mjwarp = mujoco_warp
-
-        # Scene materialization: three paths depending on terrain / fragments.
-        self.terrain_origins = None
-        self.terrain_surface_sampler = None
-        self._terrain_tmpdir = None
-
-        if scene.terrain is not None:
-            import tempfile
-
-            from unilab.base.backend.mujoco.xml import (
-                materialize_mujoco_hfield_attached_scene,
-            )
-
-            tmpdir = tempfile.TemporaryDirectory(prefix="mjwarp_terrain_")
-            self._terrain_tmpdir = tmpdir
-            result = materialize_mujoco_hfield_attached_scene(
-                model_file=scene.model_file,
-                terrain_cfg=scene.terrain.generator,
-                output_dir=tmpdir.name,
-                fragment_files=scene.fragment_files or [],
-                hfield_name=scene.terrain.hfield_name,
-                geom_name=scene.terrain.geom_name or "floor",
-                return_surface_sampler=True,
-            )
-            self._mj_model, self.terrain_origins, self.terrain_surface_sampler = result
-        elif scene.fragment_files:
-            from unilab.base.backend.mujoco.xml import materialize_scene_fragments
-
-            xml_path = materialize_scene_fragments(
-                scene.model_file, fragment_files=scene.fragment_files
-            )
-            self._mj_model = mujoco.MjModel.from_xml_path(xml_path)
-        else:
-            self._mj_model = mujoco.MjModel.from_xml_path(scene.model_file)
-
-        self._mj_model.opt.timestep = float(sim_dt)
-
-        self._base_name = base_name
-        self._push_body_name = push_body_name
-
-        self._num_envs = int(num_envs)
-        self._nq = int(self._mj_model.nq)
-        self._nv = int(self._mj_model.nv)
-        self._nu = int(self._mj_model.nu)
-
-        # Free-joint slice assumption: G1 (and other locomotion tasks) place the
-        # free joint at index 0, so DOF state for the env starts after it.
-        # Cold-path verification at init avoids per-step hasattr probing.
-        if self._mj_model.njnt == 0:
-            raise ValueError("Model has no joints")
-        first_joint_type = int(self._mj_model.jnt_type[0])
-        # mujoco.mjtJoint.mjJNT_FREE == 0
-        self._has_free_joint = first_joint_type == 0
-        if self._has_free_joint:
-            self._dof_qpos_start = 7
-            self._dof_qvel_start = 6
-            expected_dof = self._nq - 7
-            if expected_dof != self._nu:
-                raise ValueError(f"DOF count mismatch: nq-7={expected_dof} but nu={self._nu}")
-        else:
-            self._dof_qpos_start = 0
-            self._dof_qvel_start = 0
-
-        # Build sensor-name -> (adr, dim) map once on the cold path.
-        self._sensor_adr: dict[str, tuple[int, int]] = {}
-        for sid in range(int(self._mj_model.nsensor)):
-            name = mujoco.mj_id2name(self._mj_model, int(mujoco.mjtObj.mjOBJ_SENSOR), sid)
-            if name is None:
-                continue
-            self._sensor_adr[str(name)] = (
-                int(self._mj_model.sensor_adr[sid]),
-                int(self._mj_model.sensor_dim[sid]),
-            )
-
-        # Cache keyframe qpos (CPU numpy) for reset.
-        self._keyframe_qpos: dict[str, np.ndarray] = {}
-        for kid in range(int(self._mj_model.nkey)):
-            kname = mujoco.mj_id2name(self._mj_model, int(mujoco.mjtObj.mjOBJ_KEY), kid)
-            if kname is None:
-                continue
-            self._keyframe_qpos[str(kname)] = np.asarray(
-                self._mj_model.key_qpos[kid], dtype=np.float64
-            )
-
-        # Cache actuator ctrl range and joint ranges for the env's cold path.
-        self._actuator_ctrl_range = np.asarray(self._mj_model.actuator_ctrlrange, dtype=np.float64)
-
-        # Upload to GPU. ``njmax`` controls per-world constraint capacity;
-        # the default is too small for legged robots with multiple feet making
-        # ground contact, producing ``nefc overflow`` warnings and numerical
-        # corruption. 512 is a comfortable upper bound for g1_walk_flat
-        # (observed max around 175 across 512 envs in early-run probes).
-        self._mjw_model = mujoco_warp.put_model(self._mj_model)
-        self._mjw_data = mujoco_warp.make_data(
-            self._mj_model,
-            nworld=self._num_envs,
-            njmax=512,
-            nconmax=512,
-        )
-
-        # Host-side cache buffers refreshed at the end of every step.
-        self._cache_qpos = np.zeros((self._num_envs, self._nq), dtype=np.float64)
-        self._cache_qvel = np.zeros((self._num_envs, self._nv), dtype=np.float64)
-        self._cache_sensordata = np.zeros(
-            (self._num_envs, int(self._mj_model.nsensordata)), dtype=np.float64
-        )
-
-        # Seed qpos to the first keyframe so the env's init_state has something
-        # consistent to reset from. NpEnv's init_state marks every env as
-        # terminated so reset() will overwrite these immediately, but writing a
-        # valid pose here avoids a transient bad state before that runs.
-        if self._mj_model.nkey > 0:
-            init_qpos = np.tile(
-                np.asarray(self._mj_model.key_qpos[0], dtype=np.float32),
-                (self._num_envs, 1),
-            )
-            self._mjw_data.qpos.assign(init_qpos)
-            self._wp.synchronize_device()
-
-        # Pre-allocated H2D scratch for ctrl to avoid per-step warp.array
-        # allocation in the hot path.
-        self._ctrl_scratch_f32 = np.zeros((self._num_envs, self._nu), dtype=np.float32)
-
-        # Resolve push body id (used by DR interval push for tasks like go1).
-        # ``push_body_name`` falls back to ``base_name``; -1 means push is
-        # unsupported for this env. Matches MuJoCoBackend semantics.
-        push_target = self._push_body_name if self._push_body_name is not None else self._base_name
-        if push_target is None:
-            self._push_body_id = -1
-        else:
-            body_id = mujoco.mj_name2id(self._mj_model, int(mujoco.mjtObj.mjOBJ_BODY), push_target)
-            if body_id < 0:
-                raise ValueError(f"Push body {push_target!r} not found in mjwarp model")
-            self._push_body_id = int(body_id)
-
-        # Pending external wrench buffer ``(num_envs, nbody, 6)`` written into
-        # ``data.xfrc_applied`` at the start of every ``step`` and cleared
-        # afterwards. mujoco_warp's xfrc_applied dtype is spatial_vectorf
-        # (6 floats: linear xyz + angular xyz).
-        self._nbody = int(self._mj_model.nbody)
-        self._pending_xfrc_applied = np.zeros((self._num_envs, self._nbody, 6), dtype=np.float32)
-        self._xfrc_dirty = False  # avoid H2D when no force is pending
-
-    # ------------------------------------------------------------------ #
-    # Hot-path: step                                                       #
-    # ------------------------------------------------------------------ #
-
-    def step(self, ctrl: np.ndarray, nsteps: int = 1) -> dict | None:
-        ctrl = self._apply_pre_step_control(ctrl)
-        wp = self._wp
-
-        # H2D copy of ctrl (and pending external wrench if any).
-        t0 = time.perf_counter()
-        np.copyto(self._ctrl_scratch_f32, np.asarray(ctrl, dtype=np.float32))
-        self._mjw_data.ctrl.assign(self._ctrl_scratch_f32)
-        if self._xfrc_dirty:
-            self._mjw_data.xfrc_applied.assign(self._pending_xfrc_applied)
-        wp.synchronize_device()
-        set_ctrl_ms = (time.perf_counter() - t0) * 1000.0
-
-        # GPU physics.
-        t0 = time.perf_counter()
-        for _ in range(int(nsteps)):
-            self._mjwarp.step(self._mjw_model, self._mjw_data)
-        wp.synchronize_device()
-        physics_ms = (time.perf_counter() - t0) * 1000.0
-
-        # Clear pending external wrench so the next step starts clean. MuJoCo's
-        # xfrc_applied is sticky across steps; the env layer expects it to be
-        # consumed and reset by the backend.
-        if self._xfrc_dirty:
-            self._pending_xfrc_applied.fill(0.0)
-            self._mjw_data.xfrc_applied.assign(self._pending_xfrc_applied)
-            self._xfrc_dirty = False
-
-        # D2H refresh of qpos / qvel / sensordata into pre-allocated host buffers.
-        t0 = time.perf_counter()
-        self._refresh_cache()
-        wp.synchronize_device()
-        refresh_cache_ms = (time.perf_counter() - t0) * 1000.0
-
-        return {
-            "timing": {
-                "set_ctrl_ms": set_ctrl_ms,
-                "physics_ms": physics_ms,
-                "refresh_cache_ms": refresh_cache_ms,
-            }
-        }
-
-    def _refresh_cache(self) -> None:
-        np.copyto(self._cache_qpos, self._mjw_data.qpos.numpy().astype(np.float64, copy=False))
-        np.copyto(self._cache_qvel, self._mjw_data.qvel.numpy().astype(np.float64, copy=False))
-        np.copyto(
-            self._cache_sensordata,
-            self._mjw_data.sensordata.numpy().astype(np.float64, copy=False),
-        )
-
-    # ------------------------------------------------------------------ #
-    # Properties                                                            #
-    # ------------------------------------------------------------------ #
-
-    @property
-    def num_envs(self) -> int:
-        return self._num_envs
-
-    @property
-    def model(self) -> Any:
-        return self._mjw_model
-
-    @property
-    def num_actuators(self) -> int:
-        return self._nu
-
-    @property
-    def num_dof_vel(self) -> int:
-        return self._nu if self._has_free_joint else self._nv
-
-    # ------------------------------------------------------------------ #
-    # Cold-path model queries                                              #
-    # ------------------------------------------------------------------ #
-
-    def get_actuator_ctrl_range(self) -> np.ndarray:
-        return self._actuator_ctrl_range.copy()
-
-    def get_keyframe_qpos(self, name: str) -> np.ndarray:
-        if name not in self._keyframe_qpos:
-            raise ValueError(
-                f"Keyframe {name!r} not found; available: {sorted(self._keyframe_qpos)}"
-            )
-        return self._keyframe_qpos[name].copy()
-
-    def get_init_qvel(self) -> np.ndarray:
-        return np.zeros((self._nv,), dtype=np.float64)
-
-    def get_body_ids(self, names: Sequence[str]) -> np.ndarray:
-        ids = np.empty(len(names), dtype=np.int32)
-        for i, name in enumerate(names):
-            body_id = self._mujoco.mj_name2id(
-                self._mj_model, int(self._mujoco.mjtObj.mjOBJ_BODY), str(name)
-            )
-            if body_id < 0:
-                raise ValueError(f"Body {name!r} not found in mjwarp model")
-            ids[i] = body_id
-        return ids
-
-    def get_body_mass(self) -> np.ndarray:
-        return np.asarray(self._mj_model.body_mass, dtype=np.float64).copy()
-
-    def get_body_ipos(self) -> np.ndarray:
-        return np.asarray(self._mj_model.body_ipos, dtype=np.float64).copy()
-
-    def get_gravity(self) -> np.ndarray:
-        return np.asarray(self._mj_model.opt.gravity, dtype=np.float64).copy()
-
-    def get_geom_friction(self) -> np.ndarray:
-        return np.asarray(self._mj_model.geom_friction, dtype=np.float64).copy()
-
-    def get_body_subtree_ids(self, root_body_id: int) -> np.ndarray:
-        subtree_ids = {int(root_body_id)}
-        changed = True
-        while changed:
-            changed = False
-            for body_id in range(int(self._mj_model.nbody)):
-                parent_id = int(self._mj_model.body_parentid[body_id])
-                if body_id not in subtree_ids and parent_id in subtree_ids:
-                    subtree_ids.add(body_id)
-                    changed = True
-        return np.asarray(sorted(subtree_ids), dtype=np.int32)
-
-    def get_geom_names(self) -> tuple[str, ...]:
-        mujoco = self._mujoco
-        return tuple(
-            mujoco.mj_id2name(self._mj_model, int(mujoco.mjtObj.mjOBJ_GEOM), gid) or ""
-            for gid in range(int(self._mj_model.ngeom))
-        )
-
-    def get_geom_body_ids(self) -> np.ndarray:
-        return np.asarray(self._mj_model.geom_bodyid, dtype=np.int32).copy()
-
-    def get_geom_contact_masks(self) -> tuple[np.ndarray, np.ndarray]:
-        return (
-            np.asarray(self._mj_model.geom_contype, dtype=np.int32).copy(),
-            np.asarray(self._mj_model.geom_conaffinity, dtype=np.int32).copy(),
-        )
-
-    def get_joint_range(self) -> np.ndarray | None:
-        jnt_range = self._mj_model.jnt_range
-        mask = self._mj_model.jnt_type != 0  # exclude mjJNT_FREE
-        filtered = jnt_range[mask]
-        if filtered.size == 0:
-            return None
-        return np.asarray(filtered, dtype=np.float64)
-
-    def get_geom_id(self, name: str) -> int:
-        mujoco = self._mujoco
-        gid = mujoco.mj_name2id(self._mj_model, int(mujoco.mjtObj.mjOBJ_GEOM), name)
-        if gid < 0:
-            raise ValueError(f"Geom {name!r} not found in mjwarp model")
-        return int(gid)
-
-    def get_motion_body_ids(self, names: Sequence[str]) -> np.ndarray:
-        # Motion tracking uses MuJoCo body-id layout for its motion library,
-        # so the resolved IDs are the same as get_body_ids.
-        return self.get_body_ids(names)
-
-    def create_hfield_scanner(
-        self,
-        *,
-        hfield_geom_id: int,
-        offsets: np.ndarray,
-        frame_body_id: int,
-        alignment: str = "yaw",
-        output: str = "height",
-    ) -> BackendHeightScanner:
-        offsets_np = np.ascontiguousarray(np.asarray(offsets, dtype=np.float64))
-        if offsets_np.ndim != 2 or offsets_np.shape[1] != 2:
-            raise ValueError(f"offsets must have shape (num_points, 2), got {offsets_np.shape}")
-        return _MjwarpHeightScanner(
-            backend=self,
-            hfield_geom_id=int(hfield_geom_id),
-            offsets=offsets_np,
-            frame_body_id=int(frame_body_id),
-            alignment=alignment,
-            output=output,
-        )
-
-    def get_actuator_gains(self) -> tuple[np.ndarray, np.ndarray]:
-        """Return per-actuator (kp, kd) read from the underlying mujoco model.
-
-        g1_walk_flat invokes this on the cold path when ``randomize_kp`` or
-        ``randomize_kd`` is true so the DR provider can stash a baseline. The
-        returned values are never written back to mjwarp's shared model — see
-        ``get_dr_capabilities`` for why per-env kp/kd is reported unsupported.
-        """
-        kp = np.asarray(self._mj_model.actuator_gainprm[:, 0], dtype=np.float64).copy()
-        kd = np.asarray(-self._mj_model.actuator_biasprm[:, 2], dtype=np.float64).copy()
-        return kp, kd
-
-    # ------------------------------------------------------------------ #
-    # Simulation control: reset and DR                                     #
-    # ------------------------------------------------------------------ #
-
-    def set_state(
-        self,
-        env_indices: np.ndarray,
-        qpos: np.ndarray,
-        qvel: np.ndarray,
-        randomization: ResetRandomizationPayload | None = None,
-    ) -> None:
-        env_indices_np = np.asarray(env_indices, dtype=np.int64)
-        if env_indices_np.size == 0:
-            return
-
-        # The randomization terms that mjwarp Phase 1 does not support
-        # (kp/kd in particular) are filtered out by capabilities upstream,
-        # so anything reaching here is dropped silently; capability honesty
-        # lives in get_dr_capabilities below.
-        del randomization
-
-        qpos_np = np.asarray(qpos, dtype=np.float64)
-        qvel_np = np.asarray(qvel, dtype=np.float64)
-        if qpos_np.shape != (env_indices_np.size, self._nq):
-            raise ValueError(
-                f"qpos must have shape {(env_indices_np.size, self._nq)}, got {qpos_np.shape}"
-            )
-        if qvel_np.shape != (env_indices_np.size, self._nv):
-            raise ValueError(
-                f"qvel must have shape {(env_indices_np.size, self._nv)}, got {qvel_np.shape}"
-            )
-
-        # Update host cache so getters return consistent values immediately.
-        self._cache_qpos[env_indices_np] = qpos_np
-        self._cache_qvel[env_indices_np] = qvel_np
-
-        # Read current device state, splice in the new rows, write back.
-        # A targeted warp kernel could do this with one D2H+H2D for the touched
-        # rows, but Phase 1 favors a simple full-array round-trip; reset is not
-        # in the hot path.
-        device_qpos = self._mjw_data.qpos.numpy()
-        device_qvel = self._mjw_data.qvel.numpy()
-        device_qpos[env_indices_np] = qpos_np.astype(device_qpos.dtype, copy=False)
-        device_qvel[env_indices_np] = qvel_np.astype(device_qvel.dtype, copy=False)
-        self._mjw_data.qpos.assign(device_qpos)
-        self._mjw_data.qvel.assign(device_qvel)
-        self._wp.synchronize_device()
-
-    def get_dr_capabilities(self) -> DomainRandomizationCapabilities:
-        # mjwarp's batched Data shares the Model across worlds, so per-env
-        # actuator gain / mass / friction overrides cannot be physically applied.
-        # However we declare all reset terms as "supported" so that DR provider
-        # validate() passes — the actual randomization payload is silently
-        # ignored in set_state (qpos/qvel are the only values written to GPU).
-        # This is acceptable for benchmark timing: we measure step throughput,
-        # not DR numerical correctness.
-        from unilab.dr.types import (
-            RESET_TERM_BASE_COM,
-            RESET_TERM_BASE_MASS,
-            RESET_TERM_BODY_INERTIA,
-            RESET_TERM_BODY_IPOS,
-            RESET_TERM_BODY_IQUAT,
-            RESET_TERM_BODY_MASS,
-            RESET_TERM_DOF_ARMATURE,
-            RESET_TERM_GEOM_FRICTION,
-            RESET_TERM_GRAVITY,
-            RESET_TERM_KD,
-            RESET_TERM_KP,
-        )
-
-        push_supported = self._push_body_id >= 0
-        return DomainRandomizationCapabilities(
-            supported_reset_terms=frozenset(
-                {
-                    RESET_TERM_KP,
-                    RESET_TERM_KD,
-                    RESET_TERM_BASE_MASS,
-                    RESET_TERM_BASE_COM,
-                    RESET_TERM_GRAVITY,
-                    RESET_TERM_BODY_IPOS,
-                    RESET_TERM_BODY_IQUAT,
-                    RESET_TERM_BODY_INERTIA,
-                    RESET_TERM_BODY_MASS,
-                    RESET_TERM_DOF_ARMATURE,
-                    RESET_TERM_GEOM_FRICTION,
-                }
-            ),
-            supports_interval_push=push_supported,
-            supports_interval_body_velocity_delta=False,
-            supports_interval_body_force=True,
-        )
-
-    def apply_interval_randomization(self, plan: IntervalRandomizationPlan) -> None:
-        if plan.is_empty():
-            return
-        # Start from a clean wrench buffer so successive applies do not
-        # accumulate stale forces. Matches MuJoCoBackend.apply_interval_randomization
-        # ([src/unilab/base/backend/mujoco/backend.py]).
-        self._pending_xfrc_applied.fill(0.0)
-        if plan.push_perturbation_limit is not None:
-            self._sample_push_into_pending(plan.push_perturbation_limit)
-            self._xfrc_dirty = True
-        if plan.body_force is not None:
-            if plan.body_ids is None:
-                raise ValueError("Interval body-force perturbation requires body_ids")
-            self.apply_body_force(plan.body_ids, plan.body_force)
-        if plan.body_linear_velocity_delta is not None:
-            raise NotImplementedError(
-                "MjwarpBackend does not implement interval body linear velocity delta "
-                "(no current benchmark task requires it)"
-            )
-
-    def _sample_push_into_pending(self, force_range: Sequence[float] | np.ndarray) -> None:
-        if self._push_body_id < 0:
-            raise RuntimeError("Interval push requested but push_body_id is unresolved")
-        ex_force = np.random.uniform(-1.0, 1.0, size=(self._num_envs, 3))
-        ex_force *= np.asarray(force_range, dtype=np.float64)
-        self._pending_xfrc_applied[:, self._push_body_id, 0:3] = ex_force.astype(
-            np.float32, copy=False
-        )
-
-    def apply_body_force(
-        self,
-        body_ids: np.ndarray,
-        force: np.ndarray,
-    ) -> None:
-        """Accumulate one world-frame force vector per target body for the
-        upcoming step. Mirrors MuJoCoBackend.apply_body_force semantics; the
-        force is staged in the host-side buffer and uploaded to
-        ``data.xfrc_applied`` at the start of ``step``.
-        """
-        body_ids_np = np.asarray(body_ids, dtype=np.int32).reshape(-1)
-        force_np = np.asarray(force, dtype=np.float64)
-        expected_shape = (self._num_envs, body_ids_np.size, 3)
-        if force_np.shape != expected_shape:
-            raise ValueError(f"body force must have shape {expected_shape}, got {force_np.shape}")
-        for body_offset, body_id in enumerate(body_ids_np):
-            bid = int(body_id)
-            self._pending_xfrc_applied[:, bid, 0:3] += force_np[:, body_offset, :].astype(
-                np.float32, copy=False
-            )
-        self._xfrc_dirty = True
-
-    def materialize(self) -> None:
-        # mjwarp model/data are already finalized in __init__.
-        return
-
-    # ------------------------------------------------------------------ #
-    # Base kinematics                                                      #
-    # ------------------------------------------------------------------ #
-
-    def get_base_pos(self) -> np.ndarray:
-        if not self._has_free_joint:
-            raise NotImplementedError(
-                "get_base_pos requires a free joint at index 0 (g1_walk_flat layout)"
-            )
-        return self._cache_qpos[:, 0:3].copy()
-
-    def get_base_quat(self) -> np.ndarray:
-        if not self._has_free_joint:
-            raise NotImplementedError("get_base_quat requires free-joint layout")
-        return self._cache_qpos[:, 3:7].copy()
-
-    def get_base_lin_vel(self) -> np.ndarray:
-        if not self._has_free_joint:
-            raise NotImplementedError("get_base_lin_vel requires free-joint layout")
-        return self._cache_qvel[:, 0:3].copy()
-
-    def get_base_ang_vel(self) -> np.ndarray:
-        if not self._has_free_joint:
-            raise NotImplementedError("get_base_ang_vel requires free-joint layout")
-        return self._cache_qvel[:, 3:6].copy()
-
-    # ------------------------------------------------------------------ #
-    # DOF state                                                            #
-    # ------------------------------------------------------------------ #
-
-    def get_dof_pos(self) -> np.ndarray:
-        return self._cache_qpos[:, self._dof_qpos_start : self._dof_qpos_start + self._nu].copy()
-
-    def get_dof_vel(self) -> np.ndarray:
-        return self._cache_qvel[:, self._dof_qvel_start : self._dof_qvel_start + self._nu].copy()
-
-    # ------------------------------------------------------------------ #
-    # Body kinematics — world frame                                        #
-    # ------------------------------------------------------------------ #
-
-    def get_body_pos_w(self, body_ids: np.ndarray) -> np.ndarray:
-        body_ids_np = np.asarray(body_ids, dtype=np.int32).reshape(-1)
-        xpos = self._mjw_data.xpos.numpy()  # (num_envs, nbody, 3)
-        return np.asarray(xpos[:, body_ids_np, :], dtype=np.float64)
-
-    def get_body_quat_w(self, body_ids: np.ndarray) -> np.ndarray:
-        body_ids_np = np.asarray(body_ids, dtype=np.int32).reshape(-1)
-        xquat = self._mjw_data.xquat.numpy()  # (num_envs, nbody, 4)
-        return np.asarray(xquat[:, body_ids_np, :], dtype=np.float64)
-
-    def get_body_lin_vel_w(self, body_ids: np.ndarray) -> np.ndarray:
-        # MuJoCo cvel layout per body is [angular_xyz, linear_xyz]; mjwarp matches.
-        body_ids_np = np.asarray(body_ids, dtype=np.int32).reshape(-1)
-        cvel = self._mjw_data.cvel.numpy()  # (num_envs, nbody, 6)
-        return np.asarray(cvel[:, body_ids_np, 3:6], dtype=np.float64)
-
-    def get_body_ang_vel_w(self, body_ids: np.ndarray) -> np.ndarray:
-        body_ids_np = np.asarray(body_ids, dtype=np.int32).reshape(-1)
-        cvel = self._mjw_data.cvel.numpy()  # (num_envs, nbody, 6)
-        return np.asarray(cvel[:, body_ids_np, 0:3], dtype=np.float64)
-
-    # ------------------------------------------------------------------ #
-    # Body kinematics — baselink frame                                     #
-    # ------------------------------------------------------------------ #
-
-    def get_body_pos_b(self, body_ids: np.ndarray) -> np.ndarray:
-        raise NotImplementedError("MjwarpBackend Phase 1 does not implement get_body_pos_b")
-
-    def get_body_quat_b(self, body_ids: np.ndarray) -> np.ndarray:
-        raise NotImplementedError("MjwarpBackend Phase 1 does not implement get_body_quat_b")
-
-    def get_body_lin_vel_b(self, body_ids: np.ndarray) -> np.ndarray:
-        raise NotImplementedError("MjwarpBackend Phase 1 does not implement get_body_lin_vel_b")
-
-    def get_body_ang_vel_b(self, body_ids: np.ndarray) -> np.ndarray:
-        raise NotImplementedError("MjwarpBackend Phase 1 does not implement get_body_ang_vel_b")
-
-    # ------------------------------------------------------------------ #
-    # Sensors                                                              #
-    # ------------------------------------------------------------------ #
-
-    def get_sensor_data(self, name: str) -> np.ndarray:
-        slot = self._sensor_adr.get(name)
-        if slot is None:
-            raise ValueError(
-                f"Sensor {name!r} not found in mjwarp model; available: {sorted(self._sensor_adr)}"
-            )
-        adr, dim = slot
-        # Match MuJoCoBackend.get_sensor_data: always return shape
-        # (num_envs, dim). Tasks rely on NumPy broadcasting on the dim axis
-        # (e.g. go2 broadcasts dim=1 contact "found" sensors into a (num_envs, 3)
-        # feet_force buffer). Squeezing dim==1 here would break that path.
-        return self._cache_sensordata[:, adr : adr + dim].copy()
diff --git a/scripts/benchmark/physics/benchmark_motrix_set_state_ab.py b/scripts/benchmark/physics/benchmark_motrix_set_state_ab.py
index 3f400c0db..d8b3d4114 100644
--- a/scripts/benchmark/physics/benchmark_motrix_set_state_ab.py
+++ b/scripts/benchmark/physics/benchmark_motrix_set_state_ab.py
@@ -39,9 +39,10 @@
 if str(ROOT_DIR) not in sys.path:
     sys.path.append(str(ROOT_DIR))
 
+from unisim.dr.types import ResetRandomizationPayload  # noqa: E402
+
 from unilab.assets import ASSETS_ROOT_PATH  # noqa: E402
 from unilab.base.scene import SceneCfg  # noqa: E402
-from unilab.dr.types import ResetRandomizationPayload  # noqa: E402
 
 _G1_MODEL_FILE = str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat.xml")
 
@@ -171,7 +172,7 @@ def _baseline_set_state(
 
 
 def _make_backend(num_envs: int):
-    from unilab.base.backend.motrix.backend import MotrixBackend
+    from unisim.backend.motrix.backend import MotrixBackend
 
     return MotrixBackend(
         SceneCfg(model_file=_G1_MODEL_FILE),
diff --git a/scripts/benchmark/physics/benchmark_mujoco_backend_step_detail.py b/scripts/benchmark/physics/benchmark_mujoco_backend_step_detail.py
index 131c763e7..d82e61d38 100644
--- a/scripts/benchmark/physics/benchmark_mujoco_backend_step_detail.py
+++ b/scripts/benchmark/physics/benchmark_mujoco_backend_step_detail.py
@@ -44,8 +44,8 @@
 import numpy as np
 from matplotlib.patches import Rectangle
 from mujoco_uni.batch_env import BatchEnvPool
+from unisim.backend.mujoco.xml import create_discardvisual_xml
 
-from unilab.base.backend.mujoco.xml import create_discardvisual_xml
 from unilab.dtype_config import get_global_dtype
 
 matplotlib.use("Agg")
diff --git a/scripts/benchmark/physics/benchmark_mujoco_single_dispatch_callback.py b/scripts/benchmark/physics/benchmark_mujoco_single_dispatch_callback.py
new file mode 100644
index 000000000..20e15c9a3
--- /dev/null
+++ b/scripts/benchmark/physics/benchmark_mujoco_single_dispatch_callback.py
@@ -0,0 +1,212 @@
+#!/usr/bin/env python3
+"""One-off microbenchmark for issue #1262.
+
+Question: can one ``BatchEnvPool.step(nstep=N)`` dispatch reproduce the MBA
+per-substep control semantics that today require N ``pool.step(nstep=1)``
+dispatches (``MuJoCoBackend._step_with_pre_step_control``)?
+
+Measured on the g1_walk_flat MuJoCo contract (scene_flat.xml + injected body
+tracking sensors, sim_dt=1/150, sim_substeps=3):
+
+- ``multi``:   current MBA path — N dispatches of nstep=1, with a Python-side
+               per-substep control recompute (JointPositionAction-style:
+               ``target = processed - encoder_bias``, constant within one
+               control step) between dispatches.
+- ``traj``:    single dispatch, nstep=N, control baked as an (nbatch, N, nu)
+               trajectory with identical rows.
+- ``const``:   single dispatch, nstep=N, control passed as one (nbatch, nu)
+               array (native ``control_is_constant`` fast path).
+
+All paths start from the same contact-rich state (standing keyframe settled
+with foot contact) and use the same pool, model, sensors, and chunk size.
+The numerical check compares final full-physics state and sensordata between
+``multi`` and the single-dispatch paths.
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import time
+
+import mujoco
+import numpy as np
+from mujoco_uni import BatchEnvPool
+from unisim.backend.mujoco.xml import (
+    create_discardvisual_xml,
+    inject_mujoco_tracking_sensors,
+)
+
+SCENE_XML = "src/unilab/assets/robots/g1/scene_flat.xml"
+BASE_BODY = "pelvis"
+KEYFRAME = "stand"
+SIM_DT = 1.0 / 150.0
+SUBSTEPS = 3  # ctrl_dt 0.02 / sim_dt 0.006667, matches g1_walk_flat
+CTRL_SPEC = int(mujoco.mjtState.mjSTATE_CTRL)
+FULLPHYSICS = mujoco.mjtState.mjSTATE_FULLPHYSICS
+
+
+def build_model() -> mujoco.MjModel:
+    path = create_discardvisual_xml(SCENE_XML)
+    path, _, _ = inject_mujoco_tracking_sensors(path, baselink_name=BASE_BODY)
+    model = mujoco.MjModel.from_xml_path(path)
+    model.opt.timestep = SIM_DT
+    return model
+
+
+def keyframe_state(model: mujoco.MjModel) -> np.ndarray:
+    data = mujoco.MjData(model)
+    kid = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_KEY, KEYFRAME)
+    if kid < 0:
+        raise ValueError(f"keyframe '{KEYFRAME}' not found in {SCENE_XML}")
+    mujoco.mj_resetDataKeyframe(model, data, kid)
+    mujoco.mj_forward(model, data)
+    state = np.zeros(mujoco.mj_stateSize(model, FULLPHYSICS), dtype=np.float64)
+    mujoco.mj_getState(model, data, state, FULLPHYSICS)
+    return state
+
+
+def make_ctrl(model: mujoco.MjModel, state0: np.ndarray, nbatch: int) -> np.ndarray:
+    """Stand-pose position targets plus per-env jitter (deterministic)."""
+    nq = model.nq
+    qpos = state0[1 : 1 + nq]  # FULLPHYSICS: time, qpos, qvel, act, ...
+    stand = qpos[-model.nu :]  # free root (7) precedes actuated joints
+    rng = np.random.default_rng(0)
+    ctrl = stand[None, :] + rng.uniform(-0.05, 0.05, size=(nbatch, model.nu))
+    return np.ascontiguousarray(ctrl, dtype=np.float64)
+
+
+def settle(pool: BatchEnvPool, state0: np.ndarray, ctrl: np.ndarray, chunk_size: int) -> np.ndarray:
+    """Roll out a few control steps so feet are in steady contact."""
+    state = state0
+    for _ in range(40):
+        state = pool.step(
+            state,
+            nstep=SUBSTEPS,
+            control=ctrl,
+            control_spec=CTRL_SPEC,
+            chunk_size=chunk_size,
+        )
+    return state
+
+
+def run_multi(pool, state, ctrl, bias, chunk_size):
+    """Current MBA path: SUBSTEPS dispatches of nstep=1 with callback between."""
+    pool_ms = 0.0
+    callback_ms = 0.0
+    for _ in range(SUBSTEPS):
+        t0 = time.perf_counter()
+        native_ctrl = np.subtract(ctrl, bias)  # JointPositionAction-style recompute
+        callback_ms += (time.perf_counter() - t0) * 1e3
+        t0 = time.perf_counter()
+        state, _sensor = pool.step(
+            state,
+            nstep=1,
+            control=native_ctrl[:, None, :],
+            control_spec=CTRL_SPEC,
+            chunk_size=chunk_size,
+            return_sensor=True,
+        )
+        pool_ms += (time.perf_counter() - t0) * 1e3
+    return state, pool_ms, callback_ms
+
+
+def run_traj(pool, state, ctrl, bias, chunk_size):
+    """Single dispatch, baked (nbatch, N, nu) trajectory with identical rows."""
+    t0 = time.perf_counter()
+    native_ctrl = np.subtract(ctrl, bias)
+    callback_ms = (time.perf_counter() - t0) * 1e3
+    t0 = time.perf_counter()
+    traj = np.broadcast_to(native_ctrl[:, None, :], (ctrl.shape[0], SUBSTEPS, ctrl.shape[1]))
+    state, _sensor = pool.step(
+        state,
+        nstep=SUBSTEPS,
+        control=traj,
+        control_spec=CTRL_SPEC,
+        chunk_size=chunk_size,
+        return_sensor=True,
+    )
+    return state, (time.perf_counter() - t0) * 1e3, callback_ms
+
+
+def run_const(pool, state, ctrl, bias, chunk_size):
+    """Single dispatch, baked (nbatch, nu) constant control (native fast path)."""
+    t0 = time.perf_counter()
+    native_ctrl = np.subtract(ctrl, bias)
+    callback_ms = (time.perf_counter() - t0) * 1e3
+    t0 = time.perf_counter()
+    state, _sensor = pool.step(
+        state,
+        nstep=SUBSTEPS,
+        control=native_ctrl,
+        control_spec=CTRL_SPEC,
+        chunk_size=chunk_size,
+        return_sensor=True,
+    )
+    return state, (time.perf_counter() - t0) * 1e3, callback_ms
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--num-envs", type=int, default=8192)
+    parser.add_argument("--chunk-sizes", type=int, nargs="+", default=[13, 41])
+    parser.add_argument("--repeats", type=int, default=15)
+    parser.add_argument("--warmup", type=int, default=3)
+    args = parser.parse_args()
+
+    model = build_model()
+    nthread = min(args.num_envs, (os.cpu_count() or 1) * 2)
+    print(
+        f"model: nu={model.nu} nq={model.nq} nv={model.nv} nsensor={model.nsensor} "
+        f"nsensordata={model.nsensordata}; nbatch={args.num_envs} nthread={nthread}"
+    )
+    pool = BatchEnvPool(model, nbatch=args.num_envs, nthread=nthread)
+    try:
+        state0 = np.broadcast_to(keyframe_state(model)[None, :], (args.num_envs, pool.nstate))
+        state0 = np.ascontiguousarray(state0, dtype=np.float64)
+        ctrl = make_ctrl(model, state0[0], args.num_envs)
+        bias = np.zeros_like(ctrl)  # encoder_bias placeholder (zeros on g1_walk_flat)
+        contact_state = settle(pool, state0, ctrl, chunk_size=args.chunk_sizes[0])
+
+        paths = {"multi": run_multi, "traj": run_traj, "const": run_const}
+
+        # Numerical comparison from the same contact-rich state.
+        finals = {}
+        for name, fn in paths.items():
+            out = fn(pool, contact_state, ctrl, bias, args.chunk_sizes[0])
+            finals[name] = out[0]
+        for name in ("traj", "const"):
+            diff = np.abs(finals[name] - finals["multi"])
+            bitwise = float(np.mean(finals[name] == finals["multi"]))
+            print(
+                f"numerics {name} vs multi: max_abs_diff={diff.max():.3e} "
+                f"bitwise_equal={bitwise:.4f}"
+            )
+        print(
+            f"numerics traj vs const: bitwise={bool(np.array_equal(finals['traj'], finals['const']))}"
+        )
+
+        # Timing.
+        for chunk_size in args.chunk_sizes:
+            print(
+                f"--- chunk_size={chunk_size} (ms per control step, median of {args.repeats}) ---"
+            )
+            for name, fn in paths.items():
+                for _ in range(args.warmup):
+                    fn(pool, contact_state, ctrl, bias, chunk_size)
+                pool_ts, cb_ts = [], []
+                for _ in range(args.repeats):
+                    _s, pool_ms, cb_ms = fn(pool, contact_state, ctrl, bias, chunk_size)
+                    pool_ts.append(pool_ms)
+                    cb_ts.append(cb_ms)
+                print(
+                    f"  {name:>5}: pool={np.median(pool_ts):7.2f}  "
+                    f"callback={np.median(cb_ts):5.2f}  "
+                    f"total={np.median(pool_ts) + np.median(cb_ts):7.2f}"
+                )
+    finally:
+        pool.close()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/benchmark/physics/benchmark_physics_step_motrixsim.py b/scripts/benchmark/physics/benchmark_physics_step_motrixsim.py
index 2c0cfd7d6..1b427f7f1 100644
--- a/scripts/benchmark/physics/benchmark_physics_step_motrixsim.py
+++ b/scripts/benchmark/physics/benchmark_physics_step_motrixsim.py
@@ -57,7 +57,7 @@
 # (EnvConfig.motrix_max_iterations defaults to None, and the backend falls back
 # to DEFAULT_MOTRIX_MAX_ITERATIONS), so the benchmark reflects real workloads
 # rather than the XML's <option iterations> which would otherwise leak in.
-from unilab.base.backend.motrix.backend import (  # noqa: E402
+from unisim.backend.motrix.backend import (  # noqa: E402
     DEFAULT_MOTRIX_MAX_ITERATIONS,
 )
 
diff --git a/scripts/benchmark/rl/benchmark_offpolicy_collector_active.py b/scripts/benchmark/rl/benchmark_offpolicy_collector_active.py
index 274f21d33..bd79d178b 100644
--- a/scripts/benchmark/rl/benchmark_offpolicy_collector_active.py
+++ b/scripts/benchmark/rl/benchmark_offpolicy_collector_active.py
@@ -14,6 +14,9 @@
     uv run scripts/benchmark/rl/benchmark_offpolicy_collector_active.py --cases auto --backend motrix
     uv run scripts/benchmark/rl/benchmark_offpolicy_collector_active.py --cases sac/g1_walk_flat/mujoco
     uv run scripts/benchmark/rl/benchmark_offpolicy_collector_active.py --cases sac/g1_walk_flat/motrixsim
+    # mjwarp (GPU) is opt-in and requires the optional mjwarp extra:
+    uv run --extra mjwarp scripts/benchmark/rl/benchmark_offpolicy_collector_active.py \
+        --cases sac/g1_motion_tracking/mjwarp
     uv run scripts/benchmark/rl/benchmark_offpolicy_collector_active.py --num-envs 1024 --measure-steps 100
 """
 
@@ -32,6 +35,7 @@
 from collections import defaultdict
 from dataclasses import asdict, dataclass, field
 from datetime import datetime, timezone
+from importlib.util import find_spec
 from pathlib import Path
 from statistics import mean, median, pstdev
 from typing import Any, Sequence, cast
@@ -61,6 +65,10 @@
 DEFAULT_ALGOS = ("sac", "flashsac", "td3")
 DEFAULT_BACKEND = "motrix"
 BENCHMARK_BACKENDS = ("mujoco", "motrix")
+# mjwarp (GPU) is opt-in only: it requires the optional ``mjwarp`` extra
+# (mujoco-warp + warp-lang) and is never part of --all. Request it explicitly
+# via --backend mjwarp or an explicit <algo>/<task>/mjwarp case.
+OPTIONAL_BACKENDS = ("mjwarp",)
 DEFAULT_COLLECTOR_CPU_THREADS = 8
 COLLECTOR_CPU_THREADS_ENV = "UNILAB_COLLECTOR_TORCH_THREADS"
 BACKEND_ALIASES = {
@@ -119,6 +127,9 @@
     "set_state_qpos_convert_ms",
     "set_state_pool_reset_ms",
     "set_state_state_scatter_ms",
+    "set_state_reset_upload_ms",
+    "set_state_reset_forward_ms",
+    "set_state_host_cache_refresh_ms",
     "set_state_internal_gap_ms",
 )
 NP_ENV_STEP_COUNT_KEYS = ("reset_done_count",)
@@ -175,6 +186,9 @@
     ("set_state_qpos_convert_ms", "set_state_qpos_convert_ms"),
     ("set_state_pool_reset_ms", "set_state_pool_reset_ms"),
     ("set_state_state_scatter_ms", "set_state_state_scatter_ms"),
+    ("set_state_reset_upload_ms", "set_state_reset_upload_ms"),
+    ("set_state_reset_forward_ms", "set_state_reset_forward_ms"),
+    ("set_state_host_cache_refresh_ms", "set_state_host_cache_refresh_ms"),
     ("set_state_internal_gap_ms", "set_state_internal_gap_ms"),
 )
 
@@ -382,8 +396,7 @@ def _compose_offpolicy_cfg(
 ) -> DictConfig:
     owner_sim = _runtime_sim_backend(sim)
     overrides = [
-        f"algo={algo}",
-        f"task={algo}/{task}/{owner_sim}",
+        f"task={task}/{owner_sim}",
         "hydra.run.dir=.",
         "hydra.output_subdir=null",
         "hydra/job_logging=disabled",
@@ -395,13 +408,22 @@ def _compose_offpolicy_cfg(
         overrides.extend(extra_overrides)
 
     GlobalHydra.instance().clear()
-    with initialize_config_dir(config_dir=str(ROOT_DIR / "conf" / "offpolicy"), version_base="1.3"):
+    with initialize_config_dir(
+        config_dir=str(ROOT_DIR / "src" / "unilab" / "conf" / algo), version_base="1.3"
+    ):
         return compose(config_name="config", overrides=overrides)
 
 
 def _owner_config_path(algo: str, task: str, sim: str) -> Path:
     return (
-        ROOT_DIR / "conf" / "offpolicy" / "task" / algo / task / f"{_runtime_sim_backend(sim)}.yaml"
+        ROOT_DIR
+        / "src"
+        / "unilab"
+        / "conf"
+        / algo
+        / "task"
+        / task
+        / f"{_runtime_sim_backend(sim)}.yaml"
     )
 
 
@@ -409,7 +431,7 @@ def _discover_cases(*, algos: list[str], sim: str) -> list[str]:
     cases: list[str] = []
     owner_sim = _runtime_sim_backend(sim)
     for algo in algos:
-        task_root = ROOT_DIR / "conf" / "offpolicy" / "task" / algo
+        task_root = ROOT_DIR / "src" / "unilab" / "conf" / algo / "task"
         if not task_root.is_dir():
             continue
         for path in sorted(task_root.glob(f"*/{owner_sim}.yaml")):
@@ -420,11 +442,25 @@ def _discover_cases(*, algos: list[str], sim: str) -> list[str]:
 def _resolve_backend_selection(*, backend: str, all_backends: bool) -> tuple[str, ...]:
     if all_backends:
         return BENCHMARK_BACKENDS
+    if backend in OPTIONAL_BACKENDS:
+        _check_optional_backend_deps(backend)
+        return (backend,)
     if backend not in BENCHMARK_BACKENDS:
-        raise ValueError(f"unsupported backend {backend!r}; expected one of {BENCHMARK_BACKENDS}")
+        raise ValueError(
+            f"unsupported backend {backend!r}; expected one of "
+            f"{BENCHMARK_BACKENDS + OPTIONAL_BACKENDS}"
+        )
     return (backend,)
 
 
+def _check_optional_backend_deps(backend: str) -> None:
+    if backend == "mjwarp" and (find_spec("mujoco_warp") is None or find_spec("warp") is None):
+        raise SystemExit(
+            "backend=mjwarp requires the mjwarp extra. Install it with `uv sync --extra mjwarp` "
+            "or run this benchmark with `uv run --extra mjwarp ...`."
+        )
+
+
 def _default_case_specs(backends: Sequence[str]) -> list[str]:
     return [f"{template}/{backend}" for backend in backends for template in DEFAULT_CASE_TEMPLATES]
 
@@ -500,8 +536,9 @@ def _make_env(
     *,
     env_cfg_override: dict[str, Any] | None,
 ):
-    from unilab.base.observations import get_obs_dims
-    from unilab.training import create_env
+    from uni_rl.utils.observations import get_obs_dims
+
+    from unilab.base.config_adapter import create_env
 
     env = create_env(cfg, num_envs=int(cfg.algo.num_envs), env_cfg_override=env_cfg_override)
     if env.state is None:
@@ -523,9 +560,9 @@ def _run_active_window_case(
     measure_steps: int,
     profile_numpy_random: bool = False,
 ) -> CollectorResult:
-    from unilab.base.final_observation import resolve_terminal_observation_contract
-    from unilab.base.observations import split_obs_dict
-    from unilab.ipc.replay_buffer import ReplayBuffer
+    from uni_rl.ipc.replay_buffer import ReplayBuffer
+    from uni_rl.utils.final_observation import resolve_terminal_observation_contract
+    from uni_rl.utils.observations import split_obs_dict
 
     replay_buffer = ReplayBuffer(
         capacity=case.replay_capacity_rows,
@@ -610,7 +647,6 @@ def _run_active_window_case(
                 )
             else:
                 env_step_timing_values["env_step_internal_gap_ms"] = None
-
             phase_start_ns = time.perf_counter_ns()
             next_obs_np, next_critic_np = split_obs_dict(state.obs)
             next_obs_np = np.asarray(next_obs_np, dtype=np.float32)
@@ -691,7 +727,7 @@ def _run_active_window_case(
                     aux_samples["env_step_overhead_ms"].append(env_step_ms - physics_ms)
                 for key, value in env_step_timing_values.items():
                     if value is not None:
-                        env_step_timing_samples[key].append(value)
+                        env_step_timing_samples.setdefault(key, []).append(value)
     finally:
         if random_profiler is not None:
             random_profiler.uninstall()
@@ -750,8 +786,9 @@ def _build_and_run_case(
     variant: str = "default",
     profile_numpy_random: bool = False,
 ) -> CollectorResult:
-    from unilab.training import BackendAdapter, ensure_registries
-    from unilab.training.seed import apply_training_seed
+    from unilab.base.config_adapter import BackendAdapter
+    from unilab.training import ensure_registries
+    from unilab.utils.seed import apply_training_seed
 
     algo, task, sim = _parse_case(spec)
     owner_path = _owner_config_path(algo, task, sim)
@@ -1296,6 +1333,13 @@ def _format_dr_reset_timing_table(results: list[CollectorResult]) -> str:
     ("set_state_internal_gap_ms", "Gap"),
 )
 
+_SET_STATE_MJWARP_KEYS = (
+    ("set_state_reset_upload_ms", "Reset upload"),
+    ("set_state_reset_forward_ms", "Reset forward"),
+    ("set_state_host_cache_refresh_ms", "Host cache refresh"),
+    ("set_state_internal_gap_ms", "Gap"),
+)
+
 
 def _format_set_state_detail_table(results: list[CollectorResult]) -> str:
     """Backend set_state sub-timing table (motrix keyset).
@@ -1356,6 +1400,32 @@ def _format_set_state_mujoco_table(results: list[CollectorResult]) -> str:
     return _format_table(headers, rows)
 
 
+def _format_set_state_mjwarp_table(results: list[CollectorResult]) -> str:
+    """Backend set_state sub-timing table (mjwarp keyset)."""
+    headers = (
+        "Algo",
+        "Task",
+        "Backend",
+        "Set state ms (%env, %active)",
+        *(label for _, label in _SET_STATE_MJWARP_KEYS),
+    )
+    rows = []
+    for result in results:
+        env_step = result.phase_ms_per_vector_step.get("env_step_ms")
+        if env_step is None:
+            continue
+        rows.append(
+            (
+                result.case.algo,
+                result.case.task,
+                result.case.runtime_sim_backend,
+                _format_np_env_timing(result, "dr_reset_set_state_ms"),
+                *(_format_set_state_sub_ms(result, key) for key, _ in _SET_STATE_MJWARP_KEYS),
+            )
+        )
+    return _format_table(headers, rows)
+
+
 def _format_np_env_step_timing_table(results: list[CollectorResult]) -> str:
     headers = (
         "Algo",
@@ -1565,9 +1635,12 @@ def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace:
     )
     parser.add_argument(
         "--backend",
-        choices=BENCHMARK_BACKENDS,
+        choices=(*BENCHMARK_BACKENDS, *OPTIONAL_BACKENDS),
         default=DEFAULT_BACKEND,
-        help="Backend to benchmark for --cases default/auto. Default: motrix.",
+        help=(
+            "Backend to benchmark for --cases default/auto. Default: motrix. "
+            "mjwarp is opt-in and requires the mjwarp extra."
+        ),
     )
     parser.add_argument(
         "--all",
@@ -1577,7 +1650,7 @@ def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace:
     )
     parser.add_argument(
         "--sim",
-        choices=(*BENCHMARK_BACKENDS, *BACKEND_ALIASES.keys()),
+        choices=(*BENCHMARK_BACKENDS, *OPTIONAL_BACKENDS, *BACKEND_ALIASES.keys()),
         default=None,
         help=argparse.SUPPRESS,
     )
@@ -1637,6 +1710,12 @@ def main() -> int:
     specs = _resolve_case_specs(args.cases, algos_arg=args.algos, backends=backends)
     if not specs:
         raise SystemExit("No benchmark cases resolved.")
+    # Explicit <algo>/<task>/<sim> cases bypass --backend, so check optional
+    # backend deps per resolved case as well.
+    for spec in specs:
+        _, _, spec_sim = _parse_case(spec)
+        if spec_sim in OPTIONAL_BACKENDS:
+            _check_optional_backend_deps(spec_sim)
 
     hardware_info = _get_benchmark_hardware_info()
     print(f"Device: {get_device_info_line()}")
@@ -1722,6 +1801,8 @@ def main() -> int:
         print(_format_set_state_detail_table(results))
         print("\nBackend set_state detail — mujoco keyset:")
         print(_format_set_state_mujoco_table(results))
+        print("\nBackend set_state detail — mjwarp keyset:")
+        print(_format_set_state_mjwarp_table(results))
     else:
         print("No successful benchmark cases.")
     return 0 if not errors else 1
diff --git a/scripts/benchmark/rl/benchmark_offpolicy_dp_scaling.py b/scripts/benchmark/rl/benchmark_offpolicy_dp_scaling.py
index f89c5ff5e..daa6fc447 100644
--- a/scripts/benchmark/rl/benchmark_offpolicy_dp_scaling.py
+++ b/scripts/benchmark/rl/benchmark_offpolicy_dp_scaling.py
@@ -1,6 +1,6 @@
 """Off-policy multi-GPU data-parallel scaling benchmark (issue #968).
 
-Runs real ``scripts/train_offpolicy.py`` training via subprocess (same Hydra
+Runs real ``src/unilab/scripts/train_sac.py`` training via subprocess (same Hydra
 overrides as the production CLI entry, never importing training internals) and
 compares single-device N=1 (no ``training.devices``) against N-way data
 parallel (``training.devices=[d0..dN-1]``, default ``[0,1]``). Every config
@@ -59,11 +59,11 @@
 )
 DEFAULT_RUNS_ROOT = ROOT_DIR / "scripts" / "benchmark" / "outputs" / "offpolicy_dp_scaling" / "runs"
 
-TRAIN_SCRIPT = ROOT_DIR / "scripts" / "train_offpolicy.py"
+TRAIN_SCRIPT = ROOT_DIR / "src" / "unilab" / "scripts" / "train_sac.py"
 
 # Route overrides equivalent to `uv run train --algo sac --task g1_walk_flat
 # --sim mujoco` (see src/unilab/cli.py build_route for off-policy algos).
-ROUTE_OVERRIDES = ("algo=sac", "task=sac/g1_walk_flat/mujoco")
+ROUTE_OVERRIDES = ("task=g1_walk_flat/mujoco",)
 
 STEPS_PER_SEC_TAG = "perf/steps_per_sec"
 SAMPLES_PER_SEC_TAG = "perf/effective_samples_per_sec"
diff --git a/scripts/benchmark/rl/benchmark_replay_buffer_placement.py b/scripts/benchmark/rl/benchmark_replay_buffer_placement.py
index 4e608a7fc..e98fcc82c 100644
--- a/scripts/benchmark/rl/benchmark_replay_buffer_placement.py
+++ b/scripts/benchmark/rl/benchmark_replay_buffer_placement.py
@@ -28,12 +28,12 @@
 from datetime import datetime, timezone
 from pathlib import Path
 from statistics import mean, median, pstdev
-from typing import Any, cast
+from typing import Any
 
 import torch
 from hydra import compose, initialize_config_dir
 from hydra.core.global_hydra import GlobalHydra
-from omegaconf import DictConfig, OmegaConf
+from omegaconf import DictConfig
 
 ROOT_DIR = Path(__file__).resolve().parents[3]
 if str(ROOT_DIR) not in sys.path:
@@ -108,7 +108,6 @@ class BenchmarkCase:
     benchmark_capacity_rows: int
     configured_batch_size: int
     learner_batch_size: int
-    symmetry_batch_multiplier: int
     updates_per_step: int
     sample_count: int
     learning_starts: int
@@ -255,10 +254,9 @@ def _cleanup_device() -> None:
 
 
 def _compose_offpolicy_cfg(algo: str, task: str, sim: str) -> DictConfig:
-    config_dir = str(ROOT_DIR / "conf" / "offpolicy")
+    config_dir = str(ROOT_DIR / "src" / "unilab" / "conf" / algo)
     overrides = [
-        f"algo={algo}",
-        f"task={algo}/{task}/{sim}",
+        f"task={task}/{sim}",
         "hydra.run.dir=.",
         "hydra.output_subdir=null",
         "hydra/job_logging=disabled",
@@ -270,7 +268,7 @@ def _compose_offpolicy_cfg(algo: str, task: str, sim: str) -> DictConfig:
 
 
 def _owner_config_path(algo: str, task: str, sim: str) -> Path:
-    return ROOT_DIR / "conf" / "offpolicy" / "task" / algo / task / f"{sim}.yaml"
+    return ROOT_DIR / "src" / "unilab" / "conf" / algo / "task" / task / f"{sim}.yaml"
 
 
 def _owner_config_exists(algo: str, task: str, sim: str) -> bool:
@@ -278,7 +276,7 @@ def _owner_config_exists(algo: str, task: str, sim: str) -> bool:
 
 
 def _discover_supported_tasks(algo: str, sim: str) -> list[str]:
-    task_root = ROOT_DIR / "conf" / "offpolicy" / "task" / algo
+    task_root = ROOT_DIR / "src" / "unilab" / "conf" / algo / "task"
     if not task_root.is_dir():
         return []
     return sorted(path.parent.name for path in task_root.glob(f"*/{sim}.yaml") if path.is_file())
@@ -317,9 +315,11 @@ def _resolve_targets(
     return targets, skipped
 
 
-def _resolve_env_shape_and_symmetry(cfg: DictConfig, algo: str) -> tuple[ReplayShape, int]:
-    from unilab.base.observations import get_obs_dims
-    from unilab.training import BackendAdapter, create_env, ensure_registries
+def _resolve_env_shape(cfg: DictConfig, algo: str) -> ReplayShape:
+    from uni_rl.utils.observations import get_obs_dims
+
+    from unilab.base.config_adapter import BackendAdapter, create_env
+    from unilab.training import ensure_registries
 
     ensure_registries()
     env_cfg_override = BackendAdapter(
@@ -334,17 +334,6 @@ def _resolve_env_shape_and_symmetry(cfg: DictConfig, algo: str) -> tuple[ReplayS
         if action_shape is None:
             raise ValueError("env.action_space.shape must be defined")
         action_dim = int(action_shape[0])
-
-        symmetry_batch_multiplier = 1
-        use_symmetry = bool(OmegaConf.select(cfg, "algo.use_symmetry", default=False))
-        if algo == "sac" and use_symmetry:
-            symmetry_builder = getattr(env, "build_symmetry_augmentation", None)
-            if not callable(symmetry_builder):
-                raise ValueError(f"{cfg.training.task_name} does not provide symmetry augmentation")
-            symmetry = cast(Any, symmetry_builder(device="cpu"))
-            if symmetry is None:
-                raise ValueError(f"{cfg.training.task_name} does not provide symmetry augmentation")
-            symmetry_batch_multiplier = int(symmetry.batch_multiplier)
     finally:
         env.close()
 
@@ -352,7 +341,7 @@ def _resolve_env_shape_and_symmetry(cfg: DictConfig, algo: str) -> tuple[ReplayS
         obs_dim=int(obs_dim),
         action_dim=action_dim,
         critic_dim=int(critic_dim),
-    ), symmetry_batch_multiplier
+    )
 
 
 def _build_case(
@@ -362,7 +351,6 @@ def _build_case(
     task: str,
     sim: str,
     shape: ReplayShape,
-    symmetry_batch_multiplier: int,
     max_capacity_rows: int | None,
 ) -> BenchmarkCase:
     num_envs = int(cfg.algo.num_envs)
@@ -374,13 +362,6 @@ def _build_case(
 
     configured_batch_size = int(cfg.algo.batch_size)
     learner_batch_size = configured_batch_size
-    if algo == "sac" and bool(OmegaConf.select(cfg, "algo.use_symmetry", default=False)):
-        if configured_batch_size % symmetry_batch_multiplier != 0:
-            raise ValueError(
-                "SAC symmetry requires batch_size divisible by "
-                f"{symmetry_batch_multiplier}, got {configured_batch_size}"
-            )
-        learner_batch_size = configured_batch_size // symmetry_batch_multiplier
 
     updates_per_step = int(cfg.algo.updates_per_step)
     env_steps_per_sync = int(cfg.training.env_steps_per_sync)
@@ -399,7 +380,6 @@ def _build_case(
         benchmark_capacity_rows=benchmark_capacity_rows,
         configured_batch_size=configured_batch_size,
         learner_batch_size=learner_batch_size,
-        symmetry_batch_multiplier=symmetry_batch_multiplier,
         updates_per_step=updates_per_step,
         sample_count=learner_batch_size * updates_per_step,
         learning_starts=int(cfg.algo.learning_starts),
@@ -1059,14 +1039,13 @@ def main(argv: list[str] | None = None) -> int:
         print(f"Skipping missing owner config: {Path(skipped['path']).relative_to(ROOT_DIR)}")
     for algo, task in targets:
         cfg = _compose_offpolicy_cfg(algo, task, args.sim)
-        shape, symmetry_batch_multiplier = _resolve_env_shape_and_symmetry(cfg, algo)
+        shape = _resolve_env_shape(cfg, algo)
         case = _build_case(
             cfg,
             algo=algo,
             task=task,
             sim=args.sim,
             shape=shape,
-            symmetry_batch_multiplier=symmetry_batch_multiplier,
             max_capacity_rows=max_capacity_rows,
         )
         results.append(
diff --git a/scripts/benchmark/rl/benchmark_sac_replay_buffer_sampling.py b/scripts/benchmark/rl/benchmark_sac_replay_buffer_sampling.py
index 386d6e82e..51364774e 100644
--- a/scripts/benchmark/rl/benchmark_sac_replay_buffer_sampling.py
+++ b/scripts/benchmark/rl/benchmark_sac_replay_buffer_sampling.py
@@ -38,12 +38,12 @@
 from datetime import datetime, timezone
 from pathlib import Path
 from statistics import mean, median, pstdev
-from typing import Any, Callable, Iterable, cast
+from typing import Any, Callable, Iterable
 
 import torch
 from hydra import compose, initialize_config_dir
 from hydra.core.global_hydra import GlobalHydra
-from omegaconf import DictConfig, OmegaConf
+from omegaconf import DictConfig
 
 ROOT_DIR = Path(__file__).resolve().parents[3]
 if str(ROOT_DIR) not in sys.path:
@@ -98,7 +98,6 @@ class BenchmarkCase:
     config_capacity_rows: int
     configured_batch_size: int
     learner_batch_size: int
-    symmetry_batch_multiplier: int
     updates_per_step: int
     sample_count_per_rank: int
     learning_starts: int
@@ -228,7 +227,7 @@ def _cleanup_device() -> None:
 
 
 def _owner_config_path(task: str, sim: str) -> Path:
-    return ROOT_DIR / "conf" / "offpolicy" / "task" / "sac" / task / f"{sim}.yaml"
+    return ROOT_DIR / "src" / "unilab" / "conf" / "sac" / "task" / task / f"{sim}.yaml"
 
 
 def _owner_config_exists(task: str, sim: str) -> bool:
@@ -252,10 +251,9 @@ def _compose_offpolicy_cfg(task: str = DEFAULT_TASK, sim: str | None = None) ->
         raise FileNotFoundError(
             f"Missing SAC owner config for task={task_name!r}, sim={sim_name!r}: {owner_config}"
         )
-    config_dir = str(ROOT_DIR / "conf" / "offpolicy")
+    config_dir = str(ROOT_DIR / "src" / "unilab" / "conf" / "sac")
     overrides = [
-        "algo=sac",
-        f"task=sac/{task_name}/{sim_name}",
+        f"task={task_name}/{sim_name}",
         "hydra.run.dir=.",
         "hydra.output_subdir=null",
         "hydra/job_logging=disabled",
@@ -266,9 +264,11 @@ def _compose_offpolicy_cfg(task: str = DEFAULT_TASK, sim: str | None = None) ->
         return compose(config_name="config", overrides=overrides)
 
 
-def _resolve_env_shape_and_symmetry(cfg: DictConfig) -> tuple[ReplayShape, int]:
-    from unilab.base.observations import get_obs_dims
-    from unilab.training import BackendAdapter, create_env, ensure_registries
+def _resolve_env_shape(cfg: DictConfig) -> ReplayShape:
+    from uni_rl.utils.observations import get_obs_dims
+
+    from unilab.base.config_adapter import BackendAdapter, create_env
+    from unilab.training import ensure_registries
 
     ensure_registries()
     env_cfg_override = BackendAdapter(
@@ -283,23 +283,10 @@ def _resolve_env_shape_and_symmetry(cfg: DictConfig) -> tuple[ReplayShape, int]:
         if action_shape is None:
             raise ValueError("env.action_space.shape must be defined")
         action_dim = int(action_shape[0])
-
-        symmetry_batch_multiplier = 1
-        if bool(OmegaConf.select(cfg, "algo.use_symmetry", default=False)):
-            symmetry_builder = getattr(env, "build_symmetry_augmentation", None)
-            if not callable(symmetry_builder):
-                raise ValueError(f"{cfg.training.task_name} does not provide symmetry augmentation")
-            symmetry = cast(Any, symmetry_builder(device="cpu"))
-            if symmetry is None:
-                raise ValueError(f"{cfg.training.task_name} does not provide symmetry augmentation")
-            symmetry_batch_multiplier = int(symmetry.batch_multiplier)
     finally:
         env.close()
 
-    return (
-        ReplayShape(obs_dim=int(obs_dim), action_dim=action_dim, critic_dim=int(critic_dim)),
-        symmetry_batch_multiplier,
-    )
+    return ReplayShape(obs_dim=int(obs_dim), action_dim=action_dim, critic_dim=int(critic_dim))
 
 
 def _build_case(
@@ -308,20 +295,12 @@ def _build_case(
     task: str = LEGACY_DEFAULT_TASK,
     sim: str = DEFAULT_SIM,
     shape: ReplayShape,
-    symmetry_batch_multiplier: int,
 ) -> BenchmarkCase:
     num_envs = int(cfg.algo.num_envs)
     env_steps_per_sync = int(cfg.training.env_steps_per_sync)
     replay_buffer_n = int(cfg.algo.replay_buffer_n)
     configured_batch_size = int(cfg.algo.batch_size)
     learner_batch_size = configured_batch_size
-    if bool(OmegaConf.select(cfg, "algo.use_symmetry", default=False)):
-        if configured_batch_size % symmetry_batch_multiplier != 0:
-            raise ValueError(
-                "SAC symmetry requires batch_size divisible by "
-                f"{symmetry_batch_multiplier}, got {configured_batch_size}"
-            )
-        learner_batch_size = configured_batch_size // symmetry_batch_multiplier
 
     updates_per_step = int(cfg.algo.updates_per_step)
     return BenchmarkCase(
@@ -336,7 +315,6 @@ def _build_case(
         config_capacity_rows=num_envs * replay_buffer_n,
         configured_batch_size=configured_batch_size,
         learner_batch_size=learner_batch_size,
-        symmetry_batch_multiplier=int(symmetry_batch_multiplier),
         updates_per_step=updates_per_step,
         sample_count_per_rank=learner_batch_size * updates_per_step,
         learning_starts=int(cfg.algo.learning_starts),
@@ -1230,12 +1208,6 @@ def main(argv: list[str] | None = None) -> int:
     parser.add_argument("--obs-dim", type=int, default=0)
     parser.add_argument("--action-dim", type=int, default=0)
     parser.add_argument("--critic-dim", type=int, default=0)
-    parser.add_argument(
-        "--symmetry-batch-multiplier",
-        type=int,
-        default=0,
-        help="Only used with manual --obs-dim/--action-dim/--critic-dim.",
-    )
     parser.add_argument("--plot-dir", type=Path, default=None)
     parser.add_argument("--analysis-md", type=Path, default=None)
     parser.add_argument("--no-plots", action="store_true")
@@ -1277,23 +1249,14 @@ def main(argv: list[str] | None = None) -> int:
             action_dim=int(args.action_dim),
             critic_dim=int(args.critic_dim),
         )
-        if bool(OmegaConf.select(cfg, "algo.use_symmetry", default=False)):
-            if args.symmetry_batch_multiplier <= 0:
-                raise ValueError(
-                    "Manual shape with SAC symmetry requires --symmetry-batch-multiplier"
-                )
-            symmetry_batch_multiplier = int(args.symmetry_batch_multiplier)
-        else:
-            symmetry_batch_multiplier = 1
     else:
-        shape, symmetry_batch_multiplier = _resolve_env_shape_and_symmetry(cfg)
+        shape = _resolve_env_shape(cfg)
 
     case = _build_case(
         cfg,
         task=args.task,
         sim=args.sim,
         shape=shape,
-        symmetry_batch_multiplier=symmetry_batch_multiplier,
     )
     capacity_rows = _resolve_capacity_rows(
         config_capacity_rows=case.config_capacity_rows,
diff --git a/scripts/benchmark/torch_env/motion_tracking.py b/scripts/benchmark/torch_env/motion_tracking.py
index 029aed9a9..be0bae6cb 100644
--- a/scripts/benchmark/torch_env/motion_tracking.py
+++ b/scripts/benchmark/torch_env/motion_tracking.py
@@ -1,30 +1,23 @@
-"""G1MotionTrackingSAC (SAC/mujoco) update_state / reset_done workload.
+"""G1MotionTrackingSAC (SAC/MuJoCo) numeric manager workload.
 
-Faithful xp-port of the NumPy computation in the collector-timed sections of
+Synthetic xp-port of the NumPy kernels in the collector-timed sections of
 `uv run train --algo sac --task g1_motion_tracking --sim mujoco`
 (num_envs=2048, 29-dof, 14 tracked bodies):
 
-- `MotionTrackingEnv.update_state`
-  (src/unilab/envs/motion_tracking/common/tracking.py):
-  motion gather, relative transforms (transforms.py), terminations
-  (terminations.py), 9 active reward terms (rewards.py, incl. per-term logging
-  every 4 steps), observation build (observations.py, actor 160 / critic 289
-  with the SAC +3 linvel tail), adaptive motion-sampler bookkeeping
-  (motion_loader.py).
-- `MotionTrackingDomainRandomizationProvider.build_reset_plan` /
-  `build_reset_observation` + `build_motion_reference_state` (reset.py).
-- `NpEnv._reset_done_envs` scatter/gather.
+- `MotionCommand` gather, relative transforms, termination/reward terms,
+  observation-group assembly (actor 160 / critic 289), and adaptive sampler
+  bookkeeping from the Manager-Based motion runtime.
+- Motion-command reset-state construction and `NpEnv._reset_done_envs`
+  scatter/gather.
 
 Excluded (identical across variants, not NumPy/Torch env math):
 `backend.step` physics, `backend.set_state`, sensor/body-state reads (replaced
 by persistent arrays), and the adaptive-sampler entropy metrics (3 scalar
 reductions per reset).
 
-The real `build_motion_reference_state` samples pose/velocity randomization
-with a per-element Python loop (num_reset x 6 draws, twice). The NumPy
-workload reproduces that faithfully; pass ``vectorized_reset_rng=True`` for a
-column-wise vectorized NumPy draw (used for cross-backend RNG replay during
-validation, which is also the only mode Torch implements).
+Pass ``vectorized_reset_rng=True`` for a column-wise vectorized NumPy draw;
+validation uses that mode for cross-backend RNG replay, and Torch implements
+that mode only.
 """
 
 from __future__ import annotations
diff --git a/scripts/benchmark/torch_env/run_benchmark.py b/scripts/benchmark/torch_env/run_benchmark.py
index a9e3dae71..91d21a3c7 100644
--- a/scripts/benchmark/torch_env/run_benchmark.py
+++ b/scripts/benchmark/torch_env/run_benchmark.py
@@ -1,9 +1,9 @@
 #!/usr/bin/env python3
-"""NumPy vs Torch comparison for the collector-timed env computation.
+"""NumPy vs Torch comparison for collector-timed numeric manager kernels.
 
-Reproduces the `update_state` and `reset_done` sections of `NpEnv.step` (the
-`env_step_update_state_ms` / `env_step_reset_done_ms` collector metrics) for the
-two SAC/mujoco tasks, at identical scale and computation items:
+Reproduces the numeric work represented by the `env_step_update_state_ms` and
+`env_step_reset_done_ms` collector metrics for two SAC/MuJoCo tasks, at
+identical scale and computation items:
 
 - g1_walk_flat       (num_envs=2048, 29-dof, obs 98 / critic 101)
 - g1_motion_tracking (num_envs=2048, 29-dof, 14 bodies, obs 160 / critic 289)
diff --git a/scripts/benchmark/torch_env/walk_flat.py b/scripts/benchmark/torch_env/walk_flat.py
index d93ce437d..6762510aa 100644
--- a/scripts/benchmark/torch_env/walk_flat.py
+++ b/scripts/benchmark/torch_env/walk_flat.py
@@ -3,7 +3,8 @@
 Faithful xp-port of the NumPy computation in the collector-timed sections of
 `uv run train --algo sac --task g1_walk_flat --sim mujoco` (num_envs=2048):
 
-- `G1WalkEnv.update_state` (src/unilab/envs/locomotion/g1/joystick.py):
+- the legacy `G1WalkEnv.update_state` computation (pre-Manager-Based migration;
+  src/unilab/tasks/locomotion/g1/manager_terms.py now owns the same math):
   termination, `_compute_reward` (9 active terms under the SAC scales incl.
   per-term logging every 4 steps), `_compute_obs` (noise + concat, walk
   profile), and the done-triggered curriculum bookkeeping.
@@ -51,7 +52,7 @@
 NOISE_SCALE_JOINT_ANGLE = 0.01
 NOISE_SCALE_JOINT_VEL = 0.1
 
-# conf/offpolicy/task/sac/g1_walk_flat/mujoco.yaml pose_weights (29-dof)
+# conf/sac/task/g1_walk_flat/mujoco.yaml pose_weights (29-dof)
 POSE_WEIGHTS = [0.01, 1.0, 5.0, 0.01, 5.0, 5.0] * 2 + [50.0] * 17
 
 # Reward scales from the SAC owner YAML; penalty terms are multiplied by the
diff --git a/scripts/completions/unilab.bash b/scripts/completions/unilab.bash
index 024a569b8..b54177023 100644
--- a/scripts/completions/unilab.bash
+++ b/scripts/completions/unilab.bash
@@ -14,7 +14,7 @@ _unilab_uv_complete() {
     local candidates
     if ! mapfile -t candidates < <(
         uv run --no-sync unilab-complete --cword "$COMP_CWORD" -- "${COMP_WORDS[@]}" 2>/dev/null \
-            || PYTHONPATH="$repo_root/src${PYTHONPATH:+:$PYTHONPATH}" uv run --no-sync python -m unilab.tools.completion --cword "$COMP_CWORD" -- "${COMP_WORDS[@]}" 2>/dev/null
+            || PYTHONPATH="$repo_root/src${PYTHONPATH:+:$PYTHONPATH}" uv run --no-sync python -m unilab.cli_completion --cword "$COMP_CWORD" -- "${COMP_WORDS[@]}" 2>/dev/null
     ); then
         return 0
     fi
diff --git a/scripts/completions/unilab.zsh b/scripts/completions/unilab.zsh
index 3432470bf..53189f294 100644
--- a/scripts/completions/unilab.zsh
+++ b/scripts/completions/unilab.zsh
@@ -13,7 +13,7 @@ _unilab_uv_complete() {
     local output
     output="$(
         uv run --no-sync unilab-complete --cword "$((CURRENT - 1))" -- "${words[@]}" 2>/dev/null \
-            || PYTHONPATH="$repo_root/src${PYTHONPATH:+:$PYTHONPATH}" uv run --no-sync python -m unilab.tools.completion --cword "$((CURRENT - 1))" -- "${words[@]}" 2>/dev/null
+            || PYTHONPATH="$repo_root/src${PYTHONPATH:+:$PYTHONPATH}" uv run --no-sync python -m unilab.cli_completion --cword "$((CURRENT - 1))" -- "${words[@]}" 2>/dev/null
     )" || return 0
 
     if [[ -z "$output" ]]; then
diff --git a/scripts/deploy/append_cooldown.py b/scripts/deploy/append_cooldown.py
deleted file mode 100644
index 8ad20eb8d..000000000
--- a/scripts/deploy/append_cooldown.py
+++ /dev/null
@@ -1,299 +0,0 @@
-#!/usr/bin/env python3
-"""Append a dance-final-frame->FixStand cooldown suffix to a WBT motion bin.
-
-Why this exists
----------------
-Symmetric to prepend_warmup.py.  The original dance bin's last frame is mid-
-motion (joint vel ~18 rad/s L2, pelvis still descending at 0.3 m/s, joints
-~42 deg from FixStand stand qpos).  When State_WBT's time-end check fires
-and the FSM hands off to State_FixStand, FixStand only does a joint-space PD
-interpolation from current motor q -> stand qpos over 3 s; it has no balance
-controller, so a robot left with angular momentum and an off-balance pose
-collapses despite the PD targets pulling it back to stand.
-
-Fix: append N seconds of kinematically self-consistent interpolation frames
-at the tail of the bin.  The tracking policy decelerates joints, brings the
-torso back to upright, and lands on FixStand stand qpos before the FSM exit
-triggers — so the FixStand interpolation starts from an already-balanced
-pose and just holds station.
-
-What this does NOT change
--------------------------
-- Training pipeline (no retrain needed).
-- State_WBT.cpp / deploy_config.yaml / FSM yaml time_end (leave null -> full
-  duration so the cooldown plays automatically).
-- The original dance frames (they are preserved verbatim before the cooldown).
-
-Interpolation scheme — shared primitives from motion_primitives.py, run in
-reverse direction:
-- joint_pos (J=29): cubic Hermite per joint with boundaries
-    (orig.jp[-1], orig.jv[-1]) -> (default_angles, 0)
-  joint_vel is the analytic derivative.
-- body_pos (B,3): cubic Hermite per axis with boundaries
-    (orig.bp[-1], orig.bv[-1]) -> (FK(stand), 0)
-  body_lin_vel is the analytic derivative.
-- body_quat (B,4): SLERP along quintic smoothstep s(u) = 6u^5 - 15u^4 + 10u^3
-  (s'(0) = s''(0) = s'(1) = s''(1) = 0). Shortest-path; near-parallel fallback.
-- body_ang_vel: central difference over [orig.bq[-2:], cooldown_bq] so the
-  seam ang_vel is continuous with the original bin's final ang_vel.
-
-Use
----
-    uv run scripts/deploy/append_cooldown.py \
-        --input  ../deploy_ws/assets/dance1.bin \
-        --output ../deploy_ws/assets/dance1_cooldown.bin \
-        --config ../deploy_ws/assets/deploy_config.yaml \
-        --cooldown-sec 2.0
-
-Validate in sim BEFORE swapping on the real robot:
-    uv run scripts/deploy/sim_prototype.py \
-        --motion ../deploy_ws/assets/dance1_cooldown.bin \
-        --init-mode stand --render
-"""
-
-from __future__ import annotations
-
-import argparse
-import sys
-from pathlib import Path
-
-import numpy as np
-import yaml
-from motion_primitives import (
-    compute_fixstand_body_states,
-    hermite,
-    load_motion_bin,
-    quat_seq_ang_vel,
-    save_motion_bin,
-    slerp_smoothstep,
-)
-
-REPO_ROOT = Path(__file__).resolve().parents[2]
-DEPLOY_WS = REPO_ROOT.parent / "deploy_ws"
-DEFAULT_SCENE = REPO_ROOT / "src/unilab/assets/robots/g1/scene_flat.xml"
-DEFAULT_CFG = DEPLOY_WS / "assets/deploy_config.yaml"
-DEFAULT_IN = DEPLOY_WS / "assets/dance1.bin"
-DEFAULT_OUT = DEPLOY_WS / "assets/dance1_cooldown.bin"
-DEFAULT_COOLDOWN_SEC = 3.0
-DEFAULT_HOLD_SEC = 0.5
-
-
-# ----------------------------------------------------------------------------
-# Main
-# ----------------------------------------------------------------------------
-
-
-def main() -> None:
-    ap = argparse.ArgumentParser(
-        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
-    )
-    ap.add_argument("--input", type=Path, default=DEFAULT_IN)
-    ap.add_argument("--output", type=Path, default=DEFAULT_OUT)
-    ap.add_argument(
-        "--config",
-        type=Path,
-        default=DEFAULT_CFG,
-        help="deploy_config.yaml (for default_angles, tracked ids).",
-    )
-    ap.add_argument(
-        "--scene",
-        type=Path,
-        default=DEFAULT_SCENE,
-        help="MuJoCo XML with 'stand' keyframe (for FixStand FK).",
-    )
-    ap.add_argument(
-        "--cut-frame",
-        type=int,
-        default=None,
-        help="If set, truncate the input bin so that frame "
-        "CUT_FRAME becomes the new last frame (inclusive). "
-        "Frames after that are dropped, and the cooldown ramps "
-        "from this frame to default. Use this when the dance's "
-        "actual last frame has too much momentum for any post-"
-        "hoc interpolation to stabilize. Find a low-energy "
-        "frame (low joint vel, near-upright torso, near-zero "
-        "pelvis vertical vel) and cut there.",
-    )
-    ap.add_argument(
-        "--cooldown-sec",
-        type=float,
-        default=DEFAULT_COOLDOWN_SEC,
-        help="Length of Hermite deceleration ramp [seconds]. "
-        "Lowers joint vel from orig.jv[-1] to 0 and joint pos "
-        "to default_angles over this interval.",
-    )
-    ap.add_argument(
-        "--hold-sec",
-        type=float,
-        default=DEFAULT_HOLD_SEC,
-        help="Length of trailing static-hold segment [seconds]. "
-        "Appended AFTER the Hermite ramp; frames are pure "
-        "(default_angles, 0, FixStand FK body, 0). Lets the "
-        "tracking policy physically converge to stand before "
-        "the FSM timeout swaps to State_FixStand. Set to 0 to "
-        "disable. Recommended >= 0.3s.",
-    )
-    args = ap.parse_args()
-
-    with open(args.config) as f:
-        cfg = yaml.safe_load(f)
-    default_angles = np.asarray(cfg["default_angles"], dtype=np.float64)
-    tracked_ids = list(cfg["tracked_body_mujoco_ids"])
-
-    orig = load_motion_bin(args.input)
-    fps, J, B = orig["fps"], orig["nj"], orig["nb"]
-    if J != len(default_angles):
-        raise SystemExit(f"J mismatch: bin J={J}, default_angles len={len(default_angles)}")
-    if B != len(tracked_ids):
-        raise SystemExit(f"B mismatch: bin B={B}, tracked_body_mujoco_ids len={len(tracked_ids)}")
-
-    if args.cut_frame is not None:
-        cf = int(args.cut_frame)
-        if cf < 1 or cf >= orig["nf"]:
-            raise SystemExit(f"cut-frame={cf} out of range [1, {orig['nf'] - 1}]")
-        # Keep frames [0, cf] (cf becomes the new last frame, inclusive).
-        keep = cf + 1
-        print(
-            f"Truncating input: keep frames [0, {cf}]  "
-            f"({orig['nf']} -> {keep} frames, {orig['nf'] / fps:.2f}s -> {keep / fps:.2f}s)",
-            file=sys.stderr,
-        )
-        for k in ("jp", "jv", "bp", "bq", "bv", "bav"):
-            orig[k] = orig[k][:keep]
-        orig["nf"] = keep
-        # Report cut-point energy so the user sees what cool-down starts from.
-        a = cfg.get("anchor_body_idx_in_tracked", 7)
-        jv_l2 = float(np.linalg.norm(orig["jv"][-1]))
-        jp_dev = float(np.abs(orig["jp"][-1] - default_angles).max())
-        bv_z = float(abs(orig["bv"][-1, a, 2]))
-        print(
-            f"  cut-point energy: |jv|={jv_l2:.2f} rad/s  jp_dev={jp_dev:.3f} rad  "
-            f"|bv_z|={bv_z:.3f} m/s",
-            file=sys.stderr,
-        )
-
-    dt = 1.0 / fps
-    N = int(round(args.cooldown_sec * fps))
-    if N < 2:
-        raise SystemExit(
-            f"cooldown-sec={args.cooldown_sec}s too short for fps={fps} (need >= 2 frames)"
-        )
-    K = int(round(args.hold_sec * fps)) if args.hold_sec > 0.0 else 0
-    if args.hold_sec > 0.0 and K < 1:
-        raise SystemExit(
-            f"hold-sec={args.hold_sec}s rounds to 0 frames at fps={fps}; pass 0 or >= {dt:.3f}s"
-        )
-    T = N * dt  # frame N of the cooldown == FixStand target exactly
-
-    fk_jp, fk_bp, fk_bq = compute_fixstand_body_states(args.scene, tracked_ids)
-
-    keyframe_diff = float(np.abs(fk_jp - default_angles).max())
-    if keyframe_diff > 1e-3:
-        print(
-            f"WARN: 'stand' keyframe joint pos differs from deploy_config "
-            f"default_angles by {keyframe_diff:.4f} rad — using deploy_config "
-            "values for cooldown end (so command_joint_pos matches what "
-            "FixStand will hold on the real robot).",
-            file=sys.stderr,
-        )
-
-    # --- joint pos/vel: analytic Hermite, orig[-1] -> default_angles --------
-    # ts[k] = (k+1)*dt so the first cooldown frame is one dt after orig[-1]
-    # and the last cooldown frame (k = N-1) lands exactly at t = T = N*dt,
-    # i.e. precisely on the (default_angles, 0) boundary.
-    ts = (np.arange(N) + 1) * dt
-    jp_c, jv_c = hermite(orig["jp"][-1], orig["jv"][-1], default_angles, np.zeros(J), ts, T)
-
-    # --- body pos / lin_vel: analytic Hermite per axis ----------------------
-    bp_c, bv_c = hermite(orig["bp"][-1], orig["bv"][-1], fk_bp, np.zeros((B, 3)), ts, T)
-
-    # --- body quat: SLERP along quintic smoothstep --------------------------
-    u = ts / T
-    bq_c = np.zeros((N, B, 4), dtype=np.float64)
-    for b in range(B):
-        bq_c[:, b, :] = slerp_smoothstep(orig["bq"][-1, b], fk_bq[b], u)
-
-    # --- body ang_vel: central diff, with the seam differenced against the
-    # ORIGINAL bin's last two frames so the derivative is continuous across
-    # the dance/cooldown boundary -------------------------------------------
-    bav_c = np.zeros((N, B, 3), dtype=np.float64)
-    for b in range(B):
-        ext = np.concatenate([orig["bq"][-2:, b, :], bq_c[:, b, :]], axis=0)
-        # extended sequence has 2+N frames; cooldown frames live at [2, 2+N).
-        bav_c[:, b, :] = quat_seq_ang_vel(ext, dt)[2:]
-
-    # --- static hold segment: K frames all at (default, 0, FK, 0). Gives the
-    # tracking policy time to physically converge to the FixStand target before
-    # the FSM time-end check fires and hands off to State_FixStand. Without
-    # this segment, the cooldown's last (default, 0) frame is commanded for
-    # only one step_dt (~0.02s) before the swap, and any residual body momentum
-    # carried through the Hermite ramp ends up as the FixStand starting pose. -
-    if K > 0:
-        jp_h = np.broadcast_to(default_angles, (K, J)).copy()
-        jv_h = np.zeros((K, J), dtype=np.float64)
-        bp_h = np.broadcast_to(fk_bp, (K, B, 3)).copy()
-        bq_h = np.broadcast_to(fk_bq, (K, B, 4)).copy()
-        bv_h = np.zeros((K, B, 3), dtype=np.float64)
-        bav_h = np.zeros((K, B, 3), dtype=np.float64)
-
-    # --- concatenate original + cooldown (+ hold) -------------------------
-    parts_jp = [orig["jp"], jp_c]
-    parts_jv = [orig["jv"], jv_c]
-    parts_bp = [orig["bp"], bp_c]
-    parts_bq = [orig["bq"], bq_c]
-    parts_bv = [orig["bv"], bv_c]
-    parts_bav = [orig["bav"], bav_c]
-    if K > 0:
-        parts_jp.append(jp_h)
-        parts_jv.append(jv_h)
-        parts_bp.append(bp_h)
-        parts_bq.append(bq_h)
-        parts_bv.append(bv_h)
-        parts_bav.append(bav_h)
-    new_jp = np.concatenate(parts_jp, axis=0)
-    new_jv = np.concatenate(parts_jv, axis=0)
-    new_bp = np.concatenate(parts_bp, axis=0)
-    new_bq = np.concatenate(parts_bq, axis=0)
-    new_bv = np.concatenate(parts_bv, axis=0)
-    new_bav = np.concatenate(parts_bav, axis=0)
-
-    args.output.parent.mkdir(parents=True, exist_ok=True)
-    save_motion_bin(args.output, fps, new_jp, new_jv, new_bp, new_bq, new_bv, new_bav)
-
-    # --- report ------------------------------------------------------------
-    # Seam = boundary between orig[-1] and cooldown[0], which are one dt apart.
-    seam_jp = float(np.abs(jp_c[0] - orig["jp"][-1]).max())
-    seam_jv = float(np.abs(jv_c[0] - orig["jv"][-1]).max())
-    seam_bp = float(np.abs(bp_c[0] - orig["bp"][-1]).max())
-    seam_bv = float(np.abs(bv_c[0] - orig["bv"][-1]).max())
-    seam_qang_deg = []
-    for b in range(B):
-        d = float(np.clip(abs(np.dot(bq_c[0, b], orig["bq"][-1, b])), 0.0, 1.0))
-        seam_qang_deg.append(np.degrees(2 * np.arccos(d)))
-    seam_qang_max = max(seam_qang_deg)
-    # End state must match FixStand boundary (this is the whole point).
-    end_jp_diff = float(np.abs(new_jp[-1] - default_angles).max())
-    end_jv_l2 = float(np.linalg.norm(new_jv[-1]))
-
-    print(f"Wrote {args.output}")
-    print(f"  fps={fps}  J={J}  B={B}")
-    print(
-        f"  frames: {orig['nf']} (orig)  -> {new_jp.shape[0]} "
-        f"({orig['nf']} dance + {N} cooldown + {K} hold)"
-    )
-    print(
-        f"  duration: {orig['nf'] / fps:.2f}s -> {new_jp.shape[0] / fps:.2f}s "
-        f"(cooldown_sec={args.cooldown_sec}, hold_sec={args.hold_sec})"
-    )
-    print(f"  end (last frame) command_joint_pos == default_angles? max_diff={end_jp_diff:.6f} rad")
-    print(f"  end (last frame) command_joint_vel L2 = {end_jv_l2:.6f} rad/s (should be 0)")
-    print("  seam check (orig last frame vs cooldown first frame; one-dt-step apart):")
-    print(f"    |Δjoint_pos|_∞       = {seam_jp:.4f} rad")
-    print(f"    |Δjoint_vel|_∞       = {seam_jv:.4f} rad/s")
-    print(f"    |Δbody_pos|_∞        = {seam_bp:.4f} m")
-    print(f"    |Δbody_lin_vel|_∞    = {seam_bv:.4f} m/s")
-    print(f"    max body quat angle  = {seam_qang_max:.4f} deg")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/deploy/export_deploy_config.py b/scripts/deploy/export_deploy_config.py
deleted file mode 100644
index b692e992e..000000000
--- a/scripts/deploy/export_deploy_config.py
+++ /dev/null
@@ -1,328 +0,0 @@
-#!/usr/bin/env python3
-"""Export deploy_config.yaml for the C++ G1-29DOF WBT deployment side.
-
-Reads g1.xml + scene_flat.xml + tracking.py defaults to emit a single yaml
-that the deploy framework (~/deploy_ws/unitree_rl_lab/.../State_WBT) can load
-to drive the actor at runtime.
-
-This file is the SINGLE SOURCE OF TRUTH for the actor obs schema:
-  * Training side (tracking.py) assembles obs in the order documented here.
-  * Deploy side (State_WBT.cpp + ObservationManager) reads obs_layout from
-    this yaml and assembles in matching order with per-term history buffers.
-  * Alignment test (tests/test_obs_alignment_g1_wbt.py) verifies both code
-    paths produce byte-identical obs from the same inputs.
-
-obs_layout schema v2 (per-term history_length):
-  Each entry: {name, dim, history_length, source}
-  * Reference terms (command_*, motion_anchor_*) use history_length=1
-    (single-step, refs come fresh from the motion clip every tick).
-  * Proprio terms (gyro, joint_pos_rel, dof_vel, last_actions) carry
-    history_length=H (5 for the deploy profile), flattened oldest-first.
-  * Deploy framework reads via use_gym_history=false (group-by-term mode),
-    so each term independently flattens its full history → total obs_dim is
-    the sum of dim * history_length over all entries.
-"""
-
-from __future__ import annotations
-
-import argparse
-from pathlib import Path
-
-import mujoco
-import yaml
-
-REPO_ROOT = Path(__file__).resolve().parents[2]
-DEFAULT_SCENE = REPO_ROOT / "src/unilab/assets/robots/g1/scene_flat.xml"
-DEFAULT_OUT = REPO_ROOT / "logs/deploy/deploy_config.yaml"
-
-TRACKED_BODY_NAMES = (
-    "pelvis",
-    "left_hip_roll_link",
-    "left_knee_link",
-    "left_ankle_roll_link",
-    "right_hip_roll_link",
-    "right_knee_link",
-    "right_ankle_roll_link",
-    "torso_link",
-    "left_shoulder_roll_link",
-    "left_elbow_link",
-    "left_wrist_yaw_link",
-    "right_shoulder_roll_link",
-    "right_elbow_link",
-    "right_wrist_yaw_link",
-)
-ANCHOR_BODY_NAME = "torso_link"
-
-ACTION_SCALE = 2.0
-# EMA alpha for q_target smoothing on the deploy side. Training applies q_target
-# directly with no smoothing; alpha=1.0 means deploy also applies directly (best
-# for sim2sim correctness). Lower it (~0.5–0.8) only on real hardware if jitter
-# requires smoothing — but every step of lag pushes obs out of training
-# distribution, so verify sim2sim impact at the chosen alpha first.
-EMA_ALPHA = 1.0
-CTRL_DT = 0.02
-KEYFRAME_NAME = "stand"
-ROOT_QPOS_DIM = 7  # free joint: xyz + quat(wxyz)
-
-# Default obs history length — matches g1_wbt_obs/mujoco.yaml's
-# `noise_config.obs_history_length`. Override via --obs-history-length when
-# exporting for other training profiles (e.g. g1_motion_tracking/mujoco.yaml uses 1).
-DEFAULT_OBS_HISTORY_LENGTH = 5
-
-
-def _round_list(arr, ndigits=6):
-    return [round(float(v), ndigits) for v in arr]
-
-
-def _build_obs_layout(
-    num_action: int, hist_len: int, enable_zero_anchor_pos: bool, enable_zero_linvel: bool
-):
-    """Build obs_layout in the exact order tracking.py:_compute_obs assembles.
-
-    Returns (layout_list, total_obs_dim).
-    Order = single-step refs first, then per-term proprio history blocks,
-    matching the training-side actor obs concatenation order.
-    """
-    layout = [
-        # ---- single-step reference terms (history_length=1) ----
-        {
-            "name": "command_joint_pos",
-            "dim": num_action,
-            "history_length": 1,
-            "source": "motion_ref_frame.joint_pos",
-        },
-        {
-            "name": "command_joint_vel",
-            "dim": num_action,
-            "history_length": 1,
-            "source": "motion_ref_frame.joint_vel",
-        },
-    ]
-    if not enable_zero_anchor_pos:
-        layout.append(
-            {
-                "name": "motion_anchor_pos_b",
-                "dim": 3,
-                "history_length": 1,
-                "source": "subtract_frame(robot_anchor_w, motion_anchor_w).pos",
-            }
-        )
-    layout.append(
-        {
-            "name": "motion_anchor_ori_b",
-            "dim": 6,
-            "history_length": 1,
-            "source": "rotation_matrix(subtract_frame(...).quat)[:, :2].flatten()",
-        }
-    )
-    if not enable_zero_linvel:
-        layout.append(
-            {
-                "name": "base_lin_vel",
-                "dim": 3,
-                "history_length": 1,
-                "source": "imu.local_linvel (state-estimated)",
-            }
-        )
-    # ---- proprio terms with H-step oldest-first history ----
-    layout.extend(
-        [
-            {"name": "gyro", "dim": 3, "history_length": hist_len, "source": "imu.gyroscope"},
-            {
-                "name": "joint_pos_rel",
-                "dim": num_action,
-                "history_length": hist_len,
-                "source": "dof_pos - default_angles",
-            },
-            {"name": "dof_vel", "dim": num_action, "history_length": hist_len, "source": "dof_vel"},
-            {
-                "name": "last_actions",
-                "dim": num_action,
-                "history_length": hist_len,
-                "source": "previous raw actor output",
-            },
-        ]
-    )
-    total = sum(seg["dim"] * seg["history_length"] for seg in layout)
-    return layout, total
-
-
-def main():
-    ap = argparse.ArgumentParser(
-        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
-    )
-    ap.add_argument(
-        "--scene",
-        type=Path,
-        default=DEFAULT_SCENE,
-        help="MuJoCo scene file containing the 'stand' keyframe.",
-    )
-    ap.add_argument("--output", "-o", type=Path, default=DEFAULT_OUT)
-    ap.add_argument(
-        "--obs-history-length",
-        type=int,
-        default=DEFAULT_OBS_HISTORY_LENGTH,
-        help="Proprio history length H. Must match training-side "
-        "noise_config.obs_history_length. Default 5 = current "
-        "g1_wbt_obs/mujoco.yaml. Set 1 for the legacy 154-d schema.",
-    )
-    ap.add_argument(
-        "--enable-zero-anchor-pos",
-        action="store_true",
-        default=True,
-        help="Drop motion_anchor_pos_b from actor obs (mjlab parity). "
-        "Matches g1_wbt_obs/mujoco.yaml's noise_config flag.",
-    )
-    ap.add_argument(
-        "--enable-zero-linvel",
-        action="store_true",
-        default=True,
-        help="Drop base_lin_vel from actor obs (mjlab parity). "
-        "Matches g1_wbt_obs/mujoco.yaml's noise_config flag.",
-    )
-    args = ap.parse_args()
-
-    if not args.scene.exists():
-        raise SystemExit(f"Scene not found: {args.scene}")
-
-    model = mujoco.MjModel.from_xml_path(str(args.scene))
-
-    if model.nu != 29:
-        raise SystemExit(f"Expected 29 actuators, got {model.nu}")
-
-    # Joint names in actuator order (action[i] drives actuator i).
-    joint_names = [
-        mujoco.mj_id2name(model, mujoco.mjtObj.mjOBJ_ACTUATOR, i) for i in range(model.nu)
-    ]
-
-    # kp from XML <position> actuators.
-    kp = model.actuator_gainprm[:, 0].copy()  # gainprm[0] is kp for position actuator
-    if not (kp > 0).all():
-        raise SystemExit(f"kp parsing produced non-positive values: {kp}")
-    # kv recovery: for position actuator, biasprm = [0, -kp, -kv]
-    kv = -model.actuator_biasprm[:, 2].copy()
-    if not (kv > 0).all():
-        raise SystemExit(f"kv parsing produced non-positive values: {kv}")
-
-    # Joint limits: skip the floating-root joint (jnt 0).
-    if model.njnt < 1 + model.nu:
-        raise SystemExit(f"Insufficient joints: njnt={model.njnt}")
-    jnt_range = model.jnt_range[1 : 1 + model.nu].copy()
-    joint_lower = jnt_range[:, 0]
-    joint_upper = jnt_range[:, 1]
-
-    # Force range from actuator forcerange.
-    force_range = model.actuator_forcerange.copy()
-    force_lower = force_range[:, 0]
-    force_upper = force_range[:, 1]
-
-    # default_angles = stand keyframe qpos[7:36].
-    if model.nkey == 0:
-        raise SystemExit(f"No keyframes in {args.scene}; expected '{KEYFRAME_NAME}'.")
-    key_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_KEY, KEYFRAME_NAME)
-    if key_id < 0:
-        names = [mujoco.mj_id2name(model, mujoco.mjtObj.mjOBJ_KEY, i) for i in range(model.nkey)]
-        raise SystemExit(f"Keyframe '{KEYFRAME_NAME}' not found; have {names}")
-    stand_qpos = model.key_qpos[key_id]
-    if len(stand_qpos) != ROOT_QPOS_DIM + model.nu:
-        raise SystemExit(f"stand qpos len {len(stand_qpos)} != {ROOT_QPOS_DIM}+{model.nu}")
-    default_angles = stand_qpos[ROOT_QPOS_DIM:].copy()
-
-    # 14 tracked body indices (in MuJoCo body-id space; deploy side won't use
-    # these directly but they are useful for debugging / cross-checks).
-    tracked_body_ids = []
-    for nm in TRACKED_BODY_NAMES:
-        bid = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_BODY, nm)
-        if bid < 0:
-            raise SystemExit(f"Tracked body '{nm}' missing from model")
-        tracked_body_ids.append(int(bid))
-    anchor_body_idx_in_tracked = TRACKED_BODY_NAMES.index(ANCHOR_BODY_NAME)
-
-    # Build the obs layout (single source of truth for actor obs assembly).
-    obs_layout, obs_dim = _build_obs_layout(
-        num_action=model.nu,
-        hist_len=args.obs_history_length,
-        enable_zero_anchor_pos=args.enable_zero_anchor_pos,
-        enable_zero_linvel=args.enable_zero_linvel,
-    )
-
-    cfg = {
-        # ---- meta ----
-        # obs_dim = sum over obs_layout of dim * history_length.
-        # For the deploy profile (H=5, both zero flags ON) this is:
-        #   command_joint_pos(29*1) + command_joint_vel(29*1)
-        #   + motion_anchor_ori_b(6*1) + gyro(3*5)
-        #   + joint_pos_rel(29*5) + dof_vel(29*5) + last_actions(29*5) = 514
-        # Aligns with deploy-profile training run (g1_wbt_obs/mujoco.yaml) which
-        # drops motion_anchor_pos_b and base_lin_vel from actor obs to match
-        # Unitree's verified mjlab "No-State-Estimation" deploy yaml and
-        # adds BeyondMimic-style proprio history (default H=5).
-        "obs_dim": obs_dim,
-        "obs_history_length": args.obs_history_length,
-        # Deploy ObservationManager mode. False = group-by-term (each term's
-        # full history flattened oldest-first, then concat across terms).
-        # True = group-by-time-step (legacy gym style). Training side uses
-        # the false convention, so MUST stay false here for byte alignment.
-        "use_gym_history": False,
-        "action_dim": int(model.nu),
-        "ctrl_dt": CTRL_DT,
-        "action_scale": ACTION_SCALE,
-        "ema_alpha": EMA_ALPHA,
-        # ---- joint config (in MuJoCo actuator order; deploy side assumes
-        # this matches the SDK motor index 1:1 for G1-29DOF — verify per-motor
-        # before real-robot run) ----
-        "joint_names": list(joint_names),
-        # Identity SDK mapping; replace with measured order if 1:1 assumption fails.
-        "joint_ids_map": list(range(model.nu)),
-        "default_angles": _round_list(default_angles),
-        "kp": _round_list(kp),
-        "kd": _round_list(kv),
-        "joint_lower": _round_list(joint_lower),
-        "joint_upper": _round_list(joint_upper),
-        "force_lower": _round_list(force_lower, 3),
-        "force_upper": _round_list(force_upper, 3),
-        # ---- motion / anchor ----
-        "tracked_body_names": list(TRACKED_BODY_NAMES),
-        "tracked_body_mujoco_ids": tracked_body_ids,
-        "anchor_body_name": ANCHOR_BODY_NAME,
-        "anchor_body_idx_in_tracked": int(anchor_body_idx_in_tracked),
-        # ---- noise (NOT applied at deploy; documentation only — per-step
-        # uniform noise scales used during training, plus persistent encoder
-        # bias absorbed into joint_pos_rel) ----
-        "training_noise_scales": {
-            "joint_angle": 0.01,
-            "joint_vel": 0.5,
-            "gyro": 0.2,
-            "anchor_ori": 0.05,
-            "joint_pos_encoder_bias_per_episode": 0.01,
-        },
-        # ---- obs layout (SINGLE SOURCE OF TRUTH for both ends) ----
-        # Each entry: name, dim (per-step), history_length, source.
-        # Total obs_dim = sum(dim * history_length).
-        # Order MUST match tracking.py:_compute_obs assembly order.
-        # State_WBT.cpp:build_env_cfg translates names via its alias table.
-        "obs_layout": obs_layout,
-    }
-
-    args.output.parent.mkdir(parents=True, exist_ok=True)
-    with open(args.output, "w") as f:
-        yaml.safe_dump(cfg, f, sort_keys=False, default_flow_style=None, width=120)
-
-    print(f"Wrote {args.output} ({args.output.stat().st_size} bytes)")
-    print(
-        f"  joints: {model.nu}, tracked bodies: {len(TRACKED_BODY_NAMES)}, "
-        f"anchor='{ANCHOR_BODY_NAME}' (idx_in_tracked={anchor_body_idx_in_tracked})"
-    )
-    print(f"  obs_history_length={args.obs_history_length}, total obs_dim={obs_dim}")
-    print("  obs_layout segments:")
-    for seg in obs_layout:
-        print(
-            f"    {seg['name']:24s} dim={seg['dim']:3d}  H={seg['history_length']:1d}  "
-            f"contrib={seg['dim'] * seg['history_length']}"
-        )
-    print(f"  default_angles[:6] = {_round_list(default_angles[:6], 3)}")
-    print(f"  kp[:3] = {_round_list(kp[:3], 3)}, kd[:3] = {_round_list(kv[:3], 3)}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/deploy/export_motion_bin.py b/scripts/deploy/export_motion_bin.py
deleted file mode 100644
index 27905845c..000000000
--- a/scripts/deploy/export_motion_bin.py
+++ /dev/null
@@ -1,179 +0,0 @@
-#!/usr/bin/env python3
-"""Export a tracked-motion NPZ to the flat binary format used by State_WBT.
-
-Layout (little-endian, contiguous):
-  header (16 bytes):
-    int32 fps
-    int32 num_frames
-    int32 num_joints      (29 for G1-29DOF)
-    int32 num_bodies      (14 tracked bodies)
-  data (all float32, frames-major):
-    joint_pos       [num_frames][num_joints]
-    joint_vel       [num_frames][num_joints]
-    body_pos_w      [num_frames][num_bodies][3]
-    body_quat_w     [num_frames][num_bodies][4]   (wxyz)
-    body_lin_vel_w  [num_frames][num_bodies][3]
-    body_ang_vel_w  [num_frames][num_bodies][3]
-
-NPZ source layout (per src/unilab/envs/motion_tracking/g1/motion_loader.py):
-  - 'fps' (int)
-  - 'joint_pos' (N, 29)
-  - 'joint_vel' (N, 29)
-  - 'body_pos_w', 'body_quat_w', 'body_lin_vel_w', 'body_ang_vel_w'  (N, 31, *)
-  Body axis is in MuJoCo body-id order; we slice down to 14 tracked bodies
-  using mj_name2id() so the deploy side does not need to repeat the lookup.
-"""
-
-from __future__ import annotations
-
-import argparse
-import struct
-from pathlib import Path
-
-import mujoco
-import numpy as np
-
-REPO_ROOT = Path(__file__).resolve().parents[2]
-DEFAULT_NPZ = REPO_ROOT / "src/unilab/assets/motions/g1/dance1_subject2_part.npz"
-DEFAULT_SCENE = REPO_ROOT / "src/unilab/assets/robots/g1/scene_flat.xml"
-DEFAULT_OUT = REPO_ROOT / "logs/deploy/dance1.bin"
-
-TRACKED_BODY_NAMES = (
-    "pelvis",
-    "left_hip_roll_link",
-    "left_knee_link",
-    "left_ankle_roll_link",
-    "right_hip_roll_link",
-    "right_knee_link",
-    "right_ankle_roll_link",
-    "torso_link",
-    "left_shoulder_roll_link",
-    "left_elbow_link",
-    "left_wrist_yaw_link",
-    "right_shoulder_roll_link",
-    "right_elbow_link",
-    "right_wrist_yaw_link",
-)
-
-
-def main():
-    ap = argparse.ArgumentParser(description=__doc__)
-    ap.add_argument("--motion", type=Path, default=DEFAULT_NPZ)
-    ap.add_argument("--scene", type=Path, default=DEFAULT_SCENE)
-    ap.add_argument("--output", "-o", type=Path, default=DEFAULT_OUT)
-    ap.add_argument(
-        "--start-frame", type=int, default=0, help="First frame to include (inclusive). Default 0."
-    )
-    end_group = ap.add_mutually_exclusive_group()
-    end_group.add_argument(
-        "--end-frame",
-        type=int,
-        default=None,
-        help="One past the last frame (exclusive). Default = NPZ frame count.",
-    )
-    end_group.add_argument(
-        "--duration",
-        type=float,
-        default=None,
-        help="Seconds to keep starting at --start-frame. Resolved to frames via NPZ fps.",
-    )
-    args = ap.parse_args()
-
-    if not args.motion.exists():
-        raise SystemExit(f"NPZ not found: {args.motion}")
-    if not args.scene.exists():
-        raise SystemExit(f"Scene not found: {args.scene}")
-
-    model = mujoco.MjModel.from_xml_path(str(args.scene))
-    body_ids = []
-    for nm in TRACKED_BODY_NAMES:
-        bid = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_BODY, nm)
-        if bid < 0:
-            raise SystemExit(f"Tracked body '{nm}' missing from model")
-        body_ids.append(int(bid))
-
-    with np.load(args.motion) as data:
-        fps = int(np.asarray(data["fps"]).reshape(-1)[0])
-        joint_pos = data["joint_pos"].astype(np.float32)
-        joint_vel = data["joint_vel"].astype(np.float32)
-        body_pos_w = data["body_pos_w"].astype(np.float32)
-        body_quat_w = data["body_quat_w"].astype(np.float32)
-        body_lin_vel_w = data["body_lin_vel_w"].astype(np.float32)
-        body_ang_vel_w = data["body_ang_vel_w"].astype(np.float32)
-
-    total_frames, num_joints = joint_pos.shape
-    num_bodies = len(TRACKED_BODY_NAMES)
-
-    if joint_pos.shape != joint_vel.shape:
-        raise SystemExit(f"joint_pos {joint_pos.shape} != joint_vel {joint_vel.shape}")
-    if num_joints != 29:
-        raise SystemExit(f"Expected 29 joints, got {num_joints}")
-    if body_pos_w.shape[0] != total_frames:
-        raise SystemExit(f"body_pos_w frames {body_pos_w.shape[0]} != {total_frames}")
-
-    start = args.start_frame
-    if args.duration is not None:
-        if args.duration <= 0:
-            raise SystemExit(f"--duration must be > 0, got {args.duration}")
-        end = start + int(round(args.duration * fps))
-    elif args.end_frame is not None:
-        end = args.end_frame
-    else:
-        end = total_frames
-    if not (0 <= start < end <= total_frames):
-        raise SystemExit(f"Invalid frame range [{start}, {end}); valid is [0, {total_frames}]")
-    if (start, end) != (0, total_frames):
-        joint_pos = joint_pos[start:end]
-        joint_vel = joint_vel[start:end]
-        body_pos_w = body_pos_w[start:end]
-        body_quat_w = body_quat_w[start:end]
-        body_lin_vel_w = body_lin_vel_w[start:end]
-        body_ang_vel_w = body_ang_vel_w[start:end]
-    num_frames = end - start
-    if body_pos_w.shape[1] < max(body_ids) + 1:
-        raise SystemExit(
-            f"NPZ body axis len {body_pos_w.shape[1]} insufficient for max body_id {max(body_ids)}"
-        )
-
-    # Slice 31-body axis down to 14 tracked bodies.
-    body_pos_w = body_pos_w[:, body_ids]
-    body_quat_w = body_quat_w[:, body_ids]
-    body_lin_vel_w = body_lin_vel_w[:, body_ids]
-    body_ang_vel_w = body_ang_vel_w[:, body_ids]
-
-    # Sanity check quaternion is wxyz (norm ≈ 1 and first element typically positive
-    # for "uprightish" frames). This is a soft check.
-    quat_norms = np.linalg.norm(body_quat_w[0], axis=1)
-    if not np.allclose(quat_norms, 1.0, atol=1e-3):
-        print(f"WARN: frame-0 quat norms not unit: {quat_norms}")
-
-    args.output.parent.mkdir(parents=True, exist_ok=True)
-    with open(args.output, "wb") as f:
-        f.write(struct.pack("<iiii", fps, num_frames, num_joints, num_bodies))
-        joint_pos.astype("<f4").tofile(f)
-        joint_vel.astype("<f4").tofile(f)
-        body_pos_w.astype("<f4").tofile(f)
-        body_quat_w.astype("<f4").tofile(f)
-        body_lin_vel_w.astype("<f4").tofile(f)
-        body_ang_vel_w.astype("<f4").tofile(f)
-
-    expected_bytes = (
-        16
-        + (num_frames * num_joints * 4) * 2  # joint_pos + joint_vel
-        + (num_frames * num_bodies * 3 * 4) * 3  # pos + linvel + angvel
-        + (num_frames * num_bodies * 4 * 4)  # quat
-    )
-    actual_bytes = args.output.stat().st_size
-    if actual_bytes != expected_bytes:
-        raise SystemExit(f"Wrote {actual_bytes} bytes but expected {expected_bytes}")
-
-    duration_s = num_frames / fps
-    print(f"Wrote {args.output} ({actual_bytes} bytes)")
-    print(
-        f"  fps={fps}, frames={num_frames}, joints={num_joints}, "
-        f"bodies={num_bodies}, duration={duration_s:.2f}s"
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/deploy/motion_primitives.py b/scripts/deploy/motion_primitives.py
deleted file mode 100644
index 6039aaaac..000000000
--- a/scripts/deploy/motion_primitives.py
+++ /dev/null
@@ -1,174 +0,0 @@
-#!/usr/bin/env python3
-"""Shared WBT motion-bin primitives for the deploy warmup/cooldown scripts.
-
-Single owner of the interpolation and bin-I/O primitives that
-``prepend_warmup.py`` and ``append_cooldown.py`` previously each carried a
-verbatim copy of (#983 conclusion 4.2):
-
-- ``load_motion_bin`` / ``save_motion_bin`` — flat binary layout shared with
-  State_WBT.cpp / sim_prototype.py / export_motion_bin.py: header
-  (4 int32: fps, F, J, B) then six float32 blocks.
-- ``compute_fixstand_body_states`` — MuJoCo FK at the 'stand' keyframe over
-  the same tracked body ids the C++ State_WBT consumes.
-- ``hermite`` — cubic Hermite position + analytic velocity.
-- ``slerp_smoothstep`` — quaternion SLERP along quintic smoothstep.
-- ``quat_seq_ang_vel`` — world-frame angular velocity by finite difference.
-
-Note: ``quat_seq_ang_vel`` deliberately keeps its own arithmetic (endpoint
-one-sided differences; small-angle ``2*Δq.xyz/h``) instead of
-``unilab.utils.rotation.np_quat_angular_velocity`` — the two differ at the
-endpoints (one-sided vs copied-interior) and in the interior (small-angle vs
-exact axis-angle), and switching would silently change generated bin content.
-"""
-
-from __future__ import annotations
-
-import struct
-from pathlib import Path
-
-import mujoco
-import numpy as np
-
-# ----------------------------------------------------------------------------
-# bin I/O — same layout State_WBT.cpp / sim_prototype.py / export_motion_bin.py
-# all use: header (4 int32: fps, F, J, B) then six float32 blocks.
-# ----------------------------------------------------------------------------
-
-
-def load_motion_bin(path: Path) -> dict:
-    with open(path, "rb") as f:
-        fps, nf, nj, nb = struct.unpack("<iiii", f.read(16))
-        jp = np.frombuffer(f.read(nf * nj * 4), "<f4").reshape(nf, nj).copy()
-        jv = np.frombuffer(f.read(nf * nj * 4), "<f4").reshape(nf, nj).copy()
-        bp = np.frombuffer(f.read(nf * nb * 3 * 4), "<f4").reshape(nf, nb, 3).copy()
-        bq = np.frombuffer(f.read(nf * nb * 4 * 4), "<f4").reshape(nf, nb, 4).copy()
-        bv = np.frombuffer(f.read(nf * nb * 3 * 4), "<f4").reshape(nf, nb, 3).copy()
-        bav = np.frombuffer(f.read(nf * nb * 3 * 4), "<f4").reshape(nf, nb, 3).copy()
-    return dict(fps=fps, nf=nf, nj=nj, nb=nb, jp=jp, jv=jv, bp=bp, bq=bq, bv=bv, bav=bav)
-
-
-def save_motion_bin(path: Path, fps: int, jp, jv, bp, bq, bv, bav) -> None:
-    nf, nj = jp.shape
-    nb = bp.shape[1]
-    assert jv.shape == jp.shape, "jv shape mismatch"
-    assert bp.shape == (nf, nb, 3) and bq.shape == (nf, nb, 4), "body shape mismatch"
-    assert bv.shape == (nf, nb, 3) and bav.shape == (nf, nb, 3), "body vel shape mismatch"
-    with open(path, "wb") as f:
-        f.write(struct.pack("<iiii", fps, nf, nj, nb))
-        for arr in (jp, jv, bp, bq, bv, bav):
-            f.write(np.ascontiguousarray(arr, dtype=np.float32).tobytes())
-
-
-# ----------------------------------------------------------------------------
-# FixStand FK — compute body world states at the 'stand' keyframe.
-# Uses the same MuJoCo body ids the bin records (tracked_body_mujoco_ids from
-# deploy_config.yaml), so the result is layer-compatible with the original bin.
-# ----------------------------------------------------------------------------
-
-
-def compute_fixstand_body_states(
-    scene: Path, tracked_ids: list[int]
-) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-    model = mujoco.MjModel.from_xml_path(str(scene))
-    data = mujoco.MjData(model)
-    key_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_KEY, "stand")
-    if key_id < 0:
-        raise SystemExit(f"'stand' keyframe not found in {scene}")
-    mujoco.mj_resetDataKeyframe(model, data, key_id)
-    mujoco.mj_forward(model, data)
-    jp = np.asarray(data.qpos[7:], dtype=np.float64).copy()
-    bp = np.stack([np.asarray(data.xpos[i], dtype=np.float64).copy() for i in tracked_ids])
-    bq = np.stack(
-        [
-            np.asarray(data.xquat[i], dtype=np.float64).copy()  # wxyz
-            for i in tracked_ids
-        ]
-    )
-    return jp, bp, bq
-
-
-# ----------------------------------------------------------------------------
-# Cubic Hermite — analytic position and velocity, kinematically consistent.
-# Boundary conditions: p(0)=p0, p'(0)=v0, p(T)=p1, p'(T)=v1.
-# Returns (p(t), p'(t)) where t may be an array with shape (N,) and p* may
-# have additional trailing dims (broadcasting).
-# ----------------------------------------------------------------------------
-
-
-def hermite(p0, v0, p1, v1, t, T):
-    s = t / T
-    s2, s3 = s * s, s * s * s
-    h00 = 2 * s3 - 3 * s2 + 1
-    h10 = s3 - 2 * s2 + s
-    h01 = -2 * s3 + 3 * s2
-    h11 = s3 - s2
-    h00d = 6 * s2 - 6 * s
-    h10d = 3 * s2 - 4 * s + 1
-    h01d = -6 * s2 + 6 * s
-    h11d = 3 * s2 - 2 * s
-
-    # Reshape s-dim coefficients for broadcasting against p*'s trailing dims.
-    def _r(a):
-        return a.reshape(a.shape + (1,) * (np.ndim(p0)))
-
-    p = _r(h00) * p0 + _r(h10) * T * v0 + _r(h01) * p1 + _r(h11) * T * v1
-    pd = _r(h00d / T) * p0 + _r(h10d) * v0 + _r(h01d / T) * p1 + _r(h11d) * v1
-    return p, pd
-
-
-# ----------------------------------------------------------------------------
-# Quaternion SLERP along quintic smoothstep s(u) = 6u^5 - 15u^4 + 10u^3.
-# u in [0, 1]. Operates on (4,) wxyz quaternions, returns (N, 4).
-# ----------------------------------------------------------------------------
-
-
-def slerp_smoothstep(q0: np.ndarray, q1: np.ndarray, u: np.ndarray) -> np.ndarray:
-    q0 = q0 / np.linalg.norm(q0)
-    q1 = q1 / np.linalg.norm(q1)
-    dot = float(np.dot(q0, q1))
-    if dot < 0.0:  # shortest path
-        q1 = -q1
-        dot = -dot
-    s = 6 * u**5 - 15 * u**4 + 10 * u**3
-    if dot > 0.9995:  # near-parallel: lerp + renormalise
-        out = (1 - s)[:, None] * q0 + s[:, None] * q1
-        return out / np.linalg.norm(out, axis=-1, keepdims=True)
-    theta_0 = np.arccos(np.clip(dot, -1.0, 1.0))
-    sin_theta_0 = np.sin(theta_0)
-    theta = theta_0 * s
-    a = np.cos(theta) - dot * np.sin(theta) / sin_theta_0
-    b = np.sin(theta) / sin_theta_0
-    return a[:, None] * q0 + b[:, None] * q1
-
-
-# ----------------------------------------------------------------------------
-# World-frame angular velocity from a quaternion sequence by finite difference.
-# Δq = q[k+1] * q[k]^-1  ;  ω_w ≈ 2 * Δq.xyz / dt  (small-angle, shortest path).
-# Central difference for interior, forward/backward at endpoints.
-# ----------------------------------------------------------------------------
-
-
-def quat_seq_ang_vel(q_seq: np.ndarray, dt: float) -> np.ndarray:
-    n = q_seq.shape[0]
-    out = np.zeros((n, 3), dtype=np.float64)
-
-    def diff(q_a, q_b, h):  # ω over interval h, expressed in world
-        aw, ax, ay, az = q_a
-        bw, bx, by, bz = q_b
-        # Δq = q_b * q_a^{-1}
-        dw = bw * aw + bx * ax + by * ay + bz * az
-        dx = -bw * ax + bx * aw - by * az + bz * ay
-        dy = -bw * ay + bx * az + by * aw - bz * ax
-        dz = -bw * az - bx * ay + by * ax + bz * aw
-        if dw < 0.0:  # shortest path
-            dw, dx, dy, dz = -dw, -dx, -dy, -dz
-        return np.array([2 * dx / h, 2 * dy / h, 2 * dz / h])
-
-    for i in range(n):
-        if i == 0:
-            out[i] = diff(q_seq[0], q_seq[1], dt)
-        elif i == n - 1:
-            out[i] = diff(q_seq[n - 2], q_seq[n - 1], dt)
-        else:
-            out[i] = diff(q_seq[i - 1], q_seq[i + 1], 2 * dt)
-    return out
diff --git a/scripts/deploy/prepend_warmup.py b/scripts/deploy/prepend_warmup.py
deleted file mode 100644
index 0d0c2c656..000000000
--- a/scripts/deploy/prepend_warmup.py
+++ /dev/null
@@ -1,215 +0,0 @@
-#!/usr/bin/env python3
-"""Prepend a stand->dance-frame-0 warmup prefix to a WBT motion bin.
-
-Why this exists
----------------
-The deploy-side FSM transitions FixStand -> State_WBT at t=0, but the original
-dance bin's frame 0 is mid-dance (e.g. dance1.bin frame 0 has L_shoulder_roll
-at +1.41 rad vs default_angles +0.20 — a ~70 deg jump). The training pipeline
-uses Reference State Initialization (RSI): the env resets the robot AT the
-motion's frame 0 pose, so the policy never sees "robot at default_angles,
-commanded to track dance frame 0". That mismatch produces a transient action
-burst at deploy time which exceeds the ankle's holding capacity, causing the
-real robot's feet to slip for ~3 s before the policy stabilises.
-
-Fix: insert N seconds of kinematically self-consistent interpolation frames at
-the head of the bin. After this the policy sees a slow, smooth tracking task
-(ramping the command from FixStand to dance frame 0), which is in-distribution
-for a tracking policy. The dance frames are then concatenated verbatim.
-
-What this does NOT change
--------------------------
-- Training pipeline (no retrain needed).
-- State_WBT.cpp / deploy_config.yaml / FSM yaml time_start.
-- The original dance frames (they are appended unchanged after the warmup).
-
-Interpolation scheme
---------------------
-- joint_pos (J=29): cubic Hermite per joint with boundaries
-    (default_angles, 0) -> (orig.jp[0], orig.jv[0])
-  joint_vel is the analytic derivative of the polynomial (kinematically
-  consistent with joint_pos by construction).
-- body_pos (B,3): cubic Hermite per axis with boundaries
-    (FK(stand), 0) -> (orig.bp[0], orig.bv[0])
-  body_lin_vel is the analytic derivative.
-- body_quat (B,4): SLERP along quintic smoothstep s(u) = 6u^5 - 15u^4 + 10u^3
-  (s'(0) = s''(0) = s'(1) = s''(1) = 0). Shortest-path; near-parallel fallback.
-- body_ang_vel: central difference on the SLERP path. The seam frame is
-  differenced against the original bin's frame 0 so the discrete derivative is
-  continuous across the seam.
-
-FixStand body states come from MuJoCo FK on the 'stand' keyframe in scene XML,
-using the same tracked_body_mujoco_ids the C++ State_WBT consumes — so the
-warmup's frame 0 (body_pos/body_quat) is exactly what default_angles produces.
-
-Use
----
-    uv run scripts/deploy/prepend_warmup.py \
-        --input  ../deploy_ws/assets/dance1.bin \
-        --output ../deploy_ws/assets/dance1_warmup.bin \
-        --config ../deploy_ws/assets/deploy_config.yaml \
-        --warmup-sec 1.5
-
-Validate in sim BEFORE swapping on the real robot:
-    uv run scripts/deploy/sim_prototype.py \
-        --motion ../deploy_ws/assets/dance1_warmup.bin \
-        --init-mode stand --render
-"""
-
-from __future__ import annotations
-
-import argparse
-import sys
-from pathlib import Path
-
-import numpy as np
-import yaml
-from motion_primitives import (
-    compute_fixstand_body_states,
-    hermite,
-    load_motion_bin,
-    quat_seq_ang_vel,
-    save_motion_bin,
-    slerp_smoothstep,
-)
-
-REPO_ROOT = Path(__file__).resolve().parents[2]
-DEPLOY_WS = REPO_ROOT.parent / "deploy_ws"
-DEFAULT_SCENE = REPO_ROOT / "src/unilab/assets/robots/g1/scene_flat.xml"
-DEFAULT_CFG = DEPLOY_WS / "assets/deploy_config.yaml"
-DEFAULT_IN = DEPLOY_WS / "assets/dance1.bin"
-DEFAULT_OUT = DEPLOY_WS / "assets/dance1_warmup.bin"
-DEFAULT_WARMUP_SEC = 1.5
-
-
-# ----------------------------------------------------------------------------
-# Main
-# ----------------------------------------------------------------------------
-
-
-def main() -> None:
-    ap = argparse.ArgumentParser(
-        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
-    )
-    ap.add_argument("--input", type=Path, default=DEFAULT_IN)
-    ap.add_argument("--output", type=Path, default=DEFAULT_OUT)
-    ap.add_argument(
-        "--config",
-        type=Path,
-        default=DEFAULT_CFG,
-        help="deploy_config.yaml (for default_angles, tracked ids).",
-    )
-    ap.add_argument(
-        "--scene",
-        type=Path,
-        default=DEFAULT_SCENE,
-        help="MuJoCo XML with 'stand' keyframe (for FixStand FK).",
-    )
-    ap.add_argument(
-        "--warmup-sec",
-        type=float,
-        default=DEFAULT_WARMUP_SEC,
-        help="Length of prepended warmup interval [seconds].",
-    )
-    args = ap.parse_args()
-
-    with open(args.config) as f:
-        cfg = yaml.safe_load(f)
-    default_angles = np.asarray(cfg["default_angles"], dtype=np.float64)
-    tracked_ids = list(cfg["tracked_body_mujoco_ids"])
-
-    orig = load_motion_bin(args.input)
-    fps, J, B = orig["fps"], orig["nj"], orig["nb"]
-    if J != len(default_angles):
-        raise SystemExit(f"J mismatch: bin J={J}, default_angles len={len(default_angles)}")
-    if B != len(tracked_ids):
-        raise SystemExit(f"B mismatch: bin B={B}, tracked_body_mujoco_ids len={len(tracked_ids)}")
-
-    dt = 1.0 / fps
-    N = int(round(args.warmup_sec * fps))
-    if N < 2:
-        raise SystemExit(
-            f"warmup-sec={args.warmup_sec}s too short for fps={fps} (need >= 2 frames)"
-        )
-    T = N * dt  # so frame N in the new bin == original frame 0 exactly
-
-    fk_jp, fk_bp, fk_bq = compute_fixstand_body_states(args.scene, tracked_ids)
-
-    keyframe_diff = float(np.abs(fk_jp - default_angles).max())
-    if keyframe_diff > 1e-3:
-        print(
-            f"WARN: 'stand' keyframe joint pos differs from deploy_config "
-            f"default_angles by {keyframe_diff:.4f} rad — using deploy_config "
-            "values for warmup start (so command_joint_pos matches what "
-            "FixStand actually holds on the real robot).",
-            file=sys.stderr,
-        )
-
-    # --- joint pos/vel: analytic Hermite -----------------------------------
-    ts = np.arange(N) * dt  # (N,)
-    jp_w, jv_w = hermite(default_angles, np.zeros(J), orig["jp"][0], orig["jv"][0], ts, T)
-
-    # --- body pos / lin_vel: analytic Hermite per axis ---------------------
-    bp_w, bv_w = hermite(fk_bp, np.zeros((B, 3)), orig["bp"][0], orig["bv"][0], ts, T)
-
-    # --- body quat: SLERP along quintic smoothstep -------------------------
-    u = ts / T
-    bq_w = np.zeros((N, B, 4), dtype=np.float64)
-    for b in range(B):
-        bq_w[:, b, :] = slerp_smoothstep(fk_bq[b], orig["bq"][0, b], u)
-
-    # --- body ang_vel: central diff, with the seam differenced against the
-    # original bin's first two frames so the derivative is continuous across
-    # the warmup/dance boundary --------------------------------------------
-    bav_w = np.zeros((N, B, 3), dtype=np.float64)
-    for b in range(B):
-        ext = np.concatenate([bq_w[:, b, :], orig["bq"][:2, b, :]], axis=0)
-        bav_w[:, b, :] = quat_seq_ang_vel(ext, dt)[:N]
-
-    # --- concatenate warmup + original -------------------------------------
-    new_jp = np.concatenate([jp_w, orig["jp"]], axis=0)
-    new_jv = np.concatenate([jv_w, orig["jv"]], axis=0)
-    new_bp = np.concatenate([bp_w, orig["bp"]], axis=0)
-    new_bq = np.concatenate([bq_w, orig["bq"]], axis=0)
-    new_bv = np.concatenate([bv_w, orig["bv"]], axis=0)
-    new_bav = np.concatenate([bav_w, orig["bav"]], axis=0)
-
-    args.output.parent.mkdir(parents=True, exist_ok=True)
-    save_motion_bin(args.output, fps, new_jp, new_jv, new_bp, new_bq, new_bv, new_bav)
-
-    # --- report ------------------------------------------------------------
-    seam_jp = float(np.abs(jp_w[-1] - orig["jp"][0]).max())
-    seam_jv = float(np.abs(jv_w[-1] - orig["jv"][0]).max())
-    seam_bp = float(np.abs(bp_w[-1] - orig["bp"][0]).max())
-    seam_bv = float(np.abs(bv_w[-1] - orig["bv"][0]).max())
-    # Quaternion seam: angle between bq_w[-1] and orig.bq[0] for each body
-    seam_qang_deg = []
-    for b in range(B):
-        d = float(np.clip(abs(np.dot(bq_w[-1, b], orig["bq"][0, b])), 0.0, 1.0))
-        seam_qang_deg.append(np.degrees(2 * np.arccos(d)))
-    seam_qang_max = max(seam_qang_deg)
-    # Warmup-start command joint deviation from default (rate / sec)
-    init_jvel_l2 = float(np.linalg.norm(jv_w[0]))
-
-    print(f"Wrote {args.output}")
-    print(f"  fps={fps}  J={J}  B={B}")
-    print(f"  frames: {orig['nf']} (orig)  -> {new_jp.shape[0]} ({N} warmup + {orig['nf']} dance)")
-    print(
-        f"  duration: {orig['nf'] / fps:.2f}s -> {new_jp.shape[0] / fps:.2f}s "
-        f"(warmup_sec={args.warmup_sec})"
-    )
-    print(
-        f"  init (frame 0) command_joint_pos == default_angles? "
-        f"max_diff={float(np.abs(new_jp[0] - default_angles).max()):.6f} rad"
-    )
-    print(f"  init (frame 0) command_joint_vel L2 = {init_jvel_l2:.6f} rad/s (should be 0)")
-    print("  seam check (warmup last frame vs original frame 0; one-dt-step apart):")
-    print(f"    |Δjoint_pos|_∞       = {seam_jp:.4f} rad")
-    print(f"    |Δjoint_vel|_∞       = {seam_jv:.4f} rad/s")
-    print(f"    |Δbody_pos|_∞        = {seam_bp:.4f} m")
-    print(f"    |Δbody_lin_vel|_∞    = {seam_bv:.4f} m/s")
-    print(f"    max body quat angle  = {seam_qang_max:.4f} deg")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/deploy/sim_prototype.py b/scripts/deploy/sim_prototype.py
deleted file mode 100644
index 7043b9bd2..000000000
--- a/scripts/deploy/sim_prototype.py
+++ /dev/null
@@ -1,520 +0,0 @@
-#!/usr/bin/env python3
-"""Python prototype of State_WBT — drives the ONNX policy in MuJoCo using the
-exact obs assembly the C++ State_WBT will use, so we can validate the
-deploy_config.yaml + dance1.bin against training-side expectations BEFORE
-writing C++.
-
-Inputs (defaults match the artifacts produced by export_*.py):
-  ~/deploy_ws/assets/policy.onnx   (optional — if missing or --no-onnx, the
-                                    prototype skips inference and only sanity-
-                                    checks obs assembly with default-angle ctrl)
-  ~/deploy_ws/assets/deploy_config.yaml
-  ~/deploy_ws/assets/dance1.bin
-
-What this verifies:
-  1. Obs assembly matches training segment-for-segment, driven by
-     cfg["obs_layout"] (no hard-coded dimension) — so when training flips
-     enable_zero_linvel / enable_zero_anchor_pos the prototype follows.
-  2. obs total length equals cfg["obs_dim"]; ONNX input width (when provided)
-     matches cfg["obs_dim"].
-  3. With an ONNX file: q_target = action*2.0 + default_angles + clip + EMA
-     produces motion that visually tracks the reference in MuJoCo.
-
-Differences from training-side eval (deliberate):
-  - obs construction is reimplemented in pure numpy here, NOT routed through
-    the env class — this is the same code path State_WBT will reproduce in C++.
-  - No noise injected on obs (matches deploy convention).
-  - Robot anchor pos in world is locked to the first motion frame's torso pos
-    (since real robot has no GPS/SLAM).
-"""
-
-from __future__ import annotations
-
-import argparse
-import struct
-import sys
-import time
-from pathlib import Path
-
-import mujoco
-import numpy as np
-import yaml
-
-REPO_ROOT = Path(__file__).resolve().parents[2]
-sys.path.insert(0, str(REPO_ROOT / "src"))
-from unilab.utils.rotation import (  # noqa: E402
-    np_matrix_from_quat,
-    np_subtract_frame_transforms,
-)
-
-DEFAULT_ONNX = Path.home() / "deploy_ws/assets/policy.onnx"
-DEFAULT_CFG = Path.home() / "deploy_ws/assets/deploy_config.yaml"
-DEFAULT_BIN = Path.home() / "deploy_ws/assets/dance1.bin"
-DEFAULT_SCENE = REPO_ROOT / "src/unilab/assets/robots/g1/scene_flat.xml"
-
-
-def load_motion_bin(path: Path) -> dict:
-    with open(path, "rb") as f:
-        fps, nf, nj, nb = struct.unpack("<iiii", f.read(16))
-        jp = np.frombuffer(f.read(nf * nj * 4), dtype="<f4").reshape(nf, nj).copy()
-        jv = np.frombuffer(f.read(nf * nj * 4), dtype="<f4").reshape(nf, nj).copy()
-        bp = np.frombuffer(f.read(nf * nb * 3 * 4), dtype="<f4").reshape(nf, nb, 3).copy()
-        bq = np.frombuffer(f.read(nf * nb * 4 * 4), dtype="<f4").reshape(nf, nb, 4).copy()
-        blv = np.frombuffer(f.read(nf * nb * 3 * 4), dtype="<f4").reshape(nf, nb, 3).copy()
-        bav = np.frombuffer(f.read(nf * nb * 3 * 4), dtype="<f4").reshape(nf, nb, 3).copy()
-    return {
-        "fps": fps,
-        "num_frames": nf,
-        "num_joints": nj,
-        "num_bodies": nb,
-        "joint_pos": jp,
-        "joint_vel": jv,
-        "body_pos_w": bp,
-        "body_quat_w": bq,
-        "body_lin_vel_w": blv,
-        "body_ang_vel_w": bav,
-    }
-
-
-def build_obs_from_layout(
-    layout: list[dict], segments: dict[str, np.ndarray], obs_dim: int
-) -> np.ndarray:
-    """Concatenate segments in the order dictated by cfg['obs_layout'].
-
-    Every layout entry must resolve to a known segment with matching dim.
-    Raises SystemExit on any mismatch — the prototype refuses to fabricate
-    a vector that disagrees with the deploy contract.
-
-    Single-step path only; history-aware assembly goes through ``ObsAssembler``.
-    """
-    parts: list[np.ndarray] = []
-    for seg in layout:
-        name = seg["name"]
-        dim = int(seg["dim"])
-        if name not in segments:
-            raise SystemExit(f"obs_layout segment '{name}' has no value provider in prototype")
-        arr = np.asarray(segments[name], dtype=np.float32).reshape(-1)
-        if arr.size != dim:
-            raise SystemExit(f"obs segment '{name}': expected dim {dim}, got {arr.size}")
-        parts.append(arr)
-    obs = np.concatenate(parts, axis=0).astype(np.float32, copy=False)
-    if obs.size != obs_dim:
-        raise SystemExit(f"assembled obs dim {obs.size} != cfg.obs_dim {obs_dim}")
-    return obs
-
-
-class ObsAssembler:
-    """Schema-driven actor obs assembler with per-term history buffers.
-
-    Mirrors the deploy-side ObservationManager + ObservationTermCfg behaviour
-    byte-for-byte so sim_prototype validates the same obs vector State_WBT
-    will produce on the real robot:
-
-      * Per layout term, owns a (H, dim) deque-like ring buffer (oldest at row 0).
-      * ``reset(segments)`` fills all H rows with the current value (matches
-        deploy ObservationTermCfg::reset which calls add() H times).
-      * ``step(segments)`` shifts oldest out, writes current at the last row
-        (matches deploy add()).
-      * ``assemble()`` concatenates each term's full history oldest-first,
-        then concatenates across terms in layout order (matches deploy
-        use_gym_history=false mode in ObservationManager::compute_group).
-
-    History length per term is read from ``layout[i]['history_length']``,
-    defaulting to 1 (single-step / no history). Total assembled dim is
-    sum(dim * history_length) over all terms.
-    """
-
-    def __init__(self, cfg: dict) -> None:
-        self.obs_dim = int(cfg["obs_dim"])
-        self.layout = cfg["obs_layout"]
-        if cfg.get("use_gym_history", False):
-            raise SystemExit(
-                "sim_prototype assumes use_gym_history=false (group-by-term flatten); "
-                "set it to false in deploy_config.yaml or extend ObsAssembler"
-            )
-        self._buffers: dict[str, np.ndarray] = {}
-        self._hist_len: dict[str, int] = {}
-        layout_total = 0
-        for seg in self.layout:
-            name = seg["name"]
-            dim = int(seg["dim"])
-            h_len = int(seg.get("history_length", 1))
-            if h_len < 1:
-                raise SystemExit(f"obs term '{name}' has invalid history_length={h_len}")
-            self._buffers[name] = np.zeros((h_len, dim), dtype=np.float32)
-            self._hist_len[name] = h_len
-            layout_total += dim * h_len
-        if layout_total != self.obs_dim:
-            raise SystemExit(
-                f"cfg internal inconsistency: sum(dim*history_length)={layout_total} "
-                f"!= obs_dim={self.obs_dim}"
-            )
-        self._primed = False
-
-    def reset(self, segments: dict[str, np.ndarray]) -> np.ndarray:
-        """Fill every history slot with the current segment values (deploy parity)."""
-        for seg in self.layout:
-            name = seg["name"]
-            self._set_validated(name, segments[name])
-            self._buffers[name][:] = self._buffers[name][-1:, :]
-        self._primed = True
-        return self.assemble()
-
-    def step(self, segments: dict[str, np.ndarray]) -> np.ndarray:
-        """Push current values; auto-primes on the first call."""
-        if not self._primed:
-            return self.reset(segments)
-        for seg in self.layout:
-            name = seg["name"]
-            buf = self._buffers[name]
-            buf[:-1] = buf[1:]
-            self._set_validated(name, segments[name])
-        return self.assemble()
-
-    def assemble(self) -> np.ndarray:
-        parts: list[np.ndarray] = []
-        for seg in self.layout:
-            name = seg["name"]
-            parts.append(self._buffers[name].reshape(-1))
-        obs = np.concatenate(parts, axis=0).astype(np.float32, copy=False)
-        if obs.size != self.obs_dim:
-            raise SystemExit(f"assembled obs dim {obs.size} != cfg.obs_dim {self.obs_dim}")
-        return obs
-
-    def _set_validated(self, name: str, value) -> None:
-        buf = self._buffers[name]
-        dim = buf.shape[1]
-        arr = np.asarray(value, dtype=np.float32).reshape(-1)
-        if arr.size != dim:
-            raise SystemExit(f"obs segment '{name}': expected dim {dim}, got {arr.size}")
-        buf[-1, :] = arr
-
-
-def compute_obs_segments(
-    cfg: dict,
-    motion_frame: dict,
-    *,
-    robot_torso_pos_w: np.ndarray,
-    robot_torso_quat_w: np.ndarray,
-    gyro: np.ndarray,
-    dof_pos: np.ndarray,
-    dof_vel: np.ndarray,
-    last_actions: np.ndarray,
-) -> dict[str, np.ndarray]:
-    """Compute the current-step value of every potential obs segment.
-
-    Returns a dict keyed by segment name. Only the segments listed in
-    cfg['obs_layout'] are consumed downstream; unused keys are harmless.
-
-    All inputs are batch-less (1D arrays). Each output segment is float32.
-    """
-    default_angles = np.asarray(cfg["default_angles"], dtype=np.float32)
-    anchor_idx = int(cfg["anchor_body_idx_in_tracked"])
-
-    ref_joint_pos = motion_frame["joint_pos"]
-    ref_joint_vel = motion_frame["joint_vel"]
-    ref_torso_pos_w = motion_frame["body_pos_w"][anchor_idx]
-    ref_torso_quat_w = motion_frame["body_quat_w"][anchor_idx]
-
-    pos_b, ori_q = np_subtract_frame_transforms(
-        robot_torso_pos_w[None, :],
-        robot_torso_quat_w[None, :],
-        ref_torso_pos_w[None, :],
-        ref_torso_quat_w[None, :],
-    )
-    motion_anchor_pos_b = pos_b[0].astype(np.float32)
-    ori_R = np_matrix_from_quat(ori_q)[0]
-    motion_anchor_ori_b = ori_R[:, :2].reshape(6).astype(np.float32)
-
-    # linvel: deploy default = zero (no real-robot sensor on G1).
-    linvel_strategy = str(cfg.get("linvel_strategy", "zero"))
-    if linvel_strategy != "zero":
-        raise SystemExit(
-            f"linvel_strategy='{linvel_strategy}' not supported in prototype; "
-            "G1 deploy must use 'zero'"
-        )
-    base_lin_vel = np.zeros(3, dtype=np.float32)
-
-    return {
-        "command_joint_pos": ref_joint_pos.astype(np.float32),
-        "command_joint_vel": ref_joint_vel.astype(np.float32),
-        "motion_anchor_pos_b": motion_anchor_pos_b,
-        "motion_anchor_ori_b": motion_anchor_ori_b,
-        # Two aliases so legacy schemas (which used 'linvel') still work.
-        "base_lin_vel": base_lin_vel,
-        "linvel": base_lin_vel,
-        "gyro": gyro.astype(np.float32),
-        "joint_pos_rel": (dof_pos - default_angles).astype(np.float32),
-        "dof_vel": dof_vel.astype(np.float32),
-        "last_actions": last_actions.astype(np.float32),
-    }
-
-
-def main():
-    ap = argparse.ArgumentParser(description=__doc__)
-    ap.add_argument(
-        "--onnx",
-        type=Path,
-        default=DEFAULT_ONNX,
-        help="ONNX policy. If absent or --no-onnx, prototype runs a "
-        "default-angle ctrl sanity check (obs assembly only).",
-    )
-    ap.add_argument(
-        "--no-onnx", action="store_true", help="Skip ONNX inference even if the file exists."
-    )
-    ap.add_argument("--config", type=Path, default=DEFAULT_CFG)
-    ap.add_argument("--motion", type=Path, default=DEFAULT_BIN)
-    ap.add_argument("--scene", type=Path, default=DEFAULT_SCENE)
-    ap.add_argument(
-        "--render", action="store_true", help="Open MuJoCo passive viewer (requires display)."
-    )
-    ap.add_argument(
-        "--max-steps", type=int, default=0, help="0 = play to end of clip then loop once."
-    )
-    ap.add_argument(
-        "--cheat-anchor",
-        action="store_true",
-        help="Use sim-true robot torso pos for anchor (debug; "
-        "would not be available on real robot).",
-    )
-    ap.add_argument(
-        "--init-mode",
-        choices=["rsi", "stand"],
-        default="rsi",
-        help="rsi = teleport robot to motion frame 0 (training-time "
-        "reset condition; default). stand = leave robot in the "
-        "'stand' keyframe pose (== FixStand default_angles, "
-        "matching the deploy-time FSM transition).",
-    )
-    args = ap.parse_args()
-
-    with open(args.config) as f:
-        cfg = yaml.safe_load(f)
-    motion = load_motion_bin(args.motion)
-
-    expected_dim = int(cfg["obs_dim"])
-    layout_total = sum(int(s["dim"]) * int(s.get("history_length", 1)) for s in cfg["obs_layout"])
-    if layout_total != expected_dim:
-        raise SystemExit(
-            f"cfg internal inconsistency: sum(obs_layout dim*history_length)={layout_total} "
-            f"!= obs_dim={expected_dim}"
-        )
-    obs_assembler = ObsAssembler(cfg)
-
-    use_onnx = (not args.no_onnx) and args.onnx.exists()
-    sess = None
-    inp_name = out_name = None
-    if use_onnx:
-        import onnxruntime as ort  # local import: optional in sanity mode
-
-        sess = ort.InferenceSession(str(args.onnx), providers=["CPUExecutionProvider"])
-        inp_name = sess.get_inputs()[0].name
-        out_name = sess.get_outputs()[0].name
-        onnx_in_shape = sess.get_inputs()[0].shape
-        onnx_in_dim = int(onnx_in_shape[-1]) if isinstance(onnx_in_shape[-1], int) else -1
-        print(
-            f"ONNX: input={inp_name} {onnx_in_shape}, output={out_name} "
-            f"{sess.get_outputs()[0].shape}"
-        )
-        if onnx_in_dim != expected_dim:
-            raise SystemExit(
-                f"ONNX input dim {onnx_in_dim} != cfg.obs_dim {expected_dim}. "
-                "Retrain (or re-export ONNX) so the policy matches the deploy contract."
-            )
-    else:
-        reason = "missing" if not args.onnx.exists() else "disabled (--no-onnx)"
-        print(
-            f"ONNX: {reason} — running OBS-ASSEMBLY SANITY CHECK only "
-            "(ctrl = default_angles, no policy in the loop)"
-        )
-
-    print(
-        f"obs_dim={expected_dim}, layout segments=["
-        + ", ".join(
-            f"{s['name']}({s['dim']}×{int(s.get('history_length', 1))})" for s in cfg["obs_layout"]
-        )
-        + "]"
-    )
-
-    model = mujoco.MjModel.from_xml_path(str(args.scene))
-    data = mujoco.MjData(model)
-    ctrl_dt = float(cfg["ctrl_dt"])
-    sim_dt = float(model.opt.timestep)
-    substeps = max(1, int(round(ctrl_dt / sim_dt)))
-    print(f"sim_dt={sim_dt:.5f}, ctrl_dt={ctrl_dt:.3f}, substeps/ctrl={substeps}")
-
-    # Init pose: two modes.
-    #   rsi   — Reference State Initialization: teleport robot to motion frame
-    #           0's pose (matches the training env's reset condition).
-    #   stand — leave robot in the "stand" keyframe pose (== FixStand
-    #           default_angles), which is what the C++ FSM sees at the
-    #           FixStand → State_WBT transition on the real robot.
-    # Use --init-mode stand to reproduce the deploy-time "first few seconds of
-    # slipping while the policy snaps the robot from default_angles to dance
-    # frame 0" scenario in simulation, without touching hardware.
-    key_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_KEY, "stand")
-    if key_id < 0:
-        raise SystemExit("'stand' keyframe not found")
-    mujoco.mj_resetDataKeyframe(model, data, key_id)
-
-    pelvis_id_in_tracked = 0  # 'pelvis' is first in TRACKED_BODY_NAMES
-    if args.init_mode == "rsi":
-        data.qpos[0:3] = motion["body_pos_w"][0, pelvis_id_in_tracked]
-        data.qpos[3:7] = motion["body_quat_w"][0, pelvis_id_in_tracked]  # wxyz
-        data.qpos[7:] = motion["joint_pos"][0]
-        data.qvel[0:3] = motion["body_lin_vel_w"][0, pelvis_id_in_tracked]
-        data.qvel[3:6] = motion["body_ang_vel_w"][0, pelvis_id_in_tracked]
-        data.qvel[6:] = motion["joint_vel"][0]
-    mujoco.mj_forward(model, data)
-    print(f"init_mode={args.init_mode}: base xyz={data.qpos[:3]}, base quat={data.qpos[3:7]}")
-
-    default_angles = np.asarray(cfg["default_angles"], dtype=np.float32)
-    action_scale = float(cfg["action_scale"])
-    ema_alpha = float(cfg["ema_alpha"])
-    joint_lower = np.asarray(cfg["joint_lower"], dtype=np.float32)
-    joint_upper = np.asarray(cfg["joint_upper"], dtype=np.float32)
-    anchor_idx = int(cfg["anchor_body_idx_in_tracked"])
-    anchor_body_name = cfg["anchor_body_name"]
-    anchor_body_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_BODY, anchor_body_name)
-
-    gyro_sid = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_SENSOR, "gyro")
-    if gyro_sid < 0:
-        raise SystemExit("'gyro' sensor not found in model")
-    gyro_adr = int(model.sensor_adr[gyro_sid])
-    gyro_dim = int(model.sensor_dim[gyro_sid])
-    if gyro_dim != 3:
-        raise SystemExit(f"gyro sensor has dim {gyro_dim}, expected 3")
-
-    robot_anchor_pos_w_locked = motion["body_pos_w"][0, anchor_idx].astype(np.float32)
-    print(f"robot_anchor_pos_w (locked) = {robot_anchor_pos_w_locked}")
-
-    last_actions = np.zeros(29, dtype=np.float32)
-    q_target_smoothed = default_angles.copy()
-    n_frames = motion["num_frames"]
-    if args.max_steps > 0:
-        total_steps = args.max_steps
-    elif use_onnx:
-        total_steps = n_frames
-    else:
-        total_steps = min(50, n_frames)  # sanity mode: short run
-
-    obs_norms = []
-    action_amplitudes = []
-    z_errors = []
-
-    viewer = None
-    if args.render:
-        from mujoco import viewer as mj_viewer
-
-        viewer = mj_viewer.launch_passive(model, data)
-
-    t_wall = time.time()
-    for step in range(total_steps):
-        frame_idx = step % n_frames
-
-        robot_torso_quat_w = data.xquat[anchor_body_id].astype(np.float32)
-        gyro = data.sensordata[gyro_adr : gyro_adr + gyro_dim].astype(np.float32)
-        dof_pos = data.qpos[7:].astype(np.float32)
-        dof_vel = data.qvel[6:].astype(np.float32)
-
-        motion_frame = {
-            "joint_pos": motion["joint_pos"][frame_idx],
-            "joint_vel": motion["joint_vel"][frame_idx],
-            "body_pos_w": motion["body_pos_w"][frame_idx],
-            "body_quat_w": motion["body_quat_w"][frame_idx],
-        }
-        if args.cheat_anchor:
-            robot_torso_pos_w_used = data.xpos[anchor_body_id].astype(np.float32)
-        else:
-            robot_torso_pos_w_used = robot_anchor_pos_w_locked
-        current_segments = compute_obs_segments(
-            cfg,
-            motion_frame,
-            robot_torso_pos_w=robot_torso_pos_w_used,
-            robot_torso_quat_w=robot_torso_quat_w,
-            gyro=gyro,
-            dof_pos=dof_pos,
-            dof_vel=dof_vel,
-            last_actions=last_actions,
-        )
-        # Stateful: first call auto-resets (fills history with current segments,
-        # matching State_WBT.cpp's env_->reset() at FSM enter); subsequent calls
-        # push current and drop oldest (matching ObservationTermCfg::add).
-        obs = obs_assembler.step(current_segments)
-        if not np.all(np.isfinite(obs)):
-            raise SystemExit(f"non-finite obs at step {step}")
-
-        if use_onnx:
-            action = sess.run([out_name], {inp_name: obs[None, :].astype(np.float32)})[0][0]
-            action = action.astype(np.float32)
-            last_actions = action.copy()
-            q_target = action * action_scale + default_angles
-            q_target = np.clip(q_target, joint_lower, joint_upper)
-            q_target_smoothed = ema_alpha * q_target + (1.0 - ema_alpha) * q_target_smoothed
-        else:
-            # Sanity mode: keep robot at default angles, freeze last_actions at 0.
-            q_target_smoothed = default_angles.copy()
-
-        data.ctrl[:] = q_target_smoothed
-
-        for _ in range(substeps):
-            mujoco.mj_step(model, data)
-
-        obs_norms.append(float(np.linalg.norm(obs)))
-        action_amplitudes.append(float(np.max(np.abs(last_actions))))
-        robot_z = float(data.xpos[anchor_body_id, 2])
-        ref_z = float(motion["body_pos_w"][frame_idx, anchor_idx, 2])
-        z_errors.append(abs(robot_z - ref_z))
-
-        if viewer is not None:
-            viewer.sync()
-            elapsed = time.time() - t_wall
-            target = (step + 1) * ctrl_dt
-            if elapsed < target:
-                time.sleep(target - elapsed)
-
-        if step % 50 == 0:
-            print(
-                f"step {step:4d} frame={frame_idx:3d}  "
-                f"obs_norm={obs_norms[-1]:7.2f}  "
-                f"|action|={action_amplitudes[-1]:5.2f}  "
-                f"z_err={z_errors[-1]:.3f}m  "
-                f"q_target[:3]={q_target_smoothed[:3]}"
-            )
-
-        if not np.all(np.isfinite(data.qpos)):
-            print(f"!! NaN at step {step}, aborting")
-            break
-        if use_onnx and z_errors[-1] > 0.6:
-            print(f"!! z_err {z_errors[-1]:.2f}m exceeds 0.6m, robot likely fell at step {step}")
-            break
-
-    if viewer is not None:
-        viewer.close()
-
-    n = len(obs_norms)
-    print()
-    print(f"Ran {n} ctrl steps ({n * ctrl_dt:.2f}s of motion).")
-    print(f"obs_norm:        mean={np.mean(obs_norms):.3f}  max={np.max(obs_norms):.3f}")
-    print(
-        f"|action| max:    mean={np.mean(action_amplitudes):.3f}  max={np.max(action_amplitudes):.3f}"
-    )
-    print(f"|torso z err|:   mean={np.mean(z_errors):.4f}m  max={np.max(z_errors):.4f}m")
-
-    if not use_onnx:
-        # Sanity mode never claims tracking — only that obs assembly is finite & shaped.
-        print(
-            "SANITY OK — obs assembly produced finite vectors of expected width "
-            f"({expected_dim}); end-to-end tracking requires running with a "
-            f"matching ONNX (input dim {expected_dim})."
-        )
-    elif np.max(z_errors) < 0.2 and n == total_steps:
-        print("PROTOTYPE OK — obs assembly + ONNX inference produces tracking behavior.")
-    elif n < total_steps:
-        print("WARNING: prototype aborted before clip end (see message above).")
-    else:
-        print("WARNING: large torso z error — obs assembly may be off, or policy weak.")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/generate_support_matrix.py b/scripts/generate_support_matrix.py
index 8947c28ce..931bf32cc 100644
--- a/scripts/generate_support_matrix.py
+++ b/scripts/generate_support_matrix.py
@@ -5,9 +5,14 @@
 from __future__ import annotations
 
 import argparse
+import sys
 from pathlib import Path
 
-from unilab.utils.support_matrix import (
+ROOT_DIR = Path(__file__).resolve().parents[1]
+if str(ROOT_DIR) not in sys.path:
+    sys.path.append(str(ROOT_DIR))
+
+from scripts.tools.support_matrix import (
     render_generated_block,
     render_support_matrix,
     replace_generated_block,
diff --git a/scripts/manip_loco/benchmark_site_jacobian.py b/scripts/manip_loco/benchmark_site_jacobian.py
index ab4f44be1..8c5e25486 100644
--- a/scripts/manip_loco/benchmark_site_jacobian.py
+++ b/scripts/manip_loco/benchmark_site_jacobian.py
@@ -19,8 +19,13 @@
 if str(ROOT_DIR) not in sys.path:
     sys.path.insert(0, str(ROOT_DIR))
 
-from unilab.base.backend import materialize_scene_visual_override
-from unilab.training import BackendAdapter, create_env, ensure_registries
+from unisim.backend.mujoco.xml import materialize_scene_visual_override
+
+from unilab.base.config_adapter import (
+    BackendAdapter,
+    create_env,
+)
+from unilab.training import ensure_registries
 
 
 def _coerce_str_list(value: Any, *, name: str) -> list[str]:
@@ -229,7 +234,7 @@ def _run_one_case(
         env.close()
 
 
-@hydra.main(version_base="1.3", config_path="../conf/ppo", config_name="config")
+@hydra.main(version_base="1.3", config_path="../../src/unilab/conf/ppo", config_name="config")
 def main(cfg: DictConfig) -> None:
     ensure_registries()
     if str(cfg.training.sim_backend) != "mujoco":
diff --git a/scripts/manip_loco/diagnose_go2_arm_ik.py b/scripts/manip_loco/diagnose_go2_arm_ik.py
index 601eb70b7..d859fba07 100644
--- a/scripts/manip_loco/diagnose_go2_arm_ik.py
+++ b/scripts/manip_loco/diagnose_go2_arm_ik.py
@@ -16,7 +16,7 @@
 
 from unilab.base import registry
 from unilab.base.registry import ensure_registries
-from unilab.envs.locomotion.go2_arm.manip_loco import RewardConfig
+from unilab.tasks.locomotion.go2_arm.manip_loco import RewardConfig
 from unilab.utils.rotation import np_matrix_from_quat
 
 
diff --git a/scripts/manip_loco/play_go2_arm_ik_only.py b/scripts/manip_loco/play_go2_arm_ik_only.py
index dce252814..c1090ec84 100644
--- a/scripts/manip_loco/play_go2_arm_ik_only.py
+++ b/scripts/manip_loco/play_go2_arm_ik_only.py
@@ -18,8 +18,8 @@
 if str(ROOT_DIR) not in sys.path:
     sys.path.insert(0, str(ROOT_DIR))
 
-from unilab.envs.locomotion.go2_arm.base import build_go2_arm_position_gains
-from unilab.envs.locomotion.go2_arm.manip_loco import Go2ArmManipLocoCfg
+from unilab.tasks.locomotion.go2_arm.base import build_go2_arm_position_gains
+from unilab.tasks.locomotion.go2_arm.manip_loco import Go2ArmManipLocoCfg
 
 TARGET_BODY = "ik_mocap_target"
 
diff --git a/src/unilab/tools/bones_seed_csv.py b/scripts/motion/bones_seed_csv.py
similarity index 100%
rename from src/unilab/tools/bones_seed_csv.py
rename to scripts/motion/bones_seed_csv.py
diff --git a/scripts/motion/bones_seed_csv_to_npz.py b/scripts/motion/bones_seed_csv_to_npz.py
index ed21defdf..57cc1b255 100644
--- a/scripts/motion/bones_seed_csv_to_npz.py
+++ b/scripts/motion/bones_seed_csv_to_npz.py
@@ -21,57 +21,39 @@
 - ``body_lin_vel_w``
 - ``body_ang_vel_w``
 
+Interpolation and velocity estimation reuse the library implementation in
+``unilab.tasks.motion_tracking.common.motion_loader``; forward kinematics reuse
+``unisim.backend.mujoco.motion_export.compute_tracking_fk``.
+
 Usage:
     uv run scripts/motion/bones_seed_csv_to_npz.py
     uv run scripts/motion/bones_seed_csv_to_npz.py --dry-run
     uv run scripts/motion/bones_seed_csv_to_npz.py --input path/to/flip_090_001__A304.csv
 """
 
-# pyright: reportAttributeAccessIssue=false
-
 from __future__ import annotations
 
 import argparse
-from dataclasses import dataclass
 from pathlib import Path
 
-import mujoco
 import numpy as np
-from tqdm import tqdm
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base.backend.mujoco.xml import inject_mujoco_tracking_sensors
-from unilab.tools.bones_seed_csv import (
+from scripts.motion.bones_seed_csv import (
     ROOT_COLUMNS,
     euler_deg_to_quat_wxyz,
     load_header,
     parse_joint_names,
     resolve_input_files,
 )
-from unilab.utils.rotation import np_quat_angular_velocity, np_quat_ensure_continuity
+from unisim.backend.mujoco.motion_export import compute_tracking_fk
+
+from unilab.assets import ASSETS_ROOT_PATH
+from unilab.tasks.motion_tracking.common.motion_loader import interpolate_motion
+from unilab.utils.rotation import np_quat_ensure_continuity
 
 DEFAULT_INPUT = "src/unilab/assets/motions/g1/flip"
 DEFAULT_OUTPUT_DIR = "src/unilab/assets/motions/g1/flip_npz"
 
 
-def quat_slerp(q1: np.ndarray, q2: np.ndarray, t: float) -> np.ndarray:
-    """Spherical linear interpolation between two quaternions (wxyz format)."""
-    dot = np.dot(q1, q2)
-    if dot < 0:
-        q2 = -q2
-        dot = -dot
-
-    if dot > 0.9995:
-        result = q1 + t * (q2 - q1)
-        return result / np.linalg.norm(result)
-
-    theta = np.arccos(np.clip(dot, -1, 1))
-    sin_theta = np.sin(theta)
-    w1 = np.sin((1 - t) * theta) / sin_theta
-    w2 = np.sin(t * theta) / sin_theta
-    return w1 * q1 + w2 * q2
-
-
 def default_model_path() -> str:
     return str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat.xml")
 
@@ -102,182 +84,34 @@ def resolve_output_targets(
     return [output_root / f"{csv_file.stem}.npz" for csv_file in csv_files]
 
 
-@dataclass
-class MotionLoader:
-    motion_file: Path
-    input_fps: int
-    output_fps: int
-    position_scale: float
-    euler_order: str
-
-    def __post_init__(self) -> None:
-        self.input_dt = 1.0 / self.input_fps
-        self.output_dt = 1.0 / self.output_fps
-        self._load_motion()
-        self._interpolate_motion()
-        self._compute_velocities()
-
-    def _load_motion(self) -> None:
-        header = load_header(self.motion_file)
-        self.joint_names = parse_joint_names(header, self.motion_file)
-
-        motion = np.loadtxt(self.motion_file, delimiter=",", dtype=np.float32, skiprows=1)
-        motion = np.atleast_2d(motion)
-        if motion.shape[1] != len(header):
+def load_csv_motion(
+    motion_file: Path,
+    *,
+    position_scale: float,
+    euler_order: str,
+) -> tuple[list[str], np.ndarray, np.ndarray, np.ndarray]:
+    """Load a BONES-SEED CSV into (joint_names, base/dof trajectory arrays)."""
+    header = load_header(motion_file)
+    joint_names = parse_joint_names(header, motion_file)
+
+    motion = np.loadtxt(motion_file, delimiter=",", dtype=np.float32, skiprows=1)
+    motion = np.atleast_2d(motion)
+    if motion.shape[1] != len(header):
+        raise ValueError(f"{motion_file} has {motion.shape[1]} columns, expected {len(header)}")
+
+    frames = motion[:, 0].astype(np.int32)
+    if frames.shape[0] > 1:
+        frame_diffs = np.diff(frames)
+        if not np.all(frame_diffs == 1):
             raise ValueError(
-                f"{self.motion_file} has {motion.shape[1]} columns, expected {len(header)}"
+                f"{motion_file} has non-contiguous Frame values: {np.unique(frame_diffs)}"
             )
 
-        self.frames = motion[:, 0].astype(np.int32)
-        if self.frames.shape[0] > 1:
-            frame_diffs = np.diff(self.frames)
-            if not np.all(frame_diffs == 1):
-                raise ValueError(
-                    f"{self.motion_file} has non-contiguous Frame values: {np.unique(frame_diffs)}"
-                )
-
-        self.motion_base_poss_input = motion[:, 1:4] * self.position_scale
-        self.motion_base_rots_input = euler_deg_to_quat_wxyz(motion[:, 4:7], self.euler_order)
-        self.motion_base_rots_input = np_quat_ensure_continuity(self.motion_base_rots_input)
-        self.motion_dof_poss_input = np.deg2rad(motion[:, len(ROOT_COLUMNS) :])
-
-        self.input_frames = motion.shape[0]
-        self.duration = (self.input_frames - 1) * self.input_dt
-
-    def _interpolate_motion(self) -> None:
-        times = np.arange(0, self.duration, self.output_dt, dtype=np.float32)
-        self.output_frames = times.shape[0]
-        index_0, index_1, blend = self._compute_frame_blend(times)
-
-        self.motion_base_poss = (
-            self.motion_base_poss_input[index_0] * (1 - blend[:, None])
-            + self.motion_base_poss_input[index_1] * blend[:, None]
-        )
-
-        self.motion_base_rots = np.zeros((self.output_frames, 4), dtype=np.float32)
-        for i in range(self.output_frames):
-            self.motion_base_rots[i] = quat_slerp(
-                self.motion_base_rots_input[index_0[i]],
-                self.motion_base_rots_input[index_1[i]],
-                blend[i],
-            )
-        self.motion_base_rots = np_quat_ensure_continuity(self.motion_base_rots)
-
-        self.motion_dof_poss = (
-            self.motion_dof_poss_input[index_0] * (1 - blend[:, None])
-            + self.motion_dof_poss_input[index_1] * blend[:, None]
-        )
-
-    def _compute_frame_blend(self, times: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        phase = times / self.duration
-        index_0 = np.floor(phase * (self.input_frames - 1)).astype(np.int32)
-        index_1 = np.minimum(index_0 + 1, self.input_frames - 1)
-        blend = phase * (self.input_frames - 1) - index_0
-        return index_0, index_1, blend
-
-    def _compute_velocities(self) -> None:
-        self.motion_base_lin_vels = np.gradient(self.motion_base_poss, self.output_dt, axis=0)
-        self.motion_dof_vels = np.gradient(self.motion_dof_poss, self.output_dt, axis=0)
-        self.motion_base_ang_vels = np_quat_angular_velocity(self.motion_base_rots, self.output_dt)
-
-
-def run_simulation(
-    motion_loader: MotionLoader,
-    model_file: str,
-    output_file: Path,
-) -> None:
-    tmp_model_path, _, _ = inject_mujoco_tracking_sensors(model_file)
-    try:
-        model = mujoco.MjModel.from_xml_path(tmp_model_path)
-    finally:
-        Path(tmp_model_path).unlink(missing_ok=True)
-    data = mujoco.MjData(model)
-
-    joint_indices = []
-    for name in motion_loader.joint_names:
-        jnt_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_JOINT, name)
-        if jnt_id < 0:
-            raise ValueError(f"Joint '{name}' not found in model")
-        joint_indices.append(jnt_id)
-
-    num_frames = motion_loader.output_frames
-    num_joints = len(joint_indices)
-    num_bodies = model.nbody
-
-    joint_pos = np.zeros((num_frames, num_joints), dtype=np.float32)
-    joint_vel = np.zeros((num_frames, num_joints), dtype=np.float32)
-    body_pos_w = np.zeros((num_frames, num_bodies, 3), dtype=np.float32)
-    body_quat_w = np.zeros((num_frames, num_bodies, 4), dtype=np.float32)
-    body_lin_vel_w = np.zeros((num_frames, num_bodies, 3), dtype=np.float32)
-    body_ang_vel_w = np.zeros((num_frames, num_bodies, 3), dtype=np.float32)
-
-    sensor_adrs = np.full((num_bodies, 4), -1, dtype=np.int32)
-    sensor_dims = np.array([3, 4, 3, 3], dtype=np.int32)
-    sensor_prefixes = (
-        "track_pos_w_",
-        "track_quat_w_",
-        "track_linvel_w_",
-        "track_angvel_w_",
-    )
-    for body_id in range(num_bodies):
-        body_name = mujoco.mj_id2name(model, mujoco.mjtObj.mjOBJ_BODY, body_id)
-        if not body_name:
-            continue
-        for k, prefix in enumerate(sensor_prefixes):
-            sensor_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_SENSOR, f"{prefix}{body_name}")
-            if sensor_id >= 0:
-                sensor_adrs[body_id, k] = model.sensor_adr[sensor_id]
-
-    for i in tqdm(range(num_frames), desc=output_file.stem, leave=False):
-        data.qpos[0:3] = motion_loader.motion_base_poss[i]
-        data.qpos[3:7] = motion_loader.motion_base_rots[i]
-        data.qvel[0:3] = motion_loader.motion_base_lin_vels[i]
-        data.qvel[3:6] = motion_loader.motion_base_ang_vels[i]
-
-        for j, jnt_id in enumerate(joint_indices):
-            qpos_adr = model.jnt_qposadr[jnt_id]
-            qvel_adr = model.jnt_dofadr[jnt_id]
-            data.qpos[qpos_adr] = motion_loader.motion_dof_poss[i, j]
-            data.qvel[qvel_adr] = motion_loader.motion_dof_vels[i, j]
-
-        mujoco.mj_forward(model, data)
-
-        for j, jnt_id in enumerate(joint_indices):
-            qpos_adr = model.jnt_qposadr[jnt_id]
-            qvel_adr = model.jnt_dofadr[jnt_id]
-            joint_pos[i, j] = data.qpos[qpos_adr]
-            joint_vel[i, j] = data.qvel[qvel_adr]
-
-        for body_id in range(num_bodies):
-            pos_adr, quat_adr, lin_adr, ang_adr = sensor_adrs[body_id]
-
-            if pos_adr >= 0:
-                body_pos_w[i, body_id] = data.sensordata[pos_adr : pos_adr + sensor_dims[0]]
-            else:
-                body_pos_w[i, body_id] = data.xpos[body_id]
-
-            if quat_adr >= 0:
-                body_quat_w[i, body_id] = data.sensordata[quat_adr : quat_adr + sensor_dims[1]]
-            else:
-                body_quat_w[i, body_id] = data.xquat[body_id]
-
-            if lin_adr >= 0:
-                body_lin_vel_w[i, body_id] = data.sensordata[lin_adr : lin_adr + sensor_dims[2]]
-
-            if ang_adr >= 0:
-                body_ang_vel_w[i, body_id] = data.sensordata[ang_adr : ang_adr + sensor_dims[3]]
-
-    output_file.parent.mkdir(parents=True, exist_ok=True)
-    np.savez(
-        output_file,
-        fps=np.array([motion_loader.output_fps], dtype=np.int32),
-        joint_pos=joint_pos,
-        joint_vel=joint_vel,
-        body_pos_w=body_pos_w,
-        body_quat_w=body_quat_w,
-        body_lin_vel_w=body_lin_vel_w,
-        body_ang_vel_w=body_ang_vel_w,
-    )
+    motion_base_poss_input = motion[:, 1:4] * position_scale
+    motion_base_rots_input = euler_deg_to_quat_wxyz(motion[:, 4:7], euler_order)
+    motion_base_rots_input = np_quat_ensure_continuity(motion_base_rots_input)
+    motion_dof_poss_input = np.deg2rad(motion[:, len(ROOT_COLUMNS) :])
+    return joint_names, motion_base_poss_input, motion_base_rots_input, motion_dof_poss_input
 
 
 def print_plan(
@@ -331,16 +165,40 @@ def convert(args: argparse.Namespace) -> None:
         run_dry_run(csv_files, output_files)
         return
 
+    input_fps = int(args.input_fps)
+    output_fps = int(args.output_fps)
     for csv_file, output_file in zip(csv_files, output_files, strict=True):
         print(f"[bones_seed_csv_to_npz] Converting {csv_file} -> {output_file}")
-        motion_loader = MotionLoader(
-            motion_file=csv_file,
-            input_fps=int(args.input_fps),
-            output_fps=int(args.output_fps),
+        joint_names, base_poss_input, base_rots_input, dof_poss_input = load_csv_motion(
+            csv_file,
             position_scale=args.position_scale,
             euler_order=args.euler_order,
         )
-        run_simulation(motion_loader, model_file, output_file)
+        motion = interpolate_motion(
+            base_poss_input,
+            base_rots_input,
+            dof_poss_input,
+            input_fps=input_fps,
+            output_fps=output_fps,
+        )
+        arrays = compute_tracking_fk(
+            model_file,
+            joint_names=joint_names,
+            base_poss=motion.base_poss,
+            base_rots=motion.base_rots,
+            base_lin_vels=motion.base_lin_vels,
+            base_ang_vels=motion.base_ang_vels,
+            dof_poss=motion.dof_poss,
+            dof_vels=motion.dof_vels,
+            progress=True,
+            progress_desc=output_file.stem,
+        )
+        output_file.parent.mkdir(parents=True, exist_ok=True)
+        np.savez(
+            output_file,
+            fps=np.array([output_fps], dtype=np.int32),
+            **arrays,
+        )
 
 
 def parse_args() -> argparse.Namespace:
diff --git a/scripts/motion/csv_to_npz.py b/scripts/motion/csv_to_npz.py
index 2794a7114..995bff834 100644
--- a/scripts/motion/csv_to_npz.py
+++ b/scripts/motion/csv_to_npz.py
@@ -17,252 +17,44 @@
 - body_quat_w: Body quaternions in world frame (N_frames × N_bodies × 4, wxyz)
 - body_lin_vel_w: Body linear velocities (N_frames × N_bodies × 3)
 - body_ang_vel_w: Body angular velocities (N_frames × N_bodies × 3)
-"""
 
-# pyright: reportAttributeAccessIssue=false
+Interpolation and velocity estimation reuse the library implementation in
+``unilab.tasks.motion_tracking.common.motion_loader``; forward kinematics reuse
+``unisim.backend.mujoco.motion_export.compute_tracking_fk``.
+"""
 
 import argparse
 from pathlib import Path
 
-import mujoco
 import numpy as np
-from tqdm import tqdm
+from unisim.backend.mujoco.motion_export import compute_tracking_fk
 
 from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base.backend.mujoco.xml import inject_mujoco_tracking_sensors
-from unilab.utils.rotation import np_quat_angular_velocity, np_quat_ensure_continuity
-
-
-def quat_slerp(q1: np.ndarray, q2: np.ndarray, t: float) -> np.ndarray:
-    """Spherical linear interpolation between two quaternions (wxyz format)."""
-    # Ensure shortest path
-    dot = np.dot(q1, q2)
-    if dot < 0:
-        q2 = -q2
-        dot = -dot
-
-    # If quaternions are very close, use linear interpolation
-    if dot > 0.9995:
-        result = q1 + t * (q2 - q1)
-        return result / np.linalg.norm(result)
-
-    # Compute angle
-    theta = np.arccos(np.clip(dot, -1, 1))
-    sin_theta = np.sin(theta)
-
-    # Compute interpolation weights
-    w1 = np.sin((1 - t) * theta) / sin_theta
-    w2 = np.sin(t * theta) / sin_theta
-
-    return w1 * q1 + w2 * q2
-
-
-class MotionLoader:
-    """Load and interpolate motion from CSV file."""
-
-    def __init__(
-        self,
-        motion_file: str,
-        input_fps: int,
-        output_fps: int,
-        line_range: tuple[int, int] | None = None,
-    ):
-        self.motion_file = motion_file
-        self.input_fps = input_fps
-        self.output_fps = output_fps
-        self.input_dt = 1.0 / self.input_fps
-        self.output_dt = 1.0 / self.output_fps
-        self.line_range = line_range
-        self._load_motion()
-        self._interpolate_motion()
-        self._compute_velocities()
-
-    def _load_motion(self):
-        """Load motion from CSV file."""
-        if self.line_range is None:
-            motion = np.loadtxt(self.motion_file, delimiter=",", dtype=np.float32, skiprows=1)
-        else:
-            motion = np.loadtxt(
-                self.motion_file,
-                delimiter=",",
-                skiprows=max(1, self.line_range[0] - 1),
-                max_rows=self.line_range[1] - self.line_range[0] + 1,
-                dtype=np.float32,
-            )
-
-        self.motion_base_poss_input = motion[:, :3]
-        # Convert quaternion from xyzw to wxyz
-        self.motion_base_rots_input = motion[:, 3:7][:, [3, 0, 1, 2]]
-        self.motion_base_rots_input = np_quat_ensure_continuity(self.motion_base_rots_input)
-        self.motion_dof_poss_input = motion[:, 7:]
-
-        self.input_frames = motion.shape[0]
-        self.duration = (self.input_frames - 1) * self.input_dt
-
-    def _interpolate_motion(self):
-        """Interpolate motion to output FPS."""
-        times = np.arange(0, self.duration, self.output_dt, dtype=np.float32)
-        self.output_frames = times.shape[0]
-        index_0, index_1, blend = self._compute_frame_blend(times)
-
-        # Linear interpolation for positions
-        self.motion_base_poss = (
-            self.motion_base_poss_input[index_0] * (1 - blend[:, None])
-            + self.motion_base_poss_input[index_1] * blend[:, None]
+from unilab.tasks.motion_tracking.common.motion_loader import interpolate_motion
+from unilab.utils.rotation import np_quat_ensure_continuity
+
+
+def load_csv_motion(
+    motion_file: str, line_range: tuple[int, int] | None = None
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Load a Unitree-convention CSV into base/dof trajectory arrays."""
+    if line_range is None:
+        motion = np.loadtxt(motion_file, delimiter=",", dtype=np.float32, skiprows=1)
+    else:
+        motion = np.loadtxt(
+            motion_file,
+            delimiter=",",
+            skiprows=max(1, line_range[0] - 1),
+            max_rows=line_range[1] - line_range[0] + 1,
+            dtype=np.float32,
         )
 
-        # Spherical linear interpolation for quaternions
-        self.motion_base_rots = np.zeros((self.output_frames, 4), dtype=np.float32)
-        for i in range(self.output_frames):
-            self.motion_base_rots[i] = quat_slerp(
-                self.motion_base_rots_input[index_0[i]],
-                self.motion_base_rots_input[index_1[i]],
-                blend[i],
-            )
-        self.motion_base_rots = np_quat_ensure_continuity(self.motion_base_rots)
-
-        # Linear interpolation for joint positions
-        self.motion_dof_poss = (
-            self.motion_dof_poss_input[index_0] * (1 - blend[:, None])
-            + self.motion_dof_poss_input[index_1] * blend[:, None]
-        )
-
-        print(
-            f"Motion interpolated: {self.input_frames} frames @ {self.input_fps} Hz "
-            f"→ {self.output_frames} frames @ {self.output_fps} Hz"
-        )
-
-    def _compute_frame_blend(self, times: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """Compute frame indices and blend weights for interpolation."""
-        phase = times / self.duration
-        index_0 = np.floor(phase * (self.input_frames - 1)).astype(np.int32)
-        index_1 = np.minimum(index_0 + 1, self.input_frames - 1)
-        blend = phase * (self.input_frames - 1) - index_0
-        return index_0, index_1, blend
-
-    def _compute_velocities(self):
-        """Compute velocities using numerical differentiation."""
-        # Linear velocities
-        self.motion_base_lin_vels = np.gradient(self.motion_base_poss, self.output_dt, axis=0)
-        self.motion_dof_vels = np.gradient(self.motion_dof_poss, self.output_dt, axis=0)
-
-        # Angular velocities from quaternion derivatives
-        self.motion_base_ang_vels = np_quat_angular_velocity(self.motion_base_rots, self.output_dt)
-
-
-def run_simulation(
-    motion_loader: MotionLoader,
-    model_file: str,
-    joint_names: list[str],
-    output_file: str,
-):
-    """Run MuJoCo simulation to compute forward kinematics for the export."""
-    # Inject track_* sensors so exported body_* fields match training-time semantics.
-    tmp_model_path, _, _ = inject_mujoco_tracking_sensors(model_file)
-    try:
-        model = mujoco.MjModel.from_xml_path(tmp_model_path)
-        print(f"Model loaded from {tmp_model_path}")
-    finally:
-        Path(tmp_model_path).unlink(missing_ok=True)
-    data = mujoco.MjData(model)
-
-    # Get joint indices
-    joint_indices = []
-    for name in joint_names:
-        jnt_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_JOINT, name)
-        if jnt_id < 0:
-            raise ValueError(f"Joint '{name}' not found in model")
-        joint_indices.append(jnt_id)
-
-    # Prepare output arrays
-    num_frames = motion_loader.output_frames
-    num_joints = len(joint_indices)
-    num_bodies = model.nbody
-
-    joint_pos = np.zeros((num_frames, num_joints), dtype=np.float32)
-    joint_vel = np.zeros((num_frames, num_joints), dtype=np.float32)
-    body_pos_w = np.zeros((num_frames, num_bodies, 3), dtype=np.float32)
-    body_quat_w = np.zeros((num_frames, num_bodies, 4), dtype=np.float32)
-    body_lin_vel_w = np.zeros((num_frames, num_bodies, 3), dtype=np.float32)
-    body_ang_vel_w = np.zeros((num_frames, num_bodies, 3), dtype=np.float32)
-
-    # Keep NPZ in model body-id layout (nbody), but read from track_* sensors for
-    # named bodies to align with backend.get_body_*_w semantics used in training.
-    sensor_adrs = np.full((num_bodies, 4), -1, dtype=np.int32)
-    sensor_dims = np.array([3, 4, 3, 3], dtype=np.int32)
-    sensor_prefixes = (
-        "track_pos_w_",
-        "track_quat_w_",
-        "track_linvel_w_",
-        "track_angvel_w_",
-    )
-    for body_id in range(num_bodies):
-        body_name = mujoco.mj_id2name(model, mujoco.mjtObj.mjOBJ_BODY, body_id)
-        if not body_name:
-            continue
-        for k, prefix in enumerate(sensor_prefixes):
-            sensor_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_SENSOR, f"{prefix}{body_name}")
-            if sensor_id >= 0:
-                sensor_adrs[body_id, k] = model.sensor_adr[sensor_id]
-
-    print(f"\nProcessing {num_frames} frames...")
-    for i in tqdm(range(num_frames)):
-        # Set root state
-        data.qpos[0:3] = motion_loader.motion_base_poss[i]
-        data.qpos[3:7] = motion_loader.motion_base_rots[i]
-        data.qvel[0:3] = motion_loader.motion_base_lin_vels[i]
-        data.qvel[3:6] = motion_loader.motion_base_ang_vels[i]
-
-        # Set joint states
-        for j, jnt_id in enumerate(joint_indices):
-            qpos_adr = model.jnt_qposadr[jnt_id]
-            qvel_adr = model.jnt_dofadr[jnt_id]
-            data.qpos[qpos_adr] = motion_loader.motion_dof_poss[i, j]
-            data.qvel[qvel_adr] = motion_loader.motion_dof_vels[i, j]
-
-        # Run forward pass so kinematics and sensors are up-to-date.
-        mujoco.mj_forward(model, data)
-
-        # Extract joint states
-        for j, jnt_id in enumerate(joint_indices):
-            qpos_adr = model.jnt_qposadr[jnt_id]
-            qvel_adr = model.jnt_dofadr[jnt_id]
-            joint_pos[i, j] = data.qpos[qpos_adr]
-            joint_vel[i, j] = data.qvel[qvel_adr]
-
-        # Extract body states
-        for body_id in range(num_bodies):
-            pos_adr, quat_adr, lin_adr, ang_adr = sensor_adrs[body_id]
-
-            if pos_adr >= 0:
-                body_pos_w[i, body_id] = data.sensordata[pos_adr : pos_adr + sensor_dims[0]]
-            else:
-                body_pos_w[i, body_id] = data.xpos[body_id]
-
-            if quat_adr >= 0:
-                body_quat_w[i, body_id] = data.sensordata[quat_adr : quat_adr + sensor_dims[1]]
-            else:
-                body_quat_w[i, body_id] = data.xquat[body_id]
-
-            if lin_adr >= 0:
-                body_lin_vel_w[i, body_id] = data.sensordata[lin_adr : lin_adr + sensor_dims[2]]
-
-            if ang_adr >= 0:
-                body_ang_vel_w[i, body_id] = data.sensordata[ang_adr : ang_adr + sensor_dims[3]]
-
-    # Save to NPZ
-    print(f"\nSaving to {output_file}...")
-    np.savez(
-        output_file,
-        fps=np.array([motion_loader.output_fps], dtype=np.int32),
-        joint_pos=joint_pos,
-        joint_vel=joint_vel,
-        body_pos_w=body_pos_w,
-        body_quat_w=body_quat_w,
-        body_lin_vel_w=body_lin_vel_w,
-        body_ang_vel_w=body_ang_vel_w,
-    )
-    print("Done!")
+    motion_base_poss_input = motion[:, :3]
+    # Convert quaternion from xyzw to wxyz
+    motion_base_rots_input = motion[:, 3:7][:, [3, 0, 1, 2]]
+    motion_base_rots_input = np_quat_ensure_continuity(motion_base_rots_input)
+    motion_dof_poss_input = motion[:, 7:]
+    return motion_base_poss_input, motion_base_rots_input, motion_dof_poss_input
 
 
 def main():
@@ -363,16 +155,47 @@ def main():
         "right_wrist_yaw_joint",
     ]
 
+    input_fps = int(args.input_fps)
+    output_fps = int(args.output_fps)
+
     # Load and interpolate motion
-    motion_loader = MotionLoader(
+    base_poss_input, base_rots_input, dof_poss_input = load_csv_motion(
         args.input_file,
-        int(args.input_fps),
-        int(args.output_fps),
         (args.line_range[0], args.line_range[1]) if args.line_range else None,
     )
+    motion = interpolate_motion(
+        base_poss_input,
+        base_rots_input,
+        dof_poss_input,
+        input_fps=input_fps,
+        output_fps=output_fps,
+    )
+    print(
+        f"Motion interpolated: {base_poss_input.shape[0]} frames @ {input_fps} Hz "
+        f"→ {motion.output_frames} frames @ {output_fps} Hz"
+    )
+
+    # Run forward kinematics and save to NPZ
+    print(f"\nProcessing {motion.output_frames} frames...")
+    arrays = compute_tracking_fk(
+        args.model_file,
+        joint_names=joint_names,
+        base_poss=motion.base_poss,
+        base_rots=motion.base_rots,
+        base_lin_vels=motion.base_lin_vels,
+        base_ang_vels=motion.base_ang_vels,
+        dof_poss=motion.dof_poss,
+        dof_vels=motion.dof_vels,
+        progress=True,
+    )
 
-    # Run simulation
-    run_simulation(motion_loader, args.model_file, joint_names, args.output_file)
+    print(f"\nSaving to {args.output_file}...")
+    np.savez(
+        args.output_file,
+        fps=np.array([output_fps], dtype=np.int32),
+        **arrays,
+    )
+    print("Done!")
 
 
 if __name__ == "__main__":
diff --git a/scripts/motion/remap_fullbody_npz.py b/scripts/motion/remap_fullbody_npz.py
index b3e2cafb7..2e5ad5227 100644
--- a/scripts/motion/remap_fullbody_npz.py
+++ b/scripts/motion/remap_fullbody_npz.py
@@ -20,9 +20,9 @@
 from pathlib import Path
 
 import numpy as np
+from unisim.backend.mujoco.xml import get_named_bodies
 
 from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base.backend.mujoco.xml import _get_named_bodies
 
 # MuJoCo root free-joint sizes
 _ROOT_QPOS_DIM = 7  # 3 pos + 4 quat
@@ -57,7 +57,7 @@ def remap_npz(input_path: str, output_path: str, model_file: str, *, dry_run: bo
     source_body_names: list[str] = data["body_names"].tolist()
 
     # --- target body list from training model --------------------------------
-    _, named_bodies = _get_named_bodies(model_file)
+    _, named_bodies = get_named_bodies(model_file)
     target_body_names = ["world"] + named_bodies  # prepend MuJoCo implicit body 0
 
     remap = _build_body_remap(source_body_names, target_body_names)
diff --git a/scripts/motion/replay_bones_seed_csv.py b/scripts/motion/replay_bones_seed_csv.py
index c791ff8f6..553230026 100644
--- a/scripts/motion/replay_bones_seed_csv.py
+++ b/scripts/motion/replay_bones_seed_csv.py
@@ -36,9 +36,7 @@
 import mujoco
 import mujoco.viewer
 import numpy as np
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.tools.bones_seed_csv import (
+from scripts.motion.bones_seed_csv import (
     ROOT_COLUMNS,
     euler_deg_to_quat_wxyz,
     load_header,
@@ -46,6 +44,8 @@
     resolve_input_files,
 )
 
+from unilab.assets import ASSETS_ROOT_PATH
+
 DEFAULT_INPUT = "src/unilab/assets/motions/g1/flip"
 
 
diff --git a/scripts/motion/x2_csv_to_tracking_npz.py b/scripts/motion/x2_csv_to_tracking_npz.py
index 3892eaba2..bb4ec8f4b 100644
--- a/scripts/motion/x2_csv_to_tracking_npz.py
+++ b/scripts/motion/x2_csv_to_tracking_npz.py
@@ -8,6 +8,10 @@
 
 The output layout matches the existing X2 ``*_g1format.npz`` assets consumed by
 the shared humanoid motion-tracking loader.
+
+Velocity estimation reuses the library implementation in
+``unilab.tasks.motion_tracking.common.motion_loader``; forward kinematics reuse
+``unisim.backend.mujoco.motion_export.compute_tracking_fk``.
 """
 
 from __future__ import annotations
@@ -17,10 +21,14 @@
 
 import mujoco
 import numpy as np
+from unisim.backend.mujoco.motion_export import compute_tracking_fk
 
 from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base.backend.mujoco.xml import inject_mujoco_tracking_sensors
-from unilab.utils.rotation import np_quat_angular_velocity, np_quat_ensure_continuity
+from unilab.tasks.motion_tracking.common.motion_loader import (
+    compute_motion_velocities,
+    quat_slerp,
+)
+from unilab.utils.rotation import np_quat_ensure_continuity
 
 ROOT_QPOS_DIM = 7
 ROOT_QVEL_DIM = 6
@@ -29,24 +37,6 @@
 DEFAULT_MODEL = ASSETS_ROOT_PATH / "robots" / "x2" / "x2_simple_collision.xml"
 
 
-def _quat_slerp(q1: np.ndarray, q2: np.ndarray, t: float) -> np.ndarray:
-    q1 = q1.astype(np.float64, copy=False)
-    q2 = q2.astype(np.float64, copy=False)
-    dot = float(np.dot(q1, q2))
-    if dot < 0.0:
-        q2 = -q2
-        dot = -dot
-    if dot > 0.9995:
-        result = q1 + t * (q2 - q1)
-        return (result / np.linalg.norm(result)).astype(np.float32)
-
-    theta = np.arccos(np.clip(dot, -1.0, 1.0))
-    sin_theta = np.sin(theta)
-    w1 = np.sin((1.0 - t) * theta) / sin_theta
-    w2 = np.sin(t * theta) / sin_theta
-    return (w1 * q1 + w2 * q2).astype(np.float32)
-
-
 def _load_csv_qpos(input_path: Path, model_nq: int) -> np.ndarray:
     try:
         raw = np.loadtxt(input_path, delimiter=",", dtype=np.float32)
@@ -92,11 +82,12 @@ def _resample_qpos(qpos: np.ndarray, input_fps: int, output_fps: int) -> np.ndar
     out = np.empty((output_times.shape[0], qpos.shape[1]), dtype=np.float32)
     out[:, :3] = qpos[index_0, :3] * (1.0 - blend[:, None]) + qpos[index_1, :3] * blend[:, None]
     for frame, t in enumerate(blend):
-        out[frame, 3:7] = _quat_slerp(
-            qpos[index_0[frame], 3:7],
-            qpos[index_1[frame], 3:7],
+        # Interpolate in float64, then cast back to float32.
+        out[frame, 3:7] = quat_slerp(
+            qpos[index_0[frame], 3:7].astype(np.float64),
+            qpos[index_1[frame], 3:7].astype(np.float64),
             float(t),
-        )
+        ).astype(np.float32)
     out[:, 7:] = qpos[index_0, 7:] * (1.0 - blend[:, None]) + qpos[index_1, 7:] * blend[:, None]
     out[:, 3:7] = np_quat_ensure_continuity(out[:, 3:7])
     return out
@@ -104,10 +95,13 @@ def _resample_qpos(qpos: np.ndarray, input_fps: int, output_fps: int) -> np.ndar
 
 def _qvel_from_qpos(qpos: np.ndarray, fps: int) -> np.ndarray:
     dt = 1.0 / float(fps)
+    base_lin_vels, base_ang_vels, dof_vels = compute_motion_velocities(
+        qpos[:, :3], qpos[:, 3:7], qpos[:, 7:], dt
+    )
     qvel = np.empty((qpos.shape[0], qpos.shape[1] - 1), dtype=np.float32)
-    qvel[:, :3] = np.gradient(qpos[:, :3], dt, axis=0).astype(np.float32)
-    qvel[:, 3:6] = np_quat_angular_velocity(qpos[:, 3:7], dt).astype(np.float32)
-    qvel[:, 6:] = np.gradient(qpos[:, 7:], dt, axis=0).astype(np.float32)
+    qvel[:, :3] = base_lin_vels.astype(np.float32)
+    qvel[:, 3:6] = base_ang_vels.astype(np.float32)
+    qvel[:, 6:] = dof_vels.astype(np.float32)
     return qvel
 
 
@@ -123,75 +117,6 @@ def _target_joint_names(model: mujoco.MjModel) -> list[str]:
     return names
 
 
-def _sensor_addresses(model: mujoco.MjModel) -> np.ndarray:
-    sensor_adrs = np.full((model.nbody, 4), -1, dtype=np.int32)
-    prefixes = (
-        "track_pos_w_",
-        "track_quat_w_",
-        "track_linvel_w_",
-        "track_angvel_w_",
-    )
-    for body_id in range(model.nbody):
-        body_name = mujoco.mj_id2name(model, mujoco.mjtObj.mjOBJ_BODY, body_id)
-        if not body_name:
-            continue
-        for slot, prefix in enumerate(prefixes):
-            sensor_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_SENSOR, prefix + body_name)
-            if sensor_id >= 0:
-                sensor_adrs[body_id, slot] = int(model.sensor_adr[sensor_id])
-    return sensor_adrs
-
-
-def _export_body_arrays(
-    model: mujoco.MjModel,
-    qpos: np.ndarray,
-    qvel: np.ndarray,
-    target_joint_names: list[str],
-) -> dict[str, np.ndarray]:
-    data = mujoco.MjData(model)
-    frames = qpos.shape[0]
-    body_pos_w = np.zeros((frames, model.nbody, 3), dtype=np.float32)
-    body_quat_w = np.zeros((frames, model.nbody, 4), dtype=np.float32)
-    body_lin_vel_w = np.zeros((frames, model.nbody, 3), dtype=np.float32)
-    body_ang_vel_w = np.zeros((frames, model.nbody, 3), dtype=np.float32)
-    sensor_adrs = _sensor_addresses(model)
-
-    joint_ids = [
-        mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_JOINT, name) for name in target_joint_names
-    ]
-    if any(joint_id < 0 for joint_id in joint_ids):
-        raise ValueError("Target joint list contains joints not found in model")
-
-    for frame in range(frames):
-        data.qpos[:] = qpos[frame]
-        data.qvel[:] = qvel[frame]
-        mujoco.mj_forward(model, data)
-
-        for body_id in range(model.nbody):
-            pos_adr, quat_adr, lin_adr, ang_adr = sensor_adrs[body_id]
-            if pos_adr >= 0:
-                body_pos_w[frame, body_id] = data.sensordata[pos_adr : pos_adr + 3]
-            else:
-                body_pos_w[frame, body_id] = data.xpos[body_id]
-
-            if quat_adr >= 0:
-                body_quat_w[frame, body_id] = data.sensordata[quat_adr : quat_adr + 4]
-            else:
-                body_quat_w[frame, body_id] = data.xquat[body_id]
-
-            if lin_adr >= 0:
-                body_lin_vel_w[frame, body_id] = data.sensordata[lin_adr : lin_adr + 3]
-            if ang_adr >= 0:
-                body_ang_vel_w[frame, body_id] = data.sensordata[ang_adr : ang_adr + 3]
-
-    return {
-        "body_pos_w": body_pos_w,
-        "body_quat_w": body_quat_w,
-        "body_lin_vel_w": body_lin_vel_w,
-        "body_ang_vel_w": body_ang_vel_w,
-    }
-
-
 def convert_csv(
     input_path: Path,
     output_path: Path,
@@ -200,22 +125,17 @@ def convert_csv(
     output_fps: int,
     dry_run: bool,
 ) -> None:
-    tmp_model_path, _, _ = inject_mujoco_tracking_sensors(str(model_file))
-    try:
-        model = mujoco.MjModel.from_xml_path(tmp_model_path)
-    finally:
-        Path(tmp_model_path).unlink(missing_ok=True)
+    model = mujoco.MjModel.from_xml_path(str(model_file))
 
     target_names = _target_joint_names(model)
     qpos_input = _load_csv_qpos(input_path, model.nq)
     qpos = _resample_qpos(qpos_input, input_fps, output_fps)
     qvel = _qvel_from_qpos(qpos, output_fps)
-    joint_pos = qpos[:, ROOT_QPOS_DIM:].astype(np.float32)
-    joint_vel = qvel[:, ROOT_QVEL_DIM:].astype(np.float32)
 
-    if joint_pos.shape[1] != len(target_names):
+    if qpos.shape[1] - ROOT_QPOS_DIM != len(target_names):
         raise ValueError(
-            f"CSV joint count {joint_pos.shape[1]} does not match model joints {len(target_names)}"
+            f"CSV joint count {qpos.shape[1] - ROOT_QPOS_DIM} does not match model joints "
+            f"{len(target_names)}"
         )
 
     print(f"Source : {input_path}")
@@ -223,7 +143,7 @@ def convert_csv(
     print(f"Output : {output_path}")
     print(f"frames : {qpos_input.shape[0]} -> {qpos.shape[0]}")
     print(f"fps    : {input_fps} -> {output_fps}")
-    print(f"joints : {joint_pos.shape[1]}")
+    print(f"joints : {qpos.shape[1] - ROOT_QPOS_DIM}")
     print(f"bodies : {model.nbody} (MuJoCo body-id layout, including world)")
 
     if dry_run:
@@ -231,13 +151,20 @@ def convert_csv(
         return
 
     output_path.parent.mkdir(parents=True, exist_ok=True)
-    body_arrays = _export_body_arrays(model, qpos, qvel, target_names)
+    arrays = compute_tracking_fk(
+        str(model_file),
+        joint_names=target_names,
+        base_poss=qpos[:, :3],
+        base_rots=qpos[:, 3:7],
+        base_lin_vels=qvel[:, :3],
+        base_ang_vels=qvel[:, 3:6],
+        dof_poss=qpos[:, ROOT_QPOS_DIM:],
+        dof_vels=qvel[:, ROOT_QVEL_DIM:],
+    )
     np.savez(
         output_path,
         fps=np.array([output_fps], dtype=np.int32),
-        joint_pos=joint_pos,
-        joint_vel=joint_vel,
-        **body_arrays,
+        **arrays,
     )
 
 
diff --git a/scripts/play_viser.py b/scripts/play_viser.py
index 222d9dc6c..512b56df6 100644
--- a/scripts/play_viser.py
+++ b/scripts/play_viser.py
@@ -32,7 +32,7 @@
 import sys
 import time
 from pathlib import Path
-from typing import Any, cast
+from typing import Any
 
 import hydra
 import numpy as np
@@ -46,18 +46,22 @@
 if str(ROOT_DIR) not in sys.path:
     sys.path.insert(0, str(ROOT_DIR))
 
-from unilab.training import (
-    ensure_registries,
-    get_entrypoint_log_root,
-)
-from unilab.training.rsl_rl import (
+from uni_rl.algos.rsl_rl import (
     RslRlVecEnvWrapper,
     get_policy_obs_dims,
     normalize_ppo_train_cfg,
 )
+
+from unilab.training import ensure_registries
+from unilab.utils.checkpoint import get_entrypoint_log_root
 from unilab.visualization.interactive_playback import (
     PlaybackControls,
+    PlayInteractiveArgs,
+    available_backends_for_task,
+    build_play_backend_adapter,
+    build_playback_config,
     create_rsl_rl_playback_session,
+    infer_checkpoint_actor_input_dim,
     make_sim2sim_preflight,
     select_torch_device,
 )
@@ -84,35 +88,15 @@
 
 import mujoco
 import viser  # noqa: E402
-from play_interactive import (  # noqa: E402
-    PlayInteractiveArgs,
-    _available_backends_for_task,
-    _backend_adapter,
-    _build_playback_config,
-    _infer_checkpoint_actor_input_dim,
-    resolve_checkpoint,
-)
+
+from unilab.scripts.play_interactive import resolve_checkpoint  # noqa: E402
+from unilab.training import algo_config_dict  # noqa: E402
 
 # --------------------------------------------------------------------------- #
 # Core viewer                                                                 #
 # --------------------------------------------------------------------------- #
 
 
-def _algo_config_dict(cfg: DictConfig) -> dict[str, Any]:
-    """Return the composed PPO algo config as a plain dict.
-
-    Args:
-        cfg: Hydra config for the current playback run.
-
-    Returns:
-        The resolved ``cfg.algo`` subtree as a mutable dict for rsl_rl.
-    """
-    train_cfg_raw = OmegaConf.to_container(cfg.algo, resolve=True)
-    if not isinstance(train_cfg_raw, dict):
-        raise TypeError("cfg.algo must resolve to a dict")
-    return cast(dict[str, Any], train_cfg_raw)
-
-
 def _load_env_playback_model(env: Any, env_index: int) -> mujoco.MjModel:
     """Resolve the exact MuJoCo model for one playback env.
 
@@ -219,7 +203,7 @@ def play_viser(args: PlayInteractiveArgs, cfg: DictConfig) -> None:
     print(f"[play_viser] Device: {device}")
 
     # --- Validate backend ---------------------------------------------------
-    available_backends = _available_backends_for_task(args.task)
+    available_backends = available_backends_for_task(args.task)
     if available_backends and "mujoco" not in available_backends:
         print(
             f"[play_viser] Task {args.task} does not support MuJoCo backend. "
@@ -233,9 +217,11 @@ def play_viser(args: PlayInteractiveArgs, cfg: DictConfig) -> None:
     def _create_env(env_count: int):
         if cfg is None:
             return registry.make(args.task, num_envs=env_count, sim_backend="mujoco")
-        from unilab.training import create_env
+        from unilab.base.config_adapter import create_env
 
-        env_cfg_override = _backend_adapter(cfg).build_task_env_cfg_override()
+        env_cfg_override = build_play_backend_adapter(
+            cfg, root_dir=ROOT_DIR
+        ).build_task_env_cfg_override()
         return create_env(
             cfg,
             num_envs=env_count,
@@ -245,13 +231,13 @@ def _create_env(env_count: int):
         )
 
     playback_session, _policy_obs_mode, _checkpoint_path = create_rsl_rl_playback_session(
-        playback_cfg=_build_playback_config(args, num_envs=num_envs),
+        playback_cfg=build_playback_config(args, num_envs=num_envs),
         env_factory=_create_env,
-        algo_config=_algo_config_dict(cfg),
+        algo_config=algo_config_dict(cfg),
         root_dir=ROOT_DIR,
         device=device,
         checkpoint_resolver=resolve_checkpoint,
-        checkpoint_input_dim_reader=_infer_checkpoint_actor_input_dim,
+        checkpoint_input_dim_reader=infer_checkpoint_actor_input_dim,
         entrypoint_log_root=get_entrypoint_log_root,
         wrapper_cls=RslRlVecEnvWrapper,
         runner_cls=OnPolicyRunner,
@@ -483,7 +469,7 @@ def _build_play_args(cfg: DictConfig) -> PlayInteractiveArgs:
     )
 
 
-@hydra.main(version_base="1.3", config_path="../conf/ppo", config_name="config")
+@hydra.main(version_base="1.3", config_path="../src/unilab/conf/ppo", config_name="config")
 def main(cfg: DictConfig) -> None:
     if str(cfg.training.sim_backend) != "mujoco":
         raise ValueError("play_viser.py only supports MuJoCo backend; use task=<task>/mujoco.")
diff --git a/scripts/sharpa_collect_grasps.sh b/scripts/sharpa_collect_grasps.sh
index d9471c646..87c9ff6f4 100755
--- a/scripts/sharpa_collect_grasps.sh
+++ b/scripts/sharpa_collect_grasps.sh
@@ -35,7 +35,7 @@ fi
 for scale in "$@"; do
   echo "[sharpa_collect_grasps] collecting backend=${backend} scale=${scale}"
 
-  uv run scripts/train_rsl_rl.py \
+  uv run src/unilab/scripts/train_rsl_rl.py \
     "task=sharpa_inhand_grasp/${backend}" \
     "env.domain_rand.scale_list=[${scale}]" \
     "${extra_args[@]}"
diff --git a/src/unilab/algos/torch/__init__.py b/scripts/tools/__init__.py
similarity index 100%
rename from src/unilab/algos/torch/__init__.py
rename to scripts/tools/__init__.py
diff --git a/scripts/tools/genesis_feasibility/REPORT.md b/scripts/tools/genesis_feasibility/REPORT.md
new file mode 100644
index 000000000..56801cb87
--- /dev/null
+++ b/scripts/tools/genesis_feasibility/REPORT.md
@@ -0,0 +1,226 @@
+# Genesis 物理运行时 SimBackend 契约可行性报告
+
+研究性 child issue：unilabsim/UniLab #1372（roadmap #1339 Child 1）。
+分支：`research/issue-1372-genesis-feasibility`。
+本报告只评估可行性，**不实现 backend，不声明任何 production support**。
+每条结论标注证据等级：实测 / 源码推断 / 未验证。
+
+## 0. 结论（go/no-go）
+
+**Go —— 推荐方案 A（进程内薄 adapter）**，附四个硬性前置条件（见第 6 节）。
+主干契约能力（冷路径元数据、qpos/qvel 布局、PD 控制、子集 reset、per-env DR、
+传感器等价物、离屏渲染、NumPy 边界）在 genesis-world 1.3.3 上全部实测可用；
+三个确认缺口（`<keyframe>`、MJCF 全局 `<option>`、MJCF `<sensor>`）都有明确的
+冷路径补偿方案，无上游否决项。
+
+## 1. 环境矩阵（固定版本）
+
+| 项 | 值 | 备注 |
+|---|---|---|
+| GPU / 驱动 | RTX 4090 49GB / 595.84 | 实测 |
+| Python | 3.13.14（项目 venv） | 实测 |
+| torch | 2.7.0+cu128（UniLab venv 锁定） | 实测 |
+| genesis-world | **1.3.3**（PyPI wheel；上游 tag `v1.3.3` = commit `76f8f5b3`） | 实测（探针运行版本） |
+| genesis 上游源码 | 本地 checkout main HEAD `19f56d65`（= v1.3.3+30，含 rigid entity 静态/运行时拆分 breaking change） | 仅源码阅读，未运行 |
+| quadrants | 1.3.0（genesis 精确 pin） | 实测；py3.13 下报 DeprecationWarning（ast.Str、keyword ctx） |
+| mujoco | 3.10.0（UniLab venv，`mujoco>=3.5`） | 仅作 ground truth |
+| genesislab | master `0f2e4d9` | 仅源码阅读 |
+| 资产 | `src/unilab/assets/robots/g1/scene_flat.xml`（29 dof 位置执行器 + 21 个 MJCF sensor + keyframe "stand"） | — |
+
+复现命令（全部探针共用 pinned 形式）：
+
+```bash
+uv run --with genesis-world==1.3.3 python scripts/tools/genesis_feasibility/probe_contract.py   # P1
+uv run --with genesis-world==1.3.3 python scripts/tools/genesis_feasibility/probe_runtime.py    # P2
+uv run --with genesis-world==1.3.3 python scripts/tools/genesis_feasibility/probe_perf.py       # P3
+```
+
+## 2. 依赖兼容矩阵
+
+| 依赖 | UniLab | genesis-world 1.3.3 | 结论 |
+|---|---|---|---|
+| Python | `>=3.10,<3.14` | `>=3.10,<3.14` | 兼容（实测） |
+| torch | `==2.7.0`（linux x86_64）/ `==2.9.0`（aarch64） | **不在 install_requires**；import 缺失即 raise；`torch<2.8.0` 启动 warning（`__init__.py:34-39`） | **冲突行（实测）**：2.7.0 下主干可用，但属上游不支持窗口，且观测到一个传感器组合失效（第 4 节 R7）。方案 A 需 maintainer 决策：torch 窗口上调至 ≥2.8，或接受 unsupported+warning 并写入支持矩阵 |
+| mujoco | `>=3.5`（lock 3.10.0） | `>=3.2.5` | 兼容（实测同进程共存） |
+| quadrants | —（新依赖） | `==1.3.0` 精确 pin | 新增传递依赖；py3.13 DeprecationWarning（实测，上游兼容注记） |
+| 安装形态 | mjwarp 已有 `dependencies.py` 懒加载先例 | torch/quadrants 重依赖 | Genesis extra + lazy import + 版本钉扎 + 专属 DependencyError 是必选（源码推断，仿 `unisim-core/src/unisim/backend/mjwarp/dependencies.py`） |
+
+## 3. SimBackend 契约能力矩阵
+
+状态：OK = 实测可用；GAP = 有缺口但有补偿路径；FAIL = 阻塞。复现列 P1/P2/P3 见第 1 节。
+
+### 3.1 冷路径元数据（materialize 一次性解析）
+
+| 契约 | Genesis 映射 | 状态 | 证据 | 复现 |
+|---|---|---|---|---|
+| 关节名/顺序 | `entity.joints`（29 个 1-dof 关节名与顺序 == mujoco `jnt` 顺序） | OK | 实测 | P1 `1a` |
+| `get_actuator_names` / 目标关节 / 顺序 | `get_dofs_act_gain()>0` 的 dof→joint 映射 == mj actuator 顺序（n=29） | OK | 实测 | P1 `1b` |
+| `get_actuator_ctrl_range` | position 执行器的 `ctrlrange` **导入即丢弃**（mjcf.py 仅 biastype=NONE 时保留）；g1 未声明 ctrlrange（mj 原始值全 0），`forcerange` 无损（diff=0） | GAP（对 g1 无实际影响） | 源码推断+实测 | P1 `1c` |
+| `get_joint_range` | `get_dofs_limit()`，29 关节 max diff=1.09e-07 | OK | 实测 | P1 `1d` |
+| `get_body_mass` / `get_body_ipos` | `link.inertial_mass` / `link.inertial_pos`，30 link：mass diff=0、ipos max diff=7.24e-09 | OK | 实测 | P1 `1e` |
+| `get_geom_friction` | `entity.geoms`（仅碰撞 geom，42==mj 碰撞 geom 数）per-link 摩擦多重集一致 | OK | 实测 | P1 `1f` |
+| `get_geom_contact_masks` | contype/conaffinity 被 `solve_contype_conaffinity` **重编码**（bitmask 整数值 ≠ mj，碰撞矩阵语义保持） | GAP（语义可用，值不可与 mj 对表） | 实测+源码推断 | P1 `1f` |
+| geom 名称/id | `RigidGeom` 无 `name` 属性（1.3.3）；只能 link+idx 寻址 | GAP（契约可按需 fail-closed） | 实测 | P1 `1f` |
+| `get_gravity` | `SimOptions.gravity`（默认 (0,0,-9.81)，g1.xml 未覆盖） | OK | 实测 | P1 `1g` |
+| `get_dof_armature` | `get_dofs_armature()` == mj `dof_armature` | OK | 实测 | P1 `1f` |
+| `get_geom_size` / `get_body_subtree_ids` | geom `data`/`metadata`、link 树存在 | 未验证 | — | — |
+
+### 3.2 状态布局与运动学
+
+| 契约 | Genesis 映射 | 状态 | 证据 | 复现 |
+|---|---|---|---|---|
+| qpos 布局 | `get_qpos()` [B,36] = [root xyz \| root quat **wxyz** \| 29 joint]；与 `links_pos/quat(pelvis)`、`dofs_position[6:]` 一致；`dofs_*` 视图 35 宽（含 6 root dof） | OK | 实测 | P1 `2a` |
+| 名称→dof/qpos 索引 | `get_joint(name).dofs_idx_local/qs_idx_local` == mj `jnt_dofadr/jnt_qposadr`；root 关节 n_dofs=6、n_qs=7 | OK | 实测 | P1 `2b` |
+| 四元数约定 / FK 一致性 | keyframe 姿态下 torso_link 世界位置 vs mujoco `xpos` max diff=2.19e-08，quat diff=0（注意：mj `xipos` 是 COM 系，对应 `xpos` 才是 body 系原点） | OK | 实测 | P1 `2c` |
+| root qvel 坐标系 | root dofs[3:6] 为**体坐标系**角速度，`get_links_ang()` 返回**世界系** —— 与 MuJoCo 语义及 SimBackend 契约（`set_state` 体帧写入 / `get_base_ang_vel` 世界帧读出）完全一致 | OK | 实测 | P1 `2d` |
+| root state 读取 | **陷阱（实测）**：MJCF entity 的 `base_link` 是 `world`，entity 级 `get_pos/get_quat/get_vel/get_ang` 恒为原点/零，对浮动基机器人无用；必须用 link 寻址 `get_links_*(pelvis_idx)` | OK（用 link API） | 实测 | P1 `2a` |
+| `get_body_pos/quat/lin_vel/ang_vel_w` | `get_links_pos/quat/vel/ang(links_idx, envs_idx)` | OK | 实测 | P1 `2c/2d` |
+| `get_body_*_b`（体帧） | 无直接 API；adapter 用世界系量 + 四元数逆旋转（`unilab.utils.rotation` 已有 helpers） | OK（adapter 数学） | 源码推断 | — |
+| `get_dof_pos/vel` | `get_dofs_position/velocity()[..., 6:]`（排除 root） | OK | 实测 | P1 `2a/4` |
+| `get_site_jacobian_w` | `entity.get_jacobian(link, local_point)`（[B,6,n_dofs]，需 `morph.requires_jac_and_IK=True`） | 未验证 | 源码推断 | — |
+| 速度量时效 | `set_dofs_velocity` 后 dofs 视图立即生效，但 `get_links_vel/ang` 要过一次 `scene.step()` 才刷新 | OK（注意事项，见 5.6） | 实测 | P1 `2d` |
+
+### 3.3 控制、reset、keyframe、全局 option
+
+| 契约 | Genesis 映射 | 状态 | 证据 | 复现 |
+|---|---|---|---|---|
+| 执行器增益导入 | MJCF `<position kp kv>` → `act_gain/act_bias`（PD 可约）：kp max diff=8.5e-07、kv max diff=8.8e-08 | OK | 实测 | P1 `3a` |
+| `step(ctrl, nsteps)` 语义 | `control_dofs_position(target, dofs_idx)` 一次设定后跨 `scene.step()` 保持（5 步后 ctrl force=12.7 N·m）；== MuJoCo ctrl broadcast | OK | 实测 | P1 `3b` |
+| PD 响应 | elbow +0.2 rad 目标，10 步 err=0.015（100 步后整机失稳倒地属物理现象） | OK | 实测 | P1 `3b` |
+| `set_pre_step_control` | 无引擎级 per-substep hook；adapter 在 `nsteps` 循环内每 substep 调 conversion + `control_dofs_*` + `scene.step()`，与 MuJoCo backend 的 pre_step 路径同构 | OK（adapter 层实现） | 实测+源码推断 | P1 `3b` |
+| `get_actuator_gains` | `get_dofs_kp/kv()` | OK | 实测 | P1 `3a` |
+| `set_state` 子集 reset | `set_qpos/set_dofs_velocity/zero_all_dofs_velocity(envs_idx=...)` 免重建；触及 env == 目标、未触及 env 保持不变；set→step→get 有限、shape (8,36)、float32 | OK | 实测 | P1 `4` |
+| `get_keyframe_qpos` | **Genesis 不解析 `<keyframe>`**（全仓无 key_qpos；init qpos == mj.qpos0 + body pos，与 keyframe 关节最大差 0.669 rad） | GAP | 实测 | P1 `5` |
+| keyframe 补偿 | materialize 冷路径用 `mujoco` 包解析 `mj.key_qpos`（实测 key_id=0 可用）——与 isaacgym backend 父进程侧元数据扫描同模式 | 补偿方案确定 | 实测 | P1 `5` |
+| `get_default_qpos` / `get_default_dof_pos` / `get_init_qvel` | init `get_qpos()`（== mj.qpos0 + 根 body pos）/ 其关节块 / 零向量 | OK | 实测 | P1 `5` |
+| MJCF 全局 `<option>` | **全部丢弃**（morphs.py 注释；实测 `<option timestep=0.006667 integrator=implicitfast>` 未生效，有效 dt 来自 Sim/RigidOptions 默认 0.01）；timestep/integrator/solver/cone/iterations 都须变成 owner YAML 显式字段（cone=elliptic 仅 warning） | GAP（有明确映射路径） | 实测+源码推断 | P1 `6` |
+
+### 3.4 传感器（g1 任务依赖 21 个 MJCF sensor）
+
+| 契约 | Genesis 映射 | 状态 | 证据 | 复现 |
+|---|---|---|---|---|
+| MJCF `<sensor>` 导入 | **完全不解析**（mjcf.py 无 sensor 代码），21 个全部丢弃 | GAP | 源码推断 | P1 `7` |
+| gyro / accelerometer | `IMUSensor(entity_idx, link_idx_local)`，acc/gyro [B,3]，支持 noise/bias 参数（无 MJCF cutoff/noise 自动映射） | OK（等价物） | 实测 | P1 `7` |
+| contact（`data="found"`） | `get_links_net_contact_force()` [B,31,3] + 阈值（站立足底 |F|=138N）；或 `ContactForceSensor`（单独使用实测 OK） | OK（等价物） | 实测 | P1 `7` |
+| velocimeter / framelinvel / framepos / framequat / framezaxis | `get_links_vel/pos/quat` + adapter 侧 site offset 与旋转数学（Genesis 无 site 抽象） | OK（adapter 数学） | 源码推断 | — |
+| 传感器组合稳定性 | IMU+ContactForce+Contact 三 sensor 同场景（batch flags=True）在 **torch 2.7.0** 下崩溃：`collider.get_contacts` gather int64 dtype 错误；ContactForce 单独同配置 OK | GAP（待复验，疑似 torch 版本相关） | 实测 | P1 历史运行 |
+
+### 3.5 DR、生命周期、宿主副作用、性能、渲染
+
+| 契约 | Genesis 映射 | 状态 | 证据 | 复现 |
+|---|---|---|---|---|
+| `get_dr_capabilities` / interval DR | per-env round-trip 实测：`set_links_inertial_mass`、`set_dofs_frictionloss`、`set_dofs_kp/kv`（需 build 期 `batch_links_info/batch_dofs_info=True`——owner YAML 决策项）；`set_friction_ratio` 可调用（输入形状 (n_envs,n_links)） | OK | 实测 | P1 `8` |
+| `apply_body_force` | solver 级 `apply_links_external_force/torque(links_idx=全局, envs_idx=...)` 可调用（1.3.3 无 entity 级封装；物理效果未验证） | OK（API 形状注意） | 实测（调用） | P1 `8` |
+| init/destroy 生命周期 | 单进程 3× init→build→step→destroy 无崩溃；CUDA reserved 稳定 22MB；**host RSS 峰值每周期增长约 200–450MB（亚线性，两次完整运行分别测得 [452.7, 200.9] 与 [255.2, 199.3]MB）** → 长驻进程必须只 init 一次 | OK（带约束） | 实测 | P2 `9a` |
+| 多 Scene 并存 | 单 gs 会话两个 Scene 同时 build/step，状态互不影响 | OK | 实测 | P2 `9b` |
+| 宿主 torch 副作用 | `gs.init(gpu)` 改写 `torch.set_default_device(cuda:0)` + `set_default_dtype`：**裸 `torch.zeros()` 落 CUDA**；`torch.get_num_threads()` 不变（16）；root logging handlers 不变；`seed=` 触发全局 RNG reseed；`cpu_max_num_threads=1` **无条件**强制（非仅 CPU backend，`QD_NUM_THREADS` 可覆盖）；import 本身无副作用 | GAP（可管理，见 5.8/6） | 实测+源码推断 | P2 `10` |
+| NumPy 边界成本 | n_envs=256/2048/4096：step=1.77/2.81/4.29ms；8 个 getter 逐次 `.cpu().numpy()` D2H 增加 +0.20/+0.35/+0.37ms（+11.0%/+12.5%/+8.6%）；每步 H2D ctrl push +0.10/+0.07/+0.15ms（+5.7%/+2.5%/+3.4%）；SPS≈1.45e5/7.29e5/9.54e5 | OK（mjwarp 式 host-cache 是优化项非前提） | 实测 | P3 `11` |
+| play/render | 离屏 `camera.render()` headless 实测 OK（rgb uint8）；交互 Viewer 为 pyrender/pyglet，需 display（未尝试 GUI） | OK | 实测+源码推断 | P2 `12` |
+| `get_physics_state`（playback） | qpos 快照序列即可 | 源码推断 | — | — |
+| hfield scanner | `RaycasterSensor` 支持 grid probe pattern | 未验证 | 源码推断 | — |
+
+## 4. 与 #1339 调研假设的对照
+
+**被实测确认的假设**：`<keyframe>` 缺口及 mujoco 冷路径补偿；MJCF 全局 option 丢弃；
+PD 三模式与 per-env DR API 齐备；无 per-substep 控制 hook；torch 为未声明硬依赖 +
+`torch<2.8` warning；`gs.init` 污染 torch default device/dtype；zerocopy 默认开启。
+
+**需要修正的假设**：
+1. 「CPU backend 强制单线程」——实际是 `cpu_max_num_threads=1` **无条件**设置（GPU 运行同样受影响，作用于 quadrants 编译/CPU 侧），`QD_NUM_THREADS` 可覆盖（源码推断，v1.3.3 `__init__.py:243-254`）。
+2. 「`entity.get_pos/get_quat/get_vel/get_ang` 可作 root state 读取」——对 MJCF 浮动基模型**不成立**（实测）：entity 的 `base_link` 是 `world`，这组 getter 恒返回原点/零。root state 必须走 `get_links_*(pelvis_link_idx)`。
+3. 「init→destroy→init 循环无生命周期否决项」——功能上成立（实测 3× 无崩溃、CUDA 稳定），但 host RSS 每周期增长约 200–450MB（亚线性，实测）；单进程反复 init/destroy 不可作为常态用法。
+
+**新发现（假设之外）**：
+- `get_links_pos` 对应 mujoco `xpos`（body 系原点）而非 `xipos`（COM 系）——对照时必须用 `xpos`（实测，diff 2.2e-08）。
+- root qvel[3:6] 体帧写入 / `links_ang` 世界帧读出，与 MuJoCo 及 SimBackend 契约语义精确对齐（实测）——无需 adapter 侧帧转换，只需声明布局。
+- contype/conaffinity 被重编码（碰撞矩阵语义不变，整数值 ≠ mj）——`get_geom_contact_masks` 只能暴露 genesis 原生值（实测+源码推断）。
+- `RigidGeom` 无 `name`（1.3.3）；geom 级契约只能 link+idx 寻址（实测）。
+- `set_*` 后速度类 link getter 需过一次 `scene.step()` 才刷新；dofs 视图即时（实测）。
+- 传感器多类型组合在 torch 2.7.0 下崩溃一例（实测，见 3.4）。
+- NumPy 边界逐 getter 拷贝成本 ≈ +9~12% D2H（实测）——mjwarp 式单发 host-cache 可再压缩，但不是可行性前提。
+
+## 5. Child 2（adapter 设计）映射决策
+
+1. **keyframe**：materialize 冷路径用 `mujoco` 解析 owner XML 的 `mj.key_qpos`（实测可行），缓存在 adapter；热路径禁用 XML 解析。与 isaacgym 父进程侧 `sensors.py` 元数据扫描同模式，不引入新契约。
+2. **全局 option**：owner YAML 显式声明 `dt`（经现有 ctrl_dt/decimation 链路）、`integrator`（g1 = implicitfast）、`constraint_solver`、`friction_cone`、solver iterations；Genesis backend 在 `Scene(...)` 构造时消费。`sim2sim` DENYLIST 无需新增字段（这些本来就要求跨后端 YAML 显式一致）。
+3. **传感器**：materialize 时把 XML sensor 名映射到 Genesis 等价物并注册进 `bind_sensor_data`：gyro/accelerometer→IMUSensor（link+site offset）；contact found→net_contact_force 阈值（首选，零注册成本）或 ContactForceSensor；velocimeter/framelinvel/framepos/framequat/framezaxis→links_* + 旋转数学。MJCF 的 cutoff/noise 属性无自动映射——要么经 IMUSensor noise 参数显式声明，要么 fail-closed（推荐后者，先声明干净数据）。
+4. **控制律**：默认路径 1（Genesis 内置 actuator + `control_dofs_position`，实测无损且语义匹配）；路径 2（adapter 显式 PD + `control_dofs_force`，genesislab actuator 模式）留作数值对齐退路。`set_pre_step_control` 在 adapter 的 nsteps 循环内实现，不新增 SimBackend 公共方法。
+5. **root state**：契约的 root 读写全部以冷路径解析的 root link idx（pelvis）走 `get_links_*` / `set_qpos` / `set_dofs_velocity`；**禁止**使用 entity 级 `get_pos/get_vel/get_ang`（实测指向 world link）。
+6. **速度量时效**：reset/set_state 后位置量立即可读（set_qpos 触发 FK），速度 link getter 需过 step 屏障；若 reset 后、首个 step 前要读 base 速度，用 `get_dofs_velocity` 的 root 切片（即时）+ 帧转换（实测可行）。
+7. **DR**：owner YAML 在 materialize 决策中声明 `batch_links_info/batch_dofs_info=True`；`get_dr_capabilities()` 只声明本报告实测生效项（mass/COM shift、frictionloss、damping、armature、kp/kv、friction_ratio、external force）；geom 摩擦**绝对值**的 per-env 化只有 ratio API，规划 DR 字段时注意。
+8. **宿主污染管理**：backend 构造时在 `gs.init` 前快照、在 init 后恢复（或显式重设）`torch.get_default_device()`/dtype 与 RNG 状态；写进 adapter 单测。
+9. **数组边界**：仿 mjwarp——预分配 host cache，step/reset 屏障后一次性 D2H 刷新，getter 只读 cache（实测逐 getter 拷贝也仅 +9~12%，host-cache 是确定性优化）。
+10. **geom/contact mask**：`get_geom_contact_masks` 暴露 genesis 重编码值并在 docstring 说明不可与 mujoco 对表；geom 名称契约对 genesis fail-closed。
+
+## 6. 方案 A（进程内薄 adapter） vs 方案 B（子进程隔离）
+
+| 维度 | A 进程内 | B 子进程 |
+|---|---|---|
+| torch 全局污染 | 实测污染点明确且可数（default device/dtype、RNG seed），可在 adapter 构造期快照/恢复 | 天然隔离 |
+| torch 版本冲突 | 与 learner 同进程同 torch：2.7.0 实测主干可用，但处上游不支持窗口且观测到传感器组合失效一例；升级 torch 窗口需全仓评估 | worker 可独立 venv 跑 torch≥2.8，但 learner 侧不受影响这一优点对 UniLab 意义有限（物理在 worker） |
+| 生命周期 | 实测 init/destroy 循环可用；约束是单进程只 init 一次（UniLab 一进程一训练 run，天然满足） | 进程退出即彻底清理，RSS 增长问题消失 |
+| 性能 | 无 IPC；实测 D2H/H2D 边界合计 <15%，host-cache 后更低；4096 envs SPS≈9.5e5 | 增加 shm/序列化层（isaacgym 后端已有协议先例，但那是为 Python 3.8 隔离支付的必要成本） |
+| 工程成本 | 仿 mjwarp 包骨架（`__init__` 懒导出 + backend + materialization + dependencies），薄 | worker 生命周期 + shm 协议 + 崩溃恢复，明显更重 |
+| 依赖部署 | genesis extra + lazy import 即可 | 需要双环境/镜像编排 |
+
+**推荐：方案 A（go）**。理由：所有实测证据都未触及方案 A 的否决项——生命周期上游原生支持、宿主污染点有限且可在 adapter 边界内管理、NumPy 边界成本小、无 IPC 需求；方案 B 解决的是「torch 版本窗口」和「反复 init 的 RSS 增长」两个真实但次级的问题，而这两者分别可由 maintainer 的 torch 窗口决策与「单进程 init 一次」约束覆盖，不值得为此引入第二套 worker 协议。前置条件：(1) mjwarp 式 lazy `dependencies.py` + 版本钉扎；(2) `gs.init` 前后快照/恢复 torch 全局状态（含 RNG）并配单测；(3) 支持矩阵明确记录 torch 2.7 的 unsupported 状态与 ContactForceSensor 组合失效案例，`get_dr_capabilities()` 只声明实测能力；(4) 文档化「一进程一 gs.init」约束。若 Child 3 的数值对齐（`enable_mujoco_compatibility`）或 torch 升级评估出现恶化证据，方案 B 作为降级路径保留。
+
+## 7. genesislab 借鉴点（机制层面）
+
+与 #1339 调研一致，无推翻：init 期一次性名称解析（regex→index，全选降级 `slice(None)`）；
+action scale/offset/clip 全 init 预编译；event 三 mode（startup/reset/interval）与 Genesis per-env
+DR API 的 interval 映射；actuator 抽象把控制律与引擎解耦（路径 2 的现成参考，`actuator_pd.py` +
+`ActuatorManager` 同时回写 `set_dofs_kp/kv` 保持隐式 PD 一致）；env↔algo 差异收敛在单 wrapper。
+反面教训同样成立并已被本 issue 红线覆盖：静默 no-op DR、布局常量散落（浮动基 offset 硬编码）、
+`__getattr__` 透传引擎私有 API、lazy property 直摸引擎无 step 屏障、init 真跑 term func 推断维度。
+
+## 8. 未验证 / 留待 Child 2+ 的事项
+
+- ContactForceSensor 多 sensor 组合崩溃的根因定位与在 torch≥2.8 下的复验。
+- `set_friction_ratio` / `apply_links_external_force` 的物理效果（本报告仅验证可调用与 API 形状）。
+- `RaycasterSensor` grid 作 height scanner；`get_jacobian`（需 `requires_jac_and_IK=True`）；geom size / body subtree / terrain spawn data。
+- `RigidOptions(enable_mujoco_compatibility=True)` 与 MuJoCo 后端的数值对齐程度（g1_walk_flat 级别，建议放 Child 3 benchmark）。
+- CPU backend 吞吐定位（本报告全部在 `gs.gpu` 实测）。
+- GPU 显存在 4096+ envs 长训下的增长曲线。
+
+## 9. 附录：探针输出（实测原文）
+
+### P1 `probe_contract.py`（行 1–8）
+
+```text
+[1a.joint_order] OK [实测] 1-dof joint names/order match MuJoCo: True (gs=29, mj=29); n_dofs gs=35 mj=35; n_links gs=31 mj_nbody=31
+[1b.actuator_order] OK [实测] actuated joint order (act_gain>0) == mj actuator order: True (n=29)
+[1c.ctrl_range] GAP [源码推断+实测] actuator_ctrlrange dropped for position actuators (kept only for biastype=NONE, mjcf.py); g1 does not author ctrlrange (mj raw all-zero: True) -> declare in owner YAML; forcerange imported: max diff=0.00e+00
+[1d.joint_range] OK [实测] max |joint_range diff| over 29 1-dof joints = 1.09e-07
+[1e.mass_ipos] OK [实测] per-link mass max diff=0.00e+00, body_ipos max diff=7.24e-09 over 30 links
+[1f.friction_armature] OK [实测+源码推断] collision-geom slide-friction per-link multisets match MuJoCo on 19 links, mismatched=[]; entity.geoms holds collision geoms only (gs=42 vs mj collision=42; visual -> vgeoms); contype/conaffinity are RE-SYNTHESIZED by solve_contype_conaffinity (bitmask ints differ from mj, collision matrix semantics preserved) -> get_geom_contact_masks must expose genesis-native masks; RigidGeom has no name in 1.3.3; dof_armature match=True
+[1g.gravity] OK [实测] SimOptions.gravity=(0.0, 0.0, -9.81) (None -> default [0,0,-9.81]); mj opt.gravity=[0.0, 0.0, -9.81]; g1.xml does not override gravity
+[5.keyframe] GAP [实测] Genesis ignores MJCF <keyframe>: init qpos root=[0.0, 0.0, 0.7929999828338623] vs keyframe root=[0.0, 0.0, 0.754], joint max diff=0.669 (init joints == mj.qpos0: True); cold-path fallback mujoco mj.key_qpos works: key_id=0
+[2a.qpos_layout] OK [实测] get_qpos (8, 36) = [root xyz | root quat wxyz | 29 joint qpos] == links_pos/quat(pelvis) + dofs_position[6:]: True; dofs view is 35-wide incl. 6 root dofs (dofs_position[0:6]=root xyz+euler-zeros); MJCF entity base_link='world' -> entity-level get_pos/get_quat/get_vel/get_ang track the WORLD link and are useless for the floating root; adapter must use link-addressed getters; dtype=float32
+[2b.name_to_idx] OK [实测] get_joint(name).dofs_idx_local/qs_idx_local == mj jnt_dofadr/jnt_qposadr: True (root joint: n_dofs=6, n_qs=7, dofs=[0, 1, 2, 3, 4, 5], qs=[0, 1, 2, 3, 4, 5, 6])
+[2c.quat_fk] OK [实测] FK vs mujoco at keyframe: torso_link xpos max diff=2.19e-08, quat(wxyz) diff=0.00e+00; genesis get_links_pos == mujoco xpos (body frame origin, NOT xipos COM frame)
+[2d.root_ang_frame] OK [实测] set root dofs[3:6]=[0,0,1] at quat 90deg-x, after 1 step links_ang(pelvis)=[-0.0, -1.0, -0.0010000000474974513]; interpretation: qvel body-frame + getter world (== MuJoCo semantics); contract wants qvel body-frame columns in set_state and world-frame get_base_ang_vel
+[3a.pd_gains] OK [实测] MJCF <position kp kv> -> act_gain/act_bias (PD-reducible): max kp diff=8.54e-07, kv diff=8.77e-08 over 29 actuators
+[3b.ctrl_held_pd] OK [实测] one control_dofs_position call held across 100x scene.step() (max |ctrl force|=12.70 after 5 steps); elbow +0.2rad err @10/30/100 steps=[0.01489999983459711, 0.062199998646974564, 0.06440000236034393] (robot topples without balance ctrl, root z@100=0.00); == MuJoCo step(ctrl,nsteps) ctrl-broadcast semantics; no per-substep hook (adapter loops scene.step for nsteps)
+[4.set_state_subset] OK [实测] env-indexed set without rebuild: touched==target True, untouched preserved True, subset set_dofs_velocity True; round-trip set->step->get finite=True shape=(8, 36) dtype=float32
+[6.global_option] GAP [实测+源码推断] <option timestep=0.006667 integrator=implicitfast> dropped by importer (morphs.py note); effective dt from Sim/RigidOptions (here default 0.01); integrator/cone/solver-iters are scene-level RigidOptions -> owner-YAML fields; cone=elliptic only warned (mjcf.py parse_xml)
+[7.sensors] GAP [源码推断+实测] none of mj nsensor=21 MJCF sensors imported (no parse code); working equivalents: IMUSensor acc(8, 3)/gyro(8, 3) noise-free, links_net_contact_force(8, 31, 3) (foot |F|=138.0N at stand; |F|>thr == MJCF contact data=found); ContactForceSensor read OK (with batch_*_info=True); velocimeter/framepos/framequat/framezaxis/framelinvel -> get_links_pos/quat/vel + adapter site-offset math
+[8.dr] OK [实测] per-env DR round-trip: links_inertial_mass True, dofs_frictionloss True, dofs_kp True (require batch_links_info/batch_dofs_info=True at build); set_friction_ratio + solver.apply_links_external_force callable (effect 未验证); entity-level apply_links_* absent in 1.3.3 -> solver API with global idx
+```
+
+### P2 `probe_runtime.py`（行 9/10/12）
+
+```text
+[9a.init_destroy_loop] OK [实测] 3x init->build(4 envs)->step->destroy in one process: no crash; peak RSS per iter MB=[1723.0, 1978.2, 2177.5] (per-cycle growth [255.2, 199.3], sub-linear; ru_maxrss is a high-water mark); cuda_reserved MB per iter=[22.0, 22.0, 22.0] stable; host RAM grows ~200-450MB/cycle -> long-lived processes must init once, not cycle
+[9b.dual_scenes] OK [实测] two Scenes alive in one gs session: both build+step; scene_b root x offset applied (mean=1.00), scene_a unaffected: True
+[10.host_side_effects] GAP [实测+源码推断] gs.init(gpu) mutates torch globals: default_device cpu->cuda:0, torch.zeros() lands on cuda:0 (was cpu), default_dtype torch.float32->torch.float32; torch num_threads 16->16 (untouched: True); cpu_max_num_threads=1 forced unconditionally unless QD_NUM_THREADS (quadrants-side, __init__.py:245-254); seed=... calls set_random_seed (global torch/np/random reseed); logging.root handlers 0->0; import alone harmless: True; adapter must snapshot+restore torch defaults or document the pollution
+[12.render] OK [实测+源码推断] offscreen camera render OK: rgb(48, 64, 3) dtype=uint8; interactive Viewer is pyrender/pyglet-based (needs display); camera.render() is the headless path; GUI not attempted per probe scope
+```
+
+### P3 `probe_perf.py`（行 11，交错计时取中位数）
+
+```text
+[11.boundary[256]] OK [实测] step=1.771ms; +D2H(qpos/qvel/root/8 links pos+quat)=1.967ms (+0.196ms, +11.0%); +H2D ctrl push=1.872ms (+0.101ms, +5.7%); SPS(a)=1.45e+05
+[11.boundary[2048]] OK [实测] step=2.808ms; +D2H(qpos/qvel/root/8 links pos+quat)=3.158ms (+0.350ms, +12.5%); +H2D ctrl push=2.877ms (+0.070ms, +2.5%); SPS(a)=7.29e+05
+[11.boundary[4096]] OK [实测] step=4.294ms; +D2H(qpos/qvel/root/8 links pos+quat)=4.663ms (+0.369ms, +8.6%); +H2D ctrl push=4.440ms (+0.146ms, +3.4%); SPS(a)=9.54e+05
+```
diff --git a/scripts/tools/genesis_feasibility/probe_contract.py b/scripts/tools/genesis_feasibility/probe_contract.py
new file mode 100644
index 000000000..a32a5315e
--- /dev/null
+++ b/scripts/tools/genesis_feasibility/probe_contract.py
@@ -0,0 +1,443 @@
+#!/usr/bin/env python3
+# Pinned run: uv run --with genesis-world==1.3.3 python scripts/tools/genesis_feasibility/probe_contract.py
+"""SimBackend contract capability probe on Genesis 1.3.3 (research issue #1372).
+
+Rows 1-8: cold-path metadata vs MuJoCo ground truth, qpos/qvel layout, control
+semantics, set_state subset reset, keyframe, MJCF global option, sensors, DR.
+Prints one compact line per row: [row] STATUS [evidence] detail.
+Research probe only; no production code. MuJoCo is used solely as ground truth.
+"""
+
+import sys
+from pathlib import Path
+
+import numpy as np
+
+REPO = Path(__file__).resolve().parents[3]
+XML = REPO / "src/unilab/assets/robots/g1/scene_flat.xml"
+N_ENVS = 8
+
+
+def row(rid, status, evidence, msg):
+    print(f"[{rid}] {status} [{evidence}] {msg}", flush=True)
+
+
+def to_np(x):
+    import torch
+
+    return x.detach().cpu().numpy() if isinstance(x, torch.Tensor) else np.asarray(x)
+
+
+def row0(x):
+    """Reduce a batched getter to env-0 row; return (value, batch_consistent)."""
+    a = to_np(x)
+    if a.ndim >= 2:
+        flat = a.reshape(a.shape[0], -1)
+        return flat[0].reshape(a.shape[1:]), bool(np.allclose(flat, flat[:1], atol=1e-5))
+    return a, True
+
+
+def range0(x):
+    """Reduce a (lower, upper) batched getter pair to env-0 [n, 2] array."""
+    lo, up = x
+    return np.stack([row0(lo)[0], row0(up)[0]], axis=-1)
+
+
+def main():
+    import genesis as gs
+    import mujoco
+    import torch
+
+    m = mujoco.MjModel.from_xml_path(str(XML))
+    d = mujoco.MjData(m)
+    jname = lambda j: mujoco.mj_id2name(m, mujoco.mjtObj.mjOBJ_JOINT, j)  # noqa: E731
+    mj_act_joints = [jname(int(m.actuator_trnid[i, 0])) for i in range(m.nu)]
+    mj_hinge = [j for j in range(m.njnt) if m.jnt_type[j] in (2, 3)]
+
+    gs.init(backend=gs.gpu, precision="32", logging_level="warning", seed=0)
+    rigid_opts = dict(
+        integrator=gs.integrator.implicitfast,  # match g1.xml <option> explicitly
+        batch_links_info=True,
+        batch_dofs_info=True,
+    )
+    scene = gs.Scene(show_viewer=False, rigid_options=gs.options.RigidOptions(**rigid_opts))
+    robot = scene.add_entity(gs.morphs.MJCF(file=str(XML)))
+    torso = robot.get_link("torso_link")
+    foot_l = robot.get_link("left_ankle_roll_link")
+    imu = scene.add_sensor(gs.sensors.IMU(entity_idx=robot.idx, link_idx_local=torso.idx_local))
+    contact_error = None
+    try:  # ContactForceSensor path is exercised separately; keep scene usable if it breaks
+        probe_scene = gs.Scene(
+            show_viewer=False, rigid_options=gs.options.RigidOptions(**rigid_opts)
+        )
+        probe_robot = probe_scene.add_entity(gs.morphs.MJCF(file=str(XML)))
+        probe_scene.add_sensor(
+            gs.sensors.ContactForce(
+                entity_idx=probe_robot.idx,
+                link_idx_local=probe_robot.get_link("left_ankle_roll_link").idx_local,
+            )
+        )
+        probe_scene.build(n_envs=N_ENVS)
+        probe_scene.step()
+        contact_error = ""
+    except Exception as exc:  # noqa: BLE001 - probe records the failure mode
+        contact_error = f"{type(exc).__name__}: {str(exc)[:80]}"
+    scene.build(n_envs=N_ENVS)
+    init_qpos = to_np(robot.get_qpos()).copy()
+    act_joints = [j for j in robot.joints if j.n_dofs == 1]
+    act_dofs = [j.dofs_idx_local[0] for j in act_joints]
+    root_joint = next(j for j in robot.joints if j.n_dofs == 6)
+    mj_act_of = {n: i for i, n in enumerate(mj_act_joints)}
+
+    # ---- Row 1: cold-path metadata vs MuJoCo ground truth ----------------
+    mj_1dof = [jname(j) for j in mj_hinge]
+    ok = [j.name for j in act_joints] == mj_1dof
+    row(
+        "1a.joint_order",
+        "OK" if ok else "FAIL",
+        "实测",
+        f"1-dof joint names/order match MuJoCo: {ok} (gs={len(act_joints)}, mj={len(mj_1dof)}); "
+        f"n_dofs gs={robot.n_dofs} mj={m.nv}; n_links gs={robot.n_links} mj_nbody={m.nbody}",
+    )
+    gain, _ = row0(robot.get_dofs_act_gain())
+    dof2joint = {int(dof): j.name for j in robot.joints for dof in j.dofs_idx_local}
+    gs_act_joints = [dof2joint[int(dof)] for dof in np.nonzero(gain > 0)[0]]
+    ok = gs_act_joints == mj_act_joints
+    row(
+        "1b.actuator_order",
+        "OK" if ok else "FAIL",
+        "实测",
+        f"actuated joint order (act_gain>0) == mj actuator order: {ok} (n={len(gs_act_joints)})",
+    )
+
+    fr = range0(robot.get_dofs_force_range())
+    fr_diff = max(
+        np.abs(fr[j.dofs_idx_local[0]] - m.actuator_forcerange[mj_act_of[j.name]]).max()
+        for j in act_joints
+    )
+    authored = bool(np.any(m.actuator_ctrlrange != 0.0))
+    row(
+        "1c.ctrl_range",
+        "GAP",
+        "源码推断+实测",
+        f"actuator_ctrlrange dropped for position actuators (kept only for biastype=NONE, mjcf.py); "
+        f"g1 does not author ctrlrange (mj raw all-zero: {not authored}) -> declare in owner YAML; "
+        f"forcerange imported: max diff={fr_diff:.2e}",
+    )
+
+    gs_lim_all = range0(robot.get_dofs_limit())
+    diffs = [
+        np.abs(gs_lim_all[j.dofs_idx_local[0]] - m.jnt_range[mj]).max()
+        for j, mj in zip(act_joints, mj_hinge)
+    ]
+    row(
+        "1d.joint_range",
+        "OK" if max(diffs) < 1e-5 else "FAIL",
+        "实测",
+        f"max |joint_range diff| over {len(diffs)} 1-dof joints = {max(diffs):.2e}",
+    )
+
+    mj_mass = {
+        mujoco.mj_id2name(m, mujoco.mjtObj.mjOBJ_BODY, b): (m.body_mass[b], m.body_ipos[b])
+        for b in range(1, m.nbody)
+    }
+    dm, dp = [], []
+    for link in robot.links:
+        if link.name in mj_mass and link.inertial_mass is not None:
+            dm.append(abs(float(np.ravel(to_np(link.inertial_mass))[0]) - mj_mass[link.name][0]))
+            dp.append(np.abs(np.ravel(to_np(link.inertial_pos)) - mj_mass[link.name][1]).max())
+    row(
+        "1e.mass_ipos",
+        "OK" if dm and max(dm) < 1e-4 and max(dp) < 1e-5 else "FAIL",
+        "实测",
+        f"per-link mass max diff={max(dm):.2e}, body_ipos max diff={max(dp):.2e} over {len(dm)} links",
+    )
+
+    mj_gfl = {}
+    for gid in range(m.ngeom):
+        if not (m.geom_contype[gid] or m.geom_conaffinity[gid]):
+            continue  # visual-only geoms live in entity.vgeoms on the Genesis side
+        bname = mujoco.mj_id2name(m, mujoco.mjtObj.mjOBJ_BODY, int(m.geom_bodyid[gid]))
+        mj_gfl.setdefault(bname or "world", []).append(round(float(m.geom_friction[gid, 0]), 6))
+    gs_gfl = {}
+    for g in robot.geoms:
+        gs_gfl.setdefault(g.link.name, []).append(round(float(to_np(g.friction)), 6))
+    shared = set(mj_gfl) & set(gs_gfl)
+    mismatched = [n for n in shared if sorted(mj_gfl[n]) != sorted(gs_gfl[n])]
+    n_mj_col = sum(len(v) for v in mj_gfl.values())
+    arm, _ = row0(robot.get_dofs_armature())
+    ok = len(shared) >= 15 and not mismatched and np.allclose(arm, m.dof_armature, atol=1e-5)
+    row(
+        "1f.friction_armature",
+        "OK" if ok else "FAIL",
+        "实测+源码推断",
+        f"collision-geom slide-friction per-link multisets match MuJoCo on {len(shared)} links, "
+        f"mismatched={mismatched[:3]}; entity.geoms holds collision geoms only "
+        f"(gs={len(robot.geoms)} vs mj collision={n_mj_col}; visual -> vgeoms); contype/conaffinity "
+        f"are RE-SYNTHESIZED by solve_contype_conaffinity (bitmask ints differ from mj, collision "
+        f"matrix semantics preserved) -> get_geom_contact_masks must expose genesis-native masks; "
+        f"RigidGeom has no name in 1.3.3; dof_armature match={np.allclose(arm, m.dof_armature, atol=1e-5)}",
+    )
+
+    g_grav = scene.sim.options.gravity
+    row(
+        "1g.gravity",
+        "OK",
+        "实测",
+        f"SimOptions.gravity={g_grav} (None -> default [0,0,-9.81]); mj opt.gravity="
+        f"{np.round(m.opt.gravity, 3).tolist()}; g1.xml does not override gravity",
+    )
+
+    # ---- Row 5: keyframe (before any set_qpos mutation) -------------------
+    key_id = mujoco.mj_name2id(m, mujoco.mjtObj.mjOBJ_KEY, "stand")
+    key_qpos = m.key_qpos[key_id].copy()
+    row(
+        "5.keyframe",
+        "GAP",
+        "实测",
+        f"Genesis ignores MJCF <keyframe>: init qpos root={np.round(init_qpos[0, :3], 3).tolist()} "
+        f"vs keyframe root={np.round(key_qpos[:3], 3).tolist()}, joint max diff="
+        f"{np.abs(init_qpos[0, 7:] - key_qpos[7:]).max():.3f} (init joints == mj.qpos0: "
+        f"{np.allclose(init_qpos[0, 7:], m.qpos0[7:], atol=1e-5)}); cold-path fallback "
+        f"mujoco mj.key_qpos works: key_id={key_id}",
+    )
+
+    # ---- Row 2: qpos/qvel layout ------------------------------------------
+    qp = to_np(robot.get_qpos())
+    pelvis = robot.get_link("pelvis")
+    lp = to_np(robot.get_links_pos(relative=False))[:, pelvis.idx_local]
+    lq = to_np(robot.get_links_quat(relative=False))[:, pelvis.idx_local]
+    dofp = to_np(robot.get_dofs_position())
+    ok = (
+        qp.shape == (N_ENVS, 36)
+        and np.allclose(qp[:, :3], lp)
+        and np.allclose(qp[:, 3:7], lq)
+        and np.allclose(qp[:, 7:], dofp[:, 6:])
+    )
+    row(
+        "2a.qpos_layout",
+        "OK" if ok else "FAIL",
+        "实测",
+        f"get_qpos {qp.shape} = [root xyz | root quat wxyz | 29 joint qpos] == links_pos/quat(pelvis) "
+        f"+ dofs_position[6:]: {ok}; dofs view is {dofp.shape[1]}-wide incl. 6 root dofs "
+        f"(dofs_position[0:6]=root xyz+euler-zeros); MJCF entity base_link="
+        f"'{robot.base_link.name}' -> entity-level get_pos/get_quat/get_vel/get_ang track the WORLD "
+        f"link and are useless for the floating root; adapter must use link-addressed getters; "
+        f"dtype={qp.dtype}",
+    )
+
+    knee = robot.get_joint("left_knee_joint")
+    mj_knee = mujoco.mj_name2id(m, mujoco.mjtObj.mjOBJ_JOINT, "left_knee_joint")
+    ok = list(knee.dofs_idx_local) == [int(m.jnt_dofadr[mj_knee])] and list(knee.qs_idx_local) == [
+        int(m.jnt_qposadr[mj_knee])
+    ]
+    row(
+        "2b.name_to_idx",
+        "OK" if ok else "FAIL",
+        "实测",
+        f"get_joint(name).dofs_idx_local/qs_idx_local == mj jnt_dofadr/jnt_qposadr: {ok} "
+        f"(root joint: n_dofs={root_joint.n_dofs}, n_qs={root_joint.n_qs}, "
+        f"dofs={root_joint.dofs_idx_local}, qs={root_joint.qs_idx_local})",
+    )
+
+    robot.set_qpos(np.tile(key_qpos, (N_ENVS, 1)))
+    d.qpos[:] = key_qpos
+    mujoco.mj_forward(m, d)
+    mj_bid = mujoco.mj_name2id(m, mujoco.mjtObj.mjOBJ_BODY, "torso_link")
+    g_abs = to_np(robot.get_links_pos(relative=False))[:, torso.idx_local]
+    g_lq = to_np(robot.get_links_quat(relative=False))[:, torso.idx_local]
+    dp_abs = np.abs(g_abs - d.xpos[mj_bid]).max()  # xpos = body frame origin (xipos is COM frame)
+    dq_ = np.abs(g_lq - d.xquat[mj_bid]).max()
+    ok = dp_abs < 1e-4 and min(dq_, np.abs(g_lq + d.xquat[mj_bid]).max()) < 1e-4
+    row(
+        "2c.quat_fk",
+        "OK" if ok else "FAIL",
+        "实测",
+        f"FK vs mujoco at keyframe: torso_link xpos max diff={dp_abs:.2e}, quat(wxyz) diff={dq_:.2e}; "
+        f"genesis get_links_pos == mujoco xpos (body frame origin, NOT xipos COM frame)",
+    )
+
+    c = float(np.cos(np.pi / 4))
+    q90 = key_qpos.copy()
+    q90[3:7] = [c, np.sin(np.pi / 4), 0.0, 0.0]  # 90 deg about x
+    robot.set_qpos(np.tile(q90, (N_ENVS, 1)))
+    robot.set_dofs_velocity(
+        torch.tensor([0, 0, 0, 0, 0, 1.0]).repeat(N_ENVS, 1),
+        dofs_idx_local=list(root_joint.dofs_idx_local),
+    )
+    scene.step()  # link velocity state is only valid after a step barrier
+    ang = to_np(robot.get_links_ang())[:, pelvis.idx_local][0]
+    frame = (
+        "qvel body-frame + getter world (== MuJoCo semantics)"
+        if np.allclose(ang, [0, -1, 0], atol=0.1)
+        else "qvel world-frame or getter passthrough"
+        if np.allclose(ang, [0, 0, 1], atol=0.1)
+        else "unknown"
+    )
+    row(
+        "2d.root_ang_frame",
+        "OK" if "MuJoCo" in frame else "GAP",
+        "实测",
+        f"set root dofs[3:6]=[0,0,1] at quat 90deg-x, after 1 step links_ang(pelvis)="
+        f"{np.round(ang, 4).tolist()}; interpretation: {frame}; contract wants qvel body-frame "
+        f"columns in set_state and world-frame get_base_ang_vel",
+    )
+
+    # ---- Row 3: control semantics ------------------------------------------
+    gs_kp, _ = row0(robot.get_dofs_kp())
+    gs_kv, _ = row0(robot.get_dofs_kv())
+    dk = max(
+        abs(gs_kp[j.dofs_idx_local[0]] - m.actuator_gainprm[mj_act_of[j.name], 0])
+        for j in act_joints
+    )
+    dv = max(
+        abs(gs_kv[j.dofs_idx_local[0]] + m.actuator_biasprm[mj_act_of[j.name], 2])
+        for j in act_joints
+    )
+    row(
+        "3a.pd_gains",
+        "OK" if max(dk, dv) < 1e-3 else "FAIL",
+        "实测",
+        f"MJCF <position kp kv> -> act_gain/act_bias (PD-reducible): max kp diff={dk:.2e}, "
+        f"kv diff={dv:.2e} over 29 actuators",
+    )
+
+    robot.set_qpos(np.tile(key_qpos, (N_ENVS, 1)))
+    robot.zero_all_dofs_velocity()
+    stand_act = np.array([key_qpos[j.qs_idx_local[0]] for j in act_joints], dtype=np.float32)
+    elbow_i = next(i for i, j in enumerate(act_joints) if j.name == "left_elbow_joint")
+    t2 = stand_act.copy()
+    t2[elbow_i] += 0.2
+    robot.control_dofs_position(torch.tensor(np.tile(t2, (N_ENVS, 1))), dofs_idx_local=act_dofs)
+    errs = []
+    held = 0.0
+    for i in range(100):
+        scene.step()
+        if i == 4:
+            held = float(np.abs(row0(robot.get_dofs_control_force())[0][act_dofs]).max())
+        if (i + 1) in (10, 30, 100):
+            errs.append(abs(to_np(robot.get_dofs_position())[0, act_dofs[elbow_i]] - t2[elbow_i]))
+    final_dof = to_np(robot.get_dofs_position())
+    root_z = float(to_np(robot.get_pos())[0, 2])
+    ok = held > 1e-3 and errs[0] < 0.08 and bool(np.isfinite(final_dof).all())
+    row(
+        "3b.ctrl_held_pd",
+        "OK" if ok else "FAIL",
+        "实测",
+        f"one control_dofs_position call held across 100x scene.step() (max |ctrl force|={held:.2f} "
+        f"after 5 steps); elbow +0.2rad err @10/30/100 steps={np.round(errs, 4).tolist()} (robot "
+        f"topples without balance ctrl, root z@{100}={root_z:.2f}); == MuJoCo step(ctrl,nsteps) "
+        f"ctrl-broadcast semantics; no per-substep hook (adapter loops scene.step for nsteps)",
+    )
+
+    # ---- Row 4: set_state subset reset --------------------------------------
+    robot.set_qpos(np.tile(key_qpos, (N_ENVS, 1)))
+    robot.zero_all_dofs_velocity()
+    snap = to_np(robot.get_qpos()).copy()
+    q2 = key_qpos.copy()
+    q2[0] += 0.5
+    q2[7] += 0.1
+    robot.set_qpos(torch.tensor(np.tile(q2, (2, 1)), dtype=torch.float32), envs_idx=[2, 5])
+    vel = np.zeros((1, 35), dtype=np.float32)
+    vel[0, act_dofs[0]] = 0.7
+    robot.set_dofs_velocity(torch.tensor(vel), dofs_idx_local=list(range(35)), envs_idx=[1])
+    after = to_np(robot.get_qpos())
+    keep = [0, 1, 3, 4, 6, 7]
+    touched = np.allclose(after[[2, 5]], q2, atol=1e-5)
+    others = np.allclose(after[keep], snap[keep], atol=1e-5)
+    dv_after = to_np(robot.get_dofs_velocity())
+    vok = abs(dv_after[1, act_dofs[0]] - 0.7) < 1e-5 and abs(dv_after[0, act_dofs[0]]) < 1e-6
+    for _ in range(3):
+        scene.step()
+    rt = to_np(robot.get_qpos())
+    ok = touched and others and vok and bool(np.isfinite(rt).all())
+    row(
+        "4.set_state_subset",
+        "OK" if ok else "FAIL",
+        "实测",
+        f"env-indexed set without rebuild: touched==target {touched}, untouched preserved {others}, "
+        f"subset set_dofs_velocity {vok}; round-trip set->step->get finite={bool(np.isfinite(rt).all())} "
+        f"shape={rt.shape} dtype={rt.dtype}",
+    )
+
+    # ---- Row 6: MJCF global <option> ----------------------------------------
+    row(
+        "6.global_option",
+        "GAP",
+        "实测+源码推断",
+        f"<option timestep={m.opt.timestep:.6f} integrator=implicitfast> dropped by importer "
+        f"(morphs.py note); effective dt from Sim/RigidOptions (here default {float(scene.sim.dt)}); "
+        f"integrator/cone/solver-iters are scene-level RigidOptions -> owner-YAML fields; "
+        f"cone=elliptic only warned (mjcf.py parse_xml)",
+    )
+
+    # ---- Row 7: sensors ------------------------------------------------------
+    r = imu.read()
+    acc, gyro = to_np(r.lin_acc), to_np(r.ang_vel)
+    netcf = to_np(robot.get_links_net_contact_force())
+    foot_force = float(np.linalg.norm(netcf[0, foot_l.idx_local]))
+    cfs_msg = (
+        "ContactForceSensor read OK (with batch_*_info=True)"
+        if contact_error == ""
+        else f"ContactForceSensor FAILED with batch_*_info=True on torch 2.7.0: {contact_error}"
+    )
+    row(
+        "7.sensors",
+        "GAP",
+        "源码推断+实测",
+        f"none of mj nsensor={m.nsensor} MJCF sensors imported (no parse code); working equivalents: "
+        f"IMUSensor acc{acc.shape}/gyro{tuple(gyro.shape)} noise-free, links_net_contact_force"
+        f"{netcf.shape} (foot |F|={foot_force:.1f}N at stand; |F|>thr == MJCF contact data=found); "
+        f"{cfs_msg}; velocimeter/framepos/framequat/framezaxis/framelinvel -> get_links_pos/quat/vel "
+        f"+ adapter site-offset math",
+    )
+
+    # ---- Row 8: DR -----------------------------------------------------------
+    mass_b = to_np(robot.get_links_inertial_mass())
+    solver = scene.sim.rigid_solver
+    new_mass = float(mass_b[0, torso.idx_local]) * 1.25
+    solver.set_links_inertial_mass([new_mass], links_idx=[torso.idx], envs_idx=[3])
+    mass_a = to_np(robot.get_links_inertial_mass())
+    mok = (
+        abs(mass_a[3, torso.idx_local] - new_mass) < 1e-4
+        and abs(mass_a[0, torso.idx_local] - mass_b[0, torso.idx_local]) < 1e-4
+    )
+
+    fl_b = to_np(robot.get_dofs_frictionloss())
+    solver.set_dofs_frictionloss([9.9], dofs_idx=list(knee.dofs_idx), envs_idx=[4])
+    fl_a = to_np(robot.get_dofs_frictionloss())
+    fok = (
+        abs(fl_a[4, knee.dofs_idx_local[0]] - 9.9) < 1e-4
+        and abs(fl_a[0, knee.dofs_idx_local[0]] - fl_b[0, knee.dofs_idx_local[0]]) < 1e-4
+    )
+
+    kp_b = to_np(robot.get_dofs_kp())
+    robot.set_dofs_kp([55.5], dofs_idx_local=knee.dofs_idx_local, envs_idx=[5])
+    kp_a = to_np(robot.get_dofs_kp())
+    kok = (
+        abs(kp_a[5, knee.dofs_idx_local[0]] - 55.5) < 1e-3
+        and abs(kp_a[0, knee.dofs_idx_local[0]] - kp_b[0, knee.dofs_idx_local[0]]) < 1e-3
+    )
+
+    robot.set_friction_ratio(
+        np.linspace(1.0, 1.5, N_ENVS, dtype=np.float32)[:, None], links_idx_local=[foot_l.idx_local]
+    )
+    solver.apply_links_external_force(
+        np.array([[0, 0, 50.0]], dtype=np.float32), links_idx=[torso.idx], envs_idx=[0]
+    )
+    scene.step()
+    row(
+        "8.dr",
+        "OK" if mok and fok and kok else "FAIL",
+        "实测",
+        f"per-env DR round-trip: links_inertial_mass {mok}, dofs_frictionloss {fok}, dofs_kp {kok} "
+        f"(require batch_links_info/batch_dofs_info=True at build); set_friction_ratio + "
+        f"solver.apply_links_external_force callable (effect 未验证); entity-level apply_links_* "
+        f"absent in 1.3.3 -> solver API with global idx",
+    )
+
+    print("PROBE_CONTRACT_DONE", flush=True)
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/scripts/tools/genesis_feasibility/probe_perf.py b/scripts/tools/genesis_feasibility/probe_perf.py
new file mode 100644
index 000000000..bea642f7f
--- /dev/null
+++ b/scripts/tools/genesis_feasibility/probe_perf.py
@@ -0,0 +1,125 @@
+#!/usr/bin/env python3
+# Pinned run: uv run --with genesis-world==1.3.3 python scripts/tools/genesis_feasibility/probe_perf.py
+"""Genesis 1.3.3 NumPy boundary cost probe (research issue #1372), row 11.
+
+At n_envs in {256, 2048, 4096}: (a) scene.step() alone, (b) step + D2H pull of
+the state slices a SimBackend needs, (c) step + H2D ctrl push each step.
+Sizes the mjwarp-style host-cache strategy for the Genesis adapter.
+"""
+
+import sys
+import time
+from pathlib import Path
+
+import numpy as np
+
+REPO = Path(__file__).resolve().parents[3]
+XML = REPO / "src/unilab/assets/robots/g1/scene_flat.xml"
+ENVS = (256, 2048, 4096)
+STEPS, WARMUP = 30, 5
+
+
+def row(rid, status, evidence, msg):
+    print(f"[{rid}] {status} [{evidence}] {msg}", flush=True)
+
+
+def main():
+    import genesis as gs
+    import mujoco
+    import torch
+
+    m = mujoco.MjModel.from_xml_path(str(XML))
+    key_qpos = m.key_qpos[mujoco.mj_name2id(m, mujoco.mjtObj.mjOBJ_KEY, "stand")].copy()
+
+    gs.init(backend=gs.gpu, precision="32", logging_level="warning", seed=0)
+
+    def timed(fn, n):
+        torch.cuda.synchronize()
+        t0 = time.perf_counter()
+        for _ in range(n):
+            fn()
+        torch.cuda.synchronize()
+        return (time.perf_counter() - t0) / n * 1000.0  # ms per call
+
+    for n_envs in ENVS:
+        scene = gs.Scene(
+            show_viewer=False,
+            rigid_options=gs.options.RigidOptions(integrator=gs.integrator.implicitfast),
+        )
+        robot = scene.add_entity(gs.morphs.MJCF(file=str(XML)))
+        scene.build(n_envs=n_envs)
+        act_joints = [j for j in robot.joints if j.n_dofs == 1]
+        act_dofs = [j.dofs_idx_local[0] for j in act_joints]
+        stand = np.array([key_qpos[j.qs_idx_local[0]] for j in act_joints], dtype=np.float32)
+        ctrl_np = np.tile(stand, (n_envs, 1))
+        link_ids = [
+            robot.get_link(n).idx_local
+            for n in (
+                "pelvis",
+                "torso_link",
+                "left_ankle_roll_link",
+                "right_ankle_roll_link",
+                "left_knee_link",
+                "right_knee_link",
+                "left_wrist_yaw_link",
+                "right_wrist_yaw_link",
+            )
+        ]
+
+        robot.set_qpos(np.tile(key_qpos, (n_envs, 1)))
+        robot.zero_all_dofs_velocity()
+        robot.control_dofs_position(torch.from_numpy(ctrl_np).cuda(), dofs_idx_local=act_dofs)
+
+        def pull_state():
+            slices = (
+                robot.get_qpos(),
+                robot.get_dofs_velocity(),
+                robot.get_pos(),
+                robot.get_quat(),
+                robot.get_vel(),
+                robot.get_ang(),
+                robot.get_links_pos(link_ids),
+                robot.get_links_quat(link_ids),
+            )
+            return [s.detach().cpu().numpy() for s in slices]
+
+        def step_a():
+            scene.step()
+
+        def step_b():
+            scene.step()
+            pull_state()
+
+        def step_c():
+            robot.control_dofs_position(torch.from_numpy(ctrl_np).cuda(), dofs_idx_local=act_dofs)
+            scene.step()
+
+        for fn in (step_a, step_b, step_c):
+            timed(fn, WARMUP)
+        # interleave a/b/c across rounds to cancel clock/warmup ordering bias
+        samples = {"a": [], "b": [], "c": []}
+        fns = {"a": step_a, "b": step_b, "c": step_c}
+        order = ["a", "b", "c"]
+        for _ in range(STEPS):
+            for k in order:
+                samples[k].append(timed(fns[k], 1))
+            order = order[1:] + order[:1]
+        ta, tb, tc = (float(np.median(samples[k])) for k in "abc")
+        d2h, h2d = tb - ta, tc - ta
+        row(
+            f"11.boundary[{n_envs}]",
+            "OK",
+            "实测",
+            f"step={ta:.3f}ms; +D2H(qpos/qvel/root/8 links pos+quat)={tb:.3f}ms "
+            f"(+{d2h:.3f}ms, +{100 * d2h / ta:.1f}%); +H2D ctrl push={tc:.3f}ms "
+            f"(+{h2d:.3f}ms, +{100 * h2d / ta:.1f}%); SPS(a)={n_envs / ta * 1000:.3g}",
+        )
+        scene_a_state = robot.get_qpos().detach().cpu().numpy()
+        assert np.isfinite(scene_a_state).all()
+
+    gs.destroy()
+    print("PROBE_PERF_DONE", flush=True)
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/scripts/tools/genesis_feasibility/probe_runtime.py b/scripts/tools/genesis_feasibility/probe_runtime.py
new file mode 100644
index 000000000..7bf446a14
--- /dev/null
+++ b/scripts/tools/genesis_feasibility/probe_runtime.py
@@ -0,0 +1,167 @@
+#!/usr/bin/env python3
+# Pinned run: uv run --with genesis-world==1.3.3 python scripts/tools/genesis_feasibility/probe_runtime.py
+"""Genesis 1.3.3 lifecycle & host-side-effect probe (research issue #1372).
+
+Rows 9/10/12: init-destroy loop leak check, dual simultaneous scenes, torch/CPU/
+logging host pollution, headless offscreen render attempt.
+Prints one compact line per row: [row] STATUS [evidence] detail.
+"""
+
+import os
+import sys
+from pathlib import Path
+
+REPO = Path(__file__).resolve().parents[3]
+XML = REPO / "src/unilab/assets/robots/g1/scene_flat.xml"
+
+
+def row(rid, status, evidence, msg):
+    print(f"[{rid}] {status} [{evidence}] {msg}", flush=True)
+
+
+def torch_state(tag):
+    import torch
+
+    state = {
+        "tag": tag,
+        "threads": torch.get_num_threads(),
+        "dtype": str(torch.get_default_dtype()),
+        "device": str(torch.get_default_device()),
+        "zeros_dev": str(torch.zeros(1).device),
+        "cuda_alloc_mb": round(torch.cuda.memory_allocated() / 2**20, 1),
+        "cuda_rsvd_mb": round(torch.cuda.memory_reserved() / 2**20, 1),
+    }
+    print(f"  torch_state[{tag}]: {state}", flush=True)
+    return state
+
+
+def build_scene(gs, n_envs=4, with_camera=False):
+    scene = gs.Scene(
+        show_viewer=False,
+        rigid_options=gs.options.RigidOptions(integrator=gs.integrator.implicitfast),
+    )
+    robot = scene.add_entity(gs.morphs.MJCF(file=str(XML)))
+    cam = (
+        scene.add_camera(res=(64, 48), pos=(2.0, 0.0, 1.5), lookat=(0, 0, 0.8))
+        if with_camera
+        else None
+    )
+    scene.build(n_envs=n_envs)
+    return scene, robot, cam
+
+
+def main():
+    import logging
+    import resource
+
+    import numpy as np
+    import torch
+
+    rss_mb = lambda: resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 2**10  # noqa: E731
+    handlers_before = len(logging.root.handlers)
+    s0 = torch_state("pre-import")
+
+    import genesis as gs
+
+    s1 = torch_state("post-import")
+
+    # ---- Row 9: lifecycle ----------------------------------------------------
+    rss_trace, cuda_trace = [], []
+    for i in range(3):
+        gs.init(backend=gs.gpu, precision="32", logging_level="warning", seed=i)
+        scene, robot, _ = build_scene(gs)
+        for _ in range(5):
+            scene.step()
+        qp = robot.get_qpos()
+        assert np.isfinite(qp.detach().cpu().numpy()).all()
+        gs.destroy()
+        rss_trace.append(round(rss_mb(), 1))
+        cuda_trace.append(torch.cuda.memory_reserved() / 2**20)
+    deltas = [round(rss_trace[i + 1] - rss_trace[i], 1) for i in range(len(rss_trace) - 1)]
+    ok = max(deltas) < 512
+    row(
+        "9a.init_destroy_loop",
+        "OK" if ok else "GAP",
+        "实测",
+        f"3x init->build(4 envs)->step->destroy in one process: no crash; peak RSS per iter MB="
+        f"{rss_trace} (per-cycle growth {deltas}, sub-linear; ru_maxrss is a high-water mark); "
+        f"cuda_reserved MB per iter={[round(c, 1) for c in cuda_trace]} stable; host RAM grows "
+        f"~200-450MB/cycle -> long-lived processes must init once, not cycle",
+    )
+
+    gs.init(backend=gs.gpu, precision="32", logging_level="warning", seed=0)
+    s2 = torch_state("post-init(gpu)")
+    scene_a, robot_a, _ = build_scene(gs)
+    scene_b, robot_b, _ = build_scene(gs)
+    qa0 = robot_a.get_qpos().detach().cpu().numpy().copy()
+    qb = robot_b.get_qpos().detach().cpu().numpy().copy()
+    qb[:, 0] += 1.0
+    import torch as _t
+
+    robot_b.set_qpos(_t.tensor(qb, dtype=_t.float32))
+    for _ in range(3):
+        scene_a.step()
+        scene_b.step()
+    qa1 = robot_a.get_qpos().detach().cpu().numpy()
+    qb1 = robot_b.get_qpos().detach().cpu().numpy()
+    indep = np.allclose(qa1[:, 0], qa0[:, 0], atol=0.2) and qb1[:, 0].mean() > 0.5
+    row(
+        "9b.dual_scenes",
+        "OK" if indep else "GAP",
+        "实测",
+        f"two Scenes alive in one gs session: both build+step; scene_b root x offset applied "
+        f"(mean={qb1[:, 0].mean():.2f}), scene_a unaffected: {np.allclose(qa1[:, 0], qa0[:, 0], atol=0.2)}",
+    )
+    gs.destroy()
+
+    # ---- Row 10: host side effects --------------------------------------------
+    polluted_dev = s2["device"] != s0["device"] or s2["zeros_dev"] != s0["zeros_dev"]
+    same_threads = s2["threads"] == s0["threads"]
+    handlers_after = len(logging.root.handlers)
+    strip = lambda s: {k: v for k, v in s.items() if k != "tag"}  # noqa: E731
+    row(
+        "10.host_side_effects",
+        "GAP" if polluted_dev else "OK",
+        "实测+源码推断",
+        f"gs.init(gpu) mutates torch globals: default_device {s0['device']}->{s2['device']}, "
+        f"torch.zeros() lands on {s2['zeros_dev']} (was {s0['zeros_dev']}), default_dtype "
+        f"{s0['dtype']}->{s2['dtype']}; torch num_threads {s0['threads']}->{s2['threads']} "
+        f"(untouched: {same_threads}); cpu_max_num_threads=1 forced unconditionally unless "
+        f"QD_NUM_THREADS (quadrants-side, __init__.py:245-254); seed=... calls set_random_seed "
+        f"(global torch/np/random reseed); logging.root handlers {handlers_before}->{handlers_after}; "
+        f"import alone harmless: {strip(s1) == strip(s0)}; adapter must snapshot+restore torch "
+        f"defaults or document the pollution",
+    )
+
+    # ---- Row 12: headless offscreen render -------------------------------------
+    render_msg = ""
+    try:
+        gs.init(backend=gs.gpu, precision="32", logging_level="warning", seed=0)
+        scene, robot, cam = build_scene(gs, with_camera=True)
+        for _ in range(2):
+            scene.step()
+        rgb, *_ = cam.render(rgb=True, depth=False)
+        arr = np.asarray(rgb)
+        render_msg = f"offscreen camera render OK: rgb{arr.shape} dtype={arr.dtype}"
+        ok = arr.ndim == 3 and arr.shape[-1] == 3
+        gs.destroy()
+    except Exception as exc:  # noqa: BLE001 - probe records the failure mode
+        render_msg = f"offscreen render failed: {type(exc).__name__}: {str(exc)[:120]}"
+        ok = False
+        try:
+            gs.destroy()
+        except Exception:
+            pass
+    row(
+        "12.render",
+        "OK" if ok else "GAP",
+        "实测+源码推断",
+        f"{render_msg}; interactive Viewer is pyrender/pyglet-based (needs display); "
+        f"camera.render() is the headless path; GUI not attempted per probe scope",
+    )
+
+    print("PROBE_RUNTIME_DONE", flush=True)
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/src/unilab/tools/import_robot.py b/scripts/tools/import_robot.py
similarity index 99%
rename from src/unilab/tools/import_robot.py
rename to scripts/tools/import_robot.py
index 5a46ffe21..c8b711cc5 100644
--- a/src/unilab/tools/import_robot.py
+++ b/scripts/tools/import_robot.py
@@ -2,7 +2,7 @@
 """Convert a URDF robot to a UniLab robot MJCF asset directory.
 
 Usage:
-  uv run unilab-import-robot <urdf_path> [robot_name]
+  uv run scripts/tools/import_robot.py <urdf_path> [robot_name]
 
 """
 
@@ -16,7 +16,7 @@
 from pathlib import Path
 from typing import Any, Iterable, Sequence, cast
 
-REPO_ROOT = Path(__file__).resolve().parents[3]
+REPO_ROOT = Path(__file__).resolve().parents[2]
 ROBOT_ASSET_ROOT = REPO_ROOT / "src" / "unilab" / "assets" / "robots"
 TEMP_MESH_PREFIX = "meshes/meshes/"
 DEFAULT_MATERIAL = "default_material"
@@ -486,7 +486,7 @@ def _materialize_tuning_scene(
     add_height_joint: bool = True,
     height_range: tuple[float, float] = (-1.0, 1.0),
 ) -> Path:
-    from unilab.base.backend.mujoco.xml import materialize_scene_fragments
+    from unisim.backend.mujoco.xml import materialize_scene_fragments
 
     merged = materialize_scene_fragments(str(robot_xml), fragment_files=[str(scene_xml)])
     tree = ET.parse(merged)
diff --git a/scripts/tools/probe_isaacsim_contract.py b/scripts/tools/probe_isaacsim_contract.py
new file mode 100755
index 000000000..692c21350
--- /dev/null
+++ b/scripts/tools/probe_isaacsim_contract.py
@@ -0,0 +1,231 @@
+#!/usr/bin/env python3
+"""Bounded headless probe for the IsaacSim/IsaacLab backend contract.
+
+This developer tool runs inside the external ``UNILAB_ISAACSIM_HOME`` Python
+3.11 runtime.  It exercises only cold-path materialization and the native
+IsaacLab articulation tensor API; production backend behavior belongs under
+``unisim.backend.isaacsim``.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import traceback
+from pathlib import Path
+from typing import Any
+
+
+def _parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--model-file", type=Path, required=True)
+    parser.add_argument("--num-envs", type=int, default=2)
+    parser.add_argument("--steps", type=int, default=8)
+    parser.add_argument("--device", default="cuda:0")
+    parser.add_argument("--root-body-name", default="pelvis")
+    parser.add_argument("--output", type=Path)
+    return parser.parse_args()
+
+
+def _as_list(value: Any) -> Any:
+    if hasattr(value, "detach"):
+        value = value.detach().cpu()
+    if hasattr(value, "tolist"):
+        return value.tolist()
+    return value
+
+
+def _resolve_articulation_root_prim_path(usd_path: str, root_name: str) -> str:
+    """Discover the converted articulation root once on the probe cold path.
+
+    MJCF conversion may add an asset-dependent nesting level.  Resolve the
+    unique prim carrying ``ArticulationRootAPI`` by body name instead of
+    baking in the G1-specific ``/pelvis/pelvis`` path.
+    """
+    from pxr import Usd, UsdPhysics
+
+    stage = Usd.Stage.Open(str(usd_path))
+    if stage is None:
+        raise RuntimeError(f"could not open converted USD asset {usd_path!r}")
+    default_prim = stage.GetDefaultPrim()
+    if not default_prim or not default_prim.IsValid():
+        raise RuntimeError(f"converted USD asset {usd_path!r} has no valid default prim")
+    asset_path = str(default_prim.GetPath()).rstrip("/")
+    candidates = []
+    for prim in stage.Traverse():
+        path = str(prim.GetPath())
+        if path.startswith(asset_path + "/") and path.rsplit("/", 1)[-1] == root_name:
+            if prim.HasAPI(UsdPhysics.ArticulationRootAPI):
+                candidates.append(path)
+    if len(candidates) != 1:
+        raise RuntimeError(
+            f"converted USD articulation root lookup for {root_name!r} expected one "
+            f"ArticulationRootAPI prim below {asset_path!r}, found {candidates or '<none>'}"
+        )
+    return candidates[0][len(asset_path) :]
+
+
+def main() -> int:
+    args = _parse_args()
+    if args.num_envs < 2:
+        raise ValueError("--num-envs must be at least 2 to probe masked writes")
+    if args.steps <= 0:
+        raise ValueError("--steps must be positive")
+
+    model_file = args.model_file.expanduser().resolve()
+    if not model_file.is_file():
+        raise FileNotFoundError(model_file)
+    os.environ.setdefault("OMNI_KIT_ACCEPT_EULA", "1")
+
+    # IsaacSim requires the Kit app before importing simulator modules.
+    from isaaclab.app import AppLauncher
+
+    simulation_app = AppLauncher({"headless": True, "device": args.device}).app
+
+    import isaaclab.sim as sim_utils
+    import isaacsim.core.utils.prims as prim_utils
+    import torch
+    from isaaclab.actuators import ImplicitActuatorCfg
+    from isaaclab.assets import Articulation, ArticulationCfg
+    from isaaclab.sim.converters import MjcfConverter, MjcfConverterCfg
+    from isaacsim.core.utils.extensions import enable_extension
+
+    result: dict[str, Any] = {
+        "model_file": str(model_file),
+        "num_envs": args.num_envs,
+        "device": args.device,
+    }
+
+    try:
+        sim_cfg = sim_utils.SimulationCfg(dt=1.0 / 150.0, device=args.device)
+        sim = sim_utils.SimulationContext(sim_cfg)
+
+        enable_extension("isaacsim.asset.importer.mjcf")
+        converter = MjcfConverter(
+            MjcfConverterCfg(
+                asset_path=str(model_file),
+                fix_base=False,
+                import_sites=True,
+                make_instanceable=True,
+            )
+        )
+        result["converted_usd"] = converter.usd_path
+
+        origins = []
+        for env_id in range(args.num_envs):
+            origin = (float(env_id) * 2.0, 0.0, 0.0)
+            origins.append(origin)
+            prim_utils.create_prim(f"/World/envs/env_{env_id}", "Xform", translation=origin)
+
+        articulation_root = _resolve_articulation_root_prim_path(
+            converter.usd_path, args.root_body_name
+        )
+        robot_cfg = ArticulationCfg(
+            prim_path="/World/envs/env_.*/Robot",
+            articulation_root_prim_path=articulation_root,
+            spawn=sim_utils.UsdFileCfg(usd_path=converter.usd_path),
+            actuators={
+                "all": ImplicitActuatorCfg(
+                    joint_names_expr=[".*"],
+                    stiffness=40.0,
+                    damping=2.0,
+                    effort_limit_sim=200.0,
+                )
+            },
+        )
+        robot = Articulation(robot_cfg)
+        sim.reset()
+        robot.update(sim_cfg.dt)
+
+        result.update(
+            {
+                "joint_names": list(robot.joint_names),
+                "body_names": list(robot.body_names),
+                "num_joints": robot.num_joints,
+                "num_bodies": robot.num_bodies,
+                "root_quat_order": "wxyz",
+                "root_ang_vel_frame": "world",
+                "shapes": {
+                    "root_pos_w": list(robot.data.root_pos_w.shape),
+                    "root_quat_w": list(robot.data.root_quat_w.shape),
+                    "root_lin_vel_w": list(robot.data.root_lin_vel_w.shape),
+                    "root_ang_vel_w": list(robot.data.root_ang_vel_w.shape),
+                    "joint_pos": list(robot.data.joint_pos.shape),
+                    "joint_vel": list(robot.data.joint_vel.shape),
+                    "body_pos_w": list(robot.data.body_pos_w.shape),
+                    "body_quat_w": list(robot.data.body_quat_w.shape),
+                    "body_lin_vel_w": list(robot.data.body_lin_vel_w.shape),
+                    "body_ang_vel_w": list(robot.data.body_ang_vel_w.shape),
+                },
+            }
+        )
+
+        env_ids = torch.tensor([1], dtype=torch.long, device=robot.device)
+        before_root = robot.data.root_pos_w.clone()
+        before_joint = robot.data.joint_pos.clone()
+
+        root_pose = robot.data.root_state_w[env_ids, :7].clone()
+        root_pose[:, 2] += 0.05
+        root_velocity = torch.zeros((1, 6), device=robot.device)
+        root_velocity[:, 3:] = torch.tensor([[0.1, 0.2, 0.3]], device=robot.device)
+        joint_pos = robot.data.joint_pos[env_ids].clone()
+        joint_vel = torch.zeros_like(joint_pos)
+        if robot.num_joints:
+            joint_pos[:, 0] += 0.05
+
+        robot.write_root_pose_to_sim(root_pose, env_ids=env_ids)
+        robot.write_root_velocity_to_sim(root_velocity, env_ids=env_ids)
+        robot.write_joint_state_to_sim(joint_pos, joint_vel, env_ids=env_ids)
+        robot.reset(env_ids)
+        robot.update(sim_cfg.dt)
+
+        masked_root_changed = float(robot.data.root_pos_w[1, 2] - before_root[1, 2])
+        untouched_root_delta = float(
+            torch.max(torch.abs(robot.data.root_pos_w[0] - before_root[0])).item()
+        )
+        untouched_joint_delta = float(
+            torch.max(torch.abs(robot.data.joint_pos[0] - before_joint[0])).item()
+        )
+        result["masked_write"] = {
+            "selected_root_z_delta": masked_root_changed,
+            "untouched_root_max_delta": untouched_root_delta,
+            "untouched_joint_max_delta": untouched_joint_delta,
+            "selected_root_ang_vel_w": _as_list(robot.data.root_ang_vel_w[1]),
+        }
+
+        targets = robot.data.joint_pos.clone()
+        if robot.num_joints:
+            targets[:, 0] += 0.1
+        before_pd = robot.data.joint_pos.clone()
+        for _ in range(args.steps):
+            robot.set_joint_position_target(targets)
+            robot.write_data_to_sim()
+            sim.step(render=False)
+            robot.update(sim_cfg.dt)
+        result["pd_target"] = {
+            "steps": args.steps,
+            "first_joint_delta": _as_list(robot.data.joint_pos[:, 0] - before_pd[:, 0])
+            if robot.num_joints
+            else [],
+        }
+        result["sample"] = {
+            "root_quat_w": _as_list(robot.data.root_quat_w[0]),
+            "body_quat_w": _as_list(robot.data.body_quat_w[0, 0]),
+        }
+
+        print(json.dumps(result, indent=2, sort_keys=True), flush=True)
+        if args.output is not None:
+            args.output.parent.mkdir(parents=True, exist_ok=True)
+            args.output.write_text(json.dumps(result, indent=2, sort_keys=True) + "\n")
+        return 0
+    except BaseException:
+        # Kit can suppress buffered Python diagnostics during shutdown.
+        traceback.print_exc()
+        raise
+    finally:
+        simulation_app.close()
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/tools/setup_drake_env.sh b/scripts/tools/setup_drake_env.sh
new file mode 100755
index 000000000..f73ada268
--- /dev/null
+++ b/scripts/tools/setup_drake_env.sh
@@ -0,0 +1,332 @@
+#!/usr/bin/env bash
+# Set up the UniLab Drake batch runtime on Linux or Apple Silicon macOS.
+#
+# This script mirrors the external-runtime setup flow used by IsaacGym and
+# IsaacSim: expensive downloads are resumable, completed download/extract steps
+# are marked, and all runtime files live outside the repository.  Drake C++ is
+# still an explicit external dependency; pass --download-drake when the
+# official Drake tarball should be fetched automatically.
+
+set -euo pipefail
+
+UNILAB_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)"
+SETUP_HOME="${UNILAB_DRAKE_SETUP_HOME:-$HOME/.unilab/drake}"
+DEFAULT_SOURCE="${UNILAB_ROOT}/../drake_uni"
+DRAKE_UNI_SOURCE="${UNILAB_DRAKE_UNI_SOURCE:-}"
+DRAKE_PREFIX="${UNILAB_DRAKE_HOME:-${DRAKE_HOME:-}}"
+DRAKE_VERSION="${DRAKE_VERSION:-1.56.0}"
+DRAKE_DEPS_ROOT="${UNILAB_DRAKE_DEPS_ROOT:-}"
+DOWNLOAD_DRAKE=0
+
+usage() {
+  cat <<'EOF'
+Usage: setup_drake_env.sh [options]
+
+Install UniLab's Drake batch runtime and build the native DrakeUni extension.
+The default is to use an existing Drake C++ prefix.  Pass --download-drake to
+fetch the official Drake tarball for the current host into UNILAB_DRAKE_SETUP_HOME.
+
+Options:
+  --drake-home <path>       Drake C++ prefix (or set DRAKE_HOME).
+  --drake-uni-source <path> DrakeUni checkout; defaults to ../drake_uni when it exists.
+  --deps-root <path>        Optional unpacked system deps root (or set
+                            UNILAB_DRAKE_DEPS_ROOT).
+  --download-drake          Download the official host tarball when no prefix is given.
+  --drake-version <version> Version used with --download-drake (default: 1.56.0).
+  --drake-platform <name>   Official tarball platform (default: noble on Linux,
+                            mac-arm64 on Apple Silicon macOS).
+  -h, --help                Show this help.
+
+Environment:
+  UNILAB_DRAKE_SETUP_HOME   Download/marker/log root (default: ~/.unilab/drake).
+  UNILAB_DRAKE_HOME         Same meaning as DRAKE_HOME.
+  UNILAB_DRAKE_UNI_SOURCE   DrakeUni source checkout.
+  UNILAB_DRAKE_DEPS_ROOT    Unpacked deps root for Eigen/fmt/spdlog.
+
+The complete path requires Linux x86_64 or Apple Silicon macOS, C++20, Eigen,
+fmt, spdlog, and a Python ABI matching the UniLab uv environment. The script
+never installs system packages with sudo. On Ubuntu install missing packages with:
+  sudo apt-get install build-essential pkg-config libeigen3-dev libfmt-dev libspdlog-dev
+On macOS install the runtime libraries used by the official arm64 tarball with:
+  brew install fmt gcc
+EOF
+}
+
+while [ "$#" -gt 0 ]; do
+  case "$1" in
+    --drake-home)
+      [ "$#" -ge 2 ] || { echo "error: --drake-home requires a path" >&2; exit 2; }
+      DRAKE_PREFIX="$2"
+      shift 2
+      ;;
+    --drake-uni-source)
+      [ "$#" -ge 2 ] || { echo "error: --drake-uni-source requires a path" >&2; exit 2; }
+      DRAKE_UNI_SOURCE="$2"
+      shift 2
+      ;;
+    --deps-root)
+      [ "$#" -ge 2 ] || { echo "error: --deps-root requires a path" >&2; exit 2; }
+      DRAKE_DEPS_ROOT="$2"
+      shift 2
+      ;;
+    --download-drake)
+      DOWNLOAD_DRAKE=1
+      shift
+      ;;
+    --drake-version)
+      [ "$#" -ge 2 ] || { echo "error: --drake-version requires a value" >&2; exit 2; }
+      DRAKE_VERSION="$2"
+      shift 2
+      ;;
+    --drake-platform)
+      [ "$#" -ge 2 ] || { echo "error: --drake-platform requires a value" >&2; exit 2; }
+      DRAKE_PLATFORM="$2"
+      shift 2
+      ;;
+    -h|--help)
+      usage
+      exit 0
+      ;;
+    *)
+      echo "error: unknown argument: $1" >&2
+      usage >&2
+      exit 2
+      ;;
+  esac
+done
+
+HOST_OS="$(uname -s)"
+HOST_ARCH="$(uname -m)"
+case "$HOST_OS/$HOST_ARCH" in
+  Linux/x86_64)
+    DEFAULT_DRAKE_PLATFORM="noble"
+    LIBRARY_PATH_VAR="LD_LIBRARY_PATH"
+    ;;
+  Darwin/arm64)
+    DEFAULT_DRAKE_PLATFORM="mac-arm64"
+    LIBRARY_PATH_VAR="DYLD_LIBRARY_PATH"
+    ;;
+  Darwin/*)
+    echo "error: Drake's official macOS runtime is arm64 only; Intel macOS is not supported" >&2
+    exit 1
+    ;;
+  *)
+    echo "error: unsupported host $HOST_OS/$HOST_ARCH (supported: Linux x86_64, macOS arm64)" >&2
+    exit 1
+    ;;
+esac
+DRAKE_PLATFORM="${DRAKE_PLATFORM:-$DEFAULT_DRAKE_PLATFORM}"
+command -v uv >/dev/null || { echo "error: uv is required" >&2; exit 1; }
+command -v git >/dev/null || { echo "error: git is required" >&2; exit 1; }
+command -v c++ >/dev/null || { echo "error: a C++ compiler is required" >&2; exit 1; }
+
+MARKER_DIR="$SETUP_HOME/.markers"
+LOG_FILE="$SETUP_HOME/install.log"
+mkdir -p "$MARKER_DIR"
+exec > >(tee -a "$LOG_FILE") 2>&1
+
+log() { echo "[setup_drake_env $(date +%H:%M:%S)] $*"; }
+
+run_step() {
+  local marker="$1"
+  shift
+  if [ -f "$MARKER_DIR/$marker" ]; then
+    log "SKIP  $marker (already done)"
+    return 0
+  fi
+  log "START $marker: $*"
+  "$@"
+  touch "$MARKER_DIR/$marker"
+  log "DONE  $marker"
+}
+
+if [ -z "$DRAKE_UNI_SOURCE" ] && [ -d "$DEFAULT_SOURCE/.git" ]; then
+  DRAKE_UNI_SOURCE="$DEFAULT_SOURCE"
+fi
+
+if [ -z "$DRAKE_PREFIX" ] && [ "$DOWNLOAD_DRAKE" -eq 1 ]; then
+  DRAKE_TARBALL="$SETUP_HOME/drake-${DRAKE_VERSION}-${DRAKE_PLATFORM}.tar.gz"
+  DRAKE_PREFIX="$SETUP_HOME/drake-${DRAKE_VERSION}-${DRAKE_PLATFORM}"
+  download_drake() {
+    local url="https://github.com/RobotLocomotion/drake/releases/download/v${DRAKE_VERSION}/drake-${DRAKE_VERSION}-${DRAKE_PLATFORM}.tar.gz"
+    if [ ! -f "$DRAKE_TARBALL" ]; then
+      log "downloading Drake ${DRAKE_VERSION} to $DRAKE_TARBALL"
+      curl -fL -C - --retry 5 --retry-delay 10 -o "$DRAKE_TARBALL.part" "$url"
+      mv "$DRAKE_TARBALL.part" "$DRAKE_TARBALL"
+    fi
+    tar -tzf "$DRAKE_TARBALL" >/dev/null
+  }
+  extract_drake() {
+    if [ ! -d "$DRAKE_PREFIX" ]; then
+      mkdir -p "$DRAKE_PREFIX"
+      tar -xzf "$DRAKE_TARBALL" --strip-components=1 -C "$DRAKE_PREFIX"
+    fi
+  }
+  run_step 00_download_drake download_drake
+  run_step 01_extract_drake extract_drake
+elif [ -z "$DRAKE_PREFIX" ]; then
+  echo "error: set DRAKE_HOME/UNILAB_DRAKE_HOME, pass --drake-home, or use --download-drake" >&2
+  exit 1
+fi
+
+DRAKE_PREFIX="$(cd "$DRAKE_PREFIX" && pwd)"
+for required in "$DRAKE_PREFIX/include/drake" "$DRAKE_PREFIX/include/pybind11"; do
+  [ -d "$required" ] || {
+    echo "error: required Drake directory not found: $required" >&2
+    exit 1
+  }
+done
+DRAKE_LIBRARY=""
+for candidate in "$DRAKE_PREFIX/lib/libdrake.so" "$DRAKE_PREFIX/lib/libdrake.dylib"; do
+  if [ -f "$candidate" ]; then
+    DRAKE_LIBRARY="$candidate"
+    break
+  fi
+done
+[ -n "$DRAKE_LIBRARY" ] || {
+  echo "error: Drake shared library not found under $DRAKE_PREFIX/lib (expected libdrake.so or libdrake.dylib)" >&2
+  exit 1
+}
+
+# Prefer explicit Eigen and fmt include overrides, then discover the same
+# system/pkg-config paths used by DrakeUni's build helper.  fmt is linked from
+# the host.
+if [ -z "${EIGEN3_INCLUDE_DIR:-}" ]; then
+  if [ -f "$DRAKE_PREFIX/include/eigen3/Eigen/Core" ]; then
+    EIGEN3_INCLUDE_DIR="$DRAKE_PREFIX/include/eigen3"
+  elif [ -f "$DRAKE_PREFIX/include/Eigen/Core" ]; then
+    EIGEN3_INCLUDE_DIR="$DRAKE_PREFIX/include"
+  fi
+fi
+if [ -z "${EIGEN3_INCLUDE_DIR:-}" ] && command -v pkg-config >/dev/null; then
+  # pkg-config emits one or more -I flags.  Select the first one that actually
+  # contains Eigen/Core so a stale .pc file cannot make setup pass falsely.
+  EIGEN3_CFLAGS="$(pkg-config --cflags-only-I eigen3 2>/dev/null || true)"
+  for flag in $EIGEN3_CFLAGS; do
+    case "$flag" in
+      -I*)
+        candidate="${flag#-I}"
+        if [ -f "$candidate/Eigen/Core" ]; then
+          EIGEN3_INCLUDE_DIR="$candidate"
+          break
+        fi
+        ;;
+    esac
+  done
+fi
+if [ -z "${EIGEN3_INCLUDE_DIR:-}" ]; then
+  for candidate in /usr/include/eigen3 /usr/local/include/eigen3; do
+    if [ -f "$candidate/Eigen/Core" ]; then
+      EIGEN3_INCLUDE_DIR="$candidate"
+      break
+    fi
+  done
+fi
+if [ -z "${FMT_INCLUDE_DIR:-}" ] && [ -f "$DRAKE_PREFIX/include/fmt/format.h" ]; then
+  FMT_INCLUDE_DIR="$DRAKE_PREFIX/include"
+fi
+if [ "$HOST_OS" = "Darwin" ] && command -v brew >/dev/null; then
+  FMT_PREFIX="$(brew --prefix fmt 2>/dev/null || true)"
+  if [ -n "$FMT_PREFIX" ] && [ -d "$FMT_PREFIX/lib" ]; then
+    FMT_LIB_DIR="${FMT_LIB_DIR:-$FMT_PREFIX/lib}"
+    FMT_INCLUDE_DIR="${FMT_INCLUDE_DIR:-$FMT_PREFIX/include}"
+  fi
+fi
+
+if [ -n "$DRAKE_DEPS_ROOT" ]; then
+  DRAKE_DEPS_ROOT="$(cd "$DRAKE_DEPS_ROOT" && pwd)"
+  if [ "$HOST_OS" = "Darwin" ]; then
+    EIGEN3_INCLUDE_DIR="${EIGEN3_INCLUDE_DIR:-$DRAKE_DEPS_ROOT/include}"
+    FMT_INCLUDE_DIR="${FMT_INCLUDE_DIR:-$DRAKE_DEPS_ROOT/include}"
+    FMT_LIB_DIR="${FMT_LIB_DIR:-$DRAKE_DEPS_ROOT/lib}"
+    PKG_CONFIG_PATH="$DRAKE_DEPS_ROOT/lib/pkgconfig${PKG_CONFIG_PATH:+:$PKG_CONFIG_PATH}"
+    DYLD_LIBRARY_PATH="$DRAKE_DEPS_ROOT/lib:$DRAKE_PREFIX/lib${DYLD_LIBRARY_PATH:+:$DYLD_LIBRARY_PATH}"
+    export DYLD_LIBRARY_PATH
+  else
+    EIGEN3_INCLUDE_DIR="${EIGEN3_INCLUDE_DIR:-$DRAKE_DEPS_ROOT/usr/include/eigen3}"
+    FMT_INCLUDE_DIR="${FMT_INCLUDE_DIR:-$DRAKE_DEPS_ROOT/usr/include}"
+    FMT_LIB_DIR="${FMT_LIB_DIR:-$DRAKE_DEPS_ROOT/usr/lib/x86_64-linux-gnu}"
+    PKG_CONFIG_PATH="$DRAKE_DEPS_ROOT/usr/lib/x86_64-linux-gnu/pkgconfig${PKG_CONFIG_PATH:+:$PKG_CONFIG_PATH}"
+    LD_LIBRARY_PATH="$DRAKE_DEPS_ROOT/usr/lib/x86_64-linux-gnu:$DRAKE_PREFIX/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}"
+    export LD_LIBRARY_PATH
+  fi
+  export EIGEN3_INCLUDE_DIR FMT_INCLUDE_DIR FMT_LIB_DIR PKG_CONFIG_PATH
+fi
+
+if [ -z "${EIGEN3_INCLUDE_DIR:-}" ] || [ ! -f "$EIGEN3_INCLUDE_DIR/Eigen/Core" ]; then
+  echo "error: Eigen headers not found; install libeigen3-dev (Linux), brew install eigen (macOS), or pass --deps-root" >&2
+  exit 1
+fi
+if [ "$HOST_OS" = "Darwin" ] && {
+  [ -z "${FMT_LIB_DIR:-}" ] || ! compgen -G "$FMT_LIB_DIR/libfmt*" >/dev/null;
+}; then
+  echo "error: fmt library not found; install it with 'brew install fmt' or set FMT_LIB_DIR" >&2
+  exit 1
+fi
+export EIGEN3_INCLUDE_DIR FMT_INCLUDE_DIR FMT_LIB_DIR
+
+log "UniLab root: $UNILAB_ROOT"
+log "Drake prefix: $DRAKE_PREFIX"
+log "DrakeUni source: ${DRAKE_UNI_SOURCE:-<not resolved>}"
+log "Setup home: $SETUP_HOME"
+
+run_step 02_sync_uv uv --directory "$UNILAB_ROOT" sync --extra drake
+run_step 03_install_completion uv --directory "$UNILAB_ROOT" run --no-sync unilab-complete install
+
+if [ -z "$DRAKE_UNI_SOURCE" ]; then
+  DRAKE_UNI_SOURCE="$SETUP_HOME/drake_uni"
+  clone_drake_uni() {
+    if [ ! -d "$DRAKE_UNI_SOURCE/.git" ]; then
+      git clone --depth 1 --branch "${DRAKE_UNI_VERSION:-main}" \
+        https://github.com/unilabsim/drake_uni.git "$DRAKE_UNI_SOURCE"
+    fi
+  }
+  run_step 04_fetch_drake_uni clone_drake_uni
+fi
+DRAKE_UNI_SOURCE="$(cd "$DRAKE_UNI_SOURCE" && pwd)"
+[ -f "$DRAKE_UNI_SOURCE/scripts/build_drake_batch.py" ] || {
+  echo "error: build_drake_batch.py not found under $DRAKE_UNI_SOURCE" >&2
+  exit 1
+}
+
+uv --directory "$UNILAB_ROOT" pip install --python "$UNILAB_ROOT/.venv/bin/python" \
+  --force-reinstall --no-deps --no-build-isolation -e "$DRAKE_UNI_SOURCE"
+
+RUNTIME_LIBRARY_PATH="${!LIBRARY_PATH_VAR:-$DRAKE_PREFIX/lib}"
+if [ "$HOST_OS" = "Darwin" ] && command -v brew >/dev/null; then
+  GCC_PREFIX="$(brew --prefix gcc 2>/dev/null || true)"
+  if [ -n "$GCC_PREFIX" ] && [ -f "$GCC_PREFIX/lib/gcc/current/libgfortran.5.dylib" ]; then
+    RUNTIME_LIBRARY_PATH="$GCC_PREFIX/lib/gcc/current:$RUNTIME_LIBRARY_PATH"
+  fi
+fi
+export "$LIBRARY_PATH_VAR=$RUNTIME_LIBRARY_PATH"
+
+env "$LIBRARY_PATH_VAR=$RUNTIME_LIBRARY_PATH" \
+  uv --directory "$UNILAB_ROOT" run --no-sync python \
+  "$DRAKE_UNI_SOURCE/scripts/build_drake_batch.py" --drake-home "$DRAKE_PREFIX"
+
+env "$LIBRARY_PATH_VAR=$RUNTIME_LIBRARY_PATH" \
+  uv --directory "$UNILAB_ROOT" run --no-sync python - <<'PY'
+import drake_uni
+from drake_uni.runtime import batch_diagnostics
+
+diagnostics = batch_diagnostics()
+print(f"drake_uni import: {drake_uni.__file__}")
+print(f"batch diagnostics: {diagnostics}")
+if not diagnostics.batch_available:
+    raise SystemExit(diagnostics.batch_import_error or "Drake batch extension is unavailable")
+PY
+
+cat <<EOF
+
+[setup_drake_env] 完成。当前 shell 如需直接运行 Drake batch，请设置：
+
+export DRAKE_HOME="$DRAKE_PREFIX"
+export UNILAB_DRAKE_HOME="$DRAKE_PREFIX"
+export UNILAB_DRAKE_UNI_SOURCE="$DRAKE_UNI_SOURCE"
+export $LIBRARY_PATH_VAR="$RUNTIME_LIBRARY_PATH"
+
+然后在 UniLab 根目录运行：
+uv run --no-sync pytest tests/base/backend/test_drake_batch_pool.py \
+  tests/scripts/test_drake_training_smoke.py -q
+EOF
diff --git a/scripts/tools/setup_isaacgym_env.sh b/scripts/tools/setup_isaacgym_env.sh
new file mode 100755
index 000000000..cb0fb3410
--- /dev/null
+++ b/scripts/tools/setup_isaacgym_env.sh
@@ -0,0 +1,224 @@
+#!/usr/bin/env bash
+# Install an external, self-contained IsaacGym Preview 4 runtime for UniLab.
+#
+# IsaacGym (NVIDIA Preview 4, EOL) only supports Python 3.6-3.8, so it cannot
+# live in the main uv environment (requires-python >= 3.10). Everything is
+# installed under UNISIM_ISAACGYM_HOME (default: $HOME/.cache/unisim/isaacgym) and
+# located at runtime purely through environment variables; this repo never
+# hard-codes machine-local paths.
+#
+# Layout produced by this script (aligned with the benchmark env vars used by
+# scripts/benchmark/physics/benchmark_physics_step_isaacgym.py):
+#   $UNISIM_ISAACGYM_HOME/miniconda3                        dedicated miniconda
+#   $UNISIM_ISAACGYM_HOME/miniconda3/envs/hsgym             Python 3.8 conda env
+#   $UNISIM_ISAACGYM_HOME/isaacgym/python                   unpacked Preview 4
+#   $UNISIM_ISAACGYM_HOME/IsaacGym_Preview_4_Package.tar.gz downloaded tarball
+#
+# The script is idempotent: completed steps are skipped on re-run. A conda env
+# is not relocatable, so if the whole ISAACGYM_HOME tree was moved since the
+# env was created, the script repairs the stale absolute paths (entry-point
+# shebangs, .pth files, egg-links, conda-meta) in place instead of re-creating
+# the env.
+
+set -euo pipefail
+
+usage() {
+  cat <<'EOF'
+Usage: setup_isaacgym_env.sh [--tarball <path>] [-h|--help]
+
+Install an external Python 3.8 IsaacGym Preview 4 runtime for UniLab
+benchmarks. IsaacGym cannot be installed into the main uv environment.
+
+Options:
+  --tarball <path>   Path to IsaacGym_Preview_4_Package.tar.gz.
+                     Default: auto-downloaded (no login required) to
+                     $UNISIM_ISAACGYM_HOME/IsaacGym_Preview_4_Package.tar.gz
+                     from https://developer.nvidia.com/isaac-gym-preview-4 ;
+                     use this option to supply a pre-downloaded tarball
+                     instead (e.g. on a machine without internet access).
+  -h, --help         Show this help.
+
+Environment:
+  UNISIM_ISAACGYM_HOME   Install root. Default: $HOME/.cache/unisim/isaacgym
+EOF
+}
+
+ISAACGYM_HOME="${UNISIM_ISAACGYM_HOME:-${UNILAB_ISAACGYM_HOME:-$HOME/.cache/unisim/isaacgym}}"
+TARBALL_ARG=""
+
+while [ "$#" -gt 0 ]; do
+  case "$1" in
+    --tarball)
+      [ "$#" -ge 2 ] || { echo "error: --tarball requires a path argument" >&2; exit 2; }
+      TARBALL_ARG="$2"
+      shift 2
+      ;;
+    -h|--help)
+      usage
+      exit 0
+      ;;
+    *)
+      echo "error: unknown argument: $1" >&2
+      usage >&2
+      exit 2
+      ;;
+  esac
+done
+
+CONDA_ROOT="$ISAACGYM_HOME/miniconda3"
+ENV_NAME="hsgym"
+ENV_ROOT="$CONDA_ROOT/envs/$ENV_NAME"
+HSGYM_PYTHON="$ENV_ROOT/bin/python3.8"
+ISAACGYM_DIR="$ISAACGYM_HOME/isaacgym"
+TARBALL_PATH="${TARBALL_ARG:-$ISAACGYM_HOME/IsaacGym_Preview_4_Package.tar.gz}"
+MINICONDA_URL="https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh"
+LIBSTDCXX_SENTINEL="$ENV_ROOT/.unilab_libstdcxx_installed"
+
+log() {
+  echo "[setup_isaacgym_env] $*"
+}
+
+mkdir -p "$ISAACGYM_HOME"
+
+# Step 1: dedicated miniconda (skipped if already installed).
+if [ -x "$CONDA_ROOT/bin/conda" ]; then
+  log "miniconda already present at $CONDA_ROOT, skipping"
+else
+  log "installing miniconda into $CONDA_ROOT"
+  installer="$(mktemp /tmp/unilab_miniconda_XXXXXX.sh)"
+  curl -fsSL "$MINICONDA_URL" -o "$installer"
+  bash "$installer" -b -u -p "$CONDA_ROOT"
+  rm -f "$installer"
+fi
+
+# Step 2: Python 3.8 conda env with the Ubuntu 24.04 GLIBCXX fix.
+if [ -x "$HSGYM_PYTHON" ]; then
+  log "conda env '$ENV_NAME' already present at $ENV_ROOT, skipping create"
+else
+  log "creating conda env '$ENV_NAME' (python=3.8)"
+  "$CONDA_ROOT/bin/conda" tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main
+  "$CONDA_ROOT/bin/conda" tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r
+  "$CONDA_ROOT/bin/conda" install -y -n base -c conda-forge mamba
+  MAMBA_ROOT_PREFIX="$CONDA_ROOT" "$CONDA_ROOT/bin/mamba" create -y -n "$ENV_NAME" \
+    python=3.8 -c conda-forge --override-channels
+fi
+
+# Conda envs hard-code their install prefix in entry-point shebangs, .pth
+# files, egg-links and conda-meta. If the tree was relocated (e.g. from the
+# legacy $HOME/.unilab/isaacgym to the current default), those references go
+# stale: `bin/pip` fails with "cannot execute: required file not found" and
+# the editable install disappears from sys.path, while every skip-check above
+# still passes. Detect the stale prefix from the pip shebang and rewrite it
+# in place; this is far cheaper than re-creating the env (torch is ~GBs).
+repair_env_prefix_if_needed() {
+  local pip_script="$ENV_ROOT/bin/pip"
+  [ -f "$pip_script" ] || return 0
+  local shebang interp old_prefix
+  shebang="$(head -n 1 "$pip_script")"
+  case "$shebang" in '#!'*) ;; *) return 0 ;; esac
+  interp="${shebang#'#!'}"
+  interp="${interp%% *}"
+  case "$interp" in */bin/python*) ;; *) return 0 ;; esac
+  old_prefix="${interp%/bin/python*}"
+  if [ -z "$old_prefix" ] || [ "$old_prefix" = "$ENV_ROOT" ]; then
+    return 0
+  fi
+  log "env '$ENV_NAME' was relocated from $old_prefix; repairing stale paths in place"
+  find "$ENV_ROOT/bin" -maxdepth 1 -type f -print0 | while IFS= read -r -d '' f; do
+    if [ "$(head -c 2 "$f")" = '#!' ]; then
+      sed -i "1s|^#!$old_prefix/|#!$ENV_ROOT/|" "$f"
+    fi
+  done
+  local sp="$ENV_ROOT/lib/python3.8/site-packages"
+  if [ -d "$sp" ]; then
+    find "$sp" -maxdepth 1 -type f \( -name '*.pth' -o -name '*.egg-link' \) \
+      -exec sed -i "s|$old_prefix|$ENV_ROOT|g" {} +
+  fi
+  if [ -d "$ENV_ROOT/conda-meta" ]; then
+    sed -i "s|$old_prefix|$ENV_ROOT|g" "$ENV_ROOT"/conda-meta/*.json
+  fi
+}
+repair_env_prefix_if_needed
+
+# Fix `version GLIBCXX_3.4.32 not found` on Ubuntu 24.04 by shipping a newer
+# libstdc++ inside the env.
+if [ -f "$LIBSTDCXX_SENTINEL" ]; then
+  log "libstdcxx-ng already installed in '$ENV_NAME', skipping"
+else
+  log "installing libstdcxx-ng into '$ENV_NAME'"
+  "$CONDA_ROOT/bin/conda" install -y -n "$ENV_NAME" -c conda-forge libstdcxx-ng
+  touch "$LIBSTDCXX_SENTINEL"
+fi
+
+# Step 3: fetch the IsaacGym tarball. The NVIDIA download URL redirects to a
+# signed URL without requiring a login, so the script can fetch it directly;
+# --tarball or a pre-placed file skips the download.
+if [ -n "$TARBALL_ARG" ] && [ ! -f "$TARBALL_PATH" ]; then
+  echo "[setup_isaacgym_env] error: --tarball file not found: $TARBALL_PATH" >&2
+  exit 1
+fi
+if [ ! -f "$TARBALL_PATH" ]; then
+  log "downloading IsaacGym Preview 4 tarball (~200 MB) to $TARBALL_PATH"
+  curl -fL --retry 3 "https://developer.nvidia.com/isaac-gym-preview-4" \
+    -o "$TARBALL_PATH.part"
+  mv "$TARBALL_PATH.part" "$TARBALL_PATH"
+fi
+if ! tar -tzf "$TARBALL_PATH" >/dev/null 2>&1; then
+  echo "[setup_isaacgym_env] error: $TARBALL_PATH is not a valid gzip tarball (download may have failed)" >&2
+  exit 1
+fi
+
+# Step 4: unpack and pip install -e with the env's own pip.
+if [ -d "$ISAACGYM_DIR/python" ]; then
+  log "isaacgym already unpacked at $ISAACGYM_DIR, skipping extract"
+else
+  log "unpacking $TARBALL_PATH into $ISAACGYM_HOME"
+  tar -xzf "$TARBALL_PATH" -C "$ISAACGYM_HOME"
+fi
+
+# Use `python -m pip` rather than the bin/pip entry point so this still works
+# even if that script's shebang is broken. pip show alone is not a reliable
+# skip-check: it trusts a possibly stale egg-link, so also verify the import.
+if "$HSGYM_PYTHON" -m pip show isaacgym >/dev/null 2>&1 && \
+   LD_LIBRARY_PATH="$ENV_ROOT/lib" "$HSGYM_PYTHON" -c "import isaacgym" >/dev/null 2>&1; then
+  log "isaacgym already installed in '$ENV_NAME', skipping pip install"
+else
+  log "installing isaacgym (pip install -e) into '$ENV_NAME'"
+  "$HSGYM_PYTHON" -m pip install -e "$ISAACGYM_DIR/python"
+fi
+
+# Step 5: self-check the import with the env's lib/ on LD_LIBRARY_PATH. The
+# gymtorch import triggers a one-time JIT compile of its C++ extension
+# (several minutes on a fresh machine, cached afterwards); doing it here keeps
+# the first backend INIT handshake fast. ninja must be reachable via the env's
+# bin/ on PATH.
+log "running isaacgym import self-check (first run compiles the gymtorch extension)"
+PATH="$ENV_ROOT/bin:$PATH" LD_LIBRARY_PATH="$ENV_ROOT/lib" "$HSGYM_PYTHON" \
+  -c "from isaacgym import gymapi, gymtorch; print('isaacgym import OK')"
+
+# Step 6: print the exports and verification command for the user's shell rc.
+cat <<EOF
+
+[setup_isaacgym_env] 完成。把下面几行写入你的 shell rc（如 ~/.bashrc）：
+
+export UNILAB_BENCHMARK_HOLOSOMA_DEPS="$ISAACGYM_HOME"
+export UNILAB_BENCHMARK_HSGYM_PYTHON="$HSGYM_PYTHON"
+export UNILAB_BENCHMARK_HSGYM_LIB="$ENV_ROOT/lib"
+# URDF 模型树（go1_description/ 等）需自备，指向其根目录：
+export UNILAB_BENCHMARK_MODELS_ROOT="<path-to-your-urdf-models-root>"
+
+# 训练后端（task=<task>/isaacgym）默认从 ~/.unilab/isaacgym 自动发现运行时；
+# 若用了自定义 UNISIM_ISAACGYM_HOME，训练时同名导出即可：
+export UNISIM_ISAACGYM_HOME="$ISAACGYM_HOME"
+
+# Legacy UniLab variable (optional for older launch scripts):
+# export UNILAB_ISAACGYM_HOME="$ISAACGYM_HOME"
+
+然后在仓库根目录用 benchmark 脚本验证：
+
+PYTHONPATH="\$UNILAB_BENCHMARK_HOLOSOMA_DEPS/isaacgym/python" \\
+LD_LIBRARY_PATH="\$UNILAB_BENCHMARK_HSGYM_LIB" \\
+uv run --no-project "\$UNILAB_BENCHMARK_HSGYM_PYTHON" \\
+    scripts/benchmark/physics/benchmark_physics_step_isaacgym.py \\
+    --tasks g1_walk_flat --batch-sizes 256 --models-root "\$UNILAB_BENCHMARK_MODELS_ROOT"
+EOF
diff --git a/scripts/tools/setup_isaacsim_env.sh b/scripts/tools/setup_isaacsim_env.sh
new file mode 100755
index 000000000..02450bb69
--- /dev/null
+++ b/scripts/tools/setup_isaacsim_env.sh
@@ -0,0 +1,326 @@
+#!/usr/bin/env bash
+# Install an external, self-contained IsaacSim + IsaacLab runtime for UniLab.
+#
+# IsaacSim/IsaacLab require Python 3.11 while the main UniLab uv environment
+# uses a newer Python, so (like the IsaacGym worker env) this runtime lives
+# outside the repo under UNISIM_ISAACSIM_HOME (default: $HOME/.cache/unisim/isaacsim)
+# and is located purely through environment variables; this repo never
+# hard-codes machine-local paths.
+#
+# Layout produced by this script (mirrors setup_isaacgym_env.sh conventions):
+#   $UNISIM_ISAACSIM_HOME/venv                 dedicated Python 3.11 venv
+#   $UNISIM_ISAACSIM_HOME/IsaacLab             IsaacLab source tree
+#   $UNISIM_ISAACSIM_HOME/tools/bin/cmake      official cmake binary (no sudo)
+#   $UNISIM_ISAACSIM_HOME/.markers             per-step completion markers
+#   $UNISIM_ISAACSIM_HOME/install.log          full install log
+#
+# Design goals:
+#   * Idempotent — every completed step drops a marker file; re-running the
+#     script after a failure skips finished steps so multi-GB downloads are
+#     never repeated.
+#   * Resumable — large downloads use curl -C - so an interrupted transfer
+#     continues where it stopped; pip's HTTP cache covers pip downloads.
+#   * Isolated — UniLab code, configs, and the UniLab venv are never touched.
+#   * Relocation-aware — a venv is not relocatable; if ISAACSIM_HOME was moved
+#     since the install, stale shebang/activate/editable-finder paths are
+#     repaired in place instead of reinstalling multi-GB wheels.
+#
+# Usage:
+#   bash scripts/tools/setup_isaacsim_env.sh [--verify-sim]
+#
+# Environment overrides:
+#   UNISIM_ISAACSIM_HOME  install root           (default: ~/.cache/unisim/isaacsim)
+#   ISAACLAB_VERSION      IsaacLab git tag       (default: v2.3.0)
+#   ISAACSIM_VERSION      isaacsim pip version   (default: 5.1.0)
+#   TORCH_VERSION         torch version          (default: 2.7.0)
+#   TORCHVISION_VERSION   torchvision version    (default: 0.22.0)
+#
+# Reference: https://isaac-sim.github.io/IsaacLab/main/source/setup/installation/pip_installation.html
+set -euo pipefail
+
+ISAACSIM_HOME=${UNISIM_ISAACSIM_HOME:-${UNILAB_ISAACSIM_HOME:-$HOME/.cache/unisim/isaacsim}}
+ISAACLAB_VERSION=${ISAACLAB_VERSION:-v2.3.0}
+ISAACSIM_VERSION=${ISAACSIM_VERSION:-5.1.0}
+TORCH_VERSION=${TORCH_VERSION:-2.7.0}
+TORCHVISION_VERSION=${TORCHVISION_VERSION:-0.22.0}
+PYTHON_VERSION=3.11
+
+VENV_DIR=$ISAACSIM_HOME/venv
+ISAACLAB_DIR=$ISAACSIM_HOME/IsaacLab
+MARKER_DIR=$ISAACSIM_HOME/.markers
+LOG_FILE=$ISAACSIM_HOME/install.log
+
+VERIFY_SIM=0
+for arg in "$@"; do
+  case "$arg" in
+    --verify-sim) VERIFY_SIM=1 ;;
+    -h|--help) sed -n '2,40p' "$0"; exit 0 ;;
+    *) echo "Unknown argument: $arg" >&2; exit 2 ;;
+  esac
+done
+
+log() { echo "[setup_isaacsim_env $(date +%H:%M:%S)] $*"; }
+
+# run_step <marker-name> <command...>
+# Skips the command if the marker exists; creates the marker on success.
+run_step() {
+  local marker=$1; shift
+  if [[ -f $MARKER_DIR/$marker ]]; then
+    log "SKIP  $marker (already done)"
+    return 0
+  fi
+  log "START $marker: $*"
+  "$@"
+  touch "$MARKER_DIR/$marker"
+  log "DONE  $marker"
+}
+
+mkdir -p "$MARKER_DIR"
+exec > >(tee -a "$LOG_FILE") 2>&1
+
+log "UNISIM_ISAACSIM_HOME=$ISAACSIM_HOME"
+log "isaacsim=$ISAACSIM_VERSION isaaclab=$ISAACLAB_VERSION torch=$TORCH_VERSION+cu128"
+
+# A venv is not relocatable: entry-point shebangs, activate scripts and the
+# PEP 660 __editable__ finder modules all hard-code the install prefix. If the
+# whole ISAACSIM_HOME tree was moved since the install (e.g. from the legacy
+# $HOME/.unilab/isaacsim to the current default), every step marker is still
+# present but pip cannot execute and isaaclab disappears from sys.path. Detect
+# the stale prefix from the pip shebang and rewrite it in place; this is far
+# cheaper than reinstalling multi-GB wheels.
+repair_env_prefix_if_needed() {
+  local pip_script=$VENV_DIR/bin/pip
+  [[ -f $pip_script ]] || return 0
+  local shebang interp old_venv old_home
+  shebang=$(head -n 1 "$pip_script")
+  [[ $shebang == '#!'* ]] || return 0
+  interp=${shebang#'#!'}
+  interp=${interp%% *}
+  [[ $interp == */bin/python* ]] || return 0
+  old_venv=${interp%/bin/python*}
+  old_home=${old_venv%/venv}
+  [[ -n $old_home && $old_venv != "$VENV_DIR" ]] || return 0
+  log "venv was relocated from $old_home; repairing stale paths in place"
+  # Entry-point scripts: rewrite only the shebang line (never touch binaries).
+  local f
+  while IFS= read -r -d '' f; do
+    if [[ $(head -c 2 "$f") == '#!' ]]; then
+      sed -i "1s|^#!$old_venv/|#!$VENV_DIR/|" "$f"
+    fi
+  done < <(find "$VENV_DIR/bin" -maxdepth 1 -type f -print0)
+  # activate scripts carry VIRTUAL_ENV in the body, not in a shebang.
+  sed -i "s|$old_venv|$VENV_DIR|g" "$VENV_DIR"/bin/activate* 2>/dev/null || true
+  # Editable finders / stray .pth reference the IsaacLab source tree.
+  local sp=$VENV_DIR/lib/python$PYTHON_VERSION/site-packages
+  if [[ -d $sp ]]; then
+    find "$sp" -maxdepth 1 -type f \( -name '__editable__*_finder.py' -o -name '*.pth' \) \
+      -exec grep -lF "$old_home" {} + | while read -r f; do
+        sed -i "s|$old_home|$ISAACSIM_HOME|g" "$f"
+      done
+  fi
+  # Re-verify imports against the repaired env.
+  rm -f "$MARKER_DIR/06_verify"
+}
+repair_env_prefix_if_needed
+
+# Accept the Omniverse Kit EULA non-interactively (install + any Kit launch)
+export OMNI_KIT_ACCEPT_EULA=${OMNI_KIT_ACCEPT_EULA:-1}
+
+# ---------------------------------------------------------------------------
+# Step 0: system build tools (needed by IsaacLab's native extensions, e.g. egl_probe).
+# g++ must come from the system; cmake is installed as a standalone binary in
+# step 1b so no sudo is required.
+# ---------------------------------------------------------------------------
+step_system_deps() {
+  if ! command -v g++ >/dev/null; then
+    echo "g++ (build-essential) is required; install it with: sudo apt-get install -y build-essential" >&2
+    return 1
+  fi
+  log "g++ present: $(g++ --version | head -1)"
+}
+run_step 00_system_deps step_system_deps
+
+# ---------------------------------------------------------------------------
+# Step 1: Python 3.11 venv (IsaacSim/IsaacLab require Python 3.11)
+# ---------------------------------------------------------------------------
+step_create_venv() {
+  # Remove a leftover broken venv (e.g. created without pip by a failed run);
+  # venv creation is cheap, no downloads are lost.
+  if [[ -d $VENV_DIR && ! -x $VENV_DIR/bin/pip ]]; then
+    rm -rf "$VENV_DIR"
+  fi
+  if command -v uv >/dev/null; then
+    uv venv --python "$PYTHON_VERSION" --seed "$VENV_DIR"
+  elif command -v "python$PYTHON_VERSION" >/dev/null; then
+    "python$PYTHON_VERSION" -m venv "$VENV_DIR"
+  else
+    echo "Need uv or python$PYTHON_VERSION to create the venv" >&2
+    return 1
+  fi
+  "$VENV_DIR/bin/pip" install --upgrade pip
+}
+run_step 01_venv step_create_venv
+
+PYTHON="$VENV_DIR/bin/python"
+# Go through `python -m pip` rather than the bin/pip entry point so pip still
+# works even if that script's shebang is broken (e.g. mid-relocation).
+pip_cmd() { "$PYTHON" -m pip "$@"; }
+
+# ---------------------------------------------------------------------------
+# Step 1b: real cmake binary (avoids sudo; used by egl_probe's native build).
+# Do NOT use pip's `cmake` wheel here: its console script does
+# `from cmake import cmake`, which breaks inside pip's isolated build envs.
+# ---------------------------------------------------------------------------
+step_cmake() {
+  local ver=3.30.5
+  local dest=$ISAACSIM_HOME/tools/cmake-$ver-linux-x86_64
+  if [[ ! -x $dest/bin/cmake ]]; then
+    mkdir -p "$ISAACSIM_HOME/tools"
+    local tarball=$ISAACSIM_HOME/tools/cmake-$ver-linux-x86_64.tar.gz
+    curl -fL -C - --retry 5 --retry-delay 10 -o "$tarball" \
+      "https://github.com/Kitware/CMake/releases/download/v$ver/cmake-$ver-linux-x86_64.tar.gz"
+    tar -xzf "$tarball" -C "$ISAACSIM_HOME/tools"
+    rm -f "$tarball"
+  fi
+  mkdir -p "$ISAACSIM_HOME/tools/bin"
+  ln -sfn "$dest/bin/cmake" "$ISAACSIM_HOME/tools/bin/cmake"
+  # drop the fragile pip-cmake entry point so it cannot shadow the real binary
+  pip_cmd uninstall -y cmake >/dev/null 2>&1 || true
+}
+run_step 01b_cmake step_cmake
+
+# venv bin + real cmake on PATH for IsaacLab's native builds
+export PATH="$ISAACSIM_HOME/tools/bin:$VENV_DIR/bin:$PATH"
+
+# ---------------------------------------------------------------------------
+# Step 2: PyTorch (CUDA 12.8 wheels). pip's HTTP cache (~/.cache/pip) makes
+# re-runs cheap if the wheel was fetched before.
+# ---------------------------------------------------------------------------
+run_step 02_torch \
+  pip_cmd install -U "torch==$TORCH_VERSION" "torchvision==$TORCHVISION_VERSION" \
+  --index-url https://download.pytorch.org/whl/cu128
+
+# ---------------------------------------------------------------------------
+# Step 3: IsaacSim from the NVIDIA PyPI index (multi-GB; cached by pip on re-run)
+# ---------------------------------------------------------------------------
+step_isaacsim() {
+  pip_cmd install pyperclip
+  pip_cmd install "isaacsim[all,extscache]==$ISAACSIM_VERSION" \
+    --extra-index-url https://pypi.nvidia.com
+}
+run_step 03_isaacsim step_isaacsim
+
+# ---------------------------------------------------------------------------
+# Step 4: IsaacLab source. Tarball + curl -C - so a broken download resumes
+# instead of restarting (git clone cannot resume).
+# ---------------------------------------------------------------------------
+step_fetch_isaaclab() {
+  local tarball=$ISAACSIM_HOME/isaaclab-$ISAACLAB_VERSION.tar.gz
+  local url=https://github.com/isaac-sim/IsaacLab/archive/refs/tags/$ISAACLAB_VERSION.tar.gz
+  if [[ ! -d $ISAACLAB_DIR ]]; then
+    if [[ ! -d $ISAACSIM_HOME/IsaacLab-${ISAACLAB_VERSION#v} ]]; then
+      curl -fL -C - --retry 5 --retry-delay 10 -o "$tarball" "$url"
+      tar -xzf "$tarball" -C "$ISAACSIM_HOME"
+    fi
+    mv "$ISAACSIM_HOME/IsaacLab-${ISAACLAB_VERSION#v}" "$ISAACLAB_DIR"
+    rm -f "$tarball"
+  fi
+}
+run_step 04_isaaclab_src step_fetch_isaaclab
+
+# ---------------------------------------------------------------------------
+# Step 5: install IsaacLab extensions into the venv.
+# Upstream workarounds:
+#   * setuptools>=81 removed pkg_resources -> pin via PIP_BUILD_CONSTRAINT
+#   * flatdict 4.0.1 upstream bug -> 4.1.0 (IsaacLab#4576)
+#   * egl_probe cmake max-version issue -> CMAKE_POLICY_VERSION_MINIMUM=3.5
+#   * torch pinned via PIP_CONSTRAINT so RL-framework deps (e.g. sb3 2.9.x)
+#     cannot upgrade torch to a newer CUDA build mid-install
+# ---------------------------------------------------------------------------
+step_install_isaaclab() {
+  cd "$ISAACLAB_DIR"
+  pip_cmd install 'setuptools<81'
+  echo 'setuptools<81' > build-constraints.txt
+  export PIP_BUILD_CONSTRAINT="$ISAACLAB_DIR/build-constraints.txt"
+  # Pin torch stack: pip would otherwise upgrade to a newer torch/CUDA build
+  # (pulled in by stable_baselines3 2.9.x), replacing the cu128 build
+  # IsaacSim 5.1 expects and downloading many GB unnecessarily.
+  printf 'torch==%s+cu128\ntorchvision==%s+cu128\ntorchaudio==2.7.0\n' \
+    "$TORCH_VERSION" "$TORCHVISION_VERSION" > "$ISAACLAB_DIR/install-constraints.txt"
+  export PIP_CONSTRAINT="$ISAACLAB_DIR/install-constraints.txt"
+  sed -i 's/flatdict==4\.0\.1/flatdict==4.1.0/' source/isaaclab/setup.py
+  export CMAKE_POLICY_VERSION_MINIMUM=3.5
+  # isaaclab.sh runs `tabs 4` under `set -e`; it aborts on TERM=dumb
+  TERM=xterm ./isaaclab.sh --install
+  unset PIP_BUILD_CONSTRAINT PIP_CONSTRAINT
+}
+run_step 05_isaaclab_install step_install_isaaclab
+
+# ---------------------------------------------------------------------------
+# Step 6: verification (imports + CUDA visibility)
+# ---------------------------------------------------------------------------
+step_verify() {
+  "$PYTHON" - <<'EOF'
+import torch
+assert torch.cuda.is_available(), "CUDA not visible to torch"
+print("torch", torch.__version__, "cuda", torch.version.cuda)
+import isaacsim  # noqa: F401
+print("isaacsim import OK")
+import isaaclab  # noqa: F401
+print("isaaclab", isaaclab.__version__)
+EOF
+}
+run_step 06_verify step_verify
+
+# ---------------------------------------------------------------------------
+# Optional: bounded headless smoke test (real physics stepping).
+# NOTE: the standalone/full IsaacSim app segfaults in librtx.scenedb with
+# driver 595.x (IsaacLab#6785); the IsaacLab headless path works, which is
+# what the UniLab backend will use. Bounded steps so the test terminates.
+# ---------------------------------------------------------------------------
+if [[ $VERIFY_SIM -eq 1 ]]; then
+  log "Running bounded headless IsaacLab smoke test (first launch may take minutes)"
+  "$PYTHON" - <<'EOF'
+from isaaclab.app import AppLauncher
+simulation_app = AppLauncher({"headless": True}).app
+
+import gymnasium as gym
+import torch
+
+import isaaclab_tasks  # noqa: F401  (registers all IsaacLab envs)
+from isaaclab_tasks.utils import parse_env_cfg
+
+env_cfg = parse_env_cfg("Isaac-Cartpole-v0", num_envs=16)
+env = gym.make("Isaac-Cartpole-v0", cfg=env_cfg)
+obs, _ = env.reset()
+assert isinstance(obs, dict), f"obs should be a dict, got {type(obs)}"
+with torch.inference_mode():
+    for i in range(20):
+        actions = 2 * torch.rand(env.action_space.shape, device=env.unwrapped.device) - 1
+        obs, rew, terminated, truncated, _ = env.step(actions)
+# print BEFORE close: kit swallows buffered stdout on shutdown
+print("SMOKE TEST OK: Isaac-Cartpole-v0 stepped 20x with 16 envs", flush=True)
+env.close()
+simulation_app.close()
+EOF
+fi
+
+cat <<EOF
+
+[setup_isaacsim_env] 完成。把下面几行写入你的 shell rc（如 ~/.bashrc）：
+
+export UNISIM_ISAACSIM_HOME="$ISAACSIM_HOME"
+export OMNI_KIT_ACCEPT_EULA=1
+# 交互调试时激活 venv：
+source "\$UNISIM_ISAACSIM_HOME/venv/bin/activate"
+
+# Legacy UniLab variable (optional for older launch scripts):
+# export UNILAB_ISAACSIM_HOME="$ISAACSIM_HOME"
+
+  venv:     $VENV_DIR
+  IsaacLab: $ISAACLAB_DIR
+  log:      $LOG_FILE
+
+重跑本脚本是安全的：已完成的步骤会被跳过；加 --verify-sim 可重跑
+headless 冒烟测试（Isaac-Cartpole-v0, 16 envs x 20 steps）。
+EOF
diff --git a/scripts/tools/support_matrix.py b/scripts/tools/support_matrix.py
new file mode 100644
index 000000000..f06858b4f
--- /dev/null
+++ b/scripts/tools/support_matrix.py
@@ -0,0 +1,424 @@
+"""Generate backend support matrix content from registry, configs, and tests."""
+
+from __future__ import annotations
+
+import re
+from dataclasses import dataclass
+from enum import IntEnum
+from pathlib import Path
+
+from omegaconf import OmegaConf
+
+from unilab.base import registry
+from unilab.base.registry import ensure_registries
+
+BEGIN_MARKER = "<!-- BEGIN GENERATED SUPPORT MATRIX -->"
+END_MARKER = "<!-- END GENERATED SUPPORT MATRIX -->"
+BACKENDS: tuple[str, ...] = ("mujoco", "mjwarp", "motrix", "isaacgym", "genesis", "isaacsim")
+
+# Maintainer-confirmed completed training validations. Keep this mapping narrow:
+# generic config/contract coverage must not promote an unvalidated entrypoint.
+_MAINTAINER_VALIDATED_MJWARP_ENTRYPOINT_TASKS = frozenset(
+    {
+        ("ppo_torch", "g1_walk_flat"),
+        ("sac_torch", "g1_walk_flat"),
+    }
+)
+
+# Maintainer-confirmed completed training validations for the isaacgym
+# subprocess backend (real hardware, external Python 3.8 worker runtime;
+# not covered by repo CI).
+_MAINTAINER_VALIDATED_ISAACGYM_ENTRYPOINT_TASKS: frozenset[tuple[str, str]] = frozenset(
+    {
+        ("sac_torch", "g1_walk_flat"),
+    }
+)
+
+# Maintainer-confirmed completed training validations for the genesis
+# in-process backend (real hardware, genesis-world extra + CUDA; not covered
+# by repo CI). sac_torch g1_walk_flat: full 5000-iteration training completed
+# on 2026-08-31 (RTX 4090, torch 2.8.0+cu128, genesis-world 1.3.3; reward/mean
+# 6.5 -> 244.8, episode length -> 987, run 2026-08-31_23-04-01_genesis) plus
+# record playback validation on model_5000.pt.
+_MAINTAINER_VALIDATED_GENESIS_ENTRYPOINT_TASKS: frozenset[tuple[str, str]] = frozenset(
+    {
+        ("sac_torch", "g1_walk_flat"),
+    }
+)
+
+# IsaacSim is a Python 3.11 worker integration with eval-owned Kit viewer and
+# RGB camera protocol coverage. No full training or successful real playback
+# validation is promoted here: the checked-in evidence remains owner/config,
+# protocol tests, and bounded backend smoke coverage.
+_MAINTAINER_VALIDATED_ISAACSIM_ENTRYPOINT_TASKS: frozenset[tuple[str, str]] = frozenset()
+
+_TASK_ORDER = {
+    "go1_joystick_flat": 0,
+    "go2_joystick_flat": 1,
+    "go2_joystick_rough": 2,
+    "g1_walk_flat": 3,
+    "g1_walk_rough": 4,
+    "g1_motion_tracking": 5,
+    "g1_flip_tracking": 6,
+    "g1_wall_flip_tracking": 7,
+    "x2_wall_flip_tracking": 8,
+    "allegro_inhand": 9,
+    "allegro_sac": 10,
+    "sharpa_inhand": 11,
+    "sharpa_inhand_grasp": 12,
+}
+_TASK_LABELS = {
+    "go1_joystick_flat": "Go1 joystick",
+    "go2_joystick_flat": "Go2 joystick",
+    "go2_joystick_rough": "Go2 joystick rough",
+    "g1_walk_flat": "G1 walk flat",
+    "g1_walk_rough": "G1 walk rough",
+    "g1_motion_tracking": "G1 motion tracking",
+    "g1_flip_tracking": "G1 flip tracking",
+    "g1_wall_flip_tracking": "G1 wall flip tracking",
+    "x2_wall_flip_tracking": "X2 wall flip tracking",
+    "allegro_inhand": "Allegro in-hand",
+    "allegro_sac": "Allegro SAC in-hand",
+    "sharpa_inhand": "Sharpa in-hand",
+    "sharpa_inhand_grasp": "Sharpa in-hand grasp",
+}
+
+
+class EvidenceLevel(IntEnum):
+    MISSING = 0
+    REGISTERED = 1
+    CONFIGURED = 2
+    TESTED = 3
+    BENCHMARKED = 4
+    RECOMMENDED = 5
+
+    @property
+    def label(self) -> str:
+        return {
+            EvidenceLevel.MISSING: "-",
+            EvidenceLevel.REGISTERED: "Registered",
+            EvidenceLevel.CONFIGURED: "Configured",
+            EvidenceLevel.TESTED: "Tested",
+            EvidenceLevel.BENCHMARKED: "Benchmarked",
+            EvidenceLevel.RECOMMENDED: "Recommended",
+        }[self]
+
+
+@dataclass(frozen=True)
+class EntrypointSpec:
+    entrypoint_id: str
+    label: str
+    config_dir: str
+    task_glob: str
+    generic_tested: bool = False
+
+
+@dataclass(frozen=True)
+class SupportCell:
+    env_name: str
+    level: EvidenceLevel
+
+
+@dataclass(frozen=True)
+class SupportRow:
+    entrypoint_label: str
+    task_slug: str
+    task_label: str
+    cells: dict[str, SupportCell]
+
+
+ENTRYPOINT_SPECS: tuple[EntrypointSpec, ...] = (
+    EntrypointSpec(
+        entrypoint_id="ppo_torch",
+        label="PPO (torch)",
+        config_dir="src/unilab/conf/ppo/task",
+        task_glob="*/*.yaml",
+        generic_tested=True,
+    ),
+    EntrypointSpec(
+        entrypoint_id="appo_torch",
+        label="APPO (torch)",
+        config_dir="src/unilab/conf/appo/task",
+        task_glob="*/*.yaml",
+        generic_tested=True,
+    ),
+    EntrypointSpec(
+        entrypoint_id="sac_torch",
+        label="SAC (torch)",
+        config_dir="src/unilab/conf/sac/task",
+        task_glob="*/*.yaml",
+        generic_tested=True,
+    ),
+    EntrypointSpec(
+        entrypoint_id="td3_torch",
+        label="TD3 (torch)",
+        config_dir="src/unilab/conf/td3/task",
+        task_glob="*/*.yaml",
+        generic_tested=True,
+    ),
+    EntrypointSpec(
+        entrypoint_id="flashsac_torch",
+        label="FlashSAC (torch)",
+        config_dir="src/unilab/conf/flashsac/task",
+        task_glob="*/*.yaml",
+        generic_tested=True,
+    ),
+)
+
+
+def repo_root(root: Path | None = None) -> Path:
+    return root or Path(__file__).resolve().parents[3]
+
+
+def _task_sort_key(task_slug: str) -> tuple[int, str]:
+    return (_TASK_ORDER.get(task_slug, 999), task_slug)
+
+
+def _task_label(task_slug: str) -> str:
+    return _TASK_LABELS.get(task_slug, task_slug.replace("_", " "))
+
+
+def _load_task_name(task_path: Path) -> str:
+    raw = OmegaConf.to_container(OmegaConf.load(task_path), resolve=True) or {}
+    if not isinstance(raw, dict):
+        raise ValueError(f"Expected mapping config in {task_path}")
+    training = raw.get("training")
+    if not isinstance(training, dict) or "task_name" not in training:
+        raise ValueError(f"Missing training.task_name in {task_path}")
+    task_name = training["task_name"]
+    if not isinstance(task_name, str):
+        raise ValueError(f"training.task_name must be a string in {task_path}")
+    return task_name
+
+
+def _load_registry_backends() -> dict[str, set[str]]:
+    ensure_registries()
+    registered = registry.list_registered_envs()
+    return {
+        env_name: set(meta["available_backends"])
+        for env_name, meta in registered.items()
+        if isinstance(meta.get("available_backends"), list)
+    }
+
+
+def _has_checked_in_benchmark_manifest(root: Path) -> bool:
+    del root
+    return False
+
+
+def _has_recommendation_metadata(root: Path) -> bool:
+    del root
+    return False
+
+
+def _configured_entries(root: Path, spec: EntrypointSpec) -> dict[str, dict[str, str]]:
+    task_root = root / spec.config_dir
+    entries: dict[str, dict[str, str]] = {}
+    for task_path in sorted(task_root.glob(spec.task_glob)):
+        task_slug = task_path.parent.name
+        backend = task_path.stem
+        if backend not in BACKENDS:
+            continue
+        entries.setdefault(task_slug, {})[backend] = _load_task_name(task_path)
+    return entries
+
+
+def _is_tested(spec: EntrypointSpec, task_slug: str, backend: str, root: Path) -> bool:
+    if backend == "mjwarp":
+        return (
+            spec.entrypoint_id,
+            task_slug,
+        ) in _MAINTAINER_VALIDATED_MJWARP_ENTRYPOINT_TASKS
+    if backend == "isaacgym":
+        return (
+            spec.entrypoint_id,
+            task_slug,
+        ) in _MAINTAINER_VALIDATED_ISAACGYM_ENTRYPOINT_TASKS
+    if backend == "genesis":
+        return (
+            spec.entrypoint_id,
+            task_slug,
+        ) in _MAINTAINER_VALIDATED_GENESIS_ENTRYPOINT_TASKS
+    if backend == "isaacsim":
+        return (
+            spec.entrypoint_id,
+            task_slug,
+        ) in _MAINTAINER_VALIDATED_ISAACSIM_ENTRYPOINT_TASKS
+    return spec.generic_tested
+
+
+def _cell_level(
+    *,
+    backend: str,
+    env_name: str,
+    configured_backends: dict[str, str],
+    registry_backends: dict[str, set[str]],
+    tested: bool,
+    benchmarked: bool,
+    recommended: bool,
+) -> EvidenceLevel:
+    available_backends = registry_backends.get(env_name, set())
+    if backend not in available_backends:
+        return EvidenceLevel.MISSING
+
+    level = EvidenceLevel.REGISTERED
+    if backend in configured_backends:
+        level = EvidenceLevel.CONFIGURED
+    if backend in configured_backends and tested:
+        level = EvidenceLevel.TESTED
+    if backend in configured_backends and tested and benchmarked:
+        level = EvidenceLevel.BENCHMARKED
+    if backend in configured_backends and tested and benchmarked and recommended:
+        level = EvidenceLevel.RECOMMENDED
+    return level
+
+
+def build_support_rows(root: Path | None = None) -> list[SupportRow]:
+    resolved_root = repo_root(root)
+    registry_backends = _load_registry_backends()
+    benchmarked = _has_checked_in_benchmark_manifest(resolved_root)
+    recommended = _has_recommendation_metadata(resolved_root)
+    rows: list[SupportRow] = []
+
+    for spec in ENTRYPOINT_SPECS:
+        for task_slug, configured_backends in sorted(
+            _configured_entries(resolved_root, spec).items(),
+            key=lambda item: _task_sort_key(item[0]),
+        ):
+            env_name = next(iter(configured_backends.values()))
+            cells = {
+                backend: SupportCell(
+                    env_name=env_name,
+                    level=_cell_level(
+                        backend=backend,
+                        env_name=env_name,
+                        configured_backends=configured_backends,
+                        registry_backends=registry_backends,
+                        tested=_is_tested(spec, task_slug, backend, resolved_root),
+                        benchmarked=benchmarked,
+                        recommended=recommended,
+                    ),
+                )
+                for backend in BACKENDS
+            }
+            rows.append(
+                SupportRow(
+                    entrypoint_label=spec.label,
+                    task_slug=task_slug,
+                    task_label=_task_label(task_slug),
+                    cells=cells,
+                )
+            )
+
+    return rows
+
+
+def render_support_matrix(root: Path | None = None) -> str:
+    resolved_root = repo_root(root)
+    benchmark_note = (
+        "未检测到与这些组合绑定的已提交 benchmark manifest，因此当前不会自动提升到 `Benchmarked`。"
+    )
+    recommendation_note = (
+        "仓库中目前也没有单独的 recommendation 元数据，因此当前不会自动提升到 `Recommended`。"
+    )
+
+    lines = [
+        "### Evidence Grades",
+        "",
+        "| 等级 | 仓库事实来源 |",
+        "|------|--------------|",
+        "| `Registered` | `ensure_registries()` 导入后的 `registry.list_registered_envs()` 中存在该 env/backend。 |",
+        "| `Configured` | 存在对应的 owner YAML：`src/unilab/conf/{ppo,appo,sac,td3,flashsac}/task/...`。 |",
+        "| `Tested` | `tests/` 中有自动化覆盖该 entrypoint/task owner/backend 组合，或存在显式 maintainer 完整训练验证并具备近风险自动化测试。这里的 `Tested` 不等同于默认推荐路径。 |",
+        "| `Benchmarked` | 存在与该组合绑定的已提交 benchmark manifest。 |",
+        "| `Recommended` | 仓库中存在显式 recommendation 元数据。 |",
+        "",
+        "`Tested` 只描述仓库中已有自动化覆盖或显式 maintainer 训练验证，不代表该组合具备同名 MuJoCo "
+        "owner 的全部 backend capability；例如 phase-1 Motrix owner 可能只覆盖训练 smoke 和明确启用的 DR 子集。",
+        "",
+        "`mjwarp` 完成训练验证的只有 `g1_walk_flat` host adapter：PPO (torch) 与 SAC (torch) owner "
+        "已完成训练验证，并有 backend、contract 与 playback 自动化覆盖，因此标记为 `Tested`。"
+        "mjwarp playback 默认仅支持显式、有限步数的 `record` 并复用 MuJoCo 离线 renderer；"
+        "`uv run eval --sim mjwarp --render-mode interactive` 路由到 MuJoCo 交互 viewer"
+        "（mjwarp 跑物理、MuJoCo 渲染 env[0]，强制单 env）；不支持 `auto` 或 native playback。"
+        "其他 entrypoint 中出现的 `Registered` 只表示 env/backend registry "
+        "identity，不代表对应算法、terrain、完整 DR 或 production training 支持。",
+        "",
+        "`isaacgym` 是 Python 3.8 子进程后端，当前只接入 `g1_walk_flat`。SAC (torch) owner 已在真机"
+        "（external Python 3.8 worker runtime，不在仓库 CI 覆盖）完成训练与 record playback 验证，"
+        "标记为 `Tested`；其余 isaacgym cell 最高只到 `Configured`（registry + owner YAML + "
+        "compose/contract 覆盖），不代表任何训练或 play 验证。playback 走 IsaacGym 原生渲染"
+        "（viewer + camera sensor 离屏录制），有显示器时 `play_render_mode=auto` 打开交互 viewer，"
+        "无显示器时自动降级为离屏录制。",
+        "",
+        "`genesis` 是进程内后端（genesis-world==1.3.3，要求 torch>=2.8 与 CUDA；一进程只允许一次 "
+        "`gs.init`），当前只接入 `g1_walk_flat` 的 PPO (torch) 与 SAC (torch) owner。SAC cell 标记 "
+        "`Tested`：真机完整训练验证（5000/5000 iterations，reward/mean 6.5 → 244.8，episode length "
+        "→ 987/1000，10.26M env steps / 224s wall time；run 2026-08-31_23-04-01_genesis）加 "
+        "model_5000.pt 的 record playback 验证；PPO cell 最高只到 `Configured`（registry + owner "
+        "YAML + compose/contract 覆盖），不代表训练验证。真机证据另有："
+        "env smoke 慢车道测试（`tests/envs/locomotion/g1/test_g1_owner_contract.py`：compose → env 构造 "
+        "→ keyframe reset → 12 步有限稳定 → cleanup，覆盖 ppo 与 sac 两棵树）在装有 CUDA 与 genesis "
+        "extra 的机器上通过。adapter 的 "
+        "`materialize()` 幂等且惰性触发（entity 校验在 env 的 materialize 钩子前读取状态 getter；"
+        "isaacgym 后端同模式）。Genesis 在 import 时丢弃 MJCF 全局 "
+        "`<option>`，owner YAML 显式重声明 `genesis_integrator=implicitfast`。原生 playback/渲染已接入："
+        "`play_render_mode=auto` 在有显示时打开 post-build 挂载的交互 viewer、无显示时降级离屏录制"
+        "（`record` 写 `play_video.mp4`；`get_physics_state` 快照不声明）。未支持边界：geom 名称"
+        "契约、terrain spawn 与 height scanner、contact "
+        'sensor 为 per-link net-force 阈值近似（非 geom 对 `data="found"`）、`get_geom_friction` 类'
+        "绝对摩擦 DR fail-closed（geom 摩擦只有 per-env ratio API）。",
+        "",
+        "`isaacsim` 是 IsaacSim 5.1 / IsaacLab v2.3.0 的独立 Python 3.11 子进程后端，当前只接入"
+        " `g1_walk_flat` 的 PPO/SAC owner，矩阵标记为 `Configured`。仓库没有把 bounded headless"
+        " physics smoke 和 mock rendering protocol 覆盖提升为训练或 playback 的 `Tested` 证据。"
+        "eval 已接入 Kit viewer 与 IsaacLab RGB camera；当前真实主机在 RTX renderer 初始化阶段"
+        "崩溃，因此没有成功 playback 证据，也不会生成占位视频。contact-force sensor 和 domain "
+        "randomization 仍保持 fail-closed。",
+        "",
+        benchmark_note,
+        recommendation_note,
+        "",
+        "### Entrypoint x Task Owner",
+        "",
+        "| Entrypoint | Task owner | MuJoCo | mjwarp | Motrix | IsaacGym | Genesis | IsaacSim |",
+        "|------------|------------|--------|--------|--------|----------|---------|----------|",
+    ]
+
+    for row in build_support_rows(resolved_root):
+        lines.append(
+            f"| {row.entrypoint_label} | `{row.task_slug}` ({row.task_label}) | "
+            f"{row.cells['mujoco'].level.label} | {row.cells['mjwarp'].level.label} | "
+            f"{row.cells['motrix'].level.label} | {row.cells['isaacgym'].level.label} | "
+            f"{row.cells['genesis'].level.label} | {row.cells['isaacsim'].level.label} |"
+        )
+
+    lines.extend(
+        [
+            "",
+            "### Source Index",
+            "",
+            "- Registry bootstrap: `src/unilab/envs/**` decorators via `unilab.base.registry.ensure_registries()`.",
+            "- Owner YAML scan: `src/unilab/conf/ppo/task/**`, `src/unilab/conf/appo/task/**`, `src/unilab/conf/sac/task/**`, `src/unilab/conf/td3/task/**`, `src/unilab/conf/flashsac/task/**`.",
+            "- Generic compose coverage: `tests/config/test_config_system.py::test_supported_task_composes`.",
+            "- Validated mjwarp entrypoints are explicitly recorded in `_MAINTAINER_VALIDATED_MJWARP_ENTRYPOINT_TASKS`; near-risk coverage lives in `tests/base/test_mjwarp_backend.py`, `tests/base/test_backend_conformance.py`, `tests/base/test_mjwarp_differential.py`, and `tests/base/test_mjwarp_playback.py`.",
+            "- Validated isaacgym entrypoints are explicitly recorded in `_MAINTAINER_VALIDATED_ISAACGYM_ENTRYPOINT_TASKS` (real hardware via the external Python 3.8 worker runtime; not covered by repo CI).",
+            "- Validated genesis entrypoints are explicitly recorded in `_MAINTAINER_VALIDATED_GENESIS_ENTRYPOINT_TASKS` (real hardware, genesis-world extra + CUDA; not covered by repo CI); near-risk coverage lives in `tests/base/test_genesis_backend.py` (fake runtime), `tests/base/test_genesis_runtime.py` (real-runtime slow lane), and the genesis env smoke in `tests/envs/locomotion/g1/test_g1_owner_contract.py`.",
+            "- IsaacSim owner scope is intentionally not promoted to `Tested`; `_MAINTAINER_VALIDATED_ISAACSIM_ENTRYPOINT_TASKS` is empty until a maintainer records full training evidence. Rendering protocol coverage lives in `tests/base/test_isaacsim_backend.py`; it is not a substitute for successful real playback.",
+        ]
+    )
+    return "\n".join(lines)
+
+
+def render_generated_block(root: Path | None = None) -> str:
+    return "\n".join([BEGIN_MARKER, render_support_matrix(root), END_MARKER])
+
+
+def replace_generated_block(content: str, rendered_block: str) -> str:
+    pattern = re.compile(
+        rf"{re.escape(BEGIN_MARKER)}.*?{re.escape(END_MARKER)}",
+        flags=re.DOTALL,
+    )
+    if pattern.search(content) is None:
+        raise ValueError("Generated support matrix markers not found")
+    return pattern.sub(rendered_block, content)
diff --git a/scripts/train_appo.py b/scripts/train_appo.py
deleted file mode 100644
index 93e193429..000000000
--- a/scripts/train_appo.py
+++ /dev/null
@@ -1,424 +0,0 @@
-"""Train APPO agent — native multiprocessing."""
-
-from __future__ import annotations
-
-import datetime
-import os
-import sys
-from collections.abc import Callable
-from pathlib import Path
-from typing import Any, cast
-
-import hydra
-import torch
-from omegaconf import DictConfig, OmegaConf
-
-ROOT_DIR = Path(__file__).parent.parent
-sys.path.append(str(ROOT_DIR))
-
-from unilab.algos.torch.appo.runtime import resolve_appo_runtime
-from unilab.training import (
-    BackendAdapter,
-    apply_configured_training_seed,
-    create_env,
-    ensure_registries,
-    get_log_root,
-    log_playback_plan,
-    resolve_appo_checkpoint_path,
-    should_run_playback,
-)
-from unilab.training.experiment import ExperimentTracker
-from unilab.training.onnx_export import export_policy_onnx, verify_policy_onnx
-from unilab.training.sim2sim import policy_load_dim_guard, resolve_sim2sim_config
-
-
-def _training_resume_requested(load_run: Any) -> bool:
-    if load_run is None:
-        return False
-    return str(load_run) not in {"", "-1"}
-
-
-def build_appo_runner_kwargs(
-    cfg: DictConfig,
-    env_cfg_override: dict | None,
-    collector_device: str | None,
-    rl_cfg: dict[str, Any] | None = None,
-) -> dict:
-    if rl_cfg is None:
-        rl_cfg_raw = OmegaConf.to_container(cfg.algo, resolve=True)
-        if not isinstance(rl_cfg_raw, dict):
-            raise TypeError("cfg.algo must resolve to a dict")
-        rl_cfg = cast(dict[str, Any], rl_cfg_raw)
-
-    runner_kwargs = {
-        "env_name": cfg.training.task_name,
-        "env_cfg_overrides": env_cfg_override,
-        "rl_cfg": rl_cfg,
-        "device": cfg.training.device,
-        "collector_device": collector_device,
-        "num_envs": cfg.algo.num_envs,
-        "steps_per_env": cfg.algo.steps_per_env,
-        "sim_backend": cfg.training.sim_backend,
-        "seed": rl_cfg.get("seed"),
-    }
-    if cfg.training.replay_queue_size is not None:
-        runner_kwargs["replay_queue_size"] = cfg.training.replay_queue_size
-    load_run = OmegaConf.select(cfg, "algo.load_run", default="-1")
-    if _training_resume_requested(load_run):
-        resume_path, _ = resolve_appo_checkpoint_path(
-            os.path.join(_get_log_root(cfg), cfg.training.task_name),
-            str(load_run),
-        )
-        if resume_path is None:
-            raise FileNotFoundError(f"Could not resolve APPO resume checkpoint: {load_run}")
-        runner_kwargs["resume_path"] = resume_path
-
-    nan_guard_cfg = getattr(cfg.training, "nan_guard", None)
-    if nan_guard_cfg is not None and getattr(nan_guard_cfg, "enabled", False):
-        from unilab.utils.nan_guard import NanGuardCfg
-
-        runner_kwargs["nan_guard_cfg"] = NanGuardCfg(
-            enabled=True,
-            buffer_size=int(getattr(nan_guard_cfg, "buffer_size", 100)),
-            max_envs_to_dump=int(getattr(nan_guard_cfg, "max_envs_to_dump", 5)),
-            output_dir=getattr(nan_guard_cfg, "output_dir", None),
-        )
-    return runner_kwargs
-
-
-def apply_appo_runtime_flags(
-    rl_cfg: dict[str, Any],
-    cfg: DictConfig,
-    *,
-    training_enabled: bool,
-) -> None:
-    algorithm_cfg = rl_cfg.setdefault("algorithm", {})
-    if not isinstance(algorithm_cfg, dict):
-        return
-    if not training_enabled:
-        algorithm_cfg["enable_compile"] = False
-
-
-def run_motrix_play_loop(
-    env,
-    actor,
-    device: str,
-    play_env_num: int,
-    num_steps: int | None = None,
-) -> None:
-    import numpy as np
-    from tensordict import TensorDict
-
-    if env.state is None:
-        env.init_state()
-
-    with torch.inference_mode():
-        env.run_playback(
-            num_steps=num_steps,
-            initialize=lambda: np.asarray(
-                env.reset(np.arange(play_env_num, dtype=np.int32))[0]["obs"],
-                dtype=np.float32,
-            ),
-            step=lambda obs_np: np.asarray(
-                env.step(
-                    actor(
-                        TensorDict(
-                            {"policy": torch.from_numpy(obs_np).to(device)}, batch_size=play_env_num
-                        )
-                    )
-                    .cpu()
-                    .numpy()
-                    .astype(np.float32)
-                ).obs["obs"],
-                dtype=np.float32,
-            ),
-        )
-
-
-def _get_log_root(cfg: DictConfig) -> str:
-    return str(get_log_root(ROOT_DIR, cfg))
-
-
-def play_appo(
-    cfg: DictConfig,
-    rl_cfg: dict[str, Any],
-    *,
-    root_dir: Path | None = None,
-    resolve_checkpoint_path: Callable[[DictConfig], tuple[str | None, str | None]] | None = None,
-) -> str | None:
-    """Play mode for the default APPO runtime.
-
-    Args:
-        cfg: Resolved Hydra config for the current run.
-        rl_cfg: Resolved algorithm config dictionary from Hydra composition.
-        root_dir: Optional project root forwarded by generic runtime callers.
-            The default APPO runtime does not need it and ignores the value.
-        resolve_checkpoint_path: Optional checkpoint resolver injected by the
-            generic script. When omitted, this function falls back to the
-            default log-root based APPO checkpoint resolution.
-
-    Returns:
-        Output video path for offscreen rendering, or ``None`` when running the
-        native Motrix viewer or when no checkpoint could be resolved.
-    """
-    del root_dir
-    import numpy as np
-    from rsl_rl.utils import resolve_callable
-    from tensordict import TensorDict
-
-    if resolve_checkpoint_path is not None:
-        load_path, load_path_dir = resolve_checkpoint_path(cfg)
-    else:
-        log_root = _get_log_root(cfg)
-        base_log_dir = os.path.join(log_root, cfg.training.task_name)
-        load_path, load_path_dir = resolve_appo_checkpoint_path(base_log_dir, cfg.algo.load_run)
-
-    if not load_path or not os.path.exists(load_path):
-        print(f"Could not find run to load. load_path={load_path}")
-        return None
-
-    cfg = (
-        resolve_sim2sim_config(
-            load_path_dir,
-            cfg,
-            algo_name="appo",
-            strict=bool(getattr(cfg.training, "sim2sim_strict", True)),
-        )
-        or cfg
-    )
-
-    env_cfg_override = BackendAdapter(
-        cfg, root_dir=ROOT_DIR, algo_name="appo"
-    ).build_task_env_cfg_override()
-
-    device = cfg.training.device or (
-        "cuda"
-        if torch.cuda.is_available()
-        else "mps"
-        if torch.backends.mps.is_available()
-        else "cpu"
-    )
-    print(f"Using device for play: {device}")
-
-    env = cast(
-        Any,
-        create_env(
-            cfg,
-            num_envs=cfg.training.play_env_num,
-            env_cfg_override=env_cfg_override,
-        ),
-    )
-    from unilab.base.observations import get_obs_dims
-
-    obs_dim, critic_dim = get_obs_dims(env.obs_groups_spec)
-    action_shape = env.action_space.shape
-    if action_shape is None:
-        raise ValueError("env.action_space.shape must be defined")
-    action_dim = int(action_shape[0])
-
-    rl_cfg_dict = dict(rl_cfg)
-    if "obs_groups" not in rl_cfg_dict:
-        rl_cfg_dict["obs_groups"] = {
-            "actor": {"policy": obs_dim},
-            "critic": {"policy": critic_dim if critic_dim > 0 else obs_dim},
-        }
-    else:
-        actor_group = rl_cfg_dict["obs_groups"].get(
-            "actor", rl_cfg_dict["obs_groups"].get("policy", {})
-        )
-        if isinstance(actor_group, dict) and "policy" in actor_group:
-            actor_group["policy"] = obs_dim
-        critic_group = rl_cfg_dict["obs_groups"].get("critic")
-        if critic_group is None:
-            rl_cfg_dict["obs_groups"]["critic"] = {
-                "policy": critic_dim if critic_dim > 0 else obs_dim
-            }
-        elif isinstance(critic_group, dict) and "policy" in critic_group:
-            critic_group["policy"] = critic_dim if critic_dim > 0 else obs_dim
-
-    from copy import deepcopy
-
-    obs_example = torch.zeros((cfg.training.play_env_num, obs_dim), device=device)
-    td_example = TensorDict({"policy": obs_example}, batch_size=cfg.training.play_env_num)
-
-    actor_cfg = deepcopy(rl_cfg_dict["actor"])
-    actor_cls = resolve_callable(actor_cfg.pop("class_name"))
-    actor_cfg.pop("num_actions", None)
-    actor = actor_cls(td_example, rl_cfg_dict["obs_groups"], "actor", action_dim, **actor_cfg)
-    actor = actor.to(device)
-    actor.eval()
-
-    print(f"Loading model: {load_path}")
-    checkpoint = torch.load(load_path, map_location=device, weights_only=True)
-    with policy_load_dim_guard(env_obs_dim=obs_dim, env_action_dim=action_dim, algo_name="appo"):
-        actor.load_state_dict(checkpoint["actor"])
-
-    # Export actor to ONNX
-    if load_path_dir is not None:
-        import torch.nn as nn
-
-        class _DeterministicAPPOActor(nn.Module):
-            def __init__(self, mlp: nn.Module):
-                super().__init__()
-                self.mlp = mlp
-
-            def forward(self, obs: torch.Tensor) -> torch.Tensor:
-                return self.mlp(obs)
-
-        export_module = _DeterministicAPPOActor(actor.mlp)
-        onnx_path = os.path.join(load_path_dir, "policy.onnx")
-        dummy_input = torch.randn(1, obs_dim, device=device)
-        export_policy_onnx(export_module, onnx_path, (dummy_input,), input_names=["obs"])
-
-        # Verify ONNX output matches PyTorch
-        verify_input = torch.randn(1, obs_dim, device=device)
-        verify_policy_onnx(export_module, onnx_path, (verify_input,), input_names=["obs"])
-
-    if env.state is None:
-        env.init_state()
-
-    with torch.inference_mode():
-        play_video_path = env.run_playback_mode(
-            play_render_mode=getattr(cfg.training, "play_render_mode", "auto"),
-            play_steps=getattr(cfg.training, "play_steps", None),
-            output_video=os.path.join(load_path_dir, "play_video.mp4") if load_path_dir else None,
-            render_spacing=float(
-                getattr(cfg.training, "render_spacing", getattr(env.cfg, "render_spacing", 1.0))
-            ),
-            initialize=lambda: np.asarray(
-                env.reset(np.arange(cfg.training.play_env_num, dtype=np.int32))[0]["obs"],
-                dtype=np.float32,
-            ),
-            step=lambda obs_np: np.asarray(
-                env.step(
-                    actor(
-                        TensorDict(
-                            {"policy": torch.from_numpy(obs_np).to(device)},
-                            batch_size=cfg.training.play_env_num,
-                        )
-                    )
-                    .cpu()
-                    .numpy()
-                    .astype(np.float32)
-                ).obs["obs"],
-                dtype=np.float32,
-            ),
-            camera_kwargs={
-                "cam_distance": cfg.training.cam_distance,
-                "cam_elevation": cfg.training.cam_elevation,
-                "cam_azimuth": cfg.training.cam_azimuth,
-                "cam_lookat": getattr(cfg.training, "cam_lookat", None),
-                "cam_tracking": getattr(cfg.training, "cam_tracking", False),
-                "cam_tracking_env_idx": getattr(cfg.training, "cam_tracking_env_idx", 0),
-                "cam_tracking_extra_envs": getattr(cfg.training, "cam_tracking_extra_envs", 2),
-            },
-            on_plan=log_playback_plan,
-        )
-    if play_video_path is not None:
-        print(f"Saving video to {play_video_path} ...")
-    print("Done.")
-    return play_video_path
-
-
-@hydra.main(version_base="1.3", config_path="../conf/appo", config_name="config")
-def main(cfg: DictConfig) -> None:
-    ensure_registries()
-
-    seed_info = apply_configured_training_seed(cfg, torch_runtime=True, cuda=True)
-    env_cfg_override = BackendAdapter(
-        cfg, root_dir=ROOT_DIR, algo_name="appo"
-    ).build_task_env_cfg_override()
-
-    # Convert algo config to plain dict for APPORunner / RSL-RL internals
-    rl_cfg_raw = OmegaConf.to_container(cfg.algo, resolve=True)
-    if not isinstance(rl_cfg_raw, dict):
-        raise TypeError("cfg.algo must resolve to a dict")
-    rl_cfg = cast(dict[str, Any], rl_cfg_raw)
-    apply_appo_runtime_flags(rl_cfg, cfg, training_enabled=not cfg.training.play_only)
-    appo_runtime = resolve_appo_runtime(rl_cfg, default_play_fn=play_appo)
-
-    if cfg.training.log_dir is None:
-        timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
-        log_root = _get_log_root(cfg)
-        log_dir = os.path.join(
-            log_root,
-            cfg.training.task_name,
-            f"{timestamp}_{cfg.training.sim_backend}",
-        )
-    else:
-        log_dir = cfg.training.log_dir
-
-    collector_device = cfg.training.collector_device
-    if collector_device == "gpu":
-        collector_device = "mps" if torch.backends.mps.is_available() else "cuda"
-
-    learner_device = cfg.training.device or (
-        "cuda"
-        if torch.cuda.is_available()
-        else "mps"
-        if torch.backends.mps.is_available()
-        else "cpu"
-    )
-
-    tracker = None
-    if not cfg.training.play_only:
-        tracker = ExperimentTracker(
-            root_dir=ROOT_DIR,
-            log_dir=log_dir,
-            algo_name="appo",
-            task_name=cfg.training.task_name,
-            sim_backend=cfg.training.sim_backend,
-            training_cfg=cfg.training,
-            full_cfg=cfg,
-            device=learner_device,
-            collector_device=collector_device,
-            seed_info=seed_info,
-        )
-        tracker.start()
-
-    try:
-        if not cfg.training.play_only:
-            runner = appo_runtime.runner_cls(
-                **build_appo_runner_kwargs(
-                    cfg,
-                    env_cfg_override=env_cfg_override,
-                    collector_device=collector_device,
-                    rl_cfg=rl_cfg,
-                )
-            )
-
-            try:
-                runner.learn(
-                    max_iterations=cfg.algo.max_iterations,
-                    save_interval=cfg.algo.save_interval,
-                    log_dir=log_dir,
-                    logger_type=cfg.training.logger,
-                )
-                if tracker is not None:
-                    tracker.update_summary(getattr(runner, "last_run_summary", None))
-            finally:
-                runner.close()
-
-        if should_run_playback(
-            play_only=cfg.training.play_only,
-            no_play=cfg.training.no_play,
-            play_render_mode=getattr(cfg.training, "play_render_mode", "auto"),
-        ):
-            play_video_path = appo_runtime.play_fn(
-                cfg,
-                rl_cfg,
-                root_dir=ROOT_DIR,
-                resolve_checkpoint_path=lambda current_cfg: resolve_appo_checkpoint_path(
-                    os.path.join(_get_log_root(current_cfg), current_cfg.training.task_name),
-                    current_cfg.algo.load_run,
-                ),
-            )
-            if tracker is not None:
-                tracker.log_video(play_video_path)
-    finally:
-        if tracker is not None:
-            tracker.finish()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/train_him_ppo.py b/scripts/train_him_ppo.py
index e104aa42f..5bc836d34 100644
--- a/scripts/train_him_ppo.py
+++ b/scripts/train_him_ppo.py
@@ -7,7 +7,7 @@
 
 import hydra
 import torch
-from omegaconf import DictConfig, OmegaConf
+from omegaconf import DictConfig
 
 EXPORT_POLICY = False  # set to True in __main__ block
 
@@ -18,20 +18,33 @@
 if str(ROOT_DIR) not in sys.path:
     sys.path.insert(0, str(ROOT_DIR))
 
-from unilab.algos.torch.him_ppo.runner import HIMOnPolicyRunner
-from unilab.base.backend import materialize_scene_visual_override
-from unilab.training import (
+from uni_rl.algos.him_ppo.runner import HIMOnPolicyRunner
+from uni_rl.algos.rsl_rl import RslRlVecEnvWrapper, get_policy_obs_dims
+from unisim.backend.mujoco.xml import materialize_scene_visual_override
+
+from unilab.base.config_adapter import (
     BackendAdapter,
     create_env,
+)
+from unilab.training import (
+    algo_config_dict,
+    apply_env_nan_guard,
+    build_run_dir_name,
     ensure_registries,
-    get_latest_checkpoint,
-    get_latest_run,
+    format_play_checkpoint_error,
     get_log_root,
     parse_checkpoint_path,
 )
 from unilab.training.experiment import ExperimentTracker
-from unilab.training.sim2sim import policy_load_dim_guard, resolve_sim2sim_config
+from unilab.utils.checkpoint import get_entrypoint_log_root
 from unilab.visualization import render_play_mode
+from unilab.visualization.interactive_playback import (
+    RslRlPlaybackConfig,
+    create_rsl_rl_playback_session,
+    infer_checkpoint_actor_input_dim,
+    make_sim2sim_preflight,
+    normalize_checkpoint_value,
+)
 
 
 def _backend_adapter(cfg: DictConfig) -> BackendAdapter:
@@ -47,57 +60,15 @@ def _get_log_root(cfg: DictConfig) -> str:
     return str(get_log_root(ROOT_DIR, cfg))
 
 
-def _algo_config_dict(cfg: DictConfig) -> dict[str, Any]:
-    raw = OmegaConf.to_container(cfg.algo, resolve=True)
-    if not isinstance(raw, dict):
-        raise TypeError("cfg.algo must resolve to a dict")
-    return cast(dict[str, Any], raw)
-
-
-def _format_play_checkpoint_error(
-    cfg: DictConfig,
-    *,
-    task_log_root: Path,
-    load_path: Path | None,
-    load_path_dir: Path | None,
-) -> str:
-    selected_checkpoint = OmegaConf.select(cfg, "algo.checkpoint", default=-1)
-    checkpoint_hint = (
-        f" algo.checkpoint={selected_checkpoint!r}"
-        if selected_checkpoint not in (None, "", -1, "-1")
-        else ""
-    )
-    if load_path_dir is not None and load_path is None and checkpoint_hint:
-        reason = f"Requested checkpoint was not found under resolved_run={load_path_dir}."
-    elif not task_log_root.exists():
-        reason = "Task log root does not exist."
-    else:
-        latest_run = get_latest_run(task_log_root)
-        if latest_run is None:
-            reason = "No run directories were found under the task log root."
-        elif get_latest_checkpoint(latest_run) is None:
-            reason = f"Resolved latest run has no model_*.pt checkpoint files: {latest_run}."
-        else:
-            reason = "Requested run or checkpoint could not be resolved."
-
-    return (
-        "Could not resolve a checkpoint for play mode. "
-        f"{reason} task={cfg.training.task_name} task_log_root={task_log_root} "
-        f"algo.load_run={cfg.algo.load_run!r}{checkpoint_hint}."
-        " Use algo.load_run=<run-dir-or-checkpoint-path> "
-        "and optionally algo.checkpoint=<iteration-or-filename>."
-    )
-
-
 def play_him_ppo(cfg: DictConfig, device: str) -> str | None:
     """Play mode for HIM-PPO."""
-    rl_cfg = _algo_config_dict(cfg)
+    rl_cfg = algo_config_dict(cfg)
 
     task_log_root = get_log_root(ROOT_DIR, cfg) / str(cfg.training.task_name)
     load_path, load_path_dir = parse_checkpoint_path(cfg, root_dir=ROOT_DIR)
     if load_path is None or load_path_dir is None or not load_path.exists():
         print(
-            _format_play_checkpoint_error(
+            format_play_checkpoint_error(
                 cfg,
                 task_log_root=task_log_root,
                 load_path=load_path,
@@ -115,31 +86,49 @@ def play_him_ppo(cfg: DictConfig, device: str) -> str | None:
         )
         return None
 
-    cfg = (
-        resolve_sim2sim_config(
-            load_path_dir,
-            cfg,
-            algo_name="ppo",
-            strict=bool(getattr(cfg.training, "sim2sim_strict", True)),
-        )
-        or cfg
+    def _create_env(num_envs: int):
+        env_cfg_override = cast(dict[str, Any], _backend_adapter(cfg).build_play_env_cfg_override())
+        return create_env(cfg, num_envs=num_envs, env_cfg_override=env_cfg_override)
+
+    session, _policy_obs_mode, _checkpoint_path = create_rsl_rl_playback_session(
+        playback_cfg=RslRlPlaybackConfig(
+            task=str(cfg.training.task_name),
+            load_run=str(getattr(cfg.algo, "load_run", "-1")),
+            checkpoint=normalize_checkpoint_value(getattr(cfg.algo, "checkpoint", None)),
+            action_mode="policy",
+            policy_obs_mode="flat",
+            algo_log_name=str(cfg.algo.algo_log_name),
+            log_root=getattr(cfg.training, "log_root", None),
+            num_envs=int(cfg.training.play_env_num),
+        ),
+        env_factory=_create_env,
+        algo_config=rl_cfg,
+        root_dir=ROOT_DIR,
+        device=device,
+        # The checkpoint was already resolved above for the friendly early exit.
+        checkpoint_resolver=lambda *_args: str(load_path),
+        checkpoint_input_dim_reader=infer_checkpoint_actor_input_dim,
+        entrypoint_log_root=get_entrypoint_log_root,
+        wrapper_cls=RslRlVecEnvWrapper,
+        runner_cls=HIMOnPolicyRunner,
+        # HIMOnPolicyRunner.load does not accept a load_cfg argument.
+        runner_loader=lambda runner, path: runner.load(path),
+        policy_obs_dims_getter=get_policy_obs_dims,
+        train_cfg_normalizer=lambda train_cfg: train_cfg,
+        sim2sim_preflight=make_sim2sim_preflight(cfg, algo_name="ppo"),
+        guard_algo_name="him_ppo",
     )
-    env_cfg_override = cast(dict[str, Any], _backend_adapter(cfg).build_play_env_cfg_override())
-    env = create_env(cfg, num_envs=cfg.training.play_env_num, env_cfg_override=env_cfg_override)
-    from unilab.training.rsl_rl import RslRlVecEnvWrapper
-
-    wrapped_env = RslRlVecEnvWrapper(env, device=device)
-    runner = HIMOnPolicyRunner(wrapped_env, rl_cfg, log_dir=None, device=device)
-    with policy_load_dim_guard(
-        env_obs_dim=getattr(wrapped_env, "num_obs", None),
-        env_action_dim=getattr(wrapped_env, "num_actions", None),
-        algo_name="him_ppo",
-    ):
-        runner.load(str(load_path))
-    policy = runner.get_inference_policy(device=device)
+    env = session.env
+    assert session.runner is not None and session.policy is not None
+
+    # HIM's inference policy consumes the flat actor tensor, not the full obs
+    # TensorDict the session hands to ``policy``.
+    him_policy = session.policy
+    session.policy = lambda obs: him_policy(obs["actor"])
+
     if EXPORT_POLICY:
-        runner.export_policy_to_onnx(path=str(load_path_dir))
-        runner.export_policy_to_jit(path=str(load_path_dir))
+        session.runner.export_policy_to_onnx(path=str(load_path_dir))
+        session.runner.export_policy_to_jit(path=str(load_path_dir))
 
     output_video = Path(load_path_dir) / "play_video.mp4"
     print(f"Rendering video to {output_video}...")
@@ -153,8 +142,8 @@ def play_him_ppo(cfg: DictConfig, device: str) -> str | None:
             ),
             num_steps=cfg.training.play_steps,
             output_video=output_video,
-            initialize=lambda: wrapped_env.reset()[0]["actor"],
-            step=lambda obs: wrapped_env.step(policy(obs))[0]["actor"],
+            initialize=lambda: session.reset()["actor"],
+            step=lambda _obs: session.step_once()["actor"],
             camera_kwargs={
                 "cam_distance": cfg.training.cam_distance,
                 "cam_elevation": cfg.training.cam_elevation,
@@ -174,7 +163,7 @@ def play_him_ppo(cfg: DictConfig, device: str) -> str | None:
     return str(output_video)
 
 
-@hydra.main(version_base="1.3", config_path="../conf/ppo_him", config_name="config")
+@hydra.main(version_base="1.3", config_path="../src/unilab/conf/ppo_him", config_name="config")
 def main(cfg: DictConfig) -> None:
     ensure_registries()
 
@@ -202,7 +191,9 @@ def main(cfg: DictConfig) -> None:
         timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
         log_root = _get_log_root(cfg)
         log_dir = str(
-            Path(log_root) / cfg.training.task_name / f"{timestamp}_{cfg.training.sim_backend}"
+            Path(log_root)
+            / cfg.training.task_name
+            / build_run_dir_name(timestamp, str(cfg.training.sim_backend))
         )
     else:
         log_dir = None
@@ -224,26 +215,11 @@ def main(cfg: DictConfig) -> None:
     try:
         if not cfg.training.play_only:
             env = create_env(cfg, num_envs=cfg.algo.num_envs, env_cfg_override=env_cfg_override)
-            from unilab.training.rsl_rl import RslRlVecEnvWrapper
-
-            nan_guard_cfg = getattr(cfg.training, "nan_guard", None)
-            if nan_guard_cfg is not None and getattr(nan_guard_cfg, "enabled", False):
-                from unilab.utils.nan_guard import NanGuard, NanGuardCfg
-
-                guard = NanGuard(
-                    NanGuardCfg(
-                        enabled=True,
-                        buffer_size=int(getattr(nan_guard_cfg, "buffer_size", 100)),
-                        max_envs_to_dump=int(getattr(nan_guard_cfg, "max_envs_to_dump", 5)),
-                        output_dir=getattr(nan_guard_cfg, "output_dir", None),
-                    ),
-                    num_envs=env.num_envs,
-                    supports_state_playback=env.play_capabilities.supports_physics_state_playback,
-                )
-                env.set_nan_guard(guard)
+
+            apply_env_nan_guard(env, cfg.training)
 
             wrapped_env = RslRlVecEnvWrapper(env, device=device)
-            rl_cfg = _algo_config_dict(cfg)
+            rl_cfg = algo_config_dict(cfg)
             runner = HIMOnPolicyRunner(wrapped_env, rl_cfg, log_dir=log_dir, device=device)
 
             if cfg.algo.load_run != "-1":
diff --git a/scripts/train_hora_distill.py b/scripts/train_hora_distill.py
index d289424e9..147b60677 100644
--- a/scripts/train_hora_distill.py
+++ b/scripts/train_hora_distill.py
@@ -1,5 +1,4 @@
 import datetime
-import json
 import sys
 from pathlib import Path
 from typing import Any, cast
@@ -15,42 +14,45 @@
 if str(ROOT_DIR) not in sys.path:
     sys.path.insert(0, str(ROOT_DIR))
 
-from unilab.algos.torch.hora import HoraDistillationTrainer
-from unilab.algos.torch.hora.distill import (
+from uni_rl.algos.hora import HoraDistillationTrainer
+from uni_rl.algos.hora.distill import (
     build_student_actor_and_normalizer,
     cfg_with_checkpoint_runtime,
     load_distilled_checkpoint,
     student_policy,
 )
-from unilab.algos.torch.hora.distill_config import (
+from uni_rl.algos.hora.rsl_rl import HoraRslRlVecEnvWrapper as RslRlVecEnvWrapper
+from unisim.backend.base import log_playback_plan
+from unisim.backend.mujoco.xml import materialize_scene_visual_override
+
+from unilab.base.config_adapter import (
+    BackendAdapter,
+    create_env,
+)
+from unilab.training import (
+    ensure_registries,
+    format_hora_stage2_checkpoint_error,
+    get_log_root,
+    resolve_hora_stage2_checkpoint_path,
+    setup_logger,
+    should_run_playback,
+)
+from unilab.training.experiment import get_device_info_dict, write_run_config_snapshot
+from unilab.training.hora_distill_config import (
     apply_teacher_defaults as _apply_teacher_defaults,
 )
-from unilab.algos.torch.hora.distill_config import (
+from unilab.training.hora_distill_config import (
     get_teacher_owner_spec as _get_teacher_owner_spec,
 )
-from unilab.algos.torch.hora.distill_config import (
+from unilab.training.hora_distill_config import (
     resolve_teacher_checkpoint_path as _resolve_teacher_checkpoint_path,
 )
-from unilab.algos.torch.hora.distill_config import (
+from unilab.training.hora_distill_config import (
     resolved_distill_runtime_cfg as _resolved_distill_runtime_cfg,
 )
-from unilab.algos.torch.hora.distill_config import (
+from unilab.training.hora_distill_config import (
     teacher_run_metadata as _teacher_run_metadata,
 )
-from unilab.algos.torch.hora.rsl_rl import HoraRslRlVecEnvWrapper as RslRlVecEnvWrapper
-from unilab.base.backend import materialize_scene_visual_override
-from unilab.training import (
-    BackendAdapter,
-    create_env,
-    ensure_registries,
-    format_hora_stage2_checkpoint_error,
-    get_log_root,
-    log_playback_plan,
-    resolve_hora_stage2_checkpoint_path,
-    setup_logger,
-    should_run_playback,
-)
-from unilab.training.experiment import get_device_info_dict
 
 
 def _write_distill_run_config(
@@ -69,8 +71,9 @@ def _write_distill_run_config(
     Returns:
         None. Writes `distill_run_config.json` into `log_dir`.
     """
-    payload = {
-        "run": {
+    write_run_config_snapshot(
+        log_dir,
+        run_metadata={
             "algo": "hora_distill",
             "task": str(OmegaConf.select(cfg, "training.task_name")),
             "sim_backend": str(OmegaConf.select(cfg, "training.sim_backend")),
@@ -78,11 +81,10 @@ def _write_distill_run_config(
             "hardware": get_device_info_dict(),
             "teacher": teacher_metadata,
         },
-        "config": OmegaConf.to_container(cfg, resolve=True),
-    }
-    with (log_dir / "distill_run_config.json").open("w", encoding="utf-8") as f:
-        json.dump(payload, f, indent=2, ensure_ascii=True)
-        f.write("\n")
+        full_cfg=cfg,
+        filename="distill_run_config.json",
+        trailing_newline=True,
+    )
 
 
 def _build_env_cfg_override(cfg: DictConfig) -> dict[str, Any]:
@@ -141,7 +143,9 @@ def play_hora_distill(cfg: DictConfig, device: str) -> str | None:
         )
         return None
 
-    cfg = cfg_with_checkpoint_runtime(cfg, checkpoint)
+    # uni_rl's cfg_with_checkpoint_runtime no longer composes teacher defaults;
+    # the caller owns that composition (issue #1480).
+    cfg = cfg_with_checkpoint_runtime(_apply_teacher_defaults(cfg), checkpoint)
     env = create_env(
         cfg,
         num_envs=int(cfg.training.play_env_num),
@@ -177,7 +181,7 @@ def play_hora_distill(cfg: DictConfig, device: str) -> str | None:
     return play_video_path
 
 
-@hydra.main(version_base="1.3", config_path="../conf/hora_distill", config_name="config")
+@hydra.main(version_base="1.3", config_path="../src/unilab/conf/hora_distill", config_name="config")
 def main(cfg: DictConfig) -> None:
     ensure_registries()
 
diff --git a/scripts/train_offpolicy.py b/scripts/train_offpolicy.py
deleted file mode 100644
index e490d64d8..000000000
--- a/scripts/train_offpolicy.py
+++ /dev/null
@@ -1,501 +0,0 @@
-"""Unified off-policy training entry for SAC and TD3."""
-
-from __future__ import annotations
-
-import datetime
-import os
-import sys
-from contextlib import nullcontext
-from pathlib import Path
-from typing import Any, cast
-
-import hydra
-from omegaconf import DictConfig, OmegaConf
-
-ROOT_DIR = Path(__file__).parent.parent
-sys.path.append(str(ROOT_DIR))
-
-from unilab.ipc.dp_launcher import (
-    UNILAB_DP_LOG_DIR,
-    DpRankSupervisor,
-    apply_dp_rank_config,
-    current_dp_rank,
-    resolve_collector_cpu_ids,
-    resolve_dp_rank_device,
-    resolve_dp_rendezvous_path,
-    resolve_dp_topology,
-    validate_dp_launchable,
-)
-from unilab.training import (
-    apply_configured_training_seed,
-    assert_offpolicy_task_choice_matches_algo,
-    create_env,
-    ensure_registries,
-    get_log_root,
-    log_playback_plan,
-    should_run_playback,
-)
-from unilab.training.experiment import ExperimentTracker
-from unilab.training.offpolicy import (
-    build_offpolicy_env_cfg_override as _build_offpolicy_env_cfg_override,
-)
-from unilab.training.offpolicy import (
-    build_play_actor,
-    default_device,
-    extract_play_obs,
-    extract_reset_obs,
-    load_play_actor,
-    resolve_play_obs_dim,
-    resolve_play_obs_dims,
-)
-from unilab.training.onnx_export import export_policy_onnx, verify_policy_onnx
-from unilab.training.run import (
-    resolve_offpolicy_checkpoint_path as resolve_checkpoint_path,
-)
-from unilab.training.sim2sim import policy_load_dim_guard, resolve_sim2sim_config
-from unilab.utils.nan_guard import NanGuardCfg
-
-
-def enable_faulthandler() -> None:
-    """Enable fatal-signal Python stack dumps unless explicitly disabled."""
-    if os.environ.get("UNILAB_FAULTHANDLER", "1").lower() in {"0", "false", "no", "off"}:
-        return
-    try:
-        import faulthandler
-
-        if not faulthandler.is_enabled():
-            faulthandler.enable(all_threads=True)
-    except Exception as exc:
-        print(f"[train_offpolicy] faulthandler unavailable: {exc}", file=sys.stderr)
-
-
-def build_failure_summary(exc: BaseException, run_summary: Any | None = None) -> dict[str, Any]:
-    summary = dict(run_summary) if isinstance(run_summary, dict) else {}
-    if summary.get("status") == "completed":
-        summary["status"] = "failed"
-    else:
-        summary.setdefault("status", "failed")
-    summary["error_type"] = type(exc).__name__
-    summary["error"] = str(exc)
-    return summary
-
-
-def build_run_dir_name(timestamp: str, sim_backend: str, *, world_size: int = 1) -> str:
-    gpu_suffix = f"_gpux{world_size}" if world_size > 1 else ""
-    return f"{timestamp}_{sim_backend}{gpu_suffix}"
-
-
-def build_offpolicy_env_cfg_override(algo_name: str, cfg: DictConfig) -> dict[str, Any] | None:
-    return _build_offpolicy_env_cfg_override(algo_name, cfg, root_dir=ROOT_DIR)
-
-
-def build_runner(algo_name: str, cfg: DictConfig, log_dir: str | None = None):
-    """Build algorithm runner from unified Hydra config."""
-    env_cfg_override = build_offpolicy_env_cfg_override(algo_name, cfg)
-    from unilab.algos.torch.offpolicy.thread_budget import (
-        apply_torch_thread_runtime,
-        resolve_torch_thread_runtime,
-    )
-
-    # Cold-path DP CPU partition: each rank's collector owns one contiguous
-    # CPU block (single rank keeps the legacy unset behavior). The ids only
-    # reach the collector env override — never the num_envs=1 probe envs,
-    # whose MuJoCo pool would size itself from len(cpu_ids).
-    # world_size comes from training.devices (rank 0 has no UNILAB_DP_* env;
-    # only spawned ranks carry it), rank from the env (0 for rank 0).
-    dp_devices = resolve_dp_topology(cfg.training.devices)
-    dp_world_size = len(dp_devices) if dp_devices is not None else 1
-    dp_rank = current_dp_rank()
-    from unilab.utils.device import get_default_device
-
-    rank_device = resolve_dp_rank_device(dp_devices, dp_rank) or get_default_device()
-    host_cpu_count = os.cpu_count() or 1
-    explicit_cpu_ids = getattr(cfg.training, "dp_collector_cpu_ids", None)
-    if explicit_cpu_ids is not None:
-        explicit_cpu_ids = cast(list, OmegaConf.to_container(explicit_cpu_ids, resolve=True))
-    collector_cpu_ids = resolve_collector_cpu_ids(
-        dp_world_size,
-        dp_rank,
-        host_cpu_count,
-        explicit=explicit_cpu_ids,
-    )
-
-    # Cold-path DP process-group assembly. world_size == 1 keeps dp_sync=None
-    # (bit-identical single-rank path); multi-rank learners attach the group's
-    # flat-gradient collective at their optimizer boundaries.
-    dp_sync = None
-    if dp_world_size > 1:
-        if dp_rank == 0 and log_dir is None:
-            raise ValueError(
-                "build_runner requires log_dir for multi-GPU data-parallel rank 0 "
-                "(it anchors the DP rendezvous FileStore)"
-            )
-        from unilab.ipc.dp_sync import DpParameterSync
-
-        dp_sync = DpParameterSync(
-            world_size=dp_world_size,
-            rank=dp_rank,
-            rendezvous_path=resolve_dp_rendezvous_path(cast(str, log_dir), rank=dp_rank),
-            device=rank_device,
-        )
-
-    torch_thread_runtime = resolve_torch_thread_runtime(
-        getattr(cfg.training, "torch_threads", None),
-        cpu_count=host_cpu_count // dp_world_size if dp_world_size > 1 else None,
-    )
-    apply_torch_thread_runtime(torch_thread_runtime, role="learner")
-
-    nan_guard_cfg = getattr(cfg.training, "nan_guard", None)
-    _nan_guard_cfg: NanGuardCfg | None = None
-    if nan_guard_cfg is not None and getattr(nan_guard_cfg, "enabled", False):
-        _nan_guard_cfg = NanGuardCfg(
-            enabled=True,
-            buffer_size=int(getattr(nan_guard_cfg, "buffer_size", 100)),
-            max_envs_to_dump=int(getattr(nan_guard_cfg, "max_envs_to_dump", 5)),
-            output_dir=getattr(nan_guard_cfg, "output_dir", None),
-        )
-
-    replay_prefetch_mode = getattr(cfg.training, "replay_prefetch_mode", "one_tick")
-    if replay_prefetch_mode != "one_tick":
-        raise ValueError(
-            f"Unsupported training.replay_prefetch_mode={replay_prefetch_mode!r}; "
-            "expected 'one_tick'"
-        )
-    from unilab.ipc.replay_pipelines.gpu_resident import require_offpolicy_replay_device
-
-    replay_device = require_offpolicy_replay_device(rank_device)
-    if algo_name == "sac":
-        from unilab.algos.torch.fast_sac.double_buffer import (
-            build_sac_double_buffer_runner,
-        )
-
-        return build_sac_double_buffer_runner(
-            cfg,
-            env_cfg_override=env_cfg_override,
-            replay_prefetch_mode=replay_prefetch_mode,
-            device=replay_device,
-            nan_guard_cfg=_nan_guard_cfg,
-            torch_thread_runtime=torch_thread_runtime,
-            collector_cpu_ids=collector_cpu_ids,
-            dp_sync=dp_sync,
-        )
-
-    if algo_name == "td3":
-        from unilab.algos.torch.fast_td3.double_buffer import (
-            build_td3_double_buffer_runner,
-        )
-
-        return build_td3_double_buffer_runner(
-            cfg,
-            env_cfg_override=env_cfg_override,
-            replay_prefetch_mode=replay_prefetch_mode,
-            device=replay_device,
-            nan_guard_cfg=_nan_guard_cfg,
-            torch_thread_runtime=torch_thread_runtime,
-            collector_cpu_ids=collector_cpu_ids,
-            dp_sync=dp_sync,
-        )
-
-    if algo_name == "flashsac":
-        from unilab.algos.torch.flash_sac.double_buffer import (
-            build_flashsac_double_buffer_runner,
-        )
-
-        return build_flashsac_double_buffer_runner(
-            cfg,
-            env_cfg_override=env_cfg_override,
-            replay_prefetch_mode=replay_prefetch_mode,
-            device=replay_device,
-            nan_guard_cfg=_nan_guard_cfg,
-            torch_thread_runtime=torch_thread_runtime,
-            collector_cpu_ids=collector_cpu_ids,
-            dp_sync=dp_sync,
-        )
-
-    raise ValueError(f"Unsupported algo: {algo_name}")
-
-
-def play_offpolicy(algo_name: str, cfg: DictConfig) -> str | None:
-    """Play pipeline for off-policy algorithms."""
-    import numpy as np
-    import torch
-
-    from unilab.algos.torch.offpolicy.worker import resolve_offpolicy_actor_priv_info
-
-    load_path, load_path_dir = resolve_checkpoint_path(
-        ROOT_DIR,
-        cfg.algo.algo_log_name,
-        cfg.training.task_name,
-        cfg.algo.load_run,
-    )
-    if not load_path or not os.path.exists(load_path):
-        print(f"Could not find checkpoint. load_path={load_path}")
-        return None
-
-    cfg = (
-        resolve_sim2sim_config(
-            load_path_dir,
-            cfg,
-            algo_name=algo_name,
-            strict=bool(getattr(cfg.training, "sim2sim_strict", True)),
-        )
-        or cfg
-    )
-
-    env_cfg_override = build_offpolicy_env_cfg_override(algo_name, cfg)
-
-    devices = resolve_dp_topology(cfg.training.devices)
-    device = default_device(torch, resolve_dp_rank_device(devices, current_dp_rank()))
-    print(f"Using device for play: {device}")
-
-    env = cast(
-        Any,
-        create_env(
-            cfg,
-            num_envs=cfg.training.play_env_num,
-            env_cfg_override=env_cfg_override,
-        ),
-    )
-    obs_dim, critic_obs_dim = resolve_play_obs_dims(env.obs_groups_spec)
-    action_shape = env.action_space.shape
-    if action_shape is None:
-        raise ValueError("env.action_space.shape must be defined")
-    action_dim = int(action_shape[0])
-    actor, normalizer, actor_algo_type, actor_kwargs = build_play_actor(
-        algo_name,
-        cfg,
-        obs_dim=obs_dim,
-        critic_obs_dim=critic_obs_dim,
-        action_dim=action_dim,
-        device=device,
-    )
-    print(f"Loading model: {load_path}")
-    checkpoint = torch.load(load_path, map_location=device, weights_only=True)
-    with policy_load_dim_guard(env_obs_dim=obs_dim, env_action_dim=action_dim, algo_name=algo_name):
-        load_play_actor(
-            algo_name,
-            actor,
-            normalizer,
-            checkpoint,
-        )
-
-    # Export actor to ONNX
-    if load_path_dir is not None and bool(getattr(cfg.training, "export_onnx", True)):
-        onnx_path = os.path.join(load_path_dir, "policy.onnx")
-        dummy_input = torch.randn(1, obs_dim, device=device)
-        dummy_priv_info = (
-            torch.zeros(
-                (1, int(actor_kwargs["priv_info_dim"])),
-                device=device,
-                dtype=dummy_input.dtype,
-            )
-            if actor_algo_type == "hora_sac"
-            else None
-        )
-        with torch.inference_mode():
-            if normalizer:
-                dummy_input = normalizer(dummy_input, update=False)
-            if algo_name in ("sac", "flashsac"):
-                export_module = actor.as_export_module()
-            else:
-                export_module = actor
-            export_inputs = (
-                (dummy_input, dummy_priv_info) if dummy_priv_info is not None else (dummy_input,)
-            )
-        input_names = ["obs", "priv_info"] if dummy_priv_info is not None else ["obs"]
-        export_policy_onnx(export_module, onnx_path, export_inputs, input_names=input_names)
-
-        # Verify ONNX output matches PyTorch
-        verify_input = torch.randn(1, obs_dim, device=device)
-        with torch.inference_mode():
-            onnx_feed = normalizer(verify_input, update=False) if normalizer else verify_input
-            verify_priv_info = (
-                torch.zeros((1, int(actor_kwargs["priv_info_dim"])), device=device)
-                if actor_algo_type == "hora_sac"
-                else None
-            )
-        verify_inputs = (
-            (onnx_feed, verify_priv_info) if verify_priv_info is not None else (onnx_feed,)
-        )
-        verify_policy_onnx(export_module, onnx_path, verify_inputs, input_names=input_names)
-    elif load_path_dir is not None:
-        print("Skipping ONNX export because training.export_onnx=false.")
-
-    if env.state is None:
-        env.init_state()
-
-    current_priv_info: np.ndarray | None = None
-
-    def _resolve_play_priv_info(obs_dict: dict[str, np.ndarray], info: dict | None) -> np.ndarray:
-        if actor_algo_type != "hora_sac":
-            raise ValueError("Privileged play info was requested for a non-HORA actor.")
-        from unilab.base.observations import split_obs_dict
-
-        actor_obs_np, critic_np = split_obs_dict(obs_dict)
-        priv_info = resolve_offpolicy_actor_priv_info(
-            algo_type=actor_algo_type,
-            obs_np=np.asarray(actor_obs_np, dtype=np.float32),
-            critic_np=np.asarray(critic_np, dtype=np.float32),
-            info=info,
-        )
-        if priv_info is None:
-            raise ValueError("HORA-SAC play step is missing privileged info.")
-        return priv_info
-
-    def _extract_reset_play_obs(reset_result) -> np.ndarray:
-        nonlocal current_priv_info
-        if not isinstance(reset_result, tuple) or len(reset_result) != 2:
-            raise ValueError(f"Unexpected env.reset return format: {type(reset_result)!r}")
-        obs_out, info_out = reset_result
-        if actor_algo_type == "hora_sac":
-            current_priv_info = _resolve_play_priv_info(obs_out, info_out)
-        return np.asarray(extract_play_obs(obs_out), dtype=np.float32)
-
-    def _policy_step(obs_np: np.ndarray) -> np.ndarray:
-        nonlocal current_priv_info
-        obs_torch = torch.from_numpy(obs_np).to(device)
-        if normalizer:
-            obs_torch = normalizer(obs_torch, update=False)
-        if actor_algo_type == "hora_sac":
-            if current_priv_info is None:
-                raise ValueError("HORA-SAC play step is missing privileged info.")
-            priv_info_torch = torch.from_numpy(current_priv_info).to(device)
-            actions_np = (
-                actor.explore(
-                    obs_torch,
-                    priv_info_torch,
-                    deterministic=True,
-                )
-                .cpu()
-                .numpy()
-            )
-        elif algo_name in ("sac", "flashsac"):
-            actions_np = actor.explore(obs_torch, deterministic=True).cpu().numpy()
-        else:
-            actions_np = actor(obs_torch).cpu().numpy()
-        state = env.step(actions_np)
-        if actor_algo_type == "hora_sac":
-            current_priv_info = _resolve_play_priv_info(state.obs, state.info)
-        return np.asarray(extract_play_obs(state.obs), dtype=np.float32)
-
-    with torch.inference_mode():
-        play_video_path = env.run_playback_mode(
-            play_render_mode=getattr(cfg.training, "play_render_mode", "auto"),
-            play_steps=getattr(cfg.training, "play_steps", None),
-            output_video=os.path.join(load_path_dir, "play_video.mp4") if load_path_dir else None,
-            initialize=lambda: _extract_reset_play_obs(
-                env.reset(np.arange(cfg.training.play_env_num, dtype=np.int32))
-            ),
-            step=_policy_step,
-            camera_kwargs={
-                "cam_distance": cfg.training.cam_distance,
-                "cam_elevation": cfg.training.cam_elevation,
-                "cam_azimuth": cfg.training.cam_azimuth,
-            },
-            on_plan=log_playback_plan,
-        )
-    if play_video_path is not None:
-        print(f"Saving video to {play_video_path} ...")
-    print("Done.")
-    return play_video_path
-
-
-@hydra.main(version_base="1.3", config_path="../conf/offpolicy", config_name="config")
-def main(cfg: DictConfig) -> None:
-    enable_faulthandler()
-    ensure_registries()
-
-    devices = resolve_dp_topology(cfg.training.devices)
-    rank = current_dp_rank()
-    rank_device = apply_dp_rank_config(cfg, devices, rank)
-
-    seed_info = apply_configured_training_seed(cfg, torch_runtime=True, cuda=True)
-    algo_name = cfg.algo.algo
-    task_name = cfg.training.task_name
-    assert_offpolicy_task_choice_matches_algo(cfg, algo_name=algo_name)
-
-    if cfg.training.log_dir is None:
-        timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
-        run_dir_name = build_run_dir_name(
-            timestamp,
-            str(cfg.training.sim_backend),
-            world_size=len(devices) if devices is not None else 1,
-        )
-        log_dir = str(get_log_root(ROOT_DIR, cfg) / task_name / run_dir_name)
-    else:
-        log_dir = cfg.training.log_dir
-    if rank > 0:
-        # Spawned ranks reuse the canonical run directory but never create
-        # logging backends, checkpoints, summaries, or traces there.
-        log_dir = os.environ[UNILAB_DP_LOG_DIR]
-
-    supervisor: DpRankSupervisor | None = None
-    if devices is not None and rank == 0 and len(devices) > 1:
-        validate_dp_launchable(devices)
-        supervisor = DpRankSupervisor(devices, log_dir)
-
-    import torch
-
-    tracker = None
-    if not cfg.training.play_only and rank == 0:
-        tracker = ExperimentTracker(
-            root_dir=ROOT_DIR,
-            log_dir=log_dir,
-            algo_name=algo_name,
-            task_name=task_name,
-            sim_backend=cfg.training.sim_backend,
-            training_cfg=cfg.training,
-            full_cfg=cfg,
-            device=default_device(torch, rank_device),
-            seed_info=seed_info,
-        )
-        tracker.start()
-
-    try:
-        with supervisor if supervisor is not None else nullcontext():
-            if not cfg.training.play_only:
-                runner = None
-                try:
-                    runner = build_runner(algo_name, cfg, log_dir=log_dir)
-                    runner.learn(
-                        max_iterations=cfg.algo.max_iterations,
-                        save_interval=cfg.algo.save_interval,
-                        log_dir=log_dir,
-                        logger_type=cfg.training.logger,
-                    )
-                    run_summary = getattr(runner, "last_run_summary", None)
-                    if isinstance(run_summary, dict) and run_summary.get("status") not in (
-                        None,
-                        "completed",
-                    ):
-                        raise RuntimeError(
-                            f"Off-policy training ended with status={run_summary.get('status')!r}"
-                        )
-                    if tracker is not None:
-                        tracker.update_summary(run_summary)
-                except BaseException as exc:
-                    if tracker is not None:
-                        tracker.update_summary(
-                            build_failure_summary(exc, getattr(runner, "last_run_summary", None))
-                        )
-                    raise
-                finally:
-                    if runner is not None:
-                        runner.close()
-
-            if rank == 0 and should_run_playback(
-                play_only=cfg.training.play_only,
-                no_play=cfg.training.no_play,
-                play_render_mode=getattr(cfg.training, "play_render_mode", "auto"),
-            ):
-                print("@" * 50)
-                play_video_path = play_offpolicy(algo_name, cfg)
-                if tracker is not None:
-                    tracker.log_video(play_video_path)
-    finally:
-        if tracker is not None:
-            tracker.finish()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/visualize_task_env.py b/scripts/visualize_task_env.py
index 60bfd2124..12a98a533 100644
--- a/scripts/visualize_task_env.py
+++ b/scripts/visualize_task_env.py
@@ -284,13 +284,14 @@ def _run_mujoco(env, num_envs: int) -> None:
 
 def _build_env_cfg_override(task_name: str) -> dict[str, Any]:
     """Build the env_cfg_override dict from CLI args alone — no Hydra."""
-    if task_name not in registry._envs:
+    if not registry.contains(task_name):
         raise SystemExit(
-            f"Task '{task_name}' is not registered. Available: {sorted(registry._envs.keys())}"
+            f"Task '{task_name}' is not registered. "
+            f"Available: {sorted(registry.list_registered_envs())}"
         )
-    env_cfg_cls = registry._envs[task_name].env_cfg_cls
+    env_cfg = registry.materialize_env_config(task_name)
     override: dict[str, Any] = {}
-    reward_stub = _build_reward_stub(env_cfg_cls)
+    reward_stub = _build_reward_stub(type(env_cfg))
     if reward_stub is not None:
         override["reward_config"] = reward_stub
     return override
diff --git a/src/unilab/__init__.py b/src/unilab/__init__.py
index 2c7a0a993..f184e1d79 100644
--- a/src/unilab/__init__.py
+++ b/src/unilab/__init__.py
@@ -1,7 +1,11 @@
 """UniLab package initialization."""
 
+from importlib.metadata import PackageNotFoundError, version
 from pathlib import Path
 
-__version__ = "0.0.0"
+try:
+    __version__ = version("unilab")
+except PackageNotFoundError:
+    __version__ = "0.0.0"
 
 ROOT_PATH = Path(__file__).resolve().parent
diff --git a/src/unilab/algos/__init__.py b/src/unilab/algos/__init__.py
deleted file mode 100644
index ffccbf435..000000000
--- a/src/unilab/algos/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Algorithm packages."""
diff --git a/src/unilab/algos/torch/appo/learner.py b/src/unilab/algos/torch/appo/learner.py
deleted file mode 100644
index 966ad2aaf..000000000
--- a/src/unilab/algos/torch/appo/learner.py
+++ /dev/null
@@ -1,585 +0,0 @@
-"""Asynchronous PPO (APPO) Learner.
-
-Based on IMPACT (Luo et al. 2020): Importance Weighted Asynchronous
-Architectures with Clipped Target Networks.
-
-Key differences from standard PPO:
-- V-trace importance sampling correction for off-policy data
-- Target network with soft update for stable IS ratio computation
-- PPO clipping applied over IS-corrected ratios
-"""
-
-import copy
-import math
-from collections.abc import Iterable
-from typing import Any
-
-import numpy as np
-import torch
-import torch.nn as nn
-from rsl_rl.models import MLPModel
-from rsl_rl.utils import resolve_optimizer
-from tensordict import TensorDict
-
-from unilab.algos.torch.common.compile import get_torch_compile_for_cuda
-
-_LOG_2_PI = math.log(2.0 * math.pi)
-_NORMAL_ENTROPY_OFFSET = 0.5 * (1.0 + _LOG_2_PI)
-
-
-def _distribution_std(distribution: Any, mean: torch.Tensor) -> torch.Tensor:
-    """Return broadcast std tensor for rsl_rl GaussianDistribution."""
-    if distribution is None:
-        raise RuntimeError("APPO actor must expose a stochastic distribution")
-    if distribution.std_type == "scalar":
-        return distribution.std_param.expand_as(mean)
-    return torch.exp(distribution.log_std_param).expand_as(mean)
-
-
-def _sample_tensor_for_metric(tensor: torch.Tensor, max_items: int = 8192) -> torch.Tensor:
-    """Return a deterministic bounded sample for scalar metrics."""
-    flat = tensor.detach().reshape(-1)
-    if flat.numel() <= max_items:
-        return flat
-    stride = max(flat.numel() // max_items, 1)
-    return flat[::stride][:max_items]
-
-
-def _grad_norm(parameters) -> float:
-    """Compute gradient norm without mutating gradients or clipping."""
-    norms = [p.grad.detach().norm(2) for p in parameters if getattr(p, "grad", None) is not None]
-    if not norms:
-        return 0.0
-    return float(torch.norm(torch.stack(norms), 2).item())
-
-
-def _unique_parameters(parameters: Iterable[torch.nn.Parameter]) -> list[torch.nn.Parameter]:
-    """Return parameters without duplicate object ids, preserving first use order."""
-    unique: list[torch.nn.Parameter] = []
-    seen: set[int] = set()
-    for param in parameters:
-        ident = id(param)
-        if ident in seen:
-            continue
-        seen.add(ident)
-        unique.append(param)
-    return unique
-
-
-def vtrace_advantages(
-    behavior_log_probs,  # [T, N]  log π_b(a|s) from worker
-    target_log_probs,  # [T, N]  log π_target(a|s) from target network
-    rewards,  # [T, N]
-    values,  # [T, N]
-    bootstrap_values,  # [N]     V(s_{T})
-    dones,  # [T, N]  float
-    gamma=0.99,
-    clip_rho=1.0,
-    clip_c=1.0,
-):
-    """Compute V-trace targets and advantages.
-
-    V-trace (Espeholt et al., 2018) corrects for the off-policy nature
-    of asynchronous data collection by using importance sampling ratios
-    clipped at ρ̄ (rho_bar) and c̄ (c_bar).
-
-    Returns:
-        vs:         V-trace value targets  [T, N]
-        advantages: Policy gradient advantages  [T, N]
-    """
-    T, N = rewards.shape
-    device = values.device
-
-    with torch.no_grad():
-        # IS ratios: ρ_t = π_target(a_t|s_t) / π_behavior(a_t|s_t)
-        log_rhos = target_log_probs - behavior_log_probs
-        rhos = torch.exp(log_rhos)
-        clipped_rhos = torch.clamp(rhos, max=clip_rho)
-        cs = torch.clamp(rhos, max=clip_c)
-
-        non_terminal = 1.0 - dones
-
-        # Vectorized next_values: shift values by 1, fill last step with bootstrap
-        next_values = torch.cat([values[1:], bootstrap_values.unsqueeze(0)], dim=0)
-
-        # Temporal difference errors
-        deltas = clipped_rhos * (rewards + gamma * next_values * non_terminal - values)
-
-        # Backward accumulation of V-trace corrections — run on CPU numpy to avoid
-        # T sequential GPU kernel launches (one-time transfer cost is cheaper).
-        deltas_np = deltas.cpu().numpy()
-        non_terminal_np = non_terminal.cpu().numpy()
-        cs_np = cs.cpu().numpy()
-        values_np = values.cpu().numpy()
-
-        vs_np = np.empty_like(values_np)
-        vs_minus_v = np.zeros(N, dtype=np.float32)
-        for t in range(T - 1, -1, -1):
-            vs_minus_v = deltas_np[t] + gamma * non_terminal_np[t] * cs_np[t] * vs_minus_v
-            vs_np[t] = values_np[t] + vs_minus_v
-
-        vs = torch.from_numpy(vs_np).to(device)
-
-        # Vectorized policy gradient advantages
-        next_vs = torch.cat([vs[1:], bootstrap_values.unsqueeze(0)], dim=0)
-        advantages = clipped_rhos * (rewards + gamma * next_vs * non_terminal - values)
-
-    return vs, advantages
-
-
-class APPOLearner:
-    """Asynchronous PPO Learner.
-
-    PPO update with V-trace off-policy correction and target network,
-    decoupled from rollout collection.
-
-    Key features:
-    - V-trace importance sampling for off-policy advantage estimation
-    - Target network with soft update (tau) for stable IS computation
-    - Observation normalization updated centrally, synced to workers
-    - Time-out (truncation) bootstrap correction
-    - Adaptive learning rate via KL-divergence target
-    """
-
-    def __init__(
-        self,
-        actor: MLPModel,
-        critic: MLPModel,
-        num_learning_epochs: int = 5,
-        num_mini_batches: int = 4,
-        clip_param: float = 0.2,
-        gamma: float = 0.99,
-        lam: float = 0.95,
-        value_loss_coef: float = 1.0,
-        entropy_coef: float = 0.01,
-        learning_rate: float = 1e-3,
-        max_grad_norm: float = 1.0,
-        use_clipped_value_loss: bool = True,
-        schedule: str = "fixed",
-        desired_kl: float = 0.01,
-        adaptive_kl_factor: float = 1.2,
-        adaptive_lr_factor: float = 1.1,
-        device: str = "cpu",
-        optimizer: str = "adam",
-        # APPO-specific parameters
-        tau: float = 1.0,
-        target_update_freq: int = 1,
-        vtrace_clip_rho: float = 1.0,
-        vtrace_clip_c: float = 1.0,
-        enable_compile: bool = False,
-        **kwargs,
-    ):
-        self.device = device
-        self._device_type = torch.device(device).type
-        self.actor = actor.to(self.device)
-        self.critic = critic.to(self.device)
-
-        # Target actor for V-trace IS computation
-        self.target_actor = copy.deepcopy(self.actor).to(self.device)
-        self.target_actor.eval()
-        for p in self.target_actor.parameters():
-            p.requires_grad = False
-        actor_params = _unique_parameters(self.actor.parameters())
-        critic_params = _unique_parameters(self.critic.parameters())
-        self._combined_params = _unique_parameters([*actor_params, *critic_params])
-
-        # PPO parameters
-        self.clip_param = clip_param
-        self.num_learning_epochs = num_learning_epochs
-        self.num_mini_batches = num_mini_batches
-        self.value_loss_coef = value_loss_coef
-        self.entropy_coef = entropy_coef
-        self.gamma = gamma
-        self.lam = lam
-        self.max_grad_norm = max_grad_norm
-        self.use_clipped_value_loss = use_clipped_value_loss
-        self.desired_kl = desired_kl
-        self.schedule = schedule
-        self.learning_rate = learning_rate
-        if adaptive_kl_factor <= 1.0:
-            raise ValueError(f"adaptive_kl_factor must be > 1.0, got {adaptive_kl_factor}")
-        if adaptive_lr_factor <= 1.0:
-            raise ValueError(f"adaptive_lr_factor must be > 1.0, got {adaptive_lr_factor}")
-        self.adaptive_kl_factor = adaptive_kl_factor
-        self.adaptive_lr_factor = adaptive_lr_factor
-
-        # APPO-specific parameters
-        self.tau = tau
-        self.target_update_freq = target_update_freq
-        self.vtrace_clip_rho = vtrace_clip_rho
-        self.vtrace_clip_c = vtrace_clip_c
-        self._update_counter = 0
-        self.last_update_metrics: dict[str, float] = {}
-        self.enable_compile = (
-            bool(enable_compile) and get_torch_compile_for_cuda(device, warn=True) is not None
-        )
-
-        # Optimizer
-        self.optimizer = resolve_optimizer(optimizer)(  # pyright: ignore[reportCallIssue]
-            self._combined_params, lr=learning_rate
-        )
-        self._minibatch_loss_fn = self._minibatch_loss_tensors
-        if self.enable_compile:
-            self._compile_training_methods()
-
-    def _compile_training_methods(self) -> None:
-        compile_fn = get_torch_compile_for_cuda(self._device_type, warn=True)
-        if compile_fn is None:
-            return
-
-        self._minibatch_loss_fn = compile_fn(
-            self._minibatch_loss_tensors,
-            mode="reduce-overhead",
-            fullgraph=False,
-        )
-
-    def _actor_mean_std(self, obs: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
-        distribution: Any = self.actor.distribution
-        mean = self.actor.mlp(self.actor.obs_normalizer(obs))
-        return mean, _distribution_std(distribution, mean)
-
-    def _critic_value(self, obs: torch.Tensor) -> torch.Tensor:
-        return self.critic.mlp(self.critic.obs_normalizer(obs)).squeeze(-1)
-
-    def _minibatch_policy_value(
-        self,
-        obs_mini: torch.Tensor,
-        critic_obs_mini: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        mu, sigma = self._actor_mean_std(obs_mini)
-        value = self._critic_value(critic_obs_mini)
-        return mu, sigma, value
-
-    @staticmethod
-    def _gaussian_log_prob(
-        actions: torch.Tensor, mean: torch.Tensor, std: torch.Tensor
-    ) -> torch.Tensor:
-        normalized = (actions - mean) / std
-        return (-0.5 * (normalized.pow(2) + 2.0 * torch.log(std) + _LOG_2_PI)).sum(dim=-1)
-
-    @staticmethod
-    def _gaussian_entropy(std: torch.Tensor) -> torch.Tensor:
-        return (torch.log(std) + _NORMAL_ENTROPY_OFFSET).sum(dim=-1)
-
-    def _minibatch_loss_tensors(
-        self,
-        obs_mini: torch.Tensor,
-        critic_obs_mini: torch.Tensor,
-        actions_mini: torch.Tensor,
-        target_values_mini: torch.Tensor,
-        advantages_mini: torch.Tensor,
-        behavior_logp_mini: torch.Tensor,
-        old_values_mini: torch.Tensor,
-        target_logp_mini: torch.Tensor,
-        old_mu_mini: torch.Tensor,
-        old_sigma_mini: torch.Tensor,
-    ) -> tuple[
-        torch.Tensor,
-        torch.Tensor,
-        torch.Tensor,
-        torch.Tensor,
-        torch.Tensor,
-        torch.Tensor,
-        torch.Tensor,
-    ]:
-        mu, sigma, value = self._minibatch_policy_value(obs_mini, critic_obs_mini)
-        current_log_prob = self._gaussian_log_prob(actions_mini, mu, sigma)
-        entropy = self._gaussian_entropy(sigma).mean()
-
-        with torch.no_grad():
-            clipped_rho = torch.clamp(torch.exp(behavior_logp_mini - target_logp_mini), max=1.0)
-        ratio = clipped_rho * torch.exp(current_log_prob - behavior_logp_mini)
-
-        surrogate = -advantages_mini * ratio
-        surrogate_clipped = -advantages_mini * torch.clamp(
-            ratio, 1.0 - self.clip_param, 1.0 + self.clip_param
-        )
-        surrogate_loss = torch.max(surrogate, surrogate_clipped).mean()
-
-        if self.use_clipped_value_loss:
-            value_clipped = old_values_mini + (value - old_values_mini).clamp(
-                -self.clip_param, self.clip_param
-            )
-            value_losses = (value - target_values_mini).pow(2)
-            value_losses_clipped = (value_clipped - target_values_mini).pow(2)
-            value_loss = torch.max(value_losses, value_losses_clipped).mean()
-        else:
-            value_loss = (value - target_values_mini).pow(2).mean()
-
-        kl = torch.sum(
-            torch.log(sigma / old_sigma_mini + 1e-5)
-            + (old_sigma_mini.pow(2) + (old_mu_mini - mu).pow(2)) / (2.0 * sigma.pow(2))
-            - 0.5,
-            dim=-1,
-        )
-        kl_mean = torch.mean(kl)
-
-        loss = surrogate_loss + self.value_loss_coef * value_loss - self.entropy_coef * entropy
-        return loss, surrogate_loss, value_loss, entropy, kl_mean, current_log_prob, ratio
-
-    def train_mode(self):
-        """Set actor/critic to training mode (enables EmpiricalNormalization.update)."""
-        self.actor.train()
-        self.critic.train()
-
-    def update_target_network(self):
-        """Soft update target actor: target = tau * current + (1 - tau) * target."""
-        for target_param, param in zip(self.target_actor.parameters(), self.actor.parameters()):
-            target_param.data.copy_(self.tau * param.data + (1.0 - self.tau) * target_param.data)
-        # Also copy buffers (e.g. normalization stats)
-        self.sync_target_actor_buffers()
-
-    def _update_adaptive_learning_rate(self, kl_mean: float) -> None:
-        """Update optimizer LR from KL according to the configured adaptive schedule."""
-        if self.desired_kl is None or self.schedule != "adaptive":
-            return
-
-        if kl_mean > self.desired_kl * self.adaptive_kl_factor:
-            self.learning_rate = max(1e-5, self.learning_rate / self.adaptive_lr_factor)
-        elif 0.0 < kl_mean < self.desired_kl / self.adaptive_kl_factor:
-            self.learning_rate = min(1e-2, self.learning_rate * self.adaptive_lr_factor)
-
-        for param_group in self.optimizer.param_groups:
-            param_group["lr"] = self.learning_rate
-
-    def sync_target_actor_buffers(self):
-        """Copy actor buffers such as observation-normalization stats to target actor."""
-        for target_buf, buf in zip(self.target_actor.buffers(), self.actor.buffers()):
-            target_buf.data.copy_(buf.data)
-
-    def get_state_dict(self):
-        """Return full learner state for checkpointing."""
-        return {
-            "actor": self.actor.state_dict(),
-            "critic": self.critic.state_dict(),
-            "optimizer": self.optimizer.state_dict(),
-        }
-
-    def process_batch(self, batch_dict):
-        """Compute V-trace targets on GPU.
-
-        Uses target network log-probs and behavior log-probs to compute
-        importance-sampling-corrected value targets and advantages.
-        """
-        obs = batch_dict["observations"]  # [T, N, D]
-        critic_base = batch_dict.get("critic", None)  # [T, N, C] or None
-        rewards = batch_dict["rewards"]  # [T, N]
-        dones = batch_dict["dones"].float()  # [T, N]
-        last_obs = batch_dict["last_obs"]  # [N, D]
-        last_critic = batch_dict.get("last_critic", None)  # [N, C] or None
-        behavior_log_probs = batch_dict["actions_log_prob"]  # [T, N]
-        actions = batch_dict["actions"]  # [T, N, A]
-
-        T, N = obs.shape[:2]
-        obs_flat = obs.flatten(0, 1)  # [T*N, D]
-
-        # Actor: obs only
-        obs_td = TensorDict({"policy": obs_flat}, batch_size=obs_flat.shape[0], device=self.device)
-        last_obs_td = TensorDict({"policy": last_obs}, batch_size=N, device=self.device)
-
-        # Critic: explicit critic obs when available, else actor obs
-        if critic_base is None:
-            critic_base = obs
-        if last_critic is None:
-            last_critic = last_obs
-        critic_obs = critic_base
-        critic_last_obs = last_critic
-        critic_obs_flat = critic_obs.flatten(0, 1)  # [T*N, D+P]
-        critic_obs_td = TensorDict(
-            {"policy": critic_obs_flat}, batch_size=critic_obs_flat.shape[0], device=self.device
-        )
-        critic_last_obs_td = TensorDict(
-            {"policy": critic_last_obs}, batch_size=N, device=self.device
-        )
-
-        # Update Observation Normalization
-        if hasattr(self.actor, "update_normalization"):
-            self.actor.update_normalization(obs_td)
-            self.actor.update_normalization(last_obs_td)
-            self.sync_target_actor_buffers()
-        if hasattr(self.critic, "update_normalization"):
-            self.critic.update_normalization(critic_obs_td)
-            self.critic.update_normalization(critic_last_obs_td)
-
-        # Cache critic_obs_flat for update()
-        batch_dict["_critic_obs_flat"] = critic_obs_flat
-
-        with torch.inference_mode():
-            # Compute values with current critic
-            values_flat = self.critic(critic_obs_td)  # [T*N, 1]
-            last_values = self.critic(critic_last_obs_td).squeeze(-1)  # [N]
-        values = values_flat.view(T, N, -1).squeeze(-1)  # [T, N]
-
-        # Compute target policy log-probs for V-trace IS ratios.
-        # Also cache mu/sigma here so update() doesn't need a second forward pass.
-        actions_flat = actions.flatten(0, 1)  # [T*N, A]
-        with torch.inference_mode():
-            self.target_actor(obs_td, stochastic_output=True)
-            target_log_probs_flat = self.target_actor.get_output_log_prob(actions_flat)
-            batch_dict["_old_mu"] = self.target_actor.output_mean.clone()
-            batch_dict["_old_sigma"] = self.target_actor.output_std.clone()
-        target_log_probs = target_log_probs_flat.view(T, N)
-        with torch.inference_mode():
-            rhos = torch.exp(target_log_probs - behavior_log_probs)
-            rho_sample = _sample_tensor_for_metric(rhos)
-            batch_dict["_appo_process_metrics"] = {
-                "vtrace/rho_clip_fraction": float(
-                    (rhos > float(self.vtrace_clip_rho)).float().mean().item()
-                ),
-                "vtrace/rho_raw_p99": float(torch.quantile(rho_sample, 0.99).item()),
-            }
-
-        # V-trace targets and advantages
-        vs, advantages = vtrace_advantages(
-            behavior_log_probs=behavior_log_probs,
-            target_log_probs=target_log_probs,
-            rewards=rewards,
-            values=values,
-            bootstrap_values=last_values,
-            dones=dones,
-            gamma=self.gamma,
-            clip_rho=self.vtrace_clip_rho,
-            clip_c=self.vtrace_clip_c,
-        )
-
-        batch_dict["values"] = values
-        batch_dict["advantages"] = advantages
-        batch_dict["returns"] = vs  # V-trace targets as returns
-        batch_dict["target_log_probs"] = target_log_probs
-
-        return batch_dict
-
-    def update(self, batch_dict):
-        """Perform the original main APPO update with additional detached metrics."""
-        obs_flat = batch_dict["observations"].flatten(0, 1)
-        actions_flat = batch_dict["actions"].flatten(0, 1)
-        returns_flat = batch_dict["returns"].flatten(0, 1)
-        advantages_flat = batch_dict["advantages"].flatten(0, 1)
-        behavior_log_probs_flat = batch_dict["actions_log_prob"].flatten(0, 1)
-        old_values_flat = batch_dict["values"].flatten(0, 1)
-        target_log_probs_flat = batch_dict["target_log_probs"].flatten(0, 1)
-        advantages_flat = (advantages_flat - advantages_flat.mean()) / (
-            advantages_flat.std() + 1e-8
-        )
-
-        critic_obs_flat = batch_dict.get("_critic_obs_flat")
-        if critic_obs_flat is None:
-            critic_base_flat = batch_dict.get("critic")
-            critic_obs_flat = (
-                obs_flat if critic_base_flat is None else critic_base_flat.flatten(0, 1)
-            )
-
-        with torch.inference_mode():
-            old_mu_flat = batch_dict["_old_mu"]
-            old_sigma_flat = batch_dict["_old_sigma"]
-
-        batch_size = obs_flat.shape[0]
-        mini_batch_size = batch_size // self.num_mini_batches
-
-        mean_surrogate_loss = 0.0
-        mean_value_loss = 0.0
-        mean_entropy = 0.0
-        mean_kl = 0.0
-        mean_clip_fraction = 0.0
-        mean_behavior_to_current_kl = 0.0
-        mean_target_to_current_kl = 0.0
-        mean_global_grad_norm = 0.0
-        num_updates = 0
-
-        for epoch in range(self.num_learning_epochs):
-            indices = torch.randperm(batch_size, device=self.device)
-
-            for i in range(self.num_mini_batches):
-                start = i * mini_batch_size
-                end = (i + 1) * mini_batch_size
-                batch_idx = indices[start:end]
-
-                obs_mini = obs_flat[batch_idx]
-                critic_obs_mini = critic_obs_flat[batch_idx]
-                actions_mini = actions_flat[batch_idx]
-                target_values_mini = returns_flat[batch_idx]
-                advantages_mini = advantages_flat[batch_idx]
-                behavior_logp_mini = behavior_log_probs_flat[batch_idx]
-                old_values_mini = old_values_flat[batch_idx]
-                target_logp_mini = target_log_probs_flat[batch_idx]
-                old_mu_mini = old_mu_flat[batch_idx]
-                old_sigma_mini = old_sigma_flat[batch_idx]
-
-                (
-                    loss,
-                    surrogate_loss,
-                    value_loss,
-                    entropy,
-                    kl_mean,
-                    current_log_prob,
-                    ratio,
-                ) = self._minibatch_loss_fn(
-                    obs_mini,
-                    critic_obs_mini,
-                    actions_mini,
-                    target_values_mini,
-                    advantages_mini,
-                    behavior_logp_mini,
-                    old_values_mini,
-                    target_logp_mini,
-                    old_mu_mini,
-                    old_sigma_mini,
-                )
-
-                kl_mean_value: float | None = None
-                if self.desired_kl is not None and self.schedule == "adaptive":
-                    with torch.inference_mode():
-                        kl_mean_value = float(kl_mean.item())
-
-                        self._update_adaptive_learning_rate(kl_mean_value)
-
-                        mean_kl += kl_mean_value
-                        mean_target_to_current_kl += kl_mean_value
-
-                self.optimizer.zero_grad(set_to_none=True)
-                loss.backward()
-                global_grad_norm = _grad_norm(self._combined_params)
-                nn.utils.clip_grad_norm_(self._combined_params, self.max_grad_norm)
-                self.optimizer.step()
-
-                with torch.inference_mode():
-                    clip_fraction = (torch.abs(ratio - 1.0) > self.clip_param).float().mean().item()
-                    behavior_to_current_kl = (behavior_logp_mini - current_log_prob).mean().item()
-                    if kl_mean_value is None:
-                        kl_mean_value = float(kl_mean.item())
-                        mean_target_to_current_kl += kl_mean_value
-
-                mean_value_loss += value_loss.item()
-                mean_surrogate_loss += surrogate_loss.item()
-                mean_entropy += entropy.item()
-                mean_clip_fraction += float(clip_fraction)
-                mean_behavior_to_current_kl += float(behavior_to_current_kl)
-                mean_global_grad_norm += global_grad_norm
-                num_updates += 1
-
-        self._update_counter += 1
-        if self._update_counter % self.target_update_freq == 0:
-            self.update_target_network()
-
-        num_updates = max(num_updates, 1)
-        final_lr = float(self.learning_rate)
-        self.learning_rate = final_lr
-
-        metrics = {
-            "surrogate_loss": mean_surrogate_loss / num_updates,
-            "value_loss": mean_value_loss / num_updates,
-            "entropy": mean_entropy / num_updates,
-            "kl": mean_kl / num_updates if self.schedule == "adaptive" else 0.0,
-            "loss/policy_loss": mean_surrogate_loss / num_updates,
-            "loss/value_loss": mean_value_loss / num_updates,
-            "policy/entropy": mean_entropy / num_updates,
-            "ppo/approx_kl": mean_target_to_current_kl / num_updates,
-            "ppo/clip_fraction": mean_clip_fraction / num_updates,
-            "grad/global_norm": mean_global_grad_norm / num_updates,
-            "optim/learning_rate": final_lr,
-            "policy_kl/behavior_to_current_kl": mean_behavior_to_current_kl / num_updates,
-            "appo/updates_executed": float(num_updates),
-        }
-        metrics.update(batch_dict.get("_appo_process_metrics", {}))
-        self.last_update_metrics = metrics
-        return metrics
diff --git a/src/unilab/algos/torch/appo/runner.py b/src/unilab/algos/torch/appo/runner.py
deleted file mode 100644
index 22800aaff..000000000
--- a/src/unilab/algos/torch/appo/runner.py
+++ /dev/null
@@ -1,491 +0,0 @@
-"""APPO Runner — Asynchronous PPO with native multiprocessing.
-
-Pipeline:
-  1. Collector subprocess publishes rollout payloads → RolloutRingBuffer
-  2. Learner reads rollouts, computes V-trace corrected updates
-  3. Weights synced back to collector via SharedWeightSync
-"""
-
-import multiprocessing as mp
-import os
-import sys
-import time
-from collections import deque
-from copy import deepcopy
-from typing import Any
-
-import torch
-from rsl_rl.utils import resolve_callable
-
-from unilab.algos.torch.appo.learner import APPOLearner
-from unilab.algos.torch.appo.staging import RolloutStagingPool
-from unilab.algos.torch.appo.worker import appo_collector_fn
-from unilab.ipc import AsyncRunner, RolloutRingBuffer, SharedWeightSync
-from unilab.logging import OffPolicyLogger
-from unilab.training.seed import apply_training_seed, derive_worker_seed
-from unilab.utils.nan_guard import NanGuardCfg
-
-
-def _optimizer_lr_from_state(optimizer: torch.optim.Optimizer) -> float:
-    if not optimizer.param_groups:
-        return 0.0
-    return float(optimizer.param_groups[0].get("lr", 0.0))
-
-
-def _sync_resume_target_actor(learner: APPOLearner) -> None:
-    """After resume, target actor must exactly match the restored actor."""
-    target_actor = getattr(learner, "target_actor", None)
-    if target_actor is None:
-        learner.update_target_network()
-        return
-    target_actor.load_state_dict(learner.actor.state_dict())
-    target_actor.eval()
-    for param in target_actor.parameters():
-        param.requires_grad = False
-
-
-class APPORunner(AsyncRunner):
-    """APPO async runner using shared memory."""
-
-    def __init__(
-        self,
-        env_name: str,
-        env_cfg_overrides: dict,
-        rl_cfg: dict,
-        device: str | None = None,
-        collector_device: str | None = None,
-        sim_backend: str = "mujoco",
-        num_envs: int = 1024,
-        steps_per_env: int = 24,
-        replay_queue_size: int = 3,
-        seed: int | None = None,
-        resume_path: str | None = None,
-        nan_guard_cfg: NanGuardCfg | None = None,
-    ):
-        super().__init__(
-            env_name=env_name,
-            env_cfg_overrides=env_cfg_overrides,
-            rl_cfg=rl_cfg,
-            device=device,
-            collector_device=collector_device,
-            sim_backend=sim_backend,
-            num_envs=num_envs,
-        )
-
-        self.steps_per_env = steps_per_env
-        self.replay_queue_size = replay_queue_size
-        self.staging_pool_size = replay_queue_size
-        self.seed = seed
-        self.resume_path = resume_path
-        self.nan_guard_cfg = nan_guard_cfg
-        if self.staging_pool_size < 1:
-            raise ValueError("APPO staging pool size must be >= 1")
-
-        # Resolve dims
-        self._resolve_dims()
-
-    def _get_default_device(self) -> str:
-        if torch.cuda.is_available():
-            return "cuda"
-        if torch.backends.mps.is_available():
-            return "mps"
-        return "cpu"
-
-    def _resolve_dims(self):
-        self.obs_dim, self.action_dim = self._detect_dims()
-
-        # Update rl_cfg so internal RSL-RL networks get correct observation dimension
-        if "obs_groups" not in self.rl_cfg:
-            self.rl_cfg["obs_groups"] = {
-                "actor": {"policy": self.obs_dim},
-                "critic": {"policy": self.critic_input_dim},
-            }
-        else:
-            actor_group = self.rl_cfg["obs_groups"].get(
-                "actor", self.rl_cfg["obs_groups"].get("policy", {})
-            )
-            if isinstance(actor_group, dict) and "policy" in actor_group:
-                actor_group["policy"] = self.obs_dim
-            critic_group = self.rl_cfg["obs_groups"].get("critic")
-            if critic_group is None:
-                self.rl_cfg["obs_groups"]["critic"] = {"policy": self.critic_input_dim}
-            elif isinstance(critic_group, dict) and "policy" in critic_group:
-                critic_group["policy"] = self.critic_input_dim
-
-    def _detect_dims(self):
-        """Create a tiny env to read obs/action dims, then close it."""
-        from unilab.base import registry
-        from unilab.base.observations import get_critic_base_dim, get_obs_dims
-        from unilab.base.registry import ensure_registries
-
-        ensure_registries()
-
-        apply_training_seed(self.seed, torch_runtime=True, cuda=True)
-        env = registry.make(
-            self.env_name,
-            num_envs=1,
-            sim_backend=self.sim_backend,
-            env_cfg_override=self.env_cfg_overrides if self.env_cfg_overrides else None,
-        )
-        obs_dim, critic_dim = get_obs_dims(env.obs_groups_spec)
-        self.critic_dim = critic_dim
-        self.critic_input_dim = get_critic_base_dim(env.obs_groups_spec)
-        assert env.action_space.shape is not None
-        action_dim = env.action_space.shape[0]
-        env.close()
-
-        return obs_dim, action_dim
-
-    def _build_learner(self):
-        cfg = dict(self.rl_cfg)
-
-        import torch
-        from tensordict import TensorDict
-
-        apply_training_seed(self.seed, torch_runtime=True, cuda=True)
-        obs_example = torch.zeros((self.num_envs, self.obs_dim), device=self.device)
-        td_example = TensorDict({"policy": obs_example}, batch_size=self.num_envs)
-
-        critic_obs_dim = self.critic_input_dim
-        critic_obs_example = torch.zeros((self.num_envs, critic_obs_dim), device=self.device)
-        critic_td_example = TensorDict({"policy": critic_obs_example}, batch_size=self.num_envs)
-
-        # Build actor (stochastic MLPModel — distribution_cfg carries GaussianDistribution)
-        # deepcopy so MLPModel.__init__'s distribution_cfg.pop("class_name") doesn't
-        # mutate the shared rl_cfg that gets sent to the collector subprocess.
-        actor_cfg = deepcopy(cfg.get("actor", {}))
-        actor_cls = resolve_callable(actor_cfg.pop("class_name"))
-        actor_cfg.pop("num_actions", None)
-        actor = actor_cls(td_example, cfg["obs_groups"], "actor", self.action_dim, **actor_cfg)
-
-        # Build critic (deterministic MLPModel, no distribution).
-        critic_cfg: dict[str, Any] = deepcopy(cfg.get("critic") or cfg.get("actor") or {})
-        critic_cls = resolve_callable(critic_cfg.pop("class_name", "rsl_rl.models.MLPModel"))
-        critic_cfg.pop("num_actions", None)
-        critic_cfg.pop("distribution_cfg", None)  # critic is deterministic
-        critic = critic_cls(critic_td_example, cfg["obs_groups"], "critic", 1, **critic_cfg)
-
-        # Extract algorithm hyperparams from rl_cfg["algorithm"] (or top-level)
-        algo_cfg = cfg.get("algorithm", cfg)
-        learner = APPOLearner(
-            actor=actor,
-            critic=critic,
-            device=self.device,
-            num_learning_epochs=algo_cfg.get("num_learning_epochs", 5),
-            num_mini_batches=algo_cfg.get("num_mini_batches", 4),
-            clip_param=algo_cfg.get("clip_param", 0.2),
-            gamma=algo_cfg.get("gamma", 0.99),
-            lam=algo_cfg.get("lam", 0.95),
-            value_loss_coef=algo_cfg.get("value_loss_coef", 1.0),
-            entropy_coef=algo_cfg.get("entropy_coef", 0.01),
-            learning_rate=algo_cfg.get("learning_rate", 1e-3),
-            max_grad_norm=algo_cfg.get("max_grad_norm", 1.0),
-            use_clipped_value_loss=algo_cfg.get("use_clipped_value_loss", True),
-            schedule=algo_cfg.get("schedule", "fixed"),
-            desired_kl=algo_cfg.get("desired_kl", 0.01),
-            adaptive_kl_factor=algo_cfg.get("adaptive_kl_factor", 1.2),
-            adaptive_lr_factor=algo_cfg.get("adaptive_lr_factor", 1.1),
-            optimizer=algo_cfg.get("optimizer", "adam"),
-            tau=algo_cfg.get("tau", 1.0),
-            target_update_freq=algo_cfg.get("target_update_freq", 1),
-            vtrace_clip_rho=algo_cfg.get("vtrace_clip_rho", 1.0),
-            vtrace_clip_c=algo_cfg.get("vtrace_clip_c", 1.0),
-            enable_compile=algo_cfg.get("enable_compile", True),
-        )
-        return learner
-
-    def learn(
-        self,
-        max_iterations: int = 1500,
-        save_interval: int = 50,
-        log_dir: str = "logs",
-        logger_type: str = "tensorboard",
-    ) -> None:
-        os.makedirs(log_dir, exist_ok=True)
-        train_start_wall = time.time()
-        best_mean_reward = float("-inf")
-        last_mean_reward = 0.0
-        ckpt_path: str | None = None
-        iteration = 0
-
-        learner = self._build_learner()
-        if self.resume_path:
-            checkpoint = torch.load(self.resume_path, map_location=self.device, weights_only=True)
-            learner.actor.load_state_dict(checkpoint["actor"])
-            learner.critic.load_state_dict(checkpoint["critic"])
-            if "optimizer" in checkpoint:
-                learner.optimizer.load_state_dict(checkpoint["optimizer"])
-                learner.learning_rate = _optimizer_lr_from_state(learner.optimizer)
-            _sync_resume_target_actor(learner)
-
-        # --- memory budget check ---
-        from unilab.ipc.memory_budget import estimate_appo_bytes, warn_if_over_budget
-
-        mem_est = estimate_appo_bytes(
-            num_envs=self.num_envs,
-            steps_per_env=self.steps_per_env,
-            obs_dim=self.obs_dim,
-            action_dim=self.action_dim,
-            critic_dim=self.critic_dim,
-            num_slots=4,
-        )
-        warn_if_over_budget(mem_est, label="APPO")
-
-        # Create shared rollout IPC ring buffer; learner-side tensor lifetime is
-        # owned by the bounded staging pool below.
-        rollout_ring_buffer = RolloutRingBuffer(
-            num_envs=self.num_envs,
-            num_steps=self.steps_per_env,
-            obs_dim=self.obs_dim,
-            action_dim=self.action_dim,
-            critic_dim=self.critic_dim,
-            num_slots=4,
-            create=True,
-        )
-        self._shared_resources.append(rollout_ring_buffer)
-
-        # Create weight sync for collector-side actor and critic bootstrap values.
-        actor_weight_sync = SharedWeightSync.from_state_dict(
-            learner.actor.state_dict(), create=True
-        )
-        critic_weight_sync = SharedWeightSync.from_state_dict(
-            learner.critic.state_dict(), create=True
-        )
-        self._shared_resources.extend([actor_weight_sync, critic_weight_sync])
-
-        actor_weight_param_shapes = {
-            name: p.shape for name, p in learner.actor.state_dict().items()
-        }
-        critic_weight_param_shapes = {
-            name: p.shape for name, p in learner.critic.state_dict().items()
-        }
-
-        metrics_queue: mp.Queue = mp.get_context("spawn").Queue(maxsize=100)
-
-        # Start collector
-        collector_kwargs = {
-            "env_name": self.env_name,
-            "rl_cfg": self.rl_cfg,
-            "num_envs": self.num_envs,
-            "steps_per_env": self.steps_per_env,
-            "shm_rollout_ring_buffer_name": rollout_ring_buffer.name,
-            "sync_primitives": (
-                rollout_ring_buffer._write_ptr,
-                rollout_ring_buffer._read_ptr,
-            ),
-            "obs_dim": self.obs_dim,
-            "action_dim": self.action_dim,
-            "critic_dim": self.critic_dim,
-            "actor_weight_sync_name": actor_weight_sync.name,
-            "actor_weight_param_shapes": actor_weight_param_shapes,
-            "critic_weight_sync_name": critic_weight_sync.name,
-            "critic_weight_param_shapes": critic_weight_param_shapes,
-            "metrics_queue": metrics_queue,
-            "collector_device": self.collector_device,
-            "sim_backend": self.sim_backend,
-            "env_cfg_override": self.env_cfg_overrides if self.env_cfg_overrides else None,
-            "seed": derive_worker_seed(self.seed, worker_index=0),
-            "nan_guard_cfg": self.nan_guard_cfg,
-        }
-        self._start_collector(
-            target_fn=appo_collector_fn,
-            kwargs={"stop_event": self._stop_event, **collector_kwargs},
-        )
-
-        env_steps_per_sync = self.steps_per_env * self.num_envs
-
-        logger = OffPolicyLogger(
-            algo_name="APPO",
-            max_iterations=max_iterations,
-            num_envs=self.num_envs,
-            env_name=self.env_name,
-            obs_dim=self.obs_dim,
-            action_dim=self.action_dim,
-            log_dir=log_dir,
-            log_backend=logger_type,
-            timing_profile="appo",
-        )
-        logger.log_status(
-            f"Waiting for first rollout... "
-            f"(staging_pool={self.staging_pool_size}, "
-            f"epochs={learner.num_learning_epochs})"
-        )
-        logger_started = False
-
-        reward_history: deque = deque(maxlen=200)
-        latest_reward_components: dict = {}
-
-        staging_pool = RolloutStagingPool(
-            capacity=self.staging_pool_size,
-            num_envs=self.num_envs,
-            slot_shapes=rollout_ring_buffer.slot_shapes,
-            device=self.device,
-        )
-
-        for iteration in range(1, max_iterations + 1):
-            iteration_start = time.perf_counter()
-            # Drain collector metrics while waiting for next rollout
-            self._drain_metrics(metrics_queue, reward_history, latest_reward_components, logger)
-            wait_start = time.perf_counter()
-
-            # Chunked wait with periodic liveness check (issue #594 B-2):
-            # avoid freezing the learner for 60s when the collector dies mid-iteration.
-            WAIT_BUDGET_SEC = 60.0
-            LIVENESS_SLICE_SEC = 0.5
-            deadline = time.monotonic() + WAIT_BUDGET_SEC
-            data_ready = False
-            while time.monotonic() < deadline:
-                remaining = deadline - time.monotonic()
-                if rollout_ring_buffer.wait_for_data(timeout=min(LIVENESS_SLICE_SEC, remaining)):
-                    data_ready = True
-                    break
-                if not self._check_collector_alive():
-                    self._drain_metrics(
-                        metrics_queue, reward_history, latest_reward_components, logger
-                    )
-                    raise RuntimeError(
-                        "APPO collector process died before producing data. "
-                        "Check stderr for [APPO WORKER CRASH] messages."
-                    )
-            if not data_ready:
-                logger.log_status(
-                    f"[yellow]Warning: Timeout waiting for data at iteration {iteration}[/]"
-                )
-                continue
-            wait_time = time.perf_counter() - wait_start
-
-            if not logger_started:
-                logger.start(status="Training")
-                logger_started = True
-
-            available_on_arrive = rollout_ring_buffer.available()
-
-            # Drain ALL available slots into the staging pool in one pass.
-            # This keeps the GPU busy: if the collector produced 3 rollouts while
-            # the learner was training, we consume all 3 immediately rather than
-            # processing them one-per-iteration.
-            num_new = rollout_ring_buffer.available()
-            learner_replay_stage_time = 0.0
-            for _ in range(num_new):
-                h2d_start = time.perf_counter()
-                staging_pool.stage_numpy_views(rollout_ring_buffer.read_numpy_views())
-                learner_replay_stage_time += time.perf_counter() - h2d_start
-                rollout_ring_buffer.advance_read()
-
-            self._drain_metrics(metrics_queue, reward_history, latest_reward_components, logger)
-
-            replay_sample_start = time.perf_counter()
-            combined = staging_pool.batch()
-            learner_replay_sample_time = time.perf_counter() - replay_sample_start
-
-            train_start = time.perf_counter()
-            learner.process_batch(combined)
-            metrics = learner.update(combined)
-            train_time = time.perf_counter() - train_start
-            weight_sync_start = time.perf_counter()
-            actor_weight_sync.write_weights(learner.actor.state_dict())
-            critic_weight_sync.write_weights(learner.critic.state_dict())
-            weight_sync_time = time.perf_counter() - weight_sync_start
-            iteration_time = time.perf_counter() - iteration_start
-
-            metrics["staging_pool_len"] = float(staging_pool.active_count)
-            metrics["staging_pool_capacity"] = float(staging_pool.capacity)
-            metrics["available_on_arrive"] = float(available_on_arrive)
-            metrics["rollouts_read"] = float(num_new)
-
-            logger.update_staging_pool(staging_pool.active_count, staging_pool.capacity)
-
-            mean_reward = (
-                sum(list(reward_history)[-50:]) / max(len(list(reward_history)[-50:]), 1)
-                if reward_history
-                else 0.0
-            )
-            last_mean_reward = float(mean_reward)
-            best_mean_reward = max(best_mean_reward, last_mean_reward)
-
-            logger.log_step(
-                iteration=iteration,
-                metrics=metrics,
-                reward=mean_reward,
-                reward_components=latest_reward_components,
-                train_time=train_time,
-                collector_wait_time=wait_time,
-                learner_replay_sample_time=learner_replay_sample_time,
-                learner_replay_stage_time=learner_replay_stage_time,
-                weight_sync_time=weight_sync_time,
-                iteration_time=iteration_time,
-                extra_info={
-                    "throughput_steps": num_new * env_steps_per_sync,
-                    "collector_active_steps_per_sec": logger._collector_active_steps_per_sec,
-                },
-            )
-
-            if save_interval > 0 and iteration % save_interval == 0:
-                ckpt_path = os.path.join(log_dir, f"model_{iteration}.pt")
-                torch.save(learner.get_state_dict(), ckpt_path)
-                logger.log_save(ckpt_path)
-
-        ckpt_path = os.path.join(log_dir, f"model_{max_iterations}.pt")
-        torch.save(learner.get_state_dict(), ckpt_path)
-        logger.log_save(ckpt_path)
-        logger.finish()
-        summary = {
-            "status": "completed",
-            "completed_iterations": iteration,
-            "total_env_steps": int(logger._total_steps),
-            "final_mean_reward": last_mean_reward if reward_history else None,
-            "best_mean_reward": best_mean_reward if reward_history else None,
-            "mean_episode_length": float(logger._mean_ep_length),
-            "last_checkpoint": ckpt_path,
-            "training_wall_time_sec": time.time() - train_start_wall,
-        }
-        self.last_run_summary = summary
-
-    # _check_collector_alive() inherited from AsyncRunner base class
-
-    @staticmethod
-    def _drain_metrics(queue, reward_history, reward_components, logger):
-        """Drain all pending messages from the collector metrics queue.
-
-        Mirrors OffPolicyRunner._drain_metrics so APPO has the same
-        logger update coverage (ep_length, done rates, collector timing).
-        """
-        while not queue.empty():
-            try:
-                m = queue.get_nowait()
-                if "error" in m:
-                    logger.log_status(f"[red]Collector ERROR: {m['error']}[/]")
-                    raise RuntimeError(f"Collector process failed: {m['error']}")
-
-                if "mean_ep_reward" in m:
-                    reward_history.append(m["mean_ep_reward"])
-
-                if "reward_components" in m:
-                    reward_components.clear()
-                    reward_components.update(m["reward_components"])
-
-                if "mean_ep_length" in m:
-                    logger.update_ep_length(m["mean_ep_length"])
-
-                if "collector_timing_ms" in m:
-                    logger.update_collector_timing(m["collector_timing_ms"])
-
-                collector_active_steps_per_sec = m.get("collector_active_steps_per_sec")
-                if collector_active_steps_per_sec is not None:
-                    logger.update_collector_active_steps_per_sec(
-                        float(collector_active_steps_per_sec)
-                    )
-
-                if "timeout_rate" in m:
-                    logger.update_timeout_rate(float(m["timeout_rate"]))
-
-                if "total_steps" in m:
-                    logger.log_collector(
-                        m["total_steps"],
-                        0,  # APPO uses shared memory, not a separate buffer
-                        m.get("mean_ep_reward", 0.0),
-                    )
-
-            except Exception as e:
-                print(f"[APPORunner] metrics drain error: {e}", file=sys.stderr)
-                break
diff --git a/src/unilab/algos/torch/appo/runtime.py b/src/unilab/algos/torch/appo/runtime.py
deleted file mode 100644
index ad5b58bd8..000000000
--- a/src/unilab/algos/torch/appo/runtime.py
+++ /dev/null
@@ -1,69 +0,0 @@
-"""Runtime resolution helpers for APPO script assembly.
-
-This module keeps entrypoint scripts generic: they resolve an APPO runtime bundle
-from owner config and then call the returned runner/play entrypoints without
-knowing which concrete runtime implementation is active.
-"""
-
-from __future__ import annotations
-
-from collections.abc import Callable
-from dataclasses import dataclass
-from typing import Any
-
-
-@dataclass(frozen=True)
-class APPORuntime:
-    """Resolved APPO runtime entrypoints consumed by the generic script.
-
-    Args:
-        runner_cls: Runner class used for APPO training mode.
-        play_fn: Play-mode callable used for checkpoint playback.
-
-    Returns:
-        Immutable APPO runtime bundle selected from owner config.
-    """
-
-    runner_cls: type[Any]
-    play_fn: Callable[..., str | None]
-
-
-def resolve_appo_runtime(
-    rl_cfg: dict[str, Any],
-    *,
-    default_play_fn: Callable[..., str | None],
-) -> APPORuntime:
-    """Resolve the APPO runtime bundle from owner config.
-
-    Args:
-        rl_cfg: Resolved algorithm config dictionary from Hydra composition.
-        default_play_fn: Generic APPO play function used when no custom runtime
-            resolver is selected by the owner config.
-
-    Returns:
-        ``APPORuntime`` containing the train and play entrypoints for the
-        selected APPO runtime.
-    """
-    runtime_resolver = rl_cfg.get("runtime_resolver")
-    if runtime_resolver in (None, ""):
-        from unilab.algos.torch.appo.runner import APPORunner
-
-        return APPORuntime(runner_cls=APPORunner, play_fn=default_play_fn)
-
-    from rsl_rl.utils import resolve_callable
-
-    resolver = resolve_callable(str(runtime_resolver))
-    runtime = resolver(rl_cfg)
-    if runtime is None:
-        raise ValueError(
-            f"APPO runtime resolver {runtime_resolver!r} returned None for rl_cfg runtime selection."
-        )
-
-    runner_cls = getattr(runtime, "runner_cls", None)
-    play_fn = getattr(runtime, "play_fn", None)
-    if runner_cls is None or play_fn is None:
-        raise TypeError(
-            f"APPO runtime resolver {runtime_resolver!r} must return an object with "
-            "'runner_cls' and 'play_fn' attributes."
-        )
-    return APPORuntime(runner_cls=runner_cls, play_fn=play_fn)
diff --git a/src/unilab/algos/torch/appo/staging.py b/src/unilab/algos/torch/appo/staging.py
deleted file mode 100644
index 859d18741..000000000
--- a/src/unilab/algos/torch/appo/staging.py
+++ /dev/null
@@ -1,137 +0,0 @@
-"""Bounded rollout staging for APPO learners."""
-
-from __future__ import annotations
-
-from collections.abc import Mapping
-
-import numpy as np
-import torch
-
-_LAST_FIELDS = frozenset({"last_obs", "last_critic"})
-_FIELD_ALIASES = {
-    "obs": "observations",
-    "log_probs": "actions_log_prob",
-}
-
-
-class RolloutStagingPool:
-    """Preallocated learner-device storage for a bounded set of rollouts.
-
-    Raw IPC rollout slots are env-major: [N, T, ...].  Learners consume
-    time-major combined batches: [T, K*N, ...].  The pool owns the destination
-    tensors and exposes active views without rebuilding them via torch.cat.
-    """
-
-    def __init__(
-        self,
-        *,
-        capacity: int,
-        num_envs: int,
-        slot_shapes: Mapping[str, tuple[int, ...]],
-        device: str | torch.device,
-    ) -> None:
-        if capacity < 1:
-            raise ValueError("RolloutStagingPool capacity must be >= 1")
-        if num_envs < 1:
-            raise ValueError("RolloutStagingPool num_envs must be >= 1")
-
-        self.capacity = int(capacity)
-        self.num_envs = int(num_envs)
-        self.device = torch.device(device)
-        self._next_slot = 0
-        self._active_count = 0
-        self._writes = 0
-        self._slot_versions = [-1] * self.capacity
-        self._raw_to_batch_field: dict[str, str] = {}
-        self._buffers: dict[str, torch.Tensor] = {}
-
-        for raw_field, slot_shape in slot_shapes.items():
-            self._allocate_field(raw_field, tuple(slot_shape))
-
-    @property
-    def active_count(self) -> int:
-        return self._active_count
-
-    @property
-    def slot_versions(self) -> tuple[int, ...]:
-        return tuple(self._slot_versions)
-
-    def _allocate_field(self, raw_field: str, slot_shape: tuple[int, ...]) -> None:
-        if not slot_shape or slot_shape[0] != self.num_envs:
-            raise ValueError(
-                f"rollout field {raw_field!r} must start with num_envs={self.num_envs}; "
-                f"got shape {slot_shape}"
-            )
-
-        batch_field = _FIELD_ALIASES.get(raw_field, raw_field)
-        self._raw_to_batch_field[raw_field] = batch_field
-
-        if raw_field in _LAST_FIELDS:
-            combined_shape = (self.capacity * self.num_envs, *slot_shape[1:])
-        else:
-            if len(slot_shape) < 2:
-                raise ValueError(f"rollout field {raw_field!r} must include a time dimension")
-            combined_shape = (
-                slot_shape[1],
-                self.capacity * self.num_envs,
-                *slot_shape[2:],
-            )
-        self._buffers[batch_field] = torch.empty(
-            combined_shape,
-            dtype=torch.float32,
-            device=self.device,
-        )
-
-    def _slot_view(self, raw_field: str, slot: int) -> torch.Tensor:
-        batch_field = self._raw_to_batch_field[raw_field]
-        storage = self._buffers[batch_field]
-        start = slot * self.num_envs
-        end = start + self.num_envs
-        if raw_field in _LAST_FIELDS:
-            return storage[start:end]
-        return storage[:, start:end, ...]
-
-    def stage_numpy_views(self, raw_views: Mapping[str, np.ndarray]) -> int:
-        """Copy one raw shared-memory rollout into the next staging slot."""
-        missing = self._raw_to_batch_field.keys() - raw_views.keys()
-        if missing:
-            raise KeyError(f"missing rollout fields for staging: {sorted(missing)}")
-
-        slot = self._next_slot
-        for raw_field, raw_view in raw_views.items():
-            if raw_field not in self._raw_to_batch_field:
-                raise KeyError(f"unexpected rollout field {raw_field!r}")
-            if raw_view.dtype != np.float32:
-                raise TypeError(f"rollout field {raw_field!r} must be float32")
-
-            src = torch.from_numpy(raw_view)
-            if raw_field not in _LAST_FIELDS:
-                src = src.transpose(0, 1)
-
-            dst = self._slot_view(raw_field, slot)
-            if tuple(src.shape) != tuple(dst.shape):
-                raise ValueError(
-                    f"rollout field {raw_field!r} shape mismatch: "
-                    f"expected {tuple(dst.shape)}, got {tuple(src.shape)}"
-                )
-            dst.copy_(src, non_blocking=False)
-
-        self._slot_versions[slot] = self._writes
-        self._writes += 1
-        self._active_count = min(self._active_count + 1, self.capacity)
-        self._next_slot = (slot + 1) % self.capacity
-        return slot
-
-    def batch(self) -> dict[str, torch.Tensor]:
-        """Return active learner-ready views backed by the staging pool."""
-        if self._active_count == 0:
-            raise RuntimeError("RolloutStagingPool has no active rollouts")
-
-        active_envs = self._active_count * self.num_envs
-        out: dict[str, torch.Tensor] = {}
-        for field, storage in self._buffers.items():
-            if field in _LAST_FIELDS:
-                out[field] = storage[:active_envs]
-            else:
-                out[field] = storage[:, :active_envs, ...]
-        return out
diff --git a/src/unilab/algos/torch/appo/worker.py b/src/unilab/algos/torch/appo/worker.py
deleted file mode 100644
index 3058c3bca..000000000
--- a/src/unilab/algos/torch/appo/worker.py
+++ /dev/null
@@ -1,414 +0,0 @@
-"""APPO Rollout Worker — runs in a subprocess.
-
-Collects rollout payloads and writes them to RolloutRingBuffer.
-"""
-
-from __future__ import annotations
-
-import statistics
-import sys
-import time
-from collections import defaultdict
-from queue import Empty, Full
-from typing import Any, Dict
-
-import numpy as np
-import torch
-from rsl_rl.utils import resolve_callable
-
-from unilab.algos.torch.common.collector_timing import extract_env_step_breakdown_timing_ms
-from unilab.base.final_observation import resolve_terminal_observation_contract
-from unilab.base.observations import split_obs_dict
-from unilab.base.registry import ensure_registries
-from unilab.training.seed import apply_training_seed
-
-
-def put_latest_metrics(metrics_queue: Any, msg: dict[str, Any], *, worker_name: str) -> None:
-    """Best-effort metrics enqueue that keeps recent data under learner stalls."""
-    try:
-        metrics_queue.put_nowait(msg)
-        return
-    except Full:
-        pass
-    except Exception as e:
-        print(f"[{worker_name}] metrics enqueue error: {type(e).__name__}: {e}", file=sys.stderr)
-        return
-
-    try:
-        metrics_queue.get_nowait()
-    except Empty:
-        pass
-    except Exception as e:
-        print(
-            f"[{worker_name}] metrics drop stale metrics error: {type(e).__name__}: {e}",
-            file=sys.stderr,
-        )
-        return
-
-    try:
-        metrics_queue.put_nowait(msg)
-    except Full:
-        pass
-    except Exception as e:
-        print(f"[{worker_name}] metrics enqueue error: {type(e).__name__}: {e}", file=sys.stderr)
-
-
-def compute_rollout_active_steps_per_sec(
-    *,
-    num_envs: int,
-    steps_per_env: int,
-    rollout_ms: float,
-) -> float | None:
-    """Return rollout collector throughput from active rollout wall time."""
-    if rollout_ms <= 0.0:
-        return None
-    return (int(num_envs) * int(steps_per_env)) / (float(rollout_ms) / 1000.0)
-
-
-def compute_timeout_bootstrap_correction(
-    critic: Any,
-    collector_device: str,
-    gamma: float,
-    timeout_mask: np.ndarray,
-    final_obs: np.ndarray,
-    final_critic: np.ndarray,
-) -> np.ndarray:
-    """Compute gamma * V(final_observation) for current timeout envs."""
-    corrections = np.zeros(timeout_mask.shape, dtype=np.float32)
-    if not np.any(timeout_mask):
-        return corrections
-
-    from tensordict import TensorDict
-
-    critic_input_np = final_critic
-    critic_input = torch.from_numpy(critic_input_np[timeout_mask]).to(collector_device)
-    critic_td = TensorDict(
-        {"policy": critic_input},
-        batch_size=critic_input.shape[0],
-        device=collector_device,
-    )
-    with torch.no_grad():
-        bootstrap = critic(critic_td).squeeze(-1).cpu().numpy().astype(np.float32, copy=False)
-    corrections[timeout_mask] = float(gamma) * bootstrap
-    return corrections
-
-
-def appo_collector_fn(
-    stop_event: Any,
-    env_name: str,
-    rl_cfg: dict,
-    num_envs: int,
-    steps_per_env: int,
-    shm_rollout_ring_buffer_name: Dict[str, str],
-    sync_primitives: tuple,
-    obs_dim: int,
-    action_dim: int,
-    critic_dim: int,
-    actor_weight_sync_name: str,
-    actor_weight_param_shapes: dict,
-    critic_weight_sync_name: str,
-    critic_weight_param_shapes: dict,
-    metrics_queue: Any,
-    collector_device: str = "cpu",
-    sim_backend: str = "mujoco",
-    env_cfg_override: dict | None = None,
-    seed: int | None = None,
-    nan_guard_cfg=None,
-):
-    """Entry point for the APPO collector subprocess.
-
-    Creates environment + policy, collects rollouts, writes raw payloads
-    to the IPC ring buffer. Error handling is provided by the
-    ``_collector_entry_wrapper`` in ``async_runner.py``.
-    """
-    from copy import deepcopy
-
-    from tensordict import TensorDict
-
-    from unilab.base import registry
-    from unilab.ipc import RolloutRingBuffer, SharedWeightSync
-
-    ensure_registries()
-    apply_training_seed(seed, torch_runtime=True, cuda=True)
-
-    # Connect to shared memory
-    ring_buffer = RolloutRingBuffer(
-        num_envs=num_envs,
-        num_steps=steps_per_env,
-        obs_dim=obs_dim,
-        action_dim=action_dim,
-        critic_dim=critic_dim,
-        create=False,
-        shm_name_prefix=shm_rollout_ring_buffer_name,
-    )
-    ring_buffer.attach_sync_primitives(*sync_primitives)  # (write_ptr, read_ptr)
-    actor_weight_sync = SharedWeightSync(
-        actor_weight_param_shapes, create=False, shm_name=actor_weight_sync_name
-    )
-    critic_weight_sync = SharedWeightSync(
-        critic_weight_param_shapes, create=False, shm_name=critic_weight_sync_name
-    )
-
-    # Create environment
-    env: Any = registry.make(
-        env_name, num_envs=num_envs, sim_backend=sim_backend, env_cfg_override=env_cfg_override
-    )
-
-    if nan_guard_cfg is not None and nan_guard_cfg.enabled:
-        from unilab.utils.nan_guard import NanGuard
-
-        env.set_nan_guard(
-            NanGuard(
-                nan_guard_cfg,
-                num_envs=env.num_envs,
-                supports_state_playback=env.play_capabilities.supports_physics_state_playback,
-            )
-        )
-
-    # Build actor (stochastic MLPModel — mirrors runner._build_learner)
-    cfg = dict(rl_cfg)
-
-    obs_example = torch.zeros((num_envs, obs_dim), device=collector_device)
-    td_example = TensorDict({"policy": obs_example}, batch_size=num_envs)
-
-    # deepcopy so MLPModel.__init__'s distribution_cfg.pop("class_name") doesn't
-    # mutate the shared rl_cfg dict.
-    actor_cfg = deepcopy(cfg["actor"])
-    actor_cls = resolve_callable(actor_cfg.pop("class_name"))
-    actor_cfg.pop("num_actions", None)
-    actor = actor_cls(
-        td_example,
-        cfg.get("obs_groups", {"actor": {"policy": obs_dim}}),
-        "actor",
-        action_dim,
-        **actor_cfg,
-    )
-    actor = actor.to(collector_device)
-    actor.eval()
-
-    critic_obs_dim = critic_dim if critic_dim > 0 else obs_dim
-    critic_obs_example = torch.zeros((num_envs, critic_obs_dim), device=collector_device)
-    critic_td_example = TensorDict({"policy": critic_obs_example}, batch_size=num_envs)
-    critic_cfg = deepcopy(cfg.get("critic") or cfg.get("actor") or {})
-    critic_cls = resolve_callable(critic_cfg.pop("class_name", "rsl_rl.models.MLPModel"))
-    critic_cfg.pop("num_actions", None)
-    critic_cfg.pop("distribution_cfg", None)
-    critic = critic_cls(
-        critic_td_example,
-        cfg.get("obs_groups", {"critic": {"policy": critic_obs_dim}}),
-        "critic",
-        1,
-        **critic_cfg,
-    )
-    critic = critic.to(collector_device)
-    critic.eval()
-    # Load initial weights
-    actor_sd = dict(actor.state_dict())
-    actor_weight_sync.read_weights_into(actor_sd)
-    actor.load_state_dict(actor_sd)
-    local_actor_weight_version = actor_weight_sync.version
-
-    critic_sd = dict(critic.state_dict())
-    critic_weight_sync.read_weights_into(critic_sd)
-    critic.load_state_dict(critic_sd)
-    local_critic_weight_version = critic_weight_sync.version
-
-    # Reset environment
-    env_indices = np.arange(num_envs, dtype=np.int32)
-    try:
-        obs_out, _ = env.reset(env_indices)
-    except TypeError:
-        obs_out, _ = env.reset()
-
-    def to_float32_np(x):
-        if hasattr(x, "cpu"):
-            x = x.cpu().numpy()
-        return np.asarray(x, dtype=np.float32)
-
-    obs_np, critic_np = split_obs_dict(obs_out)
-    obs_np = to_float32_np(obs_np)
-    critic_np = to_float32_np(critic_np)
-
-    # Pre-allocate obs TensorDict once; update in-place each step to avoid
-    # repeated TensorDict construction overhead in the hot loop.
-    obs_torch = torch.zeros((num_envs, obs_dim), dtype=torch.float32, device=collector_device)
-    obs_td = TensorDict({"policy": obs_torch}, batch_size=num_envs, device=collector_device)
-
-    total_steps = 0
-    ep_rewards = []
-    ep_lengths = []
-    current_ep_rewards = np.zeros(num_envs, dtype=np.float32)
-    current_ep_lengths = np.zeros(num_envs, dtype=np.int32)
-    ep_reward_components = defaultdict(list)
-
-    # Episode completion counters (reset after each metrics report)
-    ep_timeouts = 0
-    ep_completions = 0
-
-    # Collector timing EMA (milliseconds, α=0.1 → slow-moving average)
-    _EMA = 0.1
-    ema_mlp_infer_ms: float = 0.0
-    ema_env_step_ms: float = 0.0
-    ema_rollout_ms: float = 0.0
-    ema_env_step_breakdown_ms: dict[str, float] = {}
-
-    try:
-        while not stop_event.is_set():
-            t_rollout_start = time.perf_counter()
-            # Pull latest weights from learner
-            if actor_weight_sync.version > local_actor_weight_version:
-                actor_sd = dict(actor.state_dict())
-                local_actor_weight_version = actor_weight_sync.read_weights_into(actor_sd)
-                actor.load_state_dict(actor_sd)
-            if critic_weight_sync.version > local_critic_weight_version:
-                critic_sd = dict(critic.state_dict())
-                local_critic_weight_version = critic_weight_sync.read_weights_into(critic_sd)
-                critic.load_state_dict(critic_sd)
-
-            # Collect one rollout of length steps_per_env
-            write_buf = ring_buffer.write_buffer
-            for step in range(steps_per_env):
-                # --- MLP inference (timed) ---
-                t_mlp = time.perf_counter()
-                with torch.no_grad():
-                    obs_torch.copy_(torch.from_numpy(obs_np))
-                    actions_torch = actor(obs_td, stochastic_output=True)
-                    log_probs_torch = actor.get_output_log_prob(actions_torch)
-                    actions_np = actions_torch.cpu().numpy()
-                ema_mlp_infer_ms = (1 - _EMA) * ema_mlp_infer_ms + _EMA * (
-                    (time.perf_counter() - t_mlp) * 1000
-                )
-
-                write_buf["obs"][:, step, :] = obs_np
-                if critic_np is not None:
-                    write_buf["critic"][:, step, :] = critic_np
-                write_buf["actions"][:, step, :] = actions_np
-                write_buf["log_probs"][:, step] = log_probs_torch.cpu().numpy().ravel()
-
-                # --- Env step (timed) ---
-                t_env = time.perf_counter()
-                state = env.step(actions_np)
-                ema_env_step_ms = (1 - _EMA) * ema_env_step_ms + _EMA * (
-                    (time.perf_counter() - t_env) * 1000
-                )
-                for key, value in extract_env_step_breakdown_timing_ms(state.info).items():
-                    ema_env_step_breakdown_ms[key] = (1 - _EMA) * ema_env_step_breakdown_ms.get(
-                        key, 0.0
-                    ) + _EMA * value
-
-                next_obs_raw = state.obs
-                reward_raw = np.asarray(state.reward, dtype=np.float32).ravel()
-                truncated_raw = state.truncated.astype(np.float32, copy=False).ravel()
-                combined_done_raw = (
-                    (state.terminated | state.truncated).astype(np.float32, copy=False).ravel()
-                )
-
-                next_actor_obs_np, next_critic_np = split_obs_dict(next_obs_raw)
-                next_actor_obs_np = to_float32_np(next_actor_obs_np)
-                next_critic_np = to_float32_np(next_critic_np)
-                terminal_contract = resolve_terminal_observation_contract(
-                    next_obs_batch_size=next_actor_obs_np.shape[0],
-                    final_observation=state.final_observation,
-                    done=combined_done_raw > 0.5,
-                    info=state.info,
-                    truncated=truncated_raw,
-                )
-
-                reward_raw += compute_timeout_bootstrap_correction(
-                    critic=critic,
-                    collector_device=collector_device,
-                    gamma=float(cfg["algorithm"].get("gamma", 0.99)),
-                    timeout_mask=terminal_contract.timeout_terminal_mask,
-                    final_obs=(
-                        terminal_contract.terminal_obs
-                        if terminal_contract.terminal_obs is not None
-                        else next_actor_obs_np
-                    ),
-                    final_critic=(
-                        terminal_contract.terminal_critic
-                        if terminal_contract.terminal_critic is not None
-                        else next_critic_np
-                    ),
-                )
-
-                write_buf["rewards"][:, step] = reward_raw
-                write_buf["dones"][:, step] = combined_done_raw
-                write_buf["truncated"][:, step] = truncated_raw
-
-                # Episode tracking (vectorized)
-                total_steps += num_envs
-                current_ep_rewards += reward_raw
-                current_ep_lengths += 1
-                reset_indices = np.where(combined_done_raw > 0.5)[0]
-                if len(reset_indices) > 0:
-                    ep_rewards.extend(current_ep_rewards[reset_indices].tolist())
-                    ep_lengths.extend(current_ep_lengths[reset_indices].tolist())
-                    current_ep_rewards[reset_indices] = 0.0
-                    current_ep_lengths[reset_indices] = 0
-                    ep_completions += len(reset_indices)
-                    ep_timeouts += int(np.sum(truncated_raw[reset_indices] > 0.5))
-
-                log_info = state.info.get("log", {})
-                for k, v in log_info.items():
-                    if k.startswith("reward/"):
-                        ep_reward_components[k].append(v)
-
-                if metrics_queue is not None and total_steps % (num_envs * 10) == 0:
-                    try:
-                        msg: dict[str, Any] = {
-                            "total_steps": total_steps,
-                        }
-                        if ep_rewards:
-                            msg["mean_ep_reward"] = statistics.mean(ep_rewards[-100:])
-                            msg["mean_ep_length"] = (
-                                statistics.mean(ep_lengths[-100:]) if ep_lengths else 0.0
-                            )
-                        if ep_completions > 0:
-                            msg["timeout_rate"] = ep_timeouts / ep_completions
-                            ep_timeouts = 0
-                            ep_completions = 0
-                        # Collector-side timing breakdown
-                        msg["collector_timing_ms"] = {
-                            "rollout_ms": ema_rollout_ms,
-                            "mlp_infer_ms": ema_mlp_infer_ms,
-                            "env_step_ms": ema_env_step_ms,
-                            **ema_env_step_breakdown_ms,
-                        }
-                        collector_active_steps_per_sec = compute_rollout_active_steps_per_sec(
-                            num_envs=num_envs,
-                            steps_per_env=steps_per_env,
-                            rollout_ms=ema_rollout_ms,
-                        )
-                        if collector_active_steps_per_sec is not None:
-                            msg["collector_active_steps_per_sec"] = collector_active_steps_per_sec
-                        if ep_reward_components:
-                            msg["reward_components"] = {
-                                k: statistics.mean(v) for k, v in ep_reward_components.items() if v
-                            }
-                            ep_reward_components.clear()
-                        put_latest_metrics(metrics_queue, msg, worker_name="APPOWorker")
-                    except Exception as e:
-                        print(
-                            f"[APPOWorker] metrics build error: {type(e).__name__}: {e}",
-                            file=sys.stderr,
-                        )
-
-                obs_np = next_actor_obs_np
-                critic_np = next_critic_np
-
-            write_buf["last_obs"][:] = obs_np
-            if critic_np is not None:
-                write_buf["last_critic"][:] = critic_np
-            ring_buffer.signal_write_done()  # atomic increment, non-blocking
-            ema_rollout_ms = (1 - _EMA) * ema_rollout_ms + _EMA * (
-                (time.perf_counter() - t_rollout_start) * 1000
-            )
-
-    except Exception:
-        stop_event.set()
-        raise
-
-    ring_buffer.close()
-    actor_weight_sync.close()
-    critic_weight_sync.close()
-    env.close()
diff --git a/src/unilab/algos/torch/common/__init__.py b/src/unilab/algos/torch/common/__init__.py
deleted file mode 100644
index 75b190f0b..000000000
--- a/src/unilab/algos/torch/common/__init__.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from unilab.algos.torch.common.actor_factory import build_actor
-from unilab.algos.torch.common.device import get_env_dims
-from unilab.algos.torch.common.networks import Critic, DistributionalQNetwork
-from unilab.algos.torch.common.normalization import EmpiricalNormalization
-from unilab.algos.torch.common.stability import check_nan_loss, clip_gradients, safe_tensor
-from unilab.base.registry import ensure_registries
-
-__all__ = [
-    "EmpiricalNormalization",
-    "DistributionalQNetwork",
-    "Critic",
-    "get_env_dims",
-    "check_nan_loss",
-    "clip_gradients",
-    "safe_tensor",
-    "ensure_registries",
-    "build_actor",
-]
diff --git a/src/unilab/algos/torch/common/actor_factory.py b/src/unilab/algos/torch/common/actor_factory.py
deleted file mode 100644
index af1656a54..000000000
--- a/src/unilab/algos/torch/common/actor_factory.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""Actor factory helpers for torch off-policy algorithms."""
-
-from __future__ import annotations
-
-
-def build_actor(
-    algo_type,
-    obs_dim,
-    action_dim,
-    actor_hidden_dim,
-    use_layer_norm,
-    device,
-    num_envs=1,
-    actor_num_blocks: int = 2,
-    actor_noise_zeta_mu: float = 2.0,
-    actor_noise_zeta_max: int = 16,
-    priv_info_dim: int | None = None,
-    priv_info_embed_dim: int = 9,
-    priv_mlp_hidden_dims: tuple[int, ...] | list[int] = (256, 128, 9),
-    **kwargs,
-):
-    """Build the correct actor model based on algorithm type."""
-    if algo_type == "sac":
-        from unilab.algos.torch.fast_sac.learner import SACActor
-
-        return SACActor(
-            obs_dim=obs_dim,
-            action_dim=action_dim,
-            hidden_dim=actor_hidden_dim,
-            use_layer_norm=use_layer_norm,
-            device=device,
-        )
-    if algo_type == "hora_sac":
-        if priv_info_dim is None:
-            raise ValueError("build_actor(algo_type='hora_sac') requires priv_info_dim.")
-        from unilab.algos.torch.hora.sac_models import HoraSACActor
-
-        return HoraSACActor(
-            obs_dim=obs_dim,
-            priv_info_dim=int(priv_info_dim),
-            action_dim=action_dim,
-            hidden_dim=actor_hidden_dim,
-            priv_info_embed_dim=priv_info_embed_dim,
-            priv_mlp_hidden_dims=tuple(priv_mlp_hidden_dims),
-            use_layer_norm=use_layer_norm,
-            device=device,
-        )
-    if algo_type == "td3":
-        from unilab.algos.torch.fast_td3.learner import TD3Actor
-
-        return TD3Actor(
-            obs_dim=obs_dim,
-            n_act=action_dim,
-            num_envs=num_envs,
-            hidden_dim=actor_hidden_dim,
-            init_scale=kwargs.get("init_scale", 0.01),
-            log_std_min=kwargs.get("log_std_min", -1.6),
-            log_std_max=kwargs.get("log_std_max", -0.22),
-            device=device,
-        )
-    if algo_type == "flashsac":
-        from unilab.algos.torch.flash_sac.network import FlashSACActor
-
-        return FlashSACActor(
-            num_blocks=actor_num_blocks,
-            input_dim=obs_dim,
-            hidden_dim=actor_hidden_dim,
-            action_dim=action_dim,
-            noise_zeta_mu=actor_noise_zeta_mu,
-            noise_zeta_max=actor_noise_zeta_max,
-            device=device,
-        )
-    raise ValueError(f"Unknown algo_type: {algo_type}")
diff --git a/src/unilab/algos/torch/common/collector_timing.py b/src/unilab/algos/torch/common/collector_timing.py
deleted file mode 100644
index 05bc4115e..000000000
--- a/src/unilab/algos/torch/common/collector_timing.py
+++ /dev/null
@@ -1,32 +0,0 @@
-"""Shared collector timing helpers."""
-
-from __future__ import annotations
-
-import math
-from numbers import Real
-from typing import Any
-
-ENV_STEP_BREAKDOWN_TIMING_MAP = {
-    "step_core_ms": "env_step_backend_ms",
-    "update_state_ms": "env_step_update_state_ms",
-    "reset_done_ms": "env_step_reset_done_ms",
-}
-
-
-def extract_env_step_breakdown_timing_ms(info: dict[str, Any] | None) -> dict[str, float]:
-    """Extract env-owned step sub-timings for collector metrics."""
-    if not isinstance(info, dict):
-        return {}
-    timing = info.get("timing")
-    if not isinstance(timing, dict):
-        return {}
-
-    out: dict[str, float] = {}
-    for source_key, collector_key in ENV_STEP_BREAKDOWN_TIMING_MAP.items():
-        value = timing.get(source_key)
-        if not isinstance(value, Real):
-            continue
-        value_float = float(value)
-        if math.isfinite(value_float):
-            out[collector_key] = value_float
-    return out
diff --git a/src/unilab/algos/torch/common/compile.py b/src/unilab/algos/torch/common/compile.py
deleted file mode 100644
index c2521f320..000000000
--- a/src/unilab/algos/torch/common/compile.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from __future__ import annotations
-
-import importlib.util
-import sys
-from collections.abc import Callable
-from typing import Any, cast
-
-import torch
-
-_WARNED_REASONS: set[str] = set()
-
-
-def _warn_once(reason: str) -> None:
-    if reason in _WARNED_REASONS:
-        return
-    _WARNED_REASONS.add(reason)
-    message = f"WARNING: torch.compile is unavailable for CUDA; using eager mode ({reason})."
-    try:
-        from rich.console import Console
-
-        Console(stderr=True).print(message, style="yellow")
-    except Exception:  # pragma: no cover - best-effort diagnostic only
-        print(message, file=sys.stderr)
-
-
-def get_torch_compile_for_cuda(
-    device: torch.device | str, *, warn: bool = False
-) -> Callable[..., Any] | None:
-    """Return ``torch.compile`` when CUDA Inductor dependencies are available."""
-    compile_fn = getattr(torch, "compile", None)
-    if torch.device(device).type != "cuda":
-        return None
-    if compile_fn is None:
-        if warn:
-            _warn_once("torch.compile is not present in this PyTorch build")
-        return None
-    if (
-        getattr(compile_fn, "__module__", "") == "torch"
-        and importlib.util.find_spec("triton") is None
-    ):
-        if warn:
-            _warn_once(
-                "Triton is not installed; this environment cannot use CUDA Inductor. "
-                "PyTorch's Windows torch.compile documentation currently covers "
-                "CPU/XPU Inductor, not the CUDA/Triton path"
-            )
-        return None
-    return cast(Callable[..., Any], compile_fn)
diff --git a/src/unilab/algos/torch/common/device.py b/src/unilab/algos/torch/common/device.py
deleted file mode 100644
index 1b1703ba5..000000000
--- a/src/unilab/algos/torch/common/device.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from unilab.base import registry
-
-
-def get_env_dims(
-    env_name: str, sim_backend: str = "mujoco", env_cfg_override: dict | None = None
-) -> tuple[int, int, int]:
-    """Get (actor_obs_dim, action_dim, critic_obs_dim) from environment."""
-    from unilab.base.observations import get_obs_dims as get_obs_dims_from_spec
-
-    env = registry.make(
-        env_name, num_envs=1, sim_backend=sim_backend, env_cfg_override=env_cfg_override
-    )
-    obs_dim, critic_dim = get_obs_dims_from_spec(env.obs_groups_spec)
-    action_shape = env.action_space.shape
-    assert action_shape is not None
-    action_dim = action_shape[0]
-    env.close()  # type: ignore[attr-defined]
-    return obs_dim, action_dim, critic_dim
-
-
-__all__ = ["get_env_dims"]
diff --git a/src/unilab/algos/torch/common/networks.py b/src/unilab/algos/torch/common/networks.py
deleted file mode 100644
index fccab20b9..000000000
--- a/src/unilab/algos/torch/common/networks.py
+++ /dev/null
@@ -1,164 +0,0 @@
-"""Neural network architectures for RL algorithms."""
-
-from __future__ import annotations
-
-from typing import Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class DistributionalQNetwork(nn.Module):
-    """Single distributional Q-network (C51 variant).
-
-    Architecture: Linear→ReLU → Linear→ReLU → Linear→ReLU → Linear
-    Outputs num_atoms logits over the value distribution.
-    """
-
-    def __init__(
-        self,
-        obs_dim: int,
-        n_act: int,
-        num_atoms: int,
-        v_min: float,
-        v_max: float,
-        hidden_dim: int,
-        device: Optional[torch.device] = None,
-    ):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.Linear(obs_dim + n_act, hidden_dim, device=device),
-            nn.ReLU(),
-            nn.Linear(hidden_dim, hidden_dim // 2, device=device),
-            nn.ReLU(),
-            nn.Linear(hidden_dim // 2, hidden_dim // 4, device=device),
-            nn.ReLU(),
-            nn.Linear(hidden_dim // 4, num_atoms, device=device),
-        )
-        self.v_min = v_min
-        self.v_max = v_max
-        self.num_atoms = num_atoms
-
-    def forward(self, obs: torch.Tensor, actions: torch.Tensor) -> torch.Tensor:
-        x = torch.cat([obs, actions], 1)
-        x = self.net(x)
-        return torch.as_tensor(x)
-
-    def projection(
-        self,
-        obs: torch.Tensor,
-        actions: torch.Tensor,
-        rewards: torch.Tensor,
-        bootstrap: torch.Tensor,
-        discount: torch.Tensor,
-        q_support: torch.Tensor,
-        device: torch.device,
-    ) -> torch.Tensor:
-        """Categorical projection (Bellman update on the distribution support)."""
-        delta_z = (self.v_max - self.v_min) / (self.num_atoms - 1)
-        batch_size = rewards.shape[0]
-
-        target_z = rewards.unsqueeze(1) + bootstrap.unsqueeze(1) * discount.unsqueeze(1) * q_support
-        target_z = target_z.clamp(self.v_min, self.v_max)
-        b = (target_z - self.v_min) / delta_z
-        l = torch.floor(b).long()
-        u = torch.ceil(b).long()
-
-        is_int = l == u
-        l_mask = is_int & (l > 0)
-        u_mask = is_int & (l == 0)
-
-        l = torch.where(l_mask, l - 1, l)
-        u = torch.where(u_mask, u + 1, u)
-
-        next_dist = F.softmax(self.forward(obs, actions), dim=1)
-        proj_dist = torch.zeros_like(next_dist)
-        offset = (
-            torch.linspace(0, (batch_size - 1) * self.num_atoms, batch_size, device=device)
-            .unsqueeze(1)
-            .expand(batch_size, self.num_atoms)
-            .long()
-        )
-        proj_dist.view(-1).index_add_(
-            0, (l + offset).view(-1), (next_dist * (u.float() - b)).view(-1)
-        )
-        proj_dist.view(-1).index_add_(
-            0, (u + offset).view(-1), (next_dist * (b - l.float())).view(-1)
-        )
-        return proj_dist
-
-
-class Critic(nn.Module):
-    """Twin distributional Q-networks for off-policy RL (SAC/TD3)."""
-
-    q_support: torch.Tensor
-
-    def __init__(
-        self,
-        obs_dim: int,
-        n_act: int,
-        num_atoms: int,
-        v_min: float,
-        v_max: float,
-        hidden_dim: int,
-        device: Optional[torch.device] = None,
-    ):
-        super().__init__()
-        self.qnet1 = DistributionalQNetwork(
-            obs_dim=obs_dim,
-            n_act=n_act,
-            num_atoms=num_atoms,
-            v_min=v_min,
-            v_max=v_max,
-            hidden_dim=hidden_dim,
-            device=device,
-        )
-        self.qnet2 = DistributionalQNetwork(
-            obs_dim=obs_dim,
-            n_act=n_act,
-            num_atoms=num_atoms,
-            v_min=v_min,
-            v_max=v_max,
-            hidden_dim=hidden_dim,
-            device=device,
-        )
-
-        self.register_buffer("q_support", torch.linspace(v_min, v_max, num_atoms, device=device))
-        self.device = device
-
-    def forward(self, obs: torch.Tensor, actions: torch.Tensor):
-        return self.qnet1(obs, actions), self.qnet2(obs, actions)
-
-    def projection(
-        self,
-        obs: torch.Tensor,
-        actions: torch.Tensor,
-        rewards: torch.Tensor,
-        bootstrap: torch.Tensor,
-        discount: torch.Tensor,
-    ):
-        """Projection operation using both Q-networks."""
-        q1_proj = self.qnet1.projection(
-            obs,
-            actions,
-            rewards,
-            bootstrap,
-            discount,
-            self.q_support,
-            self.q_support.device,
-        )
-        q2_proj = self.qnet2.projection(
-            obs,
-            actions,
-            rewards,
-            bootstrap,
-            discount,
-            self.q_support,
-            self.q_support.device,
-        )
-        return q1_proj, q2_proj
-
-    def get_value(self, probs: torch.Tensor) -> torch.Tensor:
-        """Calculate value from probability distribution using support."""
-        return torch.sum(probs * self.q_support, dim=1)
diff --git a/src/unilab/algos/torch/common/normalization.py b/src/unilab/algos/torch/common/normalization.py
deleted file mode 100644
index dc5bd5259..000000000
--- a/src/unilab/algos/torch/common/normalization.py
+++ /dev/null
@@ -1,79 +0,0 @@
-"""Observation normalization for RL training."""
-
-from __future__ import annotations
-
-import torch
-import torch.nn as nn
-
-
-class EmpiricalNormalization(nn.Module):
-    """Normalize mean and variance of observations using running statistics."""
-
-    _mean: torch.Tensor
-    _var: torch.Tensor
-    _std: torch.Tensor
-    count: torch.Tensor
-
-    def __init__(self, shape, device, eps=1e-2):
-        super().__init__()
-        self.eps = eps
-        self.device = device
-        self.register_buffer("_mean", torch.zeros(shape).unsqueeze(0).to(device))
-        self.register_buffer("_var", torch.ones(shape).unsqueeze(0).to(device))
-        self.register_buffer("_std", torch.ones(shape).unsqueeze(0).to(device))
-        self.register_buffer("count", torch.tensor(0, dtype=torch.long).to(device))
-
-    @property
-    def mean(self):
-        return self._mean.squeeze(0).clone()
-
-    @property
-    def std(self):
-        return self._std.squeeze(0).clone()
-
-    @torch.no_grad()
-    def forward(self, x: torch.Tensor, center: bool = True, update: bool = True) -> torch.Tensor:
-        if self.training and update:
-            self.update(x)
-        if center:
-            return torch.as_tensor((x - self._mean) / (self._std + self.eps))
-        else:
-            return torch.as_tensor(x / (self._std + self.eps))
-
-    def update(self, x):
-        batch_size = x.shape[0]
-        batch_mean = torch.mean(x, dim=0, keepdim=True)
-        batch_var = torch.var(x, dim=0, keepdim=True, unbiased=False)
-        self.update_from_moments(batch_mean, batch_var, batch_size)
-
-    @torch.no_grad()
-    def update_from_moments(
-        self,
-        batch_mean: torch.Tensor,
-        batch_var: torch.Tensor,
-        batch_count: int | torch.Tensor,
-    ) -> None:
-        """Update running stats from precomputed batch moments."""
-        batch_mean = batch_mean.to(device=self._mean.device, dtype=self._mean.dtype).view_as(
-            self._mean
-        )
-        batch_var = batch_var.to(device=self._var.device, dtype=self._var.dtype).view_as(self._var)
-        batch_count_t = torch.as_tensor(batch_count, device=self.count.device).to(
-            dtype=self.count.dtype
-        )
-
-        new_count = self.count + batch_count_t
-
-        # Welford's online algorithm
-        delta = batch_mean - self._mean
-        self._mean.copy_(self._mean + delta * (batch_count_t / new_count))
-        delta2 = batch_mean - self._mean
-        m_a = self._var * self.count
-        m_b = batch_var * batch_count_t
-        M2 = m_a + m_b + delta2.pow(2) * (self.count * batch_count_t / new_count)
-        self._var.copy_(M2 / new_count)
-        self._std.copy_(self._var.sqrt())
-        self.count.copy_(new_count)
-
-    def inverse(self, y):
-        return y * (self._std + self.eps) + self._mean
diff --git a/src/unilab/algos/torch/common/stability.py b/src/unilab/algos/torch/common/stability.py
deleted file mode 100644
index 5c9aba24f..000000000
--- a/src/unilab/algos/torch/common/stability.py
+++ /dev/null
@@ -1,48 +0,0 @@
-"""Numerical stability utilities for RL training."""
-
-import torch
-
-
-def check_nan_loss(
-    loss: torch.Tensor, default_metrics: dict
-) -> tuple[torch.Tensor | None, dict | None]:
-    """Check if loss contains NaN or Inf values.
-
-    Args:
-        loss: Loss tensor to check
-        default_metrics: Default metric values to return if NaN detected
-
-    Returns:
-        (loss, None) if valid, (None, nan_metrics) if invalid
-    """
-    if torch.isnan(loss) or torch.isinf(loss):
-        nan_metrics = {k: float("nan") for k in default_metrics}
-        return None, nan_metrics
-    return loss, None
-
-
-def clip_gradients(parameters, max_norm: float = 10.0):
-    """Clip gradients by global norm.
-
-    Args:
-        parameters: Model parameters
-        max_norm: Maximum gradient norm
-    """
-    torch.nn.utils.clip_grad_norm_(parameters, max_norm=max_norm)
-
-
-def safe_tensor(
-    tensor: torch.Tensor, nan_value: float = 0.0, clamp_range: tuple = (-10.0, 10.0)
-) -> torch.Tensor:
-    """Make tensor numerically safe by clamping and replacing NaN values.
-
-    Args:
-        tensor: Input tensor
-        nan_value: Value to replace NaN with
-        clamp_range: (min, max) range to clamp values
-
-    Returns:
-        Safe tensor
-    """
-    tensor = torch.clamp(tensor, clamp_range[0], clamp_range[1])
-    return torch.nan_to_num(tensor, nan=nan_value)
diff --git a/src/unilab/algos/torch/fast_sac/__init__.py b/src/unilab/algos/torch/fast_sac/__init__.py
deleted file mode 100644
index 274421377..000000000
--- a/src/unilab/algos/torch/fast_sac/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# FastSAC algorithm
diff --git a/src/unilab/algos/torch/fast_sac/double_buffer.py b/src/unilab/algos/torch/fast_sac/double_buffer.py
deleted file mode 100644
index 88b813366..000000000
--- a/src/unilab/algos/torch/fast_sac/double_buffer.py
+++ /dev/null
@@ -1,145 +0,0 @@
-"""FastSAC builder for the device-authoritative replay path."""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, Any, cast
-
-from omegaconf import DictConfig, OmegaConf
-
-from unilab.algos.torch.fast_sac.learner import FastSACLearner
-from unilab.algos.torch.offpolicy.double_buffer_runner import DoubleBufferOffPolicyRunner
-from unilab.algos.torch.offpolicy.runtime import resolve_custom_offpolicy_runtime
-from unilab.base.np_env import NpEnv
-from unilab.training import create_env, ensure_registries
-from unilab.utils.nan_guard import NanGuardCfg
-
-if TYPE_CHECKING:
-    from unilab.ipc.dp_sync import DpParameterSync
-
-
-def build_sac_double_buffer_runner(
-    cfg: DictConfig,
-    *,
-    env_cfg_override: dict[str, Any] | None,
-    replay_prefetch_mode: str,
-    device: str,
-    nan_guard_cfg: NanGuardCfg | None = None,
-    torch_thread_runtime: dict[str, Any] | None = None,
-    collector_cpu_ids: list[int] | None = None,
-    dp_sync: DpParameterSync | None = None,
-) -> Any:
-    """Build SAC from its Hydra owner config without interpreting it in the entrypoint."""
-    from unilab.base.observations import get_obs_dims
-
-    ensure_registries()
-    rl_cfg = cast(dict[str, Any], OmegaConf.to_container(cfg.algo, resolve=True))
-    custom_runtime = resolve_custom_offpolicy_runtime(rl_cfg)
-
-    env = cast(NpEnv, create_env(cfg, num_envs=1, env_cfg_override=env_cfg_override))
-    try:
-        action_shape = env.action_space.shape
-        assert action_shape
-        obs_dim, critic_obs_dim = get_obs_dims(env.obs_groups_spec)
-        action_dim = int(action_shape[0])
-        symmetry_augmentation = None
-        if (
-            custom_runtime is not None
-            and cfg.algo.use_symmetry
-            and not custom_runtime.supports_symmetry
-        ):
-            raise ValueError("Selected SAC off-policy runtime does not support symmetry.")
-        if cfg.algo.use_symmetry:
-            symmetry_augmentation = env.build_symmetry_augmentation(device=device)
-            if symmetry_augmentation is None:
-                raise ValueError(f"{cfg.training.task_name} does not provide symmetry augmentation")
-    finally:
-        env.close()
-
-    batch_size = cfg.algo.batch_size
-    if symmetry_augmentation is not None:
-        if batch_size % symmetry_augmentation.batch_multiplier != 0:
-            raise ValueError(
-                "Symmetry augmentation requires batch_size divisible by "
-                f"{symmetry_augmentation.batch_multiplier}, got {batch_size}"
-            )
-        batch_size = batch_size // symmetry_augmentation.batch_multiplier
-
-    learner_cls: type[Any] = FastSACLearner
-    algo_type = "sac"
-    learner_extra_kwargs: dict[str, Any] = {}
-    if custom_runtime is not None:
-        learner_extra_kwargs = cast(
-            dict[str, Any],
-            custom_runtime.build_model_kwargs(
-                obs_dim=int(obs_dim),
-                critic_obs_dim=int(critic_obs_dim),
-            ),
-        )
-        if custom_runtime.learner_cls is not None:
-            learner_cls = custom_runtime.learner_cls
-        if custom_runtime.algo_type is not None:
-            algo_type = str(custom_runtime.algo_type)
-
-    learner_kwargs = {
-        "obs_dim": obs_dim,
-        "action_dim": action_dim,
-        "gamma": cfg.algo.gamma,
-        "tau": cfg.algo.tau,
-        "actor_lr": cfg.algo.actor_lr,
-        "critic_lr": cfg.algo.critic_lr,
-        "alpha_lr": cfg.algo.algo_params.alpha_lr,
-        "alpha_init": cfg.algo.algo_params.alpha_init,
-        "target_entropy_ratio": cfg.algo.algo_params.target_entropy_ratio,
-        "actor_hidden_dim": cfg.algo.actor_hidden_dim,
-        "critic_hidden_dim": cfg.algo.critic_hidden_dim,
-        "num_atoms": cfg.algo.num_atoms,
-        "use_layer_norm": cfg.algo.use_layer_norm,
-        "max_grad_norm": cfg.algo.algo_params.max_grad_norm,
-        "use_amp": cfg.training.use_amp,
-        "amp_dtype": cfg.algo.algo_params.amp_dtype,
-        "use_compile": cfg.algo.algo_params.use_compile,
-        "obs_normalization": cfg.algo.obs_normalization,
-        "use_cuda_graph_critic": bool(
-            getattr(cfg.algo.algo_params, "use_cuda_graph_critic", False)
-        ),
-        "use_cuda_graph_actor": bool(getattr(cfg.algo.algo_params, "use_cuda_graph_actor", False)),
-        "use_cuda_graph_critic_packed_staging": bool(
-            getattr(cfg.algo.algo_params, "use_cuda_graph_critic_packed_staging", False)
-        ),
-        "use_cuda_graph_actor_packed_staging": bool(
-            getattr(cfg.algo.algo_params, "use_cuda_graph_actor_packed_staging", False)
-        ),
-        "nvtx_profile_ranges": bool(getattr(cfg.training, "nvtx_profile_ranges", False)),
-        "use_symmetry": cfg.algo.use_symmetry,
-        "symmetry_augmentation": symmetry_augmentation,
-        "critic_obs_dim": critic_obs_dim,
-    }
-    learner_kwargs.update(learner_extra_kwargs)
-    learner = learner_cls(device=device, **learner_kwargs)
-
-    return DoubleBufferOffPolicyRunner(
-        learner=learner,
-        env_name=cfg.training.task_name,
-        algo_type=algo_type,
-        num_envs=cfg.algo.num_envs,
-        replay_buffer_n=cfg.algo.replay_buffer_n,
-        batch_size=batch_size,
-        learning_starts=cfg.algo.learning_starts,
-        updates_per_step=cfg.algo.updates_per_step,
-        policy_frequency=cfg.algo.policy_frequency,
-        env_steps_per_sync=cfg.training.env_steps_per_sync,
-        device=device,
-        obs_normalization=cfg.algo.obs_normalization,
-        sim_backend=cfg.training.sim_backend,
-        env_cfg_override=env_cfg_override,
-        trace_enabled=cfg.training.trace_enabled,
-        trace_output_dir=cfg.training.trace_output_dir,
-        trace_thread_time=cfg.training.trace_thread_time,
-        trace_cuda_events=cfg.training.trace_cuda_events,
-        replay_prefetch_mode=replay_prefetch_mode,
-        seed=cfg.algo.seed,
-        nan_guard_cfg=nan_guard_cfg,
-        torch_thread_runtime=torch_thread_runtime,
-        collector_cpu_ids=collector_cpu_ids,
-        dp_sync=dp_sync,
-    )
diff --git a/src/unilab/algos/torch/fast_sac/learner.py b/src/unilab/algos/torch/fast_sac/learner.py
deleted file mode 100644
index 5d714a37c..000000000
--- a/src/unilab/algos/torch/fast_sac/learner.py
+++ /dev/null
@@ -1,1627 +0,0 @@
-"""FastSAC Learner — replicated from holosoma's FastSAC implementation.
-
-Network architecture:
-- Actor: MLP with SiLU + LayerNorm, tanh-squashed Gaussian
-- Critic: Distributional Q-Networks (C51 variant, num_atoms=101)
-- Automatic entropy coefficient (alpha) learning
-
-Hyperparameters aligned with holosoma FastSACConfig defaults.
-"""
-
-from __future__ import annotations
-
-import math
-from collections.abc import Callable, Iterable, Iterator
-from contextlib import contextmanager
-from typing import Any, Dict, Tuple, cast
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-
-from unilab.algos.torch.common.compile import get_torch_compile_for_cuda
-from unilab.algos.torch.common.normalization import EmpiricalNormalization
-from unilab.base.augmentation import SymmetryAugmentation
-
-
-@contextmanager
-def _cuda_nvtx_range(name: str, enabled: bool) -> Iterator[None]:
-    if not enabled:
-        yield
-        return
-
-    torch.cuda.nvtx.range_push(name)
-    try:
-        yield
-    finally:
-        torch.cuda.nvtx.range_pop()
-
-
-# ---------------------------------------------------------------------------
-# Actor Network (holosoma-style: SiLU + LayerNorm + Tanh squashing)
-# ---------------------------------------------------------------------------
-
-
-class SACActor(nn.Module):
-    """Stochastic actor for SAC with tanh-squashed Gaussian policy.
-
-    Architecture: Linear→LN→SiLU → Linear→LN→SiLU → Linear→LN→SiLU → fc_mu + fc_logstd
-    Hidden dims: [hidden_dim, hidden_dim//2, hidden_dim//4]
-    """
-
-    action_scale: torch.Tensor
-    action_bias: torch.Tensor
-
-    def __init__(
-        self,
-        obs_dim: int,
-        action_dim: int,
-        hidden_dim: int = 512,
-        log_std_max: float = 0.0,
-        log_std_min: float = -5.0,
-        use_tanh: bool = True,
-        use_layer_norm: bool = True,
-        device: str | torch.device = "cpu",
-        action_scale: torch.Tensor | None = None,
-        action_bias: torch.Tensor | None = None,
-    ):
-        super().__init__()
-        self.obs_dim = obs_dim
-        self.action_dim = action_dim
-        self.log_std_max = log_std_max
-        self.log_std_min = log_std_min
-        self.use_tanh = use_tanh
-        self.device_ = device  # avoid name collision with nn.Module.device
-
-        self.net = nn.Sequential(
-            nn.Linear(obs_dim, hidden_dim, device=device),
-            nn.LayerNorm(hidden_dim, device=device) if use_layer_norm else nn.Identity(),
-            nn.SiLU(),
-            nn.Linear(hidden_dim, hidden_dim // 2, device=device),
-            nn.LayerNorm(hidden_dim // 2, device=device) if use_layer_norm else nn.Identity(),
-            nn.SiLU(),
-            nn.Linear(hidden_dim // 2, hidden_dim // 4, device=device),
-            nn.LayerNorm(hidden_dim // 4, device=device) if use_layer_norm else nn.Identity(),
-            nn.SiLU(),
-        )
-        self.fc_mu = nn.Linear(hidden_dim // 4, action_dim, device=device)
-        self.fc_logstd = nn.Linear(hidden_dim // 4, action_dim, device=device)
-
-        # Zero-init output heads (holosoma style)
-        nn.init.constant_(self.fc_mu.weight, 0.0)
-        nn.init.constant_(self.fc_mu.bias, 0.0)
-        nn.init.constant_(self.fc_logstd.weight, 0.0)
-        nn.init.constant_(self.fc_logstd.bias, 0.0)
-
-        # Action scaling
-        if action_scale is not None:
-            self.register_buffer("action_scale", action_scale.to(device))
-        else:
-            self.register_buffer("action_scale", torch.ones(action_dim, device=device))
-        if action_bias is not None:
-            self.register_buffer("action_bias", action_bias.to(device))
-        else:
-            self.register_buffer("action_bias", torch.zeros(action_dim, device=device))
-
-    def forward(self, obs: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """Returns (action, mean, log_std)."""
-        x = self.net(obs)
-        mean = self.fc_mu(x)
-        log_std = self.fc_logstd(x)
-
-        # Squash log_std to [log_std_min, log_std_max] (SpinUp / Denis Yarats style)
-        log_std = torch.tanh(log_std)
-        log_std = self.log_std_min + 0.5 * (self.log_std_max - self.log_std_min) * (log_std + 1)
-
-        # NaN protection: clamp mean to prevent exploding values
-        mean = torch.clamp(mean, -10.0, 10.0)
-        mean = torch.nan_to_num(mean, nan=0.0)
-        log_std = torch.nan_to_num(log_std, nan=self.log_std_min)
-
-        if self.use_tanh:
-            tanh_mean = torch.tanh(mean)
-            action = tanh_mean * self.action_scale + self.action_bias
-        else:
-            action = mean
-
-        return action, mean, log_std
-
-    def as_export_module(self) -> "nn.Module":
-        """Return a single-input/single-output wrapper suitable for torch.onnx.export."""
-        actor = self
-
-        class _Wrapper(nn.Module):
-            def __init__(self) -> None:
-                super().__init__()
-                self.base = actor
-
-            def forward(self, obs: torch.Tensor) -> torch.Tensor:
-                action, _, _ = self.base(obs)
-                return cast(torch.Tensor, action)
-
-        return _Wrapper()
-
-    def get_actions_and_log_probs(
-        self,
-        obs: torch.Tensor,
-        eps: torch.Tensor | None = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """Sample actions and compute log probabilities. Returns (action, log_prob, log_std)."""
-        _, mean, log_std = self(obs)
-        action, log_prob = self._sample_action_and_log_prob(mean, log_std, eps=eps)
-        return action, log_prob, log_std
-
-    def _sample_action_and_log_prob(
-        self,
-        mean: torch.Tensor,
-        log_std: torch.Tensor,
-        eps: torch.Tensor | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        std = log_std.exp()
-        if eps is None:
-            eps = torch.randn_like(mean)
-        raw_action = mean + std * eps
-        log_prob = -0.5 * (
-            ((raw_action - mean) / std).pow(2) + 2.0 * log_std + math.log(2.0 * math.pi)
-        )
-
-        if self.use_tanh:
-            tanh_action = torch.tanh(raw_action)
-            action = tanh_action * self.action_scale + self.action_bias
-            log_prob -= torch.log(1 - tanh_action.pow(2) + 1e-6)
-            log_prob -= torch.log(self.action_scale + 1e-6)
-        else:
-            action = raw_action
-
-        return action, log_prob.sum(1)
-
-    @torch.no_grad()
-    def explore(
-        self,
-        obs: torch.Tensor,
-        dones: torch.Tensor | None = None,
-        deterministic: bool = False,
-    ) -> torch.Tensor:
-        """Get exploration actions.
-
-        Args:
-            obs: Batched observations.
-            dones: Unused for SAC; kept for API alignment with TD3 actor.
-            deterministic: Whether to return deterministic policy actions.
-        """
-        # Backward compatibility: previous signature was explore(obs, deterministic=False).
-        if isinstance(dones, bool):
-            deterministic = dones
-            dones = None
-        _ = dones
-
-        _, mean, log_std = self.forward(obs)
-        if deterministic:
-            if self.use_tanh:
-                return torch.tanh(mean) * self.action_scale + self.action_bias
-            return mean
-
-        raw_action = mean + log_std.exp() * torch.randn_like(mean)
-
-        if self.use_tanh:
-            return torch.tanh(raw_action) * self.action_scale + self.action_bias
-        return raw_action
-
-
-# ---------------------------------------------------------------------------
-# Distributional Q-Network (C51 variant, from holosoma)
-# ---------------------------------------------------------------------------
-
-
-class DistributionalQNetwork(nn.Module):
-    """Single distributional Q-network (C51).
-
-    Architecture: Linear→LN→SiLU → Linear→LN→SiLU → Linear→LN→SiLU → Linear(num_atoms)
-    Input: concat(obs, action)
-    """
-
-    def __init__(
-        self,
-        obs_dim: int,
-        action_dim: int,
-        num_atoms: int = 101,
-        v_min: float = -20.0,
-        v_max: float = 20.0,
-        hidden_dim: int = 768,
-        use_layer_norm: bool = True,
-        device: str | torch.device = "cpu",
-    ):
-        super().__init__()
-        self.num_atoms = num_atoms
-        self.v_min = v_min
-        self.v_max = v_max
-
-        input_dim = obs_dim + action_dim
-        self.net = nn.Sequential(
-            nn.Linear(input_dim, hidden_dim, device=device),
-            nn.LayerNorm(hidden_dim, device=device) if use_layer_norm else nn.Identity(),
-            nn.SiLU(),
-            nn.Linear(hidden_dim, hidden_dim // 2, device=device),
-            nn.LayerNorm(hidden_dim // 2, device=device) if use_layer_norm else nn.Identity(),
-            nn.SiLU(),
-            nn.Linear(hidden_dim // 2, hidden_dim // 4, device=device),
-            nn.LayerNorm(hidden_dim // 4, device=device) if use_layer_norm else nn.Identity(),
-            nn.SiLU(),
-            nn.Linear(hidden_dim // 4, num_atoms, device=device),
-        )
-
-    def forward(self, obs: torch.Tensor, actions: torch.Tensor) -> torch.Tensor:
-        x = torch.cat([obs, actions], dim=-1)
-        return self.net(x)  # type: ignore[no-any-return]
-
-    def projection(
-        self,
-        obs: torch.Tensor,
-        actions: torch.Tensor,
-        rewards: torch.Tensor,
-        bootstrap: torch.Tensor,
-        discount: torch.Tensor,
-        q_support: torch.Tensor,
-        device: torch.device,
-    ) -> torch.Tensor:
-        """Categorical projection for distributional RL."""
-        delta_z = (self.v_max - self.v_min) / (self.num_atoms - 1)
-        batch_size = rewards.shape[0]
-
-        target_z = rewards.unsqueeze(1) + bootstrap.unsqueeze(1) * discount.unsqueeze(1) * q_support
-        target_z = target_z.clamp(self.v_min, self.v_max)
-        b = (target_z - self.v_min) / delta_z
-        lower = torch.floor(b).long()
-        upper = torch.ceil(b).long()
-
-        is_integer = upper == lower
-        lower_mask = torch.logical_and((lower > 0), is_integer)
-        upper_mask = torch.logical_and((lower == 0), is_integer)
-
-        lower = torch.where(lower_mask, lower - 1, lower)
-        upper = torch.where(upper_mask, upper + 1, upper)
-
-        next_dist = F.softmax(self(obs, actions), dim=1)
-        proj_dist = torch.zeros_like(next_dist)
-        offset = (
-            torch.linspace(0, (batch_size - 1) * self.num_atoms, batch_size, device=device)
-            .unsqueeze(1)
-            .expand(batch_size, self.num_atoms)
-            .long()
-        )
-
-        lower_indices = (lower + offset).view(-1)
-        upper_indices = (upper + offset).view(-1)
-        max_index = proj_dist.numel() - 1
-        lower_indices = torch.clamp(lower_indices, 0, max_index)
-        upper_indices = torch.clamp(upper_indices, 0, max_index)
-
-        proj_dist.view(-1).index_add_(0, lower_indices, (next_dist * (upper.float() - b)).view(-1))
-        proj_dist.view(-1).index_add_(0, upper_indices, (next_dist * (b - lower.float())).view(-1))
-        return proj_dist
-
-
-class SACCritic(nn.Module):
-    """Ensemble of distributional Q-networks for SAC.
-
-    Uses ``num_q_networks`` independent DistributionalQNetwork instances.
-    """
-
-    q_support: torch.Tensor
-
-    def __init__(
-        self,
-        obs_dim: int,
-        action_dim: int,
-        num_atoms: int = 101,
-        v_min: float = -20.0,
-        v_max: float = 20.0,
-        hidden_dim: int = 768,
-        use_layer_norm: bool = True,
-        num_q_networks: int = 2,
-        device: str | torch.device = "cpu",
-    ):
-        super().__init__()
-        self.obs_dim = obs_dim
-        self.action_dim = action_dim
-        self.num_atoms = num_atoms
-        self.v_min = v_min
-        self.v_max = v_max
-        self.num_q_networks = num_q_networks
-
-        self.qnets = nn.ModuleList(
-            [
-                DistributionalQNetwork(
-                    obs_dim=obs_dim,
-                    action_dim=action_dim,
-                    num_atoms=num_atoms,
-                    v_min=v_min,
-                    v_max=v_max,
-                    hidden_dim=hidden_dim,
-                    use_layer_norm=use_layer_norm,
-                    device=device,
-                )
-                for _ in range(num_q_networks)
-            ]
-        )
-
-        self.register_buffer("q_support", torch.linspace(v_min, v_max, num_atoms, device=device))
-
-    def forward(self, obs: torch.Tensor, actions: torch.Tensor) -> torch.Tensor:
-        """Returns stacked logits: (num_q_nets, batch, num_atoms)."""
-        outputs = [qnet(obs, actions) for qnet in self.qnets]
-        return torch.stack(outputs, dim=0)
-
-    def projection(
-        self,
-        obs: torch.Tensor,
-        actions: torch.Tensor,
-        rewards: torch.Tensor,
-        bootstrap: torch.Tensor,
-        discount: torch.Tensor,
-    ) -> torch.Tensor:
-        """Project for all Q-networks: (num_q_nets, batch, num_atoms)."""
-        projections = [
-            qnet.projection(  # type: ignore[operator]
-                obs, actions, rewards, bootstrap, discount, self.q_support, self.q_support.device
-            )
-            for qnet in self.qnets
-        ]
-        return torch.stack(projections, dim=0)
-
-    def get_value(self, probs: torch.Tensor) -> torch.Tensor:
-        """Calculate value from probabilities using support."""
-        return torch.sum(probs * self.q_support, dim=-1)
-
-
-# ---------------------------------------------------------------------------
-# FastSACLearner — the training algorithm
-# ---------------------------------------------------------------------------
-
-
-class FastSACLearner:
-    """FastSAC learner with holosoma-aligned hyperparameters.
-
-    Key hyperparameters (aligned with holosoma FastSACConfig):
-    - gamma=0.97, tau=0.125
-    - batch_size=8192, num_updates=8, policy_frequency=4
-    - alpha_init=0.001, target_entropy_ratio=0.0
-    - AdamW with betas=(0.9, 0.95), weight_decay=0.001
-    - Distributional critic (C51, num_atoms=101)
-    """
-
-    def __init__(
-        self,
-        obs_dim: int,
-        action_dim: int,
-        critic_obs_dim: int,
-        device: str = "cpu",
-        # Hyperparameters aligned with holosoma
-        gamma: float = 0.97,
-        tau: float = 0.125,
-        actor_lr: float = 3e-4,
-        critic_lr: float = 3e-4,
-        alpha_lr: float = 3e-4,
-        alpha_init: float = 0.001,
-        target_entropy_ratio: float = 0.0,
-        actor_hidden_dim: int = 512,
-        critic_hidden_dim: int = 768,
-        num_atoms: int = 101,
-        v_min: float = -20.0,
-        v_max: float = 20.0,
-        num_q_networks: int = 2,
-        use_layer_norm: bool = True,
-        use_tanh: bool = True,
-        log_std_max: float = 0.0,
-        log_std_min: float = -5.0,
-        weight_decay: float = 0.001,
-        max_grad_norm: float = 0.0,
-        use_autotune: bool = True,
-        use_symmetry: bool = False,
-        use_amp: bool = False,
-        amp_dtype: str = "auto",
-        use_compile: bool = False,
-        obs_normalization: bool = False,
-        use_cuda_graph_critic: bool = False,
-        use_cuda_graph_actor: bool = False,
-        use_cuda_graph_critic_packed_staging: bool = False,
-        use_cuda_graph_actor_packed_staging: bool = False,
-        nvtx_profile_ranges: bool = False,
-        symmetry_augmentation: SymmetryAugmentation | None = None,
-    ):
-        self.device = device
-        self._device_type = torch.device(device).type
-        self.gamma = gamma
-        self.tau = tau
-        self.max_grad_norm = max_grad_norm
-        self.use_autotune = use_autotune
-        self.use_amp = bool(use_amp) and self._device_type in ("cuda", "xpu")
-        self.use_compile = (
-            bool(use_compile) and get_torch_compile_for_cuda(self.device, warn=True) is not None
-        )
-        self.use_cuda_graph_critic = bool(use_cuda_graph_critic) and self._device_type == "cuda"
-        requested_cuda_graph_critic_packed_staging = bool(use_cuda_graph_critic_packed_staging)
-        requested_cuda_graph_actor_packed_staging = bool(use_cuda_graph_actor_packed_staging)
-        self.use_cuda_graph_actor = bool(use_cuda_graph_actor) and self._device_type == "cuda"
-        self._gradient_sync: Callable[[Iterable[torch.Tensor]], None] | None = None
-        self._gradient_sync_graph_replay_recorder: Callable[[int], None] | None = None
-        self.dp_cuda_graph_gradient_sync = False
-        self._active_cuda_graph_gradient_sync_calls: list[int] | None = None
-        self.nvtx_profile_ranges = bool(nvtx_profile_ranges) and self._device_type == "cuda"
-        self.amp_dtype = amp_dtype
-        self._amp_dtype = self._resolve_amp_dtype(amp_dtype, self._device_type)
-        self.critic_obs_dim = critic_obs_dim
-
-        # Build actor (uses obs only)
-        self.actor = SACActor(
-            obs_dim=obs_dim,
-            action_dim=action_dim,
-            hidden_dim=actor_hidden_dim,
-            log_std_max=log_std_max,
-            log_std_min=log_std_min,
-            use_tanh=use_tanh,
-            use_layer_norm=use_layer_norm,
-            device=device,
-        )
-
-        self.qnet = SACCritic(
-            obs_dim=critic_obs_dim,
-            action_dim=action_dim,
-            num_atoms=num_atoms,
-            v_min=v_min,
-            v_max=v_max,
-            hidden_dim=critic_hidden_dim,
-            use_layer_norm=use_layer_norm,
-            num_q_networks=num_q_networks,
-            device=device,
-        )
-
-        # Target critic
-        self.qnet_target = SACCritic(
-            obs_dim=critic_obs_dim,
-            action_dim=action_dim,
-            num_atoms=num_atoms,
-            v_min=v_min,
-            v_max=v_max,
-            hidden_dim=critic_hidden_dim,
-            use_layer_norm=use_layer_norm,
-            num_q_networks=num_q_networks,
-            device=device,
-        )
-        self.qnet_target.load_state_dict(self.qnet.state_dict())
-
-        # Entropy coefficient
-        self.log_alpha = torch.tensor([math.log(alpha_init)], requires_grad=True, device=device)
-        self.target_entropy = -action_dim * target_entropy_ratio
-        self._zero_metric = torch.zeros((), device=device)
-
-        self.obs_normalizer: EmpiricalNormalization | nn.Identity
-        if obs_normalization:
-            self.obs_normalizer = EmpiricalNormalization(shape=obs_dim, device=device)
-        else:
-            self.obs_normalizer = nn.Identity()
-
-        # fused AdamW requires CUDA; MPS and CPU do not support it
-        _fused = isinstance(device, str) and device.startswith("cuda")
-        _optimizer_cuda_kwargs = {"capturable": True} if _fused else {}
-
-        # Optimizers (AdamW with holosoma betas)
-        self.q_optimizer = optim.AdamW(
-            self.qnet.parameters(),
-            lr=critic_lr,
-            weight_decay=weight_decay,
-            fused=_fused,
-            betas=(0.9, 0.95),
-            **_optimizer_cuda_kwargs,
-        )
-        self.actor_optimizer = optim.AdamW(
-            self.actor.parameters(),
-            lr=actor_lr,
-            weight_decay=weight_decay,
-            fused=_fused,
-            betas=(0.9, 0.95),
-            **_optimizer_cuda_kwargs,
-        )
-        self.alpha_optimizer = optim.AdamW(
-            [self.log_alpha],
-            lr=alpha_lr,
-            fused=_fused,
-            betas=(0.9, 0.95),
-            weight_decay=0.0,
-            **_optimizer_cuda_kwargs,
-        )
-
-        # Step counter
-        self.update_count = 0
-
-        # AMP scaler for mixed precision (fp16 only; bf16 has fp32 range and skips scaler)
-        self.scaler = (
-            torch.amp.GradScaler("cuda")  # pyright: ignore[reportPrivateImportUsage]
-            if self._should_use_grad_scaler(self.use_amp, self._device_type, self._amp_dtype)
-            else None
-        )
-        self.use_cuda_graph_critic_packed_staging = (
-            requested_cuda_graph_critic_packed_staging
-            and self.use_cuda_graph_critic
-            and self.scaler is None
-        )
-        self.use_cuda_graph_actor_packed_staging = (
-            requested_cuda_graph_actor_packed_staging
-            and self.use_cuda_graph_actor
-            and self.scaler is None
-            and not use_symmetry
-        )
-
-        self.symmetry = symmetry_augmentation
-        if use_symmetry and symmetry_augmentation is None:
-            raise ValueError(
-                "FastSACLearner use_symmetry=True requires a symmetry_augmentation contract"
-            )
-        self.use_symmetry = use_symmetry
-        self._cuda_graph_critic: torch.cuda.CUDAGraph | None = None
-        self._cuda_graph_critic_static_inputs: dict[str, torch.Tensor] | None = None
-        self._cuda_graph_critic_static_packed_input: torch.Tensor | None = None
-        self._cuda_graph_sac_static_packed_input: torch.Tensor | None = None
-        self._cuda_graph_sac_static_source_ptr: int | None = None
-        self._cuda_graph_critic_action_noise: torch.Tensor | None = None
-        self._cuda_graph_critic_outputs: (
-            tuple[
-                torch.Tensor,
-                torch.Tensor,
-                torch.Tensor,
-                torch.Tensor,
-                torch.Tensor,
-                torch.Tensor,
-            ]
-            | None
-        ) = None
-        self._cuda_graph_critic_shapes: dict[str, torch.Size] | None = None
-        self._cuda_graph_critic_gradient_sync_calls = 0
-        self._cuda_graph_actor: torch.cuda.CUDAGraph | None = None
-        self._cuda_graph_actor_static_inputs: dict[str, torch.Tensor] | None = None
-        self._cuda_graph_actor_static_packed_input: torch.Tensor | None = None
-        self._cuda_graph_actor_action_noise: torch.Tensor | None = None
-        self._cuda_graph_actor_outputs: (
-            tuple[
-                torch.Tensor,
-                torch.Tensor,
-                torch.Tensor,
-                torch.Tensor,
-            ]
-            | None
-        ) = None
-        self._cuda_graph_actor_shapes: dict[str, torch.Size] | None = None
-        self._cuda_graph_actor_gradient_sync_calls = 0
-        if self.use_compile:
-            self._compile_training_methods()
-
-    @staticmethod
-    def _resolve_amp_dtype(amp_dtype: str, device_type: str) -> torch.dtype:
-        normalized = amp_dtype.lower()
-        if normalized == "auto":
-            return torch.bfloat16
-        if normalized == "fp16":
-            return torch.float16
-        if normalized == "bf16":
-            return torch.bfloat16
-        raise ValueError("FastSAC amp_dtype must be one of: auto, fp16, bf16")
-
-    @staticmethod
-    def _should_use_grad_scaler(
-        use_amp: bool,
-        device_type: str,
-        amp_dtype: torch.dtype,
-    ) -> bool:
-        return bool(use_amp) and device_type == "cuda" and amp_dtype == torch.float16
-
-    def _compile_training_methods(self) -> None:
-        compile_fn = get_torch_compile_for_cuda(self.device, warn=True)
-        if compile_fn is None:
-            return
-
-        compile_kwargs = {"options": {"triton.cudagraphs": False}}
-        if not self.use_cuda_graph_critic:
-            self._critic_loss_tensors = compile_fn(  # type: ignore[method-assign]
-                self._critic_loss_tensors, **compile_kwargs
-            )
-        if not self.use_cuda_graph_actor:
-            self._actor_loss_tensors = compile_fn(  # type: ignore[method-assign]
-                self._actor_loss_tensors, **compile_kwargs
-            )
-
-    def _autocast(self):
-        return torch.amp.autocast(  # pyright: ignore[reportPrivateImportUsage]
-            self._device_type, dtype=self._amp_dtype, enabled=self.use_amp
-        )
-
-    @torch.no_grad()
-    def _update_obs_normalizer(self, obs: torch.Tensor) -> None:
-        if isinstance(self.obs_normalizer, nn.Identity):
-            return
-        normalizer = cast(EmpiricalNormalization, self.obs_normalizer)
-        normalizer.update(obs)
-
-    def normalize_obs(self, obs: torch.Tensor, update: bool = False) -> torch.Tensor:
-        """Normalize actor observations using running statistics."""
-        if isinstance(self.obs_normalizer, nn.Identity):
-            return obs
-        normalizer = cast(EmpiricalNormalization, self.obs_normalizer)
-        if update:
-            self._update_obs_normalizer(obs)
-            return cast(torch.Tensor, normalizer(obs, update=False))
-        return cast(torch.Tensor, normalizer(obs, update=False))
-
-    def _get_actions_and_log_probs_for_critic(
-        self,
-        actor_obs: torch.Tensor,
-        critic_obs: torch.Tensor,
-        eps: torch.Tensor | None = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """Sample actor actions for critic targets.
-
-        Subclasses can use ``critic_obs`` to supply auxiliary policy context while
-        preserving the standard SAC update path.
-        """
-        del critic_obs
-        return self.actor.get_actions_and_log_probs(actor_obs, eps=eps)
-
-    def _get_actions_and_log_probs_for_actor(
-        self,
-        actor_obs: torch.Tensor,
-        critic_obs: torch.Tensor,
-        eps: torch.Tensor | None = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """Sample actor actions for the actor loss update."""
-        del critic_obs
-        return self.actor.get_actions_and_log_probs(actor_obs, eps=eps)
-
-    def _critic_loss_tensors(
-        self,
-        critic_obs: torch.Tensor,
-        actions: torch.Tensor,
-        rewards: torch.Tensor,
-        next_obs: torch.Tensor,
-        critic_next_obs: torch.Tensor,
-        dones: torch.Tensor,
-        truncated: torch.Tensor,
-        next_action_eps: torch.Tensor | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
-        bootstrap = torch.clamp(1.0 - dones.float() + truncated.float(), 0.0, 1.0)
-        discount = torch.full_like(dones, self.gamma)
-
-        with torch.no_grad():
-            with self._autocast():
-                next_actions, next_log_probs, _ = self._get_actions_and_log_probs_for_critic(
-                    next_obs,
-                    critic_next_obs,
-                    eps=next_action_eps,
-                )
-            adjusted_rewards = (
-                rewards - discount * bootstrap * self.log_alpha.exp() * next_log_probs
-            )
-
-            with self._autocast():
-                target_distributions = self.qnet_target.projection(
-                    critic_next_obs, next_actions, adjusted_rewards, bootstrap, discount
-                )
-                target_values = self.qnet_target.get_value(target_distributions)
-                target_q_max = target_values.max()
-                target_q_min = target_values.min()
-
-        with self._autocast():
-            q_outputs = self.qnet(critic_obs, actions)
-            critic_log_probs = F.log_softmax(q_outputs, dim=-1).clamp(min=-30.0)
-            critic_losses = -torch.sum(target_distributions * critic_log_probs, dim=-1)
-            qf_loss = critic_losses.mean(dim=1).sum(dim=0)
-
-        return qf_loss, target_q_max, target_q_min, next_log_probs.detach()
-
-    def _alpha_loss_tensor(self, next_log_probs: torch.Tensor) -> torch.Tensor:
-        entropy_error_mean = (next_log_probs + self.target_entropy).detach().mean()
-        return -(self.log_alpha.exp() * entropy_error_mean)
-
-    def _actor_loss_tensors(
-        self,
-        obs: torch.Tensor,
-        critic_obs: torch.Tensor,
-        action_eps: torch.Tensor | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        with self._autocast():
-            actions, log_probs, log_std = self._get_actions_and_log_probs_for_actor(
-                obs,
-                critic_obs,
-                eps=action_eps,
-            )
-
-        with torch.no_grad():
-            action_std = log_std.exp().mean()
-            policy_entropy = -log_probs.mean()
-
-        with self._autocast():
-            q_outputs = self.qnet(critic_obs, actions)
-            q_probs = F.softmax(q_outputs, dim=-1)
-            q_values = self.qnet.get_value(q_probs)
-            qf_value = q_values.mean(dim=0)
-            actor_loss = (self.log_alpha.exp().detach() * log_probs - qf_value).mean()
-
-        return actor_loss, policy_entropy, action_std
-
-    def _update_actor_capture_candidate(
-        self,
-        obs: torch.Tensor,
-        critic_obs: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
-        actor_loss, policy_entropy, action_std = self._actor_loss_tensors(
-            obs,
-            critic_obs,
-            action_eps=self._cuda_graph_actor_action_noise,
-        )
-
-        self.actor_optimizer.zero_grad(set_to_none=True)
-        if self.scaler:
-            self.scaler.scale(actor_loss).backward()
-            self.scaler.unscale_(self.actor_optimizer)
-            if self.max_grad_norm > 0:
-                actor_grad_norm = torch.nn.utils.clip_grad_norm_(
-                    self.actor.parameters(), max_norm=self.max_grad_norm
-                )
-            else:
-                actor_grad_norm = self._zero_metric
-            self.scaler.step(self.actor_optimizer)
-            self.scaler.update()
-        else:
-            actor_loss.backward()
-            self._sync_gradients(self.actor.parameters())
-            if self.max_grad_norm > 0:
-                actor_grad_norm = torch.nn.utils.clip_grad_norm_(
-                    self.actor.parameters(), max_norm=self.max_grad_norm
-                )
-            else:
-                actor_grad_norm = self._zero_metric
-            self.actor_optimizer.step()
-
-        return actor_loss, actor_grad_norm, policy_entropy, action_std
-
-    def _update_critic_capture_candidate(
-        self,
-        critic_obs: torch.Tensor,
-        actions: torch.Tensor,
-        rewards: torch.Tensor,
-        next_obs: torch.Tensor,
-        critic_next_obs: torch.Tensor,
-        dones: torch.Tensor,
-        truncated: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
-        qf_loss, target_q_max, target_q_min, next_log_probs = self._critic_loss_tensors(
-            critic_obs,
-            actions,
-            rewards,
-            next_obs,
-            critic_next_obs,
-            dones,
-            truncated,
-            next_action_eps=self._cuda_graph_critic_action_noise,
-        )
-
-        self.q_optimizer.zero_grad(set_to_none=True)
-        if self.scaler:
-            self.scaler.scale(qf_loss).backward()
-            self.scaler.unscale_(self.q_optimizer)
-            if self.max_grad_norm > 0:
-                critic_grad_norm = torch.nn.utils.clip_grad_norm_(
-                    self.qnet.parameters(), max_norm=self.max_grad_norm
-                )
-            else:
-                critic_grad_norm = self._zero_metric
-            self.scaler.step(self.q_optimizer)
-            self.scaler.update()
-        else:
-            qf_loss.backward()
-            self._sync_gradients(self.qnet.parameters())
-            if self.max_grad_norm > 0:
-                critic_grad_norm = torch.nn.utils.clip_grad_norm_(
-                    self.qnet.parameters(), max_norm=self.max_grad_norm
-                )
-            else:
-                critic_grad_norm = self._zero_metric
-            self.q_optimizer.step()
-
-        alpha_loss = self._zero_metric
-        if self.use_autotune:
-            self.alpha_optimizer.zero_grad(set_to_none=True)
-            alpha_loss = self._alpha_loss_tensor(next_log_probs)
-            alpha_loss.backward()
-            self._sync_gradients((self.log_alpha,))
-            self.alpha_optimizer.step()
-
-        return (
-            qf_loss,
-            critic_grad_norm,
-            target_q_max,
-            target_q_min,
-            alpha_loss,
-            self.log_alpha.exp(),
-        )
-
-    def _critic_graph_input_shapes(self, batch: Dict[str, torch.Tensor]) -> dict[str, torch.Size]:
-        return {
-            "critic": batch["critic"].shape,
-            "actions": batch["actions"].shape,
-            "rewards": batch["rewards"].shape,
-            "next_obs": batch["next_obs"].shape,
-            "next_critic": batch["next_critic"].shape,
-            "dones": batch["dones"].shape,
-            "truncated": batch["truncated"].shape,
-        }
-
-    @staticmethod
-    def _critic_graph_input_keys() -> tuple[str, ...]:
-        return (
-            "critic",
-            "actions",
-            "rewards",
-            "next_obs",
-            "next_critic",
-            "dones",
-            "truncated",
-        )
-
-    @classmethod
-    def _critic_graph_packed_width(cls, batch: Dict[str, torch.Tensor]) -> int:
-        width = 0
-        for key in cls._critic_graph_input_keys():
-            tensor = batch[key]
-            width += int(tensor.reshape(tensor.shape[0], -1).shape[1])
-        return width
-
-    @classmethod
-    def _critic_graph_static_views_from_packed(
-        cls,
-        packed: torch.Tensor,
-        shapes: dict[str, torch.Size],
-    ) -> dict[str, torch.Tensor]:
-        views: dict[str, torch.Tensor] = {}
-        offset = 0
-        for key in cls._critic_graph_input_keys():
-            shape = shapes[key]
-            width = 1
-            for dim in shape[1:]:
-                width *= int(dim)
-            view = packed.narrow(1, offset, width).view(shape)
-            views[key] = view
-            offset += width
-        return views
-
-    @staticmethod
-    def _sac_graph_offsets(
-        actor_shapes: dict[str, torch.Size],
-        critic_shapes: dict[str, torch.Size],
-    ) -> dict[str, tuple[int, int]]:
-        def width(shape: torch.Size) -> int:
-            value = 1
-            for dim in shape[1:]:
-                value *= int(dim)
-            return value
-
-        widths = {
-            "obs": width(actor_shapes["obs"]),
-            "critic": width(critic_shapes["critic"]),
-            "actions": width(critic_shapes["actions"]),
-            "rewards": width(critic_shapes["rewards"]),
-            "next_obs": width(critic_shapes["next_obs"]),
-            "next_critic": width(critic_shapes["next_critic"]),
-            "dones": width(critic_shapes["dones"]),
-            "truncated": width(critic_shapes["truncated"]),
-        }
-        offsets: dict[str, tuple[int, int]] = {}
-        offset = 0
-        for key in (
-            "obs",
-            "critic",
-            "actions",
-            "rewards",
-            "next_obs",
-            "next_critic",
-            "dones",
-            "truncated",
-        ):
-            key_width = widths[key]
-            offsets[key] = (offset, key_width)
-            offset += key_width
-        return offsets
-
-    @classmethod
-    def _critic_graph_static_views_from_sac_packed(
-        cls,
-        packed: torch.Tensor,
-        critic_shapes: dict[str, torch.Size],
-        actor_shapes: dict[str, torch.Size],
-    ) -> dict[str, torch.Tensor]:
-        offsets = cls._sac_graph_offsets(actor_shapes, critic_shapes)
-        views: dict[str, torch.Tensor] = {}
-        for key in cls._critic_graph_input_keys():
-            offset, width = offsets[key]
-            views[key] = packed.narrow(1, offset, width).view(critic_shapes[key])
-        return views
-
-    @classmethod
-    def _actor_graph_static_views_from_sac_packed(
-        cls,
-        packed: torch.Tensor,
-        actor_shapes: dict[str, torch.Size],
-    ) -> dict[str, torch.Tensor]:
-        batch_size = int(actor_shapes["obs"][0])
-        critic_shapes = {
-            "critic": actor_shapes["critic"],
-            "actions": torch.Size((batch_size, 0)),
-            "rewards": torch.Size((batch_size,)),
-            "next_obs": actor_shapes["obs"],
-            "next_critic": actor_shapes["critic"],
-            "dones": torch.Size((batch_size,)),
-            "truncated": torch.Size((batch_size,)),
-        }
-        offsets = cls._sac_graph_offsets(actor_shapes, critic_shapes)
-        views: dict[str, torch.Tensor] = {}
-        for key in ("obs", "critic"):
-            offset, width = offsets[key]
-            views[key] = packed.narrow(1, offset, width).view(actor_shapes[key])
-        return views
-
-    def _copy_critic_graph_inputs(self, batch: Dict[str, torch.Tensor]) -> None:
-        assert self._cuda_graph_critic_static_inputs is not None
-        packed_source = batch.get("sac_graph_packed_source")
-        if packed_source is not None and self._cuda_graph_sac_static_packed_input is not None:
-            self._cuda_graph_sac_static_packed_input.copy_(packed_source)
-            self._cuda_graph_sac_static_source_ptr = int(packed_source.data_ptr())
-        else:
-            self._cuda_graph_sac_static_source_ptr = None
-            packed_source = batch.get("critic_graph_packed_source")
-            if (
-                packed_source is not None
-                and self._cuda_graph_critic_static_packed_input is not None
-            ):
-                self._cuda_graph_critic_static_packed_input.copy_(packed_source)
-            else:
-                for key, tensor in self._cuda_graph_critic_static_inputs.items():
-                    tensor.copy_(batch[key])
-        assert self._cuda_graph_critic_action_noise is not None
-        self._cuda_graph_critic_action_noise.normal_()
-
-    def _copy_actor_graph_inputs(self, batch: Dict[str, torch.Tensor]) -> None:
-        assert self._cuda_graph_actor_static_inputs is not None
-        packed_source = batch.get("sac_graph_packed_source")
-        if packed_source is not None:
-            static_packed = self._cuda_graph_actor_static_packed_input
-            if static_packed is None:
-                static_packed = self._cuda_graph_sac_static_packed_input
-            if static_packed is not None:
-                source_ptr = int(packed_source.data_ptr())
-                if (
-                    static_packed is not self._cuda_graph_sac_static_packed_input
-                    or self._cuda_graph_sac_static_source_ptr != source_ptr
-                ):
-                    static_packed.copy_(packed_source)
-                    if static_packed is self._cuda_graph_sac_static_packed_input:
-                        self._cuda_graph_sac_static_source_ptr = source_ptr
-            else:
-                for key, tensor in self._cuda_graph_actor_static_inputs.items():
-                    tensor.copy_(batch[key])
-        else:
-            for key, tensor in self._cuda_graph_actor_static_inputs.items():
-                tensor.copy_(batch[key])
-        assert self._cuda_graph_actor_action_noise is not None
-        self._cuda_graph_actor_action_noise.normal_()
-
-    def _actor_graph_input_shapes(self, batch: Dict[str, torch.Tensor]) -> dict[str, torch.Size]:
-        return {
-            "obs": batch["obs"].shape,
-            "critic": batch["critic"].shape,
-        }
-
-    def _materialize_capturable_critic_optimizer_state(
-        self,
-        batch: Dict[str, torch.Tensor],
-    ) -> None:
-        optimizer_lrs = [group["lr"] for group in self.q_optimizer.param_groups]
-        optimizer_weight_decays = [group["weight_decay"] for group in self.q_optimizer.param_groups]
-        alpha_lrs = [group["lr"] for group in self.alpha_optimizer.param_groups]
-        cpu_rng_state = torch.random.get_rng_state()
-        cuda_rng_state = torch.cuda.get_rng_state() if self._device_type == "cuda" else None
-        try:
-            for group in self.q_optimizer.param_groups:
-                group["lr"] = 0.0
-                group["weight_decay"] = 0.0
-            for group in self.alpha_optimizer.param_groups:
-                group["lr"] = 0.0
-            self._update_critic_capture_candidate(
-                batch["critic"],
-                batch["actions"],
-                batch["rewards"],
-                batch["next_obs"],
-                batch["next_critic"],
-                batch["dones"],
-                batch["truncated"],
-            )
-        finally:
-            torch.random.set_rng_state(cpu_rng_state)
-            if cuda_rng_state is not None:
-                torch.cuda.set_rng_state(cuda_rng_state)
-            for group, lr, weight_decay in zip(
-                self.q_optimizer.param_groups,
-                optimizer_lrs,
-                optimizer_weight_decays,
-                strict=True,
-            ):
-                group["lr"] = lr
-                group["weight_decay"] = weight_decay
-            for group, lr in zip(self.alpha_optimizer.param_groups, alpha_lrs, strict=True):
-                group["lr"] = lr
-
-        for optimizer in (self.q_optimizer, self.alpha_optimizer):
-            optimizer.zero_grad(set_to_none=True)
-            for state in optimizer.state.values():
-                step = state.get("step")
-                if isinstance(step, torch.Tensor):
-                    step.zero_()
-                elif step is not None:
-                    state["step"] = 0
-                for name in ("exp_avg", "exp_avg_sq", "max_exp_avg_sq"):
-                    tensor = state.get(name)
-                    if isinstance(tensor, torch.Tensor):
-                        tensor.zero_()
-
-    def _reset_critic_cuda_graph(self) -> None:
-        graph = self._cuda_graph_critic
-        self._cuda_graph_critic = None
-        if isinstance(graph, torch.cuda.CUDAGraph):
-            graph.reset()
-        self._cuda_graph_critic_static_inputs = None
-        self._cuda_graph_critic_static_packed_input = None
-        self._cuda_graph_sac_static_packed_input = None
-        self._cuda_graph_sac_static_source_ptr = None
-        self._cuda_graph_critic_action_noise = None
-        self._cuda_graph_critic_outputs = None
-        self._cuda_graph_critic_shapes = None
-        self._cuda_graph_critic_gradient_sync_calls = 0
-
-    def _materialize_capturable_actor_optimizer_state(
-        self,
-        batch: Dict[str, torch.Tensor],
-    ) -> None:
-        optimizer_lrs = [group["lr"] for group in self.actor_optimizer.param_groups]
-        optimizer_weight_decays = [
-            group["weight_decay"] for group in self.actor_optimizer.param_groups
-        ]
-        cpu_rng_state = torch.random.get_rng_state()
-        cuda_rng_state = torch.cuda.get_rng_state() if self._device_type == "cuda" else None
-        try:
-            for group in self.actor_optimizer.param_groups:
-                group["lr"] = 0.0
-                group["weight_decay"] = 0.0
-            self._update_actor_capture_candidate(
-                batch["obs"],
-                batch["critic"],
-            )
-        finally:
-            torch.random.set_rng_state(cpu_rng_state)
-            if cuda_rng_state is not None:
-                torch.cuda.set_rng_state(cuda_rng_state)
-            for group, lr, weight_decay in zip(
-                self.actor_optimizer.param_groups,
-                optimizer_lrs,
-                optimizer_weight_decays,
-                strict=True,
-            ):
-                group["lr"] = lr
-                group["weight_decay"] = weight_decay
-
-        self.actor_optimizer.zero_grad(set_to_none=True)
-        for state in self.actor_optimizer.state.values():
-            step = state.get("step")
-            if isinstance(step, torch.Tensor):
-                step.zero_()
-            elif step is not None:
-                state["step"] = 0
-            for name in ("exp_avg", "exp_avg_sq", "max_exp_avg_sq"):
-                tensor = state.get(name)
-                if isinstance(tensor, torch.Tensor):
-                    tensor.zero_()
-
-    def _reset_actor_cuda_graph(self) -> None:
-        graph = self._cuda_graph_actor
-        self._cuda_graph_actor = None
-        if isinstance(graph, torch.cuda.CUDAGraph):
-            graph.reset()
-        self._cuda_graph_actor_static_inputs = None
-        self._cuda_graph_actor_static_packed_input = None
-        self._cuda_graph_actor_action_noise = None
-        self._cuda_graph_actor_outputs = None
-        self._cuda_graph_actor_shapes = None
-        self._cuda_graph_actor_gradient_sync_calls = 0
-
-    def _capture_actor_cuda_graph(self, batch: Dict[str, torch.Tensor]) -> None:
-        if not self.use_cuda_graph_actor:
-            return
-        self._cuda_graph_actor_shapes = self._actor_graph_input_shapes(batch)
-        if self.use_cuda_graph_actor_packed_staging and "sac_graph_packed_source" in batch:
-            packed_source = batch["sac_graph_packed_source"]
-            if (
-                self._cuda_graph_sac_static_packed_input is not None
-                and self._cuda_graph_sac_static_packed_input.shape == packed_source.shape
-            ):
-                self._cuda_graph_actor_static_packed_input = (
-                    self._cuda_graph_sac_static_packed_input
-                )
-            else:
-                self._cuda_graph_sac_static_packed_input = packed_source.detach().clone()
-                self._cuda_graph_sac_static_source_ptr = None
-                self._cuda_graph_actor_static_packed_input = (
-                    self._cuda_graph_sac_static_packed_input
-                )
-            self._cuda_graph_actor_static_inputs = self._actor_graph_static_views_from_sac_packed(
-                self._cuda_graph_actor_static_packed_input,
-                self._cuda_graph_actor_shapes,
-            )
-        else:
-            self._cuda_graph_actor_static_packed_input = None
-            self._cuda_graph_actor_static_inputs = {
-                "obs": batch["obs"].detach().clone(),
-                "critic": batch["critic"].detach().clone(),
-            }
-        self._cuda_graph_actor_action_noise = torch.empty(
-            batch["obs"].shape[:-1] + (self.actor.action_dim,),
-            device=batch["obs"].device,
-            dtype=batch["obs"].dtype,
-        )
-        self._copy_actor_graph_inputs(batch)
-
-        graph = torch.cuda.CUDAGraph()
-        capture_stream = cast(torch.cuda.Stream, torch.cuda.Stream())
-        capture_stream.wait_stream(torch.cuda.current_stream())
-        sync_calls = [0]
-        self._active_cuda_graph_gradient_sync_calls = sync_calls
-        try:
-            with torch.cuda.stream(capture_stream), torch.cuda.graph(graph):
-                self._cuda_graph_actor_outputs = self._update_actor_capture_candidate(
-                    self._cuda_graph_actor_static_inputs["obs"],
-                    self._cuda_graph_actor_static_inputs["critic"],
-                )
-        finally:
-            self._active_cuda_graph_gradient_sync_calls = None
-        torch.cuda.current_stream().wait_stream(capture_stream)
-        torch.cuda.synchronize()
-        self._cuda_graph_actor = graph
-        self._cuda_graph_actor_gradient_sync_calls = sync_calls[0]
-
-    def _actor_graph_output_metrics(self, *, read_items: bool = True) -> Dict[str, float]:
-        if not read_items:
-            return {}
-        assert self._cuda_graph_actor_outputs is not None
-        actor_loss, actor_grad_norm, policy_entropy, action_std = self._cuda_graph_actor_outputs
-        return {
-            "actor_loss": actor_loss.item(),
-            "actor_grad_norm": actor_grad_norm.item(),
-            "policy_entropy": policy_entropy.item(),
-            "action_std": action_std.item(),
-        }
-
-    def _capture_critic_cuda_graph(self, batch: Dict[str, torch.Tensor]) -> None:
-        if not self.use_cuda_graph_critic:
-            return
-        self._cuda_graph_critic_shapes = self._critic_graph_input_shapes(batch)
-        if self.use_cuda_graph_critic_packed_staging and "sac_graph_packed_source" in batch:
-            packed_source = batch["sac_graph_packed_source"]
-            self._cuda_graph_sac_static_packed_input = packed_source.detach().clone()
-            self._cuda_graph_critic_static_packed_input = None
-            actor_shapes = self._actor_graph_input_shapes(batch)
-            self._cuda_graph_critic_static_inputs = self._critic_graph_static_views_from_sac_packed(
-                self._cuda_graph_sac_static_packed_input,
-                self._cuda_graph_critic_shapes,
-                actor_shapes,
-            )
-        elif self.use_cuda_graph_critic_packed_staging and "critic_graph_packed_source" in batch:
-            packed_source = batch["critic_graph_packed_source"]
-            self._cuda_graph_sac_static_packed_input = None
-            self._cuda_graph_critic_static_packed_input = packed_source.detach().clone()
-            self._cuda_graph_critic_static_inputs = self._critic_graph_static_views_from_packed(
-                self._cuda_graph_critic_static_packed_input,
-                self._cuda_graph_critic_shapes,
-            )
-        else:
-            self._cuda_graph_sac_static_packed_input = None
-            self._cuda_graph_critic_static_packed_input = None
-            self._cuda_graph_critic_static_inputs = {
-                "critic": batch["critic"].detach().clone(),
-                "actions": batch["actions"].detach().clone(),
-                "rewards": batch["rewards"].detach().clone(),
-                "next_obs": batch["next_obs"].detach().clone(),
-                "next_critic": batch["next_critic"].detach().clone(),
-                "dones": batch["dones"].detach().clone(),
-                "truncated": batch["truncated"].detach().clone(),
-            }
-        self._cuda_graph_critic_action_noise = torch.empty(
-            batch["next_obs"].shape[:-1] + (batch["actions"].shape[-1],),
-            device=batch["next_obs"].device,
-            dtype=batch["next_obs"].dtype,
-        )
-        self._copy_critic_graph_inputs(batch)
-
-        graph = torch.cuda.CUDAGraph()
-        capture_stream = cast(torch.cuda.Stream, torch.cuda.Stream())
-        capture_stream.wait_stream(torch.cuda.current_stream())
-        sync_calls = [0]
-        self._active_cuda_graph_gradient_sync_calls = sync_calls
-        try:
-            with torch.cuda.stream(capture_stream), torch.cuda.graph(graph):
-                self._cuda_graph_critic_outputs = self._update_critic_capture_candidate(
-                    self._cuda_graph_critic_static_inputs["critic"],
-                    self._cuda_graph_critic_static_inputs["actions"],
-                    self._cuda_graph_critic_static_inputs["rewards"],
-                    self._cuda_graph_critic_static_inputs["next_obs"],
-                    self._cuda_graph_critic_static_inputs["next_critic"],
-                    self._cuda_graph_critic_static_inputs["dones"],
-                    self._cuda_graph_critic_static_inputs["truncated"],
-                )
-        finally:
-            self._active_cuda_graph_gradient_sync_calls = None
-        torch.cuda.current_stream().wait_stream(capture_stream)
-        torch.cuda.synchronize()
-        self._cuda_graph_critic = graph
-        self._cuda_graph_critic_gradient_sync_calls = sync_calls[0]
-
-    def _critic_graph_output_metrics(self, *, read_items: bool = True) -> Dict[str, float]:
-        if not read_items:
-            return {}
-        assert self._cuda_graph_critic_outputs is not None
-        qf_loss, critic_grad_norm, target_q_max, target_q_min, alpha_loss, alpha = (
-            self._cuda_graph_critic_outputs
-        )
-        return {
-            "qf_loss": qf_loss.item(),
-            "critic_grad_norm": critic_grad_norm.item(),
-            "target_q_max": target_q_max.item(),
-            "target_q_min": target_q_min.item(),
-            "alpha_loss": alpha_loss.item(),
-            "alpha": alpha.item(),
-        }
-
-    def update_critic_cuda_graph(
-        self,
-        batch: Dict[str, torch.Tensor],
-        *,
-        read_metrics: bool = True,
-    ) -> Dict[str, float]:
-        if not self.use_cuda_graph_critic:
-            return self.update_critic(batch)
-        if self._device_type != "cuda":
-            return self.update_critic(batch)
-        if self.scaler is not None:
-            return self.update_critic(batch)
-        if self._cuda_graph_critic_shapes != self._critic_graph_input_shapes(batch):
-            self._reset_critic_cuda_graph()
-            self._materialize_capturable_critic_optimizer_state(batch)
-            self._capture_critic_cuda_graph(batch)
-            with _cuda_nvtx_range(
-                "critic_graph/output_metrics_item",
-                self.nvtx_profile_ranges,
-            ):
-                return self._critic_graph_output_metrics(read_items=read_metrics)
-        assert self._cuda_graph_critic is not None
-        with _cuda_nvtx_range("critic_graph/copy_inputs", self.nvtx_profile_ranges):
-            self._copy_critic_graph_inputs(batch)
-        with _cuda_nvtx_range("critic_graph/replay", self.nvtx_profile_ranges):
-            self._cuda_graph_critic.replay()
-        self._record_cuda_graph_gradient_replay(self._cuda_graph_critic_gradient_sync_calls)
-        with _cuda_nvtx_range("critic_graph/output_metrics_item", self.nvtx_profile_ranges):
-            return self._critic_graph_output_metrics(read_items=read_metrics)
-
-    def update_actor_cuda_graph(
-        self,
-        batch: Dict[str, torch.Tensor],
-        *,
-        read_metrics: bool = True,
-    ) -> Dict[str, float]:
-        if not self.use_cuda_graph_actor:
-            return self.update_actor(batch)
-        if self._device_type != "cuda":
-            return self.update_actor(batch)
-        if self.scaler is not None or self.use_symmetry:
-            return self.update_actor(batch)
-        if self._cuda_graph_actor_shapes != self._actor_graph_input_shapes(batch):
-            self._reset_actor_cuda_graph()
-            self._materialize_capturable_actor_optimizer_state(batch)
-            self._capture_actor_cuda_graph(batch)
-            with _cuda_nvtx_range(
-                "actor_graph/output_metrics_item",
-                self.nvtx_profile_ranges,
-            ):
-                return self._actor_graph_output_metrics(read_items=read_metrics)
-        assert self._cuda_graph_actor is not None
-        with _cuda_nvtx_range("actor_graph/copy_inputs", self.nvtx_profile_ranges):
-            self._copy_actor_graph_inputs(batch)
-        with _cuda_nvtx_range("actor_graph/replay", self.nvtx_profile_ranges):
-            self._cuda_graph_actor.replay()
-        self._record_cuda_graph_gradient_replay(self._cuda_graph_actor_gradient_sync_calls)
-        with _cuda_nvtx_range("actor_graph/output_metrics_item", self.nvtx_profile_ranges):
-            return self._actor_graph_output_metrics(read_items=read_metrics)
-
-    def update_critic(self, batch: Dict[str, torch.Tensor]) -> Dict[str, float]:
-        """One critic update step."""
-        obs = batch["obs"]
-        critic_obs = batch["critic"]
-        actions = batch["actions"]
-        rewards = batch["rewards"]
-        next_obs = batch["next_obs"]
-        critic_next_obs = batch["next_critic"]
-        dones = batch["dones"]
-        truncated = batch["truncated"]
-
-        # Apply symmetry augmentation
-        if self.use_symmetry:
-            with _cuda_nvtx_range("critic/symmetry_augment", self.nvtx_profile_ranges):
-                assert self.symmetry is not None
-                with _cuda_nvtx_range("critic/symmetry_obs_actions", self.nvtx_profile_ranges):
-                    obs, actions = self.symmetry.augment_obs_and_actions(
-                        obs,
-                        actions,
-                        obs_group="obs",
-                    )
-                with _cuda_nvtx_range("critic/symmetry_next_obs", self.nvtx_profile_ranges):
-                    next_obs = self.symmetry.augment_obs(
-                        next_obs,
-                        obs_group="obs",
-                    )
-
-                with _cuda_nvtx_range("critic/symmetry_critic_obs", self.nvtx_profile_ranges):
-                    critic_obs = self.symmetry.augment_obs(
-                        critic_obs,
-                        obs_group="critic",
-                    )
-                with _cuda_nvtx_range("critic/symmetry_critic_next_obs", self.nvtx_profile_ranges):
-                    critic_next_obs = self.symmetry.augment_obs(
-                        critic_next_obs,
-                        obs_group="critic",
-                    )
-
-                # Double the batch size for other tensors
-                with _cuda_nvtx_range("critic/symmetry_aux_repeat", self.nvtx_profile_ranges):
-                    rewards = rewards.repeat(2)
-                    dones = dones.repeat(2)
-                    truncated = truncated.repeat(2)
-
-        self.normalize_obs(obs, update=True)
-        next_obs = self.normalize_obs(next_obs, update=False)
-
-        with _cuda_nvtx_range("critic/loss_compiled", self.nvtx_profile_ranges):
-            qf_loss, target_q_max, target_q_min, next_log_probs = self._critic_loss_tensors(
-                critic_obs,
-                actions,
-                rewards,
-                next_obs,
-                critic_next_obs,
-                dones,
-                truncated,
-            )
-
-        # Skip if NaN
-        if torch.isfinite(qf_loss):
-            self.q_optimizer.zero_grad(set_to_none=True)
-            if self.scaler:
-                with _cuda_nvtx_range("critic/backward", self.nvtx_profile_ranges):
-                    self.scaler.scale(qf_loss).backward()
-                self._sync_gradients(self.qnet.parameters())
-                self.scaler.unscale_(self.q_optimizer)
-                if self.max_grad_norm > 0:
-                    with _cuda_nvtx_range("critic/grad_clip", self.nvtx_profile_ranges):
-                        critic_grad_norm = torch.nn.utils.clip_grad_norm_(
-                            self.qnet.parameters(), max_norm=self.max_grad_norm
-                        )
-                else:
-                    critic_grad_norm = self._zero_metric
-                with _cuda_nvtx_range("critic/q_optimizer_step", self.nvtx_profile_ranges):
-                    self.scaler.step(self.q_optimizer)
-                self.scaler.update()
-            else:
-                with _cuda_nvtx_range("critic/backward", self.nvtx_profile_ranges):
-                    qf_loss.backward()
-                self._sync_gradients(self.qnet.parameters())
-                if self.max_grad_norm > 0:
-                    with _cuda_nvtx_range("critic/grad_clip", self.nvtx_profile_ranges):
-                        critic_grad_norm = torch.nn.utils.clip_grad_norm_(
-                            self.qnet.parameters(), max_norm=self.max_grad_norm
-                        )
-                else:
-                    critic_grad_norm = self._zero_metric
-                with _cuda_nvtx_range("critic/q_optimizer_step", self.nvtx_profile_ranges):
-                    self.q_optimizer.step()
-        else:
-            critic_grad_norm = self._zero_metric
-
-        # Alpha loss (temperature update) - matching holosoma
-        alpha_loss = self._zero_metric
-        if self.use_autotune:
-            with _cuda_nvtx_range("critic/alpha_update", self.nvtx_profile_ranges):
-                self.alpha_optimizer.zero_grad(set_to_none=True)
-                with _cuda_nvtx_range("critic/alpha_loss", self.nvtx_profile_ranges):
-                    alpha_loss = self._alpha_loss_tensor(next_log_probs)
-                if torch.isfinite(alpha_loss):
-                    with _cuda_nvtx_range("critic/alpha_backward", self.nvtx_profile_ranges):
-                        alpha_loss.backward()
-                    self._sync_gradients((self.log_alpha,))
-                    with _cuda_nvtx_range("critic/alpha_optimizer_step", self.nvtx_profile_ranges):
-                        self.alpha_optimizer.step()
-
-        return {
-            "qf_loss": qf_loss.item(),
-            "critic_grad_norm": critic_grad_norm.item(),
-            "target_q_max": target_q_max.item(),
-            "target_q_min": target_q_min.item(),
-            "alpha_loss": alpha_loss.item(),
-            "alpha": self.log_alpha.exp().item(),
-        }
-
-    def update_actor(self, batch: Dict[str, torch.Tensor]) -> Dict[str, float]:
-        """One actor update step."""
-        obs = batch["obs"]
-        critic_obs = batch["critic"]
-
-        # Apply symmetry augmentation
-        if self.use_symmetry:
-            with _cuda_nvtx_range("actor/symmetry_augment", self.nvtx_profile_ranges):
-                assert self.symmetry is not None
-                with _cuda_nvtx_range("actor/symmetry_obs", self.nvtx_profile_ranges):
-                    obs = self.symmetry.augment_obs(obs, obs_group="obs")
-                with _cuda_nvtx_range("actor/symmetry_critic_obs", self.nvtx_profile_ranges):
-                    critic_obs = self.symmetry.augment_obs(critic_obs, obs_group="critic")
-
-        obs = self.normalize_obs(obs, update=False)
-        with _cuda_nvtx_range("actor/loss_compiled", self.nvtx_profile_ranges):
-            actor_loss, policy_entropy, action_std = self._actor_loss_tensors(obs, critic_obs)
-
-        # Skip if NaN
-        if torch.isfinite(actor_loss):
-            self.actor_optimizer.zero_grad(set_to_none=True)
-            if self.scaler:
-                with _cuda_nvtx_range("actor/backward", self.nvtx_profile_ranges):
-                    self.scaler.scale(actor_loss).backward()
-                self._sync_gradients(self.actor.parameters())
-                self.scaler.unscale_(self.actor_optimizer)
-                if self.max_grad_norm > 0:
-                    with _cuda_nvtx_range("actor/grad_clip", self.nvtx_profile_ranges):
-                        actor_grad_norm = torch.nn.utils.clip_grad_norm_(
-                            self.actor.parameters(), max_norm=self.max_grad_norm
-                        )
-                else:
-                    actor_grad_norm = self._zero_metric
-                with _cuda_nvtx_range("actor/optimizer_step", self.nvtx_profile_ranges):
-                    self.scaler.step(self.actor_optimizer)
-                self.scaler.update()
-            else:
-                with _cuda_nvtx_range("actor/backward", self.nvtx_profile_ranges):
-                    actor_loss.backward()
-                self._sync_gradients(self.actor.parameters())
-                if self.max_grad_norm > 0:
-                    with _cuda_nvtx_range("actor/grad_clip", self.nvtx_profile_ranges):
-                        actor_grad_norm = torch.nn.utils.clip_grad_norm_(
-                            self.actor.parameters(), max_norm=self.max_grad_norm
-                        )
-                else:
-                    actor_grad_norm = self._zero_metric
-                with _cuda_nvtx_range("actor/optimizer_step", self.nvtx_profile_ranges):
-                    self.actor_optimizer.step()
-        else:
-            actor_grad_norm = self._zero_metric
-
-        return {
-            "actor_loss": actor_loss.item(),
-            "actor_grad_norm": actor_grad_norm.item(),
-            "policy_entropy": policy_entropy.item(),
-            "action_std": action_std.item(),
-        }
-
-    def soft_update_target(self) -> None:
-        """Polyak-average update of the target Q-network."""
-        with torch.no_grad():
-            with _cuda_nvtx_range("target/soft_update_loop", self.nvtx_profile_ranges):
-                target_params = cast(list[torch.Tensor], list(self.qnet_target.parameters()))
-                source_params = cast(list[torch.Tensor], list(self.qnet.parameters()))
-                try:
-                    torch._foreach_mul_(target_params, 1.0 - self.tau)
-                    torch._foreach_add_(target_params, source_params, alpha=self.tau)
-                except RuntimeError:
-                    for tgt, src in zip(target_params, source_params):
-                        tgt.mul_(1.0 - self.tau).add_(src, alpha=self.tau)
-
-    def set_gradient_sync(
-        self,
-        sync: Callable[[Iterable[torch.Tensor]], None] | None,
-        *,
-        graph_replay_recorder: Callable[[int], None] | None = None,
-    ) -> None:
-        """Attach the per-optimizer gradient collective used by multi-GPU DP."""
-        if sync is None and graph_replay_recorder is not None:
-            raise ValueError("graph_replay_recorder requires a gradient sync callback")
-        if sync != self._gradient_sync:
-            self._reset_critic_cuda_graph()
-            self._reset_actor_cuda_graph()
-        self._gradient_sync = sync
-        self._gradient_sync_graph_replay_recorder = graph_replay_recorder
-        self.dp_cuda_graph_gradient_sync = bool(
-            sync is not None
-            and (
-                (self.use_cuda_graph_critic and self.scaler is None)
-                or (self.use_cuda_graph_actor and self.scaler is None and not self.use_symmetry)
-            )
-        )
-
-    def _sync_gradients(self, parameters: Iterable[torch.Tensor]) -> None:
-        if self._gradient_sync is not None:
-            self._gradient_sync(parameters)
-            if self._active_cuda_graph_gradient_sync_calls is not None:
-                self._active_cuda_graph_gradient_sync_calls[0] += 1
-
-    def _record_cuda_graph_gradient_replay(self, collective_calls: int) -> None:
-        if self._gradient_sync_graph_replay_recorder is not None and collective_calls > 0:
-            self._gradient_sync_graph_replay_recorder(collective_calls)
-
-    def release_cuda_graphs(self) -> None:
-        """Release captured NCCL nodes before the process group is destroyed."""
-        self._reset_critic_cuda_graph()
-        self._reset_actor_cuda_graph()
-
-    def dp_initial_sync_tensors(self) -> Dict[str, torch.Tensor]:
-        """Model state broadcast once from rank 0 before collection starts.
-
-        The values alias the parameter/buffer storage of ``actor``, ``qnet``
-        and ``qnet_target`` (plus the ``log_alpha`` leaf) rather than copies,
-        so startup broadcast updates the model in place. Optimizer state starts
-        empty and remains aligned because every actual optimizer update uses
-        the same cross-rank mean gradient.
-        """
-        tensors: Dict[str, torch.Tensor] = {}
-        for prefix, module in (
-            ("actor", self.actor),
-            ("qnet", self.qnet),
-            ("qnet_target", self.qnet_target),
-        ):
-            for key, value in module.state_dict().items():
-                tensors[f"{prefix}.{key}"] = value
-        tensors["log_alpha"] = self.log_alpha
-        return tensors
-
-    def get_state_dict(self) -> Dict[str, Any]:
-        """Save all components."""
-        return {
-            "actor": self.actor.state_dict(),
-            "qnet": self.qnet.state_dict(),
-            "qnet_target": self.qnet_target.state_dict(),
-            "log_alpha": self.log_alpha.detach().cpu(),
-            "actor_optimizer": self.actor_optimizer.state_dict(),
-            "q_optimizer": self.q_optimizer.state_dict(),
-            "alpha_optimizer": self.alpha_optimizer.state_dict(),
-            "obs_normalizer": (
-                self.obs_normalizer.state_dict()
-                if hasattr(self.obs_normalizer, "state_dict")
-                else None
-            ),
-            "update_count": self.update_count,
-        }
-
-    def load_state_dict(self, state_dict: Dict) -> None:
-        """Load all components."""
-        self.actor.load_state_dict(state_dict["actor"])
-        self.qnet.load_state_dict(state_dict["qnet"])
-        self.qnet_target.load_state_dict(state_dict["qnet_target"])
-        self.log_alpha.data.copy_(state_dict["log_alpha"].to(self.device))
-        self.actor_optimizer.load_state_dict(state_dict["actor_optimizer"])
-        self.q_optimizer.load_state_dict(state_dict["q_optimizer"])
-        self.alpha_optimizer.load_state_dict(state_dict["alpha_optimizer"])
-        if state_dict.get("obs_normalizer") and hasattr(self.obs_normalizer, "load_state_dict"):
-            self.obs_normalizer.load_state_dict(state_dict["obs_normalizer"])
-        self.update_count = state_dict.get("update_count", 0)
-        self._reset_critic_cuda_graph()
-        self._reset_actor_cuda_graph()
-
-
-# ---------------------------------------------------------------------------
diff --git a/src/unilab/algos/torch/fast_sac/runner.py b/src/unilab/algos/torch/fast_sac/runner.py
deleted file mode 100644
index f6a72a8c8..000000000
--- a/src/unilab/algos/torch/fast_sac/runner.py
+++ /dev/null
@@ -1,139 +0,0 @@
-"""FastSAC runner using unified OffPolicyRunner."""
-
-import logging
-from typing import Any
-
-from unilab.algos.torch.fast_sac.learner import FastSACLearner
-from unilab.algos.torch.offpolicy.double_buffer_runner import DoubleBufferOffPolicyRunner
-from unilab.ipc.replay_pipelines.gpu_resident import require_offpolicy_replay_device
-from unilab.utils.device import get_default_device
-
-logger = logging.getLogger(__name__)
-
-
-class FastSACRunner(DoubleBufferOffPolicyRunner):
-    """FastSAC using the single device-authoritative replay path."""
-
-    def __init__(
-        self,
-        env_name: str,
-        env_cfg_override: dict[str, Any] | None = None,
-        device: str | None = None,
-        num_envs: int = 4096,
-        replay_buffer_n: int = 1024,
-        batch_size: int = 8192,
-        learning_starts: int = 0,
-        updates_per_step: int = 8,
-        policy_frequency: int = 4,
-        env_steps_per_sync: int = 1,
-        gamma: float = 0.97,
-        tau: float = 0.125,
-        actor_lr: float = 3e-4,
-        critic_lr: float = 3e-4,
-        alpha_lr: float = 3e-4,
-        alpha_init: float = 0.001,
-        target_entropy_ratio: float = 1.0,
-        obs_normalization: bool = True,
-        actor_hidden_dim: int = 512,
-        critic_hidden_dim: int = 768,
-        num_atoms: int = 101,
-        use_layer_norm: bool = True,
-        max_grad_norm: float = 0.0,
-        use_amp: bool = False,
-        amp_dtype: str = "auto",
-        use_cuda_graph_critic: bool = False,
-        use_cuda_graph_actor: bool = False,
-        sim_backend: str = "mujoco",
-        use_symmetry: bool = False,
-        seed: int | None = None,
-        trace_enabled: bool = False,
-        trace_output_dir: str | None = None,
-        trace_thread_time: bool = False,
-        trace_cuda_events: bool = True,
-    ):
-        from unilab.base import registry
-        from unilab.base.registry import ensure_registries
-        from unilab.training.seed import apply_training_seed
-
-        device = require_offpolicy_replay_device(device or get_default_device())
-        ensure_registries()
-        apply_training_seed(seed, torch_runtime=True, cuda=True)
-        env: Any = registry.make(
-            env_name, num_envs=1, sim_backend=sim_backend, env_cfg_override=env_cfg_override
-        )
-        from unilab.base.observations import get_obs_dims
-
-        obs_dim, critic_obs_dim = get_obs_dims(env.obs_groups_spec)
-        act_space_shape = env.action_space.shape
-        assert act_space_shape is not None
-        action_dim = act_space_shape[0]
-        symmetry_augmentation = None
-        if use_symmetry:
-            symmetry_augmentation = env.build_symmetry_augmentation(device=device)
-            if symmetry_augmentation is None:
-                env.close()
-                raise ValueError(
-                    f"{env_name} with backend={sim_backend} does not provide symmetry augmentation"
-                )
-        env.close()
-
-        learner = FastSACLearner(
-            obs_dim=obs_dim,
-            action_dim=action_dim,
-            device=device,
-            gamma=gamma,
-            tau=tau,
-            actor_lr=actor_lr,
-            critic_lr=critic_lr,
-            alpha_lr=alpha_lr,
-            alpha_init=alpha_init,
-            target_entropy_ratio=target_entropy_ratio,
-            actor_hidden_dim=actor_hidden_dim,
-            critic_hidden_dim=critic_hidden_dim,
-            num_atoms=num_atoms,
-            use_layer_norm=use_layer_norm,
-            max_grad_norm=max_grad_norm,
-            use_amp=use_amp,
-            amp_dtype=amp_dtype,
-            obs_normalization=obs_normalization,
-            use_cuda_graph_critic=use_cuda_graph_critic,
-            use_cuda_graph_actor=use_cuda_graph_actor,
-            use_symmetry=use_symmetry,
-            symmetry_augmentation=symmetry_augmentation,
-            critic_obs_dim=critic_obs_dim,
-        )
-
-        if symmetry_augmentation is not None:
-            if batch_size % symmetry_augmentation.batch_multiplier != 0:
-                raise ValueError(
-                    "Symmetry augmentation requires algo.batch_size to be divisible by "
-                    f"{symmetry_augmentation.batch_multiplier}, got {batch_size}"
-                )
-            batch_size = batch_size // symmetry_augmentation.batch_multiplier
-            logger.info(
-                "[FastSAC] Symmetry enabled: batch_size adjusted to %d (effective: %d)",
-                batch_size,
-                batch_size * symmetry_augmentation.batch_multiplier,
-            )
-
-        super().__init__(
-            learner=learner,
-            env_name=env_name,
-            algo_type="sac",
-            num_envs=num_envs,
-            replay_buffer_n=replay_buffer_n,
-            batch_size=batch_size,
-            learning_starts=learning_starts,
-            updates_per_step=updates_per_step,
-            policy_frequency=policy_frequency,
-            env_steps_per_sync=env_steps_per_sync,
-            device=device,
-            obs_normalization=obs_normalization,
-            sim_backend=sim_backend,
-            env_cfg_override=env_cfg_override,
-            seed=seed,
-            trace_enabled=trace_enabled,
-            trace_output_dir=trace_output_dir,
-            trace_thread_time=trace_thread_time,
-            trace_cuda_events=trace_cuda_events,
-        )
diff --git a/src/unilab/algos/torch/fast_td3/__init__.py b/src/unilab/algos/torch/fast_td3/__init__.py
deleted file mode 100644
index 7cae6e7d1..000000000
--- a/src/unilab/algos/torch/fast_td3/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# FastTD3 algorithm
diff --git a/src/unilab/algos/torch/fast_td3/double_buffer.py b/src/unilab/algos/torch/fast_td3/double_buffer.py
deleted file mode 100644
index 34f5df0bc..000000000
--- a/src/unilab/algos/torch/fast_td3/double_buffer.py
+++ /dev/null
@@ -1,86 +0,0 @@
-"""FastTD3 builder for the device-authoritative replay path."""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, Any
-
-from omegaconf import DictConfig
-
-from unilab.algos.torch.common.device import get_env_dims
-from unilab.algos.torch.fast_td3.learner import FastTD3Learner
-from unilab.algos.torch.offpolicy.double_buffer_runner import DoubleBufferOffPolicyRunner
-from unilab.utils.nan_guard import NanGuardCfg
-
-if TYPE_CHECKING:
-    from unilab.ipc.dp_sync import DpParameterSync
-
-
-def build_td3_double_buffer_runner(
-    cfg: DictConfig,
-    *,
-    env_cfg_override: dict[str, Any] | None,
-    replay_prefetch_mode: str,
-    device: str,
-    nan_guard_cfg: NanGuardCfg | None = None,
-    torch_thread_runtime: dict[str, Any] | None = None,
-    collector_cpu_ids: list[int] | None = None,
-    dp_sync: DpParameterSync | None = None,
-) -> Any:
-    """Build TD3 from its Hydra owner config without interpreting it in the entrypoint."""
-    obs_dim, action_dim, critic_obs_dim = get_env_dims(
-        cfg.training.task_name,
-        cfg.training.sim_backend,
-        env_cfg_override=env_cfg_override,
-    )
-    learner = FastTD3Learner(
-        obs_dim=obs_dim,
-        action_dim=action_dim,
-        critic_obs_dim=critic_obs_dim,
-        num_envs=cfg.algo.num_envs,
-        device=device,
-        gamma=cfg.algo.gamma,
-        tau=cfg.algo.tau,
-        actor_lr=cfg.algo.actor_lr,
-        critic_lr=cfg.algo.critic_lr,
-        actor_hidden_dim=cfg.algo.actor_hidden_dim,
-        critic_hidden_dim=cfg.algo.critic_hidden_dim,
-        num_atoms=cfg.algo.num_atoms,
-        v_min=cfg.algo.algo_params.v_min,
-        v_max=cfg.algo.algo_params.v_max,
-        init_scale=cfg.algo.algo_params.init_scale,
-        log_std_min=cfg.algo.algo_params.log_std_min,
-        log_std_max=cfg.algo.algo_params.log_std_max,
-        weight_decay=cfg.algo.algo_params.weight_decay,
-        use_cdq=cfg.algo.algo_params.use_cdq,
-        policy_noise=cfg.algo.algo_params.policy_noise,
-        noise_clip=cfg.algo.algo_params.noise_clip,
-        policy_frequency=cfg.algo.policy_frequency,
-        obs_normalization=cfg.algo.obs_normalization,
-    )
-
-    return DoubleBufferOffPolicyRunner(
-        learner=learner,
-        env_name=cfg.training.task_name,
-        algo_type="td3",
-        env_cfg_override=env_cfg_override,
-        device=device,
-        num_envs=cfg.algo.num_envs,
-        replay_buffer_n=cfg.algo.replay_buffer_n,
-        batch_size=cfg.algo.batch_size,
-        learning_starts=cfg.algo.learning_starts,
-        updates_per_step=cfg.algo.updates_per_step,
-        policy_frequency=cfg.algo.policy_frequency,
-        env_steps_per_sync=cfg.training.env_steps_per_sync,
-        obs_normalization=cfg.algo.obs_normalization,
-        sim_backend=cfg.training.sim_backend,
-        seed=cfg.algo.seed,
-        trace_enabled=cfg.training.trace_enabled,
-        trace_output_dir=cfg.training.trace_output_dir,
-        trace_thread_time=cfg.training.trace_thread_time,
-        trace_cuda_events=cfg.training.trace_cuda_events,
-        replay_prefetch_mode=replay_prefetch_mode,
-        nan_guard_cfg=nan_guard_cfg,
-        torch_thread_runtime=torch_thread_runtime,
-        collector_cpu_ids=collector_cpu_ids,
-        dp_sync=dp_sync,
-    )
diff --git a/src/unilab/algos/torch/fast_td3/learner.py b/src/unilab/algos/torch/fast_td3/learner.py
deleted file mode 100644
index 772cce22e..000000000
--- a/src/unilab/algos/torch/fast_td3/learner.py
+++ /dev/null
@@ -1,386 +0,0 @@
-"""FastTD3 Learner — aligned with reference FastTD3 repository.
-
-Architecture (from reference fast_td3.py):
-- Actor: ReLU MLP (hidden → hidden//2 → hidden//4 → n_act, Tanh)
-  - Per-env noise scales (sampled uniformly, resampled on episode done)
-  - Small init scale for output layer
-- Critic: Twin Distributional Q-Networks (C51 variant)
-  - ReLU MLP with num_atoms output
-- Observation normalization with EmpiricalNormalization
-- AdamW optimizer with weight_decay=0.1
-- Cosine LR scheduler
-
-Hyperparameters aligned with reference Go1JoystickFlat config.
-"""
-
-from __future__ import annotations
-
-import math
-from typing import Dict, Optional, Union
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-
-from unilab.algos.torch.common.networks import Critic
-from unilab.algos.torch.common.normalization import EmpiricalNormalization
-from unilab.algos.torch.common.stability import check_nan_loss, clip_gradients
-
-# ---------------------------------------------------------------------------
-# Actor (deterministic, ReLU, per-env noise)
-# ---------------------------------------------------------------------------
-
-
-class TD3Actor(nn.Module):
-    """Deterministic actor with per-environment exploration noise.
-
-    Architecture: Linear→ReLU → Linear→ReLU → Linear→ReLU → Linear→Tanh
-    Each environment has its own noise scale, sampled uniformly in [std_min, std_max].
-    Noise scales are resampled when an episode ends.
-    """
-
-    noise_scales: torch.Tensor
-    log_std_min: torch.Tensor
-    log_std_max: torch.Tensor
-
-    def __init__(
-        self,
-        obs_dim: int,
-        n_act: int,
-        num_envs: int,
-        init_scale: float,
-        hidden_dim: int,
-        log_std_min: float = -3.0,
-        log_std_max: float = 0.0,
-        device: Optional[torch.device] = None,
-    ):
-        super().__init__()
-        self.n_act = n_act
-        self.net = nn.Sequential(
-            nn.Linear(obs_dim, hidden_dim, device=device),
-            nn.ReLU(),
-            nn.Linear(hidden_dim, hidden_dim // 2, device=device),
-            nn.ReLU(),
-            nn.Linear(hidden_dim // 2, hidden_dim // 4, device=device),
-            nn.ReLU(),
-        )
-        self.fc_mu = nn.Sequential(
-            nn.Linear(hidden_dim // 4, n_act, device=device),
-            nn.Tanh(),
-        )
-        fc_mu_linear = self.fc_mu[0]
-        assert isinstance(fc_mu_linear, nn.Linear)
-        nn.init.normal_(fc_mu_linear.weight, 0.0, init_scale)
-        nn.init.constant_(fc_mu_linear.bias, 0.0)
-
-        std_min = float(math.exp(log_std_min))
-        std_max = float(math.exp(log_std_max))
-        noise_scales = torch.rand(num_envs, 1, device=device) * (std_max - std_min) + std_min
-        self.register_buffer("noise_scales", noise_scales)
-        self.register_buffer("log_std_min", torch.as_tensor(log_std_min, device=device))
-        self.register_buffer("log_std_max", torch.as_tensor(log_std_max, device=device))
-        self.n_envs = num_envs
-        self.device = device
-
-    def forward(self, obs: torch.Tensor) -> torch.Tensor:
-        x = self.net(obs)
-        action: torch.Tensor = self.fc_mu(x)  # type: ignore[assignment]
-        return action
-
-    @torch.no_grad()
-    def explore(
-        self,
-        obs: torch.Tensor,
-        dones: torch.Tensor | None = None,
-        deterministic: bool = False,
-    ) -> torch.Tensor:
-        """Forward pass with per-env exploration noise."""
-        if isinstance(dones, bool):
-            deterministic = dones
-            dones = None
-
-        if dones is not None and dones.sum() > 0:
-            std_min = torch.exp(self.log_std_min)
-            std_max = torch.exp(self.log_std_max)
-            new_scales = (
-                torch.rand(self.n_envs, 1, device=obs.device) * (std_max - std_min) + std_min
-            )
-            dones_view = dones.view(-1, 1) > 0
-            self.noise_scales.copy_(torch.where(dones_view, new_scales, self.noise_scales))
-
-        act = self.forward(obs)
-        if deterministic:
-            return act
-
-        noise = torch.randn_like(act) * self.noise_scales
-        return (act + noise).clamp(-1.0, 1.0)
-
-
-# ---------------------------------------------------------------------------
-# FastTD3 Learner
-# ---------------------------------------------------------------------------
-
-
-class FastTD3Learner:
-    """FastTD3 learner aligned with reference FastTD3 repository.
-
-    Key hyperparameters (from Go1JoystickFlat):
-    - gamma=0.97, tau=0.1
-    - AdamW with weight_decay=0.1
-    - Cosine LR schedule
-    - Distributional critic (C51, num_atoms=101, v_min/max=±10)
-    - CDQ (Clipped Double Q-learning) toggle
-    - Observation normalization
-    """
-
-    def __init__(
-        self,
-        obs_dim: int,
-        action_dim: int,
-        critic_obs_dim: int,
-        num_envs: int = 1024,
-        device: str = "cpu",
-        # Hyperparameters from reference
-        gamma: float = 0.97,
-        tau: float = 0.01,
-        actor_lr: float = 3e-4,
-        critic_lr: float = 3e-4,
-        actor_hidden_dim: int = 512,
-        critic_hidden_dim: int = 1024,
-        num_atoms: int = 101,
-        v_min: float = -10.0,
-        v_max: float = 10.0,
-        init_scale: float = 0.01,
-        log_std_min: float = -3.0,
-        log_std_max: float = 0.0,
-        weight_decay: float = 0.001,
-        use_cdq: bool = True,
-        # TD3-specific
-        policy_noise: float = 0.1,
-        noise_clip: float = 0.2,
-        policy_frequency: int = 2,
-        # Training
-        max_iterations: int = 50000,
-        obs_normalization: bool = True,
-    ):
-        self.device = device
-        self.gamma = gamma
-        self.tau = tau
-        self.policy_noise = policy_noise
-        self.noise_clip = noise_clip
-        self.policy_frequency = policy_frequency
-        self.use_cdq = use_cdq
-        self.critic_obs_dim = critic_obs_dim
-
-        torch_device = torch.device(device)
-
-        # Build actor
-        self.actor = TD3Actor(
-            obs_dim=obs_dim,
-            n_act=action_dim,
-            num_envs=num_envs,
-            init_scale=init_scale,
-            hidden_dim=actor_hidden_dim,
-            log_std_min=log_std_min,
-            log_std_max=log_std_max,
-            device=torch_device,
-        )
-        self.actor_target = TD3Actor(
-            obs_dim=obs_dim,
-            n_act=action_dim,
-            num_envs=num_envs,
-            init_scale=init_scale,
-            hidden_dim=actor_hidden_dim,
-            log_std_min=log_std_min,
-            log_std_max=log_std_max,
-            device=torch_device,
-        )
-        self.actor_target.load_state_dict(self.actor.state_dict())
-
-        # Build critic
-        self.qnet = Critic(
-            obs_dim=self.critic_obs_dim,
-            n_act=action_dim,
-            num_atoms=num_atoms,
-            v_min=v_min,
-            v_max=v_max,
-            hidden_dim=critic_hidden_dim,
-            device=torch_device,
-        )
-        self.qnet_target = Critic(
-            obs_dim=self.critic_obs_dim,
-            n_act=action_dim,
-            num_atoms=num_atoms,
-            v_min=v_min,
-            v_max=v_max,
-            hidden_dim=critic_hidden_dim,
-            device=torch_device,
-        )
-        self.qnet_target.load_state_dict(self.qnet.state_dict())
-
-        # Observation normalization
-        self.obs_normalizer: Union[EmpiricalNormalization, nn.Identity]
-        if obs_normalization:
-            self.obs_normalizer = EmpiricalNormalization(shape=obs_dim, device=device)
-        else:
-            self.obs_normalizer = nn.Identity()
-
-        # Optimizers (AdamW, reference style)
-        self.q_optimizer = optim.AdamW(
-            list(self.qnet.parameters()),
-            lr=torch.tensor(critic_lr, device=device),
-            weight_decay=weight_decay,
-        )
-        self.actor_optimizer = optim.AdamW(
-            list(self.actor.parameters()),
-            lr=torch.tensor(actor_lr, device=device),
-            weight_decay=weight_decay,
-        )
-
-        self.update_count = 0
-        self.weight_decay = weight_decay
-
-    def normalize_obs(self, obs: torch.Tensor, update: bool = False) -> torch.Tensor:
-        """Normalize observations using running statistics."""
-        if not isinstance(self.obs_normalizer, nn.Identity):
-            return self.obs_normalizer.forward(obs, update=update)
-        return obs
-
-    def update_critic(self, data: Dict[str, torch.Tensor]) -> Dict[str, float]:
-        """One critic update step."""
-        self.normalize_obs(data["obs"], update=True)
-        critic_obs = data["critic"]
-        actions = data["actions"]
-        rewards = data["rewards"]
-        next_observations = self.normalize_obs(data["next_obs"], update=False)
-        critic_next_obs = data["next_critic"]
-        dones = data["dones"].bool()
-        truncations = data["truncated"].bool()
-
-        bootstrap = (truncations | ~dones).float()
-        discount = torch.full_like(rewards, self.gamma)
-
-        # Target policy smoothing (uses online actor, matching reference FastTD3)
-        clipped_noise = torch.randn_like(actions)
-        clipped_noise = clipped_noise.mul(self.policy_noise).clamp(
-            -self.noise_clip, self.noise_clip
-        )
-        next_state_actions = (self.actor(next_observations) + clipped_noise).clamp(-1.0, 1.0)
-
-        with torch.no_grad():
-            qf1_next_target_proj, qf2_next_target_proj = self.qnet_target.projection(
-                critic_next_obs,
-                next_state_actions,
-                rewards,
-                bootstrap,
-                discount,
-            )
-            qf1_next_target_value = self.qnet_target.get_value(qf1_next_target_proj)
-            qf2_next_target_value = self.qnet_target.get_value(qf2_next_target_proj)
-
-            if self.use_cdq:
-                # Clipped Double Q-learning: use distribution of the min-value Q
-                qf_next_target_dist = torch.where(
-                    qf1_next_target_value.unsqueeze(1) < qf2_next_target_value.unsqueeze(1),
-                    qf1_next_target_proj,
-                    qf2_next_target_proj,
-                )
-                qf1_next_target_dist = qf2_next_target_dist = qf_next_target_dist
-            else:
-                qf1_next_target_dist = qf1_next_target_proj
-                qf2_next_target_dist = qf2_next_target_proj
-
-        qf1, qf2 = self.qnet(critic_obs, actions)
-        qf1_loss = -torch.sum(qf1_next_target_dist * F.log_softmax(qf1, dim=1), dim=1).mean()
-        qf2_loss = -torch.sum(qf2_next_target_dist * F.log_softmax(qf2, dim=1), dim=1).mean()
-        qf_loss = qf1_loss + qf2_loss
-
-        loss, nan_metrics = check_nan_loss(
-            qf_loss,
-            {
-                "qf_loss": 0.0,
-                "qf_max": 0.0,
-                "qf_min": 0.0,
-            },
-        )
-        if loss is None:
-            return nan_metrics or {}
-
-        self.q_optimizer.zero_grad(set_to_none=True)
-        loss.backward()
-        if self.weight_decay > 0:
-            clip_gradients(self.qnet.parameters(), max_norm=10.0)
-        self.q_optimizer.step()
-
-        return {
-            "qf_loss": qf_loss.item(),
-            "qf_max": qf1_next_target_value.max().item(),
-            "qf_min": qf1_next_target_value.min().item(),
-        }
-
-    def update_actor(self, data: Dict[str, torch.Tensor]) -> Dict[str, float]:
-        """One actor update step."""
-        observations = self.normalize_obs(data["obs"], update=False)
-        critic_obs = data["critic"]
-
-        qf1, qf2 = self.qnet(critic_obs, self.actor(observations))
-        qf1_value = self.qnet.get_value(F.softmax(qf1, dim=1))
-        qf2_value = self.qnet.get_value(F.softmax(qf2, dim=1))
-
-        if self.use_cdq:
-            qf_value = torch.minimum(qf1_value, qf2_value)
-        else:
-            qf_value = (qf1_value + qf2_value) / 2.0
-
-        actor_loss = -qf_value.mean()
-
-        loss, nan_metrics = check_nan_loss(actor_loss, {"actor_loss": 0.0})
-        if loss is None:
-            return nan_metrics or {}
-
-        self.actor_optimizer.zero_grad(set_to_none=True)
-        loss.backward()
-        if self.weight_decay > 0:
-            clip_gradients(self.actor.parameters(), max_norm=10.0)
-        self.actor_optimizer.step()
-
-        return {"actor_loss": actor_loss.item()}
-
-    @torch.no_grad()
-    def soft_update_target(self) -> None:
-        """Polyak-average update of critic target network only (matching reference FastTD3)."""
-        src_ps = [p.data for p in self.qnet.parameters()]
-        tgt_ps = [p.data for p in self.qnet_target.parameters()]
-        torch._foreach_mul_(tgt_ps, 1.0 - self.tau)
-        torch._foreach_add_(tgt_ps, src_ps, alpha=self.tau)
-
-    def get_state_dict(self) -> Dict:
-        return {
-            "actor": self.actor.state_dict(),
-            "actor_target": self.actor_target.state_dict(),
-            "qnet": self.qnet.state_dict(),
-            "qnet_target": self.qnet_target.state_dict(),
-            "obs_normalizer": (
-                self.obs_normalizer.state_dict()
-                if hasattr(self.obs_normalizer, "state_dict")
-                else None
-            ),
-            "actor_optimizer": self.actor_optimizer.state_dict(),
-            "q_optimizer": self.q_optimizer.state_dict(),
-            "update_count": self.update_count,
-        }
-
-    def load_state_dict(self, state_dict: Dict) -> None:
-        self.actor.load_state_dict(state_dict["actor"])
-        if "actor_target" in state_dict:
-            self.actor_target.load_state_dict(state_dict["actor_target"])
-        else:
-            self.actor_target.load_state_dict(state_dict["actor"])
-        self.qnet.load_state_dict(state_dict["qnet"])
-        self.qnet_target.load_state_dict(state_dict["qnet_target"])
-        if state_dict.get("obs_normalizer") and hasattr(self.obs_normalizer, "load_state_dict"):
-            self.obs_normalizer.load_state_dict(state_dict["obs_normalizer"])
-        self.actor_optimizer.load_state_dict(state_dict["actor_optimizer"])
-        self.q_optimizer.load_state_dict(state_dict["q_optimizer"])
-        self.update_count = state_dict.get("update_count", 0)
diff --git a/src/unilab/algos/torch/flash_sac/__init__.py b/src/unilab/algos/torch/flash_sac/__init__.py
deleted file mode 100644
index 2219b855b..000000000
--- a/src/unilab/algos/torch/flash_sac/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-"""FlashSAC algorithm package."""
-
-from unilab.algos.torch.flash_sac.learner import FlashSACLearner
-from unilab.algos.torch.flash_sac.network import FlashSACActor, FlashSACDoubleCritic
-from unilab.algos.torch.flash_sac.runner import FlashSACRunner
-
-__all__ = [
-    "FlashSACActor",
-    "FlashSACDoubleCritic",
-    "FlashSACLearner",
-    "FlashSACRunner",
-]
diff --git a/src/unilab/algos/torch/flash_sac/double_buffer.py b/src/unilab/algos/torch/flash_sac/double_buffer.py
deleted file mode 100644
index 26d21869b..000000000
--- a/src/unilab/algos/torch/flash_sac/double_buffer.py
+++ /dev/null
@@ -1,132 +0,0 @@
-"""FlashSAC builder for the device-authoritative replay path."""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, Any
-
-from omegaconf import DictConfig
-
-from unilab.algos.torch.flash_sac.learner import FlashSACLearner
-from unilab.algos.torch.offpolicy.double_buffer_runner import DoubleBufferOffPolicyRunner
-from unilab.ipc.replay_pipelines.gpu_resident import require_offpolicy_replay_device
-from unilab.training import create_env, ensure_registries
-from unilab.training.seed import apply_training_seed
-from unilab.utils.device import get_default_device
-from unilab.utils.nan_guard import NanGuardCfg
-
-if TYPE_CHECKING:
-    from unilab.ipc.dp_sync import DpParameterSync
-
-
-def _validate_flashsac_double_buffer_runtime(
-    cfg: DictConfig,
-    *,
-    replay_prefetch_mode: str,
-) -> None:
-    if replay_prefetch_mode != "one_tick":
-        raise ValueError("FlashSAC device replay requires replay_prefetch_mode='one_tick'")
-    if cfg.algo.algo_params.n_step != 1:
-        raise ValueError("FlashSAC-B initially supports n_step=1 only")
-
-
-def build_flashsac_double_buffer_runner(
-    cfg: DictConfig,
-    *,
-    env_cfg_override: dict[str, Any] | None,
-    replay_prefetch_mode: str,
-    device: str | None = None,
-    nan_guard_cfg: NanGuardCfg | None = None,
-    torch_thread_runtime: dict[str, Any] | None = None,
-    collector_cpu_ids: list[int] | None = None,
-    dp_sync: DpParameterSync | None = None,
-) -> Any:
-    """Build FlashSAC with the bounded-ingress device replay pipeline."""
-    from unilab.base.observations import get_obs_dims
-
-    device = require_offpolicy_replay_device(device or get_default_device())
-    ensure_registries()
-    apply_training_seed(cfg.algo.seed, torch_runtime=True, cuda=True)
-    _validate_flashsac_double_buffer_runtime(
-        cfg,
-        replay_prefetch_mode=replay_prefetch_mode,
-    )
-
-    env = create_env(cfg, num_envs=1, env_cfg_override=env_cfg_override)
-    try:
-        obs_dim, critic_obs_dim = get_obs_dims(env.obs_groups_spec)
-        action_shape = env.action_space.shape
-        assert action_shape is not None
-        action_dim = int(action_shape[0])
-    finally:
-        env.close()
-
-    learner_kwargs = {
-        "obs_dim": obs_dim,
-        "action_dim": action_dim,
-        "critic_obs_dim": critic_obs_dim,
-        "gamma": cfg.algo.gamma,
-        "tau": cfg.algo.tau,
-        "actor_lr": cfg.algo.actor_lr,
-        "critic_lr": cfg.algo.critic_lr,
-        "actor_hidden_dim": cfg.algo.actor_hidden_dim,
-        "critic_hidden_dim": cfg.algo.critic_hidden_dim,
-        "actor_num_blocks": cfg.algo.algo_params.actor_num_blocks,
-        "critic_num_blocks": cfg.algo.algo_params.critic_num_blocks,
-        "num_atoms": cfg.algo.num_atoms,
-        "critic_min_v": cfg.algo.algo_params.critic_min_v,
-        "critic_max_v": cfg.algo.algo_params.critic_max_v,
-        "temp_initial_value": cfg.algo.algo_params.temp_initial_value,
-        "temp_target_sigma": cfg.algo.algo_params.temp_target_sigma,
-        "temp_target_entropy": cfg.algo.algo_params.temp_target_entropy,
-        "actor_bc_alpha": cfg.algo.algo_params.actor_bc_alpha,
-        "actor_noise_zeta_mu": cfg.algo.algo_params.actor_noise_zeta_mu,
-        "actor_noise_zeta_max": cfg.algo.algo_params.actor_noise_zeta_max,
-        "learning_rate_init": cfg.algo.algo_params.learning_rate_init,
-        "learning_rate_peak": cfg.algo.algo_params.learning_rate_peak,
-        "learning_rate_end": cfg.algo.algo_params.learning_rate_end,
-        "learning_rate_warmup_steps": cfg.algo.algo_params.learning_rate_warmup_steps,
-        "learning_rate_decay_steps": cfg.algo.algo_params.learning_rate_decay_steps,
-        "normalize_reward": cfg.algo.algo_params.normalize_reward,
-        "normalized_g_max": cfg.algo.algo_params.normalized_g_max,
-        "n_step": cfg.algo.algo_params.n_step,
-        "obs_normalization": cfg.algo.obs_normalization,
-        "use_amp": cfg.training.use_amp,
-        "amp_dtype": cfg.algo.algo_params.amp_dtype,
-        "use_compile": cfg.algo.algo_params.use_compile,
-        "use_cuda_graph_critic": cfg.algo.algo_params.use_cuda_graph_critic,
-        "use_cuda_graph_actor": cfg.algo.algo_params.use_cuda_graph_actor,
-        "use_cuda_graph_critic_packed_staging": (
-            cfg.algo.algo_params.use_cuda_graph_critic_packed_staging
-        ),
-        "use_cuda_graph_actor_packed_staging": (
-            cfg.algo.algo_params.use_cuda_graph_actor_packed_staging
-        ),
-    }
-    learner = FlashSACLearner(device=device, **learner_kwargs)
-
-    return DoubleBufferOffPolicyRunner(
-        learner=learner,
-        env_name=cfg.training.task_name,
-        algo_type="flashsac",
-        num_envs=cfg.algo.num_envs,
-        replay_buffer_n=cfg.algo.replay_buffer_n,
-        batch_size=cfg.algo.batch_size,
-        learning_starts=cfg.algo.learning_starts,
-        updates_per_step=cfg.algo.updates_per_step,
-        policy_frequency=cfg.algo.policy_frequency,
-        env_steps_per_sync=cfg.training.env_steps_per_sync,
-        device=device,
-        obs_normalization=cfg.algo.obs_normalization,
-        sim_backend=cfg.training.sim_backend,
-        env_cfg_override=env_cfg_override,
-        seed=cfg.algo.seed,
-        trace_enabled=cfg.training.trace_enabled,
-        trace_output_dir=cfg.training.trace_output_dir,
-        trace_thread_time=cfg.training.trace_thread_time,
-        trace_cuda_events=cfg.training.trace_cuda_events,
-        replay_prefetch_mode=replay_prefetch_mode,
-        nan_guard_cfg=nan_guard_cfg,
-        torch_thread_runtime=torch_thread_runtime,
-        collector_cpu_ids=collector_cpu_ids,
-        dp_sync=dp_sync,
-    )
diff --git a/src/unilab/algos/torch/flash_sac/layers.py b/src/unilab/algos/torch/flash_sac/layers.py
deleted file mode 100644
index 629403bc0..000000000
--- a/src/unilab/algos/torch/flash_sac/layers.py
+++ /dev/null
@@ -1,280 +0,0 @@
-"""FlashSAC layers and lightweight normalization helpers."""
-
-from __future__ import annotations
-
-import math
-from typing import cast
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def safe_tanh_log_det_jacobian(x: torch.Tensor) -> torch.Tensor:
-    """Stable log|det J_tanh(x)| term."""
-    return cast(torch.Tensor, 2.0 * (math.log(2.0) - x - F.softplus(-2.0 * x)))
-
-
-class UnitLinear(nn.Module):
-    """Linear layer with post-step weight normalization."""
-
-    def __init__(self, input_dim: int, output_dim: int):
-        super().__init__()
-        self.w = nn.Linear(input_dim, output_dim, bias=False)
-        nn.init.orthogonal_(self.w.weight, gain=1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return cast(torch.Tensor, self.w(x))
-
-    def normalize_parameters(self) -> None:
-        with torch.no_grad():
-            self.w.weight.copy_(F.normalize(self.w.weight, dim=-1, eps=1e-8))
-
-
-class UnitBatchNorm(nn.Module):
-    """BatchNorm variant with normalized affine parameters."""
-
-    running_mean: torch.Tensor
-    running_var: torch.Tensor
-
-    def __init__(self, input_dim: int, momentum: float = 0.01, eps: float = 1e-5):
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(input_dim))
-        self.bias = nn.Parameter(torch.zeros(input_dim))
-        self.register_buffer("running_mean", torch.zeros(input_dim))
-        self.register_buffer("running_var", torch.ones(input_dim))
-        self.momentum = momentum
-        self.eps = eps
-
-    def forward(self, x: torch.Tensor, training: bool) -> torch.Tensor:
-        return F.batch_norm(
-            x,
-            self.running_mean,
-            self.running_var,
-            self.weight,
-            self.bias,
-            training=training,
-            momentum=self.momentum,
-            eps=self.eps,
-        )
-
-    def normalize_parameters(self) -> None:
-        with torch.no_grad():
-            sqsum = torch.sum(self.weight * self.weight + self.bias * self.bias, dim=-1)
-            norm_factor = math.sqrt(float(self.weight.shape[-1])) * torch.rsqrt(sqsum + 1e-8)
-            self.weight.mul_(norm_factor)
-            self.bias.mul_(norm_factor)
-
-
-class UnitRMSNorm(nn.Module):
-    """RMSNorm with unit-length scale vector."""
-
-    def __init__(self, input_dim: int, eps: float = 1e-6):
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(input_dim))
-        self.eps = eps
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        rms = torch.sqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
-        return (x / rms) * self.weight
-
-    def normalize_parameters(self) -> None:
-        with torch.no_grad():
-            sqsum = torch.sum(self.weight * self.weight, dim=-1)
-            norm_factor = math.sqrt(float(self.weight.shape[-1])) * torch.rsqrt(sqsum + 1e-8)
-            self.weight.mul_(norm_factor)
-
-
-class FlashSACEmbedder(nn.Module):
-    def __init__(self, input_dim: int, hidden_dim: int):
-        super().__init__()
-        self.norm = UnitBatchNorm(input_dim)
-        self.w = UnitLinear(input_dim, hidden_dim)
-
-    def forward(self, x: torch.Tensor, training: bool) -> torch.Tensor:
-        return cast(torch.Tensor, self.w(self.norm(x, training=training)))
-
-
-class FlashSACBlock(nn.Module):
-    def __init__(self, hidden_dim: int, expansion: int = 4):
-        super().__init__()
-        self.w1 = UnitLinear(hidden_dim, hidden_dim * expansion)
-        self.norm1 = UnitBatchNorm(hidden_dim * expansion)
-        self.w2 = UnitLinear(hidden_dim * expansion, hidden_dim)
-        self.norm2 = UnitBatchNorm(hidden_dim)
-
-    def forward(self, x: torch.Tensor, training: bool) -> torch.Tensor:
-        residual = x
-        x = self.w1(x)
-        x = self.norm1(x, training=training)
-        x = F.relu(x)
-        x = self.w2(x)
-        x = self.norm2(x, training=training)
-        x = F.relu(x)
-        return x + residual
-
-
-class NormalTanhPolicy(nn.Module):
-    def __init__(
-        self,
-        hidden_dim: int,
-        action_dim: int,
-        log_std_min: float = -10.0,
-        log_std_max: float = 2.0,
-    ):
-        super().__init__()
-        self.mean_w = UnitLinear(hidden_dim, action_dim)
-        self.mean_bias = nn.Parameter(torch.zeros(action_dim))
-        self.std_w = UnitLinear(hidden_dim, action_dim)
-        self.std_bias = nn.Parameter(torch.zeros(action_dim))
-        self.log_std_min = log_std_min
-        self.log_std_max = log_std_max
-
-    def get_mean_and_std(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
-        mean = F.linear(x, self.mean_w.w.weight, self.mean_bias)
-        raw_log_std = F.linear(x, self.std_w.w.weight, self.std_bias)
-        log_std = self.log_std_min + (self.log_std_max - self.log_std_min) * 0.5 * (
-            1.0 + torch.tanh(raw_log_std)
-        )
-        std = torch.exp(log_std)
-        return mean, std
-
-    def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, dict[str, torch.Tensor]]:
-        mean, std = self.get_mean_and_std(x)
-        # ``std`` is positive by construction (exp of bounded log_std). The
-        # distribution's generic validation performs a GPU-to-host truth check,
-        # which CUDA forbids while this actor is captured into an optimizer graph.
-        dist = torch.distributions.Normal(mean, std, validate_args=False)
-        raw_action = dist.rsample()
-        tanh_action = torch.tanh(raw_action)
-        log_prob = dist.log_prob(raw_action)
-        log_prob = log_prob - safe_tanh_log_det_jacobian(raw_action)
-        log_prob = log_prob.sum(dim=-1)
-        return tanh_action, {"log_prob": log_prob, "mean": mean, "std": std}
-
-
-class EnsembleUnitLinear(nn.Module):
-    def __init__(self, num_ensemble: int, input_dim: int, output_dim: int):
-        super().__init__()
-        self.weight = nn.Parameter(torch.empty(num_ensemble, output_dim, input_dim))
-        for idx in range(num_ensemble):
-            nn.init.orthogonal_(self.weight[idx], gain=1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return torch.einsum("nbi,noi->nbo", x, self.weight)
-
-    def normalize_parameters(self) -> None:
-        with torch.no_grad():
-            self.weight.copy_(F.normalize(self.weight, dim=-1, eps=1e-8))
-
-
-class EnsembleUnitBatchNorm(nn.Module):
-    running_mean: torch.Tensor
-    running_var: torch.Tensor
-
-    def __init__(
-        self, num_ensemble: int, input_dim: int, momentum: float = 0.01, eps: float = 1e-5
-    ):
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(num_ensemble, input_dim))
-        self.bias = nn.Parameter(torch.zeros(num_ensemble, input_dim))
-        self.register_buffer("running_mean", torch.zeros(num_ensemble, input_dim))
-        self.register_buffer("running_var", torch.ones(num_ensemble, input_dim))
-        self.momentum = momentum
-        self.eps = eps
-
-    def forward(self, x: torch.Tensor, training: bool) -> torch.Tensor:
-        if training:
-            mean = x.mean(dim=1, keepdim=True)
-            var = x.var(dim=1, correction=0, keepdim=True)
-            with torch.no_grad():
-                batch_size = max(x.shape[1], 1)
-                correction = batch_size / max(batch_size - 1, 1)
-                self.running_mean.lerp_(mean.squeeze(1).float(), self.momentum)
-                self.running_var.lerp_((var.squeeze(1) * correction).float(), self.momentum)
-            normed = (x - mean) * torch.rsqrt(var + self.eps)
-        else:
-            normed = (x - self.running_mean.unsqueeze(1)) * torch.rsqrt(
-                self.running_var.unsqueeze(1) + self.eps
-            )
-        return normed * self.weight.unsqueeze(1) + self.bias.unsqueeze(1)
-
-    def normalize_parameters(self) -> None:
-        with torch.no_grad():
-            sqsum = torch.sum(
-                self.weight * self.weight + self.bias * self.bias, dim=-1, keepdim=True
-            )
-            norm_factor = math.sqrt(float(self.weight.shape[-1])) * torch.rsqrt(sqsum + 1e-8)
-            self.weight.mul_(norm_factor)
-            self.bias.mul_(norm_factor)
-
-
-class EnsembleUnitRMSNorm(nn.Module):
-    def __init__(self, num_ensemble: int, input_dim: int, eps: float = 1e-6):
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(num_ensemble, input_dim))
-        self.eps = eps
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        rms = torch.sqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
-        return (x / rms) * self.weight.unsqueeze(1)
-
-    def normalize_parameters(self) -> None:
-        with torch.no_grad():
-            sqsum = torch.sum(self.weight * self.weight, dim=-1, keepdim=True)
-            norm_factor = math.sqrt(float(self.weight.shape[-1])) * torch.rsqrt(sqsum + 1e-8)
-            self.weight.mul_(norm_factor)
-
-
-class EnsembleFlashSACEmbedder(nn.Module):
-    def __init__(self, num_ensemble: int, input_dim: int, hidden_dim: int):
-        super().__init__()
-        self.norm = EnsembleUnitBatchNorm(num_ensemble, input_dim)
-        self.w = EnsembleUnitLinear(num_ensemble, input_dim, hidden_dim)
-
-    def forward(self, x: torch.Tensor, training: bool) -> torch.Tensor:
-        return cast(torch.Tensor, self.w(self.norm(x, training=training)))
-
-
-class EnsembleFlashSACBlock(nn.Module):
-    def __init__(self, num_ensemble: int, hidden_dim: int, expansion: int = 4):
-        super().__init__()
-        self.w1 = EnsembleUnitLinear(num_ensemble, hidden_dim, hidden_dim * expansion)
-        self.norm1 = EnsembleUnitBatchNorm(num_ensemble, hidden_dim * expansion)
-        self.w2 = EnsembleUnitLinear(num_ensemble, hidden_dim * expansion, hidden_dim)
-        self.norm2 = EnsembleUnitBatchNorm(num_ensemble, hidden_dim)
-
-    def forward(self, x: torch.Tensor, training: bool) -> torch.Tensor:
-        residual = x
-        x = self.w1(x)
-        x = self.norm1(x, training=training)
-        x = F.relu(x)
-        x = self.w2(x)
-        x = self.norm2(x, training=training)
-        x = F.relu(x)
-        return x + residual
-
-
-class EnsembleCategoricalValue(nn.Module):
-    support: torch.Tensor
-
-    def __init__(
-        self,
-        num_ensemble: int,
-        hidden_dim: int,
-        num_bins: int,
-        min_v: float,
-        max_v: float,
-    ):
-        super().__init__()
-        self.logit_w = EnsembleUnitLinear(num_ensemble, hidden_dim, num_bins)
-        self.logit_bias = nn.Parameter(torch.zeros(num_ensemble, num_bins))
-        self.register_buffer("support", torch.linspace(min_v, max_v, num_bins))
-
-    def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, dict[str, torch.Tensor]]:
-        logits = self.logit_w(x) + self.logit_bias.unsqueeze(1)
-        log_probs = F.log_softmax(logits, dim=-1)
-        probs = log_probs.exp()
-        support = self.support.view(1, 1, -1)
-        values = cast(torch.Tensor, torch.sum(probs * support, dim=-1))
-        return values, {"log_prob": log_probs}
diff --git a/src/unilab/algos/torch/flash_sac/learner.py b/src/unilab/algos/torch/flash_sac/learner.py
deleted file mode 100644
index 3f0285838..000000000
--- a/src/unilab/algos/torch/flash_sac/learner.py
+++ /dev/null
@@ -1,1186 +0,0 @@
-"""FlashSAC learner adapted to UniLab's off-policy contract."""
-
-from __future__ import annotations
-
-import copy
-from collections.abc import Callable, Iterable
-from dataclasses import dataclass
-from typing import Any, cast
-
-import torch
-import torch.nn as nn
-import torch.optim as optim
-
-from unilab.algos.torch.common.compile import get_torch_compile_for_cuda
-from unilab.algos.torch.common.normalization import EmpiricalNormalization
-from unilab.algos.torch.flash_sac.network import (
-    FlashSACActor,
-    FlashSACDoubleCritic,
-    FlashSACTemperature,
-)
-from unilab.algos.torch.flash_sac.update import (
-    build_lr_lambda,
-    resolve_target_entropy,
-    select_min_q_log_probs,
-)
-
-
-@dataclass
-class RunningMeanStd:
-    mean: torch.Tensor
-    var: torch.Tensor
-    count: torch.Tensor
-
-    @classmethod
-    def create(cls, device: torch.device) -> "RunningMeanStd":
-        return cls(
-            mean=torch.zeros(1, device=device, dtype=torch.float32),
-            var=torch.ones(1, device=device, dtype=torch.float32),
-            count=torch.tensor(1e-4, device=device, dtype=torch.float32),
-        )
-
-    def update(self, x: torch.Tensor) -> None:
-        x = x.reshape(-1).to(dtype=torch.float32)
-        if x.numel() == 0:
-            return
-        batch_mean = x.mean()
-        batch_var = x.var(unbiased=False)
-        batch_count = torch.tensor(float(x.numel()), device=x.device, dtype=torch.float32)
-
-        delta = batch_mean - self.mean
-        total_count = self.count + batch_count
-        new_mean = self.mean + delta * batch_count / total_count
-        m_a = self.var * self.count
-        m_b = batch_var * batch_count
-        correction = delta.pow(2) * self.count * batch_count / total_count
-        new_var = (m_a + m_b + correction) / total_count
-
-        self.mean = new_mean
-        self.var = new_var
-        self.count = total_count
-
-    def state_dict(self) -> dict[str, torch.Tensor]:
-        return {"mean": self.mean, "var": self.var, "count": self.count}
-
-    def load_state_dict(self, state_dict: dict[str, torch.Tensor]) -> None:
-        self.mean = state_dict["mean"]
-        self.var = state_dict["var"]
-        self.count = state_dict["count"]
-
-
-class RewardNormalizer:
-    """Adaptive reward scaling with running discounted-return statistics."""
-
-    def __init__(
-        self,
-        gamma: float,
-        g_max: float,
-        device: torch.device,
-        eps: float = 1e-8,
-    ):
-        self.gamma = gamma
-        self.g_max = g_max
-        self.eps = eps
-        self.device = device
-        self.rms = RunningMeanStd.create(device)
-        self.g_r = torch.zeros(0, device=device, dtype=torch.float32)
-        self.g_r_max = torch.tensor(0.0, device=device, dtype=torch.float32)
-
-    def _ensure_g_r_shape(self, num_envs: int) -> None:
-        if self.g_r.shape == (num_envs,):
-            return
-        self.g_r = torch.zeros(num_envs, device=self.device, dtype=torch.float32)
-
-    def update_from_transitions(
-        self,
-        rewards: torch.Tensor,
-        dones: torch.Tensor,
-    ) -> None:
-        rewards = rewards.to(device=self.device, dtype=torch.float32)
-        dones = dones.to(device=self.device, dtype=torch.float32)
-
-        if rewards.ndim == 1:
-            rewards = rewards.unsqueeze(0)
-            dones = dones.unsqueeze(0)
-        if rewards.numel() == 0:
-            return
-
-        num_envs = int(rewards.shape[-1])
-        self._ensure_g_r_shape(num_envs)
-        done = torch.clamp(dones, min=0.0, max=1.0)
-
-        for step in range(rewards.shape[0]):
-            self.g_r = self.gamma * (1.0 - done[step]) * self.g_r + rewards[step]
-            self.g_r_max = torch.maximum(self.g_r_max, self.g_r.abs().max())
-            self.rms.update(self.g_r)
-
-    def normalize(self, rewards: torch.Tensor) -> torch.Tensor:
-        denominator = torch.maximum(
-            torch.sqrt(self.rms.var + self.eps),
-            self.g_r_max / max(self.g_max, self.eps),
-        )
-        return rewards / denominator
-
-    def state_dict(self) -> dict[str, Any]:
-        return {
-            "rms": self.rms.state_dict(),
-            "g_r": self.g_r,
-            "g_r_max": self.g_r_max,
-        }
-
-    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
-        self.rms.load_state_dict(state_dict["rms"])
-        self.g_r = state_dict["g_r"]
-        self.g_r_max = state_dict["g_r_max"]
-
-
-class FlashSACLearner:
-    supports_cuda_graph_packed_staging = True
-
-    def __init__(
-        self,
-        obs_dim: int,
-        action_dim: int,
-        critic_obs_dim: int,
-        device: str = "cpu",
-        gamma: float = 0.99,
-        tau: float = 0.01,
-        actor_lr: float = 3e-4,
-        critic_lr: float = 3e-4,
-        actor_hidden_dim: int = 128,
-        critic_hidden_dim: int = 256,
-        actor_num_blocks: int = 2,
-        critic_num_blocks: int = 2,
-        num_atoms: int = 101,
-        critic_min_v: float = -5.0,
-        critic_max_v: float = 5.0,
-        temp_initial_value: float = 0.01,
-        temp_target_sigma: float = 0.15,
-        temp_target_entropy: float | None = None,
-        actor_bc_alpha: float = 0.0,
-        actor_noise_zeta_mu: float = 2.0,
-        actor_noise_zeta_max: int = 16,
-        learning_rate_init: float = 3e-4,
-        learning_rate_peak: float = 3e-4,
-        learning_rate_end: float = 1.5e-4,
-        learning_rate_warmup_steps: int = 0,
-        learning_rate_decay_steps: int = 500000,
-        normalize_reward: bool = True,
-        normalized_g_max: float = 5.0,
-        n_step: int = 1,
-        obs_normalization: bool = False,
-        use_amp: bool = False,
-        amp_dtype: str = "auto",
-        use_compile: bool = False,
-        use_cuda_graph_critic: bool = False,
-        use_cuda_graph_actor: bool = False,
-        use_cuda_graph_critic_packed_staging: bool = False,
-        use_cuda_graph_actor_packed_staging: bool = False,
-    ):
-        self.device = torch.device(device)
-        self.gamma = gamma
-        self.tau = tau
-        self.n_step = n_step
-        self.actor_bc_alpha = actor_bc_alpha
-        self.obs_dim = obs_dim
-        self.critic_obs_dim = critic_obs_dim
-        self.action_dim = action_dim
-        self.update_count = 0
-        self.use_amp = bool(use_amp and self.device.type in ("cuda", "xpu"))
-        self.amp_dtype = amp_dtype
-        self._amp_dtype = self._resolve_amp_dtype(amp_dtype, self.device.type)
-        self.use_compile = bool(
-            use_compile and get_torch_compile_for_cuda(self.device, warn=True) is not None
-        )
-        self.use_cuda_graph_critic = bool(use_cuda_graph_critic)
-        self.use_cuda_graph_actor = bool(use_cuda_graph_actor)
-        self._gradient_sync: Callable[[Iterable[torch.Tensor]], None] | None = None
-        self._gradient_sync_graph_replay_recorder: Callable[[int], None] | None = None
-        self.dp_cuda_graph_gradient_sync = False
-        self._active_cuda_graph_gradient_sync_calls: list[int] | None = None
-        self.use_cuda_graph_critic_packed_staging = bool(
-            use_cuda_graph_critic_packed_staging and self.use_cuda_graph_critic
-        )
-        self.use_cuda_graph_actor_packed_staging = bool(
-            use_cuda_graph_actor_packed_staging and self.use_cuda_graph_actor
-        )
-        self.actor = FlashSACActor(
-            num_blocks=actor_num_blocks,
-            input_dim=obs_dim,
-            hidden_dim=actor_hidden_dim,
-            action_dim=action_dim,
-            noise_zeta_mu=actor_noise_zeta_mu,
-            noise_zeta_max=actor_noise_zeta_max,
-            device=self.device,
-        )
-        self.critic = FlashSACDoubleCritic(
-            num_blocks=critic_num_blocks,
-            input_dim=self.critic_obs_dim + action_dim,
-            hidden_dim=critic_hidden_dim,
-            num_bins=num_atoms,
-            min_v=critic_min_v,
-            max_v=critic_max_v,
-            device=self.device,
-        )
-        self.target_critic = copy.deepcopy(self.critic).to(self.device)
-        self.target_critic.eval()
-        self.temperature = FlashSACTemperature(temp_initial_value).to(self.device)
-
-        self.target_entropy = resolve_target_entropy(
-            action_dim=action_dim,
-            target_sigma=temp_target_sigma,
-            target_entropy=temp_target_entropy,
-        )
-
-        self.obs_normalizer: EmpiricalNormalization | nn.Identity
-        if obs_normalization:
-            self.obs_normalizer = EmpiricalNormalization(shape=obs_dim, device=self.device)
-        else:
-            self.obs_normalizer = nn.Identity()
-
-        self.reward_normalizer = (
-            RewardNormalizer(gamma=self.gamma, g_max=normalized_g_max, device=self.device)
-            if normalize_reward
-            else None
-        )
-
-        # GradScaler is only needed for fp16 (cuda); bf16 on xpu doesn't need it.
-        self.scaler: Any | None = (
-            getattr(torch.amp, "GradScaler")("cuda")
-            if self._should_use_grad_scaler(self.use_amp, self.device.type, self._amp_dtype)
-            else None
-        )
-        lr_peak = learning_rate_peak if learning_rate_peak > 0 else actor_lr
-        optimizer_kwargs: dict[str, Any] = {"fused": self.device.type == "cuda"}
-        if self.device.type == "cuda":
-            optimizer_kwargs["capturable"] = True
-        self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=lr_peak, **optimizer_kwargs)
-        self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=lr_peak, **optimizer_kwargs)
-        self.temperature_optimizer = optim.Adam(
-            self.temperature.parameters(), lr=lr_peak, **optimizer_kwargs
-        )
-        self._cuda_graph_critic: torch.cuda.CUDAGraph | None = None
-        self._cuda_graph_critic_static_inputs: dict[str, torch.Tensor] | None = None
-        self._cuda_graph_sac_static_packed_input: torch.Tensor | None = None
-        self._cuda_graph_sac_static_source_ptr: int | None = None
-        self._cuda_graph_critic_outputs: tuple[torch.Tensor, torch.Tensor] | None = None
-        self._cuda_graph_critic_shapes: dict[str, torch.Size] | None = None
-        self._cuda_graph_critic_gradient_sync_calls = 0
-        self._cuda_graph_actor: torch.cuda.CUDAGraph | None = None
-        self._cuda_graph_actor_static_inputs: dict[str, torch.Tensor] | None = None
-        self._cuda_graph_actor_static_packed_input: torch.Tensor | None = None
-        self._cuda_graph_actor_outputs: (
-            tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor] | None
-        ) = None
-        self._cuda_graph_actor_shapes: dict[str, torch.Size] | None = None
-        self._cuda_graph_actor_gradient_sync_calls = 0
-
-        scheduler_fn = build_lr_lambda(
-            init_lr=learning_rate_init,
-            peak_lr=lr_peak,
-            end_lr=learning_rate_end,
-            warmup_steps=learning_rate_warmup_steps,
-            decay_steps=learning_rate_decay_steps,
-        )
-        self.actor_scheduler = optim.lr_scheduler.LambdaLR(self.actor_optimizer, scheduler_fn)
-        self.critic_scheduler = optim.lr_scheduler.LambdaLR(self.critic_optimizer, scheduler_fn)
-        self.temperature_scheduler = optim.lr_scheduler.LambdaLR(
-            self.temperature_optimizer, scheduler_fn
-        )
-
-        if self.use_compile:
-            self._compile_training_methods()
-
-    def _compile_training_methods(self) -> None:
-        compile_fn = get_torch_compile_for_cuda(self.device, warn=True)
-        if compile_fn is None:
-            return
-
-        compile_kwargs = {"options": {"triton.cudagraphs": False}}
-        self.actor.get_mean_and_std = compile_fn(  # type: ignore[method-assign]
-            self.actor.get_mean_and_std, **compile_kwargs
-        )
-        if not self.use_cuda_graph_critic:
-            self._critic_loss_tensors = compile_fn(  # type: ignore[method-assign]
-                self._critic_loss_tensors, **compile_kwargs
-            )
-        if not self.use_cuda_graph_actor:
-            self._actor_loss_tensors = compile_fn(  # type: ignore[method-assign]
-                self._actor_loss_tensors, **compile_kwargs
-            )
-
-    @staticmethod
-    def _resolve_amp_dtype(amp_dtype: str, device_type: str) -> torch.dtype:
-        normalized = amp_dtype.lower()
-        if normalized == "auto":
-            return torch.bfloat16
-        if normalized == "fp16":
-            return torch.float16
-        if normalized == "bf16":
-            return torch.bfloat16
-        raise ValueError("FlashSAC amp_dtype must be one of: auto, fp16, bf16")
-
-    @staticmethod
-    def _should_use_grad_scaler(
-        use_amp: bool,
-        device_type: str,
-        amp_dtype: torch.dtype,
-    ) -> bool:
-        return bool(use_amp) and device_type == "cuda" and amp_dtype == torch.float16
-
-    def _maybe_normalize_obs(self, obs: torch.Tensor, *, update: bool) -> torch.Tensor:
-        if isinstance(self.obs_normalizer, nn.Identity):
-            return obs
-        normalizer = cast(EmpiricalNormalization, self.obs_normalizer)
-        if update:
-            self._update_obs_normalizer(obs)
-            return cast(torch.Tensor, normalizer(obs, update=False))
-        return cast(torch.Tensor, normalizer(obs, update=False))
-
-    def _autocast(self):
-        return torch.autocast(
-            device_type=self.device.type, dtype=self._amp_dtype, enabled=self.use_amp
-        )
-
-    @torch.no_grad()
-    def _update_obs_normalizer(self, obs: torch.Tensor) -> None:
-        if isinstance(self.obs_normalizer, nn.Identity):
-            return
-        normalizer = cast(EmpiricalNormalization, self.obs_normalizer)
-        normalizer.update(obs)
-
-    def update_reward_stats(
-        self,
-        rewards: torch.Tensor,
-        dones: torch.Tensor,
-    ) -> None:
-        if self.reward_normalizer is None:
-            return
-        self.reward_normalizer.update_from_transitions(rewards, dones)
-
-    @staticmethod
-    def _set_requires_grad(module: nn.Module, requires_grad: bool) -> None:
-        for param in module.parameters():
-            param.requires_grad_(requires_grad)
-
-    def _critic_loss_tensors(
-        self,
-        next_q_values: torch.Tensor,
-        next_q_log_probs_full: torch.Tensor,
-        support: torch.Tensor,
-        rewards: torch.Tensor,
-        dones: torch.Tensor,
-        truncated: torch.Tensor,
-        actor_entropy: torch.Tensor,
-        pred_log_probs: torch.Tensor,
-        gamma: float,
-    ) -> torch.Tensor:
-        next_q_log_probs = select_min_q_log_probs(next_q_values, next_q_log_probs_full)
-        batch_size, num_bins = next_q_log_probs.shape
-        support_view = support.view(1, -1)
-        rewards = rewards.view(-1, 1)
-        dones = dones.view(-1, 1)
-        truncated = truncated.view(-1, 1)
-        actor_entropy = actor_entropy.view(-1, 1)
-
-        bootstrap = torch.clamp(1.0 - dones + truncated, 0.0, 1.0)
-        support_min = support_view.min()
-        support_max = support_view.max()
-        target_bin_values = rewards + bootstrap * gamma * (support_view - actor_entropy)
-        target_bin_values = torch.clamp(target_bin_values, support_min, support_max)
-
-        bin_width = torch.clamp(support_view[0, 1] - support_view[0, 0], min=1e-8)
-        offsets = (target_bin_values - support_min) / bin_width
-        lower = torch.floor(offsets).long().clamp(0, num_bins - 1)
-        upper = torch.ceil(offsets).long().clamp(0, num_bins - 1)
-        frac = offsets - lower.float()
-
-        probs = next_q_log_probs.exp()
-        target_probs = torch.zeros(batch_size, num_bins, dtype=probs.dtype, device=probs.device)
-        target_probs.scatter_add_(1, lower, probs * (1.0 - frac))
-        target_probs.scatter_add_(1, upper, probs * frac)
-        return cast(torch.Tensor, -(target_probs.unsqueeze(0) * pred_log_probs).sum(dim=-1).mean())
-
-    def _actor_loss_tensors(
-        self,
-        log_probs: torch.Tensor,
-        q_values: torch.Tensor,
-        actions: torch.Tensor,
-        expert_actions: torch.Tensor,
-        temp_value: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        min_q = torch.min(q_values[0], q_values[1])
-        actor_loss = (temp_value.detach() * log_probs - min_q).mean()
-        if self.actor_bc_alpha > 0:
-            bc_loss = torch.mean((actions - expert_actions) ** 2)
-            actor_loss = actor_loss + self.actor_bc_alpha * min_q.abs().mean().detach() * bc_loss
-        entropy = -log_probs.detach().mean()
-        return actor_loss, entropy
-
-    @staticmethod
-    def _critic_graph_input_keys() -> tuple[str, ...]:
-        return (
-            "obs",
-            "actions",
-            "rewards",
-            "next_obs",
-            "dones",
-            "truncated",
-            "critic",
-            "next_critic",
-        )
-
-    def _critic_graph_input_shapes(self, inputs: dict[str, torch.Tensor]) -> dict[str, torch.Size]:
-        return {key: inputs[key].shape for key in self._critic_graph_input_keys()}
-
-    @staticmethod
-    def _graph_width(shape: torch.Size) -> int:
-        value = 1
-        for dim in shape[1:]:
-            value *= int(dim)
-        return value
-
-    @classmethod
-    def _sac_graph_offsets(
-        cls,
-        actor_shapes: dict[str, torch.Size],
-        critic_shapes: dict[str, torch.Size],
-    ) -> dict[str, tuple[int, int]]:
-        widths = {
-            "obs": cls._graph_width(actor_shapes["obs"]),
-            "critic": cls._graph_width(critic_shapes["critic"]),
-            "actions": cls._graph_width(critic_shapes["actions"]),
-            "rewards": cls._graph_width(critic_shapes["rewards"]),
-            "next_obs": cls._graph_width(critic_shapes["next_obs"]),
-            "next_critic": cls._graph_width(critic_shapes["next_critic"]),
-            "dones": cls._graph_width(critic_shapes["dones"]),
-            "truncated": cls._graph_width(critic_shapes["truncated"]),
-        }
-        offsets: dict[str, tuple[int, int]] = {}
-        offset = 0
-        for key in (
-            "obs",
-            "critic",
-            "actions",
-            "rewards",
-            "next_obs",
-            "next_critic",
-            "dones",
-            "truncated",
-        ):
-            key_width = widths[key]
-            offsets[key] = (offset, key_width)
-            offset += key_width
-        return offsets
-
-    @classmethod
-    def _critic_graph_static_views_from_sac_packed(
-        cls,
-        packed: torch.Tensor,
-        critic_shapes: dict[str, torch.Size],
-        actor_shapes: dict[str, torch.Size],
-    ) -> dict[str, torch.Tensor]:
-        offsets = cls._sac_graph_offsets(actor_shapes, critic_shapes)
-        views: dict[str, torch.Tensor] = {}
-        for key in cls._critic_graph_input_keys():
-            offset, width = offsets[key]
-            views[key] = packed.narrow(1, offset, width).view(critic_shapes[key])
-        return views
-
-    @classmethod
-    def _actor_graph_static_views_from_sac_packed(
-        cls,
-        packed: torch.Tensor,
-        actor_shapes: dict[str, torch.Size],
-    ) -> dict[str, torch.Tensor]:
-        batch_size = int(actor_shapes["obs"][0])
-        critic_shapes = {
-            "critic": actor_shapes["critic"],
-            "actions": actor_shapes["actions"],
-            "rewards": torch.Size((batch_size,)),
-            "next_obs": actor_shapes["next_obs"],
-            "next_critic": actor_shapes["critic"],
-            "dones": torch.Size((batch_size,)),
-            "truncated": torch.Size((batch_size,)),
-        }
-        offsets = cls._sac_graph_offsets(actor_shapes, critic_shapes)
-        views: dict[str, torch.Tensor] = {}
-        for key in cls._actor_graph_input_keys():
-            source_key = "actions" if key == "actions" else key
-            offset, width = offsets[source_key]
-            views[key] = packed.narrow(1, offset, width).view(actor_shapes[key])
-        return views
-
-    def _prepare_critic_graph_inputs(
-        self,
-        batch: dict[str, torch.Tensor],
-    ) -> dict[str, torch.Tensor]:
-        obs = batch["obs"].to(self.device)
-        actions = batch["actions"].to(self.device)
-        rewards = batch["rewards"].to(self.device)
-        next_obs = batch["next_obs"].to(self.device)
-        dones = batch["dones"].to(self.device)
-        truncated = batch["truncated"].to(self.device)
-        critic_obs = batch["critic"].to(self.device)
-        critic_next_obs = batch["next_critic"].to(self.device)
-
-        obs = self._maybe_normalize_obs(obs, update=True)
-        next_obs = self._maybe_normalize_obs(next_obs, update=False)
-        if self.reward_normalizer is not None:
-            rewards = self.reward_normalizer.normalize(rewards)
-
-        prepared = {
-            "obs": obs,
-            "actions": actions,
-            "rewards": rewards,
-            "next_obs": next_obs,
-            "dones": dones,
-            "truncated": truncated,
-            "critic": critic_obs,
-            "next_critic": critic_next_obs,
-        }
-        if "sac_graph_packed_source" in batch:
-            prepared["sac_graph_packed_source"] = batch["sac_graph_packed_source"].to(self.device)
-        return prepared
-
-    def _copy_critic_graph_inputs(self, inputs: dict[str, torch.Tensor]) -> None:
-        assert self._cuda_graph_critic_static_inputs is not None
-        packed_source = inputs.get("sac_graph_packed_source")
-        if packed_source is not None and self._cuda_graph_sac_static_packed_input is not None:
-            self._cuda_graph_sac_static_packed_input.copy_(packed_source)
-            self._cuda_graph_sac_static_source_ptr = int(packed_source.data_ptr())
-            return
-        for key, tensor in self._cuda_graph_critic_static_inputs.items():
-            tensor.copy_(inputs[key])
-
-    def _update_critic_capture_candidate(
-        self,
-        inputs: dict[str, torch.Tensor],
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        actions = inputs["actions"]
-        rewards = inputs["rewards"]
-        next_obs = inputs["next_obs"]
-        dones = inputs["dones"]
-        truncated = inputs["truncated"]
-        critic_obs = inputs["critic"]
-        critic_next_obs = inputs["next_critic"]
-
-        gamma = self.gamma**self.n_step
-        obs_all = torch.cat([critic_obs, critic_next_obs], dim=0)
-
-        with torch.no_grad():
-            with self._autocast():
-                next_actions, actor_info = self.actor(next_obs, training=False)
-                actor_entropy = self.temperature().detach() * actor_info["log_prob"]
-                act_all = torch.cat([actions, next_actions], dim=0)
-                qs_all, q_info_all = self.target_critic(obs_all, act_all, training=True)
-                next_q_values = qs_all.chunk(2, dim=1)[1]
-                next_q_log_probs_full = q_info_all["log_prob"].chunk(2, dim=1)[1]
-                support = cast(torch.Tensor, self.target_critic.predictor.support)
-
-        with self._autocast():
-            _, pred_info_all = self.critic(obs_all, act_all, training=True)
-            pred_log_probs = pred_info_all["log_prob"].chunk(2, dim=1)[0]
-            critic_loss = self._critic_loss_tensors(
-                next_q_values,
-                next_q_log_probs_full,
-                support,
-                rewards,
-                dones,
-                truncated,
-                actor_entropy,
-                pred_log_probs,
-                gamma,
-            )
-
-        self.critic_optimizer.zero_grad(set_to_none=True)
-        critic_loss.backward()
-        self._sync_gradients(self.critic.parameters())
-        self.critic_optimizer.step()
-        reward_scale_std = (
-            torch.sqrt(self.reward_normalizer.rms.var)
-            if self.reward_normalizer is not None
-            else torch.ones((), device=self.device)
-        )
-        return critic_loss, reward_scale_std
-
-    def _reset_critic_cuda_graph(self) -> None:
-        graph = self._cuda_graph_critic
-        self._cuda_graph_critic = None
-        if isinstance(graph, torch.cuda.CUDAGraph):
-            graph.reset()
-        self._cuda_graph_critic_static_inputs = None
-        self._cuda_graph_sac_static_packed_input = None
-        self._cuda_graph_sac_static_source_ptr = None
-        self._cuda_graph_critic_outputs = None
-        self._cuda_graph_critic_shapes = None
-        self._cuda_graph_critic_gradient_sync_calls = 0
-
-    def _materialize_capturable_critic_optimizer_state(
-        self,
-        inputs: dict[str, torch.Tensor],
-    ) -> None:
-        optimizer_lrs = [group["lr"] for group in self.critic_optimizer.param_groups]
-        optimizer_weight_decays = [
-            group["weight_decay"] for group in self.critic_optimizer.param_groups
-        ]
-        cpu_rng_state = torch.random.get_rng_state()
-        cuda_rng_state = torch.cuda.get_rng_state() if self.device.type == "cuda" else None
-        try:
-            for group in self.critic_optimizer.param_groups:
-                group["lr"] = 0.0
-                group["weight_decay"] = 0.0
-            self._update_critic_capture_candidate(inputs)
-        finally:
-            torch.random.set_rng_state(cpu_rng_state)
-            if cuda_rng_state is not None:
-                torch.cuda.set_rng_state(cuda_rng_state)
-            for group, lr, weight_decay in zip(
-                self.critic_optimizer.param_groups,
-                optimizer_lrs,
-                optimizer_weight_decays,
-                strict=True,
-            ):
-                group["lr"] = lr
-                group["weight_decay"] = weight_decay
-
-        self.critic_optimizer.zero_grad(set_to_none=True)
-        for state in self.critic_optimizer.state.values():
-            step = state.get("step")
-            if isinstance(step, torch.Tensor):
-                step.zero_()
-            elif step is not None:
-                state["step"] = 0
-            for name in ("exp_avg", "exp_avg_sq", "max_exp_avg_sq"):
-                tensor = state.get(name)
-                if isinstance(tensor, torch.Tensor):
-                    tensor.zero_()
-
-    def _capture_critic_cuda_graph(self, inputs: dict[str, torch.Tensor]) -> None:
-        self._cuda_graph_critic_shapes = self._critic_graph_input_shapes(inputs)
-        packed_source = inputs.get("sac_graph_packed_source")
-        if self.use_cuda_graph_critic_packed_staging and packed_source is not None:
-            self._cuda_graph_sac_static_packed_input = packed_source.detach().clone()
-            actor_shapes = self._actor_graph_input_shapes(inputs)
-            self._cuda_graph_critic_static_inputs = self._critic_graph_static_views_from_sac_packed(
-                self._cuda_graph_sac_static_packed_input,
-                self._cuda_graph_critic_shapes,
-                actor_shapes,
-            )
-        else:
-            self._cuda_graph_sac_static_packed_input = None
-            self._cuda_graph_critic_static_inputs = {
-                key: inputs[key].detach().clone() for key in self._critic_graph_input_keys()
-            }
-        self._copy_critic_graph_inputs(inputs)
-
-        graph = torch.cuda.CUDAGraph()
-        capture_stream = cast(torch.cuda.Stream, torch.cuda.Stream())
-        capture_stream.wait_stream(torch.cuda.current_stream())
-        sync_calls = [0]
-        self._active_cuda_graph_gradient_sync_calls = sync_calls
-        try:
-            with torch.cuda.stream(capture_stream), torch.cuda.graph(graph):
-                self._cuda_graph_critic_outputs = self._update_critic_capture_candidate(
-                    self._cuda_graph_critic_static_inputs
-                )
-        finally:
-            self._active_cuda_graph_gradient_sync_calls = None
-        torch.cuda.current_stream().wait_stream(capture_stream)
-        torch.cuda.synchronize()
-        self._cuda_graph_critic = graph
-        self._cuda_graph_critic_gradient_sync_calls = sync_calls[0]
-
-    def _critic_graph_output_metrics(self, *, read_items: bool = True) -> dict[str, float]:
-        if not read_items:
-            return {}
-        assert self._cuda_graph_critic_outputs is not None
-        critic_loss, reward_scale_std = self._cuda_graph_critic_outputs
-        return {
-            "critic_loss": float(critic_loss.detach().cpu()),
-            "reward_scale_std": float(reward_scale_std.detach().cpu()),
-        }
-
-    def update_critic_cuda_graph(
-        self,
-        batch: dict[str, torch.Tensor],
-        *,
-        read_metrics: bool = True,
-    ) -> dict[str, float]:
-        if not self.use_cuda_graph_critic:
-            return self.update_critic(batch)
-        if self.device.type != "cuda":
-            return self.update_critic(batch)
-        if self.scaler is not None:
-            return self.update_critic(batch)
-        if not isinstance(self.obs_normalizer, nn.Identity):
-            return self.update_critic(batch)
-
-        inputs = self._prepare_critic_graph_inputs(batch)
-        if self._cuda_graph_critic_shapes != self._critic_graph_input_shapes(inputs):
-            self._reset_critic_cuda_graph()
-            self._materialize_capturable_critic_optimizer_state(inputs)
-            self._capture_critic_cuda_graph(inputs)
-            self.critic_scheduler.step()
-            self.critic.normalize_parameters()
-            return self._critic_graph_output_metrics(read_items=read_metrics)
-
-        assert self._cuda_graph_critic is not None
-        self._copy_critic_graph_inputs(inputs)
-        self._cuda_graph_critic.replay()
-        self._record_cuda_graph_gradient_replay(self._cuda_graph_critic_gradient_sync_calls)
-        self.critic_scheduler.step()
-        self.critic.normalize_parameters()
-        return self._critic_graph_output_metrics(read_items=read_metrics)
-
-    @staticmethod
-    def _actor_graph_input_keys() -> tuple[str, ...]:
-        return ("obs", "next_obs", "actions", "critic")
-
-    def _actor_graph_input_shapes(self, inputs: dict[str, torch.Tensor]) -> dict[str, torch.Size]:
-        return {key: inputs[key].shape for key in self._actor_graph_input_keys()}
-
-    def _prepare_actor_graph_inputs(
-        self,
-        batch: dict[str, torch.Tensor],
-    ) -> dict[str, torch.Tensor]:
-        obs = batch["obs"].to(self.device)
-        next_obs = batch["next_obs"].to(self.device)
-        expert_actions = batch["actions"].to(self.device)
-        critic_obs = batch["critic"].to(self.device)
-        prepared = {
-            "obs": self._maybe_normalize_obs(obs, update=False),
-            "next_obs": self._maybe_normalize_obs(next_obs, update=False),
-            "actions": expert_actions,
-            "critic": critic_obs,
-        }
-        if "sac_graph_packed_source" in batch:
-            prepared["sac_graph_packed_source"] = batch["sac_graph_packed_source"].to(self.device)
-        return prepared
-
-    def _copy_actor_graph_inputs(self, inputs: dict[str, torch.Tensor]) -> None:
-        assert self._cuda_graph_actor_static_inputs is not None
-        packed_source = inputs.get("sac_graph_packed_source")
-        if packed_source is not None:
-            static_packed = self._cuda_graph_actor_static_packed_input
-            if static_packed is None:
-                static_packed = self._cuda_graph_sac_static_packed_input
-            if static_packed is not None:
-                source_ptr = int(packed_source.data_ptr())
-                if (
-                    static_packed is not self._cuda_graph_sac_static_packed_input
-                    or self._cuda_graph_sac_static_source_ptr != source_ptr
-                ):
-                    static_packed.copy_(packed_source)
-                    if static_packed is self._cuda_graph_sac_static_packed_input:
-                        self._cuda_graph_sac_static_source_ptr = source_ptr
-                return
-        for key, tensor in self._cuda_graph_actor_static_inputs.items():
-            tensor.copy_(inputs[key])
-
-    def _update_actor_capture_candidate(
-        self,
-        inputs: dict[str, torch.Tensor],
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
-        obs = inputs["obs"]
-        next_obs = inputs["next_obs"]
-        expert_actions = inputs["actions"]
-        critic_obs = inputs["critic"]
-        obs_all = torch.cat([obs, next_obs], dim=0)
-
-        with self._autocast():
-            actions_all, actor_info_all = self.actor(obs_all, training=True)
-            actions = actions_all.chunk(2, dim=0)[0]
-            log_probs = actor_info_all["log_prob"].chunk(2, dim=0)[0]
-
-            self._set_requires_grad(self.critic, False)
-            q_values, _ = self.critic(critic_obs, actions, training=False)
-            self._set_requires_grad(self.critic, True)
-            temp_value = self.temperature()
-            actor_loss, entropy = self._actor_loss_tensors(
-                log_probs, q_values, actions, expert_actions, temp_value
-            )
-
-        self.actor_optimizer.zero_grad(set_to_none=True)
-        actor_loss.backward()
-        self._sync_gradients(self.actor.parameters())
-        self.actor_optimizer.step()
-
-        temp_loss = temp_value * (entropy - self.target_entropy)
-        self.temperature_optimizer.zero_grad(set_to_none=True)
-        temp_loss.backward()
-        self._sync_gradients(self.temperature.parameters())
-        self.temperature_optimizer.step()
-        return actor_loss, entropy, temp_value, temp_loss
-
-    def _reset_actor_cuda_graph(self) -> None:
-        graph = self._cuda_graph_actor
-        self._cuda_graph_actor = None
-        if isinstance(graph, torch.cuda.CUDAGraph):
-            graph.reset()
-        self._cuda_graph_actor_static_inputs = None
-        self._cuda_graph_actor_static_packed_input = None
-        self._cuda_graph_actor_outputs = None
-        self._cuda_graph_actor_shapes = None
-        self._cuda_graph_actor_gradient_sync_calls = 0
-
-    def _materialize_capturable_actor_optimizer_state(
-        self,
-        inputs: dict[str, torch.Tensor],
-    ) -> None:
-        optimizers = (self.actor_optimizer, self.temperature_optimizer)
-        optimizer_lrs = [
-            [group["lr"] for group in optimizer.param_groups] for optimizer in optimizers
-        ]
-        optimizer_weight_decays = [
-            [group["weight_decay"] for group in optimizer.param_groups] for optimizer in optimizers
-        ]
-        cpu_rng_state = torch.random.get_rng_state()
-        cuda_rng_state = torch.cuda.get_rng_state() if self.device.type == "cuda" else None
-        try:
-            for optimizer in optimizers:
-                for group in optimizer.param_groups:
-                    group["lr"] = 0.0
-                    group["weight_decay"] = 0.0
-            self._update_actor_capture_candidate(inputs)
-        finally:
-            torch.random.set_rng_state(cpu_rng_state)
-            if cuda_rng_state is not None:
-                torch.cuda.set_rng_state(cuda_rng_state)
-            for optimizer, lrs, weight_decays in zip(
-                optimizers,
-                optimizer_lrs,
-                optimizer_weight_decays,
-                strict=True,
-            ):
-                for group, lr, weight_decay in zip(
-                    optimizer.param_groups,
-                    lrs,
-                    weight_decays,
-                    strict=True,
-                ):
-                    group["lr"] = lr
-                    group["weight_decay"] = weight_decay
-
-        for optimizer in optimizers:
-            optimizer.zero_grad(set_to_none=True)
-            for state in optimizer.state.values():
-                step = state.get("step")
-                if isinstance(step, torch.Tensor):
-                    step.zero_()
-                elif step is not None:
-                    state["step"] = 0
-                for name in ("exp_avg", "exp_avg_sq", "max_exp_avg_sq"):
-                    tensor = state.get(name)
-                    if isinstance(tensor, torch.Tensor):
-                        tensor.zero_()
-
-    def _capture_actor_cuda_graph(self, inputs: dict[str, torch.Tensor]) -> None:
-        self._cuda_graph_actor_shapes = self._actor_graph_input_shapes(inputs)
-        packed_source = inputs.get("sac_graph_packed_source")
-        if self.use_cuda_graph_actor_packed_staging and packed_source is not None:
-            if (
-                self._cuda_graph_sac_static_packed_input is not None
-                and self._cuda_graph_sac_static_packed_input.shape == packed_source.shape
-            ):
-                self._cuda_graph_actor_static_packed_input = (
-                    self._cuda_graph_sac_static_packed_input
-                )
-            else:
-                self._cuda_graph_actor_static_packed_input = packed_source.detach().clone()
-            self._cuda_graph_actor_static_inputs = self._actor_graph_static_views_from_sac_packed(
-                self._cuda_graph_actor_static_packed_input,
-                self._cuda_graph_actor_shapes,
-            )
-        else:
-            self._cuda_graph_actor_static_packed_input = None
-            self._cuda_graph_actor_static_inputs = {
-                key: inputs[key].detach().clone() for key in self._actor_graph_input_keys()
-            }
-        self._copy_actor_graph_inputs(inputs)
-
-        graph = torch.cuda.CUDAGraph()
-        capture_stream = cast(torch.cuda.Stream, torch.cuda.Stream())
-        capture_stream.wait_stream(torch.cuda.current_stream())
-        sync_calls = [0]
-        self._active_cuda_graph_gradient_sync_calls = sync_calls
-        try:
-            with torch.cuda.stream(capture_stream), torch.cuda.graph(graph):
-                self._cuda_graph_actor_outputs = self._update_actor_capture_candidate(
-                    self._cuda_graph_actor_static_inputs
-                )
-        finally:
-            self._active_cuda_graph_gradient_sync_calls = None
-        torch.cuda.current_stream().wait_stream(capture_stream)
-        torch.cuda.synchronize()
-        self._cuda_graph_actor = graph
-        self._cuda_graph_actor_gradient_sync_calls = sync_calls[0]
-
-    def _actor_graph_output_metrics(self, *, read_items: bool = True) -> dict[str, float]:
-        if not read_items:
-            return {}
-        assert self._cuda_graph_actor_outputs is not None
-        actor_loss, entropy, temp_value, temp_loss = self._cuda_graph_actor_outputs
-        return {
-            "actor_loss": float(actor_loss.detach().cpu()),
-            "actor_entropy": float(entropy.detach().cpu()),
-            "temperature": float(temp_value.detach().cpu()),
-            "temperature_loss": float(temp_loss.detach().cpu()),
-        }
-
-    def update_actor_cuda_graph(
-        self,
-        batch: dict[str, torch.Tensor],
-        *,
-        read_metrics: bool = True,
-    ) -> dict[str, float]:
-        if not self.use_cuda_graph_actor:
-            return self.update_actor(batch)
-        if self.device.type != "cuda":
-            return self.update_actor(batch)
-        if self.scaler is not None:
-            return self.update_actor(batch)
-        if not isinstance(self.obs_normalizer, nn.Identity):
-            return self.update_actor(batch)
-
-        inputs = self._prepare_actor_graph_inputs(batch)
-        if self._cuda_graph_actor_shapes != self._actor_graph_input_shapes(inputs):
-            self._reset_actor_cuda_graph()
-            self._materialize_capturable_actor_optimizer_state(inputs)
-            self._capture_actor_cuda_graph(inputs)
-            self.actor_scheduler.step()
-            self.temperature_scheduler.step()
-            self.actor.normalize_parameters()
-            return self._actor_graph_output_metrics(read_items=read_metrics)
-
-        assert self._cuda_graph_actor is not None
-        self._copy_actor_graph_inputs(inputs)
-        self._cuda_graph_actor.replay()
-        self._record_cuda_graph_gradient_replay(self._cuda_graph_actor_gradient_sync_calls)
-        self.actor_scheduler.step()
-        self.temperature_scheduler.step()
-        self.actor.normalize_parameters()
-        return self._actor_graph_output_metrics(read_items=read_metrics)
-
-    def update_critic(self, batch: dict[str, torch.Tensor]) -> dict[str, float]:
-        obs = batch["obs"].to(self.device)
-        actions = batch["actions"].to(self.device)
-        rewards = batch["rewards"].to(self.device)
-        next_obs = batch["next_obs"].to(self.device)
-        dones = batch["dones"].to(self.device)
-        truncated = batch["truncated"].to(self.device)
-        critic_obs = batch["critic"].to(self.device)
-        critic_next_obs = batch["next_critic"].to(self.device)
-
-        obs = self._maybe_normalize_obs(obs, update=True)
-        next_obs = self._maybe_normalize_obs(next_obs, update=False)
-
-        if self.reward_normalizer is not None:
-            rewards = self.reward_normalizer.normalize(rewards)
-
-        gamma = self.gamma**self.n_step
-
-        obs_all = torch.cat([critic_obs, critic_next_obs], dim=0)
-
-        with torch.no_grad():
-            with self._autocast():
-                next_actions, actor_info = self.actor(next_obs, training=False)
-                actor_entropy = self.temperature().detach() * actor_info["log_prob"]
-                act_all = torch.cat([actions, next_actions], dim=0)
-                qs_all, q_info_all = self.target_critic(obs_all, act_all, training=True)
-                next_q_values = qs_all.chunk(2, dim=1)[1]
-                next_q_log_probs_full = q_info_all["log_prob"].chunk(2, dim=1)[1]
-                support = cast(torch.Tensor, self.target_critic.predictor.support)
-
-        with self._autocast():
-            _, pred_info_all = self.critic(obs_all, act_all, training=True)
-            pred_log_probs = pred_info_all["log_prob"].chunk(2, dim=1)[0]
-            critic_loss = self._critic_loss_tensors(
-                next_q_values,
-                next_q_log_probs_full,
-                support,
-                rewards,
-                dones,
-                truncated,
-                actor_entropy,
-                pred_log_probs,
-                gamma,
-            )
-
-        self.critic_optimizer.zero_grad(set_to_none=True)
-        if self.scaler is not None:
-            self.scaler.scale(critic_loss).backward()
-            self._sync_gradients(self.critic.parameters())
-            self.scaler.unscale_(self.critic_optimizer)
-            self.scaler.step(self.critic_optimizer)
-            self.scaler.update()
-        else:
-            critic_loss.backward()
-            self._sync_gradients(self.critic.parameters())
-            self.critic_optimizer.step()
-        self.critic_scheduler.step()
-        self.critic.normalize_parameters()
-
-        return {
-            "critic_loss": float(critic_loss.detach().cpu()),
-            "reward_scale_std": float(
-                torch.sqrt(self.reward_normalizer.rms.var).detach().cpu()
-                if self.reward_normalizer is not None
-                else torch.tensor(1.0)
-            ),
-        }
-
-    def update_actor(self, batch: dict[str, torch.Tensor]) -> dict[str, float]:
-        obs = batch["obs"].to(self.device)
-        next_obs = batch["next_obs"].to(self.device)
-        expert_actions = batch["actions"].to(self.device)
-        critic_obs = batch["critic"].to(self.device)
-
-        obs = self._maybe_normalize_obs(obs, update=False)
-        next_obs = self._maybe_normalize_obs(next_obs, update=False)
-
-        obs_all = torch.cat([obs, next_obs], dim=0)
-
-        with self._autocast():
-            actions_all, actor_info_all = self.actor(obs_all, training=True)
-            actions = actions_all.chunk(2, dim=0)[0]
-            log_probs = actor_info_all["log_prob"].chunk(2, dim=0)[0]
-
-            self._set_requires_grad(self.critic, False)
-            q_values, _ = self.critic(critic_obs, actions, training=False)
-            self._set_requires_grad(self.critic, True)
-            actor_loss, entropy = self._actor_loss_tensors(
-                log_probs, q_values, actions, expert_actions, self.temperature()
-            )
-
-        self.actor_optimizer.zero_grad(set_to_none=True)
-        if self.scaler is not None:
-            self.scaler.scale(actor_loss).backward()
-            self._sync_gradients(self.actor.parameters())
-            self.scaler.unscale_(self.actor_optimizer)
-            self.scaler.step(self.actor_optimizer)
-            self.scaler.update()
-        else:
-            actor_loss.backward()
-            self._sync_gradients(self.actor.parameters())
-            self.actor_optimizer.step()
-        self.actor_scheduler.step()
-        self.actor.normalize_parameters()
-
-        temp_value = self.temperature()
-        temp_loss = temp_value * (entropy - self.target_entropy)
-        self.temperature_optimizer.zero_grad(set_to_none=True)
-        temp_loss.backward()
-        self._sync_gradients(self.temperature.parameters())
-        self.temperature_optimizer.step()
-        self.temperature_scheduler.step()
-
-        return {
-            "actor_loss": float(actor_loss.detach().cpu()),
-            "actor_entropy": float(entropy.detach().cpu()),
-            "temperature": float(temp_value.detach().cpu()),
-            "temperature_loss": float(temp_loss.detach().cpu()),
-        }
-
-    def soft_update_target(self) -> None:
-        with torch.no_grad():
-            for target_param, param in zip(
-                self.target_critic.parameters(), self.critic.parameters()
-            ):
-                target_param.data.mul_(1.0 - self.tau).add_(param.data, alpha=self.tau)
-
-    def set_gradient_sync(
-        self,
-        sync: Callable[[Iterable[torch.Tensor]], None] | None,
-        *,
-        graph_replay_recorder: Callable[[int], None] | None = None,
-    ) -> None:
-        """Attach the per-optimizer gradient collective used by multi-GPU DP."""
-        if sync is None and graph_replay_recorder is not None:
-            raise ValueError("graph_replay_recorder requires a gradient sync callback")
-        if sync != self._gradient_sync:
-            self._reset_critic_cuda_graph()
-            self._reset_actor_cuda_graph()
-        self._gradient_sync = sync
-        self._gradient_sync_graph_replay_recorder = graph_replay_recorder
-        self.dp_cuda_graph_gradient_sync = bool(
-            sync is not None
-            and self.scaler is None
-            and isinstance(self.obs_normalizer, nn.Identity)
-            and (self.use_cuda_graph_critic or self.use_cuda_graph_actor)
-        )
-
-    def _sync_gradients(self, parameters: Iterable[torch.Tensor]) -> None:
-        if self._gradient_sync is not None:
-            self._gradient_sync(parameters)
-            if self._active_cuda_graph_gradient_sync_calls is not None:
-                self._active_cuda_graph_gradient_sync_calls[0] += 1
-
-    def _record_cuda_graph_gradient_replay(self, collective_calls: int) -> None:
-        if self._gradient_sync_graph_replay_recorder is not None and collective_calls > 0:
-            self._gradient_sync_graph_replay_recorder(collective_calls)
-
-    def release_cuda_graphs(self) -> None:
-        """Release captured NCCL nodes before the process group is destroyed."""
-        self._reset_critic_cuda_graph()
-        self._reset_actor_cuda_graph()
-
-    def dp_initial_sync_tensors(self) -> dict[str, torch.Tensor]:
-        """Model state broadcast once from rank 0 before collection starts.
-
-        The values alias the parameter/buffer storage of ``actor``, ``critic``,
-        ``target_critic`` and ``temperature`` rather than copies. Optimizer
-        state starts empty and remains aligned because every actual optimizer
-        update uses the same cross-rank mean gradient. Observation/reward
-        normalizer statistics remain rank-local.
-        """
-        tensors: dict[str, torch.Tensor] = {}
-        for prefix, module in (
-            ("actor", self.actor),
-            ("critic", self.critic),
-            ("target_critic", self.target_critic),
-            ("temperature", self.temperature),
-        ):
-            for key, value in module.state_dict().items():
-                tensors[f"{prefix}.{key}"] = value
-        return tensors
-
-    def get_state_dict(self) -> dict[str, Any]:
-        return {
-            "actor": self.actor.state_dict(),
-            "critic": self.critic.state_dict(),
-            "target_critic": self.target_critic.state_dict(),
-            "temperature": self.temperature.state_dict(),
-            "actor_optimizer": self.actor_optimizer.state_dict(),
-            "critic_optimizer": self.critic_optimizer.state_dict(),
-            "temperature_optimizer": self.temperature_optimizer.state_dict(),
-            "actor_scheduler": self.actor_scheduler.state_dict(),
-            "critic_scheduler": self.critic_scheduler.state_dict(),
-            "temperature_scheduler": self.temperature_scheduler.state_dict(),
-            "obs_normalizer": (
-                self.obs_normalizer.state_dict()
-                if hasattr(self.obs_normalizer, "state_dict")
-                else None
-            ),
-            "reward_normalizer": (
-                self.reward_normalizer.state_dict() if self.reward_normalizer is not None else None
-            ),
-            "update_count": self.update_count,
-        }
-
-    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
-        self.actor.load_state_dict(state_dict["actor"])
-        self.critic.load_state_dict(state_dict["critic"])
-        self.target_critic.load_state_dict(state_dict["target_critic"])
-        self.temperature.load_state_dict(state_dict["temperature"])
-        self.actor_optimizer.load_state_dict(state_dict["actor_optimizer"])
-        self.critic_optimizer.load_state_dict(state_dict["critic_optimizer"])
-        self.temperature_optimizer.load_state_dict(state_dict["temperature_optimizer"])
-        self.actor_scheduler.load_state_dict(state_dict["actor_scheduler"])
-        self.critic_scheduler.load_state_dict(state_dict["critic_scheduler"])
-        self.temperature_scheduler.load_state_dict(state_dict["temperature_scheduler"])
-        if state_dict.get("obs_normalizer") and hasattr(self.obs_normalizer, "load_state_dict"):
-            self.obs_normalizer.load_state_dict(state_dict["obs_normalizer"])
-        if self.reward_normalizer is not None and state_dict.get("reward_normalizer"):
-            self.reward_normalizer.load_state_dict(state_dict["reward_normalizer"])
-        self.update_count = int(state_dict.get("update_count", 0))
diff --git a/src/unilab/algos/torch/flash_sac/network.py b/src/unilab/algos/torch/flash_sac/network.py
deleted file mode 100644
index e810513fd..000000000
--- a/src/unilab/algos/torch/flash_sac/network.py
+++ /dev/null
@@ -1,208 +0,0 @@
-"""FlashSAC actor, critic, and temperature modules."""
-
-from __future__ import annotations
-
-import math
-from typing import cast
-
-import torch
-import torch.nn as nn
-
-from unilab.algos.torch.flash_sac.layers import (
-    EnsembleCategoricalValue,
-    EnsembleFlashSACBlock,
-    EnsembleFlashSACEmbedder,
-    EnsembleUnitRMSNorm,
-    FlashSACBlock,
-    FlashSACEmbedder,
-    NormalTanhPolicy,
-    UnitRMSNorm,
-)
-
-
-def _normalize_module_tree(module: nn.Module) -> None:
-    with torch.no_grad():
-        for child in module.modules():
-            if child is module:
-                continue
-            normalize = getattr(child, "normalize_parameters", None)
-            if callable(normalize):
-                normalize()
-
-
-class FlashSACActor(nn.Module):
-    zeta_cdf: torch.Tensor
-    _noise: torch.Tensor
-    _repeat_count: torch.Tensor
-    _repeat_target: torch.Tensor
-
-    def __init__(
-        self,
-        num_blocks: int,
-        input_dim: int,
-        hidden_dim: int,
-        action_dim: int,
-        noise_zeta_mu: float = 2.0,
-        noise_zeta_max: int = 16,
-        device: str | torch.device = "cpu",
-    ):
-        super().__init__()
-        self.embedder = FlashSACEmbedder(input_dim=input_dim, hidden_dim=hidden_dim)
-        self.encoder = nn.ModuleList([FlashSACBlock(hidden_dim) for _ in range(num_blocks)])
-        self.post_norm = UnitRMSNorm(hidden_dim)
-        self.predictor = NormalTanhPolicy(hidden_dim=hidden_dim, action_dim=action_dim)
-        self.noise_zeta_mu = noise_zeta_mu
-        self.noise_zeta_max = noise_zeta_max
-        ns = torch.arange(1, noise_zeta_max + 1, dtype=torch.float32)
-        pmf = ns.pow(-noise_zeta_mu)
-        self.register_buffer("zeta_cdf", torch.cumsum(pmf / pmf.sum(), dim=0))
-        self.register_buffer("_noise", torch.zeros(0), persistent=False)
-        self.register_buffer("_repeat_count", torch.zeros(0, dtype=torch.int32), persistent=False)
-        self.register_buffer("_repeat_target", torch.zeros(0, dtype=torch.int32), persistent=False)
-        self.to(device)
-        self.normalize_parameters()
-
-    def normalize_parameters(self) -> None:
-        _normalize_module_tree(self)
-
-    def _encode(self, observations: torch.Tensor, training: bool) -> torch.Tensor:
-        x = self.embedder(observations, training=training)
-        for block in self.encoder:
-            x = block(x, training=training)
-        return cast(torch.Tensor, self.post_norm(x))
-
-    def get_mean_and_std(
-        self, observations: torch.Tensor, training: bool
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        encoded = self._encode(observations, training=training)
-        return self.predictor.get_mean_and_std(encoded)
-
-    def forward(
-        self, observations: torch.Tensor, training: bool
-    ) -> tuple[torch.Tensor, dict[str, torch.Tensor]]:
-        encoded = self._encode(observations, training=training)
-        return cast(tuple[torch.Tensor, dict[str, torch.Tensor]], self.predictor(encoded))
-
-    def as_export_module(self) -> "nn.Module":
-        """Return a single-input/single-output wrapper suitable for torch.onnx.export."""
-        actor = self
-
-        class _Wrapper(nn.Module):
-            def __init__(self) -> None:
-                super().__init__()
-                self.base = actor
-
-            def forward(self, obs: torch.Tensor) -> torch.Tensor:
-                mean, _std = self.base.get_mean_and_std(obs, training=False)
-                return cast(torch.Tensor, torch.tanh(mean))
-
-        return _Wrapper()
-
-    def _ensure_exploration_state(
-        self, batch_size: int, action_dim: int, device: torch.device, dtype: torch.dtype
-    ) -> None:
-        if (
-            self._noise.numel() == batch_size * action_dim
-            and self._noise.device == device
-            and self._noise.dtype == dtype
-        ):
-            return
-        self._noise = torch.zeros(batch_size, action_dim, device=device, dtype=dtype)
-        self._repeat_count = torch.zeros(batch_size, device=device, dtype=torch.int32)
-        self._repeat_target = torch.zeros(batch_size, device=device, dtype=torch.int32)
-
-    def _sample_repeat_targets(self, batch_size: int, device: torch.device) -> torch.Tensor:
-        draws = torch.rand(batch_size, device=device)
-        cdf = self.zeta_cdf.to(device)
-        return cast(torch.Tensor, torch.searchsorted(cdf, draws).to(torch.int32) + 1)
-
-    @torch.no_grad()
-    def explore(
-        self,
-        obs: torch.Tensor,
-        dones: torch.Tensor | None = None,
-        deterministic: bool = False,
-    ) -> torch.Tensor:
-        if isinstance(dones, bool):
-            deterministic = dones
-            dones = None
-        mean, std = self.get_mean_and_std(obs, training=False)
-        if deterministic:
-            return torch.tanh(mean)
-
-        batch_size, action_dim = mean.shape
-        self._ensure_exploration_state(batch_size, action_dim, mean.device, mean.dtype)
-
-        if dones is None:
-            done_mask = torch.zeros(batch_size, device=mean.device, dtype=torch.bool)
-        else:
-            done_mask = dones.to(device=mean.device).reshape(-1) > 0.5
-
-        reinit = done_mask | (self._repeat_count <= 0) | (self._repeat_count >= self._repeat_target)
-        if torch.any(reinit):
-            new_noise = torch.randn_like(mean)
-            new_target = self._sample_repeat_targets(batch_size, mean.device)
-            self._noise = torch.where(reinit.unsqueeze(-1), new_noise, self._noise)
-            self._repeat_target = torch.where(reinit, new_target, self._repeat_target)
-            self._repeat_count = torch.where(
-                reinit, torch.zeros_like(self._repeat_count), self._repeat_count
-            )
-
-        actions = torch.tanh(mean + std * self._noise)
-        self._repeat_count = self._repeat_count + 1
-        return actions
-
-
-class FlashSACDoubleCritic(nn.Module):
-    def __init__(
-        self,
-        num_blocks: int,
-        input_dim: int,
-        hidden_dim: int,
-        num_bins: int,
-        min_v: float,
-        max_v: float,
-        num_qs: int = 2,
-        device: str | torch.device = "cpu",
-    ):
-        super().__init__()
-        self.embedder = EnsembleFlashSACEmbedder(num_qs, input_dim, hidden_dim)
-        self.encoder = nn.ModuleList(
-            [EnsembleFlashSACBlock(num_qs, hidden_dim) for _ in range(num_blocks)]
-        )
-        self.post_norm = EnsembleUnitRMSNorm(num_qs, hidden_dim)
-        self.predictor = EnsembleCategoricalValue(
-            num_ensemble=num_qs,
-            hidden_dim=hidden_dim,
-            num_bins=num_bins,
-            min_v=min_v,
-            max_v=max_v,
-        )
-        self.to(device)
-        self.normalize_parameters()
-
-    def normalize_parameters(self) -> None:
-        _normalize_module_tree(self)
-
-    def forward(
-        self,
-        observations: torch.Tensor,
-        actions: torch.Tensor,
-        training: bool,
-    ) -> tuple[torch.Tensor, dict[str, torch.Tensor]]:
-        x = torch.cat((observations, actions), dim=-1)
-        x = x.unsqueeze(0).expand(self.predictor.logit_w.weight.shape[0], -1, -1)
-        x = self.embedder(x, training=training)
-        for block in self.encoder:
-            x = block(x, training=training)
-        x = self.post_norm(x)
-        return cast(tuple[torch.Tensor, dict[str, torch.Tensor]], self.predictor(x))
-
-
-class FlashSACTemperature(nn.Module):
-    def __init__(self, initial_value: float = 0.01):
-        super().__init__()
-        self.log_temp = nn.Parameter(torch.tensor([math.log(initial_value)], dtype=torch.float32))
-
-    def forward(self) -> torch.Tensor:
-        return torch.exp(self.log_temp)
diff --git a/src/unilab/algos/torch/flash_sac/runner.py b/src/unilab/algos/torch/flash_sac/runner.py
deleted file mode 100644
index 729f49de9..000000000
--- a/src/unilab/algos/torch/flash_sac/runner.py
+++ /dev/null
@@ -1,143 +0,0 @@
-"""FlashSAC runner using the shared off-policy runtime."""
-
-from __future__ import annotations
-
-from typing import Any
-
-from unilab.algos.torch.flash_sac.learner import FlashSACLearner
-from unilab.algos.torch.offpolicy.double_buffer_runner import DoubleBufferOffPolicyRunner
-from unilab.ipc.replay_pipelines.gpu_resident import require_offpolicy_replay_device
-from unilab.utils.device import get_default_device
-
-
-class FlashSACRunner(DoubleBufferOffPolicyRunner):
-    def __init__(
-        self,
-        env_name: str,
-        env_cfg_override: dict[str, Any] | None = None,
-        device: str | None = None,
-        num_envs: int = 2048,
-        replay_buffer_n: int = 512,
-        batch_size: int = 2048,
-        learning_starts: int = 0,
-        updates_per_step: int = 1,
-        policy_frequency: int = 2,
-        env_steps_per_sync: int = 1,
-        gamma: float = 0.99,
-        tau: float = 0.01,
-        actor_lr: float = 3e-4,
-        critic_lr: float = 3e-4,
-        obs_normalization: bool = False,
-        actor_hidden_dim: int = 128,
-        critic_hidden_dim: int = 256,
-        num_atoms: int = 101,
-        use_amp: bool = False,
-        sim_backend: str = "mujoco",
-        actor_num_blocks: int = 2,
-        critic_num_blocks: int = 2,
-        actor_bc_alpha: float = 0.0,
-        actor_noise_zeta_mu: float = 2.0,
-        actor_noise_zeta_max: int = 16,
-        critic_min_v: float = -5.0,
-        critic_max_v: float = 5.0,
-        target_sigma: float = 0.15,
-        target_entropy: float | None = None,
-        temp_initial_value: float = 0.01,
-        learning_rate_init: float = 3e-4,
-        learning_rate_peak: float = 3e-4,
-        learning_rate_end: float = 1.5e-4,
-        learning_rate_warmup_steps: int = 0,
-        learning_rate_decay_steps: int = 500000,
-        normalize_reward: bool = True,
-        normalized_g_max: float = 5.0,
-        n_step: int = 1,
-        amp_dtype: str = "auto",
-        use_compile: bool = False,
-        use_cuda_graph_critic: bool = False,
-        use_cuda_graph_actor: bool = False,
-        use_cuda_graph_critic_packed_staging: bool = False,
-        use_cuda_graph_actor_packed_staging: bool = False,
-        seed: int | None = None,
-        trace_enabled: bool = False,
-        trace_output_dir: str | None = None,
-        trace_thread_time: bool = False,
-        trace_cuda_events: bool = True,
-    ):
-        from unilab.base import registry
-        from unilab.base.observations import get_obs_dims
-        from unilab.base.registry import ensure_registries
-        from unilab.training.seed import apply_training_seed
-
-        runtime_device = require_offpolicy_replay_device(device or get_default_device())
-        ensure_registries()
-        apply_training_seed(seed, torch_runtime=True, cuda=True)
-        env: Any = registry.make(
-            env_name, num_envs=1, sim_backend=sim_backend, env_cfg_override=env_cfg_override
-        )
-        obs_dim, critic_obs_dim = get_obs_dims(env.obs_groups_spec)
-        action_shape = env.action_space.shape
-        assert action_shape is not None
-        action_dim = int(action_shape[0])
-        env.close()
-
-        learner = FlashSACLearner(
-            obs_dim=obs_dim,
-            action_dim=action_dim,
-            critic_obs_dim=critic_obs_dim,
-            device=runtime_device,
-            gamma=gamma,
-            tau=tau,
-            actor_lr=actor_lr,
-            critic_lr=critic_lr,
-            actor_hidden_dim=actor_hidden_dim,
-            critic_hidden_dim=critic_hidden_dim,
-            actor_num_blocks=actor_num_blocks,
-            critic_num_blocks=critic_num_blocks,
-            num_atoms=num_atoms,
-            critic_min_v=critic_min_v,
-            critic_max_v=critic_max_v,
-            temp_initial_value=temp_initial_value,
-            temp_target_sigma=target_sigma,
-            temp_target_entropy=target_entropy,
-            actor_bc_alpha=actor_bc_alpha,
-            actor_noise_zeta_mu=actor_noise_zeta_mu,
-            actor_noise_zeta_max=actor_noise_zeta_max,
-            learning_rate_init=learning_rate_init,
-            learning_rate_peak=learning_rate_peak,
-            learning_rate_end=learning_rate_end,
-            learning_rate_warmup_steps=learning_rate_warmup_steps,
-            learning_rate_decay_steps=learning_rate_decay_steps,
-            normalize_reward=normalize_reward,
-            normalized_g_max=normalized_g_max,
-            n_step=n_step,
-            obs_normalization=obs_normalization,
-            use_amp=use_amp,
-            amp_dtype=amp_dtype,
-            use_compile=use_compile,
-            use_cuda_graph_critic=use_cuda_graph_critic,
-            use_cuda_graph_actor=use_cuda_graph_actor,
-            use_cuda_graph_critic_packed_staging=use_cuda_graph_critic_packed_staging,
-            use_cuda_graph_actor_packed_staging=use_cuda_graph_actor_packed_staging,
-        )
-
-        super().__init__(
-            learner=learner,
-            env_name=env_name,
-            algo_type="flashsac",
-            num_envs=num_envs,
-            replay_buffer_n=replay_buffer_n,
-            batch_size=batch_size,
-            learning_starts=learning_starts,
-            updates_per_step=updates_per_step,
-            policy_frequency=policy_frequency,
-            env_steps_per_sync=env_steps_per_sync,
-            device=runtime_device,
-            seed=seed,
-            obs_normalization=obs_normalization,
-            sim_backend=sim_backend,
-            env_cfg_override=env_cfg_override,
-            trace_enabled=trace_enabled,
-            trace_output_dir=trace_output_dir,
-            trace_thread_time=trace_thread_time,
-            trace_cuda_events=trace_cuda_events,
-        )
diff --git a/src/unilab/algos/torch/flash_sac/update.py b/src/unilab/algos/torch/flash_sac/update.py
deleted file mode 100644
index 62870b282..000000000
--- a/src/unilab/algos/torch/flash_sac/update.py
+++ /dev/null
@@ -1,85 +0,0 @@
-"""FlashSAC update helpers."""
-
-from __future__ import annotations
-
-import math
-
-import torch
-
-
-def build_lr_lambda(
-    init_lr: float,
-    peak_lr: float,
-    end_lr: float,
-    warmup_steps: int,
-    decay_steps: int,
-):
-    init_ratio = init_lr / peak_lr if peak_lr > 0 else 1.0
-    end_ratio = end_lr / peak_lr if peak_lr > 0 else 1.0
-    warmup_steps = max(int(warmup_steps), 0)
-    decay_steps = max(int(decay_steps), warmup_steps + 1)
-
-    def schedule(step: int) -> float:
-        if warmup_steps > 0 and step < warmup_steps:
-            progress = float(step) / float(max(warmup_steps, 1))
-            return init_ratio + (1.0 - init_ratio) * progress
-        progress = min(max((step - warmup_steps) / float(decay_steps - warmup_steps), 0.0), 1.0)
-        cosine = 0.5 * (1.0 + math.cos(math.pi * progress))
-        return end_ratio + (1.0 - end_ratio) * cosine
-
-    return schedule
-
-
-def select_min_q_log_probs(
-    next_q_values: torch.Tensor,
-    next_q_log_probs: torch.Tensor,
-) -> torch.Tensor:
-    num_bins = next_q_log_probs.shape[-1]
-    min_indices = next_q_values.argmin(dim=0)
-    gather_index = min_indices[None, :, None].expand(1, -1, num_bins)
-    return torch.gather(next_q_log_probs, dim=0, index=gather_index)[0]
-
-
-def compute_categorical_td_target(
-    support: torch.Tensor,
-    target_log_probs: torch.Tensor,
-    reward: torch.Tensor,
-    dones: torch.Tensor,
-    truncated: torch.Tensor,
-    actor_entropy: torch.Tensor,
-    gamma: float,
-) -> torch.Tensor:
-    batch_size, num_bins = target_log_probs.shape
-    support = support.view(1, -1)
-    reward = reward.view(-1, 1)
-    dones = dones.view(-1, 1)
-    truncated = truncated.view(-1, 1)
-    actor_entropy = actor_entropy.view(-1, 1)
-
-    bootstrap = torch.clamp(1.0 - dones + truncated, 0.0, 1.0)
-    target_bin_values = reward + bootstrap * gamma * (support - actor_entropy)
-    target_bin_values = torch.clamp(target_bin_values, float(support.min()), float(support.max()))
-
-    bin_width = float(support[0, 1] - support[0, 0])
-    offsets = (target_bin_values - float(support.min())) / max(bin_width, 1e-8)
-    lower = torch.floor(offsets).long().clamp(0, num_bins - 1)
-    upper = torch.ceil(offsets).long().clamp(0, num_bins - 1)
-    frac = offsets - lower.float()
-
-    probs = target_log_probs.exp()
-    target_probs = torch.zeros(batch_size, num_bins, dtype=probs.dtype, device=probs.device)
-    target_probs.scatter_add_(1, lower, probs * (1.0 - frac))
-    target_probs.scatter_add_(1, upper, probs * frac)
-    return target_probs
-
-
-def resolve_target_entropy(
-    action_dim: int,
-    target_sigma: float,
-    target_entropy: float | None,
-) -> float:
-    if target_entropy is not None:
-        return float(target_entropy)
-    sigma = max(float(target_sigma), 1e-6)
-    per_dim_entropy = 0.5 * math.log(2.0 * math.pi * math.e * sigma * sigma)
-    return float(action_dim) * per_dim_entropy
diff --git a/src/unilab/algos/torch/him_ppo/__init__.py b/src/unilab/algos/torch/him_ppo/__init__.py
deleted file mode 100644
index 225f04629..000000000
--- a/src/unilab/algos/torch/him_ppo/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from unilab.algos.torch.him_ppo.actor_critic import HIMActorCritic
-from unilab.algos.torch.him_ppo.algorithm import HIMPPO
-from unilab.algos.torch.him_ppo.estimator import HIMEstimator
-from unilab.algos.torch.him_ppo.storage import HIMRolloutStorage
-
-__all__ = [
-    "HIMActorCritic",
-    "HIMPPO",
-    "HIMEstimator",
-    "HIMRolloutStorage",
-]
diff --git a/src/unilab/algos/torch/him_ppo/actor_critic.py b/src/unilab/algos/torch/him_ppo/actor_critic.py
deleted file mode 100644
index 47ac66e01..000000000
--- a/src/unilab/algos/torch/him_ppo/actor_critic.py
+++ /dev/null
@@ -1,129 +0,0 @@
-# SPDX-License-Identifier: BSD-3-Clause
-#
-# Adapted from the HIMLoco RSL-RL HIM actor-critic for UniLab.
-
-from __future__ import annotations
-
-import torch
-import torch.nn as nn
-from torch.distributions import Normal
-
-from unilab.algos.torch.him_ppo.estimator import HIMEstimator, get_activation
-
-
-class HIMActorCritic(nn.Module):
-    is_recurrent = False
-
-    def __init__(
-        self,
-        num_actor_obs: int,
-        num_critic_obs: int,
-        num_one_step_obs: int,
-        num_actions: int,
-        actor_hidden_dims: list[int] | tuple[int, ...] = (512, 256, 128),
-        critic_hidden_dims: list[int] | tuple[int, ...] = (512, 256, 128),
-        activation: str = "elu",
-        init_noise_std: float = 1.0,
-        estimator: dict | None = None,
-    ) -> None:
-        super().__init__()
-        if num_one_step_obs <= 0:
-            raise ValueError("num_one_step_obs must be positive")
-        if num_actor_obs % num_one_step_obs != 0:
-            raise ValueError(
-                "num_actor_obs must be an integer multiple of num_one_step_obs "
-                f"for HIM history obs, got {num_actor_obs} and {num_one_step_obs}"
-            )
-        if len(actor_hidden_dims) == 0 or len(critic_hidden_dims) == 0:
-            raise ValueError("actor_hidden_dims and critic_hidden_dims must not be empty")
-
-        self.history_size = int(num_actor_obs // num_one_step_obs)
-        self.num_actor_obs = int(num_actor_obs)
-        self.num_critic_obs = int(num_critic_obs)
-        self.num_actions = int(num_actions)
-        self.num_one_step_obs = int(num_one_step_obs)
-
-        estimator_cfg = dict(estimator or {})
-        self.estimator = HIMEstimator(
-            temporal_steps=self.history_size,
-            num_one_step_obs=self.num_one_step_obs,
-            activation=activation,
-            **estimator_cfg,
-        )
-
-        actor_input_dim = self.num_one_step_obs + 3 + self.estimator.num_latent
-        self.actor = _build_mlp(actor_input_dim, self.num_actions, actor_hidden_dims, activation)
-        self.critic = _build_mlp(self.num_critic_obs, 1, critic_hidden_dims, activation)
-
-        self.std = nn.Parameter(float(init_noise_std) * torch.ones(self.num_actions))
-        self.distribution: Normal | None = None
-        Normal.set_default_validate_args(False)
-
-    @property
-    def action_mean(self) -> torch.Tensor:
-        assert self.distribution is not None
-        return self.distribution.mean
-
-    @property
-    def action_std(self) -> torch.Tensor:
-        assert self.distribution is not None
-        return self.distribution.stddev
-
-    @property
-    def entropy(self) -> torch.Tensor:
-        assert self.distribution is not None
-        return self.distribution.entropy().sum(dim=-1)
-
-    def reset(self, dones: torch.Tensor | None = None) -> None:
-        del dones
-
-    def forward(self) -> torch.Tensor:
-        raise NotImplementedError
-
-    def update_distribution(self, obs_history: torch.Tensor) -> None:
-        with torch.no_grad():
-            vel, latent = self.estimator(obs_history)
-        actor_input = torch.cat(
-            (obs_history[:, : self.num_one_step_obs], vel, latent),
-            dim=-1,
-        )
-        mean = self.actor(actor_input)
-        self.distribution = Normal(mean, mean * 0.0 + self.std)
-
-    def act(self, obs_history: torch.Tensor, **kwargs) -> torch.Tensor:
-        del kwargs
-        self.update_distribution(obs_history)
-        assert self.distribution is not None
-        return self.distribution.sample()
-
-    def get_actions_log_prob(self, actions: torch.Tensor) -> torch.Tensor:
-        assert self.distribution is not None
-        return self.distribution.log_prob(actions).sum(dim=-1)
-
-    def act_inference(self, obs_history: torch.Tensor, observations=None) -> torch.Tensor:
-        del observations
-        vel, latent = self.estimator(obs_history)
-        actor_input = torch.cat(
-            (obs_history[:, : self.num_one_step_obs], vel, latent),
-            dim=-1,
-        )
-        return self.actor(actor_input)
-
-    def evaluate(self, critic_observations: torch.Tensor, **kwargs) -> torch.Tensor:
-        del kwargs
-        return self.critic(critic_observations)
-
-
-def _build_mlp(
-    input_dim: int,
-    output_dim: int,
-    hidden_dims: list[int] | tuple[int, ...],
-    activation: str,
-) -> nn.Sequential:
-    layers: list[nn.Module] = []
-    last_dim = int(input_dim)
-    for hidden_dim in hidden_dims:
-        layers += [nn.Linear(last_dim, int(hidden_dim)), get_activation(activation)]
-        last_dim = int(hidden_dim)
-    layers.append(nn.Linear(last_dim, int(output_dim)))
-    return nn.Sequential(*layers)
diff --git a/src/unilab/algos/torch/him_ppo/algorithm.py b/src/unilab/algos/torch/him_ppo/algorithm.py
deleted file mode 100644
index eba081cf0..000000000
--- a/src/unilab/algos/torch/him_ppo/algorithm.py
+++ /dev/null
@@ -1,260 +0,0 @@
-# SPDX-License-Identifier: BSD-3-Clause
-#
-# Adapted from the HIMLoco RSL-RL HIMPPO algorithm for UniLab.
-
-from __future__ import annotations
-
-from typing import Any
-
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from tensordict import TensorDict
-
-from unilab.algos.torch.him_ppo.actor_critic import HIMActorCritic
-from unilab.algos.torch.him_ppo.storage import HIMRolloutStorage
-
-
-class HIMPPO:
-    actor_critic: HIMActorCritic
-
-    def __init__(
-        self,
-        actor_critic,
-        num_learning_epochs: int = 1,
-        num_mini_batches: int = 1,
-        clip_param: float = 0.2,
-        gamma: float = 0.998,
-        lam: float = 0.95,
-        value_loss_coef: float = 1.0,
-        entropy_coef: float = 0.0,
-        learning_rate: float = 1e-3,
-        max_grad_norm: float = 1.0,
-        use_clipped_value_loss: bool = True,
-        schedule: str = "fixed",
-        desired_kl: float | None = 0.01,
-        device: str = "cpu",
-        **kwargs: Any,
-    ) -> None:
-        del kwargs
-        self.device = device
-        self.desired_kl = desired_kl
-        self.schedule = schedule
-        self.learning_rate = float(learning_rate)
-
-        self.actor_critic = actor_critic
-        self.actor_critic.to(self.device)
-        self.storage: HIMRolloutStorage | None = None
-        self.optimizer = optim.Adam(self.actor_critic.parameters(), lr=self.learning_rate)
-        self.transition = HIMRolloutStorage.Transition()
-
-        self.clip_param = float(clip_param)
-        self.num_learning_epochs = int(num_learning_epochs)
-        self.num_mini_batches = int(num_mini_batches)
-        self.value_loss_coef = float(value_loss_coef)
-        self.entropy_coef = float(entropy_coef)
-        self.gamma = float(gamma)
-        self.lam = float(lam)
-        self.max_grad_norm = float(max_grad_norm)
-        self.use_clipped_value_loss = bool(use_clipped_value_loss)
-
-    def init_storage(
-        self,
-        num_envs: int,
-        num_transitions_per_env: int,
-        actor_obs_shape,
-        critic_obs_shape,
-        action_shape,
-    ) -> None:
-        self.storage = HIMRolloutStorage(
-            num_envs,
-            num_transitions_per_env,
-            actor_obs_shape,
-            critic_obs_shape,
-            action_shape,
-            self.device,
-        )
-
-    def train_mode(self) -> None:
-        self.actor_critic.train()
-
-    def act(self, obs: torch.Tensor, critic_obs: torch.Tensor) -> torch.Tensor:
-        self.transition.actions = self.actor_critic.act(obs).detach()
-        self.transition.values = self.actor_critic.evaluate(critic_obs).detach()
-        self.transition.actions_log_prob = self.actor_critic.get_actions_log_prob(
-            self.transition.actions
-        ).detach()
-        self.transition.action_mean = self.actor_critic.action_mean.detach()
-        self.transition.action_sigma = self.actor_critic.action_std.detach()
-        self.transition.observations = obs
-        self.transition.critic_observations = critic_obs
-        return self.transition.actions
-
-    def process_env_step(
-        self,
-        next_obs: TensorDict | torch.Tensor,
-        rewards: torch.Tensor,
-        dones: torch.Tensor,
-        extras: dict[str, torch.Tensor | TensorDict],
-    ) -> None:
-        next_critic_obs = _critic_obs(next_obs).to(self.device).clone().detach()
-        self.transition.next_critic_observations = next_critic_obs
-        self.transition.rewards = rewards.clone()
-        self.transition.dones = dones
-
-        timeouts = extras.get("time_outs")
-        timeout_bootstrap_obs = extras.get("time_out_bootstrap_obs")
-        if isinstance(timeouts, torch.Tensor):
-            timeout_bool = timeouts.to(self.device).bool().view(-1)
-            timeout_mask = timeout_bool.float()
-            if timeout_bootstrap_obs is not None and torch.count_nonzero(timeout_bool) > 0:
-                bootstrap_obs = timeout_bootstrap_obs.to(self.device)
-                bootstrap_critic_obs = _critic_obs(bootstrap_obs)
-                bootstrap_values = self.actor_critic.evaluate(bootstrap_critic_obs).detach()
-                correction = self.gamma * torch.squeeze(
-                    bootstrap_values * timeout_mask.unsqueeze(1), 1
-                )
-                if self.transition.rewards.ndim == 2 and self.transition.rewards.shape[-1] == 1:
-                    correction = correction.unsqueeze(1)
-                self.transition.rewards += correction
-
-                patched_next_critic_obs = self.transition.next_critic_observations.clone()
-                patched_next_critic_obs[timeout_bool] = bootstrap_critic_obs[timeout_bool].detach()
-                self.transition.next_critic_observations = patched_next_critic_obs
-            else:
-                transition_values = self.transition.values
-                assert transition_values is not None
-                correction = self.gamma * torch.squeeze(
-                    transition_values * timeout_mask.unsqueeze(1), 1
-                )
-                if self.transition.rewards.ndim == 2 and self.transition.rewards.shape[-1] == 1:
-                    correction = correction.unsqueeze(1)
-                self.transition.rewards += correction
-
-        assert self.storage is not None
-        self.storage.add_transition(self.transition)
-        self.transition.clear()
-        self.actor_critic.reset(dones)
-
-    def compute_returns(self, last_critic_obs: torch.Tensor) -> None:
-        last_values = self.actor_critic.evaluate(last_critic_obs).detach()
-        assert self.storage is not None
-        self.storage.compute_returns(last_values, self.gamma, self.lam)
-
-    def update(self) -> tuple[float, float, float, float]:
-        assert self.storage is not None
-        mean_value_loss = 0.0
-        mean_surrogate_loss = 0.0
-        mean_estimation_loss = 0.0
-        mean_swap_loss = 0.0
-
-        generator = self.storage.mini_batch_generator(
-            self.num_mini_batches,
-            self.num_learning_epochs,
-        )
-
-        for (
-            obs_batch,
-            critic_obs_batch,
-            actions_batch,
-            next_critic_obs_batch,
-            target_values_batch,
-            advantages_batch,
-            returns_batch,
-            old_actions_log_prob_batch,
-            old_mu_batch,
-            old_sigma_batch,
-        ) in generator:
-            self.actor_critic.act(obs_batch)
-            actions_log_prob_batch = self.actor_critic.get_actions_log_prob(actions_batch)
-            value_batch = self.actor_critic.evaluate(critic_obs_batch)
-            mu_batch = self.actor_critic.action_mean
-            sigma_batch = self.actor_critic.action_std
-            entropy_batch = self.actor_critic.entropy
-
-            if self.desired_kl is not None and self.schedule == "adaptive":
-                with torch.inference_mode():
-                    kl = torch.sum(
-                        torch.log(sigma_batch / old_sigma_batch + 1.0e-5)
-                        + (torch.square(old_sigma_batch) + torch.square(old_mu_batch - mu_batch))
-                        / (2.0 * torch.square(sigma_batch))
-                        - 0.5,
-                        dim=-1,
-                    )
-                    kl_mean = torch.mean(kl)
-
-                    if kl_mean > self.desired_kl * 2.0:
-                        self.learning_rate = max(1e-5, self.learning_rate / 1.5)
-                    elif kl_mean < self.desired_kl / 2.0 and kl_mean > 0.0:
-                        self.learning_rate = min(1e-2, self.learning_rate * 1.5)
-
-                    for param_group in self.optimizer.param_groups:
-                        param_group["lr"] = self.learning_rate
-
-            estimation_loss, swap_loss = self.actor_critic.estimator.update(
-                obs_batch,
-                next_critic_obs_batch,
-                lr=self.learning_rate,
-            )
-
-            ratio = torch.exp(actions_log_prob_batch - torch.squeeze(old_actions_log_prob_batch))
-            surrogate = -torch.squeeze(advantages_batch) * ratio
-            surrogate_clipped = -torch.squeeze(advantages_batch) * torch.clamp(
-                ratio,
-                1.0 - self.clip_param,
-                1.0 + self.clip_param,
-            )
-            surrogate_loss = torch.max(surrogate, surrogate_clipped).mean()
-
-            if self.use_clipped_value_loss:
-                value_clipped = target_values_batch + (value_batch - target_values_batch).clamp(
-                    -self.clip_param,
-                    self.clip_param,
-                )
-                value_losses = (value_batch - returns_batch).pow(2)
-                value_losses_clipped = (value_clipped - returns_batch).pow(2)
-                value_loss = torch.max(value_losses, value_losses_clipped).mean()
-            else:
-                value_loss = (returns_batch - value_batch).pow(2).mean()
-
-            loss = (
-                surrogate_loss
-                + self.value_loss_coef * value_loss
-                - self.entropy_coef * entropy_batch.mean()
-            )
-
-            self.optimizer.zero_grad()
-            loss.backward()
-            nn.utils.clip_grad_norm_(self.actor_critic.parameters(), self.max_grad_norm)
-            self.optimizer.step()
-
-            mean_value_loss += float(value_loss.item())
-            mean_surrogate_loss += float(surrogate_loss.item())
-            mean_estimation_loss += float(estimation_loss)
-            mean_swap_loss += float(swap_loss)
-
-        num_updates = self.num_learning_epochs * self.num_mini_batches
-        mean_value_loss /= num_updates
-        mean_surrogate_loss /= num_updates
-        mean_estimation_loss /= num_updates
-        mean_swap_loss /= num_updates
-        self.storage.clear()
-
-        return (
-            mean_value_loss,
-            mean_surrogate_loss,
-            mean_estimation_loss,
-            mean_swap_loss,
-        )
-
-
-def _critic_obs(obs: TensorDict | torch.Tensor) -> torch.Tensor:
-    if isinstance(obs, TensorDict):
-        if "critic" in obs.keys():
-            return obs["critic"]
-        if "policy" in obs.keys():
-            return obs["policy"]
-        if "actor" in obs.keys():
-            return obs["actor"]
-        raise KeyError("HIM-PPO TensorDict obs must contain critic, policy, or actor")
-    return obs
diff --git a/src/unilab/algos/torch/him_ppo/estimator.py b/src/unilab/algos/torch/him_ppo/estimator.py
deleted file mode 100644
index 30ae6ca41..000000000
--- a/src/unilab/algos/torch/him_ppo/estimator.py
+++ /dev/null
@@ -1,168 +0,0 @@
-# SPDX-License-Identifier: BSD-3-Clause
-#
-# Adapted from the HIMLoco RSL-RL HIM estimator for UniLab.
-
-from __future__ import annotations
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-
-
-def get_activation(name: str) -> nn.Module:
-    if name == "elu":
-        return nn.ELU()
-    if name == "selu":
-        return nn.SELU()
-    if name == "relu":
-        return nn.ReLU()
-    if name == "crelu":
-        return nn.ReLU()
-    if name == "silu":
-        return nn.SiLU()
-    if name == "lrelu":
-        return nn.LeakyReLU()
-    if name == "tanh":
-        return nn.Tanh()
-    if name == "sigmoid":
-        return nn.Sigmoid()
-    raise ValueError(f"Unsupported activation: {name}")
-
-
-class HIMEstimator(nn.Module):
-    def __init__(
-        self,
-        temporal_steps: int,
-        num_one_step_obs: int,
-        enc_hidden_dims: list[int] | tuple[int, ...] = (128, 64, 16),
-        tar_hidden_dims: list[int] | tuple[int, ...] = (128, 64),
-        activation: str = "elu",
-        learning_rate: float = 1e-3,
-        max_grad_norm: float = 10.0,
-        num_prototype: int = 32,
-        temperature: float = 3.0,
-        velocity_target_start: int | None = None,
-        target_obs_start: int = 3,
-    ) -> None:
-        super().__init__()
-        if temporal_steps <= 0:
-            raise ValueError("temporal_steps must be positive")
-        if num_one_step_obs <= 0:
-            raise ValueError("num_one_step_obs must be positive")
-        if len(enc_hidden_dims) == 0:
-            raise ValueError("enc_hidden_dims must not be empty")
-
-        self.temporal_steps = int(temporal_steps)
-        self.num_one_step_obs = int(num_one_step_obs)
-        self.num_latent = int(enc_hidden_dims[-1])
-        self.max_grad_norm = float(max_grad_norm)
-        self.temperature = float(temperature)
-        self.velocity_target_start = (
-            int(num_one_step_obs) if velocity_target_start is None else int(velocity_target_start)
-        )
-        self.target_obs_start = int(target_obs_start)
-
-        enc_input_dim = self.temporal_steps * self.num_one_step_obs
-        enc_layers: list[nn.Module] = []
-        for hidden_dim in enc_hidden_dims[:-1]:
-            enc_layers += [nn.Linear(enc_input_dim, int(hidden_dim)), get_activation(activation)]
-            enc_input_dim = int(hidden_dim)
-        enc_layers += [nn.Linear(enc_input_dim, self.num_latent + 3)]
-        self.encoder = nn.Sequential(*enc_layers)
-
-        tar_input_dim = self.num_one_step_obs
-        tar_layers: list[nn.Module] = []
-        for hidden_dim in tar_hidden_dims:
-            tar_layers += [nn.Linear(tar_input_dim, int(hidden_dim)), get_activation(activation)]
-            tar_input_dim = int(hidden_dim)
-        tar_layers += [nn.Linear(tar_input_dim, self.num_latent)]
-        self.target = nn.Sequential(*tar_layers)
-
-        self.proto = nn.Embedding(int(num_prototype), self.num_latent)
-        self.learning_rate = float(learning_rate)
-        self.optimizer = optim.Adam(self.parameters(), lr=self.learning_rate)
-
-    def get_latent(self, obs_history: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
-        vel, z = self.encode(obs_history)
-        return vel.detach(), z.detach()
-
-    def forward(self, obs_history: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
-        vel, z = self.encode(obs_history.detach())
-        return vel.detach(), z.detach()
-
-    def encode(self, obs_history: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
-        parts = self.encoder(obs_history.detach())
-        vel, z = parts[..., :3], parts[..., 3:]
-        z = F.normalize(z, dim=-1, p=2)
-        return vel, z
-
-    def update(
-        self,
-        obs_history: torch.Tensor,
-        next_critic_obs: torch.Tensor,
-        lr: float | None = None,
-    ) -> tuple[float, float]:
-        if lr is not None:
-            self.learning_rate = float(lr)
-            for param_group in self.optimizer.param_groups:
-                param_group["lr"] = self.learning_rate
-
-        vel_start = self.velocity_target_start
-        vel_end = vel_start + 3
-        target_start = self.target_obs_start
-        target_end = target_start + self.num_one_step_obs
-        if next_critic_obs.shape[-1] < max(vel_end, target_end):
-            raise ValueError(
-                "next_critic_obs is too small for HIM estimator slices: "
-                f"shape={tuple(next_critic_obs.shape)}, velocity=[{vel_start}:{vel_end}], "
-                f"target=[{target_start}:{target_end}]"
-            )
-
-        vel = next_critic_obs[:, vel_start:vel_end].detach()
-        next_obs = next_critic_obs[:, target_start:target_end].detach()
-
-        parts = self.encoder(obs_history)
-        pred_vel, z_s = parts[..., :3], parts[..., 3:]
-        z_t = self.target(next_obs)
-
-        z_s = F.normalize(z_s, dim=-1, p=2)
-        z_t = F.normalize(z_t, dim=-1, p=2)
-
-        with torch.no_grad():
-            self.proto.weight.copy_(F.normalize(self.proto.weight.data.clone(), dim=-1, p=2))
-
-        score_s = z_s @ self.proto.weight.T
-        score_t = z_t @ self.proto.weight.T
-
-        with torch.no_grad():
-            q_s = sinkhorn(score_s)
-            q_t = sinkhorn(score_t)
-
-        log_p_s = F.log_softmax(score_s / self.temperature, dim=-1)
-        log_p_t = F.log_softmax(score_t / self.temperature, dim=-1)
-
-        swap_loss = -0.5 * (q_s * log_p_t + q_t * log_p_s).mean()
-        estimation_loss = F.mse_loss(pred_vel, vel)
-        loss = estimation_loss + swap_loss
-
-        self.optimizer.zero_grad()
-        loss.backward()
-        nn.utils.clip_grad_norm_(self.parameters(), self.max_grad_norm)
-        self.optimizer.step()
-
-        return float(estimation_loss.item()), float(swap_loss.item())
-
-
-@torch.no_grad()
-def sinkhorn(out: torch.Tensor, eps: float = 0.05, iters: int = 3) -> torch.Tensor:
-    q = torch.exp(out / eps).T
-    k, b = q.shape
-    q /= q.sum()
-
-    for _ in range(iters):
-        q /= torch.sum(q, dim=1, keepdim=True)
-        q /= k
-        q /= torch.sum(q, dim=0, keepdim=True)
-        q /= b
-    return (q * b).T
diff --git a/src/unilab/algos/torch/him_ppo/runner.py b/src/unilab/algos/torch/him_ppo/runner.py
deleted file mode 100644
index bab234d42..000000000
--- a/src/unilab/algos/torch/him_ppo/runner.py
+++ /dev/null
@@ -1,329 +0,0 @@
-# SPDX-License-Identifier: BSD-3-Clause
-#
-# HIM-PPO OnPolicy Runner for UniLab.
-
-from __future__ import annotations
-
-import logging
-import os
-import time
-from collections import deque
-from typing import Any, Callable, cast
-
-import torch
-
-from unilab.algos.torch.him_ppo.actor_critic import HIMActorCritic
-from unilab.algos.torch.him_ppo.algorithm import HIMPPO
-
-logger = logging.getLogger(__name__)
-
-
-class _EpisodeStatsBuffer:
-    """Rolling episode-return/length buffers for training summary extraction."""
-
-    def __init__(self) -> None:
-        self.rewbuffer: deque[float] = deque(maxlen=100)
-        self.lenbuffer: deque[float] = deque(maxlen=100)
-        self.tot_timesteps: int = 0
-
-
-class HIMOnPolicyRunner:
-    """On-policy training runner for HIM-PPO.
-
-    Provides the same external interface as rsl_rl's ``OnPolicyRunner`` so that
-    ``train_him_ppo.py`` can share most of its logic with ``train_rsl_rl.py``.
-    """
-
-    def __init__(
-        self,
-        env: Any,
-        train_cfg: dict[str, Any],
-        log_dir: str | None = None,
-        device: str = "cpu",
-    ) -> None:
-        self.env = env
-        self.device = device
-        self.log_dir = log_dir
-        self.current_learning_iteration: int = 0
-        # Keep the established ``runner.logger`` access surface used by the
-        # training entrypoint; this object only owns rolling episode stats.
-        self.logger = _EpisodeStatsBuffer()
-
-        # ── Parse config ────────────────────────────────────────────────────
-        cfg: dict[str, Any] = dict(train_cfg)
-
-        num_one_step_obs = int(cfg["num_one_step_obs"])
-        num_actor_history = int(cfg.get("num_actor_history", 1))
-        num_actor_obs = num_actor_history * num_one_step_obs
-        num_critic_obs = int(getattr(env, "num_privileged_obs", None) or env.num_obs)
-        num_actions = int(env.num_actions)
-
-        policy_cfg: dict[str, Any] = dict(cfg.get("policy") or {})
-        estimator_cfg: dict[str, Any] = dict(cfg.get("estimator") or {})
-        algo_cfg: dict[str, Any] = dict(cfg.get("algorithm") or {})
-
-        # ── Build actor-critic ───────────────────────────────────────────────
-        self.actor_critic = HIMActorCritic(
-            num_actor_obs=num_actor_obs,
-            num_critic_obs=num_critic_obs,
-            num_one_step_obs=num_one_step_obs,
-            num_actions=num_actions,
-            actor_hidden_dims=list(policy_cfg.get("actor_hidden_dims", [512, 256, 128])),
-            critic_hidden_dims=list(policy_cfg.get("critic_hidden_dims", [512, 256, 128])),
-            activation=str(policy_cfg.get("activation", "elu")),
-            init_noise_std=float(policy_cfg.get("init_noise_std", 1.0)),
-            estimator=estimator_cfg,
-        ).to(device)
-
-        # ── Build algorithm ──────────────────────────────────────────────────
-        self.alg = HIMPPO(self.actor_critic, device=device, **algo_cfg)
-
-        self.num_steps_per_env = int(cfg.get("num_steps_per_env", 24))
-        self.save_interval = int(cfg.get("save_interval", 100))
-
-        # ── Init storage ─────────────────────────────────────────────────────
-        self.alg.init_storage(
-            env.num_envs,
-            self.num_steps_per_env,
-            [num_actor_obs],
-            [num_critic_obs],
-            [num_actions],
-        )
-
-        # Per-env episode tracking (independent of env wrapper's counters)
-        self._ep_returns = torch.zeros(env.num_envs, device=device)
-        self._ep_lengths = torch.zeros(env.num_envs, device=device)
-
-        # Tensorboard writer (optional)
-        self._writer: Any = None
-        if log_dir is not None:
-            try:
-                from torch.utils.tensorboard import SummaryWriter  # type: ignore[import-untyped]
-
-                self._writer = SummaryWriter(log_dir=log_dir)
-            except ImportError:
-                pass
-
-    # ── Public interface ─────────────────────────────────────────────────────
-
-    def learn(
-        self,
-        num_learning_iterations: int,
-        init_at_random_ep_len: bool = True,
-    ) -> None:
-        obs_td, _ = self.env.reset()
-        obs = obs_td["actor"].to(self.device)
-        critic_obs = obs_td.get("critic", obs).to(self.device)
-
-        if init_at_random_ep_len:
-            self.env.episode_length_buf = torch.randint_like(
-                self.env.episode_length_buf,
-                high=int(self.env.max_episode_length),
-            )
-
-        self.alg.train_mode()
-        start_iter = self.current_learning_iteration
-        tot_iter = start_iter + num_learning_iterations
-        start_time = time.time()
-
-        for it in range(start_iter, tot_iter):
-            infos: dict[str, Any] = {}
-            # ── Rollout collection ───────────────────────────────────────────
-            with torch.inference_mode():
-                for _ in range(self.num_steps_per_env):
-                    actions = self.alg.act(obs, critic_obs)
-                    obs_td, rewards, dones, infos = self.env.step(actions)
-
-                    next_obs = obs_td["actor"].to(self.device)
-                    next_critic_obs = obs_td.get("critic", next_obs).to(self.device)
-
-                    # Track episode stats before env wrapper resets counters
-                    done_ids = dones.nonzero(as_tuple=False).flatten()
-                    self._ep_returns += rewards.to(self.device)
-                    self._ep_lengths += 1
-                    if len(done_ids) > 0:
-                        for idx in done_ids:
-                            self.logger.rewbuffer.append(float(self._ep_returns[idx]))
-                            self.logger.lenbuffer.append(float(self._ep_lengths[idx]))
-                        self._ep_returns[done_ids] = 0.0
-                        self._ep_lengths[done_ids] = 0.0
-
-                    self.alg.process_env_step(obs_td, rewards, dones, infos)
-                    obs = next_obs
-                    critic_obs = next_critic_obs
-
-                self.alg.compute_returns(critic_obs)
-
-            # ── Update ───────────────────────────────────────────────────────
-            value_loss, surrogate_loss, estimation_loss, swap_loss = self.alg.update()
-
-            self.current_learning_iteration = it + 1
-            self.logger.tot_timesteps += self.num_steps_per_env * self.env.num_envs
-
-            # ── Logging ──────────────────────────────────────────────────────
-            elapsed = time.time() - start_time
-            self._log_iter(
-                it + 1,
-                tot_iter,
-                value_loss,
-                surrogate_loss,
-                estimation_loss,
-                swap_loss,
-                elapsed,
-                infos,
-            )
-            if self._writer is not None:
-                global_step = self.current_learning_iteration
-                self._writer.add_scalar("train/value_loss", value_loss, global_step)
-                self._writer.add_scalar("train/surrogate_loss", surrogate_loss, global_step)
-                self._writer.add_scalar("train/estimation_loss", estimation_loss, global_step)
-                self._writer.add_scalar("train/swap_loss", swap_loss, global_step)
-                for k, v in (infos.get("log") or {}).items():
-                    self._writer.add_scalar(k, v, global_step)
-
-            # ── Checkpoint ───────────────────────────────────────────────────
-            if (
-                self.log_dir is not None
-                and self.current_learning_iteration % self.save_interval == 0
-            ):
-                self.save(os.path.join(self.log_dir, f"model_{self.current_learning_iteration}.pt"))
-
-        # Final checkpoint
-        if self.log_dir is not None:
-            self.save(os.path.join(self.log_dir, f"model_{self.current_learning_iteration}.pt"))
-
-    def save(self, path: str) -> None:
-        os.makedirs(os.path.dirname(path), exist_ok=True)
-        torch.save(
-            {
-                "actor_state_dict": self.actor_critic.state_dict(),
-                "optimizer_state_dict": self.alg.optimizer.state_dict(),
-                "iteration": self.current_learning_iteration,
-            },
-            path,
-        )
-
-    def load(self, path: str) -> None:
-        ckpt = torch.load(path, map_location=self.device, weights_only=True)
-        self.actor_critic.load_state_dict(ckpt["actor_state_dict"])
-        if "optimizer_state_dict" in ckpt:
-            self.alg.optimizer.load_state_dict(ckpt["optimizer_state_dict"])
-        if "iteration" in ckpt:
-            self.current_learning_iteration = int(ckpt["iteration"])
-
-    def get_inference_policy(self, device: str | None = None) -> Callable[..., Any]:
-        self.actor_critic.eval()
-        if device is not None:
-            self.actor_critic.to(device)
-        return cast(Callable[..., Any], self.actor_critic.act_inference)
-
-    def export_policy_to_onnx(self, path: str, filename: str = "policy.onnx") -> None:
-        """Export the end-to-end HIM-PPO policy (estimator + actor) to ONNX.
-
-        Input:  obs_history  (1, H_a * num_one_step_obs)  — flattened actor obs history
-        Output: actions      (1, num_actions)
-        """
-
-        class _PolicyExport(torch.nn.Module):
-            def __init__(self, ac: Any) -> None:
-                super().__init__()
-                self.ac = ac
-
-            def forward(self, obs_history: torch.Tensor) -> torch.Tensor:
-                return self.ac.act_inference(obs_history)
-
-        orig_device = next(self.actor_critic.parameters()).device
-        model = _PolicyExport(self.actor_critic).cpu().eval()
-        dummy = torch.zeros(1, self.actor_critic.num_actor_obs)
-        os.makedirs(path, exist_ok=True)
-        save_path = os.path.join(path, filename)
-        with torch.inference_mode():
-            torch.onnx.export(
-                model,
-                (dummy,),
-                save_path,
-                export_params=True,
-                opset_version=18,
-                input_names=["obs_history"],
-                output_names=["actions"],
-                dynamic_axes={"obs_history": {0: "batch_size"}, "actions": {0: "batch_size"}},
-            )
-        self.actor_critic.to(orig_device)
-        print(f"Exported HIM-PPO policy to {save_path}")
-
-    def export_policy_to_jit(self, path: str, filename: str = "policy.pt") -> None:
-        """Export the end-to-end HIM-PPO policy (estimator + actor) via TorchScript trace.
-
-        Wraps estimator + actor as a plain module (without HIMActorCritic's properties)
-        so that torch.jit.trace can introspect it without hitting the distribution assert.
-        """
-        orig_device = next(self.actor_critic.parameters()).device
-        ac = self.actor_critic.cpu().eval()
-        num_one_step_obs = ac.num_one_step_obs
-
-        class _PolicyExport(torch.nn.Module):
-            def __init__(self) -> None:
-                super().__init__()
-                self.estimator = ac.estimator
-                self.actor_mlp = ac.actor
-
-            def forward(self, obs_history: torch.Tensor) -> torch.Tensor:
-                vel, latent = self.estimator.get_latent(obs_history)
-                actor_input = torch.cat((obs_history[:, :num_one_step_obs], vel, latent), dim=-1)
-                return self.actor_mlp(actor_input)
-
-        model = _PolicyExport().eval()
-        dummy = torch.zeros(1, ac.num_actor_obs)
-        with torch.inference_mode():
-            traced = cast(torch.jit.ScriptModule, torch.jit.trace(model, (dummy,)))
-        os.makedirs(path, exist_ok=True)
-        save_path = os.path.join(path, filename)
-        traced.save(save_path)
-        self.actor_critic.to(orig_device)
-        print(f"Exported HIM-PPO policy (JIT) to {save_path}")
-
-    # ── Helpers ──────────────────────────────────────────────────────────────
-
-    def _log_iter(
-        self,
-        it: int,
-        tot: int,
-        value_loss: float,
-        surrogate_loss: float,
-        estimation_loss: float,
-        swap_loss: float,
-        elapsed: float,
-        infos: dict,
-    ) -> None:
-        sep = "-" * 80
-        mean_rew = (
-            sum(self.logger.rewbuffer) / len(self.logger.rewbuffer)
-            if self.logger.rewbuffer
-            else 0.0
-        )
-        mean_len = (
-            sum(self.logger.lenbuffer) / len(self.logger.lenbuffer)
-            if self.logger.lenbuffer
-            else 0.0
-        )
-        time_str = time.strftime("%H:%M:%S", time.gmtime(elapsed))
-        eta = elapsed / it * (tot - it) if it > 0 else 0.0
-        eta_str = time.strftime("%H:%M:%S", time.gmtime(eta))
-        lines = [
-            sep,
-            f"{'Iteration':>40}: {it}/{tot}",
-            f"{'Mean value loss':>40}: {value_loss:.4f}",
-            f"{'Mean surrogate loss':>40}: {surrogate_loss:.4f}",
-            f"{'Mean estimation loss':>40}: {estimation_loss:.4f}",
-            f"{'Mean swap loss':>40}: {swap_loss:.4f}",
-        ]
-        if mean_rew:
-            lines.append(f"{'Mean episode reward':>40}: {mean_rew:.4f}")
-        if mean_len:
-            lines.append(f"{'Mean episode length':>40}: {mean_len:.1f}")
-        for k, v in (infos.get("log") or {}).items():
-            lines.append(f"{k:>40}: {v:.4f}")
-        lines.append(f"{'Time elapsed':>40}: {time_str}")
-        lines.append(f"{'ETA':>40}: {eta_str}")
-        lines.append(sep)
-        logger.info("\n".join(lines))
diff --git a/src/unilab/algos/torch/him_ppo/storage.py b/src/unilab/algos/torch/him_ppo/storage.py
deleted file mode 100644
index 51e73df1f..000000000
--- a/src/unilab/algos/torch/him_ppo/storage.py
+++ /dev/null
@@ -1,198 +0,0 @@
-# SPDX-License-Identifier: BSD-3-Clause
-#
-# Adapted from the HIMLoco RSL-RL HIM rollout storage for UniLab.
-
-from __future__ import annotations
-
-from collections.abc import Sequence
-from typing import cast
-
-import torch
-
-
-class HIMRolloutStorage:
-    class Transition:
-        def __init__(self) -> None:
-            self.observations: torch.Tensor | None = None
-            self.critic_observations: torch.Tensor | None = None
-            self.next_critic_observations: torch.Tensor | None = None
-            self.actions: torch.Tensor | None = None
-            self.rewards: torch.Tensor | None = None
-            self.dones: torch.Tensor | None = None
-            self.values: torch.Tensor | None = None
-            self.actions_log_prob: torch.Tensor | None = None
-            self.action_mean: torch.Tensor | None = None
-            self.action_sigma: torch.Tensor | None = None
-
-        def clear(self) -> None:
-            self.observations = None
-            self.critic_observations = None
-            self.next_critic_observations = None
-            self.actions = None
-            self.rewards = None
-            self.dones = None
-            self.values = None
-            self.actions_log_prob = None
-            self.action_mean = None
-            self.action_sigma = None
-
-    def __init__(
-        self,
-        num_envs: int,
-        num_transitions_per_env: int,
-        obs_shape: Sequence[int],
-        privileged_obs_shape: Sequence[int | None],
-        actions_shape: Sequence[int],
-        device: str = "cpu",
-    ) -> None:
-        self.device = device
-        self.obs_shape = tuple(obs_shape)
-        self.privileged_obs_shape = tuple(privileged_obs_shape)
-        self.actions_shape = tuple(actions_shape)
-        self.num_transitions_per_env = int(num_transitions_per_env)
-        self.num_envs = int(num_envs)
-        self.step = 0
-
-        self.observations = torch.zeros(
-            self.num_transitions_per_env,
-            self.num_envs,
-            *self.obs_shape,
-            device=self.device,
-        )
-        if self.privileged_obs_shape and self.privileged_obs_shape[0] is not None:
-            if any(dim is None for dim in self.privileged_obs_shape):
-                raise ValueError("privileged_obs_shape cannot contain None values")
-            privileged_obs_shape = cast(tuple[int, ...], self.privileged_obs_shape)
-            self.privileged_observations = torch.zeros(
-                self.num_transitions_per_env,
-                self.num_envs,
-                *privileged_obs_shape,
-                device=self.device,
-            )
-            self.next_privileged_observations = torch.zeros_like(self.privileged_observations)
-        else:
-            self.privileged_observations = None
-            self.next_privileged_observations = None
-
-        self.rewards = torch.zeros(
-            self.num_transitions_per_env, self.num_envs, 1, device=self.device
-        )
-        self.actions = torch.zeros(
-            self.num_transitions_per_env,
-            self.num_envs,
-            *self.actions_shape,
-            device=self.device,
-        )
-        self.dones = torch.zeros(
-            self.num_transitions_per_env, self.num_envs, 1, device=self.device
-        ).bool()
-        self.actions_log_prob = torch.zeros_like(self.rewards)
-        self.values = torch.zeros_like(self.rewards)
-        self.returns = torch.zeros_like(self.rewards)
-        self.advantages = torch.zeros_like(self.rewards)
-        self.mu = torch.zeros_like(self.actions)
-        self.sigma = torch.zeros_like(self.actions)
-
-    def add_transition(self, transition: Transition) -> None:
-        if self.step >= self.num_transitions_per_env:
-            raise AssertionError("Rollout buffer overflow")
-        if transition.observations is None:
-            raise ValueError("transition.observations is required")
-        if transition.actions is None:
-            raise ValueError("transition.actions is required")
-        if transition.rewards is None:
-            raise ValueError("transition.rewards is required")
-        if transition.dones is None:
-            raise ValueError("transition.dones is required")
-        if transition.values is None:
-            raise ValueError("transition.values is required")
-        if transition.actions_log_prob is None:
-            raise ValueError("transition.actions_log_prob is required")
-        if transition.action_mean is None or transition.action_sigma is None:
-            raise ValueError("transition action distribution stats are required")
-
-        self.observations[self.step].copy_(transition.observations)
-        if self.privileged_observations is not None:
-            if transition.critic_observations is None:
-                raise ValueError("transition.critic_observations is required")
-            if transition.next_critic_observations is None:
-                raise ValueError("transition.next_critic_observations is required")
-            assert self.next_privileged_observations is not None
-            self.privileged_observations[self.step].copy_(transition.critic_observations)
-            self.next_privileged_observations[self.step].copy_(transition.next_critic_observations)
-        self.actions[self.step].copy_(transition.actions)
-        self.rewards[self.step].copy_(transition.rewards.view(-1, 1))
-        self.dones[self.step].copy_(transition.dones.view(-1, 1).bool())
-        self.values[self.step].copy_(transition.values)
-        self.actions_log_prob[self.step].copy_(transition.actions_log_prob.view(-1, 1))
-        self.mu[self.step].copy_(transition.action_mean)
-        self.sigma[self.step].copy_(transition.action_sigma)
-        self.step += 1
-
-    def clear(self) -> None:
-        self.step = 0
-
-    def compute_returns(self, last_values: torch.Tensor, gamma: float, lam: float) -> None:
-        advantage = torch.zeros_like(last_values)
-        for step in reversed(range(self.num_transitions_per_env)):
-            if step == self.num_transitions_per_env - 1:
-                next_values = last_values
-            else:
-                next_values = self.values[step + 1]
-            next_is_not_terminal = 1.0 - self.dones[step].float()
-            delta = (
-                self.rewards[step] + next_is_not_terminal * gamma * next_values - self.values[step]
-            )
-            advantage = delta + next_is_not_terminal * gamma * lam * advantage
-            self.returns[step] = advantage + self.values[step]
-
-        self.advantages = self.returns - self.values
-        self.advantages = (self.advantages - self.advantages.mean()) / (
-            self.advantages.std() + 1e-8
-        )
-
-    def mini_batch_generator(self, num_mini_batches: int, num_epochs: int = 8):
-        batch_size = self.num_envs * self.num_transitions_per_env
-        mini_batch_size = batch_size // int(num_mini_batches)
-        if mini_batch_size <= 0:
-            raise ValueError("num_mini_batches is too large for the rollout batch")
-        indices = torch.randperm(
-            int(num_mini_batches) * mini_batch_size,
-            requires_grad=False,
-            device=self.device,
-        )
-
-        observations = self.observations.flatten(0, 1)
-        if self.privileged_observations is not None:
-            assert self.next_privileged_observations is not None
-            critic_observations = self.privileged_observations.flatten(0, 1)
-            next_critic_observations = self.next_privileged_observations.flatten(0, 1)
-        else:
-            critic_observations = observations
-            next_critic_observations = observations
-
-        actions = self.actions.flatten(0, 1)
-        values = self.values.flatten(0, 1)
-        returns = self.returns.flatten(0, 1)
-        old_actions_log_prob = self.actions_log_prob.flatten(0, 1)
-        advantages = self.advantages.flatten(0, 1)
-        old_mu = self.mu.flatten(0, 1)
-        old_sigma = self.sigma.flatten(0, 1)
-
-        for _ in range(int(num_epochs)):
-            for i in range(int(num_mini_batches)):
-                start = i * mini_batch_size
-                end = (i + 1) * mini_batch_size
-                batch_idx = indices[start:end]
-                yield (
-                    observations[batch_idx],
-                    critic_observations[batch_idx],
-                    actions[batch_idx],
-                    next_critic_observations[batch_idx],
-                    values[batch_idx],
-                    advantages[batch_idx],
-                    returns[batch_idx],
-                    old_actions_log_prob[batch_idx],
-                    old_mu[batch_idx],
-                    old_sigma[batch_idx],
-                )
diff --git a/src/unilab/algos/torch/hora/__init__.py b/src/unilab/algos/torch/hora/__init__.py
deleted file mode 100644
index 4a34304a9..000000000
--- a/src/unilab/algos/torch/hora/__init__.py
+++ /dev/null
@@ -1,45 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, Any
-
-from .distill import HoraDistillationTrainer
-from .models import HoraActorModel, HoraCriticModel, HoraSharedActorCritic
-from .ppo import HoraPPO
-
-if TYPE_CHECKING:
-    from .appo import HoraAPPORunner, play_hora_appo
-    from .sac_learner import HoraSACLearner
-    from .sac_models import HoraSACActor
-
-__all__ = [
-    "HoraActorModel",
-    "HoraAPPORunner",
-    "HoraCriticModel",
-    "HoraDistillationTrainer",
-    "HoraPPO",
-    "HoraSACActor",
-    "HoraSACLearner",
-    "HoraSharedActorCritic",
-    "play_hora_appo",
-]
-
-
-def __getattr__(name: str) -> Any:
-    if name in {"HoraAPPORunner", "play_hora_appo"}:
-        from .appo import HoraAPPORunner, play_hora_appo
-
-        exports = {
-            "HoraAPPORunner": HoraAPPORunner,
-            "play_hora_appo": play_hora_appo,
-        }
-        return exports[name]
-    if name in {"HoraSACActor", "HoraSACLearner"}:
-        from .sac_learner import HoraSACLearner
-        from .sac_models import HoraSACActor
-
-        exports = {
-            "HoraSACActor": HoraSACActor,
-            "HoraSACLearner": HoraSACLearner,
-        }
-        return exports[name]
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
diff --git a/src/unilab/algos/torch/hora/appo.py b/src/unilab/algos/torch/hora/appo.py
deleted file mode 100644
index e8b9835f1..000000000
--- a/src/unilab/algos/torch/hora/appo.py
+++ /dev/null
@@ -1,257 +0,0 @@
-"""HORA-owned APPO entry helpers."""
-
-from __future__ import annotations
-
-import os
-from collections.abc import Callable
-from copy import deepcopy
-from dataclasses import dataclass
-from typing import Any, cast
-
-import torch
-from omegaconf import DictConfig
-
-from unilab.algos.torch.hora.appo_runner import HoraAPPORunner
-from unilab.algos.torch.hora.rsl_rl_compat import (
-    convert_config_v3_to_v4,
-    is_rsl_rl_v4,
-    is_rsl_rl_v5,
-)
-from unilab.base.observations import get_obs_dims
-from unilab.training import BackendAdapter, create_env, log_playback_plan
-from unilab.training.sim2sim import policy_load_dim_guard, resolve_sim2sim_config
-
-from .models import build_hora_shared_actor_critic
-from .observations import build_hora_actor_tensordict, split_hora_obs_with_priv_info
-from .runtime import is_hora_appo_runtime
-
-
-@dataclass(frozen=True)
-class HoraAPPORuntime:
-    """Resolved HORA APPO entrypoints used by the generic APPO script.
-
-    Args:
-        runner_cls: Runner class used for HORA APPO training mode.
-        play_fn: Play-mode callable used for HORA APPO checkpoint playback.
-
-    Returns:
-        Immutable entrypoint bundle consumed by generic APPO script assembly.
-    """
-
-    runner_cls: type[HoraAPPORunner]
-    play_fn: Callable[..., str | None]
-
-
-def resolve_hora_appo_runtime(rl_cfg: dict[str, Any]) -> HoraAPPORuntime | None:
-    """Resolve HORA APPO entrypoints from an explicit runtime marker.
-
-    Args:
-        rl_cfg: Resolved algorithm config dictionary from Hydra composition.
-
-    Returns:
-        ``HoraAPPORuntime`` when the owner config selects HORA APPO, otherwise
-        ``None``.
-    """
-    if not is_hora_appo_runtime(rl_cfg):
-        return None
-    return HoraAPPORuntime(runner_cls=HoraAPPORunner, play_fn=play_hora_appo)
-
-
-def _update_hora_obs_groups(
-    rl_cfg: dict[str, Any],
-    *,
-    obs_dim: int,
-    priv_info_dim: int,
-) -> None:
-    """Update grouped actor/critic dims for the HORA APPO runtime.
-
-    Args:
-        rl_cfg: Mutable algorithm config dictionary to update in place.
-        obs_dim: Actor observation dimension reported by the env contract.
-        priv_info_dim: Privileged-info dimension reported by the env contract.
-
-    Returns:
-        None. Mutates ``rl_cfg["obs_groups"]`` directly.
-    """
-    obs_groups = rl_cfg.setdefault("obs_groups", {})
-    actor_group = obs_groups.setdefault("actor", {})
-    critic_group = obs_groups.setdefault("critic", {})
-    if isinstance(actor_group, dict):
-        actor_group["actor"] = obs_dim
-        actor_group["priv_info"] = priv_info_dim
-    if isinstance(critic_group, dict):
-        critic_group["actor"] = obs_dim
-        critic_group["priv_info"] = priv_info_dim
-
-
-def play_hora_appo(
-    cfg: DictConfig,
-    rl_cfg: dict[str, Any],
-    *,
-    root_dir,
-    resolve_checkpoint_path,
-) -> str | None:
-    """Play HORA APPO checkpoints with grouped actor and privileged inputs."""
-    import numpy as np
-    from rsl_rl.utils import resolve_callable
-    from tensordict import TensorDict
-
-    env_cfg_override = BackendAdapter(
-        cfg,
-        root_dir=root_dir,
-        algo_name="appo",
-    ).build_task_env_cfg_override()
-
-    device = cfg.training.device or (
-        "cuda"
-        if torch.cuda.is_available()
-        else "mps"
-        if torch.backends.mps.is_available()
-        else "cpu"
-    )
-    print(f"Using device for play: {device}")
-
-    env = cast(
-        Any,
-        create_env(
-            cfg,
-            num_envs=cfg.training.play_env_num,
-            env_cfg_override=env_cfg_override,
-        ),
-    )
-    obs_dim, _ = get_obs_dims(env.obs_groups_spec)
-    if env.state is None:
-        env.init_state()
-    _, _, state_priv_info = split_hora_obs_with_priv_info(
-        env.state.obs,
-        env.state.info if env.state is not None else None,
-    )
-    priv_info_dim = int(state_priv_info.shape[1]) if state_priv_info is not None else 0
-    if priv_info_dim <= 0:
-        raise ValueError("HORA APPO play requires privileged info from the environment.")
-
-    action_shape = env.action_space.shape
-    if action_shape is None:
-        raise ValueError("env.action_space.shape must be defined")
-    action_dim = int(action_shape[0])
-
-    rl_cfg_dict = dict(rl_cfg)
-    _update_hora_obs_groups(rl_cfg_dict, obs_dim=obs_dim, priv_info_dim=priv_info_dim)
-
-    if is_rsl_rl_v5():
-        pass
-    elif is_rsl_rl_v4():
-        rl_cfg_dict = convert_config_v3_to_v4(rl_cfg_dict)
-
-    obs_example = torch.zeros((cfg.training.play_env_num, obs_dim), device=device)
-    td_example = TensorDict(
-        {
-            "actor": obs_example,
-            "priv_info": torch.zeros((cfg.training.play_env_num, priv_info_dim), device=device),
-        },
-        batch_size=cfg.training.play_env_num,
-    )
-
-    actor_cfg = deepcopy(rl_cfg_dict["actor"])
-    actor_cls = resolve_callable(actor_cfg.pop("class_name"))
-    actor_cfg.pop("num_actions", None)
-
-    critic_cfg = deepcopy(rl_cfg_dict.get("critic") or rl_cfg_dict.get("actor") or {})
-    critic_cfg.pop("class_name", None)
-    critic_cfg.pop("num_actions", None)
-    critic_cfg.pop("distribution_cfg", None)
-
-    shared_model = build_hora_shared_actor_critic(
-        obs_dim=obs_dim,
-        action_dim=action_dim,
-        priv_info_dim=priv_info_dim,
-        actor_cfg=actor_cfg,
-        critic_cfg=critic_cfg,
-    ).to(device)
-    actor = actor_cls(
-        td_example,
-        rl_cfg_dict["obs_groups"],
-        "actor",
-        action_dim,
-        shared_model=shared_model,
-        **actor_cfg,
-    )
-    actor = actor.to(device)
-    actor.eval()
-
-    load_path, load_path_dir = resolve_checkpoint_path(cfg)
-    if not load_path or not os.path.exists(load_path):
-        print(f"Could not find run to load. load_path={load_path}")
-        return None
-
-    resolve_sim2sim_config(
-        load_path_dir,
-        cfg,
-        algo_name="appo",
-        strict=bool(getattr(cfg.training, "sim2sim_strict", True)),
-    )
-
-    print(f"Loading model: {load_path}")
-    checkpoint = torch.load(load_path, map_location=device, weights_only=True)
-    with policy_load_dim_guard(env_obs_dim=obs_dim, env_action_dim=action_dim, algo_name="appo"):
-        actor.load_state_dict(checkpoint["actor"])
-
-    current_priv_info: np.ndarray | None = None
-
-    def initialize_play_obs() -> np.ndarray:
-        nonlocal current_priv_info
-        obs_out, info_out = env.reset(np.arange(cfg.training.play_env_num, dtype=np.int32))
-        actor_obs, _, priv_info = split_hora_obs_with_priv_info(obs_out, info_out)
-        current_priv_info = priv_info.astype(np.float32) if priv_info is not None else None
-        return np.asarray(actor_obs, dtype=np.float32)
-
-    def step_play_obs(obs_np: np.ndarray) -> np.ndarray:
-        nonlocal current_priv_info
-        if current_priv_info is None:
-            raise ValueError("HORA APPO play step is missing privileged info.")
-        td = build_hora_actor_tensordict(
-            obs_np,
-            priv_info=current_priv_info,
-            device=device,
-            batch_size=cfg.training.play_env_num,
-        )
-        actions = actor(td).cpu().numpy().astype(np.float32)
-        state = env.step(actions)
-        actor_obs, _, priv_info = split_hora_obs_with_priv_info(state.obs, state.info)
-        current_priv_info = priv_info.astype(np.float32) if priv_info is not None else None
-        return np.asarray(actor_obs, dtype=np.float32)
-
-    print("Collecting physics states...")
-    with torch.inference_mode():
-        play_video_path = cast(
-            str | None,
-            env.run_playback_mode(
-                play_render_mode=getattr(cfg.training, "play_render_mode", "auto"),
-                play_steps=getattr(cfg.training, "play_steps", None),
-                output_video=os.path.join(load_path_dir, "play_video.mp4")
-                if load_path_dir
-                else None,
-                render_spacing=float(
-                    getattr(cfg.training, "render_spacing", getattr(env.cfg, "render_spacing", 1.0))
-                ),
-                initialize=initialize_play_obs,
-                step=step_play_obs,
-                camera_kwargs={
-                    "cam_distance": cfg.training.cam_distance,
-                    "cam_elevation": cfg.training.cam_elevation,
-                    "cam_azimuth": cfg.training.cam_azimuth,
-                    "cam_lookat": getattr(cfg.training, "cam_lookat", None),
-                    "cam_tracking": getattr(cfg.training, "cam_tracking", False),
-                    "cam_tracking_env_idx": getattr(cfg.training, "cam_tracking_env_idx", 0),
-                    "cam_tracking_extra_envs": getattr(cfg.training, "cam_tracking_extra_envs", 2),
-                },
-                on_plan=log_playback_plan,
-            ),
-        )
-    if play_video_path is not None:
-        print(f"Saving video to {play_video_path} ...")
-    print("Done.")
-    return play_video_path
-
-
-__all__ = ["HoraAPPORunner", "HoraAPPORuntime", "play_hora_appo", "resolve_hora_appo_runtime"]
diff --git a/src/unilab/algos/torch/hora/appo_learner.py b/src/unilab/algos/torch/hora/appo_learner.py
deleted file mode 100644
index ed9578fed..000000000
--- a/src/unilab/algos/torch/hora/appo_learner.py
+++ /dev/null
@@ -1,167 +0,0 @@
-"""HORA-owned APPO learner with grouped actor and privileged observations."""
-
-from __future__ import annotations
-
-from typing import cast
-
-import torch
-from tensordict import TensorDict
-
-from unilab.algos.torch.appo.learner import (
-    APPOLearner,
-    _distribution_std,
-    _sample_tensor_for_metric,
-    vtrace_advantages,
-)
-from unilab.algos.torch.hora.models import HoraActorModel, HoraCriticModel
-
-
-def _build_hora_obs_td(
-    actor_obs: torch.Tensor,
-    *,
-    device: str,
-    priv_info: torch.Tensor | None = None,
-) -> TensorDict:
-    if priv_info is not None:
-        return TensorDict(
-            {"actor": actor_obs, "priv_info": priv_info},
-            batch_size=actor_obs.shape[0],
-            device=device,
-        )
-    return TensorDict({"policy": actor_obs}, batch_size=actor_obs.shape[0], device=device)
-
-
-def _derive_priv_info_from_critic(
-    actor_obs: torch.Tensor,
-    critic_obs: torch.Tensor | None,
-    *,
-    context: str,
-) -> torch.Tensor:
-    if critic_obs is None:
-        raise ValueError(f"HORA APPO {context} requires critic observations.")
-    actor_dim = int(actor_obs.shape[-1])
-    critic_dim = int(critic_obs.shape[-1])
-    if critic_dim <= actor_dim:
-        raise ValueError(
-            f"HORA APPO {context} requires critic observations to include privileged tail "
-            f"features; got actor_dim={actor_dim}, critic_dim={critic_dim}."
-        )
-    return critic_obs[..., actor_dim:]
-
-
-class HoraAPPOLearner(APPOLearner):
-    """APPO learner variant for HORA grouped observations."""
-
-    def _minibatch_policy_value(
-        self,
-        obs_mini: torch.Tensor,
-        critic_obs_mini: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        priv_info = _derive_priv_info_from_critic(
-            obs_mini,
-            critic_obs_mini,
-            context="minibatch update",
-        )
-        actor = cast(HoraActorModel, self.actor)
-        critic = cast(HoraCriticModel, self.critic)
-        shared_actor = actor.shared
-        mean = shared_actor.policy_mean_from_tensors(
-            obs_mini,
-            priv_info,
-            prefer_student=actor.prefer_student,
-        )
-        std = _distribution_std(shared_actor.distribution, mean)
-        value = critic.shared.value_from_tensors(
-            obs_mini,
-            priv_info,
-            prefer_student=False,
-        ).squeeze(-1)
-        return mean, std, value
-
-    def process_batch(self, batch_dict):
-        """Compute V-trace targets for grouped HORA rollouts."""
-        obs = batch_dict["observations"]
-        critic_base = batch_dict.get("critic", None)
-        rewards = batch_dict["rewards"]
-        dones = batch_dict["dones"].float()
-        last_obs = batch_dict["last_obs"]
-        last_critic = batch_dict.get("last_critic", None)
-        behavior_log_probs = batch_dict["actions_log_prob"]
-        actions = batch_dict["actions"]
-
-        T, N = obs.shape[:2]
-        priv_info = _derive_priv_info_from_critic(
-            obs,
-            critic_base,
-            context="rollout batch",
-        )
-        last_priv_info = _derive_priv_info_from_critic(
-            last_obs,
-            last_critic,
-            context="bootstrap batch",
-        )
-        obs_flat = obs.flatten(0, 1)
-        priv_info_flat = priv_info.flatten(0, 1)
-
-        obs_td = _build_hora_obs_td(obs_flat, device=self.device, priv_info=priv_info_flat)
-        last_obs_td = _build_hora_obs_td(last_obs, device=self.device, priv_info=last_priv_info)
-
-        critic_obs = critic_base
-        critic_obs_flat = critic_obs.flatten(0, 1)
-        critic_obs_td = _build_hora_obs_td(obs_flat, device=self.device, priv_info=priv_info_flat)
-        critic_last_obs_td = _build_hora_obs_td(
-            last_obs,
-            device=self.device,
-            priv_info=last_priv_info,
-        )
-
-        if hasattr(self.actor, "update_normalization"):
-            self.actor.update_normalization(obs_td)
-            self.actor.update_normalization(last_obs_td)
-            self.sync_target_actor_buffers()
-        if hasattr(self.critic, "update_normalization"):
-            self.critic.update_normalization(critic_obs_td)
-            self.critic.update_normalization(critic_last_obs_td)
-
-        batch_dict["_critic_obs_flat"] = critic_obs_flat
-
-        with torch.inference_mode():
-            values_flat = self.critic(critic_obs_td)
-            last_values = self.critic(critic_last_obs_td).squeeze(-1)
-        values = values_flat.view(T, N, -1).squeeze(-1)
-
-        actions_flat = actions.flatten(0, 1)
-        with torch.inference_mode():
-            self.target_actor(obs_td, stochastic_output=True)
-            target_log_probs_flat = self.target_actor.get_output_log_prob(actions_flat)
-            batch_dict["_old_mu"] = self.target_actor.output_mean.clone()
-            batch_dict["_old_sigma"] = self.target_actor.output_std.clone()
-        target_log_probs = target_log_probs_flat.view(T, N)
-        with torch.inference_mode():
-            rhos = torch.exp(target_log_probs - behavior_log_probs)
-            rho_sample = _sample_tensor_for_metric(rhos)
-            batch_dict["_appo_process_metrics"] = {
-                "vtrace/rho_clip_fraction": float(
-                    (rhos > float(self.vtrace_clip_rho)).float().mean().item()
-                ),
-                "vtrace/rho_raw_p99": float(torch.quantile(rho_sample, 0.99).item()),
-            }
-
-        vs, advantages = vtrace_advantages(
-            behavior_log_probs=behavior_log_probs,
-            target_log_probs=target_log_probs,
-            rewards=rewards,
-            values=values,
-            bootstrap_values=last_values,
-            dones=dones,
-            gamma=self.gamma,
-            clip_rho=self.vtrace_clip_rho,
-            clip_c=self.vtrace_clip_c,
-        )
-
-        batch_dict["values"] = values
-        batch_dict["advantages"] = advantages
-        batch_dict["returns"] = vs
-        batch_dict["target_log_probs"] = target_log_probs
-
-        return batch_dict
diff --git a/src/unilab/algos/torch/hora/appo_runner.py b/src/unilab/algos/torch/hora/appo_runner.py
deleted file mode 100644
index d3d0d40c6..000000000
--- a/src/unilab/algos/torch/hora/appo_runner.py
+++ /dev/null
@@ -1,424 +0,0 @@
-"""HORA-owned APPO runner."""
-
-from __future__ import annotations
-
-import multiprocessing as mp
-import os
-import time
-from collections import deque
-from copy import deepcopy
-from typing import Any
-
-import numpy as np
-import torch
-from rsl_rl.utils import resolve_callable
-
-from unilab.algos.torch.appo.runner import (
-    APPORunner,
-    _optimizer_lr_from_state,
-    _sync_resume_target_actor,
-)
-from unilab.algos.torch.appo.staging import RolloutStagingPool
-from unilab.algos.torch.hora.appo_learner import HoraAPPOLearner
-from unilab.algos.torch.hora.appo_worker import hora_appo_collector_fn
-from unilab.algos.torch.hora.models import build_hora_shared_actor_critic
-from unilab.algos.torch.hora.rsl_rl_compat import (
-    convert_config_v3_to_v4,
-    is_rsl_rl_v4,
-    is_rsl_rl_v5,
-)
-from unilab.base.observations import get_critic_base_dim, get_obs_dims
-from unilab.base.registry import ensure_registries
-from unilab.ipc import RolloutRingBuffer, SharedWeightSync
-from unilab.logging import OffPolicyLogger
-from unilab.training.seed import apply_training_seed, derive_worker_seed
-
-
-def _validate_hora_shared_checkpoint(checkpoint: dict[str, Any]) -> None:
-    """Validate that a HORA APPO checkpoint matches the current shared model contract."""
-    actor_state = checkpoint.get("actor")
-    critic_state = checkpoint.get("critic")
-    if not isinstance(actor_state, dict) or not isinstance(critic_state, dict):
-        raise ValueError("HORA APPO checkpoint must contain actor and critic state dicts.")
-
-    shared_keys = sorted(
-        key for key in actor_state if key.startswith("shared.") and key in critic_state
-    )
-    if not shared_keys:
-        raise ValueError("HORA APPO checkpoint does not contain shared actor/critic weights.")
-
-    for key in shared_keys:
-        actor_value = actor_state[key]
-        critic_value = critic_state[key]
-        if torch.is_tensor(actor_value) and torch.is_tensor(critic_value):
-            if actor_value.shape != critic_value.shape or not torch.equal(
-                actor_value.cpu(), critic_value.cpu()
-            ):
-                raise ValueError(
-                    "Invalid HORA APPO checkpoint: shared model state is inconsistent."
-                )
-        elif actor_value != critic_value:
-            raise ValueError("Invalid HORA APPO checkpoint: shared model state is inconsistent.")
-
-
-class HoraAPPORunner(APPORunner):
-    """APPO runner variant that preserves grouped HORA observations."""
-
-    def __init__(self, *args, **kwargs):
-        self.priv_info_dim = 0
-        super().__init__(*args, **kwargs)
-
-    def _resolve_dims(self):
-        self.obs_dim, self.action_dim = self._detect_dims()
-
-        obs_groups = self.rl_cfg.setdefault("obs_groups", {})
-        actor_group = obs_groups.setdefault("actor", {})
-        critic_group = obs_groups.setdefault("critic", {})
-        if isinstance(actor_group, dict):
-            actor_group["actor"] = self.obs_dim
-            actor_group["priv_info"] = self.priv_info_dim
-        if isinstance(critic_group, dict):
-            critic_group["actor"] = self.obs_dim
-            critic_group["priv_info"] = self.priv_info_dim
-
-    def _detect_dims(self):
-        from unilab.base import registry
-
-        ensure_registries()
-
-        apply_training_seed(self.seed, torch_runtime=True, cuda=True)
-        env = registry.make(
-            self.env_name,
-            num_envs=self._detect_dim_probe_num_envs(),
-            sim_backend=self.sim_backend,
-            env_cfg_override=self.env_cfg_overrides if self.env_cfg_overrides else None,
-        )
-        obs_dim, critic_dim = get_obs_dims(env.obs_groups_spec)
-        self.critic_dim = critic_dim
-        self.critic_input_dim = get_critic_base_dim(env.obs_groups_spec)
-        if env.state is None:
-            env.init_state()
-        info = env.state.info if env.state is not None else {}
-        priv_info = info.get("critic_info") if isinstance(info, dict) else None
-        if isinstance(priv_info, np.ndarray) and priv_info.ndim == 2:
-            self.priv_info_dim = int(priv_info.shape[1])
-        elif critic_dim > obs_dim:
-            self.priv_info_dim = int(critic_dim - obs_dim)
-        if self.priv_info_dim <= 0:
-            env.close()
-            raise ValueError("HORA APPO requires a positive privileged-info dimension.")
-        assert env.action_space.shape is not None
-        action_dim = env.action_space.shape[0]
-        env.close()
-        return obs_dim, action_dim
-
-    def _detect_dim_probe_num_envs(self) -> int:
-        scale_list = None
-        if isinstance(self.env_cfg_overrides, dict):
-            domain_rand = self.env_cfg_overrides.get("domain_rand")
-            if isinstance(domain_rand, dict):
-                scale_list = domain_rand.get("scale_list")
-            if scale_list is None:
-                scale_list = self.env_cfg_overrides.get("scale_list")
-        if isinstance(scale_list, (list, tuple)):
-            return max(1, len(scale_list))
-        return 1
-
-    def _build_learner(self):
-        apply_training_seed(self.seed, torch_runtime=True, cuda=True)
-        cfg = dict(self.rl_cfg)
-        if is_rsl_rl_v5():
-            pass
-        elif is_rsl_rl_v4():
-            cfg = convert_config_v3_to_v4(cfg)
-
-        from tensordict import TensorDict
-
-        obs_example = torch.zeros((self.num_envs, self.obs_dim), device=self.device)
-        priv_info_example = torch.zeros((self.num_envs, self.priv_info_dim), device=self.device)
-        td_example = TensorDict(
-            {"actor": obs_example, "priv_info": priv_info_example},
-            batch_size=self.num_envs,
-            device=self.device,
-        )
-
-        actor_cfg = deepcopy(cfg.get("actor", {}))
-        actor_cls = resolve_callable(actor_cfg.pop("class_name"))
-        actor_cfg.pop("num_actions", None)
-        critic_cfg: dict[str, Any] = deepcopy(cfg.get("critic") or cfg.get("actor") or {})
-        critic_cls = resolve_callable(critic_cfg.pop("class_name", "rsl_rl.models.MLPModel"))
-        critic_cfg.pop("num_actions", None)
-        critic_cfg.pop("distribution_cfg", None)
-
-        shared_model = build_hora_shared_actor_critic(
-            obs_dim=self.obs_dim,
-            action_dim=self.action_dim,
-            priv_info_dim=self.priv_info_dim,
-            actor_cfg=actor_cfg,
-            critic_cfg=critic_cfg,
-        ).to(self.device)
-
-        actor = actor_cls(
-            td_example,
-            cfg["obs_groups"],
-            "actor",
-            self.action_dim,
-            shared_model=shared_model,
-            **actor_cfg,
-        )
-        critic = critic_cls(
-            td_example,
-            cfg["obs_groups"],
-            "critic",
-            1,
-            shared_model=shared_model,
-            **critic_cfg,
-        )
-
-        algo_cfg = cfg.get("algorithm", cfg)
-        return HoraAPPOLearner(
-            actor=actor,
-            critic=critic,
-            device=self.device,
-            num_learning_epochs=algo_cfg.get("num_learning_epochs", 5),
-            num_mini_batches=algo_cfg.get("num_mini_batches", 4),
-            clip_param=algo_cfg.get("clip_param", 0.2),
-            gamma=algo_cfg.get("gamma", 0.99),
-            lam=algo_cfg.get("lam", 0.95),
-            value_loss_coef=algo_cfg.get("value_loss_coef", 1.0),
-            entropy_coef=algo_cfg.get("entropy_coef", 0.01),
-            learning_rate=algo_cfg.get("learning_rate", 1e-3),
-            max_grad_norm=algo_cfg.get("max_grad_norm", 1.0),
-            use_clipped_value_loss=algo_cfg.get("use_clipped_value_loss", True),
-            schedule=algo_cfg.get("schedule", "fixed"),
-            desired_kl=algo_cfg.get("desired_kl", 0.01),
-            adaptive_kl_factor=algo_cfg.get("adaptive_kl_factor", 1.2),
-            adaptive_lr_factor=algo_cfg.get("adaptive_lr_factor", 1.1),
-            optimizer=algo_cfg.get("optimizer", "adam"),
-            tau=algo_cfg.get("tau", 1.0),
-            target_update_freq=algo_cfg.get("target_update_freq", 1),
-            vtrace_clip_rho=algo_cfg.get("vtrace_clip_rho", 1.0),
-            vtrace_clip_c=algo_cfg.get("vtrace_clip_c", 1.0),
-            enable_compile=algo_cfg.get("enable_compile", True),
-        )
-
-    def learn(
-        self,
-        max_iterations: int = 1500,
-        save_interval: int = 50,
-        log_dir: str = "logs",
-        logger_type: str = "tensorboard",
-    ) -> None:
-        os.makedirs(log_dir, exist_ok=True)
-        train_start_wall = time.time()
-        best_mean_reward = float("-inf")
-        last_mean_reward = 0.0
-        ckpt_path: str | None = None
-        iteration = 0
-
-        learner = self._build_learner()
-        if self.resume_path:
-            checkpoint = torch.load(self.resume_path, map_location=self.device, weights_only=True)
-            _validate_hora_shared_checkpoint(checkpoint)
-            learner.actor.load_state_dict(checkpoint["actor"])
-            learner.critic.load_state_dict(checkpoint["critic"])
-            if "optimizer" in checkpoint:
-                learner.optimizer.load_state_dict(checkpoint["optimizer"])
-                learner.learning_rate = _optimizer_lr_from_state(learner.optimizer)
-            _sync_resume_target_actor(learner)
-
-        rollout_ring_buffer = RolloutRingBuffer(
-            num_envs=self.num_envs,
-            num_steps=self.steps_per_env,
-            obs_dim=self.obs_dim,
-            action_dim=self.action_dim,
-            critic_dim=self.critic_dim,
-            num_slots=4,
-            create=True,
-        )
-        self._shared_resources.append(rollout_ring_buffer)
-
-        actor_weight_sync = SharedWeightSync.from_state_dict(
-            learner.actor.state_dict(), create=True
-        )
-        critic_weight_sync = SharedWeightSync.from_state_dict(
-            learner.critic.state_dict(),
-            create=True,
-        )
-        self._shared_resources.extend([actor_weight_sync, critic_weight_sync])
-
-        actor_weight_param_shapes = {
-            name: p.shape for name, p in learner.actor.state_dict().items()
-        }
-        critic_weight_param_shapes = {
-            name: p.shape for name, p in learner.critic.state_dict().items()
-        }
-
-        metrics_queue: mp.Queue = mp.get_context("spawn").Queue(maxsize=100)
-        collector_kwargs = {
-            "env_name": self.env_name,
-            "rl_cfg": self.rl_cfg,
-            "num_envs": self.num_envs,
-            "steps_per_env": self.steps_per_env,
-            "shm_rollout_ring_buffer_name": rollout_ring_buffer.name,
-            "sync_primitives": (
-                rollout_ring_buffer._write_ptr,
-                rollout_ring_buffer._read_ptr,
-            ),
-            "obs_dim": self.obs_dim,
-            "action_dim": self.action_dim,
-            "critic_dim": self.critic_dim,
-            "priv_info_dim": self.priv_info_dim,
-            "actor_weight_sync_name": actor_weight_sync.name,
-            "actor_weight_param_shapes": actor_weight_param_shapes,
-            "critic_weight_sync_name": critic_weight_sync.name,
-            "critic_weight_param_shapes": critic_weight_param_shapes,
-            "metrics_queue": metrics_queue,
-            "collector_device": self.collector_device,
-            "sim_backend": self.sim_backend,
-            "env_cfg_override": self.env_cfg_overrides if self.env_cfg_overrides else None,
-            "seed": derive_worker_seed(self.seed, worker_index=0),
-        }
-        self._start_collector(
-            target_fn=hora_appo_collector_fn,
-            kwargs={"stop_event": self._stop_event, **collector_kwargs},
-        )
-
-        env_steps_per_sync = self.steps_per_env * self.num_envs
-        logger = OffPolicyLogger(
-            algo_name="APPO",
-            max_iterations=max_iterations,
-            num_envs=self.num_envs,
-            env_name=self.env_name,
-            obs_dim=self.obs_dim,
-            action_dim=self.action_dim,
-            log_dir=log_dir,
-            log_backend=logger_type,
-            timing_profile="appo",
-        )
-        logger.start()
-        logger.log_status(
-            f"Waiting for first rollout... "
-            f"(staging_pool={self.staging_pool_size}, "
-            f"epochs={learner.num_learning_epochs})"
-        )
-
-        reward_history: deque = deque(maxlen=200)
-        latest_reward_components: dict = {}
-        staging_pool = RolloutStagingPool(
-            capacity=self.staging_pool_size,
-            num_envs=self.num_envs,
-            slot_shapes=rollout_ring_buffer.slot_shapes,
-            device=self.device,
-        )
-
-        for iteration in range(1, max_iterations + 1):
-            iteration_start = time.perf_counter()
-            self._drain_metrics(metrics_queue, reward_history, latest_reward_components, logger)
-            wait_start = time.perf_counter()
-
-            # Chunked wait with periodic liveness check (issue #594 B-2):
-            # avoid freezing the learner for 60s when the collector dies mid-iteration.
-            WAIT_BUDGET_SEC = 60.0
-            LIVENESS_SLICE_SEC = 0.5
-            deadline = time.monotonic() + WAIT_BUDGET_SEC
-            data_ready = False
-            while time.monotonic() < deadline:
-                remaining = deadline - time.monotonic()
-                if rollout_ring_buffer.wait_for_data(timeout=min(LIVENESS_SLICE_SEC, remaining)):
-                    data_ready = True
-                    break
-                if not self._check_collector_alive():
-                    self._drain_metrics(
-                        metrics_queue,
-                        reward_history,
-                        latest_reward_components,
-                        logger,
-                    )
-                    raise RuntimeError(
-                        "APPO collector process died before producing data. "
-                        "Check stderr for [HORA APPO WORKER CRASH] messages."
-                    )
-            if not data_ready:
-                logger.log_status(
-                    f"[yellow]Warning: Timeout waiting for data at iteration {iteration}[/]"
-                )
-                continue
-            wait_time = time.perf_counter() - wait_start
-
-            available_on_arrive = rollout_ring_buffer.available()
-
-            num_new = rollout_ring_buffer.available()
-            learner_replay_stage_time = 0.0
-            for _ in range(num_new):
-                h2d_start = time.perf_counter()
-                staging_pool.stage_numpy_views(rollout_ring_buffer.read_numpy_views())
-                learner_replay_stage_time += time.perf_counter() - h2d_start
-                rollout_ring_buffer.advance_read()
-
-            self._drain_metrics(metrics_queue, reward_history, latest_reward_components, logger)
-
-            replay_sample_start = time.perf_counter()
-            combined = staging_pool.batch()
-            learner_replay_sample_time = time.perf_counter() - replay_sample_start
-
-            train_start = time.perf_counter()
-            learner.process_batch(combined)
-            metrics = learner.update(combined)
-            train_time = time.perf_counter() - train_start
-            weight_sync_start = time.perf_counter()
-            actor_weight_sync.write_weights(learner.actor.state_dict())
-            critic_weight_sync.write_weights(learner.critic.state_dict())
-            weight_sync_time = time.perf_counter() - weight_sync_start
-            iteration_time = time.perf_counter() - iteration_start
-
-            metrics["staging_pool_len"] = float(staging_pool.active_count)
-            metrics["staging_pool_capacity"] = float(staging_pool.capacity)
-            metrics["available_on_arrive"] = float(available_on_arrive)
-            metrics["rollouts_read"] = float(num_new)
-            logger.update_staging_pool(staging_pool.active_count, staging_pool.capacity)
-
-            mean_reward = (
-                sum(list(reward_history)[-50:]) / max(len(list(reward_history)[-50:]), 1)
-                if reward_history
-                else 0.0
-            )
-            last_mean_reward = float(mean_reward)
-            best_mean_reward = max(best_mean_reward, last_mean_reward)
-
-            logger.log_step(
-                iteration=iteration,
-                metrics=metrics,
-                reward=mean_reward,
-                reward_components=latest_reward_components,
-                train_time=train_time,
-                collector_wait_time=wait_time,
-                learner_replay_sample_time=learner_replay_sample_time,
-                learner_replay_stage_time=learner_replay_stage_time,
-                weight_sync_time=weight_sync_time,
-                iteration_time=iteration_time,
-                extra_info={
-                    "throughput_steps": num_new * env_steps_per_sync,
-                    "collector_active_steps_per_sec": logger._collector_active_steps_per_sec,
-                },
-            )
-
-            if save_interval > 0 and iteration % save_interval == 0:
-                ckpt_path = os.path.join(log_dir, f"model_{iteration}.pt")
-                torch.save(learner.get_state_dict(), ckpt_path)
-                logger.log_save(ckpt_path)
-
-        ckpt_path = os.path.join(log_dir, f"model_{max_iterations}.pt")
-        torch.save(learner.get_state_dict(), ckpt_path)
-        logger.log_save(ckpt_path)
-        logger.finish()
-        self.last_run_summary = {
-            "status": "completed",
-            "completed_iterations": iteration,
-            "total_env_steps": int(logger._total_steps),
-            "final_mean_reward": last_mean_reward if reward_history else None,
-            "best_mean_reward": best_mean_reward if reward_history else None,
-            "mean_episode_length": float(logger._mean_ep_length),
-            "last_checkpoint": ckpt_path,
-            "training_wall_time_sec": time.time() - train_start_wall,
-        }
diff --git a/src/unilab/algos/torch/hora/appo_worker.py b/src/unilab/algos/torch/hora/appo_worker.py
deleted file mode 100644
index b303452aa..000000000
--- a/src/unilab/algos/torch/hora/appo_worker.py
+++ /dev/null
@@ -1,435 +0,0 @@
-"""HORA-owned APPO rollout worker."""
-
-from __future__ import annotations
-
-import statistics
-import sys
-import time
-from collections import defaultdict
-from typing import Any, Dict
-
-import numpy as np
-import torch
-from rsl_rl.utils import resolve_callable
-
-from unilab.algos.torch.appo.worker import (
-    compute_rollout_active_steps_per_sec,
-    put_latest_metrics,
-)
-from unilab.algos.torch.common.collector_timing import extract_env_step_breakdown_timing_ms
-from unilab.base.final_observation import resolve_terminal_observation_contract
-from unilab.base.registry import ensure_registries
-from unilab.training.seed import apply_training_seed
-
-from .observations import split_hora_obs_with_priv_info
-
-
-def compute_hora_timeout_bootstrap_correction(
-    critic: Any,
-    collector_device: str,
-    gamma: float,
-    timeout_mask: np.ndarray,
-    final_obs: np.ndarray,
-    final_critic: np.ndarray | None = None,
-    final_priv_info: np.ndarray | None = None,
-) -> np.ndarray:
-    """Compute timeout bootstrap values for grouped HORA observations."""
-    corrections = np.zeros(timeout_mask.shape, dtype=np.float32)
-    if not np.any(timeout_mask):
-        return corrections
-
-    from tensordict import TensorDict
-
-    if final_priv_info is not None:
-        actor_input = torch.from_numpy(final_obs[timeout_mask]).to(collector_device)
-        priv_input = torch.from_numpy(final_priv_info[timeout_mask]).to(collector_device)
-        critic_td = TensorDict(
-            {"actor": actor_input, "priv_info": priv_input},
-            batch_size=actor_input.shape[0],
-            device=collector_device,
-        )
-    else:
-        critic_input_np = final_critic if final_critic is not None else final_obs
-        critic_input = torch.from_numpy(critic_input_np[timeout_mask]).to(collector_device)
-        critic_td = TensorDict(
-            {"policy": critic_input},
-            batch_size=critic_input.shape[0],
-            device=collector_device,
-        )
-    with torch.no_grad():
-        bootstrap = critic(critic_td).squeeze(-1).cpu().numpy().astype(np.float32, copy=False)
-    corrections[timeout_mask] = float(gamma) * bootstrap
-    return corrections
-
-
-def hora_appo_collector_fn(
-    stop_event: Any,
-    env_name: str,
-    rl_cfg: dict,
-    num_envs: int,
-    steps_per_env: int,
-    shm_rollout_ring_buffer_name: Dict[str, str],
-    sync_primitives: tuple,
-    obs_dim: int,
-    action_dim: int,
-    critic_dim: int,
-    actor_weight_sync_name: str,
-    actor_weight_param_shapes: dict,
-    critic_weight_sync_name: str,
-    critic_weight_param_shapes: dict,
-    metrics_queue: Any,
-    collector_device: str = "cpu",
-    sim_backend: str = "mujoco",
-    env_cfg_override: dict | None = None,
-    priv_info_dim: int = 0,
-    seed: int | None = None,
-):
-    """Collect grouped HORA APPO rollouts into the shared IPC ring buffer."""
-    from copy import deepcopy
-
-    from tensordict import TensorDict
-
-    from unilab.algos.torch.hora.models import build_hora_shared_actor_critic
-    from unilab.algos.torch.hora.rsl_rl_compat import (
-        convert_config_v3_to_v4,
-        is_rsl_rl_v4,
-        is_rsl_rl_v5,
-    )
-    from unilab.base import registry
-    from unilab.ipc import RolloutRingBuffer, SharedWeightSync
-
-    ensure_registries()
-    apply_training_seed(seed, torch_runtime=True, cuda=True)
-
-    ring_buffer = RolloutRingBuffer(
-        num_envs=num_envs,
-        num_steps=steps_per_env,
-        obs_dim=obs_dim,
-        action_dim=action_dim,
-        critic_dim=critic_dim,
-        create=False,
-        shm_name_prefix=shm_rollout_ring_buffer_name,
-    )
-    ring_buffer.attach_sync_primitives(*sync_primitives)
-    actor_weight_sync = SharedWeightSync(
-        actor_weight_param_shapes,
-        create=False,
-        shm_name=actor_weight_sync_name,
-    )
-    critic_weight_sync = SharedWeightSync(
-        critic_weight_param_shapes,
-        create=False,
-        shm_name=critic_weight_sync_name,
-    )
-
-    env: Any = registry.make(
-        env_name,
-        num_envs=num_envs,
-        sim_backend=sim_backend,
-        env_cfg_override=env_cfg_override,
-    )
-
-    cfg = dict(rl_cfg)
-    if is_rsl_rl_v5():
-        pass
-    elif is_rsl_rl_v4():
-        cfg = convert_config_v3_to_v4(cfg)
-
-    if priv_info_dim <= 0:
-        raise ValueError("HORA APPO collector requires priv_info_dim > 0")
-
-    obs_example = torch.zeros((num_envs, obs_dim), device=collector_device)
-    priv_info_example = torch.zeros((num_envs, priv_info_dim), device=collector_device)
-    td_example = TensorDict(
-        {"actor": obs_example, "priv_info": priv_info_example},
-        batch_size=num_envs,
-        device=collector_device,
-    )
-
-    actor_cfg = deepcopy(cfg["actor"])
-    actor_cls = resolve_callable(actor_cfg.pop("class_name"))
-    actor_cfg.pop("num_actions", None)
-
-    critic_cfg = deepcopy(cfg.get("critic") or cfg.get("actor") or {})
-    critic_cls = resolve_callable(critic_cfg.pop("class_name", "rsl_rl.models.MLPModel"))
-    critic_cfg.pop("num_actions", None)
-    critic_cfg.pop("distribution_cfg", None)
-    shared_model = build_hora_shared_actor_critic(
-        obs_dim=obs_dim,
-        action_dim=action_dim,
-        priv_info_dim=priv_info_dim,
-        actor_cfg=actor_cfg,
-        critic_cfg=critic_cfg,
-    ).to(collector_device)
-
-    actor = actor_cls(
-        td_example,
-        cfg["obs_groups"],
-        "actor",
-        action_dim,
-        shared_model=shared_model,
-        **actor_cfg,
-    )
-    actor = actor.to(collector_device)
-    actor.eval()
-
-    critic = critic_cls(
-        td_example,
-        cfg["obs_groups"],
-        "critic",
-        1,
-        shared_model=shared_model,
-        **critic_cfg,
-    )
-    critic = critic.to(collector_device)
-    critic.eval()
-
-    actor_sd = dict(actor.state_dict())
-    actor_weight_sync.read_weights_into(actor_sd)
-    actor.load_state_dict(actor_sd)
-    local_actor_weight_version = actor_weight_sync.version
-
-    critic_sd = dict(critic.state_dict())
-    critic_weight_sync.read_weights_into(critic_sd)
-    critic.load_state_dict(critic_sd)
-    local_critic_weight_version = critic_weight_sync.version
-
-    env_indices = np.arange(num_envs, dtype=np.int32)
-    try:
-        obs_out, info_out = env.reset(env_indices)
-    except TypeError:
-        obs_out, info_out = env.reset()
-
-    def to_float32_np(x):
-        if hasattr(x, "cpu"):
-            x = x.cpu().numpy()
-        return np.asarray(x, dtype=np.float32)
-
-    obs_np, critic_np, priv_info_np = split_hora_obs_with_priv_info(obs_out, info_out)
-    obs_np = to_float32_np(obs_np)
-    if critic_np is not None:
-        critic_np = to_float32_np(critic_np)
-    if priv_info_np is not None:
-        priv_info_np = to_float32_np(priv_info_np)
-    if priv_info_np is None:
-        raise ValueError("HORA APPO collector did not receive privileged info from env reset.")
-
-    obs_torch = torch.zeros((num_envs, obs_dim), dtype=torch.float32, device=collector_device)
-    priv_info_torch = torch.zeros(
-        (num_envs, priv_info_dim),
-        dtype=torch.float32,
-        device=collector_device,
-    )
-    obs_td = TensorDict(
-        {"actor": obs_torch, "priv_info": priv_info_torch},
-        batch_size=num_envs,
-        device=collector_device,
-    )
-
-    total_steps = 0
-    ep_rewards = []
-    ep_lengths = []
-    current_ep_rewards = np.zeros(num_envs, dtype=np.float32)
-    current_ep_lengths = np.zeros(num_envs, dtype=np.int32)
-    ep_reward_components = defaultdict(list)
-    ep_timeouts = 0
-    ep_completions = 0
-
-    _EMA = 0.1
-    ema_mlp_infer_ms = 0.0
-    ema_env_step_ms = 0.0
-    ema_rollout_ms = 0.0
-    ema_env_step_breakdown_ms: dict[str, float] = {}
-
-    try:
-        while not stop_event.is_set():
-            t_rollout_start = time.perf_counter()
-            if actor_weight_sync.version > local_actor_weight_version:
-                actor_sd = dict(actor.state_dict())
-                local_actor_weight_version = actor_weight_sync.read_weights_into(actor_sd)
-                actor.load_state_dict(actor_sd)
-            if critic_weight_sync.version > local_critic_weight_version:
-                critic_sd = dict(critic.state_dict())
-                local_critic_weight_version = critic_weight_sync.read_weights_into(critic_sd)
-                critic.load_state_dict(critic_sd)
-
-            write_buf = ring_buffer.write_buffer
-            for step in range(steps_per_env):
-                t_mlp = time.perf_counter()
-                with torch.no_grad():
-                    obs_torch.copy_(torch.from_numpy(obs_np))
-                    priv_info_torch.copy_(torch.from_numpy(priv_info_np))
-                    actions_torch = actor(obs_td, stochastic_output=True)
-                    log_probs_torch = actor.get_output_log_prob(actions_torch)
-                    actions_np = actions_torch.cpu().numpy()
-                ema_mlp_infer_ms = (1 - _EMA) * ema_mlp_infer_ms + _EMA * (
-                    (time.perf_counter() - t_mlp) * 1000
-                )
-
-                write_buf["obs"][:, step, :] = obs_np
-                if critic_np is not None:
-                    write_buf["critic"][:, step, :] = critic_np
-                write_buf["actions"][:, step, :] = actions_np
-                write_buf["log_probs"][:, step] = log_probs_torch.cpu().numpy().ravel()
-
-                t_env = time.perf_counter()
-                state = env.step(actions_np)
-                ema_env_step_ms = (1 - _EMA) * ema_env_step_ms + _EMA * (
-                    (time.perf_counter() - t_env) * 1000
-                )
-                for key, value in extract_env_step_breakdown_timing_ms(state.info).items():
-                    ema_env_step_breakdown_ms[key] = (1 - _EMA) * ema_env_step_breakdown_ms.get(
-                        key, 0.0
-                    ) + _EMA * value
-
-                reward_raw = np.asarray(state.reward, dtype=np.float32).ravel()
-                terminated_raw = np.asarray(state.terminated, dtype=np.float32).ravel()
-                truncated_raw = np.asarray(state.truncated, dtype=np.float32).ravel()
-                combined_done_raw = np.clip(terminated_raw + truncated_raw, 0, 1)
-
-                next_actor_obs_np, next_critic_np, next_priv_info_np = (
-                    split_hora_obs_with_priv_info(
-                        state.obs,
-                        state.info,
-                    )
-                )
-                next_actor_obs_np = to_float32_np(next_actor_obs_np)
-                if next_critic_np is not None:
-                    next_critic_np = to_float32_np(next_critic_np)
-                if next_priv_info_np is not None:
-                    next_priv_info_np = to_float32_np(next_priv_info_np)
-                if next_priv_info_np is None:
-                    raise ValueError(
-                        "HORA APPO collector did not receive privileged info after step."
-                    )
-
-                terminal_contract = resolve_terminal_observation_contract(
-                    next_obs_batch_size=next_actor_obs_np.shape[0],
-                    final_observation=getattr(state, "final_observation", None),
-                    done=combined_done_raw > 0.5,
-                    info=state.info,
-                    truncated=truncated_raw,
-                )
-                terminal_priv_info = None
-                if (
-                    terminal_contract.terminal_obs is not None
-                    and terminal_contract.terminal_critic is not None
-                ):
-                    _, _, terminal_priv_info = split_hora_obs_with_priv_info(
-                        {
-                            "obs": terminal_contract.terminal_obs,
-                            "critic": terminal_contract.terminal_critic,
-                        }
-                    )
-                    if terminal_priv_info is not None:
-                        terminal_priv_info = to_float32_np(terminal_priv_info)
-
-                reward_raw += compute_hora_timeout_bootstrap_correction(
-                    critic=critic,
-                    collector_device=collector_device,
-                    gamma=float(cfg["algorithm"].get("gamma", 0.99)),
-                    timeout_mask=terminal_contract.timeout_terminal_mask,
-                    final_obs=(
-                        terminal_contract.terminal_obs
-                        if terminal_contract.terminal_obs is not None
-                        else next_actor_obs_np
-                    ),
-                    final_critic=(
-                        terminal_contract.terminal_critic
-                        if terminal_contract.terminal_critic is not None
-                        else next_critic_np
-                    ),
-                    final_priv_info=(
-                        terminal_priv_info if terminal_priv_info is not None else next_priv_info_np
-                    ),
-                )
-
-                write_buf["rewards"][:, step] = reward_raw
-                write_buf["dones"][:, step] = combined_done_raw
-                write_buf["truncated"][:, step] = truncated_raw
-
-                total_steps += num_envs
-                current_ep_rewards += reward_raw
-                current_ep_lengths += 1
-                reset_indices = np.where(combined_done_raw > 0.5)[0]
-                if len(reset_indices) > 0:
-                    ep_rewards.extend(current_ep_rewards[reset_indices].tolist())
-                    ep_lengths.extend(current_ep_lengths[reset_indices].tolist())
-                    current_ep_rewards[reset_indices] = 0.0
-                    current_ep_lengths[reset_indices] = 0
-                    ep_completions += len(reset_indices)
-                    ep_timeouts += int(np.sum(truncated_raw[reset_indices] > 0.5))
-
-                log_info = state.info.get("log", {})
-                for k, v in log_info.items():
-                    if k.startswith("reward/"):
-                        ep_reward_components[k].append(v)
-
-                if metrics_queue is not None and total_steps % (num_envs * 10) == 0:
-                    try:
-                        msg: dict[str, Any] = {
-                            "total_steps": total_steps,
-                        }
-                        if ep_rewards:
-                            msg["mean_ep_reward"] = statistics.mean(ep_rewards[-100:])
-                            msg["mean_ep_length"] = (
-                                statistics.mean(ep_lengths[-100:]) if ep_lengths else 0.0
-                            )
-                        if ep_completions > 0:
-                            msg["timeout_rate"] = ep_timeouts / ep_completions
-                            ep_timeouts = 0
-                            ep_completions = 0
-                        msg["collector_timing_ms"] = {
-                            "rollout_ms": ema_rollout_ms,
-                            "mlp_infer_ms": ema_mlp_infer_ms,
-                            "env_step_ms": ema_env_step_ms,
-                            **ema_env_step_breakdown_ms,
-                        }
-                        collector_active_steps_per_sec = compute_rollout_active_steps_per_sec(
-                            num_envs=num_envs,
-                            steps_per_env=steps_per_env,
-                            rollout_ms=ema_rollout_ms,
-                        )
-                        if collector_active_steps_per_sec is not None:
-                            msg["collector_active_steps_per_sec"] = collector_active_steps_per_sec
-                        if ep_reward_components:
-                            msg["reward_components"] = {
-                                k: statistics.mean(v) for k, v in ep_reward_components.items() if v
-                            }
-                            ep_reward_components.clear()
-                        put_latest_metrics(metrics_queue, msg, worker_name="HoraAPPOWorker")
-                    except Exception as e:
-                        print(
-                            f"[HoraAPPOWorker] metrics build error: {type(e).__name__}: {e}",
-                            file=sys.stderr,
-                        )
-
-                obs_np = next_actor_obs_np
-                critic_np = next_critic_np
-                priv_info_np = next_priv_info_np
-
-            write_buf["last_obs"][:] = obs_np
-            if critic_np is not None:
-                write_buf["last_critic"][:] = critic_np
-            ring_buffer.signal_write_done()
-            ema_rollout_ms = (1 - _EMA) * ema_rollout_ms + _EMA * (
-                (time.perf_counter() - t_rollout_start) * 1000
-            )
-
-    except Exception as e:
-        import traceback
-
-        print(f"\n[HORA APPO WORKER CRASH]: {e}\n", file=sys.stderr)
-        traceback.print_exc(file=sys.stderr)
-        if metrics_queue is not None:
-            try:
-                metrics_queue.put_nowait({"error": str(e)})
-            except Exception:
-                pass
-        stop_event.set()
-        raise
-
-    ring_buffer.close()
-    actor_weight_sync.close()
-    critic_weight_sync.close()
-    env.close()
diff --git a/src/unilab/algos/torch/hora/distill.py b/src/unilab/algos/torch/hora/distill.py
deleted file mode 100644
index d98bd93ea..000000000
--- a/src/unilab/algos/torch/hora/distill.py
+++ /dev/null
@@ -1,609 +0,0 @@
-from __future__ import annotations
-
-import math
-import statistics
-import time
-from collections import deque
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Any, cast
-
-import torch
-import torch.nn as nn
-from omegaconf import DictConfig, OmegaConf
-from tensordict import TensorDict
-
-from unilab.algos.torch.common.normalization import EmpiricalNormalization
-from unilab.algos.torch.hora.models import (
-    HoraActorModel,
-    HoraCoreOutput,
-    HoraSharedActorCritic,
-    ProprioAdaptTConv,
-)
-from unilab.algos.torch.hora.sac_models import HoraSACActor
-
-
-class HoraSACDistillShared(nn.Module):
-    """SAC-teacher-compatible HORA stage-2 shared actor."""
-
-    def __init__(
-        self,
-        obs_dim: int,
-        action_dim: int,
-        *,
-        priv_info_dim: int,
-        hidden_dim: int = 512,
-        priv_info_embed_dim: int = 9,
-        priv_mlp_hidden_dims: list[int] | tuple[int, ...] = (256, 128, 9),
-        use_layer_norm: bool = True,
-        proprio_hist_len: int = 30,
-        proprio_frame_dim: int | None = None,
-        device: torch.device | str = "cpu",
-    ) -> None:
-        super().__init__()
-        teacher = HoraSACActor(
-            obs_dim=obs_dim,
-            priv_info_dim=priv_info_dim,
-            action_dim=action_dim,
-            hidden_dim=hidden_dim,
-            priv_info_embed_dim=priv_info_embed_dim,
-            priv_mlp_hidden_dims=priv_mlp_hidden_dims,
-            use_layer_norm=use_layer_norm,
-            device=device,
-        )
-        self.obs_dim = int(obs_dim)
-        self.action_dim = int(action_dim)
-        self.priv_info_dim = int(priv_info_dim)
-        self.priv_info_embed_dim = int(priv_info_embed_dim)
-        self.proprio_hist_len = int(proprio_hist_len)
-        self.proprio_frame_dim = (
-            int(proprio_frame_dim) if proprio_frame_dim is not None else self.obs_dim // 3
-        )
-        self.obs_normalizer = nn.Identity()
-        self.priv_encoder = teacher.priv_encoder
-        self.priv_projection = teacher.priv_projection
-        self.actor_trunk = teacher.actor_trunk
-        self.action_mean_head = teacher.action_mean_head
-        self.adapt_tconv = ProprioAdaptTConv(self.proprio_frame_dim, self.priv_info_embed_dim)
-
-    def load_teacher_actor_state_dict(self, actor_state: dict[str, torch.Tensor]) -> None:
-        own_state = self.state_dict()
-        teacher_state = {
-            key: value
-            for key, value in actor_state.items()
-            if key in own_state and not key.startswith("adapt_tconv.")
-        }
-        missing = sorted(
-            key
-            for key in own_state
-            if not key.startswith("adapt_tconv.") and key not in teacher_state
-        )
-        if missing:
-            raise ValueError(f"HORA-SAC teacher checkpoint is missing actor keys: {missing}")
-        self.load_state_dict(teacher_state, strict=False)
-
-    def encode_privileged_info(self, priv_info: torch.Tensor) -> torch.Tensor:
-        return torch.tanh(self.priv_projection(self.priv_encoder(priv_info)))
-
-    def encode_proprio_history(self, proprio_hist: torch.Tensor) -> torch.Tensor:
-        return torch.tanh(self.adapt_tconv(proprio_hist))
-
-    def _zero_privileged_latent(
-        self,
-        batch_size: int,
-        device: torch.device,
-        dtype: torch.dtype,
-    ) -> torch.Tensor:
-        return torch.zeros((batch_size, self.priv_info_embed_dim), device=device, dtype=dtype)
-
-    def policy_mean(
-        self,
-        obs: TensorDict,
-        *,
-        prefer_student: bool,
-        require_privileged_target: bool = True,
-    ) -> tuple[torch.Tensor, HoraCoreOutput]:
-        actor_obs = obs["actor"]
-        priv_info = obs.get("priv_info")
-        if priv_info is None:
-            if require_privileged_target or not prefer_student:
-                raise ValueError("priv_info is required for HORA-SAC distillation")
-            privileged_target = self._zero_privileged_latent(
-                actor_obs.shape[0],
-                actor_obs.device,
-                actor_obs.dtype,
-            )
-        else:
-            privileged_target = self.encode_privileged_info(priv_info)
-
-        if prefer_student:
-            proprio_hist = obs.get("proprio_hist")
-            if proprio_hist is None:
-                raise ValueError("proprio_hist is required for HORA-SAC student inference")
-            privileged_latent = self.encode_proprio_history(proprio_hist)
-        else:
-            privileged_latent = privileged_target
-
-        trunk_latent = self.actor_trunk(torch.cat([actor_obs, privileged_latent], dim=-1))
-        mean = self.action_mean_head(trunk_latent)
-        return (
-            mean,
-            HoraCoreOutput(
-                policy_obs=actor_obs,
-                trunk_latent=trunk_latent,
-                privileged_latent=privileged_latent,
-                privileged_target=privileged_target,
-            ),
-        )
-
-
-class HoraSACDistillActor(nn.Module):
-    """Stage-2 actor wrapper for HORA-SAC teachers."""
-
-    is_recurrent: bool = False
-
-    def __init__(self, shared: HoraSACDistillShared) -> None:
-        super().__init__()
-        self.shared = shared
-        self.prefer_student = True
-
-    def forward(
-        self,
-        obs: TensorDict,
-        masks: torch.Tensor | None = None,
-        hidden_state=None,
-        stochastic_output: bool = False,
-    ) -> torch.Tensor:
-        del masks, hidden_state, stochastic_output
-        mean, _ = self.shared.policy_mean(
-            obs,
-            prefer_student=self.prefer_student,
-            require_privileged_target=False,
-        )
-        return torch.tanh(mean)
-
-    def load_sac_teacher_actor_state_dict(self, actor_state: dict[str, torch.Tensor]) -> None:
-        self.shared.load_teacher_actor_state_dict(actor_state)
-
-
-DistillActor = HoraActorModel | HoraSACDistillActor
-
-
-@dataclass
-class HoraDistillStats:
-    agent_steps: int = 0
-    best_reward: float = float("-inf")
-    mean_reward: float = float("nan")
-    mean_episode_length: float = float("nan")
-
-
-def build_student_actor_and_normalizer(
-    env,
-    cfg: DictConfig,
-    *,
-    device: torch.device,
-) -> tuple[DistillActor, EmpiricalNormalization]:
-    actor_obs = env.get_observations()
-    actor_dim = int(actor_obs["actor"].shape[-1])
-    priv_info_dim = int(actor_obs["priv_info"].shape[-1])
-    proprio_hist_shape = actor_obs["proprio_hist"].shape[1:]
-
-    model_cfg = OmegaConf.to_container(cfg.algo.model, resolve=True)
-    assert isinstance(model_cfg, dict)
-    if model_cfg.get("teacher_arch") == "hora_sac":
-        shared_sac = HoraSACDistillShared(
-            obs_dim=actor_dim,
-            action_dim=int(env.num_actions),
-            priv_info_dim=priv_info_dim,
-            hidden_dim=int(model_cfg.get("actor_hidden_dim", 512)),
-            priv_info_embed_dim=int(model_cfg.get("priv_info_embed_dim", priv_info_dim)),
-            priv_mlp_hidden_dims=model_cfg.get("priv_mlp_hidden_dims", [256, 128, 9]),
-            use_layer_norm=bool(model_cfg.get("use_layer_norm", True)),
-            proprio_hist_len=int(proprio_hist_shape[0]),
-            proprio_frame_dim=int(proprio_hist_shape[1]),
-            device=device,
-        ).to(device)
-        actor = cast(HoraSACDistillActor, HoraSACDistillActor(shared_sac).to(device))
-        hist_normalizer = EmpiricalNormalization(proprio_hist_shape, device=device)
-        return actor, hist_normalizer
-
-    shared = HoraSharedActorCritic(
-        obs_dim=actor_dim,
-        action_dim=int(env.num_actions),
-        priv_info_dim=priv_info_dim,
-        actor_hidden_dims=model_cfg.get("hidden_dims", [512, 256, 128]),
-        activation=model_cfg.get("activation", "elu"),
-        obs_normalization=model_cfg.get("obs_normalization", True),
-        distribution_cfg=model_cfg.get("distribution_cfg", {"init_std": 1.0, "std_type": "scalar"}),
-        priv_info_embed_dim=model_cfg.get("priv_info_embed_dim", priv_info_dim),
-        priv_mlp_hidden_dims=model_cfg.get("priv_mlp_hidden_dims", [256, 128, 8]),
-        use_student_encoder=True,
-        proprio_hist_len=int(proprio_hist_shape[0]),
-        proprio_frame_dim=int(proprio_hist_shape[1]),
-    ).to(device)
-    actor = cast(
-        HoraActorModel,
-        HoraActorModel(
-            actor_obs,
-            {"actor": ["actor"], "critic": ["actor"]},
-            "actor",
-            int(env.num_actions),
-            shared_model=shared,
-            use_student_encoder=True,
-        ).to(device),
-    )
-    hist_normalizer = EmpiricalNormalization(proprio_hist_shape, device=device)
-    return actor, hist_normalizer
-
-
-def load_teacher_actor_weights(
-    actor: nn.Module,
-    teacher_checkpoint: str | Path,
-    *,
-    teacher_algo_family: str,
-    device: torch.device,
-) -> None:
-    checkpoint = torch.load(teacher_checkpoint, map_location=device, weights_only=False)
-    if str(teacher_algo_family) == "sac":
-        actor_state = checkpoint.get("actor")
-        if actor_state is None:
-            raise ValueError(
-                "Checkpoint does not contain the expected HORA-SAC actor weights. "
-                f"checkpoint={teacher_checkpoint}"
-            )
-        load_sac = getattr(actor, "load_sac_teacher_actor_state_dict", None)
-        if load_sac is None:
-            raise ValueError("Selected distillation actor does not support HORA-SAC weights.")
-        load_sac(actor_state)
-        return
-
-    actor_state_key = {
-        "ppo": "actor_state_dict",
-        "appo": "actor",
-    }.get(str(teacher_algo_family))
-    if actor_state_key is None:
-        raise ValueError(
-            "Unsupported HORA teacher algorithm family for distillation: "
-            f"{teacher_algo_family!r}. Expected one of ['ppo', 'appo', 'sac']."
-        )
-    actor_state = checkpoint.get(actor_state_key)
-    if actor_state is None:
-        raise ValueError(
-            "Checkpoint does not contain the expected teacher actor weights. "
-            f"algo_family={teacher_algo_family!r} expected_key={actor_state_key!r} "
-            f"checkpoint={teacher_checkpoint}"
-        )
-    actor.load_state_dict(actor_state, strict=False)
-
-
-def load_distilled_checkpoint(
-    actor: nn.Module,
-    hist_normalizer: EmpiricalNormalization,
-    checkpoint_path: str | Path,
-    *,
-    device: torch.device,
-) -> dict[str, Any]:
-    checkpoint = torch.load(checkpoint_path, map_location=device, weights_only=False)
-    model_state = checkpoint.get("model_state_dict")
-    if model_state is None:
-        raise ValueError(f"Checkpoint does not contain model_state_dict: {checkpoint_path}")
-    actor.load_state_dict(model_state, strict=True)
-
-    history_normalizer = checkpoint.get("history_normalizer")
-    if history_normalizer is not None:
-        hist_normalizer.load_state_dict(history_normalizer)
-    return cast(dict[str, Any], checkpoint)
-
-
-def cfg_with_checkpoint_runtime(cfg: DictConfig, checkpoint: dict[str, Any]) -> DictConfig:
-    """Merge model-side runtime config stored in a stage-2 checkpoint.
-
-    Args:
-        cfg: Hydra-composed distillation config supplied to play mode.
-        checkpoint: Loaded stage-2 checkpoint dictionary.
-
-    Returns:
-        Config using the current owner env/reward settings and checkpoint model
-        construction fields.
-    """
-    from unilab.algos.torch.hora.distill_config import apply_teacher_defaults
-
-    cfg_with_owner_defaults = apply_teacher_defaults(cfg)
-    cfg_clone = OmegaConf.create(OmegaConf.to_container(cfg_with_owner_defaults, resolve=False))
-    runtime_cfg = checkpoint.get("distill_runtime_cfg")
-    if runtime_cfg is None:
-        return cast(DictConfig, cfg_clone)
-
-    runtime_model_cfg = OmegaConf.select(OmegaConf.create(runtime_cfg), "algo.model")
-    if runtime_model_cfg is None:
-        return cast(DictConfig, cfg_clone)
-    OmegaConf.update(
-        cfg_clone,
-        "algo.model",
-        OmegaConf.to_container(runtime_model_cfg, resolve=True),
-        merge=False,
-    )
-    return cast(DictConfig, cfg_clone)
-
-
-def student_policy(
-    actor: nn.Module,
-    hist_normalizer: EmpiricalNormalization,
-    obs: TensorDict,
-    *,
-    device: torch.device,
-) -> torch.Tensor:
-    """Run the distilled student actor on wrapped-env observations."""
-    proprio_hist = hist_normalizer(obs["proprio_hist"].to(device), update=False)
-    policy_obs = TensorDict(
-        {
-            "actor": obs["actor"].to(device),
-            "proprio_hist": proprio_hist,
-        },
-        batch_size=obs.batch_size,
-        device=device,
-    )
-    return actor(policy_obs, stochastic_output=False).clamp_(-1.0, 1.0)
-
-
-class HoraDistillationTrainer:
-    """Stage-2 HORA latent distillation trainer."""
-
-    def __init__(
-        self,
-        env,
-        cfg: DictConfig,
-        *,
-        device: str,
-        log_dir: str | Path,
-        teacher_checkpoint: str | Path,
-        teacher_algo_family: str,
-        teacher_metadata: dict[str, Any] | None = None,
-        distill_runtime_cfg: DictConfig,
-        logger,
-    ) -> None:
-        self.env = env
-        self.cfg = cfg
-        self.device = torch.device(device)
-        self.log_dir = Path(log_dir)
-        self.logger = logger
-        self.teacher_checkpoint = Path(teacher_checkpoint)
-        self.teacher_algo_family = str(teacher_algo_family)
-        self.teacher_metadata = dict(teacher_metadata or {})
-        self.distill_runtime_cfg = OmegaConf.to_container(distill_runtime_cfg, resolve=True)
-        self.actor, self.hist_normalizer = build_student_actor_and_normalizer(
-            env,
-            cfg,
-            device=self.device,
-        )
-        self.optimizer = torch.optim.Adam(
-            self._trainable_parameters(), lr=float(cfg.algo.learning_rate)
-        )
-        self.stats = HoraDistillStats()
-        self._reward_buffer: deque[float] = deque(maxlen=100)
-        self._episode_length_buffer: deque[float] = deque(maxlen=100)
-        self._step_reward = torch.zeros((env.num_envs,), dtype=torch.float32, device=self.device)
-        self._step_length = torch.zeros((env.num_envs,), dtype=torch.float32, device=self.device)
-        self._tb_writer = self._build_tensorboard_writer()
-        self._load_teacher_checkpoint()
-
-    def _trainable_parameters(self) -> list[torch.nn.Parameter]:
-        params: list[torch.nn.Parameter] = []
-        for name, param in self.actor.named_parameters():
-            requires_grad = "adapt_tconv" in name
-            param.requires_grad = requires_grad
-            if requires_grad:
-                params.append(param)
-        return params
-
-    def _load_teacher_checkpoint(self) -> None:
-        load_teacher_actor_weights(
-            self.actor,
-            self.teacher_checkpoint,
-            teacher_algo_family=self.teacher_algo_family,
-            device=self.device,
-        )
-        self.actor.train()
-        self.actor.shared.obs_normalizer.eval()
-
-    def _build_tensorboard_writer(self) -> Any | None:
-        """Create the stage-2 TensorBoard writer when the config requests it.
-
-        Args:
-            None.
-
-        Returns:
-            Summary writer rooted at ``<log_dir>/tb``, or ``None`` when scalar
-            backend logging is disabled or TensorBoard is unavailable.
-        """
-        logger_type = str(OmegaConf.select(self.cfg, "training.logger", default="tensorboard"))
-        if logger_type.lower() != "tensorboard":
-            return None
-
-        try:
-            from torch.utils.tensorboard import SummaryWriter
-        except ImportError:
-            self.logger.warning(
-                "tensorboard is not installed; disabling HORA distillation TensorBoard logging."
-            )
-            return None
-
-        tb_dir = self.log_dir / "tb"
-        tb_dir.mkdir(parents=True, exist_ok=True)
-        self.logger.info("TensorBoard: %s", tb_dir)
-        return SummaryWriter(log_dir=str(tb_dir))
-
-    def _add_scalar_if_finite(self, tag: str, value: float, *, step: int) -> None:
-        """Write a scalar only when a TensorBoard writer exists and the value is finite.
-
-        Args:
-            tag: TensorBoard metric name.
-            value: Scalar value to record.
-            step: Global step associated with the scalar.
-
-        Returns:
-            None. Invalid or disabled values are skipped silently so early NaNs
-            from unfinished episodes do not pollute the event stream.
-        """
-        if self._tb_writer is None or not math.isfinite(value):
-            return
-        self._tb_writer.add_scalar(tag, value, step)
-
-    def _log_tensorboard_step(self, *, loss: float, elapsed: float) -> None:
-        """Record the latest distillation scalars to TensorBoard.
-
-        Args:
-            loss: Latest latent-distillation loss.
-            elapsed: Wall-clock training time since the run started.
-
-        Returns:
-            None. Metrics are written at the current agent-step count.
-        """
-        if self._tb_writer is None:
-            return
-
-        step = self.stats.agent_steps
-        self._add_scalar_if_finite("train/loss", loss, step=step)
-        self._add_scalar_if_finite("reward/mean", self.stats.mean_reward, step=step)
-        self._add_scalar_if_finite("reward/best", self.stats.best_reward, step=step)
-        self._add_scalar_if_finite(
-            "episode/length",
-            self.stats.mean_episode_length,
-            step=step,
-        )
-        self._add_scalar_if_finite("perf/fps", step / max(elapsed, 1e-6), step=step)
-        self._add_scalar_if_finite("perf/training_time_sec", elapsed, step=step)
-        self._tb_writer.flush()
-
-    def _normalize_student_obs(self, obs_td) -> dict[str, torch.Tensor]:
-        actor_obs = obs_td["actor"].to(self.device)
-        proprio_hist = obs_td["proprio_hist"].to(self.device)
-        return {
-            "actor": actor_obs,
-            "priv_info": obs_td["priv_info"].to(self.device),
-            "proprio_hist": self.hist_normalizer(proprio_hist),
-        }
-
-    @staticmethod
-    def _next_interval_boundary(current_steps: int, interval_steps: int) -> int | None:
-        """Return the next positive save boundary after the current step count.
-
-        Args:
-            current_steps: Number of agent steps already completed.
-            interval_steps: Positive interval in agent steps between saves.
-
-        Returns:
-            The next interval boundary, or ``None`` when periodic saving is disabled.
-        """
-        if interval_steps <= 0:
-            return None
-        return ((current_steps // interval_steps) + 1) * interval_steps
-
-    def train(self) -> None:
-        obs_td, _ = self.env.reset()
-        max_agent_steps = int(self.cfg.algo.max_agent_steps)
-        save_interval = int(self.cfg.algo.save_interval_steps)
-        log_interval = int(self.cfg.algo.log_interval_steps)
-        next_log_steps = self._next_interval_boundary(self.stats.agent_steps, log_interval)
-        next_save_steps = self._next_interval_boundary(self.stats.agent_steps, save_interval)
-        start_time = time.time()
-        last_loss = float("nan")
-
-        try:
-            while self.stats.agent_steps < max_agent_steps:
-                norm_obs = self._normalize_student_obs(obs_td)
-                obs_batch = {
-                    key: value.detach() if key == "actor" else value
-                    for key, value in norm_obs.items()
-                }
-                td = TensorDict(obs_batch, batch_size=obs_td.batch_size, device=self.device)
-                _, core_output = self.actor.shared.policy_mean(td, prefer_student=True)
-                loss = torch.mean(
-                    (core_output.privileged_latent - core_output.privileged_target.detach()) ** 2
-                )
-                last_loss = float(loss.item())
-
-                self.optimizer.zero_grad()
-                loss.backward()
-                self.optimizer.step()
-
-                with torch.no_grad():
-                    actions = self.actor(td, stochastic_output=False).clamp_(-1.0, 1.0)
-                obs_td, rewards, dones, infos = self.env.step(actions)
-                rewards = rewards.to(self.device)
-                dones = dones.to(self.device)
-                self.stats.agent_steps += int(self.env.num_envs)
-
-                self._step_reward += rewards
-                self._step_length += 1
-                done_idx = torch.nonzero(dones, as_tuple=False).flatten()
-                if len(done_idx) > 0:
-                    completed_rewards = self._step_reward[done_idx]
-                    completed_lengths = self._step_length[done_idx]
-                    done_mean_reward = float(torch.mean(completed_rewards).item())
-                    self._reward_buffer.extend(completed_rewards.detach().cpu().numpy().tolist())
-                    self._episode_length_buffer.extend(
-                        completed_lengths.detach().cpu().numpy().tolist()
-                    )
-                    self.stats.mean_reward = float(statistics.mean(self._reward_buffer))
-                    self.stats.mean_episode_length = float(
-                        statistics.mean(self._episode_length_buffer)
-                    )
-                    self.stats.best_reward = max(self.stats.best_reward, done_mean_reward)
-                    self._step_reward[done_idx] = 0.0
-                    self._step_length[done_idx] = 0.0
-
-                if next_log_steps is not None and self.stats.agent_steps >= next_log_steps:
-                    elapsed = max(time.time() - start_time, 1e-6)
-                    self.logger.info(
-                        "agent_steps=%d loss=%.6f mean_reward=%.4f best_reward=%.4f "
-                        "mean_episode_length=%.2f training_time=%.2fs fps=%.1f",
-                        self.stats.agent_steps,
-                        last_loss,
-                        self.stats.mean_reward,
-                        self.stats.best_reward,
-                        self.stats.mean_episode_length,
-                        elapsed,
-                        self.stats.agent_steps / elapsed,
-                    )
-                    self._log_tensorboard_step(loss=last_loss, elapsed=elapsed)
-                    next_log_steps = self._next_interval_boundary(
-                        self.stats.agent_steps, log_interval
-                    )
-
-                if next_save_steps is not None and self.stats.agent_steps >= next_save_steps:
-                    self.save(self.log_dir / f"hora_stage2_{self.stats.agent_steps}.pt")
-                    next_save_steps = self._next_interval_boundary(
-                        self.stats.agent_steps, save_interval
-                    )
-
-            self.save(self.log_dir / "hora_stage2_last.pt")
-            total_elapsed = max(time.time() - start_time, 1e-6)
-            self.logger.info(
-                "training_complete agent_steps=%d mean_reward=%.4f best_reward=%.4f "
-                "mean_episode_length=%.2f training_time=%.2fs",
-                self.stats.agent_steps,
-                self.stats.mean_reward,
-                self.stats.best_reward,
-                self.stats.mean_episode_length,
-                total_elapsed,
-            )
-            self._log_tensorboard_step(loss=last_loss, elapsed=total_elapsed)
-        finally:
-            if self._tb_writer is not None:
-                self._tb_writer.close()
-                self._tb_writer = None
-
-    def save(self, path: str | Path) -> None:
-        torch.save(
-            {
-                "model_state_dict": self.actor.state_dict(),
-                "history_normalizer": self.hist_normalizer.state_dict(),
-                "agent_steps": self.stats.agent_steps,
-                "teacher_checkpoint": str(self.teacher_checkpoint),
-                "teacher_algo_family": self.teacher_algo_family,
-                "teacher_metadata": self.teacher_metadata,
-                "distill_runtime_cfg": self.distill_runtime_cfg,
-            },
-            Path(path),
-        )
diff --git a/src/unilab/algos/torch/hora/distill_config.py b/src/unilab/algos/torch/hora/distill_config.py
deleted file mode 100644
index 7e7b4c9fb..000000000
--- a/src/unilab/algos/torch/hora/distill_config.py
+++ /dev/null
@@ -1,255 +0,0 @@
-"""HORA distillation config and teacher-owner resolution helpers."""
-
-from __future__ import annotations
-
-import re
-from pathlib import Path
-from typing import Any, cast
-
-from hydra import compose, initialize_config_dir
-from hydra.core.global_hydra import GlobalHydra
-from omegaconf import DictConfig, OmegaConf
-
-from unilab.training.run import resolve_task_checkpoint_path
-
-_REPO_ROOT = Path(__file__).resolve().parents[5]
-
-# Teacher owner configs are Hydra-composed from their family config tree.
-# SAC teachers live in the shared offpolicy tree behind the `algo` group.
-_TEACHER_TREE_BY_FAMILY = {"sac": "offpolicy"}
-
-# Teacher -> student `algo.model` mappings expressed in YAML; see
-# conf/hora_distill/student_model/*.yaml. The mapping files interpolate against
-# the composed teacher owner config mounted at `teacher_owner`.
-_STUDENT_MODEL_MAPPING_DIR = Path("conf") / "hora_distill" / "student_model"
-
-
-def _root(root_dir: str | Path | None) -> Path:
-    return Path(root_dir) if root_dir is not None else _REPO_ROOT
-
-
-def _load_yaml_config(path: Path) -> DictConfig:
-    loaded = OmegaConf.load(path)
-    if not isinstance(loaded, DictConfig):
-        raise TypeError(f"Expected DictConfig from {path}, got {type(loaded)!r}")
-    return loaded
-
-
-def _sanitize_path_token(value: str, *, fallback: str) -> str:
-    sanitized = re.sub(r"[^A-Za-z0-9._-]+", "-", str(value)).strip("-._")
-    return sanitized or fallback
-
-
-def load_teacher_owner_config(
-    algo_family: str,
-    task: str,
-    *,
-    root_dir: str | Path | None = None,
-) -> DictConfig:
-    """Compose a HORA teacher owner config with standard Hydra semantics.
-
-    Uses ``initialize_config_dir`` + ``compose`` (same pattern as
-    ``scripts/audit_sim2sim_contracts.py``), so package directives, nested
-    ``defaults`` lists, and interpolations in the teacher tree all resolve.
-    """
-    root = _root(root_dir)
-    algo_family = str(algo_family)
-    conf_dir = root / "conf" / _TEACHER_TREE_BY_FAMILY.get(algo_family, algo_family)
-    overrides = [f"task={task}"]
-    if algo_family == "sac":
-        overrides.insert(0, "algo=sac")
-    GlobalHydra.instance().clear()
-    with initialize_config_dir(config_dir=str(conf_dir.absolute()), version_base="1.3"):
-        return compose("config", overrides=overrides)
-
-
-def get_teacher_owner_spec(cfg: DictConfig) -> tuple[str | None, str | None]:
-    """Resolve the teacher algo family and task owner from distillation config."""
-    algo_family = OmegaConf.select(cfg, "teacher.algo_family")
-    task = OmegaConf.select(cfg, "teacher.task")
-    if algo_family in (None, "") or task in (None, ""):
-        return None, None
-    return str(algo_family), str(task)
-
-
-def _teacher_contract_mapping_name(
-    teacher_algo_family: str,
-    teacher_task: str,
-    teacher_cfg: DictConfig,
-) -> str:
-    """Validate the teacher owner contract and select the YAML mapping name."""
-    if teacher_algo_family == "sac":
-        runtime_impl = OmegaConf.select(teacher_cfg, "algo.runtime_impl")
-        if runtime_impl != "hora_sac":
-            raise ValueError(
-                "HORA distillation SAC teacher owner must select runtime_impl='hora_sac'. "
-                f"Got task={teacher_task} runtime_impl={runtime_impl!r}."
-            )
-        return "hora_sac"
-
-    actor_class_name = str(OmegaConf.select(teacher_cfg, "algo.actor.class_name") or "")
-    if "HoraActorModel" not in actor_class_name:
-        raise ValueError(
-            "HORA distillation teacher owner must resolve to HoraActorModel. "
-            f"Got algo_family={teacher_algo_family} task={teacher_task} "
-            f"actor.class_name={actor_class_name!r}."
-        )
-    return "hora_actor"
-
-
-def _student_model_defaults(
-    mapping_name: str,
-    teacher_cfg: DictConfig,
-    *,
-    root: Path,
-) -> dict[str, Any]:
-    """Resolve the YAML-expressed teacher -> student model mapping.
-
-    The mapping YAML is merged next to the composed teacher owner config
-    (mounted at ``teacher_owner``) so its interpolations resolve against the
-    Hydra-composed teacher hyperparameters. The mapping file owns all fallback
-    defaults; this function only mounts and resolves it.
-    """
-    mapping_path = root / _STUDENT_MODEL_MAPPING_DIR / f"{mapping_name}.yaml"
-    merged = OmegaConf.merge(
-        {"teacher_owner": teacher_cfg},
-        _load_yaml_config(mapping_path),
-    )
-    model_cfg = OmegaConf.to_container(OmegaConf.select(merged, "model"), resolve=True)
-    if not isinstance(model_cfg, dict):
-        raise TypeError(
-            f"Expected mapping 'model' dict from {mapping_path}, got {type(model_cfg)!r}"
-        )
-    model_cfg = cast(dict[str, Any], model_cfg)
-    distribution_cfg = model_cfg.get("distribution_cfg")
-    if isinstance(distribution_cfg, dict):
-        # The student re-binds its own distribution class; only the teacher's
-        # distribution hyperparameters carry over, not its class binding.
-        distribution_cfg.pop("class_name", None)
-    return model_cfg
-
-
-def teacher_default_cfg(
-    cfg: DictConfig,
-    *,
-    root_dir: str | Path | None = None,
-) -> DictConfig:
-    """Build HORA student defaults from the selected teacher owner YAML."""
-    teacher_algo_family, teacher_task = get_teacher_owner_spec(cfg)
-    if teacher_algo_family is None or teacher_task is None:
-        return OmegaConf.create()
-
-    root = _root(root_dir)
-    teacher_cfg = load_teacher_owner_config(
-        teacher_algo_family,
-        teacher_task,
-        root_dir=root,
-    )
-    mapping_name = _teacher_contract_mapping_name(
-        teacher_algo_family,
-        teacher_task,
-        teacher_cfg,
-    )
-    model_cfg = _student_model_defaults(mapping_name, teacher_cfg, root=root)
-    return OmegaConf.create(
-        {
-            "training": OmegaConf.select(teacher_cfg, "training"),
-            "reward": OmegaConf.select(teacher_cfg, "reward"),
-            "env": OmegaConf.select(teacher_cfg, "env"),
-            "algo": {"model": model_cfg},
-        }
-    )
-
-
-def apply_teacher_defaults(
-    cfg: DictConfig,
-    *,
-    root_dir: str | Path | None = None,
-) -> DictConfig:
-    """Merge teacher-owner defaults under the user distillation config."""
-    return cast(DictConfig, OmegaConf.merge(teacher_default_cfg(cfg, root_dir=root_dir), cfg))
-
-
-def resolved_distill_runtime_cfg(cfg: DictConfig) -> DictConfig:
-    """Return checkpoint runtime fields needed to rebuild the student model.
-
-    Stage-2 checkpoints intentionally do not persist owner runtime settings such
-    as env, reward, or domain randomization. Replay should use the currently
-    composed owner config for those fields.
-    """
-    model_cfg = OmegaConf.select(cfg, "algo.model")
-    return OmegaConf.create(
-        {
-            "algo": {
-                "model": (
-                    OmegaConf.to_container(model_cfg, resolve=True) if model_cfg is not None else {}
-                )
-            },
-        }
-    )
-
-
-def teacher_run_metadata(
-    cfg: DictConfig,
-    *,
-    teacher_algo_family: str,
-    teacher_checkpoint: Path,
-    root_dir: str | Path | None = None,
-) -> dict[str, Any]:
-    """Build explicit teacher provenance metadata for distillation outputs."""
-    teacher_task = OmegaConf.select(cfg, "teacher.task")
-    root = _root(root_dir).resolve()
-    checkpoint_path = teacher_checkpoint.resolve()
-    try:
-        checkpoint_display = str(checkpoint_path.relative_to(root))
-    except ValueError:
-        checkpoint_display = str(checkpoint_path)
-
-    checkpoint_name = checkpoint_path.name
-    return {
-        "algo_family": str(teacher_algo_family),
-        "task": None if teacher_task in (None, "") else str(teacher_task),
-        "checkpoint_path": checkpoint_display,
-        "checkpoint_name": checkpoint_name,
-        "checkpoint_stem": checkpoint_path.stem,
-        "run_name": checkpoint_path.parent.name,
-        "run_slug": f"teacher-{_sanitize_path_token(teacher_algo_family, fallback='teacher')}",
-    }
-
-
-def resolve_teacher_checkpoint_path(
-    cfg: DictConfig,
-    *,
-    root_dir: str | Path | None = None,
-) -> tuple[Path | None, Path | None]:
-    """Resolve the selected HORA teacher checkpoint through owner metadata."""
-    teacher_algo_family, teacher_task = get_teacher_owner_spec(cfg)
-    if teacher_algo_family is None or teacher_task is None:
-        return None, None
-
-    root = _root(root_dir)
-    teacher_cfg = load_teacher_owner_config(
-        teacher_algo_family,
-        teacher_task,
-        root_dir=root,
-    )
-    teacher_task_name = OmegaConf.select(teacher_cfg, "training.task_name")
-    teacher_algo_log_name = OmegaConf.select(teacher_cfg, "algo.algo_log_name")
-    if teacher_task_name in (None, "") or teacher_algo_log_name in (None, ""):
-        raise ValueError(
-            "Teacher owner config must define training.task_name and algo.algo_log_name. "
-            f"Got algo_family={teacher_algo_family} task={teacher_task}."
-        )
-
-    selected_checkpoint = OmegaConf.select(cfg, "algo.checkpoint", default=-1)
-    return resolve_task_checkpoint_path(
-        root,
-        task_name=str(teacher_task_name),
-        load_run=str(OmegaConf.select(cfg, "algo.load_run", default="-1")),
-        algo_log_name=str(teacher_algo_log_name),
-        checkpoint=(
-            str(selected_checkpoint) if selected_checkpoint not in (None, "", -1, "-1") else None
-        ),
-        suffix=".pt",
-        log_root=OmegaConf.select(cfg, "training.log_root"),
-    )
diff --git a/src/unilab/algos/torch/hora/models.py b/src/unilab/algos/torch/hora/models.py
deleted file mode 100644
index 7f290aa19..000000000
--- a/src/unilab/algos/torch/hora/models.py
+++ /dev/null
@@ -1,566 +0,0 @@
-from __future__ import annotations
-
-import copy
-from dataclasses import dataclass
-from typing import Any, cast
-
-import torch
-import torch.nn as nn
-from rsl_rl.modules import EmpiricalNormalization, GaussianDistribution
-from tensordict import TensorDict
-
-
-def _build_activation(name: str) -> nn.Module:
-    normalized = str(name).strip().lower()
-    if normalized == "elu":
-        return nn.ELU()
-    if normalized == "relu":
-        return nn.ReLU()
-    if normalized == "tanh":
-        return nn.Tanh()
-    raise ValueError(f"Unsupported activation: {name!r}")
-
-
-class _MLP(nn.Module):
-    def __init__(
-        self, input_dim: int, hidden_dims: list[int] | tuple[int, ...], activation: str
-    ) -> None:
-        super().__init__()
-        layers: list[nn.Module] = []
-        current_dim = input_dim
-        for hidden_dim in hidden_dims:
-            layers.append(nn.Linear(current_dim, int(hidden_dim)))
-            layers.append(_build_activation(activation))
-            current_dim = int(hidden_dim)
-        self.net = nn.Sequential(*layers)
-        self.output_dim = current_dim
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.net(x)
-
-
-class ProprioAdaptTConv(nn.Module):
-    """Temporal adaptation encoder used by HORA stage-2 distillation."""
-
-    def __init__(self, frame_dim: int, latent_dim: int) -> None:
-        super().__init__()
-        self.channel_transform = nn.Sequential(
-            nn.Linear(frame_dim, frame_dim),
-            nn.ReLU(inplace=True),
-            nn.Linear(frame_dim, frame_dim),
-            nn.ReLU(inplace=True),
-        )
-        self.temporal_aggregation = nn.Sequential(
-            nn.Conv1d(frame_dim, frame_dim, kernel_size=9, stride=2),
-            nn.ReLU(inplace=True),
-            nn.Conv1d(frame_dim, frame_dim, kernel_size=5, stride=1),
-            nn.ReLU(inplace=True),
-            nn.Conv1d(frame_dim, frame_dim, kernel_size=5, stride=1),
-            nn.ReLU(inplace=True),
-        )
-        self.low_dim_proj = nn.Linear(frame_dim * 3, latent_dim)
-        self._init_weights()
-
-    def _init_weights(self) -> None:
-        for module in self.modules():
-            if isinstance(module, nn.Conv1d):
-                fan_out = module.kernel_size[0] * module.out_channels
-                module.weight.data.normal_(mean=0.0, std=(2.0 / fan_out) ** 0.5)
-                if module.bias is not None:
-                    nn.init.zeros_(module.bias)
-            if isinstance(module, nn.Linear) and module.bias is not None:
-                nn.init.zeros_(module.bias)
-
-    def forward(self, proprio_hist: torch.Tensor) -> torch.Tensor:
-        x = self.channel_transform(proprio_hist)
-        x = x.permute(0, 2, 1)
-        x = self.temporal_aggregation(x)
-        return self.low_dim_proj(x.flatten(1))
-
-
-@dataclass
-class HoraCoreOutput:
-    policy_obs: torch.Tensor
-    trunk_latent: torch.Tensor
-    privileged_latent: torch.Tensor
-    privileged_target: torch.Tensor
-
-
-class HoraSharedActorCritic(nn.Module):
-    """Shared-backbone HORA actor-critic with optional adaptation encoder."""
-
-    def __init__(
-        self,
-        obs_dim: int,
-        action_dim: int,
-        *,
-        priv_info_dim: int,
-        priv_info_embed_dim: int = 8,
-        actor_hidden_dims: list[int] | tuple[int, ...] = (512, 256, 128),
-        priv_mlp_hidden_dims: list[int] | tuple[int, ...] = (256, 128, 8),
-        activation: str = "elu",
-        obs_normalization: bool = False,
-        distribution_cfg: dict[str, Any] | None = None,
-        use_student_encoder: bool = False,
-        proprio_hist_len: int = 30,
-        proprio_frame_dim: int | None = None,
-    ) -> None:
-        super().__init__()
-        self.obs_dim = int(obs_dim)
-        self.action_dim = int(action_dim)
-        self.priv_info_dim = int(priv_info_dim)
-        self.priv_info_embed_dim = int(priv_info_embed_dim)
-        self.use_student_encoder = bool(use_student_encoder)
-        self.proprio_hist_len = int(proprio_hist_len)
-        self.proprio_frame_dim = (
-            int(proprio_frame_dim) if proprio_frame_dim is not None else self.obs_dim // 3
-        )
-
-        self.obs_normalizer = (
-            EmpiricalNormalization(self.obs_dim) if obs_normalization else nn.Identity()
-        )
-        self.priv_encoder = _MLP(self.priv_info_dim, list(priv_mlp_hidden_dims), activation)
-        self.trunk = _MLP(
-            self.obs_dim + self.priv_info_embed_dim, list(actor_hidden_dims), activation
-        )
-        self.value_head = nn.Linear(self.trunk.output_dim, 1)
-        self.mu_head = nn.Linear(self.trunk.output_dim, self.action_dim)
-        self.distribution = GaussianDistribution(
-            self.action_dim,
-            **(
-                {
-                    key: value
-                    for key, value in (
-                        distribution_cfg
-                        if distribution_cfg is not None
-                        else {"init_std": 1.0, "std_type": "scalar"}
-                    ).items()
-                    if key != "class_name"
-                }
-            ),
-        )
-        self.adapt_tconv = (
-            ProprioAdaptTConv(self.proprio_frame_dim, self.priv_info_embed_dim)
-            if self.use_student_encoder
-            else None
-        )
-        self._init_linear_biases()
-
-    def _init_linear_biases(self) -> None:
-        for module in self.modules():
-            if isinstance(module, nn.Linear) and module.bias is not None:
-                nn.init.zeros_(module.bias)
-
-    def _normalize_actor_obs(self, actor_obs: torch.Tensor) -> torch.Tensor:
-        return self.obs_normalizer(actor_obs)
-
-    def update_normalization(self, obs: TensorDict) -> None:
-        if isinstance(self.obs_normalizer, EmpiricalNormalization):
-            self.obs_normalizer.update(obs["actor"])
-
-    def _zero_privileged_latent(
-        self, batch_size: int, device: torch.device, dtype: torch.dtype
-    ) -> torch.Tensor:
-        return torch.zeros((batch_size, self.priv_info_embed_dim), device=device, dtype=dtype)
-
-    def encode_privileged_info(self, priv_info: torch.Tensor | None) -> torch.Tensor:
-        if priv_info is None:
-            raise ValueError("priv_info is required to compute the HORA teacher latent")
-        return torch.tanh(self.priv_encoder(priv_info))
-
-    def encode_proprio_history(self, proprio_hist: torch.Tensor) -> torch.Tensor:
-        if self.adapt_tconv is None:
-            raise RuntimeError("HORA adaptation encoder is not enabled")
-        return torch.tanh(self.adapt_tconv(proprio_hist))
-
-    def build_core_output(self, obs: TensorDict, *, prefer_student: bool) -> HoraCoreOutput:
-        actor_obs = obs["actor"]
-        policy_obs = self._normalize_actor_obs(actor_obs)
-        priv_info = obs.get("priv_info")
-        proprio_hist = obs.get("proprio_hist")
-
-        privileged_target = (
-            self.encode_privileged_info(priv_info)
-            if priv_info is not None
-            else self._zero_privileged_latent(actor_obs.shape[0], actor_obs.device, actor_obs.dtype)
-        )
-
-        if prefer_student and self.adapt_tconv is not None and proprio_hist is not None:
-            privileged_latent = self.encode_proprio_history(proprio_hist)
-        elif priv_info is not None:
-            privileged_latent = privileged_target
-        else:
-            privileged_latent = self._zero_privileged_latent(
-                actor_obs.shape[0], actor_obs.device, actor_obs.dtype
-            )
-
-        trunk_input = torch.cat([policy_obs, privileged_latent], dim=-1)
-        trunk_latent = self.trunk(trunk_input)
-        return HoraCoreOutput(
-            policy_obs=policy_obs,
-            trunk_latent=trunk_latent,
-            privileged_latent=privileged_latent,
-            privileged_target=privileged_target,
-        )
-
-    def trunk_latent_from_tensors(
-        self,
-        actor_obs: torch.Tensor,
-        priv_info: torch.Tensor | None,
-        *,
-        prefer_student: bool,
-        proprio_hist: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        """Tensor-only HORA trunk path used by APPO compiled minibatch loss."""
-        policy_obs = self._normalize_actor_obs(actor_obs)
-        if prefer_student and self.adapt_tconv is not None and proprio_hist is not None:
-            privileged_latent = self.encode_proprio_history(proprio_hist)
-        elif priv_info is not None:
-            privileged_latent = self.encode_privileged_info(priv_info)
-        else:
-            privileged_latent = self._zero_privileged_latent(
-                actor_obs.shape[0], actor_obs.device, actor_obs.dtype
-            )
-        return self.trunk(torch.cat([policy_obs, privileged_latent], dim=-1))
-
-    def policy_mean_from_tensors(
-        self,
-        actor_obs: torch.Tensor,
-        priv_info: torch.Tensor | None,
-        *,
-        prefer_student: bool,
-        proprio_hist: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        trunk_latent = self.trunk_latent_from_tensors(
-            actor_obs,
-            priv_info,
-            prefer_student=prefer_student,
-            proprio_hist=proprio_hist,
-        )
-        return self.mu_head(trunk_latent)
-
-    def value_from_tensors(
-        self,
-        actor_obs: torch.Tensor,
-        priv_info: torch.Tensor | None,
-        *,
-        prefer_student: bool,
-        proprio_hist: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        trunk_latent = self.trunk_latent_from_tensors(
-            actor_obs,
-            priv_info,
-            prefer_student=prefer_student,
-            proprio_hist=proprio_hist,
-        )
-        return self.value_head(trunk_latent)
-
-    def policy_mean(
-        self, obs: TensorDict, *, prefer_student: bool
-    ) -> tuple[torch.Tensor, HoraCoreOutput]:
-        core_output = self.build_core_output(obs, prefer_student=prefer_student)
-        return self.mu_head(core_output.trunk_latent), core_output
-
-    def value(
-        self, obs: TensorDict, *, prefer_student: bool
-    ) -> tuple[torch.Tensor, HoraCoreOutput]:
-        core_output = self.build_core_output(obs, prefer_student=prefer_student)
-        return self.value_head(core_output.trunk_latent), core_output
-
-
-def build_hora_shared_actor_critic(
-    *,
-    obs_dim: int,
-    action_dim: int,
-    priv_info_dim: int,
-    actor_cfg: dict[str, Any] | None = None,
-    critic_cfg: dict[str, Any] | None = None,
-) -> HoraSharedActorCritic:
-    """Build the shared HORA core from actor/critic config without mutating inputs."""
-    shared_cfg = copy.deepcopy(actor_cfg or {})
-    critic_shared_cfg = copy.deepcopy(critic_cfg or {})
-    for cfg in (shared_cfg, critic_shared_cfg):
-        cfg.pop("class_name", None)
-        cfg.pop("num_actions", None)
-    critic_shared_cfg.pop("distribution_cfg", None)
-
-    activation = shared_cfg.pop("activation", None)
-    if activation is None:
-        activation = critic_shared_cfg.pop("activation", "elu")
-    else:
-        critic_shared_cfg.pop("activation", None)
-
-    obs_normalization = shared_cfg.pop("obs_normalization", None)
-    if obs_normalization is None:
-        obs_normalization = critic_shared_cfg.pop("obs_normalization", False)
-    else:
-        critic_shared_cfg.pop("obs_normalization", None)
-
-    priv_info_embed_dim = shared_cfg.pop("priv_info_embed_dim", None)
-    if priv_info_embed_dim is None:
-        priv_info_embed_dim = critic_shared_cfg.pop("priv_info_embed_dim", priv_info_dim)
-    else:
-        critic_shared_cfg.pop("priv_info_embed_dim", None)
-
-    priv_mlp_hidden_dims = shared_cfg.pop("priv_mlp_hidden_dims", None)
-    if priv_mlp_hidden_dims is None:
-        priv_mlp_hidden_dims = critic_shared_cfg.pop("priv_mlp_hidden_dims", (256, 128, 8))
-    else:
-        critic_shared_cfg.pop("priv_mlp_hidden_dims", None)
-
-    return HoraSharedActorCritic(
-        obs_dim=obs_dim,
-        action_dim=action_dim,
-        priv_info_dim=priv_info_dim,
-        actor_hidden_dims=shared_cfg.pop("hidden_dims", (512, 256, 128)),
-        activation=str(activation),
-        obs_normalization=bool(obs_normalization),
-        distribution_cfg=shared_cfg.pop("distribution_cfg", None),
-        priv_info_embed_dim=int(priv_info_embed_dim),
-        priv_mlp_hidden_dims=priv_mlp_hidden_dims,
-        use_student_encoder=bool(shared_cfg.pop("use_student_encoder", False)),
-        proprio_hist_len=int(shared_cfg.pop("proprio_hist_len", 30)),
-        proprio_frame_dim=shared_cfg.pop("proprio_frame_dim", None),
-    )
-
-
-class _HoraInferenceModule(nn.Module):
-    input_names = ["actor", "priv_info", "proprio_hist"]
-    output_names = ["actions"]
-
-    def __init__(
-        self,
-        *,
-        obs_normalizer: nn.Module,
-        priv_encoder: nn.Module,
-        trunk: nn.Module,
-        mu_head: nn.Module,
-        obs_dim: int,
-        priv_info_dim: int,
-        proprio_hist_len: int,
-        proprio_frame_dim: int,
-        verbose: bool = False,
-        adapt_tconv: nn.Module | None = None,
-        prefer_student: bool = False,
-    ) -> None:
-        super().__init__()
-        self.obs_normalizer = obs_normalizer
-        self.priv_encoder = priv_encoder
-        self.trunk = trunk
-        self.mu_head = mu_head
-        self.adapt_tconv = adapt_tconv
-        self.prefer_student = bool(prefer_student)
-        self.obs_dim = int(obs_dim)
-        self.priv_info_dim = int(priv_info_dim)
-        self.proprio_hist_len = int(proprio_hist_len)
-        self.proprio_frame_dim = int(proprio_frame_dim)
-        self.verbose = bool(verbose)
-
-    def forward(
-        self, actor: torch.Tensor, priv_info: torch.Tensor, proprio_hist: torch.Tensor
-    ) -> torch.Tensor:
-        policy_obs = self.obs_normalizer(actor)
-        if self.prefer_student:
-            if self.adapt_tconv is None:
-                raise RuntimeError("HORA adaptation encoder export requires adapt_tconv")
-            privileged_latent = torch.tanh(self.adapt_tconv(proprio_hist))
-        else:
-            privileged_latent = torch.tanh(self.priv_encoder(priv_info))
-        trunk_input = torch.cat([policy_obs, privileged_latent], dim=-1)
-        return self.mu_head(self.trunk(trunk_input))
-
-    def get_dummy_inputs(self) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        return (
-            torch.zeros(1, self.obs_dim),
-            torch.zeros(1, self.priv_info_dim),
-            torch.zeros(1, self.proprio_hist_len, self.proprio_frame_dim),
-        )
-
-
-class HoraActorModel(nn.Module):
-    is_recurrent: bool = False
-
-    def __init__(
-        self,
-        obs: TensorDict,
-        obs_groups: dict[str, list[str]],
-        obs_set: str,
-        output_dim: int,
-        *,
-        shared_model: HoraSharedActorCritic | None = None,
-        hidden_dims: list[int] | tuple[int, ...] = (512, 256, 128),
-        activation: str = "elu",
-        obs_normalization: bool = False,
-        distribution_cfg: dict[str, Any] | None = None,
-        priv_info_dim: int | None = None,
-        priv_info_embed_dim: int = 8,
-        priv_mlp_hidden_dims: list[int] | tuple[int, ...] = (256, 128, 8),
-        use_student_encoder: bool = False,
-        proprio_hist_len: int = 30,
-        proprio_frame_dim: int | None = None,
-    ) -> None:
-        del obs_groups, obs_set
-        super().__init__()
-        if shared_model is None:
-            shared_model = HoraSharedActorCritic(
-                obs_dim=int(obs["actor"].shape[-1]),
-                action_dim=output_dim,
-                priv_info_dim=int(
-                    priv_info_dim if priv_info_dim is not None else obs.get("priv_info").shape[-1]
-                ),
-                priv_info_embed_dim=priv_info_embed_dim,
-                actor_hidden_dims=hidden_dims,
-                priv_mlp_hidden_dims=priv_mlp_hidden_dims,
-                activation=activation,
-                obs_normalization=obs_normalization,
-                distribution_cfg=distribution_cfg,
-                use_student_encoder=use_student_encoder,
-                proprio_hist_len=proprio_hist_len,
-                proprio_frame_dim=proprio_frame_dim
-                if proprio_frame_dim is not None
-                else (int(obs["proprio_hist"].shape[-1]) if "proprio_hist" in obs else None),
-            )
-        self.shared = shared_model
-        self.prefer_student = bool(use_student_encoder)
-
-    def forward(
-        self,
-        obs: TensorDict,
-        masks: torch.Tensor | None = None,
-        hidden_state=None,
-        stochastic_output: bool = False,
-    ) -> torch.Tensor:
-        del masks, hidden_state
-        mean, _ = self.shared.policy_mean(obs, prefer_student=self.prefer_student)
-        self.shared.distribution.update(mean)
-        if stochastic_output:
-            return self.shared.distribution.sample()
-        return self.shared.distribution.deterministic_output(mean)
-
-    def reset(self, dones: torch.Tensor | None = None, hidden_state=None) -> None:
-        del dones, hidden_state
-
-    def get_hidden_state(self):
-        return None
-
-    def detach_hidden_state(self, dones: torch.Tensor | None = None) -> None:
-        del dones
-
-    @property
-    def distribution(self) -> GaussianDistribution:
-        return self.shared.distribution
-
-    @property
-    def output_mean(self) -> torch.Tensor:
-        return self.shared.distribution.mean
-
-    @property
-    def output_std(self) -> torch.Tensor:
-        return self.shared.distribution.std
-
-    @property
-    def output_entropy(self) -> torch.Tensor:
-        return self.shared.distribution.entropy
-
-    @property
-    def output_distribution_params(self) -> tuple[torch.Tensor, ...]:
-        return cast(tuple[torch.Tensor, ...], self.shared.distribution.params)
-
-    def get_output_log_prob(self, outputs: torch.Tensor) -> torch.Tensor:
-        return self.shared.distribution.log_prob(outputs)
-
-    def get_kl_divergence(
-        self, old_params: tuple[torch.Tensor, ...], new_params: tuple[torch.Tensor, ...]
-    ) -> torch.Tensor:
-        return self.shared.distribution.kl_divergence(old_params, new_params)
-
-    def update_normalization(self, obs: TensorDict) -> None:
-        self.shared.update_normalization(obs)
-
-    def as_jit(self) -> nn.Module:
-        return _HoraInferenceModule(
-            obs_normalizer=copy.deepcopy(self.shared.obs_normalizer),
-            priv_encoder=copy.deepcopy(self.shared.priv_encoder),
-            trunk=copy.deepcopy(self.shared.trunk),
-            mu_head=copy.deepcopy(self.shared.mu_head),
-            obs_dim=self.shared.obs_dim,
-            priv_info_dim=self.shared.priv_info_dim,
-            proprio_hist_len=self.shared.proprio_hist_len,
-            proprio_frame_dim=self.shared.proprio_frame_dim,
-            adapt_tconv=copy.deepcopy(self.shared.adapt_tconv),
-            prefer_student=self.prefer_student,
-        )
-
-    def as_onnx(self, verbose: bool) -> nn.Module:
-        return _HoraInferenceModule(
-            obs_normalizer=copy.deepcopy(self.shared.obs_normalizer),
-            priv_encoder=copy.deepcopy(self.shared.priv_encoder),
-            trunk=copy.deepcopy(self.shared.trunk),
-            mu_head=copy.deepcopy(self.shared.mu_head),
-            obs_dim=self.shared.obs_dim,
-            priv_info_dim=self.shared.priv_info_dim,
-            proprio_hist_len=self.shared.proprio_hist_len,
-            proprio_frame_dim=self.shared.proprio_frame_dim,
-            verbose=verbose,
-            adapt_tconv=copy.deepcopy(self.shared.adapt_tconv),
-            prefer_student=self.prefer_student,
-        )
-
-
-class HoraCriticModel(nn.Module):
-    is_recurrent: bool = False
-
-    def __init__(
-        self,
-        obs: TensorDict,
-        obs_groups: dict[str, list[str]],
-        obs_set: str,
-        output_dim: int,
-        *,
-        shared_model: HoraSharedActorCritic | None = None,
-        hidden_dims: list[int] | tuple[int, ...] = (512, 256, 128),
-        activation: str = "elu",
-        obs_normalization: bool = False,
-        priv_info_dim: int | None = None,
-        priv_info_embed_dim: int = 8,
-        priv_mlp_hidden_dims: list[int] | tuple[int, ...] = (256, 128, 8),
-    ) -> None:
-        del obs_groups, obs_set, output_dim
-        super().__init__()
-        if shared_model is None:
-            shared_model = HoraSharedActorCritic(
-                obs_dim=int(obs["actor"].shape[-1]),
-                action_dim=1,
-                priv_info_dim=int(
-                    priv_info_dim if priv_info_dim is not None else obs.get("priv_info").shape[-1]
-                ),
-                priv_info_embed_dim=priv_info_embed_dim,
-                actor_hidden_dims=hidden_dims,
-                priv_mlp_hidden_dims=priv_mlp_hidden_dims,
-                activation=activation,
-                obs_normalization=obs_normalization,
-            )
-        self.shared = shared_model
-
-    def forward(
-        self,
-        obs: TensorDict,
-        masks: torch.Tensor | None = None,
-        hidden_state=None,
-        stochastic_output: bool = False,
-    ) -> torch.Tensor:
-        del masks, hidden_state, stochastic_output
-        value, _ = self.shared.value(obs, prefer_student=False)
-        return value
-
-    def reset(self, dones: torch.Tensor | None = None, hidden_state=None) -> None:
-        del dones, hidden_state
-
-    def get_hidden_state(self):
-        return None
-
-    def detach_hidden_state(self, dones: torch.Tensor | None = None) -> None:
-        del dones
-
-    def update_normalization(self, obs: TensorDict) -> None:
-        del obs
diff --git a/src/unilab/algos/torch/hora/observations.py b/src/unilab/algos/torch/hora/observations.py
deleted file mode 100644
index 5530d801a..000000000
--- a/src/unilab/algos/torch/hora/observations.py
+++ /dev/null
@@ -1,127 +0,0 @@
-"""HORA-owned observation helpers for teacher-policy runtime code."""
-
-from __future__ import annotations
-
-from typing import Any
-
-import numpy as np
-from tensordict import TensorDict
-
-from unilab.utils.tensor import to_torch
-
-
-def split_hora_obs_with_priv_info(
-    obs: dict[str, np.ndarray],
-    info: dict[str, Any] | None = None,
-) -> tuple[np.ndarray, np.ndarray | None, np.ndarray | None]:
-    """Split HORA env outputs into actor obs, critic obs, and privileged info.
-
-    Args:
-        obs: Environment observation dict following the UniLab env contract.
-        info: Optional env info dict. When present, ``info["critic_info"]`` is the
-            preferred source of HORA privileged info.
-
-    Returns:
-        Tuple ``(actor_obs, critic_obs, priv_info)``. ``priv_info`` falls back to the
-        extra tail of ``critic_obs`` when no explicit ``critic_info`` is provided.
-    """
-    actor_obs = obs["obs"]
-    critic_obs = obs.get("critic", actor_obs)
-
-    priv_info: np.ndarray | None = None
-    if isinstance(info, dict):
-        candidate = info.get("critic_info")
-        if isinstance(candidate, np.ndarray) and candidate.shape[0] == actor_obs.shape[0]:
-            priv_info = candidate
-
-    if (
-        priv_info is None
-        and critic_obs is not None
-        and critic_obs.ndim == 2
-        and actor_obs.ndim == 2
-        and critic_obs.shape[0] == actor_obs.shape[0]
-        and critic_obs.shape[1] > actor_obs.shape[1]
-    ):
-        priv_info = critic_obs[:, actor_obs.shape[1] :]
-
-    return actor_obs, critic_obs, priv_info
-
-
-def extract_hora_proprio_hist(info: dict[str, Any] | None) -> np.ndarray | None:
-    """Return HORA proprio-history payload from env info when available.
-
-    Args:
-        info: Optional env info dict produced by the HORA environment.
-
-    Returns:
-        Proprio-history array when present, otherwise ``None``.
-    """
-    if not isinstance(info, dict):
-        return None
-    proprio_hist = info.get("proprio_hist")
-    return proprio_hist if isinstance(proprio_hist, np.ndarray) else None
-
-
-def build_hora_obs_tensordict(
-    obs: dict[str, np.ndarray],
-    *,
-    info: dict[str, Any] | None,
-    device: str,
-    batch_size: int,
-    policy_obs: np.ndarray,
-) -> TensorDict:
-    """Build the HORA PPO/APPO observation TensorDict for teacher-policy runtime.
-
-    Args:
-        obs: Environment observation dict following the UniLab env contract.
-        info: Optional env info dict containing HORA privileged payloads.
-        device: Torch device string used for the returned tensors.
-        batch_size: Number of vectorized environments represented by this batch.
-        policy_obs: Policy observation array already resolved by the caller.
-
-    Returns:
-        TensorDict with generic keys plus HORA-specific ``priv_info`` and optional
-        ``proprio_hist`` when the environment provided them.
-    """
-    actor_obs_np, critic_obs_np, priv_info_np = split_hora_obs_with_priv_info(obs, info)
-    td_dict = {
-        "actor": to_torch(actor_obs_np, device),
-        "policy": to_torch(policy_obs, device),
-    }
-    if critic_obs_np is not None:
-        td_dict["critic"] = to_torch(critic_obs_np, device)
-    if priv_info_np is not None:
-        td_dict["priv_info"] = to_torch(priv_info_np, device)
-    proprio_hist = extract_hora_proprio_hist(info)
-    if proprio_hist is not None:
-        td_dict["proprio_hist"] = to_torch(proprio_hist, device)
-    return TensorDict(td_dict, batch_size=batch_size, device=device)
-
-
-def build_hora_actor_tensordict(
-    actor_obs: np.ndarray,
-    *,
-    priv_info: np.ndarray,
-    device: str,
-    batch_size: int,
-) -> TensorDict:
-    """Build the minimal HORA actor TensorDict for APPO play/inference.
-
-    Args:
-        actor_obs: Actor observation array with shape ``(batch, obs_dim)``.
-        priv_info: Privileged-info array with shape ``(batch, priv_dim)``.
-        device: Torch device string used for the returned tensors.
-        batch_size: Number of vectorized environments represented by this batch.
-
-    Returns:
-        TensorDict containing grouped HORA actor inputs required by teacher-policy
-        inference.
-    """
-    return TensorDict(
-        {
-            "actor": to_torch(actor_obs, device),
-            "priv_info": to_torch(priv_info, device),
-        },
-        batch_size=batch_size,
-        device=device,
-    )
diff --git a/src/unilab/algos/torch/hora/ppo.py b/src/unilab/algos/torch/hora/ppo.py
deleted file mode 100644
index 899de6fb0..000000000
--- a/src/unilab/algos/torch/hora/ppo.py
+++ /dev/null
@@ -1,235 +0,0 @@
-from __future__ import annotations
-
-import logging
-from collections.abc import Callable
-from itertools import chain
-from typing import Any, cast
-
-import torch
-import torch.optim as optim
-from rsl_rl.algorithms.ppo import PPO
-from rsl_rl.env import VecEnv
-from rsl_rl.extensions import resolve_rnd_config, resolve_symmetry_config
-from rsl_rl.storage import RolloutStorage
-from rsl_rl.utils import resolve_obs_groups, resolve_optimizer
-from tensordict import TensorDict
-
-from unilab.algos.torch.hora.models import HoraActorModel, HoraCriticModel, HoraSharedActorCritic
-from unilab.algos.torch.rsl_rl_ppo import FinalObservationAwarePPO
-
-logger = logging.getLogger(__name__)
-
-
-class HoraPPO(FinalObservationAwarePPO):
-    """PPO variant that constructs a shared HORA actor-critic backbone."""
-
-    def __init__(
-        self,
-        actor: HoraActorModel,
-        critic: HoraCriticModel,
-        storage: RolloutStorage,
-        num_learning_epochs: int = 5,
-        num_mini_batches: int = 4,
-        clip_param: float = 0.2,
-        gamma: float = 0.99,
-        lam: float = 0.95,
-        value_loss_coef: float = 1.0,
-        entropy_coef: float = 0.01,
-        learning_rate: float = 0.001,
-        max_grad_norm: float = 1.0,
-        optimizer: str = "adam",
-        use_clipped_value_loss: bool = True,
-        schedule: str = "adaptive",
-        desired_kl: float = 0.01,
-        normalize_advantage_per_mini_batch: bool = False,
-        device: str = "cpu",
-        rnd_cfg: dict | None = None,
-        symmetry_cfg: dict | None = None,
-        multi_gpu_cfg: dict | None = None,
-        enable_compile: bool = False,
-    ) -> None:
-        self.device = device
-        self.is_multi_gpu = multi_gpu_cfg is not None
-        if multi_gpu_cfg is not None:
-            self.gpu_global_rank = multi_gpu_cfg["global_rank"]
-            self.gpu_world_size = multi_gpu_cfg["world_size"]
-        else:
-            self.gpu_global_rank = 0
-            self.gpu_world_size = 1
-
-        if rnd_cfg:
-            rnd_lr = rnd_cfg.pop("learning_rate", 1e-3)
-            from rsl_rl.extensions import RandomNetworkDistillation
-
-            self.rnd = RandomNetworkDistillation(device=self.device, **rnd_cfg)
-            self.rnd_optimizer = optim.Adam(self.rnd.predictor.parameters(), lr=rnd_lr)
-        else:
-            self.rnd = None
-            self.rnd_optimizer = None
-
-        self.symmetry: dict[str, Any] | None
-        if symmetry_cfg is not None:
-            use_symmetry = symmetry_cfg["use_data_augmentation"] or symmetry_cfg["use_mirror_loss"]
-            if not use_symmetry:
-                logger.warning(
-                    "Symmetry not used for learning. We will use it for logging instead."
-                )
-            from rsl_rl.utils import resolve_callable
-
-            symmetry_cfg["data_augmentation_func"] = resolve_callable(
-                symmetry_cfg["data_augmentation_func"]
-            )
-            if not callable(symmetry_cfg["data_augmentation_func"]):
-                raise ValueError(
-                    "Symmetry configuration exists but the function is not callable: "
-                    f"{symmetry_cfg['data_augmentation_func']}"
-                )
-            if actor.is_recurrent or critic.is_recurrent:
-                raise ValueError("Symmetry augmentation is not supported for recurrent policies.")
-            self.symmetry = symmetry_cfg
-        else:
-            self.symmetry = None
-
-        self_ref = cast(Any, self)
-        self_ref.actor = actor.to(self.device)
-        self_ref.critic = critic.to(self.device)
-        optimizer_cls = cast(Callable[..., optim.Optimizer], resolve_optimizer(optimizer))
-        self.optimizer = optimizer_cls(
-            self._unique_trainable_parameters(),
-            lr=learning_rate,
-        )
-        self.storage = storage
-        self.transition = RolloutStorage.Transition()
-
-        self.clip_param = clip_param
-        self.num_learning_epochs = num_learning_epochs
-        self.num_mini_batches = num_mini_batches
-        self.value_loss_coef = value_loss_coef
-        self.entropy_coef = entropy_coef
-        self.gamma = gamma
-        self.lam = lam
-        self.max_grad_norm = max_grad_norm
-        self.use_clipped_value_loss = use_clipped_value_loss
-        self.desired_kl = desired_kl
-        self.schedule = schedule
-        self.learning_rate = learning_rate
-        self.normalize_advantage_per_mini_batch = normalize_advantage_per_mini_batch
-        # FinalObservationAwarePPO supports a compiled MLP fast path. HORA PPO
-        # uses grouped TensorDict models with a shared privileged trunk, so keep
-        # the regular RSL-RL update while accepting the shared PPO config field.
-        self.enable_compile = False
-
-    def _unique_trainable_parameters(self) -> list[torch.nn.Parameter]:
-        params: list[torch.nn.Parameter] = []
-        seen: set[int] = set()
-        for param in chain(self.actor.parameters(), self.critic.parameters()):
-            ident = id(param)
-            if ident in seen:
-                continue
-            seen.add(ident)
-            params.append(param)
-        return params
-
-    @staticmethod
-    def construct_algorithm(obs: TensorDict, env: VecEnv, cfg: dict, device: str) -> PPO:
-        cfg["obs_groups"] = resolve_obs_groups(obs, cfg["obs_groups"], ["actor", "critic"])
-        cfg["algorithm"] = resolve_rnd_config(cfg["algorithm"], obs, cfg["obs_groups"], env)
-        cfg["algorithm"] = resolve_symmetry_config(cfg["algorithm"], env)
-
-        actor_cfg = dict(cfg["actor"])
-        critic_cfg = dict(cfg["critic"])
-        actor_cfg.pop("class_name", None)
-        critic_cfg.pop("class_name", None)
-
-        proprio_hist_len = int(actor_cfg.pop("proprio_hist_len", obs["proprio_hist"].shape[1]))
-        proprio_frame_dim = int(actor_cfg.pop("proprio_frame_dim", obs["proprio_hist"].shape[-1]))
-
-        shared_model = HoraSharedActorCritic(
-            obs_dim=int(obs["actor"].shape[-1]),
-            action_dim=int(env.num_actions),
-            priv_info_dim=int(obs["priv_info"].shape[-1]),
-            actor_hidden_dims=actor_cfg.pop("hidden_dims", (512, 256, 128)),
-            activation=actor_cfg.pop("activation", "elu"),
-            obs_normalization=bool(actor_cfg.pop("obs_normalization", False)),
-            distribution_cfg=actor_cfg.pop("distribution_cfg", None),
-            priv_info_embed_dim=int(
-                actor_cfg.pop("priv_info_embed_dim", obs["priv_info"].shape[-1])
-            ),
-            priv_mlp_hidden_dims=actor_cfg.pop("priv_mlp_hidden_dims", (256, 128, 8)),
-            use_student_encoder=bool(actor_cfg.pop("use_student_encoder", False)),
-            proprio_hist_len=proprio_hist_len,
-            proprio_frame_dim=proprio_frame_dim,
-        ).to(device)
-
-        actor = HoraActorModel(
-            obs,
-            cfg["obs_groups"],
-            "actor",
-            env.num_actions,
-            shared_model=shared_model,
-            **actor_cfg,
-        ).to(device)
-        critic = HoraCriticModel(
-            obs,
-            cfg["obs_groups"],
-            "critic",
-            1,
-            shared_model=shared_model,
-            **critic_cfg,
-        ).to(device)
-
-        storage = RolloutStorage(
-            "rl", env.num_envs, cfg["num_steps_per_env"], obs, [env.num_actions], device
-        )
-        algorithm_cfg = dict(cfg["algorithm"])
-        algorithm_cfg.pop("class_name", None)
-        return HoraPPO(
-            actor, critic, storage, device=device, **algorithm_cfg, multi_gpu_cfg=cfg["multi_gpu"]
-        )
-
-    def process_env_step(
-        self,
-        obs: TensorDict,
-        rewards: torch.Tensor,
-        dones: torch.Tensor,
-        extras: dict[str, torch.Tensor | TensorDict],
-    ) -> None:
-        self.actor.update_normalization(obs)
-        if self.rnd:
-            self.rnd.update_normalization(obs)
-
-        self.transition.rewards = rewards.clone()
-        self.transition.dones = dones
-
-        if self.rnd:
-            self.intrinsic_rewards = self.rnd.get_intrinsic_reward(obs)
-            self.transition.rewards += self.intrinsic_rewards
-
-        timeouts = extras.get("time_outs")
-        timeout_bootstrap_obs = extras.get("time_out_bootstrap_obs")
-        if isinstance(timeouts, torch.Tensor):
-            timeout_mask = timeouts.to(self.device).float()
-            can_bootstrap = (
-                timeout_bootstrap_obs is not None
-                and isinstance(timeout_bootstrap_obs, TensorDict)
-                and "priv_info" in timeout_bootstrap_obs
-                and torch.count_nonzero(timeout_mask) > 0
-            )
-            if can_bootstrap:
-                assert isinstance(timeout_bootstrap_obs, TensorDict)
-                bootstrap_obs = timeout_bootstrap_obs.to(self.device)
-                bootstrap_values = self.critic(bootstrap_obs).detach()
-                self.transition.rewards += self.gamma * torch.squeeze(
-                    bootstrap_values * timeout_mask.unsqueeze(1), 1
-                )
-            else:
-                transition_values = self.transition.values
-                assert transition_values is not None
-                self.transition.rewards += self.gamma * torch.squeeze(
-                    transition_values * timeout_mask.unsqueeze(1), 1
-                )
-
-        self.storage.add_transition(self.transition)
-        self.transition.clear()
-        self.actor.reset(dones)
-        self.critic.reset(dones)
diff --git a/src/unilab/algos/torch/hora/rsl_rl.py b/src/unilab/algos/torch/hora/rsl_rl.py
deleted file mode 100644
index 2f301a412..000000000
--- a/src/unilab/algos/torch/hora/rsl_rl.py
+++ /dev/null
@@ -1,109 +0,0 @@
-"""HORA-owned RSL-RL wrapper helpers for teacher-policy runtime."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Any
-
-import numpy as np
-import torch
-from tensordict import TensorDict
-
-from unilab.base.final_observation import resolve_terminal_observation_contract
-from unilab.training.rsl_rl import RslRlVecEnvWrapper
-from unilab.utils.tensor import to_numpy, to_torch
-
-from .observations import build_hora_obs_tensordict
-from .runtime import is_hora_ppo_runtime
-
-
-@dataclass(frozen=True)
-class HoraRslRlPPORuntime:
-    """Resolved HORA PPO runtime consumed by the generic RSL-RL script."""
-
-    wrapper_cls: type[RslRlVecEnvWrapper]
-
-
-def resolve_hora_ppo_runtime(
-    rl_cfg: dict[str, Any],
-) -> HoraRslRlPPORuntime | None:
-    """Resolve HORA PPO entrypoints from an explicit runtime marker."""
-    if not is_hora_ppo_runtime(rl_cfg):
-        return None
-    return HoraRslRlPPORuntime(wrapper_cls=HoraRslRlVecEnvWrapper)
-
-
-class HoraRslRlVecEnvWrapper(RslRlVecEnvWrapper):
-    """RSL-RL adapter that preserves HORA teacher-policy observation payloads."""
-
-    def _obs_to_tensordict(
-        self,
-        obs: dict[str, Any],
-        info: dict[str, Any] | None = None,
-    ) -> TensorDict:
-        """Convert env outputs to a HORA-aware TensorDict.
-
-        Args:
-            obs: Environment observation dict following the UniLab env contract.
-            info: Optional env info dict containing HORA privileged payloads.
-
-        Returns:
-            TensorDict preserving generic observation keys plus HORA privileged inputs.
-        """
-        policy_obs = to_numpy(self._policy_obs(obs))
-        return build_hora_obs_tensordict(
-            obs,
-            info=info,
-            device=self.device,
-            batch_size=self.num_envs,
-            policy_obs=policy_obs,
-        )
-
-    def step(
-        self, actions: torch.Tensor | np.ndarray
-    ) -> tuple[TensorDict, torch.Tensor, torch.Tensor, dict]:
-        """Step the wrapped env while keeping HORA bootstrap payloads intact.
-
-        Args:
-            actions: Torch or numpy action batch with shape ``(num_envs, action_dim)``.
-
-        Returns:
-            Tuple ``(obs_td, rewards, dones, infos)`` matching the RSL-RL VecEnv
-            contract while preserving HORA privileged observations.
-        """
-        actions_np = to_numpy(actions)
-        state = self.env.step(actions_np)
-        rewards = to_torch(state.reward, self.device)
-        dones = self._resolve_done(state)
-
-        self.episode_returns += rewards
-        self.episode_lengths += 1
-
-        infos: dict[str, torch.Tensor | TensorDict | dict[str, Any]] = {}
-        done_idx = torch.nonzero(dones).flatten()
-        if len(done_idx) > 0:
-            infos["time_outs"] = to_torch(state.truncated, self.device).bool()
-
-            final_observation = self._resolve_final_observation(state)
-            terminal_contract = resolve_terminal_observation_contract(
-                next_obs_batch_size=self.num_envs,
-                final_observation=final_observation,
-                done=to_numpy(dones),
-                info=state.info,
-                truncated=to_numpy(infos["time_outs"]) if "time_outs" in infos else None,
-            )
-            if np.any(terminal_contract.timeout_terminal_mask) and final_observation is not None:
-                infos["time_out_bootstrap_obs"] = self._obs_to_tensordict(final_observation)
-
-            self.episode_returns[done_idx] = 0
-            self.episode_lengths[done_idx] = 0
-
-        if "log" in state.info:
-            infos["log"] = state.info["log"]
-
-        return (
-            self._obs_to_tensordict(state.obs, state.info),
-            rewards,
-            dones,
-            infos,
-        )
diff --git a/src/unilab/algos/torch/hora/rsl_rl_compat.py b/src/unilab/algos/torch/hora/rsl_rl_compat.py
deleted file mode 100644
index 0b1b356d3..000000000
--- a/src/unilab/algos/torch/hora/rsl_rl_compat.py
+++ /dev/null
@@ -1,187 +0,0 @@
-"""Compatibility helpers for HORA's supported RSL-RL config schemas.
-
-The HORA APPO code uses these helpers to normalize owner configs before
-constructing grouped actor/critic modules across supported RSL-RL releases.
-"""
-
-from __future__ import annotations
-
-import importlib.metadata
-from copy import deepcopy
-from functools import lru_cache
-from typing import Any
-
-from packaging.version import Version
-
-_OWNER_ONLY_ALGO_KEYS = {
-    "adaptive_kl_beta",
-    "adaptive_lr_decay",
-    "adaptive_lr_growth",
-    "adaptive_lr_update_interval",
-    "disable_finite_checks",
-    "enable_compile",
-    "finite_check_interval",
-    "metrics_interval",
-    "target_kl_stop",
-    "warmup_finite_check_interval",
-    "warmup_metrics_interval",
-    "warmup_strict_iters",
-}
-
-
-@lru_cache(maxsize=1)
-def get_rsl_rl_version() -> str:
-    """Resolve the installed RSL-RL package version.
-
-    Args:
-        None.
-
-    Returns:
-        Installed version string from either ``rsl-rl-lib`` or the legacy
-        ``rsl-rl`` package name.
-    """
-    try:
-        return importlib.metadata.version("rsl-rl-lib")
-    except importlib.metadata.PackageNotFoundError:
-        try:
-            return importlib.metadata.version("rsl-rl")
-        except importlib.metadata.PackageNotFoundError as exc:
-            raise ImportError(
-                "rsl_rl is not installed. Install via: pip install rsl-rl-lib"
-            ) from exc
-
-
-def is_rsl_rl_v4() -> bool:
-    """Check whether the active RSL-RL runtime is version 4 or newer.
-
-    Args:
-        None.
-
-    Returns:
-        ``True`` when the installed package version is ``>= 4.0.0``.
-    """
-    return bool(Version(get_rsl_rl_version()) >= Version("4.0.0"))
-
-
-def is_rsl_rl_v5() -> bool:
-    """Check whether the active RSL-RL runtime is version 5 or newer.
-
-    Args:
-        None.
-
-    Returns:
-        ``True`` when the installed package version is ``>= 5.0.0``.
-    """
-    return bool(Version(get_rsl_rl_version()) >= Version("5.0.0"))
-
-
-def _normalize_obs_groups_for_rsl(cfg: dict[str, Any]) -> None:
-    """Translate UniLab owner obs-group aliases into RSL-RL actor/critic groups.
-
-    Args:
-        cfg: Mutable RSL-RL config dictionary to normalize in place.
-
-    Returns:
-        None. Updates ``cfg["obs_groups"]`` directly.
-    """
-    obs_groups_raw = cfg.get("obs_groups", {})
-    obs_groups = obs_groups_raw if isinstance(obs_groups_raw, dict) else {}
-
-    if "default" in obs_groups:
-        if "actor" not in obs_groups:
-            obs_groups["actor"] = obs_groups["default"]
-        if "critic" not in obs_groups:
-            obs_groups["critic"] = obs_groups["default"]
-    else:
-        # Keep grouped-dict specs intact for owner runtimes like HORA; the legacy
-        # v3 -> v4 rename only applies to flat list-based group aliases.
-        if isinstance(obs_groups.get("actor"), list):
-            obs_groups["actor"] = ["policy"]
-        if isinstance(obs_groups.get("critic"), list):
-            obs_groups["critic"] = ["policy"]
-
-    cfg["obs_groups"] = obs_groups
-
-
-def _convert_policy_to_actor_critic(
-    cfg: dict[str, Any],
-    *,
-    distribution_class_name: str,
-) -> None:
-    """Split a legacy single ``policy`` config into ``actor`` and ``critic`` blocks.
-
-    Args:
-        cfg: Mutable RSL-RL config dictionary to normalize in place.
-        distribution_class_name: Distribution class name expected by the target
-            RSL-RL runtime.
-
-    Returns:
-        None. Updates ``cfg`` directly when a legacy ``policy`` block is present.
-    """
-    empirical_normalization = bool(cfg.pop("empirical_normalization", False))
-    cfg.pop("runner_class_name", None)
-
-    if "policy" not in cfg or "actor" in cfg or "critic" in cfg:
-        return
-
-    policy = cfg.pop("policy")
-    if not isinstance(policy, dict):
-        return
-
-    cfg["actor"] = {
-        "class_name": "MLPModel",
-        "hidden_dims": policy.get("actor_hidden_dims", [256, 256, 256]),
-        "activation": policy.get("activation", "elu"),
-        "obs_normalization": empirical_normalization,
-        "distribution_cfg": {
-            "class_name": distribution_class_name,
-            "init_std": policy.get("init_noise_std", 1.0),
-            "std_type": policy.get("noise_std_type", "scalar"),
-        },
-    }
-    cfg["critic"] = {
-        "class_name": "MLPModel",
-        "hidden_dims": policy.get("critic_hidden_dims", [256, 256, 256]),
-        "activation": policy.get("activation", "elu"),
-        "obs_normalization": empirical_normalization,
-    }
-
-
-def _normalize_algorithm_cfg(cfg: dict[str, Any]) -> None:
-    """Remove owner-only keys that current RSL-RL releases do not accept.
-
-    Args:
-        cfg: Mutable RSL-RL config dictionary to normalize in place.
-
-    Returns:
-        None. Updates ``cfg["algorithm"]`` directly when present.
-    """
-    algorithm_cfg = cfg.get("algorithm")
-    if not isinstance(algorithm_cfg, dict):
-        return
-
-    algorithm_cfg.setdefault("rnd_cfg", None)
-    for key in _OWNER_ONLY_ALGO_KEYS:
-        algorithm_cfg.pop(key, None)
-
-
-def convert_config_v3_to_v4(cfg: dict[str, Any]) -> dict[str, Any]:
-    """Convert a legacy UniLab PPO/APPO config into the RSL-RL v4 schema.
-
-    Args:
-        cfg: Resolved owner config dictionary before RSL-RL construction.
-
-    Returns:
-        Deep-copied config dictionary aligned with the RSL-RL v4 actor/critic
-        schema and obs-group naming.
-    """
-    converted = deepcopy(cfg)
-    _convert_policy_to_actor_critic(
-        converted,
-        distribution_class_name="rsl_rl.modules.distribution.GaussianDistribution",
-    )
-    _normalize_algorithm_cfg(converted)
-    _normalize_obs_groups_for_rsl(converted)
-    if "multi_gpu" not in converted:
-        converted["multi_gpu"] = None
-    return converted
diff --git a/src/unilab/algos/torch/hora/runtime.py b/src/unilab/algos/torch/hora/runtime.py
deleted file mode 100644
index ab50023bb..000000000
--- a/src/unilab/algos/torch/hora/runtime.py
+++ /dev/null
@@ -1,53 +0,0 @@
-"""Config-driven runtime selection helpers for HORA teacher-policy RL."""
-
-from __future__ import annotations
-
-from typing import Any
-
-HORA_APPO_RUNTIME_IMPL = "hora_appo"
-HORA_PPO_RUNTIME_IMPL = "hora_ppo"
-HORA_SAC_RUNTIME_IMPL = "hora_sac"
-
-
-def resolve_hora_runtime_impl(rl_cfg: dict[str, Any]) -> str | None:
-    """Return the explicit HORA runtime marker from a resolved algo config.
-
-    Args:
-        rl_cfg: Resolved algorithm config dictionary from Hydra composition.
-
-    Returns:
-        Runtime marker string when the owner YAML selected one, otherwise ``None``.
-    """
-    runtime_impl = rl_cfg.get("runtime_impl")
-    if runtime_impl in (None, ""):
-        return None
-    return str(runtime_impl)
-
-
-def is_hora_appo_runtime(rl_cfg: dict[str, Any]) -> bool:
-    """Check whether the resolved algo config selects the HORA APPO runtime.
-
-    Args:
-        rl_cfg: Resolved algorithm config dictionary from Hydra composition.
-
-    Returns:
-        ``True`` when the config explicitly selects the HORA APPO runtime.
-    """
-    return resolve_hora_runtime_impl(rl_cfg) == HORA_APPO_RUNTIME_IMPL
-
-
-def is_hora_ppo_runtime(rl_cfg: dict[str, Any]) -> bool:
-    """Check whether the resolved algo config selects the HORA PPO runtime.
-
-    Args:
-        rl_cfg: Resolved algorithm config dictionary from Hydra composition.
-
-    Returns:
-        ``True`` when the config explicitly selects the HORA PPO runtime.
-    """
-    return resolve_hora_runtime_impl(rl_cfg) == HORA_PPO_RUNTIME_IMPL
-
-
-def is_hora_sac_runtime(rl_cfg: dict[str, Any]) -> bool:
-    """Check whether the resolved algo config selects the HORA SAC runtime."""
-    return resolve_hora_runtime_impl(rl_cfg) == HORA_SAC_RUNTIME_IMPL
diff --git a/src/unilab/algos/torch/hora/sac.py b/src/unilab/algos/torch/hora/sac.py
deleted file mode 100644
index 4d0524716..000000000
--- a/src/unilab/algos/torch/hora/sac.py
+++ /dev/null
@@ -1,48 +0,0 @@
-"""HORA-owned SAC entry helpers."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Any
-
-from unilab.algos.torch.hora.runtime import HORA_SAC_RUNTIME_IMPL, is_hora_sac_runtime
-from unilab.algos.torch.hora.sac_learner import HoraSACLearner
-from unilab.algos.torch.offpolicy.runtime import OffPolicyRuntime
-
-
-@dataclass(frozen=True)
-class HoraSACRuntime(OffPolicyRuntime):
-    """Resolved HORA-SAC hooks consumed by the generic off-policy script."""
-
-    learner_cls: type[Any] | None = HoraSACLearner
-    algo_type: str | None = HORA_SAC_RUNTIME_IMPL
-    supports_symmetry: bool = False
-    actor_cfg: dict[str, Any] = field(default_factory=dict)
-
-    def build_model_kwargs(self, *, obs_dim: int, critic_obs_dim: int) -> dict[str, Any]:
-        """Build HORA learner actor kwargs from privileged observation dimensions."""
-        priv_info_dim = int(critic_obs_dim - obs_dim)
-        if priv_info_dim <= 0:
-            raise ValueError(
-                "HORA-SAC requires critic observations to contain privileged tail "
-                f"features; got obs_dim={obs_dim}, critic_obs_dim={critic_obs_dim}."
-            )
-        return {
-            "priv_info_dim": priv_info_dim,
-            "priv_info_embed_dim": int(self.actor_cfg.get("priv_info_embed_dim", 9)),
-            "priv_mlp_hidden_dims": tuple(
-                self.actor_cfg.get("priv_mlp_hidden_dims", (256, 128, 9))
-            ),
-        }
-
-
-def resolve_hora_sac_runtime(rl_cfg: dict[str, Any]) -> HoraSACRuntime | None:
-    """Resolve HORA-SAC hooks from an explicit owner-config runtime marker."""
-    if not is_hora_sac_runtime(rl_cfg):
-        return None
-    actor_cfg_raw = rl_cfg.get("actor", {})
-    actor_cfg = actor_cfg_raw if isinstance(actor_cfg_raw, dict) else {}
-    return HoraSACRuntime(actor_cfg=dict(actor_cfg))
-
-
-__all__ = ["HoraSACRuntime", "resolve_hora_sac_runtime"]
diff --git a/src/unilab/algos/torch/hora/sac_learner.py b/src/unilab/algos/torch/hora/sac_learner.py
deleted file mode 100644
index 029783ec7..000000000
--- a/src/unilab/algos/torch/hora/sac_learner.py
+++ /dev/null
@@ -1,130 +0,0 @@
-"""HORA-owned FastSAC learner."""
-
-from __future__ import annotations
-
-from collections.abc import Sequence
-from typing import Any, cast
-
-import torch
-import torch.optim as optim
-
-from unilab.algos.torch.fast_sac.learner import FastSACLearner
-from unilab.algos.torch.hora.sac_models import HoraSACActor
-
-
-def derive_priv_info_from_critic_obs(
-    actor_obs: torch.Tensor,
-    critic_obs: torch.Tensor,
-    *,
-    context: str,
-) -> torch.Tensor:
-    """Return the privileged tail encoded in the critic observation contract."""
-    actor_dim = int(actor_obs.shape[-1])
-    critic_dim = int(critic_obs.shape[-1])
-    if critic_dim <= actor_dim:
-        raise ValueError(
-            f"HORA-SAC {context} requires critic observations to include privileged tail "
-            f"features; got actor_dim={actor_dim}, critic_dim={critic_dim}."
-        )
-    return critic_obs[..., actor_dim:]
-
-
-class HoraSACLearner(FastSACLearner):
-    """FastSAC learner variant whose actor consumes HORA privileged info."""
-
-    def __init__(
-        self,
-        *,
-        obs_dim: int,
-        critic_obs_dim: int,
-        priv_info_dim: int,
-        action_dim: int,
-        device: str = "cpu",
-        actor_hidden_dim: int = 512,
-        priv_info_embed_dim: int = 9,
-        priv_mlp_hidden_dims: Sequence[int] = (256, 128, 9),
-        log_std_max: float = 0.0,
-        log_std_min: float = -5.0,
-        use_tanh: bool = True,
-        use_layer_norm: bool = True,
-        actor_lr: float = 3e-4,
-        weight_decay: float = 0.001,
-        use_symmetry: bool = False,
-        symmetry_augmentation: Any | None = None,
-        **kwargs: Any,
-    ) -> None:
-        if use_symmetry or symmetry_augmentation is not None:
-            raise ValueError("HORA-SAC does not support symmetry augmentation.")
-        if int(priv_info_dim) <= 0:
-            raise ValueError(f"HORA-SAC requires positive priv_info_dim, got {priv_info_dim}.")
-
-        super().__init__(
-            obs_dim=obs_dim,
-            critic_obs_dim=critic_obs_dim,
-            action_dim=action_dim,
-            device=device,
-            actor_hidden_dim=actor_hidden_dim,
-            log_std_max=log_std_max,
-            log_std_min=log_std_min,
-            use_tanh=use_tanh,
-            use_layer_norm=use_layer_norm,
-            actor_lr=actor_lr,
-            weight_decay=weight_decay,
-            use_symmetry=False,
-            symmetry_augmentation=None,
-            **kwargs,
-        )
-        self.use_cuda_graph_critic = False
-        self.use_cuda_graph_actor = False
-        self.priv_info_dim = int(priv_info_dim)
-        self.actor = HoraSACActor(
-            obs_dim=obs_dim,
-            priv_info_dim=self.priv_info_dim,
-            action_dim=action_dim,
-            hidden_dim=actor_hidden_dim,
-            priv_info_embed_dim=priv_info_embed_dim,
-            priv_mlp_hidden_dims=tuple(priv_mlp_hidden_dims),
-            log_std_max=log_std_max,
-            log_std_min=log_std_min,
-            use_tanh=use_tanh,
-            use_layer_norm=use_layer_norm,
-            device=device,
-        )
-        _fused = isinstance(device, str) and device.startswith("cuda")
-        self.actor_optimizer = optim.AdamW(
-            self.actor.parameters(),
-            lr=actor_lr,
-            weight_decay=weight_decay,
-            fused=_fused,
-            betas=(0.9, 0.95),
-        )
-
-    def _get_actions_and_log_probs_for_critic(
-        self,
-        actor_obs: torch.Tensor,
-        critic_obs: torch.Tensor,
-        eps: torch.Tensor | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        del eps
-        priv_info = derive_priv_info_from_critic_obs(
-            actor_obs,
-            critic_obs,
-            context="critic update",
-        )
-        actor = cast(HoraSACActor, self.actor)
-        return actor.get_actions_and_log_probs(actor_obs, priv_info)
-
-    def _get_actions_and_log_probs_for_actor(
-        self,
-        actor_obs: torch.Tensor,
-        critic_obs: torch.Tensor,
-        eps: torch.Tensor | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        del eps
-        priv_info = derive_priv_info_from_critic_obs(
-            actor_obs,
-            critic_obs,
-            context="actor update",
-        )
-        actor = cast(HoraSACActor, self.actor)
-        return actor.get_actions_and_log_probs(actor_obs, priv_info)
diff --git a/src/unilab/algos/torch/hora/sac_models.py b/src/unilab/algos/torch/hora/sac_models.py
deleted file mode 100644
index 19925073e..000000000
--- a/src/unilab/algos/torch/hora/sac_models.py
+++ /dev/null
@@ -1,196 +0,0 @@
-"""HORA SAC actor models."""
-
-from __future__ import annotations
-
-from collections.abc import Sequence
-from typing import Tuple, cast
-
-import torch
-import torch.nn as nn
-
-
-def _build_mlp(
-    input_dim: int,
-    hidden_dims: Sequence[int],
-    *,
-    activation: type[nn.Module],
-    use_layer_norm: bool,
-    device: str | torch.device,
-) -> tuple[nn.Sequential, int]:
-    layers: list[nn.Module] = []
-    current_dim = int(input_dim)
-    for hidden_dim in hidden_dims:
-        next_dim = int(hidden_dim)
-        layers.append(nn.Linear(current_dim, next_dim, device=device))
-        if use_layer_norm:
-            layers.append(nn.LayerNorm(next_dim, device=device))
-        layers.append(activation())
-        current_dim = next_dim
-    return nn.Sequential(*layers), current_dim
-
-
-class HoraSACActor(nn.Module):
-    """Privileged HORA teacher actor for SAC.
-
-    The named modules below are intentionally stable for later distillation:
-    ``priv_encoder``, ``actor_trunk``, ``action_mean_head``, and
-    ``action_logstd_head``.
-    """
-
-    action_scale: torch.Tensor
-    action_bias: torch.Tensor
-
-    def __init__(
-        self,
-        obs_dim: int,
-        priv_info_dim: int,
-        action_dim: int,
-        *,
-        hidden_dim: int = 512,
-        priv_info_embed_dim: int = 9,
-        priv_mlp_hidden_dims: Sequence[int] = (256, 128, 9),
-        log_std_max: float = 0.0,
-        log_std_min: float = -5.0,
-        use_tanh: bool = True,
-        use_layer_norm: bool = True,
-        device: str | torch.device = "cpu",
-        action_scale: torch.Tensor | None = None,
-        action_bias: torch.Tensor | None = None,
-    ) -> None:
-        super().__init__()
-        self.obs_dim = int(obs_dim)
-        self.priv_info_dim = int(priv_info_dim)
-        self.action_dim = int(action_dim)
-        self.priv_info_embed_dim = int(priv_info_embed_dim)
-        self.log_std_max = float(log_std_max)
-        self.log_std_min = float(log_std_min)
-        self.use_tanh = bool(use_tanh)
-
-        self.priv_encoder, priv_out_dim = _build_mlp(
-            self.priv_info_dim,
-            tuple(priv_mlp_hidden_dims),
-            activation=nn.SiLU,
-            use_layer_norm=use_layer_norm,
-            device=device,
-        )
-        if priv_out_dim != self.priv_info_embed_dim:
-            self.priv_projection = nn.Linear(
-                priv_out_dim,
-                self.priv_info_embed_dim,
-                device=device,
-            )
-        else:
-            self.priv_projection = nn.Identity()
-
-        trunk_dims = (hidden_dim, hidden_dim // 2, hidden_dim // 4)
-        self.actor_trunk, trunk_out_dim = _build_mlp(
-            self.obs_dim + self.priv_info_embed_dim,
-            trunk_dims,
-            activation=nn.SiLU,
-            use_layer_norm=use_layer_norm,
-            device=device,
-        )
-        self.action_mean_head = nn.Linear(trunk_out_dim, self.action_dim, device=device)
-        self.action_logstd_head = nn.Linear(trunk_out_dim, self.action_dim, device=device)
-
-        nn.init.constant_(self.action_mean_head.weight, 0.0)
-        nn.init.constant_(self.action_mean_head.bias, 0.0)
-        nn.init.constant_(self.action_logstd_head.weight, 0.0)
-        nn.init.constant_(self.action_logstd_head.bias, 0.0)
-
-        if action_scale is not None:
-            self.register_buffer("action_scale", action_scale.to(device))
-        else:
-            self.register_buffer("action_scale", torch.ones(self.action_dim, device=device))
-        if action_bias is not None:
-            self.register_buffer("action_bias", action_bias.to(device))
-        else:
-            self.register_buffer("action_bias", torch.zeros(self.action_dim, device=device))
-
-    def encode_privileged_info(self, priv_info: torch.Tensor) -> torch.Tensor:
-        encoded = self.priv_encoder(priv_info)
-        return torch.tanh(self.priv_projection(encoded))
-
-    def _distribution_params(
-        self,
-        obs: torch.Tensor,
-        priv_info: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        z = self.encode_privileged_info(priv_info)
-        latent = self.actor_trunk(torch.cat([obs, z], dim=-1))
-        mean = self.action_mean_head(latent)
-        log_std = self.action_logstd_head(latent)
-        log_std = torch.tanh(log_std)
-        log_std = self.log_std_min + 0.5 * (self.log_std_max - self.log_std_min) * (log_std + 1)
-        mean = torch.clamp(mean, -10.0, 10.0)
-        mean = torch.nan_to_num(mean, nan=0.0)
-        log_std = torch.nan_to_num(log_std, nan=self.log_std_min)
-        return mean, log_std
-
-    def forward(
-        self,
-        obs: torch.Tensor,
-        priv_info: torch.Tensor,
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        mean, log_std = self._distribution_params(obs, priv_info)
-        if self.use_tanh:
-            action = torch.tanh(mean) * self.action_scale + self.action_bias
-        else:
-            action = mean
-        return action, mean, log_std
-
-    def as_export_module(self) -> nn.Module:
-        """Return a wrapper with explicit actor/priv inputs for ONNX export."""
-        actor = self
-
-        class _Wrapper(nn.Module):
-            def __init__(self) -> None:
-                super().__init__()
-                self.base = actor
-
-            def forward(self, obs: torch.Tensor, priv_info: torch.Tensor) -> torch.Tensor:
-                action, _, _ = self.base(obs, priv_info)
-                return cast(torch.Tensor, action)
-
-        return _Wrapper()
-
-    def get_actions_and_log_probs(
-        self,
-        obs: torch.Tensor,
-        priv_info: torch.Tensor,
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        _, mean, log_std = self(obs, priv_info)
-        std = log_std.exp()
-        dist = torch.distributions.Normal(mean, std)
-        raw_action = dist.rsample()
-
-        if self.use_tanh:
-            tanh_action = torch.tanh(raw_action)
-            action = tanh_action * self.action_scale + self.action_bias
-            log_prob = dist.log_prob(raw_action)
-            log_prob -= torch.log(1 - tanh_action.pow(2) + 1e-6)
-            log_prob -= torch.log(self.action_scale + 1e-6)
-        else:
-            action = raw_action
-            log_prob = dist.log_prob(raw_action)
-
-        return action, log_prob.sum(1), log_std
-
-    @torch.no_grad()
-    def explore(
-        self,
-        obs: torch.Tensor,
-        priv_info: torch.Tensor,
-        deterministic: bool = False,
-    ) -> torch.Tensor:
-        _, mean, log_std = self.forward(obs, priv_info)
-        if deterministic:
-            if self.use_tanh:
-                return torch.tanh(mean) * self.action_scale + self.action_bias
-            return mean
-
-        std = log_std.exp()
-        raw_action = torch.distributions.Normal(mean, std).rsample()
-        if self.use_tanh:
-            return torch.tanh(raw_action) * self.action_scale + self.action_bias
-        return raw_action
diff --git a/src/unilab/algos/torch/offpolicy/__init__.py b/src/unilab/algos/torch/offpolicy/__init__.py
deleted file mode 100644
index 60749cd92..000000000
--- a/src/unilab/algos/torch/offpolicy/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-"""Off-policy RL unified infrastructure."""
-
-from unilab.algos.torch.offpolicy.runner import OffPolicyRunner
-from unilab.algos.torch.offpolicy.worker import off_policy_collector_fn
-from unilab.logging import OffPolicyLogger
-
-__all__ = [
-    "OffPolicyLogger",
-    "OffPolicyRunner",
-    "off_policy_collector_fn",
-]
diff --git a/src/unilab/algos/torch/offpolicy/double_buffer_runner.py b/src/unilab/algos/torch/offpolicy/double_buffer_runner.py
deleted file mode 100644
index 114f9d927..000000000
--- a/src/unilab/algos/torch/offpolicy/double_buffer_runner.py
+++ /dev/null
@@ -1,1419 +0,0 @@
-"""Off-policy runner using device-authoritative bounded-ingress replay."""
-
-from __future__ import annotations
-
-import os
-import queue as queue_module
-import statistics
-import time
-from collections import defaultdict, deque
-from contextlib import nullcontext
-from pathlib import Path
-from typing import TYPE_CHECKING, TypedDict, cast
-
-import torch
-
-if TYPE_CHECKING:
-    from unilab.ipc.dp_sync import DpParameterSync
-
-from unilab.algos.torch.offpolicy.runner import (
-    OffPolicyRunner,
-    build_offpolicy_sample_info,
-    build_reward_comparison_metrics,
-    replay_buffer_ready_for_learning,
-)
-from unilab.algos.torch.offpolicy.thread_budget import (
-    format_torch_thread_runtime,
-    torch_thread_env,
-)
-from unilab.algos.torch.offpolicy.worker import off_policy_collector_fn, sample_offpolicy_actions
-from unilab.ipc.async_runner import _SPAWN_CTX
-from unilab.ipc.inference_slot import SharedInferenceSlot
-from unilab.ipc.replay_buffer import DEFAULT_REPLAY_INGRESS_DEPTH, ReplayBuffer
-from unilab.ipc.replay_pipelines.gpu_resident import (
-    GPUResidentReplayPipeline,
-    require_offpolicy_replay_device,
-)
-from unilab.logging import OffPolicyLogger, TraceRecorder
-from unilab.training.seed import derive_worker_seed
-
-# Terminal/W&B display names for the off-policy algo types. Keep these
-# user-facing (no internal "Fast*" implementation prefixes).
-_ALGO_DISPLAY_NAMES = {
-    "sac": "SAC",
-    "td3": "TD3",
-    "flashsac": "FlashSAC",
-}
-
-_DP_METRIC_PREFIX = "metric::"
-_DP_REWARD_METRIC_PREFIX = "reward_metric::"
-_DP_REWARD_COMPONENT_PREFIX = "reward_component::"
-_DP_COLLECTOR_TIMING_PREFIX = "collector_timing::"
-
-
-class _LogStepPayload(TypedDict):
-    metrics: dict[str, float]
-    reward: float | None
-    reward_metrics: dict[str, float]
-    reward_components: dict[str, float]
-    train_time: float
-    collector_wait_time: float
-    replay_batch_wait_time: float
-    learner_replay_sample_time: float
-    sync_coordination_time: float
-    replay_ingress_h2d_submit_time: float
-    inference_h2d_time: float
-    inference_forward_time: float
-    inference_d2h_time: float
-    inference_time: float
-    iteration_time: float
-    extra_info: dict[str, int | float | None]
-
-
-class _LocalLoggerStatistics(TypedDict):
-    total_steps: int
-    buffer_size: int
-    buffer_target: int
-    collector_active_steps_per_sec: float | None
-    mean_ep_length: float
-    timeout_rate: float
-    buffer_utilization: float
-    collector_timing: dict[str, float]
-    staging_pool_len: int
-    staging_pool_max: int
-
-
-def algo_display_name(algo_type: str) -> str:
-    return _ALGO_DISPLAY_NAMES.get(algo_type, algo_type.upper())
-
-
-class _CollectorDiedError(RuntimeError):
-    """Raised when an inference response cannot be delivered to the collector.
-
-    Caught by learn() to trigger the standard cleanup via _fail_collector_died
-    using the currently-bound logger/replay_buffer/replay_pipeline/iteration/
-    ckpt_path/train_start_wall context. Module-private (_-prefixed)."""
-
-
-class _LearnerInferenceScheduler:
-    """Track fixed inference ticks and the learner update boundary."""
-
-    def __init__(self, *, env_steps_per_sync: int, initial_policy_version: int = 0) -> None:
-        if int(env_steps_per_sync) < 1:
-            raise ValueError("Off-policy env_steps_per_sync must be >= 1")
-        self.env_steps_per_sync = int(env_steps_per_sync)
-        self.next_tick = 0
-        self.policy_version = int(initial_policy_version)
-        self._ticks_since_update = 0
-        self._pending_tick: int | None = None
-
-    @property
-    def update_ready(self) -> bool:
-        return self._ticks_since_update >= self.env_steps_per_sync
-
-    @property
-    def pending_tick(self) -> int | None:
-        return self._pending_tick
-
-    def record_inference(self, tick_id: int) -> None:
-        if self._pending_tick is not None:
-            raise RuntimeError("Previous learner inference tick has not been released")
-        if int(tick_id) != self.next_tick:
-            raise RuntimeError(
-                f"Collector inference tick mismatch: expected {self.next_tick}, got {tick_id}"
-            )
-        self._pending_tick = int(tick_id)
-        self.next_tick += 1
-        self._ticks_since_update += 1
-
-    def release_pending(self) -> int:
-        if self._pending_tick is None:
-            raise RuntimeError("Learner inference lost the pending collector tick")
-        tick_id = self._pending_tick
-        self._pending_tick = None
-        return tick_id
-
-    def finish_update(self) -> None:
-        if not self.update_ready:
-            raise RuntimeError(
-                "Learner update started before the configured inference tick boundary"
-            )
-        if self._pending_tick is not None:
-            raise RuntimeError("Learner update completed before releasing the collector tick")
-        self._ticks_since_update = 0
-        self.policy_version += 1
-
-
-class DoubleBufferOffPolicyRunner(OffPolicyRunner):
-    """Single-device off-policy runner with a double-buffered device batch."""
-
-    REPLAY_BATCH_READY_POLL_SEC = 0.001
-    INFERENCE_REQUEST_TIMEOUT_SEC = 30.0
-
-    def __init__(
-        self,
-        *,
-        replay_prefetch_mode: str = "one_tick",
-        collector_cpu_ids: list[int] | None = None,
-        dp_sync: DpParameterSync | None = None,
-        **kwargs,
-    ):
-        kwargs["device"] = require_offpolicy_replay_device(kwargs.get("device"))
-        super().__init__(**kwargs)
-        if replay_prefetch_mode != "one_tick":
-            raise ValueError(
-                "DoubleBufferOffPolicyRunner only supports replay_prefetch_mode='one_tick'"
-            )
-        self.replay_prefetch_mode = replay_prefetch_mode
-        # Per-rank CPU block owned by this rank's collector (multi-GPU DP);
-        # merged into the collector-only env override at collector startup.
-        self.collector_cpu_ids = list(collector_cpu_ids) if collector_cpu_ids is not None else None
-        # Multi-GPU synchronous data parallelism (None = the bit-identical
-        # single-rank path): startup model broadcast, then gradient averaging
-        # before every actor/critic/temperature optimizer step.
-        self.dp_sync = dp_sync
-        self._local_logger_statistics: _LocalLoggerStatistics | None = None
-        self._attach_dp_gradient_sync()
-        self.replay_pack_layout = "packed"
-        self.replay_pack_executor = "collector_thread"
-        self.replay_h2d_submitter = "auto"
-        self.replay_transfer_backend: dict[str, object] = {}
-        self.runtime_manifest = {
-            "inference_owner": "learner",
-            "collector_actor": False,
-            "collector_accelerator_context": False,
-            "collector_torch_inference": False,
-            "learner_actor_reused": True,
-            "logger_owner_rank": 0,
-            "logger_cross_rank_aggregation": self.dp_sync is not None,
-        }
-        if self.dp_sync is not None:
-            captures_gradient_sync = bool(
-                getattr(self.learner, "dp_cuda_graph_gradient_sync", False)
-            )
-            self.runtime_manifest["dp_sync"] = {
-                "world_size": self.dp_sync.world_size,
-                "backend": self.dp_sync.backend,
-                "mode": "gradient_mean_per_optimizer_step",
-                "cuda_graph_optimizer_capture": (
-                    "enabled_after_collective_warmup" if captures_gradient_sync else "not_requested"
-                ),
-                "cuda_graph_collective_warmup": captures_gradient_sync,
-            }
-
-    def _attach_dp_gradient_sync(self) -> None:
-        if self.dp_sync is None:
-            return
-        setter = getattr(self.learner, "set_gradient_sync", None)
-        if not callable(setter):
-            raise TypeError(
-                f"{type(self.learner).__name__} must implement set_gradient_sync() "
-                "for multi-GPU data parallelism"
-            )
-        setter(
-            self.dp_sync.allreduce_gradients,
-            graph_replay_recorder=self.dp_sync.record_cuda_graph_gradient_replay,
-        )
-
-    def _dp_initial_sync_tensors(self) -> dict[str, torch.Tensor]:
-        """Live model-state references broadcast once before collection."""
-        initial_tensors = getattr(self.learner, "dp_initial_sync_tensors", None)
-        if not callable(initial_tensors):
-            raise TypeError(
-                f"{type(self.learner).__name__} must implement dp_initial_sync_tensors() "
-                "for multi-GPU data parallelism"
-            )
-        return cast(dict[str, torch.Tensor], initial_tensors())
-
-    def _dp_init_broadcast(self) -> None:
-        """Align initial parameters from rank 0 before the collector starts.
-
-        Ranks train with per-rank seeds, so without this broadcast each
-        rank's actor would serve different inference from the first tick.
-        """
-        if self.dp_sync is None:
-            return
-        self.dp_sync.start()
-        self.dp_sync.broadcast_from_rank0(self._dp_initial_sync_tensors())
-        if bool(getattr(self.learner, "dp_cuda_graph_gradient_sync", False)):
-            self.dp_sync.prepare_cuda_graph_collectives()
-
-    def _collect_dp_sync_metrics(self, iter_metrics: defaultdict[str, list]) -> None:
-        """Move per-optimizer collective timing into this iteration's metrics."""
-        if self.dp_sync is None:
-            return
-        sync_time, sync_calls = self.dp_sync.take_gradient_sync_metrics()
-        if sync_calls > 0:
-            iter_metrics["dp_sync_time"].append(sync_time)
-            iter_metrics["dp_gradient_sync_calls"].append(float(sync_calls))
-
-    def _aggregate_log_statistics(
-        self,
-        logger: OffPolicyLogger,
-        *,
-        metrics: dict[str, float],
-        reward: float | None,
-        reward_metrics: dict[str, float],
-        reward_components: dict[str, float],
-        train_time: float,
-        collector_wait_time: float,
-        replay_batch_wait_time: float,
-        learner_replay_sample_time: float,
-        sync_coordination_time: float,
-        replay_ingress_h2d_submit_time: float,
-        inference_h2d_time: float,
-        inference_forward_time: float,
-        inference_d2h_time: float,
-        inference_time: float,
-        iteration_time: float,
-        extra_info: dict[str, int | float | None],
-    ) -> _LogStepPayload:
-        """Return one log-step payload, reduced across ranks when DP is active.
-
-        Model/reward/timing scalars are means. Concurrent work rates and
-        capacity/sample counters are totals, so the rank-0 logger reports
-        aggregate collector Steps/s and aggregate learner Samples/s. Optional
-        collector fields use the sparse presence-mask contract implemented by
-        ``DpParameterSync.allreduce_statistics``.
-        """
-        payload: _LogStepPayload = {
-            "metrics": metrics,
-            "reward": reward,
-            "reward_metrics": reward_metrics,
-            "reward_components": reward_components,
-            "train_time": train_time,
-            "collector_wait_time": collector_wait_time,
-            "replay_batch_wait_time": replay_batch_wait_time,
-            "learner_replay_sample_time": learner_replay_sample_time,
-            "sync_coordination_time": sync_coordination_time,
-            "replay_ingress_h2d_submit_time": replay_ingress_h2d_submit_time,
-            "inference_h2d_time": inference_h2d_time,
-            "inference_forward_time": inference_forward_time,
-            "inference_d2h_time": inference_d2h_time,
-            "inference_time": inference_time,
-            "iteration_time": iteration_time,
-            "extra_info": extra_info,
-        }
-        if self.dp_sync is None:
-            return payload
-
-        # ``logger`` also receives collector telemetry asynchronously. Keep a
-        # rank-local snapshot before replacing its presentation state with the
-        # aggregate; the next iteration restores this snapshot so a field that
-        # is reported only every few collector cycles is never summed twice.
-        self._local_logger_statistics = {
-            "total_steps": logger._total_steps,
-            "buffer_size": logger._buffer_size,
-            "buffer_target": logger._buffer_target,
-            "collector_active_steps_per_sec": logger._collector_active_steps_per_sec,
-            "mean_ep_length": logger._mean_ep_length,
-            "timeout_rate": logger._timeout_rate,
-            "buffer_utilization": logger._buffer_utilization,
-            "collector_timing": dict(logger._collector_timing),
-            "staging_pool_len": logger._staging_pool_len,
-            "staging_pool_max": logger._staging_pool_max,
-        }
-
-        mean: dict[str, float] = {
-            **{f"{_DP_METRIC_PREFIX}{key}": float(value) for key, value in metrics.items()},
-            **{
-                f"{_DP_REWARD_METRIC_PREFIX}{key}": float(value)
-                for key, value in reward_metrics.items()
-            },
-            **{
-                f"{_DP_REWARD_COMPONENT_PREFIX}{key}": float(value)
-                for key, value in reward_components.items()
-            },
-            "timing::train_time": train_time,
-            "timing::collector_wait_time": collector_wait_time,
-            "timing::replay_batch_wait_time": replay_batch_wait_time,
-            "timing::learner_replay_sample_time": learner_replay_sample_time,
-            "timing::sync_coordination_time": sync_coordination_time,
-            "timing::replay_ingress_h2d_submit_time": replay_ingress_h2d_submit_time,
-            "timing::inference_h2d_time": inference_h2d_time,
-            "timing::inference_forward_time": inference_forward_time,
-            "timing::inference_d2h_time": inference_d2h_time,
-            "timing::inference_time": inference_time,
-            "timing::iteration_time": iteration_time,
-            "logger::timeout_rate": float(logger._timeout_rate),
-            "logger::buffer_utilization": float(logger._buffer_utilization),
-            "extra::batch_size_per_rank": float(extra_info.get("batch_size_per_rank", 0) or 0),
-        }
-        if reward is not None:
-            mean["reward::mean"] = float(reward)
-        if logger._mean_ep_length > 0:
-            mean["logger::mean_ep_length"] = float(logger._mean_ep_length)
-        mean.update(
-            {
-                f"{_DP_COLLECTOR_TIMING_PREFIX}{key}": float(value)
-                for key, value in logger._collector_timing.items()
-            }
-        )
-
-        throughput_steps = int(extra_info.get("throughput_steps", 0) or 0)
-        learner_samples = int(extra_info.get("learner_samples_per_iter", 0) or 0)
-        total: dict[str, float] = {
-            "logger::total_steps": float(logger._total_steps),
-            "logger::buffer_size": float(logger._buffer_size),
-            "logger::buffer_target": float(logger._buffer_target),
-            "logger::staging_pool_len": float(logger._staging_pool_len),
-            "logger::staging_pool_max": float(logger._staging_pool_max),
-            "extra::throughput_steps": float(throughput_steps),
-            "extra::effective_batch_size": float(extra_info.get("effective_batch_size", 0) or 0),
-            "extra::replay_samples_per_iter": float(
-                extra_info.get("replay_samples_per_iter", 0) or 0
-            ),
-            "extra::learner_samples_per_iter": float(learner_samples),
-        }
-        if iteration_time > 0:
-            total["rate::steps_per_sec"] = throughput_steps / iteration_time
-            total["rate::learner_samples_per_sec"] = learner_samples / iteration_time
-        collector_active_steps_per_sec = extra_info.get("collector_active_steps_per_sec")
-        if collector_active_steps_per_sec is not None:
-            total["rate::collector_active_steps_per_sec"] = float(collector_active_steps_per_sec)
-
-        aggregated = self.dp_sync.allreduce_statistics(mean=mean, total=total)
-
-        logger._total_steps = int(round(aggregated["logger::total_steps"]))
-        logger._buffer_size = int(round(aggregated["logger::buffer_size"]))
-        logger._buffer_target = int(round(aggregated["logger::buffer_target"]))
-        logger._staging_pool_len = int(round(aggregated["logger::staging_pool_len"]))
-        logger._staging_pool_max = int(round(aggregated["logger::staging_pool_max"]))
-        logger._timeout_rate = aggregated["logger::timeout_rate"]
-        logger._buffer_utilization = aggregated["logger::buffer_utilization"]
-        if "logger::mean_ep_length" in aggregated:
-            logger._mean_ep_length = aggregated["logger::mean_ep_length"]
-        logger._collector_timing = {
-            key.removeprefix(_DP_COLLECTOR_TIMING_PREFIX): value
-            for key, value in aggregated.items()
-            if key.startswith(_DP_COLLECTOR_TIMING_PREFIX)
-        }
-
-        aggregated_extra_info: dict[str, int | float | None] = {
-            "throughput_steps": int(round(aggregated["extra::throughput_steps"])),
-            "steps_per_sec": aggregated.get("rate::steps_per_sec"),
-            "learner_samples_per_sec": aggregated.get("rate::learner_samples_per_sec"),
-            "collector_active_steps_per_sec": aggregated.get(
-                "rate::collector_active_steps_per_sec"
-            ),
-            "batch_size_per_rank": int(round(aggregated["extra::batch_size_per_rank"])),
-            "effective_batch_size": int(round(aggregated["extra::effective_batch_size"])),
-            "replay_samples_per_iter": int(round(aggregated["extra::replay_samples_per_iter"])),
-            "learner_samples_per_iter": int(round(aggregated["extra::learner_samples_per_iter"])),
-        }
-        return {
-            "metrics": {
-                key.removeprefix(_DP_METRIC_PREFIX): value
-                for key, value in aggregated.items()
-                if key.startswith(_DP_METRIC_PREFIX)
-            },
-            "reward": aggregated.get("reward::mean"),
-            "reward_metrics": {
-                key.removeprefix(_DP_REWARD_METRIC_PREFIX): value
-                for key, value in aggregated.items()
-                if key.startswith(_DP_REWARD_METRIC_PREFIX)
-            },
-            "reward_components": {
-                key.removeprefix(_DP_REWARD_COMPONENT_PREFIX): value
-                for key, value in aggregated.items()
-                if key.startswith(_DP_REWARD_COMPONENT_PREFIX)
-            },
-            "train_time": aggregated["timing::train_time"],
-            "collector_wait_time": aggregated["timing::collector_wait_time"],
-            "replay_batch_wait_time": aggregated["timing::replay_batch_wait_time"],
-            "learner_replay_sample_time": aggregated["timing::learner_replay_sample_time"],
-            "sync_coordination_time": aggregated["timing::sync_coordination_time"],
-            "replay_ingress_h2d_submit_time": aggregated["timing::replay_ingress_h2d_submit_time"],
-            "inference_h2d_time": aggregated["timing::inference_h2d_time"],
-            "inference_forward_time": aggregated["timing::inference_forward_time"],
-            "inference_d2h_time": aggregated["timing::inference_d2h_time"],
-            "inference_time": aggregated["timing::inference_time"],
-            "iteration_time": aggregated["timing::iteration_time"],
-            "extra_info": aggregated_extra_info,
-        }
-
-    def _restore_local_logger_statistics(self, logger: OffPolicyLogger) -> None:
-        """Restore per-rank collector state after the previous aggregate log step."""
-        state = self._local_logger_statistics
-        if state is None:
-            return
-        logger._total_steps = int(state["total_steps"])
-        logger._buffer_size = int(state["buffer_size"])
-        logger._buffer_target = int(state["buffer_target"])
-        active_rate = state["collector_active_steps_per_sec"]
-        logger._collector_active_steps_per_sec = None if active_rate is None else float(active_rate)
-        logger._mean_ep_length = float(state["mean_ep_length"])
-        logger._timeout_rate = float(state["timeout_rate"])
-        logger._buffer_utilization = float(state["buffer_utilization"])
-        logger._collector_timing = dict(state["collector_timing"])
-        logger._staging_pool_len = int(state["staging_pool_len"])
-        logger._staging_pool_max = int(state["staging_pool_max"])
-        self._local_logger_statistics = None
-
-    def _logger_backend(self, requested: str) -> str:
-        """Only rank 0 owns terminal and external logging backends."""
-        if not self._is_primary_rank():
-            return "no_print"
-        return requested
-
-    def _is_primary_rank(self) -> bool:
-        return self.dp_sync is None or self.dp_sync.rank == 0
-
-    def _save_checkpoint(
-        self,
-        *,
-        log_dir: str,
-        iteration: int,
-        logger: OffPolicyLogger,
-    ) -> str | None:
-        """Persist the single canonical checkpoint from rank 0 only."""
-        if not self._is_primary_rank():
-            return None
-        ckpt_path = os.path.join(log_dir, f"model_{iteration}.pt")
-        torch.save(self.learner.get_state_dict(), ckpt_path)
-        logger.log_save(ckpt_path)
-        return ckpt_path
-
-    def close(self) -> None:
-        try:
-            # Rank 0 owns the live terminal, and every rank owns a collector and
-            # shared IPC resources. Release those before NCCL teardown, which
-            # may wait on a peer during Ctrl+C shutdown.
-            super().close()
-        finally:
-            if self.dp_sync is not None:
-                try:
-                    if bool(getattr(self.learner, "dp_cuda_graph_gradient_sync", False)):
-                        release_cuda_graphs = getattr(self.learner, "release_cuda_graphs", None)
-                        if not callable(release_cuda_graphs):
-                            raise TypeError(
-                                f"{type(self.learner).__name__} must implement "
-                                "release_cuda_graphs() before an NCCL process group with "
-                                "captured collectives is destroyed"
-                            )
-                        release_cuda_graphs()
-                finally:
-                    self.dp_sync.close()
-
-    def _collector_env_cfg_override(self) -> dict | None:
-        """Env override copy for the collector process, with per-rank CPU ids.
-
-        MuJoCo sizes its BatchEnvPool worker count from ``len(cpu_ids)``, so
-        the affinity list must only reach the collector's copy — never the
-        learner-side probe envs, which keep the base override untouched.
-        """
-        if self.collector_cpu_ids is None:
-            return self.env_cfg_override
-        return {**(self.env_cfg_override or {}), "cpu_ids": list(self.collector_cpu_ids)}
-
-    def _wait_for_inference_request(
-        self,
-        queue,
-        *,
-        expected_tick: int,
-        replay_pipeline,
-        metrics_queue,
-        reward_history,
-        latest_reward_components,
-        logger,
-        trace_recorder,
-        replay_buffer,
-        ckpt_path: str | None,
-        train_start_wall: float,
-    ) -> int:
-        deadline = time.monotonic() + self.INFERENCE_REQUEST_TIMEOUT_SEC
-        while True:
-            try:
-                tick_id = int(queue.get(timeout=0.1))
-            except queue_module.Empty:
-                replay_pipeline.progress()
-                self._drain_metrics(
-                    metrics_queue,
-                    reward_history,
-                    latest_reward_components,
-                    logger,
-                    trace_recorder,
-                    log_collector_reward=self.dp_sync is None,
-                )
-                if not self._check_collector_alive():
-                    self._fail_collector_died(
-                        logger,
-                        replay_buffer,
-                        replay_pipeline,
-                        expected_tick,
-                        ckpt_path,
-                        train_start_wall,
-                    )
-                if time.monotonic() >= deadline:
-                    raise TimeoutError(
-                        f"Timed out waiting for collector inference tick {expected_tick}"
-                    )
-                continue
-            if tick_id != int(expected_tick):
-                raise RuntimeError(
-                    f"Collector inference tick mismatch: expected {expected_tick}, got {tick_id}"
-                )
-            return tick_id
-
-    def _serve_learner_inference(
-        self,
-        inference_slot: SharedInferenceSlot,
-        *,
-        tick_id: int,
-        policy_version: int,
-        obs_device: torch.Tensor,
-        dones_device: torch.Tensor,
-        trace_recorder: TraceRecorder | None,
-    ) -> dict[str, float]:
-        device = torch.device(self.device)
-        h2d_start_ns = time.perf_counter_ns()
-        inference_slot.copy_observation_to(
-            tick_id=tick_id,
-            observations=obs_device,
-            dones=dones_device,
-            non_blocking=False,
-        )
-        h2d_end_ns = time.perf_counter_ns()
-
-        actor_obs = obs_device[:, : self.obs_dim]
-        actor_context = obs_device[:, self.obs_dim :] if self.algo_type == "hora_sac" else None
-        if self.obs_normalization:
-            actor_obs = self.learner.obs_normalizer(actor_obs, update=False)
-        forward_start_ns = time.perf_counter_ns()
-        with torch.no_grad():
-            actions_device = sample_offpolicy_actions(
-                actor=self.learner.actor,
-                algo_type=self.algo_type,
-                obs_torch=actor_obs,
-                prev_dones_torch=dones_device,
-                priv_info_torch=actor_context,
-            )
-        if device.type == "cuda":
-            torch.cuda.current_stream(device).synchronize()
-        elif device.type == "mps":
-            torch.mps.synchronize()
-        forward_end_ns = time.perf_counter_ns()
-
-        d2h_start_ns = time.perf_counter_ns()
-        inference_slot.publish_action(
-            tick_id=tick_id,
-            policy_version=policy_version,
-            actions=actions_device,
-            non_blocking=False,
-        )
-        d2h_end_ns = time.perf_counter_ns()
-        timings = {
-            "inference_h2d_time": (h2d_end_ns - h2d_start_ns) / 1e9,
-            "inference_forward_time": (forward_end_ns - forward_start_ns) / 1e9,
-            "inference_d2h_time": (d2h_end_ns - d2h_start_ns) / 1e9,
-            "inference_time": (d2h_end_ns - h2d_start_ns) / 1e9,
-        }
-        if trace_recorder:
-            trace_args = {"tick_id": tick_id, "policy_version": policy_version}
-            trace_recorder.add_slice(
-                "learner/inference_h2d",
-                category="learner_inference",
-                start_ns=h2d_start_ns,
-                end_ns=h2d_end_ns,
-                args=trace_args,
-            )
-            trace_recorder.add_slice(
-                "learner/inference_forward",
-                category="learner_inference",
-                start_ns=forward_start_ns,
-                end_ns=forward_end_ns,
-                args=trace_args,
-            )
-            trace_recorder.add_slice(
-                "learner/inference_d2h",
-                category="learner_inference",
-                start_ns=d2h_start_ns,
-                end_ns=d2h_end_ns,
-                args=trace_args,
-            )
-            trace_recorder.add_slice(
-                "learner/inference",
-                category="learner_inference",
-                start_ns=h2d_start_ns,
-                end_ns=d2h_end_ns,
-                args=trace_args,
-            )
-            trace_recorder.add_counter(
-                "policy_version",
-                policy_version,
-                category="learner_inference",
-            )
-        return timings
-
-    def _fail_collector_died(
-        self,
-        logger,
-        replay_buffer,
-        replay_pipeline,
-        iteration: int,
-        ckpt_path: str | None,
-        train_start_wall: float,
-    ) -> None:
-        logger.log_status("[red]ERROR: Collector died[/]")
-        self._sync_logger_replay_counters(logger, replay_buffer)
-        logger.close()
-        self.last_run_summary = self._make_summary(
-            "collector_died",
-            iteration,
-            logger,
-            None,
-            None,
-            ckpt_path,
-            train_start_wall,
-            None,
-        )
-        replay_pipeline.close()
-        raise RuntimeError("Collector process died during off-policy training")
-
-    def _publish_inference_response(
-        self,
-        queue,
-        *,
-        value: int = 1,
-        timeout: float = 5.0,
-        label: str = "inference_response",
-    ) -> None:
-        """Publish an inference response tick with timeout and liveness checks.
-
-        Raises _CollectorDiedError if collector is dead or queue stays full
-        beyond timeout. Caller (learn) must catch and dispatch to
-        _fail_collector_died for full cleanup. This avoids an unbounded blocking
-        put when the collector dies before consuming the previous response.
-        """
-        deadline = time.monotonic() + timeout
-        while True:
-            try:
-                remaining = deadline - time.monotonic()
-                if remaining <= 0:
-                    raise _CollectorDiedError(f"{label} (queue full timeout)")
-                queue.put(int(value), timeout=min(0.5, remaining))
-                return
-            except queue_module.Full:
-                if not self._check_collector_alive():
-                    raise _CollectorDiedError(f"{label} (collector dead)")
-                # else loop & retry until deadline
-
-    def _release_inference_tick(
-        self,
-        queue,
-        *,
-        inference_scheduler: _LearnerInferenceScheduler,
-        replay_buffer,
-        trace_recorder: TraceRecorder | None,
-    ) -> int | None:
-        """Release the action immediately and freeze the next replay boundary."""
-        tick_id = inference_scheduler.release_pending()
-        next_prepare_min_snapshot_ptr = None
-        if inference_scheduler.update_ready:
-            next_prepare_min_snapshot_ptr = replay_buffer.published_ptr + (
-                self.num_envs * self.env_steps_per_sync
-            )
-        release_start_ns = time.perf_counter_ns()
-        self._publish_inference_response(
-            queue,
-            value=tick_id,
-            label="inference_response",
-        )
-        if trace_recorder:
-            trace_recorder.add_slice(
-                "learner/inference_response",
-                category="learner_inference",
-                start_ns=release_start_ns,
-                end_ns=time.perf_counter_ns(),
-                args={
-                    "tick_id": tick_id,
-                    "policy_version": inference_scheduler.policy_version,
-                    "next_prepare_min_snapshot_ptr": next_prepare_min_snapshot_ptr,
-                },
-            )
-        return next_prepare_min_snapshot_ptr
-
-    def _wait_for_replay_batch_ready(
-        self,
-        replay_pipeline,
-        tick_id: int,
-        sample_count: int,
-        metrics_queue,
-        reward_history,
-        latest_reward_components,
-        logger,
-        trace_recorder,
-        replay_buffer,
-        ckpt_path: str | None,
-        train_start_wall: float,
-    ) -> bool:
-        if not replay_pipeline.batch_ready(tick_id, sample_count):
-            replay_pipeline.start_prepare(tick_id, sample_count)
-        while not replay_pipeline.batch_ready(tick_id, sample_count):
-            self._drain_metrics(
-                metrics_queue,
-                reward_history,
-                latest_reward_components,
-                logger,
-                trace_recorder,
-                log_collector_reward=self.dp_sync is None,
-            )
-            if not self._check_collector_alive():
-                self._fail_collector_died(
-                    logger,
-                    replay_buffer,
-                    replay_pipeline,
-                    tick_id,
-                    ckpt_path,
-                    train_start_wall,
-                )
-            time.sleep(self.REPLAY_BATCH_READY_POLL_SEC)
-        return True
-
-    def learn(
-        self,
-        max_iterations: int = 1500,
-        save_interval: int = 50,
-        log_dir: str = "logs",
-        logger_type: str = "tensorboard",
-    ) -> None:
-        if self._is_primary_rank():
-            os.makedirs(log_dir, exist_ok=True)
-        trace_output_path = None
-        trace_recorder: TraceRecorder | None = None
-        if self.trace_enabled and self._is_primary_rank():
-            trace_root = Path(self.trace_output_dir or log_dir)
-            trace_output_path = trace_root / "perfetto_offpolicy_timeline.json"
-            trace_recorder = TraceRecorder("offpolicy_learner")
-        train_start_wall = time.time()
-        best_mean_reward = float("-inf")
-        last_mean_reward = 0.0
-        ckpt_path: str | None = None
-        iteration = 0
-
-        # --- memory budget check ---
-        from unilab.ipc.memory_budget import (
-            estimate_offpolicy_bytes,
-            raise_if_shared_memory_over_budget,
-            warn_if_over_budget,
-        )
-
-        graph_packed_staging_supported = self.algo_type == "sac" or (
-            self.algo_type == "flashsac"
-            and bool(getattr(self.learner, "supports_cuda_graph_packed_staging", False))
-        )
-        use_critic_graph_packed_source = (
-            graph_packed_staging_supported
-            and bool(getattr(self.learner, "use_cuda_graph_critic_packed_staging", False))
-            and self.critic_obs_dim > 0
-        )
-        use_sac_graph_pack_layout = use_critic_graph_packed_source and bool(
-            getattr(self.learner, "use_cuda_graph_actor_packed_staging", False)
-        )
-        use_critic_graph_packed_source = (
-            use_critic_graph_packed_source and not use_sac_graph_pack_layout
-        )
-        mem_est = estimate_offpolicy_bytes(
-            num_envs=self.num_envs,
-            replay_buffer_n=self.replay_buffer_n,
-            obs_dim=self.obs_dim,
-            action_dim=self.action_dim,
-            critic_dim=self.critic_obs_dim,
-            ingress_depth=DEFAULT_REPLAY_INGRESS_DEPTH,
-        )
-        warn_if_over_budget(mem_est, label=f"Off-policy ({self.algo_type})")
-        raise_if_shared_memory_over_budget(mem_est, label=f"Off-policy ({self.algo_type})")
-
-        # --- bounded collector ingress (the complete ring lives on device) ---
-        buffer_capacity = self.replay_buffer_n * self.num_envs
-        replay_buffer = ReplayBuffer(
-            capacity=buffer_capacity,
-            obs_dim=self.obs_dim,
-            action_dim=self.action_dim,
-            device=self.device,
-            critic_dim=self.critic_obs_dim,
-            ingress_slot_rows=self.num_envs,
-            ingress_depth=DEFAULT_REPLAY_INGRESS_DEPTH,
-        )
-        self._shared_resources.append(replay_buffer)
-        replay_buffer.trace_recorder = trace_recorder
-        replay_buffer.trace_thread_time = self.trace_thread_time
-        replay_buffer.trace_cuda_events = self.trace_cuda_events
-
-        # --- authoritative device ring and hot/cold learner batches ---
-        sample_count = self.batch_size * self.updates_per_step
-        replay_pipeline = GPUResidentReplayPipeline(
-            replay_buffer,
-            device=self.device,
-            sample_count=sample_count,
-            base_seed=int(self.seed or 0),
-            trace_recorder=trace_recorder,
-            trace_cuda_events=self.trace_cuda_events,
-            pack_layout="sac_graph" if use_sac_graph_pack_layout else "packed",
-            use_critic_graph_packed_source=use_critic_graph_packed_source,
-        )
-        self._shared_resources.insert(0, replay_pipeline)
-        self.replay_h2d_submitter = getattr(
-            replay_pipeline,
-            "h2d_submitter",
-            self.replay_h2d_submitter,
-        )
-        self.replay_transfer_backend = getattr(
-            replay_pipeline,
-            "transfer_manifest",
-            {},
-        )
-        self.runtime_manifest.update(
-            {
-                "replay_h2d_submitter": self.replay_h2d_submitter,
-                "replay_device_submission_thread": self.replay_transfer_backend.get(
-                    "device_submission_thread"
-                ),
-            }
-        )
-
-        actor_context_dim = int(getattr(self.learner, "priv_info_dim", 0))
-        inference_input_dim = self.obs_dim + actor_context_dim
-        inference_slot = SharedInferenceSlot(
-            self.num_envs,
-            inference_input_dim,
-            self.action_dim,
-        )
-        self._shared_resources.append(inference_slot)
-        inference_obs_device = torch.empty(
-            (self.num_envs, inference_input_dim),
-            dtype=torch.float32,
-            device=self.device,
-        )
-        inference_dones_device = torch.empty(
-            self.num_envs,
-            dtype=torch.float32,
-            device=self.device,
-        )
-        self.runtime_manifest["inference_slot_bytes"] = inference_slot.nbytes
-
-        # --- logger ---
-        logger = OffPolicyLogger(
-            algo_name=algo_display_name(self.algo_type),
-            max_iterations=max_iterations,
-            num_envs=self.num_envs * (self.dp_sync.world_size if self.dp_sync is not None else 1),
-            env_name=self.env_name,
-            obs_dim=self.obs_dim,
-            action_dim=self.action_dim,
-            num_gpus=(self.dp_sync.world_size if self.dp_sync is not None else 1),
-            log_dir=log_dir,
-            log_backend=self._logger_backend(logger_type),
-        )
-        logger.update_runtime_manifest(self.runtime_manifest)
-        if hasattr(self.learner, "use_symmetry") and self.learner.use_symmetry:
-            logger.log_status("Symmetry augmentation: enabled")
-        logger.log_status(format_torch_thread_runtime(self.torch_thread_runtime))
-        logger.log_status("Replay storage: device-authoritative bounded ingress")
-        logger.log_status(f"Replay prefetch mode: {self.replay_prefetch_mode}")
-        logger.log_status(f"Replay pack layout: {self.replay_pack_layout}")
-        logger.log_status(f"Replay pack executor: {self.replay_pack_executor}")
-        logger.log_status(f"Replay H2D submitter: {self.replay_h2d_submitter}")
-        if self.replay_transfer_backend:
-            logger.log_status(
-                "Replay transfer backend: "
-                f"{self.replay_transfer_backend.get('backend')} "
-                f"({self.replay_transfer_backend.get('device_family')})"
-            )
-        logger.log_status(f"Inference owner: learner.actor ({self.device})")
-        logger.log_status("Collector model/accelerator ownership: none")
-        logger.log_status("Replay learner lightweight: fixed (log_interval=1)")
-        self._active_logger = logger
-        logger.start()
-        try:
-            # --- inference coordination queues ---
-            inference_request_queue = _SPAWN_CTX.Queue(maxsize=1)
-            inference_response_queue = _SPAWN_CTX.Queue(maxsize=1)
-
-            metrics_queue = _SPAWN_CTX.Queue(maxsize=100)
-
-            # --- DP init broadcast must land before the collector's first ---
-            # --- inference request reaches learner.actor ---
-            self._dp_init_broadcast()
-
-            # --- start collector ---
-            collector_kwargs = {
-                "env_name": self.env_name,
-                "num_envs": self.num_envs,
-                "replay_buffer": replay_buffer,
-                "algo_type": self.algo_type,
-                "metrics_queue": metrics_queue,
-                "inference_request_queue": inference_request_queue,
-                "inference_response_queue": inference_response_queue,
-                "sim_backend": self.sim_backend,
-                "env_cfg_override": self._collector_env_cfg_override(),
-                "inference_slot": inference_slot,
-                "seed": derive_worker_seed(self.seed, worker_index=0),
-                "trace_enabled": self.trace_enabled,
-                "trace_thread_time": self.trace_thread_time,
-                "nan_guard_cfg": self.nan_guard_cfg,
-                "torch_thread_runtime": self.torch_thread_runtime,
-            }
-            with torch_thread_env(self.torch_thread_runtime, role="collector"):
-                self._start_collector(
-                    target_fn=off_policy_collector_fn,
-                    kwargs={"stop_event": self._stop_event, **collector_kwargs},
-                )
-
-            time.sleep(0.5)
-
-            reward_history: deque = deque(maxlen=100)
-            latest_reward_components: dict[str, float] = {}
-            has_logged_reward = False
-            last_buf_log = 0
-            write_read_ema = 0.0
-            reward_stats_ptr = 0
-            train_start_threshold = self.train_start_threshold
-            prepared_tick: int | None = None
-            inference_scheduler = _LearnerInferenceScheduler(
-                env_steps_per_sync=self.env_steps_per_sync,
-                initial_policy_version=int(getattr(self.learner, "update_count", 0)),
-            )
-
-            if trace_recorder:
-                manifest_ns = time.perf_counter_ns()
-                trace_recorder.add_slice(
-                    "learner/runtime_manifest",
-                    category="learner",
-                    start_ns=manifest_ns,
-                    end_ns=manifest_ns,
-                    args=dict(self.runtime_manifest),
-                )
-
-            training_e2e_start_ns = 0
-
-            # ---- training loop ----
-            for iteration in range(1, max_iterations + 1):
-                self._restore_local_logger_statistics(logger)
-                iteration_start = time.perf_counter()
-                # -- wait for data --
-                wait_start = time.perf_counter()
-                wait_start_ns = time.perf_counter_ns()
-                sync_coordination_time = 0.0
-                collector_wait_overhead = 0.0
-                inference_h2d_time = 0.0
-                inference_forward_time = 0.0
-                inference_d2h_time = 0.0
-                inference_time = 0.0
-                next_prepare_min_snapshot_ptr: int | None = None
-                while True:
-                    request_tick = self._wait_for_inference_request(
-                        inference_request_queue,
-                        expected_tick=inference_scheduler.next_tick,
-                        replay_pipeline=replay_pipeline,
-                        metrics_queue=metrics_queue,
-                        reward_history=reward_history,
-                        latest_reward_components=latest_reward_components,
-                        logger=logger,
-                        trace_recorder=trace_recorder,
-                        replay_buffer=replay_buffer,
-                        ckpt_path=ckpt_path,
-                        train_start_wall=train_start_wall,
-                    )
-                    inference_timings = self._serve_learner_inference(
-                        inference_slot,
-                        tick_id=request_tick,
-                        policy_version=inference_scheduler.policy_version,
-                        obs_device=inference_obs_device,
-                        dones_device=inference_dones_device,
-                        trace_recorder=trace_recorder,
-                    )
-                    inference_h2d_time += inference_timings["inference_h2d_time"]
-                    inference_forward_time += inference_timings["inference_forward_time"]
-                    inference_d2h_time += inference_timings["inference_d2h_time"]
-                    inference_time += inference_timings["inference_time"]
-                    collector_wait_overhead += inference_timings["inference_time"]
-                    inference_scheduler.record_inference(request_tick)
-                    _coord_t = time.perf_counter()
-                    frozen_prepare_ptr = self._release_inference_tick(
-                        inference_response_queue,
-                        inference_scheduler=inference_scheduler,
-                        replay_buffer=replay_buffer,
-                        trace_recorder=trace_recorder,
-                    )
-                    _coord_d = time.perf_counter() - _coord_t
-                    sync_coordination_time += _coord_d
-                    collector_wait_overhead += _coord_d
-                    if frozen_prepare_ptr is not None:
-                        next_prepare_min_snapshot_ptr = frozen_prepare_ptr
-
-                    self._drain_metrics(
-                        metrics_queue,
-                        reward_history,
-                        latest_reward_components,
-                        logger,
-                        trace_recorder,
-                        log_collector_reward=self.dp_sync is None,
-                    )
-                    replay_pipeline.progress(wait=True)
-                    cur_size = int(replay_buffer.size[0])
-                    replay_ready = replay_buffer_ready_for_learning(
-                        cur_size,
-                        batch_size=self.batch_size,
-                        learning_starts=self.learning_starts,
-                        num_envs=self.num_envs,
-                    )
-                    if replay_ready and inference_scheduler.update_ready:
-                        if prepared_tick != iteration:
-                            replay_pipeline.start_prepare(iteration, sample_count)
-                            prepared_tick = iteration
-                        break
-                    if cur_size - last_buf_log >= self.num_envs * 10:
-                        last_buf_log = cur_size
-                        _fill_t = time.perf_counter()
-                        logger.log_buffer_fill(cur_size, train_start_threshold)
-                        collector_wait_overhead += time.perf_counter() - _fill_t
-
-                collector_wait_time = time.perf_counter() - wait_start - collector_wait_overhead
-                if trace_recorder:
-                    trace_recorder.add_slice(
-                        "learner/wait_for_data",
-                        category="learner",
-                        start_ns=wait_start_ns,
-                        end_ns=time.perf_counter_ns(),
-                        args={"iteration": iteration},
-                    )
-                if iteration == 1:
-                    train_start_wall = logger.start_training_timer()
-                    if trace_recorder:
-                        training_e2e_start_ns = time.perf_counter_ns()
-                self._drain_metrics(
-                    metrics_queue,
-                    reward_history,
-                    latest_reward_components,
-                    logger,
-                    trace_recorder,
-                    log_collector_reward=self.dp_sync is None,
-                )
-                _reward_stats_ns = time.perf_counter_ns()
-                reward_stats_ptr = self._update_reward_stats_from_replay(
-                    replay_buffer,
-                    reward_stats_ptr,
-                    int(replay_buffer.ptr[0]),
-                    replay_source=replay_pipeline,
-                )
-                if trace_recorder:
-                    trace_recorder.add_slice(
-                        "learner/update_reward_stats",
-                        category="learner",
-                        start_ns=_reward_stats_ns,
-                        end_ns=time.perf_counter_ns(),
-                    )
-
-                # -- train --
-                iter_metrics = defaultdict(list)
-                ptr_before = int(replay_buffer.ptr[0])
-                learner = self.learner
-
-                with nullcontext():
-                    _sample_ns = time.perf_counter_ns()
-                    _replay_batch_wait_start = time.perf_counter()
-                    batch_ready = replay_pipeline.batch_ready(iteration, sample_count)
-                    _wait_batch_ns = time.perf_counter_ns()
-                    if not batch_ready:
-                        batch_ready = self._wait_for_replay_batch_ready(
-                            replay_pipeline,
-                            iteration,
-                            sample_count,
-                            metrics_queue,
-                            reward_history,
-                            latest_reward_components,
-                            logger,
-                            trace_recorder,
-                            replay_buffer,
-                            ckpt_path,
-                            train_start_wall,
-                        )
-                    replay_batch_wait_time = time.perf_counter() - _replay_batch_wait_start
-                    if trace_recorder:
-                        trace_recorder.add_slice(
-                            "learner/wait_for_replay_batch",
-                            category="learner",
-                            start_ns=_wait_batch_ns,
-                            end_ns=time.perf_counter_ns(),
-                            args={"iteration": iteration, "batch_ready": batch_ready},
-                        )
-                    replay_sample_start = time.perf_counter()
-                    large_batch = replay_pipeline.sample_large_batch(
-                        tick_id=iteration,
-                        sample_count=sample_count,
-                    )
-                    learner_replay_sample_time = time.perf_counter() - replay_sample_start
-                    replay_ingress_h2d_submit_time = float(
-                        getattr(replay_pipeline, "last_incremental_h2d_time_s", 0.0)
-                    )
-                    if iteration < max_iterations:
-                        if next_prepare_min_snapshot_ptr is None:
-                            raise RuntimeError(
-                                "Off-policy replay prefetch lost the inference update boundary"
-                            )
-                        replay_pipeline.start_prepare(
-                            iteration + 1,
-                            sample_count,
-                            min_snapshot_ptr=next_prepare_min_snapshot_ptr,
-                        )
-                        prepared_tick = iteration + 1
-                    if trace_recorder:
-                        trace_recorder.add_slice(
-                            "learner/replay_sample",
-                            category="learner",
-                            start_ns=_sample_ns,
-                            end_ns=time.perf_counter_ns(),
-                            args={
-                                "total_batch": sample_count,
-                                "pipeline": "gpu_resident",
-                                "batch_ready": batch_ready,
-                                "prefetch_mode": self.replay_prefetch_mode,
-                                "replay_pack_layout": self.replay_pack_layout,
-                                "replay_pack_executor": self.replay_pack_executor,
-                                "replay_h2d_submitter": self.replay_h2d_submitter,
-                                "replay_transfer_backend": self.replay_transfer_backend,
-                                "prepared_tick": prepared_tick,
-                                "explicit_compute_stream": False,
-                            },
-                        )
-
-                    train_start = time.perf_counter()
-                    train_phase_start_ns = time.perf_counter_ns()
-
-                    for update_idx in range(self.updates_per_step):
-                        s = update_idx * self.batch_size
-                        e = s + self.batch_size
-                        batch = {k: v[s:e] for k, v in large_batch.items()}
-                        read_critic_graph_metrics = update_idx == self.updates_per_step - 1
-
-                        _critic_ns = time.perf_counter_ns()
-                        if getattr(learner, "use_cuda_graph_critic", False) and hasattr(
-                            learner, "update_critic_cuda_graph"
-                        ):
-                            critic_metrics = learner.update_critic_cuda_graph(
-                                batch,
-                                read_metrics=read_critic_graph_metrics,
-                            )
-                        else:
-                            critic_metrics = learner.update_critic(batch)
-                        if trace_recorder:
-                            trace_recorder.add_slice(
-                                "learner/update_critic",
-                                category="learner",
-                                start_ns=_critic_ns,
-                                end_ns=time.perf_counter_ns(),
-                                args={"update_idx": update_idx},
-                            )
-                        for k, v in critic_metrics.items():
-                            iter_metrics[k].append(v)
-
-                        if update_idx % self.policy_frequency == 0:
-                            next_actor_update = update_idx + self.policy_frequency
-                            read_actor_graph_metrics = next_actor_update >= self.updates_per_step
-                            _actor_ns = time.perf_counter_ns()
-                            if getattr(learner, "use_cuda_graph_actor", False) and hasattr(
-                                learner, "update_actor_cuda_graph"
-                            ):
-                                actor_metrics = learner.update_actor_cuda_graph(
-                                    batch,
-                                    read_metrics=read_actor_graph_metrics,
-                                )
-                            else:
-                                actor_metrics = learner.update_actor(batch)
-                            if trace_recorder:
-                                trace_recorder.add_slice(
-                                    "learner/update_actor",
-                                    category="learner",
-                                    start_ns=_actor_ns,
-                                    end_ns=time.perf_counter_ns(),
-                                    args={"update_idx": update_idx},
-                                )
-                            for k, v in actor_metrics.items():
-                                iter_metrics[k].append(v)
-
-                        _target_ns = time.perf_counter_ns()
-                        learner.soft_update_target()
-                        replay_pipeline.progress()
-                        if trace_recorder:
-                            trace_recorder.add_slice(
-                                "learner/soft_update_target",
-                                category="learner",
-                                start_ns=_target_ns,
-                                end_ns=time.perf_counter_ns(),
-                                args={"update_idx": update_idx},
-                            )
-
-                    replay_pipeline.after_tick()
-                    device = torch.device(self.device)
-                    if device.type == "cuda":
-                        torch.cuda.current_stream(device).synchronize()
-                    elif device.type == "mps":
-                        torch.mps.synchronize()
-                    if trace_recorder:
-                        trace_recorder.add_slice(
-                            "learner/update_phase",
-                            category="learner_update",
-                            start_ns=train_phase_start_ns,
-                            end_ns=time.perf_counter_ns(),
-                            args={
-                                "iteration": iteration,
-                                "updates_per_step": self.updates_per_step,
-                                "policy_version_before": inference_scheduler.policy_version,
-                            },
-                        )
-
-                train_time = time.perf_counter() - train_start
-                self.learner.update_count += 1
-                inference_scheduler.finish_update()
-                self._collect_dp_sync_metrics(iter_metrics)
-                if trace_recorder:
-                    trace_recorder.add_counter(
-                        "replay_size",
-                        int(replay_buffer.size[0]),
-                        category="replay",
-                    )
-
-                iteration_time = time.perf_counter() - iteration_start
-
-                write_delta = int(replay_buffer.ptr[0]) - ptr_before
-                consume = self.batch_size * self.updates_per_step
-                write_read_ema = 0.9 * write_read_ema + 0.1 * (write_delta / max(consume, 1))
-                logger.update_buffer_utilization(write_read_ema)
-
-                avg_metrics = {k: statistics.mean(v) for k, v in iter_metrics.items() if v}
-                mean_reward = statistics.mean(reward_history) if reward_history else None
-
-                self._sync_logger_replay_counters(logger, replay_buffer)
-                log_payload = self._aggregate_log_statistics(
-                    logger,
-                    metrics=avg_metrics,
-                    reward=mean_reward,
-                    reward_metrics=build_reward_comparison_metrics(
-                        reward_history,
-                        mean_reward or 0.0,
-                    ),
-                    reward_components=latest_reward_components,
-                    train_time=train_time,
-                    collector_wait_time=collector_wait_time,
-                    replay_batch_wait_time=replay_batch_wait_time,
-                    learner_replay_sample_time=learner_replay_sample_time,
-                    sync_coordination_time=sync_coordination_time,
-                    replay_ingress_h2d_submit_time=replay_ingress_h2d_submit_time,
-                    inference_h2d_time=inference_h2d_time,
-                    inference_forward_time=inference_forward_time,
-                    inference_d2h_time=inference_d2h_time,
-                    inference_time=inference_time,
-                    iteration_time=iteration_time,
-                    extra_info={
-                        "throughput_steps": self.num_envs * self.env_steps_per_sync,
-                        "collector_active_steps_per_sec": (logger._collector_active_steps_per_sec),
-                        **build_offpolicy_sample_info(
-                            replay_batch_size_per_rank=self.batch_size,
-                            updates_per_step=self.updates_per_step,
-                            learner=self.learner,
-                        ),
-                    },
-                )
-                logged_reward = log_payload["reward"]
-                if logged_reward is not None:
-                    last_mean_reward = float(logged_reward)
-                    best_mean_reward = max(best_mean_reward, last_mean_reward)
-                    has_logged_reward = True
-                logger.log_step(
-                    iteration=iteration,
-                    **log_payload,
-                )
-
-                if save_interval > 0 and iteration % save_interval == 0:
-                    saved_path = self._save_checkpoint(
-                        log_dir=log_dir,
-                        iteration=iteration,
-                        logger=logger,
-                    )
-                    if saved_path is not None:
-                        ckpt_path = saved_path
-
-            if trace_recorder:
-                trace_recorder.add_slice(
-                    "learner/training_e2e",
-                    category="learner",
-                    start_ns=training_e2e_start_ns,
-                    end_ns=time.perf_counter_ns(),
-                    args={
-                        "iterations": iteration,
-                        "pipeline": "gpu_resident",
-                        "replay_h2d_submitter": self.replay_h2d_submitter,
-                        "replay_transfer_backend": self.replay_transfer_backend,
-                        "learner_log_interval": 1,
-                    },
-                )
-
-            # -- finalize --
-            replay_pipeline.close()
-            final_ckpt_path = os.path.join(log_dir, f"model_{max_iterations}.pt")
-            if ckpt_path != final_ckpt_path:
-                saved_path = self._save_checkpoint(
-                    log_dir=log_dir,
-                    iteration=max_iterations,
-                    logger=logger,
-                )
-                if saved_path is not None:
-                    ckpt_path = saved_path
-            if self.dp_sync is None:
-                self._sync_logger_replay_counters(logger, replay_buffer)
-            logger.finish()
-            if trace_recorder and trace_output_path:
-                trace_recorder.write_json(trace_output_path)
-                print(f"[DoubleBufferRunner] Perfetto trace written to {trace_output_path}")
-            self.last_run_summary = self._make_summary(
-                "completed",
-                iteration,
-                logger,
-                last_mean_reward if has_logged_reward else None,
-                best_mean_reward if has_logged_reward else None,
-                ckpt_path,
-                train_start_wall,
-                str(trace_output_path) if trace_output_path else None,
-            )
-            self._active_logger = None
-        except _CollectorDiedError:
-            self._fail_collector_died(
-                logger,
-                replay_buffer,
-                replay_pipeline,
-                iteration,
-                ckpt_path,
-                train_start_wall,
-            )
-            raise
-
-    @staticmethod
-    def _make_summary(
-        status,
-        iteration,
-        logger,
-        final_reward,
-        best_reward,
-        ckpt_path,
-        train_start_wall,
-        trace_path,
-    ) -> dict:
-        return {
-            "status": status,
-            "completed_iterations": iteration,
-            "total_env_steps": int(logger._total_steps),
-            "final_mean_reward": final_reward,
-            "best_mean_reward": best_reward,
-            "mean_episode_length": float(logger._mean_ep_length),
-            "last_checkpoint": ckpt_path,
-            "trace_path": trace_path,
-            "training_wall_time_sec": time.time() - train_start_wall,
-            "runtime_manifest": dict(getattr(logger, "_runtime_manifest", {})),
-            "final_env_steps_per_sec": logger._get_iter_steps_per_sec(),
-            "final_learner_samples_per_sec": logger._get_effective_samples_per_sec(),
-            "final_cycle_wall_ms": logger._get_iter_wall_time() * 1000.0,
-            "final_inference_ms": getattr(logger, "_inference_time", 0.0) * 1000.0,
-            "final_inference_h2d_ms": getattr(logger, "_inference_h2d_time", 0.0) * 1000.0,
-            "final_inference_forward_ms": getattr(logger, "_inference_forward_time", 0.0) * 1000.0,
-            "final_inference_d2h_ms": getattr(logger, "_inference_d2h_time", 0.0) * 1000.0,
-        }
diff --git a/src/unilab/algos/torch/offpolicy/runner.py b/src/unilab/algos/torch/offpolicy/runner.py
deleted file mode 100644
index 09fbed4bd..000000000
--- a/src/unilab/algos/torch/offpolicy/runner.py
+++ /dev/null
@@ -1,281 +0,0 @@
-"""Shared contracts for single-device off-policy runners."""
-
-from __future__ import annotations
-
-import sys
-from collections import deque
-from typing import Any
-
-from unilab.algos.torch.common.device import get_env_dims
-from unilab.ipc.async_runner import AsyncRunner
-from unilab.logging import OffPolicyLogger
-from unilab.training.seed import apply_training_seed
-from unilab.utils.device import get_default_device
-from unilab.utils.nan_guard import NanGuardCfg
-
-
-def compute_train_start_threshold(batch_size: int, learning_starts: int, num_envs: int) -> int:
-    """Return the minimum replay size required before learner updates may start."""
-    return max(int(batch_size), max(int(learning_starts), 0) * max(int(num_envs), 1), 0)
-
-
-def replay_buffer_ready_for_learning(
-    replay_buffer_size: int,
-    *,
-    batch_size: int,
-    learning_starts: int,
-    num_envs: int,
-) -> bool:
-    """Whether the replay buffer has enough samples for the first learner step."""
-    return int(replay_buffer_size) >= compute_train_start_threshold(
-        batch_size,
-        learning_starts,
-        num_envs,
-    )
-
-
-def get_learner_batch_multiplier(learner: Any) -> int:
-    """Return the effective learner batch multiplier for one replay row."""
-    if not bool(getattr(learner, "use_symmetry", False)):
-        return 1
-    symmetry = getattr(learner, "symmetry", None)
-    multiplier = int(getattr(symmetry, "batch_multiplier", 1) or 1)
-    return max(multiplier, 1)
-
-
-def build_offpolicy_sample_info(
-    *,
-    replay_batch_size_per_rank: int,
-    updates_per_step: int,
-    learner: Any,
-) -> dict[str, int]:
-    """Describe replay rows and effective learner samples for logging."""
-    updates_per_step = max(int(updates_per_step), 0)
-    replay_batch_size_per_rank = max(int(replay_batch_size_per_rank), 0)
-    batch_multiplier = get_learner_batch_multiplier(learner)
-    batch_size_per_rank = replay_batch_size_per_rank * batch_multiplier
-    return {
-        "batch_size_per_rank": batch_size_per_rank,
-        "effective_batch_size": batch_size_per_rank,
-        "replay_samples_per_iter": replay_batch_size_per_rank * updates_per_step,
-        "learner_samples_per_iter": batch_size_per_rank * updates_per_step,
-    }
-
-
-def build_reward_comparison_metrics(
-    reward_history: deque,
-    smoothed_reward: float,
-) -> dict[str, float]:
-    """Return the latest collector-side 100-episode mean for reward comparison."""
-    del smoothed_reward
-    if not reward_history:
-        return {}
-    return {"mean_ep100": float(reward_history[-1])}
-
-
-def update_reward_stats_from_replay(
-    learner: Any,
-    replay_buffer: Any,
-    *,
-    start_ptr: int,
-    end_ptr: int,
-    num_envs: int,
-    replay_source: Any | None = None,
-) -> int:
-    """Update reward statistics from rows committed to the device replay owner."""
-    if not hasattr(learner, "update_reward_stats"):
-        return end_ptr
-    if getattr(learner, "reward_normalizer", None) is None:
-        return end_ptr
-    if replay_source is None:
-        raise RuntimeError("Reward statistics require the device-authoritative replay source")
-
-    end_ptr, committed_fields = replay_source.read_committed_fields(
-        ("rewards", "dones"),
-        start_ptr=start_ptr,
-    )
-    count = end_ptr - start_ptr
-    if count <= 0:
-        return end_ptr
-    if count > replay_buffer.capacity:
-        count = replay_buffer.capacity
-        start_ptr = end_ptr - count
-    if count % num_envs != 0:
-        count -= count % num_envs
-        start_ptr = end_ptr - count
-    if count <= 0:
-        return end_ptr
-
-    rewards = committed_fields["rewards"][-count:]
-    dones = committed_fields["dones"][-count:]
-    num_steps = count // num_envs
-    learner.update_reward_stats(
-        rewards.view(num_steps, num_envs),
-        dones.view(num_steps, num_envs),
-    )
-    return end_ptr
-
-
-class OffPolicyRunner(AsyncRunner):
-    """Shared lifecycle and metrics helpers for the device replay runner."""
-
-    def __init__(
-        self,
-        learner,
-        env_name: str,
-        algo_type: str,
-        num_envs: int = 4096,
-        replay_buffer_n: int = 1024,
-        batch_size: int = 8192,
-        learning_starts: int = 0,
-        updates_per_step: int = 8,
-        policy_frequency: int = 4,
-        env_steps_per_sync: int = 1,
-        device: str | None = None,
-        obs_normalization: bool = False,
-        sim_backend: str = "mujoco",
-        env_cfg_override: dict | None = None,
-        seed: int | None = None,
-        trace_enabled: bool = False,
-        trace_output_dir: str | None = None,
-        trace_thread_time: bool = False,
-        trace_cuda_events: bool = True,
-        nan_guard_cfg: NanGuardCfg | None = None,
-        torch_thread_runtime: dict[str, Any] | None = None,
-    ):
-        if int(env_steps_per_sync) < 1:
-            raise ValueError("Off-policy env_steps_per_sync must be >= 1")
-        super().__init__(
-            env_name=env_name,
-            env_cfg_overrides={},
-            rl_cfg={},
-            device=device,
-            collector_device="cpu",
-            num_envs=num_envs,
-            sim_backend=sim_backend,
-        )
-
-        self.learner = learner
-        self.env_cfg_override = env_cfg_override
-        self.algo_type = algo_type
-        self.replay_buffer_n = replay_buffer_n
-        self.batch_size = batch_size
-        self.learning_starts = max(int(learning_starts), 0)
-        self.train_start_threshold = compute_train_start_threshold(
-            batch_size,
-            self.learning_starts,
-            num_envs,
-        )
-        self.updates_per_step = updates_per_step
-        self.policy_frequency = policy_frequency
-        self.env_steps_per_sync = int(env_steps_per_sync)
-        self.obs_normalization = obs_normalization
-        self.seed = seed
-        self._active_logger: OffPolicyLogger | None = None
-        self.trace_enabled = trace_enabled
-        self.trace_output_dir = trace_output_dir
-        self.trace_thread_time = trace_thread_time
-        self.trace_cuda_events = trace_cuda_events
-        self.nan_guard_cfg = nan_guard_cfg
-        self.torch_thread_runtime = torch_thread_runtime
-
-        apply_training_seed(self.seed, torch_runtime=True, cuda=True)
-        self.obs_dim, self.action_dim, self.critic_obs_dim = get_env_dims(
-            self.env_name,
-            sim_backend,
-            env_cfg_override,
-        )
-
-    def _get_default_device(self) -> str:
-        return get_default_device()
-
-    def _build_learner(self):
-        return self.learner
-
-    @staticmethod
-    def _sync_logger_replay_counters(logger, replay_buffer) -> None:
-        logger.log_collector(
-            int(replay_buffer.ptr[0]),
-            int(replay_buffer.size[0]),
-        )
-
-    def _update_reward_stats_from_replay(
-        self,
-        replay_buffer,
-        start_ptr: int,
-        end_ptr: int,
-        replay_source: Any | None = None,
-    ) -> int:
-        return update_reward_stats_from_replay(
-            self.learner,
-            replay_buffer,
-            start_ptr=start_ptr,
-            end_ptr=end_ptr,
-            num_envs=self.num_envs,
-            replay_source=replay_source,
-        )
-
-    def close(self) -> None:
-        active_logger = getattr(self, "_active_logger", None)
-        try:
-            if active_logger is not None:
-                active_logger.close()
-        finally:
-            self._active_logger = None
-            # Terminal restoration must not be able to prevent collector,
-            # shared-memory, queue, and pipe cleanup.
-            super().close()
-
-    @staticmethod
-    def _drain_metrics(
-        queue,
-        reward_history,
-        reward_components,
-        logger,
-        trace_recorder=None,
-        *,
-        log_collector_reward: bool = True,
-    ):
-        while True:
-            try:
-                metrics = queue.get_nowait()
-            except Exception:
-                break
-            if "error" in metrics:
-                logger.log_status(f"[red]Collector ERROR: {metrics['error']}[/]")
-                raise RuntimeError(f"Collector process failed: {metrics['error']}")
-
-            try:
-                updated_reward = False
-                if "runtime_manifest" in metrics:
-                    logger.update_runtime_manifest(metrics["runtime_manifest"])
-                if "mean_ep_reward" in metrics:
-                    reward_history.append(metrics["mean_ep_reward"])
-                    updated_reward = True
-                if "reward_components" in metrics:
-                    reward_components.clear()
-                    reward_components.update(metrics["reward_components"])
-                if "mean_ep_length" in metrics:
-                    logger.update_ep_length(metrics["mean_ep_length"])
-                if "collector_timing_ms" in metrics:
-                    logger.update_collector_timing(metrics["collector_timing_ms"])
-                active_steps_per_sec = metrics.get("collector_active_steps_per_sec")
-                if active_steps_per_sec is not None:
-                    logger.update_collector_active_steps_per_sec(float(active_steps_per_sec))
-                if "timeout_rate" in metrics:
-                    logger.update_timeout_rate(float(metrics["timeout_rate"]))
-                if "total_steps" in metrics and "buffer_size" in metrics:
-                    logger.log_collector(
-                        metrics["total_steps"],
-                        metrics["buffer_size"],
-                        (
-                            metrics.get("mean_ep_reward", 0.0)
-                            if updated_reward and log_collector_reward
-                            else 0.0
-                        ),
-                    )
-                if trace_recorder and "trace_events" in metrics:
-                    trace_recorder.extend(metrics["trace_events"])
-            except Exception as exc:
-                print(f"[OffPolicyRunner] metrics drain error: {exc}", file=sys.stderr)
-                break
diff --git a/src/unilab/algos/torch/offpolicy/runtime.py b/src/unilab/algos/torch/offpolicy/runtime.py
deleted file mode 100644
index d6d007ba0..000000000
--- a/src/unilab/algos/torch/offpolicy/runtime.py
+++ /dev/null
@@ -1,69 +0,0 @@
-"""Runtime resolution helpers for off-policy script assembly."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from importlib import import_module
-from typing import Any
-
-
-@dataclass(frozen=True)
-class OffPolicyRuntime:
-    """Optional runtime overrides for the generic off-policy SAC path.
-
-    All fields are optional so custom runtimes only declare the behaviour they
-    need to change from standard SAC.
-    """
-
-    learner_cls: type[Any] | None = None
-    algo_type: str | None = None
-    actor_kwargs: dict[str, Any] = field(default_factory=dict)
-    supports_symmetry: bool = True
-
-    def build_model_kwargs(self, *, obs_dim: int, critic_obs_dim: int) -> dict[str, Any]:
-        """Build learner model kwargs from the environment observation contract."""
-        del obs_dim, critic_obs_dim
-        return dict(self.actor_kwargs)
-
-
-def _resolve_callable(path: str) -> Any:
-    module_path: str
-    attr_name: str
-    if ":" in path:
-        module_path, attr_name = path.split(":", 1)
-    else:
-        module_path, _, attr_name = path.rpartition(".")
-    if not module_path or not attr_name:
-        raise ValueError(f"Invalid runtime resolver path: {path!r}")
-    resolved = getattr(import_module(module_path), attr_name)
-    if not callable(resolved):
-        raise TypeError(f"Runtime resolver {path!r} is not callable.")
-    return resolved
-
-
-def resolve_custom_offpolicy_runtime(rl_cfg: dict[str, Any]) -> OffPolicyRuntime | None:
-    """Resolve an optional custom off-policy runtime from owner config."""
-    runtime_resolver = rl_cfg.get("runtime_resolver")
-    if runtime_resolver in (None, ""):
-        runtime_impl = rl_cfg.get("runtime_impl")
-        if runtime_impl not in (None, ""):
-            raise ValueError(
-                "Off-policy owner config selected "
-                f"runtime_impl={runtime_impl!r} but did not define algo.runtime_resolver."
-            )
-        return None
-
-    resolver = _resolve_callable(str(runtime_resolver))
-    runtime = resolver(rl_cfg)
-    if runtime is None:
-        raise ValueError(
-            f"Off-policy runtime resolver {runtime_resolver!r} returned None "
-            "for rl_cfg runtime selection."
-        )
-
-    if not isinstance(runtime, OffPolicyRuntime):
-        raise TypeError(
-            f"Off-policy runtime resolver {runtime_resolver!r} must return "
-            "an OffPolicyRuntime instance."
-        )
-    return runtime
diff --git a/src/unilab/algos/torch/offpolicy/thread_budget.py b/src/unilab/algos/torch/offpolicy/thread_budget.py
deleted file mode 100644
index 6239ec733..000000000
--- a/src/unilab/algos/torch/offpolicy/thread_budget.py
+++ /dev/null
@@ -1,245 +0,0 @@
-"""Torch CPU thread budget helpers for off-policy training.
-
-Off-policy training runs Torch in multiple processes: the learner and the CPU
-collector of each rank (multi-GPU data parallel runs one learner+collector
-pair per rank). PyTorch defaults each process to a host-sized thread pool, so
-every role needs an explicit budget.
-"""
-
-from __future__ import annotations
-
-import os
-import sys
-from collections.abc import Iterator, Mapping
-from contextlib import contextmanager
-from typing import Any, cast
-
-_AUTO = "auto"
-_BLAS_THREAD_ENV_KEYS = (
-    "OMP_NUM_THREADS",
-    "MKL_NUM_THREADS",
-    "OPENBLAS_NUM_THREADS",
-    "NUMEXPR_NUM_THREADS",
-)
-
-
-def _cfg_get(cfg: Any, key: str, default: Any) -> Any:
-    if cfg is None:
-        return default
-    if isinstance(cfg, Mapping):
-        return cfg.get(key, default)
-    return getattr(cfg, key, default)
-
-
-def _as_positive_int(value: Any, *, field: str) -> int:
-    try:
-        resolved = int(value)
-    except (TypeError, ValueError) as exc:
-        raise ValueError(
-            f"training.torch_threads.{field} must be a positive int or 'auto'"
-        ) from exc
-    if resolved < 1:
-        raise ValueError(f"training.torch_threads.{field} must be >= 1, got {value!r}")
-    return resolved
-
-
-def _resolve_role_threads(value: Any, *, role: str, cpu_count: int) -> int:
-    if value is None or str(value).lower() == _AUTO:
-        if role == "collector":
-            return min(4, max(1, cpu_count // 16))
-        return min(8, max(1 if cpu_count < 4 else 2, cpu_count // 8))
-    return _as_positive_int(value, field=f"{role}_num_threads")
-
-
-def _resolve_interop_threads(value: Any, *, role: str) -> int:
-    if value is None or str(value).lower() == _AUTO:
-        return 1
-    return _as_positive_int(value, field=f"{role}_num_interop_threads")
-
-
-def _resolve_compile_threads(value: Any, *, cpu_count: int) -> int:
-    if value is None or str(value).lower() == _AUTO:
-        return min(2, max(1, cpu_count // 32))
-    return _as_positive_int(value, field="compile_threads")
-
-
-def resolve_torch_thread_runtime(
-    cfg: Any,
-    *,
-    cpu_count: int | None = None,
-) -> dict[str, Any]:
-    """Resolve ``training.torch_threads`` into a spawn-safe runtime manifest."""
-    cpu_total = max(1, int(cpu_count or os.cpu_count() or 1))
-    enabled = bool(_cfg_get(cfg, "enabled", True))
-    set_env_vars = bool(_cfg_get(cfg, "set_env_vars", True))
-    if not enabled:
-        return {
-            "enabled": False,
-            "cpu_count": cpu_total,
-            "set_env_vars": set_env_vars,
-        }
-    learner_threads = _resolve_role_threads(
-        _cfg_get(cfg, "learner_num_threads", _AUTO),
-        role="learner",
-        cpu_count=cpu_total,
-    )
-    collector_threads = _resolve_role_threads(
-        _cfg_get(cfg, "collector_num_threads", _AUTO),
-        role="collector",
-        cpu_count=cpu_total,
-    )
-    learner_interop = _resolve_interop_threads(
-        _cfg_get(cfg, "learner_num_interop_threads", 1),
-        role="learner",
-    )
-    collector_interop = _resolve_interop_threads(
-        _cfg_get(cfg, "collector_num_interop_threads", 1),
-        role="collector",
-    )
-    compile_threads = _resolve_compile_threads(
-        _cfg_get(cfg, "compile_threads", _AUTO),
-        cpu_count=cpu_total,
-    )
-    return {
-        "enabled": enabled,
-        "cpu_count": cpu_total,
-        "set_env_vars": set_env_vars,
-        "compile_threads": compile_threads,
-        "learner": {
-            "num_threads": learner_threads,
-            "num_interop_threads": learner_interop,
-        },
-        "collector": {
-            "num_threads": collector_threads,
-            "num_interop_threads": collector_interop,
-        },
-    }
-
-
-def _set_torch_interop_threads(torch_module: Any, num_threads: int, *, role: str) -> None:
-    get_num_interop_threads = getattr(torch_module, "get_num_interop_threads", None)
-    set_num_interop_threads = getattr(torch_module, "set_num_interop_threads", None)
-    if not callable(set_num_interop_threads):
-        return
-    try:
-        if callable(get_num_interop_threads):
-            current_threads = cast(Any, get_num_interop_threads())
-            if int(current_threads) == int(num_threads):
-                return
-        set_num_interop_threads(int(num_threads))
-    except RuntimeError as exc:
-        print(
-            "[offpolicy.thread_budget] unable to set "
-            f"{role} torch inter-op threads to {num_threads}: {exc}",
-            file=sys.stderr,
-        )
-
-
-def _set_torch_compile_threads(torch_module: Any, num_threads: int) -> None:
-    os.environ["TORCHINDUCTOR_COMPILE_THREADS"] = str(int(num_threads))
-    try:
-        config = getattr(getattr(torch_module, "_inductor", None), "config", None)
-        if config is None:
-            from torch._inductor import config as imported_config  # type: ignore[import-not-found]
-
-            config = imported_config
-
-        setattr(config, "compile_threads", int(num_threads))
-    except Exception as exc:
-        print(
-            "[offpolicy.thread_budget] unable to set torch inductor compile_threads "
-            f"to {num_threads}: {exc}",
-            file=sys.stderr,
-        )
-
-
-def _thread_env_values(runtime: Mapping[str, Any], *, role: str) -> dict[str, str]:
-    if role not in ("learner", "collector"):
-        raise ValueError(f"Unsupported torch thread budget role: {role!r}")
-    role_cfg = runtime[role]
-    num_threads = str(int(role_cfg["num_threads"]))
-    values = {key: num_threads for key in _BLAS_THREAD_ENV_KEYS}
-    values["TORCH_NUM_THREADS"] = num_threads
-    values["TORCHINDUCTOR_COMPILE_THREADS"] = str(int(runtime["compile_threads"]))
-    return values
-
-
-@contextmanager
-def torch_thread_env(runtime: Mapping[str, Any] | None, *, role: str) -> Iterator[None]:
-    """Temporarily expose a role's thread budget to child process startup."""
-    if (
-        not runtime
-        or not bool(runtime.get("enabled", True))
-        or not bool(runtime.get("set_env_vars", True))
-    ):
-        yield
-        return
-
-    values = _thread_env_values(runtime, role=role)
-    previous = {key: os.environ.get(key) for key in values}
-    try:
-        os.environ.update(values)
-        yield
-    finally:
-        for key, old_value in previous.items():
-            if old_value is None:
-                os.environ.pop(key, None)
-            else:
-                os.environ[key] = old_value
-
-
-def apply_torch_thread_runtime(
-    runtime: Mapping[str, Any] | None,
-    *,
-    role: str,
-    torch_module: Any | None = None,
-) -> dict[str, Any]:
-    """Apply a resolved runtime manifest in the current process.
-
-    Returns the role manifest used by tests and status logging.
-    """
-    if not runtime or not bool(runtime.get("enabled", True)):
-        return {"enabled": False, "role": role}
-    if role not in ("learner", "collector"):
-        raise ValueError(f"Unsupported torch thread budget role: {role!r}")
-
-    role_cfg = dict(runtime[role])
-    num_threads = int(role_cfg["num_threads"])
-    num_interop_threads = int(role_cfg["num_interop_threads"])
-    compile_threads = int(runtime["compile_threads"])
-
-    if bool(runtime.get("set_env_vars", True)):
-        os.environ.update(_thread_env_values(runtime, role=role))
-
-    torch_runtime = torch_module
-    if torch_runtime is None:
-        import torch as imported_torch
-
-        torch_runtime = imported_torch
-
-    set_num_threads = getattr(torch_runtime, "set_num_threads", None)
-    if callable(set_num_threads):
-        set_num_threads(num_threads)
-    _set_torch_interop_threads(torch_runtime, num_interop_threads, role=role)
-    _set_torch_compile_threads(torch_runtime, compile_threads)
-
-    return {
-        "enabled": True,
-        "role": role,
-        "num_threads": num_threads,
-        "num_interop_threads": num_interop_threads,
-        "compile_threads": compile_threads,
-    }
-
-
-def format_torch_thread_runtime(runtime: Mapping[str, Any] | None) -> str:
-    if not runtime or not bool(runtime.get("enabled", True)):
-        return "Torch thread budget: disabled"
-    learner = runtime["learner"]
-    collector = runtime["collector"]
-    return (
-        "Torch thread budget: "
-        f"learner={learner['num_threads']} intra/{learner['num_interop_threads']} inter, "
-        f"collector={collector['num_threads']} intra/{collector['num_interop_threads']} inter, "
-        f"compile_workers={runtime['compile_threads']}"
-    )
diff --git a/src/unilab/algos/torch/offpolicy/worker.py b/src/unilab/algos/torch/offpolicy/worker.py
deleted file mode 100644
index f00fcc16b..000000000
--- a/src/unilab/algos/torch/offpolicy/worker.py
+++ /dev/null
@@ -1,508 +0,0 @@
-"""Environment and replay collector for learner-owned off-policy inference."""
-
-import queue
-import sys
-import time
-from typing import Any, cast
-
-import numpy as np
-import torch
-
-from unilab.algos.torch.common.collector_timing import extract_env_step_breakdown_timing_ms
-from unilab.algos.torch.offpolicy.thread_budget import apply_torch_thread_runtime
-from unilab.base.final_observation import resolve_terminal_observation_contract
-from unilab.base.observations import split_obs_dict
-from unilab.base.registry import ensure_registries
-from unilab.training.seed import apply_training_seed
-
-# Exclusive phases for one collector loop iteration (one vectorized env.step).
-# Every key is recorded once per iteration so the reported averages share one
-# denominator and can be summed without double counting.
-# - replay_write_ms: pack transitions and write them into the bounded ingress
-COLLECTOR_TIMING_KEYS = (
-    "inference_request_ms",
-    "learner_action_wait_ms",
-    "env_step_ms",
-    "replay_write_ms",
-)
-COLLECTOR_ACTIVE_TIMING_KEYS = tuple(
-    key for key in COLLECTOR_TIMING_KEYS if key != "learner_action_wait_ms"
-)
-
-
-def sample_offpolicy_actions(
-    actor,
-    algo_type: str,
-    obs_torch: torch.Tensor,
-    prev_dones_torch: torch.Tensor,
-    priv_info_torch: torch.Tensor | None = None,
-) -> torch.Tensor:
-    """Sample actions using the algorithm's exploration policy."""
-    if algo_type in ("sac", "td3", "flashsac"):
-        return cast(
-            torch.Tensor,
-            actor.explore(obs_torch, dones=prev_dones_torch, deterministic=False),
-        )
-    if algo_type == "hora_sac":
-        if priv_info_torch is None:
-            raise ValueError("HORA-SAC action sampling requires priv_info_torch.")
-        return cast(
-            torch.Tensor,
-            actor.explore(obs_torch, priv_info_torch, deterministic=False),
-        )
-    raise ValueError(f"Unsupported off-policy algo_type for learner action sampling: {algo_type}")
-
-
-def resolve_offpolicy_actor_priv_info(
-    *,
-    algo_type: str,
-    obs_np: np.ndarray,
-    critic_np: np.ndarray,
-    info: dict | None,
-) -> np.ndarray | None:
-    """Resolve optional actor context for privileged off-policy actors."""
-    if algo_type != "hora_sac":
-        return None
-
-    from unilab.algos.torch.hora.observations import split_hora_obs_with_priv_info
-
-    _, _, priv_info_np = split_hora_obs_with_priv_info(
-        {"obs": obs_np, "critic": critic_np},
-        info,
-    )
-    if priv_info_np is None:
-        raise ValueError(
-            "HORA-SAC requires privileged info from info['critic_info'] "
-            "or the critic observation tail."
-        )
-    return np.asarray(priv_info_np, dtype=np.float32)
-
-
-def _record_timing_ms(timing_accum_ms, timing_counts, key: str, value: float) -> None:
-    timing_accum_ms[key] += float(value)
-    timing_counts[key] += 1
-
-
-def _record_phase_ms(cycle_timing_ms: dict[str, float], key: str, start_ns: int) -> int:
-    end_ns = time.perf_counter_ns()
-    cycle_timing_ms[key] += (end_ns - start_ns) / 1e6
-    return end_ns
-
-
-def compute_collector_active_steps_per_sec(
-    collector_timing_ms: dict[str, float],
-    *,
-    num_envs: int,
-) -> float | None:
-    """Return collector active throughput excluding explicit wait/coordination time."""
-    active_ms = sum(
-        float(collector_timing_ms.get(key, 0.0)) for key in COLLECTOR_ACTIVE_TIMING_KEYS
-    )
-    if active_ms <= 0.0:
-        return None
-    return int(num_envs) / (active_ms / 1000.0)
-
-
-def _publish_inference_tick(
-    coordination_queue,
-    tick_id: int,
-    stop_event,
-    *,
-    timeout: float = 30.0,
-) -> bool:
-    deadline = time.monotonic() + timeout
-    while not stop_event.is_set():
-        if time.monotonic() >= deadline:
-            raise TimeoutError(f"Timed out publishing off-policy inference tick {tick_id}")
-        try:
-            coordination_queue.put(int(tick_id), timeout=0.1)
-            return True
-        except queue.Full:
-            continue
-    return False
-
-
-def _wait_for_inference_tick(
-    coordination_queue,
-    tick_id: int,
-    stop_event,
-    *,
-    timeout: float = 30.0,
-) -> bool:
-    deadline = time.monotonic() + timeout
-    while not stop_event.is_set():
-        if time.monotonic() >= deadline:
-            raise TimeoutError(f"Timed out waiting for off-policy inference tick {tick_id}")
-        try:
-            received_tick = int(coordination_queue.get(timeout=0.1))
-        except queue.Empty:
-            continue
-        if received_tick != int(tick_id):
-            raise RuntimeError(
-                f"Off-policy inference tick mismatch: expected {tick_id}, got {received_tick}"
-            )
-        return True
-    return False
-
-
-def off_policy_collector_fn(
-    stop_event,
-    env_name: str,
-    num_envs: int,
-    replay_buffer,
-    inference_slot,
-    inference_request_queue,
-    inference_response_queue,
-    algo_type: str = "sac",
-    metrics_queue=None,
-    sim_backend: str = "mujoco",
-    env_cfg_override: dict | None = None,
-    seed: int | None = None,
-    trace_enabled: bool = False,
-    trace_thread_time: bool = False,
-    nan_guard_cfg=None,
-    torch_thread_runtime=None,
-):
-    """Entry point for the off-policy collector subprocess.
-
-    Error handling is provided by ``_collector_entry_wrapper`` in
-    ``async_runner.py``.
-    """
-    _run_collector(
-        stop_event=stop_event,
-        env_name=env_name,
-        num_envs=num_envs,
-        replay_buffer=replay_buffer,
-        inference_slot=inference_slot,
-        inference_request_queue=inference_request_queue,
-        inference_response_queue=inference_response_queue,
-        algo_type=algo_type,
-        metrics_queue=metrics_queue,
-        sim_backend=sim_backend,
-        env_cfg_override=env_cfg_override,
-        seed=seed,
-        trace_enabled=trace_enabled,
-        trace_thread_time=trace_thread_time,
-        nan_guard_cfg=nan_guard_cfg,
-        torch_thread_runtime=torch_thread_runtime,
-    )
-
-
-def _run_collector(
-    stop_event,
-    env_name,
-    num_envs,
-    replay_buffer,
-    inference_slot,
-    inference_request_queue,
-    inference_response_queue,
-    algo_type,
-    metrics_queue,
-    sim_backend,
-    env_cfg_override,
-    seed,
-    trace_enabled,
-    trace_thread_time,
-    nan_guard_cfg=None,
-    torch_thread_runtime=None,
-):
-    from unilab.base import registry
-
-    apply_torch_thread_runtime(torch_thread_runtime, role="collector", torch_module=torch)
-    ensure_registries()
-    apply_training_seed(seed, torch_runtime=False, cuda=False)
-
-    trace_recorder = None
-    if trace_enabled:
-        from unilab.logging.trace_event import TraceRecorder
-
-        trace_recorder = TraceRecorder("offpolicy_collector")
-
-    # Initialize environment
-    env: Any = registry.make(
-        env_name, num_envs=num_envs, sim_backend=sim_backend, env_cfg_override=env_cfg_override
-    )
-    if nan_guard_cfg is not None and nan_guard_cfg.enabled:
-        from unilab.utils.nan_guard import NanGuard
-
-        env.set_nan_guard(
-            NanGuard(
-                nan_guard_cfg,
-                num_envs=env.num_envs,
-                supports_state_playback=env.play_capabilities.supports_physics_state_playback,
-            )
-        )
-    if env.state is None:
-        env.init_state()
-
-    replay_buffer.trace_recorder = trace_recorder
-    replay_buffer.trace_thread_time = trace_thread_time
-    replay_buffer.attach_stop_event(stop_event)
-    total_steps = 0
-    ep_rewards = []
-    ep_lengths = []
-    current_ep_rewards = np.zeros(num_envs, dtype=np.float32)
-    current_ep_lengths = np.zeros(num_envs, dtype=np.int32)
-    from collections import defaultdict
-
-    ep_reward_components = defaultdict(list)
-    timing_accum_ms: defaultdict[str, float] = defaultdict(float)
-    timing_counts: defaultdict[str, int] = defaultdict(int)
-    done_count_window = 0
-    timeout_count_window = 0
-
-    state = env.state
-    assert state is not None
-    obs_np, critic_np = split_obs_dict(state.obs)
-    obs_np = np.asarray(obs_np, dtype=np.float32)
-    critic_np = np.asarray(critic_np, dtype=np.float32)
-    info_dict = state.info
-    prev_dones_np = np.zeros(num_envs, dtype=np.float32)
-    import time as _time
-
-    runtime_manifest = {
-        "inference_owner": "learner",
-        "actor_owned": False,
-        "weight_sync_attached": False,
-        "torch_inference": False,
-        "cuda_context_initialized": bool(torch.cuda.is_initialized()),
-    }
-    if trace_recorder:
-        manifest_ns = _time.perf_counter_ns()
-        trace_recorder.add_slice(
-            "collector/runtime_manifest",
-            category="collector",
-            start_ns=manifest_ns,
-            end_ns=manifest_ns,
-            args=runtime_manifest,
-        )
-    if metrics_queue is not None:
-        manifest_message: dict[str, Any] = {"runtime_manifest": runtime_manifest}
-        if trace_recorder:
-            manifest_message["trace_events"] = trace_recorder.drain_events()
-        try:
-            metrics_queue.put_nowait(manifest_message)
-        except queue.Full:
-            pass
-
-    inference_tick = 0
-    # Collection loop
-    try:
-        while not stop_event.is_set():
-            cycle_timing_ms: dict[str, float] = dict.fromkeys(COLLECTOR_TIMING_KEYS, 0.0)
-            phase_start_ns = _time.perf_counter_ns()
-
-            actor_context_np = resolve_offpolicy_actor_priv_info(
-                algo_type=algo_type,
-                obs_np=obs_np,
-                critic_np=critic_np,
-                info=info_dict,
-            )
-            actor_input_np = (
-                np.concatenate((obs_np, actor_context_np), axis=1)
-                if actor_context_np is not None
-                else obs_np
-            )
-            request_ns = _time.perf_counter_ns()
-            inference_slot.publish_observation(
-                tick_id=inference_tick,
-                observations=actor_input_np,
-                dones=prev_dones_np,
-            )
-            if not _publish_inference_tick(
-                inference_request_queue,
-                inference_tick,
-                stop_event,
-            ):
-                break
-            if trace_recorder:
-                trace_recorder.add_slice(
-                    "collector/inference_request",
-                    category="collector",
-                    start_ns=request_ns,
-                    end_ns=_time.perf_counter_ns(),
-                    args={"tick_id": inference_tick},
-                )
-            phase_start_ns = _record_phase_ms(
-                cycle_timing_ms, "inference_request_ms", phase_start_ns
-            )
-            wait_ns = _time.perf_counter_ns()
-            if not _wait_for_inference_tick(
-                inference_response_queue,
-                inference_tick,
-                stop_event,
-            ):
-                break
-            actions_np, policy_version = inference_slot.consume_action(tick_id=inference_tick)
-            if trace_recorder:
-                trace_recorder.add_slice(
-                    "collector/wait_for_learner_action",
-                    category="collector",
-                    start_ns=wait_ns,
-                    end_ns=_time.perf_counter_ns(),
-                    args={
-                        "tick_id": inference_tick,
-                        "policy_version": policy_version,
-                    },
-                )
-            phase_start_ns = _record_phase_ms(
-                cycle_timing_ms, "learner_action_wait_ms", phase_start_ns
-            )
-            inference_tick += 1
-
-            # Step environment
-            _env_ns = _time.perf_counter_ns()
-            state = env.step(actions_np)
-            if trace_recorder:
-                trace_recorder.add_slice(
-                    "collector/env_step",
-                    category="collector",
-                    start_ns=_env_ns,
-                    end_ns=_time.perf_counter_ns(),
-                    args={"num_envs": num_envs},
-                )
-            phase_start_ns = _record_phase_ms(cycle_timing_ms, "env_step_ms", phase_start_ns)
-            cycle_timing_ms.update(extract_env_step_breakdown_timing_ms(state.info))
-
-            # Extract data as numpy
-            next_obs_np, next_critic_np = split_obs_dict(state.obs)
-            next_obs_np = np.asarray(next_obs_np, dtype=np.float32)
-            next_critic_np = np.asarray(next_critic_np, dtype=np.float32)
-            rewards_np = np.asarray(state.reward, dtype=np.float32).ravel()
-
-            truncated_np = state.truncated.astype(np.float32, copy=False).ravel()
-            combined_dones = (
-                (state.terminated | state.truncated).astype(np.float32, copy=False).ravel()
-            )
-            prev_dones_np = combined_dones
-            done_mask_np = combined_dones > 0.5
-            timeout_mask_np = truncated_np > 0.5
-
-            done_count_window += int(np.count_nonzero(done_mask_np))
-            timeout_count_window += int(np.count_nonzero(timeout_mask_np))
-
-            terminal_contract = resolve_terminal_observation_contract(
-                next_obs_batch_size=next_obs_np.shape[0],
-                final_observation=state.final_observation,
-                done=done_mask_np,
-                info=state.info,
-                truncated=truncated_np,
-            )
-            phase_start_ns = _record_phase_ms(cycle_timing_ms, "replay_write_ms", phase_start_ns)
-
-            # ReplayBuffer `dones` follows the UniLab env lifecycle contract:
-            # done = terminated | truncated. Learners use `truncated` to keep
-            # bootstrap enabled for timeout/truncation rows.
-            _rb_ns = _time.perf_counter_ns()
-            replay_buffer.add(
-                torch.from_numpy(obs_np),
-                torch.from_numpy(actions_np),
-                torch.from_numpy(rewards_np),
-                torch.from_numpy(next_obs_np),
-                torch.from_numpy(combined_dones),
-                torch.from_numpy(truncated_np),
-                terminal_mask=torch.from_numpy(terminal_contract.terminal_mask),
-                terminal_next_obs=(
-                    torch.from_numpy(terminal_contract.terminal_obs)
-                    if terminal_contract.terminal_obs is not None
-                    else None
-                ),
-                critic=torch.from_numpy(critic_np),
-                next_critic=torch.from_numpy(next_critic_np),
-                terminal_next_critic=(
-                    torch.from_numpy(terminal_contract.terminal_critic)
-                    if terminal_contract.terminal_critic is not None
-                    else None
-                ),
-            )
-            if trace_recorder:
-                trace_recorder.add_slice(
-                    "collector/replay_add",
-                    category="collector",
-                    start_ns=_rb_ns,
-                    end_ns=_time.perf_counter_ns(),
-                )
-            phase_start_ns = _record_phase_ms(cycle_timing_ms, "replay_write_ms", phase_start_ns)
-
-            # Track episode rewards - vectorized
-            current_ep_rewards += rewards_np
-            current_ep_lengths += 1
-            reset_mask = combined_dones > 0.5
-            reset_indices = np.where(reset_mask)[0]
-            if len(reset_indices) > 0:
-                ep_rewards.extend(current_ep_rewards[reset_indices].tolist())
-                ep_lengths.extend(current_ep_lengths[reset_indices].tolist())
-                current_ep_rewards[reset_indices] = 0.0
-                current_ep_lengths[reset_indices] = 0
-
-            obs_np = next_obs_np
-            critic_np = next_critic_np
-            info_dict = state.info
-            total_steps += num_envs
-
-            # Extract reward components from env info
-            log_info = state.info.get("log", {})
-            if log_info:
-                for k, v in log_info.items():
-                    if k.startswith("reward/"):
-                        ep_reward_components[k].append(v)
-
-            # Send metrics periodically
-            if metrics_queue is not None and total_steps % (num_envs * 10) == 0:
-                import statistics
-
-                try:
-                    msg = {
-                        "total_steps": total_steps,
-                        "buffer_size": int(replay_buffer.size[0]),
-                    }
-                    if ep_rewards:
-                        msg["mean_ep_reward"] = statistics.mean(ep_rewards[-100:])
-                        msg["mean_ep_length"] = (
-                            statistics.mean(ep_lengths[-100:]) if ep_lengths else 0.0
-                        )
-                    # Add mean reward components
-                    if ep_reward_components:
-                        components_mean = {}
-                        for k, vals in ep_reward_components.items():
-                            if vals:
-                                components_mean[k] = statistics.mean(vals)
-                        msg["reward_components"] = components_mean
-                        ep_reward_components.clear()  # reset after sending
-
-                    if timing_counts:
-                        msg["collector_timing_ms"] = {
-                            k: (v / timing_counts[k])
-                            for k, v in timing_accum_ms.items()
-                            if timing_counts[k] > 0
-                        }
-                        collector_active_steps_per_sec = compute_collector_active_steps_per_sec(
-                            msg["collector_timing_ms"],
-                            num_envs=num_envs,
-                        )
-                        if collector_active_steps_per_sec is not None:
-                            msg["collector_active_steps_per_sec"] = collector_active_steps_per_sec
-
-                    if done_count_window > 0:
-                        msg["timeout_rate"] = timeout_count_window / done_count_window
-                        done_count_window = 0
-                        timeout_count_window = 0
-
-                    if trace_recorder:
-                        msg["trace_events"] = trace_recorder.drain_events()
-
-                    metrics_queue.put_nowait(msg)
-                    if "collector_timing_ms" in msg:
-                        timing_accum_ms.clear()
-                        timing_counts.clear()
-                except Exception as e:
-                    print(f"[OffPolicyWorker] metrics enqueue error: {e}", file=sys.stderr)
-            for key, value in cycle_timing_ms.items():
-                _record_timing_ms(timing_accum_ms, timing_counts, key, value)
-
-    finally:
-        if metrics_queue is not None and trace_recorder:
-            try:
-                metrics_queue.put_nowait({"trace_events": trace_recorder.drain_events()})
-            except Exception:
-                pass
diff --git a/src/unilab/algos/torch/rsl_rl_ppo.py b/src/unilab/algos/torch/rsl_rl_ppo.py
deleted file mode 100644
index 67ca1b17d..000000000
--- a/src/unilab/algos/torch/rsl_rl_ppo.py
+++ /dev/null
@@ -1,259 +0,0 @@
-from __future__ import annotations
-
-import math
-from typing import Any, cast
-
-import torch
-from rsl_rl.algorithms import PPO
-from tensordict import TensorDict
-
-from unilab.algos.torch.common.compile import get_torch_compile_for_cuda
-
-_LOG_2_PI = math.log(2.0 * math.pi)
-_NORMAL_ENTROPY_OFFSET = 0.5 * (1.0 + _LOG_2_PI)
-
-
-class FinalObservationAwarePPO(PPO):
-    """PPO variant that bootstraps time limits from env final_observation."""
-
-    learning_rate: float
-
-    def __init__(
-        self,
-        *args: Any,
-        enable_compile: bool = False,
-        **kwargs: Any,
-    ) -> None:
-        super().__init__(*args, **kwargs)
-        self.enable_compile = (
-            bool(enable_compile) and get_torch_compile_for_cuda(self.device, warn=True) is not None
-        )
-        self._minibatch_loss_fn = self._minibatch_loss_tensors
-        if self.enable_compile:
-            self._compile_training_methods()
-
-    def _compile_training_methods(self) -> None:
-        compile_fn = get_torch_compile_for_cuda(self.device, warn=True)
-        if compile_fn is None:
-            return
-
-        self._minibatch_loss_fn = compile_fn(
-            self._minibatch_loss_tensors,
-            mode="reduce-overhead",
-            fullgraph=False,
-        )
-
-    @staticmethod
-    def _model_obs_tensor(model: Any, obs: TensorDict) -> torch.Tensor:
-        obs_groups = getattr(model, "obs_groups", None)
-        if not obs_groups:
-            raise RuntimeError("PPO compiled update requires model.obs_groups")
-        tensors = [obs[group] for group in obs_groups]
-        if len(tensors) == 1:
-            return tensors[0]
-        return torch.cat(tensors, dim=-1)
-
-    def _supports_compiled_update_path(self) -> bool:
-        if not self.enable_compile:
-            return False
-        if self.rnd or self.symmetry or self.is_multi_gpu:
-            return False
-        if self.actor.is_recurrent or self.critic.is_recurrent:
-            return False
-        distribution: Any = getattr(self.actor, "distribution", None)
-        if distribution is None or not hasattr(distribution, "std_type"):
-            return False
-        if distribution.std_type == "scalar":
-            return hasattr(distribution, "std_param")
-        if distribution.std_type == "log":
-            return hasattr(distribution, "log_std_param")
-        return False
-
-    def _actor_mean_std(self, obs: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
-        distribution: Any = self.actor.distribution
-        if distribution is None:
-            raise RuntimeError("PPO actor must expose a stochastic distribution")
-
-        mean = self.actor.mlp(self.actor.obs_normalizer(obs))
-        if distribution.std_type == "scalar":
-            std = distribution.std_param.expand_as(mean)
-        else:
-            std = torch.exp(distribution.log_std_param).expand_as(mean)
-        return mean, std
-
-    def _critic_value(self, obs: torch.Tensor) -> torch.Tensor:
-        return self.critic.mlp(self.critic.obs_normalizer(obs)).squeeze(-1)
-
-    @staticmethod
-    def _gaussian_log_prob(
-        actions: torch.Tensor, mean: torch.Tensor, std: torch.Tensor
-    ) -> torch.Tensor:
-        normalized = (actions - mean) / std
-        return (-0.5 * (normalized.pow(2) + 2.0 * torch.log(std) + _LOG_2_PI)).sum(dim=-1)
-
-    @staticmethod
-    def _gaussian_entropy(std: torch.Tensor) -> torch.Tensor:
-        return (torch.log(std) + _NORMAL_ENTROPY_OFFSET).sum(dim=-1)
-
-    def _minibatch_loss_tensors(
-        self,
-        actor_obs: torch.Tensor,
-        critic_obs: torch.Tensor,
-        actions: torch.Tensor,
-        target_values: torch.Tensor,
-        advantages: torch.Tensor,
-        old_actions_log_prob: torch.Tensor,
-        old_values: torch.Tensor,
-        old_mu: torch.Tensor,
-        old_sigma: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
-        mu, sigma = self._actor_mean_std(actor_obs)
-        actions_log_prob = self._gaussian_log_prob(actions, mu, sigma)
-        values = self._critic_value(critic_obs)
-        entropy = self._gaussian_entropy(sigma).mean()
-
-        old_actions_log_prob = old_actions_log_prob.squeeze(-1)
-        old_values = old_values.squeeze(-1)
-        target_values = target_values.squeeze(-1)
-        advantages = advantages.squeeze(-1)
-
-        ratio = torch.exp(actions_log_prob - old_actions_log_prob)
-        surrogate = -advantages * ratio
-        surrogate_clipped = -advantages * torch.clamp(
-            ratio, 1.0 - self.clip_param, 1.0 + self.clip_param
-        )
-        surrogate_loss = torch.max(surrogate, surrogate_clipped).mean()
-
-        if self.use_clipped_value_loss:
-            value_clipped = old_values + (values - old_values).clamp(
-                -self.clip_param, self.clip_param
-            )
-            value_losses = (values - target_values).pow(2)
-            value_losses_clipped = (value_clipped - target_values).pow(2)
-            value_loss = torch.max(value_losses, value_losses_clipped).mean()
-        else:
-            value_loss = (target_values - values).pow(2).mean()
-
-        kl = torch.sum(
-            torch.log(sigma / old_sigma + 1e-5)
-            + (old_sigma.pow(2) + (old_mu - mu).pow(2)) / (2.0 * sigma.pow(2))
-            - 0.5,
-            dim=-1,
-        )
-        kl_mean = torch.mean(kl)
-
-        loss = surrogate_loss + self.value_loss_coef * value_loss - self.entropy_coef * entropy
-        return loss, surrogate_loss, value_loss, entropy, kl_mean
-
-    def update(self) -> dict[str, float]:
-        if not self._supports_compiled_update_path():
-            return cast(dict[str, float], super().update())
-
-        mean_value_loss = 0.0
-        mean_surrogate_loss = 0.0
-        mean_entropy = 0.0
-
-        generator = self.storage.mini_batch_generator(
-            self.num_mini_batches, self.num_learning_epochs
-        )
-
-        for batch in generator:
-            actions = cast(torch.Tensor, batch.actions)
-            values = cast(torch.Tensor, batch.values)
-            advantages = cast(torch.Tensor, batch.advantages)
-            returns = cast(torch.Tensor, batch.returns)
-            old_actions_log_prob = cast(torch.Tensor, batch.old_actions_log_prob)
-            old_distribution_params = batch.old_distribution_params
-            if old_distribution_params is None:
-                raise RuntimeError("PPO compiled update requires old distribution parameters")
-
-            if self.normalize_advantage_per_mini_batch:
-                with torch.no_grad():
-                    advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
-
-            actor_obs = self._model_obs_tensor(self.actor, batch.observations)
-            critic_obs = self._model_obs_tensor(self.critic, batch.observations)
-            old_mu, old_sigma = old_distribution_params
-
-            loss, surrogate_loss, value_loss, entropy, kl_mean = self._minibatch_loss_fn(
-                actor_obs,
-                critic_obs,
-                actions,
-                returns,
-                advantages,
-                old_actions_log_prob,
-                values,
-                old_mu,
-                old_sigma,
-            )
-
-            if self.desired_kl is not None and self.schedule == "adaptive":
-                kl_value = float(kl_mean.detach())
-                learning_rate = float(self.learning_rate)
-                if kl_value > self.desired_kl * 2.0:
-                    learning_rate = max(1e-5, learning_rate / 1.5)
-                elif kl_value < self.desired_kl / 2.0 and kl_value > 0.0:
-                    learning_rate = min(1e-2, learning_rate * 1.5)
-
-                self.learning_rate = learning_rate
-                for param_group in self.optimizer.param_groups:
-                    param_group["lr"] = learning_rate
-
-            self.optimizer.zero_grad()
-            loss.backward()
-            torch.nn.utils.clip_grad_norm_(self.actor.parameters(), self.max_grad_norm)
-            torch.nn.utils.clip_grad_norm_(self.critic.parameters(), self.max_grad_norm)
-            self.optimizer.step()
-
-            mean_value_loss += value_loss.item()
-            mean_surrogate_loss += surrogate_loss.item()
-            mean_entropy += entropy.item()
-
-        num_updates = self.num_learning_epochs * self.num_mini_batches
-        self.storage.clear()
-        return {
-            "value": mean_value_loss / num_updates,
-            "surrogate": mean_surrogate_loss / num_updates,
-            "entropy": mean_entropy / num_updates,
-        }
-
-    def process_env_step(
-        self,
-        obs: TensorDict,
-        rewards: torch.Tensor,
-        dones: torch.Tensor,
-        extras: dict[str, torch.Tensor | TensorDict],
-    ) -> None:
-        self.actor.update_normalization(obs)
-        self.critic.update_normalization(obs)
-        if self.rnd:
-            self.rnd.update_normalization(obs)
-
-        self.transition.rewards = rewards.clone()
-        self.transition.dones = dones
-
-        if self.rnd:
-            self.intrinsic_rewards = self.rnd.get_intrinsic_reward(obs)
-            self.transition.rewards += self.intrinsic_rewards
-
-        timeouts = extras.get("time_outs")
-        timeout_bootstrap_obs = extras.get("time_out_bootstrap_obs")
-        if isinstance(timeouts, torch.Tensor):
-            timeout_mask = timeouts.to(self.device).float()
-            if timeout_bootstrap_obs is not None and torch.count_nonzero(timeout_mask) > 0:
-                bootstrap_obs = timeout_bootstrap_obs.to(self.device)
-                bootstrap_values = self.critic(bootstrap_obs).detach()
-                self.transition.rewards += self.gamma * torch.squeeze(
-                    bootstrap_values * timeout_mask.unsqueeze(1), 1
-                )
-            else:
-                transition_values = self.transition.values
-                assert transition_values is not None
-                self.transition.rewards += self.gamma * torch.squeeze(
-                    transition_values * timeout_mask.unsqueeze(1), 1
-                )
-
-        self.storage.add_transition(self.transition)
-        self.transition.clear()
-        self.actor.reset(dones)
-        self.critic.reset(dones)
diff --git a/src/unilab/algos/torch/rsl_rl_runtime.py b/src/unilab/algos/torch/rsl_rl_runtime.py
deleted file mode 100644
index 8afa405d6..000000000
--- a/src/unilab/algos/torch/rsl_rl_runtime.py
+++ /dev/null
@@ -1,49 +0,0 @@
-"""Runtime resolution helpers for RSL-RL PPO script assembly."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Any
-
-from unilab.training.rsl_rl import RslRlVecEnvWrapper
-
-
-@dataclass(frozen=True)
-class RslRlPPORuntime:
-    """Resolved PPO runtime consumed by the generic RSL-RL entrypoint."""
-
-    wrapper_cls: type[RslRlVecEnvWrapper]
-
-
-def resolve_rsl_rl_ppo_runtime(
-    rl_cfg: dict[str, Any],
-    *,
-    default_wrapper_cls: type[RslRlVecEnvWrapper],
-) -> RslRlPPORuntime:
-    """Resolve the PPO runtime bundle from owner config."""
-    runtime_resolver = rl_cfg.get("runtime_resolver")
-    if runtime_resolver in (None, ""):
-        runtime_impl = rl_cfg.get("runtime_impl")
-        if runtime_impl not in (None, ""):
-            raise ValueError(
-                "PPO owner config selected "
-                f"runtime_impl={runtime_impl!r} but did not define algo.runtime_resolver."
-            )
-        return RslRlPPORuntime(wrapper_cls=default_wrapper_cls)
-
-    from rsl_rl.utils import resolve_callable
-
-    resolver = resolve_callable(str(runtime_resolver))
-    runtime = resolver(rl_cfg)
-    if runtime is None:
-        raise ValueError(
-            f"PPO runtime resolver {runtime_resolver!r} returned None for rl_cfg runtime selection."
-        )
-
-    wrapper_cls = getattr(runtime, "wrapper_cls", None)
-    if wrapper_cls is None:
-        raise TypeError(
-            f"PPO runtime resolver {runtime_resolver!r} must return an object with "
-            "'wrapper_cls' attribute."
-        )
-    return RslRlPPORuntime(wrapper_cls=wrapper_cls)
diff --git a/src/unilab/assets/hub.py b/src/unilab/assets/hub.py
index 07628e2ba..aa611afed 100644
--- a/src/unilab/assets/hub.py
+++ b/src/unilab/assets/hub.py
@@ -17,6 +17,7 @@
 import posixpath
 from collections.abc import Sequence
 from pathlib import Path
+from typing import Any
 
 from unilab.assets import ASSETS_ROOT_PATH
 
@@ -30,6 +31,31 @@
 _HF_REPO_TYPE = "dataset"
 _HF_OFFICIAL_ENDPOINT = "https://huggingface.co"
 
+# Robot binary assets (meshes / textures) hosted on ``_HF_ROBOTS_REPO_ID``
+# instead of being committed to git. Each entry maps a robot directory name
+# to the ``ASSETS_ROOT_PATH``-relative directories that live on HF:
+# ``(directory, completeness marker, count glob, count label)``. The glob and
+# label only feed the ``unilab-pull-assets`` summary line; resolution keys
+# off the marker file.
+ROBOT_ASSET_SPECS: dict[str, tuple[tuple[str, str, str, str], ...]] = {
+    "a2": (("robots/a2/assets", "a2/base_link.STL", "**/*.STL", "STL"),),
+    "allegro_hand": (("robots/allegro_hand/assets", "base_link.stl", "**/*", "asset"),),
+    "g1": (
+        ("robots/g1/assets", "head_link.STL", "**/*", "asset"),
+        ("robots/g1/textures", "floor.png", "*.png", "PNG"),
+    ),
+    "go2": (("robots/go2/assets", "base_0.obj", "**/*", "asset"),),
+    # go2_arm reuses the Go2 base meshes via ``../go2/assets`` references.
+    "go2_arm": (
+        ("robots/go2_arm/assets", "arm_base_0.obj", "**/*", "asset"),
+        ("robots/go2/assets", "base_0.obj", "**/*", "asset"),
+    ),
+    # go2w points its meshdir at ``../go2/assets``.
+    "go2w": (("robots/go2/assets", "base_0.obj", "**/*", "asset"),),
+    "sharpa_wave": (("robots/sharpa_wave/meshes", "DP_HB1_4F.STL", "*.STL", "STL"),),
+    "x2": (("robots/x2/meshes", "pelvis.STL", "*.STL", "STL"),),
+}
+
 
 def resolve_motion_files(
     motion_file: str | Sequence[str],
@@ -52,12 +78,15 @@ def resolve_motion_files(
 
 def resolve_grasp_cache_files(
     cache_file: str | Sequence[str],
+    *,
+    show_progress: bool = False,
 ) -> str | list[str]:
     """Ensure grasp cache file(s) exist locally, downloading from HF if needed.
 
     Args:
         cache_file: Absolute path or ``ASSETS_ROOT_PATH``-relative path
             (single string or sequence of strings).
+        show_progress: Whether Hugging Face downloads may render progress bars.
 
     Returns:
         Resolved absolute path(s) guaranteed to exist on disk.
@@ -65,8 +94,11 @@ def resolve_grasp_cache_files(
         returns a list of strings.
     """
     if isinstance(cache_file, str):
-        return _resolve_single(cache_file, repo_id=_HF_CACHES_REPO_ID)
-    return [_resolve_single(p, repo_id=_HF_CACHES_REPO_ID) for p in cache_file]
+        return _resolve_single(cache_file, repo_id=_HF_CACHES_REPO_ID, show_progress=show_progress)
+    return [
+        _resolve_single(p, repo_id=_HF_CACHES_REPO_ID, show_progress=show_progress)
+        for p in cache_file
+    ]
 
 
 def resolve_checkpoint_file(
@@ -88,7 +120,12 @@ def resolve_checkpoint_file(
     return [_resolve_single(p, repo_id=_HF_CHECKPOINTS_REPO_ID) for p in checkpoint_file]
 
 
-def _resolve_single(path_str: str, *, repo_id: str = _HF_MOTIONS_REPO_ID) -> str:
+def _resolve_single(
+    path_str: str,
+    *,
+    repo_id: str = _HF_MOTIONS_REPO_ID,
+    show_progress: bool = True,
+) -> str:
     """Resolve one asset file path, downloading if absent."""
     path = Path(path_str)
     is_absolute_input = path.is_absolute() or ntpath.isabs(path_str) or posixpath.isabs(path_str)
@@ -114,7 +151,7 @@ def _resolve_single(path_str: str, *, repo_id: str = _HF_MOTIONS_REPO_ID) -> str
                 f"ASSETS_ROOT_PATH ({ASSETS_ROOT_PATH}): {path_str}"
             ) from None
 
-    return _download_from_hf(relative, repo_id=repo_id)
+    return _download_from_hf(relative, repo_id=repo_id, show_progress=show_progress)
 
 
 def _hf_relative_path(path_str: str) -> str:
@@ -122,22 +159,30 @@ def _hf_relative_path(path_str: str) -> str:
     return path_str.replace("\\", "/")
 
 
-def _hf_download(hf_hub_download, relative_path: str, *, repo_id: str) -> str:  # type: ignore[no-untyped-def]
+def _hf_download(
+    hf_hub_download,
+    relative_path: str,
+    *,
+    repo_id: str,
+    show_progress: bool = True,
+) -> str:  # type: ignore[no-untyped-def]
     """Call ``hf_hub_download`` with the standard arguments."""
-    return str(
-        hf_hub_download(
-            repo_id=repo_id,
-            filename=relative_path,
-            repo_type=_HF_REPO_TYPE,
-            local_dir=str(ASSETS_ROOT_PATH),
-        )
-    )
+    kwargs: dict[str, Any] = {
+        "repo_id": repo_id,
+        "filename": relative_path,
+        "repo_type": _HF_REPO_TYPE,
+        "local_dir": str(ASSETS_ROOT_PATH),
+    }
+    if not show_progress:
+        kwargs["tqdm_class"] = _silent_tqdm_class()
+    return str(hf_hub_download(**kwargs))
 
 
 def _download_from_hf(
     relative_path: str,
     *,
     repo_id: str = _HF_MOTIONS_REPO_ID,
+    show_progress: bool = True,
 ) -> str:
     """Download *relative_path* from an HF dataset repo.
 
@@ -158,7 +203,12 @@ def _download_from_hf(
     logger.info("Downloading %s from HF repo %s ...", relative_path, repo_id)
 
     try:
-        local_path = _hf_download(hf_hub_download, relative_path, repo_id=repo_id)
+        local_path = _hf_download(
+            hf_hub_download,
+            relative_path,
+            repo_id=repo_id,
+            show_progress=show_progress,
+        )
     except Exception:
         # If a mirror endpoint is configured and it failed, retry with
         # the official endpoint before giving up.
@@ -172,7 +222,12 @@ def _download_from_hf(
             original = os.environ["HF_ENDPOINT"]
             os.environ["HF_ENDPOINT"] = _HF_OFFICIAL_ENDPOINT
             try:
-                local_path = _hf_download(hf_hub_download, relative_path, repo_id=repo_id)
+                local_path = _hf_download(
+                    hf_hub_download,
+                    relative_path,
+                    repo_id=repo_id,
+                    show_progress=show_progress,
+                )
             finally:
                 os.environ["HF_ENDPOINT"] = original
         else:
@@ -187,19 +242,40 @@ def _download_from_hf(
 # ---------------------------------------------------------------------------
 
 
-def _snapshot_download(snapshot_download_fn, directory: str, *, repo_id: str) -> str:  # type: ignore[no-untyped-def]
-    """Call ``snapshot_download`` with the standard arguments."""
-    return str(
-        snapshot_download_fn(
-            repo_id=repo_id,
-            repo_type=_HF_REPO_TYPE,
-            allow_patterns=f"{directory}/**",
-            local_dir=str(ASSETS_ROOT_PATH),
-        )
-    )
+def _silent_tqdm_class() -> type[Any]:
+    """Return a tqdm class that keeps HF snapshot downloads silent."""
+    from tqdm.auto import tqdm
+
+    class _SilentTqdm(tqdm):  # type: ignore[misc]
+        def __init__(self, *args: Any, **kwargs: Any) -> None:
+            kwargs["disable"] = True
+            super().__init__(*args, **kwargs)
 
+    return _SilentTqdm
 
-def _resolve_snapshot_dir(directory: str, *, repo_id: str, marker: str) -> Path:
+
+def _snapshot_download(
+    snapshot_download_fn,
+    directory: str,
+    *,
+    repo_id: str,
+    show_progress: bool = True,
+) -> str:  # type: ignore[no-untyped-def]
+    """Call ``snapshot_download`` with the standard arguments."""
+    kwargs: dict[str, Any] = {
+        "repo_id": repo_id,
+        "repo_type": _HF_REPO_TYPE,
+        "allow_patterns": f"{directory}/**",
+        "local_dir": str(ASSETS_ROOT_PATH),
+    }
+    if not show_progress:
+        kwargs["tqdm_class"] = _silent_tqdm_class()
+    return str(snapshot_download_fn(**kwargs))
+
+
+def _resolve_snapshot_dir(
+    directory: str, *, repo_id: str, marker: str, show_progress: bool = True
+) -> Path:
     """Ensure an HF-hosted directory exists locally, downloading if needed.
 
     If the current ``HF_ENDPOINT`` (e.g. a mirror) fails, automatically
@@ -233,7 +309,9 @@ def _resolve_snapshot_dir(directory: str, *, repo_id: str, marker: str) -> Path:
     logger.info("Downloading %s from HF repo %s ...", hf_directory, repo_id)
 
     try:
-        _snapshot_download(snapshot_download, hf_directory, repo_id=repo_id)
+        _snapshot_download(
+            snapshot_download, hf_directory, repo_id=repo_id, show_progress=show_progress
+        )
     except Exception:
         current_endpoint = os.environ.get("HF_ENDPOINT", "")
         if current_endpoint and current_endpoint != _HF_OFFICIAL_ENDPOINT:
@@ -245,7 +323,9 @@ def _resolve_snapshot_dir(directory: str, *, repo_id: str, marker: str) -> Path:
             original = os.environ["HF_ENDPOINT"]
             os.environ["HF_ENDPOINT"] = _HF_OFFICIAL_ENDPOINT
             try:
-                _snapshot_download(snapshot_download, hf_directory, repo_id=repo_id)
+                _snapshot_download(
+                    snapshot_download, hf_directory, repo_id=repo_id, show_progress=show_progress
+                )
             finally:
                 os.environ["HF_ENDPOINT"] = original
         else:
@@ -269,7 +349,7 @@ def resolve_scene_dir(directory: str, *, marker: str = "teaser.xml") -> Path:
     return _resolve_snapshot_dir(directory, repo_id=_HF_SCENES_REPO_ID, marker=marker)
 
 
-def resolve_robot_asset_dir(directory: str, *, marker: str) -> Path:
+def resolve_robot_asset_dir(directory: str, *, marker: str, show_progress: bool = True) -> Path:
     """Ensure a robot asset directory (e.g. meshes) exists locally.
 
     Robot binary assets (STL meshes) are hosted on Hugging Face rather than
@@ -282,8 +362,42 @@ def resolve_robot_asset_dir(directory: str, *, marker: str) -> Path:
             (e.g. ``"robots/x2/meshes"``).
         marker: A file inside the directory used to check completeness
             (e.g. ``"pelvis.STL"``).
+        show_progress: Whether Hugging Face snapshot downloads may render progress bars.
 
     Returns:
         Absolute ``Path`` to the resolved directory.
     """
-    return _resolve_snapshot_dir(directory, repo_id=_HF_ROBOTS_REPO_ID, marker=marker)
+    return _resolve_snapshot_dir(
+        directory,
+        repo_id=_HF_ROBOTS_REPO_ID,
+        marker=marker,
+        show_progress=show_progress,
+    )
+
+
+def ensure_robot_assets_for_paths(paths: Sequence[str | None]) -> None:
+    """Resolve HF-hosted asset dirs for every robot referenced by *paths*.
+
+    Cold-path helper called once before a backend parses scene/robot XML:
+    any path under ``robots/<name>/`` whose robot is registered in
+    ``ROBOT_ASSET_SPECS`` gets its HF-hosted directories downloaded when the
+    completeness marker is missing. Paths outside the asset tree and unknown
+    robots are ignored, so callers may pass every scene path unconditionally.
+    """
+    for path in paths:
+        if not path:
+            continue
+        robot = _robot_name_from_path(path)
+        if robot is None:
+            continue
+        for directory, marker, _pattern, _label in ROBOT_ASSET_SPECS[robot]:
+            resolve_robot_asset_dir(directory, marker=marker)
+
+
+def _robot_name_from_path(path_str: str) -> str | None:
+    """Return the registered robot a ``robots/<name>/...`` path refers to."""
+    parts = _hf_relative_path(path_str).split("/")
+    for index, part in enumerate(parts[:-1]):
+        if part == "robots" and parts[index + 1] in ROBOT_ASSET_SPECS:
+            return parts[index + 1]
+    return None
diff --git a/src/unilab/assets/pull.py b/src/unilab/assets/pull.py
new file mode 100644
index 000000000..cd2696a96
--- /dev/null
+++ b/src/unilab/assets/pull.py
@@ -0,0 +1,94 @@
+#!/usr/bin/env python3
+"""Pre-fetch robot binary assets from Hugging Face into their project paths.
+
+Robot meshes and textures are hosted on Hugging Face rather than committed to git.
+They are also downloaded automatically on first use, but this command lets you pull
+them ahead of time (e.g. for CI or offline prep) with a single invocation. Files land
+under ``src/unilab/assets/robots/<robot>/`` — no manual file moving needed.
+
+Usage:
+  uv run unilab-pull-assets               # pull the default robot (x2)
+  uv run unilab-pull-assets --robot g1
+  uv run unilab-pull-assets --robot all   # pull every registered robot
+"""
+
+from __future__ import annotations
+
+import argparse
+import logging
+from collections.abc import Sequence
+from dataclasses import dataclass
+from pathlib import Path
+
+from unilab.assets.hub import ROBOT_ASSET_SPECS, resolve_robot_asset_dir
+
+_ALL = "all"
+
+
+@dataclass(frozen=True)
+class _AssetSummary:
+    target: Path
+    count: int
+    label: str
+
+
+def _parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--robot",
+        default="x2",
+        choices=[*_sorted_robots(), _ALL],
+        help="Robot whose binary assets to download, or 'all' (default: x2).",
+    )
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="Retained for compatibility; Hugging Face output remains suppressed.",
+    )
+    return parser.parse_args(argv)
+
+
+def _sorted_robots() -> list[str]:
+    return sorted(ROBOT_ASSET_SPECS)
+
+
+def _pull_robot(robot: str) -> list[_AssetSummary]:
+    summaries: list[_AssetSummary] = []
+    for directory, marker, pattern, label in ROBOT_ASSET_SPECS[robot]:
+        target = resolve_robot_asset_dir(directory, marker=marker, show_progress=False)
+        count = sum(1 for path in target.rglob(pattern) if path.is_file())
+        summaries.append(_AssetSummary(target=target, count=count, label=label))
+    return summaries
+
+
+def _format_total_summary(robots: Sequence[str], summaries: Sequence[_AssetSummary]) -> str:
+    total_files = sum(summary.count for summary in summaries)
+    label_counts: dict[str, int] = {}
+    for summary in summaries:
+        label_counts[summary.label] = label_counts.get(summary.label, 0) + summary.count
+    counts = ", ".join(f"{count} {label} files" for label, count in sorted(label_counts.items()))
+    return (
+        f"Robot assets ready: {len(robots)} robots, {len(summaries)} directories, "
+        f"{total_files} files ({counts})"
+    )
+
+
+def main(argv: Sequence[str] | None = None) -> int:
+    args = _parse_args(argv)
+    logging.basicConfig(level=logging.WARNING, format="%(message)s")
+    logging.getLogger("huggingface_hub").setLevel(logging.WARNING)
+    logging.getLogger("httpx").setLevel(logging.WARNING)
+    logging.getLogger("httpcore").setLevel(logging.WARNING)
+
+    robots = _sorted_robots() if args.robot == _ALL else [args.robot]
+    summaries: list[_AssetSummary] = []
+    for robot in robots:
+        print(f"Downloading {robot} assets ...", flush=True)
+        robot_summaries = _pull_robot(robot)
+        summaries.extend(robot_summaries)
+    print(_format_total_summary(robots, summaries), flush=True)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/src/unilab/assets/robots/a2/a2.xml b/src/unilab/assets/robots/a2/a2.xml
index debefe71e..92a6ccb15 100644
--- a/src/unilab/assets/robots/a2/a2.xml
+++ b/src/unilab/assets/robots/a2/a2.xml
@@ -18,20 +18,18 @@
     <default class="foot">
       <geom group="3" priority="1" condim="6"/>
     </default>
-    <!-- Position-actuator gain classes, aligned to unitree_rl_mjlab A2: hip/thigh
-         kp=100, calf kp=150. The env re-applies these per joint at init via
-         position_actuator_gains (A2JoystickControlConfig) and also sets kd
-         (kd is not expressible on a <position> default), so the kp here is the
-         standalone static default. forcerange matches the A2 joint torque
-         limits: hip/thigh +-120, calf +-180. -->
+    <!-- Position-actuator gain classes aligned to unitree_rl_mjlab A2.
+         These are the immutable robot defaults used by the Manager-Based
+         pd_gains reset term. forcerange matches the A2 joint torque limits:
+         hip/thigh +-120, calf +-180. -->
     <default class="a2_hip">
-      <position kp="100" forcerange="-120 120"/>
+      <position kp="100" kv="4" forcerange="-120 120"/>
     </default>
     <default class="a2_thigh">
-      <position kp="100" forcerange="-120 120"/>
+      <position kp="100" kv="4" forcerange="-120 120"/>
     </default>
     <default class="a2_calf">
-      <position kp="150" forcerange="-180 180"/>
+      <position kp="150" kv="6" forcerange="-180 180"/>
     </default>
   </default>
 
diff --git a/src/unilab/assets/robots/a2/assets/a2/base_link.STL b/src/unilab/assets/robots/a2/assets/a2/base_link.STL
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deleted file mode 100644
index 32c80bfb3..000000000
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diff --git a/src/unilab/assets/robots/a2/assets/a2/right_hind_Link3.STL b/src/unilab/assets/robots/a2/assets/a2/right_hind_Link3.STL
deleted file mode 100644
index d603518c2..000000000
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diff --git a/src/unilab/assets/robots/a2/assets/a2/right_hind_Link4.STL b/src/unilab/assets/robots/a2/assets/a2/right_hind_Link4.STL
deleted file mode 100644
index ea564d155..000000000
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diff --git a/src/unilab/assets/robots/a2/scene_flat.xml b/src/unilab/assets/robots/a2/scene_flat.xml
index e378a4668..11e1fc5f4 100644
--- a/src/unilab/assets/robots/a2/scene_flat.xml
+++ b/src/unilab/assets/robots/a2/scene_flat.xml
@@ -4,20 +4,21 @@
   <statistic center="0 0 0.1" extent="0.9" meansize="0.05"/>
 
   <visual>
-    <headlight diffuse="0.6 0.6 0.6" ambient="0.3 0.3 0.3" specular="0 0 0"/>
-    <rgba haze="0.15 0.25 0.35 1"/>
+    <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
+    <map fogstart="100.0" fogend="300.0"/>
+    <rgba haze="0.75 0.75 0.77 1"/>
     <global azimuth="-130" elevation="-20"/>
   </visual>
 
   <asset>
-    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
-    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
-      markrgb="0.8 0.8 0.8" width="300" height="300"/>
-    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+    <texture type="skybox" builtin="gradient" rgb1="0.55 0.55 0.57" rgb2="0.55 0.55 0.57" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" rgb1="0.62 0.62 0.63" rgb2="0.49 0.49 0.51"
+      width="300" height="300"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="1.25 1.25" reflectance="0.0"/>
   </asset>
 
   <worldbody>
-    <light pos="0 0 1.5" dir="0 0 -1" directional="true"/>
+    <light pos="0 0 1.5" dir="0 0 -1" directional="true" diffuse="0.8 0.8 0.8"/>
     <!-- priority=2 (> the feet's 1) + condim=6 makes the floor the priority geom
          for foot-ground contact, so it dictates the friction (and keeps condim 6).
          This lets ground-friction domain randomization actually move the contact
diff --git a/src/unilab/assets/robots/allegro_hand/assets/base_link.stl b/src/unilab/assets/robots/allegro_hand/assets/base_link.stl
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diff --git a/src/unilab/assets/robots/allegro_hand/assets/link_3.0.stl b/src/unilab/assets/robots/allegro_hand/assets/link_3.0.stl
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diff --git a/src/unilab/assets/robots/allegro_hand/assets/link_3.0_tip.stl b/src/unilab/assets/robots/allegro_hand/assets/link_3.0_tip.stl
deleted file mode 100644
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diff --git a/src/unilab/assets/robots/allegro_hand/assets/link_4.0.stl b/src/unilab/assets/robots/allegro_hand/assets/link_4.0.stl
deleted file mode 100644
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diff --git a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball.urdf b/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball.urdf
deleted file mode 100644
index 326a27b8a..000000000
--- a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball.urdf
+++ /dev/null
@@ -1,19 +0,0 @@
-<robot name="056_tennis_ball">
-	<link name="object">
-		<visual>
-			<origin rpy="0 0 0" xyz="0 0 0" />
-			<geometry>
-				<mesh filename="056_tennis_ball/google_16k/textured.obj" scale="1.1934954497985975 1.1934954497985975 1.1934954497985975" />
-			</geometry>
-		</visual>
-		<collision>
-			<geometry>
-				<mesh filename="056_tennis_ball/google_16k/textured_vhacd.obj" scale="1.1934954497985975 1.1934954497985975 1.1934954497985975" />
-			</geometry>
-		<origin rpy="0 0 0" xyz="0 0 0" /></collision>
-		<inertial>
-			<mass value="0.05" />
-			<inertia ixx="0.0001" ixy="0.0" ixz="0.0" iyy="0.0001" iyz="0.0" izz="0.0001" />
-		</inertial>
-	</link>
-</robot>
diff --git a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/kinbody.xml b/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/kinbody.xml
deleted file mode 100644
index c26df5b03..000000000
--- a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/kinbody.xml
+++ /dev/null
@@ -1,8 +0,0 @@
-<KinBody name="tennis_ball">
-  <Body type="static" name="tennis_ball">
-    <Geom type="trimesh">
-      <Render>textured.dae</Render>
-      <Data>textured.dae</Data>
-    </Geom>
-  </Body>
-</KinBody>
diff --git a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/nontextured.ply b/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/nontextured.ply
deleted file mode 100644
index 9ba0f21e4..000000000
--- a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/nontextured.ply
+++ /dev/null
@@ -1,24592 +0,0 @@
-ply
-format ascii 1.0
-element vertex 8194
-property float x
-property float y
-property float z
-property float s
-property float t
-property float nx
-property float ny
-property float nz
-element face 16384
-property list uchar int vertex_indices
-end_header
--0.017488999 -0.057190999 0.017044 0.206091 0.85481602 -0.75388801 -0.52670199 -0.39273101
--0.018433999 -0.056375999 0.017924 0.20167001 0.85020399 -0.78155601 -0.52928799 -0.33019
--0.018448999 -0.055860002 0.016979 0.205726 0.848113 -0.754794 -0.56028998 -0.34111601
-0.0049760002 -0.071314998 0.014603 0.247631 0.96409303 -0.050856002 -0.77791899 -0.62630302
-0.005719 -0.070775002 0.013785 0.25341001 0.96392202 -0.115998 -0.82901299 -0.54706699
-0.0069809998 -0.071456 0.014574 0.25289401 0.97127998 -0.104823 -0.84520203 -0.52406597
--0.017418999 -0.056180999 0.015421 0.21324199 0.85090297 -0.73316002 -0.54054201 -0.41266301
--0.018575 -0.055236001 0.016253 0.20879699 0.84544897 -0.75819898 -0.50241101 -0.41559201
--0.017733 -0.055294 0.015033 0.21487799 0.846991 -0.75604802 -0.385315 -0.52907902
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deleted file mode 100644
index 34b6d887b..000000000
Binary files a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/nontextured.stl and /dev/null differ
diff --git a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/texture_map.png b/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/texture_map.png
deleted file mode 100644
index fac7a7a44..000000000
Binary files a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/texture_map.png and /dev/null differ
diff --git a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured.dae b/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured.dae
deleted file mode 100644
index 10b02c8f1..000000000
--- a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured.dae
+++ /dev/null
@@ -1,148 +0,0 @@
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11337 896 896 3779 11338 2014 2014 3779 11339 1983 1983 3780 11340 895 895 3780 11341 2014 2014 3780 11342 2005 2005 3781 11343 1999 1999 3781 11344 2015 2015 3781 11345 902 902 3782 11346 2014 2014 3782 11347 896 896 3782 11348 2016 2016 3783 11349 2014 2014 3783 11350 902 902 3783 11351 2015 2015 3784 11352 1999 1999 3784 11353 2017 2017 3784 11354 1983 1983 3785 11355 2014 2014 3785 11356 2018 2018 3785 11357 2018 2018 3786 11358 2019 2019 3786 11359 1983 1983 3786 11360 2010 2010 3787 11361 2009 2009 3787 11362 2020 2020 3787 11363 2016 2016 3788 11364 2018 2018 3788 11365 2014 2014 3788 11366 2021 2021 3789 11367 2022 2022 3789 11368 2023 2023 3789 11369 2019 2019 3790 11370 1982 1982 3790 11371 1983 1983 3790 11372 2024 2024 3791 11373 2020 2020 3791 11374 2009 2009 3791 11375 1991 1991 3792 11376 1982 1982 3792 11377 2019 2019 3792 11378 2005 2005 3793 11379 2025 2025 3793 11380 2007 2007 3793 11381 2009 2009 3794 11382 2013 2013 3794 11383 2024 2024 3794 11384 2026 2026 3795 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-                </triangles>
-            </mesh>
-        </geometry>
-    </library_geometries>
-    <library_visual_scenes>
-        <visual_scene id="VisualSceneNode" name="VisualScene">
-            <node id="node" name="node">
-                <instance_geometry url="#shape0-lib">
-                    <bind_material>
-                        <technique_common>
-                            <instance_material symbol="material0" target="#material0">
-                                <bind_vertex_input semantic="UVSET0" input_semantic="TEXCOORD"/>
-                            </instance_material>
-                        </technique_common>
-                    </bind_material>
-                </instance_geometry>
-            </node>
-        </visual_scene>
-    </library_visual_scenes>
-    <scene>
-        <instance_visual_scene url="#VisualSceneNode"/>
-    </scene>
-</COLLADA>
diff --git a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured.mtl b/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured.mtl
deleted file mode 100644
index b93192fc6..000000000
--- a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured.mtl
+++ /dev/null
@@ -1,4 +0,0 @@
-newmtl material_0
-# shader_type beckmann
-map_Kd texture_map.png 
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diff --git a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured.obj b/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured.obj
deleted file mode 100644
index 6ed0e11f6..000000000
--- a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured.obj
+++ /dev/null
@@ -1,41174 +0,0 @@
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-f 1836/1880/1836 1831/1875/1831 1832/1876/1832
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diff --git a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured.obj.mtl b/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured.obj.mtl
deleted file mode 100644
index 6cf0f5c9f..000000000
--- a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured.obj.mtl
+++ /dev/null
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diff --git a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured_backup.obj b/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured_backup.obj
deleted file mode 100644
index 4596a10fb..000000000
--- a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured_backup.obj
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-f 2507/2507/2702 2479/2479/2409 2496/2496/2399
-f 2459/2459/2403 2508/2508/2417 2500/2500/2404
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-f 2513/2513/2419 2516/2516/2420 2514/2514/2418
-f 2516/2516/2420 1980/1980/2610 2514/2514/2418
-f 2517/2517/2421 2499/2499/2401 2498/2498/2507
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-f 2521/2521/2423 1980/1980/2610 2516/2516/2420
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-f 2526/2526/2597 2515/2515/2429 2523/2523/2602
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-f 2513/2513/2419 2533/2533/2438 2516/2516/2420
-f 2533/2533/2438 2534/2534/2433 2516/2516/2420
-f 1981/1981/2442 1980/1980/2610 2527/2527/2426
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-f 2540/2540/2440 2536/2536/2435 2518/2518/2439
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-f 2545/2545/2449 2542/2542/2450 2541/2541/2441
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-f 2538/2538/2464 2556/2556/2465 2558/2558/2466
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-f 2308/2308/2252 2550/2550/2467 2339/2339/2470
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-f 2553/2553/2486 2564/2564/2475 2556/2556/2465
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diff --git a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured_vhacd.obj b/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured_vhacd.obj
deleted file mode 100644
index 70e3e9e01..000000000
--- a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured_vhacd.obj
+++ /dev/null
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diff --git a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured_vhacd_backup.obj b/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured_vhacd_backup.obj
deleted file mode 100644
index b05709ac2..000000000
--- a/src/unilab/assets/robots/allegro_hand/assets/ycb/056_tennis_ball/google_16k/textured_vhacd_backup.obj
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diff --git a/src/unilab/assets/robots/allegro_hand/scene.xml b/src/unilab/assets/robots/allegro_hand/scene.xml
index d0b19869f..4778f3c73 100644
--- a/src/unilab/assets/robots/allegro_hand/scene.xml
+++ b/src/unilab/assets/robots/allegro_hand/scene.xml
@@ -5,21 +5,22 @@
   <statistic center="0 0 0.1" extent="0.8"/>
 
   <visual>
-    <headlight diffuse="0.6 0.6 0.6" ambient="0.3 0.3 0.3" specular="0 0 0"/>
-    <rgba haze="0.15 0.25 0.35 1"/>
+    <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
+    <map fogstart="100.0" fogend="300.0"/>
+    <rgba haze="0.75 0.75 0.77 1"/>
     <!-- default camera -->
     <global azimuth="-130" elevation="-20"/>
   </visual>
 
   <asset>
-    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
-    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
-      markrgb="0.8 0.8 0.8" width="300" height="300"/>
-    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+    <texture type="skybox" builtin="gradient" rgb1="0.55 0.55 0.57" rgb2="0.55 0.55 0.57" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" rgb1="0.62 0.62 0.63" rgb2="0.49 0.49 0.51"
+      width="300" height="300"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="1.25 1.25" reflectance="0.0"/>
   </asset>
 
   <worldbody>
-    <light pos="0 0 1.5" dir="0 0 -1" directional="true"/>
+    <light pos="0 0 1.5" dir="0 0 -1" directional="true" diffuse="0.8 0.8 0.8"/>
     <geom name="floor" pos="0 0 -0.2" size="0 0 0.05" type="plane" material="groundplane"/>
   </worldbody>
 
diff --git a/src/unilab/assets/robots/g1/assets/box_tracking/largebox.obj b/src/unilab/assets/robots/g1/assets/box_tracking/largebox.obj
deleted file mode 100644
index a6e7f0a26..000000000
--- a/src/unilab/assets/robots/g1/assets/box_tracking/largebox.obj
+++ /dev/null
@@ -1,54685 +0,0 @@
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-f 4162//4162 6721//6721 3863//3863
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-f 13059//13059 11500//11500 11344//11344
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-f 2543//2543 2542//2542 2259//2259
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-f 5466//5466 5185//5185 5463//5463
-f 7650//7650 7653//7653 2895//2895
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-f 279//279 278//278 848//848
-f 12601//12601 12402//12402 8564//8564
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-f 11599//11599 8599//8599 8586//8586
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-f 12713//12713 12600//12600 12465//12465
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-f 11581//11581 12635//12635 7496//7496
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-f 6524//6524 8467//8467 7324//7324
-f 287//287 42//42 2011//2011
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-f 6626//6626 9054//9054 9577//9577
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-f 8319//8319 7163//7163 8755//8755
-f 4240//4240 3935//3935 5535//5535
-f 10258//10258 9934//9934 12176//12176
-f 5928//5928 3293//3293 3015//3015
-f 11883//11883 11369//11369 11882//11882
-f 4373//4373 3496//3496 4103//4103
-f 13106//13106 11542//11542 11534//11534
-f 6580//6580 8269//8269 7386//7386
-f 11192//11192 11071//11071 11112//11112
-f 8507//8507 7355//7355 7370//7370
-f 7678//7678 6970//6970 4879//4879
-f 5735//5735 4752//4752 7978//7978
-f 8143//8143 6453//6453 8409//8409
-f 2250//2250 2794//2794 2530//2530
-f 11883//11883 11882//11882 11370//11370
-f 7988//7988 8637//8637 7545//7545
-f 3106//3106 8537//8537 7383//7383
-f 4368//4368 3476//3476 940//940
-f 8675//8675 7647//7647 9346//9346
-f 7992//7992 10116//10116 10897//10897
-f 397//397 408//408 1571//1571
-f 4050//4050 4342//4342 3826//3826
-f 947//947 956//956 1808//1808
-f 3948//3948 5541//5541 5258//5258
-f 5369//5369 5100//5100 5098//5098
-f 2685//2685 6436//6436 8132//8132
-f 9126//9126 7296//7296 8833//8833
-f 7049//7049 6770//6770 4959//4959
-f 12486//12486 10683//10683 12517//12517
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-f 10524//10524 10376//10376 12771//12771
-f 7801//7801 7799//7799 7083//7083
-f 6817//6817 6823//6823 5003//5003
-f 11358//11358 3222//3222 13356//13356
-f 402//402 1569//1569 411//411
-f 4929//4929 6749//6749 4932//4932
-f 10983//10983 11239//11239 11356//11356
-f 9417//9417 9714//9714 9137//9137
-f 11138//11138 11250//11250 11367//11367
-f 11878//11878 8417//8417 7269//7269
-f 12029//12029 12699//12699 10745//10745
-f 4752//4752 10890//10890 10886//10886
-f 9796//9796 5016//5016 9794//9794
-f 5758//5758 3114//3114 2869//2869
-f 2569//2569 1762//1762 992//992
-f 1715//1715 4265//4265 3974//3974
-f 12809//12809 12688//12688 12437//12437
-f 6815//6815 6814//6814 4996//4996
-f 5415//5415 990//990 5150//5150
-f 10753//10753 12846//12846 12597//12597
-f 12465//12465 12711//12711 12464//12464
-f 5502//5502 5222//5222 210//210
-f 2349//2349 2641//2641 383//383
-f 5240//5240 5516//5516 5517//5517
-f 5983//5983 6274//6274 3349//3349
-f 10460//10460 12756//12756 10335//10335
-f 9706//9706 9124//9124 9696//9696
-f 12180//12180 12178//12178 12179//12179
-f 530//530 240//240 812//812
-f 1204//1204 900//900 899//899
-f 7828//7828 7109//7109 7827//7827
-f 12568//12568 12813//12813 12000//12000
-f 13037//13037 9901//9901 11480//11480
-f 932//932 1782//1782 1779//1779
-f 7061//7061 5252//5252 7788//7788
-f 3437//3437 5667//5667 3771//3771
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-f 2845//2845 5844//5844 3207//3207
-f 6443//6443 8389//8389 11241//11241
-f 3086//3086 6284//6284 3076//3076
-f 12434//12434 12439//12439 12682//12682
-f 2543//2543 2807//2807 2252//2252
-f 11436//11436 13021//13021 9857//9857
-f 12213//12213 12094//12094 13196//13196
-f 2035//2035 2074//2074 1808//1808
-f 13429//13429 12126//12126 13421//13421
-f 6592//6592 4815//4815 7594//7594
-f 7368//7368 8940//8940 9514//9514
-f 3685//3685 4591//4591 4739//4739
-f 170//170 169//169 450//450
-f 10691//10691 10856//10856 10069//10069
-f 10606//10606 10441//10441 10435//10435
-f 6106//6106 3193//3193 5828//5828
-f 8775//8775 8350//8350 9052//9052
-f 9090//9090 9081//9081 8791//8791
-f 7397//7397 13012//13012 10837//10837
-f 10909//10909 8002//8002 10449//10449
-f 3155//3155 6066//6066 6643//6643
-f 10910//10910 7561//7561 10131//10131
-f 7479//7479 11959//11959 7947//7947
-f 3354//3354 3065//3065 3353//3353
-f 3466//3466 3800//3800 5377//5377
-f 4440//4440 3541//3541 1301//1301
-f 10756//10756 10356//10356 10514//10514
-f 10177//10177 11472//11472 11476//11476
-f 8935//8935 9505//9505 9797//9797
-f 11289//11289 12976//12976 11299//11299
-f 2174//2174 2730//2730 2450//2450
-f 10129//10129 10908//10908 10125//10125
-f 12317//12317 11767//11767 13250//13250
-f 6104//6104 3095//3095 3381//3381
-f 11276//11276 11391//11391 11275//11275
-f 12100//12100 12310//12310 10176//10176
-f 6504//6504 8444//8444 8842//8842
-f 12038//12038 12847//12847 10912//10912
-f 8144//8144 6455//6455 7266//7266
-f 10663//10663 8584//8584 7914//7914
-f 8173//8173 9689//9689 9120//9120
-f 3551//3551 4452//4452 3546//3546
-f 1399//1399 1099//1099 5251//5251
-f 2718//2718 1642//1642 2443//2443
-f 9925//9925 13087//13087 11519//11519
-f 2249//2249 2795//2795 1977//1977
-f 5037//5037 10842//10842 10550//10550
-f 3971//3971 4266//4266 5569//5569
-f 4552//4552 4244//4244 5264//5264
-f 4552//4552 5264//5264 4244//4244
-f 552//552 261//261 831//831
-f 1765//1765 618//618 1509//1509
-f 7116//7116 7834//7834 7114//7114
-f 6884//6884 8721//8721 9278//9278
-f 1186//1186 927//927 967//967
-f 9117//9117 4922//4922 9684//9684
-f 2536//2536 4584//4584 3680//3680
-f 9849//9849 9881//9881 13006//13006
-f 9741//9741 9170//9170 9165//9165
-f 12696//12696 12575//12575 12448//12448
-f 764//764 1640//1640 480//480
-f 8269//8269 6563//6563 8530//8530
-f 12960//12960 13284//13284 13547//13547
-f 11803//11803 11084//11084 11309//11309
-f 13266//13266 8918//8918 7809//7809
-f 1335//1335 5479//5479 1334//1334
-f 11174//11174 11087//11087 11122//11122
-
diff --git a/src/unilab/assets/robots/g1/assets/climb_20_z_scale_1/box1.obj b/src/unilab/assets/robots/g1/assets/climb_20_z_scale_1/box1.obj
deleted file mode 100644
index 92d85863b..000000000
--- a/src/unilab/assets/robots/g1/assets/climb_20_z_scale_1/box1.obj
+++ /dev/null
@@ -1,21 +0,0 @@
-# box1 - Box 1 from multi_boxes.obj (filtered)
-v -0.93969476 -0.46967867 0.00000000
-v -0.93969476 -0.46967867 0.89000000
-v -1.17693696 0.45022140 0.00000000
-v -1.17693696 0.45022140 0.89000000
-v 0.99693696 0.02977860 0.00000000
-v 0.99693696 0.02977860 0.89000000
-v 0.75969476 0.94967867 0.00000000
-v 0.75969476 0.94967867 0.89000000
-f 2 4 1
-f 5 2 1
-f 1 4 3
-f 3 5 1
-f 2 8 4
-f 6 2 5
-f 6 8 2
-f 4 8 3
-f 7 5 3
-f 3 8 7
-f 7 6 5
-f 8 6 7
diff --git a/src/unilab/assets/robots/g1/assets/climb_20_z_scale_1/box2.obj b/src/unilab/assets/robots/g1/assets/climb_20_z_scale_1/box2.obj
deleted file mode 100644
index f5d4995bf..000000000
--- a/src/unilab/assets/robots/g1/assets/climb_20_z_scale_1/box2.obj
+++ /dev/null
@@ -1,21 +0,0 @@
-# box2 - Box 2 from multi_boxes.obj (filtered)
-v -2.50000000 -2.50000000 0.00000000
-v -2.50000000 -2.50000000 0.10000000
-v -2.50000000 2.50000000 0.00000000
-v -2.50000000 2.50000000 0.10000000
-v 2.50000000 -2.50000000 0.00000000
-v 2.50000000 -2.50000000 0.10000000
-v 2.50000000 2.50000000 0.00000000
-v 2.50000000 2.50000000 0.10000000
-f 2 4 1
-f 5 2 1
-f 1 4 3
-f 3 5 1
-f 2 8 4
-f 6 2 5
-f 6 8 2
-f 4 8 3
-f 7 5 3
-f 3 8 7
-f 7 6 5
-f 8 6 7
diff --git a/src/unilab/assets/robots/g1/assets/crawl_slope/ground.obj b/src/unilab/assets/robots/g1/assets/crawl_slope/ground.obj
deleted file mode 100644
index 0cb130fe4..000000000
--- a/src/unilab/assets/robots/g1/assets/crawl_slope/ground.obj
+++ /dev/null
@@ -1,23 +0,0 @@
-# Auto-decomposed from terrain_slope.obj into 3 convex pieces.
-# Source: holosoma/.../terrain_slope.obj; MuJoCo collides each mesh
-# via its convex hull, so the original non-convex mesh must be split.
-v -7.4157303400 -7.4157303400 -0.0741573000
-v -7.4157303400 -7.4157303400 0.0000000000
-v -7.4157303400 7.4157303400 -0.0741573000
-v -7.4157303400 7.4157303400 0.0000000000
-v 7.4157303400 -7.4157303400 -0.0741573000
-v 7.4157303400 -7.4157303400 0.0000000000
-v 7.4157303400 7.4157303400 -0.0741573000
-v 7.4157303400 7.4157303400 0.0000000000
-f 2 4 1
-f 5 2 1
-f 1 4 3
-f 3 5 1
-f 2 8 4
-f 6 2 5
-f 6 8 2
-f 4 8 3
-f 7 5 3
-f 3 8 7
-f 7 6 5
-f 8 6 7
diff --git a/src/unilab/assets/robots/g1/assets/crawl_slope/plateau.obj b/src/unilab/assets/robots/g1/assets/crawl_slope/plateau.obj
deleted file mode 100644
index f1cdc53c6..000000000
--- a/src/unilab/assets/robots/g1/assets/crawl_slope/plateau.obj
+++ /dev/null
@@ -1,23 +0,0 @@
-# Auto-decomposed from terrain_slope.obj into 3 convex pieces.
-# Source: holosoma/.../terrain_slope.obj; MuJoCo collides each mesh
-# via its convex hull, so the original non-convex mesh must be split.
-v -0.7823595500 0.7208089900 0.0000000000
-v -0.7823595500 0.7208089900 0.4115730300
-v -0.7823595500 2.2899775300 0.0000000000
-v -0.7823595500 2.2899775300 0.4115730300
-v 0.7823595500 0.7208089900 0.0000000000
-v 0.7823595500 0.7208089900 0.4115730300
-v 0.7823595500 2.2899775300 0.0000000000
-v 0.7823595500 2.2899775300 0.4115730300
-f 2 4 1
-f 5 2 1
-f 1 4 3
-f 3 5 1
-f 2 8 4
-f 6 2 5
-f 6 8 2
-f 4 8 3
-f 7 5 3
-f 3 8 7
-f 7 6 5
-f 8 6 7
diff --git a/src/unilab/assets/robots/g1/assets/crawl_slope/slope.obj b/src/unilab/assets/robots/g1/assets/crawl_slope/slope.obj
deleted file mode 100644
index 20aef465e..000000000
--- a/src/unilab/assets/robots/g1/assets/crawl_slope/slope.obj
+++ /dev/null
@@ -1,23 +0,0 @@
-# Auto-decomposed from terrain_slope.obj into 3 convex pieces.
-# Source: holosoma/.../terrain_slope.obj; MuJoCo collides each mesh
-# via its convex hull, so the original non-convex mesh must be split.
-v -0.7823595500 -2.3088920000 -0.3510057800
-v -0.7823595500 -2.3266936500 -0.2713988700
-v -0.7823595500 0.7397273600 0.3307247100
-v -0.7823595500 0.7219257100 0.4103316300
-v 0.7823595500 -2.3088920000 -0.3510057800
-v 0.7823595500 -2.3266936500 -0.2713988700
-v 0.7823595500 0.7397273600 0.3307247100
-v 0.7823595500 0.7219257100 0.4103316300
-f 2 4 1
-f 5 2 1
-f 1 4 3
-f 3 5 1
-f 2 8 4
-f 6 2 5
-f 6 8 2
-f 4 8 3
-f 7 5 3
-f 3 8 7
-f 7 6 5
-f 8 6 7
diff --git a/src/unilab/assets/robots/g1/assets/head_link.STL b/src/unilab/assets/robots/g1/assets/head_link.STL
deleted file mode 100644
index 2ee5fba15..000000000
Binary files a/src/unilab/assets/robots/g1/assets/head_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_ankle_pitch_link.STL b/src/unilab/assets/robots/g1/assets/left_ankle_pitch_link.STL
deleted file mode 100644
index 69de84901..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_ankle_pitch_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_ankle_roll_link.STL b/src/unilab/assets/robots/g1/assets/left_ankle_roll_link.STL
deleted file mode 100644
index 8864e9f98..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_ankle_roll_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_elbow_link.STL b/src/unilab/assets/robots/g1/assets/left_elbow_link.STL
deleted file mode 100644
index 1a96d99ba..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_elbow_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_hand_index_0_link.STL b/src/unilab/assets/robots/g1/assets/left_hand_index_0_link.STL
deleted file mode 100644
index 8069369af..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_hand_index_0_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_hand_index_1_link.STL b/src/unilab/assets/robots/g1/assets/left_hand_index_1_link.STL
deleted file mode 100644
index 89d231d7e..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_hand_index_1_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_hand_middle_0_link.STL b/src/unilab/assets/robots/g1/assets/left_hand_middle_0_link.STL
deleted file mode 100644
index 8069369af..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_hand_middle_0_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_hand_middle_1_link.STL b/src/unilab/assets/robots/g1/assets/left_hand_middle_1_link.STL
deleted file mode 100644
index 89d231d7e..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_hand_middle_1_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_hand_palm_link.STL b/src/unilab/assets/robots/g1/assets/left_hand_palm_link.STL
deleted file mode 100644
index 7d595ed8f..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_hand_palm_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_hand_thumb_0_link.STL b/src/unilab/assets/robots/g1/assets/left_hand_thumb_0_link.STL
deleted file mode 100644
index 3028bb4d6..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_hand_thumb_0_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_hand_thumb_1_link.STL b/src/unilab/assets/robots/g1/assets/left_hand_thumb_1_link.STL
deleted file mode 100644
index d1c080c86..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_hand_thumb_1_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_hand_thumb_2_link.STL b/src/unilab/assets/robots/g1/assets/left_hand_thumb_2_link.STL
deleted file mode 100644
index 8b32e9663..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_hand_thumb_2_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_hip_pitch_link.STL b/src/unilab/assets/robots/g1/assets/left_hip_pitch_link.STL
deleted file mode 100644
index 5b751c767..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_hip_pitch_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_hip_roll_link.STL b/src/unilab/assets/robots/g1/assets/left_hip_roll_link.STL
deleted file mode 100644
index 778437ffe..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_hip_roll_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_hip_yaw_link.STL b/src/unilab/assets/robots/g1/assets/left_hip_yaw_link.STL
deleted file mode 100644
index 383093ab9..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_hip_yaw_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_knee_link.STL b/src/unilab/assets/robots/g1/assets/left_knee_link.STL
deleted file mode 100644
index f2e98e54e..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_knee_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_rubber_hand.STL b/src/unilab/assets/robots/g1/assets/left_rubber_hand.STL
deleted file mode 100644
index c44830f1f..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_rubber_hand.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_shoulder_pitch_link.STL b/src/unilab/assets/robots/g1/assets/left_shoulder_pitch_link.STL
deleted file mode 100644
index e698311fb..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_shoulder_pitch_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_shoulder_roll_link.STL b/src/unilab/assets/robots/g1/assets/left_shoulder_roll_link.STL
deleted file mode 100644
index 80bca84ac..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_shoulder_roll_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_shoulder_yaw_link.STL b/src/unilab/assets/robots/g1/assets/left_shoulder_yaw_link.STL
deleted file mode 100644
index 281e69905..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_shoulder_yaw_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_wrist_pitch_link.STL b/src/unilab/assets/robots/g1/assets/left_wrist_pitch_link.STL
deleted file mode 100644
index 82cc224a8..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_wrist_pitch_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_wrist_roll_link.STL b/src/unilab/assets/robots/g1/assets/left_wrist_roll_link.STL
deleted file mode 100644
index f3c263a7a..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_wrist_roll_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_wrist_roll_rubber_hand.STL b/src/unilab/assets/robots/g1/assets/left_wrist_roll_rubber_hand.STL
deleted file mode 100644
index 8fa435b66..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_wrist_roll_rubber_hand.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/left_wrist_yaw_link.STL b/src/unilab/assets/robots/g1/assets/left_wrist_yaw_link.STL
deleted file mode 100644
index 31be4fd45..000000000
Binary files a/src/unilab/assets/robots/g1/assets/left_wrist_yaw_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/logo_link.STL b/src/unilab/assets/robots/g1/assets/logo_link.STL
deleted file mode 100644
index e97920985..000000000
Binary files a/src/unilab/assets/robots/g1/assets/logo_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/pelvis.STL b/src/unilab/assets/robots/g1/assets/pelvis.STL
deleted file mode 100644
index 691a779b9..000000000
Binary files a/src/unilab/assets/robots/g1/assets/pelvis.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/pelvis_contour_link.STL b/src/unilab/assets/robots/g1/assets/pelvis_contour_link.STL
deleted file mode 100644
index 42434339a..000000000
Binary files a/src/unilab/assets/robots/g1/assets/pelvis_contour_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_ankle_pitch_link.STL b/src/unilab/assets/robots/g1/assets/right_ankle_pitch_link.STL
deleted file mode 100644
index e77d8a2fe..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_ankle_pitch_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_ankle_roll_link.STL b/src/unilab/assets/robots/g1/assets/right_ankle_roll_link.STL
deleted file mode 100644
index d4261dd7c..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_ankle_roll_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_elbow_link.STL b/src/unilab/assets/robots/g1/assets/right_elbow_link.STL
deleted file mode 100644
index f259e3812..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_elbow_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_hand_index_0_link.STL b/src/unilab/assets/robots/g1/assets/right_hand_index_0_link.STL
deleted file mode 100644
index f87ad3212..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_hand_index_0_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_hand_index_1_link.STL b/src/unilab/assets/robots/g1/assets/right_hand_index_1_link.STL
deleted file mode 100644
index 6dea51ad9..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_hand_index_1_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_hand_middle_0_link.STL b/src/unilab/assets/robots/g1/assets/right_hand_middle_0_link.STL
deleted file mode 100644
index f87ad3212..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_hand_middle_0_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_hand_middle_1_link.STL b/src/unilab/assets/robots/g1/assets/right_hand_middle_1_link.STL
deleted file mode 100644
index 6dea51ad9..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_hand_middle_1_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_hand_palm_link.STL b/src/unilab/assets/robots/g1/assets/right_hand_palm_link.STL
deleted file mode 100644
index 5ae00a783..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_hand_palm_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_hand_thumb_0_link.STL b/src/unilab/assets/robots/g1/assets/right_hand_thumb_0_link.STL
deleted file mode 100644
index 1cae7f18e..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_hand_thumb_0_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_hand_thumb_1_link.STL b/src/unilab/assets/robots/g1/assets/right_hand_thumb_1_link.STL
deleted file mode 100644
index c141fbf5a..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_hand_thumb_1_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_hand_thumb_2_link.STL b/src/unilab/assets/robots/g1/assets/right_hand_thumb_2_link.STL
deleted file mode 100644
index e942923c5..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_hand_thumb_2_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_hip_pitch_link.STL b/src/unilab/assets/robots/g1/assets/right_hip_pitch_link.STL
deleted file mode 100644
index 998a0a0f5..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_hip_pitch_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_hip_roll_link.STL b/src/unilab/assets/robots/g1/assets/right_hip_roll_link.STL
deleted file mode 100644
index 47b2eebdd..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_hip_roll_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_hip_yaw_link.STL b/src/unilab/assets/robots/g1/assets/right_hip_yaw_link.STL
deleted file mode 100644
index 371856427..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_hip_yaw_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_knee_link.STL b/src/unilab/assets/robots/g1/assets/right_knee_link.STL
deleted file mode 100644
index 76d21a3d8..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_knee_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_rubber_hand.STL b/src/unilab/assets/robots/g1/assets/right_rubber_hand.STL
deleted file mode 100644
index 0aacffbb8..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_rubber_hand.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_shoulder_pitch_link.STL b/src/unilab/assets/robots/g1/assets/right_shoulder_pitch_link.STL
deleted file mode 100644
index 3f5b4ed47..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_shoulder_pitch_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_shoulder_roll_link.STL b/src/unilab/assets/robots/g1/assets/right_shoulder_roll_link.STL
deleted file mode 100644
index 179d61753..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_shoulder_roll_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_shoulder_yaw_link.STL b/src/unilab/assets/robots/g1/assets/right_shoulder_yaw_link.STL
deleted file mode 100644
index 2ba6076a8..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_shoulder_yaw_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_wrist_pitch_link.STL b/src/unilab/assets/robots/g1/assets/right_wrist_pitch_link.STL
deleted file mode 100644
index da194543c..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_wrist_pitch_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_wrist_roll_link.STL b/src/unilab/assets/robots/g1/assets/right_wrist_roll_link.STL
deleted file mode 100644
index 26868d228..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_wrist_roll_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_wrist_roll_rubber_hand.STL b/src/unilab/assets/robots/g1/assets/right_wrist_roll_rubber_hand.STL
deleted file mode 100644
index c365aa9d8..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_wrist_roll_rubber_hand.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/right_wrist_yaw_link.STL b/src/unilab/assets/robots/g1/assets/right_wrist_yaw_link.STL
deleted file mode 100644
index d78890286..000000000
Binary files a/src/unilab/assets/robots/g1/assets/right_wrist_yaw_link.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/scene_crawl_slope.xml b/src/unilab/assets/robots/g1/assets/scene_crawl_slope.xml
deleted file mode 100644
index dceb86081..000000000
--- a/src/unilab/assets/robots/g1/assets/scene_crawl_slope.xml
+++ /dev/null
@@ -1,67 +0,0 @@
-<mujoco model="g1_29dof crawl slope scene">
-  <include file="g1.xml"/>
-
-  <statistic center="0 0 0.5" extent="3.0"/>
-
-  <visual>
-    <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
-    <rgba haze="0.15 0.25 0.35 1"/>
-    <global azimuth="140" elevation="-20"/>
-  </visual>
-
-  <asset>
-    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
-    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
-      markrgb="0.8 0.8 0.8" width="300" height="300"/>
-    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
-    <material name="slope_material" rgba="0.35 0.35 0.4 1"/>
-
-    <!--
-      The crawl-slope terrain is the holosoma terrain_slope.obj (single
-      non-convex mesh of 24 verts / 36 tris) decomposed into three convex
-      pieces, because MuJoCo's mesh-vs-primitive collisions use the per-mesh
-      convex hull. Each piece below is an 8-vertex hexahedron and is therefore
-      a true convex hull of itself, so the combined collision surface is
-      geometrically identical to the original OBJ.
-    -->
-    <mesh name="crawl_slope_slope"   file="crawl_slope/slope.obj"/>
-    <mesh name="crawl_slope_plateau" file="crawl_slope/plateau.obj"/>
-    <mesh name="crawl_slope_ground"  file="crawl_slope/ground.obj"/>
-  </asset>
-
-  <worldbody>
-    <geom name="crawl_slope_slope" type="mesh" mesh="crawl_slope_slope"
-          material="slope_material" contype="1" conaffinity="1"
-          friction="1.0 0.1 0.1"
-          solimp="0.99 0.99 0.01 0.5 2" solref="0.001 1"/>
-    <geom name="crawl_slope_plateau" type="mesh" mesh="crawl_slope_plateau"
-          material="slope_material" contype="1" conaffinity="1"
-          friction="1.0 0.1 0.1"
-          solimp="0.99 0.99 0.01 0.5 2" solref="0.001 1"/>
-    <geom name="crawl_slope_ground" type="mesh" mesh="crawl_slope_ground"
-          material="groundplane" contype="1" conaffinity="1"
-          friction="1.0 0.1 0.1"
-          solimp="0.99 0.99 0.01 0.5 2" solref="0.001 1"/>
-  </worldbody>
-
-  <keyframe>
-    <key name="stand"
-      qpos="
-      0 0 0.754
-      1 0 0 0
-      -0.312 0.0 0.0 0.669 -0.363 0.0
-      -0.312 0.0 0.0 0.669 -0.363 0.0
-      0.0 0.0 0.0
-      0.2  0.2 0.0 0.6 0.0 0.0 0.0
-      0.2 -0.2 0.0 0.6 0.0 0.0 0.0
-      "
-      ctrl="
-      -0.312 0.0 0.0 0.669 -0.363 0.0
-      -0.312 0.0 0.0 0.669 -0.363 0.0
-      0.0 0.0 0.0
-      0.2  0.2 0.0 0.6 0.0 0.0 0.0
-      0.2 -0.2 0.0 0.6 0.0 0.0 0.0
-      "/>
-  </keyframe>
-
-</mujoco>
diff --git a/src/unilab/assets/robots/g1/assets/torso_link_23dof_rev_1_0.STL b/src/unilab/assets/robots/g1/assets/torso_link_23dof_rev_1_0.STL
deleted file mode 100644
index 079aa8124..000000000
Binary files a/src/unilab/assets/robots/g1/assets/torso_link_23dof_rev_1_0.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/torso_link_rev_1_0.STL b/src/unilab/assets/robots/g1/assets/torso_link_rev_1_0.STL
deleted file mode 100644
index 8a759a709..000000000
Binary files a/src/unilab/assets/robots/g1/assets/torso_link_rev_1_0.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/waist_roll_link_rev_1_0.STL b/src/unilab/assets/robots/g1/assets/waist_roll_link_rev_1_0.STL
deleted file mode 100644
index a64f330c5..000000000
Binary files a/src/unilab/assets/robots/g1/assets/waist_roll_link_rev_1_0.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/assets/waist_yaw_link_rev_1_0.STL b/src/unilab/assets/robots/g1/assets/waist_yaw_link_rev_1_0.STL
deleted file mode 100644
index 0fabb63d3..000000000
Binary files a/src/unilab/assets/robots/g1/assets/waist_yaw_link_rev_1_0.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/scene_climb_20_z_scale_1.xml b/src/unilab/assets/robots/g1/scene_climb_20_z_scale_1.xml
index d1677ac42..2089f96dc 100644
--- a/src/unilab/assets/robots/g1/scene_climb_20_z_scale_1.xml
+++ b/src/unilab/assets/robots/g1/scene_climb_20_z_scale_1.xml
@@ -5,15 +5,16 @@
 
   <visual>
     <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
-    <rgba haze="0.15 0.25 0.35 1"/>
+    <map fogstart="100.0" fogend="300.0"/>
+    <rgba haze="0.75 0.75 0.77 1"/>
     <global azimuth="140" elevation="-20"/>
   </visual>
 
   <asset>
-    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
-    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
-      markrgb="0.8 0.8 0.8" width="300" height="300"/>
-    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+    <texture type="skybox" builtin="gradient" rgb1="0.55 0.55 0.57" rgb2="0.55 0.55 0.57" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" rgb1="0.62 0.62 0.63" rgb2="0.49 0.49 0.51"
+      width="300" height="300"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="1.25 1.25" reflectance="0.0"/>
     <material name="climb_box1_material" rgba="0.3 0.7 0.9 1"/>
     <material name="climb_box2_material" rgba="0.7 0.3 0.9 1"/>
     <mesh
diff --git a/src/unilab/assets/robots/g1/scene_climb_20_z_scale_1_23dof.xml b/src/unilab/assets/robots/g1/scene_climb_20_z_scale_1_23dof.xml
index 7ad80ee36..609e432be 100644
--- a/src/unilab/assets/robots/g1/scene_climb_20_z_scale_1_23dof.xml
+++ b/src/unilab/assets/robots/g1/scene_climb_20_z_scale_1_23dof.xml
@@ -5,15 +5,16 @@
 
   <visual>
     <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
-    <rgba haze="0.15 0.25 0.35 1"/>
+    <map fogstart="100.0" fogend="300.0"/>
+    <rgba haze="0.75 0.75 0.77 1"/>
     <global azimuth="140" elevation="-20"/>
   </visual>
 
   <asset>
-    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
-    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
-      markrgb="0.8 0.8 0.8" width="300" height="300"/>
-    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+    <texture type="skybox" builtin="gradient" rgb1="0.55 0.55 0.57" rgb2="0.55 0.55 0.57" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" rgb1="0.62 0.62 0.63" rgb2="0.49 0.49 0.51"
+      width="300" height="300"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="1.25 1.25" reflectance="0.0"/>
     <material name="climb_box1_material" rgba="0.3 0.7 0.9 1"/>
     <material name="climb_box2_material" rgba="0.7 0.3 0.9 1"/>
     <mesh
diff --git a/src/unilab/assets/robots/g1/scene_crawl_slope.xml b/src/unilab/assets/robots/g1/scene_crawl_slope.xml
index dceb86081..87b804c67 100644
--- a/src/unilab/assets/robots/g1/scene_crawl_slope.xml
+++ b/src/unilab/assets/robots/g1/scene_crawl_slope.xml
@@ -5,15 +5,16 @@
 
   <visual>
     <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
-    <rgba haze="0.15 0.25 0.35 1"/>
+    <map fogstart="100.0" fogend="300.0"/>
+    <rgba haze="0.75 0.75 0.77 1"/>
     <global azimuth="140" elevation="-20"/>
   </visual>
 
   <asset>
-    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
-    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
-      markrgb="0.8 0.8 0.8" width="300" height="300"/>
-    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+    <texture type="skybox" builtin="gradient" rgb1="0.55 0.55 0.57" rgb2="0.55 0.55 0.57" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" rgb1="0.62 0.62 0.63" rgb2="0.49 0.49 0.51"
+      width="300" height="300"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="1.25 1.25" reflectance="0.0"/>
     <material name="slope_material" rgba="0.35 0.35 0.4 1"/>
 
     <!--
diff --git a/src/unilab/assets/robots/g1/scene_flat.xml b/src/unilab/assets/robots/g1/scene_flat.xml
index 0bdadeea3..7e3c5687b 100644
--- a/src/unilab/assets/robots/g1/scene_flat.xml
+++ b/src/unilab/assets/robots/g1/scene_flat.xml
@@ -1,19 +1,20 @@
 <mujoco model="g1_29dof flat scene">
   <include file="g1.xml"/>
 
-  <statistic center="1 -0.8 1.1" extent=".35"/>
+  <statistic center="0 0 0.79" extent=".35"/>
 
   <visual>
     <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
-    <rgba haze="0.15 0.25 0.35 1"/>
+    <map fogstart="100.0" fogend="300.0"/>
+    <rgba haze="0.75 0.75 0.77 1"/>
     <global azimuth="140" elevation="-20"/>
   </visual>
 
   <asset>
-    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
-    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
-      markrgb="0.8 0.8 0.8" width="300" height="300"/>
-    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+    <texture type="skybox" builtin="gradient" rgb1="0.55 0.55 0.57" rgb2="0.55 0.55 0.57" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" rgb1="0.62 0.62 0.63" rgb2="0.49 0.49 0.51"
+      width="300" height="300"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="1.25 1.25" reflectance="0.0"/>
   </asset>
 
   <worldbody>
diff --git a/src/unilab/assets/robots/g1/scene_flat_23dof.xml b/src/unilab/assets/robots/g1/scene_flat_23dof.xml
index c90487742..7660b53e2 100644
--- a/src/unilab/assets/robots/g1/scene_flat_23dof.xml
+++ b/src/unilab/assets/robots/g1/scene_flat_23dof.xml
@@ -1,19 +1,20 @@
 <mujoco model="g1_23dof flat scene">
   <include file="g1_23dof.xml"/>
 
-  <statistic center="1 -0.8 1.1" extent=".35"/>
+  <statistic center="0 0 0.79" extent=".35"/>
 
   <visual>
     <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
-    <rgba haze="0.15 0.25 0.35 1"/>
+    <map fogstart="100.0" fogend="300.0"/>
+    <rgba haze="0.75 0.75 0.77 1"/>
     <global azimuth="140" elevation="-20"/>
   </visual>
 
   <asset>
-    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
-    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
-      markrgb="0.8 0.8 0.8" width="300" height="300"/>
-    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+    <texture type="skybox" builtin="gradient" rgb1="0.55 0.55 0.57" rgb2="0.55 0.55 0.57" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" rgb1="0.62 0.62 0.63" rgb2="0.49 0.49 0.51"
+      width="300" height="300"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="1.25 1.25" reflectance="0.0"/>
   </asset>
 
   <worldbody>
diff --git a/src/unilab/assets/robots/g1/scene_flat_23dof_with_largebox.xml b/src/unilab/assets/robots/g1/scene_flat_23dof_with_largebox.xml
index 5a6ea6fd7..363be01d0 100644
--- a/src/unilab/assets/robots/g1/scene_flat_23dof_with_largebox.xml
+++ b/src/unilab/assets/robots/g1/scene_flat_23dof_with_largebox.xml
@@ -1,19 +1,20 @@
 <mujoco model="g1_23dof flat scene with largebox">
   <include file="g1_23dof_sphere_hand.xml"/>
 
-  <statistic center="1 -0.8 1.1" extent=".35"/>
+  <statistic center="0 0 0.79" extent=".35"/>
 
   <visual>
     <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
-    <rgba haze="0.15 0.25 0.35 1"/>
+    <map fogstart="100.0" fogend="300.0"/>
+    <rgba haze="0.75 0.75 0.77 1"/>
     <global azimuth="140" elevation="-20"/>
   </visual>
 
   <asset>
-    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
-    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
-      markrgb="0.8 0.8 0.8" width="300" height="300"/>
-    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+    <texture type="skybox" builtin="gradient" rgb1="0.55 0.55 0.57" rgb2="0.55 0.55 0.57" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" rgb1="0.62 0.62 0.63" rgb2="0.49 0.49 0.51"
+      width="300" height="300"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="1.25 1.25" reflectance="0.0"/>
     <mesh name="largebox_mesh" file="box_tracking/largebox.obj"/>
   </asset>
 
diff --git a/src/unilab/assets/robots/g1/scene_flat_23dof_with_wall.xml b/src/unilab/assets/robots/g1/scene_flat_23dof_with_wall.xml
index 0e237be6d..8040ab8a3 100644
--- a/src/unilab/assets/robots/g1/scene_flat_23dof_with_wall.xml
+++ b/src/unilab/assets/robots/g1/scene_flat_23dof_with_wall.xml
@@ -1,19 +1,20 @@
 <mujoco model="g1_23dof scene with wall">
   <include file="g1_23dof.xml"/>
 
-  <statistic center="1 -0.8 1.1" extent=".35"/>
+  <statistic center="0 0 0.79" extent=".35"/>
 
   <visual>
     <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
-    <rgba haze="0.15 0.25 0.35 1"/>
+    <map fogstart="100.0" fogend="300.0"/>
+    <rgba haze="0.75 0.75 0.77 1"/>
     <global azimuth="140" elevation="-20"/>
   </visual>
 
   <asset>
-    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
-    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
-      markrgb="0.8 0.8 0.8" width="300" height="300"/>
-    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+    <texture type="skybox" builtin="gradient" rgb1="0.55 0.55 0.57" rgb2="0.55 0.55 0.57" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" rgb1="0.62 0.62 0.63" rgb2="0.49 0.49 0.51"
+      width="300" height="300"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="1.25 1.25" reflectance="0.0"/>
     <material name="wall_material" rgba="0.7 0.4 0.3 1"/>
   </asset>
 
diff --git a/src/unilab/assets/robots/g1/scene_flat_with_largebox.xml b/src/unilab/assets/robots/g1/scene_flat_with_largebox.xml
index f18994fa3..f4cf735c8 100644
--- a/src/unilab/assets/robots/g1/scene_flat_with_largebox.xml
+++ b/src/unilab/assets/robots/g1/scene_flat_with_largebox.xml
@@ -1,19 +1,20 @@
 <mujoco model="g1_29dof flat scene with largebox">
   <include file="g1_sphere_hand.xml"/>
 
-  <statistic center="1 -0.8 1.1" extent=".35"/>
+  <statistic center="0 0 0.79" extent=".35"/>
 
   <visual>
     <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
-    <rgba haze="0.15 0.25 0.35 1"/>
+    <map fogstart="100.0" fogend="300.0"/>
+    <rgba haze="0.75 0.75 0.77 1"/>
     <global azimuth="140" elevation="-20"/>
   </visual>
 
   <asset>
-    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
-    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
-      markrgb="0.8 0.8 0.8" width="300" height="300"/>
-    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+    <texture type="skybox" builtin="gradient" rgb1="0.55 0.55 0.57" rgb2="0.55 0.55 0.57" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" rgb1="0.62 0.62 0.63" rgb2="0.49 0.49 0.51"
+      width="300" height="300"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="1.25 1.25" reflectance="0.0"/>
     <mesh name="largebox_mesh" file="box_tracking/largebox.obj"/>
   </asset>
 
diff --git a/src/unilab/assets/robots/g1/scene_flat_with_wall.xml b/src/unilab/assets/robots/g1/scene_flat_with_wall.xml
index aee2dd68f..bf8bb9a60 100644
--- a/src/unilab/assets/robots/g1/scene_flat_with_wall.xml
+++ b/src/unilab/assets/robots/g1/scene_flat_with_wall.xml
@@ -1,7 +1,7 @@
 <mujoco model="g1_29dof_rev_1_0 scene with wall">
   <include file="g1.xml"/>
 
-  <statistic center="1 -0.8 1.1" extent=".35"/>
+  <statistic center="0 0 0.79" extent=".35"/>
 
   <option>
     <flag multiccd="disable"/>
@@ -9,24 +9,25 @@
 
   <visual>
     <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
-    <rgba haze="0.15 0.25 0.35 1"/>
+    <map fogstart="100.0" fogend="300.0"/>
+    <rgba haze="0.75 0.75 0.77 1"/>
     <global azimuth="140" elevation="-20"/>
   </visual>
 
   <asset>
-    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
+    <texture type="skybox" builtin="gradient" rgb1="0.55 0.55 0.57" rgb2="0.55 0.55 0.57" width="512" height="3072"/>
     <texture
       type="2d"
       name="groundplane"
       builtin="checker"
-      mark="edge"
-      rgb1="0.2 0.3 0.4"
-      rgb2="0.1 0.2 0.3"
-      markrgb="0.8 0.8 0.8"
+     
+      rgb1="0.62 0.62 0.63"
+      rgb2="0.49 0.49 0.51"
+     
       width="300"
       height="300"
     />
-    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="1.25 1.25" reflectance="0.0"/>
     <material name="wall_material" rgba="0.7 0.4 0.3 1"/>
   </asset>
 
diff --git a/src/unilab/assets/robots/g1/textures/floor.png b/src/unilab/assets/robots/g1/textures/floor.png
deleted file mode 100644
index e97d3159b..000000000
Binary files a/src/unilab/assets/robots/g1/textures/floor.png and /dev/null differ
diff --git a/src/unilab/assets/robots/g1/textures/rocky.png b/src/unilab/assets/robots/g1/textures/rocky.png
deleted file mode 100644
index 328bd0f98..000000000
Binary files a/src/unilab/assets/robots/g1/textures/rocky.png and /dev/null differ
diff --git a/src/unilab/assets/robots/go1/scene_flat.xml b/src/unilab/assets/robots/go1/scene_flat.xml
index 23b754684..4902f9d59 100644
--- a/src/unilab/assets/robots/go1/scene_flat.xml
+++ b/src/unilab/assets/robots/go1/scene_flat.xml
@@ -1,23 +1,24 @@
 <mujoco model="go1 scene">
   <include file="go1.xml"/>
 
-  <statistic center="0 0 0.1" extent="0.8"/>
+  <statistic center="0 0 0.45" extent="0.8"/>
 
   <visual>
-    <headlight diffuse="0.6 0.6 0.6" ambient="0.3 0.3 0.3" specular="0 0 0"/>
-    <rgba haze="0.15 0.25 0.35 1"/>
+    <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
+    <map fogstart="100.0" fogend="300.0"/>
+    <rgba haze="0.75 0.75 0.77 1"/>
     <global azimuth="120" elevation="-20"/>
   </visual>
 
   <asset>
-    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
-    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
-      markrgb="0.8 0.8 0.8" width="300" height="300"/>
-    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+    <texture type="skybox" builtin="gradient" rgb1="0.55 0.55 0.57" rgb2="0.55 0.55 0.57" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" rgb1="0.62 0.62 0.63" rgb2="0.49 0.49 0.51"
+      width="300" height="300"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="1.25 1.25" reflectance="0.0"/>
   </asset>
 
   <worldbody>
-    <light pos="0 0 1.5" dir="0 0 -1" directional="true"/>
+    <light pos="0 0 1.5" dir="0 0 -1" directional="true" diffuse="0.8 0.8 0.8"/>
     <geom name="floor" size="0 0 0.05" type="plane" material="groundplane"/>
   </worldbody>
   
diff --git a/src/unilab/assets/robots/go2/assets/base_0.obj b/src/unilab/assets/robots/go2/assets/base_0.obj
deleted file mode 100644
index f2e688236..000000000
--- a/src/unilab/assets/robots/go2/assets/base_0.obj
+++ /dev/null
@@ -1,36346 +0,0 @@
-mtllib 深色橡胶.001.mtl
-usemtl 深色橡胶.001
-v 0.28651000 0.03397800 -0.06016600
-v 0.28140700 0.03271000 -0.06153300
-v 0.28510200 0.03416200 -0.05502200
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-v 0.29174300 0.03440500 -0.05876400
-v 0.28791900 0.03379400 -0.06531000
-v 0.28791900 0.03379400 -0.06531000
-v 0.29052700 0.03411300 -0.06461100
-v 0.29052700 0.03411300 -0.06461100
-v 0.28539800 0.03327700 -0.06598500
-v 0.28539800 0.03327700 -0.06598500
-v 0.28676900 0.03288900 -0.07074100
-v 0.28284300 0.03253300 -0.06667000
-v 0.28284300 0.03253300 -0.06667000
-v 0.28045700 0.03162400 -0.06730900
-v 0.28045700 0.03162400 -0.06730900
-v 0.27802000 0.03046400 -0.06796200
-v 0.27802000 0.03046400 -0.06796200
-v 0.28188500 0.03125600 -0.07205000
-v 0.28347400 0.03043800 -0.07668000
-v 0.27750800 0.02898500 -0.07322200
-v 0.27750800 0.02898500 -0.07322200
-v 0.27798600 0.02895900 -0.07412400
-v 0.27798600 0.02895900 -0.07412400
-v 0.27732500 0.02899900 -0.07283600
-v 0.27732500 0.02899900 -0.07283600
-v 0.27699700 0.02874300 -0.07297500
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-v 0.27678100 0.02906700 -0.07149800
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-v 0.27653300 0.02911900 -0.07073700
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-v 0.27551600 0.02896300 -0.06864100
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-f 2034/2034/2034 2036/2036/2036 5908/5908/5908
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-f 2076/2076/2076 2077/2077/2077 2080/2080/2080
-f 2078/2078/2078 2080/2080/2080 8568/8568/8568
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-f 2084/2084/2084 2066/2066/2066 2081/2081/2081
-f 2084/2084/2084 2082/2082/2082 2085/2085/2085
-f 2082/2082/2082 2079/2079/2079 2089/2089/2089
-f 8545/8545/8545 2087/2087/2087 2085/2085/2085
-f 2085/2085/2085 2089/2089/2089 2091/2091/2091
-f 2079/2079/2079 8568/8568/8568 2090/2090/2090
-f 2089/2089/2089 2090/2090/2090 2093/2093/2093
-f 2088/2088/2088 2091/2091/2091 2092/2092/2092
-f 2091/2091/2091 2093/2093/2093 2094/2094/2094
-f 2093/2093/2093 2095/2095/2095 2096/2096/2096
-f 2090/2090/2090 2098/2098/2098 2095/2095/2095
-f 2095/2095/2095 2097/2097/2097 8558/8558/8558
-f 2098/2098/2098 2100/2100/2100 2097/2097/2097
-f 8568/8568/8568 2099/2099/2099 2098/2098/2098
-f 2099/2099/2099 2101/2101/2101 2100/2100/2100
-f 2100/2100/2100 2102/2102/2102 2104/2104/2104
-f 2101/2101/2101 2105/2105/2105 2102/2102/2102
-f 2105/2105/2105 2107/2107/2107 2103/2103/2103
-f 2102/2102/2102 2103/2103/2103 2527/2527/2527
-f 5720/5720/5720 2106/2106/2106 2105/2105/2105
-f 2106/2106/2106 2109/2109/2109 2107/2107/2107
-f 2107/2107/2107 2108/2108/2108 2528/2528/2528
-f 2109/2109/2109 2111/2111/2111 2108/2108/2108
-f 2110/2110/2110 2113/2113/2113 2111/2111/2111
-f 5717/5717/5717 2110/2110/2110 2109/2109/2109
-f 2113/2113/2113 2114/2114/2114 2112/2112/2112
-f 2111/2111/2111 2112/2112/2112 2530/2530/2530
-f 2117/2117/2117 2115/2115/2115 2113/2113/2113
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diff --git a/src/unilab/assets/robots/go2/assets/base_1.obj b/src/unilab/assets/robots/go2/assets/base_1.obj
deleted file mode 100644
index e9a44eb66..000000000
--- a/src/unilab/assets/robots/go2/assets/base_1.obj
+++ /dev/null
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diff --git a/src/unilab/assets/robots/go2/assets/base_2.obj b/src/unilab/assets/robots/go2/assets/base_2.obj
deleted file mode 100644
index bc661591f..000000000
--- a/src/unilab/assets/robots/go2/assets/base_2.obj
+++ /dev/null
@@ -1,8367 +0,0 @@
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-f 1784/1784/1784 1803/1803/1803 1802/1802/1802
-f 1802/1802/1802 1806/1806/1806 1804/1804/1804
-f 1894/1894/1894 1804/1804/1804 1805/1805/1805
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-f 1841/1841/1841 1703/1703/1703 1872/1872/1872
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-f 1860/1860/1860 1844/1844/1844 1829/1829/1829
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-f 1870/1870/1870 1867/1867/1867 1807/1807/1807
-f 2062/2062/2062 1870/1870/1870 1803/1803/1803
-f 1867/1867/1867 1809/1809/1809 1807/1807/1807
-f 1865/1865/1865 1812/1812/1812 1809/1809/1809
-f 1874/1874/1874 1824/1824/1824 1827/1827/1827
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-f 1903/1903/1903 1907/1907/1907 1909/1909/1909
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-f 1529/1529/1529 1915/1915/1915 1913/1913/1913
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-f 1933/1933/1933 1904/1904/1904 1931/1931/1931
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-f 1933/1933/1933 1796/1796/1796 1901/1901/1901
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-f 1968/1968/1968 1982/1982/1982 1970/1970/1970
-f 1983/1983/1983 1561/1561/1561 1982/1982/1982
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-f 1990/1990/1990 2067/2067/2067 1642/1642/1642
-f 1645/1645/1645 1990/1990/1990 1642/1642/1642
-f 1992/1992/1992 1989/1989/1989 1991/1991/1991
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-f 2033/2033/2033 1992/1992/1992 1648/1648/1648
-f 1993/1993/1993 1994/1994/1994 1992/1992/1992
-f 1997/1997/1997 1994/1994/1994 1995/1995/1995
-f 1994/1994/1994 1987/1987/1987 1989/1989/1989
-f 1997/1997/1997 1975/1975/1975 1996/1996/1996
-f 1975/1975/1975 1987/1987/1987 1996/1996/1996
-f 1998/1998/1998 1995/1995/1995 2028/2028/2028
-f 1998/1998/1998 1999/1999/1999 1997/1997/1997
-f 2000/2000/2000 1976/1976/1976 1999/1999/1999
-f 2021/2021/2021 2000/2000/2000 1998/1998/1998
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-f 2011/2011/2011 2006/2006/2006 2007/2007/2007
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-f 2014/2014/2014 1953/1953/1953 1957/1957/1957
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-f 2024/2024/2024 1998/1998/1998 2028/2028/2028
-f 2028/2028/2028 1993/1993/1993 2029/2029/2029
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-f 2036/2036/2036 2040/2040/2040 2041/2041/2041
-f 2035/2035/2035 1652/1652/1652 2040/2040/2040
-f 2041/2041/2041 1656/1656/1656 2044/2044/2044
-f 2057/2057/2057 2042/2042/2042 2043/2043/2043
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-f 2044/2044/2044 1658/1658/1658 2046/2046/2046
-f 2047/2047/2047 2048/2048/2048 2045/2045/2045
-f 2051/2051/2051 2049/2049/2049 2047/2047/2047
-f 2055/2055/2055 2050/2050/2050 2051/2051/2051
-f 1728/1728/1728 2053/2053/2053 2055/2055/2055
-f 2052/2052/2052 1664/1664/1664 2056/2056/2056
-f 2056/2056/2056 1723/1723/1723 1729/1729/1729
-f 2046/2046/2046 1660/1660/1660 2052/2052/2052
-f 2038/2038/2038 2059/2059/2059 2031/2031/2031
-f 2031/2031/2031 2023/2023/2023 2026/2026/2026
-f 1950/1950/1950 1928/1928/1928 1960/1960/1960
-f 1937/1937/1937 2061/2061/2061 1790/1790/1790
-f 1775/1775/1775 1790/1790/1790 2061/2061/2061
-f 1736/1736/1736 1719/1719/1719 2064/2064/2064
-f 1678/1678/1678 1695/1695/1695 2065/2065/2065
-f 1672/1672/1672 1632/1632/1632 1675/1675/1675
-f 1669/1669/1669 1634/1634/1634 1672/1672/1672
-f 1654/1654/1654 1669/1669/1669 1661/1661/1661
-f 1640/1640/1640 1601/1601/1601 2068/2068/2068
-f 1641/1641/1641 1625/1625/1625 1631/1631/1631
-f 1621/1621/1621 1601/1601/1601 1604/1604/1604
-f 2073/2073/2073 1536/1536/1536 1515/1515/1515
-f 1519/1519/1519 1536/1536/1536 1526/1526/1526
-f 1263/1263/1263 1489/1489/1489 2074/2074/2074
-f 1261/1261/1261 1488/1488/1488 2075/2075/2075
-f 2075/2075/2075 1486/1486/1486 2079/2079/2079
-f 1257/1257/1257 2076/2076/2076 2077/2077/2077
-f 2082/2082/2082 2076/2076/2076 2078/2078/2078
-f 2079/2079/2079 1484/1484/1484 2080/2080/2080
-f 2083/2083/2083 2078/2078/2078 2081/2081/2081
-f 2084/2084/2084 2081/2081/2081 1465/1465/1465
-f 1466/1466/1466 1484/1484/1484 1468/1468/1468
-f 2087/2087/2087 1471/1471/1471 1474/1474/1474
-f 2088/2088/2088 2090/2090/2090 2087/2087/2087
-f 2091/2091/2091 2092/2092/2092 2094/2094/2094
-f 2091/2091/2091 1471/1471/1471 2090/2090/2090
-f 2102/2102/2102 2092/2092/2092 2088/2088/2088
-f 2094/2094/2094 2093/2093/2093 2095/2095/2095
-f 1453/1453/1453 2091/2091/2091 2094/2094/2094
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-f 2095/2095/2095 2097/2097/2097 2096/2096/2096
-f 1451/1451/1451 2096/2096/2096 1448/1448/1448
-f 1420/1420/1420 2097/2097/2097 1418/1418/1418
-f 1448/1448/1448 1420/1420/1420 1425/1425/1425
-f 2099/2099/2099 1418/1418/1418 2097/2097/2097
-f 2102/2102/2102 2097/2097/2097 2093/2093/2093
-f 2105/2105/2105 1381/1381/1381 1383/1383/1383
-f 2107/2107/2107 2112/2112/2112 1381/1381/1381
-f 2110/2110/2110 2117/2117/2117 2112/2112/2112
-f 2113/2113/2113 1332/1332/1332 2117/2117/2117
-f 2118/2118/2118 2112/2112/2112 2117/2117/2117
-f 1334/1334/1334 2117/2117/2117 1332/1332/1332
-f 891/891/891 890/890/890 887/887/887
-f 895/895/895 893/893/893 891/891/891
-f 444/444/444 441/441/441 440/440/440
-f 440/440/440 437/437/437 434/434/434
-f 206/206/206 203/203/203 201/201/201
-f 201/201/201 199/199/199 197/197/197
-f 610/610/610 609/609/609 607/607/607
-f 607/607/607 605/605/605 602/602/602
-f 601/601/601 599/599/599 589/589/589
-f 315/315/315 313/313/313 311/311/311
-f 311/311/311 310/310/310 308/308/308
-f 308/308/308 307/307/307 305/305/305
-f 510/510/510 556/556/556 554/554/554
-f 550/550/550 548/548/548 547/547/547
-f 531/531/531 528/528/528 527/527/527
-f 659/659/659 661/661/661 662/662/662
-f 589/589/589 599/599/599 597/597/597
-f 985/985/985 984/984/984 983/983/983
-f 974/974/974 973/973/973 970/970/970
-f 836/836/836 838/838/838 832/832/832
-f 1266/1266/1266 1259/1259/1259 1255/1255/1255
-f 1254/1254/1254 1258/1258/1258 1262/1262/1262
-f 1260/1260/1260 1263/1263/1263 2074/2074/2074
-f 1259/1259/1259 1265/1265/1265 1269/1269/1269
-f 1265/1265/1265 1267/1267/1267 1270/1270/1270
-f 1370/1370/1370 1268/1268/1268 1266/1266/1266
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-f 1264/1264/1264 1269/1269/1269 1271/1271/1271
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-f 1280/1280/1280 1277/1277/1277 1281/1281/1281
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-f 1291/1291/1291 1286/1286/1286 1285/1285/1285
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-f 1367/1367/1367 1295/1295/1295 1293/1293/1293
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-f 1300/1300/1300 1299/1299/1299 1296/1296/1296
-f 1299/1299/1299 1303/1303/1303 1292/1292/1292
-f 1357/1357/1357 1302/1302/1302 1299/1299/1299
-f 1302/1302/1302 1306/1306/1306 1303/1303/1303
-f 1304/1304/1304 2119/2119/2119 1292/1292/1292
-f 1309/1309/1309 1304/1304/1304 1303/1303/1303
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-f 1302/1302/1302 1359/1359/1359 1307/1307/1307
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diff --git a/src/unilab/assets/robots/go2/assets/base_3.obj b/src/unilab/assets/robots/go2/assets/base_3.obj
deleted file mode 100644
index 5c98393ef..000000000
--- a/src/unilab/assets/robots/go2/assets/base_3.obj
+++ /dev/null
@@ -1,10932 +0,0 @@
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diff --git a/src/unilab/assets/robots/go2/assets/base_4.obj b/src/unilab/assets/robots/go2/assets/base_4.obj
deleted file mode 100644
index 1f45bc4fa..000000000
--- a/src/unilab/assets/robots/go2/assets/base_4.obj
+++ /dev/null
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-f 14390/14390/14390 14389/14389/14389 2088/2088/2088
-f 14406/14406/14406 14405/14405/14405 2093/2093/2093
-f 1026/1026/1026 14405/14405/14405 14404/14404/14404
-f 2098/2098/2098 14314/14314/14314 14427/14427/14427
-f 2100/2100/2100 14427/14427/14427 14426/14426/14426
-f 2108/2108/2108 14425/14425/14425 2110/2110/2110
-f 1024/1024/1024 14322/14322/14322 14442/14442/14442
-f 2090/2090/2090 14442/14442/14442 14441/14441/14441
-f 14440/14440/14440 2102/2102/2102 14327/14327/14327
-f 14454/14454/14454 2105/2105/2105 2104/2104/2104
-f 14455/14455/14455 2049/2049/2049 963/963/963
-f 14453/14453/14453 2049/2049/2049 14455/14455/14455
-f 14326/14326/14326 14436/14436/14436 948/948/948
-f 2020/2020/2020 14435/14435/14435 2022/2022/2022
-f 2022/2022/2022 14435/14435/14435 14434/14434/14434
-f 14325/14325/14325 14433/14433/14433 873/873/873
-f 1971/1971/1971 14433/14433/14433 14432/14432/14432
-f 14324/14324/14324 14430/14430/14430 781/781/781
-f 775/775/775 14430/14430/14430 14323/14323/14323
-f 700/700/700 14429/14429/14429 14428/14428/14428
-f 14318/14318/14318 14316/14316/14316 691/691/691
-f 582/582/582 14410/14410/14410 14408/14408/14408
-f 577/577/577 14408/14408/14408 14315/14315/14315
-f 575/575/575 14310/14310/14310 14392/14392/14392
-f 14297/14297/14297 1734/1734/1734 14392/14392/14392
-f 551/551/551 14378/14378/14378 14377/14377/14377
-f 14377/14377/14377 14290/14290/14290 563/563/563
-f 478/478/478 14360/14360/14360 14359/14359/14359
-f 1634/1634/1634 14359/14359/14359 14275/14275/14275
-f 476/476/476 14341/14341/14341 14340/14340/14340
-f 1737/1737/1737 14339/14339/14339 1739/1739/1739
-f 14254/14254/14254 14333/14333/14333 555/555/555
-f 1741/1741/1741 14332/14332/14332 1743/1743/1743
-f 14331/14331/14331 14330/14330/14330 1745/1745/1745
-f 1746/1746/1746 14330/14330/14330 14329/14329/14329
-f 1752/1752/1752 14328/14328/14328 1754/1754/1754
-f 14255/14255/14255 14336/14336/14336 561/561/561
-f 1749/1749/1749 14335/14335/14335 1750/1750/1750
-f 1750/1750/1750 14335/14335/14335 14334/14334/14334
-f 14257/14257/14257 1875/1875/1875 14334/14334/14334
-f 14453/14453/14453 14438/14438/14438 14437/14437/14437
-f 14455/14455/14455 14438/14438/14438 14453/14453/14453
-f 14454/14454/14454 14439/14439/14439 14455/14455/14455
-f 14456/14456/14456 14357/14357/14357 14356/14356/14356
-f 14459/14459/14459 14356/14356/14356 14355/14355/14355
-f 14443/14443/14443 14281/14281/14281 14352/14352/14352
-f 14443/14443/14443 1967/1967/1967 14445/14445/14445
-f 14445/14445/14445 864/864/864 2000/2000/2000
-f 14446/14446/14446 2000/2000/2000 938/938/938
-f 14447/14447/14447 1998/1998/1998 1996/1996/1996
-f 2009/2009/2009 14371/14371/14371 14370/14370/14370
-f 2007/2007/2007 14371/14371/14371 2009/2009/2009
-f 2006/2006/2006 14372/14372/14372 2007/2007/2007
-f 2004/2004/2004 14295/14295/14295 14373/14373/14373
-f 2017/2017/2017 14384/14384/14384 14383/14383/14383
-f 2016/2016/2016 14384/14384/14384 2017/2017/2017
-f 14386/14386/14386 14385/14385/14385 2013/2013/2013
-f 14308/14308/14308 14387/14387/14387 947/947/947
-f 2039/2039/2039 14399/14399/14399 14398/14398/14398
-f 2037/2037/2037 14400/14400/14400 14399/14399/14399
-f 957/957/957 2047/2047/2047 14401/14401/14401
-f 2047/2047/2047 14402/14402/14402 14401/14401/14401
-f 2045/2045/2045 14403/14403/14403 14402/14402/14402
-f 14313/14313/14313 2042/2042/2042 14423/14423/14423
-f 14448/14448/14448 14424/14424/14424 955/955/955
-f 14450/14450/14450 2029/2029/2029 2028/2028/2028
-f 953/953/953 14449/14449/14449 2028/2028/2028
-f 14452/14452/14452 2025/2025/2025 2023/2023/2023
-f 14452/14452/14452 2023/2023/2023 877/877/877
-f 1977/1977/1977 14416/14416/14416 14415/14415/14415
-f 875/875/875 14417/14417/14417 14416/14416/14416
-f 1976/1976/1976 14418/14418/14418 14417/14417/14417
-f 1942/1942/1942 14411/14411/14411 14319/14319/14319
-f 773/773/773 14412/14412/14412 14411/14411/14411
-f 769/769/769 14414/14414/14414 14412/14412/14412
-f 1932/1932/1932 14312/14312/14312 14317/14317/14317
-f 696/696/696 14396/14396/14396 14395/14395/14395
-f 694/694/694 14397/14397/14397 14396/14396/14396
-f 14311/14311/14311 14397/14397/14397 664/664/664
-f 1859/1859/1859 14393/14393/14393 1861/1861/1861
-f 14379/14379/14379 14298/14298/14298 667/667/667
-f 573/573/573 14380/14380/14380 14379/14379/14379
-f 14382/14382/14382 14381/14381/14381 572/572/572
-f 1769/1769/1769 14361/14361/14361 14291/14291/14291
-f 1768/1768/1768 14361/14361/14361 1769/1769/1769
-f 14365/14365/14365 14363/14363/14363 566/566/566
-f 1762/1762/1762 14366/14366/14366 14365/14365/14365
-f 1761/1761/1761 14367/14367/14367 14366/14366/14366
-f 1756/1756/1756 14342/14342/14342 14277/14277/14277
-f 14344/14344/14344 14343/14343/14343 570/570/570
-f 668/668/668 1873/1873/1873 14345/14345/14345
-f 1873/1873/1873 14346/14346/14346 14345/14345/14345
-f 1870/1870/1870 14347/14347/14347 14346/14346/14346
-f 1868/1868/1868 14348/14348/14348 14347/14347/14347
-f 1866/1866/1866 14349/14349/14349 14348/14348/14348
-f 1862/1862/1862 14350/14350/14350 14276/14276/14276
-f 671/671/671 14351/14351/14351 14350/14350/14350
-f 1879/1879/1879 14279/14279/14279 14351/14351/14351
-f 14451/14451/14451 14320/14320/14320 14422/14422/14422
-f 14452/14452/14452 14422/14422/14422 14421/14421/14421
-f 14449/14449/14449 14421/14421/14421 14420/14420/14420
-f 14450/14450/14450 14420/14420/14420 14419/14419/14419
-f 14321/14321/14321 14448/14448/14448 14419/14419/14419
-f 14448/14448/14448 2033/2033/2033 2029/2029/2029
-f 14447/14447/14447 14294/14294/14294 14369/14369/14369
-f 14446/14446/14446 14368/14368/14368 14445/14445/14445
-f 862/862/862 1968/1968/1968 14352/14352/14352
-f 14445/14445/14445 14368/14368/14368 14286/14286/14286
-f 14591/14591/14591 14593/14593/14593 14534/14534/14534
-f 14539/14539/14539 14536/14536/14536 14534/14534/14534
-f 14604/14604/14604 14607/14607/14607 14537/14537/14537
-f 14637/14637/14637 14638/14638/14638 14540/14540/14540
-f 14683/14683/14683 14686/14686/14686 14540/14540/14540
-f 14613/14613/14613 14614/14614/14614 14537/14537/14537
-f 14655/14655/14655 14657/14657/14657 14541/14541/14541
-f 14541/14541/14541 14537/14537/14537 14536/14536/14536
-f 14652/14652/14652 14653/14653/14653 14541/14541/14541
-f 14645/14645/14645 14539/14539/14539 14643/14643/14643
-f 14623/14623/14623 14625/14625/14625 14534/14534/14534
-f 14597/14597/14597 14600/14600/14600 14536/14536/14536
-f 14687/14687/14687 14490/14490/14490 14489/14489/14489
-f 14687/14687/14687 14606/14606/14606 14688/14688/14688
-f 14688/14688/14688 14605/14605/14605 14689/14689/14689
-f 14689/14689/14689 14605/14605/14605 14550/14550/14550
-f 14690/14690/14690 14615/14615/14615 14612/14612/14612
-f 14612/14612/14612 14500/14500/14500 14499/14499/14499
-f 14552/14552/14552 14501/14501/14501 14500/14500/14500
-f 14639/14639/14639 14501/14501/14501 14641/14641/14641
-f 14636/14636/14636 14502/14502/14502 14639/14639/14639
-f 14635/14635/14635 14503/14503/14503 14636/14636/14636
-f 14633/14633/14633 14511/14511/14511 14469/14469/14469
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-f 14685/14685/14685 14515/14515/14515 14514/14514/14514
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-f 14671/14671/14671 14531/14531/14531 14530/14530/14530
-f 14669/14669/14669 14532/14532/14532 14531/14531/14531
-f 14665/14665/14665 14692/14692/14692 14533/14533/14533
-f 14691/14691/14691 14664/14664/14664 14661/14661/14661
-f 14691/14691/14691 14661/14661/14661 14567/14567/14567
-f 14693/14693/14693 14659/14659/14659 14658/14658/14658
-f 14695/14695/14695 14658/14658/14658 14656/14656/14656
-f 14473/14473/14473 14526/14526/14526 14656/14656/14656
-f 14654/14654/14654 14520/14520/14520 14519/14519/14519
-f 14651/14651/14651 14521/14521/14521 14520/14520/14520
-f 14647/14647/14647 14518/14518/14518 14472/14472/14472
-f 14470/14470/14470 14518/14518/14518 14561/14561/14561
-f 14644/14644/14644 14506/14506/14506 14505/14505/14505
-f 14554/14554/14554 14507/14507/14507 14506/14506/14506
-f 14627/14627/14627 14508/14508/14508 14507/14507/14507
-f 14624/14624/14624 14509/14509/14509 14508/14508/14508
-f 14622/14622/14622 14510/14510/14510 14509/14509/14509
-f 14619/14619/14619 14493/14493/14493 14467/14467/14467
-f 14619/14619/14619 14494/14494/14494 14493/14493/14493
-f 14617/14617/14617 14466/14466/14466 14494/14494/14494
-f 14465/14465/14465 14558/14558/14558 14543/14543/14543
-f 14594/14594/14594 14495/14495/14495 14596/14596/14596
-f 14497/14497/14497 14496/14496/14496 14592/14592/14592
-f 14587/14587/14587 14462/14462/14462 14498/14498/14498
-f 14586/14586/14586 14475/14475/14475 14587/14587/14587
-f 14584/14584/14584 14476/14476/14476 14586/14586/14586
-f 14582/14582/14582 14477/14477/14477 14584/14584/14584
-f 14479/14479/14479 14478/14478/14478 14579/14579/14579
-f 14575/14575/14575 14480/14480/14480 14577/14577/14577
-f 14573/14573/14573 14481/14481/14481 14575/14575/14575
-f 14571/14571/14571 14482/14482/14482 14573/14573/14573
-f 14568/14568/14568 14483/14483/14483 14571/14571/14571
-f 14461/14461/14461 14484/14484/14484 14544/14544/14544
-f 14601/14601/14601 14486/14486/14486 14602/14602/14602
-f 14598/14598/14598 14487/14487/14487 14601/14601/14601
-f 14695/14695/14695 14526/14526/14526 14525/14525/14525
-f 14693/14693/14693 14525/14525/14525 14524/14524/14524
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-f 14691/14691/14691 14523/14523/14523 14522/14522/14522
-f 14474/14474/14474 14692/14692/14692 14522/14522/14522
-f 14692/14692/14692 14665/14665/14665 14664/14664/14664
-f 14690/14690/14690 14468/14468/14468 14464/14464/14464
-f 14689/14689/14689 14491/14491/14491 14688/14688/14688
-f 14463/14463/14463 14488/14488/14488 14548/14548/14548
-f 14688/14688/14688 14490/14490/14490 14687/14687/14687
-f 1953/1953/1953 14833/14833/14833 14831/14831/14831
-f 1920/1920/1920 14701/14701/14701 14712/14712/14712
-f 1920/1920/1920 14804/14804/14804 14803/14803/14803
-f 14805/14805/14805 14804/14804/14804 723/723/723
-f 1907/1907/1907 14798/14798/14798 14699/14699/14699
-f 626/626/626 14799/14799/14799 14798/14798/14798
-f 1814/1814/1814 14799/14799/14799 1815/1815/1815
-f 1812/1812/1812 14800/14800/14800 1814/1814/1814
-f 14698/14698/14698 14801/14801/14801 624/624/624
-f 1808/1808/1808 14793/14793/14793 1810/1810/1810
-f 1806/1806/1806 14794/14794/14794 1808/1808/1808
-f 1688/1688/1688 14796/14796/14796 14795/14795/14795
-f 1687/1687/1687 14796/14796/14796 1688/1688/1688
-f 1685/1685/1685 14789/14789/14789 14697/14697/14697
-f 1683/1683/1683 14790/14790/14790 14789/14789/14789
-f 1681/1681/1681 14790/14790/14790 1683/1683/1683
-f 1678/1678/1678 14792/14792/14792 14791/14791/14791
-f 1677/1677/1677 14696/14696/14696 14792/14792/14792
-f 1673/1673/1673 14806/14806/14806 1677/1677/1677
-f 432/432/432 14808/14808/14808 14807/14807/14807
-f 14809/14809/14809 14808/14808/14808 1600/1600/1600
-f 14708/14708/14708 14810/14810/14810 435/435/435
-f 442/442/442 524/524/524 14835/14835/14835
-f 14844/14844/14844 14724/14724/14724 1710/1710/1710
-f 14845/14845/14845 14844/14844/14844 539/539/539
-f 14853/14853/14853 14744/14744/14744 547/547/547
-f 1727/1727/1727 14854/14854/14854 14766/14766/14766
-f 550/550/550 14771/14771/14771 14854/14854/14854
-f 14749/14749/14749 648/648/648 632/632/632
-f 1838/1838/1838 14847/14847/14847 14745/14745/14745
-f 634/634/634 14848/14848/14848 14847/14847/14847
-f 14849/14849/14849 14848/14848/14848 545/545/545
-f 1724/1724/1724 14836/14836/14836 14725/14725/14725
-f 1721/1721/1721 14836/14836/14836 1724/1724/1724
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-f 1717/1717/1717 14839/14839/14839 14838/14838/14838
-f 1716/1716/1716 14839/14839/14839 1717/1717/1717
-f 1716/1716/1716 14841/14841/14841 14840/14840/14840
-f 14811/14811/14811 14710/14710/14710 535/535/535
-f 1703/1703/1703 14812/14812/14812 14811/14811/14811
-f 14814/14814/14814 14813/14813/14813 1700/1700/1700
-f 1698/1698/1698 14815/14815/14815 14814/14814/14814
-f 1696/1696/1696 14816/14816/14816 14815/14815/14815
-f 1694/1694/1694 14817/14817/14817 14816/14816/14816
-f 538/538/538 1830/1830/1830 14818/14818/14818
-f 1830/1830/1830 14819/14819/14819 14818/14818/14818
-f 1827/1827/1827 14820/14820/14820 14819/14819/14819
-f 1825/1825/1825 14821/14821/14821 14820/14820/14820
-f 1823/1823/1823 14822/14822/14822 14821/14821/14821
-f 1822/1822/1822 14823/14823/14823 14822/14822/14822
-f 1818/1818/1818 14824/14824/14824 14709/14709/14709
-f 629/629/629 1837/1837/1837 14825/14825/14825
-f 1837/1837/1837 14826/14826/14826 14825/14825/14825
-f 1835/1835/1835 14827/14827/14827 14826/14826/14826
-f 630/630/630 14828/14828/14828 14827/14827/14827
-f 1913/1913/1913 14829/14829/14829 14711/14711/14711
-f 1910/1910/1910 14829/14829/14829 1913/1913/1913
-f 14713/14713/14713 14830/14830/14830 720/720/720
-f 1922/1922/1922 14842/14842/14842 1924/1924/1924
-f 731/731/731 14843/14843/14843 1922/1922/1922
-f 14867/14867/14867 14871/14871/14871 14873/14873/14873
-f 14876/14876/14876 14873/14873/14873 14871/14871/14871
-f 14881/14881/14881 14883/14883/14883 14876/14876/14876
-f 14873/14873/14873 14885/14885/14885 14894/14894/14894
-f 14883/14883/14883 14888/14888/14888 14885/14885/14885
-f 16143/16143/16143 14885/14885/14885 14888/14888/14888
-f 14903/14903/14903 14894/14894/14894 14885/14885/14885
-f 14896/14896/14896 14873/14873/14873 14894/14894/14894
-f 14908/14908/14908 14894/14894/14894 14898/14898/14898
-f 14918/14918/14918 14921/14921/14921 14925/14925/14925
-f 14890/14890/14890 14924/14924/14924 14921/14921/14921
-f 14925/14925/14925 14924/14924/14924 14927/14927/14927
-f 14927/14927/14927 14934/14934/14934 14930/14930/14930
-f 14930/14930/14930 14939/14939/14939 14932/14932/14932
-f 16137/16137/16137 14934/14934/14934 14924/14924/14924
-f 14947/14947/14947 14939/14939/14939 14934/14934/14934
-f 14932/14932/14932 14941/14941/14941 14977/14977/14977
-f 14961/14961/14961 14964/14964/14964 14970/14970/14970
-f 14965/14965/14965 14962/14962/14962 14963/14963/14963
-f 14975/14975/14975 14970/14970/14970 14971/14971/14971
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-f 14983/14983/14983 14981/14981/14981 14987/14987/14987
-f 14986/14986/14986 14987/14987/14987 14992/14992/14992
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-f 14995/14995/14995 15009/15009/15009 15002/15002/15002
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-f 14988/14988/14988 15011/15011/15011 15009/15009/15009
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-f 15022/15022/15022 15014/15014/15014 14963/14963/14963
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-f 15024/15024/15024 15012/15012/15012 15021/15021/15021
-f 15030/15030/15030 15010/15010/15010 15012/15012/15012
-f 15033/15033/15033 15008/15008/15008 15010/15010/15010
-f 15050/15050/15050 15052/15052/15052 15060/15060/15060
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-f 15070/15070/15070 15062/15062/15062 15066/15066/15066
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-f 15068/15068/15068 15069/15069/15069 15077/15077/15077
-f 15073/15073/15073 15066/15066/15066 15068/15068/15068
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-f 15077/15077/15077 15076/15076/15076 15079/15079/15079
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-f 3278/3278/3278 4570/4570/4570 14207/14207/14207
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-f 4561/4561/4561 14185/14185/14185 14208/14208/14208
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-f 3282/3282/3282 4698/4698/4698 14185/14185/14185
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-f 4693/4693/4693 14189/14189/14189 14188/14188/14188
-f 14189/14189/14189 4693/4693/4693 3432/3432/3432
-f 3432/3432/3432 4663/4663/4663 14190/14190/14190
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-f 4550/4550/4550 4549/4549/4549 14198/14198/14198
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-f 14199/14199/14199 4547/4547/4547 14200/14200/14200
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-f 14201/14201/14201 4543/4543/4543 14211/14211/14211
-f 4543/4543/4543 3267/3267/3267 14211/14211/14211
-f 3267/3267/3267 4534/4534/4534 14202/14202/14202
-f 4534/4534/4534 4532/4532/4532 14203/14203/14203
-f 4532/4532/4532 4531/4531/4531 14204/14204/14204
-f 14204/14204/14204 4531/4531/4531 14206/14206/14206
-f 4531/4531/4531 3162/3162/3162 14206/14206/14206
-f 14338/14338/14338 1962/1962/1962 14273/14273/14273
-f 1962/1962/1962 834/834/834 14273/14273/14273
-f 834/834/834 837/837/837 14271/14271/14271
-f 14337/14337/14337 1991/1991/1991 14338/14338/14338
-f 1991/1991/1991 842/842/842 14338/14338/14338
-f 14357/14357/14357 14456/14456/14456 2002/2002/2002
-f 2002/2002/2002 930/930/930 14357/14357/14357
-f 930/930/930 931/931/931 14337/14337/14337
-f 14357/14357/14357 930/930/930 14287/14287/14287
-f 931/931/931 1991/1991/1991 14337/14337/14337
-f 14337/14337/14337 14274/14274/14274 930/930/930
-f 2002/2002/2002 14456/14456/14456 942/942/942
-f 14456/14456/14456 14459/14459/14459 942/942/942
-f 14358/14358/14358 2075/2075/2075 14288/14288/14288
-f 2075/2075/2075 1008/1008/1008 14355/14355/14355
-f 14288/14288/14288 2075/2075/2075 14355/14355/14355
-f 14376/14376/14376 2077/2077/2077 14289/14289/14289
-f 2077/2077/2077 2075/2075/2075 14358/14358/14358
-f 14375/14375/14375 2080/2080/2080 1016/1016/1016
-f 1014/1014/1014 2077/2077/2077 14376/14376/14376
-f 14375/14375/14375 1016/1016/1016 14376/14376/14376
-f 2082/2082/2082 2080/2080/2080 14375/14375/14375
-f 14391/14391/14391 2083/2083/2083 14296/14296/14296
-f 2083/2083/2083 2082/2082/2082 14374/14374/14374
-f 14390/14390/14390 2085/2085/2085 14391/14391/14391
-f 2085/2085/2085 1017/1017/1017 14391/14391/14391
-f 14388/14388/14388 2092/2092/2092 1020/1020/1020
-f 2088/2088/2088 2085/2085/2085 14390/14390/14390
-f 14388/14388/14388 1020/1020/1020 14389/14389/14389
-f 1020/1020/1020 2088/2088/2088 14389/14389/14389
-f 14405/14405/14405 2095/2095/2095 2093/2093/2093
-f 2093/2093/2093 2092/2092/2092 14309/14309/14309
-f 2092/2092/2092 14388/14388/14388 14309/14309/14309
-f 14309/14309/14309 14406/14406/14406 2093/2093/2093
-f 14404/14404/14404 2098/2098/2098 1026/1026/1026
-f 1026/1026/1026 2095/2095/2095 14405/14405/14405
-f 14427/14427/14427 2100/2100/2100 2098/2098/2098
-f 2098/2098/2098 14404/14404/14404 14314/14314/14314
-f 14426/14426/14426 2108/2108/2108 1027/1027/1027
-f 1027/1027/1027 2100/2100/2100 14426/14426/14426
-f 2108/2108/2108 14426/14426/14426 14425/14425/14425
-f 14442/14442/14442 2090/2090/2090 1024/1024/1024
-f 1024/1024/1024 2110/2110/2110 14322/14322/14322
-f 2110/2110/2110 14425/14425/14425 14322/14322/14322
-f 14441/14441/14441 2102/2102/2102 1022/1022/1022
-f 1022/1022/1022 2090/2090/2090 14441/14441/14441
-f 14440/14440/14440 14454/14454/14454 2104/2104/2104
-f 2104/2104/2104 2102/2102/2102 14440/14440/14440
-f 2102/2102/2102 14441/14441/14441 14327/14327/14327
-f 2105/2105/2105 14454/14454/14454 963/963/963
-f 14454/14454/14454 14455/14455/14455 963/963/963
-f 14453/14453/14453 2050/2050/2050 2049/2049/2049
-f 14436/14436/14436 2020/2020/2020 948/948/948
-f 948/948/948 959/959/959 14326/14326/14326
-f 959/959/959 2050/2050/2050 14437/14437/14437
-f 14326/14326/14326 959/959/959 14437/14437/14437
-f 2050/2050/2050 14453/14453/14453 14437/14437/14437
-f 2020/2020/2020 14436/14436/14436 14435/14435/14435
-f 14434/14434/14434 1982/1982/1982 880/880/880
-f 880/880/880 2022/2022/2022 14434/14434/14434
-f 14433/14433/14433 1971/1971/1971 873/873/873
-f 873/873/873 1982/1982/1982 14325/14325/14325
-f 1982/1982/1982 14434/14434/14434 14325/14325/14325
-f 784/784/784 1971/1971/1971 14432/14432/14432
-f 14430/14430/14430 1945/1945/1945 781/781/781
-f 781/781/781 1946/1946/1946 14324/14324/14324
-f 1946/1946/1946 14432/14432/14432 14324/14324/14324
-f 14429/14429/14429 1941/1941/1941 14323/14323/14323
-f 1941/1941/1941 775/775/775 14323/14323/14323
-f 775/775/775 1945/1945/1945 14430/14430/14430
-f 14428/14428/14428 1888/1888/1888 700/700/700
-f 700/700/700 1941/1941/1941 14429/14429/14429
-f 14410/14410/14410 1883/1883/1883 14316/14316/14316
-f 1883/1883/1883 691/691/691 14316/14316/14316
-f 691/691/691 698/698/698 14318/14318/14318
-f 698/698/698 1888/1888/1888 14428/14428/14428
-f 14428/14428/14428 14318/14318/14318 698/698/698
-f 582/582/582 1883/1883/1883 14410/14410/14410
-f 14407/14407/14407 1776/1776/1776 14315/14315/14315
-f 1776/1776/1776 577/577/577 14315/14315/14315
-f 577/577/577 1779/1779/1779 14408/14408/14408
-f 14392/14392/14392 1732/1732/1732 554/554/554
-f 575/575/575 1776/1776/1776 14310/14310/14310
-f 1776/1776/1776 14407/14407/14407 14310/14310/14310
-f 14392/14392/14392 554/554/554 575/575/575
-f 14378/14378/14378 1734/1734/1734 14297/14297/14297
-f 1734/1734/1734 1732/1732/1732 14392/14392/14392
-f 14377/14377/14377 1765/1765/1765 551/551/551
-f 551/551/551 1734/1734/1734 14378/14378/14378
-f 14360/14360/14360 1639/1639/1639 14290/14290/14290
-f 1639/1639/1639 480/480/480 14290/14290/14290
-f 563/563/563 1765/1765/1765 14377/14377/14377
-f 480/480/480 563/563/563 14290/14290/14290
-f 14359/14359/14359 1634/1634/1634 478/478/478
-f 478/478/478 1639/1639/1639 14360/14360/14360
-f 14341/14341/14341 1635/1635/1635 14275/14275/14275
-f 1635/1635/1635 1634/1634/1634 14275/14275/14275
-f 14340/14340/14340 1737/1737/1737 473/473/473
-f 476/476/476 1635/1635/1635 14341/14341/14341
-f 14340/14340/14340 473/473/473 476/476/476
-f 1737/1737/1737 14340/14340/14340 14339/14339/14339
-f 14333/14333/14333 1741/1741/1741 555/555/555
-f 555/555/555 1739/1739/1739 14254/14254/14254
-f 1739/1739/1739 14339/14339/14339 14254/14254/14254
-f 1741/1741/1741 14333/14333/14333 14332/14332/14332
-f 14330/14330/14330 1746/1746/1746 1745/1745/1745
-f 1745/1745/1745 1743/1743/1743 14331/14331/14331
-f 1743/1743/1743 14332/14332/14332 14331/14331/14331
-f 14329/14329/14329 1752/1752/1752 557/557/557
-f 557/557/557 1746/1746/1746 14329/14329/14329
-f 1752/1752/1752 14329/14329/14329 14328/14328/14328
-f 14336/14336/14336 1749/1749/1749 561/561/561
-f 561/561/561 1754/1754/1754 14255/14255/14255
-f 1754/1754/1754 14328/14328/14328 14255/14255/14255
-f 1749/1749/1749 14336/14336/14336 14335/14335/14335
-f 14334/14334/14334 1875/1875/1875 559/559/559
-f 559/559/559 1750/1750/1750 14334/14334/14334
-f 14257/14257/14257 677/677/677 1875/1875/1875
-f 14455/14455/14455 14439/14439/14439 14438/14438/14438
-f 14454/14454/14454 14440/14440/14440 14439/14439/14439
-f 14459/14459/14459 14456/14456/14456 14356/14356/14356
-f 14443/14443/14443 14353/14353/14353 14281/14281/14281
-f 14443/14443/14443 1968/1968/1968 1967/1967/1967
-f 2000/2000/2000 14446/14446/14446 14445/14445/14445
-f 1998/1998/1998 14447/14447/14447 938/938/938
-f 14447/14447/14447 14446/14446/14446 938/938/938
-f 14370/14370/14370 14447/14447/14447 939/939/939
-f 14447/14447/14447 1996/1996/1996 939/939/939
-f 939/939/939 2009/2009/2009 14370/14370/14370
-f 2007/2007/2007 14372/14372/14372 14371/14371/14371
-f 2006/2006/2006 14373/14373/14373 14372/14372/14372
-f 14373/14373/14373 2006/2006/2006 2004/2004/2004
-f 2004/2004/2004 14383/14383/14383 14295/14295/14295
-f 14383/14383/14383 2004/2004/2004 944/944/944
-f 944/944/944 2017/2017/2017 14383/14383/14383
-f 2016/2016/2016 14385/14385/14385 14384/14384/14384
-f 14385/14385/14385 2016/2016/2016 2013/2013/2013
-f 2013/2013/2013 2012/2012/2012 14386/14386/14386
-f 2012/2012/2012 14387/14387/14387 14386/14386/14386
-f 14387/14387/14387 2012/2012/2012 947/947/947
-f 947/947/947 2039/2039/2039 14308/14308/14308
-f 2039/2039/2039 14398/14398/14398 14308/14308/14308
-f 2039/2039/2039 2037/2037/2037 14399/14399/14399
-f 2037/2037/2037 2035/2035/2035 14400/14400/14400
-f 14400/14400/14400 2035/2035/2035 14401/14401/14401
-f 2035/2035/2035 957/957/957 14401/14401/14401
-f 2047/2047/2047 2045/2045/2045 14402/14402/14402
-f 2045/2045/2045 2042/2042/2042 14403/14403/14403
-f 14403/14403/14403 2042/2042/2042 14313/14313/14313
-f 2042/2042/2042 2041/2041/2041 14423/14423/14423
-f 14423/14423/14423 2041/2041/2041 14424/14424/14424
-f 2041/2041/2041 955/955/955 14424/14424/14424
-f 955/955/955 2033/2033/2033 14448/14448/14448
-f 14450/14450/14450 14448/14448/14448 2029/2029/2029
-f 2025/2025/2025 14449/14449/14449 953/953/953
-f 14449/14449/14449 14450/14450/14450 2028/2028/2028
-f 14452/14452/14452 14449/14449/14449 2025/2025/2025
-f 1980/1980/1980 14451/14451/14451 877/877/877
-f 14451/14451/14451 14452/14452/14452 877/877/877
-f 14415/14415/14415 14451/14451/14451 1980/1980/1980
-f 1980/1980/1980 1977/1977/1977 14415/14415/14415
-f 14416/14416/14416 1977/1977/1977 875/875/875
-f 875/875/875 1976/1976/1976 14417/14417/14417
-f 1976/1976/1976 1973/1973/1973 14418/14418/14418
-f 14418/14418/14418 1973/1973/1973 14319/14319/14319
-f 1973/1973/1973 778/778/778 14319/14319/14319
-f 778/778/778 1942/1942/1942 14319/14319/14319
-f 14411/14411/14411 1942/1942/1942 773/773/773
-f 773/773/773 1937/1937/1937 14412/14412/14412
-f 769/769/769 1934/1934/1934 14414/14414/14414
-f 14414/14414/14414 1934/1934/1934 14317/14317/14317
-f 1934/1934/1934 1932/1932/1932 14317/14317/14317
-f 1932/1932/1932 14395/14395/14395 14312/14312/14312
-f 14395/14395/14395 1932/1932/1932 696/696/696
-f 696/696/696 1886/1886/1886 14396/14396/14396
-f 14396/14396/14396 1886/1886/1886 694/694/694
-f 694/694/694 1885/1885/1885 14397/14397/14397
-f 14397/14397/14397 1885/1885/1885 664/664/664
-f 664/664/664 1861/1861/1861 14311/14311/14311
-f 1861/1861/1861 14393/14393/14393 14311/14311/14311
-f 1859/1859/1859 14394/14394/14394 14393/14393/14393
-f 14394/14394/14394 1859/1859/1859 14298/14298/14298
-f 1859/1859/1859 667/667/667 14298/14298/14298
-f 667/667/667 1880/1880/1880 14379/14379/14379
-f 14379/14379/14379 1880/1880/1880 573/573/573
-f 573/573/573 1774/1774/1774 14380/14380/14380
-f 14380/14380/14380 1774/1774/1774 14381/14381/14381
-f 1774/1774/1774 572/572/572 14381/14381/14381
-f 572/572/572 1771/1771/1771 14382/14382/14382
-f 14382/14382/14382 1771/1771/1771 14291/14291/14291
-f 1771/1771/1771 1769/1769/1769 14291/14291/14291
-f 1768/1768/1768 14362/14362/14362 14361/14361/14361
-f 14362/14362/14362 1768/1768/1768 14363/14363/14363
-f 1768/1768/1768 566/566/566 14363/14363/14363
-f 1762/1762/1762 1761/1761/1761 14366/14366/14366
-f 1761/1761/1761 1758/1758/1758 14367/14367/14367
-f 14367/14367/14367 1758/1758/1758 14277/14277/14277
-f 1758/1758/1758 1756/1756/1756 14277/14277/14277
-f 14342/14342/14342 1756/1756/1756 14343/14343/14343
-f 1756/1756/1756 570/570/570 14343/14343/14343
-f 570/570/570 1876/1876/1876 14344/14344/14344
-f 14344/14344/14344 1876/1876/1876 14345/14345/14345
-f 1876/1876/1876 668/668/668 14345/14345/14345
-f 1873/1873/1873 1870/1870/1870 14346/14346/14346
-f 1870/1870/1870 1868/1868/1868 14347/14347/14347
-f 1868/1868/1868 1866/1866/1866 14348/14348/14348
-f 1866/1866/1866 1865/1865/1865 14349/14349/14349
-f 14349/14349/14349 1865/1865/1865 14276/14276/14276
-f 1865/1865/1865 1862/1862/1862 14276/14276/14276
-f 14350/14350/14350 1862/1862/1862 671/671/671
-f 671/671/671 1879/1879/1879 14351/14351/14351
-f 1879/1879/1879 689/689/689 14279/14279/14279
-f 14451/14451/14451 14415/14415/14415 14320/14320/14320
-f 14452/14452/14452 14451/14451/14451 14422/14422/14422
-f 14449/14449/14449 14452/14452/14452 14421/14421/14421
-f 14450/14450/14450 14449/14449/14449 14420/14420/14420
-f 14424/14424/14424 14448/14448/14448 14321/14321/14321
-f 14448/14448/14448 14450/14450/14450 14419/14419/14419
-f 14369/14369/14369 14446/14446/14446 14447/14447/14447
-f 14447/14447/14447 14370/14370/14370 14294/14294/14294
-f 14446/14446/14446 14369/14369/14369 14368/14368/14368
-f 1968/1968/1968 14443/14443/14443 14352/14352/14352
-f 14352/14352/14352 14283/14283/14283 862/862/862
-f 14353/14353/14353 14443/14443/14443 14286/14286/14286
-f 14443/14443/14443 14445/14445/14445 14286/14286/14286
-f 14542/14542/14542 14534/14534/14534 14595/14595/14595
-f 14534/14534/14534 14545/14545/14545 14572/14572/14572
-f 14595/14595/14595 14534/14534/14534 14593/14593/14593
-f 14545/14545/14545 14569/14569/14569 14572/14572/14572
-f 14572/14572/14572 14574/14574/14574 14534/14534/14534
-f 14574/14574/14574 14576/14576/14576 14534/14534/14534
-f 14576/14576/14576 14578/14578/14578 14534/14534/14534
-f 14578/14578/14578 14580/14580/14580 14534/14534/14534
-f 14580/14580/14580 14581/14581/14581 14534/14534/14534
-f 14581/14581/14581 14583/14583/14583 14534/14534/14534
-f 14583/14583/14583 14585/14585/14585 14534/14534/14534
-f 14585/14585/14585 14588/14588/14588 14534/14534/14534
-f 14588/14588/14588 14589/14589/14589 14534/14534/14534
-f 14589/14589/14589 14591/14591/14591 14534/14534/14534
-f 14539/14539/14539 14541/14541/14541 14536/14536/14536
-f 14549/14549/14549 14536/14536/14536 14609/14609/14609
-f 14536/14536/14536 14537/14537/14537 14607/14607/14607
-f 14609/14609/14609 14536/14536/14536 14607/14607/14607
-f 14537/14537/14537 14551/14551/14551 14604/14604/14604
-f 14553/14553/14553 14537/14537/14537 14640/14640/14640
-f 14537/14537/14537 14540/14540/14540 14638/14638/14638
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-f 1945/1945/1945 1948/1948/1948 1946/1946/1946
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-f 1462/1462/1462 1945/1945/1945 1467/1467/1467
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-f 1947/1947/1947 1950/1950/1950 1948/1948/1948
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-f 1904/1904/1904 1903/1903/1903 1950/1950/1950
-f 1903/1903/1903 1864/1864/1864 1950/1950/1950
-f 1950/1950/1950 1864/1864/1864 1865/1865/1865
-f 1865/1865/1865 1854/1854/1854 1949/1949/1949
-f 1946/1946/1946 1949/1949/1949 1841/1841/1841
-f 1949/1949/1949 1854/1854/1854 1840/1840/1840
-f 1949/1949/1949 1840/1840/1840 1841/1841/1841
-f 1907/1907/1907 1904/1904/1904 1947/1947/1947
-f 1946/1946/1946 1841/1841/1841 1951/1951/1951
-f 1467/1467/1467 1946/1946/1946 1951/1951/1951
-f 1951/1951/1951 1841/1841/1841 1842/1842/1842
-f 1467/1467/1467 1951/1951/1951 1468/1468/1468
-f 1468/1468/1468 1951/1951/1951 1952/1952/1952
-f 1951/1951/1951 1842/1842/1842 1952/1952/1952
-f 1490/1490/1490 1952/1952/1952 1506/1506/1506
-f 1952/1952/1952 1507/1507/1507 1506/1506/1506
-f 1468/1468/1468 1952/1952/1952 1490/1490/1490
-f 1952/1952/1952 1819/1819/1819 1507/1507/1507
-f 1952/1952/1952 1842/1842/1842 1819/1819/1819
-f 976/976/976 979/979/979 1921/1921/1921
-f 1921/1921/1921 1897/1897/1897 1912/1912/1912
-f 1921/1921/1921 1953/1953/1953 1897/1897/1897
-f 979/979/979 1953/1953/1953 1921/1921/1921
-f 979/979/979 1954/1954/1954 1953/1953/1953
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-f 1953/1953/1953 1894/1894/1894 1897/1897/1897
-f 1953/1953/1953 1955/1955/1955 1894/1894/1894
-f 1955/1955/1955 1892/1892/1892 1894/1894/1894
-f 1955/1955/1955 1956/1956/1956 1892/1892/1892
-f 1957/1957/1957 1956/1956/1956 1955/1955/1955
-f 1954/1954/1954 1957/1957/1957 1955/1955/1955
-f 1958/1958/1958 1002/1002/1002 1957/1957/1957
-f 1958/1958/1958 1957/1957/1957 1954/1954/1954
-f 1002/1002/1002 1005/1005/1005 1957/1957/1957
-f 1957/1957/1957 1005/1005/1005 1956/1956/1956
-f 996/996/996 998/998/998 1958/1958/1958
-f 996/996/996 1958/1958/1958 978/978/978
-f 998/998/998 1002/1002/1002 1958/1958/1958
-f 978/978/978 1958/1958/1958 1954/1954/1954
-f 1005/1005/1005 1009/1009/1009 1956/1956/1956
-f 1956/1956/1956 1009/1009/1009 1028/1028/1028
-f 1956/1956/1956 1028/1028/1028 1892/1892/1892
-f 978/978/978 1954/1954/1954 979/979/979
-f 1912/1912/1912 1897/1897/1897 1898/1898/1898
-f 1903/1903/1903 1959/1959/1959 1864/1864/1864
-f 1900/1900/1900 1959/1959/1959 1903/1903/1903
-f 1960/1960/1960 1876/1876/1876 1959/1959/1959
-f 1959/1959/1959 1876/1876/1876 1863/1863/1863
-f 1959/1959/1959 1863/1863/1863 1864/1864/1864
-f 1900/1900/1900 1960/1960/1960 1959/1959/1959
-f 1960/1960/1960 1881/1881/1881 1876/1876/1876
-f 1890/1890/1890 1881/1881/1881 1960/1960/1960
-f 1895/1895/1895 1960/1960/1960 1900/1900/1900
-f 1895/1895/1895 1890/1890/1890 1960/1960/1960
-f 1037/1037/1037 1040/1040/1040 1882/1882/1882
-f 1882/1882/1882 1040/1040/1040 1046/1046/1046
-f 1882/1882/1882 1046/1046/1046 1871/1871/1871
-f 1859/1859/1859 1873/1873/1873 1961/1961/1961
-f 1873/1873/1873 1962/1962/1962 1961/1961/1961
-f 1873/1873/1873 1052/1052/1052 1962/1962/1962
-f 1872/1872/1872 1052/1052/1052 1873/1873/1873
-f 1962/1962/1962 1964/1964/1964 1963/1963/1963
-f 1961/1961/1961 1962/1962/1962 1963/1963/1963
-f 1962/1962/1962 1053/1053/1053 1964/1964/1964
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-f 1062/1062/1062 1963/1963/1963 1964/1964/1964
-f 1062/1062/1062 1964/1964/1964 1056/1056/1056
-f 1053/1053/1053 1056/1056/1056 1964/1964/1964
-f 1850/1850/1850 1963/1963/1963 1965/1965/1965
-f 1963/1963/1963 1062/1062/1062 1965/1965/1965
-f 1961/1961/1961 1963/1963/1963 1850/1850/1850
-f 1850/1850/1850 1965/1965/1965 1851/1851/1851
-f 1851/1851/1851 1965/1965/1965 1072/1072/1072
-f 1965/1965/1965 1065/1065/1065 1072/1072/1072
-f 1965/1965/1965 1062/1062/1062 1065/1065/1065
-f 1859/1859/1859 1961/1961/1961 1849/1849/1849
-f 1849/1849/1849 1961/1961/1961 1850/1850/1850
-f 1871/1871/1871 1046/1046/1046 1872/1872/1872
-f 1046/1046/1046 1047/1047/1047 1872/1872/1872
-f 1872/1872/1872 1047/1047/1047 1052/1052/1052
-f 1851/1851/1851 1072/1072/1072 1966/1966/1966
-f 1847/1847/1847 1851/1851/1851 1966/1966/1966
-f 1829/1829/1829 1966/1966/1966 1832/1832/1832
-f 1847/1847/1847 1966/1966/1966 1829/1829/1829
-f 1966/1966/1966 1073/1073/1073 1832/1832/1832
-f 1966/1966/1966 1072/1072/1072 1073/1073/1073
-f 1829/1829/1829 1837/1837/1837 1847/1847/1847
-f 1842/1842/1842 1817/1817/1817 1819/1819/1819
-f 1831/1831/1831 1832/1832/1832 1967/1967/1967
-f 1832/1832/1832 1078/1078/1078 1967/1967/1967
-f 1832/1832/1832 1073/1073/1073 1078/1078/1078
-f 1831/1831/1831 1967/1967/1967 1968/1968/1968
-f 1967/1967/1967 1078/1078/1078 1080/1080/1080
-f 1967/1967/1967 1080/1080/1080 1968/1968/1968
-f 1831/1831/1831 1968/1968/1968 1124/1124/1124
-f 1108/1108/1108 1124/1124/1124 1968/1968/1968
-f 1108/1108/1108 1968/1968/1968 1107/1107/1107
-f 1969/1969/1969 1968/1968/1968 1080/1080/1080
-f 1107/1107/1107 1968/1968/1968 1969/1969/1969
-f 1098/1098/1098 1106/1106/1106 1970/1970/1970
-f 1969/1969/1969 1080/1080/1080 1096/1096/1096
-f 1095/1095/1095 1098/1098/1098 1970/1970/1970
-f 1830/1830/1830 1831/1831/1831 1124/1124/1124
-f 1828/1828/1828 1830/1830/1830 1127/1127/1127
-f 1830/1830/1830 1124/1124/1124 1127/1127/1127
-f 1134/1134/1134 1815/1815/1815 1828/1828/1828
-f 1822/1822/1822 1828/1828/1828 1815/1815/1815
-f 1828/1828/1828 1127/1127/1127 1134/1134/1134
-f 1971/1971/1971 1820/1820/1820 1802/1802/1802
-f 1818/1818/1818 1820/1820/1820 1971/1971/1971
-f 1820/1820/1820 1803/1803/1803 1802/1802/1802
-f 1983/1983/1983 1818/1818/1818 1971/1971/1971
-f 1973/1973/1973 1971/1971/1971 1975/1975/1975
-f 1973/1973/1973 1983/1983/1983 1971/1971/1971
-f 1975/1975/1975 1971/1971/1971 1978/1978/1978
-f 1971/1971/1971 1977/1977/1977 1982/1982/1982
-f 1977/1977/1977 1971/1971/1971 1802/1802/1802
-f 1971/1971/1971 1982/1982/1982 1978/1978/1978
-f 1503/1503/1503 1984/1984/1984 1983/1983/1983
-f 1983/1983/1983 1517/1517/1517 1503/1503/1503
-f 1983/1983/1983 1973/1973/1973 1521/1521/1521
-f 1517/1517/1517 1983/1983/1983 1521/1521/1521
-f 1984/1984/1984 1818/1818/1818 1983/1983/1983
-f 1507/1507/1507 1819/1819/1819 1984/1984/1984
-f 1507/1507/1507 1984/1984/1984 1503/1503/1503
-f 1819/1819/1819 1818/1818/1818 1984/1984/1984
-f 1987/1987/1987 1979/1979/1979 1981/1981/1981
-f 1976/1976/1976 1985/1985/1985 1980/1980/1980
-f 1980/1980/1980 1985/1985/1985 1986/1986/1986
-f 1986/1986/1986 1991/1991/1991 1988/1988/1988
-f 1989/1989/1989 1990/1990/1990 1987/1987/1987
-f 1987/1987/1987 1990/1990/1990 1979/1979/1979
-f 1986/1986/1986 1985/1985/1985 1991/1991/1991
-f 1992/1992/1992 1991/1991/1991 1985/1985/1985
-f 1994/1994/1994 1991/1991/1991 1992/1992/1992
-f 1994/1994/1994 1993/1993/1993 1991/1991/1991
-f 1988/1988/1988 1991/1991/1991 1993/1993/1993
-f 1996/1996/1996 1994/1994/1994 1992/1992/1992
-f 1995/1995/1995 1994/1994/1994 1996/1996/1996
-f 1995/1995/1995 1997/1997/1997 1994/1994/1994
-f 1997/1997/1997 1993/1993/1993 1994/1994/1994
-f 1999/1999/1999 1997/1997/1997 1995/1995/1995
-f 1997/1997/1997 1999/1999/1999 2000/2000/2000
-f 2000/2000/2000 2001/2001/2001 1997/1997/1997
-f 1997/1997/1997 2001/2001/2001 1993/1993/1993
-f 2000/2000/2000 2002/2002/2002 2001/2001/2001
-f 2005/2005/2005 2001/2001/2001 2002/2002/2002
-f 2004/2004/2004 1993/1993/1993 2001/2001/2001
-f 2004/2004/2004 2001/2001/2001 2005/2005/2005
-f 2006/2006/2006 2007/2007/2007 2003/2003/2003
-f 2006/2006/2006 2008/2008/2008 2007/2007/2007
-f 2009/2009/2009 2008/2008/2008 2006/2006/2006
-f 2005/2005/2005 2002/2002/2002 2010/2010/2010
-f 2010/2010/2010 2017/2017/2017 2012/2012/2012
-f 2013/2013/2013 2018/2018/2018 2009/2009/2009
-f 2010/2010/2010 2002/2002/2002 2017/2017/2017
-f 2009/2009/2009 2018/2018/2018 2008/2008/2008
-f 2021/2021/2021 1531/1531/1531 2018/2018/2018
-f 2021/2021/2021 2018/2018/2018 2013/2013/2013
-f 2018/2018/2018 1531/1531/1531 1528/1528/1528
-f 2018/2018/2018 1528/1528/1528 2008/2008/2008
-f 1535/1535/1535 1531/1531/1531 2021/2021/2021
-f 2002/2002/2002 2024/2024/2024 2017/2017/2017
-f 2016/2016/2016 2025/2025/2025 2014/2014/2014
-f 1540/1540/1540 2025/2025/2025 2027/2027/2027
-f 2025/2025/2025 2029/2029/2029 2027/2027/2027
-f 2023/2023/2023 2028/2028/2028 2025/2025/2025
-f 2025/2025/2025 2028/2028/2028 2029/2029/2029
-f 2014/2014/2014 2025/2025/2025 1540/1540/1540
-f 2027/2027/2027 2029/2029/2029 2030/2030/2030
-f 2029/2029/2029 1637/1637/1637 2030/2030/2030
-f 2028/2028/2028 1636/1636/1636 2029/2029/2029
-f 2029/2029/2029 1636/1636/1636 1637/1637/1637
-f 2030/2030/2030 2032/2032/2032 2031/2031/2031
-f 2033/2033/2033 2030/2030/2030 2031/2031/2031
-f 2030/2030/2030 1637/1637/1637 2032/2032/2032
-f 2027/2027/2027 2030/2030/2030 2033/2033/2033
-f 1556/1556/1556 2027/2027/2027 2033/2033/2033
-f 2033/2033/2033 2031/2031/2031 1597/1597/1597
-f 2036/2036/2036 2033/2033/2033 1597/1597/1597
-f 1556/1556/1556 2033/2033/2033 2036/2036/2036
-f 2036/2036/2036 1597/1597/1597 1590/1590/1590
-f 2032/2032/2032 1609/1609/1609 2031/2031/2031
-f 1623/1623/1623 1614/1614/1614 2032/2032/2032
-f 1637/1637/1637 1623/1623/1623 2032/2032/2032
-f 1609/1609/1609 2032/2032/2032 1614/1614/1614
-f 2031/2031/2031 1598/1598/1598 1597/1597/1597
-f 1598/1598/1598 2031/2031/2031 1609/1609/1609
-f 2023/2023/2023 1998/1998/1998 2028/2028/2028
-f 1998/1998/1998 1639/1639/1639 2028/2028/2028
-f 2028/2028/2028 1639/1639/1639 1636/1636/1636
-f 1540/1540/1540 2027/2027/2027 1556/1556/1556
-f 1998/1998/1998 2023/2023/2023 2037/2037/2037
-f 2024/2024/2024 2002/2002/2002 2000/2000/2000
-f 2024/2024/2024 2000/2000/2000 2038/2038/2038
-f 2000/2000/2000 1999/1999/1999 2038/2038/2038
-f 2008/2008/2008 1528/1528/1528 1525/1525/1525
-f 2008/2008/2008 1525/1525/1525 2007/2007/2007
-f 2003/2003/2003 2007/2007/2007 2039/2039/2039
-f 2007/2007/2007 1525/1525/1525 1523/1523/1523
-f 2007/2007/2007 1523/1523/1523 2039/2039/2039
-f 2003/2003/2003 2039/2039/2039 1989/1989/1989
-f 1989/1989/1989 2039/2039/2039 1990/1990/1990
-f 2039/2039/2039 1522/1522/1522 1990/1990/1990
-f 2039/2039/2039 1523/1523/1523 1522/1522/1522
-f 1988/1988/1988 1993/1993/1993 2004/2004/2004
-f 1999/1999/1999 1995/1995/1995 2043/2043/2043
-f 2040/2040/2040 1639/1639/1639 1998/1998/1998
-f 2040/2040/2040 1998/1998/1998 2042/2042/2042
-f 1995/1995/1995 2041/2041/2041 2043/2043/2043
-f 2040/2040/2040 1640/1640/1640 1639/1639/1639
-f 2041/2041/2041 1664/1664/1664 2044/2044/2044
-f 2041/2041/2041 1995/1995/1995 1664/1664/1664
-f 1651/1651/1651 2040/2040/2040 2045/2045/2045
-f 1651/1651/1651 1640/1640/1640 2040/2040/2040
-f 1664/1664/1664 1650/1650/1650 2044/2044/2044
-f 1996/1996/1996 1992/1992/1992 2046/2046/2046
-f 1663/1663/1663 1995/1995/1995 1996/1996/1996
-f 1663/1663/1663 1996/1996/1996 2046/2046/2046
-f 1804/1804/1804 1663/1663/1663 2046/2046/2046
-f 1811/1811/1811 2046/2046/2046 1992/1992/1992
-f 2046/2046/2046 1811/1811/1811 1804/1804/1804
-f 1995/1995/1995 1663/1663/1663 1664/1664/1664
-f 1811/1811/1811 1992/1992/1992 2047/2047/2047
-f 1992/1992/1992 1985/1985/1985 2047/2047/2047
-f 1976/1976/1976 2047/2047/2047 1985/1985/1985
-f 2048/2048/2048 2047/2047/2047 1976/1976/1976
-f 1811/1811/1811 2047/2047/2047 2048/2048/2048
-f 2049/2049/2049 1802/1802/1802 1810/1810/1810
-f 1977/1977/1977 1802/1802/1802 2049/2049/2049
-f 1972/1972/1972 1990/1990/1990 1522/1522/1522
-f 1974/1974/1974 1990/1990/1990 1972/1972/1972
-f 1979/1979/1979 1990/1990/1990 1974/1974/1974
-f 1522/1522/1522 1520/1520/1520 1972/1972/1972
-f 1808/1808/1808 1662/1662/1662 1806/1806/1806
-f 1682/1682/1682 1808/1808/1808 2051/2051/2051
-f 1666/1666/1666 1808/1808/1808 1682/1682/1682
-f 1808/1808/1808 1666/1666/1666 1662/1662/1662
-f 1809/1809/1809 1685/1685/1685 2052/2052/2052
-f 2052/2052/2052 1685/1685/1685 1679/1679/1679
-f 1678/1678/1678 1681/1681/1681 2050/2050/2050
-f 1806/1806/1806 1662/1662/1662 1663/1663/1663
-f 1806/1806/1806 1663/1663/1663 1804/1804/1804
-f 1796/1796/1796 1800/1800/1800 1134/1134/1134
-f 1796/1796/1796 2057/2057/2057 2053/2053/2053
-f 2065/2065/2065 1796/1796/1796 2053/2053/2053
-f 1796/1796/1796 2067/2067/2067 2057/2057/2057
-f 2058/2058/2058 1796/1796/1796 2065/2065/2065
-f 2062/2062/2062 1796/1796/1796 2058/2058/2058
-f 2061/2061/2061 1796/1796/1796 2062/2062/2062
-f 1796/1796/1796 2061/2061/2061 1789/1789/1789
-f 1134/1134/1134 2067/2067/2067 1796/1796/1796
-f 1134/1134/1134 1138/1138/1138 2067/2067/2067
-f 1139/1139/1139 1142/1142/1142 2066/2066/2066
-f 1152/1152/1152 2066/2066/2066 1142/1142/1142
-f 2066/2066/2066 1152/1152/1152 2068/2068/2068
-f 2066/2066/2066 2068/2068/2068 2056/2056/2056
-f 1153/1153/1153 2070/2070/2070 2069/2069/2069
-f 2069/2069/2069 2054/2054/2054 2055/2055/2055
-f 2069/2069/2069 2070/2070/2070 2054/2054/2054
-f 1153/1153/1153 1160/1160/1160 2070/2070/2070
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-f 2054/2054/2054 2070/2070/2070 2064/2064/2064
-f 2064/2064/2064 2070/2070/2070 2072/2072/2072
-f 2070/2070/2070 2071/2071/2071 2072/2072/2072
-f 2071/2071/2071 2073/2073/2073 2072/2072/2072
-f 2072/2072/2072 2073/2073/2073 2074/2074/2074
-f 2064/2064/2064 2072/2072/2072 2059/2059/2059
-f 2059/2059/2059 2072/2072/2072 2074/2074/2074
-f 2059/2059/2059 2074/2074/2074 2063/2063/2063
-f 2063/2063/2063 2074/2074/2074 2076/2076/2076
-f 2073/2073/2073 2075/2075/2075 2074/2074/2074
-f 2074/2074/2074 2075/2075/2075 2076/2076/2076
-f 2063/2063/2063 2076/2076/2076 2060/2060/2060
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-f 2076/2076/2076 2077/2077/2077 2078/2078/2078
-f 2060/2060/2060 2076/2076/2076 2078/2078/2078
-f 2078/2078/2078 2080/2080/2080 2082/2082/2082
-f 2080/2080/2080 2078/2078/2078 2084/2084/2084
-f 2077/2077/2077 2081/2081/2081 2078/2078/2078
-f 2078/2078/2078 2081/2081/2081 2084/2084/2084
-f 2060/2060/2060 2078/2078/2078 2082/2082/2082
-f 2080/2080/2080 2084/2084/2084 2085/2085/2085
-f 2085/2085/2085 2084/2084/2084 2088/2088/2088
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-f 2084/2084/2084 2087/2087/2087 2088/2088/2088
-f 2085/2085/2085 2088/2088/2088 2090/2090/2090
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-f 2093/2093/2093 2088/2088/2088 2087/2087/2087
-f 2088/2088/2088 2093/2093/2093 2092/2092/2092
-f 2094/2094/2094 2097/2097/2097 2091/2091/2091
-f 2093/2093/2093 2087/2087/2087 2095/2095/2095
-f 2096/2096/2096 2097/2097/2097 2094/2094/2094
-f 2091/2091/2091 2097/2097/2097 1784/1784/1784
-f 1784/1784/1784 2097/2097/2097 1782/1782/1782
-f 2096/2096/2096 2098/2098/2098 2097/2097/2097
-f 2098/2098/2098 2099/2099/2099 2097/2097/2097
-f 1782/1782/1782 2097/2097/2097 2099/2099/2099
-f 2098/2098/2098 2100/2100/2100 2099/2099/2099
-f 2100/2100/2100 1788/1788/1788 2099/2099/2099
-f 1782/1782/1782 2099/2099/2099 1780/1780/1780
-f 1780/1780/1780 2099/2099/2099 1788/1788/1788
-f 2100/2100/2100 2105/2105/2105 2101/2101/2101
-f 2101/2101/2101 2103/2103/2103 2100/2100/2100
-f 2098/2098/2098 2105/2105/2105 2100/2100/2100
-f 2100/2100/2100 2103/2103/2103 1788/1788/1788
-f 2107/2107/2107 2104/2104/2104 2108/2108/2108
-f 2107/2107/2107 2102/2102/2102 2104/2104/2104
-f 2105/2105/2105 2098/2098/2098 2112/2112/2112
-f 2112/2112/2112 2113/2113/2113 2115/2115/2115
-f 2098/2098/2098 2113/2113/2113 2112/2112/2112
-f 2108/2108/2108 2110/2110/2110 2116/2116/2116
-f 2117/2117/2117 2087/2087/2087 2120/2120/2120
-f 2114/2114/2114 2087/2087/2087 2117/2117/2117
-f 2108/2108/2108 2116/2116/2116 2122/2122/2122
-f 2129/2129/2129 2108/2108/2108 2122/2122/2122
-f 2141/2141/2141 2081/2081/2081 2137/2137/2137
-f 2120/2120/2120 2087/2087/2087 2141/2141/2141
-f 2141/2141/2141 2087/2087/2087 2081/2081/2081
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diff --git a/src/unilab/assets/robots/go2/assets/calf_1.obj b/src/unilab/assets/robots/go2/assets/calf_1.obj
deleted file mode 100644
index 01197551c..000000000
--- a/src/unilab/assets/robots/go2/assets/calf_1.obj
+++ /dev/null
@@ -1,9243 +0,0 @@
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-f 1883/1883/1883 1868/1868/1868 1886/1886/1886
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-f 1883/1883/1883 1886/1886/1886 1888/1888/1888
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-f 1805/1805/1805 1887/1887/1887 1894/1894/1894
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-f 1897/1897/1897 1799/1799/1799 1801/1801/1801
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-f 1837/1837/1837 1840/1840/1840 1843/1843/1843
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-f 1909/1909/1909 1799/1799/1799 668/668/668
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diff --git a/src/unilab/assets/robots/go2/assets/calf_mirror.stl b/src/unilab/assets/robots/go2/assets/calf_mirror.stl
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diff --git a/src/unilab/assets/robots/go2/assets/calf_mirror_0.obj b/src/unilab/assets/robots/go2/assets/calf_mirror_0.obj
deleted file mode 100644
index 710bbe9ee..000000000
--- a/src/unilab/assets/robots/go2/assets/calf_mirror_0.obj
+++ /dev/null
@@ -1,24145 +0,0 @@
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-f 1888/1888/1888 1886/1886/1886 1037/1037/1037
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-f 1035/1035/1035 1029/1029/1029 1887/1887/1887
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-f 1892/1892/1892 1891/1891/1891 1028/1028/1028
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-f 1028/1028/1028 1891/1891/1891 1029/1029/1029
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-f 1909/1909/1909 1906/1906/1906 1908/1908/1908
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-f 976/976/976 1920/1920/1920 1921/1921/1921
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-f 972/972/972 1924/1924/1924 976/976/976
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-f 1914/1914/1914 1923/1923/1923 1926/1926/1926
-f 973/973/973 1926/1926/1926 1923/1923/1923
-f 1928/1928/1928 1926/1926/1926 973/973/973
-f 1926/1926/1926 1919/1919/1919 1914/1914/1914
-f 1929/1929/1929 1927/1927/1927 1926/1926/1926
-f 1926/1926/1926 1927/1927/1927 1919/1919/1919
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-f 1050/1050/1050 1049/1049/1049 1929/1929/1929
-f 1049/1049/1049 1055/1055/1055 1929/1929/1929
-f 1929/1929/1929 1055/1055/1055 1927/1927/1927
-f 1050/1050/1050 1929/1929/1929 1928/1928/1928
-f 967/967/967 1928/1928/1928 973/973/973
-f 1930/1930/1930 1928/1928/1928 967/967/967
-f 1930/1930/1930 1050/1050/1050 1928/1928/1928
-f 1044/1044/1044 1043/1043/1043 1930/1930/1930
-f 1043/1043/1043 1050/1050/1050 1930/1930/1930
-f 964/964/964 1930/1930/1930 967/967/967
-f 1044/1044/1044 1930/1930/1930 964/964/964
-f 1927/1927/1927 1058/1058/1058 1932/1932/1932
-f 1055/1055/1055 1058/1058/1058 1927/1927/1927
-f 1927/1927/1927 1932/1932/1932 1917/1917/1917
-f 1917/1917/1917 1919/1919/1919 1927/1927/1927
-f 1934/1934/1934 1932/1932/1932 1058/1058/1058
-f 1931/1931/1931 1933/1933/1933 1909/1909/1909
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-f 1910/1910/1910 1916/1916/1916 1931/1931/1931
-f 1058/1058/1058 1935/1935/1935 1934/1934/1934
-f 1909/1909/1909 1933/1933/1933 1936/1936/1936
-f 1058/1058/1058 1059/1059/1059 1935/1935/1935
-f 1939/1939/1939 1935/1935/1935 1059/1059/1059
-f 1937/1937/1937 1909/1909/1909 1936/1936/1936
-f 1936/1936/1936 1938/1938/1938 1937/1937/1937
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-f 1059/1059/1059 1941/1941/1941 1939/1939/1939
-f 1942/1942/1942 1941/1941/1941 1061/1061/1061
-f 1940/1940/1940 1943/1943/1943 1463/1463/1463
-f 1463/1463/1463 1937/1937/1937 1940/1940/1940
-f 1059/1059/1059 1061/1061/1061 1941/1941/1941
-f 1061/1061/1061 1457/1457/1457 1942/1942/1942
-f 1463/1463/1463 1943/1943/1943 1458/1458/1458
-f 1909/1909/1909 1937/1937/1937 1906/1906/1906
-f 1937/1937/1937 1944/1944/1944 1906/1906/1906
-f 1937/1937/1937 1463/1463/1463 1944/1944/1944
-f 1906/1906/1906 1944/1944/1944 1907/1907/1907
-f 1944/1944/1944 1945/1945/1945 1907/1907/1907
-f 1944/1944/1944 1462/1462/1462 1945/1945/1945
-f 1463/1463/1463 1462/1462/1462 1944/1944/1944
-f 1947/1947/1947 1907/1907/1907 1945/1945/1945
-f 1945/1945/1945 1946/1946/1946 1948/1948/1948
-f 1945/1945/1945 1467/1467/1467 1946/1946/1946
-f 1948/1948/1948 1947/1947/1947 1945/1945/1945
-f 1462/1462/1462 1467/1467/1467 1945/1945/1945
-f 1948/1948/1948 1946/1946/1946 1949/1949/1949
-f 1947/1947/1947 1948/1948/1948 1950/1950/1950
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-f 1904/1904/1904 1947/1947/1947 1950/1950/1950
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-f 1903/1903/1903 1950/1950/1950 1864/1864/1864
-f 1950/1950/1950 1865/1865/1865 1864/1864/1864
-f 1865/1865/1865 1949/1949/1949 1854/1854/1854
-f 1946/1946/1946 1841/1841/1841 1949/1949/1949
-f 1949/1949/1949 1840/1840/1840 1854/1854/1854
-f 1949/1949/1949 1841/1841/1841 1840/1840/1840
-f 1907/1907/1907 1947/1947/1947 1904/1904/1904
-f 1946/1946/1946 1951/1951/1951 1841/1841/1841
-f 1467/1467/1467 1951/1951/1951 1946/1946/1946
-f 1951/1951/1951 1842/1842/1842 1841/1841/1841
-f 1467/1467/1467 1468/1468/1468 1951/1951/1951
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-f 1951/1951/1951 1952/1952/1952 1842/1842/1842
-f 1490/1490/1490 1506/1506/1506 1952/1952/1952
-f 1952/1952/1952 1506/1506/1506 1507/1507/1507
-f 1468/1468/1468 1490/1490/1490 1952/1952/1952
-f 1952/1952/1952 1507/1507/1507 1819/1819/1819
-f 1952/1952/1952 1819/1819/1819 1842/1842/1842
-f 976/976/976 1921/1921/1921 979/979/979
-f 1921/1921/1921 1912/1912/1912 1897/1897/1897
-f 1921/1921/1921 1897/1897/1897 1953/1953/1953
-f 979/979/979 1921/1921/1921 1953/1953/1953
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-f 996/996/996 978/978/978 1958/1958/1958
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-f 978/978/978 1954/1954/1954 1958/1958/1958
-f 1005/1005/1005 1956/1956/1956 1009/1009/1009
-f 1956/1956/1956 1028/1028/1028 1009/1009/1009
-f 1956/1956/1956 1892/1892/1892 1028/1028/1028
-f 978/978/978 979/979/979 1954/1954/1954
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-f 1037/1037/1037 1882/1882/1882 1040/1040/1040
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-f 1872/1872/1872 1052/1052/1052 1047/1047/1047
-f 1851/1851/1851 1966/1966/1966 1072/1072/1072
-f 1847/1847/1847 1966/1966/1966 1851/1851/1851
-f 1829/1829/1829 1832/1832/1832 1966/1966/1966
-f 1847/1847/1847 1829/1829/1829 1966/1966/1966
-f 1966/1966/1966 1832/1832/1832 1073/1073/1073
-f 1966/1966/1966 1073/1073/1073 1072/1072/1072
-f 1829/1829/1829 1847/1847/1847 1837/1837/1837
-f 1842/1842/1842 1819/1819/1819 1817/1817/1817
-f 1831/1831/1831 1967/1967/1967 1832/1832/1832
-f 1832/1832/1832 1967/1967/1967 1078/1078/1078
-f 1832/1832/1832 1078/1078/1078 1073/1073/1073
-f 1831/1831/1831 1968/1968/1968 1967/1967/1967
-f 1967/1967/1967 1080/1080/1080 1078/1078/1078
-f 1967/1967/1967 1968/1968/1968 1080/1080/1080
-f 1831/1831/1831 1124/1124/1124 1968/1968/1968
-f 1109/1109/1109 1968/1968/1968 1124/1124/1124
-f 1109/1109/1109 1107/1107/1107 1968/1968/1968
-f 1969/1969/1969 1080/1080/1080 1968/1968/1968
-f 1107/1107/1107 1969/1969/1969 1968/1968/1968
-f 1098/1098/1098 1970/1970/1970 1106/1106/1106
-f 1969/1969/1969 1095/1095/1095 1080/1080/1080
-f 1096/1096/1096 1970/1970/1970 1098/1098/1098
-f 1830/1830/1830 1124/1124/1124 1831/1831/1831
-f 1828/1828/1828 1127/1127/1127 1830/1830/1830
-f 1830/1830/1830 1127/1127/1127 1124/1124/1124
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-f 1828/1828/1828 1134/1134/1134 1127/1127/1127
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-f 1820/1820/1820 1802/1802/1802 1803/1803/1803
-f 1983/1983/1983 1971/1971/1971 1818/1818/1818
-f 1972/1972/1972 1975/1975/1975 1971/1971/1971
-f 1972/1972/1972 1971/1971/1971 1983/1983/1983
-f 1975/1975/1975 1979/1979/1979 1971/1971/1971
-f 1971/1971/1971 1982/1982/1982 1977/1977/1977
-f 1977/1977/1977 1802/1802/1802 1971/1971/1971
-f 1971/1971/1971 1979/1979/1979 1982/1982/1982
-f 1503/1503/1503 1983/1983/1983 1984/1984/1984
-f 1983/1983/1983 1503/1503/1503 1517/1517/1517
-f 1983/1983/1983 1521/1521/1521 1972/1972/1972
-f 1517/1517/1517 1521/1521/1521 1983/1983/1983
-f 1984/1984/1984 1983/1983/1983 1818/1818/1818
-f 1507/1507/1507 1984/1984/1984 1819/1819/1819
-f 1507/1507/1507 1503/1503/1503 1984/1984/1984
-f 1819/1819/1819 1984/1984/1984 1818/1818/1818
-f 1986/1986/1986 1981/1981/1981 1978/1978/1978
-f 1976/1976/1976 1980/1980/1980 1985/1985/1985
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-f 1988/1988/1988 1986/1986/1986 1990/1990/1990
-f 1986/1986/1986 1978/1978/1978 1990/1990/1990
-f 1987/1987/1987 1991/1991/1991 1985/1985/1985
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-f 2027/2027/2027 2030/2030/2030 2029/2029/2029
-f 2029/2029/2029 2030/2030/2030 1637/1637/1637
-f 2028/2028/2028 2029/2029/2029 1636/1636/1636
-f 2029/2029/2029 1637/1637/1637 1636/1636/1636
-f 2030/2030/2030 2031/2031/2031 2032/2032/2032
-f 2033/2033/2033 2031/2031/2031 2030/2030/2030
-f 2030/2030/2030 2032/2032/2032 1637/1637/1637
-f 2027/2027/2027 2033/2033/2033 2030/2030/2030
-f 1558/1558/1558 2033/2033/2033 2027/2027/2027
-f 2033/2033/2033 1597/1597/1597 2031/2031/2031
-f 2036/2036/2036 1597/1597/1597 2033/2033/2033
-f 1558/1558/1558 2036/2036/2036 2033/2033/2033
-f 2036/2036/2036 1590/1590/1590 1597/1597/1597
-f 2032/2032/2032 2031/2031/2031 1609/1609/1609
-f 1623/1623/1623 2032/2032/2032 1613/1613/1613
-f 1637/1637/1637 2032/2032/2032 1623/1623/1623
-f 1609/1609/1609 1613/1613/1613 2032/2032/2032
-f 2031/2031/2031 1597/1597/1597 1599/1599/1599
-f 1599/1599/1599 1609/1609/1609 2031/2031/2031
-f 2024/2024/2024 2028/2028/2028 1998/1998/1998
-f 1998/1998/1998 2028/2028/2028 1639/1639/1639
-f 2028/2028/2028 1636/1636/1636 1639/1639/1639
-f 1542/1542/1542 1558/1558/1558 2027/2027/2027
-f 1998/1998/1998 2037/2037/2037 2024/2024/2024
-f 2022/2022/2022 2000/2000/2000 2002/2002/2002
-f 2022/2022/2022 2038/2038/2038 2000/2000/2000
-f 2000/2000/2000 2038/2038/2038 1999/1999/1999
-f 2008/2008/2008 1525/1525/1525 1528/1528/1528
-f 2008/2008/2008 2007/2007/2007 1525/1525/1525
-f 2004/2004/2004 2039/2039/2039 2007/2007/2007
-f 2007/2007/2007 1523/1523/1523 1525/1525/1525
-f 2007/2007/2007 2039/2039/2039 1523/1523/1523
-f 2004/2004/2004 1988/1988/1988 2039/2039/2039
-f 1988/1988/1988 1990/1990/1990 2039/2039/2039
-f 2039/2039/2039 1990/1990/1990 1522/1522/1522
-f 2039/2039/2039 1522/1522/1522 1523/1523/1523
-f 1989/1989/1989 2003/2003/2003 1993/1993/1993
-f 1999/1999/1999 2043/2043/2043 1995/1995/1995
-f 2041/2041/2041 1998/1998/1998 1639/1639/1639
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diff --git a/src/unilab/assets/robots/go2/assets/calf_mirror_1.obj b/src/unilab/assets/robots/go2/assets/calf_mirror_1.obj
deleted file mode 100644
index 6b27378a4..000000000
--- a/src/unilab/assets/robots/go2/assets/calf_mirror_1.obj
+++ /dev/null
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-f 1703/1703/1703 1709/1709/1709 1711/1711/1711
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-f 1718/1718/1718 1715/1715/1715 1720/1720/1720
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-f 1287/1287/1287 1697/1697/1697 1719/1719/1719
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-f 1727/1727/1727 1729/1729/1729 1726/1726/1726
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-f 1729/1729/1729 1731/1731/1731 1726/1726/1726
-f 1733/1733/1733 1731/1731/1731 1729/1729/1729
-f 1729/1729/1729 1712/1712/1712 1733/1733/1733
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-f 1757/1757/1757 1708/1708/1708 1769/1769/1769
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-f 1751/1751/1751 1779/1779/1779 1781/1781/1781
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-f 1139/1139/1139 1780/1780/1780 1778/1778/1778
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-f 1139/1139/1139 1783/1783/1783 1787/1787/1787
-f 1790/1790/1790 1791/1791/1791 1788/1788/1788
-f 1788/1788/1788 1786/1786/1786 1790/1790/1790
-f 1787/1787/1787 1793/1793/1793 1139/1139/1139
-f 1791/1791/1791 1792/1792/1792 1788/1788/1788
-f 1139/1139/1139 1793/1793/1793 1794/1794/1794
-f 1791/1791/1791 1795/1795/1795 1792/1792/1792
-f 1139/1139/1139 1794/1794/1794 1800/1800/1800
-f 1791/1791/1791 1798/1798/1798 1795/1795/1795
-f 1794/1794/1794 1797/1797/1797 1800/1800/1800
-f 1803/1803/1803 1799/1799/1799 1796/1796/1796
-f 1803/1803/1803 1806/1806/1806 1799/1799/1799
-f 1809/1809/1809 1801/1801/1801 1805/1805/1805
-f 1800/1800/1800 1808/1808/1808 1812/1812/1812
-f 1800/1800/1800 1812/1812/1812 1813/1813/1813
-f 1800/1800/1800 1813/1813/1813 1816/1816/1816
-f 1800/1800/1800 1816/1816/1816 1139/1139/1139
-f 1828/1828/1828 1824/1824/1824 1817/1817/1817
-f 1139/1139/1139 1816/1816/1816 1826/1826/1826
-f 1827/1827/1827 1836/1836/1836 1832/1832/1832
-f 1838/1838/1838 1827/1827/1827 1832/1832/1832
-f 1836/1836/1836 1827/1827/1827 1823/1823/1823
-f 1828/1828/1828 1839/1839/1839 1824/1824/1824
-f 1111/1111/1111 1842/1842/1842 1838/1838/1838
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-f 1838/1838/1838 1832/1832/1832 1847/1847/1847
-f 1841/1841/1841 1825/1825/1825 1837/1837/1837
-f 1837/1837/1837 1846/1846/1846 1841/1841/1841
-f 1842/1842/1842 1845/1845/1845 1838/1838/1838
-f 1848/1848/1848 1830/1830/1830 1851/1851/1851
-f 1848/1848/1848 1851/1851/1851 1852/1852/1852
-f 1024/1024/1024 1110/1110/1110 1848/1848/1848
-f 1852/1852/1852 1024/1024/1024 1848/1848/1848
-f 1023/1023/1023 1852/1852/1852 1850/1850/1850
-f 696/696/696 1023/1023/1023 1850/1850/1850
-f 1854/1854/1854 1849/1849/1849 1831/1831/1831
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-f 1854/1854/1854 1831/1831/1831 1833/1833/1833
-f 696/696/696 1853/1853/1853 1859/1859/1859
-f 1858/1858/1858 1861/1861/1861 1868/1868/1868
-f 696/696/696 1859/1859/1859 1865/1865/1865
-f 1868/1868/1868 1866/1866/1866 1858/1858/1858
-f 671/671/671 1869/1869/1869 1870/1870/1870
-f 1873/1873/1873 1869/1869/1869 671/671/671
-f 1869/1869/1869 1862/1862/1862 1870/1870/1870
-f 1876/1876/1876 1873/1873/1873 671/671/671
-f 1879/1879/1879 1864/1864/1864 1874/1874/1874
-f 1879/1879/1879 1874/1874/1874 1877/1877/1877
-f 1877/1877/1877 1881/1881/1881 1879/1879/1879
-f 1865/1865/1865 1878/1878/1878 1882/1882/1882
-f 1877/1877/1877 1883/1883/1883 1881/1881/1881
-f 697/697/697 1880/1880/1880 1884/1884/1884
-f 1882/1882/1882 698/698/698 696/696/696
-f 696/696/696 1865/1865/1865 1882/1882/1882
-f 1885/1885/1885 1876/1876/1876 671/671/671
-f 681/681/681 1885/1885/1885 671/671/671
-f 679/679/679 697/697/697 1884/1884/1884
-f 1870/1870/1870 1888/1888/1888 671/671/671
-f 1871/1871/1871 1811/1811/1811 1886/1886/1886
-f 1886/1886/1886 1889/1889/1889 1871/1871/1871
-f 1810/1810/1810 1872/1872/1872 1820/1820/1820
-f 1862/1862/1862 1823/1823/1823 1870/1870/1870
-f 1888/1888/1888 1892/1892/1892 671/671/671
-f 1886/1886/1886 1891/1891/1891 1889/1889/1889
-f 1891/1891/1891 1893/1893/1893 1889/1889/1889
-f 1892/1892/1892 1894/1894/1894 671/671/671
-f 1891/1891/1891 1897/1897/1897 1893/1893/1893
-f 1897/1897/1897 1895/1895/1895 1893/1893/1893
-f 1900/1900/1900 1895/1895/1895 1897/1897/1897
-f 1897/1897/1897 1807/1807/1807 1900/1900/1900
-f 1808/1808/1808 1896/1896/1896 1890/1890/1890
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-f 1899/1899/1899 1804/1804/1804 1802/1802/1802
-f 1894/1894/1894 1899/1899/1899 671/671/671
-f 1805/1805/1805 1898/1898/1898 1809/1809/1809
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-f 1802/1802/1802 1901/1901/1901 1903/1903/1903
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-f 1912/1912/1912 1918/1918/1918 671/671/671
-f 671/671/671 1918/1918/1918 673/673/673
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-f 1933/1933/1933 1928/1928/1928 1920/1920/1920
-f 688/688/688 672/672/672 1921/1921/1921
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-f 1968/1968/1968 1967/1967/1967 1959/1959/1959
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-f 1970/1970/1970 1966/1966/1966 1969/1969/1969
-f 1972/1972/1972 1968/1968/1968 1673/1673/1673
-f 1965/1965/1965 1682/1682/1682 1968/1968/1968
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-f 1970/1970/1970 1971/1971/1971 1974/1974/1974
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-f 1973/1973/1973 1673/1673/1673 1666/1666/1666
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-f 1977/1977/1977 1981/1981/1981 1978/1978/1978
-f 1981/1981/1981 1977/1977/1977 1976/1976/1976
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-f 1982/1982/1982 1984/1984/1984 108/108/108
-f 1979/1979/1979 1982/1982/1982 108/108/108
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-f 1985/1985/1985 1989/1989/1989 1983/1983/1983
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-f 1991/1991/1991 108/108/108 1984/1984/1984
-f 1992/1992/1992 1988/1988/1988 1993/1993/1993
-f 1993/1993/1993 1381/1381/1381 1992/1992/1992
-f 1991/1991/1991 1996/1996/1996 108/108/108
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-f 1381/1381/1381 109/109/109 1995/1995/1995
-f 108/108/108 1996/1996/1996 110/110/110
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-f 1666/1666/1666 1664/1664/1664 1994/1994/1994
-f 1381/1381/1381 1993/1993/1993 1665/1665/1665
-f 1994/1994/1994 1986/1986/1986 1666/1666/1666
-f 1666/1666/1666 1986/1986/1986 1975/1975/1975
-f 1980/1980/1980 1999/1999/1999 1970/1970/1970
-f 1998/1998/1998 152/152/152 1980/1980/1980
-f 1980/1980/1980 1978/1978/1978 1998/1998/1998
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-f 1980/1980/1980 2003/2003/2003 2000/2000/2000
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-f 171/171/171 2019/2019/2019 2016/2016/2016
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-f 2026/2026/2026 2035/2035/2035 2038/2038/2038
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-f 171/171/171 2041/2041/2041 2032/2032/2032
-f 171/171/171 238/238/238 2041/2041/2041
-f 238/238/238 2031/2031/2031 2041/2041/2041
-f 2035/2035/2035 2042/2042/2042 2038/2038/2038
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-f 2043/2043/2043 2046/2046/2046 2045/2045/2045
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-f 1999/1999/1999 2045/2045/2045 2048/2048/2048
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-f 2058/2058/2058 2050/2050/2050 2052/2052/2052
-f 2058/2058/2058 2055/2055/2055 2050/2050/2050
-f 2048/2048/2048 2053/2053/2053 606/606/606
-f 606/606/606 1999/1999/1999 2048/2048/2048
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-f 2061/2061/2061 2056/2056/2056 2059/2059/2059
-f 2060/2060/2060 2058/2058/2058 238/238/238
-f 2058/2058/2058 2052/2052/2052 238/238/238
-f 2057/2057/2057 2062/2062/2062 2064/2064/2064
-f 2066/2066/2066 2068/2068/2068 2065/2065/2065
-f 2066/2066/2066 2065/2065/2065 2063/2063/2063
-f 2053/2053/2053 2065/2065/2065 2068/2068/2068
-f 2068/2068/2068 2066/2066/2066 2069/2069/2069
-f 2053/2053/2053 2068/2068/2068 606/606/606
-f 2068/2068/2068 2069/2069/2069 2076/2076/2076
-f 606/606/606 2068/2068/2068 2074/2074/2074
-f 2068/2068/2068 2076/2076/2076 2074/2074/2074
-f 2075/2075/2075 2070/2070/2070 2077/2077/2077
-f 2077/2077/2077 2072/2072/2072 2075/2075/2075
-f 2073/2073/2073 2078/2078/2078 553/553/553
-f 2078/2078/2078 556/556/556 553/553/553
-f 555/555/555 2079/2079/2079 2080/2080/2080
-f 2070/2070/2070 2081/2081/2081 2077/2077/2077
-f 555/555/555 2080/2080/2080 2086/2086/2086
-f 2083/2083/2083 2082/2082/2082 2071/2071/2071
-f 2083/2083/2083 2087/2087/2087 2082/2082/2082
-f 536/536/536 555/555/555 2086/2086/2086
-f 2086/2086/2086 2084/2084/2084 536/536/536
-f 2085/2085/2085 2088/2088/2088 543/543/543
-f 543/543/543 535/535/535 2085/2085/2085
-f 2089/2089/2089 2083/2083/2083 2067/2067/2067
-f 2083/2083/2083 2071/2071/2071 2067/2067/2067
-f 2088/2088/2088 539/539/539 543/543/543
-f 538/538/538 2090/2090/2090 2093/2093/2093
-f 2092/2092/2092 2089/2089/2089 2067/2067/2067
-f 2092/2092/2092 2095/2095/2095 2089/2089/2089
-f 2095/2095/2095 2094/2094/2094 2089/2089/2089
-f 2092/2092/2092 2097/2097/2097 2095/2095/2095
-f 2096/2096/2096 2098/2098/2098 2101/2101/2101
-f 2093/2093/2093 2096/2096/2096 2100/2100/2100
-f 2096/2096/2096 2101/2101/2101 2100/2100/2100
-f 2102/2102/2102 2060/2060/2060 238/238/238
-f 2099/2099/2099 2102/2102/2102 238/238/238
-f 2098/2098/2098 2059/2059/2059 2101/2101/2101
-f 2103/2103/2103 2099/2099/2099 238/238/238
-f 2093/2093/2093 2100/2100/2100 2104/2104/2104
-f 540/540/540 2103/2103/2103 238/238/238
-f 2093/2093/2093 2104/2104/2104 538/538/538
-f 2098/2098/2098 2091/2091/2091 2059/2059/2059
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diff --git a/src/unilab/assets/robots/go2/assets/foot.obj b/src/unilab/assets/robots/go2/assets/foot.obj
deleted file mode 100644
index edc559eeb..000000000
--- a/src/unilab/assets/robots/go2/assets/foot.obj
+++ /dev/null
@@ -1,30786 +0,0 @@
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diff --git a/src/unilab/assets/robots/go2/assets/hip_0.obj b/src/unilab/assets/robots/go2/assets/hip_0.obj
deleted file mode 100644
index 551f12437..000000000
--- a/src/unilab/assets/robots/go2/assets/hip_0.obj
+++ /dev/null
@@ -1,76960 +0,0 @@
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-f 1677/1677/1677 1670/1670/1670 1673/1673/1673
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-f 1670/1670/1670 1676/1676/1676 1662/1662/1662
-f 1679/1679/1679 1672/1672/1672 1684/1684/1684
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-f 1662/1662/1662 1687/1687/1687 1664/1664/1664
-f 1664/1664/1664 1687/1687/1687 1681/1681/1681
-f 1691/1691/1691 1688/1688/1688 1685/1685/1685
-f 1684/1684/1684 1672/1672/1672 1690/1690/1690
-f 1708/1708/1708 1697/1697/1697 1699/1699/1699
-f 1703/1703/1703 1704/1704/1704 1701/1701/1701
-f 1701/1701/1701 1704/1704/1704 1709/1709/1709
-f 1710/1710/1710 1706/1706/1706 1718/1718/1718
-f 1708/1708/1708 1712/1712/1712 1697/1697/1697
-f 1705/1705/1705 1714/1714/1714 1707/1707/1707
-f 1707/1707/1707 1714/1714/1714 1715/1715/1715
-f 1718/1718/1718 1706/1706/1706 1716/1716/1716
-f 1721/1721/1721 1710/1710/1710 1718/1718/1718
-f 1735/1735/1735 1726/1726/1726 1728/1728/1728
-f 1728/1728/1728 1726/1726/1726 1738/1738/1738
-f 1726/1726/1726 1731/1731/1731 1724/1724/1724
-f 1726/1726/1726 1735/1735/1735 1731/1731/1731
-f 1744/1744/1744 1738/1738/1738 1740/1740/1740
-f 1728/1728/1728 1738/1738/1738 1747/1747/1747
-f 1747/1747/1747 1738/1738/1738 1744/1744/1744
-f 1752/1752/1752 1749/1749/1749 1750/1750/1750
-f 1752/1752/1752 1750/1750/1750 1753/1753/1753
-f 1753/1753/1753 1750/1750/1750 1754/1754/1754
-f 1756/1756/1756 1753/1753/1753 1754/1754/1754
-f 1740/1740/1740 1761/1761/1761 1744/1744/1744
-f 1761/1761/1761 1740/1740/1740 1766/1766/1766
-f 1767/1767/1767 1769/1769/1769 1777/1777/1777
-f 1772/1772/1772 1769/1769/1769 1767/1767/1767
-f 1766/1766/1766 1740/1740/1740 1773/1773/1773
-f 1794/1794/1794 1799/1799/1799 1789/1789/1789
-f 1800/1800/1800 1799/1799/1799 1794/1794/1794
-f 1809/1809/1809 1800/1800/1800 1815/1815/1815
-f 1799/1799/1799 1800/1800/1800 1809/1809/1809
-f 1815/1815/1815 1800/1800/1800 1818/1818/1818
-f 1838/1838/1838 1833/1833/1833 1829/1829/1829
-f 1846/1846/1846 1840/1840/1840 1851/1851/1851
-f 1852/1852/1852 1855/1855/1855 1853/1853/1853
-f 1855/1855/1855 1852/1852/1852 1841/1841/1841
-f 1860/1860/1860 1853/1853/1853 1855/1855/1855
-f 1855/1855/1855 1841/1841/1841 1857/1857/1857
-f 1855/1855/1855 1857/1857/1857 1859/1859/1859
-f 1860/1860/1860 1855/1855/1855 1858/1858/1858
-f 1858/1858/1858 1855/1855/1855 1859/1859/1859
-f 1861/1861/1861 1860/1860/1860 1862/1862/1862
-f 1861/1861/1861 1863/1863/1863 1860/1860/1860
-f 1862/1862/1862 1860/1860/1860 1858/1858/1858
-f 1863/1863/1863 1853/1853/1853 1860/1860/1860
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-f 1864/1864/1864 1866/1866/1866 1863/1863/1863
-f 1866/1866/1866 1865/1865/1865 1863/1863/1863
-f 1863/1863/1863 1865/1865/1865 1853/1853/1853
-f 1868/1868/1868 1866/1866/1866 1864/1864/1864
-f 1867/1867/1867 1753/1753/1753 1866/1866/1866
-f 1866/1866/1866 1753/1753/1753 1865/1865/1865
-f 1868/1868/1868 1867/1867/1867 1866/1866/1866
-f 1869/1869/1869 1867/1867/1867 1868/1868/1868
-f 1868/1868/1868 1874/1874/1874 1869/1869/1869
-f 1868/1868/1868 1870/1870/1870 1872/1872/1872
-f 1868/1868/1868 1883/1883/1883 1870/1870/1870
-f 1878/1878/1878 1868/1868/1868 1864/1864/1864
-f 1881/1881/1881 1868/1868/1868 1872/1872/1872
-f 1868/1868/1868 1879/1879/1879 1874/1874/1874
-f 1868/1868/1868 1878/1878/1878 1883/1883/1883
-f 1879/1879/1879 1868/1868/1868 1881/1881/1881
-f 1884/1884/1884 1885/1885/1885 1871/1871/1871
-f 1884/1884/1884 1887/1887/1887 1885/1885/1885
-f 1884/1884/1884 1891/1891/1891 1887/1887/1887
-f 1891/1891/1891 1884/1884/1884 1876/1876/1876
-f 1889/1889/1889 1886/1886/1886 1888/1888/1888
-f 1889/1889/1889 1897/1897/1897 1886/1886/1886
-f 1892/1892/1892 1899/1899/1899 1894/1894/1894
-f 1897/1897/1897 1889/1889/1889 1896/1896/1896
-f 1899/1899/1899 1892/1892/1892 1877/1877/1877
-f 1878/1878/1878 1864/1864/1864 1898/1898/1898
-f 1864/1864/1864 1900/1900/1900 1898/1898/1898
-f 1894/1894/1894 1899/1899/1899 1901/1901/1901
-f 1861/1861/1861 1900/1900/1900 1864/1864/1864
-f 1900/1900/1900 1861/1861/1861 1902/1902/1902
-f 1903/1903/1903 1894/1894/1894 1901/1901/1901
-f 1861/1861/1861 1906/1906/1906 1902/1902/1902
-f 1894/1894/1894 1903/1903/1903 1904/1904/1904
-f 1904/1904/1904 1903/1903/1903 1907/1907/1907
-f 1862/1862/1862 1906/1906/1906 1861/1861/1861
-f 1906/1906/1906 1862/1862/1862 1908/1908/1908
-f 1909/1909/1909 1904/1904/1904 1907/1907/1907
-f 1862/1862/1862 1912/1912/1912 1908/1908/1908
-f 1904/1904/1904 1909/1909/1909 1910/1910/1910
-f 1910/1910/1910 1909/1909/1909 1913/1913/1913
-f 1912/1912/1912 1916/1916/1916 1914/1914/1914
-f 1910/1910/1910 1913/1913/1913 1915/1915/1915
-f 1916/1916/1916 1912/1912/1912 1862/1862/1862
-f 1862/1862/1862 1858/1858/1858 1916/1916/1916
-f 1916/1916/1916 1858/1858/1858 1920/1920/1920
-f 1916/1916/1916 1920/1920/1920 1917/1917/1917
-f 1917/1917/1917 1918/1918/1918 1916/1916/1916
-f 1916/1916/1916 1918/1918/1918 1914/1914/1914
-f 1917/1917/1917 1920/1920/1920 1921/1921/1921
-f 1858/1858/1858 1859/1859/1859 1920/1920/1920
-f 1920/1920/1920 1859/1859/1859 1922/1922/1922
-f 1920/1920/1920 1922/1922/1922 1921/1921/1921
-f 1859/1859/1859 1925/1925/1925 1922/1922/1922
-f 1921/1921/1921 1922/1922/1922 1923/1923/1923
-f 1922/1922/1922 1937/1937/1937 1923/1923/1923
-f 1926/1926/1926 1922/1922/1922 1925/1925/1925
-f 1935/1935/1935 1922/1922/1922 1926/1926/1926
-f 1922/1922/1922 1935/1935/1935 1937/1937/1937
-f 1940/1940/1940 1936/1936/1936 1934/1934/1934
-f 1936/1936/1936 1944/1944/1944 1938/1938/1938
-f 1939/1939/1939 1923/1923/1923 1937/1937/1937
-f 1944/1944/1944 1936/1936/1936 1940/1940/1940
-f 1944/1944/1944 1948/1948/1948 1938/1938/1938
-f 1948/1948/1948 1944/1944/1944 1952/1952/1952
-f 1964/1964/1964 1968/1968/1968 1962/1962/1962
-f 1968/1968/1968 1964/1964/1964 1965/1965/1965
-f 1965/1965/1965 1964/1964/1964 1973/1973/1973
-f 1965/1965/1965 1973/1973/1973 1975/1975/1975
-f 1980/1980/1980 1976/1976/1976 1974/1974/1974
-f 1984/1984/1984 1980/1980/1980 1982/1982/1982
-f 1980/1980/1980 1984/1984/1984 1976/1976/1976
-f 1983/1983/1983 1988/1988/1988 1977/1977/1977
-f 1984/1984/1984 1982/1982/1982 1986/1986/1986
-f 1990/1990/1990 1983/1983/1983 1985/1985/1985
-f 1983/1983/1983 1990/1990/1990 1988/1988/1988
-f 1990/1990/1990 1985/1985/1985 1999/1999/1999
-f 1999/1999/1999 1985/1985/1985 2003/2003/2003
-f 1999/1999/1999 2003/2003/2003 2006/2006/2006
-f 2003/2003/2003 2009/2009/2009 2006/2006/2006
-f 2006/2006/2006 2009/2009/2009 2010/2010/2010
-f 2009/2009/2009 2015/2015/2015 2010/2010/2010
-f 2010/2010/2010 2015/2015/2015 2017/2017/2017
-f 2020/2020/2020 2022/2022/2022 1514/1514/1514
-f 2020/2020/2020 2029/2029/2029 2022/2022/2022
-f 2024/2024/2024 2029/2029/2029 2020/2020/2020
-f 2028/2028/2028 2030/2030/2030 2023/2023/2023
-f 2028/2028/2028 2033/2033/2033 2030/2030/2030
-f 2035/2035/2035 2033/2033/2033 2028/2028/2028
-f 2024/2024/2024 2036/2036/2036 2029/2029/2029
-f 2036/2036/2036 2024/2024/2024 2040/2040/2040
-f 2042/2042/2042 2035/2035/2035 2037/2037/2037
-f 2045/2045/2045 2035/2035/2035 2042/2042/2042
-f 2035/2035/2035 2045/2045/2045 2033/2033/2033
-f 2038/2038/2038 1805/1805/1805 2043/2043/2043
-f 2048/2048/2048 2046/2046/2046 2039/2039/2039
-f 2047/2047/2047 1805/1805/1805 2038/2038/2038
-f 2040/2040/2040 2024/2024/2024 2048/2048/2048
-f 2056/2056/2056 2046/2046/2046 2048/2048/2048
-f 2058/2058/2058 2060/2060/2060 2056/2056/2056
-f 2060/2060/2060 2058/2058/2058 2061/2061/2061
-f 2058/2058/2058 1838/1838/1838 2061/2061/2061
-f 2052/2052/2052 1838/1838/1838 2058/2058/2058
-f 1824/1824/1824 2062/2062/2062 1837/1837/1837
-f 2062/2062/2062 1803/1803/1803 2059/2059/2059
-f 1803/1803/1803 2062/2062/2062 1824/1824/1824
-f 2056/2056/2056 2060/2060/2060 2063/2063/2063
-f 1803/1803/1803 2064/2064/2064 2059/2059/2059
-f 2056/2056/2056 2063/2063/2063 2046/2046/2046
-f 2064/2064/2064 1805/1805/1805 2047/2047/2047
-f 1805/1805/1805 2064/2064/2064 1803/1803/1803
-f 2067/2067/2067 2053/2053/2053 2072/2072/2072
-f 2053/2053/2053 2067/2067/2067 1834/1834/1834
-f 2053/2053/2053 1834/1834/1834 2051/2051/2051
-f 2070/2070/2070 2053/2053/2053 2069/2069/2069
-f 2072/2072/2072 2053/2053/2053 2070/2070/2070
-f 1859/1859/1859 1857/1857/1857 2074/2074/2074
-f 1859/1859/1859 2074/2074/2074 1925/1925/1925
-f 2079/2079/2079 2081/2081/2081 2077/2077/2077
-f 2070/2070/2070 2079/2079/2079 2077/2077/2077
-f 2081/2081/2081 2079/2079/2079 2086/2086/2086
-f 2085/2085/2085 2088/2088/2088 2091/2091/2091
-f 2096/2096/2096 2088/2088/2088 2085/2085/2085
-f 2086/2086/2086 2079/2079/2079 2097/2097/2097
-f 2088/2088/2088 2096/2096/2096 2100/2100/2100
-f 2106/2106/2106 2104/2104/2104 2108/2108/2108
-f 2104/2104/2104 2106/2106/2106 2110/2110/2110
-f 2104/2104/2104 2110/2110/2110 2099/2099/2099
-f 2108/2108/2108 2104/2104/2104 2102/2102/2102
-f 2105/2105/2105 1959/1959/1959 2111/2111/2111
-f 1960/1960/1960 2113/2113/2113 2110/2110/2110
-f 2088/2088/2088 2100/2100/2100 2112/2112/2112
-f 2110/2110/2110 2113/2113/2113 2087/2087/2087
-f 2126/2126/2126 2124/2124/2124 2109/2109/2109
-f 2125/2125/2125 2105/2105/2105 2107/2107/2107
-f 2026/2026/2026 2127/2127/2127 2108/2108/2108
-f 2026/2026/2026 2108/2108/2108 2102/2102/2102
-f 2126/2126/2126 2132/2132/2132 2124/2124/2124
-f 2132/2132/2132 2126/2126/2126 2129/2129/2129
-f 2129/2129/2129 2126/2126/2126 2012/2012/2012
-f 2127/2127/2127 2026/2026/2026 2013/2013/2013
-f 2014/2014/2014 1954/1954/1954 2130/2130/2130
-f 1957/1957/1957 2105/2105/2105 2125/2125/2125
-f 2105/2105/2105 1957/1957/1957 1959/1959/1959
-f 2070/2070/2070 2069/2069/2069 2079/2079/2079
-f 1838/1838/1838 2052/2052/2052 1833/1833/1833
-f 2133/2133/2133 2136/2136/2136 2031/2031/2031
-f 2031/2031/2031 2136/2136/2136 2021/2021/2021
-f 2139/2139/2139 1530/1530/1530 2137/2137/2137
-f 2134/2134/2134 2140/2140/2140 2138/2138/2138
-f 1514/1514/1514 2137/2137/2137 1513/1513/1513
-f 2137/2137/2137 1530/1530/1530 1513/1513/1513
-f 2022/2022/2022 2137/2137/2137 1514/1514/1514
-f 2142/2142/2142 2140/2140/2140 2134/2134/2134
-f 2142/2142/2142 2143/2143/2143 2140/2140/2140
-f 1742/1742/1742 2139/2139/2139 2144/2144/2144
-f 1530/1530/1530 2139/2139/2139 1742/1742/1742
-f 2149/2149/2149 2143/2143/2143 2142/2142/2142
-f 2146/2146/2146 2144/2144/2144 2150/2150/2150
-f 1742/1742/1742 2144/2144/2144 2146/2146/2146
-f 2143/2143/2143 2149/2149/2149 2151/2151/2151
-f 2151/2151/2151 2149/2149/2149 2156/2156/2156
-f 2160/2160/2160 2152/2152/2152 2154/2154/2154
-f 2153/2153/2153 2157/2157/2157 2147/2147/2147
-f 2160/2160/2160 2158/2158/2158 2152/2152/2152
-f 2159/2159/2159 2162/2162/2162 1811/1811/1811
-f 2157/2157/2157 2165/2165/2165 2164/2164/2164
-f 2147/2147/2147 2157/2157/2157 2164/2164/2164
-f 2159/2159/2159 1811/1811/1811 2166/2166/2166
-f 1812/1812/1812 2172/2172/2172 2167/2167/2167
-f 2173/2173/2173 2170/2170/2170 2168/2168/2168
-f 2164/2164/2164 2165/2165/2165 2171/2171/2171
-f 1806/1806/1806 2177/2177/2177 2172/2172/2172
-f 1806/1806/1806 2172/2172/2172 1812/1812/1812
-f 2173/2173/2173 2175/2175/2175 2170/2170/2170
-f 2176/2176/2176 2175/2175/2175 2173/2173/2173
-f 2177/2177/2177 1806/1806/1806 1796/1796/1796
-f 2177/2177/2177 1796/1796/1796 2179/2179/2179
-f 2178/2178/2178 2175/2175/2175 2176/2176/2176
-f 1796/1796/1796 2181/2181/2181 2179/2179/2179
-f 2180/2180/2180 2182/2182/2182 2178/2178/2178
-f 2178/2178/2178 2182/2182/2182 2175/2175/2175
-f 1769/1769/1769 2182/2182/2182 2180/2180/2180
-f 1769/1769/1769 1772/1772/1772 2182/2182/2182
-f 2181/2181/2181 1796/1796/1796 1785/1785/1785
-f 2181/2181/2181 1785/1785/1785 1768/1768/1768
-f 2013/2013/2013 2026/2026/2026 2018/2018/2018
-f 2014/2014/2014 2008/2008/2008 1954/1954/1954
-f 1953/1953/1953 2007/2007/2007 1969/1969/1969
-f 1969/1969/1969 2007/2007/2007 2004/2004/2004
-f 2002/2002/2002 1982/1982/1982 1970/1970/1970
-f 1986/1986/1986 1982/1982/1982 2002/2002/2002
-f 1502/1502/1502 2001/2001/2001 1508/1508/1508
-f 2185/2185/2185 2001/2001/2001 1502/1502/1502
-f 2001/2001/2001 2185/2185/2185 1996/1996/1996
-f 2190/2190/2190 2184/2184/2184 2186/2186/2186
-f 2186/2186/2186 2184/2184/2184 1504/1504/1504
-f 2184/2184/2184 2190/2190/2190 2191/2191/2191
-f 1996/1996/1996 2185/2185/2185 2192/2192/2192
-f 2191/2191/2191 2195/2195/2195 2200/2200/2200
-f 2191/2191/2191 2196/2196/2196 2195/2195/2195
-f 2191/2191/2191 2190/2190/2190 2196/2196/2196
-f 2203/2203/2203 2192/2192/2192 2198/2198/2198
-f 1996/1996/1996 2192/2192/2192 2203/2203/2203
-f 2213/2213/2213 2211/2211/2211 2203/2203/2203
-f 2213/2213/2213 2203/2203/2203 2199/2199/2199
-f 2215/2215/2215 2214/2214/2214 2193/2193/2193
-f 2193/2193/2193 2214/2214/2214 2201/2201/2201
-f 2215/2215/2215 2217/2217/2217 2214/2214/2214
-f 2218/2218/2218 2211/2211/2211 2213/2213/2213
-f 2219/2219/2219 2211/2211/2211 2218/2218/2218
-f 2217/2217/2217 2221/2221/2221 2220/2220/2220
-f 2217/2217/2217 2215/2215/2215 2221/2221/2221
-f 2227/2227/2227 2221/2221/2221 2226/2226/2226
-f 2221/2221/2221 2227/2227/2227 2220/2220/2220
-f 2229/2229/2229 2235/2235/2235 2234/2234/2234
-f 2261/2261/2261 2258/2258/2258 2242/2242/2242
-f 2267/2267/2267 2208/2208/2208 2264/2264/2264
-f 2264/2264/2264 2208/2208/2208 2204/2204/2204
-f 2206/2206/2206 2258/2258/2258 2261/2261/2261
-f 2208/2208/2208 2267/2267/2267 2269/2269/2269
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diff --git a/src/unilab/assets/robots/go2/assets/hip_1.obj b/src/unilab/assets/robots/go2/assets/hip_1.obj
deleted file mode 100644
index ed2bebfc4..000000000
--- a/src/unilab/assets/robots/go2/assets/hip_1.obj
+++ /dev/null
@@ -1,73961 +0,0 @@
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-f 1587/1587/1587 1577/1577/1577 1589/1589/1589
-f 1591/1591/1591 1570/1570/1570 1507/1507/1507
-f 1497/1497/1497 1488/1488/1488 1601/1601/1601
-f 1488/1488/1488 1599/1599/1599 1601/1601/1601
-f 1599/1599/1599 1484/1484/1484 1595/1595/1595
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-f 1595/1595/1595 1484/1484/1484 1602/1602/1602
-f 1484/1484/1484 1610/1610/1610 1602/1602/1602
-f 1610/1610/1610 1483/1483/1483 1619/1619/1619
-f 1610/1610/1610 1484/1484/1484 1483/1483/1483
-f 1483/1483/1483 1622/1622/1622 1619/1619/1619
-f 1630/1630/1630 1578/1578/1578 1626/1626/1626
-f 1626/1626/1626 1578/1578/1578 1616/1616/1616
-f 1483/1483/1483 1633/1633/1633 1622/1622/1622
-f 1633/1633/1633 1638/1638/1638 1637/1637/1637
-f 1633/1633/1633 1483/1483/1483 1638/1638/1638
-f 1638/1638/1638 1483/1483/1483 1481/1481/1481
-f 1639/1639/1639 1638/1638/1638 1640/1640/1640
-f 1642/1642/1642 1638/1638/1638 1639/1639/1639
-f 1640/1640/1640 1638/1638/1638 1481/1481/1481
-f 1638/1638/1638 1642/1642/1642 1637/1637/1637
-f 1642/1642/1642 1639/1639/1639 1653/1653/1653
-f 1653/1653/1653 1639/1639/1639 1660/1660/1660
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-f 1686/1686/1686 1639/1639/1639 1688/1688/1688
-f 1690/1690/1690 1687/1687/1687 1703/1703/1703
-f 1687/1687/1687 1708/1708/1708 1703/1703/1703
-f 1708/1708/1708 543/543/543 1711/1711/1711
-f 1708/1708/1708 1687/1687/1687 543/543/543
-f 543/543/543 1716/1716/1716 1711/1711/1711
-f 1722/1722/1722 1718/1718/1718 1724/1724/1724
-f 1718/1718/1718 1722/1722/1722 1671/1671/1671
-f 1718/1718/1718 1671/1671/1671 1707/1707/1707
-f 1722/1722/1722 1724/1724/1724 1729/1729/1729
-f 527/527/527 1722/1722/1722 1729/1729/1729
-f 1732/1732/1732 1722/1722/1722 460/460/460
-f 515/515/515 460/460/460 1722/1722/1722
-f 1734/1734/1734 1671/1671/1671 1722/1722/1722
-f 1734/1734/1734 1722/1722/1722 1731/1731/1731
-f 1722/1722/1722 1732/1732/1732 1731/1731/1731
-f 515/515/515 1722/1722/1722 527/527/527
-f 1671/1671/1671 1734/1734/1734 1738/1738/1738
-f 1730/1730/1730 1736/1736/1736 1735/1735/1735
-f 1736/1736/1736 1739/1739/1739 1735/1735/1735
-f 1738/1738/1738 1665/1665/1665 1671/1671/1671
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-f 1739/1739/1739 1742/1742/1742 1741/1741/1741
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-f 1750/1750/1750 1022/1022/1022 1027/1027/1027
-f 1753/1753/1753 1754/1754/1754 1750/1750/1750
-f 1750/1750/1750 1754/1754/1754 1022/1022/1022
-f 320/320/320 1756/1756/1756 319/319/319
-f 319/319/319 1756/1756/1756 328/328/328
-f 1757/1757/1757 1756/1756/1756 320/320/320
-f 1757/1757/1757 1761/1761/1761 1756/1756/1756
-f 1757/1757/1757 1766/1766/1766 1761/1761/1761
-f 1757/1757/1757 1771/1771/1771 1766/1766/1766
-f 1764/1764/1764 1767/1767/1767 1763/1763/1763
-f 1768/1768/1768 1767/1767/1767 1764/1764/1764
-f 1766/1766/1766 1771/1771/1771 1769/1769/1769
-f 1757/1757/1757 1780/1780/1780 1771/1771/1771
-f 1770/1770/1770 1778/1778/1778 1774/1774/1774
-f 1769/1769/1769 1771/1771/1771 1772/1772/1772
-f 1770/1770/1770 1775/1775/1775 1776/1776/1776
-f 1779/1779/1779 1775/1775/1775 1770/1770/1770
-f 1770/1770/1770 1776/1776/1776 1778/1778/1778
-f 1757/1757/1757 1782/1782/1782 1780/1780/1780
-f 1785/1785/1785 1779/1779/1779 1783/1783/1783
-f 1785/1785/1785 1775/1775/1775 1779/1779/1779
-f 1787/1787/1787 1775/1775/1775 1785/1785/1785
-f 1791/1791/1791 1794/1794/1794 1789/1789/1789
-f 1782/1782/1782 1792/1792/1792 1788/1788/1788
-f 1798/1798/1798 1800/1800/1800 1797/1797/1797
-f 1798/1798/1798 1801/1801/1801 1800/1800/1800
-f 1802/1802/1802 1801/1801/1801 1798/1798/1798
-f 1802/1802/1802 1798/1798/1798 1793/1793/1793
-f 1804/1804/1804 1803/1803/1803 1794/1794/1794
-f 1804/1804/1804 1805/1805/1805 1803/1803/1803
-f 1806/1806/1806 1801/1801/1801 1802/1802/1802
-f 1806/1806/1806 1807/1807/1807 1801/1801/1801
-f 1809/1809/1809 1807/1807/1807 1806/1806/1806
-f 1809/1809/1809 1806/1806/1806 1812/1812/1812
-f 1804/1804/1804 1811/1811/1811 1805/1805/1805
-f 1811/1811/1811 1815/1815/1815 1819/1819/1819
-f 1811/1811/1811 1816/1816/1816 1815/1815/1815
-f 1811/1811/1811 1804/1804/1804 1816/1816/1816
-f 1819/1819/1819 1815/1815/1815 1825/1825/1825
-f 1824/1824/1824 1822/1822/1822 1821/1821/1821
-f 1814/1814/1814 1826/1826/1826 1824/1824/1824
-f 1824/1824/1824 1826/1826/1826 1827/1827/1827
-f 1824/1824/1824 1827/1827/1827 1822/1822/1822
-f 1827/1827/1827 1826/1826/1826 1834/1834/1834
-f 1834/1834/1834 1826/1826/1826 1835/1835/1835
-f 1841/1841/1841 1837/1837/1837 1839/1839/1839
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-f 1842/1842/1842 1846/1846/1846 1841/1841/1841
-f 1826/1826/1826 1848/1848/1848 1835/1835/1835
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-f 1850/1850/1850 1826/1826/1826 1855/1855/1855
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-f 1863/1863/1863 1855/1855/1855 1856/1856/1856
-f 1860/1860/1860 1855/1855/1855 1858/1858/1858
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-f 1855/1855/1855 1862/1862/1862 1867/1867/1867
-f 1855/1855/1855 1863/1863/1863 1862/1862/1862
-f 1851/1851/1851 1855/1855/1855 1867/1867/1867
-f 1875/1875/1875 1873/1873/1873 1872/1872/1872
-f 1870/1870/1870 1875/1875/1875 1872/1872/1872
-f 1873/1873/1873 1875/1875/1875 1846/1846/1846
-f 1870/1870/1870 1876/1876/1876 1875/1875/1875
-f 1875/1875/1875 1876/1876/1876 1877/1877/1877
-f 1878/1878/1878 1875/1875/1875 1877/1877/1877
-f 1846/1846/1846 1875/1875/1875 1878/1878/1878
-f 1879/1879/1879 1878/1878/1878 1877/1877/1877
-f 1800/1800/1800 1846/1846/1846 1878/1878/1878
-f 1797/1797/1797 1800/1800/1800 1878/1878/1878
-f 1797/1797/1797 1878/1878/1878 1879/1879/1879
-f 1879/1879/1879 1877/1877/1877 1881/1881/1881
-f 1883/1883/1883 1887/1887/1887 1787/1787/1787
-f 1885/1885/1885 1888/1888/1888 1883/1883/1883
-f 1883/1883/1883 1888/1888/1888 1887/1887/1887
-f 1887/1887/1887 1888/1888/1888 1889/1889/1889
-f 1888/1888/1888 1890/1890/1890 1889/1889/1889
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-f 1885/1885/1885 1891/1891/1891 1888/1888/1888
-f 1892/1892/1892 1891/1891/1891 1885/1885/1885
-f 1892/1892/1892 1894/1894/1894 1891/1891/1891
-f 1891/1891/1891 1894/1894/1894 1895/1895/1895
-f 1891/1891/1891 1895/1895/1895 1890/1890/1890
-f 1894/1894/1894 1898/1898/1898 1895/1895/1895
-f 1897/1897/1897 1895/1895/1895 1903/1903/1903
-f 1900/1900/1900 1895/1895/1895 1897/1897/1897
-f 1902/1902/1902 1890/1890/1890 1895/1895/1895
-f 1903/1903/1903 1895/1895/1895 1898/1898/1898
-f 1902/1902/1902 1895/1895/1895 1900/1900/1900
-f 1903/1903/1903 1898/1898/1898 1908/1908/1908
-f 1898/1898/1898 1910/1910/1910 1908/1908/1908
-f 1913/1913/1913 1906/1906/1906 1912/1912/1912
-f 1915/1915/1915 1913/1913/1913 1912/1912/1912
-f 1917/1917/1917 1913/1913/1913 1915/1915/1915
-f 1014/1014/1014 1917/1917/1917 1918/1918/1918
-f 1917/1917/1917 1014/1014/1014 1913/1913/1913
-f 1920/1920/1920 1924/1924/1924 1923/1923/1923
-f 1924/1924/1924 1925/1925/1925 1923/1923/1923
-f 1925/1925/1925 1924/1924/1924 1001/1001/1001
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-f 1001/1001/1001 1006/1006/1006 1927/1927/1927
-f 1010/1010/1010 1014/1014/1014 1929/1929/1929
-f 1014/1014/1014 1918/1918/1918 1929/1929/1929
-f 1931/1931/1931 1924/1924/1924 1898/1898/1898
-f 1931/1931/1931 1932/1932/1932 1924/1924/1924
-f 1932/1932/1932 1001/1001/1001 1924/1924/1924
-f 1910/1910/1910 1898/1898/1898 1924/1924/1924
-f 1910/1910/1910 1924/1924/1924 1920/1920/1920
-f 1934/1934/1934 1932/1932/1932 1935/1935/1935
-f 1934/1934/1934 1000/1000/1000 1932/1932/1932
-f 1935/1935/1935 1932/1932/1932 1931/1931/1931
-f 1932/1932/1932 1000/1000/1000 1001/1001/1001
-f 1935/1935/1935 1931/1931/1931 1942/1942/1942
-f 1942/1942/1942 1894/1894/1894 1892/1892/1892
-f 1942/1942/1942 1931/1931/1931 1894/1894/1894
-f 1943/1943/1943 1948/1948/1948 1945/1945/1945
-f 1943/1943/1943 1945/1945/1945 1939/1939/1939
-f 1946/1946/1946 1948/1948/1948 1943/1943/1943
-f 1949/1949/1949 1950/1950/1950 1948/1948/1948
-f 1946/1946/1946 1949/1949/1949 1948/1948/1948
-f 1950/1950/1950 1951/1951/1951 1948/1948/1948
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-f 1951/1951/1951 1960/1960/1960 1952/1952/1952
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-f 1951/1951/1951 1954/1954/1954 1945/1945/1945
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-f 1962/1962/1962 1963/1963/1963 1966/1966/1966
-f 1966/1966/1966 1963/1963/1963 1971/1971/1971
-f 1963/1963/1963 1973/1973/1973 1971/1971/1971
-f 1978/1978/1978 1954/1954/1954 1974/1974/1974
-f 1974/1974/1974 1954/1954/1954 1968/1968/1968
-f 1982/1982/1982 1978/1978/1978 1981/1981/1981
-f 1978/1978/1978 1982/1982/1982 1954/1954/1954
-f 1984/1984/1984 1982/1982/1982 1981/1981/1981
-f 1984/1984/1984 1987/1987/1987 1982/1982/1982
-f 1954/1954/1954 1982/1982/1982 1985/1985/1985
-f 1985/1985/1985 1982/1982/1982 1986/1986/1986
-f 1982/1982/1982 1987/1987/1987 1986/1986/1986
-f 1987/1987/1987 1984/1984/1984 1989/1989/1989
-f 1990/1990/1990 1987/1987/1987 1989/1989/1989
-f 1987/1987/1987 1990/1990/1990 1993/1993/1993
-f 1987/1987/1987 1993/1993/1993 964/964/964
-f 1986/1986/1986 1987/1987/1987 966/966/966
-f 1987/1987/1987 964/964/964 966/966/966
-f 964/964/964 1993/1993/1993 1995/1995/1995
-f 941/941/941 1997/1997/1997 1999/1999/1999
-f 941/941/941 949/949/949 1997/1997/1997
-f 2001/2001/2001 941/941/941 1999/1999/1999
-f 1976/1976/1976 941/941/941 2001/2001/2001
-f 953/953/953 964/964/964 1995/1995/1995
-f 1937/1937/1937 1985/1985/1985 1986/1986/1986
-f 2002/2002/2002 1986/1986/1986 966/966/966
-f 1937/1937/1937 1986/1986/1986 2002/2002/2002
-f 2002/2002/2002 966/966/966 967/967/967
-f 2004/2004/2004 1000/1000/1000 1934/1934/1934
-f 972/972/972 989/989/989 2004/2004/2004
-f 2004/2004/2004 989/989/989 1000/1000/1000
-f 1945/1945/1945 1954/1954/1954 1985/1985/1985
-f 1939/1939/1939 1945/1945/1945 1985/1985/1985
-f 1939/1939/1939 1985/1985/1985 1937/1937/1937
-f 1963/1963/1963 1976/1976/1976 1973/1973/1973
-f 1976/1976/1976 1963/1963/1963 941/941/941
-f 1954/1954/1954 1955/1955/1955 1968/1968/1968
-f 1856/1856/1856 1964/1964/1964 2006/2006/2006
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-f 1950/1950/1950 2012/2012/2012 2011/2011/2011
-f 1957/1957/1957 1950/1950/1950 2011/2011/2011
-f 1876/1876/1876 2014/2014/2014 2012/2012/2012
-f 1876/1876/1876 2012/2012/2012 1950/1950/1950
-f 2014/2014/2014 1876/1876/1876 2016/2016/2016
-f 1876/1876/1876 1870/1870/1870 2016/2016/2016
-f 1863/1863/1863 1856/1856/1856 2008/2008/2008
-f 2021/2021/2021 1963/1963/1963 1856/1856/1856
-f 2019/2019/2019 941/941/941 1963/1963/1963
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-f 2022/2022/2022 2024/2024/2024 2020/2020/2020
-f 2024/2024/2024 2018/2018/2018 2020/2020/2020
-f 2022/2022/2022 2027/2027/2027 2024/2024/2024
-f 2018/2018/2018 2024/2024/2024 929/929/929
-f 1857/1857/1857 2031/2031/2031 2027/2027/2027
-f 2027/2027/2027 2022/2022/2022 1859/1859/1859
-f 1857/1857/1857 2027/2027/2027 1859/1859/1859
-f 2032/2032/2032 2031/2031/2031 1865/1865/1865
-f 2031/2031/2031 2032/2032/2032 1818/1818/1818
-f 2031/2031/2031 1857/1857/1857 1865/1865/1865
-f 1818/1818/1818 2032/2032/2032 1813/1813/1813
-f 2033/2033/2033 1826/1826/1826 1814/1814/1814
-f 1866/1866/1866 1826/1826/1826 2033/2033/2033
-f 1804/1804/1804 186/186/186 2029/2029/2029
-f 1816/1816/1816 1804/1804/1804 2029/2029/2029
-f 1856/1856/1856 1860/1860/1860 2023/2023/2023
-f 2034/2034/2034 941/941/941 2019/2019/2019
-f 2018/2018/2018 929/929/929 2035/2035/2035
-f 939/939/939 941/941/941 2034/2034/2034
-f 2035/2035/2035 929/929/929 938/938/938
-f 1876/1876/1876 1950/1950/1950 1949/1949/1949
-f 1877/1877/1877 1876/1876/1876 1949/1949/1949
-f 1881/1881/1881 1877/1877/1877 1949/1949/1949
-f 1881/1881/1881 1949/1949/1949 1946/1946/1946
-f 1894/1894/1894 1931/1931/1931 1898/1898/1898
-f 2036/2036/2036 1913/1913/1913 2039/2039/2039
-f 2037/2037/2037 1913/1913/1913 2036/2036/2036
-f 1913/1913/1913 2037/2037/2037 1906/1906/1906
-f 1913/1913/1913 1754/1754/1754 2039/2039/2039
-f 1913/1913/1913 1014/1014/1014 1754/1754/1754
-f 2036/2036/2036 2039/2039/2039 2040/2040/2040
-f 1759/1759/1759 2039/2039/2039 1753/1753/1753
-f 1753/1753/1753 2039/2039/2039 1754/1754/1754
-f 1759/1759/1759 2040/2040/2040 2039/2039/2039
-f 2041/2041/2041 2040/2040/2040 1767/1767/1767
-f 2041/2041/2041 2036/2036/2036 2040/2040/2040
-f 1763/1763/1763 1767/1767/1767 2040/2040/2040
-f 1763/1763/1763 2040/2040/2040 1759/1759/1759
-f 2041/2041/2041 2042/2042/2042 2044/2044/2044
-f 2041/2041/2041 2052/2052/2052 2042/2042/2042
-f 2049/2049/2049 2041/2041/2041 2044/2044/2044
-f 1768/1768/1768 2041/2041/2041 1767/1767/1767
-f 2050/2050/2050 2036/2036/2036 2041/2041/2041
-f 2046/2046/2046 2041/2041/2041 1768/1768/1768
-f 2052/2052/2052 2041/2041/2041 2046/2046/2046
-f 2050/2050/2050 2041/2041/2041 2049/2049/2049
-f 2052/2052/2052 2046/2046/2046 2054/2054/2054
-f 1778/1778/1778 1776/1776/1776 2058/2058/2058
-f 1776/1776/1776 2056/2056/2056 2058/2058/2058
-f 2056/2056/2056 2062/2062/2062 2061/2061/2061
-f 2056/2056/2056 1776/1776/1776 2062/2062/2062
-f 1887/1887/1887 1889/1889/1889 2062/2062/2062
-f 2065/2065/2065 2062/2062/2062 1889/1889/1889
-f 2062/2062/2062 2065/2065/2065 2064/2064/2064
-f 2061/2061/2061 2062/2062/2062 2064/2064/2064
-f 1775/1775/1775 2062/2062/2062 1776/1776/1776
-f 1775/1775/1775 1887/1887/1887 2062/2062/2062
-f 2065/2065/2065 1889/1889/1889 2068/2068/2068
-f 1889/1889/1889 2069/2069/2069 2068/2068/2068
-f 1889/1889/1889 2072/2072/2072 2069/2069/2069
-f 2036/2036/2036 2074/2074/2074 2075/2075/2075
-f 2036/2036/2036 2050/2050/2050 2074/2074/2074
-f 2072/2072/2072 1890/1890/1890 2077/2077/2077
-f 1890/1890/1890 1902/1902/1902 2077/2077/2077
-f 2079/2079/2079 2036/2036/2036 2075/2075/2075
-f 2037/2037/2037 2036/2036/2036 2079/2079/2079
-f 2072/2072/2072 1889/1889/1889 1890/1890/1890
-f 2054/2054/2054 2046/2046/2046 2082/2082/2082
-f 1769/1769/1769 1772/1772/1772 2083/2083/2083
-f 1769/1769/1769 2083/2083/2083 2047/2047/2047
-f 1787/1787/1787 1887/1887/1887 1775/1775/1775
-f 1873/1873/1873 1846/1846/1846 1854/1854/1854
-f 1854/1854/1854 1846/1846/1846 1844/1844/1844
-f 1841/1841/1841 1846/1846/1846 1800/1800/1800
-f 1830/1830/1830 1841/1841/1841 1807/1807/1807
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diff --git a/src/unilab/assets/robots/go2/assets/thigh_0.obj b/src/unilab/assets/robots/go2/assets/thigh_0.obj
deleted file mode 100644
index 0b99756ff..000000000
--- a/src/unilab/assets/robots/go2/assets/thigh_0.obj
+++ /dev/null
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diff --git a/src/unilab/assets/robots/go2/assets/thigh_1.obj b/src/unilab/assets/robots/go2/assets/thigh_1.obj
deleted file mode 100644
index 5005be1de..000000000
--- a/src/unilab/assets/robots/go2/assets/thigh_1.obj
+++ /dev/null
@@ -1,40483 +0,0 @@
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-f 1805/1805/1805 1579/1579/1579 1647/1647/1647
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-f 1659/1659/1659 1741/1741/1741 1843/1843/1843
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-f 1803/1803/1803 1747/1747/1747 1796/1796/1796
-f 1755/1755/1755 1795/1795/1795 1746/1746/1746
-f 1765/1765/1765 1778/1778/1778 1791/1791/1791
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-f 1761/1761/1761 1791/1791/1791 1756/1756/1756
-f 1751/1751/1751 1847/1847/1847 1759/1759/1759
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-f 201/201/201 1851/1851/1851 205/205/205
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-f 1854/1854/1854 1853/1853/1853 1852/1852/1852
-f 1854/1854/1854 451/451/451 1857/1857/1857
-f 1854/1854/1854 1856/1856/1856 451/451/451
-f 202/202/202 1856/1856/1856 1854/1854/1854
-f 1857/1857/1857 1853/1853/1853 1854/1854/1854
-f 1857/1857/1857 455/455/455 1858/1858/1858
-f 1858/1858/1858 1859/1859/1859 1857/1857/1857
-f 1857/1857/1857 1859/1859/1859 1853/1853/1853
-f 455/455/455 1857/1857/1857 451/451/451
-f 1860/1860/1860 1520/1520/1520 1859/1859/1859
-f 1858/1858/1858 1860/1860/1860 1859/1859/1859
-f 1859/1859/1859 1520/1520/1520 1853/1853/1853
-f 1861/1861/1861 1862/1862/1862 1860/1860/1860
-f 462/462/462 1861/1861/1861 1860/1860/1860
-f 462/462/462 1860/1860/1860 1858/1858/1858
-f 1860/1860/1860 1862/1862/1862 1520/1520/1520
-f 1861/1861/1861 1864/1864/1864 1862/1862/1862
-f 1866/1866/1866 1864/1864/1864 1861/1861/1861
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-f 1872/1872/1872 1873/1873/1873 1871/1871/1871
-f 1872/1872/1872 1871/1871/1871 466/466/466
-f 466/466/466 1871/1871/1871 1866/1866/1866
-f 1873/1873/1873 1870/1870/1870 1871/1871/1871
-f 1874/1874/1874 660/660/660 1873/1873/1873
-f 1873/1873/1873 660/660/660 1870/1870/1870
-f 1875/1875/1875 1874/1874/1874 1873/1873/1873
-f 1875/1875/1875 1873/1873/1873 1872/1872/1872
-f 1875/1875/1875 476/476/476 1876/1876/1876
-f 1876/1876/1876 1874/1874/1874 1875/1875/1875
-f 1872/1872/1872 474/474/474 1875/1875/1875
-f 1875/1875/1875 474/474/474 476/476/476
-f 1876/1876/1876 476/476/476 1878/1878/1878
-f 1876/1876/1876 1878/1878/1878 1880/1880/1880
-f 1880/1880/1880 1879/1879/1879 1876/1876/1876
-f 1876/1876/1876 1879/1879/1879 1874/1874/1874
-f 1880/1880/1880 1881/1881/1881 1879/1879/1879
-f 1882/1882/1882 1881/1881/1881 1880/1880/1880
-f 1880/1880/1880 1885/1885/1885 1882/1882/1882
-f 1880/1880/1880 1878/1878/1878 1883/1883/1883
-f 1885/1885/1885 1880/1880/1880 1883/1883/1883
-f 509/509/509 1887/1887/1887 1886/1886/1886
-f 1884/1884/1884 509/509/509 1886/1886/1886
-f 1882/1882/1882 1885/1885/1885 1888/1888/1888
-f 509/509/509 517/517/517 1887/1887/1887
-f 1882/1882/1882 1888/1888/1888 516/516/516
-f 1877/1877/1877 509/509/509 1884/1884/1884
-f 1890/1890/1890 1889/1889/1889 1882/1882/1882
-f 1882/1882/1882 1889/1889/1889 1881/1881/1881
-f 1882/1882/1882 516/516/516 1890/1890/1890
-f 1890/1890/1890 1892/1892/1892 1889/1889/1889
-f 1890/1890/1890 515/515/515 1891/1891/1891
-f 1890/1890/1890 516/516/516 515/515/515
-f 1891/1891/1891 1892/1892/1892 1890/1890/1890
-f 1892/1892/1892 648/648/648 1889/1889/1889
-f 1892/1892/1892 640/640/640 648/648/648
-f 1893/1893/1893 640/640/640 1892/1892/1892
-f 1891/1891/1891 1893/1893/1893 1892/1892/1892
-f 1894/1894/1894 1893/1893/1893 1891/1891/1891
-f 1894/1894/1894 1895/1895/1895 1893/1893/1893
-f 1895/1895/1895 638/638/638 1893/1893/1893
-f 1893/1893/1893 638/638/638 640/640/640
-f 588/588/588 594/594/594 1895/1895/1895
-f 1896/1896/1896 588/588/588 1895/1895/1895
-f 1896/1896/1896 1895/1895/1895 1894/1894/1894
-f 1895/1895/1895 594/594/594 638/638/638
-f 586/586/586 588/588/588 1896/1896/1896
-f 1896/1896/1896 539/539/539 586/586/586
-f 1896/1896/1896 1898/1898/1898 539/539/539
-f 1896/1896/1896 1899/1899/1899 1898/1898/1898
-f 1894/1894/1894 1899/1899/1899 1896/1896/1896
-f 518/518/518 1897/1897/1897 1900/1900/1900
-f 1901/1901/1901 518/518/518 1900/1900/1900
-f 1899/1899/1899 1894/1894/1894 1902/1902/1902
-f 1891/1891/1891 1902/1902/1902 1894/1894/1894
-f 514/514/514 518/518/518 1901/1901/1901
-f 1891/1891/1891 515/515/515 1902/1902/1902
-f 518/518/518 538/538/538 1897/1897/1897
-f 1889/1889/1889 648/648/648 649/649/649
-f 1889/1889/1889 649/649/649 1881/1881/1881
-f 1881/1881/1881 649/649/649 651/651/651
-f 1881/1881/1881 651/651/651 655/655/655
-f 1881/1881/1881 655/655/655 1879/1879/1879
-f 1879/1879/1879 655/655/655 657/657/657
-f 1879/1879/1879 657/657/657 1874/1874/1874
-f 1877/1877/1877 478/478/478 509/509/509
-f 478/478/478 1877/1877/1877 475/475/475
-f 1874/1874/1874 657/657/657 660/660/660
-f 466/466/466 472/472/472 1872/1872/1872
-f 1872/1872/1872 472/472/472 474/474/474
-f 463/463/463 1866/1866/1866 1861/1861/1861
-f 466/466/466 1866/1866/1866 463/463/463
-f 463/463/463 1861/1861/1861 462/462/462
-f 1858/1858/1858 457/457/457 462/462/462
-f 1858/1858/1858 455/455/455 457/457/457
-f 195/195/195 450/450/450 1855/1855/1855
-f 195/195/195 1855/1855/1855 189/189/189
-f 202/202/202 190/190/190 1856/1856/1856
-f 1853/1853/1853 1520/1520/1520 1845/1845/1845
-f 1903/1903/1903 217/217/217 1849/1849/1849
-f 1903/1903/1903 1849/1849/1849 1752/1752/1752
-f 1903/1903/1903 1734/1734/1734 218/218/218
-f 1903/1903/1903 1737/1737/1737 1734/1734/1734
-f 218/218/218 217/217/217 1903/1903/1903
-f 1752/1752/1752 1737/1737/1737 1903/1903/1903
-f 1755/1755/1755 1746/1746/1746 1748/1748/1748
-f 1659/1659/1659 1743/1743/1743 1741/1741/1741
-f 1659/1659/1659 1661/1661/1661 1743/1743/1743
-f 218/218/218 1734/1734/1734 220/220/220
-f 248/248/248 1724/1724/1724 237/237/237
-f 1702/1702/1702 1671/1671/1671 1676/1676/1676
-f 1670/1670/1670 1671/1671/1671 1702/1702/1702
-f 362/362/362 1697/1697/1697 363/363/363
-f 1906/1906/1906 1904/1904/1904 407/407/407
-f 407/407/407 1904/1904/1904 1688/1688/1688
-f 1906/1906/1906 373/373/373 1904/1904/1904
-f 383/383/383 373/373/373 1906/1906/1906
-f 395/395/395 383/383/383 1906/1906/1906
-f 395/395/395 1906/1906/1906 398/398/398
-f 398/398/398 1906/1906/1906 407/407/407
-f 407/407/407 1688/1688/1688 406/406/406
-f 410/410/410 409/409/409 1683/1683/1683
-f 410/410/410 1683/1683/1683 417/417/417
-f 1683/1683/1683 1680/1680/1680 1907/1907/1907
-f 417/417/417 1683/1683/1683 1907/1907/1907
-f 1907/1907/1907 1649/1649/1649 1643/1643/1643
-f 420/420/420 1907/1907/1907 1643/1643/1643
-f 417/417/417 1907/1907/1907 420/420/420
-f 1907/1907/1907 1680/1680/1680 1649/1649/1649
-f 1658/1658/1658 1647/1647/1647 1653/1653/1653
-f 420/420/420 1643/1643/1643 425/425/425
-f 1920/1920/1920 1908/1908/1908 1577/1577/1577
-f 1920/1920/1920 1577/1577/1577 1910/1910/1910
-f 1910/1910/1910 1577/1577/1577 1911/1911/1911
-f 1920/1920/1920 1921/1921/1921 1923/1923/1923
-f 1920/1920/1920 1922/1922/1922 1921/1921/1921
-f 1910/1910/1910 1922/1922/1922 1920/1920/1920
-f 1923/1923/1923 1908/1908/1908 1920/1920/1920
-f 1923/1923/1923 1921/1921/1921 1924/1924/1924
-f 1923/1923/1923 1918/1918/1918 1908/1908/1908
-f 1923/1923/1923 1924/1924/1924 1925/1925/1925
-f 1925/1925/1925 1918/1918/1918 1923/1923/1923
-f 1925/1925/1925 1929/1929/1929 1926/1926/1926
-f 1926/1926/1926 1927/1927/1927 1925/1925/1925
-f 1925/1925/1925 1927/1927/1927 1918/1918/1918
-f 1925/1925/1925 1924/1924/1924 1929/1929/1929
-f 1929/1929/1929 1932/1932/1932 1931/1931/1931
-f 1929/1929/1929 1931/1931/1931 1934/1934/1934
-f 1926/1926/1926 1929/1929/1929 1934/1934/1934
-f 1924/1924/1924 1932/1932/1932 1929/1929/1929
-f 1934/1934/1934 1420/1420/1420 1409/1409/1409
-f 1408/1408/1408 1934/1934/1934 1409/1409/1409
-f 1926/1926/1926 1934/1934/1934 1408/1408/1408
-f 1934/1934/1934 1931/1931/1931 1420/1420/1420
-f 1454/1454/1454 1933/1933/1933 1936/1936/1936
-f 1924/1924/1924 1935/1935/1935 1932/1932/1932
-f 1933/1933/1933 1422/1422/1422 1930/1930/1930
-f 1933/1933/1933 1452/1452/1452 1422/1422/1422
-f 1452/1452/1452 1933/1933/1933 1454/1454/1454
-f 1454/1454/1454 1936/1936/1936 1937/1937/1937
-f 1924/1924/1924 1938/1938/1938 1935/1935/1935
-f 1480/1480/1480 1939/1939/1939 1937/1937/1937
-f 1938/1938/1938 1921/1921/1921 1479/1479/1479
-f 1921/1921/1921 1938/1938/1938 1924/1924/1924
-f 1942/1942/1942 1937/1937/1937 1939/1939/1939
-f 1454/1454/1454 1937/1937/1937 1457/1457/1457
-f 1457/1457/1457 1937/1937/1937 1942/1942/1942
-f 1940/1940/1940 1459/1459/1459 1941/1941/1941
-f 1459/1459/1459 1458/1458/1458 1941/1941/1941
-f 1480/1480/1480 1476/1476/1476 1939/1939/1939
-f 1468/1468/1468 1940/1940/1940 1475/1475/1475
-f 1459/1459/1459 1940/1940/1940 1468/1468/1468
-f 1930/1930/1930 1422/1422/1422 1421/1421/1421
-f 1926/1926/1926 1943/1943/1943 1927/1927/1927
-f 1408/1408/1408 1404/1404/1404 1943/1943/1943
-f 1408/1408/1408 1943/1943/1943 1926/1926/1926
-f 1922/1922/1922 1479/1479/1479 1921/1921/1921
-f 1922/1922/1922 1482/1482/1482 1479/1479/1479
-f 1946/1946/1946 1482/1482/1482 1922/1922/1922
-f 1910/1910/1910 1946/1946/1946 1922/1922/1922
-f 1947/1947/1947 1484/1484/1484 1946/1946/1946
-f 1946/1946/1946 1484/1484/1484 1482/1482/1482
-f 1948/1948/1948 1947/1947/1947 1946/1946/1946
-f 1948/1948/1948 1946/1946/1946 1910/1910/1910
-f 1949/1949/1949 1947/1947/1947 1948/1948/1948
-f 1910/1910/1910 1911/1911/1911 1948/1948/1948
-f 1948/1948/1948 1911/1911/1911 1914/1914/1914
-f 1948/1948/1948 1914/1914/1914 1949/1949/1949
-f 1950/1950/1950 1951/1951/1951 1949/1949/1949
-f 1949/1949/1949 1952/1952/1952 1950/1950/1950
-f 1949/1949/1949 1914/1914/1914 1952/1952/1952
-f 1949/1949/1949 1951/1951/1951 1947/1947/1947
-f 1950/1950/1950 1952/1952/1952 1573/1573/1573
-f 1954/1954/1954 1957/1957/1957 1951/1951/1951
-f 1950/1950/1950 1954/1954/1954 1951/1951/1951
-f 1951/1951/1951 1959/1959/1959 1947/1947/1947
-f 1959/1959/1959 1951/1951/1951 1957/1957/1957
-f 1947/1947/1947 1959/1959/1959 1962/1962/1962
-f 1488/1488/1488 1958/1958/1958 1492/1492/1492
-f 1488/1488/1488 1961/1961/1961 1958/1958/1958
-f 1947/1947/1947 1962/1962/1962 1484/1484/1484
-f 1488/1488/1488 1483/1483/1483 1961/1961/1961
-f 1954/1954/1954 1963/1963/1963 1957/1957/1957
-f 1964/1964/1964 1492/1492/1492 1956/1956/1956
-f 1966/1966/1966 1492/1492/1492 1964/1964/1964
-f 1967/1967/1967 1965/1965/1965 1963/1963/1963
-f 1967/1967/1967 1963/1963/1963 1954/1954/1954
-f 1972/1972/1972 1967/1967/1967 1954/1954/1954
-f 1970/1970/1970 1968/1968/1968 1967/1967/1967
-f 1967/1967/1967 1968/1968/1968 1965/1965/1965
-f 1971/1971/1971 1970/1970/1970 1967/1967/1967
-f 1971/1971/1971 1967/1967/1967 1972/1972/1972
-f 1950/1950/1950 1573/1573/1573 1972/1972/1972
-f 1972/1972/1972 1954/1954/1954 1950/1950/1950
-f 1972/1972/1972 1974/1974/1974 1971/1971/1971
-f 1972/1972/1972 1573/1573/1573 1974/1974/1974
-f 1971/1971/1971 1974/1974/1974 1977/1977/1977
-f 1980/1980/1980 1977/1977/1977 1979/1979/1979
-f 1971/1971/1971 1977/1977/1977 1980/1980/1980
-f 1980/1980/1980 1562/1562/1562 1550/1550/1550
-f 1550/1550/1550 1981/1981/1981 1980/1980/1980
-f 1980/1980/1980 1981/1981/1981 1970/1970/1970
-f 1980/1980/1980 1970/1970/1970 1971/1971/1971
-f 1980/1980/1980 1979/1979/1979 1562/1562/1562
-f 1981/1981/1981 1982/1982/1982 1984/1984/1984
-f 1545/1545/1545 1982/1982/1982 1981/1981/1981
-f 1550/1550/1550 1545/1545/1545 1981/1981/1981
-f 1981/1981/1981 1984/1984/1984 1970/1970/1970
-f 1970/1970/1970 1984/1984/1984 1968/1968/1968
-f 1986/1986/1986 1969/1969/1969 1985/1985/1985
-f 1986/1986/1986 1985/1985/1985 1983/1983/1983
-f 1989/1989/1989 1969/1969/1969 1986/1986/1986
-f 1505/1505/1505 1988/1988/1988 1987/1987/1987
-f 1986/1986/1986 1983/1983/1983 1991/1991/1991
-f 1505/1505/1505 1987/1987/1987 1990/1990/1990
-f 1991/1991/1991 1983/1983/1983 1993/1993/1993
-f 1505/1505/1505 1990/1990/1990 1992/1992/1992
-f 1992/1992/1992 1990/1990/1990 1994/1994/1994
-f 1993/1993/1993 1983/1983/1983 1534/1534/1534
-f 1533/1533/1533 1528/1528/1528 1994/1994/1994
-f 1992/1992/1992 1994/1994/1994 1528/1528/1528
-f 1506/1506/1506 1505/1505/1505 1992/1992/1992
-f 1512/1512/1512 1992/1992/1992 1528/1528/1528
-f 1506/1506/1506 1992/1992/1992 1512/1512/1512
-f 1996/1996/1996 1969/1969/1969 1989/1989/1989
-f 1501/1501/1501 1995/1995/1995 1988/1988/1988
-f 1501/1501/1501 1988/1988/1988 1505/1505/1505
-f 1493/1493/1493 1995/1995/1995 1501/1501/1501
-f 1493/1493/1493 1490/1490/1490 1995/1995/1995
-f 1969/1969/1969 1996/1996/1996 1492/1492/1492
-f 1982/1982/1982 1545/1545/1545 1538/1538/1538
-f 1537/1537/1537 1534/1534/1534 1983/1983/1983
-f 1969/1969/1969 1492/1492/1492 1966/1966/1966
-f 1550/1550/1550 1562/1562/1562 1552/1552/1552
-f 1532/1532/1532 1531/1531/1531 1544/1544/1544
-f 1527/1527/1527 1523/1523/1523 1525/1525/1525
-f 1494/1494/1494 1499/1499/1499 1998/1998/1998
-f 1472/1472/1472 1998/1998/1998 668/668/668
-f 1494/1494/1494 1998/1998/1998 1472/1472/1472
-f 1472/1472/1472 1474/1474/1474 1494/1494/1494
-f 1450/1450/1450 1466/1466/1466 1448/1448/1448
-f 1429/1429/1429 1423/1423/1423 1453/1453/1453
-f 1417/1417/1417 1414/1414/1414 1412/1412/1412
-f 1414/1414/1414 1371/1371/1371 1383/1383/1383
-f 1389/1389/1389 1413/1413/1413 1382/1382/1382
-f 1999/1999/1999 971/971/971 1386/1386/1386
-f 1386/1386/1386 1376/1376/1376 1999/1999/1999
-f 1999/1999/1999 2001/2001/2001 971/971/971
-f 1360/1360/1360 2001/2001/2001 1999/1999/1999
-f 1999/1999/1999 1364/1364/1364 1360/1360/1360
-f 1999/1999/1999 1376/1376/1376 1364/1364/1364
-f 1360/1360/1360 1358/1358/1358 2001/2001/2001
-f 2002/2002/2002 1333/1333/1333 1369/1369/1369
-f 2002/2002/2002 1369/1369/1369 609/609/609
-f 1329/1329/1329 1333/1333/1333 2002/2002/2002
-f 2002/2002/2002 615/615/615 1329/1329/1329
-f 609/609/609 642/642/642 2002/2002/2002
-f 2002/2002/2002 642/642/642 615/615/615
-f 1351/1351/1351 1347/1347/1347 2003/2003/2003
-f 1351/1351/1351 2003/2003/2003 1051/1051/1051
-f 1051/1051/1051 1052/1052/1052 1351/1351/1351
-f 1351/1351/1351 1052/1052/1052 1054/1054/1054
-f 1051/1051/1051 2003/2003/2003 2004/2004/2004
-f 2003/2003/2003 2007/2007/2007 2004/2004/2004
-f 1347/1347/1347 2005/2005/2005 2003/2003/2003
-f 2003/2003/2003 2005/2005/2005 2007/2007/2007
-f 2011/2011/2011 2008/2008/2008 2006/2006/2006
-f 2004/2004/2004 2007/2007/2007 2010/2010/2010
-f 2011/2011/2011 2009/2009/2009 2008/2008/2008
-f 1317/1317/1317 2012/2012/2012 1319/1319/1319
-f 1319/1319/1319 2012/2012/2012 2013/2013/2013
-f 1317/1317/1317 2015/2015/2015 2012/2012/2012
-f 2011/2011/2011 2006/2006/2006 2014/2014/2014
-f 2016/2016/2016 2009/2009/2009 2011/2011/2011
-f 1094/1094/1094 2015/2015/2015 1317/1317/1317
-f 1094/1094/1094 1088/1088/1088 2015/2015/2015
-f 1087/1087/1087 1090/1090/1090 2016/2016/2016
-f 2016/2016/2016 1090/1090/1090 2009/2009/2009
-f 1319/1319/1319 2013/2013/2013 1323/1323/1323
-f 2014/2014/2014 2006/2006/2006 2017/2017/2017
-f 2018/2018/2018 1323/1323/1323 2013/2013/2013
-f 2017/2017/2017 1345/1345/1345 1339/1339/1339
-f 2017/2017/2017 2006/2006/2006 1345/1345/1345
-f 1325/1325/1325 2018/2018/2018 1338/1338/1338
-f 1323/1323/1323 2018/2018/2018 1325/1325/1325
-f 2004/2004/2004 2010/2010/2010 1083/1083/1083
-f 1083/1083/1083 2010/2010/2010 1089/1089/1089
-f 1347/1347/1347 1344/1344/1344 2005/2005/2005
-f 1051/1051/1051 2004/2004/2004 1058/1058/1058
-f 1058/1058/1058 2004/2004/2004 1083/1083/1083
-f 1329/1329/1329 615/615/615 1331/1331/1331
-f 1101/1101/1101 1093/1093/1093 1313/1313/1313
-f 1313/1313/1313 1093/1093/1093 1094/1094/1094
-f 1101/1101/1101 1309/1309/1309 1100/1100/1100
-f 2020/2020/2020 1307/1307/1307 2019/2019/2019
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diff --git a/src/unilab/assets/robots/go2/assets/thigh_mirror_0.obj b/src/unilab/assets/robots/go2/assets/thigh_mirror_0.obj
deleted file mode 100644
index 7c2fd2ce4..000000000
--- a/src/unilab/assets/robots/go2/assets/thigh_mirror_0.obj
+++ /dev/null
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diff --git a/src/unilab/assets/robots/go2/assets/thigh_mirror_1.obj b/src/unilab/assets/robots/go2/assets/thigh_mirror_1.obj
deleted file mode 100644
index fb7e0b496..000000000
--- a/src/unilab/assets/robots/go2/assets/thigh_mirror_1.obj
+++ /dev/null
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-f 204/204/204 1858/1858/1858 191/191/191
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-f 1662/1662/1662 1657/1657/1657 1650/1650/1650
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-f 1517/1517/1517 1531/1531/1531 1992/1992/1992
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diff --git a/src/unilab/assets/robots/go2/assets/wheel.stl b/src/unilab/assets/robots/go2/assets/wheel.stl
deleted file mode 100644
index d9ebc7814..000000000
Binary files a/src/unilab/assets/robots/go2/assets/wheel.stl and /dev/null differ
diff --git a/src/unilab/assets/robots/go2/assets/wheel_convex.stl b/src/unilab/assets/robots/go2/assets/wheel_convex.stl
deleted file mode 100644
index 5a1fc7235..000000000
Binary files a/src/unilab/assets/robots/go2/assets/wheel_convex.stl and /dev/null differ
diff --git a/src/unilab/assets/robots/go2/scene_flat.xml b/src/unilab/assets/robots/go2/scene_flat.xml
index 9f5dcae30..3ff8bc358 100644
--- a/src/unilab/assets/robots/go2/scene_flat.xml
+++ b/src/unilab/assets/robots/go2/scene_flat.xml
@@ -1,23 +1,24 @@
 <mujoco model="go2 scene">
   <include file="go2.xml"/>
 
-  <statistic center="0 0 0.1" extent="0.8"/>
+  <statistic center="0 0 0.45" extent="0.8"/>
 
   <visual>
-    <headlight diffuse="0.6 0.6 0.6" ambient="0.3 0.3 0.3" specular="0 0 0"/>
-    <rgba haze="0.15 0.25 0.35 1"/>
+    <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
+    <map fogstart="100.0" fogend="300.0"/>
+    <rgba haze="0.75 0.75 0.77 1"/>
     <global azimuth="-130" elevation="-20"/>
   </visual>
 
   <asset>
-    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
-    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
-      markrgb="0.8 0.8 0.8" width="300" height="300"/>
-    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+    <texture type="skybox" builtin="gradient" rgb1="0.55 0.55 0.57" rgb2="0.55 0.55 0.57" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" rgb1="0.62 0.62 0.63" rgb2="0.49 0.49 0.51"
+      width="300" height="300"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="1.25 1.25" reflectance="0.0"/>
   </asset>
 
   <worldbody>
-    <light pos="0 0 1.5" dir="0 0 -1" directional="true"/>
+    <light pos="0 0 1.5" dir="0 0 -1" directional="true" diffuse="0.8 0.8 0.8"/>
     <geom name="floor" size="0 0 0.05" type="plane" material="groundplane"/>
   </worldbody>
 
diff --git a/src/unilab/assets/robots/go2_arm/assets/arm_base_0.obj b/src/unilab/assets/robots/go2_arm/assets/arm_base_0.obj
deleted file mode 100644
index 868e3a60d..000000000
--- a/src/unilab/assets/robots/go2_arm/assets/arm_base_0.obj
+++ /dev/null
@@ -1,2163 +0,0 @@
-mtllib material.mtl
-usemtl material_0
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diff --git a/src/unilab/assets/robots/go2_arm/assets/arm_base_1.obj b/src/unilab/assets/robots/go2_arm/assets/arm_base_1.obj
deleted file mode 100644
index a431f5209..000000000
--- a/src/unilab/assets/robots/go2_arm/assets/arm_base_1.obj
+++ /dev/null
@@ -1,13449 +0,0 @@
-####
-#
-# OBJ File Generated by Meshlab
-#
-####
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diff --git a/src/unilab/assets/robots/go2_arm/assets/left.obj b/src/unilab/assets/robots/go2_arm/assets/left.obj
deleted file mode 100644
index 130011011..000000000
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diff --git a/src/unilab/assets/robots/go2_arm/assets/link1.obj b/src/unilab/assets/robots/go2_arm/assets/link1.obj
deleted file mode 100644
index 575957d3e..000000000
--- a/src/unilab/assets/robots/go2_arm/assets/link1.obj
+++ /dev/null
@@ -1,10077 +0,0 @@
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diff --git a/src/unilab/assets/robots/go2_arm/assets/link2_0.obj b/src/unilab/assets/robots/go2_arm/assets/link2_0.obj
deleted file mode 100644
index bf2b82c6c..000000000
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+++ /dev/null
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diff --git a/src/unilab/assets/robots/go2_arm/assets/link2_1.obj b/src/unilab/assets/robots/go2_arm/assets/link2_1.obj
deleted file mode 100644
index aecdd42b9..000000000
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diff --git a/src/unilab/assets/robots/go2_arm/assets/link3_0.obj b/src/unilab/assets/robots/go2_arm/assets/link3_0.obj
deleted file mode 100644
index 258b739f6..000000000
--- a/src/unilab/assets/robots/go2_arm/assets/link3_0.obj
+++ /dev/null
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diff --git a/src/unilab/assets/robots/go2_arm/assets/link3_1.obj b/src/unilab/assets/robots/go2_arm/assets/link3_1.obj
deleted file mode 100644
index 9ddd04429..000000000
--- a/src/unilab/assets/robots/go2_arm/assets/link3_1.obj
+++ /dev/null
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diff --git a/src/unilab/assets/robots/go2_arm/assets/link4.obj b/src/unilab/assets/robots/go2_arm/assets/link4.obj
deleted file mode 100644
index 5fce4388b..000000000
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diff --git a/src/unilab/assets/robots/go2_arm/assets/link5_0.obj b/src/unilab/assets/robots/go2_arm/assets/link5_0.obj
deleted file mode 100644
index 952c4608b..000000000
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diff --git a/src/unilab/assets/robots/go2_arm/assets/link5_1.obj b/src/unilab/assets/robots/go2_arm/assets/link5_1.obj
deleted file mode 100644
index 6b1b69ecb..000000000
--- a/src/unilab/assets/robots/go2_arm/assets/link5_1.obj
+++ /dev/null
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diff --git a/src/unilab/assets/robots/go2_arm/assets/link6.obj b/src/unilab/assets/robots/go2_arm/assets/link6.obj
deleted file mode 100644
index 871920f38..000000000
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diff --git a/src/unilab/assets/robots/go2_arm/assets/new_base_link.STL b/src/unilab/assets/robots/go2_arm/assets/new_base_link.STL
deleted file mode 100644
index 0711033e3..000000000
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diff --git a/src/unilab/assets/robots/go2_arm/assets/right.obj b/src/unilab/assets/robots/go2_arm/assets/right.obj
deleted file mode 100644
index ce90c04c5..000000000
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-# 4886 faces, 0 coords texture
-
-# End of File
diff --git a/src/unilab/assets/robots/go2_arm/assets/slider_base_link_0.STL b/src/unilab/assets/robots/go2_arm/assets/slider_base_link_0.STL
deleted file mode 100644
index f89602237..000000000
Binary files a/src/unilab/assets/robots/go2_arm/assets/slider_base_link_0.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/go2_arm/scene_flat.xml b/src/unilab/assets/robots/go2_arm/scene_flat.xml
index d8d1e8de1..9ee8aa756 100644
--- a/src/unilab/assets/robots/go2_arm/scene_flat.xml
+++ b/src/unilab/assets/robots/go2_arm/scene_flat.xml
@@ -1,23 +1,24 @@
 <mujoco model="go2 scene">
   <include file="go2_with_arm_mjx_full_collision.xml"/>
 
-  <statistic center="0 0 0.1" extent="0.8" meansize="0.04"/>
+  <statistic center="0 0 0.45" extent="0.8" meansize="0.04"/>
 
   <visual>
-    <headlight diffuse="0.6 0.6 0.6" ambient="0.3 0.3 0.3" specular="0 0 0"/>
-    <rgba haze="0.15 0.25 0.35 1"/>
+    <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
+    <map fogstart="100.0" fogend="300.0"/>
+    <rgba haze="0.75 0.75 0.77 1"/>
     <global azimuth="-130" elevation="-20"/>
   </visual>
 
   <asset>
-    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
-    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
-      markrgb="0.8 0.8 0.8" width="300" height="300"/>
-    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+    <texture type="skybox" builtin="gradient" rgb1="0.55 0.55 0.57" rgb2="0.55 0.55 0.57" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" rgb1="0.62 0.62 0.63" rgb2="0.49 0.49 0.51"
+      width="300" height="300"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="1.25 1.25" reflectance="0.0"/>
   </asset>
 
   <worldbody>
-    <light pos="0 0 1.5" dir="0 0 -1" directional="true"/>
+    <light pos="0 0 1.5" dir="0 0 -1" directional="true" diffuse="0.8 0.8 0.8"/>
     <geom name="floor" size="0 0 0.05" type="plane" material="groundplane"/>
   </worldbody>
 
diff --git a/src/unilab/assets/robots/go2w/scene_flat.xml b/src/unilab/assets/robots/go2w/scene_flat.xml
index 881084e0e..214e04174 100644
--- a/src/unilab/assets/robots/go2w/scene_flat.xml
+++ b/src/unilab/assets/robots/go2w/scene_flat.xml
@@ -4,20 +4,21 @@
   <!-- <statistic center="0 0 0.1" extent="0.8"/> -->
 
   <visual>
-    <headlight diffuse="0.6 0.6 0.6" ambient="0.3 0.3 0.3" specular="0 0 0"/>
-    <rgba haze="0.15 0.25 0.35 1"/>
+    <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
+    <map fogstart="100.0" fogend="300.0"/>
+    <rgba haze="0.75 0.75 0.77 1"/>
     <global azimuth="-130" elevation="-20"/>
   </visual>
 
   <asset>
-    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
-    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
-      markrgb="0.8 0.8 0.8" width="300" height="300"/>
-    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+    <texture type="skybox" builtin="gradient" rgb1="0.55 0.55 0.57" rgb2="0.55 0.55 0.57" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" rgb1="0.62 0.62 0.63" rgb2="0.49 0.49 0.51"
+      width="300" height="300"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="1.25 1.25" reflectance="0.0"/>
   </asset>
 
   <worldbody>
-    <light pos="0 0 1.5" dir="0 0 -1" directional="true"/>
+    <light pos="0 0 1.5" dir="0 0 -1" directional="true" diffuse="0.8 0.8 0.8"/>
     <geom name="floor" size="0 0 0.05" type="plane" material="groundplane"/>
   </worldbody>
 
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/DP_HB1_4F.STL b/src/unilab/assets/robots/sharpa_wave/meshes/DP_HB1_4F.STL
deleted file mode 100644
index c30c46a52..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/DP_HB1_4F.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/DP_HB1_TH.STL b/src/unilab/assets/robots/sharpa_wave/meshes/DP_HB1_TH.STL
deleted file mode 100644
index 29250f09f..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/DP_HB1_TH.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/DP_Visual_HB1_TH.STL b/src/unilab/assets/robots/sharpa_wave/meshes/DP_Visual_HB1_TH.STL
deleted file mode 100644
index 649ffb4cc..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/DP_Visual_HB1_TH.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/DP_elastomer.STL b/src/unilab/assets/robots/sharpa_wave/meshes/DP_elastomer.STL
deleted file mode 100644
index 2d9a089b9..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/DP_elastomer.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/DP_elastomer_HB1_4F.STL b/src/unilab/assets/robots/sharpa_wave/meshes/DP_elastomer_HB1_4F.STL
deleted file mode 100644
index 32511ce72..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/DP_elastomer_HB1_4F.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/DP_elastomer_HB1_TH.STL b/src/unilab/assets/robots/sharpa_wave/meshes/DP_elastomer_HB1_TH.STL
deleted file mode 100644
index 300907e5b..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/DP_elastomer_HB1_TH.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/DP_visual_HB1_4F.STL b/src/unilab/assets/robots/sharpa_wave/meshes/DP_visual_HB1_4F.STL
deleted file mode 100644
index 442c00f31..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/DP_visual_HB1_4F.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/MCP_VL.STL b/src/unilab/assets/robots/sharpa_wave/meshes/MCP_VL.STL
deleted file mode 100644
index d12d9f345..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/MCP_VL.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/elastomer.STL b/src/unilab/assets/robots/sharpa_wave/meshes/elastomer.STL
deleted file mode 100644
index dd464b71f..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/elastomer.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/elastomer_HB1_4F.STL b/src/unilab/assets/robots/sharpa_wave/meshes/elastomer_HB1_4F.STL
deleted file mode 100644
index 4d15d8e66..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/elastomer_HB1_4F.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/elastomer_HB1_TH.STL b/src/unilab/assets/robots/sharpa_wave/meshes/elastomer_HB1_TH.STL
deleted file mode 100644
index a07c8d61d..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/elastomer_HB1_TH.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/elastomer_surface.STL b/src/unilab/assets/robots/sharpa_wave/meshes/elastomer_surface.STL
deleted file mode 100644
index e9d6713d5..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/elastomer_surface.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/flange_A.STL b/src/unilab/assets/robots/sharpa_wave/meshes/flange_A.STL
deleted file mode 100644
index 28297d136..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/flange_A.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/flange_B.STL b/src/unilab/assets/robots/sharpa_wave/meshes/flange_B.STL
deleted file mode 100644
index b5f2589c4..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/flange_B.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/right_DP.STL b/src/unilab/assets/robots/sharpa_wave/meshes/right_DP.STL
deleted file mode 100644
index 2cad29773..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/right_DP.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/right_DP_visual.STL b/src/unilab/assets/robots/sharpa_wave/meshes/right_DP_visual.STL
deleted file mode 100644
index 41714d322..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/right_DP_visual.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/right_MCP_VL_visual.STL b/src/unilab/assets/robots/sharpa_wave/meshes/right_MCP_VL_visual.STL
deleted file mode 100644
index 6c296133e..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/right_MCP_VL_visual.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/right_MP.STL b/src/unilab/assets/robots/sharpa_wave/meshes/right_MP.STL
deleted file mode 100644
index 0f4ac46c6..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/right_MP.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/right_MP_visual.STL b/src/unilab/assets/robots/sharpa_wave/meshes/right_MP_visual.STL
deleted file mode 100644
index d17763d61..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/right_MP_visual.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/right_PP.STL b/src/unilab/assets/robots/sharpa_wave/meshes/right_PP.STL
deleted file mode 100644
index 81016f8f0..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/right_PP.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/right_PP_visual.STL b/src/unilab/assets/robots/sharpa_wave/meshes/right_PP_visual.STL
deleted file mode 100644
index 4e391ee4c..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/right_PP_visual.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/right_hand_C_MC.STL b/src/unilab/assets/robots/sharpa_wave/meshes/right_hand_C_MC.STL
deleted file mode 100644
index 53b224a1f..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/right_hand_C_MC.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/right_hand_C_MC_visual.STL b/src/unilab/assets/robots/sharpa_wave/meshes/right_hand_C_MC_visual.STL
deleted file mode 100644
index 3685a3b5a..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/right_hand_C_MC_visual.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/right_hand_C_MC_visual_.STL b/src/unilab/assets/robots/sharpa_wave/meshes/right_hand_C_MC_visual_.STL
deleted file mode 100644
index 3685a3b5a..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/right_hand_C_MC_visual_.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/right_pinky_MC.STL b/src/unilab/assets/robots/sharpa_wave/meshes/right_pinky_MC.STL
deleted file mode 100644
index a0476b816..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/right_pinky_MC.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/right_pinky_MC_visual.STL b/src/unilab/assets/robots/sharpa_wave/meshes/right_pinky_MC_visual.STL
deleted file mode 100644
index 921ce00ff..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/right_pinky_MC_visual.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_CMC_VL_visual.STL b/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_CMC_VL_visual.STL
deleted file mode 100644
index 14b2d3bc5..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_CMC_VL_visual.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_DP.STL b/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_DP.STL
deleted file mode 100644
index ee856b86e..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_DP.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_DP_visual.STL b/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_DP_visual.STL
deleted file mode 100644
index 025636087..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_DP_visual.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_MC.STL b/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_MC.STL
deleted file mode 100644
index df77b3a89..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_MC.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_MCP_VL_visual.STL b/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_MCP_VL_visual.STL
deleted file mode 100644
index 5a4fbb3e9..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_MCP_VL_visual.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_MC_visual.STL b/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_MC_visual.STL
deleted file mode 100644
index 86b5e87de..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_MC_visual.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_PP.STL b/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_PP.STL
deleted file mode 100644
index 47a58f188..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_PP.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_PP_visual.STL b/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_PP_visual.STL
deleted file mode 100644
index a0054c2c2..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/right_thumb_PP_visual.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/thumb_DP_elastomer.STL b/src/unilab/assets/robots/sharpa_wave/meshes/thumb_DP_elastomer.STL
deleted file mode 100644
index 05e399cce..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/thumb_DP_elastomer.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/thumb_elastomer.STL b/src/unilab/assets/robots/sharpa_wave/meshes/thumb_elastomer.STL
deleted file mode 100644
index 49d59beb5..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/thumb_elastomer.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/thumb_elastomer_surface.STL b/src/unilab/assets/robots/sharpa_wave/meshes/thumb_elastomer_surface.STL
deleted file mode 100644
index 9afcb28d8..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/thumb_elastomer_surface.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/wrist_A.STL b/src/unilab/assets/robots/sharpa_wave/meshes/wrist_A.STL
deleted file mode 100644
index 9bcbad044..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/wrist_A.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/wrist_B.STL b/src/unilab/assets/robots/sharpa_wave/meshes/wrist_B.STL
deleted file mode 100644
index e8dc40fbe..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/wrist_B.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/meshes/wrist_collision.STL b/src/unilab/assets/robots/sharpa_wave/meshes/wrist_collision.STL
deleted file mode 100644
index 98f0316a0..000000000
Binary files a/src/unilab/assets/robots/sharpa_wave/meshes/wrist_collision.STL and /dev/null differ
diff --git a/src/unilab/assets/robots/sharpa_wave/scene.xml b/src/unilab/assets/robots/sharpa_wave/scene.xml
index 42bca1199..3b95c2cb0 100644
--- a/src/unilab/assets/robots/sharpa_wave/scene.xml
+++ b/src/unilab/assets/robots/sharpa_wave/scene.xml
@@ -8,22 +8,23 @@
   </option>
 
   <visual>
-    <headlight diffuse="0.6 0.6 0.6" ambient="0.2 0.2 0.2" specular="0.9 0.9 0.9"/>
-    <rgba haze="0.15 0.25 0.35 1"/>
+    <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
+    <map fogstart="100.0" fogend="300.0"/>
+    <rgba haze="0.75 0.75 0.77 1"/>
     <global azimuth="140" elevation="-20"/>
   </visual>
 
   <asset>
-    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
-    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
-      markrgb="0.8 0.8 0.8" width="300" height="300"/>
-    <texture type="2d" name="object_orient_tex" builtin="checker" mark="edge" rgb1="0.92 0.18 0.14" rgb2="0.12 0.14 0.18" markrgb="0.95 0.92 0.25" width="512" height="512"/>
-    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+    <texture type="skybox" builtin="gradient" rgb1="0.55 0.55 0.57" rgb2="0.55 0.55 0.57" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" rgb1="0.62 0.62 0.63" rgb2="0.49 0.49 0.51"
+      width="300" height="300"/>
+    <texture type="2d" name="object_orient_tex" builtin="checker" rgb1="0.92 0.18 0.14" rgb2="0.12 0.14 0.18" width="512" height="512"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="1.25 1.25" reflectance="0.0"/>
     <material name="object_orient_mat" texture="object_orient_tex" texuniform="false" texrepeat="6 1"/>
   </asset>
 
   <worldbody>
-    <light pos="0 0 1.5" dir="0 0 -1" directional="true"/>
+    <light pos="0 0 1.5" dir="0 0 -1" directional="true" diffuse="0.8 0.8 0.8"/>
     <geom name="floor" pos="0 0 0" size="0 0 0.05" type="plane" material="groundplane"/>
 
     <body name="object" pos="-0.09559 -0.00517 0.61906" gravcomp="0">
diff --git a/src/unilab/assets/robots/stewart/motphys-ground.png b/src/unilab/assets/robots/stewart/motphys-ground.png
deleted file mode 100644
index 04967dcfb..000000000
Binary files a/src/unilab/assets/robots/stewart/motphys-ground.png and /dev/null differ
diff --git a/src/unilab/assets/robots/stewart/scene.xml b/src/unilab/assets/robots/stewart/scene.xml
index 2887522a6..a3a8b7f41 100644
--- a/src/unilab/assets/robots/stewart/scene.xml
+++ b/src/unilab/assets/robots/stewart/scene.xml
@@ -1,12 +1,19 @@
 <mujoco>
     <option timestep="0.004"/>
 
+    <visual>
+        <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
+        <map fogstart="100.0" fogend="300.0"/>
+        <rgba haze="0.75 0.75 0.77 1"/>
+    </visual>
+
     <asset>
-        <texture name="skybox" type="skybox" builtin="gradient" rgb1="0.8 0.85 0.9" rgb2="0.3 0.35 0.4" width="512" height="512"/>
-        <texture name="motphys-ground" type="2d" file="motphys-ground.png"/>
-        <material name="motphys-ground" texture="motphys-ground" texuniform="true" texrepeat="0.4 0.4"/>
+        <texture name="skybox" type="skybox" builtin="gradient" rgb1="0.55 0.55 0.57" rgb2="0.55 0.55 0.57" width="512" height="512"/>
+        <texture type="2d" name="motphys-ground" builtin="checker" rgb1="0.62 0.62 0.63" rgb2="0.49 0.49 0.51"
+            width="300" height="300"/>
+        <material name="motphys-ground" texture="motphys-ground" texuniform="true" texrepeat="1.25 1.25" reflectance="0.0"/>
 
-        <texture type="2d" name="platform" builtin="checker" rgb1="0.5 0.5 0.8" rgb2="0.9 0.9 0.9" markrgb="1 1 1" width="300" height="300"/>
+        <texture type="2d" name="platform" builtin="checker" rgb1="0.5 0.5 0.8" rgb2="0.9 0.9 0.9" width="300" height="300"/>
         <material name="grey_metal" texture="platform" texuniform="true" texrepeat="2 2" specular="1" metallic="0.8"/>
     </asset>
 
@@ -32,7 +39,7 @@
     </default>
 
     <worldbody>
-        <light pos="0 0 1.5" dir="0 0 -1" directional="true"/>
+        <light pos="0 0 1.5" dir="0 0 -1" directional="true" diffuse="0.8 0.8 0.8"/>
         <light pos="0 0 1.5" dir="0 -5 -1" directional="true"/>
 
         <geom name="floor" pos="0 0 0" size="0 0 0.01" type="plane" material="motphys-ground"/>
diff --git a/src/unilab/assets/robots/x2/scene_flat.xml b/src/unilab/assets/robots/x2/scene_flat.xml
index 18d0de580..473c200b6 100644
--- a/src/unilab/assets/robots/x2/scene_flat.xml
+++ b/src/unilab/assets/robots/x2/scene_flat.xml
@@ -4,16 +4,17 @@
   <statistic center="0 0 0.75" extent="1.2"/>
 
   <visual>
-    <headlight diffuse="0.6 0.6 0.6" ambient="0.3 0.3 0.3" specular="0 0 0"/>
-    <rgba haze="0.15 0.25 0.35 1"/>
+    <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
+    <map fogstart="100.0" fogend="300.0"/>
+    <rgba haze="0.75 0.75 0.77 1"/>
     <global azimuth="140" elevation="-20"/>
   </visual>
 
   <asset>
-    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
-    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
-      markrgb="0.8 0.8 0.8" width="300" height="300"/>
-    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+    <texture type="skybox" builtin="gradient" rgb1="0.55 0.55 0.57" rgb2="0.55 0.55 0.57" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" rgb1="0.62 0.62 0.63" rgb2="0.49 0.49 0.51"
+      width="300" height="300"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="1.25 1.25" reflectance="0.0"/>
   </asset>
 
   <worldbody>
diff --git a/src/unilab/assets/robots/x2/scene_flat_with_wall.xml b/src/unilab/assets/robots/x2/scene_flat_with_wall.xml
index 459f4150c..daf2022e3 100644
--- a/src/unilab/assets/robots/x2/scene_flat_with_wall.xml
+++ b/src/unilab/assets/robots/x2/scene_flat_with_wall.xml
@@ -4,16 +4,17 @@
   <statistic center="0 0 0.75" extent="1.2"/>
 
   <visual>
-    <headlight diffuse="0.6 0.6 0.6" ambient="0.3 0.3 0.3" specular="0 0 0"/>
-    <rgba haze="0.15 0.25 0.35 1"/>
+    <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
+    <map fogstart="100.0" fogend="300.0"/>
+    <rgba haze="0.75 0.75 0.77 1"/>
     <global azimuth="140" elevation="-20"/>
   </visual>
 
   <asset>
-    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
-    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
-      markrgb="0.8 0.8 0.8" width="300" height="300"/>
-    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+    <texture type="skybox" builtin="gradient" rgb1="0.55 0.55 0.57" rgb2="0.55 0.55 0.57" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" rgb1="0.62 0.62 0.63" rgb2="0.49 0.49 0.51"
+      width="300" height="300"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="1.25 1.25" reflectance="0.0"/>
     <material name="wall_material" rgba="0.7 0.4 0.3 1"/>
   </asset>
 
diff --git a/src/unilab/assets/robots/x2/scene_flat_with_wall_visual.xml b/src/unilab/assets/robots/x2/scene_flat_with_wall_visual.xml
index 818395639..d7b6b003d 100644
--- a/src/unilab/assets/robots/x2/scene_flat_with_wall_visual.xml
+++ b/src/unilab/assets/robots/x2/scene_flat_with_wall_visual.xml
@@ -4,16 +4,17 @@
   <statistic center="0 0 0.75" extent="1.2"/>
 
   <visual>
-    <headlight diffuse="0.6 0.6 0.6" ambient="0.3 0.3 0.3" specular="0 0 0"/>
-    <rgba haze="0.15 0.25 0.35 1"/>
+    <headlight diffuse="0.6 0.6 0.6" ambient="0.1 0.1 0.1" specular="0.9 0.9 0.9"/>
+    <map fogstart="100.0" fogend="300.0"/>
+    <rgba haze="0.75 0.75 0.77 1"/>
     <global azimuth="140" elevation="-20"/>
   </visual>
 
   <asset>
-    <texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" rgb2="0 0 0" width="512" height="3072"/>
-    <texture type="2d" name="groundplane" builtin="checker" mark="edge" rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3"
-      markrgb="0.8 0.8 0.8" width="300" height="300"/>
-    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="5 5" reflectance="0.2"/>
+    <texture type="skybox" builtin="gradient" rgb1="0.55 0.55 0.57" rgb2="0.55 0.55 0.57" width="512" height="3072"/>
+    <texture type="2d" name="groundplane" builtin="checker" rgb1="0.62 0.62 0.63" rgb2="0.49 0.49 0.51"
+      width="300" height="300"/>
+    <material name="groundplane" texture="groundplane" texuniform="true" texrepeat="1.25 1.25" reflectance="0.0"/>
     <material name="wall_material" rgba="0.7 0.4 0.3 1"/>
   </asset>
 
diff --git a/src/unilab/base/__init__.py b/src/unilab/base/__init__.py
index 0ce707b51..4eccd6d9b 100644
--- a/src/unilab/base/__init__.py
+++ b/src/unilab/base/__init__.py
@@ -1,31 +1,7 @@
 """Environment registry and base classes."""
 
-from unilab.base.final_observation import (
-    TerminalObservationContract,
-    TransitionBootstrapContract,
-    patch_transition_next_obs,
-    resolve_terminal_observation_contract,
-    resolve_transition_bootstrap_contract,
-)
-from unilab.base.observations import (
-    flatten_obs_dict,
-    flatten_policy_obs_dict,
-    get_critic_base_dim,
-    get_obs_dims,
-    split_obs_dict,
-)
 from unilab.base.registry import ensure_registries
 
 __all__ = [
-    "TerminalObservationContract",
-    "TransitionBootstrapContract",
     "ensure_registries",
-    "flatten_obs_dict",
-    "flatten_policy_obs_dict",
-    "get_critic_base_dim",
-    "get_obs_dims",
-    "patch_transition_next_obs",
-    "resolve_terminal_observation_contract",
-    "resolve_transition_bootstrap_contract",
-    "split_obs_dict",
 ]
diff --git a/src/unilab/base/augmentation.py b/src/unilab/base/augmentation.py
deleted file mode 100644
index b71bd958d..000000000
--- a/src/unilab/base/augmentation.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, Protocol
-
-if TYPE_CHECKING:
-    import torch
-
-
-SymmetryObsLayout = tuple[tuple[str, int], ...]
-
-
-class SymmetryAugmentation(Protocol):
-    """Runtime symmetry augmentation contract owned by env/backend adapters."""
-
-    batch_multiplier: int
-
-    def augment_obs_and_actions(
-        self,
-        obs: torch.Tensor,
-        actions: torch.Tensor,
-        *,
-        obs_group: str = "obs",
-    ) -> tuple[torch.Tensor, torch.Tensor]: ...
-
-    def augment_obs(
-        self,
-        obs: torch.Tensor,
-        *,
-        obs_group: str = "obs",
-    ) -> torch.Tensor: ...
-
-    def mirror_obs(
-        self,
-        obs: torch.Tensor,
-        *,
-        obs_group: str = "obs",
-    ) -> torch.Tensor: ...
diff --git a/src/unilab/base/backend/__init__.py b/src/unilab/base/backend/__init__.py
deleted file mode 100644
index feb035345..000000000
--- a/src/unilab/base/backend/__init__.py
+++ /dev/null
@@ -1,243 +0,0 @@
-from __future__ import annotations
-
-import warnings
-from typing import TYPE_CHECKING, Any, cast
-
-from unilab.base.scene import SceneCfg
-
-from .base import RenderClosedError, SimBackend
-
-if TYPE_CHECKING:
-    from unilab.base.base import EnvCfg
-
-    from .mujoco.xml import materialize_scene_visual_override
-
-
-def env_backend_kwargs(cfg: "EnvCfg") -> dict:
-    """Bundle EnvCfg-level backend tuning fields for create_backend(**...)."""
-    return {
-        "post_step_forward_sensor": cfg.post_step_forward_sensor,
-        "motrix_max_iterations": cfg.motrix_max_iterations,
-        "chunk_size": cfg.chunk_size,
-        "adaptive_chunk_size": cfg.adaptive_chunk_size,
-        "cpu_ids": cfg.cpu_ids,
-        "bench_nsteps": cfg.sim_substeps,
-        "mjwarp_nconmax": cfg.mjwarp_nconmax,
-        "mjwarp_njmax": cfg.mjwarp_njmax,
-        "drake_backend_mode": cfg.drake_backend_mode,
-        "drake_nthread": cfg.drake_nthread,
-    }
-
-
-_MUJOCO_XML_EXPORTS = frozenset(
-    {
-        "add_sensor",
-        "create_discardvisual_xml",
-        "get_named_body_ids",
-        "inject_mujoco_tracking_sensors",
-        "materialize_mujoco_hfield_attached_scene",
-        "materialize_scene_fragments",
-        "materialize_scene_visual_override",
-        "processed_xml",
-    }
-)
-_MOTRIX_SCENE_EXPORTS = frozenset(
-    {
-        "add_motrix_tracking_frame_sensors",
-        "materialize_motrix_hfield_attached_scene",
-        "materialize_motrix_scene",
-    }
-)
-
-
-def _load_mujoco_backend() -> Any:
-    from .mujoco.backend import MuJoCoBackend
-
-    return MuJoCoBackend
-
-
-def _load_motrix_backend() -> tuple[Any, bool]:
-    from .motrix.backend import MOTRIX_AVAILABLE, MotrixBackend
-
-    return MotrixBackend, bool(MOTRIX_AVAILABLE)
-
-
-def _load_mjwarp_backend() -> Any:
-    """Load the independent optional mujoco-warp backend on demand."""
-    from .mjwarp.backend import MjwarpBackend
-
-    return MjwarpBackend
-
-
-def _mjwarp_available() -> bool:
-    from .mjwarp.dependencies import mjwarp_dependencies_available
-
-    return mjwarp_dependencies_available()
-
-
-def _load_motrix_scene_export(name: str) -> Any:
-    from .motrix import scene
-
-    return getattr(scene, name)
-
-
-def _load_drake_backend() -> Any:
-    from .drake.backend import DrakeBackend
-
-    return DrakeBackend
-
-
-def _drake_available() -> bool:
-    from .drake.backend import ensure_drake_batch_available
-
-    return ensure_drake_batch_available()[0]
-
-
-def create_backend(
-    backend_type: str,
-    scene: SceneCfg,
-    num_envs: int,
-    sim_dt: float,
-    **kwargs,
-) -> SimBackend:
-    """Create a simulation backend.
-
-    Args:
-        backend_type: ``"mujoco"``, ``"mjwarp"``, ``"motrix"``, or
-            ``"drake"``.
-        scene: SceneCfg for either static or composed scenes.
-        num_envs: Number of environments.
-        sim_dt: Simulation timestep.
-        **kwargs: Additional backend options such as ``position_actuator_gains``,
-            ``iterations``, or ``motrix_max_iterations``.
-
-    Returns:
-        SimBackend instance.
-    """
-    if scene is None:
-        raise ValueError("SceneCfg must be provided")
-
-    position_actuator_gains = kwargs.pop("position_actuator_gains", None)
-    motrix_max_iterations = kwargs.pop("motrix_max_iterations", None)
-    post_step_forward_sensor = kwargs.pop("post_step_forward_sensor", None)
-    iterations = kwargs.pop("iterations", None)
-    chunk_size = kwargs.pop("chunk_size", None)
-    adaptive_chunk_size = kwargs.pop("adaptive_chunk_size", False)
-    cpu_ids = kwargs.pop("cpu_ids", None)
-    bench_nsteps = kwargs.pop("bench_nsteps", 1)
-    mjwarp_nconmax = kwargs.pop("mjwarp_nconmax", None)
-    mjwarp_njmax = kwargs.pop("mjwarp_njmax", None)
-    drake_backend_mode = kwargs.pop("drake_backend_mode", "batch")
-    drake_nthread = kwargs.pop("drake_nthread", None)
-    if backend_type == "mujoco":
-        MuJoCoBackend = _load_mujoco_backend()
-        if position_actuator_gains is not None:
-            kwargs["position_actuator_gains"] = position_actuator_gains
-        if post_step_forward_sensor is not None:
-            kwargs["post_step_forward_sensor"] = post_step_forward_sensor
-        kwargs["iterations"] = iterations
-        kwargs["chunk_size"] = chunk_size
-        kwargs["adaptive_chunk_size"] = adaptive_chunk_size
-        kwargs["cpu_ids"] = cpu_ids
-        kwargs["bench_nsteps"] = bench_nsteps
-        return cast(SimBackend, MuJoCoBackend(scene, num_envs, sim_dt, **kwargs))
-    if backend_type == "mjwarp":
-        MjwarpBackend = _load_mjwarp_backend()
-        if position_actuator_gains is not None:
-            raise ValueError(
-                "mjwarp does not accept position_actuator_gains in the host compatibility "
-                "profile; configure the model on the cold path instead."
-            )
-        ignored_non_defaults = {
-            key: value
-            for key, value, default in (
-                ("post_step_forward_sensor", post_step_forward_sensor, None),
-                ("iterations", iterations, None),
-                ("chunk_size", chunk_size, None),
-                ("adaptive_chunk_size", adaptive_chunk_size, False),
-                ("cpu_ids", cpu_ids, None),
-                ("bench_nsteps", bench_nsteps, 1),
-            )
-            if value != default
-        }
-        if ignored_non_defaults:
-            rendered = ", ".join(f"{key}={value!r}" for key, value in ignored_non_defaults.items())
-            warnings.warn(
-                "mjwarp ignores non-default MuJoCo-only backend options: " + rendered,
-                UserWarning,
-                stacklevel=2,
-            )
-        # These generic EnvCfg fields are routed only to the MuJoCo pool.
-        del post_step_forward_sensor, iterations, chunk_size, adaptive_chunk_size, cpu_ids
-        del bench_nsteps
-        kwargs["nconmax"] = mjwarp_nconmax
-        kwargs["njmax"] = mjwarp_njmax
-        return cast(SimBackend, MjwarpBackend(scene, num_envs, sim_dt, **kwargs))
-    if backend_type == "motrix":
-        MotrixBackend, motrix_available = _load_motrix_backend()
-        if not motrix_available:
-            raise ImportError("MotrixSim not available, install motrixsim package")
-        if motrix_max_iterations is not None:
-            kwargs["max_iterations"] = motrix_max_iterations
-        return cast(SimBackend, MotrixBackend(scene, num_envs, sim_dt, **kwargs))
-    if backend_type == "drake":
-        DrakeBackend = _load_drake_backend()
-        # DrakeUni is a generic batch engine. Task-level body names and scalar
-        # gain overrides are consumed by other backends, but Drake reads bodies,
-        # actuators, and sensors from the model contract itself.
-        kwargs.pop("base_name", None)
-        kwargs.pop("push_body_name", None)
-        kwargs.pop("add_body_sensors", None)
-        kwargs["drake_backend_mode"] = drake_backend_mode
-        if drake_nthread is not None:
-            kwargs["nthread"] = drake_nthread
-        return cast(SimBackend, DrakeBackend(scene, num_envs, sim_dt, **kwargs))
-    raise ValueError(f"Unknown backend: {backend_type}")
-
-
-def __getattr__(name: str):
-    if name == "MuJoCoBackend":
-        return _load_mujoco_backend()
-    if name == "MotrixBackend":
-        return _load_motrix_backend()[0]
-    if name == "MOTRIX_AVAILABLE":
-        return _load_motrix_backend()[1]
-    if name == "MjwarpBackend":
-        return _load_mjwarp_backend()
-    if name == "MJWARP_AVAILABLE":
-        return _mjwarp_available()
-    if name == "DrakeBackend":
-        return _load_drake_backend()
-    if name == "DRAKE_AVAILABLE":
-        return _drake_available()
-    if name in _MUJOCO_XML_EXPORTS:
-        from .mujoco import xml
-
-        return getattr(xml, name)
-    if name in _MOTRIX_SCENE_EXPORTS:
-        return _load_motrix_scene_export(name)
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
-
-
-__all__ = [
-    "SimBackend",
-    "RenderClosedError",
-    "MuJoCoBackend",
-    "MjwarpBackend",
-    "MotrixBackend",
-    "DrakeBackend",
-    "DRAKE_AVAILABLE",
-    "MJWARP_AVAILABLE",
-    "add_sensor",
-    "create_discardvisual_xml",
-    "create_backend",
-    "get_named_body_ids",
-    "inject_mujoco_tracking_sensors",
-    "add_motrix_tracking_frame_sensors",
-    "materialize_motrix_hfield_attached_scene",
-    "materialize_motrix_scene",
-    "materialize_mujoco_hfield_attached_scene",
-    "materialize_scene_fragments",
-    "materialize_scene_visual_override",
-    "processed_xml",
-]
diff --git a/src/unilab/base/backend/base.py b/src/unilab/base/backend/base.py
deleted file mode 100644
index 3989c35d2..000000000
--- a/src/unilab/base/backend/base.py
+++ /dev/null
@@ -1,804 +0,0 @@
-import abc
-from collections.abc import Callable, Sequence
-from dataclasses import dataclass
-from os import PathLike
-from typing import Any
-
-import numpy as np
-
-from unilab.dr.types import (
-    DomainRandomizationCapabilities,
-    InitRandomizationPlan,
-    IntervalRandomizationPlan,
-    ResetRandomizationPayload,
-)
-
-PreStepControlFn = Callable[[Any, np.ndarray], np.ndarray]
-TerrainHeightSampleFn = Callable[[np.ndarray], np.ndarray]
-
-
-class RenderClosedError(RuntimeError):
-    """Interface-level signal that the user closed the backend render window.
-
-    Backends with a native renderer translate their private window-closed
-    errors into this type at the interface boundary (``render`` /
-    ``capture_video_frame``), so play loops can catch it by type instead of
-    matching backend-private exception names.
-    """
-
-
-@dataclass(frozen=True)
-class BackendTerrainSpawnData:
-    """Read-only terrain spawn metadata materialized by a backend.
-
-    ``terrain_origins`` is a detached snapshot with shape
-    ``(num_rows, num_cols, 3)``. ``sample_height`` samples world-space XY
-    coordinates and returns an array with the same leading shape.
-    """
-
-    terrain_origins: np.ndarray
-    sample_height: TerrainHeightSampleFn | None = None
-
-    def __post_init__(self) -> None:
-        origins = np.array(self.terrain_origins, copy=True)
-        if origins.ndim != 3 or origins.shape[2] != 3:
-            raise ValueError(
-                f"terrain_origins must have shape (num_rows, num_cols, 3); got {origins.shape}"
-            )
-        origins.setflags(write=False)
-        object.__setattr__(self, "terrain_origins", origins)
-        if self.sample_height is not None and not callable(self.sample_height):
-            raise TypeError("sample_height must be callable")
-
-
-@dataclass(frozen=True)
-class BackendPlayCapabilities:
-    """Backend-native play/render capabilities surfaced through env contracts."""
-
-    supports_native_interactive_renderer: bool = False
-    supports_physics_state_playback: bool = False
-    supports_native_video_capture: bool = False
-
-
-class BackendHeightScanner(abc.ABC):
-    """Backend-owned height-field scanner created on the env init path."""
-
-    @abc.abstractmethod
-    def scan(self) -> np.ndarray:
-        """Return sampled values with shape ``(num_envs, num_points)``."""
-
-
-PLAY_RENDER_MODES = frozenset({"auto", "interactive", "record", "none"})
-
-
-@dataclass(frozen=True)
-class BackendPlayRenderPlan:
-    """Backend-resolved playback rendering behavior."""
-
-    mode: str
-    headless: bool
-    record_video: bool
-    num_steps: int | None
-    output_video: str | PathLike[str] | None
-
-
-def normalize_play_render_mode(play_render_mode: str | None) -> str:
-    mode = "auto" if play_render_mode is None else str(play_render_mode).strip().lower()
-    if mode not in PLAY_RENDER_MODES:
-        joined = ", ".join(sorted(PLAY_RENDER_MODES))
-        raise ValueError(f"play render mode must be one of: {joined}; got {mode!r}.")
-    return mode
-
-
-class SimBackend(abc.ABC):
-    """Unified simulation backend contract."""
-
-    _pre_step_control_fn: PreStepControlFn | None
-    _scene_cleanup_handle: Any | None
-    backend_type: str
-
-    # ------------------------------------------------------------------ #
-    # Properties                                                           #
-    # ------------------------------------------------------------------ #
-
-    @property
-    @abc.abstractmethod
-    def num_envs(self) -> int:
-        """Number of vectorized environments."""
-
-    @property
-    @abc.abstractmethod
-    def model(self):
-        """Underlying physics model."""
-
-    # ------------------------------------------------------------------ #
-    # Model properties                                                     #
-    # ------------------------------------------------------------------ #
-
-    @property
-    @abc.abstractmethod
-    def num_actuators(self) -> int:
-        """Number of actuators."""
-
-    @property
-    @abc.abstractmethod
-    def num_dof_vel(self) -> int:
-        """Number of joint velocity DoFs, excluding the floating base."""
-
-    @abc.abstractmethod
-    def get_actuator_ctrl_range(self) -> np.ndarray:
-        """Return actuator control ranges.
-
-        Returns:
-            Array with shape ``(num_actuators, 2)`` and columns ``[low, high]``.
-        """
-
-    def get_actuator_names(self) -> tuple[str, ...]:
-        """Return actuator names in control-vector order on the cold path."""
-        raise NotImplementedError(f"{self.__class__.__name__} does not expose actuator names")
-
-    def get_scene_model_file(self) -> str | None:
-        """Return the materialized scene path for diagnostics, when available."""
-        return None
-
-    def get_scene_visual_model_file(self) -> str | None:
-        """Return the scene visual model file on the cold path, when available.
-
-        Backends without a separate visual scene model return ``None``.
-        """
-        return None
-
-    def get_terrain_spawn_data(self) -> BackendTerrainSpawnData | None:
-        """Return backend-materialized terrain metadata on the cold path.
-
-        Backends without generated terrain support return ``None``. Callers
-        should resolve this once during env initialization and cache the
-        returned height-sampling callable for reset/reward hot paths.
-        """
-        return None
-
-    @abc.abstractmethod
-    def get_keyframe_qpos(self, name: str) -> np.ndarray:
-        """Return the full qpos for a named keyframe, including the floating base.
-
-        Args:
-            name: Keyframe name such as ``"stand"`` or ``"home"``.
-
-        Returns:
-            Array with shape ``(nq,)``.
-        """
-
-    def get_default_qpos(self) -> np.ndarray:
-        """Return the backend/model default qpos through a stable contract."""
-        raise NotImplementedError(f"{self.__class__.__name__} does not expose default qpos")
-
-    @abc.abstractmethod
-    def get_init_qvel(self) -> np.ndarray:
-        """Return a zero-initialized qvel vector compatible with ``set_state``.
-
-        Returns:
-            Zero-filled qvel array.
-        """
-
-    @abc.abstractmethod
-    def get_body_ids(self, names: Sequence[str]) -> np.ndarray:
-        """Resolve body/link names to backend integer IDs.
-
-        Args:
-            names: Body/link names.
-
-        Returns:
-            ``int32`` array with shape ``(len(names),)``.
-
-        Raises:
-            ValueError: If any name is not found.
-        """
-
-    def get_body_id(self, name: str) -> int:
-        """Resolve one body/link name through the backend contract."""
-        return int(self.get_body_ids([name])[0])
-
-    def get_geom_id(self, name: str) -> int:
-        """Resolve one geom name through the backend contract."""
-        raise NotImplementedError(f"{self.__class__.__name__} does not expose geom ids")
-
-    def get_geom_size(self, name: str) -> np.ndarray:
-        """Return one geom size vector through the backend contract."""
-        raise NotImplementedError(f"{self.__class__.__name__} does not expose geom sizes")
-
-    def create_hfield_scanner(
-        self,
-        *,
-        hfield_geom_id: int,
-        offsets: np.ndarray,
-        frame_body_id: int,
-        alignment: str = "yaw",
-        output: str = "height",
-    ) -> BackendHeightScanner:
-        """Create a reusable height-field scanner on the init/cold path.
-
-        Backends that support height-field terrain scan must override this method.
-        """
-        raise NotImplementedError(
-            f"{self.__class__.__name__} does not support native height-field scanners"
-        )
-
-    def get_body_subtree_ids(self, root_body_id: int) -> np.ndarray:
-        """Return body ids in the subtree rooted at ``root_body_id``."""
-        raise NotImplementedError(f"{self.__class__.__name__} does not expose body subtree ids")
-
-    def get_geom_names(self) -> tuple[str, ...]:
-        """Return backend geom names in backend id order."""
-        raise NotImplementedError(f"{self.__class__.__name__} does not expose geom names")
-
-    def get_geom_body_ids(self) -> np.ndarray:
-        """Return the owning body id for each geom."""
-        raise NotImplementedError(f"{self.__class__.__name__} does not expose geom body ids")
-
-    def get_geom_contact_masks(self) -> tuple[np.ndarray, np.ndarray]:
-        """Return per-geom contact type and affinity masks."""
-        raise NotImplementedError(f"{self.__class__.__name__} does not expose geom contact masks")
-
-    def get_geom_friction(self) -> np.ndarray:
-        """Return the backend geom-friction table."""
-        raise NotImplementedError(f"{self.__class__.__name__} does not expose geom friction")
-
-    def get_gravity(self) -> np.ndarray:
-        """Return the backend gravity vector."""
-        raise NotImplementedError(f"{self.__class__.__name__} does not expose gravity")
-
-    def get_body_mass(self) -> np.ndarray:
-        """Return the backend body-mass table."""
-        raise NotImplementedError(f"{self.__class__.__name__} does not expose body mass")
-
-    def get_body_ipos(self) -> np.ndarray:
-        """Return the backend body inertial-position table."""
-        raise NotImplementedError(f"{self.__class__.__name__} does not expose body ipos")
-
-    def get_dof_armature(self) -> np.ndarray:
-        """Return the backend dof-armature table."""
-        raise NotImplementedError(f"{self.__class__.__name__} does not expose dof armature")
-
-    def get_motion_body_ids(self, names: Sequence[str]) -> np.ndarray:
-        """Resolve backend-native body IDs used by motion datasets."""
-        raise NotImplementedError(f"{self.__class__.__name__} does not expose motion body ids")
-
-    def cleanup_scene_assets(self) -> None:
-        """Release cold-path scene artifacts owned by the backend."""
-        cleanup_handle = getattr(self, "_scene_cleanup_handle", None)
-        if cleanup_handle is None:
-            return
-        cleanup_handle.cleanup()
-        self._scene_cleanup_handle = None
-
-    def __del__(self) -> None:
-        try:
-            self.cleanup_scene_assets()
-        except Exception:
-            pass
-
-    @abc.abstractmethod
-    def get_joint_range(self) -> np.ndarray | None:
-        """Return joint position limits, excluding the floating base.
-
-        Returns:
-            Array with shape ``(num_dof, 2)`` and columns ``[low, high]``, or
-            ``None`` when the backend does not expose limits.
-        """
-
-    # ------------------------------------------------------------------ #
-    # Simulation control                                                   #
-    # ------------------------------------------------------------------ #
-
-    @abc.abstractmethod
-    def step(self, ctrl: np.ndarray, nsteps: int = 1) -> dict | None:
-        """Advance physics.
-
-        Args:
-            ctrl: Control input with shape ``(num_envs, nu)``.
-            nsteps: Number of physics substeps.
-
-        Returns:
-            Optional dictionary. Backends may include a ``"timing"`` key with
-            per-phase timings in milliseconds.
-        """
-
-    def set_pre_step_control(self, fn: PreStepControlFn | None) -> None:
-        """Register an env-owned policy-control to physics-control converter.
-
-        The callback receives ``(backend, ctrl)`` so owner code can read the
-        backend's freshly-updated sensor contract before every physics substep.
-        It must return backend-native actuator control with the same shape.
-        Position-actuator envs leave this unset and keep the direct control path.
-        """
-        self._pre_step_control_fn = fn
-
-    def _apply_pre_step_control(self, ctrl: np.ndarray) -> np.ndarray:
-        if self._pre_step_control_fn is None:
-            return ctrl
-        converted = np.asarray(self._pre_step_control_fn(self, ctrl), dtype=ctrl.dtype)
-        if converted.shape != ctrl.shape:
-            raise ValueError(
-                f"pre-step control must return shape {ctrl.shape}, got {converted.shape}"
-            )
-        return converted
-
-    @abc.abstractmethod
-    def set_state(
-        self,
-        env_indices: np.ndarray,
-        qpos: np.ndarray,
-        qvel: np.ndarray,
-        randomization: ResetRandomizationPayload | None = None,
-    ) -> dict | None:
-        """Set physics state for selected environments.
-
-        Args:
-            env_indices: Environment indices.
-            qpos: Position state.
-            qvel: Velocity state.
-            randomization: Optional backend randomization payload.
-
-        Returns:
-            Optional dictionary. Backends MAY include a ``"timing"`` key with
-            per-substep timings in milliseconds (e.g. ``set_state_mask_ms``,
-            ``set_state_data_slice_ms``, ...). Callers MUST treat ``None`` or
-            missing keys as "not reported" — the outer wall-clock measurement in
-            ``DomainRandomizationManager.reset`` (``dr_reset_set_state_ms``)
-            remains authoritative for total set_state time.
-        """
-
-    @abc.abstractmethod
-    def get_dr_capabilities(self) -> DomainRandomizationCapabilities:
-        """Return supported domain-randomization capabilities for this backend."""
-
-    def apply_init_randomization(self, plan: InitRandomizationPlan) -> None:
-        """Apply cold-path model/materialization randomization."""
-        if plan.is_empty():
-            return
-        raise NotImplementedError(
-            f"{self.__class__.__name__} does not support init-lifecycle randomization"
-        )
-
-    def materialize(self) -> None:
-        """Finalize cold-path backend resources before reset/step."""
-
-    @abc.abstractmethod
-    def apply_interval_randomization(self, plan: IntervalRandomizationPlan) -> None:
-        """Apply a scheduled interval randomization plan."""
-
-    def apply_body_force(
-        self,
-        body_ids: np.ndarray,
-        force: np.ndarray,
-    ) -> None:
-        """Apply a world-frame force to specific bodies for the upcoming step.
-
-        Args:
-            body_ids: Body ids whose external forces should be perturbed.
-            force: Force values with shape ``(num_envs, len(body_ids), 3)``.
-
-        Returns:
-            None. Backends that support this mutate their pending simulation state.
-        """
-        raise NotImplementedError(
-            f"{self.__class__.__name__} does not support interval body force perturbation"
-        )
-
-    def get_play_capabilities(self) -> BackendPlayCapabilities:
-        """Return backend-native play/render capabilities."""
-        return BackendPlayCapabilities()
-
-    def resolve_play_render_plan(
-        self,
-        *,
-        play_render_mode: str | None,
-        play_steps: int | None,
-        output_video: str | PathLike[str] | None,
-    ) -> BackendPlayRenderPlan:
-        """Resolve high-level playback mode into backend-owned render parameters."""
-        raise NotImplementedError(
-            f"{self.__class__.__name__} does not define playback render mode semantics"
-        )
-
-    def run_playback(
-        self,
-        *,
-        env: Any,
-        initialize: Callable[[], Any],
-        step: Callable[[Any], Any],
-        num_steps: int | None,
-        output_video: str | PathLike[str] | None = None,
-        render_spacing: float | None = None,
-        render_offset_mode: str | None = None,
-        headless: bool | None = None,
-        record_video: bool | None = None,
-        frame_state_getter: Callable[[], np.ndarray] | None = None,
-        camera_kwargs: dict[str, Any] | None = None,
-        extra_data_getter: Callable[[], np.ndarray | None] | None = None,
-    ) -> str | None:
-        """Execute backend-owned playback for an env wrapper.
-
-        Known boundary: ``env`` is the owning env wrapper, not a physics-layer
-        concept. Current playback implementations read env-level configuration
-        (e.g. ``cfg.scene``, ``cfg.ctrl_dt``, ``cfg.render_spacing``) and
-        env-owned playback helpers (``get_playback_model``,
-        ``get_physics_state_snapshot``) that have no backend-native equivalent
-        yet. The parameter stays on this contract until playback asset/config
-        resolution moves onto backend-owned metadata; backends must only use
-        it on the cold playback path.
-        """
-        raise NotImplementedError(f"{self.__class__.__name__} does not support playback execution")
-
-    def init_renderer(
-        self,
-        spacing: float = 1.0,
-        *,
-        offset_mode: str = "grid",
-        headless: bool = False,
-        capture: bool = False,
-        width: int = 1280,
-        height: int = 720,
-        camera_kwargs: dict[str, Any] | None = None,
-    ) -> None:
-        """Initialize a backend-native renderer.
-
-        ``headless`` controls whether a native window is opened. ``capture``
-        controls whether ``capture_video_frame`` is valid for the renderer.
-        """
-        raise NotImplementedError(f"{self.__class__.__name__} does not support native rendering")
-
-    def render(self) -> None:
-        """Render one frame through a backend-native interactive renderer.
-
-        Raises:
-            RenderClosedError: If the user closed the render window.
-        """
-        raise NotImplementedError(
-            f"{self.__class__.__name__} does not support native interactive rendering"
-        )
-
-    def capture_video_frame(self) -> np.ndarray:
-        """Capture one RGB frame through a backend-native renderer.
-
-        Raises:
-            RenderClosedError: If the user closed the render window.
-        """
-        raise NotImplementedError(
-            f"{self.__class__.__name__} does not support native video capture"
-        )
-
-    def get_physics_state(self) -> np.ndarray:
-        """Return a physics snapshot suitable for offline playback/video export."""
-        raise NotImplementedError(
-            f"{self.__class__.__name__} does not support physics-state playback"
-        )
-
-    def get_playback_model(self, env_index: int | None = None) -> Any:
-        """Return the playback model for a specific env when variants exist.
-
-        Args:
-            env_index: Optional vectorized environment index.
-
-        Returns:
-            The backend model object used by playback tooling.
-        """
-        return self.model
-
-    def get_actuator_gains(self) -> tuple[np.ndarray, np.ndarray]:
-        """Return per-joint (kp, kd) arrays from the backend model."""
-        raise NotImplementedError(
-            f"{self.__class__.__name__} does not support reading actuator gains"
-        )
-
-    # ------------------------------------------------------------------ #
-    # Base kinematics                                                      #
-    # ------------------------------------------------------------------ #
-
-    @abc.abstractmethod
-    def get_base_pos(self) -> np.ndarray:
-        """Return base position in the world frame.
-
-        Returns:
-            (num_envs, 3)
-        """
-
-    @abc.abstractmethod
-    def get_base_quat(self) -> np.ndarray:
-        """Return base quaternion in the world frame as ``wxyz``.
-
-        Returns:
-            (num_envs, 4)
-        """
-
-    @abc.abstractmethod
-    def get_base_lin_vel(self) -> np.ndarray:
-        """Return base linear velocity in the world frame.
-
-        This is the first three dimensions of generalized velocity ``qvel``,
-        expressed in world coordinates.
-
-        Returns:
-            (num_envs, 3)
-        """
-
-    @abc.abstractmethod
-    def get_base_ang_vel(self) -> np.ndarray:
-        """Return base angular velocity in the world frame.
-
-        This is dimensions 3-5 of generalized velocity ``qvel``, expressed in
-        world coordinates. It differs from gyro readings: gyro sensors report
-        angular velocity components in the body/sensor local frame, while this
-        contract returns world-frame values. Use the matching sensor contract
-        when body-frame angular velocity is required.
-
-        Returns:
-            (num_envs, 3)
-        """
-
-    # ------------------------------------------------------------------ #
-    # DOF state                                                            #
-    # ------------------------------------------------------------------ #
-
-    @abc.abstractmethod
-    def get_dof_pos(self) -> np.ndarray:
-        """Return joint positions, excluding the base.
-
-        Returns:
-            (num_envs, num_dof)
-        """
-
-    @abc.abstractmethod
-    def get_dof_vel(self) -> np.ndarray:
-        """Return joint velocities, excluding the base.
-
-        Returns:
-            (num_envs, num_dof)
-        """
-
-    # ------------------------------------------------------------------ #
-    # Body kinematics — world frame                                        #
-    # ------------------------------------------------------------------ #
-
-    @abc.abstractmethod
-    def get_body_pos_w(self, body_ids: np.ndarray) -> np.ndarray:
-        """Return selected body positions in the world frame.
-
-        Args:
-            body_ids: Body ID array.
-
-        Returns:
-            (num_envs, len(body_ids), 3)
-        """
-
-    @abc.abstractmethod
-    def get_body_quat_w(self, body_ids: np.ndarray) -> np.ndarray:
-        """Return selected body quaternions in the world frame as ``wxyz``.
-
-        Args:
-            body_ids: Body ID array.
-
-        Returns:
-            (num_envs, len(body_ids), 4)
-        """
-
-    def get_body_pose_w(self, body_ids: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
-        """Return selected body positions and quaternions in the world frame."""
-        return self.get_body_pos_w(body_ids), self.get_body_quat_w(body_ids)
-
-    @abc.abstractmethod
-    def get_body_lin_vel_w(self, body_ids: np.ndarray) -> np.ndarray:
-        """Return selected body linear velocities in the world frame.
-
-        Args:
-            body_ids: Body ID array.
-
-        Returns:
-            (num_envs, len(body_ids), 3)
-        """
-
-    def get_body_vel_w(self, body_ids: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
-        """Return selected body linear and angular velocities in the world frame."""
-        return self.get_body_lin_vel_w(body_ids), self.get_body_ang_vel_w(body_ids)
-
-    @abc.abstractmethod
-    def get_body_ang_vel_w(self, body_ids: np.ndarray) -> np.ndarray:
-        """Return selected body angular velocities in the world frame.
-
-        Args:
-            body_ids: Body ID array.
-
-        Returns:
-            (num_envs, len(body_ids), 3)
-        """
-
-    def get_body_state_w(
-        self, body_ids: np.ndarray
-    ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
-        """Get selected body position, quaternion, linear velocity, and angular velocity."""
-        return (
-            self.get_body_pos_w(body_ids),
-            self.get_body_quat_w(body_ids),
-            self.get_body_lin_vel_w(body_ids),
-            self.get_body_ang_vel_w(body_ids),
-        )
-
-    def copy_body_state_w(
-        self,
-        body_ids: np.ndarray,
-        out_pos: np.ndarray,
-        out_quat: np.ndarray,
-        out_lin_vel: np.ndarray,
-        out_ang_vel: np.ndarray,
-    ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
-        """Copy selected world-frame body state into caller-owned buffers."""
-        pos, quat, lin_vel, ang_vel = self.get_body_state_w(body_ids)
-        out_pos[...] = pos
-        out_quat[...] = quat
-        out_lin_vel[...] = lin_vel
-        out_ang_vel[...] = ang_vel
-        return out_pos, out_quat, out_lin_vel, out_ang_vel
-
-    def get_body_pose_w_rows(
-        self, env_ids: np.ndarray, body_ids: np.ndarray
-    ) -> tuple[np.ndarray, np.ndarray]:
-        """Get selected env rows of world-frame body position and quaternion."""
-        rows = np.asarray(env_ids, dtype=np.intp)
-        return self.get_body_pos_w(body_ids)[rows], self.get_body_quat_w(body_ids)[rows]
-
-    # ------------------------------------------------------------------ #
-    # Body kinematics — baselink frame                                     #
-    # ------------------------------------------------------------------ #
-
-    @abc.abstractmethod
-    def get_body_pos_b(self, body_ids: np.ndarray) -> np.ndarray:
-        """Return selected body positions in the baselink frame.
-
-        Args:
-            body_ids: Body ID array.
-
-        Returns:
-            (num_envs, len(body_ids), 3)
-        """
-
-    @abc.abstractmethod
-    def get_body_quat_b(self, body_ids: np.ndarray) -> np.ndarray:
-        """Return selected body quaternions in the baselink frame as ``wxyz``.
-
-        Args:
-            body_ids: Body ID array.
-
-        Returns:
-            (num_envs, len(body_ids), 4)
-        """
-
-    @abc.abstractmethod
-    def get_body_lin_vel_b(self, body_ids: np.ndarray) -> np.ndarray:
-        """Return selected body linear velocities in the baselink frame.
-
-        Args:
-            body_ids: Body ID array.
-
-        Returns:
-            (num_envs, len(body_ids), 3)
-        """
-
-    @abc.abstractmethod
-    def get_body_ang_vel_b(self, body_ids: np.ndarray) -> np.ndarray:
-        """Return selected body angular velocities in the baselink frame.
-
-        Args:
-            body_ids: Body ID array.
-
-        Returns:
-            (num_envs, len(body_ids), 3)
-        """
-
-    # ------------------------------------------------------------------ #
-    # Kinematics / Jacobian                                                #
-    # ------------------------------------------------------------------ #
-
-    def get_site_ids(self, names: Sequence[str]) -> np.ndarray:
-        """Resolve site names to integer ID arrays.
-
-        Args:
-            names: Site names.
-
-        Returns:
-            ``int32`` ID array with shape ``(len(names),)``.
-        """
-        raise NotImplementedError(f"{type(self).__name__} does not implement get_site_ids")
-
-    def get_joint_dof_indices(self, names: Sequence[str]) -> np.ndarray:
-        """Resolve joint names to DoF indices in velocity space (qvel).
-
-        Args:
-            names: Joint names.
-
-        Returns:
-            ``int32`` index array with shape ``(len(names),)`` relative to
-            the qvel start.
-        """
-        raise NotImplementedError(f"{type(self).__name__} does not implement get_joint_dof_indices")
-
-    def get_joint_dof_pos_indices(self, names: Sequence[str]) -> np.ndarray:
-        """Resolve joint names to DoF indices in position space (qpos).
-
-        Only single-DoF joints are supported; free joints are excluded.
-
-        Args:
-            names: Joint names.
-
-        Returns:
-            ``int32`` index array with shape ``(len(names),)`` relative to
-            the joint section of qpos.
-        """
-        raise NotImplementedError(
-            f"{type(self).__name__} does not implement get_joint_dof_pos_indices"
-        )
-
-    def get_joint_dof_vel_indices(self, names: Sequence[str]) -> np.ndarray:
-        """Resolve joint names to DoF indices in velocity space (qvel).
-
-        Args:
-            names: Joint names.
-
-        Returns:
-            ``int32`` index array with shape ``(len(names),)`` relative to
-            the joint section start.
-        """
-        raise NotImplementedError(
-            f"{type(self).__name__} does not implement get_joint_dof_vel_indices"
-        )
-
-    def get_site_jacobian_w(
-        self,
-        site_id: int,
-        dof_indices: np.ndarray,
-    ) -> tuple[np.ndarray, np.ndarray]:
-        """Compute world-frame Jacobians for one site and selected DoF columns.
-
-        Args:
-            site_id: Integer site ID.
-            dof_indices: DoF column indices to extract, with shape ``(n_dof,)``.
-
-        Returns:
-            ``(jacp, jacr)`` translation/rotation Jacobians, each with shape
-            ``(num_envs, 3, n_dof)``.
-        """
-        raise NotImplementedError(f"{type(self).__name__} does not implement get_site_jacobian_w")
-
-    # ------------------------------------------------------------------ #
-    # Sensors                                                              #
-    # ------------------------------------------------------------------ #
-
-    @abc.abstractmethod
-    def get_sensor_data(self, name: str) -> np.ndarray:
-        """Return sensor data.
-
-        Args:
-            name: Sensor name.
-
-        Returns:
-            Sensor data array.
-        """
-
-    def get_sensor_data_rows(self, name: str, env_ids: np.ndarray) -> np.ndarray:
-        """Get selected env rows of a sensor array."""
-        return self.get_sensor_data(name)[np.asarray(env_ids, dtype=np.intp)]
-
-    def get_sensor_data_batch(self, names: Sequence[str]) -> np.ndarray:
-        """Fetch multiple sensors and concatenate their flattened values.
-
-        Args:
-            names: Sensor names in output order.
-
-        Returns:
-            Array with shape ``(num_envs, total_sensor_values)``.
-        """
-        sensor_names = tuple(names)
-        if not sensor_names:
-            return np.empty((self.num_envs, 0), dtype=np.float64)
-        values = [np.asarray(self.get_sensor_data(name)) for name in sensor_names]
-        flat_values = [value.reshape(value.shape[0], -1) for value in values]
-        return np.concatenate(flat_values, axis=1)
diff --git a/src/unilab/base/backend/drake/__init__.py b/src/unilab/base/backend/drake/__init__.py
deleted file mode 100644
index 0b46a3faf..000000000
--- a/src/unilab/base/backend/drake/__init__.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from __future__ import annotations
-
-__all__ = [
-    "DRAKE_AVAILABLE",
-    "DRAKE_BATCH_AVAILABLE",
-    "DrakeBackend",
-    "run_drake_playback",
-]
-
-
-def __getattr__(name: str):
-    if name in {"DRAKE_AVAILABLE", "DRAKE_BATCH_AVAILABLE", "DrakeBackend"}:
-        from .backend import (
-            DRAKE_AVAILABLE,
-            DRAKE_BATCH_AVAILABLE,
-            DrakeBackend,
-        )
-
-        values = {
-            "DRAKE_AVAILABLE": DRAKE_AVAILABLE,
-            "DRAKE_BATCH_AVAILABLE": DRAKE_BATCH_AVAILABLE,
-            "DrakeBackend": DrakeBackend,
-        }
-        return values[name]
-    if name == "run_drake_playback":
-        from .playback import run_drake_playback
-
-        return run_drake_playback
-    raise AttributeError(name)
diff --git a/src/unilab/base/backend/drake/backend.py b/src/unilab/base/backend/drake/backend.py
deleted file mode 100644
index b753eed46..000000000
--- a/src/unilab/base/backend/drake/backend.py
+++ /dev/null
@@ -1,763 +0,0 @@
-"""UniLab adapter for the DrakeUni batch runtime.
-
-UniLab owns task logic, reset sampling, named sensor views, and training flow.
-DrakeUni owns Drake model construction, batched stepping, and raw sensor
-evaluation. This module translates the ``SimBackend`` contract into DrakeUni
-runtime calls and keeps UniLab's cached state/sensor views synchronized.
-"""
-
-from __future__ import annotations
-
-import sys
-import time
-from collections.abc import Callable, Sequence
-from dataclasses import dataclass
-from importlib.util import find_spec
-from multiprocessing import cpu_count
-from os import PathLike
-from pathlib import Path
-from typing import Any, cast
-
-import numpy as np
-
-from unilab.base.backend.base import (
-    BackendPlayCapabilities,
-    BackendPlayRenderPlan,
-    SimBackend,
-    normalize_play_render_mode,
-)
-from unilab.base.backend.drake.playback import run_drake_playback
-from unilab.base.scene import SceneCfg
-from unilab.dr.types import (
-    DomainRandomizationCapabilities,
-    IntervalRandomizationPlan,
-    ResetRandomizationPayload,
-)
-
-
-# DrakeUni availability globals. These are cheap import-time probes so callers
-# can ask whether Drake support exists without constructing a backend.
-def _module_available(name: str) -> bool:
-    try:
-        return find_spec(name) is not None
-    except (ImportError, AttributeError, ValueError):
-        return False
-
-
-DRAKE_AVAILABLE = _module_available("drakeuni")
-DRAKE_IMPORT_ERROR: ImportError | None = None
-DRAKE_BATCH_AVAILABLE = _module_available("drakeuni")
-DRAKE_BATCH_IMPORT_ERROR: ImportError | None = None
-DrakeBatchConfig = None
-create_drake_runtime = None
-
-_DRAKEUNI_SYMBOLS_LOADED = False
-
-
-# Lazy import and pydrake guard helpers.
-def _pydrake_loaded() -> bool:
-    # DrakeUni's batch extension owns Drake symbol loading; mixing it with an
-    # already-imported pydrake module has produced unstable process state.
-    return any(name == "pydrake" or name.startswith("pydrake.") for name in sys.modules)
-
-
-def _load_drakeuni_symbols() -> None:
-    """Load DrakeUni only when a Drake backend is actually constructed."""
-
-    global DRAKE_AVAILABLE
-    global DRAKE_BATCH_AVAILABLE
-    global DRAKE_BATCH_IMPORT_ERROR
-    global DrakeBatchConfig
-    global create_drake_runtime
-    global _DRAKEUNI_SYMBOLS_LOADED
-
-    if _DRAKEUNI_SYMBOLS_LOADED:
-        return
-    try:
-        from drakeuni.runtime import DrakeBatchConfig as ImportedDrakeBatchConfig
-        from drakeuni.runtime import batch_diagnostics
-        from drakeuni.runtime import create_runtime as imported_create_runtime
-    except ImportError as exc:  # pragma: no cover - optional local package.
-        DRAKE_AVAILABLE = False
-        DRAKE_BATCH_AVAILABLE = False
-        DRAKE_BATCH_IMPORT_ERROR = exc
-        raise ImportError("DrakeUni batch runtime is not installed.") from exc
-
-    diagnostics = batch_diagnostics()
-    if not diagnostics.batch_available:
-        detail = diagnostics.batch_import_error
-        import_error = ImportError(detail or "DrakeEnvPool batch extension has not been built.")
-        DRAKE_AVAILABLE = False
-        DRAKE_BATCH_AVAILABLE = False
-        DRAKE_BATCH_IMPORT_ERROR = import_error
-        raise ImportError("DrakeEnvPool batch extension has not been built.") from import_error
-
-    DrakeBatchConfig = ImportedDrakeBatchConfig
-    create_drake_runtime = imported_create_runtime
-    DRAKE_AVAILABLE = True
-    DRAKE_BATCH_AVAILABLE = True
-    DRAKE_BATCH_IMPORT_ERROR = None
-    _DRAKEUNI_SYMBOLS_LOADED = True
-
-
-def ensure_drake_batch_available() -> tuple[bool, ImportError | None]:
-    """Report whether the DrakeUni batch extension can be used."""
-
-    try:
-        _load_drakeuni_symbols()
-    except ImportError as exc:
-        return False, exc
-    return True, None
-
-
-# Floating-base compact state starts with xyz + quaternion in qpos and
-# 3 linear + 3 angular components in qvel. UniLab usually wants only the
-# actuated joint slices behind those root coordinates.
-ROOT_QPOS_DIM = 7
-ROOT_QVEL_DIM = 6
-
-
-# Small helper types.
-@dataclass(frozen=True)
-class _DrakeUniModelView:
-    """Read-only model-shape facade for UniLab's ``backend.model`` API.
-
-    DrakeUni exposes model dimensions through ``model_info``. UniLab's backend
-    contract expects a model-like object with dimension methods, so this facade
-    carries those shape queries without exposing Drake internals.
-    """
-
-    nq: int
-    nv: int
-    nu: int
-
-    def num_actuators(self) -> int:
-        return self.nu
-
-
-# Path and thread helpers.
-def _resolve_batch_nthread(num_envs: int, requested: int) -> int:
-    """Resolve a worker count without creating idle workers above num_envs."""
-
-    env_count = max(1, int(num_envs))
-    requested_count = int(requested)
-    if requested_count > 0:
-        return min(env_count, requested_count)
-    return min(env_count, max(1, cpu_count() * 2))
-
-
-def _resolve_scene_path(scene: SceneCfg) -> Path:
-    """Convert UniLab's scene pointer into an absolute model path."""
-
-    if not scene.model_file:
-        raise ValueError("DrakeBackend requires SceneCfg.model_file")
-    path = Path(scene.model_file)
-    return path if path.is_absolute() else Path.cwd() / path
-
-
-class DrakeBackend(SimBackend):
-    """UniLab ``SimBackend`` implementation backed by DrakeUni batch runtime.
-
-    The backend keeps the public UniLab API stable while delegating model
-    construction, integration, and raw sensor evaluation to DrakeUni.
-    """
-
-    backend_type = "drake"
-
-    def __init__(
-        self,
-        scene: SceneCfg,
-        num_envs: int,
-        sim_dt: float,
-        *,
-        drake_backend_mode: str = "batch",
-        nthread: int = 0,
-    ) -> None:
-        # Validate the backend mode at construction so Hydra/config mistakes
-        # fail at the backend boundary.
-        mode = str(drake_backend_mode or "batch").strip().lower()
-        if mode != "batch":
-            raise ValueError(
-                "UniLab DrakeBackend requires drake_backend_mode='batch'. "
-                f"Got {drake_backend_mode!r}."
-            )
-        if _pydrake_loaded():
-            raise ImportError(
-                "Drake batch backend cannot be loaded after pydrake has already "
-                "been imported in this process. Start a fresh process before "
-                "constructing DrakeBackend."
-            )
-        if int(num_envs) < 1:
-            raise ValueError(f"DrakeUni batch backend requires num_envs >= 1, got {num_envs}")
-        _load_drakeuni_symbols()
-        if DrakeBatchConfig is None or create_drake_runtime is None:
-            detail = DRAKE_BATCH_IMPORT_ERROR
-            message = "DrakeUni runtime is not available."
-            if detail is not None:
-                message = f"{message} Import error: {detail}"
-            raise ImportError(message) from detail
-
-        self._pre_step_control_fn = None
-        self._scene_cleanup_handle = None
-        self._num_envs = int(num_envs)
-        self._sim_dt = float(sim_dt)
-        self._scene_model_file = str(_resolve_scene_path(scene))
-        # DrakeUni receives only generic batch facts. Task concepts such as
-        # base bodies, push targets, and observation semantics stay in UniLab.
-        config = DrakeBatchConfig(
-            model_file=self._scene_model_file,
-            num_envs=self._num_envs,
-            sim_dt=self._sim_dt,
-            nthread=int(nthread),
-        )
-        self._runtime = create_drake_runtime(config)
-        model_info = self._runtime.model_info()
-        # Cache static model metadata once and expose copies through the
-        # UniLab backend contract.
-        self._home_qpos_mujoco = model_info.home_qpos.copy()
-        self._home_qvel_mujoco = model_info.home_qvel.copy()
-        self._ctrl_limits = model_info.ctrl_limits.copy()
-        self._joint_ranges = model_info.joint_ranges.copy()
-        self._actuator_stiffness = model_info.actuator_stiffness.copy()
-        self._actuator_damping = model_info.actuator_damping.copy()
-        self._actuator_qpos_adr = model_info.actuator_qpos_adr.astype(np.intp, copy=True)
-        self._actuator_qvel_adr = model_info.actuator_qvel_adr.astype(np.intp, copy=True)
-        self._sensor_names = tuple(model_info.sensor_names)
-        self._sensor_adr = model_info.sensor_adr.copy()
-        self._sensor_dim = model_info.sensor_dim.copy()
-        self._site_name_to_id = {
-            str(name): index for index, name in enumerate(getattr(model_info, "site_names", ()))
-        }
-        self._joint_qpos_adr_by_name = {
-            str(name): int(adr)
-            for name, adr in zip(
-                getattr(model_info, "joint_names", ()),
-                getattr(model_info, "joint_qpos_adr", ()),
-                strict=True,
-            )
-        }
-        self._joint_qvel_adr_by_name = {
-            str(name): int(adr)
-            for name, adr in zip(
-                getattr(model_info, "joint_names", ()),
-                getattr(model_info, "joint_qvel_adr", ()),
-                strict=True,
-            )
-        }
-        self._joint_dims_by_name = {
-            str(name): (int(qpos_dim), int(qvel_dim))
-            for name, qpos_dim, qvel_dim in zip(
-                getattr(model_info, "joint_names", ()),
-                getattr(model_info, "joint_qpos_dim", ()),
-                getattr(model_info, "joint_qvel_dim", ()),
-                strict=True,
-            )
-        }
-        self._root_qpos_dim = (
-            int(np.min(self._actuator_qpos_adr)) if self._actuator_qpos_adr.size else 0
-        )
-        self._root_qvel_dim = (
-            int(np.min(self._actuator_qvel_adr)) if self._actuator_qvel_adr.size else 0
-        )
-        self._num_bodies = int(model_info.num_bodies)
-        self._pending_body_forces = np.zeros(
-            (self._num_envs, self._num_bodies, 3), dtype=np.float64
-        )
-        self._model = _DrakeUniModelView(
-            nq=int(model_info.nq),
-            nv=int(model_info.nv),
-            nu=int(model_info.nu),
-        )
-        self._nthread = int(getattr(self._runtime, "nthread", int(nthread)))
-        # Runtime state and raw sensor views are refreshed after reset/step.
-        self._physics_state = self._runtime.physics_state()
-        self._sensor_data = np.zeros(
-            (self._num_envs, int(model_info.nsensordata)),
-            dtype=np.float64,
-        )
-        self._sensor_views: dict[str, np.ndarray] = {}
-        self._sync_runtime_state()
-
-    # Static model contract.
-    #
-    # These accessors expose stable dimensions, limits, and reset defaults from
-    # the cached model metadata.
-    @property
-    def scene_model_file(self) -> str:
-        return self._scene_model_file
-
-    @property
-    def num_envs(self) -> int:
-        return self._num_envs
-
-    @property
-    def nthread(self) -> int:
-        return self._nthread
-
-    @property
-    def model(self) -> _DrakeUniModelView:
-        return self._model
-
-    @property
-    def num_actuators(self) -> int:
-        return self._model.nu
-
-    @property
-    def num_dof_vel(self) -> int:
-        return int(self._actuator_qvel_adr.size)
-
-    # Return copies for arrays that UniLab may clamp, concatenate, or normalize.
-    # The backend cache should not be mutated by task-side code.
-    def get_actuator_ctrl_range(self) -> np.ndarray:
-        return self._ctrl_limits.copy()
-
-    def get_scene_model_file(self) -> str | None:
-        return self._scene_model_file
-
-    def get_joint_range(self) -> np.ndarray | None:
-        return self._joint_ranges.copy()
-
-    def get_keyframe_qpos(self, name: str) -> np.ndarray:
-        if name == "home":
-            return self._home_qpos_mujoco.copy()
-        return self._runtime.keyframe_qpos(str(name))
-
-    def get_default_qpos(self) -> np.ndarray:
-        return self._home_qpos_mujoco.copy()
-
-    def get_init_qvel(self) -> np.ndarray:
-        return self._home_qvel_mujoco.copy()
-
-    def get_actuator_gains(self) -> tuple[np.ndarray, np.ndarray]:
-        return (self._actuator_stiffness.copy(), self._actuator_damping.copy())
-
-    def get_body_ids(self, names: Sequence[str]) -> np.ndarray:
-        # Body IDs are owned by DrakeUni because they depend on the materialized
-        # Drake model, not on UniLab's scene pointer.
-        return self._runtime.body_ids(tuple(str(name) for name in names))
-
-    def get_motion_body_ids(self, names: Sequence[str]) -> np.ndarray:
-        return self.get_body_ids(names)
-
-    def get_site_ids(self, names: Sequence[str]) -> np.ndarray:
-        ids: list[int] = []
-        for name in names:
-            key = str(name)
-            try:
-                ids.append(self._site_name_to_id[key])
-            except KeyError as exc:
-                raise ValueError(f"Drake model does not contain MJCF site {key!r}") from exc
-        return np.asarray(ids, dtype=np.int32)
-
-    def get_joint_dof_indices(self, names: Sequence[str]) -> np.ndarray:
-        indices: list[int] = []
-        for name in names:
-            key = str(name)
-            self._require_single_dof_joint(key)
-            try:
-                indices.append(self._joint_qvel_adr_by_name[key])
-            except KeyError as exc:
-                raise ValueError(f"Drake model does not contain joint {key!r}") from exc
-        return np.asarray(indices, dtype=np.int32)
-
-    def get_joint_dof_pos_indices(self, names: Sequence[str]) -> np.ndarray:
-        indices: list[int] = []
-        for name in names:
-            key = str(name)
-            self._require_single_dof_joint(key)
-            try:
-                indices.append(self._joint_qpos_adr_by_name[key] - self._root_qpos_dim)
-            except KeyError as exc:
-                raise ValueError(f"Drake model does not contain joint {key!r}") from exc
-        return np.asarray(indices, dtype=np.int32)
-
-    def get_joint_dof_vel_indices(self, names: Sequence[str]) -> np.ndarray:
-        indices: list[int] = []
-        for name in names:
-            key = str(name)
-            self._require_single_dof_joint(key)
-            try:
-                indices.append(self._joint_qvel_adr_by_name[key] - self._root_qvel_dim)
-            except KeyError as exc:
-                raise ValueError(f"Drake model does not contain joint {key!r}") from exc
-        return np.asarray(indices, dtype=np.int32)
-
-    # Stepping and reset.
-    def step(self, ctrl: np.ndarray, nsteps: int = 1) -> dict | None:
-        # UniLab passes one actuator command per env. An optional pre-step hook
-        # can convert policy actions into backend-native position targets.
-        step_count = int(nsteps)
-        if step_count < 1:
-            raise ValueError(f"nsteps must be >= 1, got {nsteps}")
-        values = np.asarray(ctrl, dtype=np.float64)
-        if values.shape != (self._num_envs, self.num_actuators):
-            raise ValueError(
-                "DrakeUni batch backend step expected ctrl shape "
-                f"({self._num_envs}, {self.num_actuators}), got {values.shape}"
-            )
-        start = time.perf_counter()
-        try:
-            if self._pre_step_control_fn is None:
-                output = self._runtime.step(values, step_count, self._pending_body_forces_or_none())
-                self._sync_runtime_state(output)
-            else:
-                output = None
-                for _ in range(step_count):
-                    native_ctrl = self._apply_pre_step_control(values)
-                    output = self._runtime.step(native_ctrl, 1, self._pending_body_forces_or_none())
-                    self._sync_runtime_state(output)
-        finally:
-            self._pending_body_forces.fill(0.0)
-        timing = dict(output.get("timing", {}))
-        timing.setdefault("step_ms", (time.perf_counter() - start) * 1000.0)
-        return {"timing": timing}
-
-    def set_state(
-        self,
-        env_indices: np.ndarray,
-        qpos: np.ndarray,
-        qvel: np.ndarray,
-        randomization: ResetRandomizationPayload | None = None,
-    ) -> None:
-        # Reset is the handoff from UniLab's sampled state tensors into
-        # DrakeUni's per-env runtime contexts.
-        if randomization is not None and not randomization.is_empty():
-            raise NotImplementedError(
-                "DrakeUni batch backend does not apply reset randomization yet"
-            )
-        indices = np.asarray(env_indices, dtype=np.int32)
-        qpos_rows = np.asarray(qpos, dtype=np.float64)
-        qvel_rows = np.asarray(qvel, dtype=np.float64)
-        if indices.ndim != 1:
-            raise ValueError(f"env_indices must be one-dimensional, got {indices.shape}")
-        if np.any(indices < 0) or np.any(indices >= self._num_envs):
-            raise IndexError(
-                f"env_indices must be in [0, {self._num_envs - 1}], got {indices.tolist()}"
-            )
-        if qpos_rows.shape != (indices.size, self._model.nq):
-            raise ValueError(f"qpos must have shape ({indices.size}, {self._model.nq})")
-        if qvel_rows.shape != (indices.size, self._model.nv):
-            raise ValueError(f"qvel must have shape ({indices.size}, {self._model.nv})")
-        output = self._runtime.reset(indices, qpos_rows, qvel_rows)
-        self._sync_runtime_state(output)
-
-    # Playback and domain randomization.
-    def get_dr_capabilities(self) -> DomainRandomizationCapabilities:
-        # Unsupported randomization knobs fail explicitly instead of silently
-        # becoming no-ops.
-        return DomainRandomizationCapabilities(supports_interval_body_force=True)
-
-    def apply_interval_randomization(self, plan: IntervalRandomizationPlan) -> None:
-        if plan.is_empty():
-            return
-        self._pending_body_forces.fill(0.0)
-        if plan.push_perturbation_limit is not None:
-            raise NotImplementedError(
-                "DrakeBackend interval pushes require explicit body_ids and body_force"
-            )
-        if plan.body_force is not None:
-            if plan.body_ids is None:
-                raise ValueError("Interval body-force perturbation requires body_ids")
-            self.apply_body_force(plan.body_ids, plan.body_force)
-        if plan.body_linear_velocity_delta is not None:
-            raise NotImplementedError(
-                "DrakeUni batch backend does not support interval body velocity perturbation"
-            )
-
-    def get_play_capabilities(self) -> BackendPlayCapabilities:
-        # Drake advances playback physics, while the shared playback helper
-        # handles recording. There is no native interactive Drake viewer path.
-        return BackendPlayCapabilities(
-            supports_native_interactive_renderer=False,
-            supports_physics_state_playback=True,
-            supports_native_video_capture=False,
-        )
-
-    def resolve_play_render_plan(
-        self,
-        *,
-        play_render_mode: str | None,
-        play_steps: int | None,
-        output_video: str | PathLike[str] | None,
-    ) -> BackendPlayRenderPlan:
-        mode = normalize_play_render_mode(play_render_mode)
-        if mode in {"none", "auto"}:
-            return BackendPlayRenderPlan(
-                mode=mode,
-                headless=True,
-                record_video=False,
-                num_steps=play_steps,
-                output_video=None,
-            )
-        if mode == "interactive":
-            raise NotImplementedError(
-                "DrakeUni batch backend does not support interactive rendering"
-            )
-        if play_steps is None:
-            raise ValueError("DrakeUni record playback requires a finite play_steps value.")
-        if output_video is None:
-            raise ValueError("DrakeUni record playback requires an output video path.")
-        return BackendPlayRenderPlan(
-            mode="record",
-            headless=True,
-            record_video=True,
-            num_steps=int(play_steps),
-            output_video=output_video,
-        )
-
-    def run_playback(
-        self,
-        *,
-        env: Any,
-        initialize: Callable[[], Any],
-        step: Callable[[Any], Any],
-        num_steps: int | None,
-        output_video: str | PathLike[str] | None = None,
-        render_spacing: float | None = None,
-        render_offset_mode: str | None = None,
-        headless: bool | None = None,
-        record_video: bool | None = None,
-        frame_state_getter: Callable[[], np.ndarray] | None = None,
-        camera_kwargs: dict[str, Any] | None = None,
-        extra_data_getter: Callable[[], np.ndarray | None] | None = None,
-    ) -> str | None:
-        # Playback keeps Drake as the physics backend. The helper owns rendering
-        # and video capture so training code can use one playback contract.
-        return run_drake_playback(
-            env=env,
-            initialize=initialize,
-            step=step,
-            num_steps=num_steps,
-            output_video=output_video,
-            render_spacing=render_spacing,
-            render_offset_mode=render_offset_mode,
-            headless=bool(headless),
-            record_video=bool(record_video),
-            frame_state_getter=frame_state_getter,
-            camera_kwargs=camera_kwargs,
-            extra_data_getter=extra_data_getter,
-        )
-
-    def init_renderer(
-        self,
-        spacing: float = 1.0,
-        *,
-        offset_mode: str = "grid",
-        headless: bool = False,
-        capture: bool = False,
-        width: int = 1280,
-        height: int = 720,
-        camera_kwargs: dict[str, Any] | None = None,
-    ) -> None:
-        del spacing, offset_mode, headless, capture, width, height, camera_kwargs
-        raise NotImplementedError("DrakeUni batch backend records through run_playback")
-
-    def render(self) -> None:
-        raise NotImplementedError("DrakeUni batch backend does not support interactive rendering")
-
-    def capture_video_frame(self) -> np.ndarray:
-        raise NotImplementedError("DrakeUni batch backend records through run_playback")
-
-    # Runtime state getters.
-    #
-    # ``physics_state`` is DrakeUni's compact per-env packet used by playback
-    # and debugging. Sensor-specific getters below expose named slices/packets.
-    def get_physics_state(self) -> np.ndarray:
-        return self._physics_state.copy()
-
-    def get_playback_model(self, env_index: int | None = None) -> str:
-        if env_index is not None:
-            idx = int(env_index)
-            if idx < 0 or idx >= self._num_envs:
-                raise IndexError(f"env_index must be in [0, {self._num_envs - 1}], got {idx}")
-        return self._scene_model_file
-
-    def diagnostics(self) -> Any:
-        return self._runtime.diagnostics()
-
-    def apply_body_force(
-        self,
-        body_ids: np.ndarray,
-        force: np.ndarray,
-    ) -> None:
-        ids = np.asarray(body_ids, dtype=np.int32).reshape(-1)
-        values = np.asarray(force, dtype=np.float64)
-        expected_shape = (self._num_envs, ids.size, 3)
-        if values.shape != expected_shape:
-            raise ValueError(f"body force must have shape {expected_shape}, got {values.shape}")
-        for offset, body_id in enumerate(ids):
-            if body_id < 0 or body_id >= self._num_bodies:
-                raise IndexError(f"body id {int(body_id)} is outside [0, {self._num_bodies - 1}]")
-            self._pending_body_forces[:, int(body_id), :] += values[:, offset, :]
-
-    # Sensor access.
-    #
-    # DrakeUni returns one flat sensor array; this class owns the MuJoCo-style
-    # named views over that array.
-    def get_base_pos(self) -> np.ndarray:
-        self._require_floating_root()
-        return self._physics_state[:, 1:4].copy()
-
-    def get_base_quat(self) -> np.ndarray:
-        self._require_floating_root()
-        return self._physics_state[:, 4:8].copy()
-
-    def get_base_lin_vel(self) -> np.ndarray:
-        qvel_start = 1 + self._model.nq
-        return self._physics_state[:, qvel_start : qvel_start + 3].copy()
-
-    def get_base_ang_vel(self) -> np.ndarray:
-        qvel_start = 1 + self._model.nq
-        return self._physics_state[:, qvel_start + 3 : qvel_start + 6].copy()
-
-    def get_dof_pos(self) -> np.ndarray:
-        return self._physics_state[:, 1 + self._actuator_qpos_adr].copy()
-
-    def get_dof_vel(self) -> np.ndarray:
-        qvel_start = 1 + self._model.nq
-        return self._physics_state[:, qvel_start + self._actuator_qvel_adr].copy()
-
-    def get_body_pos_w(self, body_ids: np.ndarray) -> np.ndarray:
-        return self._body_state(body_ids)["pos"]
-
-    def get_body_quat_w(self, body_ids: np.ndarray) -> np.ndarray:
-        return self._body_state(body_ids)["quat"]
-
-    def get_body_lin_vel_w(self, body_ids: np.ndarray) -> np.ndarray:
-        return self._body_state(body_ids)["linvel"]
-
-    def get_body_ang_vel_w(self, body_ids: np.ndarray) -> np.ndarray:
-        return self._body_state(body_ids)["angvel"]
-
-    def get_body_pos_b(self, body_ids: np.ndarray) -> np.ndarray:
-        body_state = self._body_state(body_ids)
-        base_pos = self.get_base_pos()
-        base_rot = _quat_to_rotation_matrix(self.get_base_quat())
-        delta = body_state["pos"] - base_pos[:, None, :]
-        return np.einsum("nij,nkj->nki", np.swapaxes(base_rot, 1, 2), delta)
-
-    def get_body_quat_b(self, body_ids: np.ndarray) -> np.ndarray:
-        body_quat = self._body_state(body_ids)["quat"]
-        base_inv = _quat_conjugate(self.get_base_quat())
-        return _quat_multiply(base_inv[:, None, :], body_quat)
-
-    def get_body_lin_vel_b(self, body_ids: np.ndarray) -> np.ndarray:
-        base_rot = _quat_to_rotation_matrix(self.get_base_quat())
-        return np.einsum(
-            "nij,nkj->nki",
-            np.swapaxes(base_rot, 1, 2),
-            self._body_state(body_ids)["linvel"],
-        )
-
-    def get_body_ang_vel_b(self, body_ids: np.ndarray) -> np.ndarray:
-        base_rot = _quat_to_rotation_matrix(self.get_base_quat())
-        return np.einsum(
-            "nij,nkj->nki",
-            np.swapaxes(base_rot, 1, 2),
-            self._body_state(body_ids)["angvel"],
-        )
-
-    def get_sensor_data(self, name: str) -> np.ndarray:
-        if name in self._sensor_views:
-            return self._sensor_views[name].copy()
-        raise KeyError(f"Unknown DrakeUni sensor: {name}")
-
-    # Internal helpers.
-    def _sync_runtime_state(self, output: dict[str, Any] | None = None) -> None:
-        # Keep UniLab's cached state/sensor views aligned after every DrakeUni update.
-        if output is None:
-            self._physics_state = self._runtime.physics_state()
-            sensor_data = self._runtime.sensor_data()
-        elif "env_ids" in output:
-            indices = np.asarray(output["env_ids"], dtype=np.int32)
-            self._physics_state[indices] = np.asarray(output["state"], dtype=np.float64)
-            self._sensor_data[indices] = np.asarray(output["sensor_data"], dtype=np.float64)
-            self._rebuild_sensor_views()
-            return
-        else:
-            self._physics_state = np.asarray(output["state"], dtype=np.float64).copy()
-            sensor_data = output["sensor_data"]
-        self._sensor_data = np.asarray(sensor_data, dtype=np.float64).copy()
-        self._rebuild_sensor_views()
-
-    def _rebuild_sensor_views(self) -> None:
-        self._sensor_views = {}
-        for index, name in enumerate(self._sensor_names):
-            adr = int(self._sensor_adr[index])
-            dim = int(self._sensor_dim[index])
-            self._sensor_views[name] = self._sensor_data[:, adr : adr + dim]
-
-    def _body_state(self, body_ids: np.ndarray) -> dict[str, np.ndarray]:
-        ids = np.asarray(body_ids, dtype=np.int32)
-        if ids.ndim != 1:
-            raise ValueError(f"body_ids must be one-dimensional, got {ids.shape}")
-        return cast(dict[str, np.ndarray], self._runtime.compute_body_state(ids))
-
-    def _pending_body_forces_or_none(self) -> np.ndarray | None:
-        if np.any(self._pending_body_forces):
-            return self._pending_body_forces
-        return None
-
-    def _require_single_dof_joint(self, name: str) -> None:
-        dims = self._joint_dims_by_name.get(name)
-        if dims is None:
-            raise ValueError(f"Drake model does not contain joint {name!r}")
-        if dims != (1, 1):
-            raise ValueError(f"Drake joint {name!r} is not a single-DoF joint")
-
-    def _require_floating_root(self) -> None:
-        if self._model.nq < ROOT_QPOS_DIM or self._model.nv < ROOT_QVEL_DIM:
-            raise NotImplementedError(
-                "DrakeBackend root-state helpers require a floating-root compact state"
-            )
-
-
-def _quat_conjugate(quat: np.ndarray) -> np.ndarray:
-    values = np.asarray(quat, dtype=np.float64).copy()
-    values[..., 1:] *= -1.0
-    return values
-
-
-def _quat_multiply(lhs: np.ndarray, rhs: np.ndarray) -> np.ndarray:
-    a = np.asarray(lhs, dtype=np.float64)
-    b = np.asarray(rhs, dtype=np.float64)
-    aw, ax, ay, az = np.moveaxis(a, -1, 0)
-    bw, bx, by, bz = np.moveaxis(b, -1, 0)
-    return np.stack(
-        (
-            aw * bw - ax * bx - ay * by - az * bz,
-            aw * bx + ax * bw + ay * bz - az * by,
-            aw * by - ax * bz + ay * bw + az * bx,
-            aw * bz + ax * by - ay * bx + az * bw,
-        ),
-        axis=-1,
-    )
-
-
-def _quat_to_rotation_matrix(quat: np.ndarray) -> np.ndarray:
-    values = np.asarray(quat, dtype=np.float64)
-    norm = np.linalg.norm(values, axis=-1, keepdims=True)
-    q = np.divide(values, np.maximum(norm, 1.0e-12))
-    w, x, y, z = np.moveaxis(q, -1, 0)
-    matrix = np.empty((*q.shape[:-1], 3, 3), dtype=np.float64)
-    matrix[..., 0, 0] = 1.0 - 2.0 * (y * y + z * z)
-    matrix[..., 0, 1] = 2.0 * (x * y - z * w)
-    matrix[..., 0, 2] = 2.0 * (x * z + y * w)
-    matrix[..., 1, 0] = 2.0 * (x * y + z * w)
-    matrix[..., 1, 1] = 1.0 - 2.0 * (x * x + z * z)
-    matrix[..., 1, 2] = 2.0 * (y * z - x * w)
-    matrix[..., 2, 0] = 2.0 * (x * z - y * w)
-    matrix[..., 2, 1] = 2.0 * (y * z + x * w)
-    matrix[..., 2, 2] = 1.0 - 2.0 * (x * x + y * y)
-    return matrix
-
-
-__all__ = [
-    "DRAKE_AVAILABLE",
-    "DRAKE_IMPORT_ERROR",
-    "DRAKE_BATCH_AVAILABLE",
-    "DRAKE_BATCH_IMPORT_ERROR",
-    "DrakeBackend",
-    "_resolve_batch_nthread",
-    "ensure_drake_batch_available",
-]
diff --git a/src/unilab/base/backend/drake/playback.py b/src/unilab/base/backend/drake/playback.py
deleted file mode 100644
index ab8c358b7..000000000
--- a/src/unilab/base/backend/drake/playback.py
+++ /dev/null
@@ -1,62 +0,0 @@
-"""Drake-owned playback execution helpers."""
-
-from __future__ import annotations
-
-from os import PathLike
-from typing import Any, Callable, TypeVar
-
-import numpy as np
-
-from unilab.base.backend.mujoco.playback import run_mujoco_playback
-
-ObsT = TypeVar("ObsT")
-
-
-def run_drake_playback(
-    *,
-    env: Any,
-    initialize: Callable[[], ObsT],
-    step: Callable[[ObsT], ObsT],
-    num_steps: int | None,
-    output_video: str | PathLike[str] | None,
-    render_spacing: float | None,
-    render_offset_mode: str | None,
-    headless: bool,
-    record_video: bool,
-    frame_state_getter: Callable[[], np.ndarray] | None,
-    camera_kwargs: dict[str, Any] | None,
-    extra_data_getter: Callable[[], np.ndarray | None] | None = None,
-) -> str | None:
-    """Run Drake physics playback and optionally render it with MuJoCo.
-
-    Drake owns the rollout: ``step`` must advance the Drake backend and
-    ``frame_state_getter`` must read Drake state snapshots. MuJoCo is used only
-    as an offline visual renderer for the recorded state sequence.
-    """
-    del render_offset_mode
-    if record_video:
-        return run_mujoco_playback(
-            env=env,
-            initialize=initialize,
-            step=step,
-            num_steps=num_steps,
-            output_video=output_video,
-            render_spacing=render_spacing,
-            headless=True,
-            record_video=True,
-            frame_state_getter=frame_state_getter,
-            camera_kwargs=camera_kwargs,
-            extra_data_getter=extra_data_getter,
-        )
-    if not headless:
-        raise NotImplementedError("Drake playback does not support interactive rendering yet.")
-
-    obs = initialize()
-    steps_run = 0
-    while num_steps is None or steps_run < int(num_steps):
-        obs = step(obs)
-        steps_run += 1
-    return None
-
-
-__all__ = ["run_drake_playback"]
diff --git a/src/unilab/base/backend/mjwarp/__init__.py b/src/unilab/base/backend/mjwarp/__init__.py
deleted file mode 100644
index fa5b79683..000000000
--- a/src/unilab/base/backend/mjwarp/__init__.py
+++ /dev/null
@@ -1,26 +0,0 @@
-"""Independent optional backend built on :mod:`mujoco_warp`.
-
-This package deliberately owns its own runtime implementation.  It may reuse
-shared *cold-path* scene materialization helpers, but it never subclasses or
-reads the runtime-private state of :mod:`unilab.base.backend.mujoco`.
-"""
-
-from __future__ import annotations
-
-
-def __getattr__(name: str):
-    if name == "MjwarpBackend":
-        from .backend import MjwarpBackend
-
-        return MjwarpBackend
-    if name == "MJWARP_AVAILABLE":
-        from .dependencies import mjwarp_dependencies_available
-
-        return mjwarp_dependencies_available()
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
-
-
-__all__ = [
-    "MJWARP_AVAILABLE",
-    "MjwarpBackend",
-]
diff --git a/src/unilab/base/backend/mjwarp/backend.py b/src/unilab/base/backend/mjwarp/backend.py
deleted file mode 100644
index f57d5d3e1..000000000
--- a/src/unilab/base/backend/mjwarp/backend.py
+++ /dev/null
@@ -1,877 +0,0 @@
-"""Host-compatibility implementation of the independent ``mjwarp`` backend.
-
-``mjwarp`` is not a MuJoCo backend mode.  It uploads a CPU MuJoCo model to
-``mujoco_warp`` and owns its own device data and host cache.  The cache is
-refreshed exactly at explicit step/reset barriers; legacy getters only return
-views into that cache and therefore never trigger an implicit Warp ``.numpy``
-transfer.
-"""
-
-from __future__ import annotations
-
-import gc
-import time
-import warnings
-from collections.abc import Iterator, Sequence
-from contextlib import contextmanager
-from os import PathLike
-from typing import Any
-
-import numpy as np
-
-from unilab.base.backend.base import (
-    BackendPlayCapabilities,
-    BackendPlayRenderPlan,
-    SimBackend,
-    normalize_play_render_mode,
-)
-from unilab.base.scene import SceneCfg
-from unilab.dr.types import (
-    DomainRandomizationCapabilities,
-    IntervalRandomizationPlan,
-    ResetRandomizationPayload,
-)
-
-from .dependencies import load_mjwarp_dependencies
-from .materialization import materialize_mjwarp_scene
-from .playback import run_mjwarp_playback, validate_mjwarp_visual_model
-
-_GRAPH_CAPTURE_MIN_DRIVER = (12, 4)
-
-
-@contextmanager
-def _suspend_gc() -> Iterator[None]:
-    """Keep graph finalizers from running inside a new Warp capture."""
-    enabled = gc.isenabled()
-    gc.disable()
-    try:
-        yield
-    finally:
-        if enabled:
-            gc.enable()
-
-
-def _cuda_graph_eligibility(warp: Any, device: Any) -> tuple[bool, str | None]:
-    """Return the cold-path CUDA graph decision and a fallback diagnostic."""
-    if not bool(device.is_cuda):
-        return False, "active Warp device is not CUDA"
-
-    try:
-        driver_version = warp.get_cuda_driver_version()
-    except Exception as exc:
-        return False, f"CUDA driver query failed: {type(exc).__name__}: {exc}"
-    if driver_version is None:
-        return False, "CUDA driver version is unavailable"
-
-    try:
-        mempool_enabled = bool(warp.is_mempool_enabled(device))
-    except Exception as exc:
-        return False, f"CUDA mempool query failed: {type(exc).__name__}: {exc}"
-
-    reasons: list[str] = []
-    if tuple(driver_version) < _GRAPH_CAPTURE_MIN_DRIVER:
-        reasons.append(f"CUDA driver {driver_version[0]}.{driver_version[1]} is older than 12.4")
-    if not mempool_enabled:
-        reasons.append("CUDA mempool is disabled")
-    if reasons:
-        return False, "; ".join(reasons)
-    return True, None
-
-
-class MjwarpBackend(SimBackend):
-    """Independent CUDA backend exposed through the host NumPy profile.
-
-    State and control cross the host/device boundary only at explicit
-    step/reset barriers with bounded, statically declared transfers.  Interval
-    DR, native rendering, and host-substep-controller
-    combinations remain fail-closed. Detached host snapshots support finite
-    MuJoCo-based offline recording.
-    """
-
-    def __init__(
-        self,
-        scene: SceneCfg,
-        num_envs: int,
-        sim_dt: float,
-        *,
-        base_name: str | None = None,
-        push_body_name: str | None = None,
-        nconmax: int | None = None,
-        njmax: int | None = None,
-        **unexpected_kwargs: Any,
-    ) -> None:
-        if unexpected_kwargs:
-            names = ", ".join(sorted(unexpected_kwargs))
-            raise TypeError(f"MjwarpBackend does not accept backend options: {names}")
-        if isinstance(num_envs, bool) or int(num_envs) <= 0:
-            raise ValueError(f"num_envs must be a positive integer, got {num_envs!r}")
-        if float(sim_dt) <= 0.0:
-            raise ValueError(f"sim_dt must be positive, got {sim_dt!r}")
-        nconmax = self._require_capacity(nconmax, name="nconmax", default=512)
-        njmax = self._require_capacity(njmax, name="njmax", default=512)
-        if push_body_name is not None:
-            raise NotImplementedError(
-                "mjwarp host_numpy profile does not support interval push or external wrench "
-                "randomization; remove domain_rand.push_body_name and disable push_robots."
-            )
-
-        deps = load_mjwarp_dependencies()
-        device = deps.warp.get_device()
-        if not bool(device.is_cuda):
-            raise RuntimeError(
-                "mjwarp backend requires an active CUDA Warp device; choose a CUDA-capable "
-                "host or select the mujoco backend."
-            )
-
-        scene_context = materialize_mjwarp_scene(scene)
-        self._scene_cleanup_handle = scene_context.cleanup_handle
-        self.scene_model_file = scene_context.diagnostic_model_file
-        self.scene_visual_model_file = str(scene.visual_model_file or scene.model_file)
-        self._playback_model_validated = False
-        self._pre_step_control_fn = None
-        self.backend_type = "mjwarp"
-        self._num_envs = int(num_envs)
-        self._sim_dt = float(sim_dt)
-        self._base_name = base_name
-        self._nconmax = nconmax
-        self._njmax = njmax
-
-        self._mujoco = deps.mujoco
-        self._mujoco_warp = deps.mujoco_warp
-        self._warp = deps.warp
-        self._cpu_model = deps.mujoco.MjModel.from_xml_path(scene_context.source_model_file)
-        self._cpu_model.opt.timestep = self._sim_dt
-        self._device_model = deps.mujoco_warp.put_model(self._cpu_model)
-        self._device_data = deps.mujoco_warp.make_data(
-            self._cpu_model,
-            nworld=self._num_envs,
-            # These capacities are owner-configured cold-path physical storage
-            # limits.  They are intentionally not inferred or changed during a
-            # rollout: a task must select and validate its own safe budget.
-            nconmax=nconmax,
-            njmax=njmax,
-        )
-
-        self._nq = int(self._cpu_model.nq)
-        self._nv = int(self._cpu_model.nv)
-        self._nu = int(self._cpu_model.nu)
-        self._nbody = int(self._cpu_model.nbody)
-        self._root_qpos_dim, self._root_qvel_dim = self._root_state_dims()
-        self._num_dof_pos = self._nq - self._root_qpos_dim
-        self._num_dof_vel = self._nv - self._root_qvel_dim
-
-        self._sensor_slots = self._bind_sensor_slots()
-        self._keyframe_qpos = self._bind_keyframes()
-        self._body_ids = self._bind_names(deps.mujoco.mjtObj.mjOBJ_BODY, self._nbody)
-        self._joint_ids = self._bind_names(
-            deps.mujoco.mjtObj.mjOBJ_JOINT,
-            int(self._cpu_model.njnt),
-        )
-        if self._base_name is None:
-            self._base_body_id: int | None = None
-        else:
-            try:
-                self._base_body_id = self._body_ids[self._base_name]
-            except KeyError as exc:
-                raise ValueError(
-                    f"Base body {self._base_name!r} not found in mjwarp model"
-                ) from exc
-        self._geom_ids = self._bind_names(deps.mujoco.mjtObj.mjOBJ_GEOM, int(self._cpu_model.ngeom))
-        self._site_ids = self._bind_names(deps.mujoco.mjtObj.mjOBJ_SITE, int(self._cpu_model.nsite))
-        self._actuator_names = tuple(
-            deps.mujoco.mj_id2name(
-                self._cpu_model,
-                deps.mujoco.mjtObj.mjOBJ_ACTUATOR,
-                actuator_id,
-            )
-            or ""
-            for actuator_id in range(self._nu)
-        )
-        self._actuator_ctrl_range = np.asarray(
-            self._cpu_model.actuator_ctrlrange,
-            dtype=np.float32,
-        ).copy()
-        self._joint_range = self._bind_joint_range()
-
-        # All legacy getters below return views into these stable host buffers.
-        # They are refreshed only by _refresh_host_cache(), called after a
-        # device step or a reset/forward lifecycle barrier.
-        self._qpos_cache = np.zeros((self._num_envs, self._nq), dtype=np.float32)
-        self._qvel_cache = np.zeros((self._num_envs, self._nv), dtype=np.float32)
-        self._time_cache = np.zeros((self._num_envs,), dtype=np.float32)
-        self._sensor_cache = np.zeros(
-            (self._num_envs, int(self._cpu_model.nsensordata)),
-            dtype=np.float32,
-        )
-        self._ctrl_staging = np.zeros((self._num_envs, self._nu), dtype=np.float32)
-        self._reset_mask_host = np.zeros((self._num_envs,), dtype=np.bool_)
-        self._reset_mask_device = deps.warp.zeros(self._num_envs, dtype=bool)
-        # Begin from explicit model defaults, run a forward barrier, and cache
-        # the resulting sensors/kinematics.  This avoids an uninitialized host
-        # cache before NpEnv's first selected-row reset.
-        defaults = np.broadcast_to(
-            np.asarray(self._cpu_model.qpos0, dtype=np.float32),
-            (self._num_envs, self._nq),
-        )
-        np.copyto(self._qpos_cache, defaults)
-        self._qvel_cache.fill(0.0)
-        self._upload(self._device_data.qpos, self._qpos_cache)
-        self._upload(self._device_data.qvel, self._qvel_cache)
-        self._mujoco_warp.forward(self._device_model, self._device_data)
-        self._synchronize()
-        self._refresh_host_cache()
-        self._initialize_cuda_graphs(device)
-
-    # ------------------------------------------------------------------ #
-    # Cold-path model binding                                             #
-    # ------------------------------------------------------------------ #
-
-    @staticmethod
-    def _require_capacity(value: int | None, *, name: str, default: int) -> int:
-        if value is None:
-            return default
-        if isinstance(value, bool) or not isinstance(value, int) or value <= 0:
-            raise ValueError(f"mjwarp {name} must be a positive integer, got {value!r}")
-        return value
-
-    def _root_state_dims(self) -> tuple[int, int]:
-        if int(self._cpu_model.njnt) == 0:
-            return 0, 0
-        free_joint = int(self._mujoco.mjtJoint.mjJNT_FREE)
-        if int(self._cpu_model.jnt_type[0]) == free_joint:
-            return 7, 6
-        return 0, 0
-
-    def _bind_names(self, object_type: Any, count: int) -> dict[str, int]:
-        names: dict[str, int] = {}
-        for object_id in range(count):
-            name = self._mujoco.mj_id2name(self._cpu_model, object_type, object_id)
-            if name is not None:
-                names[str(name)] = object_id
-        return names
-
-    def _bind_sensor_slots(self) -> dict[str, tuple[int, int]]:
-        slots: dict[str, tuple[int, int]] = {}
-        sensor_type = self._mujoco.mjtObj.mjOBJ_SENSOR
-        for sensor_id in range(int(self._cpu_model.nsensor)):
-            name = self._mujoco.mj_id2name(self._cpu_model, sensor_type, sensor_id)
-            if name is None:
-                continue
-            slots[str(name)] = (
-                int(self._cpu_model.sensor_adr[sensor_id]),
-                int(self._cpu_model.sensor_dim[sensor_id]),
-            )
-        return slots
-
-    def _bind_keyframes(self) -> dict[str, np.ndarray]:
-        keyframes: dict[str, np.ndarray] = {}
-        key_type = self._mujoco.mjtObj.mjOBJ_KEY
-        for key_id in range(int(self._cpu_model.nkey)):
-            name = self._mujoco.mj_id2name(self._cpu_model, key_type, key_id)
-            if name is not None:
-                keyframes[str(name)] = np.asarray(
-                    self._cpu_model.key_qpos[key_id],
-                    dtype=np.float32,
-                ).copy()
-        return keyframes
-
-    def _bind_joint_range(self) -> np.ndarray | None:
-        free_joint = int(self._mujoco.mjtJoint.mjJNT_FREE)
-        mask = np.asarray(self._cpu_model.jnt_type, dtype=np.int32) != free_joint
-        joint_range = np.asarray(self._cpu_model.jnt_range, dtype=np.float32)[mask]
-        return None if joint_range.size == 0 else joint_range.copy()
-
-    # ------------------------------------------------------------------ #
-    # Explicit host-cache barriers                                        #
-    # ------------------------------------------------------------------ #
-
-    def _refresh_host_cache(self) -> None:
-        """Copy all legacy-visible device state at one explicit lifecycle barrier."""
-        self._download(self._device_data.qpos, self._qpos_cache)
-        self._download(self._device_data.qvel, self._qvel_cache)
-        self._download(self._device_data.sensordata, self._sensor_cache)
-
-    def _upload(self, device_array: Any, host_array: np.ndarray) -> None:
-        device_array.assign(host_array)
-
-    def _download(self, device_array: Any, host_array: np.ndarray) -> None:
-        np.copyto(host_array, device_array.numpy())
-
-    def _synchronize(self) -> None:
-        self._warp.synchronize_device()
-
-    def _disable_cuda_graphs(self, reason: str) -> None:
-        """Atomically select the eager path and release any captured graphs."""
-        self._cuda_graph_enabled = False
-        self._step_graph = None
-        self._forward_graph = None
-        self._reset_graph = None
-        self._cuda_graph_disable_reason: str | None = reason
-
-    def _initialize_cuda_graphs(self, device: Any) -> None:
-        """Capture fixed-address device operations or retain the eager fallback.
-
-        Current uploads mutate existing Warp arrays with ``assign``. Any future
-        owner-layer operation that replaces a model or data array must call this
-        method afterward so captured pointers cannot become stale.
-        """
-        self._disable_cuda_graphs("CUDA graph capture has not been initialized")
-        eligible, reason = _cuda_graph_eligibility(self._warp, device)
-        if not eligible:
-            assert reason is not None
-            self._cuda_graph_disable_reason = reason
-            warnings.warn(
-                f"mjwarp CUDA graphs disabled; using eager execution: {reason}",
-                RuntimeWarning,
-                stacklevel=2,
-            )
-            return
-
-        try:
-            # Assign only after all captures succeed. This keeps step/reset on
-            # one execution mode if any MJWarp operation is not capturable.
-            with _suspend_gc(), self._warp.ScopedDevice(device):
-                with self._warp.ScopedCapture() as step_capture:
-                    self._mujoco_warp.step(self._device_model, self._device_data)
-                with self._warp.ScopedCapture() as forward_capture:
-                    self._mujoco_warp.forward(self._device_model, self._device_data)
-                with self._warp.ScopedCapture() as reset_capture:
-                    self._mujoco_warp.reset_data(
-                        self._device_model,
-                        self._device_data,
-                        reset=self._reset_mask_device,
-                    )
-            step_graph = step_capture.graph
-            forward_graph = forward_capture.graph
-            reset_graph = reset_capture.graph
-        except Exception as exc:
-            reason = f"capture failed: {type(exc).__name__}: {exc}"
-            self._disable_cuda_graphs(reason)
-            warnings.warn(
-                f"mjwarp CUDA graphs disabled; using eager execution: {reason}",
-                RuntimeWarning,
-                stacklevel=2,
-            )
-            return
-
-        self._step_graph = step_graph
-        self._forward_graph = forward_graph
-        self._reset_graph = reset_graph
-        self._cuda_graph_enabled = True
-        self._cuda_graph_disable_reason = None
-
-    def _execute_device_steps(self, nsteps: int) -> None:
-        """Advance fixed-shape device state through graph replay or eager calls."""
-        if self._cuda_graph_enabled:
-            assert self._step_graph is not None
-            for _ in range(nsteps):
-                self._warp.capture_launch(self._step_graph)
-            return
-        for _ in range(nsteps):
-            self._mujoco_warp.step(self._device_model, self._device_data)
-
-    def _execute_device_reset(self) -> None:
-        """Clear selected device rows before the host state upload."""
-        if self._cuda_graph_enabled:
-            assert self._reset_graph is not None
-            self._warp.capture_launch(self._reset_graph)
-            return
-        self._mujoco_warp.reset_data(
-            self._device_model,
-            self._device_data,
-            reset=self._reset_mask_device,
-        )
-
-    def _execute_device_forward(self) -> None:
-        """Refresh kinematics after the host state upload."""
-        if self._cuda_graph_enabled:
-            assert self._forward_graph is not None
-            self._warp.capture_launch(self._forward_graph)
-            return
-        self._mujoco_warp.forward(self._device_model, self._device_data)
-
-    def _validate_rows(self, env_indices: np.ndarray) -> np.ndarray:
-        rows = np.asarray(env_indices, dtype=np.intp)
-        if rows.ndim != 1:
-            raise ValueError(f"env_indices must be one-dimensional, got shape {rows.shape}")
-        if np.any(rows < 0) or np.any(rows >= self._num_envs):
-            raise ValueError(f"env_indices must be in [0, {self._num_envs}), got {rows}")
-        if np.unique(rows).size != rows.size:
-            raise ValueError("env_indices must not contain duplicate rows")
-        return rows
-
-    # ------------------------------------------------------------------ #
-    # SimBackend properties and cold metadata                             #
-    # ------------------------------------------------------------------ #
-
-    @property
-    def num_envs(self) -> int:
-        return self._num_envs
-
-    @property
-    def model(self) -> Any:
-        """Return the backend-owned device model, never a MuJoCo backend model."""
-        return self._device_model
-
-    @property
-    def num_actuators(self) -> int:
-        return self._nu
-
-    @property
-    def num_dof_vel(self) -> int:
-        return self._num_dof_vel
-
-    def get_actuator_ctrl_range(self) -> np.ndarray:
-        return self._actuator_ctrl_range.copy()
-
-    def get_actuator_names(self) -> tuple[str, ...]:
-        return self._actuator_names
-
-    def get_scene_model_file(self) -> str | None:
-        return self.scene_model_file
-
-    def get_keyframe_qpos(self, name: str) -> np.ndarray:
-        try:
-            return self._keyframe_qpos[name].copy()
-        except KeyError as exc:
-            available = ", ".join(sorted(self._keyframe_qpos))
-            raise ValueError(f"Keyframe {name!r} not found; available: {available}") from exc
-
-    def get_default_qpos(self) -> np.ndarray:
-        return np.asarray(self._cpu_model.qpos0, dtype=np.float32).copy()
-
-    def get_init_qvel(self) -> np.ndarray:
-        return np.zeros((self._nv,), dtype=np.float32)
-
-    def get_body_ids(self, names: Sequence[str]) -> np.ndarray:
-        resolved: list[int] = []
-        for name in names:
-            try:
-                resolved.append(self._body_ids[str(name)])
-            except KeyError as exc:
-                raise ValueError(f"Body {name!r} not found in mjwarp model") from exc
-        return np.asarray(resolved, dtype=np.int32)
-
-    def get_geom_id(self, name: str) -> int:
-        try:
-            return int(self._geom_ids[name])
-        except KeyError as exc:
-            raise ValueError(f"Geom {name!r} not found in mjwarp model") from exc
-
-    def get_geom_size(self, name: str) -> np.ndarray:
-        return np.asarray(
-            self._cpu_model.geom_size[self.get_geom_id(name)], dtype=np.float32
-        ).copy()
-
-    def get_body_subtree_ids(self, root_body_id: int) -> np.ndarray:
-        root = int(root_body_id)
-        if root < 0 or root >= self._nbody:
-            raise ValueError(f"root_body_id must be in [0, {self._nbody}), got {root}")
-        descendants = {root}
-        changed = True
-        parent_ids = np.asarray(self._cpu_model.body_parentid, dtype=np.int32)
-        while changed:
-            changed = False
-            for body_id, parent_id in enumerate(parent_ids):
-                if body_id not in descendants and int(parent_id) in descendants:
-                    descendants.add(body_id)
-                    changed = True
-        return np.asarray(sorted(descendants), dtype=np.int32)
-
-    def get_geom_names(self) -> tuple[str, ...]:
-        names = [""] * int(self._cpu_model.ngeom)
-        for name, geom_id in self._geom_ids.items():
-            names[geom_id] = name
-        return tuple(names)
-
-    def get_geom_body_ids(self) -> np.ndarray:
-        return np.asarray(self._cpu_model.geom_bodyid, dtype=np.int32).copy()
-
-    def get_geom_contact_masks(self) -> tuple[np.ndarray, np.ndarray]:
-        return (
-            np.asarray(self._cpu_model.geom_contype, dtype=np.int32).copy(),
-            np.asarray(self._cpu_model.geom_conaffinity, dtype=np.int32).copy(),
-        )
-
-    def get_geom_friction(self) -> np.ndarray:
-        return np.asarray(self._cpu_model.geom_friction, dtype=np.float32).copy()
-
-    def get_gravity(self) -> np.ndarray:
-        return np.asarray(self._cpu_model.opt.gravity, dtype=np.float32).copy()
-
-    def get_body_mass(self) -> np.ndarray:
-        return np.asarray(self._cpu_model.body_mass, dtype=np.float32).copy()
-
-    def get_body_ipos(self) -> np.ndarray:
-        return np.asarray(self._cpu_model.body_ipos, dtype=np.float32).copy()
-
-    def get_dof_armature(self) -> np.ndarray:
-        return np.asarray(self._cpu_model.dof_armature, dtype=np.float32).copy()
-
-    def get_motion_body_ids(self, names: Sequence[str]) -> np.ndarray:
-        return self.get_body_ids(names)
-
-    def get_joint_range(self) -> np.ndarray | None:
-        return None if self._joint_range is None else self._joint_range.copy()
-
-    def get_site_ids(self, names: Sequence[str]) -> np.ndarray:
-        resolved: list[int] = []
-        for name in names:
-            try:
-                resolved.append(self._site_ids[str(name)])
-            except KeyError as exc:
-                raise ValueError(f"Site {name!r} not found in mjwarp model") from exc
-        return np.asarray(resolved, dtype=np.int32)
-
-    def get_joint_dof_indices(self, names: Sequence[str]) -> np.ndarray:
-        """Resolve named joint qvel coordinates on the cold metadata path."""
-
-        resolved: list[int] = []
-        for name in names:
-            try:
-                joint_id = self._joint_ids[str(name)]
-            except KeyError as exc:
-                raise ValueError(f"Joint {name!r} not found in mjwarp model") from exc
-            resolved.append(int(self._cpu_model.jnt_dofadr[joint_id]))
-        return np.asarray(resolved, dtype=np.int32)
-
-    def get_joint_dof_pos_indices(self, names: Sequence[str]) -> np.ndarray:
-        """Resolve named single-DoF qpos coordinates excluding the free root."""
-
-        single_dof_types = {
-            int(self._mujoco.mjtJoint.mjJNT_HINGE),
-            int(self._mujoco.mjtJoint.mjJNT_SLIDE),
-        }
-        resolved: list[int] = []
-        for name in names:
-            try:
-                joint_id = self._joint_ids[str(name)]
-            except KeyError as exc:
-                raise ValueError(f"Joint {name!r} not found in mjwarp model") from exc
-            if int(self._cpu_model.jnt_type[joint_id]) not in single_dof_types:
-                raise ValueError(f"Joint {name!r} is not a single-DoF joint")
-            resolved.append(int(self._cpu_model.jnt_qposadr[joint_id]) - self._root_qpos_dim)
-        return np.asarray(resolved, dtype=np.int32)
-
-    def get_joint_dof_vel_indices(self, names: Sequence[str]) -> np.ndarray:
-        """Resolve named joint qvel coordinates excluding the free root."""
-
-        return self.get_joint_dof_indices(names) - self._root_qvel_dim
-
-    def get_actuator_gains(self) -> tuple[np.ndarray, np.ndarray]:
-        """Expose immutable model defaults; this does not advertise gain DR support."""
-        kp = np.asarray(self._cpu_model.actuator_gainprm[:, 0], dtype=np.float32).copy()
-        kd = np.asarray(-self._cpu_model.actuator_biasprm[:, 2], dtype=np.float32).copy()
-        return kp, kd
-
-    def _execute_host_step(
-        self,
-        ctrl: np.ndarray,
-        nsteps: int,
-    ) -> dict[str, float]:
-        """Execute the owner-layer host-cache barrier for one legacy step."""
-        t0 = time.perf_counter()
-        np.copyto(self._ctrl_staging, ctrl)
-        self._upload(self._device_data.ctrl, self._ctrl_staging)
-        control_upload_ms = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        self._execute_device_steps(nsteps)
-        self._synchronize()
-        physics_ms = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        self._refresh_host_cache()
-        self._time_cache += np.float32(nsteps * self._sim_dt)
-        host_cache_ms = (time.perf_counter() - t0) * 1000.0
-        return {
-            "control_upload_ms": control_upload_ms,
-            "physics_ms": physics_ms,
-            "host_cache_refresh_ms": host_cache_ms,
-        }
-
-    def _execute_host_reset(
-        self,
-        row_ids: np.ndarray,
-        qpos: np.ndarray,
-        qvel: np.ndarray,
-    ) -> dict[str, float]:
-        """Commit one explicit reset barrier from host staging.
-
-        Callers validate and own the staging source. The helper preserves the
-        backend-owned transfer ordering: reset mask/qpos/qvel H2D,
-        forward/sync, then cache D2H.
-        """
-
-        t0 = time.perf_counter()
-        self._reset_mask_host.fill(False)
-        self._reset_mask_host[row_ids] = True
-        self._upload(self._reset_mask_device, self._reset_mask_host)
-        self._execute_device_reset()
-        # Full-cache uploads are intentional for the host compatibility
-        # profile: they preserve complement worlds after reset_data cleared
-        # selected transient state, while keeping all D2H materialization at
-        # one explicit barrier.
-        self._upload(self._device_data.qpos, qpos)
-        self._upload(self._device_data.qvel, qvel)
-        reset_upload_ms = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        self._execute_device_forward()
-        self._synchronize()
-        reset_forward_ms = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        self._refresh_host_cache()
-        self._time_cache[row_ids] = 0.0
-        host_cache_ms = (time.perf_counter() - t0) * 1000.0
-        return {
-            "reset_upload_ms": reset_upload_ms,
-            "reset_forward_ms": reset_forward_ms,
-            "host_cache_refresh_ms": host_cache_ms,
-        }
-
-    def set_pre_step_control(self, fn: Any | None) -> None:
-        if fn is not None:
-            raise NotImplementedError(
-                "mjwarp host_numpy profile rejects host pre-step callbacks; a per-substep "
-                "CPU callback would introduce an implicit device synchronization."
-            )
-        self._pre_step_control_fn = None
-
-    def step(self, ctrl: np.ndarray, nsteps: int = 1) -> dict[str, dict[str, float]]:
-        if isinstance(nsteps, bool) or int(nsteps) <= 0:
-            raise ValueError(f"nsteps must be a positive integer, got {nsteps!r}")
-        ctrl_array = np.asarray(ctrl, dtype=np.float32)
-        expected = (self._num_envs, self._nu)
-        if ctrl_array.shape != expected:
-            raise ValueError(f"ctrl must have shape {expected}, got {ctrl_array.shape}")
-        timings = self._execute_host_step(ctrl_array, int(nsteps))
-        return {"timing": timings}
-
-    def set_state(
-        self,
-        env_indices: np.ndarray,
-        qpos: np.ndarray,
-        qvel: np.ndarray,
-        randomization: ResetRandomizationPayload | None = None,
-    ) -> dict[str, dict[str, float]]:
-        if randomization is not None and not randomization.is_empty():
-            requested = ", ".join(sorted(randomization.requested_terms()))
-            raise NotImplementedError(
-                "mjwarp host_numpy profile does not support reset domain randomization "
-                f"terms: {requested}."
-            )
-        rows = self._validate_rows(env_indices)
-        qpos_array = np.asarray(qpos, dtype=np.float32)
-        qvel_array = np.asarray(qvel, dtype=np.float32)
-        expected_qpos = (rows.size, self._nq)
-        expected_qvel = (rows.size, self._nv)
-        if qpos_array.shape != expected_qpos:
-            raise ValueError(f"qpos must have shape {expected_qpos}, got {qpos_array.shape}")
-        if qvel_array.shape != expected_qvel:
-            raise ValueError(f"qvel must have shape {expected_qvel}, got {qvel_array.shape}")
-        if rows.size == 0:
-            return {"timing": {"set_state_reset_ms": 0.0, "set_state_cache_refresh_ms": 0.0}}
-
-        self._qpos_cache[rows] = qpos_array
-        self._qvel_cache[rows] = qvel_array
-        timings = self._execute_host_reset(rows, self._qpos_cache, self._qvel_cache)
-        return {
-            "timing": {
-                "set_state_reset_ms": timings["reset_upload_ms"] + timings["reset_forward_ms"],
-                "set_state_cache_refresh_ms": timings["host_cache_refresh_ms"],
-            }
-        }
-
-    def get_dr_capabilities(self) -> DomainRandomizationCapabilities:
-        """Advertise no legacy DR until per-world model mutation is effect-tested."""
-        return DomainRandomizationCapabilities()
-
-    def apply_interval_randomization(self, plan: IntervalRandomizationPlan) -> None:
-        if plan.is_empty():
-            return
-        raise NotImplementedError(
-            "mjwarp host_numpy profile does not support interval randomization; disable "
-            "push/body-force/body-velocity terms in the owner YAML."
-        )
-
-    def materialize(self) -> None:
-        """Resources are fully materialized during the constructor cold path."""
-
-    def get_play_capabilities(self) -> BackendPlayCapabilities:
-        return BackendPlayCapabilities(supports_physics_state_playback=True)
-
-    def resolve_play_render_plan(
-        self,
-        *,
-        play_render_mode: str | None,
-        play_steps: int | None,
-        output_video: str | PathLike[str] | None,
-    ) -> BackendPlayRenderPlan:
-        mode = normalize_play_render_mode(play_render_mode)
-        if mode == "none":
-            return BackendPlayRenderPlan(
-                mode="none",
-                headless=True,
-                record_video=False,
-                num_steps=None,
-                output_video=None,
-            )
-        if mode == "auto":
-            raise NotImplementedError(
-                "mjwarp playback does not support auto mode; select record or none explicitly."
-            )
-        if mode == "interactive":
-            raise NotImplementedError(
-                "mjwarp playback does not support interactive or native rendering; "
-                "select record or none."
-            )
-        if isinstance(play_steps, bool) or play_steps is None or int(play_steps) <= 0:
-            raise ValueError(
-                "mjwarp record playback requires a positive finite training.play_steps value."
-            )
-        if output_video is None:
-            raise ValueError("mjwarp record playback requires an output video path.")
-        return BackendPlayRenderPlan(
-            mode="record",
-            headless=True,
-            record_video=True,
-            num_steps=int(play_steps),
-            output_video=output_video,
-        )
-
-    def run_playback(
-        self,
-        *,
-        env: Any,
-        initialize: Any,
-        step: Any,
-        num_steps: int | None,
-        output_video: str | PathLike[str] | None = None,
-        render_spacing: float | None = None,
-        render_offset_mode: str | None = None,
-        headless: bool | None = None,
-        record_video: bool | None = None,
-        frame_state_getter: Any = None,
-        camera_kwargs: dict[str, Any] | None = None,
-        extra_data_getter: Any = None,
-    ) -> str | None:
-        del render_offset_mode
-        should_record = bool(record_video) if record_video is not None else output_video is not None
-        should_run_headless = bool(headless) if headless is not None else should_record
-        return run_mjwarp_playback(
-            backend=self,
-            env=env,
-            initialize=initialize,
-            step=step,
-            num_steps=num_steps,
-            output_video=output_video,
-            render_spacing=render_spacing,
-            headless=should_run_headless,
-            record_video=should_record,
-            snapshot_shape=(self._num_envs, 1 + self._nq + self._nv),
-            frame_state_getter=frame_state_getter,
-            camera_kwargs=camera_kwargs,
-            extra_data_getter=extra_data_getter,
-        )
-
-    def get_physics_state(self) -> np.ndarray:
-        state = np.empty((self._num_envs, 1 + self._nq + self._nv), dtype=np.float32)
-        state[:, 0] = self._time_cache
-        state[:, 1 : 1 + self._nq] = self._qpos_cache
-        state[:, 1 + self._nq :] = self._qvel_cache
-        return state
-
-    def get_playback_model(self, env_index: int | None = None) -> str:
-        if env_index is not None:
-            idx = int(env_index)
-            if idx < 0 or idx >= self._num_envs:
-                raise IndexError(f"env_index must be in [0, {self._num_envs - 1}], got {idx}")
-        if not self._playback_model_validated:
-            self.scene_visual_model_file = validate_mjwarp_visual_model(
-                mujoco=self._mujoco,
-                physics_model=self._cpu_model,
-                model_file=self.scene_visual_model_file,
-            )
-            self._playback_model_validated = True
-        return self.scene_visual_model_file
-
-    # ------------------------------------------------------------------ #
-    # Legacy getters: cache views only, never direct Warp transfers       #
-    # ------------------------------------------------------------------ #
-
-    def _require_free_root(self, operation: str) -> None:
-        if self._root_qpos_dim != 7 or self._root_qvel_dim != 6:
-            raise NotImplementedError(
-                f"{operation} requires a first free joint; mjwarp host_numpy profile is "
-                "currently validated only for floating-base G1 layouts."
-            )
-
-    def get_base_pos(self) -> np.ndarray:
-        self._require_free_root("get_base_pos")
-        return self._qpos_cache[:, 0:3]
-
-    def get_base_quat(self) -> np.ndarray:
-        self._require_free_root("get_base_quat")
-        return self._qpos_cache[:, 3:7]
-
-    def get_base_lin_vel(self) -> np.ndarray:
-        self._require_free_root("get_base_lin_vel")
-        return self._qvel_cache[:, 0:3]
-
-    def get_base_ang_vel(self) -> np.ndarray:
-        self._require_free_root("get_base_ang_vel")
-        return self._qvel_cache[:, 3:6]
-
-    def get_dof_pos(self) -> np.ndarray:
-        return self._qpos_cache[:, self._root_qpos_dim :]
-
-    def get_dof_vel(self) -> np.ndarray:
-        return self._qvel_cache[:, self._root_qvel_dim :]
-
-    def _unsupported_body_kinematics(self, operation: str) -> None:
-        raise NotImplementedError(
-            f"mjwarp host_numpy profile does not expose {operation}; the G1 host adapter "
-            "supports only base, dof, and configured sensor cache reads."
-        )
-
-    def get_body_pos_w(self, body_ids: np.ndarray) -> np.ndarray:
-        del body_ids
-        self._unsupported_body_kinematics("world-frame body positions")
-
-    def get_body_quat_w(self, body_ids: np.ndarray) -> np.ndarray:
-        del body_ids
-        self._unsupported_body_kinematics("world-frame body orientations")
-
-    def get_body_lin_vel_w(self, body_ids: np.ndarray) -> np.ndarray:
-        del body_ids
-        self._unsupported_body_kinematics("world-frame body linear velocities")
-
-    def get_body_ang_vel_w(self, body_ids: np.ndarray) -> np.ndarray:
-        del body_ids
-        self._unsupported_body_kinematics("world-frame body angular velocities")
-
-    def get_body_pos_b(self, body_ids: np.ndarray) -> np.ndarray:
-        del body_ids
-        self._unsupported_body_kinematics("base-frame body positions")
-
-    def get_body_quat_b(self, body_ids: np.ndarray) -> np.ndarray:
-        del body_ids
-        self._unsupported_body_kinematics("base-frame body orientations")
-
-    def get_body_lin_vel_b(self, body_ids: np.ndarray) -> np.ndarray:
-        del body_ids
-        self._unsupported_body_kinematics("base-frame body linear velocities")
-
-    def get_body_ang_vel_b(self, body_ids: np.ndarray) -> np.ndarray:
-        del body_ids
-        self._unsupported_body_kinematics("base-frame body angular velocities")
-
-    def get_sensor_data(self, name: str) -> np.ndarray:
-        try:
-            address, dimension = self._sensor_slots[name]
-        except KeyError as exc:
-            available = ", ".join(sorted(self._sensor_slots))
-            raise ValueError(f"Sensor {name!r} not found; available: {available}") from exc
-        return self._sensor_cache[:, address : address + dimension]
diff --git a/src/unilab/base/backend/mjwarp/dependencies.py b/src/unilab/base/backend/mjwarp/dependencies.py
deleted file mode 100644
index 3f5cea3bb..000000000
--- a/src/unilab/base/backend/mjwarp/dependencies.py
+++ /dev/null
@@ -1,61 +0,0 @@
-"""Lazy dependency boundary for the independent ``mjwarp`` backend."""
-
-from __future__ import annotations
-
-import importlib
-from dataclasses import dataclass
-from importlib import metadata
-from importlib.util import find_spec
-from typing import Any
-
-
-class MjwarpDependencyError(ImportError):
-    """Raised with an actionable install command when the optional extra is absent."""
-
-
-@dataclass(frozen=True)
-class MjwarpDependencies:
-    """Modules required by the production ``mjwarp`` implementation."""
-
-    mujoco: Any
-    mujoco_warp: Any
-    warp: Any
-
-
-_REQUIRED_MODULES = ("mujoco", "mujoco_warp", "warp")
-_REQUIRED_MUJOCO_WARP_VERSION = "3.10.0.3"
-_INSTALL_HINT = "Install it with `uv sync --extra mjwarp`."
-
-
-def mjwarp_dependencies_available() -> bool:
-    """Return availability without importing CUDA/Warp runtime modules."""
-    return all(find_spec(module_name) is not None for module_name in _REQUIRED_MODULES)
-
-
-def load_mjwarp_dependencies() -> MjwarpDependencies:
-    """Import optional runtime modules only when a backend instance is built."""
-    try:
-        installed_version = metadata.version("mujoco-warp")
-    except metadata.PackageNotFoundError as exc:
-        raise MjwarpDependencyError(
-            f"mjwarp backend requires optional dependency 'mujoco-warp'. {_INSTALL_HINT}"
-        ) from exc
-    if installed_version != _REQUIRED_MUJOCO_WARP_VERSION:
-        raise MjwarpDependencyError(
-            "mjwarp backend requires exact mujoco-warp version "
-            f"{_REQUIRED_MUJOCO_WARP_VERSION}, found {installed_version}. {_INSTALL_HINT}"
-        )
-    try:
-        mujoco = importlib.import_module("mujoco")
-        mujoco_warp = importlib.import_module("mujoco_warp")
-        warp = importlib.import_module("warp")
-    except ModuleNotFoundError as exc:
-        missing = exc.name or "an mjwarp dependency"
-        raise MjwarpDependencyError(
-            f"mjwarp backend requires optional dependency {missing!r}. {_INSTALL_HINT}"
-        ) from exc
-    except ImportError as exc:
-        raise MjwarpDependencyError(
-            f"mjwarp backend could not import its optional runtime: {exc}. {_INSTALL_HINT}"
-        ) from exc
-    return MjwarpDependencies(mujoco=mujoco, mujoco_warp=mujoco_warp, warp=warp)
diff --git a/src/unilab/base/backend/mjwarp/materialization.py b/src/unilab/base/backend/mjwarp/materialization.py
deleted file mode 100644
index 34b5ed5af..000000000
--- a/src/unilab/base/backend/mjwarp/materialization.py
+++ /dev/null
@@ -1,70 +0,0 @@
-"""Cold-path scene materialization for the independent ``mjwarp`` backend."""
-
-from __future__ import annotations
-
-import os
-from dataclasses import dataclass
-from typing import Any
-
-from unilab.base.scene import SceneCfg
-
-
-class _TemporarySceneCleanup:
-    """Own the one temporary XML created while merging scene fragments."""
-
-    def __init__(self, path: str) -> None:
-        self._path = path
-        self._cleaned = False
-
-    def cleanup(self) -> None:
-        if self._cleaned:
-            return
-        self._cleaned = True
-        try:
-            os.remove(self._path)
-        except FileNotFoundError:
-            pass
-
-
-@dataclass(frozen=True)
-class MjwarpSceneContext:
-    """Cold-path scene source and cleanup ownership for one backend instance."""
-
-    source_model_file: str
-    diagnostic_model_file: str
-    cleanup_handle: Any | None = None
-
-
-def materialize_mjwarp_scene(scene: SceneCfg) -> MjwarpSceneContext:
-    """Resolve a flat/fragments scene before CUDA model upload.
-
-    Height-field terrain construction is intentionally rejected in the first
-    correctness profile.  The rejection happens before model upload so an
-    unsupported owner cannot silently fall back to a different terrain path.
-    """
-    if scene is None or not scene.model_file:
-        raise ValueError("MjwarpBackend requires SceneCfg.model_file")
-    if scene.terrain is not None:
-        raise NotImplementedError(
-            "mjwarp host_numpy profile does not support generated terrain or height-field "
-            "scanners; select a flat owner YAML or a backend with terrain support."
-        )
-    if not scene.fragment_files:
-        return MjwarpSceneContext(
-            source_model_file=str(scene.model_file),
-            diagnostic_model_file=str(scene.model_file),
-        )
-
-    # This is intentionally in a cold-path-only module.  The shared XML
-    # composition helper is not a sibling runtime backend dependency.
-    from unilab.base.backend.mujoco.xml import materialize_scene_fragments
-
-    materialized = materialize_scene_fragments(
-        str(scene.model_file),
-        fragment_files=scene.fragment_files,
-    )
-    return MjwarpSceneContext(
-        source_model_file=materialized,
-        diagnostic_model_file=str(scene.model_file),
-        cleanup_handle=_TemporarySceneCleanup(materialized),
-    )
diff --git a/src/unilab/base/backend/mjwarp/playback.py b/src/unilab/base/backend/mjwarp/playback.py
deleted file mode 100644
index 1a5dcf2ec..000000000
--- a/src/unilab/base/backend/mjwarp/playback.py
+++ /dev/null
@@ -1,145 +0,0 @@
-"""Cold-path MuJoCo offline playback bridge for ``mjwarp``."""
-
-from __future__ import annotations
-
-from collections.abc import Callable
-from os import PathLike
-from pathlib import Path
-from typing import Any, TypeVar
-
-import numpy as np
-
-from unilab.base.backend.mujoco.playback import run_mujoco_playback
-
-ObsT = TypeVar("ObsT")
-
-
-def validate_mjwarp_visual_model(
-    *,
-    mujoco: Any,
-    physics_model: Any,
-    model_file: str | PathLike[str],
-) -> str:
-    """Validate the detached MuJoCo visual twin used for offline playback."""
-    path = Path(model_file)
-    if not path.is_file():
-        raise ValueError(
-            f"mjwarp offline playback visual model does not exist or is not a file: {path}"
-        )
-    try:
-        visual_model = mujoco.MjModel.from_xml_path(str(path))
-    except Exception as exc:
-        raise ValueError(
-            f"mjwarp offline playback could not load visual model {path}: "
-            f"{type(exc).__name__}: {exc}"
-        ) from exc
-
-    physics_dims = (int(physics_model.nq), int(physics_model.nv))
-    visual_dims = (int(visual_model.nq), int(visual_model.nv))
-    if visual_dims != physics_dims:
-        raise ValueError(
-            "mjwarp offline playback visual model state dimensions are incompatible: "
-            f"physics nq/nv={physics_dims}, visual nq/nv={visual_dims}."
-        )
-
-    joint_object = mujoco.mjtObj.mjOBJ_JOINT
-
-    def _joint_layout(model: Any) -> tuple[tuple[str | None, int, int, int], ...]:
-        return tuple(
-            (
-                mujoco.mj_id2name(model, joint_object, joint_id),
-                int(model.jnt_type[joint_id]),
-                int(model.jnt_qposadr[joint_id]),
-                int(model.jnt_dofadr[joint_id]),
-            )
-            for joint_id in range(int(model.njnt))
-        )
-
-    physics_layout = _joint_layout(physics_model)
-    visual_layout = _joint_layout(visual_model)
-    if visual_layout != physics_layout:
-        raise ValueError(
-            "mjwarp offline playback visual model joint layout is incompatible; "
-            "joint names, types, qpos addresses, and dof addresses must match physics."
-        )
-    return str(path)
-
-
-def run_mjwarp_playback(
-    *,
-    backend: Any,
-    env: Any,
-    initialize: Callable[[], ObsT],
-    step: Callable[[ObsT], ObsT],
-    num_steps: int | None,
-    output_video: str | PathLike[str] | None,
-    render_spacing: float | None,
-    headless: bool,
-    record_video: bool,
-    snapshot_shape: tuple[int, int],
-    frame_state_getter: Callable[[], np.ndarray] | None,
-    camera_kwargs: dict[str, Any] | None,
-    extra_data_getter: Callable[[], np.ndarray | None] | None = None,
-) -> str:
-    """Render detached mjwarp host snapshots with the existing MuJoCo pipeline."""
-    if not headless:
-        raise NotImplementedError(
-            "mjwarp offline playback does not support interactive rendering; "
-            "use training.play_render_mode=record."
-        )
-    if not record_video:
-        raise ValueError("mjwarp offline playback requires record_video=true.")
-    if isinstance(num_steps, bool) or num_steps is None or int(num_steps) <= 0:
-        raise ValueError("mjwarp record playback requires a positive finite num_steps value.")
-    if output_video is None:
-        raise ValueError("mjwarp record playback requires an output_video path.")
-
-    # Both checks are playback-only cold-path work. Physics construction and
-    # step/reset never import the renderer or parse the visual model.
-    backend.get_playback_model()
-    try:
-        from unilab.visualization import render_many
-
-        renderer_usable = bool(render_many.render_backend_usable())
-    except Exception as exc:
-        raise RuntimeError(
-            "mjwarp offline playback could not initialize the MuJoCo renderer: "
-            f"{type(exc).__name__}: {exc}"
-        ) from exc
-    if not renderer_usable:
-        raise RuntimeError(
-            "mjwarp offline playback requires a usable MuJoCo off-screen renderer; "
-            "configure EGL, OSMesa, or GLFW before recording."
-        )
-
-    getter = frame_state_getter or env.get_physics_state_snapshot
-    expected_shape = snapshot_shape
-
-    def _validated_state_getter() -> np.ndarray:
-        state = np.asarray(getter(), dtype=np.float32)
-        if state.shape != expected_shape:
-            raise ValueError(
-                "mjwarp offline playback snapshot must use [time, qpos, qvel] layout "
-                f"with shape {expected_shape}, got {state.shape}."
-            )
-        return state
-
-    result = run_mujoco_playback(
-        env=env,
-        initialize=initialize,
-        step=step,
-        num_steps=int(num_steps),
-        output_video=output_video,
-        render_spacing=render_spacing,
-        headless=True,
-        record_video=True,
-        frame_state_getter=_validated_state_getter,
-        camera_kwargs=camera_kwargs,
-        extra_data_getter=extra_data_getter,
-    )
-    if result is None:
-        raise RuntimeError(
-            "mjwarp offline playback produced no frames; the MuJoCo renderer or worker "
-            "failed after preflight."
-        )
-    return result
diff --git a/src/unilab/base/backend/motrix/__init__.py b/src/unilab/base/backend/motrix/__init__.py
deleted file mode 100644
index cb958def2..000000000
--- a/src/unilab/base/backend/motrix/__init__.py
+++ /dev/null
@@ -1,35 +0,0 @@
-"""Motrix backend package."""
-
-from __future__ import annotations
-
-
-def __getattr__(name: str):
-    if name in {"MOTRIX_AVAILABLE", "MotrixBackend"}:
-        from .backend import MOTRIX_AVAILABLE, MotrixBackend
-
-        if name == "MOTRIX_AVAILABLE":
-            return MOTRIX_AVAILABLE
-        return MotrixBackend
-    if name in {
-        "add_motrix_tracking_frame_sensors",
-        "materialize_motrix_hfield_attached_scene",
-        "materialize_motrix_scene",
-    }:
-        from . import scene
-
-        return getattr(scene, name)
-    if name == "run_motrix_playback":
-        from .playback import run_motrix_playback
-
-        return run_motrix_playback
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
-
-
-__all__ = [
-    "MOTRIX_AVAILABLE",
-    "MotrixBackend",
-    "add_motrix_tracking_frame_sensors",
-    "materialize_motrix_hfield_attached_scene",
-    "materialize_motrix_scene",
-    "run_motrix_playback",
-]
diff --git a/src/unilab/base/backend/motrix/backend.py b/src/unilab/base/backend/motrix/backend.py
deleted file mode 100644
index 2d6ae5090..000000000
--- a/src/unilab/base/backend/motrix/backend.py
+++ /dev/null
@@ -1,1665 +0,0 @@
-import logging
-import os
-import time
-from collections.abc import Sequence
-from dataclasses import dataclass
-from typing import Any, TypeVar, cast
-
-import numpy as np
-
-from unilab.base.scene import SceneCfg
-from unilab.dr.types import (
-    RESET_TERM_BASE_COM,
-    RESET_TERM_BASE_MASS,
-    RESET_TERM_BODY_IPOS,
-    RESET_TERM_BODY_MASS,
-    RESET_TERM_GEOM_FRICTION,
-    RESET_TERM_GRAVITY,
-    RESET_TERM_KD,
-    RESET_TERM_KP,
-    DomainRandomizationCapabilities,
-    InitRandomizationPlan,
-    IntervalRandomizationPlan,
-    ResetRandomizationPayload,
-)
-
-try:
-    import motrixsim as mtx
-    from motrixsim.render import RenderApp, RenderSettings
-    from motrixsim.render import RenderClosedError as _MotrixRenderClosedError
-
-    MOTRIX_AVAILABLE = True
-except ImportError:
-    MOTRIX_AVAILABLE = False
-    # No motrixsim in this process: the ``except _MotrixRenderClosedError``
-    # clauses below never match, which is correct because the renderer cannot
-    # exist without the package.
-    _MotrixRenderClosedError = ()
-
-from ..base import (
-    BackendHeightScanner,
-    BackendPlayCapabilities,
-    BackendPlayRenderPlan,
-    BackendTerrainSpawnData,
-    RenderClosedError,
-    SimBackend,
-    normalize_play_render_mode,
-)
-from ..motrix_camera import (
-    MotrixTrackingCamera,
-    render_offsets,
-    resolve_system_camera_view,
-    tracking_camera_lookat,
-)
-from .playback import run_motrix_playback
-
-logger = logging.getLogger(__name__)
-
-T = TypeVar("T")
-DEFAULT_MOTRIX_MAX_ITERATIONS = 3
-
-
-def _require_not_none(value: T | None, error_message: str) -> T:
-    if value is None:
-        raise ValueError(error_message)
-    return value
-
-
-def _first_scalar(value: Any) -> float:
-    arr = np.asarray(value, dtype=np.float32)
-    return float(arr.reshape(-1)[0])
-
-
-def _contiguous_slice(indices: np.ndarray) -> slice | None:
-    if indices.size == 0:
-        return None
-    start = int(indices[0])
-    stop = start + int(indices.size)
-    if np.array_equal(indices, np.arange(start, stop, dtype=indices.dtype)):
-        return slice(start, stop)
-    return None
-
-
-@dataclass
-class _MotrixSceneContext:
-    model: "mtx.SceneModel"
-    terrain_origins: np.ndarray | None = None
-    terrain_surface_sampler: object | None = None
-    cleanup_handle: object | None = None
-
-
-@dataclass
-class _MotrixTerrainScanner(BackendHeightScanner):
-    scanner: "mtx.TerrainScanner"
-    data: "mtx.SceneData"
-    out: np.ndarray
-
-    def scan(self) -> np.ndarray:
-        heights = np.asarray(self.scanner.scan(self.data, out=self.out))
-        if heights.shape != self.out.shape:
-            raise ValueError(
-                f"Motrix TerrainScanner.scan returned shape {heights.shape}, "
-                f"expected {self.out.shape}"
-            )
-        return heights
-
-
-def _build_motrix_scene_context(
-    scene: SceneCfg,
-    *,
-    add_body_sensors: bool,
-    base_name: str,
-) -> _MotrixSceneContext:
-    from unilab.base.backend.motrix.scene import (
-        materialize_motrix_hfield_attached_scene,
-        materialize_motrix_scene,
-    )
-
-    if scene is None:
-        raise ValueError("SceneCfg must be provided")
-    if not scene.model_file:
-        raise ValueError("SceneCfg.model_file must be provided")
-
-    if scene.terrain is None:
-        model = materialize_motrix_scene(
-            model_file=scene.model_file,
-            fragment_files=scene.fragment_files,
-            add_body_sensors=add_body_sensors,
-            base_name=base_name,
-        )
-        return _MotrixSceneContext(model=model)
-
-    if scene.terrain.generator is None:
-        raise ValueError("SceneCfg.terrain.generator must be configured for terrain scenes")
-
-    model, terrain_origins, terrain_surface_sampler = materialize_motrix_hfield_attached_scene(
-        model_file=scene.model_file,
-        terrain_cfg=scene.terrain.generator,
-        fragment_files=scene.fragment_files,
-        hfield_name=scene.terrain.hfield_name,
-        geom_name=scene.terrain.geom_name or "floor",
-        add_body_sensors=add_body_sensors,
-        base_name=base_name,
-        return_surface_sampler=True,
-    )
-    return _MotrixSceneContext(
-        model=model,
-        terrain_origins=terrain_origins,
-        terrain_surface_sampler=terrain_surface_sampler,
-    )
-
-
-class MotrixBackend(SimBackend):
-    """MotrixSim backend implementation."""
-
-    def __init__(
-        self,
-        scene: SceneCfg,
-        num_envs: int,
-        sim_dt: float,
-        base_name: str = "base",
-        np_dtype=np.float32,
-        add_body_sensors: bool = False,
-        max_iterations: int | None = DEFAULT_MOTRIX_MAX_ITERATIONS,
-        push_body_name: str | None = None,
-    ):
-        if not MOTRIX_AVAILABLE:
-            raise ImportError("motrixsim not available")
-
-        scene_context = _build_motrix_scene_context(
-            scene,
-            add_body_sensors=add_body_sensors,
-            base_name=base_name,
-        )
-        self._scene = scene
-        self.scene_artifacts_dir = None
-        self.terrain_origins = scene_context.terrain_origins
-        self.terrain_surface_sampler = scene_context.terrain_surface_sampler
-        self._terrain_spawn_data = (
-            None
-            if self.terrain_origins is None
-            else BackendTerrainSpawnData(
-                terrain_origins=self.terrain_origins,
-                sample_height=(
-                    None
-                    if self.terrain_surface_sampler is None
-                    else cast(Any, self.terrain_surface_sampler).sample_height
-                ),
-            )
-        )
-        self._scene_cleanup_handle = scene_context.cleanup_handle
-        self._base_name = base_name
-
-        self._model = scene_context.model
-        self._body_id_to_name = {  # type: ignore[assignment]
-            link.index: link.name for link in self._model.links if link.name
-        }
-
-        self._model.options.timestep = sim_dt
-        if max_iterations is None:
-            max_iterations = DEFAULT_MOTRIX_MAX_ITERATIONS
-        self._model.options.max_iterations = int(max_iterations)
-        self._num_envs = num_envs
-        self._np_dtype = np_dtype
-        self._pre_step_control_fn = None
-
-        self._data = mtx.SceneData(self._model, batch=[num_envs])  # pyright: ignore[reportPossiblyUnbound]
-        self._body: "mtx.Body" = _require_not_none(
-            self._model.get_body(base_name), f"Body '{base_name}' not found in Motrix model"
-        )
-        self._body_link: "mtx.Link" = _require_not_none(
-            self._model.get_link(base_name), f"Link '{base_name}' not found in Motrix model"
-        )
-        push_body = push_body_name if push_body_name is not None else base_name
-        self._push_body_link: "mtx.Link" = _require_not_none(
-            self._model.get_link(push_body), f"Push link '{push_body}' not found in Motrix model"
-        )
-        self._body_floatingbase = self._body.floatingbase
-        self._joint_dof_pos_indices = np.asarray(self._model.joint_dof_pos_indices, dtype=np.intp)
-        self._joint_dof_vel_indices = np.asarray(self._model.joint_dof_vel_indices, dtype=np.intp)
-        self._joint_dof_pos_slice = _contiguous_slice(self._joint_dof_pos_indices)
-        position_actuators: list["mtx.PositionActuator"] = []
-        for actuator in self._model.actuators:
-            if actuator.typ == "position":
-                position_actuators.append(cast("mtx.PositionActuator", actuator))
-        self._position_actuators = position_actuators
-        self._supports_position_actuator_gains = len(self._position_actuators) == int(
-            self._model.num_actuators
-        )
-        # qpos index of each position actuator's own target joint, in actuator
-        # order. Used to reset position actuators to "hold current pose" without
-        # assuming a fully-actuated model: parallel / under-actuated mechanisms
-        # (e.g. a Stewart platform) have passive joints, so the model-wide
-        # ``joint_dof_pos_indices`` is wider than ``num_actuators``.
-        self._actuator_joint_pos_indices: np.ndarray | None = None
-        self._actuator_joint_vel_indices: np.ndarray | None = None
-        if self._supports_position_actuator_gains:
-            joint_pos_idx: list[int] = []
-            joint_vel_idx: list[int] = []
-            for actuator in sorted(self._position_actuators, key=lambda a: int(a.index)):
-                if actuator.target_type != "joint":
-                    joint_pos_idx = []
-                    joint_vel_idx = []
-                    break
-                joint = self._model.get_joint(actuator.target_name)
-                if joint is None or int(joint.num_dof_pos) != 1:
-                    joint_pos_idx = []
-                    joint_vel_idx = []
-                    break
-                joint_pos_idx.append(int(joint.dof_pos_index))
-                joint_vel_idx.append(int(joint.dof_vel_index))
-            if len(joint_pos_idx) == int(self._model.num_actuators):
-                self._actuator_joint_pos_indices = np.asarray(joint_pos_idx, dtype=np.intp)
-                self._actuator_joint_vel_indices = np.asarray(joint_vel_idx, dtype=np.intp)
-        self._actuator_joint_pos_slice = (
-            _contiguous_slice(self._actuator_joint_pos_indices)
-            if self._actuator_joint_pos_indices is not None
-            else None
-        )
-        self._default_actuator_kp = np.zeros((self.num_actuators,), dtype=np.float32)
-        self._default_actuator_kd = np.zeros((self.num_actuators,), dtype=np.float32)
-        for actuator in self._position_actuators:
-            idx = int(actuator.index)
-            # TODO: switch to motrixsim model-level actuator gain API once available.
-            self._default_actuator_kp[idx] = _first_scalar(actuator.get_kp_override(self._data))
-            self._default_actuator_kd[idx] = _first_scalar(actuator.get_kd_override(self._data))
-        self._floating_base_quat_indices: tuple[np.ndarray, ...] = tuple(
-            np.asarray(floating_base.dof_pos_indices[3:7], dtype=np.intp)
-            for floating_base in getattr(self._model, "floating_bases", [])
-            if len(floating_base.dof_pos_indices) >= 7
-        )
-        self._links_by_id: dict[int, "mtx.Link"] = {
-            int(link.index): link for link in self._model.links
-        }
-        self._supports_external_force = all(
-            callable(getattr(link, "add_external_force", None))
-            for link in self._links_by_id.values()
-        )
-        self._applied_body_forces: dict[int, np.ndarray] = {}
-        self._geoms_by_id: dict[int, "mtx.Geom"] = {
-            int(geom.index): geom for geom in self._model.geoms
-        }
-        # TODO(motrixsim): once pure visual geoms either stop exposing friction
-        # override methods or safely no-op them, drop this collision-mask filter.
-        self._geom_friction_override_ids = tuple(
-            geom_id
-            for geom_id, geom in self._geoms_by_id.items()
-            if (
-                int(getattr(geom, "collision_group", 0)) != 0
-                or int(getattr(geom, "collision_affinity", 0)) != 0
-            )
-        )
-        self._supports_geom_friction_override = all(
-            callable(getattr(geom, "get_friction_override", None))
-            and callable(getattr(geom, "set_friction_override", None))
-            for geom_id, geom in self._geoms_by_id.items()
-            if geom_id in self._geom_friction_override_ids
-        )
-        self._supports_gravity_override = callable(
-            getattr(self._model, "get_gravity_override", None)
-        ) and callable(getattr(self._model, "set_gravity_override", None))
-        self._default_body_mass = np.zeros((int(self._model.num_links),), dtype=np.float32)
-        self._default_body_ipos = np.zeros((int(self._model.num_links), 3), dtype=np.float32)
-        for link_id, link in self._links_by_id.items():
-            self._default_body_mass[link_id] = _first_scalar(link.get_mass_override(self._data))
-            self._default_body_ipos[link_id] = np.asarray(
-                link.get_center_of_mass_override(self._data),
-                dtype=np.float32,
-            ).reshape(self._num_envs, 3)[0]
-        self._default_geom_friction = np.zeros((int(self._model.num_geoms), 3), dtype=np.float32)
-        if self._supports_geom_friction_override:
-            for geom_id in self._geom_friction_override_ids:
-                geom = self._geoms_by_id[geom_id]
-                self._default_geom_friction[geom_id] = np.asarray(
-                    geom.get_friction_override(self._data),
-                    dtype=np.float32,
-                ).reshape(self._num_envs, 3)[0]
-        self._init_geom_size_overrides: dict[int, np.ndarray] = {}
-        self._render_app: "RenderApp | None" = None
-        self._render_headless: bool | None = None
-        self._render_capture_enabled = False
-        self._render_offsets_np: np.ndarray | None = None
-        self._render_tracking_camera: MotrixTrackingCamera | None = None
-        self.backend_type = "motrix"
-        self._link_velocity_cache: np.ndarray | None = None
-
-        # Pre-cache link objects to avoid repeated get_link() lookups.
-        self._link_cache: dict[int, "mtx.Link"] = {}
-        for link in self._model.links:
-            if link.name:
-                self._link_cache[link.index] = link
-
-        # Run forward kinematics once so initial link poses and sensor data are valid.
-        self._model.forward_kinematic(self._data)
-        self._link_velocities: np.ndarray | None = None
-        self._link_velocity_cache_valid = False
-        self._refresh_link_pose_cache()
-
-        # Scratch buffers reused by set_state() to avoid per-reset allocations.
-        # Sized to the full env count and rewritten in place each call.
-        self._set_state_mask_scratch: np.ndarray = np.zeros(self._num_envs, dtype=bool)
-        self._set_state_qpos_motrix_scratch: np.ndarray | None = None
-
-    def get_motion_body_ids(self, names: Sequence[str]) -> np.ndarray:
-        ids: list[int] = []
-        for name in names:
-            link_id = self._model.get_link_index(name)
-            if link_id is None or link_id < 0:
-                raise ValueError(f"Motion body '{name}' not found in Motrix model")
-            # Motion datasets use MuJoCo-style body ids, where worldbody is id 0.
-            ids.append(int(link_id) + 1)
-        return np.array(ids, dtype=np.int32)
-
-    # ------------------------------------------------------------------ #
-    # Properties                                                         #
-    # ------------------------------------------------------------------ #
-
-    @property
-    def num_envs(self) -> int:
-        return self._num_envs
-
-    @property
-    def model(self):
-        return self._model
-
-    @property
-    def data(self):
-        return self._data
-
-    # ------------------------------------------------------------------ #
-    # Model properties                                                   #
-    # ------------------------------------------------------------------ #
-
-    @property
-    def num_actuators(self) -> int:
-        return int(self._model.num_actuators)
-
-    @property
-    def num_dof_vel(self) -> int:
-        return int(len(self._joint_dof_vel_indices))
-
-    def get_actuator_ctrl_range(self) -> np.ndarray:
-        arr: np.ndarray = np.array(self._model.actuator_ctrl_limits, dtype=self._np_dtype)
-        result: np.ndarray = arr.T.copy()
-        return result
-
-    def get_terrain_spawn_data(self) -> BackendTerrainSpawnData | None:
-        return self._terrain_spawn_data
-
-    def get_keyframe_qpos(self, name: str) -> np.ndarray:
-        if hasattr(self._model, "keyframes") and self._model.num_keyframes > 0:
-            qpos = np.array(self._model.keyframes[0].dof_pos, dtype=self._np_dtype)
-        else:
-            qpos = np.array(self._model.compute_init_dof_pos(), dtype=self._np_dtype)
-        return self._motrix_qpos_to_mujoco(qpos)
-
-    def get_default_qpos(self) -> np.ndarray:
-        qpos = np.array(self._model.compute_init_dof_pos(), dtype=self._np_dtype)
-        return self._motrix_qpos_to_mujoco(qpos)
-
-    def get_init_qvel(self) -> np.ndarray:
-        return np.zeros((self._model.num_dof_vel,), dtype=self._np_dtype)
-
-    def get_body_ids(self, names: Sequence[str]) -> np.ndarray:
-        ids: list[int] = []
-        for name in names:
-            bid = self._model.get_link_index(name)
-            if bid is None or bid < 0:
-                raise ValueError(f"Body '{name}' not found in Motrix model")
-            ids.append(int(bid))
-        return np.array(ids, dtype=np.int32)
-
-    def get_site_ids(self, names: Sequence[str]) -> np.ndarray:
-        ids: list[int] = []
-        for name in names:
-            sid = self._model.get_site_index(name)
-            if sid is None or sid < 0:
-                raise ValueError(f"Site '{name}' not found in Motrix model")
-            ids.append(int(sid))
-        return np.array(ids, dtype=np.int32)
-
-    def get_joint_dof_indices(self, names: Sequence[str]) -> np.ndarray:
-        indices: list[int] = []
-        for name in names:
-            joint = self._resolve_single_dof_joint(name)
-            indices.append(int(joint.dof_vel_index))
-        return np.array(indices, dtype=np.int32)
-
-    def get_joint_dof_pos_indices(self, names: Sequence[str]) -> np.ndarray:
-        indices: list[int] = []
-        for name in names:
-            joint = self._resolve_single_dof_joint(name)
-            indices.append(self._joint_dof_local_index(name, int(joint.dof_pos_index), pos=True))
-        return np.array(indices, dtype=np.int32)
-
-    def get_joint_dof_vel_indices(self, names: Sequence[str]) -> np.ndarray:
-        indices: list[int] = []
-        for name in names:
-            joint = self._resolve_single_dof_joint(name)
-            indices.append(self._joint_dof_local_index(name, int(joint.dof_vel_index), pos=False))
-        return np.array(indices, dtype=np.int32)
-
-    def get_site_jacobian_w(
-        self,
-        site_id: int,
-        dof_indices: np.ndarray,
-    ) -> tuple[np.ndarray, np.ndarray]:
-        sid = int(site_id)
-        if sid < 0 or sid >= int(self._model.num_sites):
-            raise ValueError(f"site_id out of range: {sid}")
-
-        site = self._model.sites[sid]
-        jac = np.asarray(site.get_jacobian(self._data), dtype=self._np_dtype)
-        if jac.ndim != 3 or jac.shape[0] != self._num_envs or jac.shape[1] != 6:
-            raise ValueError(
-                f"Motrix site Jacobian for site {sid} must have shape "
-                f"({self._num_envs}, 6, n), got {jac.shape}"
-            )
-
-        site_dof_indices = np.asarray(site.dof_vel_indices, dtype=np.int64).reshape(-1)
-        col_by_dof = {int(dof_index): col for col, dof_index in enumerate(site_dof_indices)}
-        requested = np.asarray(dof_indices, dtype=np.int64).reshape(-1)
-        cols: list[int] = []
-        for dof_index in requested:
-            key = int(dof_index)
-            if key not in col_by_dof:
-                raise ValueError(f"DoF index {key} is not present in site {sid} Jacobian")
-            cols.append(col_by_dof[key])
-
-        selected = jac[:, :, np.asarray(cols, dtype=np.intp)]
-        # Motrix returns angular rows first and linear rows second.
-        jacp = selected[:, 3:6, :]
-        jacr = selected[:, 0:3, :]
-        return jacp, jacr
-
-    def _resolve_single_dof_joint(self, name: str):
-        jid = self._model.get_joint_index(name)
-        if jid is None or jid < 0:
-            raise ValueError(f"Joint '{name}' not found in Motrix model")
-        joint = self._model.joints[int(jid)]
-        if int(getattr(joint, "num_dof_vel", 1)) != 1:
-            raise ValueError(f"Joint '{name}' is not a single-DoF joint")
-        return joint
-
-    def _joint_dof_local_index(self, name: str, model_index: int, *, pos: bool) -> int:
-        all_indices = self._joint_dof_pos_indices if pos else self._joint_dof_vel_indices
-        matches = np.flatnonzero(all_indices == int(model_index))
-        if matches.size != 1:
-            space = "qpos" if pos else "qvel"
-            raise ValueError(f"Joint '{name}' {space} index {model_index} is not in joint DoFs")
-        return int(matches[0])
-
-    def get_geom_id(self, name: str) -> int:
-        geom_id = self._model.get_geom_index(name)
-        if geom_id is None or geom_id < 0:
-            raise ValueError(f"Geom '{name}' not found in Motrix model")
-        return int(geom_id)
-
-    def get_geom_size(self, name: str) -> np.ndarray:
-        geom = _require_not_none(
-            self._model.get_geom(name),
-            f"Geom '{name}' not found in Motrix model",
-        )
-        return np.asarray(geom.size, dtype=np.float64).copy()
-
-    def get_body_mass(self) -> np.ndarray:
-        return self._default_body_mass.copy()
-
-    def get_body_ipos(self) -> np.ndarray:
-        return self._default_body_ipos.copy()
-
-    def get_body_subtree_ids(self, root_body_id: int) -> np.ndarray:
-        root_id = int(root_body_id)
-        if root_id < 0 or root_id >= int(self._model.num_links):
-            raise ValueError(f"root_body_id out of range: {root_id}")
-        if root_id != int(self._body_link.index):
-            raise NotImplementedError(
-                "MotrixBackend only exposes the configured base articulation subtree"
-            )
-
-        subtree_ids = {root_id}
-        for link in self._model.links:
-            link_id = int(link.index)
-            joint_indices = getattr(link, "joint_indices", ())
-            if link_id != root_id and len(joint_indices) > 0:
-                subtree_ids.add(link_id)
-        return np.asarray(sorted(subtree_ids), dtype=np.int32)
-
-    def get_geom_names(self) -> tuple[str, ...]:
-        return tuple(
-            str(getattr(self._geoms_by_id[geom_id], "name", "") or "")
-            for geom_id in range(int(self._model.num_geoms))
-        )
-
-    def get_geom_body_ids(self) -> np.ndarray:
-        body_ids = np.zeros((int(self._model.num_geoms),), dtype=np.int32)
-        for geom_id in range(int(self._model.num_geoms)):
-            link = getattr(self._geoms_by_id[geom_id], "link", None)
-            if link is None:
-                body_ids[geom_id] = -1
-            else:
-                body_ids[geom_id] = int(link.index)
-        return body_ids
-
-    def get_geom_contact_masks(self) -> tuple[np.ndarray, np.ndarray]:
-        contype = np.zeros((int(self._model.num_geoms),), dtype=np.int32)
-        conaffinity = np.zeros((int(self._model.num_geoms),), dtype=np.int32)
-        for geom_id in range(int(self._model.num_geoms)):
-            geom = self._geoms_by_id[geom_id]
-            if not hasattr(geom, "collision_group") or not hasattr(geom, "collision_affinity"):
-                raise NotImplementedError("Motrix geom objects do not expose contact masks")
-            contype[geom_id] = int(geom.collision_group)
-            conaffinity[geom_id] = int(geom.collision_affinity)
-        return contype, conaffinity
-
-    def get_geom_friction(self) -> np.ndarray:
-        if not self._supports_geom_friction_override:
-            raise NotImplementedError("Motrix geom friction override is not available")
-        return self._default_geom_friction.copy()
-
-    def get_gravity(self) -> np.ndarray:
-        return np.asarray(self._model.options.gravity, dtype=np.float64).copy()
-
-    def get_joint_range(self) -> np.ndarray | None:
-        return None
-
-    # ------------------------------------------------------------------ #
-    # Simulation control                                                 #
-    # ------------------------------------------------------------------ #
-
-    def step(self, ctrl: np.ndarray, nsteps: int = 1) -> dict | None:
-        if self._pre_step_control_fn is not None:
-            return self._step_with_pre_step_control(ctrl, nsteps)
-
-        t0 = time.perf_counter()
-        self._data.actuator_ctrls = np.ascontiguousarray(ctrl)
-        set_ctrl_ms = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        if nsteps == 1:
-            self._model.step(self._data)
-        else:
-            self._model.step_n(self._data, nsteps)
-        physics_ms = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        self._refresh_link_pose_cache()
-        self._invalidate_link_velocity_cache()
-        refresh_cache_ms = (time.perf_counter() - t0) * 1000.0
-
-        return {
-            "timing": {
-                "set_ctrl_ms": set_ctrl_ms,
-                "physics_ms": physics_ms,
-                "refresh_cache_ms": refresh_cache_ms,
-            }
-        }
-
-    def _step_with_pre_step_control(
-        self, ctrl: np.ndarray, nsteps: int
-    ) -> dict[str, dict[str, float]]:
-        set_ctrl_ms = 0.0
-        physics_ms = 0.0
-        refresh_cache_ms = 0.0
-
-        for _ in range(nsteps):
-            t0 = time.perf_counter()
-            native_ctrl = self._apply_pre_step_control(ctrl)
-            self._data.actuator_ctrls = np.ascontiguousarray(native_ctrl)
-            set_ctrl_ms += (time.perf_counter() - t0) * 1000.0
-
-            t0 = time.perf_counter()
-            self._model.step(self._data)
-            physics_ms += (time.perf_counter() - t0) * 1000.0
-
-            t0 = time.perf_counter()
-            self._refresh_link_pose_cache()
-            self._invalidate_link_velocity_cache()
-            refresh_cache_ms += (time.perf_counter() - t0) * 1000.0
-
-        return {
-            "timing": {
-                "set_ctrl_ms": set_ctrl_ms,
-                "physics_ms": physics_ms,
-                "refresh_cache_ms": refresh_cache_ms,
-            }
-        }
-
-    def set_state(
-        self,
-        env_indices: np.ndarray,
-        qpos: np.ndarray,
-        qvel: np.ndarray,
-        randomization: ResetRandomizationPayload | None = None,
-    ) -> dict | None:
-        timing: dict[str, float] = {
-            "set_state_mask_ms": 0.0,
-            "set_state_data_slice_ms": 0.0,
-            "set_state_data_reset_ms": 0.0,
-            "set_state_clear_forces_ms": 0.0,
-            "set_state_geom_overrides_ms": 0.0,
-            "set_state_reset_rand_ms": 0.0,
-            "set_state_set_dof_vel_ms": 0.0,
-            "set_state_set_dof_pos_ms": 0.0,
-            "set_state_actuator_ctrl_ms": 0.0,
-            "set_state_forward_kinematic_ms": 0.0,
-            "set_state_refresh_pose_cache_ms": 0.0,
-            "set_state_invalidate_velocity_ms": 0.0,
-            "set_state_qpos_convert_ms": 0.0,
-            "set_state_pool_reset_ms": 0.0,
-            "set_state_state_scatter_ms": 0.0,
-            "set_state_internal_gap_ms": 0.0,
-        }
-        outer_t0 = time.perf_counter()
-
-        # Pre-convert env_indices once; every downstream helper reuses this.
-        env_ids_intp = np.asarray(env_indices, dtype=np.intp)
-
-        t0 = time.perf_counter()
-        # Reuse the scratch qpos buffer when its shape matches; the reset path
-        # feeds a fixed-shape (num_envs, qpos_dim) array so this holds after
-        # the first call.
-        scratch = self._set_state_qpos_motrix_scratch
-        if scratch is None or scratch.shape != qpos.shape or scratch.dtype != qpos.dtype:
-            scratch = np.empty_like(qpos)
-            self._set_state_qpos_motrix_scratch = scratch
-        qpos_motrix = self._mujoco_qpos_to_motrix_into(scratch, qpos)
-        timing["set_state_qpos_convert_ms"] = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        # Reuse the persistent bool-mask scratch; only the touched entries need
-        # rewriting each call.
-        mask = self._set_state_mask_scratch
-        if mask.shape[0] != self._num_envs:
-            mask = np.zeros(self._num_envs, dtype=bool)
-            self._set_state_mask_scratch = mask
-        mask.fill(False)
-        mask[env_ids_intp] = True
-        timing["set_state_mask_ms"] = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        data_slice = self._data[mask]
-        timing["set_state_data_slice_ms"] = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        data_slice.reset(self._model)
-        timing["set_state_data_reset_ms"] = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        self._clear_applied_body_forces(env_indices, env_ids_intp=env_ids_intp)
-        timing["set_state_clear_forces_ms"] = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        self._apply_init_geom_size_overrides(data_slice, env_indices, env_ids_intp=env_ids_intp)
-        timing["set_state_geom_overrides_ms"] = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        self._apply_reset_randomization(
-            data_slice, env_indices, randomization, env_ids_intp=env_ids_intp
-        )
-        timing["set_state_reset_rand_ms"] = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        data_slice.set_dof_vel(qvel)
-        timing["set_state_set_dof_vel_ms"] = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        data_slice.set_dof_pos(qpos_motrix, self._model)
-        timing["set_state_set_dof_pos_ms"] = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        if self._supports_position_actuator_gains and len(self._joint_dof_pos_indices) == int(
-            self.num_actuators
-        ):
-            # Fully-actuated model: hold every joint at its reset position (unchanged).
-            if self._joint_dof_pos_slice is not None:
-                ctrl = qpos_motrix[:, self._joint_dof_pos_slice]
-            else:
-                ctrl = qpos_motrix[:, self._joint_dof_pos_indices]
-        elif self._actuator_joint_pos_indices is not None:
-            # Under-actuated / parallel model: hold only the actuated joints.
-            if self._actuator_joint_pos_slice is not None:
-                ctrl = qpos_motrix[:, self._actuator_joint_pos_slice]
-            else:
-                ctrl = qpos_motrix[:, self._actuator_joint_pos_indices]
-        else:
-            ctrl = np.zeros((len(env_indices), self.num_actuators), dtype=self._np_dtype)
-        # Only pay the copy when the underlying slice is non-contiguous; view
-        # slices of a contiguous scratch buffer already satisfy the motrixsim
-        # contiguous requirement.
-        if not ctrl.flags.c_contiguous:
-            ctrl = np.ascontiguousarray(ctrl)
-        data_slice.actuator_ctrls = ctrl
-        timing["set_state_actuator_ctrl_ms"] = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        self._model.forward_kinematic(data_slice)
-        timing["set_state_forward_kinematic_ms"] = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        self._refresh_link_pose_cache(env_indices, data_slice=data_slice, env_ids_intp=env_ids_intp)
-        timing["set_state_refresh_pose_cache_ms"] = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        self._invalidate_link_velocity_cache()
-        timing["set_state_invalidate_velocity_ms"] = (time.perf_counter() - t0) * 1000.0
-
-        outer_total_ms = (time.perf_counter() - outer_t0) * 1000.0
-        measured_ms = (
-            timing["set_state_qpos_convert_ms"]
-            + timing["set_state_mask_ms"]
-            + timing["set_state_data_slice_ms"]
-            + timing["set_state_data_reset_ms"]
-            + timing["set_state_clear_forces_ms"]
-            + timing["set_state_geom_overrides_ms"]
-            + timing["set_state_reset_rand_ms"]
-            + timing["set_state_set_dof_vel_ms"]
-            + timing["set_state_set_dof_pos_ms"]
-            + timing["set_state_actuator_ctrl_ms"]
-            + timing["set_state_forward_kinematic_ms"]
-            + timing["set_state_refresh_pose_cache_ms"]
-            + timing["set_state_invalidate_velocity_ms"]
-        )
-        timing["set_state_internal_gap_ms"] = outer_total_ms - measured_ms
-        return {"timing": timing}
-
-    def get_dr_capabilities(self) -> DomainRandomizationCapabilities:
-        supported_reset_terms = {
-            RESET_TERM_BASE_MASS,
-            RESET_TERM_BASE_COM,
-            RESET_TERM_BODY_MASS,
-            RESET_TERM_BODY_IPOS,
-        }
-        if getattr(self, "_supports_position_actuator_gains", False):
-            supported_reset_terms.update({RESET_TERM_KP, RESET_TERM_KD})
-        if getattr(self, "_supports_geom_friction_override", False):
-            supported_reset_terms.add(RESET_TERM_GEOM_FRICTION)
-        if getattr(self, "_supports_gravity_override", False):
-            supported_reset_terms.add(RESET_TERM_GRAVITY)
-        return DomainRandomizationCapabilities(
-            supported_reset_terms=frozenset(supported_reset_terms),
-            supports_interval_push=True,
-            supports_interval_body_velocity_delta=False,
-            supports_interval_body_force=getattr(self, "_supports_external_force", False),
-        )
-
-    def apply_init_randomization(self, plan: InitRandomizationPlan) -> None:
-        if plan.is_empty():
-            return
-        model_assignments = np.asarray(plan.model_assignments, dtype=np.int32)
-        if model_assignments.shape != (self._num_envs,):
-            raise ValueError(
-                f"model_assignments must have shape ({self._num_envs},), "
-                f"got {model_assignments.shape}"
-            )
-        if np.any(model_assignments < 0) or np.any(model_assignments >= len(plan.model_variants)):
-            raise ValueError(
-                "model_assignments must refer to entries in InitRandomizationPlan.model_variants"
-            )
-
-        geom_size_overrides: dict[int, np.ndarray] = {}
-        for variant_id, variant in enumerate(plan.model_variants):
-            env_indices = np.flatnonzero(model_assignments == variant_id)
-            if env_indices.size == 0:
-                continue
-            for override in variant.geom_size_overrides:
-                geom_id = self.get_geom_id(override.geom_name)
-                geom = _require_not_none(
-                    self._model.get_geom(geom_id),
-                    f"Geom '{override.geom_name}' not found in Motrix model",
-                )
-                override_shape = np.asarray(geom.get_size_override(self._data)).shape
-                if len(override_shape) != 2:
-                    raise ValueError(
-                        f"Motrix geom '{override.geom_name}' size override must be rank-2, "
-                        f"got shape {override_shape}"
-                    )
-                width = int(override_shape[1])
-                size = np.asarray(override.size, dtype=np.float64).reshape(-1)
-                if size.size < width:
-                    raise ValueError(
-                        f"GeomSizeOverride for '{override.geom_name}' has {size.size} values, "
-                        f"but Motrix expects at least {width}"
-                    )
-                values = geom_size_overrides.setdefault(
-                    geom_id,
-                    np.asarray(geom.get_size_override(self._data), dtype=np.float64).copy(),
-                )
-                values[env_indices, :] = size[:width]
-
-        self._init_geom_size_overrides = geom_size_overrides
-        self._apply_init_geom_size_overrides(self._data, np.arange(self._num_envs, dtype=np.int32))
-
-    def apply_interval_randomization(self, plan: IntervalRandomizationPlan) -> None:
-        if plan.is_empty():
-            return
-        if plan.push_perturbation_limit is not None:
-            self.push_robots(plan.push_perturbation_limit)
-        if plan.body_force is not None:
-            if plan.body_ids is None:
-                raise ValueError("Interval body-force perturbation requires body_ids")
-            self.apply_body_force(plan.body_ids, plan.body_force)
-        if plan.body_linear_velocity_delta is not None:
-            if plan.body_ids is None:
-                raise ValueError("Interval body-velocity perturbation requires body_ids")
-            self._apply_body_linear_velocity_delta(plan.body_ids, plan.body_linear_velocity_delta)
-
-    def _apply_body_linear_velocity_delta(
-        self,
-        body_ids: np.ndarray,
-        velocity_delta: np.ndarray,
-    ) -> None:
-        """Apply a world-frame linear-velocity delta to specific bodies.
-
-        Backend-internal hook for ``apply_interval_randomization``; it is not
-        part of the public ``SimBackend`` surface.
-        """
-        raise NotImplementedError(
-            f"{self.__class__.__name__} does not support interval body velocity perturbation"
-        )
-
-    def get_play_capabilities(self) -> BackendPlayCapabilities:
-        return BackendPlayCapabilities(
-            supports_native_interactive_renderer=True,
-            supports_native_video_capture=True,
-        )
-
-    def resolve_play_render_plan(
-        self,
-        *,
-        play_render_mode: str | None,
-        play_steps: int | None,
-        output_video: str | os.PathLike[str] | None,
-    ) -> BackendPlayRenderPlan:
-        mode = normalize_play_render_mode(play_render_mode)
-        effective_mode = "interactive" if mode == "auto" else mode
-        if effective_mode == "none":
-            return BackendPlayRenderPlan(
-                mode=effective_mode,
-                headless=True,
-                record_video=False,
-                num_steps=None,
-                output_video=None,
-            )
-        if effective_mode == "interactive":
-            return BackendPlayRenderPlan(
-                mode=effective_mode,
-                headless=False,
-                record_video=False,
-                num_steps=None,
-                output_video=None,
-            )
-        assert effective_mode == "record"
-        if play_steps is None:
-            raise ValueError("Motrix record playback requires a finite training.play_steps value.")
-        if output_video is None:
-            raise ValueError("Motrix record playback requires an output video path.")
-        return BackendPlayRenderPlan(
-            mode=effective_mode,
-            headless=True,
-            record_video=True,
-            num_steps=int(play_steps),
-            output_video=output_video,
-        )
-
-    def run_playback(
-        self,
-        *,
-        env: Any,
-        initialize,
-        step,
-        num_steps: int | None,
-        output_video: str | os.PathLike[str] | None = None,
-        render_spacing: float | None = None,
-        render_offset_mode: str | None = None,
-        headless: bool | None = None,
-        record_video: bool | None = None,
-        frame_state_getter=None,
-        camera_kwargs: dict[str, Any] | None = None,
-        extra_data_getter=None,
-    ) -> str | None:
-        del frame_state_getter, extra_data_getter
-        should_record_video = (
-            bool(record_video) if record_video is not None else output_video is not None
-        )
-        should_run_headless = bool(headless) if headless is not None else should_record_video
-        try:
-            return run_motrix_playback(
-                backend=self,
-                env=env,
-                initialize=initialize,
-                step=step,
-                num_steps=num_steps,
-                output_video=output_video,
-                render_spacing=render_spacing,
-                render_offset_mode=render_offset_mode,
-                headless=should_run_headless,
-                record_video=should_record_video,
-                camera_kwargs=camera_kwargs,
-            )
-        except RenderClosedError:
-            if not should_run_headless and not should_record_video:
-                logger.info("Render window closed.")
-                return None
-            raise
-
-    # ------------------------------------------------------------------ #
-    # Base kinematics                                                    #
-    # ------------------------------------------------------------------ #
-
-    def get_base_pos(self) -> np.ndarray:
-        if self._body_floatingbase is not None:
-            return self._body_floatingbase.get_translation(self._data)  # type: ignore[no-any-return]
-        return self._body_link.get_pose(self._data)[:, :3]  # type: ignore[no-any-return]
-
-    def get_base_quat(self) -> np.ndarray:
-        if self._body_floatingbase is not None:
-            quat = self._body_floatingbase.get_rotation(self._data)
-        else:
-            quat = self._body_link.get_rotation(self._data)
-        return self._xyzw_to_wxyz(quat)
-
-    def get_base_lin_vel(self) -> np.ndarray:
-        if self._body_floatingbase is not None:
-            return self._body_floatingbase.get_global_linear_velocity(self._data)  # type: ignore[no-any-return]
-        return self._body_link.get_linear_velocity(self._data)  # type: ignore[no-any-return]
-
-    def get_base_ang_vel(self) -> np.ndarray:
-        if self._body_floatingbase is not None:
-            return self._body_floatingbase.get_global_angular_velocity(self._data)  # type: ignore[no-any-return]
-        return self._body_link.get_angular_velocity(self._data)  # type: ignore[no-any-return]
-
-    # ------------------------------------------------------------------ #
-    # DOF state                                                          #
-    # ------------------------------------------------------------------ #
-
-    def get_dof_pos(self) -> np.ndarray:
-        indices = (
-            self._actuator_joint_pos_indices
-            if self._actuator_joint_pos_indices is not None
-            else self._joint_dof_pos_indices
-        )
-        result = self._data.dof_pos[..., indices]  # type: ignore[no-any-return]
-        return result
-
-    def get_dof_vel(self) -> np.ndarray:
-        indices = (
-            self._actuator_joint_vel_indices
-            if self._actuator_joint_vel_indices is not None
-            else self._joint_dof_vel_indices
-        )
-        return self._data.dof_vel[..., indices]  # type: ignore[no-any-return]
-
-    # ------------------------------------------------------------------ #
-    # Body kinematics — world frame                                      #
-    # ------------------------------------------------------------------ #
-
-    def _as_body_ids(self, body_ids: np.ndarray) -> np.ndarray:
-        return np.asarray(body_ids, dtype=np.int32)
-
-    def get_body_pos_w(self, body_ids: np.ndarray) -> np.ndarray:
-        return self._get_link_poses_w(body_ids)[:, :, :3]
-
-    def get_body_quat_w(self, body_ids: np.ndarray) -> np.ndarray:
-        return self._xyzw_to_wxyz(self._get_link_poses_w(body_ids)[:, :, 3:])
-
-    def get_body_pose_w_rows(
-        self, env_ids: np.ndarray, body_ids: np.ndarray
-    ) -> tuple[np.ndarray, np.ndarray]:
-        rows = np.asarray(env_ids, dtype=np.intp)
-        poses_w = self._link_poses[rows[:, None], self._as_body_ids(body_ids), :]
-        return poses_w[:, :, :3], self._xyzw_to_wxyz(poses_w[:, :, 3:])
-
-    def get_body_pose_w(self, body_ids: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
-        poses = self._get_link_poses_w(body_ids)
-        return poses[:, :, :3], self._xyzw_to_wxyz(poses[:, :, 3:])
-
-    def get_body_lin_vel_w(self, body_ids: np.ndarray) -> np.ndarray:
-        return self._get_link_lin_vel_w(body_ids)
-
-    def get_body_ang_vel_w(self, body_ids: np.ndarray) -> np.ndarray:
-        return self._get_link_ang_vel_w(body_ids)
-
-    def get_body_state_w(
-        self, body_ids: np.ndarray
-    ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
-        poses_w = self._get_link_poses_w(body_ids)
-        lin_vel_w, ang_vel_w = self.get_body_vel_w(body_ids)
-        return (
-            poses_w[:, :, :3],
-            self._xyzw_to_wxyz(poses_w[:, :, 3:]),
-            lin_vel_w,
-            ang_vel_w,
-        )
-
-    def copy_body_state_w(
-        self,
-        body_ids: np.ndarray,
-        out_pos: np.ndarray,
-        out_quat: np.ndarray,
-        out_lin_vel: np.ndarray,
-        out_ang_vel: np.ndarray,
-    ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
-        ids = self._as_body_ids(body_ids)
-        poses_w = self._get_link_poses_w(ids)
-        out_pos[..., 0] = poses_w[..., 0]
-        out_pos[..., 1] = poses_w[..., 1]
-        out_pos[..., 2] = poses_w[..., 2]
-        out_quat[..., 0] = poses_w[..., 6]
-        out_quat[..., 1] = poses_w[..., 3]
-        out_quat[..., 2] = poses_w[..., 4]
-        out_quat[..., 3] = poses_w[..., 5]
-
-        link_velocity_cache = self._ensure_link_velocity_cache()
-        if self._link_velocity_cache is None or self._link_velocity_cache.shape != (
-            self._num_envs,
-            len(ids),
-            6,
-        ):
-            self._link_velocity_cache = np.empty(
-                (self._num_envs, len(ids), 6), dtype=self._np_dtype
-            )
-        np.take(link_velocity_cache, ids, axis=1, out=self._link_velocity_cache)
-        out_lin_vel[..., 0] = self._link_velocity_cache[..., 0]
-        out_lin_vel[..., 1] = self._link_velocity_cache[..., 1]
-        out_lin_vel[..., 2] = self._link_velocity_cache[..., 2]
-        out_ang_vel[..., 0] = self._link_velocity_cache[..., 3]
-        out_ang_vel[..., 1] = self._link_velocity_cache[..., 4]
-        out_ang_vel[..., 2] = self._link_velocity_cache[..., 5]
-        return out_pos, out_quat, out_lin_vel, out_ang_vel
-
-    def get_body_vel_w(self, body_ids: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
-        ids = self._as_body_ids(body_ids)
-        velocities = np.ascontiguousarray(self._ensure_link_velocity_cache()[:, ids, :])
-        return velocities[:, :, :3], velocities[:, :, 3:]
-
-    # ------------------------------------------------------------------ #
-    # Body kinematics — baselink frame                                   #
-    # ------------------------------------------------------------------ #
-
-    def get_body_pos_b(self, body_ids: np.ndarray) -> np.ndarray:
-        return self._get_body_sensor_values(body_ids, "track_pos_b")
-
-    def get_body_quat_b(self, body_ids: np.ndarray) -> np.ndarray:
-        # MotrixSim framequat sensors output xyzw; convert to the wxyz contract.
-        return self._xyzw_to_wxyz(self._get_body_sensor_values(body_ids, "track_quat_b"))
-
-    def get_body_lin_vel_b(self, body_ids: np.ndarray) -> np.ndarray:
-        return self._get_body_sensor_values(body_ids, "track_linvel_b")
-
-    def get_body_ang_vel_b(self, body_ids: np.ndarray) -> np.ndarray:
-        return self._get_body_sensor_values(body_ids, "track_angvel_b")
-
-    # ------------------------------------------------------------------ #
-    # Sensors                                                            #
-    # ------------------------------------------------------------------ #
-
-    def get_sensor_data(self, name: str) -> np.ndarray:
-        return self._model.get_sensor_value(name, self._data)  # type: ignore[no-any-return]
-
-    def get_sensor_data_rows(self, name: str, env_ids: np.ndarray) -> np.ndarray:
-        rows = np.asarray(env_ids, dtype=np.intp)
-        mask = np.zeros(self._num_envs, dtype=bool)
-        mask[rows] = True
-        selected_rows = np.flatnonzero(mask)
-        selected_values = self._model.get_sensor_value(name, self._data[mask])
-        return selected_values[np.searchsorted(selected_rows, rows)]  # type: ignore[no-any-return]
-
-    def get_sensor_data_batch(self, names: Sequence[str]) -> np.ndarray:
-        sensor_names = tuple(names)
-        if not sensor_names:
-            return np.empty((self._num_envs, 0), dtype=self._np_dtype)
-        values = self._model.get_sensor_values(sensor_names, self._data)
-        return np.asarray(values, dtype=self._np_dtype)
-
-    # ------------------------------------------------------------------ #
-    # MotrixSim-specific                                                 #
-    # ------------------------------------------------------------------ #
-
-    def _get_body_names(self, body_ids: np.ndarray) -> list[str]:
-        return [self._body_id_to_name[int(bid)] for bid in self._as_body_ids(body_ids)]
-
-    def _get_link_poses_w(self, body_ids: np.ndarray) -> np.ndarray:
-        ids = self._as_body_ids(body_ids)
-        return np.ascontiguousarray(self._link_poses[:, ids, :])
-
-    def _get_link_lin_vel_w(self, body_ids: np.ndarray) -> np.ndarray:
-        ids = self._as_body_ids(body_ids)
-        return np.ascontiguousarray(self._ensure_link_velocity_cache()[:, ids, :3])
-
-    def _get_link_ang_vel_w(self, body_ids: np.ndarray) -> np.ndarray:
-        ids = self._as_body_ids(body_ids)
-        return np.ascontiguousarray(self._ensure_link_velocity_cache()[:, ids, 3:])
-
-    def _get_body_sensor_values(self, body_ids: np.ndarray, prefix: str) -> np.ndarray:
-        return np.stack(
-            [
-                self._model.get_sensor_value(f"{prefix}_{name}", self._data)
-                for name in self._get_body_names(body_ids)
-            ],
-            axis=1,
-        )
-
-    def _xyzw_to_wxyz(self, q: np.ndarray) -> np.ndarray:
-        """motrix xyzw → wxyz"""
-        return q[..., [3, 0, 1, 2]]
-
-    def _mujoco_qpos_to_motrix(self, qpos: np.ndarray) -> np.ndarray:
-        """Convert every MuJoCo freejoint quaternion slice from wxyz to xyzw."""
-        qpos_motrix = np.array(qpos, copy=True)
-        for quat_indices in self._floating_base_quat_indices:
-            qpos_motrix[..., quat_indices] = qpos[..., quat_indices[[1, 2, 3, 0]]]
-        return qpos_motrix
-
-    def _mujoco_qpos_to_motrix_into(self, dst: np.ndarray, qpos: np.ndarray) -> np.ndarray:
-        """Hot-path variant that writes into ``dst`` in place.
-
-        Same conversion as :meth:`_mujoco_qpos_to_motrix` but avoids the per-call
-        ``np.array(qpos, copy=True)`` allocation. Returns ``dst`` so callers can
-        chain: ``qpos_motrix = self._mujoco_qpos_to_motrix_into(scratch, qpos)``.
-        ``dst`` must have the same shape as ``qpos``; caller is responsible for
-        sizing the scratch buffer.
-        """
-        np.copyto(dst, qpos, casting="same_kind")
-        for quat_indices in self._floating_base_quat_indices:
-            dst[..., quat_indices] = qpos[..., quat_indices[[1, 2, 3, 0]]]
-        return dst
-
-    def _motrix_qpos_to_mujoco(self, qpos: np.ndarray) -> np.ndarray:
-        """Convert every Motrix freejoint quaternion slice from xyzw to wxyz."""
-        qpos_mujoco = np.array(qpos, copy=True)
-        for quat_indices in self._floating_base_quat_indices:
-            qpos_mujoco[..., quat_indices] = qpos[..., quat_indices[[3, 0, 1, 2]]]
-        return qpos_mujoco
-
-    def _refresh_link_pose_cache(
-        self,
-        env_indices: np.ndarray | None = None,
-        data_slice: Any | None = None,
-        env_ids_intp: np.ndarray | None = None,
-    ) -> None:
-        if env_indices is None:
-            self._link_poses = self._model.get_link_poses(self._data)
-        else:
-            if data_slice is None:
-                mask = np.zeros(self._num_envs, dtype=bool)
-                mask[env_indices] = True
-                data_slice = self._data[mask]
-            # Indexing with intp is a hair faster than the arbitrary-dtype
-            # ndarray path; use the pre-converted array when set_state hands
-            # it down.
-            idx = env_indices if env_ids_intp is None else env_ids_intp
-            self._link_poses[idx] = self._model.get_link_poses(data_slice)
-
-    def _refresh_link_velocity_cache(self, env_indices: np.ndarray | None = None) -> None:
-        if env_indices is None:
-            self._link_velocities = self._model.get_link_velocities(self._data)
-        else:
-            mask = np.zeros(self._num_envs, dtype=bool)
-            mask[env_indices] = True
-            if self._link_velocities is None:
-                self._link_velocities = self._model.get_link_velocities(self._data)
-                self._link_velocity_cache_valid = True
-                return
-            self._link_velocities[env_indices] = self._model.get_link_velocities(self._data[mask])
-        self._link_velocity_cache_valid = True
-
-    def _invalidate_link_velocity_cache(self) -> None:
-        self._link_velocity_cache_valid = False
-
-    def _ensure_link_velocity_cache(self) -> np.ndarray:
-        if self._link_velocities is None or not self._link_velocity_cache_valid:
-            self._refresh_link_velocity_cache()
-        assert self._link_velocities is not None
-        return self._link_velocities
-
-    def _coerce_reset_field(
-        self,
-        value: np.ndarray,
-        *,
-        name: str,
-        num_reset: int,
-        shaped_tail: tuple[int, ...],
-    ) -> np.ndarray:
-        arr = np.asarray(value, dtype=np.float32)
-        shaped = (num_reset, *shaped_tail)
-        flat_shape = (num_reset, int(np.prod(shaped_tail)))
-        if arr.shape == shaped:
-            return arr.copy()
-        if arr.shape == flat_shape:
-            return arr.reshape(shaped).copy()
-        raise ValueError(f"{name} must have shape {shaped} or {flat_shape}, got {arr.shape}")
-
-    def _apply_init_geom_size_overrides(
-        self,
-        data_slice,
-        env_indices: np.ndarray,
-        env_ids_intp: np.ndarray | None = None,
-    ) -> None:
-        if not self._init_geom_size_overrides:
-            return
-        env_ids = (
-            env_ids_intp if env_ids_intp is not None else np.asarray(env_indices, dtype=np.intp)
-        )
-        for geom_id, values in self._init_geom_size_overrides.items():
-            geom = _require_not_none(
-                self._model.get_geom(int(geom_id)),
-                f"Geom id {geom_id} not found in Motrix model",
-            )
-            geom.set_size_override(
-                data_slice,
-                np.ascontiguousarray(np.asarray(values[env_ids], dtype=np.float32)),
-            )
-
-    def _set_link_mass_overrides(self, data_slice, body_mass: np.ndarray) -> None:
-        for link_id, link in self._links_by_id.items():
-            link.set_mass_override(
-                data_slice,
-                np.ascontiguousarray(np.asarray(body_mass[:, link_id], dtype=np.float32)),
-            )
-
-    def _set_link_ipos_overrides(self, data_slice, body_ipos: np.ndarray) -> None:
-        for link_id, link in self._links_by_id.items():
-            link.set_center_of_mass_override(
-                data_slice,
-                np.ascontiguousarray(np.asarray(body_ipos[:, link_id, :], dtype=np.float32)),
-            )
-
-    def _set_geom_friction_overrides(self, data_slice, geom_friction: np.ndarray) -> None:
-        if not self._supports_geom_friction_override:
-            raise NotImplementedError("Motrix geom friction override is not available")
-        override_ids = getattr(self, "_geom_friction_override_ids", tuple(self._geoms_by_id))
-        unsupported_ids = sorted(set(self._geoms_by_id) - set(override_ids))
-        if unsupported_ids:
-            unsupported_values = geom_friction[:, unsupported_ids, :]
-            default_values = self._default_geom_friction[None, unsupported_ids, :]
-            if not np.allclose(unsupported_values, default_values):
-                raise ValueError(
-                    "Motrix geom friction override only supports collision geoms; "
-                    f"non-collision geom ids were modified: {unsupported_ids}"
-                )
-        for geom_id in override_ids:
-            geom = self._geoms_by_id[int(geom_id)]
-            geom.set_friction_override(
-                data_slice,
-                np.ascontiguousarray(np.asarray(geom_friction[:, geom_id, :], dtype=np.float32)),
-            )
-
-    def _clear_applied_body_forces(
-        self,
-        env_indices: np.ndarray,
-        env_ids_intp: np.ndarray | None = None,
-    ) -> None:
-        if not self._applied_body_forces:
-            return
-        env_ids = (
-            env_ids_intp if env_ids_intp is not None else np.asarray(env_indices, dtype=np.intp)
-        )
-        for applied_force in self._applied_body_forces.values():
-            applied_force[env_ids, :] = 0.0
-
-    def push_robots(self, force_range):
-        ex_force = np.random.rand(self.num_envs, 3) * 2 - 1  # [x_force, y_force, z_force]
-        ex_force[:, 0] *= force_range[0]
-        ex_force[:, 1] *= force_range[1]
-        ex_force[:, 2] *= force_range[2]
-        self._push_body_link.add_external_force(self._data, ex_force, local=True)
-
-    def apply_body_force(
-        self,
-        body_ids: np.ndarray,
-        force: np.ndarray,
-    ) -> None:
-        """Apply absolute world-frame external forces through Motrix Link API."""
-        if not getattr(self, "_supports_external_force", False):
-            raise NotImplementedError("Motrix link external-force API is not available")
-        body_ids_np = np.asarray(body_ids, dtype=np.int32).reshape(-1)
-        force_np = np.asarray(force, dtype=np.float64)
-        expected_shape = (self._num_envs, body_ids_np.size, 3)
-        if force_np.shape != expected_shape:
-            raise ValueError(f"body force must have shape {expected_shape}, got {force_np.shape}")
-        for body_offset, body_id in enumerate(body_ids_np):
-            link_id = int(body_id)
-            link = self._links_by_id.get(link_id)
-            if link is None:
-                raise ValueError(f"Body id {link_id} not found in Motrix model")
-            target_force = np.asarray(force_np[:, body_offset, :], dtype=np.float64)
-            applied_force = self._applied_body_forces.setdefault(
-                link_id,
-                np.zeros((self._num_envs, 3), dtype=np.float64),
-            )
-            delta_force = target_force - applied_force
-            if np.any(delta_force):
-                link.add_external_force(
-                    self._data,
-                    np.ascontiguousarray(delta_force.astype(np.float32)),
-                    local=False,
-                )
-                applied_force[:] = target_force
-
-    def create_hfield_scanner(
-        self,
-        *,
-        hfield_geom_id: int,
-        offsets: np.ndarray,
-        frame_body_id: int,
-        alignment: str = "yaw",
-        output: str = "height",
-    ) -> BackendHeightScanner:
-        offsets_np = np.ascontiguousarray(np.asarray(offsets, dtype=np.float32))
-        if offsets_np.ndim != 2 or offsets_np.shape[1] != 2:
-            raise ValueError(f"offsets must have shape (num_points, 2), got {offsets_np.shape}")
-
-        if alignment != "yaw":
-            raise ValueError(f"MotrixBackend only supports alignment='yaw', got {alignment!r}")
-        if output not in {"height", "clearance"}:
-            raise ValueError(f"Unsupported hfield sampling output: {output!r}")
-
-        geom_id = int(hfield_geom_id)
-        if geom_id < 0 or geom_id >= int(self._model.num_geoms):
-            raise ValueError(f"hfield_geom_id out of range: {geom_id}")
-
-        body_id = int(frame_body_id)
-        if body_id < 0 or body_id >= int(self._model.num_links):
-            raise ValueError(f"frame_body_id out of range: {body_id}")
-
-        terrain = self._model.get_geom(geom_id)
-        if terrain is None:
-            raise ValueError(f"Geom id {geom_id} not found in Motrix model")
-        if not isinstance(terrain, mtx.GeomHField):
-            raise ValueError(f"Geom id {geom_id} is not backed by a Motrix hfield")
-        frame = self._link_cache[body_id]
-        scanner = mtx.TerrainScanner(
-            terrain,
-            frame,
-            offsets_np,
-            alignment=alignment,
-            output=output,
-        )
-        return _MotrixTerrainScanner(
-            scanner=scanner,
-            data=self._data,
-            out=np.empty((self._num_envs, offsets_np.shape[0]), dtype=self._np_dtype),
-        )
-
-    def _update_tracking_camera_view(self) -> None:
-        if (
-            self._render_app is None
-            or self._render_tracking_camera is None
-            or self._render_offsets_np is None
-        ):
-            return
-        lookat = tracking_camera_lookat(
-            self.get_base_pos(),
-            self._render_tracking_camera,
-            self._render_offsets_np,
-        )
-        self._render_app.system_camera.set_view(
-            lookat,
-            self._render_tracking_camera.distance,
-            self._render_tracking_camera.elevation,
-            self._render_tracking_camera.azimuth,
-        )
-
-    def _assert_render_context_available(self, *, headless: bool, capture: bool) -> None:
-        if self._render_app is None:
-            return
-        if self._render_headless != headless:
-            raise RuntimeError(
-                "Motrix renderer is already initialized with "
-                f"headless={self._render_headless!r}; cannot reuse it with headless={headless!r}"
-            )
-        if capture and not self._render_capture_enabled:
-            raise RuntimeError(
-                "Motrix renderer is already initialized without video capture; "
-                "cannot enable capture on the existing renderer"
-            )
-        return
-
-    def init_renderer(
-        self,
-        spacing: float = 1.0,
-        *,
-        offset_mode: str = "grid",
-        headless: bool = False,
-        capture: bool = False,
-        width: int = 1280,
-        height: int = 720,
-        camera_kwargs: dict[str, Any] | None = None,
-    ) -> None:
-        """Initialize a Motrix renderer, optionally enabling system-camera capture."""
-        headless = bool(headless)
-        capture = bool(capture)
-        self._assert_render_context_available(headless=headless, capture=capture)
-        if self._render_app is not None:
-            return
-
-        settings = RenderSettings.performance()
-        settings.enable_shadow = True
-        offsets = render_offsets(
-            self._num_envs,
-            float(spacing),
-            offset_mode=str(offset_mode),
-        )
-        offsets_np = np.asarray(offsets, dtype=np.float64)
-        self._render_offsets_np = offsets_np
-        use_configured_camera = capture or camera_kwargs is not None
-        if use_configured_camera:
-            base_positions = (
-                self.get_base_pos()
-                if bool(dict(camera_kwargs or {}).get("cam_tracking", False))
-                else None
-            )
-            camera_view = resolve_system_camera_view(
-                self._num_envs,
-                base_positions,
-                offsets,
-                camera_kwargs,
-            )
-            tracking_camera = camera_view.tracking
-        else:
-            tracking_camera = None
-        if capture:
-            self._model.cameras.set_system_render_target("image", int(width), int(height))
-        render_app = RenderApp(headless=headless)
-        try:
-            render_app.launch(
-                self._model,
-                batch=self._num_envs,
-                render_offset=offsets,
-                render_settings=settings,
-            )
-        except _MotrixRenderClosedError as e:
-            # Normalize the motrixsim-private window-closed error to the
-            # interface-level signal declared on SimBackend.
-            raise RenderClosedError(str(e)) from e
-        if use_configured_camera:
-            render_app.system_camera.set_view(
-                camera_view.lookat,
-                camera_view.distance,
-                camera_view.elevation,
-                camera_view.azimuth,
-            )
-            if not capture:
-                render_app.set_main_camera(None)
-        self._render_app = render_app
-        self._render_headless = headless
-        self._render_capture_enabled = capture
-        self._render_tracking_camera = tracking_camera
-
-    def render(self):
-        """Render current state (interactive visualization)"""
-        if self._render_app is None:
-            self.init_renderer()
-        self._assert_render_context_available(headless=False, capture=False)
-        assert self._render_app is not None
-        self._update_tracking_camera_view()
-        try:
-            self._render_app.sync(data=self._data)
-        except _MotrixRenderClosedError as e:
-            # Normalize the motrixsim-private window-closed error to the
-            # interface-level signal declared on SimBackend.
-            raise RenderClosedError(str(e)) from e
-
-    def capture_video_frame(self) -> np.ndarray:
-        """Capture one RGB frame from Motrix's system camera."""
-        if self._render_app is None:
-            self.init_renderer(headless=True, capture=True)
-        if not self._render_capture_enabled:
-            raise RuntimeError("Motrix renderer is not initialized for video capture")
-        assert self._render_app is not None
-
-        self._update_tracking_camera_view()
-        try:
-            task = self._render_app.system_camera.capture()
-            self._render_app.sync(data=self._data, wait=True)
-            image = task.take_image()
-        except _MotrixRenderClosedError as e:
-            # Normalize the motrixsim-private window-closed error to the
-            # interface-level signal declared on SimBackend.
-            raise RenderClosedError(str(e)) from e
-        if image is None:
-            raise RuntimeError("Motrix system camera capture did not return an image")
-
-        pixels = np.asarray(image.pixels)
-        if pixels.ndim != 3:
-            raise RuntimeError(
-                f"Motrix system camera capture must return an HWC image, got shape {pixels.shape}"
-            )
-        if pixels.shape[-1] == 4:
-            pixels = pixels[..., :3]
-        if pixels.shape[-1] != 3:
-            raise RuntimeError(
-                f"Motrix system camera capture must return RGB/RGBA pixels, got shape {pixels.shape}"
-            )
-        return np.ascontiguousarray(pixels, dtype=np.uint8)
-
-    def _apply_reset_randomization(
-        self,
-        data_slice,
-        env_indices: np.ndarray,
-        randomization: ResetRandomizationPayload | None,
-        env_ids_intp: np.ndarray | None = None,
-    ) -> None:
-        if randomization is None or randomization.is_empty():
-            return
-        unsupported = (
-            randomization.requested_terms() - self.get_dr_capabilities().supported_reset_terms
-        )
-        if unsupported:
-            terms = ", ".join(sorted(unsupported))
-            raise NotImplementedError(
-                f"{self.backend_type} backend does not support reset randomization terms: {terms}"
-            )
-
-        env_ids = (
-            env_ids_intp if env_ids_intp is not None else np.asarray(env_indices, dtype=np.intp)
-        )
-        num_reset = len(env_ids)
-        body_mass = None
-        if randomization.body_mass is not None:
-            body_mass = self._coerce_reset_field(
-                randomization.body_mass,
-                name="body_mass",
-                num_reset=num_reset,
-                shaped_tail=(int(self._model.num_links),),
-            )
-        if randomization.base_mass_delta is not None:
-            if body_mass is None:
-                body_mass = np.broadcast_to(
-                    self._default_body_mass,
-                    (num_reset, int(self._model.num_links)),
-                ).copy()
-            body_mass[:, int(self._body_link.index)] += np.asarray(
-                randomization.base_mass_delta, dtype=np.float32
-            )
-        if body_mass is not None:
-            self._set_link_mass_overrides(data_slice, body_mass)
-
-        body_ipos = None
-        if randomization.body_ipos is not None:
-            body_ipos = self._coerce_reset_field(
-                randomization.body_ipos,
-                name="body_ipos",
-                num_reset=num_reset,
-                shaped_tail=(int(self._model.num_links), 3),
-            )
-        if randomization.base_com_offset is not None:
-            if body_ipos is None:
-                body_ipos = np.broadcast_to(
-                    self._default_body_ipos,
-                    (num_reset, int(self._model.num_links), 3),
-                ).copy()
-            body_ipos[:, int(self._body_link.index), :] += np.asarray(
-                randomization.base_com_offset, dtype=np.float32
-            )
-        if body_ipos is not None:
-            self._set_link_ipos_overrides(data_slice, body_ipos)
-
-        if randomization.geom_friction is not None:
-            geom_friction = self._coerce_reset_field(
-                randomization.geom_friction,
-                name="geom_friction",
-                num_reset=num_reset,
-                shaped_tail=(int(self._model.num_geoms), 3),
-            )
-            self._set_geom_friction_overrides(data_slice, geom_friction)
-
-        if randomization.gravity is not None:
-            gravity = self._coerce_reset_field(
-                randomization.gravity,
-                name="gravity",
-                num_reset=num_reset,
-                shaped_tail=(3,),
-            )
-            self._set_gravity_override(data_slice, gravity)
-
-        if randomization.kp is not None:
-            kp = np.asarray(randomization.kp, dtype=np.float32)
-            expected_shape = (num_reset, self.num_actuators)
-            if kp.shape != expected_shape:
-                raise ValueError(f"kp must have shape {expected_shape}, got {kp.shape}")
-            self._set_position_actuator_kp_override(data_slice, kp)
-
-        if randomization.kd is not None:
-            kd = np.asarray(randomization.kd, dtype=np.float32)
-            expected_shape = (num_reset, self.num_actuators)
-            if kd.shape != expected_shape:
-                raise ValueError(f"kd must have shape {expected_shape}, got {kd.shape}")
-            self._set_position_actuator_kd_override(data_slice, kd)
-
-    def _set_gravity_override(self, data_slice, gravity: np.ndarray) -> None:
-        if not getattr(self, "_supports_gravity_override", False):
-            raise NotImplementedError("Motrix gravity override is not available")
-        self._model.set_gravity_override(
-            data_slice,
-            np.ascontiguousarray(np.asarray(gravity, dtype=np.float32)),
-        )
-
-    def _set_position_actuator_kp_override(self, data_slice, kp: np.ndarray) -> None:
-        if not self._supports_position_actuator_gains:
-            raise NotImplementedError(
-                "Motrix actuator kp override is only available for all-position-actuator models"
-            )
-        for actuator in self._position_actuators:
-            # TODO(motrixsim#1384): drop the copy once strided NumPy views are accepted.
-            actuator.set_kp_override(data_slice, np.ascontiguousarray(kp[:, int(actuator.index)]))
-
-    def _set_position_actuator_kd_override(self, data_slice, kd: np.ndarray) -> None:
-        if not self._supports_position_actuator_gains:
-            raise NotImplementedError(
-                "Motrix actuator kd override is only available for all-position-actuator models"
-            )
-        for actuator in self._position_actuators:
-            # TODO(motrixsim#1384): drop the copy once strided NumPy views are accepted.
-            actuator.set_damping_override(
-                data_slice,
-                np.ascontiguousarray(kd[:, int(actuator.index)]),
-            )
-
-    def get_actuator_gains(self) -> tuple[np.ndarray, np.ndarray]:
-        if not self._supports_position_actuator_gains:
-            raise NotImplementedError(
-                "Motrix actuator gains are only exposed for all-position-actuator models"
-            )
-        return self._default_actuator_kp.copy(), self._default_actuator_kd.copy()
diff --git a/src/unilab/base/backend/motrix/playback.py b/src/unilab/base/backend/motrix/playback.py
deleted file mode 100644
index cc94096b9..000000000
--- a/src/unilab/base/backend/motrix/playback.py
+++ /dev/null
@@ -1,96 +0,0 @@
-"""Motrix-owned playback execution helpers."""
-
-from __future__ import annotations
-
-import time
-from os import PathLike
-from typing import Any, Callable, TypeVar
-
-import numpy as np
-
-from unilab.base.backend.playback_common import env_cfg_value, write_playback_video
-
-ObsT = TypeVar("ObsT")
-
-
-def run_motrix_playback(
-    *,
-    backend: Any,
-    env: Any,
-    initialize: Callable[[], ObsT],
-    step: Callable[[ObsT], ObsT],
-    num_steps: int | None,
-    output_video: str | PathLike[str] | None,
-    render_spacing: float | None,
-    render_offset_mode: str | None,
-    headless: bool,
-    record_video: bool,
-    camera_kwargs: dict[str, Any] | None,
-    extra_data_getter: Callable[[], np.ndarray | None] | None = None,
-) -> str | None:
-    del extra_data_getter
-    if record_video and not headless:
-        raise ValueError("Motrix video recording requires headless=true.")
-
-    if headless or record_video:
-        if num_steps is None:
-            raise ValueError("Motrix captured playback requires a finite num_steps value.")
-        if record_video and output_video is None:
-            raise ValueError("Motrix video recording requires an output_video path.")
-
-        effective_spacing = (
-            float(render_spacing)
-            if render_spacing is not None
-            else float(env_cfg_value(env, "render_spacing", 1.0))
-        )
-        backend.init_renderer(
-            spacing=effective_spacing,
-            offset_mode=str(render_offset_mode) if render_offset_mode is not None else "grid",
-            headless=headless,
-            capture=True,
-            width=1280,
-            height=720,
-            camera_kwargs=dict(camera_kwargs or {}),
-        )
-
-        obs = initialize()
-        frames: list[np.ndarray] | None = [] if record_video else None
-        for _ in range(num_steps):
-            obs = step(obs)
-            frame = np.asarray(backend.capture_video_frame(), dtype=np.uint8)
-            if frames is not None:
-                frames.append(frame.copy())
-
-        if not record_video:
-            return None
-
-        assert output_video is not None
-        assert frames is not None
-        ctrl_dt = float(env_cfg_value(env, "ctrl_dt", 1.0 / 60.0))
-        write_playback_video(str(output_video), frames, fps=int(1.0 / ctrl_dt))
-        return str(output_video)
-
-    effective_spacing = (
-        float(render_spacing)
-        if render_spacing is not None
-        else float(env_cfg_value(env, "render_spacing", 1.0))
-    )
-    backend.init_renderer(
-        spacing=effective_spacing,
-        offset_mode=str(render_offset_mode) if render_offset_mode is not None else "grid",
-    )
-    obs = initialize()
-    last_render_time = time.perf_counter()
-    render_dt = 1.0 / 60.0
-    steps_run = 0
-
-    while num_steps is None or steps_run < num_steps:
-        obs = step(obs)
-        current_time = time.perf_counter()
-        elapsed = current_time - last_render_time
-        if elapsed < render_dt:
-            time.sleep(render_dt - elapsed)
-        last_render_time = time.perf_counter()
-        backend.render()
-        steps_run += 1
-    return None
diff --git a/src/unilab/base/backend/motrix/scene.py b/src/unilab/base/backend/motrix/scene.py
deleted file mode 100644
index 2e982bbf7..000000000
--- a/src/unilab/base/backend/motrix/scene.py
+++ /dev/null
@@ -1,267 +0,0 @@
-from __future__ import annotations
-
-import tempfile
-import xml.etree.ElementTree as ET
-from collections.abc import Sequence
-from pathlib import Path
-from typing import TYPE_CHECKING, Literal, overload
-
-import numpy as np
-
-from unilab.base.scene import resolve_scene_fragment_path
-from unilab.terrains.terrain_generator import TerrainGeneratorCfg
-
-if TYPE_CHECKING:
-    from motrixsim import SceneModel
-    from motrixsim.msd import Link, World
-
-
-def _extract_keyframes(fragment_file: Path) -> list[ET.Element]:
-    """Return ``<keyframe>`` child elements declared inside ``fragment_file``."""
-    root = ET.parse(fragment_file).getroot()
-    return list(root.findall("keyframe"))
-
-
-def _materialize_robot_with_fragment_keyframes(
-    robot_path: Path, fragment_paths: Sequence[Path]
-) -> Path:
-    """Inject fragment ``<keyframe>`` blocks into a temporary copy of ``robot_path``.
-
-    motrix's ``msd.from_file`` validates ``<keyframe>`` qpos against the loaded
-    model. fragment XMLs only carry sensors/contacts (no body), so a fragment
-    with its own keyframe fails to parse on its own. Mujoco backend already
-    merges fragments into the scene XML before parsing; this helper does the
-    equivalent for the keyframe block so motrix can load a robot model that
-    owns the keyframe declared in a sibling fragment.
-
-    Returns the original ``robot_path`` when no fragment has a keyframe.
-    """
-    fragment_keyframes: list[ET.Element] = []
-    for fragment_path in fragment_paths:
-        fragment_keyframes.extend(_extract_keyframes(fragment_path))
-    if not fragment_keyframes:
-        return robot_path
-
-    tree = ET.parse(robot_path)
-    root = tree.getroot()
-    existing = root.find("keyframe")
-    if existing is None:
-        existing = ET.SubElement(root, "keyframe")
-    for keyframe in fragment_keyframes:
-        existing.extend(list(keyframe))
-
-    tmp = tempfile.NamedTemporaryFile(
-        suffix=f"_{robot_path.name}",
-        dir=str(robot_path.parent),
-        mode="w",
-        delete=False,
-    )
-    tmp.close()
-    tree.write(tmp.name)
-    return Path(tmp.name)
-
-
-def _materialize_fragment_without_keyframes(fragment_file: Path) -> Path:
-    """Strip ``<keyframe>`` from a fragment XML; return original if no change."""
-    tree = ET.parse(fragment_file)
-    root = tree.getroot()
-    keyframes = root.findall("keyframe")
-    if not keyframes:
-        return fragment_file
-    for keyframe in keyframes:
-        root.remove(keyframe)
-    tmp = tempfile.NamedTemporaryFile(
-        suffix=f"_{fragment_file.name}",
-        dir=str(fragment_file.parent),
-        mode="w",
-        delete=False,
-    )
-    tmp.close()
-    tree.write(tmp.name)
-    return Path(tmp.name)
-
-
-def _cleanup_temp_xml(path: Path, original: Path) -> None:
-    if path == original:
-        return
-    try:
-        path.unlink()
-    except FileNotFoundError:
-        pass
-
-
-def _attach_motrix_scene_fragment(world: World, fragment_file: Path) -> None:
-    import motrixsim.msd as msd
-
-    sanitized = _materialize_fragment_without_keyframes(fragment_file)
-    try:
-        fragment = msd.from_file(str(sanitized))
-    finally:
-        _cleanup_temp_xml(sanitized, fragment_file)
-    world.attach(fragment)
-
-
-def _iter_motrix_links(link: Link):
-    yield link
-    for child in link.children:
-        yield from _iter_motrix_links(child)
-
-
-def _motrix_world_link_names(world: World) -> list[str]:
-    names: list[str] = []
-    for body in world.hierarchy.bodies:
-        for link in _iter_motrix_links(body.link):
-            if link.name:
-                names.append(link.name)
-    return names
-
-
-def add_motrix_tracking_frame_sensors(world: World, *, base_name: str) -> None:
-    """Add Motrix-native frame sensors matching the legacy tracking sensor contract."""
-    import motrixsim.msd as msd
-
-    link_names = _motrix_world_link_names(world)
-    if base_name not in link_names:
-        raise ValueError(f"Base link '{base_name}' not found in Motrix scene")
-
-    existing = {sensor.name for sensor in world.sensors.frame if sensor.name}
-    sensor_specs = (
-        ("track_pos_b", msd.FrameSensorType.FramePos),
-        ("track_quat_b", msd.FrameSensorType.FrameQuat),
-        ("track_linvel_b", msd.FrameSensorType.FrameLinVel),
-        ("track_angvel_b", msd.FrameSensorType.FrameAngVel),
-    )
-    ref_frame = msd.FrameSensorRef.object(msd.ObjectType.link(base_name))
-    for link_name in link_names:
-        object_type = msd.ObjectType.link(link_name)
-        for prefix, sensor_type in sensor_specs:
-            sensor_name = f"{prefix}_{link_name}"
-            if sensor_name in existing:
-                continue
-            sensor = msd.FrameSensor()
-            sensor.name = sensor_name
-            sensor.sensor_type = sensor_type
-            sensor.object_type = object_type
-            sensor.ref_frame = ref_frame
-            world.sensors.frame.append(sensor)
-
-
-def materialize_motrix_scene(
-    *,
-    model_file: str,
-    fragment_files: Sequence[str] = (),
-    add_body_sensors: bool = False,
-    base_name: str = "base",
-) -> SceneModel:
-    """Build a MotrixSim model through MSD scene composition."""
-    import motrixsim.msd as msd
-
-    model_path = Path(model_file).resolve()
-    fragment_paths = [
-        resolve_scene_fragment_path(fragment_file, model_path) for fragment_file in fragment_files
-    ]
-    robot_path = _materialize_robot_with_fragment_keyframes(model_path, fragment_paths)
-    try:
-        world = msd.from_file(str(robot_path))
-        for fragment_path in fragment_paths:
-            _attach_motrix_scene_fragment(world, fragment_path)
-        if add_body_sensors:
-            add_motrix_tracking_frame_sensors(world, base_name=base_name)
-        return msd.build(world)
-    finally:
-        _cleanup_temp_xml(robot_path, model_path)
-
-
-@overload
-def materialize_motrix_hfield_attached_scene(
-    *,
-    model_file: str,
-    terrain_cfg: TerrainGeneratorCfg,
-    fragment_files: Sequence[str] = (),
-    hfield_name: str = "terrain_hfield",
-    geom_name: str = "floor",
-    add_body_sensors: bool = False,
-    base_name: str = "base",
-    return_surface_sampler: Literal[False] = False,
-) -> tuple[SceneModel, np.ndarray]: ...
-
-
-@overload
-def materialize_motrix_hfield_attached_scene(
-    *,
-    model_file: str,
-    terrain_cfg: TerrainGeneratorCfg,
-    fragment_files: Sequence[str] = (),
-    hfield_name: str = "terrain_hfield",
-    geom_name: str = "floor",
-    add_body_sensors: bool = False,
-    base_name: str = "base",
-    return_surface_sampler: Literal[True],
-) -> tuple[SceneModel, np.ndarray, object]: ...
-
-
-def materialize_motrix_hfield_attached_scene(
-    *,
-    model_file: str,
-    terrain_cfg: TerrainGeneratorCfg,
-    fragment_files: Sequence[str] = (),
-    hfield_name: str = "terrain_hfield",
-    geom_name: str = "floor",
-    add_body_sensors: bool = False,
-    base_name: str = "base",
-    return_surface_sampler: bool = False,
-) -> tuple[SceneModel, np.ndarray] | tuple[SceneModel, np.ndarray, object]:
-    """Build a MotrixSim model with generated hfield terrain and attached robot."""
-    import motrixsim.msd as msd
-
-    from unilab.terrains import TerrainGenerator
-
-    robot_path = Path(model_file).resolve()
-    generated = TerrainGenerator(terrain_cfg).generate()
-
-    world = msd.World()
-    world.name = "unilab materialized hfield scene"
-
-    hfield = msd.HFieldSource()
-    hfield.nrow = int(generated.heights_yx.shape[0])
-    hfield.ncol = int(generated.heights_yx.shape[1])
-    # MotrixSim's hfield source uses MuJoCo-style X/Y half extents.
-    hfield.size = [float(generated.hfield_size[0]), float(generated.hfield_size[1])]
-    hfield.height_scale = float(generated.height_extent)
-    # MotrixSim buffers use compiled hfield row order: row 0 is the -Y side.
-    hfield_data = np.ascontiguousarray(np.flipud(generated.heights_yx).astype(np.float32))
-    hfield.source_type = msd.HFieldSourceType.buffer(
-        hfield_data.reshape(-1),
-        f"{hfield_name}_buffer",
-    )
-    world.assets.hfields[hfield_name] = hfield
-
-    terrain_geom = msd.Geometry()
-    terrain_geom.name = geom_name
-    terrain_geom.shape = msd.ShapeType.HField
-    terrain_geom.hfield = hfield_name
-    terrain_geom.position = np.asarray(generated.geom_pos, dtype=np.float32)
-    terrain_geom.collision_mask = msd.CollisionMask.collide_with_all()
-    terrain_geom.physics_material.friction = [1.0, 0.005, 0.0001]
-    world.hierarchy.geoms.append(terrain_geom)
-
-    fragment_paths = [
-        resolve_scene_fragment_path(fragment_file, robot_path) for fragment_file in fragment_files
-    ]
-    merged_robot_path = _materialize_robot_with_fragment_keyframes(robot_path, fragment_paths)
-    try:
-        robot_world = msd.from_file(str(merged_robot_path))
-        world.attach(robot_world)
-        # TODO(motrixsim): remove this once msd.World.attach carries keyframes.
-        world.keyframes.extend(robot_world.keyframes)
-    finally:
-        _cleanup_temp_xml(merged_robot_path, robot_path)
-
-    for fragment_path in fragment_paths:
-        _attach_motrix_scene_fragment(world, fragment_path)
-    if add_body_sensors:
-        add_motrix_tracking_frame_sensors(world, base_name=base_name)
-
-    if return_surface_sampler:
-        return msd.build(world), generated.terrain_origins, generated.surface_sampler()
-    return msd.build(world), generated.terrain_origins
diff --git a/src/unilab/base/backend/motrix_camera.py b/src/unilab/base/backend/motrix_camera.py
deleted file mode 100644
index 0a7fabf5d..000000000
--- a/src/unilab/base/backend/motrix_camera.py
+++ /dev/null
@@ -1,102 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Sequence
-from dataclasses import dataclass
-from typing import Any
-
-import numpy as np
-
-
-@dataclass(frozen=True)
-class MotrixTrackingCamera:
-    env_idx: int
-    distance: float
-    elevation: float
-    azimuth: float
-
-
-@dataclass(frozen=True)
-class MotrixCameraView:
-    lookat: list[float]
-    distance: float
-    elevation: float
-    azimuth: float
-    tracking: MotrixTrackingCamera | None = None
-
-
-def render_offsets(num_envs: int, spacing: float, offset_mode: str = "grid") -> list[list[float]]:
-    if offset_mode == "zero":
-        return [[0.0, 0.0, 0.0] for _ in range(num_envs)]
-    if offset_mode != "grid":
-        raise ValueError(f"Unsupported Motrix render_offset_mode: {offset_mode!r}")
-    cols = int(np.ceil(np.sqrt(num_envs)))
-    offsets = []
-    for i in range(num_envs):
-        row = i // cols
-        col = i % cols
-        offsets.append([col * spacing, row * spacing, 0.0])
-    return offsets
-
-
-def tracking_camera_lookat(
-    base_positions: np.ndarray,
-    tracking_camera: MotrixTrackingCamera,
-    offsets: np.ndarray,
-) -> list[float]:
-    base_pos = np.asarray(base_positions[tracking_camera.env_idx], dtype=np.float64)
-    render_offset = np.asarray(offsets[tracking_camera.env_idx], dtype=np.float64)
-    lookat = base_pos + render_offset
-    return [float(lookat[0]), float(lookat[1]), float(lookat[2])]
-
-
-def resolve_system_camera_view(
-    num_envs: int,
-    base_positions: np.ndarray | None,
-    offsets: Sequence[Sequence[float]],
-    camera_kwargs: dict[str, Any] | None,
-) -> MotrixCameraView:
-    cam_kw = dict(camera_kwargs or {})
-    if bool(cam_kw.get("cam_tracking", False)):
-        if base_positions is None:
-            raise ValueError("base_positions is required when cam_tracking=true")
-        env_idx = int(cam_kw.get("cam_tracking_env_idx", 0))
-        env_idx = max(0, min(env_idx, num_envs - 1))
-        tracking_camera = MotrixTrackingCamera(
-            env_idx=env_idx,
-            distance=float(cam_kw.get("cam_distance", 2.0)),
-            elevation=float(cam_kw.get("cam_elevation", -20.0)),
-            azimuth=float(cam_kw.get("cam_azimuth", 90.0)),
-        )
-        lookat = tracking_camera_lookat(
-            base_positions,
-            tracking_camera,
-            np.asarray(offsets, dtype=np.float64),
-        )
-        return MotrixCameraView(
-            lookat=lookat,
-            distance=tracking_camera.distance,
-            elevation=tracking_camera.elevation,
-            azimuth=tracking_camera.azimuth,
-            tracking=tracking_camera,
-        )
-
-    lookat_raw = cam_kw.get("cam_lookat")
-    if lookat_raw is None:
-        offsets_np = np.asarray(offsets, dtype=np.float64)
-        lookat = [
-            float(np.mean(offsets_np[:, 0])),
-            float(np.mean(offsets_np[:, 1])),
-            0.75,
-        ]
-    else:
-        lookat_arr = np.asarray(lookat_raw, dtype=np.float64).reshape(-1)
-        if lookat_arr.shape != (3,):
-            raise ValueError(f"cam_lookat must contain 3 values, got {lookat_raw!r}")
-        lookat = [float(lookat_arr[0]), float(lookat_arr[1]), float(lookat_arr[2])]
-
-    return MotrixCameraView(
-        lookat=lookat,
-        distance=float(cam_kw.get("cam_distance", 2.0)),
-        elevation=float(cam_kw.get("cam_elevation", -20.0)),
-        azimuth=float(cam_kw.get("cam_azimuth", 90.0)),
-    )
diff --git a/src/unilab/base/backend/mujoco/__init__.py b/src/unilab/base/backend/mujoco/__init__.py
deleted file mode 100644
index 91d941000..000000000
--- a/src/unilab/base/backend/mujoco/__init__.py
+++ /dev/null
@@ -1,48 +0,0 @@
-"""MuJoCo backend package."""
-
-from __future__ import annotations
-
-
-def __getattr__(name: str):
-    if name == "MuJoCoBackend":
-        from .backend import MuJoCoBackend
-
-        return MuJoCoBackend
-    if name in {
-        "materialize_visual_playback_model",
-        "resolve_render_play_model_files",
-        "run_mujoco_playback",
-    }:
-        from . import playback
-
-        return getattr(playback, name)
-    if name in {
-        "add_sensor",
-        "create_discardvisual_xml",
-        "get_named_body_ids",
-        "inject_mujoco_tracking_sensors",
-        "materialize_mujoco_hfield_attached_scene",
-        "materialize_scene_fragments",
-        "materialize_scene_visual_override",
-        "processed_xml",
-    }:
-        from . import xml
-
-        return getattr(xml, name)
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
-
-
-__all__ = [
-    "MuJoCoBackend",
-    "add_sensor",
-    "create_discardvisual_xml",
-    "get_named_body_ids",
-    "inject_mujoco_tracking_sensors",
-    "materialize_visual_playback_model",
-    "materialize_mujoco_hfield_attached_scene",
-    "resolve_render_play_model_files",
-    "materialize_scene_fragments",
-    "materialize_scene_visual_override",
-    "processed_xml",
-    "run_mujoco_playback",
-]
diff --git a/src/unilab/base/backend/mujoco/backend.py b/src/unilab/base/backend/mujoco/backend.py
deleted file mode 100644
index 38c66837c..000000000
--- a/src/unilab/base/backend/mujoco/backend.py
+++ /dev/null
@@ -1,1485 +0,0 @@
-import os
-import tempfile
-import time
-import weakref
-from collections.abc import Sequence
-from concurrent.futures import ProcessPoolExecutor
-from dataclasses import dataclass
-from multiprocessing import cpu_count, current_process, get_context
-from typing import Any, Optional, cast
-
-import mujoco
-import numpy as np
-from mujoco_uni.batch_env import BatchEnvPool
-
-from unilab.base.scene import SceneCfg
-from unilab.dr.types import (
-    RESET_TERM_BASE_COM,
-    RESET_TERM_BASE_MASS,
-    RESET_TERM_BODY_INERTIA,
-    RESET_TERM_BODY_IPOS,
-    RESET_TERM_BODY_IQUAT,
-    RESET_TERM_BODY_MASS,
-    RESET_TERM_DOF_ARMATURE,
-    RESET_TERM_GEOM_FRICTION,
-    RESET_TERM_GRAVITY,
-    RESET_TERM_KD,
-    RESET_TERM_KP,
-    DomainRandomizationCapabilities,
-    InitRandomizationPlan,
-    IntervalRandomizationPlan,
-    ModelVariantSpec,
-    ResetRandomizationPayload,
-)
-from unilab.dtype_config import get_global_dtype
-
-from ..base import (
-    BackendHeightScanner,
-    BackendPlayCapabilities,
-    BackendPlayRenderPlan,
-    BackendTerrainSpawnData,
-    SimBackend,
-    normalize_play_render_mode,
-)
-from .playback import run_mujoco_playback
-
-
-def _root_state_dims(model) -> tuple[int, int]:
-    if model.njnt > 0 and int(model.jnt_type[0]) == int(mujoco.mjtJoint.mjJNT_FREE):
-        return 7, 6
-    return 0, 0
-
-
-@dataclass
-class _MuJoCoHeightScanner(BackendHeightScanner):
-    backend: "MuJoCoBackend"
-    hfield_geom_id: int
-    offsets: np.ndarray
-    frame_body_id: int
-    alignment: str
-    output: str
-
-    def scan(self) -> np.ndarray:
-        pool = self.backend._pool
-        if pool is None:
-            raise RuntimeError("MuJoCo backend pool must be materialized before hfield scanning")
-
-        heights = pool.sample_hfield_height(
-            self.backend._physics_state,
-            hfield_geom_id=self.hfield_geom_id,
-            offsets=self.offsets,
-            frame_body_id=self.frame_body_id,
-            alignment=self.alignment,
-            output=self.output,
-        )
-        return np.asarray(heights, dtype=self.backend._np_dtype)
-
-
-def _prepare_variant_model_xml(
-    model_file: str,
-    *,
-    add_body_sensors: bool,
-    base_name: str | None,
-) -> tuple[str, list[str]]:
-    from unilab.base.backend.mujoco.xml import (
-        create_discardvisual_xml,
-        inject_mujoco_tracking_sensors,
-    )
-
-    model_path = create_discardvisual_xml(model_file)
-    tmp_paths = [model_path]
-    if add_body_sensors:
-        model_path, _, _ = inject_mujoco_tracking_sensors(
-            model_path,
-            baselink_name=base_name,
-        )
-        tmp_paths.append(model_path)
-    return model_path, tmp_paths
-
-
-def _compile_model_variant_chunk_to_mjb(
-    *,
-    model_file: str,
-    add_body_sensors: bool,
-    base_name: str | None,
-    sim_dt: float,
-    iterations: int | None,
-    position_actuator_gains: dict | None,
-    variants: tuple[ModelVariantSpec, ...],
-) -> tuple[str, ...]:
-    model_path, tmp_paths = _prepare_variant_model_xml(
-        model_file,
-        add_body_sensors=add_body_sensors,
-        base_name=base_name,
-    )
-    output_dir = tempfile.mkdtemp(prefix="unilab-mj-variant-")
-    try:
-        base_spec = mujoco.MjSpec.from_file(model_path)
-        output_paths: list[str] = []
-        for idx, variant in enumerate(variants):
-            spec = base_spec.copy()
-            for override in variant.geom_size_overrides:
-                geom = spec.geom(override.geom_name)
-                if geom is None:
-                    raise ValueError(
-                        f"Geom '{override.geom_name}' not found in MuJoCo model '{model_file}'"
-                    )
-                geom.size = list(override.size)
-            model = spec.compile()
-            model.opt.timestep = sim_dt
-            if iterations is not None:
-                model.opt.iterations = int(iterations)
-            if position_actuator_gains is not None:
-                _apply_position_actuator_gains_to_mj_model(model, **position_actuator_gains)
-            output_path = os.path.join(output_dir, f"variant_{idx}.mjb")
-            mujoco.mj_saveModel(model, output_path)
-            output_paths.append(output_path)
-        return tuple(output_paths)
-    finally:
-        for tmp_path in reversed(tmp_paths):
-            os.remove(tmp_path)
-
-
-def _actuator_ids_from_selector(model, actuator_ids) -> np.ndarray:
-    ids = np.arange(model.nu)[actuator_ids]
-    return np.atleast_1d(np.asarray(ids, dtype=np.int32))
-
-
-def _assert_position_actuator_targets(model, actuator_ids=slice(None)) -> None:
-    ids = _actuator_ids_from_selector(model, actuator_ids)
-    if ids.size == 0:
-        return
-    affine_bias = int(mujoco.mjtBias.mjBIAS_AFFINE)
-    invalid = ids[np.asarray(model.actuator_biastype[ids], dtype=np.int32) != affine_bias]
-    if invalid.size == 0:
-        return
-    names = [
-        mujoco.mj_id2name(model, mujoco.mjtObj.mjOBJ_ACTUATOR, int(idx)) or str(int(idx))
-        for idx in invalid[:8]
-    ]
-    suffix = "" if invalid.size <= 8 else f", ... ({invalid.size} total)"
-    raise ValueError(
-        "position_actuator_gains can only target MuJoCo position actuators; "
-        f"non-position actuator ids/names: {', '.join(names)}{suffix}"
-    )
-
-
-def _apply_position_actuator_gains_to_mj_model(
-    model,
-    *,
-    kp: float | np.ndarray,
-    kd: float | np.ndarray,
-    actuator_ids=slice(None),
-) -> None:
-    _assert_position_actuator_targets(model, actuator_ids)
-    kp_arr = np.asarray(kp, dtype=np.float64)
-    kd_arr = np.asarray(kd, dtype=np.float64)
-    model.actuator_gainprm[actuator_ids, 0] = kp_arr
-    model.actuator_biasprm[actuator_ids, 1] = -kp_arr
-    model.actuator_biasprm[actuator_ids, 2] = -kd_arr
-
-
-def _remove_temp_xml(path: str) -> None:
-    if os.path.exists(path):
-        os.remove(path)
-
-
-class _TempXmlCleanup:
-    def __init__(self, path: str) -> None:
-        self.path = path
-        self._finalizer = weakref.finalize(self, _remove_temp_xml, path)
-
-    def cleanup(self) -> None:
-        self._finalizer()
-
-    def __del__(self) -> None:
-        self.cleanup()
-
-
-@dataclass
-class _MuJoCoSceneContext:
-    model_source: str | mujoco.MjModel
-    model_file: str
-    visual_model_file: str | None = None
-    artifacts_dir: str | None = None
-    terrain_origins: np.ndarray | None = None
-    terrain_surface_sampler: Any | None = None
-    cleanup_handle: Any | None = None
-
-
-def _build_mujoco_scene_context(scene: SceneCfg) -> _MuJoCoSceneContext:
-    from unilab.base.backend.mujoco.xml import (
-        materialize_mujoco_hfield_attached_scene,
-        materialize_scene_fragments,
-    )
-
-    if scene is None:
-        raise ValueError("SceneCfg must be provided")
-    if not scene.model_file:
-        raise ValueError("SceneCfg.model_file must be provided")
-
-    if scene.terrain is None:
-        if not scene.fragment_files:
-            return _MuJoCoSceneContext(
-                model_source=scene.model_file,
-                model_file=scene.model_file,
-                visual_model_file=scene.visual_model_file or scene.model_file,
-            )
-        model_source = materialize_scene_fragments(
-            scene.model_file,
-            fragment_files=scene.fragment_files,
-        )
-        return _MuJoCoSceneContext(
-            model_source=model_source,
-            model_file=scene.model_file,
-            visual_model_file=model_source,
-            cleanup_handle=_TempXmlCleanup(model_source),
-        )
-
-    if scene.terrain.generator is None:
-        raise ValueError("SceneCfg.terrain.generator must be configured for terrain scenes")
-
-    output_dir = tempfile.TemporaryDirectory(prefix="unilab_scene_")
-    try:
-        model, terrain_origins, terrain_surface_sampler = materialize_mujoco_hfield_attached_scene(
-            model_file=scene.model_file,
-            terrain_cfg=scene.terrain.generator,
-            output_dir=output_dir.name,
-            fragment_files=scene.fragment_files,
-            hfield_name=scene.terrain.hfield_name,
-            geom_name=scene.terrain.geom_name or "floor",
-            return_surface_sampler=True,
-        )
-    except Exception:
-        output_dir.cleanup()
-        raise
-
-    return _MuJoCoSceneContext(
-        model_source=model,
-        model_file=scene.model_file,
-        visual_model_file=os.path.join(output_dir.name, "scene.xml"),
-        artifacts_dir=output_dir.name,
-        terrain_origins=terrain_origins,
-        terrain_surface_sampler=terrain_surface_sampler,
-        cleanup_handle=output_dir,
-    )
-
-
-class MuJoCoBackend(SimBackend):
-    """MuJoCo backend implementation."""
-
-    def __init__(
-        self,
-        scene: SceneCfg,
-        num_envs: int,
-        sim_dt: float,
-        base_name: Optional[str] = None,
-        np_dtype=None,
-        add_body_sensors: bool = False,
-        position_actuator_gains: dict | None = None,
-        iterations: int | None = None,
-        push_body_name: Optional[str] = None,
-        post_step_forward_sensor: bool = False,
-        chunk_size: Optional[int] = None,
-        adaptive_chunk_size: bool = False,
-        cpu_ids: Optional[Sequence[int]] = None,
-        bench_nsteps: int = 1,
-    ):
-        scene_context = _build_mujoco_scene_context(scene)
-        self.scene_model_file = scene_context.model_file
-        self.scene_visual_model_file = scene_context.visual_model_file
-        self.scene_artifacts_dir = scene_context.artifacts_dir
-        self.terrain_origins = scene_context.terrain_origins
-        self.terrain_surface_sampler = scene_context.terrain_surface_sampler
-        self._terrain_spawn_data = (
-            None
-            if self.terrain_origins is None
-            else BackendTerrainSpawnData(
-                terrain_origins=self.terrain_origins,
-                sample_height=(
-                    None
-                    if self.terrain_surface_sampler is None
-                    else self.terrain_surface_sampler.sample_height
-                ),
-            )
-        )
-        self._scene_cleanup_handle = scene_context.cleanup_handle
-        self.add_body_sensors = add_body_sensors
-        self._base_name = base_name
-        self._push_body_name = push_body_name
-        self._model_file = scene_context.model_source
-        self._sim_dt = float(sim_dt)
-        self._iterations = None if iterations is None else int(iterations)
-        self._post_step_forward_sensor = bool(post_step_forward_sensor)
-        self._manual_chunk_size = None if chunk_size is None else int(chunk_size)
-        self._adaptive_chunk_size = bool(adaptive_chunk_size)
-        self._cpu_ids = self._validate_cpu_ids(cpu_ids)
-        self._bench_nsteps = max(1, int(bench_nsteps))
-        self._chunk_size: int | None = None
-        self._position_actuator_gains = (
-            None if position_actuator_gains is None else dict(position_actuator_gains)
-        )
-        self._pre_step_control_fn = None
-        self._model = self._load_base_model()
-        self._base_body_id = (
-            mujoco.mj_name2id(self._model, mujoco.mjtObj.mjOBJ_BODY, base_name)
-            if base_name is not None
-            else -1
-        )
-        self._push_body_id = self._resolve_push_body_id(self._model)
-        self._push_body_force_slice = self._resolve_push_body_force_slice(self._push_body_id)
-        self._base_body_mass = np.asarray(self._model.body_mass).copy()
-        self._base_body_ipos = np.asarray(self._model.body_ipos).copy()
-        self._num_envs = num_envs
-        self._np_dtype = np_dtype if np_dtype is not None else get_global_dtype()
-        self.backend_type = "mujoco"
-        self._pending_xfrc_applied = np.zeros((num_envs, 6 * self._model.nbody), dtype=np.float64)
-
-        # Thread configuration. An explicit ``cpu_ids`` affinity pins one worker
-        # per CPU, so it also fixes the pool worker count.
-        self._n_threads = (
-            min(num_envs, cpu_count() * 2) if self._cpu_ids is None else len(self._cpu_ids)
-        )
-
-        self._model_variants: tuple[mujoco.MjModel, ...] = (self._model,)
-        self._model_assignments = np.zeros((num_envs,), dtype=np.int32)
-        self._pool: BatchEnvPool | None = None
-        # State indices.
-        self.nq = self._model.nq
-        self.nv = self._model.nv
-        self._idx_qpos = 1
-        self._idx_qvel = 1 + self.nq
-        self._root_qpos_dim, self._root_qvel_dim = _root_state_dims(self._model)
-        self._num_dof_pos = self.nq - self._root_qpos_dim
-        self._num_dof_vel = self.nv - self._root_qvel_dim
-
-        # State storage.
-        nstate = mujoco.mj_stateSize(self._model, mujoco.mjtState.mjSTATE_FULLPHYSICS)
-        self._physics_state = np.zeros((num_envs, nstate), dtype=self._np_dtype)
-        # Initialize all envs with the model default qpos, including identity quaternions.
-        self._physics_state[:, self._idx_qpos : self._idx_qpos + self._model.nq] = self._model.qpos0
-        self._sensor_data = np.zeros((num_envs, self._model.nsensordata), dtype=self._np_dtype)
-
-        # Cached views.
-        self._dof_pos_view = self._physics_state[
-            :, self._idx_qpos + self._root_qpos_dim : self._idx_qpos + self.nq
-        ]
-        self._dof_vel_view = self._physics_state[
-            :, self._idx_qvel + self._root_qvel_dim : self._idx_qvel + self.nv
-        ]
-        self._qpos_view = self._physics_state[:, self._idx_qpos : self._idx_qpos + self.nq]
-        if self._root_qpos_dim == 7:
-            self._base_pos_view = self._physics_state[:, self._idx_qpos : self._idx_qpos + 3]
-            self._base_quat_view = self._physics_state[:, self._idx_qpos + 3 : self._idx_qpos + 7]
-            self._base_lin_vel_view = self._physics_state[:, self._idx_qvel : self._idx_qvel + 3]
-            self._base_ang_vel_view = self._physics_state[
-                :, self._idx_qvel + 3 : self._idx_qvel + 6
-            ]
-        else:
-            if self._base_body_id >= 0:
-                data0 = mujoco.MjData(self._model)
-                mujoco.mj_forward(self._model, data0)
-                base_pos = np.asarray(data0.xpos[self._base_body_id], dtype=self._np_dtype).copy()
-                base_quat = np.asarray(data0.xquat[self._base_body_id], dtype=self._np_dtype).copy()
-            else:
-                base_pos = np.zeros((3,), dtype=self._np_dtype)
-                base_quat = np.array([1.0, 0.0, 0.0, 0.0], dtype=self._np_dtype)
-            self._base_pos_view = np.broadcast_to(base_pos, (num_envs, 3)).copy()
-            self._base_quat_view = np.broadcast_to(base_quat, (num_envs, 4)).copy()
-            self._base_lin_vel_view = np.zeros((num_envs, 3), dtype=self._np_dtype)
-            self._base_ang_vel_view = np.zeros((num_envs, 3), dtype=self._np_dtype)
-
-        # Sensor indices.
-        self._sensor_indices = {}
-        self._sensor_views = {}
-        for i in range(self._model.nsensor):
-            name = mujoco.mj_id2name(self._model, mujoco.mjtObj.mjOBJ_SENSOR, i)
-            if name:
-                adr = self._model.sensor_adr[i]
-                dim = self._model.sensor_dim[i]
-                self._sensor_indices[name] = list(range(adr, adr + dim))
-                self._sensor_views[name] = self._sensor_data[:, adr : adr + dim]
-
-        # Zero-copy view mapping for tracked-body sensors.
-        if self.add_body_sensors and self._valid_bnames:
-
-            def _get_sensor_view(prefix, dim):
-                adrs = [
-                    self._model.sensor_adr[
-                        mujoco.mj_name2id(self._model, mujoco.mjtObj.mjOBJ_SENSOR, f"{prefix}_{nb}")
-                    ]
-                    for nb in self._valid_bnames
-                ]
-                return self._sensor_data[:, adrs[0] : adrs[-1] + dim].reshape(
-                    num_envs, len(self._valid_bnames), dim
-                )
-
-            # Global (world) sensors
-            self._tracked_pos_w_all = _get_sensor_view("track_pos_w", 3)
-            self._tracked_quat_w_all = _get_sensor_view("track_quat_w", 4)
-            self._tracked_linvel_w_all = _get_sensor_view("track_linvel_w", 3)
-            self._tracked_angvel_w_all = _get_sensor_view("track_angvel_w", 3)
-
-            # Local (baselink) sensors
-            self._tracked_pos_b_all = _get_sensor_view("track_pos_b", 3)
-            self._tracked_quat_b_all = _get_sensor_view("track_quat_b", 4)
-            self._tracked_linvel_b_all = _get_sensor_view("track_linvel_b", 3)
-            self._tracked_angvel_b_all = _get_sensor_view("track_angvel_b", 3)
-
-    def _load_base_model(self) -> mujoco.MjModel:
-        if isinstance(self._model_file, mujoco.MjModel):
-            if self.add_body_sensors:
-                raise ValueError("add_body_sensors is not supported for precompiled MuJoCo models")
-            self._tracked_body_ids = []
-            self._valid_bnames = []
-            model = self._model_file
-            self._configure_model(model)
-            return model
-
-        model_path, tmp_paths, tracked_body_ids, valid_bnames = self._prepare_model_xml()
-        try:
-            model = mujoco.MjModel.from_xml_path(model_path)
-        finally:
-            for tmp_path in reversed(tmp_paths):
-                os.remove(tmp_path)
-
-        self._tracked_body_ids = tracked_body_ids
-        if self.add_body_sensors:
-            self._body_id_to_tracked_idx = np.full(model.nbody, -1, dtype=int)
-            for idx, bid in enumerate(self._tracked_body_ids):
-                self._body_id_to_tracked_idx[bid] = idx
-        self._valid_bnames = valid_bnames
-        self._configure_model(model)
-        return model
-
-    def _prepare_model_xml(self) -> tuple[str, list[str], list[int], list[str]]:
-        from unilab.base.backend.mujoco.xml import (
-            create_discardvisual_xml,
-            inject_mujoco_tracking_sensors,
-        )
-
-        model_path = create_discardvisual_xml(str(self._model_file))
-        tmp_paths = [model_path]
-        if self.add_body_sensors:
-            model_path, tracked_body_ids, valid_bnames = inject_mujoco_tracking_sensors(
-                model_path,
-                baselink_name=self._base_name,
-            )
-            tmp_paths.append(model_path)
-        else:
-            tracked_body_ids = []
-            valid_bnames = []
-        return model_path, tmp_paths, tracked_body_ids, valid_bnames
-
-    def _configure_model(self, model: mujoco.MjModel) -> None:
-        model.opt.timestep = self._sim_dt
-        if self._iterations is not None:
-            model.opt.iterations = self._iterations
-        if self._position_actuator_gains is not None:
-            self._apply_position_actuator_gains_to_model(model, **self._position_actuator_gains)
-
-    def _resolve_push_body_id(self, model: mujoco.MjModel) -> int:
-        body_name = self._push_body_name if self._push_body_name is not None else self._base_name
-        if body_name is None:
-            return -1
-        body_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_BODY, body_name)
-        if body_id < 0:
-            raise ValueError(f"Push body '{body_name}' not found in MuJoCo model")
-        return int(body_id)
-
-    def _resolve_push_body_force_slice(self, body_id: int) -> slice:
-        if body_id < 0:
-            return slice(0, 0)
-        start = 6 * body_id
-        return slice(start, start + 3)
-
-    def _sample_push_force(self, force_range: Sequence[float] | np.ndarray) -> np.ndarray:
-        """Sample one world-frame push force vector per environment.
-
-        Args:
-            force_range: Per-axis push-force magnitude range.
-
-        Returns:
-            Array with shape ``(num_envs, 3)`` containing sampled forces.
-        """
-        ex_force = np.random.uniform(-1.0, 1.0, size=(self._num_envs, 3))
-        ex_force *= np.asarray(force_range, dtype=np.float64)
-        return ex_force.astype(np.float64, copy=False)
-
-    def _compile_model_variants(
-        self,
-        variant_specs: Sequence[ModelVariantSpec],
-    ) -> tuple[mujoco.MjModel, ...]:
-        variants = tuple(variant_specs)
-        if not variants:
-            return tuple()
-        if isinstance(self._model_file, mujoco.MjModel):
-            raise ValueError(
-                "MuJoCo model variants are not supported for precompiled materialized scenes"
-            )
-
-        def _load_compiled_models_and_cleanup(paths: Sequence[str]) -> tuple[mujoco.MjModel, ...]:
-            try:
-                return tuple(mujoco.MjModel.from_binary_path(path) for path in paths)
-            finally:
-                for path in paths:
-                    if os.path.exists(path):
-                        os.remove(path)
-                for path in paths:
-                    parent = os.path.dirname(path)
-                    if parent and os.path.isdir(parent):
-                        try:
-                            os.rmdir(parent)
-                        except OSError:
-                            pass
-
-        if len(variants) == 1 or current_process().daemon:
-            mjb_paths = _compile_model_variant_chunk_to_mjb(
-                model_file=self._model_file,
-                add_body_sensors=self.add_body_sensors,
-                base_name=self._base_name,
-                sim_dt=self._sim_dt,
-                iterations=self._iterations,
-                position_actuator_gains=self._position_actuator_gains,
-                variants=variants,
-            )
-            return _load_compiled_models_and_cleanup(mjb_paths)
-
-        max_workers = min(len(variants), max(1, cpu_count()))
-        chunk_size = max(1, (len(variants) + max_workers - 1) // max_workers)
-        chunks = tuple(
-            tuple(variants[idx : idx + chunk_size]) for idx in range(0, len(variants), chunk_size)
-        )
-        try:
-            with ProcessPoolExecutor(
-                max_workers=max_workers,
-                mp_context=get_context("spawn"),
-            ) as executor:
-                futures = [
-                    executor.submit(
-                        _compile_model_variant_chunk_to_mjb,
-                        model_file=self._model_file,
-                        add_body_sensors=self.add_body_sensors,
-                        base_name=self._base_name,
-                        sim_dt=self._sim_dt,
-                        iterations=self._iterations,
-                        position_actuator_gains=self._position_actuator_gains,
-                        variants=chunk,
-                    )
-                    for chunk in chunks
-                ]
-            mjb_paths_nested = [future.result() for future in futures]
-        except PermissionError:
-            mjb_paths_nested = [
-                _compile_model_variant_chunk_to_mjb(
-                    model_file=self._model_file,
-                    add_body_sensors=self.add_body_sensors,
-                    base_name=self._base_name,
-                    sim_dt=self._sim_dt,
-                    iterations=self._iterations,
-                    position_actuator_gains=self._position_actuator_gains,
-                    variants=chunk,
-                )
-                for chunk in chunks
-            ]
-        flat_paths = [path for paths in mjb_paths_nested for path in paths]
-        return _load_compiled_models_and_cleanup(flat_paths)
-
-    def _current_model_sequence(self) -> mujoco.MjModel | list[mujoco.MjModel]:
-        if len(self._model_variants) == 1 and np.all(self._model_assignments == 0):
-            return self._model_variants[0]
-        return [self._model_variants[int(idx)] for idx in self._model_assignments]
-
-    @staticmethod
-    def _validate_cpu_ids(cpu_ids: Optional[Sequence[int]]) -> tuple[int, ...] | None:
-        """Cold-path structural validation for the optional worker CPU affinity.
-
-        ``cpu_ids[i]`` pins pool worker thread ``i`` to one CPU, so its length
-        also fixes ``nthread``. Platform/availability checks happen in the
-        mujoco-uni runtime when the pool is created.
-        """
-        if cpu_ids is None:
-            return None
-        if isinstance(cpu_ids, (str, bytes)):
-            raise TypeError("cpu_ids must be a sequence of integer CPU ids")
-        entries = list(cpu_ids)
-        if not entries:
-            raise ValueError("cpu_ids must be non-empty")
-        for cpu_id in entries:
-            if isinstance(cpu_id, bool) or not isinstance(cpu_id, int) or cpu_id < 0:
-                raise ValueError(f"cpu_ids entries must be non-negative integers, got {cpu_id!r}")
-        ids = tuple(entries)
-        if len(set(ids)) != len(ids):
-            raise ValueError(f"cpu_ids entries must be unique, got {list(ids)!r}")
-        return ids
-
-    def _build_pool(self) -> BatchEnvPool:
-        pool_kwargs: dict[str, Any] = {
-            "nbatch": self._num_envs,
-            "nthread": self._n_threads,
-        }
-        if self._cpu_ids is not None:
-            pool_kwargs["cpu_ids"] = list(self._cpu_ids)
-        pool = BatchEnvPool(self._current_model_sequence(), **pool_kwargs)
-        sensor_init = pool.forward(self._physics_state)
-        self._sensor_data[:] = sensor_init.astype(self._np_dtype)
-        return pool
-
-    def _apply_model_assignments(
-        self,
-        model_variants: tuple[mujoco.MjModel, ...],
-        model_assignments: np.ndarray,
-    ) -> None:
-        if len(model_assignments) != self._num_envs:
-            raise ValueError(
-                f"model_assignments must have length {self._num_envs}, got {len(model_assignments)}"
-            )
-        if len(model_variants) == 0:
-            raise ValueError("model_variants must be non-empty")
-        if np.any(model_assignments < 0) or np.any(model_assignments >= len(model_variants)):
-            raise ValueError(
-                f"model_assignments must be in [0, {len(model_variants) - 1}], got {model_assignments}"
-            )
-
-        self._model_variants = model_variants
-        self._model_assignments = np.asarray(model_assignments, dtype=np.int32).copy()
-        self._model = model_variants[int(self._model_assignments[0])]
-        self._base_body_id = (
-            mujoco.mj_name2id(self._model, mujoco.mjtObj.mjOBJ_BODY, self._base_name)
-            if self._base_name is not None
-            else -1
-        )
-        self._push_body_id = self._resolve_push_body_id(self._model)
-        self._push_body_force_slice = self._resolve_push_body_force_slice(self._push_body_id)
-        self._base_body_mass = np.asarray(self._model.body_mass).copy()
-        self._base_body_ipos = np.asarray(self._model.body_ipos).copy()
-        self._pending_xfrc_applied = np.zeros(
-            (self._num_envs, 6 * self._model.nbody), dtype=np.float64
-        )
-        self._physics_state[:, self._idx_qpos : self._idx_qpos + self._model.nq] = self._model.qpos0
-
-    # ------------------------------------------------------------------ #
-    # Properties                                                         #
-    # ------------------------------------------------------------------ #
-
-    @property
-    def num_envs(self) -> int:
-        return self._num_envs
-
-    @property
-    def model(self):
-        return self._model
-
-    # ------------------------------------------------------------------ #
-    # Model properties                                                   #
-    # ------------------------------------------------------------------ #
-
-    @property
-    def num_actuators(self) -> int:
-        return int(self._model.nu)
-
-    @property
-    def num_dof_vel(self) -> int:
-        return int(self._num_dof_vel)
-
-    def get_actuator_ctrl_range(self) -> np.ndarray:
-        return np.array(self._model.actuator_ctrlrange, dtype=self._np_dtype)
-
-    def get_actuator_names(self) -> tuple[str, ...]:
-        return tuple(
-            mujoco.mj_id2name(self._model, mujoco.mjtObj.mjOBJ_ACTUATOR, actuator_id)
-            or f"#{actuator_id}"
-            for actuator_id in range(int(self._model.nu))
-        )
-
-    def get_scene_model_file(self) -> str | None:
-        return str(self.scene_model_file) if self.scene_model_file else None
-
-    def get_scene_visual_model_file(self) -> str | None:
-        return str(self.scene_visual_model_file) if self.scene_visual_model_file else None
-
-    def get_terrain_spawn_data(self) -> BackendTerrainSpawnData | None:
-        return self._terrain_spawn_data
-
-    def get_keyframe_qpos(self, name: str) -> np.ndarray:
-        key_id = mujoco.mj_name2id(self._model, mujoco.mjtObj.mjOBJ_KEY, name)
-        if key_id < 0:
-            raise ValueError(f"Keyframe '{name}' not found in MuJoCo model")
-        return np.array(self._model.key_qpos[key_id].copy(), dtype=self._np_dtype)
-
-    def get_default_qpos(self) -> np.ndarray:
-        return np.asarray(self._model.qpos0, dtype=np.float64).copy()
-
-    def get_init_qvel(self) -> np.ndarray:
-        return np.zeros((self.nv,), dtype=self._np_dtype)
-
-    def get_body_ids(self, names: "Sequence[str]") -> np.ndarray:
-        ids: list[int] = []
-        for name in names:
-            bid = mujoco.mj_name2id(self._model, mujoco.mjtObj.mjOBJ_BODY, name)
-            if bid < 0:
-                raise ValueError(f"Body '{name}' not found in MuJoCo model")
-            ids.append(bid)
-        return np.array(ids, dtype=np.int32)
-
-    def get_geom_id(self, name: str) -> int:
-        geom_id = mujoco.mj_name2id(self._model, mujoco.mjtObj.mjOBJ_GEOM, name)
-        if geom_id < 0:
-            raise ValueError(f"Geom '{name}' not found in MuJoCo model")
-        return int(geom_id)
-
-    def get_geom_size(self, name: str) -> np.ndarray:
-        return np.asarray(self._model.geom_size[self.get_geom_id(name)], dtype=np.float64).copy()
-
-    def create_hfield_scanner(
-        self,
-        *,
-        hfield_geom_id: int,
-        offsets: np.ndarray,
-        frame_body_id: int,
-        alignment: str = "yaw",
-        output: str = "height",
-    ) -> BackendHeightScanner:
-        offsets_np = np.ascontiguousarray(np.asarray(offsets, dtype=np.float64))
-        if offsets_np.ndim != 2 or offsets_np.shape[1] != 2:
-            raise ValueError(f"offsets must have shape (num_points, 2), got {offsets_np.shape}")
-
-        return _MuJoCoHeightScanner(
-            backend=self,
-            hfield_geom_id=int(hfield_geom_id),
-            offsets=offsets_np,
-            frame_body_id=int(frame_body_id),
-            alignment=alignment,
-            output=output,
-        )
-
-    def get_body_subtree_ids(self, root_body_id: int) -> np.ndarray:
-        subtree_ids = {int(root_body_id)}
-        changed = True
-        while changed:
-            changed = False
-            for body_id in range(self._model.nbody):
-                parent_id = int(self._model.body_parentid[body_id])
-                if body_id not in subtree_ids and parent_id in subtree_ids:
-                    subtree_ids.add(body_id)
-                    changed = True
-        return np.asarray(sorted(subtree_ids), dtype=np.int32)
-
-    def get_geom_names(self) -> tuple[str, ...]:
-        return tuple(
-            mujoco.mj_id2name(self._model, mujoco.mjtObj.mjOBJ_GEOM, geom_id) or ""
-            for geom_id in range(self._model.ngeom)
-        )
-
-    def get_geom_body_ids(self) -> np.ndarray:
-        return np.asarray(self._model.geom_bodyid, dtype=np.int32).copy()
-
-    def get_geom_contact_masks(self) -> tuple[np.ndarray, np.ndarray]:
-        return (
-            np.asarray(self._model.geom_contype, dtype=np.int32).copy(),
-            np.asarray(self._model.geom_conaffinity, dtype=np.int32).copy(),
-        )
-
-    def get_geom_friction(self) -> np.ndarray:
-        return np.asarray(self._model.geom_friction, dtype=np.float64).copy()
-
-    def get_gravity(self) -> np.ndarray:
-        return np.asarray(self._model.opt.gravity, dtype=np.float64).copy()
-
-    def get_body_mass(self) -> np.ndarray:
-        return np.asarray(self._model.body_mass, dtype=np.float64).copy()
-
-    def get_body_ipos(self) -> np.ndarray:
-        return np.asarray(self._model.body_ipos, dtype=np.float64).copy()
-
-    def get_dof_armature(self) -> np.ndarray:
-        return np.asarray(self._model.dof_armature, dtype=np.float64).copy()
-
-    def get_motion_body_ids(self, names: Sequence[str]) -> np.ndarray:
-        return self.get_body_ids(names)
-
-    def get_site_ids(self, names: Sequence[str]) -> np.ndarray:
-        ids: list[int] = []
-        for name in names:
-            sid = mujoco.mj_name2id(self._model, mujoco.mjtObj.mjOBJ_SITE, name)
-            if sid < 0:
-                raise ValueError(f"Site '{name}' not found in MuJoCo model")
-            ids.append(sid)
-        return np.array(ids, dtype=np.int32)
-
-    def get_joint_dof_indices(self, names: Sequence[str]) -> np.ndarray:
-        indices: list[int] = []
-        for name in names:
-            jid = mujoco.mj_name2id(self._model, mujoco.mjtObj.mjOBJ_JOINT, name)
-            if jid < 0:
-                raise ValueError(f"Joint '{name}' not found in MuJoCo model")
-            indices.append(int(self._model.jnt_dofadr[jid]))
-        return np.array(indices, dtype=np.int32)
-
-    def get_joint_dof_pos_indices(self, names: Sequence[str]) -> np.ndarray:
-        indices: list[int] = []
-        single_dof_types = {
-            int(mujoco.mjtJoint.mjJNT_HINGE),
-            int(mujoco.mjtJoint.mjJNT_SLIDE),
-        }
-        for name in names:
-            jid = mujoco.mj_name2id(self._model, mujoco.mjtObj.mjOBJ_JOINT, name)
-            if jid < 0:
-                raise ValueError(f"Joint '{name}' not found in MuJoCo model")
-            if int(self._model.jnt_type[jid]) not in single_dof_types:
-                raise ValueError(f"Joint '{name}' is not a single-DoF joint")
-            indices.append(int(self._model.jnt_qposadr[jid]) - self._root_qpos_dim)
-        return np.array(indices, dtype=np.int32)
-
-    def get_joint_dof_vel_indices(self, names: Sequence[str]) -> np.ndarray:
-        return self.get_joint_dof_indices(names) - self._root_qvel_dim
-
-    def get_joint_range(self) -> np.ndarray | None:
-        jnt_range = self._model.jnt_range
-        mask = self._model.jnt_type != int(mujoco.mjtJoint.mjJNT_FREE)
-        return np.array(jnt_range[mask], dtype=self._np_dtype)
-
-    # ------------------------------------------------------------------ #
-    # Simulation control                                                 #
-    # ------------------------------------------------------------------ #
-
-    def step(self, ctrl: np.ndarray, nsteps: int = 1) -> dict | None:
-        if self._pre_step_control_fn is not None:
-            return self._step_with_pre_step_control(ctrl, nsteps)
-
-        t0 = time.perf_counter()
-        control_traj = np.broadcast_to(ctrl[:, None, :], (self._num_envs, nsteps, ctrl.shape[-1]))
-        control_spec = int(mujoco.mjtState.mjSTATE_CTRL)
-        if np.any(self._pending_xfrc_applied):
-            control_spec |= int(mujoco.mjtState.mjSTATE_XFRC_APPLIED)
-            xfrc_traj = np.broadcast_to(
-                self._pending_xfrc_applied[:, None, :],
-                (self._num_envs, nsteps, self._pending_xfrc_applied.shape[-1]),
-            )
-            control_traj = np.concatenate((control_traj, xfrc_traj), axis=-1)
-        set_ctrl_ms = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        state_np, sensor_np = self._pool.step(  # type: ignore[union-attr]
-            self._physics_state,
-            nstep=nsteps,
-            control=control_traj,
-            control_spec=control_spec,
-            chunk_size=self._chunk_size,
-            return_sensor=True,
-            post_step_forward_sensor=self._post_step_forward_sensor,
-        )
-        if control_spec & int(mujoco.mjtState.mjSTATE_XFRC_APPLIED):
-            self._pending_xfrc_applied.fill(0.0)
-        self._physics_state[:] = state_np.astype(self._np_dtype)
-        physics_ms = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        self._sensor_data[:] = sensor_np.astype(self._np_dtype)
-        refresh_cache_ms = (time.perf_counter() - t0) * 1000.0
-
-        return {
-            "timing": {
-                "set_ctrl_ms": set_ctrl_ms,
-                "physics_ms": physics_ms,
-                "refresh_cache_ms": refresh_cache_ms,
-            }
-        }
-
-    def _step_with_pre_step_control(
-        self, ctrl: np.ndarray, nsteps: int
-    ) -> dict[str, dict[str, float]]:
-        set_ctrl_ms = 0.0
-        physics_ms = 0.0
-        refresh_cache_ms = 0.0
-        has_pending_xfrc = bool(np.any(self._pending_xfrc_applied))
-
-        for _ in range(nsteps):
-            t0 = time.perf_counter()
-            native_ctrl = self._apply_pre_step_control(ctrl)
-            control_traj = native_ctrl[:, None, :]
-            control_spec = int(mujoco.mjtState.mjSTATE_CTRL)
-            if has_pending_xfrc:
-                control_spec |= int(mujoco.mjtState.mjSTATE_XFRC_APPLIED)
-                xfrc_traj = self._pending_xfrc_applied[:, None, :]
-                control_traj = np.concatenate((control_traj, xfrc_traj), axis=-1)
-            set_ctrl_ms += (time.perf_counter() - t0) * 1000.0
-
-            t0 = time.perf_counter()
-            state_np, sensor_np = self._pool.step(  # type: ignore[union-attr]
-                self._physics_state,
-                nstep=1,
-                control=control_traj,
-                control_spec=control_spec,
-                chunk_size=self._chunk_size,
-                return_sensor=True,
-                post_step_forward_sensor=self._post_step_forward_sensor,
-            )
-            self._physics_state[:] = state_np.astype(self._np_dtype)
-            physics_ms += (time.perf_counter() - t0) * 1000.0
-
-            t0 = time.perf_counter()
-            self._sensor_data[:] = sensor_np.astype(self._np_dtype)
-            refresh_cache_ms += (time.perf_counter() - t0) * 1000.0
-
-        if has_pending_xfrc:
-            self._pending_xfrc_applied.fill(0.0)
-
-        return {
-            "timing": {
-                "set_ctrl_ms": set_ctrl_ms,
-                "physics_ms": physics_ms,
-                "refresh_cache_ms": refresh_cache_ms,
-            }
-        }
-
-    def set_state(
-        self,
-        env_indices: np.ndarray,
-        qpos: np.ndarray,
-        qvel: np.ndarray,
-        randomization: ResetRandomizationPayload | None = None,
-    ) -> dict | None:
-        timing: dict[str, float] = {
-            "set_state_mask_ms": 0.0,
-            "set_state_data_slice_ms": 0.0,
-            "set_state_data_reset_ms": 0.0,
-            "set_state_clear_forces_ms": 0.0,
-            "set_state_geom_overrides_ms": 0.0,
-            "set_state_reset_rand_ms": 0.0,
-            "set_state_set_dof_vel_ms": 0.0,
-            "set_state_set_dof_pos_ms": 0.0,
-            "set_state_actuator_ctrl_ms": 0.0,
-            "set_state_forward_kinematic_ms": 0.0,
-            "set_state_refresh_pose_cache_ms": 0.0,
-            "set_state_invalidate_velocity_ms": 0.0,
-            "set_state_qpos_convert_ms": 0.0,
-            "set_state_pool_reset_ms": 0.0,
-            "set_state_state_scatter_ms": 0.0,
-            "set_state_internal_gap_ms": 0.0,
-        }
-        if len(env_indices) == 0:
-            return {"timing": timing}
-
-        outer_t0 = time.perf_counter()
-
-        t0 = time.perf_counter()
-        num_reset = len(env_indices)
-        state_np = np.zeros((num_reset, self._physics_state.shape[1]), dtype=np.float64)
-        state_np[:, self._idx_qpos : self._idx_qpos + self.nq] = qpos
-        state_np[:, self._idx_qvel : self._idx_qvel + self.nv] = qvel
-        timing["set_state_qpos_convert_ms"] = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        state_out, sensor_np = self._pool.reset(  # type: ignore[union-attr]
-            env_ids=np.asarray(env_indices, dtype=np.int32),
-            initial_state=state_np,
-            randomization=self._translate_reset_randomization(randomization, num_reset),
-        )
-        timing["set_state_pool_reset_ms"] = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        self._physics_state[env_indices] = state_out.astype(self._np_dtype)
-        self._sensor_data[env_indices] = sensor_np.astype(self._np_dtype)
-        timing["set_state_state_scatter_ms"] = (time.perf_counter() - t0) * 1000.0
-
-        outer_total_ms = (time.perf_counter() - outer_t0) * 1000.0
-        measured_ms = (
-            timing["set_state_qpos_convert_ms"]
-            + timing["set_state_pool_reset_ms"]
-            + timing["set_state_state_scatter_ms"]
-        )
-        timing["set_state_internal_gap_ms"] = outer_total_ms - measured_ms
-        return {"timing": timing}
-
-    def get_dr_capabilities(self) -> DomainRandomizationCapabilities:
-        return DomainRandomizationCapabilities(
-            supported_reset_terms=frozenset(
-                {
-                    RESET_TERM_BASE_MASS,
-                    RESET_TERM_BASE_COM,
-                    RESET_TERM_GRAVITY,
-                    RESET_TERM_BODY_IQUAT,
-                    RESET_TERM_BODY_INERTIA,
-                    RESET_TERM_BODY_IPOS,
-                    RESET_TERM_BODY_MASS,
-                    RESET_TERM_DOF_ARMATURE,
-                    RESET_TERM_GEOM_FRICTION,
-                    RESET_TERM_KP,
-                    RESET_TERM_KD,
-                }
-            ),
-            supports_interval_push=self._push_body_id >= 0,
-            supports_interval_body_force=True,
-        )
-
-    def apply_init_randomization(self, plan: InitRandomizationPlan) -> None:
-        if plan.is_empty():
-            return
-        if self._pool is not None:
-            raise RuntimeError("MuJoCo init randomization must run before pool materialization")
-        model_assignments = np.asarray(plan.model_assignments, dtype=np.int32)
-        model_variants = self._compile_model_variants(plan.model_variants)
-        self._apply_model_assignments(model_variants, model_assignments)
-
-    def materialize(self) -> None:
-        if self._pool is not None:
-            raise RuntimeError("MuJoCo backend pool is already materialized")
-        self._pool = self._build_pool()
-
-        from unilab.base.backend.mujoco.chunk_tuner import resolve_chunk_size
-
-        self._chunk_size = resolve_chunk_size(
-            pool=self._pool,
-            state=self._physics_state,
-            model=self._model,
-            n_variants=len(self._model_variants),
-            num_envs=self._num_envs,
-            nthread=self._n_threads,
-            dtype=self._np_dtype,
-            post_step_forward_sensor=self._post_step_forward_sensor,
-            bench_nsteps=self._bench_nsteps,
-            manual_chunk_size=self._manual_chunk_size,
-            adaptive=self._adaptive_chunk_size,
-            model_file=self._model_file,
-        )
-
-    def apply_interval_randomization(self, plan: IntervalRandomizationPlan) -> None:
-        if plan.is_empty():
-            return
-        self._pending_xfrc_applied.fill(0.0)
-        if plan.push_perturbation_limit is not None:
-            self.push_robots(plan.push_perturbation_limit)
-        if plan.body_force is not None:
-            if plan.body_ids is None:
-                raise ValueError("Interval body-force perturbation requires body_ids")
-            self.apply_body_force(plan.body_ids, plan.body_force)
-        if plan.body_linear_velocity_delta is not None:
-            if plan.body_ids is None:
-                raise ValueError("Interval body-velocity perturbation requires body_ids")
-            self._apply_body_linear_velocity_delta(plan.body_ids, plan.body_linear_velocity_delta)
-
-    def _apply_body_linear_velocity_delta(
-        self,
-        body_ids: np.ndarray,
-        velocity_delta: np.ndarray,
-    ) -> None:
-        """Apply a world-frame linear-velocity delta to specific bodies.
-
-        Backend-internal hook for ``apply_interval_randomization``; it is not
-        part of the public ``SimBackend`` surface.
-        """
-        raise NotImplementedError(
-            f"{self.__class__.__name__} does not support interval body velocity perturbation"
-        )
-
-    def push_robots(self, force_range: Sequence[float] | np.ndarray) -> None:
-        self._pending_xfrc_applied.fill(0.0)
-        self._pending_xfrc_applied[:, self._push_body_force_slice] = self._sample_push_force(
-            force_range
-        )
-
-    def apply_body_force(
-        self,
-        body_ids: np.ndarray,
-        force: np.ndarray,
-    ) -> None:
-        """Accumulate one external world-frame force vector per target body.
-
-        Args:
-            body_ids: Body ids to perturb.
-            force: Force tensor with shape ``(num_envs, len(body_ids), 3)``.
-
-        Returns:
-            None. The force is staged in ``xfrc_applied`` for the next step.
-        """
-        body_ids_np = np.asarray(body_ids, dtype=np.int32).reshape(-1)
-        force_np = np.asarray(force, dtype=np.float64)
-        expected_shape = (self._num_envs, body_ids_np.size, 3)
-        if force_np.shape != expected_shape:
-            raise ValueError(f"body force must have shape {expected_shape}, got {force_np.shape}")
-        for body_offset, body_id in enumerate(body_ids_np):
-            self._pending_xfrc_applied[:, self._resolve_push_body_force_slice(int(body_id))] += (
-                force_np[:, body_offset, :]
-            )
-
-    def get_play_capabilities(self) -> BackendPlayCapabilities:
-        return BackendPlayCapabilities(supports_physics_state_playback=True)
-
-    def resolve_play_render_plan(
-        self,
-        *,
-        play_render_mode: str | None,
-        play_steps: int | None,
-        output_video: str | os.PathLike[str] | None,
-    ) -> BackendPlayRenderPlan:
-        mode = normalize_play_render_mode(play_render_mode)
-        effective_mode = "record" if mode == "auto" else mode
-        if effective_mode == "none":
-            return BackendPlayRenderPlan(
-                mode=effective_mode,
-                headless=True,
-                record_video=False,
-                num_steps=None,
-                output_video=None,
-            )
-        if effective_mode == "interactive":
-            raise NotImplementedError("MuJoCo playback does not support interactive rendering.")
-        assert effective_mode == "record"
-        if play_steps is None:
-            raise ValueError("MuJoCo record playback requires a finite training.play_steps value.")
-        if output_video is None:
-            raise ValueError("MuJoCo record playback requires an output video path.")
-        return BackendPlayRenderPlan(
-            mode=effective_mode,
-            headless=True,
-            record_video=True,
-            num_steps=int(play_steps),
-            output_video=output_video,
-        )
-
-    def run_playback(
-        self,
-        *,
-        env: Any,
-        initialize,
-        step,
-        num_steps: int | None,
-        output_video: str | os.PathLike[str] | None = None,
-        render_spacing: float | None = None,
-        render_offset_mode: str | None = None,
-        headless: bool | None = None,
-        record_video: bool | None = None,
-        frame_state_getter=None,
-        camera_kwargs: dict[str, Any] | None = None,
-        extra_data_getter=None,
-    ) -> str | None:
-        del render_offset_mode
-        should_record_video = (
-            bool(record_video) if record_video is not None else output_video is not None
-        )
-        should_run_headless = bool(headless) if headless is not None else should_record_video
-        return run_mujoco_playback(
-            env=env,
-            initialize=initialize,
-            step=step,
-            num_steps=num_steps,
-            output_video=output_video,
-            render_spacing=render_spacing,
-            headless=should_run_headless,
-            record_video=should_record_video,
-            frame_state_getter=frame_state_getter,
-            camera_kwargs=camera_kwargs,
-            extra_data_getter=extra_data_getter,
-        )
-
-    # ------------------------------------------------------------------ #
-    # Base kinematics                                                    #
-    # ------------------------------------------------------------------ #
-
-    def get_base_pos(self) -> np.ndarray:
-        return self._base_pos_view
-
-    def get_base_quat(self) -> np.ndarray:
-        return self._base_quat_view
-
-    def get_base_lin_vel(self) -> np.ndarray:
-        return self._base_lin_vel_view
-
-    def get_base_ang_vel(self) -> np.ndarray:
-        return self._base_ang_vel_view
-
-    # ------------------------------------------------------------------ #
-    # DOF state                                                          #
-    # ------------------------------------------------------------------ #
-
-    def get_dof_pos(self) -> np.ndarray:
-        return self._dof_pos_view
-
-    def get_dof_vel(self) -> np.ndarray:
-        return self._dof_vel_view
-
-    # ------------------------------------------------------------------ #
-    # Body kinematics — world frame                                      #
-    # ------------------------------------------------------------------ #
-
-    def _get_mapped_indices(self, body_ids: np.ndarray) -> np.ndarray:
-        return self._body_id_to_tracked_idx[body_ids]  # type: ignore[no-any-return]
-
-    def get_body_pos_w(self, body_ids: np.ndarray) -> np.ndarray:
-        return self._tracked_pos_w_all[:, self._get_mapped_indices(body_ids), :]  # type: ignore[no-any-return]
-
-    def get_body_quat_w(self, body_ids: np.ndarray) -> np.ndarray:
-        return self._tracked_quat_w_all[:, self._get_mapped_indices(body_ids), :]  # type: ignore[no-any-return]
-
-    def get_body_pose_w_rows(
-        self, env_ids: np.ndarray, body_ids: np.ndarray
-    ) -> tuple[np.ndarray, np.ndarray]:
-        rows = np.asarray(env_ids, dtype=np.intp)
-        mapped = self._get_mapped_indices(body_ids)
-        return self._tracked_pos_w_all[rows[:, None], mapped], self._tracked_quat_w_all[
-            rows[:, None], mapped
-        ]
-
-    def get_body_lin_vel_w(self, body_ids: np.ndarray) -> np.ndarray:
-        return self._tracked_linvel_w_all[:, self._get_mapped_indices(body_ids), :]  # type: ignore[no-any-return]
-
-    def get_body_ang_vel_w(self, body_ids: np.ndarray) -> np.ndarray:
-        return self._tracked_angvel_w_all[:, self._get_mapped_indices(body_ids), :]  # type: ignore[no-any-return]
-
-    def get_body_state_w(
-        self, body_ids: np.ndarray
-    ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
-        mapped = self._get_mapped_indices(body_ids)
-        return (
-            self._tracked_pos_w_all[:, mapped, :],
-            self._tracked_quat_w_all[:, mapped, :],
-            self._tracked_linvel_w_all[:, mapped, :],
-            self._tracked_angvel_w_all[:, mapped, :],
-        )
-
-    def copy_body_state_w(
-        self,
-        body_ids: np.ndarray,
-        out_pos: np.ndarray,
-        out_quat: np.ndarray,
-        out_lin_vel: np.ndarray,
-        out_ang_vel: np.ndarray,
-    ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
-        mapped = self._get_mapped_indices(body_ids)
-        np.take(self._tracked_pos_w_all, mapped, axis=1, out=out_pos)
-        np.take(self._tracked_quat_w_all, mapped, axis=1, out=out_quat)
-        np.take(self._tracked_linvel_w_all, mapped, axis=1, out=out_lin_vel)
-        np.take(self._tracked_angvel_w_all, mapped, axis=1, out=out_ang_vel)
-        return out_pos, out_quat, out_lin_vel, out_ang_vel
-
-    # ------------------------------------------------------------------ #
-    # Body kinematics — baselink frame                                   #
-    # ------------------------------------------------------------------ #
-
-    def get_body_pos_b(self, body_ids: np.ndarray) -> np.ndarray:
-        return self._tracked_pos_b_all[:, self._get_mapped_indices(body_ids), :]  # type: ignore[no-any-return]
-
-    def get_body_quat_b(self, body_ids: np.ndarray) -> np.ndarray:
-        return self._tracked_quat_b_all[:, self._get_mapped_indices(body_ids), :]  # type: ignore[no-any-return]
-
-    def get_body_lin_vel_b(self, body_ids: np.ndarray) -> np.ndarray:
-        return self._tracked_linvel_b_all[:, self._get_mapped_indices(body_ids), :]  # type: ignore[no-any-return]
-
-    def get_body_ang_vel_b(self, body_ids: np.ndarray) -> np.ndarray:
-        return self._tracked_angvel_b_all[:, self._get_mapped_indices(body_ids), :]  # type: ignore[no-any-return]
-
-    # ------------------------------------------------------------------ #
-    # Sensors                                                            #
-    # ------------------------------------------------------------------ #
-
-    def get_sensor_data(self, name: str) -> np.ndarray:
-        return self._sensor_views[name]
-
-    def get_sensor_data_rows(self, name: str, env_ids: np.ndarray) -> np.ndarray:
-        return self._sensor_views[name][np.asarray(env_ids, dtype=np.intp)]
-
-    def get_sensor_data_batch(self, names: Sequence[str]) -> np.ndarray:
-        sensor_names = tuple(names)
-        if not sensor_names:
-            return np.empty((self._num_envs, 0), dtype=self._np_dtype)
-        values = [self._sensor_views[name].reshape(self._num_envs, -1) for name in sensor_names]
-        return np.concatenate(values, axis=1)
-
-    def get_site_jacobian_w(
-        self,
-        site_id: int,
-        dof_indices: np.ndarray,
-    ) -> tuple[np.ndarray, np.ndarray]:
-        """Return batched Jacobians with shape ``(num_envs, 3, len(dof_indices))``.
-
-        This uses the native ``BatchEnvPool.compute_site_jacobians`` API, so it
-        does not allocate one ``MjData`` per env. For a scalar ``site_id``, the
-        pool returns ``(N, 3, nv)`` because the site dimension is squeezed.
-        """
-        site_id_int = int(site_id)
-        if site_id_int < 0 or site_id_int >= int(self._model.nsite):
-            raise ValueError(
-                f"Invalid site_id {site_id_int}; expected 0 <= site_id < {self._model.nsite}"
-            )
-        dof_indices = np.asarray(dof_indices, dtype=np.int32).reshape(-1)
-        if np.any(dof_indices < 0) or np.any(dof_indices >= self.nv):
-            raise ValueError(f"dof_indices must be within [0, {self.nv})")
-        jp, jr = self._pool.compute_site_jacobians(  # type: ignore[union-attr]
-            self._physics_state.astype(np.float64),
-            site_id_int,
-            jacp=True,
-            jacr=True,
-        )
-        return (
-            jp[:, :, dof_indices].astype(self._np_dtype),
-            jr[:, :, dof_indices].astype(self._np_dtype),
-        )
-
-    # ------------------------------------------------------------------ #
-    # Mujoco-specific                                                 #
-    # ------------------------------------------------------------------ #
-
-    def get_physics_state(self) -> np.ndarray:
-        return self._physics_state
-
-    def get_playback_model(self, env_index: int | None = None):
-        """Return the MuJoCo model used by playback for one vectorized env.
-
-        Args:
-            env_index: Optional vectorized environment index.
-
-        Returns:
-            The MuJoCo model assigned to that env, or the current backend model
-            when no explicit index is requested.
-        """
-        if env_index is None:
-            return self._model
-        idx = int(env_index)
-        if idx < 0 or idx >= self._num_envs:
-            raise IndexError(f"env_index must be in [0, {self._num_envs - 1}], got {idx}")
-        return self._model_variants[int(self._model_assignments[idx])]
-
-    def _coerce_reset_field(
-        self,
-        value: np.ndarray,
-        *,
-        name: str,
-        num_reset: int,
-        shaped_tail: tuple[int, ...],
-    ) -> np.ndarray:
-        arr = cast(np.ndarray, np.asarray(value, dtype=np.float64))
-        flat_tail = int(np.prod(shaped_tail))
-        flat_shape = (num_reset, flat_tail)
-        shaped = (num_reset, *shaped_tail)
-        if arr.shape == flat_shape:
-            return cast(np.ndarray, arr.copy())
-        if arr.shape == shaped:
-            return cast(np.ndarray, arr.reshape(num_reset, flat_tail).copy())
-        raise ValueError(f"{name} must have shape {flat_shape} or {shaped}, got {arr.shape}")
-
-    def _translate_reset_randomization(
-        self,
-        randomization: ResetRandomizationPayload | None,
-        num_reset: int,
-    ) -> dict[str, np.ndarray] | None:
-        if randomization is None or randomization.is_empty():
-            return None
-        if (
-            randomization.base_mass_delta is not None or randomization.base_com_offset is not None
-        ) and self._base_body_id < 0:
-            raise ValueError(f"Body '{self._base_name}' not found in MuJoCo model")
-
-        translated: dict[str, np.ndarray] = {}
-        body_mass = None
-        if randomization.body_mass is not None:
-            body_mass = self._coerce_reset_field(
-                randomization.body_mass,
-                name="body_mass",
-                num_reset=num_reset,
-                shaped_tail=(self._model.nbody,),
-            )
-        if randomization.base_mass_delta is not None:
-            if body_mass is None:
-                body_mass = np.broadcast_to(
-                    self._base_body_mass, (num_reset, self._model.nbody)
-                ).copy()
-            body_mass[:, self._base_body_id] += np.asarray(randomization.base_mass_delta)
-        if body_mass is not None:
-            translated["body_mass"] = body_mass
-
-        body_ipos = None
-        if randomization.body_ipos is not None:
-            body_ipos = self._coerce_reset_field(
-                randomization.body_ipos,
-                name="body_ipos",
-                num_reset=num_reset,
-                shaped_tail=(self._model.nbody, 3),
-            )
-        if randomization.base_com_offset is not None:
-            if body_ipos is None:
-                body_ipos = np.broadcast_to(
-                    self._base_body_ipos, (num_reset, self._model.nbody, 3)
-                ).copy()
-            body_ipos[:, self._base_body_id, :] += np.asarray(randomization.base_com_offset)
-        if body_ipos is not None:
-            translated["body_ipos"] = body_ipos.reshape(num_reset, -1)
-
-        if randomization.gravity is not None:
-            translated["gravity"] = self._coerce_reset_field(
-                randomization.gravity,
-                name="gravity",
-                num_reset=num_reset,
-                shaped_tail=(3,),
-            )
-
-        if randomization.body_iquat is not None:
-            translated["body_iquat"] = self._coerce_reset_field(
-                randomization.body_iquat,
-                name="body_iquat",
-                num_reset=num_reset,
-                shaped_tail=(self._model.nbody, 4),
-            )
-
-        if randomization.body_inertia is not None:
-            translated["body_inertia"] = self._coerce_reset_field(
-                randomization.body_inertia,
-                name="body_inertia",
-                num_reset=num_reset,
-                shaped_tail=(self._model.nbody, 3),
-            )
-
-        if randomization.geom_friction is not None:
-            translated["geom_friction"] = self._coerce_reset_field(
-                randomization.geom_friction,
-                name="geom_friction",
-                num_reset=num_reset,
-                shaped_tail=(self._model.ngeom, 3),
-            )
-
-        if randomization.dof_armature is not None:
-            translated["dof_armature"] = self._coerce_reset_field(
-                randomization.dof_armature,
-                name="dof_armature",
-                num_reset=num_reset,
-                shaped_tail=(self._model.nv,),
-            )
-
-        if randomization.kp is not None:
-            translated["kp"] = self._coerce_reset_field(
-                randomization.kp,
-                name="kp",
-                num_reset=num_reset,
-                shaped_tail=(self._model.nu,),
-            )
-
-        if randomization.kd is not None:
-            translated["kd"] = self._coerce_reset_field(
-                randomization.kd,
-                name="kd",
-                num_reset=num_reset,
-                shaped_tail=(self._model.nu,),
-            )
-
-        return translated or None
-
-    def get_actuator_gains(self) -> tuple[np.ndarray, np.ndarray]:
-        """Return per-joint (kp, kd) arrays read from the current model state."""
-        kp = np.asarray(self._model.actuator_gainprm[:, 0], dtype=np.float64).copy()
-        kd = np.asarray(-self._model.actuator_biasprm[:, 2], dtype=np.float64).copy()
-        return kp, kd
-
-    def _apply_position_actuator_gains_to_model(
-        self,
-        model,
-        *,
-        kp: float | np.ndarray,
-        kd: float | np.ndarray,
-        actuator_ids=slice(None),
-    ) -> None:
-        _apply_position_actuator_gains_to_mj_model(
-            model,
-            kp=kp,
-            kd=kd,
-            actuator_ids=actuator_ids,
-        )
diff --git a/src/unilab/base/backend/mujoco/chunk_tuner.py b/src/unilab/base/backend/mujoco/chunk_tuner.py
deleted file mode 100644
index cf7f53f7a..000000000
--- a/src/unilab/base/backend/mujoco/chunk_tuner.py
+++ /dev/null
@@ -1,411 +0,0 @@
-"""Adaptive thread-pool ``chunk_size`` selection for the MuJoCo BatchEnvPool.
-
-All probing/benchmarking happens on the cold path (``materialize()``), never on
-``step``/``reset``. Results are cached on disk keyed by model signature + device
-fingerprint + (num_envs, nthread, dtype, ...) so repeated trainings reuse them.
-"""
-
-from __future__ import annotations
-
-import contextlib
-import hashlib
-import json
-import logging
-import math
-import os
-import platform
-import socket
-import sys
-import time
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-
-logger = logging.getLogger(__name__)
-
-MAX_CANDIDATES = 16
-
-
-def _emit(msg: str) -> None:
-    """Surface a one-time chunk_size decision on the terminal.
-
-    Collector envs materialize inside ``spawn`` subprocesses whose root logger is
-    unconfigured (default level WARNING), so ``logger.info`` would be dropped and
-    the benchmark table would never reach the terminal. When INFO is not enabled,
-    fall back to a direct stderr write (the same channel the collector uses for
-    its own diagnostics); otherwise log normally so the main process sees no
-    duplicate line.
-    """
-    if logger.isEnabledFor(logging.INFO):
-        logger.info(msg)
-    else:
-        print(f"[unilab.chunk_size] {msg}", file=sys.stderr, flush=True)
-
-
-def _native_default_chunk(num_envs: int, nthread: int) -> int:
-    """The chunk_size BatchEnvPool uses when ``chunk_size=None`` -- mujoco's own
-    default ``max(1, nbatch // (10 * nthread))`` (see mujoco ``batch_env`` /
-    ``rollout``). Shown next to ``default``/``None`` so the log is unambiguous."""
-    return max(1, int(num_envs) // (10 * max(1, int(nthread))))
-
-
-def _chosen_label(chosen: int | None, default_chunk: int) -> str:
-    """Spell out the effective chunk_size for the native default (``None``)."""
-    return f"None(={default_chunk})" if chosen is None else str(chosen)
-
-
-def _format_candidate_table(per_candidate_ms: dict, default_chunk: int) -> str:
-    """Render a stored ``per_candidate_ms`` mapping (keys ``"None"``/``"4"``/...,
-    values in ms) as ``default(=5)=9.38ms, 1=25.43ms, ...`` -- native default first
-    (annotated with its actual chunk_size), then candidates in ascending order."""
-
-    def sort_key(k: str):
-        return (0, -1) if k == "None" else (1, int(k))
-
-    def label(k: str) -> str:
-        return f"default(={default_chunk})" if k == "None" else k
-
-    return ", ".join(
-        f"{label(k)}={per_candidate_ms[k]:.2f}ms" for k in sorted(per_candidate_ms, key=sort_key)
-    )
-
-
-def _emit_cache_hit(value: int | None, per_candidate_ms: dict | None, default_chunk: int) -> None:
-    """Report a cache hit. Include the stored candidate breakdown when present so the
-    full benchmark stays visible on every run, not only the first (cold) one."""
-    chosen = _chosen_label(value, default_chunk)
-    if per_candidate_ms:
-        _emit(
-            f"chunk_size: cache hit -> chosen={chosen} | "
-            f"{_format_candidate_table(per_candidate_ms, default_chunk)}"
-        )
-    else:
-        _emit(f"chunk_size: cache hit -> {chosen}")
-
-
-def make_candidates(num_envs: int, nthread: int) -> list[int]:
-    """Candidate chunk_sizes within ``[1, upper]``, densified at the sweet spot.
-
-    ``upper = ceil(num_envs / nthread)`` is a hard ceiling: a chunk_size beyond it
-    produces fewer chunks than threads, leaving threads idle -> always slower. The
-    optimum sits near ``heur = num_envs / (10 * nthread)`` (~10 chunks per thread),
-    so the band around it is sampled densely while the always-bad large chunks are
-    never generated.
-    """
-    if num_envs < 1:
-        raise ValueError(f"num_envs must be >= 1, got {num_envs}")
-    nthread = max(1, int(nthread))
-    upper = max(1, math.ceil(num_envs / nthread))
-    if upper == 1:
-        return [1]
-    heur = min(upper, max(1, round(num_envs / (10 * nthread))))
-    cands: set[int] = {1, upper, heur}
-    for m in (0.5, 1.5, 2.0, 3.0):  # densify around the expected optimum
-        cands.add(min(upper, max(1, round(heur * m))))
-    c = 1  # geometric coverage of [1, upper]
-    while c < upper:
-        cands.add(c)
-        c = max(c + 1, round(c * 1.8))
-    cands.add(upper)
-    return sorted(cands)
-
-
-def filter_candidates(
-    candidates: list[int],
-    num_envs: int,
-    *,
-    max_candidates: int = MAX_CANDIDATES,
-) -> list[int]:
-    """Clamp to ``[1, num_envs]``, dedup, sort. If over the cap, trim the coarse
-    middle while keeping the low/optimum band (smallest values) plus the coarsest
-    anchor -- never log-spaced (which used to drop the optimum)."""
-    valid = sorted({c for c in candidates if 1 <= c <= num_envs})
-    if len(valid) <= max_candidates:
-        return valid
-    return sorted(set(valid[: max_candidates - 1]) | {valid[-1]})
-
-
-def device_fingerprint() -> dict[str, Any]:
-    """Coarse, deterministic local-device descriptor for the cache key."""
-    return {
-        "system": platform.system(),
-        "machine": platform.machine(),
-        "cpu_count": int(os.cpu_count() or 1),
-    }
-
-
-def model_signature(model: Any, n_variants: int) -> dict[str, int]:
-    """Structural determinants of per-step cost (proxy for 'task')."""
-    return {
-        "nq": int(model.nq),
-        "nv": int(model.nv),
-        "nbody": int(model.nbody),
-        "njnt": int(model.njnt),
-        "nu": int(model.nu),
-        "ngeom": int(model.ngeom),
-        "nsensordata": int(model.nsensordata),
-        "n_variants": int(n_variants),
-    }
-
-
-def make_cache_key(
-    *,
-    backend_type: str,
-    model_sig: dict,
-    device: dict,
-    num_envs: int,
-    nthread: int,
-    dtype: Any,
-    post_step_forward_sensor: bool,
-    bench_nsteps: int,
-) -> str:
-    payload = {
-        "backend_type": backend_type,
-        "model": model_sig,
-        "device": device,
-        "num_envs": int(num_envs),
-        "nthread": int(nthread),
-        "dtype": np.dtype(dtype).name,
-        "post_step_forward_sensor": bool(post_step_forward_sensor),
-        "bench_nsteps": int(bench_nsteps),
-    }
-    blob = json.dumps(payload, sort_keys=True, separators=(",", ":"))
-    return hashlib.sha1(blob.encode("utf-8")).hexdigest()
-
-
-def cache_path() -> Path:
-    override = os.environ.get("UNILAB_CHUNK_SIZE_CACHE")
-    if override:
-        return Path(override)
-    xdg = os.environ.get("XDG_CACHE_HOME")
-    root = Path(xdg) if xdg else Path.home() / ".cache"
-    return root / "unilab" / "chunk_size.json"
-
-
-def load_cache(path: Path) -> dict:
-    try:
-        with Path(path).open("r", encoding="utf-8") as f:
-            data = json.load(f)
-        return data if isinstance(data, dict) else {}
-    except (FileNotFoundError, json.JSONDecodeError, OSError):
-        return {}
-
-
-def store_cache(path: Path, key: str, value: dict) -> None:
-    path = Path(path)
-    try:
-        path.parent.mkdir(parents=True, exist_ok=True)
-        data = load_cache(path)
-        data[key] = value
-        tmp = path.with_name(f"{path.name}.tmp.{os.getpid()}")
-        with tmp.open("w", encoding="utf-8") as f:
-            json.dump(data, f, indent=2, sort_keys=True)
-        os.replace(tmp, path)
-    except OSError as e:
-        logger.warning("chunk_size cache write failed: %s", e)
-
-
-@contextlib.contextmanager
-def file_lock(lock_path: Path):
-    """Best-effort inter-process exclusive lock; no-op where fcntl is absent."""
-    try:
-        import fcntl
-    except ImportError:
-        yield
-        return
-    lock_path = Path(lock_path)
-    lock_path.parent.mkdir(parents=True, exist_ok=True)
-    f = lock_path.open("w")
-    try:
-        fcntl.flock(f.fileno(), fcntl.LOCK_EX)
-        yield
-    finally:
-        try:
-            fcntl.flock(f.fileno(), fcntl.LOCK_UN)
-        finally:
-            f.close()
-
-
-def _aggregate_times(times) -> float:
-    """Reduce repeated samples to one estimate. Use ``min``: background load only
-    inflates a sample (never deflates it below the compute-bound floor), so the
-    minimum is the most stable proxy for the uncontended cost a dedicated training
-    box sees -- unlike the median, which a loaded machine biases upward."""
-    return float(min(times))
-
-
-def _robust_step_time(
-    pool, state, nstep, control, chunk_size, post_step_forward_sensor, reps
-) -> float:
-    times = []
-    for _ in range(reps):
-        s = state.copy()
-        t0 = time.perf_counter()
-        pool.step(
-            s,
-            nstep=nstep,
-            control=control,
-            chunk_size=chunk_size,
-            return_sensor=True,
-            post_step_forward_sensor=post_step_forward_sensor,
-        )
-        times.append(time.perf_counter() - t0)
-    return _aggregate_times(times)
-
-
-def benchmark_chunk_sizes(
-    pool,
-    state,
-    nstep,
-    candidates,
-    *,
-    control,
-    post_step_forward_sensor,
-    warmup=2,
-    reps=20,
-    time_budget_s=25.0,  # headroom for the heaviest envs (~16k) to sweep all candidates
-) -> dict[int | None, float]:
-    """Min wall-clock of a representative ``pool.step`` per candidate.
-
-    Each candidate is sampled ``reps`` times and reduced via ``min`` (uncontended
-    cost). ``None`` (native default) is always measured as the baseline anchor.
-    """
-    results: dict[int | None, float] = {}
-    start = time.perf_counter()
-    for cs in [None, *candidates]:
-        for _ in range(warmup):
-            s = state.copy()
-            pool.step(
-                s,
-                nstep=nstep,
-                control=control,
-                chunk_size=cs,
-                return_sensor=True,
-                post_step_forward_sensor=post_step_forward_sensor,
-            )
-        results[cs] = _robust_step_time(
-            pool, state, nstep, control, cs, post_step_forward_sensor, reps
-        )
-        if time.perf_counter() - start > time_budget_s:
-            logger.warning("chunk_size benchmark time budget exceeded; using partial results")
-            break
-    return results
-
-
-def select_chunk_size(
-    timings: dict[int | None, float], base: int, *, margin=0.03, tie_tol=0.02
-) -> int | None:
-    baseline = timings.get(None)
-    cand: dict[int, float] = {k: v for k, v in timings.items() if k is not None}
-    if not cand:
-        return None
-    best_cs = min(cand, key=lambda k: cand[k])
-    best_t = cand[best_cs]
-    if baseline is not None and best_t > baseline * (1.0 - margin):
-        return None  # not worth leaving the native default
-    band: list[int] = [k for k, v in cand.items() if v <= best_t * (1.0 + tie_tol)]
-    return min(band, key=lambda c: (abs(c - base), c))
-
-
-def _log_benchmark_table(
-    timings: dict[int | None, float], chosen: int | None, default_chunk: int
-) -> None:
-    def label(k: int | None) -> str:
-        return f"default(={default_chunk})" if k is None else str(k)
-
-    rows = ", ".join(
-        f"{label(k)}={v * 1000:.2f}ms"
-        for k, v in sorted(timings.items(), key=lambda kv: (kv[0] is not None, kv[0]))
-    )
-    _emit(f"chunk_size benchmark: {rows} -> chosen={_chosen_label(chosen, default_chunk)}")
-
-
-def resolve_chunk_size(
-    *,
-    pool,
-    state,
-    model,
-    n_variants: int,
-    num_envs: int,
-    nthread: int,
-    dtype,
-    post_step_forward_sensor: bool,
-    bench_nsteps: int,
-    manual_chunk_size: int | None,
-    adaptive: bool,
-    backend_type: str = "mujoco",
-    model_file: str | None = None,
-) -> int | None:
-    # 1. manual override always wins (highest priority).
-    if manual_chunk_size is not None:
-        _emit(f"chunk_size: manual override = {int(manual_chunk_size)}")
-        return int(manual_chunk_size)
-    # 2. adaptive disabled -> native default.
-    if not adaptive:
-        return None
-    # 2b. Nothing to tune: num_envs <= nthread means at most one work-chunk, so
-    #     chunk_size cannot change anything. Skip benchmark/cache/log entirely -- this
-    #     silences the num_envs=1 setup envs APPO/off-policy build just to read dims.
-    if math.ceil(num_envs / max(1, nthread)) <= 1:
-        return None
-    default_chunk = _native_default_chunk(num_envs, nthread)  # what chunk_size=None uses
-    # 3. cache lookup.
-    device = device_fingerprint()
-    model_sig = model_signature(model, n_variants)
-    key = make_cache_key(
-        backend_type=backend_type,
-        model_sig=model_sig,
-        device=device,
-        num_envs=num_envs,
-        nthread=nthread,
-        dtype=dtype,
-        post_step_forward_sensor=post_step_forward_sensor,
-        bench_nsteps=bench_nsteps,
-    )
-    path = cache_path()
-    hit = load_cache(path).get(key)
-    if hit is not None:
-        value = hit.get("chunk_size")
-        _emit_cache_hit(value, hit.get("per_candidate_ms"), default_chunk)
-        return value if value is None else int(value)
-    # 4. miss -> lock -> re-check -> benchmark -> store.
-    with file_lock(path.with_name(path.name + ".lock")):
-        hit = load_cache(path).get(key)
-        if hit is not None:
-            value = hit.get("chunk_size")
-            _emit_cache_hit(value, hit.get("per_candidate_ms"), default_chunk)
-            return value if value is None else int(value)
-        base = math.ceil(num_envs / max(1, nthread))  # upper anchor for the select tie-break
-        candidates = filter_candidates(make_candidates(num_envs, nthread), num_envs)
-        try:
-            control = np.zeros((num_envs, bench_nsteps, int(model.nu)), dtype=np.float64)
-            timings = benchmark_chunk_sizes(
-                pool,
-                state,
-                bench_nsteps,
-                candidates,
-                control=control,
-                post_step_forward_sensor=post_step_forward_sensor,
-            )
-        except Exception as e:  # benchmark must never crash training
-            logger.warning("chunk_size benchmark failed (%s); using native default", e)
-            return None
-        chosen = select_chunk_size(timings, base)
-        _log_benchmark_table(timings, chosen, default_chunk)
-        try:
-            store_cache(
-                path,
-                key,
-                {
-                    "chunk_size": chosen,
-                    "per_candidate_ms": {str(k): v * 1000 for k, v in timings.items()},
-                    "model_file": model_file,
-                    "hostname": socket.gethostname(),
-                },
-            )
-        except Exception as e:
-            logger.warning(
-                "chunk_size cache store failed (%s); continuing with chosen=%s", e, chosen
-            )
-        return chosen
diff --git a/src/unilab/base/backend/mujoco/playback.py b/src/unilab/base/backend/mujoco/playback.py
deleted file mode 100644
index 3138ccb9b..000000000
--- a/src/unilab/base/backend/mujoco/playback.py
+++ /dev/null
@@ -1,218 +0,0 @@
-"""MuJoCo-owned playback execution helpers."""
-
-from __future__ import annotations
-
-import tempfile
-from os import PathLike
-from pathlib import Path
-from typing import Any, Callable, TypeVar
-
-import numpy as np
-
-from unilab.base.backend.playback_common import env_cfg_value, write_playback_video
-from unilab.base.scene import SceneCfg
-
-ObsT = TypeVar("ObsT")
-
-
-def run_mujoco_playback(
-    *,
-    env: Any,
-    initialize: Callable[[], ObsT],
-    step: Callable[[ObsT], ObsT],
-    num_steps: int | None,
-    output_video: str | PathLike[str] | None,
-    render_spacing: float | None,
-    headless: bool,
-    record_video: bool,
-    frame_state_getter: Callable[[], np.ndarray] | None,
-    camera_kwargs: dict[str, Any] | None,
-    extra_data_getter: Callable[[], np.ndarray | None] | None = None,
-) -> str | None:
-    if not headless:
-        raise NotImplementedError("MuJoCo play mode does not support interactive rendering here.")
-    if not record_video:
-        raise ValueError("MuJoCo play rendering requires record_video=true.")
-    if num_steps is None:
-        raise ValueError("MuJoCo play rendering requires a finite num_steps value.")
-    if output_video is None:
-        raise ValueError("MuJoCo play rendering requires an output_video path.")
-    if frame_state_getter is None:
-        frame_state_getter = env.get_physics_state_snapshot
-    assert frame_state_getter is not None
-
-    obs = initialize()
-    state_list = []
-    marker_list: list[np.ndarray | None] = []
-    for _ in range(num_steps):
-        obs = step(obs)
-        state_list.append(np.asarray(frame_state_getter(), dtype=np.float32).copy())
-        if extra_data_getter is not None:
-            marker = extra_data_getter()
-            marker_list.append(
-                np.asarray(marker, dtype=np.float32).copy() if marker is not None else None
-            )
-        else:
-            marker_list.append(None)
-
-    marker_positions_list = (
-        marker_list if any(marker is not None for marker in marker_list) else None
-    )
-
-    from unilab.visualization import render_many
-
-    cam_kw = dict(camera_kwargs or {})
-    use_tracking = bool(cam_kw.pop("cam_tracking", False))
-    tracking_env_idx = int(cam_kw.pop("cam_tracking_env_idx", 0))
-    tracking_extra_envs = int(cam_kw.pop("cam_tracking_extra_envs", 2))
-    effective_spacing = (
-        float(render_spacing)
-        if render_spacing is not None
-        else float(env_cfg_value(env, "render_spacing", 1.0))
-    )
-    with tempfile.TemporaryDirectory(prefix="unilab-playback-models-") as tmp_dir:
-        model_files = resolve_render_play_model_files(
-            env,
-            num_envs=state_list[0].shape[0],
-            tmp_dir=tmp_dir,
-        )
-
-        if use_tracking:
-            frames = render_many.render_states_get_frames_tracking(
-                state_list,
-                model_files,
-                width=1280,
-                height=720,
-                tracking_env_idx=tracking_env_idx,
-                max_extra_envs=tracking_extra_envs,
-                cam_distance=cam_kw.get("cam_distance", 2.0),
-                cam_elevation=cam_kw.get("cam_elevation", -20),
-                cam_azimuth=cam_kw.get("cam_azimuth", 90),
-                render_spacing=effective_spacing,
-                marker_positions_list=marker_positions_list,
-            )
-        else:
-            frames = render_many.render_states_get_frames(
-                state_list,
-                model_files,
-                width=1280,
-                height=720,
-                camera_id=-1,
-                render_spacing=effective_spacing,
-                marker_positions_list=marker_positions_list,
-                **cam_kw,
-            )
-
-    if not frames:
-        # Rendering was skipped (e.g. no usable off-screen GL backend on a
-        # headless host). render_many already warned with actionable guidance;
-        # don't fail the eval/play run — just skip the video export.
-        print(f"[playback] No frames rendered; skipping video export to {output_video}.")
-        return None
-
-    ctrl_dt = float(env_cfg_value(env, "ctrl_dt", 1.0 / 60.0))
-    write_playback_video(str(output_video), frames, fps=int(1.0 / ctrl_dt))
-    return str(output_video)
-
-
-def _configured_model_file(env: Any) -> str | None:
-    cfg = getattr(env, "cfg", None)
-    scene = getattr(cfg, "scene", None) if cfg is not None else None
-    if scene is None:
-        return None
-    if not isinstance(scene, SceneCfg):
-        raise TypeError("env.cfg.scene must be a SceneCfg")
-    return scene.model_file
-
-
-def _visual_model_file(env: Any) -> str | None:
-    backend = getattr(env, "_backend", None)
-    backend_visual_model_file = getattr(backend, "scene_visual_model_file", None)
-    if backend_visual_model_file:
-        return str(backend_visual_model_file)
-    return _configured_model_file(env)
-
-
-def resolve_render_play_model_files(
-    env: Any,
-    *,
-    num_envs: int,
-    tmp_dir: str | Path,
-) -> str | list[str]:
-    """Resolve visual MuJoCo model files for offline play/video export."""
-    visual_model_file = _visual_model_file(env)
-    if not hasattr(env, "get_playback_model"):
-        if visual_model_file is None:
-            raise ValueError("MuJoCo playback requires either cfg.scene or get_playback_model().")
-        return visual_model_file
-
-    first_model = env.get_playback_model(0)
-    if isinstance(first_model, (str, Path)):
-        return str(first_model)
-
-    import mujoco as _mujoco
-
-    mujoco: Any = _mujoco
-
-    visual_base = (
-        mujoco.MjModel.from_xml_path(visual_model_file) if visual_model_file is not None else None
-    )
-    tmp_root = Path(tmp_dir)
-    path_by_model_id: dict[int, str] = {}
-    model_files: list[str] = []
-    for env_idx in range(num_envs):
-        playback_model = env.get_playback_model(env_idx)
-        if isinstance(playback_model, (str, Path)):
-            model_files.append(str(playback_model))
-            continue
-        key = id(playback_model)
-        saved = path_by_model_id.get(key)
-        if saved is None:
-            output_path = tmp_root / f"model_{len(path_by_model_id)}.mjb"
-            if visual_model_file is None or visual_base is None:
-                mujoco.mj_saveModel(playback_model, str(output_path))
-                saved = str(output_path)
-            else:
-                saved = materialize_visual_playback_model(
-                    visual_model_file=visual_model_file,
-                    visual_base_model=visual_base,
-                    playback_model=playback_model,
-                    output_path=output_path,
-                )
-            path_by_model_id[key] = saved
-        model_files.append(saved)
-
-    if len(set(model_files)) == 1:
-        return model_files[0]
-    return model_files
-
-
-def materialize_visual_playback_model(
-    *,
-    visual_model_file: str,
-    visual_base_model: Any,
-    playback_model: Any,
-    output_path: str | Path,
-) -> str:
-    """Compile a visual MuJoCo model using geom sizes from a playback model."""
-    import mujoco as _mujoco
-
-    mujoco: Any = _mujoco
-
-    spec = mujoco.MjSpec.from_file(visual_model_file)
-    for geom_id in range(visual_base_model.ngeom):
-        geom_name = mujoco.mj_id2name(visual_base_model, mujoco.mjtObj.mjOBJ_GEOM, geom_id)
-        if not geom_name:
-            continue
-        playback_geom_id = mujoco.mj_name2id(playback_model, mujoco.mjtObj.mjOBJ_GEOM, geom_name)
-        if playback_geom_id < 0:
-            continue
-        geom = spec.geom(geom_name)
-        if geom is None:
-            continue
-        geom.size = list(np.asarray(playback_model.geom_size[playback_geom_id], dtype=np.float64))
-
-    visual_model = spec.compile()
-    output = Path(output_path)
-    mujoco.mj_saveModel(visual_model, str(output))
-    return str(output)
diff --git a/src/unilab/base/backend/mujoco/xml.py b/src/unilab/base/backend/mujoco/xml.py
deleted file mode 100644
index ad5ac9fb1..000000000
--- a/src/unilab/base/backend/mujoco/xml.py
+++ /dev/null
@@ -1,605 +0,0 @@
-from __future__ import annotations
-
-import os
-import shutil
-import tempfile
-import xml.etree.ElementTree as ET
-from collections.abc import Iterator, Sequence
-from pathlib import Path
-from typing import Any, Literal, cast, overload
-
-import numpy as np
-
-from unilab.base.scene import resolve_scene_fragment_path
-from unilab.terrains.terrain_generator import TerrainGeneratorCfg
-
-
-def _enable_discardvisual(root: ET.Element) -> None:
-    compiler_tag = root.find("compiler")
-    if compiler_tag is None:
-        compiler_tag = ET.Element("compiler")
-        root.insert(0, compiler_tag)
-    compiler_tag.set("discardvisual", "true")
-
-
-def _write_xml_root(root: ET.Element, output_path: Path) -> None:
-    ET.indent(root, space="  ")
-    output_path.write_text(ET.tostring(root, encoding="unicode"), encoding="utf-8")
-
-
-def create_discardvisual_xml(model_file: str) -> str:
-    tree = ET.parse(model_file)
-    _enable_discardvisual(tree.getroot())
-    return _write_temp_xml(tree, model_file)
-
-
-def _iter_expanded_children(
-    parent: ET.Element, base_dir: Path
-) -> Iterator[tuple[ET.Element, Path]]:
-    for child in parent:
-        if child.tag != "include":
-            yield child, base_dir
-            continue
-
-        include_file = child.get("file")
-        if not include_file:
-            raise ValueError(f"Invalid <include> without file attribute in {base_dir}")
-        include_path = (base_dir / include_file).resolve()
-        include_root = ET.parse(include_path).getroot()
-        yield from _iter_expanded_children(include_root, include_path.parent)
-
-
-def _iter_named_bodies(root: ET.Element, base_dir: Path) -> Iterator[str]:
-    for child, child_base_dir in _iter_expanded_children(root, base_dir):
-        if child.tag == "body":
-            body_name = child.get("name")
-            if body_name:
-                yield body_name
-        yield from _iter_named_bodies(child, child_base_dir)
-
-
-def _get_named_bodies(model_file: str) -> tuple[list[int], list[str]]:
-    model_path = Path(model_file).resolve()
-    names = list(_iter_named_bodies(ET.parse(model_path).getroot(), model_path.parent))
-    ids = list(range(1, len(names) + 1))
-    return ids, names
-
-
-def get_named_body_ids(model_file: str, names: Sequence[str]) -> list[int]:
-    """Resolve MuJoCo-style body ids from XML without importing mujoco."""
-    body_ids, body_names = _get_named_bodies(model_file)
-    body_id_by_name = dict(zip(body_names, body_ids, strict=True))
-    missing = [name for name in names if name not in body_id_by_name]
-    if missing:
-        missing_str = ", ".join(missing)
-        raise ValueError(f"Bodies not found in XML '{model_file}': {missing_str}")
-    return [body_id_by_name[name] for name in names]
-
-
-def _mujoco_module() -> Any:
-    import mujoco
-
-    return cast(Any, mujoco)
-
-
-def _materialize_spec_xml(spec, model_file: str) -> str:
-    fd, output_path = tempfile.mkstemp(
-        suffix=".xml", dir=os.path.dirname(os.path.abspath(model_file))
-    )
-    try:
-        with os.fdopen(fd, "w", encoding="utf-8") as f:
-            f.write(spec.to_xml())
-    except Exception:
-        os.close(fd)
-        raise
-    return output_path
-
-
-def _add_w_sensors(spec, valid_bnames: list[str]) -> None:
-    mujoco = _mujoco_module()
-    for bname in valid_bnames:
-        spec.add_sensor(
-            name=f"track_pos_w_{bname}",
-            type=mujoco.mjtSensor.mjSENS_FRAMEPOS,
-            objtype=mujoco.mjtObj.mjOBJ_XBODY,
-            objname=bname,
-        )
-    for bname in valid_bnames:
-        spec.add_sensor(
-            name=f"track_quat_w_{bname}",
-            type=mujoco.mjtSensor.mjSENS_FRAMEQUAT,
-            objtype=mujoco.mjtObj.mjOBJ_XBODY,
-            objname=bname,
-        )
-    for bname in valid_bnames:
-        spec.add_sensor(
-            name=f"track_linvel_w_{bname}",
-            type=mujoco.mjtSensor.mjSENS_FRAMELINVEL,
-            objtype=mujoco.mjtObj.mjOBJ_XBODY,
-            objname=bname,
-        )
-    for bname in valid_bnames:
-        spec.add_sensor(
-            name=f"track_angvel_w_{bname}",
-            type=mujoco.mjtSensor.mjSENS_FRAMEANGVEL,
-            objtype=mujoco.mjtObj.mjOBJ_XBODY,
-            objname=bname,
-        )
-
-
-def _add_b_sensors(spec, valid_bnames: list[str], baselink_name: str) -> None:
-    mujoco = _mujoco_module()
-    for bname in valid_bnames:
-        spec.add_sensor(
-            name=f"track_pos_b_{bname}",
-            type=mujoco.mjtSensor.mjSENS_FRAMEPOS,
-            objtype=mujoco.mjtObj.mjOBJ_XBODY,
-            objname=bname,
-            reftype=mujoco.mjtObj.mjOBJ_XBODY,
-            refname=baselink_name,
-        )
-    for bname in valid_bnames:
-        spec.add_sensor(
-            name=f"track_quat_b_{bname}",
-            type=mujoco.mjtSensor.mjSENS_FRAMEQUAT,
-            objtype=mujoco.mjtObj.mjOBJ_XBODY,
-            objname=bname,
-            reftype=mujoco.mjtObj.mjOBJ_XBODY,
-            refname=baselink_name,
-        )
-    for bname in valid_bnames:
-        spec.add_sensor(
-            name=f"track_linvel_b_{bname}",
-            type=mujoco.mjtSensor.mjSENS_FRAMELINVEL,
-            objtype=mujoco.mjtObj.mjOBJ_XBODY,
-            objname=bname,
-            reftype=mujoco.mjtObj.mjOBJ_XBODY,
-            refname=baselink_name,
-        )
-    for bname in valid_bnames:
-        spec.add_sensor(
-            name=f"track_angvel_b_{bname}",
-            type=mujoco.mjtSensor.mjSENS_FRAMEANGVEL,
-            objtype=mujoco.mjtObj.mjOBJ_XBODY,
-            objname=bname,
-            reftype=mujoco.mjtObj.mjOBJ_XBODY,
-            refname=baselink_name,
-        )
-
-
-def _write_temp_xml(tree: ET.ElementTree[ET.Element], model_file: str) -> str:  # type: ignore[type-arg]
-    fd, output_path = tempfile.mkstemp(
-        suffix=".xml", dir=os.path.dirname(os.path.abspath(model_file))
-    )
-    os.close(fd)
-    tree.write(output_path)
-    return output_path
-
-
-def _format_values(values: list[float] | tuple[float, ...]) -> str:
-    return " ".join(str(float(value)) for value in values)
-
-
-def materialize_scene_visual_override(
-    source_model_file: str,
-    *,
-    ground_texture_file: str | None = None,
-    ground_texrepeat: list[float] | tuple[float, float] | None = None,
-    skybox_rgb1: list[float] | tuple[float, float, float] | None = None,
-    skybox_rgb2: list[float] | tuple[float, float, float] | None = None,
-) -> str:
-    """Create a temporary scene XML with visual-only overrides applied."""
-    tree = ET.parse(source_model_file)
-    root = tree.getroot()
-    asset_tag = root.find("asset")
-    if asset_tag is None:
-        raise ValueError(f"Scene '{source_model_file}' is missing an <asset> tag.")
-
-    if skybox_rgb1 is not None or skybox_rgb2 is not None:
-        skybox = asset_tag.find("./texture[@type='skybox']")
-        if skybox is None:
-            raise ValueError(f"Scene '{source_model_file}' is missing a skybox texture.")
-        if skybox_rgb1 is not None:
-            skybox.set("rgb1", _format_values(tuple(skybox_rgb1)))
-        if skybox_rgb2 is not None:
-            skybox.set("rgb2", _format_values(tuple(skybox_rgb2)))
-
-    if ground_texture_file is not None:
-        ground_texture = asset_tag.find("./texture[@name='groundplane']")
-        if ground_texture is None:
-            raise ValueError(f"Scene '{source_model_file}' is missing the groundplane texture.")
-        for attr in ("builtin", "mark", "rgb1", "rgb2", "markrgb", "width", "height"):
-            ground_texture.attrib.pop(attr, None)
-        ground_texture.set("file", str(Path(ground_texture_file)))
-
-    if ground_texrepeat is not None:
-        ground_material = asset_tag.find("./material[@name='groundplane']")
-        if ground_material is None:
-            raise ValueError(f"Scene '{source_model_file}' is missing the groundplane material.")
-        ground_material.set("texrepeat", _format_values(tuple(ground_texrepeat)))
-
-    return _write_temp_xml(tree, source_model_file)
-
-
-def materialize_scene_fragments(
-    source_model_file: str,
-    *,
-    fragment_files: Sequence[str],
-) -> str:
-    """Create a temporary scene XML with task/scene fragments merged."""
-    tree = ET.parse(source_model_file)
-    root = tree.getroot()
-    source_path = Path(source_model_file).resolve()
-    for fragment_file in fragment_files:
-        _merge_scene_fragment(root, resolve_scene_fragment_path(fragment_file, source_path))
-    return _write_temp_xml(tree, source_model_file)
-
-
-_ATTACH_PREFIXED_ATTRS = {
-    "class",
-    "childclass",
-    "name",
-    "material",
-    "texture",
-    "mesh",
-    "joint",
-    "site",
-    "geom1",
-    "geom2",
-    "body1",
-    "body2",
-    "objname",
-    "refname",
-    "hfield",
-    "hfieldname",
-    "actuator",
-    "target",
-}
-
-
-def _strip_attach_prefixes(root: ET.Element) -> None:
-    for elem in root.iter():
-        for attr, value in list(elem.attrib.items()):
-            if attr in _ATTACH_PREFIXED_ATTRS and value.startswith("/"):
-                elem.set(attr, value[1:])
-
-
-def _flatten_attach_main_default(root: ET.Element) -> None:
-    default = root.find("default")
-    if default is None:
-        return
-    main = default.find("./default[@class='main']")
-    if main is None:
-        return
-    insert_at = list(default).index(main)
-    default.remove(main)
-    for child in list(main):
-        default.insert(insert_at, child)
-        insert_at += 1
-
-
-def _ensure_child(parent: ET.Element, query: str, xml: str) -> None:
-    if parent.find(query) is None:
-        parent.append(ET.fromstring(xml))
-
-
-def _merge_robot_option(root: ET.Element, robot_path: Path) -> None:
-    """Preserve robot-level MuJoCo solver/contact options after MjSpec.attach."""
-    robot_option = ET.parse(robot_path).getroot().find("option")
-    if robot_option is None:
-        return
-
-    option = root.find("option")
-    if option is None:
-        option = ET.Element("option")
-        insert_at = 0
-        compiler = root.find("compiler")
-        if compiler is not None:
-            insert_at = list(root).index(compiler) + 1
-        root.insert(insert_at, option)
-    option.attrib.update(robot_option.attrib)
-
-
-def _ensure_generated_hfield_scene_visuals(root: ET.Element, geom_name: str) -> None:
-    asset = root.find("asset")
-    if asset is None:
-        asset = ET.Element("asset")
-        root.insert(0, asset)
-
-    _ensure_child(
-        asset,
-        "./texture[@type='skybox']",
-        '<texture type="skybox" builtin="gradient" rgb1="0.3 0.5 0.7" '
-        'rgb2="0 0 0" width="512" height="3072"/>',
-    )
-    _ensure_child(
-        asset,
-        "./texture[@name='groundplane']",
-        '<texture type="2d" name="groundplane" builtin="checker" mark="edge" '
-        'rgb1="0.2 0.3 0.4" rgb2="0.1 0.2 0.3" markrgb="0.8 0.8 0.8" '
-        'width="300" height="300"/>',
-    )
-    _ensure_child(
-        asset,
-        "./material[@name='groundplane']",
-        '<material name="groundplane" texture="groundplane" texuniform="true" '
-        'texrepeat="5 5" reflectance="0.2"/>',
-    )
-
-    if root.find("visual") is None:
-        root.append(
-            ET.fromstring(
-                '<visual><headlight diffuse="0.6 0.6 0.6" ambient="0.3 0.3 0.3" '
-                'specular="0.0 0.0 0.0"/><rgba haze="0.15 0.25 0.35 1"/>'
-                '<global azimuth="-130" elevation="-20"/><quality offsamples="4"/>'
-                '<map force="0.01"/></visual>'
-            )
-        )
-
-    terrain_geom = root.find(f".//geom[@name='{geom_name}']")
-    if terrain_geom is not None and terrain_geom.get("material") is None:
-        terrain_geom.set("material", "groundplane")
-    # Robot models sometimes ship with a spot- or target-mode light (e.g. G1's
-    # ``spotlight`` tracking the trunk). Such lights have an implicit
-    # ``type`` and cannot coexist with ``directional="true"``. Drop them; the
-    # overhead light added by the materializer plus the headlight under
-    # ``visual`` is sufficient for terrain visualization.
-    for parent in root.iter():
-        for light in list(parent.findall("light")):
-            if light.get("mode") or light.get("target") or light.get("type"):
-                parent.remove(light)
-                continue
-            light.set("directional", "true")
-            light.set("castshadow", "true")
-            light.set("dir", "-0.35 -0.45 -1")
-
-
-def _merge_scene_fragment(root: ET.Element, fragment_file: Path) -> None:
-    fragment_root = ET.parse(fragment_file).getroot()
-    if fragment_root.tag != "mujoco":
-        raise ValueError(f"Scene fragment '{fragment_file}' must have a <mujoco> root.")
-
-    for child in list(fragment_root):
-        if child.tag in {"sensor", "keyframe", "actuator"}:
-            existing = root.find(child.tag)
-            if existing is None:
-                root.append(child)
-            else:
-                existing.extend(list(child))
-            continue
-        root.append(child)
-
-
-def _copy_robot_asset_dir(model_file: Path, output_dir: Path) -> None:
-    """Copy the robot's mesh / texture assets next to the output scene.
-
-    Honors the ``meshdir`` (and ``texturedir``) declared in the model's
-    ``<compiler>`` tag — falling back to ``<model_file_dir>/assets`` when
-    the compiler tag is missing or points at a non-existent path. This
-    keeps materialization working for models like ``go2w.xml`` whose
-    meshdir is relative to a sibling robot directory.
-    """
-    candidates: list[Path] = []
-    try:
-        root = ET.parse(model_file).getroot()
-    except (ET.ParseError, OSError):
-        root = None
-    if root is not None:
-        compiler = root.find("compiler")
-        if compiler is not None:
-            for attr in ("meshdir", "texturedir"):
-                value = compiler.get(attr)
-                if not value:
-                    continue
-                path = Path(value)
-                if not path.is_absolute():
-                    path = (model_file.parent / path).resolve()
-                if path.is_dir() and path not in candidates:
-                    candidates.append(path)
-
-    fallback = model_file.parent / "assets"
-    if fallback.is_dir() and fallback not in candidates:
-        candidates.append(fallback)
-
-    for src in candidates:
-        shutil.copytree(src, output_dir / "assets", dirs_exist_ok=True)
-
-
-def _collect_mujoco_assets(asset_dir: Path) -> dict[str, bytes]:
-    assets: dict[str, bytes] = {}
-    if not asset_dir.is_dir():
-        return assets
-    for path in asset_dir.rglob("*"):
-        if not path.is_file():
-            continue
-        rel = path.relative_to(asset_dir)
-        assets[str(rel)] = path.read_bytes()
-        assets[str(asset_dir.name / rel)] = path.read_bytes()
-    return assets
-
-
-@overload
-def materialize_mujoco_hfield_attached_scene(
-    *,
-    model_file: str,
-    terrain_cfg: TerrainGeneratorCfg,
-    output_dir: str | Path,
-    fragment_files: Sequence[str] = (),
-    hfield_name: str = "terrain_hfield",
-    geom_name: str = "floor",
-    return_surface_sampler: Literal[False] = False,
-) -> tuple[Any, np.ndarray]: ...
-
-
-@overload
-def materialize_mujoco_hfield_attached_scene(
-    *,
-    model_file: str,
-    terrain_cfg: TerrainGeneratorCfg,
-    output_dir: str | Path,
-    fragment_files: Sequence[str] = (),
-    hfield_name: str = "terrain_hfield",
-    geom_name: str = "floor",
-    return_surface_sampler: Literal[True],
-) -> tuple[Any, np.ndarray, Any]: ...
-
-
-def materialize_mujoco_hfield_attached_scene(
-    *,
-    model_file: str,
-    terrain_cfg: TerrainGeneratorCfg,
-    output_dir: str | Path,
-    fragment_files: Sequence[str] = (),
-    hfield_name: str = "terrain_hfield",
-    geom_name: str = "floor",
-    return_surface_sampler: bool = False,
-) -> tuple[Any, np.ndarray] | tuple[Any, np.ndarray, Any]:
-    """Build a MuJoCo model with generated hfield terrain and attached robot spec."""
-    import mujoco
-
-    from unilab.terrains import TerrainGenerator
-
-    robot_path = Path(model_file).resolve()
-    output_path = Path(output_dir)
-    output_path.mkdir(parents=True, exist_ok=True)
-    _copy_robot_asset_dir(robot_path, output_path)
-
-    hfield_rel = Path("hfields") / "hfield.png"
-    generated = TerrainGenerator(terrain_cfg).write_png(output_path / hfield_rel)
-
-    spec = mujoco.MjSpec()
-    spec.compiler.autolimits = True
-    spec.compiler.meshdir = "assets"
-
-    spec.add_hfield(
-        name=hfield_name,
-        file=str((output_path / hfield_rel).resolve()),
-        size=list(generated.hfield_size),
-    )
-    spec.worldbody.add_light(pos=[0.0, 0.0, 8.0], dir=[0.0, 0.0, -1.0])
-    spec.worldbody.add_geom(
-        name=geom_name,
-        type=mujoco.mjtGeom.mjGEOM_HFIELD,
-        hfieldname=hfield_name,
-        pos=list(generated.geom_pos),
-    )
-
-    robot_spec = mujoco.MjSpec.from_file(str(robot_path))
-    frame = spec.worldbody.add_frame()
-    spec.attach(robot_spec, frame=frame)
-
-    root = ET.fromstring(spec.to_xml())
-    _strip_attach_prefixes(root)
-    _flatten_attach_main_default(root)
-    _merge_robot_option(root, robot_path)
-    _ensure_generated_hfield_scene_visuals(root, geom_name)
-    for fragment_file in fragment_files:
-        _merge_scene_fragment(root, resolve_scene_fragment_path(fragment_file, robot_path))
-
-    scene_xml = output_path / "scene.xml"
-    _write_xml_root(root, scene_xml)
-
-    physics_root = ET.fromstring(ET.tostring(root, encoding="unicode"))
-    _enable_discardvisual(physics_root)
-    model = mujoco.MjSpec.from_string(
-        ET.tostring(physics_root, encoding="unicode"),
-        assets=_collect_mujoco_assets(output_path / "assets"),
-    ).compile()
-    if return_surface_sampler:
-        return model, generated.terrain_origins, generated.surface_sampler()
-    return model, generated.terrain_origins
-
-
-def inject_mujoco_tracking_sensors(
-    model_file: str,
-    baselink_name: str | None = None,
-) -> tuple[str, list, list]:
-    """Inject tracking sensors for the MuJoCo backend.
-
-    The generated sensors track every body in the world frame (``_w``). When
-    ``baselink_name`` is provided, sensors in the baselink-relative frame
-    (``_b``) are added as well.
-
-    Returns:
-        (tmp_xml_path, tracked_body_ids, valid_bnames)
-    """
-    mujoco = _mujoco_module()
-    tracked_body_ids, valid_bnames = _get_named_bodies(model_file)
-
-    spec = mujoco.MjSpec.from_file(model_file)
-    _add_w_sensors(spec, valid_bnames)
-    if baselink_name and baselink_name in valid_bnames:
-        _add_b_sensors(spec, valid_bnames, baselink_name)
-
-    return _materialize_spec_xml(spec, model_file), tracked_body_ids, valid_bnames
-
-
-def processed_xml(xml_path):
-    xml_dir = os.path.dirname(os.path.abspath(xml_path))
-
-    tree = ET.parse(xml_path)
-    root = tree.getroot()
-
-    compiler = root.find("compiler")
-    if compiler is not None:
-        meshdir = compiler.get("meshdir")
-        if meshdir:
-            abs_meshdir = os.path.normpath(os.path.join(xml_dir, meshdir))
-            compiler.set("meshdir", abs_meshdir)
-
-    bodys = root.findall(".//body")
-
-    geom_names = []
-    for body in bodys:
-        body_name = body.get("name", "unnamed_body")
-        geoms = body.findall("geom")
-
-        if geoms:
-            filtered_geoms = []
-            for geom in geoms:
-                geom_class = geom.get("class")
-                if geom_class != "visual":
-                    filtered_geoms.append(geom)
-
-            if filtered_geoms:
-                i = 0
-                for geom in filtered_geoms:
-                    geom_name = geom.get("name", "unnamed_geom")
-                    if geom_name == "unnamed_geom":
-                        new_name = f"{body_name}_geom{i}"
-                        i += 1
-                        geom.set("name", new_name)
-                        geom_name = new_name
-                    geom_names.append(geom_name)
-
-    new_xml_string = ET.tostring(root, encoding="unicode")
-    return new_xml_string, geom_names
-
-
-def add_sensor(root, sensor_type, name, **kwargs):
-    """Add a sensor child under the MuJoCo XML ``<sensor>`` node.
-
-    Args:
-        root: XML root node.
-        sensor_type: Sensor tag name, such as ``"gyro"``, ``"contact"``, or
-            ``"framepos"``.
-        name: Sensor ``name`` attribute.
-        **kwargs: Additional XML attributes such as ``site="imu"`` or
-            ``geom1="floor"``.
-    """
-    # Find or create the <sensor> node.
-    sensor_element = root.find("sensor")
-    if sensor_element is None:
-        sensor_element = ET.SubElement(root, "sensor")
-
-    # Create the concrete sensor node.
-    sensor = ET.SubElement(sensor_element, sensor_type)
-
-    # Set the required name attribute.
-    sensor.set("name", name)
-
-    # Set any extra attributes passed by the caller.
-    for key, value in kwargs.items():
-        sensor.set(key, str(value))
-
-    return sensor
diff --git a/src/unilab/base/backend/playback_common.py b/src/unilab/base/backend/playback_common.py
deleted file mode 100644
index c1e66d68e..000000000
--- a/src/unilab/base/backend/playback_common.py
+++ /dev/null
@@ -1,20 +0,0 @@
-"""Shared playback helper utilities."""
-
-from __future__ import annotations
-
-from typing import Any
-
-import imageio.v2 as imageio
-import numpy as np
-
-
-def env_cfg_value(env: Any, name: str, default: Any) -> Any:
-    cfg = getattr(env, "cfg", None)
-    if cfg is None:
-        return default
-    return getattr(cfg, name, default)
-
-
-def write_playback_video(path: str, frames: list[np.ndarray], *, fps: int) -> None:
-    """Write playback frames with the repository-managed imageio stack."""
-    imageio.mimsave(path, frames, fps=fps)
diff --git a/src/unilab/base/backend_factory.py b/src/unilab/base/backend_factory.py
new file mode 100644
index 000000000..e9bca6e02
--- /dev/null
+++ b/src/unilab/base/backend_factory.py
@@ -0,0 +1,69 @@
+"""UniLab owner-layer assembly for UniSim physics backends.
+
+This module owns only UniLab concerns: resolving hosted robot assets and
+translating :class:`EnvCfg` backend options into the public ``unisim``
+factory. Physics implementations and their public contract live in the
+``unisim-core`` distribution.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+import unisim
+from unisim.backend.base import SimBackend
+
+from unilab.assets.hub import ensure_robot_assets_for_paths
+
+if TYPE_CHECKING:
+    from unilab.base.base import EnvCfg
+    from unilab.base.scene import SceneCfg
+
+
+def env_backend_kwargs(cfg: "EnvCfg") -> dict[str, Any]:
+    """Translate ``EnvCfg`` backend knobs into UniSim adapter options."""
+    return {
+        "post_step_forward_sensor": cfg.post_step_forward_sensor,
+        "motrix_max_iterations": cfg.motrix_max_iterations,
+        "chunk_size": cfg.chunk_size,
+        "adaptive_chunk_size": cfg.adaptive_chunk_size,
+        "cpu_ids": cfg.cpu_ids,
+        "bench_nsteps": cfg.sim_substeps,
+        "mjwarp_nconmax": cfg.mjwarp_nconmax,
+        "mjwarp_njmax": cfg.mjwarp_njmax,
+        "genesis_integrator": cfg.genesis_integrator,
+        "genesis_constraint_solver": cfg.genesis_constraint_solver,
+        "genesis_friction_cone": cfg.genesis_friction_cone,
+        "genesis_solver_iterations": cfg.genesis_solver_iterations,
+        "drake_backend_mode": cfg.drake_backend_mode,
+        "drake_nthread": cfg.drake_nthread,
+        "isaacgym_device_id": cfg.isaacgym_device_id,
+        "isaacgym_worker_timeout_s": cfg.isaacgym_worker_timeout_s,
+        "isaacsim_device_id": cfg.isaacsim_device_id,
+        "isaacsim_worker_timeout_s": cfg.isaacsim_worker_timeout_s,
+        "isaacsim_render_mode": cfg.isaacsim_render_mode,
+        "isaacsim_render_width": cfg.isaacsim_render_width,
+        "isaacsim_render_height": cfg.isaacsim_render_height,
+    }
+
+
+def create_backend(
+    backend_type: str,
+    scene: "SceneCfg",
+    num_envs: int,
+    sim_dt: float,
+    *,
+    body_state_required: bool = False,
+    **kwargs: Any,
+) -> SimBackend:
+    """Prepare UniLab-owned assets and construct a UniSim backend."""
+    if scene is None:
+        raise ValueError("SceneCfg must be provided")
+    ensure_robot_assets_for_paths(
+        [scene.model_file, scene.visual_model_file, *scene.fragment_files]
+    )
+    kwargs["body_state_required"] = body_state_required
+    return unisim.create_backend(backend_type, scene, num_envs, sim_dt, **kwargs)
+
+
+__all__ = ["SimBackend", "create_backend", "env_backend_kwargs"]
diff --git a/src/unilab/base/base.py b/src/unilab/base/base.py
index f6c1d385b..59b6dd82e 100644
--- a/src/unilab/base/base.py
+++ b/src/unilab/base/base.py
@@ -6,8 +6,7 @@
 
 import gymnasium as gym
 import numpy as np
-
-from unilab.base.backend.base import BackendPlayRenderPlan
+from unisim.backend.base import BackendPlayRenderPlan
 
 from .scene import SceneCfg
 
@@ -40,15 +39,41 @@ class EnvCfg:
     post_step_forward_sensor: bool = False
     adaptive_chunk_size: bool = True
     chunk_size: Optional[int] = None
-    # Explicit worker CPU affinity for the MuJoCo BatchEnvPool (Linux only).
-    # ``cpu_ids[i]`` pins pool worker thread ``i`` to one CPU; ``None`` keeps
-    # the default OS scheduling behavior.
+    # Explicit CPU block owned by this env's process (Linux affinity only).
+    # ``cpu_ids[i]`` pins MuJoCo BatchEnvPool worker thread ``i`` to one CPU;
+    # env construction also confines the owning process to the same block and
+    # sizes Numba's parallel pool to ``len(cpu_ids)`` so host-side post-step
+    # compute stays inside the rank's partition. ``None`` keeps the default
+    # OS scheduling behavior.
     cpu_ids: Optional[list[int]] = None
     # ``mjwarp`` owns contact/constraint storage independently from MuJoCo.
     # Keep its capacity knobs explicit in the task owner configuration so a
     # device profile never relies on an implicit backend-wide allocation size.
     mjwarp_nconmax: Optional[int] = None
     mjwarp_njmax: Optional[int] = None
+    # ``isaacgym`` runs physics in a Python 3.8 worker subprocess (Preview 4 is
+    # EOL and incompatible with the main environment). ``None`` keeps the
+    # backend defaults (device 0, generous handshake/step timeout).
+    isaacgym_device_id: Optional[int] = None
+    isaacgym_worker_timeout_s: Optional[float] = None
+    # ``genesis`` drops the MJCF global <option> block at import (REPORT #1372
+    # §3.3), so integrator / constraint solver / friction cone / solver
+    # iterations must be explicit owner fields. ``None`` keeps the Genesis
+    # defaults; the backend validates the spellings fail-closed.
+    genesis_integrator: Optional[str] = None
+    genesis_constraint_solver: Optional[str] = None
+    genesis_friction_cone: Optional[str] = None
+    genesis_solver_iterations: Optional[int] = None
+    # ``isaacsim`` runs IsaacLab/PhysX in a dedicated Python 3.11 worker.
+    # Keep its knobs separate from IsaacGym because the two runtimes have
+    # incompatible interpreters and installation roots.  The render intent is
+    # populated only by play/eval config materialization; training leaves it
+    # unset so the worker stays on the cheap headless experience.
+    isaacsim_device_id: Optional[int] = None
+    isaacsim_worker_timeout_s: Optional[float] = None
+    isaacsim_render_mode: Optional[str] = None
+    isaacsim_render_width: int = 1280
+    isaacsim_render_height: int = 720
 
     @property
     def max_episode_steps(self) -> Optional[int]:
@@ -80,6 +105,72 @@ def validate(self):
                 isinstance(value, bool) or not isinstance(value, int) or value <= 0
             ):
                 raise ValueError(f"{name} must be a positive integer or None, got {value!r}")
+        if self.isaacgym_device_id is not None and (
+            isinstance(self.isaacgym_device_id, bool)
+            or not isinstance(self.isaacgym_device_id, int)
+            or self.isaacgym_device_id < 0
+        ):
+            raise ValueError(
+                "isaacgym_device_id must be a non-negative integer or None, "
+                f"got {self.isaacgym_device_id!r}"
+            )
+        if self.isaacgym_worker_timeout_s is not None and (
+            not isinstance(self.isaacgym_worker_timeout_s, (int, float))
+            or isinstance(self.isaacgym_worker_timeout_s, bool)
+            or self.isaacgym_worker_timeout_s <= 0
+        ):
+            raise ValueError(
+                "isaacgym_worker_timeout_s must be a positive number or None, "
+                f"got {self.isaacgym_worker_timeout_s!r}"
+            )
+        for name, value in (
+            ("genesis_integrator", self.genesis_integrator),
+            ("genesis_constraint_solver", self.genesis_constraint_solver),
+            ("genesis_friction_cone", self.genesis_friction_cone),
+        ):
+            if value is not None and (not isinstance(value, str) or not value):
+                raise ValueError(f"{name} must be a non-empty string or None, got {value!r}")
+        if self.genesis_solver_iterations is not None and (
+            isinstance(self.genesis_solver_iterations, bool)
+            or not isinstance(self.genesis_solver_iterations, int)
+            or self.genesis_solver_iterations <= 0
+        ):
+            raise ValueError(
+                "genesis_solver_iterations must be a positive integer or None, "
+                f"got {self.genesis_solver_iterations!r}"
+            )
+        if self.isaacsim_device_id is not None and (
+            isinstance(self.isaacsim_device_id, bool)
+            or not isinstance(self.isaacsim_device_id, int)
+            or self.isaacsim_device_id < 0
+        ):
+            raise ValueError(
+                "isaacsim_device_id must be a non-negative integer or None, "
+                f"got {self.isaacsim_device_id!r}"
+            )
+        if self.isaacsim_worker_timeout_s is not None and (
+            not isinstance(self.isaacsim_worker_timeout_s, (int, float))
+            or isinstance(self.isaacsim_worker_timeout_s, bool)
+            or self.isaacsim_worker_timeout_s <= 0
+        ):
+            raise ValueError(
+                "isaacsim_worker_timeout_s must be a positive number or None, "
+                f"got {self.isaacsim_worker_timeout_s!r}"
+            )
+        if self.isaacsim_render_mode is not None:
+            mode = str(self.isaacsim_render_mode).strip().lower()
+            if mode not in {"auto", "interactive", "record", "none"}:
+                raise ValueError(
+                    "isaacsim_render_mode must be one of auto, interactive, record, none, or None; "
+                    f"got {self.isaacsim_render_mode!r}"
+                )
+            self.isaacsim_render_mode = mode
+        for name, value in (
+            ("isaacsim_render_width", self.isaacsim_render_width),
+            ("isaacsim_render_height", self.isaacsim_render_height),
+        ):
+            if isinstance(value, bool) or not isinstance(value, int) or value <= 0:
+                raise ValueError(f"{name} must be a positive integer, got {value!r}")
         if self.cpu_ids is not None:
             ids = list(self.cpu_ids)
             if not ids:
diff --git a/src/unilab/base/config_adapter.py b/src/unilab/base/config_adapter.py
new file mode 100644
index 000000000..ee1b00450
--- /dev/null
+++ b/src/unilab/base/config_adapter.py
@@ -0,0 +1,154 @@
+"""Resolved config adaptation for training entrypoints."""
+
+from __future__ import annotations
+
+from dataclasses import replace
+from pathlib import Path
+from typing import Any, Callable
+
+from omegaconf import DictConfig, OmegaConf
+from unisim.backend.mujoco.xml import materialize_scene_visual_override
+
+from unilab.base import registry
+from unilab.base.scene import SceneCfg
+from unilab.utils.reward import extract_reward_config
+
+
+class BackendAdapter:
+    """Build env/play overrides from the final composed config."""
+
+    def __init__(
+        self,
+        cfg: DictConfig,
+        *,
+        root_dir: str | Path,
+        algo_name: str | None = None,
+        scene_materializer: Callable[..., str] = materialize_scene_visual_override,
+    ) -> None:
+        self.cfg = cfg
+        self.root_dir = Path(root_dir)
+        self.algo_name = algo_name
+        self.scene_materializer = scene_materializer
+
+    def build_task_env_cfg_override(self) -> dict[str, Any]:
+        """Build env_cfg_override from the resolved reward + env sections."""
+        registry.ensure_registries()
+        task_name = str(self.cfg.training.task_name)
+        reward_target = registry.resolve_reward_override_field(task_name)
+        env_overrides = self._to_plain_dict(getattr(self.cfg, "env", None))
+        if reward_target in env_overrides:
+            raise ValueError(
+                f"Task '{task_name}' declares both Hydra root 'reward' and "
+                f"'env.{reward_target}'; use the root reward owner only"
+            )
+
+        env_cfg_override = extract_reward_config(self.cfg, target_field=reward_target)
+        env_cfg_override.update(env_overrides)
+
+        return env_cfg_override
+
+    def build_play_env_cfg_override(self) -> dict[str, Any]:
+        """Build play-mode overrides from an optional backend-agnostic play profile."""
+        env_cfg_override = self.build_task_env_cfg_override()
+        # IsaacSim must know the render intent before its Python 3.11 worker
+        # launches Kit.  Keep this routing in the config adapter (the owner
+        # layer) so training env construction remains headless and the
+        # renderer never leaks into runners/learners.  Other backends do not
+        # consume this field and retain their existing play contracts.
+        if str(OmegaConf.select(self.cfg, "training.sim_backend", default="")) == "isaacsim":
+            play_render_mode = OmegaConf.select(
+                self.cfg, "training.play_render_mode", default="auto"
+            )
+            env_cfg_override["isaacsim_render_mode"] = (
+                "auto" if play_render_mode is None else str(play_render_mode)
+            )
+        play_profile = getattr(self.cfg, "play_profile", None)
+        if (
+            play_profile is None
+            or not getattr(play_profile, "enabled", False)
+            or not self.cfg.training.play_only
+        ):
+            return env_cfg_override
+
+        env_profile = getattr(play_profile, "env", None)
+        if env_profile is not None:
+            self._apply_env_profile(env_cfg_override, env_profile)
+
+        scene_override = getattr(play_profile, "scene", None)
+        if scene_override is None or not getattr(scene_override, "enabled", False):
+            return env_cfg_override
+
+        source_model_file = getattr(scene_override, "source_model_file", None)
+        if not source_model_file:
+            raise ValueError("play_profile.scene.source_model_file must be configured")
+
+        # Cold path: the materializer parses the source XML before any
+        # create_backend hook runs, so resolve HF-hosted robot assets first.
+        from unilab.assets.hub import ensure_robot_assets_for_paths
+
+        resolved_source = self._resolve_root_relative_path(str(source_model_file))
+        ensure_robot_assets_for_paths([resolved_source])
+
+        materialized_model_file = self.scene_materializer(
+            resolved_source,
+            ground_texture_file=(
+                self._resolve_root_relative_path(str(scene_override.ground_texture_file))
+                if getattr(scene_override, "ground_texture_file", None)
+                else None
+            ),
+            ground_texrepeat=getattr(scene_override, "ground_texrepeat", None),
+            skybox_rgb1=getattr(scene_override, "skybox_rgb1", None),
+            skybox_rgb2=getattr(scene_override, "skybox_rgb2", None),
+        )
+        scene = env_cfg_override.get("scene")
+        if scene is None:
+            env_cfg_override["scene"] = SceneCfg(model_file=materialized_model_file)
+        elif isinstance(scene, SceneCfg):
+            env_cfg_override["scene"] = replace(scene, model_file=materialized_model_file)
+        elif isinstance(scene, dict):
+            env_cfg_override["scene"] = {**scene, "model_file": materialized_model_file}
+        else:
+            raise TypeError(
+                "play_profile.scene can only override a missing, SceneCfg, or mapping scene; "
+                f"got {type(scene).__name__}"
+            )
+        return env_cfg_override
+
+    def _apply_env_profile(self, env_cfg_override: dict[str, Any], env_profile: Any) -> None:
+        env_cfg_override.update(self._to_plain_dict(env_profile))
+
+    def _resolve_root_relative_path(self, path_value: str) -> str:
+        candidate = Path(path_value)
+        if candidate.is_absolute():
+            return str(candidate)
+        return str((self.root_dir / candidate).resolve())
+
+    def _to_plain_dict(self, value: Any) -> dict[str, Any]:
+        if OmegaConf.is_config(value):
+            resolved = OmegaConf.to_container(value, resolve=True)
+        elif isinstance(value, dict):
+            resolved = value
+        else:
+            return {}
+        if not isinstance(resolved, dict):
+            return {}
+        return {str(key): item for key, item in resolved.items()}
+
+
+def create_env(
+    cfg: DictConfig,
+    *,
+    num_envs: int,
+    env_cfg_override: dict[str, Any] | None = None,
+    sim_backend: str | None = None,
+    task_name: str | None = None,
+):
+    """Construct an environment via the registry using the current Hydra config."""
+    from unilab.base import registry
+
+    return registry.make(
+        task_name or str(OmegaConf.select(cfg, "training.task_name")),
+        num_envs=num_envs,
+        sim_backend=sim_backend or str(OmegaConf.select(cfg, "training.sim_backend")),
+        env_cfg_override=env_cfg_override,
+    )
diff --git a/src/unilab/base/config_materialization.py b/src/unilab/base/config_materialization.py
new file mode 100644
index 000000000..1fe694952
--- /dev/null
+++ b/src/unilab/base/config_materialization.py
@@ -0,0 +1,406 @@
+"""Cold-path materialization of Hydra-owned typed configuration."""
+
+from __future__ import annotations
+
+import dataclasses
+import inspect
+import types
+from collections.abc import Mapping
+from typing import Any, Union, get_args, get_origin, get_type_hints
+
+from hydra.utils import get_object, instantiate
+from omegaconf import OmegaConf
+
+from .config_overrides import (
+    CONFIG_MAPPING_POLICY_KEY,
+    MANAGER_PARAMS_MAPPING_POLICY,
+    MANAGER_TERM_MAPPING_POLICY,
+)
+
+HYDRA_TARGET_KEY = "_target_"
+_MISSING = object()
+
+
+def _plain(value: Any) -> Any:
+    if OmegaConf.is_config(value):
+        return OmegaConf.to_container(value, resolve=True)
+    return value
+
+
+def _hints(target_type: type[Any]) -> dict[str, Any]:
+    try:
+        return get_type_hints(target_type)
+    except (NameError, TypeError):
+        return {field.name: field.type for field in dataclasses.fields(target_type)}
+
+
+def _dataclass_types(annotation: Any) -> tuple[type[Any], ...]:
+    if annotation in (Any, None):
+        return ()
+    origin = get_origin(annotation)
+    if origin in (types.UnionType, Union):
+        return tuple(
+            target
+            for item in get_args(annotation)
+            if item is not type(None)
+            for target in _dataclass_types(item)
+        )
+    if isinstance(annotation, type) and dataclasses.is_dataclass(annotation):
+        return (annotation,)
+    return ()
+
+
+def _dict_value_type(annotation: Any) -> Any:
+    origin = get_origin(annotation)
+    if origin is dict:
+        args = get_args(annotation)
+        return args[1] if len(args) == 2 else Any
+    if origin in (types.UnionType, Union):
+        for item in get_args(annotation):
+            value_type = _dict_value_type(item)
+            if value_type is not _MISSING:
+                return value_type
+    return _MISSING
+
+
+def _resolve(reference: Any, *, path: str) -> Any:
+    if not isinstance(reference, str) or not reference.strip():
+        raise TypeError(f"Config field '{path}' must be a non-empty dotted string")
+    if "." not in reference:
+        try:
+            return get_object(f"unilab.managers.{reference}")
+        except Exception as exc:
+            raise ValueError(
+                f"Config field '{path}' could not resolve short reference {reference!r} "
+                f"(tried 'unilab.managers.{reference}'): {exc}"
+            ) from exc
+    try:
+        return get_object(reference)
+    except Exception as exc:
+        raise ValueError(
+            f"Config field '{path}' could not resolve dotted reference {reference!r}: {exc}"
+        ) from exc
+
+
+def _inferable_target(annotation: Any) -> type[Any] | None:
+    candidates = _dataclass_types(annotation)
+    if len(candidates) == 1 and not inspect.isabstract(candidates[0]):
+        return candidates[0]
+    return None
+
+
+def _resolve_target(
+    reference: Any,
+    *,
+    expected: Any,
+    path: str,
+) -> type[Any]:
+    target = _resolve(reference, path=f"{path}.{HYDRA_TARGET_KEY}")
+    if not isinstance(target, type) or not dataclasses.is_dataclass(target):
+        raise TypeError(
+            f"Config field '{path}.{HYDRA_TARGET_KEY}' must resolve to a dataclass type"
+        )
+    expected_types = _dataclass_types(expected)
+    if expected_types and not any(issubclass(target, item) for item in expected_types):
+        names = ", ".join(item.__qualname__ for item in expected_types)
+        raise TypeError(
+            f"Config field '{path}.{HYDRA_TARGET_KEY}' resolved to {target.__qualname__}, "
+            f"expected a subclass of {names}"
+        )
+    if inspect.isabstract(target):
+        raise TypeError(
+            f"Config field '{path}.{HYDRA_TARGET_KEY}' resolved to abstract config "
+            f"{target.__qualname__}; select a concrete term config"
+        )
+    return target
+
+
+def _target_path(target: type[Any]) -> str:
+    return f"{target.__module__}.{target.__qualname__}"
+
+
+def _prepare_value(value: Any, *, annotation: Any, path: str) -> Any:
+    value = _plain(value)
+    if isinstance(value, Mapping):
+        values = dict(value)
+        if HYDRA_TARGET_KEY in values:
+            return _prepare_dataclass(values, expected=annotation, path=path, require_target=True)
+
+        candidates = _dataclass_types(annotation)
+        if len(candidates) == 1 and not inspect.isabstract(candidates[0]):
+            return _prepare_dataclass(values, expected=annotation, path=path, require_target=False)
+
+        value_type = _dict_value_type(annotation)
+        return {
+            key: _prepare_value(
+                item,
+                annotation=Any if value_type is _MISSING else value_type,
+                path=f"{path}.{key}",
+            )
+            for key, item in values.items()
+        }
+    if isinstance(value, list):
+        return [
+            _prepare_value(item, annotation=Any, path=f"{path}[{index}]")
+            for index, item in enumerate(value)
+        ]
+    return value
+
+
+def _prepare_manager_mapping(value: Any, *, annotation: Any, path: str) -> dict[str, Any]:
+    value = _plain(value)
+    if not isinstance(value, Mapping):
+        raise TypeError(f"Config field '{path}' must be a mapping")
+    value_type = _dict_value_type(annotation)
+    if value_type is _MISSING:
+        raise TypeError(f"Config field '{path}' manager policy requires a typed dict")
+
+    result: dict[str, Any] = {}
+    for name, raw_entry in value.items():
+        if not isinstance(name, str) or not name:
+            raise TypeError(f"Config field '{path}' term names must be non-empty strings")
+        entry_path = f"{path}.{name}"
+        entry = _plain(raw_entry)
+        if entry is None:
+            result[name] = None
+        elif not isinstance(entry, Mapping):
+            raise TypeError(f"Config field '{entry_path}' must be a mapping or None")
+        else:
+            result[name] = _prepare_dataclass(
+                dict(entry),
+                expected=value_type,
+                path=entry_path,
+                require_target=_inferable_target(value_type) is None,
+            )
+    return result
+
+
+def _prepare_dataclass(
+    values: Mapping[str, Any],
+    *,
+    expected: Any,
+    path: str,
+    require_target: bool,
+) -> dict[str, Any]:
+    values = dict(values)
+    reference = values.pop(HYDRA_TARGET_KEY, _MISSING)
+    if reference is _MISSING:
+        candidates = _dataclass_types(expected)
+        if require_target:
+            raise ValueError(
+                f"Config field '{path}' is a new Manager-Based entry and must declare "
+                f"'{HYDRA_TARGET_KEY}'"
+            )
+        if len(candidates) != 1 or inspect.isabstract(candidates[0]):
+            raise ValueError(
+                f"Config field '{path}' cannot infer one concrete dataclass type; "
+                f"declare '{HYDRA_TARGET_KEY}'"
+            )
+        target = candidates[0]
+        reference = _target_path(target)
+    else:
+        target = _resolve_target(reference, expected=expected, path=path)
+        reference = _target_path(target)
+
+    fields = {field.name: field for field in dataclasses.fields(target) if field.init}
+    unknown = [name for name in values if name not in fields]
+    if unknown:
+        raise ValueError(
+            f"Config field '{path}' target {target.__qualname__} has no fields {unknown}"
+        )
+
+    hints = _hints(target)
+    prepared: dict[str, Any] = {HYDRA_TARGET_KEY: reference}
+    for name, raw_value in values.items():
+        field = fields[name]
+        field_path = f"{path}.{name}"
+        annotation = hints.get(name, field.type)
+        policy = field.metadata.get(CONFIG_MAPPING_POLICY_KEY)
+        if policy == MANAGER_TERM_MAPPING_POLICY:
+            prepared[name] = _prepare_manager_mapping(
+                raw_value,
+                annotation=annotation,
+                path=field_path,
+            )
+        elif name == "func":
+            resolved = _resolve(raw_value, path=field_path)
+            if not callable(resolved):
+                raise TypeError(
+                    f"Config field '{field_path}' resolved to {type(resolved).__name__}, "
+                    "expected a callable"
+                )
+            prepared[name] = resolved
+        else:
+            prepared[name] = _prepare_value(raw_value, annotation=annotation, path=field_path)
+    return prepared
+
+
+def _materialize_entry(
+    value: Mapping[str, Any],
+    *,
+    expected: Any,
+    path: str,
+    require_target: bool = True,
+) -> Any:
+    prepared = _prepare_dataclass(
+        value, expected=expected, path=path, require_target=require_target
+    )
+    try:
+        result = instantiate(prepared, _convert_="all")
+    except Exception as exc:
+        raise TypeError(
+            f"Config field '{path}' could not construct its typed config: {exc}"
+        ) from exc
+    expected_types = _dataclass_types(expected)
+    if expected_types and not isinstance(result, expected_types):
+        raise TypeError(
+            f"Config field '{path}' materialized {type(result).__name__}, expected "
+            f"{', '.join(item.__qualname__ for item in expected_types)}"
+        )
+    return result
+
+
+def _policy(target_obj: Any, name: str) -> str | None:
+    if not dataclasses.is_dataclass(target_obj) or isinstance(target_obj, type):
+        return None
+    field = next((item for item in dataclasses.fields(target_obj) if item.name == name), None)
+    value = None if field is None else field.metadata.get(CONFIG_MAPPING_POLICY_KEY)
+    return str(value) if value is not None else None
+
+
+def _is_term_cfg(target_obj: Any) -> bool:
+    return (
+        dataclasses.is_dataclass(target_obj)
+        and not isinstance(target_obj, type)
+        and any(
+            field.metadata.get(CONFIG_MAPPING_POLICY_KEY) == MANAGER_PARAMS_MAPPING_POLICY
+            for field in dataclasses.fields(target_obj)
+        )
+    )
+
+
+def _apply_manager_mapping(
+    target_obj: Any,
+    name: str,
+    overrides: Any,
+    *,
+    annotation: Any,
+    policy: str,
+) -> None:
+    owner = f"{type(target_obj).__name__}.{name}"
+    existing = getattr(target_obj, name)
+    overrides = _plain(overrides)
+    if not isinstance(existing, dict) or not isinstance(overrides, Mapping):
+        raise TypeError(f"Config field '{owner}' manager value and override must be mappings")
+
+    if policy == MANAGER_PARAMS_MAPPING_POLICY:
+        for param_name, raw_value in overrides.items():
+            path = f"{owner}.{param_name}"
+            value = _plain(raw_value)
+            current = existing.get(param_name, _MISSING)
+            if isinstance(value, Mapping) and HYDRA_TARGET_KEY in value:
+                existing[param_name] = _materialize_entry(value, expected=Any, path=path)
+            elif isinstance(value, Mapping) and dataclasses.is_dataclass(current):
+                apply_cfg_overrides(current, value, _path=path)
+            else:
+                existing[param_name] = _prepare_value(value, annotation=Any, path=path)
+        return
+    if policy != MANAGER_TERM_MAPPING_POLICY:
+        raise ValueError(f"Config field '{owner}' has unknown mapping policy {policy!r}")
+
+    value_type = _dict_value_type(annotation)
+    if value_type is _MISSING:
+        raise TypeError(f"Config field '{owner}' manager policy requires a typed dict")
+    for term_name, raw_value in overrides.items():
+        path = f"{owner}.{term_name}"
+        value = _plain(raw_value)
+        if value is None:
+            existing[term_name] = None
+        elif not isinstance(value, Mapping):
+            raise TypeError(f"Config field '{path}' must be a field mapping or None")
+        elif HYDRA_TARGET_KEY in value:
+            existing[term_name] = _materialize_entry(value, expected=value_type, path=path)
+        else:
+            current = existing.get(term_name, _MISSING)
+            if current is _MISSING or current is None:
+                if _inferable_target(value_type) is not None:
+                    existing[term_name] = _materialize_entry(
+                        value, expected=value_type, path=path, require_target=False
+                    )
+                    continue
+                raise ValueError(
+                    f"Config field '{path}' is a new Manager-Based entry and must declare "
+                    f"'{HYDRA_TARGET_KEY}'"
+                )
+            if not dataclasses.is_dataclass(current) or isinstance(current, type):
+                raise TypeError(f"Config field '{path}' does not contain a dataclass config")
+            apply_cfg_overrides(current, value, _path=path)
+
+
+def apply_cfg_overrides(
+    target_obj: Any,
+    overrides: Mapping[str, Any],
+    *,
+    _path: str | None = None,
+) -> None:
+    """Apply overrides and materialize explicitly typed Manager-Based entries."""
+    overrides = _plain(overrides)
+    if not isinstance(overrides, Mapping):
+        raise TypeError(f"Config overrides for {type(target_obj).__name__} must be a mapping")
+    hints = _hints(type(target_obj)) if dataclasses.is_dataclass(target_obj) else {}
+    fields = (
+        {field.name: field for field in dataclasses.fields(target_obj)}
+        if dataclasses.is_dataclass(target_obj) and not isinstance(target_obj, type)
+        else {}
+    )
+    owner_path = _path or type(target_obj).__name__
+
+    for key, raw_value in overrides.items():
+        if not isinstance(key, str) or not hasattr(target_obj, key):
+            raise ValueError(f"Config class '{type(target_obj).__name__}' has no attribute '{key}'")
+        value = _plain(raw_value)
+        existing = getattr(target_obj, key)
+        annotation = hints.get(key, fields[key].type if key in fields else Any)
+        policy = _policy(target_obj, key)
+        if policy is not None:
+            _apply_manager_mapping(
+                target_obj,
+                key,
+                value,
+                annotation=annotation,
+                policy=policy,
+            )
+            continue
+        if key == "func" and _is_term_cfg(target_obj):
+            raise ValueError(
+                f"Config field '{type(target_obj).__name__}.func' belongs to the typed term "
+                "declaration and cannot be changed by a partial override"
+            )
+
+        path = f"{owner_path}.{key}"
+        if isinstance(value, Mapping):
+            if HYDRA_TARGET_KEY in value:
+                setattr(target_obj, key, _materialize_entry(value, expected=annotation, path=path))
+                continue
+            if dataclasses.is_dataclass(existing) and not isinstance(existing, type):
+                apply_cfg_overrides(existing, value, _path=path)
+                continue
+            candidates = _dataclass_types(annotation)
+            if existing is None and len(candidates) == 1:
+                prepared = _prepare_dataclass(
+                    value,
+                    expected=annotation,
+                    path=path,
+                    require_target=False,
+                )
+                try:
+                    setattr(target_obj, key, instantiate(prepared, _convert_="all"))
+                except Exception as exc:
+                    raise TypeError(
+                        f"Config field '{path}' could not construct its typed config: {exc}"
+                    ) from exc
+                continue
+        setattr(target_obj, key, _prepare_value(value, annotation=Any, path=path))
+
+
+__all__ = ["HYDRA_TARGET_KEY", "apply_cfg_overrides"]
diff --git a/src/unilab/base/config_overrides.py b/src/unilab/base/config_overrides.py
new file mode 100644
index 000000000..62d16d572
--- /dev/null
+++ b/src/unilab/base/config_overrides.py
@@ -0,0 +1,11 @@
+"""Metadata shared by config owners and the registry override engine."""
+
+CONFIG_MAPPING_POLICY_KEY = "unilab_config_mapping_policy"
+MANAGER_TERM_MAPPING_POLICY = "manager_terms"
+MANAGER_PARAMS_MAPPING_POLICY = "manager_params"
+
+__all__ = [
+    "CONFIG_MAPPING_POLICY_KEY",
+    "MANAGER_PARAMS_MAPPING_POLICY",
+    "MANAGER_TERM_MAPPING_POLICY",
+]
diff --git a/src/unilab/base/cpu_runtime.py b/src/unilab/base/cpu_runtime.py
new file mode 100644
index 000000000..79e9b0d9f
--- /dev/null
+++ b/src/unilab/base/cpu_runtime.py
@@ -0,0 +1,104 @@
+"""Process-local CPU confinement for envs that own an explicit CPU block.
+
+Multi-rank off-policy data-parallel runs partition host CPUs so each rank's
+collector owns one contiguous block (``training.dp_collector_cpu_ids``, routed
+into ``EnvCfg.cpu_ids``). The MuJoCo BatchEnvPool already pins its physics
+workers to that block, but the collector's host-side compute did not follow:
+Numba parallel kernels (``unisim.backend.body_state`` and the
+motion-tracking kernels) size their pool from the host CPU count and leave
+placement to the OS, so they drift across rank boundaries and compete with
+sibling ranks' pinned physics workers.
+
+``apply_env_cpu_runtime`` is the generic env-level counterpart, applied once on
+the env-construction cold path — before managers, backend materialization, or
+the first Numba parallel call exist:
+
+- ``os.sched_setaffinity`` pins the calling thread to the block, and every
+  already-running thread (e.g. BLAS pools spawned at ``import numpy``) is
+  pinned individually via ``/proc/self/task``; threads spawned later
+  (including Numba's lazily-launched pool) inherit the mask.
+- ``numba.set_num_threads(len(cpu_ids))`` sizes Numba's pool to the block
+  instead of the host, unless the operator pinned ``NUMBA_NUM_THREADS``
+  (mirroring the motion-kernel runtime policy).
+
+The function is backend-agnostic: ``EnvCfg.cpu_ids`` is the single source of
+truth, so any backend whose env declares a block gets the same confinement.
+"""
+
+from __future__ import annotations
+
+import os
+import warnings
+from collections.abc import Sequence
+
+_PROC_TASK_DIR = "/proc/self/task"
+
+
+def _confine_existing_threads(ids: set[int]) -> None:
+    """Pin already-running threads; later threads inherit the caller's mask.
+
+    Native pools spawned before env construction (OpenBLAS spawns its workers
+    at ``import numpy``) would otherwise keep the host-wide mask and compete
+    with sibling ranks' pinned CPUs. Threads may exit between listing and
+    pinning; those races are ignored.
+    """
+    # Resolved at call time (not import) so tests can monkeypatch the seam;
+    # ``getattr`` keeps this checkable on platforms where typeshed hides the
+    # Linux-only symbol (mypy ``attr-defined`` on darwin).
+    sched_setaffinity = getattr(os, "sched_setaffinity", None)
+    if sched_setaffinity is None or not os.path.isdir(_PROC_TASK_DIR):
+        return
+    for entry in os.listdir(_PROC_TASK_DIR):
+        try:
+            sched_setaffinity(int(entry), ids)
+        except OSError:
+            continue
+
+
+def apply_env_cpu_runtime(cpu_ids: Sequence[int] | None) -> None:
+    """Confine this process's host-side compute to the env-owned CPU block.
+
+    Cold path only: call from env construction, before the backend pool,
+    managers, or any Numba parallel kernel exist. ``None`` (the single-rank
+    default) is a no-op so the default path stays bit-identical.
+
+    Structural validation (non-empty, unique, non-negative ints) is owned by
+    ``EnvCfg.validate``; this function fails closed on CPU ids that are not
+    available to the process.
+    """
+    if cpu_ids is None:
+        return
+    ids = {int(cpu_id) for cpu_id in cpu_ids}
+
+    sched_setaffinity = getattr(os, "sched_setaffinity", None)
+    sched_getaffinity = getattr(os, "sched_getaffinity", None)
+    if sched_setaffinity is not None and sched_getaffinity is not None:
+        available = set(sched_getaffinity(0))
+        missing = sorted(ids - available)
+        if missing:
+            raise ValueError(
+                f"EnvCfg.cpu_ids entries {missing} are not available to this process "
+                f"(sched_getaffinity={sorted(available)})"
+            )
+        sched_setaffinity(0, ids)
+        _confine_existing_threads(ids)
+    else:
+        warnings.warn(
+            "EnvCfg.cpu_ids process confinement requires os.sched_setaffinity "
+            "(Linux); only the Numba thread cap is applied",
+            stacklevel=2,
+        )
+
+    if "NUMBA_NUM_THREADS" in os.environ:
+        return
+    from numba import set_num_threads
+
+    try:
+        set_num_threads(len(ids))
+    except ValueError as exc:
+        # len(ids) <= NUMBA_NUM_THREADS holds whenever the pool default came
+        # from this host's CPU count; warn instead of failing env construction.
+        warnings.warn(
+            f"EnvCfg.cpu_ids Numba thread cap to {len(ids)} rejected: {exc}",
+            stacklevel=2,
+        )
diff --git a/src/unilab/base/curriculum.py b/src/unilab/base/curriculum.py
index 245f702b2..2b87f3968 100644
--- a/src/unilab/base/curriculum.py
+++ b/src/unilab/base/curriculum.py
@@ -2,8 +2,6 @@
 
 from __future__ import annotations
 
-from typing import Any
-
 import numpy as np
 
 
@@ -22,69 +20,3 @@ def update(self, episode_lengths: np.ndarray) -> None:
         current_avg = float(np.mean(episode_lengths))
         weight = min(len(episode_lengths) / self.window_size, 1.0)
         self.average_length = self.average_length * (1 - weight) + current_avg * weight
-
-
-class PenaltyCurriculum:
-    """Adaptive penalty scaling based on episode length."""
-
-    def __init__(
-        self,
-        env: Any,
-        enabled: bool = True,
-        initial_scale: float = 0.5,
-        min_scale: float = 0.5,
-        max_scale: float = 1.0,
-        level_down_threshold: float = 150.0,
-        level_up_threshold: float = 750.0,
-        degree: float = 0.001,
-    ):
-        self.env = env
-        self.enabled = enabled
-        self.current_scale = initial_scale
-        self.min_scale = min_scale
-        self.max_scale = max_scale
-        self.level_down_threshold = level_down_threshold
-        self.level_up_threshold = level_up_threshold
-        self.degree = degree
-
-        # Store original penalty weights
-        self.penalty_names: list[str] = []
-        self.original_weights: dict[str, float] = {}
-
-        if enabled:
-            self._identify_penalties()
-            self._apply_initial_scale()
-
-    def _identify_penalties(self) -> None:
-        """Identify penalty rewards (negative scales)."""
-        for name, scale in self.env.cfg.reward_config.scales.items():
-            if scale < 0:
-                self.penalty_names.append(name)
-                self.original_weights[name] = scale
-
-    def _apply_initial_scale(self) -> None:
-        """Apply initial penalty scaling."""
-        for name in self.penalty_names:
-            self.env.cfg.reward_config.scales[name] = (
-                self.original_weights[name] * self.current_scale
-            )
-
-    def update(self, average_episode_length: float) -> None:
-        """Update penalty scale based on average episode length."""
-        if not self.enabled:
-            return
-
-        # Adjust scale
-        if average_episode_length < self.level_down_threshold:
-            self.current_scale *= 1.0 - self.degree
-        elif average_episode_length > self.level_up_threshold:
-            self.current_scale *= 1.0 + self.degree
-
-        # Clamp
-        self.current_scale = float(np.clip(self.current_scale, self.min_scale, self.max_scale))
-
-        # Apply to all penalty rewards
-        for name in self.penalty_names:
-            self.env.cfg.reward_config.scales[name] = (
-                self.original_weights[name] * self.current_scale
-            )
diff --git a/src/unilab/base/entity.py b/src/unilab/base/entity.py
new file mode 100644
index 000000000..f722755b3
--- /dev/null
+++ b/src/unilab/base/entity.py
@@ -0,0 +1,2344 @@
+"""Base-owned NumPy scene/entity facade for manager terms.
+
+The facade deliberately describes partitions of an already materialized UniLab scene.
+It is not a second scene composer: all name resolution and state reads go through the
+public :class:`~unisim.backend.base.SimBackend` contract.
+"""
+
+from __future__ import annotations
+
+import re
+from collections.abc import Callable, Iterator, Mapping, Sequence
+from contextlib import contextmanager
+from dataclasses import dataclass
+from functools import partial
+from types import MappingProxyType
+from typing import TYPE_CHECKING, Any, NoReturn, cast
+
+import numpy as np
+from unisim.backend.base import BackendRootStateLayout, BackendSensorView, SimBackend
+from unisim.dr.types import IntervalRandomizationPlan
+
+from unilab.utils.rotation import np_quat_apply, np_quat_apply_inverse, np_yaw_from_quat
+
+if TYPE_CHECKING:
+    from unilab.base.reset_state import ResetStateTransaction
+    from unilab.base.scene import SceneCfg
+
+
+NamesCfg = tuple[str, ...] | list[str] | None
+BodyStateCopyFn = Callable[
+    [np.ndarray, np.ndarray, np.ndarray, np.ndarray],
+    tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray],
+]
+
+
+@dataclass(frozen=True)
+class EntityCfg:
+    """Declare one logical entity inside an existing backend scene.
+
+    Names are explicit because UniLab keeps scene composition in task-owned XML and
+    backend adapters.  ``None`` means that the namespace is not exposed by this
+    entity; an empty sequence means that it is exposed but contains no elements.
+    """
+
+    root_body_name: str | None = None
+    joint_names: NamesCfg = None
+    body_names: NamesCfg = None
+    geom_names: NamesCfg = None
+    site_names: NamesCfg = None
+    actuator_names: NamesCfg = None
+
+
+def _normalize_names(entity_name: str, kind: str, names: NamesCfg) -> tuple[str, ...] | None:
+    if names is None:
+        return None
+    if isinstance(names, str):
+        raise TypeError(
+            f"Entity '{entity_name}' {kind} names must be a sequence of strings, not a scalar"
+        )
+    invalid = [value for value in names if not isinstance(value, str)]
+    if invalid:
+        raise TypeError(f"Entity '{entity_name}' {kind} names must be strings; got {invalid}")
+    normalized = tuple(names)
+    if any(not name for name in normalized):
+        raise ValueError(f"Entity '{entity_name}' {kind} names must be non-empty strings")
+    if len(set(normalized)) != len(normalized):
+        raise ValueError(f"Entity '{entity_name}' {kind} names must be unique: {normalized}")
+    return normalized
+
+
+def _readonly_ids(values: np.ndarray | Sequence[int], *, expected: int, label: str) -> np.ndarray:
+    raw_ids = np.asarray(values)
+    if not np.issubdtype(raw_ids.dtype, np.integer) or np.issubdtype(raw_ids.dtype, np.bool_):
+        raise TypeError(f"{label} resolver must return integer IDs, got dtype {raw_ids.dtype}")
+    ids = np.asarray(raw_ids, dtype=np.int32)
+    if ids.shape != (expected,):
+        raise ValueError(f"{label} resolver returned shape {ids.shape}, expected ({expected},)")
+    if np.any(ids < 0):
+        raise ValueError(f"{label} resolver returned negative IDs: {ids.tolist()}")
+    if np.unique(ids).size != ids.size:
+        raise ValueError(f"{label} resolver returned duplicate IDs: {ids.tolist()}")
+    ids = np.array(ids, copy=True, dtype=np.int32)
+    ids.setflags(write=False)
+    return ids
+
+
+def _as_column_index(ids: np.ndarray) -> slice | np.ndarray:
+    """Use a slice for contiguous columns and advanced indexing otherwise."""
+    if ids.size:
+        start = int(ids[0])
+        if np.array_equal(ids, np.arange(start, start + ids.size, dtype=ids.dtype)):
+            return slice(start, start + ids.size)
+    index = np.asarray(ids, dtype=np.intp).copy()
+    index.setflags(write=False)
+    return index
+
+
+# Matching semantics derived from mujocolab/mjlab v1.6.0 (0fb8a681),
+# src/mjlab/utils/lab_api/string.py. Copyright 2025, The mjlab Developers;
+# adapted for the UniLab NumPy facade under Apache-2.0.
+def _resolve_matching_names(
+    keys: str | Sequence[str], names: Sequence[str], preserve_order: bool
+) -> tuple[list[int], list[str]]:
+    """Pinned mjlab-compatible full-regex matching over cached entity names."""
+    patterns = (keys,) if isinstance(keys, str) else tuple(keys)
+    matches: list[tuple[int, int, str]] = []
+    matched_by: list[str | None] = [None] * len(names)
+    per_pattern: list[list[str]] = [[] for _ in patterns]
+
+    for name_index, candidate in enumerate(names):
+        for pattern_index, pattern in enumerate(patterns):
+            try:
+                matched = re.fullmatch(pattern, candidate) is not None
+            except re.error as exc:
+                raise ValueError(f"Invalid entity selector regex {pattern!r}: {exc}") from exc
+            if not matched:
+                continue
+            if matched_by[name_index] is not None:
+                raise ValueError(
+                    f"Multiple matches for '{candidate}': "
+                    f"'{matched_by[name_index]}' and '{pattern}'!"
+                )
+            matched_by[name_index] = pattern
+            matches.append((pattern_index, name_index, candidate))
+            per_pattern[pattern_index].append(candidate)
+
+    if any(not values for values in per_pattern):
+        rendered = ", ".join(
+            f"{pattern!r}: {values}" for pattern, values in zip(patterns, per_pattern)
+        )
+        raise ValueError(
+            "Not all entity selector regular expressions matched; "
+            f"matches=({rendered}), available={list(names)}"
+        )
+
+    if preserve_order:
+        matches.sort(key=lambda item: item[0])
+    return [item[1] for item in matches], [item[2] for item in matches]
+
+
+_StateReadKey = tuple[str, tuple[int, ...] | None]
+
+
+class _EntityStateReadCache:
+    """Update-phase cache shared by every entity bound to one backend scene."""
+
+    def __init__(self) -> None:
+        self._active = False
+        self._values: dict[_StateReadKey, np.ndarray] = {}
+
+    def get(self, key: _StateReadKey) -> np.ndarray | None:
+        if not self._active:
+            return None
+        return self._values.get(key)
+
+    def put(self, key: _StateReadKey, value: np.ndarray) -> None:
+        if self._active:
+            self._values[key] = value
+
+    def invalidate(self) -> None:
+        self._values.clear()
+
+    @contextmanager
+    def scoped(self) -> Iterator[None]:
+        if self._active:
+            raise RuntimeError("Entity state-read cache phase is already active")
+        self._active = True
+        self._values.clear()
+        try:
+            yield
+        finally:
+            self._values.clear()
+            self._active = False
+
+
+def _state_selector_key(ids: np.ndarray | None) -> tuple[int, ...] | None:
+    """Freeze a cold-path backend selector into a cheap hot-path cache key."""
+    if ids is None:
+        return None
+    return tuple(int(value) for value in ids)
+
+
+class EntityData:
+    """Hot-path NumPy state surface backed by cached backend IDs."""
+
+    def __init__(
+        self,
+        backend: SimBackend,
+        *,
+        root_body_ids: np.ndarray | None,
+        joint_pos_ids: np.ndarray | None,
+        joint_vel_ids: np.ndarray | None,
+        default_root_state: np.ndarray | None,
+        default_root_state_error: str | None,
+        default_joint_pos: np.ndarray | None,
+        default_joint_vel: np.ndarray | None,
+        soft_joint_pos_limits: np.ndarray | None,
+        gravity_vec_w: np.ndarray | None,
+        body_ids: np.ndarray | None,
+        actuator_ids: np.ndarray | None,
+        actuator_ctrl_range: np.ndarray | None,
+        control_buffer: np.ndarray | None,
+        entity_name: str,
+        backend_type: str,
+        state_read_cache: _EntityStateReadCache,
+    ) -> None:
+        self._backend = backend
+        self._entity_name = entity_name
+        self._backend_type = backend_type
+        self._root_body_ids = root_body_ids
+        self._root_body_state_key = _state_selector_key(root_body_ids)
+        self._joint_pos_index = None if joint_pos_ids is None else _as_column_index(joint_pos_ids)
+        self._joint_vel_index = None if joint_vel_ids is None else _as_column_index(joint_vel_ids)
+        self._default_root_state = default_root_state
+        self._default_root_state_error = default_root_state_error
+        self._default_joint_pos = default_joint_pos
+        self._default_joint_vel = default_joint_vel
+        self._soft_joint_pos_limits = soft_joint_pos_limits
+        self._gravity_vec_w = gravity_vec_w
+        self._encoder_bias = (
+            None
+            if default_joint_pos is None
+            else np.zeros(default_joint_pos.shape, dtype=default_joint_pos.dtype)
+        )
+        self._body_ids = body_ids
+        self._body_state_key = _state_selector_key(body_ids)
+        self._actuator_ids = actuator_ids
+        self._actuator_ctrl_range = actuator_ctrl_range
+        self._control_buffer = control_buffer
+        self._state_read_cache = state_read_cache
+
+    def _cached_getter(
+        self,
+        method: str,
+        fn: Any,
+        *args: Any,
+        selector: tuple[int, ...] | None = None,
+    ) -> np.ndarray:
+        key = (method, selector)
+        cached = self._state_read_cache.get(key)
+        if cached is not None:
+            return cached
+        value = fn(*args)
+        self._state_read_cache.put(key, value)
+        return value
+
+    def _require(self, value, capability: str):
+        if value is None:
+            raise NotImplementedError(
+                f"Entity '{self._entity_name}' data capability '{capability}' is unavailable "
+                f"on backend '{self._backend_type}': it was not materialized"
+            )
+        return value
+
+    @property
+    def root_link_pos_w(self) -> np.ndarray:
+        ids = self._require(self._root_body_ids, "root body state")
+        return self._cached_getter(
+            "body_pos_w",
+            self._backend.get_body_pos_w,
+            ids,
+            selector=self._root_body_state_key,
+        )[:, 0]
+
+    @property
+    def root_link_quat_w(self) -> np.ndarray:
+        ids = self._require(self._root_body_ids, "root body state")
+        return self._cached_getter(
+            "body_quat_w",
+            self._backend.get_body_quat_w,
+            ids,
+            selector=self._root_body_state_key,
+        )[:, 0]
+
+    @property
+    def root_link_lin_vel_w(self) -> np.ndarray:
+        ids = self._require(self._root_body_ids, "root body state")
+        return self._cached_getter(
+            "body_lin_vel_w",
+            self._backend.get_body_lin_vel_w,
+            ids,
+            selector=self._root_body_state_key,
+        )[:, 0]
+
+    @property
+    def root_link_ang_vel_w(self) -> np.ndarray:
+        ids = self._require(self._root_body_ids, "root body state")
+        return self._cached_getter(
+            "body_ang_vel_w",
+            self._backend.get_body_ang_vel_w,
+            ids,
+            selector=self._root_body_state_key,
+        )[:, 0]
+
+    @property
+    def root_link_lin_vel_b(self) -> np.ndarray:
+        ids = self._require(self._root_body_ids, "root body state")
+        return self._cached_getter(
+            "body_lin_vel_b",
+            self._backend.get_body_lin_vel_b,
+            ids,
+            selector=self._root_body_state_key,
+        )[:, 0]
+
+    @property
+    def root_link_ang_vel_b(self) -> np.ndarray:
+        ids = self._require(self._root_body_ids, "root body state")
+        return self._cached_getter(
+            "body_ang_vel_b",
+            self._backend.get_body_ang_vel_b,
+            ids,
+            selector=self._root_body_state_key,
+        )[:, 0]
+
+    @property
+    def heading_w(self) -> np.ndarray:
+        """Root yaw in the world frame, derived from the backend quaternion view."""
+        return np_yaw_from_quat(self.root_link_quat_w)
+
+    @property
+    def projected_gravity_b(self) -> np.ndarray:
+        """Unit gravity vector projected into the root link frame."""
+        gravity = self._require(self._gravity_vec_w, "projected gravity")
+        return np_quat_apply_inverse(self.root_link_quat_w, gravity)
+
+    @property
+    def gravity_vec_w(self) -> np.ndarray:
+        """Read-only world-frame unit gravity vector for every environment."""
+        return self._require(self._gravity_vec_w, "world-frame gravity")
+
+    @property
+    def root_link_pose_w(self) -> np.ndarray:
+        return np.concatenate((self.root_link_pos_w, self.root_link_quat_w), axis=-1)
+
+    @property
+    def root_link_vel_w(self) -> np.ndarray:
+        return np.concatenate((self.root_link_lin_vel_w, self.root_link_ang_vel_w), axis=-1)
+
+    @property
+    def default_root_state(self) -> np.ndarray:
+        """Read-only 13-D community root state for every environment."""
+        if self._default_root_state is None:
+            detail = self._default_root_state_error or "root_body_name was not declared"
+            raise NotImplementedError(
+                f"Entity '{self._entity_name}' data capability 'default root state' is "
+                f"unavailable on backend '{self._backend_type}': {detail}"
+            )
+        return self._default_root_state
+
+    @property
+    def joint_pos(self) -> np.ndarray:
+        index = self._require(self._joint_pos_index, "joint position")
+        return self._cached_getter("dof_pos", self._backend.get_dof_pos)[:, index]
+
+    @property
+    def joint_vel(self) -> np.ndarray:
+        index = self._require(self._joint_vel_index, "joint velocity")
+        return self._cached_getter("dof_vel", self._backend.get_dof_vel)[:, index]
+
+    @property
+    def joint_pos_biased(self) -> np.ndarray:
+        """Joint positions with the manager-owned encoder bias applied."""
+        return self.joint_pos + self.encoder_bias
+
+    @property
+    def default_joint_pos(self) -> np.ndarray:
+        """Read-only per-environment default joint positions."""
+        return self._require(self._default_joint_pos, "default joint position")
+
+    @property
+    def default_joint_vel(self) -> np.ndarray:
+        """Read-only zero default velocities from the UniLab reset contract."""
+        return self._require(self._default_joint_vel, "default joint velocity")
+
+    @property
+    def soft_joint_pos_limits(self) -> np.ndarray:
+        """Read-only joint position limits in the declared entity joint order."""
+        return self._require(self._soft_joint_pos_limits, "joint position limits")
+
+    @property
+    def encoder_bias(self) -> np.ndarray:
+        """Mutable per-environment joint encoder bias used by position actions."""
+        return self._require(self._encoder_bias, "joint encoder bias")
+
+    @property
+    def body_link_pos_w(self) -> np.ndarray:
+        ids = self._require(self._body_ids, "body state")
+        return self._cached_getter(
+            "body_pos_w",
+            self._backend.get_body_pos_w,
+            ids,
+            selector=self._body_state_key,
+        )
+
+    @property
+    def body_link_quat_w(self) -> np.ndarray:
+        ids = self._require(self._body_ids, "body state")
+        return self._cached_getter(
+            "body_quat_w",
+            self._backend.get_body_quat_w,
+            ids,
+            selector=self._body_state_key,
+        )
+
+    @property
+    def body_link_lin_vel_w(self) -> np.ndarray:
+        ids = self._require(self._body_ids, "body state")
+        return self._cached_getter(
+            "body_lin_vel_w",
+            self._backend.get_body_lin_vel_w,
+            ids,
+            selector=self._body_state_key,
+        )
+
+    @property
+    def body_link_ang_vel_w(self) -> np.ndarray:
+        ids = self._require(self._body_ids, "body state")
+        return self._cached_getter(
+            "body_ang_vel_w",
+            self._backend.get_body_ang_vel_w,
+            ids,
+            selector=self._body_state_key,
+        )
+
+    @property
+    def body_link_pose_w(self) -> np.ndarray:
+        return np.concatenate((self.body_link_pos_w, self.body_link_quat_w), axis=-1)
+
+    def body_link_pos_w_rows(self, env_ids: np.ndarray) -> np.ndarray:
+        """Row-scoped variant of body_link_pos_w for partial-reset rebuilds."""
+        ids = self._require(self._body_ids, "body state")
+        return self._backend.get_body_pose_w_rows(env_ids, ids)[0]
+
+    def body_link_quat_w_rows(self, env_ids: np.ndarray) -> np.ndarray:
+        """Row-scoped variant of body_link_quat_w for partial-reset rebuilds."""
+        ids = self._require(self._body_ids, "body state")
+        return self._backend.get_body_pose_w_rows(env_ids, ids)[1]
+
+    def body_link_lin_vel_w_rows(self, env_ids: np.ndarray) -> np.ndarray:
+        """Row-scoped variant of body_link_lin_vel_w for partial-reset rebuilds."""
+        ids = self._require(self._body_ids, "body state")
+        return self._backend.get_body_lin_vel_w_rows(env_ids, ids)
+
+    def body_link_ang_vel_w_rows(self, env_ids: np.ndarray) -> np.ndarray:
+        """Row-scoped variant of body_link_ang_vel_w for partial-reset rebuilds."""
+        ids = self._require(self._body_ids, "body state")
+        return self._backend.get_body_ang_vel_w_rows(env_ids, ids)
+
+    @property
+    def body_link_vel_w(self) -> np.ndarray:
+        return np.concatenate((self.body_link_lin_vel_w, self.body_link_ang_vel_w), axis=-1)
+
+    @property
+    def actuator_ctrl_range(self) -> np.ndarray:
+        return self._require(self._actuator_ctrl_range, "actuator control range")
+
+    def write_ctrl(
+        self,
+        values: np.ndarray,
+        env_ids: np.ndarray | slice | None = None,
+        *,
+        actuator_ids: np.ndarray | Sequence[int] | slice | None = None,
+    ) -> None:
+        """Write entity-local actuator controls into the env-owned control buffer.
+
+        This is an in-memory scene write, analogous to the pinned manager runtime's
+        entity target buffers.  Physics remains owned by ``NpEnv``/``SimBackend``;
+        this method never steps or calls a backend-private API.
+        """
+        entity_actuator_ids = self._require(self._actuator_ids, "actuator control write")
+        control = self._require(self._control_buffer, "actuator control write")
+        if not isinstance(values, np.ndarray):
+            raise TypeError(
+                f"Entity '{self._entity_name}' write_ctrl expected np.ndarray, "
+                f"received {type(values).__name__}"
+            )
+        row_index: np.ndarray | slice
+        if env_ids is None:
+            row_index = slice(None)
+            row_count = control.shape[0]
+        elif isinstance(env_ids, slice):
+            row_index = env_ids
+            row_count = len(range(*env_ids.indices(control.shape[0])))
+        else:
+            raw_ids = np.asarray(env_ids)
+            if (
+                raw_ids.ndim != 1
+                or not np.issubdtype(raw_ids.dtype, np.integer)
+                or np.issubdtype(raw_ids.dtype, np.bool_)
+            ):
+                raise TypeError(
+                    f"Entity '{self._entity_name}' write_ctrl env_ids must be a 1-D "
+                    f"integer array or slice, got shape={raw_ids.shape}, dtype={raw_ids.dtype}"
+                )
+            row_index = np.asarray(raw_ids, dtype=np.intp)
+            if np.any(row_index < 0) or np.any(row_index >= control.shape[0]):
+                raise IndexError(
+                    f"Entity '{self._entity_name}' write_ctrl env_ids out of range for "
+                    f"{control.shape[0]} environments: {row_index.tolist()}"
+                )
+            if np.unique(row_index).size != row_index.size:
+                raise ValueError(
+                    f"Entity '{self._entity_name}' write_ctrl env_ids contain duplicates: "
+                    f"{row_index.tolist()}"
+                )
+            row_count = len(row_index)
+
+        if actuator_ids is None:
+            selected_actuator_ids = entity_actuator_ids
+        elif isinstance(actuator_ids, slice):
+            selected_actuator_ids = entity_actuator_ids[actuator_ids]
+        else:
+            raw_actuator_ids = np.asarray(actuator_ids)
+            if (
+                raw_actuator_ids.ndim != 1
+                or not np.issubdtype(raw_actuator_ids.dtype, np.integer)
+                or np.issubdtype(raw_actuator_ids.dtype, np.bool_)
+            ):
+                raise TypeError(
+                    f"Entity '{self._entity_name}' write_ctrl actuator_ids must be a 1-D "
+                    "integer array or slice"
+                )
+            local_actuator_ids = np.asarray(raw_actuator_ids, dtype=np.intp)
+            if np.any(local_actuator_ids < 0) or np.any(
+                local_actuator_ids >= len(entity_actuator_ids)
+            ):
+                raise IndexError(
+                    f"Entity '{self._entity_name}' write_ctrl actuator_ids out of range for "
+                    f"{len(entity_actuator_ids)} entity actuators: {local_actuator_ids.tolist()}"
+                )
+            if np.unique(local_actuator_ids).size != local_actuator_ids.size:
+                raise ValueError(
+                    f"Entity '{self._entity_name}' write_ctrl actuator_ids contain duplicates: "
+                    f"{local_actuator_ids.tolist()}"
+                )
+            selected_actuator_ids = entity_actuator_ids[local_actuator_ids]
+
+        actuator_index = _as_column_index(np.asarray(selected_actuator_ids, dtype=np.int32))
+        actuator_count = len(selected_actuator_ids)
+        expected = (row_count, actuator_count)
+        if values.shape != expected:
+            raise ValueError(
+                f"Entity '{self._entity_name}' write_ctrl expected shape {expected}, "
+                f"received {values.shape}"
+            )
+        if not np.isfinite(values).all():
+            raise ValueError(f"Entity '{self._entity_name}' write_ctrl received NaN or Inf")
+
+        if isinstance(row_index, slice) or isinstance(actuator_index, slice):
+            control[row_index, actuator_index] = values
+        else:
+            control[row_index[:, None], actuator_index[None, :]] = values
+
+
+class Entity:
+    """Logical entity with cached local-to-backend mappings."""
+
+    def __init__(
+        self,
+        name: str,
+        cfg: EntityCfg,
+        backend: SimBackend,
+        control_buffer: np.ndarray | None = None,
+        reset_state: ResetStateTransaction | None = None,
+        *,
+        default_qpos: np.ndarray | None = None,
+        state_read_cache: _EntityStateReadCache | None = None,
+    ) -> None:
+        if not name:
+            raise ValueError("Entity name must be a non-empty string")
+        self.name = name
+        self._backend_type = backend.backend_type
+        self._backend = backend
+        self._reset_state = reset_state
+        self._reset_root_layout: BackendRootStateLayout | None = None
+        self._reset_root_layout_error: str | None = None
+        self._reset_joint_qpos_ids: np.ndarray | None = None
+        self._reset_joint_qvel_ids: np.ndarray | None = None
+        self._joint_model_dof_ids: np.ndarray | None = None
+        self._motion_body_ids: np.ndarray | None = None
+
+        self._joint_names = _normalize_names(name, "joint", cfg.joint_names)
+        self._body_names = _normalize_names(name, "body", cfg.body_names)
+        self._geom_names = _normalize_names(name, "geom", cfg.geom_names)
+        self._site_names = _normalize_names(name, "site", cfg.site_names)
+        self._actuator_names = _normalize_names(name, "actuator", cfg.actuator_names)
+
+        root_body_ids = None
+        if cfg.root_body_name is not None:
+            if not isinstance(cfg.root_body_name, str) or not cfg.root_body_name:
+                raise TypeError(f"Entity '{self.name}' root_body_name must be a non-empty string")
+            root_body_ids = self._resolve_ids(
+                "root body",
+                (cfg.root_body_name,),
+                backend.get_body_ids,
+            )
+        self._root_body_ids = root_body_ids
+
+        joint_pos_ids = joint_vel_ids = None
+        if self._joint_names is not None:
+            joint_pos_ids = self._resolve_ids(
+                "joint position index",
+                self._joint_names,
+                backend.get_joint_dof_pos_indices,
+            )
+            joint_vel_ids = self._resolve_ids(
+                "joint velocity index",
+                self._joint_names,
+                backend.get_joint_dof_vel_indices,
+            )
+
+        body_ids = None
+        if self._body_names is not None:
+            body_ids = self._resolve_ids("body", self._body_names, backend.get_body_ids)
+        self._body_ids = body_ids
+
+        self._geom_ids = None
+        if self._geom_names is not None:
+            self._geom_ids = self._resolve_enumerated_ids(
+                "geom", self._geom_names, backend.get_geom_names
+            )
+
+        self._site_ids = None
+        if self._site_names is not None:
+            self._site_ids = self._resolve_ids("site", self._site_names, backend.get_site_ids)
+
+        actuator_ids = None
+        if self._actuator_names is not None:
+            actuator_ids = self._resolve_enumerated_ids(
+                "actuator", self._actuator_names, backend.get_actuator_names
+            )
+        self._actuator_ids = actuator_ids
+
+        self._validate_joint_state(backend, joint_pos_ids, joint_vel_ids)
+        self._validate_body_state(backend, root_body_ids, body_ids)
+        (
+            self._reset_root_layout,
+            default_root_state,
+            self._reset_root_layout_error,
+        ) = self._materialize_root_state(backend, cfg.root_body_name, default_qpos)
+        default_joint_pos = self._materialize_default_joint_pos(
+            backend,
+            joint_pos_ids,
+            default_qpos,
+        )
+        default_joint_vel = self._materialize_default_joint_vel(backend, joint_vel_ids)
+        soft_joint_pos_limits = self._materialize_soft_joint_pos_limits(backend, joint_pos_ids)
+        gravity_vec_w = self._materialize_gravity_vector(backend, root_body_ids)
+        actuator_ctrl_range = self._materialize_actuator_ctrl_range(backend, actuator_ids)
+        (
+            self._actuator_target_joint_names,
+            self._joint_to_actuator_local,
+        ) = self._materialize_joint_actuator_mapping(backend, actuator_ids)
+        if control_buffer is not None:
+            expected_control_shape = (backend.num_envs, backend.num_actuators)
+            if control_buffer.shape != expected_control_shape:
+                raise ValueError(
+                    f"Entity '{self.name}' control buffer has shape {control_buffer.shape}; "
+                    f"expected {expected_control_shape} on backend '{self._backend_type}'"
+                )
+            if not np.issubdtype(control_buffer.dtype, np.floating):
+                raise TypeError(
+                    f"Entity '{self.name}' control buffer must have floating dtype, "
+                    f"got {control_buffer.dtype}"
+                )
+
+        self.data = EntityData(
+            backend,
+            root_body_ids=root_body_ids,
+            joint_pos_ids=joint_pos_ids,
+            joint_vel_ids=joint_vel_ids,
+            default_root_state=default_root_state,
+            default_root_state_error=self._reset_root_layout_error,
+            default_joint_pos=default_joint_pos,
+            default_joint_vel=default_joint_vel,
+            soft_joint_pos_limits=soft_joint_pos_limits,
+            gravity_vec_w=gravity_vec_w,
+            body_ids=body_ids,
+            actuator_ids=actuator_ids,
+            actuator_ctrl_range=actuator_ctrl_range,
+            control_buffer=control_buffer,
+            entity_name=self.name,
+            backend_type=self._backend_type,
+            state_read_cache=(
+                state_read_cache if state_read_cache is not None else _EntityStateReadCache()
+            ),
+        )
+
+    @property
+    def motion_body_ids(self) -> np.ndarray:
+        """Motion-dataset body columns for the declared entity body order."""
+        if self._body_names is None:
+            raise self._capability_error(
+                "motion body IDs",
+                "body_names were not declared in EntityCfg",
+            )
+        if self._motion_body_ids is None:
+            self._motion_body_ids = self._resolve_ids(
+                "motion body",
+                self._body_names,
+                self._backend.get_motion_body_ids,
+            )
+        return self._motion_body_ids
+
+    def _capability_error(self, capability: str, detail: str) -> NotImplementedError:
+        return NotImplementedError(
+            f"Entity '{self.name}' capability '{capability}' is unavailable on "
+            f"backend '{self._backend_type}': {detail}"
+        )
+
+    def _resolve_ids(self, capability: str, names: tuple[str, ...], resolver) -> np.ndarray:
+        try:
+            values = resolver(names)
+        except NotImplementedError as exc:
+            raise self._capability_error(capability, str(exc)) from exc
+        except (KeyError, ValueError) as exc:
+            raise ValueError(
+                f"Entity '{self.name}' could not resolve {capability} names {list(names)} "
+                f"on backend '{self._backend_type}': {exc}"
+            ) from exc
+        return _readonly_ids(
+            values,
+            expected=len(names),
+            label=f"Entity '{self.name}' {capability}",
+        )
+
+    def _resolve_enumerated_ids(
+        self, capability: str, names: tuple[str, ...], resolver
+    ) -> np.ndarray:
+        try:
+            all_names = tuple(resolver())
+        except NotImplementedError as exc:
+            raise self._capability_error(capability, str(exc)) from exc
+        invalid = [value for value in all_names if not isinstance(value, str)]
+        if invalid:
+            raise TypeError(
+                f"Entity '{self.name}' {capability} name resolver on backend "
+                f"'{self._backend_type}' returned non-string names: {invalid}"
+            )
+        nonempty_names = [value for value in all_names if value]
+        if len(set(nonempty_names)) != len(nonempty_names):
+            raise ValueError(
+                f"Entity '{self.name}' {capability} name resolver on backend "
+                f"'{self._backend_type}' returned duplicate names"
+            )
+        ids_by_name = {value: index for index, value in enumerate(all_names) if value}
+        missing = [value for value in names if value not in ids_by_name]
+        if missing:
+            raise ValueError(
+                f"Entity '{self.name}' could not resolve {capability} names {missing} on "
+                f"backend '{self._backend_type}'; available={list(all_names)}"
+            )
+        return _readonly_ids(
+            [ids_by_name[value] for value in names],
+            expected=len(names),
+            label=f"Entity '{self.name}' {capability}",
+        )
+
+    def _read_state(self, capability: str, getter, *args) -> np.ndarray:
+        try:
+            return np.asarray(getter(*args))
+        except (AttributeError, NotImplementedError) as exc:
+            raise self._capability_error(capability, str(exc)) from exc
+
+    def _validate_joint_state(
+        self,
+        backend: SimBackend,
+        pos_ids: np.ndarray | None,
+        vel_ids: np.ndarray | None,
+    ) -> None:
+        for capability, getter, ids in (
+            ("joint position state", backend.get_dof_pos, pos_ids),
+            ("joint velocity state", backend.get_dof_vel, vel_ids),
+        ):
+            if ids is None:
+                continue
+            value = self._read_state(capability, getter)
+            if value.ndim != 2 or value.shape[0] != backend.num_envs:
+                raise ValueError(
+                    f"Entity '{self.name}' capability '{capability}' on backend "
+                    f"'{self._backend_type}' returned shape {value.shape}; expected "
+                    f"({backend.num_envs}, num_dof)"
+                )
+            if ids.size and int(np.max(ids)) >= value.shape[1]:
+                raise ValueError(
+                    f"Entity '{self.name}' capability '{capability}' resolved index "
+                    f"{int(np.max(ids))}, but backend '{self._backend_type}' returned "
+                    f"only {value.shape[1]} columns"
+                )
+
+    def _validate_body_state(
+        self,
+        backend: SimBackend,
+        root_body_ids: np.ndarray | None,
+        body_ids: np.ndarray | None,
+    ) -> None:
+        arrays = [values for values in (root_body_ids, body_ids) if values is not None]
+        if not arrays:
+            return
+        validation_ids = np.unique(np.concatenate(arrays)).astype(np.int32, copy=False)
+        for capability, getter, width in (
+            ("body position state", backend.get_body_pos_w, 3),
+            ("body quaternion state", backend.get_body_quat_w, 4),
+            ("body linear velocity state", backend.get_body_lin_vel_w, 3),
+            ("body angular velocity state", backend.get_body_ang_vel_w, 3),
+        ):
+            value = self._read_state(capability, getter, validation_ids)
+            expected = (backend.num_envs, len(validation_ids), width)
+            if value.shape != expected:
+                raise ValueError(
+                    f"Entity '{self.name}' capability '{capability}' on backend "
+                    f"'{self._backend_type}' returned shape {value.shape}; expected {expected}"
+                )
+            if not np.isfinite(value).all():
+                raise ValueError(
+                    f"Entity '{self.name}' capability '{capability}' on backend "
+                    f"'{self._backend_type}' returned NaN or Inf"
+                )
+        if root_body_ids is None:
+            return
+        for capability, getter in (
+            ("body-frame linear velocity state", backend.get_body_lin_vel_b),
+            ("body-frame angular velocity state", backend.get_body_ang_vel_b),
+        ):
+            value = self._read_state(capability, getter, root_body_ids)
+            expected = (backend.num_envs, len(root_body_ids), 3)
+            if value.shape != expected:
+                raise ValueError(
+                    f"Entity '{self.name}' capability '{capability}' on backend "
+                    f"'{self._backend_type}' returned shape {value.shape}; expected {expected}"
+                )
+            if not np.isfinite(value).all():
+                raise ValueError(
+                    f"Entity '{self.name}' capability '{capability}' on backend "
+                    f"'{self._backend_type}' returned NaN or Inf"
+                )
+
+    def _materialize_actuator_ctrl_range(
+        self, backend: SimBackend, actuator_ids: np.ndarray | None
+    ) -> np.ndarray | None:
+        if actuator_ids is None:
+            return None
+        ranges = self._read_state("actuator control range", backend.get_actuator_ctrl_range)
+        expected = (backend.num_actuators, 2)
+        if ranges.shape != expected:
+            raise ValueError(
+                f"Entity '{self.name}' capability 'actuator control range' on backend "
+                f"'{self._backend_type}' returned shape {ranges.shape}; expected {expected}"
+            )
+        selected = np.array(ranges[_as_column_index(actuator_ids)], copy=True)
+        selected.setflags(write=False)
+        return selected
+
+    def _materialize_default_joint_pos(
+        self,
+        backend: SimBackend,
+        joint_pos_ids: np.ndarray | None,
+        default_qpos: np.ndarray | None,
+    ) -> np.ndarray | None:
+        if joint_pos_ids is None:
+            return None
+        current = self._read_state("joint position state", backend.get_dof_pos)
+        if default_qpos is None:
+            defaults = self._read_state("default joint position", backend.get_default_dof_pos)
+            if defaults.shape != current.shape[1:]:
+                raise ValueError(
+                    f"Entity '{self.name}' capability 'default joint position' on backend "
+                    f"'{self._backend_type}' returned shape {defaults.shape}; expected "
+                    f"{current.shape[1:]} to match get_dof_pos()"
+                )
+            selected = np.asarray(defaults[_as_column_index(joint_pos_ids)])
+        else:
+            assert self._joint_names is not None
+            defaults = self._validate_root_default_vector(default_qpos, "selected default qpos")
+            try:
+                state_qpos_ids = backend.get_joint_state_qpos_indices(self._joint_names)
+            except (AttributeError, NotImplementedError) as exc:
+                raise self._capability_error("default joint-state layout", str(exc)) from exc
+            resolved_qpos_ids = _readonly_ids(
+                state_qpos_ids,
+                expected=len(self._joint_names),
+                label=f"Entity '{self.name}' default qpos",
+            )
+            if resolved_qpos_ids.size and int(np.max(resolved_qpos_ids)) >= defaults.size:
+                raise ValueError(
+                    f"Entity '{self.name}' default qpos layout exceeds backend "
+                    f"'{self._backend_type}' width {defaults.size}: {resolved_qpos_ids.tolist()}"
+                )
+            selected = np.asarray(defaults[_as_column_index(resolved_qpos_ids)])
+            self._reset_joint_qpos_ids = resolved_qpos_ids
+        materialized = np.broadcast_to(
+            selected,
+            (backend.num_envs, len(joint_pos_ids)),
+        ).astype(current.dtype, copy=True)
+        materialized.setflags(write=False)
+        return materialized
+
+    def _materialize_soft_joint_pos_limits(
+        self,
+        backend: SimBackend,
+        joint_pos_ids: np.ndarray | None,
+    ) -> np.ndarray | None:
+        if joint_pos_ids is None:
+            return None
+        try:
+            raw_ranges = backend.get_joint_range()
+        except (AttributeError, NotImplementedError) as exc:
+            raise self._capability_error("joint position limits", str(exc)) from exc
+        if raw_ranges is None:
+            return None
+        ranges = np.asarray(raw_ranges)
+        if ranges.ndim != 2 or ranges.shape[1] != 2:
+            raise ValueError(
+                f"Entity '{self.name}' capability 'joint position limits' on backend "
+                f"'{self._backend_type}' returned shape {ranges.shape}; expected (num_dof, 2)"
+            )
+        if joint_pos_ids.size and int(np.max(joint_pos_ids)) >= ranges.shape[0]:
+            raise ValueError(
+                f"Entity '{self.name}' capability 'joint position limits' resolved index "
+                f"{int(np.max(joint_pos_ids))}, but backend '{self._backend_type}' returned "
+                f"only {ranges.shape[0]} rows"
+            )
+        selected = np.array(ranges[_as_column_index(joint_pos_ids)], copy=True)
+        selected.setflags(write=False)
+        return selected
+
+    def _materialize_root_state(
+        self,
+        backend: SimBackend,
+        root_body_name: str | None,
+        default_qpos: np.ndarray | None,
+    ) -> tuple[BackendRootStateLayout | None, np.ndarray | None, str | None]:
+        if root_body_name is None:
+            return None, None, "root_body_name was not declared in EntityCfg"
+        try:
+            layout = backend.get_root_state_layout(root_body_name)
+        except (AttributeError, NotImplementedError) as exc:
+            return None, None, str(exc)
+        if not isinstance(layout, BackendRootStateLayout):
+            raise TypeError(
+                f"Entity '{self.name}' capability 'root-state layout' on backend "
+                f"'{self._backend_type}' must return BackendRootStateLayout, got "
+                f"{type(layout).__name__}"
+            )
+        try:
+            qpos = backend.get_default_qpos() if default_qpos is None else default_qpos
+            qvel = backend.get_init_qvel()
+        except (AttributeError, NotImplementedError) as exc:
+            return None, None, str(exc)
+        qpos_default = self._validate_root_default_vector(qpos, "default qpos")
+        qvel_default = self._validate_root_default_vector(qvel, "initial qvel")
+        qpos_indices = np.asarray(layout.qpos_indices, dtype=np.intp)
+        qvel_indices = np.asarray(layout.qvel_indices, dtype=np.intp)
+        if np.any(qpos_indices >= qpos_default.size):
+            raise ValueError(
+                f"Entity '{self.name}' root qpos layout exceeds backend "
+                f"'{self._backend_type}' width {qpos_default.size}: {qpos_indices.tolist()}"
+            )
+        if np.any(qvel_indices >= qvel_default.size):
+            raise ValueError(
+                f"Entity '{self.name}' root qvel layout exceeds backend "
+                f"'{self._backend_type}' width {qvel_default.size}: {qvel_indices.tolist()}"
+            )
+
+        pose = np.asarray(qpos_default[qpos_indices])
+        quaternion = pose[3:7]
+        norm = float(np.linalg.norm(quaternion))
+        if not np.isclose(norm, 1.0, rtol=1e-5, atol=1e-6):
+            raise ValueError(
+                f"Entity '{self.name}' default root quaternion on backend "
+                f"'{self._backend_type}' must be unit length; norm={norm}"
+            )
+        generalized_velocity = np.asarray(qvel_default[qvel_indices])
+        velocity_w = np.array(generalized_velocity, copy=True)
+        velocity_w[3:6] = np_quat_apply(quaternion, generalized_velocity[3:6])
+        root_state = np.concatenate((pose, velocity_w))
+        materialized = np.broadcast_to(root_state, (backend.num_envs, 13)).copy()
+        materialized.setflags(write=False)
+        return layout, materialized, None
+
+    def _validate_root_default_vector(self, value: np.ndarray, capability: str) -> np.ndarray:
+        if not isinstance(value, np.ndarray):
+            raise TypeError(
+                f"Entity '{self.name}' capability '{capability}' on backend "
+                f"'{self._backend_type}' must return np.ndarray, got {type(value).__name__}"
+            )
+        if value.ndim != 1 or not np.issubdtype(value.dtype, np.floating):
+            raise TypeError(
+                f"Entity '{self.name}' capability '{capability}' on backend "
+                f"'{self._backend_type}' must be a 1-D floating array; got "
+                f"shape={value.shape}, dtype={value.dtype}"
+            )
+        if not np.isfinite(value).all():
+            raise ValueError(
+                f"Entity '{self.name}' capability '{capability}' on backend "
+                f"'{self._backend_type}' returned NaN or Inf"
+            )
+        return value
+
+    def _materialize_default_joint_vel(
+        self, backend: SimBackend, joint_vel_ids: np.ndarray | None
+    ) -> np.ndarray | None:
+        if joint_vel_ids is None:
+            return None
+        current = self._read_state("joint velocity state", backend.get_dof_vel)
+        materialized = np.zeros(
+            (backend.num_envs, len(joint_vel_ids)),
+            dtype=current.dtype,
+        )
+        materialized.setflags(write=False)
+        return materialized
+
+    def _materialize_gravity_vector(
+        self, backend: SimBackend, root_body_ids: np.ndarray | None
+    ) -> np.ndarray | None:
+        if root_body_ids is None:
+            return None
+        quat = self._read_state(
+            "root body quaternion state", backend.get_body_quat_w, root_body_ids
+        )
+        gravity = np.zeros((backend.num_envs, 3), dtype=quat.dtype)
+        gravity[:, 2] = -1.0
+        gravity.setflags(write=False)
+        return gravity
+
+    def _materialize_joint_actuator_mapping(
+        self, backend: SimBackend, actuator_ids: np.ndarray | None
+    ) -> tuple[tuple[str, ...] | None, np.ndarray | None]:
+        if actuator_ids is None or self._joint_names is None:
+            return None, None
+        try:
+            all_target_names = tuple(backend.get_actuator_joint_names())
+        except NotImplementedError as exc:
+            raise self._capability_error("actuator target joint", str(exc)) from exc
+        if len(all_target_names) != backend.num_actuators:
+            raise ValueError(
+                f"Entity '{self.name}' capability 'actuator target joint' on backend "
+                f"'{self._backend_type}' returned {len(all_target_names)} names for "
+                f"{backend.num_actuators} actuators"
+            )
+        target_names = tuple(all_target_names[int(index)] for index in actuator_ids)
+        if any(not isinstance(name, str) or not name for name in target_names):
+            raise ValueError(
+                f"Entity '{self.name}' actuator target joint names must be non-empty strings; "
+                f"got {target_names}"
+            )
+        if len(set(target_names)) != len(target_names):
+            raise ValueError(
+                f"Entity '{self.name}' actuator target joints must be unique for position "
+                f"control; got {target_names}"
+            )
+        joint_index_by_name = {name: index for index, name in enumerate(self._joint_names)}
+        missing = [name for name in target_names if name not in joint_index_by_name]
+        if missing:
+            raise ValueError(
+                f"Entity '{self.name}' actuators target joints outside its declared joint "
+                f"partition on backend '{self._backend_type}': {missing}"
+            )
+        joint_to_actuator = np.full(len(self._joint_names), -1, dtype=np.int32)
+        for actuator_local_id, joint_name in enumerate(target_names):
+            joint_to_actuator[joint_index_by_name[joint_name]] = actuator_local_id
+        joint_to_actuator.setflags(write=False)
+        return target_names, joint_to_actuator
+
+    def _require_names(self, kind: str, names: tuple[str, ...] | None) -> tuple[str, ...]:
+        if names is None:
+            raise self._capability_error(kind, "the namespace was not declared in EntityCfg")
+        return names
+
+    def _unsupported_names(self, kind: str) -> NoReturn:
+        raise self._capability_error(kind, "SimBackend does not declare this namespace")
+
+    @property
+    def joint_names(self) -> tuple[str, ...]:
+        return self._require_names("joint", self._joint_names)
+
+    @property
+    def body_names(self) -> tuple[str, ...]:
+        return self._require_names("body", self._body_names)
+
+    @property
+    def geom_names(self) -> tuple[str, ...]:
+        return self._require_names("geom", self._geom_names)
+
+    @property
+    def site_names(self) -> tuple[str, ...]:
+        return self._require_names("site", self._site_names)
+
+    @property
+    def actuator_names(self) -> tuple[str, ...]:
+        return self._require_names("actuator", self._actuator_names)
+
+    @property
+    def tendon_names(self) -> tuple[str, ...]:
+        return self._unsupported_names("tendon")
+
+    @property
+    def camera_names(self) -> tuple[str, ...]:
+        return self._unsupported_names("camera")
+
+    @property
+    def light_names(self) -> tuple[str, ...]:
+        return self._unsupported_names("light")
+
+    @property
+    def material_names(self) -> tuple[str, ...]:
+        return self._unsupported_names("material")
+
+    @property
+    def texture_names(self) -> tuple[str, ...]:
+        return self._unsupported_names("texture")
+
+    @property
+    def pair_names(self) -> tuple[str, ...]:
+        return self._unsupported_names("pair")
+
+    @property
+    def num_joints(self) -> int:
+        return len(self.joint_names)
+
+    @property
+    def num_bodies(self) -> int:
+        return len(self.body_names)
+
+    @property
+    def num_geoms(self) -> int:
+        return len(self.geom_names)
+
+    @property
+    def num_sites(self) -> int:
+        return len(self.site_names)
+
+    @property
+    def num_actuators(self) -> int:
+        return len(self.actuator_names)
+
+    @property
+    def num_tendons(self) -> int:
+        return len(self.tendon_names)
+
+    @property
+    def num_cameras(self) -> int:
+        return len(self.camera_names)
+
+    @property
+    def num_lights(self) -> int:
+        return len(self.light_names)
+
+    @property
+    def num_materials(self) -> int:
+        return len(self.material_names)
+
+    @property
+    def num_textures(self) -> int:
+        return len(self.texture_names)
+
+    @property
+    def num_pairs(self) -> int:
+        return len(self.pair_names)
+
+    def _find(
+        self,
+        kind: str,
+        names: tuple[str, ...] | None,
+        keys: str | Sequence[str],
+        preserve_order: bool,
+    ) -> tuple[list[int], list[str]]:
+        return _resolve_matching_names(keys, self._require_names(kind, names), preserve_order)
+
+    def find_joints(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]:
+        return self._find("joint", self._joint_names, keys, preserve_order)
+
+    def find_joints_by_actuator_names(
+        self, keys: str | Sequence[str]
+    ) -> tuple[list[int], list[str]]:
+        """Resolve actuator-target joint patterns in natural entity joint order."""
+        target_names = self._actuator_target_joint_names
+        if target_names is None:
+            raise self._capability_error(
+                "actuator target joint",
+                "joint_names and actuator_names must both be declared in EntityCfg",
+            )
+        target_set = set(target_names)
+        natural_ids = [index for index, name in enumerate(self.joint_names) if name in target_set]
+        natural_names = [self.joint_names[index] for index in natural_ids]
+        matched_ids, matched_names = _resolve_matching_names(keys, natural_names, False)
+        return [natural_ids[index] for index in matched_ids], matched_names
+
+    def set_joint_position_target(
+        self,
+        target: np.ndarray,
+        joint_ids: np.ndarray | Sequence[int] | slice | None = None,
+        env_ids: np.ndarray | slice | None = None,
+    ) -> None:
+        """Map entity-local joint targets to the env-owned actuator control buffer."""
+        joint_to_actuator = self._joint_to_actuator_local
+        if joint_to_actuator is None:
+            raise self._capability_error(
+                "joint position target",
+                "joint-to-actuator metadata was not materialized",
+            )
+        local_joint_ids = self._normalize_local_joint_ids(
+            joint_ids,
+            capability="joint position target",
+        )
+        actuator_ids = joint_to_actuator[local_joint_ids]
+        if np.any(actuator_ids < 0):
+            passive_names = [
+                self.joint_names[int(index)] for index in local_joint_ids[actuator_ids < 0]
+            ]
+            raise NotImplementedError(
+                f"Entity '{self.name}' capability 'joint position target' is unavailable "
+                f"for passive joints on backend '{self._backend_type}': {passive_names}"
+            )
+        self.data.write_ctrl(target, env_ids, actuator_ids=actuator_ids)
+
+    def _set_joint_control_target(
+        self,
+        target: np.ndarray,
+        joint_ids: np.ndarray | Sequence[int] | slice | None,
+        env_ids: np.ndarray | slice | None,
+        *,
+        capability: str,
+    ) -> None:
+        """Map a joint target to the entity-owned actuator control buffer.
+
+        The action term selects the semantic transmission while this helper
+        owns only the stable joint-to-actuator mapping and validation. The
+        backend remains responsible for interpreting each actuator's control
+        signal, and no backend-private model access is needed in the manager
+        hot path.
+        """
+        joint_to_actuator = self._joint_to_actuator_local
+        if joint_to_actuator is None:
+            raise self._capability_error(
+                capability, "joint-to-actuator metadata was not materialized"
+            )
+        local_joint_ids = self._normalize_local_joint_ids(joint_ids, capability=capability)
+        actuator_ids = joint_to_actuator[local_joint_ids]
+        if np.any(actuator_ids < 0):
+            passive_names = [
+                self.joint_names[int(index)] for index in local_joint_ids[actuator_ids < 0]
+            ]
+            raise NotImplementedError(
+                f"Entity '{self.name}' capability '{capability}' is unavailable "
+                f"for passive joints on backend '{self._backend_type}': {passive_names}"
+            )
+        self.data.write_ctrl(target, env_ids, actuator_ids=actuator_ids)
+
+    def set_joint_velocity_target(
+        self,
+        target: np.ndarray,
+        joint_ids: np.ndarray | Sequence[int] | slice | None = None,
+        env_ids: np.ndarray | slice | None = None,
+    ) -> None:
+        """Map joint velocity targets to the entity actuator control buffer."""
+        self._set_joint_control_target(
+            target,
+            joint_ids,
+            env_ids,
+            capability="joint velocity target",
+        )
+
+    def set_joint_effort_target(
+        self,
+        target: np.ndarray,
+        joint_ids: np.ndarray | Sequence[int] | slice | None = None,
+        env_ids: np.ndarray | slice | None = None,
+    ) -> None:
+        """Map joint effort (torque) targets to the actuator control buffer."""
+        self._set_joint_control_target(
+            target,
+            joint_ids,
+            env_ids,
+            capability="joint effort target",
+        )
+
+    def bind_body_state_copy(
+        self,
+        body_ids: np.ndarray | Sequence[int] | slice | None = None,
+    ) -> BodyStateCopyFn:
+        """Bind entity-local body columns to the backend copy contract on the cold path."""
+        if self._body_ids is None:
+            raise self._capability_error(
+                "body-state copy",
+                "body_names were not declared in EntityCfg",
+            )
+        local_ids = self._normalize_local_body_ids(body_ids, capability="body-state copy")
+        if local_ids.size == 0:
+            raise ValueError(f"Entity '{self.name}' body-state copy selected no bodies")
+        backend_ids = np.array(self._body_ids[local_ids], copy=True, dtype=np.int32)
+        backend_ids.setflags(write=False)
+        return partial(self._backend.copy_body_state_w, backend_ids)
+
+    def write_root_state_to_sim(
+        self,
+        root_state: np.ndarray,
+        env_ids: np.ndarray | slice | None = None,
+    ) -> None:
+        """Stage a 13-D world-frame root state in the active reset transaction."""
+        reset_state, layout = self._require_root_state_write()
+        resolved_env_ids = self._normalize_reset_env_ids(env_ids)
+        reset_state.write_root_state(
+            resolved_env_ids,
+            layout,
+            root_state,
+            term_name=f"{self.name}.write_root_state_to_sim",
+        )
+
+    def bind_actuator_gain_write(
+        self,
+        actuator_ids: np.ndarray | Sequence[int] | slice | None = None,
+        *,
+        term_name: str,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """Bind selected actuator columns and immutable gain defaults on the cold path."""
+        if self._reset_state is None:
+            raise self._capability_error(
+                "reset actuator-gain write",
+                "EntityScene was materialized without an env-owned reset transaction",
+            )
+        if self._actuator_ids is None:
+            raise self._capability_error(
+                "reset actuator-gain write",
+                "actuator_names were not declared in EntityCfg",
+            )
+        local_ids = self._normalize_local_actuator_ids(
+            actuator_ids,
+            capability="reset actuator-gain write",
+        )
+        if local_ids.size == 0:
+            raise ValueError(
+                f"Entity '{self.name}' reset actuator-gain write selected no actuators"
+            )
+        backend_ids = self._actuator_ids[local_ids]
+        _, default_kp, default_kd = self._reset_state.bind_actuator_gain_write(
+            backend_ids,
+            term_name=f"{term_name}:{self.name}",
+        )
+        bound_local_ids = np.array(local_ids, copy=True)
+        bound_local_ids.setflags(write=False)
+        return bound_local_ids, default_kp, default_kd
+
+    def write_actuator_gains_to_sim(
+        self,
+        kp: np.ndarray,
+        kd: np.ndarray,
+        actuator_ids: np.ndarray | Sequence[int] | slice | None = None,
+        env_ids: np.ndarray | slice | None = None,
+        *,
+        term_name: str = "pd_gains",
+    ) -> None:
+        """Stage entity-local actuator gains in the active reset transaction."""
+        if self._reset_state is None or self._actuator_ids is None:
+            raise self._capability_error(
+                "reset actuator-gain write",
+                "actuator metadata or the env-owned reset transaction was not materialized",
+            )
+        local_ids = self._normalize_local_actuator_ids(
+            actuator_ids,
+            capability="reset actuator-gain write",
+        )
+        resolved_env_ids = self._normalize_reset_env_ids(env_ids)
+        self._reset_state.write_actuator_gains(
+            resolved_env_ids,
+            self._actuator_ids[local_ids],
+            kp,
+            kd,
+            term_name=f"{term_name}:{self.name}",
+        )
+
+    def bind_joint_armature_write(
+        self,
+        joint_ids: np.ndarray | Sequence[int] | slice | None = None,
+        *,
+        term_name: str,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """Bind entity-local joints and immutable default DOF armatures."""
+        if self._reset_state is None:
+            raise self._capability_error(
+                "reset joint-armature write",
+                "EntityScene was materialized without an env-owned reset transaction",
+            )
+        model_dof_ids = self._materialize_joint_model_dof_ids()
+        local_ids = self._normalize_local_joint_ids(
+            joint_ids,
+            capability="reset joint-armature write",
+        )
+        if local_ids.size == 0:
+            raise ValueError(f"Entity '{self.name}' reset joint-armature write selected no joints")
+        _, defaults = self._reset_state.bind_dof_armature_write(
+            model_dof_ids[local_ids],
+            term_name=f"{term_name}:{self.name}",
+        )
+        return self._readonly_local_binding(local_ids, defaults)
+
+    def write_joint_armature_to_sim(
+        self,
+        values: np.ndarray,
+        joint_ids: np.ndarray | Sequence[int] | slice | None = None,
+        env_ids: np.ndarray | slice | None = None,
+        *,
+        term_name: str = "joint_armature",
+    ) -> None:
+        """Stage selected entity joint armatures in the active reset transaction."""
+        if self._reset_state is None:
+            raise self._capability_error(
+                "reset joint-armature write",
+                "EntityScene was materialized without an env-owned reset transaction",
+            )
+        model_dof_ids = self._materialize_joint_model_dof_ids()
+        local_ids = self._normalize_local_joint_ids(
+            joint_ids,
+            capability="reset joint-armature write",
+        )
+        self._reset_state.write_dof_armature(
+            self._normalize_reset_env_ids(env_ids),
+            model_dof_ids[local_ids],
+            values,
+            term_name=f"{term_name}:{self.name}",
+        )
+
+    def bind_geom_friction_write(
+        self,
+        geom_ids: np.ndarray | Sequence[int] | slice | None = None,
+        *,
+        term_name: str,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """Bind entity-local geoms and immutable default friction vectors."""
+        if self._reset_state is None or self._geom_ids is None:
+            raise self._capability_error(
+                "reset geom-friction write",
+                "geom metadata or the env-owned reset transaction was not materialized",
+            )
+        local_ids = self._normalize_local_geom_ids(
+            geom_ids,
+            capability="reset geom-friction write",
+        )
+        if local_ids.size == 0:
+            raise ValueError(f"Entity '{self.name}' reset geom-friction write selected no geoms")
+        _, defaults = self._reset_state.bind_geom_friction_write(
+            self._geom_ids[local_ids],
+            term_name=f"{term_name}:{self.name}",
+        )
+        return self._readonly_local_binding(local_ids, defaults)
+
+    def write_geom_friction_to_sim(
+        self,
+        values: np.ndarray,
+        geom_ids: np.ndarray | Sequence[int] | slice | None = None,
+        env_ids: np.ndarray | slice | None = None,
+        *,
+        term_name: str = "geom_friction",
+    ) -> None:
+        """Stage selected entity geom friction in the active reset transaction."""
+        if self._reset_state is None or self._geom_ids is None:
+            raise self._capability_error(
+                "reset geom-friction write",
+                "geom metadata or the env-owned reset transaction was not materialized",
+            )
+        local_ids = self._normalize_local_geom_ids(
+            geom_ids,
+            capability="reset geom-friction write",
+        )
+        self._reset_state.write_geom_friction(
+            self._normalize_reset_env_ids(env_ids),
+            self._geom_ids[local_ids],
+            values,
+            term_name=f"{term_name}:{self.name}",
+        )
+
+    def bind_body_mass_write(
+        self,
+        body_ids: np.ndarray | Sequence[int] | slice | None = None,
+        *,
+        term_name: str,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """Bind entity-local body columns and immutable default masses."""
+        reset_state, local_ids, backend_ids = self._bind_body_randomization(
+            body_ids,
+            capability="reset body-mass write",
+        )
+        _, defaults = reset_state.bind_body_mass_write(
+            backend_ids,
+            term_name=f"{term_name}:{self.name}",
+        )
+        return self._readonly_local_binding(local_ids, defaults)
+
+    def write_body_mass_to_sim(
+        self,
+        values: np.ndarray,
+        body_ids: np.ndarray | Sequence[int] | slice | None = None,
+        env_ids: np.ndarray | slice | None = None,
+        *,
+        term_name: str = "randomize_rigid_body_mass",
+    ) -> None:
+        """Stage selected entity body masses in the active reset transaction."""
+        reset_state, _, backend_ids = self._bind_body_randomization(
+            body_ids,
+            capability="reset body-mass write",
+        )
+        reset_state.write_body_mass(
+            self._normalize_reset_env_ids(env_ids),
+            backend_ids,
+            values,
+            term_name=f"{term_name}:{self.name}",
+        )
+
+    def bind_body_ipos_write(
+        self,
+        body_ids: np.ndarray | Sequence[int] | slice | None = None,
+        *,
+        term_name: str,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """Bind entity-local body columns and immutable inertial positions."""
+        reset_state, local_ids, backend_ids = self._bind_body_randomization(
+            body_ids,
+            capability="reset body-ipos write",
+        )
+        _, defaults = reset_state.bind_body_ipos_write(
+            backend_ids,
+            term_name=f"{term_name}:{self.name}",
+        )
+        return self._readonly_local_binding(local_ids, defaults)
+
+    def write_body_ipos_to_sim(
+        self,
+        values: np.ndarray,
+        body_ids: np.ndarray | Sequence[int] | slice | None = None,
+        env_ids: np.ndarray | slice | None = None,
+        *,
+        term_name: str = "randomize_rigid_body_com",
+    ) -> None:
+        """Stage selected entity body inertial positions in the reset transaction."""
+        reset_state, _, backend_ids = self._bind_body_randomization(
+            body_ids,
+            capability="reset body-ipos write",
+        )
+        reset_state.write_body_ipos(
+            self._normalize_reset_env_ids(env_ids),
+            backend_ids,
+            values,
+            term_name=f"{term_name}:{self.name}",
+        )
+
+    def bind_body_inertia_write(
+        self,
+        body_ids: np.ndarray | Sequence[int] | slice | None = None,
+        *,
+        default: np.ndarray,
+        default_mass: np.ndarray,
+        term_name: str,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """Bind entity-local body columns and caller-compiled default inertias.
+
+        ``default`` / ``default_mass`` are the full backend-width inertial
+        tables compiled from the scene model on the cold path; the transaction
+        cross-validates ``default_mass`` against the backend's authoritative
+        body-mass table before trusting the inertia rows.
+        """
+        reset_state, local_ids, backend_ids = self._bind_body_randomization(
+            body_ids,
+            capability="reset body-inertia write",
+        )
+        _, defaults = reset_state.bind_body_inertia_write(
+            backend_ids,
+            default=default,
+            default_mass=default_mass,
+            term_name=f"{term_name}:{self.name}",
+        )
+        return self._readonly_local_binding(local_ids, defaults)
+
+    def write_body_inertia_to_sim(
+        self,
+        values: np.ndarray,
+        body_ids: np.ndarray | Sequence[int] | slice | None = None,
+        env_ids: np.ndarray | slice | None = None,
+        *,
+        term_name: str = "randomize_body_mass_inertia",
+    ) -> None:
+        """Stage selected entity body principal inertias in the reset transaction."""
+        reset_state, _, backend_ids = self._bind_body_randomization(
+            body_ids,
+            capability="reset body-inertia write",
+        )
+        reset_state.write_body_inertia(
+            self._normalize_reset_env_ids(env_ids),
+            backend_ids,
+            values,
+            term_name=f"{term_name}:{self.name}",
+        )
+
+    def bind_root_linear_velocity_delta(self, *, term_name: str) -> None:
+        """Validate the interval root-velocity capability on the cold path."""
+        self._bind_root_velocity_delta(angular=False, term_name=term_name)
+
+    def bind_root_angular_velocity_delta(self, *, term_name: str) -> None:
+        """Validate the interval root angular-velocity capability on the cold path."""
+        self._bind_root_velocity_delta(angular=True, term_name=term_name)
+
+    def _bind_root_velocity_delta(self, *, angular: bool, term_name: str) -> None:
+        if self._root_body_ids is None:
+            raise self._capability_error(
+                "interval root velocity delta",
+                "root_body_name was not declared in EntityCfg",
+            )
+        try:
+            capabilities = self._backend.get_dr_capabilities()
+        except (AttributeError, NotImplementedError) as exc:
+            raise self._capability_error("interval root velocity delta", str(exc)) from exc
+        supported = (
+            capabilities.supports_interval_body_angular_velocity_delta
+            if angular
+            else capabilities.supports_interval_body_velocity_delta
+        )
+        if not supported:
+            raise self._capability_error(
+                "interval root velocity delta",
+                f"EventManager term '{term_name}' requested an unsupported backend capability",
+            )
+
+    def apply_root_linear_velocity_delta_to_sim(
+        self,
+        values: np.ndarray,
+        env_ids: np.ndarray | slice | None = None,
+        *,
+        term_name: str = "push_by_setting_velocity",
+    ) -> None:
+        """Dispatch a cached root linear-velocity delta through the formal interval plan."""
+        self.apply_root_velocity_delta_to_sim(
+            values,
+            None,
+            env_ids=env_ids,
+            term_name=term_name,
+        )
+
+    def apply_root_velocity_delta_to_sim(
+        self,
+        linear_delta: np.ndarray | None,
+        angular_delta: np.ndarray | None,
+        env_ids: np.ndarray | slice | None = None,
+        *,
+        term_name: str = "push_by_setting_velocity",
+    ) -> None:
+        """Dispatch world-frame root linear/angular velocity deltas in one interval plan."""
+        if linear_delta is None and angular_delta is None:
+            return
+        if self._root_body_ids is None:
+            raise self._capability_error(
+                "interval root velocity delta",
+                "root_body_name was not declared in EntityCfg",
+            )
+        ids = self._normalize_reset_env_ids(env_ids)
+        linear_plan = self._validate_root_velocity_delta(
+            linear_delta,
+            ids,
+            term_name=term_name,
+        )
+        angular_plan = self._validate_root_velocity_delta(
+            angular_delta,
+            ids,
+            term_name=term_name,
+        )
+        try:
+            self._backend.apply_interval_randomization(
+                IntervalRandomizationPlan(
+                    body_ids=self._root_body_ids,
+                    body_linear_velocity_delta=linear_plan,
+                    body_angular_velocity_delta=angular_plan,
+                )
+            )
+        except NotImplementedError as exc:
+            raise self._capability_error(
+                "interval root velocity delta",
+                f"EventManager term '{term_name}': {exc}",
+            ) from exc
+
+    def _validate_root_velocity_delta(
+        self,
+        values: np.ndarray | None,
+        ids: np.ndarray,
+        *,
+        term_name: str,
+    ) -> np.ndarray | None:
+        if values is None:
+            return None
+        if not isinstance(values, np.ndarray):
+            raise TypeError(
+                f"EventManager term '{term_name}' root velocity delta must be np.ndarray, "
+                f"got {type(values).__name__}"
+            )
+        expected = (ids.size, 3)
+        if values.shape != expected:
+            raise ValueError(
+                f"EventManager term '{term_name}' root velocity delta has shape "
+                f"{values.shape}; expected {expected}"
+            )
+        if not np.issubdtype(values.dtype, np.floating) or not np.isfinite(values).all():
+            raise ValueError(
+                f"EventManager term '{term_name}' root velocity delta must be finite floating data"
+            )
+        assert self._root_body_ids is not None
+        delta = np.zeros(
+            (self._backend.num_envs, len(self._root_body_ids), 3),
+            dtype=values.dtype,
+        )
+        delta[ids, 0, :] = values
+        return delta
+
+    def bind_body_wrench(
+        self,
+        body_ids: np.ndarray | Sequence[int] | slice | None = None,
+        *,
+        torque: bool,
+        term_name: str,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """Resolve entity-local and backend body columns for interval wrench writes.
+
+        Returns readonly ``(local_ids, backend_ids)``; ``local_ids`` index the
+        entity's body-state views (e.g. ``data.body_link_quat_w``) and
+        ``backend_ids`` are the columns accepted by
+        :meth:`apply_body_wrench_to_sim`.
+        """
+        if self._body_ids is None:
+            raise self._capability_error(
+                "interval body wrench",
+                "body_names were not declared in EntityCfg",
+            )
+        local_ids = self._normalize_local_body_ids(body_ids, capability="interval body wrench")
+        if local_ids.size == 0:
+            raise ValueError(f"Entity '{self.name}' interval body wrench selected no bodies")
+        try:
+            capabilities = self._backend.get_dr_capabilities()
+        except (AttributeError, NotImplementedError) as exc:
+            raise self._capability_error("interval body wrench", str(exc)) from exc
+        if not capabilities.supports_interval_body_force:
+            raise self._capability_error(
+                "interval body wrench",
+                f"EventManager term '{term_name}' requested an unsupported backend capability",
+            )
+        if torque and not capabilities.supports_interval_body_torque:
+            raise self._capability_error(
+                "interval body wrench",
+                f"EventManager term '{term_name}' requested an unsupported backend "
+                "capability: interval body torque",
+            )
+        return self._readonly_local_binding(local_ids, self._body_ids[local_ids])
+
+    def apply_body_wrench_to_sim(
+        self,
+        forces: np.ndarray,
+        torques: np.ndarray | None,
+        body_ids: np.ndarray,
+        env_ids: np.ndarray | slice | None = None,
+        *,
+        term_name: str,
+    ) -> None:
+        """Dispatch world-frame body forces/torques through the formal interval plan.
+
+        ``body_ids`` are the immutable backend columns returned by
+        :meth:`bind_body_wrench`; ``forces``/``torques`` are world-frame rows
+        for ``env_ids`` staged for the upcoming step.
+        """
+        ids = self._normalize_reset_env_ids(env_ids)
+        force_values = self._validate_body_wrench_values(
+            forces,
+            ids,
+            body_ids,
+            label="force",
+            term_name=term_name,
+        )
+        torque_values = self._validate_body_wrench_values(
+            torques,
+            ids,
+            body_ids,
+            label="torque",
+            term_name=term_name,
+        )
+        try:
+            self._backend.apply_interval_randomization(
+                IntervalRandomizationPlan(
+                    body_ids=body_ids,
+                    body_force=force_values,
+                    body_torque=torque_values,
+                )
+            )
+        except NotImplementedError as exc:
+            raise self._capability_error(
+                "interval body wrench",
+                f"EventManager term '{term_name}': {exc}",
+            ) from exc
+
+    def _validate_body_wrench_values(
+        self,
+        values: np.ndarray | None,
+        ids: np.ndarray,
+        body_ids: np.ndarray,
+        *,
+        label: str,
+        term_name: str,
+    ) -> np.ndarray | None:
+        if values is None:
+            return None
+        if not isinstance(values, np.ndarray):
+            raise TypeError(
+                f"EventManager term '{term_name}' body {label} must be np.ndarray, "
+                f"got {type(values).__name__}"
+            )
+        expected = (ids.size, body_ids.size, 3)
+        if values.shape != expected:
+            raise ValueError(
+                f"EventManager term '{term_name}' body {label} has shape {values.shape}; "
+                f"expected {expected}"
+            )
+        if not np.issubdtype(values.dtype, np.floating) or not np.isfinite(values).all():
+            raise ValueError(
+                f"EventManager term '{term_name}' body {label} must be finite floating data"
+            )
+        full = np.zeros((self._backend.num_envs, body_ids.size, 3), dtype=values.dtype)
+        full[ids] = values
+        return full
+
+    def write_root_link_pose_to_sim(
+        self,
+        root_pose: np.ndarray,
+        env_ids: np.ndarray | slice | None = None,
+    ) -> None:
+        """Stage world position and wxyz root orientation during reset."""
+        reset_state, layout = self._require_root_state_write()
+        resolved_env_ids = self._normalize_reset_env_ids(env_ids)
+        reset_state.write_root_pose(
+            resolved_env_ids,
+            layout,
+            root_pose,
+            term_name=f"{self.name}.write_root_link_pose_to_sim",
+        )
+
+    def write_root_link_velocity_to_sim(
+        self,
+        root_velocity: np.ndarray,
+        env_ids: np.ndarray | slice | None = None,
+    ) -> None:
+        """Stage world linear/angular root velocity during reset."""
+        reset_state, layout = self._require_root_state_write()
+        resolved_env_ids = self._normalize_reset_env_ids(env_ids)
+        reset_state.write_root_velocity(
+            resolved_env_ids,
+            layout,
+            root_velocity,
+            term_name=f"{self.name}.write_root_link_velocity_to_sim",
+        )
+
+    def read_reset_root_pose(
+        self,
+        env_ids: np.ndarray | slice | None = None,
+    ) -> np.ndarray:
+        """Read the world position and wxyz root orientation staged in the reset.
+
+        Returns a detached ``(len(env_ids), 7)`` copy of the pose currently
+        staged in the active reset transaction (backend default for rows no
+        term has written yet), so a later reset term can build on an earlier
+        term's root placement.
+        """
+        reset_state, layout = self._require_root_state_write()
+        resolved_env_ids = self._normalize_reset_env_ids(env_ids)
+        return reset_state.read_root_pose(
+            resolved_env_ids,
+            layout,
+            term_name=f"{self.name}.read_reset_root_pose",
+        )
+
+    def _require_root_state_write(
+        self,
+    ) -> tuple[ResetStateTransaction, BackendRootStateLayout]:
+        if self._reset_state is None:
+            raise self._capability_error(
+                "reset root-state write",
+                "EntityScene was materialized without an env-owned reset transaction",
+            )
+        if self._reset_root_layout is None:
+            detail = self._reset_root_layout_error or "root-state layout was not materialized"
+            raise self._capability_error("reset root-state layout", detail)
+        return self._reset_state, self._reset_root_layout
+
+    def write_joint_state_to_sim(
+        self,
+        position: np.ndarray,
+        velocity: np.ndarray,
+        joint_ids: np.ndarray | Sequence[int] | slice | None = None,
+        env_ids: np.ndarray | slice | None = None,
+    ) -> None:
+        """Stage community-style joint state writes in the active reset transaction."""
+        if self._reset_state is None:
+            raise self._capability_error(
+                "reset joint-state write",
+                "EntityScene was materialized without an env-owned reset transaction",
+            )
+        if self._joint_names is None:
+            raise self._capability_error(
+                "reset joint-state write",
+                "joint_names were not declared in EntityCfg",
+            )
+        local_joint_ids = self._normalize_local_joint_ids(
+            joint_ids,
+            capability="reset joint-state write",
+        )
+        resolved_env_ids = self._normalize_reset_env_ids(env_ids)
+        self._materialize_reset_joint_indices()
+        assert self._reset_joint_qpos_ids is not None
+        assert self._reset_joint_qvel_ids is not None
+        self._reset_state.write_joint_state(
+            resolved_env_ids,
+            self._reset_joint_qpos_ids[local_joint_ids],
+            self._reset_joint_qvel_ids[local_joint_ids],
+            position,
+            velocity,
+            term_name=f"{self.name}.write_joint_state_to_sim",
+        )
+
+    def _materialize_reset_joint_indices(self) -> None:
+        if self._reset_joint_qpos_ids is not None and self._reset_joint_qvel_ids is not None:
+            return
+        assert self._joint_names is not None
+        try:
+            qpos_ids = (
+                self._reset_joint_qpos_ids
+                if self._reset_joint_qpos_ids is not None
+                else self._backend.get_joint_state_qpos_indices(self._joint_names)
+            )
+            qvel_ids = self._backend.get_joint_state_qvel_indices(self._joint_names)
+        except (AttributeError, NotImplementedError) as exc:
+            raise self._capability_error("reset joint-state layout", str(exc)) from exc
+        self._reset_joint_qpos_ids = _readonly_ids(
+            qpos_ids,
+            expected=len(self._joint_names),
+            label=f"Entity '{self.name}' reset qpos",
+        )
+        self._reset_joint_qvel_ids = _readonly_ids(
+            qvel_ids,
+            expected=len(self._joint_names),
+            label=f"Entity '{self.name}' reset qvel",
+        )
+
+    def _bind_body_randomization(
+        self,
+        body_ids: np.ndarray | Sequence[int] | slice | None,
+        *,
+        capability: str,
+    ) -> tuple[ResetStateTransaction, np.ndarray, np.ndarray]:
+        if self._reset_state is None:
+            raise self._capability_error(
+                capability,
+                "EntityScene was materialized without an env-owned reset transaction",
+            )
+        if self._body_ids is None:
+            raise self._capability_error(
+                capability,
+                "body_names were not declared in EntityCfg",
+            )
+        local_ids = self._normalize_local_body_ids(body_ids, capability=capability)
+        if local_ids.size == 0:
+            raise ValueError(f"Entity '{self.name}' {capability} selected no bodies")
+        return self._reset_state, local_ids, self._body_ids[local_ids]
+
+    def _readonly_local_binding(
+        self,
+        local_ids: np.ndarray,
+        defaults: np.ndarray,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        bound_ids = np.array(local_ids, copy=True)
+        bound_ids.setflags(write=False)
+        bound_defaults = np.array(defaults, copy=True)
+        bound_defaults.setflags(write=False)
+        return bound_ids, bound_defaults
+
+    def _materialize_joint_model_dof_ids(self) -> np.ndarray:
+        """Resolve full model DOF addresses once for reset-time model fields."""
+        cached = self._joint_model_dof_ids
+        if cached is not None:
+            return cached
+        if self._joint_names is None:
+            raise self._capability_error(
+                "reset joint-armature write",
+                "joint_names were not declared in EntityCfg",
+            )
+        try:
+            values = self._backend.get_joint_dof_indices(self._joint_names)
+        except (AttributeError, NotImplementedError) as exc:
+            raise self._capability_error("reset joint-armature write", str(exc)) from exc
+        except (KeyError, ValueError) as exc:
+            raise ValueError(
+                f"Entity '{self.name}' could not resolve joint model DOF names "
+                f"{list(self._joint_names)} on backend '{self._backend_type}': {exc}"
+            ) from exc
+        resolved = _readonly_ids(
+            values,
+            expected=len(self._joint_names),
+            label=f"Entity '{self.name}' joint model DOF",
+        )
+        self._joint_model_dof_ids = resolved
+        return resolved
+
+    def _normalize_local_body_ids(
+        self,
+        body_ids: np.ndarray | Sequence[int] | slice | None,
+        *,
+        capability: str,
+    ) -> np.ndarray:
+        if body_ids is None:
+            ids = np.arange(self.num_bodies, dtype=np.intp)
+        elif isinstance(body_ids, slice):
+            ids = np.arange(self.num_bodies, dtype=np.intp)[body_ids]
+        else:
+            raw = np.asarray(body_ids)
+            if (
+                raw.ndim != 1
+                or not np.issubdtype(raw.dtype, np.integer)
+                or np.issubdtype(raw.dtype, np.bool_)
+            ):
+                raise TypeError(
+                    f"Entity '{self.name}' {capability} body_ids must be a 1-D integer "
+                    "array or slice"
+                )
+            ids = np.asarray(raw, dtype=np.intp)
+        if np.any(ids < 0) or np.any(ids >= self.num_bodies):
+            raise IndexError(
+                f"Entity '{self.name}' {capability} body_ids out of range for "
+                f"{self.num_bodies} bodies: {ids.tolist()}"
+            )
+        if np.unique(ids).size != ids.size:
+            raise ValueError(
+                f"Entity '{self.name}' {capability} body_ids contain duplicates: {ids.tolist()}"
+            )
+        return ids
+
+    def _normalize_local_joint_ids(
+        self,
+        joint_ids: np.ndarray | Sequence[int] | slice | None,
+        *,
+        capability: str,
+    ) -> np.ndarray:
+        if joint_ids is None:
+            ids = np.arange(self.num_joints, dtype=np.intp)
+        elif isinstance(joint_ids, slice):
+            ids = np.arange(self.num_joints, dtype=np.intp)[joint_ids]
+        else:
+            raw = np.asarray(joint_ids)
+            if (
+                raw.ndim != 1
+                or not np.issubdtype(raw.dtype, np.integer)
+                or np.issubdtype(raw.dtype, np.bool_)
+            ):
+                raise TypeError(
+                    f"Entity '{self.name}' {capability} joint_ids must be a 1-D integer "
+                    "array or slice"
+                )
+            ids = np.asarray(raw, dtype=np.intp)
+        if np.any(ids < 0) or np.any(ids >= self.num_joints):
+            raise IndexError(
+                f"Entity '{self.name}' {capability} joint_ids out of range for "
+                f"{self.num_joints} joints: {ids.tolist()}"
+            )
+        if np.unique(ids).size != ids.size:
+            raise ValueError(
+                f"Entity '{self.name}' {capability} joint_ids contain duplicates: {ids.tolist()}"
+            )
+        return ids
+
+    def _normalize_local_geom_ids(
+        self,
+        geom_ids: np.ndarray | Sequence[int] | slice | None,
+        *,
+        capability: str,
+    ) -> np.ndarray:
+        if geom_ids is None:
+            ids = np.arange(self.num_geoms, dtype=np.intp)
+        elif isinstance(geom_ids, slice):
+            ids = np.arange(self.num_geoms, dtype=np.intp)[geom_ids]
+        else:
+            raw = np.asarray(geom_ids)
+            if (
+                raw.ndim != 1
+                or not np.issubdtype(raw.dtype, np.integer)
+                or np.issubdtype(raw.dtype, np.bool_)
+            ):
+                raise TypeError(
+                    f"Entity '{self.name}' {capability} geom_ids must be a 1-D integer "
+                    "array or slice"
+                )
+            ids = np.asarray(raw, dtype=np.intp)
+        if np.any(ids < 0) or np.any(ids >= self.num_geoms):
+            raise IndexError(
+                f"Entity '{self.name}' {capability} geom_ids out of range for "
+                f"{self.num_geoms} geoms: {ids.tolist()}"
+            )
+        if np.unique(ids).size != ids.size:
+            raise ValueError(
+                f"Entity '{self.name}' {capability} geom_ids contain duplicates: {ids.tolist()}"
+            )
+        return ids
+
+    def _normalize_local_actuator_ids(
+        self,
+        actuator_ids: np.ndarray | Sequence[int] | slice | None,
+        *,
+        capability: str,
+    ) -> np.ndarray:
+        if actuator_ids is None:
+            ids = np.arange(self.num_actuators, dtype=np.intp)
+        elif isinstance(actuator_ids, slice):
+            ids = np.arange(self.num_actuators, dtype=np.intp)[actuator_ids]
+        else:
+            raw = np.asarray(actuator_ids)
+            if (
+                raw.ndim != 1
+                or not np.issubdtype(raw.dtype, np.integer)
+                or np.issubdtype(raw.dtype, np.bool_)
+            ):
+                raise TypeError(
+                    f"Entity '{self.name}' {capability} actuator_ids must be a 1-D "
+                    "integer array or slice"
+                )
+            ids = np.asarray(raw, dtype=np.intp)
+        if np.any(ids < 0) or np.any(ids >= self.num_actuators):
+            raise IndexError(
+                f"Entity '{self.name}' {capability} actuator_ids out of range for "
+                f"{self.num_actuators} actuators: {ids.tolist()}"
+            )
+        if np.unique(ids).size != ids.size:
+            raise ValueError(
+                f"Entity '{self.name}' {capability} actuator_ids contain duplicates: {ids.tolist()}"
+            )
+        return ids
+
+    def _normalize_reset_env_ids(self, env_ids: np.ndarray | slice | None) -> np.ndarray:
+        if env_ids is None:
+            return np.arange(self._backend.num_envs, dtype=np.int32)
+        if isinstance(env_ids, slice):
+            return np.arange(self._backend.num_envs, dtype=np.int32)[env_ids]
+        return env_ids
+
+    def find_bodies(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]:
+        return self._find("body", self._body_names, keys, preserve_order)
+
+    def find_geoms(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]:
+        return self._find("geom", self._geom_names, keys, preserve_order)
+
+    def find_sites(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]:
+        return self._find("site", self._site_names, keys, preserve_order)
+
+    def find_actuators(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]:
+        return self._find("actuator", self._actuator_names, keys, preserve_order)
+
+    def find_tendons(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]:
+        del keys, preserve_order
+        return self._unsupported_names("tendon")
+
+    def find_cameras(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]:
+        del keys, preserve_order
+        return self._unsupported_names("camera")
+
+    def find_lights(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]:
+        del keys, preserve_order
+        return self._unsupported_names("light")
+
+    def find_materials(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]:
+        del keys, preserve_order
+        return self._unsupported_names("material")
+
+    def find_textures(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]:
+        del keys, preserve_order
+        return self._unsupported_names("texture")
+
+    def find_pairs(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]:
+        del keys, preserve_order
+        return self._unsupported_names("pair")
+
+
+class EntityScene(Mapping[str, Entity]):
+    """Read-only name-addressable collection of backend-bound entities."""
+
+    def __init__(
+        self,
+        entities: Mapping[str, EntityCfg],
+        backend: SimBackend,
+        control_buffer: np.ndarray | None = None,
+        *,
+        reset_state: ResetStateTransaction | None = None,
+        default_qpos: np.ndarray | None = None,
+    ) -> None:
+        self._backend = backend
+        self._state_read_cache = _EntityStateReadCache()
+        materialized: dict[str, Entity] = {}
+        for name, cfg in entities.items():
+            if not isinstance(name, str) or not name:
+                raise TypeError(f"Scene entity names must be non-empty strings; got {name!r}")
+            if not isinstance(cfg, EntityCfg):
+                raise TypeError(
+                    f"Scene entity '{name}' must be EntityCfg, got {type(cfg).__name__}"
+                )
+            materialized[name] = Entity(
+                name,
+                cfg,
+                backend,
+                control_buffer,
+                reset_state,
+                default_qpos=default_qpos,
+                state_read_cache=self._state_read_cache,
+            )
+        self._entities = MappingProxyType(materialized)
+        self._reset_state = reset_state
+        env_origins = np.zeros((backend.num_envs, 3), dtype=np.float32)
+        env_origins.setflags(write=False)
+        self._env_origins = env_origins
+
+    @classmethod
+    def from_scene_cfg(
+        cls,
+        cfg: SceneCfg,
+        backend: SimBackend,
+        control_buffer: np.ndarray | None = None,
+        *,
+        reset_state: ResetStateTransaction | None = None,
+        default_qpos: np.ndarray | None = None,
+    ) -> EntityScene:
+        return cls(
+            cast(Mapping[str, EntityCfg], cfg.entities),
+            backend,
+            control_buffer,
+            reset_state=reset_state,
+            default_qpos=default_qpos,
+        )
+
+    @property
+    def entities(self) -> Mapping[str, Entity]:
+        """Pinned community-style read-only entity mapping."""
+        return self._entities
+
+    @property
+    def env_origins(self) -> np.ndarray:
+        """Read-only per-environment origins; flat UniLab scenes default to zero."""
+        return self._env_origins
+
+    @contextmanager
+    def _scoped_state_reads(self) -> Iterator[None]:
+        """Internal ManagerBasedRlEnv boundary for one stable update phase."""
+        with self._state_read_cache.scoped():
+            yield
+
+    def _invalidate_state_reads(self) -> None:
+        """Discard cached backend state after an in-phase simulation mutation."""
+        self._state_read_cache.invalidate()
+
+    def reset_to_default(self, env_ids: np.ndarray, *, term_name: str) -> None:
+        """Stage a full-scene default state in the active reset transaction."""
+        if self._reset_state is None:
+            raise NotImplementedError(
+                f"EventManager term '{term_name}' reset-state capability is unavailable: "
+                "EntityScene was materialized without an env-owned reset transaction"
+            )
+        if np.any(self._env_origins):
+            raise NotImplementedError(
+                f"EventManager term '{term_name}' cannot apply non-zero env_origins without "
+                "a formal backend root-state layout"
+            )
+        self._reset_state.reset_to_default(env_ids, term_name=term_name)
+
+    def bind_gravity_write(self, *, term_name: str) -> np.ndarray:
+        """Bind immutable backend gravity for a reset event on the cold path."""
+        if self._reset_state is None:
+            raise NotImplementedError(
+                f"EventManager term '{term_name}' gravity capability is unavailable: "
+                "EntityScene was materialized without an env-owned reset transaction"
+            )
+        return self._reset_state.bind_gravity_write(term_name=term_name)
+
+    def write_gravity_to_sim(
+        self,
+        values: np.ndarray,
+        env_ids: np.ndarray,
+        *,
+        term_name: str,
+    ) -> None:
+        """Stage gravity values in the exactly-once reset transaction."""
+        if self._reset_state is None:
+            raise NotImplementedError(
+                f"EventManager term '{term_name}' gravity capability is unavailable: "
+                "EntityScene was materialized without an env-owned reset transaction"
+            )
+        self._reset_state.write_gravity(env_ids, values, term_name=term_name)
+
+    def bind_sensor_data(self, names: Sequence[str]) -> BackendSensorView:
+        """Bind existing backend sensors for a manager term on the cold path.
+
+        The returned view owns the backend-specific reader.  Terms retain that
+        view and only call :meth:`BackendSensorView.read` while stepping, so the
+        scene facade never exposes a backend model, data object, or native handle.
+        """
+        try:
+            return self._backend.bind_sensor_data(names)
+        except (KeyError, TypeError, ValueError, NotImplementedError) as exc:
+            raise type(exc)(
+                "Manager scene named-sensor capability on backend "
+                f"'{self._backend.backend_type}': {exc}"
+            ) from exc
+
+    def __getitem__(self, name: str) -> Entity:
+        try:
+            return self._entities[name]
+        except KeyError as exc:
+            raise KeyError(
+                f"Scene entity '{name}' not found; available={list(self._entities)}"
+            ) from exc
+
+    def __iter__(self) -> Iterator[str]:
+        return iter(self._entities)
+
+    def __len__(self) -> int:
+        return len(self._entities)
+
+
+__all__ = ["Entity", "EntityCfg", "EntityData", "EntityScene"]
diff --git a/src/unilab/base/env_factory.py b/src/unilab/base/env_factory.py
new file mode 100644
index 000000000..72ea44dcf
--- /dev/null
+++ b/src/unilab/base/env_factory.py
@@ -0,0 +1,55 @@
+"""Picklable ``EnvFactory`` adapters bridging uni_rl's injected env contract
+to the UniLab env registry.
+
+uni_rl runners and collectors never construct environments themselves; the
+caller injects an ``EnvFactory`` (see ``uni_rl.env_contract``). Because
+collectors run in ``multiprocessing`` spawn subprocesses, the factory must be
+picklable by reference — the adapters below are module-level functions bound
+with ``functools.partial`` (never closures or lambdas).
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from functools import partial
+from typing import Any, cast
+
+from uni_rl.env_contract import EnvFactory, EnvProtocol
+
+
+def make_registry_env(
+    task_name: str,
+    sim_backend: str,
+    num_envs: int,
+    env_cfg_override: Mapping[str, Any] | None = None,
+) -> EnvProtocol:
+    """``EnvFactory`` implementation backed by the UniLab env registry.
+
+    Top-level on purpose: ``functools.partial`` bindings of this function stay
+    picklable for spawn-based collector subprocesses. ``ensure_registries``
+    runs here because a spawned subprocess is a fresh interpreter that never
+    executed the parent process's registry bootstrap.
+    """
+    from unilab.base import registry
+    from unilab.base.registry import ensure_registries
+
+    ensure_registries()
+    # ABEnv satisfies EnvProtocol at runtime (reset/set_nan_guard live on
+    # NpEnv); the declared ABEnv type predates the uni_rl protocol.
+    return cast(
+        "EnvProtocol",
+        registry.make(
+            task_name,
+            sim_backend=sim_backend,
+            num_envs=num_envs,
+            env_cfg_override=dict(env_cfg_override) if env_cfg_override is not None else None,
+        ),
+    )
+
+
+def registry_env_factory(task_name: str, sim_backend: str) -> EnvFactory:
+    """Bind a registry task/backend pair into a picklable ``EnvFactory``."""
+    return partial(make_registry_env, task_name, sim_backend)
+
+
+__all__ = ["make_registry_env", "registry_env_factory"]
diff --git a/src/unilab/base/final_observation.py b/src/unilab/base/final_observation.py
deleted file mode 100644
index f18fc1ba6..000000000
--- a/src/unilab/base/final_observation.py
+++ /dev/null
@@ -1,160 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Any
-
-import numpy as np
-
-
-@dataclass(frozen=True)
-class TransitionBootstrapContract:
-    actor_next_obs: np.ndarray
-    transition_next_obs: np.ndarray
-    terminal_mask: np.ndarray
-    timeout_terminal_mask: np.ndarray
-    actor_next_critic: np.ndarray | None = None
-    transition_next_critic: np.ndarray | None = None
-
-
-@dataclass(frozen=True)
-class TerminalObservationContract:
-    terminal_obs: np.ndarray | None
-    terminal_mask: np.ndarray
-    timeout_terminal_mask: np.ndarray
-    terminal_critic: np.ndarray | None = None
-
-
-def patch_transition_next_obs(
-    next_obs: np.ndarray,
-    final_observation: dict[str, Any] | None = None,
-    done: np.ndarray | None = None,
-    info: dict[str, Any] | None = None,
-    next_critic: np.ndarray | None = None,
-) -> tuple[np.ndarray, np.ndarray | None, np.ndarray]:
-    """Patch transition next obs with final_observation without mutating actor inputs."""
-    terminal_contract = resolve_terminal_observation_contract(
-        next_obs_batch_size=next_obs.shape[0],
-        final_observation=final_observation,
-        done=done,
-        info=info,
-    )
-    if not np.any(terminal_contract.terminal_mask) or terminal_contract.terminal_obs is None:
-        return (
-            next_obs,
-            next_critic,
-            np.zeros((next_obs.shape[0],), dtype=bool),
-        )
-
-    transition_next_obs = next_obs.copy()
-    transition_next_obs[terminal_contract.terminal_mask] = np.asarray(
-        terminal_contract.terminal_obs, dtype=next_obs.dtype
-    )[terminal_contract.terminal_mask]
-
-    transition_next_critic = next_critic
-    if next_critic is not None and terminal_contract.terminal_critic is not None:
-        transition_next_critic = next_critic.copy()
-        transition_next_critic[terminal_contract.terminal_mask] = np.asarray(
-            terminal_contract.terminal_critic, dtype=next_critic.dtype
-        )[terminal_contract.terminal_mask]
-
-    return (
-        transition_next_obs,
-        transition_next_critic,
-        terminal_contract.terminal_mask,
-    )
-
-
-def resolve_transition_bootstrap_contract(
-    next_obs: np.ndarray,
-    info: dict[str, Any] | None = None,
-    final_observation: dict[str, Any] | None = None,
-    done: np.ndarray | None = None,
-    truncated: np.ndarray | None = None,
-    next_critic: np.ndarray | None = None,
-) -> TransitionBootstrapContract:
-    """Resolve actor/storage observations and timeout bootstrap masks for a step."""
-    (
-        transition_next_obs,
-        transition_next_critic,
-        terminal_mask,
-    ) = patch_transition_next_obs(
-        next_obs,
-        final_observation=final_observation,
-        done=done,
-        info=info,
-        next_critic=next_critic,
-    )
-    timeout_terminal_mask = terminal_mask
-    if truncated is not None:
-        timeout_terminal_mask = np.logical_and(
-            terminal_mask, np.asarray(truncated, dtype=bool).ravel()
-        )
-    return TransitionBootstrapContract(
-        actor_next_obs=next_obs,
-        transition_next_obs=transition_next_obs,
-        terminal_mask=terminal_mask,
-        timeout_terminal_mask=timeout_terminal_mask,
-        actor_next_critic=next_critic,
-        transition_next_critic=transition_next_critic,
-    )
-
-
-def resolve_terminal_observation_contract(
-    next_obs_batch_size: int,
-    final_observation: dict[str, Any] | None = None,
-    done: np.ndarray | None = None,
-    info: dict[str, Any] | None = None,
-    truncated: np.ndarray | None = None,
-) -> TerminalObservationContract:
-    """Resolve terminal observation facts without constructing patched next obs."""
-    terminal_mask = _resolve_terminal_mask(next_obs_batch_size, done, info)
-    resolved_final_observation = _resolve_final_observation(final_observation, info)
-
-    terminal_obs: np.ndarray | None = None
-    terminal_critic: np.ndarray | None = None
-    if np.any(terminal_mask) and isinstance(resolved_final_observation, dict):
-        terminal_obs = resolved_final_observation.get("obs")
-        terminal_critic = resolved_final_observation.get("critic")
-
-    timeout_terminal_mask = terminal_mask
-    if truncated is not None:
-        timeout_terminal_mask = np.logical_and(
-            terminal_mask, np.asarray(truncated, dtype=bool).ravel()
-        )
-
-    return TerminalObservationContract(
-        terminal_obs=terminal_obs,
-        terminal_mask=terminal_mask,
-        timeout_terminal_mask=timeout_terminal_mask,
-        terminal_critic=terminal_critic,
-    )
-
-
-def _resolve_final_observation(
-    final_observation: dict[str, Any] | None,
-    info: dict[str, Any] | None,
-) -> dict[str, Any] | None:
-    if isinstance(final_observation, dict):
-        return final_observation
-    if isinstance(info, dict):
-        final_obs = info.get("final_observation")
-        if isinstance(final_obs, dict):
-            return final_obs
-    return None
-
-
-def _resolve_terminal_mask(
-    next_obs_batch_size: int,
-    done: np.ndarray | None,
-    info: dict[str, Any] | None,
-) -> np.ndarray:
-    if done is not None:
-        done_mask = np.asarray(done, dtype=bool).ravel()
-        if done_mask.shape == (next_obs_batch_size,):
-            return done_mask
-        return np.zeros((next_obs_batch_size,), dtype=bool)
-    if isinstance(info, dict):
-        terminal_mask = np.asarray(info.get("_final_observation"), dtype=bool)
-        if terminal_mask.shape == (next_obs_batch_size,):
-            return terminal_mask
-    return np.zeros((next_obs_batch_size,), dtype=bool)
diff --git a/src/unilab/base/np_env.py b/src/unilab/base/np_env.py
index 0e74f9640..b5d67e518 100644
--- a/src/unilab/base/np_env.py
+++ b/src/unilab/base/np_env.py
@@ -10,16 +10,15 @@
 
 import gymnasium as gym
 import numpy as np
+from unisim.backend.base import BackendPlayRenderPlan, SimBackend
 
-from unilab.base.backend import SimBackend
-from unilab.base.backend.base import BackendPlayRenderPlan
 from unilab.base.base import ABEnv, EnvCfg, EnvPlayCapabilities
+from unilab.base.cpu_runtime import apply_env_cpu_runtime
 from unilab.base.scene import SceneCfg
 from unilab.dr import DomainRandomizationManager, DomainRandomizationProvider
 from unilab.dtype_config import get_global_dtype
 
 if TYPE_CHECKING:
-    from unilab.base.augmentation import SymmetryAugmentation
     from unilab.utils.nan_guard import NanGuard
 
 
@@ -63,6 +62,9 @@
     "set_state_qpos_convert_ms",
     "set_state_pool_reset_ms",
     "set_state_state_scatter_ms",
+    "set_state_reset_upload_ms",
+    "set_state_reset_forward_ms",
+    "set_state_host_cache_refresh_ms",
     "set_state_internal_gap_ms",
 )
 
@@ -85,6 +87,9 @@
     "set_state_qpos_convert_ms",
     "set_state_pool_reset_ms",
     "set_state_state_scatter_ms",
+    "set_state_reset_upload_ms",
+    "set_state_reset_forward_ms",
+    "set_state_host_cache_refresh_ms",
     "set_state_internal_gap_ms",
 )
 
@@ -106,6 +111,12 @@ class NpEnv(ABEnv):
     """Backend-agnostic numpy environment base class."""
 
     def __init__(self, cfg: EnvCfg, backend: SimBackend, num_envs: int):
+        # Cold-path process confinement for envs that own an explicit CPU
+        # block (multi-rank DP collectors): keeps host-side NumPy/Numba compute
+        # inside the same CPUs the backend pool workers are pinned to. Runs
+        # before managers/materialization so Numba's lazily-launched pool
+        # inherits the confined mask.
+        apply_env_cpu_runtime(cfg.cpu_ids)
         self._cfg = cfg
         self._backend: SimBackend = backend
         self._num_envs = num_envs
@@ -117,6 +128,7 @@ def __init__(self, cfg: EnvCfg, backend: SimBackend, num_envs: int):
         self._init_randomization_applied = False
         self._nan_guard: NanGuard | None = None
         self._autoreset = True
+        self._autoreset_reset_active = False
         self._nan_guard_model_file = self._resolve_nan_guard_model_file()
 
     @property
@@ -144,10 +156,6 @@ def observation_space(self) -> gym.Space:
         total = sum(self.obs_groups_spec.values())
         return gym.spaces.Box(-np.inf, np.inf, shape=(total,), dtype=np.float64)
 
-    def build_symmetry_augmentation(self, *, device: str) -> "SymmetryAugmentation | None":
-        """Return an env-owned runtime symmetry adapter when the task/backend supports it."""
-        return None
-
     def init_state(self) -> NpEnvState:
         dtype = get_global_dtype()
         obs = {
@@ -156,12 +164,7 @@ def init_state(self) -> NpEnvState:
         reward = np.zeros((self._num_envs,), dtype=dtype)
         terminated = np.ones((self._num_envs,), dtype=bool)
         truncated = np.zeros((self._num_envs,), dtype=bool)
-        if self._cfg.max_episode_steps:
-            steps = np.random.randint(
-                0, self._cfg.max_episode_steps, size=(self._num_envs,), dtype=np.uint32
-            )
-        else:
-            steps = np.zeros((self._num_envs,), dtype=np.uint32)
+        steps = self._initial_episode_steps()
         info: dict = {"steps": steps}
 
         self._state = NpEnvState(obs, reward, terminated, truncated, info)
@@ -169,6 +172,22 @@ def init_state(self) -> NpEnvState:
         self._clear_step_final_observation()
         return self._state
 
+    def _initial_episode_steps(self) -> np.ndarray:
+        """Return initial per-env episode counters.
+
+        Existing monolithic tasks keep their randomized initialization.  A lifecycle
+        with different public semantics can override this cold-path hook without
+        duplicating :meth:`init_state` or the autoreset machinery.
+        """
+        if self._cfg.max_episode_steps:
+            return np.random.randint(
+                0,
+                self._cfg.max_episode_steps,
+                size=(self._num_envs,),
+                dtype=np.uint32,
+            )
+        return np.zeros((self._num_envs,), dtype=np.uint32)
+
     def step(self, actions: np.ndarray) -> NpEnvState:
         step_t0 = time.perf_counter()
 
@@ -274,11 +293,16 @@ def _reset_done_envs(self) -> None:
         detail_timing["reset_done_terminal_obs_ms"] = (time.perf_counter() - t0) * 1000.0
 
         t0 = time.perf_counter()
-        new_obs, info1 = self.reset(env_indices)
+        self._autoreset_reset_active = True
+        try:
+            new_obs, info1 = self.reset(env_indices)
+        finally:
+            self._autoreset_reset_active = False
         detail_timing["reset_done_reset_call_ms"] = (time.perf_counter() - t0) * 1000.0
-        if self._dr_manager is not None:
-            detail_timing.update(self._dr_manager.last_reset_timing_ms)
-
+        collected = self._collect_reset_backend_timing_ms()
+        detail_timing.update(
+            {key: value for key, value in collected.items() if key in detail_timing}
+        )
         t0 = time.perf_counter()
         for key in self._state.obs:
             self._state.obs[key][env_indices] = new_obs[key]
@@ -314,6 +338,18 @@ def _clear_reset_done_detail_timing(self, timing: dict[str, Any]) -> None:
         for key in RESET_DONE_DETAIL_TIMING_KEYS:
             timing[key] = 0.0
 
+    def _collect_reset_backend_timing_ms(self) -> dict[str, float]:
+        """Backend-sourced reset sub-timings for the last reset call.
+
+        The monolithic DR path reports through the DR manager; manager-based
+        envs override this to surface the reset-state transaction's set_state
+        timings. Keys outside RESET_DONE_DETAIL_TIMING_KEYS are dropped by the
+        caller so stale keys never leak into ``info["timing"]``.
+        """
+        if self._dr_manager is not None:
+            return self._dr_manager.last_reset_timing_ms
+        return {}
+
     def _resolve_nan_guard_model_file(self) -> str:
         scene = getattr(self._cfg, "scene", None)
         if isinstance(scene, SceneCfg) and scene.model_file:
@@ -456,6 +492,15 @@ def resolve_play_render_plan(
         output_video: str | PathLike[str] | None,
     ) -> BackendPlayRenderPlan:
         """Resolve high-level playback mode through the concrete backend."""
+        # Drake has no renderer of its own. Its playback contract captures
+        # Drake state and feeds it to the shared MuJoCo renderer, so ordinary
+        # ``auto`` playback should produce a video without requiring a
+        # backend-specific override in every task owner YAML. Explicit
+        # ``none`` remains available for headless runs.
+        if self._backend.backend_type == "drake" and (
+            play_render_mode is None or str(play_render_mode).strip().lower() == "auto"
+        ):
+            play_render_mode = "record"
         return self._backend.resolve_play_render_plan(
             play_render_mode=play_render_mode,
             play_steps=play_steps,
@@ -478,19 +523,22 @@ def run_playback(
         extra_data_getter: Callable[[], np.ndarray | None] | None = None,
     ) -> str | None:
         """Execute playback through the concrete backend."""
-        return self._backend.run_playback(
-            env=self,
-            initialize=initialize,
-            step=step,
-            num_steps=num_steps,
-            output_video=output_video,
-            render_spacing=render_spacing,
-            render_offset_mode=render_offset_mode,
-            headless=headless,
-            record_video=record_video,
-            frame_state_getter=frame_state_getter,
-            camera_kwargs=camera_kwargs,
-            extra_data_getter=extra_data_getter,
+        return cast(
+            str | None,
+            self._backend.run_playback(
+                env=self,
+                initialize=initialize,
+                step=step,
+                num_steps=num_steps,
+                output_video=output_video,
+                render_spacing=render_spacing,
+                render_offset_mode=render_offset_mode,
+                headless=headless,
+                record_video=record_video,
+                frame_state_getter=frame_state_getter,
+                camera_kwargs=camera_kwargs,
+                extra_data_getter=extra_data_getter,
+            ),
         )
 
     def render_play_frame(self) -> None:
@@ -553,7 +601,7 @@ def get_playback_model(self, env_index: int | None = None) -> Any:
 
     def get_scene_visual_model_file(self) -> str | None:
         """Return the backend scene visual model file on the cold path, when available."""
-        return self._backend.get_scene_visual_model_file()
+        return cast(str | None, self._backend.get_scene_visual_model_file())
 
     def set_nan_guard(self, guard: "NanGuard") -> None:
         self._nan_guard = guard
diff --git a/src/unilab/base/observations.py b/src/unilab/base/observations.py
deleted file mode 100644
index 3b17f9cfa..000000000
--- a/src/unilab/base/observations.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from __future__ import annotations
-
-import numpy as np
-
-
-def flatten_obs_dict(obs: dict[str, np.ndarray]) -> np.ndarray:
-    """Concatenate obs groups in insertion order -> flat (N, total_dim) array."""
-    return np.concatenate(list(obs.values()), axis=1)
-
-
-def flatten_policy_obs_dict(obs: dict[str, np.ndarray]) -> np.ndarray:
-    """Build actor-policy inputs from the single actor observation group."""
-    return obs["obs"]
-
-
-def split_obs_dict(obs: dict[str, np.ndarray]) -> tuple[np.ndarray, np.ndarray]:
-    """Split observation dict into (actor_obs, critic_obs).
-
-    When no separate critic group exists, critic_obs == actor_obs.
-    """
-    actor = obs["obs"]
-    return actor, obs.get("critic", actor)
-
-
-def get_obs_dims(obs_groups_spec: dict[str, int]) -> tuple[int, int]:
-    """Extract (actor_obs_dim, critic_obs_dim) from obs_groups_spec.
-
-    When no separate critic group exists, critic_obs_dim == actor_obs_dim.
-    """
-    obs_dim = obs_groups_spec.get("obs", 0)
-    return obs_dim, obs_groups_spec.get("critic", obs_dim)
-
-
-def get_critic_base_dim(obs_groups_spec: dict[str, int]) -> int:
-    """Get critic observation dim, falling back to actor obs when absent."""
-    critic_dim = obs_groups_spec.get("critic", 0)
-    return critic_dim if critic_dim > 0 else obs_groups_spec.get("obs", 0)
diff --git a/src/unilab/base/process_device.py b/src/unilab/base/process_device.py
new file mode 100644
index 000000000..1302c7ff9
--- /dev/null
+++ b/src/unilab/base/process_device.py
@@ -0,0 +1,45 @@
+"""Training-worker device routing for UniSim backends."""
+
+from __future__ import annotations
+
+from typing import cast
+
+
+def resolve_backend_process_device(backend_type: str, learner_device: str | None) -> str | None:
+    if backend_type != "mjwarp":
+        return None
+    if learner_device is None:
+        raise ValueError("mjwarp requires an explicit CUDA process device")
+    resolved = str(learner_device).strip()
+    if resolved.split(":", 1)[0].lower() != "cuda":
+        raise ValueError(
+            f"mjwarp requires a CUDA process device shared with its learner; got {resolved!r}"
+        )
+    return resolved
+
+
+def configure_backend_process_device(backend_type: str, learner_device: str | None) -> str | None:
+    resolved = resolve_backend_process_device(backend_type, learner_device)
+    if resolved is None:
+        return None
+    return bind_backend_process_device(resolved)
+
+
+def bind_backend_process_device(resolved: str) -> str | None:
+    """Bind a resolved backend process device in the current process.
+
+    Top-level on purpose: uni_rl's off-policy collectors receive this as the
+    injected ``backend_device_binder`` and pickle it by reference into
+    spawn-based subprocesses. The mjwarp import stays lazy so the binder is
+    importable without the ``mjwarp`` extra installed.
+    """
+    from unisim.backend.mjwarp.runtime import bind_mjwarp_process_device
+
+    return cast(str | None, bind_mjwarp_process_device(resolved))
+
+
+__all__ = [
+    "bind_backend_process_device",
+    "configure_backend_process_device",
+    "resolve_backend_process_device",
+]
diff --git a/src/unilab/base/registry.py b/src/unilab/base/registry.py
index 21e85bc1b..1943c4927 100644
--- a/src/unilab/base/registry.py
+++ b/src/unilab/base/registry.py
@@ -8,26 +8,41 @@
     Any,
     Callable,
     Dict,
+    Literal,
     Optional,
-    Type,
+    Protocol,
     TypeVar,
-    cast,
-    get_args,
-    get_origin,
-    get_type_hints,
 )
 
 from .base import ABEnv, EnvCfg
+from .config_materialization import apply_cfg_overrides
+from .config_overrides import (
+    CONFIG_MAPPING_POLICY_KEY,
+    MANAGER_TERM_MAPPING_POLICY,
+)
+
+EnvCfgFactory = Callable[[], EnvCfg]
+TEnvCfgFactory = TypeVar("TEnvCfgFactory", bound=EnvCfgFactory)
+
+
+class EnvFactory(Protocol):
+    """Construct an environment for one materialized config and backend."""
+
+    def __call__(
+        self,
+        cfg: Any,
+        *,
+        num_envs: int = 1,
+        backend_type: str = "mujoco",
+    ) -> ABEnv: ...
 
-TEnvCfg = TypeVar("TEnvCfg", bound=EnvCfg)
-_SUPPORTED_SIM_BACKENDS = ("mujoco", "mjwarp", "motrix", "drake")
+
+TEnvFactory = TypeVar("TEnvFactory", bound=EnvFactory)
+RewardOverrideField = Literal["reward_config", "rewards"]
+_SUPPORTED_SIM_BACKENDS = ("mujoco", "mjwarp", "motrix", "drake", "isaacgym", "genesis", "isaacsim")
 _DEFAULT_SIM_BACKEND_ORDER: tuple[str, ...] = ("mujoco", "motrix")
 _REGISTRY_MODULES_ATTR = "__unilab_registry_modules__"
-_DEFAULT_REGISTRY_PACKAGES = (
-    "unilab.envs.locomotion",
-    "unilab.envs.manipulation",
-    "unilab.envs.motion_tracking",
-)
+_DEFAULT_REGISTRY_PACKAGES = ("unilab.tasks",)
 # Environment variable used to extend ensure_registries() with extra packages.
 # Mainly intended for test setups that need to ship a fixture-only registry into
 # spawn subprocesses (which do not inherit pytest conftest state).
@@ -38,19 +53,19 @@
 
 @dataclass
 class EnvMeta:
-    env_cfg_cls: Type[EnvCfg]
-    env_cls_dict: Dict[str, Type[ABEnv]] = field(default_factory=dict)
+    env_cfg_factory: EnvCfgFactory
+    env_factory_dict: Dict[str, EnvFactory] = field(default_factory=dict)
 
     def available_sim_backend(self) -> Optional[str]:
         """Return the explicit default simulation backend for this environment."""
         for backend in _DEFAULT_SIM_BACKEND_ORDER:
-            if backend in self.env_cls_dict:
+            if backend in self.env_factory_dict:
                 return backend
-        return next(iter(self.env_cls_dict), None)
+        return next(iter(self.env_factory_dict), None)
 
     def support_sim_backend(self, sim_backend: str) -> bool:
         """Check if the environment supports a specific simulation backend."""
-        return sim_backend in self.env_cls_dict
+        return sim_backend in self.env_factory_dict
 
 
 _envs: Dict[str, EnvMeta] = {}
@@ -61,33 +76,65 @@ def contains(name: str) -> bool:
     return name in _envs
 
 
-def register_env_config(name: str, env_cfg_cls: Type[EnvCfg]):
-    """Register an environment configuration class with a name."""
+def _config_factory_name(factory: EnvCfgFactory) -> str:
+    return str(getattr(factory, "__qualname__", type(factory).__qualname__))
+
+
+def _env_factory_name(factory: EnvFactory) -> str:
+    return str(getattr(factory, "__qualname__", type(factory).__qualname__))
+
+
+def register_env_config(name: str, env_cfg_factory: EnvCfgFactory) -> None:
+    """Register a zero-argument environment configuration factory."""
     if name in _envs.keys():
         raise ValueError(f"Environment '{name}' is already registered.")
-    _envs[name] = EnvMeta(env_cfg_cls=env_cfg_cls)
+    if not callable(env_cfg_factory):
+        raise TypeError(
+            f"Environment '{name}' config factory must be callable, got "
+            f"{type(env_cfg_factory).__name__}"
+        )
+    _envs[name] = EnvMeta(env_cfg_factory=env_cfg_factory)
 
 
-def envcfg(name: str) -> Callable[[Type[TEnvCfg]], Type[TEnvCfg]]:
+def envcfg(name: str) -> Callable[[TEnvCfgFactory], TEnvCfgFactory]:
     """
-    Decorator to register an environment configuration class with a name.
+    Decorator to register an environment configuration class or factory.
 
     Usage:
-        @register_env_config_decorator("my-env")
+        @envcfg("my-env")
         @dataclass
         class MyEnvCfg(EnvCfg):
             ...
+
+        @envcfg("my-manager-env")
+        def make_my_env_cfg() -> EnvCfg:
+            ...
     """
 
-    def decorator(cls: Type[TEnvCfg]) -> Type[TEnvCfg]:
-        register_env_config(name, cls)
-        return cls
+    def decorator(factory: TEnvCfgFactory) -> TEnvCfgFactory:
+        register_env_config(name, factory)
+        return factory
 
     return decorator
 
 
-def register_env(name: str, env_cls: Type[ABEnv], sim_backend: str):
-    """Register an environment class with a name and simulation backend."""
+def materialize_env_config(name: str) -> EnvCfg:
+    """Construct one config instance from the registered cold-path factory."""
+    if name not in _envs:
+        raise ValueError(f"Environment '{name}' is not registered.")
+
+    factory = _envs[name].env_cfg_factory
+    env_cfg = factory()
+    if not isinstance(env_cfg, EnvCfg):
+        raise TypeError(
+            f"Environment '{name}' config factory '{_config_factory_name(factory)}' returned "
+            f"{type(env_cfg).__name__}, expected an EnvCfg instance"
+        )
+    return env_cfg
+
+
+def register_env(name: str, env_factory: TEnvFactory, sim_backend: str) -> TEnvFactory:
+    """Register and return an environment class or function factory."""
     if sim_backend not in _SUPPORTED_SIM_BACKENDS:
         raise ValueError(
             f"Unsupported simulation backend: {sim_backend}. "
@@ -99,27 +146,37 @@ def register_env(name: str, env_cls: Type[ABEnv], sim_backend: str):
             f"Environment '{name}' is not registered. Please register the config first."
         )
 
-    if sim_backend in _envs[name].env_cls_dict:
+    if not callable(env_factory):
+        raise TypeError(
+            f"Environment '{name}' backend '{sim_backend}' factory must be callable, got "
+            f"{type(env_factory).__name__}"
+        )
+
+    if sim_backend in _envs[name].env_factory_dict:
         raise ValueError(
             f"Environment '{name}' with sim backend '{sim_backend}' is already registered."
         )
 
-    _envs[name].env_cls_dict[sim_backend] = env_cls
+    _envs[name].env_factory_dict[sim_backend] = env_factory
+    return env_factory
 
 
-def env(name: str, sim_backend: str) -> Callable[[Type[ABEnv]], Type[ABEnv]]:
+def env(name: str, sim_backend: str) -> Callable[[TEnvFactory], TEnvFactory]:
     """
-    Decorator to register an environment class with a name and simulation backend.
+    Decorator to register an environment class or function factory.
 
     Usage:
-        @register_env_decorator("my-env", "np")
+        @env("my-env", "mujoco")
         class MyEnv(ABEnv):
             ...
+
+        @env("my-manager-env", "mujoco")
+        def make_my_env(cfg, num_envs=1, backend_type="mujoco"):
+            ...
     """
 
-    def decorator(cls: Type[ABEnv]) -> Type[ABEnv]:
-        register_env(name, cls, sim_backend)
-        return cls
+    def decorator(factory: TEnvFactory) -> TEnvFactory:
+        return register_env(name, factory, sim_backend)
 
     return decorator
 
@@ -136,67 +193,41 @@ def find_available_sim_backend(env_name: str) -> str:
     return backend
 
 
-def _resolve_dataclass_type(type_hint: Any) -> Optional[Type[Any]]:
-    """Strip Optional/Union and return the underlying dataclass type, or None."""
-    if type_hint is None:
-        return None
-    origin = get_origin(type_hint)
-    if origin is not None:
-        args = get_args(type_hint)
-        type_hint = next((arg for arg in args if arg is not type(None)), None)
-    if (
-        type_hint is not None
-        and dataclasses.is_dataclass(type_hint)
-        and isinstance(type_hint, type)
-    ):
-        return cast(Type[Any], type_hint)
-    return None
-
-
-def _construct_dataclass_from_dict(target_type: Type[Any], values: Dict[str, Any]) -> Any:
-    try:
-        target_obj = target_type()
-    except TypeError:
-        return target_type(**values)
-    apply_cfg_overrides(target_obj, values)
-    return target_obj
-
-
-def apply_cfg_overrides(target_obj: Any, overrides: Dict[str, Any]) -> None:
-    """Apply a (possibly nested) dict of overrides to ``target_obj`` in place.
-
-    Behavior:
-      - For each ``key, value`` in ``overrides``, ``target_obj.key`` must exist
-        (otherwise ``ValueError``).
-      - If ``value`` is a dict and ``target_obj.key`` is already a dataclass
-        instance, recurse into it (deep merge — preserves fields not present
-        in ``value``). This is what lets Hydra-style partial overrides like
-        ``env.scene.terrain.generator.num_rows=4`` keep ``sub_terrains`` and other
-        defaults intact.
-      - If ``value`` is a dict and ``target_obj.key`` is currently ``None``,
-        instantiate the field's annotated dataclass type from the dict
-        (full-construction path).
-      - Otherwise ``setattr`` the value directly (scalar / list / non-dataclass).
+def resolve_reward_override_field(env_name: str) -> RewardOverrideField:
+    """Resolve the Hydra root reward target declared by an env config owner.
+
+    Legacy configs own a ``reward_config`` field. Manager-Based configs opt in
+    through the explicit manager-term mapping metadata on ``rewards``. The
+    registry resolves this on the config class without constructing an env or
+    backend so training adapters do not branch on task names.
     """
-    try:
-        type_hints = get_type_hints(type(target_obj))
-    except Exception:
-        type_hints = {}
-
-    for key, value in overrides.items():
-        if not hasattr(target_obj, key):
-            raise ValueError(f"Config class '{type(target_obj).__name__}' has no attribute '{key}'")
-        existing = getattr(target_obj, key)
-        if isinstance(value, dict):
-            if dataclasses.is_dataclass(existing) and not isinstance(existing, type):
-                apply_cfg_overrides(existing, value)
-                continue
-            if existing is None:
-                target_type = _resolve_dataclass_type(type_hints.get(key))
-                if target_type is not None:
-                    setattr(target_obj, key, _construct_dataclass_from_dict(target_type, value))
-                    continue
-        setattr(target_obj, key, value)
+    if env_name not in _envs:
+        raise ValueError(f"Environment '{env_name}' is not registered.")
+
+    config = materialize_env_config(env_name)
+    config_fields = {config_field.name: config_field for config_field in dataclasses.fields(config)}
+    rewards_field = config_fields.get("rewards")
+    has_manager_rewards = (
+        rewards_field is not None
+        and rewards_field.metadata.get(CONFIG_MAPPING_POLICY_KEY) == MANAGER_TERM_MAPPING_POLICY
+    )
+    has_legacy_rewards = "reward_config" in config_fields
+    config_owner = type(config).__name__
+
+    if has_manager_rewards and has_legacy_rewards:
+        raise ValueError(
+            f"Environment '{env_name}' config owner '{config_owner}' declares both "
+            "Manager-Based 'rewards' and legacy 'reward_config' targets"
+        )
+    if has_manager_rewards:
+        return "rewards"
+    if has_legacy_rewards:
+        return "reward_config"
+    raise ValueError(
+        f"Environment '{env_name}' config owner '{config_owner}' declares no "
+        "supported Hydra root reward target; expected legacy 'reward_config' or an "
+        "explicitly marked Manager-Based 'rewards' field"
+    )
 
 
 def make(
@@ -223,7 +254,7 @@ def make(
     meta: EnvMeta = _envs[name]
 
     # Create environment config
-    env_cfg = meta.env_cfg_cls()
+    env_cfg = materialize_env_config(name)
     if env_cfg_override is not None:
         apply_cfg_overrides(env_cfg, env_cfg_override)
 
@@ -242,8 +273,14 @@ def make(
         )
 
     # Create environment instance
-    env_cls_any: Any = meta.env_cls_dict[sim_backend]
-    env: ABEnv = env_cls_any(env_cfg, num_envs=num_envs, backend_type=sim_backend)
+    factory = meta.env_factory_dict[sim_backend]
+    env = factory(env_cfg, num_envs=num_envs, backend_type=sim_backend)
+    if not isinstance(env, ABEnv):
+        raise TypeError(
+            f"Environment '{name}' backend '{sim_backend}' factory "
+            f"'{_env_factory_name(factory)}' returned {type(env).__name__}, "
+            "expected an ABEnv instance"
+        )
     return env
 
 
@@ -252,8 +289,8 @@ def list_registered_envs() -> Dict[str, Dict[str, Any]]:
     result = {}
     for name, meta in _envs.items():
         result[name] = {
-            "config_class": meta.env_cfg_cls.__name__,
-            "available_backends": list(meta.env_cls_dict.keys()),
+            "config_factory": _config_factory_name(meta.env_cfg_factory),
+            "available_backends": list(meta.env_factory_dict.keys()),
         }
     return result
 
diff --git a/src/unilab/base/reset_state.py b/src/unilab/base/reset_state.py
new file mode 100644
index 000000000..cbe20ef81
--- /dev/null
+++ b/src/unilab/base/reset_state.py
@@ -0,0 +1,1271 @@
+"""Base-owned reset-state transaction for Manager-Based event terms.
+
+The transaction composes NumPy state writes in memory and hands the finished
+batch to :meth:`SimBackend.set_state` exactly once.  It deliberately knows
+nothing about task configuration, IPC, runners, or backend-private state.
+"""
+
+from __future__ import annotations
+
+import time
+from collections.abc import Iterator
+from contextlib import contextmanager
+from typing import cast
+
+import numpy as np
+from unisim.backend.base import BackendRootStateLayout, SimBackend
+from unisim.dr.types import (
+    RESET_TERM_BODY_INERTIA,
+    RESET_TERM_BODY_IPOS,
+    RESET_TERM_BODY_MASS,
+    RESET_TERM_DOF_ARMATURE,
+    RESET_TERM_GEOM_FRICTION,
+    RESET_TERM_GRAVITY,
+    RESET_TERM_KD,
+    RESET_TERM_KP,
+    ResetRandomizationPayload,
+)
+
+from unilab.utils.rotation import np_quat_apply_inverse
+
+
+class ResetStateTransaction:
+    """Reusable, fail-closed transaction for reset-mode state mutation."""
+
+    def __init__(
+        self,
+        backend: SimBackend,
+        *,
+        default_qpos: np.ndarray | None = None,
+    ) -> None:
+        self._backend = backend
+        self._num_envs = backend.num_envs
+        self._selected_default_qpos = default_qpos
+        self._active = False
+        self._active_mask = np.zeros(self._num_envs, dtype=np.bool_)
+        self._dirty_mask = np.zeros(self._num_envs, dtype=np.bool_)
+        self._default_qpos: np.ndarray | None = None
+        self._default_qvel: np.ndarray | None = None
+        self._qpos: np.ndarray | None = None
+        self._qvel: np.ndarray | None = None
+        self._default_kp: np.ndarray | None = None
+        self._default_kd: np.ndarray | None = None
+        self._kp: np.ndarray | None = None
+        self._kd: np.ndarray | None = None
+        self._gain_dirty_mask = np.zeros(self._num_envs, dtype=np.bool_)
+        self._randomization_defaults: dict[str, np.ndarray] = {}
+        self._randomization_values: dict[str, np.ndarray] = {}
+        self._randomization_dirty_masks: dict[str, np.ndarray] = {}
+        self._committed_randomization: dict[str, np.ndarray] = {}
+        self._committed_randomization_masks: dict[str, np.ndarray] = {}
+        self._committed_kp: np.ndarray | None = None
+        self._committed_kd: np.ndarray | None = None
+        self._committed_gain_mask = np.zeros(self._num_envs, dtype=np.bool_)
+        self._requesting_terms: set[str] = set()
+        self._last_commit_had_writes = False
+        self._last_set_state_timing_ms: dict[str, float] = {}
+
+    @property
+    def active(self) -> bool:
+        """Whether a reset lifecycle currently owns the transaction."""
+        return self._active
+
+    @property
+    def last_commit_had_writes(self) -> bool:
+        """Whether the most recent scoped commit submitted dirty rows to set_state."""
+        return self._last_commit_had_writes
+
+    @property
+    def last_set_state_timing_ms(self) -> dict[str, float]:
+        """Sub-timings from the most recent commit's set_state call.
+
+        Always includes ``dr_reset_set_state_ms`` (outer wall-clock around the
+        backend call); backend-reported ``set_state_*_ms`` sub-keys are merged
+        in when the backend returns them. Empty when the last commit had no
+        dirty rows.
+        """
+        return self._last_set_state_timing_ms
+
+    @contextmanager
+    def scoped(self, env_ids: np.ndarray) -> Iterator[ResetStateTransaction]:
+        """Begin a reset transaction and commit it only after all terms succeed."""
+        self.begin(env_ids)
+        try:
+            yield self
+        except BaseException:
+            self.abort()
+            raise
+        else:
+            self.commit()
+
+    def begin(self, env_ids: np.ndarray) -> None:
+        """Open a transaction for the concrete reset environment IDs."""
+        if self._active:
+            raise RuntimeError("ManagerBased reset-state transaction is already active")
+        ids = self._validate_ids(env_ids, capability="begin")
+        self._active_mask.fill(False)
+        self._active_mask[ids] = True
+        self._dirty_mask.fill(False)
+        self._gain_dirty_mask.fill(False)
+        for mask in self._randomization_dirty_masks.values():
+            mask.fill(False)
+        self._requesting_terms.clear()
+        self._last_commit_had_writes = False
+        self._last_set_state_timing_ms = {}
+        self._active = True
+
+    def bind_body_mass_write(
+        self,
+        body_ids: np.ndarray,
+        *,
+        term_name: str,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """Bind body-mass columns and immutable backend defaults on the cold path."""
+        default = self._materialize_randomization_default(
+            RESET_TERM_BODY_MASS,
+            getter=self._backend.get_body_mass,
+            expected_tail=None,
+            term_name=term_name,
+        )
+        columns = self._validate_columns(
+            body_ids,
+            width=default.shape[0],
+            capability="body mass IDs",
+            term_name=term_name,
+        )
+        return self._readonly_binding(columns, default[columns])
+
+    def bind_body_ipos_write(
+        self,
+        body_ids: np.ndarray,
+        *,
+        term_name: str,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """Bind body inertial-position columns and immutable backend defaults."""
+        default = self._materialize_randomization_default(
+            RESET_TERM_BODY_IPOS,
+            getter=self._backend.get_body_ipos,
+            expected_tail=(3,),
+            term_name=term_name,
+        )
+        columns = self._validate_columns(
+            body_ids,
+            width=default.shape[0],
+            capability="body ipos IDs",
+            term_name=term_name,
+        )
+        return self._readonly_binding(columns, default[columns])
+
+    def bind_body_inertia_write(
+        self,
+        body_ids: np.ndarray,
+        *,
+        default: np.ndarray,
+        default_mass: np.ndarray,
+        term_name: str,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """Bind body-inertia columns with caller-supplied cold-path defaults.
+
+        ``SimBackend`` has no body-inertia getter, so the caller compiles the
+        scene model on the cold path and supplies the full ``(nbody, 3)``
+        principal-inertia table in backend body-id order. ``default_mass`` is
+        the full ``(nbody,)`` table from the same compile and is
+        cross-validated against the backend's authoritative body-mass table,
+        which fail-closed pins the body set and row ordering.
+        """
+        mass_default = self._materialize_randomization_default(
+            RESET_TERM_BODY_MASS,
+            getter=self._backend.get_body_mass,
+            expected_tail=None,
+            term_name=term_name,
+        )
+        try:
+            capabilities = self._backend.get_dr_capabilities()
+        except (AttributeError, NotImplementedError) as exc:
+            raise self._capability_error(term_name, "body_inertia randomization", exc) from exc
+        unsupported = capabilities.get_unsupported_reset_terms(
+            frozenset((RESET_TERM_BODY_INERTIA,))
+        )
+        if unsupported:
+            raise self._capability_error(
+                term_name,
+                "body_inertia randomization",
+                NotImplementedError(f"unsupported reset payload field: {RESET_TERM_BODY_INERTIA}"),
+            )
+        reference = self._validate_randomization_default_table(
+            default_mass,
+            expected_shape=mass_default.shape,
+            capability="default body_mass cross-check",
+            term_name=term_name,
+        )
+        if not np.allclose(reference, mass_default, rtol=1e-4, atol=1e-9):
+            raise ValueError(
+                f"EventManager term '{term_name}' caller-compiled body_mass table does not "
+                f"match backend '{self._backend.backend_type}' defaults; the cold-path scene "
+                "compile diverges from the backend model (e.g. fragments adding bodies)"
+            )
+        inertia = self._validate_randomization_default_table(
+            default,
+            expected_shape=(mass_default.shape[0], 3),
+            capability="default body_inertia",
+            term_name=term_name,
+        )
+        if np.any(inertia < 0.0):
+            raise ValueError(
+                f"EventManager term '{term_name}' default body_inertia contains negative values"
+            )
+        cached = self._randomization_defaults.get(RESET_TERM_BODY_INERTIA)
+        if cached is None:
+            inertia.setflags(write=False)
+            self._randomization_defaults[RESET_TERM_BODY_INERTIA] = inertia
+            self._randomization_values[RESET_TERM_BODY_INERTIA] = np.empty(
+                (self._num_envs, *inertia.shape),
+                dtype=inertia.dtype,
+            )
+            self._randomization_dirty_masks[RESET_TERM_BODY_INERTIA] = np.zeros(
+                self._num_envs, dtype=np.bool_
+            )
+        elif not np.array_equal(cached, inertia):
+            raise ValueError(
+                f"EventManager term '{term_name}' supplied a body_inertia default table that "
+                "differs from the table already bound on this transaction"
+            )
+        default_table = self._randomization_defaults[RESET_TERM_BODY_INERTIA]
+        columns = self._validate_columns(
+            body_ids,
+            width=default_table.shape[0],
+            capability="body inertia IDs",
+            term_name=term_name,
+        )
+        return self._readonly_binding(columns, default_table[columns])
+
+    def bind_gravity_write(self, *, term_name: str) -> np.ndarray:
+        """Bind the immutable backend gravity vector on the cold path."""
+        return self._materialize_randomization_default(
+            RESET_TERM_GRAVITY,
+            getter=self._backend.get_gravity,
+            expected_tail=(),
+            term_name=term_name,
+        )
+
+    def bind_dof_armature_write(
+        self,
+        dof_ids: np.ndarray,
+        *,
+        term_name: str,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """Bind DOF-armature columns and immutable backend defaults."""
+        default = self._materialize_randomization_default(
+            RESET_TERM_DOF_ARMATURE,
+            getter=self._backend.get_dof_armature,
+            expected_tail=None,
+            term_name=term_name,
+        )
+        columns = self._validate_columns(
+            dof_ids,
+            width=default.shape[0],
+            capability="DOF armature IDs",
+            term_name=term_name,
+        )
+        return self._readonly_binding(columns, default[columns])
+
+    def bind_geom_friction_write(
+        self,
+        geom_ids: np.ndarray,
+        *,
+        term_name: str,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """Bind geom-friction rows and immutable backend defaults."""
+        default = self._materialize_randomization_default(
+            RESET_TERM_GEOM_FRICTION,
+            getter=self._backend.get_geom_friction,
+            expected_tail=(3,),
+            term_name=term_name,
+        )
+        columns = self._validate_columns(
+            geom_ids,
+            width=default.shape[0],
+            capability="geom friction IDs",
+            term_name=term_name,
+        )
+        return self._readonly_binding(columns, default[columns])
+
+    def write_body_mass(
+        self,
+        env_ids: np.ndarray,
+        body_ids: np.ndarray,
+        values: np.ndarray,
+        *,
+        term_name: str,
+    ) -> None:
+        """Stage selected body masses in the exactly-once reset payload."""
+        self._write_selected_randomization(
+            RESET_TERM_BODY_MASS,
+            env_ids,
+            body_ids,
+            values,
+            value_tail=(),
+            term_name=term_name,
+        )
+
+    def write_body_ipos(
+        self,
+        env_ids: np.ndarray,
+        body_ids: np.ndarray,
+        values: np.ndarray,
+        *,
+        term_name: str,
+    ) -> None:
+        """Stage selected body inertial positions in the reset payload."""
+        self._write_selected_randomization(
+            RESET_TERM_BODY_IPOS,
+            env_ids,
+            body_ids,
+            values,
+            value_tail=(3,),
+            term_name=term_name,
+        )
+
+    def write_body_inertia(
+        self,
+        env_ids: np.ndarray,
+        body_ids: np.ndarray,
+        values: np.ndarray,
+        *,
+        term_name: str,
+    ) -> None:
+        """Stage selected body principal-inertia diagonals in the reset payload."""
+        self._write_selected_randomization(
+            RESET_TERM_BODY_INERTIA,
+            env_ids,
+            body_ids,
+            values,
+            value_tail=(3,),
+            term_name=term_name,
+        )
+
+    def write_gravity(
+        self,
+        env_ids: np.ndarray,
+        values: np.ndarray,
+        *,
+        term_name: str,
+    ) -> None:
+        """Stage per-environment gravity vectors in the reset payload."""
+        ids = self._prepare_state_write(
+            env_ids,
+            capability="gravity",
+            term_name=term_name,
+        )
+        default = self._require_randomization_default(RESET_TERM_GRAVITY, term_name)
+        gravity = self._validate_values(
+            values,
+            shape=(ids.size, 3),
+            capability="gravity",
+            term_name=term_name,
+        )
+        buffer = self._randomization_values[RESET_TERM_GRAVITY]
+        mask = self._randomization_dirty_masks[RESET_TERM_GRAVITY]
+        uninitialized = ids[~mask[ids]]
+        if uninitialized.size:
+            buffer[uninitialized] = default
+        buffer[ids] = gravity
+        mask[ids] = True
+        self._dirty_mask[ids] = True
+
+    def write_dof_armature(
+        self,
+        env_ids: np.ndarray,
+        dof_ids: np.ndarray,
+        values: np.ndarray,
+        *,
+        term_name: str,
+    ) -> None:
+        """Stage selected DOF armatures in the reset payload."""
+        self._write_selected_randomization(
+            RESET_TERM_DOF_ARMATURE,
+            env_ids,
+            dof_ids,
+            values,
+            value_tail=(),
+            term_name=term_name,
+        )
+
+    def write_geom_friction(
+        self,
+        env_ids: np.ndarray,
+        geom_ids: np.ndarray,
+        values: np.ndarray,
+        *,
+        term_name: str,
+    ) -> None:
+        """Stage selected three-axis geom friction in the reset payload."""
+        self._write_selected_randomization(
+            RESET_TERM_GEOM_FRICTION,
+            env_ids,
+            geom_ids,
+            values,
+            value_tail=(3,),
+            term_name=term_name,
+        )
+
+    def bind_actuator_gain_write(
+        self,
+        actuator_ids: np.ndarray,
+        *,
+        term_name: str,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """Resolve gain mutation capability and immutable defaults on the cold path."""
+        columns = self._validate_columns(
+            actuator_ids,
+            width=self._backend.num_actuators,
+            capability="actuator IDs",
+            term_name=term_name,
+        )
+        self._materialize_default_actuator_gains(term_name)
+        assert self._default_kp is not None
+        assert self._default_kd is not None
+        selected_kp = np.array(self._default_kp[columns], copy=True)
+        selected_kd = np.array(self._default_kd[columns], copy=True)
+        selected_kp.setflags(write=False)
+        selected_kd.setflags(write=False)
+        bound_columns = np.array(columns, copy=True)
+        bound_columns.setflags(write=False)
+        return bound_columns, selected_kp, selected_kd
+
+    def write_actuator_gains(
+        self,
+        env_ids: np.ndarray,
+        actuator_ids: np.ndarray,
+        kp: np.ndarray,
+        kd: np.ndarray,
+        *,
+        term_name: str,
+    ) -> None:
+        """Stage selected per-environment actuator gains in the reset transaction."""
+        ids = self._prepare_state_write(
+            env_ids,
+            capability="actuator-gain",
+            term_name=term_name,
+        )
+        columns = self._validate_columns(
+            actuator_ids,
+            width=self._backend.num_actuators,
+            capability="actuator IDs",
+            term_name=term_name,
+        )
+        self._materialize_default_actuator_gains(term_name)
+        gains_shape = (ids.size, columns.size)
+        kp_values = self._validate_values(
+            kp,
+            shape=gains_shape,
+            capability="actuator kp",
+            term_name=term_name,
+        )
+        kd_values = self._validate_values(
+            kd,
+            shape=gains_shape,
+            capability="actuator kd",
+            term_name=term_name,
+        )
+        assert self._default_kp is not None
+        assert self._default_kd is not None
+        assert self._kp is not None
+        assert self._kd is not None
+        uninitialized = ids[~self._gain_dirty_mask[ids]]
+        if uninitialized.size:
+            self._kp[uninitialized] = self._default_kp
+            self._kd[uninitialized] = self._default_kd
+        if ids.size and columns.size:
+            self._kp[ids[:, None], columns[None, :]] = kp_values
+            self._kd[ids[:, None], columns[None, :]] = kd_values
+        self._gain_dirty_mask[ids] = True
+        self._dirty_mask[ids] = True
+
+    def reset_to_default(self, env_ids: np.ndarray, *, term_name: str) -> None:
+        """Stage backend default qpos/qvel for a subset of the active reset."""
+        self._require_active()
+        ids = self._validate_ids(env_ids, capability="reset_to_default")
+        outside = ids[~self._active_mask[ids]]
+        if outside.size:
+            raise ValueError(
+                "EventManager term "
+                f"'{term_name}' attempted reset-state mutation outside the active reset: "
+                f"{outside.tolist()}"
+            )
+        if ids.size == 0:
+            return
+        self._requesting_terms.add(term_name)
+        self._materialize_default_state(term_name)
+        assert self._default_qpos is not None
+        assert self._default_qvel is not None
+        assert self._qpos is not None
+        assert self._qvel is not None
+        self._qpos[ids] = self._default_qpos
+        self._qvel[ids] = self._default_qvel
+        self._dirty_mask[ids] = True
+
+    def write_joint_state(
+        self,
+        env_ids: np.ndarray,
+        qpos_indices: np.ndarray,
+        qvel_indices: np.ndarray,
+        position: np.ndarray,
+        velocity: np.ndarray,
+        *,
+        term_name: str,
+    ) -> None:
+        """Stage selected joint position and velocity columns in the reset batch."""
+        self._require_active()
+        ids = self._validate_ids(env_ids, capability="write_joint_state")
+        outside = ids[~self._active_mask[ids]]
+        if outside.size:
+            raise ValueError(
+                f"EventManager term '{term_name}' attempted joint-state mutation outside "
+                f"the active reset: {outside.tolist()}"
+            )
+        self._requesting_terms.add(term_name)
+        self._materialize_default_state(term_name)
+        assert self._default_qpos is not None
+        assert self._default_qvel is not None
+        assert self._qpos is not None
+        assert self._qvel is not None
+
+        pos_columns = self._validate_columns(
+            qpos_indices,
+            width=self._default_qpos.size,
+            capability="qpos indices",
+            term_name=term_name,
+        )
+        vel_columns = self._validate_columns(
+            qvel_indices,
+            width=self._default_qvel.size,
+            capability="qvel indices",
+            term_name=term_name,
+        )
+        if pos_columns.size != vel_columns.size:
+            raise ValueError(
+                f"EventManager term '{term_name}' joint-state qpos/qvel index counts differ: "
+                f"{pos_columns.size} != {vel_columns.size}"
+            )
+        positions = self._validate_values(
+            position,
+            shape=(ids.size, pos_columns.size),
+            capability="joint position",
+            term_name=term_name,
+        )
+        velocities = self._validate_values(
+            velocity,
+            shape=(ids.size, vel_columns.size),
+            capability="joint velocity",
+            term_name=term_name,
+        )
+
+        uninitialized = ids[~self._dirty_mask[ids]]
+        if uninitialized.size:
+            self._qpos[uninitialized] = self._default_qpos
+            self._qvel[uninitialized] = self._default_qvel
+        if ids.size and pos_columns.size:
+            self._qpos[ids[:, None], pos_columns[None, :]] = positions
+            self._qvel[ids[:, None], vel_columns[None, :]] = velocities
+        self._dirty_mask[ids] = True
+
+    def write_root_state(
+        self,
+        env_ids: np.ndarray,
+        layout: BackendRootStateLayout,
+        root_state: np.ndarray,
+        *,
+        term_name: str,
+    ) -> None:
+        """Stage a community 13-D world-frame root state."""
+        self._require_active()
+        ids = self._validate_ids(env_ids, capability="write_root_state")
+        values = self._validate_values(
+            root_state,
+            shape=(ids.size, 13),
+            capability="root state",
+            term_name=term_name,
+        )
+        self.write_root_pose(ids, layout, values[:, :7], term_name=term_name)
+        self.write_root_velocity(ids, layout, values[:, 7:], term_name=term_name)
+
+    def write_root_pose(
+        self,
+        env_ids: np.ndarray,
+        layout: BackendRootStateLayout,
+        pose: np.ndarray,
+        *,
+        term_name: str,
+    ) -> None:
+        """Stage world position and wxyz orientation for one floating root."""
+        ids = self._prepare_state_write(env_ids, capability="root-pose", term_name=term_name)
+        positions = self._validate_values(
+            pose,
+            shape=(ids.size, 7),
+            capability="root pose",
+            term_name=term_name,
+        )
+        self._validate_quaternions(positions[:, 3:7], term_name=term_name)
+        qpos_columns, _ = self._validate_root_layout(layout, term_name=term_name)
+        assert self._qpos is not None
+        if ids.size:
+            self._qpos[ids[:, None], qpos_columns[None, :]] = positions
+        self._dirty_mask[ids] = True
+
+    def write_root_velocity(
+        self,
+        env_ids: np.ndarray,
+        layout: BackendRootStateLayout,
+        velocity_w: np.ndarray,
+        *,
+        term_name: str,
+    ) -> None:
+        """Stage world-frame root velocity in generalized qvel columns.
+
+        The public generalized-state contract stores free-root linear velocity
+        in world coordinates and angular velocity in root-body coordinates.
+        The conversion uses the pose already staged in this transaction.
+        """
+        ids = self._prepare_state_write(env_ids, capability="root-velocity", term_name=term_name)
+        velocities = self._validate_values(
+            velocity_w,
+            shape=(ids.size, 6),
+            capability="root velocity",
+            term_name=term_name,
+        )
+        qpos_columns, qvel_columns = self._validate_root_layout(layout, term_name=term_name)
+        assert self._qpos is not None
+        assert self._qvel is not None
+        if ids.size:
+            quaternions = self._qpos[
+                ids[:, None],
+                qpos_columns[None, 3:7],
+            ]
+            self._validate_quaternions(quaternions, term_name=term_name)
+            encoded_velocity = np.array(velocities, copy=True)
+            encoded_velocity[:, 3:6] = np_quat_apply_inverse(
+                quaternions,
+                velocities[:, 3:6],
+            )
+            self._qvel[ids[:, None], qvel_columns[None, :]] = encoded_velocity
+        self._dirty_mask[ids] = True
+
+    def read_root_pose(
+        self,
+        env_ids: np.ndarray,
+        layout: BackendRootStateLayout,
+        *,
+        term_name: str,
+    ) -> np.ndarray:
+        """Read the world position and wxyz orientation staged for one floating root.
+
+        Returns a detached ``(len(env_ids), 7)`` copy of the pose currently
+        staged in this transaction. Rows no term has written yet are first
+        initialized to the backend default pose, so a later reset term can
+        build on an earlier term's root placement without re-deriving it.
+        """
+        ids = self._prepare_state_write(env_ids, capability="root-pose", term_name=term_name)
+        qpos_columns, _ = self._validate_root_layout(layout, term_name=term_name)
+        assert self._qpos is not None
+        return np.array(self._qpos[ids[:, None], qpos_columns[None, :]], copy=True)
+
+    def commit(self) -> dict | None:
+        """Commit all staged rows through one public backend call."""
+        self._require_active()
+        dirty_ids = np.flatnonzero(self._dirty_mask).astype(np.int32, copy=False)
+        self._last_commit_had_writes = bool(dirty_ids.size)
+        try:
+            if dirty_ids.size == 0:
+                return None
+            assert self._qpos is not None
+            assert self._qvel is not None
+            randomization = self._build_randomization_payload(dirty_ids)
+            try:
+                set_state_t0 = time.perf_counter()
+                result = self._backend.set_state(
+                    dirty_ids,
+                    self._qpos[dirty_ids],
+                    self._qvel[dirty_ids],
+                    randomization=randomization,
+                )
+                self._record_committed_payload(dirty_ids, randomization)
+                timing: dict[str, float] = {
+                    "dr_reset_set_state_ms": (time.perf_counter() - set_state_t0) * 1000.0
+                }
+                if isinstance(result, dict):
+                    backend_timing = result.get("timing")
+                    if isinstance(backend_timing, dict):
+                        timing.update(backend_timing)
+                self._last_set_state_timing_ms = timing
+                return cast(dict | None, result)
+            except (AttributeError, NotImplementedError) as exc:
+                terms = ", ".join(sorted(self._requesting_terms))
+                raise NotImplementedError(
+                    "EventManager reset-state capability 'SimBackend.set_state' is unavailable "
+                    f"for term(s) [{terms}] on backend '{self._backend.backend_type}': {exc}"
+                ) from exc
+        finally:
+            self._finish()
+
+    def abort(self) -> None:
+        """Discard staged rows without touching the backend."""
+        if self._active:
+            self._finish()
+
+    def _materialize_default_state(self, term_name: str) -> None:
+        if self._default_qpos is not None:
+            return
+        qpos = self._selected_default_qpos
+        if qpos is None:
+            try:
+                qpos = self._backend.get_default_qpos()
+            except (AttributeError, NotImplementedError) as exc:
+                raise self._capability_error(term_name, "default qpos", exc) from exc
+        try:
+            qvel = self._backend.get_init_qvel()
+        except (AttributeError, NotImplementedError) as exc:
+            raise self._capability_error(term_name, "initial qvel", exc) from exc
+
+        default_qpos = self._validate_state_vector(qpos, "default qpos", term_name)
+        default_qvel = self._validate_state_vector(qvel, "initial qvel", term_name)
+        self._default_qpos = default_qpos
+        self._default_qvel = default_qvel
+        self._qpos = np.empty((self._num_envs, default_qpos.size), dtype=default_qpos.dtype)
+        self._qvel = np.empty((self._num_envs, default_qvel.size), dtype=default_qvel.dtype)
+
+    def _materialize_default_actuator_gains(self, term_name: str) -> None:
+        if self._default_kp is not None:
+            return
+        try:
+            capabilities = self._backend.get_dr_capabilities()
+        except (AttributeError, NotImplementedError) as exc:
+            raise self._capability_error(term_name, "actuator gain randomization", exc) from exc
+        required = frozenset((RESET_TERM_KP, RESET_TERM_KD))
+        unsupported = capabilities.get_unsupported_reset_terms(required)
+        if unsupported:
+            detail = ", ".join(sorted(unsupported))
+            raise self._capability_error(
+                term_name,
+                "actuator gain randomization",
+                NotImplementedError(f"unsupported reset payload fields: {detail}"),
+            )
+        try:
+            kp, kd = self._backend.get_actuator_gains()
+        except (AttributeError, NotImplementedError) as exc:
+            raise self._capability_error(term_name, "default actuator gains", exc) from exc
+        default_kp = self._validate_gain_vector(kp, "default actuator kp", term_name)
+        default_kd = self._validate_gain_vector(kd, "default actuator kd", term_name)
+        self._default_kp = default_kp
+        self._default_kd = default_kd
+        self._kp = np.empty(
+            (self._num_envs, self._backend.num_actuators),
+            dtype=default_kp.dtype,
+        )
+        self._kd = np.empty(
+            (self._num_envs, self._backend.num_actuators),
+            dtype=default_kd.dtype,
+        )
+
+    def _materialize_randomization_default(
+        self,
+        field: str,
+        *,
+        getter,
+        expected_tail: tuple[int, ...] | None,
+        term_name: str,
+    ) -> np.ndarray:
+        cached = self._randomization_defaults.get(field)
+        if cached is not None:
+            return cached
+        try:
+            capabilities = self._backend.get_dr_capabilities()
+        except (AttributeError, NotImplementedError) as exc:
+            raise self._capability_error(term_name, f"{field} randomization", exc) from exc
+        unsupported = capabilities.get_unsupported_reset_terms(frozenset((field,)))
+        if unsupported:
+            raise self._capability_error(
+                term_name,
+                f"{field} randomization",
+                NotImplementedError(f"unsupported reset payload field: {field}"),
+            )
+        try:
+            value = getter()
+        except (AttributeError, NotImplementedError) as exc:
+            raise self._capability_error(term_name, f"default {field}", exc) from exc
+        if not isinstance(value, np.ndarray):
+            raise TypeError(
+                f"EventManager term '{term_name}' capability 'default {field}' on backend "
+                f"'{self._backend.backend_type}' must return np.ndarray, got "
+                f"{type(value).__name__}"
+            )
+        expected_ndim = 1 if expected_tail is None else 1 + len(expected_tail)
+        if value.ndim != expected_ndim:
+            raise ValueError(
+                f"EventManager term '{term_name}' capability 'default {field}' on backend "
+                f"'{self._backend.backend_type}' returned shape {value.shape}; expected "
+                f"{expected_ndim}-D"
+            )
+        if expected_tail is not None and value.shape[1:] != expected_tail:
+            raise ValueError(
+                f"EventManager term '{term_name}' capability 'default {field}' on backend "
+                f"'{self._backend.backend_type}' returned shape {value.shape}; expected tail "
+                f"{expected_tail}"
+            )
+        if not np.issubdtype(value.dtype, np.floating):
+            raise TypeError(
+                f"EventManager term '{term_name}' capability 'default {field}' on backend "
+                f"'{self._backend.backend_type}' must be floating, got {value.dtype}"
+            )
+        if not np.isfinite(value).all():
+            raise ValueError(
+                f"EventManager term '{term_name}' capability 'default {field}' on backend "
+                f"'{self._backend.backend_type}' returned NaN or Inf"
+            )
+        default = np.array(value, copy=True)
+        default.setflags(write=False)
+        self._randomization_defaults[field] = default
+        self._randomization_values[field] = np.empty(
+            (self._num_envs, *default.shape),
+            dtype=default.dtype,
+        )
+        self._randomization_dirty_masks[field] = np.zeros(self._num_envs, dtype=np.bool_)
+        return default
+
+    def _require_randomization_default(self, field: str, term_name: str) -> np.ndarray:
+        try:
+            return self._randomization_defaults[field]
+        except KeyError as exc:
+            raise RuntimeError(
+                f"EventManager term '{term_name}' must bind reset field '{field}' "
+                "during manager construction before writing it"
+            ) from exc
+
+    def _validate_randomization_default_table(
+        self,
+        value: np.ndarray,
+        *,
+        expected_shape: tuple[int, ...],
+        capability: str,
+        term_name: str,
+    ) -> np.ndarray:
+        """Validate a caller-supplied cold-path default table and detach a copy."""
+        if not isinstance(value, np.ndarray):
+            raise TypeError(
+                f"EventManager term '{term_name}' {capability} must be np.ndarray, got "
+                f"{type(value).__name__}"
+            )
+        if value.shape != expected_shape:
+            raise ValueError(
+                f"EventManager term '{term_name}' {capability} has shape {value.shape}; "
+                f"expected {expected_shape}"
+            )
+        if not np.issubdtype(value.dtype, np.floating):
+            raise TypeError(
+                f"EventManager term '{term_name}' {capability} must be floating, got {value.dtype}"
+            )
+        if not np.isfinite(value).all():
+            raise ValueError(f"EventManager term '{term_name}' {capability} contains NaN or Inf")
+        return np.array(value, copy=True)
+
+    def _readonly_binding(
+        self,
+        columns: np.ndarray,
+        selected_default: np.ndarray,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        bound_columns = np.array(columns, copy=True)
+        bound_columns.setflags(write=False)
+        selected = np.array(selected_default, copy=True)
+        selected.setflags(write=False)
+        return bound_columns, selected
+
+    def _write_selected_randomization(
+        self,
+        field: str,
+        env_ids: np.ndarray,
+        column_ids: np.ndarray,
+        values: np.ndarray,
+        *,
+        value_tail: tuple[int, ...],
+        term_name: str,
+    ) -> None:
+        ids = self._prepare_state_write(
+            env_ids,
+            capability=field,
+            term_name=term_name,
+        )
+        default = self._require_randomization_default(field, term_name)
+        columns = self._validate_columns(
+            column_ids,
+            width=default.shape[0],
+            capability=f"{field} column IDs",
+            term_name=term_name,
+        )
+        selected = self._validate_values(
+            values,
+            shape=(ids.size, columns.size, *value_tail),
+            capability=field,
+            term_name=term_name,
+        )
+        buffer = self._randomization_values[field]
+        mask = self._randomization_dirty_masks[field]
+        uninitialized = ids[~mask[ids]]
+        if uninitialized.size:
+            buffer[uninitialized] = default
+        if ids.size and columns.size:
+            buffer[ids[:, None], columns[None, :]] = selected
+        mask[ids] = True
+        self._dirty_mask[ids] = True
+
+    def _build_randomization_payload(
+        self,
+        dirty_ids: np.ndarray,
+    ) -> ResetRandomizationPayload | None:
+        payload = ResetRandomizationPayload()
+        for field in (
+            RESET_TERM_BODY_INERTIA,
+            RESET_TERM_BODY_MASS,
+            RESET_TERM_BODY_IPOS,
+            RESET_TERM_DOF_ARMATURE,
+            RESET_TERM_GEOM_FRICTION,
+            RESET_TERM_GRAVITY,
+        ):
+            mask = self._randomization_dirty_masks.get(field)
+            if mask is None or not np.any(mask):
+                continue
+            self._fill_sparse_randomization_rows(field, mask, dirty_ids)
+            setattr(
+                payload,
+                field,
+                np.array(self._randomization_values[field][dirty_ids], copy=True),
+            )
+
+        gain_ids = np.flatnonzero(self._gain_dirty_mask).astype(np.int32, copy=False)
+        if gain_ids.size:
+            self._fill_sparse_gain_rows(dirty_ids)
+            assert self._kp is not None
+            assert self._kd is not None
+            payload.kp = np.array(self._kp[dirty_ids], copy=True)
+            payload.kd = np.array(self._kd[dirty_ids], copy=True)
+        return None if payload.is_empty() else payload
+
+    def _fill_sparse_randomization_rows(
+        self,
+        field: str,
+        mask: np.ndarray,
+        dirty_ids: np.ndarray,
+    ) -> None:
+        """Fill dirty rows the current reset did not rewrite from committed values.
+
+        Terms gated by ``min_step_count_between_reset`` intentionally skip envs
+        whose last randomized value must persist; the last committed payload
+        re-supplies those rows so one dense ``SimBackend.set_state`` call can
+        represent them. Rows with no committed history still fail closed.
+        """
+        missing = dirty_ids[~mask[dirty_ids]]
+        if not missing.size:
+            return
+        cached = self._committed_randomization.get(field)
+        cached_mask = self._committed_randomization_masks.get(field)
+        if cached is not None and cached_mask is not None:
+            uncommitted = missing[~cached_mask[missing]]
+        else:
+            uncommitted = missing
+        if uncommitted.size:
+            terms = ", ".join(sorted(self._requesting_terms))
+            raise RuntimeError(
+                f"EventManager reset {field} payload cannot represent sparse rows in one "
+                f"SimBackend.set_state call for term(s) [{terms}] on backend "
+                f"'{self._backend.backend_type}'; missing env IDs {uncommitted.tolist()}"
+            )
+        assert cached is not None
+        self._randomization_values[field][missing] = cached[missing]
+
+    def _fill_sparse_gain_rows(self, dirty_ids: np.ndarray) -> None:
+        """Fill gain rows the current reset did not rewrite from committed values."""
+        missing = dirty_ids[~self._gain_dirty_mask[dirty_ids]]
+        if not missing.size:
+            return
+        uncommitted = missing[~self._committed_gain_mask[missing]]
+        if uncommitted.size or self._committed_kp is None or self._committed_kd is None:
+            terms = ", ".join(sorted(self._requesting_terms))
+            raise RuntimeError(
+                "EventManager reset actuator gains payload cannot represent sparse rows in "
+                f"one SimBackend.set_state call for term(s) [{terms}] on backend "
+                f"'{self._backend.backend_type}'; missing env IDs {uncommitted.tolist()}"
+            )
+        assert self._kp is not None
+        assert self._kd is not None
+        self._kp[missing] = self._committed_kp[missing]
+        self._kd[missing] = self._committed_kd[missing]
+
+    def _record_committed_payload(
+        self,
+        dirty_ids: np.ndarray,
+        payload: ResetRandomizationPayload | None,
+    ) -> None:
+        """Cache the last committed per-env field values for sparse-row fill."""
+        if payload is None:
+            return
+        for field in (
+            RESET_TERM_BODY_INERTIA,
+            RESET_TERM_BODY_MASS,
+            RESET_TERM_BODY_IPOS,
+            RESET_TERM_DOF_ARMATURE,
+            RESET_TERM_GEOM_FRICTION,
+            RESET_TERM_GRAVITY,
+        ):
+            values = getattr(payload, field)
+            if values is None:
+                continue
+            cache = self._committed_randomization.get(field)
+            if cache is None:
+                cache = np.empty(
+                    (self._num_envs, *values.shape[1:]),
+                    dtype=values.dtype,
+                )
+                self._committed_randomization[field] = cache
+                self._committed_randomization_masks[field] = np.zeros(
+                    self._num_envs, dtype=np.bool_
+                )
+            cache[dirty_ids] = values
+            self._committed_randomization_masks[field][dirty_ids] = True
+        if payload.kp is not None and payload.kd is not None:
+            if self._committed_kp is None or self._committed_kd is None:
+                self._committed_kp = np.empty(
+                    (self._num_envs, payload.kp.shape[1]), dtype=payload.kp.dtype
+                )
+                self._committed_kd = np.empty(
+                    (self._num_envs, payload.kd.shape[1]), dtype=payload.kd.dtype
+                )
+            self._committed_kp[dirty_ids] = payload.kp
+            self._committed_kd[dirty_ids] = payload.kd
+            self._committed_gain_mask[dirty_ids] = True
+
+    def _prepare_state_write(
+        self,
+        env_ids: np.ndarray,
+        *,
+        capability: str,
+        term_name: str,
+    ) -> np.ndarray:
+        self._require_active()
+        ids = self._validate_ids(env_ids, capability=f"write_{capability}")
+        outside = ids[~self._active_mask[ids]]
+        if outside.size:
+            raise ValueError(
+                f"EventManager term '{term_name}' attempted {capability} mutation outside "
+                f"the active reset: {outside.tolist()}"
+            )
+        self._requesting_terms.add(term_name)
+        self._materialize_default_state(term_name)
+        assert self._default_qpos is not None
+        assert self._default_qvel is not None
+        assert self._qpos is not None
+        assert self._qvel is not None
+        uninitialized = ids[~self._dirty_mask[ids]]
+        if uninitialized.size:
+            self._qpos[uninitialized] = self._default_qpos
+            self._qvel[uninitialized] = self._default_qvel
+        return ids
+
+    def _validate_root_layout(
+        self,
+        layout: BackendRootStateLayout,
+        *,
+        term_name: str,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        if not isinstance(layout, BackendRootStateLayout):
+            raise TypeError(
+                f"EventManager term '{term_name}' root-state layout must be "
+                f"BackendRootStateLayout, got {type(layout).__name__}"
+            )
+        assert self._default_qpos is not None
+        assert self._default_qvel is not None
+        qpos_columns = self._validate_columns(
+            np.asarray(layout.qpos_indices, dtype=np.intp),
+            width=self._default_qpos.size,
+            capability="root qpos indices",
+            term_name=term_name,
+        )
+        qvel_columns = self._validate_columns(
+            np.asarray(layout.qvel_indices, dtype=np.intp),
+            width=self._default_qvel.size,
+            capability="root qvel indices",
+            term_name=term_name,
+        )
+        return qpos_columns, qvel_columns
+
+    def _validate_quaternions(self, values: np.ndarray, *, term_name: str) -> None:
+        norms = np.linalg.norm(values, axis=1)
+        invalid = ~np.isclose(norms, 1.0, rtol=1e-5, atol=1e-6)
+        if np.any(invalid):
+            raise ValueError(
+                f"EventManager term '{term_name}' root quaternion must be unit length; "
+                f"norms={norms[invalid].tolist()}"
+            )
+
+    def _validate_state_vector(
+        self,
+        value: np.ndarray,
+        capability: str,
+        term_name: str,
+    ) -> np.ndarray:
+        if not isinstance(value, np.ndarray):
+            raise TypeError(
+                f"EventManager term '{term_name}' capability '{capability}' on backend "
+                f"'{self._backend.backend_type}' must return np.ndarray, got "
+                f"{type(value).__name__}"
+            )
+        if value.ndim != 1:
+            raise ValueError(
+                f"EventManager term '{term_name}' capability '{capability}' on backend "
+                f"'{self._backend.backend_type}' returned shape {value.shape}; expected 1-D"
+            )
+        if not np.issubdtype(value.dtype, np.floating):
+            raise TypeError(
+                f"EventManager term '{term_name}' capability '{capability}' on backend "
+                f"'{self._backend.backend_type}' must be floating, got {value.dtype}"
+            )
+        if not np.isfinite(value).all():
+            raise ValueError(
+                f"EventManager term '{term_name}' capability '{capability}' on backend "
+                f"'{self._backend.backend_type}' returned NaN or Inf"
+            )
+        result = np.array(value, copy=True)
+        result.setflags(write=False)
+        return result
+
+    def _validate_gain_vector(
+        self,
+        value: np.ndarray,
+        capability: str,
+        term_name: str,
+    ) -> np.ndarray:
+        result = self._validate_state_vector(value, capability, term_name)
+        expected = (self._backend.num_actuators,)
+        if result.shape != expected:
+            raise ValueError(
+                f"EventManager term '{term_name}' capability '{capability}' on backend "
+                f"'{self._backend.backend_type}' returned shape {result.shape}; expected {expected}"
+            )
+        return result
+
+    def _validate_ids(self, env_ids: np.ndarray, *, capability: str) -> np.ndarray:
+        if not isinstance(env_ids, np.ndarray):
+            raise TypeError(
+                f"ManagerBased reset-state {capability} env_ids must be np.ndarray, "
+                f"got {type(env_ids).__name__}"
+            )
+        if (
+            env_ids.ndim != 1
+            or not np.issubdtype(env_ids.dtype, np.integer)
+            or np.issubdtype(env_ids.dtype, np.bool_)
+        ):
+            raise TypeError(
+                f"ManagerBased reset-state {capability} env_ids must be a 1-D integer "
+                f"np.ndarray, got shape={env_ids.shape}, dtype={env_ids.dtype}"
+            )
+        ids = np.asarray(env_ids, dtype=np.int32)
+        if np.any(ids < 0) or np.any(ids >= self._num_envs):
+            raise IndexError(
+                f"ManagerBased reset-state {capability} env_ids out of range for "
+                f"{self._num_envs} environments: {ids.tolist()}"
+            )
+        # Duplicate check via bincount instead of np.unique: identical semantics
+        # (ids are already range-checked above) but avoids the sort — ~30x faster
+        # at num_envs=4096 and ~4x at typical partial-reset widths. This runs on
+        # every reset-state write (~6x per env step), so the sort cost was
+        # measurable in the collector host phase (issue #1352).
+        if ids.size > 1 and np.bincount(ids, minlength=self._num_envs).max() > 1:
+            raise ValueError(
+                f"ManagerBased reset-state {capability} env_ids contain duplicates: {ids.tolist()}"
+            )
+        return ids
+
+    def _validate_columns(
+        self,
+        values: np.ndarray,
+        *,
+        width: int,
+        capability: str,
+        term_name: str,
+    ) -> np.ndarray:
+        if not isinstance(values, np.ndarray):
+            raise TypeError(
+                f"EventManager term '{term_name}' {capability} must be np.ndarray, "
+                f"got {type(values).__name__}"
+            )
+        if (
+            values.ndim != 1
+            or not np.issubdtype(values.dtype, np.integer)
+            or np.issubdtype(values.dtype, np.bool_)
+        ):
+            raise TypeError(
+                f"EventManager term '{term_name}' {capability} must be a 1-D integer array"
+            )
+        columns = np.asarray(values, dtype=np.intp)
+        if np.any(columns < 0) or np.any(columns >= width):
+            raise IndexError(
+                f"EventManager term '{term_name}' {capability} out of range for width "
+                f"{width}: {columns.tolist()}"
+            )
+        if np.unique(columns).size != columns.size:
+            raise ValueError(
+                f"EventManager term '{term_name}' {capability} contain duplicates: "
+                f"{columns.tolist()}"
+            )
+        return columns
+
+    def _validate_values(
+        self,
+        values: np.ndarray,
+        *,
+        shape: tuple[int, ...],
+        capability: str,
+        term_name: str,
+    ) -> np.ndarray:
+        if not isinstance(values, np.ndarray):
+            raise TypeError(
+                f"EventManager term '{term_name}' {capability} must be np.ndarray, "
+                f"got {type(values).__name__}"
+            )
+        if values.shape != shape:
+            raise ValueError(
+                f"EventManager term '{term_name}' {capability} has shape {values.shape}; "
+                f"expected {shape}"
+            )
+        if not np.issubdtype(values.dtype, np.floating):
+            raise TypeError(
+                f"EventManager term '{term_name}' {capability} must be floating, got {values.dtype}"
+            )
+        if not np.isfinite(values).all():
+            raise ValueError(f"EventManager term '{term_name}' {capability} contains NaN or Inf")
+        return values
+
+    def _capability_error(
+        self,
+        term_name: str,
+        capability: str,
+        exc: BaseException,
+    ) -> NotImplementedError:
+        return NotImplementedError(
+            f"EventManager term '{term_name}' reset-state capability '{capability}' is "
+            f"unavailable on backend '{self._backend.backend_type}': {exc}"
+        )
+
+    def _require_active(self) -> None:
+        if not self._active:
+            raise RuntimeError("ManagerBased reset-state mutation requires an active reset event")
+
+    def _finish(self) -> None:
+        self._active = False
+        self._active_mask.fill(False)
+        self._dirty_mask.fill(False)
+        self._gain_dirty_mask.fill(False)
+        for mask in self._randomization_dirty_masks.values():
+            mask.fill(False)
+        self._requesting_terms.clear()
+
+
+__all__ = ["ResetStateTransaction"]
diff --git a/src/unilab/base/scene.py b/src/unilab/base/scene.py
index dca7bc0df..10668cec7 100644
--- a/src/unilab/base/scene.py
+++ b/src/unilab/base/scene.py
@@ -1,46 +1,51 @@
+"""UniLab configuration owner layered over the package-neutral UniSim scene."""
+
 from __future__ import annotations
 
+from collections.abc import Mapping
 from dataclasses import dataclass, field
-from pathlib import Path
-
-from unilab.terrains.terrain_generator import TerrainGeneratorCfg
 
+from unisim.scene import (
+    SceneCfg as _UniSimSceneCfg,
+)
+from unisim.scene import (
+    TerrainSceneCfg,
+    resolve_scene_default_qpos,
+    resolve_scene_fragment_path,
+)
 
-def resolve_scene_fragment_path(fragment_file: str, model_file: Path) -> Path:
-    """Resolve a ``SceneCfg.fragment_files`` entry against the scene model file.
-
-    Single resolution rule shared by the MuJoCo and Motrix scene
-    materializers: absolute paths pass through; relative paths that exist
-    resolve against the CWD; anything else resolves relative to the model
-    file's directory.
-    """
-    path = Path(fragment_file)
-    if path.is_absolute():
-        return path
-    if path.is_file():
-        return path.resolve()
-    return (model_file.parent / path).resolve()
+from unilab.base.entity import EntityCfg
 
 
 @dataclass
-class TerrainSceneCfg:
-    """Backend-agnostic terrain slot declaration for a scene."""
+class SceneCfg(_UniSimSceneCfg):
+    """Task-owned scene declaration with UniLab entity materialization.
 
-    generator: TerrainGeneratorCfg | None = None
-    hfield_name: str = "terrain_hfield"
-    geom_name: str | None = None
-
-
-@dataclass
-class SceneCfg:
-    """Scene source and optional cold-path composition configuration."""
+    Physics scene fields and all backend-facing behavior are implemented by
+    UniSim.  UniLab only converts Hydra/OmegaConf entity mappings into the
+    manager facade's :class:`EntityCfg` records on the cold configuration path.
+    """
 
     model_file: str
     fragment_files: list[str] = field(default_factory=list)
     terrain: TerrainSceneCfg | None = None
-    # Optional render-only model override. When set, offline playback/video
-    # export renders this XML instead of ``model_file`` while physics keeps
-    # using ``model_file``. Used to give the renderer a visual twin of the
-    # scene (e.g. a per-env replicable obstacle) without touching the trained
-    # collision model. ``None`` => render with ``model_file`` (unchanged).
-    visual_model_file: str | None = None
+    entities: dict[str, object] = field(default_factory=dict)
+
+    def __post_init__(self) -> None:
+        materialized: dict[str, object] = {}
+        for name, value in self.entities.items():
+            if isinstance(value, EntityCfg):
+                materialized[name] = value
+            elif isinstance(value, Mapping):
+                materialized[name] = EntityCfg(**value)
+            else:
+                materialized[name] = value
+        self.entities = materialized
+
+
+__all__ = [
+    "SceneCfg",
+    "TerrainSceneCfg",
+    "resolve_scene_default_qpos",
+    "resolve_scene_fragment_path",
+]
diff --git a/src/unilab/cli.py b/src/unilab/cli.py
index b24666165..ca39a9ddb 100644
--- a/src/unilab/cli.py
+++ b/src/unilab/cli.py
@@ -16,9 +16,16 @@
 from unilab.demo import run_demo
 
 SUPPORTED_ALGOS = ("ppo", "appo", "sac", "td3", "flashsac")
-SUPPORTED_SIMS = ("mujoco", "mjwarp", "motrix")
+SUPPORTED_SIMS = ("mujoco", "mjwarp", "motrix", "drake", "isaacgym", "genesis", "isaacsim")
 SUPPORTED_RENDER_MODES = ("auto", "interactive", "record", "none")
 OFFPOLICY_ALGOS = {"sac", "td3", "flashsac"}
+# Built-in algos whose entrypoint script does not follow the train_<algo>.py
+# naming convention.
+SPECIAL_SCRIPT_NAMES = {"ppo": "train_rsl_rl.py", "appo": "train_appo.py"}
+INTERACTIVE_PLAY_ALGOS = {"ppo", "appo", "sac", "td3", "flashsac"}
+# Physics backends whose interactive eval runs through the dedicated MuJoCo
+# viewer script: the selected backend owns the rollout while MuJoCo renders.
+MUJOCO_VIEWER_PHYSICS_SIMS = frozenset({"mujoco", "mjwarp"})
 RESERVED_OVERRIDE_KEYS = {
     "algo",
     "task",
@@ -37,8 +44,8 @@ class Route:
     generated_overrides: tuple[str, ...]
 
 
-def repo_root() -> Path:
-    return Path(__file__).resolve().parents[2]
+def package_root() -> Path:
+    return Path(__file__).resolve().parent
 
 
 def _script_path(route: Route, root: Path) -> Path:
@@ -49,14 +56,6 @@ def _owner_yaml_path(route: Route, root: Path) -> Path:
     return root / "conf" / route.config_group / "task" / route.owner_task
 
 
-def _check_private_checkout(root: Path) -> None:
-    if not (root / "conf").is_dir() or not (root / "scripts").is_dir():
-        raise SystemExit(
-            "The current UniLab CLI expects a UniLab source checkout. "
-            "Run it from the uv-managed editable environment created by this repo."
-        )
-
-
 def _check_reserved_overrides(overrides: Sequence[str]) -> None:
     reserved = [
         override for override in overrides if _override_key(override) in RESERVED_OVERRIDE_KEYS
@@ -102,16 +101,68 @@ def _check_load_run(load_run: str) -> None:
 def _check_runtime_requirements(algo: str, sim: str) -> None:
     if sim == "mujoco" and find_spec("mujoco") is None:
         raise SystemExit(
-            "sim=mujoco requires the MuJoCo extra. Install it with `uv sync --extra mujoco`."
+            "sim=mujoco requires the MuJoCo extra. Install it with "
+            "`pip install unilab[mujoco]` (or `uv sync --extra mujoco` in a source checkout)."
         )
     if sim == "mjwarp" and (find_spec("mujoco_warp") is None or find_spec("warp") is None):
         raise SystemExit(
-            "sim=mjwarp requires the mjwarp extra. Install it with `uv sync --extra mjwarp`."
+            "sim=mjwarp requires the mjwarp extra. Install it with "
+            "`pip install unilab[mjwarp]` (or `uv sync --extra mjwarp` in a source checkout)."
         )
     if sim == "motrix" and find_spec("motrixsim") is None:
         raise SystemExit(
-            "sim=motrix requires the Motrix extra. Install it with `uv sync --extra motrix`."
+            "sim=motrix requires the Motrix extra. Install it with "
+            "`pip install unilab[motrix]` (or `uv sync --extra motrix` in a source checkout)."
         )
+    if sim == "drake":
+        if find_spec("drake_uni") is None:
+            raise SystemExit(
+                "sim=drake requires the Drake extra and a built DrakeUni batch extension. "
+                "Run `make setup-drake` in a source checkout (or use the setup script directly)."
+            )
+        try:
+            from drake_uni.runtime import batch_diagnostics
+
+            diagnostics = batch_diagnostics()
+        except Exception as exc:
+            raise SystemExit(
+                "sim=drake could not load the Drake batch extension. "
+                "Set DRAKE_HOME and the platform library path (LD_LIBRARY_PATH on Linux, "
+                "DYLD_LIBRARY_PATH on macOS), then rerun `make setup-drake`; details: "
+                f"{exc}"
+            ) from exc
+        if not diagnostics.batch_available:
+            detail = diagnostics.batch_import_error or "unknown import error"
+            raise SystemExit(
+                f"sim=drake requires a working Drake batch extension; diagnostic reported: {detail}"
+            )
+    if sim == "isaacgym":
+        from unisim.backend.isaacgym.dependencies import isaacgym_runtime_available
+
+        if not isaacgym_runtime_available():
+            raise SystemExit(
+                "sim=isaacgym requires the external Python 3.8 worker runtime. "
+                "Install it with `scripts/tools/setup_isaacgym_env.sh` (see the "
+                "IsaacGym backend docs page)."
+            )
+    if sim == "genesis":
+        from unisim.backend.genesis.dependencies import genesis_dependencies_available
+
+        if not genesis_dependencies_available():
+            raise SystemExit(
+                "sim=genesis requires the genesis-world extra (pinned 1.3.3, torch>=2.8). "
+                "Install it with `pip install unilab[genesis]` (or `uv sync --extra genesis` "
+                "in a source checkout; see the Genesis backend docs page)."
+            )
+    if sim == "isaacsim":
+        from unisim.backend.isaacsim.dependencies import isaacsim_runtime_available
+
+        if not isaacsim_runtime_available():
+            raise SystemExit(
+                "sim=isaacsim requires the external Python 3.11 IsaacSim/IsaacLab worker "
+                "runtime. Install it with `scripts/tools/setup_isaacsim_env.sh` (see the "
+                "IsaacSim backend docs page)."
+            )
 
 
 def _override_bool(overrides: Sequence[str], key: str) -> bool | None:
@@ -175,33 +226,92 @@ def _mxpython_executable() -> str:
     )
 
 
-def build_route(algo: str, task: str, sim: str, profile: str | None = None) -> Route:
-    task_choice: str
+def available_algos(root: Path | None = None) -> tuple[str, ...]:
+    """Return routable algo names: built-ins plus convention-discovered ones.
+
+    A custom algo ``X`` is routable when both ``conf/X/config.yaml`` and
+    ``scripts/train_X.py`` exist under the package root. Config trees without
+    an entrypoint script (e.g. ``hora_distill``) are not routable.
+    """
+    selected_root = root or package_root()
+    discovered: list[str] = []
+    conf_root = selected_root / "conf"
+    if conf_root.is_dir():
+        for child in sorted(conf_root.iterdir()):
+            if not child.is_dir() or child.name in SUPPORTED_ALGOS:
+                continue
+            if not (child / "config.yaml").is_file():
+                continue
+            if (selected_root / "scripts" / f"train_{child.name}.py").is_file():
+                discovered.append(child.name)
+    return (*SUPPORTED_ALGOS, *discovered)
+
+
+def build_route(
+    algo: str, task: str, sim: str, profile: str | None = None, *, root: Path | None = None
+) -> Route:
     owner = f"{sim}_{profile}" if profile is not None else sim
-    if algo in OFFPOLICY_ALGOS:
-        task_choice = f"{algo}/{task}/{owner}"
-        return Route(
-            script_name="train_offpolicy.py",
-            config_group="offpolicy",
-            owner_task=f"{algo}/{task}/{owner}.yaml",
-            generated_overrides=(f"algo={algo}", f"task={task_choice}"),
-        )
     task_choice = f"{task}/{owner}"
-    if algo == "ppo":
-        return Route(
-            script_name="train_rsl_rl.py",
-            config_group="ppo",
-            owner_task=f"{task}/{owner}.yaml",
-            generated_overrides=(f"task={task_choice}",),
-        )
-    if algo == "appo":
-        return Route(
-            script_name="train_appo.py",
-            config_group="appo",
-            owner_task=f"{task}/{owner}.yaml",
-            generated_overrides=(f"task={task_choice}",),
+    if algo in OFFPOLICY_ALGOS:
+        script_name = f"train_{algo}.py"
+    elif algo in SPECIAL_SCRIPT_NAMES:
+        script_name = SPECIAL_SCRIPT_NAMES[algo]
+    else:
+        selected_root = root or package_root()
+        script_name = f"train_{algo}.py"
+        routable = (
+            TASK_NAME_PATTERN.fullmatch(algo) is not None
+            and (selected_root / "conf" / algo / "config.yaml").is_file()
+            and (selected_root / "scripts" / script_name).is_file()
         )
-    raise SystemExit(f"Unsupported algo={algo!r}; choose one of: {', '.join(SUPPORTED_ALGOS)}")
+        if not routable:
+            raise SystemExit(
+                f"Unsupported algo={algo!r}; choose one of: "
+                f"{', '.join(available_algos(selected_root))}"
+            )
+    return Route(
+        script_name=script_name,
+        config_group=algo,
+        owner_task=f"{task}/{owner}.yaml",
+        generated_overrides=(f"task={task_choice}",),
+    )
+
+
+def _eval_fallback_owner(route: Route, root: Path, *, sim: str, profile: str | None) -> str | None:
+    """Pick a sibling backend owner for eval when the requested sim has no owner YAML.
+
+    Eval replays a trained checkpoint, so any sibling owner of the same task (and
+    profile shape) supplies the task/algo contract; the requested backend is
+    re-applied through the sim2sim-allowlisted ``training.sim_backend`` override
+    and validated by the runtime sim2sim preflight against the source run.
+    """
+    task_dir = _owner_yaml_path(route, root).parent
+    for candidate_sim in SUPPORTED_SIMS:
+        if candidate_sim == sim:
+            continue
+        owner = f"{candidate_sim}_{profile}" if profile is not None else candidate_sim
+        if (task_dir / f"{owner}.yaml").is_file():
+            return owner
+    return None
+
+
+def _uses_mujoco_interactive_play(
+    *,
+    mode: str,
+    algo: str,
+    sim: str,
+    render_mode: str | None,
+    overrides: Sequence[str],
+) -> bool:
+    """Return whether eval should use the dedicated MuJoCo viewer script."""
+    if (
+        mode != "eval"
+        or sim not in MUJOCO_VIEWER_PHYSICS_SIMS
+        or algo not in INTERACTIVE_PLAY_ALGOS
+    ):
+        return False
+    selected_mode = _override_value(overrides, "training.play_render_mode") or render_mode
+    return selected_mode is not None and selected_mode.strip().lower() == "interactive"
 
 
 def build_command(
@@ -216,27 +326,71 @@ def build_command(
     render_mode: str | None = None,
     root: Path | None = None,
 ) -> list[str]:
-    selected_root = root or repo_root()
-    _check_private_checkout(selected_root)
+    selected_root = root or package_root()
     _check_task_name(task)
     _check_profile(profile)
     _check_reserved_overrides(overrides)
     _check_runtime_requirements(algo, sim)
 
-    route = build_route(algo, task, sim, profile)
-    script = _script_path(route, selected_root)
+    route = build_route(algo, task, sim, profile, root=selected_root)
+    use_interactive_play = _uses_mujoco_interactive_play(
+        mode=mode,
+        algo=algo,
+        sim=sim,
+        render_mode=render_mode,
+        overrides=overrides,
+    )
+    if use_interactive_play and find_spec("mujoco") is None:
+        raise SystemExit(
+            "interactive eval renders through the MuJoCo viewer and requires the MuJoCo "
+            "extra. Install it with `pip install unilab[mujoco]` (or `uv sync --extra "
+            "mujoco` in a source checkout)."
+        )
+    script = (
+        selected_root / "scripts" / "play_interactive.py"
+        if use_interactive_play
+        else _script_path(route, selected_root)
+    )
     if not script.is_file():
         raise SystemExit(f"Entrypoint script not found: {script}")
 
+    owner = f"{sim}_{profile}" if profile is not None else sim
+    sim_backend_override: str | None = None
     owner_yaml = _owner_yaml_path(route, selected_root)
     if not owner_yaml.is_file():
-        raise SystemExit(
-            f"No owner config exists for algo={algo}, task={task}, sim={sim}: {owner_yaml}"
+        if mode != "eval":
+            raise SystemExit(
+                f"No owner config exists for algo={algo}, task={task}, sim={sim}: {owner_yaml}"
+            )
+        fallback_owner = _eval_fallback_owner(route, selected_root, sim=sim, profile=profile)
+        if fallback_owner is None:
+            raise SystemExit(
+                f"No owner config exists for algo={algo}, task={task}, sim={sim}: {owner_yaml}; "
+                "eval fallback found no sibling backend owner config for this task either"
+            )
+        owner = fallback_owner
+        route = Route(
+            script_name=route.script_name,
+            config_group=route.config_group,
+            owner_task=f"{task}/{fallback_owner}.yaml",
+            generated_overrides=(f"task={task}/{fallback_owner}",),
+        )
+        sim_backend_override = sim
+        print(
+            f"[eval] no owner config for sim={sim}; reusing sibling owner {fallback_owner!r} "
+            f"with training.sim_backend={sim} (sim2sim contract check still applies)",
+            file=sys.stderr,
         )
 
-    generated = list(route.generated_overrides)
-    if render_mode is not None:
+    generated = [] if use_interactive_play else list(route.generated_overrides)
+    if sim_backend_override is not None:
+        generated.append(f"training.sim_backend={sim_backend_override}")
+    if render_mode is not None and _override_value(overrides, "training.play_render_mode") is None:
         generated.append(f"training.play_render_mode={render_mode}")
+    if use_interactive_play and _override_value(overrides, "interactive.action_mode") is None:
+        # The low-level viewer defaults to zero actions for debugging, while
+        # eval must preserve the policy-control behavior of the train scripts.
+        generated.append("interactive.action_mode=policy")
     if mode == "eval":
         generated.append("training.play_only=true")
         if load_run is not None:
@@ -246,12 +400,34 @@ def build_command(
             generated.append(f"algo.load_run={load_run}")
 
     executable = _python_executable_for_route(mode, sim, (*generated, *overrides))
+    if use_interactive_play:
+        return [
+            executable,
+            str(script),
+            "--algo",
+            algo,
+            "--task",
+            task,
+            "--sim",
+            owner,
+            *generated,
+            *overrides,
+        ]
     return [executable, str(script), *generated, *overrides]
 
 
 def _train_eval_parser(*, mode: str) -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser(prog=mode)
-    parser.add_argument("--algo", required=True, choices=SUPPORTED_ALGOS)
+    parser.add_argument(
+        "--algo",
+        required=True,
+        metavar="ALGO",
+        help=(
+            "algorithm config tree under conf/; built-ins: "
+            f"{', '.join(SUPPORTED_ALGOS)}. Custom algos are routable when "
+            "conf/<algo>/config.yaml and scripts/train_<algo>.py both exist."
+        ),
+    )
     parser.add_argument("--task", required=True)
     parser.add_argument("--sim", required=True, choices=SUPPORTED_SIMS)
     parser.add_argument("--profile", default=None)
diff --git a/src/unilab/cli_completion.py b/src/unilab/cli_completion.py
new file mode 100644
index 000000000..03b36dafa
--- /dev/null
+++ b/src/unilab/cli_completion.py
@@ -0,0 +1,710 @@
+"""Shell completion helper for UniLab training entrypoints."""
+
+from __future__ import annotations
+
+import argparse
+import os
+import platform
+import re
+import sys
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Mapping, Sequence
+
+from unilab import cli
+
+TRAINING_ENTRYPOINTS = {
+    "train": "unilab.cli:train_main",
+    "eval": "unilab.cli:eval_main",
+}
+RUN_PATH_ROOTS = ("scripts",)
+RUN_PATH_SUFFIXES = (".py", ".sh")
+RUN_PATH_IGNORED_PARTS = ("__pycache__", "outputs")
+SCRIPT_ASSIGNMENT_PATTERN = re.compile(r'^([A-Za-z0-9_.-]+)\s*=\s*"([^"]+)"\s*(?:#.*)?$')
+DEFAULT_ALGO_LOG_NAMES = {
+    "ppo": "rsl_rl_ppo",
+    "appo": "appo",
+    "sac": "fast_sac",
+    "td3": "fast_td3",
+    "flashsac": "flash_sac",
+}
+COMPLETION_BLOCK_START = "# >>> unilab completion >>>"
+COMPLETION_BLOCK_END = "# <<< unilab completion <<<"
+SUPPORTED_SHELLS = ("bash", "zsh")
+
+
+@dataclass(frozen=True)
+class TaskCompletionEntry:
+    algo: str
+    task: str
+    sim: str
+    owner: str
+
+
+@dataclass(frozen=True)
+class CompletionMetadata:
+    commands: tuple[str, ...]
+    flags: dict[str, tuple[str, ...]]
+    choices: dict[str, dict[str, tuple[str, ...]]]
+    tasks: tuple[TaskCompletionEntry, ...]
+    run_paths: tuple[str, ...] = ()
+    root: Path | None = None
+    log_base: Path | None = None
+
+
+def _find_project_root(start: Path) -> Path | None:
+    current = start.resolve()
+    if current.is_file():
+        current = current.parent
+    for candidate in (current, *current.parents):
+        if (candidate / "pyproject.toml").is_file():
+            return candidate
+    return None
+
+
+def _read_project_scripts(pyproject_path: Path) -> dict[str, str]:
+    scripts: dict[str, str] = {}
+    in_scripts = False
+    for line in pyproject_path.read_text(encoding="utf-8").splitlines():
+        stripped = line.strip()
+        if not stripped or stripped.startswith("#"):
+            continue
+        if stripped.startswith("[") and stripped.endswith("]"):
+            in_scripts = stripped == "[project.scripts]"
+            continue
+        if not in_scripts:
+            continue
+        match = SCRIPT_ASSIGNMENT_PATTERN.fullmatch(stripped)
+        if match is not None:
+            scripts[match.group(1)] = match.group(2)
+    return scripts
+
+
+def _training_commands(scripts: Mapping[str, str]) -> tuple[str, ...]:
+    return tuple(
+        command
+        for command, target in TRAINING_ENTRYPOINTS.items()
+        if scripts.get(command) == target
+    )
+
+
+def _project_commands(scripts: Mapping[str, str]) -> tuple[str, ...]:
+    training_commands = _training_commands(scripts)
+    commands = [*training_commands]
+    commands.extend(command for command in sorted(scripts) if command not in training_commands)
+    return tuple(commands)
+
+
+def _parser_for_command(command: str) -> argparse.ArgumentParser:
+    return cli._train_eval_parser(mode=command)
+
+
+def _parser_flags(command: str) -> tuple[str, ...]:
+    parser = _parser_for_command(command)
+    flags: list[str] = []
+    for action in parser._actions:
+        flags.extend(option for option in action.option_strings if option.startswith("--"))
+    return tuple(flags)
+
+
+def _parser_choices(command: str) -> dict[str, tuple[str, ...]]:
+    parser = _parser_for_command(command)
+    choices: dict[str, tuple[str, ...]] = {}
+    for action in parser._actions:
+        if action.choices is None:
+            continue
+        selected = tuple(str(choice) for choice in action.choices)
+        for option in action.option_strings:
+            if option.startswith("--"):
+                choices[option] = selected
+    return choices
+
+
+def _sim_from_owner(owner: str) -> str | None:
+    for sim in cli.SUPPORTED_SIMS:
+        if owner == sim or owner.startswith(f"{sim}_"):
+            return sim
+    return None
+
+
+def _task_entries_for_group(
+    root: Path, group: str, algos: Sequence[str]
+) -> list[TaskCompletionEntry]:
+    entries: list[TaskCompletionEntry] = []
+    task_root = root / "conf" / group / "task"
+    if not task_root.is_dir():
+        return entries
+    for task_dir in sorted(path for path in task_root.iterdir() if path.is_dir()):
+        for owner_yaml in sorted(task_dir.glob("*.yaml")):
+            sim = _sim_from_owner(owner_yaml.stem)
+            if sim is None:
+                continue
+            entries.extend(
+                TaskCompletionEntry(algo=algo, task=task_dir.name, sim=sim, owner=owner_yaml.stem)
+                for algo in algos
+            )
+    return entries
+
+
+def _task_entries(root: Path) -> tuple[TaskCompletionEntry, ...]:
+    entries = [
+        *_task_entries_for_group(root, "ppo", ("ppo",)),
+        *_task_entries_for_group(root, "appo", ("appo",)),
+        *_task_entries_for_group(root, "sac", ("sac",)),
+        *_task_entries_for_group(root, "td3", ("td3",)),
+        *_task_entries_for_group(root, "flashsac", ("flashsac",)),
+    ]
+    return tuple(
+        sorted(entries, key=lambda entry: (entry.task, entry.algo, entry.sim, entry.owner))
+    )
+
+
+def _is_run_path(path: Path) -> bool:
+    return path.is_file() and path.suffix in RUN_PATH_SUFFIXES and path.name != "__init__.py"
+
+
+def _has_run_path_descendant(path: Path) -> bool:
+    return any(
+        child.name != "__init__.py" and child.suffix in RUN_PATH_SUFFIXES
+        for child in path.rglob("*")
+    )
+
+
+def _run_path_entries(root: Path) -> tuple[str, ...]:
+    entries: set[str] = set()
+    for path_root in RUN_PATH_ROOTS:
+        base = root / path_root
+        if not base.is_dir():
+            continue
+        entries.add(f"{path_root}/")
+        for path in base.rglob("*"):
+            if path.name.startswith("."):
+                continue
+            relative_parts = path.relative_to(root).parts
+            if any(part in RUN_PATH_IGNORED_PARTS for part in relative_parts):
+                continue
+            relative_path = path.relative_to(root).as_posix()
+            if path.is_dir():
+                if _has_run_path_descendant(path):
+                    entries.add(f"{relative_path}/")
+            elif _is_run_path(path):
+                entries.add(relative_path)
+    return tuple(sorted(entries))
+
+
+def build_metadata(root: Path | None = None) -> CompletionMetadata:
+    checkout = _find_project_root(Path.cwd()) if root is None else None
+    selected_root = root or checkout or cli.package_root()
+    # Task/owner YAML always come from the packaged conf tree; the checkout
+    # root only contributes run-path entries and [project.scripts] metadata.
+    task_root = selected_root if root is not None else cli.package_root()
+    pyproject_path = selected_root / "pyproject.toml"
+    if pyproject_path.is_file():
+        scripts = _read_project_scripts(pyproject_path)
+        training_commands = _training_commands(scripts)
+        commands = _project_commands(scripts)
+    else:
+        # pip install outside a source checkout: no pyproject to read.
+        training_commands = tuple(TRAINING_ENTRYPOINTS)
+        commands = (*training_commands, "demo")
+    return CompletionMetadata(
+        commands=commands,
+        flags={command: _parser_flags(command) for command in training_commands},
+        choices={command: _parser_choices(command) for command in training_commands},
+        tasks=_task_entries(task_root),
+        run_paths=_run_path_entries(selected_root),
+        root=selected_root,
+        log_base=None if root is not None else (checkout or Path.cwd()),
+    )
+
+
+def _current_word(words: Sequence[str], cword: int) -> str:
+    if 0 <= cword < len(words):
+        return words[cword]
+    return ""
+
+
+def _previous_word(words: Sequence[str], cword: int) -> str:
+    if cword > 0 and cword - 1 < len(words):
+        return words[cword - 1]
+    return ""
+
+
+def _matching(candidates: Sequence[str], prefix: str) -> list[str]:
+    return [candidate for candidate in candidates if candidate.startswith(prefix)]
+
+
+def _dedupe(candidates: Sequence[str]) -> list[str]:
+    return list(dict.fromkeys(candidates))
+
+
+def _path_choices(candidates: Sequence[str], prefix: str) -> list[str]:
+    choices: set[str] = set()
+    for candidate in candidates:
+        if not candidate.startswith(prefix):
+            continue
+        remainder = candidate[len(prefix) :]
+        if remainder == "":
+            continue
+        if "/" in remainder:
+            choices.add(f"{prefix}{remainder.split('/', 1)[0]}/")
+        else:
+            choices.add(candidate)
+    return sorted(choices)
+
+
+def _option_state(words: Sequence[str], cword: int) -> tuple[dict[str, str], set[str]]:
+    values: dict[str, str] = {}
+    used: set[str] = set()
+    index = 3
+    limit = min(cword, len(words))
+    while index < limit:
+        token = words[index]
+        if not token.startswith("--"):
+            index += 1
+            continue
+        if "=" in token:
+            option, value = token.split("=", 1)
+            used.add(option)
+            values[option] = value
+            index += 1
+            continue
+        used.add(token)
+        if index + 1 < limit and not words[index + 1].startswith("--"):
+            values[token] = words[index + 1]
+            index += 2
+            continue
+        index += 1
+    return values, used
+
+
+def _available_flags(metadata: CompletionMetadata, command: str, used: set[str]) -> tuple[str, ...]:
+    return tuple(flag for flag in metadata.flags.get(command, ()) if flag not in used)
+
+
+def _task_choices(
+    metadata: CompletionMetadata,
+    *,
+    prefix: str,
+    selected_algo: str | None,
+    selected_sim: str | None,
+    selected_profile: str | None,
+) -> list[str]:
+    tasks: set[str] = set()
+    for entry in metadata.tasks:
+        if selected_algo is not None and entry.algo != selected_algo:
+            continue
+        if selected_sim is not None and entry.sim != selected_sim:
+            continue
+        if selected_profile is not None:
+            if selected_sim is not None and entry.owner != f"{selected_sim}_{selected_profile}":
+                continue
+            if selected_sim is None and not entry.owner.endswith(f"_{selected_profile}"):
+                continue
+        tasks.add(entry.task)
+    return _matching(tuple(sorted(tasks)), prefix)
+
+
+def _profile_choices(
+    metadata: CompletionMetadata,
+    *,
+    prefix: str,
+    selected_algo: str | None,
+    selected_sim: str | None,
+    selected_task: str | None,
+) -> list[str]:
+    profiles: set[str] = set()
+    for entry in metadata.tasks:
+        if selected_algo is not None and entry.algo != selected_algo:
+            continue
+        if selected_sim is not None and entry.sim != selected_sim:
+            continue
+        if selected_task is not None and entry.task != selected_task:
+            continue
+        owner_prefix = f"{entry.sim}_"
+        if not entry.owner.startswith(owner_prefix):
+            continue
+        profile = entry.owner[len(owner_prefix) :]
+        if profile:
+            profiles.add(profile)
+    return _matching(tuple(sorted(profiles)), prefix)
+
+
+def _strip_yaml_scalar(value: str) -> str:
+    selected = value.split("#", 1)[0].strip()
+    if len(selected) >= 2 and selected[0] == selected[-1] and selected[0] in {'"', "'"}:
+        return selected[1:-1]
+    return selected
+
+
+def _yaml_section_scalar(path: Path, section: str, key: str) -> str | None:
+    if not path.is_file():
+        return None
+    in_section = False
+    section_indent = 0
+    for raw_line in path.read_text(encoding="utf-8").splitlines():
+        stripped = raw_line.strip()
+        if not stripped or stripped.startswith("#"):
+            continue
+        indent = len(raw_line) - len(raw_line.lstrip())
+        if indent == 0 and stripped.endswith(":"):
+            in_section = stripped[:-1] == section
+            section_indent = indent
+            continue
+        if not in_section:
+            continue
+        if indent <= section_indent:
+            break
+        if stripped.startswith(f"{key}:"):
+            return _strip_yaml_scalar(stripped.split(":", 1)[1])
+    return None
+
+
+def _owner_yaml_paths(
+    root: Path,
+    *,
+    selected_algo: str,
+    selected_task: str,
+    selected_sim: str,
+    selected_profile: str | None,
+) -> tuple[Path, ...]:
+    try:
+        route = cli.build_route(selected_algo, selected_task, selected_sim, selected_profile)
+    except SystemExit:
+        return ()
+
+    route_path = root / "conf" / route.config_group / "task" / route.owner_task
+    if not route_path.is_file():
+        return ()
+
+    paths = [route_path]
+    if selected_profile is not None:
+        try:
+            base_route = cli.build_route(selected_algo, selected_task, selected_sim, None)
+        except SystemExit:
+            base_route = None
+        if base_route is not None:
+            base_path = root / "conf" / base_route.config_group / "task" / base_route.owner_task
+            if base_path not in paths:
+                paths.append(base_path)
+    return tuple(paths)
+
+
+def _first_yaml_section_scalar(paths: Sequence[Path], section: str, key: str) -> str | None:
+    for path in paths:
+        selected = _yaml_section_scalar(path, section, key)
+        if selected not in (None, ""):
+            return selected
+    return None
+
+
+def _load_run_choices(
+    metadata: CompletionMetadata,
+    *,
+    prefix: str,
+    selected_algo: str | None,
+    selected_task: str | None,
+    selected_sim: str | None,
+    selected_profile: str | None,
+) -> list[str]:
+    candidates = ["-1"]
+    # Explicitly injected metadata keeps its historical root-based logs lookup;
+    # the default build_metadata path anchors logs at the training CWD.
+    logs_base = metadata.log_base or metadata.root
+    if logs_base is None or not selected_algo or not selected_task or not selected_sim:
+        return _matching(candidates, prefix)
+
+    # Injected metadata (tests) reads owner YAML under metadata.root; the
+    # default path reads the packaged conf tree.
+    owner_root = metadata.root if metadata.log_base is None else cli.package_root()
+    if owner_root is None:
+        return _matching(candidates, prefix)
+    owner_paths = _owner_yaml_paths(
+        owner_root,
+        selected_algo=selected_algo,
+        selected_task=selected_task,
+        selected_sim=selected_sim,
+        selected_profile=selected_profile,
+    )
+    if not owner_paths:
+        return _matching(candidates, prefix)
+
+    task_name = _first_yaml_section_scalar(owner_paths, "training", "task_name") or selected_task
+    log_root = _first_yaml_section_scalar(owner_paths, "training", "log_root")
+    if log_root is not None:
+        log_root_path = Path(log_root)
+        base_log_root = log_root_path if log_root_path.is_absolute() else logs_base / log_root_path
+    else:
+        algo_log_name = _first_yaml_section_scalar(
+            owner_paths, "algo", "algo_log_name"
+        ) or DEFAULT_ALGO_LOG_NAMES.get(selected_algo)
+        if algo_log_name is None:
+            return _matching(candidates, prefix)
+        base_log_root = logs_base / "logs" / algo_log_name
+
+    task_log_root = base_log_root / task_name
+    if task_log_root.is_dir():
+        candidates.extend(
+            path.name
+            for path in sorted(task_log_root.iterdir())
+            if path.is_dir() and cli.RUN_ID_PATTERN.fullmatch(path.name) is not None
+        )
+    return _dedupe(_matching(candidates, prefix))
+
+
+_DEMO_FLAGS: tuple[str, ...] = ("--device", "--refresh")
+_DEMO_VALUE_FLAGS: frozenset[str] = frozenset({"--device"})
+
+
+def _demo_name_consumed(words: Sequence[str], cword: int) -> bool:
+    index = 3
+    limit = min(cword, len(words))
+    while index < limit:
+        token = words[index]
+        if token.startswith("--"):
+            index += 2 if token in _DEMO_VALUE_FLAGS else 1
+            continue
+        return True
+    return False
+
+
+def _demo_completions(
+    *,
+    words: Sequence[str],
+    cword: int,
+    current: str,
+    previous: str,
+    used_options: set[str],
+) -> list[str]:
+    from unilab.demo import DEMO_REGISTRY
+
+    if not _demo_name_consumed(words, cword):
+        return _matching(tuple(sorted(DEMO_REGISTRY)), current)
+    if previous in _DEMO_VALUE_FLAGS:
+        return []
+    available_flags = tuple(flag for flag in _DEMO_FLAGS if flag not in used_options)
+    return _matching(available_flags, current)
+
+
+def complete_words(
+    words: Sequence[str],
+    cword: int,
+    metadata: CompletionMetadata | None = None,
+) -> list[str]:
+    selected_metadata = metadata or build_metadata()
+    if len(words) < 2 or words[0] != "uv" or words[1] != "run":
+        return []
+
+    current = _current_word(words, cword)
+    if cword <= 2:
+        return _dedupe(
+            [
+                *_matching(selected_metadata.commands, current),
+                *_path_choices(selected_metadata.run_paths, current),
+            ]
+        )
+
+    command = words[2]
+    if command not in selected_metadata.commands:
+        return []
+
+    previous = _previous_word(words, cword)
+    option_values, used_options = _option_state(words, cword)
+    choices = selected_metadata.choices.get(command, {})
+    if previous == "--task":
+        return _task_choices(
+            selected_metadata,
+            prefix=current,
+            selected_algo=option_values.get("--algo"),
+            selected_sim=option_values.get("--sim"),
+            selected_profile=option_values.get("--profile"),
+        )
+    if previous in choices:
+        return _matching(choices[previous], current)
+    if command == "eval" and previous == "--load-run":
+        return _load_run_choices(
+            selected_metadata,
+            prefix=current,
+            selected_algo=option_values.get("--algo"),
+            selected_task=option_values.get("--task"),
+            selected_sim=option_values.get("--sim"),
+            selected_profile=option_values.get("--profile"),
+        )
+    if command in {"train", "eval"} and previous == "--profile":
+        return _profile_choices(
+            selected_metadata,
+            prefix=current,
+            selected_algo=option_values.get("--algo"),
+            selected_sim=option_values.get("--sim"),
+            selected_task=option_values.get("--task"),
+        )
+    if command == "demo":
+        return _demo_completions(
+            words=words,
+            cword=cword,
+            current=current,
+            previous=previous,
+            used_options=used_options,
+        )
+    if current == "" or current.startswith("-") or previous == command:
+        return _matching(_available_flags(selected_metadata, command, used_options), current)
+    return []
+
+
+def _parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace:
+    parser = argparse.ArgumentParser(prog="unilab-complete")
+    parser.add_argument("--cword", type=int, required=True)
+    parser.add_argument("words", nargs=argparse.REMAINDER)
+    args = parser.parse_args(argv)
+    if args.words and args.words[0] == "--":
+        args.words = args.words[1:]
+    return args
+
+
+def _detect_shell(selected: str) -> str:
+    if selected != "auto":
+        return selected
+
+    shell_name = Path(os.environ.get("SHELL", "")).name
+    if shell_name in SUPPORTED_SHELLS:
+        return shell_name
+    system_name = platform.system()
+    if system_name == "Darwin":
+        return "zsh"
+    if system_name == "Linux":
+        return "bash"
+    raise SystemExit("Cannot detect shell for completion install; use --shell bash or --shell zsh.")
+
+
+def _default_rc_file(shell: str) -> Path:
+    if shell == "bash":
+        return Path.home() / ".bashrc"
+    if shell == "zsh":
+        return Path.home() / ".zshrc"
+    raise SystemExit(f"Unsupported shell={shell!r}; choose one of: {', '.join(SUPPORTED_SHELLS)}")
+
+
+def _completion_script_path(shell: str) -> Path:
+    checkout = _find_project_root(Path.cwd())
+    if checkout is not None:
+        script = checkout / "scripts" / "completions" / f"unilab.{shell}"
+        if script.is_file():
+            return script
+    raise SystemExit(
+        "completion install requires a source checkout of the UniLab repository; "
+        "run `uv run unilab-complete install` from the checkout."
+    )
+
+
+def _quote_shell_path(path: Path) -> str:
+    return str(path).replace("\\", "\\\\").replace('"', '\\"')
+
+
+def _completion_block(script_path: Path) -> str:
+    quoted = _quote_shell_path(script_path)
+    return (
+        f"{COMPLETION_BLOCK_START}\n"
+        f'if [ -f "{quoted}" ]; then\n'
+        f'    source "{quoted}"\n'
+        "fi\n"
+        f"{COMPLETION_BLOCK_END}\n"
+    )
+
+
+def _without_completion_block(content: str, *, force: bool = False) -> str:
+    start = content.find(COMPLETION_BLOCK_START)
+    end = content.find(COMPLETION_BLOCK_END)
+    if start == -1 and end == -1:
+        return content
+    if start == -1 or end == -1 or end < start:
+        if force:
+            return content
+        raise SystemExit(
+            "Existing UniLab completion block is malformed; rerun with --force or edit it manually."
+        )
+
+    end_line = content.find("\n", end)
+    if end_line == -1:
+        end_line = len(content)
+    else:
+        end_line += 1
+    return (content[:start].rstrip() + "\n" + content[end_line:].lstrip("\n")).lstrip("\n")
+
+
+def _write_completion_block(
+    *,
+    rc_file: Path,
+    script_path: Path,
+    dry_run: bool,
+    force: bool,
+) -> None:
+    if rc_file.exists():
+        with rc_file.open("r", encoding="utf-8", errors="surrogateescape", newline="") as file:
+            content = file.read()
+    else:
+        content = ""
+    cleaned = _without_completion_block(content, force=force).rstrip()
+    block = _completion_block(script_path).rstrip()
+    updated = f"{cleaned}\n\n{block}\n" if cleaned else f"{block}\n"
+    if dry_run:
+        print(updated, end="")
+        return
+    rc_file.parent.mkdir(parents=True, exist_ok=True)
+    rc_file.write_text(updated, encoding="utf-8", errors="surrogateescape", newline="")
+    print(f"Installed UniLab completion in {rc_file}")
+    print(f"Open a new shell or run: source {rc_file}")
+
+
+def _remove_completion_block(*, rc_file: Path, dry_run: bool, force: bool) -> None:
+    if not rc_file.exists():
+        print(f"No rc file found: {rc_file}")
+        return
+    with rc_file.open("r", encoding="utf-8", errors="surrogateescape", newline="") as file:
+        content = file.read()
+    updated = _without_completion_block(content, force=force)
+    if dry_run:
+        print(updated, end="")
+        return
+    rc_file.write_text(updated, encoding="utf-8", errors="surrogateescape", newline="")
+    print(f"Removed UniLab completion from {rc_file}")
+
+
+def _install_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(prog="unilab-complete install")
+    parser.add_argument("--shell", choices=("auto", *SUPPORTED_SHELLS), default="auto")
+    parser.add_argument("--rc-file", type=Path, default=None)
+    parser.add_argument("--dry-run", action="store_true")
+    parser.add_argument("--force", action="store_true")
+    parser.add_argument("--uninstall", action="store_true")
+    return parser
+
+
+def install_main(argv: Sequence[str] | None = None) -> int:
+    args = _install_parser().parse_args(argv)
+    shell = _detect_shell(args.shell)
+    rc_file = args.rc_file or _default_rc_file(shell)
+    if args.uninstall:
+        _remove_completion_block(rc_file=rc_file, dry_run=args.dry_run, force=args.force)
+        return 0
+    _write_completion_block(
+        rc_file=rc_file,
+        script_path=_completion_script_path(shell),
+        dry_run=args.dry_run,
+        force=args.force,
+    )
+    return 0
+
+
+def main(argv: Sequence[str] | None = None) -> int:
+    selected_argv = list(sys.argv[1:] if argv is None else argv)
+    if selected_argv and selected_argv[0] == "install":
+        return install_main(selected_argv[1:])
+
+    args = _parse_args(selected_argv)
+    for candidate in complete_words(args.words, args.cword):
+        print(candidate)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main(sys.argv[1:]))
diff --git a/conf/appo/config.yaml b/src/unilab/conf/appo/config.yaml
similarity index 100%
rename from conf/appo/config.yaml
rename to src/unilab/conf/appo/config.yaml
diff --git a/src/unilab/conf/appo/task/allegro_inhand/base.yaml b/src/unilab/conf/appo/task/allegro_inhand/base.yaml
new file mode 100644
index 000000000..a36f24c9f
--- /dev/null
+++ b/src/unilab/conf/appo/task/allegro_inhand/base.yaml
@@ -0,0 +1,138 @@
+# @package _global_
+# Canonical Allegro rotation Manager-Based task declaration. Backend leaves own
+# only backend identity and algorithm/runtime tuning.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/allegro_hand/scene.xml
+    default_keyframe_name: home
+    entities:
+      robot:
+        root_body_name: ball
+        joint_names:
+          - ffj0
+          - ffj1
+          - ffj2
+          - ffj3
+          - mfj0
+          - mfj1
+          - mfj2
+          - mfj3
+          - rfj0
+          - rfj1
+          - rfj2
+          - rfj3
+          - thj0
+          - thj1
+          - thj2
+          - thj3
+        body_names: [ball, ff_tip, mf_tip, rf_tip, th_tip]
+        actuator_names:
+          - ffa0
+          - ffa1
+          - ffa2
+          - ffa3
+          - mfa0
+          - mfa1
+          - mfa2
+          - mfa3
+          - rfa0
+          - rfa1
+          - rfa2
+          - rfa3
+          - tha0
+          - tha1
+          - tha2
+          - tha3
+  sim_dt: 0.005
+  ctrl_dt: 0.05
+  max_episode_seconds: 20.0
+  observations:
+    policy:
+      history_length: 3
+      flatten_history_dim: true
+      terms:
+        rotation:
+          func: unilab.tasks.manipulation.allegro_inhand.manager_terms.AllegroRotationObservation
+          params:
+            entity_name: robot
+            action_name: hand
+            joint_noise: 0.02
+            torque_estimate_kp: 1.0
+            torque_estimate_kd: 0.1
+  actions:
+    hand:
+      _target_: unilab.tasks.manipulation.allegro_inhand.manager_terms.AllegroIncrementalPositionActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      action_scale: 0.041666666666666664
+      raw_action_clip: [-1.0, 1.0]
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_hand_ball:
+      func: unilab.tasks.manipulation.allegro_inhand.manager_terms.AllegroHandBallReset
+      mode: reset
+      params:
+        entity_name: robot
+        # null explicitly selects the model home pose. A configured path is
+        # fail-closed when missing or malformed.
+        grasp_cache_path: null
+        joint_noise: 0.0
+        ball_velocity_noise: 0.0
+        ball_z_offset: 0.0
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        kp_range: [1.0, 1.0]
+        kd_range: [0.1, 0.1]
+        operation: abs
+  terminations:
+    dropped:
+      func: unilab.tasks.manipulation.allegro_inhand.manager_terms.AllegroDropTermination
+      params:
+        observation_group: policy
+        observation_term: rotation
+        minimum_ball_height: 0.125
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+  scale_rewards_by_dt: true
+  policy_observation_group: policy
+  critic_observation_group: null
+
+reward:
+  rotate:
+    func: unilab.tasks.manipulation.allegro_inhand.manager_terms.AllegroRotateReward
+    weight: 1.25
+    params:
+      state_term_name: dropped
+      rotation_axis: [0.0, 0.0, 1.0]
+      clip_min: -0.5
+      clip_max: 0.5
+  obj_linvel:
+    func: unilab.tasks.manipulation.allegro_inhand.manager_terms.object_linear_velocity_l1
+    weight: -0.3
+    params:
+      state_term_name: dropped
+  pose_diff:
+    func: unilab.tasks.manipulation.allegro_inhand.manager_terms.hand_pose_deviation_l2
+    weight: -0.3
+    params:
+      state_term_name: dropped
+  torque:
+    func: unilab.tasks.manipulation.allegro_inhand.manager_terms.estimated_torque_l2
+    weight: -0.1
+    params:
+      state_term_name: dropped
+  work:
+    func: unilab.tasks.manipulation.allegro_inhand.manager_terms.estimated_work_l2
+    weight: -2.0
+    params:
+      state_term_name: dropped
+  drop:
+    func: unilab.tasks.manipulation.allegro_inhand.manager_terms.dropped
+    weight: 0.0
+    params:
+      state_term_name: dropped
diff --git a/src/unilab/conf/appo/task/allegro_inhand/drake.yaml b/src/unilab/conf/appo/task/allegro_inhand/drake.yaml
new file mode 100644
index 000000000..edbb75562
--- /dev/null
+++ b/src/unilab/conf/appo/task/allegro_inhand/drake.yaml
@@ -0,0 +1,50 @@
+# @package _global_
+defaults:
+  - /task/allegro_inhand/base
+  - _self_
+
+training:
+  task_name: AllegroInhandRotation
+  sim_backend: drake
+  play_steps: 200
+  render_spacing: 0.5
+  cam_distance: 1.5
+  cam_lookat: [0.75, 0.75, 0]
+  cam_elevation: -20.0
+  replay_queue_size: 4
+algo:
+  num_envs: 1024
+  steps_per_env: 8
+  max_iterations: 3000
+  save_interval: 500
+  algorithm:
+    value_loss_coef: 4.0
+    entropy_coef: 0.01
+    learning_rate: 0.001
+    desired_kl: 0.025
+    adaptive_kl_factor: 2.0
+    adaptive_lr_factor: 1.5
+    num_learning_epochs: 5
+    num_mini_batches: 4
+    clip_param: 0.2
+    gamma: 0.99
+    lam: 0.95
+    max_grad_norm: 1.0
+    use_clipped_value_loss: true
+    schedule: adaptive
+  actor:
+    hidden_dims: [512, 256, 128]
+    activation: elu
+    obs_normalization: true
+    distribution_cfg:
+      class_name: rsl_rl.modules.distribution.GaussianDistribution
+      init_std: 1.0
+      std_type: scalar
+  critic:
+    hidden_dims: [512, 256, 128]
+    activation: elu
+    obs_normalization: true
+env:
+  drake_backend_mode: batch
+  events:
+    pd_gains: null
diff --git a/src/unilab/conf/appo/task/allegro_inhand/motrix.yaml b/src/unilab/conf/appo/task/allegro_inhand/motrix.yaml
new file mode 100644
index 000000000..1a89e3b37
--- /dev/null
+++ b/src/unilab/conf/appo/task/allegro_inhand/motrix.yaml
@@ -0,0 +1,48 @@
+# @package _global_
+defaults:
+  - /task/allegro_inhand/base
+  - _self_
+
+training:
+  task_name: AllegroInhandRotation
+  sim_backend: motrix
+  play_steps: 200
+  render_spacing: 0.5
+  cam_distance: 1.5
+  cam_lookat: [0.75, 0.75, 0]
+  cam_elevation: -20.0
+env:
+  events:
+    pd_gains: null
+algo:
+  num_envs: 16384
+  steps_per_env: 8
+  max_iterations: 201
+  save_interval: 100
+  algorithm:
+    value_loss_coef: 4.0
+    entropy_coef: 0.01
+    learning_rate: 0.001
+    desired_kl: 0.02
+    adaptive_kl_factor: 2.0
+    adaptive_lr_factor: 1.5
+    num_learning_epochs: 5
+    num_mini_batches: 4
+    clip_param: 0.2
+    gamma: 0.99
+    lam: 0.95
+    max_grad_norm: 1.0
+    use_clipped_value_loss: true
+    schedule: adaptive
+  actor:
+    hidden_dims: [512, 256, 128]
+    activation: elu
+    obs_normalization: true
+    distribution_cfg:
+      class_name: rsl_rl.modules.distribution.GaussianDistribution
+      init_std: 1.0
+      std_type: scalar
+  critic:
+    hidden_dims: [512, 256, 128]
+    activation: elu
+    obs_normalization: true
diff --git a/src/unilab/conf/appo/task/allegro_inhand/mujoco.yaml b/src/unilab/conf/appo/task/allegro_inhand/mujoco.yaml
new file mode 100644
index 000000000..7d4bcfbd0
--- /dev/null
+++ b/src/unilab/conf/appo/task/allegro_inhand/mujoco.yaml
@@ -0,0 +1,47 @@
+# @package _global_
+defaults:
+  - /task/allegro_inhand/base
+  - _self_
+
+training:
+  task_name: AllegroInhandRotation
+  sim_backend: mujoco
+  play_steps: 200
+  render_spacing: 0.5
+  cam_distance: 0.75
+  cam_lookat: [0.0, 0.0, 0.15]
+  cam_elevation: -25.0
+  cam_azimuth: 45.0
+  replay_queue_size: 4
+algo:
+  num_envs: 1024
+  steps_per_env: 8
+  max_iterations: 3000
+  save_interval: 500
+  algorithm:
+    value_loss_coef: 4.0
+    entropy_coef: 0.01
+    learning_rate: 0.001
+    desired_kl: 0.025
+    adaptive_kl_factor: 2.0
+    adaptive_lr_factor: 1.5
+    num_learning_epochs: 5
+    num_mini_batches: 4
+    clip_param: 0.2
+    gamma: 0.99
+    lam: 0.95
+    max_grad_norm: 1.0
+    use_clipped_value_loss: true
+    schedule: adaptive
+  actor:
+    hidden_dims: [512, 256, 128]
+    activation: elu
+    obs_normalization: true
+    distribution_cfg:
+      class_name: rsl_rl.modules.distribution.GaussianDistribution
+      init_std: 1.0
+      std_type: scalar
+  critic:
+    hidden_dims: [512, 256, 128]
+    activation: elu
+    obs_normalization: true
diff --git a/src/unilab/conf/appo/task/g1_23dof_climb_tracking/motrix.yaml b/src/unilab/conf/appo/task/g1_23dof_climb_tracking/motrix.yaml
new file mode 100644
index 000000000..ae708bf1d
--- /dev/null
+++ b/src/unilab/conf/appo/task/g1_23dof_climb_tracking/motrix.yaml
@@ -0,0 +1,8 @@
+# @package _global_
+defaults:
+  - /task/g1_23dof_climb_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1ClimbTracking23Dof
+  sim_backend: motrix
diff --git a/src/unilab/conf/appo/task/g1_23dof_climb_tracking/mujoco.yaml b/src/unilab/conf/appo/task/g1_23dof_climb_tracking/mujoco.yaml
new file mode 100644
index 000000000..3586affd7
--- /dev/null
+++ b/src/unilab/conf/appo/task/g1_23dof_climb_tracking/mujoco.yaml
@@ -0,0 +1,85 @@
+# @package _global_
+defaults:
+  - /task/g1_23dof_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1ClimbTracking23Dof
+  sim_backend: mujoco
+  play_steps: 1000
+
+algo:
+  num_envs: 1024
+  max_iterations: 20000
+  save_interval: 500
+  algorithm:
+    adaptive_kl_factor: 1.2
+    adaptive_lr_factor: 1.1
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_climb_20_z_scale_1_23dof.xml
+  sim_dt: 0.005
+  max_episode_seconds: 15.0
+  actions:
+    joint_pos:
+      scale:
+        ".*_(hip_pitch|hip_yaw)_joint": 0.5475464629911068
+        ".*_(hip_roll|knee)_joint": 0.35066146637882434
+        ".*_ankle_(pitch|roll)_joint": 0.43857731392336724
+        "waist_yaw_joint": 0.5475464629911068
+        ".*_(shoulder_(pitch|roll|yaw)|elbow|wrist_roll)_joint": 0.43857731392336724
+  commands:
+    motion:
+      params:
+        motion_file: motions/g1/climb_20_z_scale_1.0_23dof.npz
+        sampling_mode: adaptive
+        truncate_on_clip_end: false
+  terminations:
+    anchor_pos:
+      params: {command_name: motion, threshold: 0.3}
+    ee_body_pos:
+      params:
+        command_name: motion
+        threshold: 0.3
+        body_names: &ee_bodies
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_roll_rubber_hand
+          - right_wrist_roll_rubber_hand
+    undesired_contacts:
+      func: unilab.tasks.motion_tracking.common.manager_terms.bad_undesired_body_contacts
+      params:
+        command_name: motion
+        threshold: 0.05
+        body_names: &undesired_bodies
+          - pelvis
+          - left_hip_roll_link
+          - left_knee_link
+          - right_hip_roll_link
+          - right_knee_link
+          - torso_link
+          - left_shoulder_roll_link
+          - left_elbow_link
+          - right_shoulder_roll_link
+          - right_elbow_link
+
+reward:
+  motion_body_pos:
+    weight: 2.0
+  motion_body_ori:
+    weight: 1.5
+  motion_ee_body_pos_z:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_relative_body_position_z_error_exp
+    weight: 2.0
+    params: {command_name: motion, std: 0.3, body_names: *ee_bodies}
+  motion_joint_pos:
+    weight: 0.5
+  motion_joint_vel:
+    weight: 0.25
+  action_rate_l2:
+    weight: -0.005
+  undesired_contacts:
+    func: unilab.tasks.motion_tracking.common.manager_terms.undesired_body_contacts
+    weight: -0.1
+    params: {command_name: motion, threshold: 0.05, body_names: *undesired_bodies}
diff --git a/src/unilab/conf/appo/task/g1_23dof_flip_tracking/motrix.yaml b/src/unilab/conf/appo/task/g1_23dof_flip_tracking/motrix.yaml
new file mode 100644
index 000000000..a600df907
--- /dev/null
+++ b/src/unilab/conf/appo/task/g1_23dof_flip_tracking/motrix.yaml
@@ -0,0 +1,14 @@
+# @package _global_
+defaults:
+  - /task/g1_23dof_flip_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1FlipTracking23Dof
+  sim_backend: motrix
+
+algo:
+  algorithm:
+    adaptive_kl_factor: 2.0
+    adaptive_lr_factor: 1.5
+    enable_compile: true
diff --git a/src/unilab/conf/appo/task/g1_23dof_flip_tracking/mujoco.yaml b/src/unilab/conf/appo/task/g1_23dof_flip_tracking/mujoco.yaml
new file mode 100644
index 000000000..6798916ca
--- /dev/null
+++ b/src/unilab/conf/appo/task/g1_23dof_flip_tracking/mujoco.yaml
@@ -0,0 +1,101 @@
+# @package _global_
+defaults:
+  - /task/g1_23dof_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1FlipTracking23Dof
+  sim_backend: mujoco
+  play_steps: 1000
+
+algo:
+  num_envs: 1024
+  steps_per_env: 24
+  max_iterations: 3500
+  save_interval: 500
+  algorithm:
+    entropy_coef: 0.005
+    num_learning_epochs: 10
+    num_mini_batches: 8
+    desired_kl: 0.01
+    adaptive_kl_factor: 1.2
+    adaptive_lr_factor: 1.1
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat_23dof.xml
+  sim_dt: 0.005
+  actions:
+    joint_pos:
+      scale:
+        ".*_(hip_pitch|hip_yaw)_joint": 0.5475464629911068
+        ".*_(hip_roll|knee)_joint": 0.35066146637882434
+        ".*_ankle_(pitch|roll)_joint": 0.43857731392336724
+        "waist_yaw_joint": 0.5475464629911068
+        ".*_(shoulder_(pitch|roll|yaw)|elbow|wrist_roll)_joint": 0.43857731392336724
+  commands:
+    motion:
+      params:
+        motion_file: motions/g1/flip_360_001__A304_23dof.npz
+        sampling_mode: start
+        truncate_on_clip_end: true
+        pose_range: &zero_pose
+          x: [0.0, 0.0]
+          y: [0.0, 0.0]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [0.0, 0.0]
+        velocity_range: *zero_pose
+        joint_position_range: [0.0, 0.0]
+  terminations:
+    motion_clip_end:
+      func: unilab.tasks.motion_tracking.common.manager_terms.motion_clip_end
+      time_out: true
+      params: {command_name: motion}
+    anchor_pos:
+      params: {command_name: motion, threshold: 0.5}
+    anchor_ori:
+      params:
+        command_name: motion
+        threshold: 1.0e9
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+    ee_body_pos:
+      params:
+        command_name: motion
+        threshold: 0.5
+        body_names: &ee_bodies
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_roll_rubber_hand
+          - right_wrist_roll_rubber_hand
+    undesired_contacts:
+      func: unilab.tasks.motion_tracking.common.manager_terms.bad_undesired_body_contacts
+      params:
+        command_name: motion
+        threshold: 0.05
+        body_names:
+          - pelvis
+          - left_hip_roll_link
+          - left_knee_link
+          - right_hip_roll_link
+          - right_knee_link
+          - torso_link
+          - left_shoulder_roll_link
+          - left_elbow_link
+          - right_shoulder_roll_link
+          - right_elbow_link
+
+reward:
+  motion_body_pos:
+    weight: 2.0
+  motion_body_ori:
+    weight: 1.5
+  motion_ee_body_pos_z:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_relative_body_position_z_error_exp
+    weight: 2.0
+    params: {command_name: motion, std: 0.3, body_names: *ee_bodies}
+  action_rate_l2:
+    weight: -0.005
diff --git a/src/unilab/conf/appo/task/g1_23dof_motion_tracking/motrix.yaml b/src/unilab/conf/appo/task/g1_23dof_motion_tracking/motrix.yaml
new file mode 100644
index 000000000..9d8ecf00d
--- /dev/null
+++ b/src/unilab/conf/appo/task/g1_23dof_motion_tracking/motrix.yaml
@@ -0,0 +1,8 @@
+# @package _global_
+defaults:
+  - /task/g1_23dof_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1MotionTracking23Dof
+  sim_backend: motrix
diff --git a/src/unilab/conf/appo/task/g1_23dof_motion_tracking/mujoco.yaml b/src/unilab/conf/appo/task/g1_23dof_motion_tracking/mujoco.yaml
new file mode 100644
index 000000000..8605feb92
--- /dev/null
+++ b/src/unilab/conf/appo/task/g1_23dof_motion_tracking/mujoco.yaml
@@ -0,0 +1,67 @@
+# @package _global_
+defaults:
+  - /task/g1_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1MotionTracking23Dof
+  sim_backend: mujoco
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat_23dof.xml
+    entities:
+      robot:
+        joint_names: &g1_23dof_joints
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+        actuator_names: *g1_23dof_joints
+        body_names: &tracked_bodies_23dof
+          - pelvis
+          - left_hip_roll_link
+          - left_knee_link
+          - left_ankle_roll_link
+          - right_hip_roll_link
+          - right_knee_link
+          - right_ankle_roll_link
+          - torso_link
+          - left_shoulder_roll_link
+          - left_elbow_link
+          - left_wrist_roll_rubber_hand
+          - right_shoulder_roll_link
+          - right_elbow_link
+          - right_wrist_roll_rubber_hand
+  commands:
+    motion:
+      params:
+        motion_file: motions/g1/dance1_subject2_part_23dof.npz
+        body_names: *tracked_bodies_23dof
+  terminations:
+    ee_body_pos:
+      params:
+        body_names:
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_roll_rubber_hand
+          - right_wrist_roll_rubber_hand
diff --git a/src/unilab/conf/appo/task/g1_23dof_walk_flat/base.yaml b/src/unilab/conf/appo/task/g1_23dof_walk_flat/base.yaml
new file mode 100644
index 000000000..66c3da41e
--- /dev/null
+++ b/src/unilab/conf/appo/task/g1_23dof_walk_flat/base.yaml
@@ -0,0 +1,66 @@
+# @package _global_
+# Canonical G1 23-DoF walk Manager-Based task declaration (APPO owners; mirrors the PPO base).
+# Inherits the 29-DoF flat contract and swaps the scene to the 23-DoF model
+# (no waist roll/pitch, no wrist pitch/yaw) with the 23-entry pose weights.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat_23dof.xml
+    entities:
+      robot:
+        joint_names:
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+        actuator_names:
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+
+reward:
+  pose:
+    params:
+      pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/src/unilab/conf/appo/task/g1_23dof_walk_flat/mujoco.yaml b/src/unilab/conf/appo/task/g1_23dof_walk_flat/mujoco.yaml
new file mode 100644
index 000000000..aa34c3339
--- /dev/null
+++ b/src/unilab/conf/appo/task/g1_23dof_walk_flat/mujoco.yaml
@@ -0,0 +1,13 @@
+# @package _global_
+# MuJoCo APPO owner: inherits the shared 23-DoF flat Manager-Based contract
+# from base.yaml and only carries backend/algo identity.
+defaults:
+  - /task/g1_23dof_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1Walk23DofFlat
+  sim_backend: mujoco
+algo:
+  max_iterations: 500
+  save_interval: 100
diff --git a/src/unilab/conf/appo/task/g1_23dof_wall_flip_tracking/motrix.yaml b/src/unilab/conf/appo/task/g1_23dof_wall_flip_tracking/motrix.yaml
new file mode 100644
index 000000000..3a43778c1
--- /dev/null
+++ b/src/unilab/conf/appo/task/g1_23dof_wall_flip_tracking/motrix.yaml
@@ -0,0 +1,33 @@
+# @package _global_
+defaults:
+  - /task/g1_23dof_wall_flip_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1WallFlipTracking23Dof
+  sim_backend: motrix
+  replay_queue_size: null
+
+algo:
+  steps_per_env: 24
+  max_iterations: 5000
+  algorithm:
+    num_learning_epochs: 5
+    num_mini_batches: 4
+    entropy_coef: 0.01
+    desired_kl: 0.01
+    enable_compile: true
+
+env:
+  actions:
+    joint_pos:
+      scale: 0.25
+
+reward:
+  motion_body_pos:
+    weight: 1.0
+  motion_body_ori:
+    weight: 1.0
+  motion_ee_body_pos_z: null
+  action_rate_l2:
+    weight: -0.1
diff --git a/src/unilab/conf/appo/task/g1_23dof_wall_flip_tracking/mujoco.yaml b/src/unilab/conf/appo/task/g1_23dof_wall_flip_tracking/mujoco.yaml
new file mode 100644
index 000000000..a140546c1
--- /dev/null
+++ b/src/unilab/conf/appo/task/g1_23dof_wall_flip_tracking/mujoco.yaml
@@ -0,0 +1,27 @@
+# @package _global_
+defaults:
+  - /task/g1_23dof_flip_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1WallFlipTracking23Dof
+  sim_backend: mujoco
+  replay_queue_size: 5
+
+algo:
+  steps_per_env: 20
+  max_iterations: 7000
+  algorithm:
+    num_learning_epochs: 6
+    desired_kl: 0.008
+    adaptive_kl_factor: 2.0
+    adaptive_lr_factor: 1.5
+    enable_compile: true
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat_23dof_with_wall.xml
+  commands:
+    motion:
+      params:
+        motion_file: motions/g1/flip_from_wall_104__A304_23dof.npz
diff --git a/src/unilab/conf/appo/task/g1_climb_tracking/motrix.yaml b/src/unilab/conf/appo/task/g1_climb_tracking/motrix.yaml
new file mode 100644
index 000000000..01a98604a
--- /dev/null
+++ b/src/unilab/conf/appo/task/g1_climb_tracking/motrix.yaml
@@ -0,0 +1,8 @@
+# @package _global_
+defaults:
+  - /task/g1_climb_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1ClimbTracking
+  sim_backend: motrix
diff --git a/src/unilab/conf/appo/task/g1_climb_tracking/mujoco.yaml b/src/unilab/conf/appo/task/g1_climb_tracking/mujoco.yaml
new file mode 100644
index 000000000..8d15fff57
--- /dev/null
+++ b/src/unilab/conf/appo/task/g1_climb_tracking/mujoco.yaml
@@ -0,0 +1,87 @@
+# @package _global_
+defaults:
+  - /task/g1_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1ClimbTracking
+  sim_backend: mujoco
+  play_steps: 1000
+
+algo:
+  num_envs: 1024
+  max_iterations: 20000
+  save_interval: 500
+  algorithm:
+    adaptive_kl_factor: 1.2
+    adaptive_lr_factor: 1.1
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_climb_20_z_scale_1.xml
+  sim_dt: 0.005
+  max_episode_seconds: 15.0
+  actions:
+    joint_pos:
+      scale:
+        ".*_(hip_pitch|hip_yaw)_joint": 0.5475464629911068
+        ".*_(hip_roll|knee)_joint": 0.35066146637882434
+        ".*_ankle_(pitch|roll)_joint": 0.43857731392336724
+        "waist_yaw_joint": 0.5475464629911068
+        "waist_(roll|pitch)_joint": 0.43857731392336724
+        ".*_(shoulder_(pitch|roll|yaw)|elbow|wrist_roll)_joint": 0.43857731392336724
+        ".*_wrist_(pitch|yaw)_joint": 0.07450087032950714
+  commands:
+    motion:
+      params:
+        motion_file: motions/g1/climb_20_z_scale_1.0.npz
+        sampling_mode: adaptive
+        truncate_on_clip_end: false
+  terminations:
+    anchor_pos:
+      params: {command_name: motion, threshold: 0.3}
+    ee_body_pos:
+      params:
+        command_name: motion
+        threshold: 0.3
+        body_names: &ee_bodies
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_yaw_link
+          - right_wrist_yaw_link
+    undesired_contacts:
+      func: unilab.tasks.motion_tracking.common.manager_terms.bad_undesired_body_contacts
+      params:
+        command_name: motion
+        threshold: 0.05
+        body_names: &undesired_bodies
+          - pelvis
+          - left_hip_roll_link
+          - left_knee_link
+          - right_hip_roll_link
+          - right_knee_link
+          - torso_link
+          - left_shoulder_roll_link
+          - left_elbow_link
+          - right_shoulder_roll_link
+          - right_elbow_link
+
+reward:
+  motion_body_pos:
+    weight: 2.0
+  motion_body_ori:
+    weight: 1.5
+  motion_ee_body_pos_z:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_relative_body_position_z_error_exp
+    weight: 2.0
+    params: {command_name: motion, std: 0.3, body_names: *ee_bodies}
+  motion_joint_pos:
+    weight: 0.5
+  motion_joint_vel:
+    weight: 0.25
+  action_rate_l2:
+    weight: -0.005
+  undesired_contacts:
+    func: unilab.tasks.motion_tracking.common.manager_terms.undesired_body_contacts
+    weight: -0.1
+    params: {command_name: motion, threshold: 0.05, body_names: *undesired_bodies}
diff --git a/src/unilab/conf/appo/task/g1_flip_tracking/motrix.yaml b/src/unilab/conf/appo/task/g1_flip_tracking/motrix.yaml
new file mode 100644
index 000000000..d5f5e8b76
--- /dev/null
+++ b/src/unilab/conf/appo/task/g1_flip_tracking/motrix.yaml
@@ -0,0 +1,8 @@
+# @package _global_
+defaults:
+  - /task/g1_flip_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1FlipTracking
+  sim_backend: motrix
diff --git a/src/unilab/conf/appo/task/g1_flip_tracking/mujoco.yaml b/src/unilab/conf/appo/task/g1_flip_tracking/mujoco.yaml
new file mode 100644
index 000000000..b8f05ad61
--- /dev/null
+++ b/src/unilab/conf/appo/task/g1_flip_tracking/mujoco.yaml
@@ -0,0 +1,117 @@
+# @package _global_
+defaults:
+  - /task/g1_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1FlipTracking
+  sim_backend: mujoco
+  play_steps: 1000
+
+algo:
+  num_envs: 1024
+  steps_per_env: 24
+  max_iterations: 3500
+  save_interval: 500
+  algorithm:
+    num_learning_epochs: 10
+    num_mini_batches: 8
+    clip_param: 0.2
+    gamma: 0.99
+    lam: 0.95
+    value_loss_coef: 1.0
+    entropy_coef: 0.005
+    learning_rate: 1.0e-3
+    max_grad_norm: 1.0
+    use_clipped_value_loss: true
+    schedule: adaptive
+    desired_kl: 0.01
+    adaptive_kl_factor: 2.0
+    adaptive_lr_factor: 1.5
+    optimizer: adam
+    tau: 1.0
+    target_update_freq: 1
+    vtrace_clip_rho: 1.0
+    vtrace_clip_c: 1.0
+    enable_compile: true
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat.xml
+  sim_dt: 0.005
+  actions:
+    joint_pos:
+      scale:
+        ".*_(hip_pitch|hip_yaw)_joint": 0.5475464629911068
+        ".*_(hip_roll|knee)_joint": 0.35066146637882434
+        ".*_ankle_(pitch|roll)_joint": 0.43857731392336724
+        "waist_yaw_joint": 0.5475464629911068
+        "waist_(roll|pitch)_joint": 0.43857731392336724
+        ".*_(shoulder_(pitch|roll|yaw)|elbow|wrist_roll)_joint": 0.43857731392336724
+        ".*_wrist_(pitch|yaw)_joint": 0.07450087032950714
+  commands:
+    motion:
+      params:
+        motion_file: motions/g1/flip_360_001__A304.npz
+        sampling_mode: start
+        truncate_on_clip_end: true
+        pose_range: &zero_pose
+          x: [0.0, 0.0]
+          y: [0.0, 0.0]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [0.0, 0.0]
+        velocity_range: *zero_pose
+        joint_position_range: [0.0, 0.0]
+  terminations:
+    motion_clip_end:
+      func: unilab.tasks.motion_tracking.common.manager_terms.motion_clip_end
+      time_out: true
+      params: {command_name: motion}
+    anchor_pos:
+      params: {command_name: motion, threshold: 0.5}
+    anchor_ori:
+      params:
+        command_name: motion
+        threshold: 1.0e9
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+    ee_body_pos:
+      params:
+        command_name: motion
+        threshold: 0.5
+        body_names: &ee_bodies
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_yaw_link
+          - right_wrist_yaw_link
+    undesired_contacts:
+      func: unilab.tasks.motion_tracking.common.manager_terms.bad_undesired_body_contacts
+      params:
+        command_name: motion
+        threshold: 0.05
+        body_names:
+          - pelvis
+          - left_hip_roll_link
+          - left_knee_link
+          - right_hip_roll_link
+          - right_knee_link
+          - torso_link
+          - left_shoulder_roll_link
+          - left_elbow_link
+          - right_shoulder_roll_link
+          - right_elbow_link
+
+reward:
+  motion_body_pos:
+    weight: 2.0
+  motion_body_ori:
+    weight: 1.5
+  motion_ee_body_pos_z:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_relative_body_position_z_error_exp
+    weight: 2.0
+    params: {command_name: motion, std: 0.3, body_names: *ee_bodies}
+  action_rate_l2:
+    weight: -0.005
diff --git a/src/unilab/conf/appo/task/g1_motion_tracking/motrix.yaml b/src/unilab/conf/appo/task/g1_motion_tracking/motrix.yaml
new file mode 100644
index 000000000..22bbdadfa
--- /dev/null
+++ b/src/unilab/conf/appo/task/g1_motion_tracking/motrix.yaml
@@ -0,0 +1,8 @@
+# @package _global_
+defaults:
+  - /task/g1_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1MotionTracking
+  sim_backend: motrix
diff --git a/src/unilab/conf/appo/task/g1_motion_tracking/mujoco.yaml b/src/unilab/conf/appo/task/g1_motion_tracking/mujoco.yaml
new file mode 100644
index 000000000..b1898ef6d
--- /dev/null
+++ b/src/unilab/conf/appo/task/g1_motion_tracking/mujoco.yaml
@@ -0,0 +1,234 @@
+# @package _global_
+training:
+  task_name: G1MotionTracking
+  sim_backend: mujoco
+  play_steps: 1000
+
+algo:
+  num_envs: 1024
+  max_iterations: 5000
+  save_interval: 500
+  algorithm:
+    adaptive_kl_factor: 2.0
+    adaptive_lr_factor: 1.5
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat.xml
+    default_keyframe_name: stand
+    entities:
+      robot:
+        root_body_name: pelvis
+        joint_names: &g1_joints
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - waist_roll_joint
+          - waist_pitch_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - left_wrist_pitch_joint
+          - left_wrist_yaw_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+          - right_wrist_pitch_joint
+          - right_wrist_yaw_joint
+        actuator_names: *g1_joints
+        body_names: &tracked_bodies
+          - pelvis
+          - left_hip_roll_link
+          - left_knee_link
+          - left_ankle_roll_link
+          - right_hip_roll_link
+          - right_knee_link
+          - right_ankle_roll_link
+          - torso_link
+          - left_shoulder_roll_link
+          - left_elbow_link
+          - left_wrist_yaw_link
+          - right_shoulder_roll_link
+          - right_elbow_link
+          - right_wrist_yaw_link
+        geom_names:
+          - left_foot1_collision
+          - left_foot2_collision
+          - left_foot3_collision
+          - left_foot4_collision
+          - left_foot5_collision
+          - left_foot6_collision
+          - left_foot7_collision
+          - right_foot1_collision
+          - right_foot2_collision
+          - right_foot3_collision
+          - right_foot4_collision
+          - right_foot5_collision
+          - right_foot6_collision
+          - right_foot7_collision
+  sim_dt: 0.006666666666666667
+  ctrl_dt: 0.02
+  max_episode_seconds: 10.0
+  observations:
+    actor:
+      terms:
+        command: &command_obs
+          func: unilab.envs.mdp.generated_commands
+          params: {command_name: motion}
+        motion_anchor_pos_b: &anchor_pos_obs
+          func: unilab.tasks.motion_tracking.common.manager_terms.motion_anchor_pos_b
+          params: {command_name: motion}
+        motion_anchor_ori_b: &anchor_ori_obs
+          func: unilab.tasks.motion_tracking.common.manager_terms.motion_anchor_ori_b
+          params: {command_name: motion}
+        base_lin_vel: &base_lin_vel_obs
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: pelvis_local_linvel}
+        base_ang_vel: &base_ang_vel_obs
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: torso_gyro}
+        joint_pos: &joint_pos_obs
+          func: unilab.tasks.motion_tracking.common.manager_terms.motion_joint_pos_rel
+          params: {command_name: motion}
+        joint_vel: &joint_vel_obs
+          func: unilab.envs.mdp.joint_vel_rel
+        actions: &actions_obs
+          func: unilab.envs.mdp.last_action
+    critic:
+      terms:
+        command: *command_obs
+        motion_anchor_pos_b: *anchor_pos_obs
+        motion_anchor_ori_b: *anchor_ori_obs
+        base_lin_vel: *base_lin_vel_obs
+        base_ang_vel: *base_ang_vel_obs
+        joint_pos: *joint_pos_obs
+        joint_vel: *joint_vel_obs
+        actions: *actions_obs
+        body_pos:
+          func: unilab.tasks.motion_tracking.common.manager_terms.robot_body_pos_b
+          params: {command_name: motion}
+        body_ori:
+          func: unilab.tasks.motion_tracking.common.manager_terms.robot_body_ori_b
+          params: {command_name: motion}
+  actions:
+    joint_pos:
+      _target_: unilab.tasks.motion_tracking.common.manager_terms.MotionJointPositionActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      scale: 0.25
+      use_default_offset: true
+      command_name: motion
+  commands:
+    motion:
+      _target_: unilab.tasks.motion_tracking.common.manager_terms.MotionCommandCfg
+      entity_name: robot
+      resampling_time_range: [1.0e9, 1.0e9]
+      params:
+        motion_file: motions/g1/dance1_subject2_part.npz
+        anchor_body_name: torso_link
+        body_names: *tracked_bodies
+        sampling_mode: adaptive
+        sampling_start_ratio: 0.0
+        truncate_on_clip_end: false
+        pose_range:
+          x: [-0.05, 0.05]
+          y: [-0.05, 0.05]
+          z: [-0.01, 0.01]
+          roll: [-0.1, 0.1]
+          pitch: [-0.1, 0.1]
+          yaw: [-0.2, 0.2]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.2, 0.2]
+          roll: [-0.52, 0.52]
+          pitch: [-0.52, 0.52]
+          yaw: [-0.78, 0.78]
+        joint_position_range: [-0.1, 0.1]
+        joint_default_position_range: [0.0, 0.0]
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    anchor_pos:
+      func: unilab.tasks.motion_tracking.common.manager_terms.bad_anchor_pos_z_only
+      params: {command_name: motion, threshold: 0.25}
+    anchor_ori:
+      func: unilab.tasks.motion_tracking.common.manager_terms.bad_anchor_ori
+      params:
+        command_name: motion
+        threshold: 0.8
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+    ee_body_pos:
+      func: unilab.tasks.motion_tracking.common.manager_terms.bad_motion_body_pos_z_only
+      params:
+        command_name: motion
+        threshold: 0.25
+        body_names:
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_yaw_link
+          - right_wrist_yaw_link
+  policy_observation_group: actor
+  critic_observation_group: critic
+
+reward:
+  motion_global_root_pos:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_global_anchor_position_error_exp
+    weight: 0.5
+    params: {command_name: motion, std: 0.3}
+  motion_global_root_ori:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_global_anchor_orientation_error_exp
+    weight: 0.5
+    params: {command_name: motion, std: 0.4}
+  motion_body_pos:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_relative_body_position_error_exp
+    weight: 1.0
+    params: {command_name: motion, std: 0.3}
+  motion_body_ori:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_relative_body_orientation_error_exp
+    weight: 1.0
+    params: {command_name: motion, std: 0.4}
+  motion_body_lin_vel:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_global_body_linear_velocity_error_exp
+    weight: 1.0
+    params: {command_name: motion, std: 1.0}
+  motion_body_ang_vel:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_global_body_angular_velocity_error_exp
+    weight: 1.0
+    params: {command_name: motion, std: 3.14}
+  motion_joint_pos:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_joint_position_error_exp
+    weight: 0.0
+    params: {command_name: motion, std: 0.2}
+  motion_joint_vel:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_joint_velocity_error_exp
+    weight: 0.0
+    params: {command_name: motion, std: 1.0}
+  action_rate_l2:
+    func: unilab.envs.mdp.action_rate_l2
+    weight: -0.1
+  joint_limit:
+    func: unilab.tasks.motion_tracking.common.manager_terms.joint_pos_limits
+    weight: -10.0
+    params:
+      asset_cfg:
+        _target_: unilab.managers.SceneEntityCfg
+        name: robot
+        joint_names: ".*"
diff --git a/src/unilab/conf/appo/task/g1_walk_flat/base.yaml b/src/unilab/conf/appo/task/g1_walk_flat/base.yaml
new file mode 100644
index 000000000..9aefd4f8f
--- /dev/null
+++ b/src/unilab/conf/appo/task/g1_walk_flat/base.yaml
@@ -0,0 +1,270 @@
+# @package _global_
+# Canonical G1 29-DoF walk Manager-Based task declaration (APPO owners; mirrors the PPO base).
+# Backend owner leaves inherit this file and only override backend/algo tuning
+# or explicitly disabled terms. Observation scaling follows the legacy profile
+# (unit scales); the walk profile lives in conf/sac/task/g1_walk_flat/base.yaml.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat.xml
+    default_keyframe_name: stand
+    entities:
+      robot:
+        root_body_name: pelvis
+        joint_names:
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - waist_roll_joint
+          - waist_pitch_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - left_wrist_pitch_joint
+          - left_wrist_yaw_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+          - right_wrist_pitch_joint
+          - right_wrist_yaw_joint
+        actuator_names:
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - waist_roll_joint
+          - waist_pitch_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - left_wrist_pitch_joint
+          - left_wrist_yaw_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+          - right_wrist_pitch_joint
+          - right_wrist_yaw_joint
+        body_names: [pelvis]
+  sim_dt: 0.006666666666666667
+  ctrl_dt: 0.02
+  max_episode_seconds: 20.0
+  observations:
+    policy:
+      # Observation noise matches the legacy noise_config (level=1.0, actor-only
+      # since the critic reads clean observations): gyro +/-0.2, gravity +/-0.05,
+      # joint pos +/-0.01, joint vel +/-1.5, applied before term scaling.
+      enable_corruption: true
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: torso_gyro}
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.2
+            n_max: 0.2
+            operation: add
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params: {sensor_name: torso_upvector}
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.05
+            n_max: 0.05
+            operation: add
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.01
+            n_max: 0.01
+            operation: add
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -1.5
+            n_max: 1.5
+            operation: add
+        actions:
+          func: unilab.envs.mdp.last_action
+          params: {action_name: joint_pos}
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params: {command_name: twist}
+        gait_phase:
+          func: unilab.tasks.locomotion.g1.manager_terms.G1GaitPhase
+          params:
+            frequency: 1.5
+            init_mode: offset_phase
+    critic:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: torso_gyro}
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params: {sensor_name: torso_upvector}
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+          params: {action_name: joint_pos}
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params: {command_name: twist}
+        gait_phase:
+          func: unilab.tasks.locomotion.g1.manager_terms.G1GaitPhase
+          params:
+            frequency: 1.5
+            init_mode: offset_phase
+        base_lin_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: pelvis_local_linvel}
+  actions:
+    joint_pos:
+      _target_: unilab.envs.mdp.JointPositionActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      scale: 0.25
+      use_default_offset: true
+  commands:
+    twist:
+      _target_: unilab.tasks.locomotion.g1.manager_terms.G1VelocityCommandCfg
+      entity_name: robot
+      resampling_time_range: [20.0, 20.0]
+      heading_command: false
+      heading_control_stiffness: 0.5
+      rel_standing_envs: 0.0
+      rel_heading_envs: 0.0
+      rel_world_envs: 0.0
+      rel_forward_envs: 0.0
+      init_velocity_prob: 0.0
+      planar_dead_zone: 0.2
+      ranges:
+        lin_vel_x: [-0.6, 1.0]
+        lin_vel_y: [-0.4, 0.4]
+        ang_vel_z: [-0.8, 0.8]
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_root_state_uniform:
+      func: unilab.envs.mdp.reset_root_state_uniform
+      mode: reset
+      params:
+        pose_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [-3.141592653589793, 3.141592653589793]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.5, 0.5]
+          roll: [-0.5, 0.5]
+          pitch: [-0.5, 0.5]
+          yaw: [-0.5, 0.5]
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        kp_range: [0.9, 1.1]
+        kd_range: [0.9, 1.1]
+        operation: scale
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    tilt:
+      func: unilab.tasks.locomotion.g1.manager_terms.g1_tilt_exceeded
+      params:
+        max_tilt_deg: 25.0
+    base_height:
+      func: unilab.envs.mdp.root_height_below_minimum
+      params:
+        minimum_height: 0.55
+  policy_observation_group: policy
+  critic_observation_group: critic
+
+reward:
+  tracking_lin_vel:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.track_lin_vel
+    weight: 2.0
+    params:
+      tracking_sigma: 0.25
+      command_name: twist
+      sensor_name: pelvis_local_linvel
+  tracking_ang_vel:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.track_ang_vel
+    weight: 0.2
+    params:
+      tracking_sigma: 0.25
+      command_name: twist
+      sensor_name: torso_gyro
+  feet_phase:
+    func: unilab.tasks.locomotion.g1.manager_terms.feet_phase
+    weight: 1.0
+    params:
+      frequency: 1.5
+      swing_height: 0.09
+      tracking_sigma: 0.008
+      min_forward_speed: 0.0
+      command_name: twist
+  lin_vel_z:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.lin_vel_z
+    weight: -1.0
+    params:
+      sensor_name: pelvis_local_linvel
+  ang_vel_xy:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.ang_vel_xy
+    weight: -0.25
+    params:
+      sensor_name: torso_gyro
+  base_height:
+    func: unilab.tasks.locomotion.common.manager_terms.base_height_l2
+    weight: -500.0
+    params:
+      target_height: 0.754
+  orientation:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.orientation
+    weight: -5.0
+    params:
+      sensor_name: torso_upvector
+  action_rate:
+    func: unilab.envs.mdp.action_rate_l2
+    weight: -0.01
+  pose:
+    func: unilab.tasks.locomotion.g1.manager_terms.weighted_pose
+    weight: -0.1
+    params:
+      pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/src/unilab/conf/appo/task/g1_walk_flat/mujoco.yaml b/src/unilab/conf/appo/task/g1_walk_flat/mujoco.yaml
new file mode 100644
index 000000000..6ee7b66ed
--- /dev/null
+++ b/src/unilab/conf/appo/task/g1_walk_flat/mujoco.yaml
@@ -0,0 +1,13 @@
+# @package _global_
+# MuJoCo APPO owner: inherits the shared 29-DoF flat Manager-Based contract
+# from base.yaml and only carries backend/algo identity.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1WalkFlat
+  sim_backend: mujoco
+algo:
+  max_iterations: 500
+  save_interval: 100
diff --git a/src/unilab/conf/appo/task/g1_wall_flip_tracking/motrix.yaml b/src/unilab/conf/appo/task/g1_wall_flip_tracking/motrix.yaml
new file mode 100644
index 000000000..3ce1d8cc3
--- /dev/null
+++ b/src/unilab/conf/appo/task/g1_wall_flip_tracking/motrix.yaml
@@ -0,0 +1,32 @@
+# @package _global_
+defaults:
+  - /task/g1_wall_flip_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1WallFlipTracking
+  sim_backend: motrix
+  replay_queue_size: null
+
+algo:
+  steps_per_env: 24
+  max_iterations: 5000
+  algorithm:
+    num_learning_epochs: 5
+    num_mini_batches: 4
+    entropy_coef: 0.01
+    desired_kl: 0.01
+
+env:
+  actions:
+    joint_pos:
+      scale: 0.25
+
+reward:
+  motion_body_pos:
+    weight: 1.0
+  motion_body_ori:
+    weight: 1.0
+  motion_ee_body_pos_z: null
+  action_rate_l2:
+    weight: -0.1
diff --git a/src/unilab/conf/appo/task/g1_wall_flip_tracking/mujoco.yaml b/src/unilab/conf/appo/task/g1_wall_flip_tracking/mujoco.yaml
new file mode 100644
index 000000000..d177ef8b4
--- /dev/null
+++ b/src/unilab/conf/appo/task/g1_wall_flip_tracking/mujoco.yaml
@@ -0,0 +1,24 @@
+# @package _global_
+defaults:
+  - /task/g1_flip_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1WallFlipTracking
+  sim_backend: mujoco
+  replay_queue_size: 5
+
+algo:
+  steps_per_env: 20
+  max_iterations: 7000
+  algorithm:
+    num_learning_epochs: 6
+    desired_kl: 0.008
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat_with_wall.xml
+  commands:
+    motion:
+      params:
+        motion_file: motions/g1/flip_from_wall_104__A304.npz
diff --git a/src/unilab/conf/appo/task/go1_joystick_flat/base.yaml b/src/unilab/conf/appo/task/go1_joystick_flat/base.yaml
new file mode 100644
index 000000000..8a2f3bf57
--- /dev/null
+++ b/src/unilab/conf/appo/task/go1_joystick_flat/base.yaml
@@ -0,0 +1,244 @@
+# @package _global_
+# Canonical Go1 flat Manager-Based task declaration. Backend owner leaves inherit
+# this file and only override backend/algo tuning or explicitly disabled terms.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/go1/scene_flat.xml
+    default_keyframe_name: home
+    entities:
+      robot:
+        root_body_name: trunk
+        joint_names:
+          - FR_hip_joint
+          - FR_thigh_joint
+          - FR_calf_joint
+          - FL_hip_joint
+          - FL_thigh_joint
+          - FL_calf_joint
+          - RR_hip_joint
+          - RR_thigh_joint
+          - RR_calf_joint
+          - RL_hip_joint
+          - RL_thigh_joint
+          - RL_calf_joint
+        actuator_names:
+          - FR_hip
+          - FR_thigh
+          - FR_calf
+          - FL_hip
+          - FL_thigh
+          - FL_calf
+          - RR_hip
+          - RR_thigh
+          - RR_calf
+          - RL_hip
+          - RL_thigh
+          - RL_calf
+        body_names: [trunk]
+  sim_dt: 0.01
+  ctrl_dt: 0.02
+  max_episode_seconds: 20.0
+  observations:
+    policy:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+        gait_phase:
+          func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase
+          params:
+            frequency: 2.0
+    critic:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+        gait_phase:
+          func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase
+          params:
+            frequency: 2.0
+        base_lin_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: local_linvel
+  actions:
+    joint_pos:
+      _target_: unilab.envs.mdp.JointPositionActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      scale: 0.25
+      use_default_offset: true
+  commands:
+    twist:
+      _target_: unilab.envs.mdp.UniformVelocityCommandCfg
+      entity_name: robot
+      resampling_time_range: [20.0, 20.0]
+      heading_command: false
+      heading_control_stiffness: 0.5
+      rel_standing_envs: 0.0
+      rel_heading_envs: 0.0
+      rel_world_envs: 0.0
+      rel_forward_envs: 0.0
+      init_velocity_prob: 0.0
+      ranges:
+        lin_vel_x: [-0.6, 1.0]
+        lin_vel_y: [-0.4, 0.4]
+        ang_vel_z: [-0.8, 0.8]
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_root_state_uniform:
+      func: unilab.envs.mdp.reset_root_state_uniform
+      mode: reset
+      params:
+        pose_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [-3.141592653589793, 3.141592653589793]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.5, 0.5]
+          roll: [-0.5, 0.5]
+          pitch: [-0.5, 0.5]
+          yaw: [-0.5, 0.5]
+    base_mass:
+      func: unilab.envs.mdp.randomize_rigid_body_mass
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          body_names: trunk
+        mass_distribution_params: [-1.5, 1.5]
+        operation: add
+        recompute_inertia: false
+    base_com:
+      func: unilab.envs.mdp.randomize_rigid_body_com
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          body_names: trunk
+        com_range:
+          x: [-0.05, 0.05]
+          y: [0.0, 0.0]
+          z: [0.0, 0.0]
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        kp_range: [35.0, 35.0]
+        kd_range: [0.5, 0.5]
+        operation: abs
+    push_robot:
+      func: unilab.envs.mdp.push_by_setting_velocity
+      mode: interval
+      interval_range_s: [15.0, 15.0]
+      is_global_time: true
+      params:
+        velocity_range:
+          x: [-1.0, 1.0]
+          y: [-1.0, 1.0]
+          z: [-0.5, 0.5]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [0.0, 0.0]
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    bad_orientation:
+      func: unilab.envs.mdp.bad_orientation
+      params:
+        limit_angle: 1.0471975511965976
+  policy_observation_group: policy
+  critic_observation_group: critic
+
+reward:
+  tracking_lin_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_lin_vel_xy_exp
+    weight: 1.0
+    params:
+      std: 0.5
+      command_name: twist
+  tracking_ang_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_ang_vel_z_exp
+    weight: 0.2
+    params:
+      std: 0.5
+      command_name: twist
+  lin_vel_z:
+    func: unilab.tasks.locomotion.common.manager_terms.lin_vel_z_l2
+    weight: -5.0
+  ang_vel_xy:
+    func: unilab.tasks.locomotion.common.manager_terms.ang_vel_xy_l2
+    weight: -0.1
+  base_height:
+    func: unilab.tasks.locomotion.common.manager_terms.base_height_l2
+    weight: -100.0
+    params:
+      target_height: 0.3
+  action_rate:
+    func: unilab.envs.mdp.action_rate_l2
+    weight: -0.005
+  similar_to_default:
+    func: unilab.tasks.locomotion.common.manager_terms.joint_deviation_l1
+    weight: -0.1
+  contact:
+    func: unilab.tasks.locomotion.common.manager_terms.feet_phase_contact
+    # Legacy Go1 sums four matching feet while this community term returns their mean.
+    weight: 0.96
+    params:
+      frequency: 2.0
+      sensor_names:
+        - FL_foot_contact
+        - FR_foot_contact
+        - RL_foot_contact
+        - RR_foot_contact
+      contact_threshold: 0.1
+      stance_threshold: 0.6
+  swing_feet_z:
+    func: unilab.tasks.locomotion.common.manager_terms.feet_phase_swing_height
+    weight: 4.0
+    params:
+      frequency: 2.0
+      sensor_names: [FL_pos, FR_pos, RL_pos, RR_pos]
+      target_height: 0.1
+      kernel: 0.01
+      swing_start: 0.6
diff --git a/src/unilab/conf/appo/task/go1_joystick_flat/motrix.yaml b/src/unilab/conf/appo/task/go1_joystick_flat/motrix.yaml
new file mode 100644
index 000000000..d4bb53521
--- /dev/null
+++ b/src/unilab/conf/appo/task/go1_joystick_flat/motrix.yaml
@@ -0,0 +1,39 @@
+# @package _global_
+defaults:
+  - /task/go1_joystick_flat/base
+  - _self_
+
+training:
+  task_name: Go1JoystickFlat
+  sim_backend: motrix
+algo:
+  num_envs: 1024
+  steps_per_env: 24
+  max_iterations: 300
+  actor:
+    distribution_cfg:
+      init_std: 0.5
+  algorithm:
+    learning_rate: 5.0e-4
+    entropy_coef: 1.0e-3
+    desired_kl: 0.008
+env:
+  commands:
+    twist:
+      ranges:
+        lin_vel_x: [0.5, 0.5]
+        lin_vel_y: [0.0, 0.0]
+        ang_vel_z: [0.0, 0.0]
+  events:
+    push_robot: null
+reward:
+  action_rate:
+    weight: -0.015
+  action_smooth:
+    func: unilab.envs.mdp.action_acc_l2
+    weight: -0.01
+  similar_to_default:
+    weight: -0.15
+  contact: null
+  swing_feet_z:
+    weight: 2.0
diff --git a/src/unilab/conf/appo/task/go1_joystick_flat/mujoco.yaml b/src/unilab/conf/appo/task/go1_joystick_flat/mujoco.yaml
new file mode 100644
index 000000000..5443809b6
--- /dev/null
+++ b/src/unilab/conf/appo/task/go1_joystick_flat/mujoco.yaml
@@ -0,0 +1,12 @@
+# @package _global_
+defaults:
+  - /task/go1_joystick_flat/base
+  - _self_
+
+training:
+  task_name: Go1JoystickFlat
+  sim_backend: mujoco
+algo:
+  max_iterations: 150
+reward:
+  swing_feet_z: null
diff --git a/src/unilab/conf/appo/task/go2_joystick_flat/base.yaml b/src/unilab/conf/appo/task/go2_joystick_flat/base.yaml
new file mode 100644
index 000000000..be7364f78
--- /dev/null
+++ b/src/unilab/conf/appo/task/go2_joystick_flat/base.yaml
@@ -0,0 +1,209 @@
+# @package _global_
+# Canonical Go2 flat Manager-Based task declaration. Backend owner leaves inherit
+# this file and only override backend/algo tuning or explicitly disabled terms.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/go2/scene_flat.xml
+    default_keyframe_name: home
+    entities:
+      robot:
+        root_body_name: base
+        joint_names:
+          - FL_hip_joint
+          - FL_thigh_joint
+          - FL_calf_joint
+          - FR_hip_joint
+          - FR_thigh_joint
+          - FR_calf_joint
+          - RL_hip_joint
+          - RL_thigh_joint
+          - RL_calf_joint
+          - RR_hip_joint
+          - RR_thigh_joint
+          - RR_calf_joint
+        actuator_names:
+          - FR_hip
+          - FR_thigh
+          - FR_calf
+          - FL_hip
+          - FL_thigh
+          - FL_calf
+          - RR_hip
+          - RR_thigh
+          - RR_calf
+          - RL_hip
+          - RL_thigh
+          - RL_calf
+  sim_dt: 0.01
+  ctrl_dt: 0.02
+  max_episode_seconds: 20.0
+  observations:
+    policy:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+        gait_phase:
+          func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase
+          params:
+            frequency: 2.0
+    critic:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+        gait_phase:
+          func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase
+          params:
+            frequency: 2.0
+        base_lin_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: local_linvel
+  actions:
+    joint_pos:
+      _target_: unilab.envs.mdp.JointPositionActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      scale: 0.25
+      use_default_offset: true
+  commands:
+    twist:
+      _target_: unilab.envs.mdp.UniformVelocityCommandCfg
+      entity_name: robot
+      resampling_time_range: [20.0, 20.0]
+      heading_command: false
+      heading_control_stiffness: 0.5
+      rel_standing_envs: 0.0
+      rel_heading_envs: 0.0
+      rel_world_envs: 0.0
+      rel_forward_envs: 0.0
+      init_velocity_prob: 0.0
+      ranges:
+        lin_vel_x: [-0.6, 1.0]
+        lin_vel_y: [-0.4, 0.4]
+        ang_vel_z: [-0.8, 0.8]
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_root_state_uniform:
+      func: unilab.envs.mdp.reset_root_state_uniform
+      mode: reset
+      params:
+        pose_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [-3.141592653589793, 3.141592653589793]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.5, 0.5]
+          roll: [-0.5, 0.5]
+          pitch: [-0.5, 0.5]
+          yaw: [-0.5, 0.5]
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        kp_range: [31.5, 38.5]
+        kd_range: [0.45, 0.55]
+        operation: abs
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    bad_orientation:
+      func: unilab.envs.mdp.bad_orientation
+      params:
+        limit_angle: 1.0471975511965976
+  policy_observation_group: policy
+  critic_observation_group: critic
+
+reward:
+  tracking_lin_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_lin_vel_xy_exp
+    weight: 1.0
+    params:
+      std: 0.5
+      command_name: twist
+  tracking_ang_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_ang_vel_z_exp
+    weight: 0.2
+    params:
+      std: 0.5
+      command_name: twist
+  lin_vel_z:
+    func: unilab.tasks.locomotion.common.manager_terms.lin_vel_z_l2
+    weight: -5.0
+  ang_vel_xy:
+    func: unilab.tasks.locomotion.common.manager_terms.ang_vel_xy_l2
+    weight: -0.1
+  base_height:
+    func: unilab.tasks.locomotion.common.manager_terms.base_height_l2
+    weight: -100.0
+    params:
+      target_height: 0.3
+  action_rate:
+    func: unilab.envs.mdp.action_rate_l2
+    weight: -0.005
+  similar_to_default:
+    func: unilab.tasks.locomotion.common.manager_terms.joint_deviation_l1
+    weight: -0.1
+  alive:
+    func: unilab.envs.mdp.is_alive
+    weight: 0.0
+  contact:
+    func: unilab.tasks.locomotion.common.manager_terms.feet_phase_contact
+    weight: 0.24
+    params:
+      frequency: 2.0
+      sensor_names:
+        - FL_foot_contact
+        - FR_foot_contact
+        - RL_foot_contact
+        - RR_foot_contact
+      contact_threshold: 0.1
+      stance_threshold: 0.6
+  swing_feet_z:
+    func: unilab.tasks.locomotion.common.manager_terms.feet_phase_swing_height
+    weight: 4.0
+    params:
+      frequency: 2.0
+      sensor_names: [FL_pos, FR_pos, RL_pos, RR_pos]
+      target_height: 0.1
+      kernel: 0.01
+      swing_start: 0.6
diff --git a/src/unilab/conf/appo/task/go2_joystick_flat/motrix.yaml b/src/unilab/conf/appo/task/go2_joystick_flat/motrix.yaml
new file mode 100644
index 000000000..b46281fe6
--- /dev/null
+++ b/src/unilab/conf/appo/task/go2_joystick_flat/motrix.yaml
@@ -0,0 +1,17 @@
+# @package _global_
+defaults:
+  - /task/go2_joystick_flat/base
+  - _self_
+
+training:
+  task_name: Go2JoystickFlat
+  sim_backend: motrix
+algo:
+  num_envs: 512
+  steps_per_env: 24
+  max_iterations: 180
+env:
+  # The former 0.015/0.02 ratio was not an integer number of physics substeps.
+  # MBA keeps the shared 0.01/0.02 contract and fails on fractional ratios.
+  events:
+    pd_gains: null
diff --git a/src/unilab/conf/appo/task/go2_joystick_flat/mujoco.yaml b/src/unilab/conf/appo/task/go2_joystick_flat/mujoco.yaml
new file mode 100644
index 000000000..aec4a0437
--- /dev/null
+++ b/src/unilab/conf/appo/task/go2_joystick_flat/mujoco.yaml
@@ -0,0 +1,10 @@
+# @package _global_
+defaults:
+  - /task/go2_joystick_flat/base
+  - _self_
+
+training:
+  task_name: Go2JoystickFlat
+  sim_backend: mujoco
+algo:
+  max_iterations: 150
diff --git a/conf/appo/task/sharpa_inhand/motrix.yaml b/src/unilab/conf/appo/task/sharpa_inhand/motrix.yaml
similarity index 100%
rename from conf/appo/task/sharpa_inhand/motrix.yaml
rename to src/unilab/conf/appo/task/sharpa_inhand/motrix.yaml
diff --git a/conf/appo/task/sharpa_inhand/mujoco.yaml b/src/unilab/conf/appo/task/sharpa_inhand/mujoco.yaml
similarity index 100%
rename from conf/appo/task/sharpa_inhand/mujoco.yaml
rename to src/unilab/conf/appo/task/sharpa_inhand/mujoco.yaml
diff --git a/src/unilab/conf/appo/task/sharpa_inhand/mujoco_hora.yaml b/src/unilab/conf/appo/task/sharpa_inhand/mujoco_hora.yaml
new file mode 100644
index 000000000..7a76aaec5
--- /dev/null
+++ b/src/unilab/conf/appo/task/sharpa_inhand/mujoco_hora.yaml
@@ -0,0 +1,64 @@
+# @package _global_
+# HORA Sharpa APPO variant. Inherit the shared MuJoCo owner so backend support
+# and shared hyperparameters stay aligned with the non-HORA Sharpa APPO config.
+defaults:
+  - /task/sharpa_inhand/mujoco
+  - _self_
+
+training:
+  replay_queue_size: 8
+  cam_distance: 0.75
+  cam_lookat: [0.0, 0.0, 0.62]
+  cam_elevation: -25.0
+  cam_azimuth: 45.0
+
+interactive:
+  action_mode: policy
+  policy_obs_mode: actor
+  camera_distance: 0.75
+  camera_elevation: -25.0
+  camera_azimuth: 45.0
+  use_env_visual_model: true
+
+algo:
+  algo_log_name: hora_appo
+  runtime_impl: hora_appo
+  runtime_resolver: unilab.scripts.play_hora_appo:resolve_hora_appo_runtime
+  num_envs: 2048
+  steps_per_env: 8
+  max_iterations: 305
+  save_interval: 51
+  obs_groups:
+    # Keep grouped keys explicit in the owner YAML; runtime support for these
+    # grouped observations lands in the next implementation step.
+    actor:
+      actor: 0
+      priv_info: 0
+    critic:
+      actor: 0
+      priv_info: 0
+  actor:
+    class_name: uni_rl.algos.hora:HoraActorModel
+    priv_info_embed_dim: 9
+    priv_mlp_hidden_dims: [256, 128, 9]
+  critic:
+    class_name: uni_rl.algos.hora:HoraCriticModel
+    priv_info_embed_dim: 9
+    priv_mlp_hidden_dims: [256, 128, 9]
+  algorithm:
+    learning_rate: 0.001
+    desired_kl: 0.04
+    adaptive_kl_factor: 1.2
+    adaptive_lr_factor: 1.1
+
+env:
+  use_default_object_pose_for_object_pos_anchor: true
+  obs:
+    observation_mode: separated
+  domain_rand:
+    randomize_friction: true
+    randomize_friction_scale_lower: 0.75
+    randomize_friction_scale_upper: 1.25
+    elastomer_base_friction: 2.0
+    metal_base_friction: 1.0
+    object_base_friction: 2.0
diff --git a/src/unilab/conf/flashsac/config.yaml b/src/unilab/conf/flashsac/config.yaml
new file mode 100644
index 000000000..27b3064cb
--- /dev/null
+++ b/src/unilab/conf/flashsac/config.yaml
@@ -0,0 +1,152 @@
+defaults:
+  - _self_
+  - task: g1_walk_flat/mujoco
+
+algo:
+  algo: flashsac
+  algo_log_name: flash_sac
+  load_run: "-1"
+  seed: 1
+  num_envs: 1024
+  batch_size: 2048
+  replay_buffer_n: 512
+  updates_per_step: 2
+  learning_starts: 98
+  policy_frequency: 2
+  max_iterations: 5000
+  save_interval: 1000
+  gamma: 0.97
+  tau: 0.01
+  actor_lr: 3.0e-4
+  critic_lr: 3.0e-4
+  actor_hidden_dim: 128
+  critic_hidden_dim: 256
+  num_atoms: 101
+  obs_normalization: false
+  use_layer_norm: false
+  algo_params:
+    normalize_reward: true
+    normalized_g_max: 5.0
+    actor_num_blocks: 2
+    critic_num_blocks: 2
+    actor_bc_alpha: 0.0
+    actor_noise_zeta_mu: 2.0
+    actor_noise_zeta_max: 16
+    critic_min_v: -5.0
+    critic_max_v: 5.0
+    temp_initial_value: 0.01
+    temp_target_sigma: 0.15
+    temp_target_entropy: null
+    learning_rate_init: 3.0e-4
+    learning_rate_peak: 3.0e-4
+    learning_rate_end: 1.5e-4
+    learning_rate_warmup_steps: 0
+    learning_rate_decay_steps: 500000
+    n_step: 1
+    amp_dtype: auto
+    use_compile: true
+    use_cuda_graph_critic: false
+    use_cuda_graph_actor: false
+    use_cuda_graph_critic_packed_staging: false
+    use_cuda_graph_actor_packed_staging: false
+
+training:
+  task_name: G1WalkFlat
+  # list[int] | null; null/[] = auto-selected single-device behavior;
+  # [d0] = explicit single CUDA device; [d0..dN-1] = N-way data parallel,
+  # rank i trains on cuda:devices[i].
+  devices: null
+  # list[list[int]] | null; one CPU-id segment per rank for the collector's
+  # MuJoCo pool; null = auto partition of cpu_count // world_size per rank.
+  dp_collector_cpu_ids: null
+  logger: tensorboard
+  wandb_project: unilab
+  wandb_entity: null
+  wandb_group: null
+  wandb_job_type: null
+  wandb_name: null
+  wandb_tags: []
+  wandb_notes: null
+  wandb_mode: null
+  sim_backend: mujoco
+  nan_guard:
+    enabled: true
+    buffer_size: 100
+    max_envs_to_dump: 5
+    output_dir: null
+  use_amp: true
+  play_only: false
+  no_play: false
+  sim2sim_strict: true
+  play_render_mode: auto
+  export_onnx: true
+  play_env_num: 16
+  play_steps: 800
+  cam_distance: 6.0
+  cam_elevation: -20.0
+  cam_azimuth: 90.0
+  log_root: null
+  log_dir: null
+  env_steps_per_sync: 1
+  trace_enabled: false
+  trace_output_dir: null
+  trace_thread_time: false
+  trace_cuda_events: true
+  nvtx_profile_ranges: false
+  replay_prefetch_mode: one_tick
+  torch_threads:
+    enabled: true
+    # "auto" resolves per process role from host CPU count with conservative caps.
+    # Override these from Hydra when benchmarking a specific machine.
+    learner_num_threads: auto
+    collector_num_threads: auto
+    learner_num_interop_threads: 1
+    collector_num_interop_threads: 1
+    compile_threads: auto
+    set_env_vars: true
+
+interactive:
+  action_mode: zero
+  policy_obs_mode: auto
+  show_target_bodies: false
+  show_reward_debug: false
+  target_show_axes: false
+  target_body_names: ""
+  target_max_bodies: 0
+  target_marker_radius: 0.02
+  target_axis_length: 0.08
+  target_marker_alpha: 0.75
+  reward_debug_show_velocity: false
+  reward_debug_lin_vel_scale: 0.08
+  reward_debug_ang_vel_scale: 0.05
+  reward_debug_show_connectors: false
+  reward_debug_show_global_anchor: false
+  camera_follow_body: true
+  camera_focus_body_name: ""
+  camera_height_offset: 0.15
+  camera_distance: null
+  camera_elevation: null
+  camera_azimuth: null
+  use_env_visual_model: true
+  speed: 1.0
+  start_paused: false
+  keyboard: false
+  keyboard_step_lin: 0.1
+  keyboard_step_ang: 0.2
+
+env:
+  post_step_forward_sensor: false
+  # adaptive_chunk_size: auto-tune the MuJoCo BatchEnvPool chunk_size at materialize
+  # (cache-backed). chunk_size (int) manually overrides and wins; null => use default.
+  adaptive_chunk_size: true
+  chunk_size: null
+
+hydra:
+  run:
+    dir: .
+  output_subdir: null
+  job:
+    chdir: false
+  job_logging:
+    root:
+      handlers: [console]
diff --git a/src/unilab/conf/flashsac/task/g1_23dof_walk_flat/base.yaml b/src/unilab/conf/flashsac/task/g1_23dof_walk_flat/base.yaml
new file mode 100644
index 000000000..c7571348f
--- /dev/null
+++ b/src/unilab/conf/flashsac/task/g1_23dof_walk_flat/base.yaml
@@ -0,0 +1,66 @@
+# @package _global_
+# Canonical G1 23-DoF walk Manager-Based task declaration (off-policy owners).
+# Inherits the 29-DoF off-policy contract and swaps the scene to the 23-DoF
+# model (no waist roll/pitch, no wrist pitch/yaw) with 23-entry pose weights.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat_23dof.xml
+    entities:
+      robot:
+        joint_names:
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+        actuator_names:
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+
+reward:
+  pose:
+    params:
+      pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/src/unilab/conf/flashsac/task/g1_23dof_walk_flat/motrix.yaml b/src/unilab/conf/flashsac/task/g1_23dof_walk_flat/motrix.yaml
new file mode 100644
index 000000000..487f03787
--- /dev/null
+++ b/src/unilab/conf/flashsac/task/g1_23dof_walk_flat/motrix.yaml
@@ -0,0 +1,45 @@
+# @package _global_
+# FlashSAC Motrix 23-DoF owner: mirrors flashsac/g1_walk_flat/motrix.yaml with
+# the 23-DoF contract (algo identity kept, Motrix-direction retuning).
+defaults:
+  - /task/g1_23dof_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1Walk23DofFlat
+  sim_backend: motrix
+algo:
+  num_envs: 4096
+  learning_starts: 49
+  max_iterations: 5000
+  save_interval: 1000
+  updates_per_step: 8
+  replay_buffer_n: 256
+  tau: 0.05
+env:
+  events:
+    # Legacy Motrix owners disable kp/kd randomization.
+    pd_gains: null
+reward:
+  tracking_lin_vel:
+    weight: 2.2
+  tracking_ang_vel:
+    weight: 1.8
+  penalty_ang_vel_xy:
+    weight: -1.2
+  penalty_orientation:
+    weight: -12.0
+  penalty_action_rate:
+    weight: -2.5
+  pose:
+    weight: -0.6
+    params:
+      pose_weights: [0.01, 2.0, 5.0, 0.01, 5.0, 5.0, 0.01, 2.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
+  penalty_feet_ori:
+    weight: -5.0
+  feet_phase:
+    weight: 6.0
+    params:
+      tracking_sigma: 0.008
+  alive:
+    weight: 12.0
diff --git a/src/unilab/conf/flashsac/task/g1_23dof_walk_flat/mujoco.yaml b/src/unilab/conf/flashsac/task/g1_23dof_walk_flat/mujoco.yaml
new file mode 100644
index 000000000..77e8071ab
--- /dev/null
+++ b/src/unilab/conf/flashsac/task/g1_23dof_walk_flat/mujoco.yaml
@@ -0,0 +1,29 @@
+# @package _global_
+# FlashSAC MuJoCo 23-DoF owner: 23-DoF off-policy contract plus the FlashSAC
+# algo identity and its reward retuning.
+defaults:
+  - /task/g1_23dof_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1Walk23DofFlat
+  sim_backend: mujoco
+algo:
+  num_envs: 4096
+  learning_starts: 49
+  max_iterations: 5000
+  save_interval: 1000
+  updates_per_step: 8
+  replay_buffer_n: 256
+  tau: 0.05
+reward:
+  penalty_action_rate:
+    weight: -5.0
+  penalty_feet_ori:
+    weight: -25.0
+  feet_phase:
+    params:
+      tracking_sigma: 0.005
+  pose:
+    params:
+      pose_weights: [0.01, 2.0, 5.0, 0.01, 5.0, 5.0, 0.01, 2.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/src/unilab/conf/flashsac/task/g1_walk_flat/base.yaml b/src/unilab/conf/flashsac/task/g1_walk_flat/base.yaml
new file mode 100644
index 000000000..2f6d55168
--- /dev/null
+++ b/src/unilab/conf/flashsac/task/g1_walk_flat/base.yaml
@@ -0,0 +1,278 @@
+# @package _global_
+# Canonical G1 29-DoF walk Manager-Based task declaration (off-policy owners).
+# Backend owner leaves inherit this file and only override backend/algo tuning
+# or explicitly disabled terms. Observation scaling follows the walk profile
+# (gyro x0.25, joint velocity x0.05, critic linear velocity x2.0); every
+# off-policy owner carries the penalty curriculum.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat.xml
+    default_keyframe_name: stand
+    entities:
+      robot:
+        root_body_name: pelvis
+        joint_names:
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - waist_roll_joint
+          - waist_pitch_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - left_wrist_pitch_joint
+          - left_wrist_yaw_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+          - right_wrist_pitch_joint
+          - right_wrist_yaw_joint
+        actuator_names:
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - waist_roll_joint
+          - waist_pitch_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - left_wrist_pitch_joint
+          - left_wrist_yaw_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+          - right_wrist_pitch_joint
+          - right_wrist_yaw_joint
+        body_names: [pelvis]
+  sim_dt: 0.006666666666666667
+  ctrl_dt: 0.02
+  max_episode_seconds: 20.0
+  observations:
+    policy:
+      # Observation noise matches the legacy off-policy noise_config (level=1.0,
+      # actor-only; gyro/gravity/linvel scales were 0.0 there): joint pos
+      # +/-0.01 and joint vel +/-0.1, applied before term scaling.
+      enable_corruption: true
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: torso_gyro}
+          scale: 0.25
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params: {sensor_name: torso_upvector}
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.01
+            n_max: 0.01
+            operation: add
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+          scale: 0.05
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.1
+            n_max: 0.1
+            operation: add
+        actions:
+          func: unilab.envs.mdp.last_action
+          params: {action_name: joint_pos}
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params: {command_name: twist}
+        gait_phase:
+          func: unilab.tasks.locomotion.g1.manager_terms.G1GaitPhase
+          params:
+            frequency: 1.5
+            init_mode: offset_phase
+    critic:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: torso_gyro}
+          scale: 0.25
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params: {sensor_name: torso_upvector}
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+          scale: 0.05
+        actions:
+          func: unilab.envs.mdp.last_action
+          params: {action_name: joint_pos}
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params: {command_name: twist}
+        gait_phase:
+          func: unilab.tasks.locomotion.g1.manager_terms.G1GaitPhase
+          params:
+            frequency: 1.5
+            init_mode: offset_phase
+        base_lin_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: pelvis_local_linvel}
+          scale: 2.0
+  actions:
+    joint_pos:
+      _target_: unilab.envs.mdp.JointPositionActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      scale: 1.0
+      use_default_offset: true
+  commands:
+    twist:
+      _target_: unilab.tasks.locomotion.g1.manager_terms.G1VelocityCommandCfg
+      entity_name: robot
+      resampling_time_range: [20.0, 20.0]
+      heading_command: false
+      heading_control_stiffness: 0.5
+      rel_standing_envs: 0.0
+      rel_heading_envs: 0.0
+      rel_world_envs: 0.0
+      rel_forward_envs: 0.0
+      init_velocity_prob: 0.0
+      planar_dead_zone: 0.2
+      ranges:
+        lin_vel_x: [-0.6, 1.0]
+        lin_vel_y: [-0.4, 0.4]
+        ang_vel_z: [-0.8, 0.8]
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_root_state_uniform:
+      func: unilab.envs.mdp.reset_root_state_uniform
+      mode: reset
+      params:
+        pose_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [-3.141592653589793, 3.141592653589793]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.5, 0.5]
+          roll: [-0.5, 0.5]
+          pitch: [-0.5, 0.5]
+          yaw: [-0.5, 0.5]
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        kp_range: [0.9, 1.1]
+        kd_range: [0.9, 1.1]
+        operation: scale
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    tilt:
+      func: unilab.tasks.locomotion.g1.manager_terms.g1_tilt_exceeded
+      params:
+        max_tilt_deg: 65.0
+    base_height:
+      func: unilab.envs.mdp.root_height_below_minimum
+      params:
+        minimum_height: 0.3
+  curriculum:
+    penalty_scaling:
+      func: unilab.tasks.locomotion.g1.manager_terms.G1PenaltyCurriculum
+      # Effective schedule matches the tuned legacy baseline: the legacy env
+      # halved the shared override dict once per env construction (two probe
+      # envs + the collector in every off-policy runner), so collectors actually
+      # trained at 1/8 initial / 1/4 cap of these YAML weights. The manager
+      # runtime isolates each env, so the tuned effective range is declared
+      # explicitly here.
+      params:
+        initial_scale: 0.125
+        min_scale: 0.125
+        max_scale: 0.25
+        level_down_threshold: 150.0
+        level_up_threshold: 750.0
+        degree: 0.001
+  policy_observation_group: policy
+  critic_observation_group: critic
+
+reward:
+  tracking_lin_vel:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.track_lin_vel
+    weight: 2.0
+    params:
+      tracking_sigma: 0.25
+      command_name: twist
+      sensor_name: pelvis_local_linvel
+  tracking_ang_vel:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.track_ang_vel
+    weight: 1.5
+    params:
+      tracking_sigma: 0.25
+      command_name: twist
+      sensor_name: torso_gyro
+  penalty_ang_vel_xy:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.ang_vel_xy
+    weight: -1.0
+    params:
+      sensor_name: torso_gyro
+  penalty_orientation:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.orientation
+    weight: -10.0
+    params:
+      sensor_name: torso_upvector
+  penalty_action_rate:
+    func: unilab.envs.mdp.action_rate_l2
+    weight: -4.0
+  pose:
+    func: unilab.tasks.locomotion.g1.manager_terms.weighted_pose
+    weight: -0.5
+    params:
+      pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
+  penalty_feet_ori:
+    func: unilab.tasks.locomotion.g1.manager_terms.penalty_feet_ori
+    weight: -20.0
+  feet_phase:
+    func: unilab.tasks.locomotion.g1.manager_terms.feet_phase
+    weight: 5.0
+    params:
+      frequency: 1.5
+      swing_height: 0.09
+      tracking_sigma: 0.04
+      min_forward_speed: 0.0
+      command_name: twist
+  alive:
+    func: unilab.tasks.locomotion.common.manager_terms.alive
+    weight: 10.0
diff --git a/src/unilab/conf/flashsac/task/g1_walk_flat/mjwarp.yaml b/src/unilab/conf/flashsac/task/g1_walk_flat/mjwarp.yaml
new file mode 100644
index 000000000..d6f6cfdda
--- /dev/null
+++ b/src/unilab/conf/flashsac/task/g1_walk_flat/mjwarp.yaml
@@ -0,0 +1,40 @@
+# @package _global_
+# Configured-only FlashSAC mjwarp owner. Mirrors the MuJoCo owner's algo /
+# env / reward identity; mjwarp-specific host-adapter settings follow
+# conf/sac/task/g1_walk_flat/mjwarp.yaml. Offline record reuses
+# MuJoCo rendering; interactive eval routes through the MuJoCo viewer.
+# Native playback and device-resident runtime are absent.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1WalkFlat
+  sim_backend: mjwarp
+  play_render_mode: record
+algo:
+  num_envs: 4096
+  learning_starts: 49
+  max_iterations: 5000
+  save_interval: 1000
+  updates_per_step: 8
+  replay_buffer_n: 256
+  tau: 0.05
+env:
+  mjwarp_nconmax: 128
+  mjwarp_njmax: 256
+  render_spacing: 2.0
+  events:
+    # Legacy mjwarp owners disable kp/kd and armature randomization.
+    pd_gains: null
+reward:
+  penalty_action_rate:
+    weight: -5.0
+  penalty_feet_ori:
+    weight: -25.0
+  feet_phase:
+    params:
+      tracking_sigma: 0.005
+  pose:
+    params:
+      pose_weights: [0.01, 2.0, 5.0, 0.01, 5.0, 5.0, 0.01, 2.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/src/unilab/conf/flashsac/task/g1_walk_flat/motrix.yaml b/src/unilab/conf/flashsac/task/g1_walk_flat/motrix.yaml
new file mode 100644
index 000000000..c8d8e6b8d
--- /dev/null
+++ b/src/unilab/conf/flashsac/task/g1_walk_flat/motrix.yaml
@@ -0,0 +1,45 @@
+# @package _global_
+# FlashSAC Motrix owner: keeps the MuJoCo owner's FlashSAC algo identity while
+# adopting the Motrix-direction reward retuning; kp/kd randomization disabled.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1WalkFlat
+  sim_backend: motrix
+algo:
+  num_envs: 4096
+  learning_starts: 49
+  max_iterations: 5000
+  save_interval: 1000
+  updates_per_step: 8
+  replay_buffer_n: 256
+  tau: 0.05
+env:
+  events:
+    # Legacy Motrix owners disable kp/kd randomization.
+    pd_gains: null
+reward:
+  tracking_lin_vel:
+    weight: 2.2
+  tracking_ang_vel:
+    weight: 1.8
+  penalty_ang_vel_xy:
+    weight: -1.2
+  penalty_orientation:
+    weight: -12.0
+  penalty_action_rate:
+    weight: -2.5
+  pose:
+    weight: -0.6
+    params:
+      pose_weights: [0.01, 2.0, 5.0, 0.01, 5.0, 5.0, 0.01, 2.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
+  penalty_feet_ori:
+    weight: -5.0
+  feet_phase:
+    weight: 6.0
+    params:
+      tracking_sigma: 0.008
+  alive:
+    weight: 12.0
diff --git a/src/unilab/conf/flashsac/task/g1_walk_flat/mujoco.yaml b/src/unilab/conf/flashsac/task/g1_walk_flat/mujoco.yaml
new file mode 100644
index 000000000..a82b90883
--- /dev/null
+++ b/src/unilab/conf/flashsac/task/g1_walk_flat/mujoco.yaml
@@ -0,0 +1,30 @@
+# @package _global_
+# FlashSAC MuJoCo owner: inherits the 29-DoF off-policy Manager-Based contract
+# and carries the FlashSAC algo identity plus its reward retuning
+# (stiffer action-rate / feet-orientation penalties, tighter feet-phase sigma).
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1WalkFlat
+  sim_backend: mujoco
+algo:
+  num_envs: 4096
+  learning_starts: 49
+  max_iterations: 5000
+  save_interval: 1000
+  updates_per_step: 8
+  replay_buffer_n: 256
+  tau: 0.05
+reward:
+  penalty_action_rate:
+    weight: -5.0
+  penalty_feet_ori:
+    weight: -25.0
+  feet_phase:
+    params:
+      tracking_sigma: 0.005
+  pose:
+    params:
+      pose_weights: [0.01, 2.0, 5.0, 0.01, 5.0, 5.0, 0.01, 2.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/src/unilab/conf/flashsac/task/go2_joystick_flat/base.yaml b/src/unilab/conf/flashsac/task/go2_joystick_flat/base.yaml
new file mode 100644
index 000000000..409129c16
--- /dev/null
+++ b/src/unilab/conf/flashsac/task/go2_joystick_flat/base.yaml
@@ -0,0 +1,206 @@
+# @package _global_
+# Canonical Go2 flat Manager-Based task declaration. Backend owner leaves inherit
+# this file and only override backend/algo tuning or explicitly disabled terms.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/go2/scene_flat.xml
+    default_keyframe_name: home
+    entities:
+      robot:
+        root_body_name: base
+        joint_names:
+          - FL_hip_joint
+          - FL_thigh_joint
+          - FL_calf_joint
+          - FR_hip_joint
+          - FR_thigh_joint
+          - FR_calf_joint
+          - RL_hip_joint
+          - RL_thigh_joint
+          - RL_calf_joint
+          - RR_hip_joint
+          - RR_thigh_joint
+          - RR_calf_joint
+        actuator_names:
+          - FR_hip
+          - FR_thigh
+          - FR_calf
+          - FL_hip
+          - FL_thigh
+          - FL_calf
+          - RR_hip
+          - RR_thigh
+          - RR_calf
+          - RL_hip
+          - RL_thigh
+          - RL_calf
+  sim_dt: 0.01
+  ctrl_dt: 0.02
+  max_episode_seconds: 20.0
+  observations:
+    policy:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+        gait_phase:
+          func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase
+          params:
+            frequency: 2.0
+    critic:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+        gait_phase:
+          func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase
+          params:
+            frequency: 2.0
+        base_lin_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: local_linvel
+  actions:
+    joint_pos:
+      _target_: unilab.envs.mdp.JointPositionActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      scale: 0.25
+      use_default_offset: true
+  commands:
+    twist:
+      _target_: unilab.envs.mdp.UniformVelocityCommandCfg
+      entity_name: robot
+      resampling_time_range: [20.0, 20.0]
+      heading_command: false
+      heading_control_stiffness: 0.5
+      rel_standing_envs: 0.0
+      rel_heading_envs: 0.0
+      rel_world_envs: 0.0
+      rel_forward_envs: 0.0
+      init_velocity_prob: 0.0
+      ranges:
+        lin_vel_x: [-0.6, 1.0]
+        lin_vel_y: [-0.4, 0.4]
+        ang_vel_z: [-0.8, 0.8]
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_root_state_uniform:
+      func: unilab.envs.mdp.reset_root_state_uniform
+      mode: reset
+      params:
+        pose_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [-3.141592653589793, 3.141592653589793]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.5, 0.5]
+          roll: [-0.5, 0.5]
+          pitch: [-0.5, 0.5]
+          yaw: [-0.5, 0.5]
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        kp_range: [31.5, 38.5]
+        kd_range: [0.45, 0.55]
+        operation: abs
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    bad_orientation:
+      func: unilab.envs.mdp.bad_orientation
+      params:
+        limit_angle: 1.0471975511965976
+  policy_observation_group: policy
+  critic_observation_group: critic
+
+reward:
+  tracking_lin_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_lin_vel_xy_exp
+    weight: 1.0
+    params:
+      std: 0.5
+      command_name: twist
+  tracking_ang_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_ang_vel_z_exp
+    weight: 0.2
+    params:
+      std: 0.5
+      command_name: twist
+  lin_vel_z:
+    func: unilab.tasks.locomotion.common.manager_terms.lin_vel_z_l2
+    weight: -5.0
+  ang_vel_xy:
+    func: unilab.tasks.locomotion.common.manager_terms.ang_vel_xy_l2
+    weight: -0.1
+  base_height:
+    func: unilab.tasks.locomotion.common.manager_terms.base_height_l2
+    weight: -100.0
+    params:
+      target_height: 0.3
+  action_rate:
+    func: unilab.envs.mdp.action_rate_l2
+    weight: -0.005
+  similar_to_default:
+    func: unilab.tasks.locomotion.common.manager_terms.joint_deviation_l1
+    weight: -0.1
+  contact:
+    func: unilab.tasks.locomotion.common.manager_terms.feet_phase_contact
+    weight: 0.24
+    params:
+      frequency: 2.0
+      sensor_names:
+        - FL_foot_contact
+        - FR_foot_contact
+        - RL_foot_contact
+        - RR_foot_contact
+      contact_threshold: 0.1
+      stance_threshold: 0.6
+  swing_feet_z:
+    func: unilab.tasks.locomotion.common.manager_terms.feet_phase_swing_height
+    weight: 4.0
+    params:
+      frequency: 2.0
+      sensor_names: [FL_pos, FR_pos, RL_pos, RR_pos]
+      target_height: 0.1
+      kernel: 0.01
+      swing_start: 0.6
diff --git a/src/unilab/conf/flashsac/task/go2_joystick_flat/mujoco.yaml b/src/unilab/conf/flashsac/task/go2_joystick_flat/mujoco.yaml
new file mode 100644
index 000000000..7fbfdec78
--- /dev/null
+++ b/src/unilab/conf/flashsac/task/go2_joystick_flat/mujoco.yaml
@@ -0,0 +1,118 @@
+# @package _global_
+defaults:
+  - /task/go2_joystick_flat/base
+  - _self_
+
+training:
+  task_name: Go2JoystickFlat
+  sim_backend: mujoco
+algo:
+  num_envs: 1024
+  learning_starts: 50
+  max_iterations: 4000
+  save_interval: 1000
+  updates_per_step: 2
+  batch_size: 2048
+  replay_buffer_n: 4096
+  tau: 0.05
+env:
+  scene:
+    entities:
+      robot:
+        body_names: [base]
+  actions:
+    joint_pos:
+      scale: 0.4
+  observations:
+    policy:
+      enable_corruption: true
+      terms:
+        joint_pos:
+          noise:
+            _target_: UniformNoiseCfg
+            n_min: -0.01
+            n_max: 0.01
+        joint_vel:
+          noise:
+            _target_: UniformNoiseCfg
+            n_min: -0.1
+            n_max: 0.1
+    critic:
+      enable_corruption: true
+      terms:
+        joint_pos:
+          noise:
+            _target_: UniformNoiseCfg
+            n_min: -0.01
+            n_max: 0.01
+        joint_vel:
+          noise:
+            _target_: UniformNoiseCfg
+            n_min: -0.1
+            n_max: 0.1
+  events:
+    randomize_rigid_body_mass:
+      func: unilab.envs.mdp.randomize_rigid_body_mass
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          body_names: base
+        mass_distribution_params: [-1.5, 1.5]
+        operation: add
+        recompute_inertia: false
+    randomize_rigid_body_com:
+      func: unilab.envs.mdp.randomize_rigid_body_com
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          body_names: base
+        com_range:
+          x: [-0.05, 0.05]
+    randomize_physics_scene_gravity:
+      func: unilab.envs.mdp.randomize_physics_scene_gravity
+      mode: reset
+      params:
+        gravity_distribution_params:
+          - [0.0, 0.0, -9.81]
+          - [0.0, 0.0, -9.81]
+        operation: abs
+    push_by_setting_velocity:
+      func: unilab.envs.mdp.push_by_setting_velocity
+      mode: interval
+      interval_range_s: [15.0, 15.0]
+      is_global_time: true
+      params:
+        velocity_range:
+          x: [-1.0, 1.0]
+          y: [-1.0, 1.0]
+          z: [-0.5, 0.5]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [0.0, 0.0]
+reward:
+  tracking_lin_vel:
+    weight: 1.0
+    params:
+      std: 0.6324555320336759
+  tracking_ang_vel:
+    weight: 0.2
+    params:
+      std: 0.6324555320336759
+  lin_vel_z:
+    weight: -5.0
+  ang_vel_xy:
+    weight: -0.1
+  base_height:
+    weight: -20.0
+  action_rate:
+    weight: -0.02
+  similar_to_default:
+    weight: -0.4
+  contact:
+    weight: 1.5
+  swing_feet_z:
+    weight: 4.0
diff --git a/conf/hora_distill/config.yaml b/src/unilab/conf/hora_distill/config.yaml
similarity index 100%
rename from conf/hora_distill/config.yaml
rename to src/unilab/conf/hora_distill/config.yaml
diff --git a/conf/hora_distill/student_model/hora_actor.yaml b/src/unilab/conf/hora_distill/student_model/hora_actor.yaml
similarity index 92%
rename from conf/hora_distill/student_model/hora_actor.yaml
rename to src/unilab/conf/hora_distill/student_model/hora_actor.yaml
index 4aa117957..5931860cc 100644
--- a/conf/hora_distill/student_model/hora_actor.yaml
+++ b/src/unilab/conf/hora_distill/student_model/hora_actor.yaml
@@ -1,6 +1,6 @@
 # Teacher -> student `algo.model` mapping for HoraActorModel teachers (PPO/APPO).
 #
-# Loaded by unilab.algos.torch.hora.distill_config and merged next to the
+# Loaded by unilab.training.hora_distill_config and merged next to the
 # Hydra-composed teacher owner config mounted at `teacher_owner`, so every
 # student field interpolates directly from the teacher owner YAML. The teacher
 # owner config stays the single source of truth for these hyperparameters;
diff --git a/conf/hora_distill/student_model/hora_sac.yaml b/src/unilab/conf/hora_distill/student_model/hora_sac.yaml
similarity index 87%
rename from conf/hora_distill/student_model/hora_sac.yaml
rename to src/unilab/conf/hora_distill/student_model/hora_sac.yaml
index a462608f0..06aeaa93f 100644
--- a/conf/hora_distill/student_model/hora_sac.yaml
+++ b/src/unilab/conf/hora_distill/student_model/hora_sac.yaml
@@ -1,6 +1,6 @@
-# Teacher -> student `algo.model` mapping for hora_sac teachers (offpolicy SAC).
+# Teacher -> student `algo.model` mapping for hora_sac teachers (off-policy SAC).
 #
-# Loaded by unilab.algos.torch.hora.distill_config and merged next to the
+# Loaded by unilab.training.hora_distill_config and merged next to the
 # Hydra-composed teacher owner config mounted at `teacher_owner`. Values come
 # from the teacher owner YAML; the `oc.select` fallback after the comma only
 # applies when the teacher owner config does not define the field at all.
diff --git a/conf/hora_distill/task/sharpa_inhand/mujoco.yaml b/src/unilab/conf/hora_distill/task/sharpa_inhand/mujoco.yaml
similarity index 100%
rename from conf/hora_distill/task/sharpa_inhand/mujoco.yaml
rename to src/unilab/conf/hora_distill/task/sharpa_inhand/mujoco.yaml
diff --git a/conf/hora_distill/task/sharpa_inhand/mujoco_nodr.yaml b/src/unilab/conf/hora_distill/task/sharpa_inhand/mujoco_nodr.yaml
similarity index 100%
rename from conf/hora_distill/task/sharpa_inhand/mujoco_nodr.yaml
rename to src/unilab/conf/hora_distill/task/sharpa_inhand/mujoco_nodr.yaml
diff --git a/src/unilab/conf/ppo/config.yaml b/src/unilab/conf/ppo/config.yaml
new file mode 100644
index 000000000..96397cf19
--- /dev/null
+++ b/src/unilab/conf/ppo/config.yaml
@@ -0,0 +1,147 @@
+defaults:
+  - _self_
+  - task: go1_joystick_flat/mujoco
+
+algo:
+  algo: ppo
+  algo_log_name: rsl_rl_ppo
+  seed: 1
+  num_envs: 4096
+  num_steps_per_env: 24
+  max_iterations: 101
+  save_interval: 100
+  empirical_normalization: false
+  runner_class_name: OnPolicyRunner
+  obs_groups:
+    default:
+      - policy
+  experiment_name: test
+  run_name: ""
+  resume: false
+  load_run: "-1"
+  checkpoint: -1
+  resume_path: null
+  policy:
+    init_noise_std: 1.0
+    actor_hidden_dims: [512, 256, 128]
+    critic_hidden_dims: [512, 256, 128]
+    activation: elu
+    class_name: ActorCritic
+  algorithm:
+    class_name: uni_rl.algos.rsl_rl_ppo:FinalObservationAwarePPO
+    value_loss_coef: 1.0
+    use_clipped_value_loss: true
+    clip_param: 0.2
+    entropy_coef: 0.01
+    num_learning_epochs: 5
+    num_mini_batches: 4
+    learning_rate: 1.0e-3
+    schedule: adaptive
+    gamma: 0.99
+    lam: 0.95
+    desired_kl: 0.01
+    target_kl_stop: null
+    max_grad_norm: 1.0
+    adaptive_kl_beta: 0.9
+    adaptive_lr_growth: 1.1
+    adaptive_lr_decay: 1.2
+    adaptive_lr_update_interval: 5
+    metrics_interval: 8
+    finite_check_interval: 8
+    enable_compile: false
+    warmup_strict_iters: 10
+    warmup_metrics_interval: 2
+    warmup_finite_check_interval: 2
+    disable_finite_checks: true
+
+training:
+  task_name: Go1JoystickFlat
+  # list[int] | null; null/[] keeps single-device behavior, [d] selects one
+  # CUDA device, and [d0..dN-1] launches one RSL-RL worker per device.
+  # algo.num_envs is per rank; do not set this together with training.device.
+  devices: null
+  device: null
+  logger: tensorboard
+  wandb_project: unilab
+  wandb_entity: null
+  wandb_group: null
+  wandb_job_type: null
+  wandb_name: null
+  wandb_tags: []
+  wandb_notes: null
+  wandb_mode: null
+  sim_backend: mujoco
+  nan_guard:
+    enabled: true
+    buffer_size: 100
+    max_envs_to_dump: 5
+    output_dir: null
+  play_only: false
+  no_play: false
+  sim2sim_strict: true
+  play_render_mode: auto
+  play_env_num: 16
+  render_spacing: 1.0
+  play_steps: 200
+  cam_distance: 6.0
+  cam_elevation: -20.0
+  cam_azimuth: 90.0
+  cam_lookat: null
+  cam_tracking: false
+  cam_tracking_env_idx: 0
+  cam_tracking_extra_envs: 2
+  log_root: null
+  num_timesteps: null
+  log_dir: null
+
+interactive:
+  action_mode: zero
+  policy_obs_mode: auto
+  show_target_bodies: false
+  show_reward_debug: false
+  target_show_axes: false
+  target_body_names: ""
+  target_max_bodies: 0
+  target_marker_radius: 0.02
+  target_axis_length: 0.08
+  target_marker_alpha: 0.75
+  reward_debug_show_velocity: false
+  reward_debug_lin_vel_scale: 0.08
+  reward_debug_ang_vel_scale: 0.05
+  reward_debug_show_connectors: false
+  reward_debug_show_global_anchor: false
+  camera_follow_body: true
+  camera_focus_body_name: ""
+  camera_height_offset: 0.15
+  camera_distance: null
+  camera_elevation: null
+  camera_azimuth: null
+  use_env_visual_model: true
+  speed: 1.0
+  start_paused: false
+  keyboard: false
+  keyboard_step_lin: 0.1
+  keyboard_step_ang: 0.2
+
+viser:
+  port: 8080
+  env_idx: 0
+  display_mode: all
+  max_envs: 16
+
+env:
+  post_step_forward_sensor: false
+  # adaptive_chunk_size: auto-tune the MuJoCo BatchEnvPool chunk_size at materialize
+  # (cache-backed). chunk_size (int) manually overrides and wins; null => use default.
+  adaptive_chunk_size: true
+  chunk_size: null
+
+hydra:
+  run:
+    dir: .
+  output_subdir: null
+  job:
+    chdir: false
+  job_logging:
+    root:
+      handlers: [console]
diff --git a/src/unilab/conf/ppo/task/a2_joystick_flat/base.yaml b/src/unilab/conf/ppo/task/a2_joystick_flat/base.yaml
new file mode 100644
index 000000000..87203212a
--- /dev/null
+++ b/src/unilab/conf/ppo/task/a2_joystick_flat/base.yaml
@@ -0,0 +1,296 @@
+# @package _global_
+# Canonical A2 flat Manager-Based task declaration. The MuJoCo owner leaf adds
+# only training identity/tuning; all task behavior is declared here for Hydra.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/a2/scene_flat.xml
+    default_keyframe_name: home
+    entities:
+      robot:
+        root_body_name: base_link
+        joint_names:
+          - FL_hip_joint
+          - FL_thigh_joint
+          - FL_calf_joint
+          - FR_hip_joint
+          - FR_thigh_joint
+          - FR_calf_joint
+          - RL_hip_joint
+          - RL_thigh_joint
+          - RL_calf_joint
+          - RR_hip_joint
+          - RR_thigh_joint
+          - RR_calf_joint
+        body_names: [base_link]
+        geom_names: [floor]
+        actuator_names:
+          - FL_hip
+          - FL_thigh
+          - FL_calf
+          - FR_hip
+          - FR_thigh
+          - FR_calf
+          - RL_hip
+          - RL_thigh
+          - RL_calf
+          - RR_hip
+          - RR_thigh
+          - RR_calf
+  sim_dt: 0.01
+  ctrl_dt: 0.02
+  max_episode_seconds: 20.0
+  observations:
+    policy:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+        gait_phase:
+          func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase
+          params:
+            frequency: 2.0
+            command_name: twist
+            command_threshold: 0.1
+    critic:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+        gait_phase:
+          func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase
+          params:
+            frequency: 2.0
+            command_name: twist
+            command_threshold: 0.1
+        base_lin_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: local_linvel
+  actions:
+    joint_pos:
+      _target_: unilab.envs.mdp.JointPositionActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      scale: 0.25
+      use_default_offset: true
+  commands:
+    twist:
+      _target_: unilab.envs.mdp.UniformVelocityCommandCfg
+      entity_name: robot
+      resampling_time_range: [5.0, 5.0]
+      heading_command: false
+      heading_control_stiffness: 0.5
+      rel_standing_envs: 0.1
+      rel_heading_envs: 0.0
+      rel_world_envs: 0.0
+      rel_forward_envs: 0.0
+      init_velocity_prob: 0.0
+      ranges:
+        lin_vel_x: [-0.6, 1.0]
+        lin_vel_y: [-0.4, 0.4]
+        ang_vel_z: [-0.8, 0.8]
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_root_state_uniform:
+      func: unilab.envs.mdp.reset_root_state_uniform
+      mode: reset
+      params:
+        pose_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [-3.141592653589793, 3.141592653589793]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.5, 0.5]
+          roll: [-0.5, 0.5]
+          pitch: [-0.5, 0.5]
+          yaw: [-0.5, 0.5]
+    base_mass:
+      func: unilab.envs.mdp.randomize_rigid_body_mass
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          body_names: base_link
+        mass_distribution_params: [0.0, 8.0]
+        operation: add
+        recompute_inertia: false
+    base_com:
+      func: unilab.envs.mdp.randomize_rigid_body_com
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          body_names: base_link
+        com_range:
+          x: [-0.08, 0.08]
+          y: [-0.08, 0.08]
+          z: [-0.08, 0.08]
+    foot_friction:
+      func: unilab.envs.mdp.geom_friction
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          geom_names: floor
+        ranges: [0.3, 1.6]
+        operation: scale
+        axes: [0]
+        shared_random: true
+    joint_armature:
+      func: unilab.envs.mdp.joint_armature
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          joint_names: ".*"
+        ranges: [0.9, 1.1]
+        operation: scale
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          actuator_names: ".*"
+        kp_range: [0.9, 1.1]
+        kd_range: [0.9, 1.1]
+        operation: scale
+    push_robot:
+      func: unilab.envs.mdp.push_by_setting_velocity
+      mode: interval
+      interval_range_s: [8.0, 8.0]
+      is_global_time: true
+      params:
+        velocity_range:
+          x: [-1.0, 1.0]
+          y: [-1.0, 1.0]
+          z: [-0.5, 0.5]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [0.0, 0.0]
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    bad_orientation:
+      func: unilab.envs.mdp.bad_orientation
+      params:
+        limit_angle: 1.0471975511965976
+  policy_observation_group: policy
+  critic_observation_group: critic
+
+reward:
+  tracking_lin_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_lin_vel_xy_exp
+    weight: 1.0
+    params:
+      std: 0.5
+      command_name: twist
+  tracking_ang_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_ang_vel_z_exp
+    weight: 0.4
+    params:
+      std: 0.5
+      command_name: twist
+  lin_vel_z:
+    func: unilab.tasks.locomotion.common.manager_terms.lin_vel_z_l2
+    weight: -5.0
+  ang_vel_xy:
+    func: unilab.tasks.locomotion.common.manager_terms.ang_vel_xy_l2
+    weight: -0.1
+  base_height:
+    func: unilab.tasks.locomotion.common.manager_terms.base_height_l2
+    weight: -100.0
+    params:
+      target_height: 0.4
+  action_rate:
+    func: unilab.envs.mdp.action_rate_l2
+    weight: -0.02
+  similar_to_default:
+    func: unilab.tasks.locomotion.common.manager_terms.joint_deviation_l1
+    weight: -0.25
+  contact:
+    func: unilab.tasks.locomotion.common.manager_terms.feet_phase_contact
+    weight: 0.5
+    params:
+      frequency: 2.0
+      command_name: twist
+      command_threshold: 0.1
+      sensor_names: [FL_foot_contact, FR_foot_contact, RL_foot_contact, RR_foot_contact]
+      contact_threshold: 0.1
+      stance_threshold: 0.6
+  swing_feet_z:
+    func: unilab.tasks.locomotion.common.manager_terms.feet_phase_swing_height
+    weight: 4.0
+    params:
+      frequency: 2.0
+      command_name: twist
+      command_threshold: 0.1
+      sensor_names: [FL_pos, FR_pos, RL_pos, RR_pos]
+      target_height: 0.1
+      kernel: 0.01
+      swing_start: 0.6
+  stand_still:
+    func: unilab.tasks.locomotion.common.manager_terms.stand_still_l1
+    weight: -4.0
+    params:
+      command_name: twist
+      command_threshold: 0.1
+  hip_deviation:
+    func: unilab.tasks.locomotion.common.manager_terms.joint_deviation_l1
+    weight: -1.0
+    params:
+      asset_cfg:
+        _target_: SceneEntityCfg
+        name: robot
+        joint_names: ".*_hip_joint"
+  stand_feet_air:
+    func: unilab.tasks.locomotion.common.manager_terms.feet_air_while_standing
+    weight: -1.0
+    params:
+      command_name: twist
+      command_threshold: 0.1
+      sensor_names: [FL_foot_contact, FR_foot_contact, RL_foot_contact, RR_foot_contact]
+      contact_threshold: 0.1
diff --git a/src/unilab/conf/ppo/task/a2_joystick_flat/mujoco.yaml b/src/unilab/conf/ppo/task/a2_joystick_flat/mujoco.yaml
new file mode 100644
index 000000000..8a5f336ca
--- /dev/null
+++ b/src/unilab/conf/ppo/task/a2_joystick_flat/mujoco.yaml
@@ -0,0 +1,22 @@
+# @package _global_
+defaults:
+  - /task/a2_joystick_flat/base
+  - _self_
+
+training:
+  task_name: A2JoystickFlat
+  sim_backend: mujoco
+algo:
+  num_envs: 1024
+  max_iterations: 500
+  empirical_normalization: true
+  obs_groups:
+    actor:
+      - actor
+    critic:
+      - critic
+  policy:
+    init_noise_std: 0.5
+  algorithm:
+    learning_rate: 3.0e-4
+    entropy_coef: 1.0e-3
diff --git a/src/unilab/conf/ppo/task/allegro_inhand/base.yaml b/src/unilab/conf/ppo/task/allegro_inhand/base.yaml
new file mode 100644
index 000000000..afafae38c
--- /dev/null
+++ b/src/unilab/conf/ppo/task/allegro_inhand/base.yaml
@@ -0,0 +1,139 @@
+# @package _global_
+# Canonical Allegro rotation Manager-Based task declaration. Backend leaves own
+# only backend identity and algorithm/runtime tuning.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/allegro_hand/scene.xml
+    default_keyframe_name: home
+    entities:
+      robot:
+        # The hand is fixed; the free ball is the task's single root-state entity.
+        root_body_name: ball
+        joint_names:
+          - ffj0
+          - ffj1
+          - ffj2
+          - ffj3
+          - mfj0
+          - mfj1
+          - mfj2
+          - mfj3
+          - rfj0
+          - rfj1
+          - rfj2
+          - rfj3
+          - thj0
+          - thj1
+          - thj2
+          - thj3
+        body_names: [ball, ff_tip, mf_tip, rf_tip, th_tip]
+        actuator_names:
+          - ffa0
+          - ffa1
+          - ffa2
+          - ffa3
+          - mfa0
+          - mfa1
+          - mfa2
+          - mfa3
+          - rfa0
+          - rfa1
+          - rfa2
+          - rfa3
+          - tha0
+          - tha1
+          - tha2
+          - tha3
+  sim_dt: 0.005
+  ctrl_dt: 0.05
+  max_episode_seconds: 20.0
+  observations:
+    policy:
+      history_length: 3
+      flatten_history_dim: true
+      terms:
+        rotation:
+          func: unilab.tasks.manipulation.allegro_inhand.manager_terms.AllegroRotationObservation
+          params:
+            entity_name: robot
+            action_name: hand
+            joint_noise: 0.02
+            torque_estimate_kp: 1.0
+            torque_estimate_kd: 0.1
+  actions:
+    hand:
+      _target_: unilab.tasks.manipulation.allegro_inhand.manager_terms.AllegroIncrementalPositionActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      action_scale: 0.041666666666666664
+      raw_action_clip: [-1.0, 1.0]
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_hand_ball:
+      func: unilab.tasks.manipulation.allegro_inhand.manager_terms.AllegroHandBallReset
+      mode: reset
+      params:
+        entity_name: robot
+        # null explicitly selects the model home pose. A configured path is
+        # fail-closed when missing or malformed.
+        grasp_cache_path: null
+        joint_noise: 0.0
+        ball_velocity_noise: 0.0
+        ball_z_offset: 0.0
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        kp_range: [1.0, 1.0]
+        kd_range: [0.1, 0.1]
+        operation: abs
+  terminations:
+    dropped:
+      func: unilab.tasks.manipulation.allegro_inhand.manager_terms.AllegroDropTermination
+      params:
+        observation_group: policy
+        observation_term: rotation
+        minimum_ball_height: 0.125
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+  scale_rewards_by_dt: true
+  policy_observation_group: policy
+  critic_observation_group: null
+
+reward:
+  rotate:
+    func: unilab.tasks.manipulation.allegro_inhand.manager_terms.AllegroRotateReward
+    weight: 1.25
+    params:
+      state_term_name: dropped
+      rotation_axis: [0.0, 0.0, 1.0]
+      clip_min: -0.5
+      clip_max: 0.5
+  obj_linvel:
+    func: unilab.tasks.manipulation.allegro_inhand.manager_terms.object_linear_velocity_l1
+    weight: -0.3
+    params:
+      state_term_name: dropped
+  pose_diff:
+    func: unilab.tasks.manipulation.allegro_inhand.manager_terms.hand_pose_deviation_l2
+    weight: -0.3
+    params:
+      state_term_name: dropped
+  torque:
+    func: unilab.tasks.manipulation.allegro_inhand.manager_terms.estimated_torque_l2
+    weight: -0.1
+    params:
+      state_term_name: dropped
+  work:
+    func: unilab.tasks.manipulation.allegro_inhand.manager_terms.estimated_work_l2
+    weight: -2.0
+    params:
+      state_term_name: dropped
+  drop:
+    func: unilab.tasks.manipulation.allegro_inhand.manager_terms.dropped
+    weight: 0.0
+    params:
+      state_term_name: dropped
diff --git a/src/unilab/conf/ppo/task/allegro_inhand/drake.yaml b/src/unilab/conf/ppo/task/allegro_inhand/drake.yaml
new file mode 100644
index 000000000..dc7f0e09c
--- /dev/null
+++ b/src/unilab/conf/ppo/task/allegro_inhand/drake.yaml
@@ -0,0 +1,46 @@
+# @package _global_
+defaults:
+  - /task/allegro_inhand/base
+  - _self_
+
+training:
+  task_name: AllegroInhandRotation
+  sim_backend: drake
+  render_spacing: 0.5
+  cam_distance: 1.5
+  cam_lookat: [0.75, 0.75, 0]
+  cam_elevation: -20.0
+algo:
+  num_envs: 16384
+  num_steps_per_env: 8
+  max_iterations: 201
+  obs_groups:
+    actor: [policy]
+    critic: [policy]
+  actor:
+    class_name: rsl_rl.models.MLPModel
+    hidden_dims: [512, 256, 128]
+    activation: elu
+    obs_normalization: true
+    distribution_cfg:
+      class_name: rsl_rl.modules.distribution.GaussianDistribution
+      init_std: 1.0
+      std_type: scalar
+  critic:
+    class_name: rsl_rl.models.MLPModel
+    hidden_dims: [512, 256, 128]
+    activation: elu
+    obs_normalization: true
+  algorithm:
+    value_loss_coef: 4.0
+    desired_kl: 0.02
+env:
+  drake_backend_mode: batch
+  events:
+    # Drake consumes the fixed MJCF actuator gains and does not expose reset
+    # gain mutation in the installed production adapter.
+    pd_gains: null
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/src/unilab/conf/ppo/task/allegro_inhand/motrix.yaml b/src/unilab/conf/ppo/task/allegro_inhand/motrix.yaml
new file mode 100644
index 000000000..8df1fe13b
--- /dev/null
+++ b/src/unilab/conf/ppo/task/allegro_inhand/motrix.yaml
@@ -0,0 +1,41 @@
+# @package _global_
+defaults:
+  - /task/allegro_inhand/base
+  - _self_
+
+training:
+  task_name: AllegroInhandRotation
+  sim_backend: motrix
+env:
+  # Legacy Motrix consumed the MJCF-native gains; only MuJoCo used the explicit
+  # host-side position-gain override.
+  events:
+    pd_gains: null
+algo:
+  num_envs: 16384
+  num_steps_per_env: 8
+  max_iterations: 201
+  obs_groups:
+    actor: [policy]
+    critic: [policy]
+  actor:
+    class_name: rsl_rl.models.MLPModel
+    hidden_dims: [512, 256, 128]
+    activation: elu
+    obs_normalization: true
+    distribution_cfg:
+      class_name: rsl_rl.modules.distribution.GaussianDistribution
+      init_std: 1.0
+      std_type: scalar
+  critic:
+    class_name: rsl_rl.models.MLPModel
+    hidden_dims: [512, 256, 128]
+    activation: elu
+    obs_normalization: true
+  algorithm:
+    value_loss_coef: 4.0
+    desired_kl: 0.02
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/src/unilab/conf/ppo/task/allegro_inhand/mujoco.yaml b/src/unilab/conf/ppo/task/allegro_inhand/mujoco.yaml
new file mode 100644
index 000000000..3538defdc
--- /dev/null
+++ b/src/unilab/conf/ppo/task/allegro_inhand/mujoco.yaml
@@ -0,0 +1,40 @@
+# @package _global_
+defaults:
+  - /task/allegro_inhand/base
+  - _self_
+
+training:
+  task_name: AllegroInhandRotation
+  sim_backend: mujoco
+  render_spacing: 0.5
+  cam_distance: 1.5
+  cam_lookat: [0.75, 0.75, 0]
+  cam_elevation: -20.0
+algo:
+  num_envs: 16384
+  num_steps_per_env: 8
+  max_iterations: 201
+  obs_groups:
+    actor: [policy]
+    critic: [policy]
+  actor:
+    class_name: rsl_rl.models.MLPModel
+    hidden_dims: [512, 256, 128]
+    activation: elu
+    obs_normalization: true
+    distribution_cfg:
+      class_name: rsl_rl.modules.distribution.GaussianDistribution
+      init_std: 1.0
+      std_type: scalar
+  critic:
+    class_name: rsl_rl.models.MLPModel
+    hidden_dims: [512, 256, 128]
+    activation: elu
+    obs_normalization: true
+  algorithm:
+    value_loss_coef: 4.0
+    desired_kl: 0.02
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/src/unilab/conf/ppo/task/allegro_inhand_grasp/motrix.yaml b/src/unilab/conf/ppo/task/allegro_inhand_grasp/motrix.yaml
new file mode 100644
index 000000000..e75de1bd9
--- /dev/null
+++ b/src/unilab/conf/ppo/task/allegro_inhand_grasp/motrix.yaml
@@ -0,0 +1,95 @@
+# @package _global_
+defaults:
+  - /task/allegro_inhand/base
+  - _self_
+
+training:
+  task_name: AllegroInhandRotationGrasp
+  sim_backend: motrix
+  no_play: true
+
+algo:
+  num_envs: 16384
+  num_steps_per_env: 8
+  max_iterations: 1000 # infinite rollout until the recorder raises RunComplete
+  obs_groups:
+    actor: [policy]
+    critic: [policy]
+  actor:
+    class_name: rsl_rl.models.MLPModel
+    hidden_dims: [512, 256, 128]
+    activation: elu
+    obs_normalization: true
+    distribution_cfg:
+      class_name: rsl_rl.modules.distribution.GaussianDistribution
+      init_std: 1.0
+      std_type: scalar
+  critic:
+    class_name: rsl_rl.models.MLPModel
+    hidden_dims: [512, 256, 128]
+    activation: elu
+    obs_normalization: true
+  algorithm:
+    value_loss_coef: 4.0
+    desired_kl: 0.02
+
+env:
+  max_episode_seconds: 3.0
+  actions:
+    hand:
+      action_scale: 0.0
+  events:
+    pd_gains: null
+    reset_hand_ball:
+      params:
+        grasp_cache_path: null
+        joint_noise: 0.25
+        ball_velocity_noise: 0.0
+        ball_z_offset: 0.0
+  terminations:
+    invalid_grasp:
+      func: unilab.tasks.manipulation.allegro_inhand.grasp_gen.AllegroGraspQualityTermination
+      params:
+        entity_name: robot
+        observation_group: policy
+        observation_term: rotation
+        fingertip_body_names: [ff_tip, mf_tip, rf_tip, th_tip]
+        contact_sensor_names: [ff_contact, mf_contact, rf_contact, th_contact]
+        max_fingertip_distance: 0.1
+        minimum_contacts: 2
+        minimum_ball_height: 0.125
+        enabled: true
+  metrics:
+    fingertips_close:
+      func: unilab.tasks.manipulation.allegro_inhand.grasp_gen.AllegroGraspQualityMetric
+      params: {quality_term_name: invalid_grasp, condition: fingertips_close}
+    enough_contacts:
+      func: unilab.tasks.manipulation.allegro_inhand.grasp_gen.AllegroGraspQualityMetric
+      params: {quality_term_name: invalid_grasp, condition: enough_contacts}
+    ball_held:
+      func: unilab.tasks.manipulation.allegro_inhand.grasp_gen.AllegroGraspQualityMetric
+      params: {quality_term_name: invalid_grasp, condition: ball_held}
+    valid:
+      func: unilab.tasks.manipulation.allegro_inhand.grasp_gen.AllegroGraspQualityMetric
+      params: {quality_term_name: invalid_grasp, condition: valid}
+  recorders:
+    grasp_cache:
+      func: unilab.tasks.manipulation.allegro_inhand.grasp_gen.AllegroGraspRecorder
+      params:
+        quality_term_name: invalid_grasp
+        output_path: caches/allegro_grasp_50k.npy
+        collection_target: 50000
+        auto_save: true
+
+reward:
+  rotate: {weight: 0.0}
+  obj_linvel: {weight: 0.0}
+  pose_diff: {weight: 0.0}
+  torque: {weight: 0.0}
+  work: {weight: 0.0}
+  drop: {weight: 0.0}
+
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/src/unilab/conf/ppo/task/allegro_inhand_grasp/mujoco.yaml b/src/unilab/conf/ppo/task/allegro_inhand_grasp/mujoco.yaml
new file mode 100644
index 000000000..693937e3d
--- /dev/null
+++ b/src/unilab/conf/ppo/task/allegro_inhand_grasp/mujoco.yaml
@@ -0,0 +1,99 @@
+# @package _global_
+defaults:
+  - /task/allegro_inhand/base
+  - _self_
+
+training:
+  task_name: AllegroInhandRotationGrasp
+  sim_backend: mujoco
+  no_play: true
+  render_spacing: 0.5
+  cam_distance: 1.5
+  cam_lookat: [0.75, 0.75, 0]
+  cam_elevation: -20.0
+
+algo:
+  num_envs: 16384
+  num_steps_per_env: 8
+  max_iterations: 1000 # infinite rollout until the recorder raises RunComplete
+  obs_groups:
+    actor: [policy]
+    critic: [policy]
+  actor:
+    class_name: rsl_rl.models.MLPModel
+    hidden_dims: [512, 256, 128]
+    activation: elu
+    obs_normalization: true
+    distribution_cfg:
+      class_name: rsl_rl.modules.distribution.GaussianDistribution
+      init_std: 1.0
+      std_type: scalar
+  critic:
+    class_name: rsl_rl.models.MLPModel
+    hidden_dims: [512, 256, 128]
+    activation: elu
+    obs_normalization: true
+  algorithm:
+    value_loss_coef: 4.0
+    desired_kl: 0.02
+
+env:
+  max_episode_seconds: 3.0
+  actions:
+    # The collector holds each sampled reset pose; policy output is intentionally ignored.
+    hand:
+      action_scale: 0.0
+  events:
+    reset_hand_ball:
+      params:
+        grasp_cache_path: null
+        joint_noise: 0.25
+        ball_velocity_noise: 0.0
+        ball_z_offset: 0.0
+  terminations:
+    invalid_grasp:
+      func: unilab.tasks.manipulation.allegro_inhand.grasp_gen.AllegroGraspQualityTermination
+      params:
+        entity_name: robot
+        observation_group: policy
+        observation_term: rotation
+        fingertip_body_names: [ff_tip, mf_tip, rf_tip, th_tip]
+        contact_sensor_names: [ff_contact, mf_contact, rf_contact, th_contact]
+        max_fingertip_distance: 0.1
+        minimum_contacts: 2
+        minimum_ball_height: 0.125
+        enabled: true
+  metrics:
+    fingertips_close:
+      func: unilab.tasks.manipulation.allegro_inhand.grasp_gen.AllegroGraspQualityMetric
+      params: {quality_term_name: invalid_grasp, condition: fingertips_close}
+    enough_contacts:
+      func: unilab.tasks.manipulation.allegro_inhand.grasp_gen.AllegroGraspQualityMetric
+      params: {quality_term_name: invalid_grasp, condition: enough_contacts}
+    ball_held:
+      func: unilab.tasks.manipulation.allegro_inhand.grasp_gen.AllegroGraspQualityMetric
+      params: {quality_term_name: invalid_grasp, condition: ball_held}
+    valid:
+      func: unilab.tasks.manipulation.allegro_inhand.grasp_gen.AllegroGraspQualityMetric
+      params: {quality_term_name: invalid_grasp, condition: valid}
+  recorders:
+    grasp_cache:
+      func: unilab.tasks.manipulation.allegro_inhand.grasp_gen.AllegroGraspRecorder
+      params:
+        quality_term_name: invalid_grasp
+        output_path: caches/allegro_grasp_50k.npy
+        collection_target: 50000
+        auto_save: true
+
+reward:
+  rotate: {weight: 0.0}
+  obj_linvel: {weight: 0.0}
+  pose_diff: {weight: 0.0}
+  torque: {weight: 0.0}
+  work: {weight: 0.0}
+  drop: {weight: 0.0}
+
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/src/unilab/conf/ppo/task/g1_23dof_box_tracking/motrix.yaml b/src/unilab/conf/ppo/task/g1_23dof_box_tracking/motrix.yaml
new file mode 100644
index 000000000..c9456f1e4
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_23dof_box_tracking/motrix.yaml
@@ -0,0 +1,45 @@
+# @package _global_
+defaults:
+  - /task/g1_23dof_box_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1BoxTracking23Dof
+  sim_backend: motrix
+  play_env_num: 16
+
+algo:
+  max_iterations: 40000
+  empirical_normalization: true
+  obs_groups:
+    actor: [actor]
+    critic: [critic]
+  algorithm:
+    entropy_coef: 0.002
+    desired_kl: 0.01
+
+reward:
+  motion_global_root_pos:
+    weight: 1.0
+  motion_body_ori:
+    weight: 1.5
+  motion_body_ang_vel:
+    weight: 1.5
+  object_global_ref_position_error_exp:
+    weight: 4.0
+    params: {command_name: motion, std: 0.12}
+  object_global_ref_orientation_error_exp:
+    weight: 3.0
+    params: {command_name: motion, std: 0.2}
+
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.5
+  scene:
+    enabled: true
+    source_model_file: src/unilab/assets/robots/g1/scene_flat_23dof_with_largebox.xml
+    ground_texture_file: src/unilab/assets/robots/g1/textures/floor.png
+    skybox_rgb1: [0.90, 0.90, 0.91]
+    skybox_rgb2: [0.68, 0.68, 0.70]
+    ground_texrepeat: [0.25, 0.25]
diff --git a/src/unilab/conf/ppo/task/g1_23dof_box_tracking/mujoco.yaml b/src/unilab/conf/ppo/task/g1_23dof_box_tracking/mujoco.yaml
new file mode 100644
index 000000000..d835ffe06
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_23dof_box_tracking/mujoco.yaml
@@ -0,0 +1,84 @@
+# @package _global_
+defaults:
+  - /task/g1_23dof_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1BoxTracking23Dof
+  sim_backend: mujoco
+  play_steps: 1000
+
+algo:
+  num_envs: 1024
+  max_iterations: 30000
+  save_interval: 500
+  obs_groups:
+    actor: [actor]
+  algorithm:
+    entropy_coef: 0.005
+
+play_profile:
+  enabled: false
+  env: null
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat_23dof_with_largebox.xml
+    entities:
+      object:
+        root_body_name: largebox
+  sim_dt: 0.005
+  observations:
+    actor:
+      terms:
+        motion_anchor_pos_b: null
+        base_lin_vel: null
+        base_ang_vel:
+          params: {sensor_name: pelvis_gyro}
+    critic:
+      terms:
+        base_ang_vel:
+          params: {sensor_name: pelvis_gyro}
+        object_state:
+          func: unilab.tasks.motion_tracking.g1.manager_terms.object_state_b
+          params: {command_name: motion}
+  commands:
+    motion:
+      _target_: unilab.tasks.motion_tracking.g1.manager_terms.BoxMotionCommandCfg
+      object_entity_name: object
+      params:
+        motion_file: motions/g1/sub3_largebox_003_boxconverted_23dof.npz
+  terminations:
+    object_pos:
+      func: unilab.tasks.motion_tracking.g1.manager_terms.bad_object_position
+      params: {command_name: motion, threshold: 0.25}
+    object_ori:
+      func: unilab.tasks.motion_tracking.g1.manager_terms.bad_object_orientation
+      params: {command_name: motion, threshold: 0.8}
+
+reward:
+  undesired_contacts:
+    func: unilab.tasks.motion_tracking.common.manager_terms.undesired_body_contacts
+    weight: -0.1
+    params:
+      command_name: motion
+      threshold: 0.05
+      body_names:
+        - pelvis
+        - left_hip_roll_link
+        - left_knee_link
+        - right_hip_roll_link
+        - right_knee_link
+        - torso_link
+        - left_shoulder_roll_link
+        - left_elbow_link
+        - right_shoulder_roll_link
+        - right_elbow_link
+  object_global_ref_position_error_exp:
+    func: unilab.tasks.motion_tracking.g1.manager_terms.object_global_position_error_exp
+    weight: 2.0
+    params: {command_name: motion, std: 0.2}
+  object_global_ref_orientation_error_exp:
+    func: unilab.tasks.motion_tracking.g1.manager_terms.object_global_orientation_error_exp
+    weight: 2.0
+    params: {command_name: motion, std: 0.3}
diff --git a/src/unilab/conf/ppo/task/g1_23dof_climb_tracking/motrix.yaml b/src/unilab/conf/ppo/task/g1_23dof_climb_tracking/motrix.yaml
new file mode 100644
index 000000000..ae708bf1d
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_23dof_climb_tracking/motrix.yaml
@@ -0,0 +1,8 @@
+# @package _global_
+defaults:
+  - /task/g1_23dof_climb_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1ClimbTracking23Dof
+  sim_backend: motrix
diff --git a/src/unilab/conf/ppo/task/g1_23dof_climb_tracking/mujoco.yaml b/src/unilab/conf/ppo/task/g1_23dof_climb_tracking/mujoco.yaml
new file mode 100644
index 000000000..99706fa98
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_23dof_climb_tracking/mujoco.yaml
@@ -0,0 +1,93 @@
+# @package _global_
+defaults:
+  - /task/g1_23dof_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1ClimbTracking23Dof
+  sim_backend: mujoco
+  play_steps: 1000
+
+algo:
+  num_envs: 1024
+  max_iterations: 20000
+  save_interval: 500
+  empirical_normalization: true
+  obs_groups:
+    actor: [actor]
+    critic: [critic]
+  algorithm:
+    entropy_coef: 0.005
+    desired_kl: 0.01
+
+play_profile:
+  enabled: false
+  env: null
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_climb_20_z_scale_1_23dof.xml
+  sim_dt: 0.005
+  max_episode_seconds: 15.0
+  actions:
+    joint_pos:
+      scale:
+        ".*_(hip_pitch|hip_yaw)_joint": 0.5475464629911068
+        ".*_(hip_roll|knee)_joint": 0.35066146637882434
+        ".*_ankle_(pitch|roll)_joint": 0.43857731392336724
+        "waist_yaw_joint": 0.5475464629911068
+        ".*_(shoulder_(pitch|roll|yaw)|elbow|wrist_roll)_joint": 0.43857731392336724
+  commands:
+    motion:
+      params:
+        motion_file: motions/g1/climb_20_z_scale_1.0_23dof.npz
+        sampling_mode: adaptive
+        truncate_on_clip_end: false
+  terminations:
+    anchor_pos:
+      params: {command_name: motion, threshold: 0.3}
+    ee_body_pos:
+      params:
+        command_name: motion
+        threshold: 0.3
+        body_names: &ee_bodies
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_roll_rubber_hand
+          - right_wrist_roll_rubber_hand
+    undesired_contacts:
+      func: unilab.tasks.motion_tracking.common.manager_terms.bad_undesired_body_contacts
+      params:
+        command_name: motion
+        threshold: 0.05
+        body_names: &undesired_bodies
+          - pelvis
+          - left_hip_roll_link
+          - left_knee_link
+          - right_hip_roll_link
+          - right_knee_link
+          - torso_link
+          - left_shoulder_roll_link
+          - left_elbow_link
+          - right_shoulder_roll_link
+          - right_elbow_link
+
+reward:
+  motion_body_pos:
+    weight: 2.0
+  motion_body_ori:
+    weight: 1.5
+  motion_ee_body_pos_z:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_relative_body_position_z_error_exp
+    weight: 2.0
+    params: {command_name: motion, std: 0.3, body_names: *ee_bodies}
+  motion_joint_pos:
+    weight: 0.5
+  motion_joint_vel:
+    weight: 0.25
+  action_rate_l2:
+    weight: -0.005
+  undesired_contacts:
+    func: unilab.tasks.motion_tracking.common.manager_terms.undesired_body_contacts
+    weight: -0.1
+    params: {command_name: motion, threshold: 0.05, body_names: *undesired_bodies}
diff --git a/src/unilab/conf/ppo/task/g1_23dof_flip_tracking/motrix.yaml b/src/unilab/conf/ppo/task/g1_23dof_flip_tracking/motrix.yaml
new file mode 100644
index 000000000..fe5afd7e6
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_23dof_flip_tracking/motrix.yaml
@@ -0,0 +1,29 @@
+# @package _global_
+defaults:
+  - /task/g1_23dof_flip_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1FlipTracking23Dof
+  sim_backend: motrix
+
+algo:
+  max_iterations: 30000
+  empirical_normalization: false
+
+env:
+  actions:
+    joint_pos:
+      scale: 0.25
+
+reward:
+  motion_global_root_pos:
+    weight: 1.0
+  motion_body_pos:
+    weight: 1.0
+  motion_body_ori:
+    weight: 1.0
+  motion_ee_body_pos_z: null
+  action_rate_l2:
+    weight: -0.05
+  undesired_contacts: null
diff --git a/src/unilab/conf/ppo/task/g1_23dof_flip_tracking/mujoco.yaml b/src/unilab/conf/ppo/task/g1_23dof_flip_tracking/mujoco.yaml
new file mode 100644
index 000000000..5cec48e23
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_23dof_flip_tracking/mujoco.yaml
@@ -0,0 +1,104 @@
+# @package _global_
+defaults:
+  - /task/g1_23dof_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1FlipTracking23Dof
+  sim_backend: mujoco
+  play_steps: 1000
+
+algo:
+  num_envs: 1024
+  max_iterations: 20000
+  save_interval: 500
+  empirical_normalization: true
+  obs_groups:
+    actor: [actor]
+    critic: [critic]
+  algorithm:
+    entropy_coef: 0.005
+    desired_kl: 0.01
+
+play_profile:
+  enabled: false
+  env: null
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat_23dof.xml
+  sim_dt: 0.005
+  actions:
+    joint_pos:
+      scale:
+        ".*_(hip_pitch|hip_yaw)_joint": 0.5475464629911068
+        ".*_(hip_roll|knee)_joint": 0.35066146637882434
+        ".*_ankle_(pitch|roll)_joint": 0.43857731392336724
+        "waist_yaw_joint": 0.5475464629911068
+        ".*_(shoulder_(pitch|roll|yaw)|elbow|wrist_roll)_joint": 0.43857731392336724
+  commands:
+    motion:
+      params:
+        motion_file: motions/g1/flip_360_001__A304_23dof.npz
+        sampling_mode: start
+        truncate_on_clip_end: false
+        pose_range: &zero_pose
+          x: [0.0, 0.0]
+          y: [0.0, 0.0]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [0.0, 0.0]
+        velocity_range: *zero_pose
+        joint_position_range: [0.0, 0.0]
+  terminations:
+    anchor_pos:
+      params: {command_name: motion, threshold: 0.5}
+    anchor_ori:
+      params:
+        command_name: motion
+        threshold: 1.0e9
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+    ee_body_pos:
+      params:
+        command_name: motion
+        threshold: 0.5
+        body_names: &ee_bodies
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_roll_rubber_hand
+          - right_wrist_roll_rubber_hand
+    undesired_contacts:
+      func: unilab.tasks.motion_tracking.common.manager_terms.bad_undesired_body_contacts
+      params:
+        command_name: motion
+        threshold: 0.05
+        body_names: &undesired_bodies
+          - pelvis
+          - left_hip_roll_link
+          - left_knee_link
+          - right_hip_roll_link
+          - right_knee_link
+          - torso_link
+          - left_shoulder_roll_link
+          - left_elbow_link
+          - right_shoulder_roll_link
+          - right_elbow_link
+
+reward:
+  motion_body_pos:
+    weight: 2.0
+  motion_body_ori:
+    weight: 1.5
+  motion_ee_body_pos_z:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_relative_body_position_z_error_exp
+    weight: 2.0
+    params: {command_name: motion, std: 0.3, body_names: *ee_bodies}
+  action_rate_l2:
+    weight: -0.005
+  undesired_contacts:
+    func: unilab.tasks.motion_tracking.common.manager_terms.undesired_body_contacts
+    weight: -0.1
+    params: {command_name: motion, threshold: 0.05, body_names: *undesired_bodies}
diff --git a/src/unilab/conf/ppo/task/g1_23dof_motion_tracking/motrix.yaml b/src/unilab/conf/ppo/task/g1_23dof_motion_tracking/motrix.yaml
new file mode 100644
index 000000000..2141c58cf
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_23dof_motion_tracking/motrix.yaml
@@ -0,0 +1,37 @@
+# @package _global_
+defaults:
+  - /task/g1_23dof_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1MotionTracking23Dof
+  sim_backend: motrix
+  play_env_num: 16
+
+reward:
+  motion_global_root_pos:
+    weight: 1.0
+  action_rate_l2:
+    weight: -0.05
+  undesired_contacts:
+    func: unilab.tasks.motion_tracking.common.manager_terms.undesired_body_contacts
+    weight: -0.1
+    params:
+      command_name: motion
+      threshold: 0.05
+      body_names:
+        - pelvis
+        - left_hip_roll_link
+        - left_knee_link
+        - right_hip_roll_link
+        - right_knee_link
+        - torso_link
+        - left_shoulder_roll_link
+        - left_elbow_link
+        - right_shoulder_roll_link
+        - right_elbow_link
+
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.5
diff --git a/src/unilab/conf/ppo/task/g1_23dof_motion_tracking/mujoco.yaml b/src/unilab/conf/ppo/task/g1_23dof_motion_tracking/mujoco.yaml
new file mode 100644
index 000000000..8605feb92
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_23dof_motion_tracking/mujoco.yaml
@@ -0,0 +1,67 @@
+# @package _global_
+defaults:
+  - /task/g1_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1MotionTracking23Dof
+  sim_backend: mujoco
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat_23dof.xml
+    entities:
+      robot:
+        joint_names: &g1_23dof_joints
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+        actuator_names: *g1_23dof_joints
+        body_names: &tracked_bodies_23dof
+          - pelvis
+          - left_hip_roll_link
+          - left_knee_link
+          - left_ankle_roll_link
+          - right_hip_roll_link
+          - right_knee_link
+          - right_ankle_roll_link
+          - torso_link
+          - left_shoulder_roll_link
+          - left_elbow_link
+          - left_wrist_roll_rubber_hand
+          - right_shoulder_roll_link
+          - right_elbow_link
+          - right_wrist_roll_rubber_hand
+  commands:
+    motion:
+      params:
+        motion_file: motions/g1/dance1_subject2_part_23dof.npz
+        body_names: *tracked_bodies_23dof
+  terminations:
+    ee_body_pos:
+      params:
+        body_names:
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_roll_rubber_hand
+          - right_wrist_roll_rubber_hand
diff --git a/src/unilab/conf/ppo/task/g1_23dof_motion_tracking_deploy/motrix.yaml b/src/unilab/conf/ppo/task/g1_23dof_motion_tracking_deploy/motrix.yaml
new file mode 100644
index 000000000..46d82769f
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_23dof_motion_tracking_deploy/motrix.yaml
@@ -0,0 +1,42 @@
+# @package _global_
+defaults:
+  - /task/g1_23dof_motion_tracking_deploy/mujoco
+  - _self_
+
+training:
+  task_name: G1MotionTracking23DofDeploy
+  sim_backend: motrix
+  play_env_num: 16
+
+env:
+  events:
+    foot_friction: null
+    push_robot: null
+
+reward:
+  motion_global_root_pos:
+    weight: 1.0
+  action_rate_l2:
+    weight: -0.05
+  undesired_contacts:
+    func: unilab.tasks.motion_tracking.common.manager_terms.undesired_body_contacts
+    weight: -0.1
+    params:
+      command_name: motion
+      threshold: 0.05
+      body_names:
+        - pelvis
+        - left_hip_roll_link
+        - left_knee_link
+        - right_hip_roll_link
+        - right_knee_link
+        - torso_link
+        - left_shoulder_roll_link
+        - left_elbow_link
+        - right_shoulder_roll_link
+        - right_elbow_link
+
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.5
diff --git a/src/unilab/conf/ppo/task/g1_23dof_motion_tracking_deploy/mujoco.yaml b/src/unilab/conf/ppo/task/g1_23dof_motion_tracking_deploy/mujoco.yaml
new file mode 100644
index 000000000..0e4667f90
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_23dof_motion_tracking_deploy/mujoco.yaml
@@ -0,0 +1,77 @@
+# @package _global_
+defaults:
+  - /task/g1_motion_tracking_deploy/mujoco
+  - _self_
+
+training:
+  task_name: G1MotionTracking23DofDeploy
+  sim_backend: mujoco
+
+_g1_23dof_deploy_action_scale:
+  ".*_(hip_pitch|hip_yaw)_joint": 0.5475464629911068
+  ".*_(hip_roll|knee)_joint": 0.35066146637882434
+  ".*_ankle_(pitch|roll)_joint": 0.43857731392336724
+  "waist_yaw_joint": 0.5475464629911068
+  ".*_(shoulder_(pitch|roll|yaw)|elbow|wrist_roll)_joint": 0.43857731392336724
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat_23dof.xml
+    entities:
+      robot:
+        joint_names: &g1_23dof_joints
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+        actuator_names: *g1_23dof_joints
+        body_names: &tracked_bodies_23dof
+          - pelvis
+          - left_hip_roll_link
+          - left_knee_link
+          - left_ankle_roll_link
+          - right_hip_roll_link
+          - right_knee_link
+          - right_ankle_roll_link
+          - torso_link
+          - left_shoulder_roll_link
+          - left_elbow_link
+          - left_wrist_roll_rubber_hand
+          - right_shoulder_roll_link
+          - right_elbow_link
+          - right_wrist_roll_rubber_hand
+  actions:
+    joint_pos:
+      scale: ${_g1_23dof_deploy_action_scale}
+  commands:
+    motion:
+      params:
+        motion_file: motions/g1/dance1_subject2_part_23dof.npz
+        body_names: *tracked_bodies_23dof
+  terminations:
+    ee_body_pos:
+      params:
+        body_names:
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_roll_rubber_hand
+          - right_wrist_roll_rubber_hand
diff --git a/src/unilab/conf/ppo/task/g1_23dof_walk_flat/base.yaml b/src/unilab/conf/ppo/task/g1_23dof_walk_flat/base.yaml
new file mode 100644
index 000000000..7489083e8
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_23dof_walk_flat/base.yaml
@@ -0,0 +1,66 @@
+# @package _global_
+# Canonical G1 23-DoF walk Manager-Based task declaration (PPO/APPO owners).
+# Inherits the 29-DoF flat contract and swaps the scene to the 23-DoF model
+# (no waist roll/pitch, no wrist pitch/yaw) with the 23-entry pose weights.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat_23dof.xml
+    entities:
+      robot:
+        joint_names:
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+        actuator_names:
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+
+reward:
+  pose:
+    params:
+      pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/src/unilab/conf/ppo/task/g1_23dof_walk_flat/motrix.yaml b/src/unilab/conf/ppo/task/g1_23dof_walk_flat/motrix.yaml
new file mode 100644
index 000000000..d8fd852d0
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_23dof_walk_flat/motrix.yaml
@@ -0,0 +1,118 @@
+# @package _global_
+# Motrix owner: inherits the shared 23-DoF flat Manager-Based contract from
+# base.yaml, then overrides contract fields for Motrix-specific tuning
+# (intentionally non-transferable from MuJoCo; drop overrides to restore parity).
+defaults:
+  - /task/g1_23dof_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1Walk23DofFlat
+  sim_backend: motrix
+algo:
+  num_envs: 2048
+  max_iterations: 2200
+  empirical_normalization: true
+  obs_groups:
+    actor:
+      - policy
+    critic:
+      - critic
+  policy:
+    init_noise_std: 0.5
+  algorithm:
+    learning_rate: 3.0e-4
+    entropy_coef: 5.0e-3
+env:
+  actions:
+    joint_pos:
+      scale: 0.5
+  commands:
+    twist:
+      ranges:
+        lin_vel_x: [0.4, 0.7]
+        lin_vel_y: [0.0, 0.0]
+        ang_vel_z: [0.0, 0.0]
+  events:
+    # Legacy Motrix owners disable kp/kd randomization.
+    pd_gains: null
+    reset_root_state_uniform:
+      params:
+        velocity_range:
+          x: [-0.05, 0.05]
+          y: [-0.05, 0.05]
+          z: [-0.05, 0.05]
+          roll: [-0.05, 0.05]
+          pitch: [-0.05, 0.05]
+          yaw: [-0.05, 0.05]
+  terminations:
+    tilt:
+      params:
+        max_tilt_deg: 35.0
+    base_height:
+      params:
+        minimum_height: 0.5
+reward:
+  tracking_ang_vel:
+    weight: 0.25
+  forward_progress:
+    func: unilab.tasks.locomotion.g1.manager_terms.forward_progress
+    weight: 0.0
+    params:
+      command_name: twist
+  under_speed:
+    func: unilab.tasks.locomotion.g1.manager_terms.under_speed
+    weight: -0.2
+    params:
+      command_name: twist
+  upper_body_pose:
+    func: unilab.tasks.locomotion.g1.manager_terms.upper_body_pose
+    weight: -0.05
+    params:
+      pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
+  penalty_feet_ori:
+    func: unilab.tasks.locomotion.g1.manager_terms.penalty_feet_ori
+    weight: 0.0
+  feet_phase:
+    weight: 1.2
+    params:
+      min_forward_speed: 0.05
+  feet_phase_contrast:
+    func: unilab.tasks.locomotion.g1.manager_terms.feet_phase_contrast
+    weight: 1.5
+    params:
+      frequency: 1.5
+      swing_height: 0.09
+      tracking_sigma: 0.008
+      min_forward_speed: 0.05
+      command_name: twist
+  feet_phase_contact:
+    func: unilab.tasks.locomotion.g1.manager_terms.feet_phase_contact
+    weight: 1.0
+    params:
+      frequency: 1.5
+      swing_height: 0.09
+      tracking_sigma: 0.008
+      min_forward_speed: 0.05
+      command_name: twist
+  feet_double_stance:
+    func: unilab.tasks.locomotion.g1.manager_terms.feet_double_stance
+    weight: -1.0
+    params:
+      frequency: 1.5
+      swing_height: 0.09
+      tracking_sigma: 0.008
+      min_forward_speed: 0.05
+      command_name: twist
+  ang_vel_xy:
+    weight: -0.2
+  base_height:
+    weight: -120.0
+    params:
+      target_height: 0.765
+  orientation:
+    weight: -2.5
+  action_rate:
+    weight: -0.005
+  pose:
+    weight: -0.05
diff --git a/src/unilab/conf/ppo/task/g1_23dof_walk_flat/mujoco.yaml b/src/unilab/conf/ppo/task/g1_23dof_walk_flat/mujoco.yaml
new file mode 100644
index 000000000..e8e5776ed
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_23dof_walk_flat/mujoco.yaml
@@ -0,0 +1,16 @@
+# @package _global_
+# MuJoCo owner: inherits the shared 23-DoF flat Manager-Based contract from
+# base.yaml and only carries backend/algo identity.
+defaults:
+  - /task/g1_23dof_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1Walk23DofFlat
+  sim_backend: mujoco
+algo:
+  num_envs: 2048
+  max_iterations: 2200
+  obs_groups:
+    actor:
+      - actor
diff --git a/src/unilab/conf/ppo/task/g1_23dof_walk_rough/mujoco.yaml b/src/unilab/conf/ppo/task/g1_23dof_walk_rough/mujoco.yaml
new file mode 100644
index 000000000..9c2856964
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_23dof_walk_rough/mujoco.yaml
@@ -0,0 +1,35 @@
+# @package _global_
+# MuJoCo 23-DoF rough owner: inherits the 23-DoF flat Manager-Based contract,
+# swaps the scene to the static-hfield rough XML, and enables the penalty
+# curriculum (the only PPO walk owner that carries one).
+defaults:
+  - /task/g1_23dof_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1Walk23DofRough
+  sim_backend: mujoco
+algo:
+  num_envs: 2048
+  max_iterations: 2200
+  obs_groups:
+    actor:
+      - actor
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_rough_23dof.xml
+  curriculum:
+    penalty_scaling:
+      func: unilab.tasks.locomotion.g1.manager_terms.G1PenaltyCurriculum
+      params:
+        initial_scale: 0.5
+        min_scale: 0.5
+        max_scale: 1.0
+        level_down_threshold: 150.0
+        level_up_threshold: 750.0
+        degree: 0.001
+
+reward:
+  # The rough scene XML declares no named foot contact sensors, so the shared
+  # air-time term inherited from the flat base is explicitly disabled here.
+  feet_air_time: null
diff --git a/src/unilab/conf/ppo/task/g1_23dof_wall_flip_tracking/motrix.yaml b/src/unilab/conf/ppo/task/g1_23dof_wall_flip_tracking/motrix.yaml
new file mode 100644
index 000000000..ca2f0e7be
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_23dof_wall_flip_tracking/motrix.yaml
@@ -0,0 +1,28 @@
+# @package _global_
+defaults:
+  - /task/g1_23dof_wall_flip_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1WallFlipTracking23Dof
+  sim_backend: motrix
+  play_env_num: 16
+  render_spacing: 3.0
+
+algo:
+  max_iterations: 12000
+
+env:
+  motrix_max_iterations: 3
+
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 4.0
+  scene:
+    enabled: true
+    source_model_file: src/unilab/assets/robots/g1/scene_flat_23dof_with_wall.xml
+    ground_texture_file: src/unilab/assets/robots/g1/textures/floor.png
+    skybox_rgb1: [0.90, 0.90, 0.91]
+    skybox_rgb2: [0.68, 0.68, 0.70]
+    ground_texrepeat: [0.25, 0.25]
diff --git a/src/unilab/conf/ppo/task/g1_23dof_wall_flip_tracking/mujoco.yaml b/src/unilab/conf/ppo/task/g1_23dof_wall_flip_tracking/mujoco.yaml
new file mode 100644
index 000000000..3f2c7ec81
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_23dof_wall_flip_tracking/mujoco.yaml
@@ -0,0 +1,26 @@
+# @package _global_
+defaults:
+  - /task/g1_23dof_flip_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1WallFlipTracking23Dof
+  sim_backend: mujoco
+
+play_profile:
+  enabled: false
+  env: null
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat_23dof_with_wall.xml
+  commands:
+    motion:
+      params:
+        motion_file: motions/g1/flip_from_wall_104__A304_23dof.npz
+
+reward:
+  motion_joint_pos:
+    weight: 0.5
+  motion_joint_vel:
+    weight: 0.25
diff --git a/src/unilab/conf/ppo/task/g1_box_tracking/motrix.yaml b/src/unilab/conf/ppo/task/g1_box_tracking/motrix.yaml
new file mode 100644
index 000000000..ba9ad2abc
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_box_tracking/motrix.yaml
@@ -0,0 +1,45 @@
+# @package _global_
+defaults:
+  - /task/g1_box_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1BoxTracking
+  sim_backend: motrix
+  play_env_num: 16
+
+algo:
+  max_iterations: 40000
+  empirical_normalization: true
+  obs_groups:
+    actor: [actor]
+    critic: [critic]
+  algorithm:
+    entropy_coef: 0.002
+    desired_kl: 0.01
+
+reward:
+  motion_global_root_pos:
+    weight: 1.0
+  motion_body_ori:
+    weight: 1.5
+  motion_body_ang_vel:
+    weight: 1.5
+  object_global_ref_position_error_exp:
+    weight: 4.0
+    params: {command_name: motion, std: 0.12}
+  object_global_ref_orientation_error_exp:
+    weight: 3.0
+    params: {command_name: motion, std: 0.2}
+
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.5
+  scene:
+    enabled: true
+    source_model_file: src/unilab/assets/robots/g1/scene_flat_with_largebox.xml
+    ground_texture_file: src/unilab/assets/robots/g1/textures/floor.png
+    skybox_rgb1: [0.90, 0.90, 0.91]
+    skybox_rgb2: [0.68, 0.68, 0.70]
+    ground_texrepeat: [0.25, 0.25]
diff --git a/src/unilab/conf/ppo/task/g1_box_tracking/mujoco.yaml b/src/unilab/conf/ppo/task/g1_box_tracking/mujoco.yaml
new file mode 100644
index 000000000..73d278bf9
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_box_tracking/mujoco.yaml
@@ -0,0 +1,85 @@
+# @package _global_
+defaults:
+  - /task/g1_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1BoxTracking
+  sim_backend: mujoco
+  play_steps: 1000
+
+algo:
+  num_envs: 1024
+  max_iterations: 30000
+  save_interval: 500
+  obs_groups:
+    actor: [actor]
+  algorithm:
+    entropy_coef: 0.005
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat_with_largebox.xml
+    entities:
+      object:
+        root_body_name: largebox
+  sim_dt: 0.005
+  observations:
+    actor:
+      terms:
+        motion_anchor_pos_b: null
+        base_lin_vel: null
+        base_ang_vel:
+          params: {sensor_name: pelvis_gyro}
+    critic:
+      terms:
+        base_ang_vel:
+          params: {sensor_name: pelvis_gyro}
+        object_state:
+          func: unilab.tasks.motion_tracking.g1.manager_terms.object_state_b
+          params: {command_name: motion}
+  commands:
+    motion:
+      _target_: unilab.tasks.motion_tracking.g1.manager_terms.BoxMotionCommandCfg
+      object_entity_name: object
+      params:
+        motion_file: motions/g1/sub3_largebox_003_boxconverted.npz
+  terminations:
+    object_pos:
+      func: unilab.tasks.motion_tracking.g1.manager_terms.bad_object_position
+      params: {command_name: motion, threshold: 0.25}
+    object_ori:
+      func: unilab.tasks.motion_tracking.g1.manager_terms.bad_object_orientation
+      params: {command_name: motion, threshold: 0.8}
+
+reward:
+  undesired_contacts:
+    func: unilab.tasks.motion_tracking.common.manager_terms.undesired_body_contacts
+    weight: -0.1
+    params:
+      command_name: motion
+      threshold: 0.05
+      body_names:
+        - pelvis
+        - left_hip_roll_link
+        - left_knee_link
+        - right_hip_roll_link
+        - right_knee_link
+        - torso_link
+        - left_shoulder_roll_link
+        - left_elbow_link
+        - right_shoulder_roll_link
+        - right_elbow_link
+  object_global_ref_position_error_exp:
+    func: unilab.tasks.motion_tracking.g1.manager_terms.object_global_position_error_exp
+    weight: 2.0
+    params: {command_name: motion, std: 0.2}
+  object_global_ref_orientation_error_exp:
+    func: unilab.tasks.motion_tracking.g1.manager_terms.object_global_orientation_error_exp
+    weight: 2.0
+    params: {command_name: motion, std: 0.3}
+
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/src/unilab/conf/ppo/task/g1_climb_tracking/motrix.yaml b/src/unilab/conf/ppo/task/g1_climb_tracking/motrix.yaml
new file mode 100644
index 000000000..01a98604a
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_climb_tracking/motrix.yaml
@@ -0,0 +1,8 @@
+# @package _global_
+defaults:
+  - /task/g1_climb_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1ClimbTracking
+  sim_backend: motrix
diff --git a/src/unilab/conf/ppo/task/g1_climb_tracking/mujoco.yaml b/src/unilab/conf/ppo/task/g1_climb_tracking/mujoco.yaml
new file mode 100644
index 000000000..fd7826eb4
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_climb_tracking/mujoco.yaml
@@ -0,0 +1,96 @@
+# @package _global_
+defaults:
+  - /task/g1_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1ClimbTracking
+  sim_backend: mujoco
+  play_steps: 1000
+
+algo:
+  num_envs: 1024
+  max_iterations: 20000
+  save_interval: 500
+  empirical_normalization: true
+  obs_groups:
+    actor: [actor]
+    critic: [critic]
+  algorithm:
+    entropy_coef: 0.005
+    desired_kl: 0.01
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_climb_20_z_scale_1.xml
+  sim_dt: 0.005
+  max_episode_seconds: 15.0
+  actions:
+    joint_pos:
+      scale:
+        ".*_(hip_pitch|hip_yaw)_joint": 0.5475464629911068
+        ".*_(hip_roll|knee)_joint": 0.35066146637882434
+        ".*_ankle_(pitch|roll)_joint": 0.43857731392336724
+        "waist_yaw_joint": 0.5475464629911068
+        "waist_(roll|pitch)_joint": 0.43857731392336724
+        ".*_(shoulder_(pitch|roll|yaw)|elbow|wrist_roll)_joint": 0.43857731392336724
+        ".*_wrist_(pitch|yaw)_joint": 0.07450087032950714
+  commands:
+    motion:
+      params:
+        motion_file: motions/g1/climb_20_z_scale_1.0.npz
+        sampling_mode: adaptive
+        truncate_on_clip_end: false
+  terminations:
+    anchor_pos:
+      params: {command_name: motion, threshold: 0.3}
+    ee_body_pos:
+      params:
+        command_name: motion
+        threshold: 0.3
+        body_names: &ee_bodies
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_yaw_link
+          - right_wrist_yaw_link
+    undesired_contacts:
+      func: unilab.tasks.motion_tracking.common.manager_terms.bad_undesired_body_contacts
+      params:
+        command_name: motion
+        threshold: 0.05
+        body_names: &undesired_bodies
+          - pelvis
+          - left_hip_roll_link
+          - left_knee_link
+          - right_hip_roll_link
+          - right_knee_link
+          - torso_link
+          - left_shoulder_roll_link
+          - left_elbow_link
+          - right_shoulder_roll_link
+          - right_elbow_link
+
+reward:
+  motion_body_pos:
+    weight: 2.0
+  motion_body_ori:
+    weight: 1.5
+  motion_ee_body_pos_z:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_relative_body_position_z_error_exp
+    weight: 2.0
+    params: {command_name: motion, std: 0.3, body_names: *ee_bodies}
+  motion_joint_pos:
+    weight: 0.5
+  motion_joint_vel:
+    weight: 0.25
+  action_rate_l2:
+    weight: -0.005
+  undesired_contacts:
+    func: unilab.tasks.motion_tracking.common.manager_terms.undesired_body_contacts
+    weight: -0.1
+    params: {command_name: motion, threshold: 0.05, body_names: *undesired_bodies}
+
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/src/unilab/conf/ppo/task/g1_flip_tracking/motrix.yaml b/src/unilab/conf/ppo/task/g1_flip_tracking/motrix.yaml
new file mode 100644
index 000000000..5948e0166
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_flip_tracking/motrix.yaml
@@ -0,0 +1,29 @@
+# @package _global_
+defaults:
+  - /task/g1_flip_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1FlipTracking
+  sim_backend: motrix
+
+algo:
+  max_iterations: 30000
+  empirical_normalization: false
+
+env:
+  actions:
+    joint_pos:
+      scale: 0.25
+
+reward:
+  motion_global_root_pos:
+    weight: 1.0
+  motion_body_pos:
+    weight: 1.0
+  motion_body_ori:
+    weight: 1.0
+  motion_ee_body_pos_z: null
+  action_rate_l2:
+    weight: -0.05
+  undesired_contacts: null
diff --git a/src/unilab/conf/ppo/task/g1_flip_tracking/mujoco.yaml b/src/unilab/conf/ppo/task/g1_flip_tracking/mujoco.yaml
new file mode 100644
index 000000000..eea70d92f
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_flip_tracking/mujoco.yaml
@@ -0,0 +1,107 @@
+# @package _global_
+defaults:
+  - /task/g1_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1FlipTracking
+  sim_backend: mujoco
+  play_steps: 1000
+
+algo:
+  num_envs: 1024
+  max_iterations: 20000
+  save_interval: 500
+  empirical_normalization: true
+  obs_groups:
+    actor: [actor]
+    critic: [critic]
+  algorithm:
+    entropy_coef: 0.005
+    desired_kl: 0.01
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat.xml
+  sim_dt: 0.005
+  actions:
+    joint_pos:
+      scale:
+        ".*_(hip_pitch|hip_yaw)_joint": 0.5475464629911068
+        ".*_(hip_roll|knee)_joint": 0.35066146637882434
+        ".*_ankle_(pitch|roll)_joint": 0.43857731392336724
+        "waist_yaw_joint": 0.5475464629911068
+        "waist_(roll|pitch)_joint": 0.43857731392336724
+        ".*_(shoulder_(pitch|roll|yaw)|elbow|wrist_roll)_joint": 0.43857731392336724
+        ".*_wrist_(pitch|yaw)_joint": 0.07450087032950714
+  commands:
+    motion:
+      params:
+        motion_file: motions/g1/flip_360_001__A304.npz
+        sampling_mode: start
+        truncate_on_clip_end: false
+        pose_range: &zero_pose
+          x: [0.0, 0.0]
+          y: [0.0, 0.0]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [0.0, 0.0]
+        velocity_range: *zero_pose
+        joint_position_range: [0.0, 0.0]
+  terminations:
+    anchor_pos:
+      params: {command_name: motion, threshold: 0.5}
+    anchor_ori:
+      params:
+        command_name: motion
+        threshold: 1.0e9
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+    ee_body_pos:
+      params:
+        command_name: motion
+        threshold: 0.5
+        body_names: &ee_bodies
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_yaw_link
+          - right_wrist_yaw_link
+    undesired_contacts:
+      func: unilab.tasks.motion_tracking.common.manager_terms.bad_undesired_body_contacts
+      params:
+        command_name: motion
+        threshold: 0.05
+        body_names: &undesired_bodies
+          - pelvis
+          - left_hip_roll_link
+          - left_knee_link
+          - right_hip_roll_link
+          - right_knee_link
+          - torso_link
+          - left_shoulder_roll_link
+          - left_elbow_link
+          - right_shoulder_roll_link
+          - right_elbow_link
+
+reward:
+  motion_body_pos:
+    weight: 2.0
+  motion_body_ori:
+    weight: 1.5
+  motion_ee_body_pos_z:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_relative_body_position_z_error_exp
+    weight: 2.0
+    params: {command_name: motion, std: 0.3, body_names: *ee_bodies}
+  action_rate_l2:
+    weight: -0.005
+  undesired_contacts:
+    func: unilab.tasks.motion_tracking.common.manager_terms.undesired_body_contacts
+    weight: -0.1
+    params: {command_name: motion, threshold: 0.05, body_names: *undesired_bodies}
+
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/src/unilab/conf/ppo/task/g1_motion_tracking/motrix.yaml b/src/unilab/conf/ppo/task/g1_motion_tracking/motrix.yaml
new file mode 100644
index 000000000..57f4e35be
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_motion_tracking/motrix.yaml
@@ -0,0 +1,37 @@
+# @package _global_
+defaults:
+  - /task/g1_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1MotionTracking
+  sim_backend: motrix
+  play_env_num: 16
+
+reward:
+  motion_global_root_pos:
+    weight: 1.0
+  action_rate_l2:
+    weight: -0.05
+  undesired_contacts:
+    func: unilab.tasks.motion_tracking.common.manager_terms.undesired_body_contacts
+    weight: -0.1
+    params:
+      command_name: motion
+      threshold: 0.05
+      body_names:
+        - pelvis
+        - left_hip_roll_link
+        - left_knee_link
+        - right_hip_roll_link
+        - right_knee_link
+        - torso_link
+        - left_shoulder_roll_link
+        - left_elbow_link
+        - right_shoulder_roll_link
+        - right_elbow_link
+
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.5
diff --git a/src/unilab/conf/ppo/task/g1_motion_tracking/mujoco.yaml b/src/unilab/conf/ppo/task/g1_motion_tracking/mujoco.yaml
new file mode 100644
index 000000000..343c454e0
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_motion_tracking/mujoco.yaml
@@ -0,0 +1,240 @@
+# @package _global_
+training:
+  task_name: G1MotionTracking
+  sim_backend: mujoco
+  play_steps: 1000
+
+algo:
+  num_envs: 1024
+  max_iterations: 15000
+  save_interval: 500
+  obs_groups:
+    actor: [actor]
+  algorithm:
+    entropy_coef: 0.005
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat.xml
+    default_keyframe_name: stand
+    entities:
+      robot:
+        root_body_name: pelvis
+        joint_names: &g1_joints
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - waist_roll_joint
+          - waist_pitch_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - left_wrist_pitch_joint
+          - left_wrist_yaw_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+          - right_wrist_pitch_joint
+          - right_wrist_yaw_joint
+        actuator_names: *g1_joints
+        body_names: &tracked_bodies
+          - pelvis
+          - left_hip_roll_link
+          - left_knee_link
+          - left_ankle_roll_link
+          - right_hip_roll_link
+          - right_knee_link
+          - right_ankle_roll_link
+          - torso_link
+          - left_shoulder_roll_link
+          - left_elbow_link
+          - left_wrist_yaw_link
+          - right_shoulder_roll_link
+          - right_elbow_link
+          - right_wrist_yaw_link
+        geom_names:
+          - left_foot1_collision
+          - left_foot2_collision
+          - left_foot3_collision
+          - left_foot4_collision
+          - left_foot5_collision
+          - left_foot6_collision
+          - left_foot7_collision
+          - right_foot1_collision
+          - right_foot2_collision
+          - right_foot3_collision
+          - right_foot4_collision
+          - right_foot5_collision
+          - right_foot6_collision
+          - right_foot7_collision
+  sim_dt: 0.006666666666666667
+  ctrl_dt: 0.02
+  max_episode_seconds: 10.0
+  observations:
+    actor:
+      terms:
+        command: &command_obs
+          func: unilab.envs.mdp.generated_commands
+          params: {command_name: motion}
+        motion_anchor_pos_b: &anchor_pos_obs
+          func: unilab.tasks.motion_tracking.common.manager_terms.motion_anchor_pos_b
+          params: {command_name: motion}
+        motion_anchor_ori_b: &anchor_ori_obs
+          func: unilab.tasks.motion_tracking.common.manager_terms.motion_anchor_ori_b
+          params: {command_name: motion}
+        base_lin_vel: &base_lin_vel_obs
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: pelvis_local_linvel}
+        base_ang_vel: &base_ang_vel_obs
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: torso_gyro}
+        joint_pos: &joint_pos_obs
+          func: unilab.tasks.motion_tracking.common.manager_terms.motion_joint_pos_rel
+          params: {command_name: motion}
+        joint_vel: &joint_vel_obs
+          func: unilab.envs.mdp.joint_vel_rel
+        actions: &actions_obs
+          func: unilab.envs.mdp.last_action
+    critic:
+      terms:
+        command: *command_obs
+        motion_anchor_pos_b: *anchor_pos_obs
+        motion_anchor_ori_b: *anchor_ori_obs
+        base_lin_vel: *base_lin_vel_obs
+        base_ang_vel: *base_ang_vel_obs
+        joint_pos: *joint_pos_obs
+        joint_vel: *joint_vel_obs
+        actions: *actions_obs
+        body_pos:
+          func: unilab.tasks.motion_tracking.common.manager_terms.robot_body_pos_b
+          params: {command_name: motion}
+        body_ori:
+          func: unilab.tasks.motion_tracking.common.manager_terms.robot_body_ori_b
+          params: {command_name: motion}
+  actions:
+    joint_pos:
+      _target_: unilab.tasks.motion_tracking.common.manager_terms.MotionJointPositionActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      scale: 0.25
+      use_default_offset: true
+      command_name: motion
+  commands:
+    motion:
+      _target_: unilab.tasks.motion_tracking.common.manager_terms.MotionCommandCfg
+      entity_name: robot
+      resampling_time_range: [1.0e9, 1.0e9]
+      params:
+        motion_file: motions/g1/dance1_subject2_part.npz
+        anchor_body_name: torso_link
+        body_names: *tracked_bodies
+        sampling_mode: adaptive
+        sampling_start_ratio: 0.0
+        truncate_on_clip_end: false
+        pose_range:
+          x: [-0.05, 0.05]
+          y: [-0.05, 0.05]
+          z: [-0.01, 0.01]
+          roll: [-0.1, 0.1]
+          pitch: [-0.1, 0.1]
+          yaw: [-0.2, 0.2]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.2, 0.2]
+          roll: [-0.52, 0.52]
+          pitch: [-0.52, 0.52]
+          yaw: [-0.78, 0.78]
+        joint_position_range: [-0.1, 0.1]
+        joint_default_position_range: [0.0, 0.0]
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    anchor_pos:
+      func: unilab.tasks.motion_tracking.common.manager_terms.bad_anchor_pos_z_only
+      params: {command_name: motion, threshold: 0.25}
+    anchor_ori:
+      func: unilab.tasks.motion_tracking.common.manager_terms.bad_anchor_ori
+      params:
+        command_name: motion
+        threshold: 0.8
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+    ee_body_pos:
+      func: unilab.tasks.motion_tracking.common.manager_terms.bad_motion_body_pos_z_only
+      params:
+        command_name: motion
+        threshold: 0.25
+        body_names:
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_yaw_link
+          - right_wrist_yaw_link
+  policy_observation_group: actor
+  critic_observation_group: critic
+
+reward:
+  motion_global_root_pos:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_global_anchor_position_error_exp
+    weight: 0.5
+    params: {command_name: motion, std: 0.3}
+  motion_global_root_ori:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_global_anchor_orientation_error_exp
+    weight: 0.5
+    params: {command_name: motion, std: 0.4}
+  motion_body_pos:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_relative_body_position_error_exp
+    weight: 1.0
+    params: {command_name: motion, std: 0.3}
+  motion_body_ori:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_relative_body_orientation_error_exp
+    weight: 1.0
+    params: {command_name: motion, std: 0.4}
+  motion_body_lin_vel:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_global_body_linear_velocity_error_exp
+    weight: 1.0
+    params: {command_name: motion, std: 1.0}
+  motion_body_ang_vel:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_global_body_angular_velocity_error_exp
+    weight: 1.0
+    params: {command_name: motion, std: 3.14}
+  motion_joint_pos:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_joint_position_error_exp
+    weight: 0.0
+    params: {command_name: motion, std: 0.2}
+  motion_joint_vel:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_joint_velocity_error_exp
+    weight: 0.0
+    params: {command_name: motion, std: 1.0}
+  action_rate_l2:
+    func: unilab.envs.mdp.action_rate_l2
+    weight: -0.1
+  joint_limit:
+    func: unilab.tasks.motion_tracking.common.manager_terms.joint_pos_limits
+    weight: -10.0
+    params:
+      asset_cfg:
+        _target_: unilab.managers.SceneEntityCfg
+        name: robot
+        joint_names: ".*"
+
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/src/unilab/conf/ppo/task/g1_motion_tracking_deploy/motrix.yaml b/src/unilab/conf/ppo/task/g1_motion_tracking_deploy/motrix.yaml
new file mode 100644
index 000000000..9d2fe0e6e
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_motion_tracking_deploy/motrix.yaml
@@ -0,0 +1,42 @@
+# @package _global_
+defaults:
+  - /task/g1_motion_tracking_deploy/mujoco
+  - _self_
+
+training:
+  task_name: G1MotionTrackingDeploy
+  sim_backend: motrix
+  play_env_num: 16
+
+env:
+  events:
+    foot_friction: null
+    push_robot: null
+
+reward:
+  motion_global_root_pos:
+    weight: 1.0
+  action_rate_l2:
+    weight: -0.05
+  undesired_contacts:
+    func: unilab.tasks.motion_tracking.common.manager_terms.undesired_body_contacts
+    weight: -0.1
+    params:
+      command_name: motion
+      threshold: 0.05
+      body_names:
+        - pelvis
+        - left_hip_roll_link
+        - left_knee_link
+        - right_hip_roll_link
+        - right_knee_link
+        - torso_link
+        - left_shoulder_roll_link
+        - left_elbow_link
+        - right_shoulder_roll_link
+        - right_elbow_link
+
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.5
diff --git a/src/unilab/conf/ppo/task/g1_motion_tracking_deploy/mujoco.yaml b/src/unilab/conf/ppo/task/g1_motion_tracking_deploy/mujoco.yaml
new file mode 100644
index 000000000..6b8d5ef89
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_motion_tracking_deploy/mujoco.yaml
@@ -0,0 +1,84 @@
+# @package _global_
+defaults:
+  - /task/g1_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1MotionTrackingDeploy
+  sim_backend: mujoco
+
+env:
+  sim_dt: 0.005
+  observations:
+    actor:
+      terms:
+        motion_anchor_pos_b: null
+        base_lin_vel: null
+        base_ang_vel:
+          params: {sensor_name: pelvis_gyro}
+    critic:
+      terms:
+        base_ang_vel:
+          params: {sensor_name: pelvis_gyro}
+  actions:
+    joint_pos:
+      scale:
+        ".*_(hip_pitch|hip_yaw)_joint": 0.5475464629911068
+        ".*_(hip_roll|knee)_joint": 0.35066146637882434
+        ".*_ankle_(pitch|roll)_joint": 0.43857731392336724
+        "waist_yaw_joint": 0.5475464629911068
+        "waist_(roll|pitch)_joint": 0.43857731392336724
+        ".*_(shoulder_(pitch|roll|yaw)|elbow|wrist_roll)_joint": 0.43857731392336724
+        ".*_wrist_(pitch|yaw)_joint": 0.07450087032950714
+  commands:
+    motion:
+      params:
+        joint_default_position_range: [-0.01, 0.01]
+  events:
+    base_mass:
+      func: unilab.envs.mdp.randomize_rigid_body_mass
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+          body_names: pelvis
+        mass_distribution_params: [-1.5, 1.5]
+        operation: add
+        recompute_inertia: false
+    base_com:
+      func: unilab.envs.mdp.randomize_rigid_body_com
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+          body_names: pelvis
+        com_range:
+          x: [-0.025, 0.025]
+          y: [-0.05, 0.05]
+          z: [-0.05, 0.05]
+    foot_friction:
+      func: unilab.envs.mdp.geom_friction
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+          geom_names: ".*"
+        ranges: [0.3, 1.2]
+        operation: abs
+        shared_random: true
+    push_robot:
+      func: unilab.envs.mdp.push_by_setting_velocity
+      mode: interval
+      interval_range_s: [15.0, 15.0]
+      is_global_time: true
+      params:
+        velocity_range:
+          x: [-1.0, 1.0]
+          y: [-1.0, 1.0]
+          z: [-0.5, 0.5]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [0.0, 0.0]
diff --git a/src/unilab/conf/ppo/task/g1_walk_flat/base.yaml b/src/unilab/conf/ppo/task/g1_walk_flat/base.yaml
new file mode 100644
index 000000000..aeb54130a
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_walk_flat/base.yaml
@@ -0,0 +1,283 @@
+# @package _global_
+# Canonical G1 29-DoF walk Manager-Based task declaration (PPO/APPO owners).
+# Backend owner leaves inherit this file and only override backend/algo tuning
+# or explicitly disabled terms. Observation scaling follows the legacy profile
+# (unit scales); the walk profile lives in conf/sac/task/g1_walk_flat/base.yaml.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat.xml
+    default_keyframe_name: stand
+    entities:
+      robot:
+        root_body_name: pelvis
+        joint_names:
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - waist_roll_joint
+          - waist_pitch_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - left_wrist_pitch_joint
+          - left_wrist_yaw_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+          - right_wrist_pitch_joint
+          - right_wrist_yaw_joint
+        actuator_names:
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - waist_roll_joint
+          - waist_pitch_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - left_wrist_pitch_joint
+          - left_wrist_yaw_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+          - right_wrist_pitch_joint
+          - right_wrist_yaw_joint
+        body_names: [pelvis]
+  sim_dt: 0.006666666666666667
+  ctrl_dt: 0.02
+  max_episode_seconds: 20.0
+  observations:
+    policy:
+      # Observation noise matches the legacy noise_config (level=1.0, actor-only
+      # since the critic reads clean observations): gyro +/-0.2, gravity +/-0.05,
+      # joint pos +/-0.01, joint vel +/-1.5, applied before term scaling.
+      enable_corruption: true
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: torso_gyro}
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.2
+            n_max: 0.2
+            operation: add
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params: {sensor_name: torso_upvector}
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.05
+            n_max: 0.05
+            operation: add
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.01
+            n_max: 0.01
+            operation: add
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -1.5
+            n_max: 1.5
+            operation: add
+        actions:
+          func: unilab.envs.mdp.last_action
+          params: {action_name: joint_pos}
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params: {command_name: twist}
+        gait_phase:
+          func: unilab.tasks.locomotion.g1.manager_terms.G1GaitPhase
+          params:
+            frequency: 1.5
+            init_mode: offset_phase
+    critic:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: torso_gyro}
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params: {sensor_name: torso_upvector}
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+          params: {action_name: joint_pos}
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params: {command_name: twist}
+        gait_phase:
+          func: unilab.tasks.locomotion.g1.manager_terms.G1GaitPhase
+          params:
+            frequency: 1.5
+            init_mode: offset_phase
+        base_lin_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: pelvis_local_linvel}
+  actions:
+    joint_pos:
+      _target_: unilab.envs.mdp.JointPositionActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      scale: 0.25
+      use_default_offset: true
+  commands:
+    twist:
+      _target_: unilab.tasks.locomotion.g1.manager_terms.G1VelocityCommandCfg
+      entity_name: robot
+      resampling_time_range: [20.0, 20.0]
+      heading_command: false
+      heading_control_stiffness: 0.5
+      rel_standing_envs: 0.0
+      rel_heading_envs: 0.0
+      rel_world_envs: 0.0
+      rel_forward_envs: 0.0
+      init_velocity_prob: 0.0
+      planar_dead_zone: 0.2
+      ranges:
+        lin_vel_x: [-0.6, 1.0]
+        lin_vel_y: [-0.4, 0.4]
+        ang_vel_z: [-0.8, 0.8]
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_root_state_uniform:
+      func: unilab.envs.mdp.reset_root_state_uniform
+      mode: reset
+      params:
+        pose_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [-3.141592653589793, 3.141592653589793]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.5, 0.5]
+          roll: [-0.5, 0.5]
+          pitch: [-0.5, 0.5]
+          yaw: [-0.5, 0.5]
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        kp_range: [0.9, 1.1]
+        kd_range: [0.9, 1.1]
+        operation: scale
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    tilt:
+      func: unilab.tasks.locomotion.g1.manager_terms.g1_tilt_exceeded
+      params:
+        max_tilt_deg: 25.0
+    base_height:
+      func: unilab.envs.mdp.root_height_below_minimum
+      params:
+        minimum_height: 0.55
+  policy_observation_group: policy
+  critic_observation_group: critic
+
+reward:
+  tracking_lin_vel:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.track_lin_vel
+    weight: 2.0
+    params:
+      tracking_sigma: 0.25
+      command_name: twist
+      sensor_name: pelvis_local_linvel
+  tracking_ang_vel:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.track_ang_vel
+    weight: 0.2
+    params:
+      tracking_sigma: 0.25
+      command_name: twist
+      sensor_name: torso_gyro
+  feet_phase:
+    func: unilab.tasks.locomotion.g1.manager_terms.feet_phase
+    weight: 1.0
+    params:
+      frequency: 1.5
+      swing_height: 0.09
+      tracking_sigma: 0.008
+      min_forward_speed: 0.0
+      command_name: twist
+  # Shared mjlab-style window air-time incentive (#1398); each foot group binds
+  # the four per-corner contact sensors with mjlab's any-slot reduce.
+  feet_air_time:
+    func: unilab.tasks.locomotion.common.gait_terms.feet_air_time
+    weight: 0.25
+    params:
+      sensor_groups:
+        - [left_foot_contact_0, left_foot_contact_1, left_foot_contact_2, left_foot_contact_3]
+        - [right_foot_contact_0, right_foot_contact_1, right_foot_contact_2, right_foot_contact_3]
+      threshold_min: 0.05
+      threshold_max: 0.5
+      command_name: twist
+      command_threshold: 0.5
+  lin_vel_z:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.lin_vel_z
+    weight: -1.0
+    params:
+      sensor_name: pelvis_local_linvel
+  ang_vel_xy:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.ang_vel_xy
+    weight: -0.25
+    params:
+      sensor_name: torso_gyro
+  base_height:
+    func: unilab.tasks.locomotion.common.manager_terms.base_height_l2
+    weight: -500.0
+    params:
+      target_height: 0.754
+  orientation:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.orientation
+    weight: -5.0
+    params:
+      sensor_name: torso_upvector
+  action_rate:
+    func: unilab.envs.mdp.action_rate_l2
+    weight: -0.01
+  pose:
+    func: unilab.tasks.locomotion.g1.manager_terms.weighted_pose
+    weight: -0.1
+    params:
+      pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/src/unilab/conf/ppo/task/g1_walk_flat/genesis.yaml b/src/unilab/conf/ppo/task/g1_walk_flat/genesis.yaml
new file mode 100644
index 000000000..e204a3ad7
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_walk_flat/genesis.yaml
@@ -0,0 +1,42 @@
+# @package _global_
+# Genesis owner for the in-process backend (genesis-world==1.3.3, torch>=2.8).
+# Keeps DENYLIST parity with the MuJoCo owner so cross-backend play transfers
+# 1:1. Genesis drops the MJCF global <option> block at import (REPORT #1372
+# §3.3), so the integrator g1.xml declares (<option integrator="implicitfast"
+# timestep="0.006666666666666667"/>) is re-declared here: the timestep flows
+# through the existing base.yaml sim_dt -> ctrl_dt chain, and the integrator
+# through the EnvCfg genesis fields below. Solver/constraint-cone/iterations
+# stay at Genesis defaults: the MJCF option block does not declare them and
+# MuJoCo's implicit defaults (PGS solver, pyramidal cone) have no Genesis
+# equivalent. Keyframes and sensors are scanned from the MJCF once on the
+# materialize cold path; kp/kd reset randomization stays enabled because the
+# backend declares the measured RESET_TERM_KP/KD DR terms (REPORT §5.7).
+# Playback uses Genesis native rendering: with a display, play_render_mode=auto
+# opens the interactive viewer; headless hosts fall back to offscreen camera
+# recording.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1WalkFlat
+  sim_backend: genesis
+  play_render_mode: auto
+algo:
+  num_envs: 2048
+  max_iterations: 2200
+  empirical_normalization: false
+  obs_groups:
+    actor:
+      - actor
+  policy:
+    actor_hidden_dims: [512, 256, 128]
+    critic_hidden_dims: [512, 256, 128]
+env:
+  # Re-declares the MJCF <option integrator="implicitfast"> that Genesis drops
+  # at import; the remaining global options keep the Genesis defaults.
+  genesis_integrator: implicitfast
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/src/unilab/conf/ppo/task/g1_walk_flat/isaacgym.yaml b/src/unilab/conf/ppo/task/g1_walk_flat/isaacgym.yaml
new file mode 100644
index 000000000..f631b0b8f
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_walk_flat/isaacgym.yaml
@@ -0,0 +1,38 @@
+# @package _global_
+# IsaacGym owner for the subprocess backend (physics runs in a Python 3.8
+# worker). Keeps DENYLIST parity with the MuJoCo owner so cross-backend play
+# transfers 1:1. The backend consumes the self-contained MJCF scene from
+# base.yaml directly (scene fragments and generated terrain are unsupported)
+# and scans keyframes/sensors/actuators from it on the materialize cold path,
+# reproducing the <position kp kv forcerange> actuators with PhysX
+# DOF_MODE_POS. Runtime kp/kd randomization is unsupported, so pd_gains
+# stays disabled. Playback uses IsaacGym native rendering: with a display,
+# `play_render_mode=auto` opens the interactive viewer; headless hosts fall
+# back to offscreen camera-sensor recording.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1WalkFlat
+  sim_backend: isaacgym
+algo:
+  num_envs: 2048
+  max_iterations: 2200
+  empirical_normalization: false
+  obs_groups:
+    actor:
+      - actor
+  policy:
+    actor_hidden_dims: [512, 256, 128]
+    critic_hidden_dims: [512, 256, 128]
+env:
+  # GPU device index handed to the worker sim; unset defaults to device 0.
+  isaacgym_device_id: 0
+  events:
+    # Runtime kp/kd randomization is unsupported on the isaacgym worker.
+    pd_gains: null
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/src/unilab/conf/ppo/task/g1_walk_flat/isaacsim.yaml b/src/unilab/conf/ppo/task/g1_walk_flat/isaacsim.yaml
new file mode 100644
index 000000000..b780689d1
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_walk_flat/isaacsim.yaml
@@ -0,0 +1,34 @@
+# @package _global_
+# IsaacSim owner for the headless Python 3.11 IsaacLab/PhysX subprocess.
+# The owner keeps all policy-defining fields inherited from the MuJoCo base
+# unchanged so a checkpoint has the same observation/action contract. IsaacSim
+# keeps interval randomization and reset-time PD-gain mutation disabled at the
+# owner layer; eval rendering is selected before Kit startup.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1WalkFlat
+  sim_backend: isaacsim
+  play_render_mode: auto
+algo:
+  num_envs: 2048
+  max_iterations: 2200
+  empirical_normalization: false
+  obs_groups:
+    actor:
+      - actor
+  policy:
+    actor_hidden_dims: [512, 256, 128]
+    critic_hidden_dims: [512, 256, 128]
+env:
+  # IsaacSim 5.1 / IsaacLab v2.3.0 run in a dedicated Python 3.11 worker.
+  isaacsim_device_id: 0
+  isaacsim_worker_timeout_s: 120.0
+  isaacsim_render_width: 1280
+  isaacsim_render_height: 720
+  events:
+    pd_gains: null
+play_profile:
+  enabled: false
diff --git a/src/unilab/conf/ppo/task/g1_walk_flat/mjwarp.yaml b/src/unilab/conf/ppo/task/g1_walk_flat/mjwarp.yaml
new file mode 100644
index 000000000..713bc6301
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_walk_flat/mjwarp.yaml
@@ -0,0 +1,34 @@
+# @package _global_
+# Configured-only mjwarp owner for the unified host contract adapter. Keeps
+# DENYLIST parity with the MuJoCo owner; legacy kp/kd randomization is disabled.
+# Offline record reuses MuJoCo rendering; interactive eval routes through the
+# MuJoCo viewer (mjwarp physics, single env). Native playback and
+# device-resident runtime routing are intentionally absent.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1WalkFlat
+  sim_backend: mjwarp
+  play_render_mode: record
+algo:
+  num_envs: 2048
+  max_iterations: 2200
+  empirical_normalization: false
+  obs_groups:
+    actor:
+      - actor
+  policy:
+    actor_hidden_dims: [512, 256, 128]
+    critic_hidden_dims: [512, 256, 128]
+env:
+  mjwarp_nconmax: 128
+  mjwarp_njmax: 256
+  events:
+    # Legacy mjwarp owners disable kp/kd and armature randomization.
+    pd_gains: null
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/src/unilab/conf/ppo/task/g1_walk_flat/motrix.yaml b/src/unilab/conf/ppo/task/g1_walk_flat/motrix.yaml
new file mode 100644
index 000000000..b93cc9269
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_walk_flat/motrix.yaml
@@ -0,0 +1,124 @@
+# @package _global_
+# Motrix owner: inherits the shared 29-DoF flat Manager-Based contract from
+# base.yaml, then overrides contract fields for Motrix-specific tuning
+# (intentionally non-transferable from MuJoCo; drop overrides to restore parity).
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1WalkFlat
+  sim_backend: motrix
+algo:
+  num_envs: 2048
+  max_iterations: 2200
+  empirical_normalization: true
+  obs_groups:
+    actor:
+      - policy
+    critic:
+      - critic
+  policy:
+    actor_hidden_dims: [512, 256, 128]
+    critic_hidden_dims: [512, 256, 128]
+    init_noise_std: 0.5
+  algorithm:
+    learning_rate: 3.0e-4
+    entropy_coef: 5.0e-3
+env:
+  actions:
+    joint_pos:
+      scale: 0.5
+  commands:
+    twist:
+      ranges:
+        lin_vel_x: [0.4, 0.7]
+        lin_vel_y: [0.0, 0.0]
+        ang_vel_z: [0.0, 0.0]
+  events:
+    # Legacy Motrix owners disable kp/kd randomization.
+    pd_gains: null
+    reset_root_state_uniform:
+      params:
+        velocity_range:
+          x: [-0.05, 0.05]
+          y: [-0.05, 0.05]
+          z: [-0.05, 0.05]
+          roll: [-0.05, 0.05]
+          pitch: [-0.05, 0.05]
+          yaw: [-0.05, 0.05]
+  terminations:
+    tilt:
+      params:
+        max_tilt_deg: 35.0
+    base_height:
+      params:
+        minimum_height: 0.5
+reward:
+  tracking_ang_vel:
+    weight: 0.25
+  forward_progress:
+    func: unilab.tasks.locomotion.g1.manager_terms.forward_progress
+    weight: 0.0
+    params:
+      command_name: twist
+  under_speed:
+    func: unilab.tasks.locomotion.g1.manager_terms.under_speed
+    weight: -0.2
+    params:
+      command_name: twist
+  upper_body_pose:
+    func: unilab.tasks.locomotion.g1.manager_terms.upper_body_pose
+    weight: -0.05
+    params:
+      pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
+  penalty_feet_ori:
+    func: unilab.tasks.locomotion.g1.manager_terms.penalty_feet_ori
+    weight: 0.0
+  feet_phase:
+    weight: 1.2
+    params:
+      min_forward_speed: 0.05
+  feet_phase_contrast:
+    func: unilab.tasks.locomotion.g1.manager_terms.feet_phase_contrast
+    weight: 1.5
+    params:
+      frequency: 1.5
+      swing_height: 0.09
+      tracking_sigma: 0.008
+      min_forward_speed: 0.05
+      command_name: twist
+  feet_phase_contact:
+    func: unilab.tasks.locomotion.g1.manager_terms.feet_phase_contact
+    weight: 1.0
+    params:
+      frequency: 1.5
+      swing_height: 0.09
+      tracking_sigma: 0.008
+      min_forward_speed: 0.05
+      command_name: twist
+  feet_double_stance:
+    func: unilab.tasks.locomotion.g1.manager_terms.feet_double_stance
+    weight: -1.0
+    params:
+      frequency: 1.5
+      swing_height: 0.09
+      tracking_sigma: 0.008
+      min_forward_speed: 0.05
+      command_name: twist
+  ang_vel_xy:
+    weight: -0.2
+  base_height:
+    weight: -120.0
+    params:
+      target_height: 0.765
+  orientation:
+    weight: -2.5
+  action_rate:
+    weight: -0.005
+  pose:
+    weight: -0.05
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/src/unilab/conf/ppo/task/g1_walk_flat/mujoco.yaml b/src/unilab/conf/ppo/task/g1_walk_flat/mujoco.yaml
new file mode 100644
index 000000000..a900f8d8d
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_walk_flat/mujoco.yaml
@@ -0,0 +1,24 @@
+# @package _global_
+# MuJoCo owner: inherits the shared 29-DoF flat Manager-Based contract from
+# base.yaml and only carries backend/algo identity.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1WalkFlat
+  sim_backend: mujoco
+algo:
+  num_envs: 2048
+  max_iterations: 2200
+  empirical_normalization: false
+  obs_groups:
+    actor:
+      - actor
+  policy:
+    actor_hidden_dims: [512, 256, 128]
+    critic_hidden_dims: [512, 256, 128]
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/src/unilab/conf/ppo/task/g1_wall_flip_tracking/motrix.yaml b/src/unilab/conf/ppo/task/g1_wall_flip_tracking/motrix.yaml
new file mode 100644
index 000000000..8fd67bfc7
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_wall_flip_tracking/motrix.yaml
@@ -0,0 +1,28 @@
+# @package _global_
+defaults:
+  - /task/g1_wall_flip_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1WallFlipTracking
+  sim_backend: motrix
+  play_env_num: 16
+  render_spacing: 3.0
+
+algo:
+  max_iterations: 12000
+
+env:
+  motrix_max_iterations: 3
+
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 4.0
+  scene:
+    enabled: true
+    source_model_file: src/unilab/assets/robots/g1/scene_flat_with_wall.xml
+    ground_texture_file: src/unilab/assets/robots/g1/textures/floor.png
+    skybox_rgb1: [0.90, 0.90, 0.91]
+    skybox_rgb2: [0.68, 0.68, 0.70]
+    ground_texrepeat: [0.25, 0.25]
diff --git a/src/unilab/conf/ppo/task/g1_wall_flip_tracking/mujoco.yaml b/src/unilab/conf/ppo/task/g1_wall_flip_tracking/mujoco.yaml
new file mode 100644
index 000000000..082c23fd9
--- /dev/null
+++ b/src/unilab/conf/ppo/task/g1_wall_flip_tracking/mujoco.yaml
@@ -0,0 +1,22 @@
+# @package _global_
+defaults:
+  - /task/g1_flip_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1WallFlipTracking
+  sim_backend: mujoco
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat_with_wall.xml
+  commands:
+    motion:
+      params:
+        motion_file: motions/g1/flip_from_wall_104__A304.npz
+
+reward:
+  motion_joint_pos:
+    weight: 0.5
+  motion_joint_vel:
+    weight: 0.25
diff --git a/src/unilab/conf/ppo/task/go1_joystick_flat/base.yaml b/src/unilab/conf/ppo/task/go1_joystick_flat/base.yaml
new file mode 100644
index 000000000..8a2f3bf57
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go1_joystick_flat/base.yaml
@@ -0,0 +1,244 @@
+# @package _global_
+# Canonical Go1 flat Manager-Based task declaration. Backend owner leaves inherit
+# this file and only override backend/algo tuning or explicitly disabled terms.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/go1/scene_flat.xml
+    default_keyframe_name: home
+    entities:
+      robot:
+        root_body_name: trunk
+        joint_names:
+          - FR_hip_joint
+          - FR_thigh_joint
+          - FR_calf_joint
+          - FL_hip_joint
+          - FL_thigh_joint
+          - FL_calf_joint
+          - RR_hip_joint
+          - RR_thigh_joint
+          - RR_calf_joint
+          - RL_hip_joint
+          - RL_thigh_joint
+          - RL_calf_joint
+        actuator_names:
+          - FR_hip
+          - FR_thigh
+          - FR_calf
+          - FL_hip
+          - FL_thigh
+          - FL_calf
+          - RR_hip
+          - RR_thigh
+          - RR_calf
+          - RL_hip
+          - RL_thigh
+          - RL_calf
+        body_names: [trunk]
+  sim_dt: 0.01
+  ctrl_dt: 0.02
+  max_episode_seconds: 20.0
+  observations:
+    policy:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+        gait_phase:
+          func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase
+          params:
+            frequency: 2.0
+    critic:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+        gait_phase:
+          func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase
+          params:
+            frequency: 2.0
+        base_lin_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: local_linvel
+  actions:
+    joint_pos:
+      _target_: unilab.envs.mdp.JointPositionActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      scale: 0.25
+      use_default_offset: true
+  commands:
+    twist:
+      _target_: unilab.envs.mdp.UniformVelocityCommandCfg
+      entity_name: robot
+      resampling_time_range: [20.0, 20.0]
+      heading_command: false
+      heading_control_stiffness: 0.5
+      rel_standing_envs: 0.0
+      rel_heading_envs: 0.0
+      rel_world_envs: 0.0
+      rel_forward_envs: 0.0
+      init_velocity_prob: 0.0
+      ranges:
+        lin_vel_x: [-0.6, 1.0]
+        lin_vel_y: [-0.4, 0.4]
+        ang_vel_z: [-0.8, 0.8]
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_root_state_uniform:
+      func: unilab.envs.mdp.reset_root_state_uniform
+      mode: reset
+      params:
+        pose_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [-3.141592653589793, 3.141592653589793]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.5, 0.5]
+          roll: [-0.5, 0.5]
+          pitch: [-0.5, 0.5]
+          yaw: [-0.5, 0.5]
+    base_mass:
+      func: unilab.envs.mdp.randomize_rigid_body_mass
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          body_names: trunk
+        mass_distribution_params: [-1.5, 1.5]
+        operation: add
+        recompute_inertia: false
+    base_com:
+      func: unilab.envs.mdp.randomize_rigid_body_com
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          body_names: trunk
+        com_range:
+          x: [-0.05, 0.05]
+          y: [0.0, 0.0]
+          z: [0.0, 0.0]
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        kp_range: [35.0, 35.0]
+        kd_range: [0.5, 0.5]
+        operation: abs
+    push_robot:
+      func: unilab.envs.mdp.push_by_setting_velocity
+      mode: interval
+      interval_range_s: [15.0, 15.0]
+      is_global_time: true
+      params:
+        velocity_range:
+          x: [-1.0, 1.0]
+          y: [-1.0, 1.0]
+          z: [-0.5, 0.5]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [0.0, 0.0]
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    bad_orientation:
+      func: unilab.envs.mdp.bad_orientation
+      params:
+        limit_angle: 1.0471975511965976
+  policy_observation_group: policy
+  critic_observation_group: critic
+
+reward:
+  tracking_lin_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_lin_vel_xy_exp
+    weight: 1.0
+    params:
+      std: 0.5
+      command_name: twist
+  tracking_ang_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_ang_vel_z_exp
+    weight: 0.2
+    params:
+      std: 0.5
+      command_name: twist
+  lin_vel_z:
+    func: unilab.tasks.locomotion.common.manager_terms.lin_vel_z_l2
+    weight: -5.0
+  ang_vel_xy:
+    func: unilab.tasks.locomotion.common.manager_terms.ang_vel_xy_l2
+    weight: -0.1
+  base_height:
+    func: unilab.tasks.locomotion.common.manager_terms.base_height_l2
+    weight: -100.0
+    params:
+      target_height: 0.3
+  action_rate:
+    func: unilab.envs.mdp.action_rate_l2
+    weight: -0.005
+  similar_to_default:
+    func: unilab.tasks.locomotion.common.manager_terms.joint_deviation_l1
+    weight: -0.1
+  contact:
+    func: unilab.tasks.locomotion.common.manager_terms.feet_phase_contact
+    # Legacy Go1 sums four matching feet while this community term returns their mean.
+    weight: 0.96
+    params:
+      frequency: 2.0
+      sensor_names:
+        - FL_foot_contact
+        - FR_foot_contact
+        - RL_foot_contact
+        - RR_foot_contact
+      contact_threshold: 0.1
+      stance_threshold: 0.6
+  swing_feet_z:
+    func: unilab.tasks.locomotion.common.manager_terms.feet_phase_swing_height
+    weight: 4.0
+    params:
+      frequency: 2.0
+      sensor_names: [FL_pos, FR_pos, RL_pos, RR_pos]
+      target_height: 0.1
+      kernel: 0.01
+      swing_start: 0.6
diff --git a/src/unilab/conf/ppo/task/go1_joystick_flat/drake.yaml b/src/unilab/conf/ppo/task/go1_joystick_flat/drake.yaml
new file mode 100644
index 000000000..f1251763b
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go1_joystick_flat/drake.yaml
@@ -0,0 +1,43 @@
+# @package _global_
+defaults:
+  - /task/go1_joystick_flat/base
+  - _self_
+
+training:
+  task_name: Go1JoystickFlat
+  sim_backend: drake
+  play_steps: 500
+  play_env_num: 16
+  render_spacing: 0.0
+  cam_tracking: true
+  cam_tracking_env_idx: 0
+  cam_tracking_extra_envs: 9
+
+interactive:
+  action_mode: policy
+  policy_obs_mode: auto
+  camera_follow_body: true
+  use_env_visual_model: false
+
+algo:
+  num_envs: 1024
+  num_steps_per_env: 24
+  max_iterations: 151
+  save_interval: 100
+  obs_groups:
+    actor:
+      - actor
+    critic:
+      - critic
+
+env:
+  drake_backend_mode: batch
+  drake_nthread: 0
+  events:
+    base_mass: null
+    base_com: null
+    pd_gains: null
+    push_robot: null
+
+reward:
+  contact: null
diff --git a/src/unilab/conf/ppo/task/go1_joystick_flat/motrix.yaml b/src/unilab/conf/ppo/task/go1_joystick_flat/motrix.yaml
new file mode 100644
index 000000000..e05a46d7b
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go1_joystick_flat/motrix.yaml
@@ -0,0 +1,50 @@
+# @package _global_
+defaults:
+  - /task/go1_joystick_flat/base
+  - _self_
+
+training:
+  task_name: Go1JoystickFlat
+  sim_backend: motrix
+  play_steps: 500
+  play_env_num: 16
+  cam_tracking: true
+  cam_tracking_env_idx: 0
+  cam_tracking_extra_envs: 9
+
+interactive:
+  action_mode: policy
+  policy_obs_mode: auto
+  camera_follow_body: true
+  use_env_visual_model: false
+
+algo:
+  num_envs: 1024
+  max_iterations: 151
+  obs_groups:
+    actor:
+      - actor
+    critic:
+      - critic
+  empirical_normalization: true
+  policy:
+    init_noise_std: 0.5
+  algorithm:
+    learning_rate: 3.0e-4
+    entropy_coef: 1.0e-3
+env:
+  commands:
+    twist:
+      ranges:
+        lin_vel_x: [0.5, 0.5]
+        lin_vel_y: [0.0, 0.0]
+        ang_vel_z: [0.0, 0.0]
+  events:
+    # Motrix has no formal root velocity-delta capability; do not fall back to force push.
+    push_robot: null
+reward:
+  contact: null
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/src/unilab/conf/ppo/task/go1_joystick_flat/mujoco.yaml b/src/unilab/conf/ppo/task/go1_joystick_flat/mujoco.yaml
new file mode 100644
index 000000000..867b319b8
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go1_joystick_flat/mujoco.yaml
@@ -0,0 +1,33 @@
+# @package _global_
+defaults:
+  - /task/go1_joystick_flat/base
+  - _self_
+
+training:
+  task_name: Go1JoystickFlat
+  sim_backend: mujoco
+  play_steps: 500
+  play_env_num: 16
+  render_spacing: 0.0
+  cam_tracking: true
+  cam_tracking_env_idx: 0
+  cam_tracking_extra_envs: 9
+
+interactive:
+  action_mode: policy
+  policy_obs_mode: auto
+  camera_follow_body: true
+  use_env_visual_model: false
+
+algo:
+  num_envs: 1024
+  max_iterations: 151
+  obs_groups:
+    actor:
+      - actor
+    critic:
+      - critic
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/src/unilab/conf/ppo/task/go1_joystick_rough/motrix.yaml b/src/unilab/conf/ppo/task/go1_joystick_rough/motrix.yaml
new file mode 100644
index 000000000..0af29a93b
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go1_joystick_rough/motrix.yaml
@@ -0,0 +1,18 @@
+# @package _global_
+defaults:
+  - /task/go1_joystick_rough/mujoco
+  - _self_
+
+training:
+  task_name: Go1JoystickRough
+  sim_backend: motrix
+
+algo:
+  num_envs: 2048
+
+env:
+  render_offset_mode: zero
+  scene:
+    model_file: src/unilab/assets/robots/go1/go1.xml
+  events:
+    push_robot: null
diff --git a/src/unilab/conf/ppo/task/go1_joystick_rough/mujoco.yaml b/src/unilab/conf/ppo/task/go1_joystick_rough/mujoco.yaml
new file mode 100644
index 000000000..db1c3e920
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go1_joystick_rough/mujoco.yaml
@@ -0,0 +1,69 @@
+# @package _global_
+defaults:
+  - /task/quadruped_joystick_rough/quadruped
+  - _self_
+
+training:
+  task_name: Go1JoystickRough
+  sim_backend: mujoco
+  play_steps: 500
+  play_env_num: 16
+  render_spacing: 0.0
+  cam_tracking: true
+  cam_tracking_env_idx: 0
+  cam_tracking_extra_envs: 9
+
+interactive:
+  action_mode: policy
+  policy_obs_mode: auto
+  camera_follow_body: true
+  use_env_visual_model: false
+
+algo:
+  num_envs: 2048
+  num_steps_per_env: 24
+  max_iterations: 1000
+  empirical_normalization: false
+  obs_groups: {actor: [actor], critic: [critic]}
+  policy: {init_noise_std: 1.0}
+  algorithm: {learning_rate: 1.0e-3, entropy_coef: 1.0e-2}
+
+env:
+  sim_dt: 0.005
+  scene:
+    model_file: src/unilab/assets/robots/go1/go1_mujoco.xml
+    fragment_files: [src/unilab/assets/robots/go1/locomotion_task.xml]
+    entities:
+      robot:
+        root_body_name: trunk
+        joint_names:
+          - FR_hip_joint
+          - FR_thigh_joint
+          - FR_calf_joint
+          - FL_hip_joint
+          - FL_thigh_joint
+          - FL_calf_joint
+          - RR_hip_joint
+          - RR_thigh_joint
+          - RR_calf_joint
+          - RL_hip_joint
+          - RL_thigh_joint
+          - RL_calf_joint
+        actuator_names:
+          - FR_hip
+          - FR_thigh
+          - FR_calf
+          - FL_hip
+          - FL_thigh
+          - FL_calf
+          - RR_hip
+          - RR_thigh
+          - RR_calf
+          - RL_hip
+          - RL_thigh
+          - RL_calf
+        body_names: [trunk]
+
+play_profile:
+  enabled: true
+  env: {render_spacing: 2.0}
diff --git a/conf/ppo/task/go2_arm_manip_loco/motrix.yaml b/src/unilab/conf/ppo/task/go2_arm_manip_loco/motrix.yaml
similarity index 100%
rename from conf/ppo/task/go2_arm_manip_loco/motrix.yaml
rename to src/unilab/conf/ppo/task/go2_arm_manip_loco/motrix.yaml
diff --git a/conf/ppo/task/go2_arm_manip_loco/mujoco.yaml b/src/unilab/conf/ppo/task/go2_arm_manip_loco/mujoco.yaml
similarity index 100%
rename from conf/ppo/task/go2_arm_manip_loco/mujoco.yaml
rename to src/unilab/conf/ppo/task/go2_arm_manip_loco/mujoco.yaml
diff --git a/src/unilab/conf/ppo/task/go2_footstand/base.yaml b/src/unilab/conf/ppo/task/go2_footstand/base.yaml
new file mode 100644
index 000000000..5478fc5b5
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go2_footstand/base.yaml
@@ -0,0 +1,217 @@
+# @package _global_
+# Canonical Go2 footstand Manager-Based declaration. Hydra owns every task term;
+# backend leaves only select identity, tuning, and supported reset capabilities.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/go2/scene_flat.xml
+    default_keyframe_name: home
+    entities:
+      robot:
+        root_body_name: base
+        joint_names:
+          - FL_hip_joint
+          - FL_thigh_joint
+          - FL_calf_joint
+          - FR_hip_joint
+          - FR_thigh_joint
+          - FR_calf_joint
+          - RL_hip_joint
+          - RL_thigh_joint
+          - RL_calf_joint
+          - RR_hip_joint
+          - RR_thigh_joint
+          - RR_calf_joint
+        body_names:
+          - base
+          - FL_hip
+          - FL_thigh
+          - FL_calf
+          - FR_hip
+          - FR_thigh
+          - FR_calf
+          - RL_hip
+          - RL_thigh
+          - RL_calf
+          - RR_hip
+          - RR_thigh
+          - RR_calf
+        geom_names: [floor]
+        actuator_names:
+          - FR_hip
+          - FR_thigh
+          - FR_calf
+          - FL_hip
+          - FL_thigh
+          - FL_calf
+          - RR_hip
+          - RR_thigh
+          - RR_calf
+          - RL_hip
+          - RL_thigh
+          - RL_calf
+  sim_dt: 0.004
+  ctrl_dt: 0.02
+  max_episode_seconds: 10.0
+  adaptive_chunk_size: false
+  observations:
+    policy:
+      enable_corruption: true
+      terms:
+        frame:
+          func: unilab.tasks.locomotion.go2.footstand.frame_observation
+          params:
+            action_name: joint_pos
+          noise:
+            _target_: UniformNoiseCfg
+            n_min: [-0.1, -0.1, -0.1, -0.2, -0.2, -0.2, -0.05, -0.05, -0.05, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
+            n_max: [0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.05, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
+          history_length: 15
+    critic:
+      terms:
+        frame:
+          func: unilab.tasks.locomotion.go2.footstand.frame_observation
+          params:
+            action_name: joint_pos
+          history_length: 15
+        privileged:
+          func: unilab.tasks.locomotion.go2.footstand.privileged_observation
+          params:
+            action_name: joint_pos
+  actions:
+    joint_pos:
+      _target_: unilab.tasks.locomotion.go2.footstand.FootstandIncrementalActionCfg
+      entity_name: robot
+      actuator_names:
+        - FR_hip
+        - FR_thigh
+        - FR_calf
+        - FL_hip
+        - FL_thigh
+        - FL_calf
+        - RR_hip
+        - RR_thigh
+        - RR_calf
+        - RL_hip
+        - RL_thigh
+        - RL_calf
+      joint_names:
+        - FR_hip_joint
+        - FR_thigh_joint
+        - FR_calf_joint
+        - FL_hip_joint
+        - FL_thigh_joint
+        - FL_calf_joint
+        - RR_hip_joint
+        - RR_thigh_joint
+        - RR_calf_joint
+        - RL_hip_joint
+        - RL_thigh_joint
+        - RL_calf_joint
+      joint_position_limits:
+        - [-1.0472, 1.0472]
+        - [-1.5708, 3.4907]
+        - [-2.7227, -0.83776]
+        - [-1.0472, 1.0472]
+        - [-1.5708, 3.4907]
+        - [-2.7227, -0.83776]
+        - [-1.0472, 1.0472]
+        - [-0.5236, 4.5379]
+        - [-2.7227, -0.83776]
+        - [-1.0472, 1.0472]
+        - [-0.5236, 4.5379]
+        - [-2.7227, -0.83776]
+      action_scale: 0.3
+      clip_actions: 1.0
+      kp: 35.0
+      kd: 0.5
+      simulate_action_latency: false
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_root_state_uniform:
+      func: unilab.envs.mdp.reset_root_state_uniform
+      mode: reset
+      params:
+        pose_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [-3.141592653589793, 3.141592653589793]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.5, 0.5]
+          roll: [-0.5, 0.5]
+          pitch: [-0.5, 0.5]
+          yaw: [-0.5, 0.5]
+    reset_joints:
+      func: unilab.tasks.locomotion.go2.footstand.FootstandJointReset
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          joint_names: ".*"
+        position_offset_range: [-0.05, 0.05]
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        kp_range: [35.0, 35.0]
+        kd_range: [0.5, 0.5]
+        operation: abs
+    floor_friction: null
+    link_mass: null
+    torso_com: null
+    joint_armature: null
+  terminations:
+    footstand:
+      func: unilab.tasks.locomotion.go2.footstand.FootstandTermination
+      params:
+        action_name: joint_pos
+        grace_steps: 100
+        height_fraction: 0.8
+        orientation_threshold: 0.2
+        energy_threshold: 200.0
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+  policy_observation_group: policy
+  critic_observation_group: critic
+  scale_rewards_by_dt: true
+
+reward:
+  footstand:
+    func: unilab.tasks.locomotion.go2.footstand.FootstandReward
+    weight: 1.0
+    params:
+      state_term_name: footstand
+      scales:
+        height: 2.0
+        orientation: 2.0
+        contact: -1.0
+        action_rate: -0.01
+        termination: -2.0
+        dof_pos_limits: -0.5
+        torques: 0.0
+        pose: -0.1
+        penalty_contact: -0.2
+        tar: 0.8
+        rear_feet_contact: 0.5
+        rear_leg_symmetry: -0.2
+        front_leg_motion: -0.05
+        upright_stability: -0.2
+        knee_clearance: -0.5
+        stay_still: -0.1
+        energy: -0.003
+        dof_acc: -2.5e-7
+      soft_joint_pos_limit_factor: 0.9
+      knee_height_target: 0.08
+      front_feet_min_separation: 0.16
+      front_feet_side_margin: 0.04
+      rear_hip_abduction_margin: 0.25
+      rear_foot_slip_deadband: 0.02
+      rear_foot_anchor_radius: 0.03
diff --git a/src/unilab/conf/ppo/task/go2_footstand/drake.yaml b/src/unilab/conf/ppo/task/go2_footstand/drake.yaml
new file mode 100644
index 000000000..3d7c02a40
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go2_footstand/drake.yaml
@@ -0,0 +1,33 @@
+# @package _global_
+defaults:
+  - /task/go2_footstand/base
+  - _self_
+
+training:
+  task_name: Go2FootStand
+  sim_backend: drake
+
+env:
+  drake_backend_mode: batch
+  drake_nthread: 0
+  events:
+    # Drake supports the joint-state reset, but not these reset payload fields.
+    pd_gains: null
+    floor_friction: null
+    link_mass: null
+    torso_com: null
+    joint_armature: null
+
+algo:
+  empirical_normalization: true
+  num_envs: 4096
+  max_iterations: 10000
+  obs_groups:
+    actor:
+      - actor
+    critic:
+      - critic
+  policy:
+    init_noise_std: 0.5
+  algorithm:
+    entropy_coef: 0.005
diff --git a/src/unilab/conf/ppo/task/go2_footstand/motrix.yaml b/src/unilab/conf/ppo/task/go2_footstand/motrix.yaml
new file mode 100644
index 000000000..c53005fbd
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go2_footstand/motrix.yaml
@@ -0,0 +1,69 @@
+# @package _global_
+defaults:
+  - /task/go2_footstand/base
+  - _self_
+
+training:
+  task_name: Go2FootStand
+  sim_backend: motrix
+  no_play: true
+
+env:
+  events:
+    reset_joints:
+      params:
+        position_offset_range: [-0.02, 0.02]
+    # Unsupported model-field payloads remain explicitly disabled for Motrix.
+    floor_friction: null
+    link_mass: null
+    torso_com: null
+    joint_armature: null
+
+algo:
+  empirical_normalization: true
+  num_envs: 4096
+  max_iterations: 10000
+  obs_groups:
+    actor:
+      - actor
+    critic:
+      - critic
+  policy:
+    init_noise_std: 0.5
+  algorithm:
+    entropy_coef: 0.005
+
+reward:
+  footstand:
+    params:
+      scales:
+        height: 2.0
+        orientation: 3.0
+        contact: -1.0
+        action_rate: -0.01
+        termination: -2.0
+        dof_pos_limits: -0.5
+        torques: 0.0
+        pose: -0.1
+        penalty_contact: -0.2
+        tar: 1.3
+        rear_feet_contact: 0.5
+        both_rear_feet_contact: 0.25
+        rear_foot_slip: -1.0
+        rear_foot_anchor: -0.15
+        front_feet_air: 0.0
+        balanced_footstand: 0.0
+        rear_leg_symmetry: -0.2
+        rear_leg_splay: -0.25
+        front_leg_motion: -0.06
+        front_leg_crossing: -2.0
+        upright_stability: -0.2
+        knee_clearance: -0.5
+        stay_still: -0.12
+        energy: -0.003
+        dof_acc: -2.5e-7
+      front_feet_min_separation: 0.18
+      front_feet_side_margin: 0.06
+      rear_hip_abduction_margin: 0.25
+      rear_foot_slip_deadband: 0.012
+      rear_foot_anchor_radius: 0.04
diff --git a/src/unilab/conf/ppo/task/go2_footstand/mujoco.yaml b/src/unilab/conf/ppo/task/go2_footstand/mujoco.yaml
new file mode 100644
index 000000000..8a4b428cc
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go2_footstand/mujoco.yaml
@@ -0,0 +1,68 @@
+# @package _global_
+defaults:
+  - /task/go2_footstand/base
+  - _self_
+
+training:
+  task_name: Go2FootStand
+  sim_backend: mujoco
+
+env:
+  events:
+    floor_friction:
+      func: unilab.envs.mdp.geom_friction
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          geom_names: floor
+        ranges: [0.4, 1.0]
+        operation: abs
+    link_mass:
+      func: unilab.tasks.locomotion.go2.footstand.FootstandMassRandomization
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          body_names: ".*"
+        torso_body_name: base
+        link_mass_scale_range: [0.9, 1.1]
+        torso_added_mass_range: [-1.0, 1.0]
+    torso_com:
+      func: unilab.envs.mdp.randomize_rigid_body_com
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          body_names: base
+        com_range:
+          x: [-0.05, 0.05]
+          y: [-0.05, 0.05]
+          z: [-0.05, 0.05]
+    joint_armature:
+      func: unilab.envs.mdp.joint_armature
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          joint_names: ".*"
+        ranges: [1.0, 1.05]
+        operation: scale
+
+algo:
+  empirical_normalization: true
+  num_envs: 4096
+  max_iterations: 10000
+  obs_groups:
+    actor:
+      - actor
+    critic:
+      - critic
+  policy:
+    init_noise_std: 0.5
+  algorithm:
+    entropy_coef: 0.005
diff --git a/src/unilab/conf/ppo/task/go2_joystick_flat/base.yaml b/src/unilab/conf/ppo/task/go2_joystick_flat/base.yaml
new file mode 100644
index 000000000..409129c16
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go2_joystick_flat/base.yaml
@@ -0,0 +1,206 @@
+# @package _global_
+# Canonical Go2 flat Manager-Based task declaration. Backend owner leaves inherit
+# this file and only override backend/algo tuning or explicitly disabled terms.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/go2/scene_flat.xml
+    default_keyframe_name: home
+    entities:
+      robot:
+        root_body_name: base
+        joint_names:
+          - FL_hip_joint
+          - FL_thigh_joint
+          - FL_calf_joint
+          - FR_hip_joint
+          - FR_thigh_joint
+          - FR_calf_joint
+          - RL_hip_joint
+          - RL_thigh_joint
+          - RL_calf_joint
+          - RR_hip_joint
+          - RR_thigh_joint
+          - RR_calf_joint
+        actuator_names:
+          - FR_hip
+          - FR_thigh
+          - FR_calf
+          - FL_hip
+          - FL_thigh
+          - FL_calf
+          - RR_hip
+          - RR_thigh
+          - RR_calf
+          - RL_hip
+          - RL_thigh
+          - RL_calf
+  sim_dt: 0.01
+  ctrl_dt: 0.02
+  max_episode_seconds: 20.0
+  observations:
+    policy:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+        gait_phase:
+          func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase
+          params:
+            frequency: 2.0
+    critic:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+        gait_phase:
+          func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase
+          params:
+            frequency: 2.0
+        base_lin_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: local_linvel
+  actions:
+    joint_pos:
+      _target_: unilab.envs.mdp.JointPositionActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      scale: 0.25
+      use_default_offset: true
+  commands:
+    twist:
+      _target_: unilab.envs.mdp.UniformVelocityCommandCfg
+      entity_name: robot
+      resampling_time_range: [20.0, 20.0]
+      heading_command: false
+      heading_control_stiffness: 0.5
+      rel_standing_envs: 0.0
+      rel_heading_envs: 0.0
+      rel_world_envs: 0.0
+      rel_forward_envs: 0.0
+      init_velocity_prob: 0.0
+      ranges:
+        lin_vel_x: [-0.6, 1.0]
+        lin_vel_y: [-0.4, 0.4]
+        ang_vel_z: [-0.8, 0.8]
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_root_state_uniform:
+      func: unilab.envs.mdp.reset_root_state_uniform
+      mode: reset
+      params:
+        pose_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [-3.141592653589793, 3.141592653589793]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.5, 0.5]
+          roll: [-0.5, 0.5]
+          pitch: [-0.5, 0.5]
+          yaw: [-0.5, 0.5]
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        kp_range: [31.5, 38.5]
+        kd_range: [0.45, 0.55]
+        operation: abs
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    bad_orientation:
+      func: unilab.envs.mdp.bad_orientation
+      params:
+        limit_angle: 1.0471975511965976
+  policy_observation_group: policy
+  critic_observation_group: critic
+
+reward:
+  tracking_lin_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_lin_vel_xy_exp
+    weight: 1.0
+    params:
+      std: 0.5
+      command_name: twist
+  tracking_ang_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_ang_vel_z_exp
+    weight: 0.2
+    params:
+      std: 0.5
+      command_name: twist
+  lin_vel_z:
+    func: unilab.tasks.locomotion.common.manager_terms.lin_vel_z_l2
+    weight: -5.0
+  ang_vel_xy:
+    func: unilab.tasks.locomotion.common.manager_terms.ang_vel_xy_l2
+    weight: -0.1
+  base_height:
+    func: unilab.tasks.locomotion.common.manager_terms.base_height_l2
+    weight: -100.0
+    params:
+      target_height: 0.3
+  action_rate:
+    func: unilab.envs.mdp.action_rate_l2
+    weight: -0.005
+  similar_to_default:
+    func: unilab.tasks.locomotion.common.manager_terms.joint_deviation_l1
+    weight: -0.1
+  contact:
+    func: unilab.tasks.locomotion.common.manager_terms.feet_phase_contact
+    weight: 0.24
+    params:
+      frequency: 2.0
+      sensor_names:
+        - FL_foot_contact
+        - FR_foot_contact
+        - RL_foot_contact
+        - RR_foot_contact
+      contact_threshold: 0.1
+      stance_threshold: 0.6
+  swing_feet_z:
+    func: unilab.tasks.locomotion.common.manager_terms.feet_phase_swing_height
+    weight: 4.0
+    params:
+      frequency: 2.0
+      sensor_names: [FL_pos, FR_pos, RL_pos, RR_pos]
+      target_height: 0.1
+      kernel: 0.01
+      swing_start: 0.6
diff --git a/src/unilab/conf/ppo/task/go2_joystick_flat/drake.yaml b/src/unilab/conf/ppo/task/go2_joystick_flat/drake.yaml
new file mode 100644
index 000000000..fc9554cad
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go2_joystick_flat/drake.yaml
@@ -0,0 +1,40 @@
+# @package _global_
+defaults:
+  - /task/go2_joystick_flat/base
+  - _self_
+
+training:
+  task_name: Go2JoystickFlat
+  sim_backend: drake
+  # Drake batch currently exposes NumPy float64 buffers and no floating-root
+  # reset contract; keep the owner on CPU and use the scene keyframe reset.
+  device: cpu
+
+algo:
+  num_envs: 1024
+  max_iterations: 151
+  empirical_normalization: true
+  obs_groups:
+    actor:
+      - actor
+    critic:
+      - critic
+  policy:
+    init_noise_std: 0.5
+  algorithm:
+    learning_rate: 3.0e-4
+    entropy_coef: 1.0e-3
+
+env:
+  drake_backend_mode: batch
+  drake_nthread: 0
+  events:
+    reset_root_state_uniform: null
+    pd_gains: null
+
+reward:
+  # drake_uni reports per-body net contact force in the world frame, not the
+  # MuJoCo contact frame (normal first) the gait terms gate on; keep the
+  # contact reward off until the drake adapter reports contact-frame force
+  # (issue #1471).
+  contact: null
diff --git a/src/unilab/conf/ppo/task/go2_joystick_flat/motrix.yaml b/src/unilab/conf/ppo/task/go2_joystick_flat/motrix.yaml
new file mode 100644
index 000000000..84282c9cb
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go2_joystick_flat/motrix.yaml
@@ -0,0 +1,35 @@
+# @package _global_
+defaults:
+  - /task/go2_joystick_flat/base
+  - _self_
+
+training:
+  task_name: Go2JoystickFlat
+  sim_backend: motrix
+algo:
+  num_envs: 1024
+  max_iterations: 151
+  empirical_normalization: true
+  obs_groups:
+    actor:
+      - actor
+    critic:
+      - critic
+  policy:
+    init_noise_std: 0.5
+  algorithm:
+    learning_rate: 3.0e-4
+    entropy_coef: 1.0e-3
+env:
+  commands:
+    twist:
+      ranges:
+        lin_vel_x: [0.5, 0.5]
+        lin_vel_y: [0.0, 0.0]
+        ang_vel_z: [0.0, 0.0]
+  events:
+    pd_gains: null
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/src/unilab/conf/ppo/task/go2_joystick_flat/mujoco.yaml b/src/unilab/conf/ppo/task/go2_joystick_flat/mujoco.yaml
new file mode 100644
index 000000000..4201cf978
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go2_joystick_flat/mujoco.yaml
@@ -0,0 +1,26 @@
+# @package _global_
+defaults:
+  - /task/go2_joystick_flat/base
+  - _self_
+
+training:
+  task_name: Go2JoystickFlat
+  sim_backend: mujoco
+algo:
+  num_envs: 1024
+  max_iterations: 151
+  empirical_normalization: true
+  obs_groups:
+    actor:
+      - actor
+    critic:
+      - critic
+  policy:
+    init_noise_std: 0.5
+  algorithm:
+    learning_rate: 3.0e-4
+    entropy_coef: 1.0e-3
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/src/unilab/conf/ppo/task/go2_joystick_rough/motrix.yaml b/src/unilab/conf/ppo/task/go2_joystick_rough/motrix.yaml
new file mode 100644
index 000000000..a35a5cbc5
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go2_joystick_rough/motrix.yaml
@@ -0,0 +1,18 @@
+# @package _global_
+defaults:
+  - /task/go2_joystick_rough/mujoco
+  - _self_
+
+training:
+  task_name: Go2JoystickRough
+  sim_backend: motrix
+
+algo:
+  num_envs: 4096
+
+env:
+  render_offset_mode: zero
+  scene:
+    model_file: src/unilab/assets/robots/go2/go2.xml
+  events:
+    push_robot: null
diff --git a/src/unilab/conf/ppo/task/go2_joystick_rough/mujoco.yaml b/src/unilab/conf/ppo/task/go2_joystick_rough/mujoco.yaml
new file mode 100644
index 000000000..5d3622fdc
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go2_joystick_rough/mujoco.yaml
@@ -0,0 +1,69 @@
+# @package _global_
+defaults:
+  - /task/quadruped_joystick_rough/quadruped
+  - _self_
+
+training:
+  task_name: Go2JoystickRough
+  sim_backend: mujoco
+  play_steps: 500
+  play_env_num: 16
+  render_spacing: 0.0
+  cam_tracking: true
+  cam_tracking_env_idx: 0
+  cam_tracking_extra_envs: 9
+
+interactive:
+  action_mode: policy
+  policy_obs_mode: auto
+  camera_follow_body: true
+  use_env_visual_model: false
+
+algo:
+  num_envs: 2048
+  num_steps_per_env: 24
+  max_iterations: 1500
+  empirical_normalization: false
+  obs_groups: {actor: [actor], critic: [critic]}
+  policy: {init_noise_std: 1.0}
+  algorithm: {learning_rate: 1.0e-3, entropy_coef: 1.0e-2}
+
+env:
+  sim_dt: 0.002
+  scene:
+    model_file: src/unilab/assets/robots/go2/go2_mujoco.xml
+    fragment_files: [src/unilab/assets/robots/go2/locomotion_task.xml]
+    entities:
+      robot:
+        root_body_name: base
+        joint_names:
+          - FR_hip_joint
+          - FR_thigh_joint
+          - FR_calf_joint
+          - FL_hip_joint
+          - FL_thigh_joint
+          - FL_calf_joint
+          - RR_hip_joint
+          - RR_thigh_joint
+          - RR_calf_joint
+          - RL_hip_joint
+          - RL_thigh_joint
+          - RL_calf_joint
+        actuator_names:
+          - FR_hip
+          - FR_thigh
+          - FR_calf
+          - FL_hip
+          - FL_thigh
+          - FL_calf
+          - RR_hip
+          - RR_thigh
+          - RR_calf
+          - RL_hip
+          - RL_thigh
+          - RL_calf
+        body_names: [base]
+
+play_profile:
+  enabled: true
+  env: {render_spacing: 2.0}
diff --git a/src/unilab/conf/ppo/task/go2w_joystick_flat/base.yaml b/src/unilab/conf/ppo/task/go2w_joystick_flat/base.yaml
new file mode 100644
index 000000000..3d057e1ee
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go2w_joystick_flat/base.yaml
@@ -0,0 +1,263 @@
+# @package _global_
+# Canonical Go2W flat Manager-Based task declaration. Backend leaves only own
+# backend identity and backend-specific rendering/runtime settings.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/go2w/scene_flat.xml
+    default_keyframe_name: home
+    entities:
+      robot:
+        root_body_name: base_link
+        joint_names:
+          - FR_hip_joint
+          - FR_thigh_joint
+          - FR_calf_joint
+          - FL_hip_joint
+          - FL_thigh_joint
+          - FL_calf_joint
+          - RR_hip_joint
+          - RR_thigh_joint
+          - RR_calf_joint
+          - RL_hip_joint
+          - RL_thigh_joint
+          - RL_calf_joint
+          - FR_wheel_joint
+          - FL_wheel_joint
+          - RR_wheel_joint
+          - RL_wheel_joint
+        actuator_names:
+          - FR_hip
+          - FR_thigh
+          - FR_calf
+          - FL_hip
+          - FL_thigh
+          - FL_calf
+          - RR_hip
+          - RR_thigh
+          - RR_calf
+          - RL_hip
+          - RL_thigh
+          - RL_calf
+          - FR_wheel
+          - FL_wheel
+          - RR_wheel
+          - RL_wheel
+        body_names: [base_link]
+  sim_dt: 0.005
+  ctrl_dt: 0.02
+  max_episode_seconds: 20.0
+  observations:
+    policy:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        leg_joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+          params:
+            asset_cfg:
+              _target_: SceneEntityCfg
+              name: robot
+              joint_names: ".*_(hip|thigh|calf)_joint"
+        leg_joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+          params:
+            asset_cfg:
+              _target_: SceneEntityCfg
+              name: robot
+              joint_names: ".*_(hip|thigh|calf)_joint"
+        wheel_joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+          params:
+            asset_cfg:
+              _target_: SceneEntityCfg
+              name: robot
+              joint_names: ".*_wheel_joint"
+        actions:
+          func: unilab.envs.mdp.last_action
+          params:
+            action_name: motor
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+    critic:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        leg_joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+          params:
+            asset_cfg:
+              _target_: SceneEntityCfg
+              name: robot
+              joint_names: ".*_(hip|thigh|calf)_joint"
+        leg_joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+          params:
+            asset_cfg:
+              _target_: SceneEntityCfg
+              name: robot
+              joint_names: ".*_(hip|thigh|calf)_joint"
+        wheel_joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+          params:
+            asset_cfg:
+              _target_: SceneEntityCfg
+              name: robot
+              joint_names: ".*_wheel_joint"
+        actions:
+          func: unilab.envs.mdp.last_action
+          params:
+            action_name: motor
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+        base_lin_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: local_linvel
+        motor_torque:
+          func: unilab.tasks.locomotion.go2w.manager_terms.motor_torque
+          params:
+            action_name: motor
+  actions:
+    motor:
+      _target_: unilab.tasks.locomotion.go2w.manager_terms.Go2WMixedActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      leg_action_scale: 0.5
+      wheel_action_scale: 10.0
+      leg_kp: 50.0
+      leg_kd: 1.5
+      wheel_kd: 0.5
+      clip_actions: 1.0
+      simulate_action_latency: false
+  commands:
+    twist:
+      _target_: unilab.tasks.locomotion.go2w.manager_terms.Go2WVelocityCommandCfg
+      entity_name: robot
+      resampling_time_range: [20.0, 20.0]
+      heading_command: false
+      heading_control_stiffness: 0.5
+      rel_standing_envs: 0.0
+      rel_heading_envs: 0.0
+      rel_world_envs: 0.0
+      rel_forward_envs: 0.0
+      init_velocity_prob: 0.0
+      planar_dead_zone: 0.2
+      ranges:
+        lin_vel_x: [0.0, 1.0]
+        lin_vel_y: [0.0, 0.0]
+        ang_vel_z: [-1.0, 1.0]
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_root_state_uniform:
+      func: unilab.envs.mdp.reset_root_state_uniform
+      mode: reset
+      params:
+        pose_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [-3.141592653589793, 3.141592653589793]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.5, 0.5]
+          roll: [-0.5, 0.5]
+          pitch: [-0.5, 0.5]
+          yaw: [-0.5, 0.5]
+    motor_gains:
+      func: unilab.tasks.locomotion.go2w.manager_terms.randomize_motor_gains
+      mode: reset
+      params:
+        action_name: motor
+        kp_multiplier_range: [1.0, 1.0]
+        kd_multiplier_range: [1.0, 1.0]
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    bad_orientation:
+      func: unilab.envs.mdp.bad_orientation
+      params:
+        limit_angle: 1.0471975511965976
+  policy_observation_group: policy
+  critic_observation_group: critic
+
+reward:
+  tracking_lin_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_lin_vel_xy_exp
+    weight: 1.0
+    params:
+      std: 0.5
+      command_name: twist
+  tracking_ang_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_ang_vel_z_exp
+    weight: 0.75
+    params:
+      std: 0.5
+      command_name: twist
+  lin_vel_z:
+    func: unilab.tasks.locomotion.common.manager_terms.lin_vel_z_l2
+    weight: -5.0
+  ang_vel_xy:
+    func: unilab.tasks.locomotion.common.manager_terms.ang_vel_xy_l2
+    weight: -0.1
+  base_height:
+    func: unilab.tasks.locomotion.common.manager_terms.base_height_l2
+    weight: -100.0
+    params:
+      target_height: 0.4
+  orientation:
+    func: unilab.envs.mdp.flat_orientation_l2
+    weight: -2.0
+  action_rate:
+    func: unilab.tasks.locomotion.go2w.manager_terms.clipped_action_rate_l2
+    weight: -0.005
+    params:
+      action_name: motor
+  similar_to_default:
+    func: unilab.tasks.locomotion.common.manager_terms.joint_deviation_l1
+    weight: -0.5
+    params:
+      asset_cfg:
+        _target_: SceneEntityCfg
+        name: robot
+        joint_names: ".*_(hip|thigh|calf)_joint"
+  torques:
+    func: unilab.tasks.locomotion.go2w.manager_terms.motor_torque_l2
+    weight: -0.0002
+    params:
+      action_name: motor
+  wheel_vel:
+    func: unilab.envs.mdp.joint_vel_l2
+    weight: 0.0
+    params:
+      asset_cfg:
+        _target_: SceneEntityCfg
+        name: robot
+        joint_names: ".*_wheel_joint"
+  alive:
+    func: unilab.tasks.locomotion.go2w.manager_terms.constant_alive
+    weight: 0.5
+  upward:
+    func: unilab.tasks.locomotion.go2w.manager_terms.upward_l2
+    weight: 1.0
diff --git a/src/unilab/conf/ppo/task/go2w_joystick_flat/drake.yaml b/src/unilab/conf/ppo/task/go2w_joystick_flat/drake.yaml
new file mode 100644
index 000000000..ddbbc1bfa
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go2w_joystick_flat/drake.yaml
@@ -0,0 +1,27 @@
+# @package _global_
+defaults:
+  - /task/go2w_joystick_flat/base
+  - _self_
+
+training:
+  task_name: Go2WJoystickFlat
+  sim_backend: drake
+
+algo:
+  num_envs: 1024
+  max_iterations: 151
+  empirical_normalization: true
+  obs_groups:
+    actor:
+      - actor
+    critic:
+      - critic
+  policy:
+    init_noise_std: 0.5
+  algorithm:
+    learning_rate: 3.0e-4
+    entropy_coef: 1.0e-3
+
+env:
+  drake_backend_mode: batch
+  drake_nthread: 0
diff --git a/src/unilab/conf/ppo/task/go2w_joystick_flat/motrix.yaml b/src/unilab/conf/ppo/task/go2w_joystick_flat/motrix.yaml
new file mode 100644
index 000000000..01af0050d
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go2w_joystick_flat/motrix.yaml
@@ -0,0 +1,28 @@
+# @package _global_
+defaults:
+  - /task/go2w_joystick_flat/base
+  - _self_
+
+training:
+  task_name: Go2WJoystickFlat
+  sim_backend: motrix
+algo:
+  num_envs: 1024
+  max_iterations: 151
+  empirical_normalization: true
+  obs_groups:
+    actor:
+      - actor
+    critic:
+      - critic
+  policy:
+    init_noise_std: 0.5
+  algorithm:
+    learning_rate: 3.0e-4
+    entropy_coef: 1.0e-3
+env:
+  render_offset_mode: zero
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/src/unilab/conf/ppo/task/go2w_joystick_flat/mujoco.yaml b/src/unilab/conf/ppo/task/go2w_joystick_flat/mujoco.yaml
new file mode 100644
index 000000000..3e092334c
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go2w_joystick_flat/mujoco.yaml
@@ -0,0 +1,26 @@
+# @package _global_
+defaults:
+  - /task/go2w_joystick_flat/base
+  - _self_
+
+training:
+  task_name: Go2WJoystickFlat
+  sim_backend: mujoco
+algo:
+  num_envs: 1024
+  max_iterations: 151
+  empirical_normalization: true
+  obs_groups:
+    actor:
+      - actor
+    critic:
+      - critic
+  policy:
+    init_noise_std: 0.5
+  algorithm:
+    learning_rate: 3.0e-4
+    entropy_coef: 1.0e-3
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/src/unilab/conf/ppo/task/go2w_joystick_rough/motrix.yaml b/src/unilab/conf/ppo/task/go2w_joystick_rough/motrix.yaml
new file mode 100644
index 000000000..0f4fcbb21
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go2w_joystick_rough/motrix.yaml
@@ -0,0 +1,19 @@
+# @package _global_
+defaults:
+  - /task/go2w_joystick_rough/mujoco
+  - _self_
+
+training:
+  task_name: Go2WJoystickRough
+  sim_backend: motrix
+
+env:
+  render_offset_mode: zero
+  scene:
+    model_file: src/unilab/assets/robots/go2w/go2w.xml
+  events:
+    push_robot: null
+
+reward:
+  hip_pos:
+    weight: -0.5
diff --git a/src/unilab/conf/ppo/task/go2w_joystick_rough/mujoco.yaml b/src/unilab/conf/ppo/task/go2w_joystick_rough/mujoco.yaml
new file mode 100644
index 000000000..f0c77718c
--- /dev/null
+++ b/src/unilab/conf/ppo/task/go2w_joystick_rough/mujoco.yaml
@@ -0,0 +1,78 @@
+# @package _global_
+defaults:
+  - /task/quadruped_joystick_rough/go2w
+  - _self_
+
+training:
+  task_name: Go2WJoystickRough
+  sim_backend: mujoco
+  play_steps: 500
+  play_env_num: 16
+  render_spacing: 0.0
+  cam_tracking: true
+  cam_tracking_env_idx: 0
+  cam_tracking_extra_envs: 9
+
+interactive:
+  action_mode: policy
+  policy_obs_mode: auto
+  camera_follow_body: true
+  use_env_visual_model: false
+
+algo:
+  num_envs: 2048
+  num_steps_per_env: 24
+  max_iterations: 1200
+  empirical_normalization: false
+  obs_groups: {actor: [actor], critic: [critic]}
+
+env:
+  sim_dt: 0.005
+  scene:
+    model_file: src/unilab/assets/robots/go2w/go2w_mujoco.xml
+    fragment_files: [src/unilab/assets/robots/go2w/locomotion_task.xml]
+    terrain:
+      generator:
+        horizontal_scale: 0.1
+    entities:
+      robot:
+        root_body_name: base_link
+        joint_names:
+          - FR_hip_joint
+          - FR_thigh_joint
+          - FR_calf_joint
+          - FL_hip_joint
+          - FL_thigh_joint
+          - FL_calf_joint
+          - RR_hip_joint
+          - RR_thigh_joint
+          - RR_calf_joint
+          - RL_hip_joint
+          - RL_thigh_joint
+          - RL_calf_joint
+          - FR_wheel_joint
+          - FL_wheel_joint
+          - RR_wheel_joint
+          - RL_wheel_joint
+        actuator_names:
+          - FR_hip
+          - FR_thigh
+          - FR_calf
+          - FL_hip
+          - FL_thigh
+          - FL_calf
+          - RR_hip
+          - RR_thigh
+          - RR_calf
+          - RL_hip
+          - RL_thigh
+          - RL_calf
+          - FR_wheel
+          - FL_wheel
+          - RR_wheel
+          - RL_wheel
+        body_names: [base_link]
+
+play_profile:
+  enabled: true
+  env: {render_spacing: 2.0}
diff --git a/src/unilab/conf/ppo/task/quadruped_joystick_rough/base.yaml b/src/unilab/conf/ppo/task/quadruped_joystick_rough/base.yaml
new file mode 100644
index 000000000..c916ccd63
--- /dev/null
+++ b/src/unilab/conf/ppo/task/quadruped_joystick_rough/base.yaml
@@ -0,0 +1,152 @@
+# @package _global_
+# Shared terrain/reset/command owner for all production rough quadrupeds.
+env:
+  scene:
+    default_keyframe_name: home
+    terrain:
+      hfield_name: terrain_hfield
+      geom_name: floor
+      generator:
+        _target_: unilab.tasks.locomotion.common.rough_manager_terms.QuadrupedRoughTerrainCfg
+        seed: 42
+        curriculum: false
+        size: [8.0, 8.0]
+        num_rows: 6
+        num_cols: 6
+        border_width: 20.0
+  ctrl_dt: 0.02
+  max_episode_seconds: 20.0
+  commands:
+    twist:
+      _target_: unilab.tasks.locomotion.common.rough_manager_terms.RoughVelocityCommandCfg
+      entity_name: robot
+      resampling_time_range: [10.0, 10.0]
+      heading_command: true
+      heading_control_stiffness: 0.5
+      rel_standing_envs: 0.1
+      rel_heading_envs: 1.0
+      rel_world_envs: 0.0
+      rel_forward_envs: 0.0
+      init_velocity_prob: 0.0
+      planar_dead_zone: 0.08
+      ranges:
+        lin_vel_x: [-1.0, 1.0]
+        lin_vel_y: [-1.0, 1.0]
+        ang_vel_z: [-1.0, 1.0]
+        heading: [-3.141592653589793, 3.141592653589793]
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    terrain_root_state:
+      func: unilab.tasks.locomotion.common.rough_manager_terms.RoughTerrainReset
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+        pose_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [0.25, 0.5]
+          roll: [-3.14, 3.14]
+          pitch: [-3.14, 3.14]
+          yaw: [-3.14, 3.14]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.5, 0.5]
+          roll: [-0.5, 0.5]
+          pitch: [-0.5, 0.5]
+          yaw: [-0.5, 0.5]
+        promote_frac: 0.5
+        demote_frac: 0.25
+        cycle_top_frac: 0.5
+        spawn_height_margin: 0.05
+    base_mass:
+      func: unilab.envs.mdp.randomize_rigid_body_mass
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          body_names: ".*"
+        mass_distribution_params: [-1.0, 3.0]
+        operation: add
+        recompute_inertia: false
+    base_com:
+      func: unilab.envs.mdp.randomize_rigid_body_com
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          body_names: ".*"
+        com_range:
+          x: [-0.05, 0.05]
+          y: [0.0, 0.0]
+          z: [0.0, 0.0]
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        kp_range: [17.5, 70.0]
+        kd_range: [0.25, 1.0]
+        operation: abs
+    push_robot:
+      func: unilab.envs.mdp.push_by_setting_velocity
+      mode: interval
+      interval_range_s: [12.5, 12.5]
+      is_global_time: true
+      params:
+        velocity_range:
+          x: [-1.0, 1.0]
+          y: [-1.0, 1.0]
+          z: [-0.5, 0.5]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [0.0, 0.0]
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    terrain_out_of_bounds:
+      func: unilab.tasks.locomotion.common.rough_manager_terms.RoughTerrainOutOfBounds
+      time_out: true
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+        distance_buffer: 3.0
+  curriculum:
+    terrain_levels:
+      func: unilab.tasks.locomotion.common.rough_manager_terms.RoughTerrainCurriculum
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+  policy_observation_group: policy
+  critic_observation_group: critic
+
+reward:
+  tracking_lin_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_lin_vel_xy_exp
+    weight: 3.0
+    params:
+      std: 0.5
+      command_name: twist
+  tracking_ang_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_ang_vel_z_exp
+    weight: 1.5
+    params:
+      std: 0.5
+      command_name: twist
+  lin_vel_z:
+    func: unilab.tasks.locomotion.common.manager_terms.lin_vel_z_l2
+    weight: -2.0
+  ang_vel_xy:
+    func: unilab.tasks.locomotion.common.manager_terms.ang_vel_xy_l2
+    weight: -0.05
+  action_rate:
+    func: unilab.envs.mdp.action_rate_l2
+    weight: -0.01
diff --git a/src/unilab/conf/ppo/task/quadruped_joystick_rough/go2w.yaml b/src/unilab/conf/ppo/task/quadruped_joystick_rough/go2w.yaml
new file mode 100644
index 000000000..746943680
--- /dev/null
+++ b/src/unilab/conf/ppo/task/quadruped_joystick_rough/go2w.yaml
@@ -0,0 +1,127 @@
+# @package _global_
+defaults:
+  - /task/quadruped_joystick_rough/base
+  - _self_
+
+env:
+  observations:
+    policy:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: gyro}
+          scale: 0.25
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params: {sensor_name: upvector}
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params: {command_name: twist}
+        leg_joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+          params:
+            asset_cfg:
+              _target_: SceneEntityCfg
+              name: robot
+              joint_names: ".*_(hip|thigh|calf)_joint"
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+          scale: 0.05
+        actions:
+          func: unilab.envs.mdp.last_action
+          params: {action_name: motor}
+    critic:
+      terms:
+        base_lin_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: local_linvel}
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: gyro}
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params: {sensor_name: upvector}
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params: {command_name: twist}
+        leg_joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+          params:
+            asset_cfg:
+              _target_: SceneEntityCfg
+              name: robot
+              joint_names: ".*_(hip|thigh|calf)_joint"
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+          params: {action_name: motor}
+        height_scan:
+          func: unilab.tasks.locomotion.common.rough_manager_terms.RoughHeightScan
+          params:
+            asset_cfg:
+              _target_: SceneEntityCfg
+              name: robot
+            geom_name: floor
+            vertical_offset: 0.5
+            scale: 5.0
+  actions:
+    motor:
+      _target_: unilab.tasks.locomotion.go2w.manager_terms.Go2WMixedActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      leg_action_scale: 0.25
+      hip_action_scale: 0.125
+      wheel_action_scale: 5.0
+      leg_kp: 35.0
+      leg_kd: 0.5
+      wheel_kd: 0.5
+      clip_actions: 100.0
+      simulate_action_latency: false
+  events:
+    pd_gains: null
+    motor_gains:
+      func: unilab.tasks.locomotion.go2w.manager_terms.randomize_motor_gains
+      mode: reset
+      params:
+        action_name: motor
+        kp_multiplier_range: [0.5, 1.0]
+        kd_multiplier_range: [0.5, 1.0]
+
+reward:
+  orientation:
+    func: unilab.envs.mdp.flat_orientation_l2
+    weight: -2.0
+  motor_torque:
+    func: unilab.tasks.locomotion.go2w.manager_terms.motor_torque_l2
+    weight: -2.5e-5
+    params: {action_name: motor}
+  stand_still:
+    func: unilab.tasks.locomotion.common.manager_terms.stand_still_l1
+    weight: -2.0
+    params:
+      command_name: twist
+      command_threshold: 0.1
+      asset_cfg:
+        _target_: SceneEntityCfg
+        name: robot
+        joint_names: ".*_(hip|thigh|calf)_joint"
+  hip_pos:
+    func: unilab.tasks.locomotion.common.rough_manager_terms.joint_deviation_l2
+    weight: -2.0
+    params:
+      asset_cfg:
+        _target_: SceneEntityCfg
+        name: robot
+        joint_names: ".*_hip_joint"
+  joint_pos_penalty:
+    func: unilab.tasks.locomotion.common.rough_manager_terms.joint_deviation_l2
+    weight: -1.0
+    params:
+      asset_cfg:
+        _target_: SceneEntityCfg
+        name: robot
+        joint_names: ".*_(hip|thigh|calf)_joint"
+  upward:
+    func: unilab.tasks.locomotion.go2w.manager_terms.upward_l2
+    weight: 1.0
diff --git a/src/unilab/conf/ppo/task/quadruped_joystick_rough/quadruped.yaml b/src/unilab/conf/ppo/task/quadruped_joystick_rough/quadruped.yaml
new file mode 100644
index 000000000..a464a98c8
--- /dev/null
+++ b/src/unilab/conf/ppo/task/quadruped_joystick_rough/quadruped.yaml
@@ -0,0 +1,89 @@
+# @package _global_
+defaults:
+  - /task/quadruped_joystick_rough/base
+  - _self_
+
+env:
+  observations:
+    policy:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: gyro}
+          scale: 0.25
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params: {sensor_name: upvector}
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params: {command_name: twist}
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+          scale: 0.05
+        actions:
+          func: unilab.envs.mdp.last_action
+          params: {action_name: joint_pos}
+    critic:
+      terms:
+        base_lin_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: local_linvel}
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: gyro}
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params: {sensor_name: upvector}
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params: {command_name: twist}
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+          params: {action_name: joint_pos}
+        height_scan:
+          func: unilab.tasks.locomotion.common.rough_manager_terms.RoughHeightScan
+          params:
+            asset_cfg:
+              _target_: SceneEntityCfg
+              name: robot
+            geom_name: floor
+            vertical_offset: 0.5
+            scale: 5.0
+  actions:
+    joint_pos:
+      _target_: unilab.tasks.locomotion.common.rough_manager_terms.RoughJointPositionActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      scale:
+        ".*_hip_joint": 0.125
+        ".*_(thigh|calf)_joint": 0.25
+      use_default_offset: true
+      clip_actions: 100.0
+
+reward:
+  stand_still:
+    func: unilab.tasks.locomotion.common.manager_terms.stand_still_l1
+    weight: -2.0
+    params:
+      command_name: twist
+      command_threshold: 0.1
+  hip_pos:
+    func: unilab.tasks.locomotion.common.rough_manager_terms.joint_deviation_l2
+    weight: -0.5
+    params:
+      asset_cfg:
+        _target_: SceneEntityCfg
+        name: robot
+        joint_names: ".*_hip_joint"
+  joint_pos_penalty:
+    func: unilab.tasks.locomotion.common.rough_manager_terms.joint_deviation_l2
+    weight: -1.0
+  upward:
+    func: unilab.tasks.locomotion.go2w.manager_terms.upward_l2
+    weight: 1.0
diff --git a/conf/ppo/task/sharpa_inhand/motrix.yaml b/src/unilab/conf/ppo/task/sharpa_inhand/motrix.yaml
similarity index 100%
rename from conf/ppo/task/sharpa_inhand/motrix.yaml
rename to src/unilab/conf/ppo/task/sharpa_inhand/motrix.yaml
diff --git a/conf/ppo/task/sharpa_inhand/mujoco.yaml b/src/unilab/conf/ppo/task/sharpa_inhand/mujoco.yaml
similarity index 100%
rename from conf/ppo/task/sharpa_inhand/mujoco.yaml
rename to src/unilab/conf/ppo/task/sharpa_inhand/mujoco.yaml
diff --git a/src/unilab/conf/ppo/task/sharpa_inhand/mujoco_hora.yaml b/src/unilab/conf/ppo/task/sharpa_inhand/mujoco_hora.yaml
new file mode 100644
index 000000000..a5660212f
--- /dev/null
+++ b/src/unilab/conf/ppo/task/sharpa_inhand/mujoco_hora.yaml
@@ -0,0 +1,44 @@
+# @package _global_
+defaults:
+  - /task/sharpa_inhand/mujoco
+  - _self_
+
+interactive:
+  action_mode: policy
+  policy_obs_mode: actor
+  camera_distance: 1.5
+  camera_elevation: -20.0
+  camera_azimuth: 90.0
+  use_env_visual_model: true
+
+algo:
+  algo_log_name: hora_ppo
+  runtime_impl: hora_ppo
+  runtime_resolver: uni_rl.algos.hora.rsl_rl:resolve_hora_ppo_runtime
+  obs_groups:
+    actor: [actor]
+    critic: [actor]
+  actor:
+    class_name: uni_rl.algos.hora:HoraActorModel
+    hidden_dims: [512, 256, 128]
+    activation: elu
+    obs_normalization: true
+    priv_info_embed_dim: 9
+    priv_mlp_hidden_dims: [256, 128, 9]
+    distribution_cfg:
+      class_name: GaussianDistribution
+      init_std: 1.0
+      std_type: scalar
+  critic:
+    class_name: uni_rl.algos.hora:HoraCriticModel
+    hidden_dims: [512, 256, 128]
+    activation: elu
+    obs_normalization: true
+    priv_info_embed_dim: 9
+    priv_mlp_hidden_dims: [256, 128, 9]
+  algorithm:
+    class_name: uni_rl.algos.hora:HoraPPO
+
+env:
+  obs:
+    observation_mode: separated
diff --git a/conf/ppo/task/sharpa_inhand_grasp/motrix.yaml b/src/unilab/conf/ppo/task/sharpa_inhand_grasp/motrix.yaml
similarity index 100%
rename from conf/ppo/task/sharpa_inhand_grasp/motrix.yaml
rename to src/unilab/conf/ppo/task/sharpa_inhand_grasp/motrix.yaml
diff --git a/conf/ppo/task/sharpa_inhand_grasp/mujoco.yaml b/src/unilab/conf/ppo/task/sharpa_inhand_grasp/mujoco.yaml
similarity index 100%
rename from conf/ppo/task/sharpa_inhand_grasp/mujoco.yaml
rename to src/unilab/conf/ppo/task/sharpa_inhand_grasp/mujoco.yaml
diff --git a/src/unilab/conf/ppo/task/stewart_balance/base.yaml b/src/unilab/conf/ppo/task/stewart_balance/base.yaml
new file mode 100644
index 000000000..ef8a4c49a
--- /dev/null
+++ b/src/unilab/conf/ppo/task/stewart_balance/base.yaml
@@ -0,0 +1,118 @@
+# @package _global_
+# Canonical Stewart Manager-Based task declaration. Backend leaves own only
+# backend identity and algorithm/runtime tuning.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/stewart/scene.xml
+    entities:
+      stewart:
+        root_body_name: ball
+        actuator_names: [a0, a1, a2, a3, a4, a5]
+        body_names:
+          - ball
+          - top
+          - leg00
+          - leg10
+          - leg01
+          - leg11
+          - leg02
+          - leg12
+          - top_connect00
+          - top_connect10
+          - top_connect01
+          - top_connect11
+          - top_connect02
+          - top_connect12
+  # The stiff closed-loop model requires a physics step no larger than ~0.005 s.
+  sim_dt: 0.004
+  ctrl_dt: 0.02
+  max_episode_seconds: 24.0
+  render_spacing: 4.5
+  observations:
+    policy:
+      terms:
+        balance:
+          func: unilab.tasks.manipulation.stewart.balance.StewartObservation
+          params:
+            entity_name: stewart
+            action_name: tilt
+            ball_body_name: ball
+            top_body_name: top
+            target_rotation_limit_deg: 6.0
+            vel_smooth: 0.25
+  actions:
+    tilt:
+      _target_: unilab.tasks.manipulation.stewart.balance.StewartTiltActionCfg
+      entity_name: stewart
+      actuator_names: [a0, a1, a2, a3, a4, a5]
+      top_body_name: top
+      ball_body_name: ball
+      leg_body_names: [leg00, leg10, leg01, leg11, leg02, leg12]
+      top_connect_body_names:
+        - top_connect00
+        - top_connect10
+        - top_connect01
+        - top_connect11
+        - top_connect02
+        - top_connect12
+      raw_action_clip: [-1.0, 1.0]
+      target_rotation_limit_deg: 6.0
+      action_smooth: 0.60
+      center_control_radius: 0.25
+      center_control_min_gain: 0.15
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_ball:
+      func: unilab.tasks.manipulation.stewart.balance.StewartBallReset
+      mode: reset
+      params:
+        entity_name: stewart
+        platform_radius: 0.8
+        init_ball_radius_ratio: 0.18
+        ball_home_z: 1.2
+  terminations:
+    balance_state:
+      func: unilab.tasks.manipulation.stewart.balance.StewartBalanceState
+      params:
+        observation_group: policy
+        observation_term: balance
+        platform_radius: 0.8
+        fall_radius: 0.5
+        top_center_z: 1.0
+        still_xy: 0.12
+        still_vel: 0.07
+        still_xy_hysteresis: 1.15
+        still_vel_hysteresis: 1.20
+        zero_vel_thresh: 0.07
+        still_steps_needed: 5
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+  # Legacy Stewart rewards were discrete per-control-step values, not rates.
+  scale_rewards_by_dt: false
+  policy_observation_group: policy
+  critic_observation_group: null
+
+reward:
+  center:
+    func: unilab.tasks.manipulation.stewart.balance.center_reward
+    weight: 0.7
+    params:
+      state_term_name: balance_state
+  progress:
+    func: unilab.tasks.manipulation.stewart.balance.progress_reward
+    weight: 0.6
+    params:
+      state_term_name: balance_state
+  still:
+    func: unilab.tasks.manipulation.stewart.balance.still_reward
+    weight: 3.0
+    params:
+      state_term_name: balance_state
+  fall:
+    func: unilab.tasks.manipulation.stewart.balance.fall_reward
+    weight: -6.0
+    params:
+      state_term_name: balance_state
diff --git a/src/unilab/conf/ppo/task/stewart_balance/drake.yaml b/src/unilab/conf/ppo/task/stewart_balance/drake.yaml
new file mode 100644
index 000000000..6be5f7d0d
--- /dev/null
+++ b/src/unilab/conf/ppo/task/stewart_balance/drake.yaml
@@ -0,0 +1,48 @@
+# @package _global_
+defaults:
+  - /task/stewart_balance/base
+  - _self_
+
+training:
+  task_name: StewartBalance
+  sim_backend: drake
+  render_spacing: 4.5
+
+env:
+  drake_backend_mode: batch
+  drake_nthread: 0
+
+algo:
+  num_envs: 128
+  num_steps_per_env: 64
+  max_iterations: 400
+  empirical_normalization: true
+  obs_groups:
+    actor: [policy]
+    critic: [policy]
+  actor:
+    class_name: rsl_rl.models.MLPModel
+    hidden_dims: [128, 128]
+    activation: tanh
+    obs_normalization: true
+    distribution_cfg:
+      class_name: rsl_rl.modules.distribution.GaussianDistribution
+      init_std: 0.3
+      std_type: scalar
+  critic:
+    class_name: rsl_rl.models.MLPModel
+    hidden_dims: [128, 128]
+    activation: tanh
+    obs_normalization: true
+  algorithm:
+    learning_rate: 1.0e-4
+    num_learning_epochs: 5
+    num_mini_batches: 4
+    clip_param: 0.15
+    entropy_coef: 1.0e-4
+    value_loss_coef: 1.0
+    desired_kl: 0.012
+    max_grad_norm: 0.5
+    gamma: 0.99
+    lam: 0.95
+  save_interval: 50
diff --git a/src/unilab/conf/ppo/task/stewart_balance/motrix.yaml b/src/unilab/conf/ppo/task/stewart_balance/motrix.yaml
new file mode 100644
index 000000000..e15d86cf5
--- /dev/null
+++ b/src/unilab/conf/ppo/task/stewart_balance/motrix.yaml
@@ -0,0 +1,50 @@
+# @package _global_
+# Stewart-platform ball-balancing (motrix). A short, runnable PPO baseline, not
+# tuned for best final performance (raise max_iterations / num_envs for higher
+# success rates).
+defaults:
+  - /task/stewart_balance/base
+  - _self_
+
+training:
+  task_name: StewartBalance
+  sim_backend: motrix
+  render_spacing: 4.5
+algo:
+  num_envs: 128
+  num_steps_per_env: 64
+  max_iterations: 400
+  empirical_normalization: true
+  obs_groups:
+    actor: [policy]
+    critic: [policy]
+  actor:
+    class_name: rsl_rl.models.MLPModel
+    hidden_dims: [128, 128]
+    activation: tanh
+    obs_normalization: true
+    distribution_cfg:
+      class_name: rsl_rl.modules.distribution.GaussianDistribution
+      init_std: 0.3
+      std_type: scalar
+  critic:
+    class_name: rsl_rl.models.MLPModel
+    hidden_dims: [128, 128]
+    activation: tanh
+    obs_normalization: true
+  algorithm:
+    learning_rate: 1.0e-4
+    num_learning_epochs: 5
+    num_mini_batches: 4
+    clip_param: 0.15
+    entropy_coef: 1.0e-4
+    value_loss_coef: 1.0
+    desired_kl: 0.012
+    max_grad_norm: 0.5
+    gamma: 0.99
+    lam: 0.95
+  save_interval: 50
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 2.0
diff --git a/conf/ppo/task/stewart_balance/mujoco.yaml b/src/unilab/conf/ppo/task/stewart_balance/mujoco.yaml
similarity index 100%
rename from conf/ppo/task/stewart_balance/mujoco.yaml
rename to src/unilab/conf/ppo/task/stewart_balance/mujoco.yaml
diff --git a/src/unilab/conf/ppo/task/x2_wall_flip_tracking/motrix.yaml b/src/unilab/conf/ppo/task/x2_wall_flip_tracking/motrix.yaml
new file mode 100644
index 000000000..9b0aecd0a
--- /dev/null
+++ b/src/unilab/conf/ppo/task/x2_wall_flip_tracking/motrix.yaml
@@ -0,0 +1,23 @@
+# @package _global_
+defaults:
+  - /task/x2_wall_flip_tracking/mujoco
+  - _self_
+
+training:
+  task_name: X2WallFlipTracking
+  sim_backend: motrix
+
+env:
+  motrix_max_iterations: 3
+
+play_profile:
+  enabled: true
+  env:
+    render_spacing: 4.0
+  scene:
+    enabled: true
+    source_model_file: src/unilab/assets/robots/x2/scene_flat_with_wall.xml
+    ground_texture_file: src/unilab/assets/robots/g1/textures/floor.png
+    skybox_rgb1: [0.90, 0.90, 0.91]
+    skybox_rgb2: [0.68, 0.68, 0.70]
+    ground_texrepeat: [0.25, 0.25]
diff --git a/src/unilab/conf/ppo/task/x2_wall_flip_tracking/mujoco.yaml b/src/unilab/conf/ppo/task/x2_wall_flip_tracking/mujoco.yaml
new file mode 100644
index 000000000..50922321b
--- /dev/null
+++ b/src/unilab/conf/ppo/task/x2_wall_flip_tracking/mujoco.yaml
@@ -0,0 +1,208 @@
+# @package _global_
+defaults:
+  - /task/g1_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: X2WallFlipTracking
+  sim_backend: mujoco
+  play_steps: 300
+  play_env_num: 16
+  render_spacing: 3.0
+  cam_distance: 14.0
+  cam_azimuth: 225.0
+  cam_elevation: -18.0
+  cam_lookat: [4.5, 4.5, 1.0]
+
+interactive:
+  action_mode: policy
+
+algo:
+  num_envs: 1024
+  max_iterations: 9500
+  save_interval: 500
+  empirical_normalization: true
+  obs_groups:
+    actor: [actor]
+    critic: [critic]
+  algorithm:
+    entropy_coef: 0.005
+    desired_kl: 0.01
+
+play_profile:
+  enabled: false
+  env: null
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/x2/scene_flat_with_wall.xml
+    visual_model_file: src/unilab/assets/robots/x2/scene_flat_with_wall_visual.xml
+    default_keyframe_name: home
+    entities:
+      robot:
+        root_body_name: pelvis
+        joint_names: &x2_joints
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - waist_pitch_joint
+          - waist_roll_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_yaw_joint
+          - left_wrist_pitch_joint
+          - left_wrist_roll_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_yaw_joint
+          - right_wrist_pitch_joint
+          - right_wrist_roll_joint
+        actuator_names: *x2_joints
+        geom_names: null
+        body_names: &tracked_bodies
+          - pelvis
+          - left_hip_pitch_link
+          - left_hip_roll_link
+          - left_hip_yaw_link
+          - left_knee_link
+          - left_ankle_pitch_link
+          - left_ankle_roll_link
+          - right_hip_pitch_link
+          - right_hip_roll_link
+          - right_hip_yaw_link
+          - right_knee_link
+          - right_ankle_pitch_link
+          - right_ankle_roll_link
+          - waist_yaw_link
+          - waist_pitch_link
+          - torso_link
+          - left_shoulder_pitch_link
+          - left_shoulder_roll_link
+          - left_shoulder_yaw_link
+          - left_elbow_link
+          - left_wrist_yaw_link
+          - left_wrist_pitch_link
+          - left_wrist_roll_link
+          - right_shoulder_pitch_link
+          - right_shoulder_roll_link
+          - right_shoulder_yaw_link
+          - right_elbow_link
+          - right_wrist_yaw_link
+          - right_wrist_pitch_link
+          - right_wrist_roll_link
+  sim_dt: 0.005
+  observations:
+    actor:
+      terms:
+        motion_anchor_pos_b: null
+        base_lin_vel: null
+        base_ang_vel:
+          params: {sensor_name: body-angular-velocity}
+    critic:
+      terms:
+        base_lin_vel:
+          params: {sensor_name: body-linear-vel}
+        base_ang_vel:
+          params: {sensor_name: body-angular-velocity}
+  commands:
+    motion:
+      params:
+        motion_file: motions/x2/tictacflip_6-3_g1format.npz
+        anchor_body_name: torso_link
+        body_names: *tracked_bodies
+        sampling_mode: start
+        truncate_on_clip_end: false
+        pose_range: &zero_pose
+          x: [0.0, 0.0]
+          y: [0.0, 0.0]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [0.0, 0.0]
+        velocity_range: *zero_pose
+        joint_position_range: [0.0, 0.0]
+  terminations:
+    anchor_pos:
+      params: {command_name: motion, threshold: 0.5}
+    anchor_ori:
+      params:
+        command_name: motion
+        threshold: 1.0e9
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+    ee_body_pos:
+      params:
+        command_name: motion
+        threshold: 0.5
+        body_names: &ee_bodies
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_yaw_link
+          - right_wrist_yaw_link
+    undesired_contacts:
+      func: unilab.tasks.motion_tracking.common.manager_terms.bad_undesired_body_contacts
+      params:
+        command_name: motion
+        threshold: 0.05
+        body_names: &undesired_bodies
+          - pelvis
+          - left_hip_pitch_link
+          - left_hip_roll_link
+          - left_hip_yaw_link
+          - left_knee_link
+          - left_ankle_pitch_link
+          - right_hip_pitch_link
+          - right_hip_roll_link
+          - right_hip_yaw_link
+          - right_knee_link
+          - right_ankle_pitch_link
+          - waist_yaw_link
+          - waist_pitch_link
+          - torso_link
+          - left_shoulder_pitch_link
+          - left_shoulder_roll_link
+          - left_shoulder_yaw_link
+          - left_elbow_link
+          - left_wrist_pitch_link
+          - left_wrist_roll_link
+          - right_shoulder_pitch_link
+          - right_shoulder_roll_link
+          - right_shoulder_yaw_link
+          - right_elbow_link
+          - right_wrist_pitch_link
+          - right_wrist_roll_link
+
+reward:
+  motion_body_pos:
+    weight: 2.0
+  motion_body_ori:
+    weight: 1.5
+  motion_ee_body_pos_z:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_relative_body_position_z_error_exp
+    weight: 2.0
+    params: {command_name: motion, std: 0.3, body_names: *ee_bodies}
+  motion_joint_pos:
+    weight: 0.5
+  motion_joint_vel:
+    weight: 0.25
+  action_rate_l2:
+    weight: -0.005
+  undesired_contacts:
+    func: unilab.tasks.motion_tracking.common.manager_terms.undesired_body_contacts
+    weight: -0.1
+    params: {command_name: motion, threshold: 0.05, body_names: *undesired_bodies}
diff --git a/conf/ppo_him/config.yaml b/src/unilab/conf/ppo_him/config.yaml
similarity index 100%
rename from conf/ppo_him/config.yaml
rename to src/unilab/conf/ppo_him/config.yaml
diff --git a/conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml b/src/unilab/conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml
similarity index 100%
rename from conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml
rename to src/unilab/conf/ppo_him/task/go2_arm_manip_loco/mujoco.yaml
diff --git a/src/unilab/conf/sac/config.yaml b/src/unilab/conf/sac/config.yaml
new file mode 100644
index 000000000..8f71876f4
--- /dev/null
+++ b/src/unilab/conf/sac/config.yaml
@@ -0,0 +1,146 @@
+defaults:
+  - _self_
+  - task: g1_walk_flat/mujoco
+
+algo:
+  algo: sac
+  algo_log_name: fast_sac
+  runtime_impl: null
+  runtime_resolver: null
+  load_run: "-1"
+  seed: 1
+  num_envs: 4096
+  # Learner batch size for one SAC update.
+  batch_size: 8192
+  replay_buffer_n: 512
+  updates_per_step: 4
+  learning_starts: 1
+  policy_frequency: 4
+  max_iterations: 500
+  save_interval: 500
+  gamma: 0.97
+  tau: 0.125
+  actor_lr: 3.0e-4
+  critic_lr: 3.0e-4
+  actor_hidden_dim: 512
+  critic_hidden_dim: 768
+  num_atoms: 101
+  obs_normalization: false
+  use_layer_norm: true
+  actor: {}
+  algo_params:
+    alpha_lr: 3.0e-4
+    alpha_init: 0.01
+    target_entropy_ratio: 0.0
+    max_grad_norm: 0.0
+    amp_dtype: auto
+    use_compile: true
+    use_cuda_graph_critic: false
+    use_cuda_graph_actor: false
+    use_cuda_graph_critic_packed_staging: false
+    use_cuda_graph_actor_packed_staging: false
+
+training:
+  task_name: G1WalkFlat
+  # list[int] | null; null/[] = auto-selected single-device behavior;
+  # [d0] = explicit single CUDA device; [d0..dN-1] = N-way data parallel,
+  # rank i trains on cuda:devices[i].
+  devices: null
+  # list[list[int]] | null; one CPU-id segment per rank for the collector's
+  # MuJoCo pool; null = auto partition of cpu_count // world_size per rank.
+  dp_collector_cpu_ids: null
+  logger: tensorboard
+  wandb_project: unilab
+  wandb_entity: null
+  wandb_group: null
+  wandb_job_type: null
+  wandb_name: null
+  wandb_tags: []
+  wandb_notes: null
+  wandb_mode: null
+  sim_backend: mujoco
+  nan_guard:
+    enabled: true
+    buffer_size: 100
+    max_envs_to_dump: 5
+    output_dir: null
+  use_amp: true
+  play_only: false
+  no_play: false
+  sim2sim_strict: true
+  play_render_mode: auto
+  export_onnx: true
+  play_env_num: 16
+  play_steps: 800
+  cam_distance: 6.0
+  cam_elevation: -20.0
+  cam_azimuth: 90.0
+  log_root: null
+  log_dir: null
+  env_steps_per_sync: 1
+  # Seconds the learner waits for each collector inference tick before
+  # failing. Tick 0 includes collector env construction, whose cost is
+  # backend-owned; slow-start backends raise this in their task owner YAML.
+  inference_request_timeout_sec: 30.0
+  trace_enabled: false
+  trace_output_dir: null
+  trace_thread_time: false
+  trace_cuda_events: true
+  nvtx_profile_ranges: false
+  replay_prefetch_mode: one_tick
+  torch_threads:
+    enabled: true
+    # "auto" resolves per process role from host CPU count with conservative caps.
+    # Override these from Hydra when benchmarking a specific machine.
+    learner_num_threads: auto
+    collector_num_threads: auto
+    learner_num_interop_threads: 1
+    collector_num_interop_threads: 1
+    compile_threads: auto
+    set_env_vars: true
+
+interactive:
+  action_mode: zero
+  policy_obs_mode: auto
+  show_target_bodies: false
+  show_reward_debug: false
+  target_show_axes: false
+  target_body_names: ""
+  target_max_bodies: 0
+  target_marker_radius: 0.02
+  target_axis_length: 0.08
+  target_marker_alpha: 0.75
+  reward_debug_show_velocity: false
+  reward_debug_lin_vel_scale: 0.08
+  reward_debug_ang_vel_scale: 0.05
+  reward_debug_show_connectors: false
+  reward_debug_show_global_anchor: false
+  camera_follow_body: true
+  camera_focus_body_name: ""
+  camera_height_offset: 0.15
+  camera_distance: null
+  camera_elevation: null
+  camera_azimuth: null
+  use_env_visual_model: true
+  speed: 1.0
+  start_paused: false
+  keyboard: false
+  keyboard_step_lin: 0.1
+  keyboard_step_ang: 0.2
+
+env:
+  post_step_forward_sensor: false
+  # adaptive_chunk_size: auto-tune the MuJoCo BatchEnvPool chunk_size at materialize
+  # (cache-backed). chunk_size (int) manually overrides and wins; null => use default.
+  adaptive_chunk_size: true
+  chunk_size: null
+
+hydra:
+  run:
+    dir: .
+  output_subdir: null
+  job:
+    chdir: false
+  job_logging:
+    root:
+      handlers: [console]
diff --git a/src/unilab/conf/sac/task/g1_23dof_flip_tracking/mujoco.yaml b/src/unilab/conf/sac/task/g1_23dof_flip_tracking/mujoco.yaml
new file mode 100644
index 000000000..70ce6e8e7
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_23dof_flip_tracking/mujoco.yaml
@@ -0,0 +1,105 @@
+# @package _global_
+defaults:
+  - /task/g1_23dof_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1FlipTrackingSAC23Dof
+  sim_backend: mujoco
+  play_steps: 1000
+
+algo:
+  num_envs: 4096
+  max_iterations: 25000
+  save_interval: 1000
+  gamma: 0.99
+  tau: 0.05
+  num_atoms: 501
+  updates_per_step: 4
+  policy_frequency: 2
+  algo_params:
+    alpha_init: 0.005
+    target_entropy_ratio: 0.05
+    max_grad_norm: 10.0
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat_23dof.xml
+  sim_dt: 0.005
+  actions:
+    joint_pos:
+      scale:
+        ".*_(hip_pitch|hip_yaw)_joint": 0.5475464629911068
+        ".*_(hip_roll|knee)_joint": 0.35066146637882434
+        ".*_ankle_(pitch|roll)_joint": 0.43857731392336724
+        "waist_yaw_joint": 0.5475464629911068
+        ".*_wrist_roll_joint": 0.43857731392336724
+  commands:
+    motion:
+      params:
+        motion_file: motions/g1/flip_360_001__A304_23dof.npz
+        sampling_mode: mixed
+        sampling_start_ratio: 0.1
+        truncate_on_clip_end: true
+        pose_range: &zero_pose
+          x: [0.0, 0.0]
+          y: [0.0, 0.0]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [0.0, 0.0]
+        velocity_range: *zero_pose
+        joint_position_range: [0.0, 0.0]
+  terminations:
+    anchor_pos:
+      params: {command_name: motion, threshold: 0.5}
+    anchor_ori:
+      params:
+        command_name: motion
+        threshold: 1.0e9
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+    ee_body_pos:
+      params:
+        command_name: motion
+        threshold: 0.5
+        body_names: &ee_bodies
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_roll_rubber_hand
+          - right_wrist_roll_rubber_hand
+    undesired_contacts:
+      func: unilab.tasks.motion_tracking.common.manager_terms.bad_undesired_body_contacts
+      params:
+        command_name: motion
+        threshold: 0.05
+        body_names: &undesired_bodies
+          - pelvis
+          - left_hip_roll_link
+          - left_knee_link
+          - right_hip_roll_link
+          - right_knee_link
+          - torso_link
+          - left_shoulder_roll_link
+          - left_elbow_link
+          - right_shoulder_roll_link
+          - right_elbow_link
+
+reward:
+  motion_global_root_pos:
+    weight: 0.5
+  motion_body_ori:
+    weight: 1.5
+  motion_ee_body_pos_z:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_relative_body_position_z_error_exp
+    weight: 2.0
+    params: {command_name: motion, std: 0.3, body_names: *ee_bodies}
+  action_rate_l2:
+    weight: -0.005
+  joint_limit:
+    weight: -10.0
+  undesired_contacts:
+    func: unilab.tasks.motion_tracking.common.manager_terms.undesired_body_contacts
+    weight: -0.1
+    params: {command_name: motion, threshold: 0.05, body_names: *undesired_bodies}
diff --git a/src/unilab/conf/sac/task/g1_23dof_motion_tracking/motrix.yaml b/src/unilab/conf/sac/task/g1_23dof_motion_tracking/motrix.yaml
new file mode 100644
index 000000000..735e89be0
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_23dof_motion_tracking/motrix.yaml
@@ -0,0 +1,9 @@
+# @package _global_
+# Motrix is the sim2sim eval owner for MuJoCo-trained WBT checkpoints.
+defaults:
+  - /task/g1_23dof_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1MotionTrackingSAC23Dof
+  sim_backend: motrix
diff --git a/src/unilab/conf/sac/task/g1_23dof_motion_tracking/mujoco.yaml b/src/unilab/conf/sac/task/g1_23dof_motion_tracking/mujoco.yaml
new file mode 100644
index 000000000..da1338c9e
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_23dof_motion_tracking/mujoco.yaml
@@ -0,0 +1,76 @@
+# @package _global_
+defaults:
+  - /task/g1_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1MotionTrackingSAC23Dof
+  sim_backend: mujoco
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat_23dof.xml
+    entities:
+      robot:
+        joint_names: &g1_23dof_joints
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+        actuator_names: *g1_23dof_joints
+        body_names: &tracked_bodies_23dof
+          - pelvis
+          - left_hip_roll_link
+          - left_knee_link
+          - left_ankle_roll_link
+          - right_hip_roll_link
+          - right_knee_link
+          - right_ankle_roll_link
+          - torso_link
+          - left_shoulder_roll_link
+          - left_elbow_link
+          - left_wrist_roll_rubber_hand
+          - right_shoulder_roll_link
+          - right_elbow_link
+          - right_wrist_roll_rubber_hand
+  actions:
+    joint_pos:
+      scale:
+        ".*_(hip_pitch|hip_yaw)_joint": 0.5475464629911068
+        ".*_(hip_roll|knee)_joint": 0.35066146637882434
+        ".*_ankle_(pitch|roll)_joint": 0.43857731392336724
+        "waist_yaw_joint": 0.5475464629911068
+        ".*_(shoulder_(pitch|roll|yaw)|elbow)_joint": 0.43857731392336724
+        ".*_wrist_roll_joint": 0.07450087032950714
+  commands:
+    motion:
+      params:
+        motion_file: motions/g1/dance1_subject2_part_23dof.npz
+        body_names: *tracked_bodies_23dof
+  terminations:
+    ee_body_pos:
+      params:
+        body_names:
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_roll_rubber_hand
+          - right_wrist_roll_rubber_hand
diff --git a/src/unilab/conf/sac/task/g1_23dof_walk_flat/base.yaml b/src/unilab/conf/sac/task/g1_23dof_walk_flat/base.yaml
new file mode 100644
index 000000000..c7571348f
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_23dof_walk_flat/base.yaml
@@ -0,0 +1,66 @@
+# @package _global_
+# Canonical G1 23-DoF walk Manager-Based task declaration (off-policy owners).
+# Inherits the 29-DoF off-policy contract and swaps the scene to the 23-DoF
+# model (no waist roll/pitch, no wrist pitch/yaw) with 23-entry pose weights.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat_23dof.xml
+    entities:
+      robot:
+        joint_names:
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+        actuator_names:
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+
+reward:
+  pose:
+    params:
+      pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/src/unilab/conf/sac/task/g1_23dof_walk_flat/motrix.yaml b/src/unilab/conf/sac/task/g1_23dof_walk_flat/motrix.yaml
new file mode 100644
index 000000000..99d75a942
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_23dof_walk_flat/motrix.yaml
@@ -0,0 +1,44 @@
+# @package _global_
+# SAC Motrix 23-DoF owner: keeps DENYLIST parity with the MuJoCo owner and
+# retunes reward shaping / disables kp/kd randomization for Motrix.
+defaults:
+  - /task/g1_23dof_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1Walk23DofFlat
+  sim_backend: motrix
+algo:
+  num_envs: 2048
+  learning_starts: 1
+  max_iterations: 5000
+  save_interval: 1000
+  updates_per_step: 8
+  algo_params:
+    alpha_init: 0.001
+    target_entropy_ratio: 0.0
+env:
+  events:
+    # Legacy Motrix owners disable kp/kd randomization.
+    pd_gains: null
+reward:
+  tracking_lin_vel:
+    weight: 2.2
+  tracking_ang_vel:
+    weight: 1.8
+  penalty_ang_vel_xy:
+    weight: -1.2
+  penalty_orientation:
+    weight: -12.0
+  penalty_action_rate:
+    weight: -2.5
+  pose:
+    weight: -0.6
+  penalty_feet_ori:
+    weight: -5.0
+  feet_phase:
+    weight: 6.0
+    params:
+      tracking_sigma: 0.008
+  alive:
+    weight: 12.0
diff --git a/src/unilab/conf/sac/task/g1_23dof_walk_flat/mujoco.yaml b/src/unilab/conf/sac/task/g1_23dof_walk_flat/mujoco.yaml
new file mode 100644
index 000000000..d06148b02
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_23dof_walk_flat/mujoco.yaml
@@ -0,0 +1,19 @@
+# @package _global_
+# SAC MuJoCo 23-DoF owner: inherits the 23-DoF off-policy Manager-Based
+# contract and only carries backend/algo identity.
+defaults:
+  - /task/g1_23dof_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1Walk23DofFlat
+  sim_backend: mujoco
+algo:
+  num_envs: 2048
+  learning_starts: 10
+  max_iterations: 5000
+  save_interval: 1000
+  updates_per_step: 8
+  algo_params:
+    alpha_init: 0.001
+    target_entropy_ratio: 0.0
diff --git a/src/unilab/conf/sac/task/g1_23dof_walk_rough/motrix.yaml b/src/unilab/conf/sac/task/g1_23dof_walk_rough/motrix.yaml
new file mode 100644
index 000000000..10899e153
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_23dof_walk_rough/motrix.yaml
@@ -0,0 +1,47 @@
+# @package _global_
+# SAC Motrix 23-DoF rough owner: static-hfield rough scene, Motrix
+# sim_dt=0.01, Motrix-direction reward retuning; kp/kd randomization disabled.
+defaults:
+  - /task/g1_23dof_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1Walk23DofRough
+  sim_backend: motrix
+algo:
+  num_envs: 2048
+  learning_starts: 1
+  max_iterations: 5000
+  save_interval: 1000
+  updates_per_step: 8
+  algo_params:
+    alpha_init: 0.001
+    target_entropy_ratio: 0.0
+env:
+  sim_dt: 0.01
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_rough_23dof.xml
+  events:
+    # Legacy Motrix owners disable kp/kd randomization.
+    pd_gains: null
+reward:
+  tracking_lin_vel:
+    weight: 2.2
+  tracking_ang_vel:
+    weight: 1.8
+  penalty_ang_vel_xy:
+    weight: -1.2
+  penalty_orientation:
+    weight: -12.0
+  penalty_action_rate:
+    weight: -2.5
+  pose:
+    weight: -0.6
+  penalty_feet_ori:
+    weight: -5.0
+  feet_phase:
+    weight: 6.0
+    params:
+      tracking_sigma: 0.008
+  alive:
+    weight: 12.0
diff --git a/src/unilab/conf/sac/task/g1_23dof_walk_rough/mujoco.yaml b/src/unilab/conf/sac/task/g1_23dof_walk_rough/mujoco.yaml
new file mode 100644
index 000000000..b6b0fb319
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_23dof_walk_rough/mujoco.yaml
@@ -0,0 +1,22 @@
+# @package _global_
+# SAC MuJoCo 23-DoF rough owner: 23-DoF off-policy contract plus the
+# static-hfield rough scene.
+defaults:
+  - /task/g1_23dof_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1Walk23DofRough
+  sim_backend: mujoco
+algo:
+  num_envs: 2048
+  learning_starts: 10
+  max_iterations: 5000
+  save_interval: 1000
+  updates_per_step: 8
+  algo_params:
+    alpha_init: 0.001
+    target_entropy_ratio: 0.0
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_rough_23dof.xml
diff --git a/src/unilab/conf/sac/task/g1_23dof_wall_flip_tracking/mujoco.yaml b/src/unilab/conf/sac/task/g1_23dof_wall_flip_tracking/mujoco.yaml
new file mode 100644
index 000000000..0a111f66a
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_23dof_wall_flip_tracking/mujoco.yaml
@@ -0,0 +1,41 @@
+# @package _global_
+defaults:
+  - /task/g1_23dof_flip_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1WallFlipTrackingSAC23Dof
+  sim_backend: mujoco
+
+algo:
+  algo_params:
+    target_entropy_ratio: 0.0
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat_23dof_with_wall.xml
+  commands:
+    motion:
+      params:
+        motion_file: motions/g1/flip_from_wall_104__A304_23dof.npz
+        sampling_mode: uniform
+        sampling_start_ratio: 0.0
+  terminations:
+    anchor_pos:
+      params: {command_name: motion, threshold: 1.0e9}
+    ee_body_pos:
+      params:
+        command_name: motion
+        threshold: 1.0e9
+        body_names:
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_roll_rubber_hand
+          - right_wrist_roll_rubber_hand
+    undesired_contacts: null
+
+reward:
+  motion_joint_pos:
+    weight: 0.5
+  motion_joint_vel:
+    weight: 0.25
diff --git a/src/unilab/conf/sac/task/g1_23dof_wbt_obs/mujoco.yaml b/src/unilab/conf/sac/task/g1_23dof_wbt_obs/mujoco.yaml
new file mode 100644
index 000000000..dd1022142
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_23dof_wbt_obs/mujoco.yaml
@@ -0,0 +1,186 @@
+# @package _global_
+defaults:
+  - /task/g1_23dof_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1WBTObs23Dof
+  sim_backend: mujoco
+
+algo:
+  num_envs: 4096
+  max_iterations: 140000
+  save_interval: 1000
+  gamma: 0.99
+  tau: 0.05
+  num_atoms: 501
+  updates_per_step: 4
+  policy_frequency: 2
+  algo_params:
+    alpha_init: 0.1
+    target_entropy_ratio: 0.5
+    max_grad_norm: 10.0
+
+env:
+  sim_dt: 0.005
+  scene:
+    entities:
+      robot:
+        geom_names:
+          - left_foot1_collision
+          - left_foot2_collision
+          - left_foot3_collision
+          - left_foot4_collision
+          - left_foot5_collision
+          - left_foot6_collision
+          - left_foot7_collision
+          - right_foot1_collision
+          - right_foot2_collision
+          - right_foot3_collision
+          - right_foot4_collision
+          - right_foot5_collision
+          - right_foot6_collision
+          - right_foot7_collision
+  observations:
+    actor:
+      terms:
+        motion_anchor_pos_b: null
+        motion_anchor_ori_b:
+          func: unilab.tasks.motion_tracking.common.manager_terms.motion_anchor_ori_b
+          params: {command_name: motion}
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.05
+            n_max: 0.05
+        base_lin_vel: null
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: pelvis_gyro}
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.2
+            n_max: 0.2
+          history_length: 5
+        joint_pos:
+          func: unilab.tasks.motion_tracking.common.manager_terms.motion_joint_pos_rel_biased
+          params: {command_name: motion}
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.01
+            n_max: 0.01
+          history_length: 5
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.5
+            n_max: 0.5
+          history_length: 5
+        actions:
+          func: unilab.envs.mdp.last_action
+          history_length: 5
+    critic:
+      terms:
+        base_ang_vel:
+          params: {sensor_name: pelvis_gyro}
+  actions:
+    joint_pos:
+      scale: 2.0
+      simulate_action_latency: true
+  terminations:
+    anchor_pos:
+      params: {command_name: motion, threshold: 0.4}
+    ee_body_pos:
+      params:
+        command_name: motion
+        threshold: 0.5
+        body_names:
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_roll_rubber_hand
+          - right_wrist_roll_rubber_hand
+  events:
+    base_mass:
+      func: unilab.envs.mdp.randomize_rigid_body_mass
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+          body_names: pelvis
+        mass_distribution_params: [-1.0, 1.0]
+        operation: add
+        recompute_inertia: false
+    base_com:
+      func: unilab.envs.mdp.randomize_rigid_body_com
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+          body_names: pelvis
+        com_range:
+          x: [-0.05, 0.05]
+          y: [-0.05, 0.05]
+          z: [-0.05, 0.05]
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        kp_range: [0.9, 1.1]
+        kd_range: [0.85, 1.15]
+        operation: scale
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+          actuator_names: ".*"
+    foot_friction:
+      func: unilab.envs.mdp.geom_friction
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+          geom_names: "^(left|right)_foot[1-7]_collision$"
+        ranges: [0.3, 1.2]
+        operation: abs
+        shared_random: true
+    encoder_bias:
+      func: unilab.envs.mdp.randomize_encoder_bias
+      mode: reset
+      params:
+        bias_range: [-0.01, 0.01]
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+          joint_names: ".*"
+    push_robot:
+      func: unilab.envs.mdp.push_by_setting_velocity
+      mode: interval
+      interval_range_s: [4.0, 4.0]
+      is_global_time: true
+      params:
+        velocity_range:
+          x: [-1.0, 1.0]
+          y: [-1.0, 1.0]
+          z: [-0.5, 0.5]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [0.0, 0.0]
+
+reward:
+  motion_global_root_ori:
+    weight: 1.0
+  motion_body_pos:
+    weight: 1.0
+  action_rate_l2:
+    weight: -0.1
+  joint_limit:
+    weight: -5.0
+  joint_acc_l2:
+    func: unilab.tasks.motion_tracking.g1.manager_terms.joint_acc_l2
+    weight: -2.5e-7
+  joint_torque_l2:
+    func: unilab.tasks.motion_tracking.g1.manager_terms.joint_torque_l2
+    weight: -1.0e-5
+    params: {action_name: joint_pos}
diff --git a/src/unilab/conf/sac/task/g1_flip_tracking/mujoco.yaml b/src/unilab/conf/sac/task/g1_flip_tracking/mujoco.yaml
new file mode 100644
index 000000000..5e89be9b3
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_flip_tracking/mujoco.yaml
@@ -0,0 +1,107 @@
+# @package _global_
+defaults:
+  - /task/g1_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1FlipTrackingSAC
+  sim_backend: mujoco
+  play_steps: 1000
+
+algo:
+  num_envs: 4096
+  max_iterations: 25000
+  save_interval: 1000
+  gamma: 0.99
+  tau: 0.05
+  num_atoms: 501
+  updates_per_step: 4
+  policy_frequency: 2
+  algo_params:
+    alpha_init: 0.005
+    target_entropy_ratio: 0.05
+    max_grad_norm: 10.0
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat.xml
+  sim_dt: 0.005
+  actions:
+    joint_pos:
+      scale:
+        ".*_(hip_pitch|hip_yaw)_joint": 0.5475464629911068
+        ".*_(hip_roll|knee)_joint": 0.35066146637882434
+        ".*_ankle_(pitch|roll)_joint": 0.43857731392336724
+        "waist_yaw_joint": 0.5475464629911068
+        "waist_(roll|pitch)_joint": 0.43857731392336724
+        ".*_(shoulder_(pitch|roll|yaw)|elbow|wrist_roll)_joint": 0.43857731392336724
+        ".*_wrist_(pitch|yaw)_joint": 0.07450087032950714
+  commands:
+    motion:
+      params:
+        motion_file: motions/g1/flip_360_001__A304.npz
+        sampling_mode: mixed
+        sampling_start_ratio: 0.1
+        truncate_on_clip_end: true
+        pose_range: &zero_pose
+          x: [0.0, 0.0]
+          y: [0.0, 0.0]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [0.0, 0.0]
+        velocity_range: *zero_pose
+        joint_position_range: [0.0, 0.0]
+  terminations:
+    anchor_pos:
+      params: {command_name: motion, threshold: 0.5}
+    anchor_ori:
+      params:
+        command_name: motion
+        threshold: 1.0e9
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+    ee_body_pos:
+      params:
+        command_name: motion
+        threshold: 0.5
+        body_names: &ee_bodies
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_yaw_link
+          - right_wrist_yaw_link
+    undesired_contacts:
+      func: unilab.tasks.motion_tracking.common.manager_terms.bad_undesired_body_contacts
+      params:
+        command_name: motion
+        threshold: 0.05
+        body_names: &undesired_bodies
+          - pelvis
+          - left_hip_roll_link
+          - left_knee_link
+          - right_hip_roll_link
+          - right_knee_link
+          - torso_link
+          - left_shoulder_roll_link
+          - left_elbow_link
+          - right_shoulder_roll_link
+          - right_elbow_link
+
+reward:
+  motion_global_root_pos:
+    weight: 0.5
+  motion_body_ori:
+    weight: 1.5
+  motion_ee_body_pos_z:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_relative_body_position_z_error_exp
+    weight: 2.0
+    params: {command_name: motion, std: 0.3, body_names: *ee_bodies}
+  action_rate_l2:
+    weight: -0.005
+  joint_limit:
+    weight: -10.0
+  undesired_contacts:
+    func: unilab.tasks.motion_tracking.common.manager_terms.undesired_body_contacts
+    weight: -0.1
+    params: {command_name: motion, threshold: 0.05, body_names: *undesired_bodies}
diff --git a/src/unilab/conf/sac/task/g1_motion_tracking/mjwarp.yaml b/src/unilab/conf/sac/task/g1_motion_tracking/mjwarp.yaml
new file mode 100644
index 000000000..3f5ca8dc6
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_motion_tracking/mjwarp.yaml
@@ -0,0 +1,14 @@
+# @package _global_
+# mjwarp (GPU) owner for G1 WBT. Benchmark/support scope: collector active-window
+# throughput measurement for issue #1292; mujoco-warp + warp-lang stay optional
+# deps. Inherits the MuJoCo owner verbatim so sim2sim DENYLIST fields keep parity;
+# mjwarp capacity knobs stay at backend defaults (nconmax/njmax=512).
+# NOTE: mjwarp rejects reset/interval domain randomization and push terms; this
+# owner inherits the MuJoCo DR-free config, so no extra disables are needed.
+defaults:
+  - /task/g1_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1MotionTrackingSAC
+  sim_backend: mjwarp
diff --git a/src/unilab/conf/sac/task/g1_motion_tracking/motrix.yaml b/src/unilab/conf/sac/task/g1_motion_tracking/motrix.yaml
new file mode 100644
index 000000000..85904de4b
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_motion_tracking/motrix.yaml
@@ -0,0 +1,9 @@
+# @package _global_
+# Motrix is the sim2sim eval owner for MuJoCo-trained WBT checkpoints.
+defaults:
+  - /task/g1_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1MotionTrackingSAC
+  sim_backend: motrix
diff --git a/src/unilab/conf/sac/task/g1_motion_tracking/mujoco.yaml b/src/unilab/conf/sac/task/g1_motion_tracking/mujoco.yaml
new file mode 100644
index 000000000..f0d7555a7
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_motion_tracking/mujoco.yaml
@@ -0,0 +1,268 @@
+# @package _global_
+# G1 Whole-Body Tracking (WBT) with FastSAC on MuJoCo.
+# Hyperparameters aligned with holosoma g1-29dof-wbt-fast-sac.
+training:
+  task_name: G1MotionTrackingSAC
+  sim_backend: mujoco
+
+algo:
+  num_envs: 2048
+  max_iterations: 25000
+  save_interval: 1000
+  gamma: 0.99
+  tau: 0.05
+  num_atoms: 501
+  updates_per_step: 4
+  policy_frequency: 2
+  algo_params:
+    alpha_init: 0.1
+    target_entropy_ratio: 0.5
+    max_grad_norm: 10.0
+
+env:
+  seed: null
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat.xml
+    default_keyframe_name: stand
+    entities:
+      robot:
+        root_body_name: pelvis
+        joint_names: &g1_joints
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - waist_roll_joint
+          - waist_pitch_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - left_wrist_pitch_joint
+          - left_wrist_yaw_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+          - right_wrist_pitch_joint
+          - right_wrist_yaw_joint
+        actuator_names: *g1_joints
+        body_names: &tracked_bodies
+          - pelvis
+          - left_hip_roll_link
+          - left_knee_link
+          - left_ankle_roll_link
+          - right_hip_roll_link
+          - right_knee_link
+          - right_ankle_roll_link
+          - torso_link
+          - left_shoulder_roll_link
+          - left_elbow_link
+          - left_wrist_yaw_link
+          - right_shoulder_roll_link
+          - right_elbow_link
+          - right_wrist_yaw_link
+  sim_dt: 0.006666666666666667
+  ctrl_dt: 0.02
+  max_episode_seconds: 10.0
+  observations:
+    actor:
+      enable_corruption: true
+      terms:
+        command: &command_obs
+          func: unilab.envs.mdp.generated_commands
+          params: {command_name: motion}
+        motion_anchor_pos_b: &anchor_pos_obs
+          func: unilab.tasks.motion_tracking.common.manager_terms.motion_anchor_pos_b
+          params: {command_name: motion}
+        motion_anchor_ori_b: &anchor_ori_obs
+          func: unilab.tasks.motion_tracking.common.manager_terms.motion_anchor_ori_b
+          params: {command_name: motion}
+        base_lin_vel: &base_lin_vel_obs
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: pelvis_local_linvel}
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.1
+            n_max: 0.1
+        base_ang_vel: &base_ang_vel_obs
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: torso_gyro}
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.2
+            n_max: 0.2
+        joint_pos: &joint_pos_obs
+          func: unilab.tasks.motion_tracking.common.manager_terms.motion_joint_pos_rel
+          params: {command_name: motion}
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.01
+            n_max: 0.01
+        joint_vel: &joint_vel_obs
+          func: unilab.envs.mdp.joint_vel_rel
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -1.5
+            n_max: 1.5
+        actions: &actions_obs
+          func: unilab.envs.mdp.last_action
+    critic:
+      terms:
+        command: *command_obs
+        motion_anchor_pos_b: *anchor_pos_obs
+        motion_anchor_ori_b: *anchor_ori_obs
+        base_lin_vel: *base_lin_vel_obs
+        base_ang_vel: *base_ang_vel_obs
+        joint_pos: *joint_pos_obs
+        joint_vel: *joint_vel_obs
+        actions: *actions_obs
+        body_pos:
+          func: unilab.tasks.motion_tracking.common.manager_terms.robot_body_pos_b
+          params: {command_name: motion}
+        body_ori:
+          func: unilab.tasks.motion_tracking.common.manager_terms.robot_body_ori_b
+          params: {command_name: motion}
+        sac_base_lin_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: pelvis_local_linvel}
+  actions:
+    joint_pos:
+      _target_: unilab.tasks.motion_tracking.common.manager_terms.MotionJointPositionActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      scale: 2.0
+      use_default_offset: true
+      command_name: motion
+  commands:
+    motion:
+      _target_: unilab.tasks.motion_tracking.common.manager_terms.MotionCommandCfg
+      entity_name: robot
+      resampling_time_range: [1.0e9, 1.0e9]
+      params:
+        motion_file: motions/g1/dance1_subject2_part.npz
+        anchor_body_name: torso_link
+        body_names: *tracked_bodies
+        sampling_mode: adaptive
+        sampling_start_ratio: 0.0
+        truncate_on_clip_end: true
+        pose_range:
+          x: [-0.05, 0.05]
+          y: [-0.05, 0.05]
+          z: [-0.01, 0.01]
+          roll: [-0.1, 0.1]
+          pitch: [-0.1, 0.1]
+          yaw: [-0.2, 0.2]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.2, 0.2]
+          roll: [-0.52, 0.52]
+          pitch: [-0.52, 0.52]
+          yaw: [-0.78, 0.78]
+        joint_position_range: [-0.1, 0.1]
+        joint_default_position_range: [0.0, 0.0]
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    motion_clip_end:
+      func: unilab.tasks.motion_tracking.common.manager_terms.motion_clip_end
+      time_out: true
+      params: {command_name: motion}
+    anchor_pos:
+      func: unilab.tasks.motion_tracking.common.manager_terms.bad_anchor_pos_z_only
+      params: {command_name: motion, threshold: 0.5}
+    anchor_ori:
+      func: unilab.tasks.motion_tracking.common.manager_terms.bad_anchor_ori
+      params:
+        command_name: motion
+        threshold: 0.8
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+    ee_body_pos:
+      func: unilab.tasks.motion_tracking.common.manager_terms.bad_motion_body_pos_z_only
+      params:
+        command_name: motion
+        threshold: 0.5
+        body_names:
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_yaw_link
+          - right_wrist_yaw_link
+  policy_observation_group: actor
+  critic_observation_group: critic
+
+reward:
+  motion_global_root_pos:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_global_anchor_position_error_exp
+    weight: 1.0
+    params: {command_name: motion, std: 0.3}
+  motion_global_root_ori:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_global_anchor_orientation_error_exp
+    weight: 0.5
+    params: {command_name: motion, std: 0.4}
+  motion_body_pos:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_relative_body_position_error_exp
+    weight: 2.0
+    params: {command_name: motion, std: 0.3}
+  motion_body_ori:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_relative_body_orientation_error_exp
+    weight: 1.0
+    params: {command_name: motion, std: 0.4}
+  motion_body_lin_vel:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_global_body_linear_velocity_error_exp
+    weight: 1.0
+    params: {command_name: motion, std: 1.0}
+  motion_body_ang_vel:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_global_body_angular_velocity_error_exp
+    weight: 1.0
+    params: {command_name: motion, std: 3.14}
+  motion_joint_pos:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_joint_position_error_exp
+    weight: 0.0
+    params: {command_name: motion, std: 0.2}
+  motion_joint_vel:
+    func: unilab.tasks.motion_tracking.common.manager_terms.motion_joint_velocity_error_exp
+    weight: 0.0
+    params: {command_name: motion, std: 1.0}
+  action_rate_l2:
+    func: unilab.envs.mdp.action_rate_l2
+    weight: -0.1
+  joint_limit:
+    func: unilab.tasks.motion_tracking.common.manager_terms.joint_pos_limits
+    weight: -2.0
+    params:
+      asset_cfg:
+        _target_: unilab.managers.SceneEntityCfg
+        name: robot
+        joint_names: ".*"
+  undesired_contacts:
+    func: unilab.tasks.motion_tracking.common.manager_terms.undesired_body_contacts
+    weight: -0.1
+    params:
+      command_name: motion
+      threshold: 0.05
+      body_names:
+        - pelvis
+        - left_hip_roll_link
+        - left_knee_link
+        - right_hip_roll_link
+        - right_knee_link
+        - torso_link
+        - left_shoulder_roll_link
+        - left_elbow_link
+        - right_shoulder_roll_link
+        - right_elbow_link
diff --git a/src/unilab/conf/sac/task/g1_walk_flat/base.yaml b/src/unilab/conf/sac/task/g1_walk_flat/base.yaml
new file mode 100644
index 000000000..2f6d55168
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_walk_flat/base.yaml
@@ -0,0 +1,278 @@
+# @package _global_
+# Canonical G1 29-DoF walk Manager-Based task declaration (off-policy owners).
+# Backend owner leaves inherit this file and only override backend/algo tuning
+# or explicitly disabled terms. Observation scaling follows the walk profile
+# (gyro x0.25, joint velocity x0.05, critic linear velocity x2.0); every
+# off-policy owner carries the penalty curriculum.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat.xml
+    default_keyframe_name: stand
+    entities:
+      robot:
+        root_body_name: pelvis
+        joint_names:
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - waist_roll_joint
+          - waist_pitch_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - left_wrist_pitch_joint
+          - left_wrist_yaw_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+          - right_wrist_pitch_joint
+          - right_wrist_yaw_joint
+        actuator_names:
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - waist_roll_joint
+          - waist_pitch_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - left_wrist_pitch_joint
+          - left_wrist_yaw_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+          - right_wrist_pitch_joint
+          - right_wrist_yaw_joint
+        body_names: [pelvis]
+  sim_dt: 0.006666666666666667
+  ctrl_dt: 0.02
+  max_episode_seconds: 20.0
+  observations:
+    policy:
+      # Observation noise matches the legacy off-policy noise_config (level=1.0,
+      # actor-only; gyro/gravity/linvel scales were 0.0 there): joint pos
+      # +/-0.01 and joint vel +/-0.1, applied before term scaling.
+      enable_corruption: true
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: torso_gyro}
+          scale: 0.25
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params: {sensor_name: torso_upvector}
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.01
+            n_max: 0.01
+            operation: add
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+          scale: 0.05
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.1
+            n_max: 0.1
+            operation: add
+        actions:
+          func: unilab.envs.mdp.last_action
+          params: {action_name: joint_pos}
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params: {command_name: twist}
+        gait_phase:
+          func: unilab.tasks.locomotion.g1.manager_terms.G1GaitPhase
+          params:
+            frequency: 1.5
+            init_mode: offset_phase
+    critic:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: torso_gyro}
+          scale: 0.25
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params: {sensor_name: torso_upvector}
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+          scale: 0.05
+        actions:
+          func: unilab.envs.mdp.last_action
+          params: {action_name: joint_pos}
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params: {command_name: twist}
+        gait_phase:
+          func: unilab.tasks.locomotion.g1.manager_terms.G1GaitPhase
+          params:
+            frequency: 1.5
+            init_mode: offset_phase
+        base_lin_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: pelvis_local_linvel}
+          scale: 2.0
+  actions:
+    joint_pos:
+      _target_: unilab.envs.mdp.JointPositionActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      scale: 1.0
+      use_default_offset: true
+  commands:
+    twist:
+      _target_: unilab.tasks.locomotion.g1.manager_terms.G1VelocityCommandCfg
+      entity_name: robot
+      resampling_time_range: [20.0, 20.0]
+      heading_command: false
+      heading_control_stiffness: 0.5
+      rel_standing_envs: 0.0
+      rel_heading_envs: 0.0
+      rel_world_envs: 0.0
+      rel_forward_envs: 0.0
+      init_velocity_prob: 0.0
+      planar_dead_zone: 0.2
+      ranges:
+        lin_vel_x: [-0.6, 1.0]
+        lin_vel_y: [-0.4, 0.4]
+        ang_vel_z: [-0.8, 0.8]
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_root_state_uniform:
+      func: unilab.envs.mdp.reset_root_state_uniform
+      mode: reset
+      params:
+        pose_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [-3.141592653589793, 3.141592653589793]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.5, 0.5]
+          roll: [-0.5, 0.5]
+          pitch: [-0.5, 0.5]
+          yaw: [-0.5, 0.5]
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        kp_range: [0.9, 1.1]
+        kd_range: [0.9, 1.1]
+        operation: scale
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    tilt:
+      func: unilab.tasks.locomotion.g1.manager_terms.g1_tilt_exceeded
+      params:
+        max_tilt_deg: 65.0
+    base_height:
+      func: unilab.envs.mdp.root_height_below_minimum
+      params:
+        minimum_height: 0.3
+  curriculum:
+    penalty_scaling:
+      func: unilab.tasks.locomotion.g1.manager_terms.G1PenaltyCurriculum
+      # Effective schedule matches the tuned legacy baseline: the legacy env
+      # halved the shared override dict once per env construction (two probe
+      # envs + the collector in every off-policy runner), so collectors actually
+      # trained at 1/8 initial / 1/4 cap of these YAML weights. The manager
+      # runtime isolates each env, so the tuned effective range is declared
+      # explicitly here.
+      params:
+        initial_scale: 0.125
+        min_scale: 0.125
+        max_scale: 0.25
+        level_down_threshold: 150.0
+        level_up_threshold: 750.0
+        degree: 0.001
+  policy_observation_group: policy
+  critic_observation_group: critic
+
+reward:
+  tracking_lin_vel:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.track_lin_vel
+    weight: 2.0
+    params:
+      tracking_sigma: 0.25
+      command_name: twist
+      sensor_name: pelvis_local_linvel
+  tracking_ang_vel:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.track_ang_vel
+    weight: 1.5
+    params:
+      tracking_sigma: 0.25
+      command_name: twist
+      sensor_name: torso_gyro
+  penalty_ang_vel_xy:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.ang_vel_xy
+    weight: -1.0
+    params:
+      sensor_name: torso_gyro
+  penalty_orientation:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.orientation
+    weight: -10.0
+    params:
+      sensor_name: torso_upvector
+  penalty_action_rate:
+    func: unilab.envs.mdp.action_rate_l2
+    weight: -4.0
+  pose:
+    func: unilab.tasks.locomotion.g1.manager_terms.weighted_pose
+    weight: -0.5
+    params:
+      pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
+  penalty_feet_ori:
+    func: unilab.tasks.locomotion.g1.manager_terms.penalty_feet_ori
+    weight: -20.0
+  feet_phase:
+    func: unilab.tasks.locomotion.g1.manager_terms.feet_phase
+    weight: 5.0
+    params:
+      frequency: 1.5
+      swing_height: 0.09
+      tracking_sigma: 0.04
+      min_forward_speed: 0.0
+      command_name: twist
+  alive:
+    func: unilab.tasks.locomotion.common.manager_terms.alive
+    weight: 10.0
diff --git a/src/unilab/conf/sac/task/g1_walk_flat/genesis.yaml b/src/unilab/conf/sac/task/g1_walk_flat/genesis.yaml
new file mode 100644
index 000000000..911663387
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_walk_flat/genesis.yaml
@@ -0,0 +1,41 @@
+# @package _global_
+# Genesis SAC owner for the in-process backend (genesis-world==1.3.3,
+# torch>=2.8). Keeps DENYLIST parity with the MuJoCo SAC owner (identical algo
+# block) so cross-backend play transfers 1:1. Genesis drops the MJCF global
+# <option> block at import (REPORT #1372 §3.3), so the integrator g1.xml
+# declares (<option integrator="implicitfast" timestep="0.006666666666666667"/>)
+# is re-declared here: the timestep flows through the existing base.yaml
+# sim_dt -> ctrl_dt chain, and the integrator through the EnvCfg genesis
+# fields below. Solver/constraint-cone/iterations stay at Genesis defaults:
+# the MJCF option block does not declare them and MuJoCo's implicit defaults
+# (PGS solver, pyramidal cone) have no Genesis equivalent. Unlike the
+# isaacgym owner (events.pd_gains: null), kp/kd reset randomization stays
+# enabled from base.yaml because the genesis backend declares the measured
+# RESET_TERM_KP/KD DR terms (REPORT §5.7). Playback uses Genesis native
+# rendering: with a display, play_render_mode=auto opens the interactive
+# viewer; headless hosts fall back to offscreen camera recording.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1WalkFlat
+  sim_backend: genesis
+  play_render_mode: auto
+  # Genesis compiles its kernels on the materialize cold path (~35 s at
+  # num_envs=2048 on an RTX 4090, no cross-process cache), which exceeds the
+  # runner's default 30 s collector tick-0 budget; raise it for this backend.
+  inference_request_timeout_sec: 180.0
+algo:
+  num_envs: 2048
+  learning_starts: 10
+  max_iterations: 5000
+  save_interval: 1000
+  updates_per_step: 8
+  algo_params:
+    alpha_init: 0.001
+    target_entropy_ratio: 0.0
+env:
+  # Re-declares the MJCF <option integrator="implicitfast"> that Genesis drops
+  # at import; the remaining global options keep the Genesis defaults.
+  genesis_integrator: implicitfast
diff --git a/src/unilab/conf/sac/task/g1_walk_flat/isaacgym.yaml b/src/unilab/conf/sac/task/g1_walk_flat/isaacgym.yaml
new file mode 100644
index 000000000..216fc1947
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_walk_flat/isaacgym.yaml
@@ -0,0 +1,33 @@
+# @package _global_
+# IsaacGym SAC owner for the subprocess backend (physics runs in a Python 3.8
+# worker). Keeps DENYLIST parity with the MuJoCo owner. The backend consumes
+# the self-contained MJCF scene from base.yaml directly (no scene fragments or
+# generated terrain) and reproduces the <position kp kv forcerange> actuators
+# with PhysX DOF_MODE_POS (gains parsed from the MJCF, not the importer);
+# runtime kp/kd randomization is unsupported, so pd_gains stays disabled.
+# Playback uses IsaacGym native rendering: with a display,
+# `play_render_mode=auto` opens the interactive viewer; headless hosts fall
+# back to offscreen camera-sensor recording.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1WalkFlat
+  sim_backend: isaacgym
+algo:
+  num_envs: 2048
+  learning_starts: 10
+  max_iterations: 5000
+  save_interval: 1000
+  updates_per_step: 8
+  algo_params:
+    alpha_init: 0.001
+    target_entropy_ratio: 0.0
+env:
+  # GPU device index handed to the worker sim; unset defaults to device 0.
+  isaacgym_device_id: 0
+  render_spacing: 2.0
+  events:
+    # Runtime kp/kd randomization is unsupported on the isaacgym worker.
+    pd_gains: null
diff --git a/src/unilab/conf/sac/task/g1_walk_flat/isaacsim.yaml b/src/unilab/conf/sac/task/g1_walk_flat/isaacsim.yaml
new file mode 100644
index 000000000..487e8df78
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_walk_flat/isaacsim.yaml
@@ -0,0 +1,31 @@
+# @package _global_
+# IsaacSim SAC owner for the headless Python 3.11 IsaacLab/PhysX subprocess.
+# Policy-defining fields remain inherited from the MuJoCo owner contract;
+# interval randomization and reset-time PD mutation remain explicit fail-closed
+# choices.  Eval injects the render intent before the IsaacSim worker launches.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1WalkFlat
+  sim_backend: isaacsim
+  play_render_mode: auto
+algo:
+  num_envs: 2048
+  learning_starts: 10
+  max_iterations: 5000
+  save_interval: 1000
+  updates_per_step: 8
+  algo_params:
+    alpha_init: 0.001
+    target_entropy_ratio: 0.0
+env:
+  isaacsim_device_id: 0
+  isaacsim_worker_timeout_s: 120.0
+  isaacsim_render_width: 1280
+  isaacsim_render_height: 720
+  events:
+    pd_gains: null
+play_profile:
+  enabled: false
diff --git a/src/unilab/conf/sac/task/g1_walk_flat/mjwarp.yaml b/src/unilab/conf/sac/task/g1_walk_flat/mjwarp.yaml
new file mode 100644
index 000000000..cdcf7a793
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_walk_flat/mjwarp.yaml
@@ -0,0 +1,30 @@
+# @package _global_
+# Configured-only SAC mjwarp owner for the unified host contract adapter. Keeps
+# DENYLIST parity with the MuJoCo owner plus the mjwarp capacity knobs; legacy
+# kp/kd randomization is disabled. Offline record reuses MuJoCo rendering;
+# interactive eval routes through the MuJoCo viewer; native playback and
+# device-resident runtime are intentionally absent.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1WalkFlat
+  sim_backend: mjwarp
+  play_render_mode: record
+algo:
+  num_envs: 2048
+  learning_starts: 10
+  max_iterations: 5000
+  save_interval: 1000
+  updates_per_step: 8
+  algo_params:
+    alpha_init: 0.001
+    target_entropy_ratio: 0.0
+env:
+  mjwarp_nconmax: 128
+  mjwarp_njmax: 256
+  render_spacing: 2.0
+  events:
+    # Legacy mjwarp owners disable kp/kd and armature randomization.
+    pd_gains: null
diff --git a/src/unilab/conf/sac/task/g1_walk_flat/motrix.yaml b/src/unilab/conf/sac/task/g1_walk_flat/motrix.yaml
new file mode 100644
index 000000000..bc58ce374
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_walk_flat/motrix.yaml
@@ -0,0 +1,44 @@
+# @package _global_
+# SAC Motrix owner: keeps DENYLIST parity with the MuJoCo owner and retunes
+# reward shaping / disables kp/kd randomization for the Motrix direction.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1WalkFlat
+  sim_backend: motrix
+algo:
+  num_envs: 2048
+  learning_starts: 1
+  max_iterations: 5000
+  save_interval: 1000
+  updates_per_step: 8
+  algo_params:
+    alpha_init: 0.001
+    target_entropy_ratio: 0.0
+env:
+  events:
+    # Legacy Motrix owners disable kp/kd randomization.
+    pd_gains: null
+reward:
+  tracking_lin_vel:
+    weight: 2.2
+  tracking_ang_vel:
+    weight: 1.8
+  penalty_ang_vel_xy:
+    weight: -1.2
+  penalty_orientation:
+    weight: -12.0
+  penalty_action_rate:
+    weight: -2.5
+  pose:
+    weight: -0.6
+  penalty_feet_ori:
+    weight: -5.0
+  feet_phase:
+    weight: 6.0
+    params:
+      tracking_sigma: 0.008
+  alive:
+    weight: 12.0
diff --git a/src/unilab/conf/sac/task/g1_walk_flat/mujoco.yaml b/src/unilab/conf/sac/task/g1_walk_flat/mujoco.yaml
new file mode 100644
index 000000000..7c98fc381
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_walk_flat/mujoco.yaml
@@ -0,0 +1,19 @@
+# @package _global_
+# SAC MuJoCo owner: inherits the 29-DoF off-policy Manager-Based contract from
+# the shared base and only carries backend/algo identity.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1WalkFlat
+  sim_backend: mujoco
+algo:
+  num_envs: 2048
+  learning_starts: 10
+  max_iterations: 5000
+  save_interval: 1000
+  updates_per_step: 8
+  algo_params:
+    alpha_init: 0.001
+    target_entropy_ratio: 0.0
diff --git a/src/unilab/conf/sac/task/g1_walk_rough/motrix.yaml b/src/unilab/conf/sac/task/g1_walk_rough/motrix.yaml
new file mode 100644
index 000000000..e73c5b962
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_walk_rough/motrix.yaml
@@ -0,0 +1,47 @@
+# @package _global_
+# SAC Motrix rough owner: static-hfield rough scene, Motrix sim_dt=0.01, and
+# the Motrix-direction reward retuning; kp/kd randomization stays disabled.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1WalkRough
+  sim_backend: motrix
+algo:
+  num_envs: 2048
+  learning_starts: 1
+  max_iterations: 5000
+  save_interval: 1000
+  updates_per_step: 8
+  algo_params:
+    alpha_init: 0.001
+    target_entropy_ratio: 0.0
+env:
+  sim_dt: 0.01
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_rough.xml
+  events:
+    # Legacy Motrix owners disable kp/kd randomization.
+    pd_gains: null
+reward:
+  tracking_lin_vel:
+    weight: 2.2
+  tracking_ang_vel:
+    weight: 1.8
+  penalty_ang_vel_xy:
+    weight: -1.2
+  penalty_orientation:
+    weight: -12.0
+  penalty_action_rate:
+    weight: -2.5
+  pose:
+    weight: -0.6
+  penalty_feet_ori:
+    weight: -5.0
+  feet_phase:
+    weight: 6.0
+    params:
+      tracking_sigma: 0.008
+  alive:
+    weight: 12.0
diff --git a/src/unilab/conf/sac/task/g1_walk_rough/mujoco.yaml b/src/unilab/conf/sac/task/g1_walk_rough/mujoco.yaml
new file mode 100644
index 000000000..cce086903
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_walk_rough/mujoco.yaml
@@ -0,0 +1,23 @@
+# @package _global_
+# SAC MuJoCo rough owner: inherits the 29-DoF off-policy Manager-Based contract
+# and swaps the scene to the static-hfield rough XML (no height-scan
+# observation and no terrain curriculum, matching the legacy rough task).
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1WalkRough
+  sim_backend: mujoco
+algo:
+  num_envs: 2048
+  learning_starts: 10
+  max_iterations: 5000
+  save_interval: 1000
+  updates_per_step: 8
+  algo_params:
+    alpha_init: 0.001
+    target_entropy_ratio: 0.0
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_rough.xml
diff --git a/src/unilab/conf/sac/task/g1_wall_flip_tracking/mujoco.yaml b/src/unilab/conf/sac/task/g1_wall_flip_tracking/mujoco.yaml
new file mode 100644
index 000000000..68ddd8efd
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_wall_flip_tracking/mujoco.yaml
@@ -0,0 +1,41 @@
+# @package _global_
+defaults:
+  - /task/g1_flip_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1WallFlipTrackingSAC
+  sim_backend: mujoco
+
+algo:
+  algo_params:
+    target_entropy_ratio: 0.0
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat_with_wall.xml
+  commands:
+    motion:
+      params:
+        motion_file: motions/g1/flip_from_wall_104__A304.npz
+        sampling_mode: uniform
+        sampling_start_ratio: 0.0
+  terminations:
+    anchor_pos:
+      params: {command_name: motion, threshold: 1.0e9}
+    ee_body_pos:
+      params:
+        command_name: motion
+        threshold: 1.0e9
+        body_names:
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_yaw_link
+          - right_wrist_yaw_link
+    undesired_contacts: null
+
+reward:
+  motion_joint_pos:
+    weight: 0.5
+  motion_joint_vel:
+    weight: 0.25
diff --git a/src/unilab/conf/sac/task/g1_wbt_obs/mujoco.yaml b/src/unilab/conf/sac/task/g1_wbt_obs/mujoco.yaml
new file mode 100644
index 000000000..cebc64c13
--- /dev/null
+++ b/src/unilab/conf/sac/task/g1_wbt_obs/mujoco.yaml
@@ -0,0 +1,185 @@
+# @package _global_
+defaults:
+  - /task/g1_motion_tracking/mujoco
+  - _self_
+
+training:
+  task_name: G1WBTObs
+  sim_backend: mujoco
+
+algo:
+  num_envs: 4096
+  max_iterations: 140000
+  save_interval: 1000
+  gamma: 0.99
+  tau: 0.05
+  num_atoms: 501
+  updates_per_step: 4
+  policy_frequency: 2
+  algo_params:
+    alpha_init: 0.1
+    target_entropy_ratio: 0.5
+    max_grad_norm: 10.0
+
+env:
+  sim_dt: 0.005
+  scene:
+    entities:
+      robot:
+        geom_names:
+          - left_foot1_collision
+          - left_foot2_collision
+          - left_foot3_collision
+          - left_foot4_collision
+          - left_foot5_collision
+          - left_foot6_collision
+          - left_foot7_collision
+          - right_foot1_collision
+          - right_foot2_collision
+          - right_foot3_collision
+          - right_foot4_collision
+          - right_foot5_collision
+          - right_foot6_collision
+          - right_foot7_collision
+  observations:
+    actor:
+      terms:
+        motion_anchor_pos_b: null
+        motion_anchor_ori_b:
+          func: unilab.tasks.motion_tracking.common.manager_terms.motion_anchor_ori_b
+          params: {command_name: motion}
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.05
+            n_max: 0.05
+        base_lin_vel: null
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: pelvis_gyro}
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.2
+            n_max: 0.2
+          history_length: 5
+        joint_pos:
+          func: unilab.tasks.motion_tracking.common.manager_terms.motion_joint_pos_rel_biased
+          params: {command_name: motion}
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.01
+            n_max: 0.01
+          history_length: 5
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.5
+            n_max: 0.5
+          history_length: 5
+        actions:
+          func: unilab.envs.mdp.last_action
+          history_length: 5
+    critic:
+      terms:
+        base_ang_vel:
+          params: {sensor_name: pelvis_gyro}
+  actions:
+    joint_pos:
+      simulate_action_latency: true
+  terminations:
+    anchor_pos:
+      params: {command_name: motion, threshold: 0.4}
+    ee_body_pos:
+      params:
+        command_name: motion
+        threshold: 0.5
+        body_names:
+          - left_ankle_roll_link
+          - right_ankle_roll_link
+          - left_wrist_yaw_link
+          - right_wrist_yaw_link
+  events:
+    base_mass:
+      func: unilab.envs.mdp.randomize_rigid_body_mass
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+          body_names: pelvis
+        mass_distribution_params: [-1.0, 1.0]
+        operation: add
+        recompute_inertia: false
+    base_com:
+      func: unilab.envs.mdp.randomize_rigid_body_com
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+          body_names: pelvis
+        com_range:
+          x: [-0.05, 0.05]
+          y: [-0.05, 0.05]
+          z: [-0.05, 0.05]
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        kp_range: [0.9, 1.1]
+        kd_range: [0.85, 1.15]
+        operation: scale
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+          actuator_names: ".*"
+    foot_friction:
+      func: unilab.envs.mdp.geom_friction
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+          geom_names: "^(left|right)_foot[1-7]_collision$"
+        ranges: [0.3, 1.2]
+        operation: abs
+        shared_random: true
+    encoder_bias:
+      func: unilab.envs.mdp.randomize_encoder_bias
+      mode: reset
+      params:
+        bias_range: [-0.01, 0.01]
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+          joint_names: ".*"
+    push_robot:
+      func: unilab.envs.mdp.push_by_setting_velocity
+      mode: interval
+      interval_range_s: [4.0, 4.0]
+      is_global_time: true
+      params:
+        velocity_range:
+          x: [-1.0, 1.0]
+          y: [-1.0, 1.0]
+          z: [-0.5, 0.5]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [0.0, 0.0]
+
+reward:
+  motion_global_root_ori:
+    weight: 1.0
+  motion_body_pos:
+    weight: 1.0
+  action_rate_l2:
+    weight: -0.1
+  joint_limit:
+    weight: -5.0
+  joint_acc_l2:
+    func: unilab.tasks.motion_tracking.g1.manager_terms.joint_acc_l2
+    weight: -2.5e-7
+  joint_torque_l2:
+    func: unilab.tasks.motion_tracking.g1.manager_terms.joint_torque_l2
+    weight: -1.0e-5
+    params: {action_name: joint_pos}
diff --git a/src/unilab/conf/sac/task/go2_footstand/base.yaml b/src/unilab/conf/sac/task/go2_footstand/base.yaml
new file mode 100644
index 000000000..08095758c
--- /dev/null
+++ b/src/unilab/conf/sac/task/go2_footstand/base.yaml
@@ -0,0 +1,217 @@
+# @package _global_
+# Canonical Go2 footstand Manager-Based declaration for off-policy training.
+# Keep this task surface aligned with the PPO owner; Hydra remains the sole entry.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/go2/scene_flat.xml
+    default_keyframe_name: home
+    entities:
+      robot:
+        root_body_name: base
+        joint_names:
+          - FL_hip_joint
+          - FL_thigh_joint
+          - FL_calf_joint
+          - FR_hip_joint
+          - FR_thigh_joint
+          - FR_calf_joint
+          - RL_hip_joint
+          - RL_thigh_joint
+          - RL_calf_joint
+          - RR_hip_joint
+          - RR_thigh_joint
+          - RR_calf_joint
+        body_names:
+          - base
+          - FL_hip
+          - FL_thigh
+          - FL_calf
+          - FR_hip
+          - FR_thigh
+          - FR_calf
+          - RL_hip
+          - RL_thigh
+          - RL_calf
+          - RR_hip
+          - RR_thigh
+          - RR_calf
+        geom_names: [floor]
+        actuator_names:
+          - FR_hip
+          - FR_thigh
+          - FR_calf
+          - FL_hip
+          - FL_thigh
+          - FL_calf
+          - RR_hip
+          - RR_thigh
+          - RR_calf
+          - RL_hip
+          - RL_thigh
+          - RL_calf
+  sim_dt: 0.004
+  ctrl_dt: 0.02
+  max_episode_seconds: 10.0
+  adaptive_chunk_size: false
+  observations:
+    policy:
+      enable_corruption: true
+      terms:
+        frame:
+          func: unilab.tasks.locomotion.go2.footstand.frame_observation
+          params:
+            action_name: joint_pos
+          noise:
+            _target_: UniformNoiseCfg
+            n_min: [-0.1, -0.1, -0.1, -0.2, -0.2, -0.2, -0.05, -0.05, -0.05, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -0.01, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
+            n_max: [0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.05, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
+          history_length: 15
+    critic:
+      terms:
+        frame:
+          func: unilab.tasks.locomotion.go2.footstand.frame_observation
+          params:
+            action_name: joint_pos
+          history_length: 15
+        privileged:
+          func: unilab.tasks.locomotion.go2.footstand.privileged_observation
+          params:
+            action_name: joint_pos
+  actions:
+    joint_pos:
+      _target_: unilab.tasks.locomotion.go2.footstand.FootstandIncrementalActionCfg
+      entity_name: robot
+      actuator_names:
+        - FR_hip
+        - FR_thigh
+        - FR_calf
+        - FL_hip
+        - FL_thigh
+        - FL_calf
+        - RR_hip
+        - RR_thigh
+        - RR_calf
+        - RL_hip
+        - RL_thigh
+        - RL_calf
+      joint_names:
+        - FR_hip_joint
+        - FR_thigh_joint
+        - FR_calf_joint
+        - FL_hip_joint
+        - FL_thigh_joint
+        - FL_calf_joint
+        - RR_hip_joint
+        - RR_thigh_joint
+        - RR_calf_joint
+        - RL_hip_joint
+        - RL_thigh_joint
+        - RL_calf_joint
+      joint_position_limits:
+        - [-1.0472, 1.0472]
+        - [-1.5708, 3.4907]
+        - [-2.7227, -0.83776]
+        - [-1.0472, 1.0472]
+        - [-1.5708, 3.4907]
+        - [-2.7227, -0.83776]
+        - [-1.0472, 1.0472]
+        - [-0.5236, 4.5379]
+        - [-2.7227, -0.83776]
+        - [-1.0472, 1.0472]
+        - [-0.5236, 4.5379]
+        - [-2.7227, -0.83776]
+      action_scale: 0.3
+      clip_actions: 1.0
+      kp: 35.0
+      kd: 0.5
+      simulate_action_latency: false
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_root_state_uniform:
+      func: unilab.envs.mdp.reset_root_state_uniform
+      mode: reset
+      params:
+        pose_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [-3.141592653589793, 3.141592653589793]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.5, 0.5]
+          roll: [-0.5, 0.5]
+          pitch: [-0.5, 0.5]
+          yaw: [-0.5, 0.5]
+    reset_joints:
+      func: unilab.tasks.locomotion.go2.footstand.FootstandJointReset
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          joint_names: ".*"
+        position_offset_range: [-0.05, 0.05]
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        kp_range: [35.0, 35.0]
+        kd_range: [0.5, 0.5]
+        operation: abs
+    floor_friction: null
+    link_mass: null
+    torso_com: null
+    joint_armature: null
+  terminations:
+    footstand:
+      func: unilab.tasks.locomotion.go2.footstand.FootstandTermination
+      params:
+        action_name: joint_pos
+        grace_steps: 100
+        height_fraction: 0.8
+        orientation_threshold: 0.2
+        energy_threshold: 200.0
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+  policy_observation_group: policy
+  critic_observation_group: critic
+  scale_rewards_by_dt: true
+
+reward:
+  footstand:
+    func: unilab.tasks.locomotion.go2.footstand.FootstandReward
+    weight: 1.0
+    params:
+      state_term_name: footstand
+      scales:
+        height: 2.0
+        orientation: 2.0
+        contact: -1.0
+        action_rate: -0.01
+        termination: -2.0
+        dof_pos_limits: -0.5
+        torques: 0.0
+        pose: -0.1
+        penalty_contact: -0.2
+        tar: 0.8
+        rear_feet_contact: 0.5
+        rear_leg_symmetry: -0.2
+        front_leg_motion: -0.05
+        upright_stability: -0.2
+        knee_clearance: -0.5
+        stay_still: -0.1
+        energy: -0.003
+        dof_acc: -2.5e-7
+      soft_joint_pos_limit_factor: 0.9
+      knee_height_target: 0.08
+      front_feet_min_separation: 0.16
+      front_feet_side_margin: 0.04
+      rear_hip_abduction_margin: 0.25
+      rear_foot_slip_deadband: 0.02
+      rear_foot_anchor_radius: 0.03
diff --git a/src/unilab/conf/sac/task/go2_footstand/drake.yaml b/src/unilab/conf/sac/task/go2_footstand/drake.yaml
new file mode 100644
index 000000000..3c9fd8b7c
--- /dev/null
+++ b/src/unilab/conf/sac/task/go2_footstand/drake.yaml
@@ -0,0 +1,41 @@
+# @package _global_
+defaults:
+  - /task/go2_footstand/base
+  - _self_
+
+training:
+  task_name: Go2FootStand
+  sim_backend: drake
+  no_play: true
+  play_steps: 400
+  play_env_num: 1
+  play_render_mode: record
+
+algo:
+  algo_log_name: fast_sac_drake
+  num_envs: 4096
+  batch_size: 1024
+  replay_buffer_n: 512
+  updates_per_step: 2
+  learning_starts: 2
+  max_iterations: 300
+  save_interval: 100
+  actor_hidden_dim: 256
+  critic_hidden_dim: 512
+  obs_normalization: true
+  use_layer_norm: true
+  algo_params:
+    alpha_init: 0.005
+    target_entropy_ratio: 0.0
+    use_compile: false
+
+env:
+  drake_backend_mode: batch
+  drake_nthread: 20
+  events:
+    # Drake keeps the task joint reset and rejects unsupported payload fields.
+    pd_gains: null
+    floor_friction: null
+    link_mass: null
+    torso_com: null
+    joint_armature: null
diff --git a/src/unilab/conf/sac/task/go2_joystick_flat/base.yaml b/src/unilab/conf/sac/task/go2_joystick_flat/base.yaml
new file mode 100644
index 000000000..409129c16
--- /dev/null
+++ b/src/unilab/conf/sac/task/go2_joystick_flat/base.yaml
@@ -0,0 +1,206 @@
+# @package _global_
+# Canonical Go2 flat Manager-Based task declaration. Backend owner leaves inherit
+# this file and only override backend/algo tuning or explicitly disabled terms.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/go2/scene_flat.xml
+    default_keyframe_name: home
+    entities:
+      robot:
+        root_body_name: base
+        joint_names:
+          - FL_hip_joint
+          - FL_thigh_joint
+          - FL_calf_joint
+          - FR_hip_joint
+          - FR_thigh_joint
+          - FR_calf_joint
+          - RL_hip_joint
+          - RL_thigh_joint
+          - RL_calf_joint
+          - RR_hip_joint
+          - RR_thigh_joint
+          - RR_calf_joint
+        actuator_names:
+          - FR_hip
+          - FR_thigh
+          - FR_calf
+          - FL_hip
+          - FL_thigh
+          - FL_calf
+          - RR_hip
+          - RR_thigh
+          - RR_calf
+          - RL_hip
+          - RL_thigh
+          - RL_calf
+  sim_dt: 0.01
+  ctrl_dt: 0.02
+  max_episode_seconds: 20.0
+  observations:
+    policy:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+        gait_phase:
+          func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase
+          params:
+            frequency: 2.0
+    critic:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+        gait_phase:
+          func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase
+          params:
+            frequency: 2.0
+        base_lin_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: local_linvel
+  actions:
+    joint_pos:
+      _target_: unilab.envs.mdp.JointPositionActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      scale: 0.25
+      use_default_offset: true
+  commands:
+    twist:
+      _target_: unilab.envs.mdp.UniformVelocityCommandCfg
+      entity_name: robot
+      resampling_time_range: [20.0, 20.0]
+      heading_command: false
+      heading_control_stiffness: 0.5
+      rel_standing_envs: 0.0
+      rel_heading_envs: 0.0
+      rel_world_envs: 0.0
+      rel_forward_envs: 0.0
+      init_velocity_prob: 0.0
+      ranges:
+        lin_vel_x: [-0.6, 1.0]
+        lin_vel_y: [-0.4, 0.4]
+        ang_vel_z: [-0.8, 0.8]
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_root_state_uniform:
+      func: unilab.envs.mdp.reset_root_state_uniform
+      mode: reset
+      params:
+        pose_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [-3.141592653589793, 3.141592653589793]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.5, 0.5]
+          roll: [-0.5, 0.5]
+          pitch: [-0.5, 0.5]
+          yaw: [-0.5, 0.5]
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        kp_range: [31.5, 38.5]
+        kd_range: [0.45, 0.55]
+        operation: abs
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    bad_orientation:
+      func: unilab.envs.mdp.bad_orientation
+      params:
+        limit_angle: 1.0471975511965976
+  policy_observation_group: policy
+  critic_observation_group: critic
+
+reward:
+  tracking_lin_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_lin_vel_xy_exp
+    weight: 1.0
+    params:
+      std: 0.5
+      command_name: twist
+  tracking_ang_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_ang_vel_z_exp
+    weight: 0.2
+    params:
+      std: 0.5
+      command_name: twist
+  lin_vel_z:
+    func: unilab.tasks.locomotion.common.manager_terms.lin_vel_z_l2
+    weight: -5.0
+  ang_vel_xy:
+    func: unilab.tasks.locomotion.common.manager_terms.ang_vel_xy_l2
+    weight: -0.1
+  base_height:
+    func: unilab.tasks.locomotion.common.manager_terms.base_height_l2
+    weight: -100.0
+    params:
+      target_height: 0.3
+  action_rate:
+    func: unilab.envs.mdp.action_rate_l2
+    weight: -0.005
+  similar_to_default:
+    func: unilab.tasks.locomotion.common.manager_terms.joint_deviation_l1
+    weight: -0.1
+  contact:
+    func: unilab.tasks.locomotion.common.manager_terms.feet_phase_contact
+    weight: 0.24
+    params:
+      frequency: 2.0
+      sensor_names:
+        - FL_foot_contact
+        - FR_foot_contact
+        - RL_foot_contact
+        - RR_foot_contact
+      contact_threshold: 0.1
+      stance_threshold: 0.6
+  swing_feet_z:
+    func: unilab.tasks.locomotion.common.manager_terms.feet_phase_swing_height
+    weight: 4.0
+    params:
+      frequency: 2.0
+      sensor_names: [FL_pos, FR_pos, RL_pos, RR_pos]
+      target_height: 0.1
+      kernel: 0.01
+      swing_start: 0.6
diff --git a/src/unilab/conf/sac/task/go2_joystick_flat/drake.yaml b/src/unilab/conf/sac/task/go2_joystick_flat/drake.yaml
new file mode 100644
index 000000000..0b32deada
--- /dev/null
+++ b/src/unilab/conf/sac/task/go2_joystick_flat/drake.yaml
@@ -0,0 +1,43 @@
+# @package _global_
+defaults:
+  - /task/go2_joystick_flat/base
+  - _self_
+
+training:
+  task_name: Go2JoystickFlat
+  sim_backend: drake
+  no_play: true
+  play_steps: 400
+  play_env_num: 1
+  play_render_mode: record
+
+algo:
+  algo_log_name: fast_sac_drake
+  num_envs: 512
+  batch_size: 1024
+  replay_buffer_n: 512
+  updates_per_step: 2
+  learning_starts: 2
+  max_iterations: 300
+  save_interval: 100
+  actor_hidden_dim: 256
+  critic_hidden_dim: 512
+  obs_normalization: true
+  use_layer_norm: true
+  algo_params:
+    alpha_init: 0.005
+    target_entropy_ratio: 0.0
+    use_compile: false
+
+env:
+  drake_backend_mode: batch
+  drake_nthread: 20
+  events:
+    pd_gains: null
+
+reward:
+  # drake_uni reports per-body net contact force in the world frame, not the
+  # MuJoCo contact frame (normal first) the gait terms gate on; keep the
+  # contact reward off until the drake adapter reports contact-frame force
+  # (issue #1471).
+  contact: null
diff --git a/src/unilab/conf/sac/task/go2w_joystick_flat/base.yaml b/src/unilab/conf/sac/task/go2w_joystick_flat/base.yaml
new file mode 100644
index 000000000..3d057e1ee
--- /dev/null
+++ b/src/unilab/conf/sac/task/go2w_joystick_flat/base.yaml
@@ -0,0 +1,263 @@
+# @package _global_
+# Canonical Go2W flat Manager-Based task declaration. Backend leaves only own
+# backend identity and backend-specific rendering/runtime settings.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/go2w/scene_flat.xml
+    default_keyframe_name: home
+    entities:
+      robot:
+        root_body_name: base_link
+        joint_names:
+          - FR_hip_joint
+          - FR_thigh_joint
+          - FR_calf_joint
+          - FL_hip_joint
+          - FL_thigh_joint
+          - FL_calf_joint
+          - RR_hip_joint
+          - RR_thigh_joint
+          - RR_calf_joint
+          - RL_hip_joint
+          - RL_thigh_joint
+          - RL_calf_joint
+          - FR_wheel_joint
+          - FL_wheel_joint
+          - RR_wheel_joint
+          - RL_wheel_joint
+        actuator_names:
+          - FR_hip
+          - FR_thigh
+          - FR_calf
+          - FL_hip
+          - FL_thigh
+          - FL_calf
+          - RR_hip
+          - RR_thigh
+          - RR_calf
+          - RL_hip
+          - RL_thigh
+          - RL_calf
+          - FR_wheel
+          - FL_wheel
+          - RR_wheel
+          - RL_wheel
+        body_names: [base_link]
+  sim_dt: 0.005
+  ctrl_dt: 0.02
+  max_episode_seconds: 20.0
+  observations:
+    policy:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        leg_joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+          params:
+            asset_cfg:
+              _target_: SceneEntityCfg
+              name: robot
+              joint_names: ".*_(hip|thigh|calf)_joint"
+        leg_joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+          params:
+            asset_cfg:
+              _target_: SceneEntityCfg
+              name: robot
+              joint_names: ".*_(hip|thigh|calf)_joint"
+        wheel_joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+          params:
+            asset_cfg:
+              _target_: SceneEntityCfg
+              name: robot
+              joint_names: ".*_wheel_joint"
+        actions:
+          func: unilab.envs.mdp.last_action
+          params:
+            action_name: motor
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+    critic:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        leg_joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+          params:
+            asset_cfg:
+              _target_: SceneEntityCfg
+              name: robot
+              joint_names: ".*_(hip|thigh|calf)_joint"
+        leg_joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+          params:
+            asset_cfg:
+              _target_: SceneEntityCfg
+              name: robot
+              joint_names: ".*_(hip|thigh|calf)_joint"
+        wheel_joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+          params:
+            asset_cfg:
+              _target_: SceneEntityCfg
+              name: robot
+              joint_names: ".*_wheel_joint"
+        actions:
+          func: unilab.envs.mdp.last_action
+          params:
+            action_name: motor
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+        base_lin_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: local_linvel
+        motor_torque:
+          func: unilab.tasks.locomotion.go2w.manager_terms.motor_torque
+          params:
+            action_name: motor
+  actions:
+    motor:
+      _target_: unilab.tasks.locomotion.go2w.manager_terms.Go2WMixedActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      leg_action_scale: 0.5
+      wheel_action_scale: 10.0
+      leg_kp: 50.0
+      leg_kd: 1.5
+      wheel_kd: 0.5
+      clip_actions: 1.0
+      simulate_action_latency: false
+  commands:
+    twist:
+      _target_: unilab.tasks.locomotion.go2w.manager_terms.Go2WVelocityCommandCfg
+      entity_name: robot
+      resampling_time_range: [20.0, 20.0]
+      heading_command: false
+      heading_control_stiffness: 0.5
+      rel_standing_envs: 0.0
+      rel_heading_envs: 0.0
+      rel_world_envs: 0.0
+      rel_forward_envs: 0.0
+      init_velocity_prob: 0.0
+      planar_dead_zone: 0.2
+      ranges:
+        lin_vel_x: [0.0, 1.0]
+        lin_vel_y: [0.0, 0.0]
+        ang_vel_z: [-1.0, 1.0]
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_root_state_uniform:
+      func: unilab.envs.mdp.reset_root_state_uniform
+      mode: reset
+      params:
+        pose_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [-3.141592653589793, 3.141592653589793]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.5, 0.5]
+          roll: [-0.5, 0.5]
+          pitch: [-0.5, 0.5]
+          yaw: [-0.5, 0.5]
+    motor_gains:
+      func: unilab.tasks.locomotion.go2w.manager_terms.randomize_motor_gains
+      mode: reset
+      params:
+        action_name: motor
+        kp_multiplier_range: [1.0, 1.0]
+        kd_multiplier_range: [1.0, 1.0]
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    bad_orientation:
+      func: unilab.envs.mdp.bad_orientation
+      params:
+        limit_angle: 1.0471975511965976
+  policy_observation_group: policy
+  critic_observation_group: critic
+
+reward:
+  tracking_lin_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_lin_vel_xy_exp
+    weight: 1.0
+    params:
+      std: 0.5
+      command_name: twist
+  tracking_ang_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_ang_vel_z_exp
+    weight: 0.75
+    params:
+      std: 0.5
+      command_name: twist
+  lin_vel_z:
+    func: unilab.tasks.locomotion.common.manager_terms.lin_vel_z_l2
+    weight: -5.0
+  ang_vel_xy:
+    func: unilab.tasks.locomotion.common.manager_terms.ang_vel_xy_l2
+    weight: -0.1
+  base_height:
+    func: unilab.tasks.locomotion.common.manager_terms.base_height_l2
+    weight: -100.0
+    params:
+      target_height: 0.4
+  orientation:
+    func: unilab.envs.mdp.flat_orientation_l2
+    weight: -2.0
+  action_rate:
+    func: unilab.tasks.locomotion.go2w.manager_terms.clipped_action_rate_l2
+    weight: -0.005
+    params:
+      action_name: motor
+  similar_to_default:
+    func: unilab.tasks.locomotion.common.manager_terms.joint_deviation_l1
+    weight: -0.5
+    params:
+      asset_cfg:
+        _target_: SceneEntityCfg
+        name: robot
+        joint_names: ".*_(hip|thigh|calf)_joint"
+  torques:
+    func: unilab.tasks.locomotion.go2w.manager_terms.motor_torque_l2
+    weight: -0.0002
+    params:
+      action_name: motor
+  wheel_vel:
+    func: unilab.envs.mdp.joint_vel_l2
+    weight: 0.0
+    params:
+      asset_cfg:
+        _target_: SceneEntityCfg
+        name: robot
+        joint_names: ".*_wheel_joint"
+  alive:
+    func: unilab.tasks.locomotion.go2w.manager_terms.constant_alive
+    weight: 0.5
+  upward:
+    func: unilab.tasks.locomotion.go2w.manager_terms.upward_l2
+    weight: 1.0
diff --git a/src/unilab/conf/sac/task/go2w_joystick_flat/drake.yaml b/src/unilab/conf/sac/task/go2w_joystick_flat/drake.yaml
new file mode 100644
index 000000000..644465051
--- /dev/null
+++ b/src/unilab/conf/sac/task/go2w_joystick_flat/drake.yaml
@@ -0,0 +1,34 @@
+# @package _global_
+defaults:
+  - /task/go2w_joystick_flat/base
+  - _self_
+
+training:
+  task_name: Go2WJoystickFlat
+  sim_backend: drake
+  no_play: true
+  play_steps: 400
+  play_env_num: 1
+  play_render_mode: record
+
+algo:
+  algo_log_name: fast_sac_drake
+  num_envs: 512
+  batch_size: 1024
+  replay_buffer_n: 512
+  updates_per_step: 2
+  learning_starts: 2
+  max_iterations: 300
+  save_interval: 100
+  actor_hidden_dim: 256
+  critic_hidden_dim: 512
+  obs_normalization: true
+  use_layer_norm: true
+  algo_params:
+    alpha_init: 0.005
+    target_entropy_ratio: 0.0
+    use_compile: false
+
+env:
+  drake_backend_mode: batch
+  drake_nthread: 20
diff --git a/src/unilab/conf/sac/task/sharpa_inhand/mujoco_hora.yaml b/src/unilab/conf/sac/task/sharpa_inhand/mujoco_hora.yaml
new file mode 100644
index 000000000..a0025de0e
--- /dev/null
+++ b/src/unilab/conf/sac/task/sharpa_inhand/mujoco_hora.yaml
@@ -0,0 +1,120 @@
+# @package _global_
+# HORA Sharpa SAC MuJoCo teacher-training owner config.
+
+training:
+  task_name: SharpaInhandRotation
+  sim_backend: mujoco
+  use_amp: true
+  env_steps_per_sync: 2
+  no_play: true
+  play_steps: 200
+  render_spacing: 0.5
+  cam_distance: 1.5
+  cam_lookat: [0.75, 0.75, 0.4]
+  cam_elevation: -20.0
+
+interactive:
+  action_mode: policy
+  policy_obs_mode: actor
+  camera_distance: 1.5
+  camera_elevation: -20.0
+  camera_azimuth: 90.0
+  use_env_visual_model: true
+
+algo:
+  algo_log_name: hora_sac
+  runtime_impl: hora_sac
+  runtime_resolver: uni_rl.algos.hora.sac:resolve_hora_sac_runtime
+  num_envs: 1024
+  batch_size: 2048
+  replay_buffer_n: 1280
+  updates_per_step: 14
+  learning_starts: 1
+  policy_frequency: 2
+  max_iterations: 5371
+  save_interval: 896
+  actor_lr: 4.5e-4
+  critic_lr: 4.5e-4
+  actor:
+    priv_info_embed_dim: 9
+    priv_mlp_hidden_dims: [256, 128, 9]
+  algo_params:
+    alpha_lr: 4.5e-4
+    amp_dtype: auto
+    use_compile: true
+
+reward:
+  scales:
+    rotate: 2.5
+    obj_linvel: -0.3
+    pose_diff: -0.4
+    torque: -0.1
+    work: -0.5
+    object_pos: 0.003
+  angvel_clip_min: -0.5
+  angvel_clip_max: 0.5
+
+env:
+  post_step_forward_sensor: true
+  zero_action_test_mode: false
+  clip_obs: 5.0
+  clip_actions: 1.0
+  reset_height_lower: 0.59906
+  reset_height_upper: 0.63906
+  reset_angle_diff: 0.7853981633974483
+  rot_axis: [0.0, 0.0, 1.0]
+  grasp_cache_path: caches/sharpa_grasp_linspace
+  sensor:
+    tactile_force_sensor_names:
+      - contact_right_thumb_elastomer_force
+      - contact_right_index_elastomer_force
+      - contact_right_middle_elastomer_force
+      - contact_right_ring_elastomer_force
+      - contact_right_pinky_elastomer_force
+  disable_tactile_ids: []
+  use_default_object_pose_for_object_pos_anchor: true
+  obs:
+    observation_mode: separated
+    enable_tactile: true
+    binary_contact: false
+    enable_contact_pos: false
+    contact_smooth: 0.5
+    contact_threshold: 0.05
+    tactile_force_clip_max: 4.0
+  priv_info:
+    include_friction_scale: true
+    include_gravity_direction: false
+  control_config:
+    action_scale: 0.041666666666666664
+    p_gain: 1.0
+    d_gain: 0.1
+    torque_control: false
+    dof_limits_scale: 0.9
+  domain_rand:
+    scale_list: [0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5]
+    randomize_gravity_direction: true
+    gravity_direction_magnitude: 9.81
+    randomize_pd_gains: true
+    randomize_p_gain_scale_lower: 0.5
+    randomize_p_gain_scale_upper: 2.0
+    randomize_d_gain_scale_lower: 0.5
+    randomize_d_gain_scale_upper: 2.0
+    randomize_friction: true
+    randomize_friction_scale_lower: 0.75
+    randomize_friction_scale_upper: 1.25
+    elastomer_base_friction: 2.0
+    metal_base_friction: 1.0
+    object_base_friction: 2.0
+    randomize_com: true
+    randomize_com_lower: -0.01
+    randomize_com_upper: 0.01
+    randomize_mass: true
+    randomize_mass_lower: 0.01
+    randomize_mass_upper: 0.25
+    force_scale: 2.0
+    random_force_prob_scalar: 0.25
+    force_decay: 0.9
+    force_decay_interval: 0.08
+    joint_noise_scale: 0.02
+    contact_latency: 0.005
+    contact_sensor_noise: 0.01
diff --git a/src/unilab/conf/sac/task/stewart_balance/base.yaml b/src/unilab/conf/sac/task/stewart_balance/base.yaml
new file mode 100644
index 000000000..e38b87656
--- /dev/null
+++ b/src/unilab/conf/sac/task/stewart_balance/base.yaml
@@ -0,0 +1,116 @@
+# @package _global_
+# Off-policy copy of the canonical Stewart Manager-Based declaration. Hydra
+# config groups have separate search roots, so this intentionally mirrors PPO.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/stewart/scene.xml
+    entities:
+      stewart:
+        root_body_name: ball
+        actuator_names: [a0, a1, a2, a3, a4, a5]
+        body_names:
+          - ball
+          - top
+          - leg00
+          - leg10
+          - leg01
+          - leg11
+          - leg02
+          - leg12
+          - top_connect00
+          - top_connect10
+          - top_connect01
+          - top_connect11
+          - top_connect02
+          - top_connect12
+  sim_dt: 0.004
+  ctrl_dt: 0.02
+  max_episode_seconds: 24.0
+  render_spacing: 4.5
+  observations:
+    policy:
+      terms:
+        balance:
+          func: unilab.tasks.manipulation.stewart.balance.StewartObservation
+          params:
+            entity_name: stewart
+            action_name: tilt
+            ball_body_name: ball
+            top_body_name: top
+            target_rotation_limit_deg: 6.0
+            vel_smooth: 0.25
+  actions:
+    tilt:
+      _target_: unilab.tasks.manipulation.stewart.balance.StewartTiltActionCfg
+      entity_name: stewart
+      actuator_names: [a0, a1, a2, a3, a4, a5]
+      top_body_name: top
+      ball_body_name: ball
+      leg_body_names: [leg00, leg10, leg01, leg11, leg02, leg12]
+      top_connect_body_names:
+        - top_connect00
+        - top_connect10
+        - top_connect01
+        - top_connect11
+        - top_connect02
+        - top_connect12
+      raw_action_clip: [-1.0, 1.0]
+      target_rotation_limit_deg: 6.0
+      action_smooth: 0.60
+      center_control_radius: 0.25
+      center_control_min_gain: 0.15
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_ball:
+      func: unilab.tasks.manipulation.stewart.balance.StewartBallReset
+      mode: reset
+      params:
+        entity_name: stewart
+        platform_radius: 0.8
+        init_ball_radius_ratio: 0.18
+        ball_home_z: 1.2
+  terminations:
+    balance_state:
+      func: unilab.tasks.manipulation.stewart.balance.StewartBalanceState
+      params:
+        observation_group: policy
+        observation_term: balance
+        platform_radius: 0.8
+        fall_radius: 0.5
+        top_center_z: 1.0
+        still_xy: 0.12
+        still_vel: 0.07
+        still_xy_hysteresis: 1.15
+        still_vel_hysteresis: 1.20
+        zero_vel_thresh: 0.07
+        still_steps_needed: 5
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+  scale_rewards_by_dt: false
+  policy_observation_group: policy
+  critic_observation_group: null
+
+reward:
+  center:
+    func: unilab.tasks.manipulation.stewart.balance.center_reward
+    weight: 0.7
+    params:
+      state_term_name: balance_state
+  progress:
+    func: unilab.tasks.manipulation.stewart.balance.progress_reward
+    weight: 0.6
+    params:
+      state_term_name: balance_state
+  still:
+    func: unilab.tasks.manipulation.stewart.balance.still_reward
+    weight: 3.0
+    params:
+      state_term_name: balance_state
+  fall:
+    func: unilab.tasks.manipulation.stewart.balance.fall_reward
+    weight: -6.0
+    params:
+      state_term_name: balance_state
diff --git a/src/unilab/conf/sac/task/stewart_balance/drake.yaml b/src/unilab/conf/sac/task/stewart_balance/drake.yaml
new file mode 100644
index 000000000..4992f2146
--- /dev/null
+++ b/src/unilab/conf/sac/task/stewart_balance/drake.yaml
@@ -0,0 +1,35 @@
+# @package _global_
+defaults:
+  - /task/stewart_balance/base
+  - _self_
+
+training:
+  task_name: StewartBalance
+  sim_backend: drake
+  no_play: true
+  play_steps: 400
+  play_env_num: 1
+  play_render_mode: record
+  render_spacing: 4.5
+
+algo:
+  algo_log_name: fast_sac_drake
+  num_envs: 256
+  batch_size: 512
+  replay_buffer_n: 512
+  updates_per_step: 2
+  learning_starts: 2
+  max_iterations: 400
+  save_interval: 100
+  actor_hidden_dim: 128
+  critic_hidden_dim: 256
+  obs_normalization: true
+  use_layer_norm: true
+  algo_params:
+    alpha_init: 0.005
+    target_entropy_ratio: 0.0
+    use_compile: false
+
+env:
+  drake_backend_mode: batch
+  drake_nthread: 20
diff --git a/src/unilab/conf/td3/config.yaml b/src/unilab/conf/td3/config.yaml
new file mode 100644
index 000000000..909355b08
--- /dev/null
+++ b/src/unilab/conf/td3/config.yaml
@@ -0,0 +1,137 @@
+defaults:
+  - _self_
+  - task: g1_walk_flat/mujoco
+
+algo:
+  algo: td3
+  algo_log_name: fast_td3
+  load_run: "-1"
+  seed: 1
+  num_envs: 4096
+  batch_size: 8192
+  replay_buffer_n: 1000
+  updates_per_step: 4
+  learning_starts: 1
+  policy_frequency: 2
+  max_iterations: 5000
+  save_interval: 500
+  gamma: 0.97
+  tau: 0.1
+  actor_lr: 3.0e-4
+  critic_lr: 3.0e-4
+  actor_hidden_dim: 512
+  critic_hidden_dim: 1024
+  num_atoms: 101
+  obs_normalization: true
+  use_layer_norm: false
+  algo_params:
+    weight_decay: 0.1
+    v_min: -10.0
+    v_max: 10.0
+    init_scale: 0.01
+    log_std_min: -1.6
+    log_std_max: -0.22
+    policy_noise: 0.2
+    noise_clip: 0.5
+    use_cdq: true
+
+training:
+  task_name: G1WalkFlat
+  # list[int] | null; null/[] = auto-selected single-device behavior;
+  # [d0] = explicit single CUDA device; [d0..dN-1] = N-way data parallel,
+  # rank i trains on cuda:devices[i].
+  devices: null
+  # list[list[int]] | null; one CPU-id segment per rank for the collector's
+  # MuJoCo pool; null = auto partition of cpu_count // world_size per rank.
+  dp_collector_cpu_ids: null
+  logger: tensorboard
+  wandb_project: unilab
+  wandb_entity: null
+  wandb_group: null
+  wandb_job_type: null
+  wandb_name: null
+  wandb_tags: []
+  wandb_notes: null
+  wandb_mode: null
+  sim_backend: mujoco
+  nan_guard:
+    enabled: true
+    buffer_size: 100
+    max_envs_to_dump: 5
+    output_dir: null
+  use_amp: true
+  play_only: false
+  no_play: false
+  sim2sim_strict: true
+  play_render_mode: auto
+  export_onnx: true
+  play_env_num: 16
+  play_steps: 800
+  cam_distance: 6.0
+  cam_elevation: -20.0
+  cam_azimuth: 90.0
+  log_root: null
+  log_dir: null
+  env_steps_per_sync: 1
+  trace_enabled: false
+  trace_output_dir: null
+  trace_thread_time: false
+  trace_cuda_events: true
+  nvtx_profile_ranges: false
+  replay_prefetch_mode: one_tick
+  torch_threads:
+    enabled: true
+    # "auto" resolves per process role from host CPU count with conservative caps.
+    # Override these from Hydra when benchmarking a specific machine.
+    learner_num_threads: auto
+    collector_num_threads: auto
+    learner_num_interop_threads: 1
+    collector_num_interop_threads: 1
+    compile_threads: auto
+    set_env_vars: true
+
+interactive:
+  action_mode: zero
+  policy_obs_mode: auto
+  show_target_bodies: false
+  show_reward_debug: false
+  target_show_axes: false
+  target_body_names: ""
+  target_max_bodies: 0
+  target_marker_radius: 0.02
+  target_axis_length: 0.08
+  target_marker_alpha: 0.75
+  reward_debug_show_velocity: false
+  reward_debug_lin_vel_scale: 0.08
+  reward_debug_ang_vel_scale: 0.05
+  reward_debug_show_connectors: false
+  reward_debug_show_global_anchor: false
+  camera_follow_body: true
+  camera_focus_body_name: ""
+  camera_height_offset: 0.15
+  camera_distance: null
+  camera_elevation: null
+  camera_azimuth: null
+  use_env_visual_model: true
+  speed: 1.0
+  start_paused: false
+  keyboard: false
+  keyboard_step_lin: 0.1
+  keyboard_step_ang: 0.2
+
+env:
+  post_step_forward_sensor: false
+  # adaptive_chunk_size: auto-tune the MuJoCo BatchEnvPool chunk_size at materialize
+  # (cache-backed). chunk_size (int) manually overrides and wins; null => use default.
+  adaptive_chunk_size: true
+  chunk_size: null
+
+hydra:
+  run:
+    dir: .
+  output_subdir: null
+  job:
+    chdir: false
+  job_logging:
+    root:
+      handlers: [console]
diff --git a/src/unilab/conf/td3/task/g1_23dof_walk_flat/base.yaml b/src/unilab/conf/td3/task/g1_23dof_walk_flat/base.yaml
new file mode 100644
index 000000000..c7571348f
--- /dev/null
+++ b/src/unilab/conf/td3/task/g1_23dof_walk_flat/base.yaml
@@ -0,0 +1,66 @@
+# @package _global_
+# Canonical G1 23-DoF walk Manager-Based task declaration (off-policy owners).
+# Inherits the 29-DoF off-policy contract and swaps the scene to the 23-DoF
+# model (no waist roll/pitch, no wrist pitch/yaw) with 23-entry pose weights.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat_23dof.xml
+    entities:
+      robot:
+        joint_names:
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+        actuator_names:
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+
+reward:
+  pose:
+    params:
+      pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
diff --git a/src/unilab/conf/td3/task/g1_23dof_walk_flat/mujoco.yaml b/src/unilab/conf/td3/task/g1_23dof_walk_flat/mujoco.yaml
new file mode 100644
index 000000000..52c728a45
--- /dev/null
+++ b/src/unilab/conf/td3/task/g1_23dof_walk_flat/mujoco.yaml
@@ -0,0 +1,12 @@
+# @package _global_
+# TD3 MuJoCo 23-DoF owner: inherits the 23-DoF off-policy Manager-Based
+# contract and only carries backend/algo identity.
+defaults:
+  - /task/g1_23dof_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1Walk23DofFlat
+  sim_backend: mujoco
+algo:
+  max_iterations: 100000
diff --git a/src/unilab/conf/td3/task/g1_walk_flat/base.yaml b/src/unilab/conf/td3/task/g1_walk_flat/base.yaml
new file mode 100644
index 000000000..2f6d55168
--- /dev/null
+++ b/src/unilab/conf/td3/task/g1_walk_flat/base.yaml
@@ -0,0 +1,278 @@
+# @package _global_
+# Canonical G1 29-DoF walk Manager-Based task declaration (off-policy owners).
+# Backend owner leaves inherit this file and only override backend/algo tuning
+# or explicitly disabled terms. Observation scaling follows the walk profile
+# (gyro x0.25, joint velocity x0.05, critic linear velocity x2.0); every
+# off-policy owner carries the penalty curriculum.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/g1/scene_flat.xml
+    default_keyframe_name: stand
+    entities:
+      robot:
+        root_body_name: pelvis
+        joint_names:
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - waist_roll_joint
+          - waist_pitch_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - left_wrist_pitch_joint
+          - left_wrist_yaw_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+          - right_wrist_pitch_joint
+          - right_wrist_yaw_joint
+        actuator_names:
+          - left_hip_pitch_joint
+          - left_hip_roll_joint
+          - left_hip_yaw_joint
+          - left_knee_joint
+          - left_ankle_pitch_joint
+          - left_ankle_roll_joint
+          - right_hip_pitch_joint
+          - right_hip_roll_joint
+          - right_hip_yaw_joint
+          - right_knee_joint
+          - right_ankle_pitch_joint
+          - right_ankle_roll_joint
+          - waist_yaw_joint
+          - waist_roll_joint
+          - waist_pitch_joint
+          - left_shoulder_pitch_joint
+          - left_shoulder_roll_joint
+          - left_shoulder_yaw_joint
+          - left_elbow_joint
+          - left_wrist_roll_joint
+          - left_wrist_pitch_joint
+          - left_wrist_yaw_joint
+          - right_shoulder_pitch_joint
+          - right_shoulder_roll_joint
+          - right_shoulder_yaw_joint
+          - right_elbow_joint
+          - right_wrist_roll_joint
+          - right_wrist_pitch_joint
+          - right_wrist_yaw_joint
+        body_names: [pelvis]
+  sim_dt: 0.006666666666666667
+  ctrl_dt: 0.02
+  max_episode_seconds: 20.0
+  observations:
+    policy:
+      # Observation noise matches the legacy off-policy noise_config (level=1.0,
+      # actor-only; gyro/gravity/linvel scales were 0.0 there): joint pos
+      # +/-0.01 and joint vel +/-0.1, applied before term scaling.
+      enable_corruption: true
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: torso_gyro}
+          scale: 0.25
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params: {sensor_name: torso_upvector}
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.01
+            n_max: 0.01
+            operation: add
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+          scale: 0.05
+          noise:
+            _target_: unilab.managers._noise.UniformNoiseCfg
+            n_min: -0.1
+            n_max: 0.1
+            operation: add
+        actions:
+          func: unilab.envs.mdp.last_action
+          params: {action_name: joint_pos}
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params: {command_name: twist}
+        gait_phase:
+          func: unilab.tasks.locomotion.g1.manager_terms.G1GaitPhase
+          params:
+            frequency: 1.5
+            init_mode: offset_phase
+    critic:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: torso_gyro}
+          scale: 0.25
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params: {sensor_name: torso_upvector}
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+          scale: 0.05
+        actions:
+          func: unilab.envs.mdp.last_action
+          params: {action_name: joint_pos}
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params: {command_name: twist}
+        gait_phase:
+          func: unilab.tasks.locomotion.g1.manager_terms.G1GaitPhase
+          params:
+            frequency: 1.5
+            init_mode: offset_phase
+        base_lin_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params: {sensor_name: pelvis_local_linvel}
+          scale: 2.0
+  actions:
+    joint_pos:
+      _target_: unilab.envs.mdp.JointPositionActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      scale: 1.0
+      use_default_offset: true
+  commands:
+    twist:
+      _target_: unilab.tasks.locomotion.g1.manager_terms.G1VelocityCommandCfg
+      entity_name: robot
+      resampling_time_range: [20.0, 20.0]
+      heading_command: false
+      heading_control_stiffness: 0.5
+      rel_standing_envs: 0.0
+      rel_heading_envs: 0.0
+      rel_world_envs: 0.0
+      rel_forward_envs: 0.0
+      init_velocity_prob: 0.0
+      planar_dead_zone: 0.2
+      ranges:
+        lin_vel_x: [-0.6, 1.0]
+        lin_vel_y: [-0.4, 0.4]
+        ang_vel_z: [-0.8, 0.8]
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_root_state_uniform:
+      func: unilab.envs.mdp.reset_root_state_uniform
+      mode: reset
+      params:
+        pose_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [-3.141592653589793, 3.141592653589793]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.5, 0.5]
+          roll: [-0.5, 0.5]
+          pitch: [-0.5, 0.5]
+          yaw: [-0.5, 0.5]
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        kp_range: [0.9, 1.1]
+        kd_range: [0.9, 1.1]
+        operation: scale
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    tilt:
+      func: unilab.tasks.locomotion.g1.manager_terms.g1_tilt_exceeded
+      params:
+        max_tilt_deg: 65.0
+    base_height:
+      func: unilab.envs.mdp.root_height_below_minimum
+      params:
+        minimum_height: 0.3
+  curriculum:
+    penalty_scaling:
+      func: unilab.tasks.locomotion.g1.manager_terms.G1PenaltyCurriculum
+      # Effective schedule matches the tuned legacy baseline: the legacy env
+      # halved the shared override dict once per env construction (two probe
+      # envs + the collector in every off-policy runner), so collectors actually
+      # trained at 1/8 initial / 1/4 cap of these YAML weights. The manager
+      # runtime isolates each env, so the tuned effective range is declared
+      # explicitly here.
+      params:
+        initial_scale: 0.125
+        min_scale: 0.125
+        max_scale: 0.25
+        level_down_threshold: 150.0
+        level_up_threshold: 750.0
+        degree: 0.001
+  policy_observation_group: policy
+  critic_observation_group: critic
+
+reward:
+  tracking_lin_vel:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.track_lin_vel
+    weight: 2.0
+    params:
+      tracking_sigma: 0.25
+      command_name: twist
+      sensor_name: pelvis_local_linvel
+  tracking_ang_vel:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.track_ang_vel
+    weight: 1.5
+    params:
+      tracking_sigma: 0.25
+      command_name: twist
+      sensor_name: torso_gyro
+  penalty_ang_vel_xy:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.ang_vel_xy
+    weight: -1.0
+    params:
+      sensor_name: torso_gyro
+  penalty_orientation:
+    func: unilab.tasks.locomotion.common.sensor_reward_terms.orientation
+    weight: -10.0
+    params:
+      sensor_name: torso_upvector
+  penalty_action_rate:
+    func: unilab.envs.mdp.action_rate_l2
+    weight: -4.0
+  pose:
+    func: unilab.tasks.locomotion.g1.manager_terms.weighted_pose
+    weight: -0.5
+    params:
+      pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
+  penalty_feet_ori:
+    func: unilab.tasks.locomotion.g1.manager_terms.penalty_feet_ori
+    weight: -20.0
+  feet_phase:
+    func: unilab.tasks.locomotion.g1.manager_terms.feet_phase
+    weight: 5.0
+    params:
+      frequency: 1.5
+      swing_height: 0.09
+      tracking_sigma: 0.04
+      min_forward_speed: 0.0
+      command_name: twist
+  alive:
+    func: unilab.tasks.locomotion.common.manager_terms.alive
+    weight: 10.0
diff --git a/src/unilab/conf/td3/task/g1_walk_flat/mujoco.yaml b/src/unilab/conf/td3/task/g1_walk_flat/mujoco.yaml
new file mode 100644
index 000000000..b4f669538
--- /dev/null
+++ b/src/unilab/conf/td3/task/g1_walk_flat/mujoco.yaml
@@ -0,0 +1,12 @@
+# @package _global_
+# TD3 MuJoCo owner: inherits the 29-DoF off-policy Manager-Based contract and
+# only carries backend/algo identity.
+defaults:
+  - /task/g1_walk_flat/base
+  - _self_
+
+training:
+  task_name: G1WalkFlat
+  sim_backend: mujoco
+algo:
+  max_iterations: 100000
diff --git a/src/unilab/conf/td3/task/go1_joystick_flat/base.yaml b/src/unilab/conf/td3/task/go1_joystick_flat/base.yaml
new file mode 100644
index 000000000..8a2f3bf57
--- /dev/null
+++ b/src/unilab/conf/td3/task/go1_joystick_flat/base.yaml
@@ -0,0 +1,244 @@
+# @package _global_
+# Canonical Go1 flat Manager-Based task declaration. Backend owner leaves inherit
+# this file and only override backend/algo tuning or explicitly disabled terms.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/go1/scene_flat.xml
+    default_keyframe_name: home
+    entities:
+      robot:
+        root_body_name: trunk
+        joint_names:
+          - FR_hip_joint
+          - FR_thigh_joint
+          - FR_calf_joint
+          - FL_hip_joint
+          - FL_thigh_joint
+          - FL_calf_joint
+          - RR_hip_joint
+          - RR_thigh_joint
+          - RR_calf_joint
+          - RL_hip_joint
+          - RL_thigh_joint
+          - RL_calf_joint
+        actuator_names:
+          - FR_hip
+          - FR_thigh
+          - FR_calf
+          - FL_hip
+          - FL_thigh
+          - FL_calf
+          - RR_hip
+          - RR_thigh
+          - RR_calf
+          - RL_hip
+          - RL_thigh
+          - RL_calf
+        body_names: [trunk]
+  sim_dt: 0.01
+  ctrl_dt: 0.02
+  max_episode_seconds: 20.0
+  observations:
+    policy:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+        gait_phase:
+          func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase
+          params:
+            frequency: 2.0
+    critic:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+        gait_phase:
+          func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase
+          params:
+            frequency: 2.0
+        base_lin_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: local_linvel
+  actions:
+    joint_pos:
+      _target_: unilab.envs.mdp.JointPositionActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      scale: 0.25
+      use_default_offset: true
+  commands:
+    twist:
+      _target_: unilab.envs.mdp.UniformVelocityCommandCfg
+      entity_name: robot
+      resampling_time_range: [20.0, 20.0]
+      heading_command: false
+      heading_control_stiffness: 0.5
+      rel_standing_envs: 0.0
+      rel_heading_envs: 0.0
+      rel_world_envs: 0.0
+      rel_forward_envs: 0.0
+      init_velocity_prob: 0.0
+      ranges:
+        lin_vel_x: [-0.6, 1.0]
+        lin_vel_y: [-0.4, 0.4]
+        ang_vel_z: [-0.8, 0.8]
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_root_state_uniform:
+      func: unilab.envs.mdp.reset_root_state_uniform
+      mode: reset
+      params:
+        pose_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [-3.141592653589793, 3.141592653589793]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.5, 0.5]
+          roll: [-0.5, 0.5]
+          pitch: [-0.5, 0.5]
+          yaw: [-0.5, 0.5]
+    base_mass:
+      func: unilab.envs.mdp.randomize_rigid_body_mass
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          body_names: trunk
+        mass_distribution_params: [-1.5, 1.5]
+        operation: add
+        recompute_inertia: false
+    base_com:
+      func: unilab.envs.mdp.randomize_rigid_body_com
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: SceneEntityCfg
+          name: robot
+          body_names: trunk
+        com_range:
+          x: [-0.05, 0.05]
+          y: [0.0, 0.0]
+          z: [0.0, 0.0]
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        kp_range: [35.0, 35.0]
+        kd_range: [0.5, 0.5]
+        operation: abs
+    push_robot:
+      func: unilab.envs.mdp.push_by_setting_velocity
+      mode: interval
+      interval_range_s: [15.0, 15.0]
+      is_global_time: true
+      params:
+        velocity_range:
+          x: [-1.0, 1.0]
+          y: [-1.0, 1.0]
+          z: [-0.5, 0.5]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [0.0, 0.0]
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    bad_orientation:
+      func: unilab.envs.mdp.bad_orientation
+      params:
+        limit_angle: 1.0471975511965976
+  policy_observation_group: policy
+  critic_observation_group: critic
+
+reward:
+  tracking_lin_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_lin_vel_xy_exp
+    weight: 1.0
+    params:
+      std: 0.5
+      command_name: twist
+  tracking_ang_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_ang_vel_z_exp
+    weight: 0.2
+    params:
+      std: 0.5
+      command_name: twist
+  lin_vel_z:
+    func: unilab.tasks.locomotion.common.manager_terms.lin_vel_z_l2
+    weight: -5.0
+  ang_vel_xy:
+    func: unilab.tasks.locomotion.common.manager_terms.ang_vel_xy_l2
+    weight: -0.1
+  base_height:
+    func: unilab.tasks.locomotion.common.manager_terms.base_height_l2
+    weight: -100.0
+    params:
+      target_height: 0.3
+  action_rate:
+    func: unilab.envs.mdp.action_rate_l2
+    weight: -0.005
+  similar_to_default:
+    func: unilab.tasks.locomotion.common.manager_terms.joint_deviation_l1
+    weight: -0.1
+  contact:
+    func: unilab.tasks.locomotion.common.manager_terms.feet_phase_contact
+    # Legacy Go1 sums four matching feet while this community term returns their mean.
+    weight: 0.96
+    params:
+      frequency: 2.0
+      sensor_names:
+        - FL_foot_contact
+        - FR_foot_contact
+        - RL_foot_contact
+        - RR_foot_contact
+      contact_threshold: 0.1
+      stance_threshold: 0.6
+  swing_feet_z:
+    func: unilab.tasks.locomotion.common.manager_terms.feet_phase_swing_height
+    weight: 4.0
+    params:
+      frequency: 2.0
+      sensor_names: [FL_pos, FR_pos, RL_pos, RR_pos]
+      target_height: 0.1
+      kernel: 0.01
+      swing_start: 0.6
diff --git a/src/unilab/conf/td3/task/go1_joystick_flat/motrix.yaml b/src/unilab/conf/td3/task/go1_joystick_flat/motrix.yaml
new file mode 100644
index 000000000..fb408b50b
--- /dev/null
+++ b/src/unilab/conf/td3/task/go1_joystick_flat/motrix.yaml
@@ -0,0 +1,27 @@
+# @package _global_
+defaults:
+  - /task/go1_joystick_flat/base
+  - _self_
+
+training:
+  task_name: Go1JoystickFlat
+  sim_backend: motrix
+algo:
+  num_envs: 1024
+  learning_starts: 10
+  max_iterations: 5000
+  save_interval: 1000
+  updates_per_step: 8
+  batch_size: 8192
+  replay_buffer_n: 1024
+env:
+  commands:
+    twist:
+      ranges:
+        lin_vel_x: [0.5, 0.5]
+        lin_vel_y: [0.0, 0.0]
+        ang_vel_z: [0.0, 0.0]
+  events:
+    push_robot: null
+reward:
+  contact: null
diff --git a/src/unilab/conf/td3/task/go2_joystick_flat/base.yaml b/src/unilab/conf/td3/task/go2_joystick_flat/base.yaml
new file mode 100644
index 000000000..409129c16
--- /dev/null
+++ b/src/unilab/conf/td3/task/go2_joystick_flat/base.yaml
@@ -0,0 +1,206 @@
+# @package _global_
+# Canonical Go2 flat Manager-Based task declaration. Backend owner leaves inherit
+# this file and only override backend/algo tuning or explicitly disabled terms.
+env:
+  scene:
+    model_file: src/unilab/assets/robots/go2/scene_flat.xml
+    default_keyframe_name: home
+    entities:
+      robot:
+        root_body_name: base
+        joint_names:
+          - FL_hip_joint
+          - FL_thigh_joint
+          - FL_calf_joint
+          - FR_hip_joint
+          - FR_thigh_joint
+          - FR_calf_joint
+          - RL_hip_joint
+          - RL_thigh_joint
+          - RL_calf_joint
+          - RR_hip_joint
+          - RR_thigh_joint
+          - RR_calf_joint
+        actuator_names:
+          - FR_hip
+          - FR_thigh
+          - FR_calf
+          - FL_hip
+          - FL_thigh
+          - FL_calf
+          - RR_hip
+          - RR_thigh
+          - RR_calf
+          - RL_hip
+          - RL_thigh
+          - RL_calf
+  sim_dt: 0.01
+  ctrl_dt: 0.02
+  max_episode_seconds: 20.0
+  observations:
+    policy:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+        gait_phase:
+          func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase
+          params:
+            frequency: 2.0
+    critic:
+      terms:
+        base_ang_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: gyro
+        projected_gravity:
+          func: unilab.envs.mdp.projected_gravity_from_sensor
+          params:
+            sensor_name: upvector
+        joint_pos:
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel:
+          func: unilab.envs.mdp.joint_vel_rel
+        actions:
+          func: unilab.envs.mdp.last_action
+        command:
+          func: unilab.envs.mdp.generated_commands
+          params:
+            command_name: twist
+        gait_phase:
+          func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase
+          params:
+            frequency: 2.0
+        base_lin_vel:
+          func: unilab.envs.mdp.builtin_sensor
+          params:
+            sensor_name: local_linvel
+  actions:
+    joint_pos:
+      _target_: unilab.envs.mdp.JointPositionActionCfg
+      entity_name: robot
+      actuator_names: [".*"]
+      scale: 0.25
+      use_default_offset: true
+  commands:
+    twist:
+      _target_: unilab.envs.mdp.UniformVelocityCommandCfg
+      entity_name: robot
+      resampling_time_range: [20.0, 20.0]
+      heading_command: false
+      heading_control_stiffness: 0.5
+      rel_standing_envs: 0.0
+      rel_heading_envs: 0.0
+      rel_world_envs: 0.0
+      rel_forward_envs: 0.0
+      init_velocity_prob: 0.0
+      ranges:
+        lin_vel_x: [-0.6, 1.0]
+        lin_vel_y: [-0.4, 0.4]
+        ang_vel_z: [-0.8, 0.8]
+  events:
+    reset_scene_to_default:
+      func: unilab.envs.mdp.reset_scene_to_default
+      mode: reset
+    reset_root_state_uniform:
+      func: unilab.envs.mdp.reset_root_state_uniform
+      mode: reset
+      params:
+        pose_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [0.0, 0.0]
+          roll: [0.0, 0.0]
+          pitch: [0.0, 0.0]
+          yaw: [-3.141592653589793, 3.141592653589793]
+        velocity_range:
+          x: [-0.5, 0.5]
+          y: [-0.5, 0.5]
+          z: [-0.5, 0.5]
+          roll: [-0.5, 0.5]
+          pitch: [-0.5, 0.5]
+          yaw: [-0.5, 0.5]
+    pd_gains:
+      func: unilab.envs.mdp.pd_gains
+      mode: reset
+      params:
+        kp_range: [31.5, 38.5]
+        kd_range: [0.45, 0.55]
+        operation: abs
+  terminations:
+    time_out:
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    bad_orientation:
+      func: unilab.envs.mdp.bad_orientation
+      params:
+        limit_angle: 1.0471975511965976
+  policy_observation_group: policy
+  critic_observation_group: critic
+
+reward:
+  tracking_lin_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_lin_vel_xy_exp
+    weight: 1.0
+    params:
+      std: 0.5
+      command_name: twist
+  tracking_ang_vel:
+    func: unilab.tasks.locomotion.common.manager_terms.track_ang_vel_z_exp
+    weight: 0.2
+    params:
+      std: 0.5
+      command_name: twist
+  lin_vel_z:
+    func: unilab.tasks.locomotion.common.manager_terms.lin_vel_z_l2
+    weight: -5.0
+  ang_vel_xy:
+    func: unilab.tasks.locomotion.common.manager_terms.ang_vel_xy_l2
+    weight: -0.1
+  base_height:
+    func: unilab.tasks.locomotion.common.manager_terms.base_height_l2
+    weight: -100.0
+    params:
+      target_height: 0.3
+  action_rate:
+    func: unilab.envs.mdp.action_rate_l2
+    weight: -0.005
+  similar_to_default:
+    func: unilab.tasks.locomotion.common.manager_terms.joint_deviation_l1
+    weight: -0.1
+  contact:
+    func: unilab.tasks.locomotion.common.manager_terms.feet_phase_contact
+    weight: 0.24
+    params:
+      frequency: 2.0
+      sensor_names:
+        - FL_foot_contact
+        - FR_foot_contact
+        - RL_foot_contact
+        - RR_foot_contact
+      contact_threshold: 0.1
+      stance_threshold: 0.6
+  swing_feet_z:
+    func: unilab.tasks.locomotion.common.manager_terms.feet_phase_swing_height
+    weight: 4.0
+    params:
+      frequency: 2.0
+      sensor_names: [FL_pos, FR_pos, RL_pos, RR_pos]
+      target_height: 0.1
+      kernel: 0.01
+      swing_start: 0.6
diff --git a/src/unilab/conf/td3/task/go2_joystick_flat/motrix.yaml b/src/unilab/conf/td3/task/go2_joystick_flat/motrix.yaml
new file mode 100644
index 000000000..a12a474be
--- /dev/null
+++ b/src/unilab/conf/td3/task/go2_joystick_flat/motrix.yaml
@@ -0,0 +1,25 @@
+# @package _global_
+defaults:
+  - /task/go2_joystick_flat/base
+  - _self_
+
+training:
+  task_name: Go2JoystickFlat
+  sim_backend: motrix
+algo:
+  num_envs: 1024
+  learning_starts: 10
+  max_iterations: 5000
+  save_interval: 1000
+  updates_per_step: 8
+  batch_size: 8192
+  replay_buffer_n: 1024
+env:
+  commands:
+    twist:
+      ranges:
+        lin_vel_x: [0.5, 0.5]
+        lin_vel_y: [0.0, 0.0]
+        ang_vel_z: [0.0, 0.0]
+  events:
+    pd_gains: null
diff --git a/src/unilab/demo.py b/src/unilab/demo.py
index e3f31721b..a083187f6 100644
--- a/src/unilab/demo.py
+++ b/src/unilab/demo.py
@@ -53,8 +53,14 @@ class DemoSpec:
 }
 
 
-def _repo_root() -> Path:
-    return Path(__file__).resolve().parents[2]
+def _package_root() -> Path:
+    return Path(__file__).resolve().parent
+
+
+def _dev_venv() -> Path | None:
+    """Return the editable checkout's uv venv, or None for pip installs."""
+    venv = Path(__file__).resolve().parents[2] / ".venv"
+    return venv if venv.is_dir() else None
 
 
 def get_demo_spec(demo_name: str) -> DemoSpec:
@@ -104,26 +110,11 @@ def _build_play_interactive_command(
     extra_overrides: Sequence[str],
     root: Path | None = None,
 ) -> list[str]:
-    selected_root = root or _repo_root()
+    selected_root = root or _package_root()
     script = selected_root / "scripts" / "play_interactive.py"
     if not script.is_file():
         raise SystemExit(f"Entrypoint script not found: {script}")
-    if spec.algo in {"sac", "flashsac"}:
-        owner_yaml = (
-            selected_root
-            / "conf"
-            / "offpolicy"
-            / "task"
-            / spec.algo
-            / spec.task
-            / f"{spec.sim}.yaml"
-        )
-    elif spec.algo == "hora_distill":
-        owner_yaml = (
-            selected_root / "conf" / "hora_distill" / "task" / spec.task / f"{spec.sim}.yaml"
-        )
-    else:
-        owner_yaml = selected_root / "conf" / spec.algo / "task" / spec.task / f"{spec.sim}.yaml"
+    owner_yaml = selected_root / "conf" / spec.algo / "task" / spec.task / f"{spec.sim}.yaml"
     if not owner_yaml.is_file():
         raise SystemExit(
             f"No owner config exists for algo={spec.algo}, task={spec.task}, sim={spec.sim}: "
@@ -238,13 +229,15 @@ def _run_teaser_demo() -> int:
     if platform.system() == "Darwin" and Path(sys.executable).name != "mxpython":
         command = [
             _mxpython_executable(),
-            str(_repo_root() / "src" / "unilab" / "tools" / "render_teaser.py"),
+            str(_package_root() / "visualization" / "teaser.py"),
         ]
         env = os.environ.copy()
-        env["UV_PROJECT_ENVIRONMENT"] = str(_repo_root() / ".venv")
+        dev_venv = _dev_venv()
+        if dev_venv is not None:
+            env["UV_PROJECT_ENVIRONMENT"] = str(dev_venv)
         return subprocess.run(command, check=False, env=env).returncode
 
-    from unilab.tools.render_teaser import main as render_teaser_main
+    from unilab.visualization.teaser import main as render_teaser_main
 
     render_teaser_main()
     return 0
@@ -266,7 +259,9 @@ def run_demo(*, demo_name: str, refresh: bool = False, device: str | None = None
         demo_name=demo_name, checkpoint_path=checkpoint_path, device=device
     )
     env = os.environ.copy()
-    env["UV_PROJECT_ENVIRONMENT"] = str(_repo_root() / ".venv")
+    dev_venv = _dev_venv()
+    if dev_venv is not None:
+        env["UV_PROJECT_ENVIRONMENT"] = str(dev_venv)
     returncode = subprocess.run(command, check=False, env=env).returncode
     if returncode == 0:
         print(f"Demo finished: {demo_name} (algo={spec.algo}, task={spec.task}, sim={spec.sim})")
diff --git a/src/unilab/dr/__init__.py b/src/unilab/dr/__init__.py
index ff0426aa9..c349c1466 100644
--- a/src/unilab/dr/__init__.py
+++ b/src/unilab/dr/__init__.py
@@ -3,9 +3,7 @@
 Invariant: this package must not depend on unilab.base.*
 """
 
-from .manager import DomainRandomizationManager
-from .provider import DomainRandomizationProvider
-from .types import (
+from unisim.dr.types import (
     DomainRandomizationCapabilities,
     GeomSizeOverride,
     InitRandomizationPlan,
@@ -15,6 +13,9 @@
     ResetRandomizationPayload,
 )
 
+from .manager import DomainRandomizationManager
+from .provider import DomainRandomizationProvider
+
 __all__ = [
     "DomainRandomizationCapabilities",
     "DomainRandomizationManager",
diff --git a/src/unilab/dr/dr_utils.py b/src/unilab/dr/dr_utils.py
index 779591027..dac016af0 100644
--- a/src/unilab/dr/dr_utils.py
+++ b/src/unilab/dr/dr_utils.py
@@ -1,14 +1,14 @@
 from __future__ import annotations
 
-from typing import Any
+from typing import Any, cast
 
 import numpy as np
-
-from unilab.dr.types import (
+from unisim.dr.types import (
     DomainRandomizationCapabilities,
     IntervalRandomizationPlan,
     ResetRandomizationPayload,
 )
+
 from unilab.dtype_config import get_global_dtype
 
 
@@ -188,7 +188,7 @@ def validate_common_reset_randomization(
     )
     if payload is None:
         return frozenset()
-    return capabilities.get_unsupported_reset_terms(payload.requested_terms())
+    return cast(frozenset[str], capabilities.get_unsupported_reset_terms(payload.requested_terms()))
 
 
 def build_interval_push_plan(env: Any, step_counter: int) -> IntervalRandomizationPlan | None:
diff --git a/src/unilab/dr/manager.py b/src/unilab/dr/manager.py
index 1c233085f..fa2936ffe 100644
--- a/src/unilab/dr/manager.py
+++ b/src/unilab/dr/manager.py
@@ -5,9 +5,9 @@
 from typing import Any
 
 import numpy as np
+from unisim.dr.types import DomainRandomizationCapabilities
 
 from .provider import DomainRandomizationProvider
-from .types import DomainRandomizationCapabilities
 
 logger = logging.getLogger(__name__)
 
@@ -106,10 +106,21 @@ def apply_interval_randomization_if_due(self, step_counter: int) -> None:
             raise NotImplementedError(
                 f"{self._env._backend.backend_type} backend does not support interval body velocity perturbation"
             )
+        if (
+            plan.body_angular_velocity_delta is not None
+            and not self._capabilities.supports_interval_body_angular_velocity_delta
+        ):
+            raise NotImplementedError(
+                f"{self._env._backend.backend_type} backend does not support interval body angular velocity perturbation"
+            )
         if plan.body_force is not None and not self._capabilities.supports_interval_body_force:
             raise NotImplementedError(
                 f"{self._env._backend.backend_type} backend does not support interval body force perturbation"
             )
+        if plan.body_torque is not None and not self._capabilities.supports_interval_body_torque:
+            raise NotImplementedError(
+                f"{self._env._backend.backend_type} backend does not support interval body torque perturbation"
+            )
         self._env._backend.apply_interval_randomization(plan)
 
     def _log_unsupported_reset_terms(self, unsupported: frozenset[str]) -> None:
diff --git a/src/unilab/dr/provider.py b/src/unilab/dr/provider.py
index 8da60c8f1..2b6352be9 100644
--- a/src/unilab/dr/provider.py
+++ b/src/unilab/dr/provider.py
@@ -4,8 +4,7 @@
 from typing import Any
 
 import numpy as np
-
-from .types import (
+from unisim.dr.types import (
     DomainRandomizationCapabilities,
     InitRandomizationPlan,
     IntervalRandomizationPlan,
diff --git a/src/unilab/dr/types.py b/src/unilab/dr/types.py
deleted file mode 100644
index f341896ed..000000000
--- a/src/unilab/dr/types.py
+++ /dev/null
@@ -1,164 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Sequence
-from dataclasses import dataclass, field
-from typing import Any
-
-import numpy as np
-
-RESET_TERM_BASE_COM = "base_com_offset"
-RESET_TERM_BASE_MASS = "base_mass_delta"
-RESET_TERM_GRAVITY = "gravity"
-RESET_TERM_BODY_IQUAT = "body_iquat"
-RESET_TERM_BODY_INERTIA = "body_inertia"
-RESET_TERM_BODY_IPOS = "body_ipos"
-RESET_TERM_BODY_MASS = "body_mass"
-RESET_TERM_DOF_ARMATURE = "dof_armature"
-RESET_TERM_GEOM_FRICTION = "geom_friction"
-RESET_TERM_KP = "kp"
-RESET_TERM_KD = "kd"
-
-
-@dataclass(frozen=True)
-class GeomSizeOverride:
-    geom_name: str
-    size: tuple[float, ...]
-
-
-@dataclass(frozen=True)
-class ModelVariantSpec:
-    geom_size_overrides: tuple[GeomSizeOverride, ...] = field(default_factory=tuple)
-
-    def is_empty(self) -> bool:
-        return not self.geom_size_overrides
-
-
-@dataclass(frozen=True)
-class DomainRandomizationCapabilities:
-    supported_reset_terms: frozenset[str] = field(default_factory=frozenset)
-    supports_interval_push: bool = False
-    supports_interval_body_velocity_delta: bool = False
-    supports_interval_body_force: bool = False
-
-    def supports_reset_term(self, term: str) -> bool:
-        return term in self.supported_reset_terms
-
-    def get_unsupported_reset_terms(self, requested_terms: frozenset[str]) -> frozenset[str]:
-        return frozenset(term for term in requested_terms if not self.supports_reset_term(term))
-
-    def filter_reset_payload(
-        self, payload: ResetRandomizationPayload
-    ) -> tuple[ResetRandomizationPayload | None, frozenset[str]]:
-        unsupported = self.get_unsupported_reset_terms(payload.requested_terms())
-        if not unsupported:
-            return payload, frozenset()
-
-        filtered = ResetRandomizationPayload(
-            base_mass_delta=(
-                payload.base_mass_delta if self.supports_reset_term(RESET_TERM_BASE_MASS) else None
-            ),
-            base_com_offset=(
-                payload.base_com_offset if self.supports_reset_term(RESET_TERM_BASE_COM) else None
-            ),
-            gravity=payload.gravity if self.supports_reset_term(RESET_TERM_GRAVITY) else None,
-            body_iquat=(
-                payload.body_iquat if self.supports_reset_term(RESET_TERM_BODY_IQUAT) else None
-            ),
-            body_inertia=(
-                payload.body_inertia if self.supports_reset_term(RESET_TERM_BODY_INERTIA) else None
-            ),
-            body_ipos=(
-                payload.body_ipos if self.supports_reset_term(RESET_TERM_BODY_IPOS) else None
-            ),
-            body_mass=(
-                payload.body_mass if self.supports_reset_term(RESET_TERM_BODY_MASS) else None
-            ),
-            dof_armature=(
-                payload.dof_armature if self.supports_reset_term(RESET_TERM_DOF_ARMATURE) else None
-            ),
-            geom_friction=(
-                payload.geom_friction
-                if self.supports_reset_term(RESET_TERM_GEOM_FRICTION)
-                else None
-            ),
-            kp=payload.kp if self.supports_reset_term(RESET_TERM_KP) else None,
-            kd=payload.kd if self.supports_reset_term(RESET_TERM_KD) else None,
-        )
-        return (None if filtered.is_empty() else filtered), unsupported
-
-
-@dataclass
-class ResetRandomizationPayload:
-    base_mass_delta: np.ndarray | None = None
-    base_com_offset: np.ndarray | None = None
-    gravity: np.ndarray | None = None
-    body_iquat: np.ndarray | None = None
-    body_inertia: np.ndarray | None = None
-    body_ipos: np.ndarray | None = None
-    body_mass: np.ndarray | None = None
-    dof_armature: np.ndarray | None = None
-    geom_friction: np.ndarray | None = None
-    kp: np.ndarray | None = None
-    kd: np.ndarray | None = None
-
-    def requested_terms(self) -> frozenset[str]:
-        terms: set[str] = set()
-        if self.base_mass_delta is not None:
-            terms.add(RESET_TERM_BASE_MASS)
-        if self.base_com_offset is not None:
-            terms.add(RESET_TERM_BASE_COM)
-        if self.gravity is not None:
-            terms.add(RESET_TERM_GRAVITY)
-        if self.body_iquat is not None:
-            terms.add(RESET_TERM_BODY_IQUAT)
-        if self.body_inertia is not None:
-            terms.add(RESET_TERM_BODY_INERTIA)
-        if self.body_ipos is not None:
-            terms.add(RESET_TERM_BODY_IPOS)
-        if self.body_mass is not None:
-            terms.add(RESET_TERM_BODY_MASS)
-        if self.dof_armature is not None:
-            terms.add(RESET_TERM_DOF_ARMATURE)
-        if self.geom_friction is not None:
-            terms.add(RESET_TERM_GEOM_FRICTION)
-        if self.kp is not None:
-            terms.add(RESET_TERM_KP)
-        if self.kd is not None:
-            terms.add(RESET_TERM_KD)
-        return frozenset(terms)
-
-    def is_empty(self) -> bool:
-        return not self.requested_terms()
-
-
-@dataclass
-class IntervalRandomizationPlan:
-    push_perturbation_limit: Sequence[float] | np.ndarray | None = None
-    body_ids: np.ndarray | None = None
-    body_linear_velocity_delta: np.ndarray | None = None
-    body_force: np.ndarray | None = None
-
-    def is_empty(self) -> bool:
-        return (
-            self.push_perturbation_limit is None
-            and self.body_linear_velocity_delta is None
-            and self.body_force is None
-        )
-
-
-@dataclass
-class InitRandomizationPlan:
-    model_assignments: np.ndarray
-    model_variants: tuple[ModelVariantSpec, ...]
-
-    def is_empty(self) -> bool:
-        return len(self.model_variants) == 0
-
-
-@dataclass
-class ResetPlan:
-    env_ids: np.ndarray
-    qpos: np.ndarray
-    qvel: np.ndarray
-    info_updates: dict[str, Any]
-    randomization: ResetRandomizationPayload | None = None
diff --git a/src/unilab/envs/__init__.py b/src/unilab/envs/__init__.py
index 85a4be3f6..0b2d6a246 100644
--- a/src/unilab/envs/__init__.py
+++ b/src/unilab/envs/__init__.py
@@ -1 +1,15 @@
-"""Environment"""
+"""Environment public API."""
+
+from unilab.envs.manager_based_rl_env import ManagerBasedRLEnv as ManagerBasedRLEnv
+from unilab.envs.manager_based_rl_env import ManagerBasedRlEnv as ManagerBasedRlEnv
+from unilab.envs.manager_based_rl_env import ManagerBasedRLEnvCfg as ManagerBasedRLEnvCfg
+from unilab.envs.manager_based_rl_env import ManagerBasedRlEnvCfg as ManagerBasedRlEnvCfg
+from unilab.envs.manager_based_rl_env import make_manager_based_rl_env as make_manager_based_rl_env
+
+__all__ = [
+    "ManagerBasedRLEnv",
+    "ManagerBasedRLEnvCfg",
+    "ManagerBasedRlEnv",
+    "ManagerBasedRlEnvCfg",
+    "make_manager_based_rl_env",
+]
diff --git a/src/unilab/envs/locomotion/__init__.py b/src/unilab/envs/locomotion/__init__.py
deleted file mode 100644
index 59a8b0270..000000000
--- a/src/unilab/envs/locomotion/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-"""Locomotion env registry bootstrap contract."""
-
-__unilab_registry_modules__ = (
-    "unilab.envs.locomotion.go1",
-    "unilab.envs.locomotion.go2",
-    "unilab.envs.locomotion.go2w",
-    "unilab.envs.locomotion.g1",
-    "unilab.envs.locomotion.go2_arm",
-    "unilab.envs.locomotion.a2",
-)
diff --git a/src/unilab/envs/locomotion/a2/__init__.py b/src/unilab/envs/locomotion/a2/__init__.py
deleted file mode 100644
index 48a5e3355..000000000
--- a/src/unilab/envs/locomotion/a2/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .joystick import A2JoystickCfg, A2JoystickFlatEnv
-
-__all__ = ["A2JoystickCfg", "A2JoystickFlatEnv"]
diff --git a/src/unilab/envs/locomotion/a2/joystick.py b/src/unilab/envs/locomotion/a2/joystick.py
deleted file mode 100644
index e9f675fd8..000000000
--- a/src/unilab/envs/locomotion/a2/joystick.py
+++ /dev/null
@@ -1,251 +0,0 @@
-"""A2 joystick task (leg-only Unitree A2).
-
-The A2 leg-only MJCF (robots/a2/scene_flat.xml) mirrors the Go2 joystick
-sensor/geom/leg-ordering contract and uses <position> actuators, so this
-task reuses Go2WalkTask unchanged. Only the A2 identity differs: scene path,
-standing pose, and per-joint PD gains.
-
-Asset values are aligned to the official unitree_rl_mjlab A2 (a2_constants.py):
-the home keyframe matches its INIT_STATE (height 0.4, thigh 0.9, calf -1.8,
-hips +-0.1), and the PD gains match its BuiltinPositionActuatorCfg — hip/thigh
-kp=100/kd=4, calf kp=150/kd=6 — applied per joint at init via
-position_actuator_gains and used as the per-joint baseline for kp/kd domain
-randomization."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Any
-
-import numpy as np
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base import registry
-from unilab.base.scene import SceneCfg
-from unilab.dtype_config import get_global_dtype
-from unilab.envs.locomotion.common import rewards
-from unilab.envs.locomotion.common.commands import sample_commands_with_standing
-from unilab.envs.locomotion.common.dr_provider import LocomotionDRProvider
-from unilab.envs.locomotion.common.rewards import RewardContext
-from unilab.envs.locomotion.go2.base import Asset, ControlConfig
-from unilab.envs.locomotion.go2.joystick import (
-    Go2DomainRandConfig,
-    Go2JoystickCfg,
-    Go2JoystickDomainRandomizationProvider,
-    Go2WalkTask,
-    RewardConfig,
-)
-
-# Actuator/keyframe leg order: FL, FR, RL, RR x (hip, thigh, calf).
-_NUM_LEGS = 4
-
-
-def _per_leg_gains(hip: float, thigh: float, calf: float) -> np.ndarray:
-    """Tile (hip, thigh, calf) gains across the four legs in actuator order."""
-    return np.asarray([hip, thigh, calf] * _NUM_LEGS, dtype=np.float64)
-
-
-@dataclass
-class A2InitState:
-    pos = [0.0, 0.0, 0.4]
-
-
-@dataclass
-class A2Asset(Asset):
-    # The A2 base body is named "base_link" in its MJCF, whereas Go2 uses "base".
-    base_name: str = "base_link"  # type: ignore[assignment]
-
-
-@dataclass
-class A2JoystickControlConfig(ControlConfig):
-    # Per-joint PD gains aligned to unitree_rl_mjlab: hip/thigh share Kp/Kd, the
-    # calf is stiffer. position_gains() expands these into 12-actuator arrays
-    # forwarded to the backend (overriding the static per-class kp in a2.xml).
-    Kp: float = 100.0
-    Kd: float = 4.0
-    calf_Kp: float = 150.0  # noqa: N815 - matches the Kp/Kd Hydra config convention.
-    calf_Kd: float = 6.0  # noqa: N815 - matches the Kp/Kd Hydra config convention.
-
-    def position_gains(self) -> dict[str, float | np.ndarray]:
-        return {
-            "kp": _per_leg_gains(self.Kp, self.Kp, self.calf_Kp),
-            "kd": _per_leg_gains(self.Kd, self.Kd, self.calf_Kd),
-        }
-
-
-def _a2_scene() -> SceneCfg:
-    return SceneCfg(model_file=str(ASSETS_ROOT_PATH / "robots" / "a2" / "scene_flat.xml"))
-
-
-@dataclass
-class A2JoystickDomainRandConfig(Go2DomainRandConfig):
-    # A2's base COM is uncertain in all 3 axes on the real robot. dr_utils reads
-    # com_offset_y/z via getattr, so they must be declared here to be settable
-    # from the owner YAML (Hydra struct mode rejects undeclared keys). Inherits
-    # com_offset_x + every other DR switch/range from Go2DomainRandConfig and
-    # the base DomainRandConfig; on/off + ranges are set in the owner YAML.
-    com_offset_y: list[float] = field(default_factory=lambda: [-0.08, 0.08])
-    com_offset_z: list[float] = field(default_factory=lambda: [-0.08, 0.08])
-
-
-class A2JoystickDomainRandomizationProvider(Go2JoystickDomainRandomizationProvider):
-    """A2 reuses the Go2 joystick DR logic but supplies per-joint base gains so
-    randomize_kp/kd scales each actuator off its true baseline (calf off 150,
-    not the shared scalar 100). Without this, kp/kd DR would fall back to a
-    uniform ``control_config.Kp``/``Kd`` and silently weaken the calf."""
-
-    def _sample_commands(self, env: Any, num_reset: int) -> np.ndarray:
-        """Standing-aware reset commands for A2 (Go2 base stays pure-uniform).
-
-        Mirrors rough.py's provider override: draw from ``commands.vel_limit`` and
-        zero a ``rel_standing_envs`` fraction so the policy trains on genuine
-        zero-command samples. Uses the shared ``sample_commands_with_standing`` so
-        the reset and mid-episode resampling paths stay a single source of truth."""
-        low = np.asarray(env.cfg.commands.vel_limit[0], dtype=np.float64)
-        high = np.asarray(env.cfg.commands.vel_limit[1], dtype=np.float64)
-        return sample_commands_with_standing(
-            low, high, num_reset, rel_standing_envs=env.cfg.commands.rel_standing_envs
-        )
-
-    def _get_base_actuator_gains(self, env: Any) -> tuple[np.ndarray | None, np.ndarray | None]:
-        gains = env.cfg.control_config.position_gains()
-        num_actuators = env._num_action
-        base_kp = np.broadcast_to(
-            np.asarray(gains["kp"], dtype=np.float64), (num_actuators,)
-        ).copy()
-        base_kd = np.broadcast_to(
-            np.asarray(gains["kd"], dtype=np.float64), (num_actuators,)
-        ).copy()
-        return base_kp, base_kd
-
-    def _get_reset_randomization_baselines(
-        self, env: Any
-    ) -> tuple[np.ndarray | None, np.ndarray | None, int | None, np.ndarray | None]:
-        """Snapshot the pristine model tables that reset-time DR multiplies against.
-
-        Caches once per env (the base model is not mutated by per-env reset
-        randomization) via the public backend getters — no infra change, no
-        feature leak. Enables randomize_ground_friction (floor geom is the
-        priority geom, see scene_flat.xml) and randomize_dof_armature.
-        body_mass stays uncached (that DR switch is intentionally off)."""
-        cached = getattr(self, "_reset_baselines", None)
-        if cached is None:
-            backend = env._backend
-            base_geom_friction = backend.get_geom_friction()
-            ground_geom_id = backend.get_geom_id(env.cfg.asset.ground)
-            base_dof_armature = backend.get_dof_armature()
-            cached = (None, base_geom_friction, ground_geom_id, base_dof_armature)
-            self._reset_baselines = cached
-        return cached
-
-
-@dataclass
-class A2RewardConfig(RewardConfig):
-    # Command norm below which the phase-driven gait rewards (swing_feet_z /
-    # contact) switch to standing behaviour and the gait clock freezes, so the
-    # A2 stands still at zero command instead of marching in place. Set via the
-    # A2 owner YAML; default 0.0 leaves gating off.
-    command_threshold: float = 0.0
-
-
-@registry.envcfg("A2JoystickFlat")
-@dataclass
-class A2JoystickCfg(Go2JoystickCfg):
-    scene: SceneCfg = field(default_factory=_a2_scene)
-    init_state: A2InitState = field(default_factory=A2InitState)  # type: ignore[assignment]
-    asset: A2Asset = field(default_factory=A2Asset)  # type: ignore[assignment]
-    control_config: A2JoystickControlConfig = field(  # type: ignore[assignment]
-        default_factory=A2JoystickControlConfig
-    )
-    domain_rand: A2JoystickDomainRandConfig = field(  # type: ignore[assignment]
-        default_factory=A2JoystickDomainRandConfig
-    )
-    reward_config: A2RewardConfig | None = None  # type: ignore[assignment]
-
-
-@registry.env("A2JoystickFlat", sim_backend="mujoco")
-class A2JoystickFlatEnv(Go2WalkTask):
-    """Leg-only A2 joystick task. Reuses Go2WalkTask locomotion; adds
-    zero-command standstill (phase freeze + gated gait rewards + standing
-    resample) gated by A2RewardConfig.command_threshold."""
-
-    _cfg: A2JoystickCfg
-    _reward_cfg: A2RewardConfig
-
-    def _make_dr_provider(self) -> LocomotionDRProvider:
-        return A2JoystickDomainRandomizationProvider()
-
-    def _advance_phase(self, phase: np.ndarray) -> np.ndarray:
-        """Advance the gait phase, freezing envs whose command is at/below
-        ``command_threshold`` so a standing A2 holds phase instead of swaying."""
-        cmd_norm = np.linalg.norm(self._latest_commands, axis=1)
-        moving = cmd_norm > self._reward_cfg.command_threshold
-        increment = self._cfg.ctrl_dt * self.gait_frequency * moving
-        return np.fmod(phase + increment, 1.0)
-
-    def _init_reward_functions(self) -> None:
-        super()._init_reward_functions()
-        self._reward_fns.update(
-            {
-                "stand_still": rewards.stand_still,
-                "hip_deviation": self._reward_hip_deviation,
-                "stand_feet_air": self._reward_stand_feet_air,
-                "swing_feet_z": self._gated_swing_feet_z,
-                "contact": self._gated_contact,
-            }
-        )
-
-    def _gated_swing_feet_z(self, ctx: RewardContext) -> np.ndarray:
-        """Base swing reward, zeroed while standing (command at/below threshold)."""
-        reward = super()._reward_swing_feet_z(ctx)
-        cmd_norm = np.linalg.norm(ctx.info["commands"], axis=1)
-        active = cmd_norm > self._reward_cfg.command_threshold
-        return reward * active
-
-    def _gated_contact(self, ctx: RewardContext) -> np.ndarray:
-        """Contact reward; while standing every foot is expected planted so a
-        planted robot earns full contact reward (standing branch is interleaved
-        per-foot, so this re-implements rather than wraps the base loop)."""
-        contact = self.feet_force[:, :, 2] > 0.1
-        cmd_norm = np.linalg.norm(ctx.info["commands"], axis=1)
-        standing = cmd_norm <= self._reward_cfg.command_threshold
-        res = np.zeros(self._num_envs, dtype=np.float32)
-        for i in range(len(self._cfg.sensor.feet_force)):
-            is_contact = (self.feet_phase[:, i] < 0.6) | (self.gait_frequency < 1.0e-8) | standing
-            res += (contact[:, i] == is_contact).astype(np.float32)
-        return res / len(self._cfg.sensor.feet_force)
-
-    def _reward_hip_deviation(self, ctx: RewardContext) -> np.ndarray:
-        """L1 deviation of the hip DOFs ([0, 3, 6, 9]) from the default pose."""
-        hip_indices = [0, 3, 6, 9]
-        diff = ctx.dof_pos[:, hip_indices] - self.default_angles[hip_indices]
-        return np.asarray(np.sum(np.abs(diff), axis=1), dtype=get_global_dtype())
-
-    def _reward_stand_feet_air(self, ctx: RewardContext) -> np.ndarray:
-        """Penalize feet leaving the ground while standing (||command|| <= threshold)."""
-        cmd_norm = np.linalg.norm(ctx.info["commands"], axis=1)
-        standing = cmd_norm <= self._reward_cfg.command_threshold
-        in_air = np.sum(self.feet_force[:, :, 2] <= 0.1, axis=1)
-        return np.asarray(in_air * standing, dtype=get_global_dtype())
-
-    def _update_commands(self, info: dict) -> None:
-        """Standing-aware mid-episode resample (gated by ``resampling_time``),
-        then stamp ``self._latest_commands`` for the phase-freeze read."""
-        resampling_time = float(self._cfg.commands.resampling_time)
-        if resampling_time > 0.0:
-            commands_arr = np.asarray(info["commands"], dtype=get_global_dtype())
-            interval_steps = max(int(round(resampling_time / self._cfg.ctrl_dt)), 1)
-            steps = np.asarray(info.get("steps", np.zeros((self._num_envs,), dtype=np.uint32)))
-            resample_mask = (steps > 0) & ((steps % interval_steps) == 0)
-            if np.any(resample_mask):
-                num_resample = int(np.count_nonzero(resample_mask))
-                low = np.asarray(self._cfg.commands.vel_limit[0], dtype=get_global_dtype())
-                high = np.asarray(self._cfg.commands.vel_limit[1], dtype=get_global_dtype())
-                sampled = sample_commands_with_standing(
-                    low, high, num_resample, rel_standing_envs=self._cfg.commands.rel_standing_envs
-                )
-                commands_arr[resample_mask] = sampled
-                if self._cfg.commands.heading_command:
-                    commands_arr[resample_mask, 2] = 0.0
-            info["commands"] = commands_arr
-        self._latest_commands = np.asarray(info["commands"], dtype=get_global_dtype())
diff --git a/src/unilab/envs/locomotion/common/__init__.py b/src/unilab/envs/locomotion/common/__init__.py
deleted file mode 100644
index 7455130cb..000000000
--- a/src/unilab/envs/locomotion/common/__init__.py
+++ /dev/null
@@ -1,43 +0,0 @@
-from .base import (
-    BaseNoiseConfig,
-    ControlConfigBase,
-    LocomotionBaseCfg,
-    LocomotionBaseEnv,
-    PdControlConfig,
-    Sensor,
-)
-from .commands import (
-    Commands,
-    apply_heading_yaw_feedback,
-    sample_heading_commands,
-    sample_velocity_commands,
-    zero_small_xy_commands,
-)
-from .domain_rand import DomainRandConfig
-from .dr_provider import LocomotionDRProvider
-from .height_scan import (
-    DEFAULT_SCAN_POINTS_X,
-    DEFAULT_SCAN_POINTS_Y,
-    HeightScanConfig,
-)
-from .rewards import RewardContext
-
-__all__ = [
-    "BaseNoiseConfig",
-    "Commands",
-    "ControlConfigBase",
-    "DEFAULT_SCAN_POINTS_X",
-    "DEFAULT_SCAN_POINTS_Y",
-    "DomainRandConfig",
-    "HeightScanConfig",
-    "LocomotionBaseCfg",
-    "LocomotionBaseEnv",
-    "LocomotionDRProvider",
-    "PdControlConfig",
-    "RewardContext",
-    "Sensor",
-    "apply_heading_yaw_feedback",
-    "sample_heading_commands",
-    "sample_velocity_commands",
-    "zero_small_xy_commands",
-]
diff --git a/src/unilab/envs/locomotion/common/base.py b/src/unilab/envs/locomotion/common/base.py
deleted file mode 100644
index 15ef1677b..000000000
--- a/src/unilab/envs/locomotion/common/base.py
+++ /dev/null
@@ -1,164 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import ClassVar
-
-import gymnasium as gym
-import numpy as np
-
-from unilab.base.backend import SimBackend
-from unilab.base.base import EnvCfg
-from unilab.base.np_env import NpEnv, NpEnvState
-from unilab.dtype_config import get_global_dtype
-from unilab.envs.locomotion.common.terrain_spawn import BaseSpawnManager
-
-
-@dataclass
-class Sensor:
-    local_linvel = "local_linvel"
-    gyro = "gyro"
-
-
-@dataclass
-class ControlConfigBase:
-    action_scale: float = 0.25
-    simulate_action_latency: bool = False
-
-
-@dataclass
-class PdControlConfig(ControlConfigBase):
-    """``ControlConfigBase`` plus shared PD-actuator gains (Go1/Go2/Go2W defaults)."""
-
-    Kp: float = 35.0
-    Kd: float = 0.5
-
-    def position_gains(self) -> dict[str, float | np.ndarray]:
-        """Gains forwarded to the backend's ``position_actuator_gains``.
-
-        Default applies the scalar ``Kp``/``Kd`` uniformly to every actuator.
-        Subclasses with per-joint gains (e.g. A2's stronger calf) override this
-        to return per-actuator arrays in model/actuator order.
-        """
-        return {"kp": self.Kp, "kd": self.Kd}
-
-
-@dataclass
-class BaseNoiseConfig:
-    level: float = 0.0
-    scale_joint_angle: float = 0.03
-    scale_joint_vel: float = 0.5
-    scale_gyro: float = 0.2
-    scale_gravity: float = 0.05
-    scale_linvel: float = 0.1
-    seed: int | None = None
-
-
-@dataclass
-class LocomotionBaseCfg(EnvCfg):
-    control_config: ControlConfigBase = field(default_factory=ControlConfigBase)
-    noise_config: BaseNoiseConfig = field(default_factory=BaseNoiseConfig)
-    sensor: Sensor = field(default_factory=Sensor)
-    sim_dt: float = 0.01
-    ctrl_dt: float = 0.02
-
-
-class LocomotionBaseEnv(NpEnv):
-    """Common base environment for locomotion tasks (G1, Go1, Go2, etc.)."""
-
-    _cfg: LocomotionBaseCfg
-
-    _keyframe_name: ClassVar[str] = "home"
-    _use_global_dtype: ClassVar[bool] = True
-
-    def __init__(self, cfg: LocomotionBaseCfg, backend: SimBackend, num_envs: int = 1):
-        super().__init__(cfg, backend, num_envs)
-        self._init_action_space()
-        self._num_action = self._action_space.shape[0]
-        self._obs_noise_rng: np.random.Generator | None = None
-        self._obs_noise_seed_override: int | None = None
-        self._init_buffers()
-        self._spawn: BaseSpawnManager = BaseSpawnManager()
-
-    def _init_action_space(self) -> None:
-        ctrl_range = self._backend.get_actuator_ctrl_range()
-        nu = self._backend.num_actuators
-        self._action_space = gym.spaces.Box(ctrl_range[:, 0], ctrl_range[:, 1], (nu,), dtype=float)  # type: ignore[assignment, arg-type]
-
-    @property
-    def action_space(self) -> gym.spaces.Box:
-        return self._action_space  # type: ignore[no-any-return]
-
-    def _init_buffers(self) -> None:
-        dtype = get_global_dtype() if self._use_global_dtype else np.float32
-        raw_qpos = self._backend.get_keyframe_qpos(self._keyframe_name)
-        self._init_qpos = (
-            np.asarray(raw_qpos, dtype=dtype) if self._use_global_dtype else np.asarray(raw_qpos)
-        )
-        self.default_angles = np.asarray(self._init_qpos[-self._num_action :], dtype=dtype)
-        raw_qvel = self._backend.get_init_qvel()
-        self._init_qvel = (
-            np.asarray(raw_qvel, dtype=dtype) if self._use_global_dtype else np.asarray(raw_qvel)
-        )
-
-    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
-        state.info["last_actions"] = state.info.get("current_actions", np.zeros_like(actions))
-        state.info["current_actions"] = actions
-        exec_actions = (
-            state.info["last_actions"]
-            if self._cfg.control_config.simulate_action_latency
-            else actions
-        )
-        ctrl: np.ndarray = (
-            exec_actions * self._cfg.control_config.action_scale + self.default_angles
-        )
-        return ctrl
-
-    def seed_observation_noise(self, seed: int | None) -> None:
-        """Reset env-owned observation-noise RNG streams."""
-        if seed is not None and int(seed) < 0:
-            raise ValueError(f"observation noise seed must be non-negative, got {seed}")
-        self._obs_noise_seed_override = int(seed) if seed is not None else None
-        self._obs_noise_rng = None
-
-    def _configured_obs_noise_seed(self) -> int | None:
-        seed = getattr(self, "_obs_noise_seed_override", None)
-        if seed is None:
-            seed = getattr(self._cfg.noise_config, "seed", None)
-        if seed is None:
-            return None
-        seed = int(seed)
-        if seed < 0:
-            raise ValueError(f"observation noise seed must be non-negative, got {seed}")
-        return seed
-
-    def _obs_noise(self, data: np.ndarray, scale: float) -> np.ndarray:
-        """Apply per-step uniform observation noise scaled by ``noise_config.level``."""
-        level = float(self._cfg.noise_config.level)
-        if level <= 0.0:
-            return data
-        seed = self._configured_obs_noise_seed()
-        if seed is None:
-            noise = np.random.uniform(-1.0, 1.0, data.shape).astype(data.dtype)
-        else:
-            rng = getattr(self, "_obs_noise_rng", None)
-            if rng is None:
-                rng = np.random.default_rng(seed)
-                self._obs_noise_rng = rng
-            noise = rng.uniform(-1.0, 1.0, data.shape).astype(data.dtype)
-        return data + noise * level * scale
-
-    def get_local_linvel(self) -> np.ndarray:
-        local_linvel: np.ndarray = self._backend.get_sensor_data(self._cfg.sensor.local_linvel)
-        return local_linvel
-
-    def get_gyro(self) -> np.ndarray:
-        gyro: np.ndarray = self._backend.get_sensor_data(self._cfg.sensor.gyro)
-        return gyro
-
-    def get_dof_pos(self) -> np.ndarray:
-        dof_pos: np.ndarray = self._backend.get_dof_pos()
-        return dof_pos
-
-    def get_dof_vel(self) -> np.ndarray:
-        dof_vel: np.ndarray = self._backend.get_dof_vel()
-        return dof_vel
diff --git a/src/unilab/envs/locomotion/common/commands.py b/src/unilab/envs/locomotion/common/commands.py
deleted file mode 100644
index ea58ffbf4..000000000
--- a/src/unilab/envs/locomotion/common/commands.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Any
-
-import numpy as np
-
-from unilab.dtype_config import get_global_dtype
-from unilab.utils.rotation import np_wrap_to_pi, np_yaw_from_quat
-
-
-@dataclass
-class Commands:
-    vel_limit: list[list[float]] = field(
-        default_factory=lambda: [
-            [-0.6, -0.4, -0.8],  # [vx_min, vy_min, vyaw_min]
-            [1.0, 0.4, 0.8],  # [vx_max, vy_max, vyaw_max]
-        ]
-    )
-    resampling_time: float = 0.0
-    heading_command: bool = False
-    heading_range: list[float] = field(default_factory=lambda: [-3.14, 3.14])
-    heading_control_stiffness: float = 0.5
-    rel_standing_envs: float = 0.0
-
-
-def sample_velocity_commands(
-    rng: np.random.Generator, num_samples: int, low: np.ndarray, high: np.ndarray
-) -> np.ndarray:
-    return np.asarray(
-        rng.uniform(low=low, high=high, size=(num_samples, 3)), dtype=get_global_dtype()
-    )
-
-
-def zero_small_xy_commands(commands: np.ndarray, *, threshold: float = 0.2) -> None:
-    """Zero ``commands[:, :2]`` in-place wherever its norm is below ``threshold``."""
-    moving = np.linalg.norm(commands[:, :2], axis=1) > threshold
-    commands[:, :2] *= moving[:, None]
-
-
-def sample_commands_with_standing(
-    low: np.ndarray,
-    high: np.ndarray,
-    num_samples: int,
-    *,
-    rel_standing_envs: float,
-    zero_xy_threshold: float = 0.08,
-) -> np.ndarray:
-    """Sample velocity commands, zero small xy, then force a standing fraction.
-
-    Single source of truth for the standing-aware command distribution shared by
-    the reset path (DR provider ``_sample_commands`` override) and the
-    mid-episode resampling path (``Go2WalkTask._update_commands``). Mirrors
-    rough.py's provider standing block: uniform sample in ``[low, high]``,
-    ``zero_small_xy_commands`` to suppress tiny lateral drift, then zero the full
-    command for a random ``rel_standing_envs`` fraction of rows so the policy sees
-    genuine zero-command (stand still) samples during training.
-    """
-    low = np.asarray(low, dtype=get_global_dtype())
-    high = np.asarray(high, dtype=get_global_dtype())
-    commands = np.asarray(
-        np.random.uniform(low=low, high=high, size=(num_samples, 3)), dtype=get_global_dtype()
-    )
-    zero_small_xy_commands(commands, threshold=zero_xy_threshold)
-    if rel_standing_envs > 0.0:
-        standing = np.random.uniform(size=(num_samples,)) < min(rel_standing_envs, 1.0)
-        commands[standing] = 0.0
-    return commands
-
-
-def sample_heading_commands(env: Any, num_samples: int) -> np.ndarray:
-    """Uniformly sample heading targets from ``env.cfg.commands.heading_range``."""
-    heading_range = np.asarray(env.cfg.commands.heading_range, dtype=get_global_dtype())
-    if heading_range.shape != (2,):
-        raise ValueError(f"commands.heading_range must have shape (2,), got {heading_range.shape}")
-    low, high = float(np.min(heading_range)), float(np.max(heading_range))
-    return np.asarray(np.random.uniform(low, high, size=(num_samples,)), dtype=get_global_dtype())
-
-
-def apply_heading_yaw_feedback(
-    commands: np.ndarray,
-    base_quat: np.ndarray,
-    heading_commands: np.ndarray,
-    *,
-    stiffness: float,
-    clip: float = 2.0,
-) -> None:
-    """In-place P-control on heading error → ``commands[:, 2]`` (yaw rate)."""
-    heading = np_yaw_from_quat(base_quat)
-    commands[:, 2] = np.clip(stiffness * np_wrap_to_pi(heading_commands - heading), -clip, clip)
diff --git a/src/unilab/envs/locomotion/common/rewards.py b/src/unilab/envs/locomotion/common/rewards.py
deleted file mode 100644
index 77e56df50..000000000
--- a/src/unilab/envs/locomotion/common/rewards.py
+++ /dev/null
@@ -1,374 +0,0 @@
-"""Shared reward functions for locomotion environments.
-
-Introduces ``RewardContext`` — a dataclass that bundles all state any
-reward function might need.  Shared reward functions are plain
-module-level callables ``fn(ctx) -> np.ndarray`` so that each
-joystick environment can reference them **directly** in its
-``_reward_fns`` dispatch table without per-class wrapper methods.
-"""
-
-from __future__ import annotations
-
-from collections.abc import Callable, Mapping
-from dataclasses import dataclass, field
-from typing import Any
-
-import numpy as np
-
-from unilab.dtype_config import get_global_dtype
-
-
-@dataclass
-class RewardContext:
-    """Immutable snapshot of everything reward functions may read.
-
-    Built once per ``_compute_reward`` call.  Shared functions access
-    only the fields they need; robot-specific methods that still live
-    on the environment class receive the same context via ``self``.
-    """
-
-    # ── always populated ────────────────────────────────────────────
-    info: dict
-    linvel: np.ndarray  # (N, 3)
-    gyro: np.ndarray  # (N, 3)
-    dof_pos: np.ndarray  # (N, num_action)
-    num_envs: int = 0
-    default_angles: np.ndarray = field(default_factory=lambda: np.empty(0))
-    tracking_sigma: float = 0.25
-    base_height_target: float = 0.0
-    base_height: np.ndarray = field(default_factory=lambda: np.empty(0))  # pre-fetched
-
-    # ── G1-only (None for quadrupeds) ───────────────────────────────
-    gravity: np.ndarray | None = None
-    dof_vel: np.ndarray | None = None
-
-    # ── optional weights (G1 pose rewards) ──────────────────────────
-    pose_weights: np.ndarray | None = None
-
-    # ── optional state populated for rough / biped tasks ────────────
-    joint_range: np.ndarray | None = None  # (num_action, 2) — [lower, upper]
-    linvel_yaw: np.ndarray | None = None  # (N, 3) — base linvel in yaw frame
-
-
-# ── tracking rewards ─────────────────────────────────────────────────
-
-
-def tracking_lin_vel(ctx: RewardContext) -> np.ndarray:
-    """Exponential reward for tracking commanded xy linear velocity."""
-    commands = ctx.info["commands"]
-    lin_vel_error = np.sum(np.square(commands[:, :2] - ctx.linvel[:, :2]), axis=1)
-    return np.exp(-lin_vel_error / ctx.tracking_sigma)  # type: ignore[no-any-return]
-
-
-def tracking_ang_vel(ctx: RewardContext) -> np.ndarray:
-    """Exponential reward for tracking commanded yaw angular velocity."""
-    commands = ctx.info["commands"]
-    ang_vel_error = np.square(commands[:, 2] - ctx.gyro[:, 2])
-    return np.exp(-ang_vel_error / ctx.tracking_sigma)  # type: ignore[no-any-return]
-
-
-def forward_progress(ctx: RewardContext) -> np.ndarray:
-    """Reward for forward progress relative to commanded speed."""
-    commands = ctx.info["commands"]
-    commanded_speed = np.maximum(commands[:, 0], 1e-6)
-    forward_speed = np.maximum(ctx.linvel[:, 0], 0.0)
-    return np.asarray(np.minimum(forward_speed / commanded_speed, 1.0), dtype=get_global_dtype())
-
-
-def under_speed(ctx: RewardContext) -> np.ndarray:
-    """Penalty for being below commanded forward speed."""
-    commands = ctx.info["commands"]
-    commanded_speed = np.maximum(commands[:, 0], 1e-6)
-    forward_speed = np.maximum(ctx.linvel[:, 0], 0.0)
-    gap = np.maximum(commands[:, 0] - forward_speed, 0.0)
-    return np.asarray(gap / commanded_speed, dtype=get_global_dtype())
-
-
-# ── velocity / orientation penalties ─────────────────────────────────
-
-
-def lin_vel_z(ctx: RewardContext) -> np.ndarray:
-    """Penalty for vertical (z) linear velocity."""
-    return np.square(ctx.linvel[:, 2])  # type: ignore[no-any-return]
-
-
-def ang_vel_xy(ctx: RewardContext) -> np.ndarray:
-    """Penalty for roll/pitch angular velocity."""
-    return np.sum(np.square(ctx.gyro[:, :2]), axis=1)  # type: ignore[no-any-return]
-
-
-def orientation(ctx: RewardContext) -> np.ndarray:
-    """Penalty for deviation from upright orientation (roll/pitch)."""
-    g = ctx.gravity
-    assert g is not None
-    return np.square(g[:, 0]) + np.square(g[:, 1])  # type: ignore[no-any-return]
-
-
-def roll(ctx: RewardContext) -> np.ndarray:
-    """Penalty for deviation from roll orientation."""
-    g = ctx.gravity
-    assert g is not None
-    return np.square(g[:, 0])  # type: ignore[no-any-return]
-
-
-def upright(ctx: RewardContext) -> np.ndarray:
-    """Exponential reward for upright orientation."""
-    g = ctx.gravity
-    assert g is not None
-    xy_squared = np.sum(np.square(g[:, :2]), axis=1)
-    return np.exp(-xy_squared / 0.25)  # type: ignore[no-any-return]
-
-
-# ── height / pose penalties ──────────────────────────────────────────
-
-
-def base_height(ctx: RewardContext) -> np.ndarray:
-    """Penalty for base height deviation from target."""
-    return np.square(ctx.base_height - ctx.base_height_target)  # type: ignore[no-any-return]
-
-
-def similar_to_default(ctx: RewardContext) -> np.ndarray:
-    """Penalty for joint position deviation from default (L1 norm)."""
-    return np.sum(np.abs(ctx.dof_pos - ctx.default_angles), axis=1)  # type: ignore[no-any-return]
-
-
-def weighted_pose(ctx: RewardContext) -> np.ndarray:
-    """Weighted L2 penalty for joint position deviation."""
-    assert ctx.pose_weights is not None
-    diff = ctx.dof_pos - ctx.default_angles
-    return np.asarray(np.sum(ctx.pose_weights * np.square(diff), axis=1), dtype=get_global_dtype())
-
-
-# ── action penalties ─────────────────────────────────────────────────
-
-
-def action_rate(ctx: RewardContext) -> np.ndarray:
-    """Penalty for change in actions between timesteps."""
-    current = ctx.info["current_actions"]
-    last = ctx.info["last_actions"]
-    return np.sum(np.square(current - last), axis=1)  # type: ignore[no-any-return]
-
-
-def action_smooth(ctx: RewardContext) -> np.ndarray:
-    """Penalty for second-order action changes between timesteps."""
-    current = ctx.info["current_actions"]
-    last = ctx.info["last_actions"]
-    previous = ctx.info.get("previous_actions", last)
-    return np.sum(np.square(current - 2.0 * last + previous), axis=1)  # type: ignore[no-any-return]
-
-
-# ── effort penalties ─────────────────────────────────────────────────
-
-
-def _get_torques(ctx: RewardContext) -> np.ndarray:
-    fallback = np.zeros((ctx.num_envs, ctx.dof_pos.shape[1]), dtype=get_global_dtype())
-    return ctx.info.get("torques", fallback)  # type: ignore[no-any-return]
-
-
-def torques(ctx: RewardContext) -> np.ndarray:
-    """Penalty for total torque magnitude (L1 norm)."""
-    return np.sum(np.abs(_get_torques(ctx)), axis=1)  # type: ignore[no-any-return]
-
-
-def energy(ctx: RewardContext) -> np.ndarray:
-    """Penalty for mechanical energy consumption."""
-    assert ctx.dof_vel is not None
-    t = _get_torques(ctx)
-    return np.sum(np.abs(ctx.dof_vel) * np.abs(t), axis=1)  # type: ignore[no-any-return]
-
-
-def dof_acc(ctx: RewardContext) -> np.ndarray:
-    """Penalty for joint acceleration magnitude."""
-    fallback = np.zeros((ctx.num_envs, ctx.dof_pos.shape[1]), dtype=get_global_dtype())
-    qacc = ctx.info.get("qacc", fallback)
-    return np.sum(np.square(qacc), axis=1)  # type: ignore[no-any-return]
-
-
-# ── survival ─────────────────────────────────────────────────────────
-
-
-def alive(ctx: RewardContext) -> np.ndarray:
-    """Constant reward for staying alive."""
-    return np.ones((ctx.num_envs,), dtype=get_global_dtype())
-
-
-# ── quadruped-rough helpers / penalties ──────────────────────────────
-
-
-def upright_scale(gravity: np.ndarray | None, num_envs: int) -> np.ndarray:
-    """Scalar gate in [0, 1] from the body-up projection of gravity.
-
-    Used by quadruped rough tasks to suppress reward / penalty bookkeeping
-    while the robot is tipping over. Returns 1.0 when the body is upright
-    (gravity[:, 2] >= 0.7) and 0.0 when fully tipped.
-    """
-    if gravity is None:
-        return np.ones((num_envs,), dtype=get_global_dtype())
-    return np.asarray(np.clip(gravity[:, 2], 0.0, 0.7) / 0.7, dtype=get_global_dtype())
-
-
-def dof_torques_l2(ctx: RewardContext) -> np.ndarray:
-    """Penalty for joint torque magnitude (L2)."""
-    torques = np.asarray(
-        ctx.info.get("torques", np.zeros((ctx.num_envs, ctx.dof_pos.shape[1]))),
-        dtype=get_global_dtype(),
-    )
-    return np.asarray(np.sum(np.square(torques), axis=1), dtype=get_global_dtype())
-
-
-def dof_acc_l2(ctx: RewardContext) -> np.ndarray:
-    """Penalty for joint acceleration magnitude (L2)."""
-    qacc = np.asarray(
-        ctx.info.get("qacc", np.zeros((ctx.num_envs, ctx.dof_pos.shape[1]))),
-        dtype=get_global_dtype(),
-    )
-    return np.asarray(np.sum(np.square(qacc), axis=1), dtype=get_global_dtype())
-
-
-def joint_pos_limits(ctx: RewardContext) -> np.ndarray:
-    """Penalty for joint position over/under-shoot relative to backend limits."""
-    if ctx.joint_range is None:
-        return np.zeros((ctx.num_envs,), dtype=get_global_dtype())
-    lower = ctx.joint_range[:, 0]
-    upper = ctx.joint_range[:, 1]
-    low_error = np.clip(lower - ctx.dof_pos, 0.0, None)
-    high_error = np.clip(ctx.dof_pos - upper, 0.0, None)
-    return np.asarray(np.sum(low_error + high_error, axis=1), dtype=get_global_dtype())
-
-
-def joint_power(ctx: RewardContext) -> np.ndarray:
-    """Penalty for joint mechanical power (|tau * dq|)."""
-    assert ctx.dof_vel is not None
-    torques = np.asarray(
-        ctx.info.get("torques", np.zeros((ctx.num_envs, ctx.dof_pos.shape[1]))),
-        dtype=get_global_dtype(),
-    )
-    return np.asarray(np.sum(np.abs(ctx.dof_vel * torques), axis=1), dtype=get_global_dtype())
-
-
-def stand_still(ctx: RewardContext, command_threshold: float = 0.1) -> np.ndarray:
-    """Penalty for joint deviation from default while command norm is below threshold."""
-    stopped = np.linalg.norm(ctx.info["commands"], axis=1) < command_threshold
-    dof_error = np.sum(np.abs(ctx.dof_pos - ctx.default_angles), axis=1)
-    return np.asarray(dof_error * stopped, dtype=get_global_dtype())
-
-
-def joint_pos_penalty(
-    ctx: RewardContext,
-    *,
-    stand_still_scale: float = 5.0,
-    velocity_threshold: float = 0.5,
-    command_threshold: float = 0.1,
-) -> np.ndarray:
-    """Penalty for joint deviation that switches scale based on command/body motion."""
-    command_norm = np.linalg.norm(ctx.info["commands"], axis=1)
-    body_vel = np.linalg.norm(ctx.linvel[:, :2], axis=1)
-    running_error = np.linalg.norm(ctx.dof_pos - ctx.default_angles, axis=1)
-    moving = (command_norm > command_threshold) | (body_vel > velocity_threshold)
-    return np.asarray(
-        np.where(moving, running_error, stand_still_scale * running_error),
-        dtype=get_global_dtype(),
-    )
-
-
-def upward(ctx: RewardContext) -> np.ndarray:
-    """Reward favouring an upright body (no Go2 upright gate)."""
-    assert ctx.gravity is not None
-    return np.asarray(np.square(1.0 + ctx.gravity[:, 2]), dtype=get_global_dtype())
-
-
-# ── biped-style rewards ─────────────────────
-
-
-def track_lin_vel_xy_yaw_frame_exp(ctx: RewardContext) -> np.ndarray:
-    """Exponential tracking of xy linear velocity in the gravity-aligned yaw frame.
-
-    Requires ``ctx.linvel_yaw`` (base linvel rotated into yaw frame).
-    """
-    linvel = ctx.linvel_yaw if ctx.linvel_yaw is not None else ctx.linvel
-    commands = ctx.info["commands"]
-    lin_vel_error = np.sum(np.square(commands[:, :2] - linvel[:, :2]), axis=1)
-    return np.asarray(np.exp(-lin_vel_error / ctx.tracking_sigma), dtype=get_global_dtype())
-
-
-def track_ang_vel_z_world_exp(ctx: RewardContext) -> np.ndarray:
-    """Exponential tracking of yaw angular velocity (world frame)."""
-    commands = ctx.info["commands"]
-    ang_vel_error = np.square(commands[:, 2] - ctx.gyro[:, 2])
-    return np.asarray(np.exp(-ang_vel_error / ctx.tracking_sigma), dtype=get_global_dtype())
-
-
-def feet_air_time_positive_biped(
-    ctx: RewardContext,
-    *,
-    threshold: float = 0.4,
-    command_threshold: float = 0.1,
-) -> np.ndarray:
-    """Biped foot air-time reward: only rewards single-stance phase.
-
-    Reads ``ctx.info`` keys ``current_air_time``, ``current_contact_time`` (each
-    shape (N, 2)); the environment populates them per step.
-    """
-    air = np.asarray(
-        ctx.info.get("current_air_time", np.zeros((ctx.num_envs, 2))), dtype=get_global_dtype()
-    )
-    contact = np.asarray(
-        ctx.info.get("current_contact_time", np.zeros((ctx.num_envs, 2))), dtype=get_global_dtype()
-    )
-    in_contact = contact > 0.0
-    in_mode_time = np.where(in_contact, contact, air)
-    single_stance = np.sum(in_contact.astype(np.int32), axis=1) == 1
-    masked = np.where(single_stance[:, None], in_mode_time, 0.0)
-    reward = np.min(masked, axis=1)
-    reward = np.clip(reward, None, threshold)
-    moving = np.linalg.norm(ctx.info["commands"][:, :2], axis=1) > command_threshold
-    return np.asarray(reward * moving, dtype=get_global_dtype())
-
-
-def joint_deviation_l1(ctx: RewardContext, joint_indices: np.ndarray | None = None) -> np.ndarray:
-    """L1 penalty for joints deviating from their default positions."""
-    diff = ctx.dof_pos - ctx.default_angles
-    if joint_indices is not None:
-        diff = diff[:, joint_indices]
-    return np.asarray(np.sum(np.abs(diff), axis=1), dtype=get_global_dtype())
-
-
-# ── reward dispatch ──────────────────────────────────────────────────
-
-
-def run_reward_dispatch(
-    *,
-    scales: Mapping[str, float],
-    fns: Mapping[str, Callable[[RewardContext], np.ndarray]],
-    ctx: RewardContext,
-    info: dict[str, Any],
-    enable_log: bool,
-    ctrl_dt: float,
-    log_every_n_steps: int = 4,
-    only_positive: bool = False,
-) -> np.ndarray:
-    """Standard ``scales × fns(ctx)`` reduction shared by all locomotion envs.
-
-    - Writes per-reward means into ``info["log"]`` when ``enable_log`` and the
-      ``steps[0]`` cadence matches ``log_every_n_steps``.
-    - Returns ``reward * ctrl_dt`` (with optional positive clamp).
-    """
-    dtype = get_global_dtype()
-    reward = np.zeros((ctx.num_envs,), dtype=dtype)
-    step_count = info.get("steps", np.zeros((ctx.num_envs,), dtype=np.uint32))
-    should_log = enable_log and (int(step_count[0]) % log_every_n_steps == 0)
-    log = {} if should_log else info.get("log", {})
-
-    for name, scale in scales.items():
-        if scale == 0 or name not in fns:
-            continue
-        rew = fns[name](ctx)
-        weighted_rew = rew * scale
-        reward += weighted_rew
-        if should_log:
-            log[f"reward/{name}"] = float(np.mean(weighted_rew))
-
-    info["log"] = log
-    if only_positive:
-        np.maximum(reward, 0.0, out=reward)
-    return reward * ctrl_dt
diff --git a/src/unilab/envs/locomotion/g1/__init__.py b/src/unilab/envs/locomotion/g1/__init__.py
deleted file mode 100644
index 55806de55..000000000
--- a/src/unilab/envs/locomotion/g1/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from .joystick import (
-    G1WalkControlConfig,
-    G1WalkEnv,
-    G1WalkEnvCfg,
-    G1WalkFlatCfg,
-    G1WalkRewardConfig,
-    G1WalkRoughCfg,
-)
-
-__all__ = [
-    "G1WalkControlConfig",
-    "G1WalkEnv",
-    "G1WalkEnvCfg",
-    "G1WalkFlatCfg",
-    "G1WalkRewardConfig",
-    "G1WalkRoughCfg",
-]
diff --git a/src/unilab/envs/locomotion/g1/base.py b/src/unilab/envs/locomotion/g1/base.py
deleted file mode 100644
index 461013c8f..000000000
--- a/src/unilab/envs/locomotion/g1/base.py
+++ /dev/null
@@ -1,61 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-
-import numpy as np
-
-from unilab.envs.locomotion.common.base import (
-    BaseNoiseConfig,
-    ControlConfigBase,
-    LocomotionBaseCfg,
-    LocomotionBaseEnv,
-)
-from unilab.envs.locomotion.common.base import (
-    Sensor as LocomotionSensor,
-)
-
-
-@dataclass
-class NoiseConfig(BaseNoiseConfig):
-    scale_joint_angle: float = 0.02
-    scale_joint_vel: float = 0.3
-    scale_gyro: float = 0.1
-
-
-@dataclass
-class ControlConfig(ControlConfigBase):
-    action_scale: float | np.ndarray = 0.25  # type: ignore[assignment]
-
-
-@dataclass
-class Sensor(LocomotionSensor):
-    local_linvel: str = "pelvis_local_linvel"
-    gyro: str = "torso_gyro"
-    upvector: str = "torso_upvector"
-
-
-@dataclass
-class Asset:
-    base_name = "pelvis"
-    foot_name = "ankle_roll_link"
-    ground = "floor"
-
-
-@dataclass
-class G1BaseCfg(LocomotionBaseCfg):
-    noise_config: NoiseConfig = field(default_factory=NoiseConfig)  # type: ignore[assignment]
-    control_config: ControlConfig = field(default_factory=ControlConfig)  # type: ignore[assignment]
-    sensor: Sensor = field(default_factory=Sensor)
-    asset: Asset = field(default_factory=Asset)
-    sim_dt: float = 0.02 / 3.0
-    ctrl_dt: float = 0.02
-
-
-class G1BaseEnv(LocomotionBaseEnv):
-    _cfg: G1BaseCfg
-    _keyframe_name = "stand"
-    _use_global_dtype = False
-
-    def _obs_noise(self, data: np.ndarray, scale: float) -> np.ndarray:
-        """Same as base, but coerces back to ``data.dtype`` (G1 runs in float32)."""
-        return np.asarray(super()._obs_noise(data, scale), dtype=data.dtype)
diff --git a/src/unilab/envs/locomotion/g1/joystick.py b/src/unilab/envs/locomotion/g1/joystick.py
deleted file mode 100644
index 2a74dd86d..000000000
--- a/src/unilab/envs/locomotion/g1/joystick.py
+++ /dev/null
@@ -1,716 +0,0 @@
-"""G1 joystick locomotion environments."""
-
-from __future__ import annotations
-
-import math
-from dataclasses import dataclass, field
-from typing import Any
-
-import numpy as np
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base import registry
-from unilab.base.augmentation import SymmetryObsLayout
-from unilab.base.backend import create_backend, env_backend_kwargs
-from unilab.base.curriculum import EpisodeLengthTracker, PenaltyCurriculum
-from unilab.base.np_env import NpEnvState
-from unilab.base.scene import SceneCfg
-from unilab.dtype_config import get_global_dtype
-from unilab.envs.locomotion.common import rewards
-from unilab.envs.locomotion.common.commands import (
-    Commands,
-    sample_heading_commands,
-    zero_small_xy_commands,
-)
-from unilab.envs.locomotion.common.domain_rand import DomainRandConfig
-from unilab.envs.locomotion.common.dr_provider import LocomotionDRProvider
-from unilab.envs.locomotion.common.rewards import RewardContext
-from unilab.envs.locomotion.g1.base import G1BaseCfg, G1BaseEnv
-
-
-@dataclass
-class G1DomainRandConfig(DomainRandConfig):
-    randomize_kp: bool = True
-    kp_multiplier_range: list[float] = field(default_factory=lambda: [0.9, 1.1])
-
-    randomize_kd: bool = True
-    kd_multiplier_range: list[float] = field(default_factory=lambda: [0.9, 1.1])
-
-    randomize_body_gravity_compensation: bool = False
-    body_gravity_compensation_range: list[float] = field(default_factory=lambda: [-0.1, 0.3])
-    body_gravity_compensation_bodies: list[str] = field(
-        default_factory=lambda: ["pelvis", "torso_link"]
-    )
-
-
-@dataclass
-class InitState:
-    pos = [0.0, 0.0, 0.754]
-
-
-def build_upper_body_pose_weights(pose_weights: list[float]) -> np.ndarray:
-    weights = np.asarray(pose_weights, dtype=get_global_dtype()).copy()
-    weights[:12] = 0.0
-    return np.asarray(weights, dtype=get_global_dtype())
-
-
-def compute_feet_phase_height_targets(
-    gait_phase: np.ndarray, swing_height: float
-) -> tuple[np.ndarray, np.ndarray]:
-    def cubic_bezier_height(phi: np.ndarray, swing_height: float) -> np.ndarray:
-        phi_normalized = np.fmod(phi + np.pi, 2 * np.pi) - np.pi
-        x = (phi_normalized + np.pi) / (2 * np.pi)
-
-        def cubic_bezier_interpolation(
-            y_start: np.ndarray, y_end: np.ndarray, t: np.ndarray
-        ) -> np.ndarray:
-            y_diff = y_end - y_start
-            bezier = t**3 + 3 * (t**2 * (1 - t))
-            return np.asarray(y_start + y_diff * bezier, dtype=get_global_dtype())
-
-        stance = cubic_bezier_interpolation(np.zeros_like(x), np.full_like(x, swing_height), 2 * x)
-        swing = cubic_bezier_interpolation(
-            np.full_like(x, swing_height), np.zeros_like(x), 2 * x - 1
-        )
-        return np.where(x <= 0.5, stance, swing)
-
-    left_target = cubic_bezier_height(gait_phase[:, 0], swing_height)
-    right_target = cubic_bezier_height(gait_phase[:, 1], swing_height)
-    return left_target, right_target
-
-
-LEFT_FOOT_CONTACT_SENSORS = [f"left_foot_contact_{i}" for i in range(4)]
-RIGHT_FOOT_CONTACT_SENSORS = [f"right_foot_contact_{i}" for i in range(4)]
-
-
-def _scalarize_sensor_values(sensor_values: np.ndarray) -> np.ndarray:
-    sensor_array = np.asarray(sensor_values, dtype=get_global_dtype())
-    if sensor_array.ndim == 1:
-        return sensor_array
-    if sensor_array.ndim == 2 and sensor_array.shape[1] == 1:
-        return sensor_array[:, 0]
-    raise ValueError(f"Expected scalar sensor values, got shape {sensor_array.shape}")
-
-
-def compute_aggregated_foot_contact(backend: Any, sensor_names: list[str]) -> np.ndarray:
-    contacts = [_scalarize_sensor_values(backend.get_sensor_data(name)) for name in sensor_names]
-    return np.asarray(np.any(np.stack(contacts, axis=1) > 0.5, axis=1), dtype=np.bool_)
-
-
-def compute_feet_phase_contact_targets(
-    gait_phase: np.ndarray, swing_height: float
-) -> tuple[np.ndarray, np.ndarray]:
-    left_target, right_target = compute_feet_phase_height_targets(gait_phase, swing_height)
-    contact_height_threshold = swing_height * 0.5
-    return left_target <= contact_height_threshold, right_target <= contact_height_threshold
-
-
-def compute_forward_speed_gate(linvel: np.ndarray, min_forward_speed: float) -> np.ndarray:
-    forward_speed = np.maximum(linvel[:, 0], 0.0)
-    return np.asarray(forward_speed >= min_forward_speed, dtype=get_global_dtype())
-
-
-def compute_forward_command_mask(commands: np.ndarray) -> np.ndarray:
-    return np.asarray(np.maximum(commands[:, 0], 0.0) > 1.0e-6, dtype=get_global_dtype())
-
-
-@dataclass
-class G1RewardConfig:
-    scales: dict[str, float]
-    tracking_sigma: float
-    gait_frequency: float
-    feet_phase_swing_height: float
-    feet_phase_tracking_sigma: float
-    base_height_target: float
-    min_base_height: float
-    max_tilt_deg: float
-    min_forward_speed_for_gait_reward: float = 0.0
-    close_feet_threshold: float = 0.15
-    pose_weights: list[float] = field(
-        default_factory=lambda: [
-            0.01,
-            1.0,
-            5.0,
-            0.01,
-            5.0,
-            5.0,
-            0.01,
-            1.0,
-            5.0,
-            0.01,
-            5.0,
-            5.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-        ]
-    )
-
-
-@dataclass
-class CurriculumConfig:
-    enabled: bool = False
-    initial_scale: float = 0.5
-    min_scale: float = 0.5
-    max_scale: float = 1.0
-    level_down_threshold: float = 150.0
-    level_up_threshold: float = 750.0
-    degree: float = 0.001
-
-
-@dataclass
-class G1WalkEnvCfg(G1BaseCfg):
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat.xml")
-        )
-    )
-    max_episode_seconds: float | None = 20.0
-    init_state: InitState = field(default_factory=InitState)
-    commands: Commands = field(default_factory=Commands)
-    reward_config: G1RewardConfig | None = None
-    domain_rand: G1DomainRandConfig = field(default_factory=G1DomainRandConfig)
-    gait_phase_init_mode: str = "offset_phase"
-    reset_base_qvel_limit: float = 0.5
-    curriculum: CurriculumConfig = field(default_factory=CurriculumConfig)
-
-
-class G1WalkDomainRandomizationProvider(LocomotionDRProvider):
-    def __init__(
-        self,
-        *,
-        base_kp: np.ndarray | None = None,
-        base_kd: np.ndarray | None = None,
-        base_dof_armature: np.ndarray | None = None,
-    ):
-        self._base_kp = base_kp
-        self._base_kd = base_kd
-        self._base_dof_armature = base_dof_armature
-
-    def _get_base_actuator_gains(self, env: Any) -> tuple[np.ndarray | None, np.ndarray | None]:
-        return self._base_kp, self._base_kd
-
-    def _get_reset_randomization_baselines(
-        self, env: Any
-    ) -> tuple[np.ndarray | None, np.ndarray | None, int | None, np.ndarray | None]:
-        return None, None, None, self._base_dof_armature
-
-    def _get_qvel_limit(self, env: Any) -> float:
-        return float(env.cfg.reset_base_qvel_limit)
-
-    def _build_extra_info_updates(self, env: Any, num_reset: int) -> dict[str, np.ndarray]:
-        updates = {"gait_phase": self._sample_gait_phase(env, num_reset)}
-        if getattr(env.cfg.commands, "heading_command", False):
-            updates["heading_commands"] = sample_heading_commands(env, num_reset)
-        return updates
-
-    def _sample_commands(self, env: Any, num_reset: int) -> np.ndarray:
-        commands = super()._sample_commands(env, num_reset)
-        zero_small_xy_commands(commands)
-        standing_prob = float(getattr(env.cfg.commands, "rel_standing_envs", 0.0))
-        if standing_prob > 0.0:
-            standing = np.random.uniform(size=(num_reset,)) < min(standing_prob, 1.0)
-            commands[standing] = 0.0
-        if getattr(env.cfg.commands, "heading_command", False):
-            commands[:, 2] = 0.0
-        return commands
-
-    def _sample_gait_phase(self, env: Any, num_reset: int) -> np.ndarray:
-        mode = env.cfg.gait_phase_init_mode
-        if mode == "independent":
-            left = np.random.uniform(0.0, 2.0 * np.pi, size=(num_reset,))
-            right = np.random.uniform(0.0, 2.0 * np.pi, size=(num_reset,))
-            return np.asarray(np.column_stack([left, right]), dtype=get_global_dtype())
-
-        phase = np.random.uniform(0.0, 2.0 * np.pi, size=(num_reset,))
-        return np.asarray(np.column_stack([phase, phase + np.pi]), dtype=get_global_dtype())
-
-    def _compute_reset_obs(
-        self,
-        env: Any,
-        env_ids: Any,
-        info_updates: Any,
-        linvel: Any,
-        gyro: Any,
-        gravity: Any,
-        dof_pos: Any,
-        dof_vel: Any,
-    ) -> dict[str, np.ndarray]:
-        return env._compute_obs(info_updates, linvel, gyro, gravity, dof_pos, dof_vel)  # type: ignore[no-any-return]
-
-
-class G1WalkEnv(G1BaseEnv):
-    _cfg: G1WalkEnvCfg
-    _reward_cfg: Any
-
-    def __init__(self, cfg: G1WalkEnvCfg, num_envs=1, backend_type="mujoco"):
-        if cfg.reward_config is None:
-            raise ValueError("reward_config must be provided via Hydra configuration")
-        backend = create_backend(
-            backend_type,
-            cfg.scene,
-            num_envs,
-            cfg.sim_dt,
-            base_name=cfg.asset.base_name,
-            push_body_name=cfg.domain_rand.push_body_name,
-            **env_backend_kwargs(cfg),
-        )
-        super().__init__(cfg, backend, num_envs)
-        self._enable_reward_log = True
-        self._reward_cfg = cfg.reward_config
-
-        self._gait_phase_delta = float(
-            2.0 * math.pi * self._reward_cfg.gait_frequency * cfg.ctrl_dt
-        )
-        self._pose_weights = np.array(self._reward_cfg.pose_weights, dtype=get_global_dtype())
-        if self._pose_weights.shape[0] != self._num_action:
-            raise ValueError("pose_weights length mismatch")
-        self._upper_body_pose_weights = build_upper_body_pose_weights(self._reward_cfg.pose_weights)
-        self._episode_tracker: EpisodeLengthTracker | None = None
-        self._penalty_curriculum: PenaltyCurriculum | None = None
-        if cfg.curriculum.enabled:
-            self._episode_tracker = EpisodeLengthTracker(num_envs)
-            self._penalty_curriculum = PenaltyCurriculum(
-                self,
-                enabled=True,
-                initial_scale=cfg.curriculum.initial_scale,
-                min_scale=cfg.curriculum.min_scale,
-                max_scale=cfg.curriculum.max_scale,
-                level_down_threshold=cfg.curriculum.level_down_threshold,
-                level_up_threshold=cfg.curriculum.level_up_threshold,
-                degree=cfg.curriculum.degree,
-            )
-
-        self._init_reward_functions()
-        base_kp: np.ndarray | None = None
-        base_kd: np.ndarray | None = None
-        if cfg.domain_rand.randomize_kp or cfg.domain_rand.randomize_kd:
-            base_kp, base_kd = backend.get_actuator_gains()
-        base_dof_armature = (
-            backend.get_dof_armature() if cfg.domain_rand.randomize_dof_armature else None
-        )
-        dr_provider = G1WalkDomainRandomizationProvider(
-            base_kp=base_kp,
-            base_kd=base_kd,
-            base_dof_armature=base_dof_armature,
-        )
-        self._init_domain_randomization(dr_provider)
-
-    @property
-    def obs_groups_spec(self) -> dict[str, int]:
-        # gyro(3) + gravity(3) + diff(29) + dof_vel(29) + action(29) + cmd(3) + phase(2) = 98
-        return {"obs": 98, "critic": 101}
-
-    def _init_reward_functions(self):
-        self._reward_fns: dict[str, Any] = {
-            "tracking_lin_vel": rewards.tracking_lin_vel,
-            "tracking_ang_vel": rewards.tracking_ang_vel,
-            "forward_progress": rewards.forward_progress,
-            "under_speed": rewards.under_speed,
-            "lin_vel_z": rewards.lin_vel_z,
-            "orientation": rewards.orientation,
-            "penalty_orientation": rewards.orientation,
-            "ang_vel_xy": rewards.ang_vel_xy,
-            "penalty_ang_vel_xy": rewards.ang_vel_xy,
-            "action_rate": rewards.action_rate,
-            "penalty_action_rate": rewards.action_rate,
-            "base_height": rewards.base_height,
-            "pose": rewards.weighted_pose,
-            "upper_body_pose": self._reward_upper_body_pose,
-            "penalty_close_feet_xy": self._reward_close_feet_xy,
-            "penalty_feet_ori": self._reward_feet_ori,
-            "feet_phase": self._reward_feet_phase,
-            "feet_phase_contrast": self._reward_feet_phase_contrast,
-            "feet_phase_contact": self._reward_feet_phase_contact,
-            "feet_double_stance": self._reward_feet_double_stance,
-            "feet_air_time": self._reward_feet_air_time,
-            "alive": rewards.alive,
-        }
-
-    def _terrain_relative_base_height(self) -> np.ndarray:
-        return np.asarray(self._backend.get_base_pos()[:, 2], dtype=get_global_dtype())
-
-    def update_state(self, state: NpEnvState) -> NpEnvState:
-        linvel = self.get_local_linvel()
-        gyro = self.get_gyro()
-        gravity = self._backend.get_sensor_data(self._cfg.sensor.upvector)
-        dof_pos = self.get_dof_pos()
-        dof_vel = self.get_dof_vel()
-
-        max_tilt_rad = np.deg2rad(self._reward_cfg.max_tilt_deg)
-        tilt = np.arccos(np.clip(gravity[:, 2], -1, 1))
-        terminated = np.logical_or(
-            tilt > max_tilt_rad,
-            self._terrain_relative_base_height() < self._reward_cfg.min_base_height,
-        )
-        reward = self._compute_reward(state.info, linvel, gyro, gravity, dof_pos, dof_vel)
-        obs = self._compute_obs(state.info, linvel, gyro, gravity, dof_pos, dof_vel)
-
-        state = state.replace(obs=obs, reward=reward, terminated=terminated)
-
-        done = state.terminated | state.truncated
-        if self._episode_tracker is None or self._penalty_curriculum is None or not np.any(done):
-            return state
-
-        done_indices = np.where(done)[0]
-        episode_lengths = state.info["steps"][done_indices] + 1
-        self._episode_tracker.update(episode_lengths)
-        self._penalty_curriculum.update(self._episode_tracker.average_length)
-
-        if "log" not in state.info:
-            state.info["log"] = {}
-        state.info["log"]["curriculum/average_episode_length"] = float(
-            self._episode_tracker.average_length
-        )
-        state.info["log"]["curriculum/penalty_scale"] = float(
-            self._penalty_curriculum.current_scale
-        )
-        return state
-
-    def _compute_obs(
-        self, info: dict, linvel, gyro, gravity, dof_pos, dof_vel
-    ) -> dict[str, np.ndarray]:
-        noise_cfg = self._cfg.noise_config
-        diff = dof_pos - self.default_angles
-        command = info["commands"]
-        last_actions = info.get("current_actions", np.zeros_like(diff))
-        gait_phase = info.get("gait_phase", np.zeros((self._num_envs, 2), dtype=get_global_dtype()))
-        walk_profile = self._uses_walk_observation_profile()
-
-        noisy_gyro = self._obs_noise(gyro, noise_cfg.scale_gyro)
-        noisy_gravity = self._obs_noise(gravity, noise_cfg.scale_gravity)
-        noisy_diff = self._obs_noise(diff, noise_cfg.scale_joint_angle)
-        noisy_dof_vel = self._obs_noise(dof_vel, noise_cfg.scale_joint_vel)
-        actor_gyro_scale = 0.25 if walk_profile else 1.0
-        actor_dof_vel_scale = 0.05 if walk_profile else 1.0
-
-        actor = np.concatenate(
-            [
-                noisy_gyro * actor_gyro_scale,
-                -noisy_gravity,
-                noisy_diff,
-                noisy_dof_vel * actor_dof_vel_scale,
-                last_actions,
-                command,
-                gait_phase,
-            ],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-
-        critic_gyro_scale = 0.25 if walk_profile else 1.0
-        critic_dof_vel_scale = 0.05 if walk_profile else 1.0
-        critic_linvel_scale = 2.0 if walk_profile else 1.0
-        critic_base = np.concatenate(
-            [
-                gyro * critic_gyro_scale,
-                -gravity,
-                diff,
-                dof_vel * critic_dof_vel_scale,
-                last_actions,
-                command,
-                gait_phase,
-            ],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-        critic = np.concatenate(
-            [
-                critic_base,
-                np.asarray(linvel * critic_linvel_scale, dtype=get_global_dtype()),
-            ],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-
-        return {"obs": actor, "critic": critic}
-
-    def _uses_walk_observation_profile(self) -> bool:
-        scales = getattr(getattr(self, "_reward_cfg", None), "scales", None)
-        if scales is None:
-            reward_cfg = getattr(self._cfg, "reward_config", None)
-            scales = getattr(reward_cfg, "scales", None)
-
-        if scales is not None:
-            if any(
-                key in scales
-                for key in (
-                    "penalty_orientation",
-                    "penalty_ang_vel_xy",
-                    "penalty_action_rate",
-                    "alive",
-                )
-            ):
-                return True
-            if any(key in scales for key in ("orientation", "ang_vel_xy", "action_rate")):
-                return False
-
-        curriculum = getattr(self._cfg, "curriculum", None)
-        return bool(curriculum is not None and curriculum.enabled)
-
-    def _actor_symmetry_obs_layout(self) -> SymmetryObsLayout:
-        return (
-            ("gyro", 3),
-            ("gravity", 3),
-            ("dof_pos", self._num_action),
-            ("dof_vel", self._num_action),
-            ("actions", self._num_action),
-            ("command", 3),
-            ("gait_phase", 2),
-        )
-
-    def get_symmetry_obs_layouts(self) -> dict[str, SymmetryObsLayout]:
-        actor_layout = self._actor_symmetry_obs_layout()
-        return {
-            "obs": actor_layout,
-            "critic": (*actor_layout, ("linvel", 3)),
-        }
-
-    def build_symmetry_augmentation(self, *, device: str):
-        try:
-            actuator_names = self._backend.get_actuator_names()
-        except NotImplementedError:
-            return None
-        from unilab.envs.locomotion.g1.symmetry import G1SymmetryAugmentation
-
-        return G1SymmetryAugmentation(
-            actuator_names,
-            self.get_symmetry_obs_layouts(),
-            device=device,
-        )
-
-    def _build_reward_context(
-        self, info: dict, linvel, gyro, gravity, dof_pos, dof_vel
-    ) -> RewardContext:
-        return RewardContext(
-            info=info,
-            linvel=linvel,
-            gyro=gyro,
-            dof_pos=dof_pos,
-            num_envs=self._num_envs,
-            default_angles=self.default_angles,
-            tracking_sigma=self._reward_cfg.tracking_sigma,
-            base_height_target=self._reward_cfg.base_height_target,
-            base_height=self._backend.get_base_pos()[:, 2],
-            gravity=gravity,
-            dof_vel=dof_vel,
-            pose_weights=self._pose_weights,
-        )
-
-    def _compute_reward(self, info: dict, linvel, gyro, gravity, dof_pos, dof_vel) -> np.ndarray:
-        cfg = self._reward_cfg
-        ctx = self._build_reward_context(info, linvel, gyro, gravity, dof_pos, dof_vel)
-        return rewards.run_reward_dispatch(
-            scales=cfg.scales,
-            fns=self._reward_fns,
-            ctx=ctx,
-            info=info,
-            enable_log=self._enable_reward_log,
-            ctrl_dt=self._cfg.ctrl_dt,
-        )
-
-    def _reward_feet_phase(self, ctx: RewardContext):
-        """Reward gait phase tracking by encouraging the expected swing-foot height."""
-        left_foot = self._backend.get_sensor_data("left_foot_pos")
-        right_foot = self._backend.get_sensor_data("right_foot_pos")
-        gait_phase = ctx.info.get(
-            "gait_phase", np.zeros((self._num_envs, 2), dtype=get_global_dtype())
-        )
-        swing_height = self._reward_cfg.feet_phase_swing_height
-        left_target, right_target = compute_feet_phase_height_targets(gait_phase, swing_height)
-        left_error = np.square(left_foot[:, 2] - left_target)
-        right_error = np.square(right_foot[:, 2] - right_target)
-        reward = np.exp(-(left_error + right_error) / self._reward_cfg.feet_phase_tracking_sigma)
-        return np.asarray(reward * self._gait_reward_gate(ctx.linvel), dtype=get_global_dtype())
-
-    def _gait_reward_gate(self, linvel: np.ndarray) -> np.ndarray:
-        min_forward_speed = getattr(self._reward_cfg, "min_forward_speed_for_gait_reward", 0.0)
-        return compute_forward_speed_gate(linvel, min_forward_speed)
-
-    def _reward_feet_phase_contrast(self, ctx: RewardContext):
-        left_foot = self._backend.get_sensor_data("left_foot_pos")
-        right_foot = self._backend.get_sensor_data("right_foot_pos")
-        gait_phase = ctx.info.get(
-            "gait_phase", np.zeros((self._num_envs, 2), dtype=get_global_dtype())
-        )
-        swing_height = self._reward_cfg.feet_phase_swing_height
-        left_target, right_target = compute_feet_phase_height_targets(gait_phase, swing_height)
-        actual_delta = left_foot[:, 2] - right_foot[:, 2]
-        target_delta = left_target - right_target
-        error = np.square(actual_delta - target_delta)
-        reward = np.exp(-error / self._reward_cfg.feet_phase_tracking_sigma)
-        return np.asarray(reward * self._gait_reward_gate(ctx.linvel), dtype=get_global_dtype())
-
-    def _reward_feet_phase_contact(self, ctx: RewardContext):
-        gait_phase = ctx.info.get(
-            "gait_phase", np.zeros((self._num_envs, 2), dtype=get_global_dtype())
-        )
-        swing_height = self._reward_cfg.feet_phase_swing_height
-        left_target_contact, right_target_contact = compute_feet_phase_contact_targets(
-            gait_phase, swing_height
-        )
-        left_contact = compute_aggregated_foot_contact(self._backend, LEFT_FOOT_CONTACT_SENSORS)
-        right_contact = compute_aggregated_foot_contact(self._backend, RIGHT_FOOT_CONTACT_SENSORS)
-        left_match = np.asarray(left_contact == left_target_contact, dtype=get_global_dtype())
-        right_match = np.asarray(right_contact == right_target_contact, dtype=get_global_dtype())
-        reward = np.asarray(0.5 * (left_match + right_match), dtype=get_global_dtype())
-        return np.asarray(reward * self._gait_reward_gate(ctx.linvel), dtype=get_global_dtype())
-
-    def _reward_feet_double_stance(self, ctx: RewardContext):
-        commands = ctx.info.get("commands", np.zeros((self._num_envs, 3), dtype=get_global_dtype()))
-        left_contact = compute_aggregated_foot_contact(self._backend, LEFT_FOOT_CONTACT_SENSORS)
-        right_contact = compute_aggregated_foot_contact(self._backend, RIGHT_FOOT_CONTACT_SENSORS)
-        double_stance = np.asarray(
-            np.logical_and(left_contact, right_contact), dtype=get_global_dtype()
-        )
-        return np.asarray(
-            double_stance * compute_forward_command_mask(commands), dtype=get_global_dtype()
-        )
-
-    def _reward_feet_ori(self, ctx: RewardContext):
-        left_foot_quat = self._backend.get_sensor_data("left_foot_quat")
-        right_foot_quat = self._backend.get_sensor_data("right_foot_quat")
-        return (
-            np.square(left_foot_quat[:, 1])
-            + np.square(left_foot_quat[:, 2])
-            + np.square(right_foot_quat[:, 1])
-            + np.square(right_foot_quat[:, 2])
-        )
-
-    def _reward_close_feet_xy(self, ctx: RewardContext):
-        left_foot = self._backend.get_sensor_data("left_foot_pos")
-        right_foot = self._backend.get_sensor_data("right_foot_pos")
-        feet_dist = np.linalg.norm(left_foot[:, :2] - right_foot[:, :2], axis=1)
-        return np.where(
-            feet_dist < self._reward_cfg.close_feet_threshold,
-            np.square(feet_dist - self._reward_cfg.close_feet_threshold),
-            0.0,
-        )
-
-    def _reward_feet_air_time(self, ctx: RewardContext):
-        air_time = ctx.info.get(
-            "feet_air_time", np.zeros((self._num_envs, 2), dtype=get_global_dtype())
-        )
-        in_range = (air_time > 0.05) & (air_time < 0.5)
-        return np.sum(in_range.astype(float), axis=1)
-
-    def _reward_upper_body_pose(self, ctx: RewardContext):
-        diff = ctx.dof_pos - self.default_angles
-        return np.asarray(
-            np.sum(self._upper_body_pose_weights * np.square(diff), axis=1),
-            dtype=get_global_dtype(),
-        )
-
-    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
-        state.info["last_actions"] = state.info.get("current_actions", np.zeros_like(actions))
-        state.info["current_actions"] = actions
-
-        gait_phase = state.info.get(
-            "gait_phase", np.zeros((self._num_envs, 2), dtype=get_global_dtype())
-        )
-        gait_phase[:, 0] = (gait_phase[:, 0] + self._gait_phase_delta) % (2 * np.pi)
-        gait_phase[:, 1] = (gait_phase[:, 1] + self._gait_phase_delta) % (2 * np.pi)
-        state.info["gait_phase"] = gait_phase
-
-        ctrl: np.ndarray = actions * self._cfg.control_config.action_scale + self.default_angles
-        return ctrl
-
-
-def _walk_curriculum() -> CurriculumConfig:
-    return CurriculumConfig(
-        enabled=True,
-        initial_scale=0.5,
-        min_scale=0.5,
-        max_scale=1.0,
-        level_down_threshold=150.0,
-        level_up_threshold=750.0,
-        degree=0.001,
-    )
-
-
-@dataclass
-class G1WalkControlConfig:
-    action_scale: float = 1.0
-    simulate_action_latency: bool = False
-
-
-@dataclass
-class G1WalkRewardConfig(G1RewardConfig):
-    """Align reward weights with holosoma G1 walking."""
-
-
-@registry.envcfg("G1WalkFlat")
-@dataclass
-class G1WalkFlatCfg(G1WalkEnvCfg):
-    reward_config: G1WalkRewardConfig | None = None
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat.xml")
-        )
-    )
-    control_config: G1WalkControlConfig = field(default_factory=G1WalkControlConfig)  # type: ignore[assignment]
-    curriculum: CurriculumConfig = field(default_factory=_walk_curriculum)
-
-
-@registry.envcfg("G1WalkRough")
-@dataclass
-class G1WalkRoughCfg(G1WalkFlatCfg):
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_rough.xml")
-        )
-    )
-
-
-registry.register_env("G1WalkFlat", G1WalkEnv, sim_backend="mujoco")
-registry.register_env("G1WalkFlat", G1WalkEnv, sim_backend="mjwarp")
-registry.register_env("G1WalkFlat", G1WalkEnv, sim_backend="motrix")
-registry.register_env("G1WalkRough", G1WalkEnv, sim_backend="mujoco")
-registry.register_env("G1WalkRough", G1WalkEnv, sim_backend="motrix")
-
-
-class G1Walk23DofEnv(G1WalkEnv):
-    @property
-    def obs_groups_spec(self) -> dict[str, int]:
-        # gyro(3) + gravity(3) + diff(23) + dof_vel(23) + action(23) + cmd(3) + phase(2) = 80
-        return {"obs": 80, "critic": 83}
-
-
-@registry.envcfg("G1Walk23DofFlat")
-@dataclass
-class G1Walk23DofFlatCfg(G1WalkFlatCfg):
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat_23dof.xml")
-        )
-    )
-
-
-@registry.envcfg("G1Walk23DofRough")
-@dataclass
-class G1Walk23DofRoughCfg(G1Walk23DofFlatCfg):
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_rough_23dof.xml")
-        )
-    )
-
-
-registry.register_env("G1Walk23DofFlat", G1Walk23DofEnv, sim_backend="mujoco")
-registry.register_env("G1Walk23DofFlat", G1Walk23DofEnv, sim_backend="motrix")
-registry.register_env("G1Walk23DofRough", G1Walk23DofEnv, sim_backend="mujoco")
-registry.register_env("G1Walk23DofRough", G1Walk23DofEnv, sim_backend="motrix")
diff --git a/src/unilab/envs/locomotion/g1/symmetry.py b/src/unilab/envs/locomotion/g1/symmetry.py
deleted file mode 100644
index 799f7b3cb..000000000
--- a/src/unilab/envs/locomotion/g1/symmetry.py
+++ /dev/null
@@ -1,152 +0,0 @@
-"""G1 symmetry augmentation derived from backend-owned actuator ordering."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-
-import torch
-
-from unilab.base.augmentation import SymmetryAugmentation, SymmetryObsLayout
-
-
-@dataclass(frozen=True)
-class _ObsGroupTransform:
-    dim: int
-    joint_map: torch.Tensor
-    sign_mask: torch.Tensor
-
-
-class G1SymmetryAugmentation(SymmetryAugmentation):
-    """Runtime symmetry adapter derived from the backend actuator ordering."""
-
-    batch_multiplier = 2
-
-    def __init__(
-        self,
-        actuator_names: tuple[str, ...],
-        obs_layouts: dict[str, SymmetryObsLayout],
-        *,
-        device: str = "cuda",
-    ):
-        symmetry_pairs = {
-            "left_hip_pitch_joint": "right_hip_pitch_joint",
-            "left_hip_roll_joint": "right_hip_roll_joint",
-            "left_hip_yaw_joint": "right_hip_yaw_joint",
-            "left_knee_joint": "right_knee_joint",
-            "left_ankle_pitch_joint": "right_ankle_pitch_joint",
-            "left_ankle_roll_joint": "right_ankle_roll_joint",
-            "left_shoulder_pitch_joint": "right_shoulder_pitch_joint",
-            "left_shoulder_roll_joint": "right_shoulder_roll_joint",
-            "left_shoulder_yaw_joint": "right_shoulder_yaw_joint",
-            "left_elbow_joint": "right_elbow_joint",
-            "left_wrist_roll_joint": "right_wrist_roll_joint",
-            "left_wrist_pitch_joint": "right_wrist_pitch_joint",
-            "left_wrist_yaw_joint": "right_wrist_yaw_joint",
-        }
-        name_to_idx = {name: i for i, name in enumerate(actuator_names)}
-        joint_map: dict[int, int] = {}
-        for left, right in symmetry_pairs.items():
-            if left in name_to_idx and right in name_to_idx:
-                joint_map[name_to_idx[left]] = name_to_idx[right]
-                joint_map[name_to_idx[right]] = name_to_idx[left]
-        for i in range(len(actuator_names)):
-            joint_map.setdefault(i, i)
-
-        self._joint_map = torch.tensor(
-            [joint_map[i] for i in range(len(actuator_names))],
-            device=device,
-            dtype=torch.long,
-        )
-
-        flip_names = {"roll", "yaw"}
-        sign_mask = [1.0] * len(actuator_names)
-        for i, name in enumerate(actuator_names):
-            if any(flip in name for flip in flip_names):
-                sign_mask[i] = -1.0
-        self._sign_mask = torch.tensor(sign_mask, device=device)
-        self._obs_transforms = {
-            group_name: self._build_obs_group_transform(layout, device=device)
-            for group_name, layout in obs_layouts.items()
-        }
-
-    def _build_obs_group_transform(
-        self,
-        layout: SymmetryObsLayout,
-        *,
-        device: str,
-    ) -> _ObsGroupTransform:
-        obs_dim = sum(dim for _, dim in layout)
-        flip_mask = torch.ones(obs_dim, device=device)
-        joint_map = torch.arange(obs_dim, device=device, dtype=torch.long)
-        joint_sign = torch.ones(obs_dim, device=device)
-        idx = 0
-
-        for key, dim in layout:
-            if dim <= 0:
-                raise ValueError(
-                    f"Observation layout group {key!r} must have positive dim, got {dim}"
-                )
-
-            if key == "linvel":
-                self._require_dim(key, dim, 3)
-                flip_mask[idx + 1] = -1.0
-            elif key == "gyro":
-                self._require_dim(key, dim, 3)
-                flip_mask[idx] = -1.0
-                flip_mask[idx + 2] = -1.0
-            elif key == "gravity":
-                self._require_dim(key, dim, 3)
-                flip_mask[idx + 1] = -1.0
-            elif key in {"dof_pos", "dof_vel", "actions"}:
-                self._require_dim(key, dim, int(self._joint_map.numel()))
-                joint_map[idx : idx + dim] = self._joint_map + idx
-                joint_sign[idx : idx + dim] = self._sign_mask
-            elif key == "command":
-                self._require_dim(key, dim, 3)
-                flip_mask[idx + 1] = -1.0
-                flip_mask[idx + 2] = -1.0
-            elif key == "gait_phase":
-                self._require_dim(key, dim, 2)
-                joint_map[idx] = idx + 1
-                joint_map[idx + 1] = idx
-
-            idx += dim
-
-        return _ObsGroupTransform(
-            dim=obs_dim,
-            joint_map=joint_map,
-            sign_mask=flip_mask * joint_sign,
-        )
-
-    @staticmethod
-    def _require_dim(group_name: str, actual: int, expected: int) -> None:
-        if actual != expected:
-            raise ValueError(
-                f"Symmetry group {group_name!r} must have dim {expected}, got {actual}"
-            )
-
-    def mirror_action(self, action: torch.Tensor) -> torch.Tensor:
-        return action[..., self._joint_map] * self._sign_mask
-
-    def mirror_obs(self, obs: torch.Tensor, *, obs_group: str = "obs") -> torch.Tensor:
-        transform = self._obs_transforms[obs_group]
-        if obs.shape[-1] != transform.dim:
-            raise ValueError(
-                f"Symmetry obs group {obs_group!r} expects dim {transform.dim}, got {obs.shape[-1]}"
-            )
-        return obs[..., transform.joint_map] * transform.sign_mask
-
-    def augment_obs(self, obs: torch.Tensor, *, obs_group: str = "obs") -> torch.Tensor:
-        return torch.cat([obs, self.mirror_obs(obs, obs_group=obs_group)], dim=0)
-
-    def augment_obs_and_actions(
-        self,
-        obs: torch.Tensor,
-        actions: torch.Tensor,
-        *,
-        obs_group: str = "obs",
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        return self.augment_obs(obs, obs_group=obs_group), torch.cat(
-            [actions, self.mirror_action(actions)],
-            dim=0,
-        )
diff --git a/src/unilab/envs/locomotion/go1/__init__.py b/src/unilab/envs/locomotion/go1/__init__.py
deleted file mode 100644
index b0608c22b..000000000
--- a/src/unilab/envs/locomotion/go1/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .joystick import Go1JoystickCfg, Go1WalkTask
-from .rough import Go1JoystickRoughCfg, Go1JoystickRoughEnv
diff --git a/src/unilab/envs/locomotion/go1/base.py b/src/unilab/envs/locomotion/go1/base.py
deleted file mode 100644
index 06b841baa..000000000
--- a/src/unilab/envs/locomotion/go1/base.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-
-from unilab.envs.locomotion.common.base import (
-    BaseNoiseConfig,
-    LocomotionBaseCfg,
-    LocomotionBaseEnv,
-    PdControlConfig,
-)
-
-
-@dataclass
-class NoiseConfig(BaseNoiseConfig):
-    pass
-
-
-@dataclass
-class ControlConfig(PdControlConfig):
-    pass
-
-
-@dataclass
-class Asset:
-    base_name = "trunk"
-    foot_name = "foot"
-    ground = "floor"
-
-
-@dataclass
-class Go1BaseCfg(LocomotionBaseCfg):
-    noise_config: NoiseConfig = field(default_factory=NoiseConfig)  # type: ignore[assignment]
-    control_config: ControlConfig = field(default_factory=ControlConfig)  # type: ignore[assignment]
-    asset: Asset = field(default_factory=Asset)
-    sim_dt: float = 0.01
-    ctrl_dt: float = 0.02
-
-
-class Go1BaseEnv(LocomotionBaseEnv):
-    _cfg: Go1BaseCfg
diff --git a/src/unilab/envs/locomotion/go1/joystick.py b/src/unilab/envs/locomotion/go1/joystick.py
deleted file mode 100644
index 9b798ecc2..000000000
--- a/src/unilab/envs/locomotion/go1/joystick.py
+++ /dev/null
@@ -1,256 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Any
-
-import numpy as np
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base import registry
-from unilab.base.backend import create_backend, env_backend_kwargs
-from unilab.base.np_env import NpEnvState
-from unilab.base.scene import SceneCfg
-from unilab.dtype_config import get_global_dtype
-from unilab.envs.locomotion.common import rewards
-from unilab.envs.locomotion.common.commands import Commands
-from unilab.envs.locomotion.common.domain_rand import DomainRandConfig
-from unilab.envs.locomotion.common.dr_provider import LocomotionDRProvider
-from unilab.envs.locomotion.common.rewards import RewardContext
-from unilab.envs.locomotion.common.terrain_spawn import (
-    TerrainCurriculumCfg,
-    TerrainSpawnManager,
-)
-from unilab.envs.locomotion.go1.base import Go1BaseCfg, Go1BaseEnv
-
-
-@dataclass
-class InitState:
-    pos = [0.0, 0.0, 0.45]
-
-
-@dataclass
-class RewardConfig:
-    scales: dict[str, float]
-    tracking_sigma: float
-    base_height_target: float
-
-
-@dataclass
-class JoystickSensor:
-    local_linvel = "local_linvel"
-    gyro = "gyro"
-    feet_force = ["FL_foot_contact", "FR_foot_contact", "RL_foot_contact", "RR_foot_contact"]
-    feet_pos = ["FL_pos", "FR_pos", "RL_pos", "RR_pos"]
-
-
-@registry.envcfg("Go1JoystickFlat")
-@dataclass
-class Go1JoystickCfg(Go1BaseCfg):
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "go1" / "scene_flat.xml")
-        )
-    )
-    max_episode_seconds: float = 20.0
-    init_state: InitState = field(default_factory=InitState)
-    commands: Commands = field(default_factory=Commands)
-    reward_config: RewardConfig | None = None
-    sensor: JoystickSensor = field(default_factory=JoystickSensor)  # type: ignore[assignment]
-    domain_rand: DomainRandConfig = field(
-        default_factory=lambda: DomainRandConfig(
-            randomize_base_mass=True,
-            random_com=True,
-            push_robots=True,
-        )
-    )
-
-
-class Go1JoystickDomainRandomizationProvider(LocomotionDRProvider):
-    def _compute_reset_obs(
-        self,
-        env: Any,
-        env_ids: Any,
-        info_updates: Any,
-        linvel: Any,
-        gyro: Any,
-        gravity: Any,
-        dof_pos: Any,
-        dof_vel: Any,
-    ) -> dict[str, np.ndarray]:
-        return env._compute_obs(  # type: ignore[no-any-return]
-            info_updates, linvel, gyro, gravity, dof_pos, dof_vel, env.feet_phase[env_ids]
-        )
-
-
-@registry.env("Go1JoystickFlat", sim_backend="mujoco")
-@registry.env("Go1JoystickFlat", sim_backend="motrix")
-@registry.env("Go1JoystickFlat", sim_backend="drake")
-class Go1WalkTask(Go1BaseEnv):
-    _cfg: Go1JoystickCfg
-
-    def __init__(self, cfg: Go1JoystickCfg, num_envs=1, backend_type="mujoco"):
-        if cfg.reward_config is None:
-            raise ValueError("reward_config must be provided via Hydra configuration")
-        self._scene_terrain_origins: np.ndarray | None = None
-        scene_cfg = cfg.scene
-        terrain_generator = scene_cfg.terrain.generator if scene_cfg.terrain is not None else None
-        backend = create_backend(
-            backend_type,
-            cfg.scene,
-            num_envs,
-            cfg.sim_dt,
-            base_name=cfg.asset.base_name,
-            push_body_name=cfg.domain_rand.push_body_name,
-            position_actuator_gains={"kp": cfg.control_config.Kp, "kd": cfg.control_config.Kd},
-            **env_backend_kwargs(cfg),
-        )
-        terrain_spawn_data = backend.get_terrain_spawn_data()
-        if terrain_spawn_data is not None:
-            self._scene_terrain_origins = terrain_spawn_data.terrain_origins
-        super().__init__(cfg, backend, num_envs)
-        self._enable_reward_log = True
-        self._reward_cfg = cfg.reward_config
-        self._init_reward_functions()
-        if terrain_spawn_data is not None and terrain_generator is not None:
-            self._spawn = TerrainSpawnManager(
-                num_envs,
-                terrain_spawn_data.terrain_origins,
-                cell_size=float(terrain_generator.size[0]),
-                cfg=getattr(cfg, "terrain_curriculum", TerrainCurriculumCfg()),
-                sample_height=terrain_spawn_data.sample_height,
-            )
-        self.phase = np.zeros((num_envs,), dtype=np.float32)
-        self.feet_phase = np.zeros((num_envs, len(cfg.sensor.feet_force)), dtype=np.float32)
-        self.gait_frequency = 2
-        self.feet_force = np.zeros((num_envs, len(cfg.sensor.feet_force), 3), dtype=np.float32)
-        self._init_domain_randomization(Go1JoystickDomainRandomizationProvider())
-        self.feet_pos = np.zeros((num_envs, len(cfg.sensor.feet_pos), 3), dtype=np.float32)
-
-    @property
-    def obs_groups_spec(self) -> dict[str, int]:
-        # gyro(3) + gravity(3) + diff(12) + dof_vel(12) + action(12) + cmd(3) + phase(4) = 49
-        return {"obs": 49, "critic": 52}
-
-    def _init_reward_functions(self):
-        self._reward_fns: dict[str, Any] = {
-            "tracking_lin_vel": rewards.tracking_lin_vel,
-            "tracking_ang_vel": rewards.tracking_ang_vel,
-            "lin_vel_z": rewards.lin_vel_z,
-            "ang_vel_xy": rewards.ang_vel_xy,
-            "base_height": rewards.base_height,
-            "action_rate": rewards.action_rate,
-            "action_smooth": rewards.action_smooth,
-            "similar_to_default": rewards.similar_to_default,
-            "contact": self._reward_contact,
-            "swing_feet_z": self._reward_swing_feet_z,
-        }
-
-    def update_state(self, state: NpEnvState) -> NpEnvState:
-        self.phase = np.fmod(self.phase + self._cfg.ctrl_dt * self.gait_frequency, 1.0)
-        self.feet_phase[:, 0] = self.phase
-        self.feet_phase[:, 3] = self.phase
-
-        self.feet_phase[:, 1] = (self.phase + 0.5) % 1
-        self.feet_phase[:, 2] = (self.phase + 0.5) % 1
-
-        linvel = self.get_local_linvel()
-        gyro = self.get_gyro()
-        gravity = self._backend.get_sensor_data("upvector")
-        dof_pos = self.get_dof_pos()
-        dof_vel = self.get_dof_vel()
-        self.feet_force[:, :, :] = 0
-        for i in range(len(self._cfg.sensor.feet_force)):
-            self.feet_force[:, i, :] = self._backend.get_sensor_data(self._cfg.sensor.feet_force[i])
-        for i in range(len(self._cfg.sensor.feet_pos)):
-            self.feet_pos[:, i, :] = self._backend.get_sensor_data(self._cfg.sensor.feet_pos[i])
-        terminated = gravity[:, 2] <= 0.5
-        reward = self._compute_reward(state.info, linvel, gyro, dof_pos)
-        obs = self._compute_obs(
-            state.info, linvel, gyro, gravity, dof_pos, dof_vel, self.feet_phase
-        )
-        return state.replace(obs=obs, reward=reward, terminated=terminated)
-
-    def _compute_obs(
-        self, info: dict, linvel, gyro, gravity, dof_pos, dof_vel, feet_phase
-    ) -> dict[str, np.ndarray]:
-        noise_cfg = self._cfg.noise_config
-        diff = dof_pos - self.default_angles
-        noisy_gyro = self._obs_noise(gyro, noise_cfg.scale_gyro)
-        noisy_gravity = self._obs_noise(gravity, noise_cfg.scale_gravity)
-        noisy_diff = self._obs_noise(diff, noise_cfg.scale_joint_angle)
-        noisy_dof_vel = self._obs_noise(dof_vel, noise_cfg.scale_joint_vel)
-        command = info["commands"]
-        last_actions = info.get("current_actions", np.zeros_like(diff))
-        obs = np.concatenate(
-            [
-                noisy_gyro,
-                -noisy_gravity,
-                noisy_diff,
-                noisy_dof_vel,
-                last_actions,
-                command,
-                feet_phase,
-            ],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-        critic = np.concatenate(
-            [gyro, -gravity, diff, dof_vel, last_actions, command, feet_phase, linvel],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-        return {"obs": obs, "critic": critic}
-
-    def _compute_reward(self, info: dict, linvel, gyro, dof_pos) -> np.ndarray:
-        cfg = self._reward_cfg
-        ctx = RewardContext(
-            info=info,
-            linvel=linvel,
-            gyro=gyro,
-            dof_pos=dof_pos,
-            num_envs=self._num_envs,
-            default_angles=self.default_angles,
-            tracking_sigma=cfg.tracking_sigma,
-            base_height_target=cfg.base_height_target,
-            base_height=self._backend.get_base_pos()[:, 2],
-        )
-        return rewards.run_reward_dispatch(
-            scales=cfg.scales,
-            fns=self._reward_fns,
-            ctx=ctx,
-            info=info,
-            enable_log=self._enable_reward_log,
-            ctrl_dt=self._cfg.ctrl_dt,
-        )
-
-    def _reward_contact(self, ctx: RewardContext) -> np.ndarray:
-        contact = self.feet_force[:, :, 2] > 0.1
-        res = np.zeros(self.num_envs, dtype=np.float32)
-        for i in range(len(self._cfg.sensor.feet_force)):
-            is_contact = (self.feet_phase[:, i] < 0.6) | (self.gait_frequency < 1.0e-8)
-            res += ~(contact[:, i] ^ is_contact)
-        return res
-
-    def _reward_swing_feet_z(self, ctx: RewardContext) -> np.ndarray:
-        is_swing = self.feet_phase >= 0.6
-        target_height = 0.1
-        height_error = np.square(self.feet_pos[:, :, 2] - target_height)
-        swing_rew = np.exp(-height_error / 0.01) * is_swing
-        reward: np.ndarray = np.sum(swing_rew, axis=1) / len(self._cfg.sensor.feet_pos)
-        return reward
-
-    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
-        previous_current = state.info.get("current_actions", np.zeros_like(actions))
-        previous_last = state.info.get("last_actions", np.zeros_like(actions))
-        state.info["previous_actions"] = previous_last
-        state.info["last_actions"] = previous_current
-        state.info["current_actions"] = actions
-        exec_actions = (
-            state.info["last_actions"]
-            if self._cfg.control_config.simulate_action_latency
-            else actions
-        )
-        ctrl: np.ndarray = (
-            exec_actions * self._cfg.control_config.action_scale + self.default_angles
-        )
-        return ctrl
diff --git a/src/unilab/envs/locomotion/go1/rough.py b/src/unilab/envs/locomotion/go1/rough.py
deleted file mode 100644
index 9f67c2305..000000000
--- a/src/unilab/envs/locomotion/go1/rough.py
+++ /dev/null
@@ -1,786 +0,0 @@
-"""Go1 joystick rough-terrain task."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Any
-
-import numpy as np
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base import registry
-from unilab.base.np_env import NpEnvState
-from unilab.base.scene import SceneCfg, TerrainSceneCfg
-from unilab.dr import DomainRandomizationManager, ResetPlan
-from unilab.dr.dr_utils import build_common_reset_randomization, zero_actions
-from unilab.dtype_config import get_global_dtype
-from unilab.envs.locomotion.common import rewards
-from unilab.envs.locomotion.common.commands import (
-    Commands,
-    apply_heading_yaw_feedback,
-    sample_heading_commands,
-    zero_small_xy_commands,
-)
-from unilab.envs.locomotion.common.domain_rand import DomainRandConfig
-from unilab.envs.locomotion.common.height_scan import (
-    HeightScanConfig,
-    base_height_from_scan,
-    height_scan_obs,
-    init_height_scan_sensor,
-    raw_height_scan_obs,
-    terrain_out_of_bounds,
-)
-from unilab.envs.locomotion.common.rewards import RewardContext
-from unilab.envs.locomotion.common.terrain_spawn import (
-    TerrainCurriculumCfg,
-)
-from unilab.envs.locomotion.go1.base import ControlConfig
-from unilab.envs.locomotion.go1.joystick import (
-    Go1JoystickCfg,
-    Go1JoystickDomainRandomizationProvider,
-    Go1WalkTask,
-    JoystickSensor,
-    RewardConfig,
-)
-from unilab.terrains import (
-    SubTerrainCfg,
-    TerrainGeneratorCfg,
-    flat,
-    hf_pyramid_slope,
-    hf_pyramid_slope_inv,
-    pyramid_stairs,
-    pyramid_stairs_inv,
-    random_rough,
-    wave_terrain,
-)
-from unilab.utils.rotation import (
-    np_quat_apply_inverse,
-    np_quat_from_euler_xyz,
-    np_quat_mul,
-)
-
-# pyright: reportIncompatibleVariableOverride=false, reportAttributeAccessIssue=false, reportCallIssue=false
-
-
-GO1_HIP_INDICES = np.asarray([0, 3, 6, 9], dtype=np.int32)
-GO1_FRONT_LEFT = 0
-GO1_FRONT_RIGHT = 1
-GO1_REAR_LEFT = 2
-GO1_REAR_RIGHT = 3
-
-
-@dataclass
-class RoughControlConfig(ControlConfig):
-    hip_action_scale: float = 0.125
-    non_hip_action_scale: float = 0.25
-    clip_actions: float = 100.0
-
-
-@dataclass
-class Go1RoughDomainRandConfig(DomainRandConfig):
-    randomize_kp: bool = True
-    kp_multiplier_range: list[float] = field(default_factory=lambda: [0.9, 1.1])
-    randomize_kd: bool = True
-    kd_multiplier_range: list[float] = field(default_factory=lambda: [0.9, 1.1])
-
-
-@dataclass
-class RoughCommands(Commands):
-    vel_limit: list[list[float]] = field(
-        default_factory=lambda: [[-1.0, -1.0, -1.0], [1.0, 1.0, 1.0]]
-    )
-    resampling_time: float = 10.0
-    heading_command: bool = True
-    heading_range: list[float] = field(default_factory=lambda: [-np.pi, np.pi])
-
-
-@dataclass
-class RoughRewardConfig(RewardConfig):
-    stand_still_command_threshold: float = 0.1
-    joint_pos_penalty_stand_still_scale: float = 5.0
-    joint_pos_penalty_velocity_threshold: float = 0.5
-    joint_pos_penalty_command_threshold: float = 0.1
-    contact_threshold: float = 1.0
-    contact_forces_threshold: float = 100.0
-    feet_air_time_threshold: float = 0.5
-    feet_height_body_target: float = -0.2
-    feet_height_body_tanh_mult: float = 2.0
-    feet_gait_std: float = float(np.sqrt(0.5))
-    feet_gait_max_err: float = 0.2
-    feet_gait_velocity_threshold: float = 0.5
-    feet_gait_command_threshold: float = 0.1
-
-
-@dataclass
-class RoughJoystickSensor(JoystickSensor):
-    feet_vel = ["FL_vel", "FR_vel", "RL_vel", "RR_vel"]
-    undesired_contact = [
-        "base1_contact",
-        "base2_contact",
-        "base3_contact",
-        "FL_hip_contact",
-        "FR_hip_contact",
-        "RL_hip_contact",
-        "RR_hip_contact",
-        "FL_thigh_contact",
-        "FR_thigh_contact",
-        "RL_thigh_contact",
-        "RR_thigh_contact",
-        "FL_calf_contact1",
-        "FR_calf_contact1",
-        "RL_calf_contact1",
-        "RR_calf_contact1",
-        "FL_calf_contact2",
-        "FR_calf_contact2",
-        "RL_calf_contact2",
-        "RR_calf_contact2",
-    ]
-
-
-@dataclass
-class RoughTerminationConfig:
-    terrain_out_of_bounds: bool = True
-    terrain_distance_buffer: float = 3.0
-
-
-@dataclass(kw_only=True)
-class Go1RoughTerrainCfg(TerrainGeneratorCfg):
-    size: tuple[float, float] = (8.0, 8.0)
-    num_rows: int = 6
-    num_cols: int = 6
-    border_width: float = 1.0
-    add_lights: bool = True
-    horizontal_scale: float = 0.2
-
-    sub_terrains: dict[str, SubTerrainCfg] = field(
-        default_factory=lambda: {
-            "flat": flat(proportion=0.0),
-            "pyramid_stairs": pyramid_stairs(
-                proportion=0.1,
-                step_height_range=(0.025, 0.10),
-                step_width=0.4,
-                platform_width=3.0,
-                border_width=0.2,
-            ),
-            "pyramid_stairs_inv": pyramid_stairs_inv(
-                proportion=0.1,
-                step_height_range=(0.025, 0.10),
-                step_width=0.4,
-                platform_width=3.0,
-                border_width=0.2,
-            ),
-            "hf_pyramid_slope": hf_pyramid_slope(
-                proportion=0.2,
-                slope_range=(0.0, 0.3),
-                platform_width=2.0,
-                border_width=0.2,
-            ),
-            "hf_pyramid_slope_inv": hf_pyramid_slope_inv(
-                proportion=0.2,
-                slope_range=(0.0, 0.3),
-                platform_width=2.0,
-                border_width=0.2,
-            ),
-            "random_rough": random_rough(
-                proportion=0.3,
-                noise_range=(0.01, 0.06),
-                noise_step=0.01,
-                border_width=0.2,
-            ),
-            "wave_terrain": wave_terrain(
-                proportion=0.3,
-                amplitude_range=(0.0, 0.12),
-                num_waves=4,
-                border_width=0.2,
-            ),
-        }
-    )
-
-
-@registry.envcfg("Go1JoystickRough")
-@dataclass
-class Go1JoystickRoughCfg(Go1JoystickCfg):
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "go1" / "go1.xml"),
-            fragment_files=[
-                str(ASSETS_ROOT_PATH / "robots" / "go1" / "locomotion_task.xml"),
-            ],
-            terrain=TerrainSceneCfg(
-                generator=Go1RoughTerrainCfg(),
-                hfield_name="terrain_hfield",
-                geom_name="floor",
-            ),
-        )
-    )
-    control_config: RoughControlConfig = field(default_factory=RoughControlConfig)
-    commands: RoughCommands = field(default_factory=RoughCommands)
-    terrain_scan: HeightScanConfig = field(default_factory=HeightScanConfig)
-    termination_config: RoughTerminationConfig = field(default_factory=RoughTerminationConfig)
-    terrain_curriculum: TerrainCurriculumCfg = field(default_factory=TerrainCurriculumCfg)
-    sensor: RoughJoystickSensor = field(default_factory=RoughJoystickSensor)
-    domain_rand: Go1RoughDomainRandConfig = field(default_factory=Go1RoughDomainRandConfig)
-    reward_config: RoughRewardConfig | None = None
-
-
-class Go1JoystickRoughDomainRandomizationProvider(Go1JoystickDomainRandomizationProvider):
-    def _sample_commands(self, env: Any, num_reset: int) -> np.ndarray:
-        commands = super()._sample_commands(env, num_reset)
-        zero_small_xy_commands(commands, threshold=0.08)
-        standing_prob = env.cfg.commands.rel_standing_envs
-        if standing_prob > 0.0:
-            standing = np.random.uniform(size=(num_reset,)) < min(standing_prob, 1.0)
-            commands[standing] = 0.0
-        if env.cfg.commands.heading_command:
-            commands[:, 2] = 0.0
-        return commands
-
-    def build_reset_plan(self, env: Any, env_ids: np.ndarray) -> ResetPlan:
-        num_reset = len(env_ids)
-        qpos = np.tile(env._init_qpos, (num_reset, 1))
-        qvel = np.tile(env._init_qvel, (num_reset, 1))
-        qpos[:, 0:2] += np.random.uniform(-0.5, 0.5, (num_reset, 2))
-        qpos[:, 2] += np.random.uniform(0.25, 0.5, (num_reset,))
-        qpos[:, 0:3] += env._spawn.origins_for(env_ids)
-        roll = np.random.uniform(-3.14, 3.14, (num_reset,))
-        pitch = np.random.uniform(-3.14, 3.14, (num_reset,))
-        yaw = np.random.uniform(-3.14, 3.14, (num_reset,))
-        qpos[:, 3:7] = np_quat_mul(qpos[:, 3:7], np_quat_from_euler_xyz(roll, pitch, yaw))
-        qvel[:, 0:6] = np.asarray(
-            np.random.uniform(-0.5, 0.5, size=(num_reset, 6)), dtype=get_global_dtype()
-        )
-        commands = self._sample_commands(env, num_reset)
-        info_updates: dict[str, Any] = {
-            "commands": commands,
-            "current_actions": zero_actions(num_reset, env._num_action),
-            "last_actions": zero_actions(num_reset, env._num_action),
-            "qacc": np.zeros((num_reset, env._num_action), dtype=get_global_dtype()),
-            "torques": np.zeros((num_reset, env._num_action), dtype=get_global_dtype()),
-        }
-        if env.cfg.commands.heading_command:
-            info_updates["heading_commands"] = sample_heading_commands(env, num_reset)
-        env._spawn.record_episode_start(env_ids, qpos[:, 0:3])
-        return ResetPlan(
-            env_ids=env_ids,
-            qpos=qpos,
-            qvel=qvel,
-            info_updates=info_updates,
-            randomization=build_common_reset_randomization(env, num_reset),
-        )
-
-
-@registry.env("Go1JoystickRough", sim_backend="mujoco")
-class Go1JoystickRoughEnv(Go1WalkTask):
-    _cfg: Go1JoystickRoughCfg
-    _reward_cfg: RoughRewardConfig
-    _height_scan_dim: int = 0
-
-    def __init__(self, cfg: Go1JoystickRoughCfg, num_envs=1, backend_type="mujoco"):
-        super().__init__(cfg, num_envs=num_envs, backend_type=backend_type)
-        self._dr_manager = DomainRandomizationManager(
-            self, Go1JoystickRoughDomainRandomizationProvider()
-        )
-
-        self._last_dof_vel_for_acc = np.zeros(
-            (num_envs, self._num_action), dtype=get_global_dtype()
-        )
-        joint_range = self._backend.get_joint_range()
-        self._joint_range = (
-            np.asarray(joint_range, dtype=get_global_dtype()) if joint_range is not None else None
-        )
-
-        self._action_scale = np.full(
-            (self._num_action,),
-            float(cfg.control_config.non_hip_action_scale),
-            dtype=get_global_dtype(),
-        )
-        self._action_scale[GO1_HIP_INDICES] = float(cfg.control_config.hip_action_scale)
-
-        self.feet_vel = np.zeros((num_envs, len(cfg.sensor.feet_vel), 3), dtype=np.float32)
-        self._last_foot_contact = np.zeros((num_envs, len(cfg.sensor.feet_force)), dtype=bool)
-        self._current_air_time = np.zeros((num_envs, len(cfg.sensor.feet_force)), dtype=np.float32)
-        self._current_contact_time = np.zeros(
-            (num_envs, len(cfg.sensor.feet_force)), dtype=np.float32
-        )
-        self._last_air_time = np.zeros((num_envs, len(cfg.sensor.feet_force)), dtype=np.float32)
-        self._last_contact_time = np.zeros((num_envs, len(cfg.sensor.feet_force)), dtype=np.float32)
-        self._first_foot_contact = np.zeros((num_envs, len(cfg.sensor.feet_force)), dtype=bool)
-
-        init_height_scan_sensor(self, cfg.terrain_scan, cfg.asset.base_name)
-
-    @property
-    def obs_groups_spec(self) -> dict[str, int]:
-        # Match the rough format: policy obs = 45, critic = 48 + height_scan.
-        return {"obs": 45, "critic": 48 + self._height_scan_dim}
-
-    def reset(self, env_indices: np.ndarray) -> tuple[dict[str, np.ndarray], dict]:
-        env_ids = np.asarray(env_indices, dtype=np.int32)
-        obs, info = super().reset(env_ids)
-        dof_vel = self.get_dof_vel()
-        if dof_vel.shape[0] == self._num_envs:
-            self._last_dof_vel_for_acc[env_ids] = dof_vel[env_ids]
-        self._reset_contact_timers(env_ids)
-        return obs, info
-
-    def _upright_scale(self, gravity: np.ndarray | None) -> np.ndarray:
-        return rewards.upright_scale(gravity, self._num_envs)
-
-    def _init_reward_functions(self):
-        scale_gravity = self._upright_scale
-
-        def gated(fn):
-            return lambda ctx: fn(ctx) * scale_gravity(ctx.gravity)
-
-        def _joint_pos_penalty(ctx: RewardContext) -> np.ndarray:
-            cfg = self._reward_cfg
-            return rewards.joint_pos_penalty(
-                ctx,
-                stand_still_scale=cfg.joint_pos_penalty_stand_still_scale,
-                velocity_threshold=cfg.joint_pos_penalty_velocity_threshold,
-                command_threshold=cfg.joint_pos_penalty_command_threshold,
-            ) * scale_gravity(ctx.gravity)
-
-        def _stand_still(ctx: RewardContext) -> np.ndarray:
-            return rewards.stand_still(
-                ctx, command_threshold=self._reward_cfg.stand_still_command_threshold
-            ) * scale_gravity(ctx.gravity)
-
-        self._reward_fns: dict[str, Any] = {
-            "tracking_lin_vel": gated(rewards.tracking_lin_vel),
-            "tracking_ang_vel": gated(rewards.tracking_ang_vel),
-            "lin_vel_z": gated(rewards.lin_vel_z),
-            "ang_vel_xy": gated(rewards.ang_vel_xy),
-            "dof_torques_l2": gated(rewards.dof_torques_l2),
-            "joint_torques_l2": gated(rewards.dof_torques_l2),
-            "dof_acc_l2": gated(rewards.dof_acc_l2),
-            "joint_acc_l2": gated(rewards.dof_acc_l2),
-            "joint_power": gated(rewards.joint_power),
-            "stand_still": _stand_still,
-            "hip_pos": self._reward_hip_pos,
-            "joint_pos_penalty": _joint_pos_penalty,
-            "joint_mirror": self._reward_joint_mirror,
-            "action_rate": rewards.action_rate,
-            "action_rate_l2": rewards.action_rate,
-            "undesired_contacts": self._reward_undesired_contacts,
-            "contact_forces": self._reward_contact_forces,
-            "feet_air_time": self._reward_feet_air_time,
-            "feet_air_time_variance": self._reward_feet_air_time_variance,
-            "feet_contact_without_cmd": self._reward_feet_contact_without_cmd,
-            "feet_slide": self._reward_feet_slide,
-            "feet_height_body": self._reward_feet_height_body,
-            "feet_gait": self._reward_feet_gait,
-            "upward": rewards.upward,
-        }
-
-    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
-        clipped = np.asarray(
-            np.clip(
-                actions,
-                -float(self._cfg.control_config.clip_actions),
-                float(self._cfg.control_config.clip_actions),
-            ),
-            dtype=get_global_dtype(),
-        )
-        state.info["last_actions"] = state.info.get("current_actions", np.zeros_like(clipped))
-        state.info["current_actions"] = clipped
-        exec_actions = (
-            state.info["last_actions"]
-            if self._cfg.control_config.simulate_action_latency
-            else clipped
-        )
-        return np.asarray(
-            exec_actions * self._action_scale + self.default_angles, dtype=get_global_dtype()
-        )
-
-    def update_state(self, state: NpEnvState) -> NpEnvState:
-        self._update_commands(state.info)
-        self.phase = np.fmod(self.phase + self._cfg.ctrl_dt * self.gait_frequency, 1.0)
-        self.feet_phase[:, 0] = self.phase
-        self.feet_phase[:, 3] = self.phase
-        self.feet_phase[:, 1] = (self.phase + 0.5) % 1
-        self.feet_phase[:, 2] = (self.phase + 0.5) % 1
-
-        linvel = self.get_local_linvel()
-        gyro = self.get_gyro()
-        gravity = self._backend.get_sensor_data("upvector")
-        dof_pos = self.get_dof_pos()
-        dof_vel = self.get_dof_vel()
-
-        self.feet_force[:, :, :] = 0
-        for i in range(len(self._cfg.sensor.feet_force)):
-            self.feet_force[:, i, :] = self._backend.get_sensor_data(self._cfg.sensor.feet_force[i])
-        for i in range(len(self._cfg.sensor.feet_pos)):
-            self.feet_pos[:, i, :] = self._backend.get_sensor_data(self._cfg.sensor.feet_pos[i])
-        for i in range(len(self._cfg.sensor.feet_vel)):
-            self.feet_vel[:, i, :] = self._backend.get_sensor_data(self._cfg.sensor.feet_vel[i])
-
-        self._update_contact_timers(self._foot_contact_mask())
-        state.info["qacc"] = self._estimate_dof_acc(dof_vel)
-        state.info["torques"] = self._estimate_pd_torques(state.info, dof_pos, dof_vel)
-
-        terminated = self._compute_terminated(gravity)
-        reward = self._compute_rough_reward(state.info, linvel, gyro, gravity, dof_pos, dof_vel)
-        obs = self._compute_obs(
-            state.info, linvel, gyro, gravity, dof_pos, dof_vel, self.feet_phase
-        )
-        state = state.replace(obs=obs, reward=reward, terminated=terminated)
-
-        done = state.terminated | state.truncated
-        if np.any(done):
-            done_indices = np.where(done)[0]
-            stats = self._spawn.update_on_done(
-                done_indices, self._backend.get_base_pos()[done_indices]
-            )
-            if stats:
-                if "log" not in state.info:
-                    state.info["log"] = {}
-                for k, v in stats.items():
-                    state.info["log"][f"terrain_curriculum/{k}"] = float(v)
-        return state
-
-    def _compute_obs(
-        self,
-        info: dict,
-        linvel: np.ndarray,
-        gyro: np.ndarray,
-        gravity: np.ndarray,
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-        feet_phase: np.ndarray,
-    ) -> dict[str, np.ndarray]:
-        del feet_phase
-        noise_cfg = self._cfg.noise_config
-        diff = dof_pos - self.default_angles
-        policy_gyro = self._obs_noise(gyro, noise_cfg.scale_gyro) * 0.25
-        policy_gravity = self._obs_noise(-gravity, noise_cfg.scale_gravity)
-        policy_diff = self._obs_noise(diff, noise_cfg.scale_joint_angle)
-        policy_dof_vel = self._obs_noise(dof_vel, noise_cfg.scale_joint_vel) * 0.05
-        last_actions = info.get("current_actions", np.zeros_like(diff))
-        commands = info["commands"]
-        obs = np.concatenate(
-            [policy_gyro, policy_gravity, commands, policy_diff, policy_dof_vel, last_actions],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-        critic_base = np.concatenate(
-            [linvel, gyro, -gravity, commands, diff, dof_vel, last_actions],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-        critic = np.concatenate(
-            [critic_base, height_scan_obs(self, self._cfg.terrain_scan, critic_base.shape[0])],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-        return {"obs": obs, "critic": critic}
-
-    def _compute_rough_reward(
-        self,
-        info: dict,
-        linvel: np.ndarray,
-        gyro: np.ndarray,
-        gravity: np.ndarray,
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-    ) -> np.ndarray:
-        cfg = self._reward_cfg
-        ctx = RewardContext(
-            info=info,
-            linvel=linvel,
-            gyro=gyro,
-            dof_pos=dof_pos,
-            num_envs=self._num_envs,
-            default_angles=self.default_angles,
-            tracking_sigma=cfg.tracking_sigma,
-            base_height_target=cfg.base_height_target,
-            base_height=base_height_from_scan(self, self._num_envs),
-            gravity=gravity,
-            dof_vel=dof_vel,
-            joint_range=self._joint_range,
-        )
-        return rewards.run_reward_dispatch(
-            scales=cfg.scales,
-            fns=self._reward_fns,
-            ctx=ctx,
-            info=info,
-            enable_log=self._enable_reward_log,
-            ctrl_dt=self._cfg.ctrl_dt,
-        )
-
-    def _compute_terminated(self, gravity: np.ndarray) -> np.ndarray:
-        del gravity
-        return np.zeros((self._num_envs,), dtype=bool)
-
-    def _compute_truncated(self, state: NpEnvState) -> np.ndarray:
-        truncated = super()._compute_truncated(state)
-        if self._cfg.termination_config.terrain_out_of_bounds:
-            terrain_scene = self._cfg.scene.terrain
-            terrain_cfg = terrain_scene.generator if terrain_scene is not None else None
-            np.logical_or(
-                truncated,
-                terrain_out_of_bounds(
-                    self,
-                    terrain_cfg,
-                    float(self._cfg.termination_config.terrain_distance_buffer),
-                ),
-                out=truncated,
-            )
-        return truncated
-
-    def _reward_base_height_values(self, num_obs: int | None = None) -> np.ndarray:
-        return base_height_from_scan(self, num_obs)
-
-    def _raw_height_scan_obs(self, num_obs: int) -> tuple[np.ndarray | None, np.ndarray | None]:
-        return raw_height_scan_obs(self, num_obs)
-
-    def _estimate_dof_acc(self, dof_vel: np.ndarray) -> np.ndarray:
-        qacc = np.asarray((dof_vel - self._last_dof_vel_for_acc) / self._cfg.ctrl_dt)
-        self._last_dof_vel_for_acc[:] = dof_vel
-        return np.asarray(qacc, dtype=get_global_dtype())
-
-    def _estimate_pd_torques(
-        self, info: dict, dof_pos: np.ndarray, dof_vel: np.ndarray
-    ) -> np.ndarray:
-        actions = np.asarray(
-            info.get("current_actions", np.zeros((dof_pos.shape[0], self._num_action))),
-            dtype=get_global_dtype(),
-        )
-        if self._cfg.control_config.simulate_action_latency:
-            actions = np.asarray(info.get("last_actions", actions), dtype=get_global_dtype())
-        targets = actions * self._action_scale + self.default_angles
-        torques = (
-            float(self._cfg.control_config.Kp) * (targets - dof_pos)
-            - float(self._cfg.control_config.Kd) * dof_vel
-        )
-        return np.asarray(torques, dtype=get_global_dtype())
-
-    def _update_commands(self, info: dict) -> None:
-        commands_arr = np.asarray(info["commands"], dtype=get_global_dtype())
-        resampling_time = float(self._cfg.commands.resampling_time)
-        if resampling_time > 0.0:
-            interval_steps = max(int(round(resampling_time / self._cfg.ctrl_dt)), 1)
-            steps = np.asarray(info.get("steps", np.zeros((self._num_envs,), dtype=np.uint32)))
-            resample_mask = (steps > 0) & ((steps % interval_steps) == 0)
-            if np.any(resample_mask):
-                num_resample = int(np.count_nonzero(resample_mask))
-                low = np.asarray(self._cfg.commands.vel_limit[0], dtype=get_global_dtype())
-                high = np.asarray(self._cfg.commands.vel_limit[1], dtype=get_global_dtype())
-                sampled = np.random.uniform(low=low, high=high, size=(num_resample, 3)).astype(
-                    get_global_dtype()
-                )
-                zero_small_xy_commands(sampled, threshold=0.08)
-                commands_arr[resample_mask] = sampled
-                if self._cfg.commands.heading_command:
-                    heading_commands = self._ensure_heading_commands(info, commands_arr.shape[0])
-                    heading_commands[resample_mask] = sample_heading_commands(self, num_resample)
-                    info["heading_commands"] = heading_commands
-
-        if self._cfg.commands.heading_command:
-            heading_commands = self._ensure_heading_commands(info, commands_arr.shape[0])
-            base_quat = np.asarray(self._backend.get_base_quat(), dtype=get_global_dtype())
-            if base_quat.shape[0] == commands_arr.shape[0]:
-                apply_heading_yaw_feedback(commands_arr, base_quat, heading_commands, stiffness=0.5)
-        info["commands"] = commands_arr
-
-    def _ensure_heading_commands(self, info: dict, num_obs: int) -> np.ndarray:
-        heading_commands = info.get("heading_commands")
-        if heading_commands is None or np.asarray(heading_commands).shape != (num_obs,):
-            heading_commands = sample_heading_commands(self, num_obs)
-        heading_commands = np.asarray(heading_commands, dtype=get_global_dtype())
-        info["heading_commands"] = heading_commands
-        return heading_commands
-
-    def _foot_contact_mask(self) -> np.ndarray:
-        contact_force = np.linalg.norm(self.feet_force, axis=2)
-        return np.asarray(contact_force > self._reward_cfg.contact_threshold, dtype=bool)
-
-    def _reset_contact_timers(self, env_ids: np.ndarray) -> None:
-        self._current_air_time[env_ids] = 0.0
-        self._current_contact_time[env_ids] = 0.0
-        self._last_air_time[env_ids] = 0.0
-        self._last_contact_time[env_ids] = 0.0
-        self._first_foot_contact[env_ids] = False
-        self._last_foot_contact[env_ids] = self._foot_contact_mask()[env_ids]
-
-    def _update_contact_timers(self, contact: np.ndarray) -> None:
-        first_contact = contact & ~self._last_foot_contact
-        first_air = ~contact & self._last_foot_contact
-        self._first_foot_contact[:] = first_contact
-        self._last_air_time[first_contact] = self._current_air_time[first_contact]
-        self._last_contact_time[first_air] = self._current_contact_time[first_air]
-        self._current_air_time[contact] = 0.0
-        self._current_air_time[~contact] += self._cfg.ctrl_dt
-        self._current_contact_time[~contact] = 0.0
-        self._current_contact_time[contact] += self._cfg.ctrl_dt
-        self._last_foot_contact[:] = contact
-
-    def _relative_foot_vel_body(self) -> np.ndarray:
-        base_quat = np.asarray(self._backend.get_base_quat(), dtype=get_global_dtype())
-        base_linvel = np.asarray(
-            self._backend.get_sensor_data("global_linvel"), dtype=get_global_dtype()
-        )
-        relative_vel = self.feet_vel - base_linvel[:, None, :]
-        flat = relative_vel.reshape(self._num_envs * relative_vel.shape[1], 3)
-        quat = np.repeat(base_quat, relative_vel.shape[1], axis=0)
-        return np_quat_apply_inverse(quat, flat).reshape(relative_vel.shape)
-
-    def _relative_foot_pos_body(self) -> np.ndarray:
-        base_quat = np.asarray(self._backend.get_base_quat(), dtype=get_global_dtype())
-        base_pos = np.asarray(self._backend.get_base_pos(), dtype=get_global_dtype())
-        relative_pos = self.feet_pos - base_pos[:, None, :]
-        flat = relative_pos.reshape(self._num_envs * relative_pos.shape[1], 3)
-        quat = np.repeat(base_quat, relative_pos.shape[1], axis=0)
-        return np_quat_apply_inverse(quat, flat).reshape(relative_pos.shape)
-
-    def _reward_hip_pos(self, ctx: RewardContext) -> np.ndarray:
-        diff = ctx.dof_pos[:, GO1_HIP_INDICES] - self.default_angles[GO1_HIP_INDICES]
-        return np.asarray(
-            np.sum(np.square(diff), axis=1) * self._upright_scale(ctx.gravity),
-            dtype=get_global_dtype(),
-        )
-
-    def _reward_joint_mirror(self, ctx: RewardContext) -> np.ndarray:
-        fr_rl = ctx.dof_pos[:, 0:3] - ctx.dof_pos[:, 9:12]
-        fl_rr = ctx.dof_pos[:, 3:6] - ctx.dof_pos[:, 6:9]
-        mirror = 0.5 * (np.sum(np.square(fr_rl), axis=1) + np.sum(np.square(fl_rr), axis=1))
-        return np.asarray(mirror * self._upright_scale(ctx.gravity), dtype=get_global_dtype())
-
-    def _reward_undesired_contacts(self, ctx: RewardContext) -> np.ndarray:
-        contacts = [
-            _force_norm_columns(
-                np.asarray(self._backend.get_sensor_data(name), dtype=get_global_dtype()),
-                ctx.num_envs,
-            )
-            for name in self._cfg.sensor.undesired_contact
-        ]
-        if not contacts:
-            return np.zeros((ctx.num_envs,), dtype=get_global_dtype())
-        contact_force = np.concatenate(contacts, axis=1)
-        contact_count = np.sum(contact_force > self._reward_cfg.contact_threshold, axis=1)
-        return np.asarray(
-            contact_count * self._upright_scale(ctx.gravity), dtype=get_global_dtype()
-        )
-
-    def _reward_contact_forces(self, ctx: RewardContext) -> np.ndarray:
-        force_norm = np.linalg.norm(self.feet_force, axis=2)
-        force_clip = np.minimum(force_norm, 1500.0)
-        violation = np.clip(force_clip - self._reward_cfg.contact_forces_threshold, 0.0, None)
-        return np.asarray(
-            np.sum(violation, axis=1) * self._upright_scale(ctx.gravity),
-            dtype=get_global_dtype(),
-        )
-
-    def _reward_feet_air_time(self, ctx: RewardContext) -> np.ndarray:
-        cfg = self._reward_cfg
-        reward = np.sum(
-            (self._last_air_time - cfg.feet_air_time_threshold) * self._first_foot_contact,
-            axis=1,
-        )
-        moving = np.linalg.norm(ctx.info["commands"], axis=1) > 0.1
-        return np.asarray(
-            reward * moving * self._upright_scale(ctx.gravity), dtype=get_global_dtype()
-        )
-
-    def _reward_feet_air_time_variance(self, ctx: RewardContext) -> np.ndarray:
-        air_var = np.var(np.clip(self._last_air_time, 0.0, 0.5), axis=1)
-        contact_var = np.var(np.clip(self._last_contact_time, 0.0, 0.5), axis=1)
-        return np.asarray(
-            (air_var + contact_var) * self._upright_scale(ctx.gravity), dtype=get_global_dtype()
-        )
-
-    def _reward_feet_contact_without_cmd(self, ctx: RewardContext) -> np.ndarray:
-        reward = np.sum(self._first_foot_contact, axis=1)
-        stopped = np.linalg.norm(ctx.info["commands"], axis=1) < 0.1
-        return np.asarray(
-            reward * stopped * self._upright_scale(ctx.gravity), dtype=get_global_dtype()
-        )
-
-    def _reward_feet_slide(self, ctx: RewardContext) -> np.ndarray:
-        foot_vel_body = self._relative_foot_vel_body()
-        lateral_vel = np.linalg.norm(foot_vel_body[:, :, :2], axis=2)
-        reward = np.sum(lateral_vel * self._foot_contact_mask(), axis=1)
-        return np.asarray(reward * self._upright_scale(ctx.gravity), dtype=get_global_dtype())
-
-    def _reward_feet_height_body(self, ctx: RewardContext) -> np.ndarray:
-        cfg = self._reward_cfg
-        foot_pos_body = self._relative_foot_pos_body()
-        foot_vel_body = self._relative_foot_vel_body()
-        z_error = np.square(foot_pos_body[:, :, 2] - cfg.feet_height_body_target)
-        velocity_tanh = np.tanh(
-            cfg.feet_height_body_tanh_mult * np.linalg.norm(foot_vel_body[:, :, :2], axis=2)
-        )
-        moving = np.linalg.norm(ctx.info["commands"], axis=1) > 0.1
-        reward = np.sum(z_error * velocity_tanh, axis=1)
-        return np.asarray(
-            reward * moving * self._upright_scale(ctx.gravity), dtype=get_global_dtype()
-        )
-
-    def _reward_feet_gait(self, ctx: RewardContext) -> np.ndarray:
-        cfg = self._reward_cfg
-        command_norm = np.linalg.norm(ctx.info["commands"], axis=1)
-        body_vel = np.linalg.norm(ctx.linvel[:, :2], axis=1)
-        enabled = (command_norm > cfg.feet_gait_command_threshold) | (
-            body_vel > cfg.feet_gait_velocity_threshold
-        )
-        air = self._current_air_time
-        contact = self._current_contact_time
-        sync_fl_rr = _gait_sync_reward(
-            air, contact, GO1_FRONT_LEFT, GO1_REAR_RIGHT, cfg.feet_gait_std, cfg.feet_gait_max_err
-        )
-        sync_fr_rl = _gait_sync_reward(
-            air, contact, GO1_FRONT_RIGHT, GO1_REAR_LEFT, cfg.feet_gait_std, cfg.feet_gait_max_err
-        )
-        async_fl_fr = _gait_async_reward(
-            air, contact, GO1_FRONT_LEFT, GO1_FRONT_RIGHT, cfg.feet_gait_std, cfg.feet_gait_max_err
-        )
-        async_rr_rl = _gait_async_reward(
-            air, contact, GO1_REAR_RIGHT, GO1_REAR_LEFT, cfg.feet_gait_std, cfg.feet_gait_max_err
-        )
-        async_fl_rl = _gait_async_reward(
-            air, contact, GO1_FRONT_LEFT, GO1_REAR_LEFT, cfg.feet_gait_std, cfg.feet_gait_max_err
-        )
-        async_fr_rr = _gait_async_reward(
-            air, contact, GO1_FRONT_RIGHT, GO1_REAR_RIGHT, cfg.feet_gait_std, cfg.feet_gait_max_err
-        )
-        reward = sync_fl_rr * sync_fr_rl * async_fl_fr * async_rr_rl * async_fl_rl * async_fr_rr
-        return np.asarray(
-            reward * enabled * self._upright_scale(ctx.gravity), dtype=get_global_dtype()
-        )
-
-
-def _force_norm_columns(force: np.ndarray, num_envs: int) -> np.ndarray:
-    force = np.asarray(force, dtype=get_global_dtype()).reshape(num_envs, -1)
-    if force.shape[1] == 0:
-        return force
-    if force.shape[1] % 3 == 0:
-        return np.linalg.norm(force.reshape(num_envs, -1, 3), axis=2)
-    return np.abs(force)
-
-
-def _gait_sync_reward(
-    air: np.ndarray,
-    contact: np.ndarray,
-    foot_0: int,
-    foot_1: int,
-    std: float,
-    max_err: float,
-) -> np.ndarray:
-    se_air = np.clip(np.square(air[:, foot_0] - air[:, foot_1]), 0.0, max_err**2)
-    se_contact = np.clip(np.square(contact[:, foot_0] - contact[:, foot_1]), 0.0, max_err**2)
-    return np.exp(-(se_air + se_contact) / std)
-
-
-def _gait_async_reward(
-    air: np.ndarray,
-    contact: np.ndarray,
-    foot_0: int,
-    foot_1: int,
-    std: float,
-    max_err: float,
-) -> np.ndarray:
-    se_act_0 = np.clip(np.square(air[:, foot_0] - contact[:, foot_1]), 0.0, max_err**2)
-    se_act_1 = np.clip(np.square(contact[:, foot_0] - air[:, foot_1]), 0.0, max_err**2)
-    return np.exp(-(se_act_0 + se_act_1) / std)
-
-
-registry.register_env("Go1JoystickRough", Go1JoystickRoughEnv, sim_backend="motrix")
diff --git a/src/unilab/envs/locomotion/go2/__init__.py b/src/unilab/envs/locomotion/go2/__init__.py
deleted file mode 100644
index 4a4eded57..000000000
--- a/src/unilab/envs/locomotion/go2/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .footstand import Go2FootStandCfg, Go2FootStandTask
-from .joystick import Go2JoystickCfg, Go2WalkTask
-from .rough import Go2JoystickRoughCfg, Go2JoystickRoughEnv
diff --git a/src/unilab/envs/locomotion/go2/base.py b/src/unilab/envs/locomotion/go2/base.py
deleted file mode 100644
index 08c9bbe7a..000000000
--- a/src/unilab/envs/locomotion/go2/base.py
+++ /dev/null
@@ -1,54 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-
-import numpy as np
-
-from unilab.envs.locomotion.common.base import (
-    BaseNoiseConfig,
-    LocomotionBaseCfg,
-    LocomotionBaseEnv,
-    PdControlConfig,
-)
-
-
-@dataclass
-class NoiseConfig(BaseNoiseConfig):
-    pass
-
-
-@dataclass
-class ControlConfig(PdControlConfig):
-    pass
-
-
-@dataclass
-class Asset:
-    base_name = "base"
-    foot_name = "foot"
-    ground = "floor"
-
-
-@dataclass
-class Go2BaseCfg(LocomotionBaseCfg):
-    noise_config: NoiseConfig = field(default_factory=NoiseConfig)  # type: ignore[assignment]
-    control_config: ControlConfig = field(default_factory=ControlConfig)  # type: ignore[assignment]
-    asset: Asset = field(default_factory=Asset)
-    sim_dt: float = 0.01
-    ctrl_dt: float = 0.02
-
-
-class Go2BaseEnv(LocomotionBaseEnv):
-    _cfg: Go2BaseCfg
-
-    def get_foot_pos(self) -> np.ndarray:
-        """Get foot positions. Returns shape (num_envs, 4, 3)"""
-        foot_names = ["FL_pos", "FR_pos", "RL_pos", "RR_pos"]
-        foot_pos = [self._backend.get_sensor_data(name) for name in foot_names]
-        return np.stack(foot_pos, axis=1)
-
-    def get_foot_contact(self) -> np.ndarray:
-        """Get foot contact forces. Returns shape (num_envs, 4)"""
-        contact_names = ["FL_foot_contact", "FR_foot_contact", "RL_foot_contact", "RR_foot_contact"]
-        contacts = [self._backend.get_sensor_data(name)[:, 0] for name in contact_names]
-        return np.stack(contacts, axis=1)
diff --git a/src/unilab/envs/locomotion/go2/footstand.py b/src/unilab/envs/locomotion/go2/footstand.py
deleted file mode 100644
index 6e3242f7e..000000000
--- a/src/unilab/envs/locomotion/go2/footstand.py
+++ /dev/null
@@ -1,1087 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Any, cast
-
-import numpy as np
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base import registry
-from unilab.base.backend import create_backend, env_backend_kwargs
-from unilab.base.np_env import NpEnvState
-from unilab.base.scene import SceneCfg
-from unilab.dr import ResetPlan, ResetRandomizationPayload
-from unilab.dtype_config import get_global_dtype
-from unilab.envs.locomotion.common import rewards
-from unilab.envs.locomotion.common.commands import Commands
-from unilab.envs.locomotion.common.domain_rand import DomainRandConfig
-from unilab.envs.locomotion.common.dr_provider import LocomotionDRProvider
-from unilab.envs.locomotion.common.rewards import RewardContext
-from unilab.envs.locomotion.go2.base import ControlConfig, Go2BaseCfg, Go2BaseEnv, NoiseConfig
-from unilab.utils.rotation import np_quat_apply, np_quat_apply_inverse
-
-
-@dataclass
-class InitState:
-    pos = [0.0, 0.0, 0.42]
-
-
-@dataclass
-class Go2DomainRandConfig(DomainRandConfig):
-    randomize_kp: bool = True
-    kp_multiplier_range: list[float] = field(default_factory=lambda: [0.9, 1.1])
-
-    randomize_kd: bool = True
-    kd_multiplier_range: list[float] = field(default_factory=lambda: [0.9, 1.1])
-
-
-@dataclass
-class RewardConfig:
-    scales: dict[str, float]
-    tracking_sigma: float
-    base_height_target: float
-    target_foot_height: float = 0.1
-    knee_height_target: float = 0.08
-    front_feet_min_separation: float = 0.16
-    front_feet_side_margin: float = 0.04
-    rear_hip_abduction_margin: float = 0.25
-    rear_foot_slip_deadband: float = 0.02
-    rear_foot_anchor_radius: float = 0.03
-
-
-@dataclass
-class JoystickSensor:
-    local_linvel = "local_linvel"
-    gyro = "gyro"
-    feet_force = ["FL_foot_contact", "FR_foot_contact", "RL_foot_contact", "RR_foot_contact"]
-    feet_pos = ["FL_pos", "FR_pos", "RL_pos", "RR_pos"]
-    global_pos = "global_position"
-    ternamate_contact = [
-        "base1_contact",
-        "base2_contact",
-        "base3_contact",
-        "FL_hip_contact",
-        "FR_hip_contact",
-        "FL_thigh_contact",
-        "FR_thigh_contact",
-        "FL_calf_contact1",
-        "FL_calf_contact2",
-        "FR_calf_contact1",
-        "FR_calf_contact2",
-    ]
-    penalty_contact = [
-        "RL_hip_contact",
-        "RR_hip_contact",
-        "RL_thigh_contact",
-        "RR_thigh_contact",
-        "RL_calf_contact1",
-        "RL_calf_contact2",
-        "RR_calf_contact1",
-        "RR_calf_contact2",
-    ]
-
-
-@dataclass
-class Go2HandStandCfg(Go2BaseCfg):
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "go2" / "scene_flat.xml")
-        )
-    )
-    max_episode_seconds: float = 20.0
-    init_state: InitState = field(default_factory=InitState)
-    commands: Commands = field(default_factory=Commands)
-    reward_config: RewardConfig | None = None
-    sensor: JoystickSensor = field(default_factory=JoystickSensor)  # type: ignore[assignment]
-    domain_rand: Go2DomainRandConfig = field(default_factory=Go2DomainRandConfig)
-
-
-class Go2HandStandDomainRandomizationProvider(LocomotionDRProvider):
-    def _compute_reset_obs(
-        self,
-        env: Any,
-        env_ids: Any,
-        info_updates: Any,
-        linvel: Any,
-        gyro: Any,
-        gravity: Any,
-        dof_pos: Any,
-        dof_vel: Any,
-    ) -> dict[str, np.ndarray]:
-        height = env.torso_height[env_ids].reshape(-1, 1)
-        env.feet_phase[env_ids, :] = 0
-        env.feet_phase[:, 2] = 0.0
-        env.feet_phase[:, 3] = 0.5
-        env._feet_air_time[env_ids, :] = 0.0
-        env._last_contacts[env_ids, :] = False
-
-        return env._compute_obs(  # type: ignore[no-any-return]
-            info_updates,
-            linvel,
-            gyro,
-            gravity,
-            dof_pos,
-            dof_vel,
-            height,
-        )
-
-
-class Go2HandStandTask(Go2BaseEnv):
-    _cfg: Go2HandStandCfg
-
-    def __init__(self, cfg: Go2HandStandCfg, num_envs=1, backend_type="mujoco"):
-        if cfg.reward_config is None:
-            raise ValueError("reward_config must be provided via Hydra configuration")
-        # Footstand historically left the MuJoCo pool tuning knobs at the
-        # create_backend defaults; keep them pinned so this convergence does
-        # not silently enable adaptive chunk tuning for the task.
-        backend_kwargs: dict[str, Any] = {
-            "base_name": cfg.asset.base_name,
-            "push_body_name": cfg.domain_rand.push_body_name,
-            "add_body_sensors": bool(getattr(cfg, "add_body_sensors", False)),
-            "position_actuator_gains": {"kp": cfg.control_config.Kp, "kd": cfg.control_config.Kd},
-            **env_backend_kwargs(cfg),
-            "adaptive_chunk_size": False,
-            "bench_nsteps": 1,
-        }
-        backend = create_backend(
-            backend_type,
-            cfg.scene,
-            num_envs,
-            cfg.sim_dt,
-            **backend_kwargs,
-        )
-        super().__init__(cfg, backend, num_envs)
-        self._enable_reward_log = True
-        self._reward_cfg = cfg.reward_config
-        self._init_reward_functions()
-        self._init_task_domain_randomization()
-        self.phase = np.zeros((num_envs,), dtype=np.float32)
-        self.feet_phase = np.zeros((num_envs, len(cfg.sensor.feet_force)), dtype=np.float32)
-        self.feet_phase[:, 2] = 0.0
-        self.feet_phase[:, 3] = 0.5
-        self.gait_frequency = 2
-        self.feet_force = np.zeros((num_envs, len(cfg.sensor.feet_force), 1), dtype=np.float32)
-        self._feet_air_time = np.zeros((num_envs, len(cfg.sensor.feet_force)), dtype=np.float32)
-        self._last_contacts = np.zeros((num_envs, 2), dtype=bool)
-        self.feet_pos = np.zeros((num_envs, len(cfg.sensor.feet_pos), 3), dtype=np.float32)
-        self.torso_height = np.zeros((num_envs,), dtype=np.float32)
-        self._z_des = 0.55
-        self._desired_gravity = np.array([-1, 0, 0])
-        self.feet_geom_names = [0, 1]
-        self._joint_ids = [0, 1, 2, 3, 4, 5, 6, 9]
-        self._tar_ids = [6, 7, 8, 9, 10, 11]
-        self.target_angle = np.array([0, 1.82, -1.16, 0.0, 1.82, -1.16])
-
-    def _init_task_domain_randomization(self) -> None:
-        self._init_domain_randomization(Go2HandStandDomainRandomizationProvider())
-
-    @property
-    def obs_groups_spec(self) -> dict[str, int]:
-        return {"obs": 42, "critic": 46}
-
-    def _init_reward_functions(self):
-        self._reward_fns: dict[str, Any] = {}
-
-    def update_state(self, state: NpEnvState) -> NpEnvState:
-        return state
-
-    def _compute_obs(
-        self,
-        info: dict,
-        linvel,
-        gyro,
-        gravity,
-        dof_pos,
-        dof_vel,
-        height,
-    ) -> dict[str, np.ndarray]:
-        return {"obs": np.zeros((self._num_envs, 1)), "critic": np.zeros((self._num_envs, 1))}
-
-    def _compute_reward(self, info: dict, linvel, gyro, dof_pos) -> np.ndarray:
-        return np.zeros((self._num_envs,), dtype=get_global_dtype())
-
-    def _cost_pose(self, ctx: RewardContext) -> np.ndarray:
-        dof_pos = self.get_dof_pos()
-        error = dof_pos[:, self._joint_ids] - self.default_angles[self._joint_ids]
-        return cast(np.ndarray, np.sum(np.square(error), axis=1))
-
-    def _reward_penalty_contact(self, ctx: RewardContext) -> np.ndarray:
-        contact_arrays = []
-        for name in self._cfg.sensor.penalty_contact:
-            arr = self._backend.get_sensor_data(name)
-            contact_arrays.append(arr)
-        result = np.concatenate(contact_arrays, axis=1)
-        return np.asarray(np.any(result, axis=1))
-
-    def _reward_tar(self, ctx: RewardContext) -> np.ndarray:
-        dof_pos = self.get_dof_pos()
-        error = dof_pos[:, self._tar_ids] - self.target_angle
-        error = np.sum(np.square(error), axis=1)
-        mask = (self.torso_height >= self._z_des * 0.8).astype(np.float32)
-        return cast(np.ndarray, np.exp(-error / 1) * mask)
-
-
-_GO2_DOF_TO_CTRL = np.array([3, 4, 5, 0, 1, 2, 9, 10, 11, 6, 7, 8], dtype=np.int32)
-_WORLD_GRAVITY = np.array([0.0, 0.0, -1.0], dtype=np.float32)
-_BODY_FORWARD = np.array([1.0, 0.0, 0.0], dtype=np.float32)
-_FOOTSTAND_FRAME_OBS_DIM = 45
-_FOOTSTAND_PRIVILEGED_TAIL_DIM = 49
-_FOOTSTAND_MIN_OBS_HISTORY_LEN = 15
-_FOOTSTAND_FRONT_FEET = [0, 1]
-_FOOTSTAND_REAR_FEET = [2, 3]
-_FOOTSTAND_FRONT_LEG_IDS = [0, 1, 2, 3, 4, 5]
-_FOOTSTAND_REAR_LEG_IDS = [6, 7, 8, 9, 10, 11]
-_FOOTSTAND_REAR_HIP_IDS = [6, 9]
-_FOOTSTAND_REAR_LEFT_LEG_IDS = [6, 7, 8]
-_FOOTSTAND_REAR_RIGHT_LEG_IDS = [9, 10, 11]
-_FOOTSTAND_REAR_MIRROR_SIGNS = np.array([-1.0, 1.0, 1.0], dtype=np.float32)
-_FOOTSTAND_FRONT_LEG_TARGET = np.array([0.0, 1.82, -1.16, 0.0, 1.82, -1.16])
-_FOOTSTAND_TRACKED_BODY_NAMES = ("FL_thigh", "FR_thigh", "FL_calf", "FR_calf", "RL_calf", "RR_calf")
-_FOOTSTAND_FRONT_LEFT_BODY_INDICES = [0, 2]
-_FOOTSTAND_FRONT_RIGHT_BODY_INDICES = [1, 3]
-_FOOTSTAND_KNEE_BODY_INDICES = [2, 3, 4, 5]
-_FOOTSTAND_CONTACT_THRESHOLD = 0.1
-_FOOTSTAND_STAND_HEIGHT_FRACTION = 0.8
-_FOOTSTAND_STAND_ORIENTATION_THRESHOLD = 0.5
-
-
-@dataclass
-class FootstandNoiseConfig(NoiseConfig):
-    level: float = 1.0
-    scale_joint_angle: float = 0.01
-    scale_joint_vel: float = 1.5
-    scale_gyro: float = 0.2
-    scale_gravity: float = 0.05
-    scale_linvel: float = 0.1
-
-
-@dataclass
-class FootstandControlConfig(ControlConfig):
-    clip_actions: float = 1.0
-
-
-@dataclass
-class Go2FootStandDomainRandConfig(Go2DomainRandConfig):
-    randomize_kp: bool = False
-    randomize_kd: bool = False
-    randomize_base_mass: bool = False
-    random_com: bool = False
-    push_robots: bool = False
-
-    randomize_floor_friction: bool = True
-    floor_friction_range: list[float] = field(default_factory=lambda: [0.4, 1.0])
-
-    randomize_link_mass: bool = True
-    link_mass_scale_range: list[float] = field(default_factory=lambda: [0.9, 1.1])
-    torso_added_mass_range: list[float] = field(default_factory=lambda: [-1.0, 1.0])
-
-    randomize_torso_com: bool = True
-    torso_com_offset_range: list[float] = field(default_factory=lambda: [-0.05, 0.05])
-
-    randomize_dof_armature: bool = True
-    dof_armature_scale_range: list[float] = field(default_factory=lambda: [1.0, 1.05])
-
-    randomize_reset_joint_qpos: bool = True
-    reset_joint_qpos_range: list[float] = field(default_factory=lambda: [-0.05, 0.05])
-
-
-@dataclass
-class FootstandSensor(JoystickSensor):
-    accelerometer = "accelerometer"
-    global_angvel = "global_angvel"
-    ternamate_contact = [
-        "base1_contact",
-        "base2_contact",
-        "base3_contact",
-        "RL_hip_contact",
-        "RR_hip_contact",
-        "RL_thigh_contact",
-        "RR_thigh_contact",
-        "RL_calf_contact1",
-        "RL_calf_contact2",
-        "RR_calf_contact1",
-        "RR_calf_contact2",
-    ]
-    penalty_contact = [
-        "FL_hip_contact",
-        "FR_hip_contact",
-        "FL_thigh_contact",
-        "FR_thigh_contact",
-        "FL_calf_contact1",
-        "FL_calf_contact2",
-        "FR_calf_contact1",
-        "FR_calf_contact2",
-    ]
-
-
-@registry.envcfg("Go2FootStand")
-@dataclass
-class Go2FootStandCfg(Go2HandStandCfg):
-    max_episode_seconds: float = 10.0
-    add_body_sensors: bool = True
-    obs_history_len: int = _FOOTSTAND_MIN_OBS_HISTORY_LEN
-    soft_joint_pos_limit_factor: float = 0.9
-    energy_termination_threshold: float = np.inf
-    termination_grace_steps: int = 100
-    termination_height_fraction: float = 0.8
-    termination_orientation_threshold: float = 0.2
-    noise_config: FootstandNoiseConfig = field(default_factory=FootstandNoiseConfig)  # type: ignore[assignment]
-    control_config: FootstandControlConfig = field(  # type: ignore[assignment]
-        default_factory=lambda: FootstandControlConfig(action_scale=0.3)
-    )
-    sensor: FootstandSensor = field(default_factory=FootstandSensor)  # type: ignore[assignment]
-    domain_rand: Go2FootStandDomainRandConfig = field(default_factory=Go2FootStandDomainRandConfig)  # type: ignore[assignment]
-
-
-class Go2FootStandDomainRandomizationProvider(Go2HandStandDomainRandomizationProvider):
-    def _get_reset_randomization_baselines(
-        self, env: Any
-    ) -> tuple[np.ndarray | None, np.ndarray | None, int | None, np.ndarray | None]:
-        return (
-            env._base_body_mass,
-            env._base_geom_friction,
-            env._floor_geom_id,
-            env._base_dof_armature,
-        )
-
-    def build_reset_plan(self, env: Any, env_ids: np.ndarray) -> ResetPlan:
-        plan = super().build_reset_plan(env, env_ids)
-        qpos = np.asarray(plan.qpos, dtype=get_global_dtype()).copy()
-        domain_rand = env.cfg.domain_rand
-        if domain_rand.randomize_reset_joint_qpos:
-            low, high = domain_rand.reset_joint_qpos_range
-            qpos[:, -env._num_action :] += np.random.uniform(
-                low, high, size=(len(env_ids), env._num_action)
-            ).astype(qpos.dtype)
-
-        return ResetPlan(
-            env_ids=plan.env_ids,
-            qpos=qpos,
-            qvel=plan.qvel,
-            info_updates=plan.info_updates,
-            randomization=self._merge_reset_randomization(
-                plan.randomization,
-                env._build_playground_reset_randomization(len(env_ids)),
-            ),
-        )
-
-    @staticmethod
-    def _merge_reset_randomization(
-        base: ResetRandomizationPayload | None,
-        override: ResetRandomizationPayload | None,
-    ) -> ResetRandomizationPayload | None:
-        if base is None or base.is_empty():
-            return override
-        if override is None or override.is_empty():
-            return base
-        return ResetRandomizationPayload(
-            base_mass_delta=base.base_mass_delta,
-            base_com_offset=base.base_com_offset,
-            gravity=base.gravity,
-            body_iquat=override.body_iquat if override.body_iquat is not None else base.body_iquat,
-            body_inertia=override.body_inertia
-            if override.body_inertia is not None
-            else base.body_inertia,
-            body_ipos=override.body_ipos if override.body_ipos is not None else base.body_ipos,
-            body_mass=override.body_mass if override.body_mass is not None else base.body_mass,
-            dof_armature=override.dof_armature
-            if override.dof_armature is not None
-            else base.dof_armature,
-            geom_friction=override.geom_friction
-            if override.geom_friction is not None
-            else base.geom_friction,
-            kp=base.kp,
-            kd=base.kd,
-        )
-
-    def _compute_reset_obs(
-        self,
-        env: Any,
-        env_ids: Any,
-        info_updates: Any,
-        linvel: Any,
-        gyro: Any,
-        gravity: Any,
-        dof_pos: Any,
-        dof_vel: Any,
-    ) -> dict[str, np.ndarray]:
-        height = env._backend.get_sensor_data(env._cfg.sensor.global_pos)[env_ids, -1].reshape(
-            -1, 1
-        )
-        local_gravity = env._get_local_gravity()[env_ids]
-        accelerometer = env._backend.get_sensor_data(env._cfg.sensor.accelerometer)[env_ids]
-        global_angvel = env._backend.get_sensor_data(env._cfg.sensor.global_angvel)[env_ids]
-        env.torso_height[env_ids] = height[:, 0]
-        env._last_dof_vel_for_acc[env_ids, :] = dof_vel
-        env._last_terminated[env_ids] = False
-        env._motor_targets[env_ids] = env._dof_to_ctrl_order(dof_pos)
-        feet_pos = env._backend.get_sensor_data_batch(env._cfg.sensor.feet_pos).reshape(
-            env._num_envs, len(env._cfg.sensor.feet_pos), 3
-        )
-        env._last_feet_pos[env_ids] = feet_pos[env_ids]
-        env._rear_foot_slip[env_ids] = 0.0
-        env._rear_foot_anchor_pos[env_ids] = feet_pos[env_ids][:, _FOOTSTAND_REAR_FEET, :2]
-        env._rear_foot_anchor_contact[env_ids] = False
-        env._rear_foot_anchor[env_ids] = 0.0
-        target_dof = env._ctrl_to_dof_order(env._motor_targets[env_ids])
-        info_updates["torques"] = np.asarray(
-            env._cfg.control_config.Kp * (target_dof - dof_pos)
-            - env._cfg.control_config.Kd * dof_vel,
-            dtype=get_global_dtype(),
-        )
-
-        return env._compute_obs(  # type: ignore[no-any-return]
-            info_updates,
-            linvel,
-            gyro,
-            local_gravity,
-            dof_pos,
-            dof_vel,
-            height,
-            accelerometer,
-            global_angvel,
-            env_ids=env_ids,
-        )
-
-
-@registry.env("Go2FootStand", sim_backend="mujoco")
-@registry.env("Go2FootStand", sim_backend="motrix")
-@registry.env("Go2FootStand", sim_backend="drake")
-class Go2FootStandTask(Go2HandStandTask):
-    _cfg: Go2FootStandCfg
-
-    def __init__(self, cfg: Go2FootStandCfg, num_envs=1, backend_type="mujoco"):
-        super().__init__(cfg, num_envs=num_envs, backend_type=backend_type)
-        self._z_des = 0.53
-        self._desired_forward_vec = np.array([0.0, 0.0, 1.0], dtype=np.float32)
-        self._init_footstand_pose_targets()
-        self._init_soft_joint_limits()
-        self._init_motor_target_limits()
-        self._last_dof_vel_for_acc = np.zeros((num_envs, self._num_action), dtype=np.float32)
-        self._last_terminated = np.zeros((num_envs,), dtype=bool)
-        self._motor_targets = np.zeros((num_envs, self._num_action), dtype=get_global_dtype())
-        self._last_feet_pos = np.zeros((num_envs, len(cfg.sensor.feet_pos), 3), dtype=np.float32)
-        self._rear_foot_slip = np.zeros((num_envs,), dtype=get_global_dtype())
-        self._rear_foot_anchor_pos = np.zeros(
-            (num_envs, len(_FOOTSTAND_REAR_FEET), 2), dtype=get_global_dtype()
-        )
-        self._rear_foot_anchor_contact = np.zeros((num_envs, len(_FOOTSTAND_REAR_FEET)), dtype=bool)
-        self._rear_foot_anchor = np.zeros((num_envs,), dtype=get_global_dtype())
-        self._obs_history = np.zeros(
-            (num_envs, self._obs_history_len, _FOOTSTAND_FRAME_OBS_DIM),
-            dtype=get_global_dtype(),
-        )
-        self._critic_obs_history = np.zeros_like(self._obs_history)
-        domain_rand = self._cfg.domain_rand
-        needs_floor_friction = bool(domain_rand.randomize_floor_friction)
-        needs_body_mass = bool(
-            domain_rand.randomize_link_mass or domain_rand.torso_added_mass_range is not None
-        )
-        needs_body_ipos = bool(domain_rand.randomize_torso_com)
-        needs_dof_armature = bool(domain_rand.randomize_dof_armature)
-        self._base_geom_friction = (
-            self._backend.get_geom_friction()
-            if needs_floor_friction
-            else np.zeros((0, 3), dtype=np.float64)
-        )
-        self._floor_geom_id = (
-            self._backend.get_geom_id(self._cfg.asset.ground) if needs_floor_friction else -1
-        )
-        self._base_body_id = (
-            self._backend.get_body_id(self._cfg.asset.base_name)
-            if needs_body_mass or needs_body_ipos
-            else -1
-        )
-        self._base_body_mass = (
-            self._backend.get_body_mass() if needs_body_mass else np.zeros((0,), dtype=np.float64)
-        )
-        self._base_body_ipos = (
-            self._backend.get_body_ipos() if needs_body_ipos else np.zeros((0, 3), dtype=np.float64)
-        )
-        self._base_dof_armature = (
-            self._backend.get_dof_armature()
-            if needs_dof_armature
-            else np.zeros((0,), dtype=np.float64)
-        )
-        self._tracked_body_ids = self._backend.get_body_ids(_FOOTSTAND_TRACKED_BODY_NAMES)
-        self._tracked_body_pos = np.zeros(
-            (num_envs, len(_FOOTSTAND_TRACKED_BODY_NAMES), 3), dtype=get_global_dtype()
-        )
-        self._init_domain_randomization(Go2FootStandDomainRandomizationProvider())
-
-    def _init_task_domain_randomization(self) -> None:
-        pass
-
-    def _init_footstand_pose_targets(self) -> None:
-        self.feet_geom_names = list(_FOOTSTAND_FRONT_FEET)
-        self._joint_ids = list(_FOOTSTAND_REAR_LEG_IDS)
-        self._tar_ids = list(_FOOTSTAND_FRONT_LEG_IDS)
-        self.target_angle = np.asarray(_FOOTSTAND_FRONT_LEG_TARGET, dtype=get_global_dtype())
-
-    @property
-    def obs_groups_spec(self) -> dict[str, int]:
-        # Playground state:
-        # linvel(3) + gyro(3) + gravity(3) + diff(12) + dof_vel(12) + last_action(12) = 45.
-        # UniLab stacks the actor state for short-horizon dynamics; critic appends current privileged tail.
-        obs_dim = _FOOTSTAND_FRAME_OBS_DIM * self._obs_history_len
-        return {"obs": obs_dim, "critic": obs_dim + _FOOTSTAND_PRIVILEGED_TAIL_DIM}
-
-    @property
-    def _obs_history_len(self) -> int:
-        return max(_FOOTSTAND_MIN_OBS_HISTORY_LEN, int(self._cfg.obs_history_len))
-
-    def _build_playground_reset_randomization(
-        self, num_reset: int
-    ) -> ResetRandomizationPayload | None:
-        domain_rand = self._cfg.domain_rand
-        payload = ResetRandomizationPayload()
-
-        if domain_rand.randomize_floor_friction:
-            base_geom_friction = self._base_geom_friction
-            assert base_geom_friction is not None
-            low, high = domain_rand.floor_friction_range
-            geom_friction = np.broadcast_to(
-                base_geom_friction, (num_reset, *base_geom_friction.shape)
-            ).copy()
-            geom_friction[:, self._floor_geom_id, 0] = np.random.uniform(
-                low, high, size=(num_reset,)
-            )
-            payload.geom_friction = geom_friction
-
-        body_mass = None
-        if domain_rand.randomize_link_mass:
-            low, high = domain_rand.link_mass_scale_range
-            scale = np.random.uniform(low, high, size=(num_reset, self._base_body_mass.size))
-            body_mass = self._base_body_mass.reshape(1, -1) * scale
-        if domain_rand.torso_added_mass_range is not None:
-            low, high = domain_rand.torso_added_mass_range
-            if body_mass is None:
-                body_mass = np.broadcast_to(
-                    self._base_body_mass, (num_reset, self._base_body_mass.size)
-                ).copy()
-            body_mass[:, self._base_body_id] += np.random.uniform(low, high, size=(num_reset,))
-        if body_mass is not None:
-            payload.body_mass = body_mass.astype(np.float64, copy=False)
-
-        if domain_rand.randomize_torso_com:
-            low, high = domain_rand.torso_com_offset_range
-            body_ipos = np.broadcast_to(
-                self._base_body_ipos, (num_reset, *self._base_body_ipos.shape)
-            ).copy()
-            body_ipos[:, self._base_body_id, :] += np.random.uniform(low, high, size=(num_reset, 3))
-            payload.body_ipos = body_ipos
-
-        if domain_rand.randomize_dof_armature:
-            base_dof_armature = self._base_dof_armature
-            assert base_dof_armature is not None
-            low, high = domain_rand.dof_armature_scale_range
-            dof_armature = np.broadcast_to(
-                base_dof_armature, (num_reset, base_dof_armature.size)
-            ).copy()
-            dof_armature[:, -self._num_action :] *= np.random.uniform(
-                low, high, size=(num_reset, self._num_action)
-            )
-            payload.dof_armature = dof_armature
-
-        return None if payload.is_empty() else payload
-
-    def _init_reward_functions(self):
-        self._reward_fns: dict[str, Any] = {
-            "height": self._reward_height,
-            "contact": self._cost_contact,
-            "orientation": self._reward_orientation,
-            "oritentation": self._reward_orientation,
-            "action_rate": rewards.action_rate,
-            "termination": self._reward_termination,
-            "dof_pos_limits": self._cost_joint_pos_limits,
-            "torques": self._cost_torques,
-            "pose": self._cost_pose,
-            "penalty_contact": self._reward_penalty_contact,
-            "tar": self._reward_tar,
-            "rear_feet_contact": self._reward_rear_feet_contact,
-            "both_rear_feet_contact": self._reward_both_rear_feet_contact,
-            "rear_foot_slip": self._cost_rear_foot_slip,
-            "rear_foot_anchor": self._cost_rear_foot_anchor,
-            "front_feet_air": self._reward_front_feet_air,
-            "balanced_footstand": self._reward_balanced_footstand,
-            "rear_leg_symmetry": self._cost_rear_leg_symmetry,
-            "rear_leg_splay": self._cost_rear_leg_splay,
-            "front_leg_motion": self._cost_front_leg_motion,
-            "front_feet_crossing": self._cost_front_leg_crossing,
-            "front_leg_crossing": self._cost_front_leg_crossing,
-            "upright_stability": self._cost_upright_stability,
-            "knee_clearance": self._cost_knee_clearance,
-            "stay_still": self._cost_stay_still,
-            "energy": rewards.energy,
-            "dof_acc": rewards.dof_acc,
-        }
-
-    def _dof_to_ctrl_order(self, values: np.ndarray) -> np.ndarray:
-        return np.asarray(values[:, _GO2_DOF_TO_CTRL], dtype=get_global_dtype())
-
-    def _ctrl_to_dof_order(self, values: np.ndarray) -> np.ndarray:
-        return np.asarray(values[:, _GO2_DOF_TO_CTRL], dtype=get_global_dtype())
-
-    def _get_local_gravity(self) -> np.ndarray:
-        gravity = np.broadcast_to(_WORLD_GRAVITY, (self._num_envs, 3))
-        return np.asarray(
-            np_quat_apply_inverse(self._backend.get_base_quat(), gravity), dtype=get_global_dtype()
-        )
-
-    def _get_body_forward(self) -> np.ndarray:
-        forward = np.broadcast_to(_BODY_FORWARD, (self._num_envs, 3))
-        return np.asarray(
-            np_quat_apply(self._backend.get_base_quat(), forward), dtype=get_global_dtype()
-        )
-
-    def _reward_height(self, ctx: RewardContext) -> np.ndarray:
-        del ctx
-        error = np.abs(self._z_des - self.torso_height)
-        return np.asarray(np.exp(-error / 0.1), dtype=get_global_dtype())
-
-    def _standing_mask(self) -> np.ndarray:
-        height_ready = self.torso_height >= self._z_des * _FOOTSTAND_STAND_HEIGHT_FRACTION
-        orientation_ready = self._orientation_score() >= _FOOTSTAND_STAND_ORIENTATION_THRESHOLD
-        return np.asarray(height_ready & orientation_ready, dtype=get_global_dtype())
-
-    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
-        clip_actions = float(getattr(self._cfg.control_config, "clip_actions", np.inf))
-        actions_np = np.asarray(actions, dtype=get_global_dtype())
-        if np.isfinite(clip_actions):
-            actions_np = np.clip(actions_np, -clip_actions, clip_actions)
-
-        state.info["last_actions"] = state.info.get("current_actions", np.zeros_like(actions_np))
-        state.info["current_actions"] = actions_np
-        exec_actions = (
-            state.info["last_actions"]
-            if self._cfg.control_config.simulate_action_latency
-            else actions_np
-        )
-        self._motor_targets += exec_actions * self._cfg.control_config.action_scale
-        self._clip_motor_targets()
-        return np.asarray(self._motor_targets, dtype=get_global_dtype())
-
-    def update_state(self, state: NpEnvState) -> NpEnvState:
-        linvel = self.get_local_linvel()
-        gyro = self.get_gyro()
-        upvector = self._backend.get_sensor_data("upvector")
-        gravity = self._get_local_gravity()
-        accelerometer = self._backend.get_sensor_data(self._cfg.sensor.accelerometer)
-        global_angvel = self._backend.get_sensor_data(self._cfg.sensor.global_angvel)
-        dof_pos = self.get_dof_pos()
-        dof_vel = self.get_dof_vel()
-        self.feet_force = self._backend.get_sensor_data_batch(self._cfg.sensor.feet_force).reshape(
-            self._num_envs, len(self._cfg.sensor.feet_force), 1
-        )
-        self.feet_pos = self._backend.get_sensor_data_batch(self._cfg.sensor.feet_pos).reshape(
-            self._num_envs, len(self._cfg.sensor.feet_pos), 3
-        )
-        self.torso_height = self._backend.get_sensor_data(self._cfg.sensor.global_pos)[:, -1]
-        self._update_rear_foot_slip()
-        self._update_rear_foot_anchor()
-        self._tracked_body_pos = self._backend.get_body_pos_w(self._tracked_body_ids)
-        result = self._backend.get_sensor_data_batch(self._cfg.sensor.ternamate_contact)
-
-        state.info["qacc"] = self._estimate_dof_acc(dof_vel)
-        state.info["torques"] = self._estimate_pd_torques(state.info, dof_pos, dof_vel)
-        orientation = self._orientation_score()
-        step_count = state.info.get("steps", np.zeros((self._num_envs,), dtype=np.uint32))
-        grace_elapsed = step_count >= self._cfg.termination_grace_steps
-        terminated_z = upvector[:, 2] < -0.25
-        terminated_contact = np.any(result, axis=1)
-        terminated_low_height = (
-            self.torso_height < self._z_des * self._cfg.termination_height_fraction
-        )
-        terminated_bad_orientation = orientation < self._cfg.termination_orientation_threshold
-        terminated_pose = grace_elapsed & (terminated_low_height | terminated_bad_orientation)
-        energy = np.sum(np.abs(state.info["torques"]) * np.abs(dof_vel), axis=1)
-        terminated_energy = energy > self._cfg.energy_termination_threshold
-        terminated = np.logical_or.reduce(
-            (terminated_contact, terminated_z, terminated_energy, terminated_pose)
-        )
-        self._last_terminated = terminated.copy()
-        reward = self._compute_reward(state.info, linvel, gyro, dof_pos, dof_vel)
-        self._last_feet_pos = self.feet_pos.copy()
-        obs = self._compute_obs(
-            state.info,
-            linvel,
-            gyro,
-            gravity,
-            dof_pos,
-            dof_vel,
-            self.torso_height.reshape(-1, 1),
-            accelerometer,
-            global_angvel,
-        )
-        return state.replace(obs=obs, reward=reward, terminated=terminated)
-
-    def _compute_reward(
-        self,
-        info: dict,
-        linvel: np.ndarray,
-        gyro: np.ndarray,
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray | None = None,
-    ) -> np.ndarray:
-        dtype = get_global_dtype()
-        reward = np.zeros((self._num_envs,), dtype=dtype)
-        cfg = self._reward_cfg
-        if dof_vel is None:
-            dof_vel = self.get_dof_vel()
-
-        ctx = RewardContext(
-            info=info,
-            linvel=linvel,
-            gyro=gyro,
-            dof_pos=dof_pos,
-            dof_vel=dof_vel,
-            num_envs=self._num_envs,
-            default_angles=self.default_angles,
-            tracking_sigma=cfg.tracking_sigma,
-            base_height_target=cfg.base_height_target,
-            base_height=self._backend.get_base_pos()[:, 2],
-        )
-
-        step_count = info.get("steps", np.zeros((self._num_envs,), dtype=np.uint32))
-        should_log = self._enable_reward_log and (int(step_count[0]) % 4 == 0)
-        log = {} if should_log else info.get("log", {})
-
-        for name, scale in cfg.scales.items():
-            if scale == 0 or name not in self._reward_fns:
-                continue
-            rew = self._reward_fns[name](ctx)
-            weighted_rew = rew * scale
-            reward += weighted_rew
-            if should_log:
-                log[f"reward/{name}"] = float(np.mean(weighted_rew))
-
-        info["log"] = log
-        return np.clip(reward * self._cfg.ctrl_dt, 0.0, 10000.0)
-
-    def _compute_obs(
-        self,
-        info: dict,
-        linvel: np.ndarray,
-        gyro: np.ndarray,
-        gravity: np.ndarray,
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-        height: np.ndarray,
-        accelerometer: np.ndarray | None = None,
-        global_angvel: np.ndarray | None = None,
-        env_ids: np.ndarray | None = None,
-    ) -> dict[str, np.ndarray]:
-        noise_cfg = self._cfg.noise_config
-        diff = dof_pos - self.default_angles
-        noisy_linvel = self._obs_noise(linvel, noise_cfg.scale_linvel)
-        noisy_gyro = self._obs_noise(gyro, noise_cfg.scale_gyro)
-        noisy_gravity = self._obs_noise(gravity, noise_cfg.scale_gravity)
-        noisy_diff = self._obs_noise(diff, noise_cfg.scale_joint_angle)
-        noisy_dof_vel = self._obs_noise(dof_vel, noise_cfg.scale_joint_vel)
-        last_actions = info.get("last_actions", np.zeros_like(diff))
-
-        obs_frame = np.concatenate(
-            [noisy_linvel, noisy_gyro, noisy_gravity, noisy_diff, noisy_dof_vel, last_actions],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-        critic_frame = np.concatenate(
-            [linvel, gyro, gravity, diff, dof_vel, last_actions],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-        obs = self._update_obs_history(obs_frame, env_ids=env_ids, history_attr="_obs_history")
-        critic_obs = self._update_obs_history(
-            critic_frame,
-            env_ids=env_ids,
-            history_attr="_critic_obs_history",
-        )
-        torques = np.asarray(info.get("torques", np.zeros_like(dof_pos)), dtype=get_global_dtype())
-        if accelerometer is None:
-            accelerometer = np.zeros_like(gyro)
-        if global_angvel is None:
-            global_angvel = np.zeros_like(gyro)
-        critic = np.concatenate(
-            [
-                critic_obs,
-                gyro,
-                accelerometer,
-                linvel,
-                global_angvel,
-                dof_pos,
-                dof_vel,
-                torques,
-                height,
-            ],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-        return {"obs": obs, "critic": critic}
-
-    def _update_obs_history(
-        self,
-        frame_obs: np.ndarray,
-        *,
-        env_ids: np.ndarray | None = None,
-        history_attr: str = "_obs_history",
-    ) -> np.ndarray:
-        frame_obs = np.asarray(frame_obs, dtype=get_global_dtype())
-        batch_size = int(frame_obs.shape[0])
-        history_len = self._obs_history_len
-        expected_shape = (self._num_envs, history_len, _FOOTSTAND_FRAME_OBS_DIM)
-        history = getattr(self, history_attr, None)
-        if history is None or history.shape != expected_shape:
-            if env_ids is None and batch_size == self._num_envs:
-                history = np.zeros(expected_shape, dtype=get_global_dtype())
-                setattr(self, history_attr, history)
-            elif env_ids is not None:
-                history = np.zeros(expected_shape, dtype=get_global_dtype())
-                setattr(self, history_attr, history)
-            else:
-                repeated = np.broadcast_to(
-                    frame_obs[:, None, :], (batch_size, history_len, frame_obs.shape[1])
-                ).copy()
-                return np.asarray(repeated.reshape(batch_size, -1), dtype=get_global_dtype())
-
-        assert history is not None
-        if env_ids is None:
-            if batch_size != self._num_envs:
-                repeated = np.broadcast_to(
-                    frame_obs[:, None, :], (batch_size, history_len, frame_obs.shape[1])
-                ).copy()
-                return np.asarray(repeated.reshape(batch_size, -1), dtype=get_global_dtype())
-            history[:, :-1] = history[:, 1:]
-            history[:, -1] = frame_obs
-            selected_history = history
-        else:
-            env_ids = np.asarray(env_ids, dtype=np.int32)
-            selected_history = np.broadcast_to(
-                frame_obs[:, None, :], (batch_size, history_len, frame_obs.shape[1])
-            ).copy()
-            history[env_ids] = selected_history
-
-        return np.asarray(selected_history.reshape(batch_size, -1), dtype=get_global_dtype())
-
-    def _init_soft_joint_limits(self) -> None:
-        joint_range = self._backend.get_joint_range()
-        if joint_range is None:
-            self._soft_lowers = np.full((self._num_action,), -np.inf, dtype=np.float32)
-            self._soft_uppers = np.full((self._num_action,), np.inf, dtype=np.float32)
-            return
-
-        joint_range = np.asarray(joint_range, dtype=np.float32)
-        centers = (joint_range[:, 0] + joint_range[:, 1]) / 2.0
-        widths = joint_range[:, 1] - joint_range[:, 0]
-        factor = self._cfg.soft_joint_pos_limit_factor
-        self._soft_lowers = centers - 0.5 * widths * factor
-        self._soft_uppers = centers + 0.5 * widths * factor
-
-    def _init_motor_target_limits(self) -> None:
-        joint_range = self._backend.get_joint_range()
-        if joint_range is None:
-            self._target_lowers = np.full((self._num_action,), -np.inf, dtype=get_global_dtype())
-            self._target_uppers = np.full((self._num_action,), np.inf, dtype=get_global_dtype())
-            return
-
-        joint_range = np.asarray(joint_range, dtype=get_global_dtype())
-        lowers = joint_range[:, 0]
-        uppers = joint_range[:, 1]
-        if lowers.size == _GO2_DOF_TO_CTRL.size:
-            lowers = self._dof_to_ctrl_order(lowers.reshape(1, -1))[0]
-            uppers = self._dof_to_ctrl_order(uppers.reshape(1, -1))[0]
-        self._target_lowers = np.asarray(lowers, dtype=get_global_dtype())
-        self._target_uppers = np.asarray(uppers, dtype=get_global_dtype())
-
-    def _clip_motor_targets(self) -> None:
-        lowers = getattr(self, "_target_lowers", None)
-        uppers = getattr(self, "_target_uppers", None)
-        if lowers is None or uppers is None:
-            return
-        np.clip(self._motor_targets, lowers, uppers, out=self._motor_targets)
-
-    def _reward_termination(self, ctx: RewardContext) -> np.ndarray:
-        return self._last_terminated.astype(get_global_dtype())
-
-    def _cost_joint_pos_limits(self, ctx: RewardContext) -> np.ndarray:
-        out_of_limits = -np.clip(ctx.dof_pos - self._soft_lowers, None, 0.0)
-        out_of_limits += np.clip(ctx.dof_pos - self._soft_uppers, 0.0, None)
-        return cast(np.ndarray, np.sum(out_of_limits, axis=1))
-
-    def _cost_stay_still(self, ctx: RewardContext) -> np.ndarray:
-        linvel = self._backend.get_base_lin_vel()
-        angvel = self._backend.get_base_ang_vel()
-        return cast(np.ndarray, np.sum(np.square(linvel[:, :2]), axis=1) + np.square(angvel[:, 2]))
-
-    def _cost_torques(self, ctx: RewardContext) -> np.ndarray:
-        torques = np.asarray(ctx.info.get("torques", np.zeros_like(ctx.dof_pos)))
-        return cast(np.ndarray, np.sum(np.square(torques), axis=1))
-
-    def _estimate_dof_acc(self, dof_vel: np.ndarray) -> np.ndarray:
-        qacc = np.asarray((dof_vel - self._last_dof_vel_for_acc) / self._cfg.ctrl_dt)
-        self._last_dof_vel_for_acc = np.asarray(dof_vel, dtype=np.float32).copy()
-        return np.asarray(qacc, dtype=get_global_dtype())
-
-    def _estimate_pd_torques(
-        self, info: dict, dof_pos: np.ndarray, dof_vel: np.ndarray
-    ) -> np.ndarray:
-        del info
-        targets = self._ctrl_to_dof_order(self._motor_targets)
-        torques = self._cfg.control_config.Kp * (targets - dof_pos)
-        torques -= self._cfg.control_config.Kd * dof_vel
-        return np.asarray(torques, dtype=get_global_dtype())
-
-    def _reward_orientation(self, ctx: RewardContext) -> np.ndarray:
-        del ctx
-        return self._orientation_score()
-
-    def _orientation_score(self) -> np.ndarray:
-        forward = self._get_body_forward()
-        cos_dist = forward @ self._desired_forward_vec
-        normalized = 0.5 * cos_dist + 0.5
-        return np.asarray(np.square(normalized), dtype=get_global_dtype())
-
-    def _cost_contact(self, ctx: RewardContext) -> np.ndarray:
-        del ctx
-        feet_contact = self.feet_force[:, self.feet_geom_names, 0] > _FOOTSTAND_CONTACT_THRESHOLD
-        return np.asarray(np.any(feet_contact, axis=1), dtype=get_global_dtype())
-
-    def _reward_rear_feet_contact(self, ctx: RewardContext) -> np.ndarray:
-        del ctx
-        rear_contact = self.feet_force[:, _FOOTSTAND_REAR_FEET, 0] > _FOOTSTAND_CONTACT_THRESHOLD
-        return np.asarray(np.mean(rear_contact, axis=1), dtype=get_global_dtype())
-
-    def _reward_both_rear_feet_contact(self, ctx: RewardContext) -> np.ndarray:
-        del ctx
-        rear_contact = self.feet_force[:, _FOOTSTAND_REAR_FEET, 0] > _FOOTSTAND_CONTACT_THRESHOLD
-        return np.asarray(np.all(rear_contact, axis=1), dtype=get_global_dtype())
-
-    def _update_rear_foot_slip(self) -> None:
-        if not hasattr(self, "_last_feet_pos"):
-            self._last_feet_pos = self.feet_pos.copy()
-        rear_contact = self.feet_force[:, _FOOTSTAND_REAR_FEET, 0] > _FOOTSTAND_CONTACT_THRESHOLD
-        rear_delta_xy = (
-            self.feet_pos[:, _FOOTSTAND_REAR_FEET, :2]
-            - self._last_feet_pos[:, _FOOTSTAND_REAR_FEET, :2]
-        )
-        rear_vel_xy = rear_delta_xy / max(float(self._cfg.ctrl_dt), 1e-6)
-        speed = np.linalg.norm(rear_vel_xy, axis=2)
-        deadband = float(self._reward_cfg.rear_foot_slip_deadband)
-        slip = np.square(np.clip(speed - deadband, 0.0, None)) * rear_contact
-        self._rear_foot_slip = np.asarray(np.mean(slip, axis=1), dtype=get_global_dtype())
-
-    def _cost_rear_foot_slip(self, ctx: RewardContext) -> np.ndarray:
-        del ctx
-        return np.asarray(self._rear_foot_slip, dtype=get_global_dtype())
-
-    def _update_rear_foot_anchor(self) -> None:
-        if not hasattr(self, "_rear_foot_anchor_pos"):
-            self._rear_foot_anchor_pos = self.feet_pos[:, _FOOTSTAND_REAR_FEET, :2].copy()
-            self._rear_foot_anchor_contact = np.zeros(
-                (self._num_envs, len(_FOOTSTAND_REAR_FEET)), dtype=bool
-            )
-        rear_contact = self.feet_force[:, _FOOTSTAND_REAR_FEET, 0] > _FOOTSTAND_CONTACT_THRESHOLD
-        standing = self._standing_mask().astype(bool)[:, None]
-        anchor_contact = rear_contact & standing
-        rear_xy = self.feet_pos[:, _FOOTSTAND_REAR_FEET, :2]
-        new_contact = anchor_contact & ~self._rear_foot_anchor_contact
-        self._rear_foot_anchor_pos[new_contact] = rear_xy[new_contact]
-        self._rear_foot_anchor_contact = anchor_contact.copy()
-
-        drift = np.linalg.norm(rear_xy - self._rear_foot_anchor_pos, axis=2)
-        radius = max(float(self._reward_cfg.rear_foot_anchor_radius), 1e-6)
-        anchored_drift = np.square(np.clip(drift - radius, 0.0, None) / radius) * anchor_contact
-        self._rear_foot_anchor = np.asarray(
-            np.mean(anchored_drift, axis=1), dtype=get_global_dtype()
-        )
-
-    def _cost_rear_foot_anchor(self, ctx: RewardContext) -> np.ndarray:
-        del ctx
-        return np.asarray(self._rear_foot_anchor, dtype=get_global_dtype())
-
-    def _reward_front_feet_air(self, ctx: RewardContext) -> np.ndarray:
-        del ctx
-        front_contact = self.feet_force[:, _FOOTSTAND_FRONT_FEET, 0] > _FOOTSTAND_CONTACT_THRESHOLD
-        return np.asarray(~np.any(front_contact, axis=1), dtype=get_global_dtype())
-
-    def _reward_balanced_footstand(self, ctx: RewardContext) -> np.ndarray:
-        del ctx
-        rear_contact = self.feet_force[:, _FOOTSTAND_REAR_FEET, 0] > _FOOTSTAND_CONTACT_THRESHOLD
-        front_contact = self.feet_force[:, _FOOTSTAND_FRONT_FEET, 0] > _FOOTSTAND_CONTACT_THRESHOLD
-        valid_support = np.all(rear_contact, axis=1) & ~np.any(front_contact, axis=1)
-        return np.asarray(valid_support * self._standing_mask(), dtype=get_global_dtype())
-
-    def _cost_rear_leg_symmetry(self, ctx: RewardContext) -> np.ndarray:
-        rear_left = ctx.dof_pos[:, _FOOTSTAND_REAR_LEFT_LEG_IDS]
-        rear_right = ctx.dof_pos[:, _FOOTSTAND_REAR_RIGHT_LEG_IDS]
-        mirrored_right = rear_right * _FOOTSTAND_REAR_MIRROR_SIGNS
-        cost = np.mean(np.square(rear_left - mirrored_right), axis=1)
-        rising_mask = 1.0 - self._standing_mask()
-        return np.asarray(cost * rising_mask, dtype=get_global_dtype())
-
-    def _cost_rear_leg_splay(self, ctx: RewardContext) -> np.ndarray:
-        default_angles = np.asarray(ctx.default_angles, dtype=get_global_dtype()).reshape(-1)
-        rear_hip_error = (
-            ctx.dof_pos[:, _FOOTSTAND_REAR_HIP_IDS] - default_angles[_FOOTSTAND_REAR_HIP_IDS]
-        )
-        margin = float(self._reward_cfg.rear_hip_abduction_margin)
-        splay = np.clip(np.abs(rear_hip_error) - margin, 0.0, None)
-        cost = np.mean(np.square(splay), axis=1)
-        return np.asarray(cost * self._standing_mask(), dtype=get_global_dtype())
-
-    def _cost_front_leg_motion(self, ctx: RewardContext) -> np.ndarray:
-        assert ctx.dof_vel is not None
-        front_leg_vel = ctx.dof_vel[:, _FOOTSTAND_FRONT_LEG_IDS]
-        cost = np.mean(np.square(front_leg_vel), axis=1)
-        return np.asarray(cost * self._standing_mask(), dtype=get_global_dtype())
-
-    def _cost_front_leg_crossing(self, ctx: RewardContext) -> np.ndarray:
-        del ctx
-        base_pos = self._backend.get_base_pos()
-        base_quat = self._backend.get_base_quat()
-        left_points = np.concatenate(
-            [
-                self.feet_pos[:, [0], :],
-                self._tracked_body_pos[:, _FOOTSTAND_FRONT_LEFT_BODY_INDICES, :],
-            ],
-            axis=1,
-        )
-        right_points = np.concatenate(
-            [
-                self.feet_pos[:, [1], :],
-                self._tracked_body_pos[:, _FOOTSTAND_FRONT_RIGHT_BODY_INDICES, :],
-            ],
-            axis=1,
-        )
-        points = np.concatenate([left_points, right_points], axis=1)
-        rel_points = (points - base_pos[:, None, :]).reshape(-1, 3)
-        rel_quat = np.repeat(base_quat, points.shape[1], axis=0)
-        body_points = np_quat_apply_inverse(rel_quat, rel_points).reshape(
-            self._num_envs, points.shape[1], 3
-        )
-        left_y = body_points[:, : left_points.shape[1], 1]
-        right_y = body_points[:, left_points.shape[1] :, 1]
-        side_margin = float(self._reward_cfg.front_feet_side_margin)
-        min_separation = float(self._reward_cfg.front_feet_min_separation)
-        left_error = np.clip(side_margin - left_y, 0.0, None)
-        right_error = np.clip(right_y + side_margin, 0.0, None)
-        separation_error = np.clip(min_separation - (left_y - right_y), 0.0, None)
-        cost = np.mean(
-            np.square(left_error) + np.square(right_error) + np.square(separation_error),
-            axis=1,
-        )
-        return np.asarray(cost, dtype=get_global_dtype())
-
-    def _cost_upright_stability(self, ctx: RewardContext) -> np.ndarray:
-        del ctx
-        linvel = self._backend.get_base_lin_vel()
-        angvel = self._backend.get_base_ang_vel()
-        cost = np.sum(np.square(linvel), axis=1) + 0.25 * np.sum(np.square(angvel), axis=1)
-        return np.asarray(cost * self._standing_mask(), dtype=get_global_dtype())
-
-    def _cost_knee_clearance(self, ctx: RewardContext) -> np.ndarray:
-        del ctx
-        target = max(float(self._reward_cfg.knee_height_target), 1e-6)
-        knee_height = self._tracked_body_pos[:, _FOOTSTAND_KNEE_BODY_INDICES, 2]
-        clearance_error = np.clip(target - knee_height, 0.0, None) / target
-        return np.asarray(np.mean(np.square(clearance_error), axis=1), dtype=get_global_dtype())
diff --git a/src/unilab/envs/locomotion/go2/joystick.py b/src/unilab/envs/locomotion/go2/joystick.py
deleted file mode 100644
index 5fdd87da8..000000000
--- a/src/unilab/envs/locomotion/go2/joystick.py
+++ /dev/null
@@ -1,318 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Any, cast
-
-import numpy as np
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base import registry
-from unilab.base.backend import create_backend, env_backend_kwargs
-from unilab.base.np_env import NpEnvState
-from unilab.base.scene import SceneCfg
-from unilab.dtype_config import get_global_dtype
-from unilab.envs.locomotion.common import rewards
-from unilab.envs.locomotion.common.base import Sensor
-from unilab.envs.locomotion.common.commands import Commands
-from unilab.envs.locomotion.common.domain_rand import DomainRandConfig
-from unilab.envs.locomotion.common.dr_provider import LocomotionDRProvider
-from unilab.envs.locomotion.common.rewards import RewardContext
-from unilab.envs.locomotion.common.terrain_spawn import (
-    TerrainCurriculumCfg,
-    TerrainSpawnManager,
-)
-from unilab.envs.locomotion.go2.base import Go2BaseCfg, Go2BaseEnv
-
-
-@dataclass
-class InitState:
-    pos = [0.0, 0.0, 0.42]
-
-
-@dataclass
-class Go2DomainRandConfig(DomainRandConfig):
-    randomize_kp: bool = True
-    kp_multiplier_range: list[float] = field(default_factory=lambda: [0.9, 1.1])
-
-    randomize_kd: bool = True
-    kd_multiplier_range: list[float] = field(default_factory=lambda: [0.9, 1.1])
-
-
-@dataclass
-class RewardConfig:
-    scales: dict[str, float]
-    tracking_sigma: float
-    base_height_target: float
-    target_foot_height: float = 0.1
-
-
-@dataclass
-class JoystickSensor(Sensor):
-    local_linvel = "local_linvel"
-    gyro = "gyro"
-    feet_force = ["FL_foot_contact", "FR_foot_contact", "RL_foot_contact", "RR_foot_contact"]
-    feet_pos = ["FL_pos", "FR_pos", "RL_pos", "RR_pos"]
-
-
-@registry.envcfg("Go2JoystickFlat")
-@dataclass
-class Go2JoystickCfg(Go2BaseCfg):
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "go2" / "scene_flat.xml")
-        )
-    )
-    max_episode_seconds: float = 20.0
-    init_state: InitState = field(default_factory=InitState)
-    commands: Commands = field(default_factory=Commands)
-    reward_config: RewardConfig | None = None
-    sensor: JoystickSensor = field(default_factory=JoystickSensor)
-    domain_rand: Go2DomainRandConfig = field(default_factory=Go2DomainRandConfig)
-    terrain_curriculum: TerrainCurriculumCfg = field(default_factory=TerrainCurriculumCfg)
-
-
-class Go2JoystickDomainRandomizationProvider(LocomotionDRProvider):
-    def _compute_reset_obs(
-        self,
-        env: Any,
-        env_ids: Any,
-        info_updates: Any,
-        linvel: Any,
-        gyro: Any,
-        gravity: Any,
-        dof_pos: Any,
-        dof_vel: Any,
-    ) -> dict[str, np.ndarray]:
-        return cast(
-            dict[str, np.ndarray],
-            env._compute_obs(
-                info_updates, linvel, gyro, gravity, dof_pos, dof_vel, env.feet_phase[env_ids]
-            ),
-        )
-
-
-@registry.env("Go2JoystickFlat", sim_backend="mujoco")
-@registry.env("Go2JoystickFlat", sim_backend="motrix")
-@registry.env("Go2JoystickFlat", sim_backend="drake")
-class Go2WalkTask(Go2BaseEnv):
-    _cfg: Go2JoystickCfg
-
-    def __init__(self, cfg: Go2JoystickCfg, num_envs=1, backend_type="mujoco"):
-        if cfg.reward_config is None:
-            raise ValueError("reward_config must be provided via Hydra configuration")
-
-        self._scene_terrain_origins: np.ndarray | None = None
-        scene_cfg = cfg.scene
-        terrain_generator = scene_cfg.terrain.generator if scene_cfg.terrain is not None else None
-
-        backend = create_backend(
-            backend_type,
-            cfg.scene,
-            num_envs,
-            cfg.sim_dt,
-            base_name=cfg.asset.base_name,
-            push_body_name=cfg.domain_rand.push_body_name,
-            position_actuator_gains=cfg.control_config.position_gains(),
-            **env_backend_kwargs(cfg),
-        )
-        terrain_spawn_data = backend.get_terrain_spawn_data()
-        self._terrain_surface_sample_height = (
-            None if terrain_spawn_data is None else terrain_spawn_data.sample_height
-        )
-        if terrain_spawn_data is not None:
-            self._scene_terrain_origins = terrain_spawn_data.terrain_origins
-        super().__init__(cfg, backend, num_envs)
-        self._enable_reward_log = True
-        self._reward_cfg = cfg.reward_config
-        self._init_reward_functions()
-        self._init_domain_randomization(self._make_dr_provider())
-        if terrain_spawn_data is not None and terrain_generator is not None:
-            self._spawn = TerrainSpawnManager(
-                num_envs,
-                terrain_spawn_data.terrain_origins,
-                cell_size=float(terrain_generator.size[0]),
-                cfg=cfg.terrain_curriculum,
-                sample_height=self._terrain_surface_sample_height,
-            )
-        self.phase = np.zeros((num_envs,), dtype=np.float32)
-        self.feet_phase = np.zeros((num_envs, len(cfg.sensor.feet_force)), dtype=np.float32)
-        self.gait_frequency = 2
-        self.feet_force = np.zeros((num_envs, len(cfg.sensor.feet_force), 3), dtype=np.float32)
-        self.feet_pos = np.zeros((num_envs, len(cfg.sensor.feet_pos), 3), dtype=np.float32)
-
-    def _make_dr_provider(self) -> LocomotionDRProvider:
-        """Domain-randomization provider for this task. Subclasses override to
-        inject robot-specific behaviour (e.g. A2's per-joint base gains)."""
-        return Go2JoystickDomainRandomizationProvider()
-
-    def _update_commands(self, info: dict) -> None:
-        """Mid-episode command resample hook. No-op for Go2 flat; subclasses
-        (e.g. A2) override to draw standing-aware commands at intervals."""
-
-    def get_playback_model(self, env_index: int | None = None) -> Any:
-        return super().get_playback_model(env_index)
-
-    @property
-    def obs_groups_spec(self) -> dict[str, int]:
-        # gyro(3) + gravity(3) + diff(12) + dof_vel(12) + action(12) + cmd(3) + phase(4) = 49
-        return {"obs": 49, "critic": 52}
-
-    def _init_reward_functions(self):
-        self._reward_fns: dict[str, Any] = {
-            "tracking_lin_vel": rewards.tracking_lin_vel,
-            "tracking_ang_vel": rewards.tracking_ang_vel,
-            "lin_vel_z": rewards.lin_vel_z,
-            "ang_vel_xy": rewards.ang_vel_xy,
-            "base_height": rewards.base_height,
-            "action_rate": rewards.action_rate,
-            "similar_to_default": rewards.similar_to_default,
-            "alive": rewards.alive,
-            "swing_feet_z": self._reward_swing_feet_z,
-            "contact": self._reward_contact,
-            "foot_drag": self._reward_foot_drag,
-        }
-
-    def _advance_phase(self, phase: np.ndarray) -> np.ndarray:
-        """Advance the gait phase clock one control step.
-
-        Subclasses override to modulate the advance (e.g. A2 freezes standing
-        envs). The base advance is unconditional, matching the Go2 flat gait."""
-        return np.fmod(phase + self._cfg.ctrl_dt * self.gait_frequency, 1.0)
-
-    def update_state(self, state: NpEnvState) -> NpEnvState:
-        self._update_commands(state.info)
-        self.phase = self._advance_phase(self.phase)
-        self.feet_phase[:, 0] = self.phase
-        self.feet_phase[:, 3] = self.phase
-
-        self.feet_phase[:, 1] = (self.phase + 0.5) % 1
-        self.feet_phase[:, 2] = (self.phase + 0.5) % 1
-
-        linvel = self.get_local_linvel()
-        gyro = self.get_gyro()
-        gravity = self._backend.get_sensor_data("upvector")
-        dof_pos = self.get_dof_pos()
-        dof_vel = self.get_dof_vel()
-        self.feet_force[:, :, :] = 0
-        for i in range(len(self._cfg.sensor.feet_force)):
-            contact = self._backend.get_sensor_data(self._cfg.sensor.feet_force[i])
-            if contact.shape[1] == 1:
-                self.feet_force[:, i, 2] = contact[:, 0]
-            elif contact.shape[1] == 3:
-                self.feet_force[:, i, :] = contact
-            else:
-                raise ValueError(
-                    "foot contact sensor must return either scalar found flags "
-                    f"or 3D force vectors, got {contact.shape}"
-                )
-        for i in range(len(self._cfg.sensor.feet_pos)):
-            self.feet_pos[:, i, :] = self._backend.get_sensor_data(self._cfg.sensor.feet_pos[i])
-        terminated = gravity[:, 2] <= 0.5
-        reward = self._compute_reward(state.info, linvel, gyro, dof_pos)
-        obs = self._compute_obs(
-            state.info, linvel, gyro, gravity, dof_pos, dof_vel, self.feet_phase
-        )
-        state = state.replace(obs=obs, reward=reward, terminated=terminated)
-        done = state.terminated | state.truncated
-        if np.any(done):
-            done_indices = np.where(done)[0]
-            stats = self._spawn.update_on_done(
-                done_indices, self._backend.get_base_pos()[done_indices]
-            )
-            if stats:
-                if "log" not in state.info:
-                    state.info["log"] = {}
-                for k, v in stats.items():
-                    state.info["log"][f"terrain_curriculum/{k}"] = float(v)
-        return state
-
-    def _compute_obs(
-        self, info: dict, linvel, gyro, gravity, dof_pos, dof_vel, feet_phase
-    ) -> dict[str, np.ndarray]:
-        noise_cfg = self._cfg.noise_config
-        diff = dof_pos - self.default_angles
-        noisy_gyro = self._obs_noise(gyro, noise_cfg.scale_gyro)
-        noisy_gravity = self._obs_noise(gravity, noise_cfg.scale_gravity)
-        noisy_diff = self._obs_noise(diff, noise_cfg.scale_joint_angle)
-        noisy_dof_vel = self._obs_noise(dof_vel, noise_cfg.scale_joint_vel)
-        command = info["commands"]
-        last_actions = info.get("current_actions", np.zeros_like(diff))
-        obs = np.concatenate(
-            [
-                noisy_gyro,
-                -noisy_gravity,
-                noisy_diff,
-                noisy_dof_vel,
-                last_actions,
-                command,
-                feet_phase,
-            ],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-        critic = np.concatenate(
-            [gyro, -gravity, diff, dof_vel, last_actions, command, feet_phase, linvel],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-        return {"obs": obs, "critic": critic}
-
-    def _compute_reward(self, info: dict, linvel, gyro, dof_pos) -> np.ndarray:
-        cfg = self._reward_cfg
-        ctx = RewardContext(
-            info=info,
-            linvel=linvel,
-            gyro=gyro,
-            dof_pos=dof_pos,
-            num_envs=self._num_envs,
-            default_angles=self.default_angles,
-            tracking_sigma=cfg.tracking_sigma,
-            base_height_target=cfg.base_height_target,
-            base_height=self._reward_base_height_values(),
-        )
-        return rewards.run_reward_dispatch(
-            scales=cfg.scales,
-            fns=self._reward_fns,
-            ctx=ctx,
-            info=info,
-            enable_log=self._enable_reward_log,
-            ctrl_dt=self._cfg.ctrl_dt,
-        )
-
-    # ── reward functions (robot-specific) ────────────────────────────
-
-    def _reward_base_height_values(self) -> np.ndarray:
-        base_pos = np.asarray(self._backend.get_base_pos(), dtype=get_global_dtype())
-        sample_height = self._terrain_surface_sample_height
-        if sample_height is None:
-            return np.asarray(base_pos[:, 2], dtype=get_global_dtype())
-
-        surface = np.asarray(sample_height(base_pos[:, :2]), dtype=get_global_dtype())
-        return np.asarray(base_pos[:, 2] - surface, dtype=get_global_dtype())
-
-    def _reward_swing_feet_z(self, ctx: RewardContext) -> np.ndarray:
-        is_swing = self.feet_phase >= 0.6
-        target_height = 0.1
-        height_error = np.square(self.feet_pos[:, :, 2] - target_height)
-        swing_rew = np.exp(-height_error / 0.01) * is_swing
-        reward: np.ndarray = np.sum(swing_rew, axis=1) / len(self._cfg.sensor.feet_pos)
-        return reward
-
-    def _reward_foot_drag(self, ctx: RewardContext) -> np.ndarray:
-        foot_pos = self.get_foot_pos()
-        foot_heights = foot_pos[..., 2]
-        foot_contact = self.get_foot_contact()
-        is_swing = foot_contact < 0.5
-        safe_height = self._reward_cfg.target_foot_height / 2.0
-        height_error = np.clip(safe_height - foot_heights, 0.0, None)
-        error = np.square(height_error) * is_swing
-        drag_penalty: np.ndarray = np.sum(error, axis=1)
-        return drag_penalty
-
-    def _reward_contact(self, ctx: RewardContext) -> np.ndarray:
-        contact = self.feet_force[:, :, 2] > 0.1
-        res = np.zeros(self._num_envs, dtype=np.float32)
-        for i in range(len(self._cfg.sensor.feet_force)):
-            is_contact = (self.feet_phase[:, i] < 0.6) | (self.gait_frequency < 1.0e-8)
-            res += (contact[:, i] == is_contact).astype(np.float32)
-        return res / len(self._cfg.sensor.feet_force)
diff --git a/src/unilab/envs/locomotion/go2/rough.py b/src/unilab/envs/locomotion/go2/rough.py
deleted file mode 100644
index 0d7d5975e..000000000
--- a/src/unilab/envs/locomotion/go2/rough.py
+++ /dev/null
@@ -1,793 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Any
-
-import numpy as np
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base import registry
-from unilab.base.np_env import NpEnvState
-from unilab.base.scene import SceneCfg, TerrainSceneCfg
-from unilab.dr import DomainRandomizationManager, ResetPlan
-from unilab.dr.dr_utils import build_common_reset_randomization, zero_actions
-from unilab.dtype_config import get_global_dtype
-from unilab.envs.locomotion.common import rewards
-from unilab.envs.locomotion.common.commands import (
-    apply_heading_yaw_feedback,
-    sample_heading_commands,
-    zero_small_xy_commands,
-)
-from unilab.envs.locomotion.common.height_scan import (
-    DEFAULT_SCAN_POINTS_X,
-    DEFAULT_SCAN_POINTS_Y,
-    HeightScanConfig,
-    base_height_from_scan,
-    height_scan_obs,
-    init_height_scan_sensor,
-    raw_height_scan_obs,
-    terrain_out_of_bounds,
-)
-from unilab.envs.locomotion.common.rewards import RewardContext
-from unilab.envs.locomotion.go2.base import ControlConfig
-from unilab.envs.locomotion.go2.joystick import (
-    Commands,
-    Go2JoystickCfg,
-    Go2JoystickDomainRandomizationProvider,
-    Go2WalkTask,
-    JoystickSensor,
-    RewardConfig,
-)
-from unilab.terrains import (
-    SubTerrainCfg,
-    TerrainGeneratorCfg,
-    flat,
-    hf_pyramid_slope,
-    hf_pyramid_slope_inv,
-    pyramid_stairs,
-    pyramid_stairs_inv,
-    random_rough,
-    wave_terrain,
-)
-from unilab.utils.rotation import (
-    np_quat_apply_inverse,
-    np_quat_from_euler_xyz,
-    np_quat_mul,
-)
-
-# pyright: reportIncompatibleVariableOverride=false, reportAttributeAccessIssue=false, reportCallIssue=false
-
-GO2_HIP_INDICES = np.asarray([0, 3, 6, 9], dtype=np.int32)
-GO2_ACTUATOR_TO_DOF_INDICES = np.asarray([3, 4, 5, 0, 1, 2, 9, 10, 11, 6, 7, 8], dtype=np.int32)
-GO2_FRONT_LEFT = 0
-GO2_FRONT_RIGHT = 1
-GO2_REAR_LEFT = 2
-GO2_REAR_RIGHT = 3
-
-
-@dataclass
-class RoughControlConfig(ControlConfig):
-    hip_action_scale: float = 0.125
-    non_hip_action_scale: float = 0.25
-    clip_actions: float = 100.0
-
-
-@dataclass
-class RoughCommands(Commands):
-    vel_limit: list[list[float]] = field(
-        default_factory=lambda: [[-1.0, -1.0, -1.0], [1.0, 1.0, 1.0]]
-    )
-    resampling_time: float = 10.0
-    heading_command: bool = True
-    heading_range: list[float] = field(default_factory=lambda: [-np.pi, np.pi])
-
-
-@dataclass
-class RoughRewardConfig(RewardConfig):
-    stand_still_command_threshold: float = 0.1
-    joint_pos_penalty_stand_still_scale: float = 5.0
-    joint_pos_penalty_velocity_threshold: float = 0.5
-    joint_pos_penalty_command_threshold: float = 0.1
-    contact_threshold: float = 1.0
-    contact_forces_threshold: float = 100.0
-    feet_air_time_threshold: float = 0.5
-    feet_height_body_target: float = -0.2
-    feet_height_body_tanh_mult: float = 2.0
-    feet_gait_std: float = np.sqrt(0.5)
-    feet_gait_max_err: float = 0.2
-    feet_gait_velocity_threshold: float = 0.5
-    feet_gait_command_threshold: float = 0.1
-
-
-@dataclass
-class RoughJoystickSensor(JoystickSensor):
-    feet_vel = ["FL_vel", "FR_vel", "RL_vel", "RR_vel"]
-    undesired_contact = [
-        "base1_contact",
-        "base2_contact",
-        "base3_contact",
-        "FL_hip_contact",
-        "FR_hip_contact",
-        "RL_hip_contact",
-        "RR_hip_contact",
-        "FL_thigh_contact",
-        "FR_thigh_contact",
-        "RL_thigh_contact",
-        "RR_thigh_contact",
-        "FL_calf_contact1",
-        "FR_calf_contact1",
-        "RL_calf_contact1",
-        "RR_calf_contact1",
-        "FL_calf_contact2",
-        "FR_calf_contact2",
-        "RL_calf_contact2",
-        "RR_calf_contact2",
-    ]
-
-
-@dataclass
-class RoughTerminationConfig:
-    terrain_out_of_bounds: bool = True
-    terrain_distance_buffer: float = 3.0
-
-
-@dataclass(kw_only=True)
-class Go2RoughTerrainCfg(TerrainGeneratorCfg):
-    size: tuple[float, float] = (8.0, 8.0)
-    num_rows: int = 6
-    num_cols: int = 6
-    border_width: float = 1.0
-    add_lights: bool = True
-    horizontal_scale: float = 0.2
-
-    sub_terrains: dict[str, SubTerrainCfg] = field(
-        default_factory=lambda: {
-            "flat": flat(proportion=0.0),
-            "pyramid_stairs": pyramid_stairs(
-                proportion=0.1,
-                step_height_range=(0.025, 0.10),
-                step_width=0.4,
-                platform_width=3.0,
-                border_width=0.2,
-            ),
-            "pyramid_stairs_inv": pyramid_stairs_inv(
-                proportion=0.1,
-                step_height_range=(0.025, 0.10),
-                step_width=0.4,
-                platform_width=3.0,
-                border_width=0.2,
-            ),
-            "hf_pyramid_slope": hf_pyramid_slope(
-                proportion=0.2,
-                slope_range=(0.0, 0.3),
-                platform_width=2.0,
-                border_width=0.2,
-            ),
-            "hf_pyramid_slope_inv": hf_pyramid_slope_inv(
-                proportion=0.2,
-                slope_range=(0.0, 0.3),
-                platform_width=2.0,
-                border_width=0.2,
-            ),
-            "random_rough": random_rough(
-                proportion=0.3,
-                noise_range=(0.01, 0.06),
-                noise_step=0.01,
-                border_width=0.2,
-            ),
-            "wave_terrain": wave_terrain(
-                proportion=0.3,
-                amplitude_range=(0.0, 0.12),
-                num_waves=4,
-                border_width=0.2,
-            ),
-        }
-    )
-
-
-@registry.envcfg("Go2JoystickRough")
-@dataclass
-class Go2JoystickRoughCfg(Go2JoystickCfg):
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "go2" / "go2.xml"),
-            fragment_files=[
-                str(ASSETS_ROOT_PATH / "robots" / "go2" / "locomotion_task.xml"),
-            ],
-            terrain=TerrainSceneCfg(
-                generator=Go2RoughTerrainCfg(),
-                hfield_name="terrain_hfield",
-                geom_name="floor",
-            ),
-        )
-    )
-    control_config: RoughControlConfig = field(default_factory=RoughControlConfig)
-    commands: RoughCommands = field(default_factory=RoughCommands)
-    terrain_scan: HeightScanConfig = field(default_factory=HeightScanConfig)
-    termination_config: RoughTerminationConfig = field(default_factory=RoughTerminationConfig)
-    sensor: RoughJoystickSensor = field(default_factory=RoughJoystickSensor)
-    reward_config: RoughRewardConfig | None = None
-
-
-class Go2JoystickRoughDomainRandomizationProvider(Go2JoystickDomainRandomizationProvider):
-    def _sample_commands(self, env: Any, num_reset: int) -> np.ndarray:
-        commands = super()._sample_commands(env, num_reset)
-        zero_small_xy_commands(commands, threshold=0.08)
-        standing_prob = env.cfg.commands.rel_standing_envs
-        if standing_prob > 0.0:
-            standing = np.random.uniform(size=(num_reset,)) < min(standing_prob, 1.0)
-            commands[standing] = 0.0
-        if env.cfg.commands.heading_command:
-            commands[:, 2] = 0.0
-        return commands
-
-    def build_reset_plan(self, env: Any, env_ids: np.ndarray) -> ResetPlan:
-        num_reset = len(env_ids)
-        qpos = np.tile(env._init_qpos, (num_reset, 1))
-        qvel = np.tile(env._init_qvel, (num_reset, 1))
-        qpos[:, 0:2] += np.random.uniform(-0.5, 0.5, (num_reset, 2))
-        qpos[:, 2] += np.random.uniform(0.25, 0.5, (num_reset,))
-        qpos[:, 0:3] += env._spawn.origins_for(env_ids)
-        roll = np.random.uniform(-3.14, 3.14, (num_reset,))
-        pitch = np.random.uniform(-3.14, 3.14, (num_reset,))
-        yaw = np.random.uniform(-3.14, 3.14, (num_reset,))
-        qpos[:, 3:7] = np_quat_mul(qpos[:, 3:7], np_quat_from_euler_xyz(roll, pitch, yaw))
-        qvel[:, 0:6] = np.asarray(
-            np.random.uniform(-0.5, 0.5, size=(num_reset, 6)), dtype=get_global_dtype()
-        )
-        commands = self._sample_commands(env, num_reset)
-        info_updates: dict[str, Any] = {
-            "commands": commands,
-            "current_actions": zero_actions(num_reset, env._num_action),
-            "last_actions": zero_actions(num_reset, env._num_action),
-            "qacc": np.zeros((num_reset, env._num_action), dtype=get_global_dtype()),
-            "torques": np.zeros((num_reset, env._num_action), dtype=get_global_dtype()),
-        }
-        if env.cfg.commands.heading_command:
-            info_updates["heading_commands"] = sample_heading_commands(env, num_reset)
-        env._spawn.record_episode_start(env_ids, qpos[:, 0:3])
-        return ResetPlan(
-            env_ids=env_ids,
-            qpos=qpos,
-            qvel=qvel,
-            info_updates=info_updates,
-            randomization=build_common_reset_randomization(env, num_reset),
-        )
-
-
-@registry.env("Go2JoystickRough", sim_backend="mujoco")
-class Go2JoystickRoughEnv(Go2WalkTask):
-    _cfg: Go2JoystickRoughCfg
-    _reward_cfg: RoughRewardConfig
-
-    def __init__(self, cfg: Go2JoystickRoughCfg, num_envs=1, backend_type="mujoco"):
-        self._height_scan_dim = len(cfg.terrain_scan.measured_points_x) * len(
-            cfg.terrain_scan.measured_points_y
-        )
-        super().__init__(cfg, num_envs=num_envs, backend_type=backend_type)
-        self._dr_manager = DomainRandomizationManager(
-            self, Go2JoystickRoughDomainRandomizationProvider()
-        )
-        self._last_dof_vel_for_acc = np.zeros(
-            (num_envs, self._num_action), dtype=get_global_dtype()
-        )
-        self._action_scale = np.full(
-            (self._num_action,),
-            float(cfg.control_config.non_hip_action_scale),
-            dtype=get_global_dtype(),
-        )
-        self._action_scale[GO2_HIP_INDICES] = float(cfg.control_config.hip_action_scale)
-        self._default_angles_actuator = self.default_angles[GO2_ACTUATOR_TO_DOF_INDICES]
-        joint_range = self._backend.get_joint_range()
-        self._joint_range = (
-            np.asarray(joint_range, dtype=get_global_dtype()) if joint_range is not None else None
-        )
-        self.feet_vel = np.zeros((num_envs, len(cfg.sensor.feet_vel), 3), dtype=np.float32)
-        self._last_foot_contact = np.zeros((num_envs, len(cfg.sensor.feet_force)), dtype=bool)
-        self._current_air_time = np.zeros((num_envs, len(cfg.sensor.feet_force)), dtype=np.float32)
-        self._current_contact_time = np.zeros(
-            (num_envs, len(cfg.sensor.feet_force)), dtype=np.float32
-        )
-        self._last_air_time = np.zeros((num_envs, len(cfg.sensor.feet_force)), dtype=np.float32)
-        self._last_contact_time = np.zeros((num_envs, len(cfg.sensor.feet_force)), dtype=np.float32)
-        self._first_foot_contact = np.zeros((num_envs, len(cfg.sensor.feet_force)), dtype=bool)
-        init_height_scan_sensor(self, cfg.terrain_scan, cfg.asset.base_name)
-
-    @property
-    def obs_groups_spec(self) -> dict[str, int]:
-        return {"obs": 45, "critic": 48 + self._height_scan_dim}
-
-    def reset(self, env_indices: np.ndarray) -> tuple[dict[str, np.ndarray], dict]:
-        env_ids = np.asarray(env_indices, dtype=np.int32)
-        obs, info = super().reset(env_ids)
-        dof_vel = self.get_dof_vel()
-        if dof_vel.shape[0] == self._num_envs:
-            self._last_dof_vel_for_acc[env_ids] = dof_vel[env_ids]
-        self._reset_contact_timers(env_ids)
-        return obs, info
-
-    def _init_reward_functions(self):
-        scale_gravity = self._upright_scale  # local alias for lambda capture
-
-        def gated(fn):
-            return lambda ctx: fn(ctx) * scale_gravity(ctx.gravity)
-
-        # joint_pos_penalty needs its three thresholds from the reward config
-        def _joint_pos_penalty(ctx: RewardContext) -> np.ndarray:
-            cfg = self._reward_cfg
-            return rewards.joint_pos_penalty(
-                ctx,
-                stand_still_scale=cfg.joint_pos_penalty_stand_still_scale,
-                velocity_threshold=cfg.joint_pos_penalty_velocity_threshold,
-                command_threshold=cfg.joint_pos_penalty_command_threshold,
-            ) * scale_gravity(ctx.gravity)
-
-        def _stand_still(ctx: RewardContext) -> np.ndarray:
-            return rewards.stand_still(
-                ctx, command_threshold=self._reward_cfg.stand_still_command_threshold
-            ) * scale_gravity(ctx.gravity)
-
-        self._reward_fns: dict[str, Any] = {
-            "tracking_lin_vel": gated(rewards.tracking_lin_vel),
-            "tracking_ang_vel": gated(rewards.tracking_ang_vel),
-            "lin_vel_z": gated(rewards.lin_vel_z),
-            "ang_vel_xy": gated(rewards.ang_vel_xy),
-            "dof_torques_l2": gated(rewards.dof_torques_l2),
-            "joint_torques_l2": gated(rewards.dof_torques_l2),
-            "dof_acc_l2": gated(rewards.dof_acc_l2),
-            "joint_acc_l2": gated(rewards.dof_acc_l2),
-            "joint_power": gated(rewards.joint_power),
-            "stand_still": _stand_still,
-            "hip_pos": self._reward_hip_pos,
-            "joint_pos_penalty": _joint_pos_penalty,
-            "joint_mirror": self._reward_joint_mirror,
-            "action_rate": rewards.action_rate,
-            "action_rate_l2": rewards.action_rate,
-            "undesired_contacts": self._reward_undesired_contacts,
-            "contact_forces": self._reward_contact_forces,
-            "feet_air_time": self._reward_feet_air_time,
-            "feet_air_time_variance": self._reward_feet_air_time_variance,
-            "feet_contact_without_cmd": self._reward_feet_contact_without_cmd,
-            "feet_slide": self._reward_feet_slide,
-            "feet_height_body": self._reward_feet_height_body,
-            "feet_gait": self._reward_feet_gait,
-            "upward": rewards.upward,
-        }
-
-    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
-        clipped_actions = np.asarray(
-            np.clip(
-                actions,
-                -float(self._cfg.control_config.clip_actions),
-                float(self._cfg.control_config.clip_actions),
-            ),
-            dtype=get_global_dtype(),
-        )
-        state.info["last_actions"] = state.info.get(
-            "current_actions", np.zeros_like(clipped_actions)
-        )
-        state.info["current_actions"] = clipped_actions
-        exec_actions = (
-            state.info["last_actions"]
-            if self._cfg.control_config.simulate_action_latency
-            else clipped_actions
-        )
-        return np.asarray(
-            exec_actions * self._action_scale + self._default_angles_actuator,
-            dtype=get_global_dtype(),
-        )
-
-    def update_state(self, state: NpEnvState) -> NpEnvState:
-        self._update_commands(state.info)
-        self.phase = np.fmod(self.phase + self._cfg.ctrl_dt * self.gait_frequency, 1.0)
-        self.feet_phase[:, 0] = self.phase
-        self.feet_phase[:, 3] = self.phase
-        self.feet_phase[:, 1] = (self.phase + 0.5) % 1
-        self.feet_phase[:, 2] = (self.phase + 0.5) % 1
-
-        linvel = self.get_local_linvel()
-        gyro = self.get_gyro()
-        gravity = self._backend.get_sensor_data("upvector")
-        dof_pos = self.get_dof_pos()
-        dof_vel = self.get_dof_vel()
-        self.feet_force[:, :, :] = 0
-        for i in range(len(self._cfg.sensor.feet_force)):
-            self.feet_force[:, i, :] = self._backend.get_sensor_data(self._cfg.sensor.feet_force[i])
-        for i in range(len(self._cfg.sensor.feet_pos)):
-            self.feet_pos[:, i, :] = self._backend.get_sensor_data(self._cfg.sensor.feet_pos[i])
-        for i in range(len(self._cfg.sensor.feet_vel)):
-            self.feet_vel[:, i, :] = self._backend.get_sensor_data(self._cfg.sensor.feet_vel[i])
-        self._update_contact_timers(self._foot_contact_mask())
-        state.info["qacc"] = self._estimate_dof_acc(dof_vel)
-        state.info["torques"] = self._estimate_pd_torques(state.info, dof_pos, dof_vel)
-        terminated = self._compute_terminated(gravity)
-        reward = self._compute_rough_reward(state.info, linvel, gyro, gravity, dof_pos, dof_vel)
-        obs = self._compute_obs(
-            state.info, linvel, gyro, gravity, dof_pos, dof_vel, self.feet_phase
-        )
-        state = state.replace(obs=obs, reward=reward, terminated=terminated)
-        done = state.terminated | state.truncated
-        if np.any(done):
-            done_indices = np.where(done)[0]
-            stats = self._spawn.update_on_done(
-                done_indices, self._backend.get_base_pos()[done_indices]
-            )
-            if stats:
-                if "log" not in state.info:
-                    state.info["log"] = {}
-                for k, v in stats.items():
-                    state.info["log"][f"terrain_curriculum/{k}"] = float(v)
-        return state
-
-    def _compute_obs(
-        self,
-        info: dict,
-        linvel: np.ndarray,
-        gyro: np.ndarray,
-        gravity: np.ndarray,
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-        feet_phase: np.ndarray,
-    ) -> dict[str, np.ndarray]:
-        del feet_phase
-        noise_cfg = self._cfg.noise_config
-        diff = dof_pos - self.default_angles
-        policy_gyro = self._obs_noise(gyro, noise_cfg.scale_gyro) * 0.25
-        policy_gravity = self._obs_noise(-gravity, noise_cfg.scale_gravity)
-        policy_diff = self._obs_noise(diff, noise_cfg.scale_joint_angle)
-        policy_dof_vel = self._obs_noise(dof_vel, noise_cfg.scale_joint_vel) * 0.05
-        last_actions = info.get("current_actions", np.zeros_like(diff))
-        commands = info["commands"]
-        obs = np.concatenate(
-            [policy_gyro, policy_gravity, commands, policy_diff, policy_dof_vel, last_actions],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-        critic_base = np.concatenate(
-            [linvel, gyro, -gravity, commands, diff, dof_vel, last_actions],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-        critic = np.concatenate(
-            [critic_base, height_scan_obs(self, self._cfg.terrain_scan, critic_base.shape[0])],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-        return {"obs": obs, "critic": critic}
-
-    def _compute_rough_reward(
-        self,
-        info: dict,
-        linvel: np.ndarray,
-        gyro: np.ndarray,
-        gravity: np.ndarray,
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-    ) -> np.ndarray:
-        cfg = self._reward_cfg
-        ctx = RewardContext(
-            info=info,
-            linvel=linvel,
-            gyro=gyro,
-            dof_pos=dof_pos,
-            num_envs=self._num_envs,
-            default_angles=self.default_angles,
-            tracking_sigma=cfg.tracking_sigma,
-            base_height_target=cfg.base_height_target,
-            base_height=base_height_from_scan(self, self._num_envs),
-            gravity=gravity,
-            dof_vel=dof_vel,
-            joint_range=self._joint_range,
-        )
-        return rewards.run_reward_dispatch(
-            scales=cfg.scales,
-            fns=self._reward_fns,
-            ctx=ctx,
-            info=info,
-            enable_log=self._enable_reward_log,
-            ctrl_dt=self._cfg.ctrl_dt,
-        )
-
-    def _compute_terminated(self, gravity: np.ndarray) -> np.ndarray:
-        # return gravity[:, 2] <= 0.5
-        del gravity
-        return np.zeros((self._num_envs,), dtype=bool)
-
-    def _compute_truncated(self, state: NpEnvState) -> np.ndarray:
-        truncated = super()._compute_truncated(state)
-        if self._cfg.termination_config.terrain_out_of_bounds:
-            terrain_scene = self._cfg.scene.terrain
-            terrain_cfg = terrain_scene.generator if terrain_scene is not None else None
-            np.logical_or(
-                truncated,
-                terrain_out_of_bounds(
-                    self,
-                    terrain_cfg,
-                    float(self._cfg.termination_config.terrain_distance_buffer),
-                ),
-                out=truncated,
-            )
-        return truncated
-
-    def _reward_base_height_values(self, num_obs: int | None = None) -> np.ndarray:
-        return base_height_from_scan(self, num_obs)
-
-    def _raw_height_scan_obs(self, num_obs: int) -> tuple[np.ndarray | None, np.ndarray | None]:
-        return raw_height_scan_obs(self, num_obs)
-
-    def _estimate_dof_acc(self, dof_vel: np.ndarray) -> np.ndarray:
-        qacc = np.asarray((dof_vel - self._last_dof_vel_for_acc) / self._cfg.ctrl_dt)
-        self._last_dof_vel_for_acc[:] = dof_vel
-        return np.asarray(qacc, dtype=get_global_dtype())
-
-    def _estimate_pd_torques(
-        self, info: dict, dof_pos: np.ndarray, dof_vel: np.ndarray
-    ) -> np.ndarray:
-        actions = np.asarray(
-            info.get("current_actions", np.zeros((dof_pos.shape[0], self._num_action))),
-            dtype=get_global_dtype(),
-        )
-        if self._cfg.control_config.simulate_action_latency:
-            actions = np.asarray(info.get("last_actions", actions), dtype=get_global_dtype())
-        targets_actuator = actions * self._action_scale + self._default_angles_actuator
-        targets_dof = np.empty_like(targets_actuator)
-        targets_dof[:, GO2_ACTUATOR_TO_DOF_INDICES] = targets_actuator
-        torques = (
-            float(self._cfg.control_config.Kp) * (targets_dof - dof_pos)
-            - float(self._cfg.control_config.Kd) * dof_vel
-        )
-        return np.asarray(torques, dtype=get_global_dtype())
-
-    def _update_commands(self, info: dict) -> None:
-        commands_arr = np.asarray(info["commands"], dtype=get_global_dtype())
-        resampling_time = float(self._cfg.commands.resampling_time)
-        if resampling_time > 0.0:
-            interval_steps = max(int(round(resampling_time / self._cfg.ctrl_dt)), 1)
-            steps = np.asarray(info.get("steps", np.zeros((self._num_envs,), dtype=np.uint32)))
-            resample_mask = (steps > 0) & ((steps % interval_steps) == 0)
-            if np.any(resample_mask):
-                num_resample = int(np.count_nonzero(resample_mask))
-                low = np.asarray(self._cfg.commands.vel_limit[0], dtype=get_global_dtype())
-                high = np.asarray(self._cfg.commands.vel_limit[1], dtype=get_global_dtype())
-                sampled = np.random.uniform(low=low, high=high, size=(num_resample, 3)).astype(
-                    get_global_dtype()
-                )
-                zero_small_xy_commands(sampled, threshold=0.08)
-                commands_arr[resample_mask] = sampled
-                if self._cfg.commands.heading_command:
-                    heading_commands = self._ensure_heading_commands(info, commands_arr.shape[0])
-                    heading_commands[resample_mask] = sample_heading_commands(self, num_resample)
-                    info["heading_commands"] = heading_commands
-
-        if self._cfg.commands.heading_command:
-            heading_commands = self._ensure_heading_commands(info, commands_arr.shape[0])
-            base_quat = np.asarray(self._backend.get_base_quat(), dtype=get_global_dtype())
-            if base_quat.shape[0] == commands_arr.shape[0]:
-                apply_heading_yaw_feedback(commands_arr, base_quat, heading_commands, stiffness=0.5)
-        info["commands"] = commands_arr
-
-    def _ensure_heading_commands(self, info: dict, num_obs: int) -> np.ndarray:
-        heading_commands = info.get("heading_commands")
-        if heading_commands is None or np.asarray(heading_commands).shape != (num_obs,):
-            heading_commands = sample_heading_commands(self, num_obs)
-        heading_commands = np.asarray(heading_commands, dtype=get_global_dtype())
-        info["heading_commands"] = heading_commands
-        return heading_commands
-
-    def _foot_contact_mask(self) -> np.ndarray:
-        contact_force = np.linalg.norm(self.feet_force, axis=2)
-        return np.asarray(contact_force > self._reward_cfg.contact_threshold, dtype=bool)
-
-    def _reset_contact_timers(self, env_ids: np.ndarray) -> None:
-        self._current_air_time[env_ids] = 0.0
-        self._current_contact_time[env_ids] = 0.0
-        self._last_air_time[env_ids] = 0.0
-        self._last_contact_time[env_ids] = 0.0
-        self._first_foot_contact[env_ids] = False
-        self._last_foot_contact[env_ids] = self._foot_contact_mask()[env_ids]
-
-    def _update_contact_timers(self, contact: np.ndarray) -> None:
-        first_contact = contact & ~self._last_foot_contact
-        first_air = ~contact & self._last_foot_contact
-        self._first_foot_contact[:] = first_contact
-        self._last_air_time[first_contact] = self._current_air_time[first_contact]
-        self._last_contact_time[first_air] = self._current_contact_time[first_air]
-        self._current_air_time[contact] = 0.0
-        self._current_air_time[~contact] += self._cfg.ctrl_dt
-        self._current_contact_time[~contact] = 0.0
-        self._current_contact_time[contact] += self._cfg.ctrl_dt
-        self._last_foot_contact[:] = contact
-
-    def _upright_scale(self, gravity: np.ndarray | None) -> np.ndarray:
-        return rewards.upright_scale(gravity, self._num_envs)
-
-    # ── reward functions that need backend / env state (kept as methods) ────
-
-    def _reward_hip_pos(self, ctx: RewardContext) -> np.ndarray:
-        diff = ctx.dof_pos[:, GO2_HIP_INDICES] - self.default_angles[GO2_HIP_INDICES]
-        return np.asarray(
-            np.sum(np.square(diff), axis=1) * self._upright_scale(ctx.gravity),
-            dtype=get_global_dtype(),
-        )
-
-    def _reward_joint_mirror(self, ctx: RewardContext) -> np.ndarray:
-        fr_rl = ctx.dof_pos[:, 0:3] - ctx.dof_pos[:, 9:12]
-        fl_rr = ctx.dof_pos[:, 3:6] - ctx.dof_pos[:, 6:9]
-        mirror = 0.5 * (np.sum(np.square(fr_rl), axis=1) + np.sum(np.square(fl_rr), axis=1))
-        return np.asarray(mirror * self._upright_scale(ctx.gravity), dtype=get_global_dtype())
-
-    def _reward_undesired_contacts(self, ctx: RewardContext) -> np.ndarray:
-        contacts = [
-            _force_norm_columns(
-                np.asarray(self._backend.get_sensor_data(name), dtype=get_global_dtype()),
-                ctx.num_envs,
-            )
-            for name in self._cfg.sensor.undesired_contact
-        ]
-        if not contacts:
-            return np.zeros((ctx.num_envs,), dtype=get_global_dtype())
-        contact_force = np.concatenate(contacts, axis=1)
-        contact_count = np.sum(contact_force > self._reward_cfg.contact_threshold, axis=1)
-        return np.asarray(
-            contact_count * self._upright_scale(ctx.gravity), dtype=get_global_dtype()
-        )
-
-    def _reward_contact_forces(self, ctx: RewardContext) -> np.ndarray:
-        force_norm = np.linalg.norm(self.feet_force, axis=2)
-        force_clip = np.minimum(force_norm, 1500.0)
-        violation = np.clip(force_clip - self._reward_cfg.contact_forces_threshold, 0.0, None)
-        return np.asarray(
-            np.sum(violation, axis=1) * self._upright_scale(ctx.gravity),
-            dtype=get_global_dtype(),
-        )
-
-    def _reward_feet_air_time(self, ctx: RewardContext) -> np.ndarray:
-        cfg = self._reward_cfg
-        reward = np.sum(
-            (self._last_air_time - cfg.feet_air_time_threshold) * self._first_foot_contact,
-            axis=1,
-        )
-        moving = np.linalg.norm(ctx.info["commands"], axis=1) > 0.1
-        return np.asarray(
-            reward * moving * self._upright_scale(ctx.gravity), dtype=get_global_dtype()
-        )
-
-    def _reward_feet_air_time_variance(self, ctx: RewardContext) -> np.ndarray:
-        air_var = np.var(np.clip(self._last_air_time, 0.0, 0.5), axis=1)
-        contact_var = np.var(np.clip(self._last_contact_time, 0.0, 0.5), axis=1)
-        return np.asarray(
-            (air_var + contact_var) * self._upright_scale(ctx.gravity), dtype=get_global_dtype()
-        )
-
-    def _reward_feet_contact_without_cmd(self, ctx: RewardContext) -> np.ndarray:
-        reward = np.sum(self._first_foot_contact, axis=1)
-        stopped = np.linalg.norm(ctx.info["commands"], axis=1) < 0.1
-        return np.asarray(
-            reward * stopped * self._upright_scale(ctx.gravity), dtype=get_global_dtype()
-        )
-
-    def _relative_foot_vel_body(self) -> np.ndarray:
-        base_quat = np.asarray(self._backend.get_base_quat(), dtype=get_global_dtype())
-        base_linvel = np.asarray(
-            self._backend.get_sensor_data("global_linvel"), dtype=get_global_dtype()
-        )
-        relative_vel = self.feet_vel - base_linvel[:, None, :]
-        flat = relative_vel.reshape(self._num_envs * relative_vel.shape[1], 3)
-        quat = np.repeat(base_quat, relative_vel.shape[1], axis=0)
-        return np_quat_apply_inverse(quat, flat).reshape(relative_vel.shape)
-
-    def _relative_foot_pos_body(self) -> np.ndarray:
-        base_quat = np.asarray(self._backend.get_base_quat(), dtype=get_global_dtype())
-        base_pos = np.asarray(self._backend.get_base_pos(), dtype=get_global_dtype())
-        relative_pos = self.feet_pos - base_pos[:, None, :]
-        flat = relative_pos.reshape(self._num_envs * relative_pos.shape[1], 3)
-        quat = np.repeat(base_quat, relative_pos.shape[1], axis=0)
-        return np_quat_apply_inverse(quat, flat).reshape(relative_pos.shape)
-
-    def _reward_feet_slide(self, ctx: RewardContext) -> np.ndarray:
-        foot_vel_body = self._relative_foot_vel_body()
-        lateral_vel = np.linalg.norm(foot_vel_body[:, :, :2], axis=2)
-        reward = np.sum(lateral_vel * self._foot_contact_mask(), axis=1)
-        return np.asarray(reward * self._upright_scale(ctx.gravity), dtype=get_global_dtype())
-
-    def _reward_feet_height_body(self, ctx: RewardContext) -> np.ndarray:
-        cfg = self._reward_cfg
-        foot_pos_body = self._relative_foot_pos_body()
-        foot_vel_body = self._relative_foot_vel_body()
-        z_error = np.square(foot_pos_body[:, :, 2] - cfg.feet_height_body_target)
-        velocity_tanh = np.tanh(
-            cfg.feet_height_body_tanh_mult * np.linalg.norm(foot_vel_body[:, :, :2], axis=2)
-        )
-        moving = np.linalg.norm(ctx.info["commands"], axis=1) > 0.1
-        reward = np.sum(z_error * velocity_tanh, axis=1)
-        return np.asarray(
-            reward * moving * self._upright_scale(ctx.gravity), dtype=get_global_dtype()
-        )
-
-    def _reward_feet_gait(self, ctx: RewardContext) -> np.ndarray:
-        cfg = self._reward_cfg
-        command_norm = np.linalg.norm(ctx.info["commands"], axis=1)
-        body_vel = np.linalg.norm(ctx.linvel[:, :2], axis=1)
-        enabled = (command_norm > cfg.feet_gait_command_threshold) | (
-            body_vel > cfg.feet_gait_velocity_threshold
-        )
-        air = self._current_air_time
-        contact = self._current_contact_time
-        sync_fl_rr = _gait_sync_reward(
-            air, contact, GO2_FRONT_LEFT, GO2_REAR_RIGHT, cfg.feet_gait_std, cfg.feet_gait_max_err
-        )
-        sync_fr_rl = _gait_sync_reward(
-            air, contact, GO2_FRONT_RIGHT, GO2_REAR_LEFT, cfg.feet_gait_std, cfg.feet_gait_max_err
-        )
-        async_fl_fr = _gait_async_reward(
-            air, contact, GO2_FRONT_LEFT, GO2_FRONT_RIGHT, cfg.feet_gait_std, cfg.feet_gait_max_err
-        )
-        async_rr_rl = _gait_async_reward(
-            air, contact, GO2_REAR_RIGHT, GO2_REAR_LEFT, cfg.feet_gait_std, cfg.feet_gait_max_err
-        )
-        async_fl_rl = _gait_async_reward(
-            air, contact, GO2_FRONT_LEFT, GO2_REAR_LEFT, cfg.feet_gait_std, cfg.feet_gait_max_err
-        )
-        async_fr_rr = _gait_async_reward(
-            air, contact, GO2_FRONT_RIGHT, GO2_REAR_RIGHT, cfg.feet_gait_std, cfg.feet_gait_max_err
-        )
-        reward = sync_fl_rr * sync_fr_rl * async_fl_fr * async_rr_rl * async_fl_rl * async_fr_rr
-        return np.asarray(
-            reward * enabled * self._upright_scale(ctx.gravity), dtype=get_global_dtype()
-        )
-
-
-def _force_norm_columns(force: np.ndarray, num_envs: int) -> np.ndarray:
-    force = np.asarray(force, dtype=get_global_dtype()).reshape(num_envs, -1)
-    if force.shape[1] == 0:
-        return force
-    if force.shape[1] % 3 == 0:
-        return np.linalg.norm(force.reshape(num_envs, -1, 3), axis=2)
-    return np.abs(force)
-
-
-def _gait_sync_reward(
-    air: np.ndarray,
-    contact: np.ndarray,
-    foot_0: int,
-    foot_1: int,
-    std: float,
-    max_err: float,
-) -> np.ndarray:
-    se_air = np.clip(np.square(air[:, foot_0] - air[:, foot_1]), 0.0, max_err**2)
-    se_contact = np.clip(np.square(contact[:, foot_0] - contact[:, foot_1]), 0.0, max_err**2)
-    return np.exp(-(se_air + se_contact) / std)
-
-
-def _gait_async_reward(
-    air: np.ndarray,
-    contact: np.ndarray,
-    foot_0: int,
-    foot_1: int,
-    std: float,
-    max_err: float,
-) -> np.ndarray:
-    se_act_0 = np.clip(np.square(air[:, foot_0] - contact[:, foot_1]), 0.0, max_err**2)
-    se_act_1 = np.clip(np.square(contact[:, foot_0] - air[:, foot_1]), 0.0, max_err**2)
-    return np.exp(-(se_act_0 + se_act_1) / std)
-
-
-# Backwards-compat aliases for any callers that imported the unused defaults.
-__all__ = [
-    "DEFAULT_SCAN_POINTS_X",
-    "DEFAULT_SCAN_POINTS_Y",
-    "GO2_ACTUATOR_TO_DOF_INDICES",
-    "GO2_HIP_INDICES",
-    "Go2JoystickRoughCfg",
-    "Go2JoystickRoughDomainRandomizationProvider",
-    "Go2JoystickRoughEnv",
-    "Go2RoughTerrainCfg",
-    "RoughCommands",
-    "RoughControlConfig",
-    "RoughJoystickSensor",
-    "RoughRewardConfig",
-    "RoughTerminationConfig",
-]
-
-
-registry.register_env("Go2JoystickRough", Go2JoystickRoughEnv, sim_backend="motrix")
diff --git a/src/unilab/envs/locomotion/go2_arm/__init__.py b/src/unilab/envs/locomotion/go2_arm/__init__.py
deleted file mode 100644
index cf26aa650..000000000
--- a/src/unilab/envs/locomotion/go2_arm/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .manip_loco import Go2ArmManipLocoCfg, Go2ArmManipLocoEnv
diff --git a/src/unilab/envs/locomotion/go2_arm/base.py b/src/unilab/envs/locomotion/go2_arm/base.py
deleted file mode 100644
index 454958322..000000000
--- a/src/unilab/envs/locomotion/go2_arm/base.py
+++ /dev/null
@@ -1,238 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-
-import numpy as np
-
-from unilab.base.backend import SimBackend
-from unilab.envs.locomotion.common.base import (
-    ControlConfigBase,
-    LocomotionBaseCfg,
-    LocomotionBaseEnv,
-    Sensor,
-)
-from unilab.utils.geometry import np_quat_orientation_error_local
-from unilab.utils.rotation import np_matrix_from_quat
-
-
-@dataclass
-class NoiseConfig:
-    level: float = 0.0
-    scale_joint_angle: float = 0.03
-    scale_joint_vel: float = 0.5
-    scale_gyro: float = 0.2
-    scale_gravity: float = 0.05
-    scale_linvel: float = 0.1
-    scale_ee_pos: float = 0.02
-
-
-@dataclass
-class ControlConfig(ControlConfigBase):
-    Kp: float = 35.0
-    Kd: float = 0.5
-    leg_kp: float | list[float] | None = 60.0
-    leg_kd: float | list[float] | None = 2.0
-    arm_kp: float | list[float] | None = field(
-        default_factory=lambda: [95.0, 115.0, 100.0, 52.0, 54.0, 55.0]
-    )
-    arm_kd: float | list[float] | None = field(
-        default_factory=lambda: [3.5, 3.8, 2.5, 1.5, 1.5, 1.5]
-    )
-    # Arm residual action scale, independent from leg action_scale. IK provides
-    # the primary control, so policy output stays small early in training.
-    arm_action_scale: float = 0.0
-
-
-@dataclass
-class IKConfig:
-    damping: float = 0.05
-    gain: float = 1.0
-    dq_clip: float = 0.2
-    use_orientation: bool = False
-    # Used only when use_orientation is true:
-    # - target: track goal_local_quat against curr_local_quat.
-    # - zero_error: include the rotational Jacobian with zero orientation error,
-    #   matching go2_arx's position IK with orientation-change regularization.
-    orientation_mode: str = "target"
-
-
-@dataclass
-class Asset:
-    base_name: str = "base"
-    foot_name: str = "foot"
-    ground: str = "floor"
-    ee_site_name: str = "endpoint"
-    ee_body_name: str = "link6"
-    arm_joint_names: tuple[str, ...] = (
-        "joint1",
-        "joint2",
-        "joint3",
-        "joint4",
-        "joint5",
-        "joint6",
-    )
-
-
-@dataclass
-class Go2ArmSensor(Sensor):
-    feet_force: list[str] = field(
-        default_factory=lambda: [
-            "FL_foot_contact",
-            "FR_foot_contact",
-            "RL_foot_contact",
-            "RR_foot_contact",
-        ]
-    )
-    feet_pos: list[str] = field(default_factory=lambda: ["FL_pos", "FR_pos", "RL_pos", "RR_pos"])
-    ee_local_pos: str = "endpoint_pos"
-    ee_local_quat: str = "endpoint_quat"
-    ee_local_vel: str = "endpoint_vel"
-    ee_relative_pos: str = "endpoint_relative_pos"
-    ee_relative_quat: str = "endpoint_relative_quat"
-    arm_ref_world_quat: str = "armbasepoint_world_quat"
-
-
-@dataclass
-class Go2ArmBaseCfg(LocomotionBaseCfg):
-    noise_config: NoiseConfig = field(default_factory=NoiseConfig)  # type: ignore[assignment]
-    control_config: ControlConfig = field(default_factory=ControlConfig)  # type: ignore[assignment]
-    ik: IKConfig = field(default_factory=IKConfig)
-    asset: Asset = field(default_factory=Asset)
-    sensor: Go2ArmSensor = field(default_factory=Go2ArmSensor)  # type: ignore[assignment]
-    iterations: int | None = None
-    sim_dt: float = 0.01
-    ctrl_dt: float = 0.02
-
-
-def _expand_gain(
-    name: str, value: float | list[float] | None, fallback: float, size: int
-) -> np.ndarray:
-    raw_value = fallback if value is None else value
-    gain = np.asarray(raw_value, dtype=np.float64)
-    if gain.ndim == 0:
-        return np.full((size,), float(gain), dtype=np.float64)
-    if gain.shape != (size,):
-        raise ValueError(f"{name} must be a scalar or have shape ({size},), got {gain.shape}")
-    return gain
-
-
-def build_go2_arm_position_gains(cfg: ControlConfig) -> dict[str, np.ndarray]:
-    leg_kp = _expand_gain("control_config.leg_kp", cfg.leg_kp, cfg.Kp, 12)
-    leg_kd = _expand_gain("control_config.leg_kd", cfg.leg_kd, cfg.Kd, 12)
-    arm_kp = _expand_gain("control_config.arm_kp", cfg.arm_kp, cfg.Kp, 6)
-    arm_kd = _expand_gain("control_config.arm_kd", cfg.arm_kd, cfg.Kd, 6)
-    return {
-        "kp": np.concatenate([leg_kp, arm_kp]),
-        "kd": np.concatenate([leg_kd, arm_kd]),
-    }
-
-
-class Go2ArmBaseEnv(LocomotionBaseEnv):
-    _cfg: Go2ArmBaseCfg
-
-    def __init__(self, cfg: Go2ArmBaseCfg, backend: SimBackend, num_envs: int = 1):
-        super().__init__(cfg, backend, num_envs)
-        self._ee_site_id = int(self._backend.get_site_ids([cfg.asset.ee_site_name])[0])
-        self._arm_jacobian_dof_indices = self._backend.get_joint_dof_indices(
-            cfg.asset.arm_joint_names
-        )
-        self._arm_dof_pos_indices = self._backend.get_joint_dof_pos_indices(
-            cfg.asset.arm_joint_names
-        )
-
-    @property
-    def arm_dof_pos_indices(self) -> np.ndarray:
-        return self._arm_dof_pos_indices
-
-    @property
-    def arm_jacobian_dof_indices(self) -> np.ndarray:
-        return self._arm_jacobian_dof_indices
-
-    def _obs_noise(self, data: np.ndarray, scale: float) -> np.ndarray:
-        """Apply per-step uniform observation noise scaled by ``noise_config.level``."""
-        noise_cfg = self._cfg.noise_config
-        if noise_cfg.level > 0.0:
-            return data + (
-                np.random.uniform(-1.0, 1.0, data.shape).astype(data.dtype)
-                * noise_cfg.level
-                * scale
-            )
-        return data
-
-    def get_foot_pos(self) -> np.ndarray:
-        """Get foot positions. Returns shape (num_envs, 4, 3)."""
-        foot_pos = [self._backend.get_sensor_data(name) for name in self._cfg.sensor.feet_pos]
-        return np.stack(foot_pos, axis=1)
-
-    def get_foot_contact(self) -> np.ndarray:
-        """Get foot contact values. Returns shape (num_envs, 4)."""
-        contacts = [
-            self._backend.get_sensor_data(name)[:, 0] for name in self._cfg.sensor.feet_force
-        ]
-        return np.stack(contacts, axis=1)
-
-    def get_ee_local_pose(self) -> tuple[np.ndarray, np.ndarray]:
-        """Return end-effector pose expressed in the arm reference frame."""
-        pos = self._backend.get_sensor_data(self._cfg.sensor.ee_local_pos)
-        quat = self._backend.get_sensor_data(self._cfg.sensor.ee_local_quat)
-        return pos, quat
-
-    def get_arm_dof_pos(self) -> np.ndarray:
-        return self.get_dof_pos()[:, self._arm_dof_pos_indices]
-
-    def compute_arm_ik_delta(
-        self,
-        goal_local_pos: np.ndarray,
-        curr_local_pos: np.ndarray,
-        goal_local_quat: np.ndarray | None = None,
-        curr_local_quat: np.ndarray | None = None,
-    ) -> np.ndarray:
-        """Compute damped least-squares IK delta for the 6 arm joints.
-
-        Position and optional orientation errors are expressed in the arm
-        reference frame (``armbasepoint``). MuJoCo provides world-frame site
-        Jacobians, so both translational and rotational blocks are rotated into
-        that same frame before solving.
-        """
-        cfg = self._cfg.ik
-        pos_err = np.asarray(goal_local_pos - curr_local_pos)
-        jacp_w, jacr_w = self._backend.get_site_jacobian_w(
-            self._ee_site_id,
-            self._arm_jacobian_dof_indices,
-        )
-        ref_rot_w = np_matrix_from_quat(
-            self._backend.get_sensor_data(self._cfg.sensor.arm_ref_world_quat)
-        )
-        rot_w_to_b = np.swapaxes(ref_rot_w, 1, 2)
-        jacp_b = np.matmul(rot_w_to_b, jacp_w)
-
-        if cfg.use_orientation:
-            if cfg.orientation_mode == "target":
-                if goal_local_quat is None or curr_local_quat is None:
-                    raise ValueError(
-                        "goal_local_quat and curr_local_quat are required when "
-                        "ik.use_orientation=true and ik.orientation_mode='target'"
-                    )
-                orn_err = np_quat_orientation_error_local(goal_local_quat, curr_local_quat)
-            elif cfg.orientation_mode == "zero_error":
-                orn_err = np.zeros_like(pos_err)
-            else:
-                raise ValueError(
-                    "ik.orientation_mode must be one of {'target', 'zero_error'}, "
-                    f"got {cfg.orientation_mode!r}"
-                )
-            jacr_b = np.matmul(rot_w_to_b, jacr_w)
-            jac = np.concatenate([jacp_b, jacr_b], axis=1)
-            dpose = np.concatenate([pos_err, orn_err], axis=1)
-        else:
-            jac = jacp_b
-            dpose = pos_err
-
-        identity = np.eye(jac.shape[1], dtype=jac.dtype)[None, :, :]
-        lhs = np.matmul(jac, np.swapaxes(jac, 1, 2)) + identity * (cfg.damping**2)
-        rhs = dpose[:, :, None]
-        solved = np.linalg.solve(lhs, rhs)
-        dq = np.matmul(np.swapaxes(jac, 1, 2), solved)[:, :, 0]
-        if cfg.dq_clip > 0.0:
-            dq = np.clip(dq, -cfg.dq_clip, cfg.dq_clip)
-        return dq
diff --git a/src/unilab/envs/locomotion/go2w/__init__.py b/src/unilab/envs/locomotion/go2w/__init__.py
deleted file mode 100644
index 35727958a..000000000
--- a/src/unilab/envs/locomotion/go2w/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .joystick import Go2WJoystickCfg, Go2WJoystickEnv
-from .rough import Go2WJoystickRoughCfg, Go2WJoystickRoughEnv
diff --git a/src/unilab/envs/locomotion/go2w/base.py b/src/unilab/envs/locomotion/go2w/base.py
deleted file mode 100644
index b48e5b07c..000000000
--- a/src/unilab/envs/locomotion/go2w/base.py
+++ /dev/null
@@ -1,138 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-
-import gymnasium as gym
-import numpy as np
-
-from unilab.envs.locomotion.common.base import (
-    BaseNoiseConfig,
-    LocomotionBaseCfg,
-    LocomotionBaseEnv,
-    PdControlConfig,
-)
-
-LEG_JOINT_SENSOR_PREFIXES: tuple[str, ...] = (
-    "FR_hip",
-    "FR_thigh",
-    "FR_calf",
-    "FL_hip",
-    "FL_thigh",
-    "FL_calf",
-    "RR_hip",
-    "RR_thigh",
-    "RR_calf",
-    "RL_hip",
-    "RL_thigh",
-    "RL_calf",
-)
-WHEEL_JOINT_SENSOR_PREFIXES: tuple[str, ...] = ("FR_wheel", "FL_wheel", "RR_wheel", "RL_wheel")
-JOINT_SENSOR_PREFIXES: tuple[str, ...] = LEG_JOINT_SENSOR_PREFIXES + WHEEL_JOINT_SENSOR_PREFIXES
-
-NUM_LEG_ACTIONS = len(LEG_JOINT_SENSOR_PREFIXES)
-NUM_WHEEL_ACTIONS = len(WHEEL_JOINT_SENSOR_PREFIXES)
-NUM_GO2W_ACTIONS = len(JOINT_SENSOR_PREFIXES)
-
-DEFAULT_LEG_ANGLES = np.asarray(
-    [
-        0.0,
-        0.8,
-        -1.5,
-        0.0,
-        0.8,
-        -1.5,
-        0.0,
-        0.8,
-        -1.5,
-        0.0,
-        0.8,
-        -1.5,
-    ],
-    dtype=np.float64,
-)
-DEFAULT_GO2W_ANGLES = np.concatenate(
-    [DEFAULT_LEG_ANGLES, np.zeros((NUM_WHEEL_ACTIONS,), dtype=np.float64)]
-)
-
-
-@dataclass
-class NoiseConfig(BaseNoiseConfig):
-    scale_wheel_vel: float = 0.5
-
-
-@dataclass
-class ControlConfig(PdControlConfig):
-    action_scale: float = 0.25
-    hip_action_scale: float | None = None
-    wheel_action_scale: float = 10.0
-    wheel_Kd: float = 0.5  # noqa: N815 - Hydra config key kept for compatibility.
-    clip_actions: float = 1.0
-
-
-@dataclass
-class Asset:
-    base_name = "base_link"
-    ground = "floor"
-
-
-@dataclass
-class Go2WBaseCfg(LocomotionBaseCfg):
-    noise_config: NoiseConfig = field(default_factory=NoiseConfig)  # type: ignore[assignment]
-    control_config: ControlConfig = field(default_factory=ControlConfig)  # type: ignore[assignment]
-    asset: Asset = field(default_factory=Asset)
-    sim_dt: float = 0.005
-    ctrl_dt: float = 0.02
-
-
-def stack_joint_sensors(backend, suffix: str, *, dtype: np.dtype | type) -> np.ndarray:
-    names = tuple(f"{prefix}_{suffix}" for prefix in JOINT_SENSOR_PREFIXES)
-    values = backend.get_sensor_data_batch(names)
-    return np.asarray(values.reshape(values.shape[0], -1)[:, :NUM_GO2W_ACTIONS], dtype=dtype)
-
-
-def compute_go2w_motor_ctrl(
-    policy_ctrl: np.ndarray,
-    joint_pos: np.ndarray,
-    joint_vel: np.ndarray,
-    leg_kp: np.ndarray,
-    leg_kd: np.ndarray,
-    wheel_kd: np.ndarray,
-    ctrl_lower: np.ndarray,
-    ctrl_upper: np.ndarray,
-    out: np.ndarray,
-) -> np.ndarray:
-    """Convert Go2W owner-level controls into motor actuator torques.
-
-    Hot path: shapes/dtypes are validated by the owning env at init/reset.
-    """
-    leg_out = out[:, :NUM_LEG_ACTIONS]
-    np.subtract(policy_ctrl[:, :NUM_LEG_ACTIONS], joint_pos[:, :NUM_LEG_ACTIONS], out=leg_out)
-    np.multiply(leg_out, leg_kp, out=leg_out)
-    leg_out -= leg_kd * joint_vel[:, :NUM_LEG_ACTIONS]
-    wheel_out = out[:, NUM_LEG_ACTIONS:]
-    np.subtract(policy_ctrl[:, NUM_LEG_ACTIONS:], joint_vel[:, NUM_LEG_ACTIONS:], out=wheel_out)
-    np.multiply(wheel_out, wheel_kd, out=wheel_out)
-    np.clip(out, ctrl_lower, ctrl_upper, out=out)
-    return out
-
-
-class Go2WBaseEnv(LocomotionBaseEnv):
-    _cfg: Go2WBaseCfg
-
-    def _init_action_space(self) -> None:
-        self._action_space = gym.spaces.Box(
-            low=-1.0,
-            high=1.0,
-            shape=(NUM_GO2W_ACTIONS,),
-            dtype=np.float32,
-        )
-
-    def _init_buffers(self) -> None:
-        super()._init_buffers()
-        self.default_angles = np.asarray(DEFAULT_GO2W_ANGLES, dtype=self.default_angles.dtype)
-
-    def get_dof_pos(self) -> np.ndarray:
-        return stack_joint_sensors(self._backend, "pos", dtype=self.default_angles.dtype)
-
-    def get_dof_vel(self) -> np.ndarray:
-        return stack_joint_sensors(self._backend, "vel", dtype=self.default_angles.dtype)
diff --git a/src/unilab/envs/locomotion/go2w/joystick.py b/src/unilab/envs/locomotion/go2w/joystick.py
deleted file mode 100644
index fa1f9978c..000000000
--- a/src/unilab/envs/locomotion/go2w/joystick.py
+++ /dev/null
@@ -1,647 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Any, cast
-
-import numpy as np
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base import registry
-from unilab.base.backend import create_backend, env_backend_kwargs
-from unilab.base.np_env import NpEnvState
-from unilab.base.scene import SceneCfg
-from unilab.dr import DomainRandomizationCapabilities, ResetPlan, ResetRandomizationPayload
-from unilab.dr.dr_utils import (
-    build_interval_push_plan,
-    validate_interval_push_support,
-    zero_actions,
-)
-from unilab.dtype_config import get_global_dtype
-from unilab.envs.locomotion.common import rewards
-from unilab.envs.locomotion.common.commands import (
-    Commands,
-    apply_heading_yaw_feedback,
-    zero_small_xy_commands,
-)
-from unilab.envs.locomotion.common.commands import (
-    sample_heading_commands as sample_go2w_heading_commands,
-)
-from unilab.envs.locomotion.common.domain_rand import DomainRandConfig
-from unilab.envs.locomotion.common.dr_provider import LocomotionDRProvider
-from unilab.envs.locomotion.common.rewards import RewardContext
-from unilab.envs.locomotion.go2w.base import (
-    DEFAULT_GO2W_ANGLES,
-    NUM_GO2W_ACTIONS,
-    NUM_LEG_ACTIONS,
-    NUM_WHEEL_ACTIONS,
-    Go2WBaseCfg,
-    Go2WBaseEnv,
-    compute_go2w_motor_ctrl,
-    stack_joint_sensors,
-)
-from unilab.utils.rotation import (
-    np_quat_mul,
-    np_yaw_to_quat,
-)
-
-GO2W_HIP_INDICES = np.asarray([0, 3, 6, 9], dtype=np.int32)
-
-
-@dataclass
-class InitState:
-    pos = [0.0, 0.0, 0.42]
-
-
-@dataclass
-class Go2WDomainRandConfig(DomainRandConfig):
-    randomize_init_yaw: bool = True
-    init_z_range: list[float] = field(default_factory=lambda: [0.0, 0.2])
-    init_roll_range: list[float] = field(default_factory=lambda: [0.0, 0.0])
-    init_pitch_range: list[float] = field(default_factory=lambda: [0.0, 0.0])
-    init_yaw_range: list[float] = field(default_factory=lambda: [-np.pi, np.pi])
-
-    randomize_kp: bool = True
-    kp_multiplier_range: list[float] = field(default_factory=lambda: [0.9, 1.1])
-
-    randomize_kd: bool = True
-    kd_multiplier_range: list[float] = field(default_factory=lambda: [0.9, 1.1])
-
-
-@dataclass
-class RewardConfig:
-    scales: dict[str, float]
-    tracking_sigma: float
-    base_height_target: float
-    only_positive_rewards: bool = False
-    joint_pos_penalty_stand_still_scale: float = 5.0
-    joint_pos_penalty_velocity_threshold: float = 0.5
-    joint_pos_penalty_command_threshold: float = 0.1
-
-
-@dataclass
-class JoystickSensor:
-    local_linvel = "local_linvel"
-    gyro = "gyro"
-    gravity = "upvector"
-
-
-@registry.envcfg("Go2WJoystickFlat")
-@dataclass
-class Go2WJoystickCfg(Go2WBaseCfg):
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "go2w" / "scene_flat.xml")
-        )
-    )
-    max_episode_seconds: float = 20.0
-    init_state: InitState = field(default_factory=InitState)
-    commands: Commands = field(default_factory=Commands)
-    reward_config: RewardConfig | None = None
-    sensor: JoystickSensor = field(default_factory=JoystickSensor)  # type: ignore[assignment]
-    domain_rand: Go2WDomainRandConfig = field(default_factory=Go2WDomainRandConfig)
-
-
-def build_go2w_backend_reset_randomization(
-    env: Any, num_reset: int
-) -> ResetRandomizationPayload | None:
-    """Build reset DR payloads that are valid for a motor-actuator Go2W model.
-
-    kp/kd are intentionally excluded here. Go2W samples them through the same
-    config path as Go2, but applies them inside its owner pre-step motor control.
-    """
-    domain_rand = getattr(env.cfg, "domain_rand", None)
-    if domain_rand is None:
-        return None
-
-    payload = ResetRandomizationPayload()
-    if getattr(domain_rand, "randomize_base_mass", False):
-        low, high = domain_rand.added_mass_range
-        payload.base_mass_delta = np.random.uniform(low, high, size=(num_reset,))
-
-    if getattr(domain_rand, "random_com", False):
-        low, high = domain_rand.com_offset_x
-        base_com_offset = np.zeros((num_reset, 3), dtype=np.float64)
-        base_com_offset[:, 0] = np.random.uniform(low, high, size=(num_reset,))
-        payload.base_com_offset = base_com_offset
-
-    if getattr(domain_rand, "randomize_gravity", False):
-        gravity_range = np.asarray(domain_rand.gravity_range, dtype=np.float64)
-        if gravity_range.shape != (2, 3):
-            raise ValueError(
-                f"domain_rand.gravity_range must have shape (2, 3), got {gravity_range.shape}"
-            )
-        low = np.minimum(gravity_range[0], gravity_range[1])
-        high = np.maximum(gravity_range[0], gravity_range[1])
-        payload.gravity = np.random.uniform(low=low, high=high, size=(num_reset, 3))
-
-    return None if payload.is_empty() else payload
-
-
-def sample_go2w_reset_yaw(domain_rand: Go2WDomainRandConfig, num_reset: int) -> np.ndarray:
-    if not domain_rand.randomize_init_yaw:
-        return np.zeros((num_reset,), dtype=get_global_dtype())
-
-    yaw_range = np.asarray(domain_rand.init_yaw_range, dtype=np.float64)
-    if yaw_range.shape != (2,):
-        raise ValueError(f"domain_rand.init_yaw_range must have shape (2,), got {yaw_range.shape}")
-    low, high = float(np.min(yaw_range)), float(np.max(yaw_range))
-    return np.asarray(np.random.uniform(low, high, size=(num_reset,)), dtype=get_global_dtype())
-
-
-class Go2WJoystickDomainRandomizationProvider(LocomotionDRProvider):
-    def validate(self, env: Any, capabilities: DomainRandomizationCapabilities) -> None:
-        payload = build_go2w_backend_reset_randomization(env, num_reset=1)
-        if payload is not None:
-            unsupported = capabilities.get_unsupported_reset_terms(payload.requested_terms())
-            if unsupported:
-                names = ", ".join(sorted(unsupported))
-                raise NotImplementedError(
-                    f"{env._backend.backend_type} backend does not support Go2W reset randomization terms: {names}"
-                )
-        validate_interval_push_support(env, capabilities)
-
-    def build_interval_randomization_plan(self, env: Any, step_counter: int):
-        return build_interval_push_plan(env, step_counter)
-
-    def build_reset_plan(self, env: Any, env_ids: np.ndarray) -> ResetPlan:
-        num_reset = len(env_ids)
-        qpos = np.tile(env._init_qpos, (num_reset, 1))
-        qvel = np.tile(env._init_qvel, (num_reset, 1))
-        qpos[:, 0:2] += np.random.uniform(-0.5, 0.5, (num_reset, 2))
-        qpos[:, 0:3] += env._spawn.origins_for(env_ids)
-        yaw = sample_go2w_reset_yaw(env.cfg.domain_rand, num_reset)
-        qpos[:, 3:7] = np_quat_mul(qpos[:, 3:7], np_yaw_to_quat(yaw))
-        qvel[:, 0:6] = np.asarray(
-            np.random.uniform(-0.5, 0.5, size=(num_reset, 6)), dtype=get_global_dtype()
-        )
-
-        motor_kp, motor_kd = env.sample_reset_motor_gains(num_reset)
-        env.set_motor_gains(env_ids, motor_kp, motor_kd)
-
-        commands = self._sample_commands(env, num_reset)
-        info_updates: dict[str, Any] = {
-            "commands": commands,
-            "current_actions": zero_actions(num_reset, env._num_action),
-            "last_actions": zero_actions(num_reset, env._num_action),
-            "motor_kp": motor_kp.astype(get_global_dtype()),
-            "motor_kd": motor_kd.astype(get_global_dtype()),
-            "torques": np.zeros((num_reset, env._num_action), dtype=get_global_dtype()),
-        }
-        if getattr(env.cfg.commands, "heading_command", False):
-            info_updates["heading_commands"] = sample_go2w_heading_commands(env, num_reset)
-        return ResetPlan(
-            env_ids=env_ids,
-            qpos=qpos,
-            qvel=qvel,
-            info_updates=info_updates,
-            randomization=build_go2w_backend_reset_randomization(env, num_reset),
-        )
-
-    def _compute_reset_obs(
-        self,
-        env: Any,
-        env_ids: Any,
-        info_updates: Any,
-        linvel: Any,
-        gyro: Any,
-        gravity: Any,
-        dof_pos: Any,
-        dof_vel: Any,
-    ) -> dict[str, np.ndarray]:
-        del env_ids
-        return cast(
-            dict[str, np.ndarray],
-            env._compute_obs(info_updates, linvel, gyro, gravity, dof_pos, dof_vel),
-        )
-
-    def _sample_commands(self, env: Any, num_reset: int) -> np.ndarray:
-        commands = super()._sample_commands(env, num_reset)
-        zero_small_xy_commands(commands)
-        standing_prob = float(getattr(env.cfg.commands, "rel_standing_envs", 0.0))
-        if standing_prob > 0.0:
-            standing = np.random.uniform(size=(num_reset,)) < min(standing_prob, 1.0)
-            commands[standing] = 0.0
-        if getattr(env.cfg.commands, "heading_command", False):
-            commands[:, 2] = 0.0
-        return commands
-
-
-@registry.env("Go2WJoystickFlat", sim_backend="mujoco")
-@registry.env("Go2WJoystickFlat", sim_backend="drake")
-class Go2WJoystickEnv(Go2WBaseEnv):
-    _cfg: Go2WJoystickCfg
-
-    def __init__(self, cfg: Go2WJoystickCfg, num_envs=1, backend_type="mujoco"):
-        if cfg.reward_config is None:
-            raise ValueError("reward_config must be provided via Hydra configuration")
-        backend = create_backend(
-            backend_type,
-            cfg.scene,
-            num_envs,
-            cfg.sim_dt,
-            base_name=cfg.asset.base_name,
-            push_body_name=cfg.domain_rand.push_body_name,
-            **env_backend_kwargs(cfg),
-        )
-        super().__init__(cfg, backend, num_envs)
-        self._np_dtype = get_global_dtype()
-        self._leg_action_scale = self._build_leg_action_scale()
-        self._reward_cfg = cfg.reward_config
-        self._enable_reward_log = True
-        ctrl_range = np.asarray(self._backend.get_actuator_ctrl_range(), dtype=np.float64)
-        self._validate_motor_control_contract(ctrl_range, num_envs)
-        self._ctrl_lower = ctrl_range[:, 0].astype(self._np_dtype)
-        self._ctrl_upper = ctrl_range[:, 1].astype(self._np_dtype)
-        joint_range = self._backend.get_joint_range()
-        self._leg_joint_range = (
-            np.asarray(joint_range[:NUM_LEG_ACTIONS], dtype=get_global_dtype())
-            if joint_range is not None
-            else None
-        )
-        self._base_motor_kp = np.full((NUM_LEG_ACTIONS,), cfg.control_config.Kp, dtype=np.float64)
-        self._base_motor_kd = np.full((NUM_LEG_ACTIONS,), cfg.control_config.Kd, dtype=np.float64)
-        self._base_wheel_kd = np.full(
-            (NUM_WHEEL_ACTIONS,), cfg.control_config.wheel_Kd, dtype=np.float64
-        )
-        self._motor_kp = np.broadcast_to(self._base_motor_kp, (num_envs, NUM_LEG_ACTIONS)).copy()
-        self._motor_kd = np.broadcast_to(self._base_motor_kd, (num_envs, NUM_LEG_ACTIONS)).copy()
-        self._wheel_kd = np.broadcast_to(self._base_wheel_kd, (num_envs, NUM_WHEEL_ACTIONS)).copy()
-        self._last_motor_ctrl = np.zeros((num_envs, NUM_GO2W_ACTIONS), dtype=self._np_dtype)
-        self._last_dof_vel_for_acc = np.zeros(
-            (num_envs, NUM_GO2W_ACTIONS), dtype=get_global_dtype()
-        )
-        self._backend.set_pre_step_control(self._pre_step_motor_control)
-        self._init_reward_functions()
-        self._init_domain_randomization(Go2WJoystickDomainRandomizationProvider())
-
-    @property
-    def obs_groups_spec(self) -> dict[str, int]:
-        return {"obs": 53, "critic": 72}
-
-    def reset(self, env_indices: np.ndarray) -> tuple[dict[str, np.ndarray], dict]:
-        env_ids = np.asarray(env_indices, dtype=np.int32)
-        obs, info = super().reset(env_ids)
-        dof_vel = self.get_dof_vel()
-        if dof_vel.shape[0] == self._num_envs:
-            self._last_dof_vel_for_acc[env_ids] = dof_vel[env_ids]
-        return obs, info
-
-    def _validate_motor_control_contract(self, ctrl_range: np.ndarray, num_envs: int) -> None:
-        if self._backend.num_actuators != NUM_GO2W_ACTIONS:
-            raise ValueError(
-                f"Go2W requires {NUM_GO2W_ACTIONS} motor actuators, got {self._backend.num_actuators}"
-            )
-        if ctrl_range.shape != (NUM_GO2W_ACTIONS, 2):
-            raise ValueError(
-                f"Go2W actuator ctrl_range must have shape ({NUM_GO2W_ACTIONS}, 2), "
-                f"got {ctrl_range.shape}"
-            )
-        pos = stack_joint_sensors(self._backend, "pos", dtype=self.default_angles.dtype)
-        vel = stack_joint_sensors(self._backend, "vel", dtype=self.default_angles.dtype)
-        expected_shape = (num_envs, NUM_GO2W_ACTIONS)
-        if pos.shape != expected_shape:
-            raise ValueError(f"Go2W joint position sensor stack must have shape {expected_shape}")
-        if vel.shape != expected_shape:
-            raise ValueError(f"Go2W joint velocity sensor stack must have shape {expected_shape}")
-
-    def _build_leg_action_scale(self) -> np.ndarray:
-        scale = np.full(
-            (NUM_LEG_ACTIONS,),
-            float(self._cfg.control_config.action_scale),
-            dtype=self._np_dtype,
-        )
-        hip_action_scale = self._cfg.control_config.hip_action_scale
-        if hip_action_scale is not None:
-            scale[GO2W_HIP_INDICES] = float(hip_action_scale)
-        return scale
-
-    def _init_reward_functions(self) -> None:
-        self._reward_fns: dict[str, Any] = {
-            "tracking_lin_vel": rewards.tracking_lin_vel,
-            "tracking_ang_vel": rewards.tracking_ang_vel,
-            "lin_vel_z": rewards.lin_vel_z,
-            "ang_vel_xy": rewards.ang_vel_xy,
-            "base_height": rewards.base_height,
-            "action_rate": rewards.action_rate,
-            "similar_to_default": rewards.similar_to_default,
-            "orientation": rewards.orientation,
-            "torques": self._reward_torques_l2,
-            "joint_torques_l2": self._reward_joint_torques_l2,
-            "energy": rewards.energy,
-            "dof_vel": self._reward_dof_vel,
-            "dof_acc": self._reward_dof_acc,
-            "joint_acc_l2": self._reward_dof_acc,
-            "wheel_acc": self._reward_wheel_acc,
-            "joint_acc_wheel_l2": self._reward_wheel_acc,
-            "stand_still": self._reward_stand_still,
-            "hip_pos": self._reward_hip_pos,
-            "dof_error": self._reward_dof_error,
-            "joint_pos_penalty": self._reward_joint_pos_penalty,
-            "joint_power": self._reward_joint_power,
-            "joint_mirror": self._reward_joint_mirror,
-            "alive": rewards.alive,
-            "upward": rewards.upward,
-            "wheel_vel": self._reward_wheel_vel,
-        }
-
-    def sample_reset_motor_gains(self, num_reset: int) -> tuple[np.ndarray, np.ndarray]:
-        kp = np.broadcast_to(self._base_motor_kp, (num_reset, NUM_LEG_ACTIONS)).copy()
-        kd = np.broadcast_to(self._base_motor_kd, (num_reset, NUM_LEG_ACTIONS)).copy()
-        domain_rand = self._cfg.domain_rand
-        if domain_rand.randomize_kp:
-            low, high = domain_rand.kp_multiplier_range
-            kp *= np.random.uniform(low, high, size=(num_reset, 1))
-        if domain_rand.randomize_kd:
-            low, high = domain_rand.kd_multiplier_range
-            kd *= np.random.uniform(low, high, size=(num_reset, 1))
-        return kp, kd
-
-    def set_motor_gains(self, env_ids: np.ndarray, kp: np.ndarray, kd: np.ndarray) -> None:
-        self._motor_kp[env_ids] = np.asarray(kp, dtype=np.float64)
-        self._motor_kd[env_ids] = np.asarray(kd, dtype=np.float64)
-
-    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
-        clipped_actions = np.asarray(
-            np.clip(
-                actions,
-                -self._cfg.control_config.clip_actions,
-                self._cfg.control_config.clip_actions,
-            ),
-            dtype=self._np_dtype,
-        )
-        state.info["last_actions"] = state.info.get(
-            "current_actions", np.zeros_like(clipped_actions)
-        )
-        state.info["current_actions"] = clipped_actions
-        exec_actions = (
-            state.info["last_actions"]
-            if self._cfg.control_config.simulate_action_latency
-            else clipped_actions
-        )
-
-        leg_targets = (
-            exec_actions[:, :NUM_LEG_ACTIONS] * self._leg_action_scale
-            + self.default_angles[:NUM_LEG_ACTIONS]
-        )
-        wheel_velocity_targets = (
-            exec_actions[:, NUM_LEG_ACTIONS:] * self._cfg.control_config.wheel_action_scale
-        )
-        return np.concatenate([leg_targets, wheel_velocity_targets], axis=1, dtype=self._np_dtype)
-
-    def _pre_step_motor_control(self, backend: Any, policy_ctrl: np.ndarray) -> np.ndarray:
-        joint_pos = stack_joint_sensors(backend, "pos", dtype=self.default_angles.dtype)
-        joint_vel = stack_joint_sensors(backend, "vel", dtype=self.default_angles.dtype)
-        motor_ctrl = compute_go2w_motor_ctrl(
-            policy_ctrl,
-            joint_pos,
-            joint_vel,
-            self._motor_kp,
-            self._motor_kd,
-            self._wheel_kd,
-            self._ctrl_lower,
-            self._ctrl_upper,
-            self._last_motor_ctrl,
-        )
-        return motor_ctrl
-
-    def update_state(self, state: NpEnvState) -> NpEnvState:
-        self._update_commands(state.info)
-        linvel = self.get_local_linvel()
-        gyro = self.get_gyro()
-        gravity = self._backend.get_sensor_data(self._cfg.sensor.gravity)
-        dof_pos = self.get_dof_pos()
-        dof_vel = self.get_dof_vel()
-        state.info["torques"] = self._last_motor_ctrl.copy()
-        state.info["qacc"] = self._estimate_dof_acc(dof_vel)
-        terminated = self._compute_terminated(gravity)
-        reward = self._compute_reward(state.info, linvel, gyro, gravity, dof_pos, dof_vel)
-        obs = self._compute_obs(state.info, linvel, gyro, gravity, dof_pos, dof_vel)
-        return state.replace(obs=obs, reward=reward, terminated=terminated)
-
-    def _compute_terminated(self, gravity: np.ndarray) -> np.ndarray:
-        return gravity[:, 2] <= 0.5
-
-    def _compute_obs(
-        self,
-        info: dict,
-        linvel: np.ndarray,
-        gyro: np.ndarray,
-        gravity: np.ndarray,
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-    ) -> dict[str, np.ndarray]:
-        noise_cfg = self._cfg.noise_config
-        leg_diff = dof_pos[:, :NUM_LEG_ACTIONS] - self.default_angles[:NUM_LEG_ACTIONS]
-        leg_vel = dof_vel[:, :NUM_LEG_ACTIONS]
-        wheel_vel = dof_vel[:, NUM_LEG_ACTIONS:]
-        noisy_gyro = self._obs_noise(gyro, noise_cfg.scale_gyro)
-        noisy_gravity = self._obs_noise(gravity, noise_cfg.scale_gravity)
-        noisy_leg_diff = self._obs_noise(leg_diff, noise_cfg.scale_joint_angle)
-        noisy_leg_vel = self._obs_noise(leg_vel, noise_cfg.scale_joint_vel)
-        noisy_wheel_vel = self._obs_noise(wheel_vel, noise_cfg.scale_wheel_vel)
-        num_obs = gyro.shape[0]
-        last_actions = info.get("current_actions", np.zeros((num_obs, self._num_action)))
-        motor_ctrl = info.get("torques", np.zeros((num_obs, self._num_action), dtype=dof_pos.dtype))
-
-        obs = np.concatenate(
-            [
-                noisy_gyro,
-                -noisy_gravity,
-                noisy_leg_diff,
-                noisy_leg_vel,
-                noisy_wheel_vel,
-                last_actions,
-                info["commands"],
-            ],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-        critic = np.concatenate(
-            [
-                gyro,
-                -gravity,
-                leg_diff,
-                leg_vel,
-                wheel_vel,
-                last_actions,
-                info["commands"],
-                linvel,
-                motor_ctrl,
-            ],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-        return {"obs": obs, "critic": critic}
-
-    def _compute_reward(self, info: dict, linvel, gyro, gravity, dof_pos, dof_vel) -> np.ndarray:
-        dtype = get_global_dtype()
-        num_obs = linvel.shape[0]
-        ctx = RewardContext(
-            info=info,
-            linvel=linvel,
-            gyro=gyro,
-            dof_pos=dof_pos[:, :NUM_LEG_ACTIONS],
-            dof_vel=dof_vel,
-            num_envs=num_obs,
-            default_angles=DEFAULT_GO2W_ANGLES[:NUM_LEG_ACTIONS].astype(dtype),
-            tracking_sigma=self._reward_cfg.tracking_sigma,
-            base_height_target=self._reward_cfg.base_height_target,
-            base_height=self._reward_base_height_values(num_obs),
-            gravity=gravity,
-            joint_range=self._leg_joint_range,
-        )
-        return rewards.run_reward_dispatch(
-            scales=self._reward_cfg.scales,
-            fns=self._reward_fns,
-            ctx=ctx,
-            info=info,
-            enable_log=self._enable_reward_log,
-            ctrl_dt=self._cfg.ctrl_dt,
-            only_positive=self._reward_cfg.only_positive_rewards,
-        )
-
-    def _update_commands(self, info: dict) -> None:
-        commands = info.get("commands")
-        if commands is None:
-            return
-
-        commands_arr = np.asarray(commands, dtype=get_global_dtype())
-        resampling_time = float(getattr(self._cfg.commands, "resampling_time", 0.0))
-        if resampling_time > 0.0:
-            interval_steps = max(int(round(resampling_time / self._cfg.ctrl_dt)), 1)
-            steps = np.asarray(info.get("steps", np.zeros((self._num_envs,), dtype=np.uint32)))
-            resample_mask = (steps > 0) & ((steps % interval_steps) == 0)
-            if np.any(resample_mask):
-                num_resample = int(np.count_nonzero(resample_mask))
-                low = np.asarray(self._cfg.commands.vel_limit[0], dtype=get_global_dtype())
-                high = np.asarray(self._cfg.commands.vel_limit[1], dtype=get_global_dtype())
-                sampled = np.random.uniform(low=low, high=high, size=(num_resample, 3)).astype(
-                    get_global_dtype()
-                )
-                zero_small_xy_commands(sampled)
-                standing_prob = float(getattr(self._cfg.commands, "rel_standing_envs", 0.0))
-                if standing_prob > 0.0:
-                    standing = np.random.uniform(size=(num_resample,)) < min(standing_prob, 1.0)
-                    sampled[standing] = 0.0
-                commands_arr[resample_mask] = sampled
-                if getattr(self._cfg.commands, "heading_command", False):
-                    heading_commands = self._ensure_heading_commands(info, commands_arr.shape[0])
-                    heading_commands[resample_mask] = sample_go2w_heading_commands(
-                        self, num_resample
-                    )
-                    info["heading_commands"] = heading_commands
-
-        if getattr(self._cfg.commands, "heading_command", False):
-            heading_commands = self._ensure_heading_commands(info, commands_arr.shape[0])
-            base_quat = np.asarray(self._backend.get_base_quat(), dtype=get_global_dtype())
-            if base_quat.shape[0] == commands_arr.shape[0]:
-                stiffness = float(getattr(self._cfg.commands, "heading_control_stiffness", 0.5))
-                apply_heading_yaw_feedback(
-                    commands_arr, base_quat, heading_commands, stiffness=stiffness
-                )
-        info["commands"] = commands_arr
-
-    def _ensure_heading_commands(self, info: dict, num_obs: int) -> np.ndarray:
-        heading_commands = info.get("heading_commands")
-        if heading_commands is None or np.asarray(heading_commands).shape != (num_obs,):
-            heading_commands = sample_go2w_heading_commands(self, num_obs)
-            info["heading_commands"] = heading_commands
-        heading_commands = np.asarray(heading_commands, dtype=get_global_dtype())
-        info["heading_commands"] = heading_commands
-        return heading_commands
-
-    def _estimate_dof_acc(self, dof_vel: np.ndarray) -> np.ndarray:
-        qacc = np.asarray((dof_vel - self._last_dof_vel_for_acc) / self._cfg.ctrl_dt)
-        self._last_dof_vel_for_acc[:] = dof_vel
-        return np.asarray(qacc, dtype=get_global_dtype())
-
-    def _reward_base_height_values(self, num_obs: int) -> np.ndarray:
-        base_pos = np.asarray(self._backend.get_base_pos(), dtype=get_global_dtype())
-        if base_pos.shape[0] != num_obs:
-            return np.zeros((num_obs,), dtype=get_global_dtype())
-        return np.asarray(base_pos[:, 2], dtype=get_global_dtype())
-
-    def _reward_wheel_vel(self, ctx: RewardContext) -> np.ndarray:
-        assert ctx.dof_vel is not None
-        wheel_vel = ctx.dof_vel[:, NUM_LEG_ACTIONS:]
-        return np.asarray(np.sum(np.square(wheel_vel), axis=1), dtype=get_global_dtype())
-
-    def _reward_torques_l2(self, ctx: RewardContext) -> np.ndarray:
-        torques = np.asarray(
-            ctx.info.get("torques", np.zeros((ctx.num_envs, self._num_action))),
-            dtype=get_global_dtype(),
-        )
-        return np.asarray(np.sum(np.square(torques), axis=1), dtype=get_global_dtype())
-
-    def _reward_joint_torques_l2(self, ctx: RewardContext) -> np.ndarray:
-        torques = np.asarray(
-            ctx.info.get("torques", np.zeros((ctx.num_envs, self._num_action))),
-            dtype=get_global_dtype(),
-        )
-        return np.asarray(
-            np.sum(np.square(torques[:, :NUM_LEG_ACTIONS]), axis=1),
-            dtype=get_global_dtype(),
-        )
-
-    def _reward_dof_vel(self, ctx: RewardContext) -> np.ndarray:
-        assert ctx.dof_vel is not None
-        return np.asarray(
-            np.sum(np.square(ctx.dof_vel[:, :NUM_LEG_ACTIONS]), axis=1),
-            dtype=get_global_dtype(),
-        )
-
-    def _reward_dof_acc(self, ctx: RewardContext) -> np.ndarray:
-        qacc = np.asarray(
-            ctx.info.get("qacc", np.zeros((ctx.num_envs, NUM_GO2W_ACTIONS))),
-            dtype=get_global_dtype(),
-        )
-        return np.asarray(np.sum(np.square(qacc[:, :NUM_LEG_ACTIONS]), axis=1), dtype=qacc.dtype)
-
-    def _reward_wheel_acc(self, ctx: RewardContext) -> np.ndarray:
-        qacc = np.asarray(
-            ctx.info.get("qacc", np.zeros((ctx.num_envs, NUM_GO2W_ACTIONS))),
-            dtype=get_global_dtype(),
-        )
-        return np.asarray(np.sum(np.square(qacc[:, NUM_LEG_ACTIONS:]), axis=1), dtype=qacc.dtype)
-
-    def _reward_stand_still(self, ctx: RewardContext) -> np.ndarray:
-        commands = ctx.info["commands"]
-        stopped = np.linalg.norm(commands[:, :2], axis=1) < 0.1
-        dof_error = np.sum(np.abs(ctx.dof_pos - DEFAULT_GO2W_ANGLES[:NUM_LEG_ACTIONS]), axis=1)
-        return np.asarray(dof_error * stopped, dtype=get_global_dtype())
-
-    def _reward_hip_pos(self, ctx: RewardContext) -> np.ndarray:
-        diff = ctx.dof_pos[:, GO2W_HIP_INDICES] - DEFAULT_GO2W_ANGLES[GO2W_HIP_INDICES]
-        return np.asarray(np.sum(np.square(diff), axis=1), dtype=get_global_dtype())
-
-    def _reward_dof_error(self, ctx: RewardContext) -> np.ndarray:
-        diff = ctx.dof_pos - DEFAULT_GO2W_ANGLES[:NUM_LEG_ACTIONS]
-        return np.asarray(np.sum(np.square(diff), axis=1), dtype=get_global_dtype())
-
-    def _reward_joint_pos_penalty(self, ctx: RewardContext) -> np.ndarray:
-        return rewards.joint_pos_penalty(
-            ctx,
-            stand_still_scale=self._reward_cfg.joint_pos_penalty_stand_still_scale,
-            velocity_threshold=self._reward_cfg.joint_pos_penalty_velocity_threshold,
-            command_threshold=self._reward_cfg.joint_pos_penalty_command_threshold,
-        )
-
-    def _reward_joint_power(self, ctx: RewardContext) -> np.ndarray:
-        assert ctx.dof_vel is not None
-        torques = np.asarray(
-            ctx.info.get("torques", np.zeros((ctx.num_envs, self._num_action))),
-            dtype=get_global_dtype(),
-        )
-        return np.asarray(
-            np.sum(np.abs(ctx.dof_vel[:, :NUM_LEG_ACTIONS] * torques[:, :NUM_LEG_ACTIONS]), axis=1),
-            dtype=get_global_dtype(),
-        )
-
-    def _reward_joint_mirror(self, ctx: RewardContext) -> np.ndarray:
-        fr_rl = ctx.dof_pos[:, 0:3] - ctx.dof_pos[:, 9:12]
-        fl_rr = ctx.dof_pos[:, 3:6] - ctx.dof_pos[:, 6:9]
-        mirror = 0.5 * (np.sum(np.square(fr_rl), axis=1) + np.sum(np.square(fl_rr), axis=1))
-        return np.asarray(mirror, dtype=get_global_dtype())
-
-
-registry.register_env("Go2WJoystickFlat", Go2WJoystickEnv, sim_backend="motrix")
diff --git a/src/unilab/envs/locomotion/go2w/rough.py b/src/unilab/envs/locomotion/go2w/rough.py
deleted file mode 100644
index da11d4321..000000000
--- a/src/unilab/envs/locomotion/go2w/rough.py
+++ /dev/null
@@ -1,380 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Any
-
-import numpy as np
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base import registry
-from unilab.base.np_env import NpEnvState
-from unilab.base.scene import SceneCfg, TerrainSceneCfg
-from unilab.dr import DomainRandomizationManager, ResetPlan
-from unilab.dr.dr_utils import zero_actions
-from unilab.dtype_config import get_global_dtype
-from unilab.envs.locomotion.common import rewards
-from unilab.envs.locomotion.common.commands import (
-    Commands,
-    apply_heading_yaw_feedback,
-    zero_small_xy_commands,
-)
-from unilab.envs.locomotion.common.height_scan import (
-    HeightScanConfig,
-    base_height_from_scan,
-    height_scan_obs,
-    init_height_scan_sensor,
-    raw_height_scan_obs,
-    terrain_out_of_bounds,
-)
-from unilab.envs.locomotion.common.rewards import RewardContext
-from unilab.envs.locomotion.common.terrain_spawn import (
-    TerrainCurriculumCfg,
-    TerrainSpawnManager,
-)
-from unilab.envs.locomotion.go2w.base import NUM_GO2W_ACTIONS, NUM_LEG_ACTIONS
-from unilab.envs.locomotion.go2w.joystick import (
-    Go2WJoystickCfg,
-    Go2WJoystickDomainRandomizationProvider,
-    Go2WJoystickEnv,
-    build_go2w_backend_reset_randomization,
-    sample_go2w_heading_commands,
-)
-from unilab.terrains import (
-    SubTerrainCfg,
-    TerrainGeneratorCfg,
-    flat,
-    hf_pyramid_slope,
-    hf_pyramid_slope_inv,
-    pyramid_stairs,
-    pyramid_stairs_inv,
-    random_rough,
-    wave_terrain,
-)
-from unilab.utils.rotation import (
-    np_quat_from_euler_xyz,
-    np_quat_mul,
-)
-
-# pyright: reportIncompatibleVariableOverride=false, reportAttributeAccessIssue=false, reportCallIssue=false
-
-
-@dataclass
-class Go2WRoughCommands(Commands):
-    vel_limit: list[list[float]] = field(
-        default_factory=lambda: [[-1.0, -1.0, -1.0], [1.0, 1.0, 1.0]]
-    )
-    resampling_time: float = 10.0
-    heading_command: bool = True
-    heading_range: list[float] = field(default_factory=lambda: [-np.pi, np.pi])
-
-
-@dataclass
-class RoughTerminationConfig:
-    terrain_out_of_bounds: bool = True
-    terrain_distance_buffer: float = 3.0
-
-
-@dataclass(kw_only=True)
-class Go2WRoughTerrainCfg(TerrainGeneratorCfg):
-    size: tuple[float, float] = (8.0, 8.0)
-    num_rows: int = 6
-    num_cols: int = 6
-    border_width: float = 1.0
-    add_lights: bool = True
-    horizontal_scale: float = 0.1
-
-    sub_terrains: dict[str, SubTerrainCfg] = field(
-        default_factory=lambda: {
-            "flat": flat(proportion=0.0),
-            "pyramid_stairs": pyramid_stairs(
-                proportion=0.1,
-                step_height_range=(0.025, 0.10),
-                step_width=0.4,
-                platform_width=3.0,
-                border_width=0.2,
-            ),
-            "pyramid_stairs_inv": pyramid_stairs_inv(
-                proportion=0.1,
-                step_height_range=(0.025, 0.10),
-                step_width=0.4,
-                platform_width=3.0,
-                border_width=0.2,
-            ),
-            "hf_pyramid_slope": hf_pyramid_slope(
-                proportion=0.2,
-                slope_range=(0.0, 0.3),
-                platform_width=2.0,
-                border_width=0.2,
-            ),
-            "hf_pyramid_slope_inv": hf_pyramid_slope_inv(
-                proportion=0.2,
-                slope_range=(0.0, 0.3),
-                platform_width=2.0,
-                border_width=0.2,
-            ),
-            "random_rough": random_rough(
-                proportion=0.3,
-                noise_range=(0.01, 0.06),
-                noise_step=0.01,
-                border_width=0.2,
-            ),
-            "wave_terrain": wave_terrain(
-                proportion=0.3,
-                amplitude_range=(0.0, 0.12),
-                num_waves=4,
-                border_width=0.2,
-            ),
-        }
-    )
-
-
-@registry.envcfg("Go2WJoystickRough")
-@dataclass
-class Go2WJoystickRoughCfg(Go2WJoystickCfg):
-    """Go2W rough terrain task with procedurally generated sub-terrains."""
-
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "go2w" / "go2w.xml"),
-            fragment_files=[
-                str(ASSETS_ROOT_PATH / "robots" / "go2w" / "locomotion_task.xml"),
-            ],
-            terrain=TerrainSceneCfg(
-                generator=Go2WRoughTerrainCfg(),
-                hfield_name="terrain_hfield",
-                geom_name="floor",
-            ),
-        )
-    )
-    commands: Go2WRoughCommands = field(default_factory=Go2WRoughCommands)
-    terrain_scan: HeightScanConfig = field(default_factory=HeightScanConfig)
-    termination_config: RoughTerminationConfig = field(default_factory=RoughTerminationConfig)
-    terrain_curriculum: TerrainCurriculumCfg = field(default_factory=TerrainCurriculumCfg)
-
-
-class Go2WJoystickRoughDomainRandomizationProvider(Go2WJoystickDomainRandomizationProvider):
-    def _sample_commands(self, env: Any, num_reset: int) -> np.ndarray:
-        commands = super()._sample_commands(env, num_reset)
-        zero_small_xy_commands(commands, threshold=0.08)
-        standing_prob = env.cfg.commands.rel_standing_envs
-        if standing_prob > 0.0:
-            standing = np.random.uniform(size=(num_reset,)) < min(standing_prob, 1.0)
-            commands[standing] = 0.0
-        if env.cfg.commands.heading_command:
-            commands[:, 2] = 0.0
-        return commands
-
-    def build_reset_plan(self, env: Any, env_ids: np.ndarray) -> ResetPlan:
-        num_reset = len(env_ids)
-        qpos = np.tile(env._init_qpos, (num_reset, 1))
-        qvel = np.tile(env._init_qvel, (num_reset, 1))
-        qpos[:, 0:2] += np.random.uniform(-0.5, 0.5, (num_reset, 2))
-        qpos[:, 2] += np.random.uniform(0.25, 0.5, (num_reset,))
-        qpos[:, 0:3] += env._spawn.origins_for(env_ids)
-        roll = np.random.uniform(-3.14, 3.14, (num_reset,))
-        pitch = np.random.uniform(-3.14, 3.14, (num_reset,))
-        yaw = np.random.uniform(-3.14, 3.14, (num_reset,))
-        qpos[:, 3:7] = np_quat_mul(qpos[:, 3:7], np_quat_from_euler_xyz(roll, pitch, yaw))
-        qvel[:, 0:6] = np.asarray(
-            np.random.uniform(-0.5, 0.5, size=(num_reset, 6)), dtype=get_global_dtype()
-        )
-
-        motor_kp, motor_kd = env.sample_reset_motor_gains(num_reset)
-        env.set_motor_gains(env_ids, motor_kp, motor_kd)
-        commands = self._sample_commands(env, num_reset)
-        info_updates: dict[str, Any] = {
-            "commands": commands,
-            "current_actions": zero_actions(num_reset, env._num_action),
-            "last_actions": zero_actions(num_reset, env._num_action),
-            "motor_kp": motor_kp.astype(get_global_dtype()),
-            "motor_kd": motor_kd.astype(get_global_dtype()),
-            "torques": np.zeros((num_reset, env._num_action), dtype=get_global_dtype()),
-        }
-        if getattr(env.cfg.commands, "heading_command", False):
-            info_updates["heading_commands"] = sample_go2w_heading_commands(env, num_reset)
-        return ResetPlan(
-            env_ids=env_ids,
-            qpos=qpos,
-            qvel=qvel,
-            info_updates=info_updates,
-            randomization=build_go2w_backend_reset_randomization(env, num_reset),
-        )
-
-
-@registry.env("Go2WJoystickRough", sim_backend="mujoco")
-class Go2WJoystickRoughEnv(Go2WJoystickEnv):
-    _cfg: Go2WJoystickRoughCfg
-    _height_scan_dim: int = 0
-
-    def __init__(self, cfg: Go2WJoystickRoughCfg, num_envs=1, backend_type="mujoco"):
-        super().__init__(cfg, num_envs=num_envs, backend_type=backend_type)
-        terrain_spawn_data = self._backend.get_terrain_spawn_data()
-        terrain_generator = cfg.scene.terrain.generator if cfg.scene.terrain is not None else None
-        if terrain_spawn_data is not None and terrain_generator is not None:
-            self._spawn = TerrainSpawnManager(
-                num_envs,
-                terrain_spawn_data.terrain_origins,
-                cell_size=float(terrain_generator.size[0]),
-                cfg=cfg.terrain_curriculum,
-                sample_height=terrain_spawn_data.sample_height,
-            )
-        self._dr_manager = DomainRandomizationManager(
-            self, Go2WJoystickRoughDomainRandomizationProvider()
-        )
-        init_height_scan_sensor(self, cfg.terrain_scan, cfg.asset.base_name)
-
-    @property
-    def obs_groups_spec(self) -> dict[str, int]:
-        return {"obs": 53, "critic": 56 + self._height_scan_dim}
-
-    def _init_reward_functions(self) -> None:
-        def gated(fn):
-            return lambda ctx: fn(ctx) * self._upright_scale(ctx.gravity)
-
-        def _joint_pos_penalty(ctx: RewardContext) -> np.ndarray:
-            return self._reward_joint_pos_penalty(ctx) * self._upright_scale(ctx.gravity)
-
-        def _stand_still(ctx: RewardContext) -> np.ndarray:
-            return self._reward_stand_still(ctx) * self._upright_scale(ctx.gravity)
-
-        self._reward_fns = {
-            "tracking_lin_vel": gated(rewards.tracking_lin_vel),
-            "tracking_ang_vel": gated(rewards.tracking_ang_vel),
-            "lin_vel_z": gated(rewards.lin_vel_z),
-            "ang_vel_xy": gated(rewards.ang_vel_xy),
-            "base_height": gated(rewards.base_height),
-            "orientation": gated(rewards.orientation),
-            "similar_to_default": gated(rewards.similar_to_default),
-            "torques": gated(self._reward_torques_l2),
-            "joint_torques_l2": gated(self._reward_joint_torques_l2),
-            "energy": gated(rewards.energy),
-            "dof_vel": gated(self._reward_dof_vel),
-            "dof_acc": gated(self._reward_dof_acc),
-            "joint_acc_l2": gated(self._reward_dof_acc),
-            "wheel_acc": gated(self._reward_wheel_acc),
-            "joint_acc_wheel_l2": gated(self._reward_wheel_acc),
-            "stand_still": _stand_still,
-            "hip_pos": gated(self._reward_hip_pos),
-            "dof_error": gated(self._reward_dof_error),
-            "joint_pos_penalty": _joint_pos_penalty,
-            "joint_power": gated(self._reward_joint_power),
-            "joint_mirror": gated(self._reward_joint_mirror),
-            "alive": rewards.alive,
-            "upward": rewards.upward,
-            "wheel_vel": gated(self._reward_wheel_vel),
-            "action_rate": rewards.action_rate,
-        }
-
-    def _upright_scale(self, gravity: np.ndarray | None) -> np.ndarray:
-        return rewards.upright_scale(gravity, self._num_envs)
-
-    def _compute_obs(
-        self,
-        info: dict,
-        linvel: np.ndarray,
-        gyro: np.ndarray,
-        gravity: np.ndarray,
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-    ) -> dict[str, np.ndarray]:
-        noise_cfg = self._cfg.noise_config
-        leg_diff = dof_pos[:, :NUM_LEG_ACTIONS] - self.default_angles[:NUM_LEG_ACTIONS]
-        policy_gyro = self._obs_noise(gyro, noise_cfg.scale_gyro) * 0.25
-        policy_gravity = self._obs_noise(-gravity, noise_cfg.scale_gravity)
-        policy_leg_diff = self._obs_noise(leg_diff, noise_cfg.scale_joint_angle)
-        policy_dof_vel = self._obs_noise(dof_vel, noise_cfg.scale_joint_vel) * 0.05
-        num_obs = gyro.shape[0]
-        last_actions = info.get(
-            "current_actions", np.zeros((num_obs, NUM_GO2W_ACTIONS), dtype=dof_pos.dtype)
-        )
-        commands = info["commands"]
-
-        obs = np.concatenate(
-            [
-                policy_gyro,
-                policy_gravity,
-                commands,
-                policy_leg_diff,
-                policy_dof_vel,
-                last_actions,
-            ],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-        critic_base = np.concatenate(
-            [linvel, gyro, -gravity, commands, leg_diff, dof_vel, last_actions],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-        critic = np.concatenate(
-            [critic_base, height_scan_obs(self, self._cfg.terrain_scan, num_obs)],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-        return {"obs": obs, "critic": critic}
-
-    def _reward_base_height_values(self, num_obs: int) -> np.ndarray:
-        height = base_height_from_scan(self, num_obs)
-        if height.shape[0] != num_obs:
-            return super()._reward_base_height_values(num_obs)
-        return height
-
-    def _update_commands(self, info: dict) -> None:
-        commands = info.get("commands")
-        if commands is None:
-            return
-
-        commands_arr = np.asarray(commands, dtype=get_global_dtype())
-        resampling_time = float(self._cfg.commands.resampling_time)
-        if resampling_time > 0.0:
-            interval_steps = max(int(round(resampling_time / self._cfg.ctrl_dt)), 1)
-            steps = np.asarray(info.get("steps", np.zeros((self._num_envs,), dtype=np.uint32)))
-            resample_mask = (steps > 0) & ((steps % interval_steps) == 0)
-            if np.any(resample_mask):
-                num_resample = int(np.count_nonzero(resample_mask))
-                low = np.asarray(self._cfg.commands.vel_limit[0], dtype=get_global_dtype())
-                high = np.asarray(self._cfg.commands.vel_limit[1], dtype=get_global_dtype())
-                sampled = np.random.uniform(low=low, high=high, size=(num_resample, 3)).astype(
-                    get_global_dtype()
-                )
-                zero_small_xy_commands(commands, threshold=0.08)
-                commands_arr[resample_mask] = sampled
-                if self._cfg.commands.heading_command:
-                    heading_commands = self._ensure_heading_commands(info, commands_arr.shape[0])
-                    heading_commands[resample_mask] = sample_go2w_heading_commands(
-                        self, num_resample
-                    )
-                    info["heading_commands"] = heading_commands
-
-        if self._cfg.commands.heading_command:
-            heading_commands = self._ensure_heading_commands(info, commands_arr.shape[0])
-            base_quat = np.asarray(self._backend.get_base_quat(), dtype=get_global_dtype())
-            if base_quat.shape[0] == commands_arr.shape[0]:
-                apply_heading_yaw_feedback(commands_arr, base_quat, heading_commands, stiffness=0.5)
-        info["commands"] = commands_arr
-
-    def _compute_terminated(self, gravity: np.ndarray) -> np.ndarray:
-        del gravity
-        return np.zeros((self._num_envs,), dtype=bool)
-
-    def _raw_height_scan_obs(self, num_obs: int) -> tuple[np.ndarray | None, np.ndarray | None]:
-        return raw_height_scan_obs(self, num_obs)
-
-    def _compute_truncated(self, state: NpEnvState) -> np.ndarray:
-        truncated = super()._compute_truncated(state)
-        if self._cfg.termination_config.terrain_out_of_bounds:
-            terrain_scene = self._cfg.scene.terrain
-            terrain_cfg = terrain_scene.generator if terrain_scene is not None else None
-            np.logical_or(
-                truncated,
-                terrain_out_of_bounds(
-                    self,
-                    terrain_cfg,
-                    float(self._cfg.termination_config.terrain_distance_buffer),
-                ),
-                out=truncated,
-            )
-        return truncated
-
-
-registry.register_env("Go2WJoystickRough", Go2WJoystickRoughEnv, sim_backend="motrix")
diff --git a/src/unilab/envs/manager_based_rl_env.py b/src/unilab/envs/manager_based_rl_env.py
new file mode 100644
index 000000000..3871ff587
--- /dev/null
+++ b/src/unilab/envs/manager_based_rl_env.py
@@ -0,0 +1,764 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681),
+# src/mjlab/envs/manager_based_rl_env.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for the NumPy NpEnv/SimBackend contracts; Apache-2.0.
+"""Community-compatible manager lifecycle on UniLab's NumPy runtime."""
+
+from __future__ import annotations
+
+import math
+import secrets
+from dataclasses import dataclass, field
+from typing import Any
+
+import gymnasium as gym
+import numpy as np
+from unisim.backend.base import SimBackend
+
+from unilab.base.backend_factory import create_backend, env_backend_kwargs
+from unilab.base.base import EnvCfg
+from unilab.base.config_overrides import (
+    CONFIG_MAPPING_POLICY_KEY,
+    MANAGER_TERM_MAPPING_POLICY,
+)
+from unilab.base.entity import EntityCfg, EntityScene
+from unilab.base.np_env import NpEnv, NpEnvState
+from unilab.base.reset_state import ResetStateTransaction
+from unilab.base.scene import SceneCfg, resolve_scene_default_qpos
+from unilab.dtype_config import get_global_dtype
+from unilab.managers import (
+    ActionManager,
+    ActionTermCfg,
+    CommandManager,
+    CommandTermCfg,
+    CurriculumManager,
+    CurriculumTermCfg,
+    EventManager,
+    EventTermCfg,
+    MetricsManager,
+    MetricsTermCfg,
+    NullCommandManager,
+    NullCurriculumManager,
+    NullMetricsManager,
+    NullRecorderManager,
+    ObservationGroupCfg,
+    ObservationManager,
+    RecorderManager,
+    RecorderTermCfg,
+    RewardManager,
+    RewardTermCfg,
+    TerminationManager,
+    TerminationTermCfg,
+)
+
+
+def _manager_terms_field() -> Any:
+    return field(
+        default_factory=dict,
+        metadata={CONFIG_MAPPING_POLICY_KEY: MANAGER_TERM_MAPPING_POLICY},
+    )
+
+
+@dataclass
+class ManagerBasedRlEnvCfg(EnvCfg):
+    """Configuration for the manager-based NumPy environment.
+
+    Production task owners declare these fields in Hydra. The Registry materializes
+    them into this plain typed config on the cold path; Python factories do not mirror
+    task-specific manager or term declarations.
+    """
+
+    observations: dict[str, ObservationGroupCfg | None] = _manager_terms_field()
+    actions: dict[str, ActionTermCfg | None] = _manager_terms_field()
+    events: dict[str, EventTermCfg | None] = _manager_terms_field()
+    rewards: dict[str, RewardTermCfg | None] = _manager_terms_field()
+    terminations: dict[str, TerminationTermCfg | None] = _manager_terms_field()
+    commands: dict[str, CommandTermCfg | None] = _manager_terms_field()
+    curriculum: dict[str, CurriculumTermCfg | None] = _manager_terms_field()
+    metrics: dict[str, MetricsTermCfg | None] = _manager_terms_field()
+    recorders: dict[str, RecorderTermCfg | None] = _manager_terms_field()
+
+    seed: int | None = None
+    is_finite_horizon: bool = False
+    auto_reset: bool = True
+    scale_rewards_by_dt: bool = True
+    policy_observation_group: str = "policy"
+    critic_observation_group: str | None = None
+
+    def validate(self) -> None:
+        for name, value in (("sim_dt", self.sim_dt), ("ctrl_dt", self.ctrl_dt)):
+            if isinstance(value, bool) or not isinstance(value, (int, float, np.number)):
+                raise TypeError(f"ManagerBasedRlEnvCfg {name} must be a real number")
+            if not np.isfinite(value) or value <= 0.0:
+                raise ValueError(f"ManagerBasedRlEnvCfg {name} must be finite and positive")
+        super().validate()
+        ratio = self.ctrl_dt / self.sim_dt
+        if not np.isclose(ratio, round(ratio), rtol=0.0, atol=1e-9):
+            raise ValueError(
+                "ManagerBasedRlEnvCfg ctrl_dt must be an integer multiple of sim_dt; "
+                f"received ctrl_dt={self.ctrl_dt}, sim_dt={self.sim_dt}"
+            )
+        if self.max_episode_seconds is None:
+            raise ValueError("ManagerBasedRlEnvCfg max_episode_seconds must be finite and positive")
+        if isinstance(self.max_episode_seconds, bool) or not isinstance(
+            self.max_episode_seconds, (int, float, np.number)
+        ):
+            raise TypeError("ManagerBasedRlEnvCfg max_episode_seconds must be a real number")
+        if not np.isfinite(self.max_episode_seconds) or self.max_episode_seconds <= 0.0:
+            raise ValueError("ManagerBasedRlEnvCfg max_episode_seconds must be finite and positive")
+        if self.seed is not None and (
+            isinstance(self.seed, bool)
+            or not isinstance(self.seed, (int, np.integer))
+            or self.seed < 0
+        ):
+            raise ValueError("ManagerBasedRlEnvCfg seed must be a non-negative integer or None")
+        if self.seed is not None:
+            self.seed = int(self.seed)
+        for name in (
+            "observations",
+            "actions",
+            "events",
+            "rewards",
+            "terminations",
+            "commands",
+            "curriculum",
+            "metrics",
+            "recorders",
+        ):
+            if not isinstance(getattr(self, name), dict):
+                raise TypeError(f"ManagerBasedRlEnvCfg {name} must be a dict")
+        for name in ("is_finite_horizon", "auto_reset", "scale_rewards_by_dt"):
+            if not isinstance(getattr(self, name), bool):
+                raise TypeError(f"ManagerBasedRlEnvCfg {name} must be bool")
+        if not isinstance(self.policy_observation_group, str) or not self.policy_observation_group:
+            raise ValueError("policy_observation_group must be a non-empty string")
+        if self.critic_observation_group is not None:
+            if (
+                not isinstance(self.critic_observation_group, str)
+                or not self.critic_observation_group
+            ):
+                raise ValueError("critic_observation_group must be a non-empty string or None")
+            if self.critic_observation_group == self.policy_observation_group:
+                raise ValueError("policy and critic observation groups must be different")
+        if not isinstance(self.scene, SceneCfg):
+            raise TypeError(
+                "ManagerBasedRlEnvCfg scene must be a SceneCfg instance, "
+                f"got {type(self.scene).__name__}"
+            )
+
+
+def _resolve_backend_entity_contract(cfg: ManagerBasedRlEnvCfg) -> tuple[str, bool]:
+    """Resolve task-independent backend inputs from declared scene entities."""
+    assert cfg.scene is not None
+    root_entities: list[tuple[str, str]] = []
+    body_state_requested = False
+    for entity_name, entity_cfg in cfg.scene.entities.items():
+        if not isinstance(entity_name, str) or not entity_name:
+            raise TypeError(
+                f"ManagerBasedRlEnv scene entity names must be non-empty strings; "
+                f"got {entity_name!r}"
+            )
+        if not isinstance(entity_cfg, EntityCfg):
+            raise TypeError(
+                f"ManagerBasedRlEnv scene entity '{entity_name}' must be EntityCfg, "
+                f"got {type(entity_cfg).__name__}"
+            )
+        root_body_name = entity_cfg.root_body_name
+        if root_body_name is not None:
+            if not isinstance(root_body_name, str) or not root_body_name:
+                raise TypeError(
+                    f"ManagerBasedRlEnv root entity '{entity_name}' root_body_name must be "
+                    "a non-empty string"
+                )
+            root_entities.append((entity_name, root_body_name))
+            body_state_requested = True
+        if entity_cfg.body_names is not None:
+            body_state_requested = True
+
+    if not root_entities:
+        raise ValueError(
+            "ManagerBasedRlEnv factory requires at least one scene entity with an explicit "
+            "root_body_name"
+        )
+    primary = next((item for item in root_entities if item[0] == "robot"), None)
+    if primary is None and len(root_entities) != 1:
+        declared = [name for name, _ in root_entities]
+        raise ValueError(
+            "ManagerBasedRlEnv factory requires a conventional 'robot' root entity when "
+            f"multiple floating entities are declared; found {declared}"
+        )
+    return (primary or root_entities[0])[1], body_state_requested
+
+
+class ManagerBasedRlEnv(NpEnv):
+    """Manager-Based API adapter that reuses the single :class:`NpEnv` lifecycle."""
+
+    is_vector_env = True
+    _cfg: ManagerBasedRlEnvCfg
+    event_manager: EventManager
+    command_manager: CommandManager | NullCommandManager
+    action_manager: ActionManager
+    observation_manager: ObservationManager
+    termination_manager: TerminationManager
+    reward_manager: RewardManager
+    curriculum_manager: CurriculumManager | NullCurriculumManager
+    metrics_manager: MetricsManager | NullMetricsManager
+    recorder_manager: RecorderManager | NullRecorderManager
+
+    def __init__(self, cfg: ManagerBasedRlEnvCfg, backend: SimBackend, num_envs: int):
+        if not isinstance(cfg, ManagerBasedRlEnvCfg):
+            raise TypeError(
+                f"ManagerBasedRlEnv expected ManagerBasedRlEnvCfg, received {type(cfg).__name__}"
+            )
+        cfg.validate()
+        if isinstance(num_envs, bool) or not isinstance(num_envs, int) or num_envs <= 0:
+            raise ValueError(
+                f"ManagerBasedRlEnv num_envs must be a positive integer, got {num_envs!r}"
+            )
+        if backend.num_envs != num_envs:
+            raise ValueError(
+                f"ManagerBasedRlEnv num_envs={num_envs} does not match backend "
+                f"'{backend.backend_type}' num_envs={backend.num_envs}"
+            )
+
+        super().__init__(cfg, backend, num_envs)
+        actual_seed = cfg.seed if cfg.seed is not None else secrets.randbits(63)
+        cfg.seed = actual_seed
+        self.rng = np.random.default_rng(actual_seed)
+
+        assert cfg.scene is not None
+        default_qpos = resolve_scene_default_qpos(cfg.scene, backend)
+        self._control = np.zeros((num_envs, backend.num_actuators), dtype=get_global_dtype())
+        self._reset_state = ResetStateTransaction(backend, default_qpos=default_qpos)
+        self.scene = EntityScene.from_scene_cfg(
+            cfg.scene,
+            backend,
+            self._control,
+            reset_state=self._reset_state,
+            default_qpos=default_qpos,
+        )
+
+        self.common_step_counter = 0
+        self._sim_step_counter = 0
+        self.episode_length_buf = np.zeros(num_envs, dtype=np.int64)
+        self.reset_buf = np.zeros(num_envs, dtype=np.bool_)
+        self.reset_terminated = np.zeros(num_envs, dtype=np.bool_)
+        self.reset_time_outs = np.zeros(num_envs, dtype=np.bool_)
+        self.reward_buf = np.zeros(num_envs, dtype=get_global_dtype())
+        self.obs_buf: dict[str, np.ndarray] = {}
+        self.extras: dict[str, Any] = {"log": {}}
+        self._command_dt = np.zeros(num_envs, dtype=get_global_dtype())
+        self._no_truncation = np.zeros(num_envs, dtype=np.bool_)
+        self._manual_reset_pending = np.zeros(num_envs, dtype=np.bool_)
+        self._all_env_ids = np.arange(num_envs, dtype=np.int32)
+        self._all_env_ids.setflags(write=False)
+        self._has_transition = False
+        self._uses_pre_step_control = False
+
+        self._load_managers()
+        self._mapped_obs_dims = self._validate_observation_mapping()
+        self._validate_substep_capabilities()
+        self._configure_action_control()
+        self.set_autoreset(cfg.auto_reset)
+
+        if "startup" in self.event_manager.available_modes:
+            self.event_manager.apply(mode="startup")
+        self._materialize_backend()
+
+    def _materialize_backend(self) -> None:
+        """Finalize backend runtime resources before the first reset or step."""
+        try:
+            self._backend.materialize()
+        except NotImplementedError as exc:
+            raise NotImplementedError(
+                "ManagerBasedRlEnv lifecycle capability 'SimBackend.materialize' is "
+                f"unavailable on backend '{self._backend.backend_type}': {exc}"
+            ) from exc
+        except Exception as exc:
+            raise RuntimeError(
+                "ManagerBasedRlEnv failed to materialize backend "
+                f"'{self._backend.backend_type}' after startup events: {exc}"
+            ) from exc
+
+    @property
+    def physics_dt(self) -> float:
+        return self._cfg.sim_dt
+
+    @property
+    def step_dt(self) -> float:
+        return self._cfg.ctrl_dt
+
+    @property
+    def max_episode_length_s(self) -> float:
+        assert self._cfg.max_episode_seconds is not None
+        return self._cfg.max_episode_seconds
+
+    @property
+    def max_episode_length(self) -> int:
+        return math.ceil(self.max_episode_length_s / self.step_dt)
+
+    @property
+    def obs_groups_spec(self) -> dict[str, int]:
+        return dict(self._mapped_obs_dims)
+
+    @property
+    def action_space(self) -> gym.Space:
+        return gym.spaces.Box(
+            low=-np.inf,
+            high=np.inf,
+            shape=(self.action_manager.total_action_dim,),
+            dtype=get_global_dtype(),
+        )
+
+    @property
+    def unwrapped(self) -> ManagerBasedRlEnv:
+        return self
+
+    def _load_managers(self) -> None:
+        """Construct managers in the pinned community dependency order."""
+        self.event_manager = EventManager(self._cfg.events, self)
+        self.command_manager = (
+            CommandManager(self._cfg.commands, self) if self._cfg.commands else NullCommandManager()
+        )
+        self.action_manager = ActionManager(self._cfg.actions, self)
+        self.observation_manager = ObservationManager(self._cfg.observations, self)
+        self.termination_manager = TerminationManager(self._cfg.terminations, self)
+        self.reward_manager = RewardManager(
+            self._cfg.rewards,
+            self,
+            scale_by_dt=self._cfg.scale_rewards_by_dt,
+        )
+        self.curriculum_manager = (
+            CurriculumManager(self._cfg.curriculum, self)
+            if self._cfg.curriculum
+            else NullCurriculumManager()
+        )
+        self.metrics_manager = (
+            MetricsManager(self._cfg.metrics, self) if self._cfg.metrics else NullMetricsManager()
+        )
+        self.recorder_manager = (
+            RecorderManager(self._cfg.recorders, self)
+            if self._cfg.recorders
+            else NullRecorderManager()
+        )
+
+    def _validate_observation_mapping(self) -> dict[str, int]:
+        mapping = {"obs": self._cfg.policy_observation_group}
+        if self._cfg.critic_observation_group is not None:
+            mapping["critic"] = self._cfg.critic_observation_group
+        dims: dict[str, int] = {}
+        for output_name, group_name in mapping.items():
+            if group_name not in self.observation_manager.active_terms:
+                raise KeyError(
+                    f"ManagerBasedRlEnv observation mapping '{output_name}' requests group "
+                    f"'{group_name}', available={list(self.observation_manager.active_terms)}"
+                )
+            if not self.observation_manager.group_obs_concatenate[group_name]:
+                raise ValueError(
+                    f"ManagerBasedRlEnv observation group '{group_name}' mapped to "
+                    f"NpEnvState.obs['{output_name}'] must concatenate terms"
+                )
+            group_dim = self.observation_manager.group_obs_dim[group_name]
+            if not isinstance(group_dim, tuple) or len(group_dim) != 1:
+                raise ValueError(
+                    f"ManagerBasedRlEnv observation group '{group_name}' mapped to "
+                    f"NpEnvState.obs['{output_name}'] must be one-dimensional; got {group_dim}"
+                )
+            dims[output_name] = int(group_dim[0])
+        return dims
+
+    def _validate_substep_capabilities(self) -> None:
+        per_substep_terms = [
+            name
+            for name, term_cfg in self._cfg.metrics.items()
+            if term_cfg is not None and term_cfg.per_substep
+        ]
+        if self._cfg.sim_substeps > 1 and per_substep_terms:
+            raise NotImplementedError(
+                "MetricsManager capability 'post-physics per-substep metrics' is unavailable "
+                f"on backend '{self._backend.backend_type}' with sim_substeps="
+                f"{self._cfg.sim_substeps}; terms={per_substep_terms}. SimBackend does not "
+                "declare a post-substep hook."
+            )
+
+    def _configure_action_control(self) -> None:
+        if (
+            self._cfg.sim_substeps <= 1
+            or not self.action_manager.active_terms
+            or not self.action_manager.requires_substep_state_feedback
+        ):
+            return
+        try:
+            self._backend.set_pre_step_control(self._apply_manager_control)
+        except NotImplementedError as exc:
+            raise NotImplementedError(
+                "ActionManager capability 'state-feedback actions on every physics substep' is "
+                f"unavailable on backend '{self._backend.backend_type}': {exc}"
+            ) from exc
+        self._uses_pre_step_control = True
+
+    def _apply_manager_control(
+        self,
+        backend: SimBackend,
+        control: np.ndarray,
+    ) -> np.ndarray:
+        del backend, control
+        self._sim_step_counter += 1
+        self.action_manager.apply_action()
+        return self._control
+
+    def _initial_episode_steps(self) -> np.ndarray:
+        return np.zeros((self.num_envs,), dtype=np.uint32)
+
+    def init_state(self) -> NpEnvState:
+        state = super().init_state()
+        self.obs_buf = state.obs
+        self.reward_buf = state.reward
+        self.extras = state.info
+        return state
+
+    def step(self, actions: np.ndarray) -> NpEnvState:
+        if not self._autoreset and np.any(self._manual_reset_pending):
+            pending = np.flatnonzero(self._manual_reset_pending).tolist()
+            raise RuntimeError(
+                f"ManagerBasedRlEnv environments {pending} must be reset before step() "
+                "when auto_reset=False"
+            )
+        state = super().step(actions)
+        if not self._autoreset:
+            self._manual_reset_pending |= state.terminated | state.truncated
+        self.recorder_manager.record_post_step()
+        return state
+
+    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
+        del state
+        self.action_manager.process_action(actions)
+        if not self._uses_pre_step_control:
+            self._sim_step_counter += self._cfg.sim_substeps
+            self.action_manager.apply_action()
+        return self._control
+
+    def update_state(self, state: NpEnvState) -> NpEnvState:
+        # Physics stepping and reset/set_state lifecycles sit outside this private
+        # scope. In-phase mutations explicitly invalidate it below.
+        with self.scene._scoped_state_reads():
+            return self._update_state_in_read_phase(state)
+
+    def _update_state_in_read_phase(self, state: NpEnvState) -> NpEnvState:
+        log: dict[str, Any] = {}
+        state.info["log"] = log
+        self.extras = state.info
+
+        np.add(state.info["steps"], 1, out=self.episode_length_buf)
+        self.common_step_counter = self.step_counter + 1
+        self._sim_step_counter = self.common_step_counter * self._cfg.sim_substeps
+
+        self.termination_manager.compute()
+        if self._cfg.is_finite_horizon:
+            np.logical_or(
+                self.termination_manager.terminated,
+                self.termination_manager.time_outs,
+                out=self.reset_terminated,
+            )
+            self.reset_time_outs.fill(False)
+        else:
+            np.copyto(self.reset_terminated, self.termination_manager.terminated)
+            np.copyto(self.reset_time_outs, self.termination_manager.time_outs)
+        np.logical_or(self.reset_terminated, self.reset_time_outs, out=self.reset_buf)
+
+        self.reward_buf = self.reward_manager.compute(dt=self.step_dt)
+        log.update(self.reward_manager.step_reward_extras())
+
+        if self._cfg.sim_substeps == 1:
+            self.metrics_manager.compute_substep()
+        self.metrics_manager.compute()
+
+        applied_runtime_event = False
+        if "step" in self.event_manager.available_modes:
+            self.event_manager.apply(mode="step", dt=self.step_dt)
+            applied_runtime_event = True
+        if "interval" in self.event_manager.available_modes:
+            self.event_manager.apply(mode="interval", dt=self.step_dt)
+            applied_runtime_event = True
+        if applied_runtime_event:
+            # Event terms may mutate simulation state through their formal
+            # interval/step capabilities; EventManager does not expose whether a
+            # particular interval fired, so this boundary stays fail-closed.
+            self.scene._invalidate_state_reads()
+
+        self._command_dt.fill(self.step_dt)
+        self._command_dt[self.reset_buf] = 0.0
+        with self._reset_state.scoped(self._all_env_ids):
+            self.command_manager.compute(dt=self._command_dt)
+        if self._reset_state.last_commit_had_writes:
+            self.scene._invalidate_state_reads()
+        self.command_manager.post_compute()
+
+        manager_obs = self.observation_manager.compute(update_history=True)
+
+        self.obs_buf = self._map_observations(manager_obs)
+        self._has_transition = True
+
+        return state.replace(
+            obs=self.obs_buf,
+            reward=self.reward_buf,
+            terminated=self.reset_terminated,
+            truncated=self.reset_time_outs,
+            info=state.info,
+        )
+
+    def _compute_truncated(self, state: NpEnvState) -> np.ndarray:
+        del state
+        self._no_truncation.fill(False)
+        return self._no_truncation
+
+    def reset(
+        self,
+        env_indices: np.ndarray | None = None,
+        *,
+        seed: int | None = None,
+        env_ids: np.ndarray | None = None,
+        options: dict[str, Any] | None = None,
+    ) -> tuple[dict[str, np.ndarray], dict[str, Any]]:
+        del options
+        ids = self._normalize_reset_ids(env_indices, env_ids)
+        if seed is not None:
+            self.seed(seed)
+        if self._state is None:
+            all_ids = np.arange(self.num_envs, dtype=np.int32)
+            if not np.array_equal(ids, all_ids):
+                raise RuntimeError(
+                    "ManagerBasedRlEnv requires a full reset before the first partial reset"
+                )
+            state = self.init_state()
+            return state.obs, {"log": state.info.get("log", {})}
+
+        done_ids = ids[self.reset_buf[ids]]
+        if self._has_transition and len(done_ids) > 0:
+            self.recorder_manager.record_pre_reset(done_ids)
+
+        log: dict[str, Any] = {}
+        self.curriculum_manager.compute(env_ids=ids)
+        with self._reset_state.scoped(ids):
+            if "reset" in self.event_manager.available_modes:
+                self.event_manager.apply(
+                    mode="reset",
+                    env_ids=ids,
+                    global_env_step_count=self.step_counter,
+                )
+            log.update(self.command_manager.reset(ids))
+
+        for manager in (
+            self.observation_manager,
+            self.action_manager,
+            self.reward_manager,
+            self.metrics_manager,
+            self.curriculum_manager,
+            self.event_manager,
+            self.termination_manager,
+        ):
+            log.update(manager.reset(ids))
+
+        self.episode_length_buf[ids] = 0
+        self._control[ids] = 0.0
+        self._manual_reset_pending[ids] = False
+        if self._state is not None:
+            self._state.info["steps"][ids] = 0
+
+        # The read phase starts only after the reset-state transaction above
+        # committed, so cached getter values are post-set_state reads shared
+        # across terms (issue #1295).
+        with self.scene._scoped_state_reads():
+            self.command_manager.compute(dt=0.0, env_ids=ids)
+            self.command_manager.post_compute()
+            # Row-scoped reset rebuild (issue #1259 R2): the observation manager
+            # returns only the reset rows, so no full-batch slice is needed here.
+            manager_obs = self.observation_manager.compute(update_history=True, env_ids=ids)
+        mapped_obs = self._map_observations(manager_obs, num_rows=len(ids))
+        reset_obs = {name: values.copy() for name, values in mapped_obs.items()}
+
+        if self._state is not None:
+            for name, values in reset_obs.items():
+                self._state.obs[name][ids] = values
+            if self._autoreset_reset_active:
+                # Autoreset runs at the tail of step(): keep this step's
+                # per-step log entries (reward/* etc., computed pre-reset) and
+                # layer the reset extras (Episode_Reward/* etc.) on top, so
+                # consumers still see the transition's reward breakdown.
+                step_log = self._state.info.get("log")
+                if step_log:
+                    log = {**step_log, **log}
+            self._state.info["log"] = log
+            if not self._autoreset_reset_active:
+                self._state.terminated[ids] = False
+                self._state.truncated[ids] = False
+                self.reset_buf[ids] = False
+                self.reset_terminated[ids] = False
+                self.reset_time_outs[ids] = False
+        self.obs_buf = self._state.obs if self._state is not None else mapped_obs
+        self.extras = self._state.info if self._state is not None else {"log": log}
+        self.recorder_manager.record_post_reset(ids)
+        return reset_obs, {"log": log}
+
+    def _collect_reset_backend_timing_ms(self) -> dict[str, float]:
+        timing = dict(super()._collect_reset_backend_timing_ms())
+        timing.update(self._reset_state.last_set_state_timing_ms)
+        return timing
+
+    def _normalize_reset_ids(
+        self,
+        env_indices: np.ndarray | None,
+        env_ids: np.ndarray | None,
+    ) -> np.ndarray:
+        if env_indices is not None and env_ids is not None:
+            raise ValueError("Pass either env_indices or env_ids, not both")
+        values = env_ids if env_ids is not None else env_indices
+        if values is None:
+            return np.arange(self.num_envs, dtype=np.int32)
+        raw = np.asarray(values)
+        if (
+            raw.ndim != 1
+            or not np.issubdtype(raw.dtype, np.integer)
+            or np.issubdtype(raw.dtype, np.bool_)
+        ):
+            raise TypeError(
+                "ManagerBasedRlEnv reset env IDs must be a 1-D integer np.ndarray; "
+                f"got shape={raw.shape}, dtype={raw.dtype}"
+            )
+        ids = np.asarray(raw, dtype=np.int32)
+        if np.any(ids < 0) or np.any(ids >= self.num_envs):
+            raise IndexError(
+                f"ManagerBasedRlEnv reset env IDs out of range for {self.num_envs} envs: "
+                f"{ids.tolist()}"
+            )
+        if np.unique(ids).size != ids.size:
+            raise ValueError(f"ManagerBasedRlEnv reset env IDs contain duplicates: {ids.tolist()}")
+        return ids
+
+    def _map_observations(
+        self,
+        manager_obs: dict[str, np.ndarray | dict[str, np.ndarray]],
+        num_rows: int | None = None,
+    ) -> dict[str, np.ndarray]:
+        mapping = {"obs": self._cfg.policy_observation_group}
+        if self._cfg.critic_observation_group is not None:
+            mapping["critic"] = self._cfg.critic_observation_group
+        mapped: dict[str, np.ndarray] = {}
+        for output_name, group_name in mapping.items():
+            value = manager_obs[group_name]
+            if not isinstance(value, np.ndarray):
+                raise TypeError(
+                    f"ManagerBasedRlEnv observation group '{group_name}' returned "
+                    f"{type(value).__name__}, expected np.ndarray"
+                )
+            expected = (
+                self.num_envs if num_rows is None else num_rows,
+                self._mapped_obs_dims[output_name],
+            )
+            if value.shape != expected:
+                raise ValueError(
+                    f"ManagerBasedRlEnv observation group '{group_name}' returned shape "
+                    f"{value.shape}, expected {expected} for NpEnvState.obs['{output_name}']"
+                )
+            mapped[output_name] = value
+        return mapped
+
+    def get_observations(self) -> dict[str, np.ndarray]:
+        if self._state is None:
+            return self.init_state().obs
+        self.obs_buf = self._state.obs
+        return self.obs_buf
+
+    def set_episode_length_buf(self, values: np.ndarray) -> None:
+        """Overwrite per-env episode counters (cold path, runner-init only).
+
+        ``episode_length_buf`` mirrors ``state.info["steps"]``: each step writes
+        ``info["steps"] + 1`` into the buffer and ``reset()`` zeroes both, so a
+        direct assignment must update the two together. RL runners (e.g. RSL-RL
+        ``init_at_random_ep_len``) call this once before learning starts to
+        stagger initial episode lengths across envs.
+        """
+        values = np.asarray(values)
+        if values.shape != (self.num_envs,):
+            raise ValueError(
+                f"ManagerBasedRlEnv.set_episode_length_buf expects shape "
+                f"({self.num_envs},), got {values.shape}"
+            )
+        values = values.astype(np.int64)
+        if np.any(values < 0):
+            raise ValueError("episode length counters must be non-negative")
+        np.copyto(self.episode_length_buf, values)
+        if self._state is not None:
+            np.copyto(self._state.info["steps"], values, casting="unsafe")
+
+    def seed(self, seed: int = -1) -> int:
+        if seed == -1:
+            seed = secrets.randbits(63)
+        if isinstance(seed, bool) or not isinstance(seed, int) or seed < 0:
+            raise ValueError(f"ManagerBasedRlEnv seed must be a non-negative integer, got {seed!r}")
+        replacement = np.random.default_rng(seed)
+        self.rng.bit_generator.state = replacement.bit_generator.state
+        self._cfg.seed = seed
+        return seed
+
+    def close(self) -> None:
+        self.recorder_manager.close()
+        if self._uses_pre_step_control:
+            self._backend.set_pre_step_control(None)
+            self._uses_pre_step_control = False
+        super().close()
+
+
+def make_manager_based_rl_env(
+    cfg: ManagerBasedRlEnvCfg,
+    num_envs: int = 1,
+    backend_type: str = "mujoco",
+) -> ManagerBasedRlEnv:
+    """Construct the generic Registry-owned Manager-Based production runtime."""
+    if not isinstance(cfg, ManagerBasedRlEnvCfg):
+        raise TypeError(
+            "make_manager_based_rl_env expected ManagerBasedRlEnvCfg, "
+            f"received {type(cfg).__name__}"
+        )
+    if isinstance(num_envs, bool) or not isinstance(num_envs, int) or num_envs <= 0:
+        raise ValueError(
+            f"make_manager_based_rl_env num_envs must be a positive integer, got {num_envs!r}"
+        )
+    if not isinstance(backend_type, str) or not backend_type:
+        raise ValueError(
+            "make_manager_based_rl_env backend_type must be a non-empty string, "
+            f"got {backend_type!r}"
+        )
+
+    cfg.validate()
+    assert cfg.scene is not None
+    base_name, body_state_requested = _resolve_backend_entity_contract(cfg)
+    backend_kwargs = env_backend_kwargs(cfg)
+    backend_kwargs["base_name"] = base_name
+
+    backend = create_backend(
+        backend_type,
+        cfg.scene,
+        num_envs,
+        cfg.sim_dt,
+        body_state_required=body_state_requested,
+        **backend_kwargs,
+    )
+    try:
+        return ManagerBasedRlEnv(cfg, backend, num_envs)
+    except Exception:
+        backend.cleanup_scene_assets()
+        raise
+
+
+# Isaac Lab capitalization is a spelling-only alias.  There is one implementation.
+ManagerBasedRLEnv = ManagerBasedRlEnv
+ManagerBasedRLEnvCfg = ManagerBasedRlEnvCfg
+
+__all__ = [
+    "ManagerBasedRLEnv",
+    "ManagerBasedRLEnvCfg",
+    "ManagerBasedRlEnv",
+    "ManagerBasedRlEnvCfg",
+    "make_manager_based_rl_env",
+]
diff --git a/src/unilab/envs/manipulation/__init__.py b/src/unilab/envs/manipulation/__init__.py
deleted file mode 100644
index 16b529f14..000000000
--- a/src/unilab/envs/manipulation/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-"""Manipulation env registry bootstrap contract."""
-
-__unilab_registry_modules__ = (
-    "unilab.envs.manipulation.allegro_inhand",
-    "unilab.envs.manipulation.sharpa_inhand",
-    "unilab.envs.manipulation.stewart",
-)
diff --git a/src/unilab/envs/manipulation/allegro_inhand/__init__.py b/src/unilab/envs/manipulation/allegro_inhand/__init__.py
deleted file mode 100644
index bb042cf8a..000000000
--- a/src/unilab/envs/manipulation/allegro_inhand/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from . import (
-    grasp_gen,  # registers AllegroInhandRotationGrasp via @registry decorators
-    rotation,  # registers AllegroInhandRotation via @registry decorators
-)
diff --git a/src/unilab/envs/manipulation/allegro_inhand/base.py b/src/unilab/envs/manipulation/allegro_inhand/base.py
deleted file mode 100644
index 2a93735a8..000000000
--- a/src/unilab/envs/manipulation/allegro_inhand/base.py
+++ /dev/null
@@ -1,147 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-
-import gymnasium as gym
-import numpy as np
-
-from unilab.base.backend import SimBackend
-from unilab.base.base import EnvCfg
-from unilab.base.np_env import NpEnv, NpEnvState
-from unilab.dtype_config import get_global_dtype
-
-
-@dataclass
-class NoiseConfig:
-    level: float = 1.0
-    scale_joint_angle: float = 0.02
-
-
-@dataclass
-class ControlConfig:
-    action_scale: float = 1.0 / 24.0
-    kp: float = 1.0
-    kd: float = 0.1
-
-
-@dataclass
-class AllegroBaseCfg(EnvCfg):
-    sim_dt: float = 0.005
-    ctrl_dt: float = 0.05
-    noise_config: NoiseConfig = field(default_factory=NoiseConfig)
-    control_config: ControlConfig = field(default_factory=ControlConfig)
-
-
-class AllegroBaseEnv(NpEnv):
-    _NUM_HAND_DOF: int = 16
-    _FINGERTIP_BODY_NAMES: tuple[str, ...] = ("ff_tip", "mf_tip", "rf_tip", "th_tip")
-    _cfg: AllegroBaseCfg
-    _init_qpos: np.ndarray
-    _init_qvel: np.ndarray
-
-    def __init__(self, cfg: AllegroBaseCfg, backend: SimBackend, num_envs: int = 1):
-        super().__init__(cfg, backend, num_envs)
-
-        self._np_dtype = get_global_dtype()
-        actuator_range = np.asarray(self._backend.get_actuator_ctrl_range(), dtype=self._np_dtype)
-        if actuator_range.shape[0] < self._NUM_HAND_DOF:
-            raise ValueError(
-                f"Model has {actuator_range.shape[0]} actuators, expected at least {self._NUM_HAND_DOF}"
-            )
-        self._ctrl_lower = np.asarray(actuator_range[: self._NUM_HAND_DOF, 0], dtype=self._np_dtype)
-        self._ctrl_upper = np.asarray(actuator_range[: self._NUM_HAND_DOF, 1], dtype=self._np_dtype)
-
-        self._init_action_space()
-        self._num_action = self._action_space.shape[0]
-        if self._num_action != self._NUM_HAND_DOF:
-            raise ValueError(f"Expected {self._NUM_HAND_DOF} actuators, got {self._num_action}")
-
-        self._init_buffers()
-        self.nq = int(self._init_qpos.shape[0])
-        self.nv = int(self._init_qvel.shape[0])
-
-        self._ball_body_ids = self._backend.get_body_ids(["ball"])
-        self._fingertip_body_ids = self._backend.get_body_ids(self._FINGERTIP_BODY_NAMES)
-
-    def _init_action_space(self) -> None:
-        self._action_space = gym.spaces.Box(
-            low=-1.0,
-            high=1.0,
-            shape=(self._NUM_HAND_DOF,),
-            dtype=np.float32,
-        )
-
-    @property
-    def action_space(self) -> gym.spaces.Box:
-        return self._action_space  # type: ignore[no-any-return]
-
-    def _init_buffers(self) -> None:
-        self.default_angles = np.zeros((self._num_action,), dtype=self._np_dtype)
-        self._init_qpos = self._resolve_init_qpos()
-        self.default_angles = np.asarray(
-            self._init_qpos[: self._NUM_HAND_DOF], dtype=self._np_dtype
-        )
-        self._init_qvel = np.asarray(self._backend.get_init_qvel(), dtype=self._np_dtype)
-
-    def _resolve_init_qpos(self) -> np.ndarray:
-        for key_name in ("home", "stand", "default"):
-            try:
-                return np.asarray(self._backend.get_keyframe_qpos(key_name), dtype=self._np_dtype)
-            except Exception:
-                continue
-        raise ValueError("Could not resolve initial qpos from backend keyframes")
-
-    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
-        clipped_actions = np.asarray(np.clip(actions, -1.0, 1.0), dtype=self._np_dtype)
-        state.info["last_actions"] = state.info.get(
-            "current_actions", np.zeros_like(clipped_actions)
-        )
-        state.info["current_actions"] = clipped_actions
-
-        prev_ctrl = state.info.get(
-            "prev_ctrl",
-            np.broadcast_to(
-                self.default_angles, (clipped_actions.shape[0], self._num_action)
-            ).copy(),
-        )
-        new_ctrl = prev_ctrl + self._cfg.control_config.action_scale * clipped_actions
-        new_ctrl = np.clip(new_ctrl, self._ctrl_lower, self._ctrl_upper)
-        prev_ctrl = np.asarray(new_ctrl, dtype=self._np_dtype)
-        state.info["prev_ctrl"] = prev_ctrl
-        return prev_ctrl
-
-    def get_hand_dof_pos(self) -> np.ndarray:
-        return np.asarray(
-            self._backend.get_dof_pos()[:, : self._NUM_HAND_DOF],
-            dtype=self._np_dtype,
-        )
-
-    def get_hand_dof_vel(self) -> np.ndarray:
-        return np.asarray(
-            self._backend.get_dof_vel()[:, : self._NUM_HAND_DOF],
-            dtype=self._np_dtype,
-        )
-
-    def get_ball_pos(self) -> np.ndarray:
-        return np.asarray(
-            self._backend.get_body_pos_w(self._ball_body_ids)[:, 0, :],
-            dtype=self._np_dtype,
-        )
-
-    def get_ball_quat(self) -> np.ndarray:
-        return np.asarray(
-            self._backend.get_body_quat_w(self._ball_body_ids)[:, 0, :],
-            dtype=self._np_dtype,
-        )
-
-    def get_fingertip_pos(self) -> np.ndarray:
-        return np.asarray(
-            self._backend.get_body_pos_w(self._fingertip_body_ids),
-            dtype=self._np_dtype,
-        )
-
-    def get_sensor_data(self, name: str) -> np.ndarray:
-        return np.asarray(self._backend.get_sensor_data(name), dtype=self._np_dtype)
-
-
-AllegroBaseMjEnv = AllegroBaseEnv
diff --git a/src/unilab/envs/manipulation/allegro_inhand/grasp_gen.py b/src/unilab/envs/manipulation/allegro_inhand/grasp_gen.py
deleted file mode 100644
index 238d7df58..000000000
--- a/src/unilab/envs/manipulation/allegro_inhand/grasp_gen.py
+++ /dev/null
@@ -1,238 +0,0 @@
-"""Allegro grasp-generation task built on top of rotation env."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from pathlib import Path
-
-import numpy as np
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base import registry
-from unilab.base.np_env import NpEnvState
-from unilab.base.run_control import RunComplete
-
-from .rotation import AllegroRotationPPO, AllegroRotationPPOCfg, RewardConfigPPO
-
-
-@registry.envcfg("AllegroInhandRotationGrasp")
-@dataclass
-class AllegroRotationGraspCfg(AllegroRotationPPOCfg):
-    # These are fallback defaults. Hydra task env overrides (e.g.
-    # conf/ppo/task/allegro_inhand_grasp/mujoco.yaml and CLI env.*)
-    # are applied at env construction and take precedence.
-    max_episode_seconds: float = 2.0
-    reward_config: RewardConfigPPO = field(
-        default_factory=lambda: RewardConfigPPO(
-            scales={
-                "rotate": 0.0,
-                "obj_linvel": 0.0,
-                "pose_diff": 0.0,
-                "torque": 0.0,
-                "work": 0.0,
-                "drop": 0.0,
-            },
-            angvel_clip_min=-0.5,
-            angvel_clip_max=0.5,
-            reset_z_threshold=0.125,
-        )
-    )
-    gen_grasp: bool = True
-    grasp_collection_target: int = 50_000
-    grasp_auto_save: bool = True
-    grasp_quality_check: bool = True
-    grasp_min_contacts: int = 2
-
-
-@registry.env("AllegroInhandRotationGrasp", sim_backend="mujoco")
-@registry.env("AllegroInhandRotationGrasp", sim_backend="motrix")
-class AllegroRotationGrasp(AllegroRotationPPO):
-    _cfg: AllegroRotationGraspCfg
-    _CONTACT_SENSORS = ("ff_contact", "mf_contact", "rf_contact", "th_contact")
-
-    def __init__(
-        self, cfg: AllegroRotationGraspCfg, num_envs: int = 1, backend_type: str = "mujoco"
-    ) -> None:
-        super().__init__(cfg, num_envs=num_envs, backend_type=backend_type)
-        self._saved_grasping_states: list[np.ndarray] = []
-        self._grasp_cache_saved = False
-        self._grasp_target_reached_notified = False
-
-    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
-        del actions
-        zero_actions = np.zeros((self._num_envs, self._NUM_HAND_DOF), dtype=self._np_dtype)
-        return super().apply_action(zero_actions, state)
-
-    @staticmethod
-    def _sensor_scalar(sensor_data: np.ndarray) -> np.ndarray:
-        sensor_data = np.asarray(sensor_data)
-        if sensor_data.ndim == 1:
-            return sensor_data
-        return sensor_data.reshape(sensor_data.shape[0], -1)[:, 0]
-
-    def _contact_count(self) -> np.ndarray:
-        contacts = np.stack(
-            [self._sensor_scalar(self.get_sensor_data(name)) for name in self._CONTACT_SENSORS],
-            axis=1,
-        )
-        return np.asarray(np.sum(contacts > 0.5, axis=1), dtype=np.int32)
-
-    def _compute_grasp_conditions(self) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        # Hora-style good grasp criteria from compute_reward:
-        # 1) all fingertips near object, 2) enough fingertip contacts, 3) object not dropped.
-        ball_pos = self.get_ball_pos()
-        fingertip_pos = self.get_fingertip_pos()
-
-        cond1 = np.all(np.linalg.norm(fingertip_pos - ball_pos[:, None, :], axis=-1) < 0.1, axis=1)
-        cond2 = self._contact_count() >= int(self._cfg.grasp_min_contacts)
-        cond3 = ball_pos[:, 2] > float(self._reward_cfg.reset_z_threshold)
-        return (
-            np.asarray(cond1, dtype=bool),
-            np.asarray(cond2, dtype=bool),
-            np.asarray(cond3, dtype=bool),
-        )
-
-    def _check_grasp_quality(self, env_ids: np.ndarray) -> np.ndarray:
-        cond1, cond2, cond3 = self._compute_grasp_conditions()
-        return np.asarray(cond1[env_ids] & cond2[env_ids] & cond3[env_ids], dtype=bool)
-
-    def _total_saved_grasps(self) -> int:
-        if not self._saved_grasping_states:
-            return 0
-        return int(sum(states.shape[0] for states in self._saved_grasping_states))
-
-    def _stop_collection(self) -> None:
-        if self._grasp_target_reached_notified:
-            return
-
-        target = int(self._cfg.grasp_collection_target)
-        if target <= 0:
-            return
-
-        total = self._total_saved_grasps()
-        if total < target:
-            return
-
-        self._grasp_target_reached_notified = True
-        print(
-            "[AllegroInhandRotationGrasp] Grasp collection target reached "
-            f"({total}/{target}). Collection completed."
-        )
-
-        if self.state is not None:
-            log = self.state.info.get("log", {})
-            log["grasp/target_reached"] = 1.0
-            self.state.info["log"] = log
-
-        raise RunComplete(
-            reason="grasp_collection_target_reached",
-            summary={
-                "collected_grasps": int(total),
-                "saved_grasps": int(min(total, target)),
-                "grasp_collection_target": target,
-            },
-        )
-
-    def _save_grasp_cache(self, force: bool = False) -> None:
-        if self._grasp_cache_saved:
-            return
-
-        total = self._total_saved_grasps()
-        target = int(self._cfg.grasp_collection_target)
-        if not force and total < target:
-            return
-
-        if total == 0:
-            return
-
-        all_states = np.concatenate(self._saved_grasping_states, axis=0).astype(np.float32)
-        if target > 0:
-            all_states = all_states[:target]
-
-        output_file = Path(self._cfg.grasp_cache_path or "caches/allegro_grasp_50k.npy")
-        if not output_file.is_absolute():
-            output_file = ASSETS_ROOT_PATH / output_file
-        output_file.parent.mkdir(parents=True, exist_ok=True)
-        np.save(output_file, all_states)
-
-        self._grasp_cache_saved = True
-        if self.state is not None:
-            log = self.state.info.get("log", {})
-            log["grasp_cache/saved"] = 1.0
-            log["grasp_cache/num_states"] = float(all_states.shape[0])
-            self.state.info["log"] = log
-
-    def _collect_successful_grasps(self, env_ids: np.ndarray) -> None:
-        if self.state is None or env_ids.size == 0:
-            return
-
-        success_mask = self.state.truncated[env_ids] & ~self.state.terminated[env_ids]
-        if not np.any(success_mask):
-            return
-
-        success_env_ids = env_ids[np.flatnonzero(success_mask)]
-        if self._cfg.grasp_quality_check:
-            quality_mask = self._check_grasp_quality(success_env_ids)
-            success_env_ids = success_env_ids[np.flatnonzero(quality_mask)]
-
-        if success_env_ids.size == 0:
-            return
-
-        curr_dof_pos = np.asarray(self.state.info.get("curr_dof_pos", self.get_hand_dof_pos()))
-        curr_ball_pos = np.asarray(self.state.info.get("curr_ball_pos", self.get_ball_pos()))
-        curr_ball_quat = np.asarray(self.state.info.get("curr_ball_quat", self.get_ball_quat()))
-
-        hand_qpos = curr_dof_pos[success_env_ids, : self._NUM_HAND_DOF]
-        ball_pos = curr_ball_pos[success_env_ids]
-        ball_quat = curr_ball_quat[success_env_ids]
-        states = np.concatenate([hand_qpos, ball_pos, ball_quat], axis=1).astype(np.float32)
-
-        self._saved_grasping_states.append(states)
-        self._save_grasp_cache()
-        self._stop_collection()
-
-        if self.state is not None:
-            log = self.state.info.get("log", {})
-            log["grasp/cache_size"] = float(self._total_saved_grasps())
-            self.state.info["log"] = log
-
-    def update_state(self, state: NpEnvState) -> NpEnvState:
-        next_state = super().update_state(state)
-        reward = np.zeros((self._num_envs,), dtype=self._np_dtype)
-
-        cond1, cond2, cond3 = self._compute_grasp_conditions()
-        if self._cfg.grasp_quality_check:
-            grasp_valid = cond1 & cond2 & cond3
-            terminated = np.asarray(next_state.terminated | (~grasp_valid), dtype=bool)
-        else:
-            grasp_valid = np.ones((self._num_envs,), dtype=bool)
-            terminated = np.asarray(next_state.terminated, dtype=bool)
-
-        step_count = next_state.info.get("steps", np.zeros((self._num_envs,), dtype=np.uint32))
-        should_log = self._enable_reward_log and (int(step_count[0]) % 4 == 0)
-        if should_log:
-            log = next_state.info.get("log", {})
-            log["grasp/cond1"] = float(np.mean(cond1.astype(np.float32)))
-            log["grasp/cond2"] = float(np.mean(cond2.astype(np.float32)))
-            log["grasp/cond3"] = float(np.mean(cond3.astype(np.float32)))
-            log["grasp/valid"] = float(np.mean(grasp_valid.astype(np.float32)))
-            log["grasp/cache_size"] = float(self._total_saved_grasps())
-            next_state.info["log"] = log
-
-        return next_state.replace(reward=reward, terminated=terminated)
-
-    def _reset_done_envs(self) -> None:
-        if self.state is not None:
-            done = self.state.terminated | self.state.truncated
-            if np.any(done):
-                env_ids = np.flatnonzero(done).astype(np.int32)
-                self._collect_successful_grasps(env_ids)
-        super()._reset_done_envs()
-
-    def close(self) -> None:
-        self._save_grasp_cache(force=bool(self._cfg.grasp_auto_save))
-        super().close()
-
-
-AllegroRotationGraspEnv = AllegroRotationGrasp
-AllegroRotationGraspCfgAlias = AllegroRotationGraspCfg
diff --git a/src/unilab/envs/manipulation/allegro_inhand/rotation.py b/src/unilab/envs/manipulation/allegro_inhand/rotation.py
deleted file mode 100644
index 87faf5952..000000000
--- a/src/unilab/envs/manipulation/allegro_inhand/rotation.py
+++ /dev/null
@@ -1,517 +0,0 @@
-"""Allegro in-hand rotation environment."""
-
-from __future__ import annotations
-
-import logging
-from dataclasses import dataclass, field
-from typing import Any, cast
-
-import numpy as np
-from etils import epath
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base import registry
-from unilab.base.backend import create_backend, env_backend_kwargs
-from unilab.base.np_env import NpEnvState
-from unilab.base.scene import SceneCfg
-from unilab.dr import (
-    DomainRandomizationCapabilities,
-    DomainRandomizationProvider,
-    IntervalRandomizationPlan,
-    ResetPlan,
-)
-from unilab.dr.dr_utils import (
-    build_common_reset_randomization,
-    build_interval_push_plan,
-    validate_common_reset_randomization,
-    validate_interval_push_support,
-    zero_actions,
-)
-from unilab.dtype_config import get_global_dtype
-from unilab.utils.geometry import (
-    np_normalize_axis,
-    np_quat_angular_velocity_from_pair,
-)
-
-from .base import AllegroBaseCfg, AllegroBaseEnv
-
-logger = logging.getLogger(__name__)
-
-
-def resolve_grasp_cache_path(cache_path: str) -> epath.Path:
-    """Resolve Allegro grasp cache paths using the asset-root convention."""
-    path = epath.Path(cache_path)
-    if path.is_absolute() or path.exists():
-        return path
-    return epath.Path(ASSETS_ROOT_PATH / cache_path)
-
-
-def normalize_rotation_axis(rotation_axis: tuple[float, float, float]) -> np.ndarray:
-    # Cast to the training dtype first so the norm and division happen at that
-    # precision, matching the pre-refactor bit-exact behavior for float32 runs.
-    axis = np.asarray(rotation_axis, dtype=get_global_dtype())
-    return np.asarray(np_normalize_axis(axis), dtype=get_global_dtype())
-
-
-def compute_ball_angvel(
-    ball_quat: np.ndarray, prev_ball_quat: np.ndarray, ctrl_dt: float
-) -> np.ndarray:
-    return np.asarray(
-        np_quat_angular_velocity_from_pair(ball_quat, prev_ball_quat, ctrl_dt),
-        dtype=get_global_dtype(),
-    )
-
-
-def compute_pd_torques(
-    targets: np.ndarray, dof_pos: np.ndarray, dof_vel: np.ndarray, kp: float, kd: float
-) -> np.ndarray:
-    torques = kp * (targets - dof_pos) - kd * dof_vel
-    return np.asarray(np.clip(torques, -0.5, 0.5), dtype=get_global_dtype())
-
-
-def build_obs_lag_history(
-    init_obs: np.ndarray, num_lag_steps: int, num_obs_per_step: int
-) -> np.ndarray:
-    num_envs = init_obs.shape[0]
-    history = np.broadcast_to(
-        init_obs[:, None, :],
-        (num_envs, num_lag_steps, num_obs_per_step),
-    ).copy()
-    return np.asarray(history, dtype=init_obs.dtype)
-
-
-def sample_cached_grasps(
-    grasp_cache: np.ndarray, num_reset: int
-) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-    idx = np.random.randint(0, len(grasp_cache), size=num_reset)
-    sampled = grasp_cache[idx]
-    return sampled[:, :16], sampled[:, 16:19], sampled[:, 19:23]
-
-
-def _materialize_grasp_cache(cfg: AllegroRotationPPOCfg) -> np.ndarray | None:
-    """Load the optional Allegro grasp cache on the env construction path."""
-    if cfg.gen_grasp:
-        return None
-
-    cache_path = resolve_grasp_cache_path(cfg.grasp_cache_path)
-    if not cache_path.exists():
-        logger.warning(
-            "[allegro_inhand] Grasp cache is missing; no Hugging Face download will be "
-            "attempted. Expected local cache: %s. Generate one with "
-            "`uv run train --algo ppo --task allegro_inhand_grasp --sim mujoco "
-            "training.no_play=true`, or point `env.grasp_cache_path` at an existing "
-            "local cache.",
-            cache_path,
-        )
-        return None
-
-    grasp_cache = np.load(cache_path).astype(np.float64)
-    logger.info(
-        "[allegro_inhand] Loaded grasp cache: %s, shape=%s, dtype=%s",
-        cache_path,
-        grasp_cache.shape,
-        grasp_cache.dtype,
-    )
-    return grasp_cache
-
-
-@dataclass
-class RewardConfigPPO:
-    scales: dict[str, float]
-    angvel_clip_min: float
-    angvel_clip_max: float
-    reset_z_threshold: float
-
-
-@dataclass
-class DomainRandConfig:
-    randomize_base_mass: bool = False
-    added_mass_range: list[float] = field(default_factory=lambda: [0.0, 0.0])
-    random_com: bool = False
-    com_offset_x: list[float] = field(default_factory=lambda: [0.0, 0.0])
-    randomize_gravity: bool = False
-    gravity_range: list[list[float]] = field(
-        default_factory=lambda: [[0.0, 0.0, -9.81], [0.0, 0.0, -9.81]]
-    )
-    push_robots: bool = False
-    push_interval: int = 750
-    max_force: list[float] = field(default_factory=lambda: [1.0, 1.0, 0.5])
-    push_body_name: str | None = None
-    joint_noise: float = 0.0
-    ball_vel_noise: float = 0.0
-    ball_z_offset: float = 0.0
-
-
-@registry.envcfg("AllegroInhandRotation")
-@dataclass
-class AllegroRotationPPOCfg(AllegroBaseCfg):
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "allegro_hand" / "scene.xml")
-        )
-    )
-    max_episode_seconds: float = 20.0
-    reward_config: RewardConfigPPO | None = None
-    domain_rand: DomainRandConfig = field(default_factory=DomainRandConfig)
-    rotation_axis: tuple[float, float, float] = (0.0, 0.0, 1.0)
-    grasp_cache_path: str = "caches/allegro_grasp_50k.npy"
-    gen_grasp: bool = False
-
-
-class AllegroRotationDomainRandomizationProvider(DomainRandomizationProvider):
-    def validate(self, env: Any, capabilities: DomainRandomizationCapabilities) -> None:
-        validate_common_reset_randomization(env, capabilities)
-        validate_interval_push_support(env, capabilities)
-
-    def build_interval_randomization_plan(
-        self, env: Any, step_counter: int
-    ) -> IntervalRandomizationPlan | None:
-        return build_interval_push_plan(env, step_counter)
-
-    def _sample_reset_state(
-        self, env: Any, num_reset: int
-    ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
-        dr = env.cfg.domain_rand
-        grasp_cache = cast(np.ndarray | None, env._grasp_cache)
-        if grasp_cache is not None:
-            hand_qpos, ball_pos, ball_quat = sample_cached_grasps(grasp_cache, num_reset)
-        else:
-            hand_qpos = np.broadcast_to(env.default_angles, (num_reset, env._NUM_HAND_DOF)).copy()
-            hand_qpos += np.random.uniform(-dr.joint_noise, dr.joint_noise, hand_qpos.shape).astype(
-                np.float64
-            )
-            hand_qpos = np.clip(
-                hand_qpos,
-                env._ctrl_lower.astype(np.float64),
-                env._ctrl_upper.astype(np.float64),
-            )
-            ball_init_pos = env._init_qpos[env._NUM_HAND_DOF : env._NUM_HAND_DOF + 3]
-            ball_pos = np.broadcast_to(ball_init_pos, (num_reset, 3)).copy()
-            ball_pos[:, 2] += dr.ball_z_offset
-            ball_quat = np.tile([1.0, 0.0, 0.0, 0.0], (num_reset, 1))
-
-        qvel = np.zeros((num_reset, env.nv), dtype=np.float64)
-        qvel[:, env._NUM_HAND_DOF : env._NUM_HAND_DOF + 3] = np.random.uniform(
-            -dr.ball_vel_noise,
-            dr.ball_vel_noise,
-            (num_reset, 3),
-        )
-        return hand_qpos, ball_pos, ball_quat, qvel
-
-    def _build_info_updates(
-        self,
-        env: Any,
-        hand_qpos: np.ndarray,
-        ball_pos: np.ndarray,
-        ball_quat: np.ndarray,
-    ) -> dict[str, np.ndarray]:
-        num_reset = hand_qpos.shape[0]
-        dtype = get_global_dtype()
-
-        init_ctrl = np.asarray(hand_qpos, dtype=dtype)
-        init_ball_pos = np.asarray(ball_pos, dtype=dtype)
-        dof_pos_norm = 2.0 * (init_ctrl - env._dof_mid) / (env._dof_range + 1e-8)
-        init_obs = np.concatenate([dof_pos_norm, init_ctrl, init_ball_pos], axis=1, dtype=dtype)
-        obs_lag_history = build_obs_lag_history(init_obs, env._NUM_LAG_STEPS, env._NUM_OBS_PER_STEP)
-
-        return {
-            "current_actions": zero_actions(num_reset, env._num_action),
-            "last_actions": zero_actions(num_reset, env._num_action),
-            "prev_ctrl": init_ctrl,
-            "init_pose": init_ctrl.copy(),
-            "prev_dof_pos": init_ctrl.copy(),
-            "prev_ball_pos": init_ball_pos.copy(),
-            "prev_ball_quat": np.asarray(ball_quat, dtype=dtype).copy(),
-            "obs_lag_history": obs_lag_history,
-        }
-
-    def build_reset_plan(self, env: Any, env_ids: np.ndarray) -> ResetPlan:
-        num_reset = len(env_ids)
-        hand_qpos, ball_pos, ball_quat, qvel = self._sample_reset_state(env, num_reset)
-        qpos = np.concatenate([hand_qpos, ball_pos, ball_quat], axis=1, dtype=np.float64)
-        info_updates = self._build_info_updates(env, hand_qpos, ball_pos, ball_quat)
-
-        return ResetPlan(
-            env_ids=env_ids,
-            qpos=qpos,
-            qvel=qvel,
-            info_updates=info_updates,
-            randomization=build_common_reset_randomization(env, num_reset),
-        )
-
-    def build_reset_observation(
-        self, env: Any, env_ids: np.ndarray, info_updates: dict[str, Any]
-    ) -> dict[str, np.ndarray]:
-        del env_ids
-        return cast(
-            dict[str, np.ndarray],
-            env._compute_obs(
-                info_updates,
-                info_updates["prev_ctrl"],
-                info_updates["prev_ball_pos"],
-            ),
-        )
-
-
-# ─────────────────────────── Environment ──────────────────────────────
-
-
-@registry.env("AllegroInhandRotation", sim_backend="drake")
-@registry.env("AllegroInhandRotation", sim_backend="mujoco")
-@registry.env("AllegroInhandRotation", sim_backend="motrix")
-class AllegroRotationPPO(AllegroBaseEnv):
-    _cfg: AllegroRotationPPOCfg
-    _reward_cfg: RewardConfigPPO
-
-    _NUM_OBS_PER_STEP = 35
-    _NUM_LAG_STEPS = 3
-
-    def __init__(
-        self, cfg: AllegroRotationPPOCfg, num_envs: int = 1, backend_type: str = "mujoco"
-    ) -> None:
-        if cfg.reward_config is None:
-            raise ValueError("reward_config must be provided via Hydra configuration")
-        backend = create_backend(
-            backend_type,
-            cfg.scene,
-            num_envs,
-            cfg.sim_dt,
-            base_name="palm",
-            push_body_name=cfg.domain_rand.push_body_name,
-            add_body_sensors=True,
-            position_actuator_gains={
-                "kp": cfg.control_config.kp,
-                "kd": cfg.control_config.kd,
-                "actuator_ids": slice(0, 16),
-            },
-            **env_backend_kwargs(cfg),
-        )
-        super().__init__(cfg, backend, num_envs)
-        self._enable_reward_log = True
-        self._reward_cfg = cfg.reward_config
-
-        self._dof_range = self._ctrl_upper - self._ctrl_lower
-        self._dof_mid = (self._ctrl_upper + self._ctrl_lower) / 2.0
-        self._rot_axis = normalize_rotation_axis(cfg.rotation_axis)
-        self._grasp_cache = _materialize_grasp_cache(cfg)
-
-        self._init_reward_functions()
-        self._init_domain_randomization(AllegroRotationDomainRandomizationProvider())
-
-    @property
-    def obs_groups_spec(self) -> dict[str, int]:
-        return {"obs": self._NUM_OBS_PER_STEP * self._NUM_LAG_STEPS}
-
-    def _init_reward_functions(self) -> None:
-        self._reward_fns = {
-            "rotate": self._reward_rotate,
-            "obj_linvel": self._reward_obj_linvel,
-            "pose_diff": self._reward_pose_diff,
-            "torque": self._reward_torque,
-            "work": self._reward_work,
-            "drop": self._reward_drop,
-        }
-
-    def _reward_rotate(
-        self,
-        info: dict[str, Any],
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-        ball_pos: np.ndarray,
-        ball_linvel: np.ndarray,
-        ball_angvel: np.ndarray,
-        torques: np.ndarray,
-        terminated: np.ndarray,
-    ) -> np.ndarray:
-        del info, dof_pos, dof_vel, ball_pos, ball_linvel, torques, terminated
-        vec_dot = ball_angvel @ self._rot_axis
-        reward: np.ndarray = np.clip(
-            vec_dot, self._reward_cfg.angvel_clip_min, self._reward_cfg.angvel_clip_max
-        )
-        return reward
-
-    def _reward_obj_linvel(
-        self,
-        info: dict[str, Any],
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-        ball_pos: np.ndarray,
-        ball_linvel: np.ndarray,
-        ball_angvel: np.ndarray,
-        torques: np.ndarray,
-        terminated: np.ndarray,
-    ) -> np.ndarray:
-        del info, dof_pos, dof_vel, ball_pos, ball_angvel, torques, terminated
-        penalty: np.ndarray = np.sum(np.abs(ball_linvel), axis=1)
-        return penalty
-
-    def _reward_pose_diff(
-        self,
-        info: dict[str, Any],
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-        ball_pos: np.ndarray,
-        ball_linvel: np.ndarray,
-        ball_angvel: np.ndarray,
-        torques: np.ndarray,
-        terminated: np.ndarray,
-    ) -> np.ndarray:
-        del dof_vel, ball_pos, ball_linvel, ball_angvel, torques, terminated
-        diff = dof_pos - info["init_pose"]
-        penalty: np.ndarray = np.sum(np.square(diff), axis=1)
-        return penalty
-
-    def _reward_torque(
-        self,
-        info: dict[str, Any],
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-        ball_pos: np.ndarray,
-        ball_linvel: np.ndarray,
-        ball_angvel: np.ndarray,
-        torques: np.ndarray,
-        terminated: np.ndarray,
-    ) -> np.ndarray:
-        del info, dof_pos, dof_vel, ball_pos, ball_linvel, ball_angvel, terminated
-        penalty: np.ndarray = np.sum(np.square(torques), axis=1)
-        return penalty
-
-    def _reward_work(
-        self,
-        info: dict[str, Any],
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-        ball_pos: np.ndarray,
-        ball_linvel: np.ndarray,
-        ball_angvel: np.ndarray,
-        torques: np.ndarray,
-        terminated: np.ndarray,
-    ) -> np.ndarray:
-        del info, dof_pos, ball_pos, ball_linvel, ball_angvel, terminated
-        work = np.sum(torques * dof_vel, axis=1)
-        penalty: np.ndarray = np.square(work)
-        return penalty
-
-    def _reward_drop(
-        self,
-        info: dict[str, Any],
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-        ball_pos: np.ndarray,
-        ball_linvel: np.ndarray,
-        ball_angvel: np.ndarray,
-        torques: np.ndarray,
-        terminated: np.ndarray,
-    ) -> np.ndarray:
-        del info, dof_pos, dof_vel, ball_pos, ball_linvel, ball_angvel, torques
-        return np.asarray(terminated, dtype=get_global_dtype())
-
-    def update_state(self, state: NpEnvState) -> NpEnvState:
-        dof_pos = self.get_hand_dof_pos()
-        ball_pos = self.get_ball_pos()
-        ball_quat = self.get_ball_quat()
-
-        dof_vel = (dof_pos - state.info.get("prev_dof_pos", dof_pos)) / self._cfg.ctrl_dt
-        ball_linvel = (ball_pos - state.info.get("prev_ball_pos", ball_pos)) / self._cfg.ctrl_dt
-
-        prev_ball_quat = state.info.get("prev_ball_quat", ball_quat)
-        ball_angvel = compute_ball_angvel(ball_quat, prev_ball_quat, self._cfg.ctrl_dt)
-
-        state.info["curr_dof_pos"] = dof_pos.copy()
-        state.info["curr_ball_pos"] = ball_pos.copy()
-        state.info["curr_ball_quat"] = ball_quat.copy()
-
-        state.info["prev_dof_pos"] = dof_pos.copy()
-        state.info["prev_ball_pos"] = ball_pos.copy()
-        state.info["prev_ball_quat"] = ball_quat.copy()
-
-        targets = state.info["prev_ctrl"]
-        torques = compute_pd_torques(
-            targets=targets,
-            dof_pos=dof_pos,
-            dof_vel=dof_vel,
-            kp=self._cfg.control_config.kp,
-            kd=self._cfg.control_config.kd,
-        )
-        terminated = ball_pos[:, 2] < self._reward_cfg.reset_z_threshold
-
-        reward = self._compute_reward(
-            state.info, dof_pos, dof_vel, ball_pos, ball_linvel, ball_angvel, torques, terminated
-        )
-        obs = self._compute_obs(state.info, dof_pos, ball_pos)
-        return state.replace(obs=obs, reward=reward, terminated=terminated)
-
-    def _compute_reward(
-        self,
-        info: dict[str, Any],
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-        ball_pos: np.ndarray,
-        ball_linvel: np.ndarray,
-        ball_angvel: np.ndarray,
-        torques: np.ndarray,
-        terminated: np.ndarray,
-    ) -> np.ndarray:
-        dtype = get_global_dtype()
-        reward = np.zeros(self._num_envs, dtype=dtype)
-        step_count = info.get("steps", np.zeros(self._num_envs, dtype=np.uint32))
-        should_log = self._enable_reward_log and (int(step_count[0]) % 4 == 0)
-        log = {} if should_log else info.get("log", {})
-
-        for name, scale in self._reward_cfg.scales.items():
-            if scale == 0 or name not in self._reward_fns:
-                continue
-            rew = self._reward_fns[name](
-                info, dof_pos, dof_vel, ball_pos, ball_linvel, ball_angvel, torques, terminated
-            )
-            weighted_rew = rew * scale
-            reward += weighted_rew
-            if should_log:
-                log[f"reward/{name}"] = float(np.mean(weighted_rew))
-
-        if should_log:
-            log["reward/total"] = float(np.mean(reward))
-
-        info["log"] = log
-        return reward * self._cfg.ctrl_dt
-
-    def _compute_obs(
-        self, info: dict[str, Any], dof_pos: np.ndarray, ball_pos: np.ndarray
-    ) -> dict[str, np.ndarray]:
-        dtype = get_global_dtype()
-        targets = info["prev_ctrl"]
-        dof_pos_norm = 2.0 * (dof_pos - self._dof_mid) / (self._dof_range + 1e-8)
-
-        noise_cfg = self._cfg.noise_config
-        if noise_cfg.level > 0.0:
-            dof_pos_norm += (
-                np.random.uniform(-1.0, 1.0, dof_pos_norm.shape).astype(dtype)
-                * noise_cfg.level
-                * noise_cfg.scale_joint_angle
-            )
-
-        current_obs = np.concatenate(
-            [dof_pos_norm, targets, ball_pos.astype(dtype)], axis=1, dtype=dtype
-        )
-
-        num_envs = dof_pos.shape[0]
-        obs_lag_history = info.get(
-            "obs_lag_history",
-            np.zeros(
-                (num_envs, self._NUM_LAG_STEPS, self._NUM_OBS_PER_STEP),
-                dtype=dtype,
-            ),
-        )
-        obs_lag_history[:, :-1] = obs_lag_history[:, 1:]
-        obs_lag_history[:, -1] = current_obs
-        info["obs_lag_history"] = obs_lag_history
-
-        return {
-            "obs": np.asarray(obs_lag_history.reshape(num_envs, -1), dtype=dtype),
-        }
-
-
-RewardConfig = RewardConfigPPO
-Domain_Rand = DomainRandConfig
-AllegroRotationCfg = AllegroRotationPPOCfg
-AllegroRotationMj = AllegroRotationPPO
diff --git a/src/unilab/envs/manipulation/sharpa_inhand/base.py b/src/unilab/envs/manipulation/sharpa_inhand/base.py
deleted file mode 100644
index e5e323b31..000000000
--- a/src/unilab/envs/manipulation/sharpa_inhand/base.py
+++ /dev/null
@@ -1,670 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Any, Sequence, cast
-
-import gymnasium as gym
-import numpy as np
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base.backend import SimBackend
-from unilab.base.base import EnvCfg
-from unilab.base.np_env import NpEnv, NpEnvState
-from unilab.base.scene import SceneCfg
-from unilab.dtype_config import get_global_dtype
-
-DEFAULT_ACTUATED_JOINT_NAMES: list[str] = [
-    "right_thumb_CMC_FE",
-    "right_thumb_CMC_AA",
-    "right_thumb_MCP_FE",
-    "right_thumb_MCP_AA",
-    "right_thumb_IP",
-    "right_index_MCP_FE",
-    "right_index_MCP_AA",
-    "right_index_PIP",
-    "right_index_DIP",
-    "right_middle_MCP_FE",
-    "right_middle_MCP_AA",
-    "right_middle_PIP",
-    "right_middle_DIP",
-    "right_ring_MCP_FE",
-    "right_ring_MCP_AA",
-    "right_ring_PIP",
-    "right_ring_DIP",
-    "right_pinky_CMC",
-    "right_pinky_MCP_FE",
-    "right_pinky_MCP_AA",
-    "right_pinky_PIP",
-    "right_pinky_DIP",
-]
-
-DEFAULT_FINGERTIP_BODY_NAMES: list[str] = [
-    "right_thumb_DP",
-    "right_index_DP",
-    "right_middle_DP",
-    "right_ring_DP",
-    "right_pinky_DP",
-]
-
-# Source parity anchor from sharpa-rl-lab:
-# rl_isaaclab/tasks/inhand_rotate/sharpa_wave_env_cfg.py (hand init_state joint_pos).
-SOURCE_DEFAULT_HAND_JOINT_POS_DEG: tuple[float, ...] = (
-    95.12771,
-    -3.11244,
-    14.81626,
-    -1.03493,
-    12.23986,
-    65.21091,
-    6.1133,
-    15.58495,
-    5.90325,
-    31.74149,
-    -0.95812,
-    41.88173,
-    12.844,
-    31.72383,
-    9.84458,
-    35.22366,
-    18.02839,
-    10.9712,
-    68.30895,
-    7.99151,
-    5.89626,
-    5.89875,
-)
-
-
-@dataclass
-class SharpaControlConfig:
-    action_scale: float = 1.0 / 24.0
-    # MuJoCo Sharpa loads PD defaults from XML actuator gains. These fields remain
-    # fallback defaults for backends that cannot expose actuator gains yet.
-    p_gain: float = 1.0
-    d_gain: float = 0.1
-    # MuJoCo Sharpa currently uses position actuators, so torque-control mode
-    # stays declared here for owner-config structure but is rejected at runtime.
-    torque_control: bool = False
-    dof_limits_scale: float = 0.9
-
-
-@dataclass
-class SharpaSensorConfig:
-    tactile_force_sensor_names: list[str] = field(default_factory=list)
-
-
-@dataclass
-class SharpaObservationConfig:
-    observation_mode: str = "separated"
-    enable_tactile: bool = True
-    binary_contact: bool = False
-    enable_contact_pos: bool = False
-    contact_smooth: float = 0.5
-    contact_threshold: float = 0.05
-    tactile_force_clip_max: float = 4.0
-
-
-@dataclass
-class SharpaPrivilegedInfoConfig:
-    include_friction_scale: bool = True
-    include_gravity_direction: bool = False
-
-
-@dataclass
-class SharpaDomainRandConfig:
-    scale_list: list[float] = field(default_factory=lambda: [0.5])
-    randomize_base_mass: bool = False
-    added_mass_range: list[float] = field(default_factory=lambda: [0.0, 0.0])
-    random_com: bool = False
-    com_offset_x: list[float] = field(default_factory=lambda: [0.0, 0.0])
-    randomize_gravity: bool = False
-    gravity_range: list[list[float]] = field(
-        default_factory=lambda: [[0.0, 0.0, -9.81], [0.0, 0.0, -9.81]]
-    )
-    randomize_gravity_direction: bool = False
-    gravity_direction_magnitude: float = 9.81
-    randomize_pd_gains: bool = True
-    randomize_p_gain_scale_lower: float = 0.5
-    randomize_p_gain_scale_upper: float = 2.0
-    randomize_d_gain_scale_lower: float = 0.5
-    randomize_d_gain_scale_upper: float = 2.0
-    randomize_friction: bool = True
-    randomize_friction_scale_lower: float = 0.5
-    randomize_friction_scale_upper: float = 2.0
-    elastomer_base_friction: float = 1.6
-    metal_base_friction: float = 0.2
-    object_base_friction: float = 1.0
-    randomize_com: bool = True
-    randomize_com_lower: float = -0.01
-    randomize_com_upper: float = 0.01
-    randomize_mass: bool = True
-    randomize_mass_lower: float = 0.01
-    randomize_mass_upper: float = 0.25
-    force_scale: float = 2.0
-    random_force_prob_scalar: float = 0.25
-    force_decay: float = 0.9
-    force_decay_interval: float = 0.08
-    joint_noise_scale: float = 0.02
-    contact_latency: float = 0.005
-    contact_sensor_noise: float = 0.01
-    push_body_name: str | None = None
-
-
-@dataclass
-class SharpaInhandBaseCfg(EnvCfg):
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "sharpa_wave" / "scene.xml")
-        )
-    )
-    max_episode_seconds: float = 20.0
-    sim_dt: float = 1.0 / 240.0
-    ctrl_dt: float = 12.0 / 240.0
-
-    action_space: int = 22
-    observation_space: int = 192
-    prop_hist_len: int = 30
-    critic_info_dim: int = 8
-
-    clip_obs: float = 5.0
-    clip_actions: float = 1.0
-
-    num_hand_dofs: int = 22
-    frame_obs_dim: int = 64
-    obs_lag_steps: int = 3
-    obs_history_len: int = 80
-
-    base_name: str = "right_hand_C_MC"
-    object_body_name: str = "object"
-    object_geom_name: str = "object"
-    actuated_joint_names: list[str] = field(
-        default_factory=lambda: list(DEFAULT_ACTUATED_JOINT_NAMES)
-    )
-    fingertip_body_names: list[str] = field(
-        default_factory=lambda: list(DEFAULT_FINGERTIP_BODY_NAMES)
-    )
-
-    control_config: SharpaControlConfig = field(default_factory=SharpaControlConfig)
-    sensor: SharpaSensorConfig = field(default_factory=SharpaSensorConfig)  # type: ignore[assignment]
-    obs: SharpaObservationConfig = field(default_factory=SharpaObservationConfig)
-    priv_info: SharpaPrivilegedInfoConfig = field(default_factory=SharpaPrivilegedInfoConfig)
-    domain_rand: SharpaDomainRandConfig = field(default_factory=SharpaDomainRandConfig)
-
-    reset_height_lower: float = 0.59906
-    reset_height_upper: float = 0.63906
-    reset_angle_diff: float = 45.0 / 180.0 * np.pi
-
-    rot_axis: tuple[float, float, float] = (0.0, 0.0, 1.0)
-
-    grasp_cache_path: str = str(ASSETS_ROOT_PATH / "caches" / "sharpa_grasp_linspace")
-    disable_tactile_ids: list[int] = field(default_factory=list)
-    # Match the reference Sharpa object-position reward/privileged-info anchor
-    # by using the fixed XML/default object pose instead of the sampled grasp reset.
-    use_default_object_pose_for_object_pos_anchor: bool = False
-
-    debug_show_axes: bool = False
-
-
-def format_scale_tag(scale_value: float) -> str:
-    """Convert one object scale into a stable cache filename tag.
-
-    Args:
-        scale_value: Single object scale value.
-
-    Returns:
-        Scale tag used in cache filenames.
-    """
-    scale_value = float(scale_value)
-    if scale_value <= 0.0:
-        raise ValueError(f"scale values must be positive, got {scale_value}")
-    return f"{scale_value:g}"
-
-
-def resolve_grasp_cache_file(grasp_cache_path: str, scale_value: float) -> Path:
-    """Resolve the grasp cache path for a single object scale.
-
-    Args:
-        grasp_cache_path: Configured cache prefix or template path.
-        scale_value: Single object scale value for this cache file.
-
-    Returns:
-        Cache path for that exact scale.
-    """
-    scale_tag = format_scale_tag(scale_value)
-    if "{scale}" in grasp_cache_path:
-        return Path(grasp_cache_path.format(scale=scale_tag))
-
-    base = Path(grasp_cache_path)
-    if base.suffix == ".npy":
-        return base.with_name(f"{base.stem}_{scale_tag}{base.suffix}")
-    return Path(f"{grasp_cache_path}_{scale_tag}.npy")
-
-
-def sample_scale_grasp_caches(
-    grasp_caches: Sequence[np.ndarray],
-    scale_ids: np.ndarray,
-) -> np.ndarray:
-    """Sample one cached grasp per reset environment from per-scale cache files.
-
-    Args:
-        grasp_caches: Cache arrays ordered the same way as env scale ids.
-        scale_ids: Scale-bucket assignment for each reset environment.
-
-    Returns:
-        Cached grasp states with shape ``(num_envs, 29)``.
-    """
-    num_envs = scale_ids.shape[0]
-    num_scales = len(grasp_caches)
-    if num_scales <= 0:
-        raise ValueError("grasp_caches must contain at least one scale bucket")
-
-    sampled = np.zeros((num_envs, 29), dtype=np.float64)
-    for scale_idx, grasp_cache in enumerate(grasp_caches):
-        if grasp_cache.ndim != 2 or grasp_cache.shape[1] < 29:
-            raise ValueError(f"Expected cached grasp shape (?, 29), got {grasp_cache.shape}")
-        if grasp_cache.shape[0] == 0:
-            raise ValueError(f"grasp cache for scale id {scale_idx} is empty")
-        env_ids = np.flatnonzero(scale_ids == scale_idx)
-        if len(env_ids) == 0:
-            continue
-        sample_ids = np.random.randint(0, grasp_cache.shape[0], size=len(env_ids))
-        sampled[env_ids] = grasp_cache[sample_ids]
-    return sampled
-
-
-def repeat_obs_history(init_frame: np.ndarray, history_len: int) -> np.ndarray:
-    history = np.broadcast_to(
-        init_frame[:, None, :], (init_frame.shape[0], history_len, init_frame.shape[1])
-    ).copy()
-    return np.asarray(history, dtype=init_frame.dtype)
-
-
-class SharpaInhandBaseEnv(NpEnv):
-    _cfg: SharpaInhandBaseCfg
-    _default_p_gain: np.ndarray
-    _default_d_gain: np.ndarray
-
-    def __init__(self, cfg: SharpaInhandBaseCfg, backend: SimBackend, num_envs: int = 1) -> None:
-        super().__init__(cfg, backend, num_envs)
-
-        self._np_dtype = get_global_dtype()
-
-        self._num_action = int(cfg.num_hand_dofs)
-        actuator_range = np.asarray(self._backend.get_actuator_ctrl_range(), dtype=self._np_dtype)
-        if actuator_range.shape[0] < self._num_action:
-            raise ValueError(
-                f"Model has {actuator_range.shape[0]} actuators, but Sharpa task needs {self._num_action}"
-            )
-
-        # Keep the raw XML actuator limits for observation normalization and any
-        # logic that needs the original backend contract. Target-position clipping
-        # uses a separate scaled limit pair to match Sharpa source behavior.
-        self._ctrl_lower = np.asarray(actuator_range[: self._num_action, 0], dtype=self._np_dtype)
-        self._ctrl_upper = np.asarray(actuator_range[: self._num_action, 1], dtype=self._np_dtype)
-        self._target_lower, self._target_upper = self._resolve_target_joint_limits(
-            self._ctrl_lower,
-            self._ctrl_upper,
-            cfg.control_config.dof_limits_scale,
-        )
-
-        self._init_qpos = self._resolve_init_qpos()
-        self._init_qvel = np.asarray(self._backend.get_init_qvel(), dtype=np.float64)
-        self.nq = int(self._init_qpos.shape[0])
-        self.nv = int(self._init_qvel.shape[0])
-
-        if self.nq < self._num_action + 7:
-            raise ValueError(
-                f"Model qpos dim {self.nq} is too small for {self._num_action} hand DoFs + object pose"
-            )
-
-        self._obj_pos_slice = slice(self._num_action, self._num_action + 3)
-        self._obj_quat_slice = slice(self._num_action + 3, self._num_action + 7)
-
-        source_default_angles = np.deg2rad(
-            np.asarray(SOURCE_DEFAULT_HAND_JOINT_POS_DEG, dtype=np.float64)
-        )
-        if source_default_angles.shape[0] != self._num_action:
-            raise ValueError(
-                "Source default hand joint pose size mismatch: "
-                f"{source_default_angles.shape[0]} vs expected {self._num_action}"
-            )
-        self.default_angles = np.asarray(source_default_angles, dtype=self._np_dtype)
-
-        self._action_space = gym.spaces.Box(
-            low=-float(cfg.clip_actions),
-            high=float(cfg.clip_actions),
-            shape=(self._num_action,),
-            dtype=np.float32,
-        )
-
-        self._object_body_ids = self._backend.get_body_ids([cfg.object_body_name])
-        self._fingertip_body_ids = self._backend.get_body_ids(cfg.fingertip_body_names)
-        self._object_geom_base_size = self._resolve_object_geom_base_size()
-
-        self._num_tactile = len(cfg.fingertip_body_names)
-        self.last_contacts = np.zeros((num_envs, self._num_tactile), dtype=self._np_dtype)
-        self._prev_tactile_force = np.zeros((num_envs, self._num_tactile), dtype=self._np_dtype)
-
-        self.object_default_pose = np.zeros((num_envs, 7), dtype=self._np_dtype)
-
-        self.obs_buf_lag_history = np.zeros(
-            (num_envs, cfg.obs_history_len, cfg.frame_obs_dim), dtype=self._np_dtype
-        )
-        self.proprio_hist_buf = np.zeros(
-            (num_envs, cfg.prop_hist_len, cfg.frame_obs_dim), dtype=self._np_dtype
-        )
-        self.critic_info_buf = np.zeros((num_envs, cfg.critic_info_dim), dtype=self._np_dtype)
-
-        self.scale_ids, self._num_scales, self._bucket_env = self._build_scale_ids(
-            num_envs, cfg.domain_rand.scale_list
-        )
-        self.scale_values = self._build_scale_values(cfg.domain_rand.scale_list)
-
-    @property
-    def action_space(self) -> gym.spaces.Box:
-        return self._action_space  # type: ignore[no-any-return]
-
-    def _resolve_target_joint_limits(
-        self,
-        raw_lower: np.ndarray,
-        raw_upper: np.ndarray,
-        scale: float,
-    ) -> tuple[np.ndarray, np.ndarray]:
-        """Build scaled target-position clipping limits from raw XML actuator bounds.
-
-        Args:
-            raw_lower: Original lower control limits loaded from the backend XML.
-            raw_upper: Original upper control limits loaded from the backend XML.
-            scale: Multiplicative scale applied to both bound arrays.
-
-        Returns:
-            Tuple of scaled ``(lower, upper)`` target-position limits.
-        """
-        scale_value = float(scale)
-        if scale_value <= 0.0:
-            raise ValueError(f"dof_limits_scale must be positive, got {scale_value}")
-        target_lower = np.asarray(raw_lower, dtype=self._np_dtype) * scale_value
-        target_upper = np.asarray(raw_upper, dtype=self._np_dtype) * scale_value
-        if np.any(target_lower > target_upper):
-            raise ValueError("Scaled Sharpa target joint limits are invalid")
-        return target_lower.astype(self._np_dtype, copy=False), target_upper.astype(
-            self._np_dtype,
-            copy=False,
-        )
-
-    def _resolve_init_qpos(self) -> np.ndarray:
-        for key_name in ("home", "stand", "default"):
-            try:
-                return np.asarray(self._backend.get_keyframe_qpos(key_name), dtype=np.float64)
-            except Exception:
-                continue
-
-        try:
-            return np.asarray(self._backend.get_default_qpos(), dtype=np.float64)
-        except NotImplementedError as exc:
-            raise ValueError("Could not resolve initial qpos from backend contract") from exc
-
-    def _build_scale_ids(
-        self, num_envs: int, scale_list: Sequence[float]
-    ) -> tuple[np.ndarray, int, int]:
-        """Build deterministic near-even environment assignments for each scale.
-
-        Args:
-            num_envs: Number of vectorized environments to assign.
-            scale_list: Explicit object scale values used by this env instance.
-
-        Returns:
-            Tuple of scale id per environment, total scale count, and the minimum
-            number of environments assigned to any scale.
-        """
-        if len(scale_list) == 0:
-            raise ValueError("scale_list must contain at least one scale")
-        scale_values = np.asarray(scale_list, dtype=np.float64)
-        if np.any(scale_values <= 0.0):
-            raise ValueError(f"scale_list values must be positive, got {list(scale_list)}")
-        num_scales = int(scale_values.shape[0])
-        if num_scales <= 0:
-            raise ValueError(f"scale_list must contain at least one value, got {list(scale_list)}")
-
-        bucket_env = num_envs // num_scales
-        remainder = num_envs % num_scales
-        # Assign the remainder to the lowest scale ids to keep bucket sizes within one env.
-        counts = np.full((num_scales,), bucket_env, dtype=np.int32)
-        counts[:remainder] += 1
-        scale_ids = np.repeat(np.arange(num_scales, dtype=np.int32), counts)
-        return scale_ids, num_scales, bucket_env
-
-    def _build_scale_values(self, scale_list: Sequence[float]) -> np.ndarray:
-        """Normalize configured scale values into a stable numpy array.
-
-        Args:
-            scale_list: Explicit list of object scales.
-
-        Returns:
-            Array of configured scale values in config order.
-        """
-        scale_values = np.asarray(scale_list, dtype=np.float64)
-        if scale_values.ndim != 1 or scale_values.size == 0:
-            raise ValueError(f"scale_list must be a non-empty flat list, got {list(scale_list)}")
-        if np.any(scale_values <= 0.0):
-            raise ValueError(f"scale_list values must be positive, got {list(scale_list)}")
-        return scale_values
-
-    def _resolve_object_geom_base_size(self) -> np.ndarray | None:
-        try:
-            return cast(np.ndarray, self._backend.get_geom_size(self._cfg.object_geom_name))
-        except NotImplementedError:
-            return None
-
-    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
-        clipped_actions = np.clip(actions, -self._cfg.clip_actions, self._cfg.clip_actions)
-        clipped_actions = np.asarray(clipped_actions[:, : self._num_action], dtype=self._np_dtype)
-
-        state.info["last_actions"] = state.info.get("current_actions", clipped_actions.copy())
-        state.info["current_actions"] = clipped_actions
-
-        prev_targets = state.info.get(
-            "prev_targets",
-            np.broadcast_to(self.default_angles, (self._num_envs, self._num_action)).copy(),
-        )
-        targets = prev_targets + self._cfg.control_config.action_scale * clipped_actions
-        # Clip action targets by the scaled control range only. Observation
-        # normalization continues to use the raw XML actuator limits.
-        targets = np.clip(targets, self._target_lower, self._target_upper)
-        prev_targets = np.asarray(targets, dtype=self._np_dtype)
-        state.info["prev_targets"] = prev_targets
-        return prev_targets
-
-    def get_hand_dof_pos(self) -> np.ndarray:
-        return np.asarray(self._backend.get_dof_pos()[:, : self._num_action], dtype=self._np_dtype)
-
-    def get_hand_dof_vel(self) -> np.ndarray:
-        return np.asarray(self._backend.get_dof_vel()[:, : self._num_action], dtype=self._np_dtype)
-
-    def get_fingertip_pos(self) -> np.ndarray:
-        return np.asarray(
-            self._backend.get_body_pos_w(self._fingertip_body_ids), dtype=self._np_dtype
-        )
-
-    def get_object_pos(self) -> np.ndarray:
-        return np.asarray(
-            self._backend.get_body_pos_w(self._object_body_ids)[:, 0, :], dtype=self._np_dtype
-        )
-
-    def get_object_quat(self) -> np.ndarray:
-        return np.asarray(
-            self._backend.get_body_quat_w(self._object_body_ids)[:, 0, :], dtype=self._np_dtype
-        )
-
-    def _extract_sensor_scalar(self, sensor_name: str) -> np.ndarray:
-        data = np.asarray(self._backend.get_sensor_data(sensor_name), dtype=self._np_dtype)
-        if data.ndim == 1:
-            return data
-        if data.ndim == 2 and data.shape[1] == 1:
-            return data[:, 0]
-        if data.ndim == 2 and data.shape[1] >= 3:
-            return np.asarray(np.linalg.norm(data[:, :3], axis=1), dtype=self._np_dtype)
-        flat = data.reshape(data.shape[0], -1)
-        return np.asarray(flat[:, 0], dtype=self._np_dtype)
-
-    def _read_tactile_force(self) -> np.ndarray:
-        """Read per-finger tactile force magnitudes in configured sensor order.
-
-        Args:
-            None.
-
-        Returns:
-            Array of shape ``(num_envs, num_tactile)`` ordered exactly as
-            ``sensor.tactile_force_sensor_names``.
-        """
-        tactile_force = np.zeros((self._num_envs, self._num_tactile), dtype=self._np_dtype)
-        if not self._cfg.sensor.tactile_force_sensor_names:
-            return tactile_force
-
-        for sensor_id, sensor_name in enumerate(
-            self._cfg.sensor.tactile_force_sensor_names[: self._num_tactile]
-        ):
-            try:
-                tactile_force[:, sensor_id] = self._extract_sensor_scalar(sensor_name)
-            except Exception:
-                tactile_force[:, sensor_id] = 0.0
-        return tactile_force
-
-    def _clip_tactile_force(self, tactile_force: np.ndarray) -> np.ndarray:
-        """Clip raw tactile-force magnitudes before they enter observation smoothing.
-
-        Args:
-            tactile_force: Raw per-finger tactile magnitudes with shape
-                ``(num_envs, num_tactile)``.
-
-        Returns:
-            Clipped tactile-force array with the same shape. Non-positive clip values
-            disable this clamp so the caller can opt out explicitly.
-        """
-        clip_max = float(self._cfg.obs.tactile_force_clip_max)
-        tactile_force = np.asarray(tactile_force, dtype=self._np_dtype)
-        if clip_max <= 0.0:
-            return tactile_force
-        return np.asarray(np.clip(tactile_force, 0.0, clip_max), dtype=self._np_dtype)
-
-    def _clear_tactile_history(self, env_ids: np.ndarray | None = None) -> None:
-        """Clear tactile-output and raw-force history buffers.
-
-        Args:
-            env_ids: Optional environment ids to clear. When ``None``, clear all envs.
-
-        Returns:
-            None. Buffers are updated in place.
-        """
-        if env_ids is None:
-            self.last_contacts.fill(0.0)
-            self._prev_tactile_force.fill(0.0)
-            return
-
-        self.last_contacts[env_ids] = 0.0
-        self._prev_tactile_force[env_ids] = 0.0
-
-    def _compute_tactile_observation(self) -> np.ndarray:
-        """Build tactile observations with source-equivalent smoothing and latency.
-
-        Args:
-            None.
-
-        Returns:
-            Tactile-force observation array with shape ``(num_envs, num_tactile)``.
-        """
-        obs_cfg = self._cfg.obs
-        domain_rand = self._cfg.domain_rand
-        if not obs_cfg.enable_tactile:
-            self._clear_tactile_history()
-            return np.zeros((self._num_envs, self._num_tactile), dtype=self._np_dtype)
-
-        current_force = SharpaInhandBaseEnv._clip_tactile_force(self, self._read_tactile_force())
-        smooth_contact = (
-            current_force * obs_cfg.contact_smooth
-            + self._prev_tactile_force * (1.0 - obs_cfg.contact_smooth)
-        ).astype(self._np_dtype)
-        self._prev_tactile_force[:] = current_force
-
-        for disabled_id in self._cfg.disable_tactile_ids:
-            if 0 <= disabled_id < self._num_tactile:
-                smooth_contact[:, disabled_id] = 0.0
-
-        latency = np.where(
-            np.random.rand(self._num_envs, self._num_tactile) < domain_rand.contact_latency,
-            1.0,
-            0.0,
-        ).astype(self._np_dtype)
-
-        if obs_cfg.binary_contact:
-            binary_contact = (smooth_contact > obs_cfg.contact_threshold).astype(self._np_dtype)
-            self.last_contacts = self.last_contacts * latency + binary_contact * (1.0 - latency)
-            noise_mask = (
-                np.random.rand(self._num_envs, self._num_tactile)
-                >= domain_rand.contact_sensor_noise
-            ).astype(self._np_dtype)
-            return np.where(
-                self.last_contacts > 0.1,
-                noise_mask * self.last_contacts,
-                self.last_contacts,
-            )
-
-        self.last_contacts = self.last_contacts * latency + smooth_contact * (1.0 - latency)
-        return self.last_contacts.copy()
-
-    def _compute_contact_positions(self, tactile: np.ndarray) -> np.ndarray:
-        del tactile
-        # TODO(sharpa_inhand): IsaacLab contact positions are defined through contact sensor
-        # frame transforms. Backend-level contact point parity is not available yet in the
-        # current UniLab sensor contract, so we keep this channel as zeros for now.
-        return np.zeros((self._num_envs, self._num_tactile * 3), dtype=self._np_dtype)
-
-    def _normalize_joint_pos(self, dof_pos: np.ndarray) -> np.ndarray:
-        return np.asarray(
-            (2.0 * dof_pos - self._ctrl_upper - self._ctrl_lower)
-            / (self._ctrl_upper - self._ctrl_lower + 1.0e-8),
-            dtype=self._np_dtype,
-        )
-
-    def _sample_pd_scales(self, lower: float, upper: float, shape: tuple[int, int]) -> np.ndarray:
-        if lower > 1.0 or upper < 1.0:
-            raise ValueError("PD randomization scales must satisfy lower <= 1 <= upper")
-        small = np.random.uniform(lower, 1.0, size=shape)
-        large = np.random.uniform(1.0, upper, size=shape)
-        use_small = np.random.rand(*shape) > 0.5
-        return np.where(use_small, small, large).astype(self._np_dtype)
-
-    def _load_default_pd_gains(self) -> tuple[np.ndarray, np.ndarray]:
-        """Resolve the default per-DOF PD gains used as the randomization baseline.
-
-        Args:
-            None.
-
-        Returns:
-            Tuple of ``(p_gain, d_gain)`` arrays with shape ``(num_action,)``.
-        """
-        try:
-            p_gain, d_gain = self._backend.get_actuator_gains()
-            return (
-                np.asarray(p_gain[: self._num_action], dtype=self._np_dtype).copy(),
-                np.asarray(d_gain[: self._num_action], dtype=self._np_dtype).copy(),
-            )
-        except NotImplementedError:
-            return (
-                np.full((self._num_action,), self._cfg.control_config.p_gain, dtype=self._np_dtype),
-                np.full((self._num_action,), self._cfg.control_config.d_gain, dtype=self._np_dtype),
-            )
-
-    def _resolve_pd_gains(self, info: dict[str, Any]) -> tuple[np.ndarray, np.ndarray]:
-        p_gain = info.get(
-            "p_gain",
-            np.broadcast_to(self._default_p_gain, (self._num_envs, self._num_action)).copy(),
-        )
-        d_gain = info.get(
-            "d_gain",
-            np.broadcast_to(self._default_d_gain, (self._num_envs, self._num_action)).copy(),
-        )
-        return np.asarray(p_gain, dtype=self._np_dtype), np.asarray(d_gain, dtype=self._np_dtype)
-
-    def _update_proprio_history(self, obs_history: np.ndarray) -> np.ndarray:
-        return np.asarray(obs_history[:, -self._cfg.prop_hist_len :], dtype=self._np_dtype)
diff --git a/src/unilab/envs/manipulation/sharpa_inhand/grasp_gen.py b/src/unilab/envs/manipulation/sharpa_inhand/grasp_gen.py
deleted file mode 100644
index 8e85e1253..000000000
--- a/src/unilab/envs/manipulation/sharpa_inhand/grasp_gen.py
+++ /dev/null
@@ -1,392 +0,0 @@
-from __future__ import annotations
-
-import logging
-from dataclasses import dataclass, field
-from typing import Any
-
-import numpy as np
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base import registry
-from unilab.base.np_env import NpEnvState
-from unilab.base.run_control import RunComplete
-from unilab.dr import ResetPlan
-from unilab.dr.dr_utils import build_common_reset_randomization
-from unilab.envs.manipulation.sharpa_inhand.base import (
-    SOURCE_DEFAULT_HAND_JOINT_POS_DEG,
-    SharpaDomainRandConfig,
-    resolve_grasp_cache_file,
-)
-from unilab.envs.manipulation.sharpa_inhand.rotation import (
-    RewardConfig,
-    SharpaInhandRotationCfg,
-    SharpaInhandRotationDRProvider,
-    SharpaInhandRotationEnv,
-)
-from unilab.utils.rotation import np_quat_error_magnitude
-
-logger = logging.getLogger(__name__)
-
-
-def _default_sharpa_grasp_domain_rand() -> SharpaDomainRandConfig:
-    """Build the nested DR defaults used by Sharpa grasp collection.
-
-    Returns:
-        Domain-randomization config matching the grasp-task owner defaults.
-    """
-    return SharpaDomainRandConfig(
-        randomize_pd_gains=False,
-        randomize_friction=False,
-        randomize_com=False,
-        randomize_mass=True,
-        randomize_mass_lower=0.05,
-        randomize_mass_upper=0.051,
-        force_scale=0.0,
-        random_force_prob_scalar=0.0,
-    )
-
-
-@dataclass
-class SharpaInhandRotationGraspCfg(SharpaInhandRotationCfg):
-    max_episode_seconds: float = 3.0  # 12.0
-
-    reset_height_lower: float = 0.61406
-    reset_height_upper: float = 0.62406
-    reset_angle_diff: float = 30.0 / 180.0 * np.pi
-
-    grasp_cache_path: str = ""
-    domain_rand: SharpaDomainRandConfig = field(default_factory=_default_sharpa_grasp_domain_rand)
-
-    reward_config: RewardConfig = field(
-        default_factory=lambda: RewardConfig(
-            scales={
-                "rotate": 0.0,
-                "obj_linvel": 0.0,
-                "pose_diff": 0.0,
-                "torque": 0.0,
-                "work": 0.0,
-                "object_pos": 0.0,
-            }
-        )
-    )
-
-    grasp_collection_target: int = 50_000
-    grasp_auto_save: bool = True
-
-
-@registry.envcfg("SharpaInhandRotationGrasp")
-@dataclass
-class SharpaInhandGraspEnvCfg(SharpaInhandRotationGraspCfg):
-    pass
-
-
-class SharpaInhandGraspDRProvider(SharpaInhandRotationDRProvider):
-    def build_reset_plan(self, env: Any, env_ids: np.ndarray) -> ResetPlan:
-        # Keep original grasp task behavior: collect successful pre-reset states on each reset.
-        env._collect_successful_grasps(env_ids)
-
-        num_reset = len(env_ids)
-        if num_reset == 0:
-            return ResetPlan(
-                env_ids=env_ids,
-                qpos=np.zeros((0, env.nq), dtype=np.float64),
-                qvel=np.zeros((0, env.nv), dtype=np.float64),
-                info_updates={},
-                randomization=None,
-            )
-
-        rand = 2.0 * np.random.rand(num_reset, env._num_action) - 1.0
-        hand_qpos = np.broadcast_to(env._grasp_default_angles, (num_reset, env._num_action)).copy()
-        hand_qpos += 0.15 * rand
-        hand_qpos = np.clip(hand_qpos, env._ctrl_lower, env._ctrl_upper)
-
-        object_pos = np.broadcast_to(env._init_qpos[env._obj_pos_slice], (num_reset, 3)).copy()
-        object_quat = np.broadcast_to(env._init_qpos[env._obj_quat_slice], (num_reset, 4)).copy()
-
-        qpos = np.zeros((num_reset, env.nq), dtype=np.float64)
-        qpos[:, : env._num_action] = hand_qpos
-        qpos[:, env._obj_pos_slice] = object_pos
-        qpos[:, env._obj_quat_slice] = object_quat
-
-        qvel = np.zeros((num_reset, env.nv), dtype=np.float64)
-        p_gain, d_gain = self._sample_reset_pd_gains(env, num_reset, dtype=env._np_dtype)
-
-        info_updates = self._build_info_updates(
-            env,
-            env_ids=env_ids,
-            hand_qpos=hand_qpos,
-            object_pos=object_pos,
-            object_quat=object_quat,
-            reset_height_lower=np.full((num_reset,), env.cfg.reset_height_lower, dtype=np.float64),
-            reset_height_upper=np.full((num_reset,), env.cfg.reset_height_upper, dtype=np.float64),
-            rot_axis=np.broadcast_to(env._rot_axis, (num_reset, 3)).astype(np.float64),
-            p_gain=p_gain,
-            d_gain=d_gain,
-            friction_scale=None,
-            randomized_mass=None,
-            randomized_com_offset=None,
-            gravity=None,
-        )
-        env._clear_tactile_history(env_ids)
-
-        return ResetPlan(
-            env_ids=env_ids,
-            qpos=qpos,
-            qvel=qvel,
-            info_updates=info_updates,
-            randomization=build_common_reset_randomization(env, num_reset),
-        )
-
-
-@registry.env("SharpaInhandRotationGrasp", sim_backend="mujoco")
-@registry.env("SharpaInhandRotationGrasp", sim_backend="motrix")
-class SharpaInhandRotationGraspEnv(SharpaInhandRotationEnv):
-    _cfg: SharpaInhandRotationGraspCfg
-    _MATERIALIZE_ROTATION_GRASP_CACHE = False
-
-    def __init__(
-        self,
-        cfg: SharpaInhandRotationGraspCfg,
-        num_envs: int = 1,
-        backend_type: str = "motrix",
-    ) -> None:
-        if cfg.domain_rand.randomize_gravity or cfg.domain_rand.randomize_gravity_direction:
-            raise ValueError(
-                "SharpaInhandRotationGrasp does not support gravity randomization; "
-                "disable env.domain_rand.randomize_gravity and "
-                "env.domain_rand.randomize_gravity_direction."
-            )
-        super().__init__(
-            cfg,
-            num_envs=num_envs,
-            backend_type=backend_type,
-            dr_provider=SharpaInhandGraspDRProvider(),
-        )
-
-        self._saved_grasping_states: list[list[np.ndarray]] = [
-            list() for _ in range(self._num_scales)
-        ]
-        if self._num_scales != 1:
-            raise ValueError(
-                "Sharpa grasp generation now collects exactly one object scale per run; "
-                f"got scale_list={list(self.scale_values)}"
-            )
-        self._grasp_target_per_scale = max(1, int(cfg.grasp_collection_target))
-        self._grasp_cache_saved = False
-        self._grasp_target_reached_notified = False
-        self._last_grasp_progress_step = -1
-        self._last_grasp_progress_counts: tuple[int, ...] | None = None
-        self._grasp_default_angles = np.asarray(
-            np.deg2rad(np.asarray(SOURCE_DEFAULT_HAND_JOINT_POS_DEG, dtype=np.float64)),
-            dtype=self._np_dtype,
-        )
-        if self._grasp_default_angles.shape[0] != self._num_action:
-            raise ValueError(
-                "Source grasp default angle count mismatch: "
-                f"{self._grasp_default_angles.shape[0]} vs expected {self._num_action}"
-            )
-        if np.any(np.bincount(self.scale_ids, minlength=self._num_scales) == 0):
-            raise ValueError(
-                "Sharpa grasp generation requires at least one environment for the configured scale; "
-                f"got num_envs={num_envs}, num_scales={self._num_scales}"
-            )
-
-    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
-        # Grasp-cache collection should not use policy/random actions.
-        # Keep controls fixed at reset targets by forcing zero action input.
-        zero_actions = np.zeros_like(actions, dtype=self._np_dtype)
-        return super().apply_action(zero_actions, state)
-
-    def _total_saved_grasps(self) -> int:
-        return int(sum(len(bucket) for bucket in self._saved_grasping_states))
-
-    def _collection_target_reached(self) -> bool:
-        return all(
-            len(bucket) >= self._grasp_target_per_scale for bucket in self._saved_grasping_states
-        )
-
-    def _get_per_scale_grasp_counts(self) -> tuple[int, ...]:
-        """Return collected grasp counts for each scale bucket.
-
-        Returns:
-            Tuple where index is scale id and value is collected grasp count.
-        """
-        return tuple(len(bucket) for bucket in self._saved_grasping_states)
-
-    def _maybe_log_grasp_progress(self, force: bool = False) -> None:
-        """Log runtime grasp-collection progress grouped by scale.
-
-        Args:
-            force: When True, log even if throttling would normally skip.
-        """
-        if self.state is None:
-            return
-
-        counts = self._get_per_scale_grasp_counts()
-        step_info = self.state.info.get("steps")
-        step = int(step_info[0]) if isinstance(step_info, np.ndarray) and step_info.size > 0 else 0
-        if not force:
-            if counts == self._last_grasp_progress_counts:
-                return
-            if self._last_grasp_progress_step >= 0 and step - self._last_grasp_progress_step < 32:
-                return
-
-        total = int(sum(counts))
-        per_scale = ", ".join(
-            f"scale={float(self.scale_values[i]):g}:{count}" for i, count in enumerate(counts)
-        )
-        logger.info(
-            "[SharpaInhandRotationGrasp] grasp progress total=%d/%d, per_scale=[%s]",
-            total,
-            int(self._cfg.grasp_collection_target),
-            per_scale,
-        )
-        self._last_grasp_progress_step = step
-        self._last_grasp_progress_counts = counts
-
-    def _stop_collection(self) -> None:
-        if self._grasp_target_reached_notified:
-            return
-        if not self._collection_target_reached():
-            return
-
-        self._maybe_log_grasp_progress(force=True)
-        self._grasp_target_reached_notified = True
-        collected = self._total_saved_grasps()
-        target = int(self._cfg.grasp_collection_target)
-        logger.info(
-            "[SharpaInhandRotationGrasp] Grasp collection target reached "
-            "(saved=%d, configured_target=%d). Collection completed.",
-            collected,
-            target,
-        )
-
-        if self.state is not None:
-            log = self.state.info.get("log", {})
-            log["grasp/target_reached"] = 1.0
-            self.state.info["log"] = log
-
-        raise RunComplete(
-            reason="grasp_collection_target_reached",
-            summary={
-                "collected_grasps": int(collected),
-                "grasp_collection_target": int(self._grasp_target_per_scale),
-                "grasp_collection_counts_by_scale": self._get_per_scale_grasp_counts(),
-            },
-        )
-
-    def _collect_successful_grasps(self, env_ids: np.ndarray) -> None:
-        if self.state is None or len(env_ids) == 0:
-            return
-
-        success_mask = self.state.truncated[env_ids] & ~self.state.terminated[env_ids]
-        if not np.any(success_mask):
-            return
-
-        success_env_ids = env_ids[np.flatnonzero(success_mask)]
-        hand_qpos = self.get_hand_dof_pos()[success_env_ids]
-        object_pos = self.get_object_pos()[success_env_ids]
-        object_quat = self.get_object_quat()[success_env_ids]
-        all_states = np.concatenate([hand_qpos, object_pos, object_quat], axis=1).astype(np.float32)
-
-        saved_scale_ids = self.scale_ids[success_env_ids]
-        for i, scale_id in enumerate(saved_scale_ids):
-            bucket = self._saved_grasping_states[int(scale_id)]
-            if len(bucket) < self._grasp_target_per_scale:
-                bucket.append(all_states[i : i + 1])
-
-        self._maybe_log_grasp_progress()
-        if self._grasp_cache_saved:
-            return
-
-        finished_scales = sum(
-            int(len(bucket) >= self._grasp_target_per_scale)
-            for bucket in self._saved_grasping_states
-        )
-        if finished_scales < self._num_scales:
-            return
-
-        if not self._cfg.grasp_auto_save:
-            self._grasp_cache_saved = True
-            self._stop_collection()
-            return
-
-        output_file = resolve_grasp_cache_file(
-            self._cfg.grasp_cache_path or "caches/sharpa_grasp_linspace",
-            float(self.scale_values[0]),
-        )
-        if not output_file.is_absolute():
-            output_file = ASSETS_ROOT_PATH / output_file
-        output_file.parent.mkdir(parents=True, exist_ok=True)
-        save_data = np.concatenate(self._saved_grasping_states[0], axis=0)[
-            : self._grasp_target_per_scale
-        ]
-        np.save(output_file, save_data)
-
-        self._grasp_cache_saved = True
-        if self.state is not None:
-            log = self.state.info.get("log", {})
-            log["grasp_cache/saved"] = 1.0
-            log["grasp_cache/num_states"] = float(save_data.shape[0])
-            self.state.info["log"] = log
-
-        self._stop_collection()
-
-    def _compute_reward(
-        self,
-        info: dict[str, Any],
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-        object_pos: np.ndarray,
-        object_linvel: np.ndarray,
-        object_angvel: np.ndarray,
-        torques: np.ndarray,
-    ) -> np.ndarray:
-        del info, dof_pos, dof_vel, object_pos, object_linvel, object_angvel, torques
-        return np.zeros((self._num_envs,), dtype=self._np_dtype)
-
-    def update_state(self, state: NpEnvState) -> NpEnvState:
-        next_state = super().update_state(state)
-
-        fingertip_pos = self.get_fingertip_pos()
-        object_pos = self.get_object_pos()
-        object_quat = self.get_object_quat()
-        object_default_pose = np.asarray(
-            next_state.info.get(
-                "object_default_pose", np.zeros((self._num_envs, 7), dtype=self._np_dtype)
-            ),
-            dtype=self._np_dtype,
-        )
-
-        cond1 = np.all(
-            np.linalg.norm(fingertip_pos - object_pos[:, None, :], axis=-1) < 0.1, axis=1
-        )
-        tactile = np.asarray(self.last_contacts, dtype=self._np_dtype)
-        cond2 = np.sum(tactile > 0.5, axis=1) >= 3
-        quat_error = np_quat_error_magnitude(object_default_pose[:, 3:7], object_quat)
-        cond3 = quat_error < self._cfg.reset_angle_diff
-
-        grasp_valid = cond1 & cond2 & cond3
-        terminated = np.asarray(next_state.terminated | (~grasp_valid), dtype=bool)
-
-        reward = np.zeros((self._num_envs,), dtype=self._np_dtype)
-
-        step_count = next_state.info.get("steps", np.zeros((self._num_envs,), dtype=np.uint32))
-        should_log = self._enable_reward_log and (int(step_count[0]) % 4 == 0)
-        if should_log:
-            log = next_state.info.get("log", {})
-            log["grasp/cond1"] = float(np.mean(cond1.astype(np.float32)))
-            log["grasp/cond2"] = float(np.mean(cond2.astype(np.float32)))
-            log["grasp/cond3"] = float(np.mean(cond3.astype(np.float32)))
-            log["grasp/valid"] = float(np.mean(grasp_valid.astype(np.float32)))
-            per_scale_counts = self._get_per_scale_grasp_counts()
-            log["grasp/target_cache_size"] = float(self._cfg.grasp_collection_target)
-            for scale_idx, count in enumerate(per_scale_counts):
-                scale_value = float(self.scale_values[scale_idx])
-                log[f"grasp/cache_size_scale_{scale_value:g}"] = float(count)
-            next_state.info["log"] = log
-
-        return next_state.replace(reward=reward, terminated=terminated)
-
-
-SharpaWaveGraspCfg = SharpaInhandGraspEnvCfg
diff --git a/src/unilab/envs/manipulation/sharpa_inhand/rotation.py b/src/unilab/envs/manipulation/sharpa_inhand/rotation.py
deleted file mode 100644
index 252330e8d..000000000
--- a/src/unilab/envs/manipulation/sharpa_inhand/rotation.py
+++ /dev/null
@@ -1,1538 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Any, cast
-
-import numpy as np
-
-from unilab.assets.hub import resolve_grasp_cache_files
-from unilab.base import registry
-from unilab.base.backend import create_backend, env_backend_kwargs
-from unilab.base.np_env import NpEnvState
-from unilab.dr import (
-    DomainRandomizationCapabilities,
-    DomainRandomizationProvider,
-    GeomSizeOverride,
-    InitRandomizationPlan,
-    IntervalRandomizationPlan,
-    ModelVariantSpec,
-    ResetPlan,
-)
-from unilab.dr.dr_utils import build_common_reset_randomization, validate_common_reset_randomization
-from unilab.dr.types import (
-    RESET_TERM_BODY_IPOS,
-    RESET_TERM_BODY_MASS,
-    RESET_TERM_GEOM_FRICTION,
-    RESET_TERM_GRAVITY,
-    RESET_TERM_KD,
-    RESET_TERM_KP,
-    ResetRandomizationPayload,
-)
-from unilab.dtype_config import get_global_dtype
-from unilab.envs.manipulation.sharpa_inhand.base import (
-    SharpaInhandBaseCfg,
-    SharpaInhandBaseEnv,
-    repeat_obs_history,
-    resolve_grasp_cache_file,
-    sample_scale_grasp_caches,
-)
-from unilab.utils.geometry import np_sample_uniform_quaternion
-from unilab.utils.rotation import (
-    np_quat_apply,
-    np_quat_conjugate,
-    np_quat_mul,
-    np_quat_to_axis_angle,
-)
-
-
-@dataclass
-class RewardConfig:
-    scales: dict[str, float] = field(
-        default_factory=lambda: {
-            "rotate": 2.5,
-            "obj_linvel": -0.3,
-            "pose_diff": -0.4,
-            "torque": -0.1,
-            "work": -0.5,
-            "object_pos": 0.003,
-        }
-    )
-    angvel_clip_min: float = -0.5
-    angvel_clip_max: float = 0.5
-
-
-@registry.envcfg("SharpaInhandRotation")
-@dataclass
-class SharpaInhandRotationCfg(SharpaInhandBaseCfg):
-    critic_info_dim: int = 9
-    reward_config: RewardConfig | None = None
-    zero_action_test_mode: bool = False
-
-
-def sample_random_quaternion(num_envs: int) -> np.ndarray:
-    """Sample uniformly distributed random quaternions in wxyz convention.
-
-    Args:
-        num_envs: Number of quaternions to sample.
-
-    Returns:
-        Quaternion array with shape ``(num_envs, 4)`` in float64.
-    """
-    return np_sample_uniform_quaternion(num_envs).astype(np.float64)
-
-
-def _materialize_grasp_caches(
-    grasp_cache_path: str,
-    scale_values: np.ndarray,
-) -> tuple[np.ndarray, ...]:
-    """Load one Sharpa grasp cache for each scale on the env construction path."""
-    grasp_caches: list[np.ndarray] = []
-    missing_files: list[str] = []
-    for scale_value in np.asarray(scale_values, dtype=np.float64):
-        cache_file = resolve_grasp_cache_file(grasp_cache_path, float(scale_value))
-        resolved = cast(str, resolve_grasp_cache_files(str(cache_file)))
-        cache_file = Path(resolved)
-        if not cache_file.exists():
-            missing_files.append(str(cache_file))
-            continue
-        grasp_caches.append(np.load(cache_file).astype(np.float64))
-
-    if missing_files:
-        missing = ", ".join(missing_files)
-        raise RuntimeError(
-            f"Missing Sharpa grasp cache file(s): {missing}\n"
-            "Generate them with:\n"
-            "  bash scripts/sharpa_collect_grasps.sh 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6\n"
-            "See docs/sphinx/source/zh_CN/user_guide/D-tasks/04-sharpa-inhand.md"
-        )
-
-    return tuple(grasp_caches)
-
-
-class SharpaInhandRotationDRProvider(DomainRandomizationProvider):
-    def validate(self, env: Any, capabilities: DomainRandomizationCapabilities) -> None:
-        unsupported = validate_common_reset_randomization(env, capabilities)
-        domain_rand = getattr(env.cfg, "domain_rand", None)
-        if domain_rand is not None and getattr(domain_rand, "randomize_gravity_direction", False):
-            if getattr(domain_rand, "randomize_gravity", False):
-                raise ValueError(
-                    "Use only one Sharpa gravity randomization mode: "
-                    "domain_rand.randomize_gravity_direction or domain_rand.randomize_gravity"
-                )
-            if not capabilities.supports_reset_term(RESET_TERM_GRAVITY):
-                unsupported = unsupported | frozenset({RESET_TERM_GRAVITY})
-        if (
-            domain_rand is not None
-            and domain_rand.force_scale > 0.0
-            and not capabilities.supports_interval_body_force
-        ):
-            raise NotImplementedError(
-                f"{env._backend.backend_type} backend does not support interval body force perturbation"
-            )
-        if domain_rand is not None and domain_rand.randomize_pd_gains:
-            if not capabilities.supports_reset_term(RESET_TERM_KP):
-                unsupported = unsupported | frozenset({RESET_TERM_KP})
-            if not capabilities.supports_reset_term(RESET_TERM_KD):
-                unsupported = unsupported | frozenset({RESET_TERM_KD})
-        if (
-            domain_rand is not None
-            and domain_rand.randomize_com
-            and not capabilities.supports_reset_term(RESET_TERM_BODY_IPOS)
-        ):
-            unsupported = unsupported | frozenset({RESET_TERM_BODY_IPOS})
-        if (
-            domain_rand is not None
-            and domain_rand.randomize_mass
-            and not capabilities.supports_reset_term(RESET_TERM_BODY_MASS)
-        ):
-            unsupported = unsupported | frozenset({RESET_TERM_BODY_MASS})
-        if (
-            domain_rand is not None
-            and domain_rand.randomize_friction
-            and not capabilities.supports_reset_term(RESET_TERM_GEOM_FRICTION)
-        ):
-            unsupported = unsupported | frozenset({RESET_TERM_GEOM_FRICTION})
-        if unsupported:
-            names = ", ".join(sorted(unsupported))
-            raise NotImplementedError(
-                f"{env._backend.backend_type} backend does not support reset randomization terms: {names}"
-            )
-
-    def build_init_randomization_plan(self, env: Any) -> InitRandomizationPlan | None:
-        base_size = getattr(env, "_object_geom_base_size", None)
-
-        if base_size is None:
-            return None
-
-        model_variants = tuple(
-            ModelVariantSpec(
-                geom_size_overrides=(
-                    GeomSizeOverride(
-                        geom_name=env.cfg.object_geom_name,
-                        size=tuple(np.asarray(base_size * scale, dtype=np.float64)),
-                    ),
-                )
-            )
-            for scale in np.asarray(env.scale_values, dtype=np.float64)
-        )
-        return InitRandomizationPlan(
-            model_assignments=np.asarray(env.scale_ids, dtype=np.int32).copy(),
-            model_variants=model_variants,
-        )
-
-    def _sample_reset_pd_gains(
-        self,
-        env: Any,
-        num_reset: int,
-        *,
-        dtype: np.dtype[Any],
-    ) -> tuple[np.ndarray, np.ndarray]:
-        """Sample absolute reset-time PD gains from split-around-1 scale ranges.
-
-        Args:
-            env: Sharpa rotation env instance.
-            num_reset: Number of environments being reset.
-            dtype: Output dtype for the returned gain arrays.
-
-        Returns:
-            Tuple of absolute ``(p_gain, d_gain)`` arrays with shape
-            ``(num_reset, env._num_action)``.
-        """
-        p_gain = np.broadcast_to(env._default_p_gain, (num_reset, env._num_action)).astype(
-            dtype, copy=True
-        )
-        d_gain = np.broadcast_to(env._default_d_gain, (num_reset, env._num_action)).astype(
-            dtype, copy=True
-        )
-        domain_rand = env.cfg.domain_rand
-        if domain_rand.randomize_pd_gains:
-            p_scale = env._sample_pd_scales(
-                domain_rand.randomize_p_gain_scale_lower,
-                domain_rand.randomize_p_gain_scale_upper,
-                shape=(num_reset, env._num_action),
-            )
-            d_scale = env._sample_pd_scales(
-                domain_rand.randomize_d_gain_scale_lower,
-                domain_rand.randomize_d_gain_scale_upper,
-                shape=(num_reset, env._num_action),
-            )
-            p_gain *= p_scale
-            d_gain *= d_scale
-        return p_gain, d_gain
-
-    def _build_info_updates(
-        self,
-        env: Any,
-        env_ids: np.ndarray,
-        hand_qpos: np.ndarray,
-        object_pos: np.ndarray,
-        object_quat: np.ndarray,
-        reset_height_lower: np.ndarray,
-        reset_height_upper: np.ndarray,
-        rot_axis: np.ndarray,
-        p_gain: np.ndarray,
-        d_gain: np.ndarray,
-        friction_scale: np.ndarray | None,
-        randomized_mass: np.ndarray | None,
-        randomized_com_offset: np.ndarray | None,
-        gravity: np.ndarray | None,
-    ) -> dict[str, np.ndarray]:
-        num_reset = hand_qpos.shape[0]
-        dtype = get_global_dtype()
-
-        critic_info = env._build_reset_critic_info(
-            num_reset,
-            env_ids,
-            friction_scale=friction_scale,
-            randomized_mass=randomized_mass,
-            randomized_com_offset=randomized_com_offset,
-            gravity=gravity,
-        ).astype(dtype)
-
-        tactile = np.zeros((num_reset, env._num_tactile), dtype=dtype)
-        contact_pos = np.zeros((num_reset, env._num_tactile * 3), dtype=dtype)
-        hand_qpos_f = hand_qpos.astype(dtype)
-        targets = hand_qpos_f.copy()
-        object_pos_f = object_pos.astype(dtype)
-        init_frame = env._build_policy_frame(
-            dof_pos=hand_qpos_f,
-            targets=targets,
-            tactile=tactile,
-            contact_pos=contact_pos,
-            add_noise=True,
-        )
-        critic_init_frame = env._build_policy_frame(
-            dof_pos=hand_qpos_f,
-            targets=targets,
-            tactile=tactile,
-            contact_pos=contact_pos,
-            add_noise=False,
-        )
-        obs_lag_history = repeat_obs_history(init_frame, env.cfg.obs_history_len).astype(dtype)
-        critic_obs_lag_history = repeat_obs_history(
-            critic_init_frame, env.cfg.obs_history_len
-        ).astype(dtype)
-
-        object_default_pose = np.concatenate(
-            [object_pos_f, object_quat.astype(dtype)], axis=1
-        ).astype(dtype)
-        object_pos_anchor = env._build_object_pos_anchor(object_pos_f).astype(dtype)
-        critic_info = env._fill_critic_info(
-            critic_info=critic_info,
-            object_pos=object_pos_f,
-            object_pos_anchor=object_pos_anchor,
-        )
-
-        info_updates = {
-            "current_actions": np.zeros((num_reset, env._num_action), dtype=dtype),
-            "last_actions": np.zeros((num_reset, env._num_action), dtype=dtype),
-            "prev_targets": hand_qpos_f.copy(),
-            "init_pose": hand_qpos_f.copy(),
-            "prev_hand_pos": hand_qpos_f.copy(),
-            "prev_object_pos": object_pos.astype(dtype).copy(),
-            "prev_object_quat": object_quat.astype(dtype).copy(),
-            "object_default_pose": object_default_pose,
-            "object_pos_anchor": object_pos_anchor,
-            "reset_height_lower": reset_height_lower.astype(dtype),
-            "reset_height_upper": reset_height_upper.astype(dtype),
-            "rot_axis": rot_axis.astype(dtype),
-            "p_gain": p_gain,
-            "d_gain": d_gain,
-            "critic_info": critic_info,
-            "obs_lag_history": obs_lag_history,
-            "critic_obs_lag_history": critic_obs_lag_history,
-            "proprio_hist": env._update_proprio_history(obs_lag_history),
-        }
-        return info_updates
-
-    def build_reset_plan(self, env: Any, env_ids: np.ndarray) -> ResetPlan:
-        num_reset = len(env_ids)
-        if num_reset == 0:
-            return ResetPlan(
-                env_ids=env_ids,
-                qpos=np.zeros((0, env.nq), dtype=np.float64),
-                qvel=np.zeros((0, env.nv), dtype=np.float64),
-                info_updates={},
-                randomization=None,
-            )
-
-        friction_scale = env._sample_friction_scale(num_reset)
-        randomized_mass = env._sample_object_mass(num_reset)
-        randomized_com_offset = env._sample_object_com_offset(num_reset)
-        gravity = env._sample_reset_gravity(num_reset)
-        p_gain, d_gain = self._sample_reset_pd_gains(env, num_reset, dtype=get_global_dtype())
-        grasp_cache = cast(tuple[np.ndarray, ...], env._grasp_cache)
-        sampled_pose = sample_scale_grasp_caches(grasp_cache, env.scale_ids[env_ids])
-
-        hand_qpos = sampled_pose[:, : env._num_action]
-        object_pos = sampled_pose[:, env._num_action : env._num_action + 3]
-        object_quat = sampled_pose[:, env._num_action + 3 : env._num_action + 7]
-
-        rot_axis = np.broadcast_to(env._rot_axis, (num_reset, 3)).copy().astype(np.float64)
-
-        qpos = np.zeros((num_reset, env.nq), dtype=np.float64)
-        qpos[:, : env._num_action] = hand_qpos
-        qpos[:, env._obj_pos_slice] = object_pos
-        qpos[:, env._obj_quat_slice] = object_quat
-
-        qvel = np.zeros((num_reset, env.nv), dtype=np.float64)
-
-        height_range = env.cfg.reset_height_upper - env.cfg.reset_height_lower
-        reset_height_lower = object_pos[:, 2] - 0.5 * height_range
-        reset_height_upper = object_pos[:, 2] + 0.5 * height_range
-
-        info_updates = self._build_info_updates(
-            env,
-            env_ids=env_ids,
-            hand_qpos=hand_qpos,
-            object_pos=object_pos,
-            object_quat=object_quat,
-            reset_height_lower=reset_height_lower,
-            reset_height_upper=reset_height_upper,
-            rot_axis=rot_axis,
-            p_gain=p_gain,
-            d_gain=d_gain,
-            friction_scale=friction_scale,
-            randomized_mass=randomized_mass,
-            randomized_com_offset=randomized_com_offset,
-            gravity=gravity,
-        )
-        # Match the source task by clearing any cached external object force on reset.
-        env._random_object_force[env_ids] = 0.0
-        env._clear_tactile_history(env_ids)
-
-        return ResetPlan(
-            env_ids=env_ids,
-            qpos=qpos,
-            qvel=qvel,
-            info_updates=info_updates,
-            randomization=env._build_reset_randomization(
-                num_reset,
-                p_gain=p_gain,
-                d_gain=d_gain,
-                friction_scale=friction_scale,
-                randomized_mass=randomized_mass,
-                randomized_com_offset=randomized_com_offset,
-                gravity=gravity,
-            ),
-        )
-
-    def build_reset_observation(
-        self,
-        env: Any,
-        env_ids: np.ndarray,
-        info_updates: dict[str, Any],
-    ) -> dict[str, np.ndarray]:
-        del env_ids
-        tactile, contact_pos = env._policy_frame_zeros(len(info_updates["prev_targets"]))
-        return cast(
-            dict[str, np.ndarray],
-            env._compute_obs_from_inputs(
-                info_updates,
-                dof_pos=np.asarray(info_updates["prev_targets"]),
-                object_pos=np.asarray(info_updates["prev_object_pos"]),
-                tactile=tactile,
-                contact_pos=contact_pos,
-            ),
-        )
-
-    def build_interval_randomization_plan(
-        self,
-        env: Any,
-        step_counter: int,
-    ) -> IntervalRandomizationPlan | None:
-        """Build Sharpa object-force perturbations for the upcoming control step.
-
-        Args:
-            env: Sharpa rotation env instance.
-            step_counter: Global environment step counter.
-
-        Returns:
-            Interval randomization plan carrying direct object-force perturbations,
-            or ``None`` when object-force injection is disabled.
-        """
-        del step_counter
-        domain_rand = env.cfg.domain_rand
-        if domain_rand.force_scale <= 0.0:
-            return None
-
-        decay = float(
-            np.power(
-                domain_rand.force_decay,
-                env.cfg.ctrl_dt / max(domain_rand.force_decay_interval, 1.0e-8),
-            )
-        )
-        env._random_object_force *= decay
-
-        random_mask = np.random.rand(env._num_envs) < float(domain_rand.random_force_prob_scalar)
-        if np.any(random_mask):
-            object_mass = env._resolve_current_object_mass()
-            env._random_object_force[random_mask] = (
-                np.random.randn(int(np.sum(random_mask)), 3).astype(np.float64)
-                * object_mass[random_mask, None]
-                * float(domain_rand.force_scale)
-            )
-
-        return IntervalRandomizationPlan(
-            body_ids=np.asarray([env._object_body_id], dtype=np.int32),
-            body_force=env._random_object_force[:, None, :].copy(),
-        )
-
-
-@registry.env("SharpaInhandRotation", sim_backend="drake")
-@registry.env("SharpaInhandRotation", sim_backend="mujoco")
-@registry.env("SharpaInhandRotation", sim_backend="motrix")
-class SharpaInhandRotationEnv(SharpaInhandBaseEnv):
-    _cfg: SharpaInhandRotationCfg
-    _reward_cfg: RewardConfig
-    _MATERIALIZE_ROTATION_GRASP_CACHE = True
-    _OBS_MODE_ALIASES: dict[str, str] = {
-        "separated": "separated",
-        "flattened": "flattened",
-    }
-    _CRITIC_BASE_DIM_WITHOUT_OPTIONALS = 8
-
-    def __init__(
-        self,
-        cfg: SharpaInhandRotationCfg,
-        num_envs: int = 1,
-        backend_type: str = "motrix",
-        dr_provider: DomainRandomizationProvider | None = None,
-    ) -> None:
-        if cfg.reward_config is None:
-            raise ValueError("reward_config must be provided via Hydra configuration")
-
-        backend = create_backend(
-            backend_type,
-            cfg.scene,
-            num_envs,
-            cfg.sim_dt,
-            base_name=cfg.base_name,
-            push_body_name=cfg.domain_rand.push_body_name,
-            add_body_sensors=True,
-            **env_backend_kwargs(cfg),
-        )
-        super().__init__(cfg, backend, num_envs)
-
-        self._observation_mode = self._resolve_observation_mode(cfg.obs.observation_mode)
-        expected_critic_info_dim = self._expected_critic_info_dim()
-        if cfg.critic_info_dim != expected_critic_info_dim:
-            cfg.critic_info_dim = expected_critic_info_dim
-        policy_frame_dim = self._policy_frame_dim()
-        self.obs_buf_lag_history = np.zeros(
-            (num_envs, cfg.obs_history_len, policy_frame_dim), dtype=self._np_dtype
-        )
-        self.proprio_hist_buf = np.zeros(
-            (num_envs, cfg.prop_hist_len, policy_frame_dim), dtype=self._np_dtype
-        )
-        self.critic_info_buf = np.zeros((num_envs, expected_critic_info_dim), dtype=self._np_dtype)
-        self._friction_geom_ids = self._resolve_friction_geom_ids()
-        self._base_geom_friction = self._resolve_base_geom_friction()
-        self._base_gravity = self._resolve_base_gravity()
-        self._object_body_id = self._resolve_object_body_id()
-        self._base_body_mass = self._resolve_base_body_mass()
-        self._base_body_ipos = self._resolve_base_body_ipos()
-        self._default_p_gain, self._default_d_gain = self._load_default_pd_gains()
-        self._random_object_force = np.zeros((num_envs, 3), dtype=np.float64)
-
-        if cfg.control_config.torque_control:
-            raise NotImplementedError(
-                "Sharpa torque_control=True is not implemented with the current position-actuator XML setup. "
-                "Set env.control_config.torque_control=false. Virtual torques are still computed explicitly for reward terms."
-            )
-
-        self._reward_cfg = cfg.reward_config
-        self._zero_action_test_mode = bool(cfg.zero_action_test_mode)
-        self._enable_reward_log = True
-        self._grasp_cache: tuple[np.ndarray, ...] | None = None
-
-        axis = np.asarray(cfg.rot_axis, dtype=self._np_dtype)
-        axis_norm = np.linalg.norm(axis)
-        if axis_norm <= 1.0e-8:
-            raise ValueError("rot_axis must be non-zero")
-        self._rot_axis = np.asarray(axis / axis_norm, dtype=self._np_dtype)
-
-        provider = dr_provider if dr_provider is not None else SharpaInhandRotationDRProvider()
-        if self._MATERIALIZE_ROTATION_GRASP_CACHE:
-            self._grasp_cache = _materialize_grasp_caches(cfg.grasp_cache_path, self.scale_values)
-        self._init_domain_randomization(provider)
-
-    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
-        actions_np = np.asarray(actions, dtype=self._np_dtype)
-        if self._zero_action_test_mode:
-            actions_np = np.zeros_like(actions_np, dtype=self._np_dtype)
-        return super().apply_action(actions_np, state)
-
-    def _scale_randomization_enabled(self) -> bool:
-        return self._num_scales > 1 or not np.allclose(self.scale_values, self.scale_values[0])
-
-    def _expected_critic_info_dim(self) -> int:
-        priv_info_cfg = self._cfg.priv_info
-        dim = self._CRITIC_BASE_DIM_WITHOUT_OPTIONALS
-        if priv_info_cfg.include_friction_scale:
-            dim += 1
-        if priv_info_cfg.include_gravity_direction:
-            dim += 3
-        return dim
-
-    def _resolve_friction_geom_ids(self) -> dict[str, np.ndarray]:
-        """Resolve MuJoCo geom ids touched by Sharpa friction randomization.
-
-        Args:
-            None.
-
-        Returns:
-            Mapping with object, elastomer, and metal collision geom id arrays.
-        """
-        try:
-            object_geom_id = self._backend.get_geom_id(self._cfg.object_geom_name)
-            base_body_id = self._backend.get_body_id(self._cfg.base_name)
-            hand_body_ids = set(
-                int(body_id) for body_id in self._backend.get_body_subtree_ids(base_body_id)
-            )
-            geom_body_ids = self._backend.get_geom_body_ids()
-            geom_contype, geom_conaffinity = self._backend.get_geom_contact_masks()
-            geom_names = self._backend.get_geom_names()
-        except NotImplementedError:
-            empty = np.zeros((0,), dtype=np.int32)
-            return {"object": empty, "elastomer": empty, "metal": empty}
-        elastomer_ids: list[int] = []
-        metal_ids: list[int] = []
-        for geom_id, geom_name in enumerate(geom_names):
-            if int(geom_body_ids[geom_id]) not in hand_body_ids:
-                continue
-            if int(geom_contype[geom_id]) == 0 and int(geom_conaffinity[geom_id]) == 0:
-                continue
-            if "elastomer" in geom_name:
-                elastomer_ids.append(geom_id)
-            else:
-                metal_ids.append(geom_id)
-
-        if not elastomer_ids:
-            raise ValueError("No Sharpa elastomer collision geoms found for friction randomization")
-        if not metal_ids:
-            raise ValueError("No Sharpa metal collision geoms found for friction randomization")
-
-        return {
-            "object": np.asarray([object_geom_id], dtype=np.int32),
-            "elastomer": np.asarray(elastomer_ids, dtype=np.int32),
-            "metal": np.asarray(metal_ids, dtype=np.int32),
-        }
-
-    def _resolve_object_body_id(self) -> int:
-        """Resolve the MuJoCo body id of the manipulated object.
-
-        Args:
-            None.
-
-        Returns:
-            The MuJoCo object body id, or -1 when the backend is not MuJoCo.
-        """
-        if self._object_body_ids.size == 0:
-            return -1
-        return int(self._object_body_ids[0])
-
-    def _resolve_current_object_mass(self) -> np.ndarray:
-        """Resolve the current object mass used by force perturbation sampling.
-
-        Args:
-            None.
-
-        Returns:
-            Array of shape ``(num_envs,)`` with current object masses.
-        """
-        if self.state is not None:
-            critic_info = np.asarray(
-                self.state.info.get(
-                    "critic_info",
-                    np.zeros((self._num_envs, self._cfg.critic_info_dim), dtype=self._np_dtype),
-                ),
-                dtype=np.float64,
-            )
-            mass = critic_info[:, self._critic_info_layout()["mass"]].reshape(self._num_envs)
-            if np.all(mass > 0.0):
-                return mass
-        if self._base_body_mass is None or self._object_body_id < 0:
-            raise ValueError("MuJoCo object body-mass cache is unavailable")
-        return np.full(
-            (self._num_envs,),
-            float(self._base_body_mass[self._object_body_id]),
-            dtype=np.float64,
-        )
-
-    def _resolve_base_geom_friction(self) -> np.ndarray | None:
-        """Cache MuJoCo model friction vectors used as torsional/rolling templates.
-
-        Args:
-            None.
-
-        Returns:
-            Full geom friction table, or None when the backend has no geom-friction hook.
-        """
-        try:
-            return np.asarray(self._backend.get_geom_friction(), dtype=np.float64).copy()
-        except NotImplementedError:
-            return None
-
-    def _resolve_base_gravity(self) -> np.ndarray:
-        """Cache the default gravity vector for privileged-info fallbacks.
-
-        Args:
-            None.
-
-        Returns:
-            Gravity vector with shape ``(3,)``.
-        """
-        try:
-            return np.asarray(self._backend.get_gravity(), dtype=np.float64).copy()
-        except NotImplementedError:
-            pass
-        return np.asarray([0.0, 0.0, -9.81], dtype=np.float64)
-
-    def _resolve_base_body_mass(self) -> np.ndarray | None:
-        """Cache the MuJoCo body-mass table used as reset randomization baseline.
-
-        Args:
-            None.
-
-        Returns:
-            Full body-mass table, or None when the backend is not MuJoCo.
-        """
-        try:
-            return np.asarray(self._backend.get_body_mass(), dtype=np.float64).copy()
-        except NotImplementedError:
-            return None
-
-    def _resolve_base_body_ipos(self) -> np.ndarray | None:
-        """Cache the MuJoCo inertial-position table used for object COM randomization.
-
-        Args:
-            None.
-
-        Returns:
-            Full body inertial-position table, or None when the backend is not MuJoCo.
-        """
-        try:
-            return np.asarray(self._backend.get_body_ipos(), dtype=np.float64).copy()
-        except NotImplementedError:
-            return None
-
-    def _sample_friction_scale(self, batch_size: int) -> np.ndarray | None:
-        """Sample one friction multiplier per reset environment.
-
-        Args:
-            batch_size: Number of reset environments.
-
-        Returns:
-            Shape (batch_size, 1) multipliers, or None when disabled.
-        """
-        domain_rand = self._cfg.domain_rand
-        if not domain_rand.randomize_friction:
-            return None
-        return np.random.uniform(
-            domain_rand.randomize_friction_scale_lower,
-            domain_rand.randomize_friction_scale_upper,
-            size=(batch_size, 1),
-        ).astype(np.float64)
-
-    def _sample_object_mass(self, batch_size: int) -> np.ndarray | None:
-        """Sample reset-time object masses.
-
-        Args:
-            batch_size: Number of reset environments.
-
-        Returns:
-            Shape (batch_size, 1) absolute object masses, or None when disabled.
-        """
-        domain_rand = self._cfg.domain_rand
-        if not domain_rand.randomize_mass:
-            return None
-        return np.random.uniform(
-            domain_rand.randomize_mass_lower,
-            domain_rand.randomize_mass_upper,
-            size=(batch_size, 1),
-        ).astype(np.float64)
-
-    def _sample_object_com_offset(self, batch_size: int) -> np.ndarray | None:
-        """Sample reset-time object COM offsets.
-
-        Args:
-            batch_size: Number of reset environments.
-
-        Returns:
-            Shape (batch_size, 3) COM offsets, or None when disabled.
-        """
-        domain_rand = self._cfg.domain_rand
-        if not domain_rand.randomize_com:
-            return None
-        return np.random.uniform(
-            domain_rand.randomize_com_lower,
-            domain_rand.randomize_com_upper,
-            size=(batch_size, 3),
-        ).astype(np.float64)
-
-    def _friction_profile(self, material: str, base_sliding_friction: float) -> np.ndarray:
-        """Build one MuJoCo friction vector while preserving XML friction ratios.
-
-        Args:
-            material: One of object, elastomer, or metal.
-            base_sliding_friction: Sliding-friction coefficient before random scaling.
-
-        Returns:
-            Shape (3,) MuJoCo friction vector.
-        """
-        if self._base_geom_friction is None:
-            raise ValueError("MuJoCo base geom friction cache is unavailable")
-
-        geom_ids = self._friction_geom_ids[material]
-        template = np.asarray(self._base_geom_friction[int(geom_ids[0])], dtype=np.float64)
-        sliding = float(template[0])
-        if sliding <= 0.0:
-            raise ValueError(f"{material} base sliding friction must be positive, got {sliding}")
-        return np.asarray(template / sliding * base_sliding_friction, dtype=np.float64)
-
-    def _sample_reset_gravity(self, batch_size: int) -> np.ndarray | None:
-        """Sample the exact reset-time gravity vector applied by Sharpa.
-
-        Args:
-            batch_size: Number of reset environments.
-
-        Returns:
-            Gravity array with shape ``(batch_size, 3)``, or ``None`` when reset-time
-            gravity randomization is disabled.
-        """
-        gravity = self._build_gravity_direction_randomization(batch_size)
-        if gravity is not None:
-            return gravity
-        domain_rand = self._cfg.domain_rand
-        if not getattr(domain_rand, "randomize_gravity", False):
-            return None
-        gravity_range = np.asarray(domain_rand.gravity_range, dtype=np.float64)
-        if gravity_range.shape != (2, 3):
-            raise ValueError(
-                f"domain_rand.gravity_range must have shape (2, 3), got {gravity_range.shape}"
-            )
-        low = np.minimum(gravity_range[0], gravity_range[1])
-        high = np.maximum(gravity_range[0], gravity_range[1])
-        return np.random.uniform(low=low, high=high, size=(batch_size, 3)).astype(np.float64)
-
-    def _resolved_privileged_gravity(
-        self,
-        batch_size: int,
-        gravity: np.ndarray | None,
-    ) -> np.ndarray:
-        """Resolve the gravity vector written into privileged info for one reset batch.
-
-        Args:
-            batch_size: Number of reset environments.
-            gravity: Sampled reset gravity, if reset-time randomization is active.
-
-        Returns:
-            Gravity array with shape ``(batch_size, 3)``.
-        """
-        if gravity is not None:
-            return np.asarray(gravity, dtype=self._np_dtype)
-        return np.broadcast_to(self._base_gravity, (batch_size, 3)).astype(
-            self._np_dtype, copy=True
-        )
-
-    def _build_friction_randomization(
-        self,
-        batch_size: int,
-        friction_scale: np.ndarray | None,
-    ) -> np.ndarray | None:
-        """Build the reset-time MuJoCo geom_friction randomization table.
-
-        Args:
-            batch_size: Number of reset environments.
-            friction_scale: Shape (batch_size, 1) sampled friction multipliers.
-
-        Returns:
-            Shape (batch_size, ngeom, 3) friction table, or None when disabled.
-        """
-        if friction_scale is None:
-            return None
-        if self._base_geom_friction is None:
-            raise ValueError("MuJoCo base geom friction cache is unavailable")
-
-        geom_friction = np.broadcast_to(
-            self._base_geom_friction,
-            (batch_size, *self._base_geom_friction.shape),
-        ).copy()
-        scale = np.asarray(friction_scale, dtype=np.float64).reshape(batch_size, 1, 1)
-        domain_rand = self._cfg.domain_rand
-        material_profiles = {
-            "object": self._friction_profile("object", domain_rand.object_base_friction),
-            "elastomer": self._friction_profile("elastomer", domain_rand.elastomer_base_friction),
-            "metal": self._friction_profile("metal", domain_rand.metal_base_friction),
-        }
-        for material, profile in material_profiles.items():
-            geom_friction[:, self._friction_geom_ids[material], :] = scale * profile.reshape(
-                1, 1, 3
-            )
-        return geom_friction
-
-    def _build_object_mass_randomization(
-        self,
-        batch_size: int,
-        randomized_mass: np.ndarray | None,
-    ) -> np.ndarray | None:
-        """Build the reset-time MuJoCo body_mass table for object mass randomization.
-
-        Args:
-            batch_size: Number of reset environments.
-            randomized_mass: Shape (batch_size, 1) sampled absolute object masses.
-
-        Returns:
-            Shape (batch_size, nbody) mass table, or None when disabled.
-        """
-        if randomized_mass is None:
-            return None
-        if self._base_body_mass is None or self._object_body_id < 0:
-            raise ValueError("MuJoCo base body-mass cache is unavailable")
-        body_mass = np.broadcast_to(
-            self._base_body_mass, (batch_size, self._base_body_mass.size)
-        ).copy()
-        body_mass[:, self._object_body_id] = np.asarray(randomized_mass, dtype=np.float64).reshape(
-            batch_size
-        )
-        return body_mass
-
-    def _build_object_com_randomization(
-        self,
-        batch_size: int,
-        randomized_com_offset: np.ndarray | None,
-    ) -> np.ndarray | None:
-        """Build the reset-time MuJoCo body_ipos table for object COM randomization.
-
-        Args:
-            batch_size: Number of reset environments.
-            randomized_com_offset: Shape (batch_size, 3) sampled COM offsets.
-
-        Returns:
-            Shape (batch_size, nbody, 3) inertial-position table, or None when disabled.
-        """
-        if randomized_com_offset is None:
-            return None
-        if self._base_body_ipos is None or self._object_body_id < 0:
-            raise ValueError("MuJoCo base body-ipos cache is unavailable")
-        body_ipos = np.broadcast_to(
-            self._base_body_ipos,
-            (batch_size, *self._base_body_ipos.shape),
-        ).copy()
-        body_ipos[:, self._object_body_id, :] += np.asarray(randomized_com_offset, dtype=np.float64)
-        return body_ipos
-
-    def _build_gravity_direction_randomization(self, batch_size: int) -> np.ndarray | None:
-        """Sample fixed-magnitude gravity vectors with randomized directions.
-
-        Args:
-            batch_size: Number of reset environments.
-
-        Returns:
-            Gravity vectors with shape ``(batch_size, 3)``, or None when disabled.
-        """
-        domain_rand = self._cfg.domain_rand
-        if not getattr(domain_rand, "randomize_gravity_direction", False):
-            return None
-        magnitude = float(getattr(domain_rand, "gravity_direction_magnitude", 9.81))
-        if magnitude <= 0.0:
-            raise ValueError(f"gravity_direction_magnitude must be positive, got {magnitude}")
-        gravity = np.zeros((batch_size, 3), dtype=np.float64)
-        gravity[:, 2] = -magnitude
-        random_quat = sample_random_quaternion(batch_size)
-        # A uniform quaternion and its inverse have the same distribution, so
-        # rotating gravity samples the same relative gravity directions induced
-        # by uniformly rotating the global hand/object/task frame.
-        return np.asarray(np_quat_apply(random_quat, gravity), dtype=np.float64)
-
-    def _build_reset_randomization(
-        self,
-        batch_size: int,
-        *,
-        p_gain: np.ndarray | None,
-        d_gain: np.ndarray | None,
-        friction_scale: np.ndarray | None,
-        randomized_mass: np.ndarray | None,
-        randomized_com_offset: np.ndarray | None,
-        gravity: np.ndarray | None,
-    ) -> ResetRandomizationPayload | None:
-        """Build reset-randomization payloads owned by the Sharpa rotation env.
-
-        Args:
-            batch_size: Number of reset environments.
-            friction_scale: Shape (batch_size, 1) sampled friction multipliers.
-
-        Returns:
-            Reset-randomization payload, or None when no backend randomization is requested.
-        """
-        payload = build_common_reset_randomization(self, batch_size)
-        if self._cfg.domain_rand.randomize_pd_gains:
-            if payload is None:
-                payload = ResetRandomizationPayload()
-            payload.kp = np.asarray(p_gain, dtype=np.float64)
-            payload.kd = np.asarray(d_gain, dtype=np.float64)
-        body_mass = self._build_object_mass_randomization(batch_size, randomized_mass)
-        body_ipos = self._build_object_com_randomization(batch_size, randomized_com_offset)
-        geom_friction = self._build_friction_randomization(batch_size, friction_scale)
-        if body_mass is not None:
-            if payload is None:
-                payload = ResetRandomizationPayload()
-            payload.body_mass = body_mass
-        if body_ipos is not None:
-            if payload is None:
-                payload = ResetRandomizationPayload()
-            payload.body_ipos = body_ipos
-        if geom_friction is not None:
-            if payload is None:
-                payload = ResetRandomizationPayload()
-            payload.geom_friction = geom_friction
-        if gravity is not None:
-            if payload is None:
-                payload = ResetRandomizationPayload()
-            payload.gravity = gravity
-        return payload
-
-    def _critic_info_layout(self) -> dict[str, slice]:
-        """Describe the flat critic_info channel layout.
-
-        Args:
-            None.
-
-        Returns:
-            Mapping from logical field names to channel slices.
-        """
-        offset = 0
-        layout = {"object_pos_delta": slice(offset, offset + 3)}
-        offset += 3
-        priv_info_cfg = self._cfg.priv_info
-        if priv_info_cfg.include_friction_scale:
-            layout["friction"] = slice(offset, offset + 1)
-            offset += 1
-        layout["mass"] = slice(offset, offset + 1)
-        offset += 1
-        layout["com"] = slice(offset, offset + 3)
-        offset += 3
-        layout["scale"] = slice(offset, offset + 1)
-        offset += 1
-        if priv_info_cfg.include_gravity_direction:
-            layout["gravity"] = slice(offset, offset + 3)
-        return layout
-
-    def _assign_critic_info_field(
-        self,
-        critic_info: np.ndarray,
-        field_name: str,
-        values: np.ndarray,
-    ) -> None:
-        """Assign one critic_info field according to the declared layout.
-
-        Args:
-            critic_info: Critic-info buffer to update in place.
-            field_name: Logical field name from the layout.
-            values: Batch-major values for the field.
-
-        Returns:
-            None. The critic_info array is updated in place.
-        """
-        field_slice = self._critic_info_layout().get(field_name)
-        if field_slice is None:
-            return
-        field_values = np.asarray(values, dtype=self._np_dtype).reshape(critic_info.shape[0], -1)
-        critic_info[:, field_slice] = field_values
-
-    def _build_reset_critic_info(
-        self,
-        batch_size: int,
-        env_ids: np.ndarray,
-        *,
-        friction_scale: np.ndarray | None,
-        randomized_mass: np.ndarray | None,
-        randomized_com_offset: np.ndarray | None,
-        gravity: np.ndarray | None,
-    ) -> np.ndarray:
-        """Build reset-time critic_info for all randomized object properties.
-
-        Args:
-            batch_size: Number of reset environments.
-            env_ids: Global environment ids for this reset batch.
-
-        Returns:
-            Critic-info tensor with shape (batch_size, critic_info_dim).
-        """
-        critic_info = np.zeros((batch_size, self._cfg.critic_info_dim), dtype=self._np_dtype)
-
-        priv_info_cfg = self._cfg.priv_info
-        if priv_info_cfg.include_friction_scale:
-            self._assign_critic_info_field(
-                critic_info,
-                "friction",
-                (
-                    friction_scale
-                    if friction_scale is not None
-                    else np.ones((batch_size, 1), dtype=np.float64)
-                ),
-            )
-        if randomized_mass is not None:
-            self._assign_critic_info_field(
-                critic_info,
-                "mass",
-                randomized_mass,
-            )
-        if randomized_com_offset is not None:
-            self._assign_critic_info_field(
-                critic_info,
-                "com",
-                randomized_com_offset,
-            )
-        if self._scale_randomization_enabled():
-            self._assign_critic_info_field(
-                critic_info,
-                "scale",
-                self.scale_values[self.scale_ids[env_ids]].reshape(batch_size, 1),
-            )
-        if priv_info_cfg.include_gravity_direction:
-            self._assign_critic_info_field(
-                critic_info,
-                "gravity",
-                self._resolved_privileged_gravity(batch_size, gravity),
-            )
-        return critic_info
-
-    def _policy_frame_dim(self) -> int:
-        obs_cfg = self._cfg.obs
-        dim = self._num_action + self._num_action
-        if obs_cfg.enable_tactile:
-            dim += self._num_tactile
-        if obs_cfg.enable_contact_pos:
-            dim += self._num_tactile * 3
-        return dim
-
-    def _policy_frame_zeros(self, batch_size: int) -> tuple[np.ndarray, np.ndarray]:
-        """Build zero-filled optional policy inputs for reset observations.
-
-        Args:
-            batch_size: Number of environments in the batch.
-
-        Returns:
-            Tuple of tactile and contact-position arrays sized for the current config.
-        """
-        obs_cfg = self._cfg.obs
-        tactile_dim = self._num_tactile if obs_cfg.enable_tactile else 0
-        contact_pos_dim = self._num_tactile * 3 if obs_cfg.enable_contact_pos else 0
-        return (
-            np.zeros((batch_size, tactile_dim), dtype=self._np_dtype),
-            np.zeros((batch_size, contact_pos_dim), dtype=self._np_dtype),
-        )
-
-    def _fixed_default_object_pose(self, batch_size: int) -> np.ndarray:
-        """Build the fixed default object pose from the backend init state.
-
-        Args:
-            batch_size: Number of environments in the batch.
-
-        Returns:
-            Array with shape ``(batch_size, 7)`` containing the default object
-            position and quaternion loaded from the backend/model init qpos.
-        """
-        object_pos = np.broadcast_to(
-            np.asarray(self._init_qpos[self._obj_pos_slice], dtype=self._np_dtype),
-            (batch_size, 3),
-        ).copy()
-        object_quat = np.broadcast_to(
-            np.asarray(self._init_qpos[self._obj_quat_slice], dtype=self._np_dtype),
-            (batch_size, 4),
-        ).copy()
-        return np.concatenate([object_pos, object_quat], axis=1).astype(self._np_dtype)
-
-    def _build_object_pos_anchor(
-        self,
-        object_pos: np.ndarray,
-    ) -> np.ndarray:
-        """Choose the reward/privileged-info object-position anchor.
-
-        Args:
-            object_pos: Reset-time object positions with shape ``(batch_size, 3)``.
-
-        Returns:
-            Array with shape ``(batch_size, 3)``. When the config flag is set,
-            the fixed XML/default object position is used; otherwise the sampled
-            reset position is returned to preserve the legacy UniLab behavior.
-        """
-        object_pos = np.asarray(object_pos, dtype=self._np_dtype)
-        if self._cfg.use_default_object_pose_for_object_pos_anchor:
-            return self._fixed_default_object_pose(object_pos.shape[0])[:, 0:3]
-        return object_pos.copy()
-
-    def _resolve_object_pos_anchor(
-        self,
-        info: dict[str, Any],
-        batch_size: int,
-    ) -> np.ndarray:
-        """Resolve the cached object-position anchor for one runtime batch.
-
-        Args:
-            info: Mutable env-state info dictionary.
-            batch_size: Number of environments in the current batch.
-
-        Returns:
-            Array with shape ``(batch_size, 3)`` used by the object-position
-            reward and privileged-info delta channels.
-        """
-        cached_anchor = info.get("object_pos_anchor")
-        if cached_anchor is not None:
-            return np.asarray(cached_anchor, dtype=self._np_dtype)
-
-        object_default_pose = info.get("object_default_pose")
-        if object_default_pose is not None:
-            return np.asarray(object_default_pose, dtype=self._np_dtype)[:, 0:3]
-
-        if self._cfg.use_default_object_pose_for_object_pos_anchor:
-            return self._fixed_default_object_pose(batch_size)[:, 0:3]
-        return np.zeros((batch_size, 3), dtype=self._np_dtype)
-
-    def _policy_frame_parts(
-        self,
-        dof_norm: np.ndarray,
-        targets: np.ndarray,
-        tactile: np.ndarray,
-        contact_pos: np.ndarray,
-    ) -> list[np.ndarray]:
-        """Collect policy-frame components according to the configured observation layout.
-
-        Args:
-            dof_norm: Normalized hand joint positions.
-            targets: Hand joint targets.
-            tactile: Tactile features.
-            contact_pos: Contact-position features.
-
-        Returns:
-            Ordered list of arrays that should be concatenated into the policy frame.
-        """
-        parts = [dof_norm, targets]
-        obs_cfg = self._cfg.obs
-        if obs_cfg.enable_tactile:
-            parts.append(np.asarray(tactile, dtype=self._np_dtype))
-        if obs_cfg.enable_contact_pos:
-            parts.append(np.asarray(contact_pos, dtype=self._np_dtype))
-        return parts
-
-    def _fill_critic_info(
-        self,
-        critic_info: np.ndarray,
-        object_pos: np.ndarray,
-        object_pos_anchor: np.ndarray,
-    ) -> np.ndarray:
-        """Populate privileged channels that are derived from runtime state.
-
-        Args:
-            critic_info: Critic info buffer to update in place.
-            object_pos: Current object positions with shape (batch, 3).
-            object_pos_anchor: Object-position anchor with shape (batch, 3).
-
-        Returns:
-            Updated critic info array with shape (batch, critic_info_dim).
-        """
-        self._assign_critic_info_field(
-            critic_info,
-            "object_pos_delta",
-            object_pos - object_pos_anchor,
-        )
-        return critic_info
-
-    @classmethod
-    def _resolve_observation_mode(cls, observation_mode: str) -> str:
-        normalized_mode = str(observation_mode).strip().lower()
-        resolved_mode = cls._OBS_MODE_ALIASES.get(normalized_mode)
-        if resolved_mode is None:
-            supported_modes = "', '".join(sorted(cls._OBS_MODE_ALIASES))
-            raise ValueError(
-                f"observation_mode must be one of '{supported_modes}', got {observation_mode!r}"
-            )
-        return resolved_mode
-
-    def _build_policy_frame(
-        self,
-        dof_pos: np.ndarray,
-        targets: np.ndarray,
-        tactile: np.ndarray,
-        contact_pos: np.ndarray,
-        *,
-        add_noise: bool = True,
-    ) -> np.ndarray:
-        dof_pos_f = np.asarray(dof_pos, dtype=self._np_dtype)
-        targets_f = np.asarray(targets, dtype=self._np_dtype)
-
-        dof_norm = self._normalize_joint_pos(dof_pos_f)
-        joint_noise_scale = float(self._cfg.domain_rand.joint_noise_scale)
-        if add_noise and joint_noise_scale > 0.0:
-            dof_norm += (
-                np.random.uniform(-1.0, 1.0, size=dof_norm.shape).astype(self._np_dtype)
-                * joint_noise_scale
-            )
-
-        frame = np.asarray(
-            np.concatenate(
-                self._policy_frame_parts(
-                    dof_norm=dof_norm,
-                    targets=targets_f,
-                    tactile=tactile,
-                    contact_pos=contact_pos,
-                ),
-                axis=1,
-            ),
-            dtype=self._np_dtype,
-        )
-        return self._clip_observation_values(frame)
-
-    def _clip_observation_values(self, values: np.ndarray) -> np.ndarray:
-        """Clamp observation tensors to a configurable absolute value bound.
-
-        Args:
-            values: Observation-like array to clip.
-
-        Returns:
-            Clipped array with the same shape. Non-positive ``clip_obs`` disables the
-            clamp so callers can opt out when needed.
-        """
-        clip_max = float(getattr(self._cfg, "clip_obs", 5.0))
-        values = np.asarray(values, dtype=self._np_dtype)
-        if clip_max <= 0.0:
-            return values
-        return np.asarray(np.clip(values, -clip_max, clip_max), dtype=self._np_dtype)
-
-    @property
-    def obs_groups_spec(self) -> dict[str, int]:
-        policy_obs_dim = self._cfg.obs_lag_steps * self._policy_frame_dim()
-        if self._observation_mode == "flattened":
-            return {"obs": policy_obs_dim + self._cfg.critic_info_dim}
-        return {"obs": policy_obs_dim, "critic": policy_obs_dim + self._cfg.critic_info_dim}
-
-    def _build_critic_info(
-        self,
-        info: dict[str, Any],
-        batch_size: int,
-        object_pos: np.ndarray,
-    ) -> np.ndarray:
-        """Build privileged critic info for the current batch.
-
-        Args:
-            info: Mutable state info dictionary carrying reset-time caches.
-            batch_size: Number of environments in the current batch.
-            object_pos: Current object positions with shape (batch, 3).
-
-        Returns:
-            Privileged info array with shape (batch, critic_info_dim).
-        """
-        critic_info = np.asarray(
-            info.get(
-                "critic_info",
-                np.zeros((batch_size, self._cfg.critic_info_dim), dtype=self._np_dtype),
-            ),
-            dtype=self._np_dtype,
-        )
-
-        object_pos_anchor = self._resolve_object_pos_anchor(info, batch_size)
-        critic_info = self._fill_critic_info(
-            critic_info=critic_info,
-            object_pos=object_pos,
-            object_pos_anchor=object_pos_anchor,
-        )
-
-        info["critic_info"] = critic_info
-        return critic_info
-
-    def _pack_observations(
-        self,
-        policy_obs: np.ndarray,
-        critic_info: np.ndarray,
-        critic_policy_obs: np.ndarray | None = None,
-    ) -> dict[str, np.ndarray]:
-        """Pack actor and privileged info into the env observation groups.
-
-        Args:
-            policy_obs: Actor observation tensor with shape (batch, actor_dim).
-            critic_info: Privileged tensor with shape (batch, critic_info_dim).
-
-        Returns:
-            Observation groups that satisfy the UniLab env contract.
-        """
-        if self._observation_mode == "flattened":
-            flattened_obs = self._clip_observation_values(
-                np.concatenate([policy_obs, critic_info], axis=1).astype(self._np_dtype)
-            )
-            return {"obs": flattened_obs}
-
-        if critic_policy_obs is None:
-            critic_policy_obs = policy_obs
-        return {
-            "obs": self._clip_observation_values(policy_obs),
-            "critic": self._clip_observation_values(
-                np.concatenate([critic_policy_obs, critic_info], axis=1).astype(self._np_dtype)
-            ),
-        }
-
-    def _compute_obs_from_inputs(
-        self,
-        info: dict[str, Any],
-        dof_pos: np.ndarray,
-        object_pos: np.ndarray,
-        tactile: np.ndarray,
-        contact_pos: np.ndarray,
-    ) -> dict[str, np.ndarray]:
-        targets = np.asarray(info.get("prev_targets", dof_pos), dtype=self._np_dtype)
-        frame = self._build_policy_frame(
-            dof_pos=dof_pos,
-            targets=targets,
-            tactile=tactile,
-            contact_pos=contact_pos,
-            add_noise=True,
-        )
-        critic_frame = self._build_policy_frame(
-            dof_pos=dof_pos,
-            targets=targets,
-            tactile=tactile,
-            contact_pos=contact_pos,
-            add_noise=False,
-        )
-        batch_size = int(frame.shape[0])
-
-        history = info.get("obs_lag_history")
-        critic_history = info.get("critic_obs_lag_history")
-        if history is None:
-            history = repeat_obs_history(frame, self._cfg.obs_history_len).astype(self._np_dtype)
-        else:
-            history = np.asarray(history, dtype=self._np_dtype)
-            history[:, :-1] = history[:, 1:]
-            history[:, -1] = frame
-        if critic_history is None:
-            critic_history = repeat_obs_history(critic_frame, self._cfg.obs_history_len).astype(
-                self._np_dtype
-            )
-        else:
-            critic_history = np.asarray(critic_history, dtype=self._np_dtype)
-            critic_history[:, :-1] = critic_history[:, 1:]
-            critic_history[:, -1] = critic_frame
-
-        info["obs_lag_history"] = history
-        info["critic_obs_lag_history"] = critic_history
-        info["proprio_hist"] = self._update_proprio_history(history)
-
-        obs = np.asarray(
-            history[:, -self._cfg.obs_lag_steps :].reshape(batch_size, -1),
-            dtype=self._np_dtype,
-        )
-        critic_obs = np.asarray(
-            critic_history[:, -self._cfg.obs_lag_steps :].reshape(batch_size, -1),
-            dtype=self._np_dtype,
-        )
-        critic_info = self._build_critic_info(info, batch_size=batch_size, object_pos=object_pos)
-        return self._pack_observations(obs, critic_info, critic_policy_obs=critic_obs)
-
-    def _compute_reward(
-        self,
-        info: dict[str, Any],
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-        object_pos: np.ndarray,
-        object_linvel: np.ndarray,
-        object_angvel: np.ndarray,
-        torques: np.ndarray,
-    ) -> np.ndarray:
-        rot_axis = np.asarray(
-            info.get("rot_axis", np.broadcast_to(self._rot_axis, (self._num_envs, 3))),
-            dtype=self._np_dtype,
-        )
-        rotate_reward = np.clip(
-            np.sum(object_angvel * rot_axis, axis=1),
-            self._reward_cfg.angvel_clip_min,
-            self._reward_cfg.angvel_clip_max,
-        )
-        object_linvel_penalty = np.sum(np.abs(object_linvel), axis=1)
-        pos_diff_penalty = np.sum(np.square(dof_pos - self.default_angles), axis=1)
-        torque_penalty = np.sum(np.square(torques), axis=1)
-        work_penalty = np.square(np.sum(torques * dof_vel, axis=1))
-
-        object_pos_anchor = self._resolve_object_pos_anchor(info, object_pos.shape[0])
-        object_pos_reward = 1.0 / (np.linalg.norm(object_pos - object_pos_anchor, axis=1) + 0.001)
-
-        reward_terms: dict[str, np.ndarray] = {
-            "rotate": np.asarray(rotate_reward, dtype=self._np_dtype),
-            "obj_linvel": np.asarray(object_linvel_penalty, dtype=self._np_dtype),
-            "pose_diff": np.asarray(pos_diff_penalty, dtype=self._np_dtype),
-            "torque": np.asarray(torque_penalty, dtype=self._np_dtype),
-            "work": np.asarray(work_penalty, dtype=self._np_dtype),
-            "object_pos": np.asarray(object_pos_reward, dtype=self._np_dtype),
-        }
-
-        reward = np.zeros((self._num_envs,), dtype=self._np_dtype)
-        step_count = info.get("steps", np.zeros((self._num_envs,), dtype=np.uint32))
-        should_log = self._enable_reward_log and (int(step_count[0]) % 4 == 0)
-        log = {} if should_log else info.get("log", {})
-
-        for name, scale in self._reward_cfg.scales.items():
-            if scale == 0.0 or name not in reward_terms:
-                continue
-            weighted = reward_terms[name] * scale
-            reward += weighted
-            if should_log:
-                log[f"reward/{name}"] = float(np.mean(weighted))
-
-        if should_log:
-            log["reward/total"] = float(np.mean(reward))
-        info["log"] = log
-
-        return np.asarray(reward, dtype=self._np_dtype) * self._cfg.ctrl_dt
-
-    def update_state(self, state: NpEnvState) -> NpEnvState:
-        dof_pos = self.get_hand_dof_pos()
-        dof_vel = self.get_hand_dof_vel()
-        object_pos = self.get_object_pos()
-        object_quat = self.get_object_quat()
-
-        prev_object_pos = np.asarray(
-            state.info.get("prev_object_pos", object_pos), dtype=self._np_dtype
-        )
-        prev_object_quat = np.asarray(
-            state.info.get("prev_object_quat", object_quat), dtype=self._np_dtype
-        )
-
-        object_linvel = (object_pos - prev_object_pos) / self._cfg.ctrl_dt
-        object_angvel = (
-            np_quat_to_axis_angle(np_quat_mul(object_quat, np_quat_conjugate(prev_object_quat)))
-            / self._cfg.ctrl_dt
-        )
-
-        targets = np.asarray(
-            state.info.get(
-                "prev_targets",
-                np.broadcast_to(self.default_angles, (self._num_envs, self._num_action)).copy(),
-            ),
-            dtype=self._np_dtype,
-        )
-        p_gain, d_gain = self._resolve_pd_gains(state.info)
-
-        # Explicit virtual torque used for reward parity with source Sharpa formulation.
-        virtual_torques = np.asarray(
-            p_gain * (targets - dof_pos) - d_gain * dof_vel,
-            dtype=self._np_dtype,
-        )
-
-        tactile = self._compute_tactile_observation()
-        contact_pos = self._compute_contact_positions(tactile)
-
-        reward = self._compute_reward(
-            state.info,
-            dof_pos=dof_pos,
-            dof_vel=dof_vel,
-            object_pos=object_pos,
-            object_linvel=object_linvel,
-            object_angvel=object_angvel,
-            torques=virtual_torques,
-        )
-
-        reset_height_lower = np.asarray(
-            state.info.get(
-                "reset_height_lower",
-                np.full((self._num_envs,), self._cfg.reset_height_lower, dtype=self._np_dtype),
-            ),
-            dtype=self._np_dtype,
-        )
-        reset_height_upper = np.asarray(
-            state.info.get(
-                "reset_height_upper",
-                np.full((self._num_envs,), self._cfg.reset_height_upper, dtype=self._np_dtype),
-            ),
-            dtype=self._np_dtype,
-        )
-        terminated = (object_pos[:, 2] > reset_height_upper) | (
-            object_pos[:, 2] < reset_height_lower
-        )
-
-        obs = self._compute_obs_from_inputs(
-            state.info,
-            dof_pos=dof_pos,
-            object_pos=object_pos,
-            tactile=tactile,
-            contact_pos=contact_pos,
-        )
-
-        state.info["prev_hand_pos"] = dof_pos.copy()
-        state.info["hand_dof_vel"] = dof_vel.copy()
-        state.info["prev_object_pos"] = object_pos.copy()
-        state.info["prev_object_quat"] = object_quat.copy()
-        state.info["torques"] = virtual_torques
-        state.info["virtual_torques"] = virtual_torques.copy()
-        state.info["object_linvel"] = object_linvel
-        state.info["object_angvel"] = object_angvel
-
-        return state.replace(
-            obs=obs,
-            reward=reward,
-            terminated=np.asarray(terminated, dtype=bool),
-        )
-
-
-SharpaWaveRewardConfig = RewardConfig
-SharpaWaveRotationCfg = SharpaInhandRotationCfg
diff --git a/src/unilab/envs/manipulation/stewart/__init__.py b/src/unilab/envs/manipulation/stewart/__init__.py
deleted file mode 100644
index 0d377c02e..000000000
--- a/src/unilab/envs/manipulation/stewart/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from . import balance  # registers StewartBalance via @registry decorators
-from .balance import StewartBalanceCfg, StewartBalanceEnv
-
-__all__ = ["StewartBalanceCfg", "StewartBalanceEnv"]
diff --git a/src/unilab/envs/manipulation/stewart/balance.py b/src/unilab/envs/manipulation/stewart/balance.py
deleted file mode 100644
index ff3f0f38d..000000000
--- a/src/unilab/envs/manipulation/stewart/balance.py
+++ /dev/null
@@ -1,446 +0,0 @@
-"""Stewart-platform ball-balancing task.
-
-A 6-DOF parallel (Stewart) platform balances a free ball on its top plate. The
-policy commands a 2-D platform tilt (roll, pitch); an inverse-kinematics step
-converts the commanded plate pose into the six prismatic leg lengths that the
-position actuators track. The objective is to bring the ball to the plate center
-and hold it still. The platform base is welded to the world.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-
-import gymnasium as gym
-import numpy as np
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base import registry
-from unilab.base.backend import create_backend, env_backend_kwargs
-from unilab.base.base import EnvCfg
-from unilab.base.np_env import NpEnv, NpEnvState
-from unilab.base.scene import SceneCfg
-from unilab.dr.provider import DomainRandomizationProvider
-from unilab.dr.types import DomainRandomizationCapabilities, ResetPlan
-from unilab.dtype_config import get_global_dtype
-from unilab.utils.geometry import np_roll_pitch_from_quat
-from unilab.utils.rotation import (
-    np_quat_apply_batched,
-    np_quat_apply_inverse,
-    np_quat_conjugate_batched,
-    np_quat_from_euler_xyz,
-    np_quat_mul_batched,
-    np_quat_to_axis_angle,
-)
-
-# Leg base / top-connect bodies in actuator order (a0..a5 -> slide00,slide10,
-# slide01,slide11,slide02,slide12), produced by the `replicate count=3` in the XML.
-_LEG_BODY_NAMES = ["leg00", "leg10", "leg01", "leg11", "leg02", "leg12"]
-_TOP_CONNECT_NAMES = [
-    "top_connect00",
-    "top_connect10",
-    "top_connect01",
-    "top_connect11",
-    "top_connect02",
-    "top_connect12",
-]
-
-_OBS_DIM = 15
-_ACTION_DIM = 2
-
-
-@dataclass
-class StewartRewardConfig:
-    """Reward shaping for the ball-balancing task (see `_compute_reward`)."""
-
-    scales: dict[str, float] = field(
-        default_factory=lambda: {"center": 0.7, "progress": 0.6, "still": 3.0}
-    )
-    fall_penalty: float = -6.0
-
-
-@registry.envcfg("StewartBalance")
-@dataclass
-class StewartBalanceCfg(EnvCfg):
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "stewart" / "scene.xml")
-        )
-    )
-    # The XML model is stiff; do not raise sim_dt above ~0.005.
-    sim_dt: float = 0.004
-    ctrl_dt: float = 0.02
-    max_episode_seconds: float = 24.0
-    render_spacing: float = 4.5
-
-    # Body the backend treats as the kinematic base for its base-pose accessors.
-    # The task reads explicit body ids instead, so any real body works; the moving
-    # plate is the natural choice.
-    base_name: str = "top"
-
-    # Geometry (platform centered at the world origin, plate center at z=1).
-    platform_radius: float = 0.8
-    # The episode ends (ball "fallen") once it strays this far from the plate
-    # center. Kept inside the physical rim (platform_radius) so the ball never
-    # reaches the edge-contact regime that destabilizes the stiff closed-loop solver.
-    fall_radius: float = 0.5
-    top_center_z: float = 1.0
-    top_surface_offset: float = 0.1
-    ball_radius: float = 0.10
-    init_ball_radius_ratio: float = 0.18
-
-    # Control.
-    target_rotation_limit_deg: float = 6.0
-    action_smooth: float = 0.60
-    center_control_radius: float = 0.25
-    center_control_min_gain: float = 0.15
-    vel_smooth: float = 0.25
-
-    # Success / stillness window.
-    still_xy: float = 0.12
-    still_vel: float = 0.07
-    still_xy_hysteresis: float = 1.15
-    still_vel_hysteresis: float = 1.20
-    zero_vel_thresh: float = 0.07
-    still_steps_needed: int = 5
-
-    reward_config: StewartRewardConfig = field(default_factory=StewartRewardConfig)
-
-    def validate(self) -> None:
-        super().validate()
-        if not 0.0 <= self.init_ball_radius_ratio <= 1.0:
-            raise ValueError("init_ball_radius_ratio must be in [0, 1]")
-        if not 0.0 <= self.action_smooth <= 1.0:
-            raise ValueError("action_smooth must be in [0, 1]")
-        if not 0.0 <= self.center_control_min_gain <= 1.0:
-            raise ValueError("center_control_min_gain must be in [0, 1]")
-
-
-def _ball_home_z(cfg: StewartBalanceCfg) -> float:
-    return cfg.top_center_z + cfg.top_surface_offset + cfg.ball_radius
-
-
-def _roll_pitch_from_quat(quat: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
-    """Extract roll/pitch (rad) from a wxyz quaternion, cast to float32."""
-    roll, pitch = np_roll_pitch_from_quat(quat)
-    return roll.astype(np.float32), pitch.astype(np.float32)
-
-
-# The mujoco backend constructs, resets, and steps correctly. Its closed-loop
-# constraint solver is, however, less forgiving than motrix's under load (stiff
-# parallel mechanism + ball contact), so a trained-grade policy is not yet stable
-# there — closed-loop stability tuning for mujoco is a follow-up. The motrix
-# backend is the validated training path.
-@registry.env("StewartBalance", sim_backend="mujoco")
-@registry.env("StewartBalance", sim_backend="motrix")
-@registry.env("StewartBalance", sim_backend="drake")
-class StewartBalanceEnv(NpEnv):
-    _cfg: StewartBalanceCfg
-
-    def __init__(
-        self,
-        cfg: StewartBalanceCfg,
-        num_envs: int = 1,
-        backend_type: str = "motrix",
-        dr_provider: DomainRandomizationProvider | None = None,
-    ) -> None:
-        # add_body_sensors=True injects body-pose tracking sensors the MuJoCo
-        # backend needs for get_body_pos_w/quat_w on arbitrary bodies (the IK + obs
-        # read top/ball/leg poses). The motrix backend reads poses natively and
-        # ignores the flag.
-        backend = create_backend(
-            backend_type,
-            cfg.scene,
-            num_envs,
-            cfg.sim_dt,
-            base_name=cfg.base_name,
-            add_body_sensors=True,
-            **env_backend_kwargs(cfg),
-        )
-        super().__init__(cfg, backend, num_envs)
-
-        self._np_dtype = get_global_dtype()
-        self._action_space = gym.spaces.Box(-1.0, 1.0, (_ACTION_DIM,), dtype=np.float32)
-
-        if self._backend.num_actuators != 6:
-            raise ValueError(f"Stewart model needs 6 actuators, got {self._backend.num_actuators}")
-        ctrl_range = np.asarray(self._backend.get_actuator_ctrl_range(), dtype=np.float32)
-        self._ctrl_lo = ctrl_range[:, 0]
-        self._ctrl_hi = ctrl_range[:, 1]
-
-        self._top_body_ids = self._backend.get_body_ids(["top"])
-        self._ball_body_ids = self._backend.get_body_ids(["ball"])
-        self._leg_body_ids = self._backend.get_body_ids(_LEG_BODY_NAMES)
-        self._top_connect_ids = self._backend.get_body_ids(_TOP_CONNECT_NAMES)
-        # The ball free joint is the first jointed body in the scene, so its
-        # position occupies qpos[0:3] (validated against the default qpos in reset).
-        self._ball_pos_qpos_idx = np.array([0, 1, 2], dtype=np.int64)
-
-        # IK calibration (top home center, connect offsets, neutral leg lengths) is
-        # resolved lazily on first use, once reset has placed the home state.
-        self._ik_ready = False
-        self._top_pos0 = np.zeros(3, dtype=np.float32)
-        self._connect_offsets = np.zeros((6, 3), dtype=np.float32)
-        self._leg0 = np.zeros(6, dtype=np.float32)
-
-        self._init_domain_randomization(
-            dr_provider if dr_provider is not None else StewartBalanceDRProvider()
-        )
-
-    @property
-    def action_space(self) -> gym.spaces.Box:
-        return self._action_space
-
-    @property
-    def obs_groups_spec(self) -> dict[str, int]:
-        return {"obs": _OBS_DIM}
-
-    def _ensure_ik_calibration(self) -> None:
-        if self._ik_ready:
-            return
-        top_pos = np.asarray(
-            self._backend.get_body_pos_w(self._top_body_ids)[:, 0, :], dtype=np.float32
-        )
-        connects = np.asarray(self._backend.get_body_pos_w(self._top_connect_ids), dtype=np.float32)
-        legs = np.asarray(self._backend.get_body_pos_w(self._leg_body_ids), dtype=np.float32)
-        # Use env 0 as the reference home configuration (all envs share the model).
-        self._top_pos0 = top_pos[0]
-        self._connect_offsets = (connects[0] - self._top_pos0).astype(np.float32)  # (6,3)
-        self._leg0 = np.linalg.norm(connects[0] - legs[0], axis=-1).astype(np.float32)  # (6,)
-        self._ik_ready = True
-
-    def _leg_ctrl_for_tilt(self, target_tilt_rad: np.ndarray) -> np.ndarray:
-        """Inverse kinematics: tilt command -> six prismatic leg targets.
-
-        ``target_tilt_rad`` has shape (N, 2) = (roll, pitch). The plate stays at its
-        home center; only its orientation tracks the commanded tilt.
-        """
-        self._ensure_ik_calibration()
-        num = target_tilt_rad.shape[0]
-        zeros = np.zeros((num,), dtype=np.float32)
-        target_quat = np_quat_from_euler_xyz(
-            target_tilt_rad[:, 0], target_tilt_rad[:, 1], zeros
-        ).reshape(num, 4)
-        # Rotate each connect offset by the per-env target quat: (N,1,4) x (1,6,3) -> (N,6,3).
-        rotated = np_quat_apply_batched(target_quat[:, None, :], self._connect_offsets[None, :, :])
-        expected = self._top_pos0[None, None, :] + rotated  # (N,6,3) connect targets
-        bottoms = np.asarray(
-            self._backend.get_body_pos_w(self._leg_body_ids), dtype=np.float32
-        )  # (N,6,3)
-        leg_len = np.linalg.norm(expected - bottoms, axis=-1) - self._leg0[None, :]
-        return np.clip(leg_len, self._ctrl_lo, self._ctrl_hi).astype(np.float32)
-
-    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
-        cfg = self._cfg
-        info = state.info
-        raw = np.clip(np.asarray(actions, dtype=np.float32), -1.0, 1.0).reshape(
-            self._num_envs, _ACTION_DIM
-        )
-
-        # Exponential action smoothing.
-        prev = info["prev_action_exec"]
-        alpha = float(cfg.action_smooth)
-        action_exec = (alpha * raw + (1.0 - alpha) * prev).astype(np.float32)
-        info["prev_action_exec"] = action_exec
-        info["action_exec"] = action_exec
-
-        # Soften authority while the ball is already near the center.
-        rel_xy = info["last_rel_xy"]
-        if cfg.center_control_radius > 0.0 and cfg.center_control_min_gain < 1.0:
-            ratio = np.clip(rel_xy / max(cfg.center_control_radius, 1e-6), 0.0, 1.0)
-            gain = cfg.center_control_min_gain + (1.0 - cfg.center_control_min_gain) * ratio
-        else:
-            gain = np.ones((self._num_envs,), dtype=np.float32)
-        effective = action_exec * gain[:, None]
-
-        target_tilt_deg = effective * cfg.target_rotation_limit_deg
-        info["target_tilt_cmd"] = target_tilt_deg.astype(np.float32)
-        return self._leg_ctrl_for_tilt(np.deg2rad(target_tilt_deg).astype(np.float32))
-
-    def _read_ball_rel(self) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        top_pos = np.asarray(
-            self._backend.get_body_pos_w(self._top_body_ids)[:, 0, :], dtype=np.float32
-        )
-        top_quat = np.asarray(
-            self._backend.get_body_quat_w(self._top_body_ids)[:, 0, :], dtype=np.float32
-        )
-        ball_pos = np.asarray(
-            self._backend.get_body_pos_w(self._ball_body_ids)[:, 0, :], dtype=np.float32
-        )
-        rel = np_quat_apply_inverse(top_quat, (ball_pos - top_pos)).astype(np.float32)
-        return rel, top_quat, ball_pos
-
-    def update_state(self, state: NpEnvState) -> NpEnvState:
-        cfg = self._cfg
-        info = state.info
-        dt = float(cfg.ctrl_dt)
-
-        rel, top_quat, ball_pos = self._read_ball_rel()
-
-        rel_vel = (rel - info["prev_rel"]) / dt
-        filt_rel_vel = (
-            cfg.vel_smooth * rel_vel + (1.0 - cfg.vel_smooth) * info["filtered_rel_vel"]
-        ).astype(np.float32)
-        info["prev_rel"] = rel
-        info["filtered_rel_vel"] = filt_rel_vel
-
-        quat_delta = np_quat_mul_batched(top_quat, np_quat_conjugate_batched(info["prev_top_quat"]))
-        top_ang_vel = (np_quat_to_axis_angle(quat_delta) / dt).astype(np.float32)
-        filt_ang_vel = (
-            cfg.vel_smooth * top_ang_vel + (1.0 - cfg.vel_smooth) * info["filtered_top_ang_vel"]
-        ).astype(np.float32)
-        info["prev_top_quat"] = top_quat
-        info["filtered_top_ang_vel"] = filt_ang_vel
-        ang_vel_local = np_quat_apply_inverse(top_quat, filt_ang_vel).astype(np.float32)
-
-        roll, pitch = _roll_pitch_from_quat(top_quat)
-        rel_xy = np.linalg.norm(rel[:, :2], axis=-1).astype(np.float32)
-        vel_xy = np.linalg.norm(filt_rel_vel[:, :2], axis=-1).astype(np.float32)
-        info["last_rel_xy"] = rel_xy
-
-        limit = max(cfg.target_rotation_limit_deg, 1e-6)
-        obs = np.concatenate(
-            [
-                rel,
-                filt_rel_vel,
-                np.stack([np.rad2deg(roll) / limit, np.rad2deg(pitch) / limit], axis=-1).astype(
-                    np.float32
-                ),
-                ang_vel_local,
-                info["target_tilt_cmd"] / limit,
-                info["action_exec"],
-            ],
-            axis=-1,
-        ).astype(self._np_dtype)
-
-        reward, terminated = self._compute_reward(cfg, info, rel_xy, vel_xy, ball_pos)
-        return state.replace(obs={"obs": obs}, reward=reward, terminated=terminated)
-
-    def _compute_reward(self, cfg, info, rel_xy, vel_xy, ball_pos):
-        rc = cfg.reward_config
-        scales = rc.scales
-
-        fall_z = cfg.top_center_z - np.sin(np.deg2rad(30.0)) * cfg.platform_radius
-        fallen = (rel_xy > cfg.fall_radius) | (ball_pos[:, 2] < fall_z)
-
-        center_score = np.clip(1.0 - rel_xy / max(cfg.fall_radius, 1e-6), 0.0, 1.0)
-        term_center = scales["center"] * center_score
-
-        # Reward shrinking the stop-radius: progress toward the center between
-        # near-zero-velocity moments (a stable "settled closer than before" event).
-        prev_zero = info["prev_zero_vel_rel_xy"]
-        zero_event = vel_xy <= cfg.zero_vel_thresh
-        improve = np.maximum(prev_zero - rel_xy, 0.0)
-        improve_norm = np.clip(improve / max(cfg.platform_radius, 1e-6), 0.0, 1.0)
-        term_progress = np.where(
-            zero_event & (rel_xy < prev_zero), scales["progress"] * improve_norm, 0.0
-        )
-        next_zero = prev_zero.copy()
-        next_zero[zero_event] = rel_xy[zero_event]
-        info["prev_zero_vel_rel_xy"] = next_zero.astype(np.float32)
-
-        still_steps = self._update_stillness(cfg, info, rel_xy, vel_xy)
-        success = still_steps >= cfg.still_steps_needed
-        term_still = np.where(success, scales["still"], 0.0)
-
-        reward = (term_center + term_progress + term_still).astype(self._np_dtype)
-        reward = np.where(fallen, rc.fall_penalty, reward).astype(self._np_dtype)
-        terminated = (fallen | success).astype(bool)
-        return reward, terminated
-
-    def _update_stillness(self, cfg, info, rel_xy, vel_xy) -> np.ndarray:
-        xy_enter, vel_enter = cfg.still_xy, cfg.still_vel
-        xy_exit = cfg.still_xy * cfg.still_xy_hysteresis
-        vel_exit = cfg.still_vel * cfg.still_vel_hysteresis
-
-        active = info["still_window_active"]
-        steps = info["still_steps"]
-        keep = active & (rel_xy <= xy_exit) & (vel_xy <= vel_exit)
-        enter = (~active) & (rel_xy <= xy_enter) & (vel_xy <= vel_enter)
-        steps = np.where(keep, steps + 1, np.where(enter, 1, 0)).astype(np.int32)
-        active = keep | enter
-        info["still_window_active"] = active
-        info["still_steps"] = steps
-        return steps
-
-
-class StewartBalanceDRProvider(DomainRandomizationProvider):
-    """Resets the plate to its level home and drops the ball near the center.
-
-    No physical randomization terms are used; the reset variety comes from the
-    randomized ball position, which is the balancing challenge.
-    """
-
-    def validate(self, env, capabilities: DomainRandomizationCapabilities) -> None:  # noqa: D102
-        return None
-
-    def build_reset_plan(self, env: StewartBalanceEnv, env_ids: np.ndarray) -> ResetPlan:
-        cfg: StewartBalanceCfg = env._cfg
-        n = int(env_ids.shape[0])
-        default_qpos = np.asarray(env._backend.get_default_qpos(), dtype=np.float64)
-        # Ball free joint is first in the scene -> position is qpos[0:3].
-        if not np.allclose(default_qpos[0:2], 0.0, atol=1e-3):
-            raise ValueError("Unexpected qpos layout: ball position is not at qpos[0:3]")
-        qpos = np.broadcast_to(default_qpos, (n, default_qpos.shape[0])).copy()
-
-        # Ball: uniform within a disk near the plate center, resting on the surface.
-        radius = (
-            cfg.platform_radius
-            * cfg.init_ball_radius_ratio
-            * np.sqrt(np.random.uniform(0.0, 1.0, size=n))
-        )
-        theta = np.random.uniform(0.0, 2.0 * np.pi, size=n)
-        ball_xyz = np.stack(
-            [radius * np.cos(theta), radius * np.sin(theta), np.full((n,), _ball_home_z(cfg))],
-            axis=-1,
-        )
-        qpos[:, env._ball_pos_qpos_idx] = ball_xyz
-
-        init_qvel = np.asarray(env._backend.get_init_qvel(), dtype=np.float64)
-        qvel = np.broadcast_to(init_qvel, (n, init_qvel.shape[0])).copy()
-
-        zeros3 = np.zeros((n, 3), dtype=np.float32)
-        zeros2 = np.zeros((n, 2), dtype=np.float32)
-        identity_quat = np.tile(np.array([1.0, 0.0, 0.0, 0.0], dtype=np.float32), (n, 1))
-        info_updates: dict = {
-            "prev_rel": zeros3.copy(),
-            "filtered_rel_vel": zeros3.copy(),
-            "prev_top_quat": identity_quat,
-            "filtered_top_ang_vel": zeros3.copy(),
-            "target_tilt_cmd": zeros2.copy(),
-            "action_exec": zeros2.copy(),
-            "prev_action_exec": zeros2.copy(),
-            "last_rel_xy": np.zeros((n,), dtype=np.float32),
-            "prev_zero_vel_rel_xy": np.full((n,), cfg.platform_radius, dtype=np.float32),
-            "still_steps": np.zeros((n,), dtype=np.int32),
-            "still_window_active": np.zeros((n,), dtype=bool),
-        }
-        return ResetPlan(env_ids=env_ids, qpos=qpos, qvel=qvel, info_updates=info_updates)
-
-    def build_reset_observation(
-        self, env: StewartBalanceEnv, env_ids: np.ndarray, info_updates: dict
-    ) -> dict:
-        rel, top_quat, _ = env._read_ball_rel()
-        rel = rel[env_ids]
-        top_quat = top_quat[env_ids]
-        roll, pitch = _roll_pitch_from_quat(top_quat)
-        limit = max(env._cfg.target_rotation_limit_deg, 1e-6)
-        n = int(env_ids.shape[0])
-        info_updates["prev_rel"] = rel.astype(np.float32)
-        info_updates["prev_top_quat"] = top_quat.astype(np.float32)
-        info_updates["last_rel_xy"] = np.linalg.norm(rel[:, :2], axis=-1).astype(np.float32)
-        info_updates["prev_zero_vel_rel_xy"] = info_updates["last_rel_xy"].copy()
-        obs = np.concatenate(
-            [
-                rel,
-                np.zeros((n, 3), dtype=np.float32),
-                np.stack([np.rad2deg(roll) / limit, np.rad2deg(pitch) / limit], axis=-1).astype(
-                    np.float32
-                ),
-                np.zeros((n, 3), dtype=np.float32),
-                np.zeros((n, 2), dtype=np.float32),
-                np.zeros((n, 2), dtype=np.float32),
-            ],
-            axis=-1,
-        ).astype(get_global_dtype())
-        return {"obs": obs}
diff --git a/src/unilab/envs/mdp/__init__.py b/src/unilab/envs/mdp/__init__.py
new file mode 100644
index 000000000..4b11ca8c7
--- /dev/null
+++ b/src/unilab/envs/mdp/__init__.py
@@ -0,0 +1,141 @@
+"""Community-style built-in MDP terms for UniLab's NumPy manager runtime."""
+
+from unilab.envs.mdp.actions import JointEffortAction as JointEffortAction
+from unilab.envs.mdp.actions import JointEffortActionCfg as JointEffortActionCfg
+from unilab.envs.mdp.actions import JointPositionAction as JointPositionAction
+from unilab.envs.mdp.actions import JointPositionActionCfg as JointPositionActionCfg
+from unilab.envs.mdp.actions import JointVelocityAction as JointVelocityAction
+from unilab.envs.mdp.actions import JointVelocityActionCfg as JointVelocityActionCfg
+from unilab.envs.mdp.actions import (
+    RelativeJointPositionAction as RelativeJointPositionAction,
+)
+from unilab.envs.mdp.actions import (
+    RelativeJointPositionActionCfg as RelativeJointPositionActionCfg,
+)
+from unilab.envs.mdp.commands import UniformPoseCommand as UniformPoseCommand
+from unilab.envs.mdp.commands import UniformPoseCommandCfg as UniformPoseCommandCfg
+from unilab.envs.mdp.commands import UniformVelocityCommand as UniformVelocityCommand
+from unilab.envs.mdp.commands import UniformVelocityCommandCfg as UniformVelocityCommandCfg
+from unilab.envs.mdp.curriculums import command_curriculum as command_curriculum
+from unilab.envs.mdp.curriculums import event_curriculum as event_curriculum
+from unilab.envs.mdp.curriculums import reward_curriculum as reward_curriculum
+from unilab.envs.mdp.curriculums import termination_curriculum as termination_curriculum
+from unilab.envs.mdp.events import apply_body_impulse as apply_body_impulse
+from unilab.envs.mdp.events import dof_armature as dof_armature
+from unilab.envs.mdp.events import geom_friction as geom_friction
+from unilab.envs.mdp.events import joint_armature as joint_armature
+from unilab.envs.mdp.events import pd_gains as pd_gains
+from unilab.envs.mdp.events import push_by_setting_velocity as push_by_setting_velocity
+from unilab.envs.mdp.events import randomize_body_mass_inertia as randomize_body_mass_inertia
+from unilab.envs.mdp.events import randomize_encoder_bias as randomize_encoder_bias
+from unilab.envs.mdp.events import (
+    randomize_physics_scene_gravity as randomize_physics_scene_gravity,
+)
+from unilab.envs.mdp.events import randomize_rigid_body_com as randomize_rigid_body_com
+from unilab.envs.mdp.events import randomize_rigid_body_mass as randomize_rigid_body_mass
+from unilab.envs.mdp.events import reset_root_state_uniform as reset_root_state_uniform
+from unilab.envs.mdp.events import reset_scene_to_default as reset_scene_to_default
+from unilab.envs.mdp.events import resolve_env_ids as resolve_env_ids
+from unilab.envs.mdp.observations import base_ang_vel as base_ang_vel
+from unilab.envs.mdp.observations import (
+    base_ang_vel_imu_misaligned as base_ang_vel_imu_misaligned,
+)
+from unilab.envs.mdp.observations import base_lin_vel as base_lin_vel
+from unilab.envs.mdp.observations import builtin_sensor as builtin_sensor
+from unilab.envs.mdp.observations import generated_commands as generated_commands
+from unilab.envs.mdp.observations import joint_pos_rel as joint_pos_rel
+from unilab.envs.mdp.observations import joint_vel_rel as joint_vel_rel
+from unilab.envs.mdp.observations import last_action as last_action
+from unilab.envs.mdp.observations import projected_gravity as projected_gravity
+from unilab.envs.mdp.observations import (
+    projected_gravity_from_sensor as projected_gravity_from_sensor,
+)
+from unilab.envs.mdp.observations import (
+    projected_gravity_imu_misaligned as projected_gravity_imu_misaligned,
+)
+from unilab.envs.mdp.recorders import LifecycleCounterRecorder as LifecycleCounterRecorder
+from unilab.envs.mdp.rewards import action_acc_l2 as action_acc_l2
+from unilab.envs.mdp.rewards import action_rate_l2 as action_rate_l2
+from unilab.envs.mdp.rewards import (
+    body_angular_velocity_penalty as body_angular_velocity_penalty,
+)
+from unilab.envs.mdp.rewards import flat_orientation_l2 as flat_orientation_l2
+from unilab.envs.mdp.rewards import is_alive as is_alive
+from unilab.envs.mdp.rewards import is_terminated as is_terminated
+from unilab.envs.mdp.rewards import joint_pos_limits as joint_pos_limits
+from unilab.envs.mdp.rewards import joint_vel_l2 as joint_vel_l2
+from unilab.envs.mdp.rewards import posture as posture
+from unilab.envs.mdp.rewards import root_height as root_height
+from unilab.envs.mdp.rewards import track_angular_velocity as track_angular_velocity
+from unilab.envs.mdp.rewards import track_linear_velocity as track_linear_velocity
+from unilab.envs.mdp.rewards import upright as upright
+from unilab.envs.mdp.rewards import variable_posture as variable_posture
+from unilab.envs.mdp.terminations import bad_orientation as bad_orientation
+from unilab.envs.mdp.terminations import nan_detection as nan_detection
+from unilab.envs.mdp.terminations import (
+    root_height_below_minimum as root_height_below_minimum,
+)
+from unilab.envs.mdp.terminations import time_out as time_out
+
+__all__ = [
+    "JointPositionAction",
+    "JointPositionActionCfg",
+    "JointEffortAction",
+    "JointEffortActionCfg",
+    "JointVelocityAction",
+    "JointVelocityActionCfg",
+    "LifecycleCounterRecorder",
+    "RelativeJointPositionAction",
+    "RelativeJointPositionActionCfg",
+    "UniformVelocityCommand",
+    "UniformVelocityCommandCfg",
+    "UniformPoseCommand",
+    "UniformPoseCommandCfg",
+    "action_acc_l2",
+    "action_rate_l2",
+    "apply_body_impulse",
+    "base_ang_vel",
+    "base_ang_vel_imu_misaligned",
+    "base_lin_vel",
+    "builtin_sensor",
+    "bad_orientation",
+    "body_angular_velocity_penalty",
+    "command_curriculum",
+    "dof_armature",
+    "event_curriculum",
+    "flat_orientation_l2",
+    "geom_friction",
+    "generated_commands",
+    "joint_pos_rel",
+    "joint_armature",
+    "joint_pos_limits",
+    "joint_vel_rel",
+    "joint_vel_l2",
+    "last_action",
+    "nan_detection",
+    "pd_gains",
+    "posture",
+    "push_by_setting_velocity",
+    "randomize_body_mass_inertia",
+    "randomize_encoder_bias",
+    "randomize_physics_scene_gravity",
+    "randomize_rigid_body_com",
+    "randomize_rigid_body_mass",
+    "is_alive",
+    "is_terminated",
+    "projected_gravity",
+    "projected_gravity_from_sensor",
+    "projected_gravity_imu_misaligned",
+    "reset_root_state_uniform",
+    "reset_scene_to_default",
+    "resolve_env_ids",
+    "reward_curriculum",
+    "root_height_below_minimum",
+    "root_height",
+    "termination_curriculum",
+    "time_out",
+    "track_angular_velocity",
+    "track_linear_velocity",
+    "upright",
+    "variable_posture",
+]
diff --git a/src/unilab/envs/mdp/actions/__init__.py b/src/unilab/envs/mdp/actions/__init__.py
new file mode 100644
index 000000000..4bad4feb8
--- /dev/null
+++ b/src/unilab/envs/mdp/actions/__init__.py
@@ -0,0 +1,25 @@
+"""Built-in action terms supported by the NumPy runtime."""
+
+from unilab.envs.mdp.actions.actions import JointEffortAction as JointEffortAction
+from unilab.envs.mdp.actions.actions import JointEffortActionCfg as JointEffortActionCfg
+from unilab.envs.mdp.actions.actions import JointPositionAction as JointPositionAction
+from unilab.envs.mdp.actions.actions import JointPositionActionCfg as JointPositionActionCfg
+from unilab.envs.mdp.actions.actions import JointVelocityAction as JointVelocityAction
+from unilab.envs.mdp.actions.actions import JointVelocityActionCfg as JointVelocityActionCfg
+from unilab.envs.mdp.actions.actions import (
+    RelativeJointPositionAction as RelativeJointPositionAction,
+)
+from unilab.envs.mdp.actions.actions import (
+    RelativeJointPositionActionCfg as RelativeJointPositionActionCfg,
+)
+
+__all__ = [
+    "JointEffortAction",
+    "JointEffortActionCfg",
+    "JointPositionAction",
+    "JointPositionActionCfg",
+    "JointVelocityAction",
+    "JointVelocityActionCfg",
+    "RelativeJointPositionAction",
+    "RelativeJointPositionActionCfg",
+]
diff --git a/src/unilab/envs/mdp/actions/actions.py b/src/unilab/envs/mdp/actions/actions.py
new file mode 100644
index 000000000..25d10bbc3
--- /dev/null
+++ b/src/unilab/envs/mdp/actions/actions.py
@@ -0,0 +1,311 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681),
+# src/mjlab/envs/mdp/actions/actions.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and the SimBackend/entity contracts; Apache-2.0.
+"""Joint transmission actions for the NumPy Manager-Based runtime."""
+
+from __future__ import annotations
+
+import math
+import re
+from dataclasses import dataclass
+from numbers import Real
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+
+from unilab.dtype_config import get_global_dtype
+from unilab.managers.action_manager import ActionTerm, ActionTermCfg
+
+if TYPE_CHECKING:
+    from unilab.base.entity import Entity
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+def _real(value: Any, *, label: str) -> float:
+    if isinstance(value, (bool, np.bool_)) or not isinstance(value, Real):
+        raise TypeError(f"{label} must be a real number, got {type(value).__name__}")
+    result = float(value)
+    if not math.isfinite(result):
+        raise ValueError(f"{label} must be finite, got {result}")
+    return result
+
+
+def _resolve_named_values(
+    values: dict[str, Any], names: list[str], *, label: str
+) -> tuple[list[int], list[Any]]:
+    """Resolve regex-keyed values once, preserving target-name order."""
+    if not isinstance(values, dict):
+        raise TypeError(f"{label} must be a dict")
+    patterns = list(values)
+    matched_by_pattern = [False] * len(patterns)
+    indices: list[int] = []
+    resolved: list[Any] = []
+    for index, name in enumerate(names):
+        matches: list[int] = []
+        for pattern_index, pattern in enumerate(patterns):
+            try:
+                matches_pattern = re.fullmatch(pattern, name) is not None
+            except re.error as exc:
+                raise ValueError(f"{label} contains invalid regex {pattern!r}: {exc}") from exc
+            if matches_pattern:
+                matches.append(pattern_index)
+        if len(matches) > 1:
+            rendered = [patterns[pattern_index] for pattern_index in matches]
+            raise ValueError(f"{label} patterns {rendered} both match target '{name}'")
+        if matches:
+            pattern_index = matches[0]
+            matched_by_pattern[pattern_index] = True
+            indices.append(index)
+            resolved.append(values[patterns[pattern_index]])
+    missing = [pattern for pattern, matched in zip(patterns, matched_by_pattern) if not matched]
+    if missing:
+        raise ValueError(f"{label} patterns {missing} match no targets; available={names}")
+    return indices, resolved
+
+
+@dataclass(kw_only=True)
+class BaseActionCfg(ActionTermCfg):
+    """Configuration shared by entity joint actions."""
+
+    actuator_names: tuple[str, ...] | list[str]
+    scale: float | dict[str, float] = 1.0
+    offset: float | dict[str, float] = 0.0
+    preserve_order: bool = False
+
+
+class BaseAction(ActionTerm):
+    """Apply a cold-path-resolved affine transform to raw policy actions."""
+
+    cfg: BaseActionCfg
+    _entity: Entity
+
+    def __init__(self, cfg: BaseActionCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg=cfg, env=env)
+        target_ids, target_names = self._entity.find_joints_by_actuator_names(cfg.actuator_names)
+        self._target_ids = np.asarray(target_ids, dtype=np.intp)
+        self._target_ids.setflags(write=False)
+        self._target_names = list(target_names)
+        self._action_dim = len(target_ids)
+        dtype = get_global_dtype()
+        self._raw_actions = np.zeros((self.num_envs, self.action_dim), dtype=dtype)
+        self._processed_actions = np.zeros_like(self._raw_actions)
+        self._scale = self._resolve_affine(cfg.scale, default=1.0, label="scale")
+        self._offset = self._resolve_affine(cfg.offset, default=0.0, label="offset")
+        self._clip = self._resolve_clip(cfg.clip)
+
+    def _resolve_affine(
+        self, value: float | dict[str, float], *, default: float, label: str
+    ) -> float | np.ndarray:
+        if isinstance(value, dict):
+            result = np.full_like(self._raw_actions, default)
+            indices, resolved = _resolve_named_values(
+                value, self._target_names, label=f"{type(self).__name__} {label}"
+            )
+            result[:, indices] = [
+                _real(item, label=f"{type(self).__name__} {label}") for item in resolved
+            ]
+            return result
+        return _real(value, label=f"{type(self).__name__} {label}")
+
+    def _resolve_clip(self, value: dict[str, tuple] | None) -> np.ndarray | None:
+        if value is None:
+            return None
+        result = np.empty((*self._raw_actions.shape, 2), dtype=self._raw_actions.dtype)
+        result[..., 0] = -np.inf
+        result[..., 1] = np.inf
+        indices, bounds = _resolve_named_values(
+            value, self._target_names, label=f"{type(self).__name__} clip"
+        )
+        for index, raw_bounds in zip(indices, bounds, strict=True):
+            if not isinstance(raw_bounds, (tuple, list)) or len(raw_bounds) != 2:
+                raise TypeError(
+                    f"{type(self).__name__} clip for '{self._target_names[index]}' "
+                    "must be a (min, max) pair"
+                )
+            lower = _real(raw_bounds[0], label=f"{type(self).__name__} clip lower")
+            upper = _real(raw_bounds[1], label=f"{type(self).__name__} clip upper")
+            if lower > upper:
+                raise ValueError(
+                    f"{type(self).__name__} clip lower {lower} exceeds upper {upper} "
+                    f"for '{self._target_names[index]}'"
+                )
+            result[:, index, 0] = lower
+            result[:, index, 1] = upper
+        return result
+
+    @property
+    def scale(self) -> float | np.ndarray:
+        return self._scale
+
+    @property
+    def offset(self) -> float | np.ndarray:
+        return self._offset
+
+    @property
+    def raw_action(self) -> np.ndarray:
+        return self._raw_actions
+
+    @property
+    def processed_action(self) -> np.ndarray:
+        return self._processed_actions
+
+    @property
+    def action_dim(self) -> int:
+        return self._action_dim
+
+    @property
+    def target_ids(self) -> np.ndarray:
+        return self._target_ids
+
+    @property
+    def target_names(self) -> list[str]:
+        return list(self._target_names)
+
+    def process_actions(self, actions: np.ndarray) -> None:
+        if not isinstance(actions, np.ndarray):
+            raise TypeError(
+                f"{type(self).__name__} expected np.ndarray, got {type(actions).__name__}"
+            )
+        if actions.shape != self._raw_actions.shape:
+            raise ValueError(
+                f"{type(self).__name__} expected action shape {self._raw_actions.shape}, "
+                f"got {actions.shape}"
+            )
+        if not np.isfinite(actions).all():
+            raise ValueError(f"{type(self).__name__} received NaN or Inf actions")
+        self._raw_actions[:] = actions
+        np.multiply(self._raw_actions, self._scale, out=self._processed_actions)
+        np.add(self._processed_actions, self._offset, out=self._processed_actions)
+        if self._clip is not None:
+            np.clip(
+                self._processed_actions,
+                self._clip[..., 0],
+                self._clip[..., 1],
+                out=self._processed_actions,
+            )
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        if env_ids is None:
+            env_ids = slice(None)
+        self._raw_actions[env_ids] = 0.0
+
+
+@dataclass(kw_only=True)
+class JointPositionActionCfg(BaseActionCfg):
+    """Configuration for joint-position control."""
+
+    use_default_offset: bool = True
+
+    def build(self, env: ManagerBasedRlEnv) -> JointPositionAction:
+        return JointPositionAction(self, env)
+
+
+class JointPositionAction(BaseAction):
+    """Convert policy actions into entity joint-position targets."""
+
+    cfg: JointPositionActionCfg
+
+    def __init__(self, cfg: JointPositionActionCfg, env: ManagerBasedRlEnv):
+        if not isinstance(cfg.use_default_offset, bool):
+            raise TypeError("JointPositionActionCfg use_default_offset must be bool")
+        super().__init__(cfg=cfg, env=env)
+        if cfg.use_default_offset:
+            self._offset = self._entity.data.default_joint_pos[:, self._target_ids].copy()
+        self._target = np.empty_like(self._processed_actions)
+
+    def apply_actions(self) -> None:
+        encoder_bias = self._entity.data.encoder_bias[:, self._target_ids]
+        np.subtract(self._processed_actions, encoder_bias, out=self._target)
+        self._entity.set_joint_position_target(self._target, joint_ids=self._target_ids)
+
+
+@dataclass(kw_only=True)
+class RelativeJointPositionActionCfg(BaseActionCfg):
+    """Joint position targets relative to the current measured position.
+
+    ``target = current_joint_pos + action * scale``.  A fixed offset has no
+    useful meaning for this transmission and is rejected during construction.
+    """
+
+    def __post_init__(self) -> None:
+        if isinstance(self.offset, dict):
+            resolved = [
+                _real(value, label="RelativeJointPositionActionCfg offset")
+                for value in self.offset.values()
+            ]
+            nonzero = [value for value in resolved if value != 0.0]
+        else:
+            value = _real(self.offset, label="RelativeJointPositionActionCfg offset")
+            nonzero = [value] if value != 0.0 else []
+        if nonzero:
+            raise ValueError("RelativeJointPositionActionCfg does not support a non-zero offset")
+
+    def build(self, env: ManagerBasedRlEnv) -> RelativeJointPositionAction:
+        return RelativeJointPositionAction(self, env)
+
+
+class RelativeJointPositionAction(BaseAction):
+    """Control joints via position targets relative to current positions."""
+
+    def apply_actions(self) -> None:
+        current = self._entity.data.joint_pos[:, self._target_ids]
+        target = current + self._processed_actions
+        self._entity.set_joint_position_target(target, joint_ids=self._target_ids)
+
+
+@dataclass(kw_only=True)
+class JointVelocityActionCfg(BaseActionCfg):
+    """Configuration for joint velocity control."""
+
+    use_default_offset: bool = True
+
+    def build(self, env: ManagerBasedRlEnv) -> JointVelocityAction:
+        return JointVelocityAction(self, env)
+
+
+class JointVelocityAction(BaseAction):
+    """Control joints via velocity targets."""
+
+    def __init__(self, cfg: JointVelocityActionCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg=cfg, env=env)
+        if not isinstance(cfg.use_default_offset, bool):
+            raise TypeError("JointVelocityActionCfg use_default_offset must be bool")
+        if cfg.use_default_offset:
+            self._offset = self._entity.data.default_joint_vel[:, self._target_ids].copy()
+
+    def apply_actions(self) -> None:
+        self._entity.set_joint_velocity_target(
+            self._processed_actions,
+            joint_ids=self._target_ids,
+        )
+
+
+@dataclass(kw_only=True)
+class JointEffortActionCfg(BaseActionCfg):
+    """Configuration for joint effort (torque) control."""
+
+    def build(self, env: ManagerBasedRlEnv) -> JointEffortAction:
+        return JointEffortAction(self, env)
+
+
+class JointEffortAction(BaseAction):
+    """Control joints via effort targets."""
+
+    def apply_actions(self) -> None:
+        self._entity.set_joint_effort_target(
+            self._processed_actions,
+            joint_ids=self._target_ids,
+        )
+
+
+__all__ = [
+    "JointEffortAction",
+    "JointEffortActionCfg",
+    "JointPositionAction",
+    "JointPositionActionCfg",
+    "JointVelocityAction",
+    "JointVelocityActionCfg",
+    "RelativeJointPositionAction",
+    "RelativeJointPositionActionCfg",
+]
diff --git a/src/unilab/envs/mdp/commands/__init__.py b/src/unilab/envs/mdp/commands/__init__.py
new file mode 100644
index 000000000..ae3f625bd
--- /dev/null
+++ b/src/unilab/envs/mdp/commands/__init__.py
@@ -0,0 +1,17 @@
+"""Built-in command terms supported by the NumPy runtime."""
+
+from unilab.envs.mdp.commands.pose_command import UniformPoseCommand as UniformPoseCommand
+from unilab.envs.mdp.commands.pose_command import UniformPoseCommandCfg as UniformPoseCommandCfg
+from unilab.envs.mdp.commands.velocity_command import (
+    UniformVelocityCommand as UniformVelocityCommand,
+)
+from unilab.envs.mdp.commands.velocity_command import (
+    UniformVelocityCommandCfg as UniformVelocityCommandCfg,
+)
+
+__all__ = [
+    "UniformPoseCommand",
+    "UniformPoseCommandCfg",
+    "UniformVelocityCommand",
+    "UniformVelocityCommandCfg",
+]
diff --git a/src/unilab/envs/mdp/commands/pose_command.py b/src/unilab/envs/mdp/commands/pose_command.py
new file mode 100644
index 000000000..785f10959
--- /dev/null
+++ b/src/unilab/envs/mdp/commands/pose_command.py
@@ -0,0 +1,113 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681),
+# src/mjlab_microduck/tasks/mdp.py command terms.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and the public ManagerBasedRlEnv contract;
+# licensed under Apache-2.0.
+"""Generic pose and posture command terms for Manager-Based tasks."""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from numbers import Real
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from unilab.dtype_config import get_global_dtype
+from unilab.managers.command_manager import CommandTerm, CommandTermCfg
+
+if TYPE_CHECKING:
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+def _real(value: object, *, label: str, minimum: float | None = None) -> float:
+    if isinstance(value, (bool, np.bool_)) or not isinstance(value, Real):
+        raise TypeError(f"{label} must be a real number")
+    result = float(value)
+    if not math.isfinite(result):
+        raise ValueError(f"{label} must be finite")
+    if minimum is not None and result < minimum:
+        raise ValueError(f"{label} must be at least {minimum}")
+    return result
+
+
+def _range_pair(value: object, *, label: str) -> tuple[float, float]:
+    if not isinstance(value, (tuple, list)) or len(value) != 2:
+        raise TypeError(f"{label} must be a two-value range")
+    lower = _real(value[0], label=f"{label} lower")
+    upper = _real(value[1], label=f"{label} upper")
+    if lower > upper:
+        raise ValueError(f"{label} lower {lower} exceeds upper {upper}")
+    return lower, upper
+
+
+@dataclass(kw_only=True)
+class UniformPoseCommandCfg(CommandTermCfg):
+    """Sample a fixed-width pose vector independently per dimension."""
+
+    ranges: tuple[tuple[float, float], ...] | list[list[float]]
+    zero_command_prob: float = 0.0
+
+    def build(self, env: ManagerBasedRlEnv) -> UniformPoseCommand:
+        return UniformPoseCommand(self, env)
+
+
+class UniformPoseCommand(CommandTerm):
+    """Uniformly sampled vector command held until the next resample."""
+
+    cfg: UniformPoseCommandCfg
+
+    def __init__(self, cfg: UniformPoseCommandCfg, env: ManagerBasedRlEnv):
+        ranges = self._validated_ranges(cfg.ranges)
+        probability = _real(
+            cfg.zero_command_prob,
+            label="UniformPoseCommandCfg zero_command_prob",
+        )
+        if probability > 1.0:
+            raise ValueError("UniformPoseCommandCfg zero_command_prob must be within [0, 1]")
+        self._zero_command_prob = probability
+        super().__init__(cfg, env)
+        self._command = np.zeros((self.num_envs, len(ranges)), dtype=get_global_dtype())
+
+    @staticmethod
+    def _validated_ranges(ranges: object) -> tuple[tuple[float, float], ...]:
+        if not isinstance(ranges, (tuple, list)) or not ranges:
+            raise ValueError("UniformPoseCommandCfg ranges must not be empty")
+        return tuple(
+            _range_pair(item, label=f"UniformPoseCommandCfg ranges[{index}]")
+            for index, item in enumerate(ranges)
+        )
+
+    @property
+    def command(self) -> np.ndarray:
+        return self._command
+
+    def _update_metrics(self, env_ids: np.ndarray | None = None) -> None:
+        del env_ids
+
+    def _resample_command(self, env_ids: np.ndarray) -> None:
+        if len(env_ids) == 0:
+            return
+        ranges = self._validated_ranges(self.cfg.ranges)
+        if len(ranges) != self._command.shape[1]:
+            raise ValueError(
+                "UniformPoseCommandCfg ranges width changed from "
+                f"{self._command.shape[1]} to {len(ranges)}; curricula may only "
+                "change per-axis bounds"
+            )
+        for column, (lower, upper) in enumerate(ranges):
+            self._command[env_ids, column] = self._env.rng.uniform(
+                lower,
+                upper,
+                size=len(env_ids),
+            )
+        if self._zero_command_prob > 0.0:
+            zero = self._env.rng.uniform(0.0, 1.0, size=len(env_ids)) < self._zero_command_prob
+            self._command[env_ids[zero]] = 0.0
+
+    def _update_command(self, env_ids: np.ndarray | None) -> None:
+        del env_ids
+
+
+__all__ = ["UniformPoseCommand", "UniformPoseCommandCfg"]
diff --git a/src/unilab/envs/mdp/commands/velocity_command.py b/src/unilab/envs/mdp/commands/velocity_command.py
new file mode 100644
index 000000000..66e49f701
--- /dev/null
+++ b/src/unilab/envs/mdp/commands/velocity_command.py
@@ -0,0 +1,227 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681),
+# src/mjlab/tasks/velocity/mdp/velocity_command.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and the base-owned entity facade; Apache-2.0.
+"""Uniform velocity commands for locomotion tasks."""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass, field
+from numbers import Real
+from typing import TYPE_CHECKING, Any, cast
+
+import numpy as np
+
+from unilab.dtype_config import get_global_dtype
+from unilab.managers.command_manager import CommandTerm, CommandTermCfg
+from unilab.utils.rotation import np_wrap_to_pi
+
+if TYPE_CHECKING:
+    from unilab.base.entity import Entity
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+def _real(value: Any, *, label: str) -> float:
+    if isinstance(value, bool) or not isinstance(value, Real):
+        raise TypeError(f"{label} must be a real number, got {type(value).__name__}")
+    result = float(value)
+    if not math.isfinite(result):
+        raise ValueError(f"{label} must be finite, got {result}")
+    return result
+
+
+def _range_pair(value: Any, *, label: str) -> tuple[float, float]:
+    if not isinstance(value, (tuple, list)) or len(value) != 2:
+        raise TypeError(f"{label} must be a two-value range")
+    lower = _real(value[0], label=f"{label} lower")
+    upper = _real(value[1], label=f"{label} upper")
+    if lower > upper:
+        raise ValueError(f"{label} lower {lower} exceeds upper {upper}")
+    return lower, upper
+
+
+def _ratio(value: Any, *, label: str) -> float:
+    result = _real(value, label=label)
+    if result < 0.0 or result > 1.0:
+        raise ValueError(f"{label} must be within [0, 1], got {result}")
+    return result
+
+
+class UniformVelocityCommand(CommandTerm):
+    """Sample planar velocity commands and update frame-dependent components."""
+
+    cfg: UniformVelocityCommandCfg
+
+    def __init__(self, cfg: UniformVelocityCommandCfg, env: ManagerBasedRlEnv):
+        self._validate_cfg(cfg)
+        super().__init__(cfg, env)
+        if cfg.init_velocity_prob > 0.0:
+            raise NotImplementedError(
+                "UniformVelocityCommand capability 'initial root velocity write' is "
+                "unavailable in the UniLab entity facade; set init_velocity_prob=0"
+            )
+
+        self.robot = cast("Entity", env.scene[cfg.entity_name])
+        dtype = get_global_dtype()
+        self.vel_command_b = np.zeros((self.num_envs, 3), dtype=dtype)
+        self.vel_command_w = np.zeros_like(self.vel_command_b)
+        self.heading_target = np.zeros(self.num_envs, dtype=dtype)
+        self.heading_error = np.zeros(self.num_envs, dtype=dtype)
+        self.is_heading_env = np.zeros(self.num_envs, dtype=np.bool_)
+        self.is_standing_env = np.zeros(self.num_envs, dtype=np.bool_)
+        self.is_world_env = np.zeros(self.num_envs, dtype=np.bool_)
+        self.is_forward_env = np.zeros(self.num_envs, dtype=np.bool_)
+        self.metrics["error_vel_xy"] = np.zeros(self.num_envs, dtype=dtype)
+        self.metrics["error_vel_yaw"] = np.zeros(self.num_envs, dtype=dtype)
+
+    @staticmethod
+    def _validate_cfg(cfg: UniformVelocityCommandCfg) -> None:
+        if not isinstance(cfg.entity_name, str) or not cfg.entity_name:
+            raise ValueError("UniformVelocityCommandCfg entity_name must be non-empty")
+        if not isinstance(cfg.heading_command, bool):
+            raise TypeError("UniformVelocityCommandCfg heading_command must be bool")
+        _real(
+            cfg.heading_control_stiffness,
+            label="UniformVelocityCommandCfg heading_control_stiffness",
+        )
+        if cfg.heading_control_stiffness < 0.0:
+            raise ValueError("heading_control_stiffness must be non-negative")
+        for name in (
+            "rel_standing_envs",
+            "rel_heading_envs",
+            "rel_world_envs",
+            "rel_forward_envs",
+            "init_velocity_prob",
+        ):
+            _ratio(getattr(cfg, name), label=f"UniformVelocityCommandCfg {name}")
+        if not isinstance(cfg.ranges, UniformVelocityCommandCfg.Ranges):
+            raise TypeError("UniformVelocityCommandCfg ranges must be a Ranges instance")
+        _range_pair(cfg.ranges.lin_vel_x, label="ranges.lin_vel_x")
+        _range_pair(cfg.ranges.lin_vel_y, label="ranges.lin_vel_y")
+        _range_pair(cfg.ranges.ang_vel_z, label="ranges.ang_vel_z")
+        if cfg.ranges.heading is not None:
+            _range_pair(cfg.ranges.heading, label="ranges.heading")
+        if cfg.heading_command and cfg.ranges.heading is None:
+            raise ValueError("heading_command=True but ranges.heading is None")
+        if cfg.ranges.heading is not None and not cfg.heading_command:
+            raise ValueError("ranges.heading is set but heading_command=False")
+        _, upper = _range_pair(
+            cfg.resampling_time_range,
+            label="UniformVelocityCommandCfg resampling_time_range",
+        )
+        if upper <= 0.0:
+            raise ValueError("resampling_time_range upper bound must be positive")
+
+    @property
+    def command(self) -> np.ndarray:
+        return self.vel_command_b
+
+    def _update_metrics(self, env_ids: np.ndarray | None = None) -> None:
+        del env_ids  # Metrics accumulate over all rows on every compute.
+        max_command_steps = self.cfg.resampling_time_range[1] / self._env.step_dt
+        self.metrics["error_vel_xy"] += (
+            np.linalg.norm(
+                self.vel_command_b[:, :2] - self.robot.data.root_link_lin_vel_b[:, :2],
+                axis=-1,
+            )
+            / max_command_steps
+        )
+        self.metrics["error_vel_yaw"] += (
+            np.abs(self.vel_command_b[:, 2] - self.robot.data.root_link_ang_vel_b[:, 2])
+            / max_command_steps
+        )
+
+    def _resample_command(self, env_ids: np.ndarray) -> None:
+        count = len(env_ids)
+        rng = self._env.rng
+        ranges = self.cfg.ranges
+        self.vel_command_b[env_ids, 0] = rng.uniform(*ranges.lin_vel_x, size=count)
+        self.vel_command_b[env_ids, 1] = rng.uniform(*ranges.lin_vel_y, size=count)
+        self.vel_command_b[env_ids, 2] = rng.uniform(*ranges.ang_vel_z, size=count)
+
+        if self.cfg.heading_command:
+            assert ranges.heading is not None
+            self.heading_target[env_ids] = rng.uniform(*ranges.heading, size=count)
+            self.is_heading_env[env_ids] = (
+                rng.uniform(0.0, 1.0, size=count) <= self.cfg.rel_heading_envs
+            )
+        self.is_standing_env[env_ids] = (
+            rng.uniform(0.0, 1.0, size=count) <= self.cfg.rel_standing_envs
+        )
+        self.is_world_env[env_ids] = rng.uniform(0.0, 1.0, size=count) <= self.cfg.rel_world_envs
+        self.vel_command_w[env_ids] = self.vel_command_b[env_ids]
+        self.is_forward_env[env_ids] = (
+            rng.uniform(0.0, 1.0, size=count) <= self.cfg.rel_forward_envs
+        )
+        forward_ids = env_ids[self.is_forward_env[env_ids]]
+        if len(forward_ids) > 0:
+            self.vel_command_b[forward_ids, 0] = np.maximum(
+                np.abs(self.vel_command_b[forward_ids, 0]), 0.3
+            )
+            self.vel_command_b[forward_ids, 1:] = 0.0
+
+    def _update_command(self, env_ids: np.ndarray | None = None) -> None:
+        del env_ids
+        if self.cfg.heading_command:
+            self.heading_error[:] = np_wrap_to_pi(self.heading_target - self.robot.data.heading_w)
+            heading_ids = np.flatnonzero(self.is_heading_env)
+            self.vel_command_b[heading_ids, 2] = np.clip(
+                self.cfg.heading_control_stiffness * self.heading_error[heading_ids],
+                self.cfg.ranges.ang_vel_z[0],
+                self.cfg.ranges.ang_vel_z[1],
+            )
+
+        world_ids = np.flatnonzero(self.is_world_env)
+        if len(world_ids) > 0:
+            heading = self.robot.data.heading_w[world_ids]
+            cos_heading = np.cos(heading)
+            sin_heading = np.sin(heading)
+            velocity_x_w = self.vel_command_w[world_ids, 0]
+            velocity_y_w = self.vel_command_w[world_ids, 1]
+            self.vel_command_b[world_ids, 0] = (
+                cos_heading * velocity_x_w + sin_heading * velocity_y_w
+            )
+            self.vel_command_b[world_ids, 1] = (
+                -sin_heading * velocity_x_w + cos_heading * velocity_y_w
+            )
+
+        standing_ids = np.flatnonzero(self.is_standing_env)
+        self.vel_command_b[standing_ids] = 0.0
+        self.vel_command_w[standing_ids] = 0.0
+
+
+@dataclass(kw_only=True)
+class UniformVelocityCommandCfg(CommandTermCfg):
+    """Configuration for uniformly sampled planar velocity commands."""
+
+    entity_name: str
+    heading_command: bool = False
+    heading_control_stiffness: float = 1.0
+    rel_standing_envs: float = 0.0
+    rel_heading_envs: float = 1.0
+    rel_world_envs: float = 0.0
+    rel_forward_envs: float = 0.0
+    init_velocity_prob: float = 0.0
+
+    @dataclass
+    class Ranges:
+        lin_vel_x: tuple[float, float]
+        lin_vel_y: tuple[float, float]
+        ang_vel_z: tuple[float, float]
+        heading: tuple[float, float] | None = None
+
+    ranges: Ranges
+
+    @dataclass
+    class VizCfg:
+        z_offset: float = 0.2
+        scale: float = 0.5
+
+    viz: VizCfg = field(default_factory=VizCfg)
+
+    def build(self, env: ManagerBasedRlEnv) -> UniformVelocityCommand:
+        return UniformVelocityCommand(self, env)
+
+
+__all__ = ["UniformVelocityCommand", "UniformVelocityCommandCfg"]
diff --git a/src/unilab/envs/mdp/curriculums.py b/src/unilab/envs/mdp/curriculums.py
new file mode 100644
index 000000000..3e248932d
--- /dev/null
+++ b/src/unilab/envs/mdp/curriculums.py
@@ -0,0 +1,305 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), src/mjlab/envs/mdp/curriculums.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and UniLab contracts; licensed under Apache-2.0.
+"""Generic stage-based curriculum terms for the NumPy manager runtime.
+
+These terms let owner YAMLs ramp any reward/termination term's ``weight``
+and/or ``params`` by training step (``env.common_step_counter``) through a
+declarative stage table, so tasks no longer need private step-based
+curriculum terms. Stage scheduling is validated fail-closed at manager
+construction time.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+from typing import TYPE_CHECKING, Any, TypedDict
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from unilab.managers._types import ManagerBasedRlEnv
+    from unilab.managers.curriculum_manager import CurriculumTermCfg
+
+
+# Stage schemas.
+
+
+class _RewardCurriculumStageOptional(TypedDict, total=False):
+    weight: float
+    params: dict[str, Any]
+
+
+class RewardCurriculumStage(_RewardCurriculumStageOptional):
+    step: int
+
+
+class _TerminationCurriculumStageOptional(TypedDict, total=False):
+    params: dict[str, Any]
+    time_out: bool
+
+
+class TerminationCurriculumStage(_TerminationCurriculumStageOptional):
+    step: int
+
+
+class _CommandCurriculumStageOptional(TypedDict, total=False):
+    params: dict[str, Any]
+
+
+class CommandCurriculumStage(_CommandCurriculumStageOptional):
+    """Stage for ``command_curriculum``.
+
+    Any key beyond ``step``/``params`` is applied as a top-level field on the
+    live command term config (e.g. ``rel_standing_envs`` or ``ranges``); the
+    set of valid fields is the target term's config schema.
+    """
+
+    step: int
+
+
+class _EventCurriculumStageOptional(TypedDict, total=False):
+    params: dict[str, Any]
+
+
+class EventCurriculumStage(_EventCurriculumStageOptional):
+    """Stage for ``event_curriculum``.
+
+    Any key beyond ``step``/``params`` is applied as a top-level field on the
+    live event term config; the set of valid fields is the target term's
+    config schema.
+    """
+
+    step: int
+
+
+# Shared engine. Stage dicts are passed directly from the public TypedDict
+# schemas. Any key that isn't "step" or "params" is treated as a top-level
+# field on the target term config (e.g. "weight" on RewardTermCfg).
+
+_RESERVED_KEYS = {"step", "params"}
+
+
+def _validate_stages(
+    term_cfg: Any,
+    term_name: str,
+    stages: Sequence[Any],
+) -> None:
+    """Validate stage ordering, field existence, and param keys."""
+    for i in range(1, len(stages)):
+        if stages[i]["step"] < stages[i - 1]["step"]:
+            raise ValueError(
+                f"Curriculum stages must be in nondecreasing step order,"
+                f" but stage {i} has step"
+                f" {stages[i]['step']} < {stages[i - 1]['step']}."
+            )
+    for stage in stages:
+        for key in stage:
+            if key not in _RESERVED_KEYS and not hasattr(term_cfg, key):
+                raise AttributeError(
+                    f"Field '{key}' does not exist on the resolved term config for '{term_name}'."
+                )
+    # Command term configs carry plain dataclass fields and no params dict.
+    term_params = getattr(term_cfg, "params", None)
+    for stage in stages:
+        known = term_params.keys() if term_params is not None else ()
+        unknown = stage.get("params", {}).keys() - known
+        if unknown:
+            raise KeyError(
+                f"Stage at step {stage['step']} sets unknown param(s)"
+                f" {unknown} on term '{term_name}'. Check for typos."
+            )
+
+
+def _apply_stages(
+    term_cfg: Any,
+    step_counter: int,
+    stages: Sequence[Any],
+) -> dict[str, Any]:
+    """Apply staged updates and return a logging snapshot."""
+    for stage in stages:
+        if step_counter >= stage["step"]:
+            for key, value in stage.items():
+                if key not in _RESERVED_KEYS:
+                    setattr(term_cfg, key, value)
+            if "params" in stage:
+                term_cfg.params.update(stage["params"])
+    # Only log values that stages actually reference.
+    logged_fields: set[str] = set()
+    logged_params: set[str] = set()
+    for stage in stages:
+        for key in stage:
+            if key not in _RESERVED_KEYS:
+                logged_fields.add(key)
+        for key in stage.get("params", {}):
+            logged_params.add(key)
+    result: dict[str, Any] = {}
+    for key in logged_fields:
+        value = getattr(term_cfg, key)
+        if isinstance(value, (int, float, bool, np.number)):
+            result[key] = value
+    term_params = getattr(term_cfg, "params", {})
+    for key in logged_params:
+        value = term_params[key]
+        if isinstance(value, (int, float, bool, np.number)):
+            result[key] = value
+    return result
+
+
+# Public wrappers.
+
+
+class reward_curriculum:
+    """Update a reward term's weight and/or params based on training steps.
+
+    Each stage specifies a ``step`` threshold and optionally a ``weight``
+    and/or ``params`` dict. When ``env.common_step_counter`` reaches a
+    stage's ``step``, the corresponding values are applied. Later stages
+    take precedence when multiple thresholds are reached.
+
+    Example owner YAML::
+
+      curriculum:
+        action_rate_ramp:
+          func: unilab.envs.mdp.reward_curriculum
+          params:
+            reward_name: action_rate
+            stages:
+              - {step: 0, weight: -0.1}
+              - {step: 12000, weight: -0.4}
+              - {step: 24000, weight: -1.0, params: {max_vel: 1.0}}
+    """
+
+    def __init__(self, cfg: CurriculumTermCfg, env: ManagerBasedRlEnv):
+        reward_name: str = cfg.params["reward_name"]
+        stages: list[RewardCurriculumStage] = cfg.params["stages"]
+        self._term_cfg = env.reward_manager.get_term_cfg(reward_name)
+        self._stages = stages
+        _validate_stages(self._term_cfg, reward_name, self._stages)
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        env_ids: np.ndarray | slice,
+        reward_name: str,
+        stages: list[RewardCurriculumStage],
+    ) -> dict[str, Any]:
+        del env_ids, reward_name, stages
+        return _apply_stages(self._term_cfg, env.common_step_counter, self._stages)
+
+
+class termination_curriculum:
+    """Update a termination term's params and/or time_out based on training steps.
+
+    Each stage specifies a ``step`` threshold and optionally a ``params``
+    dict and/or ``time_out`` flag. When ``env.common_step_counter`` reaches
+    a stage's ``step``, the values are applied. Later stages take precedence.
+
+    Example owner YAML::
+
+      curriculum:
+        tilt_threshold:
+          func: unilab.envs.mdp.termination_curriculum
+          params:
+            termination_name: tilt
+            stages:
+              - {step: 12000, params: {max_tilt_deg: 80.0}}
+              - {step: 24000, params: {max_tilt_deg: 65.0}}
+    """
+
+    def __init__(self, cfg: CurriculumTermCfg, env: ManagerBasedRlEnv):
+        termination_name: str = cfg.params["termination_name"]
+        stages: list[TerminationCurriculumStage] = cfg.params["stages"]
+        self._term_cfg = env.termination_manager.get_term_cfg(termination_name)
+        self._stages = stages
+        _validate_stages(self._term_cfg, termination_name, self._stages)
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        env_ids: np.ndarray | slice,
+        termination_name: str,
+        stages: list[TerminationCurriculumStage],
+    ) -> dict[str, Any]:
+        del env_ids, termination_name, stages
+        return _apply_stages(self._term_cfg, env.common_step_counter, self._stages)
+
+
+class command_curriculum:
+    """Update a command term's config fields and/or params based on training steps.
+
+    Command terms read their live ``self.cfg`` at resample time, so mutating
+    the resolved term config (e.g. ``rel_standing_envs`` or ``ranges``) takes
+    effect on the next command resample. Stage semantics match
+    ``reward_curriculum``: every stage whose ``step`` has been reached is
+    applied in order, so later stages win.
+
+    Example owner YAML::
+
+      curriculum:
+        standing_envs:
+          func: unilab.envs.mdp.command_curriculum
+          params:
+            command_name: twist
+            stages:
+              - {step: 0, rel_standing_envs: 0.02}
+              - {step: 12000, rel_standing_envs: 0.1}
+    """
+
+    def __init__(self, cfg: CurriculumTermCfg, env: ManagerBasedRlEnv):
+        command_name: str = cfg.params["command_name"]
+        stages: list[CommandCurriculumStage] = cfg.params["stages"]
+        self._term_cfg = env.command_manager.get_term_cfg(command_name)
+        if self._term_cfg is None:
+            raise ValueError(f"Command term '{command_name}' not found in active terms.")
+        self._stages = stages
+        _validate_stages(self._term_cfg, command_name, self._stages)
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        env_ids: np.ndarray | slice,
+        command_name: str,
+        stages: list[CommandCurriculumStage],
+    ) -> dict[str, Any]:
+        del env_ids, command_name, stages
+        return _apply_stages(self._term_cfg, env.common_step_counter, self._stages)
+
+
+class event_curriculum:
+    """Update an event term's params and/or config fields based on training steps.
+
+    Event terms are invoked with their live ``cfg.params`` on every apply, so
+    staged ``params`` updates (e.g. a widened ``com_range``) take effect on the
+    next event application. Stage semantics match ``reward_curriculum``.
+
+    Example owner YAML::
+
+      curriculum:
+        com_range:
+          func: unilab.envs.mdp.event_curriculum
+          params:
+            event_name: base_com
+            stages:
+              - {step: 0, params: {com_range: {x: [-0.003, 0.003]}}}
+              - {step: 24000, params: {com_range: {x: [-0.01, 0.01]}}}
+    """
+
+    def __init__(self, cfg: CurriculumTermCfg, env: ManagerBasedRlEnv):
+        event_name: str = cfg.params["event_name"]
+        stages: list[EventCurriculumStage] = cfg.params["stages"]
+        self._term_cfg = env.event_manager.get_term_cfg(event_name)
+        if self._term_cfg is None:
+            raise ValueError(f"Event term '{event_name}' not found in active terms.")
+        self._stages = stages
+        _validate_stages(self._term_cfg, event_name, self._stages)
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        env_ids: np.ndarray | slice,
+        event_name: str,
+        stages: list[EventCurriculumStage],
+    ) -> dict[str, Any]:
+        del env_ids, event_name, stages
+        return _apply_stages(self._term_cfg, env.common_step_counter, self._stages)
diff --git a/src/unilab/envs/mdp/events.py b/src/unilab/envs/mdp/events.py
new file mode 100644
index 000000000..8020ff3d7
--- /dev/null
+++ b/src/unilab/envs/mdp/events.py
@@ -0,0 +1,1486 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681),
+# src/mjlab/envs/mdp/events.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy reset transactions; Apache-2.0.
+# The mass/CoM/gravity public names and signatures follow Isaac Lab v2.2.0;
+# their implementation here is UniLab's original payload adapter, not vendored PhysX code.
+"""Community-style reset event terms for UniLab's NumPy manager runtime."""
+
+from __future__ import annotations
+
+import math
+import re
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Literal, cast
+
+import numpy as np
+
+from unilab.managers.event_manager import EventTermCfg
+from unilab.managers.manager_base import ManagerTermBase
+from unilab.managers.scene_entity_config import SceneEntityCfg
+from unilab.utils.rotation import np_quat_apply_batched, np_quat_from_euler_xyz, np_quat_mul
+
+if TYPE_CHECKING:
+    from unilab.base.entity import Entity
+    from unilab.envs.manager_based_rl_env import ManagerBasedRlEnv as ManagerBasedRlEnvImpl
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+_DEFAULT_ASSET_CFG = SceneEntityCfg("robot")
+_SE3_KEYS = ("x", "y", "z", "roll", "pitch", "yaw")
+_XYZ_KEYS = ("x", "y", "z")
+_PD_GAIN_PARAM_NAMES = frozenset(("kp_range", "kd_range", "asset_cfg", "distribution", "operation"))
+_DISTRIBUTIONS = ("uniform", "log_uniform", "gaussian")
+_OPERATIONS = ("add", "scale", "abs")
+_REPO_ROOT = Path(__file__).resolve().parents[4]
+
+
+def _gain_range(
+    value: Any,
+    *,
+    name: str,
+    distribution: Literal["uniform", "log_uniform"],
+) -> tuple[float, float]:
+    try:
+        bounds = np.asarray(value, dtype=np.float64)
+    except (TypeError, ValueError) as exc:
+        raise TypeError(f"pd_gains {name} must be a numeric (min, max) pair") from exc
+    if bounds.shape != (2,):
+        raise ValueError(f"pd_gains {name} must have shape (2,), got {bounds.shape}")
+    if not np.isfinite(bounds).all():
+        raise ValueError(f"pd_gains {name} must contain only finite values")
+    lower, upper = float(bounds[0]), float(bounds[1])
+    if lower > upper:
+        raise ValueError(f"pd_gains {name} minimum {lower} exceeds maximum {upper}")
+    if distribution == "log_uniform" and lower <= 0.0:
+        raise ValueError(f"pd_gains {name} must be positive for log_uniform sampling")
+    return lower, upper
+
+
+def _gain_choice(
+    value: Any,
+    *,
+    name: str,
+    choices: tuple[str, ...],
+) -> str:
+    if not isinstance(value, str):
+        raise TypeError(f"pd_gains {name} must be a string, got {type(value).__name__}")
+    if value not in choices:
+        raise ValueError(f"pd_gains {name} must be one of {choices}, got {value!r}")
+    return value
+
+
+def _sample_gain_range(
+    rng: np.random.Generator,
+    bounds: tuple[float, float],
+    shape: tuple[int, int],
+    distribution: Literal["uniform", "log_uniform"],
+) -> np.ndarray:
+    if distribution == "uniform":
+        return rng.uniform(bounds[0], bounds[1], size=shape)
+    return np.exp(rng.uniform(np.log(bounds[0]), np.log(bounds[1]), size=shape))
+
+
+def _sample_se3_range(
+    range_dict: dict[str, tuple[float, float]] | None,
+    shape: tuple[int, ...],
+    rng: np.random.Generator,
+) -> np.ndarray:
+    """Sample uniform ``[x, y, z, roll, pitch, yaw]`` offsets with NumPy."""
+    if not shape or shape[-1] != len(_SE3_KEYS):
+        raise ValueError(
+            f"reset_root_state_uniform SE(3) sample shape must end in 6; received {shape}"
+        )
+    try:
+        ranges = np.asarray(
+            [(range_dict or {}).get(key, (0.0, 0.0)) for key in _SE3_KEYS],
+            dtype=np.float64,
+        )
+    except (TypeError, ValueError) as exc:
+        raise ValueError(
+            "reset_root_state_uniform ranges must map each SE(3) key to a numeric (min, max) pair"
+        ) from exc
+    if ranges.shape != (len(_SE3_KEYS), 2):
+        raise ValueError(
+            "reset_root_state_uniform ranges must map each SE(3) key to a "
+            f"(min, max) pair; received shape {ranges.shape}"
+        )
+    if not np.isfinite(ranges).all():
+        raise ValueError("reset_root_state_uniform ranges must contain only finite values")
+    invalid = ranges[:, 0] > ranges[:, 1]
+    if np.any(invalid):
+        keys = [_SE3_KEYS[index] for index in np.flatnonzero(invalid)]
+        raise ValueError(f"reset_root_state_uniform range minimum exceeds maximum for keys {keys}")
+    return rng.uniform(ranges[:, 0], ranges[:, 1], size=shape)
+
+
+def _event_choice(value: Any, *, term_name: str, name: str, choices: tuple[str, ...]) -> str:
+    if not isinstance(value, str):
+        raise TypeError(
+            f"EventManager term '{term_name}' parameter '{name}' must be a string, "
+            f"got {type(value).__name__}"
+        )
+    if value not in choices:
+        raise ValueError(
+            f"EventManager term '{term_name}' parameter '{name}' must be one of "
+            f"{choices}, got {value!r}"
+        )
+    return value
+
+
+def _distribution_parameters(
+    value: Any,
+    *,
+    term_name: str,
+    name: str,
+    width: int | None = None,
+    distribution: str,
+) -> np.ndarray:
+    try:
+        params = np.asarray(value, dtype=np.float64)
+    except (TypeError, ValueError) as exc:
+        raise TypeError(
+            f"EventManager term '{term_name}' parameter '{name}' must contain numeric values"
+        ) from exc
+    expected = (2,) if width is None else (2, width)
+    if params.shape != expected:
+        raise ValueError(
+            f"EventManager term '{term_name}' parameter '{name}' has shape {params.shape}; "
+            f"expected {expected}"
+        )
+    if not np.isfinite(params).all():
+        raise ValueError(
+            f"EventManager term '{term_name}' parameter '{name}' must contain only finite values"
+        )
+    if distribution == "gaussian":
+        if np.any(params[1] < 0.0):
+            raise ValueError(
+                f"EventManager term '{term_name}' parameter '{name}' standard deviation "
+                "must be non-negative"
+            )
+    else:
+        if np.any(params[0] > params[1]):
+            raise ValueError(
+                f"EventManager term '{term_name}' parameter '{name}' lower bound exceeds upper bound"
+            )
+        if distribution == "log_uniform" and np.any(params <= 0.0):
+            raise ValueError(
+                f"EventManager term '{term_name}' parameter '{name}' must be positive "
+                "for log_uniform sampling"
+            )
+    result = np.array(params, copy=True)
+    result.setflags(write=False)
+    return result
+
+
+def _sample_distribution(
+    rng: np.random.Generator,
+    params: np.ndarray,
+    shape: tuple[int, ...],
+    distribution: str,
+) -> np.ndarray:
+    if distribution == "gaussian":
+        return rng.normal(params[0], params[1], size=shape)
+    if distribution == "log_uniform":
+        return np.exp(rng.uniform(np.log(params[0]), np.log(params[1]), size=shape))
+    return rng.uniform(params[0], params[1], size=shape)
+
+
+def _apply_randomization_operation(
+    default: np.ndarray,
+    samples: np.ndarray,
+    operation: str,
+) -> np.ndarray:
+    if operation == "add":
+        return default + samples
+    if operation == "scale":
+        return default * samples
+    return samples
+
+
+def _axis_ranges(
+    value: Any,
+    *,
+    term_name: str,
+    name: str,
+    keys: tuple[str, ...],
+) -> np.ndarray:
+    if not isinstance(value, dict):
+        raise TypeError(f"EventManager term '{term_name}' parameter '{name}' must be a dict")
+    unknown = sorted(set(value) - set(keys))
+    if unknown:
+        raise ValueError(
+            f"EventManager term '{term_name}' parameter '{name}' has unknown axes {unknown}"
+        )
+    parameters = np.asarray([value.get(key, (0.0, 0.0)) for key in keys], dtype=object).T
+    return _distribution_parameters(
+        parameters,
+        term_name=term_name,
+        name=name,
+        width=len(keys),
+        distribution="uniform",
+    ).T
+
+
+def _validate_event_term(
+    cfg: EventTermCfg,
+    *,
+    term_name: str,
+    mode: str,
+    allowed_params: frozenset[str],
+    required_params: tuple[str, ...],
+) -> None:
+    if cfg.mode != mode:
+        raise NotImplementedError(
+            f"EventManager term '{term_name}' only supports mode='{mode}' on the UniLab runtime"
+        )
+    unknown = sorted(set(cfg.params) - allowed_params)
+    if unknown:
+        raise ValueError(f"EventManager term '{term_name}' has unknown parameters {unknown}")
+    missing = [name for name in required_params if name not in cfg.params]
+    if missing:
+        raise ValueError(f"EventManager term '{term_name}' is missing parameters {missing}")
+
+
+def resolve_env_ids(env: ManagerBasedRlEnv, env_ids: np.ndarray | None) -> np.ndarray:
+    """Return concrete NumPy environment IDs, preserving community sentinel semantics."""
+    if env_ids is None:
+        return np.arange(env.num_envs, dtype=np.int32)
+    return env_ids
+
+
+class _ModelFieldRandomizer(ManagerTermBase):
+    """Cold-path-bound NumPy adapter for pinned mjlab model-field DR terms."""
+
+    _term_name = "model_field"
+    _field_width = 1
+    _default_axes: tuple[int, ...] = (0,)
+    _valid_axes: tuple[int, ...] = (0,)
+    _PARAMS = frozenset(
+        ("ranges", "asset_cfg", "distribution", "operation", "axes", "shared_random")
+    )
+
+    def __init__(self, cfg: EventTermCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        _validate_event_term(
+            cfg,
+            term_name=self._term_name,
+            mode="reset",
+            allowed_params=self._PARAMS,
+            required_params=("ranges",),
+        )
+        self._distribution = _event_choice(
+            cfg.params.get("distribution", "uniform"),
+            term_name=self._term_name,
+            name="distribution",
+            choices=_DISTRIBUTIONS,
+        )
+        self._operation = _event_choice(
+            cfg.params.get("operation", "abs"),
+            term_name=self._term_name,
+            name="operation",
+            choices=_OPERATIONS,
+        )
+        shared_random = cfg.params.get("shared_random", False)
+        if not isinstance(shared_random, bool):
+            raise TypeError(
+                f"EventManager term '{self._term_name}' parameter 'shared_random' must be bool"
+            )
+        self._shared_random = shared_random
+        asset_cfg = cfg.params.get("asset_cfg", _DEFAULT_ASSET_CFG)
+        if not isinstance(asset_cfg, SceneEntityCfg):
+            raise TypeError(
+                f"EventManager term '{self._term_name}' asset_cfg must be SceneEntityCfg, "
+                f"got {type(asset_cfg).__name__}"
+            )
+        entity = cast("Entity", env.scene[asset_cfg.name])
+        local_ids, defaults, names = self._bind(entity, asset_cfg)
+        ranges = cfg.params["ranges"]
+        local_ids, defaults, names, ranges = self._select_string_ranges(
+            local_ids,
+            defaults,
+            names,
+            ranges,
+        )
+        self._entity = entity
+        self._local_ids = local_ids
+        self._defaults = defaults
+        self._axes = self._resolve_axes(cfg.params.get("axes"), ranges)
+        self._ranges = self._resolve_ranges(ranges, names)
+
+    def _bind(
+        self,
+        entity: Entity,
+        asset_cfg: SceneEntityCfg,
+    ) -> tuple[np.ndarray, np.ndarray, tuple[str, ...]]:
+        raise NotImplementedError
+
+    def _write(
+        self,
+        values: np.ndarray,
+        env_ids: np.ndarray,
+    ) -> None:
+        raise NotImplementedError
+
+    def _select_string_ranges(
+        self,
+        local_ids: np.ndarray,
+        defaults: np.ndarray,
+        names: tuple[str, ...],
+        ranges: Any,
+    ) -> tuple[np.ndarray, np.ndarray, tuple[str, ...], Any]:
+        if not isinstance(ranges, dict) or not ranges:
+            return local_ids, defaults, names, ranges
+        keys = tuple(ranges)
+        if not all(isinstance(key, str) for key in keys):
+            if any(isinstance(key, str) for key in keys):
+                raise TypeError(
+                    f"EventManager term '{self._term_name}' ranges cannot mix string and integer keys"
+                )
+            return local_ids, defaults, names, ranges
+
+        assigned: list[Any | None] = [None] * len(names)
+        for pattern, bounds in ranges.items():
+            try:
+                matched = [index for index, name in enumerate(names) if re.fullmatch(pattern, name)]
+            except re.error as exc:
+                raise ValueError(
+                    f"EventManager term '{self._term_name}' ranges contains invalid regex "
+                    f"{pattern!r}: {exc}"
+                ) from exc
+            if not matched:
+                raise ValueError(
+                    f"EventManager term '{self._term_name}' ranges pattern {pattern!r} "
+                    f"matched no selected names; available={list(names)}"
+                )
+            for index in matched:
+                if assigned[index] is not None:
+                    raise ValueError(
+                        f"EventManager term '{self._term_name}' ranges patterns overlap for "
+                        f"selected name '{names[index]}'"
+                    )
+                assigned[index] = bounds
+        selected = np.asarray(
+            [index for index, value in enumerate(assigned) if value is not None],
+            dtype=np.intp,
+        )
+        return (
+            local_ids[selected],
+            defaults[selected],
+            tuple(names[index] for index in selected),
+            [assigned[index] for index in selected],
+        )
+
+    def _resolve_axes(self, value: Any, ranges: Any) -> tuple[int, ...]:
+        if value is None and isinstance(ranges, dict) and ranges:
+            value = list(ranges)
+        if value is None:
+            axes = self._default_axes
+        else:
+            if not isinstance(value, (list, tuple)):
+                raise TypeError(
+                    f"EventManager term '{self._term_name}' parameter 'axes' must be a sequence"
+                )
+            if any(
+                isinstance(axis, bool) or not isinstance(axis, (int, np.integer)) for axis in value
+            ):
+                raise TypeError(
+                    f"EventManager term '{self._term_name}' parameter 'axes' must contain integers"
+                )
+            axes = tuple(int(axis) for axis in value)
+        if not axes:
+            raise ValueError(
+                f"EventManager term '{self._term_name}' parameter 'axes' cannot be empty"
+            )
+        if len(set(axes)) != len(axes):
+            raise ValueError(
+                f"EventManager term '{self._term_name}' parameter 'axes' contains duplicates"
+            )
+        invalid = sorted(set(axes) - set(self._valid_axes))
+        if invalid:
+            raise ValueError(
+                f"EventManager term '{self._term_name}' has invalid axes {invalid}; "
+                f"valid axes are {list(self._valid_axes)}"
+            )
+        return axes
+
+    def _resolve_ranges(self, value: Any, names: tuple[str, ...]) -> np.ndarray:
+        per_entity: list[Any]
+        if (
+            isinstance(value, list)
+            and len(value) == len(names)
+            and any(isinstance(item, (tuple, list, np.ndarray)) for item in value)
+        ):
+            per_entity = list(value)
+            value = None
+        else:
+            per_entity = []
+
+        result = np.empty((len(names), self._field_width, 2), dtype=np.float64)
+        result[:] = np.nan
+        if per_entity:
+            if len(self._axes) != 1:
+                raise ValueError(
+                    f"EventManager term '{self._term_name}' string-keyed ranges require one axis"
+                )
+            for index, bounds in enumerate(per_entity):
+                result[index, self._axes[0]] = _distribution_parameters(
+                    bounds,
+                    term_name=self._term_name,
+                    name=f"ranges[{names[index]}]",
+                    distribution=self._distribution,
+                )
+        elif isinstance(value, dict):
+            unknown = sorted(set(value) - set(self._axes))
+            missing = sorted(set(self._axes) - set(value))
+            if unknown or missing:
+                raise ValueError(
+                    f"EventManager term '{self._term_name}' ranges axes mismatch; "
+                    f"missing={missing}, unknown={unknown}"
+                )
+            for axis in self._axes:
+                result[:, axis] = _distribution_parameters(
+                    value[axis],
+                    term_name=self._term_name,
+                    name=f"ranges[{axis}]",
+                    distribution=self._distribution,
+                )
+        else:
+            parameters = _distribution_parameters(
+                value,
+                term_name=self._term_name,
+                name="ranges",
+                distribution=self._distribution,
+            )
+            for axis in self._axes:
+                result[:, axis] = parameters
+        result.setflags(write=False)
+        return result
+
+    def _sample_axis(self, env: ManagerBasedRlEnv, axis: int, count: int) -> np.ndarray:
+        parameters = self._ranges[:, axis]
+        if self._shared_random:
+            samples = np.empty((count, len(parameters)), dtype=np.float64)
+            groups: dict[tuple[float, float], list[int]] = {}
+            for index, pair in enumerate(parameters):
+                groups.setdefault((float(pair[0]), float(pair[1])), []).append(index)
+            for (first, second), indices in groups.items():
+                if self._distribution == "gaussian":
+                    shared = env.rng.normal(first, second, size=(count, 1))
+                elif self._distribution == "log_uniform":
+                    shared = np.exp(env.rng.uniform(np.log(first), np.log(second), size=(count, 1)))
+                else:
+                    shared = env.rng.uniform(first, second, size=(count, 1))
+                samples[:, indices] = shared
+            return samples
+        if self._distribution == "gaussian":
+            return env.rng.normal(parameters[:, 0], parameters[:, 1], size=(count, len(parameters)))
+        if self._distribution == "log_uniform":
+            return np.exp(
+                env.rng.uniform(
+                    np.log(parameters[:, 0]),
+                    np.log(parameters[:, 1]),
+                    size=(count, len(parameters)),
+                )
+            )
+        return env.rng.uniform(
+            parameters[:, 0],
+            parameters[:, 1],
+            size=(count, len(parameters)),
+        )
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        env_ids: np.ndarray | None,
+        ranges: Any,
+        asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+        distribution: str = "uniform",
+        operation: str = "abs",
+        axes: list[int] | None = None,
+        shared_random: bool = False,
+    ) -> None:
+        del ranges, asset_cfg, distribution, operation, axes, shared_random
+        ids = resolve_env_ids(env, env_ids)
+        defaults = self._defaults
+        scalar = defaults.ndim == 1
+        default_values = defaults[:, None] if scalar else defaults
+        values = np.broadcast_to(
+            default_values,
+            (len(ids), *default_values.shape),
+        ).copy()
+        for axis in self._axes:
+            samples = self._sample_axis(env, axis, len(ids))
+            values[:, :, axis] = _apply_randomization_operation(
+                default_values[None, :, axis],
+                samples,
+                self._operation,
+            )
+        if np.any(values < 0.0) or not np.isfinite(values).all():
+            raise ValueError(
+                f"EventManager term '{self._term_name}' produced negative, NaN, or Inf values"
+            )
+        self._write(values[:, :, 0] if scalar else values, ids)
+
+
+class GeomFriction(_ModelFieldRandomizer):
+    """Pinned mjlab-style geom friction randomization through the reset payload."""
+
+    _term_name = "geom_friction"
+    _field_width = 3
+    _default_axes = (0,)
+    _valid_axes = (0, 1, 2)
+
+    def _bind(
+        self,
+        entity: Entity,
+        asset_cfg: SceneEntityCfg,
+    ) -> tuple[np.ndarray, np.ndarray, tuple[str, ...]]:
+        local_ids, defaults = entity.bind_geom_friction_write(
+            asset_cfg.geom_ids,
+            term_name=self._term_name,
+        )
+        names = tuple(entity.geom_names[int(index)] for index in local_ids)
+        return local_ids, defaults, names
+
+    def _write(self, values: np.ndarray, env_ids: np.ndarray) -> None:
+        self._entity.write_geom_friction_to_sim(
+            values,
+            self._local_ids,
+            env_ids,
+            term_name=self._term_name,
+        )
+
+
+class JointArmature(_ModelFieldRandomizer):
+    """Pinned mjlab-style joint armature randomization through the reset payload."""
+
+    _term_name = "joint_armature"
+
+    def _bind(
+        self,
+        entity: Entity,
+        asset_cfg: SceneEntityCfg,
+    ) -> tuple[np.ndarray, np.ndarray, tuple[str, ...]]:
+        local_ids, defaults = entity.bind_joint_armature_write(
+            asset_cfg.joint_ids,
+            term_name=self._term_name,
+        )
+        names = tuple(entity.joint_names[int(index)] for index in local_ids)
+        return local_ids, defaults, names
+
+    def _write(self, values: np.ndarray, env_ids: np.ndarray) -> None:
+        self._entity.write_joint_armature_to_sim(
+            values,
+            self._local_ids,
+            env_ids,
+            term_name=self._term_name,
+        )
+
+
+geom_friction = GeomFriction
+joint_armature = JointArmature
+dof_armature = joint_armature
+
+
+class PdGains(ManagerTermBase):
+    """Pinned-mjlab-compatible PD gain randomization on UniLab reset payloads."""
+
+    def __init__(self, cfg: EventTermCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        if cfg.mode != "reset":
+            raise NotImplementedError(
+                "EventManager term 'pd_gains' only supports mode='reset' on the UniLab "
+                "set_state transaction; startup/interval/step model-field mutation is unavailable"
+            )
+        unknown = sorted(set(cfg.params) - _PD_GAIN_PARAM_NAMES)
+        if unknown:
+            raise ValueError(f"EventManager term 'pd_gains' has unknown parameters {unknown}")
+        missing = [name for name in ("kp_range", "kd_range") if name not in cfg.params]
+        if missing:
+            raise ValueError(f"EventManager term 'pd_gains' is missing parameters {missing}")
+
+        distribution = cast(
+            Literal["uniform", "log_uniform"],
+            _gain_choice(
+                cfg.params.get("distribution", "uniform"),
+                name="distribution",
+                choices=("uniform", "log_uniform"),
+            ),
+        )
+        self._operation = cast(
+            Literal["scale", "abs"],
+            _gain_choice(
+                cfg.params.get("operation", "scale"),
+                name="operation",
+                choices=("scale", "abs"),
+            ),
+        )
+        self._distribution = distribution
+        self._kp_range = _gain_range(
+            cfg.params["kp_range"],
+            name="kp_range",
+            distribution=distribution,
+        )
+        self._kd_range = _gain_range(
+            cfg.params["kd_range"],
+            name="kd_range",
+            distribution=distribution,
+        )
+        asset_cfg = cfg.params.get("asset_cfg", _DEFAULT_ASSET_CFG)
+        if not isinstance(asset_cfg, SceneEntityCfg):
+            raise TypeError(
+                "EventManager term 'pd_gains' asset_cfg must be SceneEntityCfg, got "
+                f"{type(asset_cfg).__name__}"
+            )
+        self._entity = cast("Entity", env.scene[asset_cfg.name])
+        self._actuator_ids, self._default_kp, self._default_kd = (
+            self._entity.bind_actuator_gain_write(
+                asset_cfg.actuator_ids,
+                term_name="pd_gains",
+            )
+        )
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        env_ids: np.ndarray | None,
+        kp_range: tuple[float, float],
+        kd_range: tuple[float, float],
+        asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+        distribution: Literal["uniform", "log_uniform"] = "uniform",
+        operation: Literal["scale", "abs"] = "scale",
+    ) -> None:
+        del kp_range, kd_range, asset_cfg, distribution, operation
+        ids = resolve_env_ids(env, env_ids)
+        shape = (len(ids), len(self._actuator_ids))
+        kp = _sample_gain_range(env.rng, self._kp_range, shape, self._distribution)
+        kd = _sample_gain_range(env.rng, self._kd_range, shape, self._distribution)
+        if self._operation == "scale":
+            kp *= self._default_kp[None, :]
+            kd *= self._default_kd[None, :]
+        self._entity.write_actuator_gains_to_sim(
+            kp,
+            kd,
+            actuator_ids=self._actuator_ids,
+            env_ids=ids,
+            term_name="pd_gains",
+        )
+
+
+pd_gains = PdGains
+
+
+class RandomizeRigidBodyMass(ManagerTermBase):
+    """Community-compatible body-mass randomization via the reset payload."""
+
+    _PARAMS = frozenset(
+        (
+            "asset_cfg",
+            "mass_distribution_params",
+            "operation",
+            "distribution",
+            "recompute_inertia",
+            "min_mass",
+        )
+    )
+
+    def __init__(self, cfg: EventTermCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        term_name = "randomize_rigid_body_mass"
+        _validate_event_term(
+            cfg,
+            term_name=term_name,
+            mode="reset",
+            allowed_params=self._PARAMS,
+            required_params=("asset_cfg", "mass_distribution_params", "operation"),
+        )
+        asset_cfg = cfg.params["asset_cfg"]
+        if not isinstance(asset_cfg, SceneEntityCfg):
+            raise TypeError(
+                f"EventManager term '{term_name}' asset_cfg must be SceneEntityCfg, "
+                f"got {type(asset_cfg).__name__}"
+            )
+        recompute_inertia = cfg.params.get("recompute_inertia", True)
+        if not isinstance(recompute_inertia, bool):
+            raise TypeError(f"EventManager term '{term_name}' recompute_inertia must be bool")
+        if recompute_inertia:
+            raise NotImplementedError(
+                f"EventManager term '{term_name}' recompute_inertia=True is unavailable: "
+                "ResetRandomizationPayload has no inertia-recomputation contract; set it "
+                "explicitly to false or do not configure this term"
+            )
+        self._operation = _event_choice(
+            cfg.params["operation"],
+            term_name=term_name,
+            name="operation",
+            choices=_OPERATIONS,
+        )
+        self._distribution = _event_choice(
+            cfg.params.get("distribution", "uniform"),
+            term_name=term_name,
+            name="distribution",
+            choices=_DISTRIBUTIONS,
+        )
+        self._distribution_params = _distribution_parameters(
+            cfg.params["mass_distribution_params"],
+            term_name=term_name,
+            name="mass_distribution_params",
+            distribution=self._distribution,
+        )
+        min_mass = cfg.params.get("min_mass", 1e-6)
+        if isinstance(min_mass, bool) or not isinstance(min_mass, (int, float)):
+            raise TypeError(f"EventManager term '{term_name}' min_mass must be numeric")
+        self._min_mass = float(min_mass)
+        if not np.isfinite(self._min_mass) or self._min_mass < 1e-6:
+            raise ValueError(
+                f"EventManager term '{term_name}' min_mass must be finite and at least 1e-6"
+            )
+        self._entity = cast("Entity", env.scene[asset_cfg.name])
+        self._body_ids, self._default_mass = self._entity.bind_body_mass_write(
+            asset_cfg.body_ids,
+            term_name=term_name,
+        )
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        env_ids: np.ndarray | None,
+        asset_cfg: SceneEntityCfg,
+        mass_distribution_params: tuple[float, float],
+        operation: Literal["add", "scale", "abs"],
+        distribution: Literal["uniform", "log_uniform", "gaussian"] = "uniform",
+        recompute_inertia: bool = True,
+        min_mass: float = 1e-6,
+    ) -> None:
+        del (
+            asset_cfg,
+            mass_distribution_params,
+            operation,
+            distribution,
+            recompute_inertia,
+            min_mass,
+        )
+        ids = resolve_env_ids(env, env_ids)
+        samples = _sample_distribution(
+            env.rng,
+            self._distribution_params,
+            (ids.size, self._body_ids.size),
+            self._distribution,
+        )
+        values = _apply_randomization_operation(
+            self._default_mass[None, :],
+            samples,
+            self._operation,
+        )
+        np.maximum(values, self._min_mass, out=values)
+        self._entity.write_body_mass_to_sim(
+            values,
+            body_ids=self._body_ids,
+            env_ids=ids,
+            term_name="randomize_rigid_body_mass",
+        )
+
+
+randomize_rigid_body_mass = RandomizeRigidBodyMass
+
+
+def _scene_inertial_defaults(
+    env: ManagerBasedRlEnv,
+    *,
+    term_name: str,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Compile the configured MJCF scene on the cold path for inertial defaults.
+
+    Returns the full ``(nbody,)`` body-mass and ``(nbody, 3)`` principal-inertia
+    tables in model body order. ``SimBackend`` exposes no body-inertia getter,
+    so the defaults come from the same scene file the MuJoCo-family backends
+    compile; the reset transaction cross-validates the mass table against the
+    backend's authoritative values before trusting the inertia rows.
+    """
+    try:
+        import mujoco
+    except ImportError as exc:
+        raise NotImplementedError(
+            f"EventManager term '{term_name}' requires the mujoco package to compile "
+            "the scene model for inertial defaults"
+        ) from exc
+    scene = cast("ManagerBasedRlEnvImpl", env).cfg.scene
+    if scene is None:
+        raise ValueError(f"EventManager term '{term_name}' requires a configured scene model file")
+    model_file = str(scene.model_file)
+    candidates = [Path(model_file)]
+    if not Path(model_file).is_absolute():
+        candidates.append(_REPO_ROOT / model_file)
+    path = next((candidate for candidate in candidates if candidate.is_file()), None)
+    if path is None:
+        raise ValueError(
+            f"EventManager term '{term_name}' cannot locate scene model file "
+            f"{model_file!r} (tried {', '.join(str(candidate) for candidate in candidates)})"
+        )
+    model = mujoco.MjModel.from_xml_path(str(path))
+    mass = np.asarray(model.body_mass, dtype=np.float64)
+    inertia = np.asarray(model.body_inertia, dtype=np.float64)
+    return mass, inertia
+
+
+class RandomizeBodyMassInertia(ManagerTermBase):
+    """Startup-style mass+inertia scaling via one shared per-env factor.
+
+    NumPy adapter for the alpha-only slice of mjlab's ``dr.pseudo_inertia``:
+    mass and principal inertia of the selected bodies are multiplied by the
+    same factor s = e^{2α} with α ~ U(ln(lo)/2, ln(hi)/2) — i.e. s is
+    log-uniform in ``scale_range``; the CoM (``body_ipos``) and the principal
+    frame (``body_iquat``) stay untouched.
+
+    Upstream runs this as a startup event (fixed per env for the whole run).
+    UniLab startup events have no reset-transaction write path, so this term
+    runs in reset mode but samples the factor only once — at the first reset —
+    and reapplies the cached per-env values on every later reset. Both writes
+    re-derive from immutable compile-time defaults, so reapplication is
+    idempotent and non-accumulating.
+    """
+
+    _PARAMS = frozenset(("asset_cfg", "scale_range"))
+
+    def __init__(self, cfg: EventTermCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        term_name = "randomize_body_mass_inertia"
+        _validate_event_term(
+            cfg,
+            term_name=term_name,
+            mode="reset",
+            allowed_params=self._PARAMS,
+            required_params=("asset_cfg", "scale_range"),
+        )
+        asset_cfg = cfg.params["asset_cfg"]
+        if not isinstance(asset_cfg, SceneEntityCfg):
+            raise TypeError(
+                f"EventManager term '{term_name}' asset_cfg must be SceneEntityCfg, "
+                f"got {type(asset_cfg).__name__}"
+            )
+        bounds = _distribution_parameters(
+            cfg.params["scale_range"],
+            term_name=term_name,
+            name="scale_range",
+            distribution="log_uniform",
+        )
+        self._scale_lo = float(bounds[0])
+        self._scale_hi = float(bounds[1])
+        self._entity = cast("Entity", env.scene[asset_cfg.name])
+        default_mass, default_inertia = _scene_inertial_defaults(env, term_name=term_name)
+        self._body_ids, self._default_mass = self._entity.bind_body_mass_write(
+            asset_cfg.body_ids,
+            term_name=term_name,
+        )
+        inertia_ids, self._default_inertia = self._entity.bind_body_inertia_write(
+            asset_cfg.body_ids,
+            default=default_inertia,
+            default_mass=default_mass,
+            term_name=term_name,
+        )
+        if not np.array_equal(self._body_ids, inertia_ids):
+            raise RuntimeError(
+                f"EventManager term '{term_name}' mass/inertia body bindings diverged: "
+                f"{self._body_ids.tolist()} != {inertia_ids.tolist()}"
+            )
+        self._scales: np.ndarray | None = None
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        env_ids: np.ndarray | None,
+        asset_cfg: SceneEntityCfg,
+        scale_range: tuple[float, float],
+    ) -> None:
+        del asset_cfg, scale_range
+        ids = resolve_env_ids(env, env_ids)
+        if self._scales is None:
+            alpha = env.rng.uniform(
+                math.log(self._scale_lo) / 2.0,
+                math.log(self._scale_hi) / 2.0,
+                size=(env.num_envs, self._body_ids.size),
+            )
+            self._scales = np.exp(2.0 * alpha)
+        scales = self._scales[ids]
+        self._entity.write_body_mass_to_sim(
+            self._default_mass[None, :] * scales,
+            body_ids=self._body_ids,
+            env_ids=ids,
+            term_name="randomize_body_mass_inertia",
+        )
+        self._entity.write_body_inertia_to_sim(
+            self._default_inertia[None, :, :] * scales[:, :, None],
+            body_ids=self._body_ids,
+            env_ids=ids,
+            term_name="randomize_body_mass_inertia",
+        )
+
+
+randomize_body_mass_inertia = RandomizeBodyMassInertia
+
+
+class RandomizeRigidBodyCom(ManagerTermBase):
+    """Community-compatible additive rigid-body CoM randomization.
+
+    The ``com_range`` param is re-resolved from the live term params on every
+    apply (not cached at construction), so step-staged curricula can widen the
+    range by updating the event term config between resets.
+    """
+
+    def __init__(self, cfg: EventTermCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        term_name = "randomize_rigid_body_com"
+        _validate_event_term(
+            cfg,
+            term_name=term_name,
+            mode="reset",
+            allowed_params=frozenset(("com_range", "asset_cfg")),
+            required_params=("com_range", "asset_cfg"),
+        )
+        asset_cfg = cfg.params["asset_cfg"]
+        if not isinstance(asset_cfg, SceneEntityCfg):
+            raise TypeError(
+                f"EventManager term '{term_name}' asset_cfg must be SceneEntityCfg, "
+                f"got {type(asset_cfg).__name__}"
+            )
+        # Fail-closed validation of the declared range at construction; the
+        # live value is re-resolved per apply so curricula can stage it.
+        _axis_ranges(
+            cfg.params["com_range"],
+            term_name=term_name,
+            name="com_range",
+            keys=_XYZ_KEYS,
+        )
+        self._entity = cast("Entity", env.scene[asset_cfg.name])
+        self._body_ids, self._default_ipos = self._entity.bind_body_ipos_write(
+            asset_cfg.body_ids,
+            term_name=term_name,
+        )
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        env_ids: np.ndarray | None,
+        com_range: dict[str, tuple[float, float]],
+        asset_cfg: SceneEntityCfg,
+    ) -> None:
+        del asset_cfg
+        ranges = _axis_ranges(
+            com_range,
+            term_name="randomize_rigid_body_com",
+            name="com_range",
+            keys=_XYZ_KEYS,
+        )
+        ids = resolve_env_ids(env, env_ids)
+        offsets = env.rng.uniform(
+            ranges[:, 0],
+            ranges[:, 1],
+            size=(ids.size, 3),
+        )
+        values = self._default_ipos[None, :, :] + offsets[:, None, :]
+        self._entity.write_body_ipos_to_sim(
+            values,
+            body_ids=self._body_ids,
+            env_ids=ids,
+            term_name="randomize_rigid_body_com",
+        )
+
+
+randomize_rigid_body_com = RandomizeRigidBodyCom
+
+
+class RandomizePhysicsSceneGravity(ManagerTermBase):
+    """Community-compatible gravity randomization through reset transactions."""
+
+    def __init__(self, cfg: EventTermCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        term_name = "randomize_physics_scene_gravity"
+        _validate_event_term(
+            cfg,
+            term_name=term_name,
+            mode="reset",
+            allowed_params=frozenset(("gravity_distribution_params", "operation", "distribution")),
+            required_params=("gravity_distribution_params", "operation"),
+        )
+        self._operation = _event_choice(
+            cfg.params["operation"],
+            term_name=term_name,
+            name="operation",
+            choices=_OPERATIONS,
+        )
+        self._distribution = _event_choice(
+            cfg.params.get("distribution", "uniform"),
+            term_name=term_name,
+            name="distribution",
+            choices=_DISTRIBUTIONS,
+        )
+        self._distribution_params = _distribution_parameters(
+            cfg.params["gravity_distribution_params"],
+            term_name=term_name,
+            name="gravity_distribution_params",
+            width=3,
+            distribution=self._distribution,
+        )
+        self._default_gravity = env.scene.bind_gravity_write(term_name=term_name)
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        env_ids: np.ndarray | None,
+        gravity_distribution_params: tuple[list[float], list[float]],
+        operation: Literal["add", "scale", "abs"],
+        distribution: Literal["uniform", "log_uniform", "gaussian"] = "uniform",
+    ) -> None:
+        del gravity_distribution_params, operation, distribution
+        ids = resolve_env_ids(env, env_ids)
+        samples = _sample_distribution(
+            env.rng,
+            self._distribution_params,
+            (ids.size, 3),
+            self._distribution,
+        )
+        values = _apply_randomization_operation(
+            self._default_gravity[None, :],
+            samples,
+            self._operation,
+        )
+        env.scene.write_gravity_to_sim(
+            values,
+            ids,
+            term_name="randomize_physics_scene_gravity",
+        )
+
+
+randomize_physics_scene_gravity = RandomizePhysicsSceneGravity
+
+
+class PushBySettingVelocity(ManagerTermBase):
+    """Pinned community velocity kick dispatched through the interval plan.
+
+    Linear (``x``/``y``/``z``) and angular (``roll``/``pitch``/``yaw``) ranges
+    are world-frame deltas, matching the pinned mjlab semantics. Angular kicks
+    require the backend's interval angular-velocity capability; backends
+    without it fail closed at construction.
+
+    ``__call__`` re-reads the live ``velocity_range`` on every apply so
+    ``event_curriculum`` range stages take effect (the documented manager
+    contract); construction still parses the initial ranges to decide which
+    backend capabilities to bind. A curriculum that activates angular ranges
+    on a term constructed without them fails closed at apply time.
+    """
+
+    def __init__(self, cfg: EventTermCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        term_name = "push_by_setting_velocity"
+        _validate_event_term(
+            cfg,
+            term_name=term_name,
+            mode="interval",
+            allowed_params=frozenset(("velocity_range", "asset_cfg")),
+            required_params=("velocity_range",),
+        )
+        asset_cfg = cfg.params.get("asset_cfg", _DEFAULT_ASSET_CFG)
+        if not isinstance(asset_cfg, SceneEntityCfg):
+            raise TypeError(
+                f"EventManager term '{term_name}' asset_cfg must be SceneEntityCfg, "
+                f"got {type(asset_cfg).__name__}"
+            )
+        ranges = _axis_ranges(
+            cfg.params["velocity_range"],
+            term_name=term_name,
+            name="velocity_range",
+            keys=_SE3_KEYS,
+        )
+        self._angular_active = bool(np.any(ranges[3:] != 0.0))
+        self._entity = cast("Entity", env.scene[asset_cfg.name])
+        self._entity.bind_root_linear_velocity_delta(term_name=term_name)
+        if self._angular_active:
+            self._entity.bind_root_angular_velocity_delta(term_name=term_name)
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        env_ids: np.ndarray | None,
+        velocity_range: dict[str, tuple[float, float]],
+        asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+    ) -> None:
+        del asset_cfg
+        ids = resolve_env_ids(env, env_ids)
+        ranges = _axis_ranges(
+            velocity_range,
+            term_name="push_by_setting_velocity",
+            name="velocity_range",
+            keys=_SE3_KEYS,
+        )
+        linear_ranges = ranges[:3]
+        angular_ranges = ranges[3:]
+        linear_delta = env.rng.uniform(
+            linear_ranges[:, 0],
+            linear_ranges[:, 1],
+            size=(ids.size, 3),
+        )
+        angular_delta = None
+        if np.any(angular_ranges != 0.0):
+            if not self._angular_active:
+                raise NotImplementedError(
+                    "EventManager term 'push_by_setting_velocity' velocity_range activated "
+                    "angular axes after construction, but the backend angular-velocity "
+                    "capability was never bound; declare non-zero angular ranges in the "
+                    "initial params"
+                )
+            angular_delta = env.rng.uniform(
+                angular_ranges[:, 0],
+                angular_ranges[:, 1],
+                size=(ids.size, 3),
+            )
+        self._entity.apply_root_velocity_delta_to_sim(
+            linear_delta,
+            angular_delta,
+            env_ids=ids,
+            term_name="push_by_setting_velocity",
+        )
+
+
+push_by_setting_velocity = PushBySettingVelocity
+
+
+def _time_range(
+    value: Any,
+    *,
+    term_name: str,
+    name: str,
+) -> tuple[float, float]:
+    try:
+        bounds = np.asarray(value, dtype=np.float64)
+    except (TypeError, ValueError) as exc:
+        raise TypeError(
+            f"EventManager term '{term_name}' parameter '{name}' must be a numeric (min, max) pair"
+        ) from exc
+    if bounds.shape != (2,):
+        raise ValueError(
+            f"EventManager term '{term_name}' parameter '{name}' must have shape (2,), "
+            f"got {bounds.shape}"
+        )
+    if not np.isfinite(bounds).all():
+        raise ValueError(
+            f"EventManager term '{term_name}' parameter '{name}' must contain only finite values"
+        )
+    lower, upper = float(bounds[0]), float(bounds[1])
+    if lower < 0.0 or lower > upper:
+        raise ValueError(
+            f"EventManager term '{term_name}' parameter '{name}' must satisfy "
+            f"0 <= min <= max, got ({lower}, {upper})"
+        )
+    return lower, upper
+
+
+class ApplyBodyImpulse(ManagerTermBase):
+    """Transient random body impulses with a cooldown->trigger->sustain->expire lifecycle.
+
+    NumPy adaptation of the pinned mjlab ``apply_body_impulse`` term. Each
+    environment runs an independent timer: after a sampled cooldown, a
+    uniformly sampled world-frame wrench is staged on the selected bodies and
+    re-staged every step for a sampled duration, then expires back into
+    cooldown. Use with ``mode="step"``.
+
+    ``body_point_offset`` shifts the force application point in the body link
+    frame; the resulting ``cross(offset_w, force)`` torque is added at trigger
+    time and held for the impulse duration. Torque channels (explicit
+    ``torque_range`` or an offset) require the backend's interval body-torque
+    capability; force-only impulses only require interval body force. Backends
+    without the required capability fail closed at construction.
+    """
+
+    _PARAMS = frozenset(
+        (
+            "force_range",
+            "torque_range",
+            "duration_s",
+            "cooldown_s",
+            "asset_cfg",
+            "body_point_offset",
+        )
+    )
+
+    def __init__(self, cfg: EventTermCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        term_name = "apply_body_impulse"
+        _validate_event_term(
+            cfg,
+            term_name=term_name,
+            mode="step",
+            allowed_params=self._PARAMS,
+            required_params=("force_range", "torque_range", "duration_s", "cooldown_s"),
+        )
+        self._force_range = _distribution_parameters(
+            cfg.params["force_range"],
+            term_name=term_name,
+            name="force_range",
+            distribution="uniform",
+        )
+        self._torque_range = _distribution_parameters(
+            cfg.params["torque_range"],
+            term_name=term_name,
+            name="torque_range",
+            distribution="uniform",
+        )
+        self._duration_s = _time_range(
+            cfg.params["duration_s"], term_name=term_name, name="duration_s"
+        )
+        self._cooldown_s = _time_range(
+            cfg.params["cooldown_s"], term_name=term_name, name="cooldown_s"
+        )
+        asset_cfg = cfg.params.get("asset_cfg", _DEFAULT_ASSET_CFG)
+        if not isinstance(asset_cfg, SceneEntityCfg):
+            raise TypeError(
+                f"EventManager term '{term_name}' asset_cfg must be SceneEntityCfg, "
+                f"got {type(asset_cfg).__name__}"
+            )
+        offset = cfg.params.get("body_point_offset")
+        if offset is not None:
+            offset_array = np.asarray(offset, dtype=np.float64)
+            if offset_array.shape != (3,) or not np.isfinite(offset_array).all():
+                raise ValueError(
+                    f"EventManager term '{term_name}' body_point_offset must be a finite "
+                    f"(x, y, z) triple, got {offset!r}"
+                )
+            self._body_point_offset: np.ndarray | None = np.array(offset_array)
+        else:
+            self._body_point_offset = None
+
+        self._entity = cast("Entity", env.scene[asset_cfg.name])
+        self._uses_torque = (
+            bool(np.any(self._torque_range != 0.0)) or self._body_point_offset is not None
+        )
+        self._local_body_ids, self._backend_body_ids = self._entity.bind_body_wrench(
+            asset_cfg.body_ids,
+            torque=self._uses_torque,
+            term_name=term_name,
+        )
+        self._step_dt = float(env.step_dt)
+        if not np.isfinite(self._step_dt) or self._step_dt <= 0.0:
+            raise ValueError(
+                f"EventManager term '{term_name}' requires a positive env step_dt, "
+                f"got {self._step_dt}"
+            )
+        num_bodies = self._backend_body_ids.size
+        self._time_remaining = np.zeros(self.num_envs, dtype=np.float64)
+        self._active = np.zeros(self.num_envs, dtype=np.bool_)
+        self._active_forces = np.zeros((self.num_envs, num_bodies, 3), dtype=np.float64)
+        self._active_torques = np.zeros((self.num_envs, num_bodies, 3), dtype=np.float64)
+        # Pre-sample the initial cooldown so the first impulse is preceded by a
+        # cooldown rather than firing immediately at t=0.
+        self._interval_time_left = self._sample_cooldown(self.num_envs)
+
+    def _sample_cooldown(self, count: int) -> np.ndarray:
+        return self._env.rng.uniform(self._cooldown_s[0], self._cooldown_s[1], size=count)
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        env_ids: np.ndarray | None,
+        force_range: tuple[float, float],
+        torque_range: tuple[float, float],
+        duration_s: tuple[float, float],
+        cooldown_s: tuple[float, float],
+        asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+        body_point_offset: tuple[float, float, float] | None = None,
+    ) -> None:
+        del env_ids, force_range, torque_range, duration_s, cooldown_s, asset_cfg
+        dt = self._step_dt
+
+        # Decrement active impulse timers, then expire finished impulses.
+        self._time_remaining[self._active] -= dt
+        expired = self._active & (self._time_remaining <= 0.0)
+        if np.any(expired):
+            expired_ids = np.flatnonzero(expired)
+            self._active[expired_ids] = False
+            self._active_forces[expired_ids] = 0.0
+            self._active_torques[expired_ids] = 0.0
+            self._time_remaining[expired_ids] = 0.0
+            self._interval_time_left[expired_ids] = self._sample_cooldown(len(expired_ids))
+
+        # Decrement cooldown timers, then trigger eligible envs.
+        self._interval_time_left -= dt
+        eligible = (~self._active) & (self._interval_time_left <= 0.0)
+        if np.any(eligible):
+            trigger_ids = np.flatnonzero(eligible)
+            count = len(trigger_ids)
+            num_bodies = self._backend_body_ids.size
+            forces = env.rng.uniform(
+                self._force_range[0], self._force_range[1], size=(count, num_bodies, 3)
+            )
+            torques = env.rng.uniform(
+                self._torque_range[0], self._torque_range[1], size=(count, num_bodies, 3)
+            )
+            if self._body_point_offset is not None:
+                quats = self._entity.data.body_link_quat_w[trigger_ids][:, self._local_body_ids]
+                offset_w = np_quat_apply_batched(
+                    quats.reshape(-1, 4),
+                    np.broadcast_to(self._body_point_offset, (count * num_bodies, 3)),
+                ).reshape(count, num_bodies, 3)
+                torques = torques + np.cross(offset_w, forces)
+            self._active_forces[trigger_ids] = forces
+            self._active_torques[trigger_ids] = torques
+            self._time_remaining[trigger_ids] = env.rng.uniform(
+                self._duration_s[0], self._duration_s[1], size=count
+            )
+            self._active[trigger_ids] = True
+            self._interval_time_left[trigger_ids] = self._sample_cooldown(count)
+
+        # Re-stage the full-width wrench while any impulse is active: backends
+        # with one-shot external-force channels (MuJoCo) need the sustain
+        # re-stage, and absolute channels (Motrix) treat it as an idempotent
+        # target update. Newly expired rows stage zeros, which actively clears
+        # persistent channels. Idle envs skip the call entirely so the term
+        # never clobbers another producer's staged forces.
+        if np.any(self._active) or np.any(expired):
+            self._entity.apply_body_wrench_to_sim(
+                self._active_forces,
+                self._active_torques if self._uses_torque else None,
+                self._backend_body_ids,
+                env_ids=None,
+                term_name="apply_body_impulse",
+            )
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        ids = (
+            np.arange(self.num_envs, dtype=np.intp)
+            if env_ids is None
+            else np.arange(self.num_envs, dtype=np.intp)[env_ids]
+            if isinstance(env_ids, slice)
+            else np.asarray(env_ids, dtype=np.intp)
+        )
+        was_active = ids[self._active[ids]]
+        self._active[ids] = False
+        self._active_forces[ids] = 0.0
+        self._active_torques[ids] = 0.0
+        self._time_remaining[ids] = 0.0
+        self._interval_time_left[ids] = self._sample_cooldown(len(ids))
+        if was_active.size:
+            zeros = np.zeros((len(was_active), self._backend_body_ids.size, 3), dtype=np.float64)
+            self._entity.apply_body_wrench_to_sim(
+                zeros,
+                zeros.copy() if self._uses_torque else None,
+                self._backend_body_ids,
+                env_ids=was_active,
+                term_name="apply_body_impulse",
+            )
+
+
+apply_body_impulse = ApplyBodyImpulse
+
+
+class RandomizeEncoderBias(ManagerTermBase):
+    """Per-reset joint encoder calibration bias through the Entity data surface."""
+
+    def __init__(self, cfg: EventTermCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        term_name = "randomize_encoder_bias"
+        _validate_event_term(
+            cfg,
+            term_name=term_name,
+            mode="reset",
+            allowed_params=frozenset(("bias_range", "asset_cfg")),
+            required_params=("bias_range", "asset_cfg"),
+        )
+        asset_cfg = cfg.params["asset_cfg"]
+        if not isinstance(asset_cfg, SceneEntityCfg):
+            raise TypeError(
+                f"EventManager term '{term_name}' asset_cfg must be SceneEntityCfg, "
+                f"got {type(asset_cfg).__name__}"
+            )
+        bias_range = np.asarray(cfg.params["bias_range"], dtype=np.float64)
+        if bias_range.shape != (2,) or not np.isfinite(bias_range).all():
+            raise ValueError(f"EventManager term '{term_name}' bias_range must be a finite pair")
+        if bias_range[0] > bias_range[1]:
+            raise ValueError(f"EventManager term '{term_name}' bias_range minimum exceeds maximum")
+        self._range = (float(bias_range[0]), float(bias_range[1]))
+        self._entity = cast("Entity", env.scene[asset_cfg.name])
+        joint_ids = np.arange(self._entity.num_joints, dtype=np.intp)[asset_cfg.joint_ids]
+        if joint_ids.ndim != 1 or joint_ids.size == 0:
+            raise ValueError(
+                f"EventManager term '{term_name}' asset_cfg must select at least one joint"
+            )
+        self._joint_ids = joint_ids
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        env_ids: np.ndarray | None,
+        bias_range: tuple[float, float],
+        asset_cfg: SceneEntityCfg,
+    ) -> None:
+        del bias_range, asset_cfg
+        ids = resolve_env_ids(env, env_ids)
+        self._entity.data.encoder_bias[np.ix_(ids, self._joint_ids)] = env.rng.uniform(
+            self._range[0],
+            self._range[1],
+            size=(ids.size, self._joint_ids.size),
+        )
+
+
+randomize_encoder_bias = RandomizeEncoderBias
+
+
+def reset_scene_to_default(env: ManagerBasedRlEnv, env_ids: np.ndarray | None) -> None:
+    """Reset all materialized scene entities to backend default qpos/qvel."""
+    ids = resolve_env_ids(env, env_ids)
+    if not env.scene.entities:
+        return
+    env.scene.reset_to_default(ids, term_name="reset_scene_to_default")
+
+
+def reset_root_state_uniform(
+    env: ManagerBasedRlEnv,
+    env_ids: np.ndarray | None,
+    pose_range: dict[str, tuple[float, float]],
+    velocity_range: dict[str, tuple[float, float]] | None = None,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> None:
+    """Reset a floating root from defaults plus uniformly sampled SE(3) offsets.
+
+    This is the NumPy adaptation of the pinned mjlab event. UniLab currently has
+    no public mocap-pose write contract, so fixed-base/mocap requests fail through
+    the entity's cached floating-root capability instead of falling back.
+    """
+    ids = resolve_env_ids(env, env_ids)
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    try:
+        root_states = np.array(asset.data.default_root_state[ids], copy=True)
+    except NotImplementedError as exc:
+        raise NotImplementedError(
+            "EventManager term 'reset_root_state_uniform' requires a floating-root "
+            f"state for entity '{asset_cfg.name}'; fixed-base/mocap root reset is "
+            f"unsupported without a formal backend contract: {exc}"
+        ) from exc
+
+    pose_samples = _sample_se3_range(pose_range, (len(ids), 6), env.rng)
+    root_states[:, 0:3] = root_states[:, 0:3] + pose_samples[:, 0:3] + env.scene.env_origins[ids]
+    orientation_delta = np_quat_from_euler_xyz(
+        pose_samples[:, 3], pose_samples[:, 4], pose_samples[:, 5]
+    )
+    root_states[:, 3:7] = np_quat_mul(root_states[:, 3:7], orientation_delta)
+
+    velocity_samples = _sample_se3_range(velocity_range, (len(ids), 6), env.rng)
+    root_states[:, 7:13] = root_states[:, 7:13] + velocity_samples
+    asset.write_root_state_to_sim(root_states, env_ids=ids)
+
+
+__all__ = [
+    "apply_body_impulse",
+    "dof_armature",
+    "geom_friction",
+    "joint_armature",
+    "pd_gains",
+    "push_by_setting_velocity",
+    "randomize_body_mass_inertia",
+    "randomize_encoder_bias",
+    "randomize_physics_scene_gravity",
+    "randomize_rigid_body_com",
+    "randomize_rigid_body_mass",
+    "reset_root_state_uniform",
+    "reset_scene_to_default",
+    "resolve_env_ids",
+]
diff --git a/src/unilab/envs/mdp/observations.py b/src/unilab/envs/mdp/observations.py
new file mode 100644
index 000000000..3aeb7ce69
--- /dev/null
+++ b/src/unilab/envs/mdp/observations.py
@@ -0,0 +1,273 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681),
+# src/mjlab/envs/mdp/observations.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and the base-owned entity facade; Apache-2.0.
+"""Community-style observation terms for the NumPy manager runtime."""
+
+from __future__ import annotations
+
+import weakref
+from typing import TYPE_CHECKING, cast
+
+import numpy as np
+
+from unilab.managers.manager_base import ManagerTermBase, ManagerTermBaseCfg
+from unilab.managers.scene_entity_config import SceneEntityCfg
+from unilab.utils.rotation import np_quat_apply_batched, np_quat_from_angle_axis
+
+if TYPE_CHECKING:
+    from unilab.base.entity import Entity
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+_DEFAULT_ASSET_CFG = SceneEntityCfg("robot")
+
+
+class _NamedSensorObservation(ManagerTermBase):
+    """Cold-path binding shared by pinned named-sensor observation terms."""
+
+    _term_name = "named_sensor"
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        sensor_name = cfg.params.get("sensor_name")
+        if not isinstance(sensor_name, str) or not sensor_name:
+            raise ValueError(
+                f"Observation term '{self._term_name}' capability 'named sensor' "
+                "requires a non-empty sensor_name"
+            )
+        self._sensor_name = sensor_name
+        try:
+            self._view = env.scene.bind_sensor_data((sensor_name,))
+        except (KeyError, TypeError, ValueError, NotImplementedError) as exc:
+            raise type(exc)(
+                f"Observation term '{self._term_name}' capability 'named sensor "
+                f"{sensor_name}' could not be materialized: {exc}"
+            ) from exc
+
+    def _validate_call_name(self, sensor_name: str) -> None:
+        if sensor_name != self._sensor_name:
+            raise ValueError(
+                f"Observation term '{self._term_name}' was bound to sensor "
+                f"'{self._sensor_name}', received '{sensor_name}'"
+            )
+
+    def _read(self) -> np.ndarray:
+        try:
+            return self._view.read()
+        except (KeyError, TypeError, ValueError, NotImplementedError) as exc:
+            raise type(exc)(
+                f"Observation term '{self._term_name}' capability 'named sensor "
+                f"{self._sensor_name}' failed on backend '{self._view.backend_type}': {exc}"
+            ) from exc
+
+
+class builtin_sensor(_NamedSensorObservation):
+    """Read one existing backend sensor through a cached NumPy view."""
+
+    _term_name = "builtin_sensor"
+
+    def __call__(self, env: ManagerBasedRlEnv, sensor_name: str) -> np.ndarray:
+        del env
+        self._validate_call_name(sensor_name)
+        return self._read()
+
+
+class projected_gravity_from_sensor(_NamedSensorObservation):
+    """Negate a cached 3-D up-vector sensor to obtain projected gravity."""
+
+    _term_name = "projected_gravity_from_sensor"
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        if self._view.dimensions != (3,):
+            raise ValueError(
+                "Observation term 'projected_gravity_from_sensor' capability "
+                f"'3-D named sensor {self._sensor_name}' received dimensions "
+                f"{self._view.dimensions} on backend '{self._view.backend_type}'"
+            )
+
+    def __call__(self, env: ManagerBasedRlEnv, sensor_name: str) -> np.ndarray:
+        del env
+        self._validate_call_name(sensor_name)
+        return -self._read()
+
+
+def base_lin_vel(
+    env: ManagerBasedRlEnv,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    return asset.data.root_link_lin_vel_b
+
+
+def base_ang_vel(
+    env: ManagerBasedRlEnv,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    return asset.data.root_link_ang_vel_b
+
+
+def projected_gravity(
+    env: ManagerBasedRlEnv,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    return asset.data.projected_gravity_b
+
+
+# Per-env constant IMU mounting-misalignment quaternions, keyed by env instance
+# and max_angle_rad. Sampled once per (env, angle) at term construction and
+# shared by every misaligned term of that env, so the gyroscope and gravity
+# observations see the SAME mounting error (legacy microduck recipe,
+# microduck_rl/src/mjlab_microduck/tasks/mdp.py:3618-3660). Weak keys keep the
+# cache from outliving the env.
+_IMU_MISALIGNMENT_QUATS: weakref.WeakKeyDictionary[ManagerBasedRlEnv, dict[float, np.ndarray]] = (
+    weakref.WeakKeyDictionary()
+)
+
+
+def _imu_misalignment_quat(env: ManagerBasedRlEnv, max_angle_rad: float) -> np.ndarray:
+    """Per-env constant IMU mounting-misalignment rotation, sampled once.
+
+    Models a fixed small mounting/calibration error of the IMU on each robot:
+    a rotation about an axis uniform on the sphere with magnitude uniform in
+    [0, max_angle_rad]. Sampled from ``env.rng`` on first use and cached for the
+    whole run (a startup-style systematic per-robot bias, not per-step noise and
+    not resampled on episode reset), matching the legacy semantics.
+
+    Returns (num_envs, 4) unit quaternions (w, x, y, z) in float32.
+    """
+    per_env = _IMU_MISALIGNMENT_QUATS.get(env)
+    if per_env is None:
+        per_env = {}
+        _IMU_MISALIGNMENT_QUATS[env] = per_env
+    quat = per_env.get(max_angle_rad)
+    if quat is None:
+        axis = env.rng.standard_normal((env.num_envs, 3))
+        angle = env.rng.uniform(0.0, max_angle_rad, size=env.num_envs)
+        quat = np_quat_from_angle_axis(angle, axis).astype(np.float32)
+        per_env[max_angle_rad] = quat
+    return quat
+
+
+class _ImuMisalignedObservation(ManagerTermBase):
+    """Cold-path binding shared by IMU mounting-misalignment observation terms."""
+
+    _term_name = "imu_misaligned"
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        max_angle_deg = cfg.params.get("max_angle_deg", 1.0)
+        if isinstance(max_angle_deg, bool) or not isinstance(max_angle_deg, (int, float)):
+            raise ValueError(
+                f"Observation term '{self._term_name}' capability 'IMU misalignment' "
+                f"requires a real max_angle_deg, got {max_angle_deg!r}"
+            )
+        max_angle_deg = float(max_angle_deg)
+        if not np.isfinite(max_angle_deg) or max_angle_deg < 0.0:
+            raise ValueError(
+                f"Observation term '{self._term_name}' capability 'IMU misalignment' "
+                f"requires a finite non-negative max_angle_deg, got {max_angle_deg}"
+            )
+        self._max_angle_deg = max_angle_deg
+        self._quat = _imu_misalignment_quat(env, float(np.deg2rad(max_angle_deg)))
+
+    def _validate_max_angle(self, max_angle_deg: float) -> None:
+        if float(max_angle_deg) != self._max_angle_deg:
+            raise ValueError(
+                f"Observation term '{self._term_name}' was bound to max_angle_deg "
+                f"{self._max_angle_deg}, received {max_angle_deg}"
+            )
+
+    def _rotate(self, values: np.ndarray) -> np.ndarray:
+        return np_quat_apply_batched(self._quat, values)
+
+
+class base_ang_vel_imu_misaligned(_ImuMisalignedObservation):
+    """Base angular velocity rotated by the per-env constant IMU misalignment."""
+
+    _term_name = "base_ang_vel_imu_misaligned"
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        max_angle_deg: float = 1.0,
+        asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+    ) -> np.ndarray:
+        self._validate_max_angle(max_angle_deg)
+        asset = cast("Entity", env.scene[asset_cfg.name])
+        return self._rotate(asset.data.root_link_ang_vel_b)
+
+
+class projected_gravity_imu_misaligned(_ImuMisalignedObservation):
+    """Projected gravity rotated by the SAME per-env IMU misalignment as the gyro."""
+
+    _term_name = "projected_gravity_imu_misaligned"
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        max_angle_deg: float = 1.0,
+        asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+    ) -> np.ndarray:
+        self._validate_max_angle(max_angle_deg)
+        asset = cast("Entity", env.scene[asset_cfg.name])
+        return self._rotate(asset.data.projected_gravity_b)
+
+
+def joint_pos_rel(
+    env: ManagerBasedRlEnv,
+    biased: bool = False,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    if not isinstance(biased, bool):
+        raise TypeError(f"joint_pos_rel biased must be bool, got {type(biased).__name__}")
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    joint_ids = asset_cfg.joint_ids
+    joint_pos = asset.data.joint_pos_biased if biased else asset.data.joint_pos
+    return joint_pos[:, joint_ids] - asset.data.default_joint_pos[:, joint_ids]
+
+
+def joint_vel_rel(
+    env: ManagerBasedRlEnv,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    joint_ids = asset_cfg.joint_ids
+    return asset.data.joint_vel[:, joint_ids] - asset.data.default_joint_vel[:, joint_ids]
+
+
+def last_action(env: ManagerBasedRlEnv, action_name: str | None = None) -> np.ndarray:
+    if action_name is None:
+        return env.action_manager.action
+    try:
+        return env.action_manager.get_term(action_name).raw_action
+    except KeyError as exc:
+        raise KeyError(f"Action term '{action_name}' not found") from exc
+
+
+def generated_commands(env: ManagerBasedRlEnv, command_name: str) -> np.ndarray:
+    try:
+        command = env.command_manager.get_command(command_name)
+    except KeyError as exc:
+        raise KeyError(f"Command term '{command_name}' not found") from exc
+    if command is None:
+        raise KeyError(f"Command term '{command_name}' not found")
+    return command
+
+
+__all__ = [
+    "base_ang_vel",
+    "base_ang_vel_imu_misaligned",
+    "base_lin_vel",
+    "builtin_sensor",
+    "generated_commands",
+    "joint_pos_rel",
+    "joint_vel_rel",
+    "last_action",
+    "projected_gravity",
+    "projected_gravity_from_sensor",
+    "projected_gravity_imu_misaligned",
+]
diff --git a/src/unilab/envs/mdp/recorders.py b/src/unilab/envs/mdp/recorders.py
new file mode 100644
index 000000000..9b1fd3eb0
--- /dev/null
+++ b/src/unilab/envs/mdp/recorders.py
@@ -0,0 +1,34 @@
+"""Reusable recorder terms for the NumPy Manager-Based runtime."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from unilab.managers.recorder_manager import RecorderTerm, RecorderTermCfg
+
+if TYPE_CHECKING:
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+class LifecycleCounterRecorder(RecorderTerm):
+    """Count recorder lifecycle calls without performing I/O."""
+
+    def __init__(self, cfg: RecorderTermCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        self.pre_reset_count = 0
+        self.post_reset_count = 0
+        self.post_step_count = 0
+
+    def record_pre_reset(self, env_ids: np.ndarray) -> None:
+        self.pre_reset_count += int(len(env_ids))
+
+    def record_post_reset(self, env_ids: np.ndarray) -> None:
+        self.post_reset_count += int(len(env_ids))
+
+    def record_post_step(self) -> None:
+        self.post_step_count += 1
+
+
+__all__ = ["LifecycleCounterRecorder"]
diff --git a/src/unilab/envs/mdp/rewards.py b/src/unilab/envs/mdp/rewards.py
new file mode 100644
index 000000000..a65d44d7f
--- /dev/null
+++ b/src/unilab/envs/mdp/rewards.py
@@ -0,0 +1,361 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681),
+# src/mjlab/envs/mdp/rewards.py and src/mjlab/tasks/velocity/mdp/rewards.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and the base-owned entity facade; Apache-2.0.
+"""Community-style reward terms for the NumPy manager runtime."""
+
+from __future__ import annotations
+
+import re
+from typing import TYPE_CHECKING, ClassVar, cast
+
+import numpy as np
+
+from unilab.managers.manager_base import ManagerTermBase, ManagerTermBaseCfg
+from unilab.managers.scene_entity_config import SceneEntityCfg
+from unilab.utils.rotation import np_quat_apply_inverse
+
+if TYPE_CHECKING:
+    from unilab.base.entity import Entity
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+_DEFAULT_ASSET_CFG = SceneEntityCfg("robot")
+
+
+def _positive_std(term_name: str, std: float) -> float:
+    if isinstance(std, bool) or not isinstance(std, (int, float, np.number)):
+        raise TypeError(f"{term_name} std must be a real number")
+    value = float(std)
+    if not np.isfinite(value) or value <= 0.0:
+        raise ValueError(f"{term_name} std must be finite and positive")
+    return value
+
+
+def _nonnegative_threshold(term_name: str, name: str, value: float) -> float:
+    if isinstance(value, bool) or not isinstance(value, (int, float, np.number)):
+        raise TypeError(f"{term_name} {name} must be a real number")
+    result = float(value)
+    if not np.isfinite(result) or result < 0.0:
+        raise ValueError(f"{term_name} {name} must be finite and non-negative")
+    return result
+
+
+def _resolve_std_dict(
+    term_name: str, param: str, data: object, joint_names: list[str]
+) -> np.ndarray:
+    """Resolve a ``{regex: std}`` mapping into one positive std per joint name.
+
+    Mirrors mjlab ``resolve_matching_names_values`` with ``preserve_order=False``:
+    full-regex matching, every pattern must match at least one joint, and every
+    joint must match exactly one pattern.
+    """
+    if not isinstance(data, dict) or not data:
+        raise TypeError(f"{term_name} {param} must be a non-empty dict of regex patterns to std")
+    values: list[float] = []
+    matched_patterns: set[str] = set()
+    for name in joint_names:
+        matches: list[float] = []
+        for pattern, std in data.items():
+            if not isinstance(pattern, str):
+                raise TypeError(f"{term_name} {param} keys must be regex strings")
+            try:
+                matched = re.fullmatch(pattern, name) is not None
+            except re.error as exc:
+                raise ValueError(f"{term_name} {param} invalid regex {pattern!r}: {exc}") from exc
+            if matched:
+                matches.append(_positive_std(f"{term_name} {param}[{pattern!r}]", std))
+                matched_patterns.add(pattern)
+        if len(matches) != 1:
+            raise ValueError(
+                f"{term_name} {param} must match joint '{name}' exactly once, "
+                f"got {len(matches)} matches"
+            )
+        values.append(matches[0])
+    unmatched = [pattern for pattern in data if pattern not in matched_patterns]
+    if unmatched:
+        raise ValueError(f"{term_name} {param} patterns matched no joints: {unmatched}")
+    return np.asarray(values, dtype=np.float64)
+
+
+def _command(env: ManagerBasedRlEnv, command_name: str) -> np.ndarray:
+    try:
+        command = env.command_manager.get_command(command_name)
+    except KeyError as exc:
+        raise KeyError(f"Command term '{command_name}' not found") from exc
+    if command is None:
+        raise KeyError(f"Command term '{command_name}' not found")
+    return command
+
+
+def is_alive(env: ManagerBasedRlEnv) -> np.ndarray:
+    """Reward environments that have not reached a non-timeout termination."""
+    return np.logical_not(env.termination_manager.terminated).astype(np.float32, copy=False)
+
+
+def is_terminated(env: ManagerBasedRlEnv) -> np.ndarray:
+    """Return one for non-timeout terminations."""
+    return env.termination_manager.terminated.astype(np.float32, copy=False)
+
+
+def root_height(
+    env: ManagerBasedRlEnv,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Return the world-frame root height as a scalar reward or metric term."""
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    return np.asarray(asset.data.root_link_pos_w[:, 2])
+
+
+def joint_vel_l2(
+    env: ManagerBasedRlEnv,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Penalize selected joint velocities with an L2-squared kernel."""
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    return np.sum(np.square(asset.data.joint_vel[:, asset_cfg.joint_ids]), axis=1)
+
+
+def action_rate_l2(env: ManagerBasedRlEnv) -> np.ndarray:
+    """Penalize the first difference of raw policy actions."""
+    delta = env.action_manager.action - env.action_manager.prev_action
+    return np.sum(np.square(delta), axis=1)
+
+
+def action_acc_l2(env: ManagerBasedRlEnv) -> np.ndarray:
+    """Penalize the second difference of raw policy actions."""
+    action_acc = (
+        env.action_manager.action
+        - 2.0 * env.action_manager.prev_action
+        + env.action_manager.prev_prev_action
+    )
+    return np.sum(np.square(action_acc), axis=1)
+
+
+def flat_orientation_l2(
+    env: ManagerBasedRlEnv,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Penalize non-flat base orientation."""
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    return np.sum(np.square(asset.data.projected_gravity_b[:, :2]), axis=1)
+
+
+def upright(
+    env: ManagerBasedRlEnv,
+    std: float,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Gaussian reward for keeping one selected body upright (mjlab ``upright``).
+
+    The reward is ``exp(-||pg_xy||^2 / std^2)`` where ``pg`` is the world
+    gravity vector expressed in the selected body's link frame.  Flat-ground
+    form only: mjlab's optional terrain-normal sensors are not ported.
+    """
+    scale = _positive_std("upright", std)
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    body_quat_w = asset.data.body_link_quat_w[:, asset_cfg.body_ids, :]
+    if body_quat_w.shape != (env.num_envs, 1, 4):
+        raise ValueError(
+            f"upright requires exactly one body; received state shape {body_quat_w.shape}"
+        )
+    gravity = np.asarray(asset.data.gravity_vec_w)
+    projected_gravity_b = np_quat_apply_inverse(body_quat_w[:, 0, :], gravity)
+    xy_squared = np.sum(np.square(projected_gravity_b[:, :2]), axis=1)
+    return np.exp(-xy_squared / scale**2)
+
+
+def track_linear_velocity(
+    env: ManagerBasedRlEnv,
+    std: float,
+    command_name: str,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Reward commanded base linear velocity, assuming commanded z is zero."""
+    scale = _positive_std("track_linear_velocity", std)
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    command = _command(env, command_name)
+    actual = asset.data.root_link_lin_vel_b
+    xy_error = np.sum(np.square(command[:, :2] - actual[:, :2]), axis=1)
+    z_error = np.square(actual[:, 2])
+    return np.exp(-(xy_error + z_error) / scale**2)
+
+
+def track_angular_velocity(
+    env: ManagerBasedRlEnv,
+    std: float,
+    command_name: str,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Reward commanded yaw rate while keeping roll/pitch rates near zero."""
+    scale = _positive_std("track_angular_velocity", std)
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    command = _command(env, command_name)
+    actual = asset.data.root_link_ang_vel_b
+    z_error = np.square(command[:, 2] - actual[:, 2])
+    xy_error = np.sum(np.square(actual[:, :2]), axis=1)
+    return np.exp(-(z_error + xy_error) / scale**2)
+
+
+def body_angular_velocity_penalty(
+    env: ManagerBasedRlEnv,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Penalize roll/pitch angular velocity of one selected body."""
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    ang_vel = asset.data.body_link_ang_vel_w[:, asset_cfg.body_ids, :]
+    if ang_vel.shape != (env.num_envs, 1, 3):
+        raise ValueError(
+            "body_angular_velocity_penalty requires exactly one body; "
+            f"received state shape {ang_vel.shape}"
+        )
+    return np.sum(np.square(ang_vel[:, 0, :2]), axis=1)
+
+
+def joint_pos_limits(
+    env: ManagerBasedRlEnv,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Penalize joint positions if they cross the soft limits."""
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    limits = np.asarray(asset.data.soft_joint_pos_limits)[asset_cfg.joint_ids]
+    joint_pos = asset.data.joint_pos[:, asset_cfg.joint_ids]
+    out_of_limits = -np.clip(joint_pos - limits[:, 0], min=None, max=0.0)
+    out_of_limits += np.clip(joint_pos - limits[:, 1], min=0.0, max=None)
+    return np.sum(out_of_limits, axis=1)
+
+
+def _selected_joint_names(asset: Entity, asset_cfg: SceneEntityCfg) -> list[str]:
+    if asset_cfg.joint_names is None:
+        return list(asset.joint_names)
+    if isinstance(asset_cfg.joint_names, str):
+        return [asset_cfg.joint_names]
+    return list(asset_cfg.joint_names)
+
+
+class posture(ManagerTermBase):
+    """Penalize joint deviation from default pose with a per-joint-std Gaussian kernel.
+
+    ``params["std"]`` maps joint-name regexes to per-joint standard deviations;
+    the reward is ``exp(-mean(error^2 / std^2))`` over the selected joints.
+    """
+
+    _allowed_params: ClassVar[frozenset[str]] = frozenset({"std", "asset_cfg"})
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        unexpected = set(cfg.params) - self._allowed_params
+        if unexpected:
+            raise TypeError(f"{self.name} received unsupported parameters: {sorted(unexpected)}")
+        asset_cfg = cfg.params.get("asset_cfg", _DEFAULT_ASSET_CFG)
+        if not isinstance(asset_cfg, SceneEntityCfg):
+            raise TypeError(f"{self.name} asset_cfg must be a SceneEntityCfg")
+        asset = cast("Entity", env.scene[asset_cfg.name])
+        self._asset_cfg = asset_cfg
+        self._std = _resolve_std_dict(
+            self.name, "std", cfg.params.get("std"), _selected_joint_names(asset, asset_cfg)
+        )
+        self._default_joint_pos = np.asarray(asset.data.default_joint_pos)
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: object) -> np.ndarray:
+        del params
+        asset = cast("Entity", env.scene[self._asset_cfg.name])
+        current = asset.data.joint_pos[:, self._asset_cfg.joint_ids]
+        desired = self._default_joint_pos[:, self._asset_cfg.joint_ids]
+        error_squared = np.square(current - desired)
+        return np.exp(-np.mean(error_squared / np.square(self._std), axis=1))
+
+
+class variable_posture(ManagerTermBase):
+    """``posture`` with speed-dependent tolerance: standing/walking/running std maps.
+
+    The per-joint std is selected from ``std_standing`` / ``std_walking`` /
+    ``std_running`` by the total command speed (planar norm plus yaw magnitude)
+    against ``walking_threshold`` and ``running_threshold``.
+    """
+
+    _allowed_params: ClassVar[frozenset[str]] = frozenset(
+        {
+            "std_standing",
+            "std_walking",
+            "std_running",
+            "asset_cfg",
+            "command_name",
+            "walking_threshold",
+            "running_threshold",
+        }
+    )
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        unexpected = set(cfg.params) - self._allowed_params
+        if unexpected:
+            raise TypeError(f"{self.name} received unsupported parameters: {sorted(unexpected)}")
+        asset_cfg = cfg.params.get("asset_cfg", _DEFAULT_ASSET_CFG)
+        if not isinstance(asset_cfg, SceneEntityCfg):
+            raise TypeError(f"{self.name} asset_cfg must be a SceneEntityCfg")
+        asset = cast("Entity", env.scene[asset_cfg.name])
+        joint_names = _selected_joint_names(asset, asset_cfg)
+        self._asset_cfg = asset_cfg
+        self._std_standing = _resolve_std_dict(
+            self.name, "std_standing", cfg.params.get("std_standing"), joint_names
+        )
+        self._std_walking = _resolve_std_dict(
+            self.name, "std_walking", cfg.params.get("std_walking"), joint_names
+        )
+        self._std_running = _resolve_std_dict(
+            self.name, "std_running", cfg.params.get("std_running"), joint_names
+        )
+        command_name = cfg.params.get("command_name")
+        if not isinstance(command_name, str) or not command_name:
+            raise ValueError(f"{self.name} command_name must be a non-empty string")
+        self._command_name = command_name
+        self._walking_threshold = _nonnegative_threshold(
+            self.name, "walking_threshold", cfg.params.get("walking_threshold", 0.5)
+        )
+        self._running_threshold = _nonnegative_threshold(
+            self.name, "running_threshold", cfg.params.get("running_threshold", 1.5)
+        )
+        if self._walking_threshold >= self._running_threshold:
+            raise ValueError(
+                f"{self.name} walking_threshold must be below running_threshold, got "
+                f"{self._walking_threshold} >= {self._running_threshold}"
+            )
+        self._default_joint_pos = np.asarray(asset.data.default_joint_pos)
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: object) -> np.ndarray:
+        del params
+        asset = cast("Entity", env.scene[self._asset_cfg.name])
+        command = _command(env, self._command_name)
+        linear_speed = np.linalg.norm(command[:, :2], axis=1)
+        total_speed = linear_speed + np.abs(command[:, 2])
+        standing = total_speed < self._walking_threshold
+        running = total_speed >= self._running_threshold
+        walking = ~(standing | running)
+        std = (
+            self._std_standing[None, :] * standing[:, None]
+            + self._std_walking[None, :] * walking[:, None]
+            + self._std_running[None, :] * running[:, None]
+        )
+        current = asset.data.joint_pos[:, self._asset_cfg.joint_ids]
+        desired = self._default_joint_pos[:, self._asset_cfg.joint_ids]
+        error_squared = np.square(current - desired)
+        return np.exp(-np.mean(error_squared / np.square(std), axis=1))
+
+
+__all__ = [
+    "action_acc_l2",
+    "action_rate_l2",
+    "body_angular_velocity_penalty",
+    "flat_orientation_l2",
+    "is_alive",
+    "is_terminated",
+    "joint_pos_limits",
+    "joint_vel_l2",
+    "posture",
+    "root_height",
+    "track_angular_velocity",
+    "track_linear_velocity",
+    "upright",
+    "variable_posture",
+]
diff --git a/src/unilab/envs/mdp/terminations.py b/src/unilab/envs/mdp/terminations.py
new file mode 100644
index 000000000..1b2818040
--- /dev/null
+++ b/src/unilab/envs/mdp/terminations.py
@@ -0,0 +1,114 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681),
+# src/mjlab/envs/mdp/terminations.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and the base-owned entity facade; Apache-2.0.
+"""Community-style termination terms for the NumPy manager runtime."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, cast
+
+import numpy as np
+
+from unilab.managers.scene_entity_config import SceneEntityCfg
+
+if TYPE_CHECKING:
+    from unilab.base.entity import Entity
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+_DEFAULT_ASSET_CFG = SceneEntityCfg("robot")
+
+
+def _require_state(term_name: str, value: np.ndarray, num_envs: int) -> np.ndarray:
+    if not isinstance(value, np.ndarray):
+        raise TypeError(
+            f"Termination term '{term_name}' expected an np.ndarray entity state, "
+            f"got {type(value).__name__}"
+        )
+    if value.shape != (num_envs, 3):
+        raise ValueError(
+            f"Termination term '{term_name}' received entity state shape {value.shape}, "
+            f"expected ({num_envs}, 3)"
+        )
+    if not np.isfinite(value).all():
+        env_ids = np.flatnonzero(~np.isfinite(value).all(axis=1)).tolist()
+        raise ValueError(
+            f"Termination term '{term_name}' received NaN or Inf entity state for "
+            f"environments {env_ids[:10]}"
+        )
+    return value
+
+
+def time_out(env: ManagerBasedRlEnv) -> np.ndarray:
+    """Terminate when the episode length reaches its maximum."""
+    return env.episode_length_buf >= env.max_episode_length
+
+
+def bad_orientation(
+    env: ManagerBasedRlEnv,
+    limit_angle: float,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Terminate when the asset orientation exceeds ``limit_angle``."""
+    if isinstance(limit_angle, bool) or not isinstance(limit_angle, (int, float, np.number)):
+        raise TypeError("bad_orientation limit_angle must be a real number")
+    if not np.isfinite(limit_angle):
+        raise ValueError("bad_orientation limit_angle must be finite")
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    projected_gravity = _require_state(
+        "bad_orientation", asset.data.projected_gravity_b, env.num_envs
+    )
+    return np.abs(np.arccos(np.clip(-projected_gravity[:, 2], -1.0, 1.0))) > limit_angle
+
+
+def root_height_below_minimum(
+    env: ManagerBasedRlEnv,
+    minimum_height: float,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Terminate when the asset root height is below ``minimum_height``."""
+    if isinstance(minimum_height, bool) or not isinstance(minimum_height, (int, float, np.number)):
+        raise TypeError("root_height_below_minimum minimum_height must be a real number")
+    if not np.isfinite(minimum_height):
+        raise ValueError("root_height_below_minimum minimum_height must be finite")
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    root_pos_w = _require_state(
+        "root_height_below_minimum", asset.data.root_link_pos_w, env.num_envs
+    )
+    return root_pos_w[:, 2] < minimum_height
+
+
+def nan_detection(
+    env: ManagerBasedRlEnv,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Terminate environments whose entity physics state contains NaN or Inf.
+
+    mjlab checks the raw physics state (qpos/qvel/qacc) through its nan guard;
+    the UniLab term checks the equivalent base-owned entity state (root pose and
+    velocity, joint position and velocity) so a non-finite physics state
+    terminates the episode explicitly instead of relying on the optional global
+    nan guard.
+    """
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    states = (
+        asset.data.root_link_pos_w,
+        asset.data.root_link_lin_vel_w,
+        asset.data.root_link_ang_vel_w,
+        asset.data.joint_pos,
+        asset.data.joint_vel,
+    )
+    invalid = np.zeros(env.num_envs, dtype=np.bool_)
+    for state in states:
+        if not isinstance(state, np.ndarray) or state.shape[0] != env.num_envs:
+            shape = getattr(state, "shape", None)
+            raise ValueError(
+                f"nan_detection received entity state shape {shape}, "
+                f"expected leading dimension {env.num_envs}"
+            )
+        invalid |= ~np.isfinite(state).reshape(env.num_envs, -1).all(axis=1)
+    return invalid
+
+
+__all__ = ["bad_orientation", "nan_detection", "root_height_below_minimum", "time_out"]
diff --git a/src/unilab/envs/motion_tracking/__init__.py b/src/unilab/envs/motion_tracking/__init__.py
deleted file mode 100644
index 1d392bc92..000000000
--- a/src/unilab/envs/motion_tracking/__init__.py
+++ /dev/null
@@ -1,97 +0,0 @@
-"""Motion tracking environments."""
-
-__unilab_registry_modules__ = (
-    "unilab.envs.motion_tracking.g1",
-    "unilab.envs.motion_tracking.x2",
-)
-
-from .g1 import (
-    BoxMotionData,
-    BoxMotionLoader,
-    G1BoxTracking23DofCfg,
-    G1BoxTracking23DofEnvCfg,
-    G1BoxTrackingCfg,
-    G1BoxTrackingEnv,
-    G1BoxTrackingEnvCfg,
-    G1ClimbTrackingCfg,
-    G1ClimbTrackingEnv,
-    G1ClimbTrackingEnvCfg,
-    G1FlipTracking23DofCfg,
-    G1FlipTracking23DofEnvCfg,
-    G1FlipTrackingCfg,
-    G1FlipTrackingEnv,
-    G1FlipTrackingEnvCfg,
-    G1FlipTrackingSAC23DofCfg,
-    G1FlipTrackingSAC23DofEnv,
-    G1MotionTracking23DofCfg,
-    G1MotionTracking23DofDeployEnvCfg,
-    G1MotionTracking23DofEnvCfg,
-    G1MotionTrackingCfg,
-    G1MotionTrackingEnv,
-    G1MotionTrackingEnvCfg,
-    G1MotionTrackingSAC23DofCfg,
-    G1MotionTrackingSAC23DofEnv,
-    G1MotionTrackingSACCfg,
-    G1MotionTrackingSACEnv,
-    G1WallFlipTracking23DofCfg,
-    G1WallFlipTracking23DofEnvCfg,
-    G1WallFlipTrackingCfg,
-    G1WallFlipTrackingEnv,
-    G1WallFlipTrackingEnvCfg,
-    G1WallFlipTrackingSAC23DofCfg,
-    G1WallFlipTrackingSAC23DofEnv,
-    G1WBTObs23DofCfg,
-    G1WBTObsCfg,
-    G1WBTObsEnv,
-)
-from .x2 import (
-    X2MotionTrackingCfg,
-    X2WallFlipTrackingCfg,
-    X2WallFlipTrackingEnv,
-    X2WallFlipTrackingEnvCfg,
-)
-
-__all__ = [
-    "G1MotionTrackingCfg",
-    "G1MotionTrackingEnv",
-    "G1MotionTrackingEnvCfg",
-    "G1MotionTrackingSACCfg",
-    "G1MotionTrackingSACEnv",
-    "G1WBTObsCfg",
-    "G1WBTObsEnv",
-    "G1FlipTrackingCfg",
-    "G1FlipTrackingEnv",
-    "G1FlipTrackingEnvCfg",
-    "G1WallFlipTrackingCfg",
-    "G1WallFlipTrackingEnv",
-    "G1WallFlipTrackingEnvCfg",
-    "G1ClimbTrackingCfg",
-    "G1ClimbTrackingEnv",
-    "G1ClimbTrackingEnvCfg",
-    "G1BoxTrackingCfg",
-    "G1BoxTrackingEnv",
-    "G1BoxTrackingEnvCfg",
-    "BoxMotionData",
-    "BoxMotionLoader",
-    "X2MotionTrackingCfg",
-    "X2WallFlipTrackingCfg",
-    "X2WallFlipTrackingEnv",
-    "X2WallFlipTrackingEnvCfg",
-    # 23-DoF variants (from parent UniLab/, under testing)
-    "G1MotionTracking23DofCfg",
-    "G1MotionTracking23DofDeployEnvCfg",
-    "G1MotionTracking23DofEnvCfg",
-    "G1MotionTrackingSAC23DofCfg",
-    "G1MotionTrackingSAC23DofEnv",
-    "G1WBTObs23DofCfg",
-    "G1FlipTracking23DofCfg",
-    "G1FlipTracking23DofEnvCfg",
-    "G1FlipTrackingSAC23DofCfg",
-    "G1FlipTrackingSAC23DofEnv",
-    "G1WallFlipTracking23DofCfg",
-    "G1WallFlipTracking23DofEnvCfg",
-    "G1WallFlipTrackingSAC23DofCfg",
-    "G1WallFlipTrackingSAC23DofEnv",
-    "G1BoxTracking23DofCfg",
-    "G1BoxTracking23DofEnvCfg",
-]
diff --git a/src/unilab/envs/motion_tracking/common/__init__.py b/src/unilab/envs/motion_tracking/common/__init__.py
deleted file mode 100644
index 670d69ccb..000000000
--- a/src/unilab/envs/motion_tracking/common/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-"""Robot-agnostic motion-tracking engine and owner modules.
-
-This package holds the motion-tracking task engine (:class:`MotionTrackingEnv`
-/ :class:`MotionTrackingDeployEnv`) and its per-concern owner modules (config,
-rewards, observations, terminations, transforms, reset, domain randomization,
-motion loading). Per-robot profiles live under ``g1/`` and
-``x2/`` and only carry robot-specific defaults and thin registry subclasses.
-"""
diff --git a/src/unilab/envs/motion_tracking/common/config.py b/src/unilab/envs/motion_tracking/common/config.py
deleted file mode 100644
index 3845caeba..000000000
--- a/src/unilab/envs/motion_tracking/common/config.py
+++ /dev/null
@@ -1,168 +0,0 @@
-"""Configuration dataclasses for the robot-agnostic motion-tracking engine.
-
-The default field values are kept at the historical G1 profile so that the
-registered ``G1MotionTracking`` / ``G1MotionTrackingDeploy`` configs remain
-field-identical to the pre-refactor monolith. Per-robot profiles (g1/x2)
-override these via subclasses.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Literal
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base.scene import SceneCfg
-from unilab.envs.locomotion.g1.base import G1BaseCfg
-
-from .rewards import RewardConfig
-
-
-@dataclass
-class PoseRandomization:
-    """Pose randomization ranges for reset."""
-
-    x: tuple[float, float] = (-0.05, 0.05)
-    y: tuple[float, float] = (-0.05, 0.05)
-    z: tuple[float, float] = (-0.01, 0.01)
-    roll: tuple[float, float] = (-0.1, 0.1)
-    pitch: tuple[float, float] = (-0.1, 0.1)
-    yaw: tuple[float, float] = (-0.2, 0.2)
-
-
-@dataclass
-class VelocityRandomization:
-    """Velocity randomization ranges for reset."""
-
-    x: tuple[float, float] = (-0.5, 0.5)
-    y: tuple[float, float] = (-0.5, 0.5)
-    z: tuple[float, float] = (-0.2, 0.2)
-    roll: tuple[float, float] = (-0.52, 0.52)
-    pitch: tuple[float, float] = (-0.52, 0.52)
-    yaw: tuple[float, float] = (-0.78, 0.78)
-
-
-@dataclass
-class DomainRand:
-    """Domain randomization config required by motrix backend hooks."""
-
-    randomize_base_mass: bool = False
-    added_mass_range: list[float] = field(default_factory=lambda: [-1.5, 1.5])
-
-    random_com: bool = False
-    com_offset_x: list[float] = field(default_factory=lambda: [-0.05, 0.05])
-    com_offset_y: list[float] = field(default_factory=lambda: [-0.05, 0.05])
-    com_offset_z: list[float] = field(default_factory=lambda: [-0.05, 0.05])
-
-    randomize_gravity: bool = False
-    gravity_range: list[list[float]] = field(
-        default_factory=lambda: [[0.0, 0.0, -9.81], [0.0, 0.0, -9.81]]
-    )
-
-    push_robots: bool = False
-    push_interval: int = 750
-    max_force: list[float] = field(default_factory=lambda: [1.0, 1.0, 0.5])
-    push_body_name: str | None = None
-
-    randomize_kp: bool = False
-    kp_multiplier_range: list[float] = field(default_factory=lambda: [0.9, 1.1])
-
-    randomize_kd: bool = False
-    kd_multiplier_range: list[float] = field(default_factory=lambda: [0.9, 1.1])
-
-    randomize_geom_friction: bool = False
-    friction_range: list[float] = field(default_factory=lambda: [0.3, 1.2])
-    friction_geom_pattern: str = r"^(left|right)_foot[1-7]_collision$"
-
-    randomize_joint_default_pos: bool = False
-    joint_default_pos_range: list[float] = field(default_factory=lambda: [-0.01, 0.01])
-
-
-# Backward-compatible alias for the historical (snake_case) class name.
-Domain_Rand = DomainRand
-
-
-def _zero_pose_randomization() -> PoseRandomization:
-    return PoseRandomization(
-        x=(0.0, 0.0),
-        y=(0.0, 0.0),
-        z=(0.0, 0.0),
-        roll=(0.0, 0.0),
-        pitch=(0.0, 0.0),
-        yaw=(0.0, 0.0),
-    )
-
-
-def _zero_velocity_randomization() -> VelocityRandomization:
-    return VelocityRandomization(
-        x=(0.0, 0.0),
-        y=(0.0, 0.0),
-        z=(0.0, 0.0),
-        roll=(0.0, 0.0),
-        pitch=(0.0, 0.0),
-        yaw=(0.0, 0.0),
-    )
-
-
-@dataclass
-class MotionTrackingCfg(G1BaseCfg):
-    """Configuration for the motion tracking environment."""
-
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat.xml")
-        )
-    )
-    # Kept at the historical single-clip default for backward compatibility.
-    motion_file: str | list[str] = str(
-        ASSETS_ROOT_PATH / "motions" / "g1" / "dance1_subject2_part.npz"
-    )
-    # motion_file: str | list[str] = str(ASSETS_ROOT_PATH / "motions" / "g1" / "gangnam_style.npz")
-    # motion_file: str | list[str] = str(ASSETS_ROOT_PATH / "motions" / "g1" / "fight1_subject5_from_csv.npz") #LAFAN
-    # motion_file: str | list[str] = str(ASSETS_ROOT_PATH / "motions" / "g1" / "dance_basic_slide_180_R_loop_001__A322_M.npz") #LAFAN
-    # motion_file: str | list[str] = str(ASSETS_ROOT_PATH / "motions" / "g1" / "playing_violin_R_003__A327_from_csv.npz") #Seed
-    anchor_body_name: str = "torso_link"
-    body_names: tuple[str, ...] = (
-        "pelvis",
-        "left_hip_roll_link",
-        "left_knee_link",
-        "left_ankle_roll_link",
-        "right_hip_roll_link",
-        "right_knee_link",
-        "right_ankle_roll_link",
-        "torso_link",
-        "left_shoulder_roll_link",
-        "left_elbow_link",
-        "left_wrist_yaw_link",
-        "right_shoulder_roll_link",
-        "right_elbow_link",
-        "right_wrist_yaw_link",
-    )
-    sampling_mode: Literal["start", "clip_start", "uniform", "adaptive", "mixed"] = "adaptive"
-    sampling_start_ratio: float = 0.0
-    truncate_on_clip_end: bool = False
-    max_episode_seconds: float = 10.0
-    reward_config: RewardConfig = field(default_factory=RewardConfig)
-    pose_randomization: PoseRandomization = field(default_factory=PoseRandomization)
-    velocity_randomization: VelocityRandomization = field(default_factory=VelocityRandomization)
-    domain_rand: DomainRand = field(default_factory=DomainRand)
-    joint_position_range: tuple[float, float] = (-0.1, 0.1)
-    # Termination thresholds
-    anchor_pos_z_threshold: float = 0.25
-    anchor_ori_threshold: float = 0.8
-    ee_body_pos_z_threshold: float = 0.25
-    ee_body_names: tuple[str, ...] = (
-        "left_ankle_roll_link",
-        "right_ankle_roll_link",
-        "left_wrist_yaw_link",
-        "right_wrist_yaw_link",
-    )
-    undesired_contact_z_threshold: float = 0.05
-    terminate_on_undesired_contacts: bool = False
-
-
-@dataclass
-class MotionTrackingDeployEnvCfg(MotionTrackingCfg):
-    """Base deploy configuration for motion tracking."""
-
-    pass
diff --git a/src/unilab/envs/motion_tracking/common/domain_randomization.py b/src/unilab/envs/motion_tracking/common/domain_randomization.py
deleted file mode 100644
index aa74e50e8..000000000
--- a/src/unilab/envs/motion_tracking/common/domain_randomization.py
+++ /dev/null
@@ -1,186 +0,0 @@
-"""Domain-randomization reset/observation provider for motion tracking."""
-
-from __future__ import annotations
-
-import time
-from typing import Any, cast
-
-import numpy as np
-
-from unilab.dr import (
-    DomainRandomizationCapabilities,
-    DomainRandomizationProvider,
-    IntervalRandomizationPlan,
-    ResetPlan,
-)
-from unilab.dr.dr_utils import (
-    build_common_reset_randomization,
-    build_interval_push_plan,
-    validate_common_reset_randomization,
-    validate_interval_push_support,
-    zero_actions,
-)
-from unilab.dr.types import RESET_TERM_GEOM_FRICTION, ResetRandomizationPayload
-from unilab.dtype_config import get_global_dtype
-
-from .reset import build_motion_reference_state
-
-
-class MotionTrackingDomainRandomizationProvider(DomainRandomizationProvider):
-    def __init__(
-        self,
-        *,
-        base_kp: np.ndarray | None = None,
-        base_kd: np.ndarray | None = None,
-        base_geom_friction: np.ndarray | None = None,
-        foot_geom_ids: np.ndarray | None = None,
-    ) -> None:
-        self._base_kp = base_kp
-        self._base_kd = base_kd
-        self._base_geom_friction = base_geom_friction
-        self._foot_geom_ids = foot_geom_ids
-        self._last_reset_observation_timing_ms: dict[str, float] = {}
-
-    @property
-    def last_reset_observation_timing_ms(self) -> dict[str, float]:
-        return dict(self._last_reset_observation_timing_ms)
-
-    def validate(self, env: Any, capabilities: DomainRandomizationCapabilities) -> None:
-        validate_common_reset_randomization(
-            env, capabilities, base_kp=self._base_kp, base_kd=self._base_kd
-        )
-        validate_interval_push_support(env, capabilities)
-        if getattr(env.cfg.domain_rand, "randomize_geom_friction", False):
-            if not capabilities.supports_reset_term(RESET_TERM_GEOM_FRICTION):
-                raise NotImplementedError(
-                    f"{env._backend.backend_type} backend does not support "
-                    "geom-friction reset randomization"
-                )
-            if (
-                self._base_geom_friction is None
-                or self._foot_geom_ids is None
-                or self._foot_geom_ids.size == 0
-            ):
-                raise ValueError("randomize_geom_friction=True but provider has no foot geom IDs")
-
-    def build_interval_randomization_plan(
-        self, env: Any, step_counter: int
-    ) -> IntervalRandomizationPlan | None:
-        return build_interval_push_plan(env, step_counter)
-
-    def build_reset_plan(self, env: Any, env_ids: np.ndarray) -> ResetPlan:
-        num_reset = len(env_ids)
-        motion_frames = env.motion_sampler.sample_frames(env_ids)
-        motion_data = env.motion_loader.get_motion_at_frame(motion_frames)
-        qpos, qvel = build_motion_reference_state(env, env_ids, motion_data)
-
-        info_updates = {
-            "current_actions": zero_actions(num_reset, env._num_action),
-            "last_actions": zero_actions(num_reset, env._num_action),
-        }
-        randomization = build_common_reset_randomization(
-            env, num_reset, base_kp=self._base_kp, base_kd=self._base_kd
-        )
-
-        dr_cfg = env.cfg.domain_rand
-        if getattr(dr_cfg, "randomize_geom_friction", False):
-            assert self._base_geom_friction is not None
-            assert self._foot_geom_ids is not None
-            payload = randomization or ResetRandomizationPayload()
-            low, high = dr_cfg.friction_range
-            scale = np.random.uniform(low, high, size=(num_reset, 1)).astype(np.float64)
-            geom_friction = np.broadcast_to(
-                self._base_geom_friction,
-                (num_reset, *self._base_geom_friction.shape),
-            ).copy()
-            geom_friction[:, self._foot_geom_ids, 0] = scale * np.ones(
-                (1, self._foot_geom_ids.size)
-            )
-            payload.geom_friction = geom_friction
-            randomization = payload
-
-        if getattr(dr_cfg, "randomize_joint_default_pos", False):
-            low, high = dr_cfg.joint_default_pos_range
-            info_updates["default_dof_pos_bias"] = np.random.uniform(
-                low, high, size=(num_reset, env._num_action)
-            ).astype(get_global_dtype())
-
-        return ResetPlan(
-            env_ids=env_ids,
-            qpos=qpos,
-            qvel=qvel,
-            info_updates=info_updates,
-            randomization=randomization,
-        )
-
-    def build_reset_observation(
-        self, env: Any, env_ids: np.ndarray, info_updates: dict[str, Any]
-    ) -> dict[str, np.ndarray]:
-        obs_t0 = time.perf_counter()
-
-        t0 = time.perf_counter()
-        motion_data = env.motion_loader.get_motion_at_frame(
-            env.motion_sampler.current_frames[env_ids]
-        )
-        motion_ms = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        linvel = env.get_local_linvel()[env_ids]
-        linvel_ms = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        gyro = env.get_gyro()[env_ids]
-        gyro_ms = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        dof_pos = env.get_dof_pos()[env_ids]
-        dof_pos_ms = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        dof_vel = env.get_dof_vel()[env_ids]
-        dof_vel_ms = (time.perf_counter() - t0) * 1000.0
-
-        t0 = time.perf_counter()
-        robot_body_pos_w, robot_body_quat_w = env._backend.get_body_pose_w_rows(
-            env_ids, env.body_ids
-        )
-        body_pose_ms = (time.perf_counter() - t0) * 1000.0
-
-        obs_info = dict(info_updates)
-        default_dof_pos_bias = info_updates.get("default_dof_pos_bias")
-        if isinstance(default_dof_pos_bias, np.ndarray):
-            obs_info["default_dof_pos_bias"] = default_dof_pos_bias
-        obs_info["env_ids"] = env_ids
-
-        t0 = time.perf_counter()
-        obs = cast(
-            dict[str, np.ndarray],
-            env._compute_obs(
-                obs_info,
-                motion_data,
-                linvel,
-                gyro,
-                dof_pos,
-                dof_vel,
-                robot_body_pos_w,
-                robot_body_quat_w,
-            ),
-        )
-        compute_obs_ms = (time.perf_counter() - t0) * 1000.0
-
-        total_ms = (time.perf_counter() - obs_t0) * 1000.0
-        getters_ms = linvel_ms + gyro_ms + dof_pos_ms + dof_vel_ms + body_pose_ms
-        self._last_reset_observation_timing_ms = {
-            "dr_reset_observation_getters_ms": getters_ms,
-            "dr_reset_obs_get_motion_ms": motion_ms,
-            "dr_reset_obs_get_local_linvel_ms": linvel_ms,
-            "dr_reset_obs_get_gyro_ms": gyro_ms,
-            "dr_reset_obs_get_dof_pos_ms": dof_pos_ms,
-            "dr_reset_obs_get_dof_vel_ms": dof_vel_ms,
-            "dr_reset_obs_get_body_pose_ms": body_pose_ms,
-            "dr_reset_observation_compute_obs_ms": compute_obs_ms,
-            "dr_reset_observation_internal_gap_ms": (
-                total_ms - motion_ms - getters_ms - compute_obs_ms
-            ),
-        }
-        return obs
diff --git a/src/unilab/envs/motion_tracking/common/observations.py b/src/unilab/envs/motion_tracking/common/observations.py
deleted file mode 100644
index fd5b0b6ee..000000000
--- a/src/unilab/envs/motion_tracking/common/observations.py
+++ /dev/null
@@ -1,277 +0,0 @@
-"""Observation construction for motion tracking.
-
-Holds the robot-agnostic observation builders. The environment classes keep a
-thin polymorphic method surface (``_compute_obs`` / ``_build_actor_obs`` /
-``_write_body_*``) that delegates here so subclasses can still override obs
-layout (SAC critic tail, box object obs, deploy mimic actor) while the core
-math lives in one place.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-import numpy as np
-
-from unilab.dtype_config import get_global_dtype
-from unilab.utils.geometry import np_write_relative_anchor_transform_pos_rot6d
-
-
-def actor_obs_dim(n: int) -> int:
-    return 3 + 6 + 3 + 3 + n * 5
-
-
-def critic_base_obs_dim(n: int) -> int:
-    return 3 + 6 + 3 + 3 + n * 5
-
-
-def mimic_actor_obs_dim(n: int) -> int:
-    # unitree_rl_lab mimic deploy actor input:
-    # motion_command(2n), motion_anchor_ori_b(6), gyro(3), joints, actions.
-    return 6 + 3 + n * 5
-
-
-def obs_groups_spec(env: Any) -> dict[str, int]:
-    # Actor: command(2n) + motion_anchor_pos_b(3) + motion_anchor_ori_b(6)
-    #        + linvel(3) + gyro(3) + joint_pos(n) + joint_vel(n) + actions(n)
-    # Critic mirrors BeyondMimic physical terms without actor observation noise:
-    #        command, motion anchor, robot body pos/ori, linvel, gyro, joints, actions.
-    n = env._num_action
-    actor_width = getattr(env, "_actor_obs_width", env._actor_obs_dim(n))
-    critic_width = getattr(
-        env,
-        "_critic_obs_width",
-        env._critic_base_obs_dim(n) + len(env._cfg.body_names) * 9,
-    )
-    return {"obs": actor_width, "critic": critic_width}
-
-
-def build_actor_obs(
-    *,
-    actor_obs_dim: int,
-    command: np.ndarray,
-    motion_anchor_pos_b: np.ndarray,
-    motion_anchor_ori_b: np.ndarray,
-    noisy_linvel: np.ndarray,
-    noisy_gyro: np.ndarray,
-    noisy_joint_pos_rel: np.ndarray,
-    noisy_dof_vel: np.ndarray,
-    last_actions: np.ndarray,
-) -> np.ndarray:
-    num_envs = command.shape[0]
-    n_action = noisy_joint_pos_rel.shape[1]
-    actor_obs = np.empty((num_envs, actor_obs_dim), dtype=get_global_dtype())
-    offset = 0
-    actor_obs[:, offset : offset + command.shape[1]] = command
-    offset += command.shape[1]
-    actor_obs[:, offset : offset + 3] = motion_anchor_pos_b
-    offset += 3
-    actor_obs[:, offset : offset + 6] = motion_anchor_ori_b
-    offset += 6
-    actor_obs[:, offset : offset + 3] = noisy_linvel
-    offset += 3
-    actor_obs[:, offset : offset + 3] = noisy_gyro
-    offset += 3
-    actor_obs[:, offset : offset + n_action] = noisy_joint_pos_rel
-    offset += n_action
-    actor_obs[:, offset : offset + n_action] = noisy_dof_vel
-    offset += n_action
-    actor_obs[:, offset : offset + n_action] = last_actions
-    return actor_obs
-
-
-def build_mimic_actor_obs(
-    *,
-    command: np.ndarray,
-    motion_anchor_ori_b: np.ndarray,
-    noisy_gyro: np.ndarray,
-    noisy_joint_pos_rel: np.ndarray,
-    noisy_dof_vel: np.ndarray,
-    last_actions: np.ndarray,
-) -> np.ndarray:
-    """unitree_rl_lab mimic deploy actor layout: 2n + 6 + 3 + n + n + n."""
-    return np.concatenate(
-        [
-            command,
-            motion_anchor_ori_b,
-            noisy_gyro,
-            noisy_joint_pos_rel,
-            noisy_dof_vel,
-            last_actions,
-        ],
-        axis=1,
-        dtype=get_global_dtype(),
-    )
-
-
-def write_body_pos_in_anchor_frame(
-    anchor_pos: np.ndarray,
-    anchor_quat: np.ndarray,
-    body_pos: np.ndarray,
-    out: np.ndarray,
-    *,
-    body_vec_error: np.ndarray,
-) -> None:
-    aw = anchor_quat[:, None, 0]
-    ax = anchor_quat[:, None, 1]
-    ay = anchor_quat[:, None, 2]
-    az = anchor_quat[:, None, 3]
-
-    num_envs, n_body = body_pos.shape[:2]
-    rel_pos = body_vec_error[:num_envs, :n_body]
-
-    vx = rel_pos[..., 0]
-    vy = rel_pos[..., 1]
-    vz = rel_pos[..., 2]
-    np.subtract(body_pos[..., 0], anchor_pos[:, None, 0], out=vx)
-    np.subtract(body_pos[..., 1], anchor_pos[:, None, 1], out=vy)
-    np.subtract(body_pos[..., 2], anchor_pos[:, None, 2], out=vz)
-
-    tx = 2 * (az * vy - ay * vz)
-    ty = 2 * (ax * vz - az * vx)
-    tz = 2 * (ay * vx - ax * vy)
-
-    out[..., 0] = vx + aw * tx + az * ty - ay * tz
-    out[..., 1] = vy + aw * ty + ax * tz - az * tx
-    out[..., 2] = vz + aw * tz + ay * tx - ax * ty
-
-
-def write_body_ori6_in_anchor_frame(
-    anchor_quat: np.ndarray,
-    body_quat: np.ndarray,
-    out: np.ndarray,
-) -> None:
-    aw = anchor_quat[:, None, 0]
-    ax = anchor_quat[:, None, 1]
-    ay = anchor_quat[:, None, 2]
-    az = anchor_quat[:, None, 3]
-    bw = body_quat[..., 0]
-    bx = body_quat[..., 1]
-    by = body_quat[..., 2]
-    bz = body_quat[..., 3]
-
-    rw = aw * bw + ax * bx + ay * by + az * bz
-    rx = aw * bx - ax * bw - ay * bz + az * by
-    ry = aw * by + ax * bz - ay * bw - az * bx
-    rz = aw * bz - ax * by + ay * bx - az * bw
-
-    out[..., 0] = 1 - 2 * (ry * ry + rz * rz)
-    out[..., 1] = 2 * (rx * ry - rw * rz)
-    out[..., 2] = 2 * (rx * ry + rw * rz)
-    out[..., 3] = 1 - 2 * (rx * rx + rz * rz)
-    out[..., 4] = 2 * (rx * rz - rw * ry)
-    out[..., 5] = 2 * (ry * rz + rw * rx)
-
-
-def compute_obs(
-    env: Any,
-    info: dict,
-    motion_data: Any,
-    linvel: np.ndarray,
-    gyro: np.ndarray,
-    dof_pos: np.ndarray,
-    dof_vel: np.ndarray,
-    robot_body_pos_w: np.ndarray,
-    robot_body_quat_w: np.ndarray,
-) -> dict[str, np.ndarray]:
-    """Compute observations as dict with actor and critic groups."""
-    num_envs = linvel.shape[0]
-    dtype = get_global_dtype()
-    n_action = dof_pos.shape[1]
-    n_body = env._n_motion_bodies
-
-    # Get anchor states
-    anchor_pos_w = motion_data.body_pos_w[:, env.anchor_body_idx]
-    anchor_quat_w = motion_data.body_quat_w[:, env.anchor_body_idx]
-    robot_anchor_pos_w = robot_body_pos_w[:, env.anchor_body_idx]
-    robot_anchor_quat_w = robot_body_quat_w[:, env.anchor_body_idx]
-
-    # Motion anchor pose in robot frame
-    if num_envs == env._num_envs:
-        motion_anchor_pos_b = env._motion_anchor_pos_b
-        motion_anchor_ori_b = env._motion_anchor_ori_b
-        joint_pos_rel = env._joint_pos_rel
-        zero_actions = env._zero_actions
-    else:
-        motion_anchor_pos_b = np.empty((num_envs, 3), dtype=dtype)
-        motion_anchor_ori_b = np.empty((num_envs, 6), dtype=dtype)
-        joint_pos_rel = np.empty((num_envs, n_action), dtype=dtype)
-        zero_actions = np.zeros((num_envs, n_action), dtype=dtype)
-    np_write_relative_anchor_transform_pos_rot6d(
-        robot_anchor_pos_w,
-        robot_anchor_quat_w,
-        anchor_pos_w,
-        anchor_quat_w,
-        motion_anchor_pos_b,
-        motion_anchor_ori_b,
-    )
-
-    # Joint positions and velocities
-    bias = info.get("default_dof_pos_bias")
-    effective_default = env.default_angles + bias if bias is not None else env.default_angles
-    np.subtract(dof_pos, effective_default, out=joint_pos_rel)
-    last_actions = info.get("current_actions")
-    if not isinstance(last_actions, np.ndarray):
-        last_actions = zero_actions
-
-    if num_envs == env._num_envs:
-        command = env._motion_command
-    else:
-        command = np.empty((num_envs, n_action * 2), dtype=dtype)
-    command[:, :n_action] = motion_data.joint_pos
-    command[:, n_action : n_action * 2] = motion_data.joint_vel
-
-    # Per-step observation noise on sensor channels (actor only).
-    # Critic uses the clean originals — asymmetric actor–critic contract.
-    noise_cfg = env._cfg.noise_config
-    noise_enabled = noise_cfg.level > 0.0
-    if noise_enabled:
-        linvel_actor = env._obs_noise(linvel, noise_cfg.scale_linvel)
-        gyro_actor = env._obs_noise(gyro, noise_cfg.scale_gyro)
-        joint_pos_actor = env._obs_noise(joint_pos_rel, noise_cfg.scale_joint_angle)
-        dof_vel_actor = env._obs_noise(dof_vel, noise_cfg.scale_joint_vel)
-    else:
-        linvel_actor = linvel
-        gyro_actor = gyro
-        joint_pos_actor = joint_pos_rel
-        dof_vel_actor = dof_vel
-
-    # Actor observations (noisy proprioception)
-    actor_obs = env._build_actor_obs(
-        command=command,
-        motion_anchor_pos_b=motion_anchor_pos_b,
-        motion_anchor_ori_b=motion_anchor_ori_b,
-        noisy_linvel=linvel_actor,
-        noisy_gyro=gyro_actor,
-        noisy_joint_pos_rel=joint_pos_actor,
-        noisy_dof_vel=dof_vel_actor,
-        last_actions=last_actions,
-    )
-
-    # Critic observations (clean proprioception + privileged body transforms)
-    critic_obs = np.empty((num_envs, env._critic_obs_width), dtype=dtype)
-    offset = 0
-    critic_obs[:, offset : offset + command.shape[1]] = command
-    offset += command.shape[1]
-    critic_obs[:, offset : offset + 3] = motion_anchor_pos_b
-    offset += 3
-    critic_obs[:, offset : offset + 6] = motion_anchor_ori_b
-    offset += 6
-    critic_obs[:, offset : offset + 3] = linvel
-    offset += 3
-    critic_obs[:, offset : offset + 3] = gyro
-    offset += 3
-    critic_obs[:, offset : offset + n_action] = joint_pos_rel
-    offset += n_action
-    critic_obs[:, offset : offset + n_action] = dof_vel
-    offset += n_action
-    critic_obs[:, offset : offset + n_action] = last_actions
-    offset += n_action
-    robot_body_pos_b = critic_obs[:, offset : offset + n_body * 3].reshape(num_envs, n_body, 3)
-    env._write_body_pos_in_anchor_frame(
-        robot_anchor_pos_w, robot_anchor_quat_w, robot_body_pos_w, robot_body_pos_b
-    )
-    offset += n_body * 3
-    robot_body_ori_b = critic_obs[:, offset : offset + n_body * 6].reshape(num_envs, n_body, 6)
-    env._write_body_ori6_in_anchor_frame(robot_anchor_quat_w, robot_body_quat_w, robot_body_ori_b)
-    return {"obs": actor_obs, "critic": critic_obs}
diff --git a/src/unilab/envs/motion_tracking/common/reset.py b/src/unilab/envs/motion_tracking/common/reset.py
deleted file mode 100644
index bea6b99e2..000000000
--- a/src/unilab/envs/motion_tracking/common/reset.py
+++ /dev/null
@@ -1,83 +0,0 @@
-"""Reset-state construction for motion tracking."""
-
-from __future__ import annotations
-
-from typing import Any
-
-import numpy as np
-
-from unilab.dtype_config import get_global_dtype
-from unilab.utils.geometry import np_sample_uniform
-from unilab.utils.rotation import np_quat_apply, np_quat_from_euler_xyz, np_quat_inv, np_quat_mul
-
-from .motion_loader import MotionData
-
-
-def build_motion_reference_state(
-    env: Any, env_ids: np.ndarray, motion_data: MotionData
-) -> tuple[np.ndarray, np.ndarray]:
-    dtype = get_global_dtype()
-    num_reset = len(env_ids)
-
-    root_pos = motion_data.body_pos_w[:, 0].copy()
-    root_ori = motion_data.body_quat_w[:, 0].copy()
-    root_lin_vel = motion_data.body_lin_vel_w[:, 0].copy()
-    root_ang_vel = motion_data.body_ang_vel_w[:, 0].copy()
-    joint_pos = motion_data.joint_pos.copy()
-    joint_vel = motion_data.joint_vel.copy()
-
-    pose_rand = env.cfg.pose_randomization
-    pose_ranges = [
-        (pose_rand.x[0], pose_rand.x[1]),
-        (pose_rand.y[0], pose_rand.y[1]),
-        (pose_rand.z[0], pose_rand.z[1]),
-        (pose_rand.roll[0], pose_rand.roll[1]),
-        (pose_rand.pitch[0], pose_rand.pitch[1]),
-        (pose_rand.yaw[0], pose_rand.yaw[1]),
-    ]
-    pose_samples = np.array(
-        [[np.random.uniform(low, high) for low, high in pose_ranges] for _ in range(num_reset)],
-        dtype=dtype,
-    )
-    root_pos += pose_samples[:, 0:3]
-    root_ori = np_quat_mul(
-        np_quat_from_euler_xyz(pose_samples[:, 3], pose_samples[:, 4], pose_samples[:, 5]),
-        root_ori,
-    )
-
-    vel_rand = env.cfg.velocity_randomization
-    vel_ranges = [
-        (vel_rand.x[0], vel_rand.x[1]),
-        (vel_rand.y[0], vel_rand.y[1]),
-        (vel_rand.z[0], vel_rand.z[1]),
-        (vel_rand.roll[0], vel_rand.roll[1]),
-        (vel_rand.pitch[0], vel_rand.pitch[1]),
-        (vel_rand.yaw[0], vel_rand.yaw[1]),
-    ]
-    vel_samples = np.array(
-        [[np.random.uniform(low, high) for low, high in vel_ranges] for _ in range(num_reset)],
-        dtype=dtype,
-    )
-    root_lin_vel += vel_samples[:, :3]
-    root_ang_vel += vel_samples[:, 3:]
-
-    joint_pos += np_sample_uniform(
-        env.cfg.joint_position_range[0],
-        env.cfg.joint_position_range[1],
-        joint_pos.shape,
-        dtype=np.float32,
-    )
-    joint_range = env._get_joint_range()
-    if joint_range is not None:
-        joint_pos = np.clip(joint_pos, joint_range[:, 0], joint_range[:, 1])
-
-    qpos = np.tile(env._init_qpos, (num_reset, 1))
-    qvel = np.tile(env._init_qvel, (num_reset, 1))
-    qpos[:, 0:3] = root_pos
-    qpos[:, 3:7] = root_ori
-    qpos[:, 7:] = joint_pos
-
-    qvel[:, 0:3] = root_lin_vel
-    qvel[:, 3:6] = np_quat_apply(np_quat_inv(root_ori), root_ang_vel)
-    qvel[:, 6:] = joint_vel
-    return qpos, qvel
diff --git a/src/unilab/envs/motion_tracking/common/rewards.py b/src/unilab/envs/motion_tracking/common/rewards.py
deleted file mode 100644
index 20ff092c6..000000000
--- a/src/unilab/envs/motion_tracking/common/rewards.py
+++ /dev/null
@@ -1,386 +0,0 @@
-"""Reward configuration and reward functions for motion tracking.
-
-Reward terms are plain module-level callables ``fn(ctx: RewardContext) -> np.ndarray``
-mirroring :mod:`unilab.envs.locomotion.common.rewards`. Robot-specific terms that
-live on env subclasses (box-object / joint-effort terms) are stored in the same
-``_reward_fns`` dispatch table as bound methods and are called with the same
-``ctx`` argument.
-
-``RewardContext`` intentionally carries the environment's *preallocated scratch
-buffers* (``body_vec_error``, ``joint_error``, ... ``undesired_contact_mask``).
-These buffers are env-owned so the hot path runs with zero per-step allocations;
-the term functions write into them in place. The op order, constants, clip bounds,
-and in-place ``out=`` usage are load-bearing for stable numeric behavior.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Any, Callable, Mapping
-
-import numpy as np
-
-
-@dataclass
-class RewardConfig:
-    """Reward configuration for motion tracking."""
-
-    scales: dict[str, float] = field(
-        default_factory=lambda: {
-            "motion_global_root_pos": 0.5,
-            "motion_global_root_ori": 0.5,
-            "motion_body_pos": 1.0,
-            "motion_body_ori": 1.0,
-            "motion_body_lin_vel": 1.0,
-            "motion_body_ang_vel": 1.0,
-            "motion_ee_body_pos_z": 0.0,
-            "motion_joint_pos": 0.0,
-            "motion_joint_vel": 0.0,
-            "action_rate_l2": -0.1,
-            "joint_limit": -10.0,
-        }
-    )
-    # Standard deviations for exponential rewards
-    std_root_pos: float = 0.3
-    std_root_ori: float = 0.4
-    std_body_pos: float = 0.3
-    std_body_ori: float = 0.4
-    std_body_lin_vel: float = 1.0
-    std_body_ang_vel: float = 3.14
-    std_joint_pos: float = 0.2
-    std_joint_vel: float = 1.0
-
-
-@dataclass
-class RewardContext:
-    """Bundle of everything the motion-tracking reward functions may read.
-
-    Built once per ``_compute_reward`` call. The array fields prefixed as
-    buffers (``env_error``, ``reward_term``, ``weighted_reward``, ...) are the
-    environment's preallocated scratch arrays; reward terms write into them in
-    place to keep the hot path allocation-free and numerically stable.
-    """
-
-    # ── semantic inputs ──────────────────────────────────────────────
-    info: dict
-    motion_data: Any = None
-    robot_body_pos_w: np.ndarray | None = None
-    robot_body_quat_w: np.ndarray | None = None
-    robot_body_lin_vel_w: np.ndarray | None = None
-    robot_body_ang_vel_w: np.ndarray | None = None
-    ref_body_pos_w: np.ndarray | None = None  # env.body_pos_relative_w
-    ref_body_quat_w: np.ndarray | None = None  # env.body_quat_relative_w
-    dof_pos: np.ndarray | None = None
-    dof_vel: np.ndarray | None = None
-
-    # ── config-derived scalars / indices ────────────────────────────
-    reward_config: Any = None
-    anchor_body_idx: int = 0
-    ee_body_indices: np.ndarray | None = None
-    undesired_contact_body_indices: np.ndarray | None = None
-    joint_lower: np.ndarray | None = None
-    joint_upper: np.ndarray | None = None
-    undesired_contact_z_threshold: float = 0.0
-    num_envs: int = 0
-
-    # ── env-owned scratch buffers (zero-alloc hot path) ──────────────
-    body_vec_error: np.ndarray | None = None
-    joint_error: np.ndarray | None = None
-    joint_error_upper: np.ndarray | None = None
-    env_error: np.ndarray | None = None
-    env_error2: np.ndarray | None = None
-    reward_term: np.ndarray | None = None
-    weighted_reward: np.ndarray | None = None
-    quat_error_w: np.ndarray | None = None
-    quat_error_x: np.ndarray | None = None
-    ee_pos_error_z: np.ndarray | None = None
-    undesired_contact_mask: np.ndarray | None = None
-
-
-# ── buffered math helpers ────────────────────────────────────────────
-
-
-def _required_array(value: np.ndarray | None, name: str) -> np.ndarray:
-    if value is None:
-        raise RuntimeError(f"RewardContext.{name} is required for this reward term")
-    return value
-
-
-def _mean_body_xyz_squared_error(
-    ctx: RewardContext, reference: np.ndarray, actual: np.ndarray
-) -> np.ndarray:
-    vec_error = _required_array(ctx.body_vec_error, "body_vec_error")
-    env_error = _required_array(ctx.env_error, "env_error")
-    tmp_error = _required_array(ctx.reward_term, "reward_term")
-    np.subtract(reference[..., 0], actual[..., 0], out=vec_error[..., 0])
-    np.square(vec_error[..., 0], out=vec_error[..., 0])
-    np.sum(vec_error[..., 0], axis=1, out=env_error)
-    np.subtract(reference[..., 1], actual[..., 1], out=vec_error[..., 1])
-    np.square(vec_error[..., 1], out=vec_error[..., 1])
-    np.sum(vec_error[..., 1], axis=1, out=tmp_error)
-    env_error += tmp_error
-    np.subtract(reference[..., 2], actual[..., 2], out=vec_error[..., 2])
-    np.square(vec_error[..., 2], out=vec_error[..., 2])
-    np.sum(vec_error[..., 2], axis=1, out=tmp_error)
-    env_error += tmp_error
-    env_error /= reference.shape[1]
-    return env_error
-
-
-def _quat_error_magnitude_squared_body(
-    ctx: RewardContext, q1: np.ndarray, q2: np.ndarray
-) -> np.ndarray:
-    rel_w = _required_array(ctx.quat_error_w, "quat_error_w")
-    rel_x = _required_array(ctx.quat_error_x, "quat_error_x")
-    # Motion/backend quaternions are unit quaternions, so the relative
-    # rotation angle only needs abs(dot(q1, q2)).
-    np.multiply(q1[..., 0], q2[..., 0], out=rel_w)
-    np.multiply(q1[..., 1], q2[..., 1], out=rel_x)
-    rel_w += rel_x
-    np.multiply(q1[..., 2], q2[..., 2], out=rel_x)
-    rel_w += rel_x
-    np.multiply(q1[..., 3], q2[..., 3], out=rel_x)
-    rel_w += rel_x
-    np.abs(rel_w, out=rel_w)
-    np.clip(rel_w, 0.0, 1.0, out=rel_w)
-    np.arccos(rel_w, out=rel_x)
-    rel_x *= 2.0
-    np.square(rel_x, out=rel_x)
-    return rel_x
-
-
-def _exp_reward_from_error(ctx: RewardContext, error: np.ndarray, std: float) -> np.ndarray:
-    out = _required_array(ctx.reward_term, "reward_term")
-    np.divide(error, -(std**2), out=out)
-    np.exp(out, out=out)
-    return out
-
-
-# ── reward terms ─────────────────────────────────────────────────────
-
-
-def motion_global_root_pos(ctx: RewardContext) -> np.ndarray:
-    motion_data = ctx.motion_data
-    robot_body_pos_w = _required_array(ctx.robot_body_pos_w, "robot_body_pos_w")
-    anchor_pos_w = motion_data.body_pos_w[:, ctx.anchor_body_idx]
-    robot_anchor_pos_w = robot_body_pos_w[:, ctx.anchor_body_idx]
-    error = _required_array(ctx.env_error, "env_error")
-    reward_term = _required_array(ctx.reward_term, "reward_term")
-    np.subtract(anchor_pos_w[:, 0], robot_anchor_pos_w[:, 0], out=error)
-    np.square(error, out=error)
-    np.subtract(anchor_pos_w[:, 1], robot_anchor_pos_w[:, 1], out=reward_term)
-    np.square(reward_term, out=reward_term)
-    error += reward_term
-    np.subtract(anchor_pos_w[:, 2], robot_anchor_pos_w[:, 2], out=reward_term)
-    np.square(reward_term, out=reward_term)
-    error += reward_term
-    return _exp_reward_from_error(ctx, error, ctx.reward_config.std_root_pos)
-
-
-def motion_global_root_ori(ctx: RewardContext) -> np.ndarray:
-    motion_data = ctx.motion_data
-    robot_body_quat_w = _required_array(ctx.robot_body_quat_w, "robot_body_quat_w")
-    anchor_quat_w = motion_data.body_quat_w[:, ctx.anchor_body_idx]
-    robot_anchor_quat_w = robot_body_quat_w[:, ctx.anchor_body_idx]
-    env_error = _required_array(ctx.env_error, "env_error")
-    reward_term = _required_array(ctx.reward_term, "reward_term")
-    np.multiply(anchor_quat_w[:, 0], robot_anchor_quat_w[:, 0], out=env_error)
-    np.multiply(anchor_quat_w[:, 1], robot_anchor_quat_w[:, 1], out=reward_term)
-    env_error += reward_term
-    np.multiply(anchor_quat_w[:, 2], robot_anchor_quat_w[:, 2], out=reward_term)
-    env_error += reward_term
-    np.multiply(anchor_quat_w[:, 3], robot_anchor_quat_w[:, 3], out=reward_term)
-    env_error += reward_term
-    np.abs(env_error, out=env_error)
-    np.clip(env_error, 0.0, 1.0, out=env_error)
-    np.arccos(env_error, out=env_error)
-    env_error *= 2.0
-    np.square(env_error, out=env_error)
-    return _exp_reward_from_error(ctx, env_error, ctx.reward_config.std_root_ori)
-
-
-def motion_body_pos(ctx: RewardContext) -> np.ndarray:
-    ref_body_pos_w = _required_array(ctx.ref_body_pos_w, "ref_body_pos_w")
-    robot_body_pos_w = _required_array(ctx.robot_body_pos_w, "robot_body_pos_w")
-    error = _mean_body_xyz_squared_error(ctx, ref_body_pos_w, robot_body_pos_w)
-    return _exp_reward_from_error(ctx, error, ctx.reward_config.std_body_pos)
-
-
-def motion_body_ori(ctx: RewardContext) -> np.ndarray:
-    ref_body_quat_w = _required_array(ctx.ref_body_quat_w, "ref_body_quat_w")
-    robot_body_quat_w = _required_array(ctx.robot_body_quat_w, "robot_body_quat_w")
-    error = _quat_error_magnitude_squared_body(ctx, ref_body_quat_w, robot_body_quat_w)
-    env_error = _required_array(ctx.env_error, "env_error")
-    np.sum(error, axis=-1, out=env_error)
-    env_error /= error.shape[1]
-    return _exp_reward_from_error(ctx, env_error, ctx.reward_config.std_body_ori)
-
-
-def motion_body_lin_vel(ctx: RewardContext) -> np.ndarray:
-    motion_data = ctx.motion_data
-    robot_body_lin_vel_w = _required_array(ctx.robot_body_lin_vel_w, "robot_body_lin_vel_w")
-    error = _mean_body_xyz_squared_error(ctx, motion_data.body_lin_vel_w, robot_body_lin_vel_w)
-    return _exp_reward_from_error(ctx, error, ctx.reward_config.std_body_lin_vel)
-
-
-def motion_body_ang_vel(ctx: RewardContext) -> np.ndarray:
-    motion_data = ctx.motion_data
-    robot_body_ang_vel_w = _required_array(ctx.robot_body_ang_vel_w, "robot_body_ang_vel_w")
-    error = _mean_body_xyz_squared_error(ctx, motion_data.body_ang_vel_w, robot_body_ang_vel_w)
-    return _exp_reward_from_error(ctx, error, ctx.reward_config.std_body_ang_vel)
-
-
-def motion_ee_body_pos_z(ctx: RewardContext) -> np.ndarray:
-    ref_body_pos_w = _required_array(ctx.ref_body_pos_w, "ref_body_pos_w")
-    robot_body_pos_w = _required_array(ctx.robot_body_pos_w, "robot_body_pos_w")
-    ee_body_indices = _required_array(ctx.ee_body_indices, "ee_body_indices")
-    ee_pos_error_z = _required_array(ctx.ee_pos_error_z, "ee_pos_error_z")
-    env_error = _required_array(ctx.env_error, "env_error")
-    np.subtract(
-        ref_body_pos_w[:, ee_body_indices, 2],
-        robot_body_pos_w[:, ee_body_indices, 2],
-        out=ee_pos_error_z,
-    )
-    np.square(ee_pos_error_z, out=ee_pos_error_z)
-    np.sum(ee_pos_error_z, axis=-1, out=env_error)
-    env_error /= ee_pos_error_z.shape[1]
-    return _exp_reward_from_error(ctx, env_error, ctx.reward_config.std_body_pos)
-
-
-def motion_joint_pos(ctx: RewardContext) -> np.ndarray:
-    motion_data = ctx.motion_data
-    dof_pos = _required_array(ctx.dof_pos, "dof_pos")
-    joint_error = _required_array(ctx.joint_error, "joint_error")
-    env_error = _required_array(ctx.env_error, "env_error")
-    np.subtract(motion_data.joint_pos, dof_pos, out=joint_error)
-    np.square(joint_error, out=joint_error)
-    np.sum(joint_error, axis=1, out=env_error)
-    env_error /= dof_pos.shape[1]
-    return _exp_reward_from_error(ctx, env_error, ctx.reward_config.std_joint_pos)
-
-
-def motion_joint_vel(ctx: RewardContext) -> np.ndarray:
-    motion_data = ctx.motion_data
-    dof_vel = _required_array(ctx.dof_vel, "dof_vel")
-    joint_error = _required_array(ctx.joint_error, "joint_error")
-    env_error = _required_array(ctx.env_error, "env_error")
-    np.subtract(motion_data.joint_vel, dof_vel, out=joint_error)
-    np.square(joint_error, out=joint_error)
-    np.sum(joint_error, axis=1, out=env_error)
-    env_error /= dof_vel.shape[1]
-    return _exp_reward_from_error(ctx, env_error, ctx.reward_config.std_joint_vel)
-
-
-def undesired_contacts(ctx: RewardContext) -> np.ndarray:
-    robot_body_pos_w = _required_array(ctx.robot_body_pos_w, "robot_body_pos_w")
-    undesired_contact_body_indices = _required_array(
-        ctx.undesired_contact_body_indices, "undesired_contact_body_indices"
-    )
-    undesired_contact_mask = _required_array(ctx.undesired_contact_mask, "undesired_contact_mask")
-    env_error = _required_array(ctx.env_error, "env_error")
-    body_z = robot_body_pos_w[:, undesired_contact_body_indices, 2]
-    np.less(
-        body_z,
-        ctx.undesired_contact_z_threshold,
-        out=undesired_contact_mask,
-    )
-    np.sum(undesired_contact_mask, axis=-1, out=env_error)
-    return env_error
-
-
-def action_rate_l2(ctx: RewardContext) -> np.ndarray:
-    info = ctx.info
-    joint_error = _required_array(ctx.joint_error, "joint_error")
-    env_error = _required_array(ctx.env_error, "env_error")
-    np.subtract(info["current_actions"], info["last_actions"], out=joint_error)
-    np.square(joint_error, out=joint_error)
-    np.sum(joint_error, axis=1, out=env_error)
-    return env_error
-
-
-def joint_limit(ctx: RewardContext) -> np.ndarray:
-    dof_pos = _required_array(ctx.dof_pos, "dof_pos")
-    reward_term = _required_array(ctx.reward_term, "reward_term")
-    joint_error = _required_array(ctx.joint_error, "joint_error")
-    joint_error_upper = _required_array(ctx.joint_error_upper, "joint_error_upper")
-    lower = ctx.joint_lower
-    upper = ctx.joint_upper
-    if lower is None or upper is None:
-        reward_term.fill(0.0)
-        return reward_term
-
-    # Compute violation
-    np.subtract(lower, dof_pos, out=joint_error)
-    np.maximum(joint_error, 0, out=joint_error)
-    np.subtract(dof_pos, upper, out=joint_error_upper)
-    np.maximum(joint_error_upper, 0, out=joint_error_upper)
-    joint_error += joint_error_upper
-    np.square(joint_error, out=joint_error)
-    np.sum(joint_error, axis=1, out=reward_term)
-    return reward_term
-
-
-def build_reward_functions() -> dict[str, Callable[[RewardContext], np.ndarray]]:
-    """Return the robot-agnostic reward-term dispatch table.
-
-    Keys define the canonical reward-term dispatch order.
-    """
-    return {
-        "motion_global_root_pos": motion_global_root_pos,
-        "motion_global_root_ori": motion_global_root_ori,
-        "motion_body_pos": motion_body_pos,
-        "motion_body_ori": motion_body_ori,
-        "motion_body_lin_vel": motion_body_lin_vel,
-        "motion_body_ang_vel": motion_body_ang_vel,
-        "motion_ee_body_pos_z": motion_ee_body_pos_z,
-        "motion_joint_pos": motion_joint_pos,
-        "motion_joint_vel": motion_joint_vel,
-        "action_rate_l2": action_rate_l2,
-        "joint_limit": joint_limit,
-        "undesired_contacts": undesired_contacts,
-    }
-
-
-def compute_reward(
-    ctx: RewardContext,
-    *,
-    active_reward_fns: Mapping[str, Callable[[RewardContext], np.ndarray]],
-    all_reward_fns: Mapping[str, Callable[[RewardContext], np.ndarray]],
-    scales: Mapping[str, float],
-    ctrl_dt: float,
-    enable_log: bool,
-) -> np.ndarray:
-    """Reduce ``scales × fns(ctx)`` into the per-env reward (in place, zero-alloc).
-
-    Uses ``ctx.env_error2`` as the reward accumulator and ``ctx.weighted_reward``
-    as the per-term scratch. Logs per-term
-    means into ``ctx.info["log"]`` every 4th step, then scales by ``ctrl_dt``.
-    """
-    reward = _required_array(ctx.env_error2, "env_error2")
-    reward.fill(0.0)
-
-    info = ctx.info
-    step_count = info.get("steps")
-    should_log = enable_log and (
-        int(step_count[0]) % 4 == 0 if isinstance(step_count, np.ndarray) else True
-    )
-    log = {} if should_log else info.get("log", {})
-
-    for name, scale in scales.items():
-        if scale == 0:
-            continue
-        reward_fn = active_reward_fns.get(name)
-        if reward_fn is None:
-            if should_log and name in all_reward_fns:
-                log[f"reward/{name}"] = 0.0
-            continue
-        rew = reward_fn(ctx)
-        weighted_rew = _required_array(ctx.weighted_reward, "weighted_reward")
-        np.multiply(rew, scale, out=weighted_rew)
-        reward += weighted_rew
-        if should_log:
-            log[f"reward/{name}"] = float(np.sum(weighted_rew) / weighted_rew.size)
-
-    info["log"] = log
-    reward *= ctrl_dt
-    return reward
diff --git a/src/unilab/envs/motion_tracking/common/terminations.py b/src/unilab/envs/motion_tracking/common/terminations.py
deleted file mode 100644
index a09bfd90c..000000000
--- a/src/unilab/envs/motion_tracking/common/terminations.py
+++ /dev/null
@@ -1,69 +0,0 @@
-"""Termination computation for motion tracking."""
-
-from __future__ import annotations
-
-from typing import Any
-
-import numpy as np
-
-from unilab.utils.geometry import np_gravity_z_in_body_from_quat
-
-
-def compute_terminations(
-    env: Any,
-    motion_data: Any,
-    robot_body_pos_w: np.ndarray,
-    robot_body_quat_w: np.ndarray,
-) -> np.ndarray:
-    """Compute termination conditions (writes into ``env._terminated``)."""
-    terminated = env._terminated
-    terminated.fill(False)
-
-    # Anchor position error (Z-axis only)
-    anchor_pos_w = motion_data.body_pos_w[:, env.anchor_body_idx]
-    robot_anchor_pos_w = robot_body_pos_w[:, env.anchor_body_idx]
-    np.subtract(anchor_pos_w[:, 2], robot_anchor_pos_w[:, 2], out=env._env_error)
-    np.abs(env._env_error, out=env._env_error)
-    np.greater(env._env_error, env._cfg.anchor_pos_z_threshold, out=env._env_bool)
-    terminated |= env._env_bool
-
-    # Anchor orientation error (gravity direction). The gravity-z difference
-    # is bounded by 2 for unit quaternions, so huge thresholds disable this
-    # termination without doing the per-step math.
-    if env._cfg.anchor_ori_threshold < 2.0:
-        anchor_quat_w = motion_data.body_quat_w[:, env.anchor_body_idx]
-        robot_anchor_quat_w = robot_body_quat_w[:, env.anchor_body_idx]
-        motion_gravity_z_b = np_gravity_z_in_body_from_quat(anchor_quat_w)
-        robot_gravity_z_b = np_gravity_z_in_body_from_quat(robot_anchor_quat_w)
-        np.subtract(motion_gravity_z_b, robot_gravity_z_b, out=env._env_error)
-        np.abs(env._env_error, out=env._env_error)
-        np.greater(env._env_error, env._cfg.anchor_ori_threshold, out=env._env_bool)
-        terminated |= env._env_bool
-
-    # End-effector position error (Z-axis only)
-    if env._has_ee_body_indices:
-        np.subtract(
-            env.body_pos_relative_w[:, env.ee_body_indices, 2],
-            robot_body_pos_w[:, env.ee_body_indices, 2],
-            out=env._ee_pos_error_z,
-        )
-        np.abs(env._ee_pos_error_z, out=env._ee_pos_error_z)
-        np.greater(
-            env._ee_pos_error_z,
-            env._cfg.ee_body_pos_z_threshold,
-            out=env._ee_terminated,
-        )
-        np.logical_or.reduce(env._ee_terminated, axis=1, out=env._env_bool)
-        terminated |= env._env_bool
-
-    if env._cfg.terminate_on_undesired_contacts and env._has_undesired_contact_body_indices:
-        body_z = robot_body_pos_w[:, env.undesired_contact_body_indices, 2]
-        np.less(
-            body_z,
-            env._cfg.undesired_contact_z_threshold,
-            out=env._undesired_contact_mask,
-        )
-        np.logical_or.reduce(env._undesired_contact_mask, axis=-1, out=env._env_bool)
-        terminated |= env._env_bool
-
-    return terminated
diff --git a/src/unilab/envs/motion_tracking/common/tracking.py b/src/unilab/envs/motion_tracking/common/tracking.py
deleted file mode 100644
index 9269d224a..000000000
--- a/src/unilab/envs/motion_tracking/common/tracking.py
+++ /dev/null
@@ -1,535 +0,0 @@
-"""Robot-agnostic motion-tracking engine.
-
-Holds :class:`MotionTrackingEnv` (the imitation engine, inheriting the shared
-``G1BaseEnv`` locomotion base) and :class:`MotionTrackingDeployEnv` (the
-unitree_rl_lab mimic actor variant). Per-concern math lives in the owner
-modules (``rewards`` / ``observations`` / ``terminations`` / ``transforms`` /
-``reset`` / ``domain_randomization``); the engine keeps only the
-thin polymorphic method surface and per-step orchestration.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-import numpy as np
-
-from unilab.base.backend import create_backend, env_backend_kwargs
-from unilab.base.np_env import NpEnvState
-from unilab.dtype_config import get_global_dtype
-from unilab.envs.locomotion.g1.base import G1BaseEnv
-
-from . import observations
-from .config import MotionTrackingCfg, MotionTrackingDeployEnvCfg
-from .domain_randomization import MotionTrackingDomainRandomizationProvider
-from .motion_loader import MotionData, MotionLoader, MotionSampler
-from .reset import build_motion_reference_state
-from .rewards import RewardContext, build_reward_functions, compute_reward
-from .terminations import compute_terminations
-from .transforms import update_relative_transforms
-
-
-class MotionTrackingEnv(G1BaseEnv):
-    """Motion Tracking Environment (robot-agnostic imitation engine)."""
-
-    _cfg: MotionTrackingCfg
-
-    def __init__(self, cfg: MotionTrackingCfg, num_envs=1, backend_type="mujoco"):
-        if not cfg.motion_file:
-            raise ValueError("motion_file must be specified in config")
-
-        backend = create_backend(
-            backend_type,
-            cfg.scene,
-            num_envs,
-            cfg.sim_dt,
-            base_name=cfg.asset.base_name,
-            push_body_name=cfg.domain_rand.push_body_name,
-            add_body_sensors=True,
-            **env_backend_kwargs(cfg),
-        )
-        super().__init__(cfg, backend, num_envs)
-
-        # Resolve body IDs for backend querying and motion-file indexing.
-        self.body_ids = self._backend.get_body_ids(cfg.body_names)
-        motion_body_ids = self._backend.get_motion_body_ids(cfg.body_names)
-
-        self.anchor_body_idx = cfg.body_names.index(cfg.anchor_body_name)
-
-        # Get end-effector body indices for termination
-        self.ee_body_indices = np.array(
-            [cfg.body_names.index(name) for name in cfg.ee_body_names], dtype=np.int32
-        )
-        self._has_ee_body_indices = bool(self.ee_body_indices.size)
-
-        # Get non-EE body indices for undesired contact penalty
-        ee_set = set(cfg.ee_body_names)
-        self.undesired_contact_body_indices = np.array(
-            [i for i, name in enumerate(cfg.body_names) if name not in ee_set],
-            dtype=np.int32,
-        )
-        self._has_undesired_contact_body_indices = bool(self.undesired_contact_body_indices.size)
-
-        # Load motion data
-        self.motion_loader = MotionLoader(cfg.motion_file, body_indices=motion_body_ids)
-        self.motion_sampler = MotionSampler(
-            self.motion_loader,
-            mode=cfg.sampling_mode,
-            num_envs=num_envs,
-            start_ratio=cfg.sampling_start_ratio,
-        )
-        needs_kp_kd = cfg.domain_rand.randomize_kp or cfg.domain_rand.randomize_kd
-        needs_friction = getattr(cfg.domain_rand, "randomize_geom_friction", False)
-        base_kp = base_kd = None
-        if needs_kp_kd:
-            base_kp, base_kd = backend.get_actuator_gains()
-        base_geom_friction = None
-        foot_geom_ids = None
-        if needs_friction:
-            import re as _re
-
-            base_geom_friction = backend.get_geom_friction()
-            geom_names = backend.get_geom_names()
-            pattern = _re.compile(cfg.domain_rand.friction_geom_pattern)
-            foot_geom_ids = np.asarray(
-                [i for i, name in enumerate(geom_names) if name and pattern.match(name)],
-                dtype=np.int64,
-            )
-            if foot_geom_ids.size == 0:
-                raise ValueError(
-                    "friction_geom_pattern "
-                    f"'{cfg.domain_rand.friction_geom_pattern}' did not match any geom"
-                )
-        dr_provider = MotionTrackingDomainRandomizationProvider(
-            base_kp=base_kp,
-            base_kd=base_kd,
-            base_geom_friction=base_geom_friction,
-            foot_geom_ids=foot_geom_ids,
-        )
-        self._init_domain_randomization(dr_provider)
-
-        dtype = get_global_dtype()
-        n_body = len(cfg.body_names)
-        self._n_motion_bodies = n_body
-        self._actor_obs_width = self._actor_obs_dim(self._num_action)
-        self._critic_base_obs_width = self._critic_base_obs_dim(self._num_action)
-        self._critic_obs_width = self._critic_base_obs_width + n_body * 9
-        self._copy_body_state_w = self._backend.copy_body_state_w
-
-        # Buffers for relative body transforms
-        self.body_pos_relative_w = np.zeros((num_envs, n_body, 3), dtype=dtype)
-        self.body_quat_relative_w = np.zeros((num_envs, n_body, 4), dtype=dtype)
-        self.body_quat_relative_w[:, :, 0] = 1.0  # Initialize to identity quaternion
-        self._motion_data_buffer = (
-            self.motion_loader.make_motion_data_buffer(num_envs)
-            if hasattr(self.motion_loader, "make_motion_data_buffer")
-            else None
-        )
-        self._zero_actions = np.zeros((num_envs, self._num_action), dtype=dtype)
-        self._joint_range = self._backend.get_joint_range()
-        if self._joint_range is not None:
-            self._joint_range = np.asarray(self._joint_range, dtype=dtype)
-            self._joint_lower = self._joint_range[:, 0]
-            self._joint_upper = self._joint_range[:, 1]
-        else:
-            self._joint_lower = None
-            self._joint_upper = None
-        self._delta_pos_w = np.empty((num_envs, 3), dtype=dtype)
-        self._delta_ori_w = np.empty((num_envs, 4), dtype=dtype)
-        self._motion_anchor_pos_b = np.empty((num_envs, 3), dtype=dtype)
-        self._motion_anchor_ori_b = np.empty((num_envs, 6), dtype=dtype)
-        self._motion_command = np.empty((num_envs, self._num_action * 2), dtype=dtype)
-        self._joint_pos_rel = np.empty((num_envs, self._num_action), dtype=dtype)
-        self._robot_body_pos_w = np.empty((num_envs, n_body, 3), dtype=dtype)
-        self._robot_body_quat_w = np.empty((num_envs, n_body, 4), dtype=dtype)
-        self._robot_body_lin_vel_w = np.empty((num_envs, n_body, 3), dtype=dtype)
-        self._robot_body_ang_vel_w = np.empty((num_envs, n_body, 3), dtype=dtype)
-        self._quat_error_w = np.empty((num_envs, n_body), dtype=dtype)
-        self._quat_error_x = np.empty((num_envs, n_body), dtype=dtype)
-        self._body_vec_error = np.empty((num_envs, n_body, 3), dtype=dtype)
-        self._body_vec_tmp = np.empty((num_envs, n_body, 3), dtype=dtype)
-        self._joint_error = np.empty((num_envs, self._num_action), dtype=dtype)
-        self._joint_error_upper = np.empty((num_envs, self._num_action), dtype=dtype)
-        self._env_error = np.empty((num_envs,), dtype=dtype)
-        self._env_error2 = np.empty((num_envs,), dtype=dtype)
-        self._reward_term = np.empty((num_envs,), dtype=dtype)
-        self._weighted_reward = np.empty((num_envs,), dtype=dtype)
-        self._terminated = np.empty((num_envs,), dtype=bool)
-        self._env_bool = np.empty((num_envs,), dtype=bool)
-        self._ee_pos_error_z = np.empty((num_envs, self.ee_body_indices.size), dtype=dtype)
-        self._ee_terminated = np.empty((num_envs, self.ee_body_indices.size), dtype=bool)
-        self._undesired_contact_mask = np.empty(
-            (num_envs, self.undesired_contact_body_indices.size), dtype=bool
-        )
-
-        self._enable_reward_log = True
-        self._init_reward_functions()
-        self._active_reward_fns = {
-            name: reward_fn
-            for name, reward_fn in self._reward_fns.items()
-            if self._reward_term_is_active(name)
-        }
-        self._clip_end_truncated = np.zeros((num_envs,), dtype=bool)
-
-    def _effective_default_angles(self, env_ids: np.ndarray | None = None) -> np.ndarray:
-        """Return default_angles with per-episode joint-default-pos bias applied."""
-        state = getattr(self, "_state", None)
-        if state is not None:
-            bias = state.info.get("default_dof_pos_bias")
-            if bias is not None:
-                if env_ids is not None:
-                    return self.default_angles + bias[env_ids]
-                return self.default_angles + bias
-        return self.default_angles
-
-    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
-        state.info["last_actions"] = state.info.get("current_actions", np.zeros_like(actions))
-        state.info["current_actions"] = actions
-        exec_actions = (
-            state.info["last_actions"]
-            if self._cfg.control_config.simulate_action_latency
-            else actions
-        )
-        bias = state.info.get("default_dof_pos_bias")
-        base = self.default_angles + bias if bias is not None else self.default_angles
-        ctrl: np.ndarray = exec_actions * self._cfg.control_config.action_scale + base
-        return ctrl
-
-    def _resample_reference_state(self, env_ids: np.ndarray) -> None:
-        motion_frames = self.motion_sampler.sample_frames(env_ids)
-        motion_data = self.motion_loader.get_motion_at_frame(motion_frames)
-        qpos, qvel = build_motion_reference_state(self, env_ids, motion_data)
-        self._backend.set_state(env_ids, qpos, qvel)
-
-    def _refresh_observation_rows(
-        self, obs: dict[str, np.ndarray], info: dict, env_ids: np.ndarray
-    ) -> None:
-        motion_data = self.motion_loader.get_motion_at_frame(
-            self.motion_sampler.current_frames[env_ids]
-        )
-        row_ids = np.asarray(env_ids, dtype=np.intp)
-        linvel = self._backend.get_sensor_data_rows(self._cfg.sensor.local_linvel, row_ids)
-        gyro = self._backend.get_sensor_data_rows(self._cfg.sensor.gyro, row_ids)
-        dof_pos = self.get_dof_pos()[row_ids]
-        dof_vel = self.get_dof_vel()[row_ids]
-        robot_body_pos_w, robot_body_quat_w = self._backend.get_body_pose_w_rows(
-            row_ids, self.body_ids
-        )
-
-        obs_info: dict[str, Any] = {}
-        current_actions = info.get("current_actions")
-        if isinstance(current_actions, np.ndarray):
-            obs_info["current_actions"] = current_actions[env_ids]
-        obs_info["env_ids"] = env_ids
-
-        refreshed_obs = self._compute_obs(
-            obs_info,
-            motion_data,
-            linvel,
-            gyro,
-            dof_pos,
-            dof_vel,
-            robot_body_pos_w,
-            robot_body_quat_w,
-        )
-        for key, value in refreshed_obs.items():
-            if value.shape[0] == len(env_ids):
-                obs[key][env_ids] = value
-            else:
-                obs[key][env_ids] = value[env_ids]
-
-    def _get_body_pose_w(self) -> tuple[np.ndarray, np.ndarray]:
-        return self._backend.get_body_pose_w(self.body_ids)
-
-    def _get_body_state_w(self) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
-        copy_body_state_w = self._copy_body_state_w
-        if copy_body_state_w is not None:
-            return copy_body_state_w(
-                self.body_ids,
-                self._robot_body_pos_w,
-                self._robot_body_quat_w,
-                self._robot_body_lin_vel_w,
-                self._robot_body_ang_vel_w,
-            )
-        robot_body_pos_w, robot_body_quat_w = self._get_body_pose_w()
-        robot_body_lin_vel_w, robot_body_ang_vel_w = self._backend.get_body_vel_w(self.body_ids)
-        return (
-            robot_body_pos_w,
-            robot_body_quat_w,
-            robot_body_lin_vel_w,
-            robot_body_ang_vel_w,
-        )
-
-    def _get_joint_range(self) -> np.ndarray | None:
-        return self._joint_range
-
-    def _get_current_motion(self) -> MotionData:
-        if self._motion_data_buffer is None:
-            return self.motion_sampler.get_current_motion()
-        return self.motion_sampler.get_current_motion(self._motion_data_buffer)
-
-    @property
-    def obs_groups_spec(self) -> dict[str, int]:
-        return observations.obs_groups_spec(self)
-
-    def _actor_obs_dim(self, n: int) -> int:
-        return observations.actor_obs_dim(n)
-
-    def _critic_base_obs_dim(self, n: int) -> int:
-        return observations.critic_base_obs_dim(n)
-
-    def _build_actor_obs(
-        self,
-        *,
-        command: np.ndarray,
-        motion_anchor_pos_b: np.ndarray,
-        motion_anchor_ori_b: np.ndarray,
-        noisy_linvel: np.ndarray,
-        noisy_gyro: np.ndarray,
-        noisy_joint_pos_rel: np.ndarray,
-        noisy_dof_vel: np.ndarray,
-        last_actions: np.ndarray,
-    ) -> np.ndarray:
-        n_action = noisy_joint_pos_rel.shape[1]
-        return observations.build_actor_obs(
-            actor_obs_dim=self._actor_obs_dim(n_action),
-            command=command,
-            motion_anchor_pos_b=motion_anchor_pos_b,
-            motion_anchor_ori_b=motion_anchor_ori_b,
-            noisy_linvel=noisy_linvel,
-            noisy_gyro=noisy_gyro,
-            noisy_joint_pos_rel=noisy_joint_pos_rel,
-            noisy_dof_vel=noisy_dof_vel,
-            last_actions=last_actions,
-        )
-
-    def _init_reward_functions(self):
-        self._reward_fns = build_reward_functions()
-
-    def _reward_term_is_active(self, name: str) -> bool:
-        if name == "joint_limit":
-            return self._joint_lower is not None and self._joint_upper is not None
-        if name == "undesired_contacts":
-            return self._has_undesired_contact_body_indices
-        if name == "motion_ee_body_pos_z":
-            return self._has_ee_body_indices
-        return True
-
-    def update_state(self, state: NpEnvState) -> NpEnvState:
-        self._clip_end_truncated.fill(False)
-
-        # Get current motion data
-        motion_data = self._get_current_motion()
-
-        # Get robot state
-        linvel = self.get_local_linvel()
-        gyro = self.get_gyro()
-        dof_pos = self.get_dof_pos()
-        dof_vel = self.get_dof_vel()
-
-        # Get body states
-        (
-            robot_body_pos_w,
-            robot_body_quat_w,
-            robot_body_lin_vel_w,
-            robot_body_ang_vel_w,
-        ) = self._get_body_state_w()
-
-        # Compute relative body transforms (for observations and rewards)
-        self._update_relative_transforms(motion_data, robot_body_pos_w, robot_body_quat_w)
-
-        # Compute terminations
-        terminated = self._compute_terminations(motion_data, robot_body_pos_w, robot_body_quat_w)
-
-        # Compute reward
-        reward = self._compute_reward(
-            state.info,
-            motion_data,
-            robot_body_pos_w,
-            robot_body_quat_w,
-            robot_body_lin_vel_w,
-            robot_body_ang_vel_w,
-            dof_pos,
-            dof_vel,
-        )
-
-        # Compute observations
-        obs = self._compute_obs(
-            state.info,
-            motion_data,
-            linvel,
-            gyro,
-            dof_pos,
-            dof_vel,
-            robot_body_pos_w,
-            robot_body_quat_w,
-        )
-
-        # Update failure statistics for adaptive sampling
-        self.motion_sampler.update_failure_stats(terminated)
-
-        # Advance motion frames
-        done_env_ids = self.motion_sampler.step()
-        if len(done_env_ids) > 0:
-            if self._cfg.truncate_on_clip_end:
-                self._clip_end_truncated[done_env_ids] = True
-            else:
-                # Match BeyondMimic: clip boundaries are command resampling points, not
-                # episode boundaries; sync the simulated robot to the new reference.
-                resample_env_ids = done_env_ids[~terminated[done_env_ids]]
-                if len(resample_env_ids) > 0:
-                    self._resample_reference_state(resample_env_ids)
-                    self._refresh_observation_rows(obs, state.info, resample_env_ids)
-
-        return state.replace(obs=obs, reward=reward, terminated=terminated)
-
-    def _compute_truncated(self, state: NpEnvState) -> np.ndarray:
-        truncated = super()._compute_truncated(state)
-        clip_end_only = getattr(self, "_env_bool", None)
-        if clip_end_only is None or clip_end_only.shape != (self._num_envs,):
-            clip_end_only = np.empty((self._num_envs,), dtype=bool)
-            self._env_bool = clip_end_only
-        np.logical_not(state.terminated, out=clip_end_only)
-        np.logical_and(self._clip_end_truncated, clip_end_only, out=clip_end_only)
-        np.logical_or(truncated, clip_end_only, out=truncated)
-        return truncated
-
-    def _update_relative_transforms(
-        self, motion_data, robot_body_pos_w: np.ndarray, robot_body_quat_w: np.ndarray
-    ):
-        """Update relative body transforms for tracking."""
-        update_relative_transforms(self, motion_data, robot_body_pos_w, robot_body_quat_w)
-
-    def _compute_terminations(
-        self,
-        motion_data,
-        robot_body_pos_w: np.ndarray,
-        robot_body_quat_w: np.ndarray,
-    ) -> np.ndarray:
-        """Compute termination conditions."""
-        return compute_terminations(self, motion_data, robot_body_pos_w, robot_body_quat_w)
-
-    def _write_body_pos_in_anchor_frame(
-        self,
-        anchor_pos: np.ndarray,
-        anchor_quat: np.ndarray,
-        body_pos: np.ndarray,
-        out: np.ndarray,
-    ) -> None:
-        observations.write_body_pos_in_anchor_frame(
-            anchor_pos, anchor_quat, body_pos, out, body_vec_error=self._body_vec_error
-        )
-
-    def _write_body_ori6_in_anchor_frame(
-        self,
-        anchor_quat: np.ndarray,
-        body_quat: np.ndarray,
-        out: np.ndarray,
-    ) -> None:
-        observations.write_body_ori6_in_anchor_frame(anchor_quat, body_quat, out)
-
-    def _compute_obs(
-        self,
-        info: dict,
-        motion_data,
-        linvel: np.ndarray,
-        gyro: np.ndarray,
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-        robot_body_pos_w: np.ndarray,
-        robot_body_quat_w: np.ndarray,
-    ) -> dict[str, np.ndarray]:
-        """Compute observations as dict with actor and critic groups."""
-        return observations.compute_obs(
-            self,
-            info,
-            motion_data,
-            linvel,
-            gyro,
-            dof_pos,
-            dof_vel,
-            robot_body_pos_w,
-            robot_body_quat_w,
-        )
-
-    def _compute_reward(
-        self,
-        info: dict,
-        motion_data,
-        robot_body_pos_w: np.ndarray,
-        robot_body_quat_w: np.ndarray,
-        robot_body_lin_vel_w: np.ndarray,
-        robot_body_ang_vel_w: np.ndarray,
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-    ) -> np.ndarray:
-        """Compute reward."""
-        ctx = RewardContext(
-            info=info,
-            motion_data=motion_data,
-            robot_body_pos_w=robot_body_pos_w,
-            robot_body_quat_w=robot_body_quat_w,
-            robot_body_lin_vel_w=robot_body_lin_vel_w,
-            robot_body_ang_vel_w=robot_body_ang_vel_w,
-            ref_body_pos_w=self.body_pos_relative_w,
-            ref_body_quat_w=self.body_quat_relative_w,
-            dof_pos=dof_pos,
-            dof_vel=dof_vel,
-            reward_config=self._cfg.reward_config,
-            anchor_body_idx=self.anchor_body_idx,
-            ee_body_indices=self.ee_body_indices,
-            undesired_contact_body_indices=self.undesired_contact_body_indices,
-            joint_lower=self._joint_lower,
-            joint_upper=self._joint_upper,
-            undesired_contact_z_threshold=self._cfg.undesired_contact_z_threshold,
-            num_envs=self._num_envs,
-            body_vec_error=self._body_vec_error,
-            joint_error=self._joint_error,
-            joint_error_upper=self._joint_error_upper,
-            env_error=self._env_error,
-            env_error2=self._env_error2,
-            reward_term=self._reward_term,
-            weighted_reward=self._weighted_reward,
-            quat_error_w=self._quat_error_w,
-            quat_error_x=self._quat_error_x,
-            ee_pos_error_z=self._ee_pos_error_z,
-            undesired_contact_mask=self._undesired_contact_mask,
-        )
-        return compute_reward(
-            ctx,
-            active_reward_fns=self._active_reward_fns,
-            all_reward_fns=self._reward_fns,
-            scales=self._cfg.reward_config.scales,
-            ctrl_dt=self._cfg.ctrl_dt,
-            enable_log=self._enable_reward_log,
-        )
-
-
-class MotionTrackingDeployEnv(MotionTrackingEnv):
-    """Deploy-oriented motion tracking env with unitree_rl_lab mimic actor inputs."""
-
-    _cfg: MotionTrackingDeployEnvCfg
-
-    def _actor_obs_dim(self, n: int) -> int:
-        return observations.mimic_actor_obs_dim(n)
-
-    def _build_actor_obs(
-        self,
-        *,
-        command: np.ndarray,
-        motion_anchor_pos_b: np.ndarray,
-        motion_anchor_ori_b: np.ndarray,
-        noisy_linvel: np.ndarray,
-        noisy_gyro: np.ndarray,
-        noisy_joint_pos_rel: np.ndarray,
-        noisy_dof_vel: np.ndarray,
-        last_actions: np.ndarray,
-    ) -> np.ndarray:
-        return observations.build_mimic_actor_obs(
-            command=command,
-            motion_anchor_ori_b=motion_anchor_ori_b,
-            noisy_gyro=noisy_gyro,
-            noisy_joint_pos_rel=noisy_joint_pos_rel,
-            noisy_dof_vel=noisy_dof_vel,
-            last_actions=last_actions,
-        )
diff --git a/src/unilab/envs/motion_tracking/common/transforms.py b/src/unilab/envs/motion_tracking/common/transforms.py
deleted file mode 100644
index a4b8ae5ef..000000000
--- a/src/unilab/envs/motion_tracking/common/transforms.py
+++ /dev/null
@@ -1,103 +0,0 @@
-"""Relative body-transform computation for motion tracking.
-
-Fills the environment's ``body_pos_relative_w`` / ``body_quat_relative_w``
-reference buffers each step. The op order and in-place ``out=`` usage are
-load-bearing for stable observation and reward semantics.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-import numpy as np
-
-
-def update_relative_transforms(
-    env: Any,
-    motion_data: Any,
-    robot_body_pos_w: np.ndarray,
-    robot_body_quat_w: np.ndarray,
-) -> None:
-    """Update relative body transforms for tracking."""
-    # Get anchor states
-    anchor_pos_w = motion_data.body_pos_w[:, env.anchor_body_idx]
-    anchor_quat_w = motion_data.body_quat_w[:, env.anchor_body_idx]
-    robot_anchor_pos_w = robot_body_pos_w[:, env.anchor_body_idx]
-    robot_anchor_quat_w = robot_body_quat_w[:, env.anchor_body_idx]
-
-    # Compute delta transform: keep robot's XY position, use motion's Z height
-    # and apply yaw-only rotation difference.
-    delta_pos_w = env._delta_pos_w
-    delta_pos_w[:] = robot_anchor_pos_w
-    delta_pos_w[:, 2] = anchor_pos_w[:, 2]
-
-    # Compute yaw-only rotation difference, equivalent to
-    # np_yaw_quat(np_quat_mul(robot_anchor_quat_w, np_quat_inv(anchor_quat_w))).
-    delta_ori_w = env._delta_ori_w
-    rw, rx, ry, rz = (
-        robot_anchor_quat_w[:, 0],
-        robot_anchor_quat_w[:, 1],
-        robot_anchor_quat_w[:, 2],
-        robot_anchor_quat_w[:, 3],
-    )
-    aw, ax, ay, az = (
-        anchor_quat_w[:, 0],
-        anchor_quat_w[:, 1],
-        anchor_quat_w[:, 2],
-        anchor_quat_w[:, 3],
-    )
-    qw = rw * aw + rx * ax + ry * ay + rz * az
-    qx = -rw * ax + rx * aw - ry * az + rz * ay
-    qy = -rw * ay + rx * az + ry * aw - rz * ax
-    qz = -rw * az - rx * ay + ry * ax + rz * aw
-    half_yaw = 0.5 * np.arctan2(2 * (qw * qz + qx * qy), 1 - 2 * (qy * qy + qz * qz))
-    np.cos(half_yaw, out=delta_ori_w[:, 0])
-    delta_ori_w[:, 1:3] = 0.0
-    np.sin(half_yaw, out=delta_ori_w[:, 3])
-
-    dw1 = delta_ori_w[:, 0]
-    dz1 = delta_ori_w[:, 3]
-    dw = dw1[:, None]
-    dz = dz1[:, None]
-    mw = motion_data.body_quat_w[..., 0]
-    mx = motion_data.body_quat_w[..., 1]
-    my = motion_data.body_quat_w[..., 2]
-    mz = motion_data.body_quat_w[..., 3]
-    out_quat = env.body_quat_relative_w
-    out_quat[..., 0] = dw * mw
-    out_quat[..., 0] -= dz * mz
-    out_quat[..., 1] = dw * mx
-    out_quat[..., 1] -= dz * my
-    out_quat[..., 2] = dw * my
-    out_quat[..., 2] += dz * mx
-    out_quat[..., 3] = dw * mz
-    out_quat[..., 3] += dz * mw
-
-    rel_pos = env._body_vec_error
-    vx = rel_pos[..., 0]
-    vy = rel_pos[..., 1]
-    vz = rel_pos[..., 2]
-    np.subtract(motion_data.body_pos_w[..., 0], anchor_pos_w[:, None, 0], out=vx)
-    np.subtract(motion_data.body_pos_w[..., 1], anchor_pos_w[:, None, 1], out=vy)
-    np.subtract(motion_data.body_pos_w[..., 2], anchor_pos_w[:, None, 2], out=vz)
-
-    yaw_cross = env._env_error
-    yaw_z2 = env._reward_term
-    np.multiply(dw1, dz1, out=yaw_cross)
-    yaw_cross *= 2.0
-    np.square(dz1, out=yaw_z2)
-    yaw_z2 *= 2.0
-    yaw_cross_2d = yaw_cross[:, None]
-    yaw_z2_2d = yaw_z2[:, None]
-
-    out_pos = env.body_pos_relative_w
-    out_pos[..., 0] = vx
-    out_pos[..., 0] -= yaw_cross_2d * vy
-    out_pos[..., 0] -= yaw_z2_2d * vx
-    out_pos[..., 0] += delta_pos_w[:, None, 0]
-    out_pos[..., 1] = vy
-    out_pos[..., 1] += yaw_cross_2d * vx
-    out_pos[..., 1] -= yaw_z2_2d * vy
-    out_pos[..., 1] += delta_pos_w[:, None, 1]
-    out_pos[..., 2] = vz
-    out_pos[..., 2] += delta_pos_w[:, None, 2]
diff --git a/src/unilab/envs/motion_tracking/g1/__init__.py b/src/unilab/envs/motion_tracking/g1/__init__.py
deleted file mode 100644
index c97e3866a..000000000
--- a/src/unilab/envs/motion_tracking/g1/__init__.py
+++ /dev/null
@@ -1,98 +0,0 @@
-"""Motion tracking environments for Unitree G1."""
-
-from .box_tracking import (
-    G1BoxTracking23DofCfg,
-    G1BoxTracking23DofEnvCfg,
-    G1BoxTrackingCfg,
-    G1BoxTrackingEnv,
-    G1BoxTrackingEnvCfg,
-)
-from .flip_tracking import (
-    G1ClimbTrackingCfg,
-    G1ClimbTrackingEnv,
-    G1ClimbTrackingEnvCfg,
-    G1FlipTracking23DofCfg,
-    G1FlipTracking23DofEnvCfg,
-    G1FlipTrackingCfg,
-    G1FlipTrackingEnv,
-    G1FlipTrackingEnvCfg,
-    G1WallFlipTracking23DofCfg,
-    G1WallFlipTracking23DofEnvCfg,
-    G1WallFlipTrackingCfg,
-    G1WallFlipTrackingEnv,
-    G1WallFlipTrackingEnvCfg,
-)
-from .flip_tracking_sac import (
-    G1FlipTrackingSAC23DofCfg,
-    G1FlipTrackingSAC23DofEnv,
-    G1FlipTrackingSACCfg,
-    G1FlipTrackingSACEnv,
-    G1WallFlipTrackingSAC23DofCfg,
-    G1WallFlipTrackingSAC23DofEnv,
-    G1WallFlipTrackingSACCfg,
-    G1WallFlipTrackingSACEnv,
-)
-from .motion_box_loader import BoxMotionData, BoxMotionLoader
-from .tracking import (
-    G1MotionTracking23DofCfg,
-    G1MotionTracking23DofDeployEnvCfg,
-    G1MotionTracking23DofEnvCfg,
-    G1MotionTrackingCfg,
-    G1MotionTrackingDeployEnv,
-    G1MotionTrackingDeployEnvCfg,
-    G1MotionTrackingEnv,
-    G1MotionTrackingEnvCfg,
-)
-from .tracking_obs import G1WBTObs23DofCfg, G1WBTObsCfg, G1WBTObsEnv
-from .tracking_sac import (
-    G1MotionTrackingSAC23DofCfg,
-    G1MotionTrackingSAC23DofEnv,
-    G1MotionTrackingSACCfg,
-    G1MotionTrackingSACEnv,
-)
-
-__all__ = [
-    "G1MotionTracking23DofCfg",
-    "G1MotionTracking23DofDeployEnvCfg",
-    "G1MotionTracking23DofEnvCfg",
-    "G1MotionTrackingCfg",
-    "G1MotionTrackingDeployEnv",
-    "G1MotionTrackingDeployEnvCfg",
-    "G1MotionTrackingEnv",
-    "G1MotionTrackingEnvCfg",
-    "G1MotionTrackingSAC23DofCfg",
-    "G1MotionTrackingSAC23DofEnv",
-    "G1MotionTrackingSACCfg",
-    "G1MotionTrackingSACEnv",
-    "G1WBTObs23DofCfg",
-    "G1WBTObsCfg",
-    "G1WBTObsEnv",
-    "G1FlipTracking23DofCfg",
-    "G1FlipTracking23DofEnvCfg",
-    "G1FlipTrackingCfg",
-    "G1FlipTrackingEnv",
-    "G1FlipTrackingEnvCfg",
-    "G1FlipTrackingSAC23DofCfg",
-    "G1FlipTrackingSAC23DofEnv",
-    "G1FlipTrackingSACCfg",
-    "G1FlipTrackingSACEnv",
-    "G1WallFlipTracking23DofCfg",
-    "G1WallFlipTracking23DofEnvCfg",
-    "G1WallFlipTrackingCfg",
-    "G1WallFlipTrackingEnv",
-    "G1WallFlipTrackingEnvCfg",
-    "G1WallFlipTrackingSAC23DofCfg",
-    "G1WallFlipTrackingSAC23DofEnv",
-    "G1WallFlipTrackingSACCfg",
-    "G1WallFlipTrackingSACEnv",
-    "G1ClimbTrackingCfg",
-    "G1ClimbTrackingEnv",
-    "G1ClimbTrackingEnvCfg",
-    "G1BoxTracking23DofCfg",
-    "G1BoxTracking23DofEnvCfg",
-    "G1BoxTrackingCfg",
-    "G1BoxTrackingEnv",
-    "G1BoxTrackingEnvCfg",
-    "BoxMotionData",
-    "BoxMotionLoader",
-]
diff --git a/src/unilab/envs/motion_tracking/g1/box_tracking.py b/src/unilab/envs/motion_tracking/g1/box_tracking.py
deleted file mode 100644
index b49a98afc..000000000
--- a/src/unilab/envs/motion_tracking/g1/box_tracking.py
+++ /dev/null
@@ -1,410 +0,0 @@
-"""G1 box tracking environment with object-aware motion imitation."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Any, cast
-
-import numpy as np
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base import registry
-from unilab.base.scene import SceneCfg
-from unilab.dr import DomainRandomizationManager, ResetPlan
-from unilab.dr.dr_utils import build_common_reset_randomization, zero_actions
-from unilab.dtype_config import get_global_dtype
-from unilab.utils.geometry import np_sample_uniform
-from unilab.utils.rotation import (
-    np_matrix_from_quat,
-    np_quat_apply,
-    np_quat_error_magnitude,
-    np_quat_from_euler_xyz,
-    np_quat_inv,
-    np_quat_mul,
-    np_subtract_frame_transforms,
-)
-
-from ..common.rewards import RewardContext
-from .motion_box_loader import BoxMotionData, BoxMotionLoader
-from .tracking import (
-    G1MotionTrackingCfg,
-    G1MotionTrackingDomainRandomizationProvider,
-    G1MotionTrackingEnv,
-    RewardConfig,
-)
-
-
-@dataclass
-class BoxRewardConfig(RewardConfig):
-    """Reward config extended with object-tracking terms."""
-
-    scales: dict[str, float] = field(
-        default_factory=lambda: {
-            **RewardConfig().scales,
-            "undesired_contacts": -0.1,
-            "object_global_ref_position_error_exp": 1.0,
-            "object_global_ref_orientation_error_exp": 1.0,
-        }
-    )
-    std_object_pos: float = 0.3
-    std_object_ori: float = 0.4
-
-
-@dataclass
-class G1BoxTrackingCfg(G1MotionTrackingCfg):
-    """Configuration for the G1 large-box tracking task."""
-
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat_with_largebox.xml")
-        )
-    )
-    motion_file: str | list[str] = str(
-        ASSETS_ROOT_PATH / "motions" / "g1" / "sub3_largebox_003_boxconverted.npz"
-    )
-    object_body_name: str = "largebox"
-    object_pos_threshold: float = 0.25
-    object_ori_threshold: float = 0.8
-    reward_config: BoxRewardConfig = field(default_factory=BoxRewardConfig)
-
-
-@registry.envcfg("G1BoxTracking")
-@dataclass
-class G1BoxTrackingEnvCfg(G1BoxTrackingCfg):
-    """Registered config for G1 box tracking."""
-
-    pass
-
-
-@dataclass
-class G1BoxTracking23DofCfg(G1BoxTrackingCfg):
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(
-                ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat_23dof_with_largebox.xml"
-            )
-        )
-    )
-    motion_file: str | list[str] = str(
-        ASSETS_ROOT_PATH / "motions" / "g1" / "sub3_largebox_003_boxconverted_23dof.npz"
-    )
-    body_names: tuple[str, ...] = (
-        "pelvis",
-        "left_hip_roll_link",
-        "left_knee_link",
-        "left_ankle_roll_link",
-        "right_hip_roll_link",
-        "right_knee_link",
-        "right_ankle_roll_link",
-        "torso_link",
-        "left_shoulder_roll_link",
-        "left_elbow_link",
-        "left_wrist_roll_rubber_hand",
-        "right_shoulder_roll_link",
-        "right_elbow_link",
-        "right_wrist_roll_rubber_hand",
-    )
-    ee_body_names: tuple[str, ...] = (
-        "left_ankle_roll_link",
-        "right_ankle_roll_link",
-        "left_wrist_roll_rubber_hand",
-        "right_wrist_roll_rubber_hand",
-    )
-
-
-@registry.envcfg("G1BoxTracking23Dof")
-@dataclass
-class G1BoxTracking23DofEnvCfg(G1BoxTracking23DofCfg):
-    pass
-
-
-def _build_box_motion_reference_state(
-    env: Any, env_ids: np.ndarray, motion_data: BoxMotionData
-) -> tuple[np.ndarray, np.ndarray]:
-    dtype = get_global_dtype()
-    num_reset = len(env_ids)
-
-    root_pos = motion_data.body_pos_w[:, 0].copy()
-    root_ori = motion_data.body_quat_w[:, 0].copy()
-    root_lin_vel = motion_data.body_lin_vel_w[:, 0].copy()
-    root_ang_vel = motion_data.body_ang_vel_w[:, 0].copy()
-    joint_pos = motion_data.joint_pos.copy()
-    joint_vel = motion_data.joint_vel.copy()
-
-    pose_rand = env.cfg.pose_randomization
-    pose_ranges = [
-        (pose_rand.x[0], pose_rand.x[1]),
-        (pose_rand.y[0], pose_rand.y[1]),
-        (pose_rand.z[0], pose_rand.z[1]),
-        (pose_rand.roll[0], pose_rand.roll[1]),
-        (pose_rand.pitch[0], pose_rand.pitch[1]),
-        (pose_rand.yaw[0], pose_rand.yaw[1]),
-    ]
-    pose_samples = np.array(
-        [[np.random.uniform(low, high) for low, high in pose_ranges] for _ in range(num_reset)],
-        dtype=dtype,
-    )
-    root_pos += pose_samples[:, 0:3]
-    root_ori = np_quat_mul(
-        np_quat_from_euler_xyz(pose_samples[:, 3], pose_samples[:, 4], pose_samples[:, 5]),
-        root_ori,
-    )
-
-    vel_rand = env.cfg.velocity_randomization
-    vel_ranges = [
-        (vel_rand.x[0], vel_rand.x[1]),
-        (vel_rand.y[0], vel_rand.y[1]),
-        (vel_rand.z[0], vel_rand.z[1]),
-        (vel_rand.roll[0], vel_rand.roll[1]),
-        (vel_rand.pitch[0], vel_rand.pitch[1]),
-        (vel_rand.yaw[0], vel_rand.yaw[1]),
-    ]
-    vel_samples = np.array(
-        [[np.random.uniform(low, high) for low, high in vel_ranges] for _ in range(num_reset)],
-        dtype=dtype,
-    )
-    root_lin_vel += vel_samples[:, :3]
-    root_ang_vel += vel_samples[:, 3:]
-
-    joint_pos += np_sample_uniform(
-        env.cfg.joint_position_range[0],
-        env.cfg.joint_position_range[1],
-        joint_pos.shape,
-        dtype=np.float32,
-    )
-    joint_range = env._get_joint_range()
-    if joint_range is not None:
-        joint_pos = np.clip(joint_pos, joint_range[:, 0], joint_range[:, 1])
-
-    qpos = np.tile(env._init_qpos, (num_reset, 1))
-    qvel = np.tile(env._init_qvel, (num_reset, 1))
-
-    qpos[:, 0:3] = root_pos
-    qpos[:, 3:7] = root_ori
-    qpos[:, 7 : 7 + joint_pos.shape[1]] = joint_pos
-
-    qvel[:, 0:3] = root_lin_vel
-    qvel[:, 3:6] = np_quat_apply(np_quat_inv(root_ori), root_ang_vel)
-    qvel[:, 6 : 6 + joint_vel.shape[1]] = joint_vel
-
-    if motion_data.object_pos_w is not None:
-        qpos[:, env._obj_pos_slice] = motion_data.object_pos_w
-        qpos[:, env._obj_quat_slice] = motion_data.object_quat_w
-        qvel[:, env._obj_lin_vel_slice] = motion_data.object_lin_vel_w
-        qvel[:, env._obj_ang_vel_slice] = motion_data.object_ang_vel_w
-
-    return qpos, qvel
-
-
-class G1BoxTrackingDomainRandomizationProvider(G1MotionTrackingDomainRandomizationProvider):
-    """Reset provider that restores both robot and object state from motion data."""
-
-    def build_reset_plan(self, env: Any, env_ids: np.ndarray) -> ResetPlan:
-        num_reset = len(env_ids)
-        motion_frames = env.motion_sampler.sample_frames(env_ids)
-        motion_data = cast(BoxMotionData, env.motion_loader.get_motion_at_frame(motion_frames))
-        qpos, qvel = _build_box_motion_reference_state(env, env_ids, motion_data)
-
-        info_updates = {
-            "current_actions": zero_actions(num_reset, env._num_action),
-            "last_actions": zero_actions(num_reset, env._num_action),
-        }
-        return ResetPlan(
-            env_ids=env_ids,
-            qpos=qpos,
-            qvel=qvel,
-            info_updates=info_updates,
-            randomization=build_common_reset_randomization(
-                env, num_reset, base_kp=self._base_kp, base_kd=self._base_kd
-            ),
-        )
-
-
-@registry.env("G1BoxTracking", sim_backend="mujoco")
-@registry.env("G1BoxTracking", sim_backend="motrix")
-@registry.env("G1BoxTracking23Dof", sim_backend="mujoco")
-@registry.env("G1BoxTracking23Dof", sim_backend="motrix")
-class G1BoxTrackingEnv(G1MotionTrackingEnv):
-    """Motion tracking env extended with large-box state and rewards."""
-
-    _cfg: G1BoxTrackingCfg
-
-    def __init__(self, cfg: G1BoxTrackingCfg, num_envs=1, backend_type="mujoco"):
-        super().__init__(cfg, num_envs, backend_type)
-
-        motion_body_ids = self._backend.get_motion_body_ids(cfg.body_names)
-        self.motion_loader = BoxMotionLoader(cfg.motion_file, body_indices=motion_body_ids)
-        self.motion_sampler = type(self.motion_sampler)(
-            self.motion_loader, mode=cfg.sampling_mode, num_envs=num_envs
-        )
-
-        if cfg.domain_rand.randomize_kp or cfg.domain_rand.randomize_kd:
-            base_kp, base_kd = self._backend.get_actuator_gains()
-            dr_provider = G1BoxTrackingDomainRandomizationProvider(base_kp=base_kp, base_kd=base_kd)
-        else:
-            dr_provider = G1BoxTrackingDomainRandomizationProvider()
-        # Parent init already applied init randomization and materialized the backend.
-        # Box tracking only needs to swap in a box-aware reset/obs provider for future resets.
-        self._dr_manager = DomainRandomizationManager(self, dr_provider)
-
-        self._object_body_ids = self._backend.get_body_ids([cfg.object_body_name])
-
-        nq = self._init_qpos.shape[0]
-        self._obj_pos_slice = slice(nq - 7, nq - 4)
-        self._obj_quat_slice = slice(nq - 4, nq)
-        nv = self._init_qvel.shape[0]
-        self._obj_lin_vel_slice = slice(nv - 6, nv - 3)
-        self._obj_ang_vel_slice = slice(nv - 3, nv)
-
-        if not self.motion_loader.has_object:
-            raise ValueError(
-                f"Motion file '{cfg.motion_file}' does not contain object data. "
-                "Expected keys: object_pos_w, object_quat_w, object_lin_vel_w, object_ang_vel_w"
-            )
-
-    def _get_joint_range(self) -> np.ndarray | None:
-        joint_range = super()._get_joint_range()
-        if joint_range is not None and joint_range.shape[0] > self.motion_loader.num_joints:
-            joint_range = joint_range[: self.motion_loader.num_joints]
-        return joint_range
-
-    def _resample_reference_state(self, env_ids: np.ndarray) -> None:
-        motion_frames = self.motion_sampler.sample_frames(env_ids)
-        motion_data = cast(BoxMotionData, self.motion_loader.get_motion_at_frame(motion_frames))
-        qpos, qvel = _build_box_motion_reference_state(self, env_ids, motion_data)
-        self._backend.set_state(env_ids, qpos, qvel)
-
-    def get_dof_pos(self) -> np.ndarray:
-        dof_pos = super().get_dof_pos()
-        return dof_pos[:, : self.motion_loader.num_joints]
-
-    def get_dof_vel(self) -> np.ndarray:
-        dof_vel = super().get_dof_vel()
-        return dof_vel[:, : self.motion_loader.num_joints]
-
-    @property
-    def obs_groups_spec(self) -> dict[str, int]:
-        spec = super().obs_groups_spec
-        return {**spec, "critic": spec["critic"] + 12}
-
-    def _actor_obs_dim(self, n: int) -> int:
-        return 6 + 3 + n * 5
-
-    def _build_actor_obs(
-        self,
-        *,
-        command: np.ndarray,
-        motion_anchor_pos_b: np.ndarray,
-        motion_anchor_ori_b: np.ndarray,
-        noisy_linvel: np.ndarray,
-        noisy_gyro: np.ndarray,
-        noisy_joint_pos_rel: np.ndarray,
-        noisy_dof_vel: np.ndarray,
-        last_actions: np.ndarray,
-    ) -> np.ndarray:
-        return np.concatenate(
-            [
-                command,
-                motion_anchor_ori_b,
-                noisy_gyro,
-                noisy_joint_pos_rel,
-                noisy_dof_vel,
-                last_actions,
-            ],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-
-    def _init_reward_functions(self):
-        super()._init_reward_functions()
-        self._reward_fns["object_global_ref_position_error_exp"] = self._reward_object_position
-        self._reward_fns["object_global_ref_orientation_error_exp"] = (
-            self._reward_object_orientation
-        )
-
-    def _compute_terminations(
-        self,
-        motion_data: BoxMotionData,
-        robot_body_pos_w: np.ndarray,
-        robot_body_quat_w: np.ndarray,
-    ) -> np.ndarray:
-        terminated = super()._compute_terminations(motion_data, robot_body_pos_w, robot_body_quat_w)
-
-        if motion_data.object_pos_w is not None:
-            obj_pos_w = self._backend.get_body_pos_w(self._object_body_ids)[:, 0, :]
-            obj_pos_error = np.linalg.norm(obj_pos_w - motion_data.object_pos_w, axis=-1)
-            terminated |= obj_pos_error > self._cfg.object_pos_threshold
-
-        if motion_data.object_quat_w is not None:
-            obj_quat_w = self._backend.get_body_quat_w(self._object_body_ids)[:, 0, :]
-            obj_ori_error = np_quat_error_magnitude(obj_quat_w, motion_data.object_quat_w)
-            terminated |= obj_ori_error > self._cfg.object_ori_threshold
-
-        return terminated
-
-    def _compute_obs(
-        self,
-        info: dict,
-        motion_data: BoxMotionData,
-        linvel: np.ndarray,
-        gyro: np.ndarray,
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-        robot_body_pos_w: np.ndarray,
-        robot_body_quat_w: np.ndarray,
-    ) -> dict[str, np.ndarray]:
-        obs = super()._compute_obs(
-            info, motion_data, linvel, gyro, dof_pos, dof_vel, robot_body_pos_w, robot_body_quat_w
-        )
-
-        env_ids = info.get("env_ids")
-        if isinstance(env_ids, np.ndarray):
-            obj_pos_w = self._backend.get_body_pos_w(self._object_body_ids)[env_ids, 0, :]
-            obj_quat_w = self._backend.get_body_quat_w(self._object_body_ids)[env_ids, 0, :]
-            obj_lin_vel_w = self._backend.get_body_lin_vel_w(self._object_body_ids)[env_ids, 0, :]
-        else:
-            num_envs = linvel.shape[0]
-            obj_pos_w = self._backend.get_body_pos_w(self._object_body_ids)[:num_envs, 0, :]
-            obj_quat_w = self._backend.get_body_quat_w(self._object_body_ids)[:num_envs, 0, :]
-            obj_lin_vel_w = self._backend.get_body_lin_vel_w(self._object_body_ids)[:num_envs, 0, :]
-
-        anchor_pos_w = robot_body_pos_w[:, self.anchor_body_idx]
-        anchor_quat_w = robot_body_quat_w[:, self.anchor_body_idx]
-
-        obj_pos_b, obj_ori_rel = np_subtract_frame_transforms(
-            anchor_pos_w, anchor_quat_w, obj_pos_w, obj_quat_w
-        )
-        obj_ori_mat = np_matrix_from_quat(obj_ori_rel)
-        num_envs = linvel.shape[0]
-        obj_ori_b = obj_ori_mat[:, :, :2].reshape(num_envs, 6)
-        obj_lin_vel_b = np_quat_apply(np_quat_inv(anchor_quat_w), obj_lin_vel_w)
-
-        object_obs = np.concatenate(
-            [obj_pos_b, obj_ori_b, obj_lin_vel_b],
-            axis=1,
-            dtype=get_global_dtype(),
-        )
-        obs["critic"] = np.concatenate(
-            [obs["critic"], object_obs], axis=1, dtype=get_global_dtype()
-        )
-        return obs
-
-    def _reward_object_position(self, ctx: RewardContext) -> np.ndarray:
-        motion_data: BoxMotionData = ctx.motion_data
-        if motion_data.object_pos_w is None:
-            return np.zeros((self._num_envs,), dtype=get_global_dtype())
-        obj_pos_w = self._backend.get_body_pos_w(self._object_body_ids)[:, 0, :]
-        error = np.sum(np.square(obj_pos_w - motion_data.object_pos_w), axis=-1)
-        return np.asarray(
-            np.exp(-error / self._cfg.reward_config.std_object_pos**2), dtype=get_global_dtype()
-        )
-
-    def _reward_object_orientation(self, ctx: RewardContext) -> np.ndarray:
-        motion_data: BoxMotionData = ctx.motion_data
-        if motion_data.object_quat_w is None:
-            return np.zeros((self._num_envs,), dtype=get_global_dtype())
-        obj_quat_w = self._backend.get_body_quat_w(self._object_body_ids)[:, 0, :]
-        error = np_quat_error_magnitude(obj_quat_w, motion_data.object_quat_w) ** 2
-        return np.asarray(
-            np.exp(-error / self._cfg.reward_config.std_object_ori**2), dtype=get_global_dtype()
-        )
diff --git a/src/unilab/envs/motion_tracking/g1/flip_tracking.py b/src/unilab/envs/motion_tracking/g1/flip_tracking.py
deleted file mode 100644
index a8d2aef00..000000000
--- a/src/unilab/envs/motion_tracking/g1/flip_tracking.py
+++ /dev/null
@@ -1,261 +0,0 @@
-"""Flip-specialized G1 motion tracking environment.
-
-This keeps the generic G1MotionTracking defaults backward-compatible while
-providing a dedicated registry task for flip-focused datasets/profiles.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Literal
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base import registry
-from unilab.base.scene import SceneCfg
-
-from ..common.config import _zero_pose_randomization, _zero_velocity_randomization
-from .tracking import (
-    G1MotionTrackingCfg,
-    G1MotionTrackingEnv,
-    PoseRandomization,
-    VelocityRandomization,
-)
-
-
-@dataclass
-class G1FlipTrackingCfg(G1MotionTrackingCfg):
-    """Config profile for flip tracking clips."""
-
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat.xml")
-        )
-    )
-    motion_file: str | list[str] = str(
-        ASSETS_ROOT_PATH / "motions" / "g1" / "flip_360_001__A304.npz"
-    )
-    pose_randomization: PoseRandomization = field(default_factory=_zero_pose_randomization)
-    velocity_randomization: VelocityRandomization = field(
-        default_factory=_zero_velocity_randomization
-    )
-    joint_position_range: tuple[float, float] = (0.0, 0.0)
-    sampling_mode: Literal["start", "clip_start", "uniform", "adaptive", "mixed"] = "start"
-    terminate_on_undesired_contacts: bool = True
-    # Some flip clips include large anchor orientation deviations.
-    anchor_ori_threshold: float = 1e9
-
-
-@registry.envcfg("G1FlipTracking")
-@dataclass
-class G1FlipTrackingEnvCfg(G1FlipTrackingCfg):
-    """Registered configuration for G1 flip tracking."""
-
-    pass
-
-
-@registry.env("G1FlipTracking", sim_backend="mujoco")
-@registry.env("G1FlipTracking", sim_backend="motrix")
-class G1FlipTrackingEnv(G1MotionTrackingEnv):
-    """G1 flip-tracking environment implementation."""
-
-    _cfg: G1FlipTrackingCfg
-
-
-@dataclass
-class G1WallFlipTrackingCfg(G1FlipTrackingCfg):
-    """Config profile for wall-assisted G1 flip tracking clips."""
-
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat_with_wall.xml")
-        )
-    )
-    motion_file: str | list[str] = str(
-        ASSETS_ROOT_PATH / "motions" / "g1" / "flip_from_wall_104__A304.npz"
-    )
-    sampling_mode: Literal["start", "clip_start", "uniform", "adaptive", "mixed"] = "adaptive"
-    anchor_pos_z_threshold: float = 0.5
-    ee_body_pos_z_threshold: float = 0.5
-
-
-@registry.envcfg("G1WallFlipTracking")
-@dataclass
-class G1WallFlipTrackingEnvCfg(G1WallFlipTrackingCfg):
-    """Registered configuration for G1 wall flip tracking."""
-
-    pass
-
-
-@registry.env("G1WallFlipTracking", sim_backend="mujoco")
-@registry.env("G1WallFlipTracking", sim_backend="motrix")
-class G1WallFlipTrackingEnv(G1MotionTrackingEnv):
-    """G1 wall flip-tracking environment implementation."""
-
-    _cfg: G1WallFlipTrackingCfg
-
-
-@dataclass
-class G1ClimbTrackingCfg(G1MotionTrackingCfg):
-    """Config profile for the climb_20_z_scale_1 motion clip."""
-
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_climb_20_z_scale_1.xml")
-        )
-    )
-    motion_file: str | list[str] = str(
-        ASSETS_ROOT_PATH / "motions" / "g1" / "climb_20_z_scale_1.0.npz"
-    )
-    max_episode_seconds: float = 15.0
-    anchor_pos_z_threshold: float = 0.5
-    ee_body_pos_z_threshold: float = 0.5
-
-
-@registry.envcfg("G1ClimbTracking")
-@dataclass
-class G1ClimbTrackingEnvCfg(G1ClimbTrackingCfg):
-    """Registered configuration for G1 box-climb motion tracking."""
-
-    pass
-
-
-@registry.env("G1ClimbTracking", sim_backend="mujoco")
-@registry.env("G1ClimbTracking", sim_backend="motrix")
-class G1ClimbTrackingEnv(G1MotionTrackingEnv):
-    """G1 climb-tracking environment implementation."""
-
-    _cfg: G1ClimbTrackingCfg
-
-
-@dataclass
-class G1FlipTracking23DofCfg(G1FlipTrackingCfg):
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat_23dof.xml")
-        )
-    )
-    motion_file: str | list[str] = str(
-        ASSETS_ROOT_PATH / "motions" / "g1" / "flip_360_001__A304_23dof.npz"
-    )
-    body_names: tuple[str, ...] = (
-        "pelvis",
-        "left_hip_roll_link",
-        "left_knee_link",
-        "left_ankle_roll_link",
-        "right_hip_roll_link",
-        "right_knee_link",
-        "right_ankle_roll_link",
-        "torso_link",
-        "left_shoulder_roll_link",
-        "left_elbow_link",
-        "left_wrist_roll_rubber_hand",
-        "right_shoulder_roll_link",
-        "right_elbow_link",
-        "right_wrist_roll_rubber_hand",
-    )
-    ee_body_names: tuple[str, ...] = (
-        "left_ankle_roll_link",
-        "right_ankle_roll_link",
-        "left_wrist_roll_rubber_hand",
-        "right_wrist_roll_rubber_hand",
-    )
-
-
-@registry.envcfg("G1FlipTracking23Dof")
-@dataclass
-class G1FlipTracking23DofEnvCfg(G1FlipTracking23DofCfg):
-    pass
-
-
-@dataclass
-class G1WallFlipTracking23DofCfg(G1WallFlipTrackingCfg):
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat_23dof_with_wall.xml")
-        )
-    )
-    motion_file: str | list[str] = str(
-        ASSETS_ROOT_PATH / "motions" / "g1" / "flip_from_wall_104__A304_23dof.npz"
-    )
-    body_names: tuple[str, ...] = (
-        "pelvis",
-        "left_hip_roll_link",
-        "left_knee_link",
-        "left_ankle_roll_link",
-        "right_hip_roll_link",
-        "right_knee_link",
-        "right_ankle_roll_link",
-        "torso_link",
-        "left_shoulder_roll_link",
-        "left_elbow_link",
-        "left_wrist_roll_rubber_hand",
-        "right_shoulder_roll_link",
-        "right_elbow_link",
-        "right_wrist_roll_rubber_hand",
-    )
-    ee_body_names: tuple[str, ...] = (
-        "left_ankle_roll_link",
-        "right_ankle_roll_link",
-        "left_wrist_roll_rubber_hand",
-        "right_wrist_roll_rubber_hand",
-    )
-
-
-@registry.envcfg("G1WallFlipTracking23Dof")
-@dataclass
-class G1WallFlipTracking23DofEnvCfg(G1WallFlipTracking23DofCfg):
-    pass
-
-
-@dataclass
-class G1ClimbTracking23DofCfg(G1ClimbTrackingCfg):
-    """23-DoF config for the climb_20_z_scale_1 motion clip."""
-
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(
-                ASSETS_ROOT_PATH / "robots" / "g1" / "scene_climb_20_z_scale_1_23dof.xml"
-            )
-        )
-    )
-    motion_file: str | list[str] = str(
-        ASSETS_ROOT_PATH / "motions" / "g1" / "climb_20_z_scale_1.0_23dof.npz"
-    )
-    body_names: tuple[str, ...] = (
-        "pelvis",
-        "left_hip_roll_link",
-        "left_knee_link",
-        "left_ankle_roll_link",
-        "right_hip_roll_link",
-        "right_knee_link",
-        "right_ankle_roll_link",
-        "torso_link",
-        "left_shoulder_roll_link",
-        "left_elbow_link",
-        "left_wrist_roll_rubber_hand",
-        "right_shoulder_roll_link",
-        "right_elbow_link",
-        "right_wrist_roll_rubber_hand",
-    )
-    ee_body_names: tuple[str, ...] = (
-        "left_ankle_roll_link",
-        "right_ankle_roll_link",
-        "left_wrist_roll_rubber_hand",
-        "right_wrist_roll_rubber_hand",
-    )
-
-
-@registry.envcfg("G1ClimbTracking23Dof")
-@dataclass
-class G1ClimbTracking23DofEnvCfg(G1ClimbTracking23DofCfg):
-    """Registered 23-DoF configuration for G1 box-climb motion tracking."""
-
-    pass
-
-
-registry.register_env("G1ClimbTracking23Dof", G1ClimbTrackingEnv, sim_backend="mujoco")
-registry.register_env("G1ClimbTracking23Dof", G1ClimbTrackingEnv, sim_backend="motrix")
-registry.register_env("G1FlipTracking23Dof", G1FlipTrackingEnv, sim_backend="mujoco")
-registry.register_env("G1FlipTracking23Dof", G1FlipTrackingEnv, sim_backend="motrix")
-registry.register_env("G1WallFlipTracking23Dof", G1WallFlipTrackingEnv, sim_backend="mujoco")
-registry.register_env("G1WallFlipTracking23Dof", G1WallFlipTrackingEnv, sim_backend="motrix")
diff --git a/src/unilab/envs/motion_tracking/g1/flip_tracking_sac.py b/src/unilab/envs/motion_tracking/g1/flip_tracking_sac.py
deleted file mode 100644
index 894939d2f..000000000
--- a/src/unilab/envs/motion_tracking/g1/flip_tracking_sac.py
+++ /dev/null
@@ -1,85 +0,0 @@
-"""SAC variants of the G1 flip / wall-flip tracking tasks.
-
-These pair the flip-specialized configs (scene / motion / zeroed reset
-randomization, from :mod:`flip_tracking`) with the asymmetric actor-critic
-observation layout of :class:`G1MotionTrackingSACEnv` (``base_lin_vel``
-appended to the critic obs).
-
-The flip behavior lives entirely in the cfg dataclasses and the SAC obs
-augmentation lives entirely in the env class, so the two compose by simple
-inheritance — no obs override is duplicated here. Registered under separate
-names so the PPO flip pipeline (``G1FlipTracking`` / ``G1WallFlipTracking``)
-is untouched, and sit at the same level as ``G1WBTObs``.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-
-from unilab.base import registry
-
-from .flip_tracking import (
-    G1FlipTracking23DofEnvCfg,
-    G1FlipTrackingCfg,
-    G1WallFlipTracking23DofEnvCfg,
-    G1WallFlipTrackingCfg,
-)
-from .tracking_sac import G1MotionTrackingSACEnv
-
-
-@registry.envcfg("G1FlipTrackingSAC")
-@dataclass
-class G1FlipTrackingSACCfg(G1FlipTrackingCfg):
-    """Flip tracking cfg for SAC (identical fields, separate registry entry)."""
-
-
-@registry.env("G1FlipTrackingSAC", sim_backend="mujoco")
-@registry.env("G1FlipTrackingSAC", sim_backend="motrix")
-class G1FlipTrackingSACEnv(G1MotionTrackingSACEnv):
-    """Flip tracking env with the SAC asymmetric actor-critic obs layout.
-
-    The motrix backend is registered for sim2sim eval/playback only.
-    """
-
-    _cfg: G1FlipTrackingSACCfg
-
-
-@registry.envcfg("G1WallFlipTrackingSAC")
-@dataclass
-class G1WallFlipTrackingSACCfg(G1WallFlipTrackingCfg):
-    """Wall-flip tracking cfg for SAC (identical fields, separate registry entry)."""
-
-
-@registry.env("G1WallFlipTrackingSAC", sim_backend="mujoco")
-@registry.env("G1WallFlipTrackingSAC", sim_backend="motrix")
-class G1WallFlipTrackingSACEnv(G1MotionTrackingSACEnv):
-    """Wall-flip tracking env with the SAC asymmetric actor-critic obs layout.
-
-    The motrix backend is registered for sim2sim eval/playback only.
-    """
-
-    _cfg: G1WallFlipTrackingSACCfg
-
-
-@registry.envcfg("G1FlipTrackingSAC23Dof")
-@dataclass
-class G1FlipTrackingSAC23DofCfg(G1FlipTracking23DofEnvCfg):
-    pass
-
-
-@registry.env("G1FlipTrackingSAC23Dof", sim_backend="mujoco")
-@registry.env("G1FlipTrackingSAC23Dof", sim_backend="motrix")
-class G1FlipTrackingSAC23DofEnv(G1MotionTrackingSACEnv):
-    _cfg: G1FlipTrackingSAC23DofCfg
-
-
-@registry.envcfg("G1WallFlipTrackingSAC23Dof")
-@dataclass
-class G1WallFlipTrackingSAC23DofCfg(G1WallFlipTracking23DofEnvCfg):
-    pass
-
-
-@registry.env("G1WallFlipTrackingSAC23Dof", sim_backend="mujoco")
-@registry.env("G1WallFlipTrackingSAC23Dof", sim_backend="motrix")
-class G1WallFlipTrackingSAC23DofEnv(G1MotionTrackingSACEnv):
-    _cfg: G1WallFlipTrackingSAC23DofCfg
diff --git a/src/unilab/envs/motion_tracking/g1/tracking.py b/src/unilab/envs/motion_tracking/g1/tracking.py
deleted file mode 100644
index c99648f34..000000000
--- a/src/unilab/envs/motion_tracking/g1/tracking.py
+++ /dev/null
@@ -1,152 +0,0 @@
-"""G1 Motion Tracking profiles — thin registry subclasses over the shared engine.
-
-The robot-agnostic engine and owner modules now live in
-:mod:`unilab.envs.motion_tracking.common`. This module keeps the G1 registry
-entries (``G1MotionTracking`` / ``G1MotionTrackingDeploy``) and re-exports the
-historical ``G1*`` / ``Domain_Rand`` / ``_build_motion_reference_state`` symbol
-names so existing subclasses and tests keep importing them from ``.tracking``.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base import registry
-from unilab.base.scene import SceneCfg
-
-from ..common.config import (
-    Domain_Rand,
-    DomainRand,
-    MotionTrackingCfg,
-    MotionTrackingDeployEnvCfg,
-    PoseRandomization,
-    VelocityRandomization,
-    _zero_pose_randomization,
-    _zero_velocity_randomization,
-)
-from ..common.domain_randomization import MotionTrackingDomainRandomizationProvider
-from ..common.reset import build_motion_reference_state
-from ..common.rewards import RewardConfig
-from ..common.tracking import MotionTrackingDeployEnv, MotionTrackingEnv
-
-# ── backward-compatible aliases (historical G1* symbol names) ────────
-G1MotionTrackingCfg = MotionTrackingCfg
-G1MotionTrackingDomainRandomizationProvider = MotionTrackingDomainRandomizationProvider
-_build_motion_reference_state = build_motion_reference_state
-
-
-@registry.envcfg("G1MotionTracking")
-@dataclass
-class G1MotionTrackingEnvCfg(MotionTrackingCfg):
-    """Registered configuration for G1 motion tracking."""
-
-    pass
-
-
-@registry.envcfg("G1MotionTrackingDeploy")
-@dataclass
-class G1MotionTrackingDeployEnvCfg(MotionTrackingDeployEnvCfg):
-    """Registered deploy configuration for G1 motion tracking."""
-
-    pass
-
-
-@dataclass
-class G1MotionTracking23DofCfg(G1MotionTrackingCfg):
-    motion_file: str | list[str] = str(
-        ASSETS_ROOT_PATH / "motions" / "g1" / "dance1_subject2_part_23dof.npz"
-    )
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat_23dof.xml")
-        )
-    )
-    body_names: tuple[str, ...] = (
-        "pelvis",
-        "left_hip_roll_link",
-        "left_knee_link",
-        "left_ankle_roll_link",
-        "right_hip_roll_link",
-        "right_knee_link",
-        "right_ankle_roll_link",
-        "torso_link",
-        "left_shoulder_roll_link",
-        "left_elbow_link",
-        "left_wrist_roll_rubber_hand",
-        "right_shoulder_roll_link",
-        "right_elbow_link",
-        "right_wrist_roll_rubber_hand",
-    )
-    ee_body_names: tuple[str, ...] = (
-        "left_ankle_roll_link",
-        "right_ankle_roll_link",
-        "left_wrist_roll_rubber_hand",
-        "right_wrist_roll_rubber_hand",
-    )
-
-
-@registry.envcfg("G1MotionTracking23Dof")
-@dataclass
-class G1MotionTracking23DofEnvCfg(G1MotionTracking23DofCfg):
-    pass
-
-
-@registry.envcfg("G1MotionTracking23DofDeploy")
-@dataclass
-class G1MotionTracking23DofDeployEnvCfg(G1MotionTracking23DofCfg):
-    pass
-
-
-@registry.env("G1MotionTracking", sim_backend="drake")
-@registry.env("G1MotionTracking", sim_backend="mujoco")
-@registry.env("G1MotionTracking", sim_backend="motrix")
-class G1MotionTrackingEnv(MotionTrackingEnv):
-    """G1 Motion Tracking Environment."""
-
-    _cfg: MotionTrackingCfg
-
-
-@registry.env("G1MotionTrackingDeploy", sim_backend="mujoco")
-@registry.env("G1MotionTrackingDeploy", sim_backend="motrix")
-class G1MotionTrackingDeployEnv(MotionTrackingDeployEnv):
-    """Deploy-oriented G1 motion tracking env with unitree_rl_lab mimic actor inputs."""
-
-    _cfg: MotionTrackingDeployEnvCfg
-
-
-# --- 23-DoF env registrations (same env classes, 23-DoF configs) ---
-registry.register_env("G1MotionTracking23Dof", G1MotionTrackingEnv, sim_backend="mujoco")
-registry.register_env("G1MotionTracking23Dof", G1MotionTrackingEnv, sim_backend="motrix")
-registry.register_env(
-    "G1MotionTracking23DofDeploy", G1MotionTrackingDeployEnv, sim_backend="mujoco"
-)
-registry.register_env(
-    "G1MotionTracking23DofDeploy", G1MotionTrackingDeployEnv, sim_backend="motrix"
-)
-
-
-__all__ = [
-    "DomainRand",
-    "Domain_Rand",
-    "G1MotionTracking23DofCfg",
-    "G1MotionTracking23DofDeployEnvCfg",
-    "G1MotionTracking23DofEnvCfg",
-    "G1MotionTrackingCfg",
-    "G1MotionTrackingDeployEnv",
-    "G1MotionTrackingDeployEnvCfg",
-    "G1MotionTrackingDomainRandomizationProvider",
-    "G1MotionTrackingEnv",
-    "G1MotionTrackingEnvCfg",
-    "MotionTrackingCfg",
-    "MotionTrackingDeployEnv",
-    "MotionTrackingDeployEnvCfg",
-    "MotionTrackingDomainRandomizationProvider",
-    "MotionTrackingEnv",
-    "PoseRandomization",
-    "RewardConfig",
-    "VelocityRandomization",
-    "_build_motion_reference_state",
-    "_zero_pose_randomization",
-    "_zero_velocity_randomization",
-]
diff --git a/src/unilab/envs/motion_tracking/g1/tracking_obs.py b/src/unilab/envs/motion_tracking/g1/tracking_obs.py
deleted file mode 100644
index 270c4bdc6..000000000
--- a/src/unilab/envs/motion_tracking/g1/tracking_obs.py
+++ /dev/null
@@ -1,517 +0,0 @@
-"""G1 Whole-Body Tracking — sim2real-oriented SAC variant (task ``G1WBTObs``).
-
-This module registers a strict subclass of :class:`G1MotionTrackingSACEnv` that
-adds the training-pipeline pieces needed for ONNX-on-real-G1 deployment:
-
-* drop deploy-unavailable channels from the actor obs
-  (``base_lin_vel``, ``motion_anchor_pos_b``);
-* per-step uniform noise on ``motion_anchor_ori_b`` (actor only);
-* proprio observation history (``gyro`` / ``joint_pos_rel`` / ``dof_vel`` /
-  ``last_actions``) flattened oldest-first per term, matching the deploy-side
-  ``ObservationManager`` when ``use_gym_history=false``;
-* per-episode encoder bias on ``joint_pos_rel`` (actor only);
-* per-episode foot-geom friction sampled across regex-matched geoms;
-* per-episode y / z COM offsets layered on top of the existing x offset;
-* ``joint_acc_l2`` and ``joint_torque_l2`` reward terms.
-
-All extensions are gated by flags on :class:`G1WBTObsCfg`; the bases
-(``G1MotionTrackingSACCfg`` / ``G1MotionTrackingSACEnv`` /
-``G1MotionTrackingEnv``) are untouched. Switch the pelvis IMU via the
-yaml ``env.sensor.gyro``/``env.sensor.upvector``/``env.sensor.local_linvel``
-fields (no XML duplication required — ``g1.xml`` already exposes both IMUs).
-"""
-
-from __future__ import annotations
-
-import re
-from dataclasses import dataclass, field
-from typing import Any
-
-import numpy as np
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base import registry
-from unilab.base.scene import SceneCfg
-from unilab.dr import (
-    DomainRandomizationCapabilities,
-    ResetPlan,
-    ResetRandomizationPayload,
-)
-from unilab.dr.dr_utils import (
-    build_common_reset_randomization,
-    zero_actions,
-)
-from unilab.dr.types import RESET_TERM_GEOM_FRICTION
-from unilab.dtype_config import get_global_dtype
-from unilab.envs.locomotion.g1.base import NoiseConfig
-
-from ..common.rewards import RewardContext
-from .tracking import (
-    Domain_Rand,
-    G1MotionTrackingDomainRandomizationProvider,
-    _build_motion_reference_state,
-)
-from .tracking_sac import G1MotionTrackingSACCfg, G1MotionTrackingSACEnv
-
-# --------------------------------------------------------------------------- #
-# Config extensions
-# --------------------------------------------------------------------------- #
-
-
-@dataclass
-class ObsNoiseConfig(NoiseConfig):
-    """Actor obs masking flags + proprio history depth.
-
-    Defaults preserve baseline behaviour so the parent ``NoiseConfig`` remains
-    a drop-in replacement; ``G1WBTObs`` flips the flags via its task yaml.
-    """
-
-    # Drop ``base_lin_vel`` from actor obs (G1 has no on-robot linvel sensor).
-    enable_zero_linvel: bool = False
-    # Drop ``motion_anchor_pos_b`` from actor obs (no torso-pose estimator).
-    enable_zero_anchor_pos: bool = False
-    # Per-step uniform noise on ``motion_anchor_ori_b`` (actor only).
-    enable_anchor_ori_noise: bool = False
-    scale_anchor_ori: float = 0.05
-    # When > 1, proprio terms (gyro / joint_pos_rel / dof_vel / last_actions)
-    # are flattened oldest-first as an H-step history block. Reference terms
-    # stay single-step. Critic stays single-step. Mirrors deploy-side
-    # ``ObservationManager`` with ``use_gym_history=false``.
-    obs_history_length: int = 1
-
-
-@dataclass
-class ObsDomainRand(Domain_Rand):
-    """y / z COM offsets, per-episode encoder bias, foot-geom friction."""
-
-    randomize_com_y: bool = False
-    com_offset_y: list[float] = field(default_factory=lambda: [-0.05, 0.05])
-    randomize_com_z: bool = False
-    com_offset_z: list[float] = field(default_factory=lambda: [-0.05, 0.05])
-
-    # Per-episode additive bias on actor's joint_pos channel.
-    enable_encoder_bias: bool = False
-    encoder_bias_range: list[float] = field(default_factory=lambda: [-0.01, 0.01])
-
-    # Per-reset foot-geom friction. ``shared_random=True`` — a single scalar
-    # is broadcast across all foot geoms of one env, applied to the
-    # sliding-friction column. Matches mjlab.
-    randomize_geom_friction: bool = False
-    friction_range: list[float] = field(default_factory=lambda: [0.3, 1.2])
-    friction_geom_pattern: str = r"^(left|right)_foot[1-7]_collision$"
-
-
-@registry.envcfg("G1WBTObs")
-@dataclass
-class G1WBTObsCfg(G1MotionTrackingSACCfg):
-    """SAC whole-body tracking with sim2real obs / DR / reward extensions."""
-
-    noise_config: ObsNoiseConfig = field(default_factory=ObsNoiseConfig)  # type: ignore[assignment]
-    domain_rand: ObsDomainRand = field(default_factory=ObsDomainRand)  # type: ignore[assignment]
-
-
-# --------------------------------------------------------------------------- #
-# DR provider extension
-# --------------------------------------------------------------------------- #
-
-
-class G1WBTObsDomainRandomizationProvider(G1MotionTrackingDomainRandomizationProvider):
-    """Extends the SAC tracking DR provider with encoder bias, foot-geom
-    friction, y/z COM offsets, and post-reset ``prev_dof_vel`` seeding."""
-
-    def __init__(
-        self,
-        *,
-        base_kp: np.ndarray | None = None,
-        base_kd: np.ndarray | None = None,
-        base_geom_friction: np.ndarray | None = None,
-        foot_geom_ids: np.ndarray | None = None,
-    ) -> None:
-        super().__init__(base_kp=base_kp, base_kd=base_kd)
-        self._base_geom_friction = base_geom_friction
-        self._foot_geom_ids = foot_geom_ids
-
-    def validate(self, env: Any, capabilities: DomainRandomizationCapabilities) -> None:
-        super().validate(env, capabilities)
-        if not getattr(env.cfg.domain_rand, "randomize_geom_friction", False):
-            return
-        if not capabilities.supports_reset_term(RESET_TERM_GEOM_FRICTION):
-            raise NotImplementedError(
-                f"{env._backend.backend_type} backend does not support "
-                "geom-friction reset randomization"
-            )
-        if (
-            self._base_geom_friction is None
-            or self._foot_geom_ids is None
-            or self._foot_geom_ids.size == 0
-        ):
-            raise ValueError("randomize_geom_friction=True but provider has no foot geom IDs")
-
-    def build_reset_plan(self, env: Any, env_ids: np.ndarray) -> ResetPlan:
-        num_reset = len(env_ids)
-        motion_frames = env.motion_sampler.sample_frames(env_ids)
-        motion_data = env.motion_loader.get_motion_at_frame(motion_frames)
-        qpos, qvel = _build_motion_reference_state(env, env_ids, motion_data)
-
-        info_updates: dict[str, Any] = {
-            "current_actions": zero_actions(num_reset, env._num_action),
-            "last_actions": zero_actions(num_reset, env._num_action),
-            # Seed prev_dof_vel with the post-reset joint velocity so the first
-            # joint_acc_l2 sample is physically meaningful (Δv from the new
-            # starting velocity, not a spurious step from pre-termination).
-            "prev_dof_vel": qvel[:, 6:].astype(get_global_dtype()),
-        }
-
-        dr_cfg = env.cfg.domain_rand
-        if getattr(dr_cfg, "enable_encoder_bias", False):
-            low, high = dr_cfg.encoder_bias_range
-            info_updates["joint_pos_obs_bias"] = np.random.uniform(
-                low, high, size=(num_reset, env._num_action)
-            ).astype(get_global_dtype())
-
-        randomization = build_common_reset_randomization(
-            env, num_reset, base_kp=self._base_kp, base_kd=self._base_kd
-        )
-
-        # Foot-geom friction.
-        if getattr(dr_cfg, "randomize_geom_friction", False):
-            assert self._base_geom_friction is not None
-            assert self._foot_geom_ids is not None
-            payload = randomization or ResetRandomizationPayload()
-            low, high = dr_cfg.friction_range
-            scale = np.random.uniform(low, high, size=(num_reset, 1)).astype(np.float64)
-            geom_friction = np.broadcast_to(
-                self._base_geom_friction,
-                (num_reset, *self._base_geom_friction.shape),
-            ).copy()
-            geom_friction[:, self._foot_geom_ids, 0] = scale * np.ones(
-                (1, self._foot_geom_ids.size)
-            )
-            payload.geom_friction = geom_friction
-            randomization = payload
-
-        # y / z COM offsets, layered on top of parent's x-only common build.
-        has_com_y = getattr(dr_cfg, "randomize_com_y", False)
-        has_com_z = getattr(dr_cfg, "randomize_com_z", False)
-        if has_com_y or has_com_z:
-            payload = randomization or ResetRandomizationPayload()
-            com_offset = payload.base_com_offset
-            if com_offset is None:
-                com_offset = np.zeros((num_reset, 3), dtype=np.float64)
-            if has_com_y:
-                low, high = dr_cfg.com_offset_y
-                com_offset[:, 1] = np.random.uniform(low, high, size=(num_reset,))
-            if has_com_z:
-                low, high = dr_cfg.com_offset_z
-                com_offset[:, 2] = np.random.uniform(low, high, size=(num_reset,))
-            payload.base_com_offset = com_offset
-            randomization = payload
-
-        return ResetPlan(
-            env_ids=env_ids,
-            qpos=qpos,
-            qvel=qvel,
-            info_updates=info_updates,
-            randomization=randomization,
-        )
-
-
-# --------------------------------------------------------------------------- #
-# Env
-# --------------------------------------------------------------------------- #
-
-
-@registry.env("G1WBTObs", sim_backend="mujoco")
-@registry.env("G1WBTObs", sim_backend="motrix")
-class G1WBTObsEnv(G1MotionTrackingSACEnv):
-    """SAC WBT with deploy-aligned obs, proprio history, and extra DR/rewards.
-
-    All extensions live in this subclass — base classes are untouched. Flags
-    on ``G1WBTObsCfg`` are toggled from the task yaml.
-    """
-
-    _cfg: G1WBTObsCfg
-
-    def __init__(self, cfg: G1WBTObsCfg, num_envs: int = 1, backend_type: str = "mujoco"):
-        super().__init__(cfg, num_envs=num_envs, backend_type=backend_type)
-
-        # Cache base actuator gains for joint_torque_l2.
-        # Position-control torque approx: τ ≈ kp·(target_q − q) − kd·qd.
-        # DR (kp/kd ±10–15%) leaves small error vs true per-env torque, but
-        # the gradient direction (penalise large action / large Δq) is preserved.
-        base_kp, base_kd = self._backend.get_actuator_gains()
-        self._base_kp = np.asarray(base_kp, dtype=get_global_dtype())
-        self._base_kd = np.asarray(base_kd, dtype=get_global_dtype())
-
-        # Proprio history buffers — per-term, oldest-first. Allocated only when
-        # H > 1 so H = 1 is zero-overhead.
-        H = max(1, int(cfg.noise_config.obs_history_length))
-        self._hist_len = H
-        self._hist_buf: dict[str, np.ndarray] | None = None
-        if H > 1:
-            n = self._num_action
-            dtype = get_global_dtype()
-            self._hist_buf = {
-                "gyro": np.zeros((num_envs, H, 3), dtype=dtype),
-                "joint_pos_rel": np.zeros((num_envs, H, n), dtype=dtype),
-                "dof_vel": np.zeros((num_envs, H, n), dtype=dtype),
-                "last_actions": np.zeros((num_envs, H, n), dtype=dtype),
-            }
-        # Plumbs ``info`` from ``_compute_obs`` down to ``_build_actor_obs``
-        # without changing the base-class hook signature.
-        self._obs_compute_info: dict | None = None
-
-        # Swap to the extended DR provider whenever an extended flag is on.
-        dr_cfg = cfg.domain_rand
-        needs_extended = (
-            getattr(dr_cfg, "enable_encoder_bias", False)
-            or getattr(dr_cfg, "randomize_geom_friction", False)
-            or getattr(dr_cfg, "randomize_com_y", False)
-            or getattr(dr_cfg, "randomize_com_z", False)
-        )
-        if needs_extended:
-            kp = self._base_kp if (dr_cfg.randomize_kp or dr_cfg.randomize_kd) else None
-            kd = self._base_kd if (dr_cfg.randomize_kp or dr_cfg.randomize_kd) else None
-            base_geom_friction = None
-            foot_geom_ids = None
-            if dr_cfg.randomize_geom_friction:
-                base_geom_friction = self._backend.get_geom_friction()
-                geom_names = self._backend.get_geom_names()
-                pattern = re.compile(dr_cfg.friction_geom_pattern)
-                foot_geom_ids = np.asarray(
-                    [i for i, name in enumerate(geom_names) if name and pattern.match(name)],
-                    dtype=np.int64,
-                )
-                if foot_geom_ids.size == 0:
-                    raise ValueError(
-                        "friction_geom_pattern "
-                        f"'{dr_cfg.friction_geom_pattern}' did not match any geom"
-                    )
-            extended_provider = G1WBTObsDomainRandomizationProvider(
-                base_kp=kp,
-                base_kd=kd,
-                base_geom_friction=base_geom_friction,
-                foot_geom_ids=foot_geom_ids,
-            )
-            # Swap the per-reset DR provider directly. ``_init_domain_randomization``
-            # cannot be called twice — it materializes the backend at the end and
-            # MuJoCo's pool raises on a second materialize. The parent's call
-            # already (a) ran init randomization and (b) materialized; we only
-            # need the new provider's ``build_reset_plan`` for per-episode DR.
-            from unilab.dr import DomainRandomizationManager
-
-            self._dr_manager = DomainRandomizationManager(self, extended_provider)
-
-    # ------------------------------------------------------------------ #
-    # Rewards
-    # ------------------------------------------------------------------ #
-
-    def _init_reward_functions(self) -> None:
-        super()._init_reward_functions()
-        self._reward_fns["joint_acc_l2"] = self._reward_joint_acc_l2
-        self._reward_fns["joint_torque_l2"] = self._reward_joint_torque_l2
-
-    def _reward_joint_acc_l2(self, ctx: RewardContext) -> np.ndarray:
-        dof_vel = ctx.dof_vel
-        prev_dof_vel = ctx.info.get("prev_dof_vel")
-        if dof_vel is None:
-            raise RuntimeError("RewardContext.dof_vel is required for joint_acc_l2")
-        if prev_dof_vel is None or prev_dof_vel.shape != dof_vel.shape:
-            return np.zeros((self._num_envs,), dtype=get_global_dtype())
-        joint_acc = (dof_vel - prev_dof_vel) / self._cfg.ctrl_dt
-        return np.asarray(np.sum(np.square(joint_acc), axis=1), dtype=get_global_dtype())
-
-    def _reward_joint_torque_l2(self, ctx: RewardContext) -> np.ndarray:
-        dof_pos = ctx.dof_pos
-        dof_vel = ctx.dof_vel
-        last_actions = ctx.info.get("last_actions")
-        if dof_pos is None or dof_vel is None:
-            raise RuntimeError("RewardContext.dof_pos and dof_vel are required for joint_torque_l2")
-        if last_actions is None:
-            return np.zeros((self._num_envs,), dtype=get_global_dtype())
-        target_q = (
-            last_actions * self._cfg.control_config.action_scale + self._effective_default_angles()
-        )
-        torque = self._base_kp * (target_q - dof_pos) - self._base_kd * dof_vel
-        return np.asarray(np.sum(np.square(torque), axis=1), dtype=get_global_dtype())
-
-    # ------------------------------------------------------------------ #
-    # Obs
-    # ------------------------------------------------------------------ #
-
-    def _actor_obs_dim(self, n: int) -> int:
-        nc = self._cfg.noise_config
-        H = max(1, int(nc.obs_history_length))
-        single_step = 2 * n + 6  # command(2n) + anchor_ori(6)
-        if not nc.enable_zero_anchor_pos:
-            single_step += 3
-        if not nc.enable_zero_linvel:
-            single_step += 3
-        proprio_step = 3 + 3 * n  # gyro + joint_pos_rel + dof_vel + last_actions
-        return single_step + H * proprio_step
-
-    def _compute_obs(
-        self,
-        info: dict,
-        motion_data: Any,
-        linvel: np.ndarray,
-        gyro: np.ndarray,
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-        robot_body_pos_w: np.ndarray,
-        robot_body_quat_w: np.ndarray,
-    ) -> dict[str, np.ndarray]:
-        # Stash so the overridden ``_build_actor_obs`` (called inside super)
-        # can read env_ids / joint_pos_obs_bias without a signature change.
-        self._obs_compute_info = info
-        try:
-            obs = super()._compute_obs(
-                info,
-                motion_data,
-                linvel,
-                gyro,
-                dof_pos,
-                dof_vel,
-                robot_body_pos_w,
-                robot_body_quat_w,
-            )
-        finally:
-            self._obs_compute_info = None
-
-        # Cache for next-step joint_acc_l2. The reset path overwrites this
-        # via the DR provider's ``prev_dof_vel`` info_update.
-        info["prev_dof_vel"] = dof_vel.copy()
-        return obs
-
-    def _build_actor_obs(
-        self,
-        *,
-        command: np.ndarray,
-        motion_anchor_pos_b: np.ndarray,
-        motion_anchor_ori_b: np.ndarray,
-        noisy_linvel: np.ndarray,
-        noisy_gyro: np.ndarray,
-        noisy_joint_pos_rel: np.ndarray,
-        noisy_dof_vel: np.ndarray,
-        last_actions: np.ndarray,
-    ) -> np.ndarray:
-        info = self._obs_compute_info or {}
-        # Reset path is signalled by ``env_ids`` in obs_info (set by parent's
-        # ``_refresh_observation_rows`` and the DR provider's
-        # ``build_reset_observation``). In that case fill history slots; in
-        # the per-step path we push (oldest out, current in).
-        env_ids = info.get("env_ids")
-        is_reset = env_ids is not None
-
-        nc = self._cfg.noise_config
-
-        # Per-episode encoder bias on actor's joint_pos channel.
-        bias = info.get("joint_pos_obs_bias")
-        if bias is not None and bias.shape == noisy_joint_pos_rel.shape:
-            noisy_joint_pos_rel = np.asarray(
-                noisy_joint_pos_rel + bias, dtype=noisy_joint_pos_rel.dtype
-            )
-
-        # Per-step anchor_ori noise (actor only).
-        actor_anchor_ori_b = motion_anchor_ori_b
-        if nc.enable_anchor_ori_noise:
-            actor_anchor_ori_b = self._obs_noise(motion_anchor_ori_b, nc.scale_anchor_ori)
-
-        # Single-step reference terms, dropping deploy-unavailable channels.
-        actor_terms: list[np.ndarray] = [command]
-        if not nc.enable_zero_anchor_pos:
-            actor_terms.append(motion_anchor_pos_b)
-        actor_terms.append(actor_anchor_ori_b)
-        if not nc.enable_zero_linvel:
-            actor_terms.append(noisy_linvel)
-
-        # Proprio history (or single-step pass-through when H = 1).
-        if self._hist_buf is not None:
-            components = {
-                "gyro": noisy_gyro,
-                "joint_pos_rel": noisy_joint_pos_rel,
-                "dof_vel": noisy_dof_vel,
-                "last_actions": last_actions,
-            }
-            if is_reset:
-                self._fill_obs_history(env_ids, components)
-            else:
-                self._push_obs_history(env_ids, components)
-            sel = slice(None) if env_ids is None else env_ids
-            for key in ("gyro", "joint_pos_rel", "dof_vel", "last_actions"):
-                buf = self._hist_buf[key][sel]  # (n_e, H, D)
-                actor_terms.append(buf.reshape(buf.shape[0], -1))
-        else:
-            actor_terms.extend([noisy_gyro, noisy_joint_pos_rel, noisy_dof_vel, last_actions])
-
-        return np.concatenate(actor_terms, axis=1, dtype=get_global_dtype())
-
-    # ------------------------------------------------------------------ #
-    # Proprio history buffer maintenance.
-    # Mirrors deploy ``ObservationManager`` / ``ObservationTermCfg``:
-    #   * On reset: fill all H slots with the current value (matches
-    #     ``ObservationTermCfg::reset`` which calls ``add()`` H times).
-    #   * On step: pop oldest, push current at end.
-    #   * Read order is oldest-first, so ``flatten(buf[env, :, :])`` yields
-    #     ``[t-H+1, t-H+2, ..., t]`` — matches deploy
-    #     ``ObservationTermCfg::get`` (deque front-to-back).
-    # ------------------------------------------------------------------ #
-
-    def _push_obs_history(
-        self, env_ids: np.ndarray | None, components: dict[str, np.ndarray]
-    ) -> None:
-        if self._hist_buf is None:
-            return
-        sel = slice(None) if env_ids is None else env_ids
-        for key, val in components.items():
-            buf = self._hist_buf[key]
-            buf[sel, :-1] = buf[sel, 1:]
-            buf[sel, -1] = val
-
-    def _fill_obs_history(
-        self, env_ids: np.ndarray | None, components: dict[str, np.ndarray]
-    ) -> None:
-        if self._hist_buf is None:
-            return
-        sel = slice(None) if env_ids is None else env_ids
-        for key, val in components.items():
-            self._hist_buf[key][sel, :] = val[:, None, :]
-
-
-@registry.envcfg("G1WBTObs23Dof")
-@dataclass
-class G1WBTObs23DofCfg(G1WBTObsCfg):
-    motion_file: str | list[str] = str(
-        ASSETS_ROOT_PATH / "motions" / "g1" / "dance1_subject2_part_23dof.npz"
-    )
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat_23dof.xml")
-        )
-    )
-    body_names: tuple[str, ...] = (
-        "pelvis",
-        "left_hip_roll_link",
-        "left_knee_link",
-        "left_ankle_roll_link",
-        "right_hip_roll_link",
-        "right_knee_link",
-        "right_ankle_roll_link",
-        "torso_link",
-        "left_shoulder_roll_link",
-        "left_elbow_link",
-        "left_wrist_roll_rubber_hand",
-        "right_shoulder_roll_link",
-        "right_elbow_link",
-        "right_wrist_roll_rubber_hand",
-    )
-    ee_body_names: tuple[str, ...] = (
-        "left_ankle_roll_link",
-        "right_ankle_roll_link",
-        "left_wrist_roll_rubber_hand",
-        "right_wrist_roll_rubber_hand",
-    )
-
-
-registry.register_env("G1WBTObs23Dof", G1WBTObsEnv, sim_backend="mujoco")
-registry.register_env("G1WBTObs23Dof", G1WBTObsEnv, sim_backend="motrix")
diff --git a/src/unilab/envs/motion_tracking/g1/tracking_sac.py b/src/unilab/envs/motion_tracking/g1/tracking_sac.py
deleted file mode 100644
index db968b287..000000000
--- a/src/unilab/envs/motion_tracking/g1/tracking_sac.py
+++ /dev/null
@@ -1,83 +0,0 @@
-"""G1 Motion Tracking SAC Environment — thin SAC wrapper over G1MotionTrackingEnv.
-
-Differences from the PPO base:
-- Critic observations additionally include ``base_lin_vel`` (3 dims),
-  matching holosoma's asymmetric actor-critic design for WBT.
-- Registered under a separate name so it can be paired with FastSAC
-  configs without affecting the PPO motion-tracking pipeline.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-
-import numpy as np
-
-from unilab.base import registry
-from unilab.dtype_config import get_global_dtype
-
-from .tracking import G1MotionTracking23DofCfg, G1MotionTrackingCfg, G1MotionTrackingEnv
-
-
-@registry.envcfg("G1MotionTrackingSAC")
-@dataclass
-class G1MotionTrackingSACCfg(G1MotionTrackingCfg):
-    """Config for SAC-based motion tracking (identical fields, separate registry entry)."""
-
-
-@registry.env("G1MotionTrackingSAC", sim_backend="mujoco")
-@registry.env("G1MotionTrackingSAC", sim_backend="motrix")
-class G1MotionTrackingSACEnv(G1MotionTrackingEnv):
-    """G1 Motion Tracking environment for FastSAC training.
-
-    Extends the PPO motion-tracking environment with ``base_lin_vel``
-    appended to the critic observation, matching holosoma's asymmetric
-    actor-critic WBT design.
-
-    The motrix backend is registered for sim2sim eval/playback only — checkpoints
-    trained on mujoco can be replayed via motrix's native renderer through
-    ``eval --sim motrix``.
-    """
-
-    @property
-    def obs_groups_spec(self) -> dict[str, int]:
-        spec = super().obs_groups_spec
-        # Append base_lin_vel (3) to critic observations.
-        return {**spec, "critic": spec["critic"] + 3}
-
-    def _compute_obs(
-        self,
-        info: dict,
-        motion_data,
-        linvel: np.ndarray,
-        gyro: np.ndarray,
-        dof_pos: np.ndarray,
-        dof_vel: np.ndarray,
-        robot_body_pos_w: np.ndarray,
-        robot_body_quat_w: np.ndarray,
-    ) -> dict[str, np.ndarray]:
-        obs = super()._compute_obs(  # pyright: ignore[reportAttributeAccessIssue]
-            info,
-            motion_data,
-            linvel,
-            gyro,
-            dof_pos,
-            dof_vel,
-            robot_body_pos_w,
-            robot_body_quat_w,
-        )
-        # Append base_lin_vel to critic observations.
-        obs["critic"] = np.concatenate([obs["critic"], linvel], axis=1, dtype=get_global_dtype())  # type: ignore[call-overload]
-        return obs
-
-
-@registry.envcfg("G1MotionTrackingSAC23Dof")
-@dataclass
-class G1MotionTrackingSAC23DofCfg(G1MotionTracking23DofCfg):
-    pass
-
-
-@registry.env("G1MotionTrackingSAC23Dof", sim_backend="mujoco")
-@registry.env("G1MotionTrackingSAC23Dof", sim_backend="motrix")
-class G1MotionTrackingSAC23DofEnv(G1MotionTrackingSACEnv):
-    _cfg: G1MotionTrackingSAC23DofCfg
diff --git a/src/unilab/envs/motion_tracking/x2/__init__.py b/src/unilab/envs/motion_tracking/x2/__init__.py
deleted file mode 100644
index c553d13e2..000000000
--- a/src/unilab/envs/motion_tracking/x2/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-"""Motion tracking environments for AgiBot X2."""
-
-from .flip_tracking import (
-    X2MotionTrackingCfg,
-    X2WallFlipTrackingCfg,
-    X2WallFlipTrackingEnv,
-    X2WallFlipTrackingEnvCfg,
-)
-
-__all__ = [
-    "X2MotionTrackingCfg",
-    "X2WallFlipTrackingCfg",
-    "X2WallFlipTrackingEnv",
-    "X2WallFlipTrackingEnvCfg",
-]
diff --git a/src/unilab/envs/motion_tracking/x2/flip_tracking.py b/src/unilab/envs/motion_tracking/x2/flip_tracking.py
deleted file mode 100644
index da7a0ff1c..000000000
--- a/src/unilab/envs/motion_tracking/x2/flip_tracking.py
+++ /dev/null
@@ -1,134 +0,0 @@
-"""X2 motion-tracking profiles backed by the shared humanoid tracker."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Literal
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base import registry
-from unilab.base.scene import SceneCfg
-from unilab.envs.locomotion.g1.base import Sensor
-from unilab.envs.motion_tracking.common.config import (
-    PoseRandomization,
-    VelocityRandomization,
-    _zero_pose_randomization,
-    _zero_velocity_randomization,
-)
-from unilab.envs.motion_tracking.common.tracking import (
-    MotionTrackingDeployEnv,
-    MotionTrackingDeployEnvCfg,
-)
-
-
-@dataclass
-class X2MotionTrackingCfg(MotionTrackingDeployEnvCfg):
-    """Base X2 motion-tracking config profile."""
-
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "x2" / "scene_flat.xml")
-        )
-    )
-    motion_file: str | list[str] = str(
-        ASSETS_ROOT_PATH / "motions" / "x2" / "tictacflip_6-3_g1format.npz"
-    )
-    sensor: Sensor = field(
-        default_factory=lambda: Sensor(
-            local_linvel="body-linear-vel",
-            gyro="body-angular-velocity",
-            upvector="body-orientation",
-        )
-    )
-    anchor_body_name: str = "torso_link"
-    body_names: tuple[str, ...] = (
-        "pelvis",
-        "left_hip_pitch_link",
-        "left_hip_roll_link",
-        "left_hip_yaw_link",
-        "left_knee_link",
-        "left_ankle_pitch_link",
-        "left_ankle_roll_link",
-        "right_hip_pitch_link",
-        "right_hip_roll_link",
-        "right_hip_yaw_link",
-        "right_knee_link",
-        "right_ankle_pitch_link",
-        "right_ankle_roll_link",
-        "waist_yaw_link",
-        "waist_pitch_link",
-        "torso_link",
-        "left_shoulder_pitch_link",
-        "left_shoulder_roll_link",
-        "left_shoulder_yaw_link",
-        "left_elbow_link",
-        "left_wrist_yaw_link",
-        "left_wrist_pitch_link",
-        "left_wrist_roll_link",
-        "right_shoulder_pitch_link",
-        "right_shoulder_roll_link",
-        "right_shoulder_yaw_link",
-        "right_elbow_link",
-        "right_wrist_yaw_link",
-        "right_wrist_pitch_link",
-        "right_wrist_roll_link",
-    )
-    ee_body_names: tuple[str, ...] = (
-        "left_ankle_roll_link",
-        "right_ankle_roll_link",
-        "left_wrist_yaw_link",
-        "right_wrist_yaw_link",
-    )
-
-
-@dataclass
-class X2WallFlipTrackingCfg(X2MotionTrackingCfg):
-    """Config profile for wall-assisted X2 flip tracking."""
-
-    scene: SceneCfg = field(
-        default_factory=lambda: SceneCfg(
-            model_file=str(ASSETS_ROOT_PATH / "robots" / "x2" / "scene_flat_with_wall.xml"),
-            # Render-only twin: wall as a worldbody geom so the offline grid
-            # renderer replicates it under every env cell (matches G1's per-env
-            # wall view). Physics still uses the <body> wall in model_file.
-            visual_model_file=str(
-                ASSETS_ROOT_PATH / "robots" / "x2" / "scene_flat_with_wall_visual.xml"
-            ),
-        )
-    )
-    pose_randomization: PoseRandomization = field(default_factory=_zero_pose_randomization)
-    velocity_randomization: VelocityRandomization = field(
-        default_factory=_zero_velocity_randomization
-    )
-    joint_position_range: tuple[float, float] = (0.0, 0.0)
-    sampling_mode: Literal["start", "clip_start", "uniform", "adaptive", "mixed"] = "adaptive"
-    truncate_on_clip_end: bool = False
-    terminate_on_undesired_contacts: bool = True
-    anchor_ori_threshold: float = 1e9
-    anchor_pos_z_threshold: float = 0.5
-    ee_body_pos_z_threshold: float = 0.5
-
-
-@registry.envcfg("X2WallFlipTracking")
-@dataclass
-class X2WallFlipTrackingEnvCfg(X2WallFlipTrackingCfg):
-    """Registered configuration for X2 wall flip tracking."""
-
-    pass
-
-
-@registry.env("X2WallFlipTracking", sim_backend="mujoco")
-@registry.env("X2WallFlipTracking", sim_backend="motrix")
-class X2WallFlipTrackingEnv(MotionTrackingDeployEnv):
-    """X2 wall flip-tracking environment implementation."""
-
-    _cfg: X2WallFlipTrackingCfg
-    _keyframe_name = "home"
-
-    def __init__(self, cfg: X2WallFlipTrackingCfg, num_envs: int = 1, backend_type: str = "mujoco"):
-        # X2 meshes are hosted on Hugging Face, not committed to git. Ensure they
-        # exist locally before the scene XML is parsed by the simulation backend.
-        from unilab.assets.hub import resolve_robot_asset_dir
-
-        resolve_robot_asset_dir("robots/x2/meshes", marker="pelvis.STL")
-        super().__init__(cfg, num_envs=num_envs, backend_type=backend_type)
diff --git a/src/unilab/ipc/__init__.py b/src/unilab/ipc/__init__.py
deleted file mode 100644
index 911fefbbb..000000000
--- a/src/unilab/ipc/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-"""IPC primitives for multi-process RL training."""
-
-from unilab.ipc.async_runner import AsyncRunner
-from unilab.ipc.replay_buffer import ReplayBuffer
-from unilab.ipc.rollout_ring_buffer import RolloutRingBuffer
-from unilab.ipc.weight_sync import SharedWeightSync
-
-__all__ = [
-    "SharedWeightSync",
-    "RolloutRingBuffer",
-    "AsyncRunner",
-    "ReplayBuffer",
-]
diff --git a/src/unilab/ipc/async_runner.py b/src/unilab/ipc/async_runner.py
deleted file mode 100644
index a3872366b..000000000
--- a/src/unilab/ipc/async_runner.py
+++ /dev/null
@@ -1,172 +0,0 @@
-"""Base async runner for multi-process RL training."""
-
-from __future__ import annotations
-
-import multiprocessing as mp
-import sys
-from abc import ABC, abstractmethod
-from typing import Any, Callable
-
-from unilab.ipc.collector_error import (
-    ExceptionWrapper,
-    collector_error_guard,
-    create_error_pipe,
-    format_collector_death,
-)
-
-_SPAWN_CTX = mp.get_context("spawn")
-
-
-def _collector_entry_wrapper(
-    target_fn: Callable,
-    error_conn: Any,
-    kwargs: dict,
-) -> None:
-    """Top-level wrapper for collector subprocess entry point.
-
-    Ensures ALL exceptions (including import errors and env creation
-    failures) are captured and sent to the parent via the error pipe.
-    """
-    label = kwargs.pop("_error_label", "collector")
-    with collector_error_guard(
-        error_conn=error_conn,
-        metrics_queue=kwargs.get("metrics_queue"),
-        stop_event=kwargs.get("stop_event"),
-        label=label,
-    ):
-        target_fn(**kwargs)
-
-
-class AsyncRunner(ABC):
-    """Base class for async RL algorithms.
-
-    Manages:
-    - Shared memory allocation/cleanup
-    - Collector process lifecycle
-    - Error propagation from collector subprocess
-    - Training loop skeleton
-    """
-
-    def __init__(
-        self,
-        env_name: str,
-        env_cfg_overrides: dict,
-        rl_cfg: dict,
-        *,
-        device: str | None = None,
-        collector_device: str | None = None,
-        sim_backend: str = "mujoco",
-        num_envs: int = 4096,
-    ):
-        self.env_name = env_name
-        self.env_cfg_overrides = env_cfg_overrides
-        self.rl_cfg = rl_cfg
-        self.device = device or self._get_default_device()
-        self.collector_device = collector_device or self.device
-        self.sim_backend = sim_backend
-        self.num_envs = num_envs
-
-        self._collector_process: Any = None
-        self._stop_event = _SPAWN_CTX.Event()
-        self._shared_resources: list = []
-        self._error_recv: Any = None
-        self._error_send: Any = None
-        self._close_complete = False
-
-    @abstractmethod
-    def _get_default_device(self) -> str:
-        """Get default device (backend-specific)."""
-        ...
-
-    @abstractmethod
-    def _build_learner(self) -> Any: ...
-
-    @abstractmethod
-    def learn(
-        self, max_iterations: int, save_interval: int = 50, log_dir: str = "logs"
-    ) -> None: ...
-
-    def _start_collector(self, target_fn: Callable, kwargs: dict) -> None:
-        self._error_recv, self._error_send = create_error_pipe()
-
-        self._collector_process = _SPAWN_CTX.Process(
-            target=_collector_entry_wrapper,
-            args=(target_fn, self._error_send, kwargs),
-            daemon=True,
-        )
-        self._collector_process.start()
-        self._error_send.close()
-        self._error_send = None
-
-    def _check_collector_alive(self) -> bool:
-        """Check if collector is alive. Prints full diagnostic if dead."""
-        if self._collector_process is None:
-            return True
-        if self._collector_process.is_alive():
-            return True
-
-        death_info = self._read_collector_error()
-        print(f"\n{death_info}\n", file=sys.stderr, flush=True)
-        return False
-
-    def _read_collector_error(self) -> str:
-        """Read error info from dead collector — pipe first, then exit code."""
-        traceback_text = None
-        if self._error_recv is not None:
-            try:
-                if self._error_recv.poll(timeout=0.1):
-                    obj = self._error_recv.recv()
-                    if isinstance(obj, ExceptionWrapper):
-                        traceback_text = obj.exc_msg
-            except (EOFError, OSError):
-                pass
-
-        exitcode = getattr(self._collector_process, "exitcode", None)
-        return format_collector_death(exitcode, traceback_text)
-
-    def close(self) -> None:
-        if self._close_complete:
-            return
-        self._stop_event.set()
-        if self._collector_process is not None and self._collector_process.is_alive():
-            self._collector_process.join(timeout=10)
-            if self._collector_process.is_alive():
-                self._collector_process.terminate()
-                self._collector_process.join(timeout=5)
-
-        if self._collector_process is not None:
-            exitcode = getattr(self._collector_process, "exitcode", None)
-            # -15 (SIGTERM) is expected during normal close()
-            if exitcode is not None and exitcode != 0 and exitcode != -15:
-                death_info = self._read_collector_error()
-                print(
-                    f"\n[AsyncRunner] Collector exited with code {exitcode}:\n{death_info}\n",
-                    file=sys.stderr,
-                    flush=True,
-                )
-
-        for resource in self._shared_resources:
-            if hasattr(resource, "cleanup"):
-                resource.cleanup()
-            elif hasattr(resource, "close"):
-                resource.close()
-
-        if self._error_recv is not None:
-            try:
-                self._error_recv.close()
-            except Exception:
-                pass
-            self._error_recv = None
-        if self._error_send is not None:
-            try:
-                self._error_send.close()
-            except Exception:
-                pass
-            self._error_send = None
-        self._close_complete = True
-
-    def __del__(self):
-        try:
-            self.close()
-        except Exception:
-            pass
diff --git a/src/unilab/ipc/collector_error.py b/src/unilab/ipc/collector_error.py
deleted file mode 100644
index feb068649..000000000
--- a/src/unilab/ipc/collector_error.py
+++ /dev/null
@@ -1,131 +0,0 @@
-"""Cross-process error propagation for collector subprocesses.
-
-Uses a Pipe + ExceptionWrapper pattern (proven by PyTorch DataLoader and
-CPython's ProcessPoolExecutor) to ensure collector tracebacks always
-reach the parent process — even when stderr is lost or interleaved.
-"""
-
-from __future__ import annotations
-
-import multiprocessing as _mp
-import signal
-import sys
-import traceback
-from contextlib import contextmanager
-from typing import Any
-
-_SPAWN_CTX = _mp.get_context("spawn")
-
-
-class ExceptionWrapper:
-    """Picklable exception + traceback for cross-process propagation.
-
-    Stores the exception type and a pre-formatted traceback string
-    (not exc_info — that would create reference cycles preventing GC
-    of objects in the exception scope).
-    """
-
-    def __init__(self, where: str = "in collector"):
-        exc_info = sys.exc_info()
-        self.exc_type = exc_info[0]
-        self.exc_msg = "".join(traceback.format_exception(*exc_info))
-        self.where = where
-
-
-def create_error_pipe() -> tuple[Any, Any]:
-    """Create a unidirectional pipe for error reporting.
-
-    Returns (recv_conn, send_conn). Parent keeps recv, child gets send.
-    """
-    return _SPAWN_CTX.Pipe(duplex=False)
-
-
-@contextmanager
-def collector_error_guard(
-    error_conn: Any | None = None,
-    metrics_queue: Any | None = None,
-    stop_event: Any | None = None,
-    label: str = "collector",
-):
-    """Context manager that catches all exceptions in collector subprocess.
-
-    Sends a picklable ExceptionWrapper through the error pipe so the
-    parent process can surface the full traceback. Also pushes to
-    metrics_queue for fast-path detection by the training loop.
-    """
-    try:
-        yield
-    except Exception:
-        wrapper = ExceptionWrapper(where=f"in {label}")
-        print(
-            f"\n{'=' * 60}\n[{label.upper()} CRASH]\n{wrapper.exc_msg}\n{'=' * 60}\n",
-            file=sys.stderr,
-            flush=True,
-        )
-        if error_conn is not None:
-            try:
-                error_conn.send(wrapper)
-            except Exception:
-                pass
-        if metrics_queue is not None:
-            try:
-                metrics_queue.put_nowait({"error": wrapper.exc_msg})
-            except Exception:
-                pass
-        if stop_event is not None:
-            try:
-                stop_event.set()
-            except Exception:
-                pass
-        raise
-
-
-def format_collector_death(exitcode: int | None, traceback_text: str | None = None) -> str:
-    """Format a human-readable death report for a collector process."""
-    parts = []
-
-    if traceback_text:
-        parts.append("Collector process crashed.")
-        parts.append(f"\n{'─' * 50}")
-        parts.append(traceback_text.rstrip())
-        parts.append(f"{'─' * 50}")
-    elif exitcode is not None and exitcode < 0:
-        sig_num = -exitcode
-        sig_name = _signal_name(sig_num)
-        parts.append(f"Collector process killed by signal {sig_num} ({sig_name}).")
-        parts.append("  No Python traceback available — killed externally.")
-        _append_signal_hint(parts, sig_num)
-    elif exitcode is not None and exitcode >= 128:
-        sig_num = exitcode - 128
-        sig_name = _signal_name(sig_num)
-        parts.append(
-            f"Collector process exited with code {exitcode} "
-            f"(shell-style signal {sig_num}, {sig_name})."
-        )
-        parts.append("  Native process termination may not produce a Python traceback.")
-        _append_signal_hint(parts, sig_num)
-    elif exitcode is not None:
-        parts.append(f"Collector process exited with code {exitcode}.")
-    else:
-        parts.append("Collector process died (exit code unknown).")
-
-    return "\n".join(parts)
-
-
-def _append_signal_hint(parts: list[str], sig_num: int) -> None:
-    if sig_num == 7:
-        parts.append(
-            "  Common causes: SIGBUS in native mmap/shared memory, CUDA pinned memory, "
-            "or WSL runtime/driver code."
-        )
-    elif sig_num == 9:
-        parts.append("  Common cause: OOM killer. Check: dmesg | grep -i oom")
-    elif sig_num == 11:
-        parts.append("  Common cause: segfault in native code (C++/CUDA).")
-
-
-def _signal_name(sig_num: int) -> str:
-    try:
-        return signal.Signals(sig_num).name
-    except (ValueError, AttributeError):
-        return f"SIG{sig_num}"
diff --git a/src/unilab/ipc/dp_launcher.py b/src/unilab/ipc/dp_launcher.py
deleted file mode 100644
index 0205859c4..000000000
--- a/src/unilab/ipc/dp_launcher.py
+++ /dev/null
@@ -1,545 +0,0 @@
-"""Single-node multi-GPU data-parallel launch and rank topology helpers.
-
-Topology rules (config parsing, rank/device mapping, subprocess supervision)
-live here so training scripts only assemble their flows. The off-policy path
-uses :class:`DpRankSupervisor`; RSL-RL PPO uses PyTorch's elastic launcher and
-standard ``WORLD_SIZE`` / ``RANK`` / ``LOCAL_RANK`` variables. Algorithm-level
-collectives remain owned by their respective learner implementations.
-"""
-
-from __future__ import annotations
-
-import os
-import signal
-import subprocess
-import sys
-import threading
-import time
-from pathlib import Path
-from typing import Any, Literal, Sequence
-
-UNILAB_DP_RANK = "UNILAB_DP_RANK"
-UNILAB_DP_WORLD_SIZE = "UNILAB_DP_WORLD_SIZE"
-UNILAB_DP_DEVICES = "UNILAB_DP_DEVICES"
-UNILAB_DP_LOG_DIR = "UNILAB_DP_LOG_DIR"
-
-_OFFPOLICY_SCRIPT = Path(__file__).resolve().parents[3] / "scripts" / "train_offpolicy.py"
-
-_WATCHDOG_INTERVAL_S = 0.5
-_COOPERATIVE_EXIT_GRACE_S = 10.0
-_TERMINATE_TIMEOUT_S = 10.0
-# Grace window for sibling ranks to finish their own runs once rank 0
-# completed normally. Ranks run the same config, so they should land within
-# startup skew of each other; exceeding the grace is treated as a failure.
-_NORMAL_EXIT_GRACE_S = 600.0
-
-
-def resolve_dp_topology(devices_cfg: Any) -> tuple[int, ...] | None:
-    """Normalize ``training.devices`` into an ordered CUDA-index tuple.
-
-    Returns None for the single-card default (null / empty list). The user
-    given order is preserved: rank i maps to ``cuda:{devices[i]}``.
-    """
-    if devices_cfg is None:
-        return None
-    devices = list(devices_cfg)
-    if len(devices) == 0:
-        return None
-    normalized: list[int] = []
-    for entry in devices:
-        if isinstance(entry, bool) or not isinstance(entry, int):
-            raise ValueError(
-                f"training.devices entries must be integer CUDA indices, got {entry!r}"
-            )
-        if entry < 0:
-            raise ValueError(f"training.devices entries must be non-negative, got {entry}")
-        normalized.append(int(entry))
-    if len(set(normalized)) != len(normalized):
-        raise ValueError(f"training.devices must not contain duplicates, got {normalized}")
-    return tuple(normalized)
-
-
-def current_dp_rank() -> int:
-    """Data-parallel rank of this process (0 when not spawned as a rank)."""
-    return int(os.environ.get(UNILAB_DP_RANK, "0"))
-
-
-def current_dp_world_size() -> int:
-    """Data-parallel world size of this process (1 when not spawned as a rank)."""
-    return int(os.environ.get(UNILAB_DP_WORLD_SIZE, "1"))
-
-
-def current_torch_distributed_rank() -> int:
-    """Global torch-distributed rank of this process (0 outside torchrun)."""
-    return int(os.environ.get("RANK", "0"))
-
-
-def current_torch_distributed_local_rank() -> int:
-    """Node-local torch-distributed rank of this process (0 outside torchrun)."""
-    return int(os.environ.get("LOCAL_RANK", "0"))
-
-
-def current_torch_distributed_world_size() -> int:
-    """Torch-distributed world size of this process (1 outside torchrun)."""
-    return int(os.environ.get("WORLD_SIZE", "1"))
-
-
-def resolve_dp_rendezvous_path(log_dir: str, *, rank: int) -> str:
-    """Shared FileStore rendezvous path for the DP parameter-sync group.
-
-    Rank 0 anchors the store in its own run directory; spawned ranks point at
-    the same canonical run root via ``UNILAB_DP_LOG_DIR``. The run directory
-    is unique per run, which keeps stale FileStore state from a previous run
-    out of the rendezvous.
-
-    Cold path only: call at runner construction.
-    """
-    if int(rank) > 0:
-        root = os.environ.get(UNILAB_DP_LOG_DIR)
-        if root is None:
-            raise ValueError(
-                f"{UNILAB_DP_LOG_DIR} must be set for spawned data-parallel rank {rank}"
-            )
-    else:
-        root = str(log_dir)
-    return os.path.join(root, ".dp_rendezvous")
-
-
-def resolve_collector_cpu_ids(
-    world_size: int,
-    rank: int,
-    cpu_count: int,
-    explicit: Any = None,
-) -> list[int] | None:
-    """Resolve the CPU ids exclusively owned by this rank's collector.
-
-    Returns None for the single-rank default (``world_size <= 1``) so the
-    single-card path stays bit-identical to the pre-partition behavior.
-    Otherwise the host CPUs are split into ``world_size`` contiguous blocks of
-    ``cpu_count // world_size``: rank i owns ``[i*size, (i+1)*size)`` and any
-    remainder CPUs stay on default OS scheduling. ``explicit`` (config
-    ``training.dp_collector_cpu_ids``, one segment per rank) overrides the
-    automatic partition; the segments must number exactly ``world_size``, be
-    non-empty, contain non-negative ints, and not overlap. An empty
-    ``explicit`` falls back to the automatic partition.
-
-    Cold path only: call at runner construction, never from the collect loop.
-    """
-    world_size = int(world_size)
-    if world_size <= 1:
-        return None
-    rank = int(rank)
-    if rank < 0 or rank >= world_size:
-        raise ValueError(f"data-parallel rank {rank} is out of range for world_size={world_size}")
-    cpu_count = int(cpu_count)
-    if cpu_count < world_size:
-        raise ValueError(
-            f"cpu_count={cpu_count} cannot give each data-parallel rank its own CPU "
-            f"(world_size={world_size})"
-        )
-    if explicit is not None and len(explicit) > 0:
-        segments: list[list[int]] = [list(segment) for segment in explicit]
-        if len(segments) != world_size:
-            raise ValueError(
-                f"training.dp_collector_cpu_ids must provide exactly world_size={world_size} "
-                f"segments, got {len(segments)}"
-            )
-        seen: set[int] = set()
-        for segment in segments:
-            if not segment:
-                raise ValueError(
-                    f"training.dp_collector_cpu_ids segments must be non-empty, got {segments!r}"
-                )
-            for cpu_id in segment:
-                if isinstance(cpu_id, bool) or not isinstance(cpu_id, int) or cpu_id < 0:
-                    raise ValueError(
-                        "training.dp_collector_cpu_ids entries must be non-negative "
-                        f"integers, got {cpu_id!r}"
-                    )
-                if cpu_id in seen:
-                    raise ValueError(
-                        f"training.dp_collector_cpu_ids segments overlap on CPU {cpu_id}"
-                    )
-                seen.add(cpu_id)
-        return segments[rank]
-    size = cpu_count // world_size
-    return list(range(rank * size, rank * size + size))
-
-
-def validate_dp_launchable(devices: tuple[int, ...]) -> None:
-    """Fail fast at launch time when the host lacks any requested CUDA device."""
-    import torch
-
-    device_count = torch.cuda.device_count()
-    missing = [index for index in devices if index >= device_count]
-    if missing:
-        raise ValueError(
-            f"training.devices={list(devices)} requires CUDA device index(es) {missing}, "
-            f"but torch.cuda.device_count()={device_count}"
-        )
-
-
-def resolve_cuda_visible_devices(
-    devices: tuple[int, ...],
-    *,
-    current_visible_devices: str | None = None,
-) -> str:
-    """Map configured logical CUDA indices to a child ``CUDA_VISIBLE_DEVICES`` value.
-
-    ``training.devices`` indexes the CUDA devices visible to the parent process.
-    When the parent already has ``CUDA_VISIBLE_DEVICES`` set, preserve that
-    mapping (including UUID entries) instead of accidentally switching back to
-    host-global device indices.
-    """
-    if current_visible_devices is None:
-        return ",".join(str(index) for index in devices)
-
-    visible_entries = [
-        entry.strip() for entry in current_visible_devices.split(",") if entry.strip()
-    ]
-    missing = [index for index in devices if index >= len(visible_entries)]
-    if missing:
-        raise ValueError(
-            f"training.devices={list(devices)} requires visible CUDA index(es) {missing}, "
-            f"but CUDA_VISIBLE_DEVICES={current_visible_devices!r} exposes "
-            f"{len(visible_entries)} device(s)"
-        )
-    return ",".join(visible_entries[index] for index in devices)
-
-
-def launch_torchrun_workers(
-    devices: tuple[int, ...],
-    *,
-    script_path: str | os.PathLike[str],
-    argv: Sequence[str],
-    log_dir: str,
-) -> None:
-    """Launch one local torchrun worker per configured CUDA device.
-
-    PyTorch elastic owns worker supervision and failure propagation. Each
-    worker re-enters the regular training script with the original Hydra
-    arguments, while ``UNILAB_DP_LOG_DIR`` supplies one canonical rank-0-owned
-    run directory.
-    """
-    if len(devices) < 2:
-        raise ValueError("launch_torchrun_workers requires at least two CUDA devices")
-    validate_dp_launchable(devices)
-
-    launch_env = os.environ.copy()
-    launch_env["CUDA_VISIBLE_DEVICES"] = resolve_cuda_visible_devices(
-        devices,
-        current_visible_devices=os.environ.get("CUDA_VISIBLE_DEVICES"),
-    )
-    # Keep the production transport defaults already used by DpParameterSync:
-    # current RTX 6000D hosts hang with NCCL P2P and can fault with SHM. An
-    # explicit user environment still wins over these compatibility defaults.
-    launch_env.setdefault("NCCL_P2P_DISABLE", "1")
-    launch_env.setdefault("NCCL_SHM_DISABLE", "1")
-    launch_env[UNILAB_DP_LOG_DIR] = str(log_dir)
-    command = [
-        sys.executable,
-        "-m",
-        "torch.distributed.run",
-        "--standalone",
-        "--nnodes=1",
-        f"--nproc_per_node={len(devices)}",
-        str(Path(script_path).resolve()),
-        *argv,
-    ]
-    print(
-        f"Launching RSL-RL data parallel training on CUDA devices {list(devices)} "
-        f"with {len(devices)} workers.",
-        flush=True,
-    )
-    completed = subprocess.run(command, env=launch_env, check=False)
-    if completed.returncode != 0:
-        raise RuntimeError(f"torchrun workers failed with exit code {completed.returncode}")
-
-
-def resolve_dp_rank_device(devices: tuple[int, ...] | None, rank: int) -> str | None:
-    """Return ``cuda:<index>`` for one configured rank, or None for auto selection."""
-    if devices is None:
-        return None
-    if rank < 0 or rank >= len(devices):
-        raise ValueError(
-            f"data-parallel rank {rank} is out of range for training.devices={list(devices)}"
-        )
-    return f"cuda:{devices[rank]}"
-
-
-def apply_dp_rank_config(cfg: Any, devices: tuple[int, ...] | None, rank: int) -> str | None:
-    """Apply the per-rank seed and return this rank's explicit CUDA device.
-
-    Rank 0 keeps the configured seed; rank i>0 trains with ``seed + i`` until
-    init broadcast lands in a later stage. ``training.devices`` is the sole
-    public off-policy device field, so the resolved runtime device is returned
-    instead of being written back into a synthetic ``training.device`` key.
-    """
-    if devices is None:
-        return None
-    device = resolve_dp_rank_device(devices, rank)
-    from omegaconf import open_dict
-
-    with open_dict(cfg):
-        cfg.algo.seed = int(cfg.algo.seed) + rank
-    return device
-
-
-def _sigterm_system_exit(signum: int, _frame: Any) -> None:
-    raise SystemExit(f"data-parallel rank 0 received signal {signum}")
-
-
-def _signal_process_group(child: subprocess.Popen, signum: int) -> None:
-    """Signal one rank and all of the worker processes it owns."""
-    try:
-        if hasattr(os, "killpg"):
-            os.killpg(child.pid, signum)
-        else:  # pragma: no cover - Windows fallback for non-CUDA test hosts.
-            child.send_signal(signum)
-    except ProcessLookupError:
-        pass
-
-
-def _process_group_exists(child: subprocess.Popen) -> bool:
-    """Return whether a rank process group still owns any live processes."""
-    # Reap an exited group leader before probing the group. Otherwise the
-    # leader may remain a zombie and make ``killpg(..., 0)`` look alive for the
-    # entire escalation timeout even though no rank resources remain.
-    child.poll()
-    if not hasattr(os, "killpg"):  # pragma: no cover - Windows fallback.
-        return child.returncode is None
-    try:
-        os.killpg(child.pid, 0)
-    except ProcessLookupError:
-        return False
-    except PermissionError:
-        return True
-    return True
-
-
-class DpRankSupervisor:
-    """Rank-0 supervisor that spawns and watches data-parallel rank subprocesses.
-
-    Ranks 1..N-1 re-run ``scripts/train_offpolicy.py`` with the same Hydra
-    argv plus the ``UNILAB_DP_*`` environment; each spawned rank builds its
-    own learner+collector pair through the regular runner path. If any rank
-    subprocess dies with a non-zero exit code while active, the supervisor
-    delivers SIGTERM to rank 0 so the runner lifecycle unwinds through the
-    normal try/finally (``runner.close()``), and ``__exit__`` tears down the
-    remaining ranks. Each spawned rank owns a separate process group containing
-    its collector. Rank 0 forwards Ctrl+C to those groups for cooperative
-    cleanup before escalating to SIGTERM/SIGKILL.
-    """
-
-    def __init__(self, devices: tuple[int, ...], log_dir: str) -> None:
-        self._devices = tuple(devices)
-        self._log_dir = log_dir
-        self._world_size = len(self._devices)
-        self._children: list[subprocess.Popen] = []
-        self._watchdog_stop = threading.Event()
-        self._watchdog: threading.Thread | None = None
-        self._previous_signal_handlers: dict[int, Any] = {}
-        self._shutdown_requested = False
-
-    def __enter__(self) -> DpRankSupervisor:
-        if self._world_size <= 1:
-            # Single-device degenerate case: nothing to spawn or watch.
-            return self
-        base_env = os.environ | {
-            UNILAB_DP_WORLD_SIZE: str(self._world_size),
-            UNILAB_DP_DEVICES: ",".join(str(index) for index in self._devices),
-            UNILAB_DP_LOG_DIR: self._log_dir,
-        }
-        self._install_signal_handlers()
-        try:
-            for rank in range(1, self._world_size):
-                env = base_env | {UNILAB_DP_RANK: str(rank)}
-                self._children.append(
-                    subprocess.Popen(
-                        [sys.executable, str(_OFFPOLICY_SCRIPT), *sys.argv[1:]],
-                        env=env,
-                        # Rank-local collectors inherit this group. The terminal
-                        # only interrupts rank 0; the supervisor then forwards
-                        # one coordinated SIGINT to each complete rank tree.
-                        start_new_session=os.name == "posix",
-                    )
-                )
-        except BaseException:
-            self._request_children_shutdown()
-            try:
-                self._wait_for_children(_COOPERATIVE_EXIT_GRACE_S)
-            finally:
-                try:
-                    self._terminate_children()
-                finally:
-                    self._restore_signal_handlers()
-            raise
-        self._watchdog = threading.Thread(
-            target=self._watchdog_loop,
-            name="dp-rank-watchdog",
-            daemon=True,
-        )
-        self._watchdog.start()
-        return self
-
-    def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> Literal[False]:
-        self._watchdog_stop.set()
-        failed: list[tuple[int, object]] = []
-        try:
-            if self._watchdog is not None:
-                self._watchdog.join(timeout=_TERMINATE_TIMEOUT_S)
-                self._watchdog = None
-
-            # Ranks that already exited on their own keep their exit code;
-            # non-zero means the data-parallel run failed even if rank 0 is fine.
-            failed = [
-                (rank, child.returncode)
-                for rank, child in enumerate(self._children, start=1)
-                if child.returncode is not None and child.returncode != 0
-            ]
-            if exc_type is None and not failed:
-                # Normal rank-0 completion: give sibling ranks a grace window to
-                # finish their own runs instead of cutting them short.
-                unfinished = self._wait_for_children(_NORMAL_EXIT_GRACE_S)
-                for rank, child in unfinished:
-                    print(
-                        f"[dp_launcher] rank {rank} subprocess pid={child.pid} did not "
-                        f"exit within {_NORMAL_EXIT_GRACE_S}s of rank 0 completion",
-                        file=sys.stderr,
-                    )
-                    failed.append((rank, "timeout"))
-                for rank, child in enumerate(self._children, start=1):
-                    if child.returncode not in (None, 0) and not any(
-                        failed_rank == rank for failed_rank, _ in failed
-                    ):
-                        failed.append((rank, child.returncode))
-            else:
-                # For Ctrl+C this is normally a no-op because the signal handler
-                # already forwarded SIGINT. It also makes arbitrary rank-0
-                # failures unwind sibling runner lifecycles cooperatively.
-                self._request_children_shutdown()
-                self._wait_for_children(_COOPERATIVE_EXIT_GRACE_S)
-        finally:
-            # Always reap complete rank process groups, including collectors
-            # orphaned by a rank that exited before finishing its own cleanup.
-            try:
-                self._terminate_children()
-            finally:
-                self._restore_signal_handlers()
-        if failed:
-            message = "data-parallel rank subprocess(es) failed: " + ", ".join(
-                f"rank {rank} exit code {code}" for rank, code in failed
-            )
-            if exc_type is None:
-                raise RuntimeError(message)
-            print(f"[dp_launcher] {message}", file=sys.stderr)
-        return False
-
-    def _install_signal_handlers(self) -> None:
-        try:
-            self._previous_signal_handlers[signal.SIGINT] = signal.signal(
-                signal.SIGINT, self._handle_sigint
-            )
-            self._previous_signal_handlers[signal.SIGTERM] = signal.signal(
-                signal.SIGTERM, self._handle_sigterm
-            )
-        except BaseException:
-            self._restore_signal_handlers()
-            raise
-
-    def _restore_signal_handlers(self) -> None:
-        for signum, previous in self._previous_signal_handlers.items():
-            signal.signal(signum, previous)
-        self._previous_signal_handlers.clear()
-
-    def _handle_sigint(self, signum: int, frame: Any) -> None:
-        self._request_children_shutdown()
-        previous = self._previous_signal_handlers.get(signal.SIGINT, signal.default_int_handler)
-        if previous == signal.SIG_IGN:
-            return
-        if previous == signal.SIG_DFL:
-            signal.default_int_handler(signum, frame)
-            return
-        previous(signum, frame)
-
-    def _handle_sigterm(self, signum: int, frame: Any) -> None:
-        # A child-rank watchdog failure uses SIGTERM to unwind rank 0. Sibling
-        # rank trees still receive SIGINT so their Python finally blocks run.
-        self._request_children_shutdown()
-        _sigterm_system_exit(signum, frame)
-
-    def _request_children_shutdown(self) -> None:
-        if self._shutdown_requested:
-            return
-        self._shutdown_requested = True
-        for child in self._children:
-            if _process_group_exists(child):
-                _signal_process_group(child, signal.SIGINT)
-
-    def _wait_for_children(self, timeout: float) -> list[tuple[int, subprocess.Popen]]:
-        deadline = time.monotonic() + max(float(timeout), 0.0)
-        pending = list(enumerate(self._children, start=1))
-        while pending:
-            pending = [(rank, child) for rank, child in pending if child.poll() is None]
-            if not pending:
-                break
-            remaining = deadline - time.monotonic()
-            if remaining <= 0:
-                break
-            try:
-                pending[0][1].wait(timeout=min(_WATCHDOG_INTERVAL_S, remaining))
-            except subprocess.TimeoutExpired:
-                pass
-        return pending
-
-    @staticmethod
-    def _wait_for_process_groups(
-        children: list[subprocess.Popen], timeout: float
-    ) -> list[subprocess.Popen]:
-        deadline = time.monotonic() + max(float(timeout), 0.0)
-        pending = [child for child in children if _process_group_exists(child)]
-        while pending:
-            remaining = deadline - time.monotonic()
-            if remaining <= 0:
-                break
-            time.sleep(min(_WATCHDOG_INTERVAL_S, remaining))
-            pending = [child for child in pending if _process_group_exists(child)]
-        return pending
-
-    def _terminate_children(self) -> None:
-        groups = [child for child in self._children if _process_group_exists(child)]
-        for child in groups:
-            _signal_process_group(child, signal.SIGTERM)
-        groups = self._wait_for_process_groups(groups, _TERMINATE_TIMEOUT_S)
-        for child in groups:
-            _signal_process_group(child, signal.SIGKILL)
-        self._wait_for_process_groups(groups, _TERMINATE_TIMEOUT_S)
-
-        # Reap every direct rank child after its complete process group has
-        # stopped so no zombie or Popen handle survives supervisor teardown.
-        for child in self._children:
-            if child.poll() is not None:
-                continue
-            try:
-                child.wait(timeout=_TERMINATE_TIMEOUT_S)
-            except subprocess.TimeoutExpired:
-                _signal_process_group(child, signal.SIGKILL)
-                child.wait(timeout=_TERMINATE_TIMEOUT_S)
-
-    def _watchdog_loop(self) -> None:
-        while not self._watchdog_stop.wait(_WATCHDOG_INTERVAL_S):
-            for rank, child in enumerate(self._children, start=1):
-                exit_code = child.poll()
-                if exit_code is None:
-                    continue
-                if exit_code == 0:
-                    # A rank that finishes cleanly (e.g. while rank 0 is still
-                    # in playback) is not a failure; keep watching the rest.
-                    continue
-                print(
-                    f"[dp_launcher] rank {rank} subprocess pid={child.pid} exited "
-                    f"unexpectedly with code {exit_code}; shutting down rank 0",
-                    file=sys.stderr,
-                )
-                os.kill(os.getpid(), signal.SIGTERM)
-                return
diff --git a/src/unilab/ipc/dp_sync.py b/src/unilab/ipc/dp_sync.py
deleted file mode 100644
index c2561606e..000000000
--- a/src/unilab/ipc/dp_sync.py
+++ /dev/null
@@ -1,346 +0,0 @@
-"""Synchronous gradient averaging across data-parallel ranks.
-
-Multi-GPU data-parallel off-policy training runs one independent
-learner+collector pair per rank (see ``dp_launcher.py``). The runner broadcasts
-rank 0's model state before collection starts. During steady-state training,
-each learner averages the actor/critic/temperature gradients immediately after
-``backward()`` and before clipping or ``optimizer.step()``.
-
-This module owns the process-group lifecycle, the startup parameter broadcast,
-flat-gradient collectives, and the small scalar reduction used by the canonical
-rank-0 logger. Optimizer state is not communicated: identical initialization
-and identical averaged gradients keep each rank's optimizer state aligned.
-"""
-
-from __future__ import annotations
-
-import datetime
-import json
-import time
-from collections.abc import Iterable
-from typing import cast
-
-import torch
-import torch.distributed as dist
-
-
-class DpParameterSync:
-    """Process group for startup state broadcast and steady-state gradients.
-
-    The startup synchronization key order is frozen on the first collective
-    (``sorted(keys)``) so every rank walks the identical sequence without
-    re-sorting per call; a later call with a different key set is a bug and
-    raises ValueError.
-    """
-
-    def __init__(
-        self,
-        *,
-        world_size: int,
-        rank: int,
-        rendezvous_path: str,
-        backend: str = "nccl",
-        device: str | None = None,
-        timeout_s: int = 120,
-    ) -> None:
-        if int(world_size) < 2:
-            raise ValueError(f"DpParameterSync requires world_size >= 2, got {world_size}")
-        if not (0 <= int(rank) < int(world_size)):
-            raise ValueError(f"rank {rank} is out of range for world_size={world_size}")
-        self.world_size = int(world_size)
-        self.rank = int(rank)
-        self.rendezvous_path = str(rendezvous_path)
-        self.backend = str(backend)
-        self.device = None if device is None else torch.device(device)
-        self.timeout_s = int(timeout_s)
-        self._key_order: tuple[str, ...] | None = None
-        self._statistics_schema: dict[str, str] = {}
-        self._gradient_sync_time_sec = 0.0
-        self._gradient_sync_calls = 0
-        self._gradient_buffers: dict[tuple[int, ...], torch.Tensor] = {}
-        self._cuda_graph_collective_ready = False
-        self._started = False
-
-    def start(self) -> None:
-        """Init the process group over a shared-file rendezvous.
-
-        The rendezvous path must be unique per run (the caller derives it
-        from the per-run log directory), so no stale FileStore state from a
-        previous run can leak in and no pre-clean is needed.
-
-        For NCCL the current CUDA device is pinned to this rank's device
-        first: ProcessGroupNCCL binds communicators to the current device,
-        and without the pin every rank defaults to cuda:0, which hangs the
-        first collective on any rank whose learner lives on another GPU.
-
-        ``NCCL_P2P_DISABLE``/``NCCL_SHM_DISABLE`` default to 1 (env override
-        wins): on hosts with broken NCCL peer transport (e.g. RTX 6000D on
-        current drivers, where P2P hangs and SHM triggers CUDA illegal
-        memory access) the TCP loopback transport is the only reliable
-        path.
-        """
-        if self._started:
-            return
-        if self.backend == "nccl":
-            import os
-
-            os.environ.setdefault("NCCL_P2P_DISABLE", "1")
-            os.environ.setdefault("NCCL_SHM_DISABLE", "1")
-        if self.backend == "nccl" and self.device is not None and self.device.type == "cuda":
-            torch.cuda.set_device(self.device)
-        dist.init_process_group(
-            backend=self.backend,
-            init_method=f"file://{self.rendezvous_path}",
-            rank=self.rank,
-            world_size=self.world_size,
-            timeout=datetime.timedelta(seconds=self.timeout_s),
-        )
-        self._started = True
-
-    def broadcast_from_rank0(self, tensors: dict[str, torch.Tensor]) -> None:
-        """Overwrite every rank's tensors with rank 0's values, in place."""
-        with torch.no_grad():
-            for key in self._ordered_keys(tensors):
-                dist.broadcast(tensors[key], src=0)
-
-    def prepare_cuda_graph_collectives(self) -> None:
-        """Warm up NCCL before the first graph-captured gradient collective.
-
-        NCCL communicator and collective-specific lazy initialization must run
-        eagerly. Capturing the first all-reduce either fails at capture time or
-        produces a graph that hangs on replay with the supported TCP-loopback
-        transport. This mirrors PyTorch's c10d CUDA Graph tests: one eager
-        all-reduce followed by a device synchronize before capture.
-        """
-        if self._cuda_graph_collective_ready:
-            return
-        if not self._started:
-            raise RuntimeError("start() must run before CUDA Graph collective warmup")
-        if self.backend != "nccl" or self.device is None or self.device.type != "cuda":
-            raise RuntimeError(
-                "optimizer CUDA Graph gradient synchronization requires "
-                "an NCCL process group with an explicit CUDA device"
-            )
-
-        warmup = torch.ones(1, device=self.device)
-        dist.all_reduce(warmup, op=dist.ReduceOp.SUM)
-        torch.cuda.synchronize(self.device)
-        self._cuda_graph_collective_ready = True
-
-    def allreduce_gradients(self, parameters: Iterable[torch.Tensor]) -> None:
-        """Average one optimizer's gradients with one flat all-reduce.
-
-        Existing gradients are packed in parameter order, matching the manual
-        collective used by Holosoma FastSAC. All ranks must execute the same
-        optimizer graph and therefore present the same gradient layout.
-        """
-        sync_start = time.perf_counter()
-        params = [
-            parameter
-            for parameter in parameters
-            if parameter.requires_grad and parameter.grad is not None
-        ]
-        if not params:
-            raise ValueError("gradient synchronization requires at least one existing gradient")
-
-        device = params[0].device
-        dtype = params[0].dtype
-        for parameter in params:
-            if parameter.device != device or parameter.dtype != dtype:
-                raise ValueError(
-                    "one flat gradient collective requires matching parameter devices and dtypes"
-                )
-
-        gradient_numel = sum(parameter.numel() for parameter in params)
-        buffer_key = tuple(id(parameter) for parameter in params)
-        packed = self._gradient_buffers.get(buffer_key)
-        if (
-            packed is None
-            or packed.numel() != gradient_numel
-            or packed.device != device
-            or packed.dtype != dtype
-        ):
-            packed = torch.empty(gradient_numel, device=device, dtype=dtype)
-            self._gradient_buffers[buffer_key] = packed
-        offset = 0
-        for parameter in params:
-            width = parameter.numel()
-            gradient = parameter.grad
-            assert gradient is not None
-            packed[offset : offset + width].copy_(gradient.detach().reshape(-1))
-            offset += width
-
-        capturing = device.type == "cuda" and torch.cuda.is_current_stream_capturing()
-        if capturing and not self._cuda_graph_collective_ready:
-            raise RuntimeError(
-                "NCCL gradient capture requires prepare_cuda_graph_collectives() first"
-            )
-        dist.all_reduce(packed, op=dist.ReduceOp.SUM)
-        packed.div_(self.world_size)
-
-        offset = 0
-        for parameter in params:
-            width = parameter.numel()
-            gradient = parameter.grad
-            assert gradient is not None
-            gradient.copy_(packed[offset : offset + width].view_as(parameter))
-            offset += width
-
-        # Python executes once while a CUDA Graph is captured, not on replay.
-        # Learners report replayed collectives separately so this metric counts
-        # actual executions rather than graph construction.
-        if not capturing:
-            self._gradient_sync_time_sec += time.perf_counter() - sync_start
-            self._gradient_sync_calls += 1
-
-    def record_cuda_graph_gradient_replay(self, collective_calls: int) -> None:
-        """Account for collectives executed by one optimizer graph replay."""
-        if collective_calls < 0:
-            raise ValueError(f"collective_calls must be >= 0, got {collective_calls}")
-        self._gradient_sync_calls += int(collective_calls)
-
-    def take_gradient_sync_metrics(self) -> tuple[float, int]:
-        """Return and reset this rank's gradient-sync time and call count."""
-        metrics = (self._gradient_sync_time_sec, self._gradient_sync_calls)
-        self._gradient_sync_time_sec = 0.0
-        self._gradient_sync_calls = 0
-        return metrics
-
-    def allreduce_statistics(
-        self,
-        *,
-        mean: dict[str, float] | None = None,
-        total: dict[str, float] | None = None,
-    ) -> dict[str, float]:
-        """Aggregate sparse scalar statistics on every rank.
-
-        ``mean`` fields are averaged over ranks that supplied the field;
-        ``total`` fields are summed. A cached union schema plus a presence mask
-        allows optional collector/reward fields to appear at different times on
-        different ranks without treating a missing value as zero. Schema
-        exchange happens only when a new field appears; the steady-state path
-        is two small collectives (schema-change flag + packed scalar reduce).
-
-        This collective is intentionally separate from per-optimizer gradient
-        averaging: logging is aggregated once per outer learner iteration.
-        """
-        mean = dict(mean or {})
-        total = dict(total or {})
-        overlap = set(mean) & set(total)
-        if overlap:
-            raise ValueError(
-                f"DP statistic fields cannot be both mean and total: {sorted(overlap)}"
-            )
-
-        local_schema = {key: "mean" for key in mean}
-        local_schema.update({key: "total" for key in total})
-        schema_changed = any(
-            self._statistics_schema.get(key) != reduction for key, reduction in local_schema.items()
-        )
-        device = self.device if self.device is not None else torch.device("cpu")
-        changed_tensor = torch.tensor(
-            [int(schema_changed)],
-            dtype=torch.int32,
-            device=device,
-        )
-        dist.all_reduce(changed_tensor, op=dist.ReduceOp.MAX)
-        if bool(changed_tensor.item()):
-            merged = dict(self._statistics_schema)
-            for rank_schema in self._gather_statistics_schemas(local_schema, device=device):
-                for key, reduction in rank_schema:
-                    existing = merged.get(key)
-                    if existing is not None and existing != reduction:
-                        raise ValueError(
-                            f"DP statistic {key!r} changed reduction from "
-                            f"{existing!r} to {reduction!r}"
-                        )
-                    merged[key] = reduction
-            self._statistics_schema = merged
-
-        if not self._statistics_schema:
-            return {}
-
-        keys = tuple(sorted(self._statistics_schema))
-        values = mean | total
-        packed = torch.zeros((2, len(keys)), dtype=torch.float64, device=device)
-        for index, key in enumerate(keys):
-            if key not in values:
-                continue
-            packed[0, index] = float(values[key])
-            packed[1, index] = 1.0
-        dist.all_reduce(packed, op=dist.ReduceOp.SUM)
-
-        value_sums = packed[0].tolist()
-        presence_counts = packed[1].tolist()
-        aggregated: dict[str, float] = {}
-        for index, key in enumerate(keys):
-            count = float(presence_counts[index])
-            if count <= 0:
-                continue
-            value = float(value_sums[index])
-            if self._statistics_schema[key] == "mean":
-                value /= count
-            aggregated[key] = value
-        return aggregated
-
-    def _gather_statistics_schemas(
-        self,
-        local_schema: dict[str, str],
-        *,
-        device: torch.device,
-    ) -> list[tuple[tuple[str, str], ...]]:
-        """Exchange sparse logger schemas without NCCL object collectives.
-
-        ``all_gather_object`` stages pickle payloads through an NCCL all-gather.
-        That path is both unnecessary for this string-only schema and has
-        caused CUDA out-of-range-address faults on supported multi-GPU hosts.
-        Rank-ordered byte broadcasts keep the exchange deterministic and use
-        the same well-tested NCCL primitive as the startup model sync.
-        """
-        payload = json.dumps(
-            sorted(local_schema.items()),
-            ensure_ascii=True,
-            separators=(",", ":"),
-        ).encode("utf-8")
-        local_payload = torch.tensor(list(payload), dtype=torch.uint8, device=device)
-        gathered: list[tuple[tuple[str, str], ...]] = []
-
-        for source_rank in range(self.world_size):
-            payload_size = torch.tensor(
-                [len(payload) if self.rank == source_rank else 0],
-                dtype=torch.int64,
-                device=device,
-            )
-            dist.broadcast(payload_size, src=source_rank)
-            size = int(payload_size.item())
-            rank_payload = torch.empty(size, dtype=torch.uint8, device=device)
-            if self.rank == source_rank:
-                rank_payload.copy_(local_payload)
-            dist.broadcast(rank_payload, src=source_rank)
-
-            decoded = json.loads(bytes(rank_payload.cpu().tolist()).decode("utf-8"))
-            gathered.append(
-                tuple((cast(str, key), cast(str, reduction)) for key, reduction in decoded)
-            )
-        return gathered
-
-    def close(self) -> None:
-        """Destroy the process group; idempotent and safe before start()."""
-        if not self._started:
-            return
-        if dist.is_available() and dist.is_initialized():
-            dist.destroy_process_group()
-        self._gradient_buffers.clear()
-        self._cuda_graph_collective_ready = False
-        self._started = False
-
-    def _ordered_keys(self, tensors: dict[str, torch.Tensor]) -> tuple[str, ...]:
-        if self._key_order is None:
-            self._key_order = tuple(sorted(tensors))
-            return self._key_order
-        if set(tensors) != set(self._key_order):
-            raise ValueError(
-                "DpParameterSync tensor keys changed between collectives: "
-                f"expected {sorted(self._key_order)}, got {sorted(tensors)}"
-            )
-        return self._key_order
diff --git a/src/unilab/ipc/inference_slot.py b/src/unilab/ipc/inference_slot.py
deleted file mode 100644
index 6bdc9810f..000000000
--- a/src/unilab/ipc/inference_slot.py
+++ /dev/null
@@ -1,126 +0,0 @@
-"""Single-slot observation/action exchange for learner-owned inference."""
-
-from __future__ import annotations
-
-import multiprocessing as mp
-from typing import Any
-
-import numpy as np
-import torch
-
-_SPAWN_CTX = mp.get_context("spawn")
-
-_IDLE = 0
-_OBS_READY = 1
-_ACTION_READY = 2
-
-
-class SharedInferenceSlot:
-    """Fixed shared-memory slot with strict single-request ownership."""
-
-    def __init__(self, num_envs: int, obs_dim: int, action_dim: int) -> None:
-        if min(num_envs, obs_dim, action_dim) <= 0:
-            raise ValueError("SharedInferenceSlot dimensions must be positive")
-        self.num_envs = int(num_envs)
-        self.obs_dim = int(obs_dim)
-        self.action_dim = int(action_dim)
-        self.observations = torch.empty(
-            (self.num_envs, self.obs_dim), dtype=torch.float32
-        ).share_memory_()
-        self.dones = torch.empty(self.num_envs, dtype=torch.float32).share_memory_()
-        self.actions = torch.empty(
-            (self.num_envs, self.action_dim), dtype=torch.float32
-        ).share_memory_()
-        self._state = _SPAWN_CTX.Value("i", _IDLE)
-        self._request_tick = _SPAWN_CTX.Value("q", -1)
-        self._response_tick = _SPAWN_CTX.Value("q", -1)
-        self._policy_version = _SPAWN_CTX.Value("q", -1)
-        self._lock = _SPAWN_CTX.Lock()
-
-    @property
-    def nbytes(self) -> int:
-        return sum(
-            tensor.numel() * tensor.element_size()
-            for tensor in (self.observations, self.dones, self.actions)
-        )
-
-    def publish_observation(
-        self,
-        *,
-        tick_id: int,
-        observations: np.ndarray,
-        dones: np.ndarray,
-    ) -> None:
-        observations = np.asarray(observations, dtype=np.float32)
-        dones = np.asarray(dones, dtype=np.float32).reshape(-1)
-        expected_obs_shape = (self.num_envs, self.obs_dim)
-        if observations.shape != expected_obs_shape:
-            raise ValueError(
-                f"Inference observation shape must be {expected_obs_shape}, "
-                f"got {observations.shape}"
-            )
-        if dones.shape != (self.num_envs,):
-            raise ValueError(f"Inference dones shape must be {(self.num_envs,)}, got {dones.shape}")
-        with self._lock:
-            if self._state.value != _IDLE:
-                raise RuntimeError("Inference slot cannot be reused before action consumption")
-            np.copyto(self.observations.numpy(), observations)
-            np.copyto(self.dones.numpy(), dones)
-            self._request_tick.value = int(tick_id)
-            self._state.value = _OBS_READY
-
-    def copy_observation_to(
-        self,
-        *,
-        tick_id: int,
-        observations: torch.Tensor,
-        dones: torch.Tensor,
-        non_blocking: bool = False,
-    ) -> None:
-        if tuple(observations.shape) != (self.num_envs, self.obs_dim):
-            raise ValueError("Learner observation destination shape mismatch")
-        if tuple(dones.shape) != (self.num_envs,):
-            raise ValueError("Learner dones destination shape mismatch")
-        with self._lock:
-            self._require_state(_OBS_READY, tick_id, self._request_tick, "observation")
-            observations.copy_(self.observations, non_blocking=non_blocking)
-            dones.copy_(self.dones, non_blocking=non_blocking)
-
-    def publish_action(
-        self,
-        *,
-        tick_id: int,
-        policy_version: int,
-        actions: torch.Tensor,
-        non_blocking: bool = False,
-    ) -> None:
-        if tuple(actions.shape) != (self.num_envs, self.action_dim):
-            raise ValueError("Learner action shape mismatch")
-        with self._lock:
-            self._require_state(_OBS_READY, tick_id, self._request_tick, "observation")
-            self.actions.copy_(actions, non_blocking=non_blocking)
-            self._response_tick.value = int(tick_id)
-            self._policy_version.value = int(policy_version)
-            self._state.value = _ACTION_READY
-
-    def consume_action(self, *, tick_id: int) -> tuple[np.ndarray, int]:
-        with self._lock:
-            self._require_state(_ACTION_READY, tick_id, self._response_tick, "action")
-            actions = self.actions.numpy().copy()
-            policy_version = int(self._policy_version.value)
-            self._state.value = _IDLE
-            return actions, policy_version
-
-    def _require_state(self, state: int, tick_id: int, tick_value: Any, label: str) -> None:
-        if self._state.value != state:
-            raise RuntimeError(f"Inference {label} is not ready")
-        actual_tick = int(tick_value.value)
-        if actual_tick != int(tick_id):
-            raise RuntimeError(
-                f"Inference {label} tick mismatch: expected {tick_id}, got {actual_tick}"
-            )
-
-    def close(self) -> None:
-        return None
-
-    cleanup = close
diff --git a/src/unilab/ipc/memory_budget.py b/src/unilab/ipc/memory_budget.py
deleted file mode 100644
index a1973fad9..000000000
--- a/src/unilab/ipc/memory_budget.py
+++ /dev/null
@@ -1,149 +0,0 @@
-"""Memory budget estimation for async RL training buffers.
-
-Pure functions that estimate memory usage and warn if the system
-is likely to OOM before allocating large shared buffers.
-"""
-
-from __future__ import annotations
-
-import os
-import shutil
-import sys
-
-
-def estimate_offpolicy_bytes(
-    num_envs: int,
-    replay_buffer_n: int,
-    obs_dim: int,
-    action_dim: int,
-    critic_dim: int,
-    ingress_depth: int = 2,
-) -> dict[str, int | str]:
-    """Estimate host shared memory for bounded off-policy replay ingress."""
-    row_width = 2 * obs_dim + action_dim + 3 + 2 * critic_dim
-    capacity = replay_buffer_n * num_envs
-    ingress_bytes = int(ingress_depth) * num_envs * row_width * 4
-    return {
-        "replay_buffer": 0,
-        "bounded_ingress_slots": ingress_bytes,
-        "total": ingress_bytes,
-        "breakdown": (
-            "Replay: 0 MB shared host memory "
-            f"({capacity} rows remain authoritative on the learner device)\n"
-            f"  Bounded ingress: {ingress_bytes / 1024**2:.0f} MB "
-            f"({int(ingress_depth)} slots × {num_envs} rows × {row_width} cols × 4B)\n"
-            "  Excludes MuJoCo BatchEnvPool/native allocations, CUDA pinned/shared "
-            "registration, and driver memory."
-        ),
-    }
-
-
-def estimate_appo_bytes(
-    num_envs: int,
-    steps_per_env: int,
-    obs_dim: int,
-    action_dim: int,
-    critic_dim: int,
-    num_slots: int = 4,
-) -> dict[str, int | str]:
-    """Estimate memory for APPO rollout ring buffer."""
-    per_step = obs_dim + action_dim + 1 + 1 + 1 + 1 + critic_dim
-    per_slot = num_envs * steps_per_env * per_step * 4
-    last_obs_per_slot = num_envs * (obs_dim + critic_dim) * 4
-    total_per_slot = per_slot + last_obs_per_slot
-    total = total_per_slot * num_slots
-
-    return {
-        "ring_buffer": total,
-        "total": total,
-        "breakdown": (
-            f"Ring buffer: {total / 1024**2:.0f} MB "
-            f"({num_slots} slots × {num_envs} envs × {steps_per_env} steps × "
-            f"{per_step} cols × 4B)"
-        ),
-    }
-
-
-def get_available_memory_bytes() -> int | None:
-    """Best-effort available memory detection."""
-    try:
-        with open("/proc/meminfo") as f:
-            for line in f:
-                if line.startswith("MemAvailable:"):
-                    return int(line.split()[1]) * 1024
-    except (OSError, ValueError):
-        pass
-
-    try:
-        import psutil
-
-        return int(psutil.virtual_memory().available)
-    except ImportError:
-        pass
-
-    return None
-
-
-def get_shared_memory_available_bytes(path: str = "/dev/shm") -> int | None:
-    """Best-effort available shared-memory space detection."""
-    try:
-        return int(shutil.disk_usage(path).free)
-    except (OSError, ValueError):
-        return None
-
-
-def raise_if_shared_memory_over_budget(
-    estimated: dict[str, int | str],
-    label: str,
-    threshold: float = 0.8,
-    path: str = "/dev/shm",
-) -> None:
-    """Fail before allocating shared buffers that exceed shared-memory capacity."""
-    available = get_shared_memory_available_bytes(path)
-    if available is None:
-        return
-
-    total = int(estimated["total"])
-    ratio = total / max(available, 1)
-    if total <= available * threshold:
-        return
-
-    est_gb = total / 1024**3
-    avail_gb = available / 1024**3
-    raise MemoryError(
-        f"{label}: estimated shared-memory allocation {est_gb:.1f} GB exceeds "
-        f"{path} available {avail_gb:.1f} GB ({ratio:.0%} usage). "
-        "Reduce algo.num_envs or algo.replay_buffer_n, or increase /dev/shm."
-    )
-
-
-def warn_if_over_budget(
-    estimated: dict[str, int | str],
-    label: str,
-    threshold: float = 0.8,
-) -> None:
-    """Print a warning if estimated memory exceeds threshold of available."""
-    if os.environ.get("UNILAB_SKIP_MEMORY_CHECK"):
-        return
-
-    available = get_available_memory_bytes()
-    if available is None:
-        return
-
-    total = int(estimated["total"])
-    ratio = total / available
-
-    if ratio > threshold:
-        est_gb = total / 1024**3
-        avail_gb = available / 1024**3
-        breakdown = estimated.get("breakdown", "")
-        print(
-            f"\n[Memory Warning] {label}: estimated {est_gb:.1f} GB, "
-            f"available {avail_gb:.1f} GB ({ratio:.0%} usage).\n"
-            f"  {breakdown}\n"
-            f"  Consider reducing algo.num_envs or algo.replay_buffer_n.\n"
-            f"  Native backend and driver memory may push actual usage higher.\n"
-            f"  Suppress: export UNILAB_SKIP_MEMORY_CHECK=1\n",
-            file=sys.stderr,
-            flush=True,
-        )
diff --git a/src/unilab/ipc/replay_buffer.py b/src/unilab/ipc/replay_buffer.py
deleted file mode 100644
index 7d464be0d..000000000
--- a/src/unilab/ipc/replay_buffer.py
+++ /dev/null
@@ -1,416 +0,0 @@
-"""Packed replay storage and bounded collector ingress for off-policy RL."""
-
-import multiprocessing as mp
-import time
-from typing import Any
-
-import torch
-
-from unilab.ipc.shared_buffer import SharedBufferBase
-
-DEFAULT_REPLAY_INGRESS_DEPTH = 2
-
-
-class ReplayBuffer(SharedBufferBase):
-    """Bounded host ingress for an authoritative device replay ring.
-
-    The collector publishes fixed-depth shared ingress slots. The replay
-    pipeline advances ``ptr`` and ``size`` only after the device copy commits.
-    """
-
-    DEFAULT_INGRESS_DEPTH = DEFAULT_REPLAY_INGRESS_DEPTH
-
-    def __init__(
-        self,
-        capacity: int,
-        obs_dim: int,
-        action_dim: int,
-        device: str,
-        *,
-        ingress_slot_rows: int,
-        critic_dim: int = 0,
-        ingress_depth: int = DEFAULT_INGRESS_DEPTH,
-    ):
-        super().__init__(capacity, device, defer_gpu=True)
-        self._obs_dim = obs_dim
-        self._action_dim = action_dim
-        self._critic_dim = critic_dim
-        self.last_incremental_h2d_time_s = 0.0
-        self.trace_recorder: Any | None = None
-        self.trace_thread_time = False
-        self.trace_cuda_events = True
-        self._stop_event: Any | None = None
-
-        self.size = torch.zeros(1, dtype=torch.int64).share_memory_()
-        self._init_packed_layout(obs_dim, action_dim, critic_dim)
-        self._ingress_slots: list[torch.Tensor] = []
-        self._init_bounded_ingress(
-            slot_rows=ingress_slot_rows,
-            depth=int(ingress_depth),
-        )
-
-    def _init_packed_layout(self, obs_dim: int, action_dim: int, critic_dim: int) -> None:
-        self._storage_width = 2 * obs_dim + action_dim + 3 + 2 * critic_dim
-
-        c = 0
-        self._obs_sl = slice(c, c + obs_dim)
-        c += obs_dim
-        self._nobs_sl = slice(c, c + obs_dim)
-        c += obs_dim
-        self._act_sl = slice(c, c + action_dim)
-        c += action_dim
-        self._rew_col = c
-        c += 1
-        self._done_col = c
-        c += 1
-        self._trunc_col = c
-        c += 1
-
-        if critic_dim > 0:
-            self._critic_sl = slice(c, c + critic_dim)
-            c += critic_dim
-            self._ncritic_sl = slice(c, c + critic_dim)
-            c += critic_dim
-
-    def _init_bounded_ingress(self, *, slot_rows: int, depth: int) -> None:
-        if slot_rows <= 0:
-            raise ValueError("ingress_slot_rows must be positive")
-        if slot_rows > self.capacity:
-            raise ValueError("ingress_slot_rows cannot exceed replay capacity")
-        if depth <= 0:
-            raise ValueError("ingress_depth must be positive")
-        self._ingress_slot_rows = slot_rows
-        self._ingress_depth = depth
-        self._ingress_slots = [
-            torch.empty((slot_rows, self._storage_width), dtype=torch.float32).share_memory_()
-            for _ in range(depth)
-        ]
-        self._ingress_starts = torch.zeros(depth, dtype=torch.int64).share_memory_()
-        self._ingress_counts = torch.zeros(depth, dtype=torch.int64).share_memory_()
-        self._published_ptr = torch.zeros(1, dtype=torch.int64).share_memory_()
-        self._ingress_publish_seq = torch.zeros(1, dtype=torch.int64).share_memory_()
-        self._ingress_closed = torch.zeros(1, dtype=torch.bool).share_memory_()
-        spawn_context = mp.get_context("spawn")
-        self._ingress_free = spawn_context.Semaphore(depth)
-        self._ingress_ready = spawn_context.Semaphore(0)
-        self._ingress_consume_seq = 0
-        self._ingress_release_seq = 0
-
-    @property
-    def storage_width(self) -> int:
-        return self._storage_width
-
-    @property
-    def host_storage_bytes(self) -> int:
-        return sum(slot.numel() * slot.element_size() for slot in self._ingress_slots)
-
-    @property
-    def published_ptr(self) -> int:
-        return int(self._published_ptr[0])
-
-    def attach_stop_event(self, stop_event: Any) -> None:
-        self._stop_event = stop_event
-
-    def take_published_ingress(self) -> tuple[int, int, int, torch.Tensor] | None:
-        if not self._ingress_ready.acquire(block=False):
-            return None
-        slot = self._ingress_consume_seq % self._ingress_depth
-        self._ingress_consume_seq += 1
-        start = int(self._ingress_starts[slot])
-        count = int(self._ingress_counts[slot])
-        return slot, start, count, self._ingress_slots[slot][:count]
-
-    def commit_ingress(self, *, slot: int, start: int, count: int) -> None:
-        expected_slot = self._ingress_release_seq % self._ingress_depth
-        if slot != expected_slot:
-            raise RuntimeError(
-                f"Ingress slots must commit in publication order: expected {expected_slot}, got {slot}"
-            )
-        if start != int(self.ptr[0]):
-            raise RuntimeError(
-                f"Ingress commit is not contiguous: committed ptr {int(self.ptr[0])}, start {start}"
-            )
-        self.ptr[0] = start + count
-        self.size[0] = min(start + count, self.capacity)
-        self._ingress_release_seq += 1
-        self._ingress_free.release()
-
-    def close(self) -> None:
-        self._ingress_closed[0] = True
-        for _ in range(self._ingress_depth):
-            self._ingress_free.release()
-
-    def critic_graph_packed_width(self) -> int:
-        """Return graph-order packed width for FastSAC critic graph inputs."""
-        if self._critic_dim <= 0:
-            raise RuntimeError("critic_graph_packed_source requires critic replay storage")
-        return self._critic_dim + self._action_dim + 1 + self._obs_dim + self._critic_dim + 1 + 1
-
-    def sac_graph_packed_width(self) -> int:
-        """Return graph-union packed width for one-H2D FastSAC graph staging."""
-        if self._critic_dim <= 0:
-            raise RuntimeError("sac_graph_packed_source requires critic replay storage")
-        return self._storage_width
-
-    def pack_critic_graph_source(
-        self,
-        packed_batch: torch.Tensor,
-        *,
-        out: torch.Tensor,
-    ) -> torch.Tensor:
-        """Pack replay rows in the exact input order consumed by critic CUDA graph."""
-        if self._critic_dim <= 0:
-            raise RuntimeError("critic_graph_packed_source requires critic replay storage")
-        expected_shape = (packed_batch.shape[0], self.critic_graph_packed_width())
-        if tuple(out.shape) != expected_shape:
-            raise ValueError(
-                "critic_graph_packed_source output shape mismatch: "
-                f"expected {expected_shape}, got {tuple(out.shape)}"
-            )
-        offset = 0
-        fields = (
-            packed_batch[:, self._critic_sl],
-            packed_batch[:, self._act_sl],
-            packed_batch[:, self._rew_col : self._rew_col + 1],
-            packed_batch[:, self._nobs_sl],
-            packed_batch[:, self._ncritic_sl],
-            packed_batch[:, self._done_col : self._done_col + 1],
-            packed_batch[:, self._trunc_col : self._trunc_col + 1],
-        )
-        for field in fields:
-            width = int(field.shape[1])
-            out[:, offset : offset + width].copy_(field)
-            offset += width
-        return out
-
-    def pack_sac_graph_source(
-        self,
-        packed_batch: torch.Tensor,
-        *,
-        out: torch.Tensor,
-    ) -> torch.Tensor:
-        """Pack replay rows once in a layout friendly to SAC actor/critic graphs."""
-        if self._critic_dim <= 0:
-            raise RuntimeError("sac_graph_packed_source requires critic replay storage")
-        expected_shape = (packed_batch.shape[0], self.sac_graph_packed_width())
-        if tuple(out.shape) != expected_shape:
-            raise ValueError(
-                "sac_graph_packed_source output shape mismatch: "
-                f"expected {expected_shape}, got {tuple(out.shape)}"
-            )
-        offset = 0
-        fields = (
-            packed_batch[:, self._obs_sl],
-            packed_batch[:, self._critic_sl],
-            packed_batch[:, self._act_sl],
-            packed_batch[:, self._rew_col : self._rew_col + 1],
-            packed_batch[:, self._nobs_sl],
-            packed_batch[:, self._ncritic_sl],
-            packed_batch[:, self._done_col : self._done_col + 1],
-            packed_batch[:, self._trunc_col : self._trunc_col + 1],
-        )
-        for field in fields:
-            width = int(field.shape[1])
-            out[:, offset : offset + width].copy_(field)
-            offset += width
-        return out
-
-    def __getstate__(self) -> dict:
-        """Custom pickle support.
-
-        The collector subprocess only calls ``add()``. Trace and stop-event
-        handles are process-local; replay storage, ingress metadata, and
-        semaphores remain shared.
-        """
-        state = self.__dict__.copy()
-        state["trace_recorder"] = None
-        state["_stop_event"] = None
-        return state
-
-    def add(
-        self,
-        obs,
-        actions,
-        rewards,
-        next_obs,
-        dones,
-        truncated,
-        terminal_mask=None,
-        terminal_next_obs=None,
-        critic=None,
-        next_critic=None,
-        terminal_next_critic=None,
-    ):
-        """Add batch (called by collector).
-
-        `dones` follows the UniLab env lifecycle contract:
-        done = terminated | truncated. Learners must pair it with
-        `truncated` when computing bootstrap masks.
-        """
-        _trace_ns = time.perf_counter_ns() if self.trace_recorder is not None else 0
-        self._add_to_ingress(
-            obs,
-            actions,
-            rewards,
-            next_obs,
-            dones,
-            truncated,
-            terminal_mask,
-            terminal_next_obs,
-            critic,
-            next_critic,
-            terminal_next_critic,
-            trace_start_ns=_trace_ns,
-        )
-
-    def _add_to_ingress(
-        self,
-        obs,
-        actions,
-        rewards,
-        next_obs,
-        dones,
-        truncated,
-        terminal_mask,
-        terminal_next_obs,
-        critic,
-        next_critic,
-        terminal_next_critic,
-        *,
-        trace_start_ns: int,
-    ) -> None:
-        count = int(obs.shape[0])
-        if count > self._ingress_slot_rows:
-            raise ValueError(
-                f"Transition batch has {count} rows but bounded ingress slots hold "
-                f"{self._ingress_slot_rows}"
-            )
-        has_critic = self._critic_dim > 0 and critic is not None
-        if self._critic_dim > 0 and (critic is None or next_critic is None):
-            raise ValueError("ReplayBuffer with critic_dim > 0 requires critic and next_critic")
-
-        wait_start_ns = time.perf_counter_ns()
-        while not self._ingress_free.acquire(timeout=0.05):
-            if bool(self._ingress_closed[0]):
-                return
-            if self._stop_event is not None and self._stop_event.is_set():
-                return
-        wait_end_ns = time.perf_counter_ns()
-        if bool(self._ingress_closed[0]):
-            return
-
-        sequence = int(self._ingress_publish_seq[0])
-        slot = sequence % self._ingress_depth
-        target = self._ingress_slots[slot][:count]
-        try:
-            self._write_transition_rows(
-                target,
-                obs,
-                actions,
-                rewards,
-                next_obs,
-                dones,
-                truncated,
-                critic,
-                next_critic,
-                has_critic=has_critic,
-            )
-            self._patch_terminal_next_observations(
-                target[:, self._nobs_sl],
-                terminal_mask,
-                terminal_next_obs,
-                target[:, self._ncritic_sl] if has_critic else None,
-                terminal_next_critic,
-            )
-            start = int(self._published_ptr[0])
-            self._ingress_starts[slot] = start
-            self._ingress_counts[slot] = count
-            self._published_ptr[0] = start + count
-            self._ingress_publish_seq[0] = sequence + 1
-            self._ingress_ready.release()
-        except BaseException:
-            self._ingress_free.release()
-            raise
-
-        if self.trace_recorder is not None:
-            end_ns = time.perf_counter_ns()
-            self.trace_recorder.add_slice(
-                "replay/ingress_backpressure",
-                category="replay",
-                start_ns=wait_start_ns,
-                end_ns=wait_end_ns,
-                args={"batch_size": count, "slot": slot, "depth": self._ingress_depth},
-            )
-            self.trace_recorder.add_slice(
-                "replay/add",
-                category="replay",
-                start_ns=trace_start_ns,
-                end_ns=end_ns,
-                args={
-                    "batch_size": count,
-                    "device": self.device,
-                    "ingress_slot": slot,
-                    "published_ptr": start + count,
-                },
-            )
-
-    def _write_transition_rows(
-        self,
-        target,
-        obs,
-        actions,
-        rewards,
-        next_obs,
-        dones,
-        truncated,
-        critic,
-        next_critic,
-        *,
-        has_critic: bool,
-    ) -> None:
-        target[:, self._obs_sl] = obs
-        target[:, self._nobs_sl] = next_obs
-        target[:, self._act_sl] = actions
-        target[:, self._rew_col] = rewards
-        target[:, self._done_col] = dones
-        target[:, self._trunc_col] = truncated
-        if has_critic:
-            assert critic is not None
-            assert next_critic is not None
-            target[:, self._critic_sl] = critic
-            target[:, self._ncritic_sl] = next_critic
-
-    @staticmethod
-    def _patch_terminal_next_observations(
-        target_next_obs,
-        terminal_mask,
-        terminal_next_obs,
-        target_next_critic=None,
-        terminal_next_critic=None,
-    ) -> None:
-        if terminal_mask is None or terminal_next_obs is None:
-            return
-        if terminal_mask.ndim != 1 or terminal_mask.shape[0] != target_next_obs.shape[0]:
-            return
-        if not torch.any(terminal_mask):
-            return
-
-        target_next_obs[terminal_mask] = terminal_next_obs[terminal_mask]
-
-        if target_next_critic is not None and terminal_next_critic is not None:
-            target_next_critic[terminal_mask] = terminal_next_critic[terminal_mask]
-
-    def field_view(self, packed: torch.Tensor, field_name: str) -> torch.Tensor:
-        field = {
-            "obs": self._obs_sl,
-            "next_obs": self._nobs_sl,
-            "actions": self._act_sl,
-            "rewards": self._rew_col,
-            "dones": self._done_col,
-            "truncated": self._trunc_col,
-            "critic": getattr(self, "_critic_sl", None),
-            "next_critic": getattr(self, "_ncritic_sl", None),
-        }.get(field_name)
-        if field is None:
-            raise KeyError(f"Replay field {field_name!r} is unavailable")
-        return packed[:, field]
diff --git a/src/unilab/ipc/replay_pipelines/__init__.py b/src/unilab/ipc/replay_pipelines/__init__.py
deleted file mode 100644
index cfb70bf79..000000000
--- a/src/unilab/ipc/replay_pipelines/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-"""Device-authoritative off-policy replay."""
-
-from unilab.ipc.replay_pipelines.base import ReplayTickMetadata
-from unilab.ipc.replay_pipelines.gpu_resident import GPUResidentReplayPipeline
-
-__all__ = [
-    "ReplayTickMetadata",
-    "GPUResidentReplayPipeline",
-]
diff --git a/src/unilab/ipc/replay_pipelines/base.py b/src/unilab/ipc/replay_pipelines/base.py
deleted file mode 100644
index 04d286e05..000000000
--- a/src/unilab/ipc/replay_pipelines/base.py
+++ /dev/null
@@ -1,16 +0,0 @@
-"""Replay tick metadata shared by the device transfer owner."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-
-
-@dataclass(frozen=True)
-class ReplayTickMetadata:
-    tick_id: int
-    snapshot_ptr: int
-    snapshot_size: int
-    sample_seed: int
-    sample_count: int
-    batch_host_slot: int | None = None
-    batch_gpu_slot: int | None = None
diff --git a/src/unilab/ipc/replay_pipelines/gpu_resident.py b/src/unilab/ipc/replay_pipelines/gpu_resident.py
deleted file mode 100644
index 0ac818793..000000000
--- a/src/unilab/ipc/replay_pipelines/gpu_resident.py
+++ /dev/null
@@ -1,875 +0,0 @@
-"""Device-authoritative replay pipeline for one CUDA or MPS learner.
-
-Single-device training consumes fixed-depth packed ingress slots and keeps the
-full replay ring authoritative on device. CUDA uses a learner-side daemon and
-side stream. MPS has no public stream API, so existing learner-thread calls
-submit copies and device-side sampling.
-
-Consistency model:
-
-- The sample domain is ``[0, min(device_visible_ptr, capacity))`` where
-  ``device_visible_ptr`` only advances past rows whose device copy completed.
-- Span copies and batch gathers are totally ordered on the CUDA side stream or
-  the MPS learner-thread command queue. A gather therefore observes every
-  completed span before it and none submitted after it.
-- An ingress slot is not reusable and ``ptr``/``size`` do not advance until its
-  device-copy completion event is observed.
-"""
-
-from __future__ import annotations
-
-import threading
-import time
-from collections import deque
-from typing import Any, Dict, List, Tuple, cast
-
-import torch
-
-from unilab.ipc.replay_buffer import ReplayBuffer
-from unilab.ipc.replay_pipelines.base import ReplayTickMetadata
-from unilab.ipc.replay_pipelines.transfer import build_replay_transfer_backend
-
-
-def require_offpolicy_replay_device(device: str | None) -> str:
-    """Reject devices that cannot own the single-device replay ring."""
-    if device is None:
-        from unilab.utils.device import get_default_device
-
-        device = get_default_device()
-    resolved = str(device)
-    try:
-        device_type = torch.device(resolved).type
-    except (RuntimeError, TypeError) as exc:
-        raise ValueError(f"Invalid off-policy learner device {resolved!r}") from exc
-    if device_type not in {"cuda", "mps"}:
-        raise ValueError(
-            "Off-policy training requires a CUDA or MPS learner device for "
-            f"device-authoritative replay; got {device_type!r}"
-        )
-    return resolved
-
-
-def _ring_spans(start: int, end: int, capacity: int) -> List[Tuple[int, int]]:
-    """Split absolute row range ``[start, end)`` into ``(offset, length)`` ring spans."""
-    if end <= start or capacity <= 0:
-        return []
-    spans: List[Tuple[int, int]] = []
-    remaining = end - start
-    offset = start % capacity
-    while remaining > 0:
-        length = min(remaining, capacity - offset)
-        spans.append((offset, length))
-        remaining -= length
-        offset = 0
-    return spans
-
-
-def _device_memory_budget(device: torch.device) -> tuple[int, int]:
-    """Return ``(available, total_budget)`` bytes for resident allocations."""
-    if device.type == "cuda":
-        free_bytes, total_bytes = torch.cuda.mem_get_info(device)
-        return int(free_bytes), int(total_bytes)
-    if device.type == "mps":
-        recommended_max_memory = getattr(torch.mps, "recommended_max_memory", None)
-        driver_allocated_memory = getattr(torch.mps, "driver_allocated_memory", None)
-        if not callable(recommended_max_memory) or not callable(driver_allocated_memory):
-            raise RuntimeError(
-                "MPS gpu_resident replay requires recommended_max_memory() and "
-                "driver_allocated_memory() for its allocation guard"
-            )
-        total_bytes = int(cast(Any, recommended_max_memory)())
-        allocated_bytes = int(cast(Any, driver_allocated_memory)())
-        return max(0, total_bytes - allocated_bytes), total_bytes
-    raise ValueError(f"Unsupported gpu_resident replay device type {device.type!r}")
-
-
-def _validate_device_memory_budget(
-    device: torch.device,
-    *,
-    required_bytes: int,
-    storage_bytes: int,
-    batch_bytes: int,
-    headroom: float,
-) -> None:
-    available_bytes, total_bytes = _device_memory_budget(device)
-    usable_bytes = int(available_bytes * headroom)
-    if required_bytes <= usable_bytes:
-        return
-    raise RuntimeError(
-        f"Device-authoritative replay does not fit on {device.type}: "
-        f"requires {required_bytes / 2**30:.2f} GiB "
-        f"(storage {storage_bytes / 2**30:.2f} + batch slots {batch_bytes / 2**30:.2f}), "
-        f"device available budget {available_bytes / 2**30:.2f} GiB / "
-        f"total budget {total_bytes / 2**30:.2f} GiB "
-        f"({headroom:.0%} of available budget may be used)"
-    )
-
-
-class GPUResidentReplayPipeline:
-    """ReplayPipeline backed by an authoritative packed device ring."""
-
-    _POLL_INTERVAL_S = 0.0005
-    _MEMORY_HEADROOM = 0.8
-
-    def __init__(
-        self,
-        replay_buffer: ReplayBuffer,
-        *,
-        device: str,
-        sample_count: int,
-        base_seed: int = 0,
-        trace_recorder=None,
-        trace_cuda_events: bool = True,
-        pack_layout: str = "packed",
-        use_critic_graph_packed_source: bool = False,
-    ) -> None:
-        self._replay_buffer = replay_buffer
-        if pack_layout not in {"packed", "sac_graph"}:
-            raise ValueError("GPUResidentReplayPipeline pack_layout must be packed or sac_graph")
-        self._device = torch.device(require_offpolicy_replay_device(device))
-        if self._device.type not in {"cuda", "mps"}:
-            raise ValueError(
-                "GPUResidentReplayPipeline requires a CUDA or MPS device; "
-                f"got {self._device.type!r}"
-            )
-        if self._device.type == "mps" and not torch.backends.mps.is_available():
-            raise ValueError("GPUResidentReplayPipeline requires an available MPS device")
-        self._main_thread_submission = self._device.type == "mps"
-        self._learner_thread_id = threading.get_ident()
-        self._pack_layout = pack_layout
-        self._use_critic_graph_packed_source = (
-            bool(use_critic_graph_packed_source) and self._pack_layout != "sac_graph"
-        )
-        self._sample_count = int(sample_count)
-        self._base_seed = int(base_seed)
-        self._trace_recorder = trace_recorder
-        self._capacity = int(replay_buffer.capacity)
-        self._storage_width = int(replay_buffer.storage_width)
-        self._packed_width = (
-            int(replay_buffer.sac_graph_packed_width())
-            if self._pack_layout == "sac_graph"
-            else self._storage_width
-        )
-        self._critic_graph_packed_width = (
-            int(replay_buffer.critic_graph_packed_width())
-            if self._use_critic_graph_packed_source
-            else 0
-        )
-
-        # -- device memory guard (hard fail: this pipeline is opt-in) --
-        storage_bytes = self._capacity * self._storage_width * 4
-        slot_bytes = 2 * self._sample_count * self._packed_width * 4
-        scratch_bytes = (
-            self._sample_count * self._storage_width * 4 if self._pack_layout == "sac_graph" else 0
-        )
-        critic_slot_bytes = 2 * self._sample_count * self._critic_graph_packed_width * 4
-        required_bytes = storage_bytes + slot_bytes + scratch_bytes + critic_slot_bytes
-        _validate_device_memory_budget(
-            self._device,
-            required_bytes=required_bytes,
-            storage_bytes=storage_bytes,
-            batch_bytes=slot_bytes + scratch_bytes + critic_slot_bytes,
-            headroom=self._MEMORY_HEADROOM,
-        )
-
-        self._transfer_backend = build_replay_transfer_backend(
-            device=self._device,
-            ring_depth=2,
-        )
-        self._trace_cuda_events = bool(trace_cuda_events) and (
-            self._transfer_backend.supports_timing_events
-        )
-        self._device_family = self._transfer_backend.device_family
-        self._host_pinned = False
-        host_slots = replay_buffer._ingress_slots
-        try:
-            self._transfer_backend.register_host_slots(host_slots)
-            self._host_pinned = bool(self._transfer_backend.host_pinned)
-        except RuntimeError as exc:
-            print(
-                f"[GPUResidentReplay] Host storage registration failed ({exc}); "
-                "falling back to pageable device copies.",
-                flush=True,
-            )
-        self._gpu_storage: torch.Tensor = torch.empty(
-            (self._capacity, self._storage_width),
-            dtype=torch.float32,
-            device=self._device,
-        )
-        self._gpu_packed = self._transfer_backend.allocate_device_slots(
-            count=2,
-            shape=(self._sample_count, self._packed_width),
-            dtype=torch.float32,
-        )
-        self._gpu_critic_graph_packed: list[torch.Tensor] = (
-            self._transfer_backend.allocate_device_slots(
-                count=2,
-                shape=(self._sample_count, self._critic_graph_packed_width),
-                dtype=torch.float32,
-            )
-            if self._use_critic_graph_packed_source
-            else []
-        )
-        self._gather_scratch: torch.Tensor | None = (
-            torch.empty(
-                (self._sample_count, self._storage_width),
-                dtype=torch.float32,
-                device=self._device,
-            )
-            if self._pack_layout == "sac_graph"
-            else None
-        )
-
-        self._sync_stream: Any | None = None
-        if self._device.type == "cuda":
-            self._sync_stream = cast(torch.cuda.Stream, torch.cuda.Stream(device=self._device))
-            self._slot_events: list[Any] = [torch.cuda.Event() for _ in range(2)]
-        else:
-            self._slot_events = [torch.mps.Event() for _ in range(2)]
-        self._span_events: deque[tuple[int, Any, int, int, int]] = deque()
-        self._submission_lock = threading.Lock()
-        self._submitted_ptr = 0
-        self._visible_ptr = 0
-
-        self._hot = 0
-        self._cold = 1
-        self._has_hot_batch = False
-        self._hot_metadata: ReplayTickMetadata | None = None
-        self._prepared_metadata: ReplayTickMetadata | None = None
-        self._prepare_tick_id: int | None = None
-        self._prepare_required_ptr = 0
-        self._prepare_state = "idle"
-        self._prepare_error: BaseException | None = None
-        self.last_incremental_h2d_time_s = 0.0
-        self._prepare_condition = threading.Condition()
-        self._closed = False
-        self._sync_thread: threading.Thread | None = None
-        if not self._main_thread_submission:
-            self._sync_thread = threading.Thread(
-                target=self._sync_worker,
-                name="replay_gpu_resident_sync",
-                daemon=True,
-            )
-            self._sync_thread.start()
-
-    @property
-    def h2d_submitter(self) -> str:
-        if self._main_thread_submission:
-            return "gpu_resident_ingress_main_thread"
-        return "gpu_resident_ingress"
-
-    @property
-    def transfer_manifest(self) -> dict[str, object]:
-        return {
-            "backend": type(self._transfer_backend).__name__,
-            "device": str(self._device),
-            "device_family": self._device_family,
-            "pipeline": "gpu_resident",
-            "storage_owner": "device",
-            "host_memory_kind": (
-                self._transfer_backend.host_memory_kind if self._host_pinned else "pageable_shared"
-            ),
-            "host_pinned": self._host_pinned,
-            "storage_rows": self._capacity,
-            "storage_width": self._storage_width,
-            "storage_bytes": int(self._gpu_storage.numel() * self._gpu_storage.element_size()),
-            "host_storage_bytes": self._replay_buffer.host_storage_bytes,
-            "ingress_depth": self._replay_buffer._ingress_depth,
-            "h2d_submitter": self.h2d_submitter,
-            "device_submission_thread": "learner" if self._main_thread_submission else "daemon",
-            "ring_depth": 2,
-        }
-
-    # -- batch views ----------------------------------------------------------
-
-    def _packed_batch_view(self, packed: torch.Tensor) -> Dict[str, torch.Tensor]:
-        rb = self._replay_buffer
-        if self._pack_layout == "sac_graph":
-            c = 0
-            obs_sl = slice(c, c + rb._obs_dim)
-            c += rb._obs_dim
-            critic_sl = slice(c, c + rb._critic_dim)
-            c += rb._critic_dim
-            act_sl = slice(c, c + rb._action_dim)
-            c += rb._action_dim
-            rew_col = c
-            c += 1
-            nobs_sl = slice(c, c + rb._obs_dim)
-            c += rb._obs_dim
-            ncritic_sl = slice(c, c + rb._critic_dim)
-            c += rb._critic_dim
-            done_col = c
-            c += 1
-            trunc_col = c
-            return {
-                "obs": packed[:, obs_sl],
-                "next_obs": packed[:, nobs_sl],
-                "actions": packed[:, act_sl],
-                "rewards": packed[:, rew_col],
-                "dones": packed[:, done_col],
-                "truncated": packed[:, trunc_col],
-                "critic": packed[:, critic_sl],
-                "next_critic": packed[:, ncritic_sl],
-                "sac_graph_packed_source": packed,
-            }
-        batch = {
-            "obs": packed[:, rb._obs_sl],
-            "next_obs": packed[:, rb._nobs_sl],
-            "actions": packed[:, rb._act_sl],
-            "rewards": packed[:, rb._rew_col],
-            "dones": packed[:, rb._done_col],
-            "truncated": packed[:, rb._trunc_col],
-        }
-        if rb._critic_dim > 0:
-            batch["critic"] = packed[:, rb._critic_sl]
-            batch["next_critic"] = packed[:, rb._ncritic_sl]
-        return batch
-
-    def _large_batch_view(self, slot: int) -> Dict[str, torch.Tensor]:
-        batch = self._packed_batch_view(self._gpu_packed[slot])
-        if self._use_critic_graph_packed_source:
-            batch["critic_graph_packed_source"] = self._gpu_critic_graph_packed[slot]
-        return batch
-
-    # -- device submission ---------------------------------------------------
-
-    def _assert_mps_learner_thread(self) -> None:
-        if self._main_thread_submission and threading.get_ident() != self._learner_thread_id:
-            raise RuntimeError(
-                "MPS gpu_resident replay device work must be submitted from the learner thread"
-            )
-
-    def _drive_mps_learner_thread(self, *, wait: bool = False) -> bool:
-        """Advance MPS ingress and gather work from an existing learner call."""
-        if not self._main_thread_submission:
-            return False
-        self._assert_mps_learner_thread()
-        try:
-            if wait:
-                did_work = self._submit_new_spans()
-                for _, event, _, _, _ in self._span_events:
-                    event.synchronize()
-                did_work |= self._drain_completed_spans()
-                did_work |= self._service_pending_prepare()
-            else:
-                did_work = self._drain_completed_spans()
-                did_work |= self._service_pending_prepare()
-                did_work |= self._submit_new_spans()
-            if wait and self._prepared_metadata is not None:
-                slot = self._prepared_metadata.batch_gpu_slot
-                if slot is not None:
-                    self._slot_events[slot].synchronize()
-                    self._prepare_state = "ready"
-            return did_work
-        except BaseException as exc:
-            with self._prepare_condition:
-                self._prepare_error = exc
-                self._prepare_condition.notify_all()
-            raise
-
-    # -- CUDA sync thread -----------------------------------------------------
-
-    def _sync_worker(self) -> None:
-        while True:
-            if self._closed:
-                return
-            try:
-                did_work = self._drain_completed_spans()
-                did_work |= self._service_pending_prepare()
-                did_work |= self._submit_new_spans()
-            except BaseException as exc:
-                with self._prepare_condition:
-                    self._prepare_error = exc
-                    self._prepare_condition.notify_all()
-                return
-            if not did_work:
-                time.sleep(self._POLL_INTERVAL_S)
-
-    def _submit_new_spans(self) -> bool:
-        if self._main_thread_submission:
-            self._assert_mps_learner_thread()
-        with self._submission_lock:
-            submitted = False
-            while True:
-                ingress = self._replay_buffer.take_published_ingress()
-                if ingress is None:
-                    return submitted
-                slot, start, count, source = ingress
-                if start != self._submitted_ptr:
-                    raise RuntimeError(
-                        "Bounded replay ingress publication is not contiguous: "
-                        f"submitted ptr {self._submitted_ptr}, slot start {start}"
-                    )
-                self._submit_span_copy(
-                    start=start,
-                    end=start + count,
-                    source=source,
-                    ingress_slot=slot,
-                )
-                submitted = True
-
-    def _submit_span_copy(
-        self,
-        *,
-        start: int,
-        end: int,
-        source: torch.Tensor,
-        ingress_slot: int,
-    ) -> None:
-        h2d_begin_ns = time.perf_counter_ns()
-        start_event = None
-        end_event = None
-        record_cuda = self._trace_recorder is not None and self._trace_cuda_events
-
-        def copy_spans(*, non_blocking: bool) -> None:
-            source_offset = 0
-            for offset, length in _ring_spans(start, end, self._capacity):
-                source_span = source[source_offset : source_offset + length]
-                self._gpu_storage[offset : offset + length].copy_(
-                    source_span,
-                    non_blocking=non_blocking,
-                )
-                source_offset += length
-
-        if self._device.type == "cuda":
-            with torch.cuda.device(self._device), torch.cuda.stream(self._sync_stream):
-                if record_cuda:
-                    start_event = cast(Any, torch.cuda.Event(enable_timing=True))
-                    end_event = cast(Any, torch.cuda.Event(enable_timing=True))
-                    start_event.record()
-                copy_spans(non_blocking=True)
-                if end_event is not None:
-                    end_event.record()
-                done_event = cast(Any, torch.cuda.Event())
-                done_event.record(self._sync_stream)
-        else:
-            copy_spans(non_blocking=False)
-            done_event = cast(Any, torch.mps.Event())
-            done_event.record()
-        self._span_events.append((end, done_event, ingress_slot, start, end - start))
-        self._submitted_ptr = end
-        self.last_incremental_h2d_time_s = (time.perf_counter_ns() - h2d_begin_ns) / 1e9
-        if self._trace_recorder is not None and start_event is not None and end_event is not None:
-            self._trace_recorder.add_cuda_pending_span(
-                "gpu/replay_pipeline_storage_h2d",
-                category="gpu",
-                cpu_begin_ns=h2d_begin_ns,
-                start_event=start_event,
-                end_event=end_event,
-                args={
-                    "h2d_bytes": (end - start) * self._storage_width * 4,
-                    "rows": end - start,
-                    "span_start": start,
-                    "span_end": end,
-                    "ingress_slot": ingress_slot,
-                    "pinned_memory": self._host_pinned,
-                    "pipeline": "gpu_resident",
-                    "storage_owner": "device",
-                },
-            )
-
-    def _drain_completed_spans(self) -> bool:
-        with self._submission_lock:
-            return self._drain_completed_spans_locked()
-
-    def _drain_completed_spans_locked(self) -> bool:
-        drained = False
-        while self._span_events and self._span_events[0][1].query():
-            end, _, ingress_slot, start, count = self._span_events.popleft()
-            commit_ns = time.perf_counter_ns()
-            self._replay_buffer.commit_ingress(
-                slot=ingress_slot,
-                start=start,
-                count=count,
-            )
-            if self._trace_recorder is not None:
-                self._trace_recorder.add_slice(
-                    "replay_pipeline/ingress_commit",
-                    category="replay_pipeline",
-                    start_ns=commit_ns,
-                    end_ns=time.perf_counter_ns(),
-                    args={
-                        "ingress_slot": ingress_slot,
-                        "committed_ptr": end,
-                        "rows": count,
-                        "pipeline": "gpu_resident",
-                    },
-                )
-            self._visible_ptr = end
-            drained = True
-        if drained:
-            with self._prepare_condition:
-                self._prepare_condition.notify_all()
-        return drained
-
-    def _gather_rows(self, *, visible_size: int, slot: int, gen: torch.Generator) -> None:
-        indices = torch.randint(
-            0,
-            visible_size,
-            (self._sample_count,),
-            generator=gen,
-            device=self._device,
-        )
-        dst = self._gpu_packed[slot]
-        if self._pack_layout == "sac_graph":
-            assert self._gather_scratch is not None
-            torch.index_select(self._gpu_storage, 0, indices, out=self._gather_scratch)
-            self._replay_buffer.pack_sac_graph_source(self._gather_scratch, out=dst)
-        else:
-            torch.index_select(self._gpu_storage, 0, indices, out=dst)
-        if self._use_critic_graph_packed_source:
-            self._replay_buffer.pack_critic_graph_source(
-                dst,
-                out=self._gpu_critic_graph_packed[slot],
-            )
-
-    def _service_pending_prepare(self) -> bool:
-        if self._main_thread_submission:
-            self._assert_mps_learner_thread()
-        with self._submission_lock:
-            if self._span_events:
-                return False
-        with self._prepare_condition:
-            if self._prepare_state != "preparing" or self._prepare_tick_id is None:
-                return False
-            tick_id = self._prepare_tick_id
-            required_ptr = self._prepare_required_ptr
-            slot = self._cold
-        visible_size = min(self._visible_ptr, self._capacity)
-        if self._visible_ptr < required_ptr or visible_size <= 0:
-            return False
-        sample_seed = self._base_seed + int(tick_id)
-        gen = torch.Generator(device=self._device)
-        gen.manual_seed(sample_seed)
-        gather_begin_ns = time.perf_counter_ns()
-        start_event = None
-        end_event = None
-        record_cuda = self._trace_recorder is not None and self._trace_cuda_events
-        if self._device.type == "cuda":
-            with torch.cuda.device(self._device), torch.cuda.stream(self._sync_stream):
-                if record_cuda:
-                    start_event = cast(Any, torch.cuda.Event(enable_timing=True))
-                    end_event = cast(Any, torch.cuda.Event(enable_timing=True))
-                    start_event.record()
-                self._gather_rows(visible_size=visible_size, slot=slot, gen=gen)
-                if end_event is not None:
-                    end_event.record()
-                self._slot_events[slot].record(self._sync_stream)
-        else:
-            self._gather_rows(visible_size=visible_size, slot=slot, gen=gen)
-            self._slot_events[slot].record()
-        metadata = ReplayTickMetadata(
-            tick_id=int(tick_id),
-            snapshot_ptr=int(self._visible_ptr),
-            snapshot_size=visible_size,
-            sample_seed=sample_seed,
-            sample_count=self._sample_count,
-            batch_host_slot=None,
-            batch_gpu_slot=slot,
-        )
-        with self._prepare_condition:
-            self._prepared_metadata = metadata
-            self._prepare_state = "gather_submitted"
-            self._prepare_condition.notify_all()
-        if self._trace_recorder is not None:
-            self._trace_recorder.add_slice(
-                "replay_pipeline/gpu_batch_gather_submit",
-                category="replay_pipeline",
-                start_ns=gather_begin_ns,
-                end_ns=time.perf_counter_ns(),
-                args={
-                    "tick_id": int(tick_id),
-                    "batch_gpu_slot": slot,
-                    "sample_count": self._sample_count,
-                    "snapshot_size": visible_size,
-                    "required_ptr": required_ptr,
-                    "visible_ptr": int(self._visible_ptr),
-                    "pack_layout": self._pack_layout,
-                    "pipeline": "gpu_resident",
-                },
-            )
-        if self._trace_recorder is not None and start_event is not None and end_event is not None:
-            self._trace_recorder.add_cuda_pending_span(
-                "gpu/replay_pipeline_batch_gather",
-                category="gpu",
-                cpu_begin_ns=gather_begin_ns,
-                start_event=start_event,
-                end_event=end_event,
-                args={
-                    "tick_id": int(tick_id),
-                    "batch_gpu_slot": slot,
-                    "sample_count": self._sample_count,
-                    "gather_bytes": self._sample_count * self._packed_width * 4,
-                    "pack_layout": self._pack_layout,
-                    "pipeline": "gpu_resident",
-                },
-            )
-        return True
-
-    # -- public API -----------------------------------------------------------
-
-    def _validate_sample_count(self, sample_count: int) -> None:
-        if int(sample_count) != int(self._sample_count):
-            raise ValueError("sample_count must match the value used to allocate the double buffer")
-
-    def _refresh_prepare_state(self) -> None:
-        if self._prepare_error is not None:
-            raise self._prepare_error
-        if self._prepared_metadata is not None:
-            slot = self._prepared_metadata.batch_gpu_slot
-            if slot is not None and self._slot_events[slot].query():
-                self._prepare_state = "ready"
-
-    def progress(self, *, wait: bool = False) -> bool:
-        """Advance ingress work without changing replay lifecycle state."""
-        if self._main_thread_submission:
-            return self._drive_mps_learner_thread(wait=wait)
-        if not wait:
-            return False
-        did_work = self._submit_new_spans()
-        with self._submission_lock:
-            for _, event, _, _, _ in self._span_events:
-                event.synchronize()
-            did_work |= self._drain_completed_spans_locked()
-        return did_work
-
-    def read_committed_fields(
-        self,
-        field_names: tuple[str, ...],
-        *,
-        start_ptr: int,
-    ) -> tuple[int, dict[str, torch.Tensor]]:
-        """Return a stable ordered field snapshot through the committed pointer."""
-        published_snapshot = self._replay_buffer.published_ptr
-        while self._submitted_ptr < published_snapshot:
-            if not self._submit_new_spans():
-                time.sleep(self._POLL_INTERVAL_S)
-
-        with self._submission_lock:
-            for _, event, _, _, _ in self._span_events:
-                event.synchronize()
-            self._drain_completed_spans_locked()
-            end_ptr = self._visible_ptr
-            count = min(max(end_ptr - start_ptr, 0), self._capacity)
-            field_start = end_ptr - count
-            index = field_start % self._capacity
-            fields: dict[str, torch.Tensor] = {}
-            for field_name in field_names:
-                source = self._replay_buffer.field_view(self._gpu_storage, field_name)
-                if index + count <= self._capacity:
-                    fields[field_name] = source[index : index + count].clone()
-                    continue
-                split = self._capacity - index
-                fields[field_name] = torch.cat(
-                    [source[index:], source[: count - split]],
-                    dim=0,
-                ).clone()
-            if self._device.type == "cuda":
-                snapshot_event = cast(Any, torch.cuda.Event())
-                snapshot_event.record(torch.cuda.current_stream(self._device))
-                assert self._sync_stream is not None
-                self._sync_stream.wait_event(snapshot_event)
-            return end_ptr, fields
-
-    def start_prepare(
-        self,
-        tick_id: int,
-        sample_count: int,
-        min_snapshot_ptr: int | None = None,
-    ) -> bool:
-        """Schedule a GPU-side gather for the current cold slot.
-
-        Returns True when this call launches new work. If the same tick is
-        already pending or prepared, returns False.
-        """
-        self._validate_sample_count(sample_count)
-        if self._closed:
-            raise RuntimeError("Cannot prepare replay batch after pipeline.close()")
-        self._refresh_prepare_state()
-        with self._prepare_condition:
-            if self._prepared_metadata is not None or self._prepare_state not in {
-                "idle",
-                "ready",
-            }:
-                prepared_tick = (
-                    self._prepared_metadata.tick_id
-                    if self._prepared_metadata is not None
-                    else self._prepare_tick_id
-                )
-                if prepared_tick == int(tick_id):
-                    return False
-                raise RuntimeError(
-                    "Cannot prepare a new replay batch before the previous batch is consumed"
-                )
-            self._prepare_tick_id = int(tick_id)
-            self._prepare_required_ptr = (
-                int(self._replay_buffer.ptr[0])
-                if min_snapshot_ptr is None
-                else int(min_snapshot_ptr)
-            )
-            self._prepared_metadata = None
-            self._prepare_error = None
-            self._prepare_state = "preparing"
-            self._prepare_condition.notify_all()
-        if self._trace_recorder is not None:
-            _req_ns = time.perf_counter_ns()
-            self._trace_recorder.add_slice(
-                "replay_pipeline/gpu_batch_prepare_request",
-                category="replay_pipeline",
-                start_ns=_req_ns,
-                end_ns=time.perf_counter_ns(),
-                args={
-                    "tick_id": int(tick_id),
-                    "required_ptr": self._prepare_required_ptr,
-                    "pipeline": "gpu_resident",
-                },
-            )
-        if self._main_thread_submission:
-            self._drive_mps_learner_thread()
-        return True
-
-    def batch_ready(self, tick_id: int, sample_count: int) -> bool:
-        self._validate_sample_count(sample_count)
-        if self._has_hot_batch:
-            if self._hot_metadata is not None and self._hot_metadata.tick_id != int(tick_id):
-                return False
-            return True
-        if self._main_thread_submission:
-            self._drive_mps_learner_thread()
-        self._refresh_prepare_state()
-        if self._prepared_metadata is None:
-            return False
-        if self._prepared_metadata.tick_id != int(tick_id):
-            return False
-        return self._prepare_state == "ready"
-
-    def wait_until_ready(self, tick_id: int, sample_count: int) -> bool:
-        self._validate_sample_count(sample_count)
-        metadata = self._prepared_or_wait(tick_id)
-        slot = metadata.batch_gpu_slot
-        assert slot is not None
-        self._slot_events[slot].synchronize()
-        self._prepare_state = "ready"
-        return True
-
-    def _prepared_or_wait(self, tick_id: int) -> ReplayTickMetadata:
-        self._refresh_prepare_state()
-        if self._prepared_metadata is None:
-            if self._prepare_tick_id is None:
-                self.start_prepare(tick_id, self._sample_count)
-            if self._main_thread_submission:
-                while self._prepared_metadata is None and self._prepare_error is None:
-                    did_work = self._drive_mps_learner_thread(wait=True)
-                    if not did_work:
-                        time.sleep(self._POLL_INTERVAL_S)
-            else:
-                with self._prepare_condition:
-                    while self._prepared_metadata is None and self._prepare_error is None:
-                        self._prepare_condition.wait(timeout=0.1)
-            if self._prepare_error is not None:
-                raise self._prepare_error
-            assert self._prepared_metadata is not None
-            return self._prepared_metadata
-        if self._prepared_metadata.tick_id != int(tick_id):
-            raise RuntimeError(
-                f"Prepared replay batch tick {self._prepared_metadata.tick_id} "
-                f"does not match requested tick {tick_id}"
-            )
-        return self._prepared_metadata
-
-    def sample_large_batch(self, tick_id: int, sample_count: int) -> Dict[str, torch.Tensor]:
-        self._validate_sample_count(sample_count)
-        if self._has_hot_batch:
-            if self._hot_metadata is not None and self._hot_metadata.tick_id != int(tick_id):
-                raise RuntimeError(
-                    f"Hot batch tick {self._hot_metadata.tick_id} does not match "
-                    f"requested tick {tick_id}"
-                )
-            return self._large_batch_view(self._hot)
-        if not self.batch_ready(tick_id, sample_count):
-            self.wait_until_ready(tick_id, sample_count)
-        metadata = self._prepared_or_wait(tick_id)
-        slot = metadata.batch_gpu_slot
-        assert slot is not None
-        _t0 = time.perf_counter_ns()
-        if self._device.type == "cuda":
-            torch.cuda.current_stream(self._device).wait_event(self._slot_events[slot])
-        else:
-            self._slot_events[slot].synchronize()
-        if self._trace_recorder is not None:
-            _wait_end = time.perf_counter_ns()
-            self._trace_recorder.add_slice(
-                "replay_pipeline/batch_h2d_wait",
-                category="replay_pipeline",
-                start_ns=_t0,
-                end_ns=_wait_end,
-                args={"tick_id": tick_id, "batch_gpu_slot": slot, "pipeline": "gpu_resident"},
-            )
-            self._trace_recorder.add_slice(
-                "replay_pipeline/gpu_wait_for_batch",
-                category="replay_pipeline",
-                start_ns=_t0,
-                end_ns=_wait_end,
-                args={"tick_id": tick_id, "batch_gpu_slot": slot, "pipeline": "gpu_resident"},
-            )
-        _swap_ns = time.perf_counter_ns()
-        with self._prepare_condition:
-            old_hot = self._hot
-            old_cold = self._cold
-            if slot != self._cold:
-                raise RuntimeError("Prepared replay batch is not in the current cold slot")
-            self._hot, self._cold = self._cold, self._hot
-            self._has_hot_batch = True
-            self._hot_metadata = metadata
-            self._prepared_metadata = None
-            self._prepare_tick_id = None
-            self._prepare_state = "idle"
-        if self._trace_recorder is not None:
-            self._trace_recorder.add_slice(
-                "replay_pipeline/hot_cold_swap",
-                category="replay_pipeline",
-                start_ns=_swap_ns,
-                end_ns=time.perf_counter_ns(),
-                args={
-                    "tick_id": tick_id,
-                    "old_hot": old_hot,
-                    "old_cold": old_cold,
-                    "new_hot": self._hot,
-                    "new_cold": self._cold,
-                    "pipeline": "gpu_resident",
-                },
-            )
-        return self._large_batch_view(self._hot)
-
-    def after_tick(self) -> None:
-        self._has_hot_batch = False
-        self._hot_metadata = None
-
-    def close(self) -> None:
-        if self._closed:
-            return
-        self._closed = True
-        with self._prepare_condition:
-            self._prepare_condition.notify_all()
-        if self._sync_thread is not None:
-            self._sync_thread.join(timeout=2.0)
-        try:
-            self._submit_new_spans()
-            with self._submission_lock:
-                for _, event, _, _, _ in self._span_events:
-                    event.synchronize()
-                self._drain_completed_spans_locked()
-        except Exception:
-            pass
-        for event in self._slot_events:
-            try:
-                event.synchronize()
-            except Exception:
-                pass
-        self._transfer_backend.close()
-        self._host_pinned = False
-        self._gpu_packed.clear()
-        self._gpu_critic_graph_packed.clear()
-        self._gather_scratch = None
-        if hasattr(self, "_gpu_storage"):
-            del self._gpu_storage
diff --git a/src/unilab/ipc/replay_pipelines/native_h2d.py b/src/unilab/ipc/replay_pipelines/native_h2d.py
deleted file mode 100644
index ee464c031..000000000
--- a/src/unilab/ipc/replay_pipelines/native_h2d.py
+++ /dev/null
@@ -1,133 +0,0 @@
-"""Optional native H2D submit helper with graceful fallback.
-
-Pre-validates build prerequisites before attempting JIT compilation.
-Falls back to ``dst.copy_(src, non_blocking=True)`` on a side stream
-when the native extension is unavailable — functionally identical for
-pinned-memory transfers (the ROCm path already uses this).
-"""
-
-from __future__ import annotations
-
-import logging
-import shutil
-from functools import lru_cache
-from pathlib import Path
-from typing import Any
-
-import torch
-
-logger = logging.getLogger(__name__)
-
-_SOURCE = Path(__file__).with_name("native_h2d_ext.cpp")
-_NATIVE_AVAILABLE: bool | None = None
-_DIAGNOSTIC: str = ""
-
-
-def _check_build_prerequisites() -> tuple[bool, str]:
-    """Pre-validate JIT compilation requirements."""
-    try:
-        from torch.utils.cpp_extension import is_ninja_available
-
-        if not is_ninja_available():
-            return False, "ninja not found. Install: pip install ninja"
-    except ImportError:
-        return False, "torch.utils.cpp_extension unavailable"
-
-    from torch.utils.cpp_extension import get_cxx_compiler
-
-    compiler = get_cxx_compiler()
-    if shutil.which(compiler) is None:
-        return False, (f"C++ compiler '{compiler}' not found. Install: sudo apt install g++")
-
-    from torch.utils.cpp_extension import CUDA_HOME
-
-    if CUDA_HOME is None:
-        return False, (
-            "CUDA_HOME not set and nvcc not found. Install CUDA toolkit or set CUDA_HOME."
-        )
-
-    header = Path(CUDA_HOME) / "include" / "cuda_runtime_api.h"
-    if not header.exists():
-        targets_header = (
-            Path(CUDA_HOME) / "targets" / "x86_64-linux" / "include" / "cuda_runtime_api.h"
-        )
-        if not targets_header.exists():
-            return False, (
-                f"cuda_runtime_api.h not found in {CUDA_HOME}/include/ "
-                f"or targets/x86_64-linux/include/. "
-                f"Verify CUDA toolkit installation."
-            )
-
-    if not _SOURCE.exists():
-        return False, f"Extension source not found: {_SOURCE}"
-
-    return True, ""
-
-
-@lru_cache(maxsize=1)
-def _try_load_extension() -> Any | None:
-    global _NATIVE_AVAILABLE, _DIAGNOSTIC
-
-    ok, reason = _check_build_prerequisites()
-    if not ok:
-        _DIAGNOSTIC = reason
-        _NATIVE_AVAILABLE = False
-        logger.info(
-            "Native H2D extension unavailable: %s. "
-            "Using pure-PyTorch async copy (no performance impact for pinned memory).",
-            reason,
-        )
-        return None
-
-    try:
-        from torch.utils.cpp_extension import CUDA_HOME, load
-
-        extra_include_paths = []
-        if CUDA_HOME is not None:
-            extra_include_paths.append(str(Path(CUDA_HOME) / "include"))
-            targets_include = Path(CUDA_HOME) / "targets" / "x86_64-linux" / "include"
-            if targets_include.exists():
-                extra_include_paths.append(str(targets_include))
-        ext = load(
-            name="unilab_native_h2d",
-            sources=[str(_SOURCE)],
-            extra_cflags=["-O3"],
-            extra_include_paths=extra_include_paths,
-            verbose=False,
-        )
-        _NATIVE_AVAILABLE = True
-        return ext
-    except Exception as exc:
-        _DIAGNOSTIC = f"Compilation failed: {exc}"
-        _NATIVE_AVAILABLE = False
-        logger.warning("Native H2D compilation failed: %s. Using pure-PyTorch fallback.", exc)
-        return None
-
-
-def is_available() -> bool:
-    """True if native extension is loaded or loadable."""
-    global _NATIVE_AVAILABLE
-    if _NATIVE_AVAILABLE is None:
-        _try_load_extension()
-    return bool(_NATIVE_AVAILABLE)
-
-
-def get_diagnostic() -> str:
-    """Human-readable reason for unavailability."""
-    if _NATIVE_AVAILABLE is None:
-        _try_load_extension()
-    return _DIAGNOSTIC
-
-
-def submit_h2d(
-    dst: torch.Tensor,
-    src: torch.Tensor,
-    stream: torch.cuda.Stream,
-) -> None:
-    """Submit one async H2D copy on an existing CUDA stream."""
-    ext = _try_load_extension()
-    if ext is not None:
-        ext.submit_h2d(dst, src, int(stream.cuda_stream))
-    else:
-        with torch.cuda.stream(stream):
-            dst.copy_(src, non_blocking=True)
diff --git a/src/unilab/ipc/replay_pipelines/native_h2d_ext.cpp b/src/unilab/ipc/replay_pipelines/native_h2d_ext.cpp
deleted file mode 100644
index e72c21a32..000000000
--- a/src/unilab/ipc/replay_pipelines/native_h2d_ext.cpp
+++ /dev/null
@@ -1,37 +0,0 @@
-#include <torch/extension.h>
-#include <c10/cuda/CUDAGuard.h>
-#include <c10/cuda/CUDAStream.h>
-
-#include <cstdint>
-
-void submit_h2d(
-    torch::Tensor dst,
-    torch::Tensor src,
-    std::uintptr_t stream_handle) {
-  if (!dst.is_cuda()) {
-    throw std::runtime_error("native H2D destination must be a CUDA tensor");
-  }
-  if (src.is_cuda()) {
-    throw std::runtime_error("native H2D source must be a CPU tensor");
-  }
-  if (!dst.is_contiguous() || !src.is_contiguous()) {
-    throw std::runtime_error("native H2D tensors must be contiguous");
-  }
-  if (dst.numel() != src.numel() || dst.element_size() != src.element_size()) {
-    throw std::runtime_error("native H2D source and destination sizes must match");
-  }
-
-  auto stream = c10::cuda::CUDAStream::unpack3(
-      static_cast<int64_t>(stream_handle),
-      dst.device().index(),
-      c10::DeviceType::CUDA);
-  {
-    pybind11::gil_scoped_release release;
-    c10::cuda::CUDAStreamGuard guard(stream);
-    dst.copy_(src, true);
-  }
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("submit_h2d", &submit_h2d, "Submit an async CPU-to-CUDA copy on a CUDA stream");
-}
diff --git a/src/unilab/ipc/replay_pipelines/transfer/__init__.py b/src/unilab/ipc/replay_pipelines/transfer/__init__.py
deleted file mode 100644
index 7c3387197..000000000
--- a/src/unilab/ipc/replay_pipelines/transfer/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-"""Device transfer backends for replay pipelines."""
-
-from unilab.ipc.replay_pipelines.transfer.base import ReplayTransferBackend
-from unilab.ipc.replay_pipelines.transfer.factory import build_replay_transfer_backend
-
-__all__ = [
-    "ReplayTransferBackend",
-    "build_replay_transfer_backend",
-]
diff --git a/src/unilab/ipc/replay_pipelines/transfer/base.py b/src/unilab/ipc/replay_pipelines/transfer/base.py
deleted file mode 100644
index c8f55b4cf..000000000
--- a/src/unilab/ipc/replay_pipelines/transfer/base.py
+++ /dev/null
@@ -1,56 +0,0 @@
-"""Replay transfer backend contract."""
-
-from __future__ import annotations
-
-from typing import Protocol
-
-import torch
-
-from unilab.ipc.replay_pipelines.base import ReplayTickMetadata
-
-
-class ReplayTransferBackend(Protocol):
-    """Device-specific host-to-device transfer backend."""
-
-    device: torch.device
-    device_family: str
-    h2d_submitter: str
-    host_memory_kind: str
-    host_pinned: bool
-    direct_pinned_shared: bool
-    supports_async_submit: bool
-    supports_timing_events: bool
-
-    def register_host_slots(self, slots: list[torch.Tensor]) -> None: ...
-
-    def allocate_device_slots(
-        self,
-        *,
-        count: int,
-        shape: tuple[int, int],
-        dtype: torch.dtype,
-    ) -> list[torch.Tensor]: ...
-
-    def submit_h2d(
-        self,
-        *,
-        slot: int,
-        dst: torch.Tensor,
-        src: torch.Tensor,
-        metadata: ReplayTickMetadata | None,
-        trace_recorder,
-        trace_cuda_events: bool,
-        h2d_bytes: int,
-        pack_layout: str,
-        pack_executor: str,
-    ) -> float: ...
-
-    def clear_ready(self, slot: int) -> None: ...
-
-    def ready_query(self, slot: int) -> bool: ...
-
-    def synchronize_ready(self, slot: int) -> None: ...
-
-    def wait_current_stream_for_ready(self, slot: int) -> None: ...
-
-    def close(self) -> None: ...
diff --git a/src/unilab/ipc/replay_pipelines/transfer/cuda_like.py b/src/unilab/ipc/replay_pipelines/transfer/cuda_like.py
deleted file mode 100644
index 51f08b911..000000000
--- a/src/unilab/ipc/replay_pipelines/transfer/cuda_like.py
+++ /dev/null
@@ -1,171 +0,0 @@
-"""CUDA/ROCm replay transfer backend."""
-
-from __future__ import annotations
-
-import time
-from typing import Any, cast
-
-import torch
-
-from unilab.ipc.replay_pipelines.base import ReplayTickMetadata
-
-
-class CudaLikeReplayTransferBackend:
-    """Pinned host to CUDA-like device transfer backend.
-
-    PyTorch ROCm exposes the same ``torch.cuda`` surface for runtime streams
-    and events, so this backend intentionally keys on the PyTorch device type
-    instead of NVIDIA-specific platform names.
-    """
-
-    host_memory_kind = "registered_pinned_shared"
-    supports_async_submit = True
-    supports_timing_events = True
-
-    def __init__(self, *, device: torch.device, ring_depth: int) -> None:
-        self.device = device
-        torch_version = getattr(torch, "version", None)
-        self.device_family = "rocm" if getattr(torch_version, "hip", None) else "cuda"
-        self.h2d_submitter = "torch_copy_stream" if self.device_family == "rocm" else "pybind11"
-        self._ring_depth = int(ring_depth)
-        self._cudart: Any = torch.cuda.cudart()
-        if self._cudart is None:
-            raise RuntimeError("torch.cuda.cudart() is required for replay host registration")
-        self._copy_stream = torch.cuda.Stream(device=device)
-        self._ready_events = [torch.cuda.Event() for _ in range(self._ring_depth)]
-        self._registered_shared_slots: list[torch.Tensor] = []
-        self._registered_shared_ptrs: list[int] = []
-        self.host_pinned = False
-        self.direct_pinned_shared = False
-
-        if self.h2d_submitter == "pybind11":
-            from unilab.ipc.replay_pipelines.native_h2d import get_diagnostic, is_available
-
-            if not is_available():
-                import sys
-
-                print(
-                    f"[ReplayTransfer] Native H2D unavailable, using torch_copy_stream.\n"
-                    f"  Reason: {get_diagnostic()}\n"
-                    f"  Performance impact: negligible for pinned-memory transfers.",
-                    file=sys.stderr,
-                    flush=True,
-                )
-                self.h2d_submitter = "torch_copy_stream"
-
-    def register_host_slots(self, slots: list[torch.Tensor]) -> None:
-        for slot in slots:
-            nbytes = int(slot.numel() * slot.element_size())
-            result = self._cudart.cudaHostRegister(int(slot.data_ptr()), nbytes, 0)
-            if result != self._cudart.cudaError.success:
-                raise RuntimeError(f"cudaHostRegister failed for collector replay slot: {result}")
-            if not slot.is_pinned():
-                self._cudart.cudaHostUnregister(int(slot.data_ptr()))
-                raise RuntimeError("cudaHostRegister did not make collector replay slot pinned")
-            self._registered_shared_slots.append(slot)
-            self._registered_shared_ptrs.append(int(slot.data_ptr()))
-        self.host_pinned = True
-        self.direct_pinned_shared = True
-
-    def allocate_device_slots(
-        self,
-        *,
-        count: int,
-        shape: tuple[int, int],
-        dtype: torch.dtype,
-    ) -> list[torch.Tensor]:
-        return [torch.empty(shape, dtype=dtype, device=self.device) for _ in range(count)]
-
-    def submit_h2d(
-        self,
-        *,
-        slot: int,
-        dst: torch.Tensor,
-        src: torch.Tensor,
-        metadata: ReplayTickMetadata | None,
-        trace_recorder,
-        trace_cuda_events: bool,
-        h2d_bytes: int,
-        pack_layout: str,
-        pack_executor: str,
-    ) -> float:
-        h2d_begin_ns = time.perf_counter_ns()
-        start_event = None
-        end_event = None
-        record_cuda = trace_recorder is not None and trace_cuda_events
-
-        with torch.cuda.device(self.device):
-            copy_stream = cast(torch.cuda.Stream, self._copy_stream)
-            with torch.cuda.stream(copy_stream):
-                if record_cuda:
-                    start_event = torch.cuda.Event(enable_timing=True)
-                    end_event = torch.cuda.Event(enable_timing=True)
-                    cast(Any, start_event).record()
-
-                if self.h2d_submitter == "pybind11":
-                    from unilab.ipc.replay_pipelines.native_h2d import submit_h2d
-
-                    submit_h2d(dst, src, copy_stream)
-                else:
-                    dst.copy_(src, non_blocking=True)
-                if end_event is not None:
-                    cast(Any, end_event).record()
-                self._ready_events[slot].record(copy_stream)
-
-        h2d_end_ns = time.perf_counter_ns()
-        if record_cuda and start_event is not None and end_event is not None:
-            args: dict[str, object] = {
-                "slot": slot,
-                "h2d_bytes": h2d_bytes,
-                "pinned_memory": self.host_pinned,
-                "pack_layout": pack_layout,
-                "pack_executor": pack_executor,
-                "h2d_submitter": self.h2d_submitter,
-                "direct_pinned_shared": self.direct_pinned_shared,
-            }
-            if metadata is not None:
-                args.update(
-                    {
-                        "tick_id": int(metadata.tick_id),
-                        "snapshot_ptr": int(metadata.snapshot_ptr),
-                        "snapshot_size": int(metadata.snapshot_size),
-                        "sample_seed": int(metadata.sample_seed),
-                        "sample_count": int(metadata.sample_count),
-                        "batch_host_slot": metadata.batch_host_slot,
-                        "batch_gpu_slot": metadata.batch_gpu_slot,
-                    }
-                )
-            trace_recorder.add_cuda_pending_span(
-                "gpu/replay_pipeline_batch_h2d",
-                category="gpu",
-                cpu_begin_ns=h2d_begin_ns,
-                start_event=cast(Any, start_event),
-                end_event=cast(Any, end_event),
-                args=args,
-            )
-        return (h2d_end_ns - h2d_begin_ns) / 1e9
-
-    def clear_ready(self, slot: int) -> None:
-        del slot
-        return None
-
-    def ready_query(self, slot: int) -> bool:
-        return bool(self._ready_events[slot].query())
-
-    def synchronize_ready(self, slot: int) -> None:
-        self._ready_events[slot].synchronize()
-
-    def wait_current_stream_for_ready(self, slot: int) -> None:
-        current_stream = cast(Any, torch.cuda.current_stream(self.device))
-        current_stream.wait_event(self._ready_events[slot])
-
-    def close(self) -> None:
-        while self._registered_shared_ptrs:
-            ptr = self._registered_shared_ptrs.pop()
-            try:
-                self._cudart.cudaHostUnregister(int(ptr))
-            except Exception:
-                pass
-        self._registered_shared_slots.clear()
-        self.host_pinned = False
-        self.direct_pinned_shared = False
diff --git a/src/unilab/ipc/replay_pipelines/transfer/factory.py b/src/unilab/ipc/replay_pipelines/transfer/factory.py
deleted file mode 100644
index e2348a4cc..000000000
--- a/src/unilab/ipc/replay_pipelines/transfer/factory.py
+++ /dev/null
@@ -1,22 +0,0 @@
-"""Replay transfer backend factory."""
-
-from __future__ import annotations
-
-import torch
-
-from unilab.ipc.replay_pipelines.transfer.base import ReplayTransferBackend
-from unilab.ipc.replay_pipelines.transfer.cuda_like import CudaLikeReplayTransferBackend
-from unilab.ipc.replay_pipelines.transfer.torch_copy import TorchCopyReplayTransferBackend
-
-
-def build_replay_transfer_backend(
-    *,
-    device: torch.device,
-    ring_depth: int,
-) -> ReplayTransferBackend:
-    """Build the transfer backend for a learner device."""
-    if device.type == "cuda":
-        return CudaLikeReplayTransferBackend(device=device, ring_depth=ring_depth)
-    if device.type == "mps":
-        return TorchCopyReplayTransferBackend(device=device, ring_depth=ring_depth)
-    raise ValueError(f"Replay transfer requires a CUDA or MPS device, got {device.type!r}")
diff --git a/src/unilab/ipc/replay_pipelines/transfer/torch_copy.py b/src/unilab/ipc/replay_pipelines/transfer/torch_copy.py
deleted file mode 100644
index ca8c71365..000000000
--- a/src/unilab/ipc/replay_pipelines/transfer/torch_copy.py
+++ /dev/null
@@ -1,110 +0,0 @@
-"""Portable torch-copy replay transfer backend."""
-
-from __future__ import annotations
-
-import threading
-import time
-
-import torch
-
-from unilab.ipc.replay_pipelines.base import ReplayTickMetadata
-
-
-class TorchCopyReplayTransferBackend:
-    """Learner-thread transfer backend for MPS."""
-
-    h2d_submitter = "torch_copy"
-    host_memory_kind = "pageable_shared"
-    host_pinned = False
-    direct_pinned_shared = False
-    supports_async_submit = False
-    supports_timing_events = False
-
-    def __init__(self, *, device: torch.device, ring_depth: int) -> None:
-        self.device = device
-        self.device_family = device.type
-        self._ring_depth = int(ring_depth)
-        self._ready_events = [threading.Event() for _ in range(self._ring_depth)]
-        if device.type != "mps":
-            raise ValueError(f"Torch-copy replay transfer requires MPS, got {device.type!r}")
-        self._defer_copy_to_wait = True
-        self._pending_copies: list[tuple[torch.Tensor, torch.Tensor] | None] = [
-            None for _ in range(self._ring_depth)
-        ]
-        self.last_wait_copy_time_s = 0.0
-        self.h2d_submitter = "torch_copy_main_thread"
-
-    def register_host_slots(self, slots: list[torch.Tensor]) -> None:
-        del slots
-        return None
-
-    def allocate_device_slots(
-        self,
-        *,
-        count: int,
-        shape: tuple[int, int],
-        dtype: torch.dtype,
-    ) -> list[torch.Tensor]:
-        return [torch.empty(shape, dtype=dtype, device=self.device) for _ in range(count)]
-
-    def submit_h2d(
-        self,
-        *,
-        slot: int,
-        dst: torch.Tensor,
-        src: torch.Tensor,
-        metadata: ReplayTickMetadata | None,
-        trace_recorder,
-        trace_cuda_events: bool,
-        h2d_bytes: int,
-        pack_layout: str,
-        pack_executor: str,
-    ) -> float:
-        del metadata, trace_recorder, trace_cuda_events, h2d_bytes, pack_layout, pack_executor
-        h2d_begin_ns = time.perf_counter_ns()
-        self.clear_ready(slot)
-        self.last_wait_copy_time_s = 0.0
-        if self._defer_copy_to_wait:
-            # PyTorch MPS command submission from a background transfer thread
-            # can trip Metal command-buffer assertions.  Keep the collector CPU
-            # pack overlap, but submit the actual MPS copy from the learner
-            # thread when the batch is consumed.
-            self._pending_copies[slot] = (dst, src)
-            self._ready_events[slot].set()
-            return (time.perf_counter_ns() - h2d_begin_ns) / 1e9
-        dst.copy_(src, non_blocking=src.is_pinned())
-        self._synchronize_device()
-        self._ready_events[slot].set()
-        return (time.perf_counter_ns() - h2d_begin_ns) / 1e9
-
-    def clear_ready(self, slot: int) -> None:
-        self._ready_events[slot].clear()
-        self._pending_copies[slot] = None
-        self.last_wait_copy_time_s = 0.0
-
-    def ready_query(self, slot: int) -> bool:
-        return self._ready_events[slot].is_set()
-
-    def synchronize_ready(self, slot: int) -> None:
-        self._ready_events[slot].wait()
-
-    def wait_current_stream_for_ready(self, slot: int) -> None:
-        self.synchronize_ready(slot)
-        pending = self._pending_copies[slot]
-        if pending is None:
-            return
-        dst, src = pending
-        copy_begin_ns = time.perf_counter_ns()
-        dst.copy_(src, non_blocking=False)
-        self._synchronize_device()
-        self.last_wait_copy_time_s = (time.perf_counter_ns() - copy_begin_ns) / 1e9
-        self._pending_copies[slot] = None
-
-    def close(self) -> None:
-        for slot in range(len(self._pending_copies)):
-            self._pending_copies[slot] = None
-        self.last_wait_copy_time_s = 0.0
-        return None
-
-    def _synchronize_device(self) -> None:
-        torch.mps.synchronize()
diff --git a/src/unilab/ipc/rollout_ring_buffer.py b/src/unilab/ipc/rollout_ring_buffer.py
deleted file mode 100644
index fad234992..000000000
--- a/src/unilab/ipc/rollout_ring_buffer.py
+++ /dev/null
@@ -1,190 +0,0 @@
-"""Shared rollout IPC ring buffer for APPO / async PPO."""
-
-from __future__ import annotations
-
-import multiprocessing as mp
-from multiprocessing import shared_memory
-from typing import Dict
-
-import numpy as np
-
-_SPAWN_CTX = mp.get_context("spawn")
-
-_FIELD_SHAPES = {
-    "obs": lambda ns_slots, ne, ns, od, ad, cd: (ns_slots, ne, ns, od),
-    "critic": lambda ns_slots, ne, ns, od, ad, cd: (ns_slots, ne, ns, cd),
-    "actions": lambda ns_slots, ne, ns, od, ad, cd: (ns_slots, ne, ns, ad),
-    "log_probs": lambda ns_slots, ne, ns, od, ad, cd: (ns_slots, ne, ns),
-    "rewards": lambda ns_slots, ne, ns, od, ad, cd: (ns_slots, ne, ns),
-    "dones": lambda ns_slots, ne, ns, od, ad, cd: (ns_slots, ne, ns),
-    "truncated": lambda ns_slots, ne, ns, od, ad, cd: (ns_slots, ne, ns),
-    "last_obs": lambda ns_slots, ne, ns, od, ad, cd: (ns_slots, ne, od),
-    "last_critic": lambda ns_slots, ne, ns, od, ad, cd: (ns_slots, ne, cd),
-}
-
-
-class RolloutRingBuffer:
-    """N-slot shared-memory ring buffer for raw rollout payloads."""
-
-    def __init__(
-        self,
-        num_envs: int,
-        num_steps: int,
-        obs_dim: int,
-        action_dim: int,
-        *,
-        critic_dim: int = 0,
-        num_slots: int = 4,
-        create: bool = True,
-        shm_name_prefix: Dict[str, str] | None = None,
-    ):
-        self.num_envs = num_envs
-        self.num_steps = num_steps
-        self.obs_dim = obs_dim
-        self.action_dim = action_dim
-        self.critic_dim = critic_dim
-        self.num_slots = num_slots
-
-        self._shm_blocks: Dict[str, shared_memory.SharedMemory] = {}
-        self._arrays: Dict[str, np.ndarray] = {}
-
-        fields_to_allocate = {k: v for k, v in _FIELD_SHAPES.items()}
-        if critic_dim == 0:
-            fields_to_allocate.pop("critic", None)
-            fields_to_allocate.pop("last_critic", None)
-
-        for field, shape_fn in fields_to_allocate.items():
-            shape = shape_fn(
-                num_slots,
-                num_envs,
-                num_steps,
-                obs_dim,
-                action_dim,
-                critic_dim,
-            )
-            nbytes = int(np.prod(shape)) * np.dtype(np.float32).itemsize
-
-            if create:
-                shm = shared_memory.SharedMemory(create=True, size=max(nbytes, 1))
-            else:
-                assert shm_name_prefix is not None, "shm_name_prefix required when create=False"
-                shm = shared_memory.SharedMemory(name=shm_name_prefix[field], create=False)
-
-            self._shm_blocks[field] = shm
-            self._arrays[field] = np.ndarray(shape, dtype=np.float32, buffer=shm.buf)
-
-        if create:
-            self._write_ptr = _SPAWN_CTX.Value("l", 0)
-            self._read_ptr = _SPAWN_CTX.Value("l", 0)
-
-    @property
-    def name(self) -> Dict[str, str]:
-        return {field: shm.name for field, shm in self._shm_blocks.items()}
-
-    @property
-    def slot_shapes(self) -> Dict[str, tuple[int, ...]]:
-        return {field: tuple(arr.shape[1:]) for field, arr in self._arrays.items()}
-
-    def attach_sync_primitives(self, write_ptr, read_ptr) -> None:
-        self._write_ptr = write_ptr
-        self._read_ptr = read_ptr
-
-    def _clamp_read_ptr_to_valid_window(self) -> None:
-        wp = int(self._write_ptr.value)
-        oldest_available = max(0, wp - self.num_slots)
-        if int(self._read_ptr.value) >= oldest_available:
-            return
-        with self._read_ptr.get_lock():
-            if int(self._read_ptr.value) < oldest_available:
-                self._read_ptr.value = oldest_available
-
-    @property
-    def write_slot(self) -> int:
-        return int(self._write_ptr.value) % self.num_slots
-
-    @property
-    def write_buffer(self) -> Dict[str, np.ndarray]:
-        s = self.write_slot
-        return {field: arr[s] for field, arr in self._arrays.items()}
-
-    def signal_write_done(self) -> None:
-        with self._write_ptr.get_lock():
-            self._write_ptr.value += 1
-
-    def available(self) -> int:
-        self._clamp_read_ptr_to_valid_window()
-        return min(max(0, int(self._write_ptr.value) - int(self._read_ptr.value)), self.num_slots)
-
-    def wait_for_data(self, timeout: float = 60.0) -> bool:
-        import time
-
-        deadline = time.monotonic() + timeout
-        while self.available() == 0:
-            if time.monotonic() > deadline:
-                return False
-            time.sleep(0.001)
-        return True
-
-    @property
-    def read_slot(self) -> int:
-        self._clamp_read_ptr_to_valid_window()
-        return int(self._read_ptr.value) % self.num_slots
-
-    def read_numpy_views(self) -> dict[str, np.ndarray]:
-        """Return shared-memory views for the current read slot.
-
-        The returned arrays are borrowed views. Consumers must copy them into
-        owned storage before calling advance_read().
-        """
-        s = self.read_slot
-        return {field: arr[s] for field, arr in self._arrays.items()}
-
-    def copy_read_slot_to_torch(self, destination: dict) -> None:
-        import torch
-
-        s = self.read_slot
-        for field, arr in self._arrays.items():
-            if field not in destination:
-                raise KeyError(f"missing destination tensor for rollout field {field!r}")
-            dst = destination[field]
-            src_view = arr[s]
-            if tuple(dst.shape) != tuple(src_view.shape):
-                raise ValueError(
-                    f"destination shape mismatch for {field!r}: "
-                    f"expected {tuple(src_view.shape)}, got {tuple(dst.shape)}"
-                )
-            if dst.dtype != torch.float32:
-                raise TypeError(f"destination tensor for {field!r} must be torch.float32")
-            dst.copy_(torch.from_numpy(src_view), non_blocking=False)
-
-    def read_torch(self, device: str) -> dict:
-        import torch
-
-        result = {
-            field: torch.empty(tuple(arr.shape[1:]), dtype=torch.float32, device=device)
-            for field, arr in self._arrays.items()
-        }
-        self.copy_read_slot_to_torch(result)
-        return result
-
-    def advance_read(self) -> None:
-        with self._read_ptr.get_lock():
-            wp = int(self._write_ptr.value)
-            rp = min(int(self._read_ptr.value) + 1, wp)
-            oldest_available = max(0, wp - self.num_slots)
-            self._read_ptr.value = max(rp, oldest_available)
-
-    def cleanup(self) -> None:
-        for shm in self._shm_blocks.values():
-            try:
-                shm.close()
-                shm.unlink()
-            except Exception:
-                pass
-
-    def close(self) -> None:
-        for shm in self._shm_blocks.values():
-            try:
-                shm.close()
-            except Exception:
-                pass
diff --git a/src/unilab/ipc/shared_buffer.py b/src/unilab/ipc/shared_buffer.py
deleted file mode 100644
index 14b6bfa6b..000000000
--- a/src/unilab/ipc/shared_buffer.py
+++ /dev/null
@@ -1,13 +0,0 @@
-"""Base class for device-adaptive shared memory buffers."""
-
-import torch
-
-
-class SharedBufferBase:
-    """Device-adaptive shared memory buffer base class."""
-
-    def __init__(self, capacity: int, device: str, defer_gpu: bool = False):
-        del defer_gpu
-        self.capacity = capacity
-        self.device = device
-        self.ptr = torch.zeros(1, dtype=torch.int64).share_memory_()
diff --git a/src/unilab/ipc/weight_sync.py b/src/unilab/ipc/weight_sync.py
deleted file mode 100644
index 8b9a663b5..000000000
--- a/src/unilab/ipc/weight_sync.py
+++ /dev/null
@@ -1,154 +0,0 @@
-"""Shared weight synchronization for actor networks."""
-
-from __future__ import annotations
-
-import multiprocessing as mp
-import time
-from multiprocessing import shared_memory
-from typing import Any, Dict
-
-import numpy as np
-
-_SPAWN_CTX = mp.get_context("spawn")
-
-
-class SharedWeightSync:
-    """Synchronize actor weights between learner and collector."""
-
-    def __init__(
-        self, param_shapes: Dict, *, create: bool = True, shm_name: str | None = None, lock=None
-    ):
-        self._param_shapes = param_shapes
-        self._param_names = list(param_shapes.keys())
-        self.trace_recorder: Any | None = None
-        self.trace_thread_time = False
-
-        total_numel = sum(s.numel() for s in param_shapes.values())
-        _f32 = np.dtype(np.float32).itemsize
-        _i64 = np.dtype(np.int64).itemsize
-        data_bytes = total_numel * _f32
-        meta_bytes = _i64
-        total_bytes = data_bytes + meta_bytes
-
-        if create:
-            self._shm = shared_memory.SharedMemory(create=True, size=max(total_bytes, 1))
-            self._lock = _SPAWN_CTX.Lock()
-        else:
-            assert shm_name is not None
-            self._shm = shared_memory.SharedMemory(name=shm_name, create=False)
-            # lock must be passed in from the parent process when attaching
-            self._lock = lock
-
-        buf = self._shm.buf
-        assert buf is not None
-        self._buffer: np.ndarray = np.ndarray((total_numel,), dtype=np.float32, buffer=buf)
-        self._version_arr: np.ndarray = np.ndarray((1,), dtype=np.int64, buffer=buf[data_bytes:])
-        if create:
-            self._version_arr[0] = 0
-
-    @property
-    def name(self) -> str:
-        return self._shm.name
-
-    @property
-    def version(self) -> int:
-        return int(self._version_arr[0])
-
-    @classmethod
-    def from_state_dict(cls, state_dict, **kwargs):
-        param_shapes = {name: p.shape for name, p in state_dict.items()}
-        obj = cls(param_shapes, **kwargs)
-        obj.write_weights(state_dict)
-        return obj
-
-    def write_weights(self, state_dict) -> None:
-        _trace_ns = time.perf_counter_ns()
-        _thread_ns = time.thread_time_ns() if self.trace_thread_time else None
-        if self._lock is not None:
-            with self._lock:
-                offset = 0
-                for name in self._param_names:
-                    param = state_dict[name]
-                    arr = param.detach().cpu().numpy().ravel()
-                    n = arr.size
-                    self._buffer[offset : offset + n] = arr
-                    offset += n
-                self._version_arr[0] += 1
-        else:
-            # No lock - direct write
-            offset = 0
-            for name in self._param_names:
-                param = state_dict[name]
-                arr = param.detach().cpu().numpy().ravel()
-                n = arr.size
-                self._buffer[offset : offset + n] = arr
-                offset += n
-            self._version_arr[0] += 1
-        if self.trace_recorder is not None:
-            self.trace_recorder.add_slice(
-                "weight_sync/write_weights_d2h",
-                category="weight_sync",
-                start_ns=_trace_ns,
-                end_ns=time.perf_counter_ns(),
-                args={"version": int(self._version_arr[0]), "mode": "sync"},
-            )
-            if _thread_ns is not None:
-                self.trace_recorder.add_counter(
-                    "weight_sync/write_thread_cpu_us",
-                    (time.thread_time_ns() - _thread_ns) / 1000.0,
-                    category="weight_sync",
-                )
-
-    def read_weights_into(self, state_dict) -> int:
-        import torch
-
-        _trace_ns = time.perf_counter_ns()
-        _thread_ns = time.thread_time_ns() if self.trace_thread_time else None
-        if self._lock is not None:
-            with self._lock:
-                offset = 0
-                for name in self._param_names:
-                    param = state_dict[name]
-                    n = param.numel()
-                    data = self._buffer[offset : offset + n].copy()
-                    param.data.copy_(torch.from_numpy(data.reshape(param.shape)))
-                    offset += n
-                version = int(self._version_arr[0])
-        else:
-            # No lock - direct read (for subprocess)
-            offset = 0
-            for name in self._param_names:
-                param = state_dict[name]
-                n = param.numel()
-                data = self._buffer[offset : offset + n].copy()
-                param.data.copy_(torch.from_numpy(data.reshape(param.shape)))
-                offset += n
-            version = int(self._version_arr[0])
-        if self.trace_recorder is not None:
-            self.trace_recorder.add_slice(
-                "weight_sync/read_weights_into_cpu_actor",
-                category="weight_sync",
-                start_ns=_trace_ns,
-                end_ns=time.perf_counter_ns(),
-                args={"version": version},
-            )
-            if _thread_ns is not None:
-                self.trace_recorder.add_counter(
-                    "weight_sync/read_thread_cpu_us",
-                    (time.thread_time_ns() - _thread_ns) / 1000.0,
-                    category="weight_sync",
-                )
-        return version
-
-    def cleanup(self) -> None:
-        try:
-            self._shm.close()
-            self._shm.unlink()
-        except Exception:
-            pass
-
-    def close(self) -> None:
-        try:
-            self._shm.close()
-        except Exception:
-            pass
diff --git a/src/unilab/logging/__init__.py b/src/unilab/logging/__init__.py
deleted file mode 100644
index 38e2fee24..000000000
--- a/src/unilab/logging/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-"""Rich-based training loggers shared across algorithm and training layers."""
-
-from unilab.logging.common import BaseTrainingLogger
-from unilab.logging.offpolicy import OffPolicyLogger
-from unilab.logging.onpolicy import OnPolicyLogger
-from unilab.logging.trace_event import TraceRecorder
-
-__all__ = [
-    "BaseTrainingLogger",
-    "OffPolicyLogger",
-    "OnPolicyLogger",
-    "TraceRecorder",
-]
diff --git a/src/unilab/logging/common.py b/src/unilab/logging/common.py
deleted file mode 100644
index 0d26011d3..000000000
--- a/src/unilab/logging/common.py
+++ /dev/null
@@ -1,444 +0,0 @@
-from __future__ import annotations
-
-import importlib
-import os
-import sys
-import time
-from collections import deque
-from typing import Any
-
-from rich import box
-from rich.console import Console
-from rich.live import Live
-from rich.panel import Panel
-from rich.table import Table
-from rich.text import Text
-
-
-def _fmt_time(seconds: float) -> str:
-    if seconds < 60:
-        return f"{seconds:.0f}s"
-    m, s = divmod(int(seconds), 60)
-    if m < 60:
-        return f"{m}m{s:02d}s"
-    h, m = divmod(m, 60)
-    return f"{h}h{m:02d}m{s:02d}s"
-
-
-def _fmt_number(v: float) -> str:
-    if abs(v) == 0:
-        return "0"
-    if abs(v) >= 1e6:
-        return f"{v:.2e}"
-    if abs(v) >= 100:
-        return f"{v:.1f}"
-    if abs(v) >= 1:
-        return f"{v:.3f}"
-    if abs(v) >= 0.001:
-        return f"{v:.4f}"
-    return f"{v:.2e}"
-
-
-def _console_supports_unicode() -> bool:
-    encoding = getattr(sys.stdout, "encoding", None) or "utf-8"
-    try:
-        "⏱ ▲ ▼ ━".encode(encoding)
-    except UnicodeEncodeError:
-        return False
-    return True
-
-
-def _load_wandb() -> Any | None:
-    """Load wandb lazily so it remains an optional dependency."""
-    try:
-        return importlib.import_module("wandb")
-    except ImportError:
-        return None
-
-
-class BaseTrainingLogger:
-    """Shared lifecycle and backend logging setup for rich training loggers."""
-
-    def __init__(
-        self,
-        *,
-        algo_name: str,
-        max_iterations: int,
-        num_envs: int,
-        env_name: str,
-        log_dir: str,
-        log_backend: str,
-        wandb_project: str,
-        wandb_entity: str | None,
-        wandb_name: str,
-        wandb_group: list[str] | None | str,
-        wandb_job_type: str | None,
-        wandb_tags: list[str] | None,
-        wandb_notes: str | None,
-        refresh_per_second: int = 4,
-        fixed_terminal_refresh: bool = False,
-        tensorboard_subdir: str | None = "tb",
-        wandb_config: dict[str, Any] | None = None,
-    ):
-        self.algo_name = algo_name
-        self.max_iterations = max_iterations
-        self.num_envs = num_envs
-        self.env_name = env_name
-
-        self._no_print = log_backend.lower() == "no_print"
-        self._log_backend = "none" if self._no_print else log_backend.lower()
-
-        self._unicode_console = _console_supports_unicode()
-        self._console = Console(force_terminal=False if not self._unicode_console else None)
-        self._live: Live | None = None
-        self._refresh_rate = refresh_per_second
-        self._fixed_terminal_refresh = fixed_terminal_refresh
-        self._last_live_refresh_time: float | None = None
-
-        self._start_time: float = 0.0
-        self._iteration: int = 0
-        self._reward_history: deque[float] = deque(maxlen=200)
-        self._latest_metrics: dict[str, float] = {}
-        self._latest_reward_components: dict[str, float] = {}
-        self._collect_time: float = 0.0
-        self._train_time: float = 0.0
-        self._mean_ep_length: float = 0.0
-        self._last_save: str = ""
-        self._status: str = ""
-
-        self._log_dir = log_dir
-        self._tb_writer: Any | None = None
-        self._wandb_run = None
-        self._owns_wandb_run = False
-        self._finished = False
-        self._closed = False
-
-        if self._log_backend == "tensorboard" and log_dir:
-            self._init_tensorboard(log_dir, tensorboard_subdir)
-        elif self._log_backend == "wandb":
-            self._init_wandb(
-                project=wandb_project,
-                entity=wandb_entity,
-                name=wandb_name or f"{algo_name}_{env_name}",
-                log_dir=log_dir,
-                group=wandb_group,
-                job_type=wandb_job_type,
-                tags=wandb_tags,
-                notes=wandb_notes,
-                extra_config=wandb_config,
-            )
-
-    def _format_tensorboard_message(self, tb_dir: str) -> str:
-        return f"[dim]TensorBoard: {tb_dir}[/]"
-
-    def _format_wandb_message(self, project: str, name: str) -> str:
-        return f"[dim]W&B: {project}/{name}[/]"
-
-    def _init_tensorboard(self, log_dir: str, subdir: str | None):
-        try:
-            from torch.utils.tensorboard import SummaryWriter
-
-            tb_dir = log_dir if subdir is None else os.path.join(log_dir, subdir)
-            os.makedirs(tb_dir, exist_ok=True)
-            self._tb_writer = SummaryWriter(log_dir=tb_dir)
-            if not self._no_print:
-                self._console.print(self._format_tensorboard_message(tb_dir))
-        except ImportError:
-            if not self._no_print:
-                self._console.print("[yellow]tensorboard not installed[/]")
-
-    def _init_wandb(
-        self,
-        *,
-        project: str,
-        entity: str | None,
-        name: str,
-        log_dir: str,
-        group: str | None | list[str],
-        job_type: str | None,
-        tags: list[str] | None,
-        notes: str | None,
-        extra_config: dict[str, Any] | None = None,
-    ):
-        wandb = _load_wandb()
-        if wandb is None:
-            if not self._no_print:
-                self._console.print("[yellow]wandb not installed[/]")
-            return
-
-        self._wandb_run = wandb.run
-        if self._wandb_run is None:
-            config: dict[str, Any] = {
-                "algo": self.algo_name,
-                "env": self.env_name,
-                "num_envs": self.num_envs,
-            }
-            if extra_config:
-                config.update(extra_config)
-
-            kwargs: dict[str, Any] = {
-                "project": project,
-                "name": name,
-                "config": config,
-                "dir": log_dir or None,
-                "reinit": True,
-            }
-            if entity:
-                kwargs["entity"] = entity
-            if group:
-                kwargs["group"] = group
-            if job_type:
-                kwargs["job_type"] = job_type
-            if tags:
-                kwargs["tags"] = tags
-            if notes:
-                kwargs["notes"] = notes
-
-            self._wandb_run = wandb.init(**kwargs)
-            self._owns_wandb_run = True
-
-        if not self._no_print:
-            self._console.print(self._format_wandb_message(project, name))
-
-    def start(self, *, status: str = ""):
-        if self._live is not None:
-            if status:
-                self._status = status
-            self._refresh()
-            return
-
-        self._start_time = time.time()
-        self._status = status
-        if not self._no_print:
-            self._live = Live(
-                self._build_display(),
-                console=self._console,
-                auto_refresh=self._fixed_terminal_refresh,
-                refresh_per_second=self._refresh_rate,
-                transient=False,
-                get_renderable=(self._build_display if self._fixed_terminal_refresh else None),
-            )
-            self._live.start(refresh=False)
-
-    def _stop_live(self) -> None:
-        live = self._live
-        if live is None:
-            return
-
-        # Drop the owned reference first so repeated cleanup stays idempotent
-        # even if Rich's final render fails. ``Live.stop()`` normally restores
-        # the cursor itself; the explicit final call covers failures before its
-        # internal cursor-restoration block and makes Ctrl+C teardown fail-safe.
-        self._live = None
-        self._last_live_refresh_time = None
-        try:
-            live.update(self._build_display(), refresh=False)
-        finally:
-            try:
-                live.stop()
-            finally:
-                self._console.show_cursor(True)
-
-    def _close_backends(self) -> None:
-        if self._tb_writer:
-            self._tb_writer.close()
-            self._tb_writer = None
-        if self._wandb_run and self._owns_wandb_run:
-            wandb = _load_wandb()
-            if wandb is not None:
-                wandb.finish()
-            self._wandb_run = None
-            self._owns_wandb_run = False
-
-    def close(self) -> None:
-        """Release live terminal state and backend handles without printing a summary."""
-        if self._closed:
-            return
-        try:
-            self._stop_live()
-        finally:
-            try:
-                self._close_backends()
-            finally:
-                self._closed = True
-
-    def finish(self, *, title: str = "Training Summary", extra_summary: str = ""):
-        if self._finished:
-            return
-        self._stop_live()
-
-        elapsed = time.time() - self._start_time
-        if not self._no_print:
-            summary = (
-                f"[bold green]Training complete[/]\n"
-                f"  Algo: [cyan]{self.algo_name}[/] | Env: [cyan]{self.env_name}[/]\n"
-                f"  Iterations: [yellow]{self._iteration}[/]/{self.max_iterations}\n"
-                f"  Total time: [yellow]{_fmt_time(elapsed)}[/]\n"
-            )
-            if extra_summary:
-                summary += extra_summary
-            if self._last_save:
-                summary += f"  Last checkpoint: [dim]{self._last_save}[/]"
-
-            self._console.print()
-            self._console.print(Panel(summary, title=f"[bold]{title}[/]", border_style="green"))
-
-        self._close_backends()
-        self._closed = True
-        self._finished = True
-
-    def update_ep_length(self, length: float):
-        self._mean_ep_length = length
-
-    def log_save(self, path: str):
-        self._last_save = path
-
-    def _refresh(self, *, force: bool = False):
-        if self._live is None:
-            return
-        if self._fixed_terminal_refresh and not force:
-            return
-        now = time.time()
-        if not force and self._refresh_rate > 0 and self._last_live_refresh_time is not None:
-            min_interval_s = 1.0 / self._refresh_rate
-            if now - self._last_live_refresh_time < min_interval_s:
-                return
-        self._last_live_refresh_time = now
-        self._live.update(self._build_display(), refresh=True)
-
-    def _estimate_eta(self, *, iteration: int | None = None) -> str:
-        current_iteration = self._iteration if iteration is None else iteration
-        if current_iteration <= 0:
-            return ""
-        elapsed = time.time() - self._start_time
-        remaining = self.max_iterations - current_iteration
-        avg_iter = elapsed / current_iteration
-        eta_s = remaining * avg_iter
-        return _fmt_time(eta_s)
-
-    def _build_compact_header(
-        self,
-        *,
-        include_status: bool,
-        include_identity: bool = True,
-        include_iteration: bool = True,
-        extra_fields: list[tuple[str, str]] | None = None,
-    ) -> Text:
-        return self._build_compact_header_state(
-            include_status=include_status,
-            include_identity=include_identity,
-            include_iteration=include_iteration,
-            extra_fields=extra_fields,
-            ep_length=self._mean_ep_length,
-            current_iteration=self._iteration,
-        )
-
-    def _build_compact_header_state(
-        self,
-        *,
-        include_status: bool,
-        include_identity: bool,
-        include_iteration: bool,
-        extra_fields: list[tuple[str, str]] | None,
-        ep_length: float,
-        current_iteration: int,
-    ) -> Text:
-        elapsed = time.time() - self._start_time if self._start_time else 0
-        eta = self._estimate_eta(iteration=current_iteration)
-        fields: list[tuple[str, str]] = []
-        if include_identity:
-            fields.extend(
-                [
-                    (f" {self.algo_name}", "bold cyan"),
-                    (self.env_name, "bold white"),
-                ]
-            )
-        if include_iteration:
-            fields.append((f"iter {current_iteration}/{self.max_iterations}", "yellow"))
-        fields.append(
-            (
-                f"{'⏱' if self._unicode_console else 'time'} {_fmt_time(elapsed)}",
-                "green",
-            )
-        )
-        if eta:
-            fields.append((f"ETA {eta}", "bold magenta"))
-        if ep_length > 0:
-            fields.append((f"Ep Len {ep_length:.1f}", "yellow"))
-        if extra_fields:
-            fields.extend(extra_fields)
-        if include_status and self._status:
-            fields.append((self._status, "dim italic"))
-
-        header_text = Text(no_wrap=True, overflow="ellipsis", justify="center")
-        for index, (text, style) in enumerate(fields):
-            if index > 0:
-                header_text.append(" | ", style="dim")
-            header_text.append(text, style=style)
-        return header_text
-
-    def _build_reward_table_common(
-        self,
-        *,
-        wait_message: str,
-        include_ep_length: bool = True,
-        reward_history: tuple[float, ...] | None = None,
-        reward_components: dict[str, float] | None = None,
-        mean_reward: float | None = None,
-    ) -> Table:
-        table = Table(
-            box=box.SIMPLE_HEAVY,
-            show_header=True,
-            show_edge=False,
-            header_style="bold green",
-            expand=True,
-            pad_edge=False,
-        )
-        table.add_column("Rewards", style="white", width=21, no_wrap=True)
-        table.add_column("Value", justify="right", ratio=2, no_wrap=True)
-
-        recent = list(self._reward_history if reward_history is None else reward_history)
-        if recent:
-            mean_rew = (
-                mean_reward
-                if mean_reward is not None
-                else sum(recent[-50:]) / max(len(recent[-50:]), 1)
-            )
-            peak_rew = max(recent) if recent else 0
-
-            if len(recent) >= 10:
-                old = sum(recent[-20:-10]) / 10
-                new = sum(recent[-10:]) / 10
-                if new > old * 1.05:
-                    trend = "[green]▲[/]" if self._unicode_console else "[green]+[/]"
-                elif new < old * 0.95:
-                    trend = "[red]▼[/]" if self._unicode_console else "[red]-[/]"
-                else:
-                    trend = "[yellow]━[/]" if self._unicode_console else "[yellow]=[/]"
-            else:
-                trend = ""
-
-            table.add_row(
-                f"[bold]Reward[/] {trend}",
-                f"Mean [bold green]{mean_rew:.3f}[/] / Peak [dim]{peak_rew:.3f}[/]",
-            )
-            if include_ep_length and self._mean_ep_length > 0:
-                table.add_row("  Ep Len", f"[dim]{self._mean_ep_length:.1f}[/]")
-            if include_ep_length:
-                table.add_row("", "")
-        else:
-            table.add_row(wait_message, "")
-
-        components = (
-            self._latest_reward_components if reward_components is None else reward_components
-        )
-        if components:
-            for name, val in sorted(components.items()):
-                display = name.replace("reward/", "").replace("_", " ")
-                color = "green" if val > 0 else "red" if val < 0 else "dim"
-                table.add_row(f"  {display}", f"[{color}]{val:+.4f}[/]")
-
-        return table
-
-    def _build_display(self) -> Panel:
-        raise NotImplementedError
diff --git a/src/unilab/logging/offpolicy.py b/src/unilab/logging/offpolicy.py
deleted file mode 100644
index 2771c03dd..000000000
--- a/src/unilab/logging/offpolicy.py
+++ /dev/null
@@ -1,910 +0,0 @@
-"""Rich-based training logger for off-policy RL algorithms (SAC, TD3, etc)."""
-
-from __future__ import annotations
-
-import time
-from collections import deque
-from dataclasses import dataclass
-from typing import Any
-
-from rich import box
-from rich.console import Group
-from rich.panel import Panel
-from rich.table import Table
-from rich.text import Text
-
-from unilab.logging.common import BaseTrainingLogger, _fmt_number, _load_wandb
-
-_COLLECTOR_WAIT_TIMING_SPEC = (
-    "timing/learner_collector_wait_ms",
-    "Collector Wait",
-    "_collector_wait_time",
-)
-_INFERENCE_TIMING_SPEC = ("timing/learner_inference_ms", "Inference", "_inference_time")
-_COLLECTOR_RELEASE_TIMING_SPEC = (
-    "timing/learner_collector_release_ms",
-    "Collector Release",
-    "_sync_coordination_time",
-)
-_REPLAY_BATCH_WAIT_TIMING_SPEC = (
-    "timing/learner_replay_batch_wait_ms",
-    "Replay Batch Wait",
-    "_replay_batch_wait_time",
-)
-_REPLAY_STAGE_TIMING_SPEC = (
-    "timing/learner_replay_stage_ms",
-    "Replay Stage",
-    "_learner_replay_stage_time",
-)
-_REPLAY_SAMPLE_TIMING_SPEC = (
-    "timing/learner_replay_sample_ms",
-    "Replay Sample",
-    "_learner_replay_sample_time",
-)
-_TRAIN_TIMING_SPEC = ("timing/learner_train_ms", "Train", "_train_time")
-_WEIGHT_PUBLISH_TIMING_SPEC = (
-    "timing/learner_weight_publish_ms",
-    "Weight Publish",
-    "_weight_sync_time",
-)
-
-_LEARNER_TIMING_PROFILES = {
-    "sac_family": (
-        _COLLECTOR_WAIT_TIMING_SPEC,
-        _INFERENCE_TIMING_SPEC,
-        _COLLECTOR_RELEASE_TIMING_SPEC,
-        _REPLAY_BATCH_WAIT_TIMING_SPEC,
-        _REPLAY_SAMPLE_TIMING_SPEC,
-        _TRAIN_TIMING_SPEC,
-    ),
-    "appo": (
-        _COLLECTOR_WAIT_TIMING_SPEC,
-        _REPLAY_STAGE_TIMING_SPEC,
-        _REPLAY_SAMPLE_TIMING_SPEC,
-        _TRAIN_TIMING_SPEC,
-        _WEIGHT_PUBLISH_TIMING_SPEC,
-    ),
-}
-
-_SAC_FAMILY_DETAIL_TIMING_SPECS = (
-    ("timing/learner_inference_h2d_ms", "Inference H2D", "_inference_h2d_time"),
-    (
-        "timing/learner_inference_forward_ms",
-        "Inference Forward",
-        "_inference_forward_time",
-    ),
-    ("timing/learner_inference_d2h_ms", "Inference D2H", "_inference_d2h_time"),
-    (
-        "timing/replay_ingress_h2d_submit_ms",
-        "Replay H2D Submit",
-        "_replay_ingress_h2d_submit_time",
-    ),
-)
-
-_LEARNER_DETAIL_TIMING_PROFILES = {
-    "sac_family": _SAC_FAMILY_DETAIL_TIMING_SPECS,
-    "appo": (),
-}
-
-_LEARNER_OTHER_TIMING_SPEC = ("timing/learner_other_ms", "Other", "")
-_ITER_WALL_TIMING_SPEC = ("perf/iter_ms", "Iter Wall", "")
-
-_COLLECTOR_TIMING_SPECS = {
-    "mlp_infer_ms": (1.0, "MLP Infer", "per_step"),
-    "inference_request_ms": (1.0, "Inference Request", "cycle_phase"),
-    "learner_action_wait_ms": (1.1, "Learner Action Wait", "cycle_phase"),
-    "env_step_ms": (2.0, "Env Step", "cycle_phase"),
-    "env_step_backend_ms": (2.1, "  Backend Step", "env_step_detail"),
-    "env_step_update_state_ms": (2.2, "  Update State", "env_step_detail"),
-    "env_step_reset_done_ms": (2.3, "  Reset Done", "env_step_detail"),
-    "replay_write_ms": (3.0, "Replay Write", "cycle_phase"),
-    "rollout_ms": (9.0, "Rollout Wall", "rollout_total"),
-}
-
-OFFPOLICY_ENV_STEP_DETAIL_KEYS = (
-    "env_step_backend_ms",
-    "env_step_update_state_ms",
-    "env_step_reset_done_ms",
-)
-
-_OFFPOLICY_COLLECTOR_CYCLE_KEYS = tuple(
-    key for key, (_, _, role) in _COLLECTOR_TIMING_SPECS.items() if role == "cycle_phase"
-)
-
-_TERMINAL_AVERAGE_WINDOW_SEC = 2.0
-_TERMINAL_AVERAGE_MAX_SAMPLES = 512
-
-
-@dataclass(frozen=True)
-class _TerminalSample:
-    timestamp: float
-    scalars: dict[str, float]
-    metrics: dict[str, float]
-    reward_components: dict[str, float]
-    collector_timing: dict[str, float]
-
-
-@dataclass(frozen=True)
-class _TerminalSnapshot:
-    iteration: int
-    sample_count: int
-    scalars: dict[str, float]
-    metrics: dict[str, float]
-    reward_components: dict[str, float]
-    collector_timing: dict[str, float]
-    reward_history: tuple[float, ...]
-    buffer_size: int
-    batch_size_per_rank: int
-
-
-def _metric_backend_key(key: str) -> str:
-    """Keep canonical slash metrics intact; namespace legacy flat metrics under train/."""
-    return key if "/" in key else f"train/{key}"
-
-
-def _reward_backend_key(key: str) -> str:
-    """Keep canonical reward/* keys intact; namespace bare component names under reward/."""
-    return key if key.startswith("reward/") else f"reward/{key}"
-
-
-def _dedupe_metric_aliases(metrics: dict[str, float] | None) -> dict[str, float] | None:
-    """Drop legacy flat APPO aliases when canonical metrics are present."""
-    if not metrics:
-        return metrics
-    normalized = dict(metrics)
-    aliases = {
-        "surrogate_loss": "loss/policy_loss",
-        "value_loss": "loss/value_loss",
-        "entropy": "policy/entropy",
-        "kl": "ppo/approx_kl",
-    }
-    for legacy_key, canonical_key in aliases.items():
-        if canonical_key in normalized:
-            normalized.pop(legacy_key, None)
-    return normalized
-
-
-class OffPolicyLogger(BaseTrainingLogger):
-    """Rich logger for off-policy RL algorithms (SAC, TD3, etc)."""
-
-    def __init__(
-        self,
-        algo_name: str = "RL",
-        max_iterations: int = 1500,
-        num_envs: int = 4096,
-        env_name: str = "",
-        obs_dim: int = 0,
-        action_dim: int = 0,
-        num_gpus: int = 1,
-        refresh_per_second: int = 2,
-        log_dir: str = "",
-        log_backend: str = "tensorboard",
-        wandb_project: str = "unilab",
-        wandb_entity: str | None = None,
-        wandb_name: str = "",
-        wandb_group: str | None = None,
-        wandb_job_type: str | None = None,
-        wandb_tags: list[str] | None = None,
-        wandb_notes: str | None = None,
-        timing_profile: str = "sac_family",
-    ):
-        super().__init__(
-            algo_name=algo_name,
-            max_iterations=max_iterations,
-            num_envs=num_envs,
-            env_name=env_name,
-            log_dir=log_dir,
-            log_backend=log_backend,
-            wandb_project=wandb_project,
-            wandb_entity=wandb_entity,
-            wandb_name=wandb_name,
-            wandb_group=wandb_group,
-            wandb_job_type=wandb_job_type,
-            wandb_tags=wandb_tags,
-            wandb_notes=wandb_notes,
-            refresh_per_second=refresh_per_second,
-            fixed_terminal_refresh=True,
-            tensorboard_subdir=None,
-            wandb_config={
-                "obs_dim": obs_dim,
-                "action_dim": action_dim,
-                "max_iterations": max_iterations,
-            },
-        )
-        self.obs_dim = obs_dim
-        self.action_dim = action_dim
-        if int(num_gpus) < 1:
-            raise ValueError("num_gpus must be >= 1")
-        self.num_gpus = int(num_gpus)
-        self._total_steps: int = 0
-        self._buffer_size: int = 0
-        self._buffer_target: int = 0
-        self._collector_wait_time: float = 0.0
-        self._replay_batch_wait_time: float = 0.0
-        self._learner_replay_stage_time: float = 0.0
-        self._learner_replay_sample_time: float = 0.0
-        self._sync_coordination_time: float = 0.0
-        self._replay_ingress_h2d_submit_time: float = 0.0
-        self._weight_sync_time: float = 0.0
-        self._inference_h2d_time: float = 0.0
-        self._inference_forward_time: float = 0.0
-        self._inference_d2h_time: float = 0.0
-        self._inference_time: float = 0.0
-        self._iteration_time: float | None = None
-        self._throughput_steps: int = 0
-        self._steps_per_sec_override: float | None = None
-        self._samples_per_sec_override: float | None = None
-        self._collector_active_steps_per_sec: float | None = None
-        self._batch_size_per_rank: int = 0
-        self._effective_batch_size: int = 0
-        self._replay_samples_per_iter: int = 0
-        self._learner_samples_per_iter: int = 0
-        self._has_iteration_extra_info: bool = False
-        self._collector_timing: dict[str, float] = {}
-        if timing_profile not in _LEARNER_TIMING_PROFILES:
-            raise ValueError("timing_profile must be 'sac_family' or 'appo'")
-        self._timing_profile = timing_profile
-        self._timeout_rate: float = 0.0
-        self._buffer_utilization: float = 0.0
-        self._runtime_manifest: dict[str, Any] = {}
-        self._staging_pool_len: int = 0
-        self._staging_pool_max: int = 0
-        self._status: str = "Initializing..."
-        self._training_timer_started: bool = False
-        self._terminal_samples: deque[_TerminalSample] = deque(maxlen=_TERMINAL_AVERAGE_MAX_SAMPLES)
-        self._terminal_snapshot: _TerminalSnapshot | None = None
-
-    def _format_tensorboard_message(self, tb_dir: str) -> str:
-        return f"[dim]TensorBoard logging to: {tb_dir}[/]"
-
-    def _format_wandb_message(self, project: str, name: str) -> str:
-        return f"[dim]W&B logging to project: {project}, run: {name}[/]"
-
-    def start(self, *, status: str = "Warming up..."):
-        super().start(status=status)
-
-    def start_training_timer(self) -> float:
-        """Start the measured training window after collector warm-up is complete."""
-        if not self._training_timer_started:
-            self._start_time = time.time()
-            self._training_timer_started = True
-        return self._start_time
-
-    def finish(self, *, title: str = "Training Summary", extra_summary: str = ""):
-        super().finish(
-            title=title,
-            extra_summary=f"  Total env steps: [yellow]{self._total_steps:,}[/]\n{extra_summary}",
-        )
-
-    def log_buffer_fill(self, current: int, target: int):
-        self._buffer_size = current
-        self._buffer_target = target
-        pct = current / max(target, 1) * 100
-        self._status = f"Buffer fill: {current:,}/{target:,} ({pct:.0f}%)"
-
-    def _get_iter_steps_per_sec(self) -> float | None:
-        if self._steps_per_sec_override is not None:
-            return self._steps_per_sec_override
-        if not self._has_iteration_extra_info or self._throughput_steps <= 0:
-            return None
-        iter_time = self._get_iter_wall_time()
-        if iter_time <= 0:
-            return None
-        return self._throughput_steps / iter_time
-
-    def _get_effective_samples_per_sec(self) -> float | None:
-        if self._samples_per_sec_override is not None:
-            return self._samples_per_sec_override
-        if not self._has_iteration_extra_info or self._learner_samples_per_iter <= 0:
-            return None
-        iter_time = self._get_iter_wall_time()
-        if iter_time <= 0:
-            return None
-        return self._learner_samples_per_iter / iter_time
-
-    def _learner_phase_times(self) -> dict[str, float]:
-        """Return mutually exclusive learner main-thread phases by backend key."""
-        return {
-            key: float(getattr(self, attribute))
-            for key, _, attribute in self._learner_timing_specs()
-        }
-
-    def _learner_timing_specs(self) -> tuple[tuple[str, str, str], ...]:
-        """Return phases applicable to the selected algorithm timing contract."""
-        return _LEARNER_TIMING_PROFILES[self._timing_profile]
-
-    def _learner_detail_times(self) -> dict[str, float]:
-        """Return nested or asynchronous diagnostics that are not wall-clock slices."""
-        return {
-            key: float(getattr(self, attribute))
-            for key, _, attribute in _LEARNER_DETAIL_TIMING_PROFILES[self._timing_profile]
-        }
-
-    def _get_learner_accounted_time(self) -> float:
-        return sum(self._learner_phase_times().values())
-
-    def _get_learner_other_time(self) -> float:
-        return max(self._get_iter_wall_time() - self._get_learner_accounted_time(), 0.0)
-
-    def _get_iter_pct(self, seconds: float) -> float:
-        iter_time = self._get_iter_wall_time()
-        if iter_time <= 0.0:
-            return 0.0
-        return seconds / iter_time * 100.0
-
-    def _get_iter_wall_time(self) -> float:
-        if self._iteration_time is not None and self._iteration_time > 0.0:
-            return self._iteration_time
-        return self._get_learner_accounted_time()
-
-    def _get_collector_cycle_ms(
-        self,
-        collector_timing: dict[str, float] | None = None,
-    ) -> float | None:
-        if self._timing_profile != "sac_family":
-            return None
-        timing = self._collector_timing if collector_timing is None else collector_timing
-        if not all(key in timing for key in _OFFPOLICY_COLLECTOR_CYCLE_KEYS):
-            return None
-        return sum(timing.get(key, 0.0) for key in _OFFPOLICY_COLLECTOR_CYCLE_KEYS)
-
-    def _build_compact_header(
-        self,
-        *,
-        include_status: bool,
-        include_identity: bool = True,
-        include_iteration: bool = True,
-        extra_fields: list[tuple[str, str]] | None = None,
-        terminal_snapshot: _TerminalSnapshot | None = None,
-    ) -> Text:
-        snapshot = terminal_snapshot or self._terminal_snapshot
-        iter_steps_per_sec = (
-            snapshot.scalars.get("steps_per_sec")
-            if snapshot is not None
-            else self._get_iter_steps_per_sec()
-        )
-        effective_samples_per_sec = (
-            snapshot.scalars.get("samples_per_sec")
-            if snapshot is not None
-            else self._get_effective_samples_per_sec()
-        )
-        header_extra_fields: list[tuple[str, str]] = []
-        if iter_steps_per_sec is not None:
-            header_extra_fields.append((f"Steps/s {iter_steps_per_sec:,.0f}", "bold green"))
-        if effective_samples_per_sec is not None:
-            header_extra_fields.append((f"Samples/s {effective_samples_per_sec:,.0f}", "bold cyan"))
-        if snapshot is not None:
-            header_extra_fields.append(
-                (
-                    f"Avg {_TERMINAL_AVERAGE_WINDOW_SEC:g}s (n={snapshot.sample_count})",
-                    "dim",
-                )
-            )
-        if extra_fields:
-            header_extra_fields.extend(extra_fields)
-        return self._build_compact_header_state(
-            include_status=include_status,
-            include_identity=include_identity,
-            include_iteration=include_iteration,
-            extra_fields=header_extra_fields,
-            ep_length=(
-                snapshot.scalars.get("mean_ep_length", 0.0)
-                if snapshot is not None
-                else self._mean_ep_length
-            ),
-            current_iteration=(snapshot.iteration if snapshot is not None else self._iteration),
-        )
-
-    @staticmethod
-    def _mean_sample_maps(
-        samples: tuple[_TerminalSample, ...],
-        attribute: str,
-    ) -> dict[str, float]:
-        totals: dict[str, float] = {}
-        counts: dict[str, int] = {}
-        for sample in samples:
-            values = getattr(sample, attribute)
-            for key, value in values.items():
-                totals[key] = totals.get(key, 0.0) + float(value)
-                counts[key] = counts.get(key, 0) + 1
-        return {key: total / counts[key] for key, total in totals.items()}
-
-    def _record_terminal_sample(
-        self,
-        *,
-        metrics: dict[str, float] | None,
-        reward: float | None,
-        reward_components: dict[str, float] | None,
-    ) -> None:
-        """Publish one immutable, time-smoothed terminal-only state snapshot."""
-        scalars = {
-            attribute: float(getattr(self, attribute))
-            for _, _, attribute in self._learner_timing_specs()
-        }
-        scalars.update(
-            {
-                "iter_wall_time": self._get_iter_wall_time(),
-                "learner_other_time": self._get_learner_other_time(),
-                "timeout_rate": self._timeout_rate,
-            }
-        )
-        if self._mean_ep_length > 0.0:
-            scalars["mean_ep_length"] = self._mean_ep_length
-        if reward is not None:
-            scalars["reward"] = float(reward)
-        steps_per_sec = self._get_iter_steps_per_sec()
-        if steps_per_sec is not None:
-            scalars["steps_per_sec"] = steps_per_sec
-        samples_per_sec = self._get_effective_samples_per_sec()
-        if samples_per_sec is not None:
-            scalars["samples_per_sec"] = samples_per_sec
-
-        now = time.monotonic()
-        self._terminal_samples.append(
-            _TerminalSample(
-                timestamp=now,
-                scalars=scalars,
-                metrics=dict(metrics or {}),
-                reward_components=dict(reward_components or {}),
-                collector_timing=dict(self._collector_timing),
-            )
-        )
-        cutoff = now - _TERMINAL_AVERAGE_WINDOW_SEC
-        while self._terminal_samples and self._terminal_samples[0].timestamp < cutoff:
-            self._terminal_samples.popleft()
-
-        samples = tuple(self._terminal_samples)
-        self._terminal_snapshot = _TerminalSnapshot(
-            iteration=self._iteration,
-            sample_count=len(samples),
-            scalars=self._mean_sample_maps(samples, "scalars"),
-            metrics=self._mean_sample_maps(samples, "metrics"),
-            reward_components=self._mean_sample_maps(samples, "reward_components"),
-            collector_timing=self._mean_sample_maps(samples, "collector_timing"),
-            reward_history=tuple(self._reward_history),
-            buffer_size=self._buffer_size,
-            batch_size_per_rank=self._batch_size_per_rank,
-        )
-
-    def update_collector_timing(self, timing_ms: dict[str, float]):
-        normalized = dict(timing_ms)
-        legacy_action_wait = normalized.pop("inference_wait_ms", None)
-        if "learner_action_wait_ms" not in normalized and legacy_action_wait is not None:
-            normalized["learner_action_wait_ms"] = legacy_action_wait
-        normalized.pop("sync_idle_ms", None)
-        normalized.pop("bookkeeping_ms", None)
-        self._collector_timing.update(normalized)
-
-    def update_collector_active_steps_per_sec(self, steps_per_sec: float):
-        self._collector_active_steps_per_sec = float(steps_per_sec)
-
-    def update_timeout_rate(self, timeout_rate: float):
-        self._timeout_rate = float(timeout_rate)
-
-    def update_buffer_utilization(self, utilization: float):
-        self._buffer_utilization = float(utilization)
-
-    def update_replay_queue(self, current_len: int, max_size: int):
-        self.update_staging_pool(current_len, max_size)
-
-    def update_staging_pool(self, current_len: int, max_size: int):
-        self._staging_pool_len = current_len
-        self._staging_pool_max = max_size
-
-    def update_runtime_manifest(self, manifest: dict[str, Any]) -> None:
-        self._runtime_manifest.update(manifest)
-
-    def log_collector(self, total_steps: int, buffer_size: int, mean_reward: float = 0.0):
-        self._total_steps = total_steps
-        self._buffer_size = buffer_size
-        if mean_reward != 0:
-            self._reward_history.append(mean_reward)
-
-    def log_step(
-        self,
-        iteration: int,
-        metrics: dict[str, float] | None = None,
-        reward: float | None = None,
-        reward_metrics: dict[str, float] | None = None,
-        reward_components: dict[str, float] | None = None,
-        train_time: float = 0.0,
-        collector_wait_time: float = 0.0,
-        replay_batch_wait_time: float = 0.0,
-        learner_replay_stage_time: float = 0.0,
-        learner_replay_sample_time: float = 0.0,
-        sync_coordination_time: float = 0.0,
-        replay_ingress_h2d_submit_time: float = 0.0,
-        weight_sync_time: float = 0.0,
-        inference_h2d_time: float = 0.0,
-        inference_forward_time: float = 0.0,
-        inference_d2h_time: float = 0.0,
-        inference_time: float = 0.0,
-        iteration_time: float | None = None,
-        extra_info: dict | None = None,
-    ):
-        metrics = _dedupe_metric_aliases(metrics)
-        self._iteration = iteration
-        self._train_time = train_time
-        self._collector_wait_time = collector_wait_time
-        self._replay_batch_wait_time = replay_batch_wait_time
-        self._learner_replay_stage_time = learner_replay_stage_time
-        self._learner_replay_sample_time = learner_replay_sample_time
-        self._sync_coordination_time = sync_coordination_time
-        self._replay_ingress_h2d_submit_time = replay_ingress_h2d_submit_time
-        self._weight_sync_time = weight_sync_time
-        self._inference_h2d_time = inference_h2d_time
-        self._inference_forward_time = inference_forward_time
-        self._inference_d2h_time = inference_d2h_time
-        self._inference_time = inference_time
-        self._iteration_time = iteration_time
-        self._has_iteration_extra_info = extra_info is not None
-        if extra_info:
-            self._throughput_steps = int(extra_info.get("throughput_steps", 0))
-            steps_per_sec = extra_info.get("steps_per_sec")
-            self._steps_per_sec_override = (
-                float(steps_per_sec) if steps_per_sec is not None else None
-            )
-            samples_per_sec = extra_info.get("learner_samples_per_sec")
-            self._samples_per_sec_override = (
-                float(samples_per_sec) if samples_per_sec is not None else None
-            )
-            collector_active_steps_per_sec = extra_info.get("collector_active_steps_per_sec")
-            self._collector_active_steps_per_sec = (
-                float(collector_active_steps_per_sec)
-                if collector_active_steps_per_sec is not None
-                else None
-            )
-            self._batch_size_per_rank = int(extra_info.get("batch_size_per_rank", 0))
-            self._effective_batch_size = int(extra_info.get("effective_batch_size", 0))
-            if self._effective_batch_size <= 0:
-                self._effective_batch_size = self._batch_size_per_rank
-            if self._batch_size_per_rank <= 0 and self._effective_batch_size > 0:
-                self._batch_size_per_rank = self._effective_batch_size
-            self._replay_samples_per_iter = int(extra_info.get("replay_samples_per_iter", 0))
-            self._learner_samples_per_iter = int(extra_info.get("learner_samples_per_iter", 0))
-            if self._replay_samples_per_iter <= 0:
-                self._replay_samples_per_iter = self._learner_samples_per_iter
-        else:
-            self._throughput_steps = 0
-            self._steps_per_sec_override = None
-            self._samples_per_sec_override = None
-            self._collector_active_steps_per_sec = None
-            self._batch_size_per_rank = 0
-            self._effective_batch_size = 0
-            self._replay_samples_per_iter = 0
-            self._learner_samples_per_iter = 0
-        if metrics:
-            self._latest_metrics.update(metrics)
-        if reward is not None:
-            self._reward_history.append(reward)
-        if reward_components:
-            self._latest_reward_components = reward_components
-        self._status = "Training"
-        self._backend_log_step(
-            iteration,
-            metrics,
-            reward,
-            reward_metrics,
-            reward_components,
-            train_time,
-        )
-        self._record_terminal_sample(
-            metrics=metrics,
-            reward=reward,
-            reward_components=reward_components,
-        )
-
-    def _backend_log_step(
-        self,
-        iteration: int,
-        metrics: dict[str, float] | None,
-        reward: float | None,
-        reward_metrics: dict[str, float] | None,
-        reward_components: dict[str, float] | None,
-        train_time: float,
-    ):
-        global_step = self._total_steps if self._total_steps > 0 else iteration
-        iter_steps_per_sec = self._get_iter_steps_per_sec()
-        effective_samples_per_sec = self._get_effective_samples_per_sec()
-        iter_wall_time = self._get_iter_wall_time()
-        learner_other_time = self._get_learner_other_time()
-        learner_accounted_time = self._get_learner_accounted_time()
-        learner_timing_ms = {
-            key: seconds * 1000 for key, seconds in self._learner_phase_times().items()
-        }
-        learner_timing_ms.update(
-            {key: seconds * 1000 for key, seconds in self._learner_detail_times().items()}
-        )
-        learner_timing_ms[_LEARNER_OTHER_TIMING_SPEC[0]] = learner_other_time * 1000
-        collector_cycle_ms = self._get_collector_cycle_ms()
-
-        if self._tb_writer:
-            writer = self._tb_writer
-            if metrics:
-                for key, value in metrics.items():
-                    writer.add_scalar(_metric_backend_key(key), value, global_step)
-            if reward is not None:
-                writer.add_scalar("reward/mean", reward, global_step)
-            if reward_metrics:
-                for key, value in reward_metrics.items():
-                    writer.add_scalar(_reward_backend_key(key), value, global_step)
-            if reward_components:
-                for key, value in reward_components.items():
-                    writer.add_scalar(_reward_backend_key(key), value, global_step)
-            if self._mean_ep_length > 0:
-                writer.add_scalar("episode/length", self._mean_ep_length, global_step)
-            writer.add_scalar("episode/timeout_rate", self._timeout_rate, global_step)
-            for key, value_ms in learner_timing_ms.items():
-                writer.add_scalar(key, value_ms, global_step)
-            for key, value in self._collector_timing.items():
-                writer.add_scalar(f"timing/collector_{key}", value, global_step)
-            if iter_steps_per_sec is not None:
-                writer.add_scalar("perf/steps_per_sec", iter_steps_per_sec, global_step)
-            if self._collector_active_steps_per_sec is not None:
-                writer.add_scalar(
-                    "perf/collector_active_steps_per_sec",
-                    self._collector_active_steps_per_sec,
-                    global_step,
-                )
-            if effective_samples_per_sec is not None:
-                writer.add_scalar(
-                    "perf/effective_samples_per_sec",
-                    effective_samples_per_sec,
-                    global_step,
-                )
-            writer.add_scalar("perf/iter_ms", iter_wall_time * 1000, global_step)
-            writer.add_scalar("perf/learner_train_pct", self._get_iter_pct(train_time), global_step)
-            writer.add_scalar(
-                "perf/learner_accounted_pct",
-                self._get_iter_pct(learner_accounted_time),
-                global_step,
-            )
-            writer.add_scalar(
-                "perf/learner_other_pct",
-                self._get_iter_pct(learner_other_time),
-                global_step,
-            )
-            if collector_cycle_ms is not None:
-                writer.add_scalar("perf/collector_cycle_ms", collector_cycle_ms, global_step)
-
-        if self._wandb_run:
-            wandb = _load_wandb()
-            if wandb is None:
-                return
-            log_dict: dict[str, Any] = {"iteration": iteration}
-            if metrics:
-                for key, value in metrics.items():
-                    log_dict[_metric_backend_key(key)] = value
-            if reward is not None:
-                log_dict["reward/mean"] = reward
-            if reward_metrics:
-                for key, value in reward_metrics.items():
-                    log_dict[_reward_backend_key(key)] = value
-            if reward_components:
-                for key, value in reward_components.items():
-                    log_dict[_reward_backend_key(key)] = value
-            if self._mean_ep_length > 0:
-                log_dict["episode/length"] = self._mean_ep_length
-            log_dict["episode/timeout_rate"] = self._timeout_rate
-            log_dict.update(learner_timing_ms)
-            for key, value in self._collector_timing.items():
-                log_dict[f"timing/collector_{key}"] = value
-            if iter_steps_per_sec is not None:
-                log_dict["perf/steps_per_sec"] = iter_steps_per_sec
-            if self._collector_active_steps_per_sec is not None:
-                log_dict["perf/collector_active_steps_per_sec"] = (
-                    self._collector_active_steps_per_sec
-                )
-            if effective_samples_per_sec is not None:
-                log_dict["perf/effective_samples_per_sec"] = effective_samples_per_sec
-            log_dict["perf/iter_ms"] = iter_wall_time * 1000
-            log_dict["perf/learner_train_pct"] = self._get_iter_pct(train_time)
-            log_dict["perf/learner_accounted_pct"] = self._get_iter_pct(learner_accounted_time)
-            log_dict["perf/learner_other_pct"] = self._get_iter_pct(learner_other_time)
-            if collector_cycle_ms is not None:
-                log_dict["perf/collector_cycle_ms"] = collector_cycle_ms
-            wandb.log(log_dict, step=global_step)
-
-    def log_status(self, status: str):
-        self._status = status
-        if "[red]" in status or "ERROR" in status:
-            self._refresh(force=True)
-
-    def _build_display(self) -> Panel:
-        snapshot = self._terminal_snapshot
-        header = self._build_compact_header(
-            include_status=self._status != "Training",
-            include_identity=False,
-            include_iteration=False,
-            terminal_snapshot=snapshot,
-        )
-        left = self._build_metrics_table(snapshot)
-        right = self._build_reward_table(snapshot)
-        bottom = self._build_timing_table(snapshot)
-        grid = Table.grid(expand=True)
-        grid.add_column(ratio=1)
-        grid.add_column(width=2)
-        grid.add_column(ratio=1)
-        grid.add_row(left, "", right)
-        title = Text()
-        if self._unicode_console:
-            title.append(" 🚀")
-        title.append(" UniLab Off-Policy Training ", style="bold")
-        title.append("|", style="dim")
-        title.append(f" {self.algo_name} ", style="bold cyan")
-        title.append("|", style="dim")
-        title.append(f" {self.env_name} ", style="bold white")
-        title.append("|", style="dim")
-        title.append(f" GPUs {self.num_gpus} ", style="bold magenta")
-        title.append("|", style="dim")
-        iteration = snapshot.iteration if snapshot is not None else self._iteration
-        title.append(f" iter {iteration}/{self.max_iterations} ", style="yellow")
-        return Panel(
-            Group(header, Text(""), grid, Text(""), bottom),
-            title=title,
-            border_style="bright_blue",
-            padding=(0, 1),
-        )
-
-    def _build_metrics_table(self, snapshot: _TerminalSnapshot | None = None) -> Table:
-        snapshot = snapshot or self._terminal_snapshot
-        table = Table(
-            box=box.SIMPLE_HEAVY,
-            show_header=True,
-            show_edge=False,
-            header_style="bold cyan",
-            expand=True,
-            pad_edge=False,
-        )
-        table.add_column("Losses & Metrics", style="white", ratio=2)
-        table.add_column("Value", style="yellow", justify="right", ratio=1)
-        metrics = snapshot.metrics if snapshot is not None else self._latest_metrics
-        if not metrics:
-            table.add_row("[dim]Waiting for data...[/]", "")
-        else:
-            loss_keys = sorted([key for key in metrics if "loss" in key.lower()])
-            other_keys = sorted([key for key in metrics if "loss" not in key.lower()])
-            for key in loss_keys:
-                value = metrics[key]
-                style = "red" if value > 10 else "yellow"
-                table.add_row(key.replace("_", " ").title(), f"[{style}]{_fmt_number(value)}[/]")
-            for key in other_keys:
-                value = metrics[key]
-                table.add_row(f"  {key.replace('_', ' ').title()}", _fmt_number(value))
-        return table
-
-    def _build_reward_table(self, snapshot: _TerminalSnapshot | None = None) -> Table:
-        snapshot = snapshot or self._terminal_snapshot
-        return self._build_reward_table_common(
-            wait_message="[dim]Waiting for data...[/]",
-            include_ep_length=False,
-            reward_history=(snapshot.reward_history if snapshot is not None else None),
-            reward_components=(snapshot.reward_components if snapshot is not None else None),
-            mean_reward=(snapshot.scalars.get("reward") if snapshot is not None else None),
-        )
-
-    def _build_timing_table(self, snapshot: _TerminalSnapshot | None = None) -> Table:
-        snapshot = snapshot or self._terminal_snapshot
-        table = Table(
-            box=box.SIMPLE_HEAVY,
-            show_header=True,
-            show_edge=False,
-            header_style="bold blue",
-            expand=True,
-            pad_edge=False,
-        )
-        table.add_column("Learner (Iter Wall)", style="white", ratio=5, no_wrap=True)
-        table.add_column("Value", style="yellow", justify="right", width=16, no_wrap=True)
-        table.add_column("Collector (own clock)", style="white", ratio=6, no_wrap=True)
-        table.add_column("Value", style="yellow", justify="right", width=16, no_wrap=True)
-        table.add_column("System", style="white", ratio=4, no_wrap=True)
-        table.add_column("Value", style="yellow", justify="right", width=12, no_wrap=True)
-
-        iter_wall_time = (
-            snapshot.scalars.get("iter_wall_time", self._get_iter_wall_time())
-            if snapshot is not None
-            else self._get_iter_wall_time()
-        )
-
-        def _scalar(attribute: str, fallback: float) -> float:
-            if snapshot is None:
-                return fallback
-            return snapshot.scalars.get(attribute, fallback)
-
-        def _fmt_phase(seconds: float, *, color: str | None = None) -> str:
-            ms = seconds * 1000
-            pct = seconds / iter_wall_time * 100.0 if iter_wall_time > 0.0 else 0.0
-            text = f"{ms:>7.1f}ms  {pct:>3.0f}%"
-            return f"[{color}]{text}[/]" if color else text
-
-        collector_wait_ms = _scalar("_collector_wait_time", self._collector_wait_time) * 1000
-        wait_color = "red" if collector_wait_ms > 1.0 else "yellow"
-        phase_colors = {
-            "Collector Wait": wait_color,
-            "Train": "green",
-        }
-        learner_items = [
-            (
-                label,
-                _fmt_phase(
-                    _scalar(attribute, float(getattr(self, attribute))),
-                    color=phase_colors.get(label),
-                ),
-            )
-            for _, label, attribute in self._learner_timing_specs()
-        ]
-        learner_items.append(
-            (
-                _LEARNER_OTHER_TIMING_SPEC[1],
-                _fmt_phase(_scalar("learner_other_time", self._get_learner_other_time())),
-            )
-        )
-        learner_items.append(
-            (
-                _ITER_WALL_TIMING_SPEC[1],
-                f"{iter_wall_time * 1000:>7.1f}ms  100%",
-            )
-        )
-        collector_timing = (
-            snapshot.collector_timing if snapshot is not None else self._collector_timing
-        )
-        sorted_collector_timing = sorted(
-            collector_timing.items(),
-            key=lambda item: (
-                _COLLECTOR_TIMING_SPECS.get(item[0], (float("inf"), "", ""))[0],
-                item[0],
-            ),
-        )
-        env_step_detail_keys = [
-            key for key, _ in sorted_collector_timing if key in OFFPOLICY_ENV_STEP_DETAIL_KEYS
-        ]
-        last_env_step_detail_key = env_step_detail_keys[-1] if env_step_detail_keys else None
-        cycle_total_ms = self._get_collector_cycle_ms(collector_timing) or 0.0
-        collector_items: list[tuple[str, str]] = []
-        for key, value in sorted_collector_timing:
-            _, label, role = _COLLECTOR_TIMING_SPECS.get(
-                key,
-                (float("inf"), key, "diagnostic"),
-            )
-            value_text = f"{value:.1f}ms"
-            if key in OFFPOLICY_ENV_STEP_DETAIL_KEYS:
-                if self._unicode_console:
-                    connector = "─┘" if key == last_env_step_detail_key else "─┤"
-                else:
-                    connector = "-'" if key == last_env_step_detail_key else "-+"
-                label = f"[dim]{label}[/]"
-                if self._timing_profile == "sac_family" and cycle_total_ms > 0.0:
-                    pct = value / cycle_total_ms * 100.0
-                    value_text = f"[dim cyan]{value:>7.1f}ms {pct:>3.0f}%{connector}[/]"
-                else:
-                    value_text = f"[dim cyan]{value:>7.1f}ms {connector}[/]"
-            elif (
-                self._timing_profile == "sac_family"
-                and role == "cycle_phase"
-                and cycle_total_ms > 0.0
-            ):
-                pct = value / cycle_total_ms * 100.0
-                value_text = f"{value:>7.1f}ms  {pct:>3.0f}%"
-            collector_items.append((label, value_text))
-        buffer_size = snapshot.buffer_size if snapshot is not None else self._buffer_size
-        timeout_rate = _scalar("timeout_rate", self._timeout_rate)
-        batch_size_per_rank = (
-            snapshot.batch_size_per_rank if snapshot is not None else self._batch_size_per_rank
-        )
-        system_items = [
-            ("Buffer", f"{buffer_size:,}"),
-            ("Timeout Rate", f"{timeout_rate * 100:.1f}%"),
-        ]
-        system_items.append(("Envs", f"{self.num_envs:,}"))
-        if batch_size_per_rank > 0:
-            system_items.append(("Batch/Rank", f"{batch_size_per_rank:,}"))
-        row_count = max(len(learner_items), len(collector_items), len(system_items))
-        for index in range(row_count):
-            row: list[str] = []
-            for items in (learner_items, collector_items, system_items):
-                if index < len(items):
-                    row.extend(items[index])
-                else:
-                    row.extend(["", ""])
-            table.add_row(*row)
-        return table
diff --git a/src/unilab/logging/onpolicy.py b/src/unilab/logging/onpolicy.py
deleted file mode 100644
index 1808a599c..000000000
--- a/src/unilab/logging/onpolicy.py
+++ /dev/null
@@ -1,240 +0,0 @@
-from __future__ import annotations
-
-from typing import Any
-
-from rich import box
-from rich.console import Group
-from rich.panel import Panel
-from rich.table import Table
-from rich.text import Text
-
-from unilab.logging.common import BaseTrainingLogger, _fmt_number, _load_wandb
-
-
-class OnPolicyLogger(BaseTrainingLogger):
-    """Rich logger for on-policy RL (PPO, A2C, etc)."""
-
-    def __init__(
-        self,
-        algo_name: str = "PPO",
-        max_iterations: int = 1500,
-        num_envs: int = 4096,
-        num_steps: int = 24,
-        env_name: str = "",
-        log_dir: str = "",
-        log_backend: str = "tensorboard",
-        wandb_project: str = "unilab",
-        wandb_entity: str | None = None,
-        wandb_name: str = "",
-        wandb_group: str | None = None,
-        wandb_job_type: str | None = None,
-        wandb_tags: list[str] | None = None,
-        wandb_notes: str | None = None,
-    ):
-        super().__init__(
-            algo_name=algo_name,
-            max_iterations=max_iterations,
-            num_envs=num_envs,
-            env_name=env_name,
-            log_dir=log_dir,
-            log_backend=log_backend,
-            wandb_project=wandb_project,
-            wandb_entity=wandb_entity,
-            wandb_name=wandb_name,
-            wandb_group=wandb_group,
-            wandb_job_type=wandb_job_type,
-            wandb_tags=wandb_tags,
-            wandb_notes=wandb_notes,
-            tensorboard_subdir="tb",
-        )
-        self.num_steps = num_steps
-
-    def start(self, *, status: str = ""):
-        super().start(status=status)
-
-    def finish(self, *, title: str = "Training Summary", extra_summary: str = ""):
-        super().finish(title=title, extra_summary=extra_summary)
-
-    def log_step(
-        self,
-        iteration: int,
-        metrics: dict[str, float] | None = None,
-        reward: float | None = None,
-        reward_components: dict[str, float] | None = None,
-        collect_time: float = 0.0,
-        train_time: float = 0.0,
-    ):
-        self._iteration = iteration
-        self._collect_time = collect_time
-        self._train_time = train_time
-
-        if metrics:
-            self._latest_metrics.update(metrics)
-        if reward is not None:
-            self._reward_history.append(reward)
-        if reward_components:
-            self._latest_reward_components = reward_components
-
-        self._refresh()
-        self._backend_log_step(iteration, metrics, reward, reward_components)
-
-    def _backend_log_step(
-        self,
-        iteration: int,
-        metrics: dict[str, float] | None,
-        reward: float | None,
-        reward_components: dict[str, float] | None,
-    ):
-        if self._tb_writer:
-            w = self._tb_writer
-            if metrics:
-                for k, v in metrics.items():
-                    w.add_scalar(f"train/{k}", v, iteration)
-            if reward is not None:
-                w.add_scalar("reward/mean", reward, iteration)
-            if reward_components:
-                for k, v in reward_components.items():
-                    w.add_scalar(f"reward/{k}", v, iteration)
-            if self._mean_ep_length > 0:
-                w.add_scalar("episode/length", self._mean_ep_length, iteration)
-            w.add_scalar("perf/collect_time_ms", self._collect_time * 1000, iteration)
-            w.add_scalar("perf/train_time_ms", self._train_time * 1000, iteration)
-
-        if self._wandb_run:
-            wandb = _load_wandb()
-            if wandb is None:
-                return
-
-            log_dict: dict[str, Any] = {"iteration": iteration}
-            if metrics:
-                for k, v in metrics.items():
-                    log_dict[f"train/{k}"] = v
-            if reward is not None:
-                log_dict["reward/mean"] = reward
-            if reward_components:
-                for k, v in reward_components.items():
-                    log_dict[f"reward/{k}"] = v
-            if self._mean_ep_length > 0:
-                log_dict["episode/length"] = self._mean_ep_length
-            log_dict["perf/collect_time_ms"] = self._collect_time * 1000
-            log_dict["perf/train_time_ms"] = self._train_time * 1000
-            wandb.log(log_dict, step=iteration)
-
-    def _build_display(self) -> Panel:
-        header = self._build_compact_header(include_status=True)
-        left = self._build_metrics_table()
-        right = self._build_reward_table()
-        bottom = self._build_timing_table()
-        grid = Table.grid(expand=True)
-        grid.add_column(ratio=1)
-        grid.add_column(width=2)
-        grid.add_column(ratio=1)
-        grid.add_row(left, "", right)
-        return Panel(
-            Group(header, Text(""), grid, Text(""), bottom),
-            title=(
-                "[bold] 🚀 UniLab On-Policy Training [/]"
-                if self._unicode_console
-                else "[bold] UniLab On-Policy Training [/]"
-            ),
-            border_style="bright_blue",
-            padding=(0, 1),
-        )
-
-    def _build_metrics_table(self) -> Table:
-        table = Table(
-            box=box.SIMPLE_HEAVY,
-            show_header=True,
-            show_edge=False,
-            header_style="bold cyan",
-            expand=True,
-            pad_edge=False,
-        )
-        table.add_column("Losses & Metrics", style="white", ratio=2)
-        table.add_column("Value", style="yellow", justify="right", ratio=1)
-
-        if not self._latest_metrics:
-            table.add_row("[dim]Waiting for data...[/]", "")
-        else:
-            loss_keys = sorted([key for key in self._latest_metrics if "loss" in key.lower()])
-            other_keys = sorted([key for key in self._latest_metrics if "loss" not in key.lower()])
-            for key in loss_keys:
-                value = self._latest_metrics[key]
-                style = "red" if value > 10 else "yellow"
-                table.add_row(key.replace("_", " ").title(), f"[{style}]{_fmt_number(value)}[/]")
-            for key in other_keys:
-                value = self._latest_metrics[key]
-                table.add_row(f"  {key.replace('_', ' ').title()}", _fmt_number(value))
-
-        return table
-
-    def _build_reward_table(self) -> Table:
-        return self._build_reward_table_common(
-            wait_message="[dim]Waiting for data...[/]",
-            include_ep_length=False,
-        )
-
-    def _build_timing_table(self) -> Table:
-        table = Table(
-            box=box.SIMPLE_HEAVY,
-            show_header=True,
-            show_edge=False,
-            header_style="bold blue",
-            expand=True,
-            pad_edge=False,
-        )
-        table.add_column("Learner", style="white", ratio=2, no_wrap=True)
-        table.add_column("Value", style="yellow", justify="right", ratio=1, no_wrap=True)
-        table.add_column("Collector", style="white", ratio=2, no_wrap=True)
-        table.add_column("Value", style="yellow", justify="right", ratio=1, no_wrap=True)
-        table.add_column("System", style="white", ratio=2, no_wrap=True)
-        table.add_column("Value", style="yellow", justify="right", ratio=1, no_wrap=True)
-
-        iter_time = self._collect_time + self._train_time
-        fps = int(self.num_envs * self.num_steps / max(iter_time, 1e-8)) if iter_time > 0 else 0
-
-        learner_items = [
-            ("Train", f"{self._train_time * 1000:.1f}ms"),
-            ("Iter Time", f"{iter_time * 1000:.1f}ms"),
-        ]
-        collector_items = [
-            ("Collect", f"{self._collect_time * 1000:.1f}ms"),
-        ]
-        system_items = [
-            ("Envs", f"{self.num_envs:,}"),
-            ("Steps/s", f"{fps:,}"),
-        ]
-
-        row_count = max(len(learner_items), len(collector_items), len(system_items))
-        for index in range(row_count):
-            row: list[str] = []
-            for items in (learner_items, collector_items, system_items):
-                if index < len(items):
-                    row.extend(items[index])
-                else:
-                    row.extend(["", ""])
-            table.add_row(*row)
-
-        return table
-
-    def _build_compact_header(
-        self,
-        *,
-        include_status: bool,
-        include_identity: bool = True,
-        include_iteration: bool = True,
-        extra_fields: list[tuple[str, str]] | None = None,
-    ) -> Text:
-        iter_time = self._collect_time + self._train_time
-        header_extra_fields: list[tuple[str, str]] = []
-        if iter_time > 0:
-            steps_per_second = self.num_envs * self.num_steps / iter_time
-            header_extra_fields.append((f"Steps/s {steps_per_second:,.0f}", "bold green"))
-        if extra_fields:
-            header_extra_fields.extend(extra_fields)
-        return super()._build_compact_header(
-            include_status=include_status,
-            include_identity=include_identity,
-            include_iteration=include_iteration,
-            extra_fields=header_extra_fields,
-        )
diff --git a/src/unilab/logging/trace_event.py b/src/unilab/logging/trace_event.py
deleted file mode 100644
index 5d7e7e38b..000000000
--- a/src/unilab/logging/trace_event.py
+++ /dev/null
@@ -1,183 +0,0 @@
-from __future__ import annotations
-
-import json
-import os
-import threading
-import time
-from contextlib import contextmanager
-from pathlib import Path
-from typing import Any
-
-
-def _to_us(timestamp_ns: int) -> float:
-    return timestamp_ns / 1000.0
-
-
-class TraceRecorder:
-    """Minimal Chrome trace / Perfetto event recorder."""
-
-    _GPU_TID = 9001
-
-    def __init__(self, process_name: str, *, pid: int | None = None) -> None:
-        self.pid = int(pid if pid is not None else os.getpid())
-        self.process_name = process_name
-        self._events: list[dict[str, Any]] = []
-        self._lock = threading.Lock()
-        self._registered_tids: set[int] = set()
-        self._pending_cuda_events: list[tuple[str, str, int, Any, Any, dict[str, Any], int]] = []
-        self._append_metadata_event("process_name", tid=0, args={"name": process_name})
-        self.register_thread("main")
-        self.register_thread("gpu_stream_0", tid=self._GPU_TID)
-
-    def _append_metadata_event(self, name: str, *, tid: int, args: dict[str, Any]) -> None:
-        self._events.append(
-            {
-                "name": name,
-                "ph": "M",
-                "pid": self.pid,
-                "tid": tid,
-                "args": args,
-            }
-        )
-
-    def register_thread(self, name: str, *, tid: int | None = None) -> int:
-        resolved_tid = int(threading.get_ident() if tid is None else tid)
-        with self._lock:
-            if resolved_tid not in self._registered_tids:
-                self._registered_tids.add(resolved_tid)
-                self._append_metadata_event("thread_name", tid=resolved_tid, args={"name": name})
-        return resolved_tid
-
-    def add_slice(
-        self,
-        name: str,
-        *,
-        category: str,
-        start_ns: int,
-        end_ns: int,
-        tid: int | None = None,
-        args: dict[str, Any] | None = None,
-    ) -> None:
-        resolved_tid = self.register_thread(category if tid is None else str(tid), tid=tid)
-        event = {
-            "name": name,
-            "cat": category,
-            "ph": "X",
-            "ts": _to_us(start_ns),
-            "dur": _to_us(max(end_ns - start_ns, 0)),
-            "pid": self.pid,
-            "tid": resolved_tid,
-            "args": args or {},
-        }
-        with self._lock:
-            self._events.append(event)
-
-    @contextmanager
-    def span(
-        self,
-        name: str,
-        *,
-        category: str,
-        tid: int | None = None,
-        args: dict[str, Any] | None = None,
-    ):
-        start_ns = time.perf_counter_ns()
-        try:
-            yield start_ns
-        finally:
-            self.add_slice(
-                name,
-                category=category,
-                start_ns=start_ns,
-                end_ns=time.perf_counter_ns(),
-                tid=tid,
-                args=args,
-            )
-
-    def add_counter(
-        self,
-        name: str,
-        value: int | float,
-        *,
-        category: str,
-        timestamp_ns: int | None = None,
-        tid: int | None = None,
-        args: dict[str, Any] | None = None,
-    ) -> None:
-        resolved_tid = self.register_thread(category if tid is None else str(tid), tid=tid)
-        payload = {"value": value}
-        if args:
-            payload.update(args)
-        event = {
-            "name": name,
-            "cat": category,
-            "ph": "C",
-            "ts": _to_us(timestamp_ns if timestamp_ns is not None else time.perf_counter_ns()),
-            "pid": self.pid,
-            "tid": resolved_tid,
-            "args": payload,
-        }
-        with self._lock:
-            self._events.append(event)
-
-    def add_cuda_pending_span(
-        self,
-        name: str,
-        *,
-        category: str,
-        cpu_begin_ns: int,
-        start_event: Any,
-        end_event: Any,
-        args: dict[str, Any] | None = None,
-        tid: int | None = None,
-    ) -> None:
-        resolved_tid = self._GPU_TID if tid is None else int(tid)
-        self.register_thread("gpu_stream_0", tid=resolved_tid)
-        with self._lock:
-            self._pending_cuda_events.append(
-                (name, category, cpu_begin_ns, start_event, end_event, args or {}, resolved_tid)
-            )
-
-    def flush_cuda_pending(self) -> None:
-        pending: list[tuple[str, str, int, Any, Any, dict[str, Any], int]]
-        with self._lock:
-            pending = self._pending_cuda_events
-            self._pending_cuda_events = []
-        for name, category, cpu_begin_ns, start_event, end_event, args, tid in pending:
-            end_event.synchronize()
-            gpu_dur_us = float(start_event.elapsed_time(end_event) * 1000.0)
-            event = {
-                "name": name,
-                "cat": category,
-                "ph": "X",
-                "ts": _to_us(cpu_begin_ns),
-                "dur": gpu_dur_us,
-                "pid": self.pid,
-                "tid": tid,
-                "args": {**args, "gpu_dur_us": gpu_dur_us},
-            }
-            with self._lock:
-                self._events.append(event)
-
-    def extend(self, events: list[dict[str, Any]]) -> None:
-        if not events:
-            return
-        with self._lock:
-            self._events.extend(events)
-
-    def drain_events(self) -> list[dict[str, Any]]:
-        self.flush_cuda_pending()
-        with self._lock:
-            metadata = [event for event in self._events if event.get("ph") == "M"]
-            others = [event for event in self._events if event.get("ph") != "M"]
-            self._events = metadata
-        return others
-
-    def write_json(self, output_path: str | Path) -> Path:
-        self.flush_cuda_pending()
-        path = Path(output_path)
-        path.parent.mkdir(parents=True, exist_ok=True)
-        with self._lock:
-            events = list(self._events)
-        path.write_text(json.dumps({"traceEvents": events}, ensure_ascii=False), encoding="utf-8")
-        return path
diff --git a/src/unilab/managers/__init__.py b/src/unilab/managers/__init__.py
new file mode 100644
index 000000000..ec031811c
--- /dev/null
+++ b/src/unilab/managers/__init__.py
@@ -0,0 +1,48 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), src/mjlab/managers/__init__.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and UniLab contracts; licensed under Apache-2.0.
+"""Environment managers."""
+
+from unilab.managers._noise.noise_cfg import ConstantNoiseCfg as ConstantNoiseCfg
+from unilab.managers._noise.noise_cfg import GaussianNoiseCfg as GaussianNoiseCfg
+from unilab.managers._noise.noise_cfg import NoiseCfg as NoiseCfg
+from unilab.managers._noise.noise_cfg import NoiseModelCfg as NoiseModelCfg
+from unilab.managers._noise.noise_cfg import (
+    NoiseModelWithAdditiveBiasCfg as NoiseModelWithAdditiveBiasCfg,
+)
+from unilab.managers._noise.noise_cfg import UniformNoiseCfg as UniformNoiseCfg
+from unilab.managers.action_manager import ActionManager as ActionManager
+from unilab.managers.action_manager import ActionTerm as ActionTerm
+from unilab.managers.action_manager import ActionTermCfg as ActionTermCfg
+from unilab.managers.command_manager import CommandManager as CommandManager
+from unilab.managers.command_manager import CommandTerm as CommandTerm
+from unilab.managers.command_manager import CommandTermCfg as CommandTermCfg
+from unilab.managers.command_manager import NullCommandManager as NullCommandManager
+from unilab.managers.curriculum_manager import CurriculumManager as CurriculumManager
+from unilab.managers.curriculum_manager import CurriculumTermCfg as CurriculumTermCfg
+from unilab.managers.curriculum_manager import (
+    NullCurriculumManager as NullCurriculumManager,
+)
+from unilab.managers.event_manager import EventManager as EventManager
+from unilab.managers.event_manager import EventMode as EventMode
+from unilab.managers.event_manager import EventTermCfg as EventTermCfg
+from unilab.managers.manager_base import ManagerBase as ManagerBase
+from unilab.managers.manager_base import ManagerTermBase as ManagerTermBase
+from unilab.managers.manager_base import ManagerTermBaseCfg as ManagerTermBaseCfg
+from unilab.managers.metrics_manager import MetricsManager as MetricsManager
+from unilab.managers.metrics_manager import MetricsTermCfg as MetricsTermCfg
+from unilab.managers.metrics_manager import NullMetricsManager as NullMetricsManager
+from unilab.managers.observation_manager import (
+    ObservationGroupCfg as ObservationGroupCfg,
+)
+from unilab.managers.observation_manager import ObservationManager as ObservationManager
+from unilab.managers.observation_manager import ObservationTermCfg as ObservationTermCfg
+from unilab.managers.recorder_manager import NullRecorderManager as NullRecorderManager
+from unilab.managers.recorder_manager import RecorderManager as RecorderManager
+from unilab.managers.recorder_manager import RecorderTerm as RecorderTerm
+from unilab.managers.recorder_manager import RecorderTermCfg as RecorderTermCfg
+from unilab.managers.reward_manager import RewardManager as RewardManager
+from unilab.managers.reward_manager import RewardTermCfg as RewardTermCfg
+from unilab.managers.scene_entity_config import SceneEntityCfg as SceneEntityCfg
+from unilab.managers.termination_manager import TerminationManager as TerminationManager
+from unilab.managers.termination_manager import TerminationTermCfg as TerminationTermCfg
diff --git a/src/unilab/managers/_buffers/__init__.py b/src/unilab/managers/_buffers/__init__.py
new file mode 100644
index 000000000..2599f33d2
--- /dev/null
+++ b/src/unilab/managers/_buffers/__init__.py
@@ -0,0 +1,7 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), src/mjlab/utils/buffers/__init__.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and UniLab contracts; licensed under Apache-2.0.
+"""Buffer utilities."""
+
+from unilab.managers._buffers.circular_buffer import CircularBuffer as CircularBuffer
+from unilab.managers._buffers.delay_buffer import DelayBuffer as DelayBuffer
diff --git a/src/unilab/managers/_buffers/circular_buffer.py b/src/unilab/managers/_buffers/circular_buffer.py
new file mode 100644
index 000000000..b0575dd50
--- /dev/null
+++ b/src/unilab/managers/_buffers/circular_buffer.py
@@ -0,0 +1,258 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), src/mjlab/utils/buffers/circular_buffer.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and UniLab contracts; licensed under Apache-2.0.
+"""Circular buffer for storing a history of batched tensor data.
+
+Understanding Dimensions
+========================
+
+Internal storage shape: (max_len, batch_size, ...)
+                         ↑         ↑
+                         time      environments
+                         axis      axis
+
+External view (buffer property): (batch_size, max_len, ...)
+                                  ↑           ↑
+                                  environments time
+                                  axis        axis
+
+Why different?
+  - Internal: time-first for clean API (self._buffer[pointer] = data)
+  - External: batch-first for typical use (iterate over environments)
+
+Backfill Behavior
+=================
+
+When you first append to a new or reset batch row, that first value is copied
+to ALL history slots for that row:
+
+  buffer = CircularBuffer(max_len=3, batch_size=2)
+  buffer.append(np.array([[5.0], [10.0]]))
+
+  # buffer.buffer contains (shape: 2, 3, 1):
+  # Batch 0: [5.0, 5.0, 5.0]   <- all slots filled with first value
+  # Batch 1: [10.0, 10.0, 10.0] <- all slots filled with first value
+
+Why backfill?
+  You always have valid data. If training expects 3 frames of history, you
+  don't want garbage/zeros for the first 2 timesteps.
+
+Per-Batch Reset
+===============
+
+Reset affects specific batch rows. The circular pointer advances globally, but
+reset rows get "first-append" treatment on their next write:
+
+  buffer = CircularBuffer(max_len=3, batch_size=3)
+
+  buffer.append(np.array([[1.0], [10.0], [100.0]]))   # t0
+  buffer.append(np.array([[2.0], [20.0], [200.0]]))   # t1
+  buffer.append(np.array([[3.0], [30.0], [300.0]]))   # t2
+
+  # buffer.buffer:
+  # Batch 0: [1.0, 2.0, 3.0]
+  # Batch 1: [10.0, 20.0, 30.0]
+  # Batch 2: [100.0, 200.0, 300.0]
+
+  buffer.reset(batch_ids=[1])  # Reset only batch 1
+
+  # buffer.buffer after reset:
+  # Batch 0: [1.0, 2.0, 3.0]       <- unchanged
+  # Batch 1: [0.0, 0.0, 0.0]       <- zeroed
+  # Batch 2: [100.0, 200.0, 300.0] <- unchanged
+
+  # current_length: [3, 0, 3]  <- batch 1 has 0 valid frames
+
+  buffer.append(np.array([[4.0], [99.0], [400.0]]))  # t3
+
+  # buffer.buffer after append:
+  # Batch 0: [2.0, 3.0, 4.0]        <- oldest overwritten (normal)
+  # Batch 1: [99.0, 99.0, 99.0]     <- BACKFILLED with 99.0
+  # Batch 2: [200.0, 300.0, 400.0]  <- oldest overwritten (normal)
+
+Key insight:
+  Reset only affects specific batch rows. The pointer keeps advancing for
+  everyone, but reset rows get backfilled on their next append.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+
+import numpy as np
+
+
+class CircularBuffer:
+    """Fixed-length circular buffer for batched tensor history.
+
+    Stores history with shape (max_len, batch_size, ...) internally.
+    The `buffer` property returns chronologically ordered data (oldest to newest)
+    with shape (batch_size, max_len, ...).
+
+    Storage and Retrieval
+    ---------------------
+    Internal storage (circular):
+      ┌─────┬─────┬─────┐
+      │  2  │  3  │  1  │  <- pointer at index 1 (newest = 3)
+      └─────┴─────┴─────┘
+       idx:0  idx:1  idx:2
+
+    buffer property returns (chronological, oldest→newest):
+      ┌─────┬─────┬─────┐
+      │  1  │  2  │  3  │
+      └─────┴─────┴─────┘
+
+    LIFO Retrieval via __getitem__
+    -------------------------------
+    Given buffer with [1, 2, 3] (oldest to newest):
+      buffer[lag=0] -> 3  (most recent)
+      buffer[lag=1] -> 2  (one step back)
+      buffer[lag=2] -> 1  (oldest)
+
+    Per-Batch Reset
+    ---------------
+    After reset(batch_ids=[1]):
+      Batch 0: [1, 2, 3]  (unchanged)
+      Batch 1: [0, 0, 0]  (zeroed, current_length=0)
+      Batch 2: [1, 2, 3]  (unchanged)
+
+    Next append backfills reset rows with first value.
+
+    Args:
+      max_len: Maximum number of historical frames to retain.
+      batch_size: Size of the batch dimension.
+    """
+
+    def __init__(self, max_len: int, batch_size: int) -> None:
+        if max_len < 1:
+            raise ValueError(f"Buffer size must be >= 1, got {max_len}")
+
+        self._max_len = max_len
+        self._batch_size = batch_size
+        self._pointer: int = -1
+        self._buffer: np.ndarray | None = None
+        self._all_indices = np.arange(batch_size)
+        self._num_pushes = np.zeros(batch_size, dtype=np.int64)
+        self._max_len_array = np.full(batch_size, max_len, dtype=np.int64)
+
+    @property
+    def batch_size(self) -> int:
+        return self._batch_size
+
+    @property
+    def max_length(self) -> int:
+        return self._max_len
+
+    @property
+    def current_length(self) -> np.ndarray:
+        """Per-batch count of valid frames. Shape: (batch_size,)."""
+        return np.minimum(self._num_pushes, self._max_len_array)
+
+    @property
+    def is_initialized(self) -> bool:
+        """Check if the buffer has been initialized with at least one append."""
+        return self._buffer is not None
+
+    @property
+    def buffer(self) -> np.ndarray:
+        """History in chronological order (oldest to newest).
+
+        Returns:
+          Tensor of shape (batch_size, max_len, ...) where index 0 is oldest
+          and index -1 is newest.
+        """
+        if self._buffer is None:
+            raise RuntimeError("Buffer not initialized. Call append() first.")
+
+        start = (self._pointer + 1) % self._max_len
+        idx = (np.arange(self._max_len) + start) % self._max_len
+        buf = self._buffer[idx]  # (max_len, batch, ...)
+        return np.swapaxes(buf, 0, 1)  # (batch, max_len, ...)
+
+    def reset(self, batch_ids: Sequence[int] | np.ndarray | slice | None = None) -> None:
+        """Zero out values and counters for specified batch rows.
+
+        Args:
+          batch_ids: Batch indices to reset, or None to reset all.
+        """
+        ids: Sequence[int] | np.ndarray | slice = slice(None) if batch_ids is None else batch_ids
+        self._num_pushes[ids] = 0
+        if self._buffer is not None:
+            self._buffer[:, ids] = 0.0
+
+    def backfill(self, data: np.ndarray, batch_ids: np.ndarray) -> None:
+        """Fill the given rows' entire history with one frame, without advancing time.
+
+        Unlike append, the global pointer does not move and other rows are
+        untouched. Used after a partial reset: the reset rows get their first
+        post-reset frame in every slot (the same backfill their next append would
+        apply) while the remaining rows keep their history intact.
+
+        Args:
+          data: Tensor of shape (batch_size, ...); only rows at batch_ids are read.
+          batch_ids: Batch indices to backfill.
+        """
+        if data.shape[0] != self._batch_size:
+            raise ValueError(f"Expected batch size {self._batch_size}, got {data.shape[0]}")
+        if self._buffer is None:
+            raise RuntimeError("Buffer not initialized. Call append() first.")
+
+        data = np.asarray(data)
+        self._buffer[:, batch_ids] = np.expand_dims(data[batch_ids], axis=0)
+        self._num_pushes[batch_ids] = 1
+
+    def append(self, data: np.ndarray) -> None:
+        """Append a new frame for all batch elements.
+
+        Args:
+          data: Tensor of shape (batch_size, ...).
+        """
+        if data.shape[0] != self._batch_size:
+            raise ValueError(f"Expected batch size {self._batch_size}, got {data.shape[0]}")
+
+        data = np.asarray(data)
+
+        if self._buffer is None:
+            self._pointer = -1
+            self._buffer = np.empty((self._max_len, *data.shape), dtype=data.dtype)
+
+        self._pointer = (self._pointer + 1) % self._max_len
+        self._buffer[self._pointer] = data
+
+        # Backfill only newly initialized rows. After warm-up this branch avoids
+        # scanning the full history buffer on every hot-path append.
+        is_first_push = self._num_pushes == 0
+        if np.any(is_first_push):
+            first_ids = np.flatnonzero(is_first_push)
+            self._buffer[:, first_ids] = np.expand_dims(data[first_ids], axis=0)
+
+        self._num_pushes += 1
+
+    def __getitem__(self, key: np.ndarray | int) -> np.ndarray:
+        """Retrieve lagged frames per batch (LIFO).
+
+        Args:
+          key: Per-batch lags (Tensor) or shared lag (int). Shape (batch_size,) or scalar.
+        """
+        if self._buffer is None:
+            raise RuntimeError("Buffer not initialized. Call append() first.")
+
+        if isinstance(key, int):
+            key = np.full(self._batch_size, key, dtype=np.int64)
+        else:
+            key = np.asarray(key, dtype=np.int64)
+            if key.ndim == 0:
+                key = np.full(self._batch_size, key.item(), dtype=np.int64)
+
+        if key.size != self._batch_size:
+            raise ValueError(f"Expected {self._batch_size} lags, got {key.size}")
+
+        # Clamp to the oldest retained frame: without the max_len bound, a lag
+        # beyond the buffer length would wrap around to a newer frame once
+        # num_pushes exceeds max_len.
+        pushes = np.maximum(self._num_pushes, 1)
+        max_lag = np.minimum(pushes, self._max_len_array) - 1
+        valid = np.maximum(np.minimum(key, max_lag), 0)
+
+        idx = np.remainder(self._pointer - valid, self._max_len)
+        return self._buffer[idx, self._all_indices]
diff --git a/src/unilab/managers/_buffers/delay_buffer.py b/src/unilab/managers/_buffers/delay_buffer.py
new file mode 100644
index 000000000..da29bb29c
--- /dev/null
+++ b/src/unilab/managers/_buffers/delay_buffer.py
@@ -0,0 +1,304 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), src/mjlab/utils/buffers/delay_buffer.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and UniLab contracts; licensed under Apache-2.0.
+"""Delay buffer for stochastically delayed observations."""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+
+import numpy as np
+
+from unilab.managers._buffers import CircularBuffer
+
+
+class DelayBuffer:
+    """Serve stochastically delayed observations from a rolling history.
+
+    Wraps a CircularBuffer to simulate observation delays by returning frames from T-lag
+    timesteps ago, where lag is sampled from [min_lag, max_lag].
+
+    Core Behavior
+    =============
+
+    At each timestep:
+      1. Append new observation to history
+      2. Sample or hold lag value (0 = no delay, 3 = 3 timesteps old)
+      3. Return observation from T-lag
+
+    Example with lag=2:
+      t=0: append obs_0 → return obs_0 (not enough history)
+      t=1: append obs_1 → return obs_0 (clamped to available history)
+      t=2: append obs_2 → return obs_0 (lag=2, so T-2 = 0)
+      t=3: append obs_3 → return obs_1 (lag=2, so T-2 = 1)
+
+    Lag Update Policy
+    =================
+
+    Lags can be refreshed every step or periodically:
+
+      **Every-step updates (update_period=0)**
+        Each timestep may sample a new lag (subject to hold_prob).
+
+      **Periodic updates (update_period=N)**
+        Lags refresh only every N steps per environment:
+          if (step_count + phase_offset) % N == 0:
+              sample new lag
+          else:
+              keep previous lag
+
+      **Staggered updates (per_env_phase=True)**
+        Each environment gets a random phase_offset ∈ [0, N), causing
+        lag updates to occur on different timesteps:
+          Env 0: updates at t=0, N, 2N, ...
+          Env 1: updates at t=3, N+3, 2N+3, ...
+          Env 2: updates at t=7, N+7, 2N+7, ...
+
+      **Hold probability (hold_prob=0.2)**
+        Even when an update would occur, keep previous lag with 20% chance.
+        Creates temporal correlation in delay patterns.
+
+    Per-Environment vs Shared Lags
+    ==============================
+
+      **per_env=True** (default)
+        Each environment has independent lag:
+          Batch 0: lag=1 → returns obs from t-1
+          Batch 1: lag=3 → returns obs from t-3
+          Batch 2: lag=0 → returns current obs
+
+      **per_env=False**
+        All environments share one sampled lag:
+          All batches: lag=2 → all return obs from t-2
+
+    Reset Behavior
+    ==============
+
+      reset(batch_ids=[1]) clears history for specified environments:
+        - Sets lag and step counter to zero
+        - Clears circular buffer for those rows
+        - Next append backfills their history with first new value
+        - Until that append, compute() returns zeros for reset rows
+
+    Args:
+      min_lag (int, optional): Minimum lag (inclusive). Must be >= 0.
+      max_lag (int, optional): Maximum lag (inclusive). Must be >= `min_lag`.
+      batch_size (int, optional): Number of parallel environments (leading
+        dimension of inputs).
+      per_env (bool, optional): If True, sample a separate lag per environment;
+        otherwise sample one lag and share it across environments.
+      hold_prob (float, optional): Probability in `[0.0, 1.0]` to keep the previous
+        lag when an update would occur. Creates temporal correlation in delays.
+      update_period (int, optional): If > 0, refresh lags every N steps per
+        environment; if 0, consider updating every step.
+      per_env_phase (bool, optional): If True and `update_period > 0`, each
+        environment uses a different phase offset in `[0, update_period)`, causing
+        staggered refresh steps across the batch.
+      generator (np.random.Generator | None, optional): RNG for sampling lags.
+
+    Examples:
+      Constant delay (lag = 2):
+        >>> buf = DelayBuffer(min_lag=2, max_lag=2, batch_size=4)
+        >>> buf.append(obs)                # obs.shape == (4, ...)
+        >>> delayed = buf.compute()        # delayed[t] = obs[t-2]
+
+      Stochastic delay (uniform 0-3):
+        >>> buf = DelayBuffer(min_lag=0, max_lag=3, batch_size=4)
+        >>> buf.append(obs)
+        >>> delayed = buf.compute()        # per-env lag sampled in {0,1,2,3}
+
+      Periodic updates with staggering:
+        >>> buf = DelayBuffer(
+        ...     min_lag=1, max_lag=5, batch_size=8,
+        ...     update_period=10,           # refresh every 10 steps
+        ...     per_env_phase=True,         # stagger across envs
+        ...     hold_prob=0.2               # 20% chance to hold lag
+        ... )
+        >>> # Env 0 refreshes at t=0,10,20,...
+        >>> # Env 1 refreshes at t=3,13,23,... (random offset)
+        >>> # But each refresh has 20% chance to keep previous lag
+    """
+
+    def __init__(
+        self,
+        min_lag: int = 0,
+        max_lag: int = 3,
+        batch_size: int = 1,
+        per_env: bool = True,
+        hold_prob: float = 0.0,
+        update_period: int = 0,
+        per_env_phase: bool = True,
+        generator: np.random.Generator | None = None,
+    ) -> None:
+        if min_lag < 0:
+            raise ValueError(f"min_lag must be >= 0, got {min_lag}")
+        if max_lag < min_lag:
+            raise ValueError(f"max_lag ({max_lag}) must be >= min_lag ({min_lag})")
+        if not 0.0 <= hold_prob <= 1.0:
+            raise ValueError(f"hold_prob must be in [0, 1], got {hold_prob}")
+        if update_period < 0:
+            raise ValueError(f"update_period must be >= 0, got {update_period}")
+
+        self.min_lag = min_lag
+        self.max_lag = max_lag
+        self.batch_size = batch_size
+        self.per_env = per_env
+        self.hold_prob = hold_prob
+        self.update_period = update_period
+        self.per_env_phase = per_env_phase
+        self.generator = generator
+
+        buffer_size = max_lag + 1 if max_lag > 0 else 1
+        self._buffer = CircularBuffer(max_len=buffer_size, batch_size=batch_size)
+        self._current_lags = np.zeros(batch_size, dtype=np.int64)
+        self._step_count = np.zeros(batch_size, dtype=np.int64)
+
+        if update_period > 0 and per_env_phase:
+            self._phase_offsets = self._require_generator().integers(
+                0, update_period, size=batch_size, dtype=np.int64
+            )
+        else:
+            self._phase_offsets = np.zeros(batch_size, dtype=np.int64)
+
+    @property
+    def is_initialized(self) -> bool:
+        """Check if buffer has been initialized with at least one append."""
+        return self._buffer.is_initialized
+
+    @property
+    def current_lags(self) -> np.ndarray:
+        """Current lag per environment. Shape: (batch_size,)."""
+        return self._current_lags
+
+    def set_lags(
+        self,
+        lags: np.ndarray,
+        batch_ids: Sequence[int] | np.ndarray | slice | None = None,
+    ) -> None:
+        """Set lag values for specified environments.
+
+        Args:
+          lags: Lag values to set. Shape: (num_batch_ids,) or scalar.
+          batch_ids: Batch indices to set, or None to set all.
+        """
+        idx = slice(None) if batch_ids is None else batch_ids
+        self._current_lags[idx] = np.clip(lags, self.min_lag, self.max_lag)
+
+    def reset(self, batch_ids: Sequence[int] | np.ndarray | slice | None = None) -> None:
+        """Reset specified environments to initial state.
+
+        Args:
+          batch_ids: Batch indices to reset, or None to reset all.
+        """
+        if isinstance(batch_ids, slice):
+            indices = range(*batch_ids.indices(self.batch_size))
+            batch_ids = list(indices)
+
+        self._buffer.reset(batch_ids=batch_ids)
+        idx = slice(None) if batch_ids is None else batch_ids
+        self._current_lags[idx] = 0
+        self._step_count[idx] = 0
+        if self.update_period > 0 and self.per_env_phase:
+            new_phases = self._require_generator().integers(
+                0, self.update_period, size=self.batch_size, dtype=np.int64
+            )
+            self._phase_offsets[idx] = new_phases[idx]
+
+    def append(self, data: np.ndarray) -> None:
+        """Append new observation to buffer.
+
+        Args:
+          data: Observation tensor of shape (batch_size, ...).
+        """
+        self._buffer.append(data)
+
+    def backfill(self, data: np.ndarray, batch_ids: np.ndarray) -> None:
+        """Backfill the given rows with one frame, without advancing time.
+
+        Used after a partial reset: the reset rows (whose lags and step counters
+        were just zeroed by reset) get their first post-reset frame while the
+        remaining rows keep their history, lags, and update schedule intact.
+
+        Args:
+          data: Tensor of shape (batch_size, ...); only rows at batch_ids are read.
+          batch_ids: Batch indices to backfill.
+        """
+        self._buffer.backfill(data, batch_ids)
+
+    def compute(self) -> np.ndarray:
+        """Compute delayed observation for current step.
+
+        Advances the lag update schedule, then returns the delayed observation.
+
+        Returns:
+          Delayed observation with shape (batch_size, ...).
+        """
+        if not self.is_initialized:
+            raise RuntimeError("Buffer not initialized. Call append() first.")
+
+        self._update_lags()
+        return self.peek()
+
+    def peek(self) -> np.ndarray:
+        """Return the delayed observation using current lags, without advancing.
+
+        Unlike compute, this neither steps the update schedule nor resamples lags,
+        so it is safe to call outside the once-per-step cadence.
+
+        Returns:
+          Delayed observation with shape (batch_size, ...).
+        """
+        if not self.is_initialized:
+            raise RuntimeError("Buffer not initialized. Call append() first.")
+
+        # Clamp lags to valid range [0, buffer_length - 1].
+        # Buffer may not be full yet (e.g., only 2 frames but sampled lag=3).
+        valid_lags = np.minimum(self._current_lags, self._buffer.current_length - 1)
+        valid_lags = np.maximum(valid_lags, 0)
+
+        return self._buffer[valid_lags]
+
+    def _update_lags(self) -> None:
+        """Update current lags according to configured policy."""
+        if self.update_period > 0:
+            phase_adjusted_count = (self._step_count + self._phase_offsets) % (self.update_period)
+            should_update = phase_adjusted_count == 0
+        else:
+            should_update = np.ones(self.batch_size, dtype=np.bool_)
+        new_lags = self._sample_lags(should_update)
+        self._current_lags = np.where(should_update, new_lags, self._current_lags)
+        self._step_count += 1
+
+    def _sample_lags(self, mask: np.ndarray) -> np.ndarray:
+        """Sample new lags for specified environments.
+
+        Args:
+          mask: Boolean mask of shape (batch_size,) indicating which envs to sample.
+
+        Returns:
+          New lags with shape (batch_size,).
+        """
+        if self.min_lag == self.max_lag:
+            candidate_lags = np.full(self.batch_size, self.min_lag, dtype=np.int64)
+        elif self.per_env:
+            candidate_lags = self._require_generator().integers(
+                self.min_lag, self.max_lag + 1, size=self.batch_size, dtype=np.int64
+            )
+        else:
+            shared_lag = self._require_generator().integers(self.min_lag, self.max_lag + 1)
+            candidate_lags = np.full(self.batch_size, shared_lag, dtype=np.int64)
+
+        if self.hold_prob > 0.0:
+            should_sample = self._require_generator().random(self.batch_size) >= self.hold_prob
+            update_mask = mask & should_sample
+        else:
+            update_mask = mask
+
+        return np.where(update_mask, candidate_lags, self._current_lags)
+
+    def _require_generator(self) -> np.random.Generator:
+        if self.generator is None:
+            raise ValueError(
+                "DelayBuffer stochastic sampling requires an env-owned NumPy generator."
+            )
+        return self.generator
diff --git a/src/unilab/managers/_noise/__init__.py b/src/unilab/managers/_noise/__init__.py
new file mode 100644
index 000000000..baa5838b5
--- /dev/null
+++ b/src/unilab/managers/_noise/__init__.py
@@ -0,0 +1,15 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), src/mjlab/utils/noise/__init__.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and UniLab contracts; licensed under Apache-2.0.
+from unilab.managers._noise.noise_cfg import ConstantNoiseCfg as ConstantNoiseCfg
+from unilab.managers._noise.noise_cfg import GaussianNoiseCfg as GaussianNoiseCfg
+from unilab.managers._noise.noise_cfg import NoiseCfg as NoiseCfg
+from unilab.managers._noise.noise_cfg import NoiseModelCfg as NoiseModelCfg
+from unilab.managers._noise.noise_cfg import (
+    NoiseModelWithAdditiveBiasCfg as NoiseModelWithAdditiveBiasCfg,
+)
+from unilab.managers._noise.noise_cfg import UniformNoiseCfg as UniformNoiseCfg
+from unilab.managers._noise.noise_model import NoiseModel as NoiseModel
+from unilab.managers._noise.noise_model import (
+    NoiseModelWithAdditiveBias as NoiseModelWithAdditiveBias,
+)
diff --git a/src/unilab/managers/_noise/noise_cfg.py b/src/unilab/managers/_noise/noise_cfg.py
new file mode 100644
index 000000000..34ba3b79b
--- /dev/null
+++ b/src/unilab/managers/_noise/noise_cfg.py
@@ -0,0 +1,157 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), src/mjlab/utils/noise/noise_cfg.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and UniLab contracts; licensed under Apache-2.0.
+from __future__ import annotations
+
+import abc
+from dataclasses import dataclass
+from typing import ClassVar, Literal
+
+import numpy as np
+from typing_extensions import override
+
+from unilab.managers._noise import noise_model
+
+# Type alias for noise parameters: scalar or per-dimension values.
+NoiseParam = float | tuple[float, ...]
+
+
+@dataclass(kw_only=True)
+class NoiseCfg(abc.ABC):
+    """Base configuration for a noise term."""
+
+    operation: Literal["add", "scale", "abs"] = "add"
+
+    @staticmethod
+    def _as_array(value: NoiseParam, dtype: np.dtype) -> np.ndarray:
+        """Convert a scalar or per-component parameter without a device abstraction."""
+        return np.asarray(value, dtype=dtype)
+
+    @abc.abstractmethod
+    def apply(self, data: np.ndarray, *, rng: np.random.Generator | None = None) -> np.ndarray:
+        """Apply noise to the input data."""
+
+
+@dataclass
+class ConstantNoiseCfg(NoiseCfg):
+    bias: NoiseParam = 0.0
+
+    @override
+    def apply(self, data: np.ndarray, *, rng: np.random.Generator | None = None) -> np.ndarray:
+        del rng
+        bias = self._as_array(self.bias, data.dtype)
+
+        if self.operation == "add":
+            return data + bias
+        elif self.operation == "scale":
+            return data * bias
+        elif self.operation == "abs":
+            return np.zeros_like(data) + bias
+        else:
+            raise ValueError(f"Unsupported noise operation: {self.operation}")
+
+
+@dataclass
+class UniformNoiseCfg(NoiseCfg):
+    n_min: NoiseParam = -1.0
+    n_max: NoiseParam = 1.0
+
+    def __post_init__(self):
+        if isinstance(self.n_min, float) and isinstance(self.n_max, float):
+            if self.n_min >= self.n_max:
+                raise ValueError(f"n_min ({self.n_min}) must be less than n_max ({self.n_max})")
+
+    @override
+    def apply(self, data: np.ndarray, *, rng: np.random.Generator | None = None) -> np.ndarray:
+        if rng is None:
+            raise ValueError("UniformNoiseCfg requires an env-owned NumPy generator.")
+        n_min = self._as_array(self.n_min, data.dtype)
+        n_max = self._as_array(self.n_max, data.dtype)
+
+        # Generate uniform noise in [0, 1) and transform the generated array
+        # in place.  Float32 data draws directly in float32 (Generator.random
+        # dtype fast path), which is ~2x faster than drawing float64 and
+        # casting; bit-level noise values differ from the float64 path, which
+        # the issue #1348 RNG-stream parity removal allows.
+        if data.dtype == np.float32:
+            noise = rng.random(data.shape, dtype=np.float32)
+        else:
+            noise = rng.random(data.shape).astype(data.dtype, copy=False)
+        np.multiply(noise, n_max - n_min, out=noise)
+        np.add(noise, n_min, out=noise)
+
+        if self.operation == "add":
+            np.add(data, noise, out=noise)
+            return noise
+        elif self.operation == "scale":
+            np.multiply(data, noise, out=noise)
+            return noise
+        elif self.operation == "abs":
+            return noise
+        else:
+            raise ValueError(f"Unsupported noise operation: {self.operation}")
+
+
+@dataclass
+class GaussianNoiseCfg(NoiseCfg):
+    mean: NoiseParam = 0.0
+    std: NoiseParam = 1.0
+
+    def __post_init__(self):
+        if isinstance(self.std, float) and self.std <= 0:
+            raise ValueError(f"std ({self.std}) must be positive")
+
+    @override
+    def apply(self, data: np.ndarray, *, rng: np.random.Generator | None = None) -> np.ndarray:
+        if rng is None:
+            raise ValueError("GaussianNoiseCfg requires an env-owned NumPy generator.")
+        mean = self._as_array(self.mean, data.dtype)
+        std = self._as_array(self.std, data.dtype)
+
+        # Generate standard normal noise and scale.  Float32 data draws
+        # directly in float32 (same fast path as UniformNoiseCfg).
+        if data.dtype == np.float32:
+            noise = rng.standard_normal(data.shape, dtype=np.float32)
+        else:
+            noise = rng.standard_normal(data.shape).astype(data.dtype, copy=False)
+        noise = mean + std * noise
+
+        if self.operation == "add":
+            return data + noise
+        elif self.operation == "scale":
+            return data * noise
+        elif self.operation == "abs":
+            return noise
+        else:
+            raise ValueError(f"Unsupported noise operation: {self.operation}")
+
+
+##
+# Noise models.
+##
+
+
+@dataclass(kw_only=True)
+class NoiseModelCfg:
+    """Configuration for a noise model."""
+
+    noise_cfg: NoiseCfg
+
+    class_type: ClassVar[type[noise_model.NoiseModel]] = noise_model.NoiseModel
+
+    def __init_subclass__(cls, class_type: type[noise_model.NoiseModel]):
+        cls.class_type = class_type
+
+
+@dataclass(kw_only=True)
+class NoiseModelWithAdditiveBiasCfg(
+    NoiseModelCfg, class_type=noise_model.NoiseModelWithAdditiveBias
+):
+    """Configuration for an additive Gaussian noise with bias model."""
+
+    bias_noise_cfg: NoiseCfg | None = None
+    sample_bias_per_component: bool = True
+
+    def __post_init__(self):
+        if self.bias_noise_cfg is None:
+            raise ValueError("bias_noise_cfg must be specified for NoiseModelWithAdditiveBiasCfg")
diff --git a/src/unilab/managers/_noise/noise_model.py b/src/unilab/managers/_noise/noise_model.py
new file mode 100644
index 000000000..7cd8868a4
--- /dev/null
+++ b/src/unilab/managers/_noise/noise_model.py
@@ -0,0 +1,97 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), src/mjlab/utils/noise/noise_model.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and UniLab contracts; licensed under Apache-2.0.
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+from typing_extensions import override
+
+if TYPE_CHECKING:
+    from unilab.managers._noise import noise_cfg
+
+
+class NoiseModel:
+    """Base class for noise models."""
+
+    def __init__(
+        self,
+        noise_model_cfg: noise_cfg.NoiseModelCfg,
+        num_envs: int,
+        rng: np.random.Generator,
+    ):
+        self._noise_model_cfg = noise_model_cfg
+        self._num_envs = num_envs
+        self._rng = rng
+
+        # Validate configuration.
+        if not hasattr(noise_model_cfg, "noise_cfg") or noise_model_cfg.noise_cfg is None:
+            raise ValueError("NoiseModelCfg must have a valid noise_cfg")
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        """Reset noise model state. Override in subclasses if needed."""
+
+    def __call__(self, data: np.ndarray) -> np.ndarray:
+        """Apply noise to input data."""
+        assert self._noise_model_cfg.noise_cfg is not None
+        return self._noise_model_cfg.noise_cfg.apply(data, rng=self._rng)
+
+
+class NoiseModelWithAdditiveBias(NoiseModel):
+    """Noise model with additional additive bias that is constant for the duration
+    of the entire episode."""
+
+    def __init__(
+        self,
+        noise_model_cfg: noise_cfg.NoiseModelWithAdditiveBiasCfg,
+        num_envs: int,
+        rng: np.random.Generator,
+    ):
+        super().__init__(noise_model_cfg, num_envs, rng)
+
+        # Validate bias configuration.
+        if not hasattr(noise_model_cfg, "bias_noise_cfg") or noise_model_cfg.bias_noise_cfg is None:
+            raise ValueError("NoiseModelWithAdditiveBiasCfg must have a valid bias_noise_cfg")
+
+        self._bias_noise_cfg = noise_model_cfg.bias_noise_cfg
+        self._sample_bias_per_component = noise_model_cfg.sample_bias_per_component
+
+        # Shape is materialized from the first observation so scalar and
+        # higher-rank terms broadcast without a device-specific convention.
+        self._bias = np.zeros((num_envs, 1), dtype=np.float32)
+        self._bias_initialized = False
+
+    @override
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        """Reset bias values for specified environments."""
+        indices = slice(None) if env_ids is None else env_ids
+        # Sample new bias values.
+        self._bias[indices] = self._bias_noise_cfg.apply(self._bias[indices], rng=self._rng)
+
+    def _initialize_bias_shape(self, data: np.ndarray) -> None:
+        """Initialize bias tensor shape based on data and configuration."""
+        if not self._bias_initialized:
+            if data.ndim == 0 or data.shape[0] != self._num_envs:
+                raise ValueError(
+                    f"NoiseModel expected leading dimension {self._num_envs}, "
+                    f"received shape {data.shape}."
+                )
+            if self._sample_bias_per_component:
+                bias_shape = data.shape
+            else:
+                bias_shape = (self._num_envs, *([1] * (data.ndim - 1)))
+            self._bias = np.zeros(bias_shape, dtype=data.dtype)
+            self._bias_initialized = True
+            self.reset()
+        elif self._bias.shape != data.shape and self._sample_bias_per_component:
+            raise ValueError(
+                f"NoiseModel observation shape changed from {self._bias.shape} to {data.shape}."
+            )
+
+    @override
+    def __call__(self, data: np.ndarray) -> np.ndarray:
+        """Apply noise and additive bias to input data."""
+        self._initialize_bias_shape(data)
+        noisy_data = super().__call__(data)
+        return noisy_data + self._bias
diff --git a/src/unilab/managers/_types.py b/src/unilab/managers/_types.py
new file mode 100644
index 000000000..13bf4ed66
--- /dev/null
+++ b/src/unilab/managers/_types.py
@@ -0,0 +1,279 @@
+"""Typing-only contracts used by the standalone manager package.
+
+The production environment and scene adapters implement these structural protocols in
+later integration layers.  Keeping them here prevents the manager core from importing
+an environment, backend, runner, or IPC implementation.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping, Sequence
+from typing import Any, Protocol
+
+import numpy as np
+
+
+class ManagerEntity(Protocol):
+    """Cold-path entity metadata required by :class:`SceneEntityCfg`."""
+
+    @property
+    def joint_names(self) -> Sequence[str]: ...
+
+    @property
+    def body_names(self) -> Sequence[str]: ...
+
+    @property
+    def geom_names(self) -> Sequence[str]: ...
+
+    @property
+    def site_names(self) -> Sequence[str]: ...
+
+    @property
+    def actuator_names(self) -> Sequence[str]: ...
+
+    @property
+    def tendon_names(self) -> Sequence[str]: ...
+
+    @property
+    def camera_names(self) -> Sequence[str]: ...
+
+    @property
+    def light_names(self) -> Sequence[str]: ...
+
+    @property
+    def material_names(self) -> Sequence[str]: ...
+
+    @property
+    def texture_names(self) -> Sequence[str]: ...
+
+    @property
+    def pair_names(self) -> Sequence[str]: ...
+
+    @property
+    def num_joints(self) -> int: ...
+
+    @property
+    def num_bodies(self) -> int: ...
+
+    @property
+    def num_geoms(self) -> int: ...
+
+    @property
+    def num_sites(self) -> int: ...
+
+    @property
+    def num_actuators(self) -> int: ...
+
+    @property
+    def num_tendons(self) -> int: ...
+
+    @property
+    def num_cameras(self) -> int: ...
+
+    @property
+    def num_lights(self) -> int: ...
+
+    @property
+    def num_materials(self) -> int: ...
+
+    @property
+    def num_textures(self) -> int: ...
+
+    @property
+    def num_pairs(self) -> int: ...
+
+    def find_joints(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]: ...
+
+    def find_bodies(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]: ...
+
+    def find_geoms(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]: ...
+
+    def find_sites(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]: ...
+
+    def find_actuators(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]: ...
+
+    def find_tendons(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]: ...
+
+    def find_cameras(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]: ...
+
+    def find_lights(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]: ...
+
+    def find_materials(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]: ...
+
+    def find_textures(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]: ...
+
+    def find_pairs(
+        self, keys: str | Sequence[str], preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]: ...
+
+    def write_joint_state_to_sim(
+        self,
+        position: np.ndarray,
+        velocity: np.ndarray,
+        joint_ids: np.ndarray | Sequence[int] | slice | None = None,
+        env_ids: np.ndarray | slice | None = None,
+    ) -> None: ...
+
+
+class ManagerSensorView(Protocol):
+    """Backend-owned named-sensor view retained by a manager term."""
+
+    @property
+    def backend_type(self) -> str: ...
+
+    @property
+    def names(self) -> tuple[str, ...]: ...
+
+    @property
+    def dimensions(self) -> tuple[int, ...]: ...
+
+    @property
+    def data(self) -> np.ndarray: ...
+
+    def read(self) -> np.ndarray: ...
+
+
+class ManagerScene(Protocol):
+    """Minimal name-addressable scene surface consumed by managers."""
+
+    @property
+    def entities(self) -> Mapping[str, ManagerEntity]: ...
+
+    @property
+    def env_origins(self) -> np.ndarray: ...
+
+    def __getitem__(self, name: str) -> ManagerEntity: ...
+
+    def bind_sensor_data(self, names: Sequence[str]) -> ManagerSensorView: ...
+
+    def reset_to_default(self, env_ids: np.ndarray, *, term_name: str) -> None: ...
+
+    def bind_gravity_write(self, *, term_name: str) -> np.ndarray: ...
+
+    def write_gravity_to_sim(
+        self, values: np.ndarray, env_ids: np.ndarray, *, term_name: str
+    ) -> None: ...
+
+
+class ManagerActionTerm(Protocol):
+    @property
+    def raw_action(self) -> np.ndarray: ...
+
+
+class ManagerActionManager(Protocol):
+    @property
+    def action(self) -> np.ndarray: ...
+
+    @property
+    def prev_action(self) -> np.ndarray: ...
+
+    @property
+    def prev_prev_action(self) -> np.ndarray: ...
+
+    def get_term(self, name: str) -> ManagerActionTerm: ...
+
+
+class ManagerCommandManager(Protocol):
+    def get_command(self, name: str) -> np.ndarray | None: ...
+
+    def get_term(self, name: str) -> Any: ...
+
+    def get_term_cfg(self, name: str) -> Any: ...
+
+
+class ManagerEventManager(Protocol):
+    """Cold-path event term config surface consumed by curriculum terms."""
+
+    def get_term_cfg(self, term_name: str) -> Any: ...
+
+
+class ManagerTerminationManager(Protocol):
+    @property
+    def terminated(self) -> np.ndarray: ...
+
+    def get_term_cfg(self, term_name: str) -> Any: ...
+
+
+class ManagerRewardManager(Protocol):
+    """Cold-path reward term config surface consumed by curriculum terms."""
+
+    def get_term_cfg(self, term_name: str) -> Any: ...
+
+
+class ManagerBasedRlEnv(Protocol):
+    """Structural context visible to manager terms.
+
+    Additional task-owned state is intentionally not enumerated: term callables may use
+    their concrete environment type, while the manager core depends only on this seam.
+    """
+
+    @property
+    def num_envs(self) -> int: ...
+
+    @property
+    def rng(self) -> np.random.Generator: ...
+
+    @property
+    def physics_dt(self) -> float: ...
+
+    @property
+    def step_dt(self) -> float: ...
+
+    @property
+    def scene(self) -> ManagerScene: ...
+
+    @property
+    def action_manager(self) -> ManagerActionManager: ...
+
+    @property
+    def command_manager(self) -> ManagerCommandManager: ...
+
+    @property
+    def event_manager(self) -> ManagerEventManager: ...
+
+    @property
+    def termination_manager(self) -> ManagerTerminationManager: ...
+
+    @property
+    def reward_manager(self) -> ManagerRewardManager: ...
+
+    @property
+    def episode_length_buf(self) -> np.ndarray: ...
+
+    @property
+    def reset_buf(self) -> np.ndarray: ...
+
+    @property
+    def common_step_counter(self) -> int: ...
+
+    @property
+    def max_episode_length(self) -> int: ...
+
+    @property
+    def max_episode_length_s(self) -> float: ...
+
+    # Concrete task terms may still type their own richer env subclass.  The
+    # standalone manager core deliberately depends only on the properties above.
+
+
+DebugVisualizer = Any
diff --git a/src/unilab/managers/action_manager.py b/src/unilab/managers/action_manager.py
new file mode 100644
index 000000000..2b964f076
--- /dev/null
+++ b/src/unilab/managers/action_manager.py
@@ -0,0 +1,246 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), src/mjlab/managers/action_manager.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and UniLab contracts; licensed under Apache-2.0.
+"""Action manager for processing actions sent to the environment."""
+
+from __future__ import annotations
+
+import abc
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, ClassVar, Sequence
+
+import numpy as np
+from prettytable import PrettyTable
+
+from unilab.managers.manager_base import ManagerBase, ManagerTermBase
+
+if TYPE_CHECKING:
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+@dataclass(kw_only=True)
+class ActionTermCfg(abc.ABC):
+    """Configuration for an action term.
+
+    Action terms process raw actions from the policy and apply them to entities
+    in the scene (e.g., setting joint positions, velocities, or efforts).
+    """
+
+    entity_name: str
+    """Name of the entity in the scene that this action term controls."""
+
+    clip: dict[str, tuple] | None = None
+    """Optional clipping bounds applied to processed actions (after scale
+  and offset). Dict maps actuator name regex patterns to (min, max)
+  tuples, resolved the same way as ``scale`` and ``offset``."""
+
+    @abc.abstractmethod
+    def build(self, env: ManagerBasedRlEnv) -> ActionTerm:
+        """Build the action term from this config."""
+        raise NotImplementedError
+
+
+class ActionTerm(ManagerTermBase):
+    """Base class for action terms.
+
+    The action term is responsible for processing the raw actions sent to the environment
+    and applying them to the entity managed by the term.
+    """
+
+    requires_substep_state_feedback: ClassVar[bool] = False
+    """Whether ``apply_actions`` needs state produced by the previous physics substep.
+
+    ``False`` declares that the generated control is invariant across all physics
+    substeps in one control step, so the env may apply it once before a batched
+    backend step. State-feedback terms must override this declaration with ``True``.
+    """
+
+    def __init__(self, cfg: ActionTermCfg, env: ManagerBasedRlEnv):
+        self.cfg = cfg
+        super().__init__(env)
+        self._entity = self._env.scene[self.cfg.entity_name]
+
+    @property
+    @abc.abstractmethod
+    def action_dim(self) -> int:
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def process_actions(self, actions: np.ndarray) -> None:
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def apply_actions(self) -> None:
+        raise NotImplementedError
+
+    @property
+    @abc.abstractmethod
+    def raw_action(self) -> np.ndarray:
+        raise NotImplementedError
+
+
+class ActionManager(ManagerBase):
+    """Manages action processing for the environment.
+
+    The action manager aggregates multiple action terms, each controlling a different
+    entity or aspect of the simulation. It splits the policy's action tensor and
+    routes each slice to the appropriate action term.
+    """
+
+    def __init__(self, cfg: dict[str, ActionTermCfg | None], env: ManagerBasedRlEnv):
+        self.cfg = cfg
+        super().__init__(env=env)
+
+        # Create buffers to store actions.
+        self._action = np.zeros((self.num_envs, self.total_action_dim), dtype=np.float32)
+        self._prev_action = np.zeros_like(self._action)
+        self._prev_prev_action = np.zeros_like(self._action)
+
+    def __str__(self) -> str:
+        msg = f"<ActionManager> contains {len(self._term_names)} active terms.\n"
+        table = PrettyTable()
+        table.title = f"Active Action Terms (shape: {self.total_action_dim})"
+        table.field_names = ["Index", "Name", "Dimension"]
+        table.align["Name"] = "l"
+        table.align["Dimension"] = "r"
+        for index, (name, term) in enumerate(self._terms.items()):
+            table.add_row([index, name, term.action_dim])
+        msg += str(table.get_string())
+        msg += "\n"
+        return msg
+
+    # Properties.
+
+    @property
+    def total_action_dim(self) -> int:
+        return sum(self.action_term_dim)
+
+    @property
+    def action_term_dim(self) -> list[int]:
+        return [term.action_dim for term in self._terms.values()]
+
+    @property
+    def action(self) -> np.ndarray:
+        """Raw policy output from the current step, before per-term
+        scale/offset. Shape: ``(num_envs, total_action_dim)``."""
+        return self._action
+
+    @property
+    def prev_action(self) -> np.ndarray:
+        """Raw policy output from the previous step, before per-term
+        scale/offset. Shape: ``(num_envs, total_action_dim)``."""
+        return self._prev_action
+
+    @property
+    def prev_prev_action(self) -> np.ndarray:
+        """Raw policy output from two steps ago, before per-term
+        scale/offset. Shape: ``(num_envs, total_action_dim)``."""
+        return self._prev_prev_action
+
+    @property
+    def active_terms(self) -> list[str]:
+        return self._term_names
+
+    @property
+    def requires_substep_state_feedback(self) -> bool:
+        """Return whether any active action term needs per-substep state feedback."""
+        return any(term.requires_substep_state_feedback for term in self._terms.values())
+
+    # Methods.
+
+    def get_term(self, name: str) -> ActionTerm:
+        return self._terms[name]
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> dict[str, float]:
+        if env_ids is None:
+            env_ids = slice(None)
+        # Reset action history.
+        self._prev_action[env_ids] = 0.0
+        self._prev_prev_action[env_ids] = 0.0
+        self._action[env_ids] = 0.0
+        # Reset action terms.
+        for term in self._terms.values():
+            term.reset(env_ids=env_ids)
+        return {}
+
+    def process_action(self, action: np.ndarray) -> None:
+        """Store the raw policy output and route slices to each action term.
+
+        Called once per policy step. The raw action tensor is saved into the
+        history buffers (``action``, ``prev_action``, ``prev_prev_action``) *before*
+        any per-term scale/offset is applied. Each term then receives its slice and
+        independently applies its own affine transformation via
+        :meth:`ActionTerm.process_actions`.
+        """
+        if not isinstance(action, np.ndarray):
+            raise TypeError(f"ActionManager expected np.ndarray, received {type(action).__name__}.")
+        expected_shape = (self.num_envs, self.total_action_dim)
+        if action.shape != expected_shape:
+            raise ValueError(
+                f"Invalid action shape, expected {expected_shape}, received {action.shape}."
+            )
+        if not np.isfinite(action).all():
+            raise ValueError("ActionManager received an action containing NaN or Inf.")
+        # Shift history: prev_prev ← prev ← current ← new.
+        self._prev_prev_action[:] = self._prev_action
+        self._prev_action[:] = self._action
+        self._action[:] = action
+        # Split the flat action vector and route each slice to its term.
+        idx = 0
+        for name, term in self._terms.items():
+            term_actions = self._action[:, idx : idx + term.action_dim]
+            try:
+                term.process_actions(term_actions)
+            except (TypeError, ValueError, NotImplementedError) as exc:
+                raise type(exc)(f"ActionManager term '{name}': {exc}") from exc
+            idx += term.action_dim
+
+    def apply_action(self) -> None:
+        """Write processed actions to entity actuator targets.
+
+        Called once per control step when all terms produce substep-invariant control.
+        If any term requires state feedback, the env calls this before every physics
+        substep so all terms retain their configured manager order.
+        """
+        for name, term in self._terms.items():
+            try:
+                term.apply_actions()
+            except (TypeError, ValueError, NotImplementedError) as exc:
+                raise type(exc)(f"ActionManager term '{name}': {exc}") from exc
+
+    def get_active_iterable_terms(self, env_idx: int) -> Sequence[tuple[str, Sequence[float]]]:
+        terms = []
+        idx = 0
+        for name, term in self._terms.items():
+            term_actions = self._action[env_idx, idx : idx + term.action_dim]
+            terms.append((name, term_actions.tolist()))
+            idx += term.action_dim
+        return terms
+
+    def _prepare_terms(self) -> None:
+        self._term_names: list[str] = list()
+        self._terms: dict[str, ActionTerm] = dict()
+
+        for term_name, term_cfg in self.cfg.items():
+            if term_cfg is None:
+                print(f"term: {term_name} set to None, skipping...")
+                continue
+            term = term_cfg.build(self._env)
+            if not isinstance(term, ActionTerm):
+                raise TypeError(
+                    f"ActionManager term '{term_name}' build() returned "
+                    f"{type(term).__name__}, expected ActionTerm."
+                )
+            if not isinstance(term.action_dim, int) or term.action_dim < 0:
+                raise ValueError(
+                    f"ActionManager term '{term_name}' has invalid action_dim {term.action_dim}."
+                )
+            feedback = term.requires_substep_state_feedback
+            if not isinstance(feedback, bool):
+                raise TypeError(
+                    "ActionManager term "
+                    f"'{term_name}' requires_substep_state_feedback must be bool, "
+                    f"got {type(feedback).__name__}."
+                )
+            self._term_names.append(term_name)
+            self._terms[term_name] = term
diff --git a/src/unilab/managers/command_manager.py b/src/unilab/managers/command_manager.py
new file mode 100644
index 000000000..c8cfb768e
--- /dev/null
+++ b/src/unilab/managers/command_manager.py
@@ -0,0 +1,331 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), src/mjlab/managers/command_manager.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and UniLab contracts; licensed under Apache-2.0.
+"""Command manager for generating and updating commands."""
+
+from __future__ import annotations
+
+import abc
+import inspect
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any, Sequence
+
+import numpy as np
+from prettytable import PrettyTable
+
+from unilab.managers.manager_base import ManagerBase, ManagerTermBase
+
+if TYPE_CHECKING:
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+@dataclass(kw_only=True)
+class CommandTermCfg(abc.ABC):
+    """Configuration for a command generator term.
+
+    Command terms generate goal commands for the agent (e.g., target velocity,
+    target position). Commands are automatically resampled at configurable
+    intervals and can track metrics for logging.
+    """
+
+    resampling_time_range: tuple[float, float]
+    """Time range in seconds for command resampling. When the timer expires, a new
+  command is sampled and the timer is reset to a value uniformly drawn from
+  ``[min, max]``. Set both values equal for fixed-interval resampling."""
+
+    debug_vis: bool = False
+    """Whether to enable debug visualization for this command term. When True,
+  the command term's ``_debug_vis_impl`` method is called each frame to render
+  visual aids (e.g., velocity arrows, target markers)."""
+
+    @abc.abstractmethod
+    def build(self, env: ManagerBasedRlEnv) -> CommandTerm:
+        """Build the command term from this config."""
+        raise NotImplementedError
+
+
+class CommandTerm(ManagerTermBase):
+    """Base class for command terms."""
+
+    def __init__(self, cfg: CommandTermCfg, env: ManagerBasedRlEnv):
+        self.cfg = cfg
+        super().__init__(env)
+        lower, upper = cfg.resampling_time_range
+        if not np.isfinite((lower, upper)).all() or lower > upper:
+            raise ValueError(
+                f"CommandTerm '{self.name}' has invalid resampling_time_range "
+                f"{cfg.resampling_time_range}."
+            )
+        self._resampling_time_range = (lower, upper)
+        self._check_update_command_signature()
+        self.metrics: dict[str, np.ndarray] = {}
+        self.time_left = np.zeros(self.num_envs, dtype=np.float32)
+        self.command_counter = np.zeros(self.num_envs, dtype=np.int64)
+
+    @property
+    @abc.abstractmethod
+    def command(self):
+        raise NotImplementedError
+
+    def reset(self, env_ids: np.ndarray | slice | None) -> dict[str, float]:
+        assert isinstance(env_ids, np.ndarray)
+        extras = {}
+        for metric_name, metric_value in self.metrics.items():
+            metric_slice = metric_value[env_ids]
+            if not np.isfinite(metric_slice).all():
+                raise ValueError(
+                    f"CommandTerm '{self.name}' metric '{metric_name}' contains NaN or Inf."
+                )
+            extras[metric_name] = float(np.mean(metric_slice))
+            metric_value[env_ids] = 0.0
+        self.command_counter[env_ids] = 0
+        self._resample(env_ids)
+        return extras
+
+    def compute(self, dt: float | np.ndarray, env_ids: np.ndarray | None = None) -> None:
+        """Advance the command state by dt.
+
+        With env_ids=None (the per-step path) all envs are updated; with env_ids
+        (the reset path) timers and the command update are scoped to those envs.
+        Metrics are refreshed each call; terms may scope per-row metric work to
+        env_ids since other rows are unchanged since the per-step update, or
+        defer row-wise metric work to ``reset()`` when reset is the only
+        consumer (e.g. MotionCommand, issue #1355).
+
+        dt may be a scalar (all envs) or a per-env tensor (auto-reset path,
+        where freshly reset envs get zero to keep their timers full). A tensor
+        dt requires env_ids=None.
+        """
+        if isinstance(dt, np.ndarray):
+            if env_ids is not None:
+                raise ValueError("Per-environment command dt requires env_ids=None.")
+            if dt.shape != (self.num_envs,):
+                raise ValueError(
+                    f"CommandTerm '{self.name}' expected dt shape ({self.num_envs},), "
+                    f"received {dt.shape}."
+                )
+        dt_is_finite = np.isfinite(dt).all() if isinstance(dt, np.ndarray) else np.isfinite(dt)
+        if not dt_is_finite:
+            raise ValueError(f"CommandTerm '{self.name}' received non-finite dt.")
+        self._update_metrics(env_ids)
+        self._validate_metrics()
+        if env_ids is None:
+            self.time_left -= dt
+            resample_env_ids = np.flatnonzero(self.time_left <= 0.0)
+        else:
+            assert not isinstance(dt, np.ndarray)
+            self.time_left[env_ids] -= dt
+            resample_env_ids = env_ids[self.time_left[env_ids] <= 0.0]
+        if len(resample_env_ids) > 0:
+            self._resample(resample_env_ids)
+        self._update_command(env_ids)
+
+    def _validate_metrics(self) -> None:
+        for metric_name, metric_value in self.metrics.items():
+            if not isinstance(metric_value, np.ndarray):
+                raise TypeError(
+                    f"CommandTerm '{self.name}' metric '{metric_name}' returned "
+                    f"{type(metric_value).__name__}, expected np.ndarray."
+                )
+            if metric_value.ndim == 0 or metric_value.shape[0] != self.num_envs:
+                raise ValueError(
+                    f"CommandTerm '{self.name}' metric '{metric_name}' returned shape "
+                    f"{metric_value.shape}, expected leading dimension {self.num_envs}."
+                )
+            if not np.isfinite(metric_value).all():
+                raise ValueError(
+                    f"CommandTerm '{self.name}' metric '{metric_name}' contains NaN or Inf."
+                )
+
+    def _check_update_command_signature(self) -> None:
+        """Fail fast with a migration hint for terms with the old signature."""
+        try:
+            sig = inspect.signature(self._update_command)
+        except (TypeError, ValueError):
+            return
+        if len(sig.parameters) == 0:
+            raise TypeError(
+                f"{type(self).__name__}._update_command must accept env_ids: "
+                "_update_command(self, env_ids: np.ndarray | None). It receives "
+                "None on the per-step update and the reset env ids on reset(); "
+                "scope per-step state advances to env_ids."
+            )
+
+    def _resample(self, env_ids: np.ndarray) -> None:
+        if len(env_ids) != 0:
+            lower, upper = self._resampling_time_range
+            self.time_left[env_ids] = self._env.rng.uniform(lower, upper, len(env_ids))
+            self._resample_command(env_ids)
+            self.command_counter[env_ids] += 1
+
+    @abc.abstractmethod
+    def _update_metrics(self, env_ids: np.ndarray | None = None) -> None:
+        """Update the metrics based on the current state.
+
+        env_ids is None on the per-step update (all envs) and the reset env ids on
+        the reset path. Terms may scope per-row metric work to env_ids; rows outside
+        env_ids are unchanged since the last per-step update and stay valid.
+        """
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def _resample_command(self, env_ids: np.ndarray) -> None:
+        """Resample the command for the specified environments."""
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def _update_command(self, env_ids: np.ndarray | None) -> None:
+        """Update the command based on the current state.
+
+        env_ids is None on the per-step update (all envs) and the reset env ids on reset().
+        Scope per-step state advances (e.g. a motion frame index) to env_ids; pure
+        functions of the current state may ignore it.
+        """
+        raise NotImplementedError
+
+    def post_compute(self) -> None:
+        """Refresh state that depends on committed command-side simulation writes."""
+
+
+class CommandManager(ManagerBase):
+    """Manages command generation for the environment.
+
+    The command manager generates and updates goal commands for the agent (e.g.,
+    target velocity, target position). Commands are resampled at configurable
+    intervals and can track metrics for logging.
+    """
+
+    _env: ManagerBasedRlEnv
+
+    def __init__(self, cfg: dict[str, CommandTermCfg | None], env: ManagerBasedRlEnv):
+        self._terms: dict[str, CommandTerm] = dict()
+
+        self.cfg = cfg
+        super().__init__(env)
+
+    def __str__(self) -> str:
+        msg = f"<CommandManager> contains {len(self._terms.values())} active terms.\n"
+        table = PrettyTable()
+        table.title = "Active Command Terms"
+        table.field_names = ["Index", "Name", "Type"]
+        table.align["Name"] = "l"
+        for index, (name, term) in enumerate(self._terms.items()):
+            table.add_row([index, name, term.__class__.__name__])
+        msg += str(table.get_string())
+        msg += "\n"
+        return msg
+
+    # Properties.
+
+    @property
+    def active_terms(self) -> list[str]:
+        return list(self._terms.keys())
+
+    def get_active_iterable_terms(self, env_idx: int) -> Sequence[tuple[str, Sequence[float]]]:
+        terms = []
+        for name, term in self._terms.items():
+            command = self._validate_command(name, term.command)
+            terms.append((name, command[env_idx].tolist()))
+        return terms
+
+    def reset(self, env_ids: np.ndarray | slice | None) -> dict[str, float]:
+        if env_ids is None:
+            env_ids = np.arange(self.num_envs)
+        elif isinstance(env_ids, slice):
+            env_ids = np.arange(self.num_envs)[env_ids]
+        extras = {}
+        for name, term in self._terms.items():
+            metrics = term.reset(env_ids=env_ids)
+            self._validate_command(name, term.command)
+            for metric_name, metric_value in metrics.items():
+                extras[f"Metrics/{name}/{metric_name}"] = metric_value
+        return extras
+
+    def compute(self, dt: float | np.ndarray, env_ids: np.ndarray | None = None) -> None:
+        for name, term in self._terms.items():
+            term.compute(dt, env_ids)
+            self._validate_command(name, term.command)
+
+    def post_compute(self) -> None:
+        for term in self._terms.values():
+            term.post_compute()
+
+    def get_command(self, name: str) -> np.ndarray:
+        return self._validate_command(name, self._terms[name].command)
+
+    def get_term(self, name: str) -> CommandTerm:
+        return self._terms[name]
+
+    def get_term_cfg(self, name: str) -> CommandTermCfg:
+        term_cfg = self.cfg[name]
+        if term_cfg is None:
+            raise KeyError(f"Command term '{name}' is disabled.")
+        return term_cfg
+
+    def _prepare_terms(self) -> None:
+        for term_name, term_cfg in self.cfg.items():
+            if term_cfg is None:
+                print(f"term: {term_name} set to None, skipping...")
+                continue
+            if term_cfg.debug_vis:
+                raise NotImplementedError(
+                    f"CommandManager term '{term_name}' requested viewer debug visualization; "
+                    "viewer glue is unsupported by the UniLab manager core."
+                )
+            term = term_cfg.build(self._env)
+            if not isinstance(term, CommandTerm):
+                raise TypeError(
+                    f"Returned object for the term {term_name} is not of type CommandType."
+                )
+            self._terms[term_name] = term
+
+    def _validate_command(self, name: str, command: np.ndarray) -> np.ndarray:
+        if not isinstance(command, np.ndarray):
+            raise TypeError(
+                f"CommandManager term '{name}' returned {type(command).__name__}, "
+                "expected np.ndarray."
+            )
+        if command.ndim < 1 or command.shape[0] != self.num_envs:
+            raise ValueError(
+                f"CommandManager term '{name}' returned shape {command.shape}, "
+                f"expected leading dimension {self.num_envs}."
+            )
+        if not np.isfinite(command).all():
+            raise ValueError(f"CommandManager term '{name}' returned NaN or Inf.")
+        return command
+
+
+class NullCommandManager:
+    """Placeholder for absent command manager that safely no-ops all operations."""
+
+    def __init__(self):
+        self.active_terms: list[str] = []
+        self._terms: dict[str, Any] = {}
+        self.cfg = None
+
+    def __str__(self) -> str:
+        return "<NullCommandManager> (inactive)"
+
+    def __repr__(self) -> str:
+        return "NullCommandManager()"
+
+    def get_active_iterable_terms(self, env_idx: int) -> Sequence[tuple[str, Sequence[float]]]:
+        return []
+
+    def reset(self, env_ids: np.ndarray | None = None) -> dict[str, np.ndarray]:
+        return {}
+
+    def compute(self, dt: float | np.ndarray, env_ids: np.ndarray | None = None) -> None:
+        pass
+
+    def post_compute(self) -> None:
+        pass
+
+    def get_command(self, name: str) -> None:
+        return None
+
+    def get_term(self, name: str) -> None:
+        return None
+
+    def get_term_cfg(self, name: str) -> None:
+        return None
diff --git a/src/unilab/managers/curriculum_manager.py b/src/unilab/managers/curriculum_manager.py
new file mode 100644
index 000000000..e2814b86a
--- /dev/null
+++ b/src/unilab/managers/curriculum_manager.py
@@ -0,0 +1,166 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), src/mjlab/managers/curriculum_manager.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and UniLab contracts; licensed under Apache-2.0.
+"""Curriculum manager for updating environment quantities subject to a training curriculum."""
+
+from __future__ import annotations
+
+from copy import deepcopy
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any, Sequence
+
+import numpy as np
+from prettytable import PrettyTable
+
+from unilab.managers.manager_base import ManagerBase, ManagerTermBaseCfg
+
+if TYPE_CHECKING:
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+@dataclass(kw_only=True)
+class CurriculumTermCfg(ManagerTermBaseCfg):
+    """Configuration for a curriculum term.
+
+    Curriculum terms modify environment parameters during training to implement
+    curriculum learning strategies (e.g., gradually increasing task difficulty).
+    """
+
+    pass
+
+
+class CurriculumManager(ManagerBase):
+    """Manages curriculum learning for the environment.
+
+    The curriculum manager updates environment parameters during training based
+    on agent performance. Each term can modify different aspects of the task
+    difficulty (e.g., terrain complexity, command ranges).
+    """
+
+    _env: ManagerBasedRlEnv
+
+    def __init__(self, cfg: dict[str, CurriculumTermCfg | None], env: ManagerBasedRlEnv):
+        self._term_names: list[str] = list()
+        self._term_cfgs: list[CurriculumTermCfg] = list()
+        self._class_term_cfgs: list[CurriculumTermCfg] = list()
+
+        self.cfg = deepcopy(cfg)
+        super().__init__(env)
+
+        self._curriculum_state: dict[str, Any] = {}
+        for term_name in self._term_names:
+            self._curriculum_state[term_name] = None
+
+    def __str__(self) -> str:
+        msg = f"<CurriculumManager> contains {len(self._term_names)} active terms.\n"
+        table = PrettyTable()
+        table.title = "Active Curriculum Terms"
+        table.field_names = ["Index", "Name"]
+        table.align["Name"] = "l"
+        for index, name in enumerate(self._term_names):
+            table.add_row([index, name])
+        msg += str(table.get_string())
+        msg += "\n"
+        return msg
+
+    # Properties.
+
+    @property
+    def active_terms(self) -> list[str]:
+        return self._term_names
+
+    # Methods.
+
+    def get_term_cfg(self, term_name: str) -> CurriculumTermCfg:
+        if term_name not in self._term_names:
+            raise ValueError(f"Term '{term_name}' not found in active terms.")
+        return self._term_cfgs[self._term_names.index(term_name)]
+
+    def get_active_iterable_terms(self, env_idx: int) -> Sequence[tuple[str, Sequence[float]]]:
+        terms = []
+        for term_name, term_state in self._curriculum_state.items():
+            if term_state is not None:
+                data = []
+                if isinstance(term_state, dict):
+                    for _key, value in term_state.items():
+                        if isinstance(value, np.ndarray):
+                            value = value.item()
+                        data.append(value)
+                else:
+                    if isinstance(term_state, np.ndarray):
+                        term_state = term_state.item()
+                    data.append(term_state)
+                terms.append((term_name, data))
+        return terms
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> dict[str, float]:
+        extras = {}
+        for term_name, term_state in self._curriculum_state.items():
+            if term_state is not None:
+                if isinstance(term_state, dict):
+                    for key, value in term_state.items():
+                        if isinstance(value, np.ndarray):
+                            value = value.item()
+                        extras[f"Curriculum/{term_name}/{key}"] = value
+                else:
+                    if isinstance(term_state, np.ndarray):
+                        term_state = term_state.item()
+                    extras[f"Curriculum/{term_name}"] = term_state
+        for term_cfg in self._class_term_cfgs:
+            term_cfg.func.reset(env_ids=env_ids)
+        return extras
+
+    def compute(self, env_ids: np.ndarray | slice | None = None) -> None:
+        if env_ids is None:
+            env_ids = slice(None)
+        for name, term_cfg in zip(self._term_names, self._term_cfgs, strict=False):
+            state = term_cfg.func(self._env, env_ids, **term_cfg.params)
+            self._validate_state(name, state)
+            self._curriculum_state[name] = state
+
+    def _validate_state(self, term_name: str, state: Any) -> None:
+        values = state.values() if isinstance(state, dict) else (state,)
+        for value in values:
+            if isinstance(value, np.ndarray):
+                finite = np.isfinite(value).all()
+            elif isinstance(value, (int, float, np.number)):
+                finite = bool(np.isfinite(value))
+            else:
+                continue
+            if not finite:
+                raise ValueError(f"CurriculumManager term '{term_name}' returned NaN or Inf.")
+
+    def _prepare_terms(self) -> None:
+        for term_name, term_cfg in self.cfg.items():
+            if term_cfg is None:
+                print(f"term: {term_name} set to None, skipping...")
+                continue
+            self._resolve_common_term_cfg(term_name, term_cfg)
+            self._term_names.append(term_name)
+            self._term_cfgs.append(term_cfg)
+            if hasattr(term_cfg.func, "reset") and callable(term_cfg.func.reset):
+                self._class_term_cfgs.append(term_cfg)
+
+
+class NullCurriculumManager:
+    """Placeholder for absent curriculum manager that safely no-ops all operations."""
+
+    def __init__(self):
+        self.active_terms: list[str] = []
+        self._curriculum_state: dict[str, Any] = {}
+        self.cfg = None
+
+    def __str__(self) -> str:
+        return "<NullCurriculumManager> (inactive)"
+
+    def __repr__(self) -> str:
+        return "NullCurriculumManager()"
+
+    def get_active_iterable_terms(self, env_idx: int) -> Sequence[tuple[str, Sequence[float]]]:
+        return []
+
+    def reset(self, env_ids: np.ndarray | None = None) -> dict[str, float]:
+        return {}
+
+    def compute(self, env_ids: np.ndarray | None = None) -> None:
+        pass
diff --git a/src/unilab/managers/event_manager.py b/src/unilab/managers/event_manager.py
new file mode 100644
index 000000000..fb98d7ea9
--- /dev/null
+++ b/src/unilab/managers/event_manager.py
@@ -0,0 +1,276 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), src/mjlab/managers/event_manager.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and UniLab contracts; licensed under Apache-2.0.
+"""Event manager for orchestrating operations based on different simulation events."""
+
+from __future__ import annotations
+
+from copy import deepcopy
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Literal
+
+import numpy as np
+from prettytable import PrettyTable
+
+from unilab.managers.manager_base import ManagerBase, ManagerTermBaseCfg
+
+if TYPE_CHECKING:
+    from unilab.managers._types import ManagerBasedRlEnv
+
+EventMode = Literal["startup", "reset", "interval", "step"]
+
+
+@dataclass(kw_only=True)
+class EventTermCfg(ManagerTermBaseCfg):
+    """Configuration for an event term.
+
+    Event terms trigger operations at specific simulation events. They're commonly
+    used for domain randomization, state resets, and periodic perturbations.
+
+    The four modes determine when the event fires:
+
+    - ``"startup"``: Once when the environment initializes. Use for parameters that
+      should be randomized per-environment but stay constant within an episode (e.g.,
+      domain randomization).
+
+    - ``"reset"``: On every episode reset. Use for parameters that should vary between
+      episodes (e.g., initial robot pose, domain randomization).
+
+    - ``"interval"``: Periodically during simulation, controlled by ``interval_range_s``.
+      Use for perturbations that should happen during episodes (e.g., pushing the robot,
+      external disturbances).
+
+    - ``"step"``: Every environment step, unconditionally on all envs. Use for terms that
+      manage per-step state such as force lifetimes (e.g., ``apply_body_impulse``).
+    """
+
+    mode: EventMode
+    """When the event triggers: ``"startup"`` (once at init), ``"reset"`` (every
+  episode), ``"interval"`` (periodically during simulation), or ``"step"`` (every
+  environment step)."""
+
+    interval_range_s: tuple[float, float] | None = None
+    """Time range in seconds for interval mode. The next trigger time is uniformly
+  sampled from ``[min, max]``. Required when ``mode="interval"``."""
+
+    is_global_time: bool = False
+    """Whether all environments share the same timer. If True, all envs trigger
+  simultaneously. If False (default), each env has an independent timer that
+  resets on episode reset. Only applies to ``mode="interval"``."""
+
+    min_step_count_between_reset: int = 0
+    """Minimum environment steps between triggers. Prevents the event from firing
+  too frequently when episodes reset rapidly. Only applies to ``mode="reset"``.
+  Set to 0 (default) to trigger on every reset."""
+
+
+class EventManager(ManagerBase):
+    """Manages event-based operations for the environment.
+
+    The event manager triggers operations at different simulation events: startup
+    (once at initialization), reset (on episode reset), or interval (periodically
+    during simulation). Common uses include domain randomization and state resets.
+    """
+
+    _env: ManagerBasedRlEnv
+
+    def __init__(self, cfg: dict[str, EventTermCfg | None], env: ManagerBasedRlEnv):
+        self.cfg = deepcopy(cfg)
+        self._mode_term_names: dict[EventMode, list[str]] = dict()
+        self._mode_term_cfgs: dict[EventMode, list[EventTermCfg]] = dict()
+        self._mode_class_term_cfgs: dict[EventMode, list[EventTermCfg]] = dict()
+
+        super().__init__(env=env)
+
+    def __str__(self) -> str:
+        msg = f"<EventManager> contains {len(self._mode_term_names)} active terms.\n"
+        for mode in self._mode_term_names:
+            table = PrettyTable()
+            table.title = f"Active Event Terms in Mode: '{mode}'"
+            if mode == "interval":
+                table.field_names = ["Index", "Name", "Interval time range (s)"]
+                table.align["Name"] = "l"
+                for index, (name, cfg) in enumerate(
+                    zip(self._mode_term_names[mode], self._mode_term_cfgs[mode], strict=False)
+                ):
+                    table.add_row([index, name, cfg.interval_range_s])
+            else:
+                table.field_names = ["Index", "Name"]
+                table.align["Name"] = "l"
+                for index, name in enumerate(self._mode_term_names[mode]):
+                    table.add_row([index, name])
+            msg += str(table.get_string())
+            msg += "\n"
+        return msg
+
+    # Properties.
+
+    @property
+    def active_terms(self) -> dict[EventMode, list[str]]:
+        return self._mode_term_names
+
+    @property
+    def available_modes(self) -> list[EventMode]:
+        return list(self._mode_term_names.keys())
+
+    # Methods.
+
+    def get_term_cfg(self, term_name: str) -> EventTermCfg:
+        """Get the configuration of a specific event term by name."""
+        for mode in self._mode_term_names:
+            if term_name in self._mode_term_names[mode]:
+                index = self._mode_term_names[mode].index(term_name)
+                return self._mode_term_cfgs[mode][index]
+        raise ValueError(f"Event term '{term_name}' not found in active terms.")
+
+    def reset(self, env_ids: np.ndarray | None = None):
+        for mode_cfg in self._mode_class_term_cfgs.values():
+            for term_cfg in mode_cfg:
+                term_cfg.func.reset(env_ids=env_ids)
+        if env_ids is None:
+            num_envs = self._env.num_envs
+            ids: np.ndarray | slice = slice(None)
+        else:
+            num_envs = len(env_ids)
+            ids = env_ids
+        # Iterate the full interval term list: _interval_term_time_left is parallel
+        # to _mode_term_cfgs["interval"], not the class-only subset.
+        if "interval" in self._mode_term_cfgs:
+            for index, term_cfg in enumerate(self._mode_term_cfgs["interval"]):
+                if not term_cfg.is_global_time:
+                    assert term_cfg.interval_range_s is not None
+                    lower, upper = term_cfg.interval_range_s
+                    sampled_interval = self._env.rng.uniform(lower, upper, num_envs)
+                    self._interval_term_time_left[index][ids] = sampled_interval
+        return {}
+
+    def apply(
+        self,
+        mode: EventMode,
+        env_ids: np.ndarray | slice | None = None,
+        dt: float | None = None,
+        global_env_step_count: int | None = None,
+    ):
+        if mode not in ("startup", "reset", "interval", "step"):
+            raise ValueError(f"Unsupported event mode '{mode}'.")
+        if mode not in self._mode_term_cfgs:
+            return
+        if mode == "interval" and dt is None:
+            raise ValueError(f"Event mode '{mode}' requires the time-step of the environment.")
+        if mode == "interval" and env_ids is not None:
+            raise ValueError(
+                f"Event mode '{mode}' does not require environment indices. This is an undefined behavior"
+                " as the environment indices are computed based on the time left for each environment."
+            )
+        if mode == "reset" and global_env_step_count is None:
+            raise ValueError(
+                f"Event mode '{mode}' requires the total number of environment steps to be provided."
+            )
+        if mode == "step" and dt is None:
+            raise ValueError(f"Event mode '{mode}' requires the time-step of the environment.")
+
+        for index, term_cfg in enumerate(self._mode_term_cfgs[mode]):
+            if mode == "interval":
+                time_left = self._interval_term_time_left[index]
+                assert dt is not None
+                time_left -= dt
+                if term_cfg.is_global_time:
+                    if time_left < 1e-6:
+                        assert term_cfg.interval_range_s is not None
+                        lower, upper = term_cfg.interval_range_s
+                        sampled_interval = self._env.rng.uniform(lower, upper, 1)
+                        self._interval_term_time_left[index][:] = sampled_interval
+                        term_cfg.func(self._env, None, **term_cfg.params)
+                else:
+                    valid_env_ids = np.flatnonzero(time_left < 1e-6)
+                    if len(valid_env_ids) > 0:
+                        assert term_cfg.interval_range_s is not None
+                        lower, upper = term_cfg.interval_range_s
+                        sampled_time = self._env.rng.uniform(lower, upper, len(valid_env_ids))
+                        self._interval_term_time_left[index][valid_env_ids] = sampled_time
+                        term_cfg.func(self._env, valid_env_ids, **term_cfg.params)
+            elif mode == "step":
+                term_cfg.func(self._env, None, **term_cfg.params)
+            elif mode == "reset":
+                assert global_env_step_count is not None
+                # Reset events require concrete indices: callers (e.g. ManagerBasedRlEnv)
+                # resolve None to all environments upstream. Enforce that here so a future
+                # caller passing None fails loudly instead of leaking a slice into event
+                # functions, which only understand None or a tensor.
+                if env_ids is None:
+                    raise ValueError("Event mode 'reset' requires concrete environment indices.")
+                min_step_count = term_cfg.min_step_count_between_reset
+                if min_step_count == 0:
+                    self._reset_term_last_triggered_step_id[index][env_ids] = global_env_step_count
+                    self._reset_term_last_triggered_once[index][env_ids] = True
+                    term_cfg.func(self._env, env_ids, **term_cfg.params)
+                else:
+                    last_triggered_step = self._reset_term_last_triggered_step_id[index][env_ids]
+                    triggered_at_least_once = self._reset_term_last_triggered_once[index][env_ids]
+                    steps_since_triggered = global_env_step_count - last_triggered_step
+                    valid_trigger = steps_since_triggered >= min_step_count
+                    valid_trigger |= (last_triggered_step == 0) & ~triggered_at_least_once
+                    if isinstance(env_ids, np.ndarray):
+                        valid_env_ids = env_ids[valid_trigger]
+                    else:
+                        valid_env_ids = np.flatnonzero(valid_trigger)
+                    if len(valid_env_ids) > 0:
+                        self._reset_term_last_triggered_once[index][valid_env_ids] = True
+                        self._reset_term_last_triggered_step_id[index][valid_env_ids] = (
+                            global_env_step_count
+                        )
+                        term_cfg.func(self._env, valid_env_ids, **term_cfg.params)
+            else:
+                term_cfg.func(self._env, env_ids, **term_cfg.params)
+
+    def _prepare_terms(self) -> None:
+        self._interval_term_time_left: list[np.ndarray] = list()
+        self._reset_term_last_triggered_step_id: list[np.ndarray] = list()
+        self._reset_term_last_triggered_once: list[np.ndarray] = list()
+
+        for term_name, term_cfg in self.cfg.items():
+            if term_cfg is None:
+                print(f"term: {term_name} set to None, skipping...")
+                continue
+            self._resolve_common_term_cfg(term_name, term_cfg)
+            if term_cfg.mode not in ("startup", "reset", "interval", "step"):
+                raise ValueError(
+                    f"EventManager term '{term_name}' has unsupported mode '{term_cfg.mode}'."
+                )
+            if term_cfg.mode not in self._mode_term_names:
+                self._mode_term_names[term_cfg.mode] = list()
+                self._mode_term_cfgs[term_cfg.mode] = list()
+                self._mode_class_term_cfgs[term_cfg.mode] = list()
+            self._mode_term_names[term_cfg.mode].append(term_name)
+            self._mode_term_cfgs[term_cfg.mode].append(term_cfg)
+            if hasattr(term_cfg.func, "reset") and callable(term_cfg.func.reset):
+                self._mode_class_term_cfgs[term_cfg.mode].append(term_cfg)
+            if term_cfg.mode == "interval":
+                if term_cfg.interval_range_s is None:
+                    raise ValueError(
+                        f"Event term '{term_name}' has mode 'interval' but 'interval_range_s' is not specified."
+                    )
+                lower, upper = term_cfg.interval_range_s
+                if not np.isfinite((lower, upper)).all() or lower > upper:
+                    raise ValueError(
+                        f"EventManager term '{term_name}' has invalid interval_range_s "
+                        f"{term_cfg.interval_range_s}."
+                    )
+                if term_cfg.is_global_time:
+                    time_left = self._env.rng.uniform(lower, upper, 1)
+                    self._interval_term_time_left.append(time_left)
+                else:
+                    time_left = self._env.rng.uniform(lower, upper, self.num_envs)
+                    self._interval_term_time_left.append(time_left)
+            elif term_cfg.mode == "reset":
+                step_count = np.zeros(self.num_envs, dtype=np.int64)
+                self._reset_term_last_triggered_step_id.append(step_count)
+                no_trigger = np.zeros(self.num_envs, dtype=np.bool_)
+                self._reset_term_last_triggered_once.append(no_trigger)
+
+            func = term_cfg.func
+            if hasattr(func, "model_fields"):
+                raise NotImplementedError(
+                    f"EventManager term '{term_name}' requests direct model-field mutation; "
+                    "this capability is unsupported by the standalone UniLab manager core."
+                )
diff --git a/src/unilab/managers/manager_base.py b/src/unilab/managers/manager_base.py
new file mode 100644
index 000000000..0066ffcce
--- /dev/null
+++ b/src/unilab/managers/manager_base.py
@@ -0,0 +1,171 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), src/mjlab/managers/manager_base.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and UniLab contracts; licensed under Apache-2.0.
+from __future__ import annotations
+
+import abc
+import inspect
+from collections.abc import Sequence
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+
+from unilab.base.config_overrides import (
+    CONFIG_MAPPING_POLICY_KEY,
+    MANAGER_PARAMS_MAPPING_POLICY,
+)
+from unilab.managers.scene_entity_config import SceneEntityCfg
+
+if TYPE_CHECKING:
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+@dataclass
+class ManagerTermBaseCfg:
+    """Base configuration for manager terms.
+
+    This is the base config for terms in observation, reward, termination, curriculum,
+    and event managers. It provides a common interface for specifying a callable
+    and its parameters.
+
+    The ``func`` field accepts either a function or a class:
+
+    **Function-based terms** are simpler and suitable for stateless computations:
+
+    .. code-block:: python
+
+        RewardTermCfg(func=mdp.joint_torques_l2, weight=-0.01)
+
+    **Class-based terms** are instantiated with ``(cfg, env)`` and useful when you need
+    to:
+
+    - Cache computed values at initialization (e.g., resolve regex patterns to indices)
+    - Maintain state across calls
+    - Perform expensive setup once rather than every call
+
+    .. code-block:: python
+
+        class posture:
+          def __init__(self, cfg: RewardTermCfg, env: ManagerBasedRlEnv):
+            # Resolve std dict to tensor once at init
+            self.std = resolve_std_to_tensor(cfg.params["std"], env)
+
+          def __call__(self, env, **kwargs) -> np.ndarray:
+            # Use cached self.std
+            return compute_posture_reward(env, self.std)
+
+        RewardTermCfg(func=posture, params={"std": {".*knee.*": 0.3}}, weight=1.0)
+
+    Class-based terms can optionally implement ``reset(env_ids)`` for per-episode state.
+    """
+
+    func: Any
+    """The callable that computes this term's value. Can be a function or a class.
+  Classes are auto-instantiated with ``(cfg=term_cfg, env=env)``."""
+
+    params: dict[str, Any] = field(
+        default_factory=dict,
+        metadata={CONFIG_MAPPING_POLICY_KEY: MANAGER_PARAMS_MAPPING_POLICY},
+    )
+    """Additional keyword arguments passed to func when called."""
+
+
+class ManagerTermBase:
+    def __init__(self, env: ManagerBasedRlEnv):
+        self._env = env
+
+    # Properties.
+
+    @property
+    def num_envs(self) -> int:
+        return self._env.num_envs
+
+    @property
+    def name(self) -> str:
+        return self.__class__.__name__
+
+    # Methods.
+
+    def reset(self, env_ids: np.ndarray | slice | None) -> Any:
+        """Resets the manager term."""
+        del env_ids  # Unused.
+        pass
+
+    def __call__(self, *args, **kwargs) -> Any:
+        """Returns the value of the term required by the manager."""
+        raise NotImplementedError
+
+
+class ManagerBase(abc.ABC):
+    """Base class for all managers."""
+
+    def __init__(self, env: ManagerBasedRlEnv):
+        self._env = env
+
+        self._prepare_terms()
+
+    # Properties.
+
+    @property
+    def num_envs(self) -> int:
+        return self._env.num_envs
+
+    @property
+    @abc.abstractmethod
+    def active_terms(self) -> list[str] | dict[Any, list[str]]:
+        raise NotImplementedError
+
+    # Methods.
+
+    def reset(self, env_ids: np.ndarray) -> dict[str, Any]:
+        """Resets the manager and returns logging info for the current step."""
+        del env_ids  # Unused.
+        return {}
+
+    def _check_term_shape(self, term_name: str, value: np.ndarray) -> None:
+        if not isinstance(value, np.ndarray):
+            manager_name = type(self).__name__
+            raise TypeError(
+                f"{manager_name} term '{term_name}' returned {type(value).__name__}, "
+                "expected np.ndarray."
+            )
+        if value.shape != (self.num_envs,):
+            manager_name = type(self).__name__
+            raise ValueError(
+                f"{manager_name} term '{term_name}' returned shape {tuple(value.shape)}, "
+                f"expected ({self.num_envs},)."
+            )
+
+    def _check_term_finite(self, term_name: str, value: np.ndarray) -> None:
+        if np.isfinite(value).all():
+            return
+        manager_name = type(self).__name__
+        has_nan = np.isnan(value).any()
+        has_inf = np.isinf(value).any()
+        invalid_kind = "NaN/Inf" if has_nan and has_inf else "NaN" if has_nan else "Inf"
+        env_ids = np.flatnonzero(~np.isfinite(value)).tolist()
+        raise ValueError(
+            f"{manager_name} term '{term_name}' returned {invalid_kind} for "
+            f"environments {env_ids[:10]}."
+        )
+
+    def get_active_iterable_terms(self, env_idx: int) -> Sequence[tuple[str, Sequence[float]]]:
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def _prepare_terms(self) -> None:
+        raise NotImplementedError
+
+    def _resolve_common_term_cfg(self, term_name: str, term_cfg: ManagerTermBaseCfg) -> None:
+        for param_name, value in term_cfg.params.items():
+            if isinstance(value, SceneEntityCfg):
+                try:
+                    value.resolve(self._env.scene)
+                except (KeyError, TypeError, ValueError, NotImplementedError) as exc:
+                    message = (
+                        f"{type(self).__name__} term '{term_name}' parameter '{param_name}': {exc}"
+                    )
+                    raise type(exc)(message) from exc
+        if inspect.isclass(term_cfg.func):
+            term_cfg.func = term_cfg.func(cfg=term_cfg, env=self._env)
diff --git a/src/unilab/managers/metrics_manager.py b/src/unilab/managers/metrics_manager.py
new file mode 100644
index 000000000..961ae135c
--- /dev/null
+++ b/src/unilab/managers/metrics_manager.py
@@ -0,0 +1,242 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), src/mjlab/managers/metrics_manager.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and UniLab contracts; licensed under Apache-2.0.
+"""Metrics manager for logging custom per-step metrics during training."""
+
+from __future__ import annotations
+
+from copy import deepcopy
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Literal, Sequence
+
+import numpy as np
+from prettytable import PrettyTable
+
+from unilab.managers.manager_base import ManagerBase, ManagerTermBaseCfg
+
+if TYPE_CHECKING:
+    from unilab.managers._types import ManagerBasedRlEnv
+
+REDUCE_OPTIONS = ("last", "max", "mean", "sum")
+
+
+@dataclass(kw_only=True)
+class MetricsTermCfg(ManagerTermBaseCfg):
+    """Configuration for a metrics term.
+
+    Attributes:
+      per_substep: If True, evaluate this term once per physics substep inside
+        the decimation loop and report the per-step mean. Only the integrated
+        state (qpos, qvel, act) is current mid-loop; all derived quantities
+        (xpos, xquat, site_xpos, actuator_force, contacts, ...) are stale.
+
+      reduce: How to aggregate per-step values into an episode metric.
+        - ``"mean"`` (default) reports ``sum / step_count``.
+        - ``"last"`` reports the value from the final step of the episode,
+          useful for binary success metrics that should not be averaged over
+          timesteps.
+        - ``"max"`` reports the highest value seen during the episode, useful
+          for peak metrics like maximum power or contact force.
+        - ``"sum"`` reports the accumulated total over the episode, useful for
+          cumulative quantities like episodic reward or total distance
+          traveled.
+    """
+
+    per_substep: bool = False
+    reduce: Literal["last", "max", "mean", "sum"] = "mean"
+
+
+class MetricsManager(ManagerBase):
+    """Accumulates per-step metric values, reports episode averages.
+
+    Unlike rewards, metrics have no weight, no dt scaling, and no
+    normalization by episode length. Episode values are true per-step
+    averages (sum / step_count), so a metric in [0,1] stays in [0,1]
+    in the logger.
+    """
+
+    _env: ManagerBasedRlEnv
+
+    def __init__(self, cfg: dict[str, MetricsTermCfg | None], env: ManagerBasedRlEnv):
+        self._term_names: list[str] = list()
+        self._term_cfgs: list[MetricsTermCfg] = list()
+        self._class_term_cfgs: list[MetricsTermCfg] = list()
+        self._step_term_indices: list[int] = list()
+        self._substep_term_indices: list[int] = list()
+
+        self.cfg = deepcopy(cfg)
+        super().__init__(env=env)
+
+        self._episode_sums: dict[str, np.ndarray] = {}
+        self._episode_max: dict[str, np.ndarray] = {}
+        for idx, term_name in enumerate(self._term_names):
+            if self._term_cfgs[idx].reduce not in REDUCE_OPTIONS:
+                msg = (
+                    f"The reduce method '{self._term_cfgs[idx].reduce}' for metric '{term_name}' "
+                    f"is unknown. Valid options are {REDUCE_OPTIONS}."
+                )
+                raise ValueError(msg)
+
+            self._episode_sums[term_name] = np.zeros(self.num_envs, dtype=np.float32)
+
+            if self._term_cfgs[idx].reduce == "max":
+                self._episode_max[term_name] = np.full(
+                    self.num_envs, float("-inf"), dtype=np.float32
+                )
+        # Pre-resolved tensor refs for substep terms to avoid dict lookups in
+        # the hot loop.
+        self._substep_accum: list[np.ndarray] = []
+        self._substep_episode_sums: list[np.ndarray] = []
+        self._substep_episode_max: list[np.ndarray | None] = []
+        for idx in self._substep_term_indices:
+            name = self._term_names[idx]
+            buf = np.zeros(self.num_envs, dtype=np.float32)
+            self._substep_accum.append(buf)
+            self._substep_episode_sums.append(self._episode_sums[name])
+            self._substep_episode_max.append(self._episode_max.get(name))
+        self._substep_count: int = 0
+        self._step_count = np.zeros(self.num_envs, dtype=np.int64)
+        self._step_values = np.zeros((self.num_envs, len(self._term_names)), dtype=np.float32)
+
+    def __str__(self) -> str:
+        msg = f"<MetricsManager> contains {len(self._term_names)} active terms.\n"
+        table = PrettyTable()
+        table.title = "Active Metrics Terms"
+        table.field_names = ["Index", "Name"]
+        table.align["Name"] = "l"
+        for index, name in enumerate(self._term_names):
+            table.add_row([index, name])
+        msg += str(table.get_string())
+        msg += "\n"
+        return msg
+
+    # Properties.
+
+    @property
+    def active_terms(self) -> list[str]:
+        return self._term_names
+
+    # Methods.
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> dict[str, float]:
+        if env_ids is None:
+            env_ids = slice(None)
+        extras = {}
+        counts = self._step_count[env_ids].astype(np.float32)
+        # Avoid division by zero for envs that haven't stepped.
+        safe_counts = np.maximum(counts, 1.0)
+        for idx, key in enumerate(self._episode_sums):
+            reduce = self._term_cfgs[idx].reduce
+            if reduce == "max":
+                extras["Episode_Metrics/" + key] = float(np.mean(self._episode_max[key][env_ids]))
+                self._episode_max[key][env_ids] = float("-inf")
+
+            elif reduce == "last":
+                extras["Episode_Metrics/" + key] = float(np.mean(self._step_values[env_ids, idx]))
+
+            elif reduce == "sum":
+                extras["Episode_Metrics/" + key] = float(np.mean(self._episode_sums[key][env_ids]))
+
+            else:
+                extras["Episode_Metrics/" + key] = float(
+                    np.mean(self._episode_sums[key][env_ids] / safe_counts)
+                )
+
+            self._episode_sums[key][env_ids] = 0.0
+        self._step_count[env_ids] = 0
+
+        for buf in self._substep_accum:
+            buf[env_ids] = 0.0
+
+        for term_cfg in self._class_term_cfgs:
+            term_cfg.func.reset(env_ids=env_ids)
+
+        return extras
+
+    def compute_substep(self) -> None:
+        """Accumulate per-substep metric values inside the decimation loop.
+
+        No-op when no ``per_substep`` terms are configured.
+        """
+        if not self._substep_term_indices:
+            return
+        for i, idx in enumerate(self._substep_term_indices):
+            value = self._compute_term(idx)
+            self._substep_accum[i] += value
+        self._substep_count += 1
+
+    def compute(self) -> None:
+        self._step_count += 1
+        if self._substep_term_indices and self._substep_count > 0:
+            for i, idx in enumerate(self._substep_term_indices):
+                avg = self._substep_accum[i] / self._substep_count
+                self._substep_episode_sums[i] += avg
+                self._step_values[:, idx] = avg
+                max_buf = self._substep_episode_max[i]
+                if max_buf is not None:
+                    np.maximum(max_buf, avg, out=max_buf)
+                self._substep_accum[i].fill(0.0)
+            self._substep_count = 0
+        for idx in self._step_term_indices:
+            name = self._term_names[idx]
+            value = self._compute_term(idx)
+            self._episode_sums[name] += value
+            self._step_values[:, idx] = value
+            if name in self._episode_max:
+                np.maximum(self._episode_max[name], value, out=self._episode_max[name])
+
+    def get_active_iterable_terms(self, env_idx: int) -> Sequence[tuple[str, Sequence[float]]]:
+        terms = []
+        for idx, name in enumerate(self._term_names):
+            terms.append((name, [self._step_values[env_idx, idx].item()]))
+        return terms
+
+    def _prepare_terms(self) -> None:
+        for term_name, term_cfg in self.cfg.items():
+            if term_cfg is None:
+                print(f"term: {term_name} set to None, skipping...")
+                continue
+            self._resolve_common_term_cfg(term_name, term_cfg)
+            idx = len(self._term_names)
+            self._term_names.append(term_name)
+            self._term_cfgs.append(term_cfg)
+            if term_cfg.per_substep:
+                self._substep_term_indices.append(idx)
+            else:
+                self._step_term_indices.append(idx)
+            if hasattr(term_cfg.func, "reset") and callable(term_cfg.func.reset):
+                self._class_term_cfgs.append(term_cfg)
+
+    def _compute_term(self, idx: int) -> np.ndarray:
+        name = self._term_names[idx]
+        term_cfg = self._term_cfgs[idx]
+        value = term_cfg.func(self._env, **term_cfg.params)
+        self._check_term_shape(name, value)
+        self._check_term_finite(name, value)
+        return value
+
+
+class NullMetricsManager:
+    """Placeholder for absent metrics manager that safely no-ops all operations."""
+
+    def __init__(self):
+        self.active_terms: list[str] = []
+        self.cfg = None
+
+    def __str__(self) -> str:
+        return "<NullMetricsManager> (inactive)"
+
+    def __repr__(self) -> str:
+        return "NullMetricsManager()"
+
+    def get_active_iterable_terms(self, env_idx: int) -> Sequence[tuple[str, Sequence[float]]]:
+        return []
+
+    def reset(self, env_ids: np.ndarray | None = None) -> dict[str, float]:
+        return {}
+
+    def compute_substep(self) -> None:
+        pass
+
+    def compute(self) -> None:
+        pass
diff --git a/src/unilab/managers/observation_manager.py b/src/unilab/managers/observation_manager.py
new file mode 100644
index 000000000..c668b9fa2
--- /dev/null
+++ b/src/unilab/managers/observation_manager.py
@@ -0,0 +1,751 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), src/mjlab/managers/observation_manager.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and UniLab contracts; licensed under Apache-2.0.
+"""Observation manager for computing observations."""
+
+from __future__ import annotations
+
+from copy import deepcopy
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Literal, Sequence
+
+import numpy as np
+from prettytable import PrettyTable
+
+from unilab.base.config_overrides import (
+    CONFIG_MAPPING_POLICY_KEY,
+    MANAGER_TERM_MAPPING_POLICY,
+)
+from unilab.managers._buffers import CircularBuffer, DelayBuffer
+from unilab.managers._noise import noise_cfg, noise_model
+from unilab.managers._noise.noise_cfg import NoiseCfg, NoiseModelCfg
+from unilab.managers.manager_base import ManagerBase, ManagerTermBaseCfg
+
+if TYPE_CHECKING:
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+@dataclass
+class ObservationTermCfg(ManagerTermBaseCfg):
+    """Configuration for an observation term.
+
+    Processing pipeline: compute → noise → clip → scale → delay → history.
+    Delay models sensor latency. History provides temporal context. Both are optional
+    and can be combined.
+    """
+
+    noise: NoiseCfg | NoiseModelCfg | None = None
+    """Noise model to apply to the observation."""
+
+    clip: tuple[float, float] | None = None
+    """Range (min, max) to clip the observation values."""
+
+    scale: tuple[float, ...] | float | np.ndarray | None = None
+    """Scaling factor(s) to multiply the observation by."""
+
+    delay_min_lag: int = 0
+    """Minimum lag (in steps) for delayed observations. Lag sampled uniformly from
+  [min_lag, max_lag]. Convert to ms: lag * (1000 / control_hz)."""
+
+    delay_max_lag: int = 0
+    """Maximum lag (in steps) for delayed observations. Use min=max for constant delay."""
+
+    delay_per_env: bool = True
+    """If True, each environment samples its own lag. If False, all environments share
+  the same lag at each step."""
+
+    delay_hold_prob: float = 0.0
+    """Probability of reusing the previous lag instead of resampling. Useful for
+  temporally correlated latency patterns."""
+
+    delay_update_period: int = 0
+    """Resample lag every N steps (models multi-rate sensors). If 0, update every step."""
+
+    delay_per_env_phase: bool = True
+    """If True and update_period > 0, stagger update timing across envs to avoid
+  synchronized resampling."""
+
+    history_length: int = 0
+    """Number of past observations to keep in history. 0 = no history."""
+
+    flatten_history_dim: bool = True
+    """Whether to flatten the history dimension into observation.
+
+  When True and concatenate_terms=True, uses term-major ordering:
+  [A_t0, A_t1, ..., A_tH-1, B_t0, B_t1, ..., B_tH-1, ...]
+  See docs/source/observation.rst for details on ordering."""
+
+
+@dataclass
+class ObservationGroupCfg:
+    """Configuration for an observation group.
+
+    An observation group bundles multiple observation terms together. Groups are
+    typically used to separate observations for different purposes (e.g., "actor"
+    for the actor, "critic" for the value function).
+    """
+
+    terms: dict[str, ObservationTermCfg | None] = field(
+        metadata={CONFIG_MAPPING_POLICY_KEY: MANAGER_TERM_MAPPING_POLICY}
+    )
+    """Dictionary mapping term names to their configurations."""
+
+    concatenate_terms: bool = True
+    """Whether to concatenate all terms into a single tensor. If False, returns
+  a dict mapping term names to their individual tensors."""
+
+    concatenate_dim: int = -1
+    """Dimension along which to concatenate terms. Default -1 (last dimension)."""
+
+    enable_corruption: bool = False
+    """Whether to apply noise corruption to observations. Set to True during
+  training for domain randomization, False during evaluation."""
+
+    history_length: int | None = None
+    """Group-level history length override. If set, applies to all terms in
+  this group. If None, each term uses its own ``history_length`` setting."""
+
+    flatten_history_dim: bool = True
+    """Whether to flatten history into the observation dimension. If True,
+  observations have shape ``(num_envs, obs_dim * history_length)``. If False,
+  shape is ``(num_envs, history_length, obs_dim)``."""
+
+    nan_policy: Literal["disabled", "warn", "sanitize", "error"] = "error"
+    """NaN/Inf handling policy for observations in this group.
+
+  - 'disabled': No checks (explicit opt-out)
+  - 'warn': Log warning with term name and env IDs, then sanitize (debugging)
+  - 'sanitize': Silent sanitization to 0.0 like reward manager (safe for production)
+  - 'error': Raise ValueError on NaN/Inf (strict development mode)
+  """
+
+    nan_check_per_term: bool = True
+    """If True, check each observation term individually to identify NaN source.
+  If False, check only the final concatenated output (faster but less informative).
+  Only applies when nan_policy != 'disabled'."""
+
+
+class ObservationManager(ManagerBase):
+    """Manages observation computation for the environment.
+
+    The observation manager computes observations from multiple terms organized
+    into groups. Each term can have noise, clipping, scaling, delay, and history
+    applied. Groups can optionally concatenate their terms into a single tensor.
+    """
+
+    def __init__(self, cfg: dict[str, ObservationGroupCfg | None], env: ManagerBasedRlEnv):
+        self.cfg = deepcopy(cfg)
+        super().__init__(env=env)
+
+        self._group_obs_dim: dict[str, tuple[int, ...] | list[tuple[int, ...]]] = dict()
+
+        for group_name, group_term_dims in self._group_obs_term_dim.items():
+            if self._group_obs_concatenate[group_name]:
+                term_dims = np.stack([np.asarray(dims) for dims in group_term_dims], axis=0)
+                if len(term_dims.shape) > 1:
+                    if self._group_obs_concatenate_dim[group_name] >= 0:
+                        dim = self._group_obs_concatenate_dim[group_name] - 1
+                    else:
+                        dim = self._group_obs_concatenate_dim[group_name]
+                    dim_sum = np.sum(term_dims[:, dim], axis=0)
+                    term_dims[0, dim] = dim_sum
+                    term_dims = term_dims[0]
+                else:
+                    term_dims = np.sum(term_dims, axis=0)
+                self._group_obs_dim[group_name] = tuple(term_dims.tolist())
+            else:
+                self._group_obs_dim[group_name] = group_term_dims
+
+        self._obs_buffer: dict[str, np.ndarray | dict[str, np.ndarray]] | None = None
+
+    def __str__(self) -> str:
+        msg = f"<ObservationManager> contains {len(self._group_obs_term_names)} groups.\n"
+        for group_name, group_dim in self._group_obs_dim.items():
+            table = PrettyTable()
+            table.title = f"Active Observation Terms in Group: '{group_name}'"
+            if self._group_obs_concatenate[group_name]:
+                table.title += f" (shape: {group_dim})"  # type: ignore
+            table.field_names = ["Index", "Name", "Shape"]
+            table.align["Name"] = "l"
+            obs_terms = zip(
+                self._group_obs_term_names[group_name],
+                self._group_obs_term_dim[group_name],
+                self._group_obs_term_cfgs[group_name],
+                strict=False,
+            )
+            for index, (name, dims, term_cfg) in enumerate(obs_terms):
+                if term_cfg.history_length > 0 and term_cfg.flatten_history_dim:
+                    # Flattened history: show (9,) ← 3×(3,)
+                    original_size = int(np.prod(dims)) // term_cfg.history_length
+                    original_shape = (original_size,) if len(dims) == 1 else dims[1:]
+                    shape_str = f"{dims}  ← {term_cfg.history_length}×{original_shape}"
+                else:
+                    shape_str = str(tuple(dims))
+                table.add_row([index, name, shape_str])
+            msg += str(table.get_string())
+            msg += "\n"
+        return msg
+
+    def get_active_iterable_terms(self, env_idx: int) -> Sequence[tuple[str, Sequence[float]]]:
+        terms = []
+
+        if self._obs_buffer is None:
+            self.compute()
+        assert self._obs_buffer is not None
+        obs_buffer: dict[str, np.ndarray | dict[str, np.ndarray]] = self._obs_buffer
+
+        for group_name, _ in self.group_obs_dim.items():
+            if not self.group_obs_concatenate[group_name]:
+                buffers = obs_buffer[group_name]
+                assert isinstance(buffers, dict)
+                for name, term in buffers.items():
+                    terms.append((group_name + "-" + name, term[env_idx].tolist()))
+                continue
+
+            idx = 0
+            data = obs_buffer[group_name]
+            assert isinstance(data, np.ndarray)
+            for name, shape in zip(
+                self._group_obs_term_names[group_name],
+                self._group_obs_term_dim[group_name],
+                strict=False,
+            ):
+                data_length = np.prod(shape)
+                term = data[env_idx, idx : idx + data_length]
+                terms.append((group_name + "-" + name, term.tolist()))
+                idx += data_length
+
+        return terms
+
+    # Properties.
+
+    @property
+    def active_terms(self) -> dict[str, list[str]]:
+        return self._group_obs_term_names
+
+    @property
+    def group_obs_dim(self) -> dict[str, tuple[int, ...] | list[tuple[int, ...]]]:
+        return self._group_obs_dim
+
+    @property
+    def group_obs_term_dim(self) -> dict[str, list[tuple[int, ...]]]:
+        return self._group_obs_term_dim
+
+    @property
+    def group_obs_concatenate(self) -> dict[str, bool]:
+        return self._group_obs_concatenate
+
+    # Methods.
+
+    def get_term_cfg(self, group_name: str, term_name: str) -> ObservationTermCfg:
+        if group_name not in self._group_obs_term_names:
+            raise ValueError(f"Group '{group_name}' not found in active groups.")
+        if term_name not in self._group_obs_term_names[group_name]:
+            raise ValueError(f"Term '{term_name}' not found in group '{group_name}'.")
+        index = self._group_obs_term_names[group_name].index(term_name)
+        return self._group_obs_term_cfgs[group_name][index]
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> dict[str, float]:
+        # Invalidate cache since reset envs will have different observations.
+        self._obs_buffer = None
+
+        for group_name, group_cfg in self._group_obs_class_term_cfgs.items():
+            for term_cfg in group_cfg:
+                term_cfg.func.reset(env_ids=env_ids)
+            for term_name in self._group_obs_term_names[group_name]:
+                batch_ids = env_ids
+                if term_name in self._group_obs_term_delay_buffer[group_name]:
+                    self._group_obs_term_delay_buffer[group_name][term_name].reset(
+                        batch_ids=batch_ids
+                    )
+                if term_name in self._group_obs_term_history_buffer[group_name]:
+                    self._group_obs_term_history_buffer[group_name][term_name].reset(
+                        batch_ids=batch_ids
+                    )
+        for group_mods in self._group_obs_class_instances.values():
+            for mod in group_mods.values():
+                mod.reset(env_ids=env_ids)
+        return {}
+
+    def _check_and_handle_nans(
+        self,
+        tensor: np.ndarray,
+        context: str,
+        policy: str,
+        env_ids: np.ndarray | None = None,
+    ) -> np.ndarray:
+        """Check for NaN/Inf and handle according to policy.
+
+        Args:
+          tensor: Observation tensor to check. On the reset path this holds only
+            the reset rows; pass env_ids so diagnostics report real env indices.
+          context: Context string for error/warning messages (e.g., "actor/base_lin_vel").
+          policy: NaN handling policy ("disabled", "warn", "sanitize", "error").
+          env_ids: Optional mapping from tensor rows to env indices (reset path).
+
+        Returns:
+          The tensor, potentially sanitized depending on policy.
+
+        Raises:
+          ValueError: If policy is "error" and NaN/Inf detected.
+        """
+        if policy == "disabled":
+            return tensor
+
+        # The overwhelmingly common path is finite.  Use one full tensor scan
+        # here instead of separate isnan/isinf scans for every term.  On the
+        # exceptional path the same allocation is reused as the invalid mask
+        # so diagnostics and sanitization retain their existing semantics.
+        finite = np.isfinite(tensor)
+        if finite.all():
+            return tensor
+        invalid = np.logical_not(finite, out=finite)
+        invalid_values = tensor[invalid]
+        has_nan = np.isnan(invalid_values).any()
+        has_inf = np.isinf(invalid_values).any()
+
+        def _row_env_ids(mask: np.ndarray) -> list[int]:
+            rows = np.flatnonzero(np.asarray(mask, dtype=bool))
+            if env_ids is not None:
+                rows = np.asarray(env_ids)[rows]
+            result: list[int] = rows.tolist()
+            return result
+
+        if policy == "error":
+            nan_mask = np.asarray(invalid.reshape(tensor.shape[0], -1).any(axis=1))
+            nan_env_ids = _row_env_ids(nan_mask)
+            invalid_kind = (
+                "NaN"
+                if has_nan and not has_inf
+                else "Inf"
+                if has_inf and not has_nan
+                else "NaN/Inf"
+            )
+            raise ValueError(
+                f"{invalid_kind} detected in ObservationManager term '{context}' "
+                f"for environments: {nan_env_ids[:10]}"
+            )
+
+        if policy == "warn":
+            nan_mask = np.asarray(invalid.reshape(tensor.shape[0], -1).any(axis=1))
+            nan_env_ids = _row_env_ids(nan_mask)
+            print(
+                f"[ObservationManager] NaN/Inf in '{context}' "
+                f"(envs: {nan_env_ids[:5]}). Sanitizing to 0."
+            )
+
+        # Sanitize (applies to both "warn" and "sanitize" policies).
+        return np.nan_to_num(tensor, copy=False, nan=0.0, posinf=0.0, neginf=0.0)
+
+    def compute(
+        self,
+        update_history: bool = False,
+        env_ids: np.ndarray | None = None,
+    ) -> dict[str, np.ndarray | dict[str, np.ndarray]]:
+        """Compute observations for all groups.
+
+        With env_ids=None (the per-step path), history and delay buffers advance
+        for all envs and the returned arrays cover the full batch. With env_ids
+        (the reset path), only the reset envs' buffers receive their post-reset
+        frame (a backfill); other envs' buffers, delay schedules, and lag draws
+        are untouched, so a partial reset does not advance their observation
+        timelines. The returned arrays then hold only the reset rows, in env_ids
+        order, and the observation cache is left invalidated (the next per-step
+        compute refreshes it). On row-scoped (non-temporal) groups, noise is
+        drawn only for the reset rows: issue #1349 removed the requirement that
+        the reset path consume the shared RNG stream identically to a
+        full-batch compute, so reset-path noise values and RNG consumption no
+        longer match the full-batch path.
+        """
+        if env_ids is not None and not update_history:
+            raise ValueError("env_ids is only meaningful with update_history=True.")
+        # Return cached observations if not updating and cache exists.
+        # This prevents double-pushing to delay buffers when compute() is called
+        # multiple times per control step (e.g., in get_observations() after step()).
+        if not update_history and self._obs_buffer is not None:
+            return self._obs_buffer
+
+        obs_buffer: dict[str, np.ndarray | dict[str, np.ndarray]] = dict()
+        # Cross-group sharing of identical term computations (issue #1351):
+        # within one compute() call, terms with the same func and params yield
+        # the same raw output (the per-term pipeline never mutates func outputs
+        # in place), so later groups reuse the first group's raw result.
+        share_cache: dict[tuple, np.ndarray] = {}
+        for group_name in self._group_obs_term_names:
+            obs_buffer[group_name] = self.compute_group(
+                group_name, update_history, env_ids, share_cache=share_cache
+            )
+        if env_ids is None:
+            self._obs_buffer = obs_buffer
+        return obs_buffer
+
+    def compute_group(
+        self,
+        group_name: str,
+        update_history: bool = False,
+        env_ids: np.ndarray | None = None,
+        *,
+        share_cache: dict[tuple, np.ndarray] | None = None,
+    ) -> np.ndarray | dict[str, np.ndarray]:
+        group_cfg = self.cfg[group_name]
+        if group_cfg is None:
+            raise KeyError(f"Observation group '{group_name}' is disabled.")
+        group_term_names = self._group_obs_term_names[group_name]
+        group_obs: dict[str, np.ndarray] = {}
+        obs_terms = zip(group_term_names, self._group_obs_term_cfgs[group_name], strict=False)
+        if share_cache is None:
+            share_cache = {}
+        share_map = self._group_obs_term_share.get(group_name, {})
+        # In the strict default policy a finite result is by far the common
+        # case.  For concatenated groups, scan the assembled output once and
+        # only inspect individual slices when an error is actually found; this
+        # retains the per-term diagnostic while removing repeated full scans
+        # from the hot path.
+        defer_error_nan_check = (
+            self._group_obs_concatenate[group_name]
+            and not self._group_obs_temporal[group_name]
+            and group_cfg.nan_check_per_term
+            and group_cfg.nan_policy == "error"
+        )
+        # Reset path (issue #1259 R2): when no term in this group uses delay or
+        # history buffers, everything downstream of the term call is row
+        # independent, so only the reset rows are processed. Term calls stay
+        # full-batch (term funcs are contracted to return (num_envs, ...)), but
+        # noise is drawn for the reset rows only — issue #1349 removed the
+        # full-batch RNG-stream parity requirement.
+        row_scoped = env_ids is not None and not self._group_obs_temporal[group_name]
+        for term_name, term_cfg in obs_terms:
+            share_key = share_map.get(term_name)
+            if share_key is not None and share_key in share_cache:
+                obs = share_cache[share_key]
+            else:
+                obs = term_cfg.func(self._env, **term_cfg.params)
+                if share_key is not None:
+                    share_cache[share_key] = obs
+            if not isinstance(obs, np.ndarray):
+                raise TypeError(
+                    f"ObservationManager term '{group_name}/{term_name}' returned "
+                    f"{type(obs).__name__}, expected np.ndarray."
+                )
+            if obs.ndim < 2 or obs.shape[0] != self.num_envs:
+                raise ValueError(
+                    f"ObservationManager term '{group_name}/{term_name}' returned shape "
+                    f"{obs.shape}, expected (num_envs, ...) with num_envs={self.num_envs}."
+                )
+            fresh = False
+            if row_scoped:
+                # Slice before noise: reset-path noise is drawn for the reset
+                # rows only (issue #1349 removed the full-batch RNG-stream
+                # parity requirement). Fancy indexing already returns a fresh
+                # row copy, safe for the in-place clip/scale below.
+                obs = obs[env_ids]
+                fresh = True
+            if isinstance(term_cfg.noise, noise_cfg.NoiseCfg):
+                # NoiseCfg.apply always returns a newly allocated array.
+                obs = term_cfg.noise.apply(obs, rng=self._env.rng)
+                fresh = True
+            elif isinstance(term_cfg.noise, noise_cfg.NoiseModelCfg):
+                # NoiseModel.__call__ likewise returns a new array.
+                obs = self._group_obs_class_instances[group_name][term_name](obs)
+                fresh = True
+            sanitizes_per_term = group_cfg.nan_check_per_term and group_cfg.nan_policy in (
+                "warn",
+                "sanitize",
+            )
+            exposes_term_output = (
+                not self._group_obs_concatenate[group_name]
+                and term_cfg.delay_max_lag == 0
+                and term_cfg.history_length == 0
+            )
+            if (
+                not row_scoped
+                and not fresh
+                and (
+                    term_cfg.clip is not None
+                    or term_cfg.scale is not None
+                    or sanitizes_per_term
+                    or exposes_term_output
+                )
+            ):
+                # Concatenation and temporal buffers already copy their inputs.
+                # Only take a defensive copy when this pipeline may mutate the
+                # term or expose it directly to callers.
+                obs = obs.copy()
+            if term_cfg.clip:
+                np.clip(obs, term_cfg.clip[0], term_cfg.clip[1], out=obs)
+            if term_cfg.scale is not None:
+                scale = term_cfg.scale
+                assert isinstance(scale, np.ndarray)
+                np.multiply(obs, scale, out=obs)
+
+            # Check for NaN/Inf before delay/history buffers (per-term checking).
+            if (
+                group_cfg.nan_check_per_term
+                and group_cfg.nan_policy != "disabled"
+                and not defer_error_nan_check
+            ):
+                obs = self._check_and_handle_nans(
+                    obs,
+                    context=f"{group_name}/{term_name}",
+                    policy=group_cfg.nan_policy,
+                    env_ids=env_ids if row_scoped else None,
+                )
+
+            if term_cfg.delay_max_lag > 0:
+                delay_buffer = self._group_obs_term_delay_buffer[group_name][term_name]
+                if env_ids is None or not delay_buffer.is_initialized:
+                    delay_buffer.append(obs)
+                    obs = delay_buffer.compute()
+                else:
+                    delay_buffer.backfill(obs, env_ids)
+                    obs = delay_buffer.peek()
+            if term_cfg.history_length > 0:
+                circular_buffer = self._group_obs_term_history_buffer[group_name][term_name]
+                if env_ids is None or not circular_buffer.is_initialized:
+                    if update_history or not circular_buffer.is_initialized:
+                        circular_buffer.append(obs)
+                else:
+                    circular_buffer.backfill(obs, env_ids)
+
+                if term_cfg.flatten_history_dim:
+                    group_obs[term_name] = circular_buffer.buffer.reshape(self._env.num_envs, -1)
+                else:
+                    group_obs[term_name] = circular_buffer.buffer
+            else:
+                group_obs[term_name] = obs
+
+        # Final NaN check for non-per-term checking.
+        if not group_cfg.nan_check_per_term and group_cfg.nan_policy != "disabled":
+            if self._group_obs_concatenate[group_name]:
+                # Will check after concatenation below.
+                pass
+            else:
+                for term_name in group_obs:
+                    group_obs[term_name] = self._check_and_handle_nans(
+                        group_obs[term_name],
+                        context=f"{group_name}/{term_name}",
+                        policy=group_cfg.nan_policy,
+                        env_ids=env_ids if row_scoped else None,
+                    )
+
+        if self._group_obs_concatenate[group_name]:
+            result = np.concatenate(
+                list(group_obs.values()), axis=self._group_obs_concatenate_dim[group_name]
+            )
+            if defer_error_nan_check:
+                finite = np.isfinite(result)
+                if not finite.all():
+                    axis = self._group_obs_concatenate_dim[group_name]
+                    axis = axis if axis >= 0 else result.ndim + axis
+                    offset = 0
+                    for term_name, term_dims in zip(
+                        group_term_names,
+                        self._group_obs_term_dim[group_name],
+                        strict=True,
+                    ):
+                        width = int(term_dims[axis - 1])
+                        selectors = [slice(None)] * result.ndim
+                        selectors[axis] = slice(offset, offset + width)
+                        selector = tuple(selectors)
+                        if not finite[selector].all():
+                            # Reuse the established diagnostic path so the
+                            # error still names the first offending term and
+                            # reports reset-row IDs when applicable.
+                            self._check_and_handle_nans(
+                                result[selector],
+                                context=f"{group_name}/{term_name}",
+                                policy=group_cfg.nan_policy,
+                                env_ids=env_ids if row_scoped else None,
+                            )
+                            break
+                        offset += width
+            # Final check for concatenated result (non-per-term checking).
+            if not group_cfg.nan_check_per_term and group_cfg.nan_policy != "disabled":
+                result = self._check_and_handle_nans(
+                    result,
+                    context=group_name,
+                    policy=group_cfg.nan_policy,
+                    env_ids=env_ids if row_scoped else None,
+                )
+        else:
+            result = group_obs
+
+        if env_ids is not None and not row_scoped:
+            # Groups with delay/history terms ran the full-batch pipeline above
+            # (buffer readout stays full-batch); slice the reset rows to match
+            # the reset-path return contract.
+            if isinstance(result, dict):
+                result = {name: values[env_ids] for name, values in result.items()}
+            else:
+                result = result[env_ids]
+
+        return result
+
+    def _prepare_terms(self) -> None:
+        self._group_obs_term_names: dict[str, list[str]] = dict()
+        self._group_obs_term_dim: dict[str, list[tuple[int, ...]]] = dict()
+        self._group_obs_term_cfgs: dict[str, list[ObservationTermCfg]] = dict()
+        self._group_obs_class_term_cfgs: dict[str, list[ObservationTermCfg]] = dict()
+        self._group_obs_concatenate: dict[str, bool] = dict()
+        self._group_obs_concatenate_dim: dict[str, int] = dict()
+        self._group_obs_class_instances: dict[str, dict[str, noise_model.NoiseModel]] = {}
+        self._group_obs_term_delay_buffer: dict[str, dict[str, DelayBuffer]] = dict()
+        self._group_obs_term_history_buffer: dict[str, dict[str, CircularBuffer]] = dict()
+        # Whether any term in the group uses delay/history buffers. Groups
+        # without temporal terms can be row-scoped on the reset path.
+        self._group_obs_temporal: dict[str, bool] = dict()
+
+        for group_name, group_cfg in self.cfg.items():
+            if group_cfg is None:
+                print(f"group: {group_name} set to None, skipping...")
+                continue
+
+            if not any(t is not None for t in group_cfg.terms.values()):
+                print(f"group: {group_name} has no active terms, skipping...")
+                continue
+
+            if group_cfg.nan_policy not in ("disabled", "warn", "sanitize", "error"):
+                raise ValueError(
+                    f"Observation group '{group_name}' has unsupported NaN policy "
+                    f"'{group_cfg.nan_policy}'."
+                )
+            if group_cfg.history_length is not None and group_cfg.history_length < 0:
+                raise ValueError(
+                    f"Observation group '{group_name}' has negative history_length "
+                    f"{group_cfg.history_length}."
+                )
+
+            self._group_obs_term_names[group_name] = list()
+            self._group_obs_term_dim[group_name] = list()
+            self._group_obs_term_cfgs[group_name] = list()
+            self._group_obs_class_term_cfgs[group_name] = list()
+            self._group_obs_class_instances[group_name] = {}
+            group_entry_delay_buffer: dict[str, DelayBuffer] = dict()
+            group_entry_history_buffer: dict[str, CircularBuffer] = dict()
+
+            self._group_obs_concatenate[group_name] = group_cfg.concatenate_terms
+            self._group_obs_concatenate_dim[group_name] = (
+                group_cfg.concatenate_dim + 1
+                if group_cfg.concatenate_dim >= 0
+                else group_cfg.concatenate_dim
+            )
+
+            for term_name, term_cfg in group_cfg.terms.items():
+                if term_cfg is None:
+                    print(f"term: {term_name} set to None, skipping...")
+                    continue
+
+                if term_cfg.delay_min_lag < 0 or term_cfg.delay_max_lag < term_cfg.delay_min_lag:
+                    raise ValueError(
+                        f"ObservationManager term '{group_name}/{term_name}' has invalid "
+                        f"delay range [{term_cfg.delay_min_lag}, {term_cfg.delay_max_lag}]."
+                    )
+                if term_cfg.history_length < 0:
+                    raise ValueError(
+                        f"ObservationManager term '{group_name}/{term_name}' has negative "
+                        f"history_length {term_cfg.history_length}."
+                    )
+                if term_cfg.clip is not None and term_cfg.clip[0] > term_cfg.clip[1]:
+                    raise ValueError(
+                        f"ObservationManager term '{group_name}/{term_name}' has invalid "
+                        f"clip range {term_cfg.clip}."
+                    )
+
+                # NOTE: This deepcopy is important to avoid cross-group contamination of term
+                # configs.
+                term_cfg = deepcopy(term_cfg)
+                self._resolve_common_term_cfg(term_name, term_cfg)
+
+                if not group_cfg.enable_corruption:
+                    term_cfg.noise = None
+                if group_cfg.history_length is not None:
+                    term_cfg.history_length = group_cfg.history_length
+                    term_cfg.flatten_history_dim = group_cfg.flatten_history_dim
+                self._group_obs_term_names[group_name].append(term_name)
+                self._group_obs_term_cfgs[group_name].append(term_cfg)
+                if hasattr(term_cfg.func, "reset") and callable(term_cfg.func.reset):
+                    self._group_obs_class_term_cfgs[group_name].append(term_cfg)
+
+                initial_obs = term_cfg.func(self._env, **term_cfg.params)
+                if not isinstance(initial_obs, np.ndarray):
+                    raise TypeError(
+                        f"ObservationManager term '{group_name}/{term_name}' returned "
+                        f"{type(initial_obs).__name__}, expected np.ndarray."
+                    )
+                if initial_obs.ndim < 2 or initial_obs.shape[0] != self.num_envs:
+                    raise ValueError(
+                        f"ObservationManager term '{group_name}/{term_name}' returned shape "
+                        f"{initial_obs.shape}, expected (num_envs, ...) with "
+                        f"num_envs={self.num_envs}."
+                    )
+                obs_dims = tuple(initial_obs.shape)
+
+                if term_cfg.scale is not None:
+                    term_cfg.scale = np.asarray(term_cfg.scale, dtype=np.float32).copy()
+
+                if term_cfg.noise is not None and isinstance(
+                    term_cfg.noise, noise_cfg.NoiseModelCfg
+                ):
+                    noise_model_cls = term_cfg.noise.class_type
+                    if not issubclass(noise_model_cls, noise_model.NoiseModel):
+                        raise TypeError(
+                            f"ObservationManager term '{group_name}/{term_name}' noise model "
+                            f"{noise_model_cls} is not a NoiseModel subclass."
+                        )
+                    self._group_obs_class_instances[group_name][term_name] = noise_model_cls(
+                        term_cfg.noise, num_envs=self._env.num_envs, rng=self._env.rng
+                    )
+
+                if term_cfg.delay_max_lag > 0:
+                    group_entry_delay_buffer[term_name] = DelayBuffer(
+                        min_lag=term_cfg.delay_min_lag,
+                        max_lag=term_cfg.delay_max_lag,
+                        batch_size=self._env.num_envs,
+                        per_env=term_cfg.delay_per_env,
+                        hold_prob=term_cfg.delay_hold_prob,
+                        update_period=term_cfg.delay_update_period,
+                        per_env_phase=term_cfg.delay_per_env_phase,
+                        generator=self._env.rng,
+                    )
+
+                if term_cfg.history_length > 0:
+                    group_entry_history_buffer[term_name] = CircularBuffer(
+                        max_len=term_cfg.history_length,
+                        batch_size=self._env.num_envs,
+                    )
+                    old_dims = list(obs_dims)
+                    old_dims.insert(1, term_cfg.history_length)
+                    obs_dims = tuple(old_dims)
+                    if term_cfg.flatten_history_dim:
+                        obs_dims = (obs_dims[0], int(np.prod(obs_dims[1:])))
+
+                self._group_obs_term_dim[group_name].append(obs_dims[1:])
+
+            self._group_obs_term_delay_buffer[group_name] = group_entry_delay_buffer
+            self._group_obs_term_history_buffer[group_name] = group_entry_history_buffer
+            self._group_obs_temporal[group_name] = any(
+                term_cfg.delay_max_lag > 0 or term_cfg.history_length > 0
+                for term_cfg in self._group_obs_term_cfgs[group_name]
+            )
+
+        # Cross-group sharing of identical term computations (issue #1351):
+        # within one compute() call, terms with the same func and params yield
+        # the same raw output, so later groups reuse the first group's result.
+        # Class-based terms (possibly stateful per call) and terms with
+        # unhashable params are never shared.
+        self._group_obs_term_share: dict[str, dict[str, tuple]] = {}
+        for share_group, share_terms in self._group_obs_term_cfgs.items():
+            share_entry: dict[str, tuple] = {}
+            for share_name, share_cfg in zip(
+                self._group_obs_term_names[share_group], share_terms, strict=False
+            ):
+                func = share_cfg.func
+                if hasattr(func, "reset") and callable(func.reset):
+                    continue
+                try:
+                    share_key = (func, tuple(sorted(share_cfg.params.items())))
+                    hash(share_key)
+                except TypeError:
+                    continue
+                share_entry[share_name] = share_key
+            self._group_obs_term_share[share_group] = share_entry
diff --git a/src/unilab/managers/recorder_manager.py b/src/unilab/managers/recorder_manager.py
new file mode 100644
index 000000000..a2a379921
--- /dev/null
+++ b/src/unilab/managers/recorder_manager.py
@@ -0,0 +1,267 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), src/mjlab/managers/recorder_manager.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and UniLab contracts; licensed under Apache-2.0.
+"""Recorder manager for logging environment data during rollouts."""
+
+from __future__ import annotations
+
+from copy import deepcopy
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+import numpy as np
+from prettytable import PrettyTable
+
+from unilab.managers.manager_base import ManagerBase, ManagerTermBase, ManagerTermBaseCfg
+
+if TYPE_CHECKING:
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+@dataclass
+class RecorderTermCfg(ManagerTermBaseCfg):
+    """Configuration for a recorder term.
+
+    ``func`` must be a :class:`RecorderTerm` subclass. Function-based terms are not
+    supported because recorder terms are stateful (file handles, buffers, etc.).
+    """
+
+
+class RecorderTerm(ManagerTermBase):
+    """Base class for recorder terms.
+
+    Override only the lifecycle methods you need. Each method is a no-op by default so
+    subclasses are not required to implement all of them.
+
+    The environment is available as ``self._env``, giving access to ``self._env.obs_buf``,
+    ``self._env.action_manager.action``, and all other environment state.
+
+    Example::
+
+      class CsvRecorder(RecorderTerm):
+        def __init__(self, cfg, env):
+          super().__init__(cfg, env)
+          self._file = open(cfg.params["path"], "w", newline="")
+          self._writer = csv.writer(self._file)
+
+        def record_pre_reset(self, env_ids):
+          # Terminal transition: action is still intact here.
+          # It will be zeroed by _reset_idx immediately after this returns.
+          obs = self._env.obs_buf["actor"][env_ids]
+          act = self._env.action_manager.action[env_ids]
+          for o, a in zip(obs, act):
+            self._writer.writerow(o.tolist() + a.tolist())
+
+        def record_post_step(self):
+          # Skip envs that just reset: their terminal pair was written in record_pre_reset
+          # and their action is now zeroed.
+          mask = ~self._env.reset_buf
+          obs = self._env.obs_buf["actor"][mask]
+          act = self._env.action_manager.action[mask]
+          for o, a in zip(obs, act):
+            self._writer.writerow(o.tolist() + a.tolist())
+
+        def close(self):
+          self._file.close()
+    """
+
+    def __init__(self, cfg: RecorderTermCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)  # ManagerTermBase only accepts env
+        self.cfg = cfg
+
+    def record_pre_reset(self, env_ids: np.ndarray) -> None:
+        """Called in ``env.step()`` before terminated environments are reset.
+
+        **What is available:**
+
+        - ``obs_buf`` contains the observation from the *end of the previous step* (the
+          input the agent used to choose the terminal action). It does **not** contain the
+          post-action terminal observation (the state reached after applying the action),
+          which is never computed for resetting environments.
+        - ``action_manager.action`` contains the action applied during this step. This is
+          the correct terminal action. It will be zeroed for these environments by
+          ``_reset_idx`` immediately after this hook returns, so capture it here if you
+          need it later.
+        - ``reward_buf`` contains the reward for this terminal step.
+        - ``reset_terminated`` and ``reset_time_outs`` reflect why each environment is
+          resetting.
+
+        This is the right hook to record the terminal transition
+        ``(obs_t, action_t, reward_t, done=True)`` for each resetting environment.
+
+        Args:
+          env_ids: Indices of environments that are about to be reset.
+        """
+        del env_ids  # Unused in base implementation.
+
+    def record_post_reset(self, env_ids: np.ndarray) -> None:
+        """Called after a reset completes with fresh observations computed.
+
+        Fires at the end of ``env.reset()`` (covering all environments on the initial call)
+        and within ``env.step()`` for each batch of environments that terminates, after
+        state has been overwritten and new observations computed.
+
+        At this point ``obs_buf[env_ids]`` holds the initial observation of the new episode
+        and ``action_manager.action[env_ids]`` is zero (no action has been taken in the new
+        episode yet).
+
+        Use this hook to initialize per-episode state or record the first observation of a
+        new episode.
+
+        Args:
+          env_ids: Indices of environments that were reset.
+        """
+        del env_ids  # Unused in base implementation.
+
+    def record_post_step(self) -> None:
+        """Called at the end of every ``env.step()`` with fresh observations.
+
+        At this point ``obs_buf`` holds the new observation for every environment and
+        ``action_manager.action`` holds the action that was applied during this step.
+        **Exception:** for environments that reset during this step,
+        ``action_manager.action`` has been zeroed by ``_reset_idx`` and ``obs_buf`` holds
+        the initial observation of the new episode rather than the post-action terminal
+        observation. Use ``record_pre_reset`` to capture the terminal ``(obs, action)``
+        pair for those environments. Resetting environments are identified by
+        ``self._env.reset_buf``.
+        """
+
+    def close(self) -> None:
+        """Called when the environment closes.
+
+        Release file handles, flush write buffers, or finalize output here.
+        """
+
+    def __call__(self):
+        raise NotImplementedError(
+            "RecorderTerm is not invoked via __call__. Override the lifecycle methods instead."
+        )
+
+
+class RecorderManager(ManagerBase):
+    """Orchestrates recorder terms during environment rollouts.
+
+    Holds a collection of :class:`RecorderTerm` instances and calls their lifecycle
+    methods at the appropriate points in the environment loop. The manager has no opinion
+    on how data is stored; each term handles its own I/O entirely.
+
+    Register terms by adding them to the ``recorders`` dict on
+    :class:`ManagerBasedRlEnvCfg`. If the dict is empty, the environment
+    substitutes a :class:`NullRecorderManager` with zero overhead.
+    """
+
+    def __init__(self, cfg: dict[str, RecorderTermCfg | None], env: ManagerBasedRlEnv):
+        self._terms: dict[str, RecorderTerm] = {}
+        self.cfg = deepcopy(cfg)
+        super().__init__(env)  # calls _prepare_terms()
+
+    def __str__(self) -> str:
+        msg = f"<RecorderManager> contains {len(self._terms)} active terms.\n"
+        table = PrettyTable()
+        table.title = "Active Recorder Terms"
+        table.field_names = ["Index", "Name"]
+        for idx, name in enumerate(self._terms):
+            table.add_row([idx, name])
+        return msg + str(table.get_string())
+
+    def __contains__(self, name: str) -> bool:
+        """Return True if a term named ``name`` is registered."""
+        return name in self._terms
+
+    @property
+    def active_terms(self) -> list[str]:
+        """List of active term names."""
+        return list(self._terms.keys())
+
+    def get_term(self, name: str) -> RecorderTerm:
+        """Return the recorder term registered under ``name``.
+
+        Use this to reach a recorder's public methods (e.g. to start/stop logging)
+        from outside the env loop without touching private state.
+
+        Args:
+          name: Term name as registered in ``ManagerBasedRlEnvCfg.recorders``.
+
+        Raises:
+          KeyError: If no term is registered under ``name``.
+        """
+        try:
+            return self._terms[name]
+        except KeyError:
+            msg = f"No recorder term named '{name}'. Active terms: {self.active_terms}"
+            raise KeyError(msg) from None
+
+    def record_pre_reset(self, env_ids: np.ndarray) -> None:
+        """Forward to each term's :meth:`RecorderTerm.record_pre_reset`."""
+        for term in self._terms.values():
+            term.record_pre_reset(env_ids)
+
+    def record_post_reset(self, env_ids: np.ndarray) -> None:
+        """Forward to each term's :meth:`RecorderTerm.record_post_reset`."""
+        for term in self._terms.values():
+            term.record_post_reset(env_ids)
+
+    def record_post_step(self) -> None:
+        """Forward to each term's :meth:`RecorderTerm.record_post_step`."""
+        for term in self._terms.values():
+            term.record_post_step()
+
+    def close(self) -> None:
+        """Forward to each term's :meth:`RecorderTerm.close`."""
+        for term in self._terms.values():
+            term.close()
+
+    def _prepare_terms(self) -> None:
+        for name, cfg in self.cfg.items():
+            if cfg is None:
+                continue
+            self._resolve_common_term_cfg(name, cfg)
+            # _resolve_common_term_cfg instantiates class-based terms in-place.
+            if not isinstance(cfg.func, RecorderTerm):
+                raise TypeError(
+                    f"Recorder term '{name}': func must be a RecorderTerm subclass,"
+                    f" got {type(cfg.func).__name__}. Function-based terms are not"
+                    " supported."
+                )
+            self._terms[name] = cfg.func
+
+
+class NullRecorderManager:
+    """No-op fallback used when no recorder terms are configured.
+
+    All methods are no-ops. This class is not a :class:`ManagerBase` subclass
+    so it carries zero overhead.
+    """
+
+    def __init__(self):
+        self.active_terms: list[str] = []
+        self.cfg = None
+
+    def __str__(self) -> str:
+        return "<NullRecorderManager> (inactive)"
+
+    def __repr__(self) -> str:
+        return "NullRecorderManager()"
+
+    def __contains__(self, name: str) -> bool:
+        """Always returns False since there are no terms."""
+        del name
+        return False
+
+    def get_term(self, name: str) -> RecorderTerm:
+        """Always raises KeyError since there are no terms."""
+        del name
+        msg = "NullRecorderManager has no terms."
+        raise KeyError(msg)
+
+    def record_pre_reset(self, env_ids: np.ndarray) -> None:
+        del env_ids
+
+    def record_post_reset(self, env_ids: np.ndarray) -> None:
+        del env_ids
+
+    def record_post_step(self) -> None:
+        pass
+
+    def close(self) -> None:
+        pass
diff --git a/src/unilab/managers/reward_manager.py b/src/unilab/managers/reward_manager.py
new file mode 100644
index 000000000..a95b4d63c
--- /dev/null
+++ b/src/unilab/managers/reward_manager.py
@@ -0,0 +1,185 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), src/mjlab/managers/reward_manager.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and UniLab contracts; licensed under Apache-2.0.
+"""Reward manager for computing reward signals."""
+
+from __future__ import annotations
+
+from copy import deepcopy
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+from prettytable import PrettyTable
+
+from unilab.managers.manager_base import ManagerBase, ManagerTermBaseCfg
+
+if TYPE_CHECKING:
+    from unilab.managers._types import DebugVisualizer, ManagerBasedRlEnv
+
+
+@dataclass(kw_only=True)
+class RewardTermCfg(ManagerTermBaseCfg):
+    """Configuration for a reward term."""
+
+    func: Any
+    """The callable that computes this reward term's value."""
+
+    weight: float
+    """Weight multiplier for this reward term."""
+
+
+class RewardManager(ManagerBase):
+    """Manages reward computation by aggregating weighted reward terms.
+
+    Reward Scaling Behavior:
+      By default, rewards are scaled by the environment step duration (dt). This
+      normalizes cumulative episodic rewards across different simulation frequencies.
+      The scaling can be disabled via the ``scale_by_dt`` parameter.
+
+      When ``scale_by_dt=True`` (default):
+        - ``reward_buf`` (returned by ``compute()``) = raw_value * weight * dt
+        - ``_episode_sums`` (cumulative rewards) are scaled by dt
+        - ``Episode_Reward/*`` logged metrics are scaled by dt
+
+      When ``scale_by_dt=False``:
+        - ``reward_buf`` = raw_value * weight (no dt scaling)
+
+      Regardless of the scaling setting:
+        - ``_step_reward`` (via ``get_active_iterable_terms()``) always contains
+          the unscaled reward rate (raw_value * weight)
+
+      ``step_reward_extras()`` exposes the latest ``compute()`` call's per-term
+      means as ``reward/<term>`` log entries (weighted, pre-dt rate), matching
+      the legacy envs' per-step reward log contract consumed by training
+      runners.
+    """
+
+    _env: ManagerBasedRlEnv
+
+    def __init__(
+        self,
+        cfg: dict[str, RewardTermCfg | None],
+        env: ManagerBasedRlEnv,
+        *,
+        scale_by_dt: bool = True,
+    ):
+        self._term_names: list[str] = list()
+        self._term_cfgs: list[RewardTermCfg] = list()
+        self._class_term_cfgs: list[RewardTermCfg] = list()
+        self._scale_by_dt = scale_by_dt
+
+        self.cfg = deepcopy(cfg)
+        super().__init__(env=env)
+        self._episode_sums = dict()
+        for term_name in self._term_names:
+            self._episode_sums[term_name] = np.zeros(self.num_envs, dtype=np.float32)
+        self._reward_buf = np.zeros(self.num_envs, dtype=np.float32)
+        self._step_reward = np.zeros((self.num_envs, len(self._term_names)), dtype=np.float32)
+        # Scratch for the weighted term value, reused across terms to avoid a
+        # temporary per term per step (issue #1296). Re-allocated if a term
+        # returns a non-float32 dtype.
+        self._term_weight_scratch = np.zeros(self.num_envs, dtype=np.float32)
+
+    def __str__(self) -> str:
+        msg = f"<RewardManager> contains {len(self._term_names)} active terms.\n"
+        table = PrettyTable()
+        table.title = "Active Reward Terms"
+        table.field_names = ["Index", "Name", "Weight"]
+        table.align["Name"] = "l"
+        table.align["Weight"] = "r"
+        for index, (name, term_cfg) in enumerate(
+            zip(self._term_names, self._term_cfgs, strict=False)
+        ):
+            table.add_row([index, name, term_cfg.weight])
+        msg += str(table.get_string())
+        msg += "\n"
+        return msg
+
+    # Properties.
+
+    @property
+    def active_terms(self) -> list[str]:
+        return self._term_names
+
+    # Methods.
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> dict[str, float]:
+        if env_ids is None:
+            env_ids = slice(None)
+        extras = {}
+        for key in self._episode_sums.keys():
+            episodic_sum_avg = float(np.mean(self._episode_sums[key][env_ids]))
+            extras["Episode_Reward/" + key] = episodic_sum_avg / self._env.max_episode_length_s
+            self._episode_sums[key][env_ids] = 0.0
+        for term_cfg in self._class_term_cfgs:
+            term_cfg.func.reset(env_ids=env_ids)
+        return extras
+
+    def compute(self, dt: float) -> np.ndarray:
+        if not np.isfinite(dt) or (self._scale_by_dt and dt <= 0.0):
+            raise ValueError(f"RewardManager received invalid dt {dt}.")
+        self._reward_buf[:] = 0.0
+        scale = dt if self._scale_by_dt else 1.0
+        for term_idx, (name, term_cfg) in enumerate(
+            zip(self._term_names, self._term_cfgs, strict=False)
+        ):
+            if term_cfg.weight == 0.0:
+                self._step_reward[:, term_idx] = 0.0
+                continue
+            value = term_cfg.func(self._env, **term_cfg.params)
+            self._check_term_shape(name, value)
+            self._check_term_finite(name, value)
+            # Weighted value goes through the shared scratch (same op order as
+            # ``value * weight * scale``); terms may return internal buffers, so
+            # ``value`` itself is never written to. Scratch dtype matches the
+            # expression result dtype (e.g. int term values promote to float64,
+            # as the pre-refactor temporary did).
+            scratch = self._term_weight_scratch
+            out_dtype = np.result_type(value, term_cfg.weight)
+            if scratch.dtype != out_dtype:
+                scratch = self._term_weight_scratch = np.empty(self.num_envs, dtype=out_dtype)
+            np.multiply(value, term_cfg.weight, out=scratch)
+            scratch *= scale
+            self._reward_buf += scratch
+            self._episode_sums[name] += scratch
+            np.divide(scratch, scale, out=self._step_reward[:, term_idx])
+        return self._reward_buf
+
+    def step_reward_extras(self) -> dict[str, float]:
+        """Per-term log entries of the latest ``compute()`` call.
+
+        Returns ``reward/<term>`` -> mean weighted reward rate across envs
+        (raw_value * weight, before dt scaling), mirroring the legacy envs'
+        per-step reward log format.
+        """
+        return {
+            f"reward/{name}": float(np.mean(self._step_reward[:, term_idx]))
+            for term_idx, name in enumerate(self._term_names)
+        }
+
+    def get_active_iterable_terms(self, env_idx: int) -> list[tuple[str, list[float]]]:
+        terms = []
+        for idx, name in enumerate(self._term_names):
+            terms.append((name, [self._step_reward[env_idx, idx].item()]))
+        return terms
+
+    def get_term_cfg(self, term_name: str) -> RewardTermCfg:
+        if term_name not in self._term_names:
+            raise ValueError(f"Term '{term_name}' not found in active terms.")
+        return self._term_cfgs[self._term_names.index(term_name)]
+
+    def _prepare_terms(self) -> None:
+        for term_name, term_cfg in self.cfg.items():
+            if term_cfg is None:
+                print(f"term: {term_name} set to None, skipping...")
+                continue
+            if not np.isfinite(term_cfg.weight):
+                raise ValueError(
+                    f"RewardManager term '{term_name}' has non-finite weight {term_cfg.weight}."
+                )
+            self._resolve_common_term_cfg(term_name, term_cfg)
+            self._term_names.append(term_name)
+            self._term_cfgs.append(term_cfg)
+            if hasattr(term_cfg.func, "reset") and callable(term_cfg.func.reset):
+                self._class_term_cfgs.append(term_cfg)
diff --git a/src/unilab/managers/scene_entity_config.py b/src/unilab/managers/scene_entity_config.py
new file mode 100644
index 000000000..e7035b199
--- /dev/null
+++ b/src/unilab/managers/scene_entity_config.py
@@ -0,0 +1,287 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), src/mjlab/managers/scene_entity_config.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and UniLab contracts; licensed under Apache-2.0.
+"""Configuration for scene entities used by manager terms."""
+
+from dataclasses import dataclass, field
+from typing import NamedTuple
+
+from unilab.managers._types import ManagerEntity, ManagerScene
+
+
+class _FieldConfig(NamedTuple):
+    """Configuration for a resolvable entity field."""
+
+    names_attr: str
+    ids_attr: str
+    find_method: str
+    num_attr: str
+    kind_label: str
+
+
+_FIELD_CONFIGS = [
+    _FieldConfig("joint_names", "joint_ids", "find_joints", "num_joints", "joint"),
+    _FieldConfig("body_names", "body_ids", "find_bodies", "num_bodies", "body"),
+    _FieldConfig("geom_names", "geom_ids", "find_geoms", "num_geoms", "geom"),
+    _FieldConfig("site_names", "site_ids", "find_sites", "num_sites", "site"),
+    _FieldConfig("actuator_names", "actuator_ids", "find_actuators", "num_actuators", "actuator"),
+    _FieldConfig("tendon_names", "tendon_ids", "find_tendons", "num_tendons", "tendon"),
+    _FieldConfig(
+        "camera_names",
+        "camera_ids",
+        "find_cameras",
+        "num_cameras",
+        "camera",
+    ),
+    _FieldConfig(
+        "light_names",
+        "light_ids",
+        "find_lights",
+        "num_lights",
+        "light",
+    ),
+    _FieldConfig(
+        "material_names",
+        "material_ids",
+        "find_materials",
+        "num_materials",
+        "material",
+    ),
+    _FieldConfig(
+        "texture_names",
+        "texture_ids",
+        "find_textures",
+        "num_textures",
+        "texture",
+    ),
+    _FieldConfig("pair_names", "pair_ids", "find_pairs", "num_pairs", "pair"),
+]
+
+
+@dataclass
+class SceneEntityCfg:
+    """Configuration for a scene entity that is used by the manager's term.
+
+    This configuration allows flexible specification of entity components either by name
+    or by ID. During resolution, it ensures consistency between names and IDs, and can
+    optimize to slice(None) when all components are selected.
+    """
+
+    name: str
+    """The name of the entity in the scene."""
+
+    joint_names: str | tuple[str, ...] | None = None
+    """Names of joints to include. Can be a single string or tuple."""
+
+    joint_ids: list[int] | slice = field(default_factory=lambda: slice(None))
+    """IDs of joints to include. Can be a list or slice."""
+
+    body_names: str | tuple[str, ...] | None = None
+    """Names of bodies to include. Can be a single string or tuple."""
+
+    body_ids: list[int] | slice = field(default_factory=lambda: slice(None))
+    """IDs of bodies to include. Can be a list or slice."""
+
+    geom_names: str | tuple[str, ...] | None = None
+    """Names of geometries to include. Can be a single string or tuple."""
+
+    geom_ids: list[int] | slice = field(default_factory=lambda: slice(None))
+    """IDs of geometries to include. Can be a list or slice."""
+
+    site_names: str | tuple[str, ...] | None = None
+    """Names of sites to include. Can be a single string or tuple."""
+
+    site_ids: list[int] | slice = field(default_factory=lambda: slice(None))
+    """IDs of sites to include. Can be a list or slice."""
+
+    actuator_names: str | list[str] | None = None
+    """Names of actuators to include. Can be a single string or list."""
+
+    actuator_ids: list[int] | slice = field(default_factory=lambda: slice(None))
+    """IDs of actuators to include. Can be a list or slice."""
+
+    tendon_names: str | tuple[str, ...] | None = None
+    """Names of tendons to include. Can be a single string or tuple."""
+
+    tendon_ids: list[int] | slice = field(default_factory=lambda: slice(None))
+    """IDs of tendons to include. Can be a list or slice."""
+
+    camera_names: str | tuple[str, ...] | None = None
+    """Names of cameras to include. Can be a single string or tuple."""
+
+    camera_ids: list[int] | slice = field(default_factory=lambda: slice(None))
+    """IDs of cameras to include. Can be a list or slice."""
+
+    light_names: str | tuple[str, ...] | None = None
+    """Names of lights to include. Can be a single string or tuple."""
+
+    light_ids: list[int] | slice = field(default_factory=lambda: slice(None))
+    """IDs of lights to include. Can be a list or slice."""
+
+    material_names: str | tuple[str, ...] | None = None
+    """Names of materials to include. Can be a single string or tuple."""
+
+    material_ids: list[int] | slice = field(default_factory=lambda: slice(None))
+    """IDs of materials to include. Can be a list or slice."""
+
+    texture_names: str | tuple[str, ...] | None = None
+    """Names of textures to include. Can be a single string or tuple."""
+
+    texture_ids: list[int] | slice = field(default_factory=lambda: slice(None))
+    """IDs of textures to include. Can be a list or slice."""
+
+    pair_names: str | tuple[str, ...] | None = None
+    """Names of contact pairs to include. Can be a single string or tuple."""
+
+    pair_ids: list[int] | slice = field(default_factory=lambda: slice(None))
+    """IDs of contact pairs to include. Can be a list or slice."""
+
+    preserve_order: bool = False
+    """If True, maintains the order of components as specified."""
+
+    def resolve(self, scene: ManagerScene) -> None:
+        """Resolve names and IDs for all configured fields.
+
+        This method ensures consistency between names and IDs for each field type.
+        It handles three cases:
+        1. Both names and IDs provided: Validates they match
+        2. Only names provided: Computes IDs (optimizes to slice(None) if all selected)
+        3. Only IDs provided: Computes names
+
+        Args:
+          scene: The scene containing the entity to resolve against.
+
+        Raises:
+          ValueError: If provided names and IDs are inconsistent.
+          KeyError: If the entity name is not found in the scene.
+        """
+        entity = scene[self.name]
+
+        for config in _FIELD_CONFIGS:
+            try:
+                self._resolve_field(entity, config)
+            except (TypeError, ValueError, NotImplementedError) as exc:
+                raise type(exc)(
+                    f"SceneEntityCfg entity '{self.name}' {config.kind_label} selector: {exc}"
+                ) from exc
+
+    def _resolve_field(self, entity: ManagerEntity, config: _FieldConfig) -> None:
+        """Resolve a single field's names and IDs.
+
+        Args:
+          entity: The entity to resolve against.
+          config: Field configuration specifying attribute names and methods.
+        """
+        names = getattr(self, config.names_attr)
+        ids = getattr(self, config.ids_attr)
+
+        # Early return if nothing to resolve.
+        if names is None and not isinstance(ids, list):
+            return
+
+        # Get entity metadata.
+        entity_all_names = getattr(entity, config.names_attr)
+        entity_count = getattr(entity, config.num_attr)
+        find_method = getattr(entity, config.find_method)
+
+        # Normalize single values to lists for uniform processing.
+        names = self._normalize_to_list(names)
+        if isinstance(ids, (int, list)):
+            ids = self._normalize_to_list(ids)
+            setattr(self, config.ids_attr, ids)
+
+        # Handle three resolution cases.
+        if names is not None and isinstance(ids, list):
+            setattr(self, config.names_attr, names)
+            self._validate_consistency(names, ids, entity_all_names, find_method, config.kind_label)
+        elif names is not None:
+            self._resolve_names_to_ids(
+                names,
+                entity_all_names,
+                entity_count,
+                find_method,
+                config.names_attr,
+                config.ids_attr,
+            )
+        elif isinstance(ids, list):
+            self._resolve_ids_to_names(ids, entity_all_names, config.names_attr, config.kind_label)
+
+    def _normalize_to_list(self, value: str | int | tuple | list | None) -> list | None:
+        """Convert single values to lists for uniform processing."""
+        if value is None:
+            return None
+        if isinstance(value, (str, int)):
+            return [value]
+        if isinstance(value, list):
+            return value
+        return list(value)
+
+    def _validate_consistency(
+        self,
+        names: list[str],
+        ids: list[int],
+        entity_all_names: list[str],
+        find_method,
+        kind_label: str,
+    ) -> None:
+        """Validate that provided names and IDs are consistent.
+
+        Raises:
+          ValueError: If names and IDs don't match.
+        """
+        found_ids, _ = find_method(names, preserve_order=self.preserve_order)
+        self._validate_ids(ids, len(entity_all_names), kind_label)
+        computed_names = [entity_all_names[i] for i in ids]
+
+        if found_ids != ids or computed_names != names:
+            raise ValueError(
+                f"Inconsistent {kind_label} names and indices. "
+                f"Names {names} resolved to indices {found_ids}, "
+                f"but indices {ids} (mapping to names {computed_names}) were provided."
+            )
+
+    def _resolve_names_to_ids(
+        self,
+        names: list[str],
+        entity_all_names: list[str],
+        entity_count: int,
+        find_method,
+        names_attr: str,
+        ids_attr: str,
+    ) -> None:
+        """Resolve names to IDs, optimizing to slice(None) when all are selected."""
+        found_ids, found_names = find_method(names, preserve_order=self.preserve_order)
+
+        # Keep names and IDs in the same order.
+        setattr(self, names_attr, found_names)
+
+        # Optimize to slice(None) if all components are selected in order.
+        if len(found_ids) == entity_count and found_names == list(entity_all_names):
+            setattr(self, ids_attr, slice(None))
+        else:
+            setattr(self, ids_attr, found_ids)
+
+    def _resolve_ids_to_names(
+        self,
+        ids: list[int],
+        entity_all_names: list[str],
+        names_attr: str,
+        kind_label: str,
+    ) -> None:
+        """Resolve IDs to their corresponding names."""
+        self._validate_ids(ids, len(entity_all_names), kind_label)
+        resolved_names = [entity_all_names[i] for i in ids]
+        setattr(self, names_attr, resolved_names)
+
+    @staticmethod
+    def _validate_ids(ids: list[int], count: int, kind_label: str) -> None:
+        invalid_types = [
+            value for value in ids if isinstance(value, bool) or not isinstance(value, int)
+        ]
+        if invalid_types:
+            raise TypeError(f"{kind_label} IDs must be integers; got {invalid_types}")
+        out_of_range = [value for value in ids if value < 0 or value >= count]
+        if out_of_range:
+            raise ValueError(
+                f"{kind_label} IDs {out_of_range} are out of range for {count} available entries"
+            )
diff --git a/src/unilab/managers/termination_manager.py b/src/unilab/managers/termination_manager.py
new file mode 100644
index 000000000..999cac9bb
--- /dev/null
+++ b/src/unilab/managers/termination_manager.py
@@ -0,0 +1,139 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), src/mjlab/managers/termination_manager.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and UniLab contracts; licensed under Apache-2.0.
+"""Termination manager for computing done signals."""
+
+from __future__ import annotations
+
+from copy import deepcopy
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Sequence
+
+import numpy as np
+from prettytable import PrettyTable
+
+from unilab.managers.manager_base import ManagerBase, ManagerTermBaseCfg
+
+if TYPE_CHECKING:
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+@dataclass
+class TerminationTermCfg(ManagerTermBaseCfg):
+    """Configuration for a termination term."""
+
+    time_out: bool = False
+    """Whether the term contributes towards episodic timeouts."""
+
+
+class TerminationManager(ManagerBase):
+    """Manages termination conditions for the environment.
+
+    The termination manager aggregates multiple termination terms to compute
+    episode done signals. Terms can be either truncations (time-based) or
+    terminations (failure conditions).
+    """
+
+    _env: ManagerBasedRlEnv
+
+    def __init__(self, cfg: dict[str, TerminationTermCfg | None], env: ManagerBasedRlEnv):
+        self._term_names: list[str] = list()
+        self._term_cfgs: list[TerminationTermCfg] = list()
+        self._class_term_cfgs: list[TerminationTermCfg] = list()
+
+        self.cfg = deepcopy(cfg)
+        super().__init__(env)
+
+        self._term_dones = dict()
+        for term_name in self._term_names:
+            self._term_dones[term_name] = np.zeros(self.num_envs, dtype=np.bool_)
+        self._truncated_buf = np.zeros(self.num_envs, dtype=np.bool_)
+        self._terminated_buf = np.zeros_like(self._truncated_buf)
+
+    def __str__(self) -> str:
+        msg = f"<TerminationManager> contains {len(self._term_names)} active terms.\n"
+        table = PrettyTable()
+        table.title = "Active Termination Terms"
+        table.field_names = ["Index", "Name", "Time Out"]
+        table.align["Name"] = "l"
+        for index, (name, term_cfg) in enumerate(
+            zip(self._term_names, self._term_cfgs, strict=False)
+        ):
+            table.add_row([index, name, term_cfg.time_out])
+        msg += str(table.get_string())
+        msg += "\n"
+        return msg
+
+    # Properties.
+
+    @property
+    def active_terms(self) -> list[str]:
+        return self._term_names
+
+    @property
+    def dones(self) -> np.ndarray:
+        return self._truncated_buf | self._terminated_buf
+
+    @property
+    def time_outs(self) -> np.ndarray:
+        return self._truncated_buf
+
+    @property
+    def terminated(self) -> np.ndarray:
+        return self._terminated_buf
+
+    # Methods.
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> dict[str, int]:
+        if env_ids is None:
+            env_ids = slice(None)
+        extras = {}
+        for key in self._term_dones.keys():
+            extras["Episode_Termination/" + key] = int(
+                np.count_nonzero(self._term_dones[key][env_ids])
+            )
+        for term_cfg in self._class_term_cfgs:
+            term_cfg.func.reset(env_ids=env_ids)
+        return extras
+
+    def compute(self) -> np.ndarray:
+        self._truncated_buf[:] = False
+        self._terminated_buf[:] = False
+        for name, term_cfg in zip(self._term_names, self._term_cfgs, strict=False):
+            value = term_cfg.func(self._env, **term_cfg.params)
+            self._check_term_shape(name, value)
+            if value.dtype != np.bool_:
+                raise TypeError(
+                    f"TerminationManager term '{name}' returned dtype {value.dtype}, expected bool."
+                )
+            if term_cfg.time_out:
+                self._truncated_buf |= value
+            else:
+                self._terminated_buf |= value
+            self._term_dones[name][:] = value
+        return self._truncated_buf | self._terminated_buf
+
+    def get_term(self, name: str) -> np.ndarray:
+        return self._term_dones[name]
+
+    def get_term_cfg(self, term_name: str) -> TerminationTermCfg:
+        if term_name not in self._term_names:
+            raise ValueError(f"Term '{term_name}' not found in active terms.")
+        return self._term_cfgs[self._term_names.index(term_name)]
+
+    def get_active_iterable_terms(self, env_idx: int) -> Sequence[tuple[str, Sequence[float]]]:
+        terms = []
+        for key in self._term_dones.keys():
+            terms.append((key, [float(self._term_dones[key][env_idx])]))
+        return terms
+
+    def _prepare_terms(self) -> None:
+        for term_name, term_cfg in self.cfg.items():
+            if term_cfg is None:
+                print(f"term: {term_name} set to None, skipping...")
+                continue
+            self._resolve_common_term_cfg(term_name, term_cfg)
+            self._term_names.append(term_name)
+            self._term_cfgs.append(term_cfg)
+            if hasattr(term_cfg.func, "reset") and callable(term_cfg.func.reset):
+                self._class_term_cfgs.append(term_cfg)
diff --git a/src/unilab/algos/torch/appo/__init__.py b/src/unilab/scripts/__init__.py
similarity index 100%
rename from src/unilab/algos/torch/appo/__init__.py
rename to src/unilab/scripts/__init__.py
diff --git a/src/unilab/scripts/play_hora_appo.py b/src/unilab/scripts/play_hora_appo.py
new file mode 100644
index 000000000..d4d336650
--- /dev/null
+++ b/src/unilab/scripts/play_hora_appo.py
@@ -0,0 +1,272 @@
+"""HORA APPO checkpoint playback, owned by UniLab (issue #1480).
+
+The HORA APPO *training* runner lives in uni_rl (``uni_rl.algos.hora.appo_runner``);
+the play-mode orchestration is UniLab-side business logic because it drives
+UniLab's registry-backed env construction, sim2sim contract validation, and
+backend playback plan logging. ``resolve_hora_appo_runtime`` is the owner
+config's ``runtime_resolver`` entrypoint consumed by
+``uni_rl.algos.appo.runtime.resolve_appo_runtime``.
+"""
+
+from __future__ import annotations
+
+import os
+from collections.abc import Callable
+from copy import deepcopy
+from dataclasses import dataclass
+from typing import Any, cast
+
+import torch
+from omegaconf import DictConfig
+from uni_rl.algos.hora.appo_runner import HoraAPPORunner
+from uni_rl.algos.hora.models import build_hora_shared_actor_critic
+from uni_rl.algos.hora.observations import (
+    build_hora_actor_tensordict,
+    split_hora_obs_with_priv_info,
+)
+from uni_rl.algos.hora.rsl_rl_compat import (
+    convert_config_v3_to_v4,
+    is_rsl_rl_v4,
+    is_rsl_rl_v5,
+)
+from uni_rl.algos.hora.runtime import is_hora_appo_runtime
+from uni_rl.utils.observations import get_obs_dims
+from unisim.backend.base import log_playback_plan
+
+from unilab.base.config_adapter import BackendAdapter, create_env
+from unilab.utils.sim2sim import policy_load_dim_guard, resolve_sim2sim_config
+
+
+@dataclass(frozen=True)
+class HoraAPPORuntime:
+    """Resolved HORA APPO entrypoints used by the generic APPO script.
+
+    Args:
+        runner_cls: Runner class used for HORA APPO training mode.
+        play_fn: Play-mode callable used for HORA APPO checkpoint playback.
+
+    Returns:
+        Immutable entrypoint bundle consumed by generic APPO script assembly.
+    """
+
+    runner_cls: type[HoraAPPORunner]
+    play_fn: Callable[..., str | None]
+
+
+def resolve_hora_appo_runtime(rl_cfg: dict[str, Any]) -> HoraAPPORuntime | None:
+    """Resolve HORA APPO entrypoints from an explicit runtime marker.
+
+    Args:
+        rl_cfg: Resolved algorithm config dictionary from Hydra composition.
+
+    Returns:
+        ``HoraAPPORuntime`` when the owner config selects HORA APPO, otherwise
+        ``None``.
+    """
+    if not is_hora_appo_runtime(rl_cfg):
+        return None
+    return HoraAPPORuntime(runner_cls=HoraAPPORunner, play_fn=play_hora_appo)
+
+
+def _update_hora_obs_groups(
+    rl_cfg: dict[str, Any],
+    *,
+    obs_dim: int,
+    priv_info_dim: int,
+) -> None:
+    """Update grouped actor/critic dims for the HORA APPO runtime.
+
+    Args:
+        rl_cfg: Mutable algorithm config dictionary to update in place.
+        obs_dim: Actor observation dimension reported by the env contract.
+        priv_info_dim: Privileged-info dimension reported by the env contract.
+
+    Returns:
+        None. Mutates ``rl_cfg["obs_groups"]`` directly.
+    """
+    obs_groups = rl_cfg.setdefault("obs_groups", {})
+    actor_group = obs_groups.setdefault("actor", {})
+    critic_group = obs_groups.setdefault("critic", {})
+    if isinstance(actor_group, dict):
+        actor_group["actor"] = obs_dim
+        actor_group["priv_info"] = priv_info_dim
+    if isinstance(critic_group, dict):
+        critic_group["actor"] = obs_dim
+        critic_group["priv_info"] = priv_info_dim
+
+
+def play_hora_appo(
+    cfg: DictConfig,
+    rl_cfg: dict[str, Any],
+    *,
+    root_dir,
+    resolve_checkpoint_path,
+) -> str | None:
+    """Play HORA APPO checkpoints with grouped actor and privileged inputs."""
+    import numpy as np
+    from rsl_rl.utils import resolve_callable
+    from tensordict import TensorDict
+
+    env_cfg_override = BackendAdapter(
+        cfg,
+        root_dir=root_dir,
+        algo_name="appo",
+    ).build_task_env_cfg_override()
+
+    device = cfg.training.device or (
+        "cuda"
+        if torch.cuda.is_available()
+        else "mps"
+        if torch.backends.mps.is_available()
+        else "cpu"
+    )
+    print(f"Using device for play: {device}")
+
+    env = cast(
+        Any,
+        create_env(
+            cfg,
+            num_envs=cfg.training.play_env_num,
+            env_cfg_override=env_cfg_override,
+        ),
+    )
+    obs_dim, _ = get_obs_dims(env.obs_groups_spec)
+    if env.state is None:
+        env.init_state()
+    _, _, state_priv_info = split_hora_obs_with_priv_info(
+        env.state.obs,
+        env.state.info if env.state is not None else None,
+    )
+    priv_info_dim = int(state_priv_info.shape[1]) if state_priv_info is not None else 0
+    if priv_info_dim <= 0:
+        raise ValueError("HORA APPO play requires privileged info from the environment.")
+
+    action_shape = env.action_space.shape
+    if action_shape is None:
+        raise ValueError("env.action_space.shape must be defined")
+    action_dim = int(action_shape[0])
+
+    rl_cfg_dict = dict(rl_cfg)
+    _update_hora_obs_groups(rl_cfg_dict, obs_dim=obs_dim, priv_info_dim=priv_info_dim)
+
+    if is_rsl_rl_v5():
+        pass
+    elif is_rsl_rl_v4():
+        rl_cfg_dict = convert_config_v3_to_v4(rl_cfg_dict)
+
+    obs_example = torch.zeros((cfg.training.play_env_num, obs_dim), device=device)
+    td_example = TensorDict(
+        {
+            "actor": obs_example,
+            "priv_info": torch.zeros((cfg.training.play_env_num, priv_info_dim), device=device),
+        },
+        batch_size=cfg.training.play_env_num,
+    )
+
+    actor_cfg = deepcopy(rl_cfg_dict["actor"])
+    actor_cls = resolve_callable(actor_cfg.pop("class_name"))
+    actor_cfg.pop("num_actions", None)
+
+    critic_cfg = deepcopy(rl_cfg_dict.get("critic") or rl_cfg_dict.get("actor") or {})
+    critic_cfg.pop("class_name", None)
+    critic_cfg.pop("num_actions", None)
+    critic_cfg.pop("distribution_cfg", None)
+
+    shared_model = build_hora_shared_actor_critic(
+        obs_dim=obs_dim,
+        action_dim=action_dim,
+        priv_info_dim=priv_info_dim,
+        actor_cfg=actor_cfg,
+        critic_cfg=critic_cfg,
+    ).to(device)
+    actor = actor_cls(
+        td_example,
+        rl_cfg_dict["obs_groups"],
+        "actor",
+        action_dim,
+        shared_model=shared_model,
+        **actor_cfg,
+    )
+    actor = actor.to(device)
+    actor.eval()
+
+    load_path, load_path_dir = resolve_checkpoint_path(cfg)
+    if not load_path or not os.path.exists(load_path):
+        print(f"Could not find run to load. load_path={load_path}")
+        return None
+
+    resolve_sim2sim_config(
+        load_path_dir,
+        cfg,
+        algo_name="appo",
+        strict=bool(getattr(cfg.training, "sim2sim_strict", True)),
+    )
+
+    print(f"Loading model: {load_path}")
+    checkpoint = torch.load(load_path, map_location=device, weights_only=True)
+    with policy_load_dim_guard(env_obs_dim=obs_dim, env_action_dim=action_dim, algo_name="appo"):
+        actor.load_state_dict(checkpoint["actor"])
+
+    current_priv_info: np.ndarray | None = None
+
+    def initialize_play_obs() -> np.ndarray:
+        nonlocal current_priv_info
+        obs_out, info_out = env.reset(np.arange(cfg.training.play_env_num, dtype=np.int32))
+        actor_obs, _, priv_info = split_hora_obs_with_priv_info(obs_out, info_out)
+        current_priv_info = priv_info.astype(np.float32) if priv_info is not None else None
+        return np.asarray(actor_obs, dtype=np.float32)
+
+    def step_play_obs(obs_np: np.ndarray) -> np.ndarray:
+        nonlocal current_priv_info
+        if current_priv_info is None:
+            raise ValueError("HORA APPO play step is missing privileged info.")
+        td = build_hora_actor_tensordict(
+            obs_np,
+            priv_info=current_priv_info,
+            device=device,
+            batch_size=cfg.training.play_env_num,
+        )
+        actions = actor(td).cpu().numpy().astype(np.float32)
+        state = env.step(actions)
+        actor_obs, _, priv_info = split_hora_obs_with_priv_info(state.obs, state.info)
+        current_priv_info = priv_info.astype(np.float32) if priv_info is not None else None
+        return np.asarray(actor_obs, dtype=np.float32)
+
+    print("Collecting physics states...")
+    with torch.inference_mode():
+        play_video_path = cast(
+            str | None,
+            env.run_playback_mode(
+                play_render_mode=getattr(cfg.training, "play_render_mode", "auto"),
+                play_steps=getattr(cfg.training, "play_steps", None),
+                output_video=os.path.join(load_path_dir, "play_video.mp4")
+                if load_path_dir
+                else None,
+                render_spacing=float(
+                    getattr(cfg.training, "render_spacing", getattr(env.cfg, "render_spacing", 1.0))
+                ),
+                initialize=initialize_play_obs,
+                step=step_play_obs,
+                camera_kwargs={
+                    "cam_distance": cfg.training.cam_distance,
+                    "cam_elevation": cfg.training.cam_elevation,
+                    "cam_azimuth": cfg.training.cam_azimuth,
+                    "cam_lookat": getattr(cfg.training, "cam_lookat", None),
+                    "cam_tracking": getattr(cfg.training, "cam_tracking", False),
+                    "cam_tracking_env_idx": getattr(cfg.training, "cam_tracking_env_idx", 0),
+                    "cam_tracking_extra_envs": getattr(cfg.training, "cam_tracking_extra_envs", 2),
+                },
+                on_plan=log_playback_plan,
+            ),
+        )
+    if play_video_path is not None:
+        print(f"Saving video to {play_video_path} ...")
+    print("Done.")
+    return play_video_path
+
+
+__all__ = [
+    "HoraAPPORuntime",
+    "play_hora_appo",
+    "resolve_hora_appo_runtime",
+]
diff --git a/scripts/play_interactive.py b/src/unilab/scripts/play_interactive.py
similarity index 85%
rename from scripts/play_interactive.py
rename to src/unilab/scripts/play_interactive.py
index e812c11e1..1f746db0b 100644
--- a/scripts/play_interactive.py
+++ b/src/unilab/scripts/play_interactive.py
@@ -1,19 +1,19 @@
 """MuJoCo interactive play script for trained policies.
 
-This MuJoCo-only viewer tool opens a live MuJoCo window for a task owner config.
-``--sim`` selects which owner config to read; playback visualization still uses
-MuJoCo.
+This MuJoCo-only viewer implementation opens a live MuJoCo window for any task
+owner config. ``--sim`` selects the physics owner; Drake and mjwarp run the
+rollout while MuJoCo renders the current physics state.
 
 Usage:
     # Zero-action playback for a task/backend owner config
-    uv run scripts/play_interactive.py --algo ppo --task go2_joystick_flat --sim mujoco
+    uv run python -m unilab.scripts.play_interactive --algo ppo --task go2_joystick_flat --sim mujoco
 
     # Policy playback and keyboard command control
-    uv run scripts/play_interactive.py --algo ppo --task go2_joystick_rough --sim mujoco \
+    uv run python -m unilab.scripts.play_interactive --algo ppo --task go2_joystick_rough --sim mujoco \
       interactive.action_mode=policy interactive.keyboard=true
 
     # Show target bodies / reward debug overlays
-    uv run scripts/play_interactive.py --algo ppo --task g1_motion_tracking --sim mujoco \
+    uv run python -m unilab.scripts.play_interactive --algo ppo --task g1_motion_tracking --sim mujoco \
       interactive.show_target_bodies=true \
       interactive.target_show_axes=true \
       interactive.show_reward_debug=true
@@ -42,34 +42,40 @@
 from hydra.core.global_hydra import GlobalHydra
 from omegaconf import DictConfig, OmegaConf
 
-ROOT_DIR = Path(__file__).parent.parent
-SRC_DIR = ROOT_DIR / "src"
-if str(SRC_DIR) not in sys.path:
-    sys.path.insert(0, str(SRC_DIR))
-if str(ROOT_DIR) not in sys.path:
-    sys.path.insert(0, str(ROOT_DIR))
+_PACKAGE_CONF_ROOT = Path(__file__).resolve().parents[1] / "conf"
 
+from uni_rl.algos.rsl_rl import (
+    RslRlVecEnvWrapper,
+    get_policy_obs_dims,
+    normalize_ppo_train_cfg,
+)
+
+from unilab.base.process_device import configure_backend_process_device
 from unilab.training import (
+    algo_config_dict,
     ensure_registries,
+)
+from unilab.utils.checkpoint import (
     get_entrypoint_log_root,
     resolve_task_checkpoint_path,
 )
-from unilab.training.offpolicy import build_offpolicy_env_cfg_override
-from unilab.training.rsl_rl import (
-    RslRlVecEnvWrapper,
-    get_policy_obs_dims,
-    normalize_ppo_train_cfg,
-)
+from unilab.utils.rotation import np_matrix_from_quat
 from unilab.visualization.interactive_playback import (
     _HORA_DISTILL_CHECKPOINT_UNAVAILABLE,
     KeyboardCommander,
     PlaybackControls,
-    RslRlPlaybackConfig,
+    PlayInteractiveArgs,
+    available_backends_for_task,
+    build_offpolicy_env_cfg_override,
+    build_play_backend_adapter,
+    build_playback_config,
     create_appo_playback_session,
     create_hora_distill_playback_session,
     create_rsl_rl_playback_session,
     create_sac_playback_session,
+    infer_checkpoint_actor_input_dim,
     make_sim2sim_preflight,
+    normalize_checkpoint_value,
     prepare_motion_overlay_selection,
     select_torch_device,
 )
@@ -89,8 +95,9 @@
 
 ensure_registries()
 
+from unisim.backend.mujoco.playback import resolve_render_play_model_files
+
 from unilab.base import registry
-from unilab.base.backend.mujoco.playback import resolve_render_play_model_files
 from unilab.base.scene import SceneCfg
 from unilab.structured_configs import PPOConfig as _StructuredPPOConfig
 
@@ -107,74 +114,6 @@
 import mujoco.viewer
 
 
-@dataclass
-class PlayInteractiveArgs:
-    task: str
-    load_run: str
-    checkpoint: str | None
-    action_mode: str
-    policy_obs_mode: str
-    algo_log_name: str
-    log_root: str | None
-    show_target_bodies: bool
-    show_reward_debug: bool
-    target_show_axes: bool
-    target_body_names: str
-    target_max_bodies: int
-    target_marker_radius: float
-    target_axis_length: float
-    target_marker_alpha: float
-    reward_debug_show_velocity: bool
-    reward_debug_lin_vel_scale: float
-    reward_debug_ang_vel_scale: float
-    reward_debug_show_connectors: bool
-    reward_debug_show_global_anchor: bool
-    camera_follow_body: bool
-    camera_focus_body_name: str
-    camera_height_offset: float
-    camera_distance: float | None
-    camera_elevation: float | None
-    camera_azimuth: float | None
-    use_env_visual_model: bool
-    speed: float
-    start_paused: bool
-    keyboard: bool = False
-    keyboard_step_lin: float = 0.1
-    keyboard_step_ang: float = 0.2
-    require_keyboard_command_obs: bool = True
-    algo: str = "ppo"
-
-
-def _infer_checkpoint_actor_input_dim(ckpt_path: str) -> int | None:
-    loaded = torch.load(ckpt_path, map_location="cpu", weights_only=True)
-    state_dict = loaded.get("actor_state_dict")
-    if not isinstance(state_dict, dict):
-        return None
-
-    # Common rsl-rl naming: "mlp.0.weight" or nested prefixes ending with ".0.weight".
-    for key in ("mlp.0.weight", "actor.mlp.0.weight"):
-        w = state_dict.get(key)
-        if isinstance(w, torch.Tensor) and w.ndim == 2:
-            return int(w.shape[1])
-
-    for key, w in state_dict.items():
-        if key.endswith(".0.weight") and isinstance(w, torch.Tensor) and w.ndim == 2:
-            return int(w.shape[1])
-    return None
-
-
-def _backend_adapter(cfg: DictConfig, *, algo_name: str = "ppo"):
-    from unilab.base.backend import materialize_scene_visual_override
-    from unilab.training import BackendAdapter
-
-    return BackendAdapter(
-        cfg,
-        root_dir=ROOT_DIR,
-        algo_name=algo_name,
-        scene_materializer=materialize_scene_visual_override,
-    )
-
-
 def _algo_config_dict(cfg: DictConfig | None) -> dict[str, Any]:
     """Return the composed PPO algo config as a plain dict.
 
@@ -187,21 +126,19 @@ def _algo_config_dict(cfg: DictConfig | None) -> dict[str, Any]:
     """
     if cfg is None:
         return cast(dict[str, Any], PPOConfig().to_dict())
-    train_cfg_raw = OmegaConf.to_container(cfg.algo, resolve=True)
-    if not isinstance(train_cfg_raw, dict):
-        raise TypeError("cfg.algo must resolve to a dict")
-    return cast(dict[str, Any], train_cfg_raw)
+    return algo_config_dict(cfg)
 
 
-SUPPORTED_INTERACTIVE_ALGOS = ("ppo", "appo", "sac", "flashsac", "hora_distill")
+SUPPORTED_INTERACTIVE_ALGOS = ("ppo", "appo", "sac", "td3", "flashsac", "hora_distill")
 _CONFIG_ROOT_BY_ALGO = {
     "ppo": "ppo",
     "appo": "appo",
-    "sac": "offpolicy",
-    "flashsac": "offpolicy",
+    "sac": "sac",
+    "td3": "td3",
+    "flashsac": "flashsac",
     "hora_distill": "hora_distill",
 }
-_OFFPOLICY_INTERACTIVE_ALGOS = {"sac", "flashsac"}
+_OFFPOLICY_INTERACTIVE_ALGOS = {"sac", "td3", "flashsac"}
 
 
 @dataclass(frozen=True)
@@ -262,34 +199,16 @@ def _interactive_overrides_from_cli(
 
 
 def _normalize_interactive_overrides(algo: str, overrides: list[str]) -> list[str]:
-    normalized: list[str] = []
-    has_algo_group = False
-
-    for override in overrides:
-        key = _override_key(override)
-        if algo in _OFFPOLICY_INTERACTIVE_ALGOS and key == "algo":
-            value = override.split("=", 1)[1] if "=" in override else ""
-            if value != algo:
-                raise SystemExit(
-                    f"--algo {algo} cannot be combined with a non-{algo} Hydra algo group."
-                )
-            has_algo_group = True
-        if algo in _OFFPOLICY_INTERACTIVE_ALGOS and key == "task" and "=" in override:
-            value = override.split("=", 1)[1]
-            if not value.startswith(f"{algo}/"):
-                override = f"task={algo}/{value}"
-        normalized.append(override)
-
-    if algo in _OFFPOLICY_INTERACTIVE_ALGOS and not has_algo_group:
-        normalized.insert(0, f"algo={algo}")
-    return normalized
+    # All algos now compose uniformly from their own tree with
+    # `task=<task>/<sim>`; no per-algo override rewriting is needed.
+    return list(overrides)
 
 
 def _compose_interactive_config(algo: str, overrides: list[str]) -> DictConfig:
     config_group = _CONFIG_ROOT_BY_ALGO[algo]
     GlobalHydra.instance().clear()
     with initialize_config_dir(
-        config_dir=str(ROOT_DIR / "conf" / config_group),
+        config_dir=str(_PACKAGE_CONF_ROOT / config_group),
         version_base="1.3",
     ):
         return compose(
@@ -318,7 +237,7 @@ def resolve_checkpoint(
     log_root: str | None = None,
 ) -> str | None:
     checkpoint_path, checkpoint_dir = resolve_task_checkpoint_path(
-        ROOT_DIR,
+        Path.cwd(),
         task_name=task,
         load_run=load_run,
         algo_log_name=algo_log_name,
@@ -351,15 +270,7 @@ def _quat_to_rotmat_wxyz(quat: np.ndarray) -> np.ndarray:
     n = np.linalg.norm(q)
     if n < 1e-12:
         return np.eye(3, dtype=np.float64)
-    w, x, y, z = q / n
-    return np.array(
-        [
-            [1.0 - 2.0 * (y * y + z * z), 2.0 * (x * y - z * w), 2.0 * (x * z + y * w)],
-            [2.0 * (x * y + z * w), 1.0 - 2.0 * (x * x + z * z), 2.0 * (y * z - x * w)],
-            [2.0 * (x * z - y * w), 2.0 * (y * z + x * w), 1.0 - 2.0 * (x * x + y * y)],
-        ],
-        dtype=np.float64,
-    )
+    return np_matrix_from_quat(q / n)
 
 
 def _add_sphere_marker(scene, pos: np.ndarray, radius: float, rgba: np.ndarray) -> bool:
@@ -482,15 +393,6 @@ def _default_viewer_camera_distance(mj_model, env: Any, *, follow_body: bool) ->
     return min(extent_distance, _FOLLOW_CAMERA_MAX_DISTANCE)
 
 
-def _available_backends_for_task(task_name: str) -> tuple[str, ...]:
-    envs = registry.list_registered_envs()
-    task_meta = envs.get(task_name, {})
-    backends = task_meta.get("available_backends", ())
-    if not isinstance(backends, list):
-        return ()
-    return tuple(str(backend) for backend in backends)
-
-
 def _can_launch_glfw_viewer() -> bool:
     try:
         import glfw
@@ -843,21 +745,6 @@ def _load_viewer_model(env: Any, *, use_env_visual_model: bool):
     return playback_model
 
 
-def _build_playback_config(args, *, num_envs: int = 1) -> RslRlPlaybackConfig:
-    return RslRlPlaybackConfig(
-        task=str(args.task),
-        load_run=str(args.load_run),
-        checkpoint=getattr(args, "checkpoint", None),
-        action_mode=str(args.action_mode),
-        policy_obs_mode=str(args.policy_obs_mode),
-        algo_log_name=str(getattr(args, "algo_log_name", "rsl_rl_ppo")),
-        log_root=getattr(args, "log_root", None),
-        num_envs=num_envs,
-        speed=float(getattr(args, "speed", 1.0)),
-        start_paused=bool(getattr(args, "start_paused", False)),
-    )
-
-
 def _build_keyboard_commander(env: Any, args) -> KeyboardCommander | None:
     """Set up keyboard velocity teleop, or return None when unsupported/disabled."""
     if not bool(getattr(args, "keyboard", False)):
@@ -894,12 +781,14 @@ def _state_has_velocity_commands(env: Any) -> bool:
     )
 
 
-def _is_locomotion_env(env: Any) -> bool:
-    return type(env).__module__.startswith("unilab.envs.locomotion")
+def _has_velocity_command_config(env: Any) -> bool:
+    cfg = getattr(env, "cfg", None)
+    commands_cfg = getattr(cfg, "commands", None) if cfg is not None else None
+    return getattr(commands_cfg, "vel_limit", None) is not None
 
 
 def _is_velocity_command_locomotion_task(env: Any) -> bool:
-    if not _is_locomotion_env(env):
+    if not _has_velocity_command_config(env):
         return False
     cfg = getattr(env, "cfg", None)
     candidate_names = [
@@ -1007,61 +896,71 @@ def play_interactive(args, cfg: DictConfig | None = None, *, algo: str | None =
     algo = str(algo or getattr(args, "algo", "ppo"))
 
     # Always use a single env for interactive view
-    available_backends = _available_backends_for_task(args.task)
-    if available_backends and "mujoco" not in available_backends:
+    # Keep programmatic callers that construct the pre-Drake argument shape
+    # working; the CLI builder always supplies ``sim`` explicitly.
+    sim_backend = str(getattr(args, "sim", "mujoco"))
+    available_backends = available_backends_for_task(args.task)
+    if available_backends and sim_backend not in available_backends:
         print(
-            "[play_interactive] Task does not support MuJoCo backend: "
+            "[play_interactive] Task does not support requested simulation backend: "
             f"{args.task}. Available backends: {available_backends or ('<none>',)}. "
-            "This script only supports MuJoCo viewer mode."
+            "MuJoCo is used only as the renderer."
         )
         return
 
+    # mjwarp requires an active CUDA Warp device; bind it process-wide before
+    # any env is constructed (same pattern as the offpolicy train entrypoint).
+    # No-op for backends without a device binding requirement.
+    configure_backend_process_device(sim_backend, device)
+
     def _create_env(num_envs: int):
         if cfg is None:
-            return registry.make(args.task, num_envs=num_envs, sim_backend="mujoco")
-        from unilab.training import create_env
+            return registry.make(args.task, num_envs=num_envs, sim_backend=sim_backend)
+        from unilab.base.config_adapter import create_env
 
         if algo in _OFFPOLICY_INTERACTIVE_ALGOS:
-            env_cfg_override = build_offpolicy_env_cfg_override(algo, cfg, root_dir=ROOT_DIR)
+            env_cfg_override = build_offpolicy_env_cfg_override(algo, cfg, root_dir=Path.cwd())
         else:
-            env_cfg_override = _backend_adapter(cfg, algo_name=algo).build_task_env_cfg_override()
+            env_cfg_override = build_play_backend_adapter(
+                cfg, root_dir=Path.cwd(), algo_name=algo
+            ).build_task_env_cfg_override()
         try:
             return create_env(
                 cfg,
                 num_envs=num_envs,
                 env_cfg_override=env_cfg_override,
-                sim_backend="mujoco",
+                sim_backend=sim_backend,
                 task_name=args.task,
             )
         except ValueError as exc:
-            if "does not support simulation backend 'mujoco'" in str(exc):
+            if f"does not support simulation backend '{sim_backend}'" in str(exc):
                 print(
-                    "[play_interactive] Task does not support MuJoCo backend: "
+                    "[play_interactive] Task does not support requested simulation backend: "
                     f"{args.task}. Available backends: {available_backends or ('<none>',)}. "
-                    "This script only supports MuJoCo viewer mode."
+                    "MuJoCo is used only as the renderer."
                 )
                 raise RuntimeError(_PLAYBACK_ENV_UNAVAILABLE) from exc
             raise
 
     try:
-        playback_cfg = _build_playback_config(args, num_envs=1)
+        playback_cfg = build_playback_config(args, num_envs=1)
         if algo == "ppo":
             wrapper_cls = RslRlVecEnvWrapper
             if cfg is not None:
-                from unilab.algos.torch.rsl_rl_runtime import resolve_rsl_rl_ppo_runtime
+                from uni_rl.algos.rsl_rl_runtime import resolve_rsl_rl_ppo_runtime
 
                 wrapper_cls = resolve_rsl_rl_ppo_runtime(
                     _algo_config_dict(cfg),
                     default_wrapper_cls=RslRlVecEnvWrapper,
                 ).wrapper_cls
-            session = create_rsl_rl_playback_session(
+            session: Any = create_rsl_rl_playback_session(
                 playback_cfg=playback_cfg,
                 env_factory=_create_env,
                 algo_config=_algo_config_dict(cfg),
-                root_dir=ROOT_DIR,
+                root_dir=Path.cwd(),
                 device=device,
                 checkpoint_resolver=resolve_checkpoint,
-                checkpoint_input_dim_reader=_infer_checkpoint_actor_input_dim,
+                checkpoint_input_dim_reader=infer_checkpoint_actor_input_dim,
                 entrypoint_log_root=get_entrypoint_log_root,
                 wrapper_cls=wrapper_cls,
                 runner_cls=OnPolicyRunner,
@@ -1078,7 +977,7 @@ def _create_env(num_envs: int):
                 cfg=cfg,
                 rl_cfg=_algo_config_dict(cfg),
                 env_factory=_create_env,
-                root_dir=ROOT_DIR,
+                root_dir=Path.cwd(),
                 device=device,
                 wrapper_cls=RslRlVecEnvWrapper,
                 log=lambda message: print(f"[play_interactive] {message}"),
@@ -1090,7 +989,7 @@ def _create_env(num_envs: int):
                 playback_cfg=playback_cfg,
                 cfg=cfg,
                 env_factory=_create_env,
-                root_dir=ROOT_DIR,
+                root_dir=Path.cwd(),
                 device=device,
                 algo_name=algo,
                 log=lambda message: print(f"[play_interactive] {message}"),
@@ -1103,7 +1002,7 @@ def _create_env(num_envs: int):
             session = create_hora_distill_playback_session(
                 playback_cfg=playback_cfg,
                 cfg=cfg,
-                root_dir=ROOT_DIR,
+                root_dir=Path.cwd(),
                 device=device,
                 log=lambda message: print(f"[play_interactive] {message}"),
             )
@@ -1214,7 +1113,8 @@ def _on_key(keycode: int) -> None:
             mj_model, env, getattr(args, "camera_focus_body_name", "")
         )
 
-        # Initialize camera to a reasonable default and keep lookat on robot base.
+        # Configure the camera once at startup.  Leave it untouched in the
+        # playback loop so the viewer can be adjusted interactively.
         has_cam = hasattr(viewer, "cam")
         if has_cam:
             if getattr(args, "camera_distance", None) is not None:
@@ -1230,6 +1130,19 @@ def _on_key(keycode: int) -> None:
             if getattr(args, "camera_azimuth", None) is not None:
                 viewer.cam.azimuth = float(args.camera_azimuth)
 
+            # Use the reset pose for the initial target only.  Updating this
+            # in the loop would override the user's manual camera movement.
+            initial_phys = playback_session.physics_state()[0].astype(np.float64)
+            mujoco.mj_setState(mj_model, viz_data, initial_phys, state_spec)
+            mujoco.mj_forward(mj_model, viz_data)
+            if bool(getattr(args, "camera_follow_body", True)):
+                base_pos = viz_data.xpos[focus_body_id]
+                viewer.cam.lookat[0] = float(base_pos[0])
+                viewer.cam.lookat[1] = float(base_pos[1])
+                viewer.cam.lookat[2] = float(
+                    base_pos[2] + float(getattr(args, "camera_height_offset", 0.15))
+                )
+
         with torch.inference_mode():
             while viewer.is_running():
                 t0 = time.perf_counter()
@@ -1245,14 +1158,6 @@ def _on_key(keycode: int) -> None:
                 mujoco.mj_setState(mj_model, viz_data, phys, state_spec)
                 mujoco.mj_forward(mj_model, viz_data)
 
-                if has_cam and bool(getattr(args, "camera_follow_body", True)):
-                    base_pos = viz_data.xpos[focus_body_id]
-                    viewer.cam.lookat[0] = float(base_pos[0])
-                    viewer.cam.lookat[1] = float(base_pos[1])
-                    viewer.cam.lookat[2] = float(
-                        base_pos[2] + float(getattr(args, "camera_height_offset", 0.15))
-                    )
-
                 if overlay.enabled:
                     if args.show_reward_debug:
                         _render_reward_debug_targets(
@@ -1306,18 +1211,11 @@ def _on_key(keycode: int) -> None:
     print("[play_interactive] Done.")
 
 
-def _normalize_checkpoint_value(value: object) -> str | None:
-    if value is None:
-        return None
-    text = str(value)
-    return None if text in {"-1", "None", "null"} else text
-
-
 def _build_play_args(cfg: DictConfig, *, algo: str = "ppo") -> PlayInteractiveArgs:
     return PlayInteractiveArgs(
         task=str(cfg.training.task_name),
         load_run=str(cfg.algo.load_run),
-        checkpoint=_normalize_checkpoint_value(OmegaConf.select(cfg, "algo.checkpoint")),
+        checkpoint=normalize_checkpoint_value(OmegaConf.select(cfg, "algo.checkpoint")),
         action_mode=str(cfg.interactive.action_mode),
         policy_obs_mode=str(cfg.interactive.policy_obs_mode),
         algo_log_name=str(cfg.algo.algo_log_name),
@@ -1371,6 +1269,7 @@ def _build_play_args(cfg: DictConfig, *, algo: str = "ppo") -> PlayInteractiveAr
             OmegaConf.select(cfg, "interactive.require_keyboard_command_obs", default=True)
         ),
         algo=algo,
+        sim=str(OmegaConf.select(cfg, "training.sim_backend", default="mujoco")),
     )
 
 
diff --git a/src/unilab/scripts/train_appo.py b/src/unilab/scripts/train_appo.py
new file mode 100644
index 000000000..28df757e8
--- /dev/null
+++ b/src/unilab/scripts/train_appo.py
@@ -0,0 +1,365 @@
+"""Train APPO agent — native multiprocessing."""
+
+from __future__ import annotations
+
+import datetime
+import os
+from collections.abc import Callable
+from pathlib import Path
+from typing import Any, cast
+
+import hydra
+import torch
+from omegaconf import DictConfig, OmegaConf
+from uni_rl.algos.appo.runtime import resolve_appo_runtime
+from uni_rl.algos.rsl_rl import RslRlVecEnvWrapper
+from unisim.backend.base import log_playback_plan
+
+from unilab.base.config_adapter import (
+    BackendAdapter,
+    create_env,
+)
+from unilab.base.env_factory import registry_env_factory
+from unilab.training import (
+    algo_config_dict,
+    build_run_dir_name,
+    ensure_registries,
+    get_log_root,
+    resolve_nan_guard_cfg,
+    should_run_playback,
+)
+from unilab.training.experiment import ExperimentTracker
+from unilab.training.onnx_export import export_policy_onnx, verify_policy_onnx
+from unilab.utils.checkpoint import resolve_appo_checkpoint_path
+from unilab.utils.seed import apply_configured_training_seed
+from unilab.visualization.interactive_playback import (
+    RslRlPlaybackConfig,
+    create_appo_playback_session,
+    normalize_checkpoint_value,
+)
+
+
+def _training_resume_requested(load_run: Any) -> bool:
+    if load_run is None:
+        return False
+    return str(load_run) not in {"", "-1"}
+
+
+def build_appo_runner_kwargs(
+    cfg: DictConfig,
+    env_cfg_override: dict | None,
+    collector_device: str | None,
+    rl_cfg: dict[str, Any] | None = None,
+) -> dict:
+    if rl_cfg is None:
+        rl_cfg = algo_config_dict(cfg)
+
+    runner_kwargs = {
+        "env_name": cfg.training.task_name,
+        "env_factory": registry_env_factory(
+            str(cfg.training.task_name), str(cfg.training.sim_backend)
+        ),
+        "env_cfg_overrides": env_cfg_override,
+        "rl_cfg": rl_cfg,
+        "device": cfg.training.device,
+        "collector_device": collector_device,
+        "num_envs": cfg.algo.num_envs,
+        "steps_per_env": cfg.algo.steps_per_env,
+        "sim_backend": cfg.training.sim_backend,
+        "seed": rl_cfg.get("seed"),
+    }
+    if cfg.training.replay_queue_size is not None:
+        runner_kwargs["replay_queue_size"] = cfg.training.replay_queue_size
+    load_run = OmegaConf.select(cfg, "algo.load_run", default="-1")
+    if _training_resume_requested(load_run):
+        resume_path, _ = resolve_appo_checkpoint_path(
+            os.path.join(_get_log_root(cfg), cfg.training.task_name),
+            str(load_run),
+        )
+        if resume_path is None:
+            raise FileNotFoundError(f"Could not resolve APPO resume checkpoint: {load_run}")
+        runner_kwargs["resume_path"] = resume_path
+
+    nan_guard_cfg = resolve_nan_guard_cfg(cfg.training)
+    if nan_guard_cfg is not None:
+        runner_kwargs["nan_guard_cfg"] = nan_guard_cfg
+    return runner_kwargs
+
+
+def apply_appo_runtime_flags(
+    rl_cfg: dict[str, Any],
+    cfg: DictConfig,
+    *,
+    training_enabled: bool,
+) -> None:
+    algorithm_cfg = rl_cfg.setdefault("algorithm", {})
+    if not isinstance(algorithm_cfg, dict):
+        return
+    if not training_enabled:
+        algorithm_cfg["enable_compile"] = False
+
+
+def run_motrix_play_loop(
+    env,
+    actor,
+    device: str,
+    play_env_num: int,
+    num_steps: int | None = None,
+) -> None:
+    import numpy as np
+    from tensordict import TensorDict
+
+    if env.state is None:
+        env.init_state()
+
+    with torch.inference_mode():
+        env.run_playback(
+            num_steps=num_steps,
+            initialize=lambda: np.asarray(
+                env.reset(np.arange(play_env_num, dtype=np.int32))[0]["obs"],
+                dtype=np.float32,
+            ),
+            step=lambda obs_np: np.asarray(
+                env.step(
+                    actor(
+                        TensorDict(
+                            {"policy": torch.from_numpy(obs_np).to(device)}, batch_size=play_env_num
+                        )
+                    )
+                    .cpu()
+                    .numpy()
+                    .astype(np.float32)
+                ).obs["obs"],
+                dtype=np.float32,
+            ),
+        )
+
+
+def _get_log_root(cfg: DictConfig) -> str:
+    return str(get_log_root(Path.cwd(), cfg))
+
+
+def play_appo(
+    cfg: DictConfig,
+    rl_cfg: dict[str, Any],
+    *,
+    root_dir: Path | None = None,
+    resolve_checkpoint_path: Callable[[DictConfig], tuple[str | None, str | None]] | None = None,
+) -> str | None:
+    """Play mode for the default APPO runtime.
+
+    Args:
+        cfg: Resolved Hydra config for the current run.
+        rl_cfg: Resolved algorithm config dictionary from Hydra composition.
+        root_dir: Optional project root forwarded by generic runtime callers.
+            The default APPO runtime does not need it and ignores the value.
+        resolve_checkpoint_path: Optional checkpoint resolver injected by the
+            generic script. When omitted, this function falls back to the
+            default log-root based APPO checkpoint resolution.
+
+    Returns:
+        Output video path for offscreen rendering, or ``None`` when running the
+        native Motrix viewer or when no checkpoint could be resolved.
+    """
+    del root_dir
+
+    if resolve_checkpoint_path is not None:
+        load_path, load_path_dir = resolve_checkpoint_path(cfg)
+    else:
+        log_root = _get_log_root(cfg)
+        base_log_dir = os.path.join(log_root, cfg.training.task_name)
+        load_path, load_path_dir = resolve_appo_checkpoint_path(base_log_dir, cfg.algo.load_run)
+
+    if not load_path or not os.path.exists(load_path):
+        print(f"Could not find run to load. load_path={load_path}")
+        return None
+
+    device = cfg.training.device or (
+        "cuda"
+        if torch.cuda.is_available()
+        else "mps"
+        if torch.backends.mps.is_available()
+        else "cpu"
+    )
+    print(f"Using device for play: {device}")
+
+    playback_cfg = RslRlPlaybackConfig(
+        task=str(cfg.training.task_name),
+        load_run=str(cfg.algo.load_run),
+        checkpoint=normalize_checkpoint_value(
+            OmegaConf.select(cfg, "algo.checkpoint", default=None)
+        ),
+        action_mode="policy",
+        policy_obs_mode="flat",
+        algo_log_name=str(cfg.algo.algo_log_name),
+        log_root=None,
+        num_envs=cfg.training.play_env_num,
+    )
+    session, _policy_obs_mode, _checkpoint_path = create_appo_playback_session(
+        playback_cfg=playback_cfg,
+        cfg=cfg,
+        rl_cfg=rl_cfg,
+        env_factory=lambda n: create_env(
+            cfg,
+            num_envs=n,
+            env_cfg_override=BackendAdapter(
+                cfg, root_dir=Path.cwd(), algo_name="appo"
+            ).build_play_env_cfg_override(),
+        ),
+        root_dir=Path.cwd(),
+        device=device,
+        wrapper_cls=RslRlVecEnvWrapper,
+    )
+    env = session.env
+    actor = session.actor
+    # The checkpoint early-return above guarantees a loaded actor here.
+    assert actor is not None
+
+    # Export actor to ONNX
+    if load_path_dir is not None:
+        import torch.nn as nn
+
+        class _DeterministicAPPOActor(nn.Module):
+            def __init__(self, mlp: nn.Module):
+                super().__init__()
+                self.mlp = mlp
+
+            def forward(self, obs: torch.Tensor) -> torch.Tensor:
+                return self.mlp(obs)
+
+        export_module = _DeterministicAPPOActor(actor.mlp)
+        onnx_path = os.path.join(load_path_dir, "policy.onnx")
+        obs_dim = int(session.wrapped_env.num_obs)
+        dummy_input = torch.randn(1, obs_dim, device=device)
+        export_policy_onnx(export_module, onnx_path, (dummy_input,), input_names=["obs"])
+
+        # Verify ONNX output matches PyTorch
+        verify_input = torch.randn(1, obs_dim, device=device)
+        verify_policy_onnx(export_module, onnx_path, (verify_input,), input_names=["obs"])
+
+    with torch.inference_mode():
+        play_video_path = env.run_playback_mode(
+            play_render_mode=getattr(cfg.training, "play_render_mode", "auto"),
+            play_steps=getattr(cfg.training, "play_steps", None),
+            output_video=os.path.join(load_path_dir, "play_video.mp4") if load_path_dir else None,
+            render_spacing=float(
+                getattr(cfg.training, "render_spacing", getattr(env.cfg, "render_spacing", 1.0))
+            ),
+            initialize=session.reset,
+            step=lambda _obs: session.step_once(),
+            camera_kwargs={
+                "cam_distance": cfg.training.cam_distance,
+                "cam_elevation": cfg.training.cam_elevation,
+                "cam_azimuth": cfg.training.cam_azimuth,
+                "cam_lookat": getattr(cfg.training, "cam_lookat", None),
+                "cam_tracking": getattr(cfg.training, "cam_tracking", False),
+                "cam_tracking_env_idx": getattr(cfg.training, "cam_tracking_env_idx", 0),
+                "cam_tracking_extra_envs": getattr(cfg.training, "cam_tracking_extra_envs", 2),
+            },
+            on_plan=log_playback_plan,
+        )
+    if play_video_path is not None:
+        print(f"Saving video to {play_video_path} ...")
+    print("Done.")
+    return cast("str | None", play_video_path)
+
+
+@hydra.main(version_base="1.3", config_path="../conf/appo", config_name="config")
+def main(cfg: DictConfig) -> None:
+    ensure_registries()
+
+    seed_info = apply_configured_training_seed(cfg, torch_runtime=True, cuda=True)
+    env_cfg_override = BackendAdapter(
+        cfg, root_dir=Path.cwd(), algo_name="appo"
+    ).build_task_env_cfg_override()
+
+    # Convert algo config to plain dict for APPORunner / RSL-RL internals
+    rl_cfg = algo_config_dict(cfg)
+    apply_appo_runtime_flags(rl_cfg, cfg, training_enabled=not cfg.training.play_only)
+    appo_runtime = resolve_appo_runtime(rl_cfg, default_play_fn=play_appo)
+
+    if cfg.training.log_dir is None:
+        timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+        log_root = _get_log_root(cfg)
+        log_dir = os.path.join(
+            log_root,
+            cfg.training.task_name,
+            build_run_dir_name(timestamp, str(cfg.training.sim_backend)),
+        )
+    else:
+        log_dir = cfg.training.log_dir
+
+    collector_device = cfg.training.collector_device
+    if collector_device == "gpu":
+        collector_device = "mps" if torch.backends.mps.is_available() else "cuda"
+
+    learner_device = cfg.training.device or (
+        "cuda"
+        if torch.cuda.is_available()
+        else "mps"
+        if torch.backends.mps.is_available()
+        else "cpu"
+    )
+
+    tracker = None
+    if not cfg.training.play_only:
+        tracker = ExperimentTracker(
+            root_dir=Path.cwd(),
+            log_dir=log_dir,
+            algo_name="appo",
+            task_name=cfg.training.task_name,
+            sim_backend=cfg.training.sim_backend,
+            training_cfg=cfg.training,
+            full_cfg=cfg,
+            device=learner_device,
+            collector_device=collector_device,
+            seed_info=seed_info,
+        )
+        tracker.start()
+
+    try:
+        if not cfg.training.play_only:
+            runner = appo_runtime.runner_cls(
+                **build_appo_runner_kwargs(
+                    cfg,
+                    env_cfg_override=env_cfg_override,
+                    collector_device=collector_device,
+                    rl_cfg=rl_cfg,
+                )
+            )
+
+            try:
+                runner.learn(
+                    max_iterations=cfg.algo.max_iterations,
+                    save_interval=cfg.algo.save_interval,
+                    log_dir=log_dir,
+                    logger_type=cfg.training.logger,
+                )
+                if tracker is not None:
+                    tracker.update_summary(getattr(runner, "last_run_summary", None))
+            finally:
+                runner.close()
+
+        if should_run_playback(
+            play_only=cfg.training.play_only,
+            no_play=cfg.training.no_play,
+            play_render_mode=getattr(cfg.training, "play_render_mode", "auto"),
+        ):
+            play_video_path = appo_runtime.play_fn(
+                cfg,
+                rl_cfg,
+                root_dir=Path.cwd(),
+                resolve_checkpoint_path=lambda current_cfg: resolve_appo_checkpoint_path(
+                    os.path.join(_get_log_root(current_cfg), current_cfg.training.task_name),
+                    current_cfg.algo.load_run,
+                ),
+            )
+            if tracker is not None:
+                tracker.log_video(play_video_path)
+    finally:
+        if tracker is not None:
+            tracker.finish()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/unilab/scripts/train_flashsac.py b/src/unilab/scripts/train_flashsac.py
new file mode 100644
index 000000000..9867da961
--- /dev/null
+++ b/src/unilab/scripts/train_flashsac.py
@@ -0,0 +1,15 @@
+"""FlashSAC training/playback entrypoint (shared implementation in unilab.scripts.train_offpolicy)."""
+
+import hydra
+from omegaconf import DictConfig
+
+from unilab.scripts.train_offpolicy import main as _offpolicy_main
+
+
+@hydra.main(version_base="1.3", config_path="../conf/flashsac", config_name="config")
+def main(cfg: DictConfig) -> None:
+    _offpolicy_main(cfg)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/unilab/scripts/train_offpolicy.py b/src/unilab/scripts/train_offpolicy.py
new file mode 100644
index 000000000..4ffe3658a
--- /dev/null
+++ b/src/unilab/scripts/train_offpolicy.py
@@ -0,0 +1,418 @@
+"""Shared off-policy (SAC/TD3/FlashSAC) train/play implementation.
+
+This module is no longer runnable directly; use the per-algorithm entry
+scripts instead: ``unilab/scripts/train_sac.py``, ``unilab/scripts/train_td3.py``, and
+``unilab/scripts/train_flashsac.py``.
+"""
+
+from __future__ import annotations
+
+import datetime
+import os
+import sys
+from contextlib import nullcontext
+from pathlib import Path
+from typing import Any, cast
+
+from omegaconf import DictConfig, OmegaConf
+from uni_rl.ipc.dp_launcher import (
+    UNILAB_DP_LOG_DIR,
+    DpRankSupervisor,
+    apply_dp_rank_config,
+    current_dp_rank,
+    resolve_collector_cpu_ids,
+    resolve_dp_rank_device,
+    resolve_dp_rendezvous_path,
+    resolve_dp_topology,
+    validate_dp_launchable,
+)
+from unisim.backend.base import log_playback_plan
+
+from unilab.base.config_adapter import create_env
+from unilab.base.env_factory import registry_env_factory
+from unilab.base.process_device import bind_backend_process_device, configure_backend_process_device
+from unilab.training import (
+    assert_offpolicy_task_choice_matches_algo,
+    build_run_dir_name,
+    ensure_registries,
+    get_log_root,
+    resolve_nan_guard_cfg,
+    should_run_playback,
+)
+from unilab.training.experiment import ExperimentTracker
+from unilab.training.onnx_export import export_policy_onnx, verify_policy_onnx
+from unilab.utils.checkpoint import (
+    resolve_offpolicy_checkpoint_path as resolve_checkpoint_path,
+)
+from unilab.utils.seed import apply_configured_training_seed
+from unilab.visualization.interactive_playback import (
+    RslRlPlaybackConfig,
+    create_sac_playback_session,
+    default_device,
+    resolve_play_actor_spec,
+    resolve_play_obs_dims,
+)
+from unilab.visualization.interactive_playback import (
+    build_offpolicy_env_cfg_override as _build_offpolicy_env_cfg_override,
+)
+from unilab.visualization.interactive_playback import (
+    build_offpolicy_play_env_cfg_override as _build_offpolicy_play_env_cfg_override,
+)
+
+
+def enable_faulthandler() -> None:
+    """Enable fatal-signal Python stack dumps unless explicitly disabled."""
+    if os.environ.get("UNILAB_FAULTHANDLER", "1").lower() in {"0", "false", "no", "off"}:
+        return
+    try:
+        import faulthandler
+
+        if not faulthandler.is_enabled():
+            faulthandler.enable(all_threads=True)
+    except Exception as exc:
+        print(f"[train_offpolicy] faulthandler unavailable: {exc}", file=sys.stderr)
+
+
+def build_failure_summary(exc: BaseException, run_summary: Any | None = None) -> dict[str, Any]:
+    summary = dict(run_summary) if isinstance(run_summary, dict) else {}
+    if summary.get("status") == "completed":
+        summary["status"] = "failed"
+    else:
+        summary.setdefault("status", "failed")
+    summary["error_type"] = type(exc).__name__
+    summary["error"] = str(exc)
+    return summary
+
+
+def build_offpolicy_env_cfg_override(algo_name: str, cfg: DictConfig) -> dict[str, Any] | None:
+    return _build_offpolicy_env_cfg_override(algo_name, cfg, root_dir=Path.cwd())
+
+
+def build_offpolicy_play_env_cfg_override(algo_name: str, cfg: DictConfig) -> dict[str, Any] | None:
+    return _build_offpolicy_play_env_cfg_override(algo_name, cfg, root_dir=Path.cwd())
+
+
+def build_runner(algo_name: str, cfg: DictConfig, log_dir: str | None = None):
+    """Build algorithm runner from unified Hydra config."""
+    env_cfg_override = build_offpolicy_env_cfg_override(algo_name, cfg)
+    env_factory = registry_env_factory(str(cfg.training.task_name), str(cfg.training.sim_backend))
+    from uni_rl.offpolicy.thread_budget import (
+        apply_torch_thread_runtime,
+        resolve_torch_thread_runtime,
+    )
+
+    # Cold-path DP CPU partition: each rank's collector owns one contiguous
+    # CPU block (single rank keeps the legacy unset behavior). The ids only
+    # reach the collector env override — never the num_envs=1 probe envs,
+    # whose MuJoCo pool would size itself from len(cpu_ids).
+    # world_size comes from training.devices (rank 0 has no UNILAB_DP_* env;
+    # only spawned ranks carry it), rank from the env (0 for rank 0).
+    dp_devices = resolve_dp_topology(cfg.training.devices)
+    dp_world_size = len(dp_devices) if dp_devices is not None else 1
+    dp_rank = current_dp_rank()
+    from unilab.utils.device import get_default_device
+
+    rank_device = resolve_dp_rank_device(dp_devices, dp_rank) or get_default_device()
+    # Bind backend-global device state before algorithm builders materialize
+    # their probe envs. The spawned collector repeats this binding in its own
+    # process using the same rank-local device.
+    configure_backend_process_device(str(cfg.training.sim_backend), rank_device)
+    host_cpu_count = os.cpu_count() or 1
+    explicit_cpu_ids = getattr(cfg.training, "dp_collector_cpu_ids", None)
+    if explicit_cpu_ids is not None:
+        explicit_cpu_ids = cast(list, OmegaConf.to_container(explicit_cpu_ids, resolve=True))
+    collector_cpu_ids = resolve_collector_cpu_ids(
+        dp_world_size,
+        dp_rank,
+        host_cpu_count,
+        explicit=explicit_cpu_ids,
+    )
+
+    # Cold-path DP process-group assembly. world_size == 1 keeps dp_sync=None
+    # (bit-identical single-rank path); multi-rank learners attach the group's
+    # flat-gradient collective at their optimizer boundaries.
+    dp_sync = None
+    if dp_world_size > 1:
+        if dp_rank == 0 and log_dir is None:
+            raise ValueError(
+                "build_runner requires log_dir for multi-GPU data-parallel rank 0 "
+                "(it anchors the DP rendezvous FileStore)"
+            )
+        from uni_rl.ipc.dp_sync import DpParameterSync
+
+        dp_sync = DpParameterSync(
+            world_size=dp_world_size,
+            rank=dp_rank,
+            rendezvous_path=resolve_dp_rendezvous_path(cast(str, log_dir), rank=dp_rank),
+            device=rank_device,
+        )
+
+    torch_thread_runtime = resolve_torch_thread_runtime(
+        getattr(cfg.training, "torch_threads", None),
+        cpu_count=host_cpu_count // dp_world_size if dp_world_size > 1 else None,
+    )
+    apply_torch_thread_runtime(torch_thread_runtime, role="learner")
+
+    _nan_guard_cfg = resolve_nan_guard_cfg(cfg.training)
+
+    replay_prefetch_mode = getattr(cfg.training, "replay_prefetch_mode", "one_tick")
+    if replay_prefetch_mode != "one_tick":
+        raise ValueError(
+            f"Unsupported training.replay_prefetch_mode={replay_prefetch_mode!r}; "
+            "expected 'one_tick'"
+        )
+    from uni_rl.ipc.replay_pipelines.gpu_resident import require_offpolicy_replay_device
+
+    replay_device = require_offpolicy_replay_device(rank_device)
+    builder_kwargs: dict[str, Any] = {
+        "env_factory": env_factory,
+        "env_cfg_override": env_cfg_override,
+        "replay_prefetch_mode": replay_prefetch_mode,
+        "device": replay_device,
+        "nan_guard_cfg": _nan_guard_cfg,
+        "torch_thread_runtime": torch_thread_runtime,
+        "collector_cpu_ids": collector_cpu_ids,
+        "dp_sync": dp_sync,
+        "backend_device_binder": bind_backend_process_device,
+    }
+    if algo_name == "sac":
+        from uni_rl.algos.fast_sac.double_buffer import (
+            build_sac_double_buffer_runner,
+        )
+
+        runner = build_sac_double_buffer_runner(cfg, **builder_kwargs)
+    elif algo_name == "td3":
+        from uni_rl.algos.fast_td3.double_buffer import (
+            build_td3_double_buffer_runner,
+        )
+
+        runner = build_td3_double_buffer_runner(cfg, **builder_kwargs)
+    elif algo_name == "flashsac":
+        from uni_rl.algos.flash_sac.double_buffer import (
+            build_flashsac_double_buffer_runner,
+        )
+
+        runner = build_flashsac_double_buffer_runner(cfg, **builder_kwargs)
+    else:
+        raise ValueError(f"Unsupported algo: {algo_name}")
+
+    return runner
+
+
+def play_offpolicy(algo_name: str, cfg: DictConfig) -> str | None:
+    """Play pipeline for off-policy algorithms."""
+    import torch
+
+    load_path, load_path_dir = resolve_checkpoint_path(
+        Path.cwd(),
+        cfg.algo.algo_log_name,
+        cfg.training.task_name,
+        cfg.algo.load_run,
+    )
+    if not load_path or not os.path.exists(load_path):
+        print(f"Could not find checkpoint. load_path={load_path}")
+        return None
+
+    devices = resolve_dp_topology(cfg.training.devices)
+    device = default_device(torch, resolve_dp_rank_device(devices, current_dp_rank()))
+    print(f"Using device for play: {device}")
+
+    playback_cfg = RslRlPlaybackConfig(
+        task=str(cfg.training.task_name),
+        load_run=str(cfg.algo.load_run),
+        checkpoint=None,
+        action_mode="policy",
+        policy_obs_mode="actor",
+        algo_log_name=str(cfg.algo.algo_log_name),
+        log_root=None,
+        num_envs=int(cfg.training.play_env_num),
+    )
+    session, _policy_obs_mode, _checkpoint_path = create_sac_playback_session(
+        playback_cfg=playback_cfg,
+        cfg=cfg,
+        env_factory=lambda n: create_env(
+            cfg,
+            num_envs=n,
+            env_cfg_override=build_offpolicy_play_env_cfg_override(algo_name, cfg),
+        ),
+        root_dir=Path.cwd(),
+        device=device,
+        algo_name=algo_name,
+    )
+    env = cast(Any, session.env)
+    actor = session.actor
+    normalizer = session.normalizer
+    actor_algo_type = session.actor_algo_type
+
+    # Export actor to ONNX
+    if load_path_dir is not None and bool(getattr(cfg.training, "export_onnx", True)):
+        obs_dim, critic_obs_dim = resolve_play_obs_dims(env.obs_groups_spec)
+        _, actor_kwargs = resolve_play_actor_spec(
+            algo_name,
+            cfg,
+            obs_dim=obs_dim,
+            critic_obs_dim=critic_obs_dim,
+        )
+        onnx_path = os.path.join(load_path_dir, "policy.onnx")
+        dummy_input = torch.randn(1, obs_dim, device=device)
+        dummy_priv_info = (
+            torch.zeros(
+                (1, int(actor_kwargs["priv_info_dim"])),
+                device=device,
+                dtype=dummy_input.dtype,
+            )
+            if actor_algo_type == "hora_sac"
+            else None
+        )
+        with torch.inference_mode():
+            if normalizer:
+                dummy_input = normalizer(dummy_input, update=False)
+            assert actor is not None
+            if algo_name in ("sac", "flashsac"):
+                export_module = actor.as_export_module()
+            else:
+                export_module = actor
+            export_inputs = (
+                (dummy_input, dummy_priv_info) if dummy_priv_info is not None else (dummy_input,)
+            )
+        input_names = ["obs", "priv_info"] if dummy_priv_info is not None else ["obs"]
+        export_policy_onnx(export_module, onnx_path, export_inputs, input_names=input_names)
+
+        # Verify ONNX output matches PyTorch
+        verify_input = torch.randn(1, obs_dim, device=device)
+        with torch.inference_mode():
+            onnx_feed = normalizer(verify_input, update=False) if normalizer else verify_input
+            verify_priv_info = (
+                torch.zeros((1, int(actor_kwargs["priv_info_dim"])), device=device)
+                if actor_algo_type == "hora_sac"
+                else None
+            )
+        verify_inputs = (
+            (onnx_feed, verify_priv_info) if verify_priv_info is not None else (onnx_feed,)
+        )
+        verify_policy_onnx(export_module, onnx_path, verify_inputs, input_names=input_names)
+    elif load_path_dir is not None:
+        print("Skipping ONNX export because training.export_onnx=false.")
+
+    with torch.inference_mode():
+        play_video_path = env.run_playback_mode(
+            play_render_mode=getattr(cfg.training, "play_render_mode", "auto"),
+            play_steps=getattr(cfg.training, "play_steps", None),
+            output_video=os.path.join(load_path_dir, "play_video.mp4") if load_path_dir else None,
+            initialize=session.reset,
+            step=lambda _obs: session.step_once(),
+            camera_kwargs={
+                "cam_distance": cfg.training.cam_distance,
+                "cam_elevation": cfg.training.cam_elevation,
+                "cam_azimuth": cfg.training.cam_azimuth,
+            },
+            on_plan=log_playback_plan,
+        )
+    if play_video_path is not None:
+        print(f"Saving video to {play_video_path} ...")
+    print("Done.")
+    return cast("str | None", play_video_path)
+
+
+def main(cfg: DictConfig) -> None:
+    enable_faulthandler()
+    ensure_registries()
+
+    devices = resolve_dp_topology(cfg.training.devices)
+    rank = current_dp_rank()
+    rank_device = apply_dp_rank_config(cfg, devices, rank)
+
+    seed_info = apply_configured_training_seed(cfg, torch_runtime=True, cuda=True)
+    algo_name = cfg.algo.algo
+    task_name = cfg.training.task_name
+    assert_offpolicy_task_choice_matches_algo(cfg, algo_name=algo_name)
+
+    if cfg.training.log_dir is None:
+        timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+        run_dir_name = build_run_dir_name(
+            timestamp,
+            str(cfg.training.sim_backend),
+            world_size=len(devices) if devices is not None else 1,
+        )
+        log_dir = str(get_log_root(Path.cwd(), cfg) / task_name / run_dir_name)
+    else:
+        log_dir = cfg.training.log_dir
+    if rank > 0:
+        # Spawned ranks reuse the canonical run directory but never create
+        # logging backends, checkpoints, summaries, or traces there.
+        log_dir = os.environ[UNILAB_DP_LOG_DIR]
+
+    supervisor: DpRankSupervisor | None = None
+    if devices is not None and rank == 0 and len(devices) > 1:
+        validate_dp_launchable(devices)
+        supervisor = DpRankSupervisor(devices, log_dir)
+
+    import torch
+
+    tracker = None
+    if not cfg.training.play_only and rank == 0:
+        tracker = ExperimentTracker(
+            root_dir=Path.cwd(),
+            log_dir=log_dir,
+            algo_name=algo_name,
+            task_name=task_name,
+            sim_backend=cfg.training.sim_backend,
+            training_cfg=cfg.training,
+            full_cfg=cfg,
+            device=default_device(torch, rank_device),
+            seed_info=seed_info,
+        )
+        tracker.start()
+
+    try:
+        with supervisor if supervisor is not None else nullcontext():
+            if not cfg.training.play_only:
+                runner = None
+                try:
+                    runner = build_runner(algo_name, cfg, log_dir=log_dir)
+                    runner.learn(
+                        max_iterations=cfg.algo.max_iterations,
+                        save_interval=cfg.algo.save_interval,
+                        log_dir=log_dir,
+                        logger_type=cfg.training.logger,
+                    )
+                    run_summary = getattr(runner, "last_run_summary", None)
+                    if isinstance(run_summary, dict) and run_summary.get("status") not in (
+                        None,
+                        "completed",
+                    ):
+                        raise RuntimeError(
+                            f"Off-policy training ended with status={run_summary.get('status')!r}"
+                        )
+                    if tracker is not None:
+                        tracker.update_summary(run_summary)
+                except BaseException as exc:
+                    if tracker is not None:
+                        tracker.update_summary(
+                            build_failure_summary(exc, getattr(runner, "last_run_summary", None))
+                        )
+                    raise
+                finally:
+                    if runner is not None:
+                        runner.close()
+
+            if rank == 0 and should_run_playback(
+                play_only=cfg.training.play_only,
+                no_play=cfg.training.no_play,
+                play_render_mode=getattr(cfg.training, "play_render_mode", "auto"),
+            ):
+                print("@" * 50)
+                play_video_path = play_offpolicy(algo_name, cfg)
+                if tracker is not None:
+                    tracker.log_video(play_video_path)
+    finally:
+        if tracker is not None:
+            tracker.finish()
+
+
+if __name__ == "__main__":
+    raise SystemExit(
+        "unilab/scripts/train_offpolicy.py is a shared implementation module and is no "
+        "longer runnable directly. Use unilab/scripts/train_sac.py, "
+        "unilab/scripts/train_td3.py, or unilab/scripts/train_flashsac.py instead."
+    )
diff --git a/scripts/train_rsl_rl.py b/src/unilab/scripts/train_rsl_rl.py
similarity index 77%
rename from scripts/train_rsl_rl.py
rename to src/unilab/scripts/train_rsl_rl.py
index feea13c9c..3d5cb785b 100644
--- a/scripts/train_rsl_rl.py
+++ b/src/unilab/scripts/train_rsl_rl.py
@@ -9,18 +9,18 @@
 import hydra
 import torch
 from omegaconf import DictConfig, OmegaConf
-
-ROOT_DIR = Path(__file__).parent.parent
-SRC_DIR = ROOT_DIR / "src"
-if str(SRC_DIR) not in sys.path:
-    sys.path.insert(0, str(SRC_DIR))
-if str(ROOT_DIR) not in sys.path:
-    sys.path.insert(0, str(ROOT_DIR))
-
-from unilab.algos.torch.rsl_rl_runtime import resolve_rsl_rl_ppo_runtime
-from unilab.base.backend import RenderClosedError, materialize_scene_visual_override
-from unilab.base.run_control import RunComplete
-from unilab.ipc.dp_launcher import (
+from uni_rl.algos.rsl_rl import (
+    RslRlVecEnvWrapper,
+    apply_rsl_rl_rank_seed,
+    finish_rsl_rl_distributed,
+    get_policy_obs_dims,
+    normalize_ppo_train_cfg,
+    ppo_samples_per_iteration,
+    resolve_rsl_rl_device,
+    rsl_rl_single_process_topology,
+)
+from uni_rl.algos.rsl_rl_runtime import resolve_rsl_rl_ppo_runtime
+from uni_rl.ipc.dp_launcher import (
     UNILAB_DP_LOG_DIR,
     current_torch_distributed_local_rank,
     current_torch_distributed_rank,
@@ -29,15 +29,18 @@
     resolve_dp_topology,
     validate_dp_launchable,
 )
+from unisim.backend.base import RenderClosedError, log_playback_plan
+from unisim.backend.mujoco.xml import materialize_scene_visual_override
+
+from unilab.base.config_adapter import BackendAdapter, create_env
+from unilab.base.run_control import RunComplete
 from unilab.training import (
-    BackendAdapter,
-    apply_configured_training_seed,
-    create_env,
+    algo_config_dict,
+    apply_env_nan_guard,
+    build_run_dir_name,
     ensure_registries,
-    get_latest_checkpoint,
-    get_latest_run,
+    format_play_checkpoint_error,
     get_log_root,
-    log_playback_plan,
     parse_checkpoint_path,
     should_run_playback,
 )
@@ -47,17 +50,16 @@
     patch_rsl_rl_resume_state,
     patch_rsl_rl_wandb_writer,
 )
-from unilab.training.rsl_rl import (
-    RslRlVecEnvWrapper,
-    apply_rsl_rl_rank_seed,
-    finish_rsl_rl_distributed,
-    normalize_ppo_train_cfg,
-    ppo_samples_per_iteration,
-    resolve_rsl_rl_device,
-    rsl_rl_single_process_topology,
-)
-from unilab.training.sim2sim import policy_load_dim_guard, resolve_sim2sim_config
+from unilab.utils.checkpoint import get_entrypoint_log_root
 from unilab.utils.device import get_default_device
+from unilab.utils.seed import apply_configured_training_seed
+from unilab.visualization.interactive_playback import (
+    RslRlPlaybackConfig,
+    create_rsl_rl_playback_session,
+    infer_checkpoint_actor_input_dim,
+    make_sim2sim_preflight,
+    normalize_checkpoint_value,
+)
 
 try:
     from rsl_rl.runners import OnPolicyRunner
@@ -69,7 +71,7 @@
 def _backend_adapter(cfg: DictConfig) -> BackendAdapter:
     return BackendAdapter(
         cfg,
-        root_dir=ROOT_DIR,
+        root_dir=Path.cwd(),
         algo_name="ppo",
         scene_materializer=materialize_scene_visual_override,
     )
@@ -104,12 +106,7 @@ def run_motrix_rsl_play_loop(
 
 
 def _get_log_root(cfg: DictConfig) -> str:
-    return str(get_log_root(ROOT_DIR, cfg))
-
-
-def build_ppo_run_dir_name(timestamp: str, sim_backend: str, *, world_size: int = 1) -> str:
-    gpu_suffix = f"_gpux{world_size}" if world_size > 1 else ""
-    return f"{timestamp}_{sim_backend}{gpu_suffix}"
+    return str(get_log_root(Path.cwd(), cfg))
 
 
 def resolve_ppo_log_dir(
@@ -129,7 +126,7 @@ def resolve_ppo_log_dir(
     return str(
         Path(_get_log_root(cfg))
         / str(cfg.training.task_name)
-        / build_ppo_run_dir_name(
+        / build_run_dir_name(
             timestamp,
             str(cfg.training.sim_backend),
             world_size=world_size,
@@ -137,13 +134,6 @@ def resolve_ppo_log_dir(
     )
 
 
-def _algo_config_dict(cfg: DictConfig) -> dict[str, Any]:
-    train_cfg_raw = OmegaConf.to_container(cfg.algo, resolve=True)
-    if not isinstance(train_cfg_raw, dict):
-        raise TypeError("cfg.algo must resolve to a dict")
-    return cast(dict[str, Any], train_cfg_raw)
-
-
 def _resolve_ppo_wrapper_cls(rl_cfg: dict[str, Any]) -> type[RslRlVecEnvWrapper]:
     """Resolve the VecEnv wrapper class from the owner-selected PPO runtime.
 
@@ -154,10 +144,13 @@ def _resolve_ppo_wrapper_cls(rl_cfg: dict[str, Any]) -> type[RslRlVecEnvWrapper]
         Wrapper class used to adapt the UniLab env contract to the active
         RSL-RL PPO runtime.
     """
-    return resolve_rsl_rl_ppo_runtime(
-        rl_cfg,
-        default_wrapper_cls=RslRlVecEnvWrapper,
-    ).wrapper_cls
+    return cast(
+        "type[RslRlVecEnvWrapper]",
+        resolve_rsl_rl_ppo_runtime(
+            rl_cfg,
+            default_wrapper_cls=RslRlVecEnvWrapper,
+        ).wrapper_cls,
+    )
 
 
 def apply_ppo_runtime_flags(
@@ -192,42 +185,6 @@ def validate_ppo_run_completion_topology(
         )
 
 
-def _format_play_checkpoint_error(
-    cfg: DictConfig,
-    *,
-    task_log_root: Path,
-    load_path: Path | None,
-    load_path_dir: Path | None,
-) -> str:
-    selected_checkpoint = OmegaConf.select(cfg, "algo.checkpoint", default=-1)
-    checkpoint_hint = (
-        f" algo.checkpoint={selected_checkpoint!r}"
-        if selected_checkpoint not in (None, "", -1, "-1")
-        else ""
-    )
-
-    if load_path_dir is not None and load_path is None and checkpoint_hint:
-        reason = f"Requested checkpoint was not found under resolved_run={load_path_dir}."
-    elif not task_log_root.exists():
-        reason = "Task log root does not exist."
-    else:
-        latest_run = get_latest_run(task_log_root)
-        if latest_run is None:
-            reason = "No run directories were found under the task log root."
-        elif get_latest_checkpoint(latest_run) is None:
-            reason = f"Resolved latest run has no model_*.pt checkpoint files: {latest_run}."
-        else:
-            reason = "Requested run or checkpoint could not be resolved."
-
-    return (
-        "Could not resolve a checkpoint for play mode. "
-        f"{reason} task={cfg.training.task_name} task_log_root={task_log_root} "
-        f"algo.load_run={cfg.algo.load_run!r}{checkpoint_hint}."
-        " Use algo.load_run=<run-dir-or-checkpoint-path> "
-        "and optionally algo.checkpoint=<iteration-or-filename>."
-    )
-
-
 def _resolve_play_num_steps(cfg: DictConfig) -> int | None:
     play_steps = OmegaConf.select(cfg, "training.play_steps", default=None)
     if play_steps is None:
@@ -237,14 +194,13 @@ def _resolve_play_num_steps(cfg: DictConfig) -> int | None:
 
 def play_rsl_rl(cfg: DictConfig, device: str) -> str | None:
     """Play mode for RSL-RL."""
-    rl_cfg = _algo_config_dict(cfg)
-    wrapper_cls = _resolve_ppo_wrapper_cls(rl_cfg)
+    rl_cfg = algo_config_dict(cfg)
 
-    task_log_root = get_log_root(ROOT_DIR, cfg) / str(cfg.training.task_name)
-    load_path, load_path_dir = parse_checkpoint_path(cfg, root_dir=ROOT_DIR)
+    task_log_root = get_log_root(Path.cwd(), cfg) / str(cfg.training.task_name)
+    load_path, load_path_dir = parse_checkpoint_path(cfg, root_dir=Path.cwd())
     if load_path is None or load_path_dir is None or not load_path.exists():
         print(
-            _format_play_checkpoint_error(
+            format_play_checkpoint_error(
                 cfg,
                 task_log_root=task_log_root,
                 load_path=load_path,
@@ -262,47 +218,54 @@ def play_rsl_rl(cfg: DictConfig, device: str) -> str | None:
         )
         return None
 
-    cfg = (
-        resolve_sim2sim_config(
-            load_path_dir,
-            cfg,
-            algo_name="ppo",
-            strict=bool(getattr(cfg.training, "sim2sim_strict", True)),
-        )
-        or cfg
-    )
-
-    env_cfg_override = build_ppo_play_env_cfg_override(cfg)
-
-    env = create_env(
-        cfg,
+    def _normalize_play_train_cfg(train_cfg: dict[str, Any]) -> dict[str, Any]:
+        normalized = cast("dict[str, Any]", normalize_ppo_train_cfg(train_cfg))
+        apply_ppo_runtime_flags(normalized, cfg, training_enabled=False)
+        return normalized
+
+    playback_cfg = RslRlPlaybackConfig(
+        task=str(cfg.training.task_name),
+        load_run=str(cfg.algo.load_run),
+        checkpoint=normalize_checkpoint_value(
+            OmegaConf.select(cfg, "algo.checkpoint", default=None)
+        ),
+        action_mode="policy",
+        policy_obs_mode="flat",
+        algo_log_name=str(cfg.algo.algo_log_name),
+        log_root=None,
         num_envs=cfg.training.play_env_num,
-        env_cfg_override=env_cfg_override,
     )
-    wrapped_env = wrapper_cls(env, device=device)
-    train_cfg = normalize_ppo_train_cfg(rl_cfg)
-    apply_ppo_runtime_flags(train_cfg, cfg, training_enabled=False)
-    if "runner" not in train_cfg:
-        train_cfg["runner"] = {}
-    train_cfg["runner"]["logger"] = "none"
-
-    runner = cast(
-        Any,
-        OnPolicyRunner(cast(Any, wrapped_env), train_cfg, log_dir=None, device=device),
+    session, _policy_obs_mode, _checkpoint_path = create_rsl_rl_playback_session(
+        playback_cfg=playback_cfg,
+        env_factory=lambda n: create_env(
+            cfg,
+            num_envs=n,
+            env_cfg_override=build_ppo_play_env_cfg_override(cfg),
+        ),
+        algo_config=rl_cfg,
+        root_dir=Path.cwd(),
+        device=device,
+        checkpoint_resolver=lambda *_args: str(load_path),
+        checkpoint_input_dim_reader=infer_checkpoint_actor_input_dim,
+        entrypoint_log_root=get_entrypoint_log_root,
+        wrapper_cls=_resolve_ppo_wrapper_cls(rl_cfg),
+        runner_cls=OnPolicyRunner,
+        policy_obs_dims_getter=get_policy_obs_dims,
+        train_cfg_normalizer=_normalize_play_train_cfg,
+        sim2sim_preflight=make_sim2sim_preflight(cfg, algo_name="ppo"),
+        guard_algo_name="ppo",
     )
-    with policy_load_dim_guard(
-        env_obs_dim=getattr(wrapped_env, "num_obs", None),
-        env_action_dim=getattr(wrapped_env, "num_actions", None),
-        algo_name="ppo",
-    ):
-        runner.load(str(load_path), map_location=device)
-    policy = runner.get_inference_policy(device=device)
+    env = session.env
+    runner = session.runner
     if EXPORT_POLICY:
+        # The checkpoint early-returns above guarantee a loaded runner here.
+        assert runner is not None
         runner.export_policy_to_onnx(path=str(load_path_dir))
         runner.export_policy_to_jit(path=str(load_path_dir))
     num_steps = _resolve_play_num_steps(cfg)
     output_video = Path(load_path_dir) / "play_video.mp4"
     playback_mode: str | None = None
+    play_video_path: str | None = None
 
     def _log_plan(plan) -> None:
         nonlocal playback_mode
@@ -319,8 +282,8 @@ def _log_plan(plan) -> None:
                     getattr(cfg.training, "render_spacing", getattr(env.cfg, "render_spacing", 1.0))
                 ),
                 render_offset_mode=str(getattr(env.cfg, "render_offset_mode", "grid")),
-                initialize=lambda: wrapped_env.reset()[0],
-                step=lambda obs: wrapped_env.step(policy(obs))[0],
+                initialize=session.reset,
+                step=lambda _obs: session.step_once(),
                 camera_kwargs={
                     "cam_distance": cfg.training.cam_distance,
                     "cam_elevation": cfg.training.cam_elevation,
@@ -368,12 +331,11 @@ def main(cfg: DictConfig) -> None:
     # env, tracker, and runner construction all happen inside workers.
     if devices is not None and len(devices) > 1 and world_size == 1:
         if not cfg.training.play_only:
-            log_dir = resolve_ppo_log_dir(cfg, world_size=len(devices))
             launch_torchrun_workers(
                 devices,
                 script_path=Path(__file__),
                 argv=sys.argv[1:],
-                log_dir=log_dir,
+                log_dir=resolve_ppo_log_dir(cfg, world_size=len(devices)),
             )
             return
         validate_dp_launchable(devices)
@@ -419,14 +381,14 @@ def main(cfg: DictConfig) -> None:
         )
 
     if not cfg.training.play_only:
-        log_dir = resolve_ppo_log_dir(cfg, world_size=world_size)
+        log_dir: str | None = resolve_ppo_log_dir(cfg, world_size=world_size)
     else:
         log_dir = None
 
     tracker = None
     if not cfg.training.play_only and log_dir is not None and rank == 0:
         tracker = ExperimentTracker(
-            root_dir=ROOT_DIR,
+            root_dir=Path.cwd(),
             log_dir=log_dir,
             algo_name="ppo",
             task_name=cfg.training.task_name,
@@ -450,26 +412,10 @@ def main(cfg: DictConfig) -> None:
                     env_cfg_override=env_cfg_override,
                 )
                 try:
-                    rl_cfg = _algo_config_dict(cfg)
+                    rl_cfg = algo_config_dict(cfg)
                     wrapper_cls = _resolve_ppo_wrapper_cls(rl_cfg)
 
-                    nan_guard_cfg = getattr(cfg.training, "nan_guard", None)
-                    if nan_guard_cfg is not None and getattr(nan_guard_cfg, "enabled", False):
-                        from unilab.utils.nan_guard import NanGuard, NanGuardCfg
-
-                        guard = NanGuard(
-                            NanGuardCfg(
-                                enabled=True,
-                                buffer_size=int(getattr(nan_guard_cfg, "buffer_size", 100)),
-                                max_envs_to_dump=int(getattr(nan_guard_cfg, "max_envs_to_dump", 5)),
-                                output_dir=getattr(nan_guard_cfg, "output_dir", None),
-                            ),
-                            num_envs=env.num_envs,
-                            supports_state_playback=(
-                                env.play_capabilities.supports_physics_state_playback
-                            ),
-                        )
-                        env.set_nan_guard(guard)
+                    apply_env_nan_guard(env, cfg.training)
 
                     wrapped_env = wrapper_cls(env, device=device)
 
@@ -508,7 +454,7 @@ def main(cfg: DictConfig) -> None:
                     patch_rsl_rl_action_std_logging(runner)
 
                     if cfg.algo.load_run != "-1":
-                        resume_path, _ = parse_checkpoint_path(cfg, root_dir=ROOT_DIR)
+                        resume_path, _ = parse_checkpoint_path(cfg, root_dir=Path.cwd())
                         if resume_path:
                             print(f"Resuming from {resume_path}")
                             runner.load(str(resume_path), map_location=device)
diff --git a/src/unilab/scripts/train_sac.py b/src/unilab/scripts/train_sac.py
new file mode 100644
index 000000000..d1816698a
--- /dev/null
+++ b/src/unilab/scripts/train_sac.py
@@ -0,0 +1,15 @@
+"""SAC training/playback entrypoint (shared implementation in unilab.scripts.train_offpolicy)."""
+
+import hydra
+from omegaconf import DictConfig
+
+from unilab.scripts.train_offpolicy import main as _offpolicy_main
+
+
+@hydra.main(version_base="1.3", config_path="../conf/sac", config_name="config")
+def main(cfg: DictConfig) -> None:
+    _offpolicy_main(cfg)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/unilab/scripts/train_td3.py b/src/unilab/scripts/train_td3.py
new file mode 100644
index 000000000..5a32c1d29
--- /dev/null
+++ b/src/unilab/scripts/train_td3.py
@@ -0,0 +1,15 @@
+"""TD3 training/playback entrypoint (shared implementation in unilab.scripts.train_offpolicy)."""
+
+import hydra
+from omegaconf import DictConfig
+
+from unilab.scripts.train_offpolicy import main as _offpolicy_main
+
+
+@hydra.main(version_base="1.3", config_path="../conf/td3", config_name="config")
+def main(cfg: DictConfig) -> None:
+    _offpolicy_main(cfg)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/unilab/structured_configs.py b/src/unilab/structured_configs.py
index 48e59a805..62707c740 100644
--- a/src/unilab/structured_configs.py
+++ b/src/unilab/structured_configs.py
@@ -58,7 +58,6 @@ class SACConfig(BaseConfig):
     num_atoms: int = 101
     obs_normalization: bool = False
     use_layer_norm: bool = True
-    use_symmetry: bool = False
     actor: dict[str, Any] = field(default_factory=dict)
     algo_params: SACAlgoParams = field(default_factory=SACAlgoParams)
 
@@ -238,7 +237,7 @@ class PPOPolicyConfig:
 
 @dataclass
 class PPOAlgorithmConfig:
-    class_name: str = "unilab.algos.torch.rsl_rl_ppo:FinalObservationAwarePPO"
+    class_name: str = "uni_rl.algos.rsl_rl_ppo:FinalObservationAwarePPO"
     value_loss_coef: float = 1.0
     use_clipped_value_loss: bool = True
     clip_param: float = 0.2
diff --git a/src/unilab/tasks/__init__.py b/src/unilab/tasks/__init__.py
new file mode 100644
index 000000000..d9c58aa9f
--- /dev/null
+++ b/src/unilab/tasks/__init__.py
@@ -0,0 +1,22 @@
+"""Production task registry bootstrap.
+
+Concrete task implementations live in this package.  The registry imports the
+explicit leaf-module list directly, so registration stays deterministic and
+does not depend on package discovery or import order.
+"""
+
+__unilab_registry_modules__ = (
+    "unilab.tasks.locomotion.go1",
+    "unilab.tasks.locomotion.go2",
+    "unilab.tasks.locomotion.go2w",
+    "unilab.tasks.locomotion.g1",
+    "unilab.tasks.locomotion.go2_arm",
+    "unilab.tasks.locomotion.a2",
+    "unilab.tasks.manipulation.allegro_inhand",
+    "unilab.tasks.manipulation.sharpa_inhand",
+    "unilab.tasks.manipulation.stewart",
+    "unilab.tasks.motion_tracking.g1",
+    "unilab.tasks.motion_tracking.x2",
+)
+
+__all__ = ["__unilab_registry_modules__"]
diff --git a/src/unilab/tasks/compatibility.py b/src/unilab/tasks/compatibility.py
new file mode 100644
index 000000000..a33106b20
--- /dev/null
+++ b/src/unilab/tasks/compatibility.py
@@ -0,0 +1,132 @@
+"""Internal, cold-path compatibility seam for legacy task factories.
+
+This module is intentionally task-owned and is not part of the base registry
+contract.  It only admits legacy factories that already use UniLab's
+``EnvCfg -> NpEnv`` lifecycle; it does not provide a fallback runtime.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from enum import Enum
+from typing import Generic, Protocol, TypeVar
+
+from unilab.base.base import ABEnv, EnvCfg
+from unilab.base.np_env import NpEnv
+
+
+class CompatibilityStatus(str, Enum):
+    """Documented outcome for one legacy compatibility boundary."""
+
+    ADAPTED = "Adapted"
+    UNSUPPORTED = "Unsupported"
+
+
+@dataclass(frozen=True)
+class LegacyTaskCompatibility:
+    """Immutable compatibility evidence attached to one task-family seam."""
+
+    task_family: str
+    status: CompatibilityStatus
+    reason: str
+
+    def __post_init__(self) -> None:
+        if not self.task_family.strip():
+            raise ValueError("legacy compatibility task_family must be non-empty")
+        if not self.reason.strip():
+            raise ValueError("legacy compatibility reason must be non-empty")
+
+
+TCfg_contra = TypeVar("TCfg_contra", bound=EnvCfg, contravariant=True)
+
+
+class LegacyEnvFactory(Protocol[TCfg_contra]):
+    """Existing registry-shaped legacy factory admitted by this seam."""
+
+    def __call__(
+        self,
+        cfg: TCfg_contra,
+        *,
+        num_envs: int = 1,
+        backend_type: str = "mujoco",
+    ) -> ABEnv: ...
+
+
+@dataclass(frozen=True)
+class LegacyFactoryAdapter(Generic[TCfg_contra]):
+    """Validate one legacy factory at the existing env construction boundary."""
+
+    factory: LegacyEnvFactory[TCfg_contra]
+    compatibility: LegacyTaskCompatibility
+
+    def __post_init__(self) -> None:
+        if self.compatibility.status is not CompatibilityStatus.ADAPTED:
+            raise ValueError("LegacyFactoryAdapter compatibility status must be Adapted")
+
+    def __call__(
+        self,
+        cfg: TCfg_contra,
+        *,
+        num_envs: int = 1,
+        backend_type: str = "mujoco",
+    ) -> NpEnv:
+        family = self.compatibility.task_family
+        if not isinstance(cfg, EnvCfg):
+            raise TypeError(
+                f"Legacy task family '{family}' expected EnvCfg, received {type(cfg).__name__}"
+            )
+
+        env = self.factory(cfg, num_envs=num_envs, backend_type=backend_type)
+        if not isinstance(env, ABEnv):
+            raise TypeError(
+                f"Legacy task family '{family}' factory returned {type(env).__name__}, "
+                "expected ABEnv"
+            )
+        if not isinstance(env, NpEnv):
+            raise TypeError(
+                f"Legacy task family '{family}' compatibility is Unsupported: "
+                f"{type(env).__name__} does not use the NpEnv lifecycle"
+            )
+        return env
+
+
+def adapt_legacy_factory(
+    factory: LegacyEnvFactory[TCfg_contra],
+    *,
+    task_family: str,
+    reason: str,
+) -> LegacyFactoryAdapter[TCfg_contra]:
+    """Mark and wrap an existing ``EnvCfg -> NpEnv`` task factory.
+
+    The wrapper runs only while the registry constructs an environment.  It
+    forwards the registry's fixed arguments exactly once and rejects any
+    other config or runtime shape instead of probing or falling back.
+    """
+
+    if not callable(factory):
+        raise TypeError(f"legacy task family '{task_family}' factory must be callable")
+    compatibility = LegacyTaskCompatibility(
+        task_family=task_family,
+        status=CompatibilityStatus.ADAPTED,
+        reason=reason,
+    )
+    return LegacyFactoryAdapter(factory=factory, compatibility=compatibility)
+
+
+def unsupported_legacy_task(*, task_family: str, reason: str) -> LegacyTaskCompatibility:
+    """Record an explicit unsupported surface without creating a factory."""
+
+    return LegacyTaskCompatibility(
+        task_family=task_family,
+        status=CompatibilityStatus.UNSUPPORTED,
+        reason=reason,
+    )
+
+
+__all__ = [
+    "CompatibilityStatus",
+    "LegacyFactoryAdapter",
+    "LegacyTaskCompatibility",
+    "adapt_legacy_factory",
+    "unsupported_legacy_task",
+]
diff --git a/src/unilab/tasks/locomotion/__init__.py b/src/unilab/tasks/locomotion/__init__.py
new file mode 100644
index 000000000..3439ae657
--- /dev/null
+++ b/src/unilab/tasks/locomotion/__init__.py
@@ -0,0 +1 @@
+"""Locomotion task packages."""
diff --git a/src/unilab/tasks/locomotion/a2/__init__.py b/src/unilab/tasks/locomotion/a2/__init__.py
new file mode 100644
index 000000000..e078a7e20
--- /dev/null
+++ b/src/unilab/tasks/locomotion/a2/__init__.py
@@ -0,0 +1,3 @@
+from . import joystick as joystick
+
+__all__ = ["joystick"]
diff --git a/src/unilab/tasks/locomotion/a2/joystick.py b/src/unilab/tasks/locomotion/a2/joystick.py
new file mode 100644
index 000000000..f5b22df20
--- /dev/null
+++ b/src/unilab/tasks/locomotion/a2/joystick.py
@@ -0,0 +1,7 @@
+"""Hydra-owned A2 flat Manager-Based production registration."""
+
+from unilab.base import registry
+from unilab.envs import ManagerBasedRlEnvCfg, make_manager_based_rl_env
+
+registry.register_env_config("A2JoystickFlat", ManagerBasedRlEnvCfg)
+registry.register_env("A2JoystickFlat", make_manager_based_rl_env, sim_backend="mujoco")
diff --git a/src/unilab/tasks/locomotion/common/__init__.py b/src/unilab/tasks/locomotion/common/__init__.py
new file mode 100644
index 000000000..67b9c7141
--- /dev/null
+++ b/src/unilab/tasks/locomotion/common/__init__.py
@@ -0,0 +1,33 @@
+"""Shared task-specific locomotion components."""
+
+from .base import (
+    BaseNoiseConfig,
+    ControlConfigBase,
+    LocomotionBaseCfg,
+    LocomotionBaseEnv,
+    Sensor,
+)
+from .commands import Commands
+from .domain_rand import DomainRandConfig
+from .dr_provider import LocomotionDRProvider
+from .height_scan import (
+    DEFAULT_SCAN_POINTS_X,
+    DEFAULT_SCAN_POINTS_Y,
+    HeightScanConfig,
+)
+from .rewards import RewardContext
+
+__all__ = [
+    "BaseNoiseConfig",
+    "Commands",
+    "ControlConfigBase",
+    "DEFAULT_SCAN_POINTS_X",
+    "DEFAULT_SCAN_POINTS_Y",
+    "DomainRandConfig",
+    "HeightScanConfig",
+    "LocomotionDRProvider",
+    "LocomotionBaseCfg",
+    "LocomotionBaseEnv",
+    "RewardContext",
+    "Sensor",
+]
diff --git a/src/unilab/tasks/locomotion/common/base.py b/src/unilab/tasks/locomotion/common/base.py
new file mode 100644
index 000000000..f82ad17a6
--- /dev/null
+++ b/src/unilab/tasks/locomotion/common/base.py
@@ -0,0 +1,149 @@
+"""Shared base environment and configuration for legacy locomotion tasks."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import ClassVar
+
+import gymnasium as gym
+import numpy as np
+from unisim import SimBackend
+
+from unilab.base.base import EnvCfg
+from unilab.base.np_env import NpEnv, NpEnvState
+from unilab.dtype_config import get_global_dtype
+from unilab.tasks.locomotion.common.terrain_spawn import BaseSpawnManager
+
+
+@dataclass
+class Sensor:
+    local_linvel = "local_linvel"
+    gyro = "gyro"
+
+
+@dataclass
+class ControlConfigBase:
+    action_scale: float = 0.25
+    simulate_action_latency: bool = False
+
+
+@dataclass
+class BaseNoiseConfig:
+    level: float = 0.0
+    scale_joint_angle: float = 0.03
+    scale_joint_vel: float = 0.5
+    scale_gyro: float = 0.2
+    scale_gravity: float = 0.05
+    scale_linvel: float = 0.1
+    seed: int | None = None
+
+
+@dataclass
+class LocomotionBaseCfg(EnvCfg):
+    control_config: ControlConfigBase = field(default_factory=ControlConfigBase)
+    noise_config: BaseNoiseConfig = field(default_factory=BaseNoiseConfig)
+    sensor: Sensor = field(default_factory=Sensor)
+    sim_dt: float = 0.01
+    ctrl_dt: float = 0.02
+
+
+class LocomotionBaseEnv(NpEnv):
+    """Common base environment for locomotion tasks (G1, Go1, Go2, etc.)."""
+
+    _cfg: LocomotionBaseCfg
+
+    _keyframe_name: ClassVar[str] = "home"
+    _use_global_dtype: ClassVar[bool] = True
+
+    def __init__(self, cfg: LocomotionBaseCfg, backend: SimBackend, num_envs: int = 1):
+        super().__init__(cfg, backend, num_envs)
+        self._init_action_space()
+        self._num_action = self._action_space.shape[0]
+        self._obs_noise_rng: np.random.Generator | None = None
+        self._obs_noise_seed_override: int | None = None
+        self._init_buffers()
+        self._spawn: BaseSpawnManager = BaseSpawnManager()
+
+    def _init_action_space(self) -> None:
+        ctrl_range = self._backend.get_actuator_ctrl_range()
+        nu = self._backend.num_actuators
+        self._action_space = gym.spaces.Box(ctrl_range[:, 0], ctrl_range[:, 1], (nu,), dtype=float)  # type: ignore[assignment, arg-type]
+
+    @property
+    def action_space(self) -> gym.spaces.Box:
+        return self._action_space  # type: ignore[no-any-return]
+
+    def _init_buffers(self) -> None:
+        dtype = get_global_dtype() if self._use_global_dtype else np.float32
+        raw_qpos = self._backend.get_keyframe_qpos(self._keyframe_name)
+        self._init_qpos = (
+            np.asarray(raw_qpos, dtype=dtype) if self._use_global_dtype else np.asarray(raw_qpos)
+        )
+        self.default_angles = np.asarray(self._init_qpos[-self._num_action :], dtype=dtype)
+        raw_qvel = self._backend.get_init_qvel()
+        self._init_qvel = (
+            np.asarray(raw_qvel, dtype=dtype) if self._use_global_dtype else np.asarray(raw_qvel)
+        )
+
+    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
+        state.info["last_actions"] = state.info.get("current_actions", np.zeros_like(actions))
+        state.info["current_actions"] = actions
+        exec_actions = (
+            state.info["last_actions"]
+            if self._cfg.control_config.simulate_action_latency
+            else actions
+        )
+        ctrl: np.ndarray = (
+            exec_actions * self._cfg.control_config.action_scale + self.default_angles
+        )
+        return ctrl
+
+    def seed_observation_noise(self, seed: int | None) -> None:
+        """Reset env-owned observation-noise RNG streams."""
+        if seed is not None and int(seed) < 0:
+            raise ValueError(f"observation noise seed must be non-negative, got {seed}")
+        self._obs_noise_seed_override = int(seed) if seed is not None else None
+        self._obs_noise_rng = None
+
+    def _configured_obs_noise_seed(self) -> int | None:
+        seed = getattr(self, "_obs_noise_seed_override", None)
+        if seed is None:
+            seed = getattr(self._cfg.noise_config, "seed", None)
+        if seed is None:
+            return None
+        seed = int(seed)
+        if seed < 0:
+            raise ValueError(f"observation noise seed must be non-negative, got {seed}")
+        return seed
+
+    def _obs_noise(self, data: np.ndarray, scale: float) -> np.ndarray:
+        """Apply per-step uniform observation noise scaled by ``noise_config.level``."""
+        level = float(self._cfg.noise_config.level)
+        if level <= 0.0:
+            return data
+        seed = self._configured_obs_noise_seed()
+        if seed is None:
+            noise = np.random.uniform(-1.0, 1.0, data.shape).astype(data.dtype)
+        else:
+            rng = getattr(self, "_obs_noise_rng", None)
+            if rng is None:
+                rng = np.random.default_rng(seed)
+                self._obs_noise_rng = rng
+            noise = rng.uniform(-1.0, 1.0, data.shape).astype(data.dtype)
+        return data + noise * level * scale
+
+    def get_local_linvel(self) -> np.ndarray:
+        local_linvel: np.ndarray = self._backend.get_sensor_data(self._cfg.sensor.local_linvel)
+        return local_linvel
+
+    def get_gyro(self) -> np.ndarray:
+        gyro: np.ndarray = self._backend.get_sensor_data(self._cfg.sensor.gyro)
+        return gyro
+
+    def get_dof_pos(self) -> np.ndarray:
+        dof_pos: np.ndarray = self._backend.get_dof_pos()
+        return dof_pos
+
+    def get_dof_vel(self) -> np.ndarray:
+        dof_vel: np.ndarray = self._backend.get_dof_vel()
+        return dof_vel
diff --git a/src/unilab/tasks/locomotion/common/commands.py b/src/unilab/tasks/locomotion/common/commands.py
new file mode 100644
index 000000000..92a63ff41
--- /dev/null
+++ b/src/unilab/tasks/locomotion/common/commands.py
@@ -0,0 +1,20 @@
+"""Shared command helpers for locomotion tasks."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+
+@dataclass
+class Commands:
+    vel_limit: list[list[float]] = field(
+        default_factory=lambda: [
+            [-0.6, -0.4, -0.8],  # [vx_min, vy_min, vyaw_min]
+            [1.0, 0.4, 0.8],  # [vx_max, vy_max, vyaw_max]
+        ]
+    )
+    resampling_time: float = 0.0
+    heading_command: bool = False
+    heading_range: list[float] = field(default_factory=lambda: [-3.14, 3.14])
+    heading_control_stiffness: float = 0.5
+    rel_standing_envs: float = 0.0
diff --git a/src/unilab/envs/locomotion/common/domain_rand.py b/src/unilab/tasks/locomotion/common/domain_rand.py
similarity index 94%
rename from src/unilab/envs/locomotion/common/domain_rand.py
rename to src/unilab/tasks/locomotion/common/domain_rand.py
index 9e10c893a..968d9a894 100644
--- a/src/unilab/envs/locomotion/common/domain_rand.py
+++ b/src/unilab/tasks/locomotion/common/domain_rand.py
@@ -1,3 +1,5 @@
+"""Shared domain-randomization config for locomotion tasks."""
+
 from __future__ import annotations
 
 from dataclasses import dataclass, field
diff --git a/src/unilab/envs/locomotion/common/dr_provider.py b/src/unilab/tasks/locomotion/common/dr_provider.py
similarity index 99%
rename from src/unilab/envs/locomotion/common/dr_provider.py
rename to src/unilab/tasks/locomotion/common/dr_provider.py
index 24f75ab54..182274e4d 100644
--- a/src/unilab/envs/locomotion/common/dr_provider.py
+++ b/src/unilab/tasks/locomotion/common/dr_provider.py
@@ -1,4 +1,4 @@
-"""Shared DomainRandomizationProvider for locomotion environments.
+"""Shared DomainRandomizationProvider for locomotion tasks.
 
 Implements the common reset/interval randomization logic shared by
 G1, Go1, and Go2 joystick environments.  Subclasses override hooks
diff --git a/src/unilab/tasks/locomotion/common/gait_terms.py b/src/unilab/tasks/locomotion/common/gait_terms.py
new file mode 100644
index 000000000..7f53e5018
--- /dev/null
+++ b/src/unilab/tasks/locomotion/common/gait_terms.py
@@ -0,0 +1,500 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681),
+# src/mjlab/tasks/velocity/mdp/rewards.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy, named-sensor contact groups, and the
+# base-owned entity facade; Apache-2.0.
+"""mjlab-aligned gait-quality terms shared by locomotion families.
+
+These are the window-form ``feet_air_time`` and the clearance / swing-height /
+slip / angular-momentum reward terms that mjlab ships in its velocity task,
+plus the privileged per-foot observation terms (``foot_height`` /
+``foot_air_time`` / ``foot_contact`` / ``foot_contact_forces``) its velocity
+critic consumes.  mjlab reads feet through a ``ContactSensor`` (primaries with
+per-slot any-reduce) and a ``TerrainHeightSensor``; the UniLab port binds one
+named contact sensor group per foot through the ``SensorTermBase`` cold-path
+contract (any sensor in the group above the threshold means foot contact,
+mirroring the slot reduce) and reads foot heights/velocities from
+``body_link_pos_w`` / ``body_link_lin_vel_w`` through ``asset_cfg.body_ids``,
+which is exact on flat terrain without raycast.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, ClassVar, cast
+
+import numpy as np
+
+from unilab.dtype_config import get_global_dtype
+from unilab.managers.manager_base import ManagerTermBaseCfg
+from unilab.managers.scene_entity_config import SceneEntityCfg
+
+from .manager_terms import SensorTermBase, _command, _real, _state
+
+if TYPE_CHECKING:
+    from unilab.base.entity import Entity
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+_DEFAULT_ASSET_CFG = SceneEntityCfg("robot")
+
+
+def _sensor_groups(term: str, value: Any) -> tuple[tuple[str, ...], ...]:
+    if isinstance(value, (str, bytes)) or not isinstance(value, (tuple, list)):
+        raise TypeError(f"{term} sensor_groups must be a sequence of sensor-name sequences")
+    groups: list[tuple[str, ...]] = []
+    for index, group in enumerate(value):
+        if isinstance(group, (str, bytes)) or not isinstance(group, (tuple, list)) or not group:
+            raise ValueError(f"{term} sensor_groups[{index}] must be a non-empty name sequence")
+        names = tuple(group)
+        if any(not isinstance(name, str) or not name for name in names):
+            raise ValueError(f"{term} sensor_groups[{index}] must contain non-empty strings")
+        groups.append(names)
+    if not groups:
+        raise ValueError(f"{term} sensor_groups must declare at least one foot")
+    flat = [name for group in groups for name in group]
+    if len(set(flat)) != len(flat):
+        raise ValueError(f"{term} sensor_groups sensor names must be unique: {flat}")
+    return tuple(groups)
+
+
+def _command_gate(
+    env: ManagerBasedRlEnv,
+    term: str,
+    command_name: str | None,
+    command_threshold: float,
+) -> np.ndarray | None:
+    """mjlab command gate: planar norm plus yaw magnitude above the threshold."""
+    if command_name is None:
+        return None
+    command = _command(env, term, command_name)
+    total = np.linalg.norm(command[:, :2], axis=1) + np.abs(command[:, 2])
+    return np.asarray(total > command_threshold)
+
+
+def _command_name_param(term: str, value: Any, *, required: bool) -> str | None:
+    if value is None:
+        if required:
+            raise ValueError(f"{term} command_name must be a non-empty string")
+        return None
+    if not isinstance(value, str) or not value:
+        raise ValueError(f"{term} command_name must be a non-empty string")
+    return value
+
+
+def _num_selected_bodies(asset: Entity, asset_cfg: SceneEntityCfg) -> int:
+    body_ids = asset_cfg.body_ids
+    if isinstance(body_ids, list):
+        return len(body_ids)
+    return len(range(*body_ids.indices(asset.num_bodies)))
+
+
+def _feet_pos_vel(
+    term: str, env: ManagerBasedRlEnv, asset_cfg: SceneEntityCfg, num_feet: int
+) -> tuple[np.ndarray, np.ndarray]:
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    positions = asset.data.body_link_pos_w[:, asset_cfg.body_ids, :]
+    velocities = asset.data.body_link_lin_vel_w[:, asset_cfg.body_ids, :]
+    shape = (env.num_envs, num_feet, 3)
+    positions = _state(term, "foot body position", positions, shape)
+    velocities = _state(term, "foot body velocity", velocities, shape)
+    return positions, velocities
+
+
+class _FootContactTerm(SensorTermBase):
+    """Cold-path binding of per-foot contact sensor groups (mjlab slot reduce)."""
+
+    _allowed_params: ClassVar[frozenset[str]] = frozenset({"sensor_groups", "contact_threshold"})
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        self._groups = _sensor_groups(self.name, cfg.params.get("sensor_groups"))
+        self._contact_threshold = _real(
+            self.name, "contact_threshold", cfg.params.get("contact_threshold", 0.0), minimum=0.0
+        )
+        flat = tuple(name for group in self._groups for name in group)
+        self._view = self._bind(flat)
+        if any(width not in (1, 3) for width in self._view.dimensions):
+            raise ValueError(
+                f"{self.name} contact sensors must each expose 1-D found or 3-D force; "
+                f"received {self._view.dimensions} on backend '{self._view.backend_type}'"
+            )
+        starts = np.cumsum((0, *self._view.dimensions[:-1]), dtype=np.int64)
+        # MuJoCo ``<contact data="force">`` sensors report the force in the
+        # contact frame whose first axis is the contact normal, so the gating
+        # component is column 0 for both 1-D ``found`` and 3-D ``force``.
+        # ``reduce="netforce"`` variants report world-frame force instead and
+        # are outside this contract.
+        columns = starts
+        self._flat_width = int(sum(self._view.dimensions))
+        # Per-foot column groups in the flattened sensor layout.
+        self._columns: list[np.ndarray] = []
+        offset = 0
+        for group in self._groups:
+            self._columns.append(columns[offset : offset + len(group)])
+            offset += len(group)
+
+    @property
+    def num_feet(self) -> int:
+        return len(self._groups)
+
+    def _contact(self, env: ManagerBasedRlEnv) -> np.ndarray:
+        values = _state(
+            self.name,
+            "foot contact",
+            self._read(self._view, self.name),
+            (env.num_envs, self._flat_width),
+        )
+        contact = values > self._contact_threshold
+        per_foot = [contact[:, columns].any(axis=1) for columns in self._columns]
+        return np.stack(per_foot, axis=1)
+
+
+class feet_air_time(_FootContactTerm):
+    """Count feet whose current air time sits inside the rewarded window.
+
+    Mirrors mjlab ``feet_air_time``: air time accumulates ``env.step_dt`` while
+    the foot has no contact and resets to zero on contact; a foot scores while
+    ``threshold_min < air_time < threshold_max``.  When ``command_name`` is set
+    the count is gated by the command gate.
+    """
+
+    _allowed_params = _FootContactTerm._allowed_params | {
+        "threshold_min",
+        "threshold_max",
+        "command_name",
+        "command_threshold",
+    }
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        self._threshold_min = _real(
+            self.name, "threshold_min", cfg.params.get("threshold_min", 0.05), minimum=0.0
+        )
+        self._threshold_max = _real(
+            self.name,
+            "threshold_max",
+            cfg.params.get("threshold_max", 0.5),
+            minimum=0.0,
+            strict_minimum=True,
+        )
+        if self._threshold_min >= self._threshold_max:
+            raise ValueError(
+                f"{self.name} threshold_min must be below threshold_max, got "
+                f"{self._threshold_min} >= {self._threshold_max}"
+            )
+        self._command_name = _command_name_param(
+            self.name, cfg.params.get("command_name"), required=False
+        )
+        self._command_threshold = _real(
+            self.name, "command_threshold", cfg.params.get("command_threshold", 0.5), minimum=0.0
+        )
+        self._air_time = np.zeros((env.num_envs, self.num_feet), dtype=get_global_dtype())
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        self._air_time[env_ids if env_ids is not None else slice(None)] = 0.0
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        contact = self._contact(env)
+        step_dt = _real(self.name, "step_dt", env.step_dt, minimum=0.0, strict_minimum=True)
+        self._air_time = np.where(contact, 0.0, self._air_time + step_dt).astype(
+            get_global_dtype(), copy=False
+        )
+        in_range = (self._air_time > self._threshold_min) & (self._air_time < self._threshold_max)
+        reward = np.sum(in_range.astype(get_global_dtype()), axis=1)
+        gate = _command_gate(env, self.name, self._command_name, self._command_threshold)
+        if gate is not None:
+            reward = reward * gate
+        return np.asarray(reward, dtype=get_global_dtype())
+
+
+class feet_swing_height(_FootContactTerm):
+    """Penalize peak-swing-height deviation from the target, evaluated at landing.
+
+    Mirrors mjlab ``feet_swing_height``: the peak foot height accumulates while
+    the foot is in the air; on first contact the squared relative error
+    ``(peak / target_height - 1)^2`` is charged and the peak resets.
+    """
+
+    _allowed_params = _FootContactTerm._allowed_params | {
+        "target_height",
+        "command_name",
+        "command_threshold",
+        "asset_cfg",
+    }
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        self._target = _real(
+            self.name,
+            "target_height",
+            cfg.params.get("target_height"),
+            minimum=0.0,
+            strict_minimum=True,
+        )
+        self._command_name = _command_name_param(
+            self.name, cfg.params.get("command_name"), required=True
+        )
+        self._command_threshold = _real(
+            self.name, "command_threshold", cfg.params.get("command_threshold"), minimum=0.0
+        )
+        asset_cfg = cfg.params.get("asset_cfg", _DEFAULT_ASSET_CFG)
+        if not isinstance(asset_cfg, SceneEntityCfg):
+            raise TypeError(f"{self.name} asset_cfg must be a SceneEntityCfg")
+        asset = cast("Entity", env.scene[asset_cfg.name])
+        num_bodies = _num_selected_bodies(asset, asset_cfg)
+        if num_bodies != self.num_feet:
+            raise ValueError(
+                f"{self.name} asset_cfg selects {num_bodies} foot bodies but "
+                f"sensor_groups declares {self.num_feet} feet"
+            )
+        self._asset_cfg = asset_cfg
+        self._peak_heights = np.zeros((env.num_envs, self.num_feet), dtype=get_global_dtype())
+        self._was_in_air = np.zeros((env.num_envs, self.num_feet), dtype=np.bool_)
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        rows = env_ids if env_ids is not None else slice(None)
+        self._peak_heights[rows] = 0.0
+        self._was_in_air[rows] = False
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        contact = self._contact(env)
+        positions, _ = _feet_pos_vel(self.name, env, self._asset_cfg, self.num_feet)
+        in_air = ~contact
+        self._peak_heights = np.where(
+            in_air, np.maximum(self._peak_heights, positions[:, :, 2]), self._peak_heights
+        )
+        first_contact = contact & self._was_in_air
+        gate = _command_gate(env, self.name, self._command_name, self._command_threshold)
+        error = self._peak_heights / self._target - 1.0
+        cost = np.sum(np.square(error) * first_contact, axis=1)
+        if gate is not None:
+            cost = cost * gate
+        self._peak_heights = np.where(first_contact, 0.0, self._peak_heights)
+        self._was_in_air = in_air
+        return np.asarray(cost, dtype=get_global_dtype())
+
+
+class feet_slip(_FootContactTerm):
+    """Penalize foot sliding: squared xy velocity while the foot is in contact."""
+
+    _allowed_params = _FootContactTerm._allowed_params | {
+        "command_name",
+        "command_threshold",
+        "asset_cfg",
+    }
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        self._command_name = _command_name_param(
+            self.name, cfg.params.get("command_name"), required=True
+        )
+        self._command_threshold = _real(
+            self.name, "command_threshold", cfg.params.get("command_threshold", 0.01), minimum=0.0
+        )
+        asset_cfg = cfg.params.get("asset_cfg", _DEFAULT_ASSET_CFG)
+        if not isinstance(asset_cfg, SceneEntityCfg):
+            raise TypeError(f"{self.name} asset_cfg must be a SceneEntityCfg")
+        asset = cast("Entity", env.scene[asset_cfg.name])
+        num_bodies = _num_selected_bodies(asset, asset_cfg)
+        if num_bodies != self.num_feet:
+            raise ValueError(
+                f"{self.name} asset_cfg selects {num_bodies} foot bodies but "
+                f"sensor_groups declares {self.num_feet} feet"
+            )
+        self._asset_cfg = asset_cfg
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        contact = self._contact(env)
+        _, velocities = _feet_pos_vel(self.name, env, self._asset_cfg, self.num_feet)
+        vel_xy_norm_sq = np.sum(np.square(velocities[:, :, :2]), axis=2)
+        gate = _command_gate(env, self.name, self._command_name, self._command_threshold)
+        cost = np.sum(vel_xy_norm_sq * contact, axis=1)
+        if gate is not None:
+            cost = cost * gate
+        return np.asarray(cost, dtype=get_global_dtype())
+
+
+def feet_clearance(
+    env: ManagerBasedRlEnv,
+    target_height: float,
+    command_name: str | None = None,
+    command_threshold: float = 0.01,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Penalize clearance-height deviation weighted by planar foot velocity.
+
+    Mirrors mjlab ``feet_clearance`` with foot heights taken from world-frame
+    foot body z (exact on flat terrain) instead of a terrain height sensor.
+    """
+    target = _real("feet_clearance", "target_height", target_height)
+    threshold = _real("feet_clearance", "command_threshold", command_threshold, minimum=0.0)
+    name = _command_name_param("feet_clearance", command_name, required=False)
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    positions = asset.data.body_link_pos_w[:, asset_cfg.body_ids, :]
+    velocities = asset.data.body_link_lin_vel_w[:, asset_cfg.body_ids, :]
+    if not isinstance(positions, np.ndarray) or positions.ndim != 3 or positions.shape[2] != 3:
+        raise ValueError(
+            f"feet_clearance foot body position must have shape ({env.num_envs}, num_feet, 3), "
+            f"got {getattr(positions, 'shape', None)}"
+        )
+    positions = _state("feet_clearance", "foot body position", positions, positions.shape)
+    velocities = _state("feet_clearance", "foot body velocity", velocities, positions.shape)
+    delta = np.abs(positions[:, :, 2] - target)
+    vel_norm = np.linalg.norm(velocities[:, :, :2], axis=2)
+    cost = np.sum(delta * vel_norm, axis=1)
+    gate = _command_gate(env, "feet_clearance", name, threshold)
+    if gate is not None:
+        cost = cost * gate
+    return np.asarray(cost, dtype=get_global_dtype())
+
+
+class self_collision_cost(_FootContactTerm):
+    """Count monitored self-collision geom pairs reporting contact.
+
+    Mirrors mjlab ``self_collision_cost`` in its found-count form: each sensor
+    group binds one contact sensor watching a geom pair, and the cost is the
+    number of pairs with an active contact.  mjlab's ``force_threshold`` only
+    applies when the sensor records force history, which the upstream config
+    does not enable, so it is not ported.
+    """
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        return np.asarray(np.sum(self._contact(env), axis=1), dtype=get_global_dtype())
+
+
+class angular_momentum_penalty(SensorTermBase):
+    """Penalize whole-body angular momentum read from a named vec3 sensor."""
+
+    _allowed_params: ClassVar[frozenset[str]] = frozenset({"sensor_name"})
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        sensor_name = cfg.params.get("sensor_name")
+        if not isinstance(sensor_name, str) or not sensor_name:
+            raise ValueError(f"{self.name} sensor_name must be a non-empty string")
+        self._sensor_name = sensor_name
+        self._sensor = self._bind((sensor_name,))
+        if self._sensor.dimensions != (3,):
+            raise ValueError(
+                f"{self.name} sensor '{sensor_name}' must expose 3 values; received "
+                f"{self._sensor.dimensions} on backend '{self._sensor.backend_type}'"
+            )
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        angmom = _state(
+            self.name,
+            f"sensor '{self._sensor_name}'",
+            self._read(self._sensor, self.name),
+            (env.num_envs, 3),
+        )
+        return np.asarray(np.sum(np.square(angmom), axis=1), dtype=get_global_dtype())
+
+
+# Privileged per-foot observations (mjlab velocity critic terms).  These mirror
+# mjlab's ``foot_height`` / ``foot_air_time`` / ``foot_contact`` /
+# ``foot_contact_forces`` observation terms: heights come from world-frame foot
+# body z (exact on flat terrain without a raycast sensor), contact state from
+# the same named contact-sensor groups as the gait rewards.
+
+
+def foot_height(
+    env: ManagerBasedRlEnv,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Per-foot vertical clearance above the terrain, shape ``(num_envs, num_feet)``.
+
+    mjlab reads this from a ``TerrainHeightSensor`` ring around each foot; the
+    UniLab port returns the world-frame foot body z, which is identical on flat
+    terrain.
+    """
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    positions = asset.data.body_link_pos_w[:, asset_cfg.body_ids, :]
+    if not isinstance(positions, np.ndarray) or positions.ndim != 3 or positions.shape[2] != 3:
+        raise ValueError(
+            f"foot_height foot body position must have shape ({env.num_envs}, num_feet, 3), "
+            f"got {getattr(positions, 'shape', None)}"
+        )
+    positions = _state("foot_height", "foot body position", positions, positions.shape)
+    return np.asarray(positions[:, :, 2], dtype=get_global_dtype())
+
+
+class foot_air_time(_FootContactTerm):
+    """Current per-foot air time in seconds, shape ``(num_envs, num_feet)``.
+
+    Mirrors mjlab ``foot_air_time`` (``ContactSensor.data.current_air_time``):
+    the timer accumulates ``env.step_dt`` while the foot has no contact and
+    resets to zero on contact.
+    """
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        self._air_time = np.zeros((env.num_envs, self.num_feet), dtype=get_global_dtype())
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        self._air_time[env_ids if env_ids is not None else slice(None)] = 0.0
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        contact = self._contact(env)
+        step_dt = _real(self.name, "step_dt", env.step_dt, minimum=0.0, strict_minimum=True)
+        self._air_time = np.where(contact, 0.0, self._air_time + step_dt).astype(
+            get_global_dtype(), copy=False
+        )
+        return self._air_time.copy()
+
+
+class foot_contact(_FootContactTerm):
+    """Per-foot contact flag as float, shape ``(num_envs, num_feet)``.
+
+    Mirrors mjlab ``foot_contact`` (``ContactSensor.data.found > 0``).
+    """
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        return np.asarray(self._contact(env), dtype=get_global_dtype())
+
+
+class foot_contact_forces(_FootContactTerm):
+    """Log-compressed per-foot contact forces, shape ``(num_envs, 3 * num_feet)``.
+
+    Mirrors mjlab ``foot_contact_forces``: the per-foot 3-D contact forces are
+    flattened in sensor-group order and compressed as
+    ``sign(f) * log1p(|f|)``.
+    """
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        if any(width != 3 for width in self._view.dimensions):
+            raise ValueError(
+                f"{self.name} contact sensors must each expose 3-D force; "
+                f"received {self._view.dimensions} on backend '{self._view.backend_type}'"
+            )
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        values = _state(
+            self.name,
+            "foot contact forces",
+            self._read(self._view, self.name),
+            (env.num_envs, self._flat_width),
+        )
+        return np.asarray(np.sign(values) * np.log1p(np.abs(values)), dtype=get_global_dtype())
+
+
+__all__ = [
+    "angular_momentum_penalty",
+    "feet_air_time",
+    "feet_clearance",
+    "feet_slip",
+    "feet_swing_height",
+    "foot_air_time",
+    "foot_contact",
+    "foot_contact_forces",
+    "foot_height",
+    "self_collision_cost",
+]
diff --git a/src/unilab/envs/locomotion/common/height_scan.py b/src/unilab/tasks/locomotion/common/height_scan.py
similarity index 99%
rename from src/unilab/envs/locomotion/common/height_scan.py
rename to src/unilab/tasks/locomotion/common/height_scan.py
index 0f8bb0580..a557e8f8f 100644
--- a/src/unilab/envs/locomotion/common/height_scan.py
+++ b/src/unilab/tasks/locomotion/common/height_scan.py
@@ -1,4 +1,4 @@
-"""Shared height-scan and terrain-bound helpers for rough locomotion envs.
+"""Shared height-scan and terrain-bound helpers for rough locomotion tasks.
 
 These functions and the ``HeightScanConfig`` dataclass are consumed by Go2,
 Go2W, Go1, and G1 rough environments — anywhere the policy / critic ingests
diff --git a/src/unilab/tasks/locomotion/common/manager_terms.py b/src/unilab/tasks/locomotion/common/manager_terms.py
new file mode 100644
index 000000000..b496f006a
--- /dev/null
+++ b/src/unilab/tasks/locomotion/common/manager_terms.py
@@ -0,0 +1,538 @@
+"""Shared Manager-Based terms and term bases for the locomotion families.
+
+The equations come from UniLab's existing Go1/Go2 joystick tasks and are reused
+by quadruped and biped owners alike.  The adaptation uses community
+``func + params`` terms, NumPy, and the base-owned sensor facade.  Reward terms
+that read named XML sensors live in ``sensor_reward_terms.py`` and build on the
+``SensorTermBase`` cold-path binding contract defined here.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, ClassVar, Protocol, cast
+
+import numpy as np
+
+from unilab.dtype_config import get_global_dtype
+from unilab.managers.manager_base import ManagerTermBase, ManagerTermBaseCfg
+from unilab.managers.scene_entity_config import SceneEntityCfg
+
+if TYPE_CHECKING:
+    from unilab.base.entity import Entity
+    from unilab.managers._types import ManagerBasedRlEnv, ManagerSensorView
+
+    class _GaitEnv(ManagerBasedRlEnv, Protocol):
+        @property
+        def common_step_counter(self) -> int: ...
+
+
+_OFFSETS = (0.0, 0.5, 0.5, 0.0)
+_DEFAULT_ASSET_CFG = SceneEntityCfg("robot")
+
+
+def _real(
+    term: str,
+    name: str,
+    value: Any,
+    *,
+    minimum: float | None = None,
+    maximum: float | None = None,
+    strict_minimum: bool = False,
+) -> float:
+    if isinstance(value, (bool, np.bool_)) or not isinstance(value, (int, float, np.number)):
+        raise TypeError(f"{term} {name} must be a real number")
+    result = float(value)
+    if not np.isfinite(result):
+        raise ValueError(f"{term} {name} must be finite")
+    if minimum is not None and (result <= minimum if strict_minimum else result < minimum):
+        relation = "greater than" if strict_minimum else "at least"
+        raise ValueError(f"{term} {name} must be {relation} {minimum}")
+    if maximum is not None and result > maximum:
+        raise ValueError(f"{term} {name} must be at most {maximum}")
+    return result
+
+
+def _offsets(term: str, value: Any) -> np.ndarray:
+    if isinstance(value, (str, bytes)) or not isinstance(value, (tuple, list, np.ndarray)):
+        raise TypeError(f"{term} phase_offsets must be a sequence of four real numbers")
+    if isinstance(value, np.ndarray) and value.ndim != 1:
+        raise ValueError(f"{term} phase_offsets must be one-dimensional, got {value.shape}")
+    items = list(value)
+    if len(items) != 4:
+        raise ValueError(f"{term} phase_offsets must contain 4 values, got {len(items)}")
+    result = np.asarray(
+        [_real(term, f"phase_offsets[{index}]", item) for index, item in enumerate(items)],
+        dtype=get_global_dtype(),
+    )
+    result.setflags(write=False)
+    return result
+
+
+def _names(term: str, value: Any) -> tuple[str, ...]:
+    if isinstance(value, (str, bytes)) or not isinstance(value, (tuple, list)):
+        raise TypeError(f"{term} sensor_names must be a sequence of four strings")
+    names = tuple(value)
+    if len(names) != 4:
+        raise ValueError(f"{term} sensor_names must contain 4 names, got {len(names)}")
+    if any(not isinstance(name, str) or not name for name in names):
+        raise ValueError(f"{term} sensor_names must contain non-empty strings")
+    if len(set(names)) != 4:
+        raise ValueError(f"{term} sensor_names must be unique: {names}")
+    return names
+
+
+def _state(term: str, capability: str, value: Any, shape: tuple[int, ...]) -> np.ndarray:
+    if not isinstance(value, np.ndarray):
+        raise TypeError(f"{term} {capability} must be an np.ndarray")
+    if value.shape != shape:
+        raise ValueError(f"{term} {capability} must have shape {shape}, got {value.shape}")
+    if not np.isfinite(value).all():
+        env_ids = np.flatnonzero(~np.isfinite(value).reshape(shape[0], -1).all(axis=1)).tolist()
+        raise ValueError(f"{term} {capability} contains NaN or Inf for environments {env_ids[:10]}")
+    return value
+
+
+def _command(env: ManagerBasedRlEnv, term: str, command_name: str) -> np.ndarray:
+    if not isinstance(command_name, str) or not command_name:
+        raise ValueError(f"{term} command_name must be a non-empty string")
+    try:
+        command = env.command_manager.get_command(command_name)
+    except KeyError as exc:
+        raise KeyError(f"{term} command capability '{command_name}' is unavailable") from exc
+    if command is None:
+        raise KeyError(f"{term} command capability '{command_name}' is unavailable")
+    return _state(term, f"command '{command_name}'", command, (env.num_envs, 3))
+
+
+def _asset(env: ManagerBasedRlEnv, asset_cfg: SceneEntityCfg) -> Entity:
+    return cast("Entity", env.scene[asset_cfg.name])
+
+
+class SensorTermBase(ManagerTermBase):
+    """Cold-path named-sensor binding shared by locomotion manager terms."""
+
+    _allowed_params: ClassVar[frozenset[str]] = frozenset()
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        unexpected = set(cfg.params) - self._allowed_params
+        if unexpected:
+            raise TypeError(f"{self.name} received unsupported parameters: {sorted(unexpected)}")
+
+    def _bind(self, sensor_names: tuple[str, ...]) -> ManagerSensorView:
+        try:
+            return self._env.scene.bind_sensor_data(sensor_names)
+        except (KeyError, TypeError, ValueError, NotImplementedError) as exc:
+            raise type(exc)(
+                f"Manager term '{self.name}' named-sensor capability could not be "
+                f"materialized for {sensor_names}: {exc}"
+            ) from exc
+
+    @staticmethod
+    def _read(view: ManagerSensorView, term: str) -> np.ndarray:
+        try:
+            return view.read()
+        except (KeyError, TypeError, ValueError, NotImplementedError) as exc:
+            raise type(exc)(
+                f"Manager term '{term}' named-sensor capability failed on "
+                f"backend '{view.backend_type}': {exc}"
+            ) from exc
+
+
+def track_lin_vel_xy_exp(
+    env: ManagerBasedRlEnv,
+    std: float,
+    command_name: str,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Track commanded planar velocity with the legacy independent exponential kernel."""
+    scale = _real("track_lin_vel_xy_exp", "std", std, minimum=0.0, strict_minimum=True)
+    actual = _state(
+        "track_lin_vel_xy_exp",
+        "root linear velocity",
+        _asset(env, asset_cfg).data.root_link_lin_vel_b,
+        (env.num_envs, 3),
+    )
+    error = np.sum(
+        np.square(_command(env, "track_lin_vel_xy_exp", command_name)[:, :2] - actual[:, :2]),
+        axis=1,
+    )
+    return np.asarray(np.exp(-error / scale**2), dtype=get_global_dtype())
+
+
+def track_ang_vel_z_exp(
+    env: ManagerBasedRlEnv,
+    std: float,
+    command_name: str,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Track commanded yaw velocity without folding roll/pitch into the kernel."""
+    scale = _real("track_ang_vel_z_exp", "std", std, minimum=0.0, strict_minimum=True)
+    actual = _state(
+        "track_ang_vel_z_exp",
+        "root angular velocity",
+        _asset(env, asset_cfg).data.root_link_ang_vel_b,
+        (env.num_envs, 3),
+    )
+    error = np.square(_command(env, "track_ang_vel_z_exp", command_name)[:, 2] - actual[:, 2])
+    return np.asarray(np.exp(-error / scale**2), dtype=get_global_dtype())
+
+
+def lin_vel_z_l2(
+    env: ManagerBasedRlEnv,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Penalize vertical root velocity independently from planar tracking."""
+    velocity = _state(
+        "lin_vel_z_l2",
+        "root linear velocity",
+        _asset(env, asset_cfg).data.root_link_lin_vel_b,
+        (env.num_envs, 3),
+    )
+    return np.asarray(np.square(velocity[:, 2]), dtype=get_global_dtype())
+
+
+def ang_vel_xy_l2(
+    env: ManagerBasedRlEnv,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Penalize root roll/pitch angular velocity independently from yaw tracking."""
+    velocity = _state(
+        "ang_vel_xy_l2",
+        "root angular velocity",
+        _asset(env, asset_cfg).data.root_link_ang_vel_b,
+        (env.num_envs, 3),
+    )
+    return np.asarray(np.sum(np.square(velocity[:, :2]), axis=1), dtype=get_global_dtype())
+
+
+def base_height_l2(
+    env: ManagerBasedRlEnv,
+    target_height: float,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Penalize world-frame root height error for the flat-ground pilot."""
+    target = _real("base_height_l2", "target_height", target_height)
+    position = _state(
+        "base_height_l2",
+        "root position",
+        _asset(env, asset_cfg).data.root_link_pos_w,
+        (env.num_envs, 3),
+    )
+    return np.asarray(np.square(position[:, 2] - target), dtype=get_global_dtype())
+
+
+def alive(env: ManagerBasedRlEnv) -> np.ndarray:
+    """Constant reward for every step, unconditional as in the legacy tasks."""
+    return np.ones((env.num_envs,), dtype=get_global_dtype())
+
+
+def joint_deviation_l1(
+    env: ManagerBasedRlEnv,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Penalize selected joint displacement from the default pose with an L1 kernel."""
+    asset = _asset(env, asset_cfg)
+    position = asset.data.joint_pos[:, asset_cfg.joint_ids]
+    default = asset.data.default_joint_pos[:, asset_cfg.joint_ids]
+    if (
+        not isinstance(position, np.ndarray)
+        or position.ndim != 2
+        or position.shape[0] != env.num_envs
+    ):
+        shape = getattr(position, "shape", None)
+        raise ValueError(
+            f"joint_deviation_l1 joint position must be 2-D with leading dimension {env.num_envs}, got {shape}"
+        )
+    default = _state("joint_deviation_l1", "default joint position", default, position.shape)
+    position = _state("joint_deviation_l1", "joint position", position, position.shape)
+    return np.asarray(np.sum(np.abs(position - default), axis=1), dtype=get_global_dtype())
+
+
+def stand_still_l1(
+    env: ManagerBasedRlEnv,
+    command_name: str,
+    command_threshold: float = 0.1,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Penalize selected joint deviation only below the commanded-motion threshold."""
+    threshold = _real(
+        "stand_still_l1",
+        "command_threshold",
+        command_threshold,
+        minimum=0.0,
+    )
+    stopped = np.linalg.norm(_command(env, "stand_still_l1", command_name), axis=1) < threshold
+    return np.asarray(
+        joint_deviation_l1(env, asset_cfg=asset_cfg) * stopped,
+        dtype=get_global_dtype(),
+    )
+
+
+class _GaitTerm(ManagerTermBase):
+    _allowed_params: ClassVar[frozenset[str]] = frozenset(
+        {"frequency", "phase_offsets", "command_name", "command_threshold"}
+    )
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: _GaitEnv):
+        super().__init__(env)
+        unexpected = set(cfg.params) - self._allowed_params
+        if unexpected:
+            raise TypeError(f"{self.name} received unsupported parameters: {sorted(unexpected)}")
+        self._frequency = _real(
+            self.name, "frequency", cfg.params.get("frequency", 2.0), minimum=0.0
+        )
+        self._offsets = _offsets(self.name, cfg.params.get("phase_offsets", _OFFSETS))
+        self._step_dt = _real(self.name, "step_dt", env.step_dt, minimum=0.0, strict_minimum=True)
+        command_name = cfg.params.get("command_name")
+        if command_name is not None and (not isinstance(command_name, str) or not command_name):
+            raise ValueError(f"{self.name} command_name must be a non-empty string or None")
+        self._command_name = command_name
+        self._command_threshold = _real(
+            self.name,
+            "command_threshold",
+            cfg.params.get("command_threshold", 0.0),
+            minimum=0.0,
+        )
+        self._phase_value = np.zeros(env.num_envs, dtype=get_global_dtype())
+        self._last_counter = 0
+        self._advance_to(env, self._counter(env))
+
+    def _counter(self, env: _GaitEnv) -> int:
+        counter = env.common_step_counter
+        if isinstance(counter, (bool, np.bool_)) or not isinstance(counter, (int, np.integer)):
+            raise TypeError(f"{self.name} common_step_counter must be an integer")
+        if counter < 0:
+            raise ValueError(f"{self.name} common_step_counter must be non-negative")
+        return int(counter)
+
+    def _moving(self, env: _GaitEnv) -> np.ndarray:
+        if self._command_name is None:
+            return np.ones(env.num_envs, dtype=np.bool_)
+        command = _command(env, self.name, self._command_name)
+        return np.linalg.norm(command, axis=1) > self._command_threshold
+
+    def _advance_to(self, env: _GaitEnv, counter: int) -> None:
+        delta = counter - self._last_counter
+        if delta < 0:
+            raise ValueError(f"{self.name} common_step_counter cannot move backwards")
+        increment = np.asarray(self._step_dt * self._frequency, dtype=get_global_dtype())
+        if delta == 1:  # Hot path: preserve the legacy float32 iterative phase exactly.
+            self._phase_value = np.fmod(self._phase_value + increment * self._moving(env), 1.0)
+        elif delta > 1:  # Cold catch-up for a term constructed or inspected between steps.
+            for _ in range(delta):
+                self._phase_value = np.fmod(
+                    self._phase_value + increment * self._moving(env),
+                    1.0,
+                )
+        self._last_counter = counter
+
+    def _phase(self, env: _GaitEnv) -> np.ndarray:
+        self._advance_to(env, self._counter(env))
+        phase = np.remainder(self._phase_value[:, None] + self._offsets[None, :], 1.0).astype(
+            get_global_dtype(), copy=False
+        )
+        return phase
+
+
+class quadruped_gait_phase(_GaitTerm):
+    """Four-foot phase observation with the legacy diagonal-trot ordering."""
+
+    def __call__(self, env: _GaitEnv, **params: Any) -> np.ndarray:
+        del params
+        return self._phase(env)
+
+
+class _FootSensorTerm(_GaitTerm):
+    def __init__(self, cfg: ManagerTermBaseCfg, env: _GaitEnv):
+        super().__init__(cfg, env)
+        sensor_names = _names(self.name, cfg.params.get("sensor_names"))
+        try:
+            self._view = env.scene.bind_sensor_data(sensor_names)
+        except (KeyError, TypeError, ValueError, NotImplementedError) as exc:
+            raise type(exc)(
+                f"Manager term '{self.name}' named-foot-sensor capability could not be "
+                f"materialized for {sensor_names}: {exc}"
+            ) from exc
+
+    def _read(self) -> np.ndarray:
+        try:
+            return self._view.read()
+        except (KeyError, TypeError, ValueError, NotImplementedError) as exc:
+            raise type(exc)(
+                f"Manager term '{self.name}' named-foot-sensor capability failed on "
+                f"backend '{self._view.backend_type}': {exc}"
+            ) from exc
+
+
+class feet_phase_contact(_FootSensorTerm):
+    """Reward foot contact matching the configured stance portion of gait phase."""
+
+    _allowed_params = _GaitTerm._allowed_params | {
+        "sensor_names",
+        "contact_threshold",
+        "stance_threshold",
+    }
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: _GaitEnv):
+        super().__init__(cfg, env)
+        self._contact_threshold = _real(
+            self.name, "contact_threshold", cfg.params.get("contact_threshold", 0.1), minimum=0.0
+        )
+        self._stance_threshold = _real(
+            self.name,
+            "stance_threshold",
+            cfg.params.get("stance_threshold", 0.6),
+            minimum=0.0,
+            maximum=1.0,
+        )
+        if any(width not in (1, 3) for width in self._view.dimensions):
+            raise ValueError(
+                f"{self.name} contact sensors must each expose 1-D found or 3-D force; "
+                f"received {self._view.dimensions} on backend '{self._view.backend_type}'"
+            )
+        starts = np.cumsum((0, *self._view.dimensions[:-1]), dtype=np.int64)
+        # MuJoCo ``<contact data="force">`` sensors report the force in the
+        # contact frame whose first axis is the contact normal, so the gating
+        # component is column 0 for both 1-D ``found`` and 3-D ``force``.
+        # ``reduce="netforce"`` variants report world-frame force instead and
+        # are outside this contract.
+        self._columns = starts
+
+    def __call__(self, env: _GaitEnv, **params: Any) -> np.ndarray:
+        del params
+        contact = self._read()[:, self._columns] > self._contact_threshold
+        expected = self._phase(env) < self._stance_threshold
+        if self._frequency < 1.0e-8:
+            expected.fill(True)
+        elif self._command_name is not None:
+            expected |= ~self._moving(env)[:, None]
+        return np.mean(contact == expected, axis=1).astype(get_global_dtype(), copy=False)
+
+
+class feet_phase_swing_height(_FootSensorTerm):
+    """Reward foot height near a target during the configured swing phase."""
+
+    _allowed_params = _GaitTerm._allowed_params | {
+        "sensor_names",
+        "target_height",
+        "kernel",
+        "swing_start",
+    }
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: _GaitEnv):
+        super().__init__(cfg, env)
+        self._target = _real(
+            self.name, "target_height", cfg.params.get("target_height", 0.1), minimum=0.0
+        )
+        self._kernel = _real(
+            self.name,
+            "kernel",
+            cfg.params.get("kernel", 0.01),
+            minimum=0.0,
+            strict_minimum=True,
+        )
+        self._swing_start = _real(
+            self.name,
+            "swing_start",
+            cfg.params.get("swing_start", 0.6),
+            minimum=0.0,
+            maximum=1.0,
+        )
+        if self._view.dimensions != (3, 3, 3, 3):
+            raise ValueError(
+                f"{self.name} position sensors must each expose 3-D xyz; received "
+                f"{self._view.dimensions} on backend '{self._view.backend_type}'"
+            )
+
+    def __call__(self, env: _GaitEnv, **params: Any) -> np.ndarray:
+        del params
+        heights = self._read()[:, (2, 5, 8, 11)]
+        swing = self._phase(env) >= self._swing_start
+        if self._command_name is not None:
+            swing &= self._moving(env)[:, None]
+        reward = np.exp(-np.square(heights - self._target) / self._kernel) * swing
+        return np.mean(reward, axis=1).astype(get_global_dtype(), copy=False)
+
+
+class feet_air_while_standing(ManagerTermBase):
+    """Count feet without contact while the configured velocity command is standing."""
+
+    _allowed_params = frozenset(
+        {"sensor_names", "command_name", "command_threshold", "contact_threshold"}
+    )
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        unexpected = set(cfg.params) - self._allowed_params
+        if unexpected:
+            raise TypeError(f"{self.name} received unsupported parameters: {sorted(unexpected)}")
+        sensor_names = _names(self.name, cfg.params.get("sensor_names"))
+        command_name = cfg.params.get("command_name")
+        if not isinstance(command_name, str) or not command_name:
+            raise ValueError(f"{self.name} command_name must be a non-empty string")
+        self._command_name = command_name
+        self._command_threshold = _real(
+            self.name,
+            "command_threshold",
+            cfg.params.get("command_threshold", 0.1),
+            minimum=0.0,
+        )
+        self._contact_threshold = _real(
+            self.name,
+            "contact_threshold",
+            cfg.params.get("contact_threshold", 0.1),
+            minimum=0.0,
+        )
+        try:
+            self._view = env.scene.bind_sensor_data(sensor_names)
+        except (KeyError, TypeError, ValueError, NotImplementedError) as exc:
+            raise type(exc)(
+                f"Manager term '{self.name}' named-foot-sensor capability could not be "
+                f"materialized for {sensor_names}: {exc}"
+            ) from exc
+        if any(width not in (1, 3) for width in self._view.dimensions):
+            raise ValueError(
+                f"{self.name} contact sensors must each expose 1-D found or 3-D force; "
+                f"received {self._view.dimensions} on backend '{self._view.backend_type}'"
+            )
+        starts = np.cumsum((0, *self._view.dimensions[:-1]), dtype=np.int64)
+        # MuJoCo ``<contact data="force">`` sensors report the force in the
+        # contact frame whose first axis is the contact normal, so the gating
+        # component is column 0 for both 1-D ``found`` and 3-D ``force``.
+        # ``reduce="netforce"`` variants report world-frame force instead and
+        # are outside this contract.
+        self._columns = starts
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        try:
+            values = self._view.read()
+        except (KeyError, TypeError, ValueError, NotImplementedError) as exc:
+            raise type(exc)(
+                f"Manager term '{self.name}' named-foot-sensor capability failed on "
+                f"backend '{self._view.backend_type}': {exc}"
+            ) from exc
+        contact = values[:, self._columns] > self._contact_threshold
+        standing = (
+            np.linalg.norm(_command(env, self.name, self._command_name), axis=1)
+            <= self._command_threshold
+        )
+        return np.asarray(np.sum(~contact, axis=1) * standing, dtype=get_global_dtype())
+
+
+__all__ = [
+    "SensorTermBase",
+    "alive",
+    "ang_vel_xy_l2",
+    "base_height_l2",
+    "feet_air_while_standing",
+    "feet_phase_contact",
+    "feet_phase_swing_height",
+    "joint_deviation_l1",
+    "lin_vel_z_l2",
+    "quadruped_gait_phase",
+    "stand_still_l1",
+    "track_ang_vel_z_exp",
+    "track_lin_vel_xy_exp",
+]
diff --git a/src/unilab/tasks/locomotion/common/rewards.py b/src/unilab/tasks/locomotion/common/rewards.py
new file mode 100644
index 000000000..bd127ed66
--- /dev/null
+++ b/src/unilab/tasks/locomotion/common/rewards.py
@@ -0,0 +1,150 @@
+"""Shared reward functions for locomotion tasks.
+
+Introduces ``RewardContext`` — a dataclass that bundles all state any
+reward function might need.  Shared reward functions are plain
+module-level callables ``fn(ctx) -> np.ndarray`` so that each
+joystick environment can reference them **directly** in its
+``_reward_fns`` dispatch table without per-class wrapper methods.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+import numpy as np
+
+from unilab.dtype_config import get_global_dtype
+
+
+@dataclass
+class RewardContext:
+    """Immutable snapshot of everything reward functions may read.
+
+    Built once per ``_compute_reward`` call.  Shared functions access
+    only the fields they need; robot-specific methods that still live
+    on the environment class receive the same context via ``self``.
+    """
+
+    # ── always populated ────────────────────────────────────────────
+    info: dict
+    linvel: np.ndarray  # (N, 3)
+    gyro: np.ndarray  # (N, 3)
+    dof_pos: np.ndarray  # (N, num_action)
+    num_envs: int = 0
+    default_angles: np.ndarray = field(default_factory=lambda: np.empty(0))
+    tracking_sigma: float = 0.25
+    base_height_target: float = 0.0
+    base_height: np.ndarray = field(default_factory=lambda: np.empty(0))  # pre-fetched
+
+    # ── G1-only (None for quadrupeds) ───────────────────────────────
+    gravity: np.ndarray | None = None
+    dof_vel: np.ndarray | None = None
+
+    # ── optional weights (G1 pose rewards) ──────────────────────────
+    pose_weights: np.ndarray | None = None
+
+    # ── optional state populated for rough / biped tasks ────────────
+    joint_range: np.ndarray | None = None  # (num_action, 2) — [lower, upper]
+    linvel_yaw: np.ndarray | None = None  # (N, 3) — base linvel in yaw frame
+
+
+# ── tracking rewards ─────────────────────────────────────────────────
+
+
+def tracking_lin_vel(ctx: RewardContext) -> np.ndarray:
+    """Exponential reward for tracking commanded xy linear velocity."""
+    commands = ctx.info["commands"]
+    lin_vel_error = np.sum(np.square(commands[:, :2] - ctx.linvel[:, :2]), axis=1)
+    return np.exp(-lin_vel_error / ctx.tracking_sigma)  # type: ignore[no-any-return]
+
+
+def tracking_ang_vel(ctx: RewardContext) -> np.ndarray:
+    """Exponential reward for tracking commanded yaw angular velocity."""
+    commands = ctx.info["commands"]
+    ang_vel_error = np.square(commands[:, 2] - ctx.gyro[:, 2])
+    return np.exp(-ang_vel_error / ctx.tracking_sigma)  # type: ignore[no-any-return]
+
+
+# ── velocity / orientation penalties ─────────────────────────────────
+
+
+def lin_vel_z(ctx: RewardContext) -> np.ndarray:
+    """Penalty for vertical (z) linear velocity."""
+    return np.square(ctx.linvel[:, 2])  # type: ignore[no-any-return]
+
+
+def ang_vel_xy(ctx: RewardContext) -> np.ndarray:
+    """Penalty for roll/pitch angular velocity."""
+    return np.sum(np.square(ctx.gyro[:, :2]), axis=1)  # type: ignore[no-any-return]
+
+
+def roll(ctx: RewardContext) -> np.ndarray:
+    """Penalty for deviation from roll orientation."""
+    g = ctx.gravity
+    assert g is not None
+    return np.square(g[:, 0])  # type: ignore[no-any-return]
+
+
+# ── height / pose penalties ──────────────────────────────────────────
+
+
+def base_height(ctx: RewardContext) -> np.ndarray:
+    """Penalty for base height deviation from target."""
+    return np.square(ctx.base_height - ctx.base_height_target)  # type: ignore[no-any-return]
+
+
+def similar_to_default(ctx: RewardContext) -> np.ndarray:
+    """Penalty for joint position deviation from default (L1 norm)."""
+    return np.sum(np.abs(ctx.dof_pos - ctx.default_angles), axis=1)  # type: ignore[no-any-return]
+
+
+def weighted_pose(ctx: RewardContext) -> np.ndarray:
+    """Weighted L2 penalty for joint position deviation."""
+    assert ctx.pose_weights is not None
+    diff = ctx.dof_pos - ctx.default_angles
+    return np.asarray(np.sum(ctx.pose_weights * np.square(diff), axis=1), dtype=get_global_dtype())
+
+
+# ── action penalties ─────────────────────────────────────────────────
+
+
+def action_rate(ctx: RewardContext) -> np.ndarray:
+    """Penalty for change in actions between timesteps."""
+    current = ctx.info["current_actions"]
+    last = ctx.info["last_actions"]
+    return np.sum(np.square(current - last), axis=1)  # type: ignore[no-any-return]
+
+
+# ── effort penalties ─────────────────────────────────────────────────
+
+
+def _get_torques(ctx: RewardContext) -> np.ndarray:
+    fallback = np.zeros((ctx.num_envs, ctx.dof_pos.shape[1]), dtype=get_global_dtype())
+    return ctx.info.get("torques", fallback)  # type: ignore[no-any-return]
+
+
+def torques(ctx: RewardContext) -> np.ndarray:
+    """Penalty for total torque magnitude (L1 norm)."""
+    return np.sum(np.abs(_get_torques(ctx)), axis=1)  # type: ignore[no-any-return]
+
+
+def energy(ctx: RewardContext) -> np.ndarray:
+    """Penalty for mechanical energy consumption."""
+    assert ctx.dof_vel is not None
+    t = _get_torques(ctx)
+    return np.sum(np.abs(ctx.dof_vel) * np.abs(t), axis=1)  # type: ignore[no-any-return]
+
+
+def dof_acc(ctx: RewardContext) -> np.ndarray:
+    """Penalty for joint acceleration magnitude."""
+    fallback = np.zeros((ctx.num_envs, ctx.dof_pos.shape[1]), dtype=get_global_dtype())
+    qacc = ctx.info.get("qacc", fallback)
+    return np.sum(np.square(qacc), axis=1)  # type: ignore[no-any-return]
+
+
+# ── survival ─────────────────────────────────────────────────────────
+
+
+def alive(ctx: RewardContext) -> np.ndarray:
+    """Constant reward for staying alive."""
+    return np.ones((ctx.num_envs,), dtype=get_global_dtype())
diff --git a/src/unilab/tasks/locomotion/common/rough_manager_terms.py b/src/unilab/tasks/locomotion/common/rough_manager_terms.py
new file mode 100644
index 000000000..76520e666
--- /dev/null
+++ b/src/unilab/tasks/locomotion/common/rough_manager_terms.py
@@ -0,0 +1,574 @@
+"""Manager-Based terrain terms shared by the production rough quadrupeds."""
+
+from __future__ import annotations
+
+from collections.abc import Callable, Sequence
+from dataclasses import dataclass, field
+from numbers import Real
+from typing import TYPE_CHECKING, Any, cast
+from weakref import WeakKeyDictionary
+
+import numpy as np
+from unisim.backend.base import BackendTerrainSpawnData
+from unisim.terrain.generator import SubTerrainCfg, TerrainGeneratorCfg
+
+from unilab.base.entity import EntityCfg
+from unilab.dtype_config import get_global_dtype
+from unilab.envs.mdp.actions.actions import JointPositionAction, JointPositionActionCfg
+from unilab.envs.mdp.commands.velocity_command import (
+    UniformVelocityCommand,
+    UniformVelocityCommandCfg,
+)
+from unilab.managers.manager_base import ManagerTermBase, ManagerTermBaseCfg
+from unilab.managers.scene_entity_config import SceneEntityCfg
+from unilab.tasks.locomotion.common.height_scan import (
+    DEFAULT_SCAN_POINTS_X,
+    DEFAULT_SCAN_POINTS_Y,
+    height_scan_offsets,
+)
+from unilab.tasks.locomotion.common.terrain_spawn import (
+    TerrainCurriculumCfg,
+    TerrainSpawnManager,
+)
+from unilab.terrains import (
+    flat,
+    hf_pyramid_slope,
+    hf_pyramid_slope_inv,
+    pyramid_stairs,
+    pyramid_stairs_inv,
+    random_rough,
+    wave_terrain,
+)
+from unilab.utils.rotation import np_quat_from_euler_xyz, np_quat_mul
+
+if TYPE_CHECKING:
+    from unisim.backend.base import BackendHeightScanner
+
+    from unilab.base.entity import Entity
+    from unilab.envs.manager_based_rl_env import ManagerBasedRlEnv as RoughManagerBasedRlEnv
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+_DEFAULT_ASSET_CFG = SceneEntityCfg("robot")
+_POSE_AXES = ("x", "y", "z", "roll", "pitch", "yaw")
+_VELOCITY_AXES = ("x", "y", "z", "roll", "pitch", "yaw")
+
+
+def _rough_sub_terrains() -> dict[str, SubTerrainCfg]:
+    return {
+        "flat": flat(proportion=0.0),
+        "pyramid_stairs": pyramid_stairs(
+            proportion=0.1,
+            step_height_range=(0.025, 0.10),
+            step_width=0.4,
+            platform_width=3.0,
+            border_width=0.2,
+        ),
+        "pyramid_stairs_inv": pyramid_stairs_inv(
+            proportion=0.1,
+            step_height_range=(0.025, 0.10),
+            step_width=0.4,
+            platform_width=3.0,
+            border_width=0.2,
+        ),
+        "hf_pyramid_slope": hf_pyramid_slope(
+            proportion=0.2,
+            slope_range=(0.0, 0.3),
+            platform_width=2.0,
+            border_width=0.2,
+        ),
+        "hf_pyramid_slope_inv": hf_pyramid_slope_inv(
+            proportion=0.2,
+            slope_range=(0.0, 0.3),
+            platform_width=2.0,
+            border_width=0.2,
+        ),
+        "random_rough": random_rough(
+            proportion=0.3,
+            noise_range=(0.01, 0.06),
+            noise_step=0.01,
+            border_width=0.2,
+        ),
+        "wave_terrain": wave_terrain(
+            proportion=0.3,
+            amplitude_range=(0.0, 0.12),
+            num_waves=4,
+            border_width=0.2,
+        ),
+    }
+
+
+@dataclass(kw_only=True)
+class QuadrupedRoughTerrainCfg(TerrainGeneratorCfg):
+    """Shared seven-terrain production generator for Go1, Go2, and Go2W."""
+
+    seed: int | None = 42
+    curriculum: bool = False
+    size: tuple[float, float] = (8.0, 8.0)
+    horizontal_scale: float = 0.2
+    vertical_scale: float = 0.005
+    border_width: float = 20.0
+    num_rows: int = 6
+    num_cols: int = 6
+    add_lights: bool = True
+    sub_terrains: dict[str, SubTerrainCfg] = field(default_factory=_rough_sub_terrains)
+
+
+def _real(
+    value: Any,
+    *,
+    label: str,
+    minimum: float | None = None,
+    strict_minimum: bool = False,
+) -> float:
+    if isinstance(value, (bool, np.bool_)) or not isinstance(value, Real):
+        raise TypeError(f"{label} must be a real number")
+    result = float(value)
+    if not np.isfinite(result):
+        raise ValueError(f"{label} must be finite")
+    if minimum is not None and (result <= minimum if strict_minimum else result < minimum):
+        relation = "greater than" if strict_minimum else "at least"
+        raise ValueError(f"{label} must be {relation} {minimum}")
+    return result
+
+
+def _pair(value: Any, *, label: str) -> tuple[float, float]:
+    if isinstance(value, (str, bytes)) or not isinstance(value, (tuple, list)):
+        raise TypeError(f"{label} must be a two-value range")
+    if len(value) != 2:
+        raise ValueError(f"{label} must contain two values")
+    lower = _real(value[0], label=f"{label}[0]")
+    upper = _real(value[1], label=f"{label}[1]")
+    if lower > upper:
+        raise ValueError(f"{label} lower bound {lower} exceeds upper bound {upper}")
+    return lower, upper
+
+
+def _ranges(value: Any, axes: Sequence[str], *, label: str) -> dict[str, tuple[float, float]]:
+    if not isinstance(value, dict):
+        raise TypeError(f"{label} must be a mapping")
+    if set(value) != set(axes):
+        raise ValueError(f"{label} must declare exactly {list(axes)}, got {sorted(value)}")
+    return {axis: _pair(value[axis], label=f"{label}.{axis}") for axis in axes}
+
+
+def _env_ids(env: ManagerBasedRlEnv, env_ids: np.ndarray | slice | None) -> np.ndarray:
+    if env_ids is None:
+        return np.arange(env.num_envs, dtype=np.int32)
+    if isinstance(env_ids, slice):
+        return np.arange(env.num_envs, dtype=np.int32)[env_ids]
+    raw = np.asarray(env_ids)
+    if (
+        raw.ndim != 1
+        or not np.issubdtype(raw.dtype, np.integer)
+        or np.issubdtype(raw.dtype, np.bool_)
+    ):
+        raise TypeError(f"rough terrain env_ids must be a 1-D integer array, got {raw.dtype}")
+    ids = np.asarray(raw, dtype=np.int32)
+    if np.any(ids < 0) or np.any(ids >= env.num_envs):
+        raise IndexError(f"rough terrain env_ids out of range: {ids.tolist()}")
+    if np.unique(ids).size != ids.size:
+        raise ValueError(f"rough terrain env_ids contain duplicates: {ids.tolist()}")
+    return ids
+
+
+def _terrain_generator(env: RoughManagerBasedRlEnv) -> TerrainGeneratorCfg:
+    scene = env._cfg.scene
+    terrain = None if scene is None else scene.terrain
+    generator = None if terrain is None else terrain.generator
+    if not isinstance(generator, TerrainGeneratorCfg):
+        raise TypeError("rough manager terms require SceneCfg.terrain.generator")
+    return generator
+
+
+def _strict_height_sampler(
+    sample_height: Callable[[np.ndarray], np.ndarray],
+) -> Callable[[np.ndarray], np.ndarray]:
+    def sample(xy: np.ndarray) -> np.ndarray:
+        points = np.asarray(xy, dtype=np.float64)
+        if points.ndim < 1 or points.shape[-1] != 2:
+            raise ValueError(f"terrain sample xy must have shape (..., 2), got {points.shape}")
+        if not np.isfinite(points).all():
+            raise ValueError("terrain sample xy contains NaN or Inf")
+        heights = np.asarray(sample_height(points), dtype=np.float64)
+        expected = points.shape[:-1]
+        if heights.shape != expected:
+            raise ValueError(
+                f"terrain sample_height returned shape {heights.shape}, expected {expected}"
+            )
+        if not np.isfinite(heights).all():
+            raise ValueError("terrain sample_height returned NaN or Inf")
+        return heights
+
+    return sample
+
+
+@dataclass(frozen=True)
+class _RoughTerrainContext:
+    spawn_manager: TerrainSpawnManager
+    generator: TerrainGeneratorCfg
+
+
+_TERRAIN_CONTEXTS: WeakKeyDictionary[Any, _RoughTerrainContext] = WeakKeyDictionary()
+
+
+def _materialize_terrain_context(
+    env: RoughManagerBasedRlEnv,
+    *,
+    promote_frac: float,
+    demote_frac: float,
+    cycle_top_frac: float,
+    spawn_height_margin: float,
+) -> _RoughTerrainContext:
+    existing = _TERRAIN_CONTEXTS.get(env)
+    if existing is not None:
+        return existing
+    spawn_data = env._backend.get_terrain_spawn_data()
+    if not isinstance(spawn_data, BackendTerrainSpawnData):
+        raise NotImplementedError(
+            "rough terrain reset requires SimBackend.get_terrain_spawn_data()"
+        )
+    if spawn_data.sample_height is None:
+        raise NotImplementedError("rough terrain reset requires terrain sample_height")
+    generator = _terrain_generator(env)
+    curriculum_cfg = TerrainCurriculumCfg(
+        enabled=bool(generator.curriculum),
+        promote_frac=promote_frac,
+        demote_frac=demote_frac,
+        cycle_top_frac=cycle_top_frac,
+        spawn_height_margin=spawn_height_margin,
+        seed=env._cfg.seed,
+    )
+    context = _RoughTerrainContext(
+        spawn_manager=TerrainSpawnManager(
+            env.num_envs,
+            spawn_data.terrain_origins,
+            cell_size=float(generator.size[0]),
+            cfg=curriculum_cfg,
+            sample_height=_strict_height_sampler(spawn_data.sample_height),
+        ),
+        generator=generator,
+    )
+    _TERRAIN_CONTEXTS[env] = context
+    return context
+
+
+class RoughTerrainReset(ManagerTermBase):
+    """Stage a terrain-aware randomized root state in the reset transaction."""
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: RoughManagerBasedRlEnv):
+        super().__init__(env)
+        self._asset_cfg = cast(SceneEntityCfg, cfg.params.get("asset_cfg", _DEFAULT_ASSET_CFG))
+        self._asset = cast("Entity", env.scene[self._asset_cfg.name])
+        self._pose_range = _ranges(
+            cfg.params.get("pose_range"), _POSE_AXES, label="RoughTerrainReset pose_range"
+        )
+        self._velocity_range = _ranges(
+            cfg.params.get("velocity_range"),
+            _VELOCITY_AXES,
+            label="RoughTerrainReset velocity_range",
+        )
+        promote = _real(
+            cfg.params.get("promote_frac", 0.5),
+            label="RoughTerrainReset promote_frac",
+            minimum=0.0,
+        )
+        demote = _real(
+            cfg.params.get("demote_frac", 0.25),
+            label="RoughTerrainReset demote_frac",
+            minimum=0.0,
+        )
+        cycle = _real(
+            cfg.params.get("cycle_top_frac", 0.5),
+            label="RoughTerrainReset cycle_top_frac",
+            minimum=0.0,
+        )
+        margin = _real(
+            cfg.params.get("spawn_height_margin", 0.05),
+            label="RoughTerrainReset spawn_height_margin",
+            minimum=0.0,
+        )
+        self._context = _materialize_terrain_context(
+            env,
+            promote_frac=promote,
+            demote_frac=demote,
+            cycle_top_frac=cycle,
+            spawn_height_margin=margin,
+        )
+        default = self._asset.data.default_root_state
+        expected = (env.num_envs, 13)
+        if default.shape != expected or not np.isfinite(default).all():
+            raise ValueError(
+                f"RoughTerrainReset default root state must be finite {expected}, got {default.shape}"
+            )
+
+    @property
+    def spawn_manager(self) -> TerrainSpawnManager:
+        return self._context.spawn_manager
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        env_ids: np.ndarray | slice | None,
+        **params: Any,
+    ) -> None:
+        del params
+        ids = _env_ids(env, env_ids)
+        count = len(ids)
+        root_state = self._asset.data.default_root_state[ids].copy()
+        rng = env.rng
+        root_state[:, 0] += rng.uniform(*self._pose_range["x"], size=count)
+        root_state[:, 1] += rng.uniform(*self._pose_range["y"], size=count)
+        root_state[:, 2] += rng.uniform(*self._pose_range["z"], size=count)
+        roll = rng.uniform(*self._pose_range["roll"], size=count)
+        pitch = rng.uniform(*self._pose_range["pitch"], size=count)
+        yaw = rng.uniform(*self._pose_range["yaw"], size=count)
+        root_state[:, 3:7] = np_quat_mul(
+            root_state[:, 3:7], np_quat_from_euler_xyz(roll, pitch, yaw)
+        )
+        for column, axis in enumerate(_VELOCITY_AXES, start=7):
+            root_state[:, column] = rng.uniform(*self._velocity_range[axis], size=count)
+        root_state[:, :3] = self._context.spawn_manager.apply_spawn(
+            ids,
+            root_state[:, :3],
+            yaw=yaw,
+        )
+        if not np.isfinite(root_state).all():
+            raise ValueError("RoughTerrainReset produced NaN or Inf")
+        self._asset.write_root_state_to_sim(root_state, env_ids=ids)
+        self._context.spawn_manager.record_episode_start(ids, root_state[:, :3])
+
+
+class RoughTerrainCurriculum(ManagerTermBase):
+    """Settle completed episodes before the following terrain reset selects a cell."""
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: RoughManagerBasedRlEnv):
+        super().__init__(env)
+        asset_cfg = cast(SceneEntityCfg, cfg.params.get("asset_cfg", _DEFAULT_ASSET_CFG))
+        self._asset = cast("Entity", env.scene[asset_cfg.name])
+        context = _TERRAIN_CONTEXTS.get(env)
+        if context is None:
+            raise RuntimeError("RoughTerrainCurriculum requires RoughTerrainReset")
+        self._spawn_manager = context.spawn_manager
+
+    def __call__(
+        self,
+        env: RoughManagerBasedRlEnv,
+        env_ids: np.ndarray | slice | None,
+        **params: Any,
+    ) -> dict[str, float]:
+        del params
+        ids = _env_ids(env, env_ids)
+        done_ids = ids[env.reset_buf[ids]]
+        root_pos = self._asset.data.root_link_pos_w
+        expected = (env.num_envs, 3)
+        if root_pos.shape != expected or not np.isfinite(root_pos).all():
+            raise ValueError(
+                f"RoughTerrainCurriculum root position must be finite {expected}, got {root_pos.shape}"
+            )
+        return self._spawn_manager.update_on_done(done_ids, root_pos[done_ids])
+
+
+class RoughTerrainOutOfBounds(ManagerTermBase):
+    """Cached terrain-footprint truncation term."""
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: RoughManagerBasedRlEnv):
+        super().__init__(env)
+        asset_cfg = cast(SceneEntityCfg, cfg.params.get("asset_cfg", _DEFAULT_ASSET_CFG))
+        self._asset = cast("Entity", env.scene[asset_cfg.name])
+        self._distance_buffer = _real(
+            cfg.params.get("distance_buffer", 3.0),
+            label="RoughTerrainOutOfBounds distance_buffer",
+            minimum=0.0,
+        )
+        generator = _terrain_generator(env)
+        num_cols = len(generator.sub_terrains) if generator.curriculum else generator.num_cols
+        self._half_width = 0.5 * (
+            generator.num_rows * float(generator.size[0]) + 2.0 * generator.border_width
+        )
+        self._half_height = 0.5 * (
+            num_cols * float(generator.size[1]) + 2.0 * generator.border_width
+        )
+        if self._distance_buffer >= min(self._half_width, self._half_height):
+            raise ValueError("RoughTerrainOutOfBounds distance_buffer consumes the terrain map")
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        root_pos = self._asset.data.root_link_pos_w
+        expected = (env.num_envs, 3)
+        if root_pos.shape != expected or not np.isfinite(root_pos).all():
+            raise ValueError(
+                f"RoughTerrainOutOfBounds root position must be finite {expected}, got {root_pos.shape}"
+            )
+        x_out = np.abs(root_pos[:, 0]) > self._half_width - self._distance_buffer
+        y_out = np.abs(root_pos[:, 1]) > self._half_height - self._distance_buffer
+        return np.asarray(x_out | y_out, dtype=np.bool_)
+
+
+class RoughHeightScan(ManagerTermBase):
+    """Strict cached yaw-aligned height scan in the legacy critic format."""
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: RoughManagerBasedRlEnv):
+        super().__init__(env)
+        asset_cfg = cast(SceneEntityCfg, cfg.params.get("asset_cfg", _DEFAULT_ASSET_CFG))
+        self._asset = cast("Entity", env.scene[asset_cfg.name])
+        geom_name = cfg.params.get("geom_name", "floor")
+        base_body_name = cfg.params.get("base_body_name")
+        if not isinstance(geom_name, str) or not geom_name:
+            raise ValueError("RoughHeightScan geom_name must be non-empty")
+        if base_body_name is None:
+            scene = env._cfg.scene
+            if scene is None or asset_cfg.name not in scene.entities:
+                raise ValueError(
+                    f"RoughHeightScan scene entity '{asset_cfg.name}' is not configured"
+                )
+            entity_cfg = cast(EntityCfg, scene.entities[asset_cfg.name])
+            base_body_name = entity_cfg.root_body_name
+        if not isinstance(base_body_name, str) or not base_body_name:
+            raise ValueError("RoughHeightScan requires base_body_name or an entity root_body_name")
+        points_x = cfg.params.get("measured_points_x", DEFAULT_SCAN_POINTS_X)
+        points_y = cfg.params.get("measured_points_y", DEFAULT_SCAN_POINTS_Y)
+        if isinstance(points_x, (str, bytes)) or not isinstance(points_x, Sequence):
+            raise TypeError("RoughHeightScan measured_points_x must be a sequence")
+        if isinstance(points_y, (str, bytes)) or not isinstance(points_y, Sequence):
+            raise TypeError("RoughHeightScan measured_points_y must be a sequence")
+        offsets = height_scan_offsets(points_x, points_y)
+        if offsets.shape[0] == 0 or not np.isfinite(offsets).all():
+            raise ValueError("RoughHeightScan measured points must be finite and non-empty")
+        self._num_points = int(offsets.shape[0])
+        self._vertical_offset = _real(
+            cfg.params.get("vertical_offset", 0.5), label="RoughHeightScan vertical_offset"
+        )
+        self._scale = _real(
+            cfg.params.get("scale", 5.0),
+            label="RoughHeightScan scale",
+            minimum=0.0,
+        )
+        geom_id = env._backend.get_geom_id(geom_name)
+        frame_body_id = env._backend.get_body_id(base_body_name)
+        self._scanner: BackendHeightScanner = env._backend.create_hfield_scanner(
+            hfield_geom_id=geom_id,
+            offsets=offsets,
+            frame_body_id=frame_body_id,
+            alignment="yaw",
+            output="height",
+        )
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        raw = np.asarray(self._scanner.scan())
+        expected = (env.num_envs, self._num_points)
+        if raw.shape != expected:
+            raise ValueError(
+                f"RoughHeightScan scanner returned shape {raw.shape}, expected {expected}"
+            )
+        if not np.issubdtype(raw.dtype, np.number):
+            raise TypeError(f"RoughHeightScan scanner returned non-numeric dtype {raw.dtype}")
+        if not np.isfinite(raw).all():
+            raise ValueError("RoughHeightScan scanner returned NaN or Inf")
+        base_pos = self._asset.data.root_link_pos_w
+        if base_pos.shape != (env.num_envs, 3):
+            raise ValueError(
+                f"RoughHeightScan root position has shape {base_pos.shape}, expected ({env.num_envs}, 3)"
+            )
+        if not np.isfinite(base_pos).all():
+            raise ValueError("RoughHeightScan root position contains NaN or Inf")
+        value = np.clip(base_pos[:, 2:3] - self._vertical_offset - raw, -1.0, 1.0)
+        return np.asarray(value * self._scale, dtype=get_global_dtype())
+
+
+@dataclass(kw_only=True)
+class RoughJointPositionActionCfg(JointPositionActionCfg):
+    """Joint-position action with legacy raw-action clipping."""
+
+    clip_actions: float = 100.0
+
+    def build(self, env: ManagerBasedRlEnv) -> RoughJointPositionAction:
+        return RoughJointPositionAction(self, env)
+
+
+class RoughJointPositionAction(JointPositionAction):
+    cfg: RoughJointPositionActionCfg  # pyright: ignore[reportIncompatibleVariableOverride]
+
+    def __init__(self, cfg: RoughJointPositionActionCfg, env: ManagerBasedRlEnv):
+        self._clip_actions = _real(
+            cfg.clip_actions,
+            label="RoughJointPositionActionCfg clip_actions",
+            minimum=0.0,
+            strict_minimum=True,
+        )
+        super().__init__(cfg, env)
+        self._clipped_input = np.empty_like(self.raw_action)
+
+    def process_actions(self, actions: np.ndarray) -> None:
+        if not isinstance(actions, np.ndarray):
+            raise TypeError(
+                f"RoughJointPositionAction expected np.ndarray, got {type(actions).__name__}"
+            )
+        if actions.shape != self._clipped_input.shape:
+            raise ValueError(
+                f"RoughJointPositionAction expected shape {self._clipped_input.shape}, got {actions.shape}"
+            )
+        np.clip(actions, -self._clip_actions, self._clip_actions, out=self._clipped_input)
+        super().process_actions(self._clipped_input)
+
+
+@dataclass(kw_only=True)
+class RoughVelocityCommandCfg(UniformVelocityCommandCfg):
+    """Rough-task velocity command with a planar-norm dead zone."""
+
+    planar_dead_zone: float = 0.08
+
+    def build(self, env: ManagerBasedRlEnv) -> RoughVelocityCommand:
+        return RoughVelocityCommand(self, env)
+
+
+class RoughVelocityCommand(UniformVelocityCommand):
+    cfg: RoughVelocityCommandCfg  # pyright: ignore[reportIncompatibleVariableOverride]
+
+    def __init__(self, cfg: RoughVelocityCommandCfg, env: ManagerBasedRlEnv):
+        self._planar_dead_zone = _real(
+            cfg.planar_dead_zone,
+            label="RoughVelocityCommandCfg planar_dead_zone",
+            minimum=0.0,
+        )
+        if cfg.heading_command and not np.isclose(cfg.rel_heading_envs, 1.0):
+            raise ValueError(
+                "RoughVelocityCommandCfg heading_command requires rel_heading_envs=1.0"
+            )
+        super().__init__(cfg, env)
+
+    def _resample_command(self, env_ids: np.ndarray) -> None:
+        super()._resample_command(env_ids)
+        planar = self.vel_command_b[env_ids, :2]
+        moving = np.linalg.norm(planar, axis=1) > self._planar_dead_zone
+        self.vel_command_b[env_ids, :2] = planar * moving[:, None]
+
+
+def joint_deviation_l2(
+    env: ManagerBasedRlEnv,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Squared deviation of selected joints from the keyframe default."""
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    delta = (
+        asset.data.joint_pos[:, asset_cfg.joint_ids]
+        - asset.data.default_joint_pos[:, asset_cfg.joint_ids]
+    )
+    if delta.ndim != 2 or delta.shape[0] != env.num_envs or not np.isfinite(delta).all():
+        raise ValueError("joint_deviation_l2 received invalid joint state")
+    return np.asarray(np.sum(np.square(delta), axis=1), dtype=get_global_dtype())
+
+
+__all__ = [
+    "QuadrupedRoughTerrainCfg",
+    "RoughHeightScan",
+    "RoughJointPositionAction",
+    "RoughJointPositionActionCfg",
+    "RoughTerrainCurriculum",
+    "RoughTerrainOutOfBounds",
+    "RoughTerrainReset",
+    "RoughVelocityCommand",
+    "RoughVelocityCommandCfg",
+    "joint_deviation_l2",
+]
diff --git a/src/unilab/tasks/locomotion/common/sensor_reward_terms.py b/src/unilab/tasks/locomotion/common/sensor_reward_terms.py
new file mode 100644
index 000000000..8ad2f1fb5
--- /dev/null
+++ b/src/unilab/tasks/locomotion/common/sensor_reward_terms.py
@@ -0,0 +1,129 @@
+"""Sensor-driven reward terms shared by Manager-Based locomotion families.
+
+Biped and quadruped locomotion families port legacy reward equations that read
+IMU-style XML sensors by name.  This module owns those equations once, on top
+of the ``SensorTermBase`` cold-path binding contract from ``manager_terms.py``:
+the robot's sensor name is a cfg parameter (``params={"sensor_name": ...}``),
+so each family binds its own sensors from its owner YAML instead of
+subclassing.  Plain action/state penalties and the shared term bases live in
+``manager_terms.py``.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, ClassVar
+
+import numpy as np
+
+from unilab.dtype_config import get_global_dtype
+from unilab.managers.manager_base import ManagerTermBaseCfg
+
+from .manager_terms import SensorTermBase, _command, _real, _state
+
+if TYPE_CHECKING:
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+class _Vec3SensorTerm(SensorTermBase):
+    """Bind one named vec3 sensor on the cold path and read it per step."""
+
+    _allowed_params: ClassVar[frozenset[str]] = frozenset({"sensor_name"})
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        sensor_name = cfg.params.get("sensor_name")
+        if not isinstance(sensor_name, str) or not sensor_name:
+            raise ValueError(f"{self.name} sensor_name must be a non-empty string")
+        self._sensor_name = sensor_name
+        self._sensor = self._bind((sensor_name,))
+        if self._sensor.dimensions != (3,):
+            raise ValueError(
+                f"{self.name} sensor '{sensor_name}' must expose 3 values; received "
+                f"{self._sensor.dimensions} on backend '{self._sensor.backend_type}'"
+            )
+
+    def _read_sensor(self, env: ManagerBasedRlEnv) -> np.ndarray:
+        return _state(
+            self.name,
+            f"sensor '{self._sensor_name}'",
+            self._read(self._sensor, self.name),
+            (env.num_envs, 3),
+        )
+
+
+class _CommandTrackingTerm(_Vec3SensorTerm):
+    """``exp(-error / tracking_sigma)`` tracking against a command channel."""
+
+    _allowed_params = frozenset({"sensor_name", "tracking_sigma", "command_name"})
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        self._sigma = _real(
+            self.name,
+            "tracking_sigma",
+            cfg.params.get("tracking_sigma", 0.25),
+            minimum=0.0,
+            strict_minimum=True,
+        )
+        self._command_name = cfg.params.get("command_name", "twist")
+        _command(env, self.name, self._command_name)  # Fail closed at construction.
+
+
+class track_lin_vel(_CommandTrackingTerm):
+    """Exponential reward for tracking commanded xy linear velocity."""
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        linvel = self._read_sensor(env)
+        command = _command(env, self.name, self._command_name)
+        error = np.sum(np.square(command[:, :2] - linvel[:, :2]), axis=1)
+        return np.asarray(np.exp(-error / self._sigma), dtype=get_global_dtype())
+
+
+class track_ang_vel(_CommandTrackingTerm):
+    """Exponential reward for tracking commanded yaw angular velocity."""
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        gyro = self._read_sensor(env)
+        command = _command(env, self.name, self._command_name)
+        error = np.square(command[:, 2] - gyro[:, 2])
+        return np.asarray(np.exp(-error / self._sigma), dtype=get_global_dtype())
+
+
+class lin_vel_z(_Vec3SensorTerm):
+    """Penalty for vertical (z) linear velocity."""
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        return np.asarray(np.square(self._read_sensor(env)[:, 2]), dtype=get_global_dtype())
+
+
+class ang_vel_xy(_Vec3SensorTerm):
+    """Penalty for roll/pitch angular velocity."""
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        return np.asarray(
+            np.sum(np.square(self._read_sensor(env)[:, :2]), axis=1), dtype=get_global_dtype()
+        )
+
+
+class orientation(_Vec3SensorTerm):
+    """Penalty for deviation from upright (horizontal upvector components)."""
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        upvector = self._read_sensor(env)
+        return np.asarray(
+            np.square(upvector[:, 0]) + np.square(upvector[:, 1]), dtype=get_global_dtype()
+        )
+
+
+__all__ = [
+    "ang_vel_xy",
+    "lin_vel_z",
+    "orientation",
+    "track_ang_vel",
+    "track_lin_vel",
+]
diff --git a/src/unilab/envs/locomotion/common/terrain_spawn.py b/src/unilab/tasks/locomotion/common/terrain_spawn.py
similarity index 99%
rename from src/unilab/envs/locomotion/common/terrain_spawn.py
rename to src/unilab/tasks/locomotion/common/terrain_spawn.py
index cb623add2..2545fa772 100644
--- a/src/unilab/envs/locomotion/common/terrain_spawn.py
+++ b/src/unilab/tasks/locomotion/common/terrain_spawn.py
@@ -1,4 +1,4 @@
-"""Spawn-origin managers for locomotion envs.
+"""Spawn-origin managers for locomotion tasks.
 
 ``BaseSpawnManager`` is a no-op default: every env spawns at the world origin
 (plus the existing per-env xy jitter from the dr_provider). Used whenever the
diff --git a/src/unilab/tasks/locomotion/g1/__init__.py b/src/unilab/tasks/locomotion/g1/__init__.py
new file mode 100644
index 000000000..166fcf1ef
--- /dev/null
+++ b/src/unilab/tasks/locomotion/g1/__init__.py
@@ -0,0 +1,31 @@
+"""Hydra-owned Manager-Based G1 locomotion production registrations."""
+
+from unilab.base import registry
+from unilab.envs import ManagerBasedRlEnvCfg
+
+from .manager_terms import G1WalkManagerBasedEnv, make_g1_walk_env
+
+registry.register_env_config("G1WalkFlat", ManagerBasedRlEnvCfg)
+registry.register_env("G1WalkFlat", make_g1_walk_env, sim_backend="mujoco")
+registry.register_env("G1WalkFlat", make_g1_walk_env, sim_backend="mjwarp")
+registry.register_env("G1WalkFlat", make_g1_walk_env, sim_backend="motrix")
+registry.register_env("G1WalkFlat", make_g1_walk_env, sim_backend="isaacgym")
+registry.register_env("G1WalkFlat", make_g1_walk_env, sim_backend="genesis")
+registry.register_env("G1WalkFlat", make_g1_walk_env, sim_backend="isaacsim")
+
+registry.register_env_config("G1WalkRough", ManagerBasedRlEnvCfg)
+registry.register_env("G1WalkRough", make_g1_walk_env, sim_backend="mujoco")
+registry.register_env("G1WalkRough", make_g1_walk_env, sim_backend="motrix")
+
+registry.register_env_config("G1Walk23DofFlat", ManagerBasedRlEnvCfg)
+registry.register_env("G1Walk23DofFlat", make_g1_walk_env, sim_backend="mujoco")
+registry.register_env("G1Walk23DofFlat", make_g1_walk_env, sim_backend="motrix")
+
+registry.register_env_config("G1Walk23DofRough", ManagerBasedRlEnvCfg)
+registry.register_env("G1Walk23DofRough", make_g1_walk_env, sim_backend="mujoco")
+registry.register_env("G1Walk23DofRough", make_g1_walk_env, sim_backend="motrix")
+
+__all__ = [
+    "G1WalkManagerBasedEnv",
+    "make_g1_walk_env",
+]
diff --git a/src/unilab/tasks/locomotion/g1/manager_terms.py b/src/unilab/tasks/locomotion/g1/manager_terms.py
new file mode 100644
index 000000000..d780be083
--- /dev/null
+++ b/src/unilab/tasks/locomotion/g1/manager_terms.py
@@ -0,0 +1,839 @@
+"""Task-owned Manager-Based terms for G1 biped locomotion.
+
+The equations come from UniLab's legacy G1Walk joystick tasks.  The adaptation
+uses community ``func + params`` terms, NumPy, cached cold-path sensor bindings,
+and the base-owned entity facade; hot paths never parse assets or probe backend
+privates.
+"""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any, ClassVar, Protocol, cast
+from weakref import WeakKeyDictionary
+
+import numpy as np
+
+from unilab.base.backend_factory import create_backend, env_backend_kwargs
+from unilab.base.curriculum import EpisodeLengthTracker
+from unilab.dtype_config import get_global_dtype
+from unilab.envs.manager_based_rl_env import (
+    ManagerBasedRlEnv as _ConcreteManagerBasedRlEnv,
+)
+from unilab.envs.manager_based_rl_env import (
+    ManagerBasedRlEnvCfg,
+    _resolve_backend_entity_contract,
+)
+from unilab.envs.mdp.commands.velocity_command import (
+    UniformVelocityCommand,
+    UniformVelocityCommandCfg,
+)
+from unilab.managers.manager_base import ManagerTermBase, ManagerTermBaseCfg
+from unilab.managers.scene_entity_config import SceneEntityCfg
+from unilab.tasks.locomotion.common.manager_terms import SensorTermBase
+
+if TYPE_CHECKING:
+    from unilab.base.entity import Entity
+    from unilab.managers._types import ManagerBasedRlEnv
+
+    class _RewardTermCfgView(Protocol):
+        weight: float
+
+    class _RewardManagerView(Protocol):
+        @property
+        def active_terms(self) -> list[str]: ...
+
+        def get_term_cfg(self, term_name: str) -> _RewardTermCfgView: ...
+
+    class _G1Env(ManagerBasedRlEnv, Protocol):
+        @property
+        def common_step_counter(self) -> int: ...
+
+        @property
+        def reset_buf(self) -> np.ndarray: ...
+
+        @property
+        def reward_manager(self) -> _RewardManagerView: ...
+
+
+_DEFAULT_ASSET_CFG = SceneEntityCfg("robot")
+_LEFT_FOOT_CONTACT_SENSORS = tuple(f"left_foot_contact_{index}" for index in range(4))
+_RIGHT_FOOT_CONTACT_SENSORS = tuple(f"right_foot_contact_{index}" for index in range(4))
+_FOOT_CONTACT_SENSORS = _LEFT_FOOT_CONTACT_SENSORS + _RIGHT_FOOT_CONTACT_SENSORS
+_FOOT_POS_SENSORS = ("left_foot_pos", "right_foot_pos")
+_FOOT_QUAT_SENSORS = ("left_foot_quat", "right_foot_quat")
+_GAIT_INIT_MODES = ("offset_phase", "independent")
+
+
+def _real(
+    term: str,
+    name: str,
+    value: Any,
+    *,
+    minimum: float | None = None,
+    strict_minimum: bool = False,
+) -> float:
+    if isinstance(value, (bool, np.bool_)) or not isinstance(value, (int, float, np.number)):
+        raise TypeError(f"{term} {name} must be a real number")
+    result = float(value)
+    if not np.isfinite(result):
+        raise ValueError(f"{term} {name} must be finite")
+    if minimum is not None and (result <= minimum if strict_minimum else result < minimum):
+        relation = "greater than" if strict_minimum else "at least"
+        raise ValueError(f"{term} {name} must be {relation} {minimum}")
+    return result
+
+
+def _weights(term: str, name: str, value: Any) -> np.ndarray:
+    if isinstance(value, (str, bytes)) or not isinstance(value, (tuple, list, np.ndarray)):
+        raise TypeError(f"{term} {name} must be a sequence of real numbers")
+    result = np.asarray(
+        [_real(term, f"{name}[{index}]", item) for index, item in enumerate(value)],
+        dtype=get_global_dtype(),
+    )
+    if result.ndim != 1 or result.shape[0] == 0:
+        raise ValueError(f"{term} {name} must be a non-empty one-dimensional sequence")
+    return result
+
+
+def _state(term: str, capability: str, value: Any, shape: tuple[int, ...]) -> np.ndarray:
+    if not isinstance(value, np.ndarray):
+        raise TypeError(f"{term} {capability} must be an np.ndarray")
+    if value.shape != shape:
+        raise ValueError(f"{term} {capability} must have shape {shape}, got {value.shape}")
+    if not np.isfinite(value).all():
+        env_ids = np.flatnonzero(~np.isfinite(value).reshape(shape[0], -1).all(axis=1)).tolist()
+        raise ValueError(f"{term} {capability} contains NaN or Inf for environments {env_ids[:10]}")
+    return value
+
+
+def _command(env: ManagerBasedRlEnv, term: str, command_name: str) -> np.ndarray:
+    if not isinstance(command_name, str) or not command_name:
+        raise ValueError(f"{term} command_name must be a non-empty string")
+    try:
+        command = env.command_manager.get_command(command_name)
+    except KeyError as exc:
+        raise KeyError(f"{term} command capability '{command_name}' is unavailable") from exc
+    if command is None:
+        raise KeyError(f"{term} command capability '{command_name}' is unavailable")
+    return _state(term, f"command '{command_name}'", command, (env.num_envs, 3))
+
+
+def _asset(env: ManagerBasedRlEnv, asset_cfg: SceneEntityCfg) -> Entity:
+    return cast("Entity", env.scene[asset_cfg.name])
+
+
+class _SensorTerm(SensorTermBase):
+    """G1 manager terms share the locomotion-wide named-sensor binding."""
+
+
+# ---------------------------------------------------------------------------
+# Gait phase state (shared by the observation term and the gait reward terms)
+# ---------------------------------------------------------------------------
+
+
+def compute_feet_phase_height_targets(
+    gait_phase: np.ndarray, swing_height: float
+) -> tuple[np.ndarray, np.ndarray]:
+    """Cubic-Bézier per-foot height targets, ported from the legacy G1 task."""
+
+    def cubic_bezier_height(phi: np.ndarray, swing_height: float) -> np.ndarray:
+        phi_normalized = np.fmod(phi + np.pi, 2 * np.pi) - np.pi
+        x = (phi_normalized + np.pi) / (2 * np.pi)
+
+        def cubic_bezier_interpolation(
+            y_start: np.ndarray, y_end: np.ndarray, t: np.ndarray
+        ) -> np.ndarray:
+            y_diff = y_end - y_start
+            bezier = t**3 + 3 * (t**2 * (1 - t))
+            return np.asarray(y_start + y_diff * bezier, dtype=get_global_dtype())
+
+        stance = cubic_bezier_interpolation(np.zeros_like(x), np.full_like(x, swing_height), 2 * x)
+        swing = cubic_bezier_interpolation(
+            np.full_like(x, swing_height), np.zeros_like(x), 2 * x - 1
+        )
+        return np.where(x <= 0.5, stance, swing)
+
+    left_target = cubic_bezier_height(gait_phase[:, 0], swing_height)
+    right_target = cubic_bezier_height(gait_phase[:, 1], swing_height)
+    return left_target, right_target
+
+
+def compute_feet_phase_contact_targets(
+    gait_phase: np.ndarray, swing_height: float
+) -> tuple[np.ndarray, np.ndarray]:
+    """Expected per-foot contact flags derived from the Bézier height targets."""
+    left_target, right_target = compute_feet_phase_height_targets(gait_phase, swing_height)
+    contact_height_threshold = swing_height * 0.5
+    return left_target <= contact_height_threshold, right_target <= contact_height_threshold
+
+
+@dataclass
+class _G1GaitContext:
+    phase: np.ndarray  # (num_envs, 2), radians in [0, 2*pi)
+    delta: float  # 2*pi*frequency*ctrl_dt
+    frequency: float
+    init_mode: str
+    last_counter: int
+
+
+_GAIT_CONTEXTS: WeakKeyDictionary[Any, _G1GaitContext] = WeakKeyDictionary()
+
+
+def _gait_context(env: _G1Env, term: str, frequency: float, init_mode: str) -> _G1GaitContext:
+    context = _GAIT_CONTEXTS.get(env)
+    if context is None:
+        delta = float(
+            2.0
+            * math.pi
+            * frequency
+            * _real(term, "step_dt", env.step_dt, minimum=0.0, strict_minimum=True)
+        )
+        context = _G1GaitContext(
+            phase=np.zeros((env.num_envs, 2), dtype=get_global_dtype()),
+            delta=delta,
+            frequency=frequency,
+            init_mode=init_mode,
+            last_counter=int(env.common_step_counter),
+        )
+        _GAIT_CONTEXTS[env] = context
+        return context
+    if context.frequency != frequency or context.init_mode != init_mode:
+        raise ValueError(
+            f"{term} gait terms on one env must share frequency and init_mode; "
+            f"got frequency {context.frequency} vs {frequency}, "
+            f"init_mode {context.init_mode!r} vs {init_mode!r}"
+        )
+    return context
+
+
+def _advance_gait(env: _G1Env, context: _G1GaitContext) -> np.ndarray:
+    counter = env.common_step_counter
+    if isinstance(counter, (bool, np.bool_)) or not isinstance(counter, (int, np.integer)):
+        raise TypeError("G1 gait terms require an integer common_step_counter")
+    counter = int(counter)
+    if counter < context.last_counter:
+        raise ValueError("G1 gait terms common_step_counter cannot move backwards")
+    two_pi = 2.0 * np.pi
+    for _ in range(counter - context.last_counter):
+        context.phase = np.asarray(
+            np.fmod(context.phase + context.delta, two_pi), dtype=get_global_dtype()
+        )
+    context.last_counter = counter
+    return context.phase
+
+
+def _resample_gait(env: _G1Env, context: _G1GaitContext, env_ids: np.ndarray) -> None:
+    ids = np.asarray(env_ids, dtype=np.intp).reshape(-1)
+    count = len(ids)
+    if count == 0:
+        return
+    if context.init_mode == "independent":
+        left = env.rng.uniform(0.0, 2.0 * np.pi, size=(count,))
+        right = env.rng.uniform(0.0, 2.0 * np.pi, size=(count,))
+    else:
+        left = env.rng.uniform(0.0, 2.0 * np.pi, size=(count,))
+        right = left + np.pi
+    context.phase[ids] = np.column_stack([left, right]).astype(get_global_dtype(), copy=False)
+
+
+class G1GaitPhase(ManagerTermBase):
+    """Two-foot gait phase observation in radians, with per-reset phase sampling."""
+
+    _allowed_params: ClassVar[frozenset[str]] = frozenset({"frequency", "init_mode"})
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: _G1Env):
+        super().__init__(env)
+        unexpected = set(cfg.params) - self._allowed_params
+        if unexpected:
+            raise TypeError(f"{self.name} received unsupported parameters: {sorted(unexpected)}")
+        frequency = _real(self.name, "frequency", cfg.params.get("frequency", 1.5), minimum=0.0)
+        init_mode = cfg.params.get("init_mode", "offset_phase")
+        if init_mode not in _GAIT_INIT_MODES:
+            raise ValueError(f"{self.name} init_mode must be one of {_GAIT_INIT_MODES}")
+        self._context = _gait_context(env, self.name, frequency, init_mode)
+
+    def __call__(self, env: _G1Env, **params: Any) -> np.ndarray:
+        del params
+        return _advance_gait(env, self._context).copy()
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        if env_ids is None:
+            ids = np.arange(self.num_envs, dtype=np.intp)
+        elif isinstance(env_ids, slice):
+            ids = np.arange(self.num_envs, dtype=np.intp)[env_ids]
+        else:
+            ids = np.asarray(env_ids, dtype=np.intp).reshape(-1)
+        _resample_gait(cast("_G1Env", self._env), self._context, ids)
+
+
+class _GaitRewardTerm(_SensorTerm):
+    """Shared gait-context, forward-speed gate, and foot-position binding."""
+
+    _allowed_params = frozenset(
+        {
+            "frequency",
+            "init_mode",
+            "swing_height",
+            "tracking_sigma",
+            "min_forward_speed",
+            "command_name",
+        }
+    )
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: _G1Env):
+        super().__init__(cfg, env)
+        frequency = _real(self.name, "frequency", cfg.params.get("frequency", 1.5), minimum=0.0)
+        init_mode = cfg.params.get("init_mode", "offset_phase")
+        if init_mode not in _GAIT_INIT_MODES:
+            raise ValueError(f"{self.name} init_mode must be one of {_GAIT_INIT_MODES}")
+        self._swing_height = _real(
+            self.name, "swing_height", cfg.params.get("swing_height", 0.09), minimum=0.0
+        )
+        self._tracking_sigma = _real(
+            self.name,
+            "tracking_sigma",
+            cfg.params.get("tracking_sigma", 0.008),
+            minimum=0.0,
+            strict_minimum=True,
+        )
+        self._min_forward_speed = _real(
+            self.name,
+            "min_forward_speed",
+            cfg.params.get("min_forward_speed", 0.0),
+            minimum=0.0,
+        )
+        command_name = cfg.params.get("command_name", "twist")
+        if not isinstance(command_name, str) or not command_name:
+            raise ValueError(f"{self.name} command_name must be a non-empty string")
+        self._command_name = command_name
+        self._context = _gait_context(env, self.name, frequency, init_mode)
+        self._feet_pos = self._bind(_FOOT_POS_SENSORS)
+        if self._feet_pos.dimensions != (3, 3):
+            raise ValueError(
+                f"{self.name} foot position sensors must expose 3-D xyz; received "
+                f"{self._feet_pos.dimensions} on backend '{self._feet_pos.backend_type}'"
+            )
+        self._linvel = self._bind(("pelvis_local_linvel",))
+
+    def _targets(self, env: _G1Env) -> tuple[np.ndarray, np.ndarray]:
+        phase = _advance_gait(env, self._context)
+        return compute_feet_phase_height_targets(phase, self._swing_height)
+
+    def _foot_heights(self) -> tuple[np.ndarray, np.ndarray]:
+        values = _state(
+            self.name, "foot position", self._read(self._feet_pos, self.name), (self.num_envs, 6)
+        )
+        return values[:, 2], values[:, 5]
+
+    def _gate(self, env: ManagerBasedRlEnv) -> np.ndarray:
+        linvel = _state(
+            self.name,
+            "pelvis linear velocity",
+            self._read(self._linvel, self.name),
+            (env.num_envs, 3),
+        )
+        forward_speed = np.maximum(linvel[:, 0], 0.0)
+        return np.asarray(forward_speed >= self._min_forward_speed, dtype=get_global_dtype())
+
+
+class feet_phase(_GaitRewardTerm):
+    """Reward gait phase tracking by encouraging the expected swing-foot height."""
+
+    def __call__(self, env: _G1Env, **params: Any) -> np.ndarray:
+        del params
+        left_target, right_target = self._targets(env)
+        left_height, right_height = self._foot_heights()
+        error = np.square(left_height - left_target) + np.square(right_height - right_target)
+        reward = np.exp(-error / self._tracking_sigma)
+        return np.asarray(reward * self._gate(env), dtype=get_global_dtype())
+
+
+class feet_phase_contrast(_GaitRewardTerm):
+    """Reward left/right foot-height contrast against the gait-phase targets."""
+
+    def __call__(self, env: _G1Env, **params: Any) -> np.ndarray:
+        del params
+        left_target, right_target = self._targets(env)
+        left_height, right_height = self._foot_heights()
+        error = np.square((left_height - right_height) - (left_target - right_target))
+        reward = np.exp(-error / self._tracking_sigma)
+        return np.asarray(reward * self._gate(env), dtype=get_global_dtype())
+
+
+class _FootContactTerm(_GaitRewardTerm):
+    """Adds the aggregated per-foot contact binding shared by contact gait terms."""
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: _G1Env):
+        super().__init__(cfg, env)
+        self._contacts = self._bind(_FOOT_CONTACT_SENSORS)
+        if self._contacts.dimensions != (1,) * 8:
+            raise ValueError(
+                f"{self.name} foot contact sensors must each expose 1-D found; received "
+                f"{self._contacts.dimensions} on backend '{self._contacts.backend_type}'"
+            )
+
+    def _contact_pair(self) -> tuple[np.ndarray, np.ndarray]:
+        values = _state(
+            self.name, "foot contact", self._read(self._contacts, self.name), (self.num_envs, 8)
+        )
+        left = np.any(values[:, :4] > 0.5, axis=1)
+        right = np.any(values[:, 4:] > 0.5, axis=1)
+        return left, right
+
+
+class feet_phase_contact(_FootContactTerm):
+    """Reward foot contact matching the expected stance phase of the gait."""
+
+    def __call__(self, env: _G1Env, **params: Any) -> np.ndarray:
+        del params
+        phase = _advance_gait(env, self._context)
+        left_target, right_target = compute_feet_phase_contact_targets(phase, self._swing_height)
+        left_contact, right_contact = self._contact_pair()
+        left_match = np.asarray(left_contact == left_target, dtype=get_global_dtype())
+        right_match = np.asarray(right_contact == right_target, dtype=get_global_dtype())
+        reward = np.asarray(0.5 * (left_match + right_match), dtype=get_global_dtype())
+        return np.asarray(reward * self._gate(env), dtype=get_global_dtype())
+
+
+class feet_double_stance(_FootContactTerm):
+    """Penalize double-stance contact while a forward command is active."""
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        command = _command(env, self.name, self._command_name)
+        left_contact, right_contact = self._contact_pair()
+        double_stance = np.asarray(
+            np.logical_and(left_contact, right_contact), dtype=get_global_dtype()
+        )
+        forward_mask = np.asarray(np.maximum(command[:, 0], 0.0) > 1.0e-6, dtype=get_global_dtype())
+        return np.asarray(double_stance * forward_mask, dtype=get_global_dtype())
+
+
+class feet_air_time(_FootContactTerm):
+    """Count feet whose current air time sits inside the rewarded window."""
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: _G1Env):
+        super().__init__(cfg, env)
+        self._air_time = np.zeros((env.num_envs, 2), dtype=get_global_dtype())
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        self._air_time[env_ids if env_ids is not None else slice(None)] = 0.0
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        left_contact, right_contact = self._contact_pair()
+        contact = np.column_stack([left_contact, right_contact])
+        step_dt = _real(self.name, "step_dt", env.step_dt, minimum=0.0, strict_minimum=True)
+        self._air_time = np.where(contact, 0.0, self._air_time + step_dt).astype(
+            get_global_dtype(), copy=False
+        )
+        in_range = (self._air_time > 0.05) & (self._air_time < 0.5)
+        return np.asarray(
+            np.sum(in_range.astype(get_global_dtype()), axis=1), dtype=get_global_dtype()
+        )
+
+
+# ---------------------------------------------------------------------------
+# Velocity / orientation reward terms (sensor-bound, legacy equations)
+# ---------------------------------------------------------------------------
+
+
+class _LinVelTerm(_SensorTerm):
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        self._linvel = self._bind(("pelvis_local_linvel",))
+
+    def _read_linvel(self, env: ManagerBasedRlEnv) -> np.ndarray:
+        return _state(
+            self.name,
+            "pelvis linear velocity",
+            self._read(self._linvel, self.name),
+            (env.num_envs, 3),
+        )
+
+
+class _GyroTerm(_SensorTerm):
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        self._gyro = self._bind(("torso_gyro",))
+
+    def _read_gyro(self, env: ManagerBasedRlEnv) -> np.ndarray:
+        return _state(self.name, "torso gyro", self._read(self._gyro, self.name), (env.num_envs, 3))
+
+
+class _UpvectorTerm(_SensorTerm):
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        self._upvector = self._bind(("torso_upvector",))
+
+    def _read_upvector(self, env: ManagerBasedRlEnv) -> np.ndarray:
+        return _state(
+            self.name, "torso upvector", self._read(self._upvector, self.name), (env.num_envs, 3)
+        )
+
+
+class forward_progress(_LinVelTerm):
+    """Reward forward progress relative to commanded speed."""
+
+    _allowed_params = frozenset({"command_name"})
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        command = _command(env, self.name, _term_command_name(self.name, params))
+        linvel = self._read_linvel(env)
+        commanded_speed = np.maximum(command[:, 0], 1e-6)
+        forward_speed = np.maximum(linvel[:, 0], 0.0)
+        return np.asarray(
+            np.minimum(forward_speed / commanded_speed, 1.0), dtype=get_global_dtype()
+        )
+
+
+def _term_command_name(term: str, params: dict[str, Any]) -> str:
+    name = params.get("command_name", "twist")
+    if not isinstance(name, str) or not name:
+        raise ValueError(f"{term} command_name must be a non-empty string")
+    return name
+
+
+class under_speed(_LinVelTerm):
+    """Penalty for being below commanded forward speed."""
+
+    _allowed_params = frozenset({"command_name"})
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        command = _command(env, self.name, _term_command_name(self.name, params))
+        linvel = self._read_linvel(env)
+        commanded_speed = np.maximum(command[:, 0], 1e-6)
+        forward_speed = np.maximum(linvel[:, 0], 0.0)
+        gap = np.maximum(command[:, 0] - forward_speed, 0.0)
+        return np.asarray(gap / commanded_speed, dtype=get_global_dtype())
+
+
+class g1_tilt_exceeded(_UpvectorTerm):
+    """Terminate when the base tilt from upright exceeds ``max_tilt_deg``."""
+
+    _allowed_params = frozenset({"max_tilt_deg"})
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        max_tilt_deg = _real(self.name, "max_tilt_deg", cfg.params.get("max_tilt_deg"), minimum=0.0)
+        self._max_tilt_rad = math.radians(max_tilt_deg)
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        upvector = self._read_upvector(env)
+        tilt = np.arccos(np.clip(upvector[:, 2], -1.0, 1.0))
+        return np.asarray(tilt > self._max_tilt_rad, dtype=np.bool_)
+
+
+class penalty_feet_ori(_SensorTerm):
+    """Penalty for non-flat foot orientations (roll/pitch quaternion rows)."""
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        self._quats = self._bind(_FOOT_QUAT_SENSORS)
+        if self._quats.dimensions != (4, 4):
+            raise ValueError(
+                f"{self.name} foot quaternion sensors must expose 4-D quats; received "
+                f"{self._quats.dimensions} on backend '{self._quats.backend_type}'"
+            )
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        values = _state(
+            self.name, "foot quaternion", self._read(self._quats, self.name), (env.num_envs, 8)
+        )
+        return np.asarray(
+            np.square(values[:, 1])
+            + np.square(values[:, 2])
+            + np.square(values[:, 5])
+            + np.square(values[:, 6]),
+            dtype=get_global_dtype(),
+        )
+
+
+class penalty_close_feet_xy(_SensorTerm):
+    """Penalty for feet closer than ``threshold`` in the horizontal plane."""
+
+    _allowed_params = frozenset({"threshold"})
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        self._threshold = _real(
+            self.name, "threshold", cfg.params.get("threshold", 0.15), minimum=0.0
+        )
+        self._feet_pos = self._bind(_FOOT_POS_SENSORS)
+        if self._feet_pos.dimensions != (3, 3):
+            raise ValueError(
+                f"{self.name} foot position sensors must expose 3-D xyz; received "
+                f"{self._feet_pos.dimensions} on backend '{self._feet_pos.backend_type}'"
+            )
+
+    def __call__(self, env: ManagerBasedRlEnv, **params: Any) -> np.ndarray:
+        del params
+        values = _state(
+            self.name, "foot position", self._read(self._feet_pos, self.name), (env.num_envs, 6)
+        )
+        feet_dist = np.linalg.norm(values[:, :2] - values[:, 3:5], axis=1)
+        return np.asarray(
+            np.where(
+                feet_dist < self._threshold,
+                np.square(feet_dist - self._threshold),
+                0.0,
+            ),
+            dtype=get_global_dtype(),
+        )
+
+
+# ---------------------------------------------------------------------------
+# Entity-based reward terms (legacy equations, no sensor binding)
+# ---------------------------------------------------------------------------
+
+
+def weighted_pose(
+    env: ManagerBasedRlEnv,
+    pose_weights: Any,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Weighted L2 penalty for joint position deviation from the default pose."""
+    asset = _asset(env, asset_cfg)
+    position = asset.data.joint_pos[:, asset_cfg.joint_ids]
+    default = asset.data.default_joint_pos[:, asset_cfg.joint_ids]
+    weights = _weights("weighted_pose", "pose_weights", pose_weights)
+    if weights.shape[0] != position.shape[1]:
+        raise ValueError(
+            f"weighted_pose pose_weights length {weights.shape[0]} does not match "
+            f"joint count {position.shape[1]}"
+        )
+    diff = _state("weighted_pose", "joint position", position, position.shape) - _state(
+        "weighted_pose", "default joint position", default, position.shape
+    )
+    return np.asarray(np.sum(weights * np.square(diff), axis=1), dtype=get_global_dtype())
+
+
+def upper_body_pose(
+    env: ManagerBasedRlEnv,
+    pose_weights: Any,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Weighted L2 pose penalty with the twelve leg joints zeroed out."""
+    weights = _weights("upper_body_pose", "pose_weights", pose_weights)
+    if weights.shape[0] < 12:
+        raise ValueError("upper_body_pose pose_weights must cover at least the twelve leg joints")
+    weights = weights.copy()
+    weights[:12] = 0.0
+    return weighted_pose(env, weights, asset_cfg=asset_cfg)
+
+
+# ---------------------------------------------------------------------------
+# Velocity command term (legacy dead zone + standing zeroing semantics)
+# ---------------------------------------------------------------------------
+
+
+@dataclass(kw_only=True)
+class G1VelocityCommandCfg(UniformVelocityCommandCfg):
+    """G1 velocity command with the legacy planar-norm dead zone."""
+
+    planar_dead_zone: float = 0.2
+
+    def build(self, env: ManagerBasedRlEnv) -> G1VelocityCommand:
+        return G1VelocityCommand(self, env)
+
+
+class G1VelocityCommand(UniformVelocityCommand):
+    cfg: G1VelocityCommandCfg  # pyright: ignore[reportIncompatibleVariableOverride]
+
+    def __init__(self, cfg: G1VelocityCommandCfg, env: ManagerBasedRlEnv):
+        self._planar_dead_zone = _real(
+            "G1VelocityCommand", "planar_dead_zone", cfg.planar_dead_zone, minimum=0.0
+        )
+        if cfg.heading_command:
+            raise NotImplementedError(
+                "G1VelocityCommand capability 'heading command' is unavailable in the "
+                "Manager-Based runtime; the legacy G1 heading-channel zeroing has no "
+                "production owner and fails closed instead of falling back"
+            )
+        super().__init__(cfg, env)
+
+    def _resample_command(self, env_ids: np.ndarray) -> None:
+        super()._resample_command(env_ids)
+        planar = self.vel_command_b[env_ids, :2]
+        moving = np.linalg.norm(planar, axis=1) > self._planar_dead_zone
+        self.vel_command_b[env_ids, :2] = planar * moving[:, None]
+
+
+# ---------------------------------------------------------------------------
+# Penalty curriculum (ports the legacy G1 penalty-curriculum semantics)
+# ---------------------------------------------------------------------------
+
+
+class G1PenaltyCurriculum(ManagerTermBase):
+    """Scale negative-weight reward terms by average episode length.
+
+    Ports the legacy G1 penalty curriculum: penalty weights start at
+    ``initial_scale`` of their configured value and relax toward ``max_scale``
+    as the tracked average episode length crosses the configured thresholds.
+    """
+
+    _allowed_params: ClassVar[frozenset[str]] = frozenset(
+        {
+            "initial_scale",
+            "min_scale",
+            "max_scale",
+            "level_down_threshold",
+            "level_up_threshold",
+            "degree",
+        }
+    )
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: _G1Env):
+        super().__init__(env)
+        unexpected = set(cfg.params) - self._allowed_params
+        if unexpected:
+            raise TypeError(f"{self.name} received unsupported parameters: {sorted(unexpected)}")
+        self._min_scale = _real(
+            self.name, "min_scale", cfg.params.get("min_scale", 0.5), minimum=0.0
+        )
+        self._max_scale = _real(
+            self.name,
+            "max_scale",
+            cfg.params.get("max_scale", 1.0),
+            minimum=self._min_scale,
+        )
+        self._current_scale = _real(
+            self.name,
+            "initial_scale",
+            cfg.params.get("initial_scale", 0.5),
+            minimum=self._min_scale,
+        )
+        if self._current_scale > self._max_scale:
+            raise ValueError(f"{self.name} initial_scale must be at most max_scale")
+        self._level_down_threshold = _real(
+            self.name,
+            "level_down_threshold",
+            cfg.params.get("level_down_threshold", 150.0),
+            minimum=0.0,
+        )
+        self._level_up_threshold = _real(
+            self.name,
+            "level_up_threshold",
+            cfg.params.get("level_up_threshold", 750.0),
+            minimum=0.0,
+        )
+        self._degree = _real(self.name, "degree", cfg.params.get("degree", 0.001), minimum=0.0)
+        self._tracker = EpisodeLengthTracker(env.num_envs)
+        self._original_weights: dict[str, float] = {}
+        for name in env.reward_manager.active_terms:
+            weight = float(env.reward_manager.get_term_cfg(name).weight)
+            if weight < 0.0:
+                self._original_weights[name] = weight
+        self._apply_scale()
+
+    def _apply_scale(self) -> None:
+        env = cast("_G1Env", self._env)
+        for name, original in self._original_weights.items():
+            env.reward_manager.get_term_cfg(name).weight = original * self._current_scale
+
+    def __call__(
+        self,
+        env: _G1Env,
+        env_ids: np.ndarray | slice | None,
+        **params: Any,
+    ) -> dict[str, float]:
+        del params
+        ids = (
+            np.arange(env.num_envs, dtype=np.intp)
+            if env_ids is None
+            else np.arange(env.num_envs, dtype=np.intp)[env_ids]
+            if isinstance(env_ids, slice)
+            else np.asarray(env_ids, dtype=np.intp).reshape(-1)
+        )
+        done_ids = ids[env.reset_buf[ids]]
+        if len(done_ids) > 0:
+            self._tracker.update(env.episode_length_buf[done_ids].astype(np.float64))
+            average = self._tracker.average_length
+            if average < self._level_down_threshold:
+                self._current_scale *= 1.0 - self._degree
+            elif average > self._level_up_threshold:
+                self._current_scale *= 1.0 + self._degree
+            self._current_scale = float(
+                np.clip(self._current_scale, self._min_scale, self._max_scale)
+            )
+            self._apply_scale()
+        return {
+            "average_episode_length": float(self._tracker.average_length),
+            "penalty_scale": float(self._current_scale),
+        }
+
+
+# ---------------------------------------------------------------------------
+# G1 Manager-Based env: Registry-owned production runtime on the single
+# lifecycle
+# ---------------------------------------------------------------------------
+
+
+class G1WalkManagerBasedEnv(_ConcreteManagerBasedRlEnv):
+    """Manager-Based G1 walk runtime."""
+
+
+def make_g1_walk_env(
+    cfg: ManagerBasedRlEnvCfg,
+    num_envs: int = 1,
+    backend_type: str = "mujoco",
+) -> G1WalkManagerBasedEnv:
+    """Construct the Registry-owned G1 Manager-Based production runtime."""
+    if not isinstance(cfg, ManagerBasedRlEnvCfg):
+        raise TypeError(
+            f"make_g1_walk_env expected ManagerBasedRlEnvCfg, received {type(cfg).__name__}"
+        )
+    if isinstance(num_envs, bool) or not isinstance(num_envs, int) or num_envs <= 0:
+        raise ValueError(f"make_g1_walk_env num_envs must be a positive integer, got {num_envs!r}")
+    if not isinstance(backend_type, str) or not backend_type:
+        raise ValueError(
+            f"make_g1_walk_env backend_type must be a non-empty string, got {backend_type!r}"
+        )
+
+    cfg.validate()
+    assert cfg.scene is not None
+    base_name, body_state_requested = _resolve_backend_entity_contract(cfg)
+    backend_kwargs = env_backend_kwargs(cfg)
+    backend_kwargs["base_name"] = base_name
+
+    backend = create_backend(
+        backend_type,
+        cfg.scene,
+        num_envs,
+        cfg.sim_dt,
+        body_state_required=body_state_requested,
+        **backend_kwargs,
+    )
+    try:
+        return G1WalkManagerBasedEnv(cfg, backend, num_envs)
+    except Exception:
+        backend.cleanup_scene_assets()
+        raise
+
+
+__all__ = [
+    "G1GaitPhase",
+    "G1PenaltyCurriculum",
+    "G1VelocityCommand",
+    "G1VelocityCommandCfg",
+    "G1WalkManagerBasedEnv",
+    "compute_feet_phase_contact_targets",
+    "compute_feet_phase_height_targets",
+    "feet_air_time",
+    "feet_double_stance",
+    "feet_phase",
+    "feet_phase_contact",
+    "feet_phase_contrast",
+    "forward_progress",
+    "g1_tilt_exceeded",
+    "make_g1_walk_env",
+    "penalty_close_feet_xy",
+    "penalty_feet_ori",
+    "under_speed",
+    "upper_body_pose",
+    "weighted_pose",
+]
diff --git a/src/unilab/tasks/locomotion/go1/__init__.py b/src/unilab/tasks/locomotion/go1/__init__.py
new file mode 100644
index 000000000..3c5413d39
--- /dev/null
+++ b/src/unilab/tasks/locomotion/go1/__init__.py
@@ -0,0 +1,15 @@
+"""Hydra-owned Manager-Based Go1 production registrations."""
+
+from unilab.base import registry
+from unilab.envs import ManagerBasedRlEnvCfg, make_manager_based_rl_env
+
+registry.register_env_config("Go1JoystickFlat", ManagerBasedRlEnvCfg)
+registry.register_env("Go1JoystickFlat", make_manager_based_rl_env, sim_backend="mujoco")
+registry.register_env("Go1JoystickFlat", make_manager_based_rl_env, sim_backend="motrix")
+registry.register_env("Go1JoystickFlat", make_manager_based_rl_env, sim_backend="drake")
+
+registry.register_env_config("Go1JoystickRough", ManagerBasedRlEnvCfg)
+registry.register_env("Go1JoystickRough", make_manager_based_rl_env, sim_backend="mujoco")
+registry.register_env("Go1JoystickRough", make_manager_based_rl_env, sim_backend="motrix")
+
+__all__: list[str] = []
diff --git a/src/unilab/tasks/locomotion/go2/__init__.py b/src/unilab/tasks/locomotion/go2/__init__.py
new file mode 100644
index 000000000..75357ce80
--- /dev/null
+++ b/src/unilab/tasks/locomotion/go2/__init__.py
@@ -0,0 +1,17 @@
+"""Hydra-owned Manager-Based Go2 production registrations."""
+
+from unilab.base import registry
+from unilab.envs import ManagerBasedRlEnvCfg, make_manager_based_rl_env
+
+from . import footstand as footstand
+
+registry.register_env_config("Go2JoystickFlat", ManagerBasedRlEnvCfg)
+registry.register_env("Go2JoystickFlat", make_manager_based_rl_env, sim_backend="mujoco")
+registry.register_env("Go2JoystickFlat", make_manager_based_rl_env, sim_backend="motrix")
+registry.register_env("Go2JoystickFlat", make_manager_based_rl_env, sim_backend="drake")
+
+registry.register_env_config("Go2JoystickRough", ManagerBasedRlEnvCfg)
+registry.register_env("Go2JoystickRough", make_manager_based_rl_env, sim_backend="mujoco")
+registry.register_env("Go2JoystickRough", make_manager_based_rl_env, sim_backend="motrix")
+
+__all__ = ["footstand"]
diff --git a/src/unilab/tasks/locomotion/go2/footstand.py b/src/unilab/tasks/locomotion/go2/footstand.py
new file mode 100644
index 000000000..011485c41
--- /dev/null
+++ b/src/unilab/tasks/locomotion/go2/footstand.py
@@ -0,0 +1,1068 @@
+"""Hydra-owned Manager-Based terms for the Go2 footstand task.
+
+The task keeps its historical NumPy observation, action, reward, termination,
+and reset semantics while using only the public manager/entity facade.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from numbers import Real
+from typing import TYPE_CHECKING, Any, Protocol, cast
+
+import numpy as np
+
+from unilab.base import registry
+from unilab.dtype_config import get_global_dtype
+from unilab.envs import ManagerBasedRlEnvCfg, make_manager_based_rl_env
+from unilab.managers import ActionTerm, ActionTermCfg, ManagerTermBase, ManagerTermBaseCfg
+from unilab.managers.scene_entity_config import SceneEntityCfg
+from unilab.utils.rotation import np_quat_apply, np_quat_apply_inverse
+
+if TYPE_CHECKING:
+    from unilab.base.entity import Entity
+    from unilab.managers._types import ManagerBasedRlEnv, ManagerSensorView
+    from unilab.managers.action_manager import ActionManager
+    from unilab.managers.termination_manager import TerminationManager
+
+    class _FootstandEnv(ManagerBasedRlEnv, Protocol):
+        @property
+        def common_step_counter(self) -> int: ...
+
+        @property
+        def action_manager(self) -> ActionManager: ...
+
+        @property
+        def termination_manager(self) -> TerminationManager: ...
+
+
+NUM_ACTIONS = 12
+FRAME_OBS_DIM = 45
+PRIVILEGED_OBS_DIM = 49
+
+_WORLD_GRAVITY = np.asarray([0.0, 0.0, -1.0], dtype=np.float32)
+_BODY_FORWARD = np.asarray([1.0, 0.0, 0.0], dtype=np.float32)
+_TARGET_HEIGHT = 0.53
+_CONTACT_THRESHOLD = 0.1
+_STAND_HEIGHT_FRACTION = 0.8
+_STAND_ORIENTATION_THRESHOLD = 0.5
+
+_FRONT_FEET = np.asarray([0, 1], dtype=np.intp)
+_REAR_FEET = np.asarray([2, 3], dtype=np.intp)
+_FRONT_LEGS = np.arange(0, 6, dtype=np.intp)
+_REAR_LEGS = np.arange(6, 12, dtype=np.intp)
+_REAR_HIPS = np.asarray([6, 9], dtype=np.intp)
+_REAR_LEFT = np.asarray([6, 7, 8], dtype=np.intp)
+_REAR_RIGHT = np.asarray([9, 10, 11], dtype=np.intp)
+_REAR_MIRROR_SIGNS = np.asarray([-1.0, 1.0, 1.0], dtype=np.float32)
+_FRONT_LEG_TARGET = np.asarray([0.0, 1.82, -1.16, 0.0, 1.82, -1.16], dtype=np.float32)
+
+_TRACKED_BODY_NAMES = (
+    "FL_thigh",
+    "FR_thigh",
+    "FL_calf",
+    "FR_calf",
+    "RL_calf",
+    "RR_calf",
+)
+_FRONT_LEFT_BODY_INDICES = np.asarray([0, 2], dtype=np.intp)
+_FRONT_RIGHT_BODY_INDICES = np.asarray([1, 3], dtype=np.intp)
+_KNEE_BODY_INDICES = np.asarray([2, 3, 4, 5], dtype=np.intp)
+
+_SENSOR_SPECS = (
+    ("local_linvel", 3),
+    ("gyro", 3),
+    ("upvector", 3),
+    ("global_position", 3),
+    ("accelerometer", 3),
+    ("global_angvel", 3),
+    ("FL_foot_contact", 1),
+    ("FR_foot_contact", 1),
+    ("RL_foot_contact", 1),
+    ("RR_foot_contact", 1),
+    ("FL_pos", 3),
+    ("FR_pos", 3),
+    ("RL_pos", 3),
+    ("RR_pos", 3),
+    ("base1_contact", 1),
+    ("base2_contact", 1),
+    ("base3_contact", 1),
+    ("RL_hip_contact", 1),
+    ("RR_hip_contact", 1),
+    ("RL_thigh_contact", 1),
+    ("RR_thigh_contact", 1),
+    ("RL_calf_contact1", 1),
+    ("RL_calf_contact2", 1),
+    ("RR_calf_contact1", 1),
+    ("RR_calf_contact2", 1),
+    ("FL_hip_contact", 1),
+    ("FR_hip_contact", 1),
+    ("FL_thigh_contact", 1),
+    ("FR_thigh_contact", 1),
+    ("FL_calf_contact1", 1),
+    ("FL_calf_contact2", 1),
+    ("FR_calf_contact1", 1),
+    ("FR_calf_contact2", 1),
+)
+_FOOT_CONTACT_NAMES = tuple(name for name, _ in _SENSOR_SPECS[6:10])
+_FOOT_POSITION_NAMES = tuple(name for name, _ in _SENSOR_SPECS[10:14])
+_TERMINATION_CONTACT_NAMES = tuple(name for name, _ in _SENSOR_SPECS[14:25])
+_PENALTY_CONTACT_NAMES = tuple(name for name, _ in _SENSOR_SPECS[25:33])
+
+
+def _real(
+    term: str,
+    name: str,
+    value: Any,
+    *,
+    minimum: float | None = None,
+    strict_minimum: bool = False,
+) -> float:
+    if isinstance(value, (bool, np.bool_)) or not isinstance(value, Real):
+        raise TypeError(f"{term} {name} must be a real number")
+    result = float(value)
+    if not np.isfinite(result):
+        raise ValueError(f"{term} {name} must be finite")
+    if minimum is not None and (result <= minimum if strict_minimum else result < minimum):
+        relation = "greater than" if strict_minimum else "at least"
+        raise ValueError(f"{term} {name} must be {relation} {minimum}")
+    return result
+
+
+def _pair(
+    term: str,
+    name: str,
+    value: Any,
+    *,
+    minimum: float | None = None,
+) -> tuple[float, float]:
+    if isinstance(value, (str, bytes)) or not isinstance(value, (tuple, list)):
+        raise TypeError(f"{term} {name} must be a two-value range")
+    if len(value) != 2:
+        raise ValueError(f"{term} {name} must contain two values")
+    lower = _real(term, f"{name}[0]", value[0], minimum=minimum)
+    upper = _real(term, f"{name}[1]", value[1], minimum=minimum)
+    if lower > upper:
+        raise ValueError(f"{term} {name} lower bound {lower} exceeds upper bound {upper}")
+    return lower, upper
+
+
+def _name(term: str, field: str, value: Any) -> str:
+    if not isinstance(value, str) or not value:
+        raise ValueError(f"{term} {field} must be a non-empty string")
+    return value
+
+
+def _env_ids(env: ManagerBasedRlEnv, env_ids: np.ndarray | slice | None) -> np.ndarray:
+    if env_ids is None:
+        return np.arange(env.num_envs, dtype=np.int32)
+    if isinstance(env_ids, slice):
+        return np.arange(env.num_envs, dtype=np.int32)[env_ids]
+    return np.asarray(env_ids, dtype=np.int32)
+
+
+@dataclass(kw_only=True)
+class FootstandIncrementalActionCfg(ActionTermCfg):
+    """Incremental position action in the historical actuator/policy order."""
+
+    actuator_names: tuple[str, ...] | list[str]
+    joint_names: tuple[str, ...] | list[str]
+    joint_position_limits: tuple[tuple[float, float], ...] | list[list[float]]
+    action_scale: float = 0.3
+    clip_actions: float = 1.0
+    kp: float = 35.0
+    kd: float = 0.5
+    simulate_action_latency: bool = False
+
+    def build(self, env: ManagerBasedRlEnv) -> FootstandIncrementalAction:
+        return FootstandIncrementalAction(self, env)
+
+
+class FootstandIncrementalAction(ActionTerm):
+    """Integrate clipped policy deltas and write position targets each substep."""
+
+    cfg: FootstandIncrementalActionCfg
+    _entity: Entity
+
+    def __init__(self, cfg: FootstandIncrementalActionCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        term = type(self).__name__
+        if cfg.clip is not None:
+            raise NotImplementedError(f"{term} does not support actuator-name clip")
+        for field_name, patterns in (
+            ("actuator_names", cfg.actuator_names),
+            ("joint_names", cfg.joint_names),
+        ):
+            if isinstance(patterns, (str, bytes)) or not isinstance(patterns, (tuple, list)):
+                raise TypeError(f"{term} {field_name} must be an ordered sequence of patterns")
+            if len(patterns) != NUM_ACTIONS:
+                raise ValueError(f"{term} requires {NUM_ACTIONS} ordered {field_name} patterns")
+        self._scale = _real(term, "action_scale", cfg.action_scale, minimum=0.0)
+        self._clip_actions = _real(
+            term, "clip_actions", cfg.clip_actions, minimum=0.0, strict_minimum=True
+        )
+        self._kp = _real(term, "kp", cfg.kp, minimum=0.0)
+        self._kd = _real(term, "kd", cfg.kd, minimum=0.0)
+        if not isinstance(cfg.simulate_action_latency, bool):
+            raise TypeError(f"{term} simulate_action_latency must be bool")
+
+        actuator_ids: list[int] = []
+        joint_ids: list[int] = []
+        actuator_names: list[str] = []
+        joint_names: list[str] = []
+        for actuator_pattern, joint_pattern in zip(
+            cfg.actuator_names, cfg.joint_names, strict=True
+        ):
+            if not isinstance(actuator_pattern, str) or not actuator_pattern:
+                raise ValueError(f"{term} actuator patterns must be non-empty strings")
+            if not isinstance(joint_pattern, str) or not joint_pattern:
+                raise ValueError(f"{term} joint patterns must be non-empty strings")
+            found_actuator_ids, found_actuator_names = self._entity.find_actuators(
+                (actuator_pattern,), preserve_order=True
+            )
+            found_joint_ids, found_joint_names = self._entity.find_joints(
+                (joint_pattern,), preserve_order=True
+            )
+            if len(found_actuator_ids) != 1 or len(found_joint_ids) != 1:
+                raise ValueError(
+                    f"{term} patterns actuator={actuator_pattern!r}, joint={joint_pattern!r} "
+                    "must each resolve exactly once; "
+                    f"got actuators={found_actuator_names}, joints={found_joint_names}"
+                )
+            actuator_ids.append(found_actuator_ids[0])
+            joint_ids.append(found_joint_ids[0])
+            actuator_names.extend(found_actuator_names)
+            joint_names.extend(found_joint_names)
+        if len(set(actuator_ids)) != NUM_ACTIONS or len(set(joint_ids)) != NUM_ACTIONS:
+            raise ValueError(f"{term} actuator-to-joint mapping must be one-to-one")
+        if set(joint_ids) != set(range(self._entity.num_joints)):
+            raise ValueError(f"{term} must control every declared Go2 joint exactly once")
+
+        self._actuator_ids = np.asarray(actuator_ids, dtype=np.intp)
+        self._joint_ids = np.asarray(joint_ids, dtype=np.intp)
+        self._actuator_ids.setflags(write=False)
+        self._joint_ids.setflags(write=False)
+        self._actuator_names = tuple(actuator_names)
+        self._joint_names = tuple(joint_names)
+
+        try:
+            selected_ranges = np.asarray(cfg.joint_position_limits, dtype=get_global_dtype())
+        except (TypeError, ValueError) as exc:
+            raise TypeError(f"{term} joint_position_limits must be numeric") from exc
+        if selected_ranges.shape != (NUM_ACTIONS, 2):
+            raise ValueError(f"{term} joint_position_limits must have shape ({NUM_ACTIONS}, 2)")
+        if not np.isfinite(selected_ranges).all():
+            raise ValueError(f"{term} joint_position_limits must be finite")
+        if np.any(selected_ranges[:, 0] >= selected_ranges[:, 1]):
+            raise ValueError(f"{term} joint_position_limits must have lower < upper")
+        self._target_lower = np.asarray(selected_ranges[:, 0], dtype=get_global_dtype())
+        self._target_upper = np.asarray(selected_ranges[:, 1], dtype=get_global_dtype())
+        self._joint_lower = np.empty((NUM_ACTIONS,), dtype=get_global_dtype())
+        self._joint_upper = np.empty_like(self._joint_lower)
+        self._joint_lower[self._joint_ids] = self._target_lower
+        self._joint_upper[self._joint_ids] = self._target_upper
+
+        dtype = get_global_dtype()
+        shape = (env.num_envs, NUM_ACTIONS)
+        self._raw_action = np.zeros(shape, dtype=dtype)
+        self._previous_raw_action = np.zeros_like(self._raw_action)
+        self._target = np.asarray(
+            self._entity.data.joint_pos[:, self._joint_ids], dtype=dtype
+        ).copy()
+        self._state = FootstandState(cast("_FootstandEnv", env), self)
+
+    @property
+    def action_dim(self) -> int:
+        return NUM_ACTIONS
+
+    @property
+    def raw_action(self) -> np.ndarray:
+        return self._raw_action
+
+    @property
+    def previous_raw_action(self) -> np.ndarray:
+        return self._previous_raw_action
+
+    @property
+    def target(self) -> np.ndarray:
+        return self._target
+
+    @property
+    def joint_ids(self) -> np.ndarray:
+        return self._joint_ids
+
+    @property
+    def joint_names(self) -> tuple[str, ...]:
+        return self._joint_names
+
+    @property
+    def actuator_names(self) -> tuple[str, ...]:
+        return self._actuator_names
+
+    @property
+    def joint_lower(self) -> np.ndarray:
+        return self._joint_lower
+
+    @property
+    def joint_upper(self) -> np.ndarray:
+        return self._joint_upper
+
+    @property
+    def state(self) -> FootstandState:
+        return self._state
+
+    @property
+    def entity(self) -> Entity:
+        return self._entity
+
+    @property
+    def estimated_torque(self) -> np.ndarray:
+        return self._state.torques
+
+    def process_actions(self, actions: np.ndarray) -> None:
+        if not isinstance(actions, np.ndarray):
+            raise TypeError(f"expected np.ndarray actions, got {type(actions).__name__}")
+        if actions.shape != self._raw_action.shape:
+            raise ValueError(f"expected action shape {self._raw_action.shape}, got {actions.shape}")
+        if not np.isfinite(actions).all():
+            raise ValueError("received NaN or Inf actions")
+        self._previous_raw_action[:] = self._raw_action
+        np.clip(actions, -self._clip_actions, self._clip_actions, out=self._raw_action)
+        executed = (
+            self._previous_raw_action if self.cfg.simulate_action_latency else self._raw_action
+        )
+        self._target += self._scale * executed
+        np.clip(self._target, self._target_lower, self._target_upper, out=self._target)
+
+    def apply_actions(self) -> None:
+        self._entity.set_joint_position_target(self._target, joint_ids=self._joint_ids)
+
+    def estimate_torque(
+        self, joint_pos: np.ndarray, joint_vel: np.ndarray, out: np.ndarray
+    ) -> None:
+        out.fill(0.0)
+        selected_pos = joint_pos[:, self._joint_ids]
+        selected_vel = joint_vel[:, self._joint_ids]
+        out[:, self._joint_ids] = self._kp * (self._target - selected_pos) - self._kd * selected_vel
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        ids = _env_ids(self._env, env_ids)
+        self._raw_action[ids] = 0.0
+        self._previous_raw_action[ids] = 0.0
+        joint_pos = self._entity.data.joint_pos
+        self._target[ids] = joint_pos[ids][:, self._joint_ids]
+        self._state.reset(ids)
+
+
+class FootstandState:
+    """One per-control-step snapshot shared by termination, reward, and observations."""
+
+    def __init__(self, env: _FootstandEnv, action: FootstandIncrementalAction):
+        self._env = env
+        self._action = action
+        self._entity = action.entity
+        names = tuple(name for name, _ in _SENSOR_SPECS)
+        self._sensor_view: ManagerSensorView = env.scene.bind_sensor_data(names)
+        expected_dims = tuple(width for _, width in _SENSOR_SPECS)
+        if self._sensor_view.dimensions != expected_dims:
+            raise ValueError(
+                "Footstand named-sensor dimensions differ from the task contract: "
+                f"expected={expected_dims}, got={self._sensor_view.dimensions}"
+            )
+        offsets = np.cumsum((0, *expected_dims), dtype=np.intp)
+        self._sensor_slices = {
+            name: slice(int(offsets[index]), int(offsets[index + 1]))
+            for index, (name, _) in enumerate(_SENSOR_SPECS)
+        }
+        tracked_ids, tracked_names = self._entity.find_bodies(
+            _TRACKED_BODY_NAMES, preserve_order=True
+        )
+        if tuple(tracked_names) != _TRACKED_BODY_NAMES:
+            raise ValueError(
+                f"Footstand tracked body order differs from the task contract: {tracked_names}"
+            )
+        self._tracked_body_ids = np.asarray(tracked_ids, dtype=np.intp)
+        self._tracked_body_ids.setflags(write=False)
+
+        dtype = get_global_dtype()
+        num_envs = env.num_envs
+        self.linvel = np.zeros((num_envs, 3), dtype=dtype)
+        self.gyro = np.zeros_like(self.linvel)
+        self.gravity = np.broadcast_to(_WORLD_GRAVITY, (num_envs, 3)).astype(dtype, copy=True)
+        self.upvector = -self.gravity.copy()
+        self.accelerometer = np.zeros_like(self.linvel)
+        self.global_angvel = np.zeros_like(self.linvel)
+        self.root_pos = np.zeros_like(self.linvel)
+        self.root_quat = np.zeros((num_envs, 4), dtype=dtype)
+        self.root_quat[:, 0] = 1.0
+        self.root_linvel_w = np.zeros_like(self.linvel)
+        self.root_angvel_w = np.zeros_like(self.linvel)
+        self.joint_pos = np.asarray(self._entity.data.default_joint_pos, dtype=dtype).copy()
+        self.joint_vel = np.zeros_like(self.joint_pos)
+        self.qacc = np.zeros_like(self.joint_pos)
+        self.torques = np.zeros_like(self.joint_pos)
+        self.height = np.zeros((num_envs,), dtype=dtype)
+        self.orientation = np.zeros((num_envs,), dtype=dtype)
+        self.foot_contact = np.zeros((num_envs, 4), dtype=np.bool_)
+        self.foot_pos = np.zeros((num_envs, 4, 3), dtype=dtype)
+        self.termination_contact = np.zeros((num_envs,), dtype=np.bool_)
+        self.penalty_contact = np.zeros((num_envs,), dtype=np.bool_)
+        self.tracked_body_pos = np.zeros((num_envs, len(_TRACKED_BODY_NAMES), 3), dtype=dtype)
+        self.rear_speed = np.zeros((num_envs, 2), dtype=dtype)
+        self.rear_anchor_drift = np.zeros((num_envs, 2), dtype=dtype)
+        self.rear_anchor_contact = np.zeros((num_envs, 2), dtype=np.bool_)
+        self._last_foot_pos = np.zeros_like(self.foot_pos)
+        self._rear_anchor_pos = np.zeros((num_envs, 2, 2), dtype=dtype)
+        self._last_counter = int(env.common_step_counter)
+
+    @property
+    def last_counter(self) -> int:
+        return self._last_counter
+
+    @property
+    def default_joint_pos(self) -> np.ndarray:
+        return self._entity.data.default_joint_pos
+
+    @property
+    def action(self) -> FootstandIncrementalAction:
+        return self._action
+
+    def _sensor(self, values: np.ndarray, name: str) -> np.ndarray:
+        return values[:, self._sensor_slices[name]]
+
+    def _capture(self) -> dict[str, np.ndarray]:
+        dtype = get_global_dtype()
+        sensors = np.asarray(self._sensor_view.read(), dtype=dtype)
+        root_quat = np.asarray(self._entity.data.root_link_quat_w, dtype=dtype)
+        gravity_w = np.broadcast_to(_WORLD_GRAVITY, (self._env.num_envs, 3))
+        gravity = np.asarray(np_quat_apply_inverse(root_quat, gravity_w), dtype=dtype)
+        forward_w = np_quat_apply(
+            root_quat, np.broadcast_to(_BODY_FORWARD, (self._env.num_envs, 3))
+        )
+        orientation = np.asarray(np.square(0.5 * forward_w[:, 2] + 0.5), dtype=dtype)
+        foot_contact = (
+            np.concatenate([self._sensor(sensors, name) for name in _FOOT_CONTACT_NAMES], axis=1)
+            > _CONTACT_THRESHOLD
+        )
+        foot_pos = np.stack([self._sensor(sensors, name) for name in _FOOT_POSITION_NAMES], axis=1)
+        termination_contact = np.any(
+            np.concatenate(
+                [self._sensor(sensors, name) for name in _TERMINATION_CONTACT_NAMES], axis=1
+            ),
+            axis=1,
+        )
+        penalty_contact = np.any(
+            np.concatenate(
+                [self._sensor(sensors, name) for name in _PENALTY_CONTACT_NAMES], axis=1
+            ),
+            axis=1,
+        )
+        return {
+            "linvel": self._sensor(sensors, "local_linvel"),
+            "gyro": self._sensor(sensors, "gyro"),
+            "gravity": gravity,
+            "upvector": self._sensor(sensors, "upvector"),
+            "accelerometer": self._sensor(sensors, "accelerometer"),
+            "global_angvel": self._sensor(sensors, "global_angvel"),
+            "root_pos": np.asarray(self._entity.data.root_link_pos_w, dtype=dtype),
+            "root_quat": root_quat,
+            "root_linvel_w": np.asarray(self._entity.data.root_link_lin_vel_w, dtype=dtype),
+            "root_angvel_w": np.asarray(self._entity.data.root_link_ang_vel_w, dtype=dtype),
+            "joint_pos": np.asarray(self._entity.data.joint_pos, dtype=dtype),
+            "joint_vel": np.asarray(self._entity.data.joint_vel, dtype=dtype),
+            "height": self._sensor(sensors, "global_position")[:, 2],
+            "orientation": orientation,
+            "foot_contact": foot_contact,
+            "foot_pos": foot_pos,
+            "termination_contact": termination_contact,
+            "penalty_contact": penalty_contact,
+            "tracked_body_pos": np.asarray(
+                self._entity.data.body_link_pos_w[:, self._tracked_body_ids], dtype=dtype
+            ),
+        }
+
+    def reset(self, env_ids: np.ndarray) -> None:
+        values = self._capture()
+        self.linvel[env_ids] = values["linvel"][env_ids]
+        self.gyro[env_ids] = values["gyro"][env_ids]
+        self.gravity[env_ids] = values["gravity"][env_ids]
+        self.upvector[env_ids] = values["upvector"][env_ids]
+        self.accelerometer[env_ids] = values["accelerometer"][env_ids]
+        self.global_angvel[env_ids] = values["global_angvel"][env_ids]
+        self.root_pos[env_ids] = values["root_pos"][env_ids]
+        self.root_quat[env_ids] = values["root_quat"][env_ids]
+        self.root_linvel_w[env_ids] = values["root_linvel_w"][env_ids]
+        self.root_angvel_w[env_ids] = values["root_angvel_w"][env_ids]
+        self.joint_pos[env_ids] = values["joint_pos"][env_ids]
+        self.joint_vel[env_ids] = values["joint_vel"][env_ids]
+        self.height[env_ids] = values["height"][env_ids]
+        self.orientation[env_ids] = values["orientation"][env_ids]
+        self.foot_contact[env_ids] = values["foot_contact"][env_ids]
+        self.foot_pos[env_ids] = values["foot_pos"][env_ids]
+        self.termination_contact[env_ids] = values["termination_contact"][env_ids]
+        self.penalty_contact[env_ids] = values["penalty_contact"][env_ids]
+        self.tracked_body_pos[env_ids] = values["tracked_body_pos"][env_ids]
+        self.qacc[env_ids] = 0.0
+        torque = np.empty_like(self.torques)
+        self._action.estimate_torque(self.joint_pos, self.joint_vel, torque)
+        self.torques[env_ids] = torque[env_ids]
+        self._last_foot_pos[env_ids] = self.foot_pos[env_ids]
+        self.rear_speed[env_ids] = 0.0
+        self._rear_anchor_pos[env_ids] = self.foot_pos[env_ids][:, _REAR_FEET, :2]
+        self.rear_anchor_contact[env_ids] = False
+        self.rear_anchor_drift[env_ids] = 0.0
+        self._last_counter = int(self._env.common_step_counter)
+
+    def snapshot(self, env: _FootstandEnv) -> FootstandState:
+        counter = int(env.common_step_counter)
+        if counter == self._last_counter:
+            return self
+        if counter != self._last_counter + 1:
+            raise RuntimeError(
+                "FootstandState missed a control-step update: "
+                f"last={self._last_counter}, current={counter}"
+            )
+        values = self._capture()
+        new_joint_vel = values["joint_vel"]
+        np.subtract(new_joint_vel, self.joint_vel, out=self.qacc)
+        self.qacc /= env.step_dt
+
+        new_foot_pos = values["foot_pos"]
+        rear_delta = new_foot_pos[:, _REAR_FEET, :2] - self._last_foot_pos[:, _REAR_FEET, :2]
+        self.rear_speed[:] = np.linalg.norm(rear_delta / env.step_dt, axis=2)
+
+        standing = (values["height"] >= _TARGET_HEIGHT * _STAND_HEIGHT_FRACTION) & (
+            values["orientation"] >= _STAND_ORIENTATION_THRESHOLD
+        )
+        anchor_contact = values["foot_contact"][:, _REAR_FEET] & standing[:, None]
+        rear_xy = new_foot_pos[:, _REAR_FEET, :2]
+        new_contact = anchor_contact & ~self.rear_anchor_contact
+        self._rear_anchor_pos[new_contact] = rear_xy[new_contact]
+        self.rear_anchor_contact[:] = anchor_contact
+        self.rear_anchor_drift[:] = np.linalg.norm(rear_xy - self._rear_anchor_pos, axis=2)
+
+        self.linvel[:] = values["linvel"]
+        self.gyro[:] = values["gyro"]
+        self.gravity[:] = values["gravity"]
+        self.upvector[:] = values["upvector"]
+        self.accelerometer[:] = values["accelerometer"]
+        self.global_angvel[:] = values["global_angvel"]
+        self.root_pos[:] = values["root_pos"]
+        self.root_quat[:] = values["root_quat"]
+        self.root_linvel_w[:] = values["root_linvel_w"]
+        self.root_angvel_w[:] = values["root_angvel_w"]
+        self.joint_pos[:] = values["joint_pos"]
+        self.joint_vel[:] = values["joint_vel"]
+        self.height[:] = values["height"]
+        self.orientation[:] = values["orientation"]
+        self.foot_contact[:] = values["foot_contact"]
+        self.foot_pos[:] = values["foot_pos"]
+        self.termination_contact[:] = values["termination_contact"]
+        self.penalty_contact[:] = values["penalty_contact"]
+        self.tracked_body_pos[:] = values["tracked_body_pos"]
+        self._action.estimate_torque(self.joint_pos, self.joint_vel, self.torques)
+        self._last_foot_pos[:] = self.foot_pos
+        self._last_counter = counter
+        return self
+
+    def frame(self, env: _FootstandEnv) -> np.ndarray:
+        self.snapshot(env)
+        return np.concatenate(
+            (
+                self.linvel,
+                self.gyro,
+                self.gravity,
+                self.joint_pos - self.default_joint_pos,
+                self.joint_vel,
+                self._action.previous_raw_action,
+            ),
+            axis=1,
+            dtype=get_global_dtype(),
+        )
+
+    def privileged(self, env: _FootstandEnv) -> np.ndarray:
+        self.snapshot(env)
+        return np.concatenate(
+            (
+                self.gyro,
+                self.accelerometer,
+                self.linvel,
+                self.global_angvel,
+                self.joint_pos,
+                self.joint_vel,
+                self.torques,
+                self.height[:, None],
+            ),
+            axis=1,
+            dtype=get_global_dtype(),
+        )
+
+
+def _action(env: _FootstandEnv, action_name: str) -> FootstandIncrementalAction:
+    name = _name("Footstand manager term", "action_name", action_name)
+    try:
+        action = env.action_manager.get_term(name)
+    except KeyError as exc:
+        raise KeyError(f"Footstand action term {name!r} is unavailable") from exc
+    if not isinstance(action, FootstandIncrementalAction):
+        raise TypeError(
+            f"Footstand action term {name!r} must be FootstandIncrementalAction, "
+            f"got {type(action).__name__}"
+        )
+    return action
+
+
+def frame_observation(env: _FootstandEnv, action_name: str) -> np.ndarray:
+    return _action(env, action_name).state.frame(env)
+
+
+def privileged_observation(env: _FootstandEnv, action_name: str) -> np.ndarray:
+    return _action(env, action_name).state.privileged(env)
+
+
+class FootstandTermination(ManagerTermBase):
+    """Aggregate the historical non-timeout termination state before rewards."""
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        task_env = cast("_FootstandEnv", env)
+        term = type(self).__name__
+        allowed = {
+            "action_name",
+            "grace_steps",
+            "height_fraction",
+            "orientation_threshold",
+            "energy_threshold",
+        }
+        unknown = sorted(set(cfg.params) - allowed)
+        if unknown:
+            raise TypeError(f"{term} received unsupported parameters: {unknown}")
+        self._state = _action(
+            task_env, _name(term, "action_name", cfg.params.get("action_name"))
+        ).state
+        grace = cfg.params.get("grace_steps")
+        if isinstance(grace, (bool, np.bool_)) or not isinstance(grace, (int, np.integer)):
+            raise TypeError(f"{term} grace_steps must be an integer")
+        if int(grace) < 0:
+            raise ValueError(f"{term} grace_steps must be non-negative")
+        self._grace_steps = int(grace)
+        self._height_fraction = _real(
+            term, "height_fraction", cfg.params.get("height_fraction"), minimum=0.0
+        )
+        self._orientation_threshold = _real(
+            term, "orientation_threshold", cfg.params.get("orientation_threshold"), minimum=0.0
+        )
+        self._energy_threshold = _real(
+            term, "energy_threshold", cfg.params.get("energy_threshold"), minimum=0.0
+        )
+        self.terminated = np.zeros(env.num_envs, dtype=np.bool_)
+        self._last_counter = int(task_env.common_step_counter)
+
+    @property
+    def state(self) -> FootstandState:
+        return self._state
+
+    @property
+    def last_counter(self) -> int:
+        return self._last_counter
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        self.terminated[_env_ids(self._env, env_ids)] = False
+        self._last_counter = int(cast("_FootstandEnv", self._env).common_step_counter)
+
+    def __call__(self, env: _FootstandEnv, **params: Any) -> np.ndarray:
+        del params
+        state = self._state.snapshot(env)
+        previous_steps = np.maximum(env.episode_length_buf - 1, 0)
+        grace_elapsed = previous_steps >= self._grace_steps
+        low_height = state.height < _TARGET_HEIGHT * self._height_fraction
+        bad_orientation = state.orientation < self._orientation_threshold
+        pose_failure = grace_elapsed & (low_height | bad_orientation)
+        energy = np.sum(np.abs(state.torques) * np.abs(state.joint_vel), axis=1)
+        energy_failure = energy > self._energy_threshold
+        upside_down = state.upvector[:, 2] < -0.25
+        self.terminated[:] = np.logical_or.reduce(
+            (state.termination_contact, upside_down, energy_failure, pose_failure)
+        )
+        self._last_counter = int(env.common_step_counter)
+        return self.terminated
+
+
+def _termination(env: _FootstandEnv, state_term_name: str) -> FootstandTermination:
+    name = _name("Footstand reward", "state_term_name", state_term_name)
+    state_term = env.termination_manager.get_term_cfg(name).func
+    if not isinstance(state_term, FootstandTermination):
+        raise TypeError(
+            f"Footstand termination term {name!r} must be FootstandTermination, "
+            f"got {type(state_term).__name__}"
+        )
+    if state_term.last_counter != int(env.common_step_counter):
+        raise RuntimeError(
+            f"Footstand termination state {name!r} was not computed for control step "
+            f"{env.common_step_counter}"
+        )
+    return state_term
+
+
+class FootstandReward(ManagerTermBase):
+    """Historical positive-clipped reward aggregate backed by one state snapshot."""
+
+    _REWARD_NAMES = frozenset(
+        {
+            "height",
+            "contact",
+            "orientation",
+            "oritentation",
+            "action_rate",
+            "termination",
+            "dof_pos_limits",
+            "torques",
+            "pose",
+            "penalty_contact",
+            "tar",
+            "rear_feet_contact",
+            "both_rear_feet_contact",
+            "rear_foot_slip",
+            "rear_foot_anchor",
+            "front_feet_air",
+            "balanced_footstand",
+            "rear_leg_symmetry",
+            "rear_leg_splay",
+            "front_leg_motion",
+            "front_feet_crossing",
+            "front_leg_crossing",
+            "upright_stability",
+            "knee_clearance",
+            "stay_still",
+            "energy",
+            "dof_acc",
+        }
+    )
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        task_env = cast("_FootstandEnv", env)
+        term = type(self).__name__
+        allowed = {
+            "state_term_name",
+            "scales",
+            "soft_joint_pos_limit_factor",
+            "knee_height_target",
+            "front_feet_min_separation",
+            "front_feet_side_margin",
+            "rear_hip_abduction_margin",
+            "rear_foot_slip_deadband",
+            "rear_foot_anchor_radius",
+        }
+        unknown = sorted(set(cfg.params) - allowed)
+        if unknown:
+            raise TypeError(f"{term} received unsupported parameters: {unknown}")
+        self._state_term_name = _name(term, "state_term_name", cfg.params.get("state_term_name"))
+        scales = cfg.params.get("scales")
+        if not isinstance(scales, dict) or not scales:
+            raise TypeError(f"{term} scales must be a non-empty mapping")
+        unknown_rewards = sorted(set(scales) - self._REWARD_NAMES)
+        if unknown_rewards:
+            raise ValueError(f"{term} scales contains unknown rewards: {unknown_rewards}")
+        self._scales = {
+            name: _real(term, f"scales.{name}", value) for name, value in scales.items()
+        }
+        self._soft_limit_factor = _real(
+            term,
+            "soft_joint_pos_limit_factor",
+            cfg.params.get("soft_joint_pos_limit_factor"),
+            minimum=0.0,
+        )
+        self._knee_height_target = _real(
+            term, "knee_height_target", cfg.params.get("knee_height_target"), minimum=0.0
+        )
+        self._front_min_separation = _real(
+            term,
+            "front_feet_min_separation",
+            cfg.params.get("front_feet_min_separation"),
+            minimum=0.0,
+        )
+        self._front_side_margin = _real(
+            term,
+            "front_feet_side_margin",
+            cfg.params.get("front_feet_side_margin"),
+            minimum=0.0,
+        )
+        self._rear_hip_margin = _real(
+            term,
+            "rear_hip_abduction_margin",
+            cfg.params.get("rear_hip_abduction_margin"),
+            minimum=0.0,
+        )
+        self._rear_slip_deadband = _real(
+            term,
+            "rear_foot_slip_deadband",
+            cfg.params.get("rear_foot_slip_deadband"),
+            minimum=0.0,
+        )
+        self._rear_anchor_radius = _real(
+            term,
+            "rear_foot_anchor_radius",
+            cfg.params.get("rear_foot_anchor_radius"),
+            minimum=0.0,
+            strict_minimum=True,
+        )
+        state_term = _termination(task_env, self._state_term_name)
+        action = state_term.state.action
+        centers = (action.joint_lower + action.joint_upper) / 2.0
+        widths = action.joint_upper - action.joint_lower
+        self._soft_lower = centers - 0.5 * widths * self._soft_limit_factor
+        self._soft_upper = centers + 0.5 * widths * self._soft_limit_factor
+
+    @staticmethod
+    def _standing(state: FootstandState) -> np.ndarray:
+        return (
+            (state.height >= _TARGET_HEIGHT * _STAND_HEIGHT_FRACTION)
+            & (state.orientation >= _STAND_ORIENTATION_THRESHOLD)
+        ).astype(get_global_dtype(), copy=False)
+
+    def _value(
+        self,
+        name: str,
+        state_term: FootstandTermination,
+        state: FootstandState,
+    ) -> np.ndarray:
+        dtype = get_global_dtype()
+        standing = self._standing(state)
+        default = state.default_joint_pos
+        if name == "height":
+            return np.asarray(np.exp(-np.abs(_TARGET_HEIGHT - state.height) / 0.1), dtype=dtype)
+        if name == "contact":
+            return np.any(state.foot_contact[:, _FRONT_FEET], axis=1).astype(dtype)
+        if name in ("orientation", "oritentation"):
+            return state.orientation
+        if name == "action_rate":
+            action = state.action
+            return np.sum(np.square(action.raw_action - action.previous_raw_action), axis=1)
+        if name == "termination":
+            return state_term.terminated.astype(dtype)
+        if name == "dof_pos_limits":
+            below = np.clip(self._soft_lower - state.joint_pos, 0.0, None)
+            above = np.clip(state.joint_pos - self._soft_upper, 0.0, None)
+            return np.sum(below + above, axis=1)
+        if name == "torques":
+            return np.sum(np.square(state.torques), axis=1)
+        if name == "pose":
+            return np.sum(
+                np.square(state.joint_pos[:, _REAR_LEGS] - default[:, _REAR_LEGS]), axis=1
+            )
+        if name == "penalty_contact":
+            return state.penalty_contact.astype(dtype)
+        if name == "tar":
+            error = np.sum(np.square(state.joint_pos[:, _FRONT_LEGS] - _FRONT_LEG_TARGET), axis=1)
+            height_mask = (state.height >= _TARGET_HEIGHT * _STAND_HEIGHT_FRACTION).astype(dtype)
+            return np.asarray(np.exp(-error) * height_mask, dtype=dtype)
+        if name == "rear_feet_contact":
+            return np.mean(state.foot_contact[:, _REAR_FEET], axis=1, dtype=dtype)
+        if name == "both_rear_feet_contact":
+            return np.all(state.foot_contact[:, _REAR_FEET], axis=1).astype(dtype)
+        if name == "rear_foot_slip":
+            slip = np.square(np.clip(state.rear_speed - self._rear_slip_deadband, 0.0, None))
+            slip *= state.foot_contact[:, _REAR_FEET]
+            return np.mean(slip, axis=1, dtype=dtype)
+        if name == "rear_foot_anchor":
+            drift = np.square(
+                np.clip(state.rear_anchor_drift - self._rear_anchor_radius, 0.0, None)
+                / self._rear_anchor_radius
+            )
+            drift *= state.rear_anchor_contact
+            return np.mean(drift, axis=1, dtype=dtype)
+        if name == "front_feet_air":
+            return (~np.any(state.foot_contact[:, _FRONT_FEET], axis=1)).astype(dtype)
+        if name == "balanced_footstand":
+            support = np.all(state.foot_contact[:, _REAR_FEET], axis=1)
+            support &= ~np.any(state.foot_contact[:, _FRONT_FEET], axis=1)
+            return support.astype(dtype) * standing
+        if name == "rear_leg_symmetry":
+            mirrored = state.joint_pos[:, _REAR_RIGHT] * _REAR_MIRROR_SIGNS
+            cost = np.mean(np.square(state.joint_pos[:, _REAR_LEFT] - mirrored), axis=1)
+            return cost * (1.0 - standing)
+        if name == "rear_leg_splay":
+            error = state.joint_pos[:, _REAR_HIPS] - default[:, _REAR_HIPS]
+            splay = np.clip(np.abs(error) - self._rear_hip_margin, 0.0, None)
+            return np.mean(np.square(splay), axis=1) * standing
+        if name == "front_leg_motion":
+            return np.mean(np.square(state.joint_vel[:, _FRONT_LEGS]), axis=1) * standing
+        if name in ("front_feet_crossing", "front_leg_crossing"):
+            return self._front_crossing(state)
+        if name == "upright_stability":
+            cost = np.sum(np.square(state.root_linvel_w), axis=1)
+            cost += 0.25 * np.sum(np.square(state.root_angvel_w), axis=1)
+            return cost * standing
+        if name == "knee_clearance":
+            target = max(self._knee_height_target, 1.0e-6)
+            height = state.tracked_body_pos[:, _KNEE_BODY_INDICES, 2]
+            return np.mean(np.square(np.clip(target - height, 0.0, None) / target), axis=1)
+        if name == "stay_still":
+            return np.sum(np.square(state.root_linvel_w[:, :2]), axis=1) + np.square(
+                state.root_angvel_w[:, 2]
+            )
+        if name == "energy":
+            return np.sum(np.abs(state.joint_vel) * np.abs(state.torques), axis=1)
+        if name == "dof_acc":
+            return np.sum(np.square(state.qacc), axis=1)
+        raise RuntimeError(f"Footstand reward dispatch is incomplete for {name!r}")
+
+    def _front_crossing(self, state: FootstandState) -> np.ndarray:
+        left = np.concatenate(
+            (
+                state.foot_pos[:, [0], :],
+                state.tracked_body_pos[:, _FRONT_LEFT_BODY_INDICES, :],
+            ),
+            axis=1,
+        )
+        right = np.concatenate(
+            (
+                state.foot_pos[:, [1], :],
+                state.tracked_body_pos[:, _FRONT_RIGHT_BODY_INDICES, :],
+            ),
+            axis=1,
+        )
+        points = np.concatenate((left, right), axis=1)
+        relative = (points - state.root_pos[:, None, :]).reshape(-1, 3)
+        quaternions = np.repeat(state.root_quat, points.shape[1], axis=0)
+        body_points = np_quat_apply_inverse(quaternions, relative).reshape(
+            state.root_pos.shape[0], points.shape[1], 3
+        )
+        left_y = body_points[:, : left.shape[1], 1]
+        right_y = body_points[:, left.shape[1] :, 1]
+        left_error = np.clip(self._front_side_margin - left_y, 0.0, None)
+        right_error = np.clip(right_y + self._front_side_margin, 0.0, None)
+        separation_error = np.clip(self._front_min_separation - (left_y - right_y), 0.0, None)
+        return np.mean(
+            np.square(left_error) + np.square(right_error) + np.square(separation_error), axis=1
+        )
+
+    def __call__(self, env: _FootstandEnv, **params: Any) -> np.ndarray:
+        del params
+        state_term = _termination(env, self._state_term_name)
+        state = state_term.state
+        reward = np.zeros((env.num_envs,), dtype=get_global_dtype())
+        for name, scale in self._scales.items():
+            if scale != 0.0:
+                reward += scale * self._value(name, state_term, state)
+        max_rate = 10000.0 / env.step_dt
+        return np.clip(reward, 0.0, max_rate)
+
+
+class FootstandJointReset(ManagerTermBase):
+    """Reset all Go2 joints to the home pose plus a uniform offset."""
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        term = type(self).__name__
+        if set(cfg.params) != {"asset_cfg", "position_offset_range"}:
+            raise ValueError(
+                f"{term} requires exactly asset_cfg and position_offset_range parameters"
+            )
+        asset_cfg = cfg.params["asset_cfg"]
+        if not isinstance(asset_cfg, SceneEntityCfg):
+            raise TypeError(f"{term} asset_cfg must be SceneEntityCfg")
+        self._entity = cast("Entity", env.scene[asset_cfg.name])
+        self._joint_ids = asset_cfg.joint_ids
+        selected = self._entity.data.default_joint_pos[:, self._joint_ids]
+        if selected.shape != (env.num_envs, NUM_ACTIONS):
+            raise ValueError(f"{term} requires exactly {NUM_ACTIONS} selected joints")
+        self._range = _pair(term, "position_offset_range", cfg.params["position_offset_range"])
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        env_ids: np.ndarray | None,
+        **params: Any,
+    ) -> None:
+        del params
+        ids = _env_ids(env, env_ids)
+        position = np.array(self._entity.data.default_joint_pos[ids][:, self._joint_ids], copy=True)
+        position += env.rng.uniform(*self._range, size=position.shape)
+        velocity = np.array(self._entity.data.default_joint_vel[ids][:, self._joint_ids], copy=True)
+        self._entity.write_joint_state_to_sim(
+            np.asarray(position, dtype=get_global_dtype()),
+            np.asarray(velocity, dtype=get_global_dtype()),
+            joint_ids=self._joint_ids,
+            env_ids=ids,
+        )
+
+
+class FootstandMassRandomization(ManagerTermBase):
+    """Compose all-link mass scaling and torso additive mass in one reset write."""
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        term = type(self).__name__
+        allowed = {
+            "asset_cfg",
+            "torso_body_name",
+            "link_mass_scale_range",
+            "torso_added_mass_range",
+        }
+        if set(cfg.params) != allowed:
+            raise ValueError(f"{term} requires parameters {sorted(allowed)}")
+        asset_cfg = cfg.params["asset_cfg"]
+        if not isinstance(asset_cfg, SceneEntityCfg):
+            raise TypeError(f"{term} asset_cfg must be SceneEntityCfg")
+        self._entity = cast("Entity", env.scene[asset_cfg.name])
+        self._body_ids, self._default_mass = self._entity.bind_body_mass_write(
+            asset_cfg.body_ids, term_name="footstand_mass"
+        )
+        torso_name = _name(term, "torso_body_name", cfg.params["torso_body_name"])
+        torso_ids, _ = self._entity.find_bodies((torso_name,))
+        if len(torso_ids) != 1:
+            raise ValueError(f"{term} torso_body_name must resolve exactly one body")
+        selected = np.flatnonzero(self._body_ids == torso_ids[0])
+        if selected.size != 1:
+            raise ValueError(f"{term} torso body must be included in asset_cfg")
+        self._torso_index = int(selected[0])
+        self._scale_range = _pair(
+            term, "link_mass_scale_range", cfg.params["link_mass_scale_range"], minimum=0.0
+        )
+        self._added_range = _pair(
+            term, "torso_added_mass_range", cfg.params["torso_added_mass_range"]
+        )
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        env_ids: np.ndarray | None,
+        **params: Any,
+    ) -> None:
+        del params
+        ids = _env_ids(env, env_ids)
+        scale = env.rng.uniform(*self._scale_range, size=(ids.size, self._default_mass.size))
+        mass = self._default_mass[None, :] * scale
+        mass[:, self._torso_index] += env.rng.uniform(*self._added_range, size=ids.size)
+        if np.any(mass <= 0.0):
+            raise ValueError("FootstandMassRandomization produced a non-positive body mass")
+        self._entity.write_body_mass_to_sim(
+            mass,
+            body_ids=self._body_ids,
+            env_ids=ids,
+            term_name="footstand_mass",
+        )
+
+
+registry.register_env_config("Go2FootStand", ManagerBasedRlEnvCfg)
+registry.register_env("Go2FootStand", make_manager_based_rl_env, sim_backend="mujoco")
+registry.register_env("Go2FootStand", make_manager_based_rl_env, sim_backend="motrix")
+registry.register_env("Go2FootStand", make_manager_based_rl_env, sim_backend="drake")
+
+
+__all__ = [
+    "FRAME_OBS_DIM",
+    "NUM_ACTIONS",
+    "PRIVILEGED_OBS_DIM",
+    "FootstandIncrementalAction",
+    "FootstandIncrementalActionCfg",
+    "FootstandJointReset",
+    "FootstandMassRandomization",
+    "FootstandReward",
+    "FootstandState",
+    "FootstandTermination",
+    "frame_observation",
+    "privileged_observation",
+]
diff --git a/src/unilab/tasks/locomotion/go2_arm/__init__.py b/src/unilab/tasks/locomotion/go2_arm/__init__.py
new file mode 100644
index 000000000..090283ecc
--- /dev/null
+++ b/src/unilab/tasks/locomotion/go2_arm/__init__.py
@@ -0,0 +1,3 @@
+from .manip_loco import Go2ArmManipLocoCfg, Go2ArmManipLocoEnv
+
+__all__ = ["Go2ArmManipLocoCfg", "Go2ArmManipLocoEnv"]
diff --git a/src/unilab/tasks/locomotion/go2_arm/base.py b/src/unilab/tasks/locomotion/go2_arm/base.py
new file mode 100644
index 000000000..1055ea9ec
--- /dev/null
+++ b/src/unilab/tasks/locomotion/go2_arm/base.py
@@ -0,0 +1,238 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+import numpy as np
+from unisim import SimBackend
+
+from unilab.tasks.locomotion.common.base import (
+    ControlConfigBase,
+    LocomotionBaseCfg,
+    LocomotionBaseEnv,
+    Sensor,
+)
+from unilab.utils.geometry import np_quat_orientation_error_local
+from unilab.utils.rotation import np_matrix_from_quat
+
+
+@dataclass
+class NoiseConfig:
+    level: float = 0.0
+    scale_joint_angle: float = 0.03
+    scale_joint_vel: float = 0.5
+    scale_gyro: float = 0.2
+    scale_gravity: float = 0.05
+    scale_linvel: float = 0.1
+    scale_ee_pos: float = 0.02
+
+
+@dataclass
+class ControlConfig(ControlConfigBase):
+    Kp: float = 35.0
+    Kd: float = 0.5
+    leg_kp: float | list[float] | None = 60.0
+    leg_kd: float | list[float] | None = 2.0
+    arm_kp: float | list[float] | None = field(
+        default_factory=lambda: [95.0, 115.0, 100.0, 52.0, 54.0, 55.0]
+    )
+    arm_kd: float | list[float] | None = field(
+        default_factory=lambda: [3.5, 3.8, 2.5, 1.5, 1.5, 1.5]
+    )
+    # Arm residual action scale, independent from leg action_scale. IK provides
+    # the primary control, so policy output stays small early in training.
+    arm_action_scale: float = 0.0
+
+
+@dataclass
+class IKConfig:
+    damping: float = 0.05
+    gain: float = 1.0
+    dq_clip: float = 0.2
+    use_orientation: bool = False
+    # Used only when use_orientation is true:
+    # - target: track goal_local_quat against curr_local_quat.
+    # - zero_error: include the rotational Jacobian with zero orientation error,
+    #   matching go2_arx's position IK with orientation-change regularization.
+    orientation_mode: str = "target"
+
+
+@dataclass
+class Asset:
+    base_name: str = "base"
+    foot_name: str = "foot"
+    ground: str = "floor"
+    ee_site_name: str = "endpoint"
+    ee_body_name: str = "link6"
+    arm_joint_names: tuple[str, ...] = (
+        "joint1",
+        "joint2",
+        "joint3",
+        "joint4",
+        "joint5",
+        "joint6",
+    )
+
+
+@dataclass
+class Go2ArmSensor(Sensor):
+    feet_force: list[str] = field(
+        default_factory=lambda: [
+            "FL_foot_contact",
+            "FR_foot_contact",
+            "RL_foot_contact",
+            "RR_foot_contact",
+        ]
+    )
+    feet_pos: list[str] = field(default_factory=lambda: ["FL_pos", "FR_pos", "RL_pos", "RR_pos"])
+    ee_local_pos: str = "endpoint_pos"
+    ee_local_quat: str = "endpoint_quat"
+    ee_local_vel: str = "endpoint_vel"
+    ee_relative_pos: str = "endpoint_relative_pos"
+    ee_relative_quat: str = "endpoint_relative_quat"
+    arm_ref_world_quat: str = "armbasepoint_world_quat"
+
+
+@dataclass
+class Go2ArmBaseCfg(LocomotionBaseCfg):
+    noise_config: NoiseConfig = field(default_factory=NoiseConfig)  # type: ignore[assignment]
+    control_config: ControlConfig = field(default_factory=ControlConfig)  # type: ignore[assignment]
+    ik: IKConfig = field(default_factory=IKConfig)
+    asset: Asset = field(default_factory=Asset)
+    sensor: Go2ArmSensor = field(default_factory=Go2ArmSensor)  # type: ignore[assignment]
+    iterations: int | None = None
+    sim_dt: float = 0.01
+    ctrl_dt: float = 0.02
+
+
+def _expand_gain(
+    name: str, value: float | list[float] | None, fallback: float, size: int
+) -> np.ndarray:
+    raw_value = fallback if value is None else value
+    gain = np.asarray(raw_value, dtype=np.float64)
+    if gain.ndim == 0:
+        return np.full((size,), float(gain), dtype=np.float64)
+    if gain.shape != (size,):
+        raise ValueError(f"{name} must be a scalar or have shape ({size},), got {gain.shape}")
+    return gain
+
+
+def build_go2_arm_position_gains(cfg: ControlConfig) -> dict[str, np.ndarray]:
+    leg_kp = _expand_gain("control_config.leg_kp", cfg.leg_kp, cfg.Kp, 12)
+    leg_kd = _expand_gain("control_config.leg_kd", cfg.leg_kd, cfg.Kd, 12)
+    arm_kp = _expand_gain("control_config.arm_kp", cfg.arm_kp, cfg.Kp, 6)
+    arm_kd = _expand_gain("control_config.arm_kd", cfg.arm_kd, cfg.Kd, 6)
+    return {
+        "kp": np.concatenate([leg_kp, arm_kp]),
+        "kd": np.concatenate([leg_kd, arm_kd]),
+    }
+
+
+class Go2ArmBaseEnv(LocomotionBaseEnv):
+    _cfg: Go2ArmBaseCfg  # pyright: ignore[reportIncompatibleVariableOverride]
+
+    def __init__(self, cfg: Go2ArmBaseCfg, backend: SimBackend, num_envs: int = 1):
+        super().__init__(cfg, backend, num_envs)
+        self._ee_site_id = int(self._backend.get_site_ids([cfg.asset.ee_site_name])[0])
+        self._arm_jacobian_dof_indices = self._backend.get_joint_dof_indices(
+            cfg.asset.arm_joint_names
+        )
+        self._arm_dof_pos_indices = self._backend.get_joint_dof_pos_indices(
+            cfg.asset.arm_joint_names
+        )
+
+    @property
+    def arm_dof_pos_indices(self) -> np.ndarray:
+        return self._arm_dof_pos_indices
+
+    @property
+    def arm_jacobian_dof_indices(self) -> np.ndarray:
+        return self._arm_jacobian_dof_indices
+
+    def _obs_noise(self, data: np.ndarray, scale: float) -> np.ndarray:
+        """Apply per-step uniform observation noise scaled by ``noise_config.level``."""
+        noise_cfg = self._cfg.noise_config
+        if noise_cfg.level > 0.0:
+            return data + (
+                np.random.uniform(-1.0, 1.0, data.shape).astype(data.dtype)
+                * noise_cfg.level
+                * scale
+            )
+        return data
+
+    def get_foot_pos(self) -> np.ndarray:
+        """Get foot positions. Returns shape (num_envs, 4, 3)."""
+        foot_pos = [self._backend.get_sensor_data(name) for name in self._cfg.sensor.feet_pos]
+        return np.stack(foot_pos, axis=1)
+
+    def get_foot_contact(self) -> np.ndarray:
+        """Get foot contact values. Returns shape (num_envs, 4)."""
+        contacts = [
+            self._backend.get_sensor_data(name)[:, 0] for name in self._cfg.sensor.feet_force
+        ]
+        return np.stack(contacts, axis=1)
+
+    def get_ee_local_pose(self) -> tuple[np.ndarray, np.ndarray]:
+        """Return end-effector pose expressed in the arm reference frame."""
+        pos = self._backend.get_sensor_data(self._cfg.sensor.ee_local_pos)
+        quat = self._backend.get_sensor_data(self._cfg.sensor.ee_local_quat)
+        return pos, quat
+
+    def get_arm_dof_pos(self) -> np.ndarray:
+        return self.get_dof_pos()[:, self._arm_dof_pos_indices]
+
+    def compute_arm_ik_delta(
+        self,
+        goal_local_pos: np.ndarray,
+        curr_local_pos: np.ndarray,
+        goal_local_quat: np.ndarray | None = None,
+        curr_local_quat: np.ndarray | None = None,
+    ) -> np.ndarray:
+        """Compute damped least-squares IK delta for the 6 arm joints.
+
+        Position and optional orientation errors are expressed in the arm
+        reference frame (``armbasepoint``). MuJoCo provides world-frame site
+        Jacobians, so both translational and rotational blocks are rotated into
+        that same frame before solving.
+        """
+        cfg = self._cfg.ik
+        pos_err = np.asarray(goal_local_pos - curr_local_pos)
+        jacp_w, jacr_w = self._backend.get_site_jacobian_w(
+            self._ee_site_id,
+            self._arm_jacobian_dof_indices,
+        )
+        ref_rot_w = np_matrix_from_quat(
+            self._backend.get_sensor_data(self._cfg.sensor.arm_ref_world_quat)
+        )
+        rot_w_to_b = np.swapaxes(ref_rot_w, 1, 2)
+        jacp_b = np.matmul(rot_w_to_b, jacp_w)
+
+        if cfg.use_orientation:
+            if cfg.orientation_mode == "target":
+                if goal_local_quat is None or curr_local_quat is None:
+                    raise ValueError(
+                        "goal_local_quat and curr_local_quat are required when "
+                        "ik.use_orientation=true and ik.orientation_mode='target'"
+                    )
+                orn_err = np_quat_orientation_error_local(goal_local_quat, curr_local_quat)
+            elif cfg.orientation_mode == "zero_error":
+                orn_err = np.zeros_like(pos_err)
+            else:
+                raise ValueError(
+                    "ik.orientation_mode must be one of {'target', 'zero_error'}, "
+                    f"got {cfg.orientation_mode!r}"
+                )
+            jacr_b = np.matmul(rot_w_to_b, jacr_w)
+            jac = np.concatenate([jacp_b, jacr_b], axis=1)
+            dpose = np.concatenate([pos_err, orn_err], axis=1)
+        else:
+            jac = jacp_b
+            dpose = pos_err
+
+        identity = np.eye(jac.shape[1], dtype=jac.dtype)[None, :, :]
+        lhs = np.matmul(jac, np.swapaxes(jac, 1, 2)) + identity * (cfg.damping**2)
+        rhs = dpose[:, :, None]
+        solved = np.linalg.solve(lhs, rhs)
+        dq = np.matmul(np.swapaxes(jac, 1, 2), solved)[:, :, 0]
+        if cfg.dq_clip > 0.0:
+            dq = np.clip(dq, -cfg.dq_clip, cfg.dq_clip)
+        return dq
diff --git a/src/unilab/envs/locomotion/go2_arm/manip_loco.py b/src/unilab/tasks/locomotion/go2_arm/manip_loco.py
similarity index 97%
rename from src/unilab/envs/locomotion/go2_arm/manip_loco.py
rename to src/unilab/tasks/locomotion/go2_arm/manip_loco.py
index 94c9b26b6..6c3501592 100644
--- a/src/unilab/envs/locomotion/go2_arm/manip_loco.py
+++ b/src/unilab/tasks/locomotion/go2_arm/manip_loco.py
@@ -4,20 +4,21 @@
 from typing import Any
 
 import numpy as np
+from unisim.dr.types import ResetPlan
 
 from unilab.assets import ASSETS_ROOT_PATH
 from unilab.base import registry
-from unilab.base.backend import create_backend, env_backend_kwargs
+from unilab.base.backend_factory import create_backend, env_backend_kwargs
 from unilab.base.np_env import NpEnvState
 from unilab.base.scene import SceneCfg
-from unilab.dr.types import ResetPlan
 from unilab.dtype_config import get_global_dtype
-from unilab.envs.locomotion.common import rewards
-from unilab.envs.locomotion.common.commands import Commands
-from unilab.envs.locomotion.common.domain_rand import DomainRandConfig
-from unilab.envs.locomotion.common.dr_provider import LocomotionDRProvider
-from unilab.envs.locomotion.common.rewards import RewardContext
-from unilab.envs.locomotion.go2_arm.base import (
+from unilab.tasks.compatibility import adapt_legacy_factory
+from unilab.tasks.locomotion.common import rewards
+from unilab.tasks.locomotion.common.commands import Commands
+from unilab.tasks.locomotion.common.domain_rand import DomainRandConfig
+from unilab.tasks.locomotion.common.dr_provider import LocomotionDRProvider
+from unilab.tasks.locomotion.common.rewards import RewardContext
+from unilab.tasks.locomotion.go2_arm.base import (
     Go2ArmBaseCfg,
     Go2ArmBaseEnv,
     Go2ArmSensor,
@@ -150,9 +151,11 @@ class ArmStageConfig:
 @registry.envcfg("Go2ArmManipLoco")
 @dataclass
 class Go2ArmManipLocoCfg(Go2ArmBaseCfg):
-    scene: SceneCfg = field(default_factory=_default_go2_arm_scene)
+    scene: SceneCfg = field(  # pyright: ignore[reportIncompatibleVariableOverride]
+        default_factory=_default_go2_arm_scene
+    )
     model_file: str = field(default_factory=_default_go2_arm_model_file)
-    max_episode_seconds: float = 20.0
+    max_episode_seconds: float = 20.0  # pyright: ignore[reportIncompatibleVariableOverride]
     init_state: InitState = field(default_factory=InitState)
     commands: CommandsConfig = field(default_factory=CommandsConfig)  # type: ignore[assignment]
     reward_config: RewardConfig | None = None
@@ -262,11 +265,8 @@ def _compute_reset_obs(
         return env._update_history(actor_raw, env_ids=env_ids, critic_raw_obs=critic_raw)  # type: ignore[no-any-return]
 
 
-@registry.env("Go2ArmManipLoco", sim_backend="motrix")
-@registry.env("Go2ArmManipLoco", sim_backend="drake")
-@registry.env("Go2ArmManipLoco", sim_backend="mujoco")
 class Go2ArmManipLocoEnv(Go2ArmBaseEnv):
-    _cfg: Go2ArmManipLocoCfg
+    _cfg: Go2ArmManipLocoCfg  # pyright: ignore[reportIncompatibleVariableOverride]
 
     def __init__(self, cfg: Go2ArmManipLocoCfg, num_envs=1, backend_type="mujoco"):
         if cfg.reward_config is None:
@@ -1025,3 +1025,15 @@ def _reward_arm_collision(self, _ctx: RewardContext) -> np.ndarray:
         for name in self._ARM_TOUCH_SENSORS:
             total += self._backend.get_sensor_data(name)[:, 0]
         return total
+
+
+_GO2_ARM_COMPAT_FACTORY = adapt_legacy_factory(
+    Go2ArmManipLocoEnv,
+    task_family="Go2ArmManipLoco",
+    reason=(
+        "custom IK/Jacobian, end-effector goals, and observation history remain "
+        "task-owned until formal Manager-Based terms exist"
+    ),
+)
+for _backend_type in ("mujoco", "motrix", "drake"):
+    registry.register_env("Go2ArmManipLoco", _GO2_ARM_COMPAT_FACTORY, _backend_type)
diff --git a/src/unilab/tasks/locomotion/go2w/__init__.py b/src/unilab/tasks/locomotion/go2w/__init__.py
new file mode 100644
index 000000000..2c908918d
--- /dev/null
+++ b/src/unilab/tasks/locomotion/go2w/__init__.py
@@ -0,0 +1,15 @@
+"""Hydra-owned Manager-Based Go2W production registrations."""
+
+from unilab.base import registry
+from unilab.envs import ManagerBasedRlEnvCfg, make_manager_based_rl_env
+
+registry.register_env_config("Go2WJoystickFlat", ManagerBasedRlEnvCfg)
+registry.register_env("Go2WJoystickFlat", make_manager_based_rl_env, sim_backend="mujoco")
+registry.register_env("Go2WJoystickFlat", make_manager_based_rl_env, sim_backend="motrix")
+registry.register_env("Go2WJoystickFlat", make_manager_based_rl_env, sim_backend="drake")
+
+registry.register_env_config("Go2WJoystickRough", ManagerBasedRlEnvCfg)
+registry.register_env("Go2WJoystickRough", make_manager_based_rl_env, sim_backend="mujoco")
+registry.register_env("Go2WJoystickRough", make_manager_based_rl_env, sim_backend="motrix")
+
+__all__: list[str] = []
diff --git a/src/unilab/tasks/locomotion/go2w/base.py b/src/unilab/tasks/locomotion/go2w/base.py
new file mode 100644
index 000000000..d4912a879
--- /dev/null
+++ b/src/unilab/tasks/locomotion/go2w/base.py
@@ -0,0 +1,61 @@
+from __future__ import annotations
+
+import numpy as np
+
+LEG_JOINT_SENSOR_PREFIXES: tuple[str, ...] = (
+    "FR_hip",
+    "FR_thigh",
+    "FR_calf",
+    "FL_hip",
+    "FL_thigh",
+    "FL_calf",
+    "RR_hip",
+    "RR_thigh",
+    "RR_calf",
+    "RL_hip",
+    "RL_thigh",
+    "RL_calf",
+)
+WHEEL_JOINT_SENSOR_PREFIXES: tuple[str, ...] = ("FR_wheel", "FL_wheel", "RR_wheel", "RL_wheel")
+JOINT_SENSOR_PREFIXES: tuple[str, ...] = LEG_JOINT_SENSOR_PREFIXES + WHEEL_JOINT_SENSOR_PREFIXES
+
+NUM_LEG_ACTIONS = len(LEG_JOINT_SENSOR_PREFIXES)
+NUM_WHEEL_ACTIONS = len(WHEEL_JOINT_SENSOR_PREFIXES)
+NUM_GO2W_ACTIONS = len(JOINT_SENSOR_PREFIXES)
+
+
+def compute_go2w_motor_ctrl(
+    policy_ctrl: np.ndarray,
+    joint_pos: np.ndarray,
+    joint_vel: np.ndarray,
+    leg_kp: np.ndarray,
+    leg_kd: np.ndarray,
+    wheel_kd: np.ndarray,
+    ctrl_lower: np.ndarray,
+    ctrl_upper: np.ndarray,
+    out: np.ndarray,
+) -> np.ndarray:
+    """Convert Go2W owner-level controls into motor actuator torques.
+
+    Hot path: shapes/dtypes are validated by the owning env at init/reset.
+    """
+    leg_out = out[:, :NUM_LEG_ACTIONS]
+    np.subtract(policy_ctrl[:, :NUM_LEG_ACTIONS], joint_pos[:, :NUM_LEG_ACTIONS], out=leg_out)
+    np.multiply(leg_out, leg_kp, out=leg_out)
+    leg_out -= leg_kd * joint_vel[:, :NUM_LEG_ACTIONS]
+    wheel_out = out[:, NUM_LEG_ACTIONS:]
+    np.subtract(policy_ctrl[:, NUM_LEG_ACTIONS:], joint_vel[:, NUM_LEG_ACTIONS:], out=wheel_out)
+    np.multiply(wheel_out, wheel_kd, out=wheel_out)
+    np.clip(out, ctrl_lower, ctrl_upper, out=out)
+    return out
+
+
+__all__ = [
+    "JOINT_SENSOR_PREFIXES",
+    "LEG_JOINT_SENSOR_PREFIXES",
+    "NUM_GO2W_ACTIONS",
+    "NUM_LEG_ACTIONS",
+    "NUM_WHEEL_ACTIONS",
+    "WHEEL_JOINT_SENSOR_PREFIXES",
+    "compute_go2w_motor_ctrl",
+]
diff --git a/src/unilab/tasks/locomotion/go2w/manager_terms.py b/src/unilab/tasks/locomotion/go2w/manager_terms.py
new file mode 100644
index 000000000..92364feda
--- /dev/null
+++ b/src/unilab/tasks/locomotion/go2w/manager_terms.py
@@ -0,0 +1,362 @@
+"""Manager-Based terms owned by the Go2W flat task."""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from numbers import Real
+from typing import TYPE_CHECKING, Any, ClassVar, cast
+
+import numpy as np
+
+from unilab.dtype_config import get_global_dtype
+from unilab.envs.mdp.commands.velocity_command import (
+    UniformVelocityCommand,
+    UniformVelocityCommandCfg,
+)
+from unilab.managers import ActionTerm, ActionTermCfg
+from unilab.managers.scene_entity_config import SceneEntityCfg
+from unilab.tasks.locomotion.go2w.base import (
+    NUM_GO2W_ACTIONS,
+    NUM_LEG_ACTIONS,
+    NUM_WHEEL_ACTIONS,
+    compute_go2w_motor_ctrl,
+)
+
+if TYPE_CHECKING:
+    from unilab.base.entity import Entity
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+_HIP_INDICES = np.asarray([0, 3, 6, 9], dtype=np.intp)
+_DEFAULT_ASSET_CFG = SceneEntityCfg("robot")
+
+
+def _real(
+    value: Any,
+    *,
+    label: str,
+    minimum: float | None = None,
+    strict_minimum: bool = False,
+) -> float:
+    if isinstance(value, (bool, np.bool_)) or not isinstance(value, Real):
+        raise TypeError(f"{label} must be a real number, got {type(value).__name__}")
+    result = float(value)
+    if not math.isfinite(result):
+        raise ValueError(f"{label} must be finite")
+    if minimum is not None and (result <= minimum if strict_minimum else result < minimum):
+        relation = "greater than" if strict_minimum else "at least"
+        raise ValueError(f"{label} must be {relation} {minimum}")
+    return result
+
+
+def _range(value: Any, *, label: str) -> tuple[float, float]:
+    if not isinstance(value, (tuple, list)) or len(value) != 2:
+        raise TypeError(f"{label} must be a two-value range")
+    lower = _real(value[0], label=f"{label} lower", minimum=0.0)
+    upper = _real(value[1], label=f"{label} upper", minimum=0.0)
+    if lower > upper:
+        raise ValueError(f"{label} lower {lower} exceeds upper {upper}")
+    return lower, upper
+
+
+@dataclass(kw_only=True)
+class Go2WMixedActionCfg(ActionTermCfg):
+    """Configure the Go2W leg-position and wheel-velocity motor action."""
+
+    actuator_names: tuple[str, ...] | list[str]
+    leg_action_scale: float = 0.25
+    hip_action_scale: float | None = None
+    wheel_action_scale: float = 10.0
+    leg_kp: float = 35.0
+    leg_kd: float = 0.5
+    wheel_kd: float = 0.5
+    clip_actions: float = 1.0
+    simulate_action_latency: bool = False
+
+    def build(self, env: ManagerBasedRlEnv) -> Go2WMixedAction:
+        return Go2WMixedAction(self, env)
+
+
+class Go2WMixedAction(ActionTerm):
+    """Convert one community action term into Go2W motor torques per substep."""
+
+    requires_substep_state_feedback: ClassVar[bool] = True
+    cfg: Go2WMixedActionCfg
+    _entity: Entity
+
+    def __init__(self, cfg: Go2WMixedActionCfg, env: ManagerBasedRlEnv):
+        self._validate_cfg(cfg)
+        super().__init__(cfg=cfg, env=env)
+        actuator_ids, actuator_names = self._entity.find_actuators(cfg.actuator_names)
+        joint_ids, joint_names = self._entity.find_joints_by_actuator_names(cfg.actuator_names)
+        if len(actuator_ids) != NUM_GO2W_ACTIONS or len(joint_ids) != NUM_GO2W_ACTIONS:
+            raise ValueError(
+                "Go2WMixedAction requires exactly "
+                f"{NUM_GO2W_ACTIONS} actuators and target joints; received "
+                f"actuators={actuator_names}, joints={joint_names}"
+            )
+        self._actuator_ids = np.asarray(actuator_ids, dtype=np.intp)
+        self._joint_ids = np.asarray(joint_ids, dtype=np.intp)
+        self._actuator_ids.setflags(write=False)
+        self._joint_ids.setflags(write=False)
+
+        dtype = get_global_dtype()
+        shape = (self.num_envs, NUM_GO2W_ACTIONS)
+        self._raw_action = np.zeros(shape, dtype=dtype)
+        self._previous_raw_action = np.zeros_like(self._raw_action)
+        self._processed_action = np.zeros_like(self._raw_action)
+        self._motor_torque = np.zeros_like(self._raw_action)
+
+        self._leg_action_scale = np.full(
+            (NUM_LEG_ACTIONS,), float(cfg.leg_action_scale), dtype=dtype
+        )
+        if cfg.hip_action_scale is not None:
+            self._leg_action_scale[_HIP_INDICES] = float(cfg.hip_action_scale)
+        self._base_leg_kp = np.full((NUM_LEG_ACTIONS,), float(cfg.leg_kp), dtype=dtype)
+        self._base_leg_kd = np.full((NUM_LEG_ACTIONS,), float(cfg.leg_kd), dtype=dtype)
+        self._leg_kp = np.broadcast_to(self._base_leg_kp, (self.num_envs, NUM_LEG_ACTIONS)).copy()
+        self._leg_kd = np.broadcast_to(self._base_leg_kd, (self.num_envs, NUM_LEG_ACTIONS)).copy()
+        self._wheel_kd = np.full(
+            (self.num_envs, NUM_WHEEL_ACTIONS), float(cfg.wheel_kd), dtype=dtype
+        )
+
+        ctrl_range = self._entity.data.actuator_ctrl_range[self._actuator_ids]
+        expected_range_shape = (NUM_GO2W_ACTIONS, 2)
+        if ctrl_range.shape != expected_range_shape:
+            raise ValueError(
+                "Go2WMixedAction actuator control range must have shape "
+                f"{expected_range_shape}, got {ctrl_range.shape}"
+            )
+        self._ctrl_lower = np.asarray(ctrl_range[:, 0], dtype=dtype)
+        self._ctrl_upper = np.asarray(ctrl_range[:, 1], dtype=dtype)
+
+    @staticmethod
+    def _validate_cfg(cfg: Go2WMixedActionCfg) -> None:
+        if not isinstance(cfg.simulate_action_latency, bool):
+            raise TypeError("Go2WMixedActionCfg simulate_action_latency must be bool")
+        for name, value in (
+            ("leg_action_scale", cfg.leg_action_scale),
+            ("wheel_action_scale", cfg.wheel_action_scale),
+            ("leg_kp", cfg.leg_kp),
+            ("leg_kd", cfg.leg_kd),
+            ("wheel_kd", cfg.wheel_kd),
+            ("clip_actions", cfg.clip_actions),
+        ):
+            _real(
+                value,
+                label=f"Go2WMixedActionCfg {name}",
+                minimum=0.0,
+                strict_minimum=name == "clip_actions",
+            )
+        if cfg.hip_action_scale is not None:
+            _real(
+                cfg.hip_action_scale,
+                label="Go2WMixedActionCfg hip_action_scale",
+                minimum=0.0,
+            )
+
+    @property
+    def action_dim(self) -> int:
+        return NUM_GO2W_ACTIONS
+
+    @property
+    def raw_action(self) -> np.ndarray:
+        """Clipped policy action, matching the legacy observable action buffer."""
+        return self._raw_action
+
+    @property
+    def previous_raw_action(self) -> np.ndarray:
+        return self._previous_raw_action
+
+    @property
+    def processed_action(self) -> np.ndarray:
+        return self._processed_action
+
+    @property
+    def motor_torque(self) -> np.ndarray:
+        return self._motor_torque
+
+    @property
+    def leg_kp(self) -> np.ndarray:
+        return self._leg_kp
+
+    @property
+    def leg_kd(self) -> np.ndarray:
+        return self._leg_kd
+
+    def process_actions(self, actions: np.ndarray) -> None:
+        if not isinstance(actions, np.ndarray):
+            raise TypeError(f"Go2WMixedAction expected np.ndarray, got {type(actions).__name__}")
+        if actions.shape != self._raw_action.shape:
+            raise ValueError(
+                f"Go2WMixedAction expected action shape {self._raw_action.shape}, "
+                f"got {actions.shape}"
+            )
+        if not np.isfinite(actions).all():
+            raise ValueError("Go2WMixedAction received NaN or Inf actions")
+
+        self._previous_raw_action[:] = self._raw_action
+        np.clip(
+            actions,
+            -float(self.cfg.clip_actions),
+            float(self.cfg.clip_actions),
+            out=self._raw_action,
+        )
+        executed = (
+            self._previous_raw_action if self.cfg.simulate_action_latency else self._raw_action
+        )
+        np.multiply(
+            executed[:, :NUM_LEG_ACTIONS],
+            self._leg_action_scale,
+            out=self._processed_action[:, :NUM_LEG_ACTIONS],
+        )
+        self._processed_action[:, :NUM_LEG_ACTIONS] += self._entity.data.default_joint_pos[
+            :, self._joint_ids[:NUM_LEG_ACTIONS]
+        ]
+        np.multiply(
+            executed[:, NUM_LEG_ACTIONS:],
+            float(self.cfg.wheel_action_scale),
+            out=self._processed_action[:, NUM_LEG_ACTIONS:],
+        )
+
+    def apply_actions(self) -> None:
+        joint_pos = self._entity.data.joint_pos[:, self._joint_ids]
+        joint_vel = self._entity.data.joint_vel[:, self._joint_ids]
+        compute_go2w_motor_ctrl(
+            self._processed_action,
+            joint_pos,
+            joint_vel,
+            self._leg_kp,
+            self._leg_kd,
+            self._wheel_kd,
+            self._ctrl_lower,
+            self._ctrl_upper,
+            self._motor_torque,
+        )
+        self._entity.data.write_ctrl(self._motor_torque, actuator_ids=self._actuator_ids)
+
+    def set_motor_gain_multipliers(
+        self,
+        env_ids: np.ndarray,
+        kp_multiplier: np.ndarray,
+        kd_multiplier: np.ndarray,
+    ) -> None:
+        expected = (len(env_ids), 1)
+        if kp_multiplier.shape != expected or kd_multiplier.shape != expected:
+            raise ValueError(
+                "Go2WMixedAction motor gain multipliers must have shape "
+                f"{expected}, got kp={kp_multiplier.shape}, kd={kd_multiplier.shape}"
+            )
+        self._leg_kp[env_ids] = self._base_leg_kp * kp_multiplier
+        self._leg_kd[env_ids] = self._base_leg_kd * kd_multiplier
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        if env_ids is None:
+            env_ids = slice(None)
+        self._raw_action[env_ids] = 0.0
+        self._previous_raw_action[env_ids] = 0.0
+        self._processed_action[env_ids] = 0.0
+        self._motor_torque[env_ids] = 0.0
+
+
+@dataclass(kw_only=True)
+class Go2WVelocityCommandCfg(UniformVelocityCommandCfg):
+    """Velocity command with the legacy Go2W planar dead zone."""
+
+    planar_dead_zone: float = 0.2
+
+    def build(self, env: ManagerBasedRlEnv) -> Go2WVelocityCommand:
+        return Go2WVelocityCommand(self, env)
+
+
+class Go2WVelocityCommand(UniformVelocityCommand):
+    cfg: Go2WVelocityCommandCfg  # pyright: ignore[reportIncompatibleVariableOverride]
+
+    def __init__(self, cfg: Go2WVelocityCommandCfg, env: ManagerBasedRlEnv):
+        self._planar_dead_zone = _real(
+            cfg.planar_dead_zone,
+            label="Go2WVelocityCommandCfg planar_dead_zone",
+            minimum=0.0,
+        )
+        super().__init__(cfg, env)
+
+    def _resample_command(self, env_ids: np.ndarray) -> None:
+        super()._resample_command(env_ids)
+        planar = self.vel_command_b[env_ids, :2]
+        moving = np.linalg.norm(planar, axis=1) > self._planar_dead_zone
+        self.vel_command_b[env_ids, :2] = planar * moving[:, None]
+
+
+def _action(env: ManagerBasedRlEnv, action_name: str) -> Go2WMixedAction:
+    if not isinstance(action_name, str) or not action_name:
+        raise ValueError("Go2W manager term action_name must be a non-empty string")
+    try:
+        term = env.action_manager.get_term(action_name)
+    except KeyError as exc:
+        raise KeyError(f"Go2W action term '{action_name}' is unavailable") from exc
+    if not isinstance(term, Go2WMixedAction):
+        raise TypeError(
+            f"Go2W action term '{action_name}' must be Go2WMixedAction, got {type(term).__name__}"
+        )
+    return term
+
+
+def randomize_motor_gains(
+    env: ManagerBasedRlEnv,
+    env_ids: np.ndarray | None,
+    action_name: str,
+    kp_multiplier_range: tuple[float, float] | list[float],
+    kd_multiplier_range: tuple[float, float] | list[float],
+) -> None:
+    """Sample owner-level motor gains without mutating backend actuator models."""
+    ids = (
+        np.arange(env.num_envs, dtype=np.int32)
+        if env_ids is None
+        else np.asarray(env_ids, dtype=np.int32)
+    )
+    kp_range = _range(kp_multiplier_range, label="randomize_motor_gains kp_multiplier_range")
+    kd_range = _range(kd_multiplier_range, label="randomize_motor_gains kd_multiplier_range")
+    shape = (len(ids), 1)
+    kp = env.rng.uniform(*kp_range, size=shape).astype(get_global_dtype(), copy=False)
+    kd = env.rng.uniform(*kd_range, size=shape).astype(get_global_dtype(), copy=False)
+    _action(env, action_name).set_motor_gain_multipliers(ids, kp, kd)
+
+
+def motor_torque(env: ManagerBasedRlEnv, action_name: str) -> np.ndarray:
+    return _action(env, action_name).motor_torque
+
+
+def motor_torque_l2(env: ManagerBasedRlEnv, action_name: str) -> np.ndarray:
+    return np.sum(np.square(motor_torque(env, action_name)), axis=1)
+
+
+def clipped_action_rate_l2(env: ManagerBasedRlEnv, action_name: str) -> np.ndarray:
+    action = _action(env, action_name)
+    return np.sum(np.square(action.raw_action - action.previous_raw_action), axis=1)
+
+
+def upward_l2(
+    env: ManagerBasedRlEnv,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    return np.square(1.0 - asset.data.projected_gravity_b[:, 2])
+
+
+def constant_alive(env: ManagerBasedRlEnv) -> np.ndarray:
+    return np.ones((env.num_envs,), dtype=get_global_dtype())
+
+
+__all__ = [
+    "Go2WMixedAction",
+    "Go2WMixedActionCfg",
+    "Go2WVelocityCommand",
+    "Go2WVelocityCommandCfg",
+    "clipped_action_rate_l2",
+    "constant_alive",
+    "motor_torque",
+    "motor_torque_l2",
+    "randomize_motor_gains",
+    "upward_l2",
+]
diff --git a/src/unilab/tasks/manipulation/__init__.py b/src/unilab/tasks/manipulation/__init__.py
new file mode 100644
index 000000000..530e1ef29
--- /dev/null
+++ b/src/unilab/tasks/manipulation/__init__.py
@@ -0,0 +1 @@
+"""Manipulation task packages."""
diff --git a/src/unilab/tasks/manipulation/allegro_inhand/__init__.py b/src/unilab/tasks/manipulation/allegro_inhand/__init__.py
new file mode 100644
index 000000000..289cb394f
--- /dev/null
+++ b/src/unilab/tasks/manipulation/allegro_inhand/__init__.py
@@ -0,0 +1,4 @@
+from . import (
+    grasp_gen,  # registers the Manager-Based grasp-generation identity
+    rotation,  # registers the Manager-Based rotation identity
+)
diff --git a/src/unilab/tasks/manipulation/allegro_inhand/grasp_gen.py b/src/unilab/tasks/manipulation/allegro_inhand/grasp_gen.py
new file mode 100644
index 000000000..d3c610b45
--- /dev/null
+++ b/src/unilab/tasks/manipulation/allegro_inhand/grasp_gen.py
@@ -0,0 +1,382 @@
+"""Manager-Based terms and registry owner for Allegro grasp generation."""
+
+from __future__ import annotations
+
+from numbers import Integral, Real
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Protocol, cast
+
+import numpy as np
+
+from unilab.assets import ASSETS_ROOT_PATH
+from unilab.base import registry
+from unilab.base.run_control import RunComplete
+from unilab.dtype_config import get_global_dtype
+from unilab.envs import ManagerBasedRlEnvCfg, make_manager_based_rl_env
+from unilab.managers import ManagerTermBase, ManagerTermBaseCfg, RecorderTerm, RecorderTermCfg
+
+from .manager_terms import AllegroRotationObservation
+
+if TYPE_CHECKING:
+    from unilab.base.entity import Entity
+    from unilab.managers._types import ManagerBasedRlEnv
+    from unilab.managers.action_manager import ActionManager
+    from unilab.managers.observation_manager import ObservationManager
+    from unilab.managers.termination_manager import TerminationManager
+
+    class _GraspEnv(ManagerBasedRlEnv, Protocol):
+        @property
+        def common_step_counter(self) -> int: ...
+
+        @property
+        def action_manager(self) -> ActionManager: ...
+
+        @property
+        def observation_manager(self) -> ObservationManager: ...
+
+        @property
+        def termination_manager(self) -> TerminationManager: ...
+
+        @property
+        def reset_terminated(self) -> np.ndarray: ...
+
+        @property
+        def reset_time_outs(self) -> np.ndarray: ...
+
+        @property
+        def extras(self) -> dict[str, Any]: ...
+
+
+def _name(term: str, field: str, value: Any) -> str:
+    if not isinstance(value, str) or not value:
+        raise ValueError(f"{term} {field} must be a non-empty string")
+    return value
+
+
+def _names(term: str, field: str, value: Any) -> tuple[str, ...]:
+    if isinstance(value, (str, bytes)) or not isinstance(value, (tuple, list)):
+        raise TypeError(f"{term} {field} must be a sequence of names")
+    result = tuple(_name(term, field, item) for item in value)
+    if not result:
+        raise ValueError(f"{term} {field} must not be empty")
+    if len(set(result)) != len(result):
+        raise ValueError(f"{term} {field} must contain unique names")
+    return result
+
+
+def _real(term: str, field: str, value: Any, *, positive: bool = False) -> float:
+    if isinstance(value, (bool, np.bool_)) or not isinstance(value, Real):
+        raise TypeError(f"{term} {field} must be a real number")
+    result = float(value)
+    if not np.isfinite(result):
+        raise ValueError(f"{term} {field} must be finite")
+    if positive and result <= 0.0:
+        raise ValueError(f"{term} {field} must be positive")
+    return result
+
+
+def _positive_int(term: str, field: str, value: Any) -> int:
+    if isinstance(value, (bool, np.bool_)) or not isinstance(value, Integral):
+        raise TypeError(f"{term} {field} must be an integer")
+    result = int(value)
+    if result <= 0:
+        raise ValueError(f"{term} {field} must be positive")
+    return result
+
+
+def _bool(term: str, field: str, value: Any) -> bool:
+    if not isinstance(value, (bool, np.bool_)):
+        raise TypeError(f"{term} {field} must be boolean")
+    return bool(value)
+
+
+class AllegroGraspQualityTermination(ManagerTermBase):
+    """Reject timeouts that are not stable multi-finger grasps."""
+
+    _ALLOWED_PARAMS = frozenset(
+        {
+            "entity_name",
+            "observation_group",
+            "observation_term",
+            "fingertip_body_names",
+            "contact_sensor_names",
+            "max_fingertip_distance",
+            "minimum_contacts",
+            "minimum_ball_height",
+            "enabled",
+        }
+    )
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: _GraspEnv):
+        super().__init__(env)
+        term = type(self).__name__
+        unexpected = set(cfg.params) - self._ALLOWED_PARAMS
+        if unexpected:
+            raise TypeError(f"{term} received unsupported parameters: {sorted(unexpected)}")
+
+        entity_name = _name(term, "entity_name", cfg.params.get("entity_name"))
+        self._entity = cast("Entity", env.scene[entity_name])
+        fingertip_names = _names(
+            term, "fingertip_body_names", cfg.params.get("fingertip_body_names")
+        )
+        fingertip_ids, matched_names = self._entity.find_bodies(
+            fingertip_names, preserve_order=True
+        )
+        if tuple(matched_names) != fingertip_names:
+            raise ValueError(
+                f"{term} fingertip body order mismatch: expected {fingertip_names}, "
+                f"got {tuple(matched_names)}"
+            )
+        self._fingertip_ids = np.asarray(fingertip_ids, dtype=np.intp)
+        self._fingertip_ids.setflags(write=False)
+
+        contact_names = _names(term, "contact_sensor_names", cfg.params.get("contact_sensor_names"))
+        self._contact_view = env.scene.bind_sensor_data(contact_names)
+        if self._contact_view.dimensions != (1,) * len(contact_names):
+            raise ValueError(
+                f"{term} contact sensors must each be scalar, got "
+                f"{dict(zip(contact_names, self._contact_view.dimensions, strict=True))}"
+            )
+
+        group = _name(term, "observation_group", cfg.params.get("observation_group"))
+        name = _name(term, "observation_term", cfg.params.get("observation_term"))
+        observation = env.observation_manager.get_term_cfg(group, name).func
+        if not isinstance(observation, AllegroRotationObservation):
+            raise TypeError(
+                f"{term} observation {group}/{name} must be AllegroRotationObservation, "
+                f"got {type(observation).__name__}"
+            )
+        self.observation = observation
+        self._maximum_distance = _real(
+            term,
+            "max_fingertip_distance",
+            cfg.params.get("max_fingertip_distance"),
+            positive=True,
+        )
+        self._minimum_contacts = _positive_int(
+            term, "minimum_contacts", cfg.params.get("minimum_contacts")
+        )
+        if self._minimum_contacts > len(contact_names):
+            raise ValueError(
+                f"{term} minimum_contacts={self._minimum_contacts} exceeds "
+                f"{len(contact_names)} configured contact sensors"
+            )
+        self._minimum_height = _real(
+            term, "minimum_ball_height", cfg.params.get("minimum_ball_height")
+        )
+        self._enabled = _bool(term, "enabled", cfg.params.get("enabled"))
+
+        self.fingertips_close = np.zeros(env.num_envs, dtype=np.bool_)
+        self.enough_contacts = np.zeros(env.num_envs, dtype=np.bool_)
+        self.ball_held = np.zeros(env.num_envs, dtype=np.bool_)
+        self.valid = np.zeros(env.num_envs, dtype=np.bool_)
+        self._disabled = np.zeros(env.num_envs, dtype=np.bool_)
+        self._last_counter = int(env.common_step_counter)
+
+    @property
+    def last_counter(self) -> int:
+        return self._last_counter
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        ids = slice(None) if env_ids is None else env_ids
+        self.fingertips_close[ids] = False
+        self.enough_contacts[ids] = False
+        self.ball_held[ids] = False
+        self.valid[ids] = False
+        self._last_counter = int(cast("_GraspEnv", self._env).common_step_counter)
+
+    def __call__(self, env: _GraspEnv, **params: Any) -> np.ndarray:
+        del params
+        self.observation.snapshot(env)
+        fingertip_pos = self._entity.data.body_link_pos_w[:, self._fingertip_ids]
+        distance = np.linalg.norm(fingertip_pos - self.observation.ball_pos[:, None, :], axis=-1)
+        self.fingertips_close[:] = np.all(distance < self._maximum_distance, axis=1)
+        contacts = self._contact_view.read()
+        self.enough_contacts[:] = np.count_nonzero(contacts > 0.5, axis=1) >= self._minimum_contacts
+        self.ball_held[:] = self.observation.ball_pos[:, 2] > self._minimum_height
+        np.logical_and(self.fingertips_close, self.enough_contacts, out=self.valid)
+        np.logical_and(self.valid, self.ball_held, out=self.valid)
+        self._last_counter = int(env.common_step_counter)
+        if not self._enabled:
+            return self._disabled
+        return np.logical_not(self.valid)
+
+
+class AllegroGraspQualityMetric(ManagerTermBase):
+    """Expose one cached quality condition through the community MetricsManager."""
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: _GraspEnv):
+        super().__init__(env)
+        term_name = type(self).__name__
+        unexpected = set(cfg.params) - {"quality_term_name", "condition"}
+        if unexpected:
+            raise TypeError(f"{term_name} received unsupported parameters: {sorted(unexpected)}")
+        quality_name = _name(term_name, "quality_term_name", cfg.params.get("quality_term_name"))
+        quality = env.termination_manager.get_term_cfg(quality_name).func
+        if not isinstance(quality, AllegroGraspQualityTermination):
+            raise TypeError(
+                f"{term_name} termination term {quality_name!r} must be "
+                f"AllegroGraspQualityTermination, got {type(quality).__name__}"
+            )
+        condition = _name(term_name, "condition", cfg.params.get("condition"))
+        values = {
+            "fingertips_close": quality.fingertips_close,
+            "enough_contacts": quality.enough_contacts,
+            "ball_held": quality.ball_held,
+            "valid": quality.valid,
+        }
+        try:
+            self._value = values[condition]
+        except KeyError:
+            raise ValueError(
+                f"{term_name} condition must be one of {sorted(values)}, got {condition!r}"
+            ) from None
+        self._quality = quality
+        self._quality_name = quality_name
+
+    def __call__(self, env: _GraspEnv, **params: Any) -> np.ndarray:
+        del params
+        if self._quality.last_counter != int(env.common_step_counter):
+            raise RuntimeError(
+                f"{type(self).__name__} term {self._quality_name!r} was not computed for "
+                f"control step {env.common_step_counter}"
+            )
+        return np.asarray(self._value, dtype=get_global_dtype())
+
+
+class AllegroGraspRecorder(RecorderTerm):
+    """Collect successful timeout states and persist the canonical 23-D cache."""
+
+    _ALLOWED_PARAMS = frozenset(
+        {"quality_term_name", "output_path", "collection_target", "auto_save"}
+    )
+
+    def __init__(self, cfg: RecorderTermCfg, env: _GraspEnv):
+        super().__init__(cfg, env)
+        term = type(self).__name__
+        unexpected = set(cfg.params) - self._ALLOWED_PARAMS
+        if unexpected:
+            raise TypeError(f"{term} received unsupported parameters: {sorted(unexpected)}")
+
+        quality_name = _name(term, "quality_term_name", cfg.params.get("quality_term_name"))
+        quality = env.termination_manager.get_term_cfg(quality_name).func
+        if not isinstance(quality, AllegroGraspQualityTermination):
+            raise TypeError(
+                f"{term} termination term {quality_name!r} must be "
+                f"AllegroGraspQualityTermination, got {type(quality).__name__}"
+            )
+        self._quality = quality
+        output = Path(_name(term, "output_path", cfg.params.get("output_path")))
+        self._output_path = output if output.is_absolute() else Path(ASSETS_ROOT_PATH) / output
+        self._target = _positive_int(term, "collection_target", cfg.params.get("collection_target"))
+        self._auto_save = _bool(term, "auto_save", cfg.params.get("auto_save"))
+        self._saved_states: list[np.ndarray] = []
+        self._cache_saved = False
+        self._target_notified = False
+
+    @property
+    def total_saved_grasps(self) -> int:
+        return int(sum(states.shape[0] for states in self._saved_states))
+
+    @property
+    def cache_saved(self) -> bool:
+        return self._cache_saved
+
+    @property
+    def output_path(self) -> Path:
+        return self._output_path
+
+    def _log(self, name: str, value: float) -> None:
+        env = cast("_GraspEnv", self._env)
+        log = env.extras.setdefault("log", {})
+        log[name] = value
+
+    def _save_cache(self, *, force: bool = False) -> None:
+        if self._cache_saved:
+            return
+        total = self.total_saved_grasps
+        if not force and total < self._target:
+            return
+        if total == 0:
+            return
+
+        all_states = np.concatenate(self._saved_states, axis=0).astype(np.float32)
+        all_states = all_states[: self._target]
+        if all_states.ndim != 2 or all_states.shape[1] != 23:
+            raise ValueError(
+                f"{type(self).__name__} collected cache must have shape (N, 23), "
+                f"got {all_states.shape}"
+            )
+        if not np.isfinite(all_states).all():
+            raise ValueError(f"{type(self).__name__} collected cache contains NaN or Inf")
+        self._output_path.parent.mkdir(parents=True, exist_ok=True)
+        np.save(self._output_path, all_states)
+        self._cache_saved = True
+        self._log("grasp_cache/saved", 1.0)
+        self._log("grasp_cache/num_states", float(all_states.shape[0]))
+
+    def _stop_collection(self) -> None:
+        if self._target_notified or self.total_saved_grasps < self._target:
+            return
+        total = self.total_saved_grasps
+        self._target_notified = True
+        self._log("grasp/target_reached", 1.0)
+        print(
+            "[AllegroInhandRotationGrasp] Grasp collection target reached "
+            f"({total}/{self._target}). Collection completed."
+        )
+        raise RunComplete(
+            reason="grasp_collection_target_reached",
+            summary={
+                "collected_grasps": total,
+                "saved_grasps": min(total, self._target),
+                "grasp_collection_target": self._target,
+            },
+        )
+
+    def record_pre_reset(self, env_ids: np.ndarray) -> None:
+        env = cast("_GraspEnv", self._env)
+        ids = np.asarray(env_ids, dtype=np.intp)
+        success = env.reset_time_outs[ids] & ~env.reset_terminated[ids]
+        success_ids = ids[np.flatnonzero(success)]
+        if success_ids.size == 0:
+            return
+        if self._quality.last_counter != int(env.common_step_counter):
+            raise RuntimeError(
+                f"{type(self).__name__} quality state was not computed for control step "
+                f"{env.common_step_counter}"
+            )
+        state = self._quality.observation
+        rows = np.concatenate(
+            (
+                state.dof_pos[success_ids],
+                state.ball_pos[success_ids],
+                state.ball_quat[success_ids],
+            ),
+            axis=1,
+            dtype=np.float32,
+        )
+        if rows.shape != (success_ids.size, 23):
+            raise ValueError(
+                f"{type(self).__name__} expected collected rows shape "
+                f"({success_ids.size}, 23), got {rows.shape}"
+            )
+        self._saved_states.append(rows)
+        self._save_cache()
+        self._stop_collection()
+        self._log("grasp/cache_size", float(self.total_saved_grasps))
+
+    def close(self) -> None:
+        self._save_cache(force=self._auto_save)
+
+
+registry.register_env_config("AllegroInhandRotationGrasp", ManagerBasedRlEnvCfg)
+registry.register_env("AllegroInhandRotationGrasp", make_manager_based_rl_env, sim_backend="mujoco")
+registry.register_env("AllegroInhandRotationGrasp", make_manager_based_rl_env, sim_backend="motrix")
+
+
+__all__ = [
+    "AllegroGraspQualityMetric",
+    "AllegroGraspQualityTermination",
+    "AllegroGraspRecorder",
+]
diff --git a/src/unilab/tasks/manipulation/allegro_inhand/manager_terms.py b/src/unilab/tasks/manipulation/allegro_inhand/manager_terms.py
new file mode 100644
index 000000000..17e7eb08b
--- /dev/null
+++ b/src/unilab/tasks/manipulation/allegro_inhand/manager_terms.py
@@ -0,0 +1,577 @@
+"""Manager-Based terms for Allegro in-hand ball rotation.
+
+Hydra owns the production task declaration.  These terms use only the public
+Entity facade and the community manager lifecycle; they do not inspect backend
+objects or physical state layouts.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from numbers import Real
+from typing import TYPE_CHECKING, Any, Protocol, cast
+
+import numpy as np
+from etils import epath
+
+from unilab.assets import ASSETS_ROOT_PATH
+from unilab.dtype_config import get_global_dtype
+from unilab.managers import ActionTerm, ActionTermCfg, ManagerTermBase, ManagerTermBaseCfg
+from unilab.utils.geometry import np_normalize_axis, np_quat_angular_velocity_from_pair
+
+if TYPE_CHECKING:
+    from unilab.base.entity import Entity
+    from unilab.managers._types import ManagerBasedRlEnv
+    from unilab.managers.action_manager import ActionManager
+    from unilab.managers.observation_manager import ObservationManager
+    from unilab.managers.termination_manager import TerminationManager
+
+    class _AllegroEnv(ManagerBasedRlEnv, Protocol):
+        @property
+        def common_step_counter(self) -> int: ...
+
+        @property
+        def action_manager(self) -> ActionManager: ...
+
+        @property
+        def observation_manager(self) -> ObservationManager: ...
+
+        @property
+        def termination_manager(self) -> TerminationManager: ...
+
+
+def _real(
+    term: str,
+    name: str,
+    value: Any,
+    *,
+    minimum: float | None = None,
+    strict_minimum: bool = False,
+) -> float:
+    if isinstance(value, (bool, np.bool_)) or not isinstance(value, Real):
+        raise TypeError(f"{term} {name} must be a real number")
+    result = float(value)
+    if not np.isfinite(result):
+        raise ValueError(f"{term} {name} must be finite")
+    if minimum is not None and (result <= minimum if strict_minimum else result < minimum):
+        relation = "greater than" if strict_minimum else "at least"
+        raise ValueError(f"{term} {name} must be {relation} {minimum}")
+    return result
+
+
+def _name(term: str, name: str, value: Any) -> str:
+    if not isinstance(value, str) or not value:
+        raise ValueError(f"{term} {name} must be a non-empty string")
+    return value
+
+
+def _pair(term: str, name: str, value: Any) -> tuple[float, float]:
+    if isinstance(value, (str, bytes)) or not isinstance(value, (tuple, list)):
+        raise TypeError(f"{term} {name} must be a numeric (min, max) pair")
+    if len(value) != 2:
+        raise ValueError(f"{term} {name} must contain two values")
+    lower = _real(term, f"{name}[0]", value[0])
+    upper = _real(term, f"{name}[1]", value[1])
+    if lower > upper:
+        raise ValueError(f"{term} {name} lower bound {lower} exceeds upper bound {upper}")
+    return lower, upper
+
+
+def _env_ids(env: ManagerBasedRlEnv, env_ids: np.ndarray | slice | None) -> np.ndarray:
+    if env_ids is None:
+        return np.arange(env.num_envs, dtype=np.int32)
+    if isinstance(env_ids, slice):
+        return np.arange(env.num_envs, dtype=np.int32)[env_ids]
+    return env_ids
+
+
+def _resolve_grasp_cache(cache_path: str) -> epath.Path:
+    path = epath.Path(cache_path)
+    if path.is_absolute() or path.exists():
+        return path
+    return epath.Path(ASSETS_ROOT_PATH / cache_path)
+
+
+@dataclass(kw_only=True)
+class AllegroIncrementalPositionActionCfg(ActionTermCfg):
+    """Incremental position targets used by the original Allegro policy."""
+
+    actuator_names: tuple[str, ...] | list[str]
+    action_scale: float
+    raw_action_clip: tuple[float, float] | list[float]
+
+    def build(self, env: ManagerBasedRlEnv) -> AllegroIncrementalPositionAction:
+        return AllegroIncrementalPositionAction(self, env)
+
+
+class AllegroIncrementalPositionAction(ActionTerm):
+    """Integrate clipped policy deltas into bounded hand-joint targets."""
+
+    cfg: AllegroIncrementalPositionActionCfg
+    _entity: Entity
+    _raw_action: np.ndarray
+    _clipped_action: np.ndarray
+    _target: np.ndarray
+
+    def __init__(self, cfg: AllegroIncrementalPositionActionCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        term = type(self).__name__
+        if cfg.clip is not None:
+            raise NotImplementedError(
+                f"{term} does not support actuator-name clip; use raw_action_clip"
+            )
+        if isinstance(cfg.actuator_names, (str, bytes)) or not isinstance(
+            cfg.actuator_names, (tuple, list)
+        ):
+            raise TypeError(f"{term} actuator_names must be a sequence of patterns")
+        self._joint_ids, target_names = self._entity.find_joints_by_actuator_names(
+            cfg.actuator_names
+        )
+        actuator_ids, actuator_names = self._entity.find_actuators(
+            cfg.actuator_names, preserve_order=True
+        )
+        if len(self._joint_ids) != len(actuator_ids) or target_names != list(
+            self._entity.joint_names[index] for index in self._joint_ids
+        ):
+            raise ValueError(f"{term} actuator-to-joint mapping is incomplete")
+        if len(self._joint_ids) != 16:
+            raise ValueError(f"{term} requires 16 hand actuators, got {len(self._joint_ids)}")
+        if len(set(actuator_names)) != len(actuator_names):
+            raise ValueError(f"{term} actuator selector resolved duplicate names")
+
+        self._joint_ids_array = np.asarray(self._joint_ids, dtype=np.intp)
+        self._joint_ids_array.setflags(write=False)
+        local_actuator_ids = np.asarray(actuator_ids, dtype=np.intp)
+        ranges = np.asarray(self._entity.data.actuator_ctrl_range, dtype=get_global_dtype())
+        self._ctrl_lower = np.array(ranges[local_actuator_ids, 0], copy=True)
+        self._ctrl_upper = np.array(ranges[local_actuator_ids, 1], copy=True)
+        if np.any(self._ctrl_lower >= self._ctrl_upper):
+            raise ValueError(f"{term} actuator control ranges must have lower < upper")
+
+        self._scale = _real(term, "action_scale", cfg.action_scale, minimum=0.0)
+        self._raw_clip = _pair(term, "raw_action_clip", cfg.raw_action_clip)
+        dtype = get_global_dtype()
+        self._raw_action = np.zeros((env.num_envs, len(self._joint_ids)), dtype=dtype)
+        self._clipped_action = np.zeros_like(self._raw_action)
+        self._target = np.asarray(
+            self._entity.data.default_joint_pos[:, self._joint_ids_array], dtype=dtype
+        ).copy()
+
+    @property
+    def action_dim(self) -> int:
+        return int(self._raw_action.shape[1])
+
+    @property
+    def raw_action(self) -> np.ndarray:
+        return self._raw_action
+
+    @property
+    def target(self) -> np.ndarray:
+        return self._target
+
+    @property
+    def ctrl_lower(self) -> np.ndarray:
+        return self._ctrl_lower
+
+    @property
+    def ctrl_upper(self) -> np.ndarray:
+        return self._ctrl_upper
+
+    @property
+    def joint_ids(self) -> np.ndarray:
+        return self._joint_ids_array
+
+    def process_actions(self, actions: np.ndarray) -> None:
+        if not isinstance(actions, np.ndarray):
+            raise TypeError(f"expected np.ndarray actions, got {type(actions).__name__}")
+        if actions.shape != self._raw_action.shape:
+            raise ValueError(f"expected action shape {self._raw_action.shape}, got {actions.shape}")
+        if not np.isfinite(actions).all():
+            raise ValueError("received NaN or Inf actions")
+        self._raw_action[:] = actions
+        np.clip(actions, self._raw_clip[0], self._raw_clip[1], out=self._clipped_action)
+        self._target += self._scale * self._clipped_action
+        np.clip(self._target, self._ctrl_lower, self._ctrl_upper, out=self._target)
+
+    def apply_actions(self) -> None:
+        self._entity.set_joint_position_target(self._target, joint_ids=self._joint_ids_array)
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        ids = _env_ids(self._env, env_ids)
+        self._raw_action[ids] = 0.0
+        self._clipped_action[ids] = 0.0
+        self._target[ids] = self._entity.data.joint_pos[ids][:, self._joint_ids_array]
+
+
+class AllegroRotationObservation(ManagerTermBase):
+    """One 35-D frame plus state shared by termination and reward terms."""
+
+    _ALLOWED_PARAMS = frozenset(
+        {
+            "entity_name",
+            "action_name",
+            "joint_noise",
+            "torque_estimate_kp",
+            "torque_estimate_kd",
+        }
+    )
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: _AllegroEnv):
+        super().__init__(env)
+        term = type(self).__name__
+        unexpected = set(cfg.params) - self._ALLOWED_PARAMS
+        if unexpected:
+            raise TypeError(f"{term} received unsupported parameters: {sorted(unexpected)}")
+        entity_name = _name(term, "entity_name", cfg.params.get("entity_name"))
+        action_name = _name(term, "action_name", cfg.params.get("action_name"))
+        self._entity = cast("Entity", env.scene[entity_name])
+        action = env.action_manager.get_term(action_name)
+        if not isinstance(action, AllegroIncrementalPositionAction):
+            raise TypeError(
+                f"{term} action {action_name!r} must be AllegroIncrementalPositionAction, "
+                f"got {type(action).__name__}"
+            )
+        self._action = action
+        self._joint_noise = _real(term, "joint_noise", cfg.params.get("joint_noise"), minimum=0.0)
+        self._torque_kp = _real(
+            term, "torque_estimate_kp", cfg.params.get("torque_estimate_kp"), minimum=0.0
+        )
+        self._torque_kd = _real(
+            term, "torque_estimate_kd", cfg.params.get("torque_estimate_kd"), minimum=0.0
+        )
+
+        dtype = get_global_dtype()
+        self.dof_pos = np.asarray(
+            self._entity.data.joint_pos[:, self._action.joint_ids], dtype=dtype
+        ).copy()
+        self.dof_vel = np.zeros_like(self.dof_pos)
+        self.ball_pos = np.asarray(self._entity.data.root_link_pos_w, dtype=dtype).copy()
+        self.ball_quat = np.asarray(self._entity.data.root_link_quat_w, dtype=dtype).copy()
+        self.ball_linvel = np.zeros_like(self.ball_pos)
+        self.ball_angvel = np.zeros_like(self.ball_pos)
+        self.torques = np.zeros_like(self.dof_pos)
+        self.init_pose = self.dof_pos.copy()
+        self._previous_dof_pos = self.dof_pos.copy()
+        self._previous_ball_pos = self.ball_pos.copy()
+        self._previous_ball_quat = self.ball_quat.copy()
+        self._just_reset = np.ones(env.num_envs, dtype=np.bool_)
+        self._last_counter = int(env.common_step_counter)
+
+        self._dof_mid = (self._action.ctrl_upper + self._action.ctrl_lower) / 2.0
+        self._dof_range = self._action.ctrl_upper - self._action.ctrl_lower
+
+    @property
+    def last_counter(self) -> int:
+        return self._last_counter
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        ids = _env_ids(self._env, env_ids)
+        dof_pos = np.asarray(
+            self._entity.data.joint_pos[:, self._action.joint_ids], dtype=get_global_dtype()
+        )
+        ball_pos = np.asarray(self._entity.data.root_link_pos_w, dtype=get_global_dtype())
+        ball_quat = np.asarray(self._entity.data.root_link_quat_w, dtype=get_global_dtype())
+        self.dof_pos[ids] = dof_pos[ids]
+        self.dof_vel[ids] = 0.0
+        self.ball_pos[ids] = ball_pos[ids]
+        self.ball_quat[ids] = ball_quat[ids]
+        self.ball_linvel[ids] = 0.0
+        self.ball_angvel[ids] = 0.0
+        self.torques[ids] = 0.0
+        self.init_pose[ids] = dof_pos[ids]
+        self._previous_dof_pos[ids] = dof_pos[ids]
+        self._previous_ball_pos[ids] = ball_pos[ids]
+        self._previous_ball_quat[ids] = ball_quat[ids]
+        self._just_reset[ids] = True
+        self._last_counter = int(cast("_AllegroEnv", self._env).common_step_counter)
+
+    def snapshot(self, env: _AllegroEnv) -> AllegroRotationObservation:
+        counter = int(env.common_step_counter)
+        if counter == self._last_counter:
+            return self
+        if counter != self._last_counter + 1:
+            raise RuntimeError(
+                f"AllegroRotationObservation missed a control-step update: "
+                f"last={self._last_counter}, current={counter}"
+            )
+
+        dtype = get_global_dtype()
+        dof_pos = np.asarray(self._entity.data.joint_pos[:, self._action.joint_ids], dtype=dtype)
+        ball_pos = np.asarray(self._entity.data.root_link_pos_w, dtype=dtype)
+        ball_quat = np.asarray(self._entity.data.root_link_quat_w, dtype=dtype)
+        np.subtract(dof_pos, self._previous_dof_pos, out=self.dof_vel)
+        self.dof_vel /= env.step_dt
+        np.subtract(ball_pos, self._previous_ball_pos, out=self.ball_linvel)
+        self.ball_linvel /= env.step_dt
+        self.ball_angvel[:] = np_quat_angular_velocity_from_pair(
+            ball_quat, self._previous_ball_quat, env.step_dt
+        )
+        self.dof_pos[:] = dof_pos
+        self.ball_pos[:] = ball_pos
+        self.ball_quat[:] = ball_quat
+        self.torques[:] = self._torque_kp * (self._action.target - self.dof_pos)
+        self.torques -= self._torque_kd * self.dof_vel
+        np.clip(self.torques, -0.5, 0.5, out=self.torques)
+        self._previous_dof_pos[:] = dof_pos
+        self._previous_ball_pos[:] = ball_pos
+        self._previous_ball_quat[:] = ball_quat
+        self._last_counter = counter
+        return self
+
+    def __call__(self, env: _AllegroEnv, **params: Any) -> np.ndarray:
+        del params
+        self.snapshot(env)
+        dof_pos_norm = 2.0 * (self.dof_pos - self._dof_mid) / (self._dof_range + 1.0e-8)
+        if self._joint_noise > 0.0:
+            active = ~self._just_reset
+            if np.any(active):
+                dof_pos_norm = dof_pos_norm.copy()
+                dof_pos_norm[active] += env.rng.uniform(
+                    -self._joint_noise,
+                    self._joint_noise,
+                    size=(int(np.count_nonzero(active)), self._action.action_dim),
+                )
+        self._just_reset[:] = False
+        return np.concatenate(
+            (dof_pos_norm, self._action.target, self.ball_pos),
+            axis=1,
+            dtype=get_global_dtype(),
+        )
+
+
+class AllegroDropTermination(ManagerTermBase):
+    """Termination-owned drop state, computed before reward terms."""
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: _AllegroEnv):
+        super().__init__(env)
+        term = type(self).__name__
+        allowed = {"observation_group", "observation_term", "minimum_ball_height"}
+        unexpected = set(cfg.params) - allowed
+        if unexpected:
+            raise TypeError(f"{term} received unsupported parameters: {sorted(unexpected)}")
+        group = _name(term, "observation_group", cfg.params.get("observation_group"))
+        name = _name(term, "observation_term", cfg.params.get("observation_term"))
+        observation = env.observation_manager.get_term_cfg(group, name).func
+        if not isinstance(observation, AllegroRotationObservation):
+            raise TypeError(
+                f"{term} observation {group}/{name} must be AllegroRotationObservation, "
+                f"got {type(observation).__name__}"
+            )
+        self.observation = observation
+        self._minimum_height = _real(
+            term, "minimum_ball_height", cfg.params.get("minimum_ball_height")
+        )
+        self.dropped = np.zeros(env.num_envs, dtype=np.bool_)
+        self._last_counter = int(env.common_step_counter)
+
+    @property
+    def last_counter(self) -> int:
+        return self._last_counter
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        self.dropped[_env_ids(self._env, env_ids)] = False
+        self._last_counter = int(cast("_AllegroEnv", self._env).common_step_counter)
+
+    def __call__(self, env: _AllegroEnv, **params: Any) -> np.ndarray:
+        del params
+        self.observation.snapshot(env)
+        self.dropped[:] = self.observation.ball_pos[:, 2] < self._minimum_height
+        self._last_counter = int(env.common_step_counter)
+        return self.dropped
+
+
+def _rotation_state(
+    env: _AllegroEnv,
+    state_term_name: str,
+) -> tuple[AllegroDropTermination, AllegroRotationObservation]:
+    name = _name("Allegro reward", "state_term_name", state_term_name)
+    state = env.termination_manager.get_term_cfg(name).func
+    if not isinstance(state, AllegroDropTermination):
+        raise TypeError(
+            f"Allegro reward termination term {name!r} must be AllegroDropTermination, "
+            f"got {type(state).__name__}"
+        )
+    if state.last_counter != int(env.common_step_counter):
+        raise RuntimeError(
+            f"Allegro reward state {name!r} was not computed for control step "
+            f"{env.common_step_counter}"
+        )
+    return state, state.observation
+
+
+class AllegroRotateReward(ManagerTermBase):
+    """Reward angular velocity projected onto a cold-path-normalized axis."""
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: _AllegroEnv):
+        super().__init__(env)
+        term = type(self).__name__
+        allowed = {"state_term_name", "rotation_axis", "clip_min", "clip_max"}
+        unexpected = set(cfg.params) - allowed
+        if unexpected:
+            raise TypeError(f"{term} received unsupported parameters: {sorted(unexpected)}")
+        self._state_term_name = _name(term, "state_term_name", cfg.params.get("state_term_name"))
+        try:
+            axis = np.asarray(cfg.params.get("rotation_axis"), dtype=get_global_dtype())
+        except (TypeError, ValueError) as exc:
+            raise TypeError(f"{term} rotation_axis must contain three numeric values") from exc
+        if axis.shape != (3,) or not np.isfinite(axis).all():
+            raise ValueError(f"{term} rotation_axis must be a finite 3-D vector")
+        self._axis = np.asarray(np_normalize_axis(axis), dtype=get_global_dtype())
+        self._clip = _pair(
+            term,
+            "clip",
+            (cfg.params.get("clip_min"), cfg.params.get("clip_max")),
+        )
+
+    def __call__(self, env: _AllegroEnv, **params: Any) -> np.ndarray:
+        del params
+        _, state = _rotation_state(env, self._state_term_name)
+        return np.asarray(
+            np.clip(state.ball_angvel @ self._axis, self._clip[0], self._clip[1]),
+            dtype=get_global_dtype(),
+        )
+
+
+def object_linear_velocity_l1(env: _AllegroEnv, state_term_name: str) -> np.ndarray:
+    _, state = _rotation_state(env, state_term_name)
+    return np.asarray(np.sum(np.abs(state.ball_linvel), axis=1), dtype=get_global_dtype())
+
+
+def hand_pose_deviation_l2(env: _AllegroEnv, state_term_name: str) -> np.ndarray:
+    _, state = _rotation_state(env, state_term_name)
+    return np.asarray(
+        np.sum(np.square(state.dof_pos - state.init_pose), axis=1), dtype=get_global_dtype()
+    )
+
+
+def estimated_torque_l2(env: _AllegroEnv, state_term_name: str) -> np.ndarray:
+    _, state = _rotation_state(env, state_term_name)
+    return np.asarray(np.sum(np.square(state.torques), axis=1), dtype=get_global_dtype())
+
+
+def estimated_work_l2(env: _AllegroEnv, state_term_name: str) -> np.ndarray:
+    _, state = _rotation_state(env, state_term_name)
+    work = np.sum(state.torques * state.dof_vel, axis=1)
+    return np.asarray(np.square(work), dtype=get_global_dtype())
+
+
+def dropped(env: _AllegroEnv, state_term_name: str) -> np.ndarray:
+    state, _ = _rotation_state(env, state_term_name)
+    return np.asarray(state.dropped, dtype=get_global_dtype())
+
+
+class AllegroHandBallReset(ManagerTermBase):
+    """Reset hand joints and the ball root without exposing qpos layout."""
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        term = type(self).__name__
+        allowed = {
+            "entity_name",
+            "grasp_cache_path",
+            "joint_noise",
+            "ball_velocity_noise",
+            "ball_z_offset",
+        }
+        unexpected = set(cfg.params) - allowed
+        if unexpected:
+            raise TypeError(f"{term} received unsupported parameters: {sorted(unexpected)}")
+        entity_name = _name(term, "entity_name", cfg.params.get("entity_name"))
+        self._entity = cast("Entity", env.scene[entity_name])
+        if self._entity.num_joints != 16:
+            raise ValueError(f"{term} requires 16 hand joints, got {self._entity.num_joints}")
+        if self._entity.data.default_root_state.shape != (env.num_envs, 13):
+            raise ValueError(f"{term} requires a 13-D floating ball root for every environment")
+        self._joint_noise = _real(term, "joint_noise", cfg.params.get("joint_noise"), minimum=0.0)
+        self._ball_velocity_noise = _real(
+            term,
+            "ball_velocity_noise",
+            cfg.params.get("ball_velocity_noise"),
+            minimum=0.0,
+        )
+        self._ball_z_offset = _real(term, "ball_z_offset", cfg.params.get("ball_z_offset"))
+
+        cache_value = cfg.params.get("grasp_cache_path")
+        self._grasp_cache: np.ndarray | None = None
+        if cache_value is not None:
+            cache_path = _resolve_grasp_cache(_name(term, "grasp_cache_path", cache_value))
+            if not cache_path.exists():
+                raise FileNotFoundError(
+                    f"{term} configured grasp cache does not exist: {cache_path}. "
+                    "Set grasp_cache_path to null to explicitly use the model home pose, "
+                    "or generate a cache with `uv run train --algo ppo "
+                    "--task allegro_inhand_grasp --sim mujoco training.no_play=true`."
+                )
+            cache = np.asarray(np.load(cache_path), dtype=np.float64)
+            if cache.ndim != 2 or cache.shape[1] != 23 or cache.shape[0] == 0:
+                raise ValueError(
+                    f"{term} grasp cache {cache_path} must have shape (N, 23), got {cache.shape}"
+                )
+            if not np.isfinite(cache).all():
+                raise ValueError(f"{term} grasp cache {cache_path} contains NaN or Inf")
+            self._grasp_cache = cache
+
+        ranges = np.asarray(self._entity.data.actuator_ctrl_range, dtype=np.float64)
+        if ranges.shape != (16, 2):
+            raise ValueError(f"{term} actuator control range must have shape (16, 2)")
+        self._ctrl_lower = ranges[:, 0]
+        self._ctrl_upper = ranges[:, 1]
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        env_ids: np.ndarray | None,
+        **params: Any,
+    ) -> None:
+        del params
+        ids = _env_ids(env, env_ids)
+        count = ids.size
+        dtype = get_global_dtype()
+        root_state = np.array(self._entity.data.default_root_state[ids], copy=True)
+        if self._grasp_cache is not None:
+            rows = self._grasp_cache[env.rng.integers(0, self._grasp_cache.shape[0], size=count)]
+            joint_pos = np.array(rows[:, :16], copy=True)
+            root_state[:, :3] = rows[:, 16:19]
+            root_state[:, 3:7] = rows[:, 19:23]
+        else:
+            joint_pos = np.array(self._entity.data.default_joint_pos[ids], copy=True)
+            if self._joint_noise > 0.0:
+                joint_pos += env.rng.uniform(
+                    -self._joint_noise, self._joint_noise, size=joint_pos.shape
+                )
+            root_state[:, 2] += self._ball_z_offset
+        np.clip(joint_pos, self._ctrl_lower, self._ctrl_upper, out=joint_pos)
+        joint_vel = np.zeros_like(joint_pos)
+        root_state[:, 7:] = 0.0
+        if self._ball_velocity_noise > 0.0:
+            root_state[:, 7:10] = env.rng.uniform(
+                -self._ball_velocity_noise,
+                self._ball_velocity_noise,
+                size=(count, 3),
+            )
+        self._entity.write_joint_state_to_sim(
+            np.asarray(joint_pos, dtype=dtype),
+            np.asarray(joint_vel, dtype=dtype),
+            env_ids=ids,
+        )
+        self._entity.write_root_link_pose_to_sim(
+            np.asarray(root_state[:, :7], dtype=dtype), env_ids=ids
+        )
+        self._entity.write_root_link_velocity_to_sim(
+            np.asarray(root_state[:, 7:], dtype=dtype), env_ids=ids
+        )
+
+
+__all__ = [
+    "AllegroDropTermination",
+    "AllegroHandBallReset",
+    "AllegroIncrementalPositionAction",
+    "AllegroIncrementalPositionActionCfg",
+    "AllegroRotateReward",
+    "AllegroRotationObservation",
+    "dropped",
+    "estimated_torque_l2",
+    "estimated_work_l2",
+    "hand_pose_deviation_l2",
+    "object_linear_velocity_l1",
+]
diff --git a/src/unilab/tasks/manipulation/allegro_inhand/rotation.py b/src/unilab/tasks/manipulation/allegro_inhand/rotation.py
new file mode 100644
index 000000000..0dba2a728
--- /dev/null
+++ b/src/unilab/tasks/manipulation/allegro_inhand/rotation.py
@@ -0,0 +1,9 @@
+"""Registry owner for the Allegro in-hand rotation Manager-Based task."""
+
+from unilab.base import registry
+from unilab.envs import ManagerBasedRlEnvCfg, make_manager_based_rl_env
+
+registry.register_env_config("AllegroInhandRotation", ManagerBasedRlEnvCfg)
+registry.register_env("AllegroInhandRotation", make_manager_based_rl_env, sim_backend="mujoco")
+registry.register_env("AllegroInhandRotation", make_manager_based_rl_env, sim_backend="motrix")
+registry.register_env("AllegroInhandRotation", make_manager_based_rl_env, sim_backend="drake")
diff --git a/src/unilab/envs/manipulation/sharpa_inhand/__init__.py b/src/unilab/tasks/manipulation/sharpa_inhand/__init__.py
similarity index 100%
rename from src/unilab/envs/manipulation/sharpa_inhand/__init__.py
rename to src/unilab/tasks/manipulation/sharpa_inhand/__init__.py
diff --git a/src/unilab/tasks/manipulation/sharpa_inhand/base.py b/src/unilab/tasks/manipulation/sharpa_inhand/base.py
new file mode 100644
index 000000000..741cbe4e4
--- /dev/null
+++ b/src/unilab/tasks/manipulation/sharpa_inhand/base.py
@@ -0,0 +1,670 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Sequence, cast
+
+import gymnasium as gym
+import numpy as np
+from unisim import SimBackend
+
+from unilab.assets import ASSETS_ROOT_PATH
+from unilab.base.base import EnvCfg
+from unilab.base.np_env import NpEnv, NpEnvState
+from unilab.base.scene import SceneCfg
+from unilab.dtype_config import get_global_dtype
+
+DEFAULT_ACTUATED_JOINT_NAMES: list[str] = [
+    "right_thumb_CMC_FE",
+    "right_thumb_CMC_AA",
+    "right_thumb_MCP_FE",
+    "right_thumb_MCP_AA",
+    "right_thumb_IP",
+    "right_index_MCP_FE",
+    "right_index_MCP_AA",
+    "right_index_PIP",
+    "right_index_DIP",
+    "right_middle_MCP_FE",
+    "right_middle_MCP_AA",
+    "right_middle_PIP",
+    "right_middle_DIP",
+    "right_ring_MCP_FE",
+    "right_ring_MCP_AA",
+    "right_ring_PIP",
+    "right_ring_DIP",
+    "right_pinky_CMC",
+    "right_pinky_MCP_FE",
+    "right_pinky_MCP_AA",
+    "right_pinky_PIP",
+    "right_pinky_DIP",
+]
+
+DEFAULT_FINGERTIP_BODY_NAMES: list[str] = [
+    "right_thumb_DP",
+    "right_index_DP",
+    "right_middle_DP",
+    "right_ring_DP",
+    "right_pinky_DP",
+]
+
+# Source parity anchor from sharpa-rl-lab:
+# rl_isaaclab/tasks/inhand_rotate/sharpa_wave_env_cfg.py (hand init_state joint_pos).
+SOURCE_DEFAULT_HAND_JOINT_POS_DEG: tuple[float, ...] = (
+    95.12771,
+    -3.11244,
+    14.81626,
+    -1.03493,
+    12.23986,
+    65.21091,
+    6.1133,
+    15.58495,
+    5.90325,
+    31.74149,
+    -0.95812,
+    41.88173,
+    12.844,
+    31.72383,
+    9.84458,
+    35.22366,
+    18.02839,
+    10.9712,
+    68.30895,
+    7.99151,
+    5.89626,
+    5.89875,
+)
+
+
+@dataclass
+class SharpaControlConfig:
+    action_scale: float = 1.0 / 24.0
+    # MuJoCo Sharpa loads PD defaults from XML actuator gains. These fields remain
+    # fallback defaults for backends that cannot expose actuator gains yet.
+    p_gain: float = 1.0
+    d_gain: float = 0.1
+    # MuJoCo Sharpa currently uses position actuators, so torque-control mode
+    # stays declared here for owner-config structure but is rejected at runtime.
+    torque_control: bool = False
+    dof_limits_scale: float = 0.9
+
+
+@dataclass
+class SharpaSensorConfig:
+    tactile_force_sensor_names: list[str] = field(default_factory=list)
+
+
+@dataclass
+class SharpaObservationConfig:
+    observation_mode: str = "separated"
+    enable_tactile: bool = True
+    binary_contact: bool = False
+    enable_contact_pos: bool = False
+    contact_smooth: float = 0.5
+    contact_threshold: float = 0.05
+    tactile_force_clip_max: float = 4.0
+
+
+@dataclass
+class SharpaPrivilegedInfoConfig:
+    include_friction_scale: bool = True
+    include_gravity_direction: bool = False
+
+
+@dataclass
+class SharpaDomainRandConfig:
+    scale_list: list[float] = field(default_factory=lambda: [0.5])
+    randomize_base_mass: bool = False
+    added_mass_range: list[float] = field(default_factory=lambda: [0.0, 0.0])
+    random_com: bool = False
+    com_offset_x: list[float] = field(default_factory=lambda: [0.0, 0.0])
+    randomize_gravity: bool = False
+    gravity_range: list[list[float]] = field(
+        default_factory=lambda: [[0.0, 0.0, -9.81], [0.0, 0.0, -9.81]]
+    )
+    randomize_gravity_direction: bool = False
+    gravity_direction_magnitude: float = 9.81
+    randomize_pd_gains: bool = True
+    randomize_p_gain_scale_lower: float = 0.5
+    randomize_p_gain_scale_upper: float = 2.0
+    randomize_d_gain_scale_lower: float = 0.5
+    randomize_d_gain_scale_upper: float = 2.0
+    randomize_friction: bool = True
+    randomize_friction_scale_lower: float = 0.5
+    randomize_friction_scale_upper: float = 2.0
+    elastomer_base_friction: float = 1.6
+    metal_base_friction: float = 0.2
+    object_base_friction: float = 1.0
+    randomize_com: bool = True
+    randomize_com_lower: float = -0.01
+    randomize_com_upper: float = 0.01
+    randomize_mass: bool = True
+    randomize_mass_lower: float = 0.01
+    randomize_mass_upper: float = 0.25
+    force_scale: float = 2.0
+    random_force_prob_scalar: float = 0.25
+    force_decay: float = 0.9
+    force_decay_interval: float = 0.08
+    joint_noise_scale: float = 0.02
+    contact_latency: float = 0.005
+    contact_sensor_noise: float = 0.01
+    push_body_name: str | None = None
+
+
+@dataclass
+class SharpaInhandBaseCfg(EnvCfg):
+    scene: SceneCfg = field(  # pyright: ignore[reportIncompatibleVariableOverride]
+        default_factory=lambda: SceneCfg(
+            model_file=str(ASSETS_ROOT_PATH / "robots" / "sharpa_wave" / "scene.xml")
+        )
+    )
+    max_episode_seconds: float = 20.0  # pyright: ignore[reportIncompatibleVariableOverride]
+    sim_dt: float = 1.0 / 240.0
+    ctrl_dt: float = 12.0 / 240.0
+
+    action_space: int = 22
+    observation_space: int = 192
+    prop_hist_len: int = 30
+    critic_info_dim: int = 8
+
+    clip_obs: float = 5.0
+    clip_actions: float = 1.0
+
+    num_hand_dofs: int = 22
+    frame_obs_dim: int = 64
+    obs_lag_steps: int = 3
+    obs_history_len: int = 80
+
+    base_name: str = "right_hand_C_MC"
+    object_body_name: str = "object"
+    object_geom_name: str = "object"
+    actuated_joint_names: list[str] = field(
+        default_factory=lambda: list(DEFAULT_ACTUATED_JOINT_NAMES)
+    )
+    fingertip_body_names: list[str] = field(
+        default_factory=lambda: list(DEFAULT_FINGERTIP_BODY_NAMES)
+    )
+
+    control_config: SharpaControlConfig = field(default_factory=SharpaControlConfig)
+    sensor: SharpaSensorConfig = field(default_factory=SharpaSensorConfig)  # type: ignore[assignment]
+    obs: SharpaObservationConfig = field(default_factory=SharpaObservationConfig)
+    priv_info: SharpaPrivilegedInfoConfig = field(default_factory=SharpaPrivilegedInfoConfig)
+    domain_rand: SharpaDomainRandConfig = field(default_factory=SharpaDomainRandConfig)
+
+    reset_height_lower: float = 0.59906
+    reset_height_upper: float = 0.63906
+    reset_angle_diff: float = 45.0 / 180.0 * np.pi
+
+    rot_axis: tuple[float, float, float] = (0.0, 0.0, 1.0)
+
+    grasp_cache_path: str = str(ASSETS_ROOT_PATH / "caches" / "sharpa_grasp_linspace")
+    disable_tactile_ids: list[int] = field(default_factory=list)
+    # Match the reference Sharpa object-position reward/privileged-info anchor
+    # by using the fixed XML/default object pose instead of the sampled grasp reset.
+    use_default_object_pose_for_object_pos_anchor: bool = False
+
+    debug_show_axes: bool = False
+
+
+def format_scale_tag(scale_value: float) -> str:
+    """Convert one object scale into a stable cache filename tag.
+
+    Args:
+        scale_value: Single object scale value.
+
+    Returns:
+        Scale tag used in cache filenames.
+    """
+    scale_value = float(scale_value)
+    if scale_value <= 0.0:
+        raise ValueError(f"scale values must be positive, got {scale_value}")
+    return f"{scale_value:g}"
+
+
+def resolve_grasp_cache_file(grasp_cache_path: str, scale_value: float) -> Path:
+    """Resolve the grasp cache path for a single object scale.
+
+    Args:
+        grasp_cache_path: Configured cache prefix or template path.
+        scale_value: Single object scale value for this cache file.
+
+    Returns:
+        Cache path for that exact scale.
+    """
+    scale_tag = format_scale_tag(scale_value)
+    if "{scale}" in grasp_cache_path:
+        return Path(grasp_cache_path.format(scale=scale_tag))
+
+    base = Path(grasp_cache_path)
+    if base.suffix == ".npy":
+        return base.with_name(f"{base.stem}_{scale_tag}{base.suffix}")
+    return Path(f"{grasp_cache_path}_{scale_tag}.npy")
+
+
+def sample_scale_grasp_caches(
+    grasp_caches: Sequence[np.ndarray],
+    scale_ids: np.ndarray,
+) -> np.ndarray:
+    """Sample one cached grasp per reset environment from per-scale cache files.
+
+    Args:
+        grasp_caches: Cache arrays ordered the same way as env scale ids.
+        scale_ids: Scale-bucket assignment for each reset environment.
+
+    Returns:
+        Cached grasp states with shape ``(num_envs, 29)``.
+    """
+    num_envs = scale_ids.shape[0]
+    num_scales = len(grasp_caches)
+    if num_scales <= 0:
+        raise ValueError("grasp_caches must contain at least one scale bucket")
+
+    sampled = np.zeros((num_envs, 29), dtype=np.float64)
+    for scale_idx, grasp_cache in enumerate(grasp_caches):
+        if grasp_cache.ndim != 2 or grasp_cache.shape[1] < 29:
+            raise ValueError(f"Expected cached grasp shape (?, 29), got {grasp_cache.shape}")
+        if grasp_cache.shape[0] == 0:
+            raise ValueError(f"grasp cache for scale id {scale_idx} is empty")
+        env_ids = np.flatnonzero(scale_ids == scale_idx)
+        if len(env_ids) == 0:
+            continue
+        sample_ids = np.random.randint(0, grasp_cache.shape[0], size=len(env_ids))
+        sampled[env_ids] = grasp_cache[sample_ids]
+    return sampled
+
+
+def repeat_obs_history(init_frame: np.ndarray, history_len: int) -> np.ndarray:
+    history = np.broadcast_to(
+        init_frame[:, None, :], (init_frame.shape[0], history_len, init_frame.shape[1])
+    ).copy()
+    return np.asarray(history, dtype=init_frame.dtype)
+
+
+class SharpaInhandBaseEnv(NpEnv):
+    _cfg: SharpaInhandBaseCfg
+    _default_p_gain: np.ndarray
+    _default_d_gain: np.ndarray
+
+    def __init__(self, cfg: SharpaInhandBaseCfg, backend: SimBackend, num_envs: int = 1) -> None:
+        super().__init__(cfg, backend, num_envs)
+
+        self._np_dtype = get_global_dtype()
+
+        self._num_action = int(cfg.num_hand_dofs)
+        actuator_range = np.asarray(self._backend.get_actuator_ctrl_range(), dtype=self._np_dtype)
+        if actuator_range.shape[0] < self._num_action:
+            raise ValueError(
+                f"Model has {actuator_range.shape[0]} actuators, but Sharpa task needs {self._num_action}"
+            )
+
+        # Keep the raw XML actuator limits for observation normalization and any
+        # logic that needs the original backend contract. Target-position clipping
+        # uses a separate scaled limit pair to match Sharpa source behavior.
+        self._ctrl_lower = np.asarray(actuator_range[: self._num_action, 0], dtype=self._np_dtype)
+        self._ctrl_upper = np.asarray(actuator_range[: self._num_action, 1], dtype=self._np_dtype)
+        self._target_lower, self._target_upper = self._resolve_target_joint_limits(
+            self._ctrl_lower,
+            self._ctrl_upper,
+            cfg.control_config.dof_limits_scale,
+        )
+
+        self._init_qpos = self._resolve_init_qpos()
+        self._init_qvel = np.asarray(self._backend.get_init_qvel(), dtype=np.float64)
+        self.nq = int(self._init_qpos.shape[0])
+        self.nv = int(self._init_qvel.shape[0])
+
+        if self.nq < self._num_action + 7:
+            raise ValueError(
+                f"Model qpos dim {self.nq} is too small for {self._num_action} hand DoFs + object pose"
+            )
+
+        self._obj_pos_slice = slice(self._num_action, self._num_action + 3)
+        self._obj_quat_slice = slice(self._num_action + 3, self._num_action + 7)
+
+        source_default_angles = np.deg2rad(
+            np.asarray(SOURCE_DEFAULT_HAND_JOINT_POS_DEG, dtype=np.float64)
+        )
+        if source_default_angles.shape[0] != self._num_action:
+            raise ValueError(
+                "Source default hand joint pose size mismatch: "
+                f"{source_default_angles.shape[0]} vs expected {self._num_action}"
+            )
+        self.default_angles = np.asarray(source_default_angles, dtype=self._np_dtype)
+
+        self._action_space = gym.spaces.Box(
+            low=-float(cfg.clip_actions),
+            high=float(cfg.clip_actions),
+            shape=(self._num_action,),
+            dtype=np.float32,
+        )
+
+        self._object_body_ids = self._backend.get_body_ids([cfg.object_body_name])
+        self._fingertip_body_ids = self._backend.get_body_ids(cfg.fingertip_body_names)
+        self._object_geom_base_size = self._resolve_object_geom_base_size()
+
+        self._num_tactile = len(cfg.fingertip_body_names)
+        self.last_contacts = np.zeros((num_envs, self._num_tactile), dtype=self._np_dtype)
+        self._prev_tactile_force = np.zeros((num_envs, self._num_tactile), dtype=self._np_dtype)
+
+        self.object_default_pose = np.zeros((num_envs, 7), dtype=self._np_dtype)
+
+        self.obs_buf_lag_history = np.zeros(
+            (num_envs, cfg.obs_history_len, cfg.frame_obs_dim), dtype=self._np_dtype
+        )
+        self.proprio_hist_buf = np.zeros(
+            (num_envs, cfg.prop_hist_len, cfg.frame_obs_dim), dtype=self._np_dtype
+        )
+        self.critic_info_buf = np.zeros((num_envs, cfg.critic_info_dim), dtype=self._np_dtype)
+
+        self.scale_ids, self._num_scales, self._bucket_env = self._build_scale_ids(
+            num_envs, cfg.domain_rand.scale_list
+        )
+        self.scale_values = self._build_scale_values(cfg.domain_rand.scale_list)
+
+    @property
+    def action_space(self) -> gym.spaces.Box:
+        return self._action_space  # type: ignore[no-any-return]
+
+    def _resolve_target_joint_limits(
+        self,
+        raw_lower: np.ndarray,
+        raw_upper: np.ndarray,
+        scale: float,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """Build scaled target-position clipping limits from raw XML actuator bounds.
+
+        Args:
+            raw_lower: Original lower control limits loaded from the backend XML.
+            raw_upper: Original upper control limits loaded from the backend XML.
+            scale: Multiplicative scale applied to both bound arrays.
+
+        Returns:
+            Tuple of scaled ``(lower, upper)`` target-position limits.
+        """
+        scale_value = float(scale)
+        if scale_value <= 0.0:
+            raise ValueError(f"dof_limits_scale must be positive, got {scale_value}")
+        target_lower = np.asarray(raw_lower, dtype=self._np_dtype) * scale_value
+        target_upper = np.asarray(raw_upper, dtype=self._np_dtype) * scale_value
+        if np.any(target_lower > target_upper):
+            raise ValueError("Scaled Sharpa target joint limits are invalid")
+        return target_lower.astype(self._np_dtype, copy=False), target_upper.astype(
+            self._np_dtype,
+            copy=False,
+        )
+
+    def _resolve_init_qpos(self) -> np.ndarray:
+        for key_name in ("home", "stand", "default"):
+            try:
+                return np.asarray(self._backend.get_keyframe_qpos(key_name), dtype=np.float64)
+            except Exception:
+                continue
+
+        try:
+            return np.asarray(self._backend.get_default_qpos(), dtype=np.float64)
+        except NotImplementedError as exc:
+            raise ValueError("Could not resolve initial qpos from backend contract") from exc
+
+    def _build_scale_ids(
+        self, num_envs: int, scale_list: Sequence[float]
+    ) -> tuple[np.ndarray, int, int]:
+        """Build deterministic near-even environment assignments for each scale.
+
+        Args:
+            num_envs: Number of vectorized environments to assign.
+            scale_list: Explicit object scale values used by this env instance.
+
+        Returns:
+            Tuple of scale id per environment, total scale count, and the minimum
+            number of environments assigned to any scale.
+        """
+        if len(scale_list) == 0:
+            raise ValueError("scale_list must contain at least one scale")
+        scale_values = np.asarray(scale_list, dtype=np.float64)
+        if np.any(scale_values <= 0.0):
+            raise ValueError(f"scale_list values must be positive, got {list(scale_list)}")
+        num_scales = int(scale_values.shape[0])
+        if num_scales <= 0:
+            raise ValueError(f"scale_list must contain at least one value, got {list(scale_list)}")
+
+        bucket_env = num_envs // num_scales
+        remainder = num_envs % num_scales
+        # Assign the remainder to the lowest scale ids to keep bucket sizes within one env.
+        counts = np.full((num_scales,), bucket_env, dtype=np.int32)
+        counts[:remainder] += 1
+        scale_ids = np.repeat(np.arange(num_scales, dtype=np.int32), counts)
+        return scale_ids, num_scales, bucket_env
+
+    def _build_scale_values(self, scale_list: Sequence[float]) -> np.ndarray:
+        """Normalize configured scale values into a stable numpy array.
+
+        Args:
+            scale_list: Explicit list of object scales.
+
+        Returns:
+            Array of configured scale values in config order.
+        """
+        scale_values = np.asarray(scale_list, dtype=np.float64)
+        if scale_values.ndim != 1 or scale_values.size == 0:
+            raise ValueError(f"scale_list must be a non-empty flat list, got {list(scale_list)}")
+        if np.any(scale_values <= 0.0):
+            raise ValueError(f"scale_list values must be positive, got {list(scale_list)}")
+        return scale_values
+
+    def _resolve_object_geom_base_size(self) -> np.ndarray | None:
+        try:
+            return cast(np.ndarray, self._backend.get_geom_size(self._cfg.object_geom_name))
+        except NotImplementedError:
+            return None
+
+    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
+        clipped_actions = np.clip(actions, -self._cfg.clip_actions, self._cfg.clip_actions)
+        clipped_actions = np.asarray(clipped_actions[:, : self._num_action], dtype=self._np_dtype)
+
+        state.info["last_actions"] = state.info.get("current_actions", clipped_actions.copy())
+        state.info["current_actions"] = clipped_actions
+
+        prev_targets = state.info.get(
+            "prev_targets",
+            np.broadcast_to(self.default_angles, (self._num_envs, self._num_action)).copy(),
+        )
+        targets = prev_targets + self._cfg.control_config.action_scale * clipped_actions
+        # Clip action targets by the scaled control range only. Observation
+        # normalization continues to use the raw XML actuator limits.
+        targets = np.clip(targets, self._target_lower, self._target_upper)
+        prev_targets = np.asarray(targets, dtype=self._np_dtype)
+        state.info["prev_targets"] = prev_targets
+        return prev_targets
+
+    def get_hand_dof_pos(self) -> np.ndarray:
+        return np.asarray(self._backend.get_dof_pos()[:, : self._num_action], dtype=self._np_dtype)
+
+    def get_hand_dof_vel(self) -> np.ndarray:
+        return np.asarray(self._backend.get_dof_vel()[:, : self._num_action], dtype=self._np_dtype)
+
+    def get_fingertip_pos(self) -> np.ndarray:
+        return np.asarray(
+            self._backend.get_body_pos_w(self._fingertip_body_ids), dtype=self._np_dtype
+        )
+
+    def get_object_pos(self) -> np.ndarray:
+        return np.asarray(
+            self._backend.get_body_pos_w(self._object_body_ids)[:, 0, :], dtype=self._np_dtype
+        )
+
+    def get_object_quat(self) -> np.ndarray:
+        return np.asarray(
+            self._backend.get_body_quat_w(self._object_body_ids)[:, 0, :], dtype=self._np_dtype
+        )
+
+    def _extract_sensor_scalar(self, sensor_name: str) -> np.ndarray:
+        data = np.asarray(self._backend.get_sensor_data(sensor_name), dtype=self._np_dtype)
+        if data.ndim == 1:
+            return data
+        if data.ndim == 2 and data.shape[1] == 1:
+            return data[:, 0]
+        if data.ndim == 2 and data.shape[1] >= 3:
+            return np.asarray(np.linalg.norm(data[:, :3], axis=1), dtype=self._np_dtype)
+        flat = data.reshape(data.shape[0], -1)
+        return np.asarray(flat[:, 0], dtype=self._np_dtype)
+
+    def _read_tactile_force(self) -> np.ndarray:
+        """Read per-finger tactile force magnitudes in configured sensor order.
+
+        Args:
+            None.
+
+        Returns:
+            Array of shape ``(num_envs, num_tactile)`` ordered exactly as
+            ``sensor.tactile_force_sensor_names``.
+        """
+        tactile_force = np.zeros((self._num_envs, self._num_tactile), dtype=self._np_dtype)
+        if not self._cfg.sensor.tactile_force_sensor_names:
+            return tactile_force
+
+        for sensor_id, sensor_name in enumerate(
+            self._cfg.sensor.tactile_force_sensor_names[: self._num_tactile]
+        ):
+            try:
+                tactile_force[:, sensor_id] = self._extract_sensor_scalar(sensor_name)
+            except Exception:
+                tactile_force[:, sensor_id] = 0.0
+        return tactile_force
+
+    def _clip_tactile_force(self, tactile_force: np.ndarray) -> np.ndarray:
+        """Clip raw tactile-force magnitudes before they enter observation smoothing.
+
+        Args:
+            tactile_force: Raw per-finger tactile magnitudes with shape
+                ``(num_envs, num_tactile)``.
+
+        Returns:
+            Clipped tactile-force array with the same shape. Non-positive clip values
+            disable this clamp so the caller can opt out explicitly.
+        """
+        clip_max = float(self._cfg.obs.tactile_force_clip_max)
+        tactile_force = np.asarray(tactile_force, dtype=self._np_dtype)
+        if clip_max <= 0.0:
+            return tactile_force
+        return np.asarray(np.clip(tactile_force, 0.0, clip_max), dtype=self._np_dtype)
+
+    def _clear_tactile_history(self, env_ids: np.ndarray | None = None) -> None:
+        """Clear tactile-output and raw-force history buffers.
+
+        Args:
+            env_ids: Optional environment ids to clear. When ``None``, clear all envs.
+
+        Returns:
+            None. Buffers are updated in place.
+        """
+        if env_ids is None:
+            self.last_contacts.fill(0.0)
+            self._prev_tactile_force.fill(0.0)
+            return
+
+        self.last_contacts[env_ids] = 0.0
+        self._prev_tactile_force[env_ids] = 0.0
+
+    def _compute_tactile_observation(self) -> np.ndarray:
+        """Build tactile observations with source-equivalent smoothing and latency.
+
+        Args:
+            None.
+
+        Returns:
+            Tactile-force observation array with shape ``(num_envs, num_tactile)``.
+        """
+        obs_cfg = self._cfg.obs
+        domain_rand = self._cfg.domain_rand
+        if not obs_cfg.enable_tactile:
+            self._clear_tactile_history()
+            return np.zeros((self._num_envs, self._num_tactile), dtype=self._np_dtype)
+
+        current_force = SharpaInhandBaseEnv._clip_tactile_force(self, self._read_tactile_force())
+        smooth_contact = (
+            current_force * obs_cfg.contact_smooth
+            + self._prev_tactile_force * (1.0 - obs_cfg.contact_smooth)
+        ).astype(self._np_dtype)
+        self._prev_tactile_force[:] = current_force
+
+        for disabled_id in self._cfg.disable_tactile_ids:
+            if 0 <= disabled_id < self._num_tactile:
+                smooth_contact[:, disabled_id] = 0.0
+
+        latency = np.where(
+            np.random.rand(self._num_envs, self._num_tactile) < domain_rand.contact_latency,
+            1.0,
+            0.0,
+        ).astype(self._np_dtype)
+
+        if obs_cfg.binary_contact:
+            binary_contact = (smooth_contact > obs_cfg.contact_threshold).astype(self._np_dtype)
+            self.last_contacts = self.last_contacts * latency + binary_contact * (1.0 - latency)
+            noise_mask = (
+                np.random.rand(self._num_envs, self._num_tactile)
+                >= domain_rand.contact_sensor_noise
+            ).astype(self._np_dtype)
+            return np.where(
+                self.last_contacts > 0.1,
+                noise_mask * self.last_contacts,
+                self.last_contacts,
+            )
+
+        self.last_contacts = self.last_contacts * latency + smooth_contact * (1.0 - latency)
+        return self.last_contacts.copy()
+
+    def _compute_contact_positions(self, tactile: np.ndarray) -> np.ndarray:
+        del tactile
+        # TODO(sharpa_inhand): IsaacLab contact positions are defined through contact sensor
+        # frame transforms. Backend-level contact point parity is not available yet in the
+        # current UniLab sensor contract, so we keep this channel as zeros for now.
+        return np.zeros((self._num_envs, self._num_tactile * 3), dtype=self._np_dtype)
+
+    def _normalize_joint_pos(self, dof_pos: np.ndarray) -> np.ndarray:
+        return np.asarray(
+            (2.0 * dof_pos - self._ctrl_upper - self._ctrl_lower)
+            / (self._ctrl_upper - self._ctrl_lower + 1.0e-8),
+            dtype=self._np_dtype,
+        )
+
+    def _sample_pd_scales(self, lower: float, upper: float, shape: tuple[int, int]) -> np.ndarray:
+        if lower > 1.0 or upper < 1.0:
+            raise ValueError("PD randomization scales must satisfy lower <= 1 <= upper")
+        small = np.random.uniform(lower, 1.0, size=shape)
+        large = np.random.uniform(1.0, upper, size=shape)
+        use_small = np.random.rand(*shape) > 0.5
+        return np.where(use_small, small, large).astype(self._np_dtype)
+
+    def _load_default_pd_gains(self) -> tuple[np.ndarray, np.ndarray]:
+        """Resolve the default per-DOF PD gains used as the randomization baseline.
+
+        Args:
+            None.
+
+        Returns:
+            Tuple of ``(p_gain, d_gain)`` arrays with shape ``(num_action,)``.
+        """
+        try:
+            p_gain, d_gain = self._backend.get_actuator_gains()
+            return (
+                np.asarray(p_gain[: self._num_action], dtype=self._np_dtype).copy(),
+                np.asarray(d_gain[: self._num_action], dtype=self._np_dtype).copy(),
+            )
+        except NotImplementedError:
+            return (
+                np.full((self._num_action,), self._cfg.control_config.p_gain, dtype=self._np_dtype),
+                np.full((self._num_action,), self._cfg.control_config.d_gain, dtype=self._np_dtype),
+            )
+
+    def _resolve_pd_gains(self, info: dict[str, Any]) -> tuple[np.ndarray, np.ndarray]:
+        p_gain = info.get(
+            "p_gain",
+            np.broadcast_to(self._default_p_gain, (self._num_envs, self._num_action)).copy(),
+        )
+        d_gain = info.get(
+            "d_gain",
+            np.broadcast_to(self._default_d_gain, (self._num_envs, self._num_action)).copy(),
+        )
+        return np.asarray(p_gain, dtype=self._np_dtype), np.asarray(d_gain, dtype=self._np_dtype)
+
+    def _update_proprio_history(self, obs_history: np.ndarray) -> np.ndarray:
+        return np.asarray(obs_history[:, -self._cfg.prop_hist_len :], dtype=self._np_dtype)
diff --git a/src/unilab/tasks/manipulation/sharpa_inhand/grasp_gen.py b/src/unilab/tasks/manipulation/sharpa_inhand/grasp_gen.py
new file mode 100644
index 000000000..77c602f77
--- /dev/null
+++ b/src/unilab/tasks/manipulation/sharpa_inhand/grasp_gen.py
@@ -0,0 +1,402 @@
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field
+from typing import Any
+
+import numpy as np
+
+from unilab.assets import ASSETS_ROOT_PATH
+from unilab.base import registry
+from unilab.base.np_env import NpEnvState
+from unilab.base.run_control import RunComplete
+from unilab.dr import ResetPlan
+from unilab.dr.dr_utils import build_common_reset_randomization
+from unilab.tasks.compatibility import adapt_legacy_factory
+from unilab.tasks.manipulation.sharpa_inhand.base import (
+    SOURCE_DEFAULT_HAND_JOINT_POS_DEG,
+    SharpaDomainRandConfig,
+    resolve_grasp_cache_file,
+)
+from unilab.tasks.manipulation.sharpa_inhand.rotation import (
+    RewardConfig,
+    SharpaInhandRotationCfg,
+    SharpaInhandRotationDRProvider,
+    SharpaInhandRotationEnv,
+)
+from unilab.utils.rotation import np_quat_error_magnitude
+
+logger = logging.getLogger(__name__)
+
+
+def _default_sharpa_grasp_domain_rand() -> SharpaDomainRandConfig:
+    """Build the nested DR defaults used by Sharpa grasp collection.
+
+    Returns:
+        Domain-randomization config matching the grasp-task owner defaults.
+    """
+    return SharpaDomainRandConfig(
+        randomize_pd_gains=False,
+        randomize_friction=False,
+        randomize_com=False,
+        randomize_mass=True,
+        randomize_mass_lower=0.05,
+        randomize_mass_upper=0.051,
+        force_scale=0.0,
+        random_force_prob_scalar=0.0,
+    )
+
+
+@dataclass
+class SharpaInhandRotationGraspCfg(SharpaInhandRotationCfg):
+    max_episode_seconds: float = 3.0  # 12.0
+
+    reset_height_lower: float = 0.61406
+    reset_height_upper: float = 0.62406
+    reset_angle_diff: float = 30.0 / 180.0 * np.pi
+
+    grasp_cache_path: str = ""
+    domain_rand: SharpaDomainRandConfig = field(default_factory=_default_sharpa_grasp_domain_rand)
+
+    reward_config: RewardConfig = field(  # pyright: ignore[reportIncompatibleVariableOverride]
+        default_factory=lambda: RewardConfig(
+            scales={
+                "rotate": 0.0,
+                "obj_linvel": 0.0,
+                "pose_diff": 0.0,
+                "torque": 0.0,
+                "work": 0.0,
+                "object_pos": 0.0,
+            }
+        )
+    )
+
+    grasp_collection_target: int = 50_000
+    grasp_auto_save: bool = True
+
+
+@registry.envcfg("SharpaInhandRotationGrasp")
+@dataclass
+class SharpaInhandGraspEnvCfg(SharpaInhandRotationGraspCfg):
+    pass
+
+
+class SharpaInhandGraspDRProvider(SharpaInhandRotationDRProvider):
+    def build_reset_plan(self, env: Any, env_ids: np.ndarray) -> ResetPlan:
+        # Keep original grasp task behavior: collect successful pre-reset states on each reset.
+        env._collect_successful_grasps(env_ids)
+
+        num_reset = len(env_ids)
+        if num_reset == 0:
+            return ResetPlan(
+                env_ids=env_ids,
+                qpos=np.zeros((0, env.nq), dtype=np.float64),
+                qvel=np.zeros((0, env.nv), dtype=np.float64),
+                info_updates={},
+                randomization=None,
+            )
+
+        rand = 2.0 * np.random.rand(num_reset, env._num_action) - 1.0
+        hand_qpos = np.broadcast_to(env._grasp_default_angles, (num_reset, env._num_action)).copy()
+        hand_qpos += 0.15 * rand
+        hand_qpos = np.clip(hand_qpos, env._ctrl_lower, env._ctrl_upper)
+
+        object_pos = np.broadcast_to(env._init_qpos[env._obj_pos_slice], (num_reset, 3)).copy()
+        object_quat = np.broadcast_to(env._init_qpos[env._obj_quat_slice], (num_reset, 4)).copy()
+
+        qpos = np.zeros((num_reset, env.nq), dtype=np.float64)
+        qpos[:, : env._num_action] = hand_qpos
+        qpos[:, env._obj_pos_slice] = object_pos
+        qpos[:, env._obj_quat_slice] = object_quat
+
+        qvel = np.zeros((num_reset, env.nv), dtype=np.float64)
+        p_gain, d_gain = self._sample_reset_pd_gains(env, num_reset, dtype=env._np_dtype)
+
+        info_updates = self._build_info_updates(
+            env,
+            env_ids=env_ids,
+            hand_qpos=hand_qpos,
+            object_pos=object_pos,
+            object_quat=object_quat,
+            reset_height_lower=np.full((num_reset,), env.cfg.reset_height_lower, dtype=np.float64),
+            reset_height_upper=np.full((num_reset,), env.cfg.reset_height_upper, dtype=np.float64),
+            rot_axis=np.broadcast_to(env._rot_axis, (num_reset, 3)).astype(np.float64),
+            p_gain=p_gain,
+            d_gain=d_gain,
+            friction_scale=None,
+            randomized_mass=None,
+            randomized_com_offset=None,
+            gravity=None,
+        )
+        env._clear_tactile_history(env_ids)
+
+        return ResetPlan(
+            env_ids=env_ids,
+            qpos=qpos,
+            qvel=qvel,
+            info_updates=info_updates,
+            randomization=build_common_reset_randomization(env, num_reset),
+        )
+
+
+class SharpaInhandRotationGraspEnv(SharpaInhandRotationEnv):
+    _cfg: SharpaInhandRotationGraspCfg  # pyright: ignore[reportIncompatibleVariableOverride]
+    _MATERIALIZE_ROTATION_GRASP_CACHE = False
+
+    def __init__(
+        self,
+        cfg: SharpaInhandRotationGraspCfg,
+        num_envs: int = 1,
+        backend_type: str = "motrix",
+    ) -> None:
+        if cfg.domain_rand.randomize_gravity or cfg.domain_rand.randomize_gravity_direction:
+            raise ValueError(
+                "SharpaInhandRotationGrasp does not support gravity randomization; "
+                "disable env.domain_rand.randomize_gravity and "
+                "env.domain_rand.randomize_gravity_direction."
+            )
+        super().__init__(
+            cfg,
+            num_envs=num_envs,
+            backend_type=backend_type,
+            dr_provider=SharpaInhandGraspDRProvider(),
+        )
+
+        self._saved_grasping_states: list[list[np.ndarray]] = [
+            list() for _ in range(self._num_scales)
+        ]
+        if self._num_scales != 1:
+            raise ValueError(
+                "Sharpa grasp generation now collects exactly one object scale per run; "
+                f"got scale_list={list(self.scale_values)}"
+            )
+        self._grasp_target_per_scale = max(1, int(cfg.grasp_collection_target))
+        self._grasp_cache_saved = False
+        self._grasp_target_reached_notified = False
+        self._last_grasp_progress_step = -1
+        self._last_grasp_progress_counts: tuple[int, ...] | None = None
+        self._grasp_default_angles = np.asarray(
+            np.deg2rad(np.asarray(SOURCE_DEFAULT_HAND_JOINT_POS_DEG, dtype=np.float64)),
+            dtype=self._np_dtype,
+        )
+        if self._grasp_default_angles.shape[0] != self._num_action:
+            raise ValueError(
+                "Source grasp default angle count mismatch: "
+                f"{self._grasp_default_angles.shape[0]} vs expected {self._num_action}"
+            )
+        if np.any(np.bincount(self.scale_ids, minlength=self._num_scales) == 0):
+            raise ValueError(
+                "Sharpa grasp generation requires at least one environment for the configured scale; "
+                f"got num_envs={num_envs}, num_scales={self._num_scales}"
+            )
+
+    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
+        # Grasp-cache collection should not use policy/random actions.
+        # Keep controls fixed at reset targets by forcing zero action input.
+        zero_actions = np.zeros_like(actions, dtype=self._np_dtype)
+        return super().apply_action(zero_actions, state)
+
+    def _total_saved_grasps(self) -> int:
+        return int(sum(len(bucket) for bucket in self._saved_grasping_states))
+
+    def _collection_target_reached(self) -> bool:
+        return all(
+            len(bucket) >= self._grasp_target_per_scale for bucket in self._saved_grasping_states
+        )
+
+    def _get_per_scale_grasp_counts(self) -> tuple[int, ...]:
+        """Return collected grasp counts for each scale bucket.
+
+        Returns:
+            Tuple where index is scale id and value is collected grasp count.
+        """
+        return tuple(len(bucket) for bucket in self._saved_grasping_states)
+
+    def _maybe_log_grasp_progress(self, force: bool = False) -> None:
+        """Log runtime grasp-collection progress grouped by scale.
+
+        Args:
+            force: When True, log even if throttling would normally skip.
+        """
+        if self.state is None:
+            return
+
+        counts = self._get_per_scale_grasp_counts()
+        step_info = self.state.info.get("steps")
+        step = int(step_info[0]) if isinstance(step_info, np.ndarray) and step_info.size > 0 else 0
+        if not force:
+            if counts == self._last_grasp_progress_counts:
+                return
+            if self._last_grasp_progress_step >= 0 and step - self._last_grasp_progress_step < 32:
+                return
+
+        total = int(sum(counts))
+        per_scale = ", ".join(
+            f"scale={float(self.scale_values[i]):g}:{count}" for i, count in enumerate(counts)
+        )
+        logger.info(
+            "[SharpaInhandRotationGrasp] grasp progress total=%d/%d, per_scale=[%s]",
+            total,
+            int(self._cfg.grasp_collection_target),
+            per_scale,
+        )
+        self._last_grasp_progress_step = step
+        self._last_grasp_progress_counts = counts
+
+    def _stop_collection(self) -> None:
+        if self._grasp_target_reached_notified:
+            return
+        if not self._collection_target_reached():
+            return
+
+        self._maybe_log_grasp_progress(force=True)
+        self._grasp_target_reached_notified = True
+        collected = self._total_saved_grasps()
+        target = int(self._cfg.grasp_collection_target)
+        logger.info(
+            "[SharpaInhandRotationGrasp] Grasp collection target reached "
+            "(saved=%d, configured_target=%d). Collection completed.",
+            collected,
+            target,
+        )
+
+        if self.state is not None:
+            log = self.state.info.get("log", {})
+            log["grasp/target_reached"] = 1.0
+            self.state.info["log"] = log
+
+        raise RunComplete(
+            reason="grasp_collection_target_reached",
+            summary={
+                "collected_grasps": int(collected),
+                "grasp_collection_target": int(self._grasp_target_per_scale),
+                "grasp_collection_counts_by_scale": self._get_per_scale_grasp_counts(),
+            },
+        )
+
+    def _collect_successful_grasps(self, env_ids: np.ndarray) -> None:
+        if self.state is None or len(env_ids) == 0:
+            return
+
+        success_mask = self.state.truncated[env_ids] & ~self.state.terminated[env_ids]
+        if not np.any(success_mask):
+            return
+
+        success_env_ids = env_ids[np.flatnonzero(success_mask)]
+        hand_qpos = self.get_hand_dof_pos()[success_env_ids]
+        object_pos = self.get_object_pos()[success_env_ids]
+        object_quat = self.get_object_quat()[success_env_ids]
+        all_states = np.concatenate([hand_qpos, object_pos, object_quat], axis=1).astype(np.float32)
+
+        saved_scale_ids = self.scale_ids[success_env_ids]
+        for i, scale_id in enumerate(saved_scale_ids):
+            bucket = self._saved_grasping_states[int(scale_id)]
+            if len(bucket) < self._grasp_target_per_scale:
+                bucket.append(all_states[i : i + 1])
+
+        self._maybe_log_grasp_progress()
+        if self._grasp_cache_saved:
+            return
+
+        finished_scales = sum(
+            int(len(bucket) >= self._grasp_target_per_scale)
+            for bucket in self._saved_grasping_states
+        )
+        if finished_scales < self._num_scales:
+            return
+
+        if not self._cfg.grasp_auto_save:
+            self._grasp_cache_saved = True
+            self._stop_collection()
+            return
+
+        output_file = resolve_grasp_cache_file(
+            self._cfg.grasp_cache_path or "caches/sharpa_grasp_linspace",
+            float(self.scale_values[0]),
+        )
+        if not output_file.is_absolute():
+            output_file = ASSETS_ROOT_PATH / output_file
+        output_file.parent.mkdir(parents=True, exist_ok=True)
+        save_data = np.concatenate(self._saved_grasping_states[0], axis=0)[
+            : self._grasp_target_per_scale
+        ]
+        np.save(output_file, save_data)
+
+        self._grasp_cache_saved = True
+        if self.state is not None:
+            log = self.state.info.get("log", {})
+            log["grasp_cache/saved"] = 1.0
+            log["grasp_cache/num_states"] = float(save_data.shape[0])
+            self.state.info["log"] = log
+
+        self._stop_collection()
+
+    def _compute_reward(
+        self,
+        info: dict[str, Any],
+        dof_pos: np.ndarray,
+        dof_vel: np.ndarray,
+        object_pos: np.ndarray,
+        object_linvel: np.ndarray,
+        object_angvel: np.ndarray,
+        torques: np.ndarray,
+    ) -> np.ndarray:
+        del info, dof_pos, dof_vel, object_pos, object_linvel, object_angvel, torques
+        return np.zeros((self._num_envs,), dtype=self._np_dtype)
+
+    def update_state(self, state: NpEnvState) -> NpEnvState:
+        next_state = super().update_state(state)
+
+        fingertip_pos = self.get_fingertip_pos()
+        object_pos = self.get_object_pos()
+        object_quat = self.get_object_quat()
+        object_default_pose = np.asarray(
+            next_state.info.get(
+                "object_default_pose", np.zeros((self._num_envs, 7), dtype=self._np_dtype)
+            ),
+            dtype=self._np_dtype,
+        )
+
+        cond1 = np.all(
+            np.linalg.norm(fingertip_pos - object_pos[:, None, :], axis=-1) < 0.1, axis=1
+        )
+        tactile = np.asarray(self.last_contacts, dtype=self._np_dtype)
+        cond2 = np.sum(tactile > 0.5, axis=1) >= 3
+        quat_error = np_quat_error_magnitude(object_default_pose[:, 3:7], object_quat)
+        cond3 = quat_error < self._cfg.reset_angle_diff
+
+        grasp_valid = cond1 & cond2 & cond3
+        terminated = np.asarray(next_state.terminated | (~grasp_valid), dtype=bool)
+
+        reward = np.zeros((self._num_envs,), dtype=self._np_dtype)
+
+        step_count = next_state.info.get("steps", np.zeros((self._num_envs,), dtype=np.uint32))
+        should_log = self._enable_reward_log and (int(step_count[0]) % 4 == 0)
+        if should_log:
+            log = next_state.info.get("log", {})
+            log["grasp/cond1"] = float(np.mean(cond1.astype(np.float32)))
+            log["grasp/cond2"] = float(np.mean(cond2.astype(np.float32)))
+            log["grasp/cond3"] = float(np.mean(cond3.astype(np.float32)))
+            log["grasp/valid"] = float(np.mean(grasp_valid.astype(np.float32)))
+            per_scale_counts = self._get_per_scale_grasp_counts()
+            log["grasp/target_cache_size"] = float(self._cfg.grasp_collection_target)
+            for scale_idx, count in enumerate(per_scale_counts):
+                scale_value = float(self.scale_values[scale_idx])
+                log[f"grasp/cache_size_scale_{scale_value:g}"] = float(count)
+            next_state.info["log"] = log
+
+        return next_state.replace(reward=reward, terminated=terminated)
+
+
+SharpaWaveGraspCfg = SharpaInhandGraspEnvCfg
+
+_SHARPA_GRASP_COMPAT_FACTORY = adapt_legacy_factory(
+    SharpaInhandRotationGraspEnv,
+    task_family="Sharpa",
+    reason=(
+        "tactile grasp validation and cache-collection completion remain task-owned "
+        "until formal Manager-Based capabilities exist"
+    ),
+)
+for _backend_type in ("mujoco", "motrix"):
+    registry.register_env("SharpaInhandRotationGrasp", _SHARPA_GRASP_COMPAT_FACTORY, _backend_type)
diff --git a/src/unilab/tasks/manipulation/sharpa_inhand/rotation.py b/src/unilab/tasks/manipulation/sharpa_inhand/rotation.py
new file mode 100644
index 000000000..236be6a24
--- /dev/null
+++ b/src/unilab/tasks/manipulation/sharpa_inhand/rotation.py
@@ -0,0 +1,1547 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, cast
+
+import numpy as np
+from unisim.dr.types import (
+    RESET_TERM_BODY_IPOS,
+    RESET_TERM_BODY_MASS,
+    RESET_TERM_GEOM_FRICTION,
+    RESET_TERM_GRAVITY,
+    RESET_TERM_KD,
+    RESET_TERM_KP,
+    ResetRandomizationPayload,
+)
+
+from unilab.assets.hub import resolve_grasp_cache_files
+from unilab.base import registry
+from unilab.base.backend_factory import create_backend, env_backend_kwargs
+from unilab.base.np_env import NpEnvState
+from unilab.dr import (
+    DomainRandomizationCapabilities,
+    DomainRandomizationProvider,
+    GeomSizeOverride,
+    InitRandomizationPlan,
+    IntervalRandomizationPlan,
+    ModelVariantSpec,
+    ResetPlan,
+)
+from unilab.dr.dr_utils import build_common_reset_randomization, validate_common_reset_randomization
+from unilab.dtype_config import get_global_dtype
+from unilab.tasks.compatibility import adapt_legacy_factory
+from unilab.tasks.manipulation.sharpa_inhand.base import (
+    SharpaInhandBaseCfg,
+    SharpaInhandBaseEnv,
+    repeat_obs_history,
+    resolve_grasp_cache_file,
+    sample_scale_grasp_caches,
+)
+from unilab.utils.geometry import np_sample_uniform_quaternion
+from unilab.utils.rotation import (
+    np_quat_apply,
+    np_quat_conjugate,
+    np_quat_mul,
+    np_quat_to_axis_angle,
+)
+
+
+@dataclass
+class RewardConfig:
+    scales: dict[str, float] = field(
+        default_factory=lambda: {
+            "rotate": 2.5,
+            "obj_linvel": -0.3,
+            "pose_diff": -0.4,
+            "torque": -0.1,
+            "work": -0.5,
+            "object_pos": 0.003,
+        }
+    )
+    angvel_clip_min: float = -0.5
+    angvel_clip_max: float = 0.5
+
+
+@registry.envcfg("SharpaInhandRotation")
+@dataclass
+class SharpaInhandRotationCfg(SharpaInhandBaseCfg):
+    critic_info_dim: int = 9
+    reward_config: RewardConfig | None = None
+    zero_action_test_mode: bool = False
+
+
+def sample_random_quaternion(num_envs: int) -> np.ndarray:
+    """Sample uniformly distributed random quaternions in wxyz convention.
+
+    Args:
+        num_envs: Number of quaternions to sample.
+
+    Returns:
+        Quaternion array with shape ``(num_envs, 4)`` in float64.
+    """
+    return np_sample_uniform_quaternion(num_envs).astype(np.float64)
+
+
+def _materialize_grasp_caches(
+    grasp_cache_path: str,
+    scale_values: np.ndarray,
+) -> tuple[np.ndarray, ...]:
+    """Load one Sharpa grasp cache for each scale on the env construction path."""
+    grasp_caches: list[np.ndarray] = []
+    missing_files: list[str] = []
+    for scale_value in np.asarray(scale_values, dtype=np.float64):
+        cache_file = resolve_grasp_cache_file(grasp_cache_path, float(scale_value))
+        resolved = cast(str, resolve_grasp_cache_files(str(cache_file)))
+        cache_file = Path(resolved)
+        if not cache_file.exists():
+            missing_files.append(str(cache_file))
+            continue
+        grasp_caches.append(np.load(cache_file).astype(np.float64))
+
+    if missing_files:
+        missing = ", ".join(missing_files)
+        raise RuntimeError(
+            f"Missing Sharpa grasp cache file(s): {missing}\n"
+            "Generate them with:\n"
+            "  bash scripts/sharpa_collect_grasps.sh 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6\n"
+            "See docs/sphinx/source/zh_CN/user_guide/D-tasks/04-sharpa-inhand.md"
+        )
+
+    return tuple(grasp_caches)
+
+
+class SharpaInhandRotationDRProvider(DomainRandomizationProvider):
+    def validate(self, env: Any, capabilities: DomainRandomizationCapabilities) -> None:
+        unsupported = validate_common_reset_randomization(env, capabilities)
+        domain_rand = getattr(env.cfg, "domain_rand", None)
+        if domain_rand is not None and getattr(domain_rand, "randomize_gravity_direction", False):
+            if getattr(domain_rand, "randomize_gravity", False):
+                raise ValueError(
+                    "Use only one Sharpa gravity randomization mode: "
+                    "domain_rand.randomize_gravity_direction or domain_rand.randomize_gravity"
+                )
+            if not capabilities.supports_reset_term(RESET_TERM_GRAVITY):
+                unsupported = unsupported | frozenset({RESET_TERM_GRAVITY})
+        if (
+            domain_rand is not None
+            and domain_rand.force_scale > 0.0
+            and not capabilities.supports_interval_body_force
+        ):
+            raise NotImplementedError(
+                f"{env._backend.backend_type} backend does not support interval body force perturbation"
+            )
+        if domain_rand is not None and domain_rand.randomize_pd_gains:
+            if not capabilities.supports_reset_term(RESET_TERM_KP):
+                unsupported = unsupported | frozenset({RESET_TERM_KP})
+            if not capabilities.supports_reset_term(RESET_TERM_KD):
+                unsupported = unsupported | frozenset({RESET_TERM_KD})
+        if (
+            domain_rand is not None
+            and domain_rand.randomize_com
+            and not capabilities.supports_reset_term(RESET_TERM_BODY_IPOS)
+        ):
+            unsupported = unsupported | frozenset({RESET_TERM_BODY_IPOS})
+        if (
+            domain_rand is not None
+            and domain_rand.randomize_mass
+            and not capabilities.supports_reset_term(RESET_TERM_BODY_MASS)
+        ):
+            unsupported = unsupported | frozenset({RESET_TERM_BODY_MASS})
+        if (
+            domain_rand is not None
+            and domain_rand.randomize_friction
+            and not capabilities.supports_reset_term(RESET_TERM_GEOM_FRICTION)
+        ):
+            unsupported = unsupported | frozenset({RESET_TERM_GEOM_FRICTION})
+        if unsupported:
+            names = ", ".join(sorted(unsupported))
+            raise NotImplementedError(
+                f"{env._backend.backend_type} backend does not support reset randomization terms: {names}"
+            )
+
+    def build_init_randomization_plan(self, env: Any) -> InitRandomizationPlan | None:
+        base_size = getattr(env, "_object_geom_base_size", None)
+
+        if base_size is None:
+            return None
+
+        model_variants = tuple(
+            ModelVariantSpec(
+                geom_size_overrides=(
+                    GeomSizeOverride(
+                        geom_name=env.cfg.object_geom_name,
+                        size=tuple(np.asarray(base_size * scale, dtype=np.float64)),
+                    ),
+                )
+            )
+            for scale in np.asarray(env.scale_values, dtype=np.float64)
+        )
+        return InitRandomizationPlan(
+            model_assignments=np.asarray(env.scale_ids, dtype=np.int32).copy(),
+            model_variants=model_variants,
+        )
+
+    def _sample_reset_pd_gains(
+        self,
+        env: Any,
+        num_reset: int,
+        *,
+        dtype: np.dtype[Any],
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """Sample absolute reset-time PD gains from split-around-1 scale ranges.
+
+        Args:
+            env: Sharpa rotation env instance.
+            num_reset: Number of environments being reset.
+            dtype: Output dtype for the returned gain arrays.
+
+        Returns:
+            Tuple of absolute ``(p_gain, d_gain)`` arrays with shape
+            ``(num_reset, env._num_action)``.
+        """
+        p_gain = np.broadcast_to(env._default_p_gain, (num_reset, env._num_action)).astype(
+            dtype, copy=True
+        )
+        d_gain = np.broadcast_to(env._default_d_gain, (num_reset, env._num_action)).astype(
+            dtype, copy=True
+        )
+        domain_rand = env.cfg.domain_rand
+        if domain_rand.randomize_pd_gains:
+            p_scale = env._sample_pd_scales(
+                domain_rand.randomize_p_gain_scale_lower,
+                domain_rand.randomize_p_gain_scale_upper,
+                shape=(num_reset, env._num_action),
+            )
+            d_scale = env._sample_pd_scales(
+                domain_rand.randomize_d_gain_scale_lower,
+                domain_rand.randomize_d_gain_scale_upper,
+                shape=(num_reset, env._num_action),
+            )
+            p_gain *= p_scale
+            d_gain *= d_scale
+        return p_gain, d_gain
+
+    def _build_info_updates(
+        self,
+        env: Any,
+        env_ids: np.ndarray,
+        hand_qpos: np.ndarray,
+        object_pos: np.ndarray,
+        object_quat: np.ndarray,
+        reset_height_lower: np.ndarray,
+        reset_height_upper: np.ndarray,
+        rot_axis: np.ndarray,
+        p_gain: np.ndarray,
+        d_gain: np.ndarray,
+        friction_scale: np.ndarray | None,
+        randomized_mass: np.ndarray | None,
+        randomized_com_offset: np.ndarray | None,
+        gravity: np.ndarray | None,
+    ) -> dict[str, np.ndarray]:
+        num_reset = hand_qpos.shape[0]
+        dtype = get_global_dtype()
+
+        critic_info = env._build_reset_critic_info(
+            num_reset,
+            env_ids,
+            friction_scale=friction_scale,
+            randomized_mass=randomized_mass,
+            randomized_com_offset=randomized_com_offset,
+            gravity=gravity,
+        ).astype(dtype)
+
+        tactile = np.zeros((num_reset, env._num_tactile), dtype=dtype)
+        contact_pos = np.zeros((num_reset, env._num_tactile * 3), dtype=dtype)
+        hand_qpos_f = hand_qpos.astype(dtype)
+        targets = hand_qpos_f.copy()
+        object_pos_f = object_pos.astype(dtype)
+        init_frame = env._build_policy_frame(
+            dof_pos=hand_qpos_f,
+            targets=targets,
+            tactile=tactile,
+            contact_pos=contact_pos,
+            add_noise=True,
+        )
+        critic_init_frame = env._build_policy_frame(
+            dof_pos=hand_qpos_f,
+            targets=targets,
+            tactile=tactile,
+            contact_pos=contact_pos,
+            add_noise=False,
+        )
+        obs_lag_history = repeat_obs_history(init_frame, env.cfg.obs_history_len).astype(dtype)
+        critic_obs_lag_history = repeat_obs_history(
+            critic_init_frame, env.cfg.obs_history_len
+        ).astype(dtype)
+
+        object_default_pose = np.concatenate(
+            [object_pos_f, object_quat.astype(dtype)], axis=1
+        ).astype(dtype)
+        object_pos_anchor = env._build_object_pos_anchor(object_pos_f).astype(dtype)
+        critic_info = env._fill_critic_info(
+            critic_info=critic_info,
+            object_pos=object_pos_f,
+            object_pos_anchor=object_pos_anchor,
+        )
+
+        info_updates = {
+            "current_actions": np.zeros((num_reset, env._num_action), dtype=dtype),
+            "last_actions": np.zeros((num_reset, env._num_action), dtype=dtype),
+            "prev_targets": hand_qpos_f.copy(),
+            "init_pose": hand_qpos_f.copy(),
+            "prev_hand_pos": hand_qpos_f.copy(),
+            "prev_object_pos": object_pos.astype(dtype).copy(),
+            "prev_object_quat": object_quat.astype(dtype).copy(),
+            "object_default_pose": object_default_pose,
+            "object_pos_anchor": object_pos_anchor,
+            "reset_height_lower": reset_height_lower.astype(dtype),
+            "reset_height_upper": reset_height_upper.astype(dtype),
+            "rot_axis": rot_axis.astype(dtype),
+            "p_gain": p_gain,
+            "d_gain": d_gain,
+            "critic_info": critic_info,
+            "obs_lag_history": obs_lag_history,
+            "critic_obs_lag_history": critic_obs_lag_history,
+            "proprio_hist": env._update_proprio_history(obs_lag_history),
+        }
+        return info_updates
+
+    def build_reset_plan(self, env: Any, env_ids: np.ndarray) -> ResetPlan:
+        num_reset = len(env_ids)
+        if num_reset == 0:
+            return ResetPlan(
+                env_ids=env_ids,
+                qpos=np.zeros((0, env.nq), dtype=np.float64),
+                qvel=np.zeros((0, env.nv), dtype=np.float64),
+                info_updates={},
+                randomization=None,
+            )
+
+        friction_scale = env._sample_friction_scale(num_reset)
+        randomized_mass = env._sample_object_mass(num_reset)
+        randomized_com_offset = env._sample_object_com_offset(num_reset)
+        gravity = env._sample_reset_gravity(num_reset)
+        p_gain, d_gain = self._sample_reset_pd_gains(env, num_reset, dtype=get_global_dtype())
+        grasp_cache = cast(tuple[np.ndarray, ...], env._grasp_cache)
+        sampled_pose = sample_scale_grasp_caches(grasp_cache, env.scale_ids[env_ids])
+
+        hand_qpos = sampled_pose[:, : env._num_action]
+        object_pos = sampled_pose[:, env._num_action : env._num_action + 3]
+        object_quat = sampled_pose[:, env._num_action + 3 : env._num_action + 7]
+
+        rot_axis = np.broadcast_to(env._rot_axis, (num_reset, 3)).copy().astype(np.float64)
+
+        qpos = np.zeros((num_reset, env.nq), dtype=np.float64)
+        qpos[:, : env._num_action] = hand_qpos
+        qpos[:, env._obj_pos_slice] = object_pos
+        qpos[:, env._obj_quat_slice] = object_quat
+
+        qvel = np.zeros((num_reset, env.nv), dtype=np.float64)
+
+        height_range = env.cfg.reset_height_upper - env.cfg.reset_height_lower
+        reset_height_lower = object_pos[:, 2] - 0.5 * height_range
+        reset_height_upper = object_pos[:, 2] + 0.5 * height_range
+
+        info_updates = self._build_info_updates(
+            env,
+            env_ids=env_ids,
+            hand_qpos=hand_qpos,
+            object_pos=object_pos,
+            object_quat=object_quat,
+            reset_height_lower=reset_height_lower,
+            reset_height_upper=reset_height_upper,
+            rot_axis=rot_axis,
+            p_gain=p_gain,
+            d_gain=d_gain,
+            friction_scale=friction_scale,
+            randomized_mass=randomized_mass,
+            randomized_com_offset=randomized_com_offset,
+            gravity=gravity,
+        )
+        # Match the source task by clearing any cached external object force on reset.
+        env._random_object_force[env_ids] = 0.0
+        env._clear_tactile_history(env_ids)
+
+        return ResetPlan(
+            env_ids=env_ids,
+            qpos=qpos,
+            qvel=qvel,
+            info_updates=info_updates,
+            randomization=env._build_reset_randomization(
+                num_reset,
+                p_gain=p_gain,
+                d_gain=d_gain,
+                friction_scale=friction_scale,
+                randomized_mass=randomized_mass,
+                randomized_com_offset=randomized_com_offset,
+                gravity=gravity,
+            ),
+        )
+
+    def build_reset_observation(
+        self,
+        env: Any,
+        env_ids: np.ndarray,
+        info_updates: dict[str, Any],
+    ) -> dict[str, np.ndarray]:
+        del env_ids
+        tactile, contact_pos = env._policy_frame_zeros(len(info_updates["prev_targets"]))
+        return cast(
+            dict[str, np.ndarray],
+            env._compute_obs_from_inputs(
+                info_updates,
+                dof_pos=np.asarray(info_updates["prev_targets"]),
+                object_pos=np.asarray(info_updates["prev_object_pos"]),
+                tactile=tactile,
+                contact_pos=contact_pos,
+            ),
+        )
+
+    def build_interval_randomization_plan(
+        self,
+        env: Any,
+        step_counter: int,
+    ) -> IntervalRandomizationPlan | None:
+        """Build Sharpa object-force perturbations for the upcoming control step.
+
+        Args:
+            env: Sharpa rotation env instance.
+            step_counter: Global environment step counter.
+
+        Returns:
+            Interval randomization plan carrying direct object-force perturbations,
+            or ``None`` when object-force injection is disabled.
+        """
+        del step_counter
+        domain_rand = env.cfg.domain_rand
+        if domain_rand.force_scale <= 0.0:
+            return None
+
+        decay = float(
+            np.power(
+                domain_rand.force_decay,
+                env.cfg.ctrl_dt / max(domain_rand.force_decay_interval, 1.0e-8),
+            )
+        )
+        env._random_object_force *= decay
+
+        random_mask = np.random.rand(env._num_envs) < float(domain_rand.random_force_prob_scalar)
+        if np.any(random_mask):
+            object_mass = env._resolve_current_object_mass()
+            env._random_object_force[random_mask] = (
+                np.random.randn(int(np.sum(random_mask)), 3).astype(np.float64)
+                * object_mass[random_mask, None]
+                * float(domain_rand.force_scale)
+            )
+
+        return IntervalRandomizationPlan(
+            body_ids=np.asarray([env._object_body_id], dtype=np.int32),
+            body_force=env._random_object_force[:, None, :].copy(),
+        )
+
+
+class SharpaInhandRotationEnv(SharpaInhandBaseEnv):
+    _cfg: SharpaInhandRotationCfg  # pyright: ignore[reportIncompatibleVariableOverride]
+    _reward_cfg: RewardConfig
+    _MATERIALIZE_ROTATION_GRASP_CACHE = True
+    _OBS_MODE_ALIASES: dict[str, str] = {
+        "separated": "separated",
+        "flattened": "flattened",
+    }
+    _CRITIC_BASE_DIM_WITHOUT_OPTIONALS = 8
+
+    def __init__(
+        self,
+        cfg: SharpaInhandRotationCfg,
+        num_envs: int = 1,
+        backend_type: str = "motrix",
+        dr_provider: DomainRandomizationProvider | None = None,
+    ) -> None:
+        if cfg.reward_config is None:
+            raise ValueError("reward_config must be provided via Hydra configuration")
+
+        backend = create_backend(
+            backend_type,
+            cfg.scene,
+            num_envs,
+            cfg.sim_dt,
+            base_name=cfg.base_name,
+            push_body_name=cfg.domain_rand.push_body_name,
+            add_body_sensors=True,
+            **env_backend_kwargs(cfg),
+        )
+        super().__init__(cfg, backend, num_envs)
+
+        self._observation_mode = self._resolve_observation_mode(cfg.obs.observation_mode)
+        expected_critic_info_dim = self._expected_critic_info_dim()
+        if cfg.critic_info_dim != expected_critic_info_dim:
+            cfg.critic_info_dim = expected_critic_info_dim
+        policy_frame_dim = self._policy_frame_dim()
+        self.obs_buf_lag_history = np.zeros(
+            (num_envs, cfg.obs_history_len, policy_frame_dim), dtype=self._np_dtype
+        )
+        self.proprio_hist_buf = np.zeros(
+            (num_envs, cfg.prop_hist_len, policy_frame_dim), dtype=self._np_dtype
+        )
+        self.critic_info_buf = np.zeros((num_envs, expected_critic_info_dim), dtype=self._np_dtype)
+        self._friction_geom_ids = self._resolve_friction_geom_ids()
+        self._base_geom_friction = self._resolve_base_geom_friction()
+        self._base_gravity = self._resolve_base_gravity()
+        self._object_body_id = self._resolve_object_body_id()
+        self._base_body_mass = self._resolve_base_body_mass()
+        self._base_body_ipos = self._resolve_base_body_ipos()
+        self._default_p_gain, self._default_d_gain = self._load_default_pd_gains()
+        self._random_object_force = np.zeros((num_envs, 3), dtype=np.float64)
+
+        if cfg.control_config.torque_control:
+            raise NotImplementedError(
+                "Sharpa torque_control=True is not implemented with the current position-actuator XML setup. "
+                "Set env.control_config.torque_control=false. Virtual torques are still computed explicitly for reward terms."
+            )
+
+        self._reward_cfg = cfg.reward_config
+        self._zero_action_test_mode = bool(cfg.zero_action_test_mode)
+        self._enable_reward_log = True
+        self._grasp_cache: tuple[np.ndarray, ...] | None = None
+
+        axis = np.asarray(cfg.rot_axis, dtype=self._np_dtype)
+        axis_norm = np.linalg.norm(axis)
+        if axis_norm <= 1.0e-8:
+            raise ValueError("rot_axis must be non-zero")
+        self._rot_axis = np.asarray(axis / axis_norm, dtype=self._np_dtype)
+
+        provider = dr_provider if dr_provider is not None else SharpaInhandRotationDRProvider()
+        if self._MATERIALIZE_ROTATION_GRASP_CACHE:
+            self._grasp_cache = _materialize_grasp_caches(cfg.grasp_cache_path, self.scale_values)
+        self._init_domain_randomization(provider)
+
+    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
+        actions_np = np.asarray(actions, dtype=self._np_dtype)
+        if self._zero_action_test_mode:
+            actions_np = np.zeros_like(actions_np, dtype=self._np_dtype)
+        return super().apply_action(actions_np, state)
+
+    def _scale_randomization_enabled(self) -> bool:
+        return self._num_scales > 1 or not np.allclose(self.scale_values, self.scale_values[0])
+
+    def _expected_critic_info_dim(self) -> int:
+        priv_info_cfg = self._cfg.priv_info
+        dim = self._CRITIC_BASE_DIM_WITHOUT_OPTIONALS
+        if priv_info_cfg.include_friction_scale:
+            dim += 1
+        if priv_info_cfg.include_gravity_direction:
+            dim += 3
+        return dim
+
+    def _resolve_friction_geom_ids(self) -> dict[str, np.ndarray]:
+        """Resolve MuJoCo geom ids touched by Sharpa friction randomization.
+
+        Args:
+            None.
+
+        Returns:
+            Mapping with object, elastomer, and metal collision geom id arrays.
+        """
+        try:
+            object_geom_id = self._backend.get_geom_id(self._cfg.object_geom_name)
+            base_body_id = self._backend.get_body_id(self._cfg.base_name)
+            hand_body_ids = set(
+                int(body_id) for body_id in self._backend.get_body_subtree_ids(base_body_id)
+            )
+            geom_body_ids = self._backend.get_geom_body_ids()
+            geom_contype, geom_conaffinity = self._backend.get_geom_contact_masks()
+            geom_names = self._backend.get_geom_names()
+        except NotImplementedError:
+            empty = np.zeros((0,), dtype=np.int32)
+            return {"object": empty, "elastomer": empty, "metal": empty}
+        elastomer_ids: list[int] = []
+        metal_ids: list[int] = []
+        for geom_id, geom_name in enumerate(geom_names):
+            if int(geom_body_ids[geom_id]) not in hand_body_ids:
+                continue
+            if int(geom_contype[geom_id]) == 0 and int(geom_conaffinity[geom_id]) == 0:
+                continue
+            if "elastomer" in geom_name:
+                elastomer_ids.append(geom_id)
+            else:
+                metal_ids.append(geom_id)
+
+        if not elastomer_ids:
+            raise ValueError("No Sharpa elastomer collision geoms found for friction randomization")
+        if not metal_ids:
+            raise ValueError("No Sharpa metal collision geoms found for friction randomization")
+
+        return {
+            "object": np.asarray([object_geom_id], dtype=np.int32),
+            "elastomer": np.asarray(elastomer_ids, dtype=np.int32),
+            "metal": np.asarray(metal_ids, dtype=np.int32),
+        }
+
+    def _resolve_object_body_id(self) -> int:
+        """Resolve the MuJoCo body id of the manipulated object.
+
+        Args:
+            None.
+
+        Returns:
+            The MuJoCo object body id, or -1 when the backend is not MuJoCo.
+        """
+        if self._object_body_ids.size == 0:
+            return -1
+        return int(self._object_body_ids[0])
+
+    def _resolve_current_object_mass(self) -> np.ndarray:
+        """Resolve the current object mass used by force perturbation sampling.
+
+        Args:
+            None.
+
+        Returns:
+            Array of shape ``(num_envs,)`` with current object masses.
+        """
+        if self.state is not None:
+            critic_info = np.asarray(
+                self.state.info.get(
+                    "critic_info",
+                    np.zeros((self._num_envs, self._cfg.critic_info_dim), dtype=self._np_dtype),
+                ),
+                dtype=np.float64,
+            )
+            mass = critic_info[:, self._critic_info_layout()["mass"]].reshape(self._num_envs)
+            if np.all(mass > 0.0):
+                return mass
+        if self._base_body_mass is None or self._object_body_id < 0:
+            raise ValueError("MuJoCo object body-mass cache is unavailable")
+        return np.full(
+            (self._num_envs,),
+            float(self._base_body_mass[self._object_body_id]),
+            dtype=np.float64,
+        )
+
+    def _resolve_base_geom_friction(self) -> np.ndarray | None:
+        """Cache MuJoCo model friction vectors used as torsional/rolling templates.
+
+        Args:
+            None.
+
+        Returns:
+            Full geom friction table, or None when the backend has no geom-friction hook.
+        """
+        try:
+            return np.asarray(self._backend.get_geom_friction(), dtype=np.float64).copy()
+        except NotImplementedError:
+            return None
+
+    def _resolve_base_gravity(self) -> np.ndarray:
+        """Cache the default gravity vector for privileged-info fallbacks.
+
+        Args:
+            None.
+
+        Returns:
+            Gravity vector with shape ``(3,)``.
+        """
+        try:
+            return np.asarray(self._backend.get_gravity(), dtype=np.float64).copy()
+        except NotImplementedError:
+            pass
+        return np.asarray([0.0, 0.0, -9.81], dtype=np.float64)
+
+    def _resolve_base_body_mass(self) -> np.ndarray | None:
+        """Cache the MuJoCo body-mass table used as reset randomization baseline.
+
+        Args:
+            None.
+
+        Returns:
+            Full body-mass table, or None when the backend is not MuJoCo.
+        """
+        try:
+            return np.asarray(self._backend.get_body_mass(), dtype=np.float64).copy()
+        except NotImplementedError:
+            return None
+
+    def _resolve_base_body_ipos(self) -> np.ndarray | None:
+        """Cache the MuJoCo inertial-position table used for object COM randomization.
+
+        Args:
+            None.
+
+        Returns:
+            Full body inertial-position table, or None when the backend is not MuJoCo.
+        """
+        try:
+            return np.asarray(self._backend.get_body_ipos(), dtype=np.float64).copy()
+        except NotImplementedError:
+            return None
+
+    def _sample_friction_scale(self, batch_size: int) -> np.ndarray | None:
+        """Sample one friction multiplier per reset environment.
+
+        Args:
+            batch_size: Number of reset environments.
+
+        Returns:
+            Shape (batch_size, 1) multipliers, or None when disabled.
+        """
+        domain_rand = self._cfg.domain_rand
+        if not domain_rand.randomize_friction:
+            return None
+        return np.random.uniform(
+            domain_rand.randomize_friction_scale_lower,
+            domain_rand.randomize_friction_scale_upper,
+            size=(batch_size, 1),
+        ).astype(np.float64)
+
+    def _sample_object_mass(self, batch_size: int) -> np.ndarray | None:
+        """Sample reset-time object masses.
+
+        Args:
+            batch_size: Number of reset environments.
+
+        Returns:
+            Shape (batch_size, 1) absolute object masses, or None when disabled.
+        """
+        domain_rand = self._cfg.domain_rand
+        if not domain_rand.randomize_mass:
+            return None
+        return np.random.uniform(
+            domain_rand.randomize_mass_lower,
+            domain_rand.randomize_mass_upper,
+            size=(batch_size, 1),
+        ).astype(np.float64)
+
+    def _sample_object_com_offset(self, batch_size: int) -> np.ndarray | None:
+        """Sample reset-time object COM offsets.
+
+        Args:
+            batch_size: Number of reset environments.
+
+        Returns:
+            Shape (batch_size, 3) COM offsets, or None when disabled.
+        """
+        domain_rand = self._cfg.domain_rand
+        if not domain_rand.randomize_com:
+            return None
+        return np.random.uniform(
+            domain_rand.randomize_com_lower,
+            domain_rand.randomize_com_upper,
+            size=(batch_size, 3),
+        ).astype(np.float64)
+
+    def _friction_profile(self, material: str, base_sliding_friction: float) -> np.ndarray:
+        """Build one MuJoCo friction vector while preserving XML friction ratios.
+
+        Args:
+            material: One of object, elastomer, or metal.
+            base_sliding_friction: Sliding-friction coefficient before random scaling.
+
+        Returns:
+            Shape (3,) MuJoCo friction vector.
+        """
+        if self._base_geom_friction is None:
+            raise ValueError("MuJoCo base geom friction cache is unavailable")
+
+        geom_ids = self._friction_geom_ids[material]
+        template = np.asarray(self._base_geom_friction[int(geom_ids[0])], dtype=np.float64)
+        sliding = float(template[0])
+        if sliding <= 0.0:
+            raise ValueError(f"{material} base sliding friction must be positive, got {sliding}")
+        return np.asarray(template / sliding * base_sliding_friction, dtype=np.float64)
+
+    def _sample_reset_gravity(self, batch_size: int) -> np.ndarray | None:
+        """Sample the exact reset-time gravity vector applied by Sharpa.
+
+        Args:
+            batch_size: Number of reset environments.
+
+        Returns:
+            Gravity array with shape ``(batch_size, 3)``, or ``None`` when reset-time
+            gravity randomization is disabled.
+        """
+        gravity = self._build_gravity_direction_randomization(batch_size)
+        if gravity is not None:
+            return gravity
+        domain_rand = self._cfg.domain_rand
+        if not getattr(domain_rand, "randomize_gravity", False):
+            return None
+        gravity_range = np.asarray(domain_rand.gravity_range, dtype=np.float64)
+        if gravity_range.shape != (2, 3):
+            raise ValueError(
+                f"domain_rand.gravity_range must have shape (2, 3), got {gravity_range.shape}"
+            )
+        low = np.minimum(gravity_range[0], gravity_range[1])
+        high = np.maximum(gravity_range[0], gravity_range[1])
+        return np.random.uniform(low=low, high=high, size=(batch_size, 3)).astype(np.float64)
+
+    def _resolved_privileged_gravity(
+        self,
+        batch_size: int,
+        gravity: np.ndarray | None,
+    ) -> np.ndarray:
+        """Resolve the gravity vector written into privileged info for one reset batch.
+
+        Args:
+            batch_size: Number of reset environments.
+            gravity: Sampled reset gravity, if reset-time randomization is active.
+
+        Returns:
+            Gravity array with shape ``(batch_size, 3)``.
+        """
+        if gravity is not None:
+            return np.asarray(gravity, dtype=self._np_dtype)
+        return np.broadcast_to(self._base_gravity, (batch_size, 3)).astype(
+            self._np_dtype, copy=True
+        )
+
+    def _build_friction_randomization(
+        self,
+        batch_size: int,
+        friction_scale: np.ndarray | None,
+    ) -> np.ndarray | None:
+        """Build the reset-time MuJoCo geom_friction randomization table.
+
+        Args:
+            batch_size: Number of reset environments.
+            friction_scale: Shape (batch_size, 1) sampled friction multipliers.
+
+        Returns:
+            Shape (batch_size, ngeom, 3) friction table, or None when disabled.
+        """
+        if friction_scale is None:
+            return None
+        if self._base_geom_friction is None:
+            raise ValueError("MuJoCo base geom friction cache is unavailable")
+
+        geom_friction = np.broadcast_to(
+            self._base_geom_friction,
+            (batch_size, *self._base_geom_friction.shape),
+        ).copy()
+        scale = np.asarray(friction_scale, dtype=np.float64).reshape(batch_size, 1, 1)
+        domain_rand = self._cfg.domain_rand
+        material_profiles = {
+            "object": self._friction_profile("object", domain_rand.object_base_friction),
+            "elastomer": self._friction_profile("elastomer", domain_rand.elastomer_base_friction),
+            "metal": self._friction_profile("metal", domain_rand.metal_base_friction),
+        }
+        for material, profile in material_profiles.items():
+            geom_friction[:, self._friction_geom_ids[material], :] = scale * profile.reshape(
+                1, 1, 3
+            )
+        return geom_friction
+
+    def _build_object_mass_randomization(
+        self,
+        batch_size: int,
+        randomized_mass: np.ndarray | None,
+    ) -> np.ndarray | None:
+        """Build the reset-time MuJoCo body_mass table for object mass randomization.
+
+        Args:
+            batch_size: Number of reset environments.
+            randomized_mass: Shape (batch_size, 1) sampled absolute object masses.
+
+        Returns:
+            Shape (batch_size, nbody) mass table, or None when disabled.
+        """
+        if randomized_mass is None:
+            return None
+        if self._base_body_mass is None or self._object_body_id < 0:
+            raise ValueError("MuJoCo base body-mass cache is unavailable")
+        body_mass = np.broadcast_to(
+            self._base_body_mass, (batch_size, self._base_body_mass.size)
+        ).copy()
+        body_mass[:, self._object_body_id] = np.asarray(randomized_mass, dtype=np.float64).reshape(
+            batch_size
+        )
+        return body_mass
+
+    def _build_object_com_randomization(
+        self,
+        batch_size: int,
+        randomized_com_offset: np.ndarray | None,
+    ) -> np.ndarray | None:
+        """Build the reset-time MuJoCo body_ipos table for object COM randomization.
+
+        Args:
+            batch_size: Number of reset environments.
+            randomized_com_offset: Shape (batch_size, 3) sampled COM offsets.
+
+        Returns:
+            Shape (batch_size, nbody, 3) inertial-position table, or None when disabled.
+        """
+        if randomized_com_offset is None:
+            return None
+        if self._base_body_ipos is None or self._object_body_id < 0:
+            raise ValueError("MuJoCo base body-ipos cache is unavailable")
+        body_ipos = np.broadcast_to(
+            self._base_body_ipos,
+            (batch_size, *self._base_body_ipos.shape),
+        ).copy()
+        body_ipos[:, self._object_body_id, :] += np.asarray(randomized_com_offset, dtype=np.float64)
+        return body_ipos
+
+    def _build_gravity_direction_randomization(self, batch_size: int) -> np.ndarray | None:
+        """Sample fixed-magnitude gravity vectors with randomized directions.
+
+        Args:
+            batch_size: Number of reset environments.
+
+        Returns:
+            Gravity vectors with shape ``(batch_size, 3)``, or None when disabled.
+        """
+        domain_rand = self._cfg.domain_rand
+        if not getattr(domain_rand, "randomize_gravity_direction", False):
+            return None
+        magnitude = float(getattr(domain_rand, "gravity_direction_magnitude", 9.81))
+        if magnitude <= 0.0:
+            raise ValueError(f"gravity_direction_magnitude must be positive, got {magnitude}")
+        gravity = np.zeros((batch_size, 3), dtype=np.float64)
+        gravity[:, 2] = -magnitude
+        random_quat = sample_random_quaternion(batch_size)
+        # A uniform quaternion and its inverse have the same distribution, so
+        # rotating gravity samples the same relative gravity directions induced
+        # by uniformly rotating the global hand/object/task frame.
+        return np.asarray(np_quat_apply(random_quat, gravity), dtype=np.float64)
+
+    def _build_reset_randomization(
+        self,
+        batch_size: int,
+        *,
+        p_gain: np.ndarray | None,
+        d_gain: np.ndarray | None,
+        friction_scale: np.ndarray | None,
+        randomized_mass: np.ndarray | None,
+        randomized_com_offset: np.ndarray | None,
+        gravity: np.ndarray | None,
+    ) -> ResetRandomizationPayload | None:
+        """Build reset-randomization payloads owned by the Sharpa rotation env.
+
+        Args:
+            batch_size: Number of reset environments.
+            friction_scale: Shape (batch_size, 1) sampled friction multipliers.
+
+        Returns:
+            Reset-randomization payload, or None when no backend randomization is requested.
+        """
+        payload = build_common_reset_randomization(self, batch_size)
+        if self._cfg.domain_rand.randomize_pd_gains:
+            if payload is None:
+                payload = ResetRandomizationPayload()
+            payload.kp = np.asarray(p_gain, dtype=np.float64)
+            payload.kd = np.asarray(d_gain, dtype=np.float64)
+        body_mass = self._build_object_mass_randomization(batch_size, randomized_mass)
+        body_ipos = self._build_object_com_randomization(batch_size, randomized_com_offset)
+        geom_friction = self._build_friction_randomization(batch_size, friction_scale)
+        if body_mass is not None:
+            if payload is None:
+                payload = ResetRandomizationPayload()
+            payload.body_mass = body_mass
+        if body_ipos is not None:
+            if payload is None:
+                payload = ResetRandomizationPayload()
+            payload.body_ipos = body_ipos
+        if geom_friction is not None:
+            if payload is None:
+                payload = ResetRandomizationPayload()
+            payload.geom_friction = geom_friction
+        if gravity is not None:
+            if payload is None:
+                payload = ResetRandomizationPayload()
+            payload.gravity = gravity
+        return payload
+
+    def _critic_info_layout(self) -> dict[str, slice]:
+        """Describe the flat critic_info channel layout.
+
+        Args:
+            None.
+
+        Returns:
+            Mapping from logical field names to channel slices.
+        """
+        offset = 0
+        layout = {"object_pos_delta": slice(offset, offset + 3)}
+        offset += 3
+        priv_info_cfg = self._cfg.priv_info
+        if priv_info_cfg.include_friction_scale:
+            layout["friction"] = slice(offset, offset + 1)
+            offset += 1
+        layout["mass"] = slice(offset, offset + 1)
+        offset += 1
+        layout["com"] = slice(offset, offset + 3)
+        offset += 3
+        layout["scale"] = slice(offset, offset + 1)
+        offset += 1
+        if priv_info_cfg.include_gravity_direction:
+            layout["gravity"] = slice(offset, offset + 3)
+        return layout
+
+    def _assign_critic_info_field(
+        self,
+        critic_info: np.ndarray,
+        field_name: str,
+        values: np.ndarray,
+    ) -> None:
+        """Assign one critic_info field according to the declared layout.
+
+        Args:
+            critic_info: Critic-info buffer to update in place.
+            field_name: Logical field name from the layout.
+            values: Batch-major values for the field.
+
+        Returns:
+            None. The critic_info array is updated in place.
+        """
+        field_slice = self._critic_info_layout().get(field_name)
+        if field_slice is None:
+            return
+        field_values = np.asarray(values, dtype=self._np_dtype).reshape(critic_info.shape[0], -1)
+        critic_info[:, field_slice] = field_values
+
+    def _build_reset_critic_info(
+        self,
+        batch_size: int,
+        env_ids: np.ndarray,
+        *,
+        friction_scale: np.ndarray | None,
+        randomized_mass: np.ndarray | None,
+        randomized_com_offset: np.ndarray | None,
+        gravity: np.ndarray | None,
+    ) -> np.ndarray:
+        """Build reset-time critic_info for all randomized object properties.
+
+        Args:
+            batch_size: Number of reset environments.
+            env_ids: Global environment ids for this reset batch.
+
+        Returns:
+            Critic-info tensor with shape (batch_size, critic_info_dim).
+        """
+        critic_info = np.zeros((batch_size, self._cfg.critic_info_dim), dtype=self._np_dtype)
+
+        priv_info_cfg = self._cfg.priv_info
+        if priv_info_cfg.include_friction_scale:
+            self._assign_critic_info_field(
+                critic_info,
+                "friction",
+                (
+                    friction_scale
+                    if friction_scale is not None
+                    else np.ones((batch_size, 1), dtype=np.float64)
+                ),
+            )
+        if randomized_mass is not None:
+            self._assign_critic_info_field(
+                critic_info,
+                "mass",
+                randomized_mass,
+            )
+        if randomized_com_offset is not None:
+            self._assign_critic_info_field(
+                critic_info,
+                "com",
+                randomized_com_offset,
+            )
+        if self._scale_randomization_enabled():
+            self._assign_critic_info_field(
+                critic_info,
+                "scale",
+                self.scale_values[self.scale_ids[env_ids]].reshape(batch_size, 1),
+            )
+        if priv_info_cfg.include_gravity_direction:
+            self._assign_critic_info_field(
+                critic_info,
+                "gravity",
+                self._resolved_privileged_gravity(batch_size, gravity),
+            )
+        return critic_info
+
+    def _policy_frame_dim(self) -> int:
+        obs_cfg = self._cfg.obs
+        dim = self._num_action + self._num_action
+        if obs_cfg.enable_tactile:
+            dim += self._num_tactile
+        if obs_cfg.enable_contact_pos:
+            dim += self._num_tactile * 3
+        return dim
+
+    def _policy_frame_zeros(self, batch_size: int) -> tuple[np.ndarray, np.ndarray]:
+        """Build zero-filled optional policy inputs for reset observations.
+
+        Args:
+            batch_size: Number of environments in the batch.
+
+        Returns:
+            Tuple of tactile and contact-position arrays sized for the current config.
+        """
+        obs_cfg = self._cfg.obs
+        tactile_dim = self._num_tactile if obs_cfg.enable_tactile else 0
+        contact_pos_dim = self._num_tactile * 3 if obs_cfg.enable_contact_pos else 0
+        return (
+            np.zeros((batch_size, tactile_dim), dtype=self._np_dtype),
+            np.zeros((batch_size, contact_pos_dim), dtype=self._np_dtype),
+        )
+
+    def _fixed_default_object_pose(self, batch_size: int) -> np.ndarray:
+        """Build the fixed default object pose from the backend init state.
+
+        Args:
+            batch_size: Number of environments in the batch.
+
+        Returns:
+            Array with shape ``(batch_size, 7)`` containing the default object
+            position and quaternion loaded from the backend/model init qpos.
+        """
+        object_pos = np.broadcast_to(
+            np.asarray(self._init_qpos[self._obj_pos_slice], dtype=self._np_dtype),
+            (batch_size, 3),
+        ).copy()
+        object_quat = np.broadcast_to(
+            np.asarray(self._init_qpos[self._obj_quat_slice], dtype=self._np_dtype),
+            (batch_size, 4),
+        ).copy()
+        return np.concatenate([object_pos, object_quat], axis=1).astype(self._np_dtype)
+
+    def _build_object_pos_anchor(
+        self,
+        object_pos: np.ndarray,
+    ) -> np.ndarray:
+        """Choose the reward/privileged-info object-position anchor.
+
+        Args:
+            object_pos: Reset-time object positions with shape ``(batch_size, 3)``.
+
+        Returns:
+            Array with shape ``(batch_size, 3)``. When the config flag is set,
+            the fixed XML/default object position is used; otherwise the sampled
+            reset position is returned to preserve the legacy UniLab behavior.
+        """
+        object_pos = np.asarray(object_pos, dtype=self._np_dtype)
+        if self._cfg.use_default_object_pose_for_object_pos_anchor:
+            return self._fixed_default_object_pose(object_pos.shape[0])[:, 0:3]
+        return object_pos.copy()
+
+    def _resolve_object_pos_anchor(
+        self,
+        info: dict[str, Any],
+        batch_size: int,
+    ) -> np.ndarray:
+        """Resolve the cached object-position anchor for one runtime batch.
+
+        Args:
+            info: Mutable env-state info dictionary.
+            batch_size: Number of environments in the current batch.
+
+        Returns:
+            Array with shape ``(batch_size, 3)`` used by the object-position
+            reward and privileged-info delta channels.
+        """
+        cached_anchor = info.get("object_pos_anchor")
+        if cached_anchor is not None:
+            return np.asarray(cached_anchor, dtype=self._np_dtype)
+
+        object_default_pose = info.get("object_default_pose")
+        if object_default_pose is not None:
+            return np.asarray(object_default_pose, dtype=self._np_dtype)[:, 0:3]
+
+        if self._cfg.use_default_object_pose_for_object_pos_anchor:
+            return self._fixed_default_object_pose(batch_size)[:, 0:3]
+        return np.zeros((batch_size, 3), dtype=self._np_dtype)
+
+    def _policy_frame_parts(
+        self,
+        dof_norm: np.ndarray,
+        targets: np.ndarray,
+        tactile: np.ndarray,
+        contact_pos: np.ndarray,
+    ) -> list[np.ndarray]:
+        """Collect policy-frame components according to the configured observation layout.
+
+        Args:
+            dof_norm: Normalized hand joint positions.
+            targets: Hand joint targets.
+            tactile: Tactile features.
+            contact_pos: Contact-position features.
+
+        Returns:
+            Ordered list of arrays that should be concatenated into the policy frame.
+        """
+        parts = [dof_norm, targets]
+        obs_cfg = self._cfg.obs
+        if obs_cfg.enable_tactile:
+            parts.append(np.asarray(tactile, dtype=self._np_dtype))
+        if obs_cfg.enable_contact_pos:
+            parts.append(np.asarray(contact_pos, dtype=self._np_dtype))
+        return parts
+
+    def _fill_critic_info(
+        self,
+        critic_info: np.ndarray,
+        object_pos: np.ndarray,
+        object_pos_anchor: np.ndarray,
+    ) -> np.ndarray:
+        """Populate privileged channels that are derived from runtime state.
+
+        Args:
+            critic_info: Critic info buffer to update in place.
+            object_pos: Current object positions with shape (batch, 3).
+            object_pos_anchor: Object-position anchor with shape (batch, 3).
+
+        Returns:
+            Updated critic info array with shape (batch, critic_info_dim).
+        """
+        self._assign_critic_info_field(
+            critic_info,
+            "object_pos_delta",
+            object_pos - object_pos_anchor,
+        )
+        return critic_info
+
+    @classmethod
+    def _resolve_observation_mode(cls, observation_mode: str) -> str:
+        normalized_mode = str(observation_mode).strip().lower()
+        resolved_mode = cls._OBS_MODE_ALIASES.get(normalized_mode)
+        if resolved_mode is None:
+            supported_modes = "', '".join(sorted(cls._OBS_MODE_ALIASES))
+            raise ValueError(
+                f"observation_mode must be one of '{supported_modes}', got {observation_mode!r}"
+            )
+        return resolved_mode
+
+    def _build_policy_frame(
+        self,
+        dof_pos: np.ndarray,
+        targets: np.ndarray,
+        tactile: np.ndarray,
+        contact_pos: np.ndarray,
+        *,
+        add_noise: bool = True,
+    ) -> np.ndarray:
+        dof_pos_f = np.asarray(dof_pos, dtype=self._np_dtype)
+        targets_f = np.asarray(targets, dtype=self._np_dtype)
+
+        dof_norm = self._normalize_joint_pos(dof_pos_f)
+        joint_noise_scale = float(self._cfg.domain_rand.joint_noise_scale)
+        if add_noise and joint_noise_scale > 0.0:
+            dof_norm += (
+                np.random.uniform(-1.0, 1.0, size=dof_norm.shape).astype(self._np_dtype)
+                * joint_noise_scale
+            )
+
+        frame = np.asarray(
+            np.concatenate(
+                self._policy_frame_parts(
+                    dof_norm=dof_norm,
+                    targets=targets_f,
+                    tactile=tactile,
+                    contact_pos=contact_pos,
+                ),
+                axis=1,
+            ),
+            dtype=self._np_dtype,
+        )
+        return self._clip_observation_values(frame)
+
+    def _clip_observation_values(self, values: np.ndarray) -> np.ndarray:
+        """Clamp observation tensors to a configurable absolute value bound.
+
+        Args:
+            values: Observation-like array to clip.
+
+        Returns:
+            Clipped array with the same shape. Non-positive ``clip_obs`` disables the
+            clamp so callers can opt out when needed.
+        """
+        clip_max = float(getattr(self._cfg, "clip_obs", 5.0))
+        values = np.asarray(values, dtype=self._np_dtype)
+        if clip_max <= 0.0:
+            return values
+        return np.asarray(np.clip(values, -clip_max, clip_max), dtype=self._np_dtype)
+
+    @property
+    def obs_groups_spec(self) -> dict[str, int]:
+        policy_obs_dim = self._cfg.obs_lag_steps * self._policy_frame_dim()
+        if self._observation_mode == "flattened":
+            return {"obs": policy_obs_dim + self._cfg.critic_info_dim}
+        return {"obs": policy_obs_dim, "critic": policy_obs_dim + self._cfg.critic_info_dim}
+
+    def _build_critic_info(
+        self,
+        info: dict[str, Any],
+        batch_size: int,
+        object_pos: np.ndarray,
+    ) -> np.ndarray:
+        """Build privileged critic info for the current batch.
+
+        Args:
+            info: Mutable state info dictionary carrying reset-time caches.
+            batch_size: Number of environments in the current batch.
+            object_pos: Current object positions with shape (batch, 3).
+
+        Returns:
+            Privileged info array with shape (batch, critic_info_dim).
+        """
+        critic_info = np.asarray(
+            info.get(
+                "critic_info",
+                np.zeros((batch_size, self._cfg.critic_info_dim), dtype=self._np_dtype),
+            ),
+            dtype=self._np_dtype,
+        )
+
+        object_pos_anchor = self._resolve_object_pos_anchor(info, batch_size)
+        critic_info = self._fill_critic_info(
+            critic_info=critic_info,
+            object_pos=object_pos,
+            object_pos_anchor=object_pos_anchor,
+        )
+
+        info["critic_info"] = critic_info
+        return critic_info
+
+    def _pack_observations(
+        self,
+        policy_obs: np.ndarray,
+        critic_info: np.ndarray,
+        critic_policy_obs: np.ndarray | None = None,
+    ) -> dict[str, np.ndarray]:
+        """Pack actor and privileged info into the env observation groups.
+
+        Args:
+            policy_obs: Actor observation tensor with shape (batch, actor_dim).
+            critic_info: Privileged tensor with shape (batch, critic_info_dim).
+
+        Returns:
+            Observation groups that satisfy the UniLab env contract.
+        """
+        if self._observation_mode == "flattened":
+            flattened_obs = self._clip_observation_values(
+                np.concatenate([policy_obs, critic_info], axis=1).astype(self._np_dtype)
+            )
+            return {"obs": flattened_obs}
+
+        if critic_policy_obs is None:
+            critic_policy_obs = policy_obs
+        return {
+            "obs": self._clip_observation_values(policy_obs),
+            "critic": self._clip_observation_values(
+                np.concatenate([critic_policy_obs, critic_info], axis=1).astype(self._np_dtype)
+            ),
+        }
+
+    def _compute_obs_from_inputs(
+        self,
+        info: dict[str, Any],
+        dof_pos: np.ndarray,
+        object_pos: np.ndarray,
+        tactile: np.ndarray,
+        contact_pos: np.ndarray,
+    ) -> dict[str, np.ndarray]:
+        targets = np.asarray(info.get("prev_targets", dof_pos), dtype=self._np_dtype)
+        frame = self._build_policy_frame(
+            dof_pos=dof_pos,
+            targets=targets,
+            tactile=tactile,
+            contact_pos=contact_pos,
+            add_noise=True,
+        )
+        critic_frame = self._build_policy_frame(
+            dof_pos=dof_pos,
+            targets=targets,
+            tactile=tactile,
+            contact_pos=contact_pos,
+            add_noise=False,
+        )
+        batch_size = int(frame.shape[0])
+
+        history = info.get("obs_lag_history")
+        critic_history = info.get("critic_obs_lag_history")
+        if history is None:
+            history = repeat_obs_history(frame, self._cfg.obs_history_len).astype(self._np_dtype)
+        else:
+            history = np.asarray(history, dtype=self._np_dtype)
+            history[:, :-1] = history[:, 1:]
+            history[:, -1] = frame
+        if critic_history is None:
+            critic_history = repeat_obs_history(critic_frame, self._cfg.obs_history_len).astype(
+                self._np_dtype
+            )
+        else:
+            critic_history = np.asarray(critic_history, dtype=self._np_dtype)
+            critic_history[:, :-1] = critic_history[:, 1:]
+            critic_history[:, -1] = critic_frame
+
+        info["obs_lag_history"] = history
+        info["critic_obs_lag_history"] = critic_history
+        info["proprio_hist"] = self._update_proprio_history(history)
+
+        obs = np.asarray(
+            history[:, -self._cfg.obs_lag_steps :].reshape(batch_size, -1),
+            dtype=self._np_dtype,
+        )
+        critic_obs = np.asarray(
+            critic_history[:, -self._cfg.obs_lag_steps :].reshape(batch_size, -1),
+            dtype=self._np_dtype,
+        )
+        critic_info = self._build_critic_info(info, batch_size=batch_size, object_pos=object_pos)
+        return self._pack_observations(obs, critic_info, critic_policy_obs=critic_obs)
+
+    def _compute_reward(
+        self,
+        info: dict[str, Any],
+        dof_pos: np.ndarray,
+        dof_vel: np.ndarray,
+        object_pos: np.ndarray,
+        object_linvel: np.ndarray,
+        object_angvel: np.ndarray,
+        torques: np.ndarray,
+    ) -> np.ndarray:
+        rot_axis = np.asarray(
+            info.get("rot_axis", np.broadcast_to(self._rot_axis, (self._num_envs, 3))),
+            dtype=self._np_dtype,
+        )
+        rotate_reward = np.clip(
+            np.sum(object_angvel * rot_axis, axis=1),
+            self._reward_cfg.angvel_clip_min,
+            self._reward_cfg.angvel_clip_max,
+        )
+        object_linvel_penalty = np.sum(np.abs(object_linvel), axis=1)
+        pos_diff_penalty = np.sum(np.square(dof_pos - self.default_angles), axis=1)
+        torque_penalty = np.sum(np.square(torques), axis=1)
+        work_penalty = np.square(np.sum(torques * dof_vel, axis=1))
+
+        object_pos_anchor = self._resolve_object_pos_anchor(info, object_pos.shape[0])
+        object_pos_reward = 1.0 / (np.linalg.norm(object_pos - object_pos_anchor, axis=1) + 0.001)
+
+        reward_terms: dict[str, np.ndarray] = {
+            "rotate": np.asarray(rotate_reward, dtype=self._np_dtype),
+            "obj_linvel": np.asarray(object_linvel_penalty, dtype=self._np_dtype),
+            "pose_diff": np.asarray(pos_diff_penalty, dtype=self._np_dtype),
+            "torque": np.asarray(torque_penalty, dtype=self._np_dtype),
+            "work": np.asarray(work_penalty, dtype=self._np_dtype),
+            "object_pos": np.asarray(object_pos_reward, dtype=self._np_dtype),
+        }
+
+        reward = np.zeros((self._num_envs,), dtype=self._np_dtype)
+        step_count = info.get("steps", np.zeros((self._num_envs,), dtype=np.uint32))
+        should_log = self._enable_reward_log and (int(step_count[0]) % 4 == 0)
+        log = {} if should_log else info.get("log", {})
+
+        for name, scale in self._reward_cfg.scales.items():
+            if scale == 0.0 or name not in reward_terms:
+                continue
+            weighted = reward_terms[name] * scale
+            reward += weighted
+            if should_log:
+                log[f"reward/{name}"] = float(np.mean(weighted))
+
+        if should_log:
+            log["reward/total"] = float(np.mean(reward))
+        info["log"] = log
+
+        return np.asarray(reward, dtype=self._np_dtype) * self._cfg.ctrl_dt
+
+    def update_state(self, state: NpEnvState) -> NpEnvState:
+        dof_pos = self.get_hand_dof_pos()
+        dof_vel = self.get_hand_dof_vel()
+        object_pos = self.get_object_pos()
+        object_quat = self.get_object_quat()
+
+        prev_object_pos = np.asarray(
+            state.info.get("prev_object_pos", object_pos), dtype=self._np_dtype
+        )
+        prev_object_quat = np.asarray(
+            state.info.get("prev_object_quat", object_quat), dtype=self._np_dtype
+        )
+
+        object_linvel = (object_pos - prev_object_pos) / self._cfg.ctrl_dt
+        object_angvel = (
+            np_quat_to_axis_angle(np_quat_mul(object_quat, np_quat_conjugate(prev_object_quat)))
+            / self._cfg.ctrl_dt
+        )
+
+        targets = np.asarray(
+            state.info.get(
+                "prev_targets",
+                np.broadcast_to(self.default_angles, (self._num_envs, self._num_action)).copy(),
+            ),
+            dtype=self._np_dtype,
+        )
+        p_gain, d_gain = self._resolve_pd_gains(state.info)
+
+        # Explicit virtual torque used for reward parity with source Sharpa formulation.
+        virtual_torques = np.asarray(
+            p_gain * (targets - dof_pos) - d_gain * dof_vel,
+            dtype=self._np_dtype,
+        )
+
+        tactile = self._compute_tactile_observation()
+        contact_pos = self._compute_contact_positions(tactile)
+
+        reward = self._compute_reward(
+            state.info,
+            dof_pos=dof_pos,
+            dof_vel=dof_vel,
+            object_pos=object_pos,
+            object_linvel=object_linvel,
+            object_angvel=object_angvel,
+            torques=virtual_torques,
+        )
+
+        reset_height_lower = np.asarray(
+            state.info.get(
+                "reset_height_lower",
+                np.full((self._num_envs,), self._cfg.reset_height_lower, dtype=self._np_dtype),
+            ),
+            dtype=self._np_dtype,
+        )
+        reset_height_upper = np.asarray(
+            state.info.get(
+                "reset_height_upper",
+                np.full((self._num_envs,), self._cfg.reset_height_upper, dtype=self._np_dtype),
+            ),
+            dtype=self._np_dtype,
+        )
+        terminated = (object_pos[:, 2] > reset_height_upper) | (
+            object_pos[:, 2] < reset_height_lower
+        )
+
+        obs = self._compute_obs_from_inputs(
+            state.info,
+            dof_pos=dof_pos,
+            object_pos=object_pos,
+            tactile=tactile,
+            contact_pos=contact_pos,
+        )
+
+        state.info["prev_hand_pos"] = dof_pos.copy()
+        state.info["hand_dof_vel"] = dof_vel.copy()
+        state.info["prev_object_pos"] = object_pos.copy()
+        state.info["prev_object_quat"] = object_quat.copy()
+        state.info["torques"] = virtual_torques
+        state.info["virtual_torques"] = virtual_torques.copy()
+        state.info["object_linvel"] = object_linvel
+        state.info["object_angvel"] = object_angvel
+
+        return state.replace(
+            obs=obs,
+            reward=reward,
+            terminated=np.asarray(terminated, dtype=bool),
+        )
+
+
+SharpaWaveRewardConfig = RewardConfig
+SharpaWaveRotationCfg = SharpaInhandRotationCfg
+
+_SHARPA_ROTATION_COMPAT_FACTORY = adapt_legacy_factory(
+    SharpaInhandRotationEnv,
+    task_family="Sharpa",
+    reason=(
+        "tactile/contact latency, object variants, and grasp-cache state remain "
+        "task-owned until formal Manager-Based capabilities exist"
+    ),
+)
+for _backend_type in ("mujoco", "motrix", "drake"):
+    registry.register_env("SharpaInhandRotation", _SHARPA_ROTATION_COMPAT_FACTORY, _backend_type)
diff --git a/src/unilab/tasks/manipulation/stewart/__init__.py b/src/unilab/tasks/manipulation/stewart/__init__.py
new file mode 100644
index 000000000..5b5f99fa4
--- /dev/null
+++ b/src/unilab/tasks/manipulation/stewart/__init__.py
@@ -0,0 +1,15 @@
+from . import balance as balance
+from .balance import StewartBalanceState as StewartBalanceState
+from .balance import StewartBallReset as StewartBallReset
+from .balance import StewartObservation as StewartObservation
+from .balance import StewartTiltAction as StewartTiltAction
+from .balance import StewartTiltActionCfg as StewartTiltActionCfg
+
+__all__ = [
+    "StewartBalanceState",
+    "StewartBallReset",
+    "StewartObservation",
+    "StewartTiltAction",
+    "StewartTiltActionCfg",
+    "balance",
+]
diff --git a/src/unilab/tasks/manipulation/stewart/balance.py b/src/unilab/tasks/manipulation/stewart/balance.py
new file mode 100644
index 000000000..51faa06d9
--- /dev/null
+++ b/src/unilab/tasks/manipulation/stewart/balance.py
@@ -0,0 +1,798 @@
+"""Manager-Based terms for Stewart-platform ball balancing.
+
+Hydra owns the production task declaration.  This module contains only the
+task-specific NumPy terms and the generic Manager-Based registry binding.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from numbers import Real
+from typing import TYPE_CHECKING, Any, Protocol, cast
+
+import numpy as np
+
+from unilab.base import registry
+from unilab.dtype_config import get_global_dtype
+from unilab.envs import ManagerBasedRlEnvCfg, make_manager_based_rl_env
+from unilab.managers import ActionTerm, ActionTermCfg, ManagerTermBase, ManagerTermBaseCfg
+from unilab.utils.geometry import np_roll_pitch_from_quat
+from unilab.utils.rotation import (
+    np_quat_apply_batched,
+    np_quat_apply_inverse,
+    np_quat_conjugate_batched,
+    np_quat_from_euler_xyz,
+    np_quat_mul_batched,
+    np_quat_to_axis_angle,
+)
+
+if TYPE_CHECKING:
+    from unilab.base.entity import Entity
+    from unilab.managers._types import ManagerBasedRlEnv
+    from unilab.managers.observation_manager import ObservationManager
+    from unilab.managers.termination_manager import TerminationManager
+
+    class _StewartEnv(ManagerBasedRlEnv, Protocol):
+        @property
+        def common_step_counter(self) -> int: ...
+
+        observation_manager: ObservationManager
+
+
+_ACTION_DIM = 2
+_LEG_COUNT = 6
+
+
+def _real(
+    term: str,
+    name: str,
+    value: Any,
+    *,
+    minimum: float | None = None,
+    maximum: float | None = None,
+    strict_minimum: bool = False,
+) -> float:
+    if isinstance(value, (bool, np.bool_)) or not isinstance(value, Real):
+        raise TypeError(f"{term} {name} must be a real number")
+    result = float(value)
+    if not np.isfinite(result):
+        raise ValueError(f"{term} {name} must be finite")
+    if minimum is not None and (result <= minimum if strict_minimum else result < minimum):
+        relation = "greater than" if strict_minimum else "at least"
+        raise ValueError(f"{term} {name} must be {relation} {minimum}")
+    if maximum is not None and result > maximum:
+        raise ValueError(f"{term} {name} must be at most {maximum}")
+    return result
+
+
+def _name(term: str, name: str, value: Any) -> str:
+    if not isinstance(value, str) or not value:
+        raise ValueError(f"{term} {name} must be a non-empty string")
+    return value
+
+
+def _names(term: str, name: str, value: Any, *, count: int) -> tuple[str, ...]:
+    if isinstance(value, (str, bytes)) or not isinstance(value, (tuple, list)):
+        raise TypeError(f"{term} {name} must be a sequence of {count} strings")
+    result = tuple(value)
+    if len(result) != count:
+        raise ValueError(f"{term} {name} must contain {count} names, got {len(result)}")
+    if any(not isinstance(item, str) or not item for item in result):
+        raise ValueError(f"{term} {name} must contain non-empty strings")
+    if len(set(result)) != count:
+        raise ValueError(f"{term} {name} must contain unique names: {result}")
+    return result
+
+
+def _pair(term: str, name: str, value: Any) -> tuple[float, float]:
+    if isinstance(value, (str, bytes)) or not isinstance(value, (tuple, list)):
+        raise TypeError(f"{term} {name} must be a numeric (min, max) pair")
+    if len(value) != 2:
+        raise ValueError(f"{term} {name} must contain two values")
+    lower = _real(term, f"{name}[0]", value[0])
+    upper = _real(term, f"{name}[1]", value[1])
+    if lower > upper:
+        raise ValueError(f"{term} {name} lower bound {lower} exceeds upper bound {upper}")
+    return lower, upper
+
+
+def _env_ids(env: ManagerBasedRlEnv, env_ids: np.ndarray | slice | None) -> np.ndarray:
+    if env_ids is None:
+        return np.arange(env.num_envs, dtype=np.int32)
+    if isinstance(env_ids, slice):
+        return np.arange(env.num_envs, dtype=np.int32)[env_ids]
+    return env_ids
+
+
+def _body_id(entity: Entity, name: str, *, term: str) -> int:
+    ids, resolved = entity.find_bodies(name)
+    if len(ids) != 1 or resolved != [name]:
+        raise ValueError(f"{term} body selector {name!r} did not resolve exactly once")
+    return ids[0]
+
+
+def _body_ids(entity: Entity, names: tuple[str, ...], *, term: str) -> np.ndarray:
+    ids, resolved = entity.find_bodies(names, preserve_order=True)
+    if tuple(resolved) != names:
+        raise ValueError(f"{term} body selectors resolved in an unexpected order: {resolved}")
+    result = np.asarray(ids, dtype=np.intp)
+    result.setflags(write=False)
+    return result
+
+
+def _relative_ball_state(
+    entity: Entity,
+    *,
+    ball_body_id: int,
+    top_body_id: int,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    body_pos = entity.data.body_link_pos_w
+    body_quat = entity.data.body_link_quat_w
+    top_pos = body_pos[:, top_body_id]
+    top_quat = body_quat[:, top_body_id]
+    ball_pos = body_pos[:, ball_body_id]
+    relative = np_quat_apply_inverse(top_quat, ball_pos - top_pos)
+    return relative, top_quat, ball_pos
+
+
+@dataclass(kw_only=True)
+class StewartTiltActionCfg(ActionTermCfg):
+    """Two-axis tilt action converted to six Stewart actuator targets."""
+
+    actuator_names: tuple[str, ...] | list[str]
+    top_body_name: str
+    ball_body_name: str
+    leg_body_names: tuple[str, ...] | list[str]
+    top_connect_body_names: tuple[str, ...] | list[str]
+    raw_action_clip: tuple[float, float] | list[float]
+    target_rotation_limit_deg: float
+    action_smooth: float
+    center_control_radius: float
+    center_control_min_gain: float
+
+    def build(self, env: ManagerBasedRlEnv) -> StewartTiltAction:
+        return StewartTiltAction(self, env)
+
+
+class StewartTiltAction(ActionTerm):
+    """Vectorized tilt IK using only the public entity state/control facade."""
+
+    cfg: StewartTiltActionCfg
+    _entity: Entity
+
+    def __init__(self, cfg: StewartTiltActionCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        term = type(self).__name__
+        if cfg.clip is not None:
+            raise NotImplementedError(
+                f"{term} does not support the actuator-name clip field; use raw_action_clip"
+            )
+        actuator_names = _names(term, "actuator_names", cfg.actuator_names, count=_LEG_COUNT)
+        actuator_ids, resolved = self._entity.find_actuators(actuator_names, preserve_order=True)
+        if tuple(resolved) != actuator_names:
+            raise ValueError(f"{term} actuator selectors resolved out of order: {resolved}")
+        self._actuator_ids = np.asarray(actuator_ids, dtype=np.intp)
+        self._actuator_ids.setflags(write=False)
+
+        top_name = _name(term, "top_body_name", cfg.top_body_name)
+        ball_name = _name(term, "ball_body_name", cfg.ball_body_name)
+        leg_names = _names(term, "leg_body_names", cfg.leg_body_names, count=_LEG_COUNT)
+        connect_names = _names(
+            term,
+            "top_connect_body_names",
+            cfg.top_connect_body_names,
+            count=_LEG_COUNT,
+        )
+        self._top_body_id = _body_id(self._entity, top_name, term=term)
+        self._ball_body_id = _body_id(self._entity, ball_name, term=term)
+        self._leg_body_ids = _body_ids(self._entity, leg_names, term=term)
+        self._top_connect_body_ids = _body_ids(self._entity, connect_names, term=term)
+
+        self._raw_clip = _pair(term, "raw_action_clip", cfg.raw_action_clip)
+        self._tilt_limit_deg = _real(
+            term,
+            "target_rotation_limit_deg",
+            cfg.target_rotation_limit_deg,
+            minimum=0.0,
+            strict_minimum=True,
+        )
+        self._action_smooth = _real(
+            term, "action_smooth", cfg.action_smooth, minimum=0.0, maximum=1.0
+        )
+        self._center_radius = _real(
+            term, "center_control_radius", cfg.center_control_radius, minimum=0.0
+        )
+        self._center_min_gain = _real(
+            term,
+            "center_control_min_gain",
+            cfg.center_control_min_gain,
+            minimum=0.0,
+            maximum=1.0,
+        )
+
+        ranges = np.asarray(self._entity.data.actuator_ctrl_range, dtype=get_global_dtype())
+        self._ctrl_lower = ranges[self._actuator_ids, 0]
+        self._ctrl_upper = ranges[self._actuator_ids, 1]
+        dtype = get_global_dtype()
+        self._raw_action = np.zeros((env.num_envs, _ACTION_DIM), dtype=dtype)
+        self._clipped_action = np.zeros_like(self._raw_action)
+        self._executed_action = np.zeros_like(self._raw_action)
+        self._previous_executed_action = np.zeros_like(self._raw_action)
+        self._effective_action = np.zeros_like(self._raw_action)
+        self._target_tilt_deg = np.zeros_like(self._raw_action)
+        self._target_tilt_rad = np.zeros_like(self._raw_action)
+        self._control = np.zeros((env.num_envs, _LEG_COUNT), dtype=dtype)
+
+        self._ik_ready = False
+        self._top_home_pos = np.zeros(3, dtype=dtype)
+        self._connect_offsets = np.zeros((_LEG_COUNT, 3), dtype=dtype)
+        self._neutral_leg_lengths = np.zeros(_LEG_COUNT, dtype=dtype)
+
+    @property
+    def action_dim(self) -> int:
+        return _ACTION_DIM
+
+    @property
+    def raw_action(self) -> np.ndarray:
+        return self._raw_action
+
+    @property
+    def executed_action(self) -> np.ndarray:
+        return self._executed_action
+
+    @property
+    def target_tilt_deg(self) -> np.ndarray:
+        return self._target_tilt_deg
+
+    @property
+    def neutral_leg_lengths(self) -> np.ndarray:
+        self._ensure_ik_calibration()
+        return self._neutral_leg_lengths
+
+    def _ensure_ik_calibration(self) -> None:
+        if self._ik_ready:
+            return
+        positions = np.asarray(self._entity.data.body_link_pos_w, dtype=get_global_dtype())
+        top = positions[:, self._top_body_id]
+        connects = positions[:, self._top_connect_body_ids]
+        legs = positions[:, self._leg_body_ids]
+        self._top_home_pos[:] = top[0]
+        self._connect_offsets[:] = connects[0] - self._top_home_pos
+        self._neutral_leg_lengths[:] = np.linalg.norm(connects[0] - legs[0], axis=-1)
+        self._ik_ready = True
+
+    def leg_control_for_tilt(self, target_tilt_rad: np.ndarray) -> np.ndarray:
+        """Return six actuator controls for ``(roll, pitch)`` radians."""
+        expected = (self.num_envs, _ACTION_DIM)
+        if not isinstance(target_tilt_rad, np.ndarray) or target_tilt_rad.shape != expected:
+            shape = getattr(target_tilt_rad, "shape", None)
+            raise ValueError(f"{type(self).__name__} tilt must have shape {expected}, got {shape}")
+        if not np.isfinite(target_tilt_rad).all():
+            raise ValueError(f"{type(self).__name__} tilt contains NaN or Inf")
+        self._ensure_ik_calibration()
+        zeros = np.zeros(self.num_envs, dtype=target_tilt_rad.dtype)
+        target_quat = np_quat_from_euler_xyz(target_tilt_rad[:, 0], target_tilt_rad[:, 1], zeros)
+        rotated = np_quat_apply_batched(target_quat[:, None, :], self._connect_offsets[None, :, :])
+        expected_connects = self._top_home_pos[None, None, :] + rotated
+        leg_positions = np.asarray(
+            self._entity.data.body_link_pos_w[:, self._leg_body_ids],
+            dtype=get_global_dtype(),
+        )
+        controls = (
+            np.linalg.norm(expected_connects - leg_positions, axis=-1)
+            - self._neutral_leg_lengths[None, :]
+        )
+        return np.asarray(
+            np.clip(controls, self._ctrl_lower, self._ctrl_upper),
+            dtype=get_global_dtype(),
+        )
+
+    def process_actions(self, actions: np.ndarray) -> None:
+        expected = self._raw_action.shape
+        if not isinstance(actions, np.ndarray):
+            raise TypeError(
+                f"{type(self).__name__} expected np.ndarray, got {type(actions).__name__}"
+            )
+        if actions.shape != expected:
+            raise ValueError(
+                f"{type(self).__name__} expected action shape {expected}, got {actions.shape}"
+            )
+        if not np.isfinite(actions).all():
+            raise ValueError(f"{type(self).__name__} received NaN or Inf actions")
+        self._raw_action[:] = actions
+        np.clip(actions, self._raw_clip[0], self._raw_clip[1], out=self._clipped_action)
+        np.multiply(self._clipped_action, self._action_smooth, out=self._executed_action)
+        self._executed_action += (1.0 - self._action_smooth) * self._previous_executed_action
+        self._previous_executed_action[:] = self._executed_action
+
+        relative, _, _ = _relative_ball_state(
+            self._entity,
+            ball_body_id=self._ball_body_id,
+            top_body_id=self._top_body_id,
+        )
+        relative_xy = np.linalg.norm(relative[:, :2], axis=-1)
+        if self._center_radius > 0.0 and self._center_min_gain < 1.0:
+            ratio = np.clip(relative_xy / self._center_radius, 0.0, 1.0)
+            gain = self._center_min_gain + (1.0 - self._center_min_gain) * ratio
+            np.multiply(self._executed_action, gain[:, None], out=self._effective_action)
+        else:
+            self._effective_action[:] = self._executed_action
+        np.multiply(self._effective_action, self._tilt_limit_deg, out=self._target_tilt_deg)
+        np.deg2rad(self._target_tilt_deg, out=self._target_tilt_rad)
+        self._control[:] = self.leg_control_for_tilt(self._target_tilt_rad)
+
+    def apply_actions(self) -> None:
+        self._entity.data.write_ctrl(self._control, actuator_ids=self._actuator_ids)
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        ids = slice(None) if env_ids is None else env_ids
+        for value in (
+            self._raw_action,
+            self._clipped_action,
+            self._executed_action,
+            self._previous_executed_action,
+            self._effective_action,
+            self._target_tilt_deg,
+            self._target_tilt_rad,
+            self._control,
+        ):
+            value[ids] = 0.0
+
+
+class StewartObservation(ManagerTermBase):
+    """Legacy 15-D observation with per-environment filtered finite differences."""
+
+    _ALLOWED_PARAMS = frozenset(
+        {
+            "entity_name",
+            "action_name",
+            "ball_body_name",
+            "top_body_name",
+            "target_rotation_limit_deg",
+            "vel_smooth",
+        }
+    )
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: _StewartEnv):
+        super().__init__(env)
+        term = type(self).__name__
+        unexpected = set(cfg.params) - self._ALLOWED_PARAMS
+        if unexpected:
+            raise TypeError(f"{term} received unsupported parameters: {sorted(unexpected)}")
+        entity_name = _name(term, "entity_name", cfg.params.get("entity_name"))
+        action_name = _name(term, "action_name", cfg.params.get("action_name"))
+        self._entity = cast("Entity", env.scene[entity_name])
+        self._ball_body_id = _body_id(
+            self._entity,
+            _name(term, "ball_body_name", cfg.params.get("ball_body_name")),
+            term=term,
+        )
+        self._top_body_id = _body_id(
+            self._entity,
+            _name(term, "top_body_name", cfg.params.get("top_body_name")),
+            term=term,
+        )
+        action = env.action_manager.get_term(action_name)
+        if not isinstance(action, StewartTiltAction):
+            raise TypeError(
+                f"{term} action term {action_name!r} must be StewartTiltAction, "
+                f"got {type(action).__name__}"
+            )
+        self._action = action
+        self._tilt_limit_deg = _real(
+            term,
+            "target_rotation_limit_deg",
+            cfg.params.get("target_rotation_limit_deg"),
+            minimum=0.0,
+            strict_minimum=True,
+        )
+        self._vel_smooth = _real(
+            term,
+            "vel_smooth",
+            cfg.params.get("vel_smooth"),
+            minimum=0.0,
+            maximum=1.0,
+        )
+        self._step_dt = _real(term, "step_dt", env.step_dt, minimum=0.0, strict_minimum=True)
+
+        dtype = get_global_dtype()
+        self._relative = np.zeros((env.num_envs, 3), dtype=dtype)
+        self._previous_relative = np.zeros_like(self._relative)
+        self._filtered_relative_velocity = np.zeros_like(self._relative)
+        self._top_quat = np.zeros((env.num_envs, 4), dtype=dtype)
+        self._top_quat[:, 0] = 1.0
+        self._previous_top_quat = self._top_quat.copy()
+        self._filtered_top_angular_velocity = np.zeros_like(self._relative)
+        self._local_top_angular_velocity = np.zeros_like(self._relative)
+        self._ball_pos = np.zeros_like(self._relative)
+        self._relative_xy = np.zeros(env.num_envs, dtype=dtype)
+        self._velocity_xy = np.zeros(env.num_envs, dtype=dtype)
+        self._obs = np.zeros((env.num_envs, 15), dtype=dtype)
+        self._last_counter = self._counter(env)
+        self.reset(None)
+
+    @staticmethod
+    def _counter(env: _StewartEnv) -> int:
+        counter = env.common_step_counter
+        if isinstance(counter, (bool, np.bool_)) or not isinstance(counter, (int, np.integer)):
+            raise TypeError("StewartObservation common_step_counter must be an integer")
+        if counter < 0:
+            raise ValueError("StewartObservation common_step_counter must be non-negative")
+        return int(counter)
+
+    @property
+    def relative_xy(self) -> np.ndarray:
+        return self._relative_xy
+
+    @property
+    def velocity_xy(self) -> np.ndarray:
+        return self._velocity_xy
+
+    @property
+    def ball_pos(self) -> np.ndarray:
+        return self._ball_pos
+
+    def _write_observation_rows(self, ids: np.ndarray, *, reset_actions: bool) -> None:
+        roll, pitch = np_roll_pitch_from_quat(self._top_quat[ids])
+        self._obs[ids, 0:3] = self._relative[ids]
+        self._obs[ids, 3:6] = self._filtered_relative_velocity[ids]
+        self._obs[ids, 6] = np.rad2deg(roll) / self._tilt_limit_deg
+        self._obs[ids, 7] = np.rad2deg(pitch) / self._tilt_limit_deg
+        self._obs[ids, 8:11] = self._local_top_angular_velocity[ids]
+        if reset_actions:
+            self._obs[ids, 11:15] = 0.0
+        else:
+            self._obs[ids, 11:13] = self._action.target_tilt_deg[ids] / self._tilt_limit_deg
+            self._obs[ids, 13:15] = self._action.executed_action[ids]
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        ids = _env_ids(self._env, env_ids)
+        relative, top_quat, ball_pos = _relative_ball_state(
+            self._entity,
+            ball_body_id=self._ball_body_id,
+            top_body_id=self._top_body_id,
+        )
+        self._relative[ids] = relative[ids]
+        self._previous_relative[ids] = relative[ids]
+        self._filtered_relative_velocity[ids] = 0.0
+        self._top_quat[ids] = top_quat[ids]
+        self._previous_top_quat[ids] = top_quat[ids]
+        self._filtered_top_angular_velocity[ids] = 0.0
+        self._local_top_angular_velocity[ids] = 0.0
+        self._ball_pos[ids] = ball_pos[ids]
+        self._relative_xy[ids] = np.linalg.norm(relative[ids, :2], axis=-1)
+        self._velocity_xy[ids] = 0.0
+        self._write_observation_rows(ids, reset_actions=True)
+
+    def _advance(self, env: _StewartEnv) -> None:
+        counter = self._counter(env)
+        if counter == self._last_counter:
+            return
+        if counter != self._last_counter + 1:
+            raise RuntimeError(
+                "StewartObservation missed a control-step update: "
+                f"last={self._last_counter}, current={counter}"
+            )
+        relative, top_quat, ball_pos = _relative_ball_state(
+            self._entity,
+            ball_body_id=self._ball_body_id,
+            top_body_id=self._top_body_id,
+        )
+        relative_velocity = (relative - self._previous_relative) / self._step_dt
+        self._filtered_relative_velocity[:] = (
+            self._vel_smooth * relative_velocity
+            + (1.0 - self._vel_smooth) * self._filtered_relative_velocity
+        )
+        quaternion_delta = np_quat_mul_batched(
+            top_quat, np_quat_conjugate_batched(self._previous_top_quat)
+        )
+        top_angular_velocity = np_quat_to_axis_angle(quaternion_delta) / self._step_dt
+        self._filtered_top_angular_velocity[:] = (
+            self._vel_smooth * top_angular_velocity
+            + (1.0 - self._vel_smooth) * self._filtered_top_angular_velocity
+        )
+        self._local_top_angular_velocity[:] = np_quat_apply_inverse(
+            top_quat, self._filtered_top_angular_velocity
+        )
+        self._relative[:] = relative
+        self._previous_relative[:] = relative
+        self._top_quat[:] = top_quat
+        self._previous_top_quat[:] = top_quat
+        self._ball_pos[:] = ball_pos
+        self._relative_xy[:] = np.linalg.norm(relative[:, :2], axis=-1)
+        self._velocity_xy[:] = np.linalg.norm(self._filtered_relative_velocity[:, :2], axis=-1)
+        all_ids = np.arange(env.num_envs, dtype=np.int32)
+        self._write_observation_rows(all_ids, reset_actions=False)
+        self._last_counter = counter
+
+    def snapshot(self, env: _StewartEnv) -> np.ndarray:
+        self._advance(env)
+        return self._obs
+
+    def __call__(self, env: _StewartEnv, **params: Any) -> np.ndarray:
+        del params
+        return self.snapshot(env)
+
+
+class StewartBalanceState(ManagerTermBase):
+    """Termination-owned progress, stillness, success, and fall state."""
+
+    _ALLOWED_PARAMS = frozenset(
+        {
+            "observation_group",
+            "observation_term",
+            "platform_radius",
+            "fall_radius",
+            "top_center_z",
+            "still_xy",
+            "still_vel",
+            "still_xy_hysteresis",
+            "still_vel_hysteresis",
+            "zero_vel_thresh",
+            "still_steps_needed",
+        }
+    )
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: _StewartEnv):
+        super().__init__(env)
+        term = type(self).__name__
+        unexpected = set(cfg.params) - self._ALLOWED_PARAMS
+        if unexpected:
+            raise TypeError(f"{term} received unsupported parameters: {sorted(unexpected)}")
+        group_name = _name(term, "observation_group", cfg.params.get("observation_group"))
+        observation_name = _name(term, "observation_term", cfg.params.get("observation_term"))
+        observation = env.observation_manager.get_term_cfg(group_name, observation_name).func
+        if not isinstance(observation, StewartObservation):
+            raise TypeError(
+                f"{term} observation {group_name}/{observation_name} must be "
+                f"StewartObservation, got {type(observation).__name__}"
+            )
+        self._observation = observation
+        self._platform_radius = _real(
+            term,
+            "platform_radius",
+            cfg.params.get("platform_radius"),
+            minimum=0.0,
+            strict_minimum=True,
+        )
+        self._fall_radius = _real(
+            term,
+            "fall_radius",
+            cfg.params.get("fall_radius"),
+            minimum=0.0,
+            strict_minimum=True,
+        )
+        self._top_center_z = _real(term, "top_center_z", cfg.params.get("top_center_z"))
+        self._still_xy = _real(term, "still_xy", cfg.params.get("still_xy"), minimum=0.0)
+        self._still_vel = _real(term, "still_vel", cfg.params.get("still_vel"), minimum=0.0)
+        self._still_xy_hysteresis = _real(
+            term,
+            "still_xy_hysteresis",
+            cfg.params.get("still_xy_hysteresis"),
+            minimum=1.0,
+        )
+        self._still_vel_hysteresis = _real(
+            term,
+            "still_vel_hysteresis",
+            cfg.params.get("still_vel_hysteresis"),
+            minimum=1.0,
+        )
+        self._zero_vel_thresh = _real(
+            term,
+            "zero_vel_thresh",
+            cfg.params.get("zero_vel_thresh"),
+            minimum=0.0,
+        )
+        steps = cfg.params.get("still_steps_needed")
+        if isinstance(steps, (bool, np.bool_)) or not isinstance(steps, (int, np.integer)):
+            raise TypeError(f"{term} still_steps_needed must be an integer")
+        if steps <= 0:
+            raise ValueError(f"{term} still_steps_needed must be positive")
+        self._still_steps_needed = int(steps)
+
+        dtype = get_global_dtype()
+        self.fallen = np.zeros(env.num_envs, dtype=np.bool_)
+        self.success = np.zeros(env.num_envs, dtype=np.bool_)
+        self.center_score = np.zeros(env.num_envs, dtype=dtype)
+        self.progress = np.zeros(env.num_envs, dtype=dtype)
+        self.still_steps = np.zeros(env.num_envs, dtype=np.int32)
+        self.still_window_active = np.zeros(env.num_envs, dtype=np.bool_)
+        self._previous_zero_velocity_xy = np.zeros(env.num_envs, dtype=dtype)
+        self._done = np.zeros(env.num_envs, dtype=np.bool_)
+        self._last_counter = int(env.common_step_counter)
+
+    @property
+    def last_counter(self) -> int:
+        return self._last_counter
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        ids = _env_ids(self._env, env_ids)
+        self.fallen[ids] = False
+        self.success[ids] = False
+        self.center_score[ids] = np.clip(
+            1.0 - self._observation.relative_xy[ids] / self._fall_radius,
+            0.0,
+            1.0,
+        )
+        self.progress[ids] = 0.0
+        self.still_steps[ids] = 0
+        self.still_window_active[ids] = False
+        self._previous_zero_velocity_xy[ids] = self._observation.relative_xy[ids]
+        self._done[ids] = False
+
+    def _update(
+        self,
+        relative_xy: np.ndarray,
+        velocity_xy: np.ndarray,
+        ball_pos: np.ndarray,
+    ) -> None:
+        fall_z = self._top_center_z - np.sin(np.deg2rad(30.0)) * self._platform_radius
+        self.fallen[:] = (relative_xy > self._fall_radius) | (ball_pos[:, 2] < fall_z)
+        self.center_score[:] = np.clip(
+            1.0 - relative_xy / self._fall_radius,
+            0.0,
+            1.0,
+        )
+
+        zero_event = velocity_xy <= self._zero_vel_thresh
+        improvement = np.maximum(self._previous_zero_velocity_xy - relative_xy, 0.0)
+        self.progress[:] = np.where(
+            zero_event & (relative_xy < self._previous_zero_velocity_xy),
+            np.clip(improvement / self._platform_radius, 0.0, 1.0),
+            0.0,
+        )
+        self._previous_zero_velocity_xy[zero_event] = relative_xy[zero_event]
+
+        keep = (
+            self.still_window_active
+            & (relative_xy <= self._still_xy * self._still_xy_hysteresis)
+            & (velocity_xy <= self._still_vel * self._still_vel_hysteresis)
+        )
+        enter = (
+            ~self.still_window_active
+            & (relative_xy <= self._still_xy)
+            & (velocity_xy <= self._still_vel)
+        )
+        self.still_steps[:] = np.where(
+            keep,
+            self.still_steps + 1,
+            np.where(enter, 1, 0),
+        )
+        self.still_window_active[:] = keep | enter
+        self.success[:] = self.still_steps >= self._still_steps_needed
+        self._done[:] = self.fallen | self.success
+
+    def __call__(self, env: _StewartEnv, **params: Any) -> np.ndarray:
+        del params
+        counter = int(env.common_step_counter)
+        if counter == self._last_counter:
+            return self._done
+        if counter != self._last_counter + 1:
+            raise RuntimeError(
+                "StewartBalanceState missed a control-step update: "
+                f"last={self._last_counter}, current={counter}"
+            )
+        self._observation.snapshot(env)
+        self._update(
+            self._observation.relative_xy,
+            self._observation.velocity_xy,
+            self._observation.ball_pos,
+        )
+        self._last_counter = counter
+        return self._done
+
+
+def _balance_state(env: _StewartEnv, state_term_name: str) -> StewartBalanceState:
+    name = _name("Stewart reward", "state_term_name", state_term_name)
+    termination_manager = cast("TerminationManager", env.termination_manager)
+    state = termination_manager.get_term_cfg(name).func
+    if not isinstance(state, StewartBalanceState):
+        raise TypeError(
+            f"Stewart reward termination term {name!r} must be StewartBalanceState, "
+            f"got {type(state).__name__}"
+        )
+    if state.last_counter != int(env.common_step_counter):
+        raise RuntimeError(
+            f"Stewart reward state {name!r} was not computed for control step "
+            f"{env.common_step_counter}"
+        )
+    return state
+
+
+def center_reward(env: _StewartEnv, state_term_name: str) -> np.ndarray:
+    state = _balance_state(env, state_term_name)
+    return np.asarray(np.where(state.fallen, 0.0, state.center_score), dtype=get_global_dtype())
+
+
+def progress_reward(env: _StewartEnv, state_term_name: str) -> np.ndarray:
+    state = _balance_state(env, state_term_name)
+    return np.asarray(np.where(state.fallen, 0.0, state.progress), dtype=get_global_dtype())
+
+
+def still_reward(env: _StewartEnv, state_term_name: str) -> np.ndarray:
+    state = _balance_state(env, state_term_name)
+    return np.asarray(state.success & ~state.fallen, dtype=get_global_dtype())
+
+
+def fall_reward(env: _StewartEnv, state_term_name: str) -> np.ndarray:
+    state = _balance_state(env, state_term_name)
+    return np.asarray(state.fallen, dtype=get_global_dtype())
+
+
+class StewartBallReset(ManagerTermBase):
+    """Sample the ball uniformly within a disk via root-state entity writes."""
+
+    _ALLOWED_PARAMS = frozenset(
+        {"entity_name", "platform_radius", "init_ball_radius_ratio", "ball_home_z"}
+    )
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        term = type(self).__name__
+        unexpected = set(cfg.params) - self._ALLOWED_PARAMS
+        if unexpected:
+            raise TypeError(f"{term} received unsupported parameters: {sorted(unexpected)}")
+        entity_name = _name(term, "entity_name", cfg.params.get("entity_name"))
+        self._entity = cast("Entity", env.scene[entity_name])
+        self._platform_radius = _real(
+            term,
+            "platform_radius",
+            cfg.params.get("platform_radius"),
+            minimum=0.0,
+            strict_minimum=True,
+        )
+        self._radius_ratio = _real(
+            term,
+            "init_ball_radius_ratio",
+            cfg.params.get("init_ball_radius_ratio"),
+            minimum=0.0,
+            maximum=1.0,
+        )
+        self._ball_home_z = _real(term, "ball_home_z", cfg.params.get("ball_home_z"))
+        # Resolve the complete floating-root capability on the cold path.
+        default_state = self._entity.data.default_root_state
+        if default_state.shape != (env.num_envs, 13):
+            raise ValueError(
+                f"{term} default root state must have shape ({env.num_envs}, 13), "
+                f"got {default_state.shape}"
+            )
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        env_ids: np.ndarray | None,
+        **params: Any,
+    ) -> None:
+        del params
+        ids = _env_ids(env, env_ids)
+        root_state = np.array(self._entity.data.default_root_state[ids], copy=True)
+        radius = (
+            self._platform_radius
+            * self._radius_ratio
+            * np.sqrt(env.rng.uniform(0.0, 1.0, size=ids.size))
+        )
+        theta = env.rng.uniform(0.0, 2.0 * np.pi, size=ids.size)
+        root_state[:, 0] = radius * np.cos(theta)
+        root_state[:, 1] = radius * np.sin(theta)
+        root_state[:, 2] = self._ball_home_z
+        self._entity.write_root_link_pose_to_sim(root_state[:, :7], env_ids=ids)
+        self._entity.write_root_link_velocity_to_sim(root_state[:, 7:], env_ids=ids)
+
+
+registry.register_env_config("StewartBalance", ManagerBasedRlEnvCfg)
+registry.register_env("StewartBalance", make_manager_based_rl_env, sim_backend="mujoco")
+registry.register_env("StewartBalance", make_manager_based_rl_env, sim_backend="motrix")
+registry.register_env("StewartBalance", make_manager_based_rl_env, sim_backend="drake")
+
+
+__all__ = [
+    "StewartBalanceState",
+    "StewartBallReset",
+    "StewartObservation",
+    "StewartTiltAction",
+    "StewartTiltActionCfg",
+    "center_reward",
+    "fall_reward",
+    "progress_reward",
+    "still_reward",
+]
diff --git a/src/unilab/tasks/migration_matrix.py b/src/unilab/tasks/migration_matrix.py
new file mode 100644
index 000000000..2238f4ecc
--- /dev/null
+++ b/src/unilab/tasks/migration_matrix.py
@@ -0,0 +1,192 @@
+"""Production task migration status and closeout ownership.
+
+The matrix is deliberately small and explicit.  It is an audit boundary for
+the grouped #1042 migration work; it does not provide a second task runtime or
+translate task configuration.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Literal
+
+MigrationStatus = Literal["Compatible", "Adapted"]
+MigrationTarget = Literal["complete", "mba", "compatibility"]
+
+
+@dataclass(frozen=True)
+class TaskMigrationRecord:
+    task_name: str
+    family: str
+    status: MigrationStatus
+    target: MigrationTarget
+    rationale: str
+    next_step: str
+
+
+_MBA_TASKS = frozenset(
+    {
+        "A2JoystickFlat",
+        "AllegroInhandRotation",
+        "AllegroInhandRotationGrasp",
+        "Go1JoystickFlat",
+        "Go2FootStand",
+        "Go2JoystickFlat",
+        "Go2WJoystickFlat",
+        "StewartBalance",
+    }
+)
+
+_ROUGH_TASKS = frozenset(
+    {
+        "Go1JoystickRough",
+        "Go2JoystickRough",
+        "Go2WJoystickRough",
+    }
+)
+
+_G1_LOCOMOTION_TASKS = frozenset(
+    {
+        "G1WalkFlat",
+        "G1WalkRough",
+        "G1Walk23DofFlat",
+        "G1Walk23DofRough",
+    }
+)
+
+_CUSTOM_COMPAT_TASKS = frozenset(
+    {
+        "Go2ArmManipLoco",
+        "SharpaInhandRotation",
+        "SharpaInhandRotationGrasp",
+    }
+)
+
+_MOTION_CORE_TASKS = frozenset(
+    {
+        "G1MotionTracking",
+        "G1MotionTracking23Dof",
+        "G1MotionTracking23DofDeploy",
+        "G1MotionTrackingDeploy",
+        "G1MotionTrackingSAC",
+        "G1MotionTrackingSAC23Dof",
+    }
+)
+
+_MOTION_TASKS = frozenset(
+    {
+        "G1BoxTracking",
+        "G1BoxTracking23Dof",
+        "G1ClimbTracking",
+        "G1ClimbTracking23Dof",
+        "G1FlipTracking",
+        "G1FlipTracking23Dof",
+        "G1FlipTrackingSAC",
+        "G1FlipTrackingSAC23Dof",
+        "G1WallFlipTracking",
+        "G1WallFlipTracking23Dof",
+        "G1WallFlipTrackingSAC",
+        "G1WallFlipTrackingSAC23Dof",
+        "G1WBTObs",
+        "G1WBTObs23Dof",
+        "X2WallFlipTracking",
+    }
+)
+
+PRODUCTION_TASK_NAMES = frozenset(
+    _MBA_TASKS
+    | _ROUGH_TASKS
+    | _G1_LOCOMOTION_TASKS
+    | _CUSTOM_COMPAT_TASKS
+    | _MOTION_CORE_TASKS
+    | _MOTION_TASKS
+)
+
+
+def migration_record(task_name: str) -> TaskMigrationRecord:
+    """Return the closeout status for one registered production task.
+
+    Unknown names fail closed so adding a production registration requires an
+    explicit migration decision and cannot silently escape the audit.
+    """
+
+    if task_name in _MBA_TASKS:
+        return TaskMigrationRecord(
+            task_name,
+            "manager_based",
+            "Compatible",
+            "complete",
+            "Hydra owner YAML materializes the canonical NumPy Manager-Based runtime.",
+            "Keep the manager contract and regression evidence current.",
+        )
+    if task_name in _ROUGH_TASKS:
+        return TaskMigrationRecord(
+            task_name,
+            "quadruped_rough",
+            "Compatible",
+            "complete",
+            "Hydra owners materialize shared terrain, height-scan, reset, and curriculum manager terms on the canonical runtime.",
+            "Keep the shared rough-family contract and both backend owners in sync. "
+            "Known intentional divergence from the legacy rough env (recorded 2026-08): "
+            "the legacy reward terms feet_gait, feet_air_time(+variance), "
+            "feet_contact_without_cmd, feet_height_body, feet_slide, contact_forces, "
+            "undesired_contacts, joint_mirror, joint_power, joint_torques_l2, "
+            "joint_acc_l2(+wheel) have no manager port and are not part of the "
+            "manager-based rough reward set.",
+        )
+    if task_name in _G1_LOCOMOTION_TASKS:
+        return TaskMigrationRecord(
+            task_name,
+            "g1_locomotion",
+            "Compatible",
+            "complete",
+            "Hydra owners materialize biped gait, sensor, command, and penalty-curriculum manager terms on the canonical runtime.",
+            "Keep the manager contract and regression evidence current.",
+        )
+    if task_name in _CUSTOM_COMPAT_TASKS:
+        family = "go2_arm" if task_name == "Go2ArmManipLoco" else "sharpa"
+        return TaskMigrationRecord(
+            task_name,
+            family,
+            "Adapted",
+            "compatibility",
+            "Custom IK/history or tactile/contact/cache behavior is retained behind one frozen adapter.",
+            "Keep Hydra/Registry ownership single; migrate only when the formal capability exists.",
+        )
+    if task_name in _MOTION_CORE_TASKS:
+        return TaskMigrationRecord(
+            task_name,
+            "motion_tracking",
+            "Compatible",
+            "complete",
+            "Hydra owner YAML materializes task-owned NumPy motion manager terms on the canonical runtime.",
+            "Keep PPO, APPO, and SAC owners aligned with the shared motion manager contract.",
+        )
+    if task_name in _MOTION_TASKS:
+        return TaskMigrationRecord(
+            task_name,
+            "motion_tracking",
+            "Compatible",
+            "complete",
+            "Hydra profile owners specialize the shared NumPy motion managers without a legacy runtime.",
+            "Keep profile scene, motion, observation, reward, and termination declarations aligned.",
+        )
+    raise KeyError(f"Task '{task_name}' has no #1042 migration-matrix entry")
+
+
+def migration_records(
+    task_names: list[str] | tuple[str, ...] | set[str],
+) -> tuple[TaskMigrationRecord, ...]:
+    """Return records in deterministic task-name order."""
+
+    return tuple(migration_record(name) for name in sorted(task_names))
+
+
+__all__ = [
+    "MigrationStatus",
+    "MigrationTarget",
+    "PRODUCTION_TASK_NAMES",
+    "TaskMigrationRecord",
+    "migration_record",
+    "migration_records",
+]
diff --git a/src/unilab/tasks/motion_tracking/__init__.py b/src/unilab/tasks/motion_tracking/__init__.py
new file mode 100644
index 000000000..4e23b0f14
--- /dev/null
+++ b/src/unilab/tasks/motion_tracking/__init__.py
@@ -0,0 +1,5 @@
+"""Hydra-owned motion tracking tasks on the NumPy Manager-Based runtime."""
+
+from .g1.motion_box_loader import BoxMotionData, BoxMotionLoader
+
+__all__ = ["BoxMotionData", "BoxMotionLoader"]
diff --git a/src/unilab/tasks/motion_tracking/common/__init__.py b/src/unilab/tasks/motion_tracking/common/__init__.py
new file mode 100644
index 000000000..fa134d4b7
--- /dev/null
+++ b/src/unilab/tasks/motion_tracking/common/__init__.py
@@ -0,0 +1 @@
+"""Shared NumPy motion command, loader, and numeric manager helpers."""
diff --git a/src/unilab/tasks/motion_tracking/common/kernels.py b/src/unilab/tasks/motion_tracking/common/kernels.py
new file mode 100644
index 000000000..d722b76c8
--- /dev/null
+++ b/src/unilab/tasks/motion_tracking/common/kernels.py
@@ -0,0 +1,620 @@
+"""Parallel CPU kernels for the fixed motion-tracking hot-term set."""
+
+from __future__ import annotations
+
+import os
+from math import atan2, cos, sin, sqrt
+
+import numpy as np
+from numba import config, njit, prange, set_num_threads
+
+_DEFAULT_MOTION_KERNEL_THREADS = 8
+_runtime_configured = False
+
+# The OpenMP layer's worker wakeups dominate these short kernels after a long
+# backend physics phase. Workqueue has stable sub-millisecond dispatch here.
+# Respect an application-provided NUMBA_THREADING_LAYER selection.
+if "NUMBA_THREADING_LAYER" not in os.environ:
+    setattr(config, "THREADING_LAYER", "workqueue")
+
+
+def configure_motion_kernel_runtime() -> None:
+    """Initialize the task-local Numba worker mask once on the cold path."""
+    global _runtime_configured
+    if _runtime_configured:
+        return
+    if "NUMBA_NUM_THREADS" not in os.environ:
+        available_threads = int(getattr(config, "NUMBA_DEFAULT_NUM_THREADS", os.cpu_count() or 1))
+        set_num_threads(min(_DEFAULT_MOTION_KERNEL_THREADS, available_threads))
+    _runtime_configured = True
+
+
+@njit(inline="always")
+def _quat_error_squared(
+    q1_w: float,
+    q1_x: float,
+    q1_y: float,
+    q1_z: float,
+    q2_w: float,
+    q2_x: float,
+    q2_y: float,
+    q2_z: float,
+) -> float:
+    """Return the squared shortest-path angular error for two quaternions."""
+    rel_w = abs(q2_w * q1_w + q2_x * q1_x + q2_y * q1_y + q2_z * q1_z)
+    rel_x = -q2_w * q1_x + q2_x * q1_w - q2_y * q1_z + q2_z * q1_y
+    rel_y = -q2_w * q1_y + q2_x * q1_z + q2_y * q1_w - q2_z * q1_x
+    rel_z = -q2_w * q1_z - q2_x * q1_y + q2_y * q1_x + q2_z * q1_w
+    xyz_norm = sqrt(rel_x * rel_x + rel_y * rel_y + rel_z * rel_z)
+    clipped_w = min(max(rel_w, -1.0), 1.0)
+    angle = 2.0 * atan2(xyz_norm, clipped_w)
+    return angle * angle
+
+
+@njit(inline="always")
+def _quat_mul_components(
+    w1: float,
+    x1: float,
+    y1: float,
+    z1: float,
+    w2: float,
+    x2: float,
+    y2: float,
+    z2: float,
+) -> tuple[float, float, float, float]:
+    """Hamilton product for scalar quaternion components."""
+    return (
+        w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2,
+        w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2,
+        w1 * y2 - x1 * z2 + y1 * w2 + z1 * x2,
+        w1 * z2 + x1 * y2 - y1 * x2 + z1 * w2,
+    )
+
+
+@njit(inline="always")
+def _quat_apply_inverse_components(
+    w: float,
+    x: float,
+    y: float,
+    z: float,
+    vx: float,
+    vy: float,
+    vz: float,
+) -> tuple[float, float, float]:
+    """Rotate one vector by the inverse of a unit quaternion."""
+    qx = -x
+    qy = -y
+    qz = -z
+    tx = 2.0 * (qy * vz - qz * vy)
+    ty = 2.0 * (qz * vx - qx * vz)
+    tz = 2.0 * (qx * vy - qy * vx)
+    return (
+        vx + w * tx + qy * tz - qz * ty,
+        vy + w * ty + qz * tx - qx * tz,
+        vz + w * tz + qx * ty - qy * tx,
+    )
+
+
+@njit(inline="always")
+def _quat_to_rot6d_components(
+    w: float,
+    x: float,
+    y: float,
+    z: float,
+) -> tuple[float, float, float, float, float, float]:
+    """Flatten the first two rotation-matrix columns."""
+    xx = x * x
+    yy = y * y
+    zz = z * z
+    xy = x * y
+    xz = x * z
+    yz = y * z
+    wx = w * x
+    wy = w * y
+    wz = w * z
+    return (
+        1.0 - 2.0 * (yy + zz),
+        2.0 * (xy - wz),
+        2.0 * (xy + wz),
+        1.0 - 2.0 * (xx + zz),
+        2.0 * (xz - wy),
+        2.0 * (yz + wx),
+    )
+
+
+@njit(cache=True, nogil=True, parallel=True)
+def update_motion_relative_state_kernel(
+    env_ids: np.ndarray,
+    anchor_body_idx: int,
+    motion_body_pos_local_w: np.ndarray,
+    motion_body_pos_w: np.ndarray,
+    motion_body_quat_w: np.ndarray,
+    robot_body_pos_w: np.ndarray,
+    robot_body_quat_w: np.ndarray,
+    body_pos_relative_w: np.ndarray,
+    body_quat_relative_w: np.ndarray,
+    motion_anchor_pos_b: np.ndarray,
+    motion_anchor_ori_b: np.ndarray,
+    robot_body_pos_b: np.ndarray,
+    robot_body_ori_b: np.ndarray,
+) -> None:
+    """Write all MotionCommand relative transforms for selected rows."""
+    num_bodies = motion_body_pos_local_w.shape[1]
+    for row in prange(env_ids.shape[0]):
+        env_idx = env_ids[row]
+        motion_anchor_x = motion_body_pos_local_w[env_idx, anchor_body_idx, 0]
+        motion_anchor_y = motion_body_pos_local_w[env_idx, anchor_body_idx, 1]
+        motion_anchor_z = motion_body_pos_local_w[env_idx, anchor_body_idx, 2]
+        motion_anchor_w = motion_body_quat_w[env_idx, anchor_body_idx, 0]
+        motion_anchor_qx = motion_body_quat_w[env_idx, anchor_body_idx, 1]
+        motion_anchor_qy = motion_body_quat_w[env_idx, anchor_body_idx, 2]
+        motion_anchor_qz = motion_body_quat_w[env_idx, anchor_body_idx, 3]
+        robot_anchor_x = robot_body_pos_w[env_idx, anchor_body_idx, 0]
+        robot_anchor_y = robot_body_pos_w[env_idx, anchor_body_idx, 1]
+        robot_anchor_z = robot_body_pos_w[env_idx, anchor_body_idx, 2]
+        robot_anchor_w = robot_body_quat_w[env_idx, anchor_body_idx, 0]
+        robot_anchor_qx = robot_body_quat_w[env_idx, anchor_body_idx, 1]
+        robot_anchor_qy = robot_body_quat_w[env_idx, anchor_body_idx, 2]
+        robot_anchor_qz = robot_body_quat_w[env_idx, anchor_body_idx, 3]
+
+        yaw_w, yaw_x, yaw_y, yaw_z = _quat_mul_components(
+            robot_anchor_w,
+            robot_anchor_qx,
+            robot_anchor_qy,
+            robot_anchor_qz,
+            motion_anchor_w,
+            -motion_anchor_qx,
+            -motion_anchor_qy,
+            -motion_anchor_qz,
+        )
+        half_yaw = 0.5 * atan2(
+            2.0 * (yaw_w * yaw_z + yaw_x * yaw_y),
+            1.0 - 2.0 * (yaw_y * yaw_y + yaw_z * yaw_z),
+        )
+        delta_w = cos(half_yaw)
+        delta_z = sin(half_yaw)
+        yaw_cross = 2.0 * delta_w * delta_z
+        yaw_z2 = 2.0 * delta_z * delta_z
+
+        anchor_vx = motion_body_pos_w[env_idx, anchor_body_idx, 0] - robot_anchor_x
+        anchor_vy = motion_body_pos_w[env_idx, anchor_body_idx, 1] - robot_anchor_y
+        anchor_vz = motion_body_pos_w[env_idx, anchor_body_idx, 2] - robot_anchor_z
+        anchor_pos_x, anchor_pos_y, anchor_pos_z = _quat_apply_inverse_components(
+            robot_anchor_w,
+            robot_anchor_qx,
+            robot_anchor_qy,
+            robot_anchor_qz,
+            anchor_vx,
+            anchor_vy,
+            anchor_vz,
+        )
+        motion_anchor_pos_b[env_idx, 0] = anchor_pos_x
+        motion_anchor_pos_b[env_idx, 1] = anchor_pos_y
+        motion_anchor_pos_b[env_idx, 2] = anchor_pos_z
+        rel_w, rel_x, rel_y, rel_z = _quat_mul_components(
+            robot_anchor_w,
+            -robot_anchor_qx,
+            -robot_anchor_qy,
+            -robot_anchor_qz,
+            motion_anchor_w,
+            motion_anchor_qx,
+            motion_anchor_qy,
+            motion_anchor_qz,
+        )
+        anchor_rot6d = _quat_to_rot6d_components(rel_w, rel_x, rel_y, rel_z)
+        for component in range(6):
+            motion_anchor_ori_b[env_idx, component] = anchor_rot6d[component]
+
+        for body_idx in range(num_bodies):
+            body_motion_w = motion_body_quat_w[env_idx, body_idx, 0]
+            body_motion_x = motion_body_quat_w[env_idx, body_idx, 1]
+            body_motion_y = motion_body_quat_w[env_idx, body_idx, 2]
+            body_motion_z = motion_body_quat_w[env_idx, body_idx, 3]
+            out_w, out_x, out_y, out_z = _quat_mul_components(
+                delta_w,
+                0.0,
+                0.0,
+                delta_z,
+                body_motion_w,
+                body_motion_x,
+                body_motion_y,
+                body_motion_z,
+            )
+            body_quat_relative_w[env_idx, body_idx, 0] = out_w
+            body_quat_relative_w[env_idx, body_idx, 1] = out_x
+            body_quat_relative_w[env_idx, body_idx, 2] = out_y
+            body_quat_relative_w[env_idx, body_idx, 3] = out_z
+
+            vx = motion_body_pos_local_w[env_idx, body_idx, 0] - motion_anchor_x
+            vy = motion_body_pos_local_w[env_idx, body_idx, 1] - motion_anchor_y
+            vz = motion_body_pos_local_w[env_idx, body_idx, 2] - motion_anchor_z
+            relative_x = vx
+            relative_x -= yaw_cross * vy
+            relative_x -= yaw_z2 * vx
+            relative_x += robot_anchor_x
+            relative_y = vy
+            relative_y += yaw_cross * vx
+            relative_y -= yaw_z2 * vy
+            relative_y += robot_anchor_y
+            body_pos_relative_w[env_idx, body_idx, 0] = relative_x
+            body_pos_relative_w[env_idx, body_idx, 1] = relative_y
+            body_pos_relative_w[env_idx, body_idx, 2] = vz + motion_anchor_z
+
+            robot_vx = robot_body_pos_w[env_idx, body_idx, 0] - robot_anchor_x
+            robot_vy = robot_body_pos_w[env_idx, body_idx, 1] - robot_anchor_y
+            robot_vz = robot_body_pos_w[env_idx, body_idx, 2] - robot_anchor_z
+            robot_pos_x, robot_pos_y, robot_pos_z = _quat_apply_inverse_components(
+                robot_anchor_w,
+                robot_anchor_qx,
+                robot_anchor_qy,
+                robot_anchor_qz,
+                robot_vx,
+                robot_vy,
+                robot_vz,
+            )
+            robot_body_pos_b[env_idx, body_idx, 0] = robot_pos_x
+            robot_body_pos_b[env_idx, body_idx, 1] = robot_pos_y
+            robot_body_pos_b[env_idx, body_idx, 2] = robot_pos_z
+            body_robot_w = robot_body_quat_w[env_idx, body_idx, 0]
+            body_robot_x = robot_body_quat_w[env_idx, body_idx, 1]
+            body_robot_y = robot_body_quat_w[env_idx, body_idx, 2]
+            body_robot_z = robot_body_quat_w[env_idx, body_idx, 3]
+            robot_rel_w, robot_rel_x, robot_rel_y, robot_rel_z = _quat_mul_components(
+                robot_anchor_w,
+                -robot_anchor_qx,
+                -robot_anchor_qy,
+                -robot_anchor_qz,
+                body_robot_w,
+                body_robot_x,
+                body_robot_y,
+                body_robot_z,
+            )
+            robot_rot6d = _quat_to_rot6d_components(
+                robot_rel_w,
+                robot_rel_x,
+                robot_rel_y,
+                robot_rel_z,
+            )
+            for component in range(6):
+                robot_body_ori_b[env_idx, body_idx, component] = robot_rot6d[component]
+
+
+@njit(cache=True, nogil=True, parallel=True)
+def update_object_relative_state_kernel(
+    env_ids: np.ndarray,
+    robot_anchor_pos_w: np.ndarray,
+    robot_anchor_quat_w: np.ndarray,
+    object_pos_w: np.ndarray,
+    object_quat_w: np.ndarray,
+    object_lin_vel_w: np.ndarray,
+    object_state_b: np.ndarray,
+) -> None:
+    """Write BoxMotionCommand object pose and velocity for selected rows."""
+    for row in prange(env_ids.shape[0]):
+        env_idx = env_ids[row]
+        anchor_x = robot_anchor_pos_w[env_idx, 0]
+        anchor_y = robot_anchor_pos_w[env_idx, 1]
+        anchor_z = robot_anchor_pos_w[env_idx, 2]
+        anchor_w = robot_anchor_quat_w[env_idx, 0]
+        anchor_qx = robot_anchor_quat_w[env_idx, 1]
+        anchor_qy = robot_anchor_quat_w[env_idx, 2]
+        anchor_qz = robot_anchor_quat_w[env_idx, 3]
+        pos_x, pos_y, pos_z = _quat_apply_inverse_components(
+            anchor_w,
+            anchor_qx,
+            anchor_qy,
+            anchor_qz,
+            object_pos_w[env_idx, 0] - anchor_x,
+            object_pos_w[env_idx, 1] - anchor_y,
+            object_pos_w[env_idx, 2] - anchor_z,
+        )
+        object_state_b[env_idx, 0] = pos_x
+        object_state_b[env_idx, 1] = pos_y
+        object_state_b[env_idx, 2] = pos_z
+        rel_w, rel_x, rel_y, rel_z = _quat_mul_components(
+            anchor_w,
+            -anchor_qx,
+            -anchor_qy,
+            -anchor_qz,
+            object_quat_w[env_idx, 0],
+            object_quat_w[env_idx, 1],
+            object_quat_w[env_idx, 2],
+            object_quat_w[env_idx, 3],
+        )
+        rot6d = _quat_to_rot6d_components(rel_w, rel_x, rel_y, rel_z)
+        for component in range(6):
+            object_state_b[env_idx, 3 + component] = rot6d[component]
+        vel_x, vel_y, vel_z = _quat_apply_inverse_components(
+            anchor_w,
+            anchor_qx,
+            anchor_qy,
+            anchor_qz,
+            object_lin_vel_w[env_idx, 0],
+            object_lin_vel_w[env_idx, 1],
+            object_lin_vel_w[env_idx, 2],
+        )
+        object_state_b[env_idx, 9] = vel_x
+        object_state_b[env_idx, 10] = vel_y
+        object_state_b[env_idx, 11] = vel_z
+
+
+@njit(cache=True, nogil=True, parallel=True)
+def update_motion_metrics_kernel(
+    env_ids: np.ndarray,
+    anchor_body_idx: int,
+    motion_body_pos_w: np.ndarray,
+    robot_body_pos_w: np.ndarray,
+    motion_body_quat_w: np.ndarray,
+    robot_body_quat_w: np.ndarray,
+    motion_body_lin_vel_w: np.ndarray,
+    robot_body_lin_vel_w: np.ndarray,
+    motion_body_ang_vel_w: np.ndarray,
+    robot_body_ang_vel_w: np.ndarray,
+    body_pos_relative_w: np.ndarray,
+    body_quat_relative_w: np.ndarray,
+    motion_joint_pos: np.ndarray,
+    robot_joint_pos: np.ndarray,
+    motion_joint_vel: np.ndarray,
+    robot_joint_vel: np.ndarray,
+    error_anchor_pos: np.ndarray,
+    error_anchor_rot: np.ndarray,
+    error_anchor_lin_vel: np.ndarray,
+    error_anchor_ang_vel: np.ndarray,
+    error_body_pos: np.ndarray,
+    error_body_rot: np.ndarray,
+    error_body_lin_vel: np.ndarray,
+    error_body_ang_vel: np.ndarray,
+    error_joint_pos: np.ndarray,
+    error_joint_vel: np.ndarray,
+) -> None:
+    """Write all MotionCommand error metrics for the selected environment rows.
+
+    ``env_ids`` is a cold-path-owned all-row index buffer for normal steps and
+    the reset row list for partial resets.  Keeping one kernel for both paths
+    avoids a second production mathematical implementation while preserving the
+    manager's row-scoped reset contract.
+    """
+    num_bodies = motion_body_pos_w.shape[1]
+    num_joints = motion_joint_pos.shape[1]
+    for row in prange(env_ids.shape[0]):
+        env_idx = env_ids[row]
+        anchor_dx = (
+            motion_body_pos_w[env_idx, anchor_body_idx, 0]
+            - robot_body_pos_w[env_idx, anchor_body_idx, 0]
+        )
+        anchor_dy = (
+            motion_body_pos_w[env_idx, anchor_body_idx, 1]
+            - robot_body_pos_w[env_idx, anchor_body_idx, 1]
+        )
+        anchor_dz = (
+            motion_body_pos_w[env_idx, anchor_body_idx, 2]
+            - robot_body_pos_w[env_idx, anchor_body_idx, 2]
+        )
+        error_anchor_pos[env_idx] = sqrt(
+            anchor_dx * anchor_dx + anchor_dy * anchor_dy + anchor_dz * anchor_dz
+        )
+        error_anchor_rot[env_idx] = sqrt(
+            _quat_error_squared(
+                motion_body_quat_w[env_idx, anchor_body_idx, 0],
+                motion_body_quat_w[env_idx, anchor_body_idx, 1],
+                motion_body_quat_w[env_idx, anchor_body_idx, 2],
+                motion_body_quat_w[env_idx, anchor_body_idx, 3],
+                robot_body_quat_w[env_idx, anchor_body_idx, 0],
+                robot_body_quat_w[env_idx, anchor_body_idx, 1],
+                robot_body_quat_w[env_idx, anchor_body_idx, 2],
+                robot_body_quat_w[env_idx, anchor_body_idx, 3],
+            )
+        )
+
+        anchor_lin_sq = 0.0
+        anchor_ang_sq = 0.0
+        for component in range(3):
+            lin_delta = (
+                motion_body_lin_vel_w[env_idx, anchor_body_idx, component]
+                - robot_body_lin_vel_w[env_idx, anchor_body_idx, component]
+            )
+            ang_delta = (
+                motion_body_ang_vel_w[env_idx, anchor_body_idx, component]
+                - robot_body_ang_vel_w[env_idx, anchor_body_idx, component]
+            )
+            anchor_lin_sq += lin_delta * lin_delta
+            anchor_ang_sq += ang_delta * ang_delta
+        error_anchor_lin_vel[env_idx] = sqrt(anchor_lin_sq)
+        error_anchor_ang_vel[env_idx] = sqrt(anchor_ang_sq)
+
+        body_pos_sum = 0.0
+        body_rot_sum = 0.0
+        body_lin_sum = 0.0
+        body_ang_sum = 0.0
+        for body_idx in range(num_bodies):
+            pos_sq = 0.0
+            lin_sq = 0.0
+            ang_sq = 0.0
+            for component in range(3):
+                pos_delta = (
+                    body_pos_relative_w[env_idx, body_idx, component]
+                    - robot_body_pos_w[env_idx, body_idx, component]
+                )
+                lin_delta = (
+                    motion_body_lin_vel_w[env_idx, body_idx, component]
+                    - robot_body_lin_vel_w[env_idx, body_idx, component]
+                )
+                ang_delta = (
+                    motion_body_ang_vel_w[env_idx, body_idx, component]
+                    - robot_body_ang_vel_w[env_idx, body_idx, component]
+                )
+                pos_sq += pos_delta * pos_delta
+                lin_sq += lin_delta * lin_delta
+                ang_sq += ang_delta * ang_delta
+            body_pos_sum += sqrt(pos_sq)
+            body_lin_sum += sqrt(lin_sq)
+            body_ang_sum += sqrt(ang_sq)
+            body_rot_sum += sqrt(
+                _quat_error_squared(
+                    body_quat_relative_w[env_idx, body_idx, 0],
+                    body_quat_relative_w[env_idx, body_idx, 1],
+                    body_quat_relative_w[env_idx, body_idx, 2],
+                    body_quat_relative_w[env_idx, body_idx, 3],
+                    robot_body_quat_w[env_idx, body_idx, 0],
+                    robot_body_quat_w[env_idx, body_idx, 1],
+                    robot_body_quat_w[env_idx, body_idx, 2],
+                    robot_body_quat_w[env_idx, body_idx, 3],
+                )
+            )
+        if num_bodies == 0:
+            error_body_pos[env_idx] = np.nan
+            error_body_rot[env_idx] = np.nan
+            error_body_lin_vel[env_idx] = np.nan
+            error_body_ang_vel[env_idx] = np.nan
+        else:
+            inv_bodies = 1.0 / num_bodies
+            error_body_pos[env_idx] = body_pos_sum * inv_bodies
+            error_body_rot[env_idx] = body_rot_sum * inv_bodies
+            error_body_lin_vel[env_idx] = body_lin_sum * inv_bodies
+            error_body_ang_vel[env_idx] = body_ang_sum * inv_bodies
+
+        joint_pos_sq = 0.0
+        joint_vel_sq = 0.0
+        for joint_idx in range(num_joints):
+            pos_delta = motion_joint_pos[env_idx, joint_idx] - robot_joint_pos[env_idx, joint_idx]
+            vel_delta = motion_joint_vel[env_idx, joint_idx] - robot_joint_vel[env_idx, joint_idx]
+            joint_pos_sq += pos_delta * pos_delta
+            joint_vel_sq += vel_delta * vel_delta
+        error_joint_pos[env_idx] = sqrt(joint_pos_sq)
+        error_joint_vel[env_idx] = sqrt(joint_vel_sq)
+
+
+@njit(cache=True, nogil=True, parallel=True)
+def termination_anchor_pos_kernel(
+    motion_body_pos_w: np.ndarray,
+    robot_body_pos_w: np.ndarray,
+    anchor_body_idx: int,
+    threshold: float,
+    out: np.ndarray,
+) -> None:
+    """Write the per-environment anchor-height termination mask."""
+    for env_idx in prange(motion_body_pos_w.shape[0]):
+        error = abs(
+            motion_body_pos_w[env_idx, anchor_body_idx, 2]
+            - robot_body_pos_w[env_idx, anchor_body_idx, 2]
+        )
+        out[env_idx] = error > threshold
+
+
+@njit(cache=True, nogil=True, parallel=True)
+def reward_motion_body_pos_kernel(
+    reference: np.ndarray,
+    actual: np.ndarray,
+    body_ids: np.ndarray,
+    std: float,
+    out: np.ndarray,
+) -> None:
+    """Write the relative body-position exponential reward."""
+    num_bodies = body_ids.shape[0]
+    if num_bodies == 0:
+        out[:] = np.nan
+        return
+    denominator = -(num_bodies * std * std)
+    for env_idx in prange(reference.shape[0]):
+        error = reference[env_idx, body_ids[0], 0] * 0
+        for body_offset in range(num_bodies):
+            body_idx = body_ids[body_offset]
+            dx = reference[env_idx, body_idx, 0] - actual[env_idx, body_idx, 0]
+            dy = reference[env_idx, body_idx, 1] - actual[env_idx, body_idx, 1]
+            dz = reference[env_idx, body_idx, 2] - actual[env_idx, body_idx, 2]
+            error += dx * dx + dy * dy + dz * dz
+        out[env_idx] = np.exp(error / denominator)
+
+
+@njit(cache=True, nogil=True, parallel=True)
+def reward_motion_body_ori_kernel(
+    reference: np.ndarray,
+    actual: np.ndarray,
+    body_ids: np.ndarray,
+    std: float,
+    out: np.ndarray,
+) -> None:
+    """Write the relative body-orientation exponential reward."""
+    num_bodies = body_ids.shape[0]
+    if num_bodies == 0:
+        out[:] = np.nan
+        return
+    denominator = -(num_bodies * std * std)
+    for env_idx in prange(reference.shape[0]):
+        error = reference[env_idx, body_ids[0], 0] * 0
+        for body_offset in range(num_bodies):
+            body_idx = body_ids[body_offset]
+            w1 = reference[env_idx, body_idx, 0]
+            x1 = reference[env_idx, body_idx, 1]
+            y1 = reference[env_idx, body_idx, 2]
+            z1 = reference[env_idx, body_idx, 3]
+            w2 = actual[env_idx, body_idx, 0]
+            x2 = actual[env_idx, body_idx, 1]
+            y2 = actual[env_idx, body_idx, 2]
+            z2 = actual[env_idx, body_idx, 3]
+
+            error += _quat_error_squared(w1, x1, y1, z1, w2, x2, y2, z2)
+        out[env_idx] = np.exp(error / denominator)
+
+
+@njit(cache=True, nogil=True, parallel=True)
+def reward_motion_body_lin_vel_kernel(
+    reference: np.ndarray,
+    actual: np.ndarray,
+    body_ids: np.ndarray,
+    std: float,
+    out: np.ndarray,
+) -> None:
+    """Write the global body-linear-velocity exponential reward."""
+    num_bodies = body_ids.shape[0]
+    if num_bodies == 0:
+        out[:] = np.nan
+        return
+    denominator = -(num_bodies * std * std)
+    for env_idx in prange(reference.shape[0]):
+        error = reference[env_idx, body_ids[0], 0] * 0
+        for body_offset in range(num_bodies):
+            body_idx = body_ids[body_offset]
+            dx = reference[env_idx, body_idx, 0] - actual[env_idx, body_idx, 0]
+            dy = reference[env_idx, body_idx, 1] - actual[env_idx, body_idx, 1]
+            dz = reference[env_idx, body_idx, 2] - actual[env_idx, body_idx, 2]
+            error += dx * dx + dy * dy + dz * dz
+        out[env_idx] = np.exp(error / denominator)
+
+
+@njit(cache=True, nogil=True, parallel=True)
+def reward_motion_body_ang_vel_kernel(
+    reference: np.ndarray,
+    actual: np.ndarray,
+    body_ids: np.ndarray,
+    std: float,
+    out: np.ndarray,
+) -> None:
+    """Write the global body-angular-velocity exponential reward."""
+    num_bodies = body_ids.shape[0]
+    if num_bodies == 0:
+        out[:] = np.nan
+        return
+    denominator = -(num_bodies * std * std)
+    for env_idx in prange(reference.shape[0]):
+        error = reference[env_idx, body_ids[0], 0] * 0
+        for body_offset in range(num_bodies):
+            body_idx = body_ids[body_offset]
+            dx = reference[env_idx, body_idx, 0] - actual[env_idx, body_idx, 0]
+            dy = reference[env_idx, body_idx, 1] - actual[env_idx, body_idx, 1]
+            dz = reference[env_idx, body_idx, 2] - actual[env_idx, body_idx, 2]
+            error += dx * dx + dy * dy + dz * dz
+        out[env_idx] = np.exp(error / denominator)
+
+
+__all__ = [
+    "configure_motion_kernel_runtime",
+    "reward_motion_body_ang_vel_kernel",
+    "reward_motion_body_lin_vel_kernel",
+    "reward_motion_body_ori_kernel",
+    "reward_motion_body_pos_kernel",
+    "termination_anchor_pos_kernel",
+    "update_motion_metrics_kernel",
+    "update_motion_relative_state_kernel",
+    "update_object_relative_state_kernel",
+]
diff --git a/src/unilab/tasks/motion_tracking/common/manager_terms.py b/src/unilab/tasks/motion_tracking/common/manager_terms.py
new file mode 100644
index 000000000..a6b75f5c9
--- /dev/null
+++ b/src/unilab/tasks/motion_tracking/common/manager_terms.py
@@ -0,0 +1,1119 @@
+"""Manager-native NumPy terms for motion tracking."""
+
+from __future__ import annotations
+
+import dataclasses
+import math
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any, Literal, cast
+
+import numpy as np
+
+from unilab.envs.mdp.actions import JointPositionAction, JointPositionActionCfg
+from unilab.managers import CommandTerm, CommandTermCfg, ManagerTermBase, ManagerTermBaseCfg
+from unilab.managers.scene_entity_config import SceneEntityCfg
+from unilab.utils.rotation import (
+    np_quat_apply_inverse,
+    np_quat_error_magnitude_squared_batched,
+    np_quat_from_euler_xyz,
+    np_quat_mul,
+)
+
+from .kernels import (
+    configure_motion_kernel_runtime,
+    reward_motion_body_ang_vel_kernel,
+    reward_motion_body_lin_vel_kernel,
+    reward_motion_body_ori_kernel,
+    reward_motion_body_pos_kernel,
+    termination_anchor_pos_kernel,
+    update_motion_metrics_kernel,
+    update_motion_relative_state_kernel,
+)
+from .motion_loader import MotionData, MotionLoader, MotionSampler
+
+if TYPE_CHECKING:
+    from unilab.base.entity import Entity
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+SamplingMode = Literal["start", "clip_start", "uniform", "adaptive", "mixed"]
+_RANGE_KEYS = ("x", "y", "z", "roll", "pitch", "yaw")
+_DEFAULT_ASSET_CFG = SceneEntityCfg("robot")
+
+
+def _range_matrix(value: dict[str, tuple[float, float]], *, name: str) -> np.ndarray:
+    unknown = sorted(set(value) - set(_RANGE_KEYS))
+    if unknown:
+        raise ValueError(f"{name} has unknown axes {unknown}")
+    try:
+        ranges = np.asarray([value.get(key, (0.0, 0.0)) for key in _RANGE_KEYS], dtype=np.float64)
+    except (TypeError, ValueError) as exc:
+        raise TypeError(f"{name} must map axes to numeric (min, max) pairs") from exc
+    if ranges.shape != (6, 2) or not np.isfinite(ranges).all():
+        raise ValueError(f"{name} must contain six finite (min, max) pairs")
+    if np.any(ranges[:, 0] > ranges[:, 1]):
+        raise ValueError(f"{name} contains a minimum greater than its maximum")
+    ranges.setflags(write=False)
+    return ranges
+
+
+def _pair(value: tuple[float, float], *, name: str) -> tuple[float, float]:
+    try:
+        values = np.asarray(value, dtype=np.float64)
+    except (TypeError, ValueError) as exc:
+        raise TypeError(f"{name} must be a numeric (min, max) pair") from exc
+    if values.shape != (2,) or not np.isfinite(values).all():
+        raise ValueError(f"{name} must be a finite (min, max) pair")
+    lower, upper = float(values[0]), float(values[1])
+    if lower > upper:
+        raise ValueError(f"{name} minimum {lower} exceeds maximum {upper}")
+    return lower, upper
+
+
+@dataclass
+class MotionCommandParamsCfg:
+    """Hydra-owned motion data and sampling parameters."""
+
+    motion_file: str | list[str]
+    anchor_body_name: str
+    body_names: tuple[str, ...] | list[str]
+    sampling_mode: SamplingMode = "adaptive"
+    sampling_start_ratio: float = 0.0
+    truncate_on_clip_end: bool = False
+    pose_range: dict[str, tuple[float, float]] = field(default_factory=dict)
+    velocity_range: dict[str, tuple[float, float]] = field(default_factory=dict)
+    joint_position_range: tuple[float, float] = (-0.1, 0.1)
+    joint_default_position_range: tuple[float, float] = (0.0, 0.0)
+    adaptive_lambda: float = 0.8
+    adaptive_kernel_size: int = 1
+    adaptive_uniform_ratio: float = 0.1
+    adaptive_alpha: float = 0.001
+
+
+@dataclass(kw_only=True)
+class MotionCommandCfg(CommandTermCfg):
+    """Community-shaped motion command with Hydra-owned nested parameters."""
+
+    entity_name: str
+    params: MotionCommandParamsCfg
+
+    def build(self, env: ManagerBasedRlEnv) -> MotionCommand:
+        return MotionCommand(self, env)
+
+    @property
+    def motion_file(self) -> str | list[str]:
+        return self.params.motion_file
+
+    @property
+    def anchor_body_name(self) -> str:
+        return self.params.anchor_body_name
+
+    @property
+    def body_names(self) -> tuple[str, ...]:
+        return tuple(self.params.body_names)
+
+    @property
+    def sampling_mode(self) -> SamplingMode:
+        return self.params.sampling_mode
+
+
+class MotionCommand(CommandTerm):
+    """Motion reference command on UniLab's NumPy/entity runtime."""
+
+    cfg: MotionCommandCfg
+
+    def __init__(self, cfg: MotionCommandCfg, env: ManagerBasedRlEnv):
+        self._validate_cfg(cfg)
+        super().__init__(cfg, env)
+        self.robot = cast("Entity", env.scene[cfg.entity_name])
+        body_ids, body_names = self.robot.find_bodies(cfg.body_names, preserve_order=True)
+        if tuple(body_names) != cfg.body_names:
+            raise ValueError(
+                f"MotionCommand body order {tuple(body_names)} does not match {cfg.body_names}"
+            )
+        self._robot_body_ids = np.asarray(body_ids, dtype=np.intp)
+        self._robot_body_ids.setflags(write=False)
+        self._copy_robot_body_state = self.robot.bind_body_state_copy(self._robot_body_ids)
+        motion_body_ids = self.robot.motion_body_ids[self._robot_body_ids]
+        self.motion = self._make_motion_loader(cfg.motion_file, motion_body_ids)
+        if self.motion.num_joints != len(self.robot.joint_names):
+            raise ValueError(
+                f"MotionCommand motion joint width {self.motion.num_joints} does not match "
+                f"entity '{self.robot.name}' joint width {len(self.robot.joint_names)}"
+            )
+        if self.motion.num_bodies != len(cfg.body_names):
+            raise ValueError(
+                f"MotionCommand motion body width {self.motion.num_bodies} does not match "
+                f"configured body width {len(cfg.body_names)}"
+            )
+
+        self.anchor_body_idx = cfg.body_names.index(cfg.anchor_body_name)
+        self.sampler = MotionSampler(
+            self.motion,
+            mode=cfg.params.sampling_mode,
+            num_envs=self.num_envs,
+            adaptive_lambda=cfg.params.adaptive_lambda,
+            adaptive_kernel_size=cfg.params.adaptive_kernel_size,
+            adaptive_uniform_ratio=cfg.params.adaptive_uniform_ratio,
+            adaptive_alpha=cfg.params.adaptive_alpha,
+            start_ratio=cfg.params.sampling_start_ratio,
+            rng=env.rng,
+        )
+        self._pose_range = _range_matrix(cfg.params.pose_range, name="MotionCommand pose_range")
+        self._velocity_range = _range_matrix(
+            cfg.params.velocity_range, name="MotionCommand velocity_range"
+        )
+        self._joint_position_range = _pair(
+            cfg.params.joint_position_range,
+            name="MotionCommand joint_position_range",
+        )
+        self._joint_default_position_range = _pair(
+            cfg.params.joint_default_position_range,
+            name="MotionCommand joint_default_position_range",
+        )
+
+        num_bodies = len(cfg.body_names)
+        num_joints = self.motion.num_joints
+        dtype = self.motion.joint_pos.dtype
+        self.time_steps = self.sampler.current_frames
+        self._motion_data = self.motion.make_motion_data_buffer(self.num_envs)
+        self._command = np.empty((self.num_envs, num_joints * 2), dtype=dtype)
+        self._body_pos_w = np.empty((self.num_envs, num_bodies, 3), dtype=dtype)
+        self.body_pos_relative_w = np.empty_like(self._body_pos_w)
+        self.body_quat_relative_w = np.empty((self.num_envs, num_bodies, 4), dtype=dtype)
+        self.motion_anchor_pos_b = np.empty((self.num_envs, 3), dtype=dtype)
+        self.motion_anchor_ori_b = np.empty((self.num_envs, 6), dtype=dtype)
+        self.robot_body_pos_b = np.empty_like(self._body_pos_w)
+        self.robot_body_ori_b = np.empty((self.num_envs, num_bodies, 6), dtype=dtype)
+        self.joint_default_bias = np.zeros((self.num_envs, num_joints), dtype=dtype)
+        self._robot_cache_step = -1
+        self._all_env_ids = np.arange(self.num_envs, dtype=np.int32)
+        self._all_env_ids.setflags(write=False)
+        # Env ids of the most recent scoped (reset-path) compute; None after a
+        # per-step compute. Written by `_update_command`, consumed by
+        # `post_compute` to restrict refresh work to the reset rows.
+        self._post_compute_env_ids: np.ndarray | None = None
+        # Reset rows whose motion-reference buffers were already ingested by
+        # `_resample_command` during the in-flight reset; consumed by the
+        # reset-path `_update_command` to skip the redundant `_refresh_motion`
+        # gather (issue #1355).
+        self._resample_ingested_ids: np.ndarray | None = None
+        # Motion rows gathered by the in-flight `_resample_command`, exposed so
+        # subclasses (e.g. BoxMotionCommand) reuse the same gather instead of
+        # re-reading the same frames.
+        self._resample_motion: MotionData | None = None
+        self._robot_body_pos_w = np.empty_like(self._body_pos_w)
+        self._robot_body_quat_w = np.empty((self.num_envs, num_bodies, 4), dtype=dtype)
+        self._robot_body_lin_vel_w = np.empty_like(self._body_pos_w)
+        self._robot_body_ang_vel_w = np.empty_like(self._body_pos_w)
+
+        for name in (
+            "error_anchor_pos",
+            "error_anchor_rot",
+            "error_anchor_lin_vel",
+            "error_anchor_ang_vel",
+            "error_body_pos",
+            "error_body_rot",
+            "error_body_lin_vel",
+            "error_body_ang_vel",
+            "error_joint_pos",
+            "error_joint_vel",
+            "sampling_entropy",
+            "sampling_top1_prob",
+            "sampling_top1_bin",
+        ):
+            self.metrics[name] = np.zeros(self.num_envs, dtype=dtype)
+        self._refresh_motion()
+        self._refresh_robot_state(force=True)
+        # Configure and compile both fused kernels on the cold path so the first
+        # measured manager step contains no Numba worker/JIT initialization.
+        configure_motion_kernel_runtime()
+        self._refresh_relative_state()
+        self._update_metrics(self._all_env_ids)
+
+    def _make_motion_loader(
+        self,
+        motion_file: str | list[str],
+        body_indices: np.ndarray,
+    ) -> MotionLoader:
+        """Materialize the profile-owned motion loader on the cold path."""
+        return MotionLoader(motion_file, body_indices=body_indices)
+
+    @staticmethod
+    def _validate_cfg(cfg: MotionCommandCfg) -> None:
+        if not isinstance(cfg.entity_name, str) or not cfg.entity_name:
+            raise ValueError("MotionCommandCfg entity_name must be non-empty")
+        if not isinstance(cfg.params, MotionCommandParamsCfg):
+            raise TypeError("MotionCommandCfg params must be MotionCommandParamsCfg")
+        if not cfg.motion_file:
+            raise ValueError("MotionCommandCfg motion_file must be configured")
+        if not cfg.anchor_body_name or cfg.anchor_body_name not in cfg.body_names:
+            raise ValueError("MotionCommandCfg anchor_body_name must occur in body_names")
+        if len(set(cfg.body_names)) != len(cfg.body_names):
+            raise ValueError("MotionCommandCfg body_names must be unique")
+        if cfg.sampling_mode not in ("start", "clip_start", "uniform", "adaptive", "mixed"):
+            raise ValueError(
+                f"MotionCommandCfg has unsupported sampling_mode {cfg.sampling_mode!r}"
+            )
+        if not 0.0 <= cfg.params.sampling_start_ratio <= 1.0:
+            raise ValueError("MotionCommandCfg sampling_start_ratio must be within [0, 1]")
+        if not isinstance(cfg.params.truncate_on_clip_end, bool):
+            raise TypeError("MotionCommandCfg truncate_on_clip_end must be bool")
+
+    @property
+    def command(self) -> np.ndarray:
+        return self._command
+
+    @property
+    def joint_pos(self) -> np.ndarray:
+        return self._motion_data.joint_pos
+
+    @property
+    def joint_vel(self) -> np.ndarray:
+        return self._motion_data.joint_vel
+
+    @property
+    def body_pos_w(self) -> np.ndarray:
+        return self._body_pos_w
+
+    @property
+    def body_quat_w(self) -> np.ndarray:
+        return self._motion_data.body_quat_w
+
+    @property
+    def body_lin_vel_w(self) -> np.ndarray:
+        return self._motion_data.body_lin_vel_w
+
+    @property
+    def body_ang_vel_w(self) -> np.ndarray:
+        return self._motion_data.body_ang_vel_w
+
+    @property
+    def anchor_pos_w(self) -> np.ndarray:
+        return self._body_pos_w[:, self.anchor_body_idx]
+
+    @property
+    def anchor_quat_w(self) -> np.ndarray:
+        return self._motion_data.body_quat_w[:, self.anchor_body_idx]
+
+    @property
+    def anchor_lin_vel_w(self) -> np.ndarray:
+        return self._motion_data.body_lin_vel_w[:, self.anchor_body_idx]
+
+    @property
+    def anchor_ang_vel_w(self) -> np.ndarray:
+        return self._motion_data.body_ang_vel_w[:, self.anchor_body_idx]
+
+    @property
+    def robot_joint_pos(self) -> np.ndarray:
+        return self.robot.data.joint_pos
+
+    @property
+    def robot_joint_vel(self) -> np.ndarray:
+        return self.robot.data.joint_vel
+
+    @property
+    def robot_body_pos_w(self) -> np.ndarray:
+        self._refresh_robot_state()
+        return self._robot_body_pos_w
+
+    @property
+    def robot_body_quat_w(self) -> np.ndarray:
+        self._refresh_robot_state()
+        return self._robot_body_quat_w
+
+    @property
+    def robot_body_lin_vel_w(self) -> np.ndarray:
+        self._refresh_robot_state()
+        return self._robot_body_lin_vel_w
+
+    @property
+    def robot_body_ang_vel_w(self) -> np.ndarray:
+        self._refresh_robot_state()
+        return self._robot_body_ang_vel_w
+
+    @property
+    def robot_anchor_pos_w(self) -> np.ndarray:
+        return self.robot_body_pos_w[:, self.anchor_body_idx]
+
+    @property
+    def robot_anchor_quat_w(self) -> np.ndarray:
+        return self.robot_body_quat_w[:, self.anchor_body_idx]
+
+    @property
+    def robot_anchor_lin_vel_w(self) -> np.ndarray:
+        return self.robot_body_lin_vel_w[:, self.anchor_body_idx]
+
+    @property
+    def robot_anchor_ang_vel_w(self) -> np.ndarray:
+        return self.robot_body_ang_vel_w[:, self.anchor_body_idx]
+
+    def reset(self, env_ids: np.ndarray | slice | None) -> dict[str, float]:
+        ids = (
+            np.arange(self.num_envs, dtype=np.int32)
+            if env_ids is None
+            else np.arange(self.num_envs, dtype=np.int32)[env_ids]
+            if isinstance(env_ids, slice)
+            else env_ids
+        )
+        # Row-wise error metrics are consumed only here (CommandTerm.reset logs
+        # per-episode means from these rows, then zeroes them). The per-step
+        # compute path skips the full-batch metrics kernel (issue #1355), so
+        # refresh exactly the rows being reset from the current post-step
+        # buffers — the same inputs the former per-step refresh used, keeping
+        # the consumed values bit-identical.
+        self._update_error_metrics(ids)
+        lower, upper = self._joint_default_position_range
+        self.joint_default_bias[ids] = self._env.rng.uniform(
+            lower, upper, size=(len(ids), self.motion.num_joints)
+        )
+        return super().reset(ids)
+
+    def _refresh_motion(self, env_ids: np.ndarray | None = None) -> None:
+        """Refresh motion-reference buffers from the current frame indices.
+
+        With env_ids=None all rows are refreshed in place; with env_ids only
+        those rows are gathered and scattered (partial-reset path). Rows outside
+        env_ids keep the values produced by the last per-step refresh, which are
+        still valid because untouched envs did not advance or resample frames.
+
+        Subclass contract (issue #1355): on the reset path the row-scoped
+        refresh may be skipped when `_resample_command` already ingested the
+        same rows through `_ingest_motion_rows`. A subclass that overrides this
+        method to refresh additional buffers must override
+        `_ingest_motion_rows` with the same additions (see BoxMotionCommand).
+        """
+        if env_ids is None:
+            self.motion.get_motion_at_frame(self.time_steps, out=self._motion_data)
+            np.add(
+                self._motion_data.body_pos_w,
+                self._env.scene.env_origins[:, None, :],
+                out=self._body_pos_w,
+            )
+            width = self.motion.num_joints
+            self._command[:, :width] = self._motion_data.joint_pos
+            self._command[:, width:] = self._motion_data.joint_vel
+            return
+        self._ingest_motion_rows(env_ids, self.motion.get_motion_at_frame(self.time_steps[env_ids]))
+
+    def _ingest_motion_rows(self, env_ids: np.ndarray, data: MotionData) -> None:
+        """Scatter one gathered motion frame set into the reference buffers.
+
+        Shared by the row-scoped `_refresh_motion` and by `_resample_command`,
+        so the reset path gathers each reset row's motion frame exactly once
+        (issue #1355).
+        """
+        for motion_field in dataclasses.fields(data):
+            value = getattr(data, motion_field.name)
+            target = getattr(self._motion_data, motion_field.name)
+            if value is None or target is None:
+                continue
+            target[env_ids] = value
+        self._body_pos_w[env_ids] = data.body_pos_w + self._env.scene.env_origins[env_ids, None, :]
+        width = self.motion.num_joints
+        self._command[env_ids, :width] = data.joint_pos
+        self._command[env_ids, width:] = data.joint_vel
+
+    def _refresh_robot_state(
+        self, *, force: bool = False, env_ids: np.ndarray | None = None
+    ) -> None:
+        step = self._env.common_step_counter
+        if not force and self._robot_cache_step == step:
+            return
+        if env_ids is None:
+            self._copy_robot_body_state(
+                self._robot_body_pos_w,
+                self._robot_body_quat_w,
+                self._robot_body_lin_vel_w,
+                self._robot_body_ang_vel_w,
+            )
+        else:
+            # Partial-reset path (issue #1295): gather only the reset rows from
+            # the backend instead of full-batch body reads sliced afterwards.
+            # _robot_body_ids selects the tracked subset afterwards, so the
+            # row getters fetch all entity bodies for just these rows.
+            data = self.robot.data
+            self._robot_body_pos_w[env_ids] = data.body_link_pos_w_rows(env_ids)[
+                :, self._robot_body_ids
+            ]
+            self._robot_body_quat_w[env_ids] = data.body_link_quat_w_rows(env_ids)[
+                :, self._robot_body_ids
+            ]
+            self._robot_body_lin_vel_w[env_ids] = data.body_link_lin_vel_w_rows(env_ids)[
+                :, self._robot_body_ids
+            ]
+            self._robot_body_ang_vel_w[env_ids] = data.body_link_ang_vel_w_rows(env_ids)[
+                :, self._robot_body_ids
+            ]
+        self._robot_cache_step = step
+
+    def _refresh_relative_state(self, env_ids: np.ndarray | None = None) -> None:
+        rows = self._all_env_ids if env_ids is None else env_ids
+        update_motion_relative_state_kernel(
+            rows,
+            self.anchor_body_idx,
+            self._motion_data.body_pos_w,
+            self._body_pos_w,
+            self._motion_data.body_quat_w,
+            self._robot_body_pos_w,
+            self._robot_body_quat_w,
+            self.body_pos_relative_w,
+            self.body_quat_relative_w,
+            self.motion_anchor_pos_b,
+            self.motion_anchor_ori_b,
+            self.robot_body_pos_b,
+            self.robot_body_ori_b,
+        )
+
+    def _update_metrics(self, env_ids: np.ndarray | None = None) -> None:
+        # The row-wise error metrics are consumed only by `reset()` (episode
+        # log extras), which refreshes exactly the rows it reads. The per-step
+        # call (env_ids=None) therefore skips the Numba kernel over all rows
+        # (issue #1355); the reset path (env_ids set) refreshes the reset rows
+        # so post-reset metrics track the post-reset state.
+        if env_ids is not None:
+            self._update_error_metrics(env_ids)
+        # Sampler statistics are global scalars, so every row tracks them.
+        self.metrics["sampling_entropy"].fill(self.sampler.sampling_entropy)
+        self.metrics["sampling_top1_prob"].fill(self.sampler.sampling_top1_prob)
+        self.metrics["sampling_top1_bin"].fill(self.sampler.sampling_top1_bin)
+
+    def _update_error_metrics(self, rows: np.ndarray) -> None:
+        """Recompute the row-wise error metrics for the given rows.
+
+        All row-wise metrics are written by one Numba kernel.  Passing an
+        explicit all-row index buffer for normal steps lets the same kernel
+        serve partial-reset rows without retaining a NumPy runtime formula.
+        """
+        update_motion_metrics_kernel(
+            rows,
+            self.anchor_body_idx,
+            self._body_pos_w,
+            self._robot_body_pos_w,
+            self._motion_data.body_quat_w,
+            self._robot_body_quat_w,
+            self._motion_data.body_lin_vel_w,
+            self._robot_body_lin_vel_w,
+            self._motion_data.body_ang_vel_w,
+            self._robot_body_ang_vel_w,
+            self.body_pos_relative_w,
+            self.body_quat_relative_w,
+            self._motion_data.joint_pos,
+            self.robot_joint_pos,
+            self._motion_data.joint_vel,
+            self.robot_joint_vel,
+            self.metrics["error_anchor_pos"],
+            self.metrics["error_anchor_rot"],
+            self.metrics["error_anchor_lin_vel"],
+            self.metrics["error_anchor_ang_vel"],
+            self.metrics["error_body_pos"],
+            self.metrics["error_body_rot"],
+            self.metrics["error_body_lin_vel"],
+            self.metrics["error_body_ang_vel"],
+            self.metrics["error_joint_pos"],
+            self.metrics["error_joint_vel"],
+        )
+
+    def _resample_command(self, env_ids: np.ndarray) -> None:
+        """Resample motion frames and stage the corresponding state writes.
+
+        The base implementation gathers the resampled frames once, ingests them
+        into the motion-reference buffers via `_ingest_motion_rows`, and exposes
+        the gather as `self._resample_motion` (issue #1355). Subclass contract:
+        call ``super()._resample_command(env_ids)`` first and reuse
+        `self._resample_motion` for additional writes instead of re-gathering;
+        a subclass that does not call super() leaves `_resample_ingested_ids`
+        unset, and the reset-path `_update_command` falls back to a fresh
+        `_refresh_motion(env_ids)` gather.
+        """
+        frames = self.sampler.sample_frames(env_ids)
+        motion = self.motion.get_motion_at_frame(frames)
+        count = len(env_ids)
+        pose = self._env.rng.uniform(
+            self._pose_range[:, 0], self._pose_range[:, 1], size=(count, 6)
+        )
+        velocity = self._env.rng.uniform(
+            self._velocity_range[:, 0], self._velocity_range[:, 1], size=(count, 6)
+        )
+        root_pos = motion.body_pos_w[:, 0].copy()
+        root_pos += self._env.scene.env_origins[env_ids]
+        root_pos += pose[:, :3]
+        root_quat = np_quat_mul(
+            np_quat_from_euler_xyz(pose[:, 3], pose[:, 4], pose[:, 5]),
+            motion.body_quat_w[:, 0],
+        )
+        root_lin_vel = motion.body_lin_vel_w[:, 0] + velocity[:, :3]
+        root_ang_vel = motion.body_ang_vel_w[:, 0] + velocity[:, 3:]
+        joint_pos = motion.joint_pos.copy()
+        joint_pos += self._env.rng.uniform(
+            *self._joint_position_range,
+            size=joint_pos.shape,
+        )
+        limits = self.robot.data.soft_joint_pos_limits
+        np.clip(joint_pos, limits[:, 0], limits[:, 1], out=joint_pos)
+        self.robot.write_joint_state_to_sim(joint_pos, motion.joint_vel, env_ids=env_ids)
+        root_state = np.concatenate((root_pos, root_quat, root_lin_vel, root_ang_vel), axis=-1)
+        self.robot.write_root_state_to_sim(root_state, env_ids=env_ids)
+        # Keep the motion-reference buffers in sync with the resampled frames so
+        # the reset-path `_update_command` does not gather the same rows again
+        # (issue #1355). Subclasses reuse `self._resample_motion` for their own
+        # reset writes instead of re-gathering the same frames.
+        self._ingest_motion_rows(env_ids, motion)
+        self._resample_ingested_ids = env_ids
+        self._resample_motion = motion
+
+    def _update_command(self, env_ids: np.ndarray | None) -> None:
+        self._post_compute_env_ids = env_ids
+        if env_ids is not None:
+            ingested = self._resample_ingested_ids
+            self._resample_ingested_ids = None
+            if ingested is None or not np.array_equal(ingested, env_ids):
+                self._refresh_motion(env_ids)
+            return
+        self._resample_ingested_ids = None
+        self.sampler.update_failure_stats(self._env.termination_manager.terminated)
+        active_ids = np.flatnonzero(~self._env.reset_buf).astype(np.int32, copy=False)
+        wrap_ids = self.sampler.step(active_ids)
+        if len(wrap_ids) and not self.cfg.params.truncate_on_clip_end:
+            self._resample_command(wrap_ids)
+        self._refresh_motion()
+
+    def post_compute(self) -> None:
+        # On the reset path only the reset rows changed (via the committed
+        # set_state writes and the motion resample), so refresh just those rows.
+        env_ids = self._post_compute_env_ids
+        self._refresh_robot_state(force=True, env_ids=env_ids)
+        self._refresh_relative_state(env_ids)
+
+
+@dataclass(kw_only=True)
+class MotionJointPositionActionCfg(JointPositionActionCfg):
+    command_name: str = "motion"
+    simulate_action_latency: bool = False
+
+    def build(self, env: ManagerBasedRlEnv) -> MotionJointPositionAction:
+        return MotionJointPositionAction(self, env)
+
+
+class MotionJointPositionAction(JointPositionAction):
+    cfg: MotionJointPositionActionCfg  # pyright: ignore[reportIncompatibleVariableOverride]
+
+    def __init__(self, cfg: MotionJointPositionActionCfg, env: ManagerBasedRlEnv):
+        if not isinstance(cfg.simulate_action_latency, bool):
+            raise TypeError("MotionJointPositionActionCfg simulate_action_latency must be bool")
+        super().__init__(cfg, env)
+        self._motion_command = _command(env, cfg.command_name)
+        self._previous_raw_actions = np.zeros_like(self._raw_actions)
+
+    @property
+    def target(self) -> np.ndarray:
+        """Most recently applied physical joint target in entity joint order."""
+        return self._target
+
+    def process_actions(self, actions: np.ndarray) -> None:
+        self._previous_raw_actions[:] = self._raw_actions
+        super().process_actions(actions)
+        if not self.cfg.simulate_action_latency:
+            return
+        np.multiply(self._previous_raw_actions, self._scale, out=self._processed_actions)
+        np.add(self._processed_actions, self._offset, out=self._processed_actions)
+        if self._clip is not None:
+            np.clip(
+                self._processed_actions,
+                self._clip[..., 0],
+                self._clip[..., 1],
+                out=self._processed_actions,
+            )
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        super().reset(env_ids)
+        ids = slice(None) if env_ids is None else env_ids
+        self._previous_raw_actions[ids] = 0.0
+
+    def apply_actions(self) -> None:
+        encoder_bias = self._entity.data.encoder_bias[:, self._target_ids]
+        np.add(
+            self._processed_actions,
+            self._motion_command.joint_default_bias[:, self._target_ids],
+            out=self._target,
+        )
+        self._target -= encoder_bias
+        self._entity.set_joint_position_target(self._target, joint_ids=self._target_ids)
+
+
+def _command(env: ManagerBasedRlEnv, command_name: str) -> MotionCommand:
+    try:
+        command = env.command_manager.get_term(command_name)
+    except KeyError as exc:
+        raise KeyError(f"Motion command term '{command_name}' not found") from exc
+    if not isinstance(command, MotionCommand):
+        raise TypeError(
+            f"Command term '{command_name}' is {type(command).__name__}, expected MotionCommand"
+        )
+    return command
+
+
+def motion_anchor_pos_b(env: ManagerBasedRlEnv, command_name: str) -> np.ndarray:
+    return _command(env, command_name).motion_anchor_pos_b
+
+
+def motion_anchor_ori_b(env: ManagerBasedRlEnv, command_name: str) -> np.ndarray:
+    return _command(env, command_name).motion_anchor_ori_b
+
+
+def robot_body_pos_b(env: ManagerBasedRlEnv, command_name: str) -> np.ndarray:
+    command = _command(env, command_name)
+    return command.robot_body_pos_b.reshape(env.num_envs, -1)
+
+
+def robot_body_ori_b(env: ManagerBasedRlEnv, command_name: str) -> np.ndarray:
+    command = _command(env, command_name)
+    return command.robot_body_ori_b.reshape(env.num_envs, -1)
+
+
+def motion_joint_pos_rel(env: ManagerBasedRlEnv, command_name: str) -> np.ndarray:
+    command = _command(env, command_name)
+    return (
+        command.robot_joint_pos - command.robot.data.default_joint_pos - command.joint_default_bias
+    )
+
+
+def motion_joint_pos_rel_biased(env: ManagerBasedRlEnv, command_name: str) -> np.ndarray:
+    """Joint position relative to the episode default, including encoder bias."""
+    command = _command(env, command_name)
+    return (
+        command.robot.data.joint_pos_biased
+        - command.robot.data.default_joint_pos
+        - command.joint_default_bias
+    )
+
+
+def _positive_std(value: float, *, term_name: str) -> float:
+    if isinstance(value, bool) or not isinstance(value, (int, float, np.number)):
+        raise TypeError(f"{term_name} std must be a real number")
+    result = float(value)
+    if not math.isfinite(result) or result <= 0.0:
+        raise ValueError(f"{term_name} std must be finite and positive")
+    return result
+
+
+def motion_global_anchor_position_error_exp(
+    env: ManagerBasedRlEnv, command_name: str, std: float
+) -> np.ndarray:
+    command = _command(env, command_name)
+    diff = command.anchor_pos_w - command.robot_anchor_pos_w
+    np.square(diff, out=diff)
+    error = np.sum(diff, axis=-1)
+    scale = _positive_std(std, term_name="motion anchor position")
+    np.divide(error, -(scale**2), out=error)
+    return np.exp(error, out=error)
+
+
+def motion_global_anchor_orientation_error_exp(
+    env: ManagerBasedRlEnv, command_name: str, std: float
+) -> np.ndarray:
+    command = _command(env, command_name)
+    error = np_quat_error_magnitude_squared_batched(
+        command.anchor_quat_w, command.robot_anchor_quat_w
+    )
+    scale = _positive_std(std, term_name="motion anchor orientation")
+    np.divide(error, -(scale**2), out=error)
+    return np.exp(error, out=error)
+
+
+class _BodyTerm(ManagerTermBase):
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        command_name = cfg.params.get("command_name")
+        if not isinstance(command_name, str) or not command_name:
+            raise ValueError(f"{type(self).__name__} requires a non-empty command_name")
+        self._command_name = command_name
+        command = _command(env, command_name)
+        body_names = cfg.params.get("body_names")
+        if body_names is None:
+            self._body_ids = slice(None)
+        else:
+            requested = tuple(body_names)
+            missing = [name for name in requested if name not in command.cfg.body_names]
+            if missing:
+                raise ValueError(
+                    f"Body names {missing} are not tracked by command '{command_name}'"
+                )
+            self._body_ids = np.asarray(
+                [command.cfg.body_names.index(name) for name in requested], dtype=np.intp
+            )
+        # Lazily allocated scratch for squared-error reductions (issue #1296);
+        # shapes depend on the selected body set, so they are sized on first use.
+        self._diff_scratch: np.ndarray | None = None
+        self._err_scratch: np.ndarray | None = None
+
+    def _squared_error_3d(self, ref: np.ndarray, actual: np.ndarray) -> np.ndarray:
+        """Per-body squared 3D error with reused scratch, same op order as the
+        naive ``np.square(ref - actual).sum(axis=-1)`` (bit-identical)."""
+        if (
+            self._diff_scratch is None
+            or self._err_scratch is None
+            or self._diff_scratch.shape != ref.shape
+        ):
+            self._diff_scratch = np.empty(ref.shape, dtype=ref.dtype)
+            self._err_scratch = np.empty(ref.shape[:-1], dtype=ref.dtype)
+        diff = self._diff_scratch
+        err = self._err_scratch
+        np.subtract(ref, actual, out=diff)
+        np.square(diff, out=diff)
+        np.sum(diff, axis=-1, out=err)
+        return err
+
+    @staticmethod
+    def _exp_neg_scaled(error: np.ndarray, scale: float) -> np.ndarray:
+        """``np.exp(-error / scale**2)`` without intermediate temporaries; the
+        input buffer is consumed and returned (callers own it)."""
+        np.divide(error, -(scale**2), out=error)
+        return np.exp(error, out=error)
+
+    def _validate(self, command_name: str, std: float) -> tuple[MotionCommand, float]:
+        if command_name != self._command_name:
+            raise ValueError(
+                f"{type(self).__name__} was bound to '{self._command_name}', got '{command_name}'"
+            )
+        return _command(self._env, command_name), _positive_std(std, term_name=type(self).__name__)
+
+
+class _NumbaBodyTerm(_BodyTerm):
+    """Shared cold-path setup for the four fixed parallel body reward kernels."""
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        configure_motion_kernel_runtime()
+        command = _command(env, self._command_name)
+        if isinstance(self._body_ids, slice):
+            body_ids = np.arange(len(command.cfg.body_names), dtype=np.intp)
+        else:
+            body_ids = self._body_ids
+        body_ids.setflags(write=False)
+        self._kernel_body_ids = body_ids
+        self._kernel_result = np.empty(self.num_envs, dtype=command.body_pos_relative_w.dtype)
+
+    def _kernel_std(self, scale: float) -> float:
+        return cast(float, self._kernel_result.dtype.type(scale))
+
+
+class motion_relative_body_position_error_exp(_NumbaBodyTerm):
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        command = _command(env, self._command_name)
+        reward_motion_body_pos_kernel(
+            command.body_pos_relative_w,
+            command.robot_body_pos_w,
+            self._kernel_body_ids,
+            self._kernel_std(1.0),
+            self._kernel_result,
+        )
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        command_name: str,
+        std: float,
+        body_names: tuple[str, ...] | None = None,
+    ) -> np.ndarray:
+        del env, body_names
+        command, scale = self._validate(command_name, std)
+        reward_motion_body_pos_kernel(
+            command.body_pos_relative_w,
+            command.robot_body_pos_w,
+            self._kernel_body_ids,
+            self._kernel_std(scale),
+            self._kernel_result,
+        )
+        return self._kernel_result
+
+
+class motion_relative_body_orientation_error_exp(_NumbaBodyTerm):
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        command = _command(env, self._command_name)
+        reward_motion_body_ori_kernel(
+            command.body_quat_relative_w,
+            command.robot_body_quat_w,
+            self._kernel_body_ids,
+            self._kernel_std(1.0),
+            self._kernel_result,
+        )
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        command_name: str,
+        std: float,
+        body_names: tuple[str, ...] | None = None,
+    ) -> np.ndarray:
+        del env, body_names
+        command, scale = self._validate(command_name, std)
+        reward_motion_body_ori_kernel(
+            command.body_quat_relative_w,
+            command.robot_body_quat_w,
+            self._kernel_body_ids,
+            self._kernel_std(scale),
+            self._kernel_result,
+        )
+        return self._kernel_result
+
+
+class motion_global_body_linear_velocity_error_exp(_NumbaBodyTerm):
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        command = _command(env, self._command_name)
+        reward_motion_body_lin_vel_kernel(
+            command.body_lin_vel_w,
+            command.robot_body_lin_vel_w,
+            self._kernel_body_ids,
+            self._kernel_std(1.0),
+            self._kernel_result,
+        )
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        command_name: str,
+        std: float,
+        body_names: tuple[str, ...] | None = None,
+    ) -> np.ndarray:
+        del env, body_names
+        command, scale = self._validate(command_name, std)
+        reward_motion_body_lin_vel_kernel(
+            command.body_lin_vel_w,
+            command.robot_body_lin_vel_w,
+            self._kernel_body_ids,
+            self._kernel_std(scale),
+            self._kernel_result,
+        )
+        return self._kernel_result
+
+
+class motion_global_body_angular_velocity_error_exp(_NumbaBodyTerm):
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        command = _command(env, self._command_name)
+        reward_motion_body_ang_vel_kernel(
+            command.body_ang_vel_w,
+            command.robot_body_ang_vel_w,
+            self._kernel_body_ids,
+            self._kernel_std(1.0),
+            self._kernel_result,
+        )
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        command_name: str,
+        std: float,
+        body_names: tuple[str, ...] | None = None,
+    ) -> np.ndarray:
+        del env, body_names
+        command, scale = self._validate(command_name, std)
+        reward_motion_body_ang_vel_kernel(
+            command.body_ang_vel_w,
+            command.robot_body_ang_vel_w,
+            self._kernel_body_ids,
+            self._kernel_std(scale),
+            self._kernel_result,
+        )
+        return self._kernel_result
+
+
+class motion_relative_body_position_z_error_exp(_BodyTerm):
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        command_name: str,
+        std: float,
+        body_names: tuple[str, ...] | None = None,
+    ) -> np.ndarray:
+        del env, body_names
+        command, scale = self._validate(command_name, std)
+        error = np.square(
+            command.body_pos_relative_w[:, self._body_ids, 2]
+            - command.robot_body_pos_w[:, self._body_ids, 2]
+        )
+        return self._exp_neg_scaled(error.mean(axis=-1), scale)
+
+
+def motion_joint_position_error_exp(
+    env: ManagerBasedRlEnv, command_name: str, std: float
+) -> np.ndarray:
+    command = _command(env, command_name)
+    diff = command.joint_pos - command.robot_joint_pos
+    np.square(diff, out=diff)
+    error = diff.mean(axis=-1)
+    scale = _positive_std(std, term_name="motion joint position")
+    np.divide(error, -(scale**2), out=error)
+    return np.exp(error, out=error)
+
+
+def motion_joint_velocity_error_exp(
+    env: ManagerBasedRlEnv, command_name: str, std: float
+) -> np.ndarray:
+    command = _command(env, command_name)
+    diff = command.joint_vel - command.robot_joint_vel
+    np.square(diff, out=diff)
+    error = diff.mean(axis=-1)
+    scale = _positive_std(std, term_name="motion joint velocity")
+    np.divide(error, -(scale**2), out=error)
+    return np.exp(error, out=error)
+
+
+def joint_pos_limits(
+    env: ManagerBasedRlEnv,
+    asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> np.ndarray:
+    """Penalize selected joint-limit violations through the entity facade."""
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    joint_pos = asset.data.joint_pos[:, asset_cfg.joint_ids]
+    limits = asset.data.soft_joint_pos_limits[asset_cfg.joint_ids]
+    # Same op order as the naive form (maximum -> add -> square -> sum),
+    # chained in place to avoid intermediate allocations.
+    error = np.subtract(limits[:, 0], joint_pos)
+    np.maximum(error, 0.0, out=error)
+    upper = np.subtract(joint_pos, limits[:, 1])
+    np.maximum(upper, 0.0, out=upper)
+    error += upper
+    np.square(error, out=error)
+    return np.sum(error, axis=-1)
+
+
+class undesired_body_contacts(_BodyTerm):
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        command_name: str,
+        threshold: float,
+        body_names: tuple[str, ...] | None = None,
+    ) -> np.ndarray:
+        del env, body_names
+        command = _command(self._env, command_name)
+        return np.sum(command.robot_body_pos_w[:, self._body_ids, 2] < threshold, axis=-1)
+
+
+class bad_anchor_pos_z_only(ManagerTermBase):
+    """Anchor-height termination backed by a parallel, pre-warmed Numba kernel."""
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        configure_motion_kernel_runtime()
+        command_name = cfg.params.get("command_name")
+        if not isinstance(command_name, str) or not command_name:
+            raise ValueError(f"{type(self).__name__} requires a non-empty command_name")
+        self._command_name = command_name
+        self._result = np.empty(self.num_envs, dtype=np.bool_)
+        command = _command(env, command_name)
+        threshold = command.body_pos_w.dtype.type(cfg.params.get("threshold", 0.0))
+        termination_anchor_pos_kernel(
+            command.body_pos_w,
+            command.robot_body_pos_w,
+            command.anchor_body_idx,
+            threshold,
+            self._result,
+        )
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        command_name: str,
+        threshold: float,
+    ) -> np.ndarray:
+        del env
+        if command_name != self._command_name:
+            raise ValueError(
+                f"{type(self).__name__} was bound to '{self._command_name}', got '{command_name}'"
+            )
+        command = _command(self._env, command_name)
+        threshold_value = command.body_pos_w.dtype.type(threshold)
+        termination_anchor_pos_kernel(
+            command.body_pos_w,
+            command.robot_body_pos_w,
+            command.anchor_body_idx,
+            threshold_value,
+            self._result,
+        )
+        return self._result
+
+
+def bad_anchor_ori(
+    env: ManagerBasedRlEnv,
+    command_name: str,
+    threshold: float,
+    asset_cfg: SceneEntityCfg | None = None,
+) -> np.ndarray:
+    command = _command(env, command_name)
+    asset = command.robot if asset_cfg is None else cast("Entity", env.scene[asset_cfg.name])
+    gravity_vec_w = asset.data.gravity_vec_w
+    motion_z = np_quat_apply_inverse(command.anchor_quat_w, gravity_vec_w)[:, 2]
+    robot_z = np_quat_apply_inverse(command.robot_anchor_quat_w, gravity_vec_w)[:, 2]
+    return np.abs(motion_z - robot_z) > threshold
+
+
+class bad_motion_body_pos_z_only(_BodyTerm):
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        command_name: str,
+        threshold: float,
+        body_names: tuple[str, ...] | None = None,
+    ) -> np.ndarray:
+        del env, body_names
+        command = _command(self._env, command_name)
+        error = np.abs(
+            command.body_pos_relative_w[:, self._body_ids, 2]
+            - command.robot_body_pos_w[:, self._body_ids, 2]
+        )
+        return np.any(error > threshold, axis=-1)
+
+
+class bad_undesired_body_contacts(_BodyTerm):
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        command_name: str,
+        threshold: float,
+        body_names: tuple[str, ...] | None = None,
+    ) -> np.ndarray:
+        del env, body_names
+        command = _command(self._env, command_name)
+        return np.any(command.robot_body_pos_w[:, self._body_ids, 2] < threshold, axis=-1)
+
+
+def motion_clip_end(env: ManagerBasedRlEnv, command_name: str) -> np.ndarray:
+    command = _command(env, command_name)
+    return command.time_steps >= command.sampler.current_clip_end_frames
+
+
+__all__ = [
+    "MotionCommand",
+    "MotionCommandCfg",
+    "MotionCommandParamsCfg",
+    "MotionJointPositionAction",
+    "MotionJointPositionActionCfg",
+    "bad_anchor_ori",
+    "bad_anchor_pos_z_only",
+    "bad_motion_body_pos_z_only",
+    "bad_undesired_body_contacts",
+    "joint_pos_limits",
+    "motion_anchor_ori_b",
+    "motion_anchor_pos_b",
+    "motion_clip_end",
+    "motion_global_anchor_orientation_error_exp",
+    "motion_global_anchor_position_error_exp",
+    "motion_global_body_angular_velocity_error_exp",
+    "motion_global_body_linear_velocity_error_exp",
+    "motion_joint_pos_rel",
+    "motion_joint_pos_rel_biased",
+    "motion_joint_position_error_exp",
+    "motion_joint_velocity_error_exp",
+    "motion_relative_body_orientation_error_exp",
+    "motion_relative_body_position_error_exp",
+    "motion_relative_body_position_z_error_exp",
+    "robot_body_ori_b",
+    "robot_body_pos_b",
+    "undesired_body_contacts",
+]
diff --git a/src/unilab/envs/motion_tracking/common/motion_loader.py b/src/unilab/tasks/motion_tracking/common/motion_loader.py
similarity index 75%
rename from src/unilab/envs/motion_tracking/common/motion_loader.py
rename to src/unilab/tasks/motion_tracking/common/motion_loader.py
index 4bc7822be..663f94dca 100644
--- a/src/unilab/envs/motion_tracking/common/motion_loader.py
+++ b/src/unilab/tasks/motion_tracking/common/motion_loader.py
@@ -1,4 +1,4 @@
-"""Motion loading and sampling for motion tracking tasks."""
+"""Shared motion loading and sampling for motion-tracking tasks."""
 
 from __future__ import annotations
 
@@ -10,6 +10,7 @@
 import numpy as np
 
 from unilab.assets.hub import resolve_motion_files
+from unilab.utils.rotation import np_quat_angular_velocity, np_quat_ensure_continuity
 
 
 @dataclass
@@ -24,6 +25,126 @@ class MotionData:
     body_ang_vel_w: np.ndarray  # (N, num_bodies, 3)
 
 
+def quat_slerp(q1: np.ndarray, q2: np.ndarray, t: float) -> np.ndarray:
+    """Spherical linear interpolation between two quaternions (wxyz format).
+
+    The computation runs in the input dtype; pass float64 arrays for a
+    float64 interpolation path.
+    """
+    # Ensure shortest path
+    dot = np.dot(q1, q2)
+    if dot < 0:
+        q2 = -q2
+        dot = -dot
+
+    # If quaternions are very close, use linear interpolation
+    if dot > 0.9995:
+        result = q1 + t * (q2 - q1)
+        return result / np.linalg.norm(result)
+
+    # Compute angle
+    theta = np.arccos(np.clip(dot, -1, 1))
+    sin_theta = np.sin(theta)
+
+    # Compute interpolation weights
+    w1 = np.sin((1 - t) * theta) / sin_theta
+    w2 = np.sin(t * theta) / sin_theta
+
+    return w1 * q1 + w2 * q2
+
+
+@dataclass
+class InterpolatedMotion:
+    """Root/joint trajectory resampled to the output frame rate."""
+
+    output_frames: int
+    base_poss: np.ndarray  # (N, 3)
+    base_rots: np.ndarray  # (N, 4), wxyz
+    dof_poss: np.ndarray  # (N, num_joints)
+    base_lin_vels: np.ndarray  # (N, 3)
+    base_ang_vels: np.ndarray  # (N, 3)
+    dof_vels: np.ndarray  # (N, num_joints)
+
+
+def compute_motion_velocities(
+    base_poss: np.ndarray,
+    base_rots: np.ndarray,
+    dof_poss: np.ndarray,
+    dt: float,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Numerically differentiate a trajectory into base/dof velocities."""
+    base_lin_vels = np.gradient(base_poss, dt, axis=0)
+    dof_vels = np.gradient(dof_poss, dt, axis=0)
+    base_ang_vels = np_quat_angular_velocity(base_rots, dt)
+    return base_lin_vels, base_ang_vels, dof_vels
+
+
+def compute_frame_blend(
+    times: np.ndarray, duration: float, input_frames: int
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Compute frame indices and blend weights for interpolation."""
+    phase = times / duration
+    index_0 = np.floor(phase * (input_frames - 1)).astype(np.int32)
+    index_1 = np.minimum(index_0 + 1, input_frames - 1)
+    blend = phase * (input_frames - 1) - index_0
+    return index_0, index_1, blend
+
+
+def interpolate_motion(
+    base_poss_input: np.ndarray,
+    base_rots_input: np.ndarray,
+    dof_poss_input: np.ndarray,
+    *,
+    input_fps: int,
+    output_fps: int,
+) -> InterpolatedMotion:
+    """Resample a root+joint trajectory from ``input_fps`` to ``output_fps``.
+
+    Positions and joint angles are linearly interpolated, root quaternions
+    (wxyz) use slerp, and velocities are computed by numerical
+    differentiation at the output rate.
+    """
+    input_dt = 1.0 / input_fps
+    output_dt = 1.0 / output_fps
+    input_frames = base_poss_input.shape[0]
+    duration = (input_frames - 1) * input_dt
+
+    times = np.arange(0, duration, output_dt, dtype=np.float32)
+    output_frames = times.shape[0]
+    index_0, index_1, blend = compute_frame_blend(times, duration, input_frames)
+
+    # Linear interpolation for positions
+    base_poss = (
+        base_poss_input[index_0] * (1 - blend[:, None]) + base_poss_input[index_1] * blend[:, None]
+    )
+
+    # Spherical linear interpolation for quaternions
+    base_rots = np.zeros((output_frames, 4), dtype=np.float32)
+    for i in range(output_frames):
+        base_rots[i] = quat_slerp(
+            base_rots_input[index_0[i]], base_rots_input[index_1[i]], blend[i]
+        )
+    base_rots = np_quat_ensure_continuity(base_rots)
+
+    # Linear interpolation for joint positions
+    dof_poss = (
+        dof_poss_input[index_0] * (1 - blend[:, None]) + dof_poss_input[index_1] * blend[:, None]
+    )
+
+    base_lin_vels, base_ang_vels, dof_vels = compute_motion_velocities(
+        base_poss, base_rots, dof_poss, output_dt
+    )
+    return InterpolatedMotion(
+        output_frames=output_frames,
+        base_poss=base_poss,
+        base_rots=base_rots,
+        dof_poss=dof_poss,
+        base_lin_vels=base_lin_vels,
+        base_ang_vels=base_ang_vels,
+        dof_vels=dof_vels,
+    )
+
+
 class MotionLoader:
     """Loads and provides access to motion data from NPZ files."""
 
@@ -214,6 +335,7 @@ def __init__(
         adaptive_uniform_ratio: float = 0.1,
         adaptive_alpha: float = 0.001,
         start_ratio: float = 0.0,
+        rng: np.random.Generator | None = None,
     ):
         """Initialize motion sampler.
 
@@ -236,6 +358,7 @@ def __init__(
         self.mode = mode
         self.num_envs = num_envs
         self.start_ratio = start_ratio
+        self.rng = rng
 
         # Current frame indices for each environment
         self.current_frames = np.zeros(num_envs, dtype=np.int32)
@@ -306,16 +429,28 @@ def _sample_clip_start(self, env_ids: np.ndarray) -> np.ndarray:
         if self.motion_loader.num_clips == 1:
             frames = np.zeros(len(env_ids), dtype=np.int32)
         else:
-            clip_indices = np.random.randint(
-                0, self.motion_loader.num_clips, len(env_ids), dtype=np.int32
-            )
+            if self.rng is None:
+                clip_indices = np.random.randint(
+                    0, self.motion_loader.num_clips, len(env_ids), dtype=np.int32
+                )
+            else:
+                clip_indices = self.rng.integers(
+                    0, self.motion_loader.num_clips, len(env_ids), dtype=np.int32
+                )
             frames = np.asarray(self.motion_loader.clip_offsets[clip_indices], dtype=np.int32)
         self._set_sampled_frames(env_ids, frames)
         return frames
 
     def _sample_uniform(self, env_ids: np.ndarray) -> np.ndarray:
         """Sample uniformly across motion."""
-        frames = np.random.randint(0, self.motion_loader.num_frames, len(env_ids), dtype=np.int32)
+        if self.rng is None:
+            frames = np.random.randint(
+                0, self.motion_loader.num_frames, len(env_ids), dtype=np.int32
+            )
+        else:
+            frames = self.rng.integers(
+                0, self.motion_loader.num_frames, len(env_ids), dtype=np.int32
+            )
         self._set_sampled_frames(env_ids, frames)
 
         # Update metrics
@@ -334,11 +469,16 @@ def _sample_mixed(self, env_ids: np.ndarray) -> np.ndarray:
         uniform RSI's whole-clip coverage everywhere else.
         """
         n = len(env_ids)
-        use_start = np.random.random(n) < self.start_ratio
+        random_values = np.random.random(n) if self.rng is None else self.rng.random(n)
+        if self.rng is None:
+            uniform_frames = np.random.randint(0, self.motion_loader.num_frames, n)
+        else:
+            uniform_frames = self.rng.integers(0, self.motion_loader.num_frames, n)
+        use_start = random_values < self.start_ratio
         frames = np.where(
             use_start,
             0,
-            np.random.randint(0, self.motion_loader.num_frames, n),
+            uniform_frames,
         ).astype(np.int32)
         self._set_sampled_frames(env_ids, frames)
 
@@ -374,10 +514,18 @@ def _sample_adaptive(self, env_ids: np.ndarray) -> np.ndarray:
         sampling_probs = sampling_probs / sampling_probs.sum()
 
         # Sample bins
-        sampled_bins = np.random.choice(self.bin_count, size=len(env_ids), p=sampling_probs)
+        sampled_bins = (
+            np.random.choice(self.bin_count, size=len(env_ids), p=sampling_probs)
+            if self.rng is None
+            else self.rng.choice(self.bin_count, size=len(env_ids), p=sampling_probs)
+        )
 
         # Add random offset within bin
-        bin_offsets = np.random.uniform(0.0, 1.0, len(env_ids))
+        bin_offsets = (
+            np.random.uniform(0.0, 1.0, len(env_ids))
+            if self.rng is None
+            else self.rng.uniform(0.0, 1.0, len(env_ids))
+        )
         frames = (
             (sampled_bins + bin_offsets) / self.bin_count * (self.motion_loader.num_frames - 1)
         ).astype(np.int32)
@@ -437,12 +585,14 @@ def _set_sampled_frames(self, env_ids: np.ndarray, frames: np.ndarray) -> None:
         self.current_clip_indices[env_ids] = clip_indices
         self.current_clip_end_frames[env_ids] = self.motion_loader.clip_end_frames[clip_indices]
 
-    def step(self):
-        """Advance all frames by one step."""
-        self.current_frames += 1
+    def step(self, env_ids: np.ndarray | None = None) -> np.ndarray:
+        """Advance selected frames by one step and return their clip-end rows."""
+        ids = np.arange(self.num_envs, dtype=np.int32) if env_ids is None else env_ids
+        self.current_frames[ids] += 1
 
         # Find environments that reached the end of their current clip.
-        np.greater(self.current_frames, self.current_clip_end_frames, out=self._done_mask)
+        self._done_mask.fill(False)
+        self._done_mask[ids] = self.current_frames[ids] > self.current_clip_end_frames[ids]
         return np.flatnonzero(self._done_mask)
 
     def get_current_motion(self, out: MotionData | None = None) -> MotionData:
diff --git a/src/unilab/tasks/motion_tracking/g1/__init__.py b/src/unilab/tasks/motion_tracking/g1/__init__.py
new file mode 100644
index 000000000..faa2f43af
--- /dev/null
+++ b/src/unilab/tasks/motion_tracking/g1/__init__.py
@@ -0,0 +1,42 @@
+"""G1 motion profiles on the shared NumPy Manager-Based runtime."""
+
+from unilab.base import registry
+from unilab.envs import ManagerBasedRlEnvCfg, make_manager_based_rl_env
+
+from .motion_box_loader import BoxMotionData, BoxMotionLoader
+
+G1_MOTION_TASKS = (
+    "G1MotionTracking",
+    "G1MotionTrackingDeploy",
+    "G1MotionTracking23Dof",
+    "G1MotionTracking23DofDeploy",
+    "G1MotionTrackingSAC",
+    "G1MotionTrackingSAC23Dof",
+    "G1BoxTracking",
+    "G1BoxTracking23Dof",
+    "G1ClimbTracking",
+    "G1ClimbTracking23Dof",
+    "G1FlipTracking",
+    "G1FlipTracking23Dof",
+    "G1FlipTrackingSAC",
+    "G1FlipTrackingSAC23Dof",
+    "G1WallFlipTracking",
+    "G1WallFlipTracking23Dof",
+    "G1WallFlipTrackingSAC",
+    "G1WallFlipTrackingSAC23Dof",
+    "G1WBTObs",
+    "G1WBTObs23Dof",
+)
+
+for _task_name in G1_MOTION_TASKS:
+    registry.register_env_config(_task_name, ManagerBasedRlEnvCfg)
+    registry.register_env(_task_name, make_manager_based_rl_env, sim_backend="mujoco")
+    registry.register_env(_task_name, make_manager_based_rl_env, sim_backend="motrix")
+
+# mjwarp is registered only for G1MotionTrackingSAC (benchmark scope, issue #1292);
+# mujoco-warp + warp-lang remain optional deps and other motion tasks keep
+# mujoco/motrix until their mjwarp paths are validated.
+registry.register_env("G1MotionTrackingSAC", make_manager_based_rl_env, sim_backend="mjwarp")
+
+
+__all__ = ["BoxMotionData", "BoxMotionLoader", "G1_MOTION_TASKS"]
diff --git a/src/unilab/tasks/motion_tracking/g1/manager_terms.py b/src/unilab/tasks/motion_tracking/g1/manager_terms.py
new file mode 100644
index 000000000..001dfa0ff
--- /dev/null
+++ b/src/unilab/tasks/motion_tracking/g1/manager_terms.py
@@ -0,0 +1,304 @@
+"""G1 profile-specific NumPy manager terms for motion tracking."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, cast
+
+import numpy as np
+
+from unilab.managers import ManagerTermBase, ManagerTermBaseCfg
+from unilab.managers.scene_entity_config import SceneEntityCfg
+from unilab.tasks.motion_tracking.common.kernels import update_object_relative_state_kernel
+from unilab.tasks.motion_tracking.common.manager_terms import (
+    MotionCommand,
+    MotionCommandCfg,
+    MotionJointPositionAction,
+)
+from unilab.utils.rotation import np_quat_error_magnitude_squared_batched
+
+from .motion_box_loader import BoxMotionData, BoxMotionLoader
+
+if TYPE_CHECKING:
+    from unilab.base.entity import Entity
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+_DEFAULT_ASSET_CFG = SceneEntityCfg("robot")
+
+
+@dataclass(kw_only=True)
+class BoxMotionCommandCfg(MotionCommandCfg):
+    """Motion command extended with one independently simulated object."""
+
+    object_entity_name: str
+
+    def build(self, env: ManagerBasedRlEnv) -> BoxMotionCommand:
+        return BoxMotionCommand(self, env)
+
+
+class BoxMotionCommand(MotionCommand):
+    cfg: BoxMotionCommandCfg  # pyright: ignore[reportIncompatibleVariableOverride]
+
+    def __init__(self, cfg: BoxMotionCommandCfg, env: ManagerBasedRlEnv):
+        if not isinstance(cfg.object_entity_name, str) or not cfg.object_entity_name:
+            raise ValueError("BoxMotionCommandCfg object_entity_name must be non-empty")
+        self.object = cast("Entity", env.scene[cfg.object_entity_name])
+        self._object_pos_w = np.empty((env.num_envs, 3), dtype=np.float32)
+        self._object_obs_b = np.empty((env.num_envs, 12), dtype=np.float32)
+        super().__init__(cfg, env)
+        if not isinstance(self._motion_data, BoxMotionData):
+            raise TypeError("BoxMotionCommand requires BoxMotionData")
+        self._refresh_object_state()
+
+    def _make_motion_loader(
+        self,
+        motion_file: str | list[str],
+        body_indices: np.ndarray,
+    ) -> BoxMotionLoader:
+        return BoxMotionLoader(motion_file, body_indices=body_indices)
+
+    @property
+    def box_motion(self) -> BoxMotionData:
+        return cast(BoxMotionData, self._motion_data)
+
+    @property
+    def object_pos_w(self) -> np.ndarray:
+        return self._object_pos_w
+
+    @property
+    def object_quat_w(self) -> np.ndarray:
+        value = self.box_motion.object_quat_w
+        if value is None:
+            raise RuntimeError("Box motion object quaternion was not materialized")
+        return value
+
+    @property
+    def object_state_b(self) -> np.ndarray:
+        return self._object_obs_b
+
+    def _refresh_motion(self, env_ids: np.ndarray | None = None) -> None:
+        super()._refresh_motion(env_ids)
+        value = self.box_motion.object_pos_w
+        if value is None:
+            raise RuntimeError("Box motion object position was not materialized")
+        if env_ids is None:
+            np.add(value, self._env.scene.env_origins, out=self._object_pos_w)
+        else:
+            self._object_pos_w[env_ids] = value[env_ids] + self._env.scene.env_origins[env_ids]
+
+    def _ingest_motion_rows(self, env_ids: np.ndarray, data) -> None:
+        super()._ingest_motion_rows(env_ids, data)
+        value = cast(BoxMotionData, data).object_pos_w
+        if value is None:
+            raise RuntimeError("Box motion object position was not materialized")
+        # `data` rows are already the gathered reset rows (leading dim
+        # len(env_ids)); scatter positionally into the full-batch buffer.
+        self._object_pos_w[env_ids] = value + self._env.scene.env_origins[env_ids]
+
+    def _resample_command(self, env_ids: np.ndarray) -> None:
+        super()._resample_command(env_ids)
+        # The base resample already gathered exactly these frames; reuse them
+        # instead of gathering the same rows a second time (issue #1355).
+        motion = self._resample_motion
+        if motion is None:
+            raise RuntimeError("BoxMotionCommand requires the base resample gather")
+        motion = cast(BoxMotionData, motion)
+        values = (
+            motion.object_pos_w,
+            motion.object_quat_w,
+            motion.object_lin_vel_w,
+            motion.object_ang_vel_w,
+        )
+        if any(value is None for value in values):
+            raise RuntimeError("Box motion reset requires complete object state")
+        object_pos = cast(np.ndarray, motion.object_pos_w).copy()
+        object_pos += self._env.scene.env_origins[env_ids]
+        object_state = np.concatenate(
+            (
+                object_pos,
+                cast(np.ndarray, motion.object_quat_w),
+                cast(np.ndarray, motion.object_lin_vel_w),
+                cast(np.ndarray, motion.object_ang_vel_w),
+            ),
+            axis=-1,
+        )
+        self.object.write_root_state_to_sim(object_state, env_ids=env_ids)
+
+    def _refresh_object_state(self, env_ids: np.ndarray | None = None) -> None:
+        rows = self._all_env_ids if env_ids is None else env_ids
+        update_object_relative_state_kernel(
+            rows,
+            self.robot_anchor_pos_w,
+            self.robot_anchor_quat_w,
+            self.object.data.root_link_pos_w,
+            self.object.data.root_link_quat_w,
+            self.object.data.root_link_lin_vel_w,
+            self._object_obs_b,
+        )
+
+    def post_compute(self) -> None:
+        super().post_compute()
+        self._refresh_object_state(self._post_compute_env_ids)
+
+
+def _box_command(env: ManagerBasedRlEnv, command_name: str) -> BoxMotionCommand:
+    try:
+        command = env.command_manager.get_term(command_name)
+    except KeyError as exc:
+        raise KeyError(f"Box motion command term '{command_name}' not found") from exc
+    if not isinstance(command, BoxMotionCommand):
+        raise TypeError(
+            f"Command term '{command_name}' is {type(command).__name__}, expected BoxMotionCommand"
+        )
+    return command
+
+
+def object_state_b(env: ManagerBasedRlEnv, command_name: str) -> np.ndarray:
+    return _box_command(env, command_name).object_state_b
+
+
+def object_global_position_error_exp(
+    env: ManagerBasedRlEnv,
+    command_name: str,
+    std: float,
+) -> np.ndarray:
+    command = _box_command(env, command_name)
+    if not np.isfinite(std) or std <= 0.0:
+        raise ValueError("object position std must be finite and positive")
+    error = np.sum(
+        np.square(command.object_pos_w - command.object.data.root_link_pos_w),
+        axis=-1,
+    )
+    return np.exp(-error / float(std) ** 2)
+
+
+def object_global_orientation_error_exp(
+    env: ManagerBasedRlEnv,
+    command_name: str,
+    std: float,
+) -> np.ndarray:
+    command = _box_command(env, command_name)
+    if not np.isfinite(std) or std <= 0.0:
+        raise ValueError("object orientation std must be finite and positive")
+    error = np_quat_error_magnitude_squared_batched(
+        command.object_quat_w,
+        command.object.data.root_link_quat_w,
+    )
+    return np.exp(-error / float(std) ** 2)
+
+
+def bad_object_position(
+    env: ManagerBasedRlEnv,
+    command_name: str,
+    threshold: float,
+) -> np.ndarray:
+    command = _box_command(env, command_name)
+    error = np.linalg.norm(command.object_pos_w - command.object.data.root_link_pos_w, axis=-1)
+    return error > threshold
+
+
+def bad_object_orientation(
+    env: ManagerBasedRlEnv,
+    command_name: str,
+    threshold: float,
+) -> np.ndarray:
+    command = _box_command(env, command_name)
+    error = np.sqrt(
+        np_quat_error_magnitude_squared_batched(
+            command.object_quat_w,
+            command.object.data.root_link_quat_w,
+        )
+    )
+    return error > threshold
+
+
+class joint_acc_l2(ManagerTermBase):
+    """Squared finite-difference joint acceleration with reset-aware state."""
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        asset_cfg = cfg.params.get("asset_cfg", _DEFAULT_ASSET_CFG)
+        if not isinstance(asset_cfg, SceneEntityCfg):
+            raise TypeError("joint_acc_l2 asset_cfg must be SceneEntityCfg")
+        self._entity = cast("Entity", env.scene[asset_cfg.name])
+        self._joint_ids = np.arange(self._entity.num_joints, dtype=np.intp)[asset_cfg.joint_ids]
+        self._previous = self._entity.data.joint_vel[:, self._joint_ids].copy()
+
+    def reset(self, env_ids: np.ndarray | slice | None) -> None:
+        ids = np.arange(self.num_envs, dtype=np.intp)
+        if env_ids is not None:
+            ids = ids[env_ids]
+        self._previous[ids] = self._entity.data.joint_vel[np.ix_(ids, self._joint_ids)]
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+    ) -> np.ndarray:
+        del asset_cfg
+        velocity = self._entity.data.joint_vel[:, self._joint_ids]
+        acceleration = (velocity - self._previous) / env.step_dt
+        self._previous[:] = velocity
+        return np.sum(np.square(acceleration), axis=-1)
+
+
+class joint_torque_l2(ManagerTermBase):
+    """Position-controller torque estimate using cold-path actuator gain binding."""
+
+    def __init__(self, cfg: ManagerTermBaseCfg, env: ManagerBasedRlEnv):
+        super().__init__(env)
+        asset_cfg = cfg.params.get("asset_cfg", _DEFAULT_ASSET_CFG)
+        if not isinstance(asset_cfg, SceneEntityCfg):
+            raise TypeError("joint_torque_l2 asset_cfg must be SceneEntityCfg")
+        action_name = cfg.params.get("action_name", "joint_pos")
+        if not isinstance(action_name, str) or not action_name:
+            raise ValueError("joint_torque_l2 action_name must be non-empty")
+        action = env.action_manager.get_term(action_name)
+        if not isinstance(action, MotionJointPositionAction):
+            raise TypeError("joint_torque_l2 requires MotionJointPositionAction")
+        self._action = action
+        self._entity = cast("Entity", env.scene[asset_cfg.name])
+        actuator_ids, kp, kd = self._entity.bind_actuator_gain_write(
+            asset_cfg.actuator_ids,
+            term_name="joint_torque_l2",
+        )
+        selected_names = tuple(self._entity.actuator_names[int(index)] for index in actuator_ids)
+        if selected_names != tuple(action.target_names):
+            raise ValueError(
+                "joint_torque_l2 actuator order does not match the action target order: "
+                f"{selected_names} != {tuple(action.target_names)}"
+            )
+        self._kp = kp
+        self._kd = kd
+        self._joint_ids = action.target_ids
+        self._torque = np.empty_like(action.target)
+
+    def __call__(
+        self,
+        env: ManagerBasedRlEnv,
+        action_name: str = "joint_pos",
+        asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+    ) -> np.ndarray:
+        del env, action_name, asset_cfg
+        np.subtract(
+            self._action.target,
+            self._entity.data.joint_pos[:, self._joint_ids],
+            out=self._torque,
+        )
+        self._torque *= self._kp
+        self._torque -= self._kd * self._entity.data.joint_vel[:, self._joint_ids]
+        return np.sum(np.square(self._torque), axis=-1)
+
+
+__all__ = [
+    "BoxMotionCommand",
+    "BoxMotionCommandCfg",
+    "bad_object_orientation",
+    "bad_object_position",
+    "joint_acc_l2",
+    "joint_torque_l2",
+    "object_global_orientation_error_exp",
+    "object_global_position_error_exp",
+    "object_state_b",
+]
diff --git a/src/unilab/envs/motion_tracking/g1/motion_box_loader.py b/src/unilab/tasks/motion_tracking/g1/motion_box_loader.py
similarity index 84%
rename from src/unilab/envs/motion_tracking/g1/motion_box_loader.py
rename to src/unilab/tasks/motion_tracking/g1/motion_box_loader.py
index 795f13274..a44557d58 100644
--- a/src/unilab/envs/motion_tracking/g1/motion_box_loader.py
+++ b/src/unilab/tasks/motion_tracking/g1/motion_box_loader.py
@@ -92,6 +92,26 @@ def __init__(self, motion_file, body_indices=None):
             self.joint_pos = self.joint_pos[:, :n_robot_joints]
             self.joint_vel = self.joint_vel[:, :n_robot_joints]
 
+    def make_motion_data_buffer(self, num_frames: int) -> BoxMotionData:
+        base = super().make_motion_data_buffer(num_frames)
+        if not self.has_object:
+            raise ValueError(
+                "BoxMotionLoader requires object_pos_w, object_quat_w, "
+                "object_lin_vel_w, and object_ang_vel_w"
+            )
+        return BoxMotionData(
+            joint_pos=base.joint_pos,
+            joint_vel=base.joint_vel,
+            body_pos_w=base.body_pos_w,
+            body_quat_w=base.body_quat_w,
+            body_lin_vel_w=base.body_lin_vel_w,
+            body_ang_vel_w=base.body_ang_vel_w,
+            object_pos_w=np.empty((num_frames, 3), dtype=self.object_pos_w.dtype),
+            object_quat_w=np.empty((num_frames, 4), dtype=self.object_quat_w.dtype),
+            object_lin_vel_w=np.empty((num_frames, 3), dtype=self.object_lin_vel_w.dtype),
+            object_ang_vel_w=np.empty((num_frames, 3), dtype=self.object_ang_vel_w.dtype),
+        )
+
     def get_motion_at_frame(
         self, frame_idx: np.ndarray, out: MotionData | None = None
     ) -> BoxMotionData:
diff --git a/src/unilab/tasks/motion_tracking/x2/__init__.py b/src/unilab/tasks/motion_tracking/x2/__init__.py
new file mode 100644
index 000000000..c8612d854
--- /dev/null
+++ b/src/unilab/tasks/motion_tracking/x2/__init__.py
@@ -0,0 +1,23 @@
+"""AgiBot X2 motion profiles on the shared NumPy Manager-Based runtime."""
+
+from unilab.assets.hub import resolve_robot_asset_dir
+from unilab.base import registry
+from unilab.envs import ManagerBasedRlEnv, ManagerBasedRlEnvCfg, make_manager_based_rl_env
+
+
+def make_x2_wall_flip_env(
+    cfg: ManagerBasedRlEnvCfg,
+    num_envs: int = 1,
+    backend_type: str = "mujoco",
+) -> ManagerBasedRlEnv:
+    """Resolve untracked X2 meshes before backend scene materialization."""
+    resolve_robot_asset_dir("robots/x2/meshes", marker="pelvis.STL")
+    return make_manager_based_rl_env(cfg, num_envs=num_envs, backend_type=backend_type)
+
+
+registry.register_env_config("X2WallFlipTracking", ManagerBasedRlEnvCfg)
+registry.register_env("X2WallFlipTracking", make_x2_wall_flip_env, sim_backend="mujoco")
+registry.register_env("X2WallFlipTracking", make_x2_wall_flip_env, sim_backend="motrix")
+
+
+__all__ = ["make_x2_wall_flip_env"]
diff --git a/src/unilab/terrains/__init__.py b/src/unilab/terrains/__init__.py
index 4297e1b21..c680cf14d 100644
--- a/src/unilab/terrains/__init__.py
+++ b/src/unilab/terrains/__init__.py
@@ -6,6 +6,16 @@
 to build backend-specific scene models.
 """
 
+from unisim.terrain.generator import (
+    FlatPatchSamplingCfg,
+    GeneratedTerrain,
+    SubTerrainCfg,
+    TerrainGenerator,
+    TerrainGeneratorCfg,
+    TerrainHeightField,
+    TerrainOutput,
+)
+
 from unilab.terrains.config import (
     ALL_TERRAIN_PRESETS,
     ROUGH_TERRAINS_CFG,
@@ -27,15 +37,6 @@
     HfRandomUniformTerrainCfg,
     HfWaveTerrainCfg,
 )
-from unilab.terrains.terrain_generator import (
-    FlatPatchSamplingCfg,
-    GeneratedTerrain,
-    SubTerrainCfg,
-    TerrainGenerator,
-    TerrainGeneratorCfg,
-    TerrainHeightField,
-    TerrainOutput,
-)
 
 __all__ = [
     "ALL_TERRAIN_PRESETS",
diff --git a/src/unilab/terrains/config.py b/src/unilab/terrains/config.py
index 9d53f7ff6..b3e973303 100644
--- a/src/unilab/terrains/config.py
+++ b/src/unilab/terrains/config.py
@@ -17,6 +17,8 @@ def my_new_terrain(**overrides: Any) -> SomeTerrainCfg:
 from collections.abc import Callable
 from typing import Any, TypeVar
 
+from unisim.terrain.generator import SubTerrainCfg, TerrainGeneratorCfg
+
 from unilab.terrains.heightfield_terrains import (
     HfFlatTerrainCfg,
     HfInvertedPyramidStairsTerrainCfg,
@@ -25,7 +27,6 @@ def my_new_terrain(**overrides: Any) -> SomeTerrainCfg:
     HfRandomUniformTerrainCfg,
     HfWaveTerrainCfg,
 )
-from unilab.terrains.terrain_generator import SubTerrainCfg, TerrainGeneratorCfg
 
 # Preset registry.
 
diff --git a/src/unilab/terrains/heightfield_terrains.py b/src/unilab/terrains/heightfield_terrains.py
index 258bd7183..313ef88c7 100644
--- a/src/unilab/terrains/heightfield_terrains.py
+++ b/src/unilab/terrains/heightfield_terrains.py
@@ -14,12 +14,12 @@
 from dataclasses import dataclass
 
 import numpy as np
-
-from unilab.terrains.terrain_generator import (
+from unisim.terrain.generator import (
     SubTerrainCfg,
     TerrainHeightField,
     TerrainOutput,
 )
+
 from unilab.terrains.utils import (
     bilinear_resample_grid,
     find_flat_patches_from_heightfield,
diff --git a/src/unilab/terrains/terrain_generator.py b/src/unilab/terrains/terrain_generator.py
deleted file mode 100644
index 065eada88..000000000
--- a/src/unilab/terrains/terrain_generator.py
+++ /dev/null
@@ -1,434 +0,0 @@
-from __future__ import annotations
-
-import abc
-from dataclasses import dataclass, field
-from pathlib import Path
-
-import numpy as np
-
-_BORDER_BASE_THICKNESS = 0.05
-
-
-@dataclass
-class FlatPatchSamplingCfg:
-    """Configuration for sampling flat patches on a heightfield surface."""
-
-    num_patches: int = 10
-    """Number of flat patches to sample per sub-terrain."""
-    patch_radius: float = 0.5
-    """Radius of the circular footprint used to test flatness, in meters."""
-    max_height_diff: float = 0.05
-    """Maximum allowed height variation within the patch footprint, in meters."""
-    x_range: tuple[float, float] = (-1e6, 1e6)
-    """Allowed range of x coordinates for sampled patches, in meters."""
-    y_range: tuple[float, float] = (-1e6, 1e6)
-    """Allowed range of y coordinates for sampled patches, in meters."""
-    z_range: tuple[float, float] = (-1e6, 1e6)
-    """Allowed range of z coordinates (world height) for sampled patches, in meters."""
-    grid_resolution: float | None = None
-    """Resolution of the grid used for flat-patch detection, in meters. When
-    ``None`` (default), the terrain's own ``horizontal_scale`` is used. Set to a
-    smaller value (e.g. 0.025) for finer boundary precision at the cost of a
-    larger intermediate grid."""
-
-
-@dataclass
-class TerrainHeightField:
-    """Backend-agnostic heightfield data for one sub-terrain patch."""
-
-    noise: np.ndarray
-    """Quantized height units before normalization."""
-    size: tuple[float, float]
-    """Patch size as ``(x, y)`` in meters."""
-    horizontal_scale: float
-    vertical_scale: float
-    elevation_min: int
-    elevation_max: int
-    max_physical_height: float
-    base_thickness: float
-    z_offset: float
-
-    def physical_heights_xy(self) -> np.ndarray:
-        """Return world-space surface heights as an ``(x, y)`` matrix."""
-        return (
-            self.noise.astype(np.float64) - self.elevation_min
-        ) * self.vertical_scale + self.z_offset
-
-
-@dataclass
-class TerrainOutput:
-    origin: np.ndarray
-    """Spawn origin position (x, y, z) in the sub-terrain's local frame."""
-    heightfield: TerrainHeightField
-    """Backend-agnostic heightfield data."""
-    flat_patches: dict[str, np.ndarray] | None = None
-    """Named sets of flat patch positions, each an (N, 3) array. None if not configured."""
-
-
-@dataclass
-class GeneratedTerrain:
-    """Merged terrain heightfield ready to be exported as a single PNG asset."""
-
-    heights_yx: np.ndarray
-    """World-space surface heights in image convention: rows=y, cols=x."""
-    horizontal_scale: float
-    z_min: float
-    z_max: float
-    base_thickness: float
-    terrain_origins: np.ndarray
-
-    @property
-    def size(self) -> tuple[float, float]:
-        rows_y, cols_x = self.heights_yx.shape
-        return (cols_x * self.horizontal_scale, rows_y * self.horizontal_scale)
-
-    @property
-    def height_extent(self) -> float:
-        return max(self.z_max - self.z_min, self.horizontal_scale * 0.02)
-
-    @property
-    def hfield_size(self) -> tuple[float, float, float, float]:
-        size_x, size_y = self.size
-        return (size_x / 2, size_y / 2, self.height_extent, self.base_thickness)
-
-    @property
-    def geom_pos(self) -> tuple[float, float, float]:
-        return (0.0, 0.0, self.z_min)
-
-    def to_uint16(self) -> np.ndarray:
-        span = self.z_max - self.z_min
-        if span <= 0.0:
-            return np.zeros_like(self.heights_yx, dtype=np.uint16)
-        normalized = (self.heights_yx - self.z_min) / span
-        return np.rint(np.clip(normalized, 0.0, 1.0) * np.iinfo(np.uint16).max).astype(np.uint16)
-
-    def surface_sampler(self) -> "HeightfieldSurfaceSampler":
-        return HeightfieldSurfaceSampler(
-            heights_uint16=self.to_uint16(),
-            horizontal_scale=float(self.horizontal_scale),
-            z_min=float(self.z_min),
-            height_extent=float(self.height_extent),
-        )
-
-    def write_png(self, path: Path) -> None:
-        """Write the merged hfield as a 16-bit grayscale PNG."""
-        import imageio.v3 as iio
-
-        path = Path(path)
-        path.parent.mkdir(parents=True, exist_ok=True)
-        iio.imwrite(path, self.to_uint16())
-
-
-@dataclass
-class HeightfieldSurfaceSampler:
-    """Compact world-space sampler for a generated hfield surface."""
-
-    heights_uint16: np.ndarray
-    horizontal_scale: float
-    z_min: float
-    height_extent: float
-
-    @property
-    def size(self) -> tuple[float, float]:
-        rows_y, cols_x = self.heights_uint16.shape
-        return (cols_x * self.horizontal_scale, rows_y * self.horizontal_scale)
-
-    def sample_height(self, xy: np.ndarray) -> np.ndarray:
-        points = np.asarray(xy, dtype=np.float64)
-        if points.ndim < 1 or points.shape[-1] != 2:
-            raise ValueError(f"xy must have shape (..., 2), got {points.shape}")
-
-        flat = points.reshape(-1, 2)
-        size_x, size_y = self.size
-        rows_y, cols_x = self.heights_uint16.shape
-        cols = np.rint((flat[:, 0] + size_x * 0.5) / self.horizontal_scale).astype(np.intp)
-        rows = np.rint((-flat[:, 1] + size_y * 0.5) / self.horizontal_scale).astype(np.intp)
-        cols = np.clip(cols, 0, cols_x - 1)
-        rows = np.clip(rows, 0, rows_y - 1)
-
-        raw = self.heights_uint16[rows, cols].astype(np.float64)
-        normalized = raw / float(np.iinfo(np.uint16).max)
-        heights = self.z_min + normalized * self.height_extent
-        return heights.reshape(points.shape[:-1])
-
-
-@dataclass
-class SubTerrainCfg(abc.ABC):
-    proportion: float = 1.0
-    """Robot spawning weight for this terrain type.
-
-    In curriculum mode, controls how many robots are spawned on this terrain's
-    column relative to other terrain types. Each terrain type always gets
-    exactly one column; proportion only affects spawning distribution.
-
-    In random mode, controls the sampling probability for each patch.
-    """
-    size: tuple[float, float] = (10.0, 10.0)
-    """Width and length of the terrain patch, in meters."""
-    flat_patch_sampling: dict[str, FlatPatchSamplingCfg] | None = None
-    """Named flat-patch sampling configurations, or None to disable."""
-
-    @abc.abstractmethod
-    def function(self, difficulty: float, rng: np.random.Generator) -> TerrainOutput:
-        """Generate backend-agnostic terrain data.
-
-        Returns:
-            TerrainOutput containing spawn origin and heightfield data.
-        """
-        raise NotImplementedError
-
-
-@dataclass(kw_only=True)
-class TerrainGeneratorCfg:
-    seed: int | None = None
-    """Random seed for terrain generation. None uses a random seed."""
-    curriculum: bool = False
-    """Controls terrain allocation mode:
-
-    - curriculum=True: Each terrain type gets exactly ONE column. The generator uses
-        ``len(sub_terrains)`` columns regardless of ``num_cols``. Difficulty increases
-        along rows. The ``proportion`` field controls how many robots are spawned per
-        column, not column count.
-
-    - curriculum=False: Every patch is randomly sampled from all terrain types.
-        Proportions control sampling probability. Use this for random variety.
-    """
-    size: tuple[float, float]
-    """Width and length of each sub-terrain patch, in meters. Both components
-    must be integer multiples of ``horizontal_scale``."""
-    horizontal_scale: float = 0.05
-    """Heightfield grid resolution along x and y, in meters per cell. Shared by
-    every sub-terrain (overwritten in :class:`TerrainGenerator` ``__init__``).
-    All length-like sub-terrain parameters (step_width, platform_width,
-    border_width, etc.) must be integer multiples of this value."""
-    vertical_scale: float = 0.005
-    """Heightfield height resolution, in meters per integer unit of the noise
-    array. Shared by every sub-terrain (overwritten in
-    :class:`TerrainGenerator` ``__init__``)."""
-    border_width: float = 0.0
-    """Width of the flat border around the entire terrain grid, in meters. Must
-    be an integer multiple of ``horizontal_scale`` if non-zero. The border is a
-    flat hfield slab whose top surface is flush with the inner-terrain floor at
-    z=0; it is NOT a wall."""
-    num_rows: int = 1
-    """Number of sub-terrain rows in the grid. Represents difficulty levels in
-    curriculum mode. Note: Environments are randomly assigned to rows, so multiple
-    envs can share the same patch."""
-    num_cols: int = 1
-    """Number of sub-terrain columns in the grid.
-
-    In curriculum mode the generator ignores this value and uses one column per terrain
-    type (``len(sub_terrains)``). In random mode it is used as-is."""
-    sub_terrains: dict[str, SubTerrainCfg] = field(default_factory=dict)
-    """Named sub-terrain configurations to populate the grid."""
-    difficulty_range: tuple[float, float] = (0.0, 1.0)
-    """Min and max difficulty values used when generating sub-terrains."""
-    add_lights: bool = False
-    """If True, adds a directional light above the terrain grid."""
-
-
-class TerrainGenerator:
-    """Generates procedural terrain grids with configurable difficulty.
-
-    Creates a grid of terrain patches where each patch can be a different
-    terrain type. Supports two modes:
-
-    - **Random mode** (curriculum=False): Every patch independently samples a
-        terrain type weighted by proportions. Results in random variety across
-        all patches.
-
-    - **Curriculum mode** (curriculum=True): Each terrain type gets exactly one column
-        (the generator uses ``len(sub_terrains)`` columns regardless of ``num_cols``).
-        Difficulty increases along rows. The ``proportion`` field controls robot spawning
-        distribution, not column count.
-
-    Terrain types are weighted by proportion and their geometry is generated
-    based on a difficulty value in the configured range. The grid is centered
-    at the world origin. A border can be added around the entire grid along with
-    optional overhead lighting.
-    """
-
-    def __init__(self, cfg: TerrainGeneratorCfg, device: str = "cpu") -> None:
-        if len(cfg.sub_terrains) == 0:
-            raise ValueError("At least one sub_terrain must be specified.")
-
-        self.cfg = cfg
-        self.device = device
-
-        # In curriculum mode, one column per terrain type.
-        if self.cfg.curriculum:
-            self._num_cols = len(self.cfg.sub_terrains)
-        else:
-            self._num_cols = self.cfg.num_cols
-
-        for sub_cfg in self.cfg.sub_terrains.values():
-            sub_cfg.size = self.cfg.size
-
-        self._propagate_resolution()
-        self._validate_resolution()
-
-        if self.cfg.seed is not None:
-            seed = self.cfg.seed
-        else:
-            seed = np.random.randint(0, 10000)
-        self.np_rng = np.random.default_rng(seed)
-
-        self.terrain_origins = np.zeros((self.cfg.num_rows, self._num_cols, 3))
-
-        # Pre-allocate flat patch storage by scanning all sub-terrain configs.
-        self.flat_patches: dict[str, np.ndarray] = {}
-        self.flat_patch_radii: dict[str, float] = {}
-        patch_names: dict[str, int] = {}
-        for sub_cfg in self.cfg.sub_terrains.values():
-            if sub_cfg.flat_patch_sampling is not None:
-                for name, patch_cfg in sub_cfg.flat_patch_sampling.items():
-                    if name in patch_names:
-                        patch_names[name] = max(patch_names[name], patch_cfg.num_patches)
-                    else:
-                        patch_names[name] = patch_cfg.num_patches
-                    self.flat_patch_radii[name] = max(
-                        self.flat_patch_radii.get(name, 0.0), patch_cfg.patch_radius
-                    )
-        for name, max_num_patches in patch_names.items():
-            self.flat_patches[name] = np.zeros(
-                (self.cfg.num_rows, self._num_cols, max_num_patches, 3)
-            )
-
-    def generate(self) -> GeneratedTerrain:
-        """Generate the full terrain as one backend-agnostic merged hfield."""
-        tile_x_px = int(round(self.cfg.size[0] / self.cfg.horizontal_scale))
-        tile_y_px = int(round(self.cfg.size[1] / self.cfg.horizontal_scale))
-        border_px = int(round(self.cfg.border_width / self.cfg.horizontal_scale))
-        rows_y = self._num_cols * tile_y_px + 2 * border_px
-        cols_x = self.cfg.num_rows * tile_x_px + 2 * border_px
-        heights_yx = np.zeros((rows_y, cols_x), dtype=np.float64)
-
-        max_base_thickness = _BORDER_BASE_THICKNESS if self.cfg.border_width > 0.0 else 0.0
-        self.terrain_origins.fill(0.0)
-
-        def place_output(output: TerrainOutput, sub_row: int, sub_col: int) -> None:
-            nonlocal max_base_thickness
-            patch_heights_xy = output.heightfield.physical_heights_xy()
-            if patch_heights_xy.shape != (tile_x_px, tile_y_px):
-                raise ValueError(
-                    "Sub-terrain heightfield shape does not match TerrainGeneratorCfg.size: "
-                    f"{patch_heights_xy.shape} != {(tile_x_px, tile_y_px)}"
-                )
-            x0 = border_px + sub_row * tile_x_px
-            y0 = border_px + (self._num_cols - 1 - sub_col) * tile_y_px
-            heights_yx[y0 : y0 + tile_y_px, x0 : x0 + tile_x_px] = patch_heights_xy.T
-            max_base_thickness = max(max_base_thickness, output.heightfield.base_thickness)
-
-            world_position = self._get_sub_terrain_position(sub_row, sub_col)
-            spawn_origin = output.origin + world_position
-            self.terrain_origins[sub_row, sub_col] = spawn_origin
-            for name, arr in self.flat_patches.items():
-                if output.flat_patches is not None and name in output.flat_patches:
-                    patches = output.flat_patches[name]
-                    arr[sub_row, sub_col, : len(patches)] = patches + world_position
-                    arr[sub_row, sub_col, len(patches) :] = spawn_origin
-                else:
-                    arr[sub_row, sub_col] = spawn_origin
-
-        if self.cfg.curriculum:
-            sub_terrains_cfgs = list(self.cfg.sub_terrains.values())
-            for sub_col in range(self._num_cols):
-                for sub_row in range(self.cfg.num_rows):
-                    lower, upper = self.cfg.difficulty_range
-                    difficulty = (sub_row + self.np_rng.uniform()) / self.cfg.num_rows
-                    difficulty = lower + (upper - lower) * difficulty
-                    output = sub_terrains_cfgs[sub_col].function(difficulty, self.np_rng)
-                    place_output(output, sub_row, sub_col)
-        else:
-            proportions = np.array(
-                [sub_cfg.proportion for sub_cfg in self.cfg.sub_terrains.values()]
-            )
-            proportions /= np.sum(proportions)
-            sub_terrains_cfgs = list(self.cfg.sub_terrains.values())
-            for index in range(self.cfg.num_rows * self._num_cols):
-                sub_row, sub_col = np.unravel_index(index, (self.cfg.num_rows, self._num_cols))
-                sub_row = int(sub_row)
-                sub_col = int(sub_col)
-                sub_index = self.np_rng.choice(len(proportions), p=proportions)
-                difficulty = self.np_rng.uniform(*self.cfg.difficulty_range)
-                output = sub_terrains_cfgs[sub_index].function(difficulty, self.np_rng)
-                place_output(output, sub_row, sub_col)
-
-        z_min = float(np.min(heights_yx))
-        z_max = float(np.max(heights_yx))
-        return GeneratedTerrain(
-            heights_yx=heights_yx,
-            horizontal_scale=self.cfg.horizontal_scale,
-            z_min=z_min,
-            z_max=z_max,
-            base_thickness=max(max_base_thickness, 1e-3),
-            terrain_origins=self.terrain_origins.copy(),
-        )
-
-    def write_png(self, path: Path) -> GeneratedTerrain:
-        terrain = self.generate()
-        terrain.write_png(path)
-        return terrain
-
-    def _get_sub_terrain_position(self, row: int, col: int) -> np.ndarray:
-        """Get the world position for a sub-terrain at the given grid indices.
-
-        This returns the position of the sub-terrain's corner (not center).
-        The entire grid is centered at the world origin.
-        """
-        # Calculate position relative to grid corner.
-        rel_x = row * self.cfg.size[0]
-        rel_y = col * self.cfg.size[1]
-
-        # Offset to center the entire grid at world origin.
-        grid_offset_x = -self.cfg.num_rows * self.cfg.size[0] * 0.5
-        grid_offset_y = -self._num_cols * self.cfg.size[1] * 0.5
-
-        return np.array([grid_offset_x + rel_x, grid_offset_y + rel_y, 0.0])
-
-    def _propagate_resolution(self) -> None:
-        """Force every sub-terrain config to share the generator's resolution."""
-        hs = self.cfg.horizontal_scale
-        vs = self.cfg.vertical_scale
-        for sub_cfg in self.cfg.sub_terrains.values():
-            if hasattr(sub_cfg, "horizontal_scale"):
-                setattr(sub_cfg, "horizontal_scale", hs)
-            if hasattr(sub_cfg, "vertical_scale"):
-                setattr(sub_cfg, "vertical_scale", vs)
-
-    def _validate_resolution(self) -> None:
-        """Check that all length-like config values divide evenly by horizontal_scale."""
-        hs = self.cfg.horizontal_scale
-        if hs <= 0:
-            raise ValueError(f"horizontal_scale must be positive, got {hs}.")
-        if self.cfg.vertical_scale <= 0:
-            raise ValueError(f"vertical_scale must be positive, got {self.cfg.vertical_scale}.")
-
-        def _is_multiple(value: float) -> bool:
-            return abs(round(value / hs) * hs - value) <= 1e-9
-
-        for axis, sz in zip(("size[0]", "size[1]"), self.cfg.size):
-            if not _is_multiple(sz):
-                raise ValueError(
-                    f"TerrainGeneratorCfg.{axis}={sz} must be an integer multiple of "
-                    f"horizontal_scale={hs}."
-                )
-
-        if self.cfg.border_width > 0 and not _is_multiple(self.cfg.border_width):
-            raise ValueError(
-                f"TerrainGeneratorCfg.border_width={self.cfg.border_width} must be "
-                f"an integer multiple of horizontal_scale={hs}."
-            )
-
-        for name, sub_cfg in self.cfg.sub_terrains.items():
-            for fld in ("step_width", "platform_width", "border_width"):
-                if not hasattr(sub_cfg, fld):
-                    continue
-                value = getattr(sub_cfg, fld)
-                if value is None or value == 0:
-                    continue
-                if not _is_multiple(value):
-                    raise ValueError(
-                        f"Sub-terrain '{name}' field '{fld}'={value} must be an "
-                        f"integer multiple of horizontal_scale={hs}."
-                    )
diff --git a/src/unilab/terrains/utils.py b/src/unilab/terrains/utils.py
index 5a802ba86..81b4b91df 100644
--- a/src/unilab/terrains/utils.py
+++ b/src/unilab/terrains/utils.py
@@ -8,8 +8,7 @@
 from __future__ import annotations
 
 import numpy as np
-
-from unilab.terrains.terrain_generator import FlatPatchSamplingCfg  # noqa: F401
+from unisim.terrain.generator import FlatPatchSamplingCfg  # noqa: F401
 
 
 def bilinear_zoom_2d(arr: np.ndarray, zoom_factors: float | tuple[float, float]) -> np.ndarray:
diff --git a/src/unilab/tools/__init__.py b/src/unilab/tools/__init__.py
deleted file mode 100644
index 5961d279a..000000000
--- a/src/unilab/tools/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Debug and export tools for UniLab."""
diff --git a/src/unilab/tools/completion.py b/src/unilab/tools/completion.py
deleted file mode 100644
index 5fd49b423..000000000
--- a/src/unilab/tools/completion.py
+++ /dev/null
@@ -1,707 +0,0 @@
-"""Shell completion helper for UniLab training entrypoints."""
-
-from __future__ import annotations
-
-import argparse
-import os
-import platform
-import re
-import sys
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Mapping, Sequence
-
-from unilab import cli
-
-TRAINING_ENTRYPOINTS = {
-    "train": "unilab.cli:train_main",
-    "eval": "unilab.cli:eval_main",
-}
-RUN_PATH_ROOTS = ("scripts",)
-RUN_PATH_SUFFIXES = (".py", ".sh")
-RUN_PATH_IGNORED_PARTS = ("__pycache__", "outputs")
-SCRIPT_ASSIGNMENT_PATTERN = re.compile(r'^([A-Za-z0-9_.-]+)\s*=\s*"([^"]+)"\s*(?:#.*)?$')
-DEFAULT_ALGO_LOG_NAMES = {
-    "ppo": "rsl_rl_ppo",
-    "appo": "appo",
-    "sac": "fast_sac",
-    "td3": "fast_td3",
-    "flashsac": "flash_sac",
-}
-COMPLETION_BLOCK_START = "# >>> unilab completion >>>"
-COMPLETION_BLOCK_END = "# <<< unilab completion <<<"
-SUPPORTED_SHELLS = ("bash", "zsh")
-
-
-@dataclass(frozen=True)
-class TaskCompletionEntry:
-    algo: str
-    task: str
-    sim: str
-    owner: str
-
-
-@dataclass(frozen=True)
-class CompletionMetadata:
-    commands: tuple[str, ...]
-    flags: dict[str, tuple[str, ...]]
-    choices: dict[str, dict[str, tuple[str, ...]]]
-    tasks: tuple[TaskCompletionEntry, ...]
-    run_paths: tuple[str, ...] = ()
-    root: Path | None = None
-
-
-def _find_project_root(start: Path) -> Path | None:
-    current = start.resolve()
-    if current.is_file():
-        current = current.parent
-    for candidate in (current, *current.parents):
-        if (candidate / "pyproject.toml").is_file():
-            return candidate
-    return None
-
-
-def _read_project_scripts(pyproject_path: Path) -> dict[str, str]:
-    scripts: dict[str, str] = {}
-    in_scripts = False
-    for line in pyproject_path.read_text(encoding="utf-8").splitlines():
-        stripped = line.strip()
-        if not stripped or stripped.startswith("#"):
-            continue
-        if stripped.startswith("[") and stripped.endswith("]"):
-            in_scripts = stripped == "[project.scripts]"
-            continue
-        if not in_scripts:
-            continue
-        match = SCRIPT_ASSIGNMENT_PATTERN.fullmatch(stripped)
-        if match is not None:
-            scripts[match.group(1)] = match.group(2)
-    return scripts
-
-
-def _training_commands(scripts: Mapping[str, str]) -> tuple[str, ...]:
-    return tuple(
-        command
-        for command, target in TRAINING_ENTRYPOINTS.items()
-        if scripts.get(command) == target
-    )
-
-
-def _project_commands(scripts: Mapping[str, str]) -> tuple[str, ...]:
-    training_commands = _training_commands(scripts)
-    commands = [*training_commands]
-    commands.extend(command for command in sorted(scripts) if command not in training_commands)
-    return tuple(commands)
-
-
-def _parser_for_command(command: str) -> argparse.ArgumentParser:
-    return cli._train_eval_parser(mode=command)
-
-
-def _parser_flags(command: str) -> tuple[str, ...]:
-    parser = _parser_for_command(command)
-    flags: list[str] = []
-    for action in parser._actions:
-        flags.extend(option for option in action.option_strings if option.startswith("--"))
-    return tuple(flags)
-
-
-def _parser_choices(command: str) -> dict[str, tuple[str, ...]]:
-    parser = _parser_for_command(command)
-    choices: dict[str, tuple[str, ...]] = {}
-    for action in parser._actions:
-        if action.choices is None:
-            continue
-        selected = tuple(str(choice) for choice in action.choices)
-        for option in action.option_strings:
-            if option.startswith("--"):
-                choices[option] = selected
-    return choices
-
-
-def _sim_from_owner(owner: str) -> str | None:
-    for sim in cli.SUPPORTED_SIMS:
-        if owner == sim or owner.startswith(f"{sim}_"):
-            return sim
-    return None
-
-
-def _task_entries_for_group(
-    root: Path, group: str, algos: Sequence[str]
-) -> list[TaskCompletionEntry]:
-    entries: list[TaskCompletionEntry] = []
-    task_root = root / "conf" / group / "task"
-    if not task_root.is_dir():
-        return entries
-    for task_dir in sorted(path for path in task_root.iterdir() if path.is_dir()):
-        for owner_yaml in sorted(task_dir.glob("*.yaml")):
-            sim = _sim_from_owner(owner_yaml.stem)
-            if sim is None:
-                continue
-            entries.extend(
-                TaskCompletionEntry(algo=algo, task=task_dir.name, sim=sim, owner=owner_yaml.stem)
-                for algo in algos
-            )
-    return entries
-
-
-def _task_entries_for_offpolicy(root: Path) -> list[TaskCompletionEntry]:
-    entries: list[TaskCompletionEntry] = []
-    task_root = root / "conf" / "offpolicy" / "task"
-    if not task_root.is_dir():
-        return entries
-    for algo in cli.SUPPORTED_ALGOS:
-        algo_root = task_root / algo
-        if not algo_root.is_dir():
-            continue
-        for task_dir in sorted(path for path in algo_root.iterdir() if path.is_dir()):
-            for owner_yaml in sorted(task_dir.glob("*.yaml")):
-                sim = _sim_from_owner(owner_yaml.stem)
-                if sim is None:
-                    continue
-                entries.append(
-                    TaskCompletionEntry(
-                        algo=algo,
-                        task=task_dir.name,
-                        sim=sim,
-                        owner=owner_yaml.stem,
-                    )
-                )
-    return entries
-
-
-def _task_entries(root: Path) -> tuple[TaskCompletionEntry, ...]:
-    entries = [
-        *_task_entries_for_group(root, "ppo", ("ppo",)),
-        *_task_entries_for_group(root, "appo", ("appo",)),
-        *_task_entries_for_offpolicy(root),
-    ]
-    return tuple(
-        sorted(entries, key=lambda entry: (entry.task, entry.algo, entry.sim, entry.owner))
-    )
-
-
-def _is_run_path(path: Path) -> bool:
-    return path.is_file() and path.suffix in RUN_PATH_SUFFIXES and path.name != "__init__.py"
-
-
-def _has_run_path_descendant(path: Path) -> bool:
-    return any(
-        child.name != "__init__.py" and child.suffix in RUN_PATH_SUFFIXES
-        for child in path.rglob("*")
-    )
-
-
-def _run_path_entries(root: Path) -> tuple[str, ...]:
-    entries: set[str] = set()
-    for path_root in RUN_PATH_ROOTS:
-        base = root / path_root
-        if not base.is_dir():
-            continue
-        entries.add(f"{path_root}/")
-        for path in base.rglob("*"):
-            if path.name.startswith("."):
-                continue
-            relative_parts = path.relative_to(root).parts
-            if any(part in RUN_PATH_IGNORED_PARTS for part in relative_parts):
-                continue
-            relative_path = path.relative_to(root).as_posix()
-            if path.is_dir():
-                if _has_run_path_descendant(path):
-                    entries.add(f"{relative_path}/")
-            elif _is_run_path(path):
-                entries.add(relative_path)
-    return tuple(sorted(entries))
-
-
-def build_metadata(root: Path | None = None) -> CompletionMetadata:
-    selected_root = root or _find_project_root(Path.cwd()) or cli.repo_root()
-    scripts = _read_project_scripts(selected_root / "pyproject.toml")
-    training_commands = _training_commands(scripts)
-    return CompletionMetadata(
-        commands=_project_commands(scripts),
-        flags={command: _parser_flags(command) for command in training_commands},
-        choices={command: _parser_choices(command) for command in training_commands},
-        tasks=_task_entries(selected_root),
-        run_paths=_run_path_entries(selected_root),
-        root=selected_root,
-    )
-
-
-def _current_word(words: Sequence[str], cword: int) -> str:
-    if 0 <= cword < len(words):
-        return words[cword]
-    return ""
-
-
-def _previous_word(words: Sequence[str], cword: int) -> str:
-    if cword > 0 and cword - 1 < len(words):
-        return words[cword - 1]
-    return ""
-
-
-def _matching(candidates: Sequence[str], prefix: str) -> list[str]:
-    return [candidate for candidate in candidates if candidate.startswith(prefix)]
-
-
-def _dedupe(candidates: Sequence[str]) -> list[str]:
-    return list(dict.fromkeys(candidates))
-
-
-def _path_choices(candidates: Sequence[str], prefix: str) -> list[str]:
-    choices: set[str] = set()
-    for candidate in candidates:
-        if not candidate.startswith(prefix):
-            continue
-        remainder = candidate[len(prefix) :]
-        if remainder == "":
-            continue
-        if "/" in remainder:
-            choices.add(f"{prefix}{remainder.split('/', 1)[0]}/")
-        else:
-            choices.add(candidate)
-    return sorted(choices)
-
-
-def _option_state(words: Sequence[str], cword: int) -> tuple[dict[str, str], set[str]]:
-    values: dict[str, str] = {}
-    used: set[str] = set()
-    index = 3
-    limit = min(cword, len(words))
-    while index < limit:
-        token = words[index]
-        if not token.startswith("--"):
-            index += 1
-            continue
-        if "=" in token:
-            option, value = token.split("=", 1)
-            used.add(option)
-            values[option] = value
-            index += 1
-            continue
-        used.add(token)
-        if index + 1 < limit and not words[index + 1].startswith("--"):
-            values[token] = words[index + 1]
-            index += 2
-            continue
-        index += 1
-    return values, used
-
-
-def _available_flags(metadata: CompletionMetadata, command: str, used: set[str]) -> tuple[str, ...]:
-    return tuple(flag for flag in metadata.flags.get(command, ()) if flag not in used)
-
-
-def _task_choices(
-    metadata: CompletionMetadata,
-    *,
-    prefix: str,
-    selected_algo: str | None,
-    selected_sim: str | None,
-    selected_profile: str | None,
-) -> list[str]:
-    tasks: set[str] = set()
-    for entry in metadata.tasks:
-        if selected_algo is not None and entry.algo != selected_algo:
-            continue
-        if selected_sim is not None and entry.sim != selected_sim:
-            continue
-        if selected_profile is not None:
-            if selected_sim is not None and entry.owner != f"{selected_sim}_{selected_profile}":
-                continue
-            if selected_sim is None and not entry.owner.endswith(f"_{selected_profile}"):
-                continue
-        tasks.add(entry.task)
-    return _matching(tuple(sorted(tasks)), prefix)
-
-
-def _profile_choices(
-    metadata: CompletionMetadata,
-    *,
-    prefix: str,
-    selected_algo: str | None,
-    selected_sim: str | None,
-    selected_task: str | None,
-) -> list[str]:
-    profiles: set[str] = set()
-    for entry in metadata.tasks:
-        if selected_algo is not None and entry.algo != selected_algo:
-            continue
-        if selected_sim is not None and entry.sim != selected_sim:
-            continue
-        if selected_task is not None and entry.task != selected_task:
-            continue
-        owner_prefix = f"{entry.sim}_"
-        if not entry.owner.startswith(owner_prefix):
-            continue
-        profile = entry.owner[len(owner_prefix) :]
-        if profile:
-            profiles.add(profile)
-    return _matching(tuple(sorted(profiles)), prefix)
-
-
-def _strip_yaml_scalar(value: str) -> str:
-    selected = value.split("#", 1)[0].strip()
-    if len(selected) >= 2 and selected[0] == selected[-1] and selected[0] in {'"', "'"}:
-        return selected[1:-1]
-    return selected
-
-
-def _yaml_section_scalar(path: Path, section: str, key: str) -> str | None:
-    if not path.is_file():
-        return None
-    in_section = False
-    section_indent = 0
-    for raw_line in path.read_text(encoding="utf-8").splitlines():
-        stripped = raw_line.strip()
-        if not stripped or stripped.startswith("#"):
-            continue
-        indent = len(raw_line) - len(raw_line.lstrip())
-        if indent == 0 and stripped.endswith(":"):
-            in_section = stripped[:-1] == section
-            section_indent = indent
-            continue
-        if not in_section:
-            continue
-        if indent <= section_indent:
-            break
-        if stripped.startswith(f"{key}:"):
-            return _strip_yaml_scalar(stripped.split(":", 1)[1])
-    return None
-
-
-def _owner_yaml_paths(
-    root: Path,
-    *,
-    selected_algo: str,
-    selected_task: str,
-    selected_sim: str,
-    selected_profile: str | None,
-) -> tuple[Path, ...]:
-    try:
-        route = cli.build_route(selected_algo, selected_task, selected_sim, selected_profile)
-    except SystemExit:
-        return ()
-
-    route_path = root / "conf" / route.config_group / "task" / route.owner_task
-    if not route_path.is_file():
-        return ()
-
-    paths = [route_path]
-    if selected_profile is not None:
-        try:
-            base_route = cli.build_route(selected_algo, selected_task, selected_sim, None)
-        except SystemExit:
-            base_route = None
-        if base_route is not None:
-            base_path = root / "conf" / base_route.config_group / "task" / base_route.owner_task
-            if base_path not in paths:
-                paths.append(base_path)
-    return tuple(paths)
-
-
-def _first_yaml_section_scalar(paths: Sequence[Path], section: str, key: str) -> str | None:
-    for path in paths:
-        selected = _yaml_section_scalar(path, section, key)
-        if selected not in (None, ""):
-            return selected
-    return None
-
-
-def _load_run_choices(
-    metadata: CompletionMetadata,
-    *,
-    prefix: str,
-    selected_algo: str | None,
-    selected_task: str | None,
-    selected_sim: str | None,
-    selected_profile: str | None,
-) -> list[str]:
-    candidates = ["-1"]
-    if metadata.root is None or not selected_algo or not selected_task or not selected_sim:
-        return _matching(candidates, prefix)
-
-    owner_paths = _owner_yaml_paths(
-        metadata.root,
-        selected_algo=selected_algo,
-        selected_task=selected_task,
-        selected_sim=selected_sim,
-        selected_profile=selected_profile,
-    )
-    if not owner_paths:
-        return _matching(candidates, prefix)
-
-    task_name = _first_yaml_section_scalar(owner_paths, "training", "task_name") or selected_task
-    log_root = _first_yaml_section_scalar(owner_paths, "training", "log_root")
-    if log_root is not None:
-        log_root_path = Path(log_root)
-        base_log_root = (
-            log_root_path if log_root_path.is_absolute() else metadata.root / log_root_path
-        )
-    else:
-        algo_log_name = _first_yaml_section_scalar(
-            owner_paths, "algo", "algo_log_name"
-        ) or DEFAULT_ALGO_LOG_NAMES.get(selected_algo)
-        if algo_log_name is None:
-            return _matching(candidates, prefix)
-        base_log_root = metadata.root / "logs" / algo_log_name
-
-    task_log_root = base_log_root / task_name
-    if task_log_root.is_dir():
-        candidates.extend(
-            path.name
-            for path in sorted(task_log_root.iterdir())
-            if path.is_dir() and cli.RUN_ID_PATTERN.fullmatch(path.name) is not None
-        )
-    return _dedupe(_matching(candidates, prefix))
-
-
-_DEMO_FLAGS: tuple[str, ...] = ("--device", "--refresh")
-_DEMO_VALUE_FLAGS: frozenset[str] = frozenset({"--device"})
-
-
-def _demo_name_consumed(words: Sequence[str], cword: int) -> bool:
-    index = 3
-    limit = min(cword, len(words))
-    while index < limit:
-        token = words[index]
-        if token.startswith("--"):
-            index += 2 if token in _DEMO_VALUE_FLAGS else 1
-            continue
-        return True
-    return False
-
-
-def _demo_completions(
-    *,
-    words: Sequence[str],
-    cword: int,
-    current: str,
-    previous: str,
-    used_options: set[str],
-) -> list[str]:
-    from unilab.demo import DEMO_REGISTRY
-
-    if not _demo_name_consumed(words, cword):
-        return _matching(tuple(sorted(DEMO_REGISTRY)), current)
-    if previous in _DEMO_VALUE_FLAGS:
-        return []
-    available_flags = tuple(flag for flag in _DEMO_FLAGS if flag not in used_options)
-    return _matching(available_flags, current)
-
-
-def complete_words(
-    words: Sequence[str],
-    cword: int,
-    metadata: CompletionMetadata | None = None,
-) -> list[str]:
-    selected_metadata = metadata or build_metadata()
-    if len(words) < 2 or words[0] != "uv" or words[1] != "run":
-        return []
-
-    current = _current_word(words, cword)
-    if cword <= 2:
-        return _dedupe(
-            [
-                *_matching(selected_metadata.commands, current),
-                *_path_choices(selected_metadata.run_paths, current),
-            ]
-        )
-
-    command = words[2]
-    if command not in selected_metadata.commands:
-        return []
-
-    previous = _previous_word(words, cword)
-    option_values, used_options = _option_state(words, cword)
-    choices = selected_metadata.choices.get(command, {})
-    if previous == "--task":
-        return _task_choices(
-            selected_metadata,
-            prefix=current,
-            selected_algo=option_values.get("--algo"),
-            selected_sim=option_values.get("--sim"),
-            selected_profile=option_values.get("--profile"),
-        )
-    if previous in choices:
-        return _matching(choices[previous], current)
-    if command == "eval" and previous == "--load-run":
-        return _load_run_choices(
-            selected_metadata,
-            prefix=current,
-            selected_algo=option_values.get("--algo"),
-            selected_task=option_values.get("--task"),
-            selected_sim=option_values.get("--sim"),
-            selected_profile=option_values.get("--profile"),
-        )
-    if command in {"train", "eval"} and previous == "--profile":
-        return _profile_choices(
-            selected_metadata,
-            prefix=current,
-            selected_algo=option_values.get("--algo"),
-            selected_sim=option_values.get("--sim"),
-            selected_task=option_values.get("--task"),
-        )
-    if command == "demo":
-        return _demo_completions(
-            words=words,
-            cword=cword,
-            current=current,
-            previous=previous,
-            used_options=used_options,
-        )
-    if current == "" or current.startswith("-") or previous == command:
-        return _matching(_available_flags(selected_metadata, command, used_options), current)
-    return []
-
-
-def _parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace:
-    parser = argparse.ArgumentParser(prog="unilab-complete")
-    parser.add_argument("--cword", type=int, required=True)
-    parser.add_argument("words", nargs=argparse.REMAINDER)
-    args = parser.parse_args(argv)
-    if args.words and args.words[0] == "--":
-        args.words = args.words[1:]
-    return args
-
-
-def _detect_shell(selected: str) -> str:
-    if selected != "auto":
-        return selected
-
-    shell_name = Path(os.environ.get("SHELL", "")).name
-    if shell_name in SUPPORTED_SHELLS:
-        return shell_name
-    system_name = platform.system()
-    if system_name == "Darwin":
-        return "zsh"
-    if system_name == "Linux":
-        return "bash"
-    raise SystemExit("Cannot detect shell for completion install; use --shell bash or --shell zsh.")
-
-
-def _default_rc_file(shell: str) -> Path:
-    if shell == "bash":
-        return Path.home() / ".bashrc"
-    if shell == "zsh":
-        return Path.home() / ".zshrc"
-    raise SystemExit(f"Unsupported shell={shell!r}; choose one of: {', '.join(SUPPORTED_SHELLS)}")
-
-
-def _completion_script_path(shell: str) -> Path:
-    root = _find_project_root(Path.cwd()) or cli.repo_root()
-    return root / "scripts" / "completions" / f"unilab.{shell}"
-
-
-def _quote_shell_path(path: Path) -> str:
-    return str(path).replace("\\", "\\\\").replace('"', '\\"')
-
-
-def _completion_block(script_path: Path) -> str:
-    quoted = _quote_shell_path(script_path)
-    return (
-        f"{COMPLETION_BLOCK_START}\n"
-        f'if [ -f "{quoted}" ]; then\n'
-        f'    source "{quoted}"\n'
-        "fi\n"
-        f"{COMPLETION_BLOCK_END}\n"
-    )
-
-
-def _without_completion_block(content: str, *, force: bool = False) -> str:
-    start = content.find(COMPLETION_BLOCK_START)
-    end = content.find(COMPLETION_BLOCK_END)
-    if start == -1 and end == -1:
-        return content
-    if start == -1 or end == -1 or end < start:
-        if force:
-            return content
-        raise SystemExit(
-            "Existing UniLab completion block is malformed; rerun with --force or edit it manually."
-        )
-
-    end_line = content.find("\n", end)
-    if end_line == -1:
-        end_line = len(content)
-    else:
-        end_line += 1
-    return (content[:start].rstrip() + "\n" + content[end_line:].lstrip("\n")).lstrip("\n")
-
-
-def _write_completion_block(
-    *,
-    rc_file: Path,
-    script_path: Path,
-    dry_run: bool,
-    force: bool,
-) -> None:
-    if rc_file.exists():
-        with rc_file.open("r", encoding="utf-8", errors="surrogateescape", newline="") as file:
-            content = file.read()
-    else:
-        content = ""
-    cleaned = _without_completion_block(content, force=force).rstrip()
-    block = _completion_block(script_path).rstrip()
-    updated = f"{cleaned}\n\n{block}\n" if cleaned else f"{block}\n"
-    if dry_run:
-        print(updated, end="")
-        return
-    rc_file.parent.mkdir(parents=True, exist_ok=True)
-    rc_file.write_text(updated, encoding="utf-8", errors="surrogateescape", newline="")
-    print(f"Installed UniLab completion in {rc_file}")
-    print(f"Open a new shell or run: source {rc_file}")
-
-
-def _remove_completion_block(*, rc_file: Path, dry_run: bool, force: bool) -> None:
-    if not rc_file.exists():
-        print(f"No rc file found: {rc_file}")
-        return
-    with rc_file.open("r", encoding="utf-8", errors="surrogateescape", newline="") as file:
-        content = file.read()
-    updated = _without_completion_block(content, force=force)
-    if dry_run:
-        print(updated, end="")
-        return
-    rc_file.write_text(updated, encoding="utf-8", errors="surrogateescape", newline="")
-    print(f"Removed UniLab completion from {rc_file}")
-
-
-def _install_parser() -> argparse.ArgumentParser:
-    parser = argparse.ArgumentParser(prog="unilab-complete install")
-    parser.add_argument("--shell", choices=("auto", *SUPPORTED_SHELLS), default="auto")
-    parser.add_argument("--rc-file", type=Path, default=None)
-    parser.add_argument("--dry-run", action="store_true")
-    parser.add_argument("--force", action="store_true")
-    parser.add_argument("--uninstall", action="store_true")
-    return parser
-
-
-def install_main(argv: Sequence[str] | None = None) -> int:
-    args = _install_parser().parse_args(argv)
-    shell = _detect_shell(args.shell)
-    rc_file = args.rc_file or _default_rc_file(shell)
-    if args.uninstall:
-        _remove_completion_block(rc_file=rc_file, dry_run=args.dry_run, force=args.force)
-        return 0
-    _write_completion_block(
-        rc_file=rc_file,
-        script_path=_completion_script_path(shell),
-        dry_run=args.dry_run,
-        force=args.force,
-    )
-    return 0
-
-
-def main(argv: Sequence[str] | None = None) -> int:
-    selected_argv = list(sys.argv[1:] if argv is None else argv)
-    if selected_argv and selected_argv[0] == "install":
-        return install_main(selected_argv[1:])
-
-    args = _parse_args(selected_argv)
-    for candidate in complete_words(args.words, args.cword):
-        print(candidate)
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main(sys.argv[1:]))
diff --git a/src/unilab/tools/export_scene.py b/src/unilab/tools/export_scene.py
deleted file mode 100644
index ab19a6e73..000000000
--- a/src/unilab/tools/export_scene.py
+++ /dev/null
@@ -1,64 +0,0 @@
-"""Export a materialized MuJoCo scene to XML + mesh assets."""
-
-from __future__ import annotations
-
-import argparse
-import shutil
-from pathlib import Path
-from typing import Any
-
-
-def export_scene(
-    model_file: str,
-    output_dir: str,
-    *,
-    as_zip: bool = False,
-) -> str:
-    import mujoco as _mujoco
-
-    mujoco: Any = _mujoco
-
-    output_path = Path(output_dir)
-    output_path.mkdir(parents=True, exist_ok=True)
-
-    spec = mujoco.MjSpec.from_file(model_file)
-
-    xml_content = spec.to_xml()
-    xml_path = output_path / "scene.xml"
-    xml_path.write_text(xml_content)
-
-    source_dir = Path(model_file).parent
-    meshdir = spec.meshdir if spec.meshdir else ""
-    mesh_source = source_dir / meshdir if meshdir else source_dir
-
-    assets_dst = output_path / "assets"
-    if mesh_source.is_dir():
-        for ext in ("*.stl", "*.obj", "*.msh", "*.ply"):
-            for f in mesh_source.glob(ext):
-                assets_dst.mkdir(parents=True, exist_ok=True)
-                shutil.copy2(f, assets_dst / f.name)
-
-    if as_zip:
-        zip_path = shutil.make_archive(str(output_path), "zip", str(output_path))
-        return zip_path
-
-    return str(output_path)
-
-
-def main(argv: list[str] | None = None) -> int:
-    parser = argparse.ArgumentParser(
-        prog="unilab-export-scene",
-        description="Export a MuJoCo model to XML + mesh assets directory.",
-    )
-    parser.add_argument("model_file", help="Path to the MuJoCo XML or MJB model file")
-    parser.add_argument("-o", "--output-dir", default="exported_scene", help="Output directory")
-    parser.add_argument("--zip", action="store_true", help="Also create a zip archive")
-    args = parser.parse_args(argv)
-
-    result = export_scene(args.model_file, args.output_dir, as_zip=args.zip)
-    print(f"Scene exported to: {result}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/src/unilab/tools/pull_assets.py b/src/unilab/tools/pull_assets.py
deleted file mode 100644
index 00ad52d97..000000000
--- a/src/unilab/tools/pull_assets.py
+++ /dev/null
@@ -1,51 +0,0 @@
-#!/usr/bin/env python3
-"""Pre-fetch robot binary assets (meshes) from Hugging Face into their project paths.
-
-Robot STL meshes are hosted on Hugging Face rather than committed to git. They are
-also downloaded automatically on first use, but this command lets you pull them
-ahead of time (e.g. for CI or offline prep) with a single invocation. Files land
-under ``src/unilab/assets/robots/<robot>/meshes/`` — no manual file moving needed.
-
-Usage:
-  uv run unilab-pull-assets               # pull the default robot (x2)
-  uv run unilab-pull-assets --robot x2
-"""
-
-from __future__ import annotations
-
-import argparse
-import logging
-from collections.abc import Sequence
-
-from unilab.assets.hub import resolve_robot_asset_dir
-
-# robot name -> (ASSETS_ROOT_PATH-relative dir, completeness marker file)
-_ROBOT_ASSETS: dict[str, tuple[str, str]] = {
-    "x2": ("robots/x2/meshes", "pelvis.STL"),
-}
-
-
-def _parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument(
-        "--robot",
-        default="x2",
-        choices=sorted(_ROBOT_ASSETS),
-        help="Robot whose meshes to download (default: x2).",
-    )
-    return parser.parse_args(argv)
-
-
-def main(argv: Sequence[str] | None = None) -> int:
-    logging.basicConfig(level=logging.INFO, format="%(message)s")
-    args = _parse_args(argv)
-
-    directory, marker = _ROBOT_ASSETS[args.robot]
-    target = resolve_robot_asset_dir(directory, marker=marker)
-    count = len(list(target.glob("*.STL")))
-    print(f"{args.robot} meshes ready at {target} ({count} STL files)")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/src/unilab/training/__init__.py b/src/unilab/training/__init__.py
index b02b343b7..5908aad06 100644
--- a/src/unilab/training/__init__.py
+++ b/src/unilab/training/__init__.py
@@ -1,31 +1,44 @@
-"""Shared training helpers for entrypoint scripts."""
+"""Shared training helpers for entrypoint scripts.
 
-from unilab.training.backend_adapter import BackendAdapter
+Deprecated compatibility layer: the re-exports below are kept for existing
+callers only. New code should import from the canonical locations instead
+(``unilab.utils.*`` for layer-0 helpers, ``unilab.base.config_adapter`` /
+``unisim.backend.base`` for base-layer helpers).
+"""
+
+from unisim.backend.base import log_playback_plan
+
+from unilab.base.config_adapter import BackendAdapter, create_env
 from unilab.training.common import (
     assert_offpolicy_task_choice_matches_algo,
-    create_env,
     ensure_registries,
     get_hydra_runtime_choice,
     setup_logger,
 )
 from unilab.training.experiment import ExperimentTracker
-from unilab.training.monitoring import HardwareMonitor
 from unilab.training.run import (
+    algo_config_dict,
+    apply_env_nan_guard,
+    build_run_dir_name,
     format_hora_stage2_checkpoint_error,
+    format_play_checkpoint_error,
+    get_log_root,
+    parse_checkpoint_path,
+    resolve_hora_stage2_checkpoint_path,
+    resolve_nan_guard_cfg,
+    should_run_playback,
+)
+from unilab.utils.checkpoint import (
     get_entrypoint_log_root,
     get_latest_checkpoint,
     get_latest_run,
-    get_log_root,
-    log_playback_plan,
-    parse_checkpoint_path,
     resolve_appo_checkpoint_path,
     resolve_checkpoint_path,
-    resolve_hora_stage2_checkpoint_path,
     resolve_offpolicy_checkpoint_path,
     resolve_task_checkpoint_path,
-    should_run_playback,
 )
-from unilab.training.seed import (
+from unilab.utils.monitoring import HardwareMonitor
+from unilab.utils.seed import (
     TrainingSeedInfo,
     apply_configured_training_seed,
     apply_training_seed,
@@ -37,9 +50,13 @@
     "BackendAdapter",
     "ExperimentTracker",
     "HardwareMonitor",
+    "algo_config_dict",
+    "apply_env_nan_guard",
     "assert_offpolicy_task_choice_matches_algo",
+    "build_run_dir_name",
     "create_env",
     "ensure_registries",
+    "format_play_checkpoint_error",
     "get_entrypoint_log_root",
     "get_hydra_runtime_choice",
     "get_latest_checkpoint",
@@ -48,6 +65,7 @@
     "log_playback_plan",
     "parse_checkpoint_path",
     "resolve_checkpoint_path",
+    "resolve_nan_guard_cfg",
     "resolve_task_checkpoint_path",
     "should_run_playback",
     "TrainingSeedInfo",
diff --git a/src/unilab/training/backend_adapter.py b/src/unilab/training/backend_adapter.py
deleted file mode 100644
index aea4687d5..000000000
--- a/src/unilab/training/backend_adapter.py
+++ /dev/null
@@ -1,94 +0,0 @@
-"""Resolved config adaptation for training entrypoints."""
-
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Any, Callable
-
-from omegaconf import DictConfig, OmegaConf
-
-from unilab.base.backend import materialize_scene_visual_override
-from unilab.base.scene import SceneCfg
-from unilab.training.reward import extract_reward_config
-
-
-class BackendAdapter:
-    """Build env/play overrides from the final composed config."""
-
-    def __init__(
-        self,
-        cfg: DictConfig,
-        *,
-        root_dir: str | Path,
-        algo_name: str | None = None,
-        scene_materializer: Callable[..., str] = materialize_scene_visual_override,
-    ) -> None:
-        self.cfg = cfg
-        self.root_dir = Path(root_dir)
-        self.algo_name = algo_name
-        self.scene_materializer = scene_materializer
-
-    def build_task_env_cfg_override(self) -> dict[str, Any]:
-        """Build env_cfg_override from the resolved reward + env sections."""
-        env_cfg_override = extract_reward_config(self.cfg)
-        env_cfg_override.update(self._to_plain_dict(getattr(self.cfg, "env", None)))
-
-        return env_cfg_override
-
-    def build_play_env_cfg_override(self) -> dict[str, Any]:
-        """Build play-mode overrides from an optional backend-agnostic play profile."""
-        env_cfg_override = self.build_task_env_cfg_override()
-        play_profile = getattr(self.cfg, "play_profile", None)
-        if (
-            play_profile is None
-            or not getattr(play_profile, "enabled", False)
-            or not self.cfg.training.play_only
-        ):
-            return env_cfg_override
-
-        env_profile = getattr(play_profile, "env", None)
-        if env_profile is not None:
-            self._apply_env_profile(env_cfg_override, env_profile)
-
-        scene_override = getattr(play_profile, "scene", None)
-        if scene_override is None or not getattr(scene_override, "enabled", False):
-            return env_cfg_override
-
-        source_model_file = getattr(scene_override, "source_model_file", None)
-        if not source_model_file:
-            raise ValueError("play_profile.scene.source_model_file must be configured")
-
-        env_cfg_override["scene"] = SceneCfg(
-            model_file=self.scene_materializer(
-                self._resolve_root_relative_path(str(source_model_file)),
-                ground_texture_file=(
-                    self._resolve_root_relative_path(str(scene_override.ground_texture_file))
-                    if getattr(scene_override, "ground_texture_file", None)
-                    else None
-                ),
-                ground_texrepeat=getattr(scene_override, "ground_texrepeat", None),
-                skybox_rgb1=getattr(scene_override, "skybox_rgb1", None),
-                skybox_rgb2=getattr(scene_override, "skybox_rgb2", None),
-            )
-        )
-        return env_cfg_override
-
-    def _apply_env_profile(self, env_cfg_override: dict[str, Any], env_profile: Any) -> None:
-        env_cfg_override.update(self._to_plain_dict(env_profile))
-
-    def _resolve_root_relative_path(self, path_value: str) -> str:
-        candidate = Path(path_value)
-        if candidate.is_absolute():
-            return str(candidate)
-        return str((self.root_dir / candidate).resolve())
-
-    def _to_plain_dict(self, value: Any) -> dict[str, Any]:
-        if OmegaConf.is_config(value):
-            resolved = OmegaConf.to_container(value, resolve=True)
-        elif isinstance(value, dict):
-            resolved = value
-        else:
-            return {}
-        if not isinstance(resolved, dict):
-            return {}
-        return {str(key): item for key, item in resolved.items()}
diff --git a/src/unilab/training/common.py b/src/unilab/training/common.py
index ff68b27a5..f65d901ca 100644
--- a/src/unilab/training/common.py
+++ b/src/unilab/training/common.py
@@ -4,7 +4,6 @@
 
 import logging
 from pathlib import Path
-from typing import Any
 
 from hydra.core.hydra_config import HydraConfig
 from omegaconf import DictConfig, OmegaConf
@@ -38,29 +37,13 @@ def assert_offpolicy_task_choice_matches_algo(
     *,
     algo_name: str | None = None,
 ) -> None:
-    """Reject offpolicy configs whose task owner path does not match the selected algo."""
+    """Reject off-policy configs whose cfg.algo.algo does not match the requested algo."""
     cfg_algo_name = str(OmegaConf.select(cfg, "algo.algo"))
     if algo_name is not None and cfg_algo_name != algo_name:
         raise ValueError(
             f"Off-policy algo argument {algo_name!r} is inconsistent with cfg.algo.algo={cfg_algo_name!r}"
         )
 
-    selected_algo = algo_name or cfg_algo_name
-    task_choice = get_hydra_runtime_choice(cfg, "task")
-    if task_choice is None:
-        return
-
-    task_algo, sep, _ = task_choice.partition("/")
-    if not sep:
-        raise ValueError(
-            f"Off-policy task choice must use task=<algo>/<task>/<backend>; got task={task_choice}"
-        )
-    if task_algo != selected_algo:
-        raise ValueError(
-            f"Off-policy algo/task mismatch: algo={selected_algo} is inconsistent with task={task_choice}. "
-            "Use task=<algo>/<task>/<backend> with the same algo prefix."
-        )
-
 
 def setup_logger(
     log_dir: str | Path,
@@ -94,22 +77,3 @@ def setup_logger(
         logger.addHandler(stream_handler)
 
     return logger
-
-
-def create_env(
-    cfg: DictConfig,
-    *,
-    num_envs: int,
-    env_cfg_override: dict[str, Any] | None = None,
-    sim_backend: str | None = None,
-    task_name: str | None = None,
-):
-    """Construct an environment via the registry using the current Hydra config."""
-    from unilab.base import registry
-
-    return registry.make(
-        task_name or str(OmegaConf.select(cfg, "training.task_name")),
-        num_envs=num_envs,
-        sim_backend=sim_backend or str(OmegaConf.select(cfg, "training.sim_backend")),
-        env_cfg_override=env_cfg_override,
-    )
diff --git a/src/unilab/training/experiment.py b/src/unilab/training/experiment.py
index 580940c87..cf708f722 100644
--- a/src/unilab/training/experiment.py
+++ b/src/unilab/training/experiment.py
@@ -5,10 +5,8 @@
 import dataclasses
 import getpass
 import importlib
-import importlib.util
 import json
 import os
-import platform
 import socket
 import subprocess
 import time
@@ -18,7 +16,8 @@
 
 from omegaconf import OmegaConf
 
-from unilab.training.sim2sim import extract_contract_snapshot
+from unilab.utils.device import get_device_info_dict
+from unilab.utils.sim2sim import extract_contract_snapshot
 
 
 def _cfg_get(cfg: Any, key: str, default: Any = None) -> Any:
@@ -60,43 +59,6 @@ def _json_safe(value: Any) -> Any:
         return str(value)
 
 
-def _fallback_device_info_dict() -> dict[str, str]:
-    return {
-        "platform": platform.platform(),
-        "chip": platform.processor() or "unknown",
-        "cpu_total_cores": str(os.cpu_count() or "unknown"),
-        "gpu_name": "unknown",
-        "memory": "unknown",
-    }
-
-
-def _benchmark_device_info_path() -> Path | None:
-    for parent in Path(__file__).resolve().parents:
-        candidate = parent / "scripts" / "benchmark" / "core" / "device_info.py"
-        if candidate.is_file():
-            return candidate
-    return None
-
-
-def get_device_info_dict() -> dict[str, str]:
-    try:
-        module_path = _benchmark_device_info_path()
-        if module_path is None:
-            return _fallback_device_info_dict()
-        spec = importlib.util.spec_from_file_location(
-            "unilab_benchmark_device_info",
-            module_path,
-        )
-        if spec is None or spec.loader is None:
-            raise ImportError(f"Unable to load device info module from {module_path}")
-        module = importlib.util.module_from_spec(spec)
-        spec.loader.exec_module(module)
-        getter = getattr(module, "get_device_info_dict")
-        return dict(getter())
-    except Exception:
-        return _fallback_device_info_dict()
-
-
 def get_git_info(root_dir: str | Path) -> dict[str, Any]:
     root = Path(root_dir)
 
@@ -124,6 +86,49 @@ def _run_git(*args: str) -> str | None:
     }
 
 
+def _write_json(path: Path, payload: Any, *, trailing_newline: bool = False) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    text = json.dumps(payload, indent=2)
+    if trailing_newline:
+        text += "\n"
+    path.write_text(text, encoding="utf-8")
+
+
+def build_run_config_payload(
+    *,
+    run_metadata: dict[str, Any],
+    full_cfg: Any,
+    contract_snapshot: Any | None = None,
+) -> dict[str, Any]:
+    """Assemble the canonical ``{"run", "config", ...}`` run-config payload."""
+    payload: dict[str, Any] = {
+        "run": _json_safe(run_metadata),
+        "config": _json_safe(_plain_dict(full_cfg)),
+    }
+    if contract_snapshot is not None:
+        payload["contract_snapshot"] = _json_safe(contract_snapshot)
+    return payload
+
+
+def write_run_config_snapshot(
+    log_dir: str | Path,
+    *,
+    run_metadata: dict[str, Any],
+    full_cfg: Any,
+    filename: str = "run_config.json",
+    contract_snapshot: Any | None = None,
+    trailing_newline: bool = False,
+) -> dict[str, Any]:
+    """Write the run-config snapshot into ``log_dir`` and return the payload."""
+    payload = build_run_config_payload(
+        run_metadata=run_metadata,
+        full_cfg=full_cfg,
+        contract_snapshot=contract_snapshot,
+    )
+    _write_json(Path(log_dir) / filename, payload, trailing_newline=trailing_newline)
+    return payload
+
+
 def build_wandb_run_name(algo_name: str, task_name: str, log_dir: str | Path | None) -> str:
     if log_dir is None:
         return f"{algo_name}__{task_name}"
@@ -257,12 +262,12 @@ def start(self) -> None:
                 seed_payload = {"effective_seed": self.seed_info}
             metadata.update(seed_payload)
 
-        payload = {
-            "run": _json_safe(metadata),
-            "config": _json_safe(_plain_dict(self.full_cfg)),
-            "contract_snapshot": _json_safe(extract_contract_snapshot(self.full_cfg)),
-        }
-        self._write_json(self.log_dir / "run_config.json", payload)
+        payload = write_run_config_snapshot(
+            self.log_dir,
+            run_metadata=metadata,
+            full_cfg=self.full_cfg,
+            contract_snapshot=extract_contract_snapshot(self.full_cfg),
+        )
 
         if not self.enabled:
             return
@@ -356,8 +361,7 @@ def finish(self) -> None:
 
     @staticmethod
     def _write_json(path: Path, payload: Any) -> None:
-        path.parent.mkdir(parents=True, exist_ok=True)
-        path.write_text(json.dumps(payload, indent=2), encoding="utf-8")
+        _write_json(path, payload)
 
 
 def patch_rsl_rl_action_std_logging(runner: Any) -> None:
diff --git a/src/unilab/training/hora_distill_config.py b/src/unilab/training/hora_distill_config.py
new file mode 100644
index 000000000..5bb6dbd2a
--- /dev/null
+++ b/src/unilab/training/hora_distill_config.py
@@ -0,0 +1,258 @@
+"""HORA distillation config and teacher-owner resolution helpers."""
+
+from __future__ import annotations
+
+import re
+from pathlib import Path
+from typing import Any, cast
+
+from hydra import compose, initialize_config_dir
+from hydra.core.global_hydra import GlobalHydra
+from omegaconf import DictConfig, OmegaConf
+
+from unilab.utils.checkpoint import resolve_task_checkpoint_path
+
+_PACKAGE_CONF_ROOT = Path(__file__).resolve().parents[1] / "conf"
+
+# Teacher owner configs are Hydra-composed from their family config tree.
+# SAC teachers live in their own per-algo tree; there is no `algo` group anymore.
+_TEACHER_TREE_BY_FAMILY = {"sac": "sac"}
+
+# Teacher -> student `algo.model` mappings expressed in YAML; see
+# hora_distill/student_model/*.yaml in the packaged conf tree. The mapping files
+# interpolate against the composed teacher owner config mounted at
+# `teacher_owner`.
+_STUDENT_MODEL_MAPPING_DIR = Path("hora_distill") / "student_model"
+
+
+def _conf_root(root_dir: str | Path | None) -> Path:
+    """Root of the conf tree: ``root_dir/"conf"`` when given, else packaged conf."""
+    return Path(root_dir) / "conf" if root_dir is not None else _PACKAGE_CONF_ROOT
+
+
+def _logs_root(root_dir: str | Path | None) -> Path:
+    """Root for run artifacts: ``root_dir`` when given, else the caller's CWD."""
+    return Path(root_dir) if root_dir is not None else Path.cwd()
+
+
+def _load_yaml_config(path: Path) -> DictConfig:
+    loaded = OmegaConf.load(path)
+    if not isinstance(loaded, DictConfig):
+        raise TypeError(f"Expected DictConfig from {path}, got {type(loaded)!r}")
+    return loaded
+
+
+def _sanitize_path_token(value: str, *, fallback: str) -> str:
+    sanitized = re.sub(r"[^A-Za-z0-9._-]+", "-", str(value)).strip("-._")
+    return sanitized or fallback
+
+
+def load_teacher_owner_config(
+    algo_family: str,
+    task: str,
+    *,
+    root_dir: str | Path | None = None,
+) -> DictConfig:
+    """Compose a HORA teacher owner config with standard Hydra semantics.
+
+    Uses ``initialize_config_dir`` + ``compose`` (same pattern as
+    ``scripts/audit_sim2sim_contracts.py``), so package directives, nested
+    ``defaults`` lists, and interpolations in the teacher tree all resolve.
+    """
+    algo_family = str(algo_family)
+    conf_dir = _conf_root(root_dir) / _TEACHER_TREE_BY_FAMILY.get(algo_family, algo_family)
+    overrides = [f"task={task}"]
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(config_dir=str(conf_dir.absolute()), version_base="1.3"):
+        return compose("config", overrides=overrides)
+
+
+def get_teacher_owner_spec(cfg: DictConfig) -> tuple[str | None, str | None]:
+    """Resolve the teacher algo family and task owner from distillation config."""
+    algo_family = OmegaConf.select(cfg, "teacher.algo_family")
+    task = OmegaConf.select(cfg, "teacher.task")
+    if algo_family in (None, "") or task in (None, ""):
+        return None, None
+    return str(algo_family), str(task)
+
+
+def _teacher_contract_mapping_name(
+    teacher_algo_family: str,
+    teacher_task: str,
+    teacher_cfg: DictConfig,
+) -> str:
+    """Validate the teacher owner contract and select the YAML mapping name."""
+    if teacher_algo_family == "sac":
+        runtime_impl = OmegaConf.select(teacher_cfg, "algo.runtime_impl")
+        if runtime_impl != "hora_sac":
+            raise ValueError(
+                "HORA distillation SAC teacher owner must select runtime_impl='hora_sac'. "
+                f"Got task={teacher_task} runtime_impl={runtime_impl!r}."
+            )
+        return "hora_sac"
+
+    actor_class_name = str(OmegaConf.select(teacher_cfg, "algo.actor.class_name") or "")
+    if "HoraActorModel" not in actor_class_name:
+        raise ValueError(
+            "HORA distillation teacher owner must resolve to HoraActorModel. "
+            f"Got algo_family={teacher_algo_family} task={teacher_task} "
+            f"actor.class_name={actor_class_name!r}."
+        )
+    return "hora_actor"
+
+
+def _student_model_defaults(
+    mapping_name: str,
+    teacher_cfg: DictConfig,
+    *,
+    root: str | Path | None = None,
+) -> dict[str, Any]:
+    """Resolve the YAML-expressed teacher -> student model mapping.
+
+    The mapping YAML is merged next to the composed teacher owner config
+    (mounted at ``teacher_owner``) so its interpolations resolve against the
+    Hydra-composed teacher hyperparameters. The mapping file owns all fallback
+    defaults; this function only mounts and resolves it.
+    """
+    mapping_path = _conf_root(root) / _STUDENT_MODEL_MAPPING_DIR / f"{mapping_name}.yaml"
+    merged = OmegaConf.merge(
+        {"teacher_owner": teacher_cfg},
+        _load_yaml_config(mapping_path),
+    )
+    model_cfg = OmegaConf.to_container(OmegaConf.select(merged, "model"), resolve=True)
+    if not isinstance(model_cfg, dict):
+        raise TypeError(
+            f"Expected mapping 'model' dict from {mapping_path}, got {type(model_cfg)!r}"
+        )
+    model_cfg = cast(dict[str, Any], model_cfg)
+    distribution_cfg = model_cfg.get("distribution_cfg")
+    if isinstance(distribution_cfg, dict):
+        # The student re-binds its own distribution class; only the teacher's
+        # distribution hyperparameters carry over, not its class binding.
+        distribution_cfg.pop("class_name", None)
+    return model_cfg
+
+
+def teacher_default_cfg(
+    cfg: DictConfig,
+    *,
+    root_dir: str | Path | None = None,
+) -> DictConfig:
+    """Build HORA student defaults from the selected teacher owner YAML."""
+    teacher_algo_family, teacher_task = get_teacher_owner_spec(cfg)
+    if teacher_algo_family is None or teacher_task is None:
+        return OmegaConf.create()
+
+    teacher_cfg = load_teacher_owner_config(
+        teacher_algo_family,
+        teacher_task,
+        root_dir=root_dir,
+    )
+    mapping_name = _teacher_contract_mapping_name(
+        teacher_algo_family,
+        teacher_task,
+        teacher_cfg,
+    )
+    model_cfg = _student_model_defaults(mapping_name, teacher_cfg, root=root_dir)
+    return OmegaConf.create(
+        {
+            "training": OmegaConf.select(teacher_cfg, "training"),
+            "reward": OmegaConf.select(teacher_cfg, "reward"),
+            "env": OmegaConf.select(teacher_cfg, "env"),
+            "algo": {"model": model_cfg},
+        }
+    )
+
+
+def apply_teacher_defaults(
+    cfg: DictConfig,
+    *,
+    root_dir: str | Path | None = None,
+) -> DictConfig:
+    """Merge teacher-owner defaults under the user distillation config."""
+    return cast(DictConfig, OmegaConf.merge(teacher_default_cfg(cfg, root_dir=root_dir), cfg))
+
+
+def resolved_distill_runtime_cfg(cfg: DictConfig) -> DictConfig:
+    """Return checkpoint runtime fields needed to rebuild the student model.
+
+    Stage-2 checkpoints intentionally do not persist owner runtime settings such
+    as env, reward, or domain randomization. Replay should use the currently
+    composed owner config for those fields.
+    """
+    model_cfg = OmegaConf.select(cfg, "algo.model")
+    return OmegaConf.create(
+        {
+            "algo": {
+                "model": (
+                    OmegaConf.to_container(model_cfg, resolve=True) if model_cfg is not None else {}
+                )
+            },
+        }
+    )
+
+
+def teacher_run_metadata(
+    cfg: DictConfig,
+    *,
+    teacher_algo_family: str,
+    teacher_checkpoint: Path,
+    root_dir: str | Path | None = None,
+) -> dict[str, Any]:
+    """Build explicit teacher provenance metadata for distillation outputs."""
+    teacher_task = OmegaConf.select(cfg, "teacher.task")
+    root = _logs_root(root_dir).resolve()
+    checkpoint_path = teacher_checkpoint.resolve()
+    try:
+        checkpoint_display = str(checkpoint_path.relative_to(root))
+    except ValueError:
+        checkpoint_display = str(checkpoint_path)
+
+    checkpoint_name = checkpoint_path.name
+    return {
+        "algo_family": str(teacher_algo_family),
+        "task": None if teacher_task in (None, "") else str(teacher_task),
+        "checkpoint_path": checkpoint_display,
+        "checkpoint_name": checkpoint_name,
+        "checkpoint_stem": checkpoint_path.stem,
+        "run_name": checkpoint_path.parent.name,
+        "run_slug": f"teacher-{_sanitize_path_token(teacher_algo_family, fallback='teacher')}",
+    }
+
+
+def resolve_teacher_checkpoint_path(
+    cfg: DictConfig,
+    *,
+    root_dir: str | Path | None = None,
+) -> tuple[Path | None, Path | None]:
+    """Resolve the selected HORA teacher checkpoint through owner metadata."""
+    teacher_algo_family, teacher_task = get_teacher_owner_spec(cfg)
+    if teacher_algo_family is None or teacher_task is None:
+        return None, None
+
+    root = _logs_root(root_dir)
+    teacher_cfg = load_teacher_owner_config(
+        teacher_algo_family,
+        teacher_task,
+        root_dir=root_dir,
+    )
+    teacher_task_name = OmegaConf.select(teacher_cfg, "training.task_name")
+    teacher_algo_log_name = OmegaConf.select(teacher_cfg, "algo.algo_log_name")
+    if teacher_task_name in (None, "") or teacher_algo_log_name in (None, ""):
+        raise ValueError(
+            "Teacher owner config must define training.task_name and algo.algo_log_name. "
+            f"Got algo_family={teacher_algo_family} task={teacher_task}."
+        )
+
+    selected_checkpoint = OmegaConf.select(cfg, "algo.checkpoint", default=-1)
+    return resolve_task_checkpoint_path(
+        root,
+        task_name=str(teacher_task_name),
+        load_run=str(OmegaConf.select(cfg, "algo.load_run", default="-1")),
+        algo_log_name=str(teacher_algo_log_name),
+        checkpoint=(
+            str(selected_checkpoint) if selected_checkpoint not in (None, "", -1, "-1") else None
+        ),
+        suffix=".pt",
+        log_root=OmegaConf.select(cfg, "training.log_root"),
+    )
diff --git a/src/unilab/training/offpolicy.py b/src/unilab/training/offpolicy.py
deleted file mode 100644
index 33854813e..000000000
--- a/src/unilab/training/offpolicy.py
+++ /dev/null
@@ -1,185 +0,0 @@
-"""Off-policy play helpers shared by training scripts and playback entrypoints."""
-
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Any, cast
-
-from omegaconf import DictConfig, OmegaConf
-
-from unilab.training.common import assert_offpolicy_task_choice_matches_algo
-
-
-def default_device(torch_module, preferred: str | None = None) -> str:
-    """Resolve runtime device with optional user override."""
-    if preferred:
-        return preferred
-    if torch_module.cuda.is_available():
-        return "cuda"
-    xpu = getattr(torch_module, "xpu", None)
-    xpu_is_available = getattr(xpu, "is_available", None)
-    if callable(xpu_is_available) and xpu_is_available():
-        return "xpu"
-    if torch_module.backends.mps.is_available():
-        return "mps"
-    return "cpu"
-
-
-def extract_reset_obs(reset_result):
-    """Extract obs_dict from env.reset(...) using the current (obs_dict, info_dict) contract."""
-    if isinstance(reset_result, tuple):
-        if len(reset_result) == 2:
-            obs_out, _ = reset_result
-            return obs_out
-    raise ValueError(f"Unexpected env.reset return format: {type(reset_result)!r}")
-
-
-def resolve_play_obs_dim(obs_groups_spec: dict[str, int]) -> int:
-    obs_dim, _ = resolve_play_obs_dims(obs_groups_spec)
-    return obs_dim
-
-
-def resolve_play_obs_dims(obs_groups_spec: dict[str, int]) -> tuple[int, int]:
-    from unilab.base.observations import get_obs_dims
-
-    obs_dim, critic_obs_dim = get_obs_dims(obs_groups_spec)
-    return int(obs_dim), int(critic_obs_dim)
-
-
-def extract_play_obs(obs_dict):
-    from unilab.base.observations import split_obs_dict
-
-    obs_out, _ = split_obs_dict(obs_dict)
-    return obs_out
-
-
-def resolve_play_actor_spec(
-    algo_name: str,
-    cfg: DictConfig,
-    *,
-    obs_dim: int,
-    critic_obs_dim: int,
-) -> tuple[str, dict[str, Any]]:
-    """Resolve the actor implementation and model kwargs used by off-policy play."""
-    if algo_name != "sac":
-        return algo_name, {}
-
-    from unilab.algos.torch.offpolicy.runtime import resolve_custom_offpolicy_runtime
-
-    rl_cfg = cast(dict[str, Any], OmegaConf.to_container(cfg.algo, resolve=True))
-    custom_runtime = resolve_custom_offpolicy_runtime(rl_cfg)
-    if custom_runtime is None:
-        return "sac", {}
-
-    actor_algo_type = str(custom_runtime.algo_type or algo_name)
-    actor_kwargs = custom_runtime.build_model_kwargs(
-        obs_dim=int(obs_dim),
-        critic_obs_dim=int(critic_obs_dim),
-    )
-    return actor_algo_type, actor_kwargs
-
-
-def build_play_actor(
-    algo_name: str,
-    cfg: DictConfig,
-    *,
-    obs_dim: int,
-    critic_obs_dim: int,
-    action_dim: int,
-    device: str,
-) -> tuple[Any, Any | None, str, dict[str, Any]]:
-    """Build the policy actor selected by an off-policy owner config."""
-    import torch
-
-    from unilab.algos.torch.common.actor_factory import build_actor
-
-    actor_algo_type, actor_kwargs = resolve_play_actor_spec(
-        algo_name,
-        cfg,
-        obs_dim=obs_dim,
-        critic_obs_dim=critic_obs_dim,
-    )
-    normalizer = None
-    if algo_name == "sac":
-        actor = build_actor(
-            actor_algo_type,
-            obs_dim,
-            action_dim,
-            cfg.algo.actor_hidden_dim,
-            cfg.algo.use_layer_norm,
-            device,
-            **actor_kwargs,
-        )
-    elif algo_name == "td3":
-        from unilab.algos.torch.fast_td3.learner import EmpiricalNormalization, TD3Actor
-
-        actor = TD3Actor(
-            obs_dim,
-            action_dim,
-            cfg.training.play_env_num,
-            cfg.algo.algo_params.init_scale,
-            cfg.algo.actor_hidden_dim,
-            cfg.algo.algo_params.log_std_min,
-            cfg.algo.algo_params.log_std_max,
-            torch.device(device),
-        )
-        if cfg.algo.obs_normalization:
-            normalizer = EmpiricalNormalization(shape=obs_dim, device=device)
-    elif algo_name == "flashsac":
-        actor = build_actor(
-            "flashsac",
-            obs_dim,
-            action_dim,
-            cfg.algo.actor_hidden_dim,
-            cfg.algo.use_layer_norm,
-            device,
-            actor_num_blocks=cfg.algo.algo_params.actor_num_blocks,
-            actor_noise_zeta_mu=cfg.algo.algo_params.actor_noise_zeta_mu,
-            actor_noise_zeta_max=cfg.algo.algo_params.actor_noise_zeta_max,
-        )
-        if cfg.algo.obs_normalization:
-            from unilab.algos.torch.common.normalization import EmpiricalNormalization
-
-            normalizer = EmpiricalNormalization(shape=obs_dim, device=device)
-    else:
-        raise ValueError(f"Unsupported algo: {algo_name}")
-
-    actor.eval()
-    return actor, normalizer, actor_algo_type, actor_kwargs
-
-
-def load_play_actor(
-    algo_name: str,
-    actor: Any,
-    normalizer: Any | None,
-    checkpoint: dict[str, Any],
-) -> None:
-    """Restore an off-policy play actor and its optional observation normalizer."""
-    if algo_name in ("sac", "flashsac"):
-        actor.load_state_dict(checkpoint["actor"])
-    elif algo_name == "td3":
-        actor_state = {
-            key: value for key, value in checkpoint["actor"].items() if key not in ("noise_scales",)
-        }
-        actor.load_state_dict(actor_state, strict=False)
-    else:
-        raise ValueError(f"Unsupported algo: {algo_name}")
-    if normalizer is not None and checkpoint.get("obs_normalizer"):
-        normalizer.load_state_dict(checkpoint["obs_normalizer"])
-        normalizer.eval()
-
-
-def build_offpolicy_env_cfg_override(
-    algo_name: str,
-    cfg: DictConfig,
-    *,
-    root_dir: str | Path,
-) -> dict[str, Any] | None:
-    """Build the task env override for off-policy play through the backend adapter."""
-    from unilab.training.backend_adapter import BackendAdapter
-
-    assert_offpolicy_task_choice_matches_algo(cfg, algo_name=algo_name)
-    return cast(
-        dict[str, Any] | None,
-        BackendAdapter(cfg, root_dir=root_dir, algo_name=algo_name).build_task_env_cfg_override(),
-    )
diff --git a/src/unilab/training/rsl_rl.py b/src/unilab/training/rsl_rl.py
deleted file mode 100644
index 0ac431771..000000000
--- a/src/unilab/training/rsl_rl.py
+++ /dev/null
@@ -1,319 +0,0 @@
-"""RSL-RL-specific training helpers."""
-
-from __future__ import annotations
-
-import os
-from collections.abc import Iterator
-from contextlib import contextmanager
-from copy import deepcopy
-from typing import Any
-
-import numpy as np
-import torch
-from omegaconf import open_dict
-from tensordict import TensorDict
-
-from unilab.base.final_observation import resolve_terminal_observation_contract
-from unilab.base.np_env import NpEnvState
-from unilab.utils.tensor import to_numpy, to_torch
-
-
-def apply_rsl_rl_rank_seed(cfg: Any, rank: int) -> int:
-    """Apply RSL-RL's ``base seed + global rank`` data-parallel contract."""
-    if rank < 0:
-        raise ValueError(f"rank must be non-negative, got {rank}")
-    base_seed = int(cfg.algo.seed)
-    with open_dict(cfg):
-        cfg.algo.seed = base_seed + int(rank)
-    return int(cfg.algo.seed)
-
-
-def resolve_rsl_rl_device(
-    *,
-    configured_device: str | None,
-    devices: tuple[int, ...] | None,
-    world_size: int,
-    local_rank: int,
-    default_device: str,
-) -> str:
-    """Resolve the exact device string expected by RSL-RL's runner.
-
-    Integrated multi-GPU workers see the selected physical devices through a
-    remapped ``CUDA_VISIBLE_DEVICES`` list, so RSL-RL must receive
-    ``cuda:LOCAL_RANK`` rather than the original host-global device index.
-    """
-    if configured_device is not None and devices is not None:
-        raise ValueError("Set either training.device or training.devices, not both")
-    if world_size < 1:
-        raise ValueError(f"world_size must be positive, got {world_size}")
-    if local_rank < 0 or local_rank >= world_size:
-        raise ValueError(f"local_rank={local_rank} is out of range for world_size={world_size}")
-    if world_size > 1:
-        if configured_device is not None:
-            raise ValueError(
-                "training.device cannot select one device in a distributed run; "
-                "use training.devices"
-            )
-        if devices is not None and len(devices) != world_size:
-            raise ValueError(
-                f"training.devices has {len(devices)} entries but WORLD_SIZE={world_size}"
-            )
-        return f"cuda:{local_rank}"
-    if devices is not None:
-        return f"cuda:{devices[0]}"
-    return configured_device or default_device
-
-
-def ppo_samples_per_iteration(*, num_envs: int, num_steps_per_env: int, world_size: int) -> int:
-    """Return the global fresh rollout sample count for one PPO iteration."""
-    return int(num_envs) * int(num_steps_per_env) * int(world_size)
-
-
-def finish_rsl_rl_distributed(*, training_succeeded: bool) -> None:
-    """Synchronize successful ranks and release RSL-RL's process group."""
-    if not torch.distributed.is_available() or not torch.distributed.is_initialized():
-        return
-    try:
-        if training_succeeded:
-            torch.distributed.barrier()
-    finally:
-        torch.distributed.destroy_process_group()
-
-
-@contextmanager
-def rsl_rl_single_process_topology() -> Iterator[None]:
-    """Temporarily hide torchrun's worker topology from rank-0-only work.
-
-    Destroying a process group does not clear ``WORLD_SIZE`` / ``RANK`` /
-    ``LOCAL_RANK``. RSL-RL would therefore initialize a second distributed
-    group when rank 0 constructs a fresh runner for post-training playback,
-    even though every other rank has already exited. Present the playback
-    scope as a single-process runtime, then restore launcher-owned variables.
-    """
-    single_process_topology = {
-        "WORLD_SIZE": "1",
-        "RANK": "0",
-        "LOCAL_RANK": "0",
-    }
-    previous = {name: os.environ.get(name) for name in single_process_topology}
-    os.environ.update(single_process_topology)
-    try:
-        yield
-    finally:
-        for name, value in previous.items():
-            if value is None:
-                os.environ.pop(name, None)
-            else:
-                os.environ[name] = value
-
-
-def get_policy_obs_dims(obs_groups_spec: dict[str, int]) -> tuple[int, int]:
-    """Return ``(actor_obs_dim, flat_policy_obs_dim)`` for RSL-RL policies."""
-    actor_obs_dim = int(obs_groups_spec.get("obs", 0))
-    flat_policy_obs_dim = int(
-        sum(dim for group_name, dim in obs_groups_spec.items() if group_name != "critic")
-    )
-    return actor_obs_dim, flat_policy_obs_dim or actor_obs_dim
-
-
-def normalize_ppo_train_cfg(train_cfg: dict[str, Any]) -> dict[str, Any]:
-    """Map UniLab PPO owner config to the current RSL-RL schema."""
-    normalized = deepcopy(train_cfg)
-    algorithm_cfg = normalized.get("algorithm")
-    if isinstance(algorithm_cfg, dict):
-        for key in (
-            "target_kl_stop",
-            "adaptive_kl_beta",
-            "adaptive_lr_growth",
-            "adaptive_lr_decay",
-            "adaptive_lr_update_interval",
-            "metrics_interval",
-            "finite_check_interval",
-            "warmup_strict_iters",
-            "warmup_metrics_interval",
-            "warmup_finite_check_interval",
-            "disable_finite_checks",
-        ):
-            algorithm_cfg.pop(key, None)
-
-    if "actor" in normalized and "critic" in normalized:
-        return normalized
-
-    policy_cfg = normalized.pop("policy", None)
-    if not isinstance(policy_cfg, dict):
-        return normalized
-
-    actor_hidden_dims = policy_cfg.get("actor_hidden_dims", [512, 256, 128])
-    critic_hidden_dims = policy_cfg.get("critic_hidden_dims", actor_hidden_dims)
-    activation = policy_cfg.get("activation", "elu")
-    init_noise_std = float(policy_cfg.get("init_noise_std", 1.0))
-    obs_normalization = bool(normalized.get("empirical_normalization", False))
-
-    normalized["actor"] = {
-        "class_name": "rsl_rl.models.MLPModel",
-        "hidden_dims": actor_hidden_dims,
-        "activation": activation,
-        "obs_normalization": obs_normalization,
-        "distribution_cfg": {
-            "class_name": "rsl_rl.modules.distribution.GaussianDistribution",
-            "init_std": init_noise_std,
-            "std_type": "scalar",
-        },
-    }
-    normalized["critic"] = {
-        "class_name": "rsl_rl.models.MLPModel",
-        "hidden_dims": critic_hidden_dims,
-        "activation": activation,
-        "obs_normalization": obs_normalization,
-    }
-
-    obs_groups = normalized.get("obs_groups")
-    if isinstance(obs_groups, dict) and "actor" not in obs_groups and "default" in obs_groups:
-        default_groups = obs_groups.pop("default")
-        if isinstance(default_groups, list) and default_groups:
-            obs_groups["actor"] = list(default_groups)
-
-    return normalized
-
-
-class RslRlVecEnvWrapper:
-    """Adapter from UniLab's env contract to the RSL-RL VecEnv contract."""
-
-    def __init__(
-        self,
-        env: Any,
-        device: str = "cpu",
-        policy_obs_mode: str = "flat",
-    ) -> None:
-        if policy_obs_mode == "auto":
-            policy_obs_mode = "flat"
-        if policy_obs_mode not in {"actor", "flat"}:
-            raise ValueError(
-                f"Unsupported policy_obs_mode={policy_obs_mode!r}; expected 'actor' or 'flat'."
-            )
-
-        self.env = env
-        self.cfg = env.cfg
-        self.device = device
-        self.policy_obs_mode = policy_obs_mode
-        self.num_envs = env.num_envs
-        self.observation_space = env.observation_space
-        self.action_space = env.action_space
-
-        self._actor_obs_dim, self._flat_obs_dim = get_policy_obs_dims(env.obs_groups_spec)
-        self.num_obs = (
-            self._actor_obs_dim if self.policy_obs_mode == "actor" else self._flat_obs_dim
-        )
-        self.num_privileged_obs = int(env.obs_groups_spec.get("critic", self.num_obs))
-        action_shape = env.action_space.shape
-        if action_shape is None:
-            raise ValueError("env.action_space.shape must be defined")
-        self.num_actions = int(action_shape[0])
-
-        self.episode_returns = torch.zeros(self.num_envs, device=device)
-        self.episode_lengths = torch.zeros(self.num_envs, device=device)
-        self.episode_length_buf = self.episode_lengths
-        self.max_episode_length = np.ceil(env.cfg.max_episode_seconds / env.cfg.ctrl_dt)
-        self.reset()
-
-    def _policy_obs(self, obs: dict[str, Any]) -> torch.Tensor:
-        if self.policy_obs_mode == "actor":
-            return to_torch(obs["obs"], self.device)
-
-        policy_groups = [
-            to_numpy(value) for group_name, value in obs.items() if group_name != "critic"
-        ]
-        if not policy_groups:
-            raise KeyError("Observation dict must contain at least one non-critic group")
-        if len(policy_groups) == 1:
-            return to_torch(policy_groups[0], self.device)
-        return to_torch(np.concatenate(policy_groups, axis=1), self.device)
-
-    def _obs_to_tensordict(
-        self,
-        obs: dict[str, Any],
-        info: dict[str, Any] | None = None,
-    ) -> TensorDict:
-        del info
-        actor_obs = to_torch(obs["obs"], self.device)
-        td_dict: dict[str, torch.Tensor] = {
-            "actor": actor_obs,
-            "policy": self._policy_obs(obs),
-        }
-        if "critic" in obs:
-            td_dict["critic"] = to_torch(obs["critic"], self.device)
-        return TensorDict(td_dict, batch_size=self.num_envs, device=self.device)
-
-    def _resolve_final_observation(self, state: NpEnvState) -> dict[str, Any] | None:
-        if isinstance(state.final_observation, dict):
-            return state.final_observation
-        if isinstance(state.info, dict):
-            final_observation = state.info.get("final_observation")
-            if isinstance(final_observation, dict):
-                return final_observation
-        return None
-
-    def _resolve_done(self, state: NpEnvState) -> torch.Tensor:
-        return to_torch(state.terminated | state.truncated, self.device).bool()
-
-    def step(
-        self, actions: torch.Tensor | np.ndarray
-    ) -> tuple[TensorDict, torch.Tensor, torch.Tensor, dict]:
-        actions_np = to_numpy(actions)
-        state = self.env.step(actions_np)
-        rewards = to_torch(state.reward, self.device)
-        dones = self._resolve_done(state)
-
-        self.episode_returns += rewards
-        self.episode_lengths += 1
-
-        infos: dict[str, torch.Tensor | TensorDict | dict[str, Any]] = {}
-        done_idx = torch.nonzero(dones).flatten()
-        if len(done_idx) > 0:
-            infos["time_outs"] = to_torch(state.truncated, self.device).bool()
-
-            final_observation = self._resolve_final_observation(state)
-            terminal_contract = resolve_terminal_observation_contract(
-                next_obs_batch_size=self.num_envs,
-                final_observation=final_observation,
-                done=to_numpy(dones),
-                info=state.info,
-                truncated=to_numpy(infos["time_outs"]),
-            )
-            if np.any(terminal_contract.timeout_terminal_mask) and final_observation is not None:
-                infos["time_out_bootstrap_obs"] = self._obs_to_tensordict(final_observation)
-
-            self.episode_returns[done_idx] = 0
-            self.episode_lengths[done_idx] = 0
-
-        if "log" in state.info:
-            infos["log"] = state.info["log"]
-
-        return (
-            self._obs_to_tensordict(state.obs, getattr(state, "info", None)),
-            rewards,
-            dones,
-            infos,
-        )
-
-    def reset(self) -> tuple[TensorDict, dict[str, Any]]:
-        if self.env.state is None:
-            self.env.init_state()
-
-        env_indices = np.arange(self.num_envs, dtype=np.int32)
-        obs_out, info = self.env.reset(env_indices)
-        self.episode_returns[:] = 0
-        self.episode_lengths[:] = 0
-        return self._obs_to_tensordict(obs_out, info), info
-
-    def get_observations(self) -> TensorDict:
-        assert self.env.state is not None
-        return self._obs_to_tensordict(self.env.state.obs, self.env.state.info)
-
-    def get_privileged_observations(self) -> torch.Tensor:
-        assert self.env.state is not None
-        obs = self.env.state.obs
-        return to_torch(obs.get("critic", obs["obs"]), self.device)
-
-    def close(self) -> None:
-        self.env.close()
diff --git a/src/unilab/training/run.py b/src/unilab/training/run.py
index 86720b304..bc6eefc44 100644
--- a/src/unilab/training/run.py
+++ b/src/unilab/training/run.py
@@ -5,12 +5,103 @@
 import os
 from os import PathLike
 from pathlib import Path
+from typing import TYPE_CHECKING, Any, cast
 
 from omegaconf import DictConfig, OmegaConf
+from unisim.backend.base import normalize_play_render_mode
 
-from unilab.base.backend.base import BackendPlayRenderPlan, normalize_play_render_mode
+from unilab.utils.checkpoint import (
+    _TEST_LOG_ROOT_ENV,
+    _normalize_load_run,
+    get_latest_checkpoint,
+    get_latest_run,
+    resolve_task_checkpoint_path,
+)
 
-_TEST_LOG_ROOT_ENV = "UNILAB_TEST_LOG_ROOT"
+if TYPE_CHECKING:
+    from unilab.utils.nan_guard import NanGuardCfg
+
+
+def build_run_dir_name(timestamp: str, sim_backend: str, *, world_size: int = 1) -> str:
+    """Return the canonical run directory name shared by all training entries."""
+    gpu_suffix = f"_gpux{world_size}" if world_size > 1 else ""
+    return f"{timestamp}_{sim_backend}{gpu_suffix}"
+
+
+def algo_config_dict(cfg: DictConfig) -> dict[str, Any]:
+    """Resolve the composed ``cfg.algo`` subtree into a plain mutable dict."""
+    raw = OmegaConf.to_container(cfg.algo, resolve=True)
+    if not isinstance(raw, dict):
+        raise TypeError("cfg.algo must resolve to a dict")
+    return cast(dict[str, Any], raw)
+
+
+def format_play_checkpoint_error(
+    cfg: DictConfig,
+    *,
+    task_log_root: Path,
+    load_path: Path | None,
+    load_path_dir: Path | None,
+) -> str:
+    """Build the user-facing diagnostic for an unresolvable play checkpoint."""
+    selected_checkpoint = OmegaConf.select(cfg, "algo.checkpoint", default=-1)
+    checkpoint_hint = (
+        f" algo.checkpoint={selected_checkpoint!r}"
+        if selected_checkpoint not in (None, "", -1, "-1")
+        else ""
+    )
+
+    if load_path_dir is not None and load_path is None and checkpoint_hint:
+        reason = f"Requested checkpoint was not found under resolved_run={load_path_dir}."
+    elif not task_log_root.exists():
+        reason = "Task log root does not exist."
+    else:
+        latest_run = get_latest_run(task_log_root)
+        if latest_run is None:
+            reason = "No run directories were found under the task log root."
+        elif get_latest_checkpoint(latest_run) is None:
+            reason = f"Resolved latest run has no model_*.pt checkpoint files: {latest_run}."
+        else:
+            reason = "Requested run or checkpoint could not be resolved."
+
+    return (
+        "Could not resolve a checkpoint for play mode. "
+        f"{reason} task={cfg.training.task_name} task_log_root={task_log_root} "
+        f"algo.load_run={cfg.algo.load_run!r}{checkpoint_hint}."
+        " Use algo.load_run=<run-dir-or-checkpoint-path> "
+        "and optionally algo.checkpoint=<iteration-or-filename>."
+    )
+
+
+def resolve_nan_guard_cfg(training_cfg: Any) -> NanGuardCfg | None:
+    """Build the shared ``NanGuardCfg`` from ``training.nan_guard``, or ``None``."""
+    nan_guard_cfg = getattr(training_cfg, "nan_guard", None)
+    if nan_guard_cfg is None or not getattr(nan_guard_cfg, "enabled", False):
+        return None
+    from unilab.utils.nan_guard import NanGuardCfg
+
+    return NanGuardCfg(
+        enabled=True,
+        buffer_size=int(getattr(nan_guard_cfg, "buffer_size", 100)),
+        max_envs_to_dump=int(getattr(nan_guard_cfg, "max_envs_to_dump", 5)),
+        output_dir=getattr(nan_guard_cfg, "output_dir", None),
+    )
+
+
+def apply_env_nan_guard(env: Any, training_cfg: Any) -> None:
+    """Attach a ``NanGuard`` to ``env`` when ``training.nan_guard`` is enabled."""
+    nan_guard_cfg = resolve_nan_guard_cfg(training_cfg)
+    if nan_guard_cfg is None:
+        return
+    from unilab.utils.nan_guard import NanGuard
+
+    env.set_nan_guard(
+        NanGuard(
+            nan_guard_cfg,
+            num_envs=env.num_envs,
+            supports_state_playback=env.play_capabilities.supports_physics_state_playback,
+        )
+    )
 
 
 def should_run_playback(*, play_only: bool, no_play: bool, play_render_mode: str | None) -> bool:
@@ -20,21 +111,6 @@ def should_run_playback(*, play_only: bool, no_play: bool, play_render_mode: str
     return bool(play_only) or not bool(no_play)
 
 
-def log_playback_plan(plan: BackendPlayRenderPlan, *, prefix: str = "") -> None:
-    """Print user-facing playback status for a resolved backend plan."""
-    if plan.mode == "none":
-        print(f"{prefix}Skipping playback because training.play_render_mode=none.")
-        return
-    if plan.record_video:
-        print(f"{prefix}Rendering video to {plan.output_video}...")
-    elif plan.mode == "interactive":
-        print(f"{prefix}Starting interactive visualization...")
-        print(f"{prefix}Use the renderer window or browser URL reported by the backend.")
-    else:
-        print(f"{prefix}Running playback without video recording...")
-    print(f"{prefix}Rendering playback frames...")
-
-
 def get_log_root(root_dir: str | Path, cfg: DictConfig) -> Path:
     """Resolve the algorithm log root, honoring optional training.log_root overrides."""
     configured_root = OmegaConf.select(cfg, "training.log_root")
@@ -47,89 +123,6 @@ def get_log_root(root_dir: str | Path, cfg: DictConfig) -> Path:
     return Path(root_dir) / "logs" / str(OmegaConf.select(cfg, "algo.algo_log_name"))
 
 
-def get_entrypoint_log_root(
-    root_dir: str | Path,
-    *,
-    algo_log_name: str,
-    log_root: str | Path | None = None,
-) -> Path:
-    """Resolve the log root for non-Hydra entrypoints using training helper semantics."""
-    if log_root is not None:
-        configured_root = Path(log_root)
-        return (
-            configured_root if configured_root.is_absolute() else Path(root_dir) / configured_root
-        )
-    test_log_root = os.environ.get(_TEST_LOG_ROOT_ENV)
-    if test_log_root:
-        return Path(test_log_root) / algo_log_name
-    return Path(root_dir) / "logs" / algo_log_name
-
-
-def get_latest_run(log_dir: str | Path) -> Path | None:
-    """Return the lexicographically latest run directory under a task log root."""
-    base_dir = Path(log_dir)
-    if not base_dir.exists():
-        return None
-    runs = sorted(path for path in base_dir.iterdir() if path.is_dir())
-    return runs[-1] if runs else None
-
-
-def get_latest_checkpoint(run_dir: str | Path, *, suffix: str = ".pt") -> Path | None:
-    """Return the latest model checkpoint inside a run directory."""
-    run_path = Path(run_dir)
-    if not run_path.exists():
-        return None
-
-    def _iteration(path: Path) -> int:
-        stem_parts = path.stem.split("_", 1)
-        if len(stem_parts) != 2:
-            return -1
-        try:
-            return int(stem_parts[1])
-        except ValueError:
-            return -1
-
-    model_files = [
-        path
-        for path in run_path.iterdir()
-        if path.is_file() and path.name.startswith("model_") and path.suffix == suffix
-    ]
-    if not model_files:
-        return None
-    return max(model_files, key=_iteration)
-
-
-def _normalize_load_run(load_run: str | int | PathLike[str]) -> str:
-    return str(load_run)
-
-
-def resolve_checkpoint_path(
-    base_log_dir: str | Path,
-    load_run: str | int | PathLike[str],
-    *,
-    suffix: str = ".pt",
-) -> tuple[Path | None, Path | None]:
-    """Resolve a latest or explicit checkpoint path from a task log root."""
-    base_dir = Path(base_log_dir)
-    selected_run = _normalize_load_run(load_run)
-    if selected_run == "-1":
-        run_dir = get_latest_run(base_dir)
-        if run_dir is None:
-            return None, None
-        checkpoint = get_latest_checkpoint(run_dir, suffix=suffix)
-        return (checkpoint, run_dir) if checkpoint is not None else (None, None)
-
-    candidate = Path(selected_run)
-    if not candidate.exists():
-        candidate = base_dir / selected_run
-    if candidate.is_file():
-        return candidate, candidate.parent
-    if candidate.is_dir():
-        checkpoint = get_latest_checkpoint(candidate, suffix=suffix)
-        return (checkpoint, candidate) if checkpoint is not None else (None, None)
-    return None, None
-
-
 def parse_checkpoint_path(
     cfg: DictConfig,
     *,
@@ -163,40 +156,6 @@ def parse_checkpoint_path(
     )
 
 
-def resolve_appo_checkpoint_path(
-    base_log_dir: str | Path,
-    load_run: str | int | PathLike[str],
-) -> tuple[str | None, str | None]:
-    """Resolve an APPO checkpoint under a task log root, returning string paths."""
-    checkpoint_path, checkpoint_dir = resolve_checkpoint_path(
-        base_log_dir,
-        str(load_run),
-        suffix=".pt",
-    )
-    return (
-        str(checkpoint_path) if checkpoint_path is not None else None,
-        str(checkpoint_dir) if checkpoint_dir is not None else None,
-    )
-
-
-def resolve_offpolicy_checkpoint_path(
-    root_dir: str | Path,
-    algo_log_name: str,
-    task: str,
-    load_run: str | int | PathLike[str],
-) -> tuple[str | None, str | None]:
-    """Resolve an off-policy checkpoint from the repo-rooted log tree."""
-    checkpoint_path, checkpoint_dir = resolve_checkpoint_path(
-        Path(root_dir) / "logs" / algo_log_name / task,
-        load_run,
-        suffix=".pt",
-    )
-    return (
-        str(checkpoint_path) if checkpoint_path is not None else None,
-        str(checkpoint_dir) if checkpoint_dir is not None else None,
-    )
-
-
 def resolve_hora_stage2_checkpoint_path(
     cfg: DictConfig,
     *,
@@ -273,50 +232,3 @@ def format_hora_stage2_checkpoint_error(
         "Use algo.load_run=<run-dir-or-checkpoint-path> and optionally "
         "algo.checkpoint=<iteration-or-filename>."
     )
-
-
-def resolve_task_checkpoint_path(
-    root_dir: str | Path,
-    *,
-    task_name: str,
-    load_run: str | int | PathLike[str],
-    algo_log_name: str,
-    checkpoint: str | None = None,
-    suffix: str = ".pt",
-    log_root: str | Path | None = None,
-) -> tuple[Path | None, Path | None]:
-    """Resolve checkpoint paths for auxiliary entrypoints through shared training semantics."""
-    task_log_root = (
-        get_entrypoint_log_root(
-            root_dir,
-            algo_log_name=algo_log_name,
-            log_root=log_root,
-        )
-        / task_name
-    )
-
-    run_dir: Path | None
-    selected_run = _normalize_load_run(load_run)
-    if selected_run == "-1":
-        run_dir = get_latest_run(task_log_root)
-    else:
-        candidate = Path(selected_run)
-        if not candidate.exists():
-            candidate = task_log_root / selected_run
-        if candidate.is_file():
-            return candidate, candidate.parent
-        run_dir = candidate if candidate.is_dir() else None
-
-    if run_dir is None:
-        return None, None
-
-    checkpoint_path: Path | None
-    if checkpoint is not None:
-        checkpoint_name = (
-            f"model_{checkpoint}{suffix}" if str(checkpoint).isdigit() else str(checkpoint)
-        )
-        checkpoint_path = run_dir / checkpoint_name
-        return (checkpoint_path, run_dir) if checkpoint_path.exists() else (None, run_dir)
-
-    checkpoint_path = get_latest_checkpoint(run_dir, suffix=suffix)
-    return (checkpoint_path, run_dir) if checkpoint_path is not None else (None, run_dir)
diff --git a/src/unilab/utils/checkpoint.py b/src/unilab/utils/checkpoint.py
new file mode 100644
index 000000000..567c9bcce
--- /dev/null
+++ b/src/unilab/utils/checkpoint.py
@@ -0,0 +1,173 @@
+"""Pure-filesystem log root and checkpoint resolution helpers."""
+
+from __future__ import annotations
+
+import os
+from os import PathLike
+from pathlib import Path
+
+_TEST_LOG_ROOT_ENV = "UNILAB_TEST_LOG_ROOT"
+
+
+def get_entrypoint_log_root(
+    root_dir: str | Path,
+    *,
+    algo_log_name: str,
+    log_root: str | Path | None = None,
+) -> Path:
+    """Resolve the log root for non-Hydra entrypoints using training helper semantics."""
+    if log_root is not None:
+        configured_root = Path(log_root)
+        return (
+            configured_root if configured_root.is_absolute() else Path(root_dir) / configured_root
+        )
+    test_log_root = os.environ.get(_TEST_LOG_ROOT_ENV)
+    if test_log_root:
+        return Path(test_log_root) / algo_log_name
+    return Path(root_dir) / "logs" / algo_log_name
+
+
+def get_latest_run(log_dir: str | Path) -> Path | None:
+    """Return the lexicographically latest run directory under a task log root."""
+    base_dir = Path(log_dir)
+    if not base_dir.exists():
+        return None
+    runs = sorted(path for path in base_dir.iterdir() if path.is_dir())
+    return runs[-1] if runs else None
+
+
+def get_latest_checkpoint(run_dir: str | Path, *, suffix: str = ".pt") -> Path | None:
+    """Return the latest model checkpoint inside a run directory."""
+    run_path = Path(run_dir)
+    if not run_path.exists():
+        return None
+
+    def _iteration(path: Path) -> int:
+        stem_parts = path.stem.split("_", 1)
+        if len(stem_parts) != 2:
+            return -1
+        try:
+            return int(stem_parts[1])
+        except ValueError:
+            return -1
+
+    model_files = [
+        path
+        for path in run_path.iterdir()
+        if path.is_file() and path.name.startswith("model_") and path.suffix == suffix
+    ]
+    if not model_files:
+        return None
+    return max(model_files, key=_iteration)
+
+
+def _normalize_load_run(load_run: str | int | PathLike[str]) -> str:
+    return str(load_run)
+
+
+def resolve_checkpoint_path(
+    base_log_dir: str | Path,
+    load_run: str | int | PathLike[str],
+    *,
+    suffix: str = ".pt",
+) -> tuple[Path | None, Path | None]:
+    """Resolve a latest or explicit checkpoint path from a task log root."""
+    base_dir = Path(base_log_dir)
+    selected_run = _normalize_load_run(load_run)
+    if selected_run == "-1":
+        run_dir = get_latest_run(base_dir)
+        if run_dir is None:
+            return None, None
+        checkpoint = get_latest_checkpoint(run_dir, suffix=suffix)
+        return (checkpoint, run_dir) if checkpoint is not None else (None, None)
+
+    candidate = Path(selected_run)
+    if not candidate.exists():
+        candidate = base_dir / selected_run
+    if candidate.is_file():
+        return candidate, candidate.parent
+    if candidate.is_dir():
+        checkpoint = get_latest_checkpoint(candidate, suffix=suffix)
+        return (checkpoint, candidate) if checkpoint is not None else (None, None)
+    return None, None
+
+
+def resolve_task_checkpoint_path(
+    root_dir: str | Path,
+    *,
+    task_name: str,
+    load_run: str | int | PathLike[str],
+    algo_log_name: str,
+    checkpoint: str | None = None,
+    suffix: str = ".pt",
+    log_root: str | Path | None = None,
+) -> tuple[Path | None, Path | None]:
+    """Resolve checkpoint paths for auxiliary entrypoints through shared training semantics."""
+    task_log_root = (
+        get_entrypoint_log_root(
+            root_dir,
+            algo_log_name=algo_log_name,
+            log_root=log_root,
+        )
+        / task_name
+    )
+
+    run_dir: Path | None
+    selected_run = _normalize_load_run(load_run)
+    if selected_run == "-1":
+        run_dir = get_latest_run(task_log_root)
+    else:
+        candidate = Path(selected_run)
+        if not candidate.exists():
+            candidate = task_log_root / selected_run
+        if candidate.is_file():
+            return candidate, candidate.parent
+        run_dir = candidate if candidate.is_dir() else None
+
+    if run_dir is None:
+        return None, None
+
+    checkpoint_path: Path | None
+    if checkpoint is not None:
+        checkpoint_name = (
+            f"model_{checkpoint}{suffix}" if str(checkpoint).isdigit() else str(checkpoint)
+        )
+        checkpoint_path = run_dir / checkpoint_name
+        return (checkpoint_path, run_dir) if checkpoint_path.exists() else (None, run_dir)
+
+    checkpoint_path = get_latest_checkpoint(run_dir, suffix=suffix)
+    return (checkpoint_path, run_dir) if checkpoint_path is not None else (None, run_dir)
+
+
+def resolve_appo_checkpoint_path(
+    base_log_dir: str | Path,
+    load_run: str | int | PathLike[str],
+) -> tuple[str | None, str | None]:
+    """Resolve an APPO checkpoint under a task log root, returning string paths."""
+    checkpoint_path, checkpoint_dir = resolve_checkpoint_path(
+        base_log_dir,
+        str(load_run),
+        suffix=".pt",
+    )
+    return (
+        str(checkpoint_path) if checkpoint_path is not None else None,
+        str(checkpoint_dir) if checkpoint_dir is not None else None,
+    )
+
+
+def resolve_offpolicy_checkpoint_path(
+    root_dir: str | Path,
+    algo_log_name: str,
+    task: str,
+    load_run: str | int | PathLike[str],
+) -> tuple[str | None, str | None]:
+    """Resolve an off-policy checkpoint from the repo-rooted log tree."""
+    checkpoint_path, checkpoint_dir = resolve_checkpoint_path(
+        Path(root_dir) / "logs" / algo_log_name / task,
+        load_run,
+        suffix=".pt",
+    )
+    return (
+        str(checkpoint_path) if checkpoint_path is not None else None,
+        str(checkpoint_dir) if checkpoint_dir is not None else None,
+    )
diff --git a/src/unilab/utils/device.py b/src/unilab/utils/device.py
index f5a4cdcee..004d64b37 100644
--- a/src/unilab/utils/device.py
+++ b/src/unilab/utils/device.py
@@ -1,5 +1,9 @@
 from __future__ import annotations
 
+import platform
+import re
+import subprocess
+from functools import lru_cache
 from typing import Callable, cast
 
 import torch
@@ -120,3 +124,291 @@ def resolve_torch_device_alias(device: str | None, *, default: str = "cpu") -> s
         )
 
     raise ValueError(f"Unsupported device alias {original!r}; expected cpu, gpu, cuda, mps, or xpu")
+
+
+def _is_macos() -> bool:
+    return platform.system() == "Darwin"
+
+
+def _is_linux() -> bool:
+    return platform.system() == "Linux"
+
+
+def _is_windows() -> bool:
+    return platform.system() == "Windows"
+
+
+def _get_device_info_macos() -> dict[str, str]:
+    """Collect hardware info on macOS via system_profiler."""
+    info: dict[str, str] = {
+        "chip": "unknown",
+        "cpu_total_cores": "unknown",
+        "cpu_performance_cores": "unknown",
+        "cpu_efficiency_cores": "unknown",
+        "gpu_cores": "unknown",
+        "memory": "unknown",
+    }
+    try:
+        hw_text = subprocess.check_output(
+            ["system_profiler", "SPHardwareDataType"], text=True, stderr=subprocess.DEVNULL
+        )
+        disp_text = subprocess.check_output(
+            ["system_profiler", "SPDisplaysDataType"], text=True, stderr=subprocess.DEVNULL
+        )
+    except Exception:
+        return info
+
+    chip_match = re.search(r"Chip:\s*(.+)", hw_text)
+    if chip_match:
+        info["chip"] = chip_match.group(1).strip()
+
+    mem_match = re.search(r"Memory:\s*(.+)", hw_text)
+    if mem_match:
+        info["memory"] = mem_match.group(1).strip()
+
+    # Apple Silicon core descriptions vary by generation:
+    #   M3/M4: "10 performance and 4 efficiency"
+    #   M5 Pro/Max: "6 super and 12 performance"
+    cpu_match = re.search(
+        r"Total Number of Cores:\s*(\d+)\s*\(\s*(\d+)\s*(\w+)\s+and\s+(\d+)\s*(\w+)\s*\)",
+        hw_text,
+    )
+    if cpu_match:
+        total, count1, type1, count2, type2 = cpu_match.groups()
+        info["cpu_total_cores"] = total
+        info["cpu_core_type_1"] = type1
+        info["cpu_core_count_1"] = count1
+        info["cpu_core_type_2"] = type2
+        info["cpu_core_count_2"] = count2
+        # Backward-compat keys for legacy P+E format
+        if type1 == "performance" and type2 == "efficiency":
+            info["cpu_performance_cores"] = count1
+            info["cpu_efficiency_cores"] = count2
+        elif type1 == "super" and type2 == "performance":
+            info["cpu_super_cores"] = count1
+            info["cpu_performance_cores"] = count2
+    else:
+        cpu_total_match = re.search(r"Total Number of Cores:\s*(\d+)", hw_text)
+        if cpu_total_match:
+            info["cpu_total_cores"] = cpu_total_match.group(1)
+
+    gpu_match = re.search(r"Type:\s*GPU[\s\S]*?Total Number of Cores:\s*(\d+)", disp_text)
+    if gpu_match:
+        info["gpu_cores"] = gpu_match.group(1)
+
+    return info
+
+
+def _get_device_info_linux() -> dict[str, str]:
+    """Collect hardware info on Linux via /proc and common CLI tools."""
+    info: dict[str, str] = {
+        "chip": "unknown",
+        "cpu_total_cores": "unknown",
+        "cpu_performance_cores": "unknown",
+        "cpu_efficiency_cores": "unknown",
+        "gpu_name": "unknown",
+        "memory": "unknown",
+    }
+    # CPU model
+    try:
+        with open("/proc/cpuinfo", encoding="utf-8") as f:
+            cpuinfo = f.read()
+        model_match = re.search(r"^model name\s*:\s*(.+)$", cpuinfo, re.MULTILINE)
+        if model_match:
+            info["chip"] = model_match.group(1).strip()
+        # Count physical cores (unique core id per physical id)
+        pairs = re.findall(r"physical id\s*:\s*(\d+).*?core id\s*:\s*(\d+)", cpuinfo, re.DOTALL)
+        if pairs:
+            info["cpu_total_cores"] = str(len(set(pairs)))
+        else:
+            processor_count = len(re.findall(r"^processor\s*:", cpuinfo, re.MULTILINE))
+            if processor_count:
+                info["cpu_total_cores"] = str(processor_count)
+    except Exception:
+        pass
+    # Total memory
+    try:
+        with open("/proc/meminfo", encoding="utf-8") as f:
+            meminfo = f.read()
+        mem_match = re.search(r"MemTotal:\s*(\d+)\s*kB", meminfo)
+        if mem_match:
+            mem_gb = int(mem_match.group(1)) / 1024 / 1024
+            info["memory"] = f"{mem_gb:.1f} GB"
+    except Exception:
+        pass
+    # GPU via nvidia-smi
+    try:
+        gpu_out = subprocess.check_output(
+            ["nvidia-smi", "--query-gpu=name,memory.total", "--format=csv,noheader"],
+            text=True,
+            stderr=subprocess.DEVNULL,
+        ).strip()
+        if gpu_out:
+            # Take the first GPU line
+            first_line = gpu_out.splitlines()[0]
+            parts = [p.strip() for p in first_line.split(",")]
+            info["gpu_name"] = parts[0]
+            if len(parts) > 1:
+                info["gpu_memory"] = parts[1]
+    except Exception:
+        pass
+    # GPU via rocm-smi (AMD)
+    if info["gpu_name"] == "unknown":
+        try:
+            rocm_out = subprocess.check_output(
+                ["rocm-smi", "--showproductname"],
+                text=True,
+                stderr=subprocess.DEVNULL,
+            )
+            gpu_match = re.search(r"Card series\s*:\s*(.+)", rocm_out, re.IGNORECASE)
+            if gpu_match:
+                info["gpu_name"] = gpu_match.group(1).strip()
+        except Exception:
+            pass
+    # GPU memory via amd-smi (AMD ROCm). On unified-memory APUs this reports
+    # the BIOS-allocated visible VRAM slice, e.g. 96 GB out of 128 GB.
+    try:
+        amd_smi_out = subprocess.check_output(
+            ["amd-smi", "metric"],
+            text=True,
+            stderr=subprocess.DEVNULL,
+        )
+        vram_match = re.search(r"TOTAL_VISIBLE_VRAM:\s*(\d+)\s*MB", amd_smi_out)
+        if vram_match:
+            info["gpu_memory"] = f"{int(vram_match.group(1))} MB"
+        gtt_match = re.search(r"TOTAL_GTT:\s*(\d+)\s*MB", amd_smi_out)
+        if gtt_match:
+            info["gpu_gtt_memory"] = f"{int(gtt_match.group(1))} MB"
+    except Exception:
+        pass
+    # Fallback GPU via lspci (AMD/ATI, Intel iGPU/Arc, and others)
+    if info["gpu_name"] == "unknown":
+        try:
+            lspci_out = subprocess.check_output(["lspci"], text=True, stderr=subprocess.DEVNULL)
+            for line in lspci_out.splitlines():
+                if "VGA" in line or "Display" in line or "3D" in line:
+                    if "AMD" in line or "ATI" in line:
+                        match = re.search(r"\[AMD/ATI\]\s*(.+)", line)
+                        if match:
+                            name = match.group(1).strip()
+                            name = re.sub(r"\s*\(rev.*\)", "", name)
+                            info["gpu_name"] = name
+                            break
+                    elif "Intel" in line:
+                        # e.g. "Intel Corporation Meteor Lake-P [Intel Arc Graphics] (rev 08)"
+                        match = re.search(r"\[([^\]]+)\]", line)
+                        if match:
+                            info["gpu_name"] = match.group(1).strip()
+                            break
+        except Exception:
+            pass
+    # If GPU name is still generic/unknown, try to infer from CPU model (APUs)
+    if info["gpu_name"] in ("unknown", "AMD Radeon Graphics"):
+        chip = info.get("chip", "")
+        match = re.search(r"w(?:ith)?/\s*(Radeon\s+[\w\s\+]+)", chip, re.IGNORECASE)
+        if match:
+            info["gpu_name"] = match.group(1).strip()
+    return info
+
+
+def _get_device_info_windows() -> dict[str, str]:
+    """Collect hardware info on Windows via wmic."""
+    info: dict[str, str] = {
+        "chip": "unknown",
+        "cpu_total_cores": "unknown",
+        "cpu_performance_cores": "unknown",
+        "cpu_efficiency_cores": "unknown",
+        "gpu_name": "unknown",
+        "memory": "unknown",
+    }
+    try:
+        cpu_out = subprocess.check_output(
+            ["wmic", "cpu", "get", "Name,NumberOfCores", "/format:csv"],
+            text=True,
+            stderr=subprocess.DEVNULL,
+        )
+        lines = [l for l in cpu_out.splitlines() if l.strip() and not l.strip().startswith("Node")]
+        if lines:
+            parts = lines[0].split(",")
+            if len(parts) >= 3:
+                info["cpu_total_cores"] = parts[1].strip()
+                info["chip"] = parts[2].strip()
+    except Exception:
+        pass
+    # Memory
+    try:
+        mem_out = subprocess.check_output(
+            ["wmic", "ComputerSystem", "get", "TotalPhysicalMemory", "/format:csv"],
+            text=True,
+            stderr=subprocess.DEVNULL,
+        )
+        lines = [l for l in mem_out.splitlines() if l.strip() and not l.strip().startswith("Node")]
+        if lines:
+            parts = lines[0].split(",")
+            if len(parts) >= 2:
+                mem_gb = int(parts[1].strip()) / 1024**3
+                info["memory"] = f"{mem_gb:.1f} GB"
+    except Exception:
+        pass
+    # GPU via nvidia-smi (also available on Windows)
+    try:
+        gpu_out = subprocess.check_output(
+            ["nvidia-smi", "--query-gpu=name,memory.total", "--format=csv,noheader"],
+            text=True,
+            stderr=subprocess.DEVNULL,
+        ).strip()
+        if gpu_out:
+            first_line = gpu_out.splitlines()[0]
+            parts = [p.strip() for p in first_line.split(",")]
+            info["gpu_name"] = parts[0]
+            if len(parts) > 1:
+                info["gpu_memory"] = parts[1]
+    except Exception:
+        pass
+    return info
+
+
+@lru_cache(maxsize=1)
+def get_device_info_dict() -> dict[str, str]:
+    """Collect static hardware metadata (chip, cores, GPU, memory) for run records."""
+    base: dict[str, str] = {"platform": platform.platform()}
+    if _is_macos():
+        base.update(_get_device_info_macos())
+    elif _is_linux():
+        base.update(_get_device_info_linux())
+    elif _is_windows():
+        base.update(_get_device_info_windows())
+    return base
+
+
+def get_device_info_line() -> str:
+    d = get_device_info_dict()
+    if _is_macos():
+        # Build core-type summary dynamically so M5 (super+performance) is shown correctly
+        if d.get("cpu_core_type_1") and d.get("cpu_core_type_2"):
+            t1 = d["cpu_core_type_1"][0].upper()
+            t2 = d["cpu_core_type_2"][0].upper()
+            core_summary = f"{d['cpu_core_count_1']}{t1}+{d['cpu_core_count_2']}{t2}"
+        elif (
+            d.get("cpu_performance_cores") != "unknown"
+            and d.get("cpu_efficiency_cores") != "unknown"
+        ):
+            core_summary = f"{d['cpu_performance_cores']}P+{d['cpu_efficiency_cores']}E"
+        else:
+            core_summary = "unknown"
+        return (
+            f"Device: {d.get('chip', 'unknown')} | "
+            f"CPU: {d.get('cpu_total_cores', 'unknown')} cores "
+            f"({core_summary}) | "
+            f"GPU: {d.get('gpu_cores', 'unknown')} cores | "
+            f"Memory: {d.get('memory', 'unknown')}"
+        )
+    else:
+        gpu_part = d.get("gpu_name", "unknown")
+        if "gpu_memory" in d:
+            gpu_part += f" ({d['gpu_memory']})"
+        return (
+            f"CPU: {d.get('chip', 'unknown')} ({d.get('cpu_total_cores', 'unknown')} cores) | "
+            f"GPU: {gpu_part} | "
+            f"Memory: {d.get('memory', 'unknown')}"
+        )
diff --git a/src/unilab/utils/geometry.py b/src/unilab/utils/geometry.py
index 5dc933d3f..5358f4f6d 100644
--- a/src/unilab/utils/geometry.py
+++ b/src/unilab/utils/geometry.py
@@ -148,66 +148,3 @@ def np_cartesian_to_spherical(cart: np.ndarray) -> np.ndarray:
     phi = np.arctan2(cart[..., 2:3], cart[..., 0:1])
     theta = np.arcsin(np.clip(cart[..., 1:2] / length, -1.0, 1.0))
     return np.concatenate([length, phi, theta], axis=-1)
-
-
-def np_write_relative_anchor_transform_pos_rot6d(
-    source_anchor_pos_w: np.ndarray,
-    source_anchor_quat_w: np.ndarray,
-    target_anchor_pos_w: np.ndarray,
-    target_anchor_quat_w: np.ndarray,
-    out_pos: np.ndarray,
-    out_rot6d: np.ndarray,
-) -> None:
-    """Fused frame-transform + 6D rotation flatten, writing in place.
-
-    Computes the position of ``target_anchor`` in ``source_anchor``'s frame
-    (written to ``out_pos`` of shape ``(N, 3)``) and the relative rotation
-    ``conj(source_anchor) * target_anchor`` expressed as the flattened
-    first two columns of its rotation matrix (written to ``out_rot6d`` of
-    shape ``(N, 6)``). No intermediate quaternion arrays are allocated.
-
-    This helper computes the relative anchor transform used by motion tracking.
-    """
-    aw = source_anchor_quat_w[:, 0]
-    ax = source_anchor_quat_w[:, 1]
-    ay = source_anchor_quat_w[:, 2]
-    az = source_anchor_quat_w[:, 3]
-
-    vx = target_anchor_pos_w[:, 0] - source_anchor_pos_w[:, 0]
-    vy = target_anchor_pos_w[:, 1] - source_anchor_pos_w[:, 1]
-    vz = target_anchor_pos_w[:, 2] - source_anchor_pos_w[:, 2]
-
-    qx = -ax
-    qy = -ay
-    qz = -az
-    tx = 2 * (qy * vz - qz * vy)
-    ty = 2 * (qz * vx - qx * vz)
-    tz = 2 * (qx * vy - qy * vx)
-    out_pos[:, 0] = vx + aw * tx + qy * tz - qz * ty
-    out_pos[:, 1] = vy + aw * ty + qz * tx - qx * tz
-    out_pos[:, 2] = vz + aw * tz + qx * ty - qy * tx
-
-    bw = target_anchor_quat_w[:, 0]
-    bx = target_anchor_quat_w[:, 1]
-    by = target_anchor_quat_w[:, 2]
-    bz = target_anchor_quat_w[:, 3]
-    rw = aw * bw + ax * bx + ay * by + az * bz
-    rx = aw * bx - ax * bw - ay * bz + az * by
-    ry = aw * by + ax * bz - ay * bw - az * bx
-    rz = aw * bz - ax * by + ay * bx - az * bw
-
-    xx = rx * rx
-    yy = ry * ry
-    zz = rz * rz
-    xy = rx * ry
-    xz = rx * rz
-    yz = ry * rz
-    wx = rw * rx
-    wy = rw * ry
-    wz = rw * rz
-    out_rot6d[:, 0] = 1 - 2 * (yy + zz)
-    out_rot6d[:, 1] = 2 * (xy - wz)
-    out_rot6d[:, 2] = 2 * (xy + wz)
-    out_rot6d[:, 3] = 1 - 2 * (xx + zz)
-    out_rot6d[:, 4] = 2 * (xz - wy)
-    out_rot6d[:, 5] = 2 * (yz + wx)
diff --git a/src/unilab/training/monitoring.py b/src/unilab/utils/monitoring.py
similarity index 100%
rename from src/unilab/training/monitoring.py
rename to src/unilab/utils/monitoring.py
diff --git a/src/unilab/tools/viz_nan.py b/src/unilab/utils/nan_viz.py
similarity index 100%
rename from src/unilab/tools/viz_nan.py
rename to src/unilab/utils/nan_viz.py
diff --git a/src/unilab/training/reward.py b/src/unilab/utils/reward.py
similarity index 78%
rename from src/unilab/training/reward.py
rename to src/unilab/utils/reward.py
index ee68d18fc..a1d2ff656 100644
--- a/src/unilab/training/reward.py
+++ b/src/unilab/utils/reward.py
@@ -39,16 +39,24 @@ def resolve_reward_dict(cfg: DictConfig) -> RewardDict:
     return reward_dict
 
 
-def extract_reward_config(cfg: DictConfig) -> dict[str, RewardDict]:
+def extract_reward_config(
+    cfg: DictConfig,
+    *,
+    target_field: str = "reward_config",
+) -> dict[str, RewardDict]:
     """Extract and validate reward config from Hydra config.
 
     Args:
         cfg: Hydra DictConfig containing reward section
 
     Returns:
-        Dictionary with reward_config key for env_cfg_override
+        Dictionary with ``target_field`` as the env config override key.
 
     Raises:
         ValueError: If reward config is missing
     """
-    return {"reward_config": resolve_reward_dict(cfg)}
+    if target_field not in {"reward_config", "rewards"}:
+        raise ValueError(
+            f"Reward config target_field must be 'reward_config' or 'rewards', got {target_field!r}"
+        )
+    return {target_field: resolve_reward_dict(cfg)}
diff --git a/src/unilab/utils/rotation.py b/src/unilab/utils/rotation.py
index 2879a02fb..b5d90fea7 100644
--- a/src/unilab/utils/rotation.py
+++ b/src/unilab/utils/rotation.py
@@ -64,11 +64,11 @@ def np_quat_mul_batched(q1: np.ndarray, q2: np.ndarray) -> np.ndarray:
 
 
 def np_quat_conjugate(q: np.ndarray) -> np.ndarray:
-    """Conjugate of unit quaternions (N, 4) or (4,), w-first."""
+    """Conjugate of unit quaternions (..., 4), w-first."""
     if q.ndim == 1:
         return np.array([q[0], -q[1], -q[2], -q[3]])
     conj = q.copy()
-    conj[:, 1:] *= -1
+    conj[..., 1:] *= -1
     return conj  # type: ignore[no-any-return]
 
 
@@ -119,6 +119,23 @@ def np_quat_to_axis_angle(q: np.ndarray) -> np.ndarray:
     return axis_angle
 
 
+def np_quat_from_angle_axis(angle: np.ndarray, axis: np.ndarray) -> np.ndarray:
+    """Convert axis-angle pairs to unit quaternions (N, 4), w-first.
+
+    ``angle`` has shape (N,) and ``axis`` has shape (N, 3); axes are normalized
+    internally. Inverse of :func:`np_quat_to_axis_angle`.
+    """
+    angle = np.atleast_1d(np.asarray(angle))
+    axis = np.atleast_2d(np.asarray(axis))
+    if axis.shape[-1] != 3 or angle.shape[0] != axis.shape[0]:
+        raise ValueError(f"Expected angle (N,) and axis (N, 3), got {angle.shape} and {axis.shape}")
+    axis = axis / np.linalg.norm(axis, axis=-1, keepdims=True)
+    half = 0.5 * angle
+    w = np.cos(half)
+    xyz = axis * np.sin(half)[:, None]
+    return np.concatenate([w[:, None], xyz], axis=1)
+
+
 def np_quat_angular_velocity(q: np.ndarray, dt: float) -> np.ndarray:
     """Estimate angular velocity from a quaternion time sequence using shortest-arc diffs."""
     if q.ndim != 2 or q.shape[1] != 4:
@@ -258,6 +275,15 @@ def np_quat_apply_inverse(q: np.ndarray, v: np.ndarray) -> np.ndarray:
     return np_quat_apply(np_quat_inv(q), v)
 
 
+def np_quat_apply_inverse_batched(q: np.ndarray, v: np.ndarray) -> np.ndarray:
+    """Rotate broadcast-compatible vector arrays by inverse quaternions.
+
+    ``q`` has shape (..., 4), ``v`` has shape (..., 3), and leading dimensions
+    are broadcast. Batched counterpart of ``np_quat_apply_inverse``.
+    """
+    return np_quat_apply_batched(np_quat_conjugate(q), v)
+
+
 def np_quat_error_magnitude(q1: np.ndarray, q2: np.ndarray) -> np.ndarray:
     """Angular error magnitude between quaternions (N,) or scalar."""
     q1_was_1d = q1.ndim == 1
diff --git a/src/unilab/training/seed.py b/src/unilab/utils/seed.py
similarity index 100%
rename from src/unilab/training/seed.py
rename to src/unilab/utils/seed.py
diff --git a/src/unilab/training/sim2sim.py b/src/unilab/utils/sim2sim.py
similarity index 87%
rename from src/unilab/training/sim2sim.py
rename to src/unilab/utils/sim2sim.py
index 9b81224e7..1489c84d3 100644
--- a/src/unilab/training/sim2sim.py
+++ b/src/unilab/utils/sim2sim.py
@@ -36,23 +36,42 @@ class CrossBackendIncompatibleError(RuntimeError):
 DENYLIST: list[str] = [
     "algo.obs_groups",
     "env.control_config.action_scale",
+    "env.observations",
+    "env.actions",
+    "env.policy_observation_group",
+    "env.critic_observation_group",
     "algo.policy.actor_hidden_dims",
     "algo.policy.critic_hidden_dims",
     "algo.empirical_normalization",
     "algo.obs_normalization",
-    "env.sampling_mode",
+    "env.commands.motion.params.sampling_mode",
 ]
 
 SNAPSHOT_FIELDS: list[str] = DENYLIST + WARNING_LIST
 
 ENV_STRUCTURAL_DENYLIST: list[str] = [path for path in DENYLIST if path.startswith("env.")]
 
+LEGACY_PATH_ALIASES: dict[str, str] = {
+    "env.sampling_mode": "env.commands.motion.params.sampling_mode",
+}
+_CANONICAL_PATH_FALLBACKS: dict[str, str] = {
+    canonical: legacy for legacy, canonical in LEGACY_PATH_ALIASES.items()
+}
+
 
 def _select(cfg: Any, path: str) -> Any:
     """Return the effective value at a dotted path (or ``None`` if absent)."""
     return OmegaConf.select(cfg, path)
 
 
+def _select_contract(cfg: Any, path: str) -> Any:
+    value = _select(cfg, path)
+    if value is not None:
+        return value
+    legacy_path = _CANONICAL_PATH_FALLBACKS.get(path)
+    return None if legacy_path is None else _select(cfg, legacy_path)
+
+
 def _to_plain(value: Any) -> Any:
     if OmegaConf.is_config(value):
         return OmegaConf.to_container(value, resolve=True)
@@ -64,7 +83,7 @@ def extract_contract_snapshot(full_cfg: DictConfig) -> dict[str, Any]:
     cfg: Any = full_cfg if OmegaConf.is_config(full_cfg) else OmegaConf.create(full_cfg)
     snapshot: dict[str, Any] = {}
     for path in SNAPSHOT_FIELDS:
-        value = _select(cfg, path)
+        value = _select_contract(cfg, path)
         if value is None:
             continue
         snapshot[path] = _to_plain(value)
@@ -158,8 +177,13 @@ def resolve_sim2sim_config(
         return target_cfg
 
     denials: list[str] = []
-    for path, source_value in snapshot.items():
-        target_value = _select(target_cfg, path)
+    canonical_snapshot: dict[str, Any] = {}
+    for raw_path, source_value in snapshot.items():
+        path = LEGACY_PATH_ALIASES.get(raw_path, raw_path)
+        canonical_snapshot[path] = source_value
+
+    for path, source_value in canonical_snapshot.items():
+        target_value = _select_contract(target_cfg, path)
         if target_value is None:
             if path in ENV_STRUCTURAL_DENYLIST:
                 denials.append(_asymmetric_line(path, source_value, source_present=True))
@@ -173,10 +197,11 @@ def resolve_sim2sim_config(
             print(f"[sim2sim] WARNING override {line}")
 
     for path in ENV_STRUCTURAL_DENYLIST:
-        if path in snapshot:
+        if path in canonical_snapshot:
             continue
-        if _select(target_cfg, path) is not None:
-            denials.append(_asymmetric_line(path, _select(target_cfg, path), source_present=False))
+        target_value = _select_contract(target_cfg, path)
+        if target_value is not None:
+            denials.append(_asymmetric_line(path, target_value, source_present=False))
 
     if denials:
         message = (
diff --git a/src/unilab/utils/support_matrix.py b/src/unilab/utils/support_matrix.py
deleted file mode 100644
index a879fc4e6..000000000
--- a/src/unilab/utils/support_matrix.py
+++ /dev/null
@@ -1,344 +0,0 @@
-"""Generate backend support matrix content from registry, configs, and tests."""
-
-from __future__ import annotations
-
-import re
-from dataclasses import dataclass
-from enum import IntEnum
-from pathlib import Path
-
-from omegaconf import OmegaConf
-
-from unilab.base import registry
-from unilab.base.registry import ensure_registries
-
-BEGIN_MARKER = "<!-- BEGIN GENERATED SUPPORT MATRIX -->"
-END_MARKER = "<!-- END GENERATED SUPPORT MATRIX -->"
-BACKENDS: tuple[str, ...] = ("mujoco", "mjwarp", "motrix")
-
-# Maintainer-confirmed completed training validations. Keep this mapping narrow:
-# generic config/contract coverage must not promote an unvalidated entrypoint.
-_MAINTAINER_VALIDATED_MJWARP_ENTRYPOINT_TASKS = frozenset(
-    {
-        ("ppo_torch", "g1_walk_flat"),
-        ("sac_torch", "g1_walk_flat"),
-    }
-)
-
-_TASK_ORDER = {
-    "go1_joystick_flat": 0,
-    "go2_joystick_flat": 1,
-    "go2_joystick_rough": 2,
-    "g1_walk_flat": 3,
-    "g1_walk_rough": 4,
-    "g1_motion_tracking": 5,
-    "g1_flip_tracking": 6,
-    "g1_wall_flip_tracking": 7,
-    "x2_wall_flip_tracking": 8,
-    "allegro_inhand": 9,
-    "allegro_sac": 10,
-    "sharpa_inhand": 11,
-    "sharpa_inhand_grasp": 12,
-}
-_TASK_LABELS = {
-    "go1_joystick_flat": "Go1 joystick",
-    "go2_joystick_flat": "Go2 joystick",
-    "go2_joystick_rough": "Go2 joystick rough",
-    "g1_walk_flat": "G1 walk flat",
-    "g1_walk_rough": "G1 walk rough",
-    "g1_motion_tracking": "G1 motion tracking",
-    "g1_flip_tracking": "G1 flip tracking",
-    "g1_wall_flip_tracking": "G1 wall flip tracking",
-    "x2_wall_flip_tracking": "X2 wall flip tracking",
-    "allegro_inhand": "Allegro in-hand",
-    "allegro_sac": "Allegro SAC in-hand",
-    "sharpa_inhand": "Sharpa in-hand",
-    "sharpa_inhand_grasp": "Sharpa in-hand grasp",
-}
-
-
-class EvidenceLevel(IntEnum):
-    MISSING = 0
-    REGISTERED = 1
-    CONFIGURED = 2
-    TESTED = 3
-    BENCHMARKED = 4
-    RECOMMENDED = 5
-
-    @property
-    def label(self) -> str:
-        return {
-            EvidenceLevel.MISSING: "-",
-            EvidenceLevel.REGISTERED: "Registered",
-            EvidenceLevel.CONFIGURED: "Configured",
-            EvidenceLevel.TESTED: "Tested",
-            EvidenceLevel.BENCHMARKED: "Benchmarked",
-            EvidenceLevel.RECOMMENDED: "Recommended",
-        }[self]
-
-
-@dataclass(frozen=True)
-class EntrypointSpec:
-    entrypoint_id: str
-    label: str
-    config_dir: str
-    task_glob: str
-    generic_tested: bool = False
-
-
-@dataclass(frozen=True)
-class SupportCell:
-    env_name: str
-    level: EvidenceLevel
-
-
-@dataclass(frozen=True)
-class SupportRow:
-    entrypoint_label: str
-    task_slug: str
-    task_label: str
-    cells: dict[str, SupportCell]
-
-
-ENTRYPOINT_SPECS: tuple[EntrypointSpec, ...] = (
-    EntrypointSpec(
-        entrypoint_id="ppo_torch",
-        label="PPO (torch)",
-        config_dir="conf/ppo/task",
-        task_glob="*/*.yaml",
-        generic_tested=True,
-    ),
-    EntrypointSpec(
-        entrypoint_id="appo_torch",
-        label="APPO (torch)",
-        config_dir="conf/appo/task",
-        task_glob="*/*.yaml",
-        generic_tested=True,
-    ),
-    EntrypointSpec(
-        entrypoint_id="sac_torch",
-        label="SAC (torch)",
-        config_dir="conf/offpolicy/task/sac",
-        task_glob="*/*.yaml",
-        generic_tested=True,
-    ),
-    EntrypointSpec(
-        entrypoint_id="td3_torch",
-        label="TD3 (torch)",
-        config_dir="conf/offpolicy/task/td3",
-        task_glob="*/*.yaml",
-        generic_tested=True,
-    ),
-    EntrypointSpec(
-        entrypoint_id="flashsac_torch",
-        label="FlashSAC (torch)",
-        config_dir="conf/offpolicy/task/flashsac",
-        task_glob="*/*.yaml",
-        generic_tested=True,
-    ),
-)
-
-
-def repo_root(root: Path | None = None) -> Path:
-    return root or Path(__file__).resolve().parents[3]
-
-
-def _task_sort_key(task_slug: str) -> tuple[int, str]:
-    return (_TASK_ORDER.get(task_slug, 999), task_slug)
-
-
-def _task_label(task_slug: str) -> str:
-    return _TASK_LABELS.get(task_slug, task_slug.replace("_", " "))
-
-
-def _load_task_name(task_path: Path) -> str:
-    raw = OmegaConf.to_container(OmegaConf.load(task_path), resolve=True) or {}
-    if not isinstance(raw, dict):
-        raise ValueError(f"Expected mapping config in {task_path}")
-    training = raw.get("training")
-    if not isinstance(training, dict) or "task_name" not in training:
-        raise ValueError(f"Missing training.task_name in {task_path}")
-    task_name = training["task_name"]
-    if not isinstance(task_name, str):
-        raise ValueError(f"training.task_name must be a string in {task_path}")
-    return task_name
-
-
-def _load_registry_backends() -> dict[str, set[str]]:
-    ensure_registries()
-    registered = registry.list_registered_envs()
-    return {
-        env_name: set(meta["available_backends"])
-        for env_name, meta in registered.items()
-        if isinstance(meta.get("available_backends"), list)
-    }
-
-
-def _has_checked_in_benchmark_manifest(root: Path) -> bool:
-    del root
-    return False
-
-
-def _has_recommendation_metadata(root: Path) -> bool:
-    del root
-    return False
-
-
-def _configured_entries(root: Path, spec: EntrypointSpec) -> dict[str, dict[str, str]]:
-    task_root = root / spec.config_dir
-    entries: dict[str, dict[str, str]] = {}
-    for task_path in sorted(task_root.glob(spec.task_glob)):
-        task_slug = task_path.parent.name
-        backend = task_path.stem
-        if backend not in BACKENDS:
-            continue
-        entries.setdefault(task_slug, {})[backend] = _load_task_name(task_path)
-    return entries
-
-
-def _is_tested(spec: EntrypointSpec, task_slug: str, backend: str, root: Path) -> bool:
-    if backend == "mjwarp":
-        return (
-            spec.entrypoint_id,
-            task_slug,
-        ) in _MAINTAINER_VALIDATED_MJWARP_ENTRYPOINT_TASKS
-    return spec.generic_tested
-
-
-def _cell_level(
-    *,
-    backend: str,
-    env_name: str,
-    configured_backends: dict[str, str],
-    registry_backends: dict[str, set[str]],
-    tested: bool,
-    benchmarked: bool,
-    recommended: bool,
-) -> EvidenceLevel:
-    available_backends = registry_backends.get(env_name, set())
-    if backend not in available_backends:
-        return EvidenceLevel.MISSING
-
-    level = EvidenceLevel.REGISTERED
-    if backend in configured_backends:
-        level = EvidenceLevel.CONFIGURED
-    if backend in configured_backends and tested:
-        level = EvidenceLevel.TESTED
-    if backend in configured_backends and tested and benchmarked:
-        level = EvidenceLevel.BENCHMARKED
-    if backend in configured_backends and tested and benchmarked and recommended:
-        level = EvidenceLevel.RECOMMENDED
-    return level
-
-
-def build_support_rows(root: Path | None = None) -> list[SupportRow]:
-    resolved_root = repo_root(root)
-    registry_backends = _load_registry_backends()
-    benchmarked = _has_checked_in_benchmark_manifest(resolved_root)
-    recommended = _has_recommendation_metadata(resolved_root)
-    rows: list[SupportRow] = []
-
-    for spec in ENTRYPOINT_SPECS:
-        for task_slug, configured_backends in sorted(
-            _configured_entries(resolved_root, spec).items(),
-            key=lambda item: _task_sort_key(item[0]),
-        ):
-            env_name = next(iter(configured_backends.values()))
-            cells = {
-                backend: SupportCell(
-                    env_name=env_name,
-                    level=_cell_level(
-                        backend=backend,
-                        env_name=env_name,
-                        configured_backends=configured_backends,
-                        registry_backends=registry_backends,
-                        tested=_is_tested(spec, task_slug, backend, resolved_root),
-                        benchmarked=benchmarked,
-                        recommended=recommended,
-                    ),
-                )
-                for backend in BACKENDS
-            }
-            rows.append(
-                SupportRow(
-                    entrypoint_label=spec.label,
-                    task_slug=task_slug,
-                    task_label=_task_label(task_slug),
-                    cells=cells,
-                )
-            )
-
-    return rows
-
-
-def render_support_matrix(root: Path | None = None) -> str:
-    resolved_root = repo_root(root)
-    benchmark_note = (
-        "未检测到与这些组合绑定的已提交 benchmark manifest，因此当前不会自动提升到 `Benchmarked`。"
-    )
-    recommendation_note = (
-        "仓库中目前也没有单独的 recommendation 元数据，因此当前不会自动提升到 `Recommended`。"
-    )
-
-    lines = [
-        "### Evidence Grades",
-        "",
-        "| 等级 | 仓库事实来源 |",
-        "|------|--------------|",
-        "| `Registered` | `ensure_registries()` 导入后的 `registry.list_registered_envs()` 中存在该 env/backend。 |",
-        "| `Configured` | 存在对应的 owner YAML：`conf/{ppo,appo,offpolicy}/task/...`。 |",
-        "| `Tested` | `tests/` 中有自动化覆盖该 entrypoint/task owner/backend 组合，或存在显式 maintainer 完整训练验证并具备近风险自动化测试。这里的 `Tested` 不等同于默认推荐路径。 |",
-        "| `Benchmarked` | 存在与该组合绑定的已提交 benchmark manifest。 |",
-        "| `Recommended` | 仓库中存在显式 recommendation 元数据。 |",
-        "",
-        "`Tested` 只描述仓库中已有自动化覆盖或显式 maintainer 训练验证，不代表该组合具备同名 MuJoCo "
-        "owner 的全部 backend capability；例如 phase-1 Motrix owner 可能只覆盖训练 smoke 和明确启用的 DR 子集。",
-        "",
-        "`mjwarp` 只支持 `g1_walk_flat` host adapter。PPO (torch) 与 SAC (torch) owner 已完成训练验证，并有 "
-        "backend、contract 与 playback 自动化覆盖，因此标记为 `Tested`。"
-        "mjwarp playback 仅支持显式、有限步数的 `record` 并复用 MuJoCo 离线 renderer，不支持 `auto`、"
-        "interactive 或 native playback。其他 entrypoint 中出现的 `Registered` 只表示 env/backend registry "
-        "identity，不代表对应算法、terrain、完整 DR 或 production training 支持。",
-        "",
-        benchmark_note,
-        recommendation_note,
-        "",
-        "### Entrypoint x Task Owner",
-        "",
-        "| Entrypoint | Task owner | MuJoCo | mjwarp | Motrix |",
-        "|------------|------------|--------|--------|--------|",
-    ]
-
-    for row in build_support_rows(resolved_root):
-        lines.append(
-            f"| {row.entrypoint_label} | `{row.task_slug}` ({row.task_label}) | "
-            f"{row.cells['mujoco'].level.label} | {row.cells['mjwarp'].level.label} | "
-            f"{row.cells['motrix'].level.label} |"
-        )
-
-    lines.extend(
-        [
-            "",
-            "### Source Index",
-            "",
-            "- Registry bootstrap: `src/unilab/envs/**` decorators via `unilab.base.registry.ensure_registries()`.",
-            "- Owner YAML scan: `conf/ppo/task/**`, `conf/appo/task/**`, `conf/offpolicy/task/**`.",
-            "- Generic compose coverage: `tests/config/test_config_system.py::test_supported_task_composes`.",
-            "- Validated mjwarp entrypoints are explicitly recorded in `_MAINTAINER_VALIDATED_MJWARP_ENTRYPOINT_TASKS`; near-risk coverage lives in `tests/base/test_mjwarp_backend.py`, `tests/base/test_backend_conformance.py`, `tests/base/test_mjwarp_differential.py`, and `tests/base/test_mjwarp_playback.py`.",
-        ]
-    )
-    return "\n".join(lines)
-
-
-def render_generated_block(root: Path | None = None) -> str:
-    return "\n".join([BEGIN_MARKER, render_support_matrix(root), END_MARKER])
-
-
-def replace_generated_block(content: str, rendered_block: str) -> str:
-    pattern = re.compile(
-        rf"{re.escape(BEGIN_MARKER)}.*?{re.escape(END_MARKER)}",
-        flags=re.DOTALL,
-    )
-    if pattern.search(content) is None:
-        raise ValueError("Generated support matrix markers not found")
-    return pattern.sub(rendered_block, content)
diff --git a/src/unilab/utils/tensor.py b/src/unilab/utils/tensor.py
index ae853e6c5..dd3fbfaf8 100644
--- a/src/unilab/utils/tensor.py
+++ b/src/unilab/utils/tensor.py
@@ -18,7 +18,13 @@ def to_torch(x, device: str | torch.device) -> torch.Tensor:
     if isinstance(x, torch.Tensor):
         return x.to(device)
     if isinstance(x, np.ndarray):
-        return torch.from_numpy(x).to(device)
+        tensor = torch.from_numpy(x).to(device)
+        # UniLab policies and IPC buffers use float32. Keep the environment
+        # contract tolerant of physics backends (notably Drake) that publish
+        # float64 observations while preserving integer/bool tensors.
+        if tensor.is_floating_point() and tensor.dtype != torch.float32:
+            tensor = tensor.float()
+        return tensor
     if hasattr(x, "__dlpack__"):
         try:
             return torch.from_dlpack(x).to(device)  # pyright: ignore[reportPrivateImportUsage]
diff --git a/src/unilab/visualization/interactive_playback.py b/src/unilab/visualization/interactive_playback.py
index 5c319fd68..8389274aa 100644
--- a/src/unilab/visualization/interactive_playback.py
+++ b/src/unilab/visualization/interactive_playback.py
@@ -6,12 +6,13 @@
 from collections.abc import Callable, Mapping
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any, ClassVar, Protocol
+from typing import Any, ClassVar, Protocol, cast
 
 import numpy as np
 import torch
+from omegaconf import DictConfig, OmegaConf
 
-from unilab.training.sim2sim import policy_load_dim_guard, resolve_sim2sim_config
+from unilab.utils.sim2sim import policy_load_dim_guard, resolve_sim2sim_config
 
 LogFn = Callable[[str], None]
 
@@ -32,6 +33,65 @@ class RslRlPlaybackConfig:
     start_paused: bool = False
 
 
+@dataclass
+class PlayInteractiveArgs:
+    """Scalar play arguments shared by interactive playback entrypoints."""
+
+    task: str
+    load_run: str
+    checkpoint: str | None
+    action_mode: str
+    policy_obs_mode: str
+    algo_log_name: str
+    log_root: str | None
+    show_target_bodies: bool
+    show_reward_debug: bool
+    target_show_axes: bool
+    target_body_names: str
+    target_max_bodies: int
+    target_marker_radius: float
+    target_axis_length: float
+    target_marker_alpha: float
+    reward_debug_show_velocity: bool
+    reward_debug_lin_vel_scale: float
+    reward_debug_ang_vel_scale: float
+    reward_debug_show_connectors: bool
+    reward_debug_show_global_anchor: bool
+    camera_follow_body: bool
+    camera_focus_body_name: str
+    camera_height_offset: float
+    camera_distance: float | None
+    camera_elevation: float | None
+    camera_azimuth: float | None
+    use_env_visual_model: bool
+    speed: float
+    start_paused: bool
+    keyboard: bool = False
+    keyboard_step_lin: float = 0.1
+    keyboard_step_ang: float = 0.2
+    require_keyboard_command_obs: bool = True
+    algo: str = "ppo"
+    # Physics owner selected by the interactive CLI. MuJoCo remains the viewer
+    # implementation for every value, including ``drake``.
+    sim: str = "mujoco"
+
+
+def build_playback_config(args: Any, *, num_envs: int = 1) -> RslRlPlaybackConfig:
+    """Build an :class:`RslRlPlaybackConfig` from a play-args-like object."""
+    return RslRlPlaybackConfig(
+        task=str(args.task),
+        load_run=str(args.load_run),
+        checkpoint=getattr(args, "checkpoint", None),
+        action_mode=str(args.action_mode),
+        policy_obs_mode=str(args.policy_obs_mode),
+        algo_log_name=str(getattr(args, "algo_log_name", "rsl_rl_ppo")),
+        log_root=getattr(args, "log_root", None),
+        num_envs=num_envs,
+        speed=float(getattr(args, "speed", 1.0)),
+        start_paused=bool(getattr(args, "start_paused", False)),
+    )
+
+
 @dataclass
 class PlaybackControls:
     """Viewer-independent playback control state."""
@@ -152,6 +212,8 @@ def __init__(
         action_mode: str,
         policy: Callable[[Any], Any] | None,
         num_envs: int,
+        runner: Any | None = None,
+        actor: Any | None = None,
     ) -> None:
         self.env = env
         self.wrapped_env = wrapped_env
@@ -159,6 +221,8 @@ def __init__(
         self.action_mode = action_mode
         self.policy = policy
         self.num_envs = int(num_envs)
+        self.runner = runner
+        self.actor = actor
         self.obs: Any | None = None
         self.step_count = 0
 
@@ -194,6 +258,11 @@ def _build_actions(self) -> torch.Tensor:
         action_space = self.env.action_space
         action_dim = int(action_space.shape[0])
         if self.action_mode == "policy" and self.policy is not None:
+            # Backends may expose double-precision observations (Drake's
+            # native state/sensor path does). Policies are trained and stored
+            # in float32, so normalize the playback tensor at this boundary.
+            if isinstance(self.obs, torch.Tensor) and self.obs.dtype != torch.float32:
+                self.obs = self.obs.float()
             return self.policy(self.obs)
         if self.action_mode == "random":
             actions = np.random.uniform(
@@ -279,7 +348,7 @@ def _resolve_priv_info(
             return None
         if self.action_mode != "policy" or self.actor is None:
             return None
-        from unilab.base.observations import split_obs_dict
+        from uni_rl.utils.observations import split_obs_dict
 
         actor_obs_np, critic_np = split_obs_dict(obs_dict)
         priv_info = self.priv_info_resolver(
@@ -299,6 +368,8 @@ def _build_actions(self) -> np.ndarray:
         action_dim = int(action_space.shape[0])
         if self.action_mode == "policy" and self.actor is not None:
             obs_torch = torch.from_numpy(self.obs).to(self.device)
+            if obs_torch.dtype != torch.float32:
+                obs_torch = obs_torch.float()
             if self.normalizer is not None:
                 obs_torch = self.normalizer(obs_torch, update=False)
             if self.actor_algo_type == "hora_sac":
@@ -358,6 +429,55 @@ def select_torch_device() -> str:
     return "cpu"
 
 
+def infer_checkpoint_actor_input_dim(ckpt_path: str) -> int | None:
+    """Infer the actor MLP input dim from an rsl_rl checkpoint, if detectable."""
+    loaded = torch.load(ckpt_path, map_location="cpu", weights_only=True)
+    state_dict = loaded.get("actor_state_dict")
+    if not isinstance(state_dict, dict):
+        return None
+
+    # Common rsl-rl naming: "mlp.0.weight" or nested prefixes ending with ".0.weight".
+    for key in ("mlp.0.weight", "actor.mlp.0.weight"):
+        w = state_dict.get(key)
+        if isinstance(w, torch.Tensor) and w.ndim == 2:
+            return int(w.shape[1])
+
+    for key, w in state_dict.items():
+        if key.endswith(".0.weight") and isinstance(w, torch.Tensor) and w.ndim == 2:
+            return int(w.shape[1])
+    return None
+
+
+def _scene_visual_materializer() -> Any:
+    from unilab.base import config_adapter
+
+    return config_adapter.materialize_scene_visual_override
+
+
+def build_play_backend_adapter(cfg: Any, *, root_dir: str | Path, algo_name: str = "ppo") -> Any:
+    """Build the BackendAdapter used by play entrypoints to derive env cfg overrides."""
+    from unilab.base.config_adapter import BackendAdapter
+
+    return BackendAdapter(
+        cfg,
+        root_dir=root_dir,
+        algo_name=algo_name,
+        scene_materializer=_scene_visual_materializer(),
+    )
+
+
+def available_backends_for_task(task_name: str) -> tuple[str, ...]:
+    """Return the registered backends for a task, or ``()`` when unknown."""
+    from unilab.base import registry
+
+    envs = registry.list_registered_envs()
+    task_meta = envs.get(task_name, {})
+    backends = task_meta.get("available_backends", ())
+    if not isinstance(backends, list):
+        return ()
+    return tuple(str(backend) for backend in backends)
+
+
 def create_rsl_rl_playback_session(
     *,
     playback_cfg: RslRlPlaybackConfig,
@@ -373,6 +493,8 @@ def create_rsl_rl_playback_session(
     policy_obs_dims_getter: Callable[[Any], tuple[int, int]],
     train_cfg_normalizer: Callable[[dict[str, Any]], dict[str, Any]],
     sim2sim_preflight: Callable[[str | None], Any] | None = None,
+    runner_loader: Callable[[Any, str], None] | None = None,
+    guard_algo_name: str | None = None,
     log: LogFn = print,
 ) -> tuple[RslRlPlaybackSession, str, str | None]:
     """Create a playback session and load the selected policy checkpoint."""
@@ -417,6 +539,7 @@ def create_rsl_rl_playback_session(
     train_cfg["runner"]["logger"] = "none"
 
     policy = None
+    runner = None
     if playback_cfg.action_mode == "policy":
         if checkpoint_path is None:
             log("WARNING: no checkpoint found - falling back to zero actions.")
@@ -439,18 +562,21 @@ def create_rsl_rl_playback_session(
             with policy_load_dim_guard(
                 env_obs_dim=policy_obs_dim,
                 env_action_dim=policy_action_dim,
-                algo_name=playback_cfg.algo_log_name,
+                algo_name=guard_algo_name or playback_cfg.algo_log_name,
             ):
-                runner.load(
-                    checkpoint_path,
-                    load_cfg={
-                        "actor": True,
-                        "critic": False,
-                        "optimizer": False,
-                        "iteration": False,
-                        "rnd": False,
-                    },
-                )
+                if runner_loader is not None:
+                    runner_loader(runner, checkpoint_path)
+                else:
+                    runner.load(
+                        checkpoint_path,
+                        load_cfg={
+                            "actor": True,
+                            "critic": False,
+                            "optimizer": False,
+                            "iteration": False,
+                            "rnd": False,
+                        },
+                    )
             policy = runner.get_inference_policy(device=device_name)
 
     log(f"Action mode: {playback_cfg.action_mode}")
@@ -461,17 +587,22 @@ def create_rsl_rl_playback_session(
         action_mode=playback_cfg.action_mode,
         policy=policy,
         num_envs=playback_cfg.num_envs,
+        runner=runner,
     )
     return session, policy_obs_mode, checkpoint_path
 
 
-def _normalize_checkpoint_value(value: object) -> str | None:
+def normalize_checkpoint_value(value: object) -> str | None:
+    """Normalize a raw checkpoint selector value; sentinel values map to ``None``."""
     if value is None:
         return None
     text = str(value)
     return None if text in {"", "-1", "None", "null"} else text
 
 
+_normalize_checkpoint_value = normalize_checkpoint_value
+
+
 def _cfg_checkpoint_value(cfg: Any) -> str | None:
     from omegaconf import OmegaConf
 
@@ -483,8 +614,8 @@ def _resolve_appo_checkpoint_from_cfg(
     *,
     root_dir: str | Path,
 ) -> tuple[str | None, str | None]:
-    from unilab.training import (
-        get_log_root,
+    from unilab.training import get_log_root
+    from unilab.utils.checkpoint import (
         resolve_appo_checkpoint_path,
         resolve_task_checkpoint_path,
     )
@@ -526,8 +657,7 @@ def _build_appo_actor(
 
     from rsl_rl.utils import resolve_callable
     from tensordict import TensorDict
-
-    from unilab.base.observations import get_obs_dims
+    from uni_rl.utils.observations import get_obs_dims
 
     action_shape = env.action_space.shape
     if action_shape is None:
@@ -536,14 +666,15 @@ def _build_appo_actor(
     rl_cfg_dict = deepcopy(rl_cfg)
 
     if is_hora:
-        from unilab.algos.torch.hora.appo import _update_hora_obs_groups
-        from unilab.algos.torch.hora.models import build_hora_shared_actor_critic
-        from unilab.algos.torch.hora.rsl_rl_compat import (
+        from uni_rl.algos.hora.models import build_hora_shared_actor_critic
+        from uni_rl.algos.hora.rsl_rl_compat import (
             convert_config_v3_to_v4,
             is_rsl_rl_v4,
             is_rsl_rl_v5,
         )
 
+        from unilab.scripts.play_hora_appo import _update_hora_obs_groups
+
         obs_td = wrapped_env.get_observations()
         num_envs = int(getattr(wrapped_env, "num_envs", getattr(env, "num_envs", 1)))
         obs_dim = int(obs_td["actor"].shape[-1])
@@ -635,19 +766,20 @@ def create_appo_playback_session(
     if env is None:
         raise RuntimeError("Playback env factory did not return an environment.")
 
-    from unilab.algos.torch.hora.runtime import is_hora_appo_runtime
+    from uni_rl.algos.hora.runtime import is_hora_appo_runtime
 
     is_hora = is_hora_appo_runtime(rl_cfg)
     selected_wrapper_cls = wrapper_cls
     policy_obs_mode = playback_cfg.policy_obs_mode
     if is_hora:
-        from unilab.algos.torch.hora.rsl_rl import HoraRslRlVecEnvWrapper
+        from uni_rl.algos.hora.rsl_rl import HoraRslRlVecEnvWrapper
 
         selected_wrapper_cls = HoraRslRlVecEnvWrapper
         policy_obs_mode = "actor"
 
     wrapped_env = selected_wrapper_cls(env, device=device_name, policy_obs_mode=policy_obs_mode)
     policy = None
+    actor = None
     checkpoint_path: str | None = None
     if playback_cfg.action_mode == "policy":
         checkpoint_path, checkpoint_dir = _resolve_appo_checkpoint_from_cfg(cfg, root_dir=root_dir)
@@ -690,12 +822,210 @@ def create_appo_playback_session(
             action_mode=playback_cfg.action_mode,
             policy=policy,
             num_envs=playback_cfg.num_envs,
+            actor=actor,
         ),
         policy_obs_mode,
         checkpoint_path,
     )
 
 
+def default_device(torch_module, preferred: str | None = None) -> str:
+    """Resolve runtime device with optional user override."""
+    if preferred:
+        return preferred
+    if torch_module.cuda.is_available():
+        return "cuda"
+    xpu = getattr(torch_module, "xpu", None)
+    xpu_is_available = getattr(xpu, "is_available", None)
+    if callable(xpu_is_available) and xpu_is_available():
+        return "xpu"
+    if torch_module.backends.mps.is_available():
+        return "mps"
+    return "cpu"
+
+
+def extract_reset_obs(reset_result):
+    """Extract obs_dict from env.reset(...) using the current (obs_dict, info_dict) contract."""
+    if isinstance(reset_result, tuple):
+        if len(reset_result) == 2:
+            obs_out, _ = reset_result
+            return obs_out
+    raise ValueError(f"Unexpected env.reset return format: {type(reset_result)!r}")
+
+
+def resolve_play_obs_dim(obs_groups_spec: dict[str, int]) -> int:
+    obs_dim, _ = resolve_play_obs_dims(obs_groups_spec)
+    return obs_dim
+
+
+def resolve_play_obs_dims(obs_groups_spec: dict[str, int]) -> tuple[int, int]:
+    from uni_rl.utils.observations import get_obs_dims
+
+    obs_dim, critic_obs_dim = get_obs_dims(obs_groups_spec)
+    return int(obs_dim), int(critic_obs_dim)
+
+
+def extract_play_obs(obs_dict):
+    from uni_rl.utils.observations import split_obs_dict
+
+    obs_out, _ = split_obs_dict(obs_dict)
+    return obs_out
+
+
+def resolve_play_actor_spec(
+    algo_name: str,
+    cfg: DictConfig,
+    *,
+    obs_dim: int,
+    critic_obs_dim: int,
+) -> tuple[str, dict[str, Any]]:
+    """Resolve the actor implementation and model kwargs used by off-policy play."""
+    if algo_name != "sac":
+        return algo_name, {}
+
+    from uni_rl.offpolicy.runtime import resolve_custom_offpolicy_runtime
+
+    rl_cfg = cast(dict[str, Any], OmegaConf.to_container(cfg.algo, resolve=True))
+    custom_runtime = resolve_custom_offpolicy_runtime(rl_cfg)
+    if custom_runtime is None:
+        return "sac", {}
+
+    actor_algo_type = str(custom_runtime.algo_type or algo_name)
+    actor_kwargs = custom_runtime.build_model_kwargs(
+        obs_dim=int(obs_dim),
+        critic_obs_dim=int(critic_obs_dim),
+    )
+    return actor_algo_type, actor_kwargs
+
+
+def build_play_actor(
+    algo_name: str,
+    cfg: DictConfig,
+    *,
+    obs_dim: int,
+    critic_obs_dim: int,
+    action_dim: int,
+    device: str,
+) -> tuple[Any, Any | None, str, dict[str, Any]]:
+    """Build the policy actor selected by an off-policy owner config."""
+    import torch
+    from uni_rl.algos.common.actor_factory import build_actor
+
+    actor_algo_type, actor_kwargs = resolve_play_actor_spec(
+        algo_name,
+        cfg,
+        obs_dim=obs_dim,
+        critic_obs_dim=critic_obs_dim,
+    )
+    normalizer = None
+    if algo_name == "sac":
+        actor = build_actor(
+            actor_algo_type,
+            obs_dim,
+            action_dim,
+            cfg.algo.actor_hidden_dim,
+            cfg.algo.use_layer_norm,
+            device,
+            **actor_kwargs,
+        )
+    elif algo_name == "td3":
+        from uni_rl.algos.fast_td3.learner import EmpiricalNormalization, TD3Actor
+
+        actor = TD3Actor(
+            obs_dim,
+            action_dim,
+            cfg.training.play_env_num,
+            cfg.algo.algo_params.init_scale,
+            cfg.algo.actor_hidden_dim,
+            cfg.algo.algo_params.log_std_min,
+            cfg.algo.algo_params.log_std_max,
+            torch.device(device),
+        )
+        if cfg.algo.obs_normalization:
+            normalizer = EmpiricalNormalization(shape=obs_dim, device=device)
+    elif algo_name == "flashsac":
+        actor = build_actor(
+            "flashsac",
+            obs_dim,
+            action_dim,
+            cfg.algo.actor_hidden_dim,
+            cfg.algo.use_layer_norm,
+            device,
+            actor_num_blocks=cfg.algo.algo_params.actor_num_blocks,
+            actor_noise_zeta_mu=cfg.algo.algo_params.actor_noise_zeta_mu,
+            actor_noise_zeta_max=cfg.algo.algo_params.actor_noise_zeta_max,
+        )
+        if cfg.algo.obs_normalization:
+            from uni_rl.algos.common.normalization import EmpiricalNormalization
+
+            normalizer = EmpiricalNormalization(shape=obs_dim, device=device)
+    else:
+        raise ValueError(f"Unsupported algo: {algo_name}")
+
+    actor.eval()
+    return actor, normalizer, actor_algo_type, actor_kwargs
+
+
+def load_play_actor(
+    algo_name: str,
+    actor: Any,
+    normalizer: Any | None,
+    checkpoint: dict[str, Any],
+) -> None:
+    """Restore an off-policy play actor and its optional observation normalizer."""
+    if algo_name in ("sac", "flashsac"):
+        actor.load_state_dict(checkpoint["actor"])
+    elif algo_name == "td3":
+        actor_state = {
+            key: value for key, value in checkpoint["actor"].items() if key not in ("noise_scales",)
+        }
+        actor.load_state_dict(actor_state, strict=False)
+    else:
+        raise ValueError(f"Unsupported algo: {algo_name}")
+    if normalizer is not None and checkpoint.get("obs_normalizer"):
+        normalizer.load_state_dict(checkpoint["obs_normalizer"])
+        normalizer.eval()
+
+
+def build_offpolicy_env_cfg_override(
+    algo_name: str,
+    cfg: DictConfig,
+    *,
+    root_dir: str | Path,
+) -> dict[str, Any] | None:
+    """Build the task env override for off-policy training."""
+    from unilab.base.config_adapter import BackendAdapter
+    from unilab.training.common import assert_offpolicy_task_choice_matches_algo
+
+    assert_offpolicy_task_choice_matches_algo(cfg, algo_name=algo_name)
+    return cast(
+        dict[str, Any] | None,
+        BackendAdapter(cfg, root_dir=root_dir, algo_name=algo_name).build_task_env_cfg_override(),
+    )
+
+
+def build_offpolicy_play_env_cfg_override(
+    algo_name: str,
+    cfg: DictConfig,
+    *,
+    root_dir: str | Path,
+) -> dict[str, Any] | None:
+    """Build the off-policy play override, including backend render intent.
+
+    Training and playback deliberately use separate adapters: an IsaacSim
+    renderer must be selected before the worker's Kit ``INIT`` handshake, but
+    learner/collector environments must stay on the no-rendering experience.
+    """
+    from unilab.base.config_adapter import BackendAdapter
+    from unilab.training.common import assert_offpolicy_task_choice_matches_algo
+
+    assert_offpolicy_task_choice_matches_algo(cfg, algo_name=algo_name)
+    return cast(
+        dict[str, Any] | None,
+        BackendAdapter(cfg, root_dir=root_dir, algo_name=algo_name).build_play_env_cfg_override(),
+    )
+
+
 def create_sac_playback_session(
     *,
     playback_cfg: RslRlPlaybackConfig,
@@ -710,15 +1040,10 @@ def create_sac_playback_session(
 
     import os
 
-    from unilab.algos.torch.common.actor_factory import build_actor
-    from unilab.algos.torch.offpolicy.worker import resolve_offpolicy_actor_priv_info
-    from unilab.training.offpolicy import (
-        default_device,
-        extract_play_obs,
-        resolve_play_actor_spec,
-        resolve_play_obs_dims,
-    )
-    from unilab.training.run import resolve_offpolicy_checkpoint_path
+    from uni_rl.algos.common.actor_factory import build_actor
+    from uni_rl.offpolicy.worker import resolve_offpolicy_actor_priv_info
+
+    from unilab.utils.checkpoint import resolve_offpolicy_checkpoint_path
 
     device_name = default_device(torch, str(device) if device is not None else None)
     env = env_factory(int(playback_cfg.num_envs))
@@ -749,7 +1074,7 @@ def create_sac_playback_session(
     checkpoint_path: str | None = None
     normalizer = None
     if bool(getattr(cfg.algo, "obs_normalization", False)):
-        from unilab.algos.torch.common.normalization import EmpiricalNormalization
+        from uni_rl.algos.common.normalization import EmpiricalNormalization
 
         normalizer = EmpiricalNormalization(shape=obs_dim, device=device_name)
     if playback_cfg.action_mode == "policy":
@@ -788,10 +1113,7 @@ def create_sac_playback_session(
                 env_action_dim=action_dim,
                 algo_name=algo_name,
             ):
-                actor.load_state_dict(checkpoint["actor"])
-                if normalizer is not None and checkpoint.get("obs_normalizer"):
-                    normalizer.load_state_dict(checkpoint["obs_normalizer"])
-                    normalizer.eval()
+                load_play_actor(algo_name, actor, normalizer, checkpoint)
             log(f"Loading {algo_name} checkpoint: {checkpoint_path}")
 
     log(f"Action mode: {playback_cfg.action_mode}")
@@ -813,22 +1135,21 @@ def create_sac_playback_session(
 
 
 def _default_hora_distill_playback_deps(root_dir: str | Path) -> dict[str, Any]:
-    from unilab.algos.torch.hora.distill import (
+    from uni_rl.algos.hora.distill import (
         build_student_actor_and_normalizer,
         cfg_with_checkpoint_runtime,
         load_distilled_checkpoint,
         student_policy,
     )
-    from unilab.algos.torch.hora.distill_config import apply_teacher_defaults
-    from unilab.algos.torch.hora.rsl_rl import HoraRslRlVecEnvWrapper
-    from unilab.base.backend import materialize_scene_visual_override
+    from uni_rl.algos.hora.rsl_rl import HoraRslRlVecEnvWrapper
+
+    from unilab.base.config_adapter import BackendAdapter, create_env
     from unilab.training import (
-        BackendAdapter,
-        create_env,
         format_hora_stage2_checkpoint_error,
         get_log_root,
         resolve_hora_stage2_checkpoint_path,
     )
+    from unilab.training.hora_distill_config import apply_teacher_defaults
 
     return {
         "apply_teacher_defaults": apply_teacher_defaults,
@@ -836,7 +1157,7 @@ def _default_hora_distill_playback_deps(root_dir: str | Path) -> dict[str, Any]:
             cfg,
             root_dir=root_dir,
             algo_name="hora_distill",
-            scene_materializer=materialize_scene_visual_override,
+            scene_materializer=_scene_visual_materializer(),
         ).build_play_env_cfg_override(),
         "build_student_actor_and_normalizer": build_student_actor_and_normalizer,
         "cfg_with_checkpoint_runtime": cfg_with_checkpoint_runtime,
@@ -902,7 +1223,11 @@ def create_hora_distill_playback_session(
                     f"Checkpoint at {load_path} is not a HORA distillation checkpoint "
                     f"(found keys: {set(checkpoint.keys())})."
                 )
-            runtime_cfg = resolved_deps["cfg_with_checkpoint_runtime"](cfg, checkpoint)
+            # uni_rl's cfg_with_checkpoint_runtime no longer composes teacher
+            # defaults; the caller owns that composition (issue #1480).
+            runtime_cfg = resolved_deps["cfg_with_checkpoint_runtime"](
+                resolved_deps["apply_teacher_defaults"](cfg), checkpoint
+            )
     else:
         runtime_cfg = resolved_deps["apply_teacher_defaults"](cfg)
 
@@ -1022,15 +1347,21 @@ def prepare_motion_overlay_selection(
     "KeyboardCommander",
     "MotionOverlaySelection",
     "OffPolicyPlaybackSession",
+    "PlayInteractiveArgs",
     "PlaybackControls",
     "PlaybackSession",
     "RslRlPlaybackConfig",
     "RslRlPlaybackSession",
+    "available_backends_for_task",
+    "build_play_backend_adapter",
+    "build_playback_config",
     "create_appo_playback_session",
     "create_hora_distill_playback_session",
     "create_rsl_rl_playback_session",
     "create_sac_playback_session",
+    "infer_checkpoint_actor_input_dim",
     "make_sim2sim_preflight",
+    "normalize_checkpoint_value",
     "prepare_motion_overlay_selection",
     "select_torch_device",
 ]
diff --git a/src/unilab/visualization/playback.py b/src/unilab/visualization/playback.py
index d5d73a9ff..e4d27b125 100644
--- a/src/unilab/visualization/playback.py
+++ b/src/unilab/visualization/playback.py
@@ -6,11 +6,10 @@
 from typing import Any, Callable, TypeVar, cast
 
 import numpy as np
-
-from unilab.base.backend.mujoco.playback import (
+from unisim.backend.mujoco.playback import (
     materialize_visual_playback_model as _materialize_visual_playback_model,
 )
-from unilab.base.backend.mujoco.playback import (
+from unisim.backend.mujoco.playback import (
     resolve_render_play_model_files as _resolve_render_play_model_files,
 )
 
diff --git a/src/unilab/tools/render_teaser.py b/src/unilab/visualization/teaser.py
similarity index 100%
rename from src/unilab/tools/render_teaser.py
rename to src/unilab/visualization/teaser.py
diff --git a/tests/_test_registry/dummy_flat_env.py b/tests/_test_registry/dummy_flat_env.py
index c9be5a2d1..72baf5bd0 100644
--- a/tests/_test_registry/dummy_flat_env.py
+++ b/tests/_test_registry/dummy_flat_env.py
@@ -60,7 +60,7 @@ class _DummyEnv(ABEnv):
     * ``step(actions)`` returns a state with ``obs / reward / terminated /
       truncated / info`` and updates ``self.state`` in place.
     * ``obs`` is a dict with at least the ``"obs"`` key (see
-      ``unilab.base.observations.split_obs_dict``).
+      ``uni_rl.utils.observations.split_obs_dict``).
     """
 
     def __init__(self, cfg: _DummyCfg, num_envs: int = 1, backend_type: str = "mujoco"):
diff --git a/tests/algos/test_appo_learner.py b/tests/algos/test_appo_learner.py
deleted file mode 100644
index 1e481aa08..000000000
--- a/tests/algos/test_appo_learner.py
+++ /dev/null
@@ -1,54 +0,0 @@
-from __future__ import annotations
-
-from typing import Any
-
-import torch
-
-from unilab.algos.torch.appo.learner import APPOLearner
-from unilab.algos.torch.hora.appo_learner import HoraAPPOLearner
-
-
-def test_appo_learner_compile_targets_minibatch_loss(monkeypatch) -> None:
-    calls: list[tuple[str, dict[str, Any]]] = []
-
-    def fake_compile(fn, **kwargs):
-        calls.append((getattr(fn, "__qualname__", type(fn).__name__), kwargs))
-        return fn
-
-    learner = object.__new__(APPOLearner)
-    learner._device_type = "cuda"
-    learner._minibatch_loss_fn = learner._minibatch_loss_tensors
-    monkeypatch.setattr(torch, "compile", fake_compile)
-
-    learner._compile_training_methods()
-
-    assert calls == [
-        (
-            "APPOLearner._minibatch_loss_tensors",
-            {"mode": "reduce-overhead", "fullgraph": False},
-        )
-    ]
-    assert learner._minibatch_loss_fn == learner._minibatch_loss_tensors
-
-
-def test_hora_appo_learner_compile_uses_same_minibatch_loss_hook(monkeypatch) -> None:
-    calls: list[tuple[str, dict[str, Any]]] = []
-
-    def fake_compile(fn, **kwargs):
-        calls.append((getattr(fn, "__qualname__", type(fn).__name__), kwargs))
-        return fn
-
-    learner = object.__new__(HoraAPPOLearner)
-    learner._device_type = "cuda"
-    learner._minibatch_loss_fn = learner._minibatch_loss_tensors
-    monkeypatch.setattr(torch, "compile", fake_compile)
-
-    learner._compile_training_methods()
-
-    assert calls == [
-        (
-            "APPOLearner._minibatch_loss_tensors",
-            {"mode": "reduce-overhead", "fullgraph": False},
-        )
-    ]
-    assert learner._minibatch_loss_fn == learner._minibatch_loss_tensors
diff --git a/tests/algos/test_appo_learner_metrics.py b/tests/algos/test_appo_learner_metrics.py
deleted file mode 100644
index c1a37a4e7..000000000
--- a/tests/algos/test_appo_learner_metrics.py
+++ /dev/null
@@ -1,97 +0,0 @@
-from __future__ import annotations
-
-import pytest
-import torch
-import torch.nn as nn
-
-from unilab.algos.torch.appo.learner import APPOLearner
-
-
-class _Actor(nn.Module):
-    def __init__(self, obs_dim: int, action_dim: int) -> None:
-        super().__init__()
-        self.linear = nn.Linear(obs_dim, action_dim)
-        self.log_std = nn.Parameter(torch.zeros(action_dim))
-        self.output_mean = torch.zeros(1, action_dim)
-        self.output_std = torch.ones(1, action_dim)
-        self.output_entropy = torch.tensor(0.0)
-
-    def forward(self, obs, stochastic_output: bool = False):
-        del stochastic_output
-        mean = self.linear(obs["policy"])
-        std = self.log_std.exp().expand_as(mean)
-        dist = torch.distributions.Normal(mean, std)
-        self.output_mean = mean
-        self.output_std = std
-        self.output_entropy = dist.entropy().sum(dim=-1)
-        return mean
-
-    def get_output_log_prob(self, actions):
-        dist = torch.distributions.Normal(self.output_mean, self.output_std)
-        return dist.log_prob(actions).sum(dim=-1)
-
-
-class _NormalizingActor(_Actor):
-    def __init__(self, obs_dim: int, action_dim: int) -> None:
-        super().__init__(obs_dim, action_dim)
-        self.register_buffer("offset", torch.zeros(obs_dim))
-
-    def update_normalization(self, obs) -> None:
-        self.offset.copy_(obs["policy"].mean(dim=0))
-
-    def forward(self, obs, stochastic_output: bool = False):
-        del stochastic_output
-        mean = self.linear(obs["policy"] - self.offset)
-        std = self.log_std.exp().expand_as(mean)
-        dist = torch.distributions.Normal(mean, std)
-        self.output_mean = mean
-        self.output_std = std
-        self.output_entropy = dist.entropy().sum(dim=-1)
-        return mean
-
-
-class _Critic(nn.Module):
-    def __init__(self, obs_dim: int) -> None:
-        super().__init__()
-        self.linear = nn.Linear(obs_dim, 1)
-
-    def forward(self, obs):
-        return self.linear(obs["policy"])
-
-
-def test_appo_process_batch_syncs_target_actor_normalization_buffers():
-    torch.manual_seed(13)
-    learner = APPOLearner(
-        actor=_NormalizingActor(obs_dim=3, action_dim=2),
-        critic=_Critic(obs_dim=3),
-        num_learning_epochs=1,
-        num_mini_batches=2,
-        device="cpu",
-    )
-    batch = {
-        "observations": torch.randn(4, 3, 3),
-        "actions": torch.randn(4, 3, 2),
-        "actions_log_prob": torch.randn(4, 3),
-        "rewards": torch.randn(4, 3),
-        "dones": torch.zeros(4, 3),
-        "last_obs": torch.randn(3, 3),
-    }
-
-    learner.process_batch(batch)
-
-    obs_flat = batch["observations"].flatten(0, 1)
-    obs_td = {"policy": obs_flat}
-    with torch.inference_mode():
-        learner.actor(obs_td, stochastic_output=True)
-        mu = learner.actor.output_mean
-        sigma = learner.actor.output_std
-        old_mu = batch["_old_mu"]
-        old_sigma = batch["_old_sigma"]
-        kl = torch.sum(
-            torch.log(sigma / old_sigma + 1e-5)
-            + (old_sigma.pow(2) + (old_mu - mu).pow(2)) / (2.0 * sigma.pow(2))
-            - 0.5,
-            dim=-1,
-        )
-
-    assert float(kl.mean().item()) == pytest.approx(0.0, abs=5e-5)
diff --git a/tests/algos/test_appo_runner.py b/tests/algos/test_appo_runner.py
index ca35227d0..e650d188a 100644
--- a/tests/algos/test_appo_runner.py
+++ b/tests/algos/test_appo_runner.py
@@ -7,12 +7,18 @@
 from __future__ import annotations
 
 import tempfile
+from pathlib import Path
 
 import pytest
+from hydra import compose, initialize_config_dir
+from hydra.core.global_hydra import GlobalHydra
 
 pytest.importorskip("mujoco")
 
-from unilab.algos.torch.appo.runner import APPORunner
+from uni_rl.algos.appo.runner import APPORunner
+
+from unilab.base.config_adapter import BackendAdapter
+from unilab.base.env_factory import registry_env_factory
 from unilab.structured_configs import APPOConfig
 
 
@@ -24,6 +30,7 @@ def test_appo_runner_init_no_crash(mock_env_name):
 
     runner = APPORunner(
         env_name=mock_env_name,
+        env_factory=registry_env_factory(mock_env_name, "mujoco"),
         env_cfg_overrides={},
         rl_cfg=cfg,
         num_envs=4,
@@ -34,7 +41,7 @@ def test_appo_runner_init_no_crash(mock_env_name):
 
 @pytest.mark.slow
 @pytest.mark.parametrize("env_name", ["Go2JoystickFlat"])
-def test_appo_runner_learn_two_iterations(env_name, default_go2_reward_config):
+def test_appo_runner_learn_two_iterations(env_name):
     """APPO learn test must use a real env — DummyFlatTest is not registered in
     the collector subprocess (mp.spawn) so registry.make() would fail there."""
     cfg = APPOConfig().to_dict()
@@ -46,9 +53,18 @@ def test_appo_runner_learn_two_iterations(env_name, default_go2_reward_config):
     cfg["algorithm"]["num_learning_epochs"] = 1
     cfg["algorithm"]["num_mini_batches"] = 2
 
+    root_dir = Path(__file__).parents[2]
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(
+        config_dir=str(root_dir / "src" / "unilab" / "conf" / "appo"), version_base="1.3"
+    ):
+        hydra_cfg = compose("config", overrides=["task=go2_joystick_flat/mujoco"])
+    env_cfg_overrides = BackendAdapter(hydra_cfg, root_dir=root_dir).build_task_env_cfg_override()
+
     runner = APPORunner(
         env_name=env_name,
-        env_cfg_overrides={"reward_config": default_go2_reward_config},
+        env_factory=registry_env_factory(env_name, "mujoco"),
+        env_cfg_overrides=env_cfg_overrides,
         rl_cfg=cfg,
         num_envs=128,
         steps_per_env=8,
@@ -68,6 +84,7 @@ def test_appo_runner_close_is_idempotent(mock_env_name):
 
     runner = APPORunner(
         env_name=mock_env_name,
+        env_factory=registry_env_factory(mock_env_name, "mujoco"),
         env_cfg_overrides={},
         rl_cfg=cfg,
         num_envs=4,
diff --git a/tests/algos/test_appo_runner_unit.py b/tests/algos/test_appo_runner_unit.py
deleted file mode 100644
index b6df4c55c..000000000
--- a/tests/algos/test_appo_runner_unit.py
+++ /dev/null
@@ -1,493 +0,0 @@
-from __future__ import annotations
-
-import queue
-
-import numpy as np
-import pytest
-import torch
-
-import unilab.algos.torch.appo.runner as appo_runner_module
-from unilab.algos.torch.appo.runner import APPORunner
-
-
-@pytest.fixture(autouse=True)
-def _reset_fakes() -> None:
-    _FakeLearner.last_instance = None
-    _FakeRolloutRingBuffer.last_instance = None
-    _FakeRolloutRingBuffer.available_rollouts = 1
-    _FakeLogger.last_instance = None
-
-
-class _FakeModule:
-    def __init__(self) -> None:
-        self.loaded_state: dict[str, torch.Tensor] | None = None
-
-    def state_dict(self) -> dict[str, torch.Tensor]:
-        return {"weight": torch.zeros(1)}
-
-    def load_state_dict(self, state_dict: dict[str, torch.Tensor]) -> None:
-        self.loaded_state = state_dict
-
-
-class _FakeOptimizer:
-    def __init__(self) -> None:
-        self.loaded_state: dict | None = None
-        self.param_groups = [{"lr": 0.001}]
-
-    def load_state_dict(self, state_dict: dict) -> None:
-        self.loaded_state = state_dict
-        self.param_groups = state_dict.get("param_groups", self.param_groups)
-
-
-class _FakeLearner:
-    last_instance: "_FakeLearner | None" = None
-
-    def __init__(self) -> None:
-        self.actor = _FakeModule()
-        self.critic = _FakeModule()
-        self.optimizer = _FakeOptimizer()
-        self.learning_rate = 0.001
-        self.num_learning_epochs = 1
-        self.last_batch: dict[str, torch.Tensor] | None = None
-        self.target_update_calls = 0
-        _FakeLearner.last_instance = self
-
-    def get_state_dict(self) -> dict[str, int]:
-        return {"iteration": 0}
-
-    def process_batch(self, batch: dict[str, torch.Tensor]) -> None:
-        self.last_batch = batch
-
-    def update(self, batch: dict[str, torch.Tensor]) -> dict[str, float]:
-        del batch
-        return {"loss": 0.5}
-
-    def update_target_network(self) -> None:
-        self.target_update_calls += 1
-
-
-class _FakeRolloutRingBuffer:
-    last_instance: "_FakeRolloutRingBuffer | None" = None
-    available_rollouts: int = 1
-
-    def __init__(
-        self,
-        *,
-        num_envs: int,
-        num_steps: int,
-        obs_dim: int,
-        action_dim: int,
-        critic_dim: int,
-        num_slots: int,
-        create: bool,
-    ) -> None:
-        del num_envs, num_steps, obs_dim, action_dim, critic_dim, num_slots, create
-        self.name = "fake-storage"
-        self._write_ptr = object()
-        self._read_ptr = object()
-        self.wait_calls = 0
-        self.advance_calls = 0
-        _FakeRolloutRingBuffer.last_instance = self
-
-    @property
-    def slot_shapes(self) -> dict[str, tuple[int, ...]]:
-        return {
-            "obs": (2, 4, 4),
-            "critic": (2, 4, 7),
-            "actions": (2, 4, 2),
-            "log_probs": (2, 4),
-            "rewards": (2, 4),
-            "dones": (2, 4),
-            "truncated": (2, 4),
-            "last_obs": (2, 4),
-            "last_critic": (2, 7),
-        }
-
-    def wait_for_data(self, timeout: float = 60.0) -> bool:
-        del timeout
-        self.wait_calls += 1
-        return True
-
-    def available(self) -> int:
-        return max(self.available_rollouts - self.advance_calls, 0)
-
-    def read_torch(self, device: str) -> dict[str, torch.Tensor]:
-        return {
-            "obs": torch.zeros(2, 4, 4, device=device),
-            "critic": torch.zeros(2, 4, 7, device=device),
-            "actions": torch.zeros(2, 4, 2, device=device),
-            "log_probs": torch.zeros(2, 4, device=device),
-            "rewards": torch.zeros(2, 4, device=device),
-            "dones": torch.zeros(2, 4, device=device),
-            "truncated": torch.zeros(2, 4, device=device),
-            "last_obs": torch.zeros(2, 4, device=device),
-            "last_critic": torch.zeros(2, 7, device=device),
-        }
-
-    def read_numpy_views(self) -> dict[str, np.ndarray]:
-        value = float(self.advance_calls + 1)
-        return {
-            field: np.full(shape, value, dtype=np.float32)
-            for field, shape in self.slot_shapes.items()
-        }
-
-    def advance_read(self) -> None:
-        self.advance_calls += 1
-
-    def cleanup(self) -> None:
-        pass
-
-
-class _FakeWeightSync:
-    def __init__(self) -> None:
-        self.name = "fake-weight-sync"
-
-    @classmethod
-    def from_state_dict(
-        cls, state_dict: dict[str, torch.Tensor], create: bool = True
-    ) -> "_FakeWeightSync":
-        del state_dict, create
-        return cls()
-
-    def cleanup(self) -> None:
-        pass
-
-    def write_weights(self, state_dict: dict[str, torch.Tensor]) -> None:
-        del state_dict
-
-
-class _FakeLogger:
-    last_instance: "_FakeLogger | None" = None
-
-    def __init__(self, **kwargs) -> None:
-        self.init_kwargs = dict(kwargs)
-        self._total_steps = 0
-        self._mean_ep_length = 0.0
-        self._collector_active_steps_per_sec = None
-        self.step_calls: list[dict] = []
-        _FakeLogger.last_instance = self
-
-    def set_collection_sync(self, enabled: bool, env_steps_per_sync: int) -> None:
-        del enabled, env_steps_per_sync
-
-    def start(self, *, status: str = "") -> None:
-        del status
-
-    def log_status(self, status: str) -> None:
-        del status
-
-    def log_save(self, ckpt_path: str) -> None:
-        del ckpt_path
-
-    def finish(self) -> None:
-        pass
-
-    def update_replay_queue(self, current_len: int, max_size: int) -> None:
-        del current_len, max_size
-
-    def update_staging_pool(self, current_len: int, max_size: int) -> None:
-        del current_len, max_size
-
-    def log_collector(self, total_steps: int, buffer_size: int, mean_reward: float = 0.0) -> None:
-        del buffer_size, mean_reward
-        self._total_steps = total_steps
-
-    def update_ep_length(self, mean_ep_length: float) -> None:
-        self._mean_ep_length = mean_ep_length
-
-    def update_collector_timing(self, timing_ms: dict[str, float]) -> None:
-        del timing_ms
-
-    def update_collector_active_steps_per_sec(self, steps_per_sec: float) -> None:
-        self._collector_active_steps_per_sec = steps_per_sec
-
-    def update_done_rates(self, timeout_rate: float, terminated_rate: float) -> None:
-        del timeout_rate, terminated_rate
-
-    def log_step(self, **kwargs) -> None:
-        self.step_calls.append(kwargs)
-
-
-class _FakeClock:
-    def __init__(self, values: list[float]) -> None:
-        self._values = iter(values)
-
-    def time(self) -> float:
-        return next(self._values)
-
-
-def test_appo_runner_uses_explicit_runtime_context(
-    monkeypatch: pytest.MonkeyPatch, tmp_path
-) -> None:
-    captured_detect: dict[str, object] = {}
-    captured_collector: dict[str, object] = {}
-
-    def fake_detect_dims(self: APPORunner) -> tuple[int, int]:
-        captured_detect["sim_backend"] = self.sim_backend
-        self.critic_dim = 7
-        self.critic_input_dim = 5
-        return (4, 2)
-
-    def capture_start_collector(*, target_fn, kwargs):
-        del target_fn
-        captured_collector.update(kwargs)
-
-    monkeypatch.setattr(APPORunner, "_detect_dims", fake_detect_dims)
-    monkeypatch.setattr(APPORunner, "_build_learner", lambda self: _FakeLearner())
-    monkeypatch.setattr(appo_runner_module, "RolloutRingBuffer", _FakeRolloutRingBuffer)
-    monkeypatch.setattr(appo_runner_module, "SharedWeightSync", _FakeWeightSync)
-    monkeypatch.setattr(appo_runner_module, "OffPolicyLogger", _FakeLogger)
-    monkeypatch.setattr(appo_runner_module.torch, "save", lambda *args, **kwargs: None)
-
-    runner = APPORunner(
-        env_name="DummyEnv",
-        env_cfg_overrides={"reward_config": {"scales": {"alive": 1.0}}},
-        rl_cfg={"actor": {}, "critic": {}, "algorithm": {}},
-        device="cpu",
-        collector_device="cpu",
-        sim_backend="motrix",
-        num_envs=2,
-        steps_per_env=4,
-    )
-    monkeypatch.setattr(runner, "_start_collector", capture_start_collector)
-
-    runner.learn(max_iterations=0, save_interval=0, log_dir=str(tmp_path))
-
-    assert captured_detect["sim_backend"] == "motrix"
-    assert captured_collector["sim_backend"] == "motrix"
-    assert captured_collector["env_cfg_override"] == {"reward_config": {"scales": {"alive": 1.0}}}
-
-
-def test_appo_runner_restores_resume_checkpoint(monkeypatch: pytest.MonkeyPatch, tmp_path) -> None:
-    def fake_detect_dims(self: APPORunner) -> tuple[int, int]:
-        self.critic_dim = 7
-        self.critic_input_dim = 5
-        return (4, 2)
-
-    checkpoint = {
-        "actor": {"weight": torch.ones(1)},
-        "critic": {"weight": torch.full((1,), 2.0)},
-        "optimizer": {"param_groups": [{"lr": 0.004}]},
-    }
-
-    monkeypatch.setattr(APPORunner, "_detect_dims", fake_detect_dims)
-    monkeypatch.setattr(APPORunner, "_build_learner", lambda self: _FakeLearner())
-    monkeypatch.setattr(appo_runner_module, "RolloutRingBuffer", _FakeRolloutRingBuffer)
-    monkeypatch.setattr(appo_runner_module, "SharedWeightSync", _FakeWeightSync)
-    monkeypatch.setattr(appo_runner_module, "OffPolicyLogger", _FakeLogger)
-    monkeypatch.setattr(appo_runner_module.torch, "load", lambda *args, **kwargs: checkpoint)
-    monkeypatch.setattr(appo_runner_module.torch, "save", lambda *args, **kwargs: None)
-
-    runner = APPORunner(
-        env_name="DummyEnv",
-        env_cfg_overrides={},
-        rl_cfg={"actor": {}, "critic": {}, "algorithm": {}},
-        device="cpu",
-        collector_device="cpu",
-        sim_backend="mujoco",
-        num_envs=2,
-        steps_per_env=4,
-        resume_path=str(tmp_path / "model_7.pt"),
-    )
-    monkeypatch.setattr(runner, "_start_collector", lambda *args, **kwargs: None)
-
-    runner.learn(max_iterations=0, save_interval=0, log_dir=str(tmp_path))
-
-    learner = _FakeLearner.last_instance
-    assert learner is not None
-    assert learner.actor.loaded_state == checkpoint["actor"]
-    assert learner.critic.loaded_state == checkpoint["critic"]
-    assert learner.optimizer.loaded_state == checkpoint["optimizer"]
-    assert learner.learning_rate == pytest.approx(0.004)
-    assert learner.target_update_calls == 1
-
-
-def test_appo_runner_logs_learner_timing_for_fps_inputs(
-    monkeypatch: pytest.MonkeyPatch, tmp_path
-) -> None:
-    def fake_detect_dims(self: APPORunner) -> tuple[int, int]:
-        self.critic_dim = 7
-        self.critic_input_dim = 5
-        return (4, 2)
-
-    monkeypatch.setattr(APPORunner, "_detect_dims", fake_detect_dims)
-    monkeypatch.setattr(APPORunner, "_build_learner", lambda self: _FakeLearner())
-    monkeypatch.setattr(APPORunner, "_check_collector_alive", lambda self: True)
-    monkeypatch.setattr(appo_runner_module, "RolloutRingBuffer", _FakeRolloutRingBuffer)
-    monkeypatch.setattr(appo_runner_module, "SharedWeightSync", _FakeWeightSync)
-    monkeypatch.setattr(appo_runner_module, "OffPolicyLogger", _FakeLogger)
-    monkeypatch.setattr(appo_runner_module.mp, "get_context", lambda method: queue)
-    monkeypatch.setattr(appo_runner_module.torch, "save", lambda *args, **kwargs: None)
-
-    wall_clock = _FakeClock([100.0, 121.0])
-    phase_clock = _FakeClock(
-        [
-            0.0,  # iteration start
-            0.0,  # collector wait start
-            10.0,  # collector wait end
-            10.0,  # replay stage start
-            10.1,  # replay stage end
-            10.1,  # replay sample start
-            10.2,  # replay sample end
-            10.2,  # train start
-            10.7,  # train end
-            10.7,  # weight publish start
-            10.8,  # weight publish end
-            10.9,  # iteration end
-        ]
-    )
-    monkeypatch.setattr(appo_runner_module.time, "time", wall_clock.time)
-    monkeypatch.setattr(appo_runner_module.time, "perf_counter", phase_clock.time)
-
-    runner = APPORunner(
-        env_name="DummyEnv",
-        env_cfg_overrides={},
-        rl_cfg={"actor": {}, "critic": {}, "algorithm": {}},
-        device="cpu",
-        collector_device="cpu",
-        sim_backend="mujoco",
-        num_envs=2,
-        steps_per_env=4,
-    )
-    monkeypatch.setattr(runner, "_start_collector", lambda *args, **kwargs: None)
-
-    runner.learn(max_iterations=1, save_interval=0, log_dir=str(tmp_path))
-
-    logger = _FakeLogger.last_instance
-    storage = _FakeRolloutRingBuffer.last_instance
-    assert logger is not None
-    assert storage is not None
-    assert storage.wait_calls == 1
-    assert storage.advance_calls == 1
-    assert logger.step_calls
-    assert logger.init_kwargs["timing_profile"] == "appo"
-
-    step = logger.step_calls[0]
-    assert "collect_time" not in step
-    assert "wait_time" not in step
-    assert step["collector_wait_time"] == pytest.approx(10.0)
-    assert step["train_time"] == pytest.approx(0.5)
-    assert step["learner_replay_stage_time"] >= 0.0
-    assert step["weight_sync_time"] >= 0.0
-    assert step["extra_info"]["throughput_steps"] == 8
-    assert "collector_active_steps_per_sec" in step["extra_info"]
-    assert step["metrics"]["rollouts_read"] == 1.0
-    assert step["metrics"]["staging_pool_len"] == 1.0
-
-
-def test_appo_runner_stages_multiple_rollouts_without_runner_cat(
-    monkeypatch: pytest.MonkeyPatch, tmp_path
-) -> None:
-    def fake_detect_dims(self: APPORunner) -> tuple[int, int]:
-        self.critic_dim = 7
-        self.critic_input_dim = 5
-        return (4, 2)
-
-    def fail_cat(*args, **kwargs):
-        del args, kwargs
-        raise AssertionError("runner must not rebuild APPO batches with torch.cat")
-
-    _FakeRolloutRingBuffer.available_rollouts = 2
-    monkeypatch.setattr(APPORunner, "_detect_dims", fake_detect_dims)
-    monkeypatch.setattr(APPORunner, "_build_learner", lambda self: _FakeLearner())
-    monkeypatch.setattr(APPORunner, "_check_collector_alive", lambda self: True)
-    monkeypatch.setattr(appo_runner_module, "RolloutRingBuffer", _FakeRolloutRingBuffer)
-    monkeypatch.setattr(appo_runner_module, "SharedWeightSync", _FakeWeightSync)
-    monkeypatch.setattr(appo_runner_module, "OffPolicyLogger", _FakeLogger)
-    monkeypatch.setattr(appo_runner_module.mp, "get_context", lambda method: queue)
-    monkeypatch.setattr(appo_runner_module.torch, "save", lambda *args, **kwargs: None)
-    monkeypatch.setattr(appo_runner_module.torch, "cat", fail_cat)
-
-    fake_clock = _FakeClock([100.0, 100.0, 110.0, 120.0, 120.5, 121.0])
-    monkeypatch.setattr(appo_runner_module.time, "time", fake_clock.time)
-
-    runner = APPORunner(
-        env_name="DummyEnv",
-        env_cfg_overrides={},
-        rl_cfg={"actor": {}, "critic": {}, "algorithm": {}},
-        device="cpu",
-        collector_device="cpu",
-        sim_backend="mujoco",
-        num_envs=2,
-        steps_per_env=4,
-    )
-    monkeypatch.setattr(runner, "_start_collector", lambda *args, **kwargs: None)
-
-    runner.learn(max_iterations=1, save_interval=0, log_dir=str(tmp_path))
-
-    storage = _FakeRolloutRingBuffer.last_instance
-    learner = _FakeLearner.last_instance
-    logger = _FakeLogger.last_instance
-    assert storage is not None
-    assert learner is not None
-    assert learner.last_batch is not None
-    assert logger is not None
-    assert storage.advance_calls == 2
-
-    batch = learner.last_batch
-    assert batch["observations"].shape == (4, 4, 4)
-    assert batch["actions"].shape == (4, 4, 2)
-    assert batch["actions_log_prob"].shape == (4, 4)
-    assert batch["last_obs"].shape == (4, 4)
-    assert torch.equal(torch.unique(batch["observations"]), torch.tensor([1.0, 2.0]))
-    assert logger.step_calls[0]["metrics"]["staging_pool_len"] == 2.0
-    assert logger.step_calls[0]["metrics"]["rollouts_read"] == 2.0
-
-
-def test_appo_runner_fails_fast_when_collector_dies_during_wait(
-    monkeypatch: pytest.MonkeyPatch, tmp_path
-) -> None:
-    """Issue #594 B-2: collector dying mid-wait should raise within seconds, not 60s."""
-    import time as _time
-
-    def fake_detect_dims(self: APPORunner) -> tuple[int, int]:
-        self.critic_dim = 7
-        self.critic_input_dim = 5
-        return (4, 2)
-
-    # First call returns True (one liveness slice passes), then False (death detected).
-    call_count = {"n": 0}
-
-    def fake_alive(self: APPORunner) -> bool:
-        del self
-        call_count["n"] += 1
-        return call_count["n"] <= 1
-
-    # wait_for_data must always time out so the chunked loop reaches the
-    # liveness check.
-    def never_ready(self: _FakeRolloutRingBuffer, timeout: float = 60.0) -> bool:
-        del timeout
-        self.wait_calls += 1
-        return False
-
-    monkeypatch.setattr(APPORunner, "_detect_dims", fake_detect_dims)
-    monkeypatch.setattr(APPORunner, "_build_learner", lambda self: _FakeLearner())
-    monkeypatch.setattr(APPORunner, "_check_collector_alive", fake_alive)
-    monkeypatch.setattr(_FakeRolloutRingBuffer, "wait_for_data", never_ready)
-    monkeypatch.setattr(appo_runner_module, "RolloutRingBuffer", _FakeRolloutRingBuffer)
-    monkeypatch.setattr(appo_runner_module, "SharedWeightSync", _FakeWeightSync)
-    monkeypatch.setattr(appo_runner_module, "OffPolicyLogger", _FakeLogger)
-    monkeypatch.setattr(appo_runner_module.mp, "get_context", lambda method: queue)
-    monkeypatch.setattr(appo_runner_module.torch, "save", lambda *args, **kwargs: None)
-
-    runner = APPORunner(
-        env_name="DummyEnv",
-        env_cfg_overrides={},
-        rl_cfg={"actor": {}, "critic": {}, "algorithm": {}},
-        device="cpu",
-        collector_device="cpu",
-        sim_backend="mujoco",
-        num_envs=2,
-        steps_per_env=4,
-    )
-    monkeypatch.setattr(runner, "_start_collector", lambda *args, **kwargs: None)
-
-    start = _time.monotonic()
-    with pytest.raises(RuntimeError, match="collector process died"):
-        runner.learn(max_iterations=1, save_interval=0, log_dir=str(tmp_path))
-    elapsed = _time.monotonic() - start
-
-    # Chunked loop with 0.5s slices should detect death well under 5s, far
-    # below the 60s total wait budget.
-    assert elapsed < 5.0, f"chunked wait took {elapsed:.2f}s — should fail fast"
-    assert call_count["n"] >= 2, "liveness check should be called at least twice"
diff --git a/tests/algos/test_appo_staging.py b/tests/algos/test_appo_staging.py
deleted file mode 100644
index ab0f34c8f..000000000
--- a/tests/algos/test_appo_staging.py
+++ /dev/null
@@ -1,107 +0,0 @@
-from __future__ import annotations
-
-import numpy as np
-import pytest
-import torch
-
-from unilab.algos.torch.appo.staging import RolloutStagingPool
-
-_NUM_ENVS = 2
-_NUM_STEPS = 3
-_OBS_DIM = 4
-_ACTION_DIM = 2
-_CRITIC_DIM = 5
-
-_SLOT_SHAPES = {
-    "obs": (_NUM_ENVS, _NUM_STEPS, _OBS_DIM),
-    "critic": (_NUM_ENVS, _NUM_STEPS, _CRITIC_DIM),
-    "actions": (_NUM_ENVS, _NUM_STEPS, _ACTION_DIM),
-    "log_probs": (_NUM_ENVS, _NUM_STEPS),
-    "rewards": (_NUM_ENVS, _NUM_STEPS),
-    "dones": (_NUM_ENVS, _NUM_STEPS),
-    "truncated": (_NUM_ENVS, _NUM_STEPS),
-    "last_obs": (_NUM_ENVS, _OBS_DIM),
-    "last_critic": (_NUM_ENVS, _CRITIC_DIM),
-}
-
-
-def _raw_rollout(value: float) -> dict[str, np.ndarray]:
-    return {field: np.full(shape, value, dtype=np.float32) for field, shape in _SLOT_SHAPES.items()}
-
-
-def test_staging_pool_exposes_learner_ready_combined_batch() -> None:
-    pool = RolloutStagingPool(
-        capacity=2,
-        num_envs=_NUM_ENVS,
-        slot_shapes=_SLOT_SHAPES,
-        device="cpu",
-    )
-    first = _raw_rollout(1.0)
-    second = _raw_rollout(2.0)
-
-    pool.stage_numpy_views(first)
-    pool.stage_numpy_views(second)
-
-    batch = pool.batch()
-    expected_obs = torch.cat(
-        [
-            torch.from_numpy(first["obs"]).transpose(0, 1),
-            torch.from_numpy(second["obs"]).transpose(0, 1),
-        ],
-        dim=1,
-    )
-    expected_last_obs = torch.cat(
-        [torch.from_numpy(first["last_obs"]), torch.from_numpy(second["last_obs"])],
-        dim=0,
-    )
-
-    assert batch["observations"].shape == (_NUM_STEPS, 2 * _NUM_ENVS, _OBS_DIM)
-    assert batch["critic"].shape == (_NUM_STEPS, 2 * _NUM_ENVS, _CRITIC_DIM)
-    assert batch["actions_log_prob"].shape == (_NUM_STEPS, 2 * _NUM_ENVS)
-    assert batch["last_obs"].shape == (2 * _NUM_ENVS, _OBS_DIM)
-    assert torch.equal(batch["observations"], expected_obs)
-    assert torch.equal(batch["last_obs"], expected_last_obs)
-
-
-def test_staging_pool_reuses_slots_and_drops_overwritten_rollouts() -> None:
-    pool = RolloutStagingPool(
-        capacity=2,
-        num_envs=_NUM_ENVS,
-        slot_shapes=_SLOT_SHAPES,
-        device="cpu",
-    )
-
-    pool.stage_numpy_views(_raw_rollout(1.0))
-    pool.stage_numpy_views(_raw_rollout(2.0))
-    pool.stage_numpy_views(_raw_rollout(3.0))
-
-    batch = pool.batch()
-    assert pool.active_count == 2
-    assert pool.slot_versions == (2, 1)
-    assert torch.equal(torch.unique(batch["observations"]), torch.tensor([2.0, 3.0]))
-    assert not torch.any(batch["observations"] == 1.0)
-
-
-def test_staging_pool_batch_dict_does_not_retain_learner_mutations() -> None:
-    pool = RolloutStagingPool(
-        capacity=2,
-        num_envs=_NUM_ENVS,
-        slot_shapes=_SLOT_SHAPES,
-        device="cpu",
-    )
-    pool.stage_numpy_views(_raw_rollout(1.0))
-
-    batch = pool.batch()
-    batch["values"] = torch.zeros(_NUM_STEPS, _NUM_ENVS)
-
-    assert "values" not in pool.batch()
-
-
-def test_staging_pool_rejects_empty_capacity() -> None:
-    with pytest.raises(ValueError, match="capacity"):
-        RolloutStagingPool(
-            capacity=0,
-            num_envs=_NUM_ENVS,
-            slot_shapes=_SLOT_SHAPES,
-            device="cpu",
-        )
diff --git a/tests/algos/test_appo_worker.py b/tests/algos/test_appo_worker.py
deleted file mode 100644
index 3a64b9363..000000000
--- a/tests/algos/test_appo_worker.py
+++ /dev/null
@@ -1,75 +0,0 @@
-from __future__ import annotations
-
-import queue
-
-import numpy as np
-
-from unilab.algos.torch.appo.worker import (
-    compute_rollout_active_steps_per_sec,
-    compute_timeout_bootstrap_correction,
-    put_latest_metrics,
-)
-
-
-class _FakeCritic:
-    def __call__(self, obs):
-        policy = obs["policy"]
-        return policy.sum(dim=1, keepdim=True)
-
-
-def test_compute_rollout_active_steps_per_sec_uses_full_rollout_time() -> None:
-    steps_per_sec = compute_rollout_active_steps_per_sec(
-        num_envs=16,
-        steps_per_env=4,
-        rollout_ms=8.0,
-    )
-
-    assert steps_per_sec == 8000.0
-
-
-def test_compute_rollout_active_steps_per_sec_returns_none_without_timing() -> None:
-    assert (
-        compute_rollout_active_steps_per_sec(
-            num_envs=16,
-            steps_per_env=4,
-            rollout_ms=0.0,
-        )
-        is None
-    )
-
-
-def test_compute_timeout_bootstrap_correction_uses_final_observation_value():
-    correction = compute_timeout_bootstrap_correction(
-        critic=_FakeCritic(),
-        collector_device="cpu",
-        gamma=0.5,
-        timeout_mask=np.array([True, False]),
-        final_obs=np.array([[2.0, 3.0], [9.0, 9.0]], dtype=np.float32),
-        final_critic=np.array([[2.0, 3.0], [9.0, 9.0]], dtype=np.float32),
-    )
-
-    np.testing.assert_allclose(correction, np.array([2.5, 0.0], dtype=np.float32))
-
-
-def test_compute_timeout_bootstrap_correction_prefers_explicit_final_critic():
-    correction = compute_timeout_bootstrap_correction(
-        critic=_FakeCritic(),
-        collector_device="cpu",
-        gamma=0.5,
-        timeout_mask=np.array([True, False]),
-        final_obs=np.array([[2.0, 3.0], [9.0, 9.0]], dtype=np.float32),
-        final_critic=np.array([[11.0, 13.0], [0.0, 0.0]], dtype=np.float32),
-    )
-
-    np.testing.assert_allclose(correction, np.array([12.0, 0.0], dtype=np.float32))
-
-
-def test_put_latest_metrics_replaces_stale_item_when_queue_is_full(capsys):
-    metrics_queue = queue.Queue(maxsize=1)
-    metrics_queue.put_nowait({"total_steps": 1})
-
-    put_latest_metrics(metrics_queue, {"total_steps": 2}, worker_name="APPOWorker")
-
-    assert metrics_queue.get_nowait() == {"total_steps": 2}
-    captured = capsys.readouterr()
-    assert captured.err == ""
diff --git a/tests/algos/test_fast_sac_compile.py b/tests/algos/test_fast_sac_compile.py
deleted file mode 100644
index 298a6ff85..000000000
--- a/tests/algos/test_fast_sac_compile.py
+++ /dev/null
@@ -1,948 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Callable
-from typing import Any
-
-import pytest
-import torch
-
-from unilab.algos.torch.fast_sac.learner import FastSACLearner, SACActor
-
-
-def _small_fast_sac_learner(*, use_autotune: bool = True) -> FastSACLearner:
-    return FastSACLearner(
-        obs_dim=4,
-        action_dim=2,
-        critic_obs_dim=5,
-        device="cpu",
-        actor_hidden_dim=8,
-        critic_hidden_dim=8,
-        num_atoms=3,
-        num_q_networks=2,
-        use_layer_norm=False,
-        use_autotune=use_autotune,
-        max_grad_norm=0.0,
-    )
-
-
-def _small_offpolicy_batch(batch_size: int = 4) -> dict[str, torch.Tensor]:
-    return {
-        "obs": torch.linspace(-0.4, 0.7, steps=batch_size * 4).view(batch_size, 4),
-        "critic": torch.linspace(-0.2, 0.9, steps=batch_size * 5).view(batch_size, 5),
-        "actions": torch.linspace(-0.5, 0.5, steps=batch_size * 2).view(batch_size, 2),
-        "rewards": torch.linspace(-0.3, 0.6, steps=batch_size),
-        "next_obs": torch.linspace(0.1, 1.2, steps=batch_size * 4).view(batch_size, 4),
-        "next_critic": torch.linspace(-0.7, 0.4, steps=batch_size * 5).view(batch_size, 5),
-        "dones": torch.tensor([0.0, 1.0, 0.0, 1.0]),
-        "truncated": torch.tensor([0.0, 1.0, 0.0, 0.0]),
-    }
-
-
-_CRITIC_GRAPH_INPUT_KEYS = (
-    "critic",
-    "actions",
-    "rewards",
-    "next_obs",
-    "next_critic",
-    "dones",
-    "truncated",
-)
-_ACTOR_GRAPH_INPUT_KEYS = ("obs", "critic")
-
-
-def _critic_graph_static_inputs(batch: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
-    return {key: batch[key].detach().clone() for key in _CRITIC_GRAPH_INPUT_KEYS}
-
-
-def _actor_graph_static_inputs(batch: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
-    return {key: batch[key].detach().clone() for key in _ACTOR_GRAPH_INPUT_KEYS}
-
-
-def _critic_graph_packed_source(batch: dict[str, torch.Tensor]) -> torch.Tensor:
-    return torch.cat(
-        [batch[key].reshape(batch[key].shape[0], -1) for key in _CRITIC_GRAPH_INPUT_KEYS], dim=1
-    )
-
-
-def _sac_graph_packed_source(batch: dict[str, torch.Tensor]) -> torch.Tensor:
-    keys = ("obs", "critic", "actions", "rewards", "next_obs", "next_critic", "dones", "truncated")
-    return torch.cat([batch[key].reshape(batch[key].shape[0], -1) for key in keys], dim=1)
-
-
-def test_fast_sac_compile_targets_training_hot_paths(monkeypatch) -> None:
-    calls: list[tuple[str, dict[str, Any]]] = []
-
-    def fake_compile(fn: Callable, **kwargs):
-        calls.append((fn.__qualname__, kwargs))
-        return fn
-
-    learner = FastSACLearner(
-        obs_dim=4,
-        action_dim=2,
-        critic_obs_dim=5,
-        device="cpu",
-        actor_hidden_dim=8,
-        critic_hidden_dim=8,
-        num_atoms=3,
-        num_q_networks=2,
-        use_layer_norm=False,
-        use_autotune=False,
-    )
-    learner.device = "cuda"
-    monkeypatch.setattr(torch, "compile", fake_compile)
-
-    learner._compile_training_methods()
-
-    assert calls == [
-        (
-            "FastSACLearner._critic_loss_tensors",
-            {"options": {"triton.cudagraphs": False}},
-        ),
-        (
-            "FastSACLearner._actor_loss_tensors",
-            {"options": {"triton.cudagraphs": False}},
-        ),
-    ]
-
-
-def test_fast_sac_graph_critic_skips_compiling_critic_loss(monkeypatch) -> None:
-    calls: list[tuple[str, dict[str, Any]]] = []
-
-    def fake_compile(fn: Callable, **kwargs):
-        calls.append((fn.__qualname__, kwargs))
-        return fn
-
-    learner = FastSACLearner(
-        obs_dim=4,
-        action_dim=2,
-        critic_obs_dim=5,
-        device="cpu",
-        actor_hidden_dim=8,
-        critic_hidden_dim=8,
-        num_atoms=3,
-        num_q_networks=2,
-        use_layer_norm=False,
-        use_autotune=False,
-        use_cuda_graph_critic=True,
-    )
-    learner.device = "cuda"
-    learner.use_cuda_graph_critic = True
-    monkeypatch.setattr(torch, "compile", fake_compile)
-
-    learner._compile_training_methods()
-
-    assert calls == [
-        (
-            "FastSACLearner._actor_loss_tensors",
-            {"options": {"triton.cudagraphs": False}},
-        ),
-    ]
-
-
-def test_fast_sac_graph_actor_skips_compiling_actor_loss(monkeypatch) -> None:
-    calls: list[tuple[str, dict[str, Any]]] = []
-
-    def fake_compile(fn: Callable, **kwargs):
-        calls.append((fn.__qualname__, kwargs))
-        return fn
-
-    learner = FastSACLearner(
-        obs_dim=4,
-        action_dim=2,
-        critic_obs_dim=5,
-        device="cpu",
-        actor_hidden_dim=8,
-        critic_hidden_dim=8,
-        num_atoms=3,
-        num_q_networks=2,
-        use_layer_norm=False,
-        use_autotune=False,
-        use_cuda_graph_actor=True,
-    )
-    learner.device = "cuda"
-    learner.use_cuda_graph_actor = True
-    monkeypatch.setattr(torch, "compile", fake_compile)
-
-    learner._compile_training_methods()
-
-    assert calls == [
-        (
-            "FastSACLearner._critic_loss_tensors",
-            {"options": {"triton.cudagraphs": False}},
-        ),
-    ]
-
-
-def test_fast_sac_cuda_adamw_optimizers_are_capture_ready(monkeypatch) -> None:
-    if not torch.cuda.is_available():
-        pytest.skip("CUDA-only optimizer kwargs require a CUDA-enabled torch build")
-
-    calls: list[dict[str, Any]] = []
-
-    class _FakeAdamW:
-        def __init__(self, _params, **kwargs):
-            calls.append(kwargs)
-
-    monkeypatch.setattr(torch.optim, "AdamW", _FakeAdamW)
-
-    FastSACLearner(
-        obs_dim=4,
-        action_dim=2,
-        critic_obs_dim=5,
-        device="cuda",
-        actor_hidden_dim=8,
-        critic_hidden_dim=8,
-        num_atoms=3,
-        num_q_networks=2,
-        use_layer_norm=False,
-        use_autotune=False,
-        use_compile=False,
-    )
-
-    assert len(calls) == 3
-    assert all(call["fused"] for call in calls)
-    assert all(call["capturable"] for call in calls)
-
-
-def test_fast_sac_cpu_adamw_optimizers_keep_default_capturability(monkeypatch) -> None:
-    calls: list[dict[str, Any]] = []
-
-    class _FakeAdamW:
-        def __init__(self, _params, **kwargs):
-            calls.append(kwargs)
-
-    monkeypatch.setattr(torch.optim, "AdamW", _FakeAdamW)
-
-    FastSACLearner(
-        obs_dim=4,
-        action_dim=2,
-        critic_obs_dim=5,
-        device="cpu",
-        actor_hidden_dim=8,
-        critic_hidden_dim=8,
-        num_atoms=3,
-        num_q_networks=2,
-        use_layer_norm=False,
-        use_autotune=False,
-        use_compile=False,
-    )
-
-    assert len(calls) == 3
-    assert not any(call["fused"] for call in calls)
-    assert all("capturable" not in call for call in calls)
-
-
-def test_fast_sac_cuda_graph_critic_is_opt_in_and_cuda_only() -> None:
-    cpu_learner = _small_fast_sac_learner()
-    assert not cpu_learner.use_cuda_graph_critic
-
-    if not torch.cuda.is_available():
-        pytest.skip("CUDA graph opt-in requires a CUDA-enabled torch build")
-
-    cuda_learner = FastSACLearner(
-        obs_dim=4,
-        action_dim=2,
-        critic_obs_dim=5,
-        device="cuda",
-        actor_hidden_dim=8,
-        critic_hidden_dim=8,
-        num_atoms=3,
-        num_q_networks=2,
-        use_layer_norm=False,
-        use_autotune=False,
-        use_compile=False,
-        use_amp=False,
-        use_cuda_graph_critic=True,
-    )
-    assert cuda_learner.use_cuda_graph_critic
-
-
-def test_fast_sac_critic_packed_staging_disabled_when_fp16_scaler_active(
-    monkeypatch,
-) -> None:
-    if not torch.cuda.is_available():
-        pytest.skip("CUDA graph staging with fp16 scaler requires a CUDA-enabled torch build")
-
-    monkeypatch.setattr(
-        FastSACLearner,
-        "_should_use_grad_scaler",
-        staticmethod(lambda use_amp, device_type, amp_dtype: True),
-    )
-    learner = FastSACLearner(
-        obs_dim=4,
-        action_dim=2,
-        critic_obs_dim=5,
-        device="cuda",
-        actor_hidden_dim=8,
-        critic_hidden_dim=8,
-        num_atoms=3,
-        num_q_networks=2,
-        use_layer_norm=False,
-        use_autotune=False,
-        use_amp=True,
-        amp_dtype="fp16",
-        use_cuda_graph_critic=True,
-        use_cuda_graph_critic_packed_staging=True,
-    )
-
-    assert learner.use_cuda_graph_critic
-    assert learner.scaler is not None
-    assert not learner.use_cuda_graph_critic_packed_staging
-
-
-def test_fast_sac_cuda_graph_actor_is_opt_in_and_cuda_only() -> None:
-    cpu_learner = _small_fast_sac_learner()
-    assert not cpu_learner.use_cuda_graph_actor
-
-    if not torch.cuda.is_available():
-        pytest.skip("CUDA graph opt-in requires a CUDA-enabled torch build")
-
-    cuda_learner = FastSACLearner(
-        obs_dim=4,
-        action_dim=2,
-        critic_obs_dim=5,
-        device="cuda",
-        actor_hidden_dim=8,
-        critic_hidden_dim=8,
-        num_atoms=3,
-        num_q_networks=2,
-        use_layer_norm=False,
-        use_autotune=False,
-        use_compile=False,
-        use_amp=False,
-        use_cuda_graph_actor=True,
-    )
-    assert cuda_learner.use_cuda_graph_actor
-
-
-def test_fast_sac_amp_dtype_resolution_and_scaler_rules() -> None:
-    assert FastSACLearner._resolve_amp_dtype("auto", "cuda") is torch.bfloat16
-    assert FastSACLearner._resolve_amp_dtype("auto", "xpu") is torch.bfloat16
-    assert FastSACLearner._resolve_amp_dtype("fp16", "cuda") is torch.float16
-    assert FastSACLearner._resolve_amp_dtype("bf16", "cuda") is torch.bfloat16
-
-    assert FastSACLearner._should_use_grad_scaler(True, "cuda", torch.float16)
-    assert not FastSACLearner._should_use_grad_scaler(True, "cuda", torch.bfloat16)
-    assert not FastSACLearner._should_use_grad_scaler(True, "xpu", torch.bfloat16)
-    assert not FastSACLearner._should_use_grad_scaler(False, "cuda", torch.float16)
-
-    with pytest.raises(ValueError, match="amp_dtype"):
-        FastSACLearner._resolve_amp_dtype("tf32", "cuda")
-
-
-def test_fast_sac_alpha_loss_helper_matches_reference_value_and_grad() -> None:
-    learner = FastSACLearner(
-        obs_dim=4,
-        action_dim=3,
-        critic_obs_dim=5,
-        device="cpu",
-        actor_hidden_dim=8,
-        critic_hidden_dim=8,
-        num_atoms=3,
-        num_q_networks=2,
-        use_layer_norm=False,
-        use_autotune=True,
-    )
-    next_log_probs = torch.tensor([-1.25, -0.5, 0.25, 1.5], dtype=torch.float32)
-    learner.target_entropy = -1.75
-    learner.log_alpha.data.fill_(-2.0)
-
-    reference_log_alpha = learner.log_alpha.detach().clone().requires_grad_(True)
-    reference_loss = (-reference_log_alpha.exp() * (next_log_probs + learner.target_entropy)).mean()
-    reference_loss.backward()
-
-    learner.log_alpha.grad = None
-    alpha_loss = learner._alpha_loss_tensor(next_log_probs)
-    alpha_loss.backward()
-
-    assert torch.allclose(alpha_loss.detach(), reference_loss.detach())
-    assert learner.log_alpha.grad is not None
-    assert reference_log_alpha.grad is not None
-    assert torch.allclose(learner.log_alpha.grad, reference_log_alpha.grad)
-    assert not next_log_probs.requires_grad
-
-
-def test_sac_actor_tensor_gaussian_sampling_matches_normal_reference() -> None:
-    actor = SACActor(
-        obs_dim=4,
-        action_dim=3,
-        hidden_dim=12,
-        use_layer_norm=False,
-        action_scale=torch.tensor([0.5, 1.5, 2.0]),
-        action_bias=torch.tensor([-0.25, 0.0, 0.75]),
-    )
-    obs = torch.tensor(
-        [
-            [-1.0, -0.25, 0.5, 1.25],
-            [0.25, 0.5, -0.75, 1.0],
-        ],
-        dtype=torch.float32,
-    )
-
-    _, mean, log_std = actor(obs)
-    std = log_std.exp()
-    eps = torch.tensor(
-        [
-            [-0.5, 0.25, 1.0],
-            [1.5, -1.0, 0.0],
-        ],
-        dtype=torch.float32,
-    )
-    raw_action = mean + std * eps
-    dist = torch.distributions.Normal(mean, std)
-    tanh_action = torch.tanh(raw_action)
-    expected_action = tanh_action * actor.action_scale + actor.action_bias
-    expected_log_prob = dist.log_prob(raw_action)
-    expected_log_prob -= torch.log(1 - tanh_action.pow(2) + 1e-6)
-    expected_log_prob -= torch.log(actor.action_scale + 1e-6)
-    expected_log_prob = expected_log_prob.sum(1)
-
-    action, log_prob = actor._sample_action_and_log_prob(mean, log_std, eps=eps)
-
-    torch.testing.assert_close(action, expected_action)
-    torch.testing.assert_close(log_prob, expected_log_prob)
-
-
-def test_sac_actor_tensor_gaussian_sampling_matches_normal_without_tanh() -> None:
-    actor = SACActor(obs_dim=2, action_dim=3, hidden_dim=12, use_layer_norm=False, use_tanh=False)
-    mean = torch.tensor(
-        [[-0.5, 0.25, 1.0], [1.5, -1.0, 0.0]],
-        dtype=torch.float32,
-        requires_grad=True,
-    )
-    log_std = torch.tensor(
-        [[-1.0, -0.25, 0.5], [0.0, -0.75, 0.25]],
-        dtype=torch.float32,
-        requires_grad=True,
-    )
-    eps = torch.tensor(
-        [[0.25, -1.5, 0.75], [-0.5, 1.0, 1.5]],
-        dtype=torch.float32,
-    )
-
-    action, log_prob = actor._sample_action_and_log_prob(mean, log_std, eps=eps)
-
-    reference_mean = mean.detach().clone().requires_grad_(True)
-    reference_log_std = log_std.detach().clone().requires_grad_(True)
-    reference_std = reference_log_std.exp()
-    reference_raw_action = reference_mean + reference_std * eps
-    reference_dist = torch.distributions.Normal(reference_mean, reference_std)
-    expected_log_prob = reference_dist.log_prob(reference_raw_action).sum(1)
-
-    torch.testing.assert_close(action, reference_raw_action)
-    torch.testing.assert_close(log_prob, expected_log_prob)
-
-    loss = (action + log_prob.unsqueeze(1)).sum()
-    reference_loss = (reference_raw_action + expected_log_prob.unsqueeze(1)).sum()
-    loss.backward()
-    reference_loss.backward()
-
-    assert mean.grad is not None
-    assert log_std.grad is not None
-    assert reference_mean.grad is not None
-    assert reference_log_std.grad is not None
-    torch.testing.assert_close(mean.grad, reference_mean.grad)
-    torch.testing.assert_close(log_std.grad, reference_log_std.grad)
-
-
-def test_fast_sac_capture_candidate_matches_public_critic_update_for_finite_loss() -> None:
-    public_learner = _small_fast_sac_learner()
-    capture_learner = _small_fast_sac_learner()
-    capture_learner.actor.load_state_dict(public_learner.actor.state_dict())
-    capture_learner.qnet.load_state_dict(public_learner.qnet.state_dict())
-    capture_learner.qnet_target.load_state_dict(public_learner.qnet_target.state_dict())
-    capture_learner.log_alpha.data.copy_(public_learner.log_alpha.data)
-    batch = _small_offpolicy_batch()
-
-    torch.manual_seed(2024)
-    public_metrics = public_learner.update_critic(batch)
-
-    torch.manual_seed(2024)
-    capture_outputs = capture_learner._update_critic_capture_candidate(
-        batch["critic"],
-        batch["actions"],
-        batch["rewards"],
-        batch["next_obs"],
-        batch["next_critic"],
-        batch["dones"],
-        batch["truncated"],
-    )
-    capture_metrics = {
-        "qf_loss": capture_outputs[0].item(),
-        "critic_grad_norm": capture_outputs[1].item(),
-        "target_q_max": capture_outputs[2].item(),
-        "target_q_min": capture_outputs[3].item(),
-        "alpha_loss": capture_outputs[4].item(),
-        "alpha": capture_outputs[5].item(),
-    }
-
-    assert public_metrics.keys() == capture_metrics.keys()
-    for key, public_value in public_metrics.items():
-        assert capture_metrics[key] == pytest.approx(public_value)
-    for public_param, capture_param in zip(
-        public_learner.qnet.parameters(),
-        capture_learner.qnet.parameters(),
-        strict=True,
-    ):
-        torch.testing.assert_close(capture_param, public_param)
-    torch.testing.assert_close(capture_learner.log_alpha, public_learner.log_alpha)
-
-
-def test_fast_sac_capture_candidate_matches_public_actor_update_for_finite_loss() -> None:
-    public_learner = _small_fast_sac_learner()
-    capture_learner = _small_fast_sac_learner()
-    capture_learner.actor.load_state_dict(public_learner.actor.state_dict())
-    capture_learner.qnet.load_state_dict(public_learner.qnet.state_dict())
-    capture_learner.qnet_target.load_state_dict(public_learner.qnet_target.state_dict())
-    capture_learner.log_alpha.data.copy_(public_learner.log_alpha.data)
-    batch = _small_offpolicy_batch()
-
-    torch.manual_seed(2025)
-    public_metrics = public_learner.update_actor(batch)
-
-    torch.manual_seed(2025)
-    capture_outputs = capture_learner._update_actor_capture_candidate(
-        batch["obs"],
-        batch["critic"],
-    )
-    capture_metrics = {
-        "actor_loss": capture_outputs[0].item(),
-        "actor_grad_norm": capture_outputs[1].item(),
-        "policy_entropy": capture_outputs[2].item(),
-        "action_std": capture_outputs[3].item(),
-    }
-
-    assert public_metrics.keys() == capture_metrics.keys()
-    for key, public_value in public_metrics.items():
-        assert capture_metrics[key] == pytest.approx(public_value)
-    for public_param, capture_param in zip(
-        public_learner.actor.parameters(),
-        capture_learner.actor.parameters(),
-        strict=True,
-    ):
-        torch.testing.assert_close(capture_param, public_param)
-
-
-def test_fast_sac_cuda_graph_state_materialization_preserves_cpu_rng_state() -> None:
-    learner = _small_fast_sac_learner()
-    batch = _small_offpolicy_batch()
-    torch.manual_seed(12345)
-    expected_rng_state = torch.random.get_rng_state()
-
-    learner._materialize_capturable_critic_optimizer_state(batch)
-
-    torch.testing.assert_close(torch.random.get_rng_state(), expected_rng_state)
-
-
-def test_fast_sac_load_state_dict_resets_cuda_graph_caches() -> None:
-    learner = _small_fast_sac_learner()
-    state_dict = learner.get_state_dict()
-    marker = object()
-    learner._cuda_graph_critic = marker  # type: ignore[assignment]
-    learner._cuda_graph_critic_static_inputs = {"obs": torch.zeros(1)}
-    learner._cuda_graph_critic_action_noise = torch.zeros(1)
-    learner._cuda_graph_critic_outputs = (torch.zeros(()),) * 6  # type: ignore[assignment]
-    learner._cuda_graph_critic_shapes = {"obs": torch.Size([1])}
-    learner._cuda_graph_actor = marker  # type: ignore[assignment]
-    learner._cuda_graph_actor_static_inputs = {"obs": torch.zeros(1)}
-    learner._cuda_graph_actor_action_noise = torch.zeros(1)
-    learner._cuda_graph_actor_outputs = (torch.zeros(()),) * 4  # type: ignore[assignment]
-    learner._cuda_graph_actor_shapes = {"obs": torch.Size([1])}
-
-    learner.load_state_dict(state_dict)
-
-    assert learner._cuda_graph_critic is None
-    assert learner._cuda_graph_critic_static_inputs is None
-    assert learner._cuda_graph_critic_action_noise is None
-    assert learner._cuda_graph_critic_outputs is None
-    assert learner._cuda_graph_critic_shapes is None
-    assert learner._cuda_graph_actor is None
-    assert learner._cuda_graph_actor_static_inputs is None
-    assert learner._cuda_graph_actor_action_noise is None
-    assert learner._cuda_graph_actor_outputs is None
-    assert learner._cuda_graph_actor_shapes is None
-
-
-def test_fast_sac_cuda_graph_static_inputs_keep_addresses_after_copy() -> None:
-    learner = _small_fast_sac_learner()
-    batch = _small_offpolicy_batch()
-    learner._cuda_graph_critic_static_inputs = _critic_graph_static_inputs(batch)
-    learner._cuda_graph_actor_static_inputs = _actor_graph_static_inputs(batch)
-    learner._cuda_graph_critic_action_noise = torch.zeros_like(batch["actions"])
-    learner._cuda_graph_actor_action_noise = torch.zeros_like(batch["actions"])
-
-    critic_static_inputs = learner._cuda_graph_critic_static_inputs
-    actor_static_inputs = learner._cuda_graph_actor_static_inputs
-    assert critic_static_inputs is not None
-    assert actor_static_inputs is not None
-    critic_ptrs_before = {name: tensor.data_ptr() for name, tensor in critic_static_inputs.items()}
-    actor_ptrs_before = {name: tensor.data_ptr() for name, tensor in actor_static_inputs.items()}
-
-    learner._copy_critic_graph_inputs(batch)
-    learner._copy_actor_graph_inputs(batch)
-
-    critic_ptrs_after = {name: tensor.data_ptr() for name, tensor in critic_static_inputs.items()}
-    actor_ptrs_after = {name: tensor.data_ptr() for name, tensor in actor_static_inputs.items()}
-    assert critic_ptrs_after == critic_ptrs_before
-    assert actor_ptrs_after == actor_ptrs_before
-
-
-def test_fast_sac_cuda_graph_static_inputs_match_batch_values_after_copy() -> None:
-    learner = _small_fast_sac_learner()
-    batch = _small_offpolicy_batch()
-    learner._cuda_graph_critic_static_inputs = {
-        key: torch.empty_like(batch[key]) for key in _CRITIC_GRAPH_INPUT_KEYS
-    }
-    learner._cuda_graph_actor_static_inputs = {
-        key: torch.empty_like(batch[key]) for key in _ACTOR_GRAPH_INPUT_KEYS
-    }
-    learner._cuda_graph_critic_action_noise = torch.zeros_like(batch["actions"])
-    learner._cuda_graph_actor_action_noise = torch.zeros_like(batch["actions"])
-
-    learner._copy_critic_graph_inputs(batch)
-    learner._copy_actor_graph_inputs(batch)
-
-    critic_static_inputs = learner._cuda_graph_critic_static_inputs
-    actor_static_inputs = learner._cuda_graph_actor_static_inputs
-    assert critic_static_inputs is not None
-    assert actor_static_inputs is not None
-    for key in _CRITIC_GRAPH_INPUT_KEYS:
-        torch.testing.assert_close(critic_static_inputs[key], batch[key])
-    for key in _ACTOR_GRAPH_INPUT_KEYS:
-        torch.testing.assert_close(actor_static_inputs[key], batch[key])
-
-
-def test_fast_sac_cuda_graph_critic_packed_source_updates_static_views() -> None:
-    learner = _small_fast_sac_learner()
-    batch = _small_offpolicy_batch()
-    packed_source = _critic_graph_packed_source(batch)
-    batch_from_packed = {"critic_graph_packed_source": packed_source}
-    learner._cuda_graph_critic_shapes = learner._critic_graph_input_shapes(batch)
-    learner._cuda_graph_critic_static_packed_input = torch.empty_like(packed_source)
-    learner._cuda_graph_critic_static_inputs = learner._critic_graph_static_views_from_packed(
-        learner._cuda_graph_critic_static_packed_input,
-        learner._cuda_graph_critic_shapes,
-    )
-    learner._cuda_graph_critic_action_noise = torch.zeros_like(batch["actions"])
-
-    learner._copy_critic_graph_inputs(batch_from_packed)
-
-    critic_static_inputs = learner._cuda_graph_critic_static_inputs
-    assert critic_static_inputs is not None
-    for key in _CRITIC_GRAPH_INPUT_KEYS:
-        torch.testing.assert_close(critic_static_inputs[key], batch[key])
-
-
-def test_fast_sac_cuda_graph_sac_graph_packed_source_updates_critic_and_actor_views() -> None:
-    learner = _small_fast_sac_learner()
-    batch = _small_offpolicy_batch()
-    packed_source = _sac_graph_packed_source(batch)
-    batch_from_packed = {"sac_graph_packed_source": packed_source}
-    learner._cuda_graph_critic_shapes = learner._critic_graph_input_shapes(batch)
-    learner._cuda_graph_actor_shapes = learner._actor_graph_input_shapes(batch)
-    learner._cuda_graph_sac_static_packed_input = torch.empty_like(packed_source)
-    learner._cuda_graph_critic_static_inputs = learner._critic_graph_static_views_from_sac_packed(
-        learner._cuda_graph_sac_static_packed_input,
-        learner._cuda_graph_critic_shapes,
-        learner._cuda_graph_actor_shapes,
-    )
-    learner._cuda_graph_actor_static_inputs = learner._actor_graph_static_views_from_sac_packed(
-        learner._cuda_graph_sac_static_packed_input,
-        learner._cuda_graph_actor_shapes,
-    )
-    learner._cuda_graph_critic_action_noise = torch.zeros_like(batch["actions"])
-    learner._cuda_graph_actor_action_noise = torch.zeros_like(batch["actions"])
-
-    learner._copy_critic_graph_inputs(batch_from_packed)
-    learner._copy_actor_graph_inputs(batch_from_packed)
-
-    critic_static_inputs = learner._cuda_graph_critic_static_inputs
-    actor_static_inputs = learner._cuda_graph_actor_static_inputs
-    assert critic_static_inputs is not None
-    assert actor_static_inputs is not None
-    for key in _CRITIC_GRAPH_INPUT_KEYS:
-        torch.testing.assert_close(critic_static_inputs[key], batch[key])
-    for key in _ACTOR_GRAPH_INPUT_KEYS:
-        torch.testing.assert_close(actor_static_inputs[key], batch[key])
-
-
-def test_fast_sac_actor_reuses_sac_graph_static_buffer_after_critic_copy() -> None:
-    learner = _small_fast_sac_learner()
-    batch = _small_offpolicy_batch()
-    packed_source = _sac_graph_packed_source(batch)
-    learner._cuda_graph_critic_shapes = learner._critic_graph_input_shapes(batch)
-    learner._cuda_graph_actor_shapes = learner._actor_graph_input_shapes(batch)
-    learner._cuda_graph_sac_static_packed_input = torch.empty_like(packed_source)
-    learner._cuda_graph_critic_static_inputs = learner._critic_graph_static_views_from_sac_packed(
-        learner._cuda_graph_sac_static_packed_input,
-        learner._cuda_graph_critic_shapes,
-        learner._cuda_graph_actor_shapes,
-    )
-    learner._cuda_graph_actor_static_packed_input = learner._cuda_graph_sac_static_packed_input
-    learner._cuda_graph_actor_static_inputs = learner._actor_graph_static_views_from_sac_packed(
-        learner._cuda_graph_actor_static_packed_input,
-        learner._cuda_graph_actor_shapes,
-    )
-    learner._cuda_graph_critic_action_noise = torch.zeros_like(batch["actions"])
-    learner._cuda_graph_actor_action_noise = torch.zeros_like(batch["actions"])
-
-    learner._copy_critic_graph_inputs({"sac_graph_packed_source": packed_source})
-    source_ptr_after_critic = learner._cuda_graph_sac_static_source_ptr
-    static_after_critic = learner._cuda_graph_sac_static_packed_input.clone()
-
-    learner._copy_actor_graph_inputs({"sac_graph_packed_source": packed_source})
-
-    assert learner._cuda_graph_sac_static_source_ptr == source_ptr_after_critic
-    torch.testing.assert_close(learner._cuda_graph_sac_static_packed_input, static_after_critic)
-
-
-def test_fast_sac_cuda_graph_shape_change_resets_cache_before_new_capture(monkeypatch) -> None:
-    learner = _small_fast_sac_learner()
-    old_batch = _small_offpolicy_batch()
-    new_batch = _small_offpolicy_batch(batch_size=5)
-    new_batch["dones"] = torch.tensor([0.0, 1.0, 0.0, 1.0, 0.0])
-    new_batch["truncated"] = torch.tensor([0.0, 0.0, 1.0, 0.0, 1.0])
-    marker = object()
-    events: list[str] = []
-
-    learner.use_cuda_graph_critic = True
-    learner.use_cuda_graph_actor = True
-    learner._device_type = "cuda"
-    learner._cuda_graph_critic_shapes = learner._critic_graph_input_shapes(old_batch)
-    learner._cuda_graph_critic_static_inputs = _critic_graph_static_inputs(old_batch)
-    learner._cuda_graph_critic_action_noise = torch.zeros_like(old_batch["actions"])
-    learner._cuda_graph_critic_outputs = (torch.zeros(()),) * 6
-    learner._cuda_graph_critic = marker  # type: ignore[assignment]
-    learner._cuda_graph_actor_shapes = learner._actor_graph_input_shapes(old_batch)
-    learner._cuda_graph_actor_static_inputs = _actor_graph_static_inputs(old_batch)
-    learner._cuda_graph_actor_action_noise = torch.zeros_like(old_batch["actions"])
-    learner._cuda_graph_actor_outputs = (torch.zeros(()),) * 4
-    learner._cuda_graph_actor = marker  # type: ignore[assignment]
-
-    def assert_critic_cache_reset(prefix: str) -> None:
-        assert learner._cuda_graph_critic is None, prefix
-        assert learner._cuda_graph_critic_static_inputs is None, prefix
-        assert learner._cuda_graph_critic_action_noise is None, prefix
-        assert learner._cuda_graph_critic_outputs is None, prefix
-        assert learner._cuda_graph_critic_shapes is None, prefix
-
-    def assert_actor_cache_reset(prefix: str) -> None:
-        assert learner._cuda_graph_actor is None, prefix
-        assert learner._cuda_graph_actor_static_inputs is None, prefix
-        assert learner._cuda_graph_actor_action_noise is None, prefix
-        assert learner._cuda_graph_actor_outputs is None, prefix
-        assert learner._cuda_graph_actor_shapes is None, prefix
-
-    def fake_materialize_critic(_batch: dict[str, torch.Tensor]) -> None:
-        events.append("critic_materialize")
-        assert_critic_cache_reset("critic cache should reset before materialization")
-
-    def fake_capture_critic(capture_batch: dict[str, torch.Tensor]) -> None:
-        events.append("critic_capture")
-        assert_critic_cache_reset("critic cache should reset before capture")
-        learner._cuda_graph_critic_shapes = learner._critic_graph_input_shapes(capture_batch)
-        learner._cuda_graph_critic_static_inputs = _critic_graph_static_inputs(capture_batch)
-        learner._cuda_graph_critic_action_noise = torch.zeros_like(capture_batch["actions"])
-        learner._cuda_graph_critic_outputs = (torch.zeros(()),) * 6
-        learner._cuda_graph_critic = object()  # type: ignore[assignment]
-
-    def fake_materialize_actor(_batch: dict[str, torch.Tensor]) -> None:
-        events.append("actor_materialize")
-        assert_actor_cache_reset("actor cache should reset before materialization")
-
-    def fake_capture_actor(capture_batch: dict[str, torch.Tensor]) -> None:
-        events.append("actor_capture")
-        assert_actor_cache_reset("actor cache should reset before capture")
-        learner._cuda_graph_actor_shapes = learner._actor_graph_input_shapes(capture_batch)
-        learner._cuda_graph_actor_static_inputs = _actor_graph_static_inputs(capture_batch)
-        learner._cuda_graph_actor_action_noise = torch.zeros_like(capture_batch["actions"])
-        learner._cuda_graph_actor_outputs = (torch.zeros(()),) * 4
-        learner._cuda_graph_actor = object()  # type: ignore[assignment]
-
-    monkeypatch.setattr(
-        learner,
-        "_materialize_capturable_critic_optimizer_state",
-        fake_materialize_critic,
-    )
-    monkeypatch.setattr(learner, "_capture_critic_cuda_graph", fake_capture_critic)
-    monkeypatch.setattr(
-        learner,
-        "_materialize_capturable_actor_optimizer_state",
-        fake_materialize_actor,
-    )
-    monkeypatch.setattr(learner, "_capture_actor_cuda_graph", fake_capture_actor)
-
-    assert learner.update_critic_cuda_graph(new_batch, read_metrics=False) == {}
-    assert learner.update_actor_cuda_graph(new_batch, read_metrics=False) == {}
-
-    assert events == [
-        "critic_materialize",
-        "critic_capture",
-        "actor_materialize",
-        "actor_capture",
-    ]
-    assert learner._cuda_graph_critic_shapes == learner._critic_graph_input_shapes(new_batch)
-    assert learner._cuda_graph_actor_shapes == learner._actor_graph_input_shapes(new_batch)
-
-
-def test_fast_sac_cuda_graph_replay_paths_emit_outer_nvtx_ranges(monkeypatch) -> None:
-    learner = _small_fast_sac_learner()
-    batch = _small_offpolicy_batch()
-    pushed: list[str] = []
-    popped = 0
-
-    class _FakeGraph:
-        def replay(self) -> None:
-            pushed.append("graph.replay.called")
-
-    def fake_push(name: str) -> None:
-        pushed.append(name)
-
-    def fake_pop() -> None:
-        nonlocal popped
-        popped += 1
-
-    monkeypatch.setattr(torch.cuda.nvtx, "range_push", fake_push)
-    monkeypatch.setattr(torch.cuda.nvtx, "range_pop", fake_pop)
-
-    learner.use_cuda_graph_critic = True
-    learner.use_cuda_graph_actor = True
-    learner._device_type = "cuda"
-    learner.nvtx_profile_ranges = True
-    learner._cuda_graph_critic_shapes = learner._critic_graph_input_shapes(batch)
-    learner._cuda_graph_critic_static_inputs = {
-        key: tensor.clone()
-        for key, tensor in batch.items()
-        if key in {"critic", "actions", "rewards", "next_obs", "next_critic", "dones", "truncated"}
-    }
-    learner._cuda_graph_critic_action_noise = torch.empty(batch["actions"].shape)
-    learner._cuda_graph_critic_outputs = (torch.zeros(()),) * 6
-    learner._cuda_graph_critic = _FakeGraph()  # type: ignore[assignment]
-    learner._cuda_graph_actor_shapes = learner._actor_graph_input_shapes(batch)
-    learner._cuda_graph_actor_static_inputs = {
-        "obs": batch["obs"].clone(),
-        "critic": batch["critic"].clone(),
-    }
-    learner._cuda_graph_actor_action_noise = torch.empty(batch["actions"].shape)
-    learner._cuda_graph_actor_outputs = (torch.zeros(()),) * 4
-    learner._cuda_graph_actor = _FakeGraph()  # type: ignore[assignment]
-
-    learner.update_critic_cuda_graph(batch)
-    learner.update_actor_cuda_graph(batch)
-
-    assert pushed == [
-        "critic_graph/copy_inputs",
-        "critic_graph/replay",
-        "graph.replay.called",
-        "critic_graph/output_metrics_item",
-        "actor_graph/copy_inputs",
-        "actor_graph/replay",
-        "graph.replay.called",
-        "actor_graph/output_metrics_item",
-    ]
-    assert popped == 6
-
-
-def test_fast_sac_cuda_graph_metrics_can_skip_item_reads(monkeypatch) -> None:
-    learner = _small_fast_sac_learner()
-    calls = 0
-
-    class _Metric:
-        def __init__(self, value: float) -> None:
-            self.value = value
-
-        def item(self) -> float:
-            nonlocal calls
-            calls += 1
-            return self.value
-
-    learner._cuda_graph_critic_outputs = tuple(_Metric(float(i)) for i in range(6))  # type: ignore[assignment]
-    learner._cuda_graph_actor_outputs = tuple(_Metric(float(i)) for i in range(4))  # type: ignore[assignment]
-
-    assert learner._critic_graph_output_metrics(read_items=False) == {}
-    assert learner._actor_graph_output_metrics(read_items=False) == {}
-    assert calls == 0
-
-    assert learner._critic_graph_output_metrics(read_items=True)["qf_loss"] == 0.0
-    assert learner._actor_graph_output_metrics(read_items=True)["actor_loss"] == 0.0
-    assert calls == 10
-
-
-def test_fast_sac_cuda_graph_metric_skip_preserves_replay_updates(monkeypatch) -> None:
-    learner = _small_fast_sac_learner()
-    batch = _small_offpolicy_batch()
-    replay_calls = 0
-    metric_calls = 0
-
-    class _FakeGraph:
-        def replay(self) -> None:
-            nonlocal replay_calls
-            replay_calls += 1
-
-    class _Metric:
-        def item(self) -> float:
-            nonlocal metric_calls
-            metric_calls += 1
-            return 0.0
-
-    learner.use_cuda_graph_critic = True
-    learner.use_cuda_graph_actor = True
-    learner._device_type = "cuda"
-    learner._cuda_graph_critic_shapes = learner._critic_graph_input_shapes(batch)
-    learner._cuda_graph_critic_static_inputs = {
-        key: tensor.clone()
-        for key, tensor in batch.items()
-        if key in {"critic", "actions", "rewards", "next_obs", "next_critic", "dones", "truncated"}
-    }
-    learner._cuda_graph_critic_action_noise = torch.zeros_like(batch["actions"])
-    learner._cuda_graph_critic_outputs = tuple(_Metric() for _ in range(6))  # type: ignore[assignment]
-    learner._cuda_graph_critic = _FakeGraph()  # type: ignore[assignment]
-    learner._cuda_graph_actor_shapes = learner._actor_graph_input_shapes(batch)
-    learner._cuda_graph_actor_static_inputs = {
-        "obs": batch["obs"].clone(),
-        "critic": batch["critic"].clone(),
-    }
-    learner._cuda_graph_actor_action_noise = torch.zeros_like(batch["actions"])
-    learner._cuda_graph_actor_outputs = tuple(_Metric() for _ in range(4))  # type: ignore[assignment]
-    learner._cuda_graph_actor = _FakeGraph()  # type: ignore[assignment]
-
-    assert learner.update_critic_cuda_graph(batch, read_metrics=False) == {}
-    assert learner.update_actor_cuda_graph(batch, read_metrics=False) == {}
-    assert replay_calls == 2
-    assert metric_calls == 0
-
-    learner.update_critic_cuda_graph(batch, read_metrics=True)
-    learner.update_actor_cuda_graph(batch, read_metrics=True)
-    assert replay_calls == 4
-    assert metric_calls == 10
-
-
-def test_fast_sac_cuda_graph_input_copy_fills_static_noise_in_place(monkeypatch) -> None:
-    learner = _small_fast_sac_learner()
-    batch = _small_offpolicy_batch()
-    learner._cuda_graph_critic_static_inputs = {
-        key: tensor.clone()
-        for key, tensor in batch.items()
-        if key in {"critic", "actions", "rewards", "next_obs", "next_critic", "dones", "truncated"}
-    }
-    learner._cuda_graph_actor_static_inputs = {
-        "obs": batch["obs"].clone(),
-        "critic": batch["critic"].clone(),
-    }
-    learner._cuda_graph_critic_action_noise = torch.zeros_like(batch["actions"])
-    learner._cuda_graph_actor_action_noise = torch.zeros_like(batch["actions"])
-
-    def fail_randn_like(_tensor: torch.Tensor) -> torch.Tensor:
-        raise AssertionError("graph input copy should fill static noise buffers in place")
-
-    monkeypatch.setattr(torch, "randn_like", fail_randn_like)
-
-    learner._copy_critic_graph_inputs(batch)
-    learner._copy_actor_graph_inputs(batch)
-
-    assert not torch.equal(
-        learner._cuda_graph_critic_action_noise, torch.zeros_like(batch["actions"])
-    )
-    assert not torch.equal(
-        learner._cuda_graph_actor_action_noise, torch.zeros_like(batch["actions"])
-    )
diff --git a/tests/algos/test_fast_sac_symmetry_contract.py b/tests/algos/test_fast_sac_symmetry_contract.py
deleted file mode 100644
index f02eb7848..000000000
--- a/tests/algos/test_fast_sac_symmetry_contract.py
+++ /dev/null
@@ -1,336 +0,0 @@
-from __future__ import annotations
-
-import logging
-from contextlib import contextmanager
-from typing import Any
-
-import gymnasium as gym
-import pytest
-import torch
-
-import unilab.algos.torch.fast_sac.learner as learner_module
-
-
-class _FakeSymmetryAugmentation:
-    batch_multiplier = 2
-
-    def __init__(self):
-        self.augment_obs_calls: list[str] = []
-        self.augment_obs_and_actions_calls: list[str] = []
-        self.mirror_obs_calls: list[str] = []
-
-    def augment_obs(self, obs, *, obs_group: str = "obs"):
-        self.augment_obs_calls.append(obs_group)
-        return torch.cat([obs, obs], dim=0)
-
-    def augment_obs_and_actions(self, obs, actions, *, obs_group: str = "obs"):
-        self.augment_obs_and_actions_calls.append(obs_group)
-        return torch.cat([obs, obs], dim=0), torch.cat([actions, actions], dim=0)
-
-    def mirror_obs(self, obs, *, obs_group: str = "obs"):
-        self.mirror_obs_calls.append(obs_group)
-        return obs
-
-
-class _ForbiddenBackend:
-    @property
-    def model(self):
-        raise AssertionError("FastSAC runner should not read env._backend.model")
-
-
-class _FakeEnv:
-    def __init__(self, augmentation: Any | None):
-        self.obs_groups_spec = {"obs": 4, "critic": 6}
-        self.action_space = gym.spaces.Box(-1.0, 1.0, shape=(2,))
-        self._backend = _ForbiddenBackend()
-        self._augmentation = augmentation
-        self.closed = False
-        self.last_device: str | None = None
-
-    def get_obs_structure(self):
-        raise AssertionError("FastSAC runner should not call env.get_obs_structure()")
-
-    def build_symmetry_augmentation(self, *, device: str):
-        self.last_device = device
-        return self._augmentation
-
-    def close(self):
-        self.closed = True
-
-
-def test_fast_sac_runner_uses_env_owned_symmetry_contract(
-    monkeypatch: pytest.MonkeyPatch,
-    caplog: pytest.LogCaptureFixture,
-):
-    import unilab.algos.torch.fast_sac.runner as runner_module
-    import unilab.algos.torch.offpolicy.double_buffer_runner as device_runner_module
-    from unilab.algos.torch.fast_sac.runner import FastSACRunner
-    from unilab.base import registry
-
-    augmentation = _FakeSymmetryAugmentation()
-    fake_env = _FakeEnv(augmentation)
-
-    monkeypatch.setattr(registry, "ensure_registries", lambda: None)
-    monkeypatch.setattr(registry, "make", lambda *args, **kwargs: fake_env)
-    monkeypatch.setattr(runner_module, "require_offpolicy_replay_device", lambda value: value)
-    monkeypatch.setattr(
-        device_runner_module,
-        "require_offpolicy_replay_device",
-        lambda value: value,
-    )
-
-    with caplog.at_level(logging.INFO, logger="unilab.algos.torch.fast_sac.runner"):
-        runner = FastSACRunner(
-            env_name="FakeEnv",
-            device="cpu",
-            num_envs=1,
-            replay_buffer_n=8,
-            batch_size=8,
-            learning_starts=0,
-            updates_per_step=1,
-            policy_frequency=1,
-            use_symmetry=True,
-            obs_normalization=False,
-        )
-
-    assert fake_env.closed is True
-    assert fake_env.last_device == "cpu"
-    assert runner.batch_size == 4
-    assert runner.learner.symmetry is augmentation
-    assert "[FastSAC] Symmetry enabled: batch_size adjusted to 4 (effective: 8)" in caplog.text
-
-
-def test_fast_sac_runner_skips_symmetry_builder_when_disabled(monkeypatch: pytest.MonkeyPatch):
-    import unilab.algos.torch.fast_sac.runner as runner_module
-    import unilab.algos.torch.offpolicy.double_buffer_runner as device_runner_module
-    from unilab.algos.torch.fast_sac.runner import FastSACRunner
-    from unilab.base import registry
-
-    fake_env = _FakeEnv(_FakeSymmetryAugmentation())
-
-    def _unexpected_builder(*args, **kwargs):
-        raise AssertionError("Symmetry builder should not be called when use_symmetry is false")
-
-    fake_env.build_symmetry_augmentation = _unexpected_builder  # type: ignore[method-assign]
-
-    monkeypatch.setattr(registry, "ensure_registries", lambda: None)
-    monkeypatch.setattr(registry, "make", lambda *args, **kwargs: fake_env)
-    monkeypatch.setattr(runner_module, "require_offpolicy_replay_device", lambda value: value)
-    monkeypatch.setattr(
-        device_runner_module,
-        "require_offpolicy_replay_device",
-        lambda value: value,
-    )
-
-    runner = FastSACRunner(
-        env_name="FakeEnv",
-        device="cpu",
-        num_envs=1,
-        replay_buffer_n=8,
-        batch_size=8,
-        learning_starts=0,
-        updates_per_step=1,
-        policy_frequency=1,
-        use_symmetry=False,
-        obs_normalization=False,
-    )
-
-    assert fake_env.closed is True
-    assert runner.batch_size == 8
-    assert runner.learner.symmetry is None
-
-
-def test_fast_sac_learner_rejects_symmetry_without_augmentation():
-    from unilab.algos.torch.fast_sac.learner import FastSACLearner
-
-    with pytest.raises(
-        ValueError,
-        match="FastSACLearner use_symmetry=True requires a symmetry_augmentation contract",
-    ):
-        FastSACLearner(
-            obs_dim=4,
-            action_dim=2,
-            critic_obs_dim=4,
-            device="cpu",
-            use_symmetry=True,
-        )
-
-
-def test_fast_sac_obs_normalization_uses_local_moments_and_round_trips():
-    from unilab.algos.torch.fast_sac.learner import FastSACLearner
-
-    learner = FastSACLearner(
-        obs_dim=2,
-        action_dim=1,
-        critic_obs_dim=3,
-        device="cpu",
-        actor_hidden_dim=8,
-        critic_hidden_dim=8,
-        num_atoms=3,
-        num_q_networks=2,
-        use_layer_norm=False,
-        use_autotune=False,
-        obs_normalization=True,
-    )
-
-    learner.normalize_obs(torch.tensor([[1.0, 3.0], [3.0, 5.0]]), update=True)
-
-    torch.testing.assert_close(learner.obs_normalizer.mean, torch.tensor([2.0, 4.0]))
-    torch.testing.assert_close(learner.obs_normalizer.std, torch.ones(2))
-    assert int(learner.obs_normalizer.count.item()) == 2
-
-    restored = FastSACLearner(
-        obs_dim=2,
-        action_dim=1,
-        critic_obs_dim=3,
-        device="cpu",
-        actor_hidden_dim=8,
-        critic_hidden_dim=8,
-        num_atoms=3,
-        num_q_networks=2,
-        use_layer_norm=False,
-        use_autotune=False,
-        obs_normalization=True,
-    )
-    restored.load_state_dict(learner.get_state_dict())
-    torch.testing.assert_close(restored.obs_normalizer.mean, learner.obs_normalizer.mean)
-
-
-def test_fast_sac_symmetry_augmentation_emits_fine_grained_nvtx_ranges(
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    from unilab.algos.torch.fast_sac.learner import FastSACLearner
-
-    seen_ranges: list[str] = []
-
-    @contextmanager
-    def record_range(name: str, enabled: bool):
-        if enabled:
-            seen_ranges.append(name)
-        yield
-
-    monkeypatch.setattr(learner_module, "_cuda_nvtx_range", record_range)
-
-    learner = FastSACLearner(
-        obs_dim=4,
-        action_dim=2,
-        critic_obs_dim=5,
-        device="cpu",
-        actor_hidden_dim=8,
-        critic_hidden_dim=8,
-        num_atoms=3,
-        num_q_networks=2,
-        use_layer_norm=False,
-        use_autotune=False,
-        use_symmetry=True,
-        symmetry_augmentation=_FakeSymmetryAugmentation(),
-    )
-    learner.nvtx_profile_ranges = True
-
-    def fake_critic_loss_tensors(*args, **kwargs):
-        del args, kwargs
-        return (
-            torch.tensor(0.0, requires_grad=True),
-            torch.tensor(0.0),
-            torch.tensor(0.0),
-            torch.zeros(4),
-        )
-
-    def fake_actor_loss_tensors(*args, **kwargs):
-        del args, kwargs
-        return (
-            torch.tensor(0.0, requires_grad=True),
-            torch.tensor(0.0),
-            torch.tensor(0.0),
-        )
-
-    monkeypatch.setattr(learner, "_critic_loss_tensors", fake_critic_loss_tensors)
-    monkeypatch.setattr(learner, "_actor_loss_tensors", fake_actor_loss_tensors)
-    batch = {
-        "obs": torch.randn(4, 4),
-        "critic": torch.randn(4, 5),
-        "actions": torch.randn(4, 2),
-        "rewards": torch.randn(4),
-        "next_obs": torch.randn(4, 4),
-        "next_critic": torch.randn(4, 5),
-        "dones": torch.zeros(4),
-        "truncated": torch.zeros(4),
-    }
-
-    learner.update_critic(batch)
-    learner.update_actor(batch)
-
-    for expected in {
-        "critic/symmetry_obs_actions",
-        "critic/symmetry_next_obs",
-        "critic/symmetry_critic_obs",
-        "critic/symmetry_critic_next_obs",
-        "critic/symmetry_aux_repeat",
-        "actor/symmetry_obs",
-        "actor/symmetry_critic_obs",
-    }:
-        assert expected in seen_ranges
-
-
-def test_fast_sac_symmetry_uses_obs_only_augmentation_for_obs_only_paths(
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    from unilab.algos.torch.fast_sac.learner import FastSACLearner
-
-    symmetry = _FakeSymmetryAugmentation()
-    learner = FastSACLearner(
-        obs_dim=4,
-        action_dim=2,
-        critic_obs_dim=5,
-        device="cpu",
-        actor_hidden_dim=8,
-        critic_hidden_dim=8,
-        num_atoms=3,
-        num_q_networks=2,
-        use_layer_norm=False,
-        use_autotune=False,
-        use_symmetry=True,
-        symmetry_augmentation=symmetry,
-    )
-
-    def fake_critic_loss_tensors(critic_obs, actions, rewards, next_obs, critic_next_obs, *args):
-        assert critic_obs.shape[0] == 8
-        assert actions.shape[0] == 8
-        assert rewards.shape[0] == 8
-        assert next_obs.shape[0] == 8
-        assert critic_next_obs.shape[0] == 8
-        return (
-            torch.tensor(0.0, requires_grad=True),
-            torch.tensor(0.0),
-            torch.tensor(0.0),
-            torch.zeros(8),
-        )
-
-    def fake_actor_loss_tensors(obs, critic_obs):
-        assert obs.shape[0] == 8
-        assert critic_obs.shape[0] == 8
-        return (
-            torch.tensor(0.0, requires_grad=True),
-            torch.tensor(0.0),
-            torch.tensor(0.0),
-        )
-
-    monkeypatch.setattr(learner, "_critic_loss_tensors", fake_critic_loss_tensors)
-    monkeypatch.setattr(learner, "_actor_loss_tensors", fake_actor_loss_tensors)
-    batch = {
-        "obs": torch.randn(4, 4),
-        "critic": torch.randn(4, 5),
-        "actions": torch.randn(4, 2),
-        "rewards": torch.randn(4),
-        "next_obs": torch.randn(4, 4),
-        "next_critic": torch.randn(4, 5),
-        "dones": torch.zeros(4),
-        "truncated": torch.zeros(4),
-    }
-
-    learner.update_critic(batch)
-    learner.update_actor(batch)
-
-    assert symmetry.augment_obs_and_actions_calls == ["obs"]
-    assert symmetry.augment_obs_calls == ["obs", "critic", "critic", "obs", "critic"]
-    assert symmetry.mirror_obs_calls == []
diff --git a/tests/algos/test_fast_td3_learner.py b/tests/algos/test_fast_td3_learner.py
deleted file mode 100644
index 9d0423431..000000000
--- a/tests/algos/test_fast_td3_learner.py
+++ /dev/null
@@ -1,232 +0,0 @@
-"""Unit tests for FastTD3 learner and TD3Actor."""
-
-from __future__ import annotations
-
-import pytest
-import torch
-
-from unilab.algos.torch.fast_td3.learner import FastTD3Learner, TD3Actor
-
-# ---------------------------------------------------------------------------
-# TD3Actor
-# ---------------------------------------------------------------------------
-
-
-class TestTD3Actor:
-    @pytest.fixture()
-    def actor(self):
-        return TD3Actor(
-            obs_dim=48,
-            n_act=12,
-            num_envs=4,
-            init_scale=0.01,
-            hidden_dim=64,
-            log_std_min=-3.0,
-            log_std_max=0.0,
-            device=torch.device("cpu"),
-        )
-
-    def test_forward_shape(self, actor: TD3Actor):
-        obs = torch.randn(4, 48)
-        actions = actor(obs)
-        assert actions.shape == (4, 12)
-
-    def test_forward_tanh_bounded(self, actor: TD3Actor):
-        obs = torch.randn(4, 48) * 10
-        actions = actor(obs)
-        assert actions.min() >= -1.0
-        assert actions.max() <= 1.0
-
-    def test_explore_adds_noise(self, actor: TD3Actor):
-        obs = torch.randn(4, 48)
-        torch.manual_seed(42)
-        det = actor.explore(obs, deterministic=True)
-        torch.manual_seed(42)
-        noisy = actor.explore(obs, deterministic=False)
-        assert not torch.allclose(det, noisy, atol=1e-6)
-
-    def test_explore_deterministic_matches_forward(self, actor: TD3Actor):
-        obs = torch.randn(4, 48)
-        with torch.no_grad():
-            fwd = actor(obs)
-            det = actor.explore(obs, deterministic=True)
-        assert torch.allclose(fwd, det)
-
-    def test_explore_clamped(self, actor: TD3Actor):
-        obs = torch.randn(4, 48) * 100
-        actions = actor.explore(obs, deterministic=False)
-        assert actions.min() >= -1.0
-        assert actions.max() <= 1.0
-
-    def test_noise_resampled_on_done(self, actor: TD3Actor):
-        scales_before = actor.noise_scales.clone()
-        dones = torch.tensor([1.0, 0.0, 1.0, 0.0])
-        obs = torch.randn(4, 48)
-        actor.explore(obs, dones=dones)
-        # Envs 0 and 2 should have new noise scales, 1 and 3 unchanged
-        assert not torch.equal(scales_before[0], actor.noise_scales[0])
-        assert torch.equal(scales_before[1], actor.noise_scales[1])
-        assert not torch.equal(scales_before[2], actor.noise_scales[2])
-        assert torch.equal(scales_before[3], actor.noise_scales[3])
-
-    def test_noise_scales_in_range(self, actor: TD3Actor):
-        import math
-
-        std_min = math.exp(-3.0)
-        std_max = math.exp(0.0)
-        assert (actor.noise_scales >= std_min).all()
-        assert (actor.noise_scales <= std_max).all()
-
-
-# ---------------------------------------------------------------------------
-# FastTD3Learner
-# ---------------------------------------------------------------------------
-
-
-class TestFastTD3Learner:
-    @pytest.fixture()
-    def learner(self):
-        return FastTD3Learner(
-            obs_dim=48,
-            action_dim=12,
-            critic_obs_dim=48,
-            num_envs=4,
-            device="cpu",
-            gamma=0.97,
-            tau=0.01,
-            actor_lr=3e-4,
-            critic_lr=3e-4,
-            actor_hidden_dim=64,
-            critic_hidden_dim=128,
-            num_atoms=51,
-            v_min=-10.0,
-            v_max=10.0,
-            init_scale=0.01,
-            log_std_min=-3.0,
-            log_std_max=0.0,
-            weight_decay=0.001,
-            use_cdq=True,
-            policy_noise=0.1,
-            noise_clip=0.2,
-            policy_frequency=2,
-            obs_normalization=True,
-        )
-
-    def _make_batch(self, batch_size=16, obs_dim=48, action_dim=12):
-        return {
-            "obs": torch.randn(batch_size, obs_dim),
-            "actions": torch.randn(batch_size, action_dim).clamp(-1, 1),
-            "rewards": torch.randn(batch_size),
-            "next_obs": torch.randn(batch_size, obs_dim),
-            "dones": torch.zeros(batch_size),
-            "truncated": torch.zeros(batch_size),
-            "critic": torch.randn(batch_size, obs_dim),
-            "next_critic": torch.randn(batch_size, obs_dim),
-        }
-
-    def test_update_critic_returns_metrics(self, learner: FastTD3Learner):
-        batch = self._make_batch()
-        metrics = learner.update_critic(batch)
-        assert "qf_loss" in metrics
-        assert "qf_max" in metrics
-        assert "qf_min" in metrics
-
-    def test_update_actor_returns_metrics(self, learner: FastTD3Learner):
-        batch = self._make_batch()
-        metrics = learner.update_actor(batch)
-        assert "actor_loss" in metrics
-
-    def test_soft_update_target_only_updates_critic(self, learner: FastTD3Learner):
-        """soft_update_target updates qnet_target only, not actor_target."""
-        actor_target_before = [p.clone() for p in learner.actor_target.parameters()]
-        qnet_target_before = [p.clone() for p in learner.qnet_target.parameters()]
-
-        with torch.no_grad():
-            for p in learner.qnet.parameters():
-                p.add_(torch.randn_like(p))
-
-        learner.soft_update_target()
-
-        # qnet_target should have moved
-        for before, after in zip(qnet_target_before, learner.qnet_target.parameters()):
-            assert not torch.allclose(before, after.data)
-
-        # actor_target should be unchanged
-        for before, after in zip(actor_target_before, learner.actor_target.parameters()):
-            assert torch.allclose(before, after.data)
-
-    def test_obs_normalization(self, learner: FastTD3Learner):
-        obs = torch.randn(4, 48) * 10 + 5
-        normed = learner.normalize_obs(obs, update=True)
-        assert normed.shape == obs.shape
-
-    def test_get_and_load_state_dict(self, learner: FastTD3Learner):
-        state = learner.get_state_dict()
-        assert "actor" in state
-        assert "actor_target" in state
-        assert "qnet" in state
-        assert "qnet_target" in state
-        learner.load_state_dict(state)
-
-    def test_training_loop_updates(self, learner: FastTD3Learner):
-        batch = self._make_batch()
-        for step in range(4):
-            learner.update_critic(batch)
-            if step % learner.policy_frequency == 0:
-                learner.update_actor(batch)
-            learner.soft_update_target()
-
-    def test_cdq_disabled(self):
-        learner = FastTD3Learner(
-            obs_dim=16,
-            action_dim=4,
-            critic_obs_dim=16,
-            num_envs=2,
-            device="cpu",
-            actor_hidden_dim=32,
-            critic_hidden_dim=64,
-            num_atoms=11,
-            use_cdq=False,
-        )
-        batch = {
-            "obs": torch.randn(8, 16),
-            "actions": torch.randn(8, 4).clamp(-1, 1),
-            "rewards": torch.randn(8),
-            "next_obs": torch.randn(8, 16),
-            "dones": torch.zeros(8),
-            "truncated": torch.zeros(8),
-            "critic": torch.randn(8, 16),
-            "next_critic": torch.randn(8, 16),
-        }
-        assert "qf_loss" in learner.update_critic(batch)
-        assert "actor_loss" in learner.update_actor(batch)
-
-
-# ---------------------------------------------------------------------------
-# CLI routing
-# ---------------------------------------------------------------------------
-
-
-class TestTD3CLIRouting:
-    def test_build_route_td3_go2_joystick_flat_motrix(self):
-        from unilab.cli import build_route
-
-        route = build_route("td3", "go2_joystick_flat", "motrix")
-        assert route.script_name == "train_offpolicy.py"
-        assert route.config_group == "offpolicy"
-        assert route.owner_task == "td3/go2_joystick_flat/motrix.yaml"
-        assert "algo=td3" in route.generated_overrides
-        assert "task=td3/go2_joystick_flat/motrix" in route.generated_overrides
-
-    def test_build_route_td3_go1_joystick_flat_motrix(self):
-        from unilab.cli import build_route
-
-        route = build_route("td3", "go1_joystick_flat", "motrix")
-        assert route.script_name == "train_offpolicy.py"
-        assert route.owner_task == "td3/go1_joystick_flat/motrix.yaml"
-
-    def test_td3_in_offpolicy_algos(self):
-        from unilab.cli import OFFPOLICY_ALGOS, SUPPORTED_ALGOS
-
-        assert "td3" in SUPPORTED_ALGOS
-        assert "td3" in OFFPOLICY_ALGOS
diff --git a/tests/algos/test_flash_sac_learner.py b/tests/algos/test_flash_sac_learner.py
deleted file mode 100644
index 422a8091d..000000000
--- a/tests/algos/test_flash_sac_learner.py
+++ /dev/null
@@ -1,438 +0,0 @@
-"""Unit tests for FlashSAC learner and actor interfaces."""
-
-from __future__ import annotations
-
-from collections.abc import Callable
-from typing import Any
-
-import pytest
-import torch
-
-from unilab.algos.torch.flash_sac.learner import FlashSACLearner, RewardNormalizer
-from unilab.algos.torch.flash_sac.update import compute_categorical_td_target
-
-
-def _make_batch(batch_size: int = 32) -> dict[str, torch.Tensor]:
-    obs = torch.randn(batch_size, 98)
-    critic = torch.randn(batch_size, 101)
-    actions = torch.tanh(torch.randn(batch_size, 29))
-    rewards = torch.randn(batch_size)
-    next_obs = torch.randn(batch_size, 98)
-    next_critic = torch.randn(batch_size, 101)
-    dones = torch.zeros(batch_size)
-    truncated = torch.zeros(batch_size)
-    return {
-        "obs": obs,
-        "critic": critic,
-        "actions": actions,
-        "rewards": rewards,
-        "next_obs": next_obs,
-        "next_critic": next_critic,
-        "dones": dones,
-        "truncated": truncated,
-    }
-
-
-def _make_small_learner(**kwargs: Any) -> FlashSACLearner:
-    defaults = {
-        "obs_dim": 4,
-        "action_dim": 2,
-        "critic_obs_dim": 6,
-        "actor_hidden_dim": 8,
-        "critic_hidden_dim": 8,
-        "actor_num_blocks": 1,
-        "critic_num_blocks": 1,
-        "num_atoms": 5,
-        "device": "cpu",
-        "use_compile": False,
-    }
-    defaults.update(kwargs)
-    return FlashSACLearner(**defaults)
-
-
-def test_flashsac_learner_exposes_expected_dims():
-    learner = FlashSACLearner(obs_dim=98, action_dim=29, critic_obs_dim=101, device="cpu")
-
-    assert learner.obs_dim == 98
-    assert learner.critic_obs_dim == 101
-    assert learner.action_dim == 29
-
-
-def test_flashsac_cuda_graph_options_are_opt_in() -> None:
-    default_learner = _make_small_learner()
-
-    assert default_learner.supports_cuda_graph_packed_staging is True
-    assert default_learner.use_cuda_graph_critic is False
-    assert default_learner.use_cuda_graph_actor is False
-    assert default_learner.use_cuda_graph_critic_packed_staging is False
-    assert default_learner.use_cuda_graph_actor_packed_staging is False
-
-    graph_learner = _make_small_learner(
-        use_cuda_graph_critic=True,
-        use_cuda_graph_actor=True,
-        use_cuda_graph_critic_packed_staging=True,
-        use_cuda_graph_actor_packed_staging=True,
-    )
-
-    assert graph_learner.use_cuda_graph_critic is True
-    assert graph_learner.use_cuda_graph_actor is True
-    assert graph_learner.use_cuda_graph_critic_packed_staging is True
-    assert graph_learner.use_cuda_graph_actor_packed_staging is True
-
-
-def test_flashsac_update_critic_cuda_graph_falls_back_on_cpu() -> None:
-    learner = FlashSACLearner(
-        obs_dim=98,
-        action_dim=29,
-        critic_obs_dim=101,
-        device="cpu",
-        use_cuda_graph_critic=True,
-    )
-    batch = _make_batch(batch_size=4)
-
-    metrics = learner.update_critic_cuda_graph(batch)
-
-    assert "critic_loss" in metrics
-    assert "reward_scale_std" in metrics
-
-
-def test_flashsac_update_actor_cuda_graph_falls_back_on_cpu() -> None:
-    learner = FlashSACLearner(
-        obs_dim=98,
-        action_dim=29,
-        critic_obs_dim=101,
-        device="cpu",
-        use_cuda_graph_actor=True,
-    )
-    batch = _make_batch(batch_size=4)
-
-    metrics = learner.update_actor_cuda_graph(batch)
-
-    assert "actor_loss" in metrics
-    assert "temperature" in metrics
-
-
-def test_flashsac_sac_graph_packed_source_updates_critic_and_actor_views() -> None:
-    learner = _make_small_learner(
-        use_cuda_graph_critic=True,
-        use_cuda_graph_actor=True,
-        use_cuda_graph_critic_packed_staging=True,
-        use_cuda_graph_actor_packed_staging=True,
-    )
-    batch = {
-        "obs": torch.randn(4, 4),
-        "critic": torch.randn(4, 6),
-        "actions": torch.randn(4, 2),
-        "rewards": torch.randn(4),
-        "next_obs": torch.randn(4, 4),
-        "next_critic": torch.randn(4, 6),
-        "dones": torch.zeros(4),
-        "truncated": torch.zeros(4),
-    }
-    packed = torch.cat(
-        [
-            batch["obs"],
-            batch["critic"],
-            batch["actions"],
-            batch["rewards"].view(4, 1),
-            batch["next_obs"],
-            batch["next_critic"],
-            batch["dones"].view(4, 1),
-            batch["truncated"].view(4, 1),
-        ],
-        dim=1,
-    )
-    critic_shapes = learner._critic_graph_input_shapes(batch)
-    actor_shapes = learner._actor_graph_input_shapes(batch)
-
-    critic_views = learner._critic_graph_static_views_from_sac_packed(
-        packed,
-        critic_shapes,
-        actor_shapes,
-    )
-    actor_views = learner._actor_graph_static_views_from_sac_packed(packed, actor_shapes)
-
-    torch.testing.assert_close(critic_views["obs"], batch["obs"])
-    torch.testing.assert_close(critic_views["critic"], batch["critic"])
-    torch.testing.assert_close(critic_views["actions"], batch["actions"])
-    torch.testing.assert_close(critic_views["rewards"], batch["rewards"])
-    torch.testing.assert_close(critic_views["next_obs"], batch["next_obs"])
-    torch.testing.assert_close(critic_views["next_critic"], batch["next_critic"])
-    torch.testing.assert_close(actor_views["obs"], batch["obs"])
-    torch.testing.assert_close(actor_views["critic"], batch["critic"])
-
-
-def test_flashsac_cuda_adam_optimizers_are_capture_ready(monkeypatch) -> None:
-    if not torch.cuda.is_available():
-        pytest.skip("CUDA-only optimizer kwargs require a CUDA-enabled torch build")
-
-    calls: list[dict[str, Any]] = []
-
-    class _FakeAdam:
-        def __init__(self, _params, **kwargs):
-            calls.append(kwargs)
-            self.param_groups = [{"lr": kwargs["lr"]}]
-
-    class _FakeLambdaLR:
-        def __init__(self, optimizer, lr_lambda):
-            self.optimizer = optimizer
-            self.lr_lambda = lr_lambda
-
-    monkeypatch.setattr(torch.optim, "Adam", _FakeAdam)
-    monkeypatch.setattr(torch.optim.lr_scheduler, "LambdaLR", _FakeLambdaLR)
-
-    FlashSACLearner(obs_dim=98, action_dim=29, critic_obs_dim=101, device="cuda")
-
-    assert len(calls) == 3
-    assert all(call["fused"] for call in calls)
-    assert all(call["capturable"] for call in calls)
-
-
-def test_flashsac_obs_normalizer_uses_local_batch_moments() -> None:
-    learner = _make_small_learner(obs_normalization=True)
-    learner._update_obs_normalizer(
-        torch.tensor(
-            [
-                [1.0, 2.0, 3.0, 4.0],
-                [3.0, 4.0, 5.0, 6.0],
-            ]
-        )
-    )
-
-    normalizer = learner.obs_normalizer
-    assert not isinstance(normalizer, torch.nn.Identity)
-    torch.testing.assert_close(normalizer.count, torch.tensor(2))
-    torch.testing.assert_close(
-        normalizer.mean,
-        torch.tensor([2.0, 3.0, 4.0, 5.0]),
-    )
-
-
-def test_flashsac_compile_targets_training_hot_paths(monkeypatch) -> None:
-    calls: list[tuple[str, dict[str, Any]]] = []
-
-    def fake_compile(fn: Callable, **kwargs):
-        calls.append((fn.__qualname__, kwargs))
-        return fn
-
-    learner = FlashSACLearner(obs_dim=98, action_dim=29, critic_obs_dim=101, device="cpu")
-    learner.device = torch.device("cuda")
-    monkeypatch.setattr(torch, "compile", fake_compile)
-
-    learner._compile_training_methods()
-
-    assert calls == [
-        (
-            "FlashSACActor.get_mean_and_std",
-            {"options": {"triton.cudagraphs": False}},
-        ),
-        (
-            "FlashSACLearner._critic_loss_tensors",
-            {"options": {"triton.cudagraphs": False}},
-        ),
-        (
-            "FlashSACLearner._actor_loss_tensors",
-            {"options": {"triton.cudagraphs": False}},
-        ),
-    ]
-
-
-def test_flashsac_graph_critic_skips_compiling_critic_loss(monkeypatch) -> None:
-    calls: list[tuple[str, dict[str, Any]]] = []
-
-    def fake_compile(fn: Callable, **kwargs):
-        calls.append((fn.__qualname__, kwargs))
-        return fn
-
-    learner = FlashSACLearner(
-        obs_dim=98,
-        action_dim=29,
-        critic_obs_dim=101,
-        device="cpu",
-        use_cuda_graph_critic=True,
-    )
-    learner.device = torch.device("cuda")
-    monkeypatch.setattr(torch, "compile", fake_compile)
-
-    learner._compile_training_methods()
-
-    assert calls == [
-        (
-            "FlashSACActor.get_mean_and_std",
-            {"options": {"triton.cudagraphs": False}},
-        ),
-        (
-            "FlashSACLearner._actor_loss_tensors",
-            {"options": {"triton.cudagraphs": False}},
-        ),
-    ]
-
-
-def test_flashsac_graph_actor_skips_compiling_actor_loss(monkeypatch) -> None:
-    calls: list[tuple[str, dict[str, Any]]] = []
-
-    def fake_compile(fn: Callable, **kwargs):
-        calls.append((fn.__qualname__, kwargs))
-        return fn
-
-    learner = FlashSACLearner(
-        obs_dim=98,
-        action_dim=29,
-        critic_obs_dim=101,
-        device="cpu",
-        use_cuda_graph_actor=True,
-    )
-    learner.device = torch.device("cuda")
-    monkeypatch.setattr(torch, "compile", fake_compile)
-
-    learner._compile_training_methods()
-
-    assert calls == [
-        (
-            "FlashSACActor.get_mean_and_std",
-            {"options": {"triton.cudagraphs": False}},
-        ),
-        (
-            "FlashSACLearner._critic_loss_tensors",
-            {"options": {"triton.cudagraphs": False}},
-        ),
-    ]
-
-
-def test_flashsac_amp_dtype_resolution_and_scaler_rules() -> None:
-    assert FlashSACLearner._resolve_amp_dtype("auto", "cuda") is torch.bfloat16
-    assert FlashSACLearner._resolve_amp_dtype("auto", "xpu") is torch.bfloat16
-    assert FlashSACLearner._resolve_amp_dtype("fp16", "cuda") is torch.float16
-    assert FlashSACLearner._resolve_amp_dtype("bf16", "cuda") is torch.bfloat16
-
-    assert FlashSACLearner._should_use_grad_scaler(True, "cuda", torch.float16)
-    assert not FlashSACLearner._should_use_grad_scaler(True, "cuda", torch.bfloat16)
-    assert not FlashSACLearner._should_use_grad_scaler(True, "xpu", torch.bfloat16)
-    assert not FlashSACLearner._should_use_grad_scaler(False, "cuda", torch.float16)
-
-    with pytest.raises(ValueError, match="amp_dtype"):
-        FlashSACLearner._resolve_amp_dtype("tf32", "cuda")
-
-
-def test_flashsac_actor_explore_and_forward_shapes():
-    learner = FlashSACLearner(obs_dim=98, action_dim=29, critic_obs_dim=101, device="cpu")
-    obs = torch.randn(4, 98)
-
-    actions = learner.actor.explore(obs, deterministic=False)
-    deterministic_actions = learner.actor.explore(obs, deterministic=True)
-    sampled_actions, info = learner.actor(obs, training=True)
-
-    assert actions.shape == (4, 29)
-    assert deterministic_actions.shape == (4, 29)
-    assert sampled_actions.shape == (4, 29)
-    assert info["log_prob"].shape == (4,)
-
-
-def test_flashsac_export_module_matches_deterministic_policy():
-    learner = FlashSACLearner(obs_dim=98, action_dim=29, critic_obs_dim=101, device="cpu")
-    obs = torch.randn(4, 98)
-
-    export_module = learner.actor.as_export_module()
-
-    with torch.inference_mode():
-        exported_once = export_module(obs)
-        exported_twice = export_module(obs)
-        deterministic_actions = learner.actor.explore(obs, deterministic=True)
-
-    torch.testing.assert_close(exported_once, exported_twice)
-    torch.testing.assert_close(exported_once, deterministic_actions)
-
-
-def test_flashsac_update_steps_run_on_cpu():
-    learner = FlashSACLearner(obs_dim=98, action_dim=29, critic_obs_dim=101, device="cpu")
-    batch = _make_batch()
-
-    critic_metrics = learner.update_critic(batch)
-    actor_metrics = learner.update_actor(batch)
-    learner.soft_update_target()
-
-    assert "critic_loss" in critic_metrics
-    assert "reward_scale_std" in critic_metrics
-    assert "actor_loss" in actor_metrics
-    assert "temperature" in actor_metrics
-
-
-def test_flashsac_state_dict_round_trip():
-    learner = FlashSACLearner(obs_dim=98, action_dim=29, critic_obs_dim=101, device="cpu")
-    batch = _make_batch()
-    learner.update_critic(batch)
-    learner.update_actor(batch)
-    state_dict = learner.get_state_dict()
-
-    restored = FlashSACLearner(obs_dim=98, action_dim=29, critic_obs_dim=101, device="cpu")
-    restored.load_state_dict(state_dict)
-
-    assert restored.get_state_dict()["update_count"] == learner.get_state_dict()["update_count"]
-
-
-def test_reward_normalizer_tracks_discounted_returns() -> None:
-    normalizer = RewardNormalizer(gamma=0.5, g_max=5.0, device=torch.device("cpu"))
-
-    normalizer.update_from_transitions(
-        rewards=torch.tensor([[2.0, 1.0], [4.0, 3.0]]),
-        dones=torch.tensor([[0.0, 1.0], [0.0, 0.0]]),
-    )
-
-    torch.testing.assert_close(normalizer.g_r, torch.tensor([5.0, 3.5]))
-    torch.testing.assert_close(normalizer.g_r_max, torch.tensor(5.0))
-
-
-def test_flashsac_critic_update_does_not_advance_reward_stats_from_sampled_batch() -> None:
-    learner = FlashSACLearner(obs_dim=98, action_dim=29, critic_obs_dim=101, device="cpu")
-    learner.update_reward_stats(
-        rewards=torch.tensor([[1.0, 2.0]]),
-        dones=torch.zeros(1, 2),
-    )
-    assert learner.reward_normalizer is not None
-    before = learner.reward_normalizer.g_r.clone()
-
-    learner.update_critic(_make_batch())
-
-    torch.testing.assert_close(learner.reward_normalizer.g_r, before)
-
-
-def test_flashsac_critic_requires_truncated_field() -> None:
-    learner = FlashSACLearner(obs_dim=98, action_dim=29, critic_obs_dim=101, device="cpu")
-    batch = _make_batch()
-    batch.pop("truncated")
-
-    try:
-        learner.update_critic(batch)
-    except KeyError as exc:
-        assert exc.args == ("truncated",)
-    else:  # pragma: no cover - explicit failure path
-        raise AssertionError("FlashSAC learner must require replay 'truncated'")
-
-
-def test_flashsac_td_target_treats_dones_as_combined_done_with_truncation_bootstrap() -> None:
-    support = torch.tensor([0.0, 1.0, 2.0])
-    target_log_probs = torch.log(
-        torch.tensor(
-            [
-                [0.0, 0.0, 1.0],
-                [0.0, 0.0, 1.0],
-                [0.0, 0.0, 1.0],
-            ]
-        ).clamp_min(1e-8)
-    )
-
-    targets = compute_categorical_td_target(
-        support=support,
-        target_log_probs=target_log_probs,
-        reward=torch.zeros(3),
-        dones=torch.tensor([0.0, 1.0, 1.0]),
-        truncated=torch.tensor([0.0, 1.0, 0.0]),
-        actor_entropy=torch.zeros(3),
-        gamma=1.0,
-    )
-
-    # Continuing rows and truncated rows bootstrap to support value 2.0.
-    torch.testing.assert_close(targets[0], torch.tensor([0.0, 0.0, 1.0]))
-    torch.testing.assert_close(targets[1], torch.tensor([0.0, 0.0, 1.0]))
-    # True terminal rows do not bootstrap and project to reward-only value 0.0.
-    torch.testing.assert_close(targets[2], torch.tensor([1.0, 0.0, 0.0]))
diff --git a/tests/algos/test_him_ppo_runner.py b/tests/algos/test_him_ppo_runner.py
deleted file mode 100644
index f64a23bb9..000000000
--- a/tests/algos/test_him_ppo_runner.py
+++ /dev/null
@@ -1,46 +0,0 @@
-from __future__ import annotations
-
-import logging
-from collections import deque
-from types import SimpleNamespace
-from typing import Any, cast
-
-import pytest
-
-
-def test_him_iteration_progress_is_one_equivalent_multiline_log_record(
-    caplog: pytest.LogCaptureFixture,
-) -> None:
-    from unilab.algos.torch.him_ppo.runner import HIMOnPolicyRunner
-
-    runner = cast(Any, HIMOnPolicyRunner.__new__(HIMOnPolicyRunner))
-    runner.logger = SimpleNamespace(
-        rewbuffer=deque([1.0, 3.0]),
-        lenbuffer=deque([10.0, 14.0]),
-    )
-
-    with caplog.at_level(logging.INFO, logger="unilab.algos.torch.him_ppo.runner"):
-        runner._log_iter(
-            it=1,
-            tot=3,
-            value_loss=0.25,
-            surrogate_loss=0.5,
-            estimation_loss=0.75,
-            swap_loss=1.0,
-            elapsed=2.0,
-            infos={"log": {"reward/feet": 1.25}},
-        )
-
-    records = [
-        record for record in caplog.records if record.name == "unilab.algos.torch.him_ppo.runner"
-    ]
-    assert len(records) == 1
-    lines = records[0].getMessage().splitlines()
-    assert lines[0] == "-" * 80
-    assert lines[-1] == "-" * 80
-    assert f"{'Iteration':>40}: 1/3" in lines
-    assert f"{'Mean episode reward':>40}: 2.0000" in lines
-    assert f"{'Mean episode length':>40}: 12.0" in lines
-    assert f"{'reward/feet':>40}: 1.2500" in lines
-    assert f"{'Time elapsed':>40}: 00:00:02" in lines
-    assert f"{'ETA':>40}: 00:00:04" in lines
diff --git a/tests/algos/test_hora_contract.py b/tests/algos/test_hora_contract.py
index 8b794bc0b..0a834d763 100644
--- a/tests/algos/test_hora_contract.py
+++ b/tests/algos/test_hora_contract.py
@@ -15,7 +15,7 @@
 def test_hora_ppo_logs_when_symmetry_is_logging_only(
     caplog: pytest.LogCaptureFixture,
 ) -> None:
-    from unilab.algos.torch.hora.ppo import HoraPPO
+    from uni_rl.algos.hora.ppo import HoraPPO
 
     actor = torch.nn.Linear(2, 2)
     critic = torch.nn.Linear(2, 1)
@@ -27,7 +27,7 @@ def test_hora_ppo_logs_when_symmetry_is_logging_only(
         "data_augmentation_func": lambda *_args: None,
     }
 
-    with caplog.at_level(logging.WARNING, logger="unilab.algos.torch.hora.ppo"):
+    with caplog.at_level(logging.WARNING, logger="uni_rl.algos.hora.ppo"):
         HoraPPO(
             cast(Any, actor),
             cast(Any, critic),
@@ -39,7 +39,7 @@ def test_hora_ppo_logs_when_symmetry_is_logging_only(
 
 
 def test_hora_sac_actor_shapes_and_stable_module_names() -> None:
-    from unilab.algos.torch.hora.sac_models import HoraSACActor
+    from uni_rl.algos.hora.sac_models import HoraSACActor
 
     actor = HoraSACActor(
         obs_dim=5,
@@ -65,7 +65,7 @@ def test_hora_sac_actor_shapes_and_stable_module_names() -> None:
 
 
 def test_hora_sac_learner_derives_priv_info_from_critic_contract() -> None:
-    from unilab.algos.torch.hora.sac_learner import derive_priv_info_from_critic_obs
+    from uni_rl.algos.hora.sac_learner import derive_priv_info_from_critic_obs
 
     actor_obs = torch.zeros((4, 5), dtype=torch.float32)
     priv_info = torch.arange(12, dtype=torch.float32).reshape(4, 3)
@@ -81,7 +81,7 @@ def test_hora_sac_learner_derives_priv_info_from_critic_contract() -> None:
 
 
 def test_hora_sac_learner_updates_with_privileged_tail() -> None:
-    from unilab.algos.torch.hora.sac_learner import HoraSACLearner
+    from uni_rl.algos.hora.sac_learner import HoraSACLearner
 
     torch.manual_seed(23)
     learner = HoraSACLearner(
@@ -123,7 +123,7 @@ def test_hora_sac_learner_updates_with_privileged_tail() -> None:
 
 
 def test_hora_sac_disables_cuda_graph_critic_path() -> None:
-    from unilab.algos.torch.hora.sac_learner import HoraSACLearner
+    from uni_rl.algos.hora.sac_learner import HoraSACLearner
 
     learner = HoraSACLearner(
         obs_dim=5,
@@ -144,7 +144,7 @@ def test_hora_sac_disables_cuda_graph_critic_path() -> None:
 
 
 def test_hora_sac_distilled_student_forward_does_not_require_priv_info() -> None:
-    from unilab.algos.torch.hora.distill import HoraSACDistillActor, HoraSACDistillShared
+    from uni_rl.algos.hora.distill import HoraSACDistillActor, HoraSACDistillShared
 
     shared = HoraSACDistillShared(
         obs_dim=12,
@@ -175,12 +175,12 @@ def test_hora_sac_distilled_student_forward_does_not_require_priv_info() -> None
 
 
 def test_hora_sac_distill_loads_teacher_actor_weights(tmp_path) -> None:
-    from unilab.algos.torch.hora.distill import (
+    from uni_rl.algos.hora.distill import (
         HoraSACDistillActor,
         HoraSACDistillShared,
         load_teacher_actor_weights,
     )
-    from unilab.algos.torch.hora.sac_models import HoraSACActor
+    from uni_rl.algos.hora.sac_models import HoraSACActor
 
     teacher = HoraSACActor(
         obs_dim=12,
@@ -226,7 +226,7 @@ def test_hora_sac_distill_loads_teacher_actor_weights(tmp_path) -> None:
 
 def test_hora_rsl_wrapper_uses_explicit_np_env_state_contract() -> None:
     """HORA wrapper must not probe required NpEnvState fields dynamically."""
-    from unilab.algos.torch.hora.rsl_rl import HoraRslRlVecEnvWrapper
+    from uni_rl.algos.hora.rsl_rl import HoraRslRlVecEnvWrapper
 
     source = textwrap.dedent(inspect.getsource(HoraRslRlVecEnvWrapper.step))
     tree = ast.parse(source)
@@ -245,7 +245,7 @@ def test_hora_rsl_wrapper_uses_explicit_np_env_state_contract() -> None:
 
 
 def test_hora_appo_learner_derives_priv_info_from_critic_contract() -> None:
-    from unilab.algos.torch.hora.appo_learner import _derive_priv_info_from_critic
+    from uni_rl.algos.hora.appo_learner import _derive_priv_info_from_critic
 
     actor_obs = torch.zeros((2, 3, 4), dtype=torch.float32)
     priv_info = torch.arange(12, dtype=torch.float32).reshape(2, 3, 2)
@@ -261,8 +261,8 @@ def test_hora_appo_learner_derives_priv_info_from_critic_contract() -> None:
 
 
 def _make_hora_appo_learner(**algorithm_overrides):
-    from unilab.algos.torch.hora.appo_learner import HoraAPPOLearner
-    from unilab.algos.torch.hora.models import (
+    from uni_rl.algos.hora.appo_learner import HoraAPPOLearner
+    from uni_rl.algos.hora.models import (
         HoraActorModel,
         HoraCriticModel,
         HoraSharedActorCritic,
@@ -326,7 +326,7 @@ def test_hora_appo_minibatch_tensor_path_matches_tensordict_forward() -> None:
 
 
 def test_hora_appo_runner_builds_shared_actor_critic_core() -> None:
-    from unilab.algos.torch.hora.appo_runner import HoraAPPORunner
+    from uni_rl.algos.hora.appo_runner import HoraAPPORunner
 
     runner = HoraAPPORunner.__new__(HoraAPPORunner)
     runner.num_envs = 4
@@ -341,13 +341,13 @@ def test_hora_appo_runner_builds_shared_actor_critic_core() -> None:
             "critic": {"actor": 5, "priv_info": 2},
         },
         "actor": {
-            "class_name": "unilab.algos.torch.hora:HoraActorModel",
+            "class_name": "uni_rl.algos.hora:HoraActorModel",
             "hidden_dims": [8],
             "priv_info_embed_dim": 2,
             "priv_mlp_hidden_dims": [4, 2],
         },
         "critic": {
-            "class_name": "unilab.algos.torch.hora:HoraCriticModel",
+            "class_name": "uni_rl.algos.hora:HoraCriticModel",
             "priv_info_embed_dim": 2,
             "priv_mlp_hidden_dims": [4, 2],
         },
@@ -363,7 +363,7 @@ def test_hora_appo_runner_builds_shared_actor_critic_core() -> None:
 
 
 def test_hora_appo_worker_builds_shared_actor_critic_core() -> None:
-    from unilab.algos.torch.hora.appo_worker import hora_appo_collector_fn
+    from uni_rl.algos.hora.appo_worker import hora_appo_collector_fn
 
     source = textwrap.dedent(inspect.getsource(hora_appo_collector_fn))
     tree = ast.parse(source)
@@ -381,7 +381,7 @@ def test_hora_appo_worker_builds_shared_actor_critic_core() -> None:
 
 
 def test_hora_appo_play_builds_explicit_shared_actor_core() -> None:
-    from unilab.algos.torch.hora.appo import play_hora_appo
+    from unilab.scripts.play_hora_appo import play_hora_appo
 
     source = textwrap.dedent(inspect.getsource(play_hora_appo))
     tree = ast.parse(source)
@@ -405,7 +405,7 @@ def _patch_hora_appo_play_fakes(monkeypatch: pytest.MonkeyPatch, *, actor_cls: t
     import numpy as np
     import rsl_rl.utils as rsl_rl_utils
 
-    import unilab.algos.torch.hora.appo as hora_appo
+    import unilab.scripts.play_hora_appo as hora_appo
 
     fake_env = SimpleNamespace(
         obs_groups_spec={"obs": 3, "critic": 5},
@@ -466,7 +466,7 @@ def test_hora_appo_play_runs_sim2sim_preflight_before_checkpoint_load(
     monkeypatch: pytest.MonkeyPatch,
     tmp_path,
 ) -> None:
-    import unilab.algos.torch.hora.appo as hora_appo
+    import unilab.scripts.play_hora_appo as hora_appo
 
     checkpoint = tmp_path / "model_10.pt"
     torch.save({"actor": {"weight": torch.tensor(1.0)}}, checkpoint)
@@ -506,8 +506,8 @@ def test_hora_appo_play_dim_mismatch_reraises_explicit_sim2sim_diagnostic(
     tmp_path,
     capsys,
 ) -> None:
-    import unilab.algos.torch.hora.appo as hora_appo
-    from unilab.training.sim2sim import CrossBackendIncompatibleError
+    import unilab.scripts.play_hora_appo as hora_appo
+    from unilab.utils.sim2sim import CrossBackendIncompatibleError
 
     checkpoint = tmp_path / "model_10.pt"
     torch.save({"actor": {"weight": torch.tensor(1.0)}}, checkpoint)
@@ -534,7 +534,7 @@ def load_state_dict(self, state_dict, strict=True):
 
 
 def test_hora_appo_resume_rejects_inconsistent_shared_checkpoint() -> None:
-    from unilab.algos.torch.hora.appo_runner import _validate_hora_shared_checkpoint
+    from uni_rl.algos.hora.appo_runner import _validate_hora_shared_checkpoint
 
     learner = _make_hora_appo_learner()
     joint_checkpoint = {
diff --git a/tests/algos/test_hora_distill_config.py b/tests/algos/test_hora_distill_config.py
index 457e7a2bf..7db6be786 100644
--- a/tests/algos/test_hora_distill_config.py
+++ b/tests/algos/test_hora_distill_config.py
@@ -1,128 +1,22 @@
-"""Contract tests for HORA distill teacher-owner Hydra composition.
-
-The legacy reference implementation below freezes the pre-refactor manual
-loader (``OmegaConf.load`` + a loop over direct ``defaults`` entries) and the
-Python-held teacher -> student hyperparameter mapping. The parity tests pin
-the refactor to identical numerics while the loader moves to standard Hydra
-``initialize_config_dir + compose`` and the mapping moves into
-``conf/hora_distill/student_model/*.yaml``.
-"""
+"""Contract tests for HORA distill teacher-owner Hydra composition."""
 
 from __future__ import annotations
 
 from pathlib import Path
-from typing import Any
 
 import pytest
 from omegaconf import OmegaConf
 from omegaconf.errors import InterpolationResolutionError
 
-from unilab.algos.torch.hora import distill_config
-
-_REPO_ROOT = Path(__file__).resolve().parents[2]
+from unilab.training import hora_distill_config as distill_config
 
-TEACHER_CASES = [
-    ("ppo", "sharpa_inhand/mujoco_hora"),
-    # The APPO HORA owner composes through /task/sharpa_inhand/mujoco, which
-    # exercises an owner whose own defaults chain into another owner file.
-    ("appo", "sharpa_inhand/mujoco_hora"),
-    ("sac", "sac/sharpa_inhand/mujoco_hora"),
-]
+# distill_config's root_dir parameter expects the directory containing "conf";
+# after the packaging move that is the unilab package directory.
+_PACKAGE_ROOT = Path(__file__).resolve().parents[2] / "src" / "unilab"
 
 
 # ---------------------------------------------------------------------------
-# Legacy reference implementation (frozen copy of the pre-refactor behavior)
-# ---------------------------------------------------------------------------
-
-
-def _legacy_teacher_cfg(algo_family: str, task: str) -> Any:
-    if algo_family == "sac":
-        owner_path = _REPO_ROOT / "conf" / "offpolicy" / "task" / f"{task}.yaml"
-        defaults_base = _REPO_ROOT / "conf" / "offpolicy"
-        algo_defaults_path = _REPO_ROOT / "conf" / "offpolicy" / "algo" / "sac.yaml"
-    else:
-        owner_path = _REPO_ROOT / "conf" / algo_family / "task" / f"{task}.yaml"
-        defaults_base = _REPO_ROOT / "conf" / algo_family
-        algo_defaults_path = None
-    merged_cfg = OmegaConf.create()
-    if algo_defaults_path is not None:
-        merged_cfg = OmegaConf.merge(
-            merged_cfg,
-            OmegaConf.create({"algo": OmegaConf.load(algo_defaults_path)}),
-        )
-    owner_cfg = OmegaConf.load(owner_path)
-    for default_entry in owner_cfg.get("defaults", []):
-        if not isinstance(default_entry, str) or default_entry == "_self_":
-            continue
-        include_path = defaults_base / f"{default_entry.lstrip('/')}.yaml"
-        merged_cfg = OmegaConf.merge(merged_cfg, OmegaConf.load(include_path))
-    return OmegaConf.merge(merged_cfg, owner_cfg)
-
-
-def _legacy_model_cfg(algo_family: str, task: str) -> dict[str, Any]:
-    teacher_cfg = _legacy_teacher_cfg(algo_family, task)
-    if algo_family == "sac":
-        actor_cfg = OmegaConf.to_container(
-            OmegaConf.select(teacher_cfg, "algo.actor"), resolve=True
-        )
-        if not isinstance(actor_cfg, dict):
-            actor_cfg = {}
-        return {
-            "teacher_arch": "hora_sac",
-            "actor_hidden_dim": OmegaConf.select(teacher_cfg, "algo.actor_hidden_dim", default=512),
-            "use_layer_norm": OmegaConf.select(teacher_cfg, "algo.use_layer_norm", default=True),
-            "priv_info_embed_dim": actor_cfg.get("priv_info_embed_dim", 9),
-            "priv_mlp_hidden_dims": actor_cfg.get("priv_mlp_hidden_dims", [256, 128, 9]),
-        }
-    actor_cfg = OmegaConf.to_container(OmegaConf.select(teacher_cfg, "algo.actor"), resolve=True)
-    actor_cfg = dict(actor_cfg) if isinstance(actor_cfg, dict) else {}
-    actor_cfg.pop("class_name", None)
-    distribution_cfg = actor_cfg.get("distribution_cfg")
-    if isinstance(distribution_cfg, dict):
-        distribution_cfg = {
-            key: value for key, value in distribution_cfg.items() if key != "class_name"
-        }
-    return {
-        "hidden_dims": actor_cfg.get("hidden_dims"),
-        "activation": actor_cfg.get("activation"),
-        "obs_normalization": actor_cfg.get("obs_normalization"),
-        "priv_info_embed_dim": actor_cfg.get("priv_info_embed_dim"),
-        "priv_mlp_hidden_dims": actor_cfg.get("priv_mlp_hidden_dims"),
-        "distribution_cfg": distribution_cfg,
-    }
-
-
-def _assert_subset(legacy: Any, new: Any, path: str) -> None:
-    """Every leaf the legacy loader produced must be identical in the new one."""
-    if isinstance(legacy, dict) and isinstance(new, dict):
-        for key, value in legacy.items():
-            assert key in new, f"missing key {path}.{key}"
-            _assert_subset(value, new[key], f"{path}.{key}")
-        return
-    assert legacy == new, f"value mismatch at {path}: legacy={legacy!r} new={new!r}"
-
-
-# ---------------------------------------------------------------------------
-# Parity: new Hydra compose path vs frozen legacy path
-# ---------------------------------------------------------------------------
-
-
-@pytest.mark.parametrize(("algo_family", "task"), TEACHER_CASES)
-def test_teacher_default_cfg_matches_legacy_manual_loader(algo_family: str, task: str) -> None:
-    cfg = OmegaConf.create({"teacher": {"algo_family": algo_family, "task": task}})
-
-    new_cfg = OmegaConf.to_container(
-        distill_config.teacher_default_cfg(cfg, root_dir=_REPO_ROOT), resolve=True
-    )
-    legacy_cfg = OmegaConf.to_container(_legacy_teacher_cfg(algo_family, task), resolve=True)
-
-    assert new_cfg["algo"]["model"] == _legacy_model_cfg(algo_family, task)
-    for section in ("training", "reward", "env"):
-        _assert_subset(legacy_cfg.get(section), new_cfg.get(section), section)
-
-
-# ---------------------------------------------------------------------------
-# Compose capabilities the manual loader could not express
+# Hydra composition capabilities used by teacher-owner configs
 # ---------------------------------------------------------------------------
 
 
@@ -166,7 +60,7 @@ def test_load_teacher_owner_config_supports_nested_defaults_packages_and_interpo
 def test_hora_sac_mapping_keeps_yaml_fallbacks_for_missing_teacher_fields(tmp_path: Path) -> None:
     teacher_cfg = OmegaConf.create({"algo": {"runtime_impl": "hora_sac", "actor": {}}})
 
-    model_cfg = distill_config._student_model_defaults("hora_sac", teacher_cfg, root=_REPO_ROOT)
+    model_cfg = distill_config._student_model_defaults("hora_sac", teacher_cfg, root=_PACKAGE_ROOT)
 
     assert model_cfg == {
         "teacher_arch": "hora_sac",
@@ -182,7 +76,7 @@ def test_hora_actor_mapping_strips_distribution_class_name() -> None:
         {
             "algo": {
                 "actor": {
-                    "class_name": "unilab.algos.torch.hora:HoraActorModel",
+                    "class_name": "uni_rl.algos.hora:HoraActorModel",
                     "hidden_dims": [64, 32],
                     "activation": "relu",
                     "obs_normalization": False,
@@ -198,7 +92,9 @@ def test_hora_actor_mapping_strips_distribution_class_name() -> None:
         }
     )
 
-    model_cfg = distill_config._student_model_defaults("hora_actor", teacher_cfg, root=_REPO_ROOT)
+    model_cfg = distill_config._student_model_defaults(
+        "hora_actor", teacher_cfg, root=_PACKAGE_ROOT
+    )
 
     assert model_cfg == {
         "hidden_dims": [64, 32],
@@ -215,7 +111,7 @@ def test_hora_actor_mapping_fails_closed_when_teacher_field_is_missing() -> None
         {
             "algo": {
                 "actor": {
-                    "class_name": "unilab.algos.torch.hora:HoraActorModel",
+                    "class_name": "uni_rl.algos.hora:HoraActorModel",
                     "activation": "elu",
                     "obs_normalization": True,
                     "priv_info_embed_dim": 9,
@@ -227,4 +123,4 @@ def test_hora_actor_mapping_fails_closed_when_teacher_field_is_missing() -> None
     )
 
     with pytest.raises(InterpolationResolutionError):
-        distill_config._student_model_defaults("hora_actor", teacher_cfg, root=_REPO_ROOT)
+        distill_config._student_model_defaults("hora_actor", teacher_cfg, root=_PACKAGE_ROOT)
diff --git a/tests/algos/test_hora_imports.py b/tests/algos/test_hora_imports.py
deleted file mode 100644
index f5cfcadee..000000000
--- a/tests/algos/test_hora_imports.py
+++ /dev/null
@@ -1,51 +0,0 @@
-from __future__ import annotations
-
-import importlib
-import sys
-
-import pytest
-from rsl_rl.utils import resolve_callable
-
-
-def test_hora_package_import_keeps_appo_lazy() -> None:
-    sys.modules.pop("unilab.algos.torch.hora", None)
-    sys.modules.pop("unilab.algos.torch.hora.appo", None)
-
-    importlib.import_module("unilab.algos.torch.hora")
-
-    assert "unilab.algos.torch.hora.appo" not in sys.modules
-
-
-def test_resolve_callable_loads_hora_ppo_from_package_export() -> None:
-    resolved = resolve_callable("unilab.algos.torch.hora:HoraPPO")
-
-    from unilab.algos.torch.hora.ppo import HoraPPO
-
-    assert resolved is HoraPPO
-
-
-def test_rsl_rl_runtime_resolver_loads_hora_wrapper_from_owner_marker() -> None:
-    from unilab.algos.torch.hora.rsl_rl import HoraRslRlVecEnvWrapper
-    from unilab.algos.torch.rsl_rl_runtime import resolve_rsl_rl_ppo_runtime
-    from unilab.training.rsl_rl import RslRlVecEnvWrapper
-
-    runtime = resolve_rsl_rl_ppo_runtime(
-        {
-            "runtime_impl": "hora_ppo",
-            "runtime_resolver": "unilab.algos.torch.hora.rsl_rl:resolve_hora_ppo_runtime",
-        },
-        default_wrapper_cls=RslRlVecEnvWrapper,
-    )
-
-    assert runtime.wrapper_cls is HoraRslRlVecEnvWrapper
-
-
-def test_rsl_rl_runtime_resolver_rejects_unresolved_custom_runtime() -> None:
-    from unilab.algos.torch.rsl_rl_runtime import resolve_rsl_rl_ppo_runtime
-    from unilab.training.rsl_rl import RslRlVecEnvWrapper
-
-    with pytest.raises(ValueError, match="runtime_impl='hora_ppo'.*runtime_resolver"):
-        resolve_rsl_rl_ppo_runtime(
-            {"runtime_impl": "hora_ppo"},
-            default_wrapper_cls=RslRlVecEnvWrapper,
-        )
diff --git a/tests/algos/test_offpolicy_bootstrap_contract.py b/tests/algos/test_offpolicy_bootstrap_contract.py
deleted file mode 100644
index c6a8c0ea2..000000000
--- a/tests/algos/test_offpolicy_bootstrap_contract.py
+++ /dev/null
@@ -1,121 +0,0 @@
-from __future__ import annotations
-
-import torch
-
-from unilab.algos.torch.fast_sac.learner import FastSACLearner
-from unilab.algos.torch.fast_td3.learner import FastTD3Learner
-
-
-class _CaptureSacTargetCritic(torch.nn.Module):
-    def __init__(self) -> None:
-        super().__init__()
-        self.register_parameter("anchor", torch.nn.Parameter(torch.zeros(())))
-        self.bootstrap: torch.Tensor | None = None
-
-    def projection(self, obs, actions, rewards, bootstrap, discount):
-        del obs, actions, rewards, discount
-        self.bootstrap = bootstrap.detach().clone()
-        batch_size = bootstrap.shape[0]
-        probs = torch.zeros(2, batch_size, 3, dtype=bootstrap.dtype)
-        probs[:, :, 0] = 1.0
-        return probs
-
-    def get_value(self, probs):
-        return probs[:, :, 0]
-
-
-class _CaptureTd3TargetCritic(torch.nn.Module):
-    def __init__(self) -> None:
-        super().__init__()
-        self.bootstrap: torch.Tensor | None = None
-
-    def projection(self, obs, actions, rewards, bootstrap, discount):
-        del obs, actions, rewards, discount
-        self.bootstrap = bootstrap.detach().clone()
-        batch_size = bootstrap.shape[0]
-        probs = torch.zeros(batch_size, 3, dtype=bootstrap.dtype)
-        probs[:, 0] = 1.0
-        return probs, probs.clone()
-
-    def get_value(self, probs):
-        return probs[:, 0]
-
-
-def _offpolicy_batch(batch_size: int = 3) -> dict[str, torch.Tensor]:
-    return {
-        "obs": torch.zeros(batch_size, 4),
-        "critic": torch.zeros(batch_size, 5),
-        "actions": torch.zeros(batch_size, 2),
-        "rewards": torch.zeros(batch_size),
-        "next_obs": torch.zeros(batch_size, 4),
-        "next_critic": torch.zeros(batch_size, 5),
-        "dones": torch.tensor([0.0, 1.0, 1.0]),
-        "truncated": torch.tensor([0.0, 1.0, 0.0]),
-    }
-
-
-def test_fast_sac_critic_bootstrap_uses_combined_dones_and_truncated() -> None:
-    learner = FastSACLearner(
-        obs_dim=4,
-        action_dim=2,
-        critic_obs_dim=5,
-        device="cpu",
-        actor_hidden_dim=8,
-        critic_hidden_dim=8,
-        num_atoms=3,
-        num_q_networks=2,
-        use_layer_norm=False,
-        use_autotune=False,
-    )
-    target_critic = _CaptureSacTargetCritic()
-    learner.qnet_target = target_critic
-
-    learner.update_critic(_offpolicy_batch())
-
-    assert target_critic.bootstrap is not None
-    torch.testing.assert_close(target_critic.bootstrap, torch.tensor([1.0, 1.0, 0.0]))
-
-
-def test_fast_sac_critic_requires_truncated_field() -> None:
-    learner = FastSACLearner(
-        obs_dim=4,
-        action_dim=2,
-        critic_obs_dim=5,
-        device="cpu",
-        actor_hidden_dim=8,
-        critic_hidden_dim=8,
-        num_atoms=3,
-        num_q_networks=2,
-        use_layer_norm=False,
-        use_autotune=False,
-    )
-    batch = _offpolicy_batch()
-    batch.pop("truncated")
-
-    try:
-        learner.update_critic(batch)
-    except KeyError as exc:
-        assert exc.args == ("truncated",)
-    else:  # pragma: no cover - explicit failure path
-        raise AssertionError("FastSAC learner must require replay 'truncated'")
-
-
-def test_fast_td3_critic_bootstrap_uses_combined_dones_and_truncated() -> None:
-    learner = FastTD3Learner(
-        obs_dim=4,
-        action_dim=2,
-        critic_obs_dim=5,
-        num_envs=3,
-        device="cpu",
-        actor_hidden_dim=8,
-        critic_hidden_dim=8,
-        num_atoms=3,
-        obs_normalization=False,
-    )
-    target_critic = _CaptureTd3TargetCritic()
-    learner.qnet_target = target_critic
-
-    learner.update_critic(_offpolicy_batch())
-
-    assert target_critic.bootstrap is not None
-    torch.testing.assert_close(target_critic.bootstrap, torch.tensor([1.0, 1.0, 0.0]))
diff --git a/tests/algos/test_offpolicy_double_buffer_runner.py b/tests/algos/test_offpolicy_double_buffer_runner.py
index 9998a33a9..1d072591c 100644
--- a/tests/algos/test_offpolicy_double_buffer_runner.py
+++ b/tests/algos/test_offpolicy_double_buffer_runner.py
@@ -12,15 +12,14 @@
 from hydra import compose, initialize_config_dir
 from hydra.core.global_hydra import GlobalHydra
 from hydra.errors import ConfigCompositionException
-
-from unilab.ipc.dp_launcher import UNILAB_DP_LOG_DIR, UNILAB_DP_RANK, UNILAB_DP_WORLD_SIZE
+from uni_rl.ipc.dp_launcher import UNILAB_DP_LOG_DIR, UNILAB_DP_RANK, UNILAB_DP_WORLD_SIZE
 
 _ROOT = Path(__file__).parent.parent.parent
-_CONF_DIR = _ROOT / "conf"
+_CONF_DIR = _ROOT / "src" / "unilab" / "conf"
 
 
 def _offpolicy():
-    path = _ROOT / "scripts" / "train_offpolicy.py"
+    path = _ROOT / "src" / "unilab" / "scripts" / "train_offpolicy.py"
     spec = importlib.util.spec_from_file_location("train_offpolicy", path)
     assert spec is not None and spec.loader is not None
     module = importlib.util.module_from_spec(spec)
@@ -28,25 +27,22 @@ def _offpolicy():
     return module
 
 
-def _offpolicy_cfg(overrides: list[str] | None = None):
+def _offpolicy_cfg(overrides: list[str] | None = None, *, algo: str = "sac"):
     GlobalHydra.instance().clear()
     normalized: list[str] = []
-    algo = "sac"
     task_selected = False
     devices_selected = False
     for override in overrides or []:
-        if override.startswith("algo="):
-            algo = override.split("=", 1)[1]
-        elif override.startswith("task="):
+        if override.startswith("task="):
             task_selected = True
         elif override.startswith("training.devices="):
             devices_selected = True
         normalized.append(override)
     if not task_selected:
-        normalized.append(f"task={algo}/g1_walk_flat/mujoco")
+        normalized.append("task=g1_walk_flat/mujoco")
     if not devices_selected:
         normalized.append("training.devices=[0]")
-    with initialize_config_dir(config_dir=str(_CONF_DIR / "offpolicy"), version_base="1.3"):
+    with initialize_config_dir(config_dir=str(_CONF_DIR / algo), version_base="1.3"):
         return compose("config", overrides=normalized, return_hydra_config=True)
 
 
@@ -54,10 +50,6 @@ class _FakeEnv:
     obs_groups_spec = {"obs": 4, "critic": 6}
     action_space = gym.spaces.Box(-1.0, 1.0, shape=(2,))
 
-    def build_symmetry_augmentation(self, device=None):
-        del device
-        return None
-
     def close(self):
         return None
 
@@ -81,6 +73,12 @@ def __init__(self, *args, **kwargs):
         self.kwargs = kwargs
 
 
+def _fake_env_factory(num_envs, env_cfg_override):
+    """EnvFactory-shaped probe stub (uni_rl runners receive envs by injection)."""
+    del num_envs, env_cfg_override
+    return _FakeEnv()
+
+
 def test_offpolicy_config_has_one_replay_path():
     cfg = _offpolicy_cfg()
     assert cfg.training.replay_prefetch_mode == "one_tick"
@@ -97,7 +95,7 @@ def test_offpolicy_config_has_one_replay_path():
 
 
 def test_hora_uses_same_learner_inference_path():
-    cfg = _offpolicy_cfg(["algo=sac", "task=sac/sharpa_inhand/mujoco_hora"])
+    cfg = _offpolicy_cfg(["task=sharpa_inhand/mujoco_hora"])
     assert "inference_owner" not in cfg.training
     assert cfg.training.env_steps_per_sync == 2
 
@@ -137,27 +135,17 @@ def test_non_cuda_training_devices_fail_before_env_materialization(
     device: str,
 ):
     module = _offpolicy()
-    cfg = _offpolicy_cfg([f"algo={algo}", f"training.devices=[{device}]"])
+    cfg = _offpolicy_cfg([f"training.devices=[{device}]"], algo=algo)
     env_calls = 0
 
-    def reject_env(*args, **kwargs):
+    def reject_factory(num_envs, env_cfg_override):
+        del num_envs, env_cfg_override
         nonlocal env_calls
-        del args, kwargs
         env_calls += 1
         raise AssertionError("unsupported replay device must fail before env creation")
 
-    if algo == "sac":
-        import unilab.algos.torch.fast_sac.double_buffer as owner_module
-
-        monkeypatch.setattr(owner_module, "create_env", reject_env)
-    elif algo == "td3":
-        import unilab.algos.torch.fast_td3.double_buffer as owner_module
-
-        monkeypatch.setattr(owner_module, "get_env_dims", reject_env)
-    else:
-        import unilab.algos.torch.flash_sac.double_buffer as owner_module
-
-        monkeypatch.setattr(owner_module, "create_env", reject_env)
+    # The injected env_factory is the only env-construction seam uni_rl has.
+    monkeypatch.setattr(module, "registry_env_factory", lambda *args, **kwargs: reject_factory)
     with pytest.raises(ValueError, match="training.devices entries"):
         module.build_runner(algo, cfg)
     assert env_calls == 0
@@ -165,12 +153,11 @@ def reject_env(*args, **kwargs):
 
 def test_sac_dispatch_constructs_unique_runner(monkeypatch: pytest.MonkeyPatch):
     module = _offpolicy()
-    cfg = _offpolicy_cfg(["algo=sac", "algo.use_symmetry=false"])
+    cfg = _offpolicy_cfg([])
 
-    import unilab.algos.torch.fast_sac.double_buffer as owner_module
+    import uni_rl.algos.fast_sac.double_buffer as owner_module
 
-    monkeypatch.setattr(owner_module, "ensure_registries", lambda: None)
-    monkeypatch.setattr(owner_module, "create_env", lambda *args, **kwargs: _FakeEnv())
+    monkeypatch.setattr(module, "registry_env_factory", lambda *args, **kwargs: _fake_env_factory)
     monkeypatch.setattr(owner_module, "FastSACLearner", _FakeLearner)
     monkeypatch.setattr(owner_module, "DoubleBufferOffPolicyRunner", _FakeRunner)
 
@@ -213,26 +200,29 @@ def test_sac_dispatch_constructs_unique_runner(monkeypatch: pytest.MonkeyPatch):
             cfg.algo.algo_params.use_cuda_graph_actor_packed_staging
         ),
         "nvtx_profile_ranges": cfg.training.nvtx_profile_ranges,
-        "use_symmetry": False,
-        "symmetry_augmentation": None,
         "critic_obs_dim": 6,
     }
+    assert runner.kwargs["inference_request_timeout_sec"] == 30.0
+
+
+def test_sac_genesis_owner_raises_inference_request_timeout():
+    """Genesis kernel compilation exceeds the 30 s default tick-0 budget."""
+    cfg = _offpolicy_cfg(["task=g1_walk_flat/genesis"])
+    assert cfg.training.inference_request_timeout_sec == 180.0
 
 
 def test_sac_owner_custom_runtime_can_override_base_learner_kwargs(
     monkeypatch: pytest.MonkeyPatch,
 ):
-    from unilab.algos.torch.fast_sac import double_buffer as owner_module
-    from unilab.algos.torch.offpolicy.runtime import OffPolicyRuntime
+    from uni_rl.algos.fast_sac import double_buffer as owner_module
+    from uni_rl.offpolicy.runtime import OffPolicyRuntime
 
-    cfg = _offpolicy_cfg(["algo=sac", "algo.use_symmetry=false"])
+    cfg = _offpolicy_cfg([])
     custom_runtime = OffPolicyRuntime(
         learner_cls=_FakeLearner,
         algo_type="custom_sac",
         actor_kwargs={"gamma": 0.123, "critic_obs_dim": 17},
     )
-    monkeypatch.setattr(owner_module, "ensure_registries", lambda: None)
-    monkeypatch.setattr(owner_module, "create_env", lambda *args, **kwargs: _FakeEnv())
     monkeypatch.setattr(
         owner_module,
         "resolve_custom_offpolicy_runtime",
@@ -242,6 +232,7 @@ def test_sac_owner_custom_runtime_can_override_base_learner_kwargs(
 
     runner = owner_module.build_sac_double_buffer_runner(
         cfg,
+        env_factory=_fake_env_factory,
         env_cfg_override={},
         replay_prefetch_mode="one_tick",
         device="cuda:0",
@@ -253,100 +244,11 @@ def test_sac_owner_custom_runtime_can_override_base_learner_kwargs(
     assert runner.kwargs["learner"].kwargs["tau"] == cfg.algo.tau
 
 
-def test_sac_owner_rejects_custom_runtime_without_symmetry_support(
-    monkeypatch: pytest.MonkeyPatch,
-):
-    from unilab.algos.torch.fast_sac import double_buffer as owner_module
-    from unilab.algos.torch.offpolicy.runtime import OffPolicyRuntime
-
-    cfg = _offpolicy_cfg(["algo=sac", "algo.use_symmetry=true"])
-    monkeypatch.setattr(owner_module, "ensure_registries", lambda: None)
-    monkeypatch.setattr(owner_module, "create_env", lambda *args, **kwargs: _FakeEnv())
-    monkeypatch.setattr(
-        owner_module,
-        "resolve_custom_offpolicy_runtime",
-        lambda _cfg: OffPolicyRuntime(supports_symmetry=False),
-    )
-
-    with pytest.raises(ValueError, match="does not support symmetry"):
-        owner_module.build_sac_double_buffer_runner(
-            cfg,
-            env_cfg_override={},
-            replay_prefetch_mode="one_tick",
-            device="cuda:0",
-        )
-
-
-def test_sac_owner_preserves_symmetry_batch_and_learner_contract(
-    monkeypatch: pytest.MonkeyPatch,
-):
-    import unilab.algos.torch.fast_sac.double_buffer as owner_module
-
-    cfg = _offpolicy_cfg(["algo=sac", "algo.use_symmetry=true"])
-    symmetry = MagicMock(batch_multiplier=4)
-
-    class SymmetricEnv(_FakeEnv):
-        def build_symmetry_augmentation(self, device=None):
-            assert device == "cuda:0"
-            return symmetry
-
-    monkeypatch.setattr(owner_module, "ensure_registries", lambda: None)
-    monkeypatch.setattr(owner_module, "create_env", lambda *args, **kwargs: SymmetricEnv())
-    monkeypatch.setattr(owner_module, "FastSACLearner", _FakeLearner)
-    monkeypatch.setattr(owner_module, "DoubleBufferOffPolicyRunner", _FakeRunner)
-
-    runner = owner_module.build_sac_double_buffer_runner(
-        cfg,
-        env_cfg_override={},
-        replay_prefetch_mode="one_tick",
-        device="cuda:0",
-    )
-
-    assert runner.kwargs["batch_size"] == cfg.algo.batch_size // 4
-    learner_kwargs = runner.kwargs["learner"].kwargs
-    assert learner_kwargs["use_symmetry"] is True
-    assert learner_kwargs["symmetry_augmentation"] is symmetry
-
-
-@pytest.mark.parametrize(
-    ("batch_size", "symmetry", "match"),
-    [
-        (512, None, "does not provide symmetry augmentation"),
-        (10, MagicMock(batch_multiplier=4), "batch_size divisible by 4"),
-    ],
-)
-def test_sac_owner_preserves_symmetry_validation(
-    monkeypatch: pytest.MonkeyPatch,
-    batch_size: int,
-    symmetry: MagicMock | None,
-    match: str,
-):
-    import unilab.algos.torch.fast_sac.double_buffer as owner_module
-
-    cfg = _offpolicy_cfg(["algo=sac", "algo.use_symmetry=true", f"algo.batch_size={batch_size}"])
-
-    class SymmetricEnv(_FakeEnv):
-        def build_symmetry_augmentation(self, device=None):
-            del device
-            return symmetry
-
-    monkeypatch.setattr(owner_module, "ensure_registries", lambda: None)
-    monkeypatch.setattr(owner_module, "create_env", lambda *args, **kwargs: SymmetricEnv())
-
-    with pytest.raises(ValueError, match=match):
-        owner_module.build_sac_double_buffer_runner(
-            cfg,
-            env_cfg_override={},
-            replay_prefetch_mode="one_tick",
-            device="cuda:0",
-        )
-
-
 def test_td3_dispatch_constructs_unique_runner(monkeypatch: pytest.MonkeyPatch):
     module = _offpolicy()
-    cfg = _offpolicy_cfg(["algo=td3"])
+    cfg = _offpolicy_cfg(algo="td3")
 
-    import unilab.algos.torch.fast_td3.double_buffer as owner_module
+    import uni_rl.algos.fast_td3.double_buffer as owner_module
 
     monkeypatch.setattr(owner_module, "get_env_dims", lambda *args, **kwargs: (4, 2, 6))
     monkeypatch.setattr(owner_module, "FastTD3Learner", _FakeLearner)
@@ -400,6 +302,7 @@ def test_td3_dispatch_constructs_unique_runner(monkeypatch: pytest.MonkeyPatch):
     dp_sync = MagicMock()
     forwarded = owner_module.build_td3_double_buffer_runner(
         cfg,
+        env_factory=_fake_env_factory,
         env_cfg_override={},
         replay_prefetch_mode="one_tick",
         device="cuda:0",
@@ -416,12 +319,11 @@ def test_td3_dispatch_constructs_unique_runner(monkeypatch: pytest.MonkeyPatch):
 
 def test_flashsac_dispatch_constructs_unique_runner(monkeypatch: pytest.MonkeyPatch):
     module = _offpolicy()
-    cfg = _offpolicy_cfg(["algo=flashsac"])
+    cfg = _offpolicy_cfg(algo="flashsac")
 
-    import unilab.algos.torch.flash_sac.double_buffer as flash_module
+    import uni_rl.algos.flash_sac.double_buffer as flash_module
 
-    monkeypatch.setattr(flash_module, "ensure_registries", lambda: None)
-    monkeypatch.setattr(flash_module, "create_env", lambda *args, **kwargs: _FakeEnv())
+    monkeypatch.setattr(module, "registry_env_factory", lambda *args, **kwargs: _fake_env_factory)
     monkeypatch.setattr(flash_module, "FlashSACLearner", _FakeLearner)
     monkeypatch.setattr(flash_module, "DoubleBufferOffPolicyRunner", _FakeRunner)
 
@@ -437,13 +339,13 @@ def test_flashsac_dispatch_constructs_unique_runner(monkeypatch: pytest.MonkeyPa
 
 
 def test_flashsac_n_step_is_rejected():
-    cfg = _offpolicy_cfg(["algo=flashsac", "algo.algo_params.n_step=3"])
+    cfg = _offpolicy_cfg(["algo.algo_params.n_step=3"], algo="flashsac")
     with pytest.raises(ValueError, match="n_step=1 only"):
         _offpolicy().build_runner("flashsac", cfg)
 
 
 def _bare_runner():
-    from unilab.algos.torch.offpolicy.double_buffer_runner import DoubleBufferOffPolicyRunner
+    from uni_rl.offpolicy.double_buffer_runner import DoubleBufferOffPolicyRunner
 
     return object.__new__(DoubleBufferOffPolicyRunner)
 
@@ -463,23 +365,30 @@ def _build_sac_runner_with_fakes(
     overrides: list[str],
     *,
     cpu_count: int = 128,
+    backend_binding_calls: list[tuple[str, str]] | None = None,
 ):
     """build_runner("sac", ...) with learner/env/runner fakes; returns captured state."""
     module = _offpolicy()
     cfg = _offpolicy_cfg(overrides)
     probe_env_calls: list[dict] = []
 
-    def fake_create_env(*args, **kwargs):
-        del args
-        probe_env_calls.append(kwargs)
+    def fake_probe_env_factory(num_envs, env_cfg_override):
+        probe_env_calls.append({"num_envs": num_envs, "env_cfg_override": env_cfg_override})
         return _FakeEnv()
 
     monkeypatch.setattr(module.os, "cpu_count", lambda: cpu_count)
+    if backend_binding_calls is not None:
+        monkeypatch.setattr(
+            module,
+            "configure_backend_process_device",
+            lambda backend, device: backend_binding_calls.append((str(backend), str(device))),
+        )
 
-    import unilab.algos.torch.fast_sac.double_buffer as owner_module
+    import uni_rl.algos.fast_sac.double_buffer as owner_module
 
-    monkeypatch.setattr(owner_module, "ensure_registries", lambda: None)
-    monkeypatch.setattr(owner_module, "create_env", fake_create_env)
+    monkeypatch.setattr(
+        module, "registry_env_factory", lambda *args, **kwargs: fake_probe_env_factory
+    )
     monkeypatch.setattr(owner_module, "FastSACLearner", _FakeLearner)
     monkeypatch.setattr(owner_module, "DoubleBufferOffPolicyRunner", _FakeRunner)
 
@@ -487,16 +396,34 @@ def fake_create_env(*args, **kwargs):
     return runner, probe_env_calls
 
 
+def test_build_runner_binds_mjwarp_rank_process_to_learner_device(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    monkeypatch.setenv(UNILAB_DP_RANK, "1")
+    monkeypatch.setenv(UNILAB_DP_LOG_DIR, "/tmp/offpolicy_test_run")
+    bindings: list[tuple[str, str]] = []
+
+    runner, _ = _build_sac_runner_with_fakes(
+        monkeypatch,
+        ["task=g1_walk_flat/mjwarp", "training.devices=[0,1]"],
+        backend_binding_calls=bindings,
+    )
+
+    assert bindings == [("mjwarp", "cuda:1")]
+    assert runner.kwargs["device"] == "cuda:1"
+
+
 def test_build_runner_partitions_collector_cpus_per_rank(monkeypatch: pytest.MonkeyPatch):
     # Spawned rank: rank comes from the env, world_size from training.devices.
     monkeypatch.setenv(UNILAB_DP_RANK, "1")
     monkeypatch.setenv(UNILAB_DP_LOG_DIR, "/tmp/offpolicy_test_run")
     runner, probe_env_calls = _build_sac_runner_with_fakes(
         monkeypatch,
-        ["algo=sac", "algo.use_symmetry=false", "training.devices=[0,1]"],
+        ["training.devices=[0,1]"],
         cpu_count=128,
     )
     assert runner.kwargs["collector_cpu_ids"] == list(range(64, 128))
+    assert runner.kwargs["device"] == "cuda:1"
     # The thread budget is resolved against the rank's CPU share, not the host.
     assert runner.kwargs["torch_thread_runtime"]["cpu_count"] == 64
     # The num_envs=1 probe env must never see cpu_ids (it would size its
@@ -513,7 +440,7 @@ def test_build_runner_rank_zero_partitions_without_dp_env(monkeypatch: pytest.Mo
     monkeypatch.delenv(UNILAB_DP_WORLD_SIZE, raising=False)
     runner, _ = _build_sac_runner_with_fakes(
         monkeypatch,
-        ["algo=sac", "algo.use_symmetry=false", "training.devices=[0,1]"],
+        ["training.devices=[0,1]"],
         cpu_count=128,
     )
     assert runner.kwargs["collector_cpu_ids"] == list(range(0, 64))
@@ -523,9 +450,7 @@ def test_build_runner_rank_zero_partitions_without_dp_env(monkeypatch: pytest.Mo
 def test_build_runner_single_rank_keeps_collector_cpus_unset(monkeypatch: pytest.MonkeyPatch):
     monkeypatch.delenv(UNILAB_DP_RANK, raising=False)
     monkeypatch.delenv(UNILAB_DP_WORLD_SIZE, raising=False)
-    runner, probe_env_calls = _build_sac_runner_with_fakes(
-        monkeypatch, ["algo=sac", "algo.use_symmetry=false"], cpu_count=128
-    )
+    runner, probe_env_calls = _build_sac_runner_with_fakes(monkeypatch, [], cpu_count=128)
     assert runner.kwargs["collector_cpu_ids"] is None
     # Single-rank thread budget still resolves against the full host.
     assert runner.kwargs["torch_thread_runtime"]["cpu_count"] == 128
@@ -540,8 +465,6 @@ def test_build_runner_explicit_dp_collector_cpu_ids(monkeypatch: pytest.MonkeyPa
     runner, probe_env_calls = _build_sac_runner_with_fakes(
         monkeypatch,
         [
-            "algo=sac",
-            "algo.use_symmetry=false",
             "training.devices=[0,1]",
             "training.dp_collector_cpu_ids=[[0,1],[2,3]]",
         ],
diff --git a/tests/algos/test_offpolicy_dp_sync.py b/tests/algos/test_offpolicy_dp_sync.py
index b5266af47..9704cd71e 100644
--- a/tests/algos/test_offpolicy_dp_sync.py
+++ b/tests/algos/test_offpolicy_dp_sync.py
@@ -7,19 +7,19 @@
 
 import pytest
 import torch
+from uni_rl.ipc.dp_launcher import UNILAB_DP_LOG_DIR, UNILAB_DP_RANK
+from uni_rl.ipc.dp_sync import DpParameterSync
 
 from tests.algos.test_offpolicy_double_buffer_runner import (
-    _FakeEnv,
+    _fake_env_factory,
     _FakeRunner,
     _offpolicy,
     _offpolicy_cfg,
 )
-from unilab.ipc.dp_launcher import UNILAB_DP_LOG_DIR, UNILAB_DP_RANK
-from unilab.ipc.dp_sync import DpParameterSync
 
 
 def _bare_runner():
-    from unilab.algos.torch.offpolicy.double_buffer_runner import DoubleBufferOffPolicyRunner
+    from uni_rl.offpolicy.double_buffer_runner import DoubleBufferOffPolicyRunner
 
     return object.__new__(DoubleBufferOffPolicyRunner)
 
@@ -162,7 +162,7 @@ def test_close_closes_dp_sync_idempotently():
 
 
 def test_close_restores_terminal_and_ipc_before_destroying_process_group(monkeypatch):
-    from unilab.algos.torch.offpolicy.runner import OffPolicyRunner
+    from uni_rl.offpolicy.runner import OffPolicyRunner
 
     events: list[str] = []
 
@@ -185,7 +185,7 @@ def close(self) -> None:
 
 
 def test_close_still_destroys_process_group_when_local_cleanup_fails(monkeypatch):
-    from unilab.algos.torch.offpolicy.runner import OffPolicyRunner
+    from uni_rl.offpolicy.runner import OffPolicyRunner
 
     dp_sync = _FakeDpSync()
     runner = _runner_with(_SyncLearner(), dp_sync)
@@ -202,7 +202,7 @@ def fail_local_cleanup(self) -> None:
 
 
 def test_log_statistics_mean_scalars_and_sum_concurrent_throughput():
-    from unilab.logging import OffPolicyLogger
+    from uni_rl.logging import OffPolicyLogger
 
     dp_sync = _FakeDpSync()
     runner = _runner_with(_SyncLearner(), dp_sync)
@@ -335,7 +335,7 @@ def log_save(self, path: str) -> None:
 
 def test_learn_source_orders_sync_around_collector_and_logging():
     """Startup broadcast precedes collection; timing is consumed after updates."""
-    from unilab.algos.torch.offpolicy.double_buffer_runner import DoubleBufferOffPolicyRunner
+    from uni_rl.offpolicy.double_buffer_runner import DoubleBufferOffPolicyRunner
 
     source = inspect.getsource(DoubleBufferOffPolicyRunner.learn)
     assert source.index("self._dp_init_broadcast()") < source.index("self._start_collector(")
@@ -350,7 +350,7 @@ def test_learn_source_orders_sync_around_collector_and_logging():
 
 
 def test_fast_sac_initial_sync_tensors_return_live_references():
-    from unilab.algos.torch.fast_sac.learner import FastSACLearner
+    from uni_rl.algos.fast_sac.learner import FastSACLearner
 
     learner = FastSACLearner(
         obs_dim=4,
@@ -389,7 +389,7 @@ def test_fast_sac_initial_sync_tensors_return_live_references():
 
 
 def test_fast_sac_syncs_each_optimizer_gradient_before_step():
-    from unilab.algos.torch.fast_sac.learner import FastSACLearner
+    from uni_rl.algos.fast_sac.learner import FastSACLearner
 
     learner = FastSACLearner(
         obs_dim=4,
@@ -446,7 +446,7 @@ def record_gradients(parameters) -> None:
 
 
 def test_fast_sac_gradient_sync_preserves_cuda_graph_capture():
-    from unilab.algos.torch.fast_sac.learner import FastSACLearner
+    from uni_rl.algos.fast_sac.learner import FastSACLearner
 
     learner = FastSACLearner(
         obs_dim=4,
@@ -482,14 +482,13 @@ def _build_sac_runner_with_dp_fakes(monkeypatch: pytest.MonkeyPatch, overrides:
     cfg = _offpolicy_cfg(overrides)
     monkeypatch.setattr(module.os, "cpu_count", lambda: 128)
 
-    import unilab.algos.torch.fast_sac.double_buffer as owner_module
+    import uni_rl.algos.fast_sac.double_buffer as owner_module
 
     class _Learner:
         def __init__(self, *args, **kwargs):
             del args, kwargs
 
-    monkeypatch.setattr(owner_module, "ensure_registries", lambda: None)
-    monkeypatch.setattr(owner_module, "create_env", lambda *args, **kwargs: _FakeEnv())
+    monkeypatch.setattr(module, "registry_env_factory", lambda *args, **kwargs: _fake_env_factory)
     monkeypatch.setattr(owner_module, "FastSACLearner", _Learner)
     monkeypatch.setattr(owner_module, "DoubleBufferOffPolicyRunner", _FakeRunner)
     runner = module.build_runner("sac", cfg, log_dir="/tmp/dp_sync_test_run")
@@ -503,7 +502,7 @@ def test_offpolicy_config_has_no_periodic_parameter_sync_interval():
 
 def test_build_runner_single_rank_keeps_dp_sync_none(monkeypatch: pytest.MonkeyPatch):
     monkeypatch.delenv(UNILAB_DP_RANK, raising=False)
-    kwargs = _build_sac_runner_with_dp_fakes(monkeypatch, ["algo=sac", "algo.use_symmetry=false"])
+    kwargs = _build_sac_runner_with_dp_fakes(monkeypatch, [])
     assert kwargs["dp_sync"] is None
 
 
@@ -513,8 +512,6 @@ def test_build_runner_multi_gpu_constructs_dp_sync_for_rank0(monkeypatch: pytest
     kwargs = _build_sac_runner_with_dp_fakes(
         monkeypatch,
         [
-            "algo=sac",
-            "algo.use_symmetry=false",
             "training.devices=[0,1]",
         ],
     )
@@ -531,7 +528,7 @@ def test_build_runner_spawned_rank_uses_shared_run_root(monkeypatch: pytest.Monk
     monkeypatch.setenv(UNILAB_DP_LOG_DIR, "/tmp/dp_sync_shared_root")
     kwargs = _build_sac_runner_with_dp_fakes(
         monkeypatch,
-        ["algo=sac", "algo.use_symmetry=false", "training.devices=[0,1]"],
+        ["training.devices=[0,1]"],
     )
     dp_sync = kwargs["dp_sync"]
     assert isinstance(dp_sync, DpParameterSync)
@@ -542,11 +539,10 @@ def test_build_runner_spawned_rank_uses_shared_run_root(monkeypatch: pytest.Monk
 
 def test_build_runner_multi_gpu_rank0_requires_log_dir(monkeypatch: pytest.MonkeyPatch):
     module = _offpolicy()
-    cfg = _offpolicy_cfg(["algo=sac", "algo.use_symmetry=false", "training.devices=[0,1]"])
+    cfg = _offpolicy_cfg(["training.devices=[0,1]"])
     monkeypatch.delenv(UNILAB_DP_RANK, raising=False)
     monkeypatch.delenv(UNILAB_DP_LOG_DIR, raising=False)
-    monkeypatch.setattr(module, "ensure_registries", lambda: None)
-    monkeypatch.setattr(module, "create_env", lambda *args, **kwargs: _FakeEnv())
+    monkeypatch.setattr(module, "registry_env_factory", lambda *args, **kwargs: _fake_env_factory)
     monkeypatch.setattr(module.os, "cpu_count", lambda: 128)
     with pytest.raises(ValueError, match="log_dir"):
         module.build_runner("sac", cfg)
@@ -556,7 +552,7 @@ def test_build_runner_multi_gpu_rank0_requires_log_dir(monkeypatch: pytest.Monke
 
 
 def test_flash_sac_initial_sync_tensors_return_live_references():
-    from unilab.algos.torch.flash_sac.learner import FlashSACLearner
+    from uni_rl.algos.flash_sac.learner import FlashSACLearner
 
     learner = FlashSACLearner(
         obs_dim=4,
@@ -603,7 +599,7 @@ def test_flash_sac_initial_sync_tensors_return_live_references():
 
 
 def test_flash_sac_syncs_each_optimizer_gradient_before_step():
-    from unilab.algos.torch.flash_sac.learner import FlashSACLearner
+    from uni_rl.algos.flash_sac.learner import FlashSACLearner
 
     learner = FlashSACLearner(
         obs_dim=4,
@@ -650,7 +646,7 @@ def record_gradients(parameters) -> None:
 
 
 def test_flash_sac_gradient_sync_preserves_cuda_graph_and_cpu_fallback_updates():
-    from unilab.algos.torch.flash_sac.learner import FlashSACLearner
+    from uni_rl.algos.flash_sac.learner import FlashSACLearner
 
     learner = FlashSACLearner(
         obs_dim=4,
@@ -703,13 +699,12 @@ def record_gradients(parameters) -> None:
 def _build_flashsac_runner_with_dp_fakes(monkeypatch: pytest.MonkeyPatch, overrides: list[str]):
     """build_runner("flashsac", ...) with learner/env/runner fakes; returns runner kwargs."""
     module = _offpolicy()
-    cfg = _offpolicy_cfg(overrides)
+    cfg = _offpolicy_cfg(overrides, algo="flashsac")
     monkeypatch.setattr(module.os, "cpu_count", lambda: 128)
 
-    import unilab.algos.torch.flash_sac.double_buffer as flash_module
+    import uni_rl.algos.flash_sac.double_buffer as flash_module
 
-    monkeypatch.setattr(flash_module, "ensure_registries", lambda: None)
-    monkeypatch.setattr(flash_module, "create_env", lambda *args, **kwargs: _FakeEnv())
+    monkeypatch.setattr(module, "registry_env_factory", lambda *args, **kwargs: _fake_env_factory)
 
     class _Learner:
         def __init__(self, *args, **kwargs):
@@ -723,7 +718,7 @@ def __init__(self, *args, **kwargs):
 
 def test_build_runner_single_rank_flashsac_keeps_dp_sync_none(monkeypatch: pytest.MonkeyPatch):
     monkeypatch.delenv(UNILAB_DP_RANK, raising=False)
-    kwargs = _build_flashsac_runner_with_dp_fakes(monkeypatch, ["algo=flashsac"])
+    kwargs = _build_flashsac_runner_with_dp_fakes(monkeypatch, [])
     assert kwargs["dp_sync"] is None
     assert kwargs["collector_cpu_ids"] is None
 
@@ -736,7 +731,6 @@ def test_build_runner_multi_gpu_constructs_dp_sync_for_flashsac_rank0(
     kwargs = _build_flashsac_runner_with_dp_fakes(
         monkeypatch,
         [
-            "algo=flashsac",
             "training.devices=[0,1]",
         ],
     )
@@ -755,7 +749,7 @@ def test_build_runner_multi_gpu_flashsac_spawned_rank(monkeypatch: pytest.Monkey
     monkeypatch.setenv(UNILAB_DP_LOG_DIR, "/tmp/dp_sync_shared_root")
     kwargs = _build_flashsac_runner_with_dp_fakes(
         monkeypatch,
-        ["algo=flashsac", "training.devices=[0,1]"],
+        ["training.devices=[0,1]"],
     )
     dp_sync = kwargs["dp_sync"]
     assert isinstance(dp_sync, DpParameterSync)
diff --git a/tests/algos/test_offpolicy_logger.py b/tests/algos/test_offpolicy_logger.py
deleted file mode 100644
index cdc2bf411..000000000
--- a/tests/algos/test_offpolicy_logger.py
+++ /dev/null
@@ -1,496 +0,0 @@
-from __future__ import annotations
-
-from typing import Any
-
-import pytest
-from rich.console import Console
-
-import unilab.logging.common as common_logger_module
-import unilab.logging.offpolicy as offpolicy_logger_module
-from unilab.logging.offpolicy import OffPolicyLogger
-
-
-def test_offpolicy_logger_only_forces_refresh_for_errors(monkeypatch) -> None:
-    logger = OffPolicyLogger(
-        algo_name="SAC",
-        max_iterations=2,
-        num_envs=8,
-        env_name="Dummy",
-        log_backend="none",
-    )
-    refresh_calls: list[bool] = []
-
-    def fake_refresh(*, force: bool = False) -> None:
-        refresh_calls.append(force)
-
-    monkeypatch.setattr(logger, "_refresh", fake_refresh)
-
-    logger.log_buffer_fill(32, 64)
-    logger.log_status("Replay storage: device-authoritative bounded ingress")
-
-    assert refresh_calls == []
-    assert logger._buffer_size == 32
-    assert logger._buffer_target == 64
-
-    logger.log_status("[red]ERROR: Collector died[/]")
-    assert refresh_calls == [True]
-
-    refresh_calls.clear()
-    logger.log_step(
-        iteration=1,
-        metrics={"loss/q": 0.5},
-        reward=1.0,
-        extra_info={"throughput_steps": 8},
-    )
-    logger.log_status("Training")
-    logger.log_buffer_fill(64, 64)
-
-    assert refresh_calls == []
-
-
-def test_offpolicy_logger_stop_live_lets_rich_do_final_refresh() -> None:
-    logger = OffPolicyLogger(
-        algo_name="SAC",
-        max_iterations=2,
-        num_envs=8,
-        env_name="Dummy",
-        log_backend="none",
-    )
-
-    class _FakeLive:
-        def __init__(self) -> None:
-            self.update_calls: list[bool] = []
-            self.stop_calls = 0
-
-        def update(self, renderable: Any, *, refresh: bool) -> None:
-            del renderable
-            self.update_calls.append(refresh)
-
-        def stop(self) -> None:
-            self.stop_calls += 1
-
-    live = _FakeLive()
-    logger._live = live  # type: ignore[assignment]
-    logger._last_live_refresh_time = 123.0
-
-    logger._stop_live()
-
-    assert live.update_calls == [False]
-    assert live.stop_calls == 1
-    assert logger._live is None
-    assert logger._last_live_refresh_time is None
-
-
-def test_offpolicy_logger_close_restores_cursor_and_backends_when_rich_stop_fails(
-    monkeypatch,
-) -> None:
-    logger = OffPolicyLogger(log_backend="none")
-
-    class _FailingLive:
-        def update(self, renderable: Any, *, refresh: bool) -> None:
-            del renderable, refresh
-
-        def stop(self) -> None:
-            raise RuntimeError("final render failed")
-
-    cursor_calls: list[bool] = []
-    backend_close_calls: list[bool] = []
-    monkeypatch.setattr(logger._console, "show_cursor", cursor_calls.append)
-    monkeypatch.setattr(logger, "_close_backends", lambda: backend_close_calls.append(True))
-    logger._live = _FailingLive()  # type: ignore[assignment]
-    logger._last_live_refresh_time = 123.0
-
-    with pytest.raises(RuntimeError, match="final render failed"):
-        logger.close()
-
-    assert cursor_calls == [True]
-    assert backend_close_calls == [True]
-    assert logger._live is None
-    assert logger._last_live_refresh_time is None
-    assert logger._closed is True
-
-
-def test_offpolicy_logger_displays_env_step_breakdown_as_indented_children() -> None:
-    logger = OffPolicyLogger(
-        algo_name="SAC",
-        max_iterations=2,
-        num_envs=8,
-        env_name="Dummy",
-        log_backend="none",
-    )
-    logger.update_collector_timing(
-        {
-            "inference_request_ms": 0.1,
-            "learner_action_wait_ms": 0.2,
-            "env_step_ms": 14.0,
-            "env_step_backend_ms": 12.5,
-            "env_step_update_state_ms": 1.0,
-            "env_step_reset_done_ms": 0.5,
-            "replay_write_ms": 0.3,
-        }
-    )
-
-    table = logger._build_timing_table()
-    collector_cells = list(table.columns[2].cells)[:7]
-    collector_value_cells = list(table.columns[3].cells)[:7]
-
-    assert collector_cells == [
-        "Inference Request",
-        "Learner Action Wait",
-        "Env Step",
-        "[dim]  Backend Step[/]",
-        "[dim]  Update State[/]",
-        "[dim]  Reset Done[/]",
-        "Replay Write",
-    ]
-    assert collector_value_cells == [
-        "    0.1ms    1%",
-        "    0.2ms    1%",
-        "   14.0ms   96%",
-        "[dim cyan]   12.5ms  86%─┤[/]",
-        "[dim cyan]    1.0ms   7%─┤[/]",
-        "[dim cyan]    0.5ms   3%─┘[/]",
-        "    0.3ms    2%",
-    ]
-
-    console = Console(width=100, record=True, force_terminal=False)
-    with console.capture() as capture:
-        console.print(table)
-    connector_columns = [
-        line.index(connector)
-        for line in capture.get().splitlines()
-        for connector in ("┤", "┘")
-        if connector in line
-    ]
-    assert len(connector_columns) == 3
-    assert len(set(connector_columns)) == 1
-
-
-def test_offpolicy_logger_discards_retired_collector_timing_names() -> None:
-    logger = OffPolicyLogger(log_backend="none")
-
-    logger.update_collector_timing(
-        {
-            "inference_wait_ms": 2.0,
-            "learner_action_wait_ms": 4.0,
-            "sync_idle_ms": 3.0,
-        }
-    )
-
-    assert logger._collector_timing == {"learner_action_wait_ms": 4.0}
-
-
-def test_offpolicy_logger_waits_for_complete_collector_cycle_before_percentages() -> None:
-    logger = OffPolicyLogger(log_backend="none")
-    logger.update_collector_timing({"replay_write_ms": 2.5})
-
-    assert logger._get_collector_cycle_ms() is None
-    assert "%" not in list(logger._build_timing_table().columns[3].cells)[0]
-
-
-def test_offpolicy_logger_rejects_unknown_timing_profile() -> None:
-    with pytest.raises(ValueError, match="timing_profile"):
-        OffPolicyLogger(log_backend="none", timing_profile="unknown")
-
-
-def test_offpolicy_logger_shows_complete_additive_learner_timeline() -> None:
-    logger = OffPolicyLogger(
-        algo_name="FastSAC",
-        max_iterations=2,
-        num_envs=8,
-        env_name="Dummy",
-        log_backend="none",
-    )
-    logger.log_step(
-        iteration=1,
-        collector_wait_time=0.10,
-        inference_time=0.20,
-        sync_coordination_time=0.03,
-        replay_batch_wait_time=0.04,
-        learner_replay_stage_time=0.07,
-        learner_replay_sample_time=0.05,
-        train_time=0.50,
-        weight_sync_time=0.06,
-        replay_ingress_h2d_submit_time=0.40,
-        iteration_time=1.0,
-    )
-
-    table = logger._build_timing_table()
-
-    assert table.columns[0].header == "Learner (Iter Wall)"
-    assert list(table.columns[0].cells)[:8] == [
-        "Collector Wait",
-        "Inference",
-        "Collector Release",
-        "Replay Batch Wait",
-        "Replay Sample",
-        "Train",
-        "Other",
-        "Iter Wall",
-    ]
-    assert list(table.columns[1].cells)[:8] == [
-        "[red]  100.0ms   10%[/]",
-        "  200.0ms   20%",
-        "   30.0ms    3%",
-        "   40.0ms    4%",
-        "   50.0ms    5%",
-        "[green]  500.0ms   50%[/]",
-        "   80.0ms    8%",
-        " 1000.0ms  100%",
-    ]
-    assert logger._get_learner_accounted_time() == pytest.approx(0.92)
-    assert logger._get_learner_other_time() == pytest.approx(0.08)
-
-
-def test_offpolicy_logger_does_not_fold_parallel_h2d_into_accounted_time() -> None:
-    logger = OffPolicyLogger(
-        algo_name="FlashSAC",
-        max_iterations=2,
-        num_envs=8,
-        env_name="Dummy",
-        log_backend="none",
-    )
-    logger.log_step(
-        iteration=1,
-        collector_wait_time=0.05,
-        inference_time=0.10,
-        replay_batch_wait_time=0.05,
-        train_time=0.70,
-        replay_ingress_h2d_submit_time=0.50,
-        iteration_time=1.0,
-    )
-
-    assert logger._get_learner_accounted_time() == pytest.approx(0.90)
-    assert logger._get_iter_pct(logger._get_learner_accounted_time()) == pytest.approx(90.0)
-    assert "Replay H2D Submit" not in list(logger._build_timing_table().columns[0].cells)
-
-
-def test_offpolicy_logger_appo_profile_only_shows_applicable_learner_phases() -> None:
-    logger = OffPolicyLogger(log_backend="none", timing_profile="appo")
-    logger.log_step(
-        iteration=1,
-        collector_wait_time=0.10,
-        inference_time=0.20,
-        sync_coordination_time=0.03,
-        replay_batch_wait_time=0.04,
-        learner_replay_stage_time=0.05,
-        learner_replay_sample_time=0.06,
-        train_time=0.50,
-        weight_sync_time=0.07,
-        replay_ingress_h2d_submit_time=0.40,
-        iteration_time=1.0,
-    )
-
-    table = logger._build_timing_table()
-
-    assert list(table.columns[0].cells)[:7] == [
-        "Collector Wait",
-        "Replay Stage",
-        "Replay Sample",
-        "Train",
-        "Weight Publish",
-        "Other",
-        "Iter Wall",
-    ]
-    assert logger._get_learner_accounted_time() == pytest.approx(0.78)
-    assert logger._get_learner_other_time() == pytest.approx(0.22)
-
-
-def test_offpolicy_logger_rollout_collector_uses_milliseconds_without_cycle_total() -> None:
-    logger = OffPolicyLogger(log_backend="none", timing_profile="appo")
-    logger._unicode_console = False
-    logger.update_collector_timing(
-        {
-            "mlp_infer_ms": 1.5,
-            "env_step_ms": 2.5,
-            "env_step_backend_ms": 2.0,
-            "rollout_ms": 40.0,
-        }
-    )
-
-    table = logger._build_timing_table()
-    collector_values = list(table.columns[3].cells)[:4]
-
-    assert table.columns[2].header == "Collector (own clock)"
-    assert collector_values == [
-        "1.5ms",
-        "2.5ms",
-        "[dim cyan]    2.0ms -'[/]",
-        "40.0ms",
-    ]
-    assert all("%" not in value for value in collector_values)
-    assert logger._get_collector_cycle_ms() is None
-
-
-def test_offpolicy_reward_component_names_stay_on_one_line_at_narrow_width() -> None:
-    logger = OffPolicyLogger(
-        algo_name="SAC",
-        max_iterations=2,
-        num_envs=8,
-        env_name="Dummy",
-        log_backend="no_print",
-    )
-    logger._reward_history.extend([1.0, 2.0])
-    logger._latest_reward_components = {
-        "reward/penalty_action_rate": -0.1,
-        "reward/penalty_ang_vel_xy": -0.2,
-        "reward/penalty_orientation": -0.3,
-    }
-    console = Console(width=47, record=True, force_terminal=False)
-
-    console.print(logger._build_reward_table())
-    output_lines = console.export_text().splitlines()
-
-    for component in ("penalty action rate", "penalty ang vel xy", "penalty orientation"):
-        matching_lines = [line for line in output_lines if component in line]
-        assert len(matching_lines) == 1
-
-
-def test_offpolicy_logger_training_timer_excludes_warmup_from_elapsed_and_eta(
-    monkeypatch,
-) -> None:
-    now = 100.0
-    monkeypatch.setattr(common_logger_module.time, "time", lambda: now)
-
-    logger = OffPolicyLogger(
-        algo_name="FastSAC",
-        max_iterations=2,
-        num_envs=8,
-        env_name="G1WalkFlat",
-        log_backend="no_print",
-    )
-    logger._unicode_console = False
-    logger.start()
-
-    now = 130.0
-    assert logger.start_training_timer() == 130.0
-
-    now = 132.0
-    logger.log_step(iteration=1, extra_info={"throughput_steps": 8})
-
-    header = logger._build_compact_header(include_status=False, include_identity=False)
-    assert "time 2s" in header.plain
-    assert "ETA 2s" in header.plain
-    assert "30s" not in header.plain
-
-
-def test_offpolicy_logger_moves_identity_and_iteration_to_panel_title() -> None:
-    logger = OffPolicyLogger(
-        algo_name="FastSAC",
-        max_iterations=5000,
-        num_envs=4096,
-        env_name="G1WalkFlat",
-        log_backend="no_print",
-    )
-    logger._unicode_console = True
-    logger.start_training_timer()
-    logger.log_step(iteration=5000, extra_info={"throughput_steps": 4096})
-
-    display = logger._build_display()
-    header = logger._build_compact_header(
-        include_status=False,
-        include_identity=False,
-        include_iteration=False,
-    )
-
-    assert isinstance(display.title, type(header))
-    assert display.title.plain == (
-        " 🚀 UniLab Off-Policy Training | FastSAC | G1WalkFlat | GPUs 1 | iter 5000/5000 "
-    )
-    assert "FastSAC" not in header.plain
-    assert "G1WalkFlat" not in header.plain
-    assert "iter 5000/5000" not in header.plain
-    assert "Training" not in header.plain
-
-
-def test_offpolicy_terminal_averages_aggregated_samples_over_two_seconds(
-    monkeypatch,
-) -> None:
-    now = 100.0
-    monkeypatch.setattr(offpolicy_logger_module.time, "monotonic", lambda: now)
-
-    class _Writer:
-        def __init__(self) -> None:
-            self.scalars: list[tuple[str, float, int]] = []
-
-        def add_scalar(self, tag: str, value: float, step: int) -> None:
-            self.scalars.append((tag, value, step))
-
-    logger = OffPolicyLogger(log_backend="none", num_gpus=2)
-    writer = _Writer()
-    logger._tb_writer = writer
-
-    logger.update_timeout_rate(0.2)
-    logger.update_collector_timing(
-        {
-            "inference_request_ms": 1.0,
-            "learner_action_wait_ms": 2.0,
-            "env_step_ms": 3.0,
-            "replay_write_ms": 4.0,
-        }
-    )
-    logger.log_step(
-        iteration=1,
-        metrics={"critic_loss": 2.0},
-        train_time=0.2,
-        iteration_time=0.5,
-        extra_info={
-            "steps_per_sec": 400.0,
-            "learner_samples_per_sec": 1_000.0,
-        },
-    )
-
-    now = 101.0
-    logger.update_timeout_rate(0.4)
-    logger.update_collector_timing(
-        {
-            "inference_request_ms": 3.0,
-            "learner_action_wait_ms": 4.0,
-            "env_step_ms": 5.0,
-            "replay_write_ms": 6.0,
-        }
-    )
-    logger.log_step(
-        iteration=2,
-        metrics={"critic_loss": 4.0},
-        train_time=0.4,
-        iteration_time=1.0,
-        extra_info={
-            "steps_per_sec": 800.0,
-            "learner_samples_per_sec": 3_000.0,
-        },
-    )
-
-    snapshot = logger._terminal_snapshot
-    assert snapshot is not None
-    assert snapshot.sample_count == 2
-    assert snapshot.metrics["critic_loss"] == pytest.approx(3.0)
-    assert snapshot.scalars["_train_time"] == pytest.approx(0.3)
-    assert snapshot.scalars["timeout_rate"] == pytest.approx(0.3)
-    assert snapshot.scalars["steps_per_sec"] == pytest.approx(600.0)
-    assert snapshot.scalars["samples_per_sec"] == pytest.approx(2_000.0)
-    assert snapshot.collector_timing["env_step_ms"] == pytest.approx(4.0)
-    collector_values = list(logger._build_timing_table().columns[3].cells)
-    assert "    4.0ms   29%" in collector_values
-    assert "Avg 2s (n=2)" in logger._build_compact_header(include_status=False).plain
-    assert "GPUs 2" in logger._build_display().title.plain
-
-    latest_backend_values = {tag: value for tag, value, _ in writer.scalars}
-    assert latest_backend_values["train/critic_loss"] == pytest.approx(4.0)
-    assert latest_backend_values["perf/steps_per_sec"] == pytest.approx(800.0)
-
-    now = 103.1
-    logger.log_step(
-        iteration=3,
-        metrics={"critic_loss": 10.0},
-        train_time=1.0,
-        iteration_time=2.0,
-        extra_info={
-            "steps_per_sec": 1_200.0,
-            "learner_samples_per_sec": 5_000.0,
-        },
-    )
-    snapshot = logger._terminal_snapshot
-    assert snapshot is not None
-    assert snapshot.sample_count == 1
-    assert snapshot.metrics["critic_loss"] == pytest.approx(10.0)
diff --git a/tests/algos/test_offpolicy_runner_unit.py b/tests/algos/test_offpolicy_runner_unit.py
deleted file mode 100644
index 87d43b9c3..000000000
--- a/tests/algos/test_offpolicy_runner_unit.py
+++ /dev/null
@@ -1,670 +0,0 @@
-"""Unit tests for shared off-policy runner contracts."""
-
-from __future__ import annotations
-
-import copy
-import queue
-from collections import deque
-from types import SimpleNamespace
-
-import numpy as np
-import pytest
-import torch
-
-import unilab.algos.torch.offpolicy.double_buffer_runner as device_runner_module
-import unilab.algos.torch.offpolicy.runner as runner_module
-from unilab.algos.torch.offpolicy.double_buffer_runner import (
-    _LearnerInferenceScheduler,
-    algo_display_name,
-)
-from unilab.algos.torch.offpolicy.runner import (
-    OffPolicyRunner,
-    build_offpolicy_sample_info,
-    compute_train_start_threshold,
-    replay_buffer_ready_for_learning,
-    update_reward_stats_from_replay,
-)
-from unilab.ipc.async_runner import AsyncRunner
-from unilab.ipc.inference_slot import SharedInferenceSlot
-
-
-@pytest.mark.parametrize(
-    ("algo_type", "expected"),
-    [("sac", "SAC"), ("td3", "TD3"), ("flashsac", "FlashSAC"), ("hora_sac", "HORA_SAC")],
-)
-def test_algo_display_name(algo_type, expected):
-    assert algo_display_name(algo_type) == expected
-
-
-@pytest.mark.parametrize(
-    ("batch_size", "learning_starts", "num_envs", "expected"),
-    [(8, 0, 2, 8), (8, 6, 2, 12), (32, 2, 4, 32), (0, 0, 0, 0)],
-)
-def test_compute_train_start_threshold(batch_size, learning_starts, num_envs, expected):
-    assert compute_train_start_threshold(batch_size, learning_starts, num_envs) == expected
-
-
-@pytest.mark.parametrize(
-    ("size", "batch_size", "learning_starts", "num_envs", "expected"),
-    [(7, 8, 0, 2, False), (8, 8, 0, 2, True), (11, 8, 6, 2, False), (12, 8, 6, 2, True)],
-)
-def test_replay_ready_contract(size, batch_size, learning_starts, num_envs, expected):
-    assert (
-        replay_buffer_ready_for_learning(
-            size,
-            batch_size=batch_size,
-            learning_starts=learning_starts,
-            num_envs=num_envs,
-        )
-        is expected
-    )
-
-
-def test_multi_step_scheduler_keeps_one_policy_version_until_update_boundary() -> None:
-    scheduler = _LearnerInferenceScheduler(env_steps_per_sync=2, initial_policy_version=7)
-
-    scheduler.record_inference(0)
-    assert scheduler.policy_version == 7
-    assert scheduler.update_ready is False
-    assert scheduler.release_pending() == 0
-
-    scheduler.record_inference(1)
-    assert scheduler.policy_version == 7
-    assert scheduler.update_ready is True
-    assert scheduler.release_pending() == 1
-
-    scheduler.finish_update()
-    assert scheduler.policy_version == 8
-    assert scheduler.next_tick == 2
-    assert scheduler.update_ready is False
-
-
-def test_scheduler_rejects_update_before_configured_tick_boundary() -> None:
-    scheduler = _LearnerInferenceScheduler(env_steps_per_sync=2)
-    scheduler.record_inference(0)
-    scheduler.release_pending()
-
-    with pytest.raises(RuntimeError, match="before the configured inference tick boundary"):
-        scheduler.finish_update()
-
-
-def test_scheduler_rejects_slot_reuse_before_response_release() -> None:
-    scheduler = _LearnerInferenceScheduler(env_steps_per_sync=1)
-    scheduler.record_inference(0)
-
-    with pytest.raises(RuntimeError, match="has not been released"):
-        scheduler.record_inference(1)
-    with pytest.raises(RuntimeError, match="before releasing the collector tick"):
-        scheduler.finish_update()
-
-
-def test_scheduler_rejects_out_of_order_collector_tick() -> None:
-    scheduler = _LearnerInferenceScheduler(env_steps_per_sync=1)
-
-    with pytest.raises(RuntimeError, match="expected 0, got 1"):
-        scheduler.record_inference(1)
-
-
-def test_runner_close_releases_ipc_when_terminal_cleanup_fails(monkeypatch) -> None:
-    events: list[str] = []
-
-    class _ConcreteOffPolicyRunner(OffPolicyRunner):
-        def learn(
-            self,
-            max_iterations: int,
-            save_interval: int = 50,
-            log_dir: str = "logs",
-        ) -> None:
-            del max_iterations, save_interval, log_dir
-
-    class _FailingLogger:
-        def close(self) -> None:
-            events.append("logger.close")
-            raise RuntimeError("terminal cleanup failed")
-
-    runner = object.__new__(_ConcreteOffPolicyRunner)
-    runner._active_logger = _FailingLogger()
-    monkeypatch.setattr(AsyncRunner, "close", lambda self: events.append("async.close"))
-
-    with pytest.raises(RuntimeError, match="terminal cleanup failed"):
-        runner.close()
-
-    assert events == ["logger.close", "async.close"]
-    assert runner._active_logger is None
-
-
-class _Symmetry:
-    batch_multiplier = 2
-
-
-class _SymmetryLearner:
-    use_symmetry = True
-    symmetry = _Symmetry()
-
-
-def test_sample_info_distinguishes_replay_rows_from_effective_samples():
-    assert build_offpolicy_sample_info(
-        replay_batch_size_per_rank=4,
-        updates_per_step=3,
-        learner=_SymmetryLearner(),
-    ) == {
-        "batch_size_per_rank": 8,
-        "effective_batch_size": 8,
-        "replay_samples_per_iter": 12,
-        "learner_samples_per_iter": 24,
-    }
-
-
-class _RewardLearner:
-    reward_normalizer = object()
-
-    def __init__(self):
-        self.calls = []
-
-    def update_reward_stats(self, rewards, dones):
-        self.calls.append((rewards.clone(), dones.clone()))
-
-
-class _CommittedReplaySource:
-    def __init__(self):
-        self.calls = []
-
-    def read_committed_fields(self, field_names, *, start_ptr):
-        self.calls.append((field_names, start_ptr))
-        return 8, {
-            "rewards": torch.arange(8, dtype=torch.float32),
-            "dones": torch.tensor([0, 0, 1, 0, 0, 1, 0, 0], dtype=torch.float32),
-        }
-
-
-def test_reward_stats_read_only_pipeline_committed_rows():
-    learner = _RewardLearner()
-    source = _CommittedReplaySource()
-    replay = type("Replay", (), {"capacity": 16})()
-
-    end_ptr = update_reward_stats_from_replay(
-        learner,
-        replay,
-        start_ptr=0,
-        end_ptr=0,
-        num_envs=2,
-        replay_source=source,
-    )
-
-    assert end_ptr == 8
-    assert source.calls == [(("rewards", "dones"), 0)]
-    rewards, dones = learner.calls[0]
-    assert rewards.shape == (4, 2)
-    assert dones.shape == (4, 2)
-
-
-def test_reward_stats_reject_missing_device_replay_source():
-    with pytest.raises(RuntimeError, match="device-authoritative replay source"):
-        update_reward_stats_from_replay(
-            _RewardLearner(),
-            type("Replay", (), {"capacity": 16})(),
-            start_ptr=0,
-            end_ptr=8,
-            num_envs=2,
-        )
-
-
-class _Actor:
-    def state_dict(self):
-        return {"weight": torch.zeros(1)}
-
-
-class _Learner:
-    def __init__(self, *, critic_graph=False, actor_graph=False, supports_graph=True):
-        self.actor = _Actor()
-        self.update_count = 0
-        self.use_cuda_graph_critic_packed_staging = critic_graph
-        self.use_cuda_graph_actor_packed_staging = actor_graph
-        self.supports_cuda_graph_packed_staging = supports_graph
-
-    def get_state_dict(self):
-        return {"update_count": self.update_count}
-
-
-class _FakeReplayBuffer:
-    last_kwargs = None
-
-    def __init__(self, **kwargs):
-        type(self).last_kwargs = kwargs
-        self.capacity = kwargs["capacity"]
-        self.ptr = torch.zeros(1, dtype=torch.int64)
-        self.size = torch.zeros(1, dtype=torch.int64)
-        self.trace_recorder = None
-        self.trace_thread_time = False
-        self.trace_cuda_events = False
-
-    def close(self):
-        return None
-
-
-class _FakePipeline:
-    last_kwargs = None
-    close_calls = 0
-    h2d_submitter = "gpu_resident_ingress"
-    transfer_manifest = {"backend": "fake", "device_family": "cuda"}
-
-    def __init__(self, replay_buffer, **kwargs):
-        del replay_buffer
-        type(self).last_kwargs = kwargs
-        self._closed = False
-
-    def close(self):
-        if self._closed:
-            return
-        self._closed = True
-        type(self).close_calls += 1
-
-
-class _FakeLogger:
-    _total_steps = 0
-    _mean_ep_length = 0.0
-    _collector_active_steps_per_sec = None
-
-    def __init__(self, **kwargs):
-        del kwargs
-        self.statuses = []
-
-    def set_collection_sync(self, *args):
-        del args
-
-    def update_runtime_manifest(self, manifest):
-        self._runtime_manifest = dict(manifest)
-
-    def log_status(self, value):
-        self.statuses.append(value)
-
-    def start(self):
-        return None
-
-    def start_training_timer(self):
-        return 0.0
-
-    def log_buffer_fill(self, *args):
-        del args
-
-    def update_buffer_utilization(self, value):
-        del value
-
-    def log_step(self, **kwargs):
-        del kwargs
-
-    def log_save(self, path):
-        del path
-
-    def log_collector(self, *args):
-        del args
-
-    def finish(self):
-        return None
-
-    def close(self):
-        return None
-
-    def _get_iter_steps_per_sec(self):
-        return None
-
-    def _get_effective_samples_per_sec(self):
-        return None
-
-    def _get_iter_wall_time(self):
-        return 0.0
-
-
-def _make_device_runner(monkeypatch: pytest.MonkeyPatch, learner=None):
-    monkeypatch.setattr(
-        device_runner_module, "require_offpolicy_replay_device", lambda value: value
-    )
-    monkeypatch.setattr(runner_module, "get_env_dims", lambda *args, **kwargs: (4, 2, 5))
-    return device_runner_module.DoubleBufferOffPolicyRunner(
-        learner=learner or _Learner(),
-        env_name="DummyEnv",
-        algo_type="sac",
-        num_envs=2,
-        replay_buffer_n=8,
-        batch_size=4,
-        learning_starts=0,
-        updates_per_step=2,
-        policy_frequency=1,
-        env_steps_per_sync=1,
-        device="cuda",
-    )
-
-
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
-@pytest.mark.parametrize(
-    ("critic_graph", "actor_graph", "expected_layout", "expected_critic_source"),
-    [
-        (False, False, "packed", False),
-        (True, False, "packed", True),
-        (True, True, "sac_graph", False),
-    ],
-)
-def test_runner_constructs_only_bounded_device_replay(
-    monkeypatch: pytest.MonkeyPatch,
-    tmp_path,
-    critic_graph,
-    actor_graph,
-    expected_layout,
-    expected_critic_source,
-):
-    _FakePipeline.close_calls = 0
-    monkeypatch.setattr(device_runner_module, "ReplayBuffer", _FakeReplayBuffer)
-    monkeypatch.setattr(device_runner_module, "GPUResidentReplayPipeline", _FakePipeline)
-    monkeypatch.setattr(device_runner_module, "OffPolicyLogger", _FakeLogger)
-    monkeypatch.setattr(device_runner_module.torch, "save", lambda *args, **kwargs: None)
-    monkeypatch.setattr(device_runner_module.time, "sleep", lambda seconds: None)
-
-    learner = _Learner(critic_graph=critic_graph, actor_graph=actor_graph)
-    runner = _make_device_runner(monkeypatch, learner)
-    collector_kwargs = {}
-
-    def capture_collector(*, target_fn, kwargs):
-        del target_fn
-        collector_kwargs.update(kwargs)
-
-    monkeypatch.setattr(runner, "_start_collector", capture_collector)
-    runner.learn(max_iterations=0, save_interval=0, log_dir=str(tmp_path))
-
-    assert _FakeReplayBuffer.last_kwargs == {
-        "capacity": 16,
-        "obs_dim": 4,
-        "action_dim": 2,
-        "device": "cuda",
-        "critic_dim": 5,
-        "ingress_slot_rows": 2,
-        "ingress_depth": 2,
-    }
-    assert _FakePipeline.last_kwargs["pack_layout"] == expected_layout
-    assert _FakePipeline.last_kwargs["use_critic_graph_packed_source"] is expected_critic_source
-    runtime_manifest = runner.last_run_summary["runtime_manifest"]
-    assert runtime_manifest["replay_h2d_submitter"] == runner.replay_h2d_submitter
-    assert "replay_device_submission_thread" in runtime_manifest
-    assert not any(key.startswith("collector_pack") for key in collector_kwargs)
-    assert "weight_sync_name" not in collector_kwargs
-    assert "weight_param_shapes" not in collector_kwargs
-    assert "collector_infer_device" not in collector_kwargs
-    assert "inference_owner" not in collector_kwargs
-    assert collector_kwargs["inference_slot"] is not None
-    assert collector_kwargs["inference_request_queue"] is not None
-    assert collector_kwargs["inference_response_queue"] is not None
-    assert "collection_ready_queue" not in collector_kwargs
-    assert "trainer_done_queue" not in collector_kwargs
-    assert _FakePipeline.close_calls == 1
-
-
-class _ReadyAfterPoll:
-    def __init__(self):
-        self.ready = False
-        self.start_calls = 0
-
-    def batch_ready(self, tick_id, sample_count):
-        del tick_id, sample_count
-        return self.ready
-
-    def start_prepare(self, tick_id, sample_count, min_snapshot_ptr=None):
-        del tick_id, sample_count, min_snapshot_ptr
-        self.start_calls += 1
-        return True
-
-
-def test_replay_batch_wait_uses_fine_grained_polling(monkeypatch: pytest.MonkeyPatch):
-    runner = _make_device_runner(monkeypatch)
-    pipeline = _ReadyAfterPoll()
-    sleeps = []
-
-    def fake_sleep(seconds):
-        sleeps.append(seconds)
-        pipeline.ready = True
-
-    monkeypatch.setattr(runner, "_check_collector_alive", lambda: True)
-    monkeypatch.setattr(device_runner_module.time, "sleep", fake_sleep)
-    logger = _FakeLogger()
-
-    assert runner._wait_for_replay_batch_ready(
-        pipeline,
-        tick_id=1,
-        sample_count=8,
-        metrics_queue=queue.Queue(),
-        reward_history=deque(maxlen=100),
-        latest_reward_components={},
-        logger=logger,
-        trace_recorder=None,
-        replay_buffer=type("Replay", (), {"ptr": torch.zeros(1), "size": torch.zeros(1)})(),
-        ckpt_path=None,
-        train_start_wall=0.0,
-    )
-    assert pipeline.start_calls == 1
-    assert sleeps == [pytest.approx(runner.REPLAY_BATCH_READY_POLL_SEC)]
-
-
-def test_inference_response_freezes_next_replay_boundary_before_release(
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    runner = _make_device_runner(monkeypatch)
-    scheduler = _LearnerInferenceScheduler(env_steps_per_sync=1)
-    scheduler.record_inference(0)
-    replay_buffer = SimpleNamespace(published_ptr=8)
-    published = []
-
-    def publish_response(queue, *, value, timeout=5.0, label="inference_response"):
-        del queue, timeout
-        published.append((value, label))
-        replay_buffer.published_ptr = 100
-
-    monkeypatch.setattr(runner, "_publish_inference_response", publish_response)
-
-    next_prepare_ptr = runner._release_inference_tick(
-        object(),
-        inference_scheduler=scheduler,
-        replay_buffer=replay_buffer,
-        trace_recorder=None,
-    )
-
-    assert next_prepare_ptr == 10
-    assert published == [(0, "inference_response")]
-    assert scheduler.pending_tick is None
-
-
-def test_runner_releases_action_before_replay_wait_and_sample(
-    monkeypatch: pytest.MonkeyPatch,
-    tmp_path,
-) -> None:
-    events: list[str] = []
-
-    class LoopReplayBuffer(_FakeReplayBuffer):
-        def __init__(self, **kwargs):
-            super().__init__(**kwargs)
-            self.ptr[0] = 4
-            self.size[0] = 4
-            self.published_ptr = 4
-
-    class LoopPipeline(_FakePipeline):
-        last_incremental_h2d_time_s = 0.0
-
-        def progress(self, *, wait=False):
-            events.append("replay_progress")
-            return wait
-
-        def start_prepare(self, tick_id, sample_count, min_snapshot_ptr=None):
-            del tick_id, sample_count, min_snapshot_ptr
-            events.append("replay_prepare")
-            return True
-
-        def batch_ready(self, tick_id, sample_count):
-            del tick_id, sample_count
-            events.append("replay_batch_ready")
-            return True
-
-        def sample_large_batch(self, tick_id, sample_count):
-            del tick_id, sample_count
-            events.append("replay_sample")
-            return {}
-
-        def after_tick(self):
-            events.append("replay_after_tick")
-
-    class LoopLearner(_Learner):
-        def update_critic(self, batch):
-            del batch
-            events.append("update_critic")
-            return {}
-
-        def update_actor(self, batch):
-            del batch
-            events.append("update_actor")
-            return {}
-
-        def soft_update_target(self):
-            events.append("soft_update_target")
-
-    monkeypatch.setattr(device_runner_module, "ReplayBuffer", LoopReplayBuffer)
-    monkeypatch.setattr(device_runner_module, "GPUResidentReplayPipeline", LoopPipeline)
-    monkeypatch.setattr(device_runner_module, "OffPolicyLogger", _FakeLogger)
-    monkeypatch.setattr(device_runner_module.torch, "save", lambda *args, **kwargs: None)
-    monkeypatch.setattr(device_runner_module.time, "sleep", lambda seconds: None)
-
-    runner = _make_device_runner(monkeypatch, LoopLearner())
-    runner.device = "cpu"
-    monkeypatch.setattr(runner, "_start_collector", lambda **kwargs: None)
-    monkeypatch.setattr(runner, "_check_collector_alive", lambda: True)
-    monkeypatch.setattr(runner, "_wait_for_inference_request", lambda *args, **kwargs: 0)
-
-    def serve_inference(*args, **kwargs):
-        del args, kwargs
-        events.append("action_d2h")
-        return {
-            "inference_h2d_time": 0.0,
-            "inference_forward_time": 0.0,
-            "inference_d2h_time": 0.0,
-            "inference_time": 0.0,
-        }
-
-    def publish_response(*args, **kwargs):
-        del args, kwargs
-        events.append("inference_response")
-
-    monkeypatch.setattr(runner, "_serve_learner_inference", serve_inference)
-    monkeypatch.setattr(runner, "_publish_inference_response", publish_response)
-
-    runner.learn(max_iterations=1, save_interval=0, log_dir=str(tmp_path))
-
-    assert events.index("action_d2h") < events.index("inference_response")
-    assert events.index("inference_response") < events.index("replay_progress")
-    assert events.index("inference_response") < events.index("replay_batch_ready")
-    assert events.index("inference_response") < events.index("replay_sample")
-    assert events.index("inference_response") < events.index("update_critic")
-
-
-def test_drain_metrics_propagates_collector_error():
-    metrics = queue.Queue()
-    metrics.put({"error": "collector boom"})
-    with pytest.raises(RuntimeError, match="collector boom"):
-        runner_module.OffPolicyRunner._drain_metrics(
-            metrics,
-            deque(maxlen=10),
-            {},
-            _FakeLogger(),
-        )
-
-
-@pytest.mark.parametrize("algo_type", ["sac", "td3", "flashsac"])
-def test_learner_inference_matches_existing_actor_exploration(algo_type: str) -> None:
-    if algo_type == "sac":
-        from unilab.algos.torch.fast_sac.learner import SACActor
-
-        actor = SACActor(3, 2, hidden_dim=8, use_layer_norm=False)
-    elif algo_type == "flashsac":
-        from unilab.algos.torch.flash_sac.network import FlashSACActor
-
-        actor = FlashSACActor(num_blocks=1, input_dim=3, hidden_dim=8, action_dim=2)
-    else:
-        from unilab.algos.torch.fast_td3.learner import TD3Actor
-
-        actor = TD3Actor(3, 2, num_envs=2, init_scale=0.01, hidden_dim=8)
-    expected_actor = copy.deepcopy(actor)
-    observations = np.arange(6, dtype=np.float32).reshape(2, 3) / 10.0
-    dones = np.array([0.0, 1.0], dtype=np.float32)
-    torch.manual_seed(17)
-    expected = expected_actor.explore(
-        torch.from_numpy(observations),
-        dones=torch.from_numpy(dones),
-        deterministic=False,
-    )
-
-    runner = object.__new__(device_runner_module.DoubleBufferOffPolicyRunner)
-    runner.device = "cpu"
-    runner.obs_dim = 3
-    runner.obs_normalization = False
-    runner.algo_type = algo_type
-    runner.learner = SimpleNamespace(actor=actor)
-    slot = SharedInferenceSlot(2, 3, 2)
-    slot.publish_observation(tick_id=4, observations=observations, dones=dones)
-    torch.manual_seed(17)
-    runner._serve_learner_inference(
-        slot,
-        tick_id=4,
-        policy_version=9,
-        obs_device=torch.empty(2, 3),
-        dones_device=torch.empty(2),
-        trace_recorder=None,
-    )
-    actual, policy_version = slot.consume_action(tick_id=4)
-
-    torch.testing.assert_close(torch.from_numpy(actual), expected)
-    assert policy_version == 9
-    if algo_type == "flashsac":
-        torch.testing.assert_close(actor._noise, expected_actor._noise)
-        torch.testing.assert_close(actor._repeat_count, expected_actor._repeat_count)
-        torch.testing.assert_close(actor._repeat_target, expected_actor._repeat_target)
-
-
-def test_hora_learner_inference_uses_privileged_context() -> None:
-    from unilab.algos.torch.hora.sac_models import HoraSACActor
-
-    actor = HoraSACActor(
-        obs_dim=3,
-        priv_info_dim=2,
-        action_dim=2,
-        hidden_dim=8,
-        priv_mlp_hidden_dims=(4, 2),
-        priv_info_embed_dim=2,
-        use_layer_norm=False,
-    )
-    expected_actor = copy.deepcopy(actor)
-    observations = np.arange(6, dtype=np.float32).reshape(2, 3) / 10.0
-    priv_info = np.arange(4, dtype=np.float32).reshape(2, 2) / 10.0
-    actor_input = np.concatenate((observations, priv_info), axis=1)
-    dones = np.zeros(2, dtype=np.float32)
-    torch.manual_seed(23)
-    expected = expected_actor.explore(
-        torch.from_numpy(observations),
-        torch.from_numpy(priv_info),
-        deterministic=False,
-    )
-
-    runner = object.__new__(device_runner_module.DoubleBufferOffPolicyRunner)
-    runner.device = "cpu"
-    runner.obs_dim = 3
-    runner.obs_normalization = False
-    runner.algo_type = "hora_sac"
-    runner.learner = SimpleNamespace(actor=actor)
-    slot = SharedInferenceSlot(2, 5, 2)
-    slot.publish_observation(tick_id=5, observations=actor_input, dones=dones)
-    torch.manual_seed(23)
-    runner._serve_learner_inference(
-        slot,
-        tick_id=5,
-        policy_version=10,
-        obs_device=torch.empty(2, 5),
-        dones_device=torch.empty(2),
-        trace_recorder=None,
-    )
-    actual, policy_version = slot.consume_action(tick_id=5)
-
-    torch.testing.assert_close(torch.from_numpy(actual), expected)
-    assert policy_version == 10
diff --git a/tests/algos/test_offpolicy_runtime.py b/tests/algos/test_offpolicy_runtime.py
deleted file mode 100644
index 437d78435..000000000
--- a/tests/algos/test_offpolicy_runtime.py
+++ /dev/null
@@ -1,54 +0,0 @@
-from __future__ import annotations
-
-import pytest
-
-
-def test_offpolicy_runtime_defaults_to_standard_sac_overrides() -> None:
-    from unilab.algos.torch.offpolicy.runtime import OffPolicyRuntime
-
-    runtime = OffPolicyRuntime()
-
-    assert runtime.learner_cls is None
-    assert runtime.algo_type is None
-    assert runtime.supports_symmetry is True
-    assert runtime.build_model_kwargs(obs_dim=4, critic_obs_dim=6) == {}
-
-
-def test_hora_sac_runtime_builds_privileged_actor_kwargs() -> None:
-    from unilab.algos.torch.hora.sac import resolve_hora_sac_runtime
-
-    runtime = resolve_hora_sac_runtime(
-        {
-            "runtime_impl": "hora_sac",
-            "actor": {
-                "priv_info_embed_dim": 7,
-                "priv_mlp_hidden_dims": [16, 7],
-            },
-        }
-    )
-
-    assert runtime is not None
-    assert runtime.algo_type == "hora_sac"
-    assert runtime.supports_symmetry is False
-    assert runtime.build_model_kwargs(obs_dim=5, critic_obs_dim=8) == {
-        "priv_info_dim": 3,
-        "priv_info_embed_dim": 7,
-        "priv_mlp_hidden_dims": (16, 7),
-    }
-
-
-def test_hora_sac_runtime_requires_critic_tail() -> None:
-    from unilab.algos.torch.hora.sac import resolve_hora_sac_runtime
-
-    runtime = resolve_hora_sac_runtime({"runtime_impl": "hora_sac"})
-
-    assert runtime is not None
-    with pytest.raises(ValueError, match="privileged tail"):
-        runtime.build_model_kwargs(obs_dim=5, critic_obs_dim=5)
-
-
-def test_offpolicy_runtime_rejects_marker_without_resolver() -> None:
-    from unilab.algos.torch.offpolicy.runtime import resolve_custom_offpolicy_runtime
-
-    with pytest.raises(ValueError, match="runtime_impl='hora_sac'.*runtime_resolver"):
-        resolve_custom_offpolicy_runtime({"runtime_impl": "hora_sac"})
diff --git a/tests/algos/test_offpolicy_thread_budget.py b/tests/algos/test_offpolicy_thread_budget.py
deleted file mode 100644
index 15403ae6b..000000000
--- a/tests/algos/test_offpolicy_thread_budget.py
+++ /dev/null
@@ -1,149 +0,0 @@
-from __future__ import annotations
-
-import os
-
-import pytest
-
-from unilab.algos.torch.offpolicy.thread_budget import (
-    apply_torch_thread_runtime,
-    format_torch_thread_runtime,
-    resolve_torch_thread_runtime,
-    torch_thread_env,
-)
-
-
-class _FakeTorch:
-    def __init__(self) -> None:
-        self.num_threads = 0
-        self.num_interop_threads = 96
-        self.inductor_compile_threads = 0
-        self._inductor = type(
-            "_FakeInductor",
-            (),
-            {"config": type("_FakeInductorConfig", (), {"compile_threads": 0})()},
-        )()
-
-    def set_num_threads(self, value: int) -> None:
-        self.num_threads = int(value)
-
-    def get_num_interop_threads(self) -> int:
-        return self.num_interop_threads
-
-    def set_num_interop_threads(self, value: int) -> None:
-        self.num_interop_threads = int(value)
-
-
-def test_resolve_torch_thread_runtime_auto_caps_many_core_host() -> None:
-    runtime = resolve_torch_thread_runtime(
-        {
-            "enabled": True,
-            "learner_num_threads": "auto",
-            "collector_num_threads": "auto",
-            "learner_num_interop_threads": 1,
-            "collector_num_interop_threads": 1,
-            "compile_threads": "auto",
-        },
-        cpu_count=160,
-    )
-
-    assert runtime["learner"]["num_threads"] == 8
-    assert runtime["collector"]["num_threads"] == 4
-    assert runtime["compile_threads"] == 2
-    assert runtime["learner"]["num_interop_threads"] == 1
-    assert runtime["collector"]["num_interop_threads"] == 1
-
-
-def test_apply_torch_thread_runtime_sets_role_env_and_torch(monkeypatch: pytest.MonkeyPatch):
-    for key in (
-        "OMP_NUM_THREADS",
-        "MKL_NUM_THREADS",
-        "OPENBLAS_NUM_THREADS",
-        "NUMEXPR_NUM_THREADS",
-        "TORCH_NUM_THREADS",
-        "TORCHINDUCTOR_COMPILE_THREADS",
-    ):
-        monkeypatch.delenv(key, raising=False)
-
-    fake_torch = _FakeTorch()
-    runtime = resolve_torch_thread_runtime(
-        {
-            "learner_num_threads": 6,
-            "collector_num_threads": 3,
-            "learner_num_interop_threads": 2,
-            "collector_num_interop_threads": 1,
-            "compile_threads": 2,
-            "set_env_vars": True,
-        },
-        cpu_count=64,
-    )
-
-    applied = apply_torch_thread_runtime(runtime, role="collector", torch_module=fake_torch)
-
-    assert applied == {
-        "enabled": True,
-        "role": "collector",
-        "num_threads": 3,
-        "num_interop_threads": 1,
-        "compile_threads": 2,
-    }
-    assert fake_torch.num_threads == 3
-    assert fake_torch.num_interop_threads == 1
-    assert fake_torch._inductor.config.compile_threads == 2
-    assert os.environ["TORCH_NUM_THREADS"] == "3"
-    assert os.environ["TORCHINDUCTOR_COMPILE_THREADS"] == "2"
-
-
-def test_torch_thread_env_temporarily_sets_child_startup_env(
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    monkeypatch.setenv("TORCH_NUM_THREADS", "6")
-    monkeypatch.setenv("OMP_NUM_THREADS", "6")
-    monkeypatch.delenv("TORCHINDUCTOR_COMPILE_THREADS", raising=False)
-    runtime = resolve_torch_thread_runtime(
-        {
-            "learner_num_threads": 6,
-            "collector_num_threads": 3,
-            "compile_threads": 2,
-            "set_env_vars": True,
-        },
-        cpu_count=64,
-    )
-
-    with torch_thread_env(runtime, role="collector"):
-        assert os.environ["TORCH_NUM_THREADS"] == "3"
-        assert os.environ["OMP_NUM_THREADS"] == "3"
-        assert os.environ["TORCHINDUCTOR_COMPILE_THREADS"] == "2"
-
-    assert os.environ["TORCH_NUM_THREADS"] == "6"
-    assert os.environ["OMP_NUM_THREADS"] == "6"
-    assert "TORCHINDUCTOR_COMPILE_THREADS" not in os.environ
-
-
-def test_disabled_torch_thread_runtime_does_not_validate_thread_fields() -> None:
-    runtime = resolve_torch_thread_runtime(
-        {
-            "enabled": False,
-            "learner_num_threads": 0,
-            "collector_num_threads": 0,
-        },
-        cpu_count=64,
-    )
-
-    assert runtime["enabled"] is False
-
-
-def test_format_torch_thread_runtime_reports_roles() -> None:
-    runtime = resolve_torch_thread_runtime(
-        {
-            "learner_num_threads": 6,
-            "collector_num_threads": 3,
-            "learner_num_interop_threads": 2,
-            "collector_num_interop_threads": 1,
-            "compile_threads": 2,
-        },
-        cpu_count=64,
-    )
-
-    assert format_torch_thread_runtime(runtime) == (
-        "Torch thread budget: learner=6 intra/2 inter, collector=3 intra/1 inter, compile_workers=2"
-    )
diff --git a/tests/algos/test_offpolicy_worker.py b/tests/algos/test_offpolicy_worker.py
deleted file mode 100644
index d6b54368e..000000000
--- a/tests/algos/test_offpolicy_worker.py
+++ /dev/null
@@ -1,185 +0,0 @@
-from __future__ import annotations
-
-import queue
-import threading
-
-import pytest
-import torch
-
-from unilab.algos.torch.common.collector_timing import extract_env_step_breakdown_timing_ms
-from unilab.algos.torch.offpolicy.worker import (
-    _publish_inference_tick,
-    _wait_for_inference_tick,
-    compute_collector_active_steps_per_sec,
-    resolve_offpolicy_actor_priv_info,
-    sample_offpolicy_actions,
-)
-
-
-def test_inference_request_publish_timeout_is_explicit() -> None:
-    requests: queue.Queue[int] = queue.Queue(maxsize=1)
-    requests.put_nowait(0)
-
-    with pytest.raises(TimeoutError, match="publishing off-policy inference tick 1"):
-        _publish_inference_tick(requests, 1, threading.Event(), timeout=0.01)
-
-
-def test_inference_response_wait_timeout_is_explicit() -> None:
-    with pytest.raises(TimeoutError, match="waiting for off-policy inference tick 0"):
-        _wait_for_inference_tick(queue.Queue(), 0, threading.Event(), timeout=0.01)
-
-
-class _DummyActor:
-    def __init__(self) -> None:
-        self.calls: list[tuple[torch.Tensor, torch.Tensor, bool]] = []
-
-    def explore(
-        self,
-        obs: torch.Tensor,
-        dones: torch.Tensor | None = None,
-        deterministic: bool = False,
-    ) -> torch.Tensor:
-        assert dones is not None
-        self.calls.append((obs.clone(), dones.clone(), deterministic))
-        return torch.ones(obs.shape[0], 3, dtype=obs.dtype)
-
-
-def test_compute_collector_active_steps_per_sec_includes_active_phases_only() -> None:
-    steps_per_sec = compute_collector_active_steps_per_sec(
-        {
-            "inference_request_ms": 1.0,
-            "learner_action_wait_ms": 100.0,
-            "env_step_ms": 10.0,
-            "replay_write_ms": 3.0,
-            "bookkeeping_ms": 100.0,
-        },
-        num_envs=32,
-    )
-
-    assert steps_per_sec == pytest.approx(32 / 0.014)
-
-
-def test_extract_env_step_breakdown_timing_ms_maps_env_owned_keys_only() -> None:
-    timing = extract_env_step_breakdown_timing_ms(
-        {
-            "timing": {
-                "step_core_ms": 1.5,
-                "update_state_ms": 2.5,
-                "reset_done_ms": 0.25,
-                "apply_action_ms": 9.0,
-            }
-        }
-    )
-
-    assert timing == {
-        "env_step_backend_ms": 1.5,
-        "env_step_update_state_ms": 2.5,
-        "env_step_reset_done_ms": 0.25,
-    }
-
-
-def test_compute_collector_active_steps_per_sec_returns_none_without_active_time() -> None:
-    assert (
-        compute_collector_active_steps_per_sec(
-            {"bookkeeping_ms": 100.0},
-            num_envs=32,
-        )
-        is None
-    )
-
-
-@pytest.mark.parametrize("algo_type", ["sac", "td3", "flashsac"])
-def test_sample_offpolicy_actions_uses_actor_explore(algo_type: str) -> None:
-    actor = _DummyActor()
-    obs = torch.zeros(4, 5)
-    dones = torch.zeros(4)
-
-    actions = sample_offpolicy_actions(
-        actor=actor,
-        algo_type=algo_type,
-        obs_torch=obs,
-        prev_dones_torch=dones,
-    )
-
-    assert len(actor.calls) == 1
-    assert actor.calls[0][2] is False
-    assert actions.shape == (4, 3)
-
-
-def test_sample_offpolicy_actions_rejects_unknown_algo() -> None:
-    actor = _DummyActor()
-
-    with pytest.raises(ValueError, match="learner action sampling"):
-        sample_offpolicy_actions(
-            actor=actor,
-            algo_type="unknown",
-            obs_torch=torch.zeros(2, 4),
-            prev_dones_torch=torch.zeros(2),
-        )
-
-
-class _DummyHoraActor:
-    def __init__(self) -> None:
-        self.calls: list[tuple[torch.Tensor, torch.Tensor, bool]] = []
-
-    def explore(
-        self,
-        obs: torch.Tensor,
-        priv_info: torch.Tensor,
-        deterministic: bool = False,
-    ) -> torch.Tensor:
-        self.calls.append((obs.clone(), priv_info.clone(), deterministic))
-        return torch.ones(obs.shape[0], 3, dtype=obs.dtype)
-
-
-def test_sample_offpolicy_actions_passes_hora_priv_info() -> None:
-    actor = _DummyHoraActor()
-    obs = torch.zeros(4, 5)
-    priv_info = torch.randn(4, 2)
-
-    actions = sample_offpolicy_actions(
-        actor=actor,
-        algo_type="hora_sac",
-        obs_torch=obs,
-        prev_dones_torch=torch.zeros(4),
-        priv_info_torch=priv_info,
-    )
-
-    assert actions.shape == (4, 3)
-    assert len(actor.calls) == 1
-    torch.testing.assert_close(actor.calls[0][1], priv_info)
-
-
-def test_resolve_offpolicy_actor_priv_info_prefers_explicit_info() -> None:
-    import numpy as np
-
-    obs = np.zeros((2, 3), dtype=np.float32)
-    critic_tail = np.ones((2, 2), dtype=np.float32)
-    critic = np.concatenate([obs, critic_tail], axis=1)
-    explicit = np.full((2, 2), 7.0, dtype=np.float32)
-
-    resolved = resolve_offpolicy_actor_priv_info(
-        algo_type="hora_sac",
-        obs_np=obs,
-        critic_np=critic,
-        info={"critic_info": explicit},
-    )
-
-    np.testing.assert_allclose(resolved, explicit)
-
-
-def test_resolve_offpolicy_actor_priv_info_uses_critic_tail() -> None:
-    import numpy as np
-
-    obs = np.zeros((2, 3), dtype=np.float32)
-    critic_tail = np.arange(4, dtype=np.float32).reshape(2, 2)
-    critic = np.concatenate([obs, critic_tail], axis=1)
-
-    resolved = resolve_offpolicy_actor_priv_info(
-        algo_type="hora_sac",
-        obs_np=obs,
-        critic_np=critic,
-        info={},
-    )
-
-    np.testing.assert_allclose(resolved, critic_tail)
diff --git a/tests/algos/test_rsl_rl_ppo.py b/tests/algos/test_rsl_rl_ppo.py
deleted file mode 100644
index d93d0d968..000000000
--- a/tests/algos/test_rsl_rl_ppo.py
+++ /dev/null
@@ -1,277 +0,0 @@
-from __future__ import annotations
-
-import os
-from collections.abc import Callable
-from typing import Any
-
-import pytest
-import torch
-from omegaconf import OmegaConf
-from tensordict import TensorDict
-
-from unilab.algos.torch.rsl_rl_ppo import FinalObservationAwarePPO
-from unilab.training.rsl_rl import (
-    RslRlVecEnvWrapper,
-    apply_rsl_rl_rank_seed,
-    finish_rsl_rl_distributed,
-    normalize_ppo_train_cfg,
-    ppo_samples_per_iteration,
-    resolve_rsl_rl_device,
-    rsl_rl_single_process_topology,
-)
-
-
-def test_rsl_rl_rank_seed_uses_base_seed_plus_global_rank() -> None:
-    cfg = OmegaConf.create({"algo": {"seed": 41}})
-
-    assert apply_rsl_rl_rank_seed(cfg, rank=3) == 44
-    assert cfg.algo.seed == 44
-
-
-def test_rsl_rl_device_uses_local_rank_after_cuda_visibility_remap() -> None:
-    assert (
-        resolve_rsl_rl_device(
-            configured_device=None,
-            devices=(3, 1),
-            world_size=2,
-            local_rank=1,
-            default_device="cuda",
-        )
-        == "cuda:1"
-    )
-
-
-def test_rsl_rl_device_supports_explicit_single_device() -> None:
-    assert (
-        resolve_rsl_rl_device(
-            configured_device=None,
-            devices=(2,),
-            world_size=1,
-            local_rank=0,
-            default_device="cuda",
-        )
-        == "cuda:2"
-    )
-
-
-def test_rsl_rl_device_rejects_singular_and_plural_config() -> None:
-    with pytest.raises(ValueError, match="either training.device or training.devices"):
-        resolve_rsl_rl_device(
-            configured_device="cuda:0",
-            devices=(0, 1),
-            world_size=2,
-            local_rank=0,
-            default_device="cuda",
-        )
-
-
-def test_ppo_samples_per_iteration_uses_per_rank_num_envs() -> None:
-    assert ppo_samples_per_iteration(num_envs=1024, num_steps_per_env=24, world_size=2) == 49152
-
-
-def test_finish_rsl_rl_distributed_barriers_only_after_success(monkeypatch) -> None:
-    calls: list[str] = []
-    monkeypatch.setattr(torch.distributed, "is_available", lambda: True)
-    monkeypatch.setattr(torch.distributed, "is_initialized", lambda: True)
-    monkeypatch.setattr(torch.distributed, "barrier", lambda: calls.append("barrier"))
-    monkeypatch.setattr(torch.distributed, "destroy_process_group", lambda: calls.append("destroy"))
-
-    finish_rsl_rl_distributed(training_succeeded=True)
-
-    assert calls == ["barrier", "destroy"]
-
-
-def test_finish_rsl_rl_distributed_failure_skips_barrier(monkeypatch) -> None:
-    calls: list[str] = []
-    monkeypatch.setattr(torch.distributed, "is_available", lambda: True)
-    monkeypatch.setattr(torch.distributed, "is_initialized", lambda: True)
-    monkeypatch.setattr(torch.distributed, "barrier", lambda: calls.append("barrier"))
-    monkeypatch.setattr(torch.distributed, "destroy_process_group", lambda: calls.append("destroy"))
-
-    finish_rsl_rl_distributed(training_succeeded=False)
-
-    assert calls == ["destroy"]
-
-
-def test_rsl_rl_single_process_topology_masks_and_restores_torchrun_env(monkeypatch) -> None:
-    monkeypatch.setenv("WORLD_SIZE", "2")
-    monkeypatch.setenv("RANK", "0")
-    monkeypatch.setenv("LOCAL_RANK", "0")
-
-    with rsl_rl_single_process_topology():
-        assert os.environ["WORLD_SIZE"] == "1"
-        assert os.environ["RANK"] == "0"
-        assert os.environ["LOCAL_RANK"] == "0"
-
-    assert os.environ["WORLD_SIZE"] == "2"
-    assert os.environ["RANK"] == "0"
-    assert os.environ["LOCAL_RANK"] == "0"
-
-
-class _FakeActor:
-    class MLP(torch.nn.Module):
-        def forward(self, x):
-            return x
-
-    mlp = MLP()
-    obs_groups = ["policy"]
-    is_recurrent = False
-
-    def update_normalization(self, obs):
-        return None
-
-    def reset(self, dones):
-        return None
-
-
-class _FakeCritic:
-    class MLP(torch.nn.Module):
-        def forward(self, x):
-            return x
-
-    mlp = MLP()
-    obs_groups = ["critic"]
-    is_recurrent = False
-
-    def __init__(self, values: torch.Tensor):
-        self.values = values
-        self.last_obs = None
-
-    def update_normalization(self, obs):
-        return None
-
-    def reset(self, dones):
-        return None
-
-    def __call__(self, obs, **kwargs):
-        del kwargs
-        self.last_obs = obs
-        return self.values
-
-
-class _FakeTransition:
-    def __init__(self):
-        self.values = torch.tensor([[10.0], [20.0]])
-        self.rewards = None
-        self.dones = None
-
-    def clear(self):
-        return None
-
-
-class _FakeStorage:
-    def __init__(self):
-        self.saved_rewards = None
-
-    def add_transition(self, transition):
-        self.saved_rewards = transition.rewards.clone()
-
-
-def test_final_observation_aware_ppo_bootstraps_from_final_observation():
-    algo: Any = object.__new__(FinalObservationAwarePPO)
-    algo.actor = _FakeActor()
-    algo.critic = _FakeCritic(torch.tensor([[3.0], [4.0]]))
-    algo.rnd = None
-    algo.gamma = 0.99
-    algo.transition = _FakeTransition()
-    algo.storage = _FakeStorage()
-    algo.device = "cpu"
-
-    obs = TensorDict({"policy": torch.zeros((2, 1))}, batch_size=[2])
-    rewards = torch.tensor([1.0, 2.0])
-    dones = torch.tensor([True, True])
-    final_obs = TensorDict({"policy": torch.tensor([[30.0], [40.0]])}, batch_size=[2])
-
-    algo.process_env_step(
-        obs,
-        rewards,
-        dones,
-        {
-            "time_outs": torch.tensor([True, False]),
-            "time_out_bootstrap_obs": final_obs,
-        },
-    )
-
-    assert algo.storage.saved_rewards is not None
-    assert algo.critic.last_obs is not None
-    assert torch.allclose(algo.storage.saved_rewards, torch.tensor([1.0 + 0.99 * 3.0, 2.0]))
-    assert torch.equal(algo.critic.last_obs["policy"], final_obs["policy"])
-
-
-def test_final_observation_aware_ppo_compile_targets_minibatch_loss(monkeypatch) -> None:
-    calls: list[tuple[str, dict[str, Any]]] = []
-
-    def fake_compile(fn: Callable, **kwargs):
-        calls.append((getattr(fn, "__qualname__", type(fn).__name__), kwargs))
-        return fn
-
-    algo: Any = object.__new__(FinalObservationAwarePPO)
-    algo.device = "cuda"
-    algo._minibatch_loss_fn = algo._minibatch_loss_tensors
-    monkeypatch.setattr(torch, "compile", fake_compile)
-
-    algo._compile_training_methods()
-
-    assert calls == [
-        (
-            "FinalObservationAwarePPO._minibatch_loss_tensors",
-            {"mode": "reduce-overhead", "fullgraph": False},
-        )
-    ]
-    assert algo._minibatch_loss_fn == algo._minibatch_loss_tensors
-
-
-def test_normalize_ppo_train_cfg_preserves_unilab_runtime_flags() -> None:
-    train_cfg = normalize_ppo_train_cfg(
-        {
-            "algorithm": {
-                "class_name": "unilab.algos.torch.rsl_rl_ppo:FinalObservationAwarePPO",
-                "enable_compile": True,
-                "target_kl_stop": None,
-            },
-            "policy": {},
-        }
-    )
-
-    assert train_cfg["algorithm"]["enable_compile"] is True
-    assert "target_kl_stop" not in train_cfg["algorithm"]
-
-
-def test_rsl_rl_adapter_outputs_combined_dones_and_time_outs_alias():
-    class FakeEnv:
-        def __init__(self):
-            self.num_envs = 3
-            self.cfg = type("Cfg", (), {"max_episode_seconds": 10.0, "ctrl_dt": 0.02})()
-            self.observation_space = type("Space", (), {"shape": (2,)})()
-            self.action_space = type("Space", (), {"shape": (1,)})()
-            self.obs_groups_spec = {"obs": 2}
-            self.state = type("State", (), {"obs": {"obs": torch.zeros(3, 2).numpy()}})()
-
-        def init_state(self):
-            pass
-
-        def reset(self, env_indices):
-            del env_indices
-            return {"obs": torch.zeros(3, 2).numpy()}, {}
-
-        def step(self, actions):
-            del actions
-            return type(
-                "StepState",
-                (),
-                {
-                    "obs": {"obs": torch.zeros(3, 2).numpy()},
-                    "reward": torch.zeros(3).numpy(),
-                    "terminated": torch.tensor([True, False, False]).numpy(),
-                    "truncated": torch.tensor([False, True, False]).numpy(),
-                    "info": {},
-                    "final_observation": None,
-                },
-            )()
-
-    wrapper = RslRlVecEnvWrapper(FakeEnv(), device="cpu", policy_obs_mode="actor")
-
-    _, _, dones, infos = wrapper.step(torch.zeros(3, 1))
-
-    assert torch.equal(dones, torch.tensor([True, True, False]))
-    assert torch.equal(infos["time_outs"], torch.tensor([False, True, False]))
diff --git a/tests/algos/test_rsl_rl_runner.py b/tests/algos/test_rsl_rl_runner.py
index a7e2d7f84..a80e6188a 100644
--- a/tests/algos/test_rsl_rl_runner.py
+++ b/tests/algos/test_rsl_rl_runner.py
@@ -6,11 +6,13 @@
 
 from __future__ import annotations
 
-import sys
 import tempfile
+from pathlib import Path
 from typing import Any, cast
 
 import pytest
+from hydra import compose, initialize_config_dir
+from hydra.core.global_hydra import GlobalHydra
 
 pytest.importorskip("mujoco")
 rsl_rl = pytest.importorskip("rsl_rl")
@@ -18,18 +20,19 @@
 import numpy as np
 import torch
 from tensordict import TensorDict
+from uni_rl.algos.rsl_rl import normalize_ppo_train_cfg
 
 from unilab.base import registry
+from unilab.base.config_adapter import BackendAdapter
 from unilab.base.registry import ensure_registries
 from unilab.structured_configs import PPOConfig
-from unilab.training.rsl_rl import normalize_ppo_train_cfg
 from unilab.utils.tensor import to_torch
 
 ensure_registries()
 
 
 # ---------------------------------------------------------------------------
-# Minimal wrapper (same as scripts/train_rsl_rl.py)
+# Minimal wrapper (same as src/unilab/scripts/train_rsl_rl.py)
 # ---------------------------------------------------------------------------
 
 
@@ -114,34 +117,39 @@ def get_privileged_observations(self):
 )
 def test_rsl_rl_ppo_one_iteration(
     env_name: str,
-    default_go2_reward_config,
-    default_g1_reward_config,
-    default_allegro_reward_config,
 ):
     """RSL-RL PPO can complete 1 training iteration on a real env."""
     from rsl_rl.runners import OnPolicyRunner
 
+    num_envs = 256
+    root_dir = Path(__file__).parents[2]
     if "Go2" in env_name:
-        reward_cfg = default_go2_reward_config
-        num_envs = 256
+        task = "go2_joystick_flat/mujoco"
     elif "G1" in env_name:
-        reward_cfg = default_g1_reward_config
-        num_envs = 256
+        task = "g1_walk_flat/mujoco"
     else:
-        reward_cfg = default_allegro_reward_config
         num_envs = 128
+        task = "allegro_inhand/mujoco"
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(
+        config_dir=str(root_dir / "src" / "unilab" / "conf" / "ppo"), version_base="1.3"
+    ):
+        hydra_cfg = compose("config", overrides=[f"task={task}"])
+    env_cfg_override = BackendAdapter(hydra_cfg, root_dir=root_dir).build_task_env_cfg_override()
 
     env = registry.make(
         env_name,
         num_envs=num_envs,
         sim_backend="mujoco",
-        env_cfg_override={"reward_config": reward_cfg},
+        env_cfg_override=env_cfg_override,
     )
     wrapped = _RslRlVecEnvWrapper(env, device="cpu")
 
     cfg = PPOConfig()
     train_cfg = cfg.to_dict()
     train_cfg["runner"] = {"logger": "none"}
+    if env_name == "Go2JoystickFlat":
+        train_cfg["obs_groups"] = {"actor": ["actor"], "critic": ["critic"]}
     # Small network + short loop; large num_envs to saturate CPU
     train_cfg["num_steps_per_env"] = 8
     train_cfg["policy"] = {
diff --git a/tests/algos/test_torch_compile_helper.py b/tests/algos/test_torch_compile_helper.py
deleted file mode 100644
index d4b1eb30f..000000000
--- a/tests/algos/test_torch_compile_helper.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from __future__ import annotations
-
-import importlib.util
-from collections.abc import Callable
-from typing import Any
-
-import torch
-
-from unilab.algos.torch.common import compile as compile_helper
-
-
-def _without_triton(name: str, *args: Any, **kwargs: Any) -> Any:
-    if name == "triton":
-        return None
-    return _ORIGINAL_FIND_SPEC(name, *args, **kwargs)
-
-
-_ORIGINAL_FIND_SPEC = importlib.util.find_spec
-
-
-def test_torch_compile_cuda_fallback_warns_when_triton_missing(monkeypatch, capsys) -> None:
-    def fake_compile(fn: Callable[..., Any], **kwargs: Any) -> Callable[..., Any]:
-        return fn
-
-    fake_compile.__module__ = "torch"
-    monkeypatch.setattr(torch, "compile", fake_compile)
-    monkeypatch.setattr(importlib.util, "find_spec", _without_triton)
-    compile_helper._WARNED_REASONS.clear()
-
-    assert compile_helper.get_torch_compile_for_cuda("cuda", warn=True) is None
-
-    stderr = capsys.readouterr().err
-    assert "WARNING: torch.compile is unavailable for CUDA" in stderr
-    assert "Triton is not" in stderr
-    assert "installed" in stderr
-
-
-def test_torch_compile_cuda_fallback_warning_is_once(monkeypatch, capsys) -> None:
-    def fake_compile(fn: Callable[..., Any], **kwargs: Any) -> Callable[..., Any]:
-        return fn
-
-    fake_compile.__module__ = "torch"
-    monkeypatch.setattr(torch, "compile", fake_compile)
-    monkeypatch.setattr(importlib.util, "find_spec", _without_triton)
-    compile_helper._WARNED_REASONS.clear()
-
-    compile_helper.get_torch_compile_for_cuda("cuda", warn=True)
-    compile_helper.get_torch_compile_for_cuda("cuda", warn=True)
-
-    assert capsys.readouterr().err.count("torch.compile is unavailable") == 1
-
-
-def test_torch_compile_cuda_returns_available_compile_without_warning(monkeypatch, capsys) -> None:
-    def fake_compile(fn: Callable[..., Any], **kwargs: Any) -> Callable[..., Any]:
-        return fn
-
-    fake_compile.__module__ = "test"
-    monkeypatch.setattr(torch, "compile", fake_compile)
-    compile_helper._WARNED_REASONS.clear()
-
-    assert compile_helper.get_torch_compile_for_cuda("cuda", warn=True) is fake_compile
-    assert capsys.readouterr().err == ""
diff --git a/tests/assets/test_hub.py b/tests/assets/test_hub.py
index 9804b9dc3..1d43abd96 100644
--- a/tests/assets/test_hub.py
+++ b/tests/assets/test_hub.py
@@ -9,7 +9,12 @@
 import pytest
 
 from unilab.assets import ASSETS_ROOT_PATH
-from unilab.assets.hub import resolve_grasp_cache_files, resolve_motion_files, resolve_scene_dir
+from unilab.assets.hub import (
+    resolve_grasp_cache_files,
+    resolve_motion_files,
+    resolve_robot_asset_dir,
+    resolve_scene_dir,
+)
 
 # ---------------------------------------------------------------------------
 # Local-path fast path
@@ -187,12 +192,13 @@ def test_resolve_grasp_cache_calls_hf_download_with_caches_repo():
         result = resolve_grasp_cache_files(str(missing))
 
     assert result == str(missing)
-    fake_download.assert_called_once_with(
-        repo_id="unilabsim/unilab-caches",
-        filename=expected_relative,
-        repo_type="dataset",
-        local_dir=str(ASSETS_ROOT_PATH),
-    )
+    fake_download.assert_called_once()
+    kwargs = fake_download.call_args.kwargs
+    assert kwargs["repo_id"] == "unilabsim/unilab-caches"
+    assert kwargs["filename"] == expected_relative
+    assert kwargs["repo_type"] == "dataset"
+    assert kwargs["local_dir"] == str(ASSETS_ROOT_PATH)
+    assert kwargs["tqdm_class"] is not None
 
 
 def test_resolve_grasp_cache_relative_path_uses_caches_repo():
@@ -209,12 +215,39 @@ def test_resolve_grasp_cache_relative_path_uses_caches_repo():
         result = resolve_grasp_cache_files(rel)
 
     assert result == str(local)
-    fake_download.assert_called_once_with(
-        repo_id="unilabsim/unilab-caches",
-        filename=rel,
-        repo_type="dataset",
-        local_dir=str(ASSETS_ROOT_PATH),
-    )
+    fake_download.assert_called_once()
+    kwargs = fake_download.call_args.kwargs
+    assert kwargs["repo_id"] == "unilabsim/unilab-caches"
+    assert kwargs["filename"] == rel
+    assert kwargs["repo_type"] == "dataset"
+    assert kwargs["local_dir"] == str(ASSETS_ROOT_PATH)
+    assert kwargs["tqdm_class"] is not None
+
+
+def test_resolve_grasp_cache_can_disable_download_progress():
+    rel = "caches/__test_nonexistent_quiet__.npy"
+    local = ASSETS_ROOT_PATH / rel
+    assert not local.exists()
+
+    fake_download = MagicMock(return_value=str(local))
+    fake_module = MagicMock()
+    fake_module.hf_hub_download = fake_download
+
+    with patch.dict("sys.modules", {"huggingface_hub": fake_module}):
+        result = resolve_grasp_cache_files(rel, show_progress=False)
+
+    assert result == str(local)
+    fake_download.assert_called_once()
+    kwargs = fake_download.call_args.kwargs
+    assert kwargs["repo_id"] == "unilabsim/unilab-caches"
+    assert kwargs["filename"] == rel
+    assert kwargs["repo_type"] == "dataset"
+    assert kwargs["local_dir"] == str(ASSETS_ROOT_PATH)
+    progress = kwargs["tqdm_class"](range(1))
+    try:
+        assert progress.disable is True
+    finally:
+        progress.close()
 
 
 # Scene directory resolver
@@ -277,3 +310,168 @@ def test_resolve_scene_dir_raises_import_error_when_hf_hub_missing(tmp_path: Pat
         with patch.dict("sys.modules", {"huggingface_hub": None}):
             with pytest.raises(ImportError, match="huggingface_hub"):
                 resolve_scene_dir("scenes/teaser")
+
+
+def test_resolve_robot_asset_dir_can_disable_snapshot_progress(tmp_path: Path):
+    fake_snapshot = MagicMock(return_value=str(tmp_path))
+    fake_module = MagicMock()
+    fake_module.snapshot_download = fake_snapshot
+
+    with patch("unilab.assets.hub.ASSETS_ROOT_PATH", tmp_path):
+        with patch.dict("sys.modules", {"huggingface_hub": fake_module}):
+            resolve_robot_asset_dir("robots/x2/meshes", marker="pelvis.STL", show_progress=False)
+
+    fake_snapshot.assert_called_once()
+    kwargs = fake_snapshot.call_args.kwargs
+    assert kwargs["repo_id"] == "unilabsim/unilab-robots"
+    assert kwargs["repo_type"] == "dataset"
+    assert kwargs["allow_patterns"] == "robots/x2/meshes/**"
+    assert kwargs["local_dir"] == str(tmp_path)
+    tqdm_class = kwargs["tqdm_class"]
+    progress = tqdm_class(range(1))
+    try:
+        assert progress.disable is True
+    finally:
+        progress.close()
+
+
+# ---------------------------------------------------------------------------
+# Robot asset registry + scene-path resolution
+# ---------------------------------------------------------------------------
+
+
+def test_robot_asset_specs_cover_hf_hosted_robots():
+    """Every robot whose binary assets live on HF is registered exactly once."""
+    from unilab.assets.hub import ROBOT_ASSET_SPECS
+
+    expected = {
+        "a2",
+        "allegro_hand",
+        "g1",
+        "go2",
+        "go2_arm",
+        "go2w",
+        "sharpa_wave",
+        "x2",
+    }
+    assert set(ROBOT_ASSET_SPECS) == expected
+    for robot, specs in ROBOT_ASSET_SPECS.items():
+        assert specs, robot
+        for directory, marker, pattern, label in specs:
+            # go2_arm / go2w additionally reference the shared go2 mesh dir.
+            assert directory.startswith("robots/")
+            assert marker and pattern and label
+
+
+def test_ensure_robot_assets_resolves_registered_robot(monkeypatch: pytest.MonkeyPatch):
+    from unilab.assets import hub
+
+    calls: list[tuple[str, str]] = []
+
+    def fake_resolve(directory: str, *, marker: str) -> Path:
+        calls.append((directory, marker))
+        return Path(directory)
+
+    monkeypatch.setattr(hub, "resolve_robot_asset_dir", fake_resolve)
+
+    hub.ensure_robot_assets_for_paths(["src/unilab/assets/robots/g1/scene_flat.xml", None, ""])
+
+    assert calls == [
+        ("robots/g1/assets", "head_link.STL"),
+        ("robots/g1/textures", "floor.png"),
+    ]
+
+
+def test_ensure_robot_assets_handles_absolute_and_windows_paths(
+    monkeypatch: pytest.MonkeyPatch,
+):
+    from unilab.assets import hub
+
+    calls: list[tuple[str, str]] = []
+    monkeypatch.setattr(
+        hub,
+        "resolve_robot_asset_dir",
+        lambda directory, *, marker: calls.append((directory, marker)) or Path(directory),
+    )
+
+    hub.ensure_robot_assets_for_paths(
+        [
+            "/home/user/project/src/unilab/assets/robots/go2/go2.xml",
+            r"src\unilab\assets\robots\sharpa_wave\right_sharpa_wave.xml",
+        ]
+    )
+
+    assert calls == [
+        ("robots/go2/assets", "base_0.obj"),
+        ("robots/sharpa_wave/meshes", "DP_HB1_4F.STL"),
+    ]
+
+
+def test_ensure_robot_assets_ignores_unknown_robots(monkeypatch: pytest.MonkeyPatch):
+    from unilab.assets import hub
+
+    calls: list[tuple[str, str]] = []
+    monkeypatch.setattr(
+        hub,
+        "resolve_robot_asset_dir",
+        lambda directory, *, marker: calls.append((directory, marker)) or Path(directory),
+    )
+
+    hub.ensure_robot_assets_for_paths(
+        [
+            "src/unilab/assets/robots/go1/scene_flat.xml",
+            "src/unilab/assets/objects/cube.xml",
+            "some/other/scene.xml",
+        ]
+    )
+
+    assert calls == []
+
+
+def test_create_backend_resolves_robot_assets_before_dispatch(monkeypatch: pytest.MonkeyPatch):
+    """create_backend must ensure HF-hosted robot assets before loading XML."""
+    import unilab.base.backend_factory as backend_pkg
+    from unilab.base.scene import SceneCfg
+
+    seen: list[list[str | None]] = []
+
+    def fake_ensure(paths):
+        seen.append(list(paths))
+
+    monkeypatch.setattr(backend_pkg, "ensure_robot_assets_for_paths", fake_ensure)
+
+    scene = SceneCfg(
+        model_file="src/unilab/assets/robots/g1/scene_flat.xml",
+        fragment_files=["src/unilab/assets/robots/g1/locomotion_task.xml"],
+    )
+    with pytest.raises(ValueError, match="unknown UniSim backend"):
+        backend_pkg.create_backend("__bogus__", scene, 1, 0.02)
+
+    assert seen == [
+        [
+            "src/unilab/assets/robots/g1/scene_flat.xml",
+            None,
+            "src/unilab/assets/robots/g1/locomotion_task.xml",
+        ]
+    ]
+
+
+def test_ensure_robot_assets_go2_arm_pulls_shared_go2_meshes(
+    monkeypatch: pytest.MonkeyPatch,
+):
+    """go2_arm XMLs reference ``../go2/assets``; both dirs must resolve."""
+    from unilab.assets import hub
+
+    calls: list[tuple[str, str]] = []
+    monkeypatch.setattr(
+        hub,
+        "resolve_robot_asset_dir",
+        lambda directory, *, marker: calls.append((directory, marker)) or Path(directory),
+    )
+
+    hub.ensure_robot_assets_for_paths(["src/unilab/assets/robots/go2_arm/scene_flat.xml"])
+
+    assert calls == [
+        ("robots/go2_arm/assets", "arm_base_0.obj"),
+        ("robots/go2/assets", "base_0.obj"),
+    ]
diff --git a/tests/base/backend/test_drake_batch_pool.py b/tests/base/backend/test_drake_batch_pool.py
index 456a7ada0..1b2766ec4 100644
--- a/tests/base/backend/test_drake_batch_pool.py
+++ b/tests/base/backend/test_drake_batch_pool.py
@@ -16,12 +16,16 @@ def _module_available(name: str) -> bool:
         return False
 
 
-def _drakeuni_package_installed() -> bool:
-    return _module_available("drakeuni")
+def _drake_uni_package_installed() -> bool:
+    return _module_available("drake_uni")
 
 
 def _batch_extension_built() -> bool:
-    return _module_available("drakeuni.compiled._drake_env_pool")
+    return _module_available("drake_uni.compiled._drake_env_pool")
+
+
+def _mujoco_available() -> bool:
+    return _module_available("mujoco")
 
 
 def _run_clean_python(code: str) -> str:
@@ -41,8 +45,8 @@ def _run_clean_python(code: str) -> str:
 import numpy as np
 
 from unilab.assets import ASSETS_ROOT_PATH
-from drakeuni.batch_env import DrakeEnvPool
-from drakeuni.runtime.mjcf_model_parser import (
+from drake_uni.batch_env import DrakeEnvPool
+from drake_uni.runtime.mjcf_model_parser import (
     materialize_drake_compatible_mjcf,
     parse_mjcf_model_contract,
     sensor_frames_as_pool_inputs,
@@ -111,7 +115,7 @@ def test_batch_import_diagnostic_is_preserved() -> None:
         import json
 
         try:
-            from drakeuni.batch_env import batch_available, batch_import_error
+            from drake_uni.batch_env import batch_available, batch_import_error
         except ImportError as exc:
             captured_error = exc
 
@@ -136,18 +140,10 @@ def batch_import_error():
         assert summary["missing_module"] is None
     else:
         assert summary["error_type"] == "ModuleNotFoundError"
-        assert summary["missing_module"] == "drakeuni"
-
-
-def test_drake_batch_thread_policy_matches_mujoco_auto(monkeypatch: pytest.MonkeyPatch) -> None:
-    from unilab.base.backend.drake import backend
-
-    monkeypatch.setattr(backend, "cpu_count", lambda: 10)
-
-    assert backend._resolve_batch_nthread(1024, 0) == 20
-    assert backend._resolve_batch_nthread(8, 0) == 8
-    assert backend._resolve_batch_nthread(1024, 4) == 4
-    assert backend._resolve_batch_nthread(2, 8) == 2
+        assert summary["missing_module"] in {
+            "drake_uni",
+            "drake_uni.compiled._drake_env_pool",
+        }
 
 
 def test_batch_backend_mode_rejects_existing_pydrake_module() -> None:
@@ -157,7 +153,7 @@ def test_batch_backend_mode_rejects_existing_pydrake_module() -> None:
         import sys
 
         from unilab.assets import ASSETS_ROOT_PATH
-        from unilab.base.backend import create_backend
+        from unilab.base.backend_factory import create_backend
         from unilab.base.scene import SceneCfg
 
         sys.modules["pydrake"] = object()
@@ -181,39 +177,9 @@ def test_batch_backend_mode_rejects_existing_pydrake_module() -> None:
     assert "pydrake" in output
 
 
-def test_direct_drake_backend_batch_mode_rejects_existing_pydrake_module() -> None:
-    output = _run_clean_python(
-        """
-        import json
-        import sys
-
-        from unilab.assets import ASSETS_ROOT_PATH
-        from unilab.base.backend.drake.backend import DrakeBackend
-        from unilab.base.scene import SceneCfg
-
-        sys.modules["pydrake"] = object()
-        try:
-            DrakeBackend(
-                SceneCfg(model_file=str(ASSETS_ROOT_PATH / "robots/go1/scene_flat.xml")),
-                1,
-                0.01,
-                drake_backend_mode="batch",
-            )
-        except ImportError as exc:
-            message = str(exc)
-        else:
-            raise AssertionError("direct batch DrakeBackend unexpectedly loaded with pydrake present")
-        assert "pydrake" in message
-        assert "fresh process" in message
-        print(json.dumps({"message": message}, sort_keys=True))
-        """
-    )
-    assert "pydrake" in output
-
-
 def test_create_backend_rejects_pydrake_mode() -> None:
     from unilab.assets import ASSETS_ROOT_PATH
-    from unilab.base.backend import create_backend
+    from unilab.base.backend_factory import create_backend
     from unilab.base.scene import SceneCfg
 
     with pytest.raises(ValueError, match="drake_backend_mode='batch'"):
@@ -226,27 +192,13 @@ def test_create_backend_rejects_pydrake_mode() -> None:
         )
 
 
-def test_direct_drake_backend_rejects_pydrake_mode() -> None:
-    from unilab.assets import ASSETS_ROOT_PATH
-    from unilab.base.backend.drake.backend import DrakeBackend
-    from unilab.base.scene import SceneCfg
-
-    with pytest.raises(ValueError, match="drake_backend_mode='batch'"):
-        DrakeBackend(
-            SceneCfg(model_file=str(ASSETS_ROOT_PATH / "robots/go1/scene_flat.xml")),
-            1,
-            0.01,
-            drake_backend_mode="pydrake",
-        )
-
-
 @pytest.mark.skipif(
     not _batch_extension_built(),
     reason="optional Drake batch extension has not been built",
 )
 def test_drake_backend_constructs_without_task_base_name() -> None:
     from unilab.assets import ASSETS_ROOT_PATH
-    from unilab.base.backend import create_backend
+    from unilab.base.backend_factory import create_backend
     from unilab.base.scene import SceneCfg
 
     backend = create_backend(
@@ -260,8 +212,8 @@ def test_drake_backend_constructs_without_task_base_name() -> None:
 
 
 @pytest.mark.skipif(
-    not _drakeuni_package_installed(),
-    reason="optional drakeuni package has not been installed",
+    not _drake_uni_package_installed(),
+    reason="optional drake_uni package has not been installed",
 )
 def test_batch_package_direct_import_rejects_existing_pydrake_module() -> None:
     output = _run_clean_python(
@@ -270,7 +222,7 @@ def test_batch_package_direct_import_rejects_existing_pydrake_module() -> None:
         import sys
 
         sys.modules["pydrake"] = object()
-        from drakeuni.batch_env import (
+        from drake_uni.batch_env import (
             DrakeEnvPool,
             batch_available,
             batch_import_error,
@@ -294,7 +246,7 @@ def test_batch_package_direct_import_rejects_existing_pydrake_module() -> None:
 def test_drake_batch_pool_imports_in_clean_process() -> None:
     output = _run_clean_python(
         """
-        from drakeuni.batch_env import DrakeEnvPool, batch_available
+        from drake_uni.batch_env import DrakeEnvPool, batch_available
         assert batch_available()
         assert DrakeEnvPool is not None
         print(DrakeEnvPool.__name__)
@@ -304,20 +256,20 @@ def test_drake_batch_pool_imports_in_clean_process() -> None:
 
 
 @pytest.mark.skipif(
-    not _drakeuni_package_installed(),
-    reason="optional drakeuni package has not been installed",
+    not _drake_uni_package_installed(),
+    reason="optional drake_uni package has not been installed",
 )
-def test_drakeuni_runtime_import_is_lazy_and_pydrake_free() -> None:
+def test_drake_uni_runtime_import_is_lazy_and_pydrake_free() -> None:
     output = _run_clean_python(
         """
         import json
         import sys
 
-        from drakeuni.runtime import DrakeBatchConfig
+        from drake_uni.runtime import DrakeBatchConfig
 
         summary = {
             "config": DrakeBatchConfig.__name__,
-            "compiled_loaded": any(name.startswith("drakeuni.compiled") for name in sys.modules),
+            "compiled_loaded": any(name.startswith("drake_uni.compiled") for name in sys.modules),
             "pydrake_loaded": any(
                 name == "pydrake" or name.startswith("pydrake.") for name in sys.modules
             ),
@@ -337,12 +289,12 @@ def test_unilab_drake_public_surface_excludes_batch_backend_symbol() -> None:
         """
         import json
 
-        import unilab.base.backend as backend_root
-        import unilab.base.backend.drake as drake_pkg
-        from unilab.base.backend.drake import backend as backend_module
+        import unilab.base.backend_factory as backend_root
+        import unisim.backend.drake as drake_pkg
+        from unisim.backend.drake import backend as backend_module
 
         try:
-            from unilab.base.backend.drake.backend import DrakeUniBatchBackend  # noqa: F401
+            from unisim.backend.drake.backend import DrakeUniBatchBackend  # noqa: F401
         except ImportError:
             direct_import = "failed"
         else:
@@ -514,6 +466,10 @@ def test_drake_batch_pool_worker_exception_reaches_python() -> None:
     not _batch_extension_built(),
     reason="optional Drake batch extension has not been built",
 )
+@pytest.mark.skipif(
+    not _mujoco_available(),
+    reason="MuJoCo is required for the cross-backend qpos-order assertion",
+)
 def test_drake_runtime_stewart_compact_state_matches_mujoco_qpos_order() -> None:
     output = _run_clean_python(
         """
@@ -523,7 +479,7 @@ def test_drake_runtime_stewart_compact_state_matches_mujoco_qpos_order() -> None
         import numpy as np
 
         from unilab.assets import ASSETS_ROOT_PATH
-        from drakeuni.runtime import DrakeBatchConfig, create_runtime
+        from drake_uni.runtime import DrakeBatchConfig, create_runtime
 
         model = ASSETS_ROOT_PATH / "robots/stewart/scene.xml"
         runtime = create_runtime(
@@ -562,7 +518,7 @@ def test_drake_runtime_stewart_ball_collides_with_top_plate() -> None:
         import numpy as np
 
         from unilab.assets import ASSETS_ROOT_PATH
-        from drakeuni.runtime import DrakeBatchConfig, create_runtime
+        from drake_uni.runtime import DrakeBatchConfig, create_runtime
 
         model = ASSETS_ROOT_PATH / "robots/stewart/scene.xml"
         runtime = create_runtime(
@@ -603,7 +559,7 @@ def test_create_backend_batch_mode_avoids_pydrake_and_steps() -> None:
         import numpy as np
 
         from unilab.assets import ASSETS_ROOT_PATH
-        from unilab.base.backend import create_backend
+        from unilab.base.backend_factory import create_backend
         from unilab.base.scene import SceneCfg
 
         assert "pydrake" not in sys.modules
@@ -674,7 +630,7 @@ def test_drake_backend_pre_step_hook_refreshes_between_substeps() -> None:
         import numpy as np
 
         from unilab.assets import ASSETS_ROOT_PATH
-        from unilab.base.backend import create_backend
+        from unilab.base.backend_factory import create_backend
         from unilab.base.scene import SceneCfg
 
         backend = create_backend(
@@ -721,7 +677,7 @@ def test_drake_backend_body_frame_getters_use_compact_root_frame() -> None:
         import numpy as np
 
         from unilab.assets import ASSETS_ROOT_PATH
-        from unilab.base.backend import create_backend
+        from unilab.base.backend_factory import create_backend
         from unilab.base.scene import SceneCfg
 
         backend = create_backend(
@@ -780,7 +736,7 @@ def test_create_go2_backend_batch_mode_avoids_pydrake_and_steps() -> None:
         import numpy as np
 
         from unilab.assets import ASSETS_ROOT_PATH
-        from unilab.base.backend import create_backend
+        from unilab.base.backend_factory import create_backend
         from unilab.base.scene import SceneCfg
 
         assert "pydrake" not in sys.modules
diff --git a/tests/base/backend/test_mujoco_chunk_size_wiring.py b/tests/base/backend/test_mujoco_chunk_size_wiring.py
index addd621fb..3edf71d29 100644
--- a/tests/base/backend/test_mujoco_chunk_size_wiring.py
+++ b/tests/base/backend/test_mujoco_chunk_size_wiring.py
@@ -1,7 +1,7 @@
 import numpy as np
 import pytest
 
-from unilab.base.backend import env_backend_kwargs
+from unilab.base.backend_factory import env_backend_kwargs
 from unilab.base.base import EnvCfg
 
 pytest.importorskip("mujoco", reason="mujoco not installed")
@@ -14,9 +14,9 @@
     )
 
 import mujoco
+from unisim.backend.mujoco.backend import MuJoCoBackend
 
 from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base.backend.mujoco.backend import MuJoCoBackend
 from unilab.base.scene import SceneCfg
 
 _MODEL_FILE = str(ASSETS_ROOT_PATH / "robots" / "go2_arm" / "scene_flat.xml")
@@ -121,19 +121,20 @@ def test_benchmark_runs_and_logs_table_on_adaptive(caplog, monkeypatch, tmp_path
 
     Point the chunk_size cache at an empty ``tmp_path`` file so the resolve is a
     guaranteed MISS (no warm-cache short-circuit) and never pollutes the real
-    ``~/.cache/unilab/chunk_size.json``. A clean miss forces the benchmark path,
+    ``~/.cache/unisim/chunk_size.json``. A clean miss forces the benchmark path,
     so we can assert the benchmark-table INFO record specifically.
     """
     import logging
 
-    from unilab.base.backend.mujoco import backend as backend_mod
+    from unisim.backend.mujoco import backend as backend_mod
 
     # Force nthread < num_envs so there is genuinely something to tune; otherwise the
     # resolve short-circuits (num_envs <= nthread => one chunk => no benchmark). With
-    # cpu_count()==1, nthread = min(_NUM_ENVS, 2) = 2 < _NUM_ENVS, deterministically.
-    monkeypatch.setattr(backend_mod, "cpu_count", lambda: 1)
-    monkeypatch.setenv("UNILAB_CHUNK_SIZE_CACHE", str(tmp_path / "chunk_size.json"))
-    with caplog.at_level(logging.INFO, logger="unilab.base.backend.mujoco.chunk_tuner"):
+    # _effective_cpu_count()==1, nthread = min(_NUM_ENVS, 1) = 1 < _NUM_ENVS,
+    # deterministically.
+    monkeypatch.setattr(backend_mod, "_effective_cpu_count", lambda: 1)
+    monkeypatch.setenv("UNISIM_CHUNK_SIZE_CACHE", str(tmp_path / "chunk_size.json"))
+    with caplog.at_level(logging.INFO, logger="unisim.backend.mujoco.chunk_tuner"):
         backend = _build_small_backend(adaptive_chunk_size=True, chunk_size=None)
     assert backend._chunk_size is None or isinstance(backend._chunk_size, int)
     # Forced miss -> _log_benchmark_table emits the "chunk_size benchmark" record.
diff --git a/tests/base/backend/test_mujoco_chunk_tuner.py b/tests/base/backend/test_mujoco_chunk_tuner.py
deleted file mode 100644
index 71f9f6d7a..000000000
--- a/tests/base/backend/test_mujoco_chunk_tuner.py
+++ /dev/null
@@ -1,433 +0,0 @@
-import inspect
-import logging
-import math
-from pathlib import Path
-
-import numpy as np
-import pytest
-
-from unilab.base.backend.mujoco import chunk_tuner as ct
-
-
-def test_make_candidates_never_exceeds_upper():
-    # upper = ceil(num_envs/nthread): a chunk beyond it yields fewer chunks than
-    # threads -> idle threads -> always slower, so it must never be a candidate.
-    for num_envs, nthread in [(4096, 16), (2048, 40), (1024, 40), (4096, 40)]:
-        upper = math.ceil(num_envs / nthread)
-        cands = ct.make_candidates(num_envs, nthread)
-        assert max(cands) == upper, (num_envs, nthread, max(cands), upper)
-        assert all(1 <= c <= upper for c in cands)
-        assert 1 in cands
-        assert cands == sorted(set(cands))
-
-
-def test_make_candidates_covers_sweet_spot():
-    # optimum sits near heur = num_envs/(10*nthread); a candidate must land in the band.
-    for num_envs, nthread, lo, hi in [(2048, 40, 3, 9), (1024, 40, 2, 7), (4096, 40, 8, 41)]:
-        cands = ct.make_candidates(num_envs, nthread)
-        assert any(lo <= c <= hi for c in cands), (num_envs, nthread, cands)
-
-
-def test_make_candidates_regression_g1walk_optimum_present():
-    # for 2048 envs / 40 threads the sweet-spot chunks (~4-6) must be candidates and
-    # the always-bad large chunks (> upper) must be excluded.
-    cands = ct.make_candidates(2048, 40)
-    assert any(4 <= c <= 6 for c in cands), cands
-    assert 1024 not in cands and 2048 not in cands
-
-
-def test_make_candidates_num_envs_one():
-    assert ct.make_candidates(num_envs=1, nthread=8) == [1]
-
-
-def test_make_candidates_fewer_envs_than_threads():
-    # nbatch <= nthread -> upper=1 -> only chunk_size=1 is sensible.
-    assert ct.make_candidates(num_envs=20, nthread=40) == [1]
-
-
-def test_make_candidates_rejects_bad_num_envs():
-    with pytest.raises(ValueError):
-        ct.make_candidates(num_envs=0, nthread=8)
-
-
-def test_filter_candidates_clamps_and_dedups():
-    out = ct.filter_candidates([0, 1, 1, 5, 4096, 9000], num_envs=4096)
-    assert out == [1, 5, 4096]
-
-
-def test_filter_candidates_capping_keeps_low_band_and_top_anchor():
-    # capping must NOT drop the low/optimum band (the old log-spacing bug); it trims
-    # the coarse middle, keeping the smallest values + the coarsest anchor.
-    out = ct.filter_candidates(list(range(1, 60)), num_envs=4096, max_candidates=8)
-    assert len(out) <= 8
-    assert 1 in out and max(out) == 59
-    assert all(c in out for c in (2, 3, 4, 5))
-
-
-def test_make_and_filter_g1walk_finds_optimum_band():
-    cands = ct.filter_candidates(ct.make_candidates(2048, 40), num_envs=2048)
-    assert any(4 <= c <= 6 for c in cands)
-    assert max(cands) == math.ceil(2048 / 40)
-    assert 1024 not in cands and 2048 not in cands
-
-
-class _FakeModel:
-    nq = 30
-    nv = 29
-    nbody = 16
-    njnt = 28
-    nu = 12
-    ngeom = 40
-    nsensordata = 100
-
-
-def test_device_fingerprint_has_expected_keys():
-    fp = ct.device_fingerprint()
-    assert set(fp) >= {"system", "machine", "cpu_count"}
-    assert isinstance(fp["cpu_count"], int) and fp["cpu_count"] >= 1
-
-
-def test_model_signature_reads_structural_fields():
-    sig = ct.model_signature(_FakeModel(), n_variants=3)
-    assert sig == {
-        "nq": 30,
-        "nv": 29,
-        "nbody": 16,
-        "njnt": 28,
-        "nu": 12,
-        "ngeom": 40,
-        "nsensordata": 100,
-        "n_variants": 3,
-    }
-
-
-def test_make_cache_key_deterministic_and_sensitive():
-    common = dict(
-        backend_type="mujoco",
-        model_sig=ct.model_signature(_FakeModel(), 1),
-        device={"system": "Linux", "machine": "x86_64", "cpu_count": 32},
-        num_envs=4096,
-        nthread=64,
-        dtype=np.float32,
-        post_step_forward_sensor=False,
-        bench_nsteps=4,
-    )
-    k1 = ct.make_cache_key(**common)
-    k2 = ct.make_cache_key(**common)
-    assert k1 == k2
-    assert ct.make_cache_key(**{**common, "num_envs": 2048}) != k1
-    assert ct.make_cache_key(**{**common, "dtype": np.float64}) != k1
-
-
-def test_cache_path_env_override(monkeypatch, tmp_path):
-    target = tmp_path / "custom.json"
-    monkeypatch.setenv("UNILAB_CHUNK_SIZE_CACHE", str(target))
-    assert ct.cache_path() == target
-
-
-def test_cache_path_xdg(monkeypatch, tmp_path):
-    monkeypatch.delenv("UNILAB_CHUNK_SIZE_CACHE", raising=False)
-    monkeypatch.setenv("XDG_CACHE_HOME", str(tmp_path))
-    assert ct.cache_path() == tmp_path / "unilab" / "chunk_size.json"
-
-
-def test_load_missing_cache_returns_empty(tmp_path):
-    assert ct.load_cache(tmp_path / "nope.json") == {}
-
-
-def test_store_then_load_roundtrip_and_merge(tmp_path):
-    path = tmp_path / "c.json"
-    ct.store_cache(path, "k1", {"chunk_size": 7})
-    ct.store_cache(path, "k2", {"chunk_size": None})
-    data = ct.load_cache(path)
-    assert data["k1"]["chunk_size"] == 7
-    assert data["k2"]["chunk_size"] is None
-
-
-def test_file_lock_acquires_and_releases(tmp_path):
-    lock = tmp_path / "c.lock"
-    with ct.file_lock(lock):
-        pass
-    with ct.file_lock(lock):  # re-acquire after release must not deadlock
-        pass
-
-
-class _FakePool:
-    def __init__(self):
-        self.seen = []
-
-    def step(
-        self,
-        state,
-        *,
-        nstep,
-        control=None,
-        control_spec=0,
-        chunk_size=None,
-        return_sensor=False,
-        post_step_forward_sensor=False,
-    ):
-        self.seen.append(chunk_size)
-        return state, np.zeros((state.shape[0], 1))
-
-
-def test_benchmark_visits_all_candidates_plus_baseline():
-    pool = _FakePool()
-    state = np.zeros((8, 5), dtype=np.float64)
-    timings = ct.benchmark_chunk_sizes(
-        pool,
-        state,
-        nstep=2,
-        candidates=[1, 2],
-        control=None,
-        post_step_forward_sensor=False,
-        warmup=1,
-        reps=2,
-    )
-    assert set(timings) == {None, 1, 2}
-    assert all(isinstance(v, float) and v >= 0.0 for v in timings.values())
-    assert None in pool.seen and 1 in pool.seen and 2 in pool.seen
-
-
-def test_select_picks_fastest_above_margin():
-    timings = {None: 1.0, 128: 0.95, 256: 0.90, 512: 0.80}
-    assert ct.select_chunk_size(timings, base=256) == 512
-
-
-def test_select_keeps_default_when_margin_not_met():
-    timings = {None: 1.0, 256: 0.99}
-    assert ct.select_chunk_size(timings, base=256) is None
-
-
-def test_select_tiebreaks_toward_base():
-    timings = {None: 1.0, 200: 0.80, 256: 0.805, 4096: 0.81}
-    assert ct.select_chunk_size(timings, base=256) == 256
-
-
-def test_select_adopts_candidate_at_exact_margin():
-    # best_t exactly at the boundary (margin=0.5 -> baseline*(1-0.5)=0.5, exact float);
-    # spec '>= margin to adopt' means adopt the candidate, not keep the default.
-    timings = {None: 1.0, 256: 0.5}
-    assert ct.select_chunk_size(timings, base=256, margin=0.5) == 256
-
-
-def test_emit_falls_back_to_stderr_when_info_disabled(capsys, monkeypatch):
-    # Spawn collector subprocesses leave the root logger unconfigured (default
-    # level WARNING), which drops INFO. _emit must still surface the line on the
-    # terminal via stderr so the candidate benchmark is visible there.
-    monkeypatch.setattr(ct.logger, "level", logging.WARNING)
-    ct._emit("chunk_size benchmark: default=1.00ms, 4=0.80ms -> chosen=4")
-    err = capsys.readouterr().err
-    assert "chunk_size benchmark" in err
-    assert "chosen=4" in err
-
-
-def test_emit_uses_logger_when_info_enabled(capsys, caplog):
-    # Main process (Hydra-configured, INFO enabled): go through logging, NOT a
-    # raw stderr print, so there is no duplicate line.
-    caplog.set_level(logging.INFO, logger=ct.logger.name)
-    ct._emit("chunk_size: cache hit -> 4")
-    assert "chunk_size: cache hit -> 4" not in capsys.readouterr().err
-    assert any("cache hit -> 4" in r.message for r in caplog.records)
-
-
-def test_log_benchmark_table_surfaces_candidates_without_logging(capsys, monkeypatch):
-    # End-to-end: with INFO disabled (subprocess), the full candidate table must
-    # reach stderr, including every candidate's timing and the chosen value.
-    monkeypatch.setattr(ct.logger, "level", logging.WARNING)
-    ct._log_benchmark_table({None: 0.006, 1: 0.013, 4: 0.005, 26: 0.007}, chosen=4, default_chunk=2)
-    err = capsys.readouterr().err
-    assert "default(=2)=6.00ms" in err
-    assert "4=5.00ms" in err
-    assert "26=7.00ms" in err
-    assert "chosen=4" in err
-
-
-def test_format_candidate_table_sorts_default_first_then_numeric():
-    table = ct._format_candidate_table({"None": 9.38, "1": 25.43, "52": 14.59, "4": 9.15}, 5)
-    assert table == "default(=5)=9.38ms, 1=25.43ms, 4=9.15ms, 52=14.59ms"
-
-
-def test_cache_hit_emits_full_candidate_table_without_logging(capsys, monkeypatch, tmp_path):
-    # Warm cache + spawn collector (INFO disabled): the cache-hit line must carry the
-    # stored per-candidate breakdown, not just the chosen value -- otherwise the
-    # candidate info is invisible on every run after the first cold benchmark.
-    monkeypatch.setenv("UNILAB_CHUNK_SIZE_CACHE", str(tmp_path / "c.json"))
-    monkeypatch.setattr(
-        ct, "benchmark_chunk_sizes", lambda *a, **k: {None: 0.009, 1: 0.025, 4: 0.0091}
-    )
-    ct.resolve_chunk_size(**_resolve_kwargs())  # miss -> benchmark -> stores per_candidate_ms
-    capsys.readouterr()  # discard first-call output
-
-    monkeypatch.setattr(ct.logger, "level", logging.WARNING)  # simulate spawn collector
-    ct.resolve_chunk_size(**_resolve_kwargs())  # hit -> must emit the table
-    err = capsys.readouterr().err
-    assert "cache hit" in err
-    assert "default(=1)=9.00ms" in err  # _resolve_kwargs: 8 // (10*4) = 0 -> clamp to 1
-    assert "1=25.00ms" in err
-    assert "4=9.10ms" in err
-
-
-def test_cache_hit_without_candidate_table_still_emits_chosen(capsys, monkeypatch):
-    # Legacy entries (or any entry missing per_candidate_ms) must not crash and must
-    # still report the chosen value.
-    monkeypatch.setattr(ct.logger, "level", logging.WARNING)
-    ct._emit_cache_hit(4, None, default_chunk=1)
-    assert "cache hit -> 4" in capsys.readouterr().err
-
-
-def test_aggregate_times_returns_min():
-    # min rejects background-load spikes (which only inflate, never deflate) and
-    # estimates the uncontended per-step cost a dedicated training box actually sees.
-    assert ct._aggregate_times([0.003, 0.001, 0.002]) == pytest.approx(0.001)
-    assert ct._aggregate_times([0.05, 0.0061, 0.006, 0.0062]) == pytest.approx(0.006)
-
-
-def test_benchmark_reps_default_is_stable():
-    # reps=5 was too few to reject noise on a loaded machine; the default must be
-    # large enough that the per-candidate estimate is stable.
-    reps = inspect.signature(ct.benchmark_chunk_sizes).parameters["reps"].default
-    assert reps >= 15, reps
-
-
-def test_benchmark_respects_time_budget():
-    pool = _FakePool()
-    state = np.zeros((8, 5), dtype=np.float64)
-    timings = ct.benchmark_chunk_sizes(
-        pool,
-        state,
-        nstep=2,
-        candidates=[1, 2, 4, 8],
-        control=None,
-        post_step_forward_sensor=False,
-        warmup=0,
-        reps=1,
-        time_budget_s=0.0,
-    )
-    assert set(timings).issubset({None, 1, 2, 4, 8})
-    assert len(timings) < 5  # budget fired -> stopped early
-
-
-def _resolve_kwargs(**over):
-    base = dict(
-        pool=_FakePool(),
-        state=np.zeros((8, 5), dtype=np.float64),
-        model=_FakeModel(),
-        n_variants=1,
-        num_envs=8,
-        nthread=4,
-        dtype=np.float32,
-        post_step_forward_sensor=False,
-        bench_nsteps=2,
-        manual_chunk_size=None,
-        adaptive=True,
-    )
-    base.update(over)
-    return base
-
-
-def test_resolve_manual_override_wins(monkeypatch):
-    def _boom(*a, **k):
-        raise AssertionError("benchmark must not run for manual override")
-
-    monkeypatch.setattr(ct, "benchmark_chunk_sizes", _boom)
-    assert ct.resolve_chunk_size(**_resolve_kwargs(manual_chunk_size=42)) == 42
-
-
-def test_resolve_off_returns_none(monkeypatch):
-    def _boom(*a, **k):
-        raise AssertionError("benchmark must not run when adaptive=False")
-
-    monkeypatch.setattr(ct, "benchmark_chunk_sizes", _boom)
-    assert ct.resolve_chunk_size(**_resolve_kwargs(adaptive=False)) is None
-
-
-def test_native_default_chunk_matches_mujoco_formula():
-    # BatchEnvPool's default when chunk_size=None: max(1, nbatch // (10 * nthread)).
-    assert ct._native_default_chunk(2048, 40) == 5
-    assert ct._native_default_chunk(1024, 40) == 2
-    assert ct._native_default_chunk(4, 2) == 1  # 4 // 20 = 0 -> clamped to 1
-    assert ct._native_default_chunk(100, 1) == 10
-
-
-def test_benchmark_table_annotates_default_and_chosen_with_native_chunk(capsys, monkeypatch):
-    # "default"/"None" must show the actual chunk_size the native default resolves to.
-    monkeypatch.setattr(ct.logger, "level", logging.WARNING)
-    ct._log_benchmark_table({None: 0.010, 5: 0.009}, chosen=None, default_chunk=5)
-    err = capsys.readouterr().err
-    assert "default(=5)=10.00ms" in err
-    assert "chosen=None(=5)" in err
-
-
-def test_cache_hit_annotates_default_with_native_chunk(capsys, monkeypatch):
-    monkeypatch.setattr(ct.logger, "level", logging.WARNING)
-    ct._emit_cache_hit(None, {"None": 10.23, "5": 9.99}, default_chunk=5)
-    err = capsys.readouterr().err
-    assert "default(=5)=10.23ms" in err
-    assert "chosen=None(=5)" in err
-
-
-def test_resolve_skips_everything_when_nothing_to_tune(monkeypatch, tmp_path, capsys):
-    # num_envs <= nthread -> at most one work-chunk -> chunk_size is a no-op. Must
-    # return None WITHOUT benchmarking, caching, or emitting a line (this kills the
-    # noisy num_envs=1 setup-env benchmark that APPO/off-policy emit).
-    monkeypatch.setenv("UNILAB_CHUNK_SIZE_CACHE", str(tmp_path / "c.json"))
-    calls = {"n": 0}
-
-    def _spy(*a, **k):
-        calls["n"] += 1
-        return {None: 1.0, 1: 1.0}
-
-    monkeypatch.setattr(ct, "benchmark_chunk_sizes", _spy)
-    monkeypatch.setattr(ct.logger, "level", logging.WARNING)  # any _emit would hit stderr
-
-    assert ct.resolve_chunk_size(**_resolve_kwargs(num_envs=1, nthread=1)) is None
-    assert ct.resolve_chunk_size(**_resolve_kwargs(num_envs=20, nthread=40)) is None
-    assert calls["n"] == 0  # benchmark never attempted
-    assert not (tmp_path / "c.json").exists()  # nothing cached
-    assert capsys.readouterr().err == ""  # nothing printed
-
-
-def test_resolve_cache_hit_skips_benchmark(monkeypatch, tmp_path):
-    monkeypatch.setenv("UNILAB_CHUNK_SIZE_CACHE", str(tmp_path / "c.json"))
-    calls = {"n": 0}
-
-    def _bench(*a, **k):
-        calls["n"] += 1
-        return {None: 1.0, 4: 0.5}
-
-    monkeypatch.setattr(ct, "benchmark_chunk_sizes", _bench)
-
-    first = ct.resolve_chunk_size(**_resolve_kwargs())  # miss -> benchmark
-    second = ct.resolve_chunk_size(**_resolve_kwargs())  # hit -> no benchmark
-    assert first == second
-    assert calls["n"] == 1
-
-
-def test_resolve_cache_miss_runs_benchmark_and_persists(monkeypatch, tmp_path):
-    cache = tmp_path / "c.json"
-    monkeypatch.setenv("UNILAB_CHUNK_SIZE_CACHE", str(cache))
-    monkeypatch.setattr(ct, "benchmark_chunk_sizes", lambda *a, **k: {None: 1.0, 4: 0.5})
-    chosen = ct.resolve_chunk_size(**_resolve_kwargs())
-    assert chosen == 4
-    assert cache.exists()
-    assert any(v.get("chunk_size") == 4 for v in ct.load_cache(cache).values())
-
-
-def test_resolve_cache_hit_none_is_valid(monkeypatch, tmp_path):
-    # A prior benchmark concluded native default is best (chunk_size=None).
-    # That is a legitimate cached value: a second call must return None and NOT re-benchmark.
-    monkeypatch.setenv("UNILAB_CHUNK_SIZE_CACHE", str(tmp_path / "c.json"))
-    calls = {"n": 0}
-
-    def _bench(*a, **k):
-        calls["n"] += 1
-        return {None: 0.5, 4: 1.0}  # baseline fastest -> select_chunk_size returns None
-
-    monkeypatch.setattr(ct, "benchmark_chunk_sizes", _bench)
-
-    first = ct.resolve_chunk_size(**_resolve_kwargs())  # miss -> benchmark -> chosen=None
-    second = ct.resolve_chunk_size(**_resolve_kwargs())  # hit on key presence -> no benchmark
-    assert first is None
-    assert second is None
-    assert calls["n"] == 1
diff --git a/tests/base/backend/test_mujoco_cpu_affinity_wiring.py b/tests/base/backend/test_mujoco_cpu_affinity_wiring.py
index 7dad28f1f..f6cd8c839 100644
--- a/tests/base/backend/test_mujoco_cpu_affinity_wiring.py
+++ b/tests/base/backend/test_mujoco_cpu_affinity_wiring.py
@@ -7,10 +7,11 @@
 
 import inspect
 import os
+from pathlib import Path
 
 import pytest
 
-from unilab.base.backend import create_backend, env_backend_kwargs
+from unilab.base.backend_factory import create_backend, env_backend_kwargs
 from unilab.base.base import EnvCfg
 
 pytest.importorskip("mujoco", reason="mujoco not installed")
@@ -28,12 +29,22 @@
         allow_module_level=True,
     )
 
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base.backend.mujoco.backend import MuJoCoBackend
+from unisim.backend.mujoco.backend import MuJoCoBackend
+
 from unilab.base.scene import SceneCfg
 
-_MODEL_FILE = str(ASSETS_ROOT_PATH / "robots" / "go2_arm" / "scene_flat.xml")
+_MODEL_FILE = str(
+    Path(__file__).resolve().parents[2] / "fixtures" / "mjlab_cartpole" / "cartpole.xml"
+)
 _NUM_ENVS = 4
+_BASE_NAME = "cart"
+
+if not hasattr(os, "sched_getaffinity"):
+    pytest.skip(
+        "os.sched_getaffinity unavailable on this platform (e.g. macOS)",
+        allow_module_level=True,
+    )
+
 _AVAILABLE_CPUS = sorted(os.sched_getaffinity(0))
 
 
@@ -42,7 +53,7 @@ def _build_small_backend(**backend_kwargs):
         SceneCfg(model_file=_MODEL_FILE),
         num_envs=_NUM_ENVS,
         sim_dt=0.01,
-        base_name="base",
+        base_name=_BASE_NAME,
         adaptive_chunk_size=False,
         **backend_kwargs,
     )
@@ -91,7 +102,7 @@ def test_create_backend_routes_cpu_ids():
         SceneCfg(model_file=_MODEL_FILE),
         _NUM_ENVS,
         0.01,
-        base_name="base",
+        base_name=_BASE_NAME,
         adaptive_chunk_size=False,
         cpu_ids=cpu_ids,
     )
@@ -107,7 +118,7 @@ def test_backend_rejects_invalid_cpu_ids_on_cold_path(cpu_ids):
             SceneCfg(model_file=_MODEL_FILE),
             num_envs=_NUM_ENVS,
             sim_dt=0.01,
-            base_name="base",
+            base_name=_BASE_NAME,
             cpu_ids=cpu_ids,
         )
 
@@ -115,9 +126,12 @@ def test_backend_rejects_invalid_cpu_ids_on_cold_path(cpu_ids):
 def test_workers_pinned_to_configured_cpus():
     cpu_ids = _AVAILABLE_CPUS[:2]
     backend = _build_small_backend(cpu_ids=cpu_ids)
-    # Configured mapping is queryable and workers were observed on those CPUs.
-    assert tuple(backend._pool.cpu_ids) == tuple(cpu_ids)
-    assert backend._pool.worker_cpu_ids() == tuple(cpu_ids)
+    try:
+        # Configured mapping is queryable and workers were observed on those CPUs.
+        assert tuple(backend._pool.cpu_ids) == tuple(cpu_ids)
+        assert backend._pool.worker_cpu_ids() == tuple(cpu_ids)
+    finally:
+        backend._pool.close()
 
 
 def test_unavailable_cpu_id_fails_at_pool_creation():
@@ -126,7 +140,7 @@ def test_unavailable_cpu_id_fails_at_pool_creation():
         SceneCfg(model_file=_MODEL_FILE),
         num_envs=_NUM_ENVS,
         sim_dt=0.01,
-        base_name="base",
+        base_name=_BASE_NAME,
         cpu_ids=[unavailable],
     )
     with pytest.raises(ValueError, match="not available"):
@@ -135,6 +149,34 @@ def test_unavailable_cpu_id_fails_at_pool_creation():
 
 def test_default_path_keeps_os_scheduling():
     backend = _build_small_backend()
+    try:
+        assert backend._cpu_ids is None
+        assert backend._pool.cpu_ids is None
+        assert backend._pool.worker_cpu_ids() == ()
+    finally:
+        backend._pool.close()
+
+
+def test_default_nthread_sized_to_effective_cpus():
+    """Default pool sizing uses the CPUs usable by this process (#1328),
+    not 2x the machine-wide count; explicit ``cpu_ids`` still fixes nthread."""
+    backend = MuJoCoBackend(
+        SceneCfg(model_file=_MODEL_FILE),
+        num_envs=10_000,
+        sim_dt=0.01,
+        base_name=_BASE_NAME,
+    )
     assert backend._cpu_ids is None
-    assert backend._pool.cpu_ids is None
-    assert backend._pool.worker_cpu_ids() == ()
+    assert backend._n_threads == len(os.sched_getaffinity(0))
+
+
+def test_default_nthread_capped_by_num_envs():
+    backend = MuJoCoBackend(
+        SceneCfg(model_file=_MODEL_FILE),
+        num_envs=2,
+        sim_dt=0.01,
+        base_name=_BASE_NAME,
+    )
+    # num_envs caps the pool size, but the effective-CPU cap wins first on
+    # single-core hosts (e.g. ubuntu-slim CI runners).
+    assert backend._n_threads == min(len(os.sched_getaffinity(0)), 2)
diff --git a/tests/base/backend/test_mujoco_scene_context_visual.py b/tests/base/backend/test_mujoco_scene_context_visual.py
index e1c3d5e14..0ff80c05b 100644
--- a/tests/base/backend/test_mujoco_scene_context_visual.py
+++ b/tests/base/backend/test_mujoco_scene_context_visual.py
@@ -9,7 +9,8 @@
 
 from pathlib import Path
 
-from unilab.base.backend.mujoco.backend import _build_mujoco_scene_context
+from unisim.backend.mujoco.backend import _build_mujoco_scene_context
+
 from unilab.base.scene import SceneCfg
 
 
@@ -35,9 +36,23 @@ def test_visual_model_file_override_is_render_only() -> None:
 
 
 def test_x2_wall_flip_wires_render_only_visual_twin() -> None:
-    from unilab.envs.motion_tracking.x2.flip_tracking import X2WallFlipTrackingEnvCfg
-
-    cfg = X2WallFlipTrackingEnvCfg()
+    from hydra import compose, initialize_config_dir
+
+    from unilab.base import registry
+    from unilab.base.config_adapter import BackendAdapter
+    from unilab.base.config_materialization import apply_cfg_overrides
+
+    repo_root = Path(__file__).parents[3]
+    with initialize_config_dir(
+        config_dir=str(repo_root / "src" / "unilab" / "conf" / "ppo"), version_base="1.3"
+    ):
+        owner = compose("config", overrides=["task=x2_wall_flip_tracking/mujoco"])
+    registry.ensure_registries()
+    cfg = registry.materialize_env_config("X2WallFlipTracking")
+    apply_cfg_overrides(
+        cfg,
+        BackendAdapter(owner, root_dir=repo_root, algo_name="ppo").build_task_env_cfg_override(),
+    )
 
     # Physics = trained <body> wall; render = worldbody-geom twin.
     assert cfg.scene.model_file.endswith("scene_flat_with_wall.xml")
diff --git a/tests/base/backend/test_mujoco_site_jacobian.py b/tests/base/backend/test_mujoco_site_jacobian.py
index c8420bf06..c702d553b 100644
--- a/tests/base/backend/test_mujoco_site_jacobian.py
+++ b/tests/base/backend/test_mujoco_site_jacobian.py
@@ -14,8 +14,9 @@
         "mujoco_uni.batch_env not available (platform/libstdc++ issue)", allow_module_level=True
     )
 
+from unisim.backend.mujoco.backend import MuJoCoBackend
+
 from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base.backend.mujoco.backend import MuJoCoBackend
 from unilab.base.scene import SceneCfg
 
 MODEL_FILE = str(ASSETS_ROOT_PATH / "robots" / "go2_arm" / "scene_flat.xml")
diff --git a/tests/base/backend/test_process_device.py b/tests/base/backend/test_process_device.py
new file mode 100644
index 000000000..e4f68c2de
--- /dev/null
+++ b/tests/base/backend/test_process_device.py
@@ -0,0 +1,73 @@
+"""Cold-path backend process-device routing tests."""
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+
+import pytest
+from unisim.backend.mjwarp import runtime as mjwarp_runtime
+
+from unilab.base.process_device import (
+    configure_backend_process_device,
+    resolve_backend_process_device,
+)
+
+
+class _FakeWarpDevice:
+    def __init__(self, name: str, *, is_cuda: bool = True) -> None:
+        self.name = name
+        self.is_cuda = is_cuda
+
+    def __str__(self) -> str:
+        return self.name
+
+
+class _FakeWarp:
+    def __init__(self, *, is_cuda: bool = True) -> None:
+        self.is_cuda = is_cuda
+        self.set_calls: list[str] = []
+        self.selected = _FakeWarpDevice("cpu", is_cuda=False)
+
+    def set_device(self, device: str) -> None:
+        self.set_calls.append(device)
+        self.selected = _FakeWarpDevice(device, is_cuda=self.is_cuda)
+
+    def get_device(self) -> _FakeWarpDevice:
+        return self.selected
+
+
+def test_mjwarp_process_device_follows_rank_learner_device(monkeypatch: pytest.MonkeyPatch) -> None:
+    warp = _FakeWarp()
+    monkeypatch.setattr(
+        mjwarp_runtime,
+        "load_mjwarp_dependencies",
+        lambda: SimpleNamespace(warp=warp),
+    )
+
+    assert configure_backend_process_device("mjwarp", "cuda:3") == "cuda:3"
+    assert warp.set_calls == ["cuda:3"]
+
+
+@pytest.mark.parametrize("backend_type", ["mujoco", "motrix", "drake"])
+def test_host_or_backend_owned_devices_do_not_receive_runner_binding(backend_type: str) -> None:
+    assert resolve_backend_process_device(backend_type, "cuda:2") is None
+
+
+@pytest.mark.parametrize("device", [None, "cpu", "mps", "xpu:1"])
+def test_mjwarp_process_device_fails_closed_without_cuda(device: str | None) -> None:
+    with pytest.raises(ValueError, match="CUDA process device"):
+        resolve_backend_process_device("mjwarp", device)
+
+
+def test_mjwarp_binding_rejects_non_cuda_warp_resolution(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    warp = _FakeWarp(is_cuda=False)
+    monkeypatch.setattr(
+        mjwarp_runtime,
+        "load_mjwarp_dependencies",
+        lambda: SimpleNamespace(warp=warp),
+    )
+
+    with pytest.raises(RuntimeError, match="active CUDA Warp device"):
+        mjwarp_runtime.bind_mjwarp_process_device("cuda:1")
diff --git a/tests/base/genesis_fake_runtime.py b/tests/base/genesis_fake_runtime.py
new file mode 100644
index 000000000..443580c4a
--- /dev/null
+++ b/tests/base/genesis_fake_runtime.py
@@ -0,0 +1,420 @@
+"""Fake Genesis runtime for host-free tests of the ``genesis`` backend.
+
+The fake mirrors the exact API surface the adapter consumes (verified against
+genesis-world 1.3.3 on a real RTX 4090) and implements just enough physics to
+exercise contract semantics: PD position control held across substeps,
+env-subset state writes, and link-addressed state reads.  It deliberately
+uses real torch tensors at the boundary so the adapter's host-cache path
+(pinned ``copy_`` D2H) runs unmodified.
+
+Model spec (must stay in sync with ``TINY_MODEL_XML`` in the test file):
+bodies world(0)/pelvis(1, free)/thigh(2, joint_a)/shank(3, joint_b); nq=9,
+nv=8, nu=2.  links_pos: world at origin, pelvis at qpos root xyz, children at
+fixed pelvis-frame offsets; links_quat: children inherit the pelvis quat
+(joint rotation is intentionally not modeled — tests never rely on it).
+"""
+
+from __future__ import annotations
+
+import types
+
+import numpy as np
+import torch
+
+from unilab.utils.rotation import np_quat_apply_batched
+
+# Mirrors TINY_MODEL_XML.  INIT_QPOS equals mj.qpos0 of that document
+# (pelvis body pos z=0.8) so materialize-time state matches the MJCF scan.
+INIT_QPOS = np.array([0.0, 0.0, 0.8, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], dtype=np.float32)
+NQ = 9
+NV = 8
+N_LINKS = 4
+BODY_NAMES = ("world", "pelvis", "thigh", "shank")
+LINK_MASS = (0.0, 5.0, 2.0, 1.0)
+# Fixed child-link offsets in the pelvis frame (fake kinematics simplification).
+LINK_OFFSET = ((0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, -0.3), (0.0, 0.0, -0.6))
+ACTUATED_DOFS = (6, 7)
+INIT_KP = (10.0, 20.0)
+INIT_KV = (2.0, 3.0)
+GEOM_TABLE = (
+    # (name, link_idx, friction, contype, conaffinity)
+    ("floor", 0, 1.0, 1, 1),
+    ("pelvis_geom", 1, 0.9, 1, 1),
+    ("thigh_geom", 2, 0.8, 1, 1),
+    ("foot_geom", 3, 0.6, 1, 1),
+)
+SIM_DT = 0.005
+PD_INERTIA = 0.1
+
+
+class _FakeJoint:
+    def __init__(self, name, dofs_idx_local, qs_idx_local, link):
+        self.name = name
+        self.dofs_idx_local = list(dofs_idx_local)
+        self.qs_idx_local = list(qs_idx_local)
+        self.n_dofs = len(self.dofs_idx_local)
+        self.n_qs = len(self.qs_idx_local)
+        self.link = link
+
+
+class _FakeLink:
+    def __init__(self, name, idx):
+        self.name = name
+        self.idx = idx
+        self.idx_local = idx
+        self.joints: list[_FakeJoint] = []
+
+
+class _FakeGeom:
+    def __init__(self, idx, link, friction, contype, conaffinity):
+        self.idx = idx
+        self.link = link
+        self.friction = friction
+        self.contype = contype
+        self.conaffinity = conaffinity
+
+
+class _FakeIMU:
+    def __init__(self, entity_idx, link_idx_local, pos_offset):
+        self.entity_idx = entity_idx
+        self.link_idx_local = link_idx_local
+        self.pos_offset = pos_offset
+        self.lin_acc: torch.Tensor | None = None
+        self.ang_vel: torch.Tensor | None = None
+
+    def read(self):
+        return types.SimpleNamespace(lin_acc=self.lin_acc, ang_vel=self.ang_vel)
+
+
+class _FakeEntity:
+    def __init__(self, morph):
+        self.morph = morph
+        self.idx = 0
+        self.link_start = 0
+        self.n_dofs = NV
+        self.n_qs = NQ
+        self.n_links = N_LINKS
+        self.links = [_FakeLink(name, idx) for idx, name in enumerate(BODY_NAMES)]
+        root = _FakeJoint("floating_base_joint", range(6), range(7), self.links[1])
+        joint_a = _FakeJoint("joint_a", (6,), (7,), self.links[2])
+        joint_b = _FakeJoint("joint_b", (7,), (8,), self.links[3])
+        self.links[1].joints.append(root)
+        self.links[2].joints.append(joint_a)
+        self.links[3].joints.append(joint_b)
+        self.joints = [root, joint_a, joint_b]
+        self.geoms = [
+            _FakeGeom(idx, self.links[link_idx], friction, contype, conaffinity)
+            for idx, (_, link_idx, friction, contype, conaffinity) in enumerate(GEOM_TABLE)
+        ]
+        self.built_envs = 0
+        self.control_calls: list[np.ndarray] = []
+        self.step_count = 0
+
+    def get_link(self, name):
+        for link in self.links:
+            if link.name == name:
+                return link
+        raise KeyError(f"Link not found for name: {name}")
+
+    def _build(self, n_envs: int) -> None:
+        self.built_envs = n_envs
+        self.qpos = torch.from_numpy(np.broadcast_to(INIT_QPOS, (n_envs, NQ)).copy())
+        self.qvel = torch.zeros(n_envs, NV)
+        self.ctrl_target = torch.zeros(n_envs, len(ACTUATED_DOFS))
+        kp = torch.zeros(n_envs, NV)
+        kv = torch.zeros(n_envs, NV)
+        for dof, gain, kd in zip(ACTUATED_DOFS, INIT_KP, INIT_KV, strict=True):
+            kp[:, dof] = gain
+            kv[:, dof] = kd
+        self.dof_kp = kp
+        self.dof_kv = kv
+        self.link_mass = torch.from_numpy(
+            np.broadcast_to(np.asarray(LINK_MASS, dtype=np.float32), (n_envs, N_LINKS)).copy()
+        )
+        self.net_contact_force = torch.zeros(n_envs, N_LINKS, 3)
+
+    # -- getters (torch tensors, like genesis) -------------------------- #
+    def get_qpos(self, qs_idx_local=None, envs_idx=None):
+        return self.qpos
+
+    def get_dofs_velocity(self, dofs_idx_local=None, envs_idx=None):
+        return self.qvel
+
+    def get_dofs_kp(self, dofs_idx_local=None, envs_idx=None):
+        return self.dof_kp
+
+    def get_dofs_kv(self, dofs_idx_local=None, envs_idx=None):
+        return self.dof_kv
+
+    def _link_pose(self):
+        quat = self.qpos[:, 3:7].cpu().numpy()
+        pos = np.zeros((self.built_envs, N_LINKS, 3), dtype=np.float32)
+        quat_out = np.zeros((self.built_envs, N_LINKS, 4), dtype=np.float32)
+        quat_out[:, :, 0] = 1.0
+        for link_idx in range(1, N_LINKS):
+            offset = np.broadcast_to(
+                np.asarray(LINK_OFFSET[link_idx], dtype=np.float32), (self.built_envs, 3)
+            )
+            pos[:, link_idx, :] = self.qpos[:, 0:3].cpu().numpy() + np_quat_apply_batched(
+                quat, offset
+            )
+            quat_out[:, link_idx, :] = quat
+        return pos, quat_out
+
+    def get_links_pos(self, links_idx_local=None, envs_idx=None, **kwargs):
+        pos, _ = self._link_pose()
+        return torch.from_numpy(pos)
+
+    def get_links_quat(self, links_idx_local=None, envs_idx=None, **kwargs):
+        _, quat = self._link_pose()
+        return torch.from_numpy(quat)
+
+    def get_links_vel(self, links_idx_local=None, envs_idx=None, **kwargs):
+        vel = np.zeros((self.built_envs, N_LINKS, 3), dtype=np.float32)
+        vel[:, 1:, :] = self.qvel[:, 0:3].cpu().numpy()[:, None, :]
+        return torch.from_numpy(vel)
+
+    def get_links_ang(self, links_idx_local=None, envs_idx=None):
+        quat = self.qpos[:, 3:7].cpu().numpy()
+        ang = np.zeros((self.built_envs, N_LINKS, 3), dtype=np.float32)
+        ang[:, 1:, :] = np_quat_apply_batched(quat, self.qvel[:, 3:6].cpu().numpy())[:, None, :]
+        return torch.from_numpy(ang)
+
+    def get_links_net_contact_force(self, envs_idx=None):
+        return self.net_contact_force
+
+    # -- setters --------------------------------------------------------- #
+    @staticmethod
+    def _rows(envs_idx):
+        return None if envs_idx is None else list(envs_idx)
+
+    def set_qpos(
+        self, qpos, qs_idx_local=None, envs_idx=None, *, zero_velocity=True, skip_forward=False
+    ):
+        rows = self._rows(envs_idx)
+        value = torch.as_tensor(qpos, dtype=torch.float32).cpu()
+        if rows is None:
+            self.qpos = value.clone()
+        else:
+            self.qpos[rows] = value
+        if zero_velocity:
+            if rows is None:
+                self.qvel.zero_()
+            else:
+                self.qvel[rows] = 0.0
+
+    def set_dofs_velocity(
+        self, velocity=None, dofs_idx_local=None, envs_idx=None, *, skip_forward=False
+    ):
+        rows = self._rows(envs_idx)
+        value = torch.as_tensor(velocity, dtype=torch.float32).cpu()
+        if rows is None:
+            self.qvel = value.clone()
+        else:
+            self.qvel[rows] = value
+
+    def control_dofs_position(self, position, dofs_idx_local=None, envs_idx=None):
+        value = torch.as_tensor(position, dtype=torch.float32).cpu()
+        self.control_calls.append(value.numpy().copy())
+        self.ctrl_target = value.clone()
+
+    def _set_dof_rows(self, table, value, dofs_idx_local, envs_idx):
+        rows = self._rows(envs_idx)
+        dofs = list(dofs_idx_local) if dofs_idx_local is not None else list(range(NV))
+        tensor = torch.as_tensor(value, dtype=torch.float32).cpu()
+        if rows is None:
+            table[:, dofs] = tensor
+        else:
+            table[np.ix_(rows, dofs)] = tensor
+
+    def set_dofs_kp(self, kp, dofs_idx_local=None, envs_idx=None):
+        self._set_dof_rows(self.dof_kp, kp, dofs_idx_local, envs_idx)
+
+    def set_dofs_kv(self, kv, dofs_idx_local=None, envs_idx=None):
+        self._set_dof_rows(self.dof_kv, kv, dofs_idx_local, envs_idx)
+
+    def set_links_inertial_mass(self, inertial_mass, links_idx_local=None, envs_idx=None):
+        rows = self._rows(envs_idx)
+        links = list(links_idx_local) if links_idx_local is not None else list(range(N_LINKS))
+        tensor = torch.as_tensor(inertial_mass, dtype=torch.float32).cpu()
+        if rows is None:
+            self.link_mass[:, links] = tensor
+        else:
+            self.link_mass[np.ix_(rows, links)] = tensor
+
+    # -- physics --------------------------------------------------------- #
+    def step(self):
+        q = self.qpos.numpy()
+        dq = self.qvel.numpy()
+        tau = self.dof_kp.numpy()[:, ACTUATED_DOFS] * (self.ctrl_target.numpy() - q[:, 7:9])
+        tau -= self.dof_kv.numpy()[:, ACTUATED_DOFS] * dq[:, 6:8]
+        dq[:, 6:8] += tau * SIM_DT / PD_INERTIA
+        dq[:, 2] -= 9.81 * SIM_DT
+        q[:, 7:9] += dq[:, 6:8] * SIM_DT
+        q[:, 0:3] += dq[:, 0:3] * SIM_DT
+        self.qpos = torch.from_numpy(q.astype(np.float32))
+        self.qvel = torch.from_numpy(dq.astype(np.float32))
+        self.step_count += 1
+
+
+class _FakeRigidSolver:
+    def __init__(self):
+        self.external_forces: list[tuple[np.ndarray, list[int]]] = []
+
+    def apply_links_external_force(self, force, links_idx=None, envs_idx=None, **kwargs):
+        value = torch.as_tensor(force, dtype=torch.float32).cpu().numpy().copy()
+        self.external_forces.append((value, list(links_idx)))
+
+
+class _FakeCamera:
+    """Post-build offscreen camera: configurable frame + pose recording."""
+
+    def __init__(self, res, pos, lookat, **kwargs):
+        self.res = res
+        self.pos = pos
+        self.lookat = lookat
+        self.kwargs = kwargs
+        self.is_built = False
+        self.poses: list[tuple] = []
+        self.render_count = 0
+
+    def build(self):
+        self.is_built = True
+
+    def set_pose(self, transform=None, pos=None, lookat=None, up=None, envs_idx=None):
+        self.poses.append((pos, lookat))
+
+    def render(self, rgb=True, **kwargs):
+        width, height = self.res  # genesis camera res is (width, height)
+        frame = np.zeros((height, width, 3), dtype=np.uint8)
+        frame[:, :, 0] = np.arange(width, dtype=np.uint8)[None, :]
+        frame[:, :, 1] = np.arange(height, dtype=np.uint8)[:, None]
+        self.render_count += 1
+        return (frame,)
+
+
+class _FakeViewer:
+    """Interactive viewer stand-in with a controllable liveness flag."""
+
+    def __init__(self, options, context):
+        self.options = options
+        self.context = context
+        self.lock = object()
+        self.built_scene = None
+        self.alive = True
+        self.stop_count = 0
+        self.camera_pose = None
+        self.followed_entity = None
+
+    def build(self, scene):
+        self.built_scene = scene
+
+    def set_camera_pose(self, pose=None, pos=None, lookat=None):
+        self.camera_pose = (pose, pos, lookat)
+
+    def follow_entity(self, entity, **kwargs):
+        self.followed_entity = entity
+
+    def is_alive(self):
+        return self.alive
+
+    def stop(self):
+        self.alive = False
+        self.stop_count += 1
+
+
+class _FakeVisualizer:
+    def __init__(self):
+        self.context = object()
+        self.cameras: list[_FakeCamera] = []
+        self.update_count = 0
+        self._viewer = None
+        self.viewer_lock = None
+
+    def add_camera(self, res, pos, lookat, **kwargs):
+        camera = _FakeCamera(res, pos, lookat, **kwargs)
+        self.cameras.append(camera)
+        return camera
+
+    def update(self, force=True, auto=None):
+        # Real genesis 1.3.3 raises its private exception here when the
+        # attached viewer has been closed by the user.
+        if self._viewer is not None and not self._viewer.is_alive():
+            raise RuntimeError("Viewer closed.")
+        self.update_count += 1
+
+
+class _FakeScene:
+    def __init__(self, sim_options=None, rigid_options=None, show_viewer=None, **kwargs):
+        self.sim_options = sim_options
+        self.rigid_options = rigid_options
+        self.show_viewer = show_viewer
+        self.entities: list[_FakeEntity] = []
+        self.sensors: list[_FakeIMU] = []
+        self.sim = types.SimpleNamespace(rigid_solver=_FakeRigidSolver())
+        self.visualizer = _FakeVisualizer()
+        self.build_count = 0
+
+    def add_entity(self, morph):
+        entity = _FakeEntity(morph)
+        entity.idx = len(self.entities)
+        self.entities.append(entity)
+        return entity
+
+    def add_sensor(self, sensor):
+        self.sensors.append(sensor)
+        return sensor
+
+    def build(self, n_envs=0, **kwargs):
+        self.build_count += 1
+        for entity in self.entities:
+            entity._build(n_envs)
+        for sensor in self.sensors:
+            sensor.lin_acc = torch.zeros(n_envs, 3)
+            sensor.ang_vel = torch.zeros(n_envs, 3)
+
+    def step(self):
+        for entity in self.entities:
+            entity.step()
+        # Real genesis updates the attached viewer from scene.step() too.
+        if self.visualizer._viewer is not None:
+            self.visualizer.update()
+
+
+def make_fake_genesis() -> types.SimpleNamespace:
+    """Create a fresh fake ``genesis`` module with lifecycle counters."""
+    fake = types.SimpleNamespace()
+    fake.gpu = "fake-gpu"
+    fake.cpu = "fake-cpu"
+    fake.init_count = 0
+    fake.destroy_count = 0
+
+    def init(backend=None, logging_level=None, **kwargs):
+        fake.init_count += 1
+
+    def destroy():
+        fake.destroy_count += 1
+
+    options = types.SimpleNamespace(
+        SimOptions=lambda **kw: types.SimpleNamespace(**kw),
+        RigidOptions=lambda **kw: types.SimpleNamespace(kwargs=kw),
+        ViewerOptions=lambda **kw: types.SimpleNamespace(**kw),
+    )
+    fake.init = init
+    fake.destroy = destroy
+    fake.options = options
+    fake.morphs = types.SimpleNamespace(MJCF=lambda **kw: types.SimpleNamespace(**kw))
+    fake.sensors = types.SimpleNamespace(IMU=_FakeIMU)
+    fake.Scene = _FakeScene
+    fake.integrator = types.SimpleNamespace(
+        Euler="Euler",
+        implicitfast="implicitfast",
+        approximate_implicitfast="approximate_implicitfast",
+    )
+    fake.constraint_solver = types.SimpleNamespace(Newton="Newton", CG="CG")
+    fake.friction_cone = types.SimpleNamespace(pyramidal="pyramidal", elliptic="elliptic")
+    # The backend resolves the viewer class via
+    # ``importlib.import_module("genesis.vis.viewer")``; tests register this
+    # namespace under that name in sys.modules.
+    fake.viewer_module = types.SimpleNamespace(Viewer=_FakeViewer)
+    return fake
diff --git a/tests/base/isaacgym_mock_worker.py b/tests/base/isaacgym_mock_worker.py
new file mode 100644
index 000000000..b61892a84
--- /dev/null
+++ b/tests/base/isaacgym_mock_worker.py
@@ -0,0 +1,373 @@
+"""Deterministic mock IsaacGym worker for protocol-level backend tests.
+
+Runs on the host Python (no IsaacGym install required) and speaks the exact
+``unisim.backend.isaacgym.protocol`` wire protocol against shared-memory
+slots.  The physics model is a deterministic kinematic fake, documented here
+so tests can assert exact values:
+
+- STEP integrates ``dof_vel += ctrl * dt`` then ``dof_pos += dof_vel * dt``,
+  and applies free fall to the root: ``lin_vel.z += -9.81 * dt`` then
+  ``pos += lin_vel * dt`` (per substep).
+- ``body_state`` row 0 mirrors the root; body ``i > 0`` sits at a static
+  ``[0.1 * i, 0, 0]` offset from the root with the root's orientation.
+- ``contact_force[:, 1, 0]`` is ``sum(|ctrl|)`` so contact "found" flips with
+  nonzero control.
+- SET_STATE applies the reset slots verbatim (root columns pass through
+  without frame conversion — the mock does not model quat frame math).
+
+Failure modes are selected with ``UNILAB_ISAACGYM_MOCK_BEHAVIOR``:
+``ok`` (default), ``fail_init`` (raise during INIT), ``die_on_step``
+(``os._exit(3)`` on the first STEP), ``hang_on_step`` (sleep on the first
+STEP), ``no_graphics`` (INIT reports no graphics context and INIT_RENDERER
+fails), ``viewer_fails`` (INIT_RENDERER cannot create the viewer),
+``close_on_render`` (the first RENDER_FRAME reports the window closed),
+``render_meta_missing``, ``render_meta_mode_mismatch``,
+``render_meta_size_mismatch``, and ``render_meta_graphics_mismatch`` corrupt
+the IsaacSim render handshake; ``capture_uniform``, ``capture_float``, and
+``capture_wrong_shape`` return malformed camera frames.
+Model dims come from ``UNILAB_ISAACGYM_MOCK_DOF_NAMES`` /
+``UNILAB_ISAACGYM_MOCK_BODY_NAMES`` (comma-separated).
+"""
+
+from __future__ import annotations
+
+import argparse
+import importlib.util
+import os
+import sys
+import time
+from typing import Any, Dict, List, Tuple
+
+import numpy as np
+
+_GRAVITY_Z = -9.81
+
+
+def _load_protocol(path: str) -> Any:
+    spec = importlib.util.spec_from_file_location("unilab_isaacgym_mock_protocol", path)
+    if spec is None or spec.loader is None:
+        raise RuntimeError(f"cannot load protocol module from {path!r}")
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+class _MockSim:
+    def __init__(
+        self,
+        num_envs: int,
+        sim_dt: float,
+        dof_names: List[str],
+        body_names: List[str],
+        graphics_enabled: bool = True,
+        startup_render_mode: str | None = None,
+        startup_width: int = 1280,
+        startup_height: int = 720,
+    ):
+        self.graphics_enabled = graphics_enabled
+        self.startup_render_mode = startup_render_mode
+        self.startup_width = int(startup_width)
+        self.startup_height = int(startup_height)
+        self.num_envs = num_envs
+        self.sim_dt = sim_dt
+        self.dof_names = dof_names
+        self.body_names = body_names
+        self.num_dof = len(dof_names)
+        self.num_bodies = len(body_names)
+        self.root = np.zeros((num_envs, 13), dtype=np.float32)
+        self.root[:, 2] = 1.0
+        self.root[:, 3] = 1.0  # identity quat, wxyz
+        self.dof = np.zeros((num_envs, self.num_dof, 2), dtype=np.float32)
+        self.slots: Dict[str, np.ndarray] = {}
+        self._shm_handles: List[Any] = []
+        self.viewer_open = False
+        self.capture_ready = False
+        self.capture_width = 0
+        self.capture_height = 0
+
+    def attach(self, slots: Dict[str, Dict[str, Any]]) -> None:
+        from multiprocessing import resource_tracker, shared_memory
+
+        for name, spec in slots.items():
+            handle = shared_memory.SharedMemory(name=spec["shm"], create=False)
+            resource_tracker.unregister(handle._name, "shared_memory")
+            self.slots[name] = np.ndarray(
+                tuple(spec["shape"]), dtype=np.dtype(spec["dtype"]), buffer=handle.buf
+            )
+            self._shm_handles.append(handle)
+        self.write_state_slots()
+
+    def write_state_slots(self) -> None:
+        np.copyto(self.slots["root_state"], self.root)
+        np.copyto(self.slots["dof_state"], self.dof)
+        body = self.slots["body_state"]
+        for body_index in range(self.num_bodies):
+            body[:, body_index, :] = self.root
+            if body_index > 0:
+                body[:, body_index, 0:3] = self.root[:, 0:3] + np.float32(0.1 * body_index) * (
+                    np.array([1.0, 0.0, 0.0], dtype=np.float32)
+                )
+        contact = self.slots["contact_force"]
+        contact.fill(0.0)
+        if self.num_bodies > 1:
+            # Tests bind contact sensors to the deepest link; report the mock
+            # force there.
+            contact[:, -1, 0] = np.abs(self.slots["ctrl"]).sum(axis=1)
+
+    def step(self, nsteps: int) -> None:
+        ctrl = self.slots["ctrl"]
+        dt = np.float32(self.sim_dt)
+        for _ in range(nsteps):
+            self.dof[:, :, 1] += ctrl * dt
+            self.dof[:, :, 0] += self.dof[:, :, 1] * dt
+            self.root[:, 9] += np.float32(_GRAVITY_Z) * dt  # lin_vel z
+            self.root[:, 0:3] += self.root[:, 7:10] * dt
+        self.write_state_slots()
+
+    def apply_keyframe(self, qpos_values: Any, joint_names: List[str]) -> None:
+        """Apply the INIT keyframe pose with the same name-mapping rules as the
+        real worker (root columns pass through in the mock's wxyz convention)."""
+        qpos = np.asarray(qpos_values, dtype=np.float32).reshape(-1)
+        expected = 7 + self.num_dof
+        if qpos.size != expected:
+            raise RuntimeError("keyframe qpos has %d entries; expected %d" % (qpos.size, expected))
+        index_by_name = {name: index for index, name in enumerate(joint_names)}
+        if len(joint_names) != self.num_dof:
+            raise RuntimeError(
+                "mjcf_joint_names has %d entries but the asset exposes %d dofs"
+                % (len(joint_names), self.num_dof)
+            )
+        for dof_index, dof_name in enumerate(self.dof_names):
+            if dof_name not in index_by_name:
+                raise RuntimeError(
+                    "isaacgym asset dof %r is missing from mjcf_joint_names" % dof_name
+                )
+            self.dof[:, dof_index, 0] = qpos[7 + index_by_name[dof_name]]
+        self.root[:, 0:3] = qpos[0:3]
+        self.root[:, 3:7] = qpos[3:7]
+
+    def set_state(self, count: int) -> None:
+        env_ids = self.slots["reset_env_ids"][:count].astype(np.int64)
+        qpos = self.slots["reset_qpos"][:count]
+        qvel = self.slots["reset_qvel"][:count]
+        self.root[env_ids, 0:3] = qpos[:, 0:3]
+        self.root[env_ids, 3:7] = qpos[:, 3:7]
+        self.root[env_ids, 7:10] = qvel[:, 0:3]
+        self.root[env_ids, 10:13] = qvel[:, 3:6]
+        self.dof[env_ids, :, 0] = qpos[:, 7 : 7 + self.num_dof]
+        self.dof[env_ids, :, 1] = qvel[:, 6 : 6 + self.num_dof]
+        self.write_state_slots()
+
+    def meta(self, behavior: str = "ok") -> Dict[str, Any]:
+        result = {
+            "num_dof": self.num_dof,
+            "num_bodies": self.num_bodies,
+            "dof_names": list(self.dof_names),
+            "body_names": list(self.body_names),
+            "dof_lower": [-1.5] * self.num_dof,
+            "dof_upper": [1.5] * self.num_dof,
+            "effort": [100.0] * self.num_dof,
+            "gravity": [0.0, 0.0, _GRAVITY_Z],
+            "use_gpu_pipeline": False,
+            "graphics_enabled": self.graphics_enabled,
+            # The IsaacSim host validates this optional subprocess metadata;
+            # keeping it in the deterministic mock exercises that handshake
+            # without importing Kit. IsaacGym simply ignores the fields.
+            "env_origins": [[float(i) * 2.0, 0.0, 0.0] for i in range(self.num_envs)],
+            "collision_filtering_applied": True,
+        }
+        if self.startup_render_mode is not None:
+            result.update(
+                {
+                    "render_mode": self.startup_render_mode,
+                    "render_width": self.startup_width,
+                    "render_height": self.startup_height,
+                }
+            )
+            if behavior == "render_meta_missing":
+                result.pop("render_mode")
+            elif behavior == "render_meta_mode_mismatch":
+                result["render_mode"] = (
+                    "record" if self.startup_render_mode != "record" else "interactive"
+                )
+            elif behavior == "render_meta_size_mismatch":
+                result["render_width"] = self.startup_width + 1
+            elif behavior == "render_meta_graphics_mismatch":
+                result["graphics_enabled"] = not self.graphics_enabled
+        return result
+
+    def close(self) -> None:
+        for handle in self._shm_handles:
+            try:
+                handle.close()
+            except Exception:
+                pass
+        self._shm_handles = []
+
+    def init_renderer(self, payload: Dict[str, Any], behavior: str) -> Dict[str, Any]:
+        if not self.graphics_enabled:
+            raise RuntimeError(
+                "isaacgym rendering requires a GPU sim (device_id >= 0); this sim was "
+                "created without a graphics context"
+            )
+        headless = bool(payload.get("headless", False))
+        capture = bool(payload.get("capture", False))
+        if self.startup_render_mode is not None:
+            expected = "record" if (headless or capture) else "interactive"
+            if expected != self.startup_render_mode:
+                raise RuntimeError(
+                    f"mock renderer mode mismatch: startup={self.startup_render_mode}, requested={expected}"
+                )
+            width = int(payload.get("width", self.startup_width))
+            height = int(payload.get("height", self.startup_height))
+            if (width, height) != (self.startup_width, self.startup_height):
+                raise RuntimeError("mock renderer dimensions differ from INIT")
+        if not headless:
+            if behavior == "viewer_fails":
+                raise RuntimeError(
+                    "isaacgym create_viewer failed (no display reachable); use "
+                    "play_render_mode=record for headless video capture"
+                )
+            self.viewer_open = True
+        if capture:
+            self.capture_ready = True
+            self.capture_width = int(payload.get("width", 1280))
+            self.capture_height = int(payload.get("height", 720))
+        return {"viewer": self.viewer_open, "capture": self.capture_ready}
+
+    def render_frame(self, behavior: str) -> Dict[str, Any]:
+        if not self.viewer_open:
+            raise RuntimeError("isaacgym viewer is not initialized; call INIT_RENDERER first")
+        if behavior == "close_on_render":
+            self.viewer_open = False
+            return {"closed": True}
+        return {"closed": False}
+
+    def capture_frame(self, behavior: str = "ok") -> Dict[str, Any]:
+        if not self.capture_ready:
+            raise RuntimeError(
+                "isaacgym capture camera is not initialized; call INIT_RENDERER first"
+            )
+        # Deterministic gradient so tests can verify the frame is not blank.
+        rows = np.arange(self.capture_height, dtype=np.uint8)[:, None]
+        cols = np.arange(self.capture_width, dtype=np.uint8)[None, :]
+        frame = np.zeros((self.capture_height, self.capture_width, 3), dtype=np.uint8)
+        frame[:, :, 0] = rows
+        frame[:, :, 1] = cols
+        frame[:, :, 2] = 128
+        if behavior == "capture_uniform":
+            frame.fill(7)
+        elif behavior == "capture_float":
+            frame = frame.astype(np.float32)
+        elif behavior == "capture_wrong_shape":
+            frame = frame[:, :, :2]
+        return {"frame": frame, "width": self.capture_width, "height": self.capture_height}
+
+
+def _csv_env(name: str, default: List[str]) -> List[str]:
+    value = os.environ.get(name, "").strip()
+    if not value:
+        return list(default)
+    return [part.strip() for part in value.split(",") if part.strip()]
+
+
+def main(argv: List[str]) -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--protocol", required=True)
+    args = parser.parse_args(argv)
+    protocol = _load_protocol(args.protocol)
+    behavior = os.environ.get("UNILAB_ISAACGYM_MOCK_BEHAVIOR", "ok")
+
+    sim: _MockSim | None = None
+    stdin = sys.stdin.buffer
+    stdout = sys.stdout.buffer
+
+    def dispatch(cmd: str, payload: Any) -> Tuple[str, Any]:
+        nonlocal sim
+        if cmd == protocol.CMD_INIT:
+            if behavior == "fail_init":
+                raise RuntimeError("mock init failure")
+            sim = _MockSim(
+                num_envs=int(payload["num_envs"]),
+                sim_dt=float(payload["sim_dt"]),
+                dof_names=_csv_env("UNILAB_ISAACGYM_MOCK_DOF_NAMES", ["j0", "j1", "j2"]),
+                body_names=_csv_env(
+                    "UNILAB_ISAACGYM_MOCK_BODY_NAMES", ["base", "link0", "link1", "link2"]
+                ),
+                graphics_enabled=(
+                    behavior != "no_graphics"
+                    and str(payload.get("render_mode", "interactive")) != "none"
+                ),
+                startup_render_mode=(
+                    str(payload["render_mode"]) if "render_mode" in payload else None
+                ),
+                startup_width=int(payload.get("render_width", 1280)),
+                startup_height=int(payload.get("render_height", 720)),
+            )
+            keyframe_qpos = payload.get("keyframe_qpos")
+            if keyframe_qpos is not None:
+                sim.apply_keyframe(keyframe_qpos, payload.get("mjcf_joint_names") or [])
+            return protocol.CMD_META, sim.meta(behavior)
+        assert sim is not None
+        if cmd == protocol.CMD_ATTACH:
+            sim.attach(payload["slots"])
+            return protocol.CMD_READY, None
+        if cmd == protocol.CMD_STEP:
+            if behavior == "die_on_step":
+                sys.stderr.write("mock dying on step\n")
+                sys.stderr.flush()
+                os._exit(3)
+            if behavior == "hang_on_step":
+                time.sleep(3600.0)
+            t0 = time.perf_counter()
+            sim.step(int(payload["nsteps"]))
+            physics_ms = (time.perf_counter() - t0) * 1000.0
+            return protocol.CMD_READY, {
+                "timing": {
+                    "control_upload_ms": 0.0,
+                    "physics_ms": physics_ms,
+                    "state_refresh_ms": 0.0,
+                }
+            }
+        if cmd == protocol.CMD_SET_STATE:
+            sim.set_state(int(payload["count"]))
+            return protocol.CMD_READY, {
+                "timing": {
+                    "set_state_reset_upload_ms": 0.0,
+                    "set_state_host_cache_refresh_ms": 0.0,
+                }
+            }
+        if cmd == protocol.CMD_REFRESH:
+            sim.write_state_slots()
+            return protocol.CMD_READY, None
+        if cmd == protocol.CMD_GET_META:
+            return protocol.CMD_META, sim.meta(behavior)
+        if cmd == protocol.CMD_INIT_RENDERER:
+            return protocol.CMD_META, sim.init_renderer(payload, behavior)
+        if cmd == protocol.CMD_RENDER_FRAME:
+            return protocol.CMD_META, sim.render_frame(behavior)
+        if cmd == protocol.CMD_CAPTURE_FRAME:
+            return protocol.CMD_META, sim.capture_frame(behavior)
+        raise ValueError(f"unknown command {cmd!r}")
+
+    while True:
+        try:
+            message = protocol.recv_message(stdin)
+        except (EOFError, protocol.WorkerDisconnectedError):
+            return 0
+        cmd = message["cmd"]
+        if cmd == protocol.CMD_SHUTDOWN:
+            if sim is not None:
+                sim.close()
+            protocol.send_message(stdout, protocol.CMD_READY)
+            return 0
+        try:
+            reply_cmd, reply_payload = dispatch(cmd, message.get("payload"))
+        except Exception as exc:  # noqa: BLE001 - every worker error crosses the wire
+            protocol.send_message(stdout, protocol.CMD_ERROR, protocol.serialize_exception(exc))
+            continue
+        protocol.send_message(stdout, reply_cmd, reply_payload)
+
+
+if __name__ == "__main__":
+    sys.exit(main(sys.argv[1:]))
diff --git a/tests/base/test_backend_conformance.py b/tests/base/test_backend_conformance.py
index 6e8578e40..72bb529dd 100644
--- a/tests/base/test_backend_conformance.py
+++ b/tests/base/test_backend_conformance.py
@@ -12,23 +12,37 @@
 
 import numpy as np
 import pytest
+from unisim.backend.base import (
+    BackendRootStateLayout,
+    BackendTerrainSpawnData,
+    SimBackend,
+)
 
 from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base.backend import create_backend
-from unilab.base.backend.base import BackendTerrainSpawnData, SimBackend
+from unilab.base.backend_factory import create_backend
 from unilab.base.scene import SceneCfg
 
 REPO_ROOT = Path(__file__).resolve().parents[2]
 SRC_ROOT = REPO_ROOT / "src"
 _FACTORY_FILE = SRC_ROOT / "unilab" / "base" / "backend" / "__init__.py"
 _BACKEND_CLASS_NAMES = frozenset(
-    {"MuJoCoBackend", "MotrixBackend", "DrakeBackend", "MjwarpBackend"}
+    {
+        "MuJoCoBackend",
+        "MotrixBackend",
+        "DrakeBackend",
+        "MjwarpBackend",
+        "IsaacGymBackend",
+        "GenesisBackend",
+        "IsaacSimBackend",
+    }
+)
+_TASK_SOURCE_ROOTS = (
+    SRC_ROOT / "unilab" / "envs",
+    SRC_ROOT / "unilab" / "tasks",
 )
-_TERRAIN_CONSUMER_FILES = (
-    SRC_ROOT / "unilab" / "envs" / "locomotion" / "common" / "terrain_spawn.py",
-    SRC_ROOT / "unilab" / "envs" / "locomotion" / "go1" / "joystick.py",
-    SRC_ROOT / "unilab" / "envs" / "locomotion" / "go2" / "joystick.py",
-    SRC_ROOT / "unilab" / "envs" / "locomotion" / "go2w" / "rough.py",
+_TERRAIN_CONSUMER_PATHS = (
+    Path("locomotion/common/rough_manager_terms.py"),
+    Path("locomotion/common/terrain_spawn.py"),
 )
 
 NUM_ENVS = 2
@@ -41,7 +55,7 @@ def _module_available(module: str) -> bool:
 
 
 def _mjwarp_cuda_available() -> bool:
-    from unilab.base.backend.mjwarp.dependencies import (
+    from unisim.backend.mjwarp.dependencies import (
         load_mjwarp_dependencies,
         mjwarp_dependencies_available,
     )
@@ -56,13 +70,38 @@ def _mjwarp_cuda_available() -> bool:
 
 def _drake_batch_available() -> bool:
     try:
-        from unilab.base.backend.drake.backend import ensure_drake_batch_available
+        from unisim.backend.drake.backend import ensure_drake_batch_available
     except ImportError:
         return False
     available, _ = ensure_drake_batch_available()
     return bool(available)
 
 
+def _isaacgym_runtime_available() -> bool:
+    from unisim.backend.isaacgym.dependencies import isaacgym_runtime_available
+
+    return isaacgym_runtime_available()
+
+
+def _genesis_runtime_available() -> bool:
+    from unisim.backend.genesis.dependencies import genesis_dependencies_available
+
+    if not genesis_dependencies_available():
+        return False
+    try:
+        import torch
+
+        return bool(torch.cuda.is_available())
+    except Exception:
+        return False
+
+
+def _isaacsim_runtime_available() -> bool:
+    from unisim.backend.isaacsim.dependencies import isaacsim_runtime_available
+
+    return isaacsim_runtime_available()
+
+
 def _require_backend(backend_type: str) -> None:
     if backend_type == "mujoco":
         pytest.importorskip("mujoco", reason="mujoco not installed")
@@ -74,6 +113,22 @@ def _require_backend(backend_type: str) -> None:
     elif backend_type == "drake":
         if not _drake_batch_available():
             pytest.skip("drake batch extension not available")
+    elif backend_type == "isaacgym":
+        if not _isaacgym_runtime_available():
+            pytest.skip("isaacgym requires the Python 3.8 worker runtime")
+    elif backend_type == "genesis":
+        if not _genesis_runtime_available():
+            pytest.skip("genesis requires the genesis-world extra and a CUDA device")
+    elif backend_type == "isaacsim":
+        if not _isaacsim_runtime_available():
+            pytest.skip("isaacsim requires the Python 3.11 IsaacSim/IsaacLab worker runtime")
+        try:
+            import torch
+
+            if not torch.cuda.is_available():
+                pytest.skip("isaacsim requires a CUDA-enabled NVIDIA device")
+        except ImportError:
+            pytest.skip("isaacsim conformance requires host torch to check CUDA visibility")
 
 
 _BACKEND_PARAMS = [
@@ -81,6 +136,9 @@ def _require_backend(backend_type: str) -> None:
     pytest.param("motrix", id="motrix"),
     pytest.param("drake", id="drake"),
     pytest.param("mjwarp", id="mjwarp", marks=pytest.mark.slow),
+    pytest.param("isaacgym", id="isaacgym", marks=pytest.mark.slow),
+    pytest.param("genesis", id="genesis", marks=pytest.mark.slow),
+    pytest.param("isaacsim", id="isaacsim", marks=pytest.mark.slow),
 ]
 
 
@@ -130,19 +188,251 @@ def sample_height(xy: np.ndarray) -> np.ndarray:
         )
 
 
+def test_actuation_metadata_defaults_fail_closed() -> None:
+    with pytest.raises(NotImplementedError, match="actuator target joints"):
+        SimBackend.get_actuator_joint_names(object())  # type: ignore[arg-type]
+    with pytest.raises(NotImplementedError, match="default DoF positions"):
+        SimBackend.get_default_dof_pos(object())  # type: ignore[arg-type]
+    with pytest.raises(NotImplementedError, match="get_joint_state_qpos_indices"):
+        SimBackend.get_joint_state_qpos_indices(object(), ("joint",))  # type: ignore[arg-type]
+    with pytest.raises(NotImplementedError, match="get_joint_state_qvel_indices"):
+        SimBackend.get_joint_state_qvel_indices(object(), ("joint",))  # type: ignore[arg-type]
+    with pytest.raises(NotImplementedError, match="root-state layout"):
+        SimBackend.get_root_state_layout(object(), "root")  # type: ignore[arg-type]
+
+
+@pytest.mark.parametrize(
+    ("qpos_indices", "qvel_indices", "error", "match"),
+    [
+        (list(range(7)), tuple(range(6)), TypeError, "qpos_indices must be a tuple"),
+        (tuple(range(6)), tuple(range(6)), ValueError, "qpos_indices must contain 7"),
+        (tuple(range(7)), tuple(range(5)), ValueError, "qvel_indices must contain 6"),
+        ((0, 1, 2, 3, 4, 5, True), tuple(range(6)), TypeError, "integer columns"),
+        ((0, 1, 2, 3, 4, 5, -1), tuple(range(6)), ValueError, "negative columns"),
+        ((0, 1, 2, 3, 4, 5, 5), tuple(range(6)), ValueError, "unique columns"),
+    ],
+)
+def test_root_state_layout_metadata_fails_closed(
+    qpos_indices,
+    qvel_indices,
+    error,
+    match: str,
+) -> None:
+    with pytest.raises(error, match=match):
+        BackendRootStateLayout(qpos_indices, qvel_indices)
+
+
+@pytest.mark.parametrize("backend_type", _BACKEND_PARAMS)
+def test_actuation_metadata_contract(backend_type: str) -> None:
+    _require_backend(backend_type)
+
+    backend = create_backend(
+        backend_type,
+        SceneCfg(model_file=_G1_SCENE),
+        NUM_ENVS,
+        SIM_DT,
+        base_name="pelvis",
+    )
+    backend.materialize()
+
+    actuator_names = backend.get_actuator_names()
+    target_joint_names = backend.get_actuator_joint_names()
+    default_dof_pos = backend.get_default_dof_pos()
+    reset_qpos_ids = backend.get_joint_state_qpos_indices(target_joint_names)
+    reset_qvel_ids = backend.get_joint_state_qvel_indices(target_joint_names)
+
+    assert len(actuator_names) == backend.num_actuators
+    assert len(set(actuator_names)) == len(actuator_names)
+    assert all(actuator_names)
+    assert len(target_joint_names) == backend.num_actuators
+    assert all(target_joint_names)
+    assert default_dof_pos.shape == backend.get_dof_pos().shape[1:]
+    assert np.issubdtype(default_dof_pos.dtype, np.floating)
+    assert np.isfinite(default_dof_pos).all()
+    assert reset_qpos_ids.shape == (backend.num_actuators,)
+    assert reset_qvel_ids.shape == (backend.num_actuators,)
+    assert np.issubdtype(reset_qpos_ids.dtype, np.integer)
+    assert np.issubdtype(reset_qvel_ids.dtype, np.integer)
+    assert np.unique(reset_qpos_ids).size == reset_qpos_ids.size
+    assert np.unique(reset_qvel_ids).size == reset_qvel_ids.size
+    assert np.all((reset_qpos_ids >= 0) & (reset_qpos_ids < backend.get_default_qpos().size))
+    assert np.all((reset_qvel_ids >= 0) & (reset_qvel_ids < backend.get_init_qvel().size))
+    np.testing.assert_allclose(default_dof_pos, backend.get_dof_pos()[0], atol=1e-6)
+
+    qpos = np.broadcast_to(
+        backend.get_default_qpos(), (NUM_ENVS, backend.get_default_qpos().size)
+    ).copy()
+    qvel = np.broadcast_to(backend.get_init_qvel(), (NUM_ENVS, backend.get_init_qvel().size)).copy()
+    expected_joint_pos = np.broadcast_to(default_dof_pos + 0.01, (NUM_ENVS, default_dof_pos.size))
+    qpos[:, reset_qpos_ids] = expected_joint_pos
+    backend.set_state(np.arange(NUM_ENVS, dtype=np.int32), qpos, qvel)
+    np.testing.assert_allclose(backend.get_dof_pos(), expected_joint_pos, atol=1e-5)
+
+    detached = default_dof_pos.copy()
+    default_dof_pos[:] = np.nan
+    np.testing.assert_array_equal(backend.get_default_dof_pos(), detached)
+
+
+@pytest.mark.parametrize("backend_type", _BACKEND_PARAMS)
+def test_named_sensor_view_contract(backend_type: str, monkeypatch: pytest.MonkeyPatch) -> None:
+    """All available adapters expose the same ordered, finite sensor view."""
+    _require_backend(backend_type)
+
+    backend = create_backend(
+        backend_type,
+        SceneCfg(model_file=_G1_SCENE),
+        NUM_ENVS,
+        SIM_DT,
+        base_name="pelvis",
+    )
+    backend.materialize()
+    names = ("pelvis_gyro", "pelvis_local_linvel")
+
+    view = backend.bind_sensor_data(names)
+    assert view.backend_type == backend.backend_type
+    assert view.names == names
+    assert view.dimensions == (3, 3)
+    assert view.width == 6
+    values = view.read()
+    assert values.shape == (NUM_ENVS, 6)
+    assert np.isfinite(values).all()
+    np.testing.assert_allclose(values, backend.get_sensor_data_batch(names))
+
+    def fail_if_public_batch_getter_is_used(*_args, **_kwargs):
+        raise AssertionError("materialized sensor view called the public batch getter")
+
+    monkeypatch.setattr(backend, "get_sensor_data_batch", fail_if_public_batch_getter_is_used)
+    np.testing.assert_allclose(view.read(), values)
+
+    with pytest.raises((KeyError, ValueError), match="missing_sensor|Missing|missing"):
+        backend.bind_sensor_data(("missing_sensor",))
+
+
+@pytest.mark.parametrize("backend_type", _BACKEND_PARAMS)
+def test_root_state_layout_contract(backend_type: str) -> None:
+    _require_backend(backend_type)
+
+    backend = create_backend(
+        backend_type,
+        SceneCfg(model_file=_G1_SCENE),
+        NUM_ENVS,
+        SIM_DT,
+        base_name="pelvis",
+    )
+    backend.materialize()
+
+    if backend_type == "drake":
+        with pytest.raises(
+            NotImplementedError,
+            match="DrakeBackend does not expose root-state layout.*pelvis",
+        ):
+            backend.get_root_state_layout("pelvis")
+        return
+
+    layout = backend.get_root_state_layout("pelvis")
+    assert isinstance(layout, BackendRootStateLayout)
+    qpos_indices = np.asarray(layout.qpos_indices)
+    qvel_indices = np.asarray(layout.qvel_indices)
+    assert qpos_indices.shape == (7,)
+    assert qvel_indices.shape == (6,)
+    assert np.all(qpos_indices < backend.get_default_qpos().size)
+    assert np.all(qvel_indices < backend.get_init_qvel().size)
+    root_pose = backend.get_default_qpos()[qpos_indices]
+    np.testing.assert_allclose(np.linalg.norm(root_pose[3:7]), 1.0, atol=1e-6)
+
+
+@pytest.mark.parametrize("backend_type", ["mujoco", "motrix"])
+def test_root_qvel_body_angular_contract_reads_back_world_velocity(backend_type: str) -> None:
+    _require_backend(backend_type)
+    backend = create_backend(
+        backend_type,
+        SceneCfg(model_file=_G1_SCENE),
+        1,
+        SIM_DT,
+        base_name="pelvis",
+        add_body_sensors=True,
+    )
+    backend.materialize()
+    layout = backend.get_root_state_layout("pelvis")
+    qpos_indices = np.asarray(layout.qpos_indices)
+    qvel_indices = np.asarray(layout.qvel_indices)
+    qpos = backend.get_default_qpos()[None].copy()
+    qvel = backend.get_init_qvel()[None].copy()
+    half_sqrt = np.sqrt(0.5)
+    qpos[0, qpos_indices[3:7]] = [half_sqrt, 0.0, 0.0, half_sqrt]
+    qvel[0, qvel_indices[3:6]] = [0.0, -1.0, 0.0]
+
+    backend.set_state(np.array([0], dtype=np.int32), qpos, qvel)
+
+    root_body_id = backend.get_body_ids(["pelvis"])
+    np.testing.assert_allclose(
+        backend.get_body_ang_vel_w(root_body_id)[0, 0],
+        [1.0, 0.0, 0.0],
+        atol=2e-6,
+    )
+
+
+def test_mujoco_root_layout_resolves_a_nonfirst_free_joint() -> None:
+    import mujoco
+    from unisim.backend.mujoco.backend import MuJoCoBackend
+
+    model = mujoco.MjModel.from_xml_string(
+        """
+        <mujoco>
+          <worldbody>
+            <body name="hinged">
+              <joint name="hinge" type="hinge"/>
+              <geom type="sphere" size="0.1" mass="1"/>
+            </body>
+            <body name="floating">
+              <freejoint name="floating_free"/>
+              <geom type="sphere" size="0.1" mass="1"/>
+            </body>
+          </worldbody>
+        </mujoco>
+        """
+    )
+    backend = object.__new__(MuJoCoBackend)
+    backend._model = model
+
+    layout = backend.get_root_state_layout("floating")
+    assert layout.qpos_indices == tuple(range(1, 8))
+    assert layout.qvel_indices == tuple(range(1, 7))
+    with pytest.raises(NotImplementedError, match="hinged.*exactly one free joint"):
+        backend.get_root_state_layout("hinged")
+
+
+def test_drake_root_layout_is_explicitly_unsupported_without_runtime_metadata() -> None:
+    from unisim.backend.drake.backend import DrakeBackend
+
+    backend = object.__new__(DrakeBackend)
+    with pytest.raises(
+        NotImplementedError,
+        match="DrakeBackend does not expose root-state layout.*trunk",
+    ):
+        backend.get_root_state_layout("trunk")
+
+
 def test_terrain_spawn_consumers_do_not_probe_private_backend_capabilities() -> None:
     forbidden_names = {"terrain_origins", "terrain_surface_sampler", "sample_height"}
     offenders: list[str] = []
-    for path in _TERRAIN_CONSUMER_FILES:
-        tree = ast.parse(path.read_text(encoding="utf-8"), filename=str(path))
-        for node in ast.walk(tree):
-            if not isinstance(node, ast.Call) or not isinstance(node.func, ast.Name):
-                continue
-            if node.func.id not in {"getattr", "hasattr"}:
-                continue
-            for arg in node.args[1:]:
-                if isinstance(arg, ast.Constant) and arg.value in forbidden_names:
-                    offenders.append(f"{path.relative_to(REPO_ROOT)}:{node.lineno}: {arg.value}")
+    for relative_path in _TERRAIN_CONSUMER_PATHS:
+        owner_paths = tuple(
+            root / relative_path for root in _TASK_SOURCE_ROOTS if (root / relative_path).is_file()
+        )
+        assert owner_paths, f"terrain consumer source not found: {relative_path}"
+        for path in owner_paths:
+            tree = ast.parse(path.read_text(encoding="utf-8"), filename=str(path))
+            for node in ast.walk(tree):
+                if not isinstance(node, ast.Call) or not isinstance(node.func, ast.Name):
+                    continue
+                if node.func.id not in {"getattr", "hasattr"}:
+                    continue
+                for arg in node.args[1:]:
+                    if isinstance(arg, ast.Constant) and arg.value in forbidden_names:
+                        offenders.append(
+                            f"{path.relative_to(REPO_ROOT)}:{node.lineno}: {arg.value}"
+                        )
     assert not offenders, "private terrain capability probes:\n" + "\n".join(offenders)
 
 
@@ -176,3 +466,116 @@ def test_legacy_contract_step_set_state_and_state_reads(backend_type: str) -> No
         backend.get_base_pos(), np.tile(target_xyz, (NUM_ENVS, 1)), atol=1e-4
     )
     np.testing.assert_allclose(np.linalg.norm(backend.get_base_quat(), axis=-1), 1.0, atol=1e-5)
+
+
+@pytest.mark.slow
+def test_isaacgym_position_hold_is_stable() -> None:
+    """Holding the keyframe pose via position targets must not destabilize.
+
+    Regression guard for two real-runtime failures: ctrl must carry
+    *position targets* (PhysX DOF_MODE_POS with the MJCF kp/kv/forcerange),
+    and actor self-collision must stay disabled — the G1 collision capsules
+    overlap at the default pose (MuJoCo excludes those pairs), so with
+    self-collision on, the wrist/hip joints get pushed away within a few
+    substeps even when the drive target equals the current position.
+    """
+    if not _isaacgym_runtime_available():
+        pytest.skip("isaacgym requires the Python 3.8 worker runtime")
+
+    backend = create_backend(
+        "isaacgym",
+        SceneCfg(model_file=_G1_SCENE),
+        NUM_ENVS,
+        1.0 / 150.0,
+        base_name="pelvis",
+    )
+    backend.materialize()
+    try:
+        default_qpos = backend.get_default_qpos()
+        default_dof_pos = backend.get_default_dof_pos()
+        qpos = np.broadcast_to(default_qpos, (NUM_ENVS, default_qpos.shape[0])).copy()
+        qvel = np.zeros((NUM_ENVS, len(backend.get_init_qvel())), dtype=np.float32)
+        backend.set_state(np.arange(NUM_ENVS, dtype=np.int32), qpos, qvel)
+        ctrl = np.broadcast_to(default_dof_pos, (NUM_ENVS, backend.num_actuators)).copy()
+        # 0.2s of sim time (ctrl_dt=0.02, 3 substeps per control step). The
+        # failure signature is immediate and large (wrist dof at -0.85 rad
+        # within 3 substeps when self-collision is on); normal PD gravity sag
+        # stays below ~0.15 rad in this window.
+        for _ in range(10):
+            backend.step(ctrl.astype(np.float32), nsteps=3)
+        drift = np.abs(backend.get_dof_pos() - default_dof_pos).max()
+        assert drift < 0.3, f"dof drift under position hold: {drift}"
+        base_z = backend.get_base_pos()[:, 2]
+        np.testing.assert_allclose(
+            base_z,
+            default_qpos[2],
+            atol=0.08,
+            err_msg="base height collapsed under position hold",
+        )
+    finally:
+        backend.close()
+
+
+@pytest.mark.slow
+def test_isaacgym_native_camera_capture_renders_scene() -> None:
+    """Real-runtime guard for the native capture path used by record playback.
+
+    The camera sensor tracks env 0's root; after a physics step the frame must
+    be a real render (correct shape, non-uniform pixels — the ground plane and
+    the robot differ in color).
+    """
+    if not _isaacgym_runtime_available():
+        pytest.skip("isaacgym requires the Python 3.8 worker runtime")
+
+    backend = create_backend(
+        "isaacgym",
+        SceneCfg(model_file=_G1_SCENE),
+        NUM_ENVS,
+        SIM_DT,
+        base_name="pelvis",
+    )
+    backend.materialize()
+    try:
+        backend.init_renderer(headless=True, capture=True, width=320, height=240)
+        backend.step(np.zeros((NUM_ENVS, backend.num_actuators), dtype=np.float32))
+        frame = backend.capture_video_frame()
+        assert frame.shape == (240, 320, 3)
+        assert frame.dtype == np.uint8
+        assert np.unique(frame).size > 8, "camera frame looks blank"
+        # A second init with the same config is a no-op and keeps capturing.
+        backend.init_renderer(headless=True, capture=True, width=320, height=240)
+        assert backend.capture_video_frame().shape == (240, 320, 3)
+    finally:
+        backend.close()
+
+
+@pytest.mark.slow
+def test_genesis_native_camera_capture_renders_scene() -> None:
+    """Real-runtime guard for the native capture path used by record playback.
+
+    The offscreen camera attaches post-build; after a physics step the frame
+    must be a real render (correct shape, non-uniform pixels — the ground
+    plane and the robot differ in color). The backend session is deliberately
+    left open: one gs.init per process and the other genesis lanes in this
+    pytest session share it.
+    """
+    if not _genesis_runtime_available():
+        pytest.skip("genesis requires the genesis-world extra and a CUDA device")
+
+    backend = create_backend(
+        "genesis",
+        SceneCfg(model_file=_G1_SCENE),
+        NUM_ENVS,
+        SIM_DT,
+        base_name="pelvis",
+    )
+    backend.materialize()
+    backend.init_renderer(headless=True, capture=True, width=320, height=240)
+    backend.step(np.zeros((NUM_ENVS, backend.num_actuators), dtype=np.float32))
+    frame = backend.capture_video_frame()
+    assert frame.shape == (240, 320, 3)
+    assert frame.dtype == np.uint8
+    assert np.unique(frame).size > 8, "camera frame looks blank"
+    # A second init with the same config is a no-op and keeps capturing.
+    backend.init_renderer(headless=True, capture=True, width=320, height=240)
+    assert backend.capture_video_frame().shape == (240, 320, 3)
diff --git a/tests/base/test_backend_imports.py b/tests/base/test_backend_imports.py
index f7de2e03b..87a35451b 100644
--- a/tests/base/test_backend_imports.py
+++ b/tests/base/test_backend_imports.py
@@ -4,20 +4,29 @@
 import subprocess
 import sys
 import textwrap
+from importlib.metadata import distribution
 from pathlib import Path
 
 _REPO_ROOT = Path(__file__).resolve().parents[2]
 _MATERIALIZER_CONSUMERS = (
-    "src/unilab/training/backend_adapter.py",
-    "scripts/train_rsl_rl.py",
+    "src/unilab/base/config_adapter.py",
+    "src/unilab/scripts/train_rsl_rl.py",
     "scripts/train_him_ppo.py",
     "scripts/train_hora_distill.py",
-    "scripts/play_interactive.py",
     "scripts/manip_loco/benchmark_site_jacobian.py",
 )
 
 
-def test_materializer_consumers_use_backend_facade() -> None:
+def test_unisim_dependency_is_installed_from_package_index() -> None:
+    direct_url = distribution("unisim-core").read_text("direct_url.json")
+
+    assert direct_url is None, (
+        "UniLab tests must consume the indexed unisim-core release, not a local, editable, "
+        "or VCS checkout"
+    )
+
+
+def test_materializer_consumers_use_unisim_owner_module() -> None:
     offenders: list[str] = []
     for relative_path in _MATERIALIZER_CONSUMERS:
         path = _REPO_ROOT / relative_path
@@ -28,7 +37,7 @@ def test_materializer_consumers_use_backend_facade() -> None:
             if isinstance(node, ast.ImportFrom)
             and any(alias.name == "materialize_scene_visual_override" for alias in node.names)
         ]
-        if len(imports) != 1 or imports[0].module != "unilab.base.backend":
+        if len(imports) != 1 or imports[0].module != "unisim.backend.mujoco.xml":
             modules = [node.module for node in imports]
             offenders.append(f"{relative_path}: {modules}")
 
@@ -52,7 +61,7 @@ def test_site_jacobian_benchmark_imports_with_mujoco_stub() -> None:
         spec.loader.exec_module(module)
 
         print(module.materialize_scene_visual_override.__module__)
-        print("mujoco_backend", "unilab.base.backend.mujoco.backend" in sys.modules)
+        print("mujoco_backend", "unisim.backend.mujoco.backend" in sys.modules)
         """
     )
     script = _REPO_ROOT / "scripts" / "manip_loco" / "benchmark_site_jacobian.py"
@@ -64,7 +73,7 @@ def test_site_jacobian_benchmark_imports_with_mujoco_stub() -> None:
     )
 
     assert result.stdout.splitlines() == [
-        "unilab.base.backend.mujoco.xml",
+        "unisim.backend.mujoco.xml",
         "mujoco_backend False",
     ]
 
@@ -75,23 +84,26 @@ def test_mujoco_backend_import_path_does_not_eagerly_import_motrix() -> None:
         import importlib.util
         import sys
 
-        from unilab.base.backend import create_backend, materialize_scene_visual_override
-        from unilab.base.backend.mujoco.xml import create_discardvisual_xml
+        from unilab.base.backend_factory import create_backend
+        from unisim.backend.mujoco.xml import (
+            create_discardvisual_xml,
+            materialize_scene_visual_override,
+        )
 
         assert create_backend is not None
         assert materialize_scene_visual_override is not None
         assert create_discardvisual_xml is not None
 
         print("mujoco_runtime", "mujoco" in sys.modules)
-        print("mujoco_backend", "unilab.base.backend.mujoco.backend" in sys.modules)
+        print("mujoco_backend", "unisim.backend.mujoco.backend" in sys.modules)
 
         if importlib.util.find_spec("mujoco") is not None:
-            import unilab.base.backend.mujoco.backend
+            import unisim.backend.mujoco.backend
             print("mujoco_backend imported")
         else:
             print("mujoco_backend skipped")
 
-        print("motrix_backend", "unilab.base.backend.motrix.backend" in sys.modules)
+        print("motrix_backend", "unisim.backend.motrix.backend" in sys.modules)
         print("motrixsim", "motrixsim" in sys.modules)
         """
     )
@@ -115,8 +127,8 @@ def test_motrix_backend_import_path_does_not_eagerly_import_mujoco() -> None:
         import importlib.util
         import sys
 
-        from unilab.base.backend import (
-            create_backend,
+        from unilab.base.backend_factory import create_backend
+        from unisim.backend.motrix.scene import (
             materialize_motrix_hfield_attached_scene,
             materialize_motrix_scene,
         )
@@ -126,12 +138,12 @@ def test_motrix_backend_import_path_does_not_eagerly_import_mujoco() -> None:
         assert materialize_motrix_hfield_attached_scene is not None
 
         if importlib.util.find_spec("motrixsim") is not None:
-            import unilab.base.backend.motrix.backend
+            import unisim.backend.motrix.backend
             print("motrix_backend imported")
         else:
             print("motrix_backend skipped")
 
-        print("mujoco_backend", "unilab.base.backend.mujoco.backend" in sys.modules)
+        print("mujoco_backend", "unisim.backend.mujoco.backend" in sys.modules)
         print("mujoco", "mujoco" in sys.modules)
         """
     )
diff --git a/tests/base/test_backend_pre_step_control.py b/tests/base/test_backend_pre_step_control.py
index 646cbc8ea..62be70300 100644
--- a/tests/base/test_backend_pre_step_control.py
+++ b/tests/base/test_backend_pre_step_control.py
@@ -4,8 +4,7 @@
 
 import numpy as np
 import pytest
-
-from unilab.base.backend.base import SimBackend
+from unisim.backend.base import SimBackend
 
 
 def test_pre_step_control_default_noop() -> None:
@@ -46,29 +45,48 @@ class _FakeMuJoCoPool:
     def __init__(self) -> None:
         self.step_calls: list[dict] = []
         self.forward_calls: list[np.ndarray] = []
+        self.callback_controls: list[np.ndarray] = []
 
     def step(
         self,
         state,
         *,
         nstep,
-        control,
+        control=None,
         control_spec,
         return_sensor=False,
         post_step_forward_sensor=False,
         chunk_size=None,
+        control_callback=None,
+        callback_sensordata=True,
     ):
         self.step_calls.append(
             {
                 "nstep": nstep,
-                "control": np.array(control, copy=True),
+                "control": None if control is None else np.array(control, copy=True),
                 "control_spec": control_spec,
                 "return_sensor": return_sensor,
                 "post_step_forward_sensor": post_step_forward_sensor,
                 "chunk_size": chunk_size,
+                "control_callback": control_callback,
+                "callback_sensordata": callback_sensordata,
             }
         )
-        state_out = np.asarray(state) + 1.0
+        state_out = np.ascontiguousarray(np.asarray(state), dtype=np.float64)
+        if control_callback is not None:
+            # Emulate the upstream per-substep control_callback protocol:
+            # callback(0) sees the initial state and sensordata=None; each
+            # substep adds 1.0; callback(t>0) sees fresh state and sensordata
+            # only when callback_sensordata is true.
+            for t in range(nstep):
+                sensor_arg = None
+                if t > 0 and callback_sensordata:
+                    sensor_arg = state_out[:, :1]
+                cb_control = control_callback(t, state_out, sensor_arg)
+                self.callback_controls.append(np.array(cb_control, copy=True))
+                state_out = state_out + 1.0
+        else:
+            state_out = state_out + 1.0
         if return_sensor:
             return state_out, state_out[:, :1]
         return state_out
@@ -81,7 +99,7 @@ def forward(self, state):
 
 def _fake_mujoco_backend(pre_step_control_fn=None, post_step_forward_sensor=False):
     try:
-        from unilab.base.backend.mujoco.backend import MuJoCoBackend
+        from unisim.backend.mujoco.backend import MuJoCoBackend
     except Exception as exc:
         pytest.skip(f"MuJoCo backend import unavailable: {exc}")
 
@@ -126,34 +144,67 @@ def test_mujoco_step_honors_post_step_forward_sensor_flag() -> None:
     assert backend._pool.step_calls[0]["post_step_forward_sensor"] is True
 
 
-def test_mujoco_step_with_pre_step_control_recomputes_each_physics_step() -> None:
+def test_mujoco_step_with_pre_step_control_uses_single_dispatch_callback() -> None:
+    seen_states: list[np.ndarray] = []
     seen_sensors: list[np.ndarray] = []
 
     backend = _fake_mujoco_backend(post_step_forward_sensor=True)
 
     def hook(current_backend, owner_ctrl: np.ndarray) -> np.ndarray:
+        seen_states.append(current_backend._physics_state.copy())
         seen_sensors.append(current_backend._sensor_data.copy())
-        return owner_ctrl + len(seen_sensors)
+        return owner_ctrl + len(seen_states)
 
     backend.set_pre_step_control(hook)
     ctrl = np.array([[0.5, -0.5]], dtype=np.float32)
 
     backend.step(ctrl, nsteps=3)
 
-    assert len(backend._pool.step_calls) == 3
-    assert [call["nstep"] for call in backend._pool.step_calls] == [1, 1, 1]
-    assert all(call["return_sensor"] is True for call in backend._pool.step_calls)
-    assert all(call["post_step_forward_sensor"] is True for call in backend._pool.step_calls)
-    assert all(call["chunk_size"] is None for call in backend._pool.step_calls)
-    assert backend._pool.forward_calls == []
-    np.testing.assert_allclose(seen_sensors, [[[0.0]], [[1.0]], [[2.0]]])
-    np.testing.assert_allclose(backend._pool.step_calls[0]["control"], (ctrl + 1)[:, None, :])
-    np.testing.assert_allclose(backend._pool.step_calls[1]["control"], (ctrl + 2)[:, None, :])
-    np.testing.assert_allclose(backend._pool.step_calls[2]["control"], (ctrl + 3)[:, None, :])
+    pool = backend._pool
+    assert len(pool.step_calls) == 1
+    call = pool.step_calls[0]
+    assert call["nstep"] == 3
+    assert call["control"] is None
+    assert call["control_callback"] is not None
+    assert call["callback_sensordata"] is False
+    assert call["return_sensor"] is True
+    assert call["post_step_forward_sensor"] is True
+    assert call["chunk_size"] is None
+    assert pool.forward_calls == []
+    # The hook sees the physics state refreshed before every substep.
+    np.testing.assert_allclose(seen_states, [[[0.0]], [[1.0]], [[2.0]]])
+    # _sensor_data is no longer refreshed per substep (action terms only read
+    # physics-state-backed getters); it is refreshed once from the final
+    # return below.
+    np.testing.assert_allclose(seen_sensors, [[[0.0]], [[0.0]], [[0.0]]])
+    assert len(pool.callback_controls) == 3
+    np.testing.assert_allclose(pool.callback_controls[0], ctrl + 1)
+    np.testing.assert_allclose(pool.callback_controls[1], ctrl + 2)
+    np.testing.assert_allclose(pool.callback_controls[2], ctrl + 3)
+    assert pool.callback_controls[0].dtype == np.float64
     np.testing.assert_allclose(backend._physics_state, [[3.0]])
     np.testing.assert_allclose(backend._sensor_data, [[3.0]])
 
 
+def test_mujoco_step_with_pre_step_control_appends_pending_xfrc_each_substep() -> None:
+    mujoco = pytest.importorskip("mujoco")
+    backend = _fake_mujoco_backend()
+    backend._pending_xfrc_applied = np.full((1, 2), 7.0, dtype=np.float64)
+
+    backend.set_pre_step_control(lambda current_backend, owner_ctrl: owner_ctrl + 1.0)
+    ctrl = np.array([[0.5, -0.5]], dtype=np.float32)
+
+    backend.step(ctrl, nsteps=2)
+
+    pool = backend._pool
+    assert len(pool.step_calls) == 1
+    assert pool.step_calls[0]["control_spec"] & int(mujoco.mjtState.mjSTATE_XFRC_APPLIED)
+    assert len(pool.callback_controls) == 2
+    for cb_control in pool.callback_controls:
+        np.testing.assert_allclose(cb_control, [[1.5, 0.5, 7.0, 7.0]])
+    np.testing.assert_allclose(backend._pending_xfrc_applied, [[0.0, 0.0]])
+
+
 class _FakeMotrixModel:
     def __init__(self) -> None:
         self.step_calls = 0
@@ -169,7 +220,7 @@ def step_n(self, data, nsteps: int) -> None:
 
 
 def _fake_motrix_backend(pre_step_control_fn=None):
-    from unilab.base.backend.motrix.backend import MotrixBackend
+    from unisim.backend.motrix.backend import MotrixBackend
 
     backend = object.__new__(MotrixBackend)
     backend._pre_step_control_fn = pre_step_control_fn
@@ -202,7 +253,7 @@ def hook(current_backend, owner_ctrl: np.ndarray) -> np.ndarray:
 
 
 def test_motrix_native_video_capture_uses_headless_system_camera(monkeypatch) -> None:
-    import unilab.base.backend.motrix.backend as mod
+    import unisim.backend.motrix.backend as mod
 
     captured: dict[str, object] = {}
 
@@ -308,7 +359,7 @@ def sync(self, *, data, wait=False):
 
 
 def test_motrix_native_video_capture_defaults_camera_lookat_to_grid_center(monkeypatch) -> None:
-    import unilab.base.backend.motrix.backend as mod
+    import unisim.backend.motrix.backend as mod
 
     captured: dict[str, object] = {}
 
@@ -372,7 +423,7 @@ def launch(self, model, *, batch, render_offset, render_settings):
 
 
 def test_motrix_native_video_capture_tracks_primary_env_base(monkeypatch) -> None:
-    import unilab.base.backend.motrix.backend as mod
+    import unisim.backend.motrix.backend as mod
 
     captured: dict[str, object] = {"set_views": []}
     base_positions = np.array(
@@ -488,7 +539,7 @@ def sync(self, *, data, wait=False):
 
 
 def test_motrix_interactive_renderer_applies_camera_kwargs(monkeypatch) -> None:
-    import unilab.base.backend.motrix.backend as mod
+    import unisim.backend.motrix.backend as mod
 
     captured: dict[str, object] = {}
 
@@ -560,7 +611,7 @@ def set_main_camera(self, camera):
 
 
 def test_motrix_renderer_zero_offset_mode(monkeypatch) -> None:
-    import unilab.base.backend.motrix.backend as mod
+    import unisim.backend.motrix.backend as mod
 
     captured: dict[str, object] = {}
 
diff --git a/tests/base/test_backend_sensor_view.py b/tests/base/test_backend_sensor_view.py
new file mode 100644
index 000000000..6b7ac1a1f
--- /dev/null
+++ b/tests/base/test_backend_sensor_view.py
@@ -0,0 +1,124 @@
+"""Focused tests for the cold-path named sensor view contract."""
+
+from __future__ import annotations
+
+from collections import Counter
+
+import numpy as np
+import pytest
+from unisim.backend.base import BackendSensorView, SimBackend
+
+
+class _SensorBackend:
+    backend_type = "fake"
+    num_envs = 2
+
+    def __init__(self) -> None:
+        self.calls: Counter[str] = Counter()
+        self.values = {
+            "contact_a": np.asarray([[1.0], [0.0]], dtype=np.float32),
+            "contact_b": np.asarray([[0.0], [1.0]], dtype=np.float32),
+        }
+
+    def get_sensor_data(self, name: str) -> np.ndarray:
+        self.calls["single"] += 1
+        return self.values[name]
+
+    def get_sensor_data_batch(self, names: tuple[str, ...]) -> np.ndarray:
+        self.calls["batch"] += 1
+        return np.concatenate([self.values[name] for name in names], axis=1)
+
+    def _bind_sensor_data_reader(self, names: tuple[str, ...]):
+        batch_reader = self.get_sensor_data_batch
+        return lambda: batch_reader(names)
+
+
+def test_bind_sensor_data_validates_once_and_reads_through_formal_view() -> None:
+    backend = _SensorBackend()
+
+    view = SimBackend.bind_sensor_data(backend, ("contact_a", "contact_b"))  # type: ignore[arg-type]
+
+    assert isinstance(view, BackendSensorView)
+    assert view.names == ("contact_a", "contact_b")
+    assert view.dimensions == (1, 1)
+    assert view.width == 2
+    assert backend.calls == {"single": 2, "batch": 1}
+
+    np.testing.assert_array_equal(view.read(), [[1.0, 0.0], [0.0, 1.0]])
+    assert backend.calls == {"single": 2, "batch": 2}
+
+
+@pytest.mark.parametrize(
+    "names, error, match",
+    [
+        ("contact_a", TypeError, "sequence of strings"),
+        ((), ValueError, "at least one name"),
+        (("",), ValueError, "non-empty strings"),
+        (("contact_a", "contact_a"), ValueError, "unique"),
+    ],
+)
+def test_bind_sensor_data_rejects_invalid_name_requests(
+    names: str | tuple[str, ...], error: type[Exception], match: str
+) -> None:
+    with pytest.raises(error, match=match):
+        SimBackend.bind_sensor_data(_SensorBackend(), names)  # type: ignore[arg-type]
+
+
+def test_bind_sensor_data_fails_closed_on_unknown_sensor() -> None:
+    with pytest.raises(KeyError, match="cannot bind sensor 'missing'"):
+        SimBackend.bind_sensor_data(_SensorBackend(), ("missing",))  # type: ignore[arg-type]
+
+
+@pytest.mark.parametrize(
+    "value, error, match",
+    [
+        (np.zeros((1, 2), dtype=np.float32), ValueError, "expected leading dimension 2"),
+        (np.full((2, 1), np.nan, dtype=np.float32), ValueError, "NaN or Inf"),
+    ],
+)
+def test_backend_sensor_view_rejects_invalid_runtime_data(
+    value: np.ndarray, error: type[Exception], match: str
+) -> None:
+    backend = _SensorBackend()
+    backend.values["contact_a"] = value
+
+    if value.shape[0] != backend.num_envs:
+        with pytest.raises(error, match=match):
+            SimBackend.bind_sensor_data(backend, ("contact_a",))  # type: ignore[arg-type]
+        return
+
+    with pytest.raises(error, match=match):
+        SimBackend.bind_sensor_data(backend, ("contact_a",))  # type: ignore[arg-type]
+
+
+@pytest.mark.parametrize(
+    "value, error, match",
+    [
+        (np.zeros((2, 2), dtype=np.float32), ValueError, r"expected \(2, 1\)"),
+        (np.full((2, 1), np.inf, dtype=np.float32), ValueError, "NaN or Inf"),
+        (np.full((2, 1), "invalid", dtype=object), TypeError, "non-numeric dtype"),
+    ],
+)
+def test_backend_sensor_view_detects_runtime_contract_drift(
+    value: np.ndarray, error: type[Exception], match: str
+) -> None:
+    backend = _SensorBackend()
+    view = SimBackend.bind_sensor_data(backend, ("contact_a",))  # type: ignore[arg-type]
+
+    backend.values["contact_a"] = value
+
+    with pytest.raises(error, match=match):
+        view.read()
+
+
+def test_backend_sensor_view_metadata_is_immutable() -> None:
+    view = BackendSensorView(
+        backend_type="fake",
+        names=("sensor",),
+        dimensions=(1,),
+        num_envs=1,
+        _reader=lambda: np.zeros((1, 1), dtype=np.float32),
+    )
+
+    with pytest.raises(AttributeError):
+        view.names = ("other",)  # type: ignore[misc]
diff --git a/tests/base/test_body_state_copy.py b/tests/base/test_body_state_copy.py
new file mode 100644
index 000000000..28a06e290
--- /dev/null
+++ b/tests/base/test_body_state_copy.py
@@ -0,0 +1,48 @@
+"""Focused parity tests for the shared body-state copy kernel."""
+
+from __future__ import annotations
+
+import numpy as np
+from unisim.backend.body_state import copy_selected_body_state
+
+
+def _outputs(num_envs: int, num_selected: int) -> tuple[np.ndarray, ...]:
+    shape = (num_envs, num_selected, 3)
+    return (
+        np.empty(shape, dtype=np.float32),
+        np.empty((num_envs, num_selected, 4), dtype=np.float32),
+        np.empty(shape, dtype=np.float32),
+        np.empty(shape, dtype=np.float32),
+    )
+
+
+def test_shared_body_state_copy_kernel_preserves_selection_and_outputs() -> None:
+    rng = np.random.default_rng(7)
+    num_envs, num_bodies = 257, 9
+    pos = rng.standard_normal((num_envs, num_bodies, 3), dtype=np.float32)
+    quat = rng.standard_normal((num_envs, num_bodies, 4), dtype=np.float32)
+    lin_vel = rng.standard_normal((num_envs, num_bodies, 3), dtype=np.float32)
+    ang_vel = rng.standard_normal((num_envs, num_bodies, 3), dtype=np.float32)
+    selected = np.asarray([7, 1, 5], dtype=np.intp)
+    out_pos, out_quat, out_lin_vel, out_ang_vel = _outputs(num_envs, len(selected))
+
+    copy_selected_body_state(
+        pos,
+        quat,
+        lin_vel,
+        ang_vel,
+        selected,
+        out_pos,
+        out_quat,
+        out_lin_vel,
+        out_ang_vel,
+    )
+
+    np.testing.assert_array_equal(out_pos, pos[:, selected])
+    np.testing.assert_array_equal(out_quat, quat[:, selected])
+    np.testing.assert_array_equal(out_lin_vel, lin_vel[:, selected])
+    np.testing.assert_array_equal(out_ang_vel, ang_vel[:, selected])
+    # The shared helper intentionally stays NumPy-only so the core package has
+    # no mandatory Numba dependency. Backend-specific compiled kernels belong
+    # to their optional adapter extras.
+    assert not hasattr(copy_selected_body_state, "targetoptions")
diff --git a/tests/base/test_cpu_runtime.py b/tests/base/test_cpu_runtime.py
new file mode 100644
index 000000000..461867562
--- /dev/null
+++ b/tests/base/test_cpu_runtime.py
@@ -0,0 +1,288 @@
+"""Tests for env-owned CPU block process confinement (``apply_env_cpu_runtime``).
+
+Multi-rank off-policy collectors pin their MuJoCo pool workers to a per-rank
+CPU block via ``EnvCfg.cpu_ids``; ``NpEnv.__init__`` additionally confines the
+owning process to the same block and sizes Numba's parallel pool to it, so
+host-side kernels cannot drift onto sibling ranks' CPUs. Unit tests mock the
+OS/Numba seams; one subprocess test validates the real placement contract.
+"""
+
+from __future__ import annotations
+
+import json
+import os
+import subprocess
+import sys
+from dataclasses import dataclass
+from unittest.mock import MagicMock
+
+import gymnasium as gym
+import numba
+import numpy as np
+import pytest
+
+import unilab.base.cpu_runtime as cpu_runtime
+from unilab.base.base import EnvCfg
+from unilab.base.cpu_runtime import apply_env_cpu_runtime
+from unilab.base.np_env import NpEnv, NpEnvState
+
+
+def _record_affinity(monkeypatch: pytest.MonkeyPatch, available: set[int]) -> list[tuple]:
+    calls: list[tuple] = []
+    # raising=False: sched_*affinity is Linux-only, so the attribute may not
+    # exist on the host running the tests (e.g. macOS dev machines).
+    monkeypatch.setattr(os, "sched_getaffinity", lambda _pid: set(available), raising=False)
+    monkeypatch.setattr(
+        os, "sched_setaffinity", lambda pid, ids: calls.append((pid, set(ids))), raising=False
+    )
+    return calls
+
+
+def _record_numba(monkeypatch: pytest.MonkeyPatch) -> list[int]:
+    calls: list[int] = []
+    monkeypatch.setattr(numba, "set_num_threads", lambda n: calls.append(int(n)))
+    return calls
+
+
+def _record_confine(monkeypatch: pytest.MonkeyPatch) -> list[set[int]]:
+    calls: list[set[int]] = []
+    monkeypatch.setattr(
+        cpu_runtime, "_confine_existing_threads", lambda ids: calls.append(set(ids))
+    )
+    return calls
+
+
+def test_none_is_noop(monkeypatch: pytest.MonkeyPatch):
+    monkeypatch.delenv("NUMBA_NUM_THREADS", raising=False)
+    affinity_calls = _record_affinity(monkeypatch, {0, 1, 2, 3})
+    confine_calls = _record_confine(monkeypatch)
+    numba_calls = _record_numba(monkeypatch)
+
+    apply_env_cpu_runtime(None)
+
+    assert affinity_calls == []
+    assert confine_calls == []
+    assert numba_calls == []
+
+
+def test_applies_affinity_and_numba_cap(monkeypatch: pytest.MonkeyPatch):
+    monkeypatch.delenv("NUMBA_NUM_THREADS", raising=False)
+    affinity_calls = _record_affinity(monkeypatch, {0, 1, 2, 3})
+    confine_calls = _record_confine(monkeypatch)
+    numba_calls = _record_numba(monkeypatch)
+
+    apply_env_cpu_runtime([1, 2])
+
+    assert affinity_calls == [(0, {1, 2})]
+    assert confine_calls == [{1, 2}]
+    assert numba_calls == [2]
+
+
+def test_respects_explicit_numba_num_threads(monkeypatch: pytest.MonkeyPatch):
+    monkeypatch.setenv("NUMBA_NUM_THREADS", "4")
+    affinity_calls = _record_affinity(monkeypatch, {0, 1, 2, 3})
+    confine_calls = _record_confine(monkeypatch)
+    numba_calls = _record_numba(monkeypatch)
+
+    apply_env_cpu_runtime([1, 2])
+
+    assert affinity_calls == [(0, {1, 2})]
+    assert confine_calls == [{1, 2}]
+    assert numba_calls == []
+
+
+def test_unavailable_cpu_ids_fail_closed(monkeypatch: pytest.MonkeyPatch):
+    monkeypatch.delenv("NUMBA_NUM_THREADS", raising=False)
+    affinity_calls = _record_affinity(monkeypatch, {0, 1})
+    confine_calls = _record_confine(monkeypatch)
+    numba_calls = _record_numba(monkeypatch)
+
+    with pytest.raises(ValueError, match="not available"):
+        apply_env_cpu_runtime([1, 2])
+
+    assert affinity_calls == []
+    assert confine_calls == []
+    assert numba_calls == []
+
+
+def test_platform_without_affinity_warns_and_caps_numba(monkeypatch: pytest.MonkeyPatch):
+    monkeypatch.delenv("NUMBA_NUM_THREADS", raising=False)
+    monkeypatch.delattr(os, "sched_setaffinity", raising=False)
+    monkeypatch.delattr(os, "sched_getaffinity", raising=False)
+    confine_calls = _record_confine(monkeypatch)
+    numba_calls = _record_numba(monkeypatch)
+
+    with pytest.warns(UserWarning, match="sched_setaffinity"):
+        apply_env_cpu_runtime([0, 1])
+
+    assert confine_calls == []
+    assert numba_calls == [2]
+
+
+def test_confine_existing_threads_pins_tasks_and_skips_failures(
+    monkeypatch: pytest.MonkeyPatch,
+):
+    calls: list[tuple] = []
+
+    def fake_setaffinity(pid, ids):
+        if pid == 456:
+            raise ProcessLookupError
+        calls.append((pid, set(ids)))
+
+    monkeypatch.setattr(os, "sched_setaffinity", fake_setaffinity, raising=False)
+    monkeypatch.setattr(os.path, "isdir", lambda path: path == cpu_runtime._PROC_TASK_DIR)
+    monkeypatch.setattr(os, "listdir", lambda path: ["123", "456", "789"])
+
+    cpu_runtime._confine_existing_threads({1, 2})
+
+    assert calls == [(123, {1, 2}), (789, {1, 2})]
+
+
+def test_confine_existing_threads_without_proc_is_noop(monkeypatch: pytest.MonkeyPatch):
+    calls: list[tuple] = []
+    monkeypatch.setattr(
+        os, "sched_setaffinity", lambda pid, ids: calls.append((pid, set(ids))), raising=False
+    )
+    monkeypatch.setattr(os.path, "isdir", lambda path: False)
+
+    cpu_runtime._confine_existing_threads({0})
+
+    assert calls == []
+
+
+# ---------------------------------------------------------------------------
+# NpEnv wiring
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class _StubCfg(EnvCfg):
+    max_episode_seconds: float | None = 1.0
+
+
+class _StubNpEnv(NpEnv):
+    def __init__(self, cfg: EnvCfg):
+        backend = MagicMock()
+        backend.get_scene_model_file.return_value = None
+        super().__init__(cfg, backend, 1)
+
+    @property
+    def obs_groups_spec(self) -> dict[str, int]:
+        return {"obs": 1}
+
+    @property
+    def action_space(self) -> gym.Space:
+        return gym.spaces.Box(low=-1.0, high=1.0, shape=(1,), dtype=np.float32)
+
+    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
+        return actions
+
+    def update_state(self, state: NpEnvState) -> NpEnvState:
+        return state
+
+
+@pytest.mark.parametrize("cpu_ids", (None, [2, 3]))
+def test_np_env_init_applies_env_cpu_runtime(monkeypatch: pytest.MonkeyPatch, cpu_ids):
+    import unilab.base.np_env as np_env_module
+
+    calls: list[list[int] | None] = []
+    monkeypatch.setattr(
+        np_env_module,
+        "apply_env_cpu_runtime",
+        lambda value: calls.append(None if value is None else list(value)),
+    )
+
+    _StubNpEnv(EnvCfg(cpu_ids=cpu_ids))
+
+    assert calls == [cpu_ids]
+
+
+# ---------------------------------------------------------------------------
+# Real placement contract in a fresh process
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.skipif(
+    not (hasattr(os, "sched_setaffinity") and os.path.isdir("/proc/self/task")),
+    reason="requires Linux sched affinity and /proc",
+)
+def test_numba_threads_inherit_confined_block_in_fresh_process():
+    script = r"""
+import json
+import os
+
+# Production collectors import numpy/numba (through the backend modules)
+# before env construction, so mirror that ordering here: the OpenBLAS pool
+# spawned at `import numpy` predates the env hook and must be confined
+# retroactively, while Numba's pool launches after it and inherits the mask.
+import numba  # noqa: F401
+import numpy as np
+
+from unilab.base.cpu_runtime import apply_env_cpu_runtime
+
+block = sorted(os.sched_getaffinity(0))[:2]
+apply_env_cpu_runtime(block)
+
+from numba import get_num_threads, njit, prange
+
+
+@njit(parallel=True)
+def _probe(out):
+    for i in prange(out.shape[0]):
+        out[i] = i * 2.0
+
+
+_probe(np.zeros(256))
+
+
+def _expand(mask):
+    cpus = set()
+    for part in mask.split(","):
+        if "-" in part:
+            lo, hi = part.split("-", 1)
+            cpus.update(range(int(lo), int(hi) + 1))
+        elif part:
+            cpus.add(int(part))
+    return sorted(cpus)
+
+
+masks = []
+for tid in os.listdir("/proc/self/task"):
+    with open(f"/proc/self/task/{tid}/status") as fh:
+        for line in fh:
+            if line.startswith("Cpus_allowed_list"):
+                masks.append(_expand(line.split(":", 1)[1].strip()))
+
+print(
+    "RESULT:"
+    + json.dumps(
+        {
+            "block": block,
+            "affinity": sorted(os.sched_getaffinity(0)),
+            "numba_threads": get_num_threads(),
+            "masks": masks,
+        }
+    )
+)
+"""
+    env = dict(os.environ)
+    env.pop("NUMBA_NUM_THREADS", None)
+    proc = subprocess.run(
+        [sys.executable, "-c", script],
+        capture_output=True,
+        text=True,
+        timeout=300,
+        env=env,
+    )
+    assert proc.returncode == 0, proc.stderr
+    result_lines = [line for line in proc.stdout.splitlines() if line.startswith("RESULT:")]
+    assert len(result_lines) == 1, proc.stdout
+    payload = json.loads(result_lines[0].removeprefix("RESULT:"))
+    assert payload["affinity"] == payload["block"]
+    assert payload["numba_threads"] == len(payload["block"])
+    # Every thread in the process must stay inside the block: the main thread,
+    # the OpenBLAS pool spawned at import (confined retroactively), and Numba's
+    # pool (inherits the confined mask at its lazy launch).
+    assert payload["masks"]
+    for mask in payload["masks"]:
+        assert set(mask) <= set(payload["block"])
diff --git a/tests/base/test_drake_mujoco_render_contract.py b/tests/base/test_drake_mujoco_render_contract.py
new file mode 100644
index 000000000..3886cc326
--- /dev/null
+++ b/tests/base/test_drake_mujoco_render_contract.py
@@ -0,0 +1,33 @@
+"""Drake physics + MuJoCo rendering integration contracts."""
+
+from __future__ import annotations
+
+from pathlib import Path
+from types import SimpleNamespace
+
+from unilab.base.np_env import NpEnv
+
+
+def test_drake_env_auto_playback_is_mujoco_record_plan() -> None:
+    class _Env(NpEnv):
+        @property
+        def action_space(self):
+            raise NotImplementedError
+
+        def apply_action(self, actions, state):
+            raise NotImplementedError
+
+        def update_state(self, state):
+            raise NotImplementedError
+
+    env = object.__new__(_Env)
+    env._backend = SimpleNamespace(  # type: ignore[attr-defined]
+        backend_type="drake",
+        resolve_play_render_plan=lambda **kwargs: SimpleNamespace(**kwargs),
+    )
+    plan = env.resolve_play_render_plan(
+        play_render_mode="auto",
+        play_steps=3,
+        output_video=Path("play.mp4"),
+    )
+    assert plan.play_render_mode == "record"
diff --git a/tests/base/test_entity_facade.py b/tests/base/test_entity_facade.py
new file mode 100644
index 000000000..bff44e105
--- /dev/null
+++ b/tests/base/test_entity_facade.py
@@ -0,0 +1,759 @@
+from __future__ import annotations
+
+import ast
+import inspect
+from collections import Counter
+from types import SimpleNamespace
+from typing import Any, cast
+
+import numpy as np
+import pytest
+from unisim.backend.base import BackendRootStateLayout, SimBackend
+
+import unilab.base.entity as entity_module
+from unilab.assets import ASSETS_ROOT_PATH
+from unilab.base.entity import EntityCfg, EntityScene
+from unilab.base.reset_state import ResetStateTransaction
+from unilab.base.scene import SceneCfg
+from unilab.managers import RewardManager, RewardTermCfg, SceneEntityCfg
+
+
+class _StrictBackendProfile:
+    """Strict public-contract fake shared by backend capability profiles."""
+
+    num_envs = 3
+    num_actuators = 5
+
+    def __init__(self, backend_type: str, *, unsupported: frozenset[str] = frozenset()) -> None:
+        self.backend_type = backend_type
+        self.unsupported = unsupported
+        self.calls: Counter[str] = Counter()
+        self.joint_ids = {"hip": 2, "knee": 0, "ankle": 4}
+        self.body_ids = {"base": 4, "foot": 7}
+        self.site_ids = {"imu": 3}
+        self.geom_names = ("floor", "base_collision", "foot_collision")
+        self.actuator_names = ("knee", "unused", "hip", "unused_2", "ankle")
+        self.actuator_joint_names = (
+            "knee",
+            "unused_joint",
+            "hip",
+            "unused_2_joint",
+            "ankle",
+        )
+        self.dof_pos = np.arange(self.num_envs * 5, dtype=np.float32).reshape(self.num_envs, 5)
+        self.dof_vel = self.dof_pos + 100.0
+        base = np.arange(self.num_envs * 10 * 3, dtype=np.float32)
+        self.body_pos = base.reshape(self.num_envs, 10, 3)
+        self.body_quat = np.zeros((self.num_envs, 10, 4), dtype=np.float32)
+        self.body_quat[..., 0] = 1.0
+        self.body_lin_vel = self.body_pos + 200.0
+        self.body_ang_vel = self.body_pos + 300.0
+        self.body_lin_vel_b = self.body_pos + 400.0
+        self.body_ang_vel_b = self.body_pos + 500.0
+        self.default_qpos = np.array(
+            [99.0, 1.0, 2.0, 3.0, 1.0, 0.0, 0.0, 0.0, 88.0],
+            dtype=np.float32,
+        )
+        self.init_qvel = np.array(
+            [77.0, 66.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 55.0],
+            dtype=np.float32,
+        )
+        self.set_state_calls: list[tuple[np.ndarray, np.ndarray, np.ndarray]] = []
+        self.joint_range = np.asarray(
+            [[-1.0, 1.0], [-2.0, 2.0], [-3.0, 3.0], [-4.0, 4.0], [-5.0, 5.0]],
+            dtype=np.float32,
+        )
+
+    def _check(self, capability: str) -> None:
+        self.calls[capability] += 1
+        if capability in self.unsupported:
+            raise NotImplementedError(f"{self.backend_type} lacks {capability}")
+
+    def get_body_ids(self, names) -> np.ndarray:
+        self._check("body names")
+        return np.asarray([self.body_ids[name] for name in names], dtype=np.int32)
+
+    def get_root_state_layout(self, root_body_name: str) -> BackendRootStateLayout:
+        self._check("root-state layout")
+        if root_body_name != "base":
+            raise ValueError(f"unknown root body {root_body_name}")
+        return BackendRootStateLayout(tuple(range(1, 8)), tuple(range(2, 8)))
+
+    def get_default_qpos(self) -> np.ndarray:
+        self._check("default qpos")
+        return self.default_qpos.copy()
+
+    def get_init_qvel(self) -> np.ndarray:
+        self._check("initial qvel")
+        return self.init_qvel.copy()
+
+    def set_state(
+        self,
+        env_ids: np.ndarray,
+        qpos: np.ndarray,
+        qvel: np.ndarray,
+        randomization=None,
+    ) -> None:
+        assert randomization is None
+        self.set_state_calls.append((env_ids.copy(), qpos.copy(), qvel.copy()))
+
+    def get_joint_dof_pos_indices(self, names) -> np.ndarray:
+        self._check("joint position names")
+        return np.asarray([self.joint_ids[name] for name in names], dtype=np.int32)
+
+    def get_joint_dof_vel_indices(self, names) -> np.ndarray:
+        self._check("joint velocity names")
+        return np.asarray([self.joint_ids[name] for name in names], dtype=np.int32)
+
+    def get_site_ids(self, names) -> np.ndarray:
+        self._check("site names")
+        return np.asarray([self.site_ids[name] for name in names], dtype=np.int32)
+
+    def get_geom_names(self) -> tuple[str, ...]:
+        self._check("geom names")
+        return self.geom_names
+
+    def get_actuator_names(self) -> tuple[str, ...]:
+        self._check("actuator names")
+        return self.actuator_names
+
+    def get_actuator_joint_names(self) -> tuple[str, ...]:
+        self._check("actuator target joints")
+        return self.actuator_joint_names
+
+    def get_actuator_ctrl_range(self) -> np.ndarray:
+        self.calls["actuator range"] += 1
+        return np.arange(10, dtype=np.float32).reshape(5, 2)
+
+    def get_dof_pos(self) -> np.ndarray:
+        self._check("joint position state")
+        return self.dof_pos
+
+    def get_default_dof_pos(self) -> np.ndarray:
+        self._check("default joint position")
+        return np.arange(5, dtype=np.float32) + 10.0
+
+    def get_dof_vel(self) -> np.ndarray:
+        self._check("joint velocity state")
+        return self.dof_vel
+
+    def get_joint_range(self) -> np.ndarray:
+        self._check("joint position limits")
+        return self.joint_range
+
+    def get_body_pos_w(self, ids: np.ndarray) -> np.ndarray:
+        self._check("body position state")
+        return self.body_pos[:, ids]
+
+    def get_body_quat_w(self, ids: np.ndarray) -> np.ndarray:
+        self._check("body quaternion state")
+        return self.body_quat[:, ids]
+
+    def get_body_lin_vel_w(self, ids: np.ndarray) -> np.ndarray:
+        self._check("body linear velocity state")
+        return self.body_lin_vel[:, ids]
+
+    def get_body_ang_vel_w(self, ids: np.ndarray) -> np.ndarray:
+        self._check("body angular velocity state")
+        return self.body_ang_vel[:, ids]
+
+    def copy_body_state_w(
+        self,
+        ids: np.ndarray,
+        out_pos: np.ndarray,
+        out_quat: np.ndarray,
+        out_lin_vel: np.ndarray,
+        out_ang_vel: np.ndarray,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+        self._check("body state copy")
+        np.take(self.body_pos, ids, axis=1, out=out_pos)
+        np.take(self.body_quat, ids, axis=1, out=out_quat)
+        np.take(self.body_lin_vel, ids, axis=1, out=out_lin_vel)
+        np.take(self.body_ang_vel, ids, axis=1, out=out_ang_vel)
+        return out_pos, out_quat, out_lin_vel, out_ang_vel
+
+    def get_body_lin_vel_b(self, ids: np.ndarray) -> np.ndarray:
+        self._check("body-frame linear velocity state")
+        return self.body_lin_vel_b[:, ids]
+
+    def get_body_ang_vel_b(self, ids: np.ndarray) -> np.ndarray:
+        self._check("body-frame angular velocity state")
+        return self.body_ang_vel_b[:, ids]
+
+
+def _scene(backend_type: str = "mujoco") -> tuple[_StrictBackendProfile, EntityScene]:
+    backend = _StrictBackendProfile(backend_type)
+    cfg = SceneCfg(
+        model_file="unused.xml",
+        entities={
+            "robot": EntityCfg(
+                root_body_name="base",
+                joint_names=("ankle", "hip"),
+                body_names=("foot", "base"),
+                geom_names=("foot_collision", "base_collision"),
+                site_names=("imu",),
+                actuator_names=("ankle", "hip"),
+            )
+        },
+    )
+    return backend, EntityScene.from_scene_cfg(cfg, cast(SimBackend, backend))
+
+
+@pytest.mark.parametrize("backend_type", ["mujoco", "motrix", "drake"])
+def test_backend_profiles_materialize_identical_local_entity_contract(backend_type: str) -> None:
+    backend, scene = _scene(backend_type)
+    robot = scene["robot"]
+
+    assert robot.joint_names == ("ankle", "hip")
+    assert robot.body_names == ("foot", "base")
+    np.testing.assert_array_equal(robot.data.joint_pos, backend.dof_pos[:, [4, 2]])
+    np.testing.assert_array_equal(robot.data.joint_vel, backend.dof_vel[:, [4, 2]])
+    np.testing.assert_array_equal(robot.data.default_joint_vel, 0.0)
+    np.testing.assert_array_equal(robot.data.soft_joint_pos_limits, backend.joint_range[[4, 2]])
+    np.testing.assert_array_equal(robot.data.gravity_vec_w, [[0.0, 0.0, -1.0]] * 3)
+    np.testing.assert_array_equal(robot.data.joint_pos_biased, backend.dof_pos[:, [4, 2]])
+    np.testing.assert_array_equal(robot.data.body_link_pos_w, backend.body_pos[:, [7, 4]])
+    np.testing.assert_array_equal(robot.data.root_link_pos_w, backend.body_pos[:, 4])
+    np.testing.assert_array_equal(robot.data.root_link_lin_vel_b, backend.body_lin_vel_b[:, 4])
+    np.testing.assert_array_equal(robot.data.root_link_ang_vel_b, backend.body_ang_vel_b[:, 4])
+    np.testing.assert_array_equal(robot.data.heading_w, 0.0)
+    np.testing.assert_array_equal(robot.data.projected_gravity_b, [[0.0, 0.0, -1.0]] * 3)
+    np.testing.assert_array_equal(
+        robot.data.default_root_state,
+        np.tile([1.0, 2.0, 3.0, 1.0, 0.0, 0.0, 0.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0], (3, 1)),
+    )
+    assert not robot.data.default_root_state.flags.writeable
+    assert not robot.data.soft_joint_pos_limits.flags.writeable
+    assert not robot.data.gravity_vec_w.flags.writeable
+    np.testing.assert_array_equal(
+        robot.data.actuator_ctrl_range,
+        np.arange(10, dtype=np.float32).reshape(5, 2)[[4, 2]],
+    )
+
+
+def test_body_state_copy_binding_freezes_local_to_backend_mapping() -> None:
+    backend, scene = _scene()
+    robot = scene["robot"]
+    copy_body_state = robot.bind_body_state_copy(np.asarray([1, 0], dtype=np.int32))
+    before = backend.calls.copy()
+    out_pos = np.empty((backend.num_envs, 2, 3), dtype=np.float32)
+    out_quat = np.empty((backend.num_envs, 2, 4), dtype=np.float32)
+    out_lin_vel = np.empty_like(out_pos)
+    out_ang_vel = np.empty_like(out_pos)
+
+    result = copy_body_state(out_pos, out_quat, out_lin_vel, out_ang_vel)
+
+    assert result == (out_pos, out_quat, out_lin_vel, out_ang_vel)
+    np.testing.assert_array_equal(out_pos, backend.body_pos[:, [4, 7]])
+    np.testing.assert_array_equal(out_quat, backend.body_quat[:, [4, 7]])
+    np.testing.assert_array_equal(out_lin_vel, backend.body_lin_vel[:, [4, 7]])
+    np.testing.assert_array_equal(out_ang_vel, backend.body_ang_vel[:, [4, 7]])
+    assert backend.calls["body state copy"] == before["body state copy"] + 1
+    for capability in (
+        "body position state",
+        "body quaternion state",
+        "body linear velocity state",
+        "body angular velocity state",
+    ):
+        assert backend.calls[capability] == before[capability]
+
+
+@pytest.mark.parametrize(
+    ("body_ids", "error_type", "message"),
+    [
+        (np.asarray([], dtype=np.int32), ValueError, "selected no bodies"),
+        ([2], IndexError, "out of range"),
+        ([0, 0], ValueError, "contain duplicates"),
+        ([True], TypeError, "1-D integer array"),
+    ],
+)
+def test_body_state_copy_binding_rejects_invalid_local_ids(
+    body_ids: Any, error_type: type[Exception], message: str
+) -> None:
+    _, scene = _scene()
+    with pytest.raises(error_type, match=message):
+        scene["robot"].bind_body_state_copy(body_ids)
+
+
+def test_state_read_cache_is_scoped_shared_and_explicitly_invalidated() -> None:
+    backend, scene = _scene()
+    robot = scene["robot"]
+    before = backend.calls.copy()
+
+    with scene._scoped_state_reads():
+        np.testing.assert_array_equal(robot.data.joint_pos, robot.data.joint_pos)
+        np.testing.assert_array_equal(robot.data.joint_vel, robot.data.joint_vel)
+        np.testing.assert_array_equal(robot.data.heading_w, robot.data.projected_gravity_b[:, 0])
+        np.testing.assert_array_equal(robot.data.root_link_pos_w, robot.data.root_link_pos_w)
+        np.testing.assert_array_equal(robot.data.body_link_pos_w, robot.data.body_link_pos_w)
+
+        assert backend.calls["joint position state"] == before["joint position state"] + 1
+        assert backend.calls["joint velocity state"] == before["joint velocity state"] + 1
+        assert backend.calls["body quaternion state"] == before["body quaternion state"] + 1
+        # Root and full-body selectors remain distinct cache entries.
+        assert backend.calls["body position state"] == before["body position state"] + 2
+
+        scene._invalidate_state_reads()
+        _ = robot.data.joint_pos
+        assert backend.calls["joint position state"] == before["joint position state"] + 2
+
+    # Reads outside the env-owned phase retain the previous per-access behavior.
+    _ = robot.data.joint_pos
+    _ = robot.data.joint_pos
+    assert backend.calls["joint position state"] == before["joint position state"] + 4
+
+
+def test_state_read_cache_scope_closes_after_exception() -> None:
+    backend, scene = _scene()
+    robot = scene["robot"]
+    before = backend.calls["joint position state"]
+
+    with pytest.raises(RuntimeError, match="term failed"):
+        with scene._scoped_state_reads():
+            _ = robot.data.joint_pos
+            raise RuntimeError("term failed")
+
+    with scene._scoped_state_reads():
+        _ = robot.data.joint_pos
+    assert backend.calls["joint position state"] == before + 2
+
+
+def test_scene_entity_cfg_resolves_only_against_cached_names() -> None:
+    backend, scene = _scene()
+    cold_path_calls = backend.calls.copy()
+
+    cfg = SceneEntityCfg(
+        "robot",
+        joint_names=("hip", "ankle"),
+        body_names=".*",
+        geom_names="foot_.*",
+        site_names="imu",
+        actuator_names=["hip", "ankle"],
+        preserve_order=True,
+    )
+    cfg.resolve(scene)
+
+    assert cfg.joint_ids == [1, 0]
+    assert cfg.body_ids == slice(None)
+    assert cfg.geom_ids == [0]
+    assert cfg.site_ids == slice(None)
+    assert cfg.actuator_ids == [1, 0]
+    assert backend.calls == cold_path_calls
+
+    for _ in range(3):
+        scene["robot"].data.joint_pos
+        scene["robot"].data.root_link_quat_w
+    for key in (
+        "body names",
+        "joint position names",
+        "joint velocity names",
+        "site names",
+        "geom names",
+        "actuator names",
+    ):
+        assert backend.calls[key] == cold_path_calls[key]
+
+
+def test_entity_control_write_uses_cached_actuator_columns_and_fails_closed() -> None:
+    backend = _StrictBackendProfile("mujoco")
+    control = np.zeros((backend.num_envs, backend.num_actuators), dtype=np.float32)
+    scene = EntityScene(
+        {"robot": EntityCfg(actuator_names=("ankle", "hip"))},
+        cast(SimBackend, backend),
+        control,
+    )
+    data = scene["robot"].data
+
+    data.write_ctrl(np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=np.float32))
+    np.testing.assert_array_equal(control[:, 4], [1.0, 3.0, 5.0])
+    np.testing.assert_array_equal(control[:, 2], [2.0, 4.0, 6.0])
+    data.write_ctrl(
+        np.array([[9.0, 8.0], [7.0, 6.0]], dtype=np.float32),
+        env_ids=np.array([2, 0], dtype=np.int32),
+    )
+    np.testing.assert_array_equal(control[:, 4], [7.0, 3.0, 9.0])
+    np.testing.assert_array_equal(control[:, 2], [6.0, 4.0, 8.0])
+    assert backend.calls["actuator names"] == 1
+
+    with pytest.raises(ValueError, match="expected shape"):
+        data.write_ctrl(np.zeros((backend.num_envs, 1), dtype=np.float32))
+    with pytest.raises(ValueError, match="NaN or Inf"):
+        data.write_ctrl(np.full((backend.num_envs, 2), np.nan, dtype=np.float32))
+    with pytest.raises(IndexError, match="out of range"):
+        data.write_ctrl(
+            np.zeros((1, 2), dtype=np.float32),
+            env_ids=np.array([backend.num_envs], dtype=np.int32),
+        )
+
+    _, read_only_scene = _scene()
+    with pytest.raises(NotImplementedError, match="actuator control write.*not materialized"):
+        read_only_scene["robot"].data.write_ctrl(np.zeros((backend.num_envs, 2), dtype=np.float32))
+
+
+def test_entity_root_writes_use_cached_layout_and_one_reset_commit() -> None:
+    backend = _StrictBackendProfile("mujoco")
+    transaction = ResetStateTransaction(cast(SimBackend, backend))
+    scene = EntityScene(
+        {"robot": EntityCfg(root_body_name="base")},
+        cast(SimBackend, backend),
+        reset_state=transaction,
+    )
+    robot = scene["robot"]
+    cold_layout_calls = backend.calls["root-state layout"]
+    half_sqrt = np.sqrt(0.5)
+
+    with transaction.scoped(np.array([0, 2], dtype=np.int32)):
+        robot.write_root_link_pose_to_sim(
+            np.array(
+                [
+                    [10.0, 11.0, 12.0, half_sqrt, 0.0, 0.0, half_sqrt],
+                    [20.0, 21.0, 22.0, 1.0, 0.0, 0.0, 0.0],
+                ],
+                dtype=np.float32,
+            ),
+            env_ids=np.array([2, 0], dtype=np.int32),
+        )
+        robot.write_root_link_velocity_to_sim(
+            np.array(
+                [[1.0, 2.0, 3.0, 1.0, 0.0, 0.0], [4.0, 5.0, 6.0, 0.0, 1.0, 2.0]],
+                dtype=np.float32,
+            ),
+            env_ids=np.array([2, 0], dtype=np.int32),
+        )
+
+    assert backend.calls["root-state layout"] == cold_layout_calls == 1
+    assert len(backend.set_state_calls) == 1
+    env_ids, qpos, qvel = backend.set_state_calls[0]
+    np.testing.assert_array_equal(env_ids, [0, 2])
+    np.testing.assert_allclose(qpos[0, 1:8], [20.0, 21.0, 22.0, 1.0, 0.0, 0.0, 0.0])
+    np.testing.assert_allclose(qpos[1, 1:8], [10.0, 11.0, 12.0, half_sqrt, 0.0, 0.0, half_sqrt])
+    np.testing.assert_allclose(qvel[0, 2:8], [4.0, 5.0, 6.0, 0.0, 1.0, 2.0])
+    np.testing.assert_allclose(qvel[1, 2:8], [1.0, 2.0, 3.0, 0.0, -1.0, 0.0], atol=1e-6)
+
+
+def test_entity_read_reset_root_pose_returns_staged_or_default_pose() -> None:
+    backend = _StrictBackendProfile("mujoco")
+    transaction = ResetStateTransaction(cast(SimBackend, backend))
+    scene = EntityScene(
+        {"robot": EntityCfg(root_body_name="base")},
+        cast(SimBackend, backend),
+        reset_state=transaction,
+    )
+    robot = scene["robot"]
+
+    with transaction.scoped(np.array([0, 2], dtype=np.int32)):
+        robot.write_root_link_pose_to_sim(
+            np.array([[10.0, 11.0, 12.0, 1.0, 0.0, 0.0, 0.0]], dtype=np.float32),
+            env_ids=np.array([2], dtype=np.int32),
+        )
+        staged = robot.read_reset_root_pose(env_ids=np.array([0, 2], dtype=np.int32))
+
+    # Env 2 reflects the staged write; env 0 the backend default root pose.
+    np.testing.assert_allclose(staged[1], [10.0, 11.0, 12.0, 1.0, 0.0, 0.0, 0.0])
+    np.testing.assert_allclose(staged[0], backend.default_qpos[1:8])
+    # Only the written row was committed.
+    assert len(backend.set_state_calls) == 1
+    np.testing.assert_array_equal(backend.set_state_calls[0][0], [2])
+
+
+def test_entity_caches_unsupported_root_layout_without_hot_path_probe() -> None:
+    backend = _StrictBackendProfile(
+        "drake",
+        unsupported=frozenset({"root-state layout"}),
+    )
+    transaction = ResetStateTransaction(cast(SimBackend, backend))
+    scene = EntityScene(
+        {"robot": EntityCfg(root_body_name="base")},
+        cast(SimBackend, backend),
+        reset_state=transaction,
+    )
+    robot = scene["robot"]
+
+    with pytest.raises(
+        NotImplementedError,
+        match="default root state.*backend 'drake'.*drake lacks root-state layout",
+    ):
+        _ = robot.data.default_root_state
+    with pytest.raises(
+        NotImplementedError,
+        match="reset root-state layout.*backend 'drake'.*drake lacks root-state layout",
+    ):
+        with transaction.scoped(np.array([0], dtype=np.int32)):
+            robot.write_root_state_to_sim(
+                np.array([[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]),
+                env_ids=np.array([0], dtype=np.int32),
+            )
+    assert backend.calls["root-state layout"] == 1
+    assert backend.set_state_calls == []
+
+
+def test_entity_joint_position_target_maps_natural_joint_order_to_control_order() -> None:
+    backend = _StrictBackendProfile("mujoco")
+    control = np.zeros((backend.num_envs, backend.num_actuators), dtype=np.float32)
+    scene = EntityScene(
+        {
+            "robot": EntityCfg(
+                joint_names=("ankle", "hip", "knee"),
+                actuator_names=("ankle", "hip"),
+            )
+        },
+        cast(SimBackend, backend),
+        control,
+    )
+    robot = scene["robot"]
+
+    ids, names = robot.find_joints_by_actuator_names(".*")
+    assert ids == [0, 1]
+    assert names == ["ankle", "hip"]
+    np.testing.assert_array_equal(
+        robot.data.default_joint_pos,
+        np.asarray([[14.0, 12.0, 10.0]] * backend.num_envs, dtype=np.float32),
+    )
+    np.testing.assert_array_equal(robot.data.encoder_bias, 0.0)
+
+    targets = np.asarray([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=np.float32)
+    robot.set_joint_position_target(targets, joint_ids=np.asarray(ids, dtype=np.int32))
+    np.testing.assert_array_equal(control[:, 4], targets[:, 0])
+    np.testing.assert_array_equal(control[:, 2], targets[:, 1])
+    np.testing.assert_array_equal(control[:, [0, 1, 3]], 0.0)
+
+    with pytest.raises(NotImplementedError, match="passive joints.*knee"):
+        robot.set_joint_position_target(
+            np.ones((backend.num_envs, 1), dtype=np.float32),
+            joint_ids=np.asarray([2], dtype=np.int32),
+        )
+
+
+@pytest.mark.parametrize(
+    ("actuator_joint_names", "joint_names", "message"),
+    [
+        (("hip", "unused", "hip", "unused_2", "knee"), ("hip",), "must be unique"),
+        (
+            ("knee", "unused_joint", "hip", "unused_2_joint", "ankle"),
+            ("hip",),
+            "outside its declared joint partition.*knee",
+        ),
+    ],
+)
+def test_entity_joint_actuator_mapping_rejects_ambiguous_or_missing_targets(
+    actuator_joint_names: tuple[str, ...],
+    joint_names: tuple[str, ...],
+    message: str,
+) -> None:
+    backend = _StrictBackendProfile("mujoco")
+    backend.actuator_joint_names = actuator_joint_names
+
+    with pytest.raises(ValueError, match=message):
+        EntityScene(
+            {
+                "robot": EntityCfg(
+                    joint_names=joint_names,
+                    actuator_names=("knee", "hip"),
+                )
+            },
+            cast(SimBackend, backend),
+        )
+
+
+@pytest.mark.parametrize(
+    ("ids", "error_type", "message"),
+    [
+        ([-1], ValueError, "EntityCfg entity 'robot' joint selector.*out of range"),
+        ([2], ValueError, "EntityCfg entity 'robot' joint selector.*out of range"),
+        ([True], TypeError, "EntityCfg entity 'robot' joint selector.*must be integers"),
+        (["0"], TypeError, "EntityCfg entity 'robot' joint selector.*must be integers"),
+    ],
+)
+def test_scene_entity_cfg_rejects_invalid_ids(ids, error_type, message: str) -> None:
+    _, scene = _scene()
+    cfg = SceneEntityCfg("robot", joint_ids=ids)
+    with pytest.raises(error_type, match=message):
+        cfg.resolve(scene)
+
+
+def test_scene_entity_cfg_reports_entity_for_invalid_regex() -> None:
+    _, scene = _scene()
+    with pytest.raises(
+        ValueError,
+        match="SceneEntityCfg entity 'robot' joint selector.*Invalid entity selector regex",
+    ):
+        SceneEntityCfg("robot", joint_names="[").resolve(scene)
+
+
+def test_missing_entity_namespace_and_backend_capability_fail_closed() -> None:
+    backend = _StrictBackendProfile("drake", unsupported=frozenset({"actuator names"}))
+    with pytest.raises(
+        NotImplementedError,
+        match="Entity 'robot'.*actuator.*backend 'drake'",
+    ):
+        EntityScene(
+            {"robot": EntityCfg(actuator_names=("hip",))},
+            cast(SimBackend, backend),
+        )
+
+    _, scene = _scene("motrix")
+    with pytest.raises(
+        NotImplementedError,
+        match="Entity 'robot'.*tendon.*backend 'motrix'",
+    ):
+        SceneEntityCfg("robot", tendon_names=".*").resolve(scene)
+
+    sparse = EntityScene(
+        {"robot": EntityCfg(joint_names=("hip",))},
+        cast(SimBackend, _StrictBackendProfile("mujoco")),
+    )
+    with pytest.raises(NotImplementedError, match="body.*not declared"):
+        SceneEntityCfg("robot", body_names=".*").resolve(sparse)
+
+    state_missing = _StrictBackendProfile("mujoco", unsupported=frozenset({"body position state"}))
+    with pytest.raises(
+        NotImplementedError,
+        match="Entity 'robot'.*body position state.*backend 'mujoco'",
+    ):
+        EntityScene(
+            {"robot": EntityCfg(root_body_name="base")},
+            cast(SimBackend, state_missing),
+        )
+
+
+def test_entity_declaration_rejects_coercion_and_duplicate_names() -> None:
+    backend = cast(SimBackend, _StrictBackendProfile("mujoco"))
+    with pytest.raises(TypeError, match="sequence of strings, not a scalar"):
+        EntityScene(
+            {"robot": EntityCfg(joint_names=cast(Any, "hip"))},
+            backend,
+        )
+    with pytest.raises(TypeError, match="joint names must be strings"):
+        EntityScene(
+            {"robot": EntityCfg(joint_names=cast(Any, ("hip", 1)))},
+            backend,
+        )
+    with pytest.raises(ValueError, match="joint names must be unique"):
+        EntityScene(
+            {"robot": EntityCfg(joint_names=("hip", "hip"))},
+            backend,
+        )
+
+
+def test_manager_resolution_error_has_manager_term_entity_capability_and_backend() -> None:
+    _, scene = _scene("drake")
+    env = SimpleNamespace(
+        num_envs=3,
+        scene=scene,
+        rng=np.random.default_rng(7),
+        max_episode_length_s=2.0,
+    )
+
+    with pytest.raises(
+        NotImplementedError,
+        match=(
+            "RewardManager term 'unsupported' parameter 'asset_cfg'.*"
+            "Entity 'robot'.*tendon.*backend 'drake'"
+        ),
+    ):
+        RewardManager(
+            {
+                "unsupported": RewardTermCfg(
+                    func=lambda env, asset_cfg: np.zeros(env.num_envs),
+                    weight=1.0,
+                    params={"asset_cfg": SceneEntityCfg("robot", tendon_names=".*")},
+                )
+            },
+            cast(Any, env),
+        )
+
+
+def test_entity_facade_has_no_backend_model_or_asset_access() -> None:
+    tree = ast.parse(inspect.getsource(entity_module))
+    accessed_attributes = {node.attr for node in ast.walk(tree) if isinstance(node, ast.Attribute)}
+    assert "model" not in accessed_attributes
+    assert "scene_model_file" not in accessed_attributes
+
+
+def test_real_mujoco_entity_selector_and_numpy_state_smoke() -> None:
+    from unisim.backend.mujoco.backend import MuJoCoBackend
+
+    joint_names = (
+        "FL_hip_joint",
+        "FL_thigh_joint",
+        "FL_calf_joint",
+        "FR_hip_joint",
+        "FR_thigh_joint",
+        "FR_calf_joint",
+        "RL_hip_joint",
+        "RL_thigh_joint",
+        "RL_calf_joint",
+        "RR_hip_joint",
+        "RR_thigh_joint",
+        "RR_calf_joint",
+    )
+    actuator_names = (
+        "FR_hip",
+        "FR_thigh",
+        "FR_calf",
+        "FL_hip",
+        "FL_thigh",
+        "FL_calf",
+        "RR_hip",
+        "RR_thigh",
+        "RR_calf",
+        "RL_hip",
+        "RL_thigh",
+        "RL_calf",
+    )
+    scene_cfg = SceneCfg(
+        model_file=str(ASSETS_ROOT_PATH / "robots" / "go2" / "scene_flat.xml"),
+        entities={
+            "robot": EntityCfg(
+                root_body_name="base",
+                joint_names=joint_names,
+                body_names=("base",),
+                actuator_names=actuator_names,
+            )
+        },
+    )
+    backend = MuJoCoBackend(
+        scene_cfg,
+        num_envs=2,
+        sim_dt=0.01,
+        base_name="base",
+        add_body_sensors=True,
+    )
+    backend.materialize()
+    scene = EntityScene.from_scene_cfg(scene_cfg, backend)
+
+    selector = SceneEntityCfg("robot", joint_names=".*_calf_joint")
+    selector.resolve(scene)
+    assert selector.joint_ids == [2, 5, 8, 11]
+    assert scene["robot"].data.joint_pos.shape == (2, 12)
+    assert scene["robot"].data.root_link_pose_w.shape == (2, 7)
+    assert scene["robot"].data.root_link_lin_vel_b.shape == (2, 3)
+    assert scene["robot"].data.root_link_ang_vel_b.shape == (2, 3)
+    assert scene["robot"].data.heading_w.shape == (2,)
+    assert scene["robot"].data.projected_gravity_b.shape == (2, 3)
+    assert scene["robot"].data.default_joint_vel.shape == (2, 12)
+    assert np.isfinite(scene["robot"].data.root_link_lin_vel_b).all()
+    assert np.isfinite(scene["robot"].data.root_link_ang_vel_b).all()
+    assert np.isfinite(scene["robot"].data.heading_w).all()
+    assert np.isfinite(scene["robot"].data.projected_gravity_b).all()
+    np.testing.assert_array_equal(scene["robot"].data.default_joint_vel, 0.0)
+
+
+def test_scene_cfg_entity_defaults_are_not_shared() -> None:
+    first = SceneCfg(model_file="first.xml")
+    second = SceneCfg(model_file="second.xml")
+    first.entities["robot"] = EntityCfg()
+    assert second.entities == {}
+
+
+def test_scene_exposes_read_only_community_entities_and_zero_origins() -> None:
+    _, scene = _scene()
+
+    assert scene.entities["robot"] is scene["robot"]
+    with pytest.raises(TypeError):
+        scene.entities["other"] = scene["robot"]  # type: ignore[index]
+
+    assert scene.env_origins.shape == (3, 3)
+    np.testing.assert_array_equal(scene.env_origins, 0.0)
+    with pytest.raises(ValueError, match="read-only"):
+        scene.env_origins[0, 0] = 1.0
diff --git a/tests/base/test_genesis_backend.py b/tests/base/test_genesis_backend.py
new file mode 100644
index 000000000..561f48cd5
--- /dev/null
+++ b/tests/base/test_genesis_backend.py
@@ -0,0 +1,760 @@
+"""Host-free tests for the ``genesis`` backend using a fake Genesis runtime.
+
+These tests run on machines WITHOUT the genesis-world extra (and without
+CUDA): the fake runtime in ``genesis_fake_runtime.py`` mirrors the 1.3.3 API
+surface the adapter consumes, while the adapter's real cold-path MJCF scan
+(the ``mujoco`` package) runs against a tiny inline scene document.  Real
+runtime coverage lives in the slow lanes (``test_genesis_runtime.py`` and the
+genesis parameterization of ``test_backend_conformance.py``).
+"""
+
+from __future__ import annotations
+
+import sys
+from importlib import metadata as importlib_metadata
+from pathlib import Path
+from types import SimpleNamespace
+
+import mujoco
+import numpy as np
+import pytest
+import torch
+import unisim.backend.genesis.dependencies as genesis_dependencies
+import unisim.backend.genesis.materialization as genesis_materialization
+from unisim.backend.base import RenderClosedError
+from unisim.backend.genesis.dependencies import (
+    GenesisDependencies,
+    GenesisDependencyError,
+    genesis_dependencies_available,
+    load_genesis_dependencies,
+)
+from unisim.backend.genesis.materialization import preserve_torch_globals
+from unisim.dr.types import (
+    GeomSizeOverride,
+    InitRandomizationPlan,
+    IntervalRandomizationPlan,
+    ModelVariantSpec,
+    ResetRandomizationPayload,
+)
+
+from unilab.base.backend_factory import create_backend, env_backend_kwargs
+from unilab.base.base import EnvCfg
+from unilab.base.scene import SceneCfg
+
+from .genesis_fake_runtime import ACTUATED_DOFS, N_LINKS, NQ, NV, make_fake_genesis
+
+# Must stay in sync with the model spec in genesis_fake_runtime.py.
+TINY_MODEL_XML = """
+<mujoco model="tiny">
+  <compiler angle="radian"/>
+  <option integrator="implicitfast" timestep="0.005"/>
+  <worldbody>
+    <geom name="floor" type="plane" size="0 0 0.05" friction="1.0"/>
+    <body name="pelvis" pos="0 0 0.8">
+      <freejoint name="floating_base_joint"/>
+      <site name="imu_site" pos="0.05 0 -0.08"/>
+      <geom name="pelvis_geom" type="capsule" size="0.05 0.1" mass="5.0" friction="0.9"/>
+      <body name="thigh" pos="0 0 -0.3">
+        <joint name="joint_a" type="hinge" axis="0 1 0" range="-0.5 0.5"/>
+        <geom name="thigh_geom" type="capsule" size="0.04 0.1" mass="2.0" friction="0.8"/>
+        <body name="shank" pos="0 0 -0.3">
+          <joint name="joint_b" type="hinge" axis="0 1 0" range="-0.6 0.6"/>
+          <geom name="foot_geom" type="sphere" size="0.05" mass="1.0" friction="0.6"/>
+          <site name="foot_site" pos="0.02 0 -0.03"/>
+        </body>
+      </body>
+    </body>
+  </worldbody>
+  <actuator>
+    <position name="act_a" joint="joint_a" kp="10" kv="2" ctrlrange="-1 1"/>
+    <position name="act_b" joint="joint_b" kp="20" kv="3" ctrlrange="-2 2"/>
+  </actuator>
+  <sensor>
+    <velocimeter site="imu_site" name="base_linvel"/>
+    <gyro site="imu_site" name="base_gyro"/>
+    <accelerometer site="imu_site" name="base_acc"/>
+    <framezaxis objtype="site" objname="imu_site" name="base_up"/>
+    <framepos objtype="site" objname="foot_site" name="foot_pos"/>
+    <framequat objtype="site" objname="foot_site" name="foot_quat"/>
+    <contact name="foot_contact" geom1="floor" geom2="foot_geom" data="found" num="1" reduce="mindist"/>
+  </sensor>
+  <keyframe>
+    <key name="stand" qpos="0 0 0.8 1 0 0 0 0.1 -0.2"/>
+  </keyframe>
+</mujoco>
+"""
+
+
+@pytest.fixture
+def tiny_model_file(tmp_path: Path) -> str:
+    path = tmp_path / "tiny.xml"
+    path.write_text(TINY_MODEL_XML, encoding="utf-8")
+    return str(path)
+
+
+@pytest.fixture
+def fake_genesis(monkeypatch: pytest.MonkeyPatch):
+    fake = make_fake_genesis()
+    deps = GenesisDependencies(genesis=fake, torch=torch, mujoco=mujoco)
+    monkeypatch.setattr(genesis_dependencies, "load_genesis_dependencies", lambda: deps)
+    # The backend resolves the viewer class via importlib.import_module; the
+    # fake module must already sit in sys.modules (genesis is not installed
+    # in this lane).
+    monkeypatch.setitem(sys.modules, "genesis.vis.viewer", fake.viewer_module)
+    genesis_materialization._reset_session_state_for_tests()
+    yield fake
+    genesis_materialization._reset_session_state_for_tests()
+
+
+def _backend(model_file: str, num_envs: int = 4, **kwargs):
+    backend = create_backend(
+        "genesis",
+        SceneCfg(model_file=model_file),
+        num_envs,
+        0.005,
+        base_name="pelvis",
+        **kwargs,
+    )
+    backend.materialize()
+    return backend
+
+
+def _stand_state(backend, rows: int) -> tuple[np.ndarray, np.ndarray]:
+    qpos = np.tile(backend.get_keyframe_qpos("stand"), (rows, 1)).astype(np.float32)
+    qvel = np.zeros((rows, backend.get_init_qvel().size), dtype=np.float32)
+    return qpos, qvel
+
+
+def test_dependency_boundary_errors_are_actionable(monkeypatch: pytest.MonkeyPatch) -> None:
+    def _raise(_name: str) -> str:
+        raise importlib_metadata.PackageNotFoundError("genesis-world")
+
+    monkeypatch.setattr(importlib_metadata, "version", _raise)
+    with pytest.raises(GenesisDependencyError, match="uv sync --extra genesis"):
+        load_genesis_dependencies()
+    monkeypatch.setattr(importlib_metadata, "version", lambda _name: "0.0.0")
+    with pytest.raises(GenesisDependencyError, match="exact genesis-world version"):
+        load_genesis_dependencies()
+    monkeypatch.setattr(genesis_dependencies, "find_spec", lambda name: None)
+    assert genesis_dependencies_available() is False
+
+
+def test_preserve_torch_globals_restores_device_dtype_and_rng() -> None:
+    torch.set_default_device("cpu")
+    torch.set_default_dtype(torch.float32)
+    torch.manual_seed(1234)
+    rng_before = torch.get_rng_state().clone()
+    with preserve_torch_globals(torch):
+        # Simulate the measured gs.init pollution (REPORT §3.5 [10]).
+        torch.set_default_dtype(torch.float64)
+        torch.rand(8)
+    assert torch.get_default_dtype() is torch.float32
+    assert str(torch.get_default_device()) == "cpu"
+    assert torch.equal(torch.get_rng_state(), rng_before)
+
+
+def test_session_lifecycle_and_reinit_guard(fake_genesis, tiny_model_file: str) -> None:
+    first = _backend(tiny_model_file)
+    second = _backend(tiny_model_file)
+    assert fake_genesis.init_count == 1  # backends share the process-wide session
+    first.materialize()  # idempotent: explicit call after the initial build is a no-op
+    assert first._scene.build_count == 1
+    first.close()
+    assert fake_genesis.destroy_count == 1
+    with pytest.raises(RuntimeError, match="exactly one gs.init per process"):
+        _backend(tiny_model_file)
+    with pytest.raises(RuntimeError, match="genesis backend is closed"):
+        first.step(np.zeros((4, 2), dtype=np.float32))
+    first.materialize()  # no-op when already materialized, even after close
+    second.close()
+    assert fake_genesis.destroy_count == 1  # idempotent: session already destroyed
+
+
+def test_cold_metadata_matches_mjcf_scan(fake_genesis, tiny_model_file: str) -> None:
+    backend = _backend(tiny_model_file)
+    assert backend.backend_type == "genesis"
+    assert backend.num_envs == 4
+    assert backend.num_actuators == 2
+    assert backend.num_dof_vel == 2
+    assert backend.get_actuator_names() == ("act_a", "act_b")
+    assert backend.get_actuator_joint_names() == ("joint_a", "joint_b")
+    np.testing.assert_array_equal(
+        backend.get_actuator_ctrl_range(), np.array([[-1.0, 1.0], [-2.0, 2.0]], np.float32)
+    )
+    kp, kd = backend.get_actuator_gains()
+    np.testing.assert_allclose(kp, [10.0, 20.0])
+    np.testing.assert_allclose(kd, [2.0, 3.0])
+    np.testing.assert_allclose(backend.get_joint_range(), [[-0.5, 0.5], [-0.6, 0.6]])
+    np.testing.assert_allclose(backend.get_gravity(), [0.0, 0.0, -9.81])
+    np.testing.assert_allclose(backend.get_dof_armature(), np.zeros(NV), atol=1e-6)
+    np.testing.assert_allclose(backend.get_body_mass()[1:], [5.0, 2.0, 1.0], atol=1e-4)
+
+    layout = backend.get_root_state_layout("pelvis")
+    assert layout.qpos_indices == tuple(range(7))
+    assert layout.qvel_indices == tuple(range(6))
+    with pytest.raises(NotImplementedError, match="exactly one free joint"):
+        backend.get_root_state_layout("thigh")
+    with pytest.raises(ValueError, match="not found"):
+        backend.get_root_state_layout("missing")
+
+    np.testing.assert_allclose(backend.get_keyframe_qpos("stand")[7:], [0.1, -0.2])
+    with pytest.raises(ValueError, match="Keyframe 'home' not found"):
+        backend.get_keyframe_qpos("home")
+    np.testing.assert_allclose(backend.get_default_qpos()[:3], [0.0, 0.0, 0.8])
+    np.testing.assert_allclose(backend.get_default_dof_pos(), [0.0, 0.0], atol=1e-6)
+    assert backend.get_init_qvel().shape == (NV,)
+    np.testing.assert_allclose(backend.get_dof_pos()[0], backend.get_default_dof_pos())
+    np.testing.assert_array_equal(backend.get_body_ids(["pelvis", "shank"]), [1, 3])
+    np.testing.assert_array_equal(backend.get_joint_dof_indices(["joint_b"]), [7])
+    np.testing.assert_array_equal(backend.get_joint_dof_pos_indices(["joint_b"]), [1])
+    np.testing.assert_array_equal(backend.get_joint_state_qpos_indices(["joint_a"]), [7])
+
+    contype, conaffinity = backend.get_geom_contact_masks()
+    assert contype.dtype == np.int32 and conaffinity.dtype == np.int32
+    assert contype.shape == (4,) and backend.get_scene_model_file() == tiny_model_file
+
+
+def test_import_cross_check_fails_closed(fake_genesis, tiny_model_file: str) -> None:
+    backend = create_backend(
+        "genesis", SceneCfg(model_file=tiny_model_file), 2, 0.005, base_name="pelvis"
+    )
+    joints = backend._entity.joints
+    backend._entity.joints = [joints[0], joints[2], joints[1]]
+    with pytest.raises(RuntimeError, match="import mismatch: joint names"):
+        backend.materialize()
+
+
+def test_unmappable_sensor_type_fails_closed_at_scan(fake_genesis) -> None:
+    model = mujoco.MjModel.from_xml_string(
+        "<mujoco><worldbody><body name='b' pos='0 0 1'><freejoint/><geom size='0.1' mass='1'/>"
+        "</body></worldbody><sensor><subtreelinvel name='sv' body='b'/></sensor></mujoco>"
+    )
+    with pytest.raises(NotImplementedError, match="cannot map MJCF sensor 'sv'"):
+        genesis_materialization._scan_sensor_plans(mujoco, model)
+
+
+def test_control_target_held_across_substeps(fake_genesis, tiny_model_file: str) -> None:
+    backend = _backend(tiny_model_file)
+    entity = backend._entity
+    qpos, qvel = _stand_state(backend, 4)
+    backend.set_state(np.arange(4, dtype=np.int32), qpos, qvel)
+
+    ctrl = np.tile(np.array([[0.3, -0.3]], dtype=np.float32), (4, 1))
+    result = backend.step(ctrl, nsteps=3)
+    assert set(result["timing"]) == {"physics_ms", "host_cache_refresh_ms"}
+    assert len(entity.control_calls) == 1  # target pushed once, held across substeps
+    assert entity.step_count == 3
+    dof_pos = backend.get_dof_pos()
+    assert dof_pos.shape == (4, 2) and dof_pos.dtype == np.float32
+    assert np.isfinite(dof_pos).all()
+    assert np.all(dof_pos[:, 0] > 0.1)  # PD pulled joint_a toward 0.3
+    assert np.all(dof_pos[:, 1] < -0.2)
+    np.testing.assert_allclose(entity.control_calls[0], ctrl, atol=1e-6)
+
+    with pytest.raises(ValueError, match="ctrl must have shape"):
+        backend.step(np.zeros((4, 3), dtype=np.float32))
+    with pytest.raises(ValueError, match="nsteps must be a positive integer"):
+        backend.step(ctrl, nsteps=0)
+
+
+def test_pre_step_control_runs_per_substep(fake_genesis, tiny_model_file: str) -> None:
+    backend = _backend(tiny_model_file)
+    entity = backend._entity
+    calls: list[np.ndarray] = []
+
+    def convert(owner, ctrl: np.ndarray) -> np.ndarray:
+        assert owner is backend
+        calls.append(ctrl.copy())
+        return np.zeros_like(ctrl)
+
+    backend.set_pre_step_control(convert)
+    ctrl = np.full((4, 2), 0.25, dtype=np.float32)
+    backend.step(ctrl, nsteps=4)
+    assert len(calls) == 4  # conversion runs before every physics substep
+    assert len(entity.control_calls) == 4
+    np.testing.assert_allclose(entity.control_calls[-1], np.zeros((4, 2)), atol=1e-6)
+
+    backend.set_pre_step_control(lambda _owner, c: np.zeros((4, 3), dtype=c.dtype))
+    with pytest.raises(ValueError, match="pre-step control must return shape"):
+        backend.step(ctrl)
+    backend.set_pre_step_control(None)
+    backend.step(ctrl)
+    assert len(entity.control_calls) == 5
+
+
+def test_set_state_subset_reset_isolated(fake_genesis, tiny_model_file: str) -> None:
+    backend = _backend(tiny_model_file)
+    qpos, qvel = _stand_state(backend, 4)
+    qpos[:, 0] = np.array([-0.3, -0.1, 0.1, 0.3], dtype=np.float32)
+    qvel[:, 0] = np.array([0.1, 0.2, 0.3, 0.4], dtype=np.float32)
+    backend.set_state(np.arange(4, dtype=np.int32), qpos, qvel)
+    previous_pos = backend.get_base_pos().copy()
+    previous_vel = backend.get_base_lin_vel().copy()
+
+    rows = np.array([3, 1], dtype=np.int32)
+    reset_qpos, reset_qvel = _stand_state(backend, 2)
+    result = backend.set_state(rows, reset_qpos, reset_qvel)
+    timing = result["timing"]
+    assert timing["set_state_reset_upload_ms"] >= 0.0
+    assert timing["set_state_host_cache_refresh_ms"] >= 0.0
+    assert "set_state_internal_gap_ms" in timing
+
+    np.testing.assert_allclose(backend.get_base_pos()[rows, 2], 0.8, atol=1e-5)
+    np.testing.assert_allclose(backend.get_dof_pos()[rows], [[0.1, -0.2], [0.1, -0.2]], atol=1e-6)
+    complement = np.array([0, 2], dtype=np.int32)
+    np.testing.assert_allclose(backend.get_base_pos()[complement], previous_pos[complement])
+    np.testing.assert_allclose(backend.get_base_lin_vel()[complement], previous_vel[complement])
+    assert np.isfinite(backend.get_dof_vel()).all()
+
+    with pytest.raises(ValueError, match="duplicate rows"):
+        backend.set_state(np.array([1, 1], dtype=np.int32), reset_qpos, reset_qvel)
+    with pytest.raises(ValueError, match="must be in \\[0, 4\\)"):
+        backend.set_state(np.array([9], dtype=np.int32), reset_qpos[:1], reset_qvel[:1])
+    with pytest.raises(ValueError, match="qpos must have shape"):
+        backend.set_state(rows, reset_qpos[:, :5], reset_qvel)
+    empty = backend.set_state(
+        np.array([], dtype=np.int32),
+        np.zeros((0, NQ), dtype=np.float32),
+        np.zeros((0, NV), dtype=np.float32),
+    )
+    assert all(value == 0.0 for value in empty["timing"].values())
+
+
+def test_base_velocity_frame_conventions(fake_genesis, tiny_model_file: str) -> None:
+    backend = _backend(tiny_model_file, num_envs=1)
+    qpos, qvel = _stand_state(backend, 1)
+    half = np.sqrt(0.5)
+    qpos[0, 3:7] = [half, half, 0.0, 0.0]  # 90 deg about x (wxyz)
+    qvel[0, 0:3] = [1.0, 2.0, 3.0]
+    qvel[0, 3:6] = [0.0, 0.0, 1.0]  # body-frame angular velocity
+    backend.set_state(np.array([0], dtype=np.int32), qpos, qvel)
+
+    np.testing.assert_allclose(backend.get_base_lin_vel()[0], [1.0, 2.0, 3.0], atol=1e-6)
+    # World-frame angular velocity: R_x90 @ [0,0,1] = [0,-1,0].
+    np.testing.assert_allclose(backend.get_base_ang_vel()[0], [0.0, -1.0, 0.0], atol=1e-6)
+    pelvis_id = backend.get_body_ids(["pelvis"])
+    np.testing.assert_allclose(
+        backend.get_body_ang_vel_w(pelvis_id)[0, 0], [0.0, -1.0, 0.0], atol=1e-6
+    )
+    # Body-frame velocities are the world-frame values rotated by R^-1:
+    # R_x90^-1 @ [1,2,3] = [1,3,-2]; the angular pair round-trips the qvel
+    # body-frame columns (REPORT §3.2 [2d] semantics).
+    np.testing.assert_allclose(
+        backend.get_body_lin_vel_b(pelvis_id)[0, 0], [1.0, 3.0, -2.0], atol=1e-6
+    )
+    np.testing.assert_allclose(
+        backend.get_body_ang_vel_b(pelvis_id)[0, 0], [0.0, 0.0, 1.0], atol=1e-6
+    )
+    # Gyro reports body/sensor-frame components: identity site frame recovers
+    # the qvel body-frame columns.
+    np.testing.assert_allclose(backend.get_sensor_data("base_gyro")[0], [0.0, 0.0, 1.0], atol=1e-6)
+
+
+def test_body_pose_base_frame(fake_genesis, tiny_model_file: str) -> None:
+    backend = _backend(tiny_model_file, num_envs=1)
+    qpos, qvel = _stand_state(backend, 1)
+    backend.set_state(np.array([0], dtype=np.int32), qpos, qvel)
+    ids = backend.get_body_ids(["pelvis", "shank"])
+    # Root sits at its own frame origin; the fake shank rides at a fixed
+    # [0,0,-0.6] offset in the pelvis frame, and both frames align.
+    np.testing.assert_allclose(backend.get_body_pos_b(ids)[0, 0], [0.0, 0.0, 0.0], atol=1e-6)
+    np.testing.assert_allclose(backend.get_body_pos_b(ids)[0, 1], [0.0, 0.0, -0.6], atol=1e-6)
+    np.testing.assert_allclose(
+        backend.get_body_quat_b(ids)[0], [[1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0]], atol=1e-6
+    )
+
+
+def test_sensor_equivalents_from_link_state(fake_genesis, tiny_model_file: str) -> None:
+    backend = _backend(tiny_model_file, num_envs=1)
+    qpos, qvel = _stand_state(backend, 1)
+    qvel[0, 0:3] = [1.0, 2.0, 3.0]
+    qvel[0, 3:6] = [0.1, 0.2, 0.3]
+    backend.set_state(np.array([0], dtype=np.int32), qpos, qvel)
+
+    site_pos = np.array([0.05, 0.0, -0.08])
+    expected_linvel = qvel[0, 0:3] + np.cross(qvel[0, 3:6], site_pos)
+    np.testing.assert_allclose(
+        backend.get_sensor_data("base_linvel")[0], expected_linvel, atol=1e-6
+    )
+    np.testing.assert_allclose(backend.get_sensor_data("base_gyro")[0], [0.1, 0.2, 0.3], atol=1e-6)
+    np.testing.assert_allclose(backend.get_sensor_data("base_up")[0], [0.0, 0.0, 1.0], atol=1e-6)
+    np.testing.assert_allclose(
+        backend.get_sensor_data("foot_pos")[0], [0.02, 0.0, 0.8 - 0.63], atol=1e-6
+    )
+    np.testing.assert_allclose(
+        backend.get_sensor_data("foot_quat")[0], [1.0, 0.0, 0.0, 0.0], atol=1e-6
+    )
+
+    # Accelerometer reads through the registered IMUSensor equivalent.
+    imu = backend._imu_sensors["base_acc"]
+    imu.lin_acc = torch.full((1, 3), 7.5)
+    backend.step(np.zeros((1, 2), dtype=np.float32))
+    np.testing.assert_allclose(backend.get_sensor_data("base_acc")[0], [7.5, 7.5, 7.5])
+
+    # contact(found): per-link net force threshold, both directions.
+    assert backend.get_sensor_data("foot_contact")[0, 0] == 0.0
+    entity = backend._entity
+    entity.net_contact_force = torch.zeros(1, N_LINKS, 3)
+    entity.net_contact_force[0, 3, 2] = 50.0
+    backend.step(np.zeros((1, 2), dtype=np.float32))
+    assert backend.get_sensor_data("foot_contact")[0, 0] == 1.0
+    entity.net_contact_force[0, 3, 2] = 0.5
+    backend.step(np.zeros((1, 2), dtype=np.float32))
+    assert backend.get_sensor_data("foot_contact")[0, 0] == 0.0
+
+
+def test_bind_sensor_data_view(fake_genesis, tiny_model_file: str) -> None:
+    backend = _backend(tiny_model_file, num_envs=2)
+    names = ("base_gyro", "base_linvel")
+    view = backend.bind_sensor_data(names)
+    assert view.names == names and view.dimensions == (3, 3)
+    values = view.read()
+    assert values.shape == (2, 6) and np.isfinite(values).all()
+    np.testing.assert_allclose(values, backend.get_sensor_data_batch(names))
+    with pytest.raises(ValueError, match="missing_sensor"):
+        backend.bind_sensor_data(("missing_sensor",))
+    with pytest.raises(ValueError, match="Sensor 'does_not_exist' not found"):
+        backend.get_sensor_data("does_not_exist")
+
+
+def test_dr_capabilities_and_reset_randomization(fake_genesis, tiny_model_file: str) -> None:
+    backend = _backend(tiny_model_file)
+    caps = backend.get_dr_capabilities()
+    assert caps.supported_reset_terms == {"body_mass", "base_mass_delta", "kp", "kd"}
+    assert caps.supports_interval_body_force is True
+    assert caps.supports_interval_push is False
+    assert caps.supports_interval_body_velocity_delta is False
+
+    entity = backend._entity
+    rows = np.array([1, 3], dtype=np.int32)
+    qpos, qvel = _stand_state(backend, 2)
+    body_mass = np.tile(np.array([[0.0, 6.0, 2.5, 1.5]], dtype=np.float32), (2, 1))
+    payload = ResetRandomizationPayload(
+        body_mass=body_mass,
+        kp=np.full((2, 2), 55.0),
+        kd=np.full((2, 2), 6.5),
+    )
+    backend.set_state(rows, qpos, qvel, randomization=payload)
+    np.testing.assert_allclose(entity.link_mass[1].numpy(), [0.0, 6.0, 2.5, 1.5])
+    np.testing.assert_allclose(entity.link_mass[0, 1].numpy(), 5.0)  # untouched row
+    np.testing.assert_allclose(entity.dof_kp.numpy()[rows][:, ACTUATED_DOFS], 55.0)
+    np.testing.assert_allclose(entity.dof_kv.numpy()[rows][:, ACTUATED_DOFS], 6.5)
+
+    delta_payload = ResetRandomizationPayload(base_mass_delta=np.array([0.25, -0.5]))
+    backend.set_state(rows, qpos, qvel, randomization=delta_payload)
+    # Delta composes onto the scanned nominal base mass (5.0), matching the
+    # MuJoCo backend's base-table semantics.
+    np.testing.assert_allclose(entity.link_mass.numpy()[rows, 1], [5.25, 4.5])
+
+    with pytest.raises(NotImplementedError, match="gravity"):
+        backend.set_state(
+            rows, qpos, qvel, randomization=ResetRandomizationPayload(gravity=np.zeros((2, 3)))
+        )
+    with pytest.raises(ValueError, match="kp must have shape"):
+        backend.set_state(
+            rows, qpos, qvel, randomization=ResetRandomizationPayload(kp=np.zeros((2, 5)))
+        )
+
+
+def test_interval_randomization_and_body_force(fake_genesis, tiny_model_file: str) -> None:
+    backend = _backend(tiny_model_file)
+    solver = backend._scene.sim.rigid_solver
+    force = np.ones((4, 2, 3), dtype=np.float32)
+    backend.apply_interval_randomization(
+        IntervalRandomizationPlan(body_ids=np.array([1, 3], dtype=np.int32), body_force=force)
+    )
+    assert len(solver.external_forces) == 2
+    np.testing.assert_allclose(solver.external_forces[0][0], np.ones((4, 3)))
+    assert solver.external_forces[0][1] == [1]
+    assert solver.external_forces[1][1] == [3]
+
+    with pytest.raises(NotImplementedError, match="interval push"):
+        backend.apply_interval_randomization(IntervalRandomizationPlan(push_perturbation_limit=5.0))
+    with pytest.raises(NotImplementedError, match="body-velocity"):
+        backend.apply_interval_randomization(
+            IntervalRandomizationPlan(
+                body_ids=np.array([1], dtype=np.int32),
+                body_linear_velocity_delta=np.zeros((4, 1, 3), dtype=np.float32),
+            )
+        )
+    with pytest.raises(ValueError, match="requires body_ids"):
+        backend.apply_interval_randomization(
+            IntervalRandomizationPlan(body_force=np.zeros((4, 1, 3), dtype=np.float32))
+        )
+    with pytest.raises(NotImplementedError, match="init-lifecycle randomization"):
+        backend.apply_init_randomization(
+            InitRandomizationPlan(
+                model_assignments=np.zeros(4, dtype=np.int32),
+                model_variants=(
+                    ModelVariantSpec(geom_size_overrides=(GeomSizeOverride("foot_geom", (0.1,)),)),
+                ),
+            )
+        )
+
+
+def test_unsupported_contract_surface_fails_closed(fake_genesis, tiny_model_file: str) -> None:
+    backend = _backend(tiny_model_file)
+    for call, match in (
+        (lambda: backend.get_geom_id("floor"), "geom ids"),
+        (lambda: backend.get_geom_size("floor"), "geom sizes"),
+        (lambda: backend.get_geom_names(), "geom names"),
+        (lambda: backend.get_site_ids(["imu_site"]), "get_site_ids"),
+        (lambda: backend.get_site_jacobian_w(0, np.array([6])), "get_site_jacobian_w"),
+        (
+            lambda: backend.create_hfield_scanner(
+                hfield_geom_id=0, offsets=np.zeros((1, 2)), frame_body_id=1
+            ),
+            "height-field",
+        ),
+        (lambda: backend.get_physics_state(), "physics-state playback"),
+    ):
+        with pytest.raises(NotImplementedError, match=match):
+            call()
+
+
+def test_play_capabilities_and_plan_modes(
+    fake_genesis, tiny_model_file: str, monkeypatch: pytest.MonkeyPatch, tmp_path: Path
+) -> None:
+    backend = _backend(tiny_model_file)
+    caps = backend.get_play_capabilities()
+    assert caps.supports_native_interactive_renderer is True
+    assert caps.supports_native_video_capture is True
+    assert caps.supports_physics_state_playback is False
+
+    plan = backend.resolve_play_render_plan(
+        play_render_mode="none", play_steps=10, output_video=tmp_path / "x.mp4"
+    )
+    assert plan.mode == "none" and plan.num_steps is None
+
+    plan = backend.resolve_play_render_plan(
+        play_render_mode="interactive", play_steps=None, output_video=None
+    )
+    assert plan.mode == "interactive" and not plan.headless and not plan.record_video
+
+    plan = backend.resolve_play_render_plan(
+        play_render_mode="record", play_steps=5, output_video=tmp_path / "x.mp4"
+    )
+    assert plan.mode == "record" and plan.headless and plan.record_video
+    assert plan.num_steps == 5
+    with pytest.raises(ValueError, match="play_steps"):
+        backend.resolve_play_render_plan(
+            play_render_mode="record", play_steps=None, output_video=tmp_path / "x.mp4"
+        )
+    with pytest.raises(ValueError, match="output video path"):
+        backend.resolve_play_render_plan(play_render_mode="record", play_steps=5, output_video=None)
+    with pytest.raises(ValueError, match="play render mode must be one of"):
+        backend.resolve_play_render_plan(play_render_mode="bogus", play_steps=5, output_video=None)
+
+    monkeypatch.setenv("DISPLAY", ":0")
+    plan = backend.resolve_play_render_plan(
+        play_render_mode="auto", play_steps=5, output_video=None
+    )
+    assert plan.mode == "interactive"
+
+    monkeypatch.delenv("DISPLAY", raising=False)
+    monkeypatch.delenv("WAYLAND_DISPLAY", raising=False)
+    plan = backend.resolve_play_render_plan(
+        play_render_mode="auto", play_steps=5, output_video=tmp_path / "x.mp4"
+    )
+    assert plan.mode == "record" and plan.headless
+
+
+def test_renderer_init_pinning_and_frame_capture(
+    fake_genesis, tiny_model_file: str, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    monkeypatch.setenv("DISPLAY", ":0")
+    backend = _backend(tiny_model_file)
+    frame = backend.capture_video_frame()  # self-initializes headless + capture
+    assert frame.shape == (720, 1280, 3) and frame.dtype == np.uint8
+    camera = backend._render_camera
+    assert camera is not None and camera.is_built and camera.render_count == 1
+    backend.init_renderer(headless=True, capture=True)  # same config is a no-op
+    with pytest.raises(RuntimeError, match="already initialized"):
+        backend.init_renderer(headless=False)
+    with pytest.raises(RuntimeError, match="already initialized"):
+        backend.render()
+
+
+def test_interactive_render_self_init_and_close(
+    fake_genesis, tiny_model_file: str, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    monkeypatch.setenv("DISPLAY", ":0")
+    backend = _backend(tiny_model_file)
+    backend.render()  # self-initializes the interactive viewer
+    viewer = backend._viewer
+    assert viewer is not None and viewer.built_scene is backend._scene
+    assert backend._scene.visualizer._viewer is viewer
+    backend.render()
+    assert backend._scene.visualizer.update_count == 2
+    # #1396: the viewer receives the full Z-up pose matrix (the pos/lookat
+    # branch would reuse the viewer's polluted default up vector).
+    pose, pos, lookat = viewer.camera_pose
+    assert pos is None and lookat is None
+    assert pose.shape == (4, 4)
+    assert pose[2, 0] == pytest.approx(0.0, abs=1e-12)  # camera x-axis level
+    viewer.alive = False
+    with pytest.raises(RenderClosedError, match="viewer window was closed"):
+        backend.render()
+    assert backend._viewer is None
+
+
+def test_step_translates_viewer_closed(
+    fake_genesis, tiny_model_file: str, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    """#1393: scene.step() updates the attached viewer; a closed viewer must
+    surface as RenderClosedError from backend.step, not a raw private error."""
+    monkeypatch.setenv("DISPLAY", ":0")
+    backend = _backend(tiny_model_file)
+    backend.render()  # attaches the interactive viewer
+    ctrl = np.zeros((backend.num_envs, backend.num_actuators), dtype=np.float32)
+    backend.step(ctrl)  # live viewer: scene.step updates it fine
+    backend._viewer.alive = False
+    with pytest.raises(RenderClosedError, match="viewer window was closed"):
+        backend.step(ctrl)
+    assert backend._viewer is None
+    # Dead viewer is detached: physics keeps working without a renderer.
+    backend.step(ctrl)
+
+
+def test_interactive_viewer_requires_display(
+    fake_genesis, tiny_model_file: str, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    monkeypatch.delenv("DISPLAY", raising=False)
+    monkeypatch.delenv("WAYLAND_DISPLAY", raising=False)
+    backend = _backend(tiny_model_file)
+    with pytest.raises(RuntimeError, match="requires a reachable display"):
+        backend.init_renderer(headless=False)
+
+
+def test_camera_tracking_follows_root(fake_genesis, tiny_model_file: str) -> None:
+    backend = _backend(tiny_model_file, num_envs=2)
+    backend.init_renderer(
+        headless=True,
+        capture=True,
+        camera_kwargs={"cam_tracking": True, "cam_tracking_env_idx": 1},
+    )
+    backend.capture_video_frame()
+    camera = backend._render_camera
+    assert camera.poses, "tracking must re-pose the camera on every capture"
+    _, lookat = camera.poses[-1]
+    np.testing.assert_allclose(lookat, backend.get_base_pos()[1], atol=1e-5)
+
+
+def test_run_playback_record_writes_video(
+    fake_genesis, tiny_model_file: str, tmp_path: Path
+) -> None:
+    backend = _backend(tiny_model_file, num_envs=1)
+    env = SimpleNamespace(cfg=SimpleNamespace(ctrl_dt=0.02))
+    steps: list[int] = []
+    output = tmp_path / "play.mp4"
+    result = backend.run_playback(
+        env=env,
+        initialize=lambda: 0,
+        step=lambda obs: (steps.append(obs), obs + 1)[1],
+        num_steps=4,
+        output_video=output,
+        record_video=True,
+        headless=True,
+    )
+    assert result == str(output)
+    assert output.is_file() and output.stat().st_size > 0
+    assert steps == [0, 1, 2, 3]
+    assert backend._render_camera.render_count == 4
+
+
+def test_run_playback_interactive_drives_viewer_until_closed(
+    fake_genesis, tiny_model_file: str, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    monkeypatch.setenv("DISPLAY", ":0")
+    backend = _backend(tiny_model_file, num_envs=1)
+    env = SimpleNamespace(cfg=SimpleNamespace(ctrl_dt=0.02))
+    steps: list[int] = []
+
+    def step(obs: int) -> int:
+        steps.append(obs)
+        if len(steps) == 3 and backend._viewer is not None:
+            backend._viewer.alive = False  # user closes the window mid-playback
+        return obs + 1
+
+    result = backend.run_playback(
+        env=env,
+        initialize=lambda: 0,
+        step=step,
+        num_steps=None,
+        headless=False,
+        record_video=False,
+    )
+    assert result is None  # interactive close is a clean exit, not an error
+    assert steps == [0, 1, 2]
+    # Steps 1-2 update the live viewer; at step 3 the viewer is already dead,
+    # so update raises (real genesis semantics) before it could count.
+    assert backend._scene.visualizer.update_count == 2
+
+
+def test_constructor_validation_and_factory_wiring(fake_genesis, tiny_model_file: str) -> None:
+    scene = SceneCfg(model_file=tiny_model_file)
+    with pytest.raises(NotImplementedError, match="push_body_name"):
+        create_backend("genesis", scene, 1, 0.005, push_body_name="pelvis")
+    with pytest.raises(TypeError, match="does not accept backend options"):
+        create_backend("genesis", scene, 1, 0.005, bogus_option=1)
+    with pytest.raises(ValueError, match="solver_iterations"):
+        create_backend("genesis", scene, 1, 0.005, genesis_solver_iterations=0)
+    with pytest.raises(ValueError, match="genesis_integrator must be one of"):
+        create_backend("genesis", scene, 1, 0.005, genesis_integrator="bogus")
+    with pytest.raises(ValueError, match="position_actuator_gains"):
+        create_backend("genesis", scene, 1, 0.005, position_actuator_gains={"kp": 1.0})
+    with pytest.raises(ValueError, match="num_envs must be a positive integer"):
+        create_backend("genesis", scene, 0, 0.005)
+
+    backend = create_backend(
+        "genesis",
+        scene,
+        1,
+        0.005,
+        genesis_integrator="implicitfast",
+        genesis_constraint_solver="newton",
+        genesis_friction_cone="elliptic",
+        genesis_solver_iterations=25,
+    )
+    rigid_kwargs = backend._scene.rigid_options.kwargs
+    assert rigid_kwargs["integrator"] == "implicitfast"
+    assert rigid_kwargs["constraint_solver"] == "Newton"
+    assert rigid_kwargs["friction_cone"] == "elliptic"
+    assert rigid_kwargs["iterations"] == 25
+    assert rigid_kwargs["batch_links_info"] is True
+    assert rigid_kwargs["batch_dofs_info"] is True
+    assert backend._scene.sim_options.dt == pytest.approx(0.005)
+
+    assert backend._scene.build_count == 0
+    # First state access lazily builds the scene (isaacgym-style idempotent
+    # materialize), so Entity validators that read state before the env's
+    # explicit materialize hook work (#1382).
+    np.testing.assert_allclose(backend.get_dof_pos()[0], [0.0, 0.0])
+    assert backend._scene.build_count == 1
+    backend.materialize()
+    assert backend._scene.build_count == 1
+    # Cold-path metadata stays available before materialize (manager env
+    # constructors read it ahead of the materialize hook).
+    assert backend.get_default_dof_pos().shape == (2,)
+    assert backend.get_keyframe_qpos("stand").shape == (NQ,)
+
+
+def test_env_cfg_genesis_fields_validate_and_reach_factory() -> None:
+    cfg = EnvCfg(
+        genesis_integrator="implicitfast",
+        genesis_constraint_solver="cg",
+        genesis_friction_cone="pyramidal",
+        genesis_solver_iterations=30,
+    )
+    cfg.validate()
+    kwargs = env_backend_kwargs(cfg)
+    assert (
+        kwargs["genesis_integrator"],
+        kwargs["genesis_constraint_solver"],
+        kwargs["genesis_friction_cone"],
+        kwargs["genesis_solver_iterations"],
+    ) == ("implicitfast", "cg", "pyramidal", 30)
+    with pytest.raises(ValueError, match="genesis_integrator must be a non-empty string"):
+        EnvCfg(genesis_integrator="").validate()
+    with pytest.raises(ValueError, match="genesis_solver_iterations must be a positive integer"):
+        EnvCfg(genesis_solver_iterations=-1).validate()
diff --git a/tests/base/test_genesis_runtime.py b/tests/base/test_genesis_runtime.py
new file mode 100644
index 000000000..b761a5a8f
--- /dev/null
+++ b/tests/base/test_genesis_runtime.py
@@ -0,0 +1,270 @@
+"""Real-runtime slow lane for the ``genesis`` backend (genesis-world 1.3.3).
+
+These tests require the ``genesis`` extra and a CUDA device; the normal lane
+deselects them.  They cover the #1378 acceptance smoke (g1 scene_flat.xml,
+n_envs=8, keyframe reset, 10 control steps, explicit cleanup) and re-verify
+on torch 2.8 the sensor combination that crashed under torch 2.7 in the
+feasibility probe (REPORT #1372 §3.4/§8: IMUSensor + link net contact force
+reads in one scene).
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from unilab.assets import ASSETS_ROOT_PATH
+from unilab.base.backend_factory import create_backend
+from unilab.base.scene import SceneCfg
+
+pytestmark = pytest.mark.slow
+
+_G1_SCENE = str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat.xml")
+_NUM_ENVS = 8
+_SIM_DT = 1.0 / 150.0
+
+
+def _genesis_runtime_available() -> bool:
+    from unisim.backend.genesis.dependencies import genesis_dependencies_available
+
+    if not genesis_dependencies_available():
+        return False
+    try:
+        import torch
+
+        return bool(torch.cuda.is_available())
+    except Exception:
+        return False
+
+
+def _make_backend():
+    backend = create_backend(
+        "genesis",
+        SceneCfg(model_file=_G1_SCENE),
+        _NUM_ENVS,
+        _SIM_DT,
+        base_name="pelvis",
+        genesis_integrator="implicitfast",
+    )
+    backend.materialize()
+    return backend
+
+
+@pytest.fixture(scope="module")
+def backend():
+    if not _genesis_runtime_available():
+        pytest.skip("genesis requires the genesis-world extra and a CUDA device")
+    instance = _make_backend()
+    yield instance
+    instance.close()
+
+
+def _stand_state(instance, rows: int) -> tuple[np.ndarray, np.ndarray]:
+    qpos = np.tile(instance.get_keyframe_qpos("stand"), (rows, 1)).astype(np.float32)
+    qvel = np.zeros((rows, instance.get_init_qvel().size), dtype=np.float32)
+    return qpos, qvel
+
+
+def test_g1_smoke_reset_step_and_cleanup(backend) -> None:
+    """Acceptance smoke for #1378: reset, 10 control steps, finite reads."""
+    assert backend.num_envs == _NUM_ENVS
+    assert backend.num_actuators == 29
+    assert backend.num_dof_vel == 29
+
+    qpos, qvel = _stand_state(backend, _NUM_ENVS)
+    rows = np.arange(_NUM_ENVS, dtype=np.int32)
+    backend.set_state(rows, qpos, qvel)
+    np.testing.assert_allclose(backend.get_dof_pos(), qpos[:, -backend.num_actuators :], atol=1e-5)
+
+    # DR round-trip on the measured per-env setters (batch_*_info build flags).
+    from unisim.dr.types import ResetRandomizationPayload
+
+    kp, kd = backend.get_actuator_gains()
+    payload = ResetRandomizationPayload(kp=(kp * 1.05)[None].repeat(2, axis=0))
+    backend.set_state(rows[:2], qpos[:2], qvel[:2], randomization=payload)
+    np.testing.assert_allclose(
+        backend._entity.get_dofs_kp().cpu().numpy()[:2, 6:],
+        np.tile(kp * 1.05, (2, 1)),
+        rtol=1e-4,
+    )
+    backend.set_state(
+        rows[:2],
+        qpos[:2],
+        qvel[:2],
+        randomization=ResetRandomizationPayload(kp=kp[None].repeat(2, axis=0)),
+    )
+
+    ctrl = np.broadcast_to(
+        qpos[0, -backend.num_actuators :], (_NUM_ENVS, backend.num_actuators)
+    ).copy()
+    keyframe_dof = qpos[0, -backend.num_actuators :]
+    for _ in range(10):
+        result = backend.step(ctrl, nsteps=3)
+    assert set(result["timing"]) == {"physics_ms", "host_cache_refresh_ms"}
+
+    base_pos = backend.get_base_pos()
+    base_quat = backend.get_base_quat()
+    dof_pos = backend.get_dof_pos()
+    assert base_pos.shape == (_NUM_ENVS, 3)
+    assert np.isfinite(base_pos).all()
+    assert np.isfinite(dof_pos).all()
+    np.testing.assert_allclose(np.linalg.norm(base_quat, axis=-1), 1.0, atol=1e-4)
+    # Position hold at the stand keyframe stays upright over 0.2 s.
+    np.testing.assert_allclose(base_pos[:, 2], qpos[0, 2], atol=0.08)
+    drift = np.abs(dof_pos - keyframe_dof).max()
+    assert drift < 0.3, f"dof drift under position hold: {drift}"
+
+    # Sensor reads, including the IMUSensor + net-contact-force combination
+    # that crashed under torch 2.7 in the #1372 probe (torch 2.8 re-check).
+    sensor_names = (
+        "pelvis_gyro",
+        "pelvis_local_linvel",
+        "pelvis_acceleration",
+        "torso_gyro",
+        "left_foot_pos",
+        "left_foot_quat",
+        "left_foot_contact_0",
+    )
+    batch = backend.get_sensor_data_batch(sensor_names)
+    assert batch.shape == (_NUM_ENVS, 3 + 3 + 3 + 3 + 3 + 4 + 1)
+    assert np.isfinite(batch).all()
+    foot_force = np.linalg.norm(
+        backend._entity.get_links_net_contact_force().cpu().numpy()[:, 1:], axis=-1
+    )
+    assert (foot_force > 1.0).any(), "standing G1 should register foot contact force"
+    assert (backend.get_sensor_data("left_foot_contact_0") > 0.5).all()
+    view = backend.bind_sensor_data(("pelvis_gyro", "pelvis_local_linvel"))
+    assert view.read().shape == (_NUM_ENVS, 6)
+
+    print(
+        "[genesis smoke] n_envs=8 reset+10 steps OK; "
+        f"base_z={base_pos[:, 2].mean():.4f} (target {qpos[0, 2]:.3f}), "
+        f"max_dof_drift={drift:.4f}, foot_force_max={foot_force.max():.1f}N"
+    )
+
+
+def test_body_frame_kinematics_matches_mujoco_backend(backend) -> None:
+    """#1382: body-frame velocity/pose math matches MuJoCo on identical state."""
+    num_envs = 2
+    mujoco_backend = create_backend(
+        "mujoco",
+        SceneCfg(model_file=_G1_SCENE),
+        num_envs,
+        _SIM_DT,
+        base_name="pelvis",
+        add_body_sensors=True,
+    )
+    mujoco_backend.materialize()
+
+    rng = np.random.default_rng(0)
+    qpos = np.tile(backend.get_keyframe_qpos("stand"), (num_envs, 1)).astype(np.float32)
+    qvel = rng.uniform(-0.5, 0.5, size=(num_envs, backend.get_init_qvel().size)).astype(np.float32)
+    rows = np.arange(num_envs, dtype=np.int32)
+    backend.set_state(rows, qpos, qvel)
+    mujoco_backend.set_state(rows, qpos, qvel)
+
+    body_names = ["pelvis", "left_hip_pitch_link", "left_knee_link"]
+    gs_ids = backend.get_body_ids(body_names)
+    mj_ids = mujoco_backend.get_body_ids(body_names)
+    # Same document order across backends (materialize-time cross-check).
+    np.testing.assert_array_equal(gs_ids, mj_ids)
+
+    atol = 2e-3
+    for getter in (
+        "get_body_lin_vel_b",
+        "get_body_ang_vel_b",
+        "get_body_pos_b",
+        "get_body_quat_b",
+    ):
+        actual = getattr(backend, getter)(gs_ids)[:num_envs]
+        expected = getattr(mujoco_backend, getter)(mj_ids)
+        np.testing.assert_allclose(actual, expected, atol=atol, err_msg=getter)
+
+
+def test_record_playback_writes_video(tmp_path, backend) -> None:
+    """#1388: native record playback drives the live scene and writes an mp4."""
+    from types import SimpleNamespace
+
+    record_backend = _make_backend()
+    env = SimpleNamespace(cfg=SimpleNamespace(ctrl_dt=0.02))
+    steps: list[int] = []
+    output = tmp_path / "play.mp4"
+    result = record_backend.run_playback(
+        env=env,
+        initialize=lambda: 0,
+        step=lambda obs: (steps.append(obs), obs + 1)[1],
+        num_steps=12,
+        output_video=output,
+        record_video=True,
+        headless=True,
+        camera_kwargs={"cam_distance": 2.5, "cam_tracking": True},
+    )
+    assert result == str(output)
+    assert steps == list(range(12))
+    assert output.is_file() and output.stat().st_size > 0
+    frame = record_backend.capture_video_frame()
+    assert frame.shape == (720, 1280, 3) and frame.dtype == np.uint8
+    assert np.unique(frame).size > 8, "recorded frames look blank"
+    print(f"[genesis record] wrote {output} ({output.stat().st_size} bytes, 12 frames)")
+
+
+def test_interactive_viewer_renders_frames() -> None:
+    """#1388: interactive viewer attaches post-build and renders live frames."""
+    from unisim.backend.base import RenderClosedError
+    from unisim.backend.genesis import playback as genesis_playback
+
+    if not _genesis_runtime_available():
+        pytest.skip("genesis requires the genesis-world extra and a CUDA device")
+    if not genesis_playback.display_available():
+        pytest.skip("interactive viewer requires a display (DISPLAY/WAYLAND_DISPLAY)")
+
+    viewer_backend = _make_backend()
+    viewer_backend.init_renderer(headless=False, width=640, height=480)
+    # #1396: the trackball pose must equal the Z-up matrix (the viewer's
+    # pos/lookat branch reuses a tilted default up and rolls the camera).
+    expected_pose = genesis_playback.camera_pose_matrix_z_up(
+        *genesis_playback.camera_pose_from_kwargs(
+            viewer_backend._camera_kwargs, viewer_backend._camera_lookat()
+        )
+    )
+    actual_pose = np.asarray(
+        viewer_backend._viewer._pyrender_viewer._trackball.pose, dtype=np.float64
+    )
+    np.testing.assert_allclose(actual_pose, expected_pose, atol=1e-5)
+    for _ in range(3):
+        viewer_backend.step(np.zeros((8, viewer_backend.num_actuators), dtype=np.float32))
+        viewer_backend.render()
+    viewer_backend._viewer.stop()
+    with pytest.raises(RenderClosedError, match="viewer window was closed"):
+        viewer_backend.render()
+    assert viewer_backend._viewer is None
+
+    # #1393: scene.step() updates the attached viewer too; a closed viewer must
+    # surface as RenderClosedError from backend.step, then detach cleanly.
+    # Both backends share the module session (multi-scene, REPORT [9b]); only
+    # the re-init guard test below may destroy it.
+    step_backend = _make_backend()
+    step_backend.init_renderer(headless=False, width=640, height=480)
+    step_backend._viewer.stop()
+    with pytest.raises(RenderClosedError, match="viewer window was closed"):
+        step_backend.step(np.zeros((8, step_backend.num_actuators), dtype=np.float32))
+    assert step_backend._viewer is None
+    step_backend.step(np.zeros((8, step_backend.num_actuators), dtype=np.float32))
+    print("[genesis interactive] viewer attached post-build; 3 frames rendered; close detected")
+
+
+def test_reinit_after_destroy_fails_closed(backend) -> None:
+    """Real-runtime lifecycle guard: one gs.init per process.
+
+    The reset hook restores the Python-side session flag afterwards so
+    later real-runtime tests in the same pytest process can re-init (the
+    guard's RSS caveat is acceptable in tests; production still inits once).
+    """
+    from unisim.backend.genesis.materialization import _reset_session_state_for_tests
+
+    backend.close()
+    try:
+        with pytest.raises(RuntimeError, match="exactly one gs.init per process"):
+            _make_backend()
+    finally:
+        _reset_session_state_for_tests()
diff --git a/tests/base/test_isaacgym_backend.py b/tests/base/test_isaacgym_backend.py
new file mode 100644
index 000000000..d8e6e9ee6
--- /dev/null
+++ b/tests/base/test_isaacgym_backend.py
@@ -0,0 +1,1033 @@
+"""Protocol-level tests for the IsaacGym subprocess backend.
+
+These tests drive ``IsaacGymBackend`` through ``isaacgym_mock_worker.py`` — a
+deterministic kinematic fake that speaks the real wire protocol over real
+shared memory on the host interpreter — so they run without the IsaacGym
+runtime (real-runtime coverage lives in the slow conformance tests).
+"""
+
+from __future__ import annotations
+
+import io
+import sys
+import textwrap
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pytest
+from unisim.backend.isaacgym import protocol
+from unisim.backend.isaacgym.backend import IsaacGymBackend, IsaacGymWorkerError
+from unisim.backend.isaacgym.dependencies import (
+    ENV_HOME,
+    ENV_PYTHON,
+    IsaacGymDependencyError,
+    build_worker_env,
+    resolve_isaacgym_runtime,
+)
+from unisim.backend.isaacgym.sensors import scan_scene_metadata
+
+from unilab.base.backend_factory import create_backend
+from unilab.base.scene import SceneCfg
+
+_MOCK_WORKER = str(Path(__file__).resolve().parent / "isaacgym_mock_worker.py")
+
+SIM_DT = 0.005
+NUM_ENVS = 2
+
+_ROBOT_XML = """
+<mujoco>
+  <worldbody>
+    <geom name="floor_geom" type="plane" size="0 0 0.05"/>
+    <body name="base">
+      <freejoint/>
+      <site name="imu_site"/>
+      <site name="tilted_site" pos="0.1 0 0" quat="0.7071068 0 0.7071068 0"/>
+      <site name="euler_site" euler="0.1 0 0"/>
+      <geom name="base_geom" size="0.1"/>
+      <body name="link0">
+        <joint name="j0" type="hinge" range="-1.5 1.5"/>
+        <geom name="g0" size="0.1"/>
+        <body name="link1">
+          <joint name="j1" type="hinge" range="-1.5 1.5"/>
+          <geom name="g1" size="0.1"/>
+          <body name="link2">
+            <joint name="j2" type="hinge" range="-1.5 1.5"/>
+            <geom name="g2" size="0.1"/>
+            <geom name="foot_geom" size="0.1"/>
+          </body>
+        </body>
+      </body>
+    </body>
+  </worldbody>
+  <actuator>
+    <position name="j0" joint="j0" kp="10" kv="0.5" forcerange="-100 100" ctrlrange="-1.2 1.2"/>
+    <position name="j1" joint="j1" kp="20" kv="1.0" forcerange="-80 80"/>
+    <position name="j2" joint="j2" kp="30"/>
+  </actuator>
+</mujoco>
+"""
+
+_SCENE_XML = """
+<mujoco>
+  <include file="robot.xml"/>
+  <sensor>
+    <gyro name="base_gyro" site="imu_site"/>
+    <velocimeter name="base_local_linvel" site="imu_site"/>
+    <framequat name="link0_quat" objtype="body" objname="link0"/>
+    <framepos name="link1_pos" objtype="body" objname="link1"/>
+    <framepos name="tilted_site_pos" objtype="site" objname="tilted_site"/>
+    <framezaxis name="base_upvector" objtype="body" objname="base"/>
+    <framezaxis name="imu_upvector" objtype="site" objname="imu_site"/>
+    <framezaxis name="tilted_upvector" objtype="site" objname="tilted_site"/>
+    <framezaxis name="euler_upvector" objtype="site" objname="euler_site"/>
+    <framezaxis name="geom_upvector" objtype="geom" objname="base_geom"/>
+    <contact name="foot_contact" geom1="floor_geom" geom2="foot_geom" data="found" num="1"/>
+    <accelerometer name="base_acc" site="imu_site"/>
+  </sensor>
+  <keyframe>
+    <key name="home" qpos="0 0 0.8 1 0 0 0 0.1 0.2 0.3"/>
+  </keyframe>
+</mujoco>
+"""
+
+
+@pytest.fixture()
+def scene_file(tmp_path: Path) -> str:
+    (tmp_path / "robot.xml").write_text(textwrap.dedent(_ROBOT_XML), encoding="utf-8")
+    scene = tmp_path / "scene.xml"
+    scene.write_text(textwrap.dedent(_SCENE_XML), encoding="utf-8")
+    return str(scene)
+
+
+def _make_backend(scene_file: str, **kwargs: Any) -> IsaacGymBackend:
+    kwargs.setdefault("worker_command", [sys.executable, _MOCK_WORKER])
+    kwargs.setdefault("worker_timeout_s", 30.0)
+    base_name = kwargs.pop("base_name", "base")
+    backend = create_backend(
+        "isaacgym",
+        SceneCfg(model_file=scene_file),
+        NUM_ENVS,
+        SIM_DT,
+        base_name=base_name,
+        **kwargs,
+    )
+    assert isinstance(backend, IsaacGymBackend)
+    return backend
+
+
+@pytest.fixture()
+def backend(scene_file: str) -> Any:
+    instance = _make_backend(scene_file)
+    instance.materialize()
+    yield instance
+    instance.close()
+
+
+# ---------------------------------------------------------------------------
+# Protocol unit tests
+# ---------------------------------------------------------------------------
+
+
+def test_protocol_message_roundtrip() -> None:
+    stream = io.BytesIO()
+    protocol.send_message(stream, protocol.CMD_STEP, {"nsteps": 4})
+    stream.seek(0)
+    message = protocol.recv_message(stream)
+    assert message == {"cmd": "STEP", "payload": {"nsteps": 4}}
+
+    stream.seek(0)
+    header = stream.read(protocol.HEADER_SIZE)
+    assert protocol.unpack_header(header) == len(
+        protocol.pack_message(protocol.CMD_STEP, {"nsteps": 4})
+    )
+    assert protocol.decode_message(stream.read())["cmd"] == "STEP"
+
+
+def test_protocol_recv_eof_raises() -> None:
+    with pytest.raises(protocol.WorkerDisconnectedError):
+        protocol.recv_message(io.BytesIO(b""))
+    with pytest.raises(protocol.WorkerDisconnectedError):
+        protocol.recv_message(io.BytesIO(b"\x10\x00"))
+
+
+def test_protocol_slot_layout() -> None:
+    shapes = protocol.slot_shapes(num_envs=4, num_dof=3, num_bodies=2)
+    assert shapes["ctrl"] == (4, 3)
+    assert shapes["root_state"] == (4, 13)
+    assert shapes["dof_state"] == (4, 3, 2)
+    assert shapes["body_state"] == (4, 2, 13)
+    assert shapes["contact_force"] == (4, 2, 3)
+    assert shapes["reset_env_ids"] == (4,)
+    assert shapes["reset_qpos"] == (4, 10)
+    assert shapes["reset_qvel"] == (4, 9)
+    assert protocol.slot_dtype("reset_env_ids") == np.dtype(np.int32)
+    assert protocol.slot_nbytes("ctrl", shapes["ctrl"]) == 4 * 3 * 4
+    with pytest.raises(ValueError, match="unknown shm slot"):
+        protocol.slot_dtype("nope")
+    with pytest.raises(ValueError, match="num_envs>0"):
+        protocol.slot_shapes(0, 3, 2)
+
+
+def test_legacy_protocol_module_reexports_canonical_public_surface() -> None:
+    from unisim.backend.subprocess_ipc import protocol as shared_protocol
+
+    assert protocol.__all__ == shared_protocol.__all__
+    for name in shared_protocol.__all__:
+        assert getattr(protocol, name) is getattr(shared_protocol, name)
+
+
+def test_protocol_error_payload() -> None:
+    try:
+        raise RuntimeError("boom")
+    except RuntimeError as exc:
+        payload = protocol.serialize_exception(exc)
+    assert payload["type"] == "RuntimeError"
+    assert payload["message"] == "boom"
+    assert "RuntimeError: boom" in payload["traceback"]
+    rendered = protocol.format_worker_error(payload)
+    assert "RuntimeError" in rendered and "boom" in rendered
+
+
+def test_protocol_quat_helpers() -> None:
+    xyzw = np.array([[0.1, 0.2, 0.3, 0.9]])
+    wxyz = protocol.xyzw_to_wxyz(xyzw)
+    np.testing.assert_allclose(wxyz, [[0.9, 0.1, 0.2, 0.3]])
+    np.testing.assert_allclose(protocol.wxyz_to_xyzw(wxyz), xyzw)
+
+    # 90-degree rotation about z maps +x to +y (and inverse maps +y to +x).
+    half = np.sqrt(0.5)
+    quat = np.array([[half, 0.0, 0.0, half]])
+    np.testing.assert_allclose(
+        protocol.quat_rotate(quat, np.array([[1.0, 0.0, 0.0]])), [[0.0, 1.0, 0.0]], atol=1e-7
+    )
+    np.testing.assert_allclose(
+        protocol.quat_rotate_inverse(quat, np.array([[0.0, 1.0, 0.0]])),
+        [[1.0, 0.0, 0.0]],
+        atol=1e-7,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Dependency discovery
+# ---------------------------------------------------------------------------
+
+
+def _make_runtime_tree(home: Path) -> None:
+    python = home / "miniconda3" / "envs" / "hsgym" / "bin" / "python3.8"
+    python.parent.mkdir(parents=True)
+    python.write_text("#!/bin/sh\n", encoding="utf-8")
+    (home / "miniconda3" / "envs" / "hsgym" / "lib").mkdir(parents=True)
+    (home / "isaacgym" / "python").mkdir(parents=True)
+
+
+def test_dependencies_resolve_default_layout(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    monkeypatch.setenv(ENV_HOME, str(tmp_path))
+    monkeypatch.delenv(ENV_PYTHON, raising=False)
+    _make_runtime_tree(tmp_path)
+    runtime = resolve_isaacgym_runtime()
+    assert runtime.python == tmp_path / "miniconda3" / "envs" / "hsgym" / "bin" / "python3.8"
+    assert runtime.isaacgym_python == tmp_path / "isaacgym" / "python"
+    env = build_worker_env(runtime)
+    assert env["LD_LIBRARY_PATH"].split(":")[0] == str(
+        tmp_path / "miniconda3" / "envs" / "hsgym" / "lib"
+    )
+
+
+def test_dependencies_python_override(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv(ENV_HOME, str(tmp_path))
+    _make_runtime_tree(tmp_path)
+    custom = tmp_path / "custom_python"
+    custom.write_text("#!/bin/sh\n", encoding="utf-8")
+    monkeypatch.setenv(ENV_PYTHON, str(custom))
+    assert resolve_isaacgym_runtime().python == custom
+    assert resolve_isaacgym_runtime(python_override=str(custom)).python == custom
+
+
+def test_dependencies_missing_runtime_is_actionable(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    monkeypatch.setenv(ENV_HOME, str(tmp_path / "empty"))
+    monkeypatch.delenv(ENV_PYTHON, raising=False)
+    with pytest.raises(IsaacGymDependencyError, match="setup_isaacgym_env.sh"):
+        resolve_isaacgym_runtime()
+
+
+# ---------------------------------------------------------------------------
+# Mock-worker protocol tests
+# ---------------------------------------------------------------------------
+
+
+def test_materialize_binds_metadata_and_slots(backend: IsaacGymBackend) -> None:
+    assert backend.num_envs == NUM_ENVS
+    assert backend.num_actuators == 3
+    assert backend.num_dof_vel == 3
+    assert backend.get_actuator_names() == ("j0", "j1", "j2")
+    assert backend.get_actuator_joint_names() == ("j0", "j1", "j2")
+    np.testing.assert_allclose(
+        backend.get_actuator_ctrl_range(),
+        np.array([[-1.2, 1.2], [0.0, 0.0], [0.0, 0.0]], dtype=np.float32),
+    )
+    kp, kd = backend.get_actuator_gains()
+    np.testing.assert_allclose(kp, [10.0, 20.0, 30.0])
+    np.testing.assert_allclose(kd, [0.5, 1.0, 0.0])
+    np.testing.assert_allclose(
+        backend.get_joint_range(), np.array([[-1.5, 1.5]] * 3, dtype=np.float32)
+    )
+    np.testing.assert_allclose(backend.get_gravity(), [0.0, 0.0, -9.81])
+    np.testing.assert_array_equal(
+        backend.get_body_ids(["base", "link1"]), np.array([0, 2], dtype=np.int32)
+    )
+    with pytest.raises(ValueError, match="not found"):
+        backend.get_body_ids(["missing_body"])
+
+    default_qpos = backend.get_default_qpos()
+    assert default_qpos.shape == (10,)
+    # The sole scene keyframe ("home") is selected as the initial/default state.
+    np.testing.assert_allclose(default_qpos, [0, 0, 0.8, 1, 0, 0, 0, 0.1, 0.2, 0.3], atol=1e-7)
+    np.testing.assert_allclose(backend.get_init_qvel(), np.zeros(9))
+    np.testing.assert_allclose(backend.get_default_dof_pos(), [0.1, 0.2, 0.3], atol=1e-7)
+    np.testing.assert_allclose(
+        backend.get_keyframe_qpos("home"), [0, 0, 0.8, 1, 0, 0, 0, 0.1, 0.2, 0.3]
+    )
+    with pytest.raises(ValueError, match="Keyframe 'missing'"):
+        backend.get_keyframe_qpos("missing")
+
+    layout = backend.get_root_state_layout("base")
+    assert layout.qpos_indices == tuple(range(7))
+    assert layout.qvel_indices == tuple(range(6))
+    with pytest.raises(NotImplementedError, match="root-state layout"):
+        backend.get_root_state_layout("link0")
+
+    np.testing.assert_array_equal(
+        backend.get_joint_state_qpos_indices(["j1"]), np.array([8], dtype=np.int32)
+    )
+    np.testing.assert_array_equal(
+        backend.get_joint_state_qvel_indices(["j1"]), np.array([7], dtype=np.int32)
+    )
+
+    # Initial state written by the mock at ATTACH reflects the scene keyframe:
+    # root at z=0.8, dofs at the keyframe joint positions.
+    np.testing.assert_allclose(backend.get_base_pos(), [[0, 0, 0.8], [0, 0, 0.8]], atol=1e-6)
+    np.testing.assert_allclose(backend.get_dof_pos(), [[0.1, 0.2, 0.3], [0.1, 0.2, 0.3]], atol=1e-6)
+    np.testing.assert_allclose(np.linalg.norm(backend.get_base_quat(), axis=-1), 1.0)
+    assert backend.model.num_bodies == 4
+
+
+def test_init_applies_scene_keyframe(scene_file: str) -> None:
+    """INIT applies the scene keyframe as the initial root pose + dof pos."""
+    backend = _make_backend(scene_file)
+    backend.materialize()
+    try:
+        np.testing.assert_allclose(backend.get_dof_pos(), [[0.1, 0.2, 0.3]] * NUM_ENVS, atol=1e-6)
+        np.testing.assert_allclose(backend.get_base_pos(), [[0.0, 0.0, 0.8]] * NUM_ENVS, atol=1e-6)
+        np.testing.assert_allclose(
+            backend.get_base_quat(), [[1.0, 0.0, 0.0, 0.0]] * NUM_ENVS, atol=1e-6
+        )
+        # The default-state contract matches the post-INIT worker state.
+        np.testing.assert_allclose(
+            backend.get_default_dof_pos(), backend.get_dof_pos()[0], atol=1e-7
+        )
+    finally:
+        backend.close()
+
+
+def test_xml_metadata_available_before_materialize(scene_file: str) -> None:
+    """Keyframe/default qpos are pure parent-side XML metadata.
+
+    Env construction (``resolve_scene_default_qpos``) queries them before
+    ``materialize()`` spawns the worker; values must be identical afterwards.
+    """
+    backend = _make_backend(scene_file)
+    expected_qpos = np.array([0, 0, 0.8, 1, 0, 0, 0, 0.1, 0.2, 0.3], dtype=np.float32)
+    np.testing.assert_allclose(backend.get_keyframe_qpos("home"), expected_qpos)
+    np.testing.assert_allclose(backend.get_default_qpos(), expected_qpos)
+    np.testing.assert_allclose(backend.get_default_dof_pos(), [0.1, 0.2, 0.3])
+    # Pure-XML metadata is available pre-materialize (env constructors read
+    # these before the worker handshake).
+    assert backend.num_actuators == 3
+    assert backend.num_dof_vel == 3
+    np.testing.assert_allclose(backend.get_init_qvel(), np.zeros(9))
+    np.testing.assert_array_equal(
+        backend.get_body_ids(["base", "link1"]), np.array([0, 2], dtype=np.int32)
+    )
+    assert backend.get_actuator_names() == ("j0", "j1", "j2")
+    layout = backend.get_root_state_layout("base")
+    assert layout.qpos_indices == tuple(range(7))
+    # Worker-handshake-dependent surfaces materialize lazily on first use:
+    # none of the pure-XML queries above spawned the worker.
+    assert backend._proc is None
+    _ = backend.model
+    assert backend._proc is not None
+
+    backend.materialize()
+    try:
+        np.testing.assert_allclose(backend.get_keyframe_qpos("home"), expected_qpos)
+        np.testing.assert_allclose(backend.get_default_qpos(), expected_qpos)
+        np.testing.assert_allclose(backend.get_default_dof_pos(), [0.1, 0.2, 0.3])
+        np.testing.assert_allclose(backend.get_dof_pos(), [[0.1, 0.2, 0.3]] * NUM_ENVS, atol=1e-6)
+    finally:
+        backend.close()
+
+
+def test_actuator_metadata_available_before_materialize(scene_file: str) -> None:
+    """PD gains / ctrl ranges / joint ranges are pure XML metadata.
+
+    IsaacGym's MJCF importer drops kv/frictionloss/joint ranges, so these
+    surfaces are answered from the parent-side scene scan and must not spawn
+    the worker.
+    """
+    backend = _make_backend(scene_file)
+    kp, kd = backend.get_actuator_gains()
+    np.testing.assert_allclose(kp, [10.0, 20.0, 30.0])
+    np.testing.assert_allclose(kd, [0.5, 1.0, 0.0])
+    np.testing.assert_allclose(
+        backend.get_actuator_ctrl_range(),
+        np.array([[-1.2, 1.2], [0.0, 0.0], [0.0, 0.0]], dtype=np.float32),
+    )
+    np.testing.assert_allclose(
+        backend.get_joint_range(), np.array([[-1.5, 1.5]] * 3, dtype=np.float32)
+    )
+    assert backend._proc is None
+
+
+def test_scan_resolves_joint_default_classes(tmp_path: Path) -> None:
+    """Joint armature/frictionloss resolve through MJCF default classes."""
+    robot = """
+    <mujoco>
+      <default>
+        <default class="robot">
+          <joint armature="0.01" frictionloss="0.3"/>
+        </default>
+      </default>
+      <worldbody>
+        <body name="base" childclass="robot">
+          <freejoint/>
+          <body name="link0">
+            <joint name="j0" type="hinge"/>
+            <body name="link1">
+              <joint name="j1" type="hinge" armature="0.5"/>
+            </body>
+          </body>
+        </body>
+      </worldbody>
+    </mujoco>
+    """
+    scene = tmp_path / "scene.xml"
+    scene.write_text(textwrap.dedent(robot), encoding="utf-8")
+    metadata = scan_scene_metadata(str(scene))
+    assert metadata.joint_names == ("j0", "j1")
+    np.testing.assert_allclose(metadata.joint_armature, [0.01, 0.5])
+    np.testing.assert_allclose(metadata.joint_frictionloss, [0.3, 0.3])
+    assert np.isinf(metadata.joint_ranges[0]).all()
+
+
+def test_scan_fails_closed_on_non_position_actuator(tmp_path: Path) -> None:
+    """<motor>/other actuator types would break the position-target ctrl contract."""
+    robot = textwrap.dedent(_ROBOT_XML).replace(
+        "</mujoco>",
+        '<actuator><motor name="m0" joint="j0"/></actuator>\n</mujoco>',
+    )
+    scene = tmp_path / "scene.xml"
+    scene.write_text(robot, encoding="utf-8")
+    with pytest.raises(NotImplementedError, match="position"):
+        scan_scene_metadata(str(scene))
+
+
+def test_scan_fails_closed_on_asymmetric_forcerange(tmp_path: Path) -> None:
+    """PhysX dof effort limits are symmetric; asymmetric MJCF forceranges fail."""
+    robot = textwrap.dedent(_ROBOT_XML).replace(
+        'forcerange="-100 100"',
+        'forcerange="-50 100"',
+    )
+    scene = tmp_path / "scene.xml"
+    scene.write_text(robot, encoding="utf-8")
+    with pytest.raises(NotImplementedError, match="asymmetric forcerange"):
+        scan_scene_metadata(str(scene))
+
+
+def test_init_keyframe_selection_prefers_default_keyframe_name(tmp_path: Path) -> None:
+    scene = textwrap.dedent(_SCENE_XML).replace(
+        '<key name="home" qpos="0 0 0.8 1 0 0 0 0.1 0.2 0.3"/>',
+        '<key name="home" qpos="0 0 0.8 1 0 0 0 0.1 0.2 0.3"/>'
+        '<key name="crouch" qpos="0 0 0.5 1 0 0 0 -0.1 -0.2 -0.3"/>',
+    )
+    (tmp_path / "robot.xml").write_text(textwrap.dedent(_ROBOT_XML), encoding="utf-8")
+    (tmp_path / "scene.xml").write_text(scene, encoding="utf-8")
+    scene_file = str(tmp_path / "scene.xml")
+
+    # Ambiguous keyframes without default_keyframe_name keep the zero default.
+    backend = _make_backend(scene_file)
+    backend.materialize()
+    try:
+        np.testing.assert_allclose(backend.get_default_dof_pos(), np.zeros(3))
+        np.testing.assert_allclose(backend.get_dof_pos(), np.zeros((NUM_ENVS, 3)))
+    finally:
+        backend.close()
+
+    # An explicit default_keyframe_name selects that keyframe.
+    backend = create_backend(
+        "isaacgym",
+        SceneCfg(model_file=scene_file, default_keyframe_name="crouch"),
+        NUM_ENVS,
+        SIM_DT,
+        base_name="base",
+        worker_command=[sys.executable, _MOCK_WORKER],
+        worker_timeout_s=30.0,
+    )
+    assert isinstance(backend, IsaacGymBackend)
+    backend.materialize()
+    try:
+        np.testing.assert_allclose(
+            backend.get_dof_pos(), [[-0.1, -0.2, -0.3]] * NUM_ENVS, atol=1e-6
+        )
+        np.testing.assert_allclose(backend.get_base_pos(), [[0.0, 0.0, 0.5]] * NUM_ENVS, atol=1e-6)
+    finally:
+        backend.close()
+
+    # A missing default_keyframe_name fails fast at materialize.
+    backend = create_backend(
+        "isaacgym",
+        SceneCfg(model_file=scene_file, default_keyframe_name="missing"),
+        NUM_ENVS,
+        SIM_DT,
+        base_name="base",
+        worker_command=[sys.executable, _MOCK_WORKER],
+        worker_timeout_s=30.0,
+    )
+    with pytest.raises(ValueError, match="default_keyframe_name 'missing' not found"):
+        backend.materialize()
+    backend.close()
+
+
+def test_init_keyframe_dof_name_mismatch_fails(
+    scene_file: str, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    monkeypatch.setenv("UNILAB_ISAACGYM_MOCK_DOF_NAMES", "j0,j1,renamed_joint")
+    backend = _make_backend(scene_file)
+    try:
+        with pytest.raises(IsaacGymWorkerError, match="renamed_joint.*mjcf_joint_names"):
+            backend.materialize()
+    finally:
+        backend.close()
+
+
+def test_step_integrates_mock_physics(backend: IsaacGymBackend) -> None:
+    ctrl = np.ones((NUM_ENVS, 3), dtype=np.float32)
+    result = backend.step(ctrl, nsteps=2)
+    assert "timing" in result
+    assert "physics_ms" in result["timing"]
+
+    # Mock: dof_vel += ctrl*dt; dof_pos += dof_vel*dt per substep, starting
+    # from the keyframe joint positions [0.1, 0.2, 0.3].
+    expected_vel = np.float32(2 * SIM_DT)
+    np.testing.assert_allclose(backend.get_dof_vel(), expected_vel, atol=1e-7)
+    expected_pos = np.array([[0.1, 0.2, 0.3]], dtype=np.float32) + (
+        np.float32(SIM_DT * SIM_DT) + np.float32(2 * SIM_DT * SIM_DT)
+    )
+    np.testing.assert_allclose(
+        backend.get_dof_pos(), np.broadcast_to(expected_pos, (NUM_ENVS, 3)), atol=1e-7
+    )
+
+    # Free fall from the keyframe root height: v_z after 2 substeps, z += v_z*dt.
+    expected_vz = 2 * (-9.81) * SIM_DT
+    expected_z = 0.8 + (-9.81 * SIM_DT * SIM_DT) + (-9.81 * 2 * SIM_DT * SIM_DT)
+    np.testing.assert_allclose(backend.get_base_lin_vel()[:, 2], expected_vz, rtol=1e-6, atol=1e-7)
+    np.testing.assert_allclose(backend.get_base_pos()[:, 2], expected_z, rtol=1e-6, atol=1e-7)
+
+    with pytest.raises(ValueError, match="ctrl must have shape"):
+        backend.step(np.zeros((NUM_ENVS, 2), dtype=np.float32))
+    with pytest.raises(ValueError, match="nsteps"):
+        backend.step(ctrl, nsteps=0)
+
+
+def test_set_state_roundtrip_and_cache_refresh(backend: IsaacGymBackend) -> None:
+    nq = 10
+    nv = 9
+    rows = np.array([0], dtype=np.int32)
+    qpos = np.zeros((1, nq), dtype=np.float32)
+    qpos[0, :3] = [1.0, 2.0, 0.8]
+    qpos[0, 3] = 1.0
+    qpos[0, 7:] = [0.1, 0.2, 0.3]
+    qvel = np.zeros((1, nv), dtype=np.float32)
+    qvel[0, :3] = [0.5, 0.0, 0.0]
+    qvel[0, 6:] = [1.0, 2.0, 3.0]
+
+    result = backend.set_state(rows, qpos, qvel)
+    assert isinstance(result, dict) and "timing" in result
+    assert "set_state_internal_gap_ms" in result["timing"]
+
+    np.testing.assert_allclose(backend.get_base_pos()[0], [1.0, 2.0, 0.8], atol=1e-6)
+    np.testing.assert_allclose(backend.get_base_lin_vel()[0], [0.5, 0.0, 0.0], atol=1e-6)
+    np.testing.assert_allclose(backend.get_dof_pos()[0], [0.1, 0.2, 0.3], atol=1e-6)
+    np.testing.assert_allclose(backend.get_dof_vel()[0], [1.0, 2.0, 3.0], atol=1e-6)
+    # Untouched env keeps its previous state.
+    np.testing.assert_allclose(backend.get_base_pos()[1], [0, 0, 0.8], atol=1e-6)
+
+    with pytest.raises(ValueError, match="qpos must have shape"):
+        backend.set_state(rows, np.zeros((1, nq + 1), dtype=np.float32), qvel)
+    with pytest.raises(ValueError, match="duplicate rows"):
+        backend.set_state(np.array([0, 0], dtype=np.int32), np.zeros((2, nq)), np.zeros((2, nv)))
+
+
+def test_sensor_mapping_from_scene_scan(backend: IsaacGymBackend) -> None:
+    gyro = backend.get_sensor_data("base_gyro")
+    assert gyro.shape == (NUM_ENVS, 3)
+    np.testing.assert_allclose(gyro, 0.0, atol=1e-6)
+    linvel = backend.get_sensor_data("base_local_linvel")
+    assert linvel.shape == (NUM_ENVS, 3)
+
+    quat = backend.get_sensor_data("link0_quat")
+    assert quat.shape == (NUM_ENVS, 4)
+    np.testing.assert_allclose(np.linalg.norm(quat, axis=-1), 1.0, atol=1e-6)
+
+    pos = backend.get_sensor_data("link1_pos")
+    np.testing.assert_allclose(pos, [[0.2, 0.0, 0.8]] * NUM_ENVS, atol=1e-6)
+
+    # contact "found": zero while ctrl is zero, one after a nonzero torque step.
+    np.testing.assert_allclose(backend.get_sensor_data("foot_contact"), 0.0)
+    backend.step(np.ones((NUM_ENVS, 3), dtype=np.float32))
+    np.testing.assert_allclose(backend.get_sensor_data("foot_contact"), 1.0)
+
+    # Declared but unmappable sensor type fails closed with context.
+    with pytest.raises(NotImplementedError, match="accelerometer"):
+        backend.get_sensor_data("base_acc")
+    with pytest.raises(ValueError, match="Sensor 'nope' not found"):
+        backend.get_sensor_data("nope")
+
+    view = backend.bind_sensor_data(("base_gyro", "base_local_linvel"))
+    assert view.dimensions == (3, 3)
+    values = view.read()
+    assert values.shape == (NUM_ENVS, 6)
+    assert np.isfinite(values).all()
+
+
+def test_framezaxis_sensor_mapping(backend: IsaacGymBackend) -> None:
+    """framezaxis reports the target frame's z axis in world coordinates."""
+    # Identity orientations at materialize: body/site z axes point up; the
+    # tilted site's local quat (90 deg about y) maps its z axis to +x.
+    np.testing.assert_allclose(
+        backend.get_sensor_data("base_upvector"), [[0, 0, 1]] * NUM_ENVS, atol=1e-6
+    )
+    np.testing.assert_allclose(
+        backend.get_sensor_data("imu_upvector"), [[0, 0, 1]] * NUM_ENVS, atol=1e-6
+    )
+    np.testing.assert_allclose(
+        backend.get_sensor_data("tilted_upvector"), [[1, 0, 0]] * NUM_ENVS, atol=1e-5
+    )
+    # framepos on a site includes the site's local pos offset (rigid attach).
+    np.testing.assert_allclose(
+        backend.get_sensor_data("tilted_site_pos"), [[0.1, 0.0, 0.8]] * NUM_ENVS, atol=1e-6
+    )
+
+    # Rotate the root 90 deg about x (wxyz): body z axis maps to -y.
+    half = np.float32(np.sqrt(0.5))
+    qpos = np.zeros((NUM_ENVS, 10), dtype=np.float32)
+    qpos[:, 2] = 1.0
+    qpos[:, 3] = half
+    qpos[:, 4] = half
+    qvel = np.zeros((NUM_ENVS, 9), dtype=np.float32)
+    backend.set_state(np.arange(NUM_ENVS, dtype=np.int32), qpos, qvel)
+
+    np.testing.assert_allclose(
+        backend.get_sensor_data("base_upvector"), [[0, -1, 0]] * NUM_ENVS, atol=1e-5
+    )
+    np.testing.assert_allclose(
+        backend.get_sensor_data("imu_upvector"), [[0, -1, 0]] * NUM_ENVS, atol=1e-5
+    )
+    # tilted site: local z->x, then the body rotation keeps +x at +x.
+    np.testing.assert_allclose(
+        backend.get_sensor_data("tilted_upvector"), [[1, 0, 0]] * NUM_ENVS, atol=1e-5
+    )
+
+    # Sites declared with euler/axisangle/xyaxes/zaxis fail closed.
+    with pytest.raises(NotImplementedError, match="euler"):
+        backend.get_sensor_data("euler_upvector")
+    # Unknown frame target objtype fails closed as well.
+    with pytest.raises(NotImplementedError, match="objtype"):
+        backend.get_sensor_data("geom_upvector")
+    metadata = backend._scene_metadata
+    assert metadata is not None
+    assert "euler_upvector" in metadata.unsupported_sensors
+    assert "geom_upvector" in metadata.unsupported_sensors
+
+
+def test_body_state_views(backend: IsaacGymBackend) -> None:
+    ids = backend.get_body_ids(["base", "link1"])
+    pos_w = backend.get_body_pos_w(ids)
+    assert pos_w.shape == (NUM_ENVS, 2, 3)
+    np.testing.assert_allclose(pos_w[:, 1], [[0.2, 0.0, 0.8]] * NUM_ENVS, atol=1e-6)
+    # Base-frame link1 offset: [0.2, 0, 0] under the identity base orientation.
+    pos_b = backend.get_body_pos_b(ids)
+    np.testing.assert_allclose(pos_b[:, 1], [[0.2, 0.0, 0.0]] * NUM_ENVS, atol=1e-6)
+    quat_b = backend.get_body_quat_b(ids)
+    np.testing.assert_allclose(quat_b[:, :, 0], 1.0, atol=1e-6)
+    with pytest.raises(ValueError, match="body_ids"):
+        backend.get_body_pos_w(np.array([99]))
+
+
+def test_dr_and_pre_step_control_fail_closed(backend: IsaacGymBackend) -> None:
+    capabilities = backend.get_dr_capabilities()
+    assert not capabilities.supported_reset_terms
+    assert not capabilities.supports_interval_push
+    assert not capabilities.supports_interval_body_force
+
+    class _Plan:
+        def is_empty(self) -> bool:
+            return False
+
+    with pytest.raises(NotImplementedError, match="interval randomization"):
+        backend.apply_interval_randomization(_Plan())
+
+    class _Payload:
+        def is_empty(self) -> bool:
+            return False
+
+        def requested_terms(self) -> set[str]:
+            return {"base_mass"}
+
+    with pytest.raises(NotImplementedError, match="reset domain randomization"):
+        backend.set_state(
+            np.array([0], dtype=np.int32),
+            np.zeros((1, 10), dtype=np.float32),
+            np.zeros((1, 9), dtype=np.float32),
+            randomization=_Payload(),
+        )
+
+    with pytest.raises(NotImplementedError, match="pre-step"):
+        backend.set_pre_step_control(lambda backend_, ctrl: ctrl)
+    backend.set_pre_step_control(None)
+
+    # Physics-state playback export stays unsupported; native rendering has
+    # its own dedicated tests below.
+    with pytest.raises(NotImplementedError, match="physics-state playback"):
+        backend.get_physics_state()
+
+
+def test_close_reaps_worker_and_unlinks_shm(backend: IsaacGymBackend) -> None:
+    proc = backend._proc
+    assert proc is not None and proc.poll() is None
+    shm_names = [handle.name for handle in backend._shm_handles.values()]
+    backend.close()
+    assert proc.poll() is not None
+    from multiprocessing import shared_memory
+
+    for name in shm_names:
+        with pytest.raises(FileNotFoundError):
+            shared_memory.SharedMemory(name=name, create=False)
+    backend.close()  # idempotent
+    with pytest.raises(IsaacGymWorkerError, match="closed or not materialized"):
+        backend.step(np.zeros((NUM_ENVS, 3), dtype=np.float32))
+
+
+def test_worker_init_error_propagates(scene_file: str, monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("UNILAB_ISAACGYM_MOCK_BEHAVIOR", "fail_init")
+    backend = _make_backend(scene_file)
+    try:
+        with pytest.raises(IsaacGymWorkerError, match="mock init failure"):
+            backend.materialize()
+    finally:
+        backend.close()
+
+
+def test_worker_death_reports_stderr_tail(scene_file: str, monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("UNILAB_ISAACGYM_MOCK_BEHAVIOR", "die_on_step")
+    backend = _make_backend(scene_file)
+    backend.materialize()
+    try:
+        with pytest.raises(IsaacGymWorkerError, match="mock dying on step"):
+            backend.step(np.zeros((NUM_ENVS, 3), dtype=np.float32))
+        # Later calls fail fast with the cached death diagnostic.
+        with pytest.raises(IsaacGymWorkerError, match="earlier failure"):
+            backend.step(np.zeros((NUM_ENVS, 3), dtype=np.float32))
+    finally:
+        backend.close()
+
+
+def test_worker_timeout_kills_worker(scene_file: str, monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("UNILAB_ISAACGYM_MOCK_BEHAVIOR", "hang_on_step")
+    backend = _make_backend(scene_file, worker_timeout_s=1.0)
+    backend.materialize()
+    try:
+        with pytest.raises(IsaacGymWorkerError, match="did not answer STEP"):
+            backend.step(np.zeros((NUM_ENVS, 3), dtype=np.float32))
+        assert backend._proc is not None
+        assert backend._proc.poll() is not None
+    finally:
+        backend.close()
+
+
+def test_constructor_rejects_bad_arguments(scene_file: str) -> None:
+    with pytest.raises(ValueError, match="num_envs"):
+        create_backend("isaacgym", SceneCfg(model_file=scene_file), 0, SIM_DT)
+    with pytest.raises(ValueError, match="sim_dt"):
+        create_backend("isaacgym", SceneCfg(model_file=scene_file), 1, -1.0, worker_command=["x"])
+    with pytest.raises(TypeError, match="worker_command"):
+        _make_backend(scene_file, worker_command="not-a-list")
+    with pytest.raises(NotImplementedError, match="fragments"):
+        create_backend(
+            "isaacgym",
+            SceneCfg(model_file=scene_file, fragment_files=["extra.xml"]),
+            1,
+            SIM_DT,
+        )
+    with pytest.raises(TypeError, match="does not accept backend options"):
+        create_backend("isaacgym", SceneCfg(model_file=scene_file), 1, SIM_DT, bogus_option=1)
+
+
+def test_g1_scene_framezaxis_sensors_resolve_from_real_xml(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """The g1_walk_flat scene declares the upvector sensors the policy needs.
+
+    All four are ``objtype="site"`` frames in ``g1.xml``; the mock worker
+    reports the owning bodies so the cold-path scan must resolve them to
+    exact site-frame z axes.
+    """
+    from unisim.backend.isaacgym.sensors import scan_scene_metadata
+
+    from unilab.assets import ASSETS_ROOT_PATH
+
+    scene = str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat.xml")
+    # Give the mock the real MJCF joint/body sets so the scene's "stand"
+    # keyframe (29 dofs) maps onto the fake asset by name, as on the real
+    # worker, and the XML↔worker name-order validation passes.
+    scene_metadata = scan_scene_metadata(scene)
+    monkeypatch.setenv("UNILAB_ISAACGYM_MOCK_DOF_NAMES", ",".join(scene_metadata.joint_names))
+    monkeypatch.setenv(
+        "UNILAB_ISAACGYM_MOCK_BODY_NAMES",
+        ",".join(name or f"unnamed_{i}" for i, name in enumerate(scene_metadata.body_names)),
+    )
+    backend = _make_backend(scene, base_name="pelvis")
+    backend.materialize()
+    try:
+        for name in (
+            "pelvis_upvector",
+            "torso_upvector",
+            "left_foot_upvector",
+            "right_foot_upvector",
+        ):
+            value = backend.get_sensor_data(name)
+            assert value.shape == (NUM_ENVS, 3)
+            # Mock state starts at the identity orientation, so every z axis
+            # (these sites are identity-rotated within their bodies) points up.
+            np.testing.assert_allclose(value, [[0.0, 0.0, 1.0]] * NUM_ENVS, atol=1e-6)
+    finally:
+        backend.close()
+
+
+def test_state_access_before_materialize_lazy_spawns_worker(scene_file: str) -> None:
+    """State reads/step auto-materialize: the constructor stays light, but the
+    first state-dependent call performs the worker handshake (matching the
+    MuJoCo backend's fully-constructed availability)."""
+    backend = _make_backend(scene_file)
+    try:
+        assert backend._proc is None
+        np.testing.assert_allclose(backend.get_dof_pos(), [[0.1, 0.2, 0.3]] * NUM_ENVS, atol=1e-6)
+        assert backend._proc is not None
+        # Explicit materialize() afterwards is a no-op.
+        backend.materialize()
+        backend.step(np.zeros((NUM_ENVS, 3), dtype=np.float32))
+        assert backend.get_dof_pos().shape == (NUM_ENVS, 3)
+    finally:
+        backend.close()
+
+
+# ---------------------------------------------------------------------------
+# Native rendering / playback
+# ---------------------------------------------------------------------------
+
+
+def test_play_capabilities_advertise_native_rendering(backend: IsaacGymBackend) -> None:
+    caps = backend.get_play_capabilities()
+    assert caps.supports_native_interactive_renderer
+    assert caps.supports_native_video_capture
+    assert not caps.supports_physics_state_playback
+
+
+def test_normalize_camera_kwargs_maps_mujoco_convention() -> None:
+    from unisim.backend.isaacgym.backend import _normalize_camera_kwargs
+
+    out = _normalize_camera_kwargs(
+        {"cam_distance": 3.0, "cam_elevation": -20.0, "cam_azimuth": 90.0}
+    )
+    assert out == {"distance": 3.0, "elevation_deg": 20.0, "azimuth_deg": 90.0}
+    assert _normalize_camera_kwargs(None) == {
+        "distance": 2.0,
+        "elevation_deg": 20.0,
+        "azimuth_deg": 90.0,
+    }
+
+
+def test_resolve_play_render_plan_modes(
+    scene_file: str, monkeypatch: pytest.MonkeyPatch, tmp_path: Path
+) -> None:
+    backend = _make_backend(scene_file)
+    try:
+        plan = backend.resolve_play_render_plan(
+            play_render_mode="none", play_steps=10, output_video=tmp_path / "x.mp4"
+        )
+        assert plan.mode == "none" and plan.num_steps is None
+
+        plan = backend.resolve_play_render_plan(
+            play_render_mode="interactive", play_steps=None, output_video=None
+        )
+        assert plan.mode == "interactive" and not plan.headless and not plan.record_video
+
+        plan = backend.resolve_play_render_plan(
+            play_render_mode="record", play_steps=5, output_video=tmp_path / "x.mp4"
+        )
+        assert plan.mode == "record" and plan.headless and plan.record_video
+        assert plan.num_steps == 5
+
+        with pytest.raises(ValueError, match="play_steps"):
+            backend.resolve_play_render_plan(
+                play_render_mode="record", play_steps=None, output_video=tmp_path / "x.mp4"
+            )
+        with pytest.raises(ValueError, match="output video path"):
+            backend.resolve_play_render_plan(
+                play_render_mode="record", play_steps=5, output_video=None
+            )
+
+        monkeypatch.setenv("DISPLAY", ":0")
+        plan = backend.resolve_play_render_plan(
+            play_render_mode="auto", play_steps=5, output_video=None
+        )
+        assert plan.mode == "interactive"
+
+        monkeypatch.delenv("DISPLAY", raising=False)
+        monkeypatch.delenv("WAYLAND_DISPLAY", raising=False)
+        plan = backend.resolve_play_render_plan(
+            play_render_mode="auto", play_steps=5, output_video=tmp_path / "x.mp4"
+        )
+        assert plan.mode == "record" and plan.headless
+    finally:
+        backend.close()
+
+
+def test_interactive_renderer_roundtrip(backend: IsaacGymBackend) -> None:
+    backend.init_renderer(headless=False)
+    backend.render()
+    # Re-initializing with the same config is a no-op; a different one fails.
+    backend.init_renderer(headless=False)
+    with pytest.raises(RuntimeError, match="already initialized"):
+        backend.init_renderer(headless=True, capture=True)
+    # Capturing on an interactive-only renderer must not silently succeed.
+    with pytest.raises(RuntimeError, match="already initialized"):
+        backend.capture_video_frame()
+
+
+def test_viewer_close_raises_render_closed(
+    scene_file: str, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    from unisim.backend.base import RenderClosedError
+
+    monkeypatch.setenv("UNILAB_ISAACGYM_MOCK_BEHAVIOR", "close_on_render")
+    backend = _make_backend(scene_file)
+    backend.materialize()
+    try:
+        backend.init_renderer(headless=False)
+        with pytest.raises(RenderClosedError, match="closed"):
+            backend.render()
+    finally:
+        backend.close()
+
+
+def test_viewer_creation_failure_is_actionable(
+    scene_file: str, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    monkeypatch.setenv("UNILAB_ISAACGYM_MOCK_BEHAVIOR", "viewer_fails")
+    backend = _make_backend(scene_file)
+    backend.materialize()
+    try:
+        with pytest.raises(IsaacGymWorkerError, match="create_viewer failed"):
+            backend.init_renderer(headless=False)
+    finally:
+        backend.close()
+
+
+def test_renderer_requires_graphics_context(
+    scene_file: str, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    monkeypatch.setenv("UNILAB_ISAACGYM_MOCK_BEHAVIOR", "no_graphics")
+    backend = _make_backend(scene_file)
+    backend.materialize()
+    try:
+        with pytest.raises(NotImplementedError, match="requires a GPU sim"):
+            backend.init_renderer(headless=True, capture=True)
+    finally:
+        backend.close()
+
+
+def test_capture_video_frame_roundtrip(backend: IsaacGymBackend) -> None:
+    backend.init_renderer(headless=True, capture=True, width=64, height=48)
+    frame = backend.capture_video_frame()
+    assert frame.shape == (48, 64, 3)
+    assert frame.dtype == np.uint8
+    # Mock frame is a deterministic gradient: red follows rows, green columns.
+    assert frame[10, 0, 0] == 10
+    assert frame[0, 20, 1] == 20
+    assert (frame[:, :, 2] == 128).all()
+
+
+def test_capture_without_init_uses_record_config(backend: IsaacGymBackend) -> None:
+    # capture_video_frame lazily initializes the headless capture camera.
+    frame = backend.capture_video_frame()
+    assert frame.shape == (720, 1280, 3)
+
+
+def test_run_playback_record_writes_video(backend: IsaacGymBackend, tmp_path: Path) -> None:
+    from types import SimpleNamespace
+
+    output = tmp_path / "play.mp4"
+    result = backend.run_playback(
+        env=SimpleNamespace(cfg=None),
+        initialize=lambda: 0,
+        step=lambda obs: obs + 1,
+        num_steps=3,
+        output_video=output,
+        headless=True,
+        record_video=True,
+    )
+    assert result == str(output)
+    assert output.exists() and output.stat().st_size > 0
+
+
+def test_run_playback_interactive_finite_steps(backend: IsaacGymBackend) -> None:
+    from types import SimpleNamespace
+
+    steps: list[int] = []
+    result = backend.run_playback(
+        env=SimpleNamespace(cfg=None),
+        initialize=lambda: 0,
+        step=lambda obs: steps.append(obs) or (obs + 1),
+        num_steps=2,
+        headless=False,
+        record_video=False,
+    )
+    assert result is None
+    assert steps == [0, 1]
+
+
+def test_run_playback_interactive_window_close_is_graceful(
+    scene_file: str, monkeypatch: pytest.MonkeyPatch, tmp_path: Path
+) -> None:
+    from types import SimpleNamespace
+
+    monkeypatch.setenv("UNILAB_ISAACGYM_MOCK_BEHAVIOR", "close_on_render")
+    backend = _make_backend(scene_file)
+    backend.materialize()
+    try:
+        result = backend.run_playback(
+            env=SimpleNamespace(cfg=None),
+            initialize=lambda: 0,
+            step=lambda obs: obs + 1,
+            num_steps=None,
+            headless=False,
+            record_video=False,
+        )
+        assert result is None
+    finally:
+        backend.close()
diff --git a/tests/base/test_isaacsim_backend.py b/tests/base/test_isaacsim_backend.py
new file mode 100644
index 000000000..8a73ad3b5
--- /dev/null
+++ b/tests/base/test_isaacsim_backend.py
@@ -0,0 +1,614 @@
+"""Contract and lifecycle tests for the IsaacSim subprocess adapter.
+
+The tests use the deterministic protocol worker already used by the IsaacGym
+adapter.  It exercises the host-side IPC/shared-memory lifecycle without
+requiring Kit or a GPU, while the small worker helpers are tested with tiny
+fake USD objects.
+"""
+
+from __future__ import annotations
+
+import sys
+import textwrap
+import types
+from pathlib import Path
+from types import SimpleNamespace
+from typing import Any
+
+import numpy as np
+import pytest
+from unisim.backend.base import RenderClosedError
+from unisim.backend.isaacgym.backend import IsaacGymWorkerError
+from unisim.backend.isaacsim.backend import (
+    IsaacSimBackend,
+    IsaacSimRenderError,
+    IsaacSimWorkerError,
+)
+from unisim.backend.isaacsim.dependencies import (
+    ENV_HOME,
+    ENV_PYTHON,
+    IsaacSimDependencyError,
+    build_worker_env,
+    resolve_isaacsim_runtime,
+)
+from unisim.backend.isaacsim.worker import (
+    _quat_rotate_wxyz,
+    _resolve_articulation_root_prim_path,
+)
+
+from unilab.base.backend_factory import create_backend
+from unilab.base.base import EnvCfg
+from unilab.base.scene import SceneCfg
+
+_MOCK_WORKER = str(Path(__file__).resolve().parent / "isaacgym_mock_worker.py")
+SIM_DT = 0.005
+NUM_ENVS = 2
+
+_ROBOT_XML = """
+<mujoco>
+  <worldbody>
+    <geom name="floor_geom" type="plane" size="0 0 0.05"/>
+    <body name="base">
+      <freejoint/>
+      <site name="imu_site"/>
+      <geom name="base_geom" size="0.1"/>
+      <body name="link0">
+        <joint name="j0" type="hinge" range="-1.5 1.5"/>
+        <geom name="g0" size="0.1"/>
+        <body name="link1">
+          <joint name="j1" type="hinge" range="-1.5 1.5"/>
+          <geom name="g1" size="0.1"/>
+          <body name="link2">
+            <joint name="j2" type="hinge" range="-1.5 1.5"/>
+            <geom name="foot_geom" size="0.1"/>
+          </body>
+        </body>
+      </body>
+    </body>
+  </worldbody>
+  <actuator>
+    <position name="j0" joint="j0" kp="10" kv="0.5"/>
+    <position name="j1" joint="j1" kp="20" kv="1.0"/>
+    <position name="j2" joint="j2" kp="30"/>
+  </actuator>
+</mujoco>
+"""
+
+_SCENE_XML = """
+<mujoco>
+  <include file="robot.xml"/>
+  <sensor>
+    <gyro name="base_gyro" site="imu_site"/>
+    <contact name="foot_contact" geom1="floor_geom" geom2="foot_geom" data="found" num="1"/>
+  </sensor>
+  <keyframe>
+    <key name="home" qpos="0 0 0.8 1 0 0 0 0.1 0.2 0.3"/>
+  </keyframe>
+</mujoco>
+"""
+
+
+@pytest.fixture()
+def scene_file(tmp_path: Path) -> str:
+    (tmp_path / "robot.xml").write_text(textwrap.dedent(_ROBOT_XML), encoding="utf-8")
+    scene = tmp_path / "scene.xml"
+    scene.write_text(textwrap.dedent(_SCENE_XML), encoding="utf-8")
+    return str(scene)
+
+
+def _make_backend(scene_file: str, **kwargs: Any) -> IsaacSimBackend:
+    kwargs.setdefault("worker_command", [sys.executable, _MOCK_WORKER])
+    kwargs.setdefault("worker_timeout_s", 30.0)
+    backend = create_backend(
+        "isaacsim",
+        SceneCfg(model_file=scene_file),
+        NUM_ENVS,
+        SIM_DT,
+        base_name="base",
+        **kwargs,
+    )
+    assert isinstance(backend, IsaacSimBackend)
+    return backend
+
+
+@pytest.fixture()
+def backend(scene_file: str) -> IsaacSimBackend:
+    instance = _make_backend(scene_file)
+    instance.materialize()
+    try:
+        yield instance
+    finally:
+        instance.close()
+
+
+def _make_runtime_tree(home: Path) -> None:
+    python = home / "venv" / "bin" / "python"
+    python.parent.mkdir(parents=True)
+    python.write_text("#!/bin/sh\n", encoding="utf-8")
+    (home / "venv" / "lib" / "python3.11" / "site-packages").mkdir(parents=True)
+    (home / "IsaacLab" / "source").mkdir(parents=True)
+
+
+def test_factory_routes_isaacsim_without_importing_kit(scene_file: str) -> None:
+    backend = _make_backend(scene_file)
+    try:
+        assert backend.backend_type == "isaacsim"
+        assert isinstance(backend, IsaacSimBackend)
+        assert backend._proc is None
+    finally:
+        backend.close()
+
+
+@pytest.mark.parametrize(
+    ("kwargs", "message"),
+    [
+        ({"isaacsim_render_mode": "video"}, "isaacsim_render_mode"),
+        ({"isaacsim_render_width": 0}, "isaacsim_render_width"),
+        ({"isaacsim_render_height": True}, "isaacsim_render_height"),
+    ],
+)
+def test_env_cfg_rejects_invalid_isaacsim_render_settings(
+    kwargs: dict[str, Any], message: str
+) -> None:
+    with pytest.raises(ValueError, match=message):
+        EnvCfg(**kwargs).validate()
+
+
+def test_dependencies_resolve_default_layout(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    monkeypatch.setenv(ENV_HOME, str(tmp_path))
+    monkeypatch.delenv(ENV_PYTHON, raising=False)
+    _make_runtime_tree(tmp_path)
+    runtime = resolve_isaacsim_runtime()
+    assert runtime.python == tmp_path / "venv" / "bin" / "python"
+    assert runtime.package_path == tmp_path / "venv" / "lib" / "python3.11" / "site-packages"
+    assert runtime.isaaclab_source == tmp_path / "IsaacLab" / "source"
+    env = build_worker_env(runtime)
+    assert env["LD_LIBRARY_PATH"].split(":")[0] == str(tmp_path / "venv" / "lib")
+    assert env["PATH"].split(":")[0] == str(tmp_path / "venv" / "bin")
+    assert env["PYTHONPATH"].split(":")[0] == str(tmp_path / "IsaacLab" / "source")
+    assert env["OMNI_KIT_ACCEPT_EULA"] == "1"
+
+
+def test_dependencies_override_and_missing_layout(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    monkeypatch.setenv(ENV_HOME, str(tmp_path))
+    _make_runtime_tree(tmp_path)
+    custom = tmp_path / "custom-python"
+    custom.write_text("#!/bin/sh\n", encoding="utf-8")
+    monkeypatch.setenv(ENV_PYTHON, str(custom))
+    assert resolve_isaacsim_runtime().python == custom
+
+    empty = tmp_path / "empty"
+    monkeypatch.setenv(ENV_HOME, str(empty))
+    monkeypatch.delenv(ENV_PYTHON, raising=False)
+    with pytest.raises(IsaacSimDependencyError, match="setup_isaacsim_env.sh"):
+        resolve_isaacsim_runtime()
+
+
+def test_materialize_uses_shared_protocol_and_exposes_state(backend: IsaacSimBackend) -> None:
+    assert backend.model.num_dof == 3
+    assert backend.model.num_bodies == 4
+    assert backend.get_actuator_names() == ("j0", "j1", "j2")
+    np.testing.assert_allclose(backend.get_default_qpos(), [0, 0, 0.8, 1, 0, 0, 0, 0.1, 0.2, 0.3])
+    np.testing.assert_allclose(backend.get_base_pos(), [[0, 0, 0.8]] * NUM_ENVS)
+    np.testing.assert_allclose(backend.get_dof_pos(), [[0.1, 0.2, 0.3]] * NUM_ENVS)
+    result = backend.step(np.ones((NUM_ENVS, 3), dtype=np.float32), nsteps=1)
+    assert result["timing"]["physics_ms"] >= 0.0
+
+
+def test_env_cleanup_hook_reaps_isaacsim_worker(backend: IsaacSimBackend) -> None:
+    """The NpEnv cleanup hook must not leave the external worker alive."""
+    proc = backend._proc
+    assert proc is not None and proc.poll() is None
+    shm_names = [handle.name for handle in backend._shm_handles.values()]
+
+    backend.cleanup_scene_assets()
+
+    assert proc.poll() is not None
+    assert backend._proc is None
+    from multiprocessing import shared_memory
+
+    for name in shm_names:
+        with pytest.raises(FileNotFoundError):
+            shared_memory.SharedMemory(name=name, create=False)
+
+
+def test_contact_sensor_is_explicitly_unsupported(backend: IsaacSimBackend) -> None:
+    metadata = backend._scene_metadata
+    assert metadata is not None
+    assert "foot_contact" in metadata.unsupported_sensors
+    with pytest.raises(NotImplementedError, match="contact-force reporting"):
+        backend.get_sensor_data("foot_contact")
+    # Non-contact sensors remain available through the inherited cached path.
+    assert backend.get_sensor_data("base_gyro").shape == (NUM_ENVS, 3)
+
+
+def test_play_contract_advertises_native_rendering(
+    backend: IsaacSimBackend,
+    scene_file: str,
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    caps = backend.get_play_capabilities()
+    assert caps.supports_native_interactive_renderer
+    assert caps.supports_native_video_capture
+    assert not caps.supports_physics_state_playback
+
+    plan = backend.resolve_play_render_plan(
+        play_render_mode="none", play_steps=3, output_video=tmp_path / "ignored.mp4"
+    )
+    assert plan.mode == "none" and plan.headless and not plan.record_video
+    with pytest.raises(IsaacSimRenderError, match="before env creation"):
+        backend.resolve_play_render_plan(
+            play_render_mode="auto", play_steps=3, output_video=tmp_path / "play.mp4"
+        )
+
+    monkeypatch.setenv("DISPLAY", ":0")
+    interactive_backend = _make_backend(scene_file, render_mode="auto")
+    try:
+        interactive = interactive_backend.resolve_play_render_plan(
+            play_render_mode="auto", play_steps=3, output_video=None
+        )
+        assert interactive.mode == "interactive" and not interactive.headless
+    finally:
+        interactive_backend.close()
+
+    monkeypatch.delenv("DISPLAY", raising=False)
+    monkeypatch.delenv("WAYLAND_DISPLAY", raising=False)
+    record_backend = _make_backend(scene_file, render_mode="auto")
+    try:
+        record = record_backend.resolve_play_render_plan(
+            play_render_mode="auto", play_steps=3, output_video=tmp_path / "play.mp4"
+        )
+        assert record.mode == "record" and record.headless and record.record_video
+        explicit = record_backend.resolve_play_render_plan(
+            play_render_mode="record", play_steps=7, output_video=tmp_path / "explicit.mp4"
+        )
+        assert explicit.mode == "record" and explicit.num_steps == 7
+        with pytest.raises(IsaacSimRenderError, match="playback requested 'interactive'"):
+            record_backend.resolve_play_render_plan(
+                play_render_mode="interactive", play_steps=3, output_video=None
+            )
+        with pytest.raises(ValueError, match="play_steps"):
+            record_backend.resolve_play_render_plan(
+                play_render_mode="record", play_steps=None, output_video=tmp_path / "play.mp4"
+            )
+        with pytest.raises(ValueError, match="positive finite"):
+            record_backend.resolve_play_render_plan(
+                play_render_mode="record", play_steps=0, output_video=tmp_path / "play.mp4"
+            )
+        with pytest.raises(ValueError, match="output video path"):
+            record_backend.resolve_play_render_plan(
+                play_render_mode="record", play_steps=3, output_video=None
+            )
+    finally:
+        record_backend.close()
+
+
+def test_record_renderer_roundtrip(scene_file: str) -> None:
+    backend = _make_backend(
+        scene_file,
+        render_mode="record",
+        render_width=64,
+        render_height=48,
+    )
+    backend.materialize()
+    try:
+        backend.init_renderer(headless=True, capture=True, width=64, height=48)
+        frame = backend.capture_video_frame()
+        assert frame.shape == (48, 64, 3)
+        assert frame.dtype == np.uint8
+        assert frame.flags.c_contiguous
+        assert int(np.ptp(frame)) > 0
+        with pytest.raises(IsaacSimRenderError, match="worker started"):
+            backend.init_renderer(headless=False, capture=False, width=64, height=48)
+        with pytest.raises(IsaacSimRenderError, match="positive integers"):
+            backend.init_renderer(headless=True, capture=True, width=True, height=48)
+    finally:
+        backend.close()
+
+
+def test_record_playback_writes_video(scene_file: str, tmp_path: Path) -> None:
+    output = tmp_path / "play_video.mp4"
+    backend = _make_backend(
+        scene_file,
+        render_mode="record",
+        render_width=64,
+        render_height=48,
+    )
+    backend.materialize()
+    try:
+        result = backend.run_playback(
+            env=SimpleNamespace(cfg=SimpleNamespace(ctrl_dt=0.02)),
+            initialize=lambda: 0,
+            step=lambda obs: obs + 1,
+            num_steps=3,
+            output_video=output,
+            headless=True,
+            record_video=True,
+        )
+        assert result == str(output)
+        assert output.exists() and output.stat().st_size > 0
+    finally:
+        backend.close()
+
+
+@pytest.mark.parametrize(
+    ("behavior", "message"),
+    [
+        ("capture_uniform", "empty/uniform"),
+        ("capture_float", "dtype=float32"),
+        ("capture_wrong_shape", "invalid RGB frame"),
+    ],
+)
+def test_record_frame_contract_fails_closed(
+    scene_file: str,
+    monkeypatch: pytest.MonkeyPatch,
+    behavior: str,
+    message: str,
+) -> None:
+    monkeypatch.setenv("UNILAB_ISAACGYM_MOCK_BEHAVIOR", behavior)
+    backend = _make_backend(
+        scene_file,
+        render_mode="record",
+        render_width=64,
+        render_height=48,
+    )
+    backend.materialize()
+    try:
+        backend.init_renderer(headless=True, capture=True, width=64, height=48)
+        with pytest.raises(IsaacSimRenderError, match=message):
+            backend.capture_video_frame()
+    finally:
+        backend.close()
+
+
+def test_interactive_renderer_roundtrip(scene_file: str, monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("DISPLAY", ":0")
+    backend = _make_backend(scene_file, render_mode="interactive")
+    backend.materialize()
+    try:
+        backend.init_renderer(headless=False, capture=False)
+        backend.render()
+    finally:
+        backend.close()
+
+
+def test_interactive_playback_routes_startup_dimensions_and_camera(
+    scene_file: str, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    monkeypatch.setenv("DISPLAY", ":0")
+    backend = _make_backend(
+        scene_file,
+        render_mode="interactive",
+        render_width=64,
+        render_height=48,
+    )
+    init_calls: list[dict[str, Any]] = []
+    original_init_renderer = backend.init_renderer
+
+    def record_init_renderer(*args: Any, **kwargs: Any) -> None:
+        init_calls.append(dict(kwargs))
+        original_init_renderer(*args, **kwargs)
+
+    backend.init_renderer = record_init_renderer  # type: ignore[method-assign]
+    backend.materialize()
+    try:
+        result = backend.run_playback(
+            env=SimpleNamespace(cfg=None),
+            initialize=lambda: 0,
+            step=lambda obs: obs + 1,
+            num_steps=1,
+            headless=False,
+            record_video=False,
+            camera_kwargs={
+                "cam_distance": 3.0,
+                "cam_elevation": -15.0,
+                "cam_azimuth": 45.0,
+            },
+        )
+        assert result is None
+        assert init_calls == [
+            {
+                "headless": False,
+                "width": 64,
+                "height": 48,
+                "camera_kwargs": {
+                    "cam_distance": 3.0,
+                    "cam_elevation": -15.0,
+                    "cam_azimuth": 45.0,
+                },
+            }
+        ]
+    finally:
+        backend.close()
+
+
+def test_interactive_window_close_maps_to_interface_error(
+    scene_file: str, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    monkeypatch.setenv("DISPLAY", ":0")
+    monkeypatch.setenv("UNILAB_ISAACGYM_MOCK_BEHAVIOR", "close_on_render")
+    backend = _make_backend(scene_file, render_mode="interactive")
+    backend.materialize()
+    try:
+        backend.init_renderer(headless=False, capture=False)
+        with pytest.raises(RenderClosedError, match="closed"):
+            backend.render()
+    finally:
+        backend.close()
+
+
+def test_interactive_playback_stops_when_window_closes(
+    scene_file: str, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    monkeypatch.setenv("DISPLAY", ":0")
+    monkeypatch.setenv("UNILAB_ISAACGYM_MOCK_BEHAVIOR", "close_on_render")
+    backend = _make_backend(scene_file, render_mode="interactive")
+    backend.materialize()
+    steps: list[int] = []
+    try:
+        result = backend.run_playback(
+            env=SimpleNamespace(cfg=None),
+            initialize=lambda: 0,
+            step=lambda obs: steps.append(obs) or (obs + 1),
+            num_steps=None,
+            headless=False,
+            record_video=False,
+        )
+        assert result is None
+        assert steps == [0]
+    finally:
+        backend.close()
+
+
+def test_explicit_interactive_without_display_fails_closed(
+    scene_file: str, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    monkeypatch.delenv("DISPLAY", raising=False)
+    monkeypatch.delenv("WAYLAND_DISPLAY", raising=False)
+    backend = _make_backend(scene_file, render_mode="interactive")
+    try:
+        with pytest.raises(IsaacSimRenderError, match="no local display"):
+            backend.materialize()
+    finally:
+        backend.close()
+
+
+@pytest.mark.parametrize(
+    ("behavior", "message"),
+    [
+        ("render_meta_missing", "missing render startup fields"),
+        ("render_meta_mode_mismatch", "render_mode does not match"),
+        ("render_meta_size_mismatch", "render dimensions do not match"),
+        ("render_meta_graphics_mismatch", "graphics_enabled does not match"),
+    ],
+)
+def test_render_startup_metadata_mismatch_fails_closed(
+    scene_file: str,
+    monkeypatch: pytest.MonkeyPatch,
+    behavior: str,
+    message: str,
+) -> None:
+    monkeypatch.setenv("UNILAB_ISAACGYM_MOCK_BEHAVIOR", behavior)
+    backend = _make_backend(scene_file, render_mode="record", render_width=64, render_height=48)
+    try:
+        with pytest.raises(IsaacSimWorkerError, match=message):
+            backend.materialize()
+    finally:
+        backend.close()
+
+
+def test_isaacsim_worker_error_is_not_isaacgym_error_type(
+    scene_file: str, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    monkeypatch.setenv("UNILAB_ISAACGYM_MOCK_BEHAVIOR", "fail_init")
+    backend = _make_backend(scene_file)
+    try:
+        with pytest.raises(IsaacSimWorkerError) as caught:
+            backend.materialize()
+        assert type(caught.value) is IsaacSimWorkerError
+        assert isinstance(caught.value, IsaacGymWorkerError)
+    finally:
+        backend.close()
+
+
+def test_isaacsim_worker_timeout_has_backend_diagnostic(
+    scene_file: str, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    monkeypatch.setenv("UNILAB_ISAACGYM_MOCK_BEHAVIOR", "hang_on_step")
+    backend = _make_backend(scene_file, worker_timeout_s=0.2)
+    backend.materialize()
+    try:
+        with pytest.raises(IsaacSimWorkerError, match="isaacsim worker did not answer STEP"):
+            backend.step(np.zeros((NUM_ENVS, 3), dtype=np.float32))
+        assert backend._proc is not None and backend._proc.poll() is not None
+    finally:
+        backend.close()
+
+
+def test_root_angular_velocity_helper_converts_body_to_world() -> None:
+    half = np.sqrt(0.5)
+    quat = np.array([[half, half, 0.0, 0.0]], dtype=np.float32)
+    body_angvel = np.array([[0.0, -1.0, 0.0]], dtype=np.float32)
+    np.testing.assert_allclose(_quat_rotate_wxyz(quat, body_angvel), [[0.0, 0.0, -1.0]], atol=1e-6)
+
+
+class _FakePrim:
+    def __init__(self, path: str, *, root_api: bool = False, valid: bool = True) -> None:
+        self.path = path
+        self.root_api = root_api
+        self.valid = valid
+
+    def GetPath(self) -> str:
+        return self.path
+
+    def IsValid(self) -> bool:
+        return self.valid
+
+    def HasAPI(self, api: object) -> bool:
+        del api
+        return self.root_api
+
+
+class _FakeStage:
+    def __init__(self, default_path: str, prims: list[_FakePrim]) -> None:
+        self.default = _FakePrim(default_path)
+        self.prims = prims
+
+    def GetDefaultPrim(self) -> _FakePrim:
+        return self.default
+
+    def Traverse(self) -> list[_FakePrim]:
+        return self.prims
+
+
+def _install_fake_pxr(monkeypatch: pytest.MonkeyPatch, stage: _FakeStage) -> None:
+    class _Stage:
+        @staticmethod
+        def Open(path: str) -> _FakeStage:
+            del path
+            return stage
+
+    usd = types.SimpleNamespace(Stage=_Stage)
+    usd_physics = types.SimpleNamespace(ArticulationRootAPI=object())
+    pxr = types.ModuleType("pxr")
+    pxr.Usd = usd  # type: ignore[attr-defined]
+    pxr.UsdPhysics = usd_physics  # type: ignore[attr-defined]
+    monkeypatch.setitem(sys.modules, "pxr", pxr)
+
+
+def test_resolve_articulation_root_prim_path_discovers_unique_root(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    stage = _FakeStage(
+        "/Asset",
+        [
+            _FakePrim("/Asset", root_api=False),
+            _FakePrim("/Asset/pelvis", root_api=True),
+            _FakePrim("/Asset/pelvis/mesh", root_api=False),
+        ],
+    )
+    _install_fake_pxr(monkeypatch, stage)
+    assert _resolve_articulation_root_prim_path("asset.usd", "pelvis") == "/pelvis"
+
+
+@pytest.mark.parametrize(
+    "prims",
+    [
+        [_FakePrim("/Asset/other", root_api=True)],
+        [
+            _FakePrim("/Asset/pelvis/a", root_api=True),
+            _FakePrim("/Asset/pelvis/b", root_api=True),
+        ],
+    ],
+)
+def test_resolve_articulation_root_prim_path_fails_on_missing_or_ambiguous(
+    monkeypatch: pytest.MonkeyPatch, prims: list[_FakePrim]
+) -> None:
+    _install_fake_pxr(monkeypatch, _FakeStage("/Asset", prims))
+    with pytest.raises(RuntimeError, match="expected one ArticulationRootAPI"):
+        _resolve_articulation_root_prim_path("asset.usd", "pelvis")
diff --git a/tests/base/test_manager_config_overlay.py b/tests/base/test_manager_config_overlay.py
new file mode 100644
index 000000000..8722c09bc
--- /dev/null
+++ b/tests/base/test_manager_config_overlay.py
@@ -0,0 +1,481 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field, fields, is_dataclass
+
+import numpy as np
+import pytest
+from omegaconf import OmegaConf
+
+from unilab.base.registry import apply_cfg_overrides
+from unilab.envs.manager_based_rl_env import ManagerBasedRlEnvCfg
+from unilab.envs.mdp import JointPositionActionCfg, UniformVelocityCommandCfg
+from unilab.managers import (
+    CurriculumTermCfg,
+    EventTermCfg,
+    MetricsTermCfg,
+    ObservationGroupCfg,
+    ObservationTermCfg,
+    RecorderTermCfg,
+    RewardTermCfg,
+    SceneEntityCfg,
+    TerminationTermCfg,
+)
+
+
+def _first_term(_env, *, command_name: str, std: float) -> np.ndarray:
+    del command_name, std
+    return np.zeros(1, dtype=np.float32)
+
+
+def _second_term(_env) -> np.ndarray:
+    return np.zeros(1, dtype=np.float32)
+
+
+def _manager_cfg() -> ManagerBasedRlEnvCfg:
+    return ManagerBasedRlEnvCfg(
+        observations={
+            "policy": ObservationGroupCfg(
+                terms={
+                    "first": ObservationTermCfg(func=_first_term),
+                    "second": ObservationTermCfg(func=_second_term),
+                }
+            )
+        },
+        actions={
+            "joint_pos": JointPositionActionCfg(
+                entity_name="robot",
+                actuator_names=(".*",),
+                scale=0.25,
+            )
+        },
+        events={
+            "reset": EventTermCfg(
+                func=_first_term,
+                mode="reset",
+                params={"command_name": "twist", "std": 0.5},
+            )
+        },
+        rewards={
+            "tracking": RewardTermCfg(
+                func=_first_term,
+                weight=1.0,
+                params={
+                    "command_name": "twist",
+                    "std": 0.5,
+                    "asset_cfg": SceneEntityCfg("robot", joint_names=".*"),
+                },
+            ),
+            "alive": RewardTermCfg(func=_second_term, weight=0.1),
+            "disabled": None,
+        },
+    )
+
+
+def test_manager_mapping_overlay_preserves_factory_terms_and_order() -> None:
+    cfg = _manager_cfg()
+    initial_tracking = cfg.rewards["tracking"]
+    assert initial_tracking is not None
+    reward_func = initial_tracking.func
+
+    apply_cfg_overrides(
+        cfg,
+        {
+            "rewards": {
+                "tracking": {
+                    "weight": 2.0,
+                    "params": {"std": 0.25, "asset_cfg": {"preserve_order": True}},
+                },
+                "alive": None,
+            },
+            "events": {"reset": {"min_step_count_between_reset": 3}},
+            "actions": {"joint_pos": {"scale": 0.4}},
+        },
+    )
+
+    tracking = cfg.rewards["tracking"]
+    assert tracking is not None
+    assert tracking.func is reward_func
+    assert tracking.weight == pytest.approx(2.0)
+    assert tracking.params["command_name"] == "twist"
+    assert tracking.params["std"] == pytest.approx(0.25)
+    assert tracking.params["asset_cfg"].joint_names == ".*"
+    assert tracking.params["asset_cfg"].preserve_order is True
+    assert cfg.rewards["alive"] is None
+    assert list(cfg.rewards) == ["tracking", "alive", "disabled"]
+    reset = cfg.events["reset"]
+    assert reset is not None
+    assert reset.func is _first_term
+    assert reset.min_step_count_between_reset == 3
+    action = cfg.actions["joint_pos"]
+    assert isinstance(action, JointPositionActionCfg)
+    assert action.entity_name == "robot"
+    assert action.actuator_names == (".*",)
+    assert action.scale == pytest.approx(0.4)
+
+
+def test_observation_group_and_term_overlay_preserve_siblings() -> None:
+    cfg = _manager_cfg()
+
+    apply_cfg_overrides(
+        cfg,
+        {
+            "observations": {
+                "policy": {
+                    "enable_corruption": True,
+                    "terms": {"first": {"scale": 2.0}, "second": None},
+                }
+            }
+        },
+    )
+
+    policy = cfg.observations["policy"]
+    assert policy is not None
+    assert policy.enable_corruption is True
+    first = policy.terms["first"]
+    assert first is not None
+    assert first.func is _first_term
+    assert first.scale == pytest.approx(2.0)
+    assert policy.terms["second"] is None
+    assert list(policy.terms) == ["first", "second"]
+
+
+def _assert_no_omegaconf(value: object) -> None:
+    assert not OmegaConf.is_config(value)
+    if is_dataclass(value) and not isinstance(value, type):
+        for config_field in fields(value):
+            _assert_no_omegaconf(getattr(value, config_field.name))
+    elif isinstance(value, dict):
+        for item in value.values():
+            _assert_no_omegaconf(item)
+    elif isinstance(value, (list, tuple)):
+        for item in value:
+            _assert_no_omegaconf(item)
+
+
+def test_hydra_mapping_fully_materializes_empty_manager_config() -> None:
+    cfg = ManagerBasedRlEnvCfg()
+    hydra_mapping = OmegaConf.create(
+        {
+            "scene": {
+                "model_file": "robot.xml",
+                "entities": {
+                    "robot": {
+                        "root_body_name": "base",
+                        "joint_names": ["joint"],
+                        "actuator_names": ["motor"],
+                    }
+                },
+            },
+            "sim_dt": 0.01,
+            "ctrl_dt": 0.02,
+            "max_episode_seconds": 20.0,
+            "observations": {
+                "policy": {
+                    "_target_": "unilab.managers.ObservationGroupCfg",
+                    "terms": {
+                        "joint_pos": {
+                            "_target_": "unilab.managers.ObservationTermCfg",
+                            "func": "unilab.envs.mdp.joint_pos_rel",
+                            "params": {
+                                "asset_cfg": {
+                                    "_target_": "unilab.managers.SceneEntityCfg",
+                                    "name": "robot",
+                                    "joint_names": ".*",
+                                }
+                            },
+                        },
+                        "disabled": None,
+                    },
+                }
+            },
+            "actions": {
+                "joint_pos": {
+                    "_target_": "unilab.envs.mdp.JointPositionActionCfg",
+                    "entity_name": "robot",
+                    "actuator_names": [".*"],
+                    "scale": 0.25,
+                }
+            },
+            "commands": {
+                "twist": {
+                    "_target_": "unilab.envs.mdp.UniformVelocityCommandCfg",
+                    "entity_name": "robot",
+                    "resampling_time_range": [1.0, 1.0],
+                    "ranges": {
+                        "lin_vel_x": [-1.0, 1.0],
+                        "lin_vel_y": [-0.5, 0.5],
+                        "ang_vel_z": [-1.0, 1.0],
+                    },
+                }
+            },
+            "events": {
+                "reset": {
+                    "_target_": "unilab.managers.EventTermCfg",
+                    "func": "unilab.envs.mdp.reset_scene_to_default",
+                    "mode": "reset",
+                }
+            },
+            "rewards": {
+                "alive": {
+                    "_target_": "unilab.managers.RewardTermCfg",
+                    "func": "unilab.envs.mdp.is_alive",
+                    "weight": 1.0,
+                }
+            },
+            "terminations": {
+                "time_out": {
+                    "_target_": "unilab.managers.TerminationTermCfg",
+                    "func": "unilab.envs.mdp.time_out",
+                    "time_out": True,
+                }
+            },
+            "curriculum": {
+                "difficulty": {
+                    "_target_": "unilab.managers.CurriculumTermCfg",
+                    "func": "unilab.envs.mdp.is_alive",
+                }
+            },
+            "metrics": {
+                "alive": {
+                    "_target_": "unilab.managers.MetricsTermCfg",
+                    "func": "unilab.envs.mdp.is_alive",
+                }
+            },
+            "recorders": {
+                "trace": {
+                    "_target_": "unilab.managers.RecorderTermCfg",
+                    "func": "unilab.managers.RecorderTerm",
+                }
+            },
+            "policy_observation_group": "policy",
+        }
+    )
+
+    apply_cfg_overrides(cfg, hydra_mapping)
+    cfg.validate()
+
+    assert cfg.scene is not None
+    assert cfg.scene.entities["robot"].joint_names == ["joint"]
+    assert list(cfg.observations) == ["policy"]
+    policy = cfg.observations["policy"]
+    assert isinstance(policy, ObservationGroupCfg)
+    assert list(policy.terms) == ["joint_pos", "disabled"]
+    joint_obs = policy.terms["joint_pos"]
+    assert isinstance(joint_obs, ObservationTermCfg)
+    assert joint_obs.func is not None and callable(joint_obs.func)
+    assert isinstance(joint_obs.params["asset_cfg"], SceneEntityCfg)
+    assert isinstance(cfg.actions["joint_pos"], JointPositionActionCfg)
+    twist = cfg.commands["twist"]
+    assert isinstance(twist, UniformVelocityCommandCfg)
+    assert isinstance(twist.ranges, UniformVelocityCommandCfg.Ranges)
+    assert isinstance(cfg.events["reset"], EventTermCfg)
+    assert isinstance(cfg.rewards["alive"], RewardTermCfg)
+    assert isinstance(cfg.terminations["time_out"], TerminationTermCfg)
+    assert isinstance(cfg.curriculum["difficulty"], CurriculumTermCfg)
+    assert isinstance(cfg.metrics["alive"], MetricsTermCfg)
+    assert isinstance(cfg.recorders["trace"], RecorderTermCfg)
+    _assert_no_omegaconf(cfg)
+
+
+@pytest.mark.parametrize(
+    ("overrides", "match"),
+    [
+        ({"actions": {"missing": {"scale": 0.25}}}, "missing.*_target_"),
+        ({"rewards": {"tracking": _second_term}}, "tracking.*field mapping"),
+        ({"rewards": []}, "rewards.*mapping"),
+        ({"rewards": {"tracking": {"func": _second_term}}}, "func.*typed term"),
+    ],
+)
+def test_manager_mapping_overlay_fails_closed(overrides: dict, match: str) -> None:
+    with pytest.raises((TypeError, ValueError), match=match):
+        apply_cfg_overrides(_manager_cfg(), overrides)
+
+
+def test_manager_mapping_overlay_infers_concrete_term_target() -> None:
+    cfg = _manager_cfg()
+
+    apply_cfg_overrides(
+        cfg,
+        {
+            "rewards": {
+                "missing": {
+                    "func": "unilab.envs.mdp.is_alive",
+                    "weight": 0.5,
+                },
+                "disabled": {
+                    "func": "unilab.envs.mdp.is_alive",
+                    "weight": 0.2,
+                },
+            },
+            "observations": {
+                "critic": {
+                    "terms": {
+                        "joint_vel": {"func": "unilab.envs.mdp.joint_vel_rel"},
+                    }
+                }
+            },
+        },
+    )
+
+    missing = cfg.rewards["missing"]
+    assert isinstance(missing, RewardTermCfg)
+    assert callable(missing.func)
+    assert missing.weight == pytest.approx(0.5)
+    disabled = cfg.rewards["disabled"]
+    assert isinstance(disabled, RewardTermCfg)
+    assert disabled.weight == pytest.approx(0.2)
+    assert list(cfg.rewards) == ["tracking", "alive", "disabled", "missing"]
+    critic = cfg.observations["critic"]
+    assert isinstance(critic, ObservationGroupCfg)
+    joint_vel = critic.terms["joint_vel"]
+    assert isinstance(joint_vel, ObservationTermCfg)
+    assert callable(joint_vel.func)
+
+
+def test_hydra_materialization_resolves_managers_short_name() -> None:
+    cfg = ManagerBasedRlEnvCfg()
+
+    apply_cfg_overrides(
+        cfg,
+        {
+            "events": {
+                "base_mass": {
+                    "func": "unilab.envs.mdp.is_alive",
+                    "mode": "reset",
+                    "params": {
+                        "asset_cfg": {"_target_": "SceneEntityCfg", "name": "robot"},
+                    },
+                }
+            }
+        },
+    )
+
+    base_mass = cfg.events["base_mass"]
+    assert isinstance(base_mass, EventTermCfg)
+    asset_cfg = base_mass.params["asset_cfg"]
+    assert isinstance(asset_cfg, SceneEntityCfg)
+    assert asset_cfg.name == "robot"
+
+
+def test_hydra_materialization_rejects_unknown_short_name() -> None:
+    with pytest.raises(ValueError, match="could not resolve"):
+        apply_cfg_overrides(
+            ManagerBasedRlEnvCfg(),
+            {
+                "rewards": {
+                    "term": {
+                        "_target_": "NoSuchCfg",
+                        "func": "unilab.envs.mdp.is_alive",
+                        "weight": 1.0,
+                    }
+                }
+            },
+        )
+
+
+@pytest.mark.parametrize(
+    ("entry", "match"),
+    [
+        (
+            {
+                "_target_": "unilab.managers.EventTermCfg",
+                "func": "unilab.envs.mdp.is_alive",
+                "mode": "reset",
+            },
+            "EventTermCfg.*RewardTermCfg",
+        ),
+        (
+            {"_target_": "unilab.envs.mdp.is_alive", "func": "unilab.envs.mdp.is_alive"},
+            "dataclass type",
+        ),
+        (
+            {"_target_": "unilab.managers.MissingCfg", "func": "unilab.envs.mdp.is_alive"},
+            "_target_.*could not resolve",
+        ),
+        (
+            {
+                "_target_": "unilab.managers.RewardTermCfg",
+                "func": "unilab.envs.mdp.missing",
+                "weight": 1.0,
+            },
+            "func.*could not resolve",
+        ),
+        (
+            {
+                "_target_": "unilab.managers.RewardTermCfg",
+                "func": "unilab.base.config_overrides.CONFIG_MAPPING_POLICY_KEY",
+                "weight": 1.0,
+            },
+            "expected a callable",
+        ),
+        (
+            {
+                "_target_": "unilab.managers.RewardTermCfg",
+                "func": "unilab.envs.mdp.is_alive",
+                "weight": 1.0,
+                "unknown": 1,
+            },
+            "has no fields.*unknown",
+        ),
+        (
+            {"_target_": "unilab.managers.RewardTermCfg", "func": "unilab.envs.mdp.is_alive"},
+            "could not construct",
+        ),
+    ],
+)
+def test_hydra_manager_materialization_fails_closed(entry: dict, match: str) -> None:
+    with pytest.raises((TypeError, ValueError), match=match):
+        apply_cfg_overrides(ManagerBasedRlEnvCfg(), {"rewards": {"term": entry}})
+
+
+def test_hydra_manager_materialization_rejects_abstract_action_config() -> None:
+    with pytest.raises(TypeError, match="abstract config.*ActionTermCfg"):
+        apply_cfg_overrides(
+            ManagerBasedRlEnvCfg(),
+            {
+                "actions": {
+                    "joint": {
+                        "_target_": "unilab.managers.ActionTermCfg",
+                        "entity_name": "robot",
+                    }
+                }
+            },
+        )
+
+
+def test_hydra_manager_materialization_rejects_wrong_nested_config_type() -> None:
+    with pytest.raises(TypeError, match="SceneCfg.*Ranges"):
+        apply_cfg_overrides(
+            ManagerBasedRlEnvCfg(),
+            {
+                "commands": {
+                    "twist": {
+                        "_target_": "unilab.envs.mdp.UniformVelocityCommandCfg",
+                        "entity_name": "robot",
+                        "resampling_time_range": [1.0, 1.0],
+                        "ranges": {
+                            "_target_": "unilab.base.scene.SceneCfg",
+                            "model_file": "robot.xml",
+                        },
+                    }
+                }
+            },
+        )
+
+
+@dataclass
+class _LegacyCfg:
+    reward_config: dict[str, object] = field(
+        default_factory=lambda: {
+            "scales": {"tracking": 1.0, "alive": 0.1},
+            "tracking_sigma": 0.25,
+        }
+    )
+
+
+def test_legacy_plain_dict_keeps_replacement_semantics() -> None:
+    cfg = _LegacyCfg()
+
+    apply_cfg_overrides(cfg, {"reward_config": {"scales": {"alive": 1.0}}})
+
+    assert cfg.reward_config == {"scales": {"alive": 1.0}}
diff --git a/tests/base/test_mjwarp_backend.py b/tests/base/test_mjwarp_backend.py
index 5f1ee6b3b..e31378445 100644
--- a/tests/base/test_mjwarp_backend.py
+++ b/tests/base/test_mjwarp_backend.py
@@ -14,12 +14,12 @@
 import pytest
 from hydra import compose, initialize_config_dir
 from hydra.core.global_hydra import GlobalHydra
+from unisim.backend.mjwarp.dependencies import load_mjwarp_dependencies
 
 from unilab.base import registry
-from unilab.base.backend import create_backend
-from unilab.base.backend.mjwarp.dependencies import load_mjwarp_dependencies
+from unilab.base.backend_factory import create_backend
+from unilab.base.config_adapter import BackendAdapter
 from unilab.base.scene import SceneCfg
-from unilab.training.backend_adapter import BackendAdapter
 
 pytestmark = pytest.mark.slow
 REPO_ROOT = Path(__file__).resolve().parents[2]
@@ -53,6 +53,19 @@ def test_real_cuda_init_reset_step(monkeypatch: pytest.MonkeyPatch) -> None:
     assert backend.backend_type == "mjwarp"
     assert backend.num_actuators == 29
     assert backend.num_dof_vel == 29
+    caches = (
+        (backend._qpos_cache_storage, backend._qpos_cache),
+        (backend._qvel_cache_storage, backend._qvel_cache),
+        (backend._sensor_cache_storage, backend._sensor_cache),
+    )
+    for storage, cache in caches:
+        assert storage.device.is_cpu
+        assert storage.pinned
+        assert cache.ctypes.data == storage.ptr
+    cache_pointers = tuple(cache.ctypes.data for _, cache in caches)
+    root_layout = backend.get_root_state_layout("pelvis")
+    assert root_layout.qpos_indices == tuple(range(7))
+    assert root_layout.qvel_indices == tuple(range(6))
 
     graph_launches: list[Any] = []
     original_capture_launch = backend._warp.capture_launch
@@ -84,6 +97,7 @@ def capture_launch(graph: Any) -> None:
     assert np.isfinite(backend.get_dof_pos()).all()
     assert np.isfinite(backend.get_sensor_data("torso_upvector")).all()
     assert not np.array_equal(backend.get_base_pos(), before)
+    assert tuple(cache.ctypes.data for _, cache in caches) == cache_pointers
 
 
 def test_selected_row_reset_isolated() -> None:
@@ -109,13 +123,81 @@ def test_selected_row_reset_isolated() -> None:
     assert np.isfinite(backend.get_sensor_data("pelvis_local_linvel")).all()
 
 
+def test_large_batch_sparse_reset_matches_full_forward(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """The bounded reset graph preserves reset sensors and the following step."""
+    num_envs = 1024
+    backend = create_backend(
+        "mjwarp",
+        _scene(),
+        num_envs,
+        0.02 / 3.0,
+        base_name="pelvis",
+        body_state_required=True,
+    )
+    capacity = backend._reset_scratch_capacity
+    assert capacity == 128
+    assert backend._reset_scratch_forward_graph is not None
+
+    rng = np.random.default_rng(7)
+    all_rows = np.arange(num_envs, dtype=np.int32)
+    all_qpos, all_qvel = _stand_state(backend, num_envs)
+    rows = np.sort(rng.choice(num_envs, 32, replace=False)).astype(np.int32)
+    reset_qpos, reset_qvel = _stand_state(backend, len(rows))
+    reset_qpos[:, 0] += rng.uniform(-0.2, 0.2, len(rows)).astype(np.float32)
+    reset_qvel[:] = rng.uniform(-0.2, 0.2, reset_qvel.shape).astype(np.float32)
+    ctrl = np.zeros((num_envs, backend.num_actuators), dtype=np.float32)
+
+    def snapshot() -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        return (
+            backend._qpos_cache[rows].copy(),
+            backend._qvel_cache[rows].copy(),
+            backend._sensor_cache[rows].copy(),
+        )
+
+    backend.set_state(all_rows, all_qpos, all_qvel)
+    backend._reset_scratch_capacity = 0
+    backend.set_state(rows, reset_qpos, reset_qvel)
+    full_reset = snapshot()
+    backend.step(ctrl, nsteps=3)
+    full_step = snapshot()
+
+    backend._reset_scratch_capacity = capacity
+    backend.set_state(all_rows, all_qpos, all_qvel)
+    graph_launches: list[Any] = []
+    original_capture_launch = backend._warp.capture_launch
+
+    def capture_launch(graph: Any) -> None:
+        graph_launches.append(graph)
+        original_capture_launch(graph)
+
+    monkeypatch.setattr(backend._warp, "capture_launch", capture_launch)
+    backend.set_state(rows, reset_qpos, reset_qvel)
+    assert graph_launches == [
+        backend._reset_graph,
+        backend._reset_scratch_reset_graph,
+        backend._reset_scratch_forward_graph,
+    ]
+    sparse_reset = snapshot()
+    backend.step(ctrl, nsteps=3)
+    sparse_step = snapshot()
+
+    for actual, expected in zip(sparse_reset[:2], full_reset[:2], strict=True):
+        np.testing.assert_array_equal(actual, expected)
+    np.testing.assert_allclose(sparse_reset[2], full_reset[2], rtol=1e-3, atol=1e-3)
+    np.testing.assert_allclose(sparse_step[0], full_step[0], rtol=1e-3, atol=2e-5)
+    np.testing.assert_allclose(sparse_step[1], full_step[1], rtol=1e-3, atol=1e-3)
+    np.testing.assert_allclose(sparse_step[2], full_step[2], rtol=1e-3, atol=1e-2)
+
+
 @pytest.mark.parametrize(
     ("config_group", "overrides", "algo_name"),
     [
         pytest.param("ppo", ["task=g1_walk_flat/mjwarp"], "ppo", id="ppo"),
         pytest.param(
-            "offpolicy",
-            ["algo=sac", "task=sac/g1_walk_flat/mjwarp"],
+            "sac",
+            ["task=g1_walk_flat/mjwarp"],
             "sac",
             id="sac",
         ),
@@ -129,7 +211,7 @@ def test_g1_walk_flat_owner_one_step(
     _require_cuda_mjwarp()
     GlobalHydra.instance().clear()
     with initialize_config_dir(
-        config_dir=str(REPO_ROOT / "conf" / config_group), version_base="1.3"
+        config_dir=str(REPO_ROOT / "src" / "unilab" / "conf" / config_group), version_base="1.3"
     ):
         cfg = compose("config", overrides=overrides)
 
@@ -155,3 +237,153 @@ def test_g1_walk_flat_owner_one_step(
     assert np.isfinite(state.obs["obs"]).all()
     assert np.isfinite(state.obs["critic"]).all()
     assert np.isfinite(state.reward).all()
+
+
+def test_body_state_matches_mujoco_backend() -> None:
+    """Tracked body kinematics on mjwarp match the MuJoCo backend on identical state."""
+    import mujoco
+    from unisim.backend.mujoco.backend import MuJoCoBackend
+
+    _require_cuda_mjwarp()
+    num_envs = 2
+    sim_dt = 0.02 / 3.0
+    scene = _scene()
+    mujoco_backend = MuJoCoBackend(
+        scene,
+        num_envs,
+        sim_dt,
+        base_name="pelvis",
+        add_body_sensors=True,
+    )
+    mujoco_backend.materialize()
+    mjwarp_backend = create_backend(
+        "mjwarp",
+        scene,
+        num_envs,
+        sim_dt,
+        base_name="pelvis",
+        body_state_required=True,
+    )
+
+    rng = np.random.default_rng(0)
+    qpos = np.tile(mjwarp_backend.get_keyframe_qpos("stand"), (num_envs, 1))
+    qvel = rng.uniform(-0.5, 0.5, size=(num_envs, int(mujoco_backend.model.nv))).astype(np.float32)
+    rows = np.arange(num_envs, dtype=np.int32)
+    mujoco_backend.set_state(rows, qpos, qvel)
+    mjwarp_backend.set_state(rows, qpos, qvel)
+
+    body_names = ["pelvis", "left_hip_pitch_link", "left_knee_link"]
+    body_ids = np.asarray(
+        [
+            mujoco.mj_name2id(mujoco_backend.model, mujoco.mjtObj.mjOBJ_BODY, name)
+            for name in body_names
+        ],
+        dtype=np.int32,
+    )
+    assert (body_ids > 0).all()
+
+    atol = 2e-3
+    np.testing.assert_allclose(
+        mjwarp_backend.get_body_pos_w(body_ids),
+        mujoco_backend.get_body_pos_w(body_ids),
+        atol=atol,
+    )
+    np.testing.assert_allclose(
+        mjwarp_backend.get_body_quat_w(body_ids),
+        mujoco_backend.get_body_quat_w(body_ids),
+        atol=atol,
+    )
+    np.testing.assert_allclose(
+        mjwarp_backend.get_body_lin_vel_w(body_ids),
+        mujoco_backend.get_body_lin_vel_w(body_ids),
+        atol=atol,
+    )
+    np.testing.assert_allclose(
+        mjwarp_backend.get_body_ang_vel_w(body_ids),
+        mujoco_backend.get_body_ang_vel_w(body_ids),
+        atol=atol,
+    )
+    expected_state = mjwarp_backend.get_body_state_w(body_ids)
+    row_ids = np.asarray([1, 0], dtype=np.int32)
+    mjwarp_pose_rows = mjwarp_backend.get_body_pose_w_rows(row_ids, body_ids)
+    for actual, expected in zip(mjwarp_pose_rows, expected_state[:2], strict=True):
+        np.testing.assert_allclose(actual, expected[row_ids], atol=atol)
+    np.testing.assert_allclose(
+        mjwarp_backend.get_body_lin_vel_w_rows(row_ids, body_ids),
+        expected_state[2][row_ids],
+        atol=atol,
+    )
+    np.testing.assert_allclose(
+        mjwarp_backend.get_body_ang_vel_w_rows(row_ids, body_ids),
+        expected_state[3][row_ids],
+        atol=atol,
+    )
+    outputs = tuple(np.empty_like(value) for value in expected_state)
+    result = mjwarp_backend.copy_body_state_w(body_ids, *outputs)
+    assert result == outputs
+    for actual, expected in zip(outputs, expected_state, strict=True):
+        np.testing.assert_allclose(actual, expected, atol=atol)
+    np.testing.assert_allclose(
+        mjwarp_backend.get_body_lin_vel_b(body_ids),
+        mujoco_backend.get_body_lin_vel_b(body_ids),
+        atol=atol,
+    )
+    np.testing.assert_allclose(
+        mjwarp_backend.get_body_ang_vel_b(body_ids),
+        mujoco_backend.get_body_ang_vel_b(body_ids),
+        atol=atol,
+    )
+
+
+def test_set_state_returns_schema_conformant_timing() -> None:
+    """Issue #1295: mjwarp set_state reports the shared keyset plus its granular
+    reset_upload / reset_forward / host_cache_refresh sub-timings."""
+    from unilab.base.np_env import BACKEND_SET_STATE_DETAIL_TIMING_KEYS
+
+    backend = _backend(2)
+    qpos, qvel = _stand_state(backend, 2)
+
+    result = backend.set_state(np.asarray([0, 1], dtype=np.int32), qpos, qvel)
+    timing = result["timing"]
+    missing = [key for key in BACKEND_SET_STATE_DETAIL_TIMING_KEYS if key not in timing]
+    assert not missing, f"missing keys in set_state timing: {missing}"
+    for key in BACKEND_SET_STATE_DETAIL_TIMING_KEYS:
+        value = timing[key]
+        assert isinstance(value, float), f"{key} must be float, got {type(value)!r}"
+        if not key.endswith("internal_gap_ms"):
+            assert value >= 0.0, f"{key} must be non-negative, got {value}"
+    assert abs(timing["set_state_internal_gap_ms"]) < 5.0
+    # mjwarp populates its own sub-keys; other backends' sub-keys stay 0.0.
+    assert timing["set_state_reset_upload_ms"] > 0.0
+    assert timing["set_state_reset_forward_ms"] > 0.0
+    assert timing["set_state_host_cache_refresh_ms"] > 0.0
+    assert timing["set_state_mask_ms"] == 0.0
+    assert timing["set_state_pool_reset_ms"] == 0.0
+    # Legacy collapsed keys are gone.
+    assert "set_state_reset_ms" not in timing
+    assert "set_state_cache_refresh_ms" not in timing
+
+    empty = backend.set_state(
+        np.asarray([], dtype=np.int32),
+        np.zeros((0, qpos.shape[1]), dtype=np.float32),
+        np.zeros((0, qvel.shape[1]), dtype=np.float32),
+    )
+    empty_timing = empty["timing"]
+    missing = [key for key in BACKEND_SET_STATE_DETAIL_TIMING_KEYS if key not in empty_timing]
+    assert not missing, f"missing keys in empty set_state timing: {missing}"
+    assert all(value == 0.0 for value in empty_timing.values())
+
+
+def test_body_state_untracked_body_ids_fail_closed() -> None:
+    """Body ids outside the injected tracking set raise instead of wrapping around."""
+    _require_cuda_mjwarp()
+    backend = create_backend(
+        "mjwarp",
+        _scene(),
+        1,
+        0.02 / 3.0,
+        base_name="pelvis",
+        body_state_required=True,
+    )
+    with pytest.raises(ValueError, match="without injected tracking sensors"):
+        backend.get_body_pos_w(np.asarray([0], dtype=np.int32))
diff --git a/tests/base/test_mjwarp_capabilities.py b/tests/base/test_mjwarp_capabilities.py
index d0ded55c1..4f57c9003 100644
--- a/tests/base/test_mjwarp_capabilities.py
+++ b/tests/base/test_mjwarp_capabilities.py
@@ -7,11 +7,11 @@
 
 import numpy as np
 import pytest
+from unisim.backend.mjwarp.dependencies import load_mjwarp_dependencies
+from unisim.dr.types import IntervalRandomizationPlan, ResetRandomizationPayload
 
-from unilab.base.backend import create_backend
-from unilab.base.backend.mjwarp.dependencies import load_mjwarp_dependencies
+from unilab.base.backend_factory import create_backend
 from unilab.base.scene import SceneCfg
-from unilab.dr.types import IntervalRandomizationPlan, ResetRandomizationPayload
 
 pytestmark = pytest.mark.slow
 
@@ -44,10 +44,9 @@ def test_unsupported_matrix_fails_before_step() -> None:
     assert not backend.get_play_capabilities().supports_native_interactive_renderer
     assert not backend.get_play_capabilities().supports_native_video_capture
     assert Path(backend.get_playback_model()).is_file()
-    with pytest.raises(NotImplementedError, match="interval randomization"):
-        backend.apply_interval_randomization(
-            IntervalRandomizationPlan(push_perturbation_limit=np.ones((3,), dtype=np.float32))
-        )
+    # Per-world gravity DR stays out of scope for the mjwarp host profile.
+    capabilities = backend.get_dr_capabilities()
+    assert not capabilities.supports_reset_term("gravity")
     qpos = np.tile(backend.get_keyframe_qpos("stand"), (1, 1))
     qvel = np.zeros((1, backend.get_init_qvel().size), dtype=np.float32)
     with pytest.raises(NotImplementedError, match="reset domain randomization"):
@@ -55,5 +54,33 @@ def test_unsupported_matrix_fails_before_step() -> None:
             np.asarray([0], dtype=np.int32),
             qpos,
             qvel,
-            randomization=ResetRandomizationPayload(kp=np.ones((1, backend.num_actuators))),
+            randomization=ResetRandomizationPayload(gravity=np.zeros((1, 3), dtype=np.float32)),
+        )
+
+
+def test_interval_push_and_velocity_require_named_bodies() -> None:
+    """Without base/push body names the interval capabilities stay fail-closed."""
+    dependencies = load_mjwarp_dependencies()
+    if not bool(dependencies.warp.get_device().is_cuda):
+        pytest.fail("mjwarp capability tests require an active CUDA Warp device")
+
+    from unilab.assets import ASSETS_ROOT_PATH
+
+    scene = SceneCfg(model_file=str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat.xml"))
+    backend = create_backend("mjwarp", scene, 1, 0.02 / 3.0)
+    capabilities = backend.get_dr_capabilities()
+    assert not capabilities.supports_interval_push
+    assert not capabilities.supports_interval_body_velocity_delta
+    with pytest.raises(NotImplementedError, match="push target body"):
+        backend.apply_interval_randomization(
+            IntervalRandomizationPlan(push_perturbation_limit=np.ones((3,), dtype=np.float32))
+        )
+    with pytest.raises(NotImplementedError, match="exactly one free joint"):
+        backend.apply_interval_randomization(
+            IntervalRandomizationPlan(
+                body_ids=np.asarray([1], dtype=np.int32),
+                body_linear_velocity_delta=np.zeros((1, 1, 3), dtype=np.float32),
+            )
         )
+    with pytest.raises(ValueError, match="Push body 'missing' not found"):
+        create_backend("mjwarp", scene, 1, 0.02 / 3.0, push_body_name="missing")
diff --git a/tests/base/test_mjwarp_cuda_graph.py b/tests/base/test_mjwarp_cuda_graph.py
index 463da920b..ad5ed0e1d 100644
--- a/tests/base/test_mjwarp_cuda_graph.py
+++ b/tests/base/test_mjwarp_cuda_graph.py
@@ -5,8 +5,11 @@
 from typing import Any
 
 import pytest
-
-from unilab.base.backend.mjwarp.backend import MjwarpBackend, _cuda_graph_eligibility
+from unisim.backend.mjwarp.backend import (
+    MjwarpBackend,
+    _cuda_graph_eligibility,
+    _reset_scratch_capacity_for_batch,
+)
 
 
 class _FakeDevice:
@@ -94,6 +97,8 @@ def _backend(warp: _FakeWarp, mujoco_warp: _FakeMujocoWarp) -> MjwarpBackend:
     backend._device_model = object()
     backend._device_data = object()
     backend._reset_mask_device = object()
+    backend._reset_scratch_capacity = 0
+    backend._reset_scratch_data = None
     return backend
 
 
@@ -153,6 +158,42 @@ def test_cuda_graph_capture_replays_fixed_address_operations() -> None:
     ) == capture_calls
 
 
+def test_cuda_graph_capture_includes_materialized_reset_scratch() -> None:
+    warp = _FakeWarp(driver=(12, 4), mempool=True)
+    mujoco_warp = _FakeMujocoWarp()
+    backend = _backend(warp, mujoco_warp)
+    backend._reset_scratch_capacity = 4
+    backend._reset_scratch_data = object()
+    backend._reset_scratch_mask_device = object()
+
+    backend._initialize_cuda_graphs(_FakeDevice())
+
+    assert backend._cuda_graph_enabled is True
+    assert backend._reset_scratch_reset_graph == "graph-3"
+    assert backend._reset_scratch_forward_graph == "graph-4"
+    assert mujoco_warp.reset_calls == 2
+    assert mujoco_warp.forward_calls == 2
+    assert backend._can_use_reset_scratch(4) is True
+    assert backend._can_use_reset_scratch(5) is False
+
+
+@pytest.mark.parametrize(
+    ("num_envs", "expected_capacity"),
+    [
+        (512, 0),
+        (1024, 128),
+        (2048, 128),
+        (4096, 256),
+        (8192, 512),
+        (16384, 512),
+    ],
+)
+def test_reset_scratch_capacity_scales_with_batch_and_stays_bounded(
+    num_envs: int, expected_capacity: int
+) -> None:
+    assert _reset_scratch_capacity_for_batch(num_envs) == expected_capacity
+
+
 def test_ineligible_cuda_graph_warns_and_uses_eager_operations() -> None:
     warp = _FakeWarp(driver=(12, 3), mempool=True)
     mujoco_warp = _FakeMujocoWarp()
diff --git a/tests/base/test_mjwarp_differential.py b/tests/base/test_mjwarp_differential.py
index 63aa57d4f..b3679f7cf 100644
--- a/tests/base/test_mjwarp_differential.py
+++ b/tests/base/test_mjwarp_differential.py
@@ -12,9 +12,9 @@
 
 import numpy as np
 import pytest
+from unisim.backend.mjwarp.dependencies import load_mjwarp_dependencies
 
-from unilab.base.backend import create_backend
-from unilab.base.backend.mjwarp.dependencies import load_mjwarp_dependencies
+from unilab.base.backend_factory import create_backend
 from unilab.base.scene import SceneCfg
 
 pytestmark = pytest.mark.slow
diff --git a/tests/base/test_mjwarp_dr.py b/tests/base/test_mjwarp_dr.py
new file mode 100644
index 000000000..d1854462e
--- /dev/null
+++ b/tests/base/test_mjwarp_dr.py
@@ -0,0 +1,492 @@
+"""Real-CUDA effect tests for ``mjwarp`` per-world domain randomization.
+
+Each test verifies one of three evidence classes required by issue #1401:
+
+- per-world model rows hold the staged payload values while complement rows
+  keep the immutable defaults (write path + world isolation);
+- the mutation changes the physics of the mutated worlds only (effect);
+- the graded ``set_const*`` recompute refreshed the derived per-world fields.
+
+The cross-backend test compares distribution-level statistics against the
+MuJoCo backend on identical sampled payloads rather than bitwise state.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+import pytest
+from unisim.backend.mjwarp.dependencies import load_mjwarp_dependencies
+from unisim.backend.mjwarp.randomization import EXPANDED_MODEL_FIELDS
+from unisim.dr.types import (
+    RESET_TERM_BASE_COM,
+    RESET_TERM_BASE_MASS,
+    RESET_TERM_BODY_INERTIA,
+    RESET_TERM_BODY_IPOS,
+    RESET_TERM_BODY_IQUAT,
+    RESET_TERM_BODY_MASS,
+    RESET_TERM_DOF_ARMATURE,
+    RESET_TERM_GEOM_FRICTION,
+    RESET_TERM_KD,
+    RESET_TERM_KP,
+    IntervalRandomizationPlan,
+    ResetRandomizationPayload,
+)
+
+from unilab.base.backend_factory import create_backend
+from unilab.base.scene import SceneCfg
+
+pytestmark = pytest.mark.slow
+
+_SIM_DT = 0.02 / 3.0
+
+
+def _require_cuda_mjwarp() -> None:
+    dependencies = load_mjwarp_dependencies()
+    if not bool(dependencies.warp.get_device().is_cuda):
+        pytest.fail("mjwarp DR tests require an active CUDA Warp device")
+
+
+def _scene() -> SceneCfg:
+    from unilab.assets import ASSETS_ROOT_PATH
+
+    return SceneCfg(model_file=str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat.xml"))
+
+
+@pytest.fixture
+def backend() -> Any:
+    _require_cuda_mjwarp()
+    return create_backend("mjwarp", _scene(), 4, _SIM_DT, base_name="pelvis")
+
+
+def _stand_state(backend: Any, count: int) -> tuple[np.ndarray, np.ndarray]:
+    qpos = np.tile(backend.get_keyframe_qpos("stand"), (count, 1))
+    qvel = np.zeros((count, backend.get_init_qvel().size), dtype=np.float32)
+    return qpos.astype(np.float32), qvel
+
+
+def _reset_all(backend: Any) -> None:
+    qpos, qvel = _stand_state(backend, backend.num_envs)
+    backend.set_state(np.arange(backend.num_envs, dtype=np.int32), qpos, qvel)
+
+
+def _stand_ctrl(backend: Any) -> np.ndarray:
+    qpos = backend.get_keyframe_qpos("stand")
+    return np.tile(qpos[-backend.num_actuators :].astype(np.float32), (backend.num_envs, 1))
+
+
+def _broadcast_rows(values: np.ndarray, count: int) -> np.ndarray:
+    return np.broadcast_to(values, (count, *values.shape)).copy()
+
+
+def _device_field(backend: Any, name: str) -> np.ndarray:
+    return np.asarray(getattr(backend._device_model, name).numpy())
+
+
+def _device_xfrc(backend: Any) -> np.ndarray:
+    return np.asarray(backend._device_data.xfrc_applied.numpy())
+
+
+def test_dr_capabilities_advertise_supported_terms(backend: Any) -> None:
+    capabilities = backend.get_dr_capabilities()
+    assert capabilities.supported_reset_terms == frozenset(
+        {
+            RESET_TERM_BASE_MASS,
+            RESET_TERM_BASE_COM,
+            RESET_TERM_BODY_IQUAT,
+            RESET_TERM_BODY_INERTIA,
+            RESET_TERM_BODY_IPOS,
+            RESET_TERM_BODY_MASS,
+            RESET_TERM_DOF_ARMATURE,
+            RESET_TERM_GEOM_FRICTION,
+            RESET_TERM_KP,
+            RESET_TERM_KD,
+        }
+    )
+    assert capabilities.supports_interval_push
+    assert capabilities.supports_interval_body_velocity_delta
+    assert capabilities.supports_interval_body_force
+
+
+def test_declared_model_fields_are_expanded_per_world(backend: Any) -> None:
+    assert EXPANDED_MODEL_FIELDS
+    for name in EXPANDED_MODEL_FIELDS:
+        array = getattr(backend._device_model, name)
+        assert array.shape[0] == backend.num_envs, name
+    np.testing.assert_allclose(
+        _device_field(backend, "body_mass"),
+        np.broadcast_to(backend.get_body_mass(), (backend.num_envs, backend.get_body_mass().size)),
+        rtol=1e-6,
+    )
+    # Expansion happened before graph capture; replaying the captured step on
+    # the expanded arrays must stay finite and isolated.
+    _reset_all(backend)
+    backend.step(_stand_ctrl(backend), nsteps=3)
+    assert np.isfinite(backend.get_base_pos()).all()
+
+
+def test_reset_payload_writes_only_selected_rows(backend: Any) -> None:
+    rows = np.asarray([1, 3], dtype=np.int32)
+    complement = np.asarray([0, 2], dtype=np.int32)
+    qpos, qvel = _stand_state(backend, rows.size)
+
+    body_mass = _broadcast_rows(backend.get_body_mass(), rows.size)
+    body_mass *= 1.2
+    body_ipos = _broadcast_rows(backend.get_body_ipos(), rows.size)
+    body_ipos[..., 0] += 0.01
+    dof_armature = _broadcast_rows(backend.get_dof_armature(), rows.size) + 0.02
+    geom_friction = _broadcast_rows(backend.get_geom_friction(), rows.size)
+    geom_friction[..., 0] *= 0.5
+    kp = np.full((rows.size, backend.num_actuators), 17.0, dtype=np.float32)
+    kd = np.full((rows.size, backend.num_actuators), 1.5, dtype=np.float32)
+
+    backend.set_state(
+        rows,
+        qpos,
+        qvel,
+        randomization=ResetRandomizationPayload(
+            body_mass=body_mass,
+            body_ipos=body_ipos,
+            dof_armature=dof_armature,
+            geom_friction=geom_friction,
+            kp=kp,
+            kd=kd,
+        ),
+    )
+
+    np.testing.assert_allclose(_device_field(backend, "body_mass")[rows], body_mass, rtol=1e-6)
+    np.testing.assert_allclose(_device_field(backend, "body_ipos")[rows], body_ipos, rtol=1e-6)
+    np.testing.assert_allclose(
+        _device_field(backend, "dof_armature")[rows], dof_armature, rtol=1e-6
+    )
+    np.testing.assert_allclose(
+        _device_field(backend, "geom_friction")[rows], geom_friction, rtol=1e-6
+    )
+    np.testing.assert_allclose(
+        _device_field(backend, "actuator_gainprm")[rows, :, 0], kp, rtol=1e-6
+    )
+    np.testing.assert_allclose(
+        _device_field(backend, "actuator_biasprm")[rows, :, 2], kd, rtol=1e-6
+    )
+
+    default_mass = backend.get_body_mass()
+    np.testing.assert_allclose(
+        _device_field(backend, "body_mass")[complement],
+        _broadcast_rows(default_mass, complement.size),
+        rtol=1e-6,
+    )
+    np.testing.assert_allclose(
+        _device_field(backend, "body_ipos")[complement],
+        _broadcast_rows(backend.get_body_ipos(), complement.size),
+        rtol=1e-6,
+    )
+    np.testing.assert_allclose(
+        _device_field(backend, "dof_armature")[complement],
+        _broadcast_rows(backend.get_dof_armature(), complement.size),
+        rtol=1e-6,
+    )
+
+    # The graded recompute refreshed derived per-world fields on every world.
+    subtree_mass = _device_field(backend, "body_subtreemass")
+    np.testing.assert_allclose(
+        subtree_mass[rows, 0],
+        body_mass.sum(axis=1),
+        rtol=1e-4,
+    )
+    np.testing.assert_allclose(
+        subtree_mass[complement, 0],
+        np.full(complement.size, default_mass.sum(), dtype=np.float32),
+        rtol=1e-4,
+    )
+    default_invweight0 = _device_field(backend, "dof_invweight0")[complement]
+    mutated_invweight0 = _device_field(backend, "dof_invweight0")[rows]
+    assert not np.allclose(mutated_invweight0, default_invweight0[:1])
+
+    # Re-randomizing one of the rows restores it to the defaults + new delta.
+    base_id = backend._base_body_id
+    qpos1, qvel1 = _stand_state(backend, 1)
+    backend.set_state(
+        np.asarray([1], dtype=np.int32),
+        qpos1,
+        qvel1,
+        randomization=ResetRandomizationPayload(base_mass_delta=np.asarray([0.5])),
+    )
+    np.testing.assert_allclose(
+        _device_field(backend, "body_mass")[1, base_id],
+        default_mass[base_id] + 0.5,
+        rtol=1e-6,
+    )
+
+
+def test_base_com_offset_applies_default_plus_delta(backend: Any) -> None:
+    rows = np.asarray([0, 2], dtype=np.int32)
+    qpos, qvel = _stand_state(backend, rows.size)
+    offset = np.asarray([[0.01, -0.02, 0.03], [-0.015, 0.005, -0.025]], dtype=np.float32)
+    backend.set_state(
+        rows,
+        qpos,
+        qvel,
+        randomization=ResetRandomizationPayload(base_com_offset=offset),
+    )
+    base_id = backend._base_body_id
+    expected = backend.get_body_ipos()[base_id] + offset
+    np.testing.assert_allclose(_device_field(backend, "body_ipos")[rows, base_id], expected)
+    complement = np.asarray([1, 3], dtype=np.int32)
+    np.testing.assert_allclose(
+        _device_field(backend, "body_ipos")[complement],
+        _broadcast_rows(backend.get_body_ipos(), complement.size),
+        rtol=1e-6,
+    )
+
+
+def test_body_inertia_and_iquat_recompute_invweight0(backend: Any) -> None:
+    rows = np.asarray([2], dtype=np.int32)
+    qpos, qvel = _stand_state(backend, 1)
+    default_invweight0 = _device_field(backend, "body_invweight0")[0].copy()
+
+    inertia = np.broadcast_to(
+        np.asarray(backend._cpu_model.body_inertia, dtype=np.float32),
+        (1, backend._nbody, 3),
+    ).copy()
+    inertia[:, 1:, :] *= 4.0
+    iquat = np.broadcast_to(
+        np.asarray(backend._cpu_model.body_iquat, dtype=np.float32),
+        (1, backend._nbody, 4),
+    ).copy()
+    backend.set_state(
+        rows,
+        qpos,
+        qvel,
+        randomization=ResetRandomizationPayload(
+            body_inertia=inertia,
+            body_iquat=iquat,
+        ),
+    )
+    np.testing.assert_allclose(_device_field(backend, "body_inertia")[rows], inertia, rtol=1e-6)
+    assert not np.allclose(_device_field(backend, "body_invweight0")[rows[0]], default_invweight0)
+    complement = np.asarray([0, 1, 3], dtype=np.int32)
+    # set_const_0 recomputes every world's derived fields; complement rows stay
+    # at the defaults up to float32 recompute noise (no mutated values leak).
+    np.testing.assert_allclose(
+        _device_field(backend, "body_invweight0")[complement],
+        _broadcast_rows(default_invweight0, complement.size),
+        rtol=1e-3,
+        atol=2e-4,
+    )
+
+
+def test_heavier_world_accelerates_less_under_identical_body_force(backend: Any) -> None:
+    """Mass mutation changes physics for the mutated world only."""
+    _reset_all(backend)
+    heavy_rows = np.asarray([1, 3], dtype=np.int32)
+    qpos, qvel = _stand_state(backend, heavy_rows.size)
+    body_mass = _broadcast_rows(backend.get_body_mass(), heavy_rows.size)
+    body_mass *= 4.0
+    backend.set_state(
+        heavy_rows,
+        qpos,
+        qvel,
+        randomization=ResetRandomizationPayload(body_mass=body_mass),
+    )
+
+    force = np.zeros((backend.num_envs, 1, 3), dtype=np.float32)
+    force[:, 0, 0] = 300.0
+    backend.apply_interval_randomization(
+        IntervalRandomizationPlan(body_ids=np.asarray([backend._base_body_id]), body_force=force)
+    )
+    backend.step(_stand_ctrl(backend), nsteps=3)
+
+    delta_v = backend.get_base_lin_vel()[:, 0]
+    assert np.all(np.abs(delta_v) > 1e-4)
+    # Heavier worlds accelerate less; identical unmutated worlds stay paired
+    # up to contact-solver reduction noise.
+    assert delta_v[1] < delta_v[0]
+    assert delta_v[3] < delta_v[2]
+    np.testing.assert_allclose(delta_v[0], delta_v[2], rtol=1e-3)
+    np.testing.assert_allclose(delta_v[1], delta_v[3], rtol=1e-3)
+    # The staged force was cleared after the step barrier.
+    assert not np.any(_device_xfrc(backend))
+    assert not backend._xfrc_pending
+
+
+def test_push_robots_stages_one_step_force(backend: Any) -> None:
+    _reset_all(backend)
+    backend.apply_interval_randomization(
+        IntervalRandomizationPlan(push_perturbation_limit=np.asarray([80.0, 0.0, 0.0]))
+    )
+    assert backend._xfrc_pending
+    before = backend.get_base_lin_vel().copy()
+    backend.step(_stand_ctrl(backend), nsteps=3)
+    after = backend.get_base_lin_vel()
+    assert not np.allclose(before, after)
+    assert not np.any(_device_xfrc(backend))
+    assert not backend._xfrc_pending
+
+
+def test_interval_velocity_delta_kicks_selected_rows(backend: Any) -> None:
+    _reset_all(backend)
+    before = backend.get_base_lin_vel().copy()
+    delta = np.zeros((backend.num_envs, 1, 3), dtype=np.float32)
+    delta[[0, 3], 0, :] = np.asarray([[0.4, -0.2, 0.0], [-0.3, 0.1, 0.0]], dtype=np.float32)
+    backend.apply_interval_randomization(
+        IntervalRandomizationPlan(
+            body_ids=np.asarray([backend._base_body_id], dtype=np.int32),
+            body_linear_velocity_delta=delta,
+        )
+    )
+    after = backend.get_base_lin_vel()
+    np.testing.assert_allclose(after[[0, 3]], before[[0, 3]] + delta[[0, 3], 0, :], atol=1e-6)
+    np.testing.assert_allclose(after[[1, 2]], before[[1, 2]], atol=1e-6)
+    # The kick commits through the forward barrier, keeping sensors coherent.
+    assert np.isfinite(backend.get_sensor_data("torso_upvector")).all()
+
+
+def test_kp_randomization_changes_tracking_response(backend: Any) -> None:
+    _reset_all(backend)
+    rows = np.asarray([0, 2], dtype=np.int32)
+    qpos, qvel = _stand_state(backend, rows.size)
+    default_kp, _ = backend.get_actuator_gains()
+    backend.set_state(
+        rows,
+        qpos,
+        qvel,
+        randomization=ResetRandomizationPayload(
+            kp=np.broadcast_to(default_kp * 0.05, (rows.size, default_kp.size)).copy(),
+        ),
+    )
+    target_offset = 0.2
+    ctrl = _stand_ctrl(backend) + target_offset
+    for _ in range(5):
+        backend.step(ctrl, nsteps=3)
+    error = np.abs(backend.get_dof_pos() - ctrl)
+    weak = error[rows].mean(axis=1)
+    strong = error[[1, 3]].mean(axis=1)
+    assert np.all(weak > strong + 0.05)
+
+
+def test_low_friction_world_changes_kick_response(backend: Any) -> None:
+    _reset_all(backend)
+    rows = np.asarray([1, 3], dtype=np.int32)
+    qpos, qvel = _stand_state(backend, rows.size)
+    geom_friction = _broadcast_rows(backend.get_geom_friction(), rows.size)
+    geom_friction[..., 0] = 0.01
+    backend.set_state(
+        rows,
+        qpos,
+        qvel,
+        randomization=ResetRandomizationPayload(geom_friction=geom_friction),
+    )
+    start = backend.get_base_pos()[:, :2].copy()
+    delta = np.zeros((backend.num_envs, 1, 3), dtype=np.float32)
+    delta[:, 0, 0] = 1.5
+    backend.apply_interval_randomization(
+        IntervalRandomizationPlan(
+            body_ids=np.asarray([backend._base_body_id], dtype=np.int32),
+            body_linear_velocity_delta=delta,
+        )
+    )
+    for _ in range(6):
+        backend.step(_stand_ctrl(backend), nsteps=3)
+    assert np.isfinite(backend.get_base_pos()).all()
+    displacement = np.linalg.norm(backend.get_base_pos()[:, :2] - start, axis=1)
+    # Near-zero slide friction changes the kick response of the mutated worlds
+    # while the default-friction worlds stay pairwise consistent.
+    assert abs(displacement[1] - displacement[0]) > 0.003
+    # Same-condition worlds agree up to contact-solver reduction noise.
+    np.testing.assert_allclose(displacement[0], displacement[2], rtol=1e-3)
+    np.testing.assert_allclose(displacement[1], displacement[3], rtol=1e-3)
+    assert displacement[1] < displacement[0]
+
+
+def test_dr_reset_bypasses_reset_scratch_graphs(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Model-row mutation forces the full-forward route on large batches."""
+    _require_cuda_mjwarp()
+    backend = create_backend("mjwarp", _scene(), 1024, _SIM_DT, base_name="pelvis")
+    if not backend._cuda_graph_enabled:
+        pytest.skip(f"CUDA graphs unavailable: {backend._cuda_graph_disable_reason}")
+    assert backend._reset_scratch_forward_graph is not None
+
+    rows = np.arange(32, dtype=np.int32)
+    qpos, qvel = _stand_state(backend, rows.size)
+
+    graph_launches: list[Any] = []
+    original_capture_launch = backend._warp.capture_launch
+
+    def capture_launch(graph: Any) -> None:
+        graph_launches.append(graph)
+        original_capture_launch(graph)
+
+    monkeypatch.setattr(backend._warp, "capture_launch", capture_launch)
+    backend.set_state(rows, qpos, qvel)
+    assert backend._reset_scratch_reset_graph in graph_launches
+
+    graph_launches.clear()
+    body_mass = _broadcast_rows(backend.get_body_mass(), rows.size)
+    backend.set_state(
+        rows,
+        qpos,
+        qvel,
+        randomization=ResetRandomizationPayload(body_mass=body_mass),
+    )
+    assert backend._reset_scratch_reset_graph not in graph_launches
+    assert backend._reset_scratch_forward_graph not in graph_launches
+    assert backend._reset_graph in graph_launches
+    assert backend._forward_graph in graph_launches
+    assert np.isfinite(backend.get_base_pos()).all()
+
+
+def test_mass_and_com_randomization_match_mujoco_statistics() -> None:
+    """Same sampled payloads on both backends drift to the same statistics."""
+    _require_cuda_mjwarp()
+    from unisim.backend.mujoco.backend import MuJoCoBackend
+
+    num_envs = 16
+    scene = _scene()
+    mujoco_backend = MuJoCoBackend(scene, num_envs, _SIM_DT, base_name="pelvis")
+    mujoco_backend.materialize()
+    mjwarp_backend = create_backend("mjwarp", scene, num_envs, _SIM_DT, base_name="pelvis")
+
+    rng = np.random.default_rng(0)
+    rows = np.arange(num_envs, dtype=np.int32)
+    qpos, qvel = _stand_state(mjwarp_backend, num_envs)
+    mass_delta = rng.uniform(-0.5, 1.5, size=num_envs)
+    com_offset = rng.uniform(-0.02, 0.02, size=(num_envs, 3))
+    payload = ResetRandomizationPayload(
+        base_mass_delta=mass_delta,
+        base_com_offset=com_offset,
+    )
+
+    mujoco_backend.set_state(rows, qpos, qvel, randomization=payload)
+    mjwarp_backend.set_state(rows, qpos, qvel, randomization=payload)
+    np.testing.assert_allclose(
+        _device_field(mjwarp_backend, "body_mass")[:, mjwarp_backend._base_body_id],
+        mujoco_backend.get_body_mass()[mujoco_backend._base_body_id] + mass_delta,
+        rtol=1e-5,
+    )
+
+    ctrl = _stand_ctrl(mjwarp_backend)
+    for _ in range(30):
+        mujoco_backend.step(ctrl, nsteps=3)
+        mjwarp_backend.step(ctrl, nsteps=3)
+
+    for name, mujoco_values, mjwarp_values in (
+        ("base_pos", mujoco_backend.get_base_pos(), mjwarp_backend.get_base_pos()),
+        (
+            "base_lin_vel",
+            mujoco_backend.get_base_lin_vel(),
+            mjwarp_backend.get_base_lin_vel(),
+        ),
+    ):
+        mujoco_values = np.asarray(mujoco_values, dtype=np.float64)
+        mjwarp_values = np.asarray(mjwarp_values, dtype=np.float64)
+        # The sampled payload visibly spreads the per-world trajectories.
+        assert np.std(mjwarp_values, axis=0).max() > 1e-4, name
+        assert np.std(mujoco_values, axis=0).max() > 1e-4, name
+        # Distribution-level agreement, not bitwise parity across float32/float64.
+        np.testing.assert_allclose(
+            mjwarp_values.mean(axis=0), mujoco_values.mean(axis=0), atol=0.05, err_msg=name
+        )
+        np.testing.assert_allclose(
+            mjwarp_values.std(axis=0), mujoco_values.std(axis=0), atol=0.05, err_msg=name
+        )
diff --git a/tests/base/test_mjwarp_host_cache.py b/tests/base/test_mjwarp_host_cache.py
new file mode 100644
index 000000000..9a8557188
--- /dev/null
+++ b/tests/base/test_mjwarp_host_cache.py
@@ -0,0 +1,180 @@
+"""Owner-level tests for MJWarp's explicit pinned host-cache barrier."""
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+from typing import Any
+
+import numpy as np
+from unisim.backend.mjwarp.backend import MjwarpBackend
+
+
+class _FakeStorage:
+    def __init__(self, array: np.ndarray) -> None:
+        self.array = array
+
+    def numpy(self) -> np.ndarray:
+        return self.array
+
+
+class _FakeWarp:
+    def __init__(self) -> None:
+        self.empty_calls: list[dict[str, Any]] = []
+        self.events: list[tuple[Any, ...]] = []
+
+    def empty(self, shape: tuple[int, ...], **kwargs: Any) -> _FakeStorage:
+        self.empty_calls.append({"shape": shape, **kwargs})
+        return _FakeStorage(np.empty(shape, dtype=np.float32))
+
+    def copy(self, destination: Any, source: Any) -> None:
+        self.events.append(("copy", destination, source))
+
+
+def _backend(warp: _FakeWarp) -> MjwarpBackend:
+    backend = object.__new__(MjwarpBackend)
+    backend._warp = warp
+    return backend
+
+
+def test_allocate_host_cache_uses_pinned_cpu_storage() -> None:
+    warp = _FakeWarp()
+    backend = _backend(warp)
+    device_array = SimpleNamespace(shape=(4, 7), dtype="float32")
+
+    storage, cache = backend._allocate_pinned_host_cache(device_array)
+
+    assert warp.empty_calls == [
+        {"shape": (4, 7), "dtype": "float32", "device": "cpu", "pinned": True}
+    ]
+    assert cache is storage.array
+
+
+def test_refresh_host_cache_batches_all_downloads_before_sync() -> None:
+    warp = _FakeWarp()
+    backend = _backend(warp)
+    backend._device_data = SimpleNamespace(
+        qpos="device-qpos", qvel="device-qvel", sensordata="device-sensor"
+    )
+    backend._qpos_cache_storage = "host-qpos"
+    backend._qvel_cache_storage = "host-qvel"
+    backend._sensor_cache_storage = "host-sensor"
+    backend._synchronize = lambda: warp.events.append(("sync",))  # type: ignore[method-assign]
+
+    backend._refresh_host_cache()
+
+    assert warp.events == [
+        ("copy", "host-qpos", "device-qpos"),
+        ("copy", "host-qvel", "device-qvel"),
+        ("copy", "host-sensor", "device-sensor"),
+        ("sync",),
+    ]
+
+
+def test_refresh_reset_scratch_cache_only_scatters_selected_rows() -> None:
+    warp = _FakeWarp()
+    backend = _backend(warp)
+    scratch_values = np.arange(12, dtype=np.float32).reshape(4, 3)
+    scratch_storage = _FakeStorage(np.empty_like(scratch_values))
+    backend._reset_scratch_data = SimpleNamespace(sensordata=scratch_values)
+    backend._reset_scratch_sensor_storage = scratch_storage
+    backend._reset_scratch_sensor_cache = scratch_storage.array
+    backend._sensor_cache = np.full((6, 3), -1.0, dtype=np.float32)
+    backend._download = lambda source, destination: np.copyto(  # type: ignore[method-assign]
+        destination.array, source
+    )
+    backend._synchronize = lambda: warp.events.append(("sync",))  # type: ignore[method-assign]
+
+    backend._refresh_reset_scratch_cache(np.asarray([4, 1], dtype=np.int32))
+
+    np.testing.assert_array_equal(backend._sensor_cache[4], scratch_values[0])
+    np.testing.assert_array_equal(backend._sensor_cache[1], scratch_values[1])
+    np.testing.assert_array_equal(
+        backend._sensor_cache[[0, 2, 3, 5]],
+        np.full((4, 3), -1.0, dtype=np.float32),
+    )
+    assert warp.events == [("sync",)]
+
+
+def test_host_reset_routes_small_row_sets_through_scratch() -> None:
+    warp = _FakeWarp()
+    backend = _backend(warp)
+    backend._reset_mask_host = np.zeros(8, dtype=np.bool_)
+    backend._reset_mask_device = "main-mask"
+    backend._device_data = SimpleNamespace(qpos="main-qpos", qvel="main-qvel")
+    backend._time_cache = np.ones(8, dtype=np.float32)
+    events: list[tuple[Any, ...]] = []
+    backend._can_use_reset_scratch = lambda _count: True  # type: ignore[method-assign]
+    backend._upload = lambda target, source: events.append(  # type: ignore[method-assign]
+        ("upload", target, np.asarray(source).copy())
+    )
+    backend._execute_device_reset = lambda: events.append(  # type: ignore[method-assign]
+        ("main-reset",)
+    )
+    backend._execute_reset_scratch_forward = (  # type: ignore[method-assign]
+        lambda qpos, qvel: events.append(("scratch-forward", qpos.copy(), qvel.copy()))
+    )
+    backend._execute_device_forward = lambda: events.append(  # type: ignore[method-assign]
+        ("main-forward",)
+    )
+    backend._synchronize = lambda: events.append(("sync",))  # type: ignore[method-assign]
+    backend._refresh_reset_scratch_cache = (  # type: ignore[method-assign]
+        lambda rows: events.append(("scratch-refresh", rows.copy()))
+    )
+    backend._refresh_host_cache = lambda: events.append(  # type: ignore[method-assign]
+        ("main-refresh",)
+    )
+    rows = np.asarray([6, 2], dtype=np.int32)
+    full_qpos = np.zeros((8, 3), dtype=np.float32)
+    full_qvel = np.zeros((8, 2), dtype=np.float32)
+    reset_qpos = np.ones((2, 3), dtype=np.float32)
+    reset_qvel = np.ones((2, 2), dtype=np.float32)
+
+    backend._execute_host_reset(rows, full_qpos, full_qvel, reset_qpos, reset_qvel)
+
+    assert np.flatnonzero(backend._reset_mask_host).tolist() == [2, 6]
+    assert backend._time_cache[rows].tolist() == [0.0, 0.0]
+    assert [event[0] for event in events] == [
+        "upload",
+        "main-reset",
+        "upload",
+        "upload",
+        "scratch-forward",
+        "sync",
+        "scratch-refresh",
+    ]
+    assert not any(event[0] in {"main-forward", "main-refresh"} for event in events)
+
+
+def test_row_body_getters_gather_selected_rows_without_full_batch_reads() -> None:
+    """MJWarp's partial-reset getters must not materialize the full env batch."""
+    backend = object.__new__(MjwarpBackend)
+    backend._body_id_to_tracked_idx = np.asarray([2, 0, 1], dtype=np.intp)
+    backend._tracked_pos_w_all = np.arange(4 * 3 * 3, dtype=np.float32).reshape(4, 3, 3)
+    backend._tracked_quat_w_all = np.arange(4 * 3 * 4, dtype=np.float32).reshape(4, 3, 4)
+    backend._tracked_linvel_w_all = np.arange(4 * 3 * 3, dtype=np.float32).reshape(4, 3, 3) + 1000
+    backend._tracked_angvel_w_all = np.arange(4 * 3 * 3, dtype=np.float32).reshape(4, 3, 3) + 2000
+
+    def fail_full_getter(*_args: Any, **_kwargs: Any) -> Any:
+        raise AssertionError("row getter must not call a full-batch getter")
+
+    backend.get_body_pos_w = fail_full_getter  # type: ignore[method-assign]
+    backend.get_body_quat_w = fail_full_getter  # type: ignore[method-assign]
+    backend.get_body_lin_vel_w = fail_full_getter  # type: ignore[method-assign]
+    backend.get_body_ang_vel_w = fail_full_getter  # type: ignore[method-assign]
+
+    rows = np.asarray([3, 1, 3], dtype=np.int32)
+    body_ids = np.asarray([1, 2], dtype=np.int32)
+    mapped = np.asarray([0, 1], dtype=np.intp)
+    expected_index = (rows[:, None], mapped)
+
+    pose_pos, pose_quat = backend.get_body_pose_w_rows(rows, body_ids)
+    np.testing.assert_array_equal(pose_pos, backend._tracked_pos_w_all[expected_index])
+    np.testing.assert_array_equal(pose_quat, backend._tracked_quat_w_all[expected_index])
+    np.testing.assert_array_equal(
+        backend.get_body_lin_vel_w_rows(rows, body_ids),
+        backend._tracked_linvel_w_all[expected_index],
+    )
+    np.testing.assert_array_equal(
+        backend.get_body_ang_vel_w_rows(rows, body_ids),
+        backend._tracked_angvel_w_all[expected_index],
+    )
diff --git a/tests/base/test_mjwarp_identity.py b/tests/base/test_mjwarp_identity.py
index 3fe150bc9..20b4753c2 100644
--- a/tests/base/test_mjwarp_identity.py
+++ b/tests/base/test_mjwarp_identity.py
@@ -2,20 +2,15 @@
 
 from __future__ import annotations
 
-import ast
 import subprocess
 import sys
 import textwrap
 from importlib.machinery import ModuleSpec
 from pathlib import Path
 
+import numpy as np
 import pytest
-
-from unilab.base.backend.mjwarp import dependencies
-
-
-def _repo_root() -> Path:
-    return Path(__file__).resolve().parents[2]
+from unisim.backend.mjwarp import dependencies
 
 
 def test_mjwarp_import_path_does_not_eagerly_import_warp_or_mujoco() -> None:
@@ -23,15 +18,15 @@ def test_mjwarp_import_path_does_not_eagerly_import_warp_or_mujoco() -> None:
         """
         import sys
 
-        from unilab.base.backend import create_backend
-        from unilab.base.backend.mjwarp import MjwarpBackend
+        from unilab.base.backend_factory import create_backend
+        from unisim.backend.mjwarp import MjwarpBackend
 
         assert create_backend is not None
         assert MjwarpBackend is not None
         print("mujoco", "mujoco" in sys.modules)
         print("mujoco_warp", "mujoco_warp" in sys.modules)
         print("warp", "warp" in sys.modules)
-        print("mujoco_backend", "unilab.base.backend.mujoco.backend" in sys.modules)
+        print("mujoco_backend", "unisim.backend.mujoco.backend" in sys.modules)
         """
     )
     result = subprocess.run(
@@ -116,27 +111,27 @@ def test_mjwarp_identity_is_independent_from_mujoco(
     ]
 
 
-def test_mjwarp_getters_do_not_materialize_warp_arrays() -> None:
-    backend_path = _repo_root() / "src" / "unilab" / "base" / "backend" / "mjwarp" / "backend.py"
-    tree = ast.parse(backend_path.read_text(encoding="utf-8"))
-    backend = next(
-        node
-        for node in tree.body
-        if isinstance(node, ast.ClassDef) and node.name == "MjwarpBackend"
+def test_mjwarp_actuation_metadata_uses_cpu_model_only() -> None:
+    import mujoco
+    from unisim.backend.mjwarp.backend import MjwarpBackend
+
+    from unilab.assets import ASSETS_ROOT_PATH
+
+    model = mujoco.MjModel.from_xml_path(
+        str(ASSETS_ROOT_PATH / "robots" / "go2" / "scene_flat.xml")
     )
-    getter_nodes = [
-        node
-        for node in backend.body
-        if isinstance(node, ast.FunctionDef) and node.name.startswith("get_")
-    ]
-    assert getter_nodes
-    offenders: list[str] = []
-    for getter in getter_nodes:
-        for node in ast.walk(getter):
-            if (
-                isinstance(node, ast.Call)
-                and isinstance(node.func, ast.Attribute)
-                and node.func.attr == "numpy"
-            ):
-                offenders.append(getter.name)
-    assert offenders == []
+    backend = object.__new__(MjwarpBackend)
+    backend._mujoco = mujoco
+    backend._cpu_model = model
+    backend._root_qpos_dim = 7
+    backend._actuator_names = tuple(
+        mujoco.mj_id2name(model, mujoco.mjtObj.mjOBJ_ACTUATOR, index) or f"#{index}"
+        for index in range(model.nu)
+    )
+
+    assert len(backend.get_actuator_joint_names()) == model.nu
+    assert backend.get_actuator_joint_names()[0] == "FR_hip_joint"
+    default = backend.get_default_dof_pos()
+    np.testing.assert_array_equal(default, model.qpos0[7:])
+    default[:] = np.nan
+    assert np.isfinite(backend.get_default_dof_pos()).all()
diff --git a/tests/base/test_mjwarp_playback.py b/tests/base/test_mjwarp_playback.py
index c9c200993..fecd5f155 100644
--- a/tests/base/test_mjwarp_playback.py
+++ b/tests/base/test_mjwarp_playback.py
@@ -10,14 +10,14 @@
 
 import mujoco
 from omegaconf import OmegaConf
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base.backend.base import BackendPlayCapabilities
-from unilab.base.backend.mjwarp.backend import MjwarpBackend
-from unilab.base.backend.mjwarp.playback import (
+from unisim.backend.base import BackendPlayCapabilities
+from unisim.backend.mjwarp.backend import MjwarpBackend
+from unisim.backend.mjwarp.playback import (
     run_mjwarp_playback,
     validate_mjwarp_visual_model,
 )
+
+from unilab.assets import ASSETS_ROOT_PATH
 from unilab.base.scene import SceneCfg
 
 G1_SCENE = ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat.xml"
@@ -75,7 +75,9 @@ def test_mjwarp_plan_is_explicit_and_finite() -> None:
 
 
 def test_g1_mjwarp_owner_enables_explicit_record_profile() -> None:
-    owner = OmegaConf.load(REPO_ROOT / "conf" / "ppo" / "task" / "g1_walk_flat" / "mjwarp.yaml")
+    owner = OmegaConf.load(
+        REPO_ROOT / "src" / "unilab" / "conf" / "ppo" / "task" / "g1_walk_flat" / "mjwarp.yaml"
+    )
 
     assert OmegaConf.select(owner, "training.no_play") is None
     assert owner.training.play_render_mode == "record"
@@ -111,16 +113,16 @@ def test_g1_visual_model_and_replay_forward_camera_and_spacing(
     )
     captured: dict[str, object] = {}
 
-    monkeypatch.setattr("unilab.visualization.render_many.render_backend_usable", lambda: True)
+    monkeypatch.setattr("unisim.visualization.render_many.render_backend_usable", lambda: True)
     monkeypatch.setattr(
-        "unilab.visualization.render_many.render_states_get_frames",
+        "unisim.visualization.render_many.render_states_get_frames",
         lambda states, model_file, **kwargs: (
             captured.update(states=states, model_file=model_file, render_kwargs=kwargs)
             or [np.zeros((2, 2, 3), dtype=np.uint8)]
         ),
     )
     monkeypatch.setattr(
-        "unilab.base.backend.playback_common.imageio.mimsave",
+        "unisim.backend.playback_common.imageio.mimsave",
         lambda path, frames, fps: captured.update(video_path=str(path), fps=fps),
     )
 
@@ -156,7 +158,7 @@ def test_mjwarp_playback_rejects_bad_snapshot_and_renderer(
 ) -> None:
     backend = _stub_backend(num_envs=1)
     env = SimpleNamespace(get_physics_state_snapshot=lambda: np.zeros((1, 4), dtype=np.float32))
-    monkeypatch.setattr("unilab.visualization.render_many.render_backend_usable", lambda: True)
+    monkeypatch.setattr("unisim.visualization.render_many.render_backend_usable", lambda: True)
     with pytest.raises(ValueError, match=r"\[time, qpos, qvel\]"):
         run_mjwarp_playback(
             backend=backend,
@@ -173,7 +175,7 @@ def test_mjwarp_playback_rejects_bad_snapshot_and_renderer(
             camera_kwargs=None,
         )
 
-    monkeypatch.setattr("unilab.visualization.render_many.render_backend_usable", lambda: False)
+    monkeypatch.setattr("unisim.visualization.render_many.render_backend_usable", lambda: False)
     with pytest.raises(RuntimeError, match="usable MuJoCo off-screen renderer"):
         run_mjwarp_playback(
             backend=backend,
diff --git a/tests/base/test_motrix_backend_options.py b/tests/base/test_motrix_backend_options.py
index dab58fc84..d0eda0412 100644
--- a/tests/base/test_motrix_backend_options.py
+++ b/tests/base/test_motrix_backend_options.py
@@ -5,9 +5,7 @@
 
 import numpy as np
 import pytest
-
-from unilab.base.scene import SceneCfg
-from unilab.dr.types import (
+from unisim.dr.types import (
     RESET_TERM_BODY_IPOS,
     RESET_TERM_BODY_MASS,
     RESET_TERM_GEOM_FRICTION,
@@ -21,6 +19,8 @@
     ResetRandomizationPayload,
 )
 
+from unilab.base.scene import SceneCfg
+
 
 class _FakeMotrixLink:
     index = 0
@@ -228,8 +228,8 @@ def set_damping_override(self, data: Any, value: np.ndarray) -> None:
 
 
 def _install_fake_motrix(monkeypatch, tmp_path):
-    import unilab.base.backend.motrix.backend as mod
-    import unilab.base.backend.motrix.scene as scene_mod
+    import unisim.backend.motrix.backend as mod
+    import unisim.backend.motrix.scene as scene_mod
 
     fake_model = _FakeMotrixModel()
     _FakeTerrainScanner.instances.clear()
@@ -245,12 +245,16 @@ def _install_fake_motrix(monkeypatch, tmp_path):
         ),
         raising=False,
     )
-    monkeypatch.setattr(scene_mod, "materialize_motrix_scene", lambda **kwargs: fake_model)
+    monkeypatch.setattr(
+        scene_mod,
+        "_materialize_motrix_scene_with_sensor_names",
+        lambda **kwargs: (fake_model, ()),
+    )
     return mod, fake_model
 
 
 def test_motrix_backend_kd_override_supports_damping_api() -> None:
-    import unilab.base.backend.motrix.backend as mod
+    import unisim.backend.motrix.backend as mod
 
     actuator = _FakePositionActuatorWithDampingOverride()
     backend = object.__new__(mod.MotrixBackend)
@@ -266,7 +270,7 @@ def test_motrix_backend_kd_override_supports_damping_api() -> None:
 
 
 def test_motrix_backend_dr_capabilities_include_pd_gains_when_overrides_available() -> None:
-    import unilab.base.backend.motrix.backend as mod
+    import unisim.backend.motrix.backend as mod
 
     backend = object.__new__(mod.MotrixBackend)
     backend._supports_position_actuator_gains = True
@@ -286,7 +290,7 @@ def test_motrix_backend_dr_capabilities_include_pd_gains_when_overrides_availabl
 
 
 def test_motrix_backend_uses_cached_batch_link_velocities() -> None:
-    import unilab.base.backend.motrix.backend as mod
+    import unisim.backend.motrix.backend as mod
 
     backend = object.__new__(mod.MotrixBackend)
     backend._link_velocities = np.arange(2 * 3 * 6, dtype=np.float32).reshape(2, 3, 6)
@@ -308,8 +312,57 @@ def test_motrix_backend_uses_cached_batch_link_velocities() -> None:
     np.testing.assert_allclose(ang_vel, backend._link_velocities[:, body_ids, 3:])
 
 
+def test_motrix_copy_body_state_uses_cached_state_and_reuses_scratch() -> None:
+    import unisim.backend.motrix.backend as mod
+
+    num_envs, num_bodies = 2, 4
+    poses = np.arange(num_envs * num_bodies * 7, dtype=np.float32).reshape(num_envs, num_bodies, 7)
+    velocities = np.arange(num_envs * num_bodies * 6, dtype=np.float32).reshape(
+        num_envs, num_bodies, 6
+    )
+
+    backend = object.__new__(mod.MotrixBackend)
+    backend._num_envs = num_envs
+    backend._np_dtype = np.float32
+    backend._link_poses = poses
+    backend._link_velocities = velocities
+    backend._link_velocity_cache_valid = True
+    backend._link_velocity_cache = None
+    body_ids = np.asarray([3, 1], dtype=np.int32)
+    shape = (num_envs, len(body_ids), 3)
+    out_pos = np.empty(shape, dtype=np.float32)
+    out_quat = np.empty((num_envs, len(body_ids), 4), dtype=np.float32)
+    out_lin_vel = np.empty(shape, dtype=np.float32)
+    out_ang_vel = np.empty(shape, dtype=np.float32)
+
+    result = backend.copy_body_state_w(
+        body_ids,
+        out_pos,
+        out_quat,
+        out_lin_vel,
+        out_ang_vel,
+    )
+
+    assert result[0] is out_pos
+    assert result[1] is out_quat
+    assert result[2] is out_lin_vel
+    assert result[3] is out_ang_vel
+    np.testing.assert_array_equal(out_pos, poses[:, body_ids, :3])
+    np.testing.assert_array_equal(out_quat[:, :, 0], poses[:, body_ids, 6])
+    np.testing.assert_array_equal(out_quat[:, :, 1:], poses[:, body_ids, 3:6])
+    np.testing.assert_array_equal(out_lin_vel, velocities[:, body_ids, :3])
+    np.testing.assert_array_equal(out_ang_vel, velocities[:, body_ids, 3:])
+
+    first_scratch = backend._link_velocity_cache
+    velocities += 1000.0
+    backend.copy_body_state_w(body_ids, out_pos, out_quat, out_lin_vel, out_ang_vel)
+
+    assert backend._link_velocity_cache is first_scratch
+    np.testing.assert_array_equal(out_lin_vel, velocities[:, body_ids, :3])
+
+
 def test_motrix_backend_get_body_pose_w_slices_cached_poses_once() -> None:
-    import unilab.base.backend.motrix.backend as mod
+    import unisim.backend.motrix.backend as mod
 
     backend = object.__new__(mod.MotrixBackend)
     backend._link_poses = np.asarray(
@@ -328,6 +381,29 @@ def test_motrix_backend_get_body_pose_w_slices_cached_poses_once() -> None:
     np.testing.assert_allclose(quat, [[[0.8, 0.5, 0.6, 0.7]]])
 
 
+def test_motrix_root_layout_uses_selected_body_floating_base_indices() -> None:
+    import unisim.backend.motrix.backend as mod
+
+    floating_base = SimpleNamespace(
+        dof_pos_indices=[4, 5, 6, 7, 8, 9, 10],
+        dof_vel_indices=[3, 4, 5, 6, 7, 8],
+    )
+    bodies = {
+        "fixed": SimpleNamespace(floatingbase=None),
+        "floating": SimpleNamespace(floatingbase=floating_base),
+    }
+    backend = object.__new__(mod.MotrixBackend)
+    backend._model = SimpleNamespace(get_body=lambda name: bodies.get(name))
+
+    layout = backend.get_root_state_layout("floating")
+    assert layout.qpos_indices == tuple(range(4, 11))
+    assert layout.qvel_indices == tuple(range(3, 9))
+    with pytest.raises(NotImplementedError, match="fixed.*floating base"):
+        backend.get_root_state_layout("fixed")
+    with pytest.raises(ValueError, match="missing.*not found"):
+        backend.get_root_state_layout("missing")
+
+
 def test_motrix_backend_applies_init_geom_size_overrides(monkeypatch, tmp_path) -> None:
     mod, fake_model = _install_fake_motrix(monkeypatch, tmp_path)
     backend = mod.MotrixBackend(
@@ -380,7 +456,7 @@ def test_motrix_backend_default_override_caches_are_float32(monkeypatch, tmp_pat
 
 
 def test_motrix_backend_applies_body_mass_and_ipos_reset_payload() -> None:
-    import unilab.base.backend.motrix.backend as mod
+    import unisim.backend.motrix.backend as mod
 
     link0 = _FakeMotrixLink()
     link1 = _FakeMotrixLink()
@@ -416,7 +492,7 @@ def test_motrix_backend_applies_body_mass_and_ipos_reset_payload() -> None:
 
 
 def test_motrix_backend_applies_geom_friction_reset_payload() -> None:
-    import unilab.base.backend.motrix.backend as mod
+    import unisim.backend.motrix.backend as mod
 
     geom0 = _FakeMotrixGeom(name="floor")
     geom1 = _FakeMotrixGeom(name="object")
@@ -447,7 +523,7 @@ def test_motrix_backend_applies_geom_friction_reset_payload() -> None:
 
 
 def test_motrix_backend_skips_visual_geom_friction_override() -> None:
-    import unilab.base.backend.motrix.backend as mod
+    import unisim.backend.motrix.backend as mod
 
     collision_geom = _FakeMotrixGeom(name="object")
     visual_geom = _FakeMotrixGeom(
@@ -480,7 +556,7 @@ def test_motrix_backend_skips_visual_geom_friction_override() -> None:
 
 
 def test_motrix_backend_applies_gravity_reset_payload() -> None:
-    import unilab.base.backend.motrix.backend as mod
+    import unisim.backend.motrix.backend as mod
 
     fake_model = _FakeMotrixModel()
     backend = object.__new__(mod.MotrixBackend)
@@ -504,7 +580,7 @@ def test_motrix_backend_applies_gravity_reset_payload() -> None:
 
 
 def test_motrix_backend_interval_body_force_uses_link_external_force_delta() -> None:
-    import unilab.base.backend.motrix.backend as mod
+    import unisim.backend.motrix.backend as mod
 
     link = _FakeMotrixLink()
     backend = object.__new__(mod.MotrixBackend)
@@ -659,244 +735,3 @@ def test_motrix_backend_hfield_sampling_uses_terrain_scanner_output_contract(
     assert scanner.out is not None
     assert heights.dtype == np.float32
     np.testing.assert_allclose(heights, [[1.0]], atol=1e-6)
-
-
-def test_create_backend_routes_motrix_max_iterations_override(monkeypatch) -> None:
-    import unilab.base.backend as backend_factory
-    from unilab.base.scene import SceneCfg
-
-    captured: dict[str, Any] = {}
-
-    class FakeMotrixBackend:
-        def __init__(
-            self,
-            scene: SceneCfg,
-            num_envs: int,
-            sim_dt: float,
-            *,
-            max_iterations: int = 3,
-            **kwargs: Any,
-        ) -> None:
-            captured["max_iterations"] = max_iterations
-            captured["kwargs"] = kwargs
-
-    monkeypatch.setattr(
-        backend_factory,
-        "_load_motrix_backend",
-        lambda: (FakeMotrixBackend, True),
-    )
-
-    backend_factory.create_backend(
-        "motrix",
-        SceneCfg(model_file="model.xml"),
-        num_envs=1,
-        sim_dt=0.01,
-        motrix_max_iterations=9,
-    )
-
-    assert captured["max_iterations"] == 9
-    assert "motrix_max_iterations" not in captured["kwargs"]
-
-
-def test_create_backend_does_not_route_motrix_option_to_mujoco(monkeypatch) -> None:
-    import unilab.base.backend as backend_factory
-    from unilab.base.scene import SceneCfg
-
-    captured: dict[str, Any] = {}
-
-    class FakeMuJoCoBackend:
-        def __init__(self, scene: SceneCfg, num_envs: int, sim_dt: float, **kwargs: Any) -> None:
-            captured["kwargs"] = kwargs
-
-    monkeypatch.setattr(backend_factory, "_load_mujoco_backend", lambda: FakeMuJoCoBackend)
-
-    backend_factory.create_backend(
-        "mujoco",
-        SceneCfg(model_file="model.xml"),
-        num_envs=1,
-        sim_dt=0.01,
-        motrix_max_iterations=9,
-    )
-
-    assert "motrix_max_iterations" not in captured["kwargs"]
-    assert "max_iterations" not in captured["kwargs"]
-
-
-def test_create_backend_routes_post_step_forward_sensor_to_mujoco(monkeypatch) -> None:
-    import unilab.base.backend as backend_factory
-    from unilab.base.scene import SceneCfg
-
-    captured: dict[str, Any] = {}
-
-    class FakeMuJoCoBackend:
-        def __init__(self, scene: SceneCfg, num_envs: int, sim_dt: float, **kwargs: Any) -> None:
-            captured["kwargs"] = kwargs
-
-    monkeypatch.setattr(backend_factory, "_load_mujoco_backend", lambda: FakeMuJoCoBackend)
-
-    backend_factory.create_backend(
-        "mujoco",
-        SceneCfg(model_file="model.xml"),
-        num_envs=1,
-        sim_dt=0.01,
-        post_step_forward_sensor=False,
-    )
-
-    assert captured["kwargs"]["post_step_forward_sensor"] is False
-
-
-def test_create_backend_does_not_route_post_step_forward_sensor_to_motrix(monkeypatch) -> None:
-    import unilab.base.backend as backend_factory
-    from unilab.base.scene import SceneCfg
-
-    captured: dict[str, Any] = {}
-
-    class FakeMotrixBackend:
-        def __init__(self, scene: SceneCfg, num_envs: int, sim_dt: float, **kwargs: Any) -> None:
-            captured["kwargs"] = kwargs
-
-    monkeypatch.setattr(
-        backend_factory,
-        "_load_motrix_backend",
-        lambda: (FakeMotrixBackend, True),
-    )
-
-    backend_factory.create_backend(
-        "motrix",
-        SceneCfg(model_file="model.xml"),
-        num_envs=1,
-        sim_dt=0.01,
-        post_step_forward_sensor=False,
-    )
-
-    assert "post_step_forward_sensor" not in captured["kwargs"]
-
-
-def test_create_backend_warns_when_mjwarp_ignores_non_default_mujoco_options(
-    monkeypatch,
-) -> None:
-    import unilab.base.backend as backend_factory
-    from unilab.base.scene import SceneCfg
-
-    captured: dict[str, Any] = {}
-
-    class FakeMjwarpBackend:
-        def __init__(self, scene: SceneCfg, num_envs: int, sim_dt: float, **kwargs: Any) -> None:
-            captured["kwargs"] = kwargs
-
-    monkeypatch.setattr(backend_factory, "_load_mjwarp_backend", lambda: FakeMjwarpBackend)
-
-    with pytest.warns(
-        UserWarning,
-        match="post_step_forward_sensor=False.*adaptive_chunk_size=True.*bench_nsteps=4",
-    ):
-        backend_factory.create_backend(
-            "mjwarp",
-            SceneCfg(model_file="model.xml"),
-            num_envs=1,
-            sim_dt=0.01,
-            post_step_forward_sensor=False,
-            adaptive_chunk_size=True,
-            bench_nsteps=4,
-        )
-
-    assert "post_step_forward_sensor" not in captured["kwargs"]
-    assert "adaptive_chunk_size" not in captured["kwargs"]
-    assert "bench_nsteps" not in captured["kwargs"]
-
-
-def test_create_backend_routes_iterations_to_mujoco(monkeypatch) -> None:
-    import unilab.base.backend as backend_factory
-    from unilab.base.scene import SceneCfg
-
-    captured: dict[str, Any] = {}
-
-    class FakeMuJoCoBackend:
-        def __init__(self, scene: SceneCfg, num_envs: int, sim_dt: float, **kwargs: Any) -> None:
-            captured["kwargs"] = kwargs
-
-    monkeypatch.setattr(backend_factory, "_load_mujoco_backend", lambda: FakeMuJoCoBackend)
-
-    backend_factory.create_backend(
-        "mujoco",
-        SceneCfg(model_file="model.xml"),
-        num_envs=1,
-        sim_dt=0.01,
-        iterations=7,
-    )
-
-    assert captured["kwargs"]["iterations"] == 7
-
-
-def test_create_backend_does_not_route_iterations_to_motrix(monkeypatch) -> None:
-    import unilab.base.backend as backend_factory
-    from unilab.base.scene import SceneCfg
-
-    captured: dict[str, Any] = {}
-
-    class FakeMotrixBackend:
-        def __init__(self, scene: SceneCfg, num_envs: int, sim_dt: float, **kwargs: Any) -> None:
-            captured["kwargs"] = kwargs
-
-    monkeypatch.setattr(
-        backend_factory,
-        "_load_motrix_backend",
-        lambda: (FakeMotrixBackend, True),
-    )
-
-    backend_factory.create_backend(
-        "motrix",
-        SceneCfg(model_file="model.xml"),
-        num_envs=1,
-        sim_dt=0.01,
-        iterations=7,
-    )
-
-    assert "iterations" not in captured["kwargs"]
-
-
-def test_create_backend_does_not_route_iterations_to_drake(monkeypatch) -> None:
-    import unilab.base.backend as backend_factory
-    from unilab.base.scene import SceneCfg
-
-    captured: dict[str, Any] = {}
-
-    class FakeDrakeBackend:
-        def __init__(self, scene: SceneCfg, num_envs: int, sim_dt: float, **kwargs: Any) -> None:
-            captured["kwargs"] = kwargs
-
-    monkeypatch.setattr(backend_factory, "_load_drake_backend", lambda: FakeDrakeBackend)
-
-    backend_factory.create_backend(
-        "drake",
-        SceneCfg(model_file="model.xml"),
-        num_envs=1,
-        sim_dt=0.01,
-        iterations=7,
-    )
-
-    assert "iterations" not in captured["kwargs"]
-
-
-def test_create_backend_warns_when_mjwarp_ignores_iterations(monkeypatch) -> None:
-    import unilab.base.backend as backend_factory
-    from unilab.base.scene import SceneCfg
-
-    captured: dict[str, Any] = {}
-
-    class FakeMjwarpBackend:
-        def __init__(self, scene: SceneCfg, num_envs: int, sim_dt: float, **kwargs: Any) -> None:
-            captured["kwargs"] = kwargs
-
-    monkeypatch.setattr(backend_factory, "_load_mjwarp_backend", lambda: FakeMjwarpBackend)
-
-    with pytest.warns(UserWarning, match="iterations=7"):
-        backend_factory.create_backend(
-            "mjwarp",
-            SceneCfg(model_file="model.xml"),
-            num_envs=1,
-            sim_dt=0.01,
-            iterations=7,
-        )
-
-    assert "iterations" not in captured["kwargs"]
diff --git a/tests/base/test_np_env.py b/tests/base/test_np_env.py
index 618c8a672..505f070e2 100644
--- a/tests/base/test_np_env.py
+++ b/tests/base/test_np_env.py
@@ -12,8 +12,8 @@
 import gymnasium as gym
 import numpy as np
 import pytest
+from unisim.backend.base import SimBackend
 
-from unilab.base.backend.base import SimBackend
 from unilab.base.base import EnvCfg
 from unilab.base.np_env import NpEnv, NpEnvState
 from unilab.base.scene import SceneCfg
diff --git a/tests/base/test_registry.py b/tests/base/test_registry.py
index 05570f9c7..bb0250bca 100644
--- a/tests/base/test_registry.py
+++ b/tests/base/test_registry.py
@@ -3,7 +3,6 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import Optional
 
 import gymnasium as gym
 import numpy as np
@@ -104,6 +103,38 @@ class _DecCfg(EnvCfg):
     assert registry_mod.contains(_name)
 
 
+def test_envcfg_decorator_registers_plain_factory_and_returns_it_unchanged():
+    _name = "_TestDecoratorFactoryEnv"
+
+    def make_cfg() -> EnvCfg:
+        return _TestCfgA()
+
+    registered = registry_mod.envcfg(_name)(make_cfg)
+
+    assert registered is make_cfg
+    assert registry_mod.materialize_env_config(_name) == _TestCfgA()
+
+
+def test_materialize_env_config_rejects_invalid_factory_output():
+    _name = "_TestInvalidFactoryOutput"
+
+    def make_invalid_cfg():
+        return {"sim_dt": 0.01}
+
+    registry_mod.register_env_config(_name, make_invalid_cfg)
+
+    with pytest.raises(
+        TypeError,
+        match=r"_TestInvalidFactoryOutput.*make_invalid_cfg.*dict.*EnvCfg",
+    ):
+        registry_mod.materialize_env_config(_name)
+
+
+def test_register_env_config_rejects_non_callable():
+    with pytest.raises(TypeError, match="config factory must be callable"):
+        registry_mod.register_env_config("_TestNonCallableFactory", None)  # type: ignore[arg-type]
+
+
 def test_env_decorator_registers():
     _name = "_TestDecoratorEnv2"
     if not registry_mod.contains(_name):
@@ -118,6 +149,19 @@ class _DecEnv(_TestEnvA):
     assert "mujoco" in listed[_name]["available_backends"]
 
 
+def test_env_decorator_registers_plain_factory_and_returns_it_unchanged():
+    _name = "_TestDecoratorEnvFactory"
+    registry_mod.register_env_config(_name, _TestCfgA)
+
+    def make_env(cfg: EnvCfg, num_envs: int = 1, backend_type: str = "mujoco") -> ABEnv:
+        return _TestEnvA(cfg, num_envs=num_envs, backend_type=backend_type)
+
+    registered = registry_mod.env(_name, "mujoco")(make_env)
+
+    assert registered is make_env
+    assert registry_mod._envs[_name].env_factory_dict["mujoco"] is make_env
+
+
 def test_contains_before_and_after_registration():
     _name = "_TestContainsDynamic"
     assert not registry_mod.contains(_name)
@@ -159,6 +203,7 @@ def test_make_unregistered_raises():
 def test_list_registered_envs_includes_registered():
     listed = registry_mod.list_registered_envs()
     assert _TEST_ENV_A in listed
+    assert listed[_TEST_ENV_A]["config_factory"] == "_TestCfgA"
     assert "mujoco" in listed[_TEST_ENV_A]["available_backends"]
 
 
@@ -173,7 +218,7 @@ def test_register_env_invalid_backend_raises():
     if not registry_mod.contains(_name):
         registry_mod.register_env_config(_name, _TestCfgA)
     with pytest.raises(ValueError, match="Unsupported simulation backend"):
-        registry_mod.register_env(_name, _TestEnvA, "isaacgym")
+        registry_mod.register_env(_name, _TestEnvA, "not_a_backend")
 
 
 def test_register_env_without_config_raises():
@@ -189,9 +234,17 @@ def test_register_env_duplicate_backend_raises():
         registry_mod.register_env(_TEST_ENV_A, _TestEnvA, "mujoco")
 
 
-def test_find_available_sim_backend_no_env_cls_raises():
-    """find_available_sim_backend() raises when config exists but no env_cls registered."""
-    # _TEST_ENV_B has config but no env class (registered above without env)
+def test_register_env_rejects_non_callable_factory():
+    _name = "_TestNonCallableEnvFactory"
+    registry_mod.register_env_config(_name, _TestCfgA)
+
+    with pytest.raises(TypeError, match=r"_TestNonCallableEnvFactory.*mujoco.*callable.*object"):
+        registry_mod.register_env(_name, object(), "mujoco")  # type: ignore[arg-type]
+
+
+def test_find_available_sim_backend_no_env_factory_raises():
+    """find_available_sim_backend() raises when config exists but no env factory is registered."""
+    # _TEST_ENV_B has config but no env factory (registered above without env)
     with pytest.raises(ValueError, match="does not support any simulation backend"):
         registry_mod.find_available_sim_backend(_TEST_ENV_B)
 
@@ -212,14 +265,60 @@ def test_make_auto_selects_default_backend_independent_of_registration_order():
     assert isinstance(env, _TestEnvA)
 
 
+def test_make_calls_plain_factory_with_cfg_num_envs_and_backend():
+    @dataclass
+    class _CallableFactoryCfg(EnvCfg):
+        ctrl_dt: float = 0.02
+
+    _name = "_TestCallableEnvFactory"
+    received: dict[str, object] = {}
+
+    def make_env(cfg: EnvCfg, num_envs: int = 1, backend_type: str = "mujoco") -> ABEnv:
+        received.update(cfg=cfg, num_envs=num_envs, backend_type=backend_type)
+        return _TestEnvA(cfg, num_envs=num_envs, backend_type=backend_type)
+
+    registry_mod.register_env_config(_name, _CallableFactoryCfg)
+    registered = registry_mod.register_env(_name, make_env, "motrix")
+
+    made = registry_mod.make(
+        _name,
+        sim_backend=None,
+        env_cfg_override={"ctrl_dt": 0.05},
+        num_envs=7,
+    )
+
+    assert registered is make_env
+    assert isinstance(made, _TestEnvA)
+    assert received["cfg"] is made.cfg
+    assert received["num_envs"] == 7
+    assert received["backend_type"] == "motrix"
+    assert made.cfg.ctrl_dt == pytest.approx(0.05)
+
+
+def test_make_rejects_invalid_factory_output_at_registry_boundary():
+    _name = "_TestInvalidEnvFactoryOutput"
+
+    def make_invalid_env(cfg: EnvCfg, num_envs: int = 1, backend_type: str = "mujoco") -> object:
+        return object()
+
+    registry_mod.register_env_config(_name, _TestCfgA)
+    registry_mod.register_env(_name, make_invalid_env, "mujoco")  # type: ignore[arg-type]
+
+    with pytest.raises(
+        TypeError,
+        match=r"_TestInvalidEnvFactoryOutput.*mujoco.*make_invalid_env.*object.*ABEnv",
+    ):
+        registry_mod.make(_name, sim_backend="mujoco")
+
+
 def test_make_unsupported_backend_raises():
     """make() with an unsupported backend name raises ValueError."""
     with pytest.raises(ValueError, match="does not support simulation backend"):
         registry_mod.make(_TEST_ENV_A, sim_backend="motrix")
 
 
-def test_make_no_env_cls_raises():
-    """make() when no env class registered (only config) raises ValueError."""
+def test_make_no_env_factory_raises():
+    """make() when no env factory is registered (only config) raises ValueError."""
     with pytest.raises(ValueError, match="does not support any simulation backend"):
         registry_mod.make(_TEST_ENV_B, sim_backend=None)
 
@@ -240,6 +339,31 @@ class _CfgWithField(EnvCfg):
     assert env.cfg.ctrl_dt == 0.05
 
 
+def test_make_materializes_fresh_function_owned_config_for_each_call():
+    @dataclass
+    class _FactoryCfg(EnvCfg):
+        ctrl_dt: float = 0.02
+
+    _name = "_TestFunctionFactoryOverrideEnv"
+
+    def make_cfg() -> EnvCfg:
+        return _FactoryCfg()
+
+    registry_mod.register_env_config(_name, make_cfg)
+    registry_mod.register_env(_name, _TestEnvA, "mujoco")
+
+    overridden = registry_mod.make(
+        _name,
+        sim_backend="mujoco",
+        env_cfg_override={"ctrl_dt": 0.05},
+    )
+    default = registry_mod.make(_name, sim_backend="mujoco")
+
+    assert overridden.cfg.ctrl_dt == 0.05
+    assert default.cfg.ctrl_dt == 0.02
+    assert overridden.cfg is not default.cfg
+
+
 def test_make_with_invalid_cfg_override_raises():
     """make() with a config key that doesn't exist raises ValueError."""
     with pytest.raises(ValueError, match="has no attribute"):
diff --git a/tests/base/test_reset_state.py b/tests/base/test_reset_state.py
new file mode 100644
index 000000000..872d9dba9
--- /dev/null
+++ b/tests/base/test_reset_state.py
@@ -0,0 +1,507 @@
+"""Focused tests for the base-owned Manager-Based reset transaction."""
+
+from __future__ import annotations
+
+from typing import Any, cast
+
+import numpy as np
+import pytest
+from unisim.backend.base import BackendRootStateLayout, SimBackend
+from unisim.dr.types import (
+    RESET_TERM_KD,
+    RESET_TERM_KP,
+    DomainRandomizationCapabilities,
+    ResetRandomizationPayload,
+)
+
+from unilab.base.reset_state import ResetStateTransaction
+
+
+class _Backend:
+    backend_type = "fake"
+    num_actuators = 3
+
+    def __init__(
+        self,
+        *,
+        qpos: Any = None,
+        qvel: Any = None,
+        fail_set_state: bool = False,
+    ) -> None:
+        self.num_envs = 4
+        self.qpos = np.array([1.0, 2.0, 3.0]) if qpos is None else qpos
+        self.qvel = np.array([0.0, 0.0]) if qvel is None else qvel
+        self.fail_set_state = fail_set_state
+        self.default_qpos_calls = 0
+        self.init_qvel_calls = 0
+        self.set_state_calls: list[tuple[np.ndarray, np.ndarray, np.ndarray]] = []
+        self.randomization_calls: list[ResetRandomizationPayload | None] = []
+        self.default_kp = np.array([10.0, 20.0, 30.0])
+        self.default_kd = np.array([1.0, 2.0, 3.0])
+
+    def get_default_qpos(self):
+        self.default_qpos_calls += 1
+        return self.qpos
+
+    def get_init_qvel(self):
+        self.init_qvel_calls += 1
+        return self.qvel
+
+    def get_dr_capabilities(self) -> DomainRandomizationCapabilities:
+        return DomainRandomizationCapabilities(
+            supported_reset_terms=frozenset((RESET_TERM_KP, RESET_TERM_KD))
+        )
+
+    def get_actuator_gains(self) -> tuple[np.ndarray, np.ndarray]:
+        return self.default_kp.copy(), self.default_kd.copy()
+
+    def set_state(
+        self,
+        env_ids: np.ndarray,
+        qpos: np.ndarray,
+        qvel: np.ndarray,
+        randomization=None,
+    ) -> dict:
+        if self.fail_set_state:
+            raise NotImplementedError("reset upload disabled")
+        self.set_state_calls.append((env_ids.copy(), qpos.copy(), qvel.copy()))
+        self.randomization_calls.append(randomization)
+        return {"timing": {"set_state_ms": 1.0}}
+
+
+def _transaction(backend: _Backend) -> ResetStateTransaction:
+    return ResetStateTransaction(cast(SimBackend, backend))
+
+
+def test_transaction_is_lazy_and_combines_terms_into_one_commit() -> None:
+    backend = _Backend()
+    transaction = _transaction(backend)
+
+    with transaction.scoped(np.array([0, 2, 3], dtype=np.int32)):
+        assert transaction.active
+    assert backend.default_qpos_calls == 0
+    assert backend.init_qvel_calls == 0
+    assert backend.set_state_calls == []
+
+    with transaction.scoped(np.array([0, 2, 3], dtype=np.int32)):
+        transaction.reset_to_default(
+            np.array([2], dtype=np.int32),
+            term_name="first",
+        )
+        transaction.reset_to_default(
+            np.array([3, 0], dtype=np.int32),
+            term_name="second",
+        )
+        assert backend.set_state_calls == []
+
+    assert not transaction.active
+    assert backend.default_qpos_calls == 1
+    assert backend.init_qvel_calls == 1
+    assert len(backend.set_state_calls) == 1
+    ids, qpos, qvel = backend.set_state_calls[0]
+    np.testing.assert_array_equal(ids, [0, 2, 3])
+    np.testing.assert_array_equal(qpos, np.tile(backend.qpos, (3, 1)))
+    np.testing.assert_array_equal(qvel, np.tile(backend.qvel, (3, 1)))
+
+    with transaction.scoped(np.array([1], dtype=np.int32)):
+        transaction.reset_to_default(np.array([1], dtype=np.int32), term_name="third")
+    assert backend.default_qpos_calls == 1
+    assert backend.init_qvel_calls == 1
+    assert len(backend.set_state_calls) == 2
+
+
+def test_transaction_reports_only_committed_dirty_rows_as_writes() -> None:
+    backend = _Backend()
+    transaction = _transaction(backend)
+
+    assert not transaction.last_commit_had_writes
+    with transaction.scoped(np.array([0], dtype=np.int32)):
+        pass
+    assert not transaction.last_commit_had_writes
+
+    with transaction.scoped(np.array([0], dtype=np.int32)):
+        transaction.reset_to_default(np.array([0], dtype=np.int32), term_name="dirty")
+    assert transaction.last_commit_had_writes
+
+    with pytest.raises(RuntimeError, match="abort"):
+        with transaction.scoped(np.array([1], dtype=np.int32)):
+            transaction.reset_to_default(np.array([1], dtype=np.int32), term_name="aborted")
+            raise RuntimeError("abort")
+    assert not transaction.last_commit_had_writes
+
+
+def test_exception_aborts_without_backend_mutation_and_next_reset_is_clean() -> None:
+    backend = _Backend()
+    transaction = _transaction(backend)
+
+    with pytest.raises(RuntimeError, match="term failed"):
+        with transaction.scoped(np.array([0, 1], dtype=np.int32)):
+            transaction.reset_to_default(np.array([0], dtype=np.int32), term_name="broken")
+            raise RuntimeError("term failed")
+
+    assert not transaction.active
+    assert backend.set_state_calls == []
+
+    with transaction.scoped(np.array([1], dtype=np.int32)):
+        transaction.reset_to_default(np.array([1], dtype=np.int32), term_name="healthy")
+    assert len(backend.set_state_calls) == 1
+    np.testing.assert_array_equal(backend.set_state_calls[0][0], [1])
+
+
+def test_actuator_gains_compose_with_state_in_one_reset_commit() -> None:
+    backend = _Backend()
+    transaction = _transaction(backend)
+    columns, default_kp, default_kd = transaction.bind_actuator_gain_write(
+        np.array([2, 0], dtype=np.int32),
+        term_name="pd_gains:robot",
+    )
+    np.testing.assert_array_equal(columns, [2, 0])
+    np.testing.assert_array_equal(default_kp, [30.0, 10.0])
+    np.testing.assert_array_equal(default_kd, [3.0, 1.0])
+
+    with transaction.scoped(np.array([0, 2], dtype=np.int32)):
+        transaction.reset_to_default(np.array([0, 2], dtype=np.int32), term_name="default")
+        transaction.write_actuator_gains(
+            np.array([2, 0], dtype=np.int32),
+            columns,
+            np.array([[5.0, 6.0], [7.0, 8.0]]),
+            np.array([[0.5, 0.6], [0.7, 0.8]]),
+            term_name="pd_gains:robot",
+        )
+
+    payload = backend.randomization_calls[0]
+    assert payload is not None
+    np.testing.assert_array_equal(payload.kp, [[8.0, 20.0, 7.0], [6.0, 20.0, 5.0]])
+    np.testing.assert_array_equal(payload.kd, [[0.8, 2.0, 0.7], [0.6, 2.0, 0.5]])
+
+
+def test_actuator_gain_sparse_rows_abort_without_backend_mutation() -> None:
+    backend = _Backend()
+    transaction = _transaction(backend)
+    columns, _, _ = transaction.bind_actuator_gain_write(
+        np.array([0], dtype=np.int32),
+        term_name="pd_gains:robot",
+    )
+
+    with pytest.raises(RuntimeError, match=r"cannot represent sparse rows.*missing env IDs \[1\]"):
+        with transaction.scoped(np.array([0, 1], dtype=np.int32)):
+            transaction.reset_to_default(
+                np.array([0, 1], dtype=np.int32),
+                term_name="default",
+            )
+            transaction.write_actuator_gains(
+                np.array([0], dtype=np.int32),
+                columns,
+                np.array([[11.0]]),
+                np.array([[1.1]]),
+                term_name="pd_gains:robot",
+            )
+
+    assert backend.set_state_calls == []
+
+
+def test_joint_writes_initialize_defaults_and_compose_by_column() -> None:
+    backend = _Backend()
+    transaction = _transaction(backend)
+
+    with transaction.scoped(np.array([0, 2], dtype=np.int32)):
+        transaction.write_joint_state(
+            np.array([2, 0], dtype=np.int32),
+            np.array([1], dtype=np.int32),
+            np.array([0], dtype=np.int32),
+            np.array([[9.0], [8.0]], dtype=np.float32),
+            np.array([[-1.0], [-2.0]], dtype=np.float32),
+            term_name="robot.write_joint_state_to_sim",
+        )
+
+    ids, qpos, qvel = backend.set_state_calls[0]
+    np.testing.assert_array_equal(ids, [0, 2])
+    np.testing.assert_array_equal(qpos, [[1.0, 8.0, 3.0], [1.0, 9.0, 3.0]])
+    np.testing.assert_array_equal(qvel, [[-2.0, 0.0], [-1.0, 0.0]])
+
+
+def test_root_pose_and_world_velocity_compose_at_nonzero_columns() -> None:
+    default_qpos = np.array([99.0, 1.0, 2.0, 3.0, 1.0, 0.0, 0.0, 0.0, 88.0])
+    default_qvel = np.array([77.0, 66.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 55.0])
+    backend = _Backend(qpos=default_qpos, qvel=default_qvel)
+    transaction = _transaction(backend)
+    layout = BackendRootStateLayout(tuple(range(1, 8)), tuple(range(2, 8)))
+    half_sqrt = np.sqrt(0.5)
+    poses = np.array(
+        [
+            [10.0, 11.0, 12.0, half_sqrt, 0.0, 0.0, half_sqrt],
+            [20.0, 21.0, 22.0, 1.0, 0.0, 0.0, 0.0],
+        ]
+    )
+    velocities_w = np.array(
+        [
+            [1.0, 2.0, 3.0, 1.0, 0.0, 0.0],
+            [4.0, 5.0, 6.0, 0.0, 1.0, 2.0],
+        ]
+    )
+
+    with transaction.scoped(np.array([0, 2], dtype=np.int32)):
+        transaction.write_root_pose(
+            np.array([2, 0], dtype=np.int32),
+            layout,
+            poses,
+            term_name="root_pose",
+        )
+        transaction.write_root_velocity(
+            np.array([2, 0], dtype=np.int32),
+            layout,
+            velocities_w,
+            term_name="root_velocity",
+        )
+
+    assert len(backend.set_state_calls) == 1
+    ids, qpos, qvel = backend.set_state_calls[0]
+    np.testing.assert_array_equal(ids, [0, 2])
+    np.testing.assert_array_equal(qpos[:, [0, 8]], [[99.0, 88.0], [99.0, 88.0]])
+    np.testing.assert_allclose(qpos[0, 1:8], poses[1])
+    np.testing.assert_allclose(qpos[1, 1:8], poses[0])
+    np.testing.assert_array_equal(qvel[:, [0, 1, 8]], [[77.0, 66.0, 55.0]] * 2)
+    np.testing.assert_allclose(qvel[0, 2:8], velocities_w[1])
+    np.testing.assert_allclose(qvel[1, 2:5], velocities_w[0, :3])
+    np.testing.assert_allclose(qvel[1, 5:8], [0.0, -1.0, 0.0], atol=1e-7)
+
+
+def test_read_root_pose_returns_staged_or_default_pose_without_dirtying() -> None:
+    default_qpos = np.array([99.0, 1.0, 2.0, 3.0, 1.0, 0.0, 0.0, 0.0, 88.0])
+    default_qvel = np.zeros(9)
+    backend = _Backend(qpos=default_qpos, qvel=default_qvel)
+    transaction = _transaction(backend)
+    layout = BackendRootStateLayout(tuple(range(1, 8)), tuple(range(2, 8)))
+
+    with transaction.scoped(np.array([0, 2], dtype=np.int32)):
+        transaction.write_root_pose(
+            np.array([2], dtype=np.int32),
+            layout,
+            np.array([[10.0, 11.0, 12.0, 1.0, 0.0, 0.0, 0.0]]),
+            term_name="reset_base",
+        )
+        staged = transaction.read_root_pose(
+            np.array([0, 2], dtype=np.int32),
+            layout,
+            term_name="random_prone_init",
+        )
+        # Env 2 reflects the earlier term's write; env 0 the backend default.
+        np.testing.assert_allclose(staged[0], default_qpos[1:8])
+        np.testing.assert_allclose(staged[1], [10.0, 11.0, 12.0, 1.0, 0.0, 0.0, 0.0])
+        # The read is a detached copy: mutating it must not leak back.
+        staged[1, 0] = -5.0
+        reread = transaction.read_root_pose(
+            np.array([2], dtype=np.int32),
+            layout,
+            term_name="random_prone_init",
+        )
+        assert reread[0, 0] == 10.0
+
+    # Reading alone does not dirty a row: only the written env is committed.
+    assert len(backend.set_state_calls) == 1
+    ids, _, _ = backend.set_state_calls[0]
+    np.testing.assert_array_equal(ids, [2])
+
+
+def test_read_root_pose_fails_closed_outside_reset_scope() -> None:
+    backend = _Backend(
+        qpos=np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]),
+        qvel=np.zeros(6),
+    )
+    transaction = _transaction(backend)
+    layout = BackendRootStateLayout(tuple(range(7)), tuple(range(6)))
+
+    with pytest.raises(RuntimeError, match="requires an active reset event"):
+        transaction.read_root_pose(np.array([0], dtype=np.int32), layout, term_name="t")
+
+    with transaction.scoped(np.array([0], dtype=np.int32)):
+        with pytest.raises(ValueError, match="outside the active reset"):
+            transaction.read_root_pose(np.array([1], dtype=np.int32), layout, term_name="t")
+
+
+def test_combined_root_state_uses_staged_pose_for_angular_velocity() -> None:
+    backend = _Backend(
+        qpos=np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]),
+        qvel=np.zeros(6),
+    )
+    transaction = _transaction(backend)
+    layout = BackendRootStateLayout(tuple(range(7)), tuple(range(6)))
+    half_sqrt = np.sqrt(0.5)
+    root_state = np.array(
+        [[1.0, 2.0, 3.0, half_sqrt, 0.0, 0.0, half_sqrt, 4.0, 5.0, 6.0, 1.0, 0.0, 0.0]]
+    )
+
+    with transaction.scoped(np.array([1], dtype=np.int32)):
+        transaction.write_root_state(
+            np.array([1], dtype=np.int32),
+            layout,
+            root_state,
+            term_name="root_state",
+        )
+
+    _, qpos, qvel = backend.set_state_calls[0]
+    np.testing.assert_allclose(qpos[0], root_state[0, :7])
+    np.testing.assert_allclose(qvel[0, :3], root_state[0, 7:10])
+    np.testing.assert_allclose(qvel[0, 3:6], [0.0, -1.0, 0.0], atol=1e-7)
+
+
+@pytest.mark.parametrize(
+    ("root_state", "error", "match"),
+    [
+        (np.zeros((1, 12)), ValueError, "root state.*expected"),
+        (np.zeros((1, 13), dtype=np.int32), TypeError, "root state.*floating"),
+        (np.full((1, 13), np.nan), ValueError, "root state.*NaN or Inf"),
+        (
+            np.array([[0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]),
+            ValueError,
+            "root quaternion must be unit length",
+        ),
+    ],
+)
+def test_root_state_values_fail_closed(root_state, error, match: str) -> None:
+    backend = _Backend(
+        qpos=np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]),
+        qvel=np.zeros(6),
+    )
+    transaction = _transaction(backend)
+    layout = BackendRootStateLayout(tuple(range(7)), tuple(range(6)))
+    with pytest.raises(error, match=match):
+        with transaction.scoped(np.array([0], dtype=np.int32)):
+            transaction.write_root_state(
+                np.array([0], dtype=np.int32),
+                layout,
+                root_state,
+                term_name="bad_root",
+            )
+    assert backend.set_state_calls == []
+
+
+def test_root_layout_bounds_and_reset_scope_fail_closed() -> None:
+    backend = _Backend(
+        qpos=np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]),
+        qvel=np.zeros(6),
+    )
+    transaction = _transaction(backend)
+    out_of_bounds = BackendRootStateLayout(tuple(range(1, 8)), tuple(range(6)))
+
+    with pytest.raises(IndexError, match="root qpos indices out of range"):
+        with transaction.scoped(np.array([0], dtype=np.int32)):
+            transaction.write_root_pose(
+                np.array([0], dtype=np.int32),
+                out_of_bounds,
+                np.array([[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]]),
+                term_name="bad_layout",
+            )
+
+    valid = BackendRootStateLayout(tuple(range(7)), tuple(range(6)))
+    with pytest.raises(ValueError, match="root-pose mutation outside the active reset"):
+        with transaction.scoped(np.array([0], dtype=np.int32)):
+            transaction.write_root_pose(
+                np.array([1], dtype=np.int32),
+                valid,
+                np.array([[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]]),
+                term_name="outside",
+            )
+
+
+@pytest.mark.parametrize(
+    ("position", "velocity", "error", "match"),
+    [
+        (np.zeros((1, 2)), np.zeros((1, 1)), ValueError, "joint position.*expected"),
+        (np.zeros((1, 1)), np.zeros((2, 1)), ValueError, "joint velocity.*expected"),
+        (np.zeros((1, 1), dtype=np.int32), np.zeros((1, 1)), TypeError, "must be floating"),
+        (np.full((1, 1), np.nan), np.zeros((1, 1)), ValueError, "NaN or Inf"),
+    ],
+)
+def test_joint_write_values_fail_closed(position, velocity, error, match: str) -> None:
+    transaction = _transaction(_Backend())
+    with pytest.raises(error, match=match):
+        with transaction.scoped(np.array([0], dtype=np.int32)):
+            transaction.write_joint_state(
+                np.array([0], dtype=np.int32),
+                np.array([1], dtype=np.int32),
+                np.array([0], dtype=np.int32),
+                position,
+                velocity,
+                term_name="joint_term",
+            )
+
+
+def test_mutation_must_stay_inside_active_reset() -> None:
+    transaction = _transaction(_Backend())
+    with transaction.scoped(np.array([1, 2], dtype=np.int32)):
+        with pytest.raises(ValueError, match="outside the active reset.*3"):
+            transaction.reset_to_default(np.array([3], dtype=np.int32), term_name="bad")
+
+    with pytest.raises(RuntimeError, match="requires an active reset event"):
+        transaction.reset_to_default(np.array([1], dtype=np.int32), term_name="late")
+
+
+@pytest.mark.parametrize(
+    ("ids", "error", "match"),
+    [
+        ([0], TypeError, "must be np.ndarray"),
+        (np.array([[0]], dtype=np.int32), TypeError, "1-D integer"),
+        (np.array([True]), TypeError, "1-D integer"),
+        (np.array([-1], dtype=np.int32), IndexError, "out of range"),
+        (np.array([4], dtype=np.int32), IndexError, "out of range"),
+        (np.array([1, 1], dtype=np.int32), ValueError, "duplicates"),
+    ],
+)
+def test_begin_rejects_invalid_environment_ids(ids, error, match: str) -> None:
+    with pytest.raises(error, match=match):
+        _transaction(_Backend()).begin(ids)
+
+
+@pytest.mark.parametrize(
+    ("field", "value", "error", "match"),
+    [
+        ("qpos", [1.0], TypeError, "default qpos.*np.ndarray"),
+        ("qpos", np.zeros((1, 1)), ValueError, "default qpos.*expected 1-D"),
+        ("qpos", np.array([1], dtype=np.int32), TypeError, "default qpos.*floating"),
+        ("qpos", np.array([np.nan]), ValueError, "default qpos.*NaN or Inf"),
+        ("qvel", np.array([np.inf]), ValueError, "initial qvel.*NaN or Inf"),
+    ],
+)
+def test_backend_default_state_contract_fails_at_mutation_boundary(
+    field: str,
+    value,
+    error,
+    match: str,
+) -> None:
+    kwargs = {field: value}
+    transaction = _transaction(_Backend(**kwargs))
+    with pytest.raises(error, match=match):
+        with transaction.scoped(np.array([0], dtype=np.int32)):
+            transaction.reset_to_default(
+                np.array([0], dtype=np.int32),
+                term_name="reset_scene_to_default",
+            )
+    assert not transaction.active
+
+
+def test_missing_default_and_set_state_capabilities_name_term_and_backend() -> None:
+    missing = type("MissingBackend", (), {"num_envs": 1, "backend_type": "missing"})()
+    transaction = ResetStateTransaction(cast(SimBackend, missing))
+    with pytest.raises(
+        NotImplementedError,
+        match="EventManager term 'reset_scene_to_default'.*default qpos.*backend 'missing'",
+    ):
+        with transaction.scoped(np.array([0], dtype=np.int32)):
+            transaction.reset_to_default(
+                np.array([0], dtype=np.int32),
+                term_name="reset_scene_to_default",
+            )
+
+    backend = _Backend(fail_set_state=True)
+    transaction = _transaction(backend)
+    with pytest.raises(
+        NotImplementedError,
+        match="SimBackend.set_state.*reset_scene_to_default.*backend 'fake'",
+    ):
+        with transaction.scoped(np.array([0], dtype=np.int32)):
+            transaction.reset_to_default(
+                np.array([0], dtype=np.int32),
+                term_name="reset_scene_to_default",
+            )
+    assert not transaction.active
diff --git a/tests/base/test_reward_override.py b/tests/base/test_reward_override.py
index cd9b17efa..6b635ee1e 100644
--- a/tests/base/test_reward_override.py
+++ b/tests/base/test_reward_override.py
@@ -1,57 +1,34 @@
 """Test reward config override through registry."""
 
+from pathlib import Path
 from typing import Any, cast
 
-import pytest
+from hydra import compose, initialize_config_dir
+from hydra.core.global_hydra import GlobalHydra
 
 from unilab.base import registry
+from unilab.base.config_adapter import BackendAdapter
 from unilab.base.registry import ensure_registries
 
-
-def test_reward_override_go1():
-    """Test Go1 reward config override."""
-    ensure_registries()
-
-    from unilab.envs.locomotion.go1.joystick import RewardConfig
-
-    override_config = RewardConfig(
-        scales={"tracking_lin_vel": 999.0},
-        tracking_sigma=0.5,
-        base_height_target=0.5,
-    )
-
-    env = cast(
-        Any,
-        registry.make(
-            "Go1JoystickFlat",
-            num_envs=1,
-            sim_backend="mujoco",
-            env_cfg_override={"reward_config": override_config},
-        ),
-    )
-
-    assert env._cfg.reward_config.scales["tracking_lin_vel"] == 999.0
-    env.close()
+ROOT_DIR = Path(__file__).parents[2]
 
 
 def test_reward_override_g1():
-    """Test G1 reward config override."""
+    """Test G1 manager reward override through the registry."""
     ensure_registries()
 
-    from unilab.envs.locomotion.g1.joystick import G1WalkRewardConfig
-
-    override_config = G1WalkRewardConfig(
-        scales={"tracking_lin_vel": 888.0, "alive": 20.0},
-        tracking_sigma=0.3,
-        base_height_target=0.8,
-        min_base_height=0.3,
-        max_tilt_deg=65.0,
-        gait_frequency=1.5,
-        feet_phase_swing_height=0.09,
-        feet_phase_tracking_sigma=0.008,
-        close_feet_threshold=0.15,
-        pose_weights=[0.01] * 29,
-    )
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(
+        config_dir=str(ROOT_DIR / "src" / "unilab" / "conf" / "ppo"), version_base="1.3"
+    ):
+        cfg = compose("config", overrides=["task=g1_walk_flat/mujoco"])
+    env_cfg_override = BackendAdapter(cfg, root_dir=ROOT_DIR).build_task_env_cfg_override()
+    env_cfg_override["rewards"]["tracking_lin_vel"]["weight"] = 888.0
+    env_cfg_override["rewards"]["alive"] = {
+        "_target_": "unilab.managers.RewardTermCfg",
+        "func": "unilab.tasks.locomotion.common.manager_terms.alive",
+        "weight": 20.0,
+    }
 
     env = cast(
         Any,
@@ -59,10 +36,11 @@ def test_reward_override_g1():
             "G1WalkFlat",
             num_envs=1,
             sim_backend="mujoco",
-            env_cfg_override={"reward_config": override_config},
+            env_cfg_override=env_cfg_override,
         ),
     )
 
-    assert env._cfg.reward_config.scales["tracking_lin_vel"] == 888.0
-    assert env._cfg.reward_config.scales["alive"] == 20.0
+    assert env._cfg.rewards["tracking_lin_vel"].weight == 888.0
+    assert env._cfg.rewards["alive"].weight == 20.0
+    assert env.reward_manager.get_term_cfg("tracking_lin_vel").weight == 888.0
     env.close()
diff --git a/tests/base/test_sim_backend.py b/tests/base/test_sim_backend.py
index 88eb1a8e3..3421a718c 100644
--- a/tests/base/test_sim_backend.py
+++ b/tests/base/test_sim_backend.py
@@ -93,6 +93,52 @@ def _allegro_state() -> tuple[np.ndarray, np.ndarray]:
     return qpos, qvel
 
 
+# Base-motion state used by the get_body_*_vel_b semantic tests: a 30-degree
+# yawed base with a non-zero velocity. Free-joint convention (shared by the
+# backends' set_state): qvel[:3] is linear velocity in the world frame,
+# qvel[3:6] is angular velocity in the body-local frame.
+_BASE_YAW = np.deg2rad(30.0)
+_BASE_LIN_VEL_W = np.array([0.5, -0.2, 0.8])
+_BASE_ANG_VEL_B = np.array([0.3, -0.1, 0.2])
+
+
+def _tilted_motion_state(bkd) -> tuple[np.ndarray, np.ndarray]:
+    nq = bkd.get_dof_pos().shape[-1] + 7
+    nv = bkd.get_dof_vel().shape[-1] + 6
+    qpos = np.tile(_identity_qpos_mujoco(nq), (NUM_ENVS, 1))
+    qpos[:, 3] = np.cos(_BASE_YAW / 2)
+    qpos[:, 6] = np.sin(_BASE_YAW / 2)
+    qvel = np.zeros((NUM_ENVS, nv))
+    qvel[:, :3] = _BASE_LIN_VEL_W
+    qvel[:, 3:6] = _BASE_ANG_VEL_B
+    return qpos, qvel
+
+
+def _yaw_world_to_body_rot() -> np.ndarray:
+    c, s = np.cos(_BASE_YAW), np.sin(_BASE_YAW)
+    return np.array([[c, s, 0.0], [-s, c, 0.0], [0.0, 0.0, 1.0]])
+
+
+def _assert_root_vel_b_matches_world_vel_in_body_frame(bkd, root_id: int, atol: float) -> None:
+    """Root-body vel_b must be the world-frame velocity expressed in the body
+    frame (mjlab/Isaac-style analytical contract), not degenerate zero."""
+    qpos, qvel = _tilted_motion_state(bkd)
+    bkd.set_state(np.arange(NUM_ENVS), qpos, qvel)
+    ids = np.array([root_id])
+    rot_w2b = _yaw_world_to_body_rot()
+    np.testing.assert_allclose(
+        bkd.get_body_lin_vel_b(ids)[:, 0, :],
+        np.tile(rot_w2b @ _BASE_LIN_VEL_W, (NUM_ENVS, 1)),
+        atol=atol,
+    )
+    # qvel[3:6] is already body-local, so ang_vel_b must reproduce it exactly.
+    np.testing.assert_allclose(
+        bkd.get_body_ang_vel_b(ids)[:, 0, :],
+        np.tile(_BASE_ANG_VEL_B, (NUM_ENVS, 1)),
+        atol=atol,
+    )
+
+
 # ---------------------------------------------------------------------------
 # MuJoCo — basic, 3 robots, no body sensors
 # ---------------------------------------------------------------------------
@@ -102,7 +148,7 @@ def _allegro_state() -> tuple[np.ndarray, np.ndarray]:
 class TestMuJoCoBasic:
     @pytest.fixture(params=BASIC_ROBOTS)
     def bkd(self, request):
-        from unilab.base.backend.mujoco.backend import MuJoCoBackend
+        from unisim.backend.mujoco.backend import MuJoCoBackend
 
         p = request.param
         backend = MuJoCoBackend(
@@ -117,7 +163,7 @@ def test_num_envs(self, bkd):
         assert bkd.num_envs == NUM_ENVS
 
     def test_apply_init_randomization_sets_variants_before_materialization(self):
-        from unilab.base.backend.mujoco.backend import MuJoCoBackend
+        from unisim.backend.mujoco.backend import MuJoCoBackend
 
         bkd = MuJoCoBackend(
             SceneCfg(model_file=_SHARPA["model_file"]), 4, SIM_DT, base_name=_SHARPA["base_name"]
@@ -153,7 +199,7 @@ def test_apply_init_randomization_sets_variants_before_materialization(self):
         assert bkd._pool is not None
 
     def test_get_playback_model_returns_env_specific_variant(self):
-        from unilab.base.backend.mujoco.backend import MuJoCoBackend
+        from unisim.backend.mujoco.backend import MuJoCoBackend
 
         bkd = MuJoCoBackend(
             SceneCfg(model_file=_SHARPA["model_file"]), 4, SIM_DT, base_name=_SHARPA["base_name"]
@@ -279,7 +325,7 @@ def _fake_step(
         assert calls[1]["control"].shape == (NUM_ENVS, 1, bkd.model.nu)
 
     def test_interval_push_uses_configured_body(self, monkeypatch: pytest.MonkeyPatch):
-        from unilab.base.backend.mujoco.backend import MuJoCoBackend
+        from unisim.backend.mujoco.backend import MuJoCoBackend
 
         mujoco = _mujoco_module()
         bkd = MuJoCoBackend(
@@ -460,7 +506,7 @@ def test_dof_pos_finite_after_step(self, bkd):
 def test_mujoco_backend_discards_visual_assets():
     mujoco = _mujoco_module()
 
-    from unilab.base.backend.mujoco.backend import MuJoCoBackend
+    from unisim.backend.mujoco.backend import MuJoCoBackend
 
     model_file = _xml("go2")
     full = mujoco.MjModel.from_xml_path(model_file)
@@ -474,8 +520,8 @@ def test_mujoco_backend_discards_visual_assets():
 
 @pytest.mark.slow
 def test_motrix_backend_fixed_base_set_state_matches_mujoco_for_hand_and_ball():
-    from unilab.base.backend.motrix.backend import MotrixBackend
-    from unilab.base.backend.mujoco.backend import MuJoCoBackend
+    from unisim.backend.motrix.backend import MotrixBackend
+    from unisim.backend.mujoco.backend import MuJoCoBackend
 
     pytest.importorskip("motrixsim")
     mj = MuJoCoBackend(
@@ -528,7 +574,7 @@ def test_motrix_backend_fixed_base_set_state_matches_mujoco_for_hand_and_ball():
 class TestMuJoCoBodySensors:
     @pytest.fixture
     def bkd(self):
-        from unilab.base.backend.mujoco.backend import MuJoCoBackend
+        from unisim.backend.mujoco.backend import MuJoCoBackend
 
         backend = MuJoCoBackend(
             SceneCfg(model_file=_G1["model_file"]),
@@ -583,6 +629,12 @@ def test_get_body_lin_vel_b_shape(self, bkd, body_ids):
     def test_get_body_ang_vel_b_shape(self, bkd, body_ids):
         _shape(bkd.get_body_ang_vel_b(body_ids), NUM_ENVS, len(body_ids), 3)
 
+    def test_root_body_vel_b_matches_world_vel_in_body_frame(self, bkd):
+        mujoco = _mujoco_module()
+
+        root_id = int(mujoco.mj_name2id(bkd.model, mujoco.mjtObj.mjOBJ_BODY, _G1["base_name"]))
+        _assert_root_vel_b_matches_world_vel_in_body_frame(bkd, root_id, atol=1e-5)
+
     # sensors
 
     def test_get_sensor_data_w_shape(self, bkd, body_ids):
@@ -643,7 +695,7 @@ def _require_motrix(self):
     # dependent fixtures can share the same parameter value.
     @pytest.fixture(params=BASIC_ROBOTS)
     def _ctx(self, request):
-        from unilab.base.backend.motrix.backend import MotrixBackend
+        from unisim.backend.motrix.backend import MotrixBackend
 
         p = request.param
         bkd = MotrixBackend(
@@ -856,7 +908,7 @@ def _require_motrix(self):
 
     @pytest.fixture
     def bkd(self):
-        from unilab.base.backend.motrix.backend import MotrixBackend
+        from unisim.backend.motrix.backend import MotrixBackend
 
         return MotrixBackend(
             SceneCfg(model_file=_G1["model_file"]),
@@ -887,6 +939,10 @@ def test_get_body_lin_vel_b_shape(self, bkd, body_ids):
     def test_get_body_ang_vel_b_shape(self, bkd, body_ids):
         _shape(bkd.get_body_ang_vel_b(body_ids), NUM_ENVS, len(body_ids), 3)
 
+    def test_root_body_vel_b_matches_world_vel_in_body_frame(self, bkd):
+        root_id = int(bkd.model.get_body_index(_G1["base_name"]))
+        _assert_root_vel_b_matches_world_vel_in_body_frame(bkd, root_id, atol=1e-4)
+
     # sensors
 
     def test_get_sensor_data_b_shape(self, bkd, body_ids):
@@ -928,8 +984,8 @@ class TestCrossBackend:
     @pytest.fixture(params=BASIC_ROBOTS)
     def synced(self, request):
         """Create both backends, synchronize their initial state, and return them."""
-        from unilab.base.backend.motrix.backend import MotrixBackend
-        from unilab.base.backend.mujoco.backend import MuJoCoBackend
+        from unisim.backend.motrix.backend import MotrixBackend
+        from unisim.backend.mujoco.backend import MuJoCoBackend
 
         pytest.importorskip("motrixsim")
         p = request.param
@@ -977,8 +1033,8 @@ class TestCrossBackendBodySensors:
 
     @pytest.fixture
     def synced(self):
-        from unilab.base.backend.motrix.backend import MotrixBackend
-        from unilab.base.backend.mujoco.backend import MuJoCoBackend
+        from unisim.backend.motrix.backend import MotrixBackend
+        from unisim.backend.mujoco.backend import MuJoCoBackend
 
         pytest.importorskip("motrixsim")
         mj = MuJoCoBackend(
@@ -1094,6 +1150,25 @@ def test_body_ang_vel_b(self, synced, body_pairs):
             atol=self.ATOL,
         )
 
+    def test_body_vel_b_parity_under_base_motion(self, synced, body_pairs):
+        """vel_b parity must hold with non-zero base motion (root included)."""
+        mj, mx = synced
+        qpos, qvel = _tilted_motion_state(mj)
+        env_idx = np.arange(NUM_ENVS)
+        mj.set_state(env_idx, qpos, qvel)
+        mx.set_state(env_idx, qpos, qvel)
+        mj_ids, mx_ids = body_pairs
+        np.testing.assert_allclose(
+            mj.get_body_lin_vel_b(mj_ids),
+            mx.get_body_lin_vel_b(mx_ids),
+            atol=self.ATOL,
+        )
+        np.testing.assert_allclose(
+            mj.get_body_ang_vel_b(mj_ids),
+            mx.get_body_ang_vel_b(mx_ids),
+            atol=self.ATOL,
+        )
+
 
 # ---------------------------------------------------------------------------
 # Unified model properties — MuJoCo
@@ -1104,7 +1179,7 @@ def test_body_ang_vel_b(self, synced, body_pairs):
 class TestMuJoCoModelProperties:
     @pytest.fixture(params=BASIC_ROBOTS)
     def bkd(self, request):
-        from unilab.base.backend.mujoco.backend import MuJoCoBackend
+        from unisim.backend.mujoco.backend import MuJoCoBackend
 
         p = request.param
         return MuJoCoBackend(
@@ -1177,7 +1252,7 @@ def _require_motrix(self):
 
     @pytest.fixture(params=BASIC_ROBOTS)
     def _ctx(self, request):
-        from unilab.base.backend.motrix.backend import MotrixBackend
+        from unisim.backend.motrix.backend import MotrixBackend
 
         p = request.param
         bkd = MotrixBackend(
@@ -1221,7 +1296,10 @@ def test_get_body_ids_missing(self, bkd):
             bkd.get_body_ids(["nonexistent_body_xyz"])
 
     def test_get_joint_range(self, bkd):
-        assert bkd.get_joint_range() is None
+        jr = bkd.get_joint_range()
+        assert jr is not None
+        assert jr.shape == (bkd.num_dof_vel, 2)
+        assert np.all(jr[:, 0] <= jr[:, 1])
 
 
 # ---------------------------------------------------------------------------
@@ -1237,8 +1315,8 @@ def _require_motrix(self):
 
     @pytest.fixture
     def backends(self):
-        from unilab.base.backend.motrix.backend import MotrixBackend
-        from unilab.base.backend.mujoco.backend import MuJoCoBackend
+        from unisim.backend.motrix.backend import MotrixBackend
+        from unisim.backend.mujoco.backend import MuJoCoBackend
 
         mj = MuJoCoBackend(
             SceneCfg(model_file=_G1["model_file"]), NUM_ENVS, SIM_DT, base_name=_G1["base_name"]
diff --git a/tests/base/test_sim_backend_set_state_timing.py b/tests/base/test_sim_backend_set_state_timing.py
index 987213297..fdd4f70bd 100644
--- a/tests/base/test_sim_backend_set_state_timing.py
+++ b/tests/base/test_sim_backend_set_state_timing.py
@@ -24,11 +24,11 @@
 
 import numpy as np
 import pytest
+from unisim.dr.types import ResetRandomizationPayload
 
 from unilab.assets import ASSETS_ROOT_PATH
 from unilab.base.np_env import BACKEND_SET_STATE_DETAIL_TIMING_KEYS
 from unilab.base.scene import SceneCfg
-from unilab.dr.types import ResetRandomizationPayload
 
 pytest.importorskip("mujoco", reason="mujoco not installed")
 
@@ -77,7 +77,7 @@ def _assert_gap_bounded(timing: dict[str, float]) -> None:
 
 
 def test_mujoco_set_state_returns_schema_conformant_timing() -> None:
-    from unilab.base.backend.mujoco.backend import MuJoCoBackend
+    from unisim.backend.mujoco.backend import MuJoCoBackend
 
     backend = MuJoCoBackend(
         SceneCfg(model_file=_G1_MODEL_FILE),
@@ -105,7 +105,7 @@ def test_mujoco_set_state_returns_schema_conformant_timing() -> None:
 
 def test_mujoco_set_state_empty_env_indices_still_returns_timing_dict() -> None:
     """Backends must never return None even on the empty-reset fast path."""
-    from unilab.base.backend.mujoco.backend import MuJoCoBackend
+    from unisim.backend.mujoco.backend import MuJoCoBackend
 
     backend = MuJoCoBackend(
         SceneCfg(model_file=_G1_MODEL_FILE),
@@ -131,7 +131,7 @@ def test_mujoco_set_state_empty_env_indices_still_returns_timing_dict() -> None:
 def test_motrix_set_state_returns_schema_conformant_timing() -> None:
     pytest.importorskip("motrixsim")
 
-    from unilab.base.backend.motrix.backend import MotrixBackend
+    from unisim.backend.motrix.backend import MotrixBackend
 
     backend = MotrixBackend(
         SceneCfg(model_file=_G1_MODEL_FILE),
@@ -169,7 +169,7 @@ def test_motrix_set_state_returns_schema_conformant_timing() -> None:
 def test_motrix_set_state_without_randomization_leaves_reset_rand_zero() -> None:
     pytest.importorskip("motrixsim")
 
-    from unilab.base.backend.motrix.backend import MotrixBackend
+    from unisim.backend.motrix.backend import MotrixBackend
 
     backend = MotrixBackend(
         SceneCfg(model_file=_G1_MODEL_FILE),
diff --git a/tests/base/test_sim_backend_smoke.py b/tests/base/test_sim_backend_smoke.py
index fee15ceb3..1540703cf 100644
--- a/tests/base/test_sim_backend_smoke.py
+++ b/tests/base/test_sim_backend_smoke.py
@@ -10,11 +10,11 @@
 
 import numpy as np
 import pytest
+from unisim.backend.mujoco.xml import get_named_body_ids
+from unisim.dr.types import IntervalRandomizationPlan, ResetRandomizationPayload
 
 from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base.backend.mujoco.xml import get_named_body_ids
 from unilab.base.scene import SceneCfg
-from unilab.dr.types import ResetRandomizationPayload
 
 pytest.importorskip("mujoco", reason="mujoco not installed")
 
@@ -74,7 +74,7 @@ def _mujoco_expected_dof_dims(model) -> tuple[int, int]:
 
 @pytest.mark.parametrize("robot", BASIC_ROBOTS)
 def test_mujoco_backend_smoke_contract(robot):
-    from unilab.base.backend.mujoco.backend import MuJoCoBackend
+    from unisim.backend.mujoco.backend import MuJoCoBackend
 
     bkd = MuJoCoBackend(
         SceneCfg(model_file=robot["model_file"]), NUM_ENVS, SIM_DT, base_name=robot["base_name"]
@@ -108,12 +108,16 @@ def test_mujoco_backend_smoke_contract(robot):
         "kd",
     }.issubset(caps.supported_reset_terms)
     assert caps.supports_interval_push
+    assert caps.supports_interval_body_velocity_delta
+    assert caps.supports_interval_body_angular_velocity_delta
+    assert caps.supports_interval_body_force
+    assert caps.supports_interval_body_torque
 
 
 def test_mujoco_backend_fixed_base_dof_views_do_not_skip_first_joint():
     mujoco = _mujoco_module()
 
-    from unilab.base.backend.mujoco.backend import MuJoCoBackend
+    from unisim.backend.mujoco.backend import MuJoCoBackend
 
     bkd = MuJoCoBackend(
         SceneCfg(model_file=_ALLEGRO["model_file"]),
@@ -129,13 +133,239 @@ def test_mujoco_backend_fixed_base_dof_views_do_not_skip_first_joint():
     np.testing.assert_allclose(bkd.get_base_lin_vel(), 0.0, atol=1e-8)
     np.testing.assert_allclose(bkd.get_base_ang_vel(), 0.0, atol=1e-8)
     _unit_quat(bkd.get_base_quat(), "MuJoCo fixed-base smoke")
+    assert not bkd.get_dr_capabilities().supports_interval_body_velocity_delta
+
+
+def test_mujoco_interval_root_velocity_kick_is_row_selective_and_refreshes_sensors():
+    from unisim.backend.mujoco.backend import MuJoCoBackend
+
+    bkd = MuJoCoBackend(
+        SceneCfg(model_file=_xml("go2")),
+        NUM_ENVS,
+        SIM_DT,
+        base_name="base",
+        add_body_sensors=True,
+    )
+    bkd.materialize()
+    qpos = np.broadcast_to(bkd.get_default_qpos(), (NUM_ENVS, bkd.model.nq)).copy()
+    qvel = np.zeros((NUM_ENVS, bkd.model.nv), dtype=np.float64)
+    bkd.set_state(np.arange(NUM_ENVS, dtype=np.int32), qpos, qvel)
+    bkd.step(np.zeros((NUM_ENVS, bkd.model.nu)), nsteps=1)
+    assert bkd._pool is not None
+    bkd._sensor_data[:] = bkd._pool.forward(bkd.get_physics_state())
+
+    body_ids = bkd.get_body_ids(("base",))
+    state_before = bkd.get_physics_state().copy()
+    body_velocity_before = bkd.get_body_lin_vel_w(body_ids).copy()
+    delta = np.zeros((NUM_ENVS, 1, 3), dtype=np.float64)
+    delta[1, 0] = (0.25, -0.4, 0.15)
+
+    bkd.apply_interval_randomization(
+        IntervalRandomizationPlan(
+            body_ids=body_ids,
+            body_linear_velocity_delta=delta,
+        )
+    )
+
+    state_after = bkd.get_physics_state()
+    body_velocity_after = bkd.get_body_lin_vel_w(body_ids)
+    np.testing.assert_array_equal(state_after[0], state_before[0])
+    np.testing.assert_allclose(state_after[1, 0], state_before[1, 0], atol=1e-12)
+    np.testing.assert_allclose(
+        state_after[1, bkd._idx_qpos : bkd._idx_qvel],
+        state_before[1, bkd._idx_qpos : bkd._idx_qvel],
+        atol=1e-12,
+    )
+    np.testing.assert_allclose(
+        state_after[1, bkd._idx_qvel + 3 :],
+        state_before[1, bkd._idx_qvel + 3 :],
+        atol=1e-12,
+    )
+    np.testing.assert_allclose(
+        state_after[1, bkd._idx_qvel : bkd._idx_qvel + 3],
+        state_before[1, bkd._idx_qvel : bkd._idx_qvel + 3] + delta[1, 0],
+        atol=1e-7,
+    )
+    np.testing.assert_allclose(
+        body_velocity_after[1, 0],
+        body_velocity_before[1, 0] + delta[1, 0],
+        atol=1e-6,
+    )
+
+
+def test_mujoco_interval_root_velocity_kick_rejects_invalid_contracts():
+    from unisim.backend.mujoco.backend import MuJoCoBackend
+
+    bkd = MuJoCoBackend(
+        SceneCfg(model_file=_xml("go2")),
+        NUM_ENVS,
+        SIM_DT,
+        base_name="base",
+    )
+    body_ids = bkd.get_body_ids(("base",))
+    delta = np.zeros((NUM_ENVS, 1, 3), dtype=np.float64)
+
+    with pytest.raises(RuntimeError, match="requires a materialized backend"):
+        bkd.apply_interval_randomization(
+            IntervalRandomizationPlan(
+                body_ids=body_ids,
+                body_linear_velocity_delta=delta,
+            )
+        )
+
+    bkd.materialize()
+    with pytest.raises(TypeError, match="body_ids must be a 1-D integer array"):
+        bkd.apply_interval_randomization(
+            IntervalRandomizationPlan(
+                body_ids=body_ids.astype(np.float64),
+                body_linear_velocity_delta=delta,
+            )
+        )
+    with pytest.raises(NotImplementedError, match="only supports the configured free root"):
+        bkd.apply_interval_randomization(
+            IntervalRandomizationPlan(
+                body_ids=np.asarray([body_ids[0] + 1], dtype=np.int32),
+                body_linear_velocity_delta=delta,
+            )
+        )
+    with pytest.raises(ValueError, match=r"shape .* expected \(2, 1, 3\)"):
+        bkd.apply_interval_randomization(
+            IntervalRandomizationPlan(
+                body_ids=body_ids,
+                body_linear_velocity_delta=np.zeros((NUM_ENVS, 3), dtype=np.float64),
+            )
+        )
+    invalid = delta.copy()
+    invalid[0, 0, 0] = np.nan
+    with pytest.raises(ValueError, match="contains NaN or Inf"):
+        bkd.apply_interval_randomization(
+            IntervalRandomizationPlan(
+                body_ids=body_ids,
+                body_linear_velocity_delta=invalid,
+            )
+        )
+
+
+def test_mujoco_interval_root_angular_velocity_kick_converts_to_body_frame():
+    from unisim.backend.mujoco.backend import MuJoCoBackend
+
+    bkd = MuJoCoBackend(
+        SceneCfg(model_file=_xml("go2")),
+        NUM_ENVS,
+        SIM_DT,
+        base_name="base",
+        add_body_sensors=True,
+    )
+    bkd.materialize()
+    qpos = np.broadcast_to(bkd.get_default_qpos(), (NUM_ENVS, bkd.model.nq)).copy()
+    # Hover far above the terrain and yaw the root by +90 degrees: without
+    # contact the orientation is exactly preserved across the probe step.
+    qpos[:, 2] += 2.0
+    half = np.sqrt(0.5)
+    qpos[:, 3:7] = [half, 0.0, 0.0, half]
+    qvel = np.zeros((NUM_ENVS, bkd.model.nv), dtype=np.float64)
+    bkd.set_state(np.arange(NUM_ENVS, dtype=np.int32), qpos, qvel)
+
+    body_ids = bkd.get_body_ids(("base",))
+    delta = np.zeros((NUM_ENVS, 1, 3), dtype=np.float64)
+    delta[1, 0] = (0.5, 0.0, 0.0)  # world-frame +x angular rate
+
+    bkd.apply_interval_randomization(
+        IntervalRandomizationPlan(
+            body_ids=body_ids,
+            body_angular_velocity_delta=delta,
+        )
+    )
+
+    state_after = bkd.get_physics_state()
+    # The free-root qvel angular channels are body-frame: yaw(+90deg) maps the
+    # world-frame +x kick onto body-frame -y.
+    np.testing.assert_allclose(
+        state_after[0, bkd._idx_qvel + 3 : bkd._idx_qvel + 6],
+        0.0,
+        atol=1e-12,
+    )
+    np.testing.assert_allclose(
+        state_after[1, bkd._idx_qvel + 3 : bkd._idx_qvel + 6],
+        [0.0, -0.5, 0.0],
+        atol=1e-7,
+    )
+    # The world-frame read-back recovers the sampled delta.
+    np.testing.assert_allclose(
+        bkd.get_body_ang_vel_w(body_ids)[1, 0],
+        [0.5, 0.0, 0.0],
+        atol=1e-6,
+    )
+    np.testing.assert_allclose(
+        bkd.get_body_ang_vel_w(body_ids)[0, 0],
+        [0.0, 0.0, 0.0],
+        atol=1e-6,
+    )
+
+
+def test_mujoco_interval_body_force_and_torque_have_observable_effect(tmp_path):
+    from unisim.backend.mujoco.backend import MuJoCoBackend
+
+    # A single free body gives exact rigid-body dynamics: dv = F/m * dt and
+    # dw = I^-1 tau * dt at identity orientation with diagonal inertia.
+    model_file = tmp_path / "free_body.xml"
+    model_file.write_text(
+        """
+<mujoco>
+  <worldbody>
+    <body name="ball" pos="0 0 5">
+      <freejoint/>
+      <inertial mass="2.0" diaginertia="0.5 0.5 0.5" pos="0 0 0"/>
+      <geom type="sphere" size="0.1" mass="2.0"/>
+    </body>
+  </worldbody>
+</mujoco>
+""".strip()
+    )
+    bkd = MuJoCoBackend(SceneCfg(model_file=str(model_file)), NUM_ENVS, SIM_DT, base_name="ball")
+    bkd.materialize()
+    caps = bkd.get_dr_capabilities()
+    assert caps.supports_interval_body_force
+    assert caps.supports_interval_body_torque
+
+    body_ids = bkd.get_body_ids(("ball",))
+    qpos = np.broadcast_to(bkd.get_default_qpos(), (NUM_ENVS, bkd.model.nq)).copy()
+    qvel0 = np.zeros((NUM_ENVS, bkd.model.nv), dtype=np.float64)
+    ids = np.arange(NUM_ENVS, dtype=np.int32)
+    ctrl = np.zeros((NUM_ENVS, bkd.model.nu))
+
+    bkd.set_state(ids, qpos, qvel0)
+    bkd.step(ctrl, nsteps=1)
+    baseline = bkd.get_physics_state()[:, bkd._idx_qvel : bkd._idx_qvel + 6].copy()
+
+    force = np.zeros((NUM_ENVS, 1, 3), dtype=np.float64)
+    force[:, 0, 2] = 10.0  # N, world +z
+    torque = np.zeros((NUM_ENVS, 1, 3), dtype=np.float64)
+    torque[:, 0, 2] = 1.0  # Nm, world z
+
+    bkd.set_state(ids, qpos, qvel0)
+    bkd.apply_interval_randomization(
+        IntervalRandomizationPlan(
+            body_ids=body_ids,
+            body_force=force,
+            body_torque=torque,
+        )
+    )
+    bkd.step(ctrl, nsteps=1)
+    kicked = bkd.get_physics_state()[:, bkd._idx_qvel : bkd._idx_qvel + 6]
+
+    # Free fall is common to both runs, so the staged wrench adds exactly
+    # F/m * dt to linear z and tau/I * dt to angular z.
+    np.testing.assert_allclose(kicked[:, 2] - baseline[:, 2], 10.0 / 2.0 * SIM_DT, rtol=1e-3)
+    np.testing.assert_allclose(kicked[:, 5] - baseline[:, 5], 1.0 / 0.5 * SIM_DT, rtol=1e-3)
+    np.testing.assert_allclose(kicked[:, [0, 1, 3, 4]], baseline[:, [0, 1, 3, 4]], atol=1e-9)
 
 
 @pytest.mark.parametrize("robot", BASIC_ROBOTS)
 def test_motrix_backend_smoke_contract(robot):
     pytest.importorskip("motrixsim")
 
-    from unilab.base.backend.motrix.backend import MotrixBackend
+    from unisim.backend.motrix.backend import MotrixBackend
 
     bkd = MotrixBackend(
         SceneCfg(model_file=robot["model_file"]), NUM_ENVS, SIM_DT, base_name=robot["base_name"]
@@ -175,7 +405,7 @@ def test_motrix_backend_smoke_contract(robot):
 def test_motrix_backend_fixed_base_base_views_are_available():
     pytest.importorskip("motrixsim")
 
-    from unilab.base.backend.motrix.backend import MotrixBackend
+    from unisim.backend.motrix.backend import MotrixBackend
 
     bkd = MotrixBackend(
         SceneCfg(model_file=_ALLEGRO["model_file"]),
@@ -200,8 +430,8 @@ def test_motrix_backend_fixed_base_base_views_are_available():
 def test_cross_backend_base_pose_smoke(robot):
     pytest.importorskip("motrixsim")
 
-    from unilab.base.backend.motrix.backend import MotrixBackend
-    from unilab.base.backend.mujoco.backend import MuJoCoBackend
+    from unisim.backend.motrix.backend import MotrixBackend
+    from unisim.backend.mujoco.backend import MuJoCoBackend
 
     mj = MuJoCoBackend(
         SceneCfg(model_file=robot["model_file"]), NUM_ENVS, SIM_DT, base_name=robot["base_name"]
@@ -229,8 +459,8 @@ def test_cross_backend_base_pose_smoke(robot):
 def test_cross_backend_model_properties_smoke():
     pytest.importorskip("motrixsim")
 
-    from unilab.base.backend.motrix.backend import MotrixBackend
-    from unilab.base.backend.mujoco.backend import MuJoCoBackend
+    from unisim.backend.motrix.backend import MotrixBackend
+    from unisim.backend.mujoco.backend import MuJoCoBackend
 
     mj = MuJoCoBackend(
         SceneCfg(model_file=_G1["model_file"]), NUM_ENVS, SIM_DT, base_name=_G1["base_name"]
@@ -254,8 +484,8 @@ def test_backend_batch_sensor_data_matches_individual_sensors(backend_type):
     if backend_type == "motrix":
         pytest.importorskip("motrixsim")
 
-    from unilab.base.backend import create_backend
-    from unilab.envs.locomotion.go2w.base import JOINT_SENSOR_PREFIXES
+    from unilab.base.backend_factory import create_backend
+    from unilab.tasks.locomotion.go2w.base import JOINT_SENSOR_PREFIXES
 
     bkd = create_backend(
         backend_type,
@@ -275,9 +505,16 @@ def test_backend_batch_sensor_data_matches_individual_sensors(backend_type):
     np.testing.assert_allclose(bkd.get_sensor_data_batch(names), expected)
     _shape(bkd.get_sensor_data_batch(()), NUM_ENVS, 0)
 
+    view = bkd.bind_sensor_data(names)
+    assert view.names == names
+    assert view.dimensions == tuple(
+        int(np.asarray(bkd.get_sensor_data(name)).reshape(NUM_ENVS, -1).shape[1]) for name in names
+    )
+    np.testing.assert_allclose(view.read(), expected)
+
 
 def test_mujoco_model_properties_smoke():
-    from unilab.base.backend.mujoco.backend import MuJoCoBackend
+    from unisim.backend.mujoco.backend import MuJoCoBackend
 
     bkd = MuJoCoBackend(
         SceneCfg(model_file=_G1["model_file"]), NUM_ENVS, SIM_DT, base_name=_G1["base_name"]
@@ -295,8 +532,11 @@ def test_mujoco_model_properties_smoke():
 def test_mujoco_metadata_getters_return_stable_copies():
     mujoco = _mujoco_module()
 
-    from unilab.base.backend.mujoco.backend import MuJoCoBackend
+    from unisim.backend.mujoco.backend import MuJoCoBackend
+
+    from unilab.assets.hub import ensure_robot_assets_for_paths
 
+    ensure_robot_assets_for_paths([_SHARPA["model_file"]])
     bkd = MuJoCoBackend(
         SceneCfg(model_file=_SHARPA["model_file"]), NUM_ENVS, SIM_DT, base_name=_SHARPA["base_name"]
     )
@@ -372,7 +612,7 @@ def test_mujoco_metadata_getters_return_stable_copies():
 
 
 def test_mujoco_copy_body_state_matches_split_queries():
-    from unilab.base.backend.mujoco.backend import MuJoCoBackend
+    from unisim.backend.mujoco.backend import MuJoCoBackend
 
     bkd = MuJoCoBackend(
         SceneCfg(model_file=_G1["model_file"]),
@@ -402,11 +642,22 @@ def test_mujoco_copy_body_state_matches_split_queries():
     np.testing.assert_allclose(out_lin_vel, expected_lin_vel)
     np.testing.assert_allclose(out_ang_vel, expected_ang_vel)
 
+    # Issue #1295: row-scoped velocity getters are parity with full+slice.
+    row_ids = np.array([1, 0, 1], dtype=np.int32)
+    np.testing.assert_allclose(
+        bkd.get_body_lin_vel_w_rows(row_ids, body_ids),
+        bkd.get_body_lin_vel_w(body_ids)[row_ids],
+    )
+    np.testing.assert_allclose(
+        bkd.get_body_ang_vel_w_rows(row_ids, body_ids),
+        bkd.get_body_ang_vel_w(body_ids)[row_ids],
+    )
+
 
 def test_motrix_model_properties_smoke():
     pytest.importorskip("motrixsim")
 
-    from unilab.base.backend.motrix.backend import MotrixBackend
+    from unisim.backend.motrix.backend import MotrixBackend
 
     bkd = MotrixBackend(
         SceneCfg(model_file=_G1["model_file"]), NUM_ENVS, SIM_DT, base_name=_G1["base_name"]
@@ -416,13 +667,15 @@ def test_motrix_model_properties_smoke():
     ctrl_range = bkd.get_actuator_ctrl_range()
     _shape(ctrl_range, bkd.num_actuators, 2)
     assert bkd.get_default_qpos().ndim == 1
-    assert bkd.get_joint_range() is None
+    joint_range = bkd.get_joint_range()
+    assert joint_range is not None
+    assert joint_range.shape == (bkd.num_dof_vel, 2)
 
 
 def test_motrix_copy_body_state_matches_split_queries():
     pytest.importorskip("motrixsim")
 
-    from unilab.base.backend.motrix.backend import MotrixBackend
+    from unisim.backend.motrix.backend import MotrixBackend
 
     bkd = MotrixBackend(
         SceneCfg(model_file=_G1["model_file"]),
@@ -456,13 +709,25 @@ def test_motrix_copy_body_state_matches_split_queries():
         bkd.get_sensor_data_rows("pelvis_local_linvel", row_ids),
         bkd.get_sensor_data("pelvis_local_linvel")[row_ids],
     )
+    # Issue #1295: row-scoped velocity getters are parity with full+slice.
+    np.testing.assert_allclose(
+        bkd.get_body_lin_vel_w_rows(row_ids, body_ids),
+        bkd.get_body_lin_vel_w(body_ids)[row_ids],
+    )
+    np.testing.assert_allclose(
+        bkd.get_body_ang_vel_w_rows(row_ids, body_ids),
+        bkd.get_body_ang_vel_w(body_ids)[row_ids],
+    )
 
 
 def test_motrix_default_qpos_uses_mujoco_quaternion_convention():
     pytest.importorskip("motrixsim")
 
-    from unilab.base.backend.motrix.backend import MotrixBackend
+    from unisim.backend.motrix.backend import MotrixBackend
+
+    from unilab.assets.hub import ensure_robot_assets_for_paths
 
+    ensure_robot_assets_for_paths([_SHARPA["model_file"]])
     bkd = MotrixBackend(
         SceneCfg(model_file=_SHARPA["model_file"]), NUM_ENVS, SIM_DT, base_name=_SHARPA["base_name"]
     )
diff --git a/tests/benchmark/test_device_info.py b/tests/benchmark/test_device_info.py
deleted file mode 100644
index 96c082d38..000000000
--- a/tests/benchmark/test_device_info.py
+++ /dev/null
@@ -1,88 +0,0 @@
-from __future__ import annotations
-
-import scripts.benchmark.core.device_info as device_info
-
-
-def test_linux_device_info_reads_amd_visible_vram(monkeypatch):
-    cpuinfo = "model name\t: AMD RYZEN AI MAX+ 395 w/ Radeon 8060S\nprocessor\t: 0\n"
-    meminfo = "MemTotal:       32486180 kB\n"
-    amd_smi_metric = """
-GPU: 0
-    MEM_USAGE:
-        TOTAL_VRAM: 98304 MB
-        USED_VRAM: 3603 MB
-        FREE_VRAM: 94701 MB
-        TOTAL_VISIBLE_VRAM: 98304 MB
-        USED_VISIBLE_VRAM: 3603 MB
-        FREE_VISIBLE_VRAM: 94701 MB
-        TOTAL_GTT: 15862 MB
-        USED_GTT: 158 MB
-        FREE_GTT: 15704 MB
-"""
-
-    def fake_open(path, *args, **kwargs):
-        del args, kwargs
-        if path == "/proc/cpuinfo":
-            from io import StringIO
-
-            return StringIO(cpuinfo)
-        if path == "/proc/meminfo":
-            from io import StringIO
-
-            return StringIO(meminfo)
-        raise FileNotFoundError(path)
-
-    def fake_check_output(cmd, *args, **kwargs):
-        del args, kwargs
-        if cmd[0] == "nvidia-smi":
-            raise FileNotFoundError(cmd[0])
-        if cmd[:2] == ["rocm-smi", "--showproductname"]:
-            return "Card series: AMD Radeon Graphics\n"
-        if cmd[:2] == ["amd-smi", "metric"]:
-            return amd_smi_metric
-        raise FileNotFoundError(cmd[0])
-
-    monkeypatch.setattr(device_info, "open", fake_open, raising=False)
-    monkeypatch.setattr(device_info.subprocess, "check_output", fake_check_output)
-
-    info = device_info._get_device_info_linux()
-
-    assert info["gpu_name"] == "Radeon 8060S"
-    assert info["gpu_memory"] == "98304 MB"
-    assert info["gpu_gtt_memory"] == "15862 MB"
-    assert info["memory"] == "31.0 GB"
-
-
-def test_linux_device_info_reads_intel_igpu_from_lspci(monkeypatch):
-    cpuinfo = "model name\t: Intel(R) Core(TM) Ultra 9 185H\nprocessor\t: 0\n"
-    meminfo = "MemTotal:       31692928 kB\n"
-    lspci_out = (
-        "00:02.0 VGA compatible controller: "
-        "Intel Corporation Meteor Lake-P [Intel Arc Graphics] (rev 08)\n"
-    )
-
-    def fake_open(path, *args, **kwargs):
-        del args, kwargs
-        from io import StringIO
-
-        if path == "/proc/cpuinfo":
-            return StringIO(cpuinfo)
-        if path == "/proc/meminfo":
-            return StringIO(meminfo)
-        raise FileNotFoundError(path)
-
-    def fake_check_output(cmd, *args, **kwargs):
-        del args, kwargs
-        if cmd[0] == "lspci":
-            return lspci_out
-        # No nvidia-smi, rocm-smi, or amd-smi on Intel iGPU systems
-        raise FileNotFoundError(cmd[0])
-
-    monkeypatch.setattr(device_info, "open", fake_open, raising=False)
-    monkeypatch.setattr(device_info.subprocess, "check_output", fake_check_output)
-
-    info = device_info._get_device_info_linux()
-
-    assert info["gpu_name"] == "Intel Arc Graphics"
-    assert info["chip"] == "Intel(R) Core(TM) Ultra 9 185H"
-    assert info["memory"] == "30.2 GB"
diff --git a/tests/benchmark/test_env_step_config_contract.py b/tests/benchmark/test_env_step_config_contract.py
index eef8c8f34..f7bbe7c64 100644
--- a/tests/benchmark/test_env_step_config_contract.py
+++ b/tests/benchmark/test_env_step_config_contract.py
@@ -1,41 +1,52 @@
 from __future__ import annotations
 
-from pathlib import Path
-
 import pytest
 from hydra.errors import ConfigCompositionException
-from omegaconf import OmegaConf
 from scripts.benchmark.env import benchmark_env_step as bench
 
+from unilab.envs import ManagerBasedRlEnvCfg, make_manager_based_rl_env
+from unilab.tasks.locomotion.common.rough_manager_terms import QuadrupedRoughTerrainCfg
+
+# Importing benchmark_env_step installs a process-wide create_backend patch for
+# the benchmark script. These tests only build configs, so undo the patch to
+# keep the global factory pristine for the rest of the pytest session.
+bench._uninstall_mjwarp_patch()
+
+
+def test_go2w_flat_benchmark_uses_production_manager_owner() -> None:
+    cfg = bench.TASK_CONFIGS["go2w"].build_cfg("mujoco")
+
+    assert isinstance(cfg, ManagerBasedRlEnvCfg)
+    assert list(cfg.actions) == ["motor"]
+    assert cfg.critic_observation_group == "critic"
+    assert bench.TASK_CONFIGS["go2w"].env_cls_factory() is make_manager_based_rl_env
+    assert bench.DEFAULT_NUM_ENVS == 4096
+
 
 def test_go2w_rough_cfg_matches_ppo_owner_yaml() -> None:
     cfg = bench.TASK_CONFIGS["go2w_rough"].build_cfg("mujoco")
-    owner_path = (
-        Path(bench.ROOT_DIR) / "conf" / "ppo" / "task" / "go2w_joystick_rough" / "mujoco.yaml"
-    )
-    owner_cfg = OmegaConf.load(owner_path)
 
-    assert cfg.reward_config.scales == OmegaConf.to_container(
-        owner_cfg.reward.scales,
-        resolve=True,
-    )
-    assert cfg.reward_config.tracking_sigma == owner_cfg.reward.tracking_sigma
-    assert cfg.reward_config.base_height_target == owner_cfg.reward.base_height_target
-    assert cfg.scene.model_file == owner_cfg.env.scene.model_file
+    assert isinstance(cfg, ManagerBasedRlEnvCfg)
+    assert cfg.scene is not None
+    assert cfg.scene.terrain is not None
+    assert isinstance(cfg.scene.terrain.generator, QuadrupedRoughTerrainCfg)
+    assert cfg.scene.model_file.endswith("go2w_mujoco.xml")
+    assert cfg.actions["motor"].wheel_action_scale == pytest.approx(5.0)
+    assert cfg.rewards["tracking_lin_vel"].weight == pytest.approx(3.0)
+    assert bench.TASK_CONFIGS["go2w_rough"].env_cls_factory() is make_manager_based_rl_env
 
 
 def test_env_and_reward_overrides_use_hydra_composition() -> None:
     cfg = bench.TASK_CONFIGS["go2w_rough"].build_cfg(
         "mujoco",
         [
-            "env.control_config.action_scale=0.125",
-            "reward.tracking_sigma=${reward.base_height_target}",
+            "env.actions.motor.leg_action_scale=0.125",
+            "reward.tracking_lin_vel.weight=2.25",
         ],
     )
 
-    assert cfg.control_config.action_scale == pytest.approx(0.125)
-    assert cfg.reward_config.tracking_sigma == pytest.approx(0.4)
-    assert isinstance(cfg.reward_config.tracking_sigma, float)
+    assert cfg.actions["motor"].leg_action_scale == pytest.approx(0.125)
+    assert cfg.rewards["tracking_lin_vel"].weight == pytest.approx(2.25)
 
 
 def test_unknown_env_override_fails_in_hydra() -> None:
@@ -58,8 +69,8 @@ def test_training_sim_backend_override_is_rejected(override: str) -> None:
 def test_only_owner_config_overrides_are_forwarded() -> None:
     overrides = [
         "task=go2w_joystick_rough/mujoco",
-        "env.control_config.action_scale=0.125",
-        "+reward.scales.custom_term=1.0",
+        "env.actions.motor.leg_action_scale=0.125",
+        "reward.tracking_lin_vel.weight=2.25",
     ]
 
     assert bench._owner_config_overrides(overrides) == overrides[1:]
diff --git a/tests/benchmark/test_offpolicy_collector_active_benchmark.py b/tests/benchmark/test_offpolicy_collector_active_benchmark.py
index d88e31c4c..6daf14fdc 100644
--- a/tests/benchmark/test_offpolicy_collector_active_benchmark.py
+++ b/tests/benchmark/test_offpolicy_collector_active_benchmark.py
@@ -98,6 +98,16 @@ def test_all_backend_selection_expands_default_cases() -> None:
     assert "flashsac/g1_walk_flat/motrix" in specs
 
 
+def test_mjwarp_backend_is_opt_in_and_never_part_of_all() -> None:
+    # --all stays on the default backends; mjwarp must be requested explicitly.
+    assert "mjwarp" not in bench._resolve_backend_selection(backend="mujoco", all_backends=True)
+    if bench.find_spec("mujoco_warp") is None or bench.find_spec("warp") is None:
+        with pytest.raises(SystemExit, match="mjwarp extra"):
+            bench._resolve_backend_selection(backend="mjwarp", all_backends=False)
+    else:
+        assert bench._resolve_backend_selection(backend="mjwarp", all_backends=False) == ("mjwarp",)
+
+
 def test_resolve_case_specs_deduplicates_explicit_specs() -> None:
     specs = bench._resolve_case_specs(
         "sac/g1_walk_flat/mujoco,sac/g1_walk_flat/mujoco,td3/g1_walk_flat/mujoco",
@@ -135,20 +145,15 @@ def test_auto_discovery_supports_motrixsim_alias() -> None:
 
 
 def test_noise_seed_override_composes_for_target_g1_profiles() -> None:
-    for spec in (
-        ("sac", "g1_motion_tracking", "mujoco"),
-        ("sac", "g1_walk_flat", "mujoco"),
-        ("sac", "g1_walk_flat", "motrix"),
-        ("flashsac", "g1_walk_flat", "mujoco"),
-        ("flashsac", "g1_walk_flat", "motrix"),
-        ("td3", "g1_walk_flat", "mujoco"),
-    ):
+    # Manager-Based owners seed their NumPy runtime directly rather than
+    # carrying the legacy observation-noise config.
+    for spec in (("sac", "g1_motion_tracking", "mujoco"),):
         cfg = bench._compose_offpolicy_cfg(
             *spec,
-            extra_overrides=["env.noise_config.seed=123"],
+            extra_overrides=["env.seed=123"],
         )
 
-        assert cfg.env.noise_config.seed == 123
+        assert cfg.env.seed == 123
 
 
 def test_stats_reports_distribution() -> None:
@@ -261,6 +266,7 @@ def test_write_csv_includes_all_phase_columns(tmp_path) -> None:
     assert "env_step_overhead_ms" in header
     assert "physics_pct" in header
     assert "env_step_overhead_pct" in header
+    assert "mba_" not in header
     for key in bench.COLLECTOR_PHASES:
         assert key in header
 
@@ -290,6 +296,9 @@ def test_write_csv_includes_backend_set_state_sub_timing_columns(tmp_path) -> No
         "set_state_qpos_convert_ms",
         "set_state_pool_reset_ms",
         "set_state_state_scatter_ms",
+        "set_state_reset_upload_ms",
+        "set_state_reset_forward_ms",
+        "set_state_host_cache_refresh_ms",
         "set_state_internal_gap_ms",
     )
     for key in expected_keys:
@@ -344,3 +353,30 @@ def test_format_set_state_mujoco_table_covers_mujoco_only_keys() -> None:
     assert "State scatter" in table
     assert "4.000 (80.0%)" in table
     assert "0.750 (15.0%)" in table
+
+
+def test_format_set_state_mjwarp_table_covers_mjwarp_only_keys() -> None:
+    """The mjwarp keyset table exposes reset_upload / forward / host_cache_refresh."""
+    result = _make_result(
+        runtime_sim_backend="mjwarp",
+        num_envs=2,
+        throughput=2000.0,
+        include_env_step_breakdown=True,
+    )
+    result.env_step_timing_ms_per_vector_step["dr_reset_set_state_ms"] = bench.TimingStats(
+        [8.0], 8.0, 8.0, 0.0, 8.0, 8.0
+    )
+    result.env_step_timing_ms_per_vector_step["set_state_reset_upload_ms"] = bench.TimingStats(
+        [4.0], 4.0, 4.0, 0.0, 4.0, 4.0
+    )
+    result.env_step_timing_ms_per_vector_step["set_state_host_cache_refresh_ms"] = (
+        bench.TimingStats([2.0], 2.0, 2.0, 0.0, 2.0, 2.0)
+    )
+
+    table = bench._format_set_state_mjwarp_table([result])
+
+    assert "Reset upload" in table
+    assert "Reset forward" in table
+    assert "Host cache refresh" in table
+    assert "4.000 (50.0%)" in table
+    assert "2.000 (25.0%)" in table
diff --git a/tests/benchmark/test_offpolicy_dp_scaling_benchmark.py b/tests/benchmark/test_offpolicy_dp_scaling_benchmark.py
index 3a8859e76..4414f3a9e 100644
--- a/tests/benchmark/test_offpolicy_dp_scaling_benchmark.py
+++ b/tests/benchmark/test_offpolicy_dp_scaling_benchmark.py
@@ -74,9 +74,8 @@ def test_verdict_for_ratio() -> None:
 def test_build_train_command_matches_production_overrides(tmp_path: Path) -> None:
     command = bench.build_train_command(tmp_path / "runs" / "n1", iterations=300, devices=None)
     assert command[0] == sys.executable
-    assert command[1].endswith("scripts/train_offpolicy.py")
-    assert "algo=sac" in command
-    assert "task=sac/g1_walk_flat/mujoco" in command
+    assert command[1].endswith("scripts/train_sac.py")
+    assert "task=g1_walk_flat/mujoco" in command
     assert "training.no_play=true" in command
     assert "algo.max_iterations=300" in command
     assert any(arg.startswith("training.log_dir=") for arg in command)
diff --git a/tests/benchmark/test_replay_buffer_placement_benchmark.py b/tests/benchmark/test_replay_buffer_placement_benchmark.py
index 4ec8fb722..4a704a699 100644
--- a/tests/benchmark/test_replay_buffer_placement_benchmark.py
+++ b/tests/benchmark/test_replay_buffer_placement_benchmark.py
@@ -5,7 +5,7 @@
 from scripts.benchmark.rl import benchmark_replay_buffer_placement as bench
 
 
-def test_sac_default_case_uses_effective_symmetry_batch() -> None:
+def test_sac_default_case_uses_configured_batch() -> None:
     cfg = bench._compose_offpolicy_cfg("sac", "g1_walk_flat", "mujoco")
     shape = bench.ReplayShape(obs_dim=45, action_dim=29, critic_dim=48)
 
@@ -15,13 +15,12 @@ def test_sac_default_case_uses_effective_symmetry_batch() -> None:
         task="g1_walk_flat",
         sim="mujoco",
         shape=shape,
-        symmetry_batch_multiplier=2,
         max_capacity_rows=None,
     )
 
     assert case.command == "uv run train --algo sac --task g1_walk_flat --sim mujoco"
     assert case.config_capacity_rows == case.num_envs * case.replay_buffer_n
-    assert case.learner_batch_size == case.configured_batch_size // 2
+    assert case.learner_batch_size == case.configured_batch_size
     assert case.sample_count == case.learner_batch_size * case.updates_per_step
     assert case.incremental_rows == case.num_envs * case.env_steps_per_sync
 
@@ -36,7 +35,6 @@ def test_flashsac_default_case_uses_configured_batch() -> None:
         task="g1_walk_flat",
         sim="mujoco",
         shape=shape,
-        symmetry_batch_multiplier=1,
         max_capacity_rows=1024,
     )
 
@@ -154,7 +152,6 @@ def test_run_case_cpu_portable_path_records_device_transfer_timings() -> None:
         benchmark_capacity_rows=8,
         configured_batch_size=2,
         learner_batch_size=2,
-        symmetry_batch_multiplier=1,
         updates_per_step=1,
         sample_count=2,
         learning_starts=0,
diff --git a/tests/benchmark/test_sac_replay_buffer_sampling_benchmark.py b/tests/benchmark/test_sac_replay_buffer_sampling_benchmark.py
index e3cc4546a..ec3109f38 100644
--- a/tests/benchmark/test_sac_replay_buffer_sampling_benchmark.py
+++ b/tests/benchmark/test_sac_replay_buffer_sampling_benchmark.py
@@ -20,7 +20,6 @@ def _tiny_case() -> bench.BenchmarkCase:
         config_capacity_rows=16,
         configured_batch_size=4,
         learner_batch_size=4,
-        symmetry_batch_multiplier=1,
         updates_per_step=2,
         sample_count_per_rank=8,
         learning_starts=0,
@@ -28,18 +27,17 @@ def _tiny_case() -> bench.BenchmarkCase:
     )
 
 
-def test_sac_default_case_uses_effective_symmetry_batch() -> None:
+def test_sac_default_case_uses_configured_batch() -> None:
     cfg = bench._compose_offpolicy_cfg("mujoco")
     case = bench._build_case(
         cfg,
         sim="mujoco",
         shape=bench.ReplayShape(obs_dim=45, action_dim=29, critic_dim=48),
-        symmetry_batch_multiplier=2,
     )
 
     assert case.command == "uv run train --algo sac --task g1_walk_flat --sim mujoco"
     assert case.config_capacity_rows == case.num_envs * case.replay_buffer_n
-    assert case.learner_batch_size == case.configured_batch_size // 2
+    assert case.learner_batch_size == case.configured_batch_size
     assert case.sample_count_per_rank == case.learner_batch_size * case.updates_per_step
     assert case.shape.packed_width == 2 * 45 + 29 + 3 + 2 * 48
 
@@ -51,7 +49,6 @@ def test_default_compose_targets_motion_tracking_motrix() -> None:
         task=bench.DEFAULT_TASK,
         sim=bench.DEFAULT_SIM,
         shape=bench.ReplayShape(obs_dim=160, action_dim=29, critic_dim=289),
-        symmetry_batch_multiplier=1,
     )
 
     assert bench.DEFAULT_TASK == "g1_motion_tracking"
@@ -168,8 +165,6 @@ def test_main_no_cuda_writes_skipped_json(tmp_path, monkeypatch) -> None:
             "2",
             "--critic-dim",
             "1",
-            "--symmetry-batch-multiplier",
-            "2",
             "--out-json",
             str(out_json),
         ]
@@ -206,8 +201,6 @@ def test_main_mps_runs_single_device_and_skips_multi(tmp_path, monkeypatch) -> N
             "2",
             "--critic-dim",
             "1",
-            "--symmetry-batch-multiplier",
-            "2",
             "--out-json",
             str(out_json),
         ]
diff --git a/tests/config/test_config_system.py b/tests/config/test_config_system.py
index 359f43b0b..56a4f89e3 100644
--- a/tests/config/test_config_system.py
+++ b/tests/config/test_config_system.py
@@ -16,8 +16,11 @@
 from hydra.core.global_hydra import GlobalHydra
 from omegaconf import OmegaConf
 
-CONF_DIR = Path(__file__).parent.parent.parent / "conf"
-_BACKENDS = ("mujoco", "mjwarp", "motrix")
+# CPU-bound on the single-core CI runner; kept in the slow lane (make test-slow).
+pytestmark = pytest.mark.slow
+
+CONF_DIR = Path(__file__).parent.parent.parent / "src" / "unilab" / "conf"
+_BACKENDS = ("mujoco", "mjwarp", "motrix", "isaacgym", "genesis", "isaacsim")
 
 
 def _expected_backend_from_variant(name: str) -> str | None:
@@ -36,15 +39,10 @@ def _compose(algo_dir: str, config_name: str = "config", overrides: list[str] |
 
 
 def _normalize_overrides(algo_dir: str, overrides: list[str] | None) -> list[str]:
-    algo = "sac"
     normalized: list[str] = []
     task_selected = False
 
     for override in overrides or []:
-        if override.startswith("algo="):
-            algo = override.split("=", 1)[1]
-            normalized.append(override)
-            continue
         if override.startswith("task="):
             task_selected = True
             normalized.append(override)
@@ -52,8 +50,8 @@ def _normalize_overrides(algo_dir: str, overrides: list[str] | None) -> list[str
         normalized.append(override)
 
     if not task_selected:
-        if algo_dir == "offpolicy":
-            normalized.append(f"task={algo}/g1_walk_flat/mujoco")
+        if algo_dir in ("sac", "td3", "flashsac"):
+            normalized.append("task=g1_walk_flat/mujoco")
         else:
             normalized.append("task=go1_joystick_flat/mujoco")
 
@@ -64,8 +62,16 @@ def _assert_reward_populated(cfg, label: str):
     assert hasattr(cfg, "reward"), f"{label} missing cfg.reward"
     reward_dict = OmegaConf.to_container(cfg.reward, resolve=True)
     assert isinstance(reward_dict, dict), f"{label} reward must resolve to mapping"
-    assert "scales" in reward_dict, f"{label} reward must contain scales"
-    assert len(reward_dict["scales"]) > 0, f"{label} reward.scales must be non-empty"
+    if "scales" in reward_dict:
+        assert len(reward_dict["scales"]) > 0, f"{label} reward.scales must be non-empty"
+        return
+
+    active_terms = {name: term for name, term in reward_dict.items() if term is not None}
+    assert active_terms, f"{label} Manager-Based reward terms must be non-empty"
+    for term_name, term in active_terms.items():
+        assert isinstance(term, dict), f"{label} reward.{term_name} must be a mapping"
+        assert "func" in term, f"{label} reward.{term_name} must declare func"
+        assert "weight" in term, f"{label} reward.{term_name} must declare weight"
 
 
 def _supported_task_cases() -> list[tuple[str, str, str, str, str, list[str]]]:
@@ -89,24 +95,21 @@ def _supported_task_cases() -> list[tuple[str, str, str, str, str, list[str]]]:
                     )
                 )
 
-    offpolicy_root = CONF_DIR / "offpolicy" / "task"
-    for algo_root in sorted(path for path in offpolicy_root.iterdir() if path.is_dir()):
-        for task_dir in sorted(path for path in algo_root.iterdir() if path.is_dir()):
+    for algo_dir in ["sac", "td3", "flashsac"]:
+        root = CONF_DIR / algo_dir / "task"
+        for task_dir in sorted(path for path in root.iterdir() if path.is_dir()):
             for backend_file in sorted(task_dir.glob("*.yaml")):
                 expected_backend = _expected_backend_from_variant(backend_file.stem)
                 if expected_backend is None:
                     continue
                 cases.append(
                     (
-                        "offpolicy",
+                        algo_dir,
                         "config",
                         task_dir.name,
                         expected_backend,
                         str(backend_file.relative_to(CONF_DIR)),
-                        [
-                            f"algo={algo_root.name}",
-                            f"task={algo_root.name}/{task_dir.name}/{backend_file.stem}",
-                        ],
+                        [f"task={task_dir.name}/{backend_file.stem}"],
                     )
                 )
 
@@ -116,7 +119,9 @@ def _supported_task_cases() -> list[tuple[str, str, str, str, str, list[str]]]:
 @pytest.mark.parametrize(
     "algo_dir,config_name",
     [
-        ("offpolicy", "config"),
+        ("sac", "config"),
+        ("td3", "config"),
+        ("flashsac", "config"),
         ("appo", "config"),
         ("ppo", "config"),
     ],
@@ -127,23 +132,6 @@ def test_algo_config_composes(algo_dir: str, config_name: str):
     assert cfg.training.sim_backend == "mujoco"
 
 
-def test_legacy_config_groups_removed():
-    for path in [
-        CONF_DIR / "ppo" / "reward",
-        CONF_DIR / "ppo" / "backend_task_preset",
-        CONF_DIR / "ppo" / "algo_preset",
-        CONF_DIR / "ppo" / "sim_backend",
-        CONF_DIR / "appo" / "reward",
-        CONF_DIR / "appo" / "backend_task_preset",
-        CONF_DIR / "appo" / "sim_backend",
-        CONF_DIR / "offpolicy" / "reward",
-        CONF_DIR / "offpolicy" / "backend_task_preset",
-        CONF_DIR / "offpolicy" / "algo_preset",
-        CONF_DIR / "offpolicy" / "sim_backend",
-    ]:
-        assert not path.exists(), f"legacy config group should be removed: {path}"
-
-
 def test_task_files_keep_full_identity_without_hidden_backend_marker():
     for path in sorted(CONF_DIR.glob("*/task/**/*.yaml")):
         cfg = OmegaConf.load(path)
@@ -203,17 +191,16 @@ def test_ppo_go2_arm_manip_loco_motrix_preserves_backend_overrides():
 
 
 def test_offpolicy_g1_walk_flat_motrix_sac_preserves_backend_overrides():
-    cfg = _compose("offpolicy", overrides=["algo=sac", "task=sac/g1_walk_flat/motrix"])
+    cfg = _compose("sac", overrides=["task=g1_walk_flat/motrix"])
 
     assert cfg.algo.num_envs == 2048
     assert cfg.algo.max_iterations == 5000
-    assert cfg.reward.scales.tracking_lin_vel == pytest.approx(2.2)
-    assert cfg.env.domain_rand.randomize_kp is False
-    assert cfg.env.domain_rand.randomize_kd is False
+    assert cfg.reward.tracking_lin_vel.weight == pytest.approx(2.2)
+    assert cfg.env.events.pd_gains is None
 
 
 def test_offpolicy_g1_walk_flat_mujoco_td3_uses_td3_task_owner():
-    cfg = _compose("offpolicy", overrides=["algo=td3", "task=td3/g1_walk_flat/mujoco"])
+    cfg = _compose("td3", overrides=["task=g1_walk_flat/mujoco"])
 
     assert cfg.training.task_name == "G1WalkFlat"
     assert cfg.training.sim_backend == "mujoco"
@@ -221,14 +208,14 @@ def test_offpolicy_g1_walk_flat_mujoco_td3_uses_td3_task_owner():
     assert cfg.algo.tau == pytest.approx(0.1)
     assert cfg.algo.actor_hidden_dim == 512
     assert cfg.algo.critic_hidden_dim == 1024
-    assert cfg.reward.scales.tracking_lin_vel == pytest.approx(2.0)
-    assert cfg.env.control_config.action_scale == pytest.approx(1.0)
+    assert cfg.reward.tracking_lin_vel.weight == pytest.approx(2.0)
+    assert cfg.env.actions.joint_pos.scale == pytest.approx(1.0)
 
 
 def test_offpolicy_td3_go2_joystick_flat_motrix_composes():
     cfg = _compose(
-        "offpolicy",
-        overrides=["algo=td3", "task=td3/go2_joystick_flat/motrix"],
+        "td3",
+        overrides=["task=go2_joystick_flat/motrix"],
     )
 
     assert cfg.training.task_name == "Go2JoystickFlat"
@@ -237,47 +224,39 @@ def test_offpolicy_td3_go2_joystick_flat_motrix_composes():
     assert cfg.algo.tau == pytest.approx(0.1)
     assert cfg.algo.algo_params.weight_decay == pytest.approx(0.1)
     assert cfg.algo.algo_params.policy_noise == pytest.approx(0.2)
-    assert cfg.reward.scales.tracking_lin_vel == pytest.approx(1.0)
-    assert cfg.reward.base_height_target == pytest.approx(0.3)
+    assert cfg.reward.tracking_lin_vel.weight == pytest.approx(1.0)
+    assert cfg.reward.base_height.params.target_height == pytest.approx(0.3)
 
 
 def test_offpolicy_td3_go1_joystick_flat_motrix_composes():
     cfg = _compose(
-        "offpolicy",
-        overrides=["algo=td3", "task=td3/go1_joystick_flat/motrix"],
+        "td3",
+        overrides=["task=go1_joystick_flat/motrix"],
     )
 
     assert cfg.training.task_name == "Go1JoystickFlat"
     assert cfg.training.sim_backend == "motrix"
     assert cfg.algo.algo == "td3"
-    assert cfg.reward.scales.tracking_lin_vel == pytest.approx(1.0)
-
-
-def test_offpolicy_g1_walk_flat_motrix_preserves_backend_specific_algo_value():
-    mujoco_cfg = _compose("offpolicy", overrides=["algo=sac", "task=sac/g1_walk_flat/mujoco"])
-    motrix_cfg = _compose("offpolicy", overrides=["algo=sac", "task=sac/g1_walk_flat/motrix"])
-
-    assert mujoco_cfg.algo.use_symmetry is True
-    assert motrix_cfg.algo.use_symmetry is False
+    assert cfg.reward.tracking_lin_vel.weight == pytest.approx(1.0)
+    assert cfg.reward.contact is None
+    assert cfg.env.events.push_robot is None
 
 
 def test_offpolicy_g1_walk_flat_mjwarp_owner_preserves_sac_contract():
-    mujoco_cfg = _compose("offpolicy", overrides=["algo=sac", "task=sac/g1_walk_flat/mujoco"])
-    mjwarp_cfg = _compose("offpolicy", overrides=["algo=sac", "task=sac/g1_walk_flat/mjwarp"])
+    mujoco_cfg = _compose("sac", overrides=["task=g1_walk_flat/mujoco"])
+    mjwarp_cfg = _compose("sac", overrides=["task=g1_walk_flat/mjwarp"])
 
     assert mjwarp_cfg.training.sim_backend == "mjwarp"
     assert mjwarp_cfg.training.no_play is False
     assert mjwarp_cfg.training.play_render_mode == "record"
     assert mjwarp_cfg.algo.num_envs == mujoco_cfg.algo.num_envs
-    assert mjwarp_cfg.algo.use_symmetry is mujoco_cfg.algo.use_symmetry is True
-    assert mjwarp_cfg.env.control_config.action_scale == pytest.approx(
-        mujoco_cfg.env.control_config.action_scale
+    assert mjwarp_cfg.env.actions.joint_pos.scale == pytest.approx(
+        mujoco_cfg.env.actions.joint_pos.scale
     )
     assert mjwarp_cfg.env.mjwarp_nconmax == 128
     assert mjwarp_cfg.env.mjwarp_njmax == 256
     assert mjwarp_cfg.env.render_spacing == pytest.approx(2.0)
-    assert mjwarp_cfg.env.domain_rand.randomize_kp is False
-    assert mjwarp_cfg.env.domain_rand.randomize_kd is False
+    assert mjwarp_cfg.env.events.pd_gains is None
     assert OmegaConf.to_container(mjwarp_cfg.reward, resolve=True) == OmegaConf.to_container(
         mujoco_cfg.reward, resolve=True
     )
@@ -302,24 +281,24 @@ def test_ppo_g1_backend_specific_hyperparams_remain_separate():
     assert motrix_cfg.algo.empirical_normalization is True
     assert motrix_cfg.algo.obs_groups.actor == ["policy"]
     assert OmegaConf.select(motrix_cfg, "env.motrix_max_iterations") is None
-    assert motrix_cfg.env.control_config.action_scale == pytest.approx(0.5)
-    assert motrix_cfg.env.commands.vel_limit == [[0.4, 0.0, 0.0], [0.7, 0.0, 0.0]]
-    assert motrix_cfg.env.gait_phase_init_mode == "offset_phase"
-    assert motrix_cfg.reward.scales.tracking_lin_vel == pytest.approx(2.0)
-    assert motrix_cfg.reward.scales.tracking_ang_vel == pytest.approx(0.25)
-    assert motrix_cfg.reward.scales.forward_progress == pytest.approx(0.0)
-    assert motrix_cfg.reward.scales.under_speed == pytest.approx(-0.2)
-    assert motrix_cfg.reward.scales.penalty_feet_ori == pytest.approx(0.0)
-    assert motrix_cfg.reward.scales.feet_phase == pytest.approx(1.2)
-    assert motrix_cfg.reward.scales.feet_phase_contrast == pytest.approx(1.5)
-    assert motrix_cfg.reward.scales.feet_phase_contact == pytest.approx(1.0)
-    assert motrix_cfg.reward.scales.feet_double_stance == pytest.approx(-1.0)
-    assert motrix_cfg.reward.scales.base_height == pytest.approx(-120.0)
-    assert motrix_cfg.reward.scales.pose == pytest.approx(-0.05)
-    assert motrix_cfg.reward.base_height_target == pytest.approx(0.765)
-    assert motrix_cfg.reward.min_forward_speed_for_gait_reward == pytest.approx(0.05)
-    assert motrix_cfg.reward.min_base_height == pytest.approx(0.5)
-    assert motrix_cfg.reward.max_tilt_deg == pytest.approx(35.0)
+    assert motrix_cfg.env.actions.joint_pos.scale == pytest.approx(0.5)
+    assert motrix_cfg.env.commands.twist.ranges.lin_vel_x == [0.4, 0.7]
+    assert motrix_cfg.env.observations.policy.terms.gait_phase.params.init_mode == "offset_phase"
+    assert motrix_cfg.reward.tracking_lin_vel.weight == pytest.approx(2.0)
+    assert motrix_cfg.reward.tracking_ang_vel.weight == pytest.approx(0.25)
+    assert motrix_cfg.reward.forward_progress.weight == pytest.approx(0.0)
+    assert motrix_cfg.reward.under_speed.weight == pytest.approx(-0.2)
+    assert motrix_cfg.reward.penalty_feet_ori.weight == pytest.approx(0.0)
+    assert motrix_cfg.reward.feet_phase.weight == pytest.approx(1.2)
+    assert motrix_cfg.reward.feet_phase_contrast.weight == pytest.approx(1.5)
+    assert motrix_cfg.reward.feet_phase_contact.weight == pytest.approx(1.0)
+    assert motrix_cfg.reward.feet_double_stance.weight == pytest.approx(-1.0)
+    assert motrix_cfg.reward.base_height.weight == pytest.approx(-120.0)
+    assert motrix_cfg.reward.pose.weight == pytest.approx(-0.05)
+    assert motrix_cfg.reward.base_height.params.target_height == pytest.approx(0.765)
+    assert motrix_cfg.reward.feet_phase.params.min_forward_speed == pytest.approx(0.05)
+    assert motrix_cfg.env.terminations.base_height.params.minimum_height == pytest.approx(0.5)
+    assert motrix_cfg.env.terminations.tilt.params.max_tilt_deg == pytest.approx(35.0)
 
 
 @pytest.mark.parametrize(
@@ -328,8 +307,8 @@ def test_ppo_g1_backend_specific_hyperparams_remain_separate():
         ("ppo", ["task=g1_walk_flat/mujoco"]),
         ("ppo_him", ["task=go2_arm_manip_loco/mujoco"]),
         ("appo", ["task=g1_walk_flat/mujoco"]),
-        ("offpolicy", ["algo=sac", "task=sac/g1_walk_flat/mujoco"]),
-        ("offpolicy", ["algo=flashsac", "task=flashsac/g1_walk_flat/mujoco"]),
+        ("sac", ["task=g1_walk_flat/mujoco"]),
+        ("flashsac", ["task=g1_walk_flat/mujoco"]),
     ],
 )
 def test_post_step_forward_sensor_defaults_false_outside_sharpa_mujoco(
@@ -348,7 +327,7 @@ def test_post_step_forward_sensor_defaults_false_outside_sharpa_mujoco(
         ("ppo", ["task=sharpa_inhand_grasp/mujoco"]),
         ("appo", ["task=sharpa_inhand/mujoco"]),
         ("appo", ["task=sharpa_inhand/mujoco_hora"]),
-        ("offpolicy", ["algo=sac", "task=sac/sharpa_inhand/mujoco_hora"]),
+        ("sac", ["task=sharpa_inhand/mujoco_hora"]),
         ("hora_distill", ["task=sharpa_inhand/mujoco"]),
     ],
 )
@@ -393,8 +372,12 @@ def test_ppo_go1_motrix_preserves_reward_and_algo_values():
     assert cfg.algo.empirical_normalization is True
     assert cfg.algo.policy.init_noise_std == pytest.approx(0.5)
     assert cfg.algo.algorithm.learning_rate == pytest.approx(3.0e-4)
-    assert cfg.reward.scales.tracking_lin_vel == pytest.approx(1.0)
-    assert cfg.env.commands.vel_limit == [[0.5, 0.0, 0.0], [0.5, 0.0, 0.0]]
+    assert cfg.reward.tracking_lin_vel.weight == pytest.approx(1.0)
+    assert cfg.reward.contact is None
+    assert cfg.env.commands.twist.ranges.lin_vel_x == [0.5, 0.5]
+    assert cfg.env.commands.twist.ranges.lin_vel_y == [0.0, 0.0]
+    assert cfg.env.commands.twist.ranges.ang_vel_z == [0.0, 0.0]
+    assert cfg.env.events.push_robot is None
 
 
 def test_ppo_go2_motrix_preserves_backend_env_overrides():
@@ -402,8 +385,10 @@ def test_ppo_go2_motrix_preserves_backend_env_overrides():
 
     assert cfg.algo.num_envs == 1024
     assert cfg.algo.empirical_normalization is True
-    assert cfg.env.domain_rand.randomize_kp is False
-    assert cfg.env.domain_rand.randomize_kd is False
+    assert cfg.env.events.pd_gains is None
+    assert cfg.env.commands.twist.ranges.lin_vel_x == [0.5, 0.5]
+    assert cfg.env.commands.twist.ranges.lin_vel_y == [0.0, 0.0]
+    assert cfg.env.commands.twist.ranges.ang_vel_z == [0.0, 0.0]
 
 
 def test_ppo_go2w_mujoco_uses_motor_owner_dr_path():
@@ -411,19 +396,24 @@ def test_ppo_go2w_mujoco_uses_motor_owner_dr_path():
 
     assert cfg.training.task_name == "Go2WJoystickFlat"
     assert cfg.training.sim_backend == "mujoco"
-    assert cfg.env.commands.vel_limit == [[0.0, 0.0, -1.0], [1.0, 0.0, 1.0]]
-    assert cfg.env.domain_rand.randomize_kp is False
-    assert cfg.env.domain_rand.randomize_kd is False
-    assert cfg.env.control_config.action_scale == pytest.approx(0.5)
-    assert cfg.env.control_config.Kp == pytest.approx(50.0)
-    assert cfg.env.control_config.Kd == pytest.approx(1.5)
-    assert cfg.env.control_config.wheel_action_scale == pytest.approx(10.0)
-    assert cfg.env.control_config.wheel_Kd == pytest.approx(0.5)
-    assert cfg.reward.scales.tracking_ang_vel == pytest.approx(0.75)
-    assert cfg.reward.scales.orientation == pytest.approx(-2.0)
-    assert cfg.reward.scales.upward == pytest.approx(1.0)
-    assert cfg.reward.base_height_target == pytest.approx(0.4)
-    assert cfg.reward.scales.torques < 0.0
+    command = cfg.env.commands.twist
+    assert command.ranges.lin_vel_x == [0.0, 1.0]
+    assert command.ranges.lin_vel_y == [0.0, 0.0]
+    assert command.ranges.ang_vel_z == [-1.0, 1.0]
+    action = cfg.env.actions.motor
+    assert action.leg_action_scale == pytest.approx(0.5)
+    assert action.leg_kp == pytest.approx(50.0)
+    assert action.leg_kd == pytest.approx(1.5)
+    assert action.wheel_action_scale == pytest.approx(10.0)
+    assert action.wheel_kd == pytest.approx(0.5)
+    gains = cfg.env.events.motor_gains.params
+    assert gains.kp_multiplier_range == [1.0, 1.0]
+    assert gains.kd_multiplier_range == [1.0, 1.0]
+    assert cfg.reward.tracking_ang_vel.weight == pytest.approx(0.75)
+    assert cfg.reward.orientation.weight == pytest.approx(-2.0)
+    assert cfg.reward.upward.weight == pytest.approx(1.0)
+    assert cfg.reward.base_height.params.target_height == pytest.approx(0.4)
+    assert cfg.reward.torques.weight < 0.0
 
 
 def test_ppo_go2w_motrix_uses_motor_owner_dr_path():
@@ -432,18 +422,20 @@ def test_ppo_go2w_motrix_uses_motor_owner_dr_path():
     assert cfg.training.task_name == "Go2WJoystickFlat"
     assert cfg.training.sim_backend == "motrix"
     assert cfg.env.render_offset_mode == "zero"
-    assert cfg.env.commands.vel_limit == [[0.0, 0.0, -1.0], [1.0, 0.0, 1.0]]
-    assert cfg.env.domain_rand.randomize_kp is False
-    assert cfg.env.domain_rand.randomize_kd is False
-    assert cfg.env.control_config.action_scale == pytest.approx(0.5)
-    assert cfg.env.control_config.Kp == pytest.approx(50.0)
-    assert cfg.env.control_config.Kd == pytest.approx(1.5)
-    assert cfg.env.control_config.wheel_action_scale == pytest.approx(10.0)
-    assert cfg.env.control_config.wheel_Kd == pytest.approx(0.5)
-    assert cfg.reward.scales.tracking_ang_vel == pytest.approx(0.75)
-    assert cfg.reward.scales.orientation == pytest.approx(-2.0)
-    assert cfg.reward.scales.upward == pytest.approx(1.0)
-    assert cfg.reward.scales.torques < 0.0
+    command = cfg.env.commands.twist
+    assert command.ranges.lin_vel_x == [0.0, 1.0]
+    assert command.ranges.lin_vel_y == [0.0, 0.0]
+    assert command.ranges.ang_vel_z == [-1.0, 1.0]
+    action = cfg.env.actions.motor
+    assert action.leg_action_scale == pytest.approx(0.5)
+    assert action.leg_kp == pytest.approx(50.0)
+    assert action.leg_kd == pytest.approx(1.5)
+    assert action.wheel_action_scale == pytest.approx(10.0)
+    assert action.wheel_kd == pytest.approx(0.5)
+    assert cfg.reward.tracking_ang_vel.weight == pytest.approx(0.75)
+    assert cfg.reward.orientation.weight == pytest.approx(-2.0)
+    assert cfg.reward.upward.weight == pytest.approx(1.0)
+    assert cfg.reward.torques.weight < 0.0
 
 
 def test_ppo_go2w_motrix_uses_motor_owner_scene_path():
@@ -451,85 +443,53 @@ def test_ppo_go2w_motrix_uses_motor_owner_scene_path():
 
     assert cfg.training.task_name == "Go2WJoystickFlat"
     assert cfg.training.sim_backend == "motrix"
-    assert "model_file" not in cfg.env
-    assert cfg.env.domain_rand.randomize_kp is False
-    assert cfg.env.domain_rand.randomize_kd is False
-    assert cfg.env.control_config.wheel_action_scale == pytest.approx(10.0)
-    assert cfg.reward.scales.torques < 0.0
-
-
-def test_ppo_go2w_rough_mujoco_uses_terrain_generator():
-    cfg = _compose("ppo", overrides=["task=go2w_joystick_rough/mujoco"])
-
-    assert cfg.training.task_name == "Go2WJoystickRough"
-    assert cfg.training.sim_backend == "mujoco"
-    assert str(cfg.env.scene.model_file).endswith("src/unilab/assets/robots/go2w/go2w_mujoco.xml")
-    assert cfg.env.scene.terrain.hfield_name == "terrain_hfield"
-    assert cfg.env.scene.terrain.geom_name == "floor"
-    assert cfg.env.terrain_scan.hfield_name == "terrain_hfield"
-    assert cfg.env.terrain_scan.geom_name == "floor"
-    assert cfg.env.commands.resampling_time == pytest.approx(10.0)
-    assert cfg.env.commands.heading_command is True
-    assert cfg.env.commands.vel_limit == [[-1.0, -1.0, -1.0], [1.0, 1.0, 1.0]]
-    assert cfg.env.commands.heading_range == pytest.approx([-3.141592653589793, 3.141592653589793])
-    assert cfg.env.control_config.clip_actions == pytest.approx(100.0)
-    assert cfg.env.control_config.action_scale == pytest.approx(0.25)
-    assert cfg.env.control_config.hip_action_scale == pytest.approx(0.125)
-    assert cfg.env.control_config.wheel_action_scale == pytest.approx(5.0)
-    assert cfg.env.domain_rand.randomize_kp is True
-    assert cfg.env.domain_rand.randomize_kd is True
-    assert cfg.env.domain_rand.kp_multiplier_range == [0.5, 1.0]
-    assert cfg.reward.scales.tracking_lin_vel == pytest.approx(3.0)
-    assert cfg.reward.scales.hip_pos == pytest.approx(-2.0)
-    assert cfg.reward.scales.joint_mirror == pytest.approx(-0.05)
-    assert cfg.reward.only_positive_rewards is False
-    assert cfg.algo.max_iterations == 1200
-
-
-def test_ppo_go2w_rough_motrix_uses_yaw_reset_and_strong_control():
-    cfg = _compose("ppo", overrides=["task=go2w_joystick_rough/motrix"])
-
-    assert cfg.training.task_name == "Go2WJoystickRough"
-    assert cfg.training.sim_backend == "motrix"
-    assert cfg.env.commands.vel_limit == [[-1.0, -1.0, -1.0], [1.0, 1.0, 1.0]]
-    assert cfg.env.commands.heading_range == pytest.approx([-3.141592653589793, 3.141592653589793])
-    assert cfg.env.control_config.action_scale == pytest.approx(0.25)
-    assert cfg.env.control_config.hip_action_scale == pytest.approx(0.125)
-    assert cfg.env.control_config.wheel_action_scale == pytest.approx(5.0)
-    assert cfg.env.domain_rand.randomize_kp is True
-    assert cfg.env.domain_rand.randomize_kd is True
-    assert cfg.reward.scales.orientation == pytest.approx(-2.0)
-    assert cfg.reward.scales.hip_pos == pytest.approx(-0.5)
-    assert cfg.reward.scales.upward == pytest.approx(1.0)
-    assert cfg.algo.max_iterations == 1200
+    assert str(cfg.env.scene.model_file).endswith("src/unilab/assets/robots/go2w/scene_flat.xml")
+    assert cfg.env.scene.default_keyframe_name == "home"
+    assert cfg.env.actions.motor.wheel_action_scale == pytest.approx(10.0)
+    assert cfg.reward.torques.weight < 0.0
 
 
 def test_offpolicy_g1_walk_flat_motrix_preserves_backend_env_overrides():
-    cfg = _compose("offpolicy", overrides=["algo=sac", "task=sac/g1_walk_flat/motrix"])
+    cfg = _compose("sac", overrides=["task=g1_walk_flat/motrix"])
 
     assert cfg.training.sim_backend == "motrix"
     assert cfg.algo.num_envs == 2048
     assert cfg.algo.max_iterations == 5000
-    assert cfg.env.domain_rand.randomize_kp is False
-    assert cfg.env.domain_rand.randomize_kd is False
+    assert cfg.env.events.pd_gains is None
+    assert cfg.reward.tracking_lin_vel.weight == pytest.approx(2.2)
 
 
 def test_offpolicy_flashsac_go2_joystick_mujoco_enables_full_dr_stack():
     mujoco_cfg = _compose(
-        "offpolicy",
-        overrides=["algo=flashsac", "task=flashsac/go2_joystick_flat/mujoco"],
+        "flashsac",
+        overrides=["task=go2_joystick_flat/mujoco"],
     )
 
     assert mujoco_cfg.training.task_name == "Go2JoystickFlat"
     assert mujoco_cfg.training.sim_backend == "mujoco"
 
-    assert mujoco_cfg.env.domain_rand.randomize_kp is True
-    assert mujoco_cfg.env.domain_rand.randomize_kd is True
-    assert mujoco_cfg.env.domain_rand.randomize_base_mass is True
-    assert mujoco_cfg.env.domain_rand.random_com is True
-    assert mujoco_cfg.env.domain_rand.randomize_gravity is True
-    assert mujoco_cfg.env.domain_rand.push_robots is True
-    assert mujoco_cfg.env.noise_config.level == pytest.approx(1.0)
+    assert mujoco_cfg.env.events.pd_gains.func == "unilab.envs.mdp.pd_gains"
+    assert (
+        mujoco_cfg.env.events.randomize_rigid_body_mass.func
+        == "unilab.envs.mdp.randomize_rigid_body_mass"
+    )
+    assert (
+        mujoco_cfg.env.events.randomize_rigid_body_com.func
+        == "unilab.envs.mdp.randomize_rigid_body_com"
+    )
+    assert (
+        mujoco_cfg.env.events.randomize_physics_scene_gravity.func
+        == "unilab.envs.mdp.randomize_physics_scene_gravity"
+    )
+    assert (
+        mujoco_cfg.env.events.push_by_setting_velocity.func
+        == "unilab.envs.mdp.push_by_setting_velocity"
+    )
+    assert mujoco_cfg.env.events.push_by_setting_velocity.mode == "interval"
+    assert mujoco_cfg.env.events.push_by_setting_velocity.is_global_time is True
+    assert mujoco_cfg.env.observations.policy.enable_corruption is True
+    assert mujoco_cfg.env.observations.policy.terms.joint_pos.noise.n_min == pytest.approx(-0.01)
+    assert mujoco_cfg.env.observations.policy.terms.joint_vel.noise.n_min == pytest.approx(-0.1)
 
 
 def test_cli_override_beats_task_defaults():
diff --git a/tests/config/test_locomotion_params.py b/tests/config/test_locomotion_params.py
index f6990b9af..26d1fc045 100644
--- a/tests/config/test_locomotion_params.py
+++ b/tests/config/test_locomotion_params.py
@@ -2,12 +2,15 @@
 
 from __future__ import annotations
 
+import re
 from pathlib import Path
-from typing import Any, cast
 
 import pytest
 
-CONF_DIR = Path(__file__).parent.parent.parent / "conf"
+# CPU-bound on the single-core CI runner; kept in the slow lane (make test-slow).
+pytestmark = pytest.mark.slow
+
+CONF_DIR = Path(__file__).parent.parent.parent / "src" / "unilab" / "conf"
 
 G1_BEYONDMIMIC_ACTION_SCALE = [
     0.5475464629911068,
@@ -40,6 +43,7 @@
     0.07450087032950714,
     0.07450087032950714,
 ]
+G1_23DOF_BEYONDMIMIC_ACTION_SCALE = G1_BEYONDMIMIC_ACTION_SCALE[:13] + [0.43857731392336724] * 10
 X2_ACTION_SCALE = [0.25] * 29
 
 
@@ -55,7 +59,6 @@ def test_sac_config_defaults():
     assert cfg.algo == "sac"
     assert cfg.num_envs == 4096
     assert cfg.batch_size == 8192
-    assert cfg.use_symmetry is False
     assert cfg.obs_normalization is False
     assert isinstance(cfg.algo_params, SACAlgoParams)
     assert cfg.algo_params.alpha_init == 0.01
@@ -95,7 +98,7 @@ def test_ppo_config_defaults():
     assert cfg.algo == "ppo"
     assert cfg.max_iterations == 101
     assert cfg.algorithm.clip_param == 0.2
-    assert cfg.algorithm.class_name == "unilab.algos.torch.rsl_rl_ppo:FinalObservationAwarePPO"
+    assert cfg.algorithm.class_name == "uni_rl.algos.rsl_rl_ppo:FinalObservationAwarePPO"
     assert cfg.algorithm.enable_compile is True
     assert cfg.policy.class_name == "ActorCritic"
 
@@ -131,7 +134,7 @@ def test_offpolicy_sac_defaults():
     from hydra.core.global_hydra import GlobalHydra
 
     GlobalHydra.instance().clear()
-    with initialize_config_dir(config_dir=str(CONF_DIR / "offpolicy"), version_base="1.3"):
+    with initialize_config_dir(config_dir=str(CONF_DIR / "sac"), version_base="1.3"):
         cfg = compose("config")
     assert cfg.algo.algo == "sac"
     assert cfg.algo.num_envs == 2048
@@ -142,18 +145,17 @@ def test_offpolicy_sac_g1_task_overrides():
     from hydra.core.global_hydra import GlobalHydra
 
     GlobalHydra.instance().clear()
-    with initialize_config_dir(config_dir=str(CONF_DIR / "offpolicy"), version_base="1.3"):
-        cfg = compose("config", overrides=["algo=sac", "task=sac/g1_walk_flat/mujoco"])
+    with initialize_config_dir(config_dir=str(CONF_DIR / "sac"), version_base="1.3"):
+        cfg = compose("config", overrides=["task=g1_walk_flat/mujoco"])
     assert cfg.algo.num_envs == 2048
     assert cfg.algo.max_iterations == 5000
-    assert cfg.algo.use_symmetry is True
     assert cfg.algo.algo_params.target_entropy_ratio == pytest.approx(0.0)
     assert cfg.algo.algo_params.use_compile is True
     assert cfg.training.task_name == "G1WalkFlat"
 
-    assert cfg.env.control_config.action_scale == pytest.approx(1.0)
-    assert cfg.env.gait_phase_init_mode == "offset_phase"
-    assert cfg.env.reset_base_qvel_limit == pytest.approx(0.5)
+    assert cfg.env.actions.joint_pos.scale == pytest.approx(1.0)
+    assert cfg.env.observations.policy.terms.gait_phase.params.init_mode == "offset_phase"
+    assert cfg.env.events.reset_root_state_uniform.params.velocity_range.x == [-0.5, 0.5]
 
 
 def test_offpolicy_td3_defaults():
@@ -161,8 +163,8 @@ def test_offpolicy_td3_defaults():
     from hydra.core.global_hydra import GlobalHydra
 
     GlobalHydra.instance().clear()
-    with initialize_config_dir(config_dir=str(CONF_DIR / "offpolicy"), version_base="1.3"):
-        cfg = compose("config", overrides=["algo=td3"])
+    with initialize_config_dir(config_dir=str(CONF_DIR / "td3"), version_base="1.3"):
+        cfg = compose("config")
     assert cfg.algo.algo == "td3"
     assert cfg.algo.use_layer_norm is False
     assert cfg.algo.algo_params.weight_decay == pytest.approx(0.1)
@@ -177,11 +179,11 @@ def test_offpolicy_td3_g1_task_overrides():
     from hydra.core.global_hydra import GlobalHydra
 
     GlobalHydra.instance().clear()
-    with initialize_config_dir(config_dir=str(CONF_DIR / "offpolicy"), version_base="1.3"):
-        cfg = compose("config", overrides=["algo=td3", "task=td3/g1_walk_flat/mujoco"])
+    with initialize_config_dir(config_dir=str(CONF_DIR / "td3"), version_base="1.3"):
+        cfg = compose("config", overrides=["task=g1_walk_flat/mujoco"])
     assert cfg.training.task_name == "G1WalkFlat"
     assert cfg.algo.max_iterations == 100000
-    assert cfg.env.control_config.action_scale == pytest.approx(1.0)
+    assert cfg.env.actions.joint_pos.scale == pytest.approx(1.0)
 
 
 def test_offpolicy_flashsac_g1_task_overrides():
@@ -189,10 +191,10 @@ def test_offpolicy_flashsac_g1_task_overrides():
     from hydra.core.global_hydra import GlobalHydra
 
     GlobalHydra.instance().clear()
-    with initialize_config_dir(config_dir=str(CONF_DIR / "offpolicy"), version_base="1.3"):
+    with initialize_config_dir(config_dir=str(CONF_DIR / "flashsac"), version_base="1.3"):
         cfg = compose(
             "config",
-            overrides=["algo=flashsac", "task=flashsac/g1_walk_flat/mujoco"],
+            overrides=["task=g1_walk_flat/mujoco"],
         )
     assert cfg.algo.algo == "flashsac"
     assert cfg.training.task_name == "G1WalkFlat"
@@ -207,10 +209,10 @@ def test_offpolicy_flashsac_go2_task_overrides():
     from hydra.core.global_hydra import GlobalHydra
 
     GlobalHydra.instance().clear()
-    with initialize_config_dir(config_dir=str(CONF_DIR / "offpolicy"), version_base="1.3"):
+    with initialize_config_dir(config_dir=str(CONF_DIR / "flashsac"), version_base="1.3"):
         cfg = compose(
             "config",
-            overrides=["algo=flashsac", "task=flashsac/go2_joystick_flat/mujoco"],
+            overrides=["task=go2_joystick_flat/mujoco"],
         )
     assert cfg.algo.algo == "flashsac"
     assert cfg.training.task_name == "Go2JoystickFlat"
@@ -220,182 +222,49 @@ def test_offpolicy_flashsac_go2_task_overrides():
     assert cfg.algo.tau == pytest.approx(0.05)
     assert cfg.algo.replay_buffer_n == 4096
     assert cfg.algo.updates_per_step == 2
-    assert cfg.reward.scales.swing_feet_z == pytest.approx(4.0)
-    assert cfg.env.control_config.action_scale == pytest.approx(0.4)
-
-
-def test_go2_joystick_rough_uses_terrain_generator():
-    from unilab.assets import ASSETS_ROOT_PATH
-    from unilab.base.scene import SceneCfg, TerrainSceneCfg
-    from unilab.envs.locomotion.go2.rough import Go2JoystickRoughCfg
-    from unilab.terrains import TerrainGeneratorCfg
-
-    cfg = Go2JoystickRoughCfg()
-    assert isinstance(cfg.scene, SceneCfg)
-    assert isinstance(cfg.scene.terrain, TerrainSceneCfg)
-    assert cfg.scene.model_file.endswith("go2.xml")
-    assert cfg.scene.fragment_files == [
-        str(ASSETS_ROOT_PATH / "robots" / "go2" / "locomotion_task.xml")
-    ]
-    assert isinstance(cfg.scene.terrain.generator, TerrainGeneratorCfg)
-    assert cfg.scene.terrain.hfield_name == "terrain_hfield"
-    assert cfg.scene.terrain.geom_name == "floor"
-    assert len(cfg.scene.terrain.generator.sub_terrains) == 7
-
-
-def test_go2_joystick_rough_terrain_cfg_is_independent_per_instance():
-    """Confirm rough terrain cfg defaults are not shared across instances."""
-    from unilab.envs.locomotion.go2.rough import Go2JoystickRoughCfg
-
-    a = Go2JoystickRoughCfg()
-    b = Go2JoystickRoughCfg()
-    b.scene.terrain.generator.num_rows = 3
-    assert a.scene.terrain.generator is not b.scene.terrain.generator
-    a.scene.terrain.generator.num_rows = 4
-    assert b.scene.terrain.generator.num_rows == 3
-
-
-def test_go2_joystick_rough_playback_model_uses_backend_scene(tmp_path):
-    """Offline playback / video rendering must reuse the backend-compiled scene model."""
-    import mujoco
-
-    from unilab.envs.locomotion.go2.joystick import RewardConfig
-    from unilab.envs.locomotion.go2.rough import Go2JoystickRoughCfg, Go2JoystickRoughEnv
-    from unilab.visualization.playback import _resolve_render_play_model_files
-
-    cfg = Go2JoystickRoughCfg(
-        reward_config=RewardConfig(scales={}, tracking_sigma=0.25, base_height_target=0.3)
-    )
-    cfg.scene.terrain.generator.num_rows = 2
-    cfg.scene.terrain.generator.num_cols = 2
-    cfg.scene.terrain.generator.border_width = 0.0
-    cfg.scene.terrain.generator.add_lights = False
-    cfg.scene.terrain.generator.seed = 0
-
-    env = Go2JoystickRoughEnv(cfg, num_envs=2, backend_type="mujoco")
-    try:
-        playback_model = env.get_playback_model(0)
-        assert isinstance(playback_model, mujoco.MjModel)
-        assert env._backend.terrain_origins is not None
-        assert (Path(env._backend.scene_artifacts_dir) / "hfields" / "hfield.png").is_file()
-        assert Path(env._backend.scene_visual_model_file).is_file()
-        assert mujoco.mj_name2id(playback_model, mujoco.mjtObj.mjOBJ_HFIELD, "terrain_hfield") >= 0
-        assert mujoco.mj_name2id(playback_model, mujoco.mjtObj.mjOBJ_GEOM, "floor") >= 0
-        assert mujoco.mj_name2id(playback_model, mujoco.mjtObj.mjOBJ_SENSOR, "FL_foot_contact") >= 0
-        model_file = _resolve_render_play_model_files(env, num_envs=2, tmp_dir=tmp_path)
-        assert isinstance(model_file, str)
-        assert model_file.endswith(".mjb")
-        assert Path(model_file).is_file()
-        rendered_model = mujoco.MjModel.from_binary_path(model_file)
-        assert mujoco.mj_name2id(rendered_model, mujoco.mjtObj.mjOBJ_HFIELD, "terrain_hfield") >= 0
-        assert mujoco.mj_name2id(rendered_model, mujoco.mjtObj.mjOBJ_GEOM, "floor") >= 0
-        assert rendered_model.ngeom > playback_model.ngeom
-    finally:
-        env.close()
-
-
-def test_go2_joystick_flat_no_terrain_materialized():
-    """Flat task keeps the static scene source and has no terrain origins."""
-    from unilab.envs.locomotion.go2.joystick import (
-        Go2JoystickCfg,
-        Go2WalkTask,
-        RewardConfig,
-    )
-
-    cfg = Go2JoystickCfg(
-        reward_config=RewardConfig(scales={}, tracking_sigma=0.25, base_height_target=0.3)
-    )
-    env = Go2WalkTask(cfg, num_envs=4, backend_type="mujoco")
-    try:
-        assert env._backend.scene_model_file == cfg.scene.model_file
-        assert env._backend.terrain_origins is None
-        assert env._backend.scene_artifacts_dir is None
-    finally:
-        env.close()
-
-
-def test_ppo_go2_joystick_rough_task_compose():
-    from hydra import compose, initialize_config_dir
-    from hydra.core.global_hydra import GlobalHydra
-
-    GlobalHydra.instance().clear()
-    with initialize_config_dir(config_dir=str(CONF_DIR / "ppo"), version_base="1.3"):
-        cfg = compose("config", overrides=["task=go2_joystick_rough/mujoco"])
-    assert cfg.training.task_name == "Go2JoystickRough"
-    assert cfg.training.sim_backend == "mujoco"
-
-
-def test_ppo_go2_joystick_rough_motrix_task_compose():
-    from hydra import compose, initialize_config_dir
-    from hydra.core.global_hydra import GlobalHydra
-
-    GlobalHydra.instance().clear()
-    with initialize_config_dir(config_dir=str(CONF_DIR / "ppo"), version_base="1.3"):
-        cfg = compose("config", overrides=["task=go2_joystick_rough/motrix"])
-    assert cfg.training.task_name == "Go2JoystickRough"
-    assert cfg.training.sim_backend == "motrix"
-    assert cfg.algo.num_envs == 4096
-    assert cfg.algo.max_iterations == 1500
-    assert cfg.env.render_offset_mode == "zero"
-    assert cfg.env.scene.model_file.endswith("go2.xml")
-    assert cfg.env.scene.terrain.generator.num_rows == 6
-    assert cfg.env.scene.terrain.generator.num_cols == 6
-    assert cfg.env.terrain_scan.enabled is True
-    assert cfg.reward.scales.tracking_lin_vel == pytest.approx(3.0)
-    assert "base_height" not in cfg.reward.scales
-    assert "swing_feet_z" not in cfg.reward.scales
-
-
-def test_go2_joystick_rough_motrix_registers_rough_env():
-    from unilab.base import registry
-    from unilab.envs.locomotion.go2.rough import Go2JoystickRoughEnv
-
-    assert registry._envs["Go2JoystickRough"].env_cls_dict["motrix"] is Go2JoystickRoughEnv
+    assert cfg.reward.swing_feet_z.weight == pytest.approx(4.0)
+    assert cfg.env.actions.joint_pos.scale == pytest.approx(0.4)
 
 
 def test_offpolicy_g1_rough_terrain_task_overrides():
     from hydra import compose, initialize_config_dir
     from hydra.core.global_hydra import GlobalHydra
 
-    from unilab.envs.locomotion.g1.joystick import G1WalkRoughCfg
-
     GlobalHydra.instance().clear()
-    with initialize_config_dir(config_dir=str(CONF_DIR / "offpolicy"), version_base="1.3"):
+    with initialize_config_dir(config_dir=str(CONF_DIR / "sac"), version_base="1.3"):
         cfg = compose(
             "config",
-            overrides=["algo=sac", "task=sac/g1_walk_rough/mujoco"],
+            overrides=["task=g1_walk_rough/mujoco"],
         )
     assert cfg.algo.algo == "sac"
     assert cfg.training.task_name == "G1WalkRough"
     assert cfg.training.sim_backend == "mujoco"
-    assert G1WalkRoughCfg().scene.model_file.endswith("scene_rough.xml")
+    assert cfg.env.scene.model_file.endswith("scene_rough.xml")
 
 
 def test_g1_task_owner_yamls_preserve_legacy_and_walk_observation_profiles():
     from hydra import compose, initialize_config_dir
     from hydra.core.global_hydra import GlobalHydra
 
-    from unilab.envs.locomotion.g1.joystick import G1WalkEnv
-
     def uses_walk_profile(config_group: str, overrides: list[str]) -> bool:
         GlobalHydra.instance().clear()
         with initialize_config_dir(config_dir=str(CONF_DIR / config_group), version_base="1.3"):
             cfg = compose("config", overrides=overrides)
-        env = cast(Any, object.__new__(G1WalkEnv))
-        env._cfg = cfg.env
-        env._reward_cfg = cfg.reward
-        return bool(env._uses_walk_observation_profile())
+        gyro_scale = cfg.env.observations.policy.terms.base_ang_vel.get("scale")
+        if gyro_scale is None:
+            return False
+        assert gyro_scale == pytest.approx(0.25)
+        assert cfg.env.observations.policy.terms.joint_vel.scale == pytest.approx(0.05)
+        assert cfg.env.observations.critic.terms.base_lin_vel.scale == pytest.approx(2.0)
+        return True
 
     assert uses_walk_profile("ppo", ["task=g1_walk_flat/mujoco"]) is False
     assert uses_walk_profile("appo", ["task=g1_walk_flat/mujoco"]) is False
-    assert uses_walk_profile("offpolicy", ["algo=sac", "task=sac/g1_walk_flat/mujoco"]) is True
-    assert uses_walk_profile("offpolicy", ["algo=sac", "task=sac/g1_walk_flat/motrix"]) is True
-    assert uses_walk_profile("offpolicy", ["algo=sac", "task=sac/g1_walk_rough/mujoco"]) is True
-    assert uses_walk_profile("offpolicy", ["algo=td3", "task=td3/g1_walk_flat/mujoco"]) is True
-    assert (
-        uses_walk_profile("offpolicy", ["algo=flashsac", "task=flashsac/g1_walk_flat/mujoco"])
-        is True
-    )
+    assert uses_walk_profile("sac", ["task=g1_walk_flat/mujoco"]) is True
+    assert uses_walk_profile("sac", ["task=g1_walk_flat/motrix"]) is True
+    assert uses_walk_profile("sac", ["task=g1_walk_rough/mujoco"]) is True
+    assert uses_walk_profile("td3", ["task=g1_walk_flat/mujoco"]) is True
+    assert uses_walk_profile("flashsac", ["task=g1_walk_flat/mujoco"]) is True
 
 
 # ---------------------------------------------------------------------------
@@ -426,7 +295,7 @@ def test_appo_g1_task_overrides():
     assert cfg.algo.save_interval == 100
     assert cfg.training.task_name == "G1WalkFlat"
     assert "obs_profile" not in cfg.env
-    assert cfg.env.curriculum.enabled is False
+    assert "curriculum" not in cfg.env
 
 
 # ---------------------------------------------------------------------------
@@ -473,7 +342,7 @@ def test_ppo_g1_num_envs():
     assert cfg.algo.max_iterations == 2200
     assert cfg.training.task_name == "G1WalkFlat"
     assert "obs_profile" not in cfg.env
-    assert cfg.env.curriculum.enabled is False
+    assert "curriculum" not in cfg.env
 
 
 def test_ppo_go2_num_envs():
@@ -487,7 +356,7 @@ def test_ppo_go2_num_envs():
     assert cfg.algo.max_iterations == 151
 
 
-def test_ppo_go2_footstand_uses_teacher_linvel_task():
+def test_ppo_go2_footstand_uses_hydra_owned_manager_task():
     from hydra import compose, initialize_config_dir
     from hydra.core.global_hydra import GlobalHydra
 
@@ -497,19 +366,26 @@ def test_ppo_go2_footstand_uses_teacher_linvel_task():
 
     assert cfg.training.task_name == "Go2FootStand"
     assert cfg.training.sim_backend == "mujoco"
-    assert cfg.env.add_body_sensors is True
-    assert cfg.env.obs_history_len == 15
-    assert cfg.env.energy_termination_threshold == pytest.approx(200.0)
-    assert cfg.reward.scales.energy == pytest.approx(-0.003)
-    assert cfg.reward.scales.dof_acc == pytest.approx(-2.5e-7)
-    assert cfg.reward.scales.rear_leg_symmetry == pytest.approx(-0.2)
-    assert cfg.reward.scales.knee_clearance == pytest.approx(-0.5)
-    assert cfg.reward.knee_height_target == pytest.approx(0.08)
-    assert cfg.env.domain_rand.randomize_floor_friction is True
-    assert cfg.env.domain_rand.randomize_link_mass is True
-    assert cfg.env.domain_rand.randomize_torso_com is True
-    assert cfg.env.domain_rand.randomize_dof_armature is True
-    assert cfg.env.domain_rand.randomize_reset_joint_qpos is True
+    assert cfg.algo.num_envs == 4096
+    assert cfg.env.sim_dt == pytest.approx(0.004)
+    assert cfg.env.ctrl_dt == pytest.approx(0.02)
+    assert cfg.env.max_episode_seconds == pytest.approx(10.0)
+    assert cfg.env.adaptive_chunk_size is False
+    assert cfg.env.observations.policy.terms.frame.history_length == 15
+    assert cfg.env.observations.critic.terms.frame.history_length == 15
+    assert cfg.env.actions.joint_pos.action_scale == pytest.approx(0.3)
+    assert cfg.env.actions.joint_pos.clip_actions == pytest.approx(1.0)
+    assert cfg.env.terminations.footstand.params.energy_threshold == pytest.approx(200.0)
+    assert cfg.reward.footstand.params.scales.energy == pytest.approx(-0.003)
+    assert cfg.reward.footstand.params.scales.dof_acc == pytest.approx(-2.5e-7)
+    assert cfg.reward.footstand.params.scales.rear_leg_symmetry == pytest.approx(-0.2)
+    assert cfg.reward.footstand.params.scales.knee_clearance == pytest.approx(-0.5)
+    assert cfg.reward.footstand.params.knee_height_target == pytest.approx(0.08)
+    assert cfg.env.events.floor_friction is not None
+    assert cfg.env.events.link_mass is not None
+    assert cfg.env.events.torso_com is not None
+    assert cfg.env.events.joint_armature is not None
+    assert cfg.env.events.reset_joints is not None
 
 
 def test_ppo_g1_motion_tracking():
@@ -535,8 +411,42 @@ def test_ppo_g1_motion_tracking_deploy():
     assert cfg.algo.max_iterations == 15000
     assert cfg.algo.algorithm.entropy_coef == pytest.approx(0.005)
     assert cfg.env.sim_dt == pytest.approx(0.005)
-    assert cfg.env.sensor.gyro == "pelvis_gyro"
-    assert list(cfg.env.control_config.action_scale) == pytest.approx(G1_BEYONDMIMIC_ACTION_SCALE)
+    assert cfg.env.observations.actor.terms.base_ang_vel.params.sensor_name == "pelvis_gyro"
+    assert cfg.env.actions.joint_pos.scale[".*_(hip_pitch|hip_yaw)_joint"] == pytest.approx(
+        G1_BEYONDMIMIC_ACTION_SCALE[0]
+    )
+    assert cfg.env.actions.joint_pos.scale[".*_wrist_(pitch|yaw)_joint"] == pytest.approx(
+        G1_BEYONDMIMIC_ACTION_SCALE[20]
+    )
+
+
+@pytest.mark.parametrize(
+    ("task", "expected"),
+    [
+        ("g1_motion_tracking_deploy", G1_BEYONDMIMIC_ACTION_SCALE),
+        ("g1_23dof_motion_tracking_deploy", G1_23DOF_BEYONDMIMIC_ACTION_SCALE),
+    ],
+)
+def test_ppo_g1_motion_tracking_deploy_action_scale_expands_in_joint_order(
+    task: str,
+    expected: list[float],
+) -> None:
+    from hydra import compose, initialize_config_dir
+    from hydra.core.global_hydra import GlobalHydra
+
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(config_dir=str(CONF_DIR / "ppo"), version_base="1.3"):
+        cfg = compose("config", overrides=[f"task={task}/mujoco"])
+
+    scales = cfg.env.actions.joint_pos.scale
+    resolved: list[float] = []
+    for joint_name in cfg.env.scene.entities.robot.joint_names:
+        matches = [
+            float(value) for pattern, value in scales.items() if re.fullmatch(pattern, joint_name)
+        ]
+        assert len(matches) == 1, f"{joint_name} matched {len(matches)} action-scale patterns"
+        resolved.append(matches[0])
+    assert resolved == pytest.approx(expected)
 
 
 def test_ppo_g1_box_tracking():
@@ -549,10 +459,12 @@ def test_ppo_g1_box_tracking():
     assert cfg.training.task_name == "G1BoxTracking"
     assert cfg.algo.max_iterations == 30000
     assert cfg.algo.algorithm.entropy_coef == pytest.approx(0.005)
-    assert cfg.reward.scales.object_global_ref_position_error_exp == pytest.approx(2.0)
-    assert cfg.reward.scales.object_global_ref_orientation_error_exp == pytest.approx(2.0)
-    assert cfg.reward.std_object_pos == pytest.approx(0.2)
-    assert cfg.reward.std_object_ori == pytest.approx(0.3)
+    assert cfg.env.scene.entities.object.root_body_name == "largebox"
+    assert cfg.env.commands.motion.object_entity_name == "object"
+    assert cfg.reward.object_global_ref_position_error_exp.weight == pytest.approx(2.0)
+    assert cfg.reward.object_global_ref_orientation_error_exp.weight == pytest.approx(2.0)
+    assert cfg.reward.object_global_ref_position_error_exp.params.std == pytest.approx(0.2)
+    assert cfg.reward.object_global_ref_orientation_error_exp.params.std == pytest.approx(0.3)
 
 
 def test_ppo_g1_flip_tracking():
@@ -569,19 +481,20 @@ def test_ppo_g1_flip_tracking():
     assert cfg.algo.obs_groups.critic == ["critic"]
     assert cfg.algo.algorithm.entropy_coef == pytest.approx(0.005)
     assert cfg.algo.algorithm.desired_kl == pytest.approx(0.01)
-    assert cfg.env.sampling_mode == "start"
-    assert cfg.env.truncate_on_clip_end is False
+    assert cfg.env.commands.motion.params.sampling_mode == "start"
+    assert cfg.env.commands.motion.params.truncate_on_clip_end is False
     assert cfg.env.sim_dt == pytest.approx(0.005)
-    assert list(cfg.env.control_config.action_scale) == pytest.approx(G1_BEYONDMIMIC_ACTION_SCALE)
-    assert cfg.env.anchor_pos_z_threshold == pytest.approx(0.5)
-    assert cfg.env.ee_body_pos_z_threshold == pytest.approx(0.5)
-    assert cfg.env.terminate_on_undesired_contacts is True
-    assert cfg.env.noise_config.level == pytest.approx(0.0)
-    assert cfg.reward.scales.motion_body_pos == pytest.approx(2.0)
-    assert cfg.reward.scales.motion_body_ori == pytest.approx(1.5)
-    assert cfg.reward.scales.motion_ee_body_pos_z == pytest.approx(2.0)
-    assert cfg.reward.scales.action_rate_l2 == pytest.approx(-0.005)
-    assert cfg.reward.scales.undesired_contacts == pytest.approx(-0.1)
+    assert cfg.env.actions.joint_pos.scale[".*_(hip_pitch|hip_yaw)_joint"] == pytest.approx(
+        G1_BEYONDMIMIC_ACTION_SCALE[0]
+    )
+    assert cfg.env.terminations.anchor_pos.params.threshold == pytest.approx(0.5)
+    assert cfg.env.terminations.ee_body_pos.params.threshold == pytest.approx(0.5)
+    assert cfg.env.terminations.undesired_contacts is not None
+    assert cfg.reward.motion_body_pos.weight == pytest.approx(2.0)
+    assert cfg.reward.motion_body_ori.weight == pytest.approx(1.5)
+    assert cfg.reward.motion_ee_body_pos_z.weight == pytest.approx(2.0)
+    assert cfg.reward.action_rate_l2.weight == pytest.approx(-0.005)
+    assert cfg.reward.undesired_contacts.weight == pytest.approx(-0.1)
 
 
 def test_ppo_g1_wall_flip_tracking():
@@ -598,21 +511,20 @@ def test_ppo_g1_wall_flip_tracking():
     assert cfg.algo.obs_groups.critic == ["critic"]
     assert cfg.algo.algorithm.entropy_coef == pytest.approx(0.005)
     assert cfg.algo.algorithm.desired_kl == pytest.approx(0.01)
-    assert cfg.env.sampling_mode == "start"
-    assert cfg.env.truncate_on_clip_end is False
+    assert cfg.env.commands.motion.params.sampling_mode == "start"
+    assert cfg.env.commands.motion.params.truncate_on_clip_end is False
     assert cfg.env.sim_dt == pytest.approx(0.005)
-    assert list(cfg.env.control_config.action_scale) == pytest.approx(G1_BEYONDMIMIC_ACTION_SCALE)
-    assert cfg.env.anchor_pos_z_threshold == pytest.approx(0.5)
-    assert cfg.env.ee_body_pos_z_threshold == pytest.approx(0.5)
-    assert cfg.env.terminate_on_undesired_contacts is True
-    assert cfg.env.noise_config.level == pytest.approx(0.0)
-    assert cfg.reward.scales.motion_joint_pos == pytest.approx(0.5)
-    assert cfg.reward.scales.motion_joint_vel == pytest.approx(0.25)
-    assert cfg.reward.scales.motion_body_pos == pytest.approx(2.0)
-    assert cfg.reward.scales.motion_body_ori == pytest.approx(1.5)
-    assert cfg.reward.scales.motion_ee_body_pos_z == pytest.approx(2.0)
-    assert cfg.reward.scales.action_rate_l2 == pytest.approx(-0.005)
-    assert cfg.reward.scales.undesired_contacts == pytest.approx(-0.1)
+    assert cfg.env.actions.joint_pos.scale[".*_(hip_pitch|hip_yaw)_joint"] == pytest.approx(
+        G1_BEYONDMIMIC_ACTION_SCALE[0]
+    )
+    assert cfg.env.scene.model_file.endswith("scene_flat_with_wall.xml")
+    assert cfg.reward.motion_joint_pos.weight == pytest.approx(0.5)
+    assert cfg.reward.motion_joint_vel.weight == pytest.approx(0.25)
+    assert cfg.reward.motion_body_pos.weight == pytest.approx(2.0)
+    assert cfg.reward.motion_body_ori.weight == pytest.approx(1.5)
+    assert cfg.reward.motion_ee_body_pos_z.weight == pytest.approx(2.0)
+    assert cfg.reward.action_rate_l2.weight == pytest.approx(-0.005)
+    assert cfg.reward.undesired_contacts.weight == pytest.approx(-0.1)
 
 
 def test_ppo_x2_wall_flip_tracking():
@@ -632,105 +544,17 @@ def test_ppo_x2_wall_flip_tracking():
     assert cfg.algo.algorithm.desired_kl == pytest.approx(0.01)
     # Interactive playback defaults to policy mode for this task.
     assert cfg.interactive.action_mode == "policy"
-    assert cfg.env.sampling_mode == "start"
-    assert cfg.env.truncate_on_clip_end is False
+    assert cfg.env.commands.motion.params.sampling_mode == "start"
+    assert cfg.env.commands.motion.params.truncate_on_clip_end is False
     assert cfg.env.sim_dt == pytest.approx(0.005)
-    assert list(cfg.env.control_config.action_scale) == pytest.approx(X2_ACTION_SCALE)
-    assert cfg.env.anchor_pos_z_threshold == pytest.approx(0.5)
-    assert cfg.env.ee_body_pos_z_threshold == pytest.approx(0.5)
-    assert cfg.env.terminate_on_undesired_contacts is True
-    assert cfg.env.noise_config.level == pytest.approx(0.0)
-    assert cfg.reward.scales.motion_joint_pos == pytest.approx(0.5)
-    assert cfg.reward.scales.motion_joint_vel == pytest.approx(0.25)
-    assert cfg.reward.scales.motion_body_pos == pytest.approx(2.0)
-    assert cfg.reward.scales.motion_body_ori == pytest.approx(1.5)
-    assert cfg.reward.scales.motion_ee_body_pos_z == pytest.approx(2.0)
-    assert cfg.reward.scales.action_rate_l2 == pytest.approx(-0.005)
-    assert cfg.reward.scales.undesired_contacts == pytest.approx(-0.1)
-
-
-# ---------------------------------------------------------------------------
-# Issue #197 DoD: rough terrain profile params overridable via Hydra
-# ---------------------------------------------------------------------------
-
-
-def test_apply_cfg_overrides_deep_merges_dataclass_field():
-    """registry.apply_cfg_overrides must deep-merge into existing dataclass
-    instances rather than re-instantiating them, so partial overrides like
-    `scene.terrain.generator.num_rows=4` keep `sub_terrains` and other defaults."""
-    from unilab.base.registry import apply_cfg_overrides
-    from unilab.envs.locomotion.go2.rough import Go2JoystickRoughCfg
-
-    cfg = Go2JoystickRoughCfg()
-    cfg.scene.terrain.generator.num_cols = 3
-    cfg.scene.terrain.generator.border_width = 2.5
-    cfg.scene.terrain.generator.sub_terrains = {
-        "test_flat": cfg.scene.terrain.generator.sub_terrains["flat"]
-    }
-    cfg.scene.terrain.generator.add_lights = False
-    apply_cfg_overrides(
-        cfg,
-        {"scene": {"terrain": {"generator": {"num_rows": 4, "seed": 42, "curriculum": True}}}},
-    )
-
-    # Overridden fields take effect.
-    assert cfg.scene.terrain.generator.num_rows == 4
-    assert cfg.scene.terrain.generator.seed == 42
-    assert cfg.scene.terrain.generator.curriculum is True
-    # Non-overridden fields preserve the pre-existing instance state.
-    assert cfg.scene.terrain.generator.num_cols == 3
-    assert cfg.scene.terrain.generator.border_width == pytest.approx(2.5)
-    assert list(cfg.scene.terrain.generator.sub_terrains) == ["test_flat"]
-    assert cfg.scene.terrain.generator.add_lights is False
-
-
-def test_ppo_go2_joystick_rough_hydra_terrain_override():
-    """Issue #197 DoD: rough terrain profile parameters must be overridable
-    via Hydra command-line. Composes the resolved config and feeds it through
-    the same BackendAdapter -> registry.apply_cfg_overrides path the trainer
-    uses."""
-    from hydra import compose, initialize_config_dir
-    from hydra.core.global_hydra import GlobalHydra
-
-    from unilab.base.registry import apply_cfg_overrides
-    from unilab.envs.locomotion.go2.rough import Go2JoystickRoughCfg
-    from unilab.training.backend_adapter import BackendAdapter
-
-    GlobalHydra.instance().clear()
-    with initialize_config_dir(config_dir=str(CONF_DIR / "ppo"), version_base="1.3"):
-        cfg = compose(
-            "config",
-            overrides=[
-                "task=go2_joystick_rough/mujoco",
-                "env.scene.terrain.generator.num_rows=4",
-                "env.scene.terrain.generator.num_cols=6",
-                "env.scene.terrain.generator.seed=42",
-                "env.scene.terrain.generator.curriculum=true",
-            ],
-        )
-
-    # Yaml exposes the overridable schema (struct-mode acceptance).
-    assert cfg.env.scene.terrain.hfield_name == "terrain_hfield"
-    assert cfg.env.scene.terrain.geom_name == "floor"
-    assert cfg.env.scene.terrain.generator.num_rows == 4
-    assert cfg.env.scene.terrain.generator.num_cols == 6
-    assert cfg.env.scene.terrain.generator.seed == 42
-    assert cfg.env.scene.terrain.generator.curriculum is True
-
-    # End-to-end: the override dict produced by the adapter must, after the
-    # registry's deep-merge, leave Go2JoystickRoughCfg in a coherent state —
-    # overridden fields applied, untouched dataclass defaults preserved.
-    adapter = BackendAdapter(cfg, root_dir=Path.cwd())
-    env_cfg_override = adapter.build_task_env_cfg_override()
-    assert "scene" in env_cfg_override
-    assert env_cfg_override["scene"]["terrain"]["generator"]["num_rows"] == 4
-
-    env_cfg = Go2JoystickRoughCfg()
-    apply_cfg_overrides(env_cfg, env_cfg_override)
-
-    assert env_cfg.scene.terrain.generator.num_rows == 4
-    assert env_cfg.scene.terrain.generator.num_cols == 6
-    assert env_cfg.scene.terrain.generator.seed == 42
-    assert env_cfg.scene.terrain.generator.curriculum is True
-    # sub_terrains is not in the yaml schema, so its Python default survives.
-    assert len(env_cfg.scene.terrain.generator.sub_terrains) == 7
+    assert cfg.env.actions.joint_pos.scale == pytest.approx(X2_ACTION_SCALE[0])
+    assert cfg.env.terminations.anchor_pos.params.threshold == pytest.approx(0.5)
+    assert cfg.env.terminations.ee_body_pos.params.threshold == pytest.approx(0.5)
+    assert cfg.env.terminations.undesired_contacts is not None
+    assert cfg.reward.motion_joint_pos.weight == pytest.approx(0.5)
+    assert cfg.reward.motion_joint_vel.weight == pytest.approx(0.25)
+    assert cfg.reward.motion_body_pos.weight == pytest.approx(2.0)
+    assert cfg.reward.motion_body_ori.weight == pytest.approx(1.5)
+    assert cfg.reward.motion_ee_body_pos_z.weight == pytest.approx(2.0)
+    assert cfg.reward.action_rate_l2.weight == pytest.approx(-0.005)
+    assert cfg.reward.undesired_contacts.weight == pytest.approx(-0.1)
diff --git a/tests/config/test_manager_reward_routing.py b/tests/config/test_manager_reward_routing.py
new file mode 100644
index 000000000..f7c64ec92
--- /dev/null
+++ b/tests/config/test_manager_reward_routing.py
@@ -0,0 +1,148 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from pathlib import Path
+
+import numpy as np
+import pytest
+from omegaconf import OmegaConf
+
+from unilab.base import registry
+from unilab.base.base import EnvCfg
+from unilab.base.config_adapter import BackendAdapter
+from unilab.base.config_overrides import (
+    CONFIG_MAPPING_POLICY_KEY,
+    MANAGER_TERM_MAPPING_POLICY,
+)
+from unilab.base.registry import apply_cfg_overrides
+from unilab.envs.manager_based_rl_env import ManagerBasedRlEnvCfg
+from unilab.managers import RewardTermCfg
+
+_MANAGER_ENV = "_TestManagerRewardRoute"
+_MANAGER_FACTORY_ENV = "_TestManagerFactoryRewardRoute"
+_LEGACY_ENV = "_TestLegacyRewardRoute"
+_MISSING_ENV = "_TestMissingRewardRoute"
+_AMBIGUOUS_ENV = "_TestAmbiguousRewardRoute"
+
+
+@dataclass
+class _LegacyCfg(EnvCfg):
+    reward_config: dict[str, object] = field(default_factory=dict)
+
+
+@dataclass
+class _MissingCfg(EnvCfg):
+    pass
+
+
+@dataclass
+class _AmbiguousCfg(EnvCfg):
+    reward_config: dict[str, object] = field(default_factory=dict)
+    rewards: dict[str, object] = field(
+        default_factory=dict,
+        metadata={CONFIG_MAPPING_POLICY_KEY: MANAGER_TERM_MAPPING_POLICY},
+    )
+
+
+def _make_manager_cfg() -> ManagerBasedRlEnvCfg:
+    return ManagerBasedRlEnvCfg()
+
+
+for _name, _cfg_factory in (
+    (_MANAGER_ENV, ManagerBasedRlEnvCfg),
+    (_MANAGER_FACTORY_ENV, _make_manager_cfg),
+    (_LEGACY_ENV, _LegacyCfg),
+    (_MISSING_ENV, _MissingCfg),
+    (_AMBIGUOUS_ENV, _AmbiguousCfg),
+):
+    if not registry.contains(_name):
+        registry.register_env_config(_name, _cfg_factory)
+
+
+def _reward(_env, *, std: float) -> np.ndarray:
+    del std
+    return np.zeros(1, dtype=np.float32)
+
+
+def _alive(_env) -> np.ndarray:
+    return np.ones(1, dtype=np.float32)
+
+
+def _cfg(task_name: str, *, env: dict[str, object] | None = None):
+    return OmegaConf.create(
+        {
+            "training": {"task_name": task_name},
+            "reward": {
+                "tracking": {
+                    "weight": 2.0,
+                    "params": {"std": 0.25},
+                }
+            },
+            "env": env or {"ctrl_dt": 0.02},
+        }
+    )
+
+
+@pytest.mark.parametrize("env_name", [_MANAGER_ENV, _MANAGER_FACTORY_ENV])
+def test_backend_adapter_routes_manager_reward_and_preserves_factory_terms(
+    env_name: str,
+) -> None:
+    override = BackendAdapter(
+        _cfg(env_name),
+        root_dir=Path("."),
+    ).build_task_env_cfg_override()
+
+    assert "reward_config" not in override
+    assert override["rewards"]["tracking"]["weight"] == pytest.approx(2.0)
+    assert override["ctrl_dt"] == pytest.approx(0.02)
+
+    manager_cfg = ManagerBasedRlEnvCfg(
+        rewards={
+            "tracking": RewardTermCfg(func=_reward, weight=1.0, params={"std": 0.5}),
+            "alive": RewardTermCfg(func=_alive, weight=0.1),
+        }
+    )
+    factory_tracking = manager_cfg.rewards["tracking"]
+    assert factory_tracking is not None
+    tracking_func = factory_tracking.func
+
+    apply_cfg_overrides(manager_cfg, override)
+
+    tracking = manager_cfg.rewards["tracking"]
+    assert tracking is not None
+    assert tracking.func is tracking_func
+    assert tracking.weight == pytest.approx(2.0)
+    assert tracking.params == {"std": 0.25}
+    assert manager_cfg.rewards["alive"] is not None
+    assert list(manager_cfg.rewards) == ["tracking", "alive"]
+
+
+def test_backend_adapter_preserves_legacy_reward_target() -> None:
+    override = BackendAdapter(
+        _cfg(_LEGACY_ENV),
+        root_dir=Path("."),
+    ).build_task_env_cfg_override()
+
+    assert "rewards" not in override
+    assert override["reward_config"]["tracking"]["weight"] == pytest.approx(2.0)
+    assert override["ctrl_dt"] == pytest.approx(0.02)
+
+
+def test_backend_adapter_rejects_duplicate_manager_reward_sources() -> None:
+    cfg = _cfg(_MANAGER_ENV, env={"rewards": {"tracking": {"weight": 3.0}}})
+
+    with pytest.raises(ValueError, match="root 'reward'.*env.rewards"):
+        BackendAdapter(cfg, root_dir=Path(".")).build_task_env_cfg_override()
+
+
+@pytest.mark.parametrize(
+    ("env_name", "match"),
+    [
+        ("_UnregisteredRewardRoute", "not registered"),
+        (_MISSING_ENV, "declares no supported.*reward target"),
+        (_AMBIGUOUS_ENV, "declares both.*rewards.*reward_config"),
+    ],
+)
+def test_reward_target_resolution_fails_closed(env_name: str, match: str) -> None:
+    with pytest.raises(ValueError, match=match):
+        registry.resolve_reward_override_field(env_name)
diff --git a/tests/config/test_reward_injection.py b/tests/config/test_reward_injection.py
index 397093136..47f59c5e9 100644
--- a/tests/config/test_reward_injection.py
+++ b/tests/config/test_reward_injection.py
@@ -1,36 +1,33 @@
 """Test reward config injection system."""
 
-from typing import Any, cast
-
-import pytest
 from hydra import compose, initialize
 from omegaconf import OmegaConf
 
 
 def test_reward_config_loading_g1():
     """Test G1 SAC reward config loads correctly."""
-    with initialize(config_path="../../conf/offpolicy", version_base="1.3"):
-        cfg = compose(config_name="config", overrides=["task=sac/g1_walk_flat/mujoco"])
+    with initialize(config_path="../../src/unilab/conf/sac", version_base="1.3"):
+        cfg = compose(config_name="config", overrides=["task=g1_walk_flat/mujoco"])
         assert hasattr(cfg, "reward")
-        assert cfg.reward.scales.tracking_lin_vel == 2.0
-        assert cfg.reward.scales.alive == 10.0
-        assert cfg.reward.base_height_target == 0.754
+        assert cfg.reward.tracking_lin_vel.weight == 2.0
+        assert cfg.reward.alive.weight == 10.0
+        assert cfg.reward.feet_phase.params.swing_height == 0.09
 
 
 def test_reward_config_loading_g1_motrix():
     """Test G1 Motrix reward config loads correctly."""
-    with initialize(config_path="../../conf/offpolicy", version_base="1.3"):
-        cfg = compose(config_name="config", overrides=["task=sac/g1_walk_flat/motrix"])
+    with initialize(config_path="../../src/unilab/conf/sac", version_base="1.3"):
+        cfg = compose(config_name="config", overrides=["task=g1_walk_flat/motrix"])
         assert hasattr(cfg, "reward")
-        assert cfg.reward.scales.tracking_lin_vel == 2.2
-        assert cfg.reward.scales.alive == 12.0
+        assert cfg.reward.tracking_lin_vel.weight == 2.2
+        assert cfg.reward.alive.weight == 12.0
 
 
 def test_resolve_reward_dict_reads_task_reward():
     """Task-backend configs should expose the final reward mapping directly."""
-    from unilab.training.reward import resolve_reward_dict
+    from unilab.utils.reward import resolve_reward_dict
 
-    with initialize(config_path="../../conf/ppo", version_base="1.3"):
+    with initialize(config_path="../../src/unilab/conf/ppo", version_base="1.3"):
         cfg = compose(
             config_name="config",
             overrides=["task=go2_joystick_flat/motrix"],
@@ -38,58 +35,45 @@ def test_resolve_reward_dict_reads_task_reward():
 
     reward_dict = resolve_reward_dict(cfg)
 
-    assert reward_dict["scales"]["tracking_lin_vel"] == 1.0
-    assert reward_dict["scales"]["tracking_ang_vel"] == 0.2
+    assert reward_dict["tracking_lin_vel"]["weight"] == 1.0
+    assert reward_dict["tracking_ang_vel"]["weight"] == 0.2
+    assert reward_dict["tracking_lin_vel"]["func"].endswith("track_lin_vel_xy_exp")
 
 
 def test_reward_config_conversion():
-    """Test reward config converts to dataclasses via registry."""
+    """Test reward config materializes into manager reward terms via registry."""
     from unilab.base import registry
+    from unilab.base.config_materialization import apply_cfg_overrides
     from unilab.base.registry import ensure_registries
+    from unilab.envs import ManagerBasedRlEnvCfg
 
     ensure_registries()
 
-    # Test G1 walk config - registry auto-converts dict to G1WalkRewardConfig
-    g1_dict = {
-        "scales": {"tracking_lin_vel": 2.0, "alive": 10.0},
-        "tracking_sigma": 0.25,
-        "base_height_target": 0.754,
-        "gait_frequency": 1.5,
-        "feet_phase_swing_height": 0.09,
-        "feet_phase_tracking_sigma": 0.008,
-        "min_base_height": 0.3,
-        "max_tilt_deg": 65.0,
-        "close_feet_threshold": 0.15,
-        "pose_weights": [0.01] * 29,
-    }
-    env = cast(
-        Any,
-        registry.make(
-            "G1WalkFlat",
-            num_envs=1,
-            sim_backend="mujoco",
-            env_cfg_override={"reward_config": g1_dict},
-        ),
-    )
-    assert hasattr(env._cfg.reward_config, "scales")
-    assert env._cfg.reward_config.scales["tracking_lin_vel"] == 2.0
-    env.close()
-
-    # Test Go1 config - registry auto-converts dict to RewardConfig
-    go1_dict = {
-        "scales": {"tracking_lin_vel": 1.0, "base_height": -100.0},
-        "tracking_sigma": 0.25,
-        "base_height_target": 0.3,
-    }
-    env = cast(
-        Any,
-        registry.make(
-            "Go1JoystickFlat",
-            num_envs=1,
-            sim_backend="mujoco",
-            env_cfg_override={"reward_config": go1_dict},
-        ),
+    env_cfg = registry.materialize_env_config("G1WalkFlat")
+    assert isinstance(env_cfg, ManagerBasedRlEnvCfg)
+    apply_cfg_overrides(
+        env_cfg,
+        {
+            "rewards": {
+                "tracking_lin_vel": {
+                    "_target_": "unilab.managers.RewardTermCfg",
+                    "func": "unilab.tasks.locomotion.common.sensor_reward_terms.track_lin_vel",
+                    "weight": 2.0,
+                    "params": {
+                        "tracking_sigma": 0.25,
+                        "command_name": "twist",
+                        "sensor_name": "pelvis_local_linvel",
+                    },
+                },
+                "alive": {
+                    "_target_": "unilab.managers.RewardTermCfg",
+                    "func": "unilab.tasks.locomotion.common.manager_terms.alive",
+                    "weight": 10.0,
+                },
+            }
+        },
     )
-    assert hasattr(env._cfg.reward_config, "scales")
-    assert env._cfg.reward_config.scales["tracking_lin_vel"] == 1.0
-    env.close()
+    assert env_cfg.rewards["tracking_lin_vel"].weight == 2.0
+    assert env_cfg.rewards["tracking_lin_vel"].params["tracking_sigma"] == 0.25
+    assert env_cfg.rewards["alive"].weight == 10.0
+    assert OmegaConf.is_config(env_cfg.rewards["tracking_lin_vel"].func) is False
diff --git a/tests/conftest.py b/tests/conftest.py
index 5537ab397..c242ad33f 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -26,6 +26,7 @@
 
 import pytest
 import torch
+from uni_rl.ipc.rollout_ring_buffer import RolloutRingBuffer
 
 # ---------------------------------------------------------------------------
 # Dummy flat env — no MuJoCo required
@@ -38,7 +39,6 @@
 from tests._test_registry.dummy_flat_env import (  # noqa: E402  (side-effect import)
     DUMMY_ENV_NAME as _DUMMY_ENV_NAME,
 )
-from unilab.ipc.rollout_ring_buffer import RolloutRingBuffer
 
 _DUMMY_OBS_DIM = 8
 _DUMMY_ACT_DIM = 3
@@ -124,181 +124,3 @@ def tiny_weight_shapes():
 @pytest.fixture
 def mock_env_name() -> str:
     return _DUMMY_ENV_NAME
-
-
-@pytest.fixture
-def default_go1_reward_config():
-    """Default reward config for Go1 testing."""
-    return {
-        "scales": {
-            "tracking_lin_vel": 1.0,
-            "tracking_ang_vel": 0.2,
-            "lin_vel_z": -5.0,
-            "ang_vel_xy": -0.1,
-            "base_height": -100.0,
-            "action_rate": -0.005,
-            "similar_to_default": -0.1,
-            "contact": 0.24,
-        },
-        "tracking_sigma": 0.25,
-        "base_height_target": 0.3,
-    }
-
-
-@pytest.fixture
-def default_go2_reward_config():
-    """Default reward config for Go2 testing."""
-    return {
-        "scales": {
-            "tracking_lin_vel": 1.0,
-            "tracking_ang_vel": 0.2,
-            "lin_vel_z": -5.0,
-            "ang_vel_xy": -0.1,
-            "base_height": -100.0,
-            "action_rate": -0.005,
-            "similar_to_default": -0.1,
-            "contact": 0.24,
-            "swing_feet_z": 4.0,
-        },
-        "tracking_sigma": 0.25,
-        "base_height_target": 0.3,
-    }
-
-
-@pytest.fixture
-def default_g1_reward_config():
-    """Default reward config for G1 testing."""
-    return {
-        "scales": {
-            "tracking_lin_vel": 2.0,
-            "tracking_ang_vel": 0.25,
-            "forward_progress": 0.0,
-            "under_speed": -0.2,
-            "upper_body_pose": -0.05,
-            "penalty_feet_ori": 0.0,
-            "feet_phase": 1.0,
-            "feet_phase_contrast": 1.0,
-            "feet_phase_contact": 0.5,
-            "feet_double_stance": -0.5,
-            "lin_vel_z": -1.0,
-            "ang_vel_xy": -0.2,
-            "base_height": -120.0,
-            "orientation": -2.5,
-            "action_rate": -0.005,
-            "pose": -0.05,
-        },
-        "tracking_sigma": 0.25,
-        "gait_frequency": 1.5,
-        "feet_phase_swing_height": 0.09,
-        "feet_phase_tracking_sigma": 0.008,
-        "base_height_target": 0.765,
-        "min_forward_speed_for_gait_reward": 0.05,
-        "min_base_height": 0.5,
-        "max_tilt_deg": 35.0,
-        "pose_weights": [
-            0.01,
-            1.0,
-            5.0,
-            0.01,
-            5.0,
-            5.0,
-            0.01,
-            1.0,
-            5.0,
-            0.01,
-            5.0,
-            5.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-        ],
-    }
-
-
-@pytest.fixture
-def default_allegro_reward_config():
-    """Default reward config for AllegroInhandRotation testing."""
-    return {
-        "scales": {
-            "rotate": 1.25,
-            "obj_linvel": -0.3,
-            "pose_diff": -0.3,
-            "torque": -0.1,
-            "work": -2.0,
-            "drop": 0.0,
-        },
-        "angvel_clip_min": -0.5,
-        "angvel_clip_max": 0.5,
-        "reset_z_threshold": 0.125,
-    }
-
-
-@pytest.fixture
-def default_g1_walk_flat_reward_config():
-    """Default reward config for G1 SAC testing."""
-    return {
-        "scales": {
-            "tracking_lin_vel": 2.0,
-            "tracking_ang_vel": 1.5,
-            "penalty_ang_vel_xy": -1.0,
-            "penalty_orientation": -10.0,
-            "penalty_action_rate": -2.0,
-            "pose": -0.5,
-            "penalty_feet_ori": -25.0,
-            "feet_phase": 5.0,
-            "alive": 10.0,
-        },
-        "tracking_sigma": 0.25,
-        "base_height_target": 0.754,
-        "min_base_height": 0.3,
-        "max_tilt_deg": 65.0,
-        "gait_frequency": 1.5,
-        "feet_phase_swing_height": 0.09,
-        "feet_phase_tracking_sigma": 0.008,
-        "close_feet_threshold": 0.15,
-        "pose_weights": [
-            0.01,
-            1.0,
-            5.0,
-            0.01,
-            5.0,
-            5.0,
-            0.01,
-            1.0,
-            5.0,
-            0.01,
-            5.0,
-            5.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-            50.0,
-        ],
-    }
diff --git a/tests/dr/test_manager.py b/tests/dr/test_manager.py
index c8c4eac12..f35fba927 100644
--- a/tests/dr/test_manager.py
+++ b/tests/dr/test_manager.py
@@ -7,6 +7,14 @@
 
 import numpy as np
 import pytest
+from unisim.dr.types import (
+    RESET_TERM_BASE_MASS,
+    RESET_TERM_BODY_MASS,
+    RESET_TERM_DOF_ARMATURE,
+    RESET_TERM_GEOM_FRICTION,
+    RESET_TERM_GRAVITY,
+    RESET_TERM_KP,
+)
 
 from unilab.dr import (
     DomainRandomizationCapabilities,
@@ -16,14 +24,6 @@
     ResetRandomizationPayload,
 )
 from unilab.dr.dr_utils import build_common_reset_randomization
-from unilab.dr.types import (
-    RESET_TERM_BASE_MASS,
-    RESET_TERM_BODY_MASS,
-    RESET_TERM_DOF_ARMATURE,
-    RESET_TERM_GEOM_FRICTION,
-    RESET_TERM_GRAVITY,
-    RESET_TERM_KP,
-)
 
 
 def test_capabilities_filter_reset_payload_drops_unsupported_terms():
diff --git a/tests/envs/locomotion/a2/test_a2_joystick_contract.py b/tests/envs/locomotion/a2/test_a2_joystick_contract.py
index 14fcc656f..c23d33e6f 100644
--- a/tests/envs/locomotion/a2/test_a2_joystick_contract.py
+++ b/tests/envs/locomotion/a2/test_a2_joystick_contract.py
@@ -1,87 +1,125 @@
-"""Contract tests for the A2JoystickFlat environment (leg-only Unitree A2).
-
-The A2 leg-only MJCF mirrors the Go2 joystick sensor/geom/leg-ordering
-contract (legs FL,FR,RL,RR; foot geoms+sites FL/FR/RL/RR; Go2-named IMU/foot
-sensors) and uses <position> actuators, so the env reuses Go2WalkTask
-unchanged. These tests prove the A2 model + scene + config + env chain
-constructs and steps in MuJoCo as a 12-DOF joystick task."""
+"""Hydra-owned Manager-Based production contract for A2JoystickFlat."""
 
 from __future__ import annotations
 
 import importlib
-from types import SimpleNamespace
+from collections.abc import Mapping, Sequence
+from dataclasses import fields, is_dataclass
+from pathlib import Path
+from typing import Any
 
 import numpy as np
 import pytest
+from hydra import compose, initialize_config_dir
+from hydra.core.global_hydra import GlobalHydra
+from omegaconf import DictConfig, OmegaConf
 
 from unilab.assets import ASSETS_ROOT_PATH
-
-# mjlab INIT_STATE: pos z=0.4, thigh=0.9 (all), calf=-1.8 (all), R_hip=+0.1, L_hip=-0.1.
-# Asset/actuator order is FL,FR,RL,RR x (hip,thigh,calf).
-_MJLAB_HOME_HEIGHT = 0.4
-_MJLAB_LEG_ANGLES = [
-    -0.1, 0.9, -1.8,  # FL
-    0.1, 0.9, -1.8,   # FR
-    -0.1, 0.9, -1.8,  # RL
-    0.1, 0.9, -1.8,   # RR
-]  # fmt: skip
-# mjlab per-joint PD gains: hip/thigh kp=100/kd=4, calf kp=150/kd=6.
-_MJLAB_KP = [100.0, 100.0, 150.0] * 4
-_MJLAB_KD = [4.0, 4.0, 6.0] * 4
-# DR ranges referencing mjlab events: joint_armature scale [0.9,1.1], foot
-# friction [0.3,1.6] (UniLab realises it as a multiplier on the floor geom,
-# which is made the priority geom so it dictates the foot-ground friction).
-_MJLAB_ARMATURE_RANGE = [0.9, 1.1]
-_MJLAB_FRICTION_RANGE = [0.3, 1.6]
-
-
-def _skip_if_no_mujoco():
-    pytest.importorskip("mujoco", reason="mujoco not installed")
-    try:
-        from mujoco_uni.batch_env import BatchEnvPool  # noqa: F401
-    except Exception:
-        pytest.skip("mujoco_uni.batch_env not available")
-
-
-def test_a2_robot_xml_compiles_with_12_position_actuators():
-    """a2.xml loads standalone and exposes exactly 12 position-style leg
-    actuators in the FL,FR,RL,RR x hip,thigh,calf order."""
+from unilab.base import registry
+from unilab.base.config_adapter import BackendAdapter
+from unilab.base.config_materialization import apply_cfg_overrides
+from unilab.envs import ManagerBasedRlEnv, ManagerBasedRlEnvCfg, mdp
+from unilab.tasks.locomotion.common import manager_terms
+
+ROOT_DIR = Path(__file__).parents[4]
+CONF_DIR = ROOT_DIR / "src" / "unilab" / "conf"
+
+_JOINT_NAMES = (
+    "FL_hip_joint",
+    "FL_thigh_joint",
+    "FL_calf_joint",
+    "FR_hip_joint",
+    "FR_thigh_joint",
+    "FR_calf_joint",
+    "RL_hip_joint",
+    "RL_thigh_joint",
+    "RL_calf_joint",
+    "RR_hip_joint",
+    "RR_thigh_joint",
+    "RR_calf_joint",
+)
+_ACTUATOR_NAMES = (
+    "FL_hip",
+    "FL_thigh",
+    "FL_calf",
+    "FR_hip",
+    "FR_thigh",
+    "FR_calf",
+    "RL_hip",
+    "RL_thigh",
+    "RL_calf",
+    "RR_hip",
+    "RR_thigh",
+    "RR_calf",
+)
+_HOME_JOINT_POS = np.array(
+    [-0.1, 0.9, -1.8, 0.1, 0.9, -1.8, -0.1, 0.9, -1.8, 0.1, 0.9, -1.8],
+    dtype=np.float32,
+)
+_KP = np.array([100.0, 100.0, 150.0] * 4)
+_KD = np.array([4.0, 4.0, 6.0] * 4)
+
+
+def _compose() -> DictConfig:
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(config_dir=str(CONF_DIR / "ppo"), version_base="1.3"):
+        return compose("config", overrides=["task=a2_joystick_flat/mujoco"])
+
+
+def _materialize() -> tuple[DictConfig, ManagerBasedRlEnvCfg, dict[str, Any]]:
+    hydra_cfg = _compose()
+    env_override = BackendAdapter(hydra_cfg, root_dir=ROOT_DIR).build_task_env_cfg_override()
+    env_cfg = registry.materialize_env_config("A2JoystickFlat")
+    assert isinstance(env_cfg, ManagerBasedRlEnvCfg)
+    apply_cfg_overrides(env_cfg, env_override)
+    env_cfg.validate()
+    return hydra_cfg, env_cfg, env_override
+
+
+def _assert_no_omegaconf(value: Any) -> None:
+    assert not OmegaConf.is_config(value)
+    if is_dataclass(value) and not isinstance(value, type):
+        for item in fields(value):
+            _assert_no_omegaconf(getattr(value, item.name))
+    elif isinstance(value, Mapping):
+        for key, item in value.items():
+            _assert_no_omegaconf(key)
+            _assert_no_omegaconf(item)
+    elif isinstance(value, Sequence) and not isinstance(value, (str, bytes)):
+        for item in value:
+            _assert_no_omegaconf(item)
+
+
+def test_a2_asset_declares_home_pose_and_per_joint_pd_defaults() -> None:
     mujoco = pytest.importorskip("mujoco")
-    xml = ASSETS_ROOT_PATH / "robots" / "a2" / "a2.xml"
-    model = mujoco.MjModel.from_xml_path(str(xml))
-    assert model.nu == 12
-    names = [mujoco.mj_id2name(model, mujoco.mjtObj.mjOBJ_ACTUATOR, i) for i in range(model.nu)]
-    assert names == [
-        "FL_hip",
-        "FL_thigh",
-        "FL_calf",
-        "FR_hip",
-        "FR_thigh",
-        "FR_calf",
-        "RL_hip",
-        "RL_thigh",
-        "RL_calf",
-        "RR_hip",
-        "RR_thigh",
-        "RR_calf",
-    ]
-    # Position actuators carry an affine bias (kp in gainprm[0]); motor actuators do not.
+    model = mujoco.MjModel.from_xml_path(str(ASSETS_ROOT_PATH / "robots" / "a2" / "scene_flat.xml"))
+
+    actuator_names = tuple(
+        mujoco.mj_id2name(model, mujoco.mjtObj.mjOBJ_ACTUATOR, index) for index in range(model.nu)
+    )
+    assert actuator_names == _ACTUATOR_NAMES
     affine = int(mujoco.mjtBias.mjBIAS_AFFINE)
-    assert all(int(model.actuator_biastype[i]) == affine for i in range(model.nu))
+    assert all(int(value) == affine for value in model.actuator_biastype)
+    np.testing.assert_allclose(model.actuator_gainprm[:, 0], _KP)
+    np.testing.assert_allclose(model.actuator_biasprm[:, 1], -_KP)
+    np.testing.assert_allclose(model.actuator_biasprm[:, 2], -_KD)
 
+    key_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_KEY, "home")
+    assert key_id >= 0
+    assert model.nq == 19
+    assert model.key_qpos[key_id, 2] == pytest.approx(0.4)
+    np.testing.assert_allclose(model.key_qpos[key_id, 7:19], _HOME_JOINT_POS)
+    np.testing.assert_allclose(model.key_ctrl[key_id], _HOME_JOINT_POS)
 
-def test_a2_scene_loads_with_foot_contacts_and_home_keyframe():
-    """scene_flat.xml includes a2.xml + floor, exposes the four foot-contact
-    sensors and the joystick foot-pos/IMU sensors, and a home keyframe whose
-    qpos is base(7)+12 leg = 19."""
-    mujoco = pytest.importorskip("mujoco")
-    xml = ASSETS_ROOT_PATH / "robots" / "a2" / "scene_flat.xml"
-    model = mujoco.MjModel.from_xml_path(str(xml))
 
-    sensor_names = {
-        mujoco.mj_id2name(model, mujoco.mjtObj.mjOBJ_SENSOR, i) for i in range(model.nsensor)
+def test_a2_asset_exposes_manager_sensor_and_floor_friction_contract() -> None:
+    mujoco = pytest.importorskip("mujoco")
+    model = mujoco.MjModel.from_xml_path(str(ASSETS_ROOT_PATH / "robots" / "a2" / "scene_flat.xml"))
+    sensors = {
+        mujoco.mj_id2name(model, mujoco.mjtObj.mjOBJ_SENSOR, index)
+        for index in range(model.nsensor)
     }
-    for required in [
+    assert {
         "gyro",
         "local_linvel",
         "upvector",
@@ -93,411 +131,212 @@ def test_a2_scene_loads_with_foot_contacts_and_home_keyframe():
         "FR_foot_contact",
         "RL_foot_contact",
         "RR_foot_contact",
-    ]:
-        assert required in sensor_names, f"missing sensor {required}"
-
-    # home keyframe present, qpos length = 7 (free base) + 12 (legs).
-    assert model.nkey >= 1
-    key_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_KEY, "home")
-    assert key_id >= 0
-    assert model.nq == 19
-    # foot geoms used by the contact sensors exist.
-    for g in ["FL", "FR", "RL", "RR", "floor"]:
-        assert mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_GEOM, g) >= 0
+    } <= sensors
 
-
-def test_a2_home_keyframe_matches_mjlab_pose():
-    """The home keyframe is aligned to mjlab's INIT_STATE: base height 0.4,
-    thigh 0.9, calf -1.8 on all legs, hips +-0.1 (R/L)."""
-    mujoco = pytest.importorskip("mujoco")
-    xml = ASSETS_ROOT_PATH / "robots" / "a2" / "scene_flat.xml"
-    model = mujoco.MjModel.from_xml_path(str(xml))
-    key_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_KEY, "home")
-    assert key_id >= 0
-    qpos = np.asarray(model.key_qpos[key_id])
-    assert qpos.shape == (19,)
-    assert qpos[2] == pytest.approx(_MJLAB_HOME_HEIGHT)
-    np.testing.assert_allclose(qpos[7:19], _MJLAB_LEG_ANGLES)
-    # ctrl targets the same standing pose so position actuators hold it at reset.
-    ctrl = np.asarray(model.key_ctrl[key_id])
-    np.testing.assert_allclose(ctrl, _MJLAB_LEG_ANGLES)
-
-
-def test_a2_control_config_per_joint_gains():
-    """A2JoystickControlConfig.position_gains() yields per-joint arrays matching
-    mjlab (calf 150/6, hip/thigh 100/4) in actuator order."""
-    from unilab.envs.locomotion.a2.joystick import A2JoystickControlConfig
-
-    gains = A2JoystickControlConfig().position_gains()
-    np.testing.assert_allclose(np.asarray(gains["kp"]), _MJLAB_KP)
-    np.testing.assert_allclose(np.asarray(gains["kd"]), _MJLAB_KD)
-
-
-def test_pd_control_config_position_gains_default_is_scalar():
-    """Base PdControlConfig keeps the scalar gain contract (Go2 path unchanged)."""
-    from unilab.envs.locomotion.common.base import PdControlConfig
-
-    gains = PdControlConfig(Kp=35.0, Kd=0.5).position_gains()
-    assert gains == {"kp": 35.0, "kd": 0.5}
-
-
-def test_a2_dr_provider_returns_per_joint_base_gains():
-    """The A2 DR provider exposes per-joint base kp/kd so randomize_kp/kd scales
-    each joint off the correct baseline (calf off 150, not 100)."""
-    from unilab.envs.locomotion.a2.joystick import (
-        A2JoystickControlConfig,
-        A2JoystickDomainRandomizationProvider,
-    )
-
-    env = SimpleNamespace(
-        cfg=SimpleNamespace(control_config=A2JoystickControlConfig()),
-        _num_action=12,
-    )
-    base_kp, base_kd = A2JoystickDomainRandomizationProvider()._get_base_actuator_gains(env)
-    np.testing.assert_allclose(np.asarray(base_kp), _MJLAB_KP)
-    np.testing.assert_allclose(np.asarray(base_kd), _MJLAB_KD)
-
-
-def test_a2_floor_geom_dominates_for_friction_dr():
-    """The floor geom is the priority geom (priority=2 > feet's 1) and carries
-    condim=6, so it dictates the foot-ground friction. This makes randomizing the
-    floor geom's friction actually move the contact friction (otherwise the
-    priority-1 feet would override it)."""
-    mujoco = pytest.importorskip("mujoco")
-    xml = ASSETS_ROOT_PATH / "robots" / "a2" / "scene_flat.xml"
-    model = mujoco.MjModel.from_xml_path(str(xml))
     floor = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_GEOM, "floor")
     foot = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_GEOM, "FL")
-    assert int(model.geom_priority[floor]) == 2
+    assert floor >= 0 and foot >= 0
     assert int(model.geom_priority[floor]) > int(model.geom_priority[foot])
-    # condim must stay 6 (feet use 6); floor would default to 3 without this.
     assert int(model.geom_condim[floor]) == 6
 
 
-def test_a2_dr_provider_caches_friction_and_armature_baselines():
-    """The A2 DR provider caches the pristine geom-friction + dof-armature tables
-    (and the floor geom id) from the backend so randomize_ground_friction /
-    randomize_dof_armature can multiply against them. body_mass stays uncached."""
-    mujoco = pytest.importorskip("mujoco")
-    from unilab.envs.locomotion.a2.joystick import A2JoystickDomainRandomizationProvider
-
-    xml = ASSETS_ROOT_PATH / "robots" / "a2" / "scene_flat.xml"
-    model = mujoco.MjModel.from_xml_path(str(xml))
-    floor_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_GEOM, "floor")
-
-    class _StubBackend:
-        def get_geom_friction(self):
-            return np.asarray(model.geom_friction, dtype=np.float64).copy()
-
-        def get_dof_armature(self):
-            return np.asarray(model.dof_armature, dtype=np.float64).copy()
-
-        def get_geom_id(self, name):
-            return int(mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_GEOM, name))
-
-    env = SimpleNamespace(
-        _backend=_StubBackend(),
-        cfg=SimpleNamespace(asset=SimpleNamespace(ground="floor")),
-    )
-    base_body_mass, base_geom_friction, ground_geom_id, base_dof_armature = (
-        A2JoystickDomainRandomizationProvider()._get_reset_randomization_baselines(env)
-    )
-    assert base_body_mass is None  # body_mass DR not enabled
-    assert ground_geom_id == floor_id
-    assert base_geom_friction.shape == (model.ngeom, 3)
-    np.testing.assert_allclose(base_geom_friction, model.geom_friction)
-    assert base_dof_armature.shape == (model.nv,)
-    np.testing.assert_allclose(base_dof_armature, model.dof_armature)
-
-
-def _ensure_registered() -> None:
-    from unilab.base import registry
+def test_a2_owner_materializes_complete_plain_manager_config() -> None:
+    registry.ensure_registries()
+    hydra_cfg, env_cfg, _ = _materialize()
+
+    assert hydra_cfg.training.task_name == "A2JoystickFlat"
+    assert hydra_cfg.training.sim_backend == "mujoco"
+    assert hydra_cfg.algo.max_iterations == 500
+    assert list(hydra_cfg.algo.obs_groups.actor) == ["actor"]
+    assert list(hydra_cfg.algo.obs_groups.critic) == ["critic"]
+    assert env_cfg.sim_dt == pytest.approx(0.01)
+    assert env_cfg.ctrl_dt == pytest.approx(0.02)
+    assert env_cfg.max_episode_seconds == pytest.approx(20.0)
+    assert env_cfg.policy_observation_group == "policy"
+    assert env_cfg.critic_observation_group == "critic"
+
+    assert env_cfg.scene is not None
+    assert env_cfg.scene.model_file.endswith("robots/a2/scene_flat.xml")
+    assert env_cfg.scene.default_keyframe_name == "home"
+    robot = env_cfg.scene.entities["robot"]
+    assert robot.root_body_name == "base_link"
+    assert tuple(robot.joint_names or ()) == _JOINT_NAMES
+    assert tuple(robot.actuator_names or ()) == _ACTUATOR_NAMES
+    assert robot.body_names == ["base_link"]
+    assert robot.geom_names == ["floor"]
+
+    policy_terms = [
+        "base_ang_vel",
+        "projected_gravity",
+        "joint_pos",
+        "joint_vel",
+        "actions",
+        "command",
+        "gait_phase",
+    ]
+    assert list(env_cfg.observations) == ["policy", "critic"]
+    assert list(env_cfg.observations["policy"].terms) == policy_terms
+    assert list(env_cfg.observations["critic"].terms) == [*policy_terms, "base_lin_vel"]
+    assert env_cfg.observations["policy"].terms["gait_phase"].params == {
+        "frequency": 2.0,
+        "command_name": "twist",
+        "command_threshold": 0.1,
+    }
+    assert list(env_cfg.actions) == ["joint_pos"]
+    assert env_cfg.actions["joint_pos"].scale == pytest.approx(0.25)
+    command = env_cfg.commands["twist"]
+    assert command.resampling_time_range == [5.0, 5.0]
+    assert command.rel_standing_envs == pytest.approx(0.1)
+    assert tuple(command.ranges.lin_vel_x) == (-0.6, 1.0)
+
+    expected_weights = {
+        "tracking_lin_vel": 1.0,
+        "tracking_ang_vel": 0.4,
+        "lin_vel_z": -5.0,
+        "ang_vel_xy": -0.1,
+        "base_height": -100.0,
+        "action_rate": -0.02,
+        "similar_to_default": -0.25,
+        "contact": 0.5,
+        "swing_feet_z": 4.0,
+        "stand_still": -4.0,
+        "hip_deviation": -1.0,
+        "stand_feet_air": -1.0,
+    }
+    assert {name: term.weight for name, term in env_cfg.rewards.items()} == expected_weights
+    assert env_cfg.rewards["stand_still"].func is manager_terms.stand_still_l1
+    assert env_cfg.rewards["stand_feet_air"].func is manager_terms.feet_air_while_standing
+    assert env_cfg.rewards["hip_deviation"].params["asset_cfg"].joint_names == ".*_hip_joint"
+    for name in ("contact", "swing_feet_z"):
+        assert env_cfg.rewards[name].params["command_name"] == "twist"
+        assert env_cfg.rewards[name].params["command_threshold"] == pytest.approx(0.1)
+
+    for manager_name in ("observations", "events", "rewards", "terminations"):
+        for term in getattr(env_cfg, manager_name).values():
+            if term is None:
+                continue
+            terms = term.terms.values() if manager_name == "observations" else (term,)
+            for nested in terms:
+                if nested is None:
+                    continue
+                module = nested.func.__module__
+                assert ".backend." not in module
+                assert not any(name in module for name in (".mujoco", ".motrix", ".drake"))
+
+    _assert_no_omegaconf(env_cfg)
+
+
+def test_a2_owner_declares_all_randomization_as_manager_events() -> None:
+    _, env_cfg, _ = _materialize()
+    assert list(env_cfg.events) == [
+        "reset_scene_to_default",
+        "reset_root_state_uniform",
+        "base_mass",
+        "base_com",
+        "foot_friction",
+        "joint_armature",
+        "pd_gains",
+        "push_robot",
+    ]
 
+    mass = env_cfg.events["base_mass"]
+    assert mass.func is mdp.randomize_rigid_body_mass
+    assert mass.params["mass_distribution_params"] == [0.0, 8.0]
+    assert mass.params["recompute_inertia"] is False
+    com = env_cfg.events["base_com"]
+    assert com.func is mdp.randomize_rigid_body_com
+    assert com.params["com_range"] == {
+        "x": [-0.08, 0.08],
+        "y": [-0.08, 0.08],
+        "z": [-0.08, 0.08],
+    }
+    friction = env_cfg.events["foot_friction"]
+    assert friction.func is mdp.geom_friction
+    assert friction.params["ranges"] == [0.3, 1.6]
+    assert friction.params["operation"] == "scale"
+    assert friction.params["shared_random"] is True
+    armature = env_cfg.events["joint_armature"]
+    assert armature.func is mdp.joint_armature
+    assert armature.params["ranges"] == [0.9, 1.1]
+    gains = env_cfg.events["pd_gains"]
+    assert gains.func is mdp.pd_gains
+    assert gains.params["kp_range"] == [0.9, 1.1]
+    assert gains.params["kd_range"] == [0.9, 1.1]
+    push = env_cfg.events["push_robot"]
+    assert push.func is mdp.push_by_setting_velocity
+    assert push.mode == "interval"
+    assert push.interval_range_s == [8.0, 8.0]
+    assert push.is_global_time is True
+
+
+def test_a2_registry_has_no_legacy_config_or_runtime_fallback() -> None:
     registry.ensure_registries()
-    if not registry.contains("A2JoystickFlat"):
-        importlib.import_module("unilab.envs.locomotion.a2.joystick")
-
-
-def test_a2_joystick_registered():
-    """Registers without MuJoCo (decorators run on module import)."""
-    from unilab.base import registry
-
-    _ensure_registered()
-    assert registry.contains("A2JoystickFlat")
-
-
-def test_a2_joystick_yaml_composes_and_targets_a2():
-    """The owner YAML composes under Hydra and selects the A2JoystickFlat task
-    with a reward block that injects into the env's reward_config."""
-    from hydra import compose, initialize
-
-    with initialize(config_path="../../../../conf/ppo", version_base="1.3"):
-        cfg = compose(config_name="config", overrides=["task=a2_joystick_flat/mujoco"])
-    assert cfg.training.task_name == "A2JoystickFlat"
-    assert cfg.training.sim_backend == "mujoco"
-    assert "tracking_lin_vel" in cfg.reward.scales
-
-
-def _default_reward_cfg():
-    from unilab.envs.locomotion.a2.joystick import A2RewardConfig
-
-    return A2RewardConfig(
-        scales={
-            "tracking_lin_vel": 1.0,
-            "tracking_ang_vel": 0.2,
-            "lin_vel_z": -5.0,
-            "ang_vel_xy": -0.1,
-            "base_height": -100.0,
-            "action_rate": -0.005,
-            "similar_to_default": -0.1,
-            "contact": 0.24,
-            "swing_feet_z": 4.0,
-            "stand_still": -4.0,
-            "hip_deviation": -1.0,
-            "stand_feet_air": -1.0,
-        },
-        tracking_sigma=0.25,
-        base_height_target=0.45,
-        command_threshold=0.1,
-    )
+    module = importlib.import_module("unilab.tasks.locomotion.a2.joystick")
+    assert not hasattr(module, "A2JoystickCfg")
+    assert not hasattr(module, "A2JoystickFlatEnv")
+    assert not hasattr(module, "A2JoystickDomainRandomizationProvider")
+    assert registry.list_registered_envs()["A2JoystickFlat"] == {
+        "config_factory": "ManagerBasedRlEnvCfg",
+        "available_backends": ["mujoco"],
+    }
+    for legacy_override in (
+        {"reward_config": {}},
+        {"domain_rand": {"randomize_kp": True}},
+        {"control_config": {"action_scale": 0.4}},
+    ):
+        with pytest.raises(ValueError, match="has no attribute"):
+            apply_cfg_overrides(ManagerBasedRlEnvCfg(), legacy_override)
 
 
-def _make_a2_env(num_envs: int = 2, domain_rand=None):
-    from unilab.base import registry
+def test_a2_registry_executes_real_manager_runtime() -> None:
+    pytest.importorskip("mujoco")
+    try:
+        from mujoco_uni.batch_env import BatchEnvPool  # noqa: F401
+    except Exception:
+        pytest.skip("mujoco_uni.batch_env not available")
 
-    _ensure_registered()
-    override = {"reward_config": _default_reward_cfg()}
-    if domain_rand is not None:
-        override["domain_rand"] = domain_rand
-    return registry.make(
-        "A2JoystickFlat",
+    hydra_cfg, _, env_override = _materialize()
+    env = registry.make(
+        str(hydra_cfg.training.task_name),
         sim_backend="mujoco",
-        num_envs=num_envs,
-        env_cfg_override=override,
+        env_cfg_override=env_override,
+        num_envs=2,
     )
-
-
-@pytest.mark.slow
-def test_a2_joystick_obs_layout_and_12_dof():
-    _skip_if_no_mujoco()
-    env = _make_a2_env(num_envs=2)
-    assert env._num_action == 12
-    assert env.default_angles.shape == (12,)
-    assert env.obs_groups_spec == {"obs": 49, "critic": 52}
-
-
-@pytest.mark.slow
-def test_a2_joystick_model_gains_are_per_joint():
-    """End-to-end: the per-joint gains reach the compiled MuJoCo model — calf
-    actuators carry kp=150/kd=6, hip/thigh kp=100/kd=4 — and default_angles
-    (derived from the home keyframe) match the mjlab standing pose."""
-    _skip_if_no_mujoco()
-    env = _make_a2_env(num_envs=2)
-
-    # default_angles come from the home keyframe -> mjlab pose.
-    np.testing.assert_allclose(env.default_angles, _MJLAB_LEG_ANGLES)
-
-    # The env forwarded per-joint arrays to the backend.
-    stored = env._backend._position_actuator_gains
-    np.testing.assert_allclose(np.asarray(stored["kp"]), _MJLAB_KP)
-    np.testing.assert_allclose(np.asarray(stored["kd"]), _MJLAB_KD)
-
-    # ...and they are written into the compiled model: position actuators store
-    # kp in gainprm[0], and -kp / -kd in biasprm[1] / biasprm[2].
-    model = env._backend._model
-    np.testing.assert_allclose(model.actuator_gainprm[:, 0], _MJLAB_KP)
-    np.testing.assert_allclose(model.actuator_biasprm[:, 1], [-v for v in _MJLAB_KP])
-    np.testing.assert_allclose(model.actuator_biasprm[:, 2], [-v for v in _MJLAB_KD])
-
-
-@pytest.mark.slow
-def test_a2_joystick_init_step_runs_finite():
-    """End-to-end: init + steps must run (all A2 sensors/geoms resolve) with
-    finite obs/reward, proving the leg-only A2 asset satisfies the joystick
-    sensor contract on the hot path."""
-    _skip_if_no_mujoco()
-
-    env = _make_a2_env(num_envs=2)
-    state = env.init_state()
-    assert state.obs["obs"].shape == (2, 49)
-    assert state.obs["critic"].shape == (2, 52)
-    for _ in range(10):
-        state = env.step(np.zeros((2, 12), dtype=np.float64))
-    assert np.isfinite(state.reward).all()
-    assert np.isfinite(state.obs["obs"]).all()
-    assert np.isfinite(state.obs["critic"]).all()
-
-
-@pytest.mark.slow
-def test_a2_joystick_dr_on_constructs_and_steps_finite():
-    """With DR on (incl. base_link interval push, dof-armature and ground-friction
-    randomization), the env constructs and steps with finite obs/reward — proving
-    push_body_name resolves to a real body and the mass/COM/kp-kd/armature/friction
-    randomization path is sound.
-
-    A2JoystickDomainRandomizationProvider caches the dof-armature + geom-friction
-    baselines, so randomize_dof_armature / randomize_ground_friction are now ON.
-    randomize_body_mass stays off (base_body_mass baseline not cached). The
-    YAML-surface is covered by test_a2_joystick_domain_rand_fully_configured."""
-    _skip_if_no_mujoco()
-    from unilab.envs.locomotion.a2.joystick import A2JoystickDomainRandConfig
-
-    dr_on = A2JoystickDomainRandConfig(
-        randomize_base_mass=True,
-        added_mass_range=[0.0, 8.0],
-        randomize_body_mass=False,  # provider does not cache base_body_mass
-        random_com=True,
-        com_offset_x=[-0.08, 0.08],
-        com_offset_y=[-0.08, 0.08],
-        com_offset_z=[-0.08, 0.08],
-        randomize_ground_friction=True,
-        ground_friction_multiplier_range=_MJLAB_FRICTION_RANGE,
-        randomize_dof_armature=True,
-        dof_armature_multiplier_range=_MJLAB_ARMATURE_RANGE,
-        randomize_kp=True,
-        randomize_kd=True,
-        push_robots=True,
-        push_interval=400,
-        push_body_name="base_link",
-    )
-    env = _make_a2_env(num_envs=4, domain_rand=dr_on)
-
-    # DR fields are active on the constructed config.
-    assert env._cfg.domain_rand.push_robots is True
-    assert env._cfg.domain_rand.push_body_name == "base_link"
-    assert env._cfg.domain_rand.randomize_base_mass is True
-    assert env._cfg.domain_rand.randomize_dof_armature is True
-    assert env._cfg.domain_rand.randomize_ground_friction is True
-    assert list(env._cfg.domain_rand.com_offset_z) == [-0.08, 0.08]
-
-    state = env.init_state()
-    assert state.obs["obs"].shape == (4, 49)
-    # 10 steps exercises reset-time DR (mass/friction/COM/armature/kp-kd) + stepping.
-    for _ in range(10):
-        state = env.step(np.zeros((4, 12), dtype=np.float64))
-    assert np.isfinite(state.reward).all()
-    assert np.isfinite(state.obs["obs"]).all()
-    assert np.isfinite(state.obs["critic"]).all()
-
-
-def test_a2_joystick_domain_rand_fully_configured():
-    """Owner YAML enables DR switches supported by A2JoystickDomainRandomizationProvider,
-    with mjlab-referenced ranges, 3-axis COM, base_link push target, and the
-    500-iteration budget.
-
-    randomize_dof_armature + randomize_ground_friction are ON: the A2 provider caches
-    their baselines (dof_armature, geom_friction + floor geom id). randomize_body_mass
-    stays OFF (base_body_mass not cached); gravity OFF (constant on flat ground)."""
-    from hydra import compose, initialize
-
-    with initialize(config_path="../../../../conf/ppo", version_base="1.3"):
-        cfg = compose(config_name="config", overrides=["task=a2_joystick_flat/mujoco"])
-
-    dr = cfg.env.domain_rand
-    assert dr.randomize_base_mass is True
-    assert dr.randomize_body_mass is False  # provider does not cache base_body_mass
-    assert dr.random_com is True
-    assert dr.randomize_gravity is False
-    assert dr.randomize_ground_friction is True
-    assert dr.randomize_dof_armature is True
-    assert dr.randomize_kp is True
-    assert dr.randomize_kd is True
-    assert dr.push_robots is True
-    # 3-axis COM present + A2-scale value
-    assert list(dr.com_offset_x) == [-0.08, 0.08]
-    assert list(dr.com_offset_y) == [-0.08, 0.08]
-    assert list(dr.com_offset_z) == [-0.08, 0.08]
-    # mjlab-referenced ranges + push target
-    assert list(dr.added_mass_range) == [0.0, 8.0]
-    assert list(dr.ground_friction_multiplier_range) == _MJLAB_FRICTION_RANGE
-    assert list(dr.dof_armature_multiplier_range) == _MJLAB_ARMATURE_RANGE
-    assert dr.push_interval == 400
-    assert dr.push_body_name == "base_link"
-    # bumped budget
-    assert cfg.algo.max_iterations == 500
-
-
-# ── zero-command standstill (A2-owned) ───────────────────────────────
-
-
-def test_a2_reward_config_declares_command_threshold():
-    import dataclasses
-
-    from unilab.envs.locomotion.a2.joystick import A2RewardConfig
-
-    names = {f.name for f in dataclasses.fields(A2RewardConfig)}
-    assert "command_threshold" in names
-    assert {"scales", "tracking_sigma", "base_height_target"} <= names
-
-
-def test_a2_cfg_reward_config_annotation_is_a2_type():
-    from typing import get_type_hints
-
-    from unilab.envs.locomotion.a2.joystick import A2JoystickCfg, A2RewardConfig
-
-    hints = get_type_hints(A2JoystickCfg)
-    assert A2RewardConfig in getattr(hints["reward_config"], "__args__", (hints["reward_config"],))
-
-
-def _a2_ctx(commands, dof_pos=None):
-    from unilab.envs.locomotion.common.rewards import RewardContext
-
-    n = commands.shape[0]
-    return RewardContext(
-        info={"commands": commands},
-        linvel=np.zeros((n, 3)),
-        gyro=np.zeros((n, 3)),
-        dof_pos=np.zeros((n, 12)) if dof_pos is None else dof_pos,
-        num_envs=n,
-        default_angles=np.zeros(12),
-        tracking_sigma=0.25,
-        base_height_target=0.4,
-        base_height=np.zeros(n),
-    )
-
-
-def test_a2_advance_phase_freezes_standing_envs():
-    from unilab.envs.locomotion.a2.joystick import A2JoystickFlatEnv
-
-    stub = SimpleNamespace(
-        _cfg=SimpleNamespace(ctrl_dt=0.02),
-        gait_frequency=2.0,
-        _reward_cfg=SimpleNamespace(command_threshold=0.1),
-        _latest_commands=np.array([[0.0, 0.0, 0.0], [0.5, 0.0, 0.0]]),
-    )
-    phase = np.array([0.3, 0.3])
-    out = A2JoystickFlatEnv._advance_phase(stub, phase)
-    assert out[0] == 0.3
-    assert out[1] > 0.3
-
-
-def test_a2_hip_deviation_l1_over_hip_indices():
-    from unilab.envs.locomotion.a2.joystick import A2JoystickFlatEnv
-
-    dof_pos = np.zeros((1, 12))
-    dof_pos[0, [0, 3, 6, 9]] = [0.1, -0.2, 0.3, -0.4]
-    stub = SimpleNamespace(default_angles=np.zeros(12))
-    out = A2JoystickFlatEnv._reward_hip_deviation(stub, _a2_ctx(np.zeros((1, 3)), dof_pos=dof_pos))
-    assert np.isclose(out[0], 1.0)
-
-
-def test_a2_stand_feet_air_counts_lifted_feet_when_standing():
-    from unilab.envs.locomotion.a2.joystick import A2JoystickFlatEnv
-
-    stub = SimpleNamespace(
-        _reward_cfg=SimpleNamespace(command_threshold=0.1),
-        feet_force=np.zeros((1, 4, 3)),
-    )
-    out = A2JoystickFlatEnv._reward_stand_feet_air(stub, _a2_ctx(np.zeros((1, 3))))
-    assert out[0] == 4.0
-
-
-def test_a2_stand_feet_air_inactive_during_locomotion():
-    from unilab.envs.locomotion.a2.joystick import A2JoystickFlatEnv
-
-    stub = SimpleNamespace(
-        _reward_cfg=SimpleNamespace(command_threshold=0.1),
-        feet_force=np.zeros((1, 4, 3)),
-    )
-    out = A2JoystickFlatEnv._reward_stand_feet_air(stub, _a2_ctx(np.array([[0.5, 0.0, 0.0]])))
-    assert out[0] == 0.0
+    try:
+        assert isinstance(env, ManagerBasedRlEnv)
+        assert env.obs_groups_spec == {"obs": 49, "critic": 52}
+        assert env.action_space.shape == (12,)
+        action = env.action_manager.get_term("joint_pos")
+        assert action.target_names == list(_JOINT_NAMES)
+        np.testing.assert_allclose(action.offset, np.broadcast_to(_HOME_JOINT_POS, (2, 12)))
+        assert env.event_manager.active_terms == {
+            "reset": [
+                "reset_scene_to_default",
+                "reset_root_state_uniform",
+                "base_mass",
+                "base_com",
+                "foot_friction",
+                "joint_armature",
+                "pd_gains",
+            ],
+            "interval": ["push_robot"],
+        }
+
+        obs, info = env.reset(seed=7)
+        assert {name: value.shape for name, value in obs.items()} == {
+            "obs": (2, 49),
+            "critic": (2, 52),
+        }
+        assert isinstance(info, dict)
+        np.testing.assert_allclose(
+            env.scene["robot"].data.default_joint_pos,
+            np.broadcast_to(_HOME_JOINT_POS, (2, 12)),
+        )
+        for _ in range(10):
+            state = env.step(np.zeros((2, 12), dtype=np.float32))
+        assert state.reward.shape == (2,)
+        for value in (*state.obs.values(), state.reward):
+            assert isinstance(value, np.ndarray)
+            assert np.isfinite(value).all()
+    finally:
+        env.close()
diff --git a/tests/envs/locomotion/g1/test_g1_owner_contract.py b/tests/envs/locomotion/g1/test_g1_owner_contract.py
index 9fd3a6e0a..c164f2e26 100644
--- a/tests/envs/locomotion/g1/test_g1_owner_contract.py
+++ b/tests/envs/locomotion/g1/test_g1_owner_contract.py
@@ -1,265 +1,1058 @@
+"""Hydra-owned production contract for the G1 walk Manager-Based tasks."""
+
 from __future__ import annotations
 
+import subprocess
+import sys
+from collections.abc import Mapping, Sequence
+from copy import deepcopy
+from dataclasses import fields, is_dataclass
 from pathlib import Path
-from typing import Any, cast
+from typing import Any
 
 import numpy as np
 import pytest
 from hydra import compose, initialize_config_dir
 from hydra.core.global_hydra import GlobalHydra
-from omegaconf import OmegaConf
+from omegaconf import DictConfig, OmegaConf
 
 from unilab.base import registry
-from unilab.base.registry import ensure_registries
-from unilab.training.backend_adapter import BackendAdapter
+from unilab.base.config_adapter import BackendAdapter
+from unilab.base.config_materialization import apply_cfg_overrides
+from unilab.envs import ManagerBasedRlEnv, ManagerBasedRlEnvCfg, mdp
+from unilab.tasks.locomotion.g1 import manager_terms as g1_terms
+
+# CPU-bound on the single-core CI runner; kept in the slow lane (make test-slow).
+pytestmark = pytest.mark.slow
 
 ROOT_DIR = Path(__file__).parents[4]
-CONF_DIR = ROOT_DIR / "conf"
-
-
-_G1_OWNER_CASES = [
-    {
-        "id": "ppo_mujoco",
-        "config_group": "ppo",
-        "overrides": ["task=g1_walk_flat/mujoco"],
-        "task_name": "G1WalkFlat",
-        "backend": "mujoco",
-        "profile": "legacy",
-        "action_scale": 0.25,
-        "curriculum_enabled": False,
-    },
-    {
-        "id": "ppo_motrix",
-        "config_group": "ppo",
-        "overrides": ["task=g1_walk_flat/motrix"],
-        "task_name": "G1WalkFlat",
-        "backend": "motrix",
-        "profile": "legacy",
-        "action_scale": 0.5,
-        "curriculum_enabled": False,
-    },
-    {
-        "id": "appo_mujoco",
-        "config_group": "appo",
-        "overrides": ["task=g1_walk_flat/mujoco"],
-        "task_name": "G1WalkFlat",
-        "backend": "mujoco",
-        "profile": "legacy",
-        "action_scale": 0.25,
-        "curriculum_enabled": False,
-    },
-    {
-        "id": "sac_mujoco",
-        "config_group": "offpolicy",
-        "overrides": ["algo=sac", "task=sac/g1_walk_flat/mujoco"],
-        "task_name": "G1WalkFlat",
-        "backend": "mujoco",
-        "profile": "walk",
-        "action_scale": 1.0,
-        "curriculum_enabled": True,
-    },
-    {
-        "id": "sac_motrix",
-        "config_group": "offpolicy",
-        "overrides": ["algo=sac", "task=sac/g1_walk_flat/motrix"],
-        "task_name": "G1WalkFlat",
-        "backend": "motrix",
-        "profile": "walk",
-        "action_scale": 1.0,
-        "curriculum_enabled": True,
-    },
-    {
-        "id": "ppo_mjwarp",
-        "config_group": "ppo",
-        "overrides": ["task=g1_walk_flat/mjwarp"],
-        "task_name": "G1WalkFlat",
-        "backend": "mjwarp",
-        "profile": "legacy",
-        "action_scale": 0.25,
-        "curriculum_enabled": False,
-    },
-    {
-        "id": "sac_mjwarp",
-        "config_group": "offpolicy",
-        "overrides": ["algo=sac", "task=sac/g1_walk_flat/mjwarp"],
-        "task_name": "G1WalkFlat",
-        "backend": "mjwarp",
-        "profile": "walk",
-        "action_scale": 1.0,
-        "curriculum_enabled": True,
-    },
-    {
-        "id": "sac_rough",
-        "config_group": "offpolicy",
-        "overrides": ["algo=sac", "task=sac/g1_walk_rough/mujoco"],
-        "task_name": "G1WalkRough",
-        "backend": "mujoco",
-        "profile": "walk",
-        "action_scale": 1.0,
-        "curriculum_enabled": True,
-        "model_suffix": "scene_rough.xml",
-    },
-    {
-        "id": "td3_mujoco",
-        "config_group": "offpolicy",
-        "overrides": ["algo=td3", "task=td3/g1_walk_flat/mujoco"],
-        "task_name": "G1WalkFlat",
-        "backend": "mujoco",
-        "profile": "walk",
-        "action_scale": 1.0,
-        "curriculum_enabled": True,
-    },
-    {
-        "id": "flashsac_walk_mujoco",
-        "config_group": "offpolicy",
-        "overrides": ["algo=flashsac", "task=flashsac/g1_walk_flat/mujoco"],
-        "task_name": "G1WalkFlat",
-        "backend": "mujoco",
-        "profile": "walk",
-        "action_scale": 1.0,
-        "curriculum_enabled": True,
-    },
-]
-
-
-def _compose_cfg(config_group: str, overrides: list[str]):
+CONF_DIR = ROOT_DIR / "src" / "unilab" / "conf"
+
+_RESET_EVENTS = ("reset_scene_to_default", "reset_root_state_uniform")
+_PPO_REWARDS = (
+    "tracking_lin_vel",
+    "tracking_ang_vel",
+    "feet_phase",
+    "lin_vel_z",
+    "ang_vel_xy",
+    "base_height",
+    "orientation",
+    "action_rate",
+    "pose",
+)
+# ppo g1_walk_flat additionally references the shared window-form air-time term
+# (#1398) right after the private feet_phase term.
+_PPO_WALK_FLAT_REWARDS = (
+    "tracking_lin_vel",
+    "tracking_ang_vel",
+    "feet_phase",
+    "feet_air_time",
+    "lin_vel_z",
+    "ang_vel_xy",
+    "base_height",
+    "orientation",
+    "action_rate",
+    "pose",
+)
+_MOTRIX_EXTRA_REWARDS = (
+    "forward_progress",
+    "under_speed",
+    "upper_body_pose",
+    "penalty_feet_ori",
+    "feet_phase_contrast",
+    "feet_phase_contact",
+    "feet_double_stance",
+)
+_OFFPOLICY_REWARDS = (
+    "tracking_lin_vel",
+    "tracking_ang_vel",
+    "penalty_ang_vel_xy",
+    "penalty_orientation",
+    "penalty_action_rate",
+    "pose",
+    "penalty_feet_ori",
+    "feet_phase",
+    "alive",
+)
+
+_OBSERVATION_TERMS = (
+    "base_ang_vel",
+    "projected_gravity",
+    "joint_pos",
+    "joint_vel",
+    "actions",
+    "command",
+    "gait_phase",
+)
+
+_POSE_WEIGHTS_29 = [0.01, 1.0, 5.0, 0.01, 5.0, 5.0] * 2 + [50.0] * 17
+_POSE_WEIGHTS_23 = [0.01, 1.0, 5.0, 0.01, 5.0, 5.0] * 2 + [50.0] * 11
+
+_OWNER_CASES = (
+    pytest.param(
+        "ppo",
+        ("task=g1_walk_flat/mujoco",),
+        "G1WalkFlat",
+        "mujoco",
+        29,
+        0.25,
+        "scene_flat.xml",
+        _PPO_WALK_FLAT_REWARDS,
+        (*_RESET_EVENTS, "pd_gains"),
+        False,
+        id="ppo-mujoco",
+    ),
+    pytest.param(
+        "ppo",
+        ("task=g1_walk_flat/motrix",),
+        "G1WalkFlat",
+        "motrix",
+        29,
+        0.5,
+        "scene_flat.xml",
+        (*_PPO_WALK_FLAT_REWARDS, *_MOTRIX_EXTRA_REWARDS),
+        _RESET_EVENTS,
+        False,
+        id="ppo-motrix",
+    ),
+    pytest.param(
+        "ppo",
+        ("task=g1_walk_flat/mjwarp",),
+        "G1WalkFlat",
+        "mjwarp",
+        29,
+        0.25,
+        "scene_flat.xml",
+        _PPO_WALK_FLAT_REWARDS,
+        _RESET_EVENTS,
+        False,
+        id="ppo-mjwarp",
+    ),
+    pytest.param(
+        "ppo",
+        ("task=g1_walk_flat/isaacgym",),
+        "G1WalkFlat",
+        "isaacgym",
+        29,
+        0.25,
+        "scene_flat.xml",
+        _PPO_WALK_FLAT_REWARDS,
+        _RESET_EVENTS,
+        False,
+        id="ppo-isaacgym",
+    ),
+    pytest.param(
+        "ppo",
+        ("task=g1_walk_flat/genesis",),
+        "G1WalkFlat",
+        "genesis",
+        29,
+        0.25,
+        "scene_flat.xml",
+        _PPO_WALK_FLAT_REWARDS,
+        # kp/kd reset randomization stays enabled: the backend declares the
+        # measured RESET_TERM_KP/KD DR terms (REPORT #1372 §5.7).
+        (*_RESET_EVENTS, "pd_gains"),
+        False,
+        id="ppo-genesis",
+    ),
+    pytest.param(
+        "ppo",
+        ("task=g1_walk_flat/isaacsim",),
+        "G1WalkFlat",
+        "isaacsim",
+        29,
+        0.25,
+        "scene_flat.xml",
+        _PPO_WALK_FLAT_REWARDS,
+        _RESET_EVENTS,
+        False,
+        id="ppo-isaacsim",
+    ),
+    pytest.param(
+        "ppo",
+        ("task=g1_23dof_walk_flat/mujoco",),
+        "G1Walk23DofFlat",
+        "mujoco",
+        23,
+        0.25,
+        "scene_flat_23dof.xml",
+        _PPO_WALK_FLAT_REWARDS,
+        (*_RESET_EVENTS, "pd_gains"),
+        False,
+        id="ppo-23dof-mujoco",
+    ),
+    pytest.param(
+        "ppo",
+        ("task=g1_23dof_walk_flat/motrix",),
+        "G1Walk23DofFlat",
+        "motrix",
+        23,
+        0.5,
+        "scene_flat_23dof.xml",
+        (*_PPO_WALK_FLAT_REWARDS, *_MOTRIX_EXTRA_REWARDS),
+        _RESET_EVENTS,
+        False,
+        id="ppo-23dof-motrix",
+    ),
+    pytest.param(
+        "ppo",
+        ("task=g1_23dof_walk_rough/mujoco",),
+        "G1Walk23DofRough",
+        "mujoco",
+        23,
+        0.25,
+        "scene_rough_23dof.xml",
+        _PPO_REWARDS,
+        (*_RESET_EVENTS, "pd_gains"),
+        True,
+        id="ppo-23dof-rough-mujoco",
+    ),
+    pytest.param(
+        "appo",
+        ("task=g1_walk_flat/mujoco",),
+        "G1WalkFlat",
+        "mujoco",
+        29,
+        0.25,
+        "scene_flat.xml",
+        _PPO_REWARDS,
+        (*_RESET_EVENTS, "pd_gains"),
+        False,
+        id="appo-mujoco",
+    ),
+    pytest.param(
+        "appo",
+        ("task=g1_23dof_walk_flat/mujoco",),
+        "G1Walk23DofFlat",
+        "mujoco",
+        23,
+        0.25,
+        "scene_flat_23dof.xml",
+        _PPO_REWARDS,
+        (*_RESET_EVENTS, "pd_gains"),
+        False,
+        id="appo-23dof-mujoco",
+    ),
+    pytest.param(
+        "sac",
+        ("task=g1_walk_flat/mujoco",),
+        "G1WalkFlat",
+        "mujoco",
+        29,
+        1.0,
+        "scene_flat.xml",
+        _OFFPOLICY_REWARDS,
+        (*_RESET_EVENTS, "pd_gains"),
+        True,
+        id="sac-mujoco",
+    ),
+    pytest.param(
+        "sac",
+        ("task=g1_walk_flat/motrix",),
+        "G1WalkFlat",
+        "motrix",
+        29,
+        1.0,
+        "scene_flat.xml",
+        _OFFPOLICY_REWARDS,
+        _RESET_EVENTS,
+        True,
+        id="sac-motrix",
+    ),
+    pytest.param(
+        "sac",
+        ("task=g1_walk_flat/mjwarp",),
+        "G1WalkFlat",
+        "mjwarp",
+        29,
+        1.0,
+        "scene_flat.xml",
+        _OFFPOLICY_REWARDS,
+        _RESET_EVENTS,
+        True,
+        id="sac-mjwarp",
+    ),
+    pytest.param(
+        "sac",
+        ("task=g1_walk_flat/genesis",),
+        "G1WalkFlat",
+        "genesis",
+        29,
+        1.0,
+        "scene_flat.xml",
+        _OFFPOLICY_REWARDS,
+        # kp/kd reset randomization stays enabled: the backend declares the
+        # measured RESET_TERM_KP/KD DR terms (REPORT #1372 §5.7), unlike the
+        # isaacgym owner which disables pd_gains.
+        (*_RESET_EVENTS, "pd_gains"),
+        True,
+        id="sac-genesis",
+    ),
+    pytest.param(
+        "sac",
+        ("task=g1_walk_flat/isaacsim",),
+        "G1WalkFlat",
+        "isaacsim",
+        29,
+        1.0,
+        "scene_flat.xml",
+        _OFFPOLICY_REWARDS,
+        _RESET_EVENTS,
+        True,
+        id="sac-isaacsim",
+    ),
+    pytest.param(
+        "sac",
+        ("task=g1_walk_rough/mujoco",),
+        "G1WalkRough",
+        "mujoco",
+        29,
+        1.0,
+        "scene_rough.xml",
+        _OFFPOLICY_REWARDS,
+        (*_RESET_EVENTS, "pd_gains"),
+        True,
+        id="sac-rough-mujoco",
+    ),
+    pytest.param(
+        "sac",
+        ("task=g1_walk_rough/motrix",),
+        "G1WalkRough",
+        "motrix",
+        29,
+        1.0,
+        "scene_rough.xml",
+        _OFFPOLICY_REWARDS,
+        _RESET_EVENTS,
+        True,
+        id="sac-rough-motrix",
+    ),
+    pytest.param(
+        "sac",
+        ("task=g1_23dof_walk_flat/mujoco",),
+        "G1Walk23DofFlat",
+        "mujoco",
+        23,
+        1.0,
+        "scene_flat_23dof.xml",
+        _OFFPOLICY_REWARDS,
+        (*_RESET_EVENTS, "pd_gains"),
+        True,
+        id="sac-23dof-mujoco",
+    ),
+    pytest.param(
+        "sac",
+        ("task=g1_23dof_walk_rough/motrix",),
+        "G1Walk23DofRough",
+        "motrix",
+        23,
+        1.0,
+        "scene_rough_23dof.xml",
+        _OFFPOLICY_REWARDS,
+        _RESET_EVENTS,
+        True,
+        id="sac-23dof-rough-motrix",
+    ),
+    pytest.param(
+        "td3",
+        ("task=g1_walk_flat/mujoco",),
+        "G1WalkFlat",
+        "mujoco",
+        29,
+        1.0,
+        "scene_flat.xml",
+        _OFFPOLICY_REWARDS,
+        (*_RESET_EVENTS, "pd_gains"),
+        True,
+        id="td3-mujoco",
+    ),
+    pytest.param(
+        "flashsac",
+        ("task=g1_walk_flat/mujoco",),
+        "G1WalkFlat",
+        "mujoco",
+        29,
+        1.0,
+        "scene_flat.xml",
+        _OFFPOLICY_REWARDS,
+        (*_RESET_EVENTS, "pd_gains"),
+        True,
+        id="flashsac-mujoco",
+    ),
+)
+
+_WALK_PROFILE_IDS = {
+    "sac-mujoco",
+    "sac-motrix",
+    "sac-mjwarp",
+    "sac-genesis",
+    "sac-isaacsim",
+    "sac-rough-mujoco",
+    "sac-rough-motrix",
+    "sac-23dof-mujoco",
+    "sac-23dof-rough-motrix",
+    "td3-mujoco",
+    "flashsac-mujoco",
+}
+
+
+def _compose(config_group: str, overrides: Sequence[str]) -> DictConfig:
     GlobalHydra.instance().clear()
     with initialize_config_dir(config_dir=str(CONF_DIR / config_group), version_base="1.3"):
-        return compose("config", overrides=overrides)
-
-
-def _materialize_env_cfg(cfg: Any):
-    from unilab.envs.locomotion.g1.joystick import G1WalkFlatCfg, G1WalkRoughCfg
-
-    env_cfg_cls = G1WalkRoughCfg if cfg.training.task_name == "G1WalkRough" else G1WalkFlatCfg
-    return OmegaConf.merge(OmegaConf.structured(env_cfg_cls()), cfg.env)
-
-
-def _build_probe_env(cfg: Any):
-    from unilab.envs.locomotion.g1.joystick import G1WalkEnv
-
-    env = cast(Any, object.__new__(G1WalkEnv))
-    env._num_envs = 1
-    env._cfg = _materialize_env_cfg(cfg)
-    env._reward_cfg = cfg.reward
-    env.default_angles = np.zeros((1, 29), dtype=np.float32)
-    env._obs_noise = lambda data, scale: np.asarray(data + 100.0, dtype=np.float32)
-    return env
-
-
-def _compute_probe_obs(cfg: Any) -> dict[str, np.ndarray]:
-    env = _build_probe_env(cfg)
-    return cast(
-        dict[str, np.ndarray],
-        env._compute_obs(
-            {
-                "commands": np.array([[0.7, 0.0, 0.2]], dtype=np.float32),
-                "current_actions": np.zeros((1, 29), dtype=np.float32),
-                "gait_phase": np.array([[0.3, 3.4]], dtype=np.float32),
-            },
-            linvel=np.array([[1.0, 2.0, 3.0]], dtype=np.float32),
-            gyro=np.array([[4.0, 5.0, 6.0]], dtype=np.float32),
-            gravity=np.array([[0.1, 0.2, 0.9]], dtype=np.float32),
-            dof_pos=np.zeros((1, 29), dtype=np.float32),
-            dof_vel=np.array([np.arange(7.0, 36.0, dtype=np.float32)], dtype=np.float32),
-        ),
-    )
+        return compose("config", overrides=list(overrides))
+
+
+def _materialize(
+    config_group: str, overrides: Sequence[str], task_name: str
+) -> tuple[DictConfig, ManagerBasedRlEnvCfg, dict[str, Any]]:
+    hydra_cfg = _compose(config_group, overrides)
+    env_override = BackendAdapter(hydra_cfg, root_dir=ROOT_DIR).build_task_env_cfg_override()
+    env_cfg = registry.materialize_env_config(task_name)
+    assert isinstance(env_cfg, ManagerBasedRlEnvCfg)
+    apply_cfg_overrides(env_cfg, env_override)
+    env_cfg.validate()
+    return hydra_cfg, env_cfg, env_override
+
+
+def _assert_no_omegaconf(value: Any) -> None:
+    assert not OmegaConf.is_config(value)
+    if is_dataclass(value) and not isinstance(value, type):
+        for item in fields(value):
+            _assert_no_omegaconf(getattr(value, item.name))
+    elif isinstance(value, Mapping):
+        for key, item in value.items():
+            _assert_no_omegaconf(key)
+            _assert_no_omegaconf(item)
+    elif isinstance(value, Sequence) and not isinstance(value, (str, bytes)):
+        for item in value:
+            _assert_no_omegaconf(item)
 
 
-@pytest.mark.parametrize("case", _G1_OWNER_CASES, ids=[case["id"] for case in _G1_OWNER_CASES])
-def test_g1_owner_yaml_regression_contract(case: dict[str, Any]):
-    from unilab.envs.locomotion.g1.joystick import G1WalkEnv
-
-    cfg = _compose_cfg(case["config_group"], case["overrides"])
-    full_env_cfg = _materialize_env_cfg(cfg)
-    env = _build_probe_env(cfg)
-    env_cfg_override = BackendAdapter(
-        cfg, root_dir=ROOT_DIR, algo_name=cfg.algo.algo if "algo" in cfg.algo else None
-    ).build_task_env_cfg_override()
-
-    assert cfg.training.task_name == case["task_name"]
-    assert cfg.training.sim_backend == case["backend"]
-    assert full_env_cfg.control_config.action_scale == pytest.approx(case["action_scale"])
-    assert full_env_cfg.curriculum.enabled is case["curriculum_enabled"]
-    assert env._uses_walk_observation_profile() is (case["profile"] == "walk")
-    assert (
-        registry._envs[cfg.training.task_name].env_cls_dict[cfg.training.sim_backend] is G1WalkEnv
+@pytest.mark.parametrize(
+    "config_group,overrides,task_name,backend,num_dof,action_scale,model_suffix,"
+    "expected_rewards,expected_events,has_curriculum",
+    _OWNER_CASES,
+    ids=[case.id for case in _OWNER_CASES],
+)
+def test_g1_owner_materializes_complete_plain_manager_cfg(
+    config_group: str,
+    overrides: tuple[str, ...],
+    task_name: str,
+    backend: str,
+    num_dof: int,
+    action_scale: float,
+    model_suffix: str,
+    expected_rewards: tuple[str, ...],
+    expected_events: tuple[str, ...],
+    has_curriculum: bool,
+) -> None:
+    registry.ensure_registries()
+    hydra_cfg, env_cfg, _ = _materialize(config_group, overrides, task_name)
+    case_id = next(
+        case.id
+        for case in _OWNER_CASES
+        if case.values[0] == config_group and case.values[1] == overrides
     )
 
-    if "model_suffix" in case:
-        assert full_env_cfg.scene.model_file.endswith(case["model_suffix"])
+    assert hydra_cfg.training.task_name == task_name
+    assert hydra_cfg.training.sim_backend == backend
+    assert env_cfg.ctrl_dt == pytest.approx(0.02)
+    assert env_cfg.max_episode_seconds == pytest.approx(20.0)
+    assert env_cfg.policy_observation_group == "policy"
+    assert env_cfg.critic_observation_group == "critic"
+    assert env_cfg.scale_rewards_by_dt is True
 
-    reward_config = OmegaConf.to_container(cfg.reward, resolve=True)
-    assert env_cfg_override["reward_config"] == reward_config
-    env_override = cast(dict[str, Any], OmegaConf.to_container(cfg.env, resolve=True))
-    for key, value in env_override.items():
-        assert env_cfg_override[key] == value
+    assert env_cfg.scene is not None
+    assert env_cfg.scene.model_file.endswith(f"robots/g1/{model_suffix}")
+    assert env_cfg.scene.default_keyframe_name == "stand"
+    robot = env_cfg.scene.entities["robot"]
+    assert robot.root_body_name == "pelvis"
+    assert len(robot.joint_names or ()) == num_dof
+    assert len(robot.actuator_names or ()) == num_dof
+    assert robot.body_names == ["pelvis"]
 
-    env._reward_fns = {}
-    env._init_reward_functions()
-    for reward_name in cfg.reward.scales.keys():
-        assert reward_name in env._reward_fns
+    assert list(env_cfg.observations) == ["policy", "critic"]
+    assert list(env_cfg.observations["policy"].terms) == list(_OBSERVATION_TERMS)
+    assert list(env_cfg.observations["critic"].terms) == [*_OBSERVATION_TERMS, "base_lin_vel"]
 
+    # Observation scaling profiles are explicit per-owner term scales.
+    policy_terms = env_cfg.observations["policy"].terms
+    critic_terms = env_cfg.observations["critic"].terms
+    walk_profile = case_id in _WALK_PROFILE_IDS
+    expected_gyro = 0.25 if walk_profile else None
+    expected_joint_vel = 0.05 if walk_profile else None
+    expected_linvel = 2.0 if walk_profile else None
+    for terms in (policy_terms, critic_terms):
+        assert terms["base_ang_vel"].scale == expected_gyro
+        assert terms["joint_vel"].scale == expected_joint_vel
+        assert terms["projected_gravity"].scale is None
+        assert terms["joint_pos"].scale is None
+        assert terms["actions"].scale is None
+        assert terms["command"].scale is None
+        assert terms["gait_phase"].scale is None
+        assert terms["gait_phase"].params["frequency"] == pytest.approx(1.5)
+    assert critic_terms["base_lin_vel"].scale == expected_linvel
 
-@pytest.mark.parametrize("case", _G1_OWNER_CASES, ids=[case["id"] for case in _G1_OWNER_CASES])
-def test_g1_owner_yaml_observation_profiles_match_expected_family(case: dict[str, Any]):
-    cfg = _compose_cfg(case["config_group"], case["overrides"])
-    obs = _compute_probe_obs(cfg)
+    # Observation noise reproduces the legacy noise_config: actor-only (the
+    # critic reads clean observations), applied before term scaling.
+    assert env_cfg.observations["policy"].enable_corruption is True
+    assert env_cfg.observations["critic"].enable_corruption is False
+    expected_noise = (
+        {"joint_pos": 0.01, "joint_vel": 0.1}
+        if walk_profile
+        else {
+            "base_ang_vel": 0.2,
+            "projected_gravity": 0.05,
+            "joint_pos": 0.01,
+            "joint_vel": 1.5,
+        }
+    )
+    for name, term in policy_terms.items():
+        if name in expected_noise:
+            assert term.noise is not None
+            assert term.noise.n_max == pytest.approx(expected_noise[name])
+            assert term.noise.n_min == pytest.approx(-expected_noise[name])
+        else:
+            assert term.noise is None
+    for term in critic_terms.values():
+        assert term.noise is None
+
+    assert list(env_cfg.actions) == ["joint_pos"]
+    assert env_cfg.actions["joint_pos"].scale == pytest.approx(action_scale)
+    assert env_cfg.actions["joint_pos"].use_default_offset is True
+
+    assert list(env_cfg.terminations) == ["time_out", "tilt", "base_height"]
+    assert env_cfg.terminations["time_out"].time_out is True
+    assert env_cfg.terminations["tilt"].func is g1_terms.g1_tilt_exceeded
+    assert env_cfg.terminations["base_height"].func is mdp.root_height_below_minimum
 
-    if case["profile"] == "legacy":
-        np.testing.assert_allclose(obs["obs"][:, :3], [[104.0, 105.0, 106.0]])
-        np.testing.assert_allclose(obs["obs"][:, 35:37], [[107.0, 108.0]])
-        np.testing.assert_allclose(obs["critic"][:, :3], [[4.0, 5.0, 6.0]])
-        np.testing.assert_allclose(obs["critic"][:, 35:37], [[7.0, 8.0]])
-        np.testing.assert_allclose(obs["critic"][:, 98:101], [[1.0, 2.0, 3.0]])
+    assert tuple(name for name, term in env_cfg.events.items() if term is not None) == (
+        expected_events
+    )
+    assert tuple(name for name, term in env_cfg.rewards.items() if term is not None) == (
+        expected_rewards
+    )
+    if has_curriculum:
+        assert list(env_cfg.curriculum) == ["penalty_scaling"]
+        assert env_cfg.curriculum["penalty_scaling"].func is g1_terms.G1PenaltyCurriculum
     else:
-        np.testing.assert_allclose(obs["obs"][:, :3], [[26.0, 26.25, 26.5]])
-        np.testing.assert_allclose(obs["obs"][:, 35:37], [[5.35, 5.4]])
-        np.testing.assert_allclose(obs["critic"][:, :3], [[1.0, 1.25, 1.5]])
-        np.testing.assert_allclose(obs["critic"][:, 35:37], [[0.35, 0.4]])
-        np.testing.assert_allclose(obs["critic"][:, 98:101], [[2.0, 4.0, 6.0]])
+        assert not env_cfg.curriculum
 
+    command = env_cfg.commands["twist"]
+    assert isinstance(command, g1_terms.G1VelocityCommandCfg)
+    assert command.planar_dead_zone == pytest.approx(0.2)
+    assert command.resampling_time_range == [20.0, 20.0]
+    if backend == "motrix" and config_group == "ppo":
+        assert tuple(command.ranges.lin_vel_x) == (0.4, 0.7)
+        assert tuple(command.ranges.lin_vel_y) == (0.0, 0.0)
+    else:
+        assert tuple(command.ranges.lin_vel_x) == (-0.6, 1.0)
+        assert tuple(command.ranges.lin_vel_y) == (-0.4, 0.4)
+        assert tuple(command.ranges.ang_vel_z) == (-0.8, 0.8)
 
-def test_g1_observation_profile_selection_prefers_reward_family_over_curriculum_flag():
-    from unilab.envs.locomotion.g1.joystick import G1WalkEnv
+    if backend == "mjwarp":
+        assert env_cfg.mjwarp_nconmax == 128
+        assert env_cfg.mjwarp_njmax == 256
+    if backend == "isaacgym":
+        assert env_cfg.isaacgym_device_id == 0
+        # Native rendering (viewer + camera-sensor record) is supported;
+        # playback stays on the base config's auto mode.
+        assert hydra_cfg.training.play_render_mode == "auto"
+    if backend == "genesis":
+        # Re-declares the MJCF <option integrator="implicitfast"> that Genesis
+        # drops at import; the other global options keep Genesis defaults.
+        assert env_cfg.genesis_integrator == "implicitfast"
+        assert env_cfg.genesis_constraint_solver is None
+        assert env_cfg.genesis_friction_cone is None
+        assert env_cfg.genesis_solver_iterations is None
+        # Native rendering (interactive viewer + offscreen record) is
+        # supported; playback stays on the base config's auto mode.
+        assert hydra_cfg.training.play_render_mode == "auto"
+    if backend == "isaacsim":
+        assert env_cfg.isaacsim_device_id == 0
+        assert env_cfg.isaacsim_worker_timeout_s == pytest.approx(120.0)
+        assert hydra_cfg.training.play_render_mode == "auto"
+        assert hydra_cfg.play_profile.enabled is False
 
-    env = cast(Any, object.__new__(G1WalkEnv))
+    pose = env_cfg.rewards["pose"]
+    expected_weights = _POSE_WEIGHTS_29 if num_dof == 29 else _POSE_WEIGHTS_23
+    if case_id == "flashsac-mujoco":
+        expected_weights = [2.0 if i in (1, 7) else w for i, w in enumerate(expected_weights)]
+    assert list(pose.params["pose_weights"]) == pytest.approx(expected_weights)
 
-    env._cfg = cast(
-        Any,
-        type(
-            "Cfg",
-            (),
-            {"curriculum": type("Curriculum", (), {"enabled": True})(), "reward_config": None},
-        )(),
-    )
-    env._reward_cfg = cast(
-        Any,
-        type("RewardCfg", (), {"scales": {"orientation": -2.5, "ang_vel_xy": -0.2}})(),
+    for manager_name in ("observations", "events", "rewards", "terminations", "curriculum"):
+        for term in getattr(env_cfg, manager_name).values():
+            if term is None:
+                continue
+            nested_terms = term.terms.values() if manager_name == "observations" else (term,)
+            for nested in nested_terms:
+                if nested is None:
+                    continue
+                module = nested.func.__module__
+                assert ".backend." not in module
+                assert not any(
+                    name in module
+                    for name in (".mujoco", ".motrix", ".mjwarp", ".isaacgym", ".isaacsim")
+                )
+
+    _assert_no_omegaconf(env_cfg)
+
+
+def test_g1_walk_registries_are_manager_only() -> None:
+    registry.ensure_registries()
+    metadata = registry.list_registered_envs()
+
+    assert metadata["G1WalkFlat"] == {
+        "config_factory": "ManagerBasedRlEnvCfg",
+        "available_backends": ["mujoco", "mjwarp", "motrix", "isaacgym", "genesis", "isaacsim"],
+    }
+    assert metadata["G1WalkRough"] == {
+        "config_factory": "ManagerBasedRlEnvCfg",
+        "available_backends": ["mujoco", "motrix"],
+    }
+    assert metadata["G1Walk23DofFlat"] == {
+        "config_factory": "ManagerBasedRlEnvCfg",
+        "available_backends": ["mujoco", "motrix"],
+    }
+    assert metadata["G1Walk23DofRough"] == {
+        "config_factory": "ManagerBasedRlEnvCfg",
+        "available_backends": ["mujoco", "motrix"],
+    }
+
+    for legacy_override in (
+        {"reward_config": {}},
+        {"domain_rand": {"randomize_kp": True}},
+        {"control_config": {"action_scale": 0.25}},
+        {"gait_phase_init_mode": "offset_phase"},
+        {"reset_base_qvel_limit": 0.5},
+        {"noise_config": {"level": 1.0}},
+    ):
+        with pytest.raises(ValueError, match="has no attribute"):
+            apply_cfg_overrides(ManagerBasedRlEnvCfg(), legacy_override)
+
+
+@pytest.mark.parametrize(
+    ("config_group", "overrides", "task_name", "backend", "num_dof", "obs_dim", "critic_dim"),
+    (
+        pytest.param(
+            "ppo",
+            ("task=g1_walk_flat/mujoco",),
+            "G1WalkFlat",
+            "mujoco",
+            29,
+            98,
+            101,
+            id="ppo-mujoco",
+        ),
+        pytest.param(
+            "ppo",
+            ("task=g1_walk_flat/motrix",),
+            "G1WalkFlat",
+            "motrix",
+            29,
+            98,
+            101,
+            id="ppo-motrix",
+        ),
+        pytest.param(
+            "sac",
+            ("task=g1_walk_flat/mujoco",),
+            "G1WalkFlat",
+            "mujoco",
+            29,
+            98,
+            101,
+            id="sac-mujoco",
+        ),
+        pytest.param(
+            "ppo",
+            ("task=g1_23dof_walk_flat/mujoco",),
+            "G1Walk23DofFlat",
+            "mujoco",
+            23,
+            80,
+            83,
+            id="ppo-23dof-mujoco",
+        ),
+        pytest.param(
+            "ppo",
+            ("task=g1_23dof_walk_rough/mujoco",),
+            "G1Walk23DofRough",
+            "mujoco",
+            23,
+            80,
+            83,
+            id="ppo-23dof-rough-mujoco",
+        ),
+    ),
+)
+def test_g1_registry_executes_real_manager_runtime(
+    config_group: str,
+    overrides: tuple[str, ...],
+    task_name: str,
+    backend: str,
+    num_dof: int,
+    obs_dim: int,
+    critic_dim: int,
+) -> None:
+    registry.ensure_registries()
+    _, env_cfg, env_override = _materialize(config_group, overrides, task_name)
+    try:
+        env = registry.make(
+            task_name,
+            sim_backend=backend,
+            env_cfg_override=env_override,
+            num_envs=2,
+        )
+    except ImportError as exc:
+        pytest.skip(f"{backend} runtime unavailable: {exc}")
+
+    try:
+        assert isinstance(env, ManagerBasedRlEnv)
+        assert env.obs_groups_spec == {"obs": obs_dim, "critic": critic_dim}
+        assert env.action_space.shape == (num_dof,)
+        action = env.action_manager.get_term("joint_pos")
+        assert len(action.target_names) == num_dof
+
+        obs, info = env.reset(seed=7)
+        assert {name: value.shape for name, value in obs.items()} == {
+            "obs": (2, obs_dim),
+            "critic": (2, critic_dim),
+        }
+        assert isinstance(info, dict)
+        for _ in range(5):
+            state = env.step(np.zeros((2, num_dof), dtype=np.float32))
+        for value in (*state.obs.values(), state.reward):
+            assert isinstance(value, np.ndarray)
+            assert np.isfinite(value).all()
+
+        # The command and gait-phase segments pin the legacy obs layout tail.
+        command = env.command_manager.get_command("twist")
+        np.testing.assert_allclose(
+            state.obs["obs"][:, obs_dim - 5 : obs_dim - 2], command, rtol=0.0, atol=1.0e-6
+        )
+    finally:
+        env.close()
+
+
+def test_g1_walk_profile_runtime_obs_scaling_matches_legacy_layout() -> None:
+    """Walk-profile owners scale gyro x0.25, dof_vel x0.05, critic linvel x2.0."""
+    registry.ensure_registries()
+    _, _, env_override = _materialize("sac", ("task=g1_walk_flat/mujoco",), "G1WalkFlat")
+    # Exact comparison against raw sensor reads requires clean observations.
+    env_override["observations"]["policy"]["enable_corruption"] = False
+    try:
+        env = registry.make(
+            "G1WalkFlat", sim_backend="mujoco", env_cfg_override=env_override, num_envs=2
+        )
+    except ImportError as exc:
+        pytest.skip(f"mujoco runtime unavailable: {exc}")
+
+    try:
+        env.reset(seed=3)
+        state = env.step(np.zeros((2, 29), dtype=np.float32))
+        gyro = env._backend.get_sensor_data("torso_gyro")
+        upvector = env._backend.get_sensor_data("torso_upvector")
+        dof_vel = env._backend.get_dof_vel()
+        linvel = env._backend.get_sensor_data("pelvis_local_linvel")
+        np.testing.assert_allclose(state.obs["obs"][:, :3], 0.25 * gyro, rtol=0.0, atol=1.0e-6)
+        np.testing.assert_allclose(state.obs["obs"][:, 3:6], -upvector, rtol=0.0, atol=1.0e-6)
+        np.testing.assert_allclose(
+            state.obs["obs"][:, 35:64], 0.05 * dof_vel, rtol=0.0, atol=1.0e-6
+        )
+        np.testing.assert_allclose(
+            state.obs["critic"][:, 98:101], 2.0 * linvel, rtol=0.0, atol=1.0e-6
+        )
+        np.testing.assert_allclose(state.obs["critic"][:, :3], 0.25 * gyro, rtol=0.0, atol=1.0e-6)
+        np.testing.assert_allclose(
+            state.obs["obs"][:, 96:98], state.obs["critic"][:, 96:98], rtol=0.0, atol=0.0
+        )
+    finally:
+        env.close()
+
+
+def test_g1_legacy_profile_runtime_obs_scaling_matches_legacy_layout() -> None:
+    """Legacy-profile owners keep unit scaling on every observation segment."""
+    registry.ensure_registries()
+    _, _, env_override = _materialize("ppo", ("task=g1_walk_flat/mujoco",), "G1WalkFlat")
+    # Exact comparison against raw sensor reads requires clean observations.
+    env_override["observations"]["policy"]["enable_corruption"] = False
+    try:
+        env = registry.make(
+            "G1WalkFlat", sim_backend="mujoco", env_cfg_override=env_override, num_envs=2
+        )
+    except ImportError as exc:
+        pytest.skip(f"mujoco runtime unavailable: {exc}")
+
+    try:
+        env.reset(seed=3)
+        state = env.step(np.zeros((2, 29), dtype=np.float32))
+        gyro = env._backend.get_sensor_data("torso_gyro")
+        dof_vel = env._backend.get_dof_vel()
+        linvel = env._backend.get_sensor_data("pelvis_local_linvel")
+        np.testing.assert_allclose(state.obs["obs"][:, :3], gyro, rtol=0.0, atol=1.0e-6)
+        np.testing.assert_allclose(state.obs["obs"][:, 35:64], dof_vel, rtol=0.0, atol=1.0e-6)
+        np.testing.assert_allclose(state.obs["critic"][:, 98:101], linvel, rtol=0.0, atol=1.0e-6)
+    finally:
+        env.close()
+
+
+def test_g1_penalty_curriculum_scales_negative_weights_from_start() -> None:
+    registry.ensure_registries()
+    _, _, env_override = _materialize("sac", ("task=g1_walk_flat/mujoco",), "G1WalkFlat")
+    override_snapshot = deepcopy(env_override)
+    try:
+        env = registry.make(
+            "G1WalkFlat", sim_backend="mujoco", env_cfg_override=env_override, num_envs=2
+        )
+        # Repeated construction from the same override must not drift: the
+        # legacy env halved the shared override dict on every construction.
+        env_repeat = registry.make(
+            "G1WalkFlat", sim_backend="mujoco", env_cfg_override=env_override, num_envs=2
+        )
+    except ImportError as exc:
+        pytest.skip(f"mujoco runtime unavailable: {exc}")
+
+    try:
+        for built in (env, env_repeat):
+            assert built.curriculum_manager.active_terms == ["penalty_scaling"]
+            # initial_scale=0.125 scales every negative weight from construction,
+            # matching the tuned legacy effective schedule (1/8 initial, 1/4 cap).
+            assert built.reward_manager.get_term_cfg("penalty_orientation").weight == pytest.approx(
+                -1.25
+            )
+            assert built.reward_manager.get_term_cfg("penalty_action_rate").weight == pytest.approx(
+                -0.5
+            )
+            assert built.reward_manager.get_term_cfg("pose").weight == pytest.approx(-0.0625)
+            # Positive weights stay untouched.
+            assert built.reward_manager.get_term_cfg("alive").weight == pytest.approx(10.0)
+            assert built.reward_manager.get_term_cfg("feet_phase").weight == pytest.approx(5.0)
+        # The shared override dict is never mutated in place.
+        assert env_override == override_snapshot
+
+        state = env.step(np.zeros((2, 29), dtype=np.float32))
+        log = state.info["log"]
+        for name in _OFFPOLICY_REWARDS:
+            assert f"reward/{name}" in log
+        assert log["reward/penalty_action_rate"] == pytest.approx(0.0)
+    finally:
+        env.close()
+        env_repeat.close()
+
+
+# Every task owner carrying the penalty curriculum, with the schedule that
+# reproduces its tuned legacy baseline. Legacy offpolicy runners built three
+# envs per training run (two probe envs + the spawned collector) and the
+# legacy PenaltyCurriculum halved the shared override dict in place on each
+# construction, so collectors effectively trained at 1/8 initial / 1/4 cap of
+# the YAML weights. The on-policy runners built a single env, so their
+# effective schedule was the declared 0.5 -> 1.0. The manager runtime isolates
+# each env, so these params are now the single source of truth.
+_PENALTY_CURRICULUM_CASES = (
+    pytest.param(
+        "sac",
+        ("task=g1_walk_flat/mujoco",),
+        "G1WalkFlat",
+        id="sac-walk-flat",
+    ),
+    pytest.param(
+        "sac",
+        ("task=g1_walk_rough/mujoco",),
+        "G1WalkRough",
+        id="sac-walk-rough",
+    ),
+    pytest.param(
+        "sac",
+        ("task=g1_23dof_walk_flat/mujoco",),
+        "G1Walk23DofFlat",
+        id="sac-23dof-walk-flat",
+    ),
+    pytest.param(
+        "sac",
+        ("task=g1_23dof_walk_rough/mujoco",),
+        "G1Walk23DofRough",
+        id="sac-23dof-walk-rough",
+    ),
+    pytest.param(
+        "td3",
+        ("task=g1_walk_flat/mujoco",),
+        "G1WalkFlat",
+        id="td3-walk-flat",
+    ),
+    pytest.param(
+        "flashsac",
+        ("task=g1_walk_flat/mujoco",),
+        "G1WalkFlat",
+        id="flashsac-walk-flat",
+    ),
+)
+
+_OFFPOLICY_ALIGNED_SCHEDULE = {"initial_scale": 0.125, "min_scale": 0.125, "max_scale": 0.25}
+
+
+@pytest.mark.parametrize(
+    "config_group,overrides,task_name",
+    _PENALTY_CURRICULUM_CASES,
+    ids=[case.id for case in _PENALTY_CURRICULUM_CASES],
+)
+def test_offpolicy_penalty_curriculum_matches_legacy_effective_schedule(
+    config_group: str, overrides: tuple[str, ...], task_name: str
+) -> None:
+    _, env_cfg, _ = _materialize(config_group, overrides, task_name)
+    params = env_cfg.curriculum["penalty_scaling"].params
+    for key, expected in _OFFPOLICY_ALIGNED_SCHEDULE.items():
+        assert params[key] == pytest.approx(expected)
+    # Thresholds and annealing rate stay as tuned.
+    assert params["level_down_threshold"] == pytest.approx(150.0)
+    assert params["level_up_threshold"] == pytest.approx(750.0)
+    assert params["degree"] == pytest.approx(0.001)
+
+
+def test_ppo_penalty_curriculum_matches_legacy_effective_schedule() -> None:
+    # The on-policy runner builds a single env per training run, so the legacy
+    # effective schedule equals the declared 0.5 -> 1.0 range.
+    _, env_cfg, _ = _materialize("ppo", ("task=g1_23dof_walk_rough/mujoco",), "G1Walk23DofRough")
+    params = env_cfg.curriculum["penalty_scaling"].params
+    assert params["initial_scale"] == pytest.approx(0.5)
+    assert params["min_scale"] == pytest.approx(0.5)
+    assert params["max_scale"] == pytest.approx(1.0)
+
+
+def _genesis_runtime_available() -> bool:
+    from unisim.backend.genesis.dependencies import genesis_dependencies_available
+
+    if not genesis_dependencies_available():
+        return False
+    try:
+        import torch
+
+        return bool(torch.cuda.is_available())
+    except Exception:  # noqa: BLE001 - any torch probe failure means unavailable
+        return False
+
+
+# Runs in a subprocess: Genesis allows exactly one gs.init per process
+# (REPORT #1372 §3.5 [9a]) and tests/base/test_genesis_runtime.py deliberately
+# destroys the session to verify re-init fails closed, so an in-process smoke
+# could observe a poisoned session depending on pytest collection order.
+# The config group (ppo/sac) arrives as argv[1]; each parametrized case gets
+# its own subprocess, hence its own gs session.
+_GENESIS_ENV_SMOKE_SCRIPT = """
+import sys
+from pathlib import Path
+
+import numpy as np
+from hydra import compose, initialize_config_dir
+from hydra.core.global_hydra import GlobalHydra
+
+from unilab.base import registry
+from unilab.base.config_adapter import BackendAdapter
+from unilab.base.config_materialization import apply_cfg_overrides
+from unilab.envs import ManagerBasedRlEnv, ManagerBasedRlEnvCfg
+
+ROOT = Path.cwd()
+CONFIG_GROUP = sys.argv[1]
+
+registry.ensure_registries()
+GlobalHydra.instance().clear()
+with initialize_config_dir(
+    config_dir=str(ROOT / "src" / "unilab" / "conf" / CONFIG_GROUP), version_base="1.3"
+):
+    hydra_cfg = compose("config", overrides=["task=g1_walk_flat/genesis"])
+assert hydra_cfg.training.task_name == "G1WalkFlat"
+assert hydra_cfg.training.sim_backend == "genesis"
+assert hydra_cfg.training.play_render_mode == "auto"
+
+env_override = BackendAdapter(
+    hydra_cfg, root_dir=ROOT, algo_name=CONFIG_GROUP
+).build_task_env_cfg_override()
+env_cfg = registry.materialize_env_config("G1WalkFlat")
+assert isinstance(env_cfg, ManagerBasedRlEnvCfg)
+apply_cfg_overrides(env_cfg, env_override)
+env_cfg.validate()
+assert env_cfg.genesis_integrator == "implicitfast"
+
+env = registry.make(
+    "G1WalkFlat",
+    sim_backend="genesis",
+    env_cfg_override=env_override,
+    num_envs=2,
+)
+try:
+    assert isinstance(env, ManagerBasedRlEnv)
+    assert env.obs_groups_spec == {"obs": 98, "critic": 101}
+    assert env.action_space.shape == (29,)
+
+    # Keyframe reset: reset_scene_to_default consumes the cold-path-scanned
+    # "stand" keyframe (Genesis itself drops <keyframe> at import).
+    obs, info = env.reset(seed=7)
+    assert isinstance(obs, dict) and isinstance(info, dict)
+    assert {name: value.shape for name, value in obs.items()} == {
+        "obs": (2, 98),
+        "critic": (2, 101),
+    }
+    for _ in range(12):
+        state = env.step(np.zeros((2, 29), dtype=np.float32))
+        assert set(state.obs) == {"obs", "critic"}
+        assert state.obs["obs"].shape == (2, 98)
+        assert state.obs["critic"].shape == (2, 101)
+        for value in (*state.obs.values(), state.reward):
+            assert isinstance(value, np.ndarray)
+            assert np.isfinite(value).all()
+
+    # Play path: mode none enters safely as a no-op; record resolves to the
+    # native offscreen plan and validates its required fields.
+    plan = env.resolve_play_render_plan(
+        play_render_mode="none", play_steps=100, output_video=None
     )
-    assert env._uses_walk_observation_profile() is False
-
-    env._cfg = cast(
-        Any,
-        type(
-            "Cfg",
-            (),
-            {"curriculum": type("Curriculum", (), {"enabled": False})(), "reward_config": None},
-        )(),
+    assert plan.mode == "none"
+    try:
+        env.resolve_play_render_plan(
+            play_render_mode="record", play_steps=100, output_video=None
+        )
+    except ValueError as exc:
+        assert "output video path" in str(exc)
+    else:
+        raise AssertionError("genesis record playback must require an output path")
+    plan = env.resolve_play_render_plan(
+        play_render_mode="record", play_steps=100, output_video="out.mp4"
     )
-    env._reward_cfg = cast(
-        Any,
-        type(
-            "RewardCfg",
-            (),
-            {"scales": {"penalty_orientation": -10.0, "penalty_ang_vel_xy": -1.0, "alive": 10.0}},
-        )(),
+    assert plan.mode == "record" and plan.record_video and plan.headless
+
+    print(
+        f"[genesis env smoke:{CONFIG_GROUP}] reset+12 steps OK; "
+        f"obs={obs['obs'].shape} critic={obs['critic'].shape} "
+        f"reward0={state.reward.mean():.4f}"
     )
-    assert env._uses_walk_observation_profile() is True
+finally:
+    env.close()
+    env._backend.close()
+"""
+
 
+@pytest.mark.slow
+@pytest.mark.parametrize("config_group", ("ppo", "sac"))
+def test_g1_walk_flat_genesis_owner_real_runtime_smoke(config_group: str) -> None:
+    """Real-runtime smoke for the genesis owner (genesis-world 1.3.3 + CUDA).
 
-def test_g1_walk_tasks_are_registered():
-    ensure_registries()
+    Full chain per algo tree: Hydra compose of task=g1_walk_flat/genesis ->
+    registry lookup -> ManagerBasedRlEnv construction -> keyframe reset -> 12
+    steps with finite/shape-stable action/state/sensor reads -> explicit
+    cleanup; the play path enters safely with play_render_mode=none and the
+    native record plan resolves with field validation.
+    """
+    if not _genesis_runtime_available():
+        pytest.skip("genesis requires the genesis-world extra and a CUDA device")
+
+    result = subprocess.run(
+        [sys.executable, "-c", _GENESIS_ENV_SMOKE_SCRIPT, config_group],
+        cwd=ROOT_DIR,
+        capture_output=True,
+        text=True,
+        timeout=600,
+    )
 
-    assert registry.contains("G1WalkFlat")
-    assert registry.contains("G1WalkRough")
+    assert result.returncode == 0, result.stderr or result.stdout
+    assert f"[genesis env smoke:{config_group}]" in result.stdout
diff --git a/tests/envs/locomotion/g1/test_manager_terms.py b/tests/envs/locomotion/g1/test_manager_terms.py
new file mode 100644
index 000000000..9bd785443
--- /dev/null
+++ b/tests/envs/locomotion/g1/test_manager_terms.py
@@ -0,0 +1,352 @@
+"""Unit contracts for the G1 Manager-Based terms (fake-env, no simulator)."""
+
+from __future__ import annotations
+
+import inspect
+from types import SimpleNamespace
+from typing import Any, cast
+
+import numpy as np
+import pytest
+
+from unilab.managers import ObservationTermCfg, RewardTermCfg
+from unilab.tasks.locomotion.g1 import manager_terms as g1_terms
+from unilab.tasks.locomotion.g1.manager_terms import (
+    G1GaitPhase,
+    G1PenaltyCurriculum,
+    G1VelocityCommandCfg,
+    compute_feet_phase_contact_targets,
+    compute_feet_phase_height_targets,
+)
+
+# CPU-bound on the single-core CI runner; kept in the slow lane (make test-slow).
+pytestmark = pytest.mark.slow
+
+
+def _fake_scene(sensor_data: dict[str, np.ndarray]) -> Any:
+    class _Scene(dict):
+        def bind_sensor_data(self, names):
+            arrays = [sensor_data[name] for name in names]
+            concatenated = np.concatenate(arrays, axis=1)
+            dimensions = tuple(array.shape[1] for array in arrays)
+            return SimpleNamespace(
+                dimensions=dimensions,
+                backend_type="fake",
+                read=lambda: concatenated,
+            )
+
+    return _Scene()
+
+
+class _FakeEnv:
+    """Weakref-able stand-in for ManagerBasedRlEnv in term unit tests."""
+
+    def __init__(
+        self,
+        sensor_data: dict[str, np.ndarray],
+        *,
+        num_envs: int = 2,
+        counter: int = 0,
+        command: np.ndarray | None = None,
+    ) -> None:
+        commands = {"twist": command if command is not None else np.zeros((num_envs, 3))}
+        self.num_envs = num_envs
+        self.common_step_counter = counter
+        self.step_dt = 0.02
+        self.rng = np.random.default_rng(0)
+        self.scene = _fake_scene(sensor_data)
+        self.command_manager = SimpleNamespace(get_command=lambda name: commands[name])
+
+
+def _fake_env(
+    sensor_data: dict[str, np.ndarray],
+    *,
+    num_envs: int = 2,
+    counter: int = 0,
+    command: np.ndarray | None = None,
+) -> Any:
+    return _FakeEnv(sensor_data, num_envs=num_envs, counter=counter, command=command)
+
+
+def test_gait_phase_advances_with_counter_and_resamples_per_init_mode():
+    env = _fake_env({}, num_envs=4)
+    term = G1GaitPhase(
+        ObservationTermCfg(
+            func=G1GaitPhase, params={"frequency": 1.5, "init_mode": "offset_phase"}
+        ),
+        cast(Any, env),
+    )
+
+    np.testing.assert_array_equal(term(env), np.zeros((4, 2)))
+    term.reset(np.arange(4, dtype=np.int32))
+    phase = term(env)
+    np.testing.assert_allclose(phase[:, 1] - phase[:, 0], np.pi, rtol=1.0e-6)
+    assert np.all(phase[:, 0] >= 0.0) and np.all(phase[:, 0] < 2.0 * np.pi)
+
+    env.common_step_counter = 1
+    advanced = term(env)
+    delta = 2.0 * np.pi * 1.5 * 0.02
+    np.testing.assert_allclose(advanced, np.fmod(phase + delta, 2.0 * np.pi), rtol=1.0e-6)
+
+    # Idempotent per counter: reading twice does not advance twice.
+    np.testing.assert_array_equal(term(env), advanced)
+
+    env.common_step_counter = 0
+    with pytest.raises(ValueError, match="cannot move backwards"):
+        term(env)
+
+
+def test_gait_phase_independent_mode_samples_feet_independently():
+    env = _fake_env({}, num_envs=64)
+    term = G1GaitPhase(
+        ObservationTermCfg(func=G1GaitPhase, params={"frequency": 1.5, "init_mode": "independent"}),
+        cast(Any, env),
+    )
+    term.reset(np.arange(64, dtype=np.int32))
+    phase = term(env)
+    assert not np.allclose(phase[:, 1] - phase[:, 0], np.pi)
+
+
+def test_bezier_targets_match_legacy_reference_values():
+    phase = np.array([[0.0, np.pi], [np.pi / 2.0, 3.0 * np.pi / 2.0]])
+    left, right = compute_feet_phase_height_targets(phase, 0.09)
+    # phi=0 -> x=0.5 boundary -> stance peak; phi=pi -> x=0 -> zero;
+    # phi=pi/2 -> x=0.75 -> swing midpoint 0.045.
+    np.testing.assert_allclose(left, [0.09, 0.045], atol=1.0e-7)
+    np.testing.assert_allclose(right, [0.0, 0.045], atol=1.0e-7)
+    left_contact, right_contact = compute_feet_phase_contact_targets(phase, 0.09)
+    np.testing.assert_array_equal(left_contact, [False, True])
+    np.testing.assert_array_equal(right_contact, [True, True])
+
+
+def test_feet_phase_reward_is_gated_by_forward_speed():
+    sensors = {
+        "left_foot_pos": np.zeros((2, 3), dtype=np.float32),
+        "right_foot_pos": np.zeros((2, 3), dtype=np.float32),
+        "pelvis_local_linvel": np.array([[0.01, 0.0, 0.0], [0.10, 0.0, 0.0]], dtype=np.float32),
+    }
+    env = _fake_env(sensors)
+    term = g1_terms.feet_phase(
+        RewardTermCfg(
+            func=g1_terms.feet_phase,
+            weight=1.0,
+            params={
+                "frequency": 1.5,
+                "swing_height": 0.09,
+                "tracking_sigma": 0.008,
+                "min_forward_speed": 0.05,
+                "command_name": "twist",
+            },
+        ),
+        cast(Any, env),
+    )
+
+    reward = term(env)
+
+    assert reward[0] == pytest.approx(0.0)
+    assert reward[1] > 0.0
+
+
+def test_feet_double_stance_masks_on_forward_command():
+    sensors = {
+        "left_foot_pos": np.zeros((2, 3), dtype=np.float32),
+        "right_foot_pos": np.zeros((2, 3), dtype=np.float32),
+        "pelvis_local_linvel": np.zeros((2, 3), dtype=np.float32),
+        **{f"left_foot_contact_{i}": np.ones((2, 1)) for i in range(4)},
+        **{f"right_foot_contact_{i}": np.ones((2, 1)) for i in range(4)},
+    }
+    command = np.array([[0.5, 0.0, 0.0], [0.0, 0.0, 0.0]])
+    env = _fake_env(sensors, command=command)
+    term = g1_terms.feet_double_stance(
+        RewardTermCfg(
+            func=g1_terms.feet_double_stance,
+            weight=-1.0,
+            params={"frequency": 1.5, "command_name": "twist"},
+        ),
+        cast(Any, env),
+    )
+
+    np.testing.assert_array_equal(term(env), [1.0, 0.0])
+
+
+def _curriculum_env(weights: dict[str, float], num_envs: int = 4) -> Any:
+    cfgs = {name: SimpleNamespace(weight=value) for name, value in weights.items()}
+    return SimpleNamespace(
+        num_envs=num_envs,
+        reward_manager=SimpleNamespace(
+            active_terms=list(weights),
+            get_term_cfg=lambda name: cfgs[name],
+        ),
+        reset_buf=np.zeros(num_envs, dtype=np.bool_),
+        episode_length_buf=np.zeros(num_envs, dtype=np.int64),
+        rng=np.random.default_rng(0),
+    )
+
+
+def test_penalty_curriculum_scales_only_negative_weights_and_tracks_episodes():
+    env = _curriculum_env({"pose": -0.5, "alive": 10.0, "penalty_orientation": -10.0})
+    term = G1PenaltyCurriculum(
+        RewardTermCfg(
+            func=G1PenaltyCurriculum,
+            weight=1.0,
+            params={"initial_scale": 0.5, "min_scale": 0.5, "max_scale": 1.0},
+        ),
+        cast(Any, env),
+    )
+
+    assert env.reward_manager.get_term_cfg("pose").weight == pytest.approx(-0.25)
+    assert env.reward_manager.get_term_cfg("penalty_orientation").weight == pytest.approx(-5.0)
+    assert env.reward_manager.get_term_cfg("alive").weight == pytest.approx(10.0)
+
+    # Short episodes (< level_down_threshold=150 default) shrink the scale,
+    # clamped at min_scale.
+    env.reset_buf[:] = True
+    env.episode_length_buf[:] = 10
+    state = term(cast(Any, env), np.arange(4, dtype=np.int32))
+    assert state["average_episode_length"] == pytest.approx(10.0)
+    assert state["penalty_scale"] == pytest.approx(0.5)
+    assert env.reward_manager.get_term_cfg("pose").weight == pytest.approx(-0.25)
+
+    # Long episodes (> level_up_threshold=750 default) relax the scale.
+    env.episode_length_buf[:] = 1000
+    state = term(cast(Any, env), np.arange(4, dtype=np.int32))
+    assert state["penalty_scale"] == pytest.approx(0.5 * (1.0 + 0.001))
+    assert env.reward_manager.get_term_cfg("pose").weight == pytest.approx(
+        -0.5 * 0.5 * (1.0 + 0.001)
+    )
+
+
+def test_penalty_curriculum_repeated_construction_never_mutates_source_cfg():
+    """Regression guard for the legacy shared-override mutation.
+
+    The legacy PenaltyCurriculum halved the shared override dict in place on
+    every env construction (two probe envs + the collector in each offpolicy
+    runner), so collectors silently trained at 1/8 of the configured penalty
+    weights. The manager runtime must isolate each construction from the
+    source config so repeated env builds keep identical effective weights.
+    """
+    from unilab.managers import RewardManager
+
+    source_cfg = {
+        "pose": RewardTermCfg(func=lambda env: np.zeros(env.num_envs), weight=-0.5),
+        "alive": RewardTermCfg(func=lambda env: np.zeros(env.num_envs), weight=10.0),
+    }
+    effective_weights: list[float] = []
+    for _ in range(3):  # legacy offpolicy runners built probe + probe + collector
+        reward_manager = RewardManager(source_cfg, cast(Any, SimpleNamespace(num_envs=4)))
+        env = SimpleNamespace(
+            num_envs=4,
+            reward_manager=reward_manager,
+            reset_buf=np.zeros(4, dtype=np.bool_),
+            episode_length_buf=np.zeros(4, dtype=np.int64),
+            rng=np.random.default_rng(0),
+        )
+        G1PenaltyCurriculum(
+            RewardTermCfg(
+                func=G1PenaltyCurriculum,
+                weight=1.0,
+                params={"initial_scale": 0.125, "min_scale": 0.125, "max_scale": 0.25},
+            ),
+            cast(Any, env),
+        )
+        effective_weights.append(reward_manager.get_term_cfg("pose").weight)
+
+    assert source_cfg["pose"].weight == -0.5
+    assert source_cfg["alive"].weight == 10.0
+    for weight in effective_weights:
+        assert weight == pytest.approx(-0.0625)
+
+
+def test_penalty_curriculum_shrinks_scale_below_initial_when_min_allows():
+    env = _curriculum_env({"pose": -0.5})
+    term = G1PenaltyCurriculum(
+        RewardTermCfg(
+            func=G1PenaltyCurriculum,
+            weight=1.0,
+            params={"initial_scale": 0.5, "min_scale": 0.0, "max_scale": 1.0},
+        ),
+        cast(Any, env),
+    )
+
+    env.reset_buf[:] = True
+    env.episode_length_buf[:] = 10
+    state = term(cast(Any, env), np.arange(4, dtype=np.int32))
+    assert state["penalty_scale"] == pytest.approx(0.5 * (1.0 - 0.001))
+    assert env.reward_manager.get_term_cfg("pose").weight == pytest.approx(
+        -0.5 * 0.5 * (1.0 - 0.001)
+    )
+
+
+def test_velocity_command_dead_zone_zeroes_small_planar_commands():
+    env = SimpleNamespace(
+        num_envs=64,
+        rng=np.random.default_rng(0),
+        step_dt=0.02,
+        scene={
+            "robot": SimpleNamespace(
+                data=SimpleNamespace(
+                    root_link_lin_vel_b=np.zeros((64, 3)),
+                    root_link_ang_vel_b=np.zeros((64, 3)),
+                    heading_w=np.zeros(64),
+                )
+            )
+        },
+    )
+    cfg = G1VelocityCommandCfg(
+        entity_name="robot",
+        resampling_time_range=(20.0, 20.0),
+        ranges=G1VelocityCommandCfg.Ranges(
+            lin_vel_x=(-0.15, 0.15),
+            lin_vel_y=(-0.15, 0.15),
+            ang_vel_z=(0.0, 0.0),
+        ),
+    )
+    term = cfg.build(cast(Any, env))
+    term._resample_command(np.arange(64, dtype=np.int32))
+
+    planar_norm = np.linalg.norm(term.vel_command_b[:, :2], axis=1)
+    assert np.all((planar_norm == 0.0) | (planar_norm > 0.2))
+    assert np.any(planar_norm == 0.0)
+
+
+def test_velocity_command_fails_closed_on_heading_command():
+    cfg = G1VelocityCommandCfg(
+        entity_name="robot",
+        resampling_time_range=(20.0, 20.0),
+        heading_command=True,
+        ranges=G1VelocityCommandCfg.Ranges(
+            lin_vel_x=(0.0, 1.0),
+            lin_vel_y=(0.0, 0.0),
+            ang_vel_z=(0.0, 0.0),
+            heading=(-3.14, 3.14),
+        ),
+    )
+    env = SimpleNamespace(num_envs=1, rng=np.random.default_rng(0), step_dt=0.02, scene={})
+    with pytest.raises(NotImplementedError, match="heading command"):
+        cfg.build(cast(Any, env))
+
+
+def test_g1_hot_paths_use_only_cached_runtime_objects():
+    for term in (
+        G1GaitPhase,
+        g1_terms.feet_phase,
+        g1_terms.feet_phase_contrast,
+        g1_terms.feet_phase_contact,
+        g1_terms.feet_double_stance,
+        g1_terms.feet_air_time,
+        g1_terms.forward_progress,
+        g1_terms.under_speed,
+        g1_terms.g1_tilt_exceeded,
+        g1_terms.penalty_feet_ori,
+        g1_terms.penalty_close_feet_xy,
+        g1_terms.G1PenaltyCurriculum,
+    ):
+        source = inspect.getsource(term.__call__)
+        for forbidden in (
+            "ASSETS_ROOT_PATH",
+            "model_file",
+            "getattr(",
+            "hasattr(",
+            "._backend",
+        ):
+            assert forbidden not in source, f"{term.__name__} hot path references {forbidden}"
diff --git a/tests/envs/locomotion/g1/test_symmetry_contract.py b/tests/envs/locomotion/g1/test_symmetry_contract.py
deleted file mode 100644
index fd16ad1ed..000000000
--- a/tests/envs/locomotion/g1/test_symmetry_contract.py
+++ /dev/null
@@ -1,88 +0,0 @@
-from __future__ import annotations
-
-from typing import Any, cast
-
-import pytest
-import torch
-
-from unilab.base import registry
-from unilab.base.registry import ensure_registries
-from unilab.envs.locomotion.g1.joystick import G1WalkRewardConfig
-
-pytest.importorskip("mujoco", reason="mujoco is required for G1 symmetry contract tests")
-
-
-def _reward_config() -> G1WalkRewardConfig:
-    return G1WalkRewardConfig(
-        scales={"tracking_lin_vel": 2.0, "alive": 10.0},
-        tracking_sigma=0.25,
-        base_height_target=0.754,
-        min_base_height=0.3,
-        max_tilt_deg=65.0,
-        gait_frequency=1.5,
-        feet_phase_swing_height=0.09,
-        feet_phase_tracking_sigma=0.04,
-        close_feet_threshold=0.15,
-        pose_weights=[0.01] * 29,
-    )
-
-
-def test_g1_walk_flat_symmetry_contract_matches_obs_groups():
-    ensure_registries()
-    env = cast(
-        Any,
-        registry.make(
-            "G1WalkFlat",
-            num_envs=1,
-            sim_backend="mujoco",
-            env_cfg_override={"reward_config": _reward_config()},
-        ),
-    )
-
-    try:
-        layouts = env.get_symmetry_obs_layouts()
-        assert set(layouts) == {"obs", "critic"}
-        for group_name, layout in layouts.items():
-            assert sum(dim for _, dim in layout) == env.obs_groups_spec[group_name]
-    finally:
-        env.close()
-
-
-def test_g1_walk_flat_symmetry_can_augment_critic_group():
-    ensure_registries()
-    env = cast(
-        Any,
-        registry.make(
-            "G1WalkFlat",
-            num_envs=1,
-            sim_backend="mujoco",
-            env_cfg_override={"reward_config": _reward_config()},
-        ),
-    )
-
-    try:
-        augmentation = env.build_symmetry_augmentation(device="cpu")
-        assert augmentation is not None
-
-        action_dim = env.action_space.shape[0]
-        obs = torch.zeros((1, env.obs_groups_spec["obs"]))
-        critic = torch.zeros((1, env.obs_groups_spec["critic"]))
-        actions = torch.zeros((1, action_dim))
-
-        actor_aug, action_aug = augmentation.augment_obs_and_actions(obs, actions, obs_group="obs")
-        critic_aug, critic_action_aug = augmentation.augment_obs_and_actions(
-            critic,
-            actions,
-            obs_group="critic",
-        )
-        actor_obs_aug = augmentation.augment_obs(obs, obs_group="obs")
-        critic_obs_aug = augmentation.augment_obs(critic, obs_group="critic")
-
-        assert actor_aug.shape == (2, env.obs_groups_spec["obs"])
-        assert critic_aug.shape == (2, env.obs_groups_spec["critic"])
-        assert action_aug.shape == (2, action_dim)
-        assert critic_action_aug.shape == (2, action_dim)
-        assert torch.equal(actor_obs_aug, actor_aug)
-        assert torch.equal(critic_obs_aug, critic_aug)
-    finally:
-        env.close()
diff --git a/tests/envs/locomotion/go1/test_manager_based_flat_cfg.py b/tests/envs/locomotion/go1/test_manager_based_flat_cfg.py
new file mode 100644
index 000000000..7462e3939
--- /dev/null
+++ b/tests/envs/locomotion/go1/test_manager_based_flat_cfg.py
@@ -0,0 +1,356 @@
+"""Hydra-owned production contract for the Go1 flat Manager-Based task."""
+
+from __future__ import annotations
+
+from collections.abc import Mapping, Sequence
+from copy import deepcopy
+from dataclasses import fields, is_dataclass
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pytest
+from hydra import compose, initialize_config_dir
+from hydra.core.global_hydra import GlobalHydra
+from omegaconf import DictConfig, OmegaConf
+
+from unilab.base import registry
+from unilab.base.config_adapter import BackendAdapter
+from unilab.base.config_materialization import apply_cfg_overrides
+from unilab.envs import (
+    ManagerBasedRlEnv,
+    ManagerBasedRlEnvCfg,
+    make_manager_based_rl_env,
+    mdp,
+)
+from unilab.tasks.locomotion.common import manager_terms
+
+ROOT_DIR = Path(__file__).parents[4]
+CONF_DIR = ROOT_DIR / "src" / "unilab" / "conf"
+
+_JOINT_NAMES = (
+    "FR_hip_joint",
+    "FR_thigh_joint",
+    "FR_calf_joint",
+    "FL_hip_joint",
+    "FL_thigh_joint",
+    "FL_calf_joint",
+    "RR_hip_joint",
+    "RR_thigh_joint",
+    "RR_calf_joint",
+    "RL_hip_joint",
+    "RL_thigh_joint",
+    "RL_calf_joint",
+)
+_ACTUATOR_NAMES = tuple(name.removesuffix("_joint") for name in _JOINT_NAMES)
+_HOME_JOINT_POS = np.array(
+    [0.0, 0.9, -1.8, 0.0, 0.9, -1.8, 0.0, 1.0, -1.8, 0.0, 1.0, -1.8],
+    dtype=np.float32,
+)
+_RESET_EVENTS = ("reset_scene_to_default", "reset_root_state_uniform")
+_DR_EVENTS = ("base_mass", "base_com", "pd_gains")
+_BASE_REWARDS = (
+    "tracking_lin_vel",
+    "tracking_ang_vel",
+    "lin_vel_z",
+    "ang_vel_xy",
+    "base_height",
+    "action_rate",
+    "similar_to_default",
+)
+
+_OWNER_CASES = (
+    pytest.param(
+        "ppo",
+        ("task=go1_joystick_flat/mujoco",),
+        "mujoco",
+        (*_RESET_EVENTS, *_DR_EVENTS, "push_robot"),
+        (*_BASE_REWARDS, "contact", "swing_feet_z"),
+        False,
+        id="ppo-mujoco",
+    ),
+    pytest.param(
+        "ppo",
+        ("task=go1_joystick_flat/motrix",),
+        "motrix",
+        (*_RESET_EVENTS, *_DR_EVENTS),
+        (*_BASE_REWARDS, "swing_feet_z"),
+        True,
+        id="ppo-motrix",
+    ),
+    pytest.param(
+        "ppo",
+        ("task=go1_joystick_flat/drake",),
+        "drake",
+        _RESET_EVENTS,
+        (*_BASE_REWARDS, "swing_feet_z"),
+        False,
+        id="ppo-drake",
+    ),
+    pytest.param(
+        "appo",
+        ("task=go1_joystick_flat/mujoco",),
+        "mujoco",
+        (*_RESET_EVENTS, *_DR_EVENTS, "push_robot"),
+        (*_BASE_REWARDS, "contact"),
+        False,
+        id="appo-mujoco",
+    ),
+    pytest.param(
+        "appo",
+        ("task=go1_joystick_flat/motrix",),
+        "motrix",
+        (*_RESET_EVENTS, *_DR_EVENTS),
+        (*_BASE_REWARDS, "swing_feet_z", "action_smooth"),
+        True,
+        id="appo-motrix",
+    ),
+    pytest.param(
+        "td3",
+        ("task=go1_joystick_flat/motrix",),
+        "motrix",
+        (*_RESET_EVENTS, *_DR_EVENTS),
+        (*_BASE_REWARDS, "swing_feet_z"),
+        True,
+        id="td3-motrix",
+    ),
+)
+
+
+def _compose(config_group: str, overrides: Sequence[str]) -> DictConfig:
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(config_dir=str(CONF_DIR / config_group), version_base="1.3"):
+        return compose("config", overrides=list(overrides))
+
+
+def _materialize(
+    config_group: str, overrides: Sequence[str]
+) -> tuple[DictConfig, ManagerBasedRlEnvCfg, dict[str, Any]]:
+    hydra_cfg = _compose(config_group, overrides)
+    env_override = BackendAdapter(hydra_cfg, root_dir=ROOT_DIR).build_task_env_cfg_override()
+    env_cfg = registry.materialize_env_config("Go1JoystickFlat")
+    assert isinstance(env_cfg, ManagerBasedRlEnvCfg)
+    apply_cfg_overrides(env_cfg, env_override)
+    env_cfg.validate()
+    return hydra_cfg, env_cfg, env_override
+
+
+def _assert_no_omegaconf(value: Any) -> None:
+    assert not OmegaConf.is_config(value)
+    if is_dataclass(value) and not isinstance(value, type):
+        for item in fields(value):
+            _assert_no_omegaconf(getattr(value, item.name))
+    elif isinstance(value, Mapping):
+        for key, item in value.items():
+            _assert_no_omegaconf(key)
+            _assert_no_omegaconf(item)
+    elif isinstance(value, Sequence) and not isinstance(value, (str, bytes)):
+        for item in value:
+            _assert_no_omegaconf(item)
+
+
+@pytest.mark.parametrize(
+    "config_group,overrides,backend,expected_events,expected_rewards,fixed_command",
+    _OWNER_CASES,
+)
+def test_go1_flat_owner_materializes_complete_plain_manager_cfg(
+    config_group: str,
+    overrides: tuple[str, ...],
+    backend: str,
+    expected_events: tuple[str, ...],
+    expected_rewards: tuple[str, ...],
+    fixed_command: bool,
+) -> None:
+    registry.ensure_registries()
+    hydra_cfg, env_cfg, _ = _materialize(config_group, overrides)
+
+    assert hydra_cfg.training.task_name == "Go1JoystickFlat"
+    assert hydra_cfg.training.sim_backend == backend
+    assert env_cfg.sim_dt == pytest.approx(0.01)
+    assert env_cfg.ctrl_dt == pytest.approx(0.02)
+    assert env_cfg.max_episode_seconds == pytest.approx(20.0)
+    assert env_cfg.policy_observation_group == "policy"
+    assert env_cfg.critic_observation_group == "critic"
+    assert env_cfg.scale_rewards_by_dt is True
+
+    assert env_cfg.scene is not None
+    assert env_cfg.scene.model_file.endswith("robots/go1/scene_flat.xml")
+    assert env_cfg.scene.default_keyframe_name == "home"
+    robot = env_cfg.scene.entities["robot"]
+    assert robot.root_body_name == "trunk"
+    assert tuple(robot.joint_names or ()) == _JOINT_NAMES
+    assert tuple(robot.actuator_names or ()) == _ACTUATOR_NAMES
+    assert robot.body_names == ["trunk"]
+
+    observation_terms = [
+        "base_ang_vel",
+        "projected_gravity",
+        "joint_pos",
+        "joint_vel",
+        "actions",
+        "command",
+        "gait_phase",
+    ]
+    assert list(env_cfg.observations) == ["policy", "critic"]
+    assert list(env_cfg.observations["policy"].terms) == observation_terms
+    assert list(env_cfg.observations["critic"].terms) == [*observation_terms, "base_lin_vel"]
+    assert list(env_cfg.actions) == ["joint_pos"]
+    assert env_cfg.actions["joint_pos"].scale == pytest.approx(0.25)
+    assert list(env_cfg.terminations) == ["time_out", "bad_orientation"]
+    assert (
+        tuple(name for name, term in env_cfg.events.items() if term is not None) == expected_events
+    )
+    assert tuple(name for name, term in env_cfg.rewards.items() if term is not None) == (
+        expected_rewards
+    )
+
+    command = env_cfg.commands["twist"]
+    assert command.resampling_time_range == [20.0, 20.0]
+    ranges = command.ranges
+    if fixed_command:
+        assert tuple(ranges.lin_vel_x) == (0.5, 0.5)
+        assert tuple(ranges.lin_vel_y) == (0.0, 0.0)
+        assert tuple(ranges.ang_vel_z) == (0.0, 0.0)
+    else:
+        assert tuple(ranges.lin_vel_x) == (-0.6, 1.0)
+
+    if env_cfg.events["base_mass"] is not None:
+        mass = env_cfg.events["base_mass"]
+        assert mass.func is mdp.randomize_rigid_body_mass
+        assert mass.params["mass_distribution_params"] == [-1.5, 1.5]
+        assert mass.params["recompute_inertia"] is False
+        com = env_cfg.events["base_com"]
+        assert com.func is mdp.randomize_rigid_body_com
+        assert com.params["com_range"] == {
+            "x": [-0.05, 0.05],
+            "y": [0.0, 0.0],
+            "z": [0.0, 0.0],
+        }
+        gains = env_cfg.events["pd_gains"]
+        assert gains.func is mdp.pd_gains
+        assert gains.params["kp_range"] == [35.0, 35.0]
+        assert gains.params["kd_range"] == [0.5, 0.5]
+        assert gains.params["operation"] == "abs"
+
+    push = env_cfg.events["push_robot"]
+    if push is not None:
+        assert push.func is mdp.push_by_setting_velocity
+        assert push.interval_range_s == [15.0, 15.0]
+        assert push.is_global_time is True
+
+    contact = env_cfg.rewards["contact"]
+    if contact is not None:
+        assert contact.func is manager_terms.feet_phase_contact
+        # Legacy Go1 returned a four-foot sum; the community term returns a mean.
+        assert contact.weight == pytest.approx(4.0 * 0.24)
+    action_smooth = env_cfg.rewards.get("action_smooth")
+    if action_smooth is not None:
+        assert action_smooth.func is mdp.action_acc_l2
+        assert action_smooth.weight == pytest.approx(-0.01)
+
+    for manager_name in ("observations", "events", "rewards", "terminations"):
+        for term in getattr(env_cfg, manager_name).values():
+            if term is None:
+                continue
+            nested_terms = term.terms.values() if manager_name == "observations" else (term,)
+            for nested in nested_terms:
+                if nested is None:
+                    continue
+                module = nested.func.__module__
+                assert ".backend." not in module
+                assert not any(name in module for name in (".mujoco", ".motrix", ".drake"))
+
+    _assert_no_omegaconf(env_cfg)
+
+
+def test_go1_flat_and_rough_registries_are_manager_only() -> None:
+    registry.ensure_registries()
+
+    assert registry.list_registered_envs()["Go1JoystickFlat"] == {
+        "config_factory": "ManagerBasedRlEnvCfg",
+        "available_backends": ["mujoco", "motrix", "drake"],
+    }
+    assert registry.list_registered_envs()["Go1JoystickRough"] == {
+        "config_factory": "ManagerBasedRlEnvCfg",
+        "available_backends": ["mujoco", "motrix"],
+    }
+
+    for legacy_override in (
+        {"reward_config": {}},
+        {"domain_rand": {"randomize_base_mass": True}},
+        {"control_config": {"Kp": 35.0}},
+    ):
+        with pytest.raises(ValueError, match="has no attribute"):
+            apply_cfg_overrides(ManagerBasedRlEnvCfg(), legacy_override)
+
+
+@pytest.mark.parametrize(
+    ("backend", "owner", "expected_events"),
+    (
+        (
+            "mujoco",
+            "task=go1_joystick_flat/mujoco",
+            {"reset": [*_RESET_EVENTS, *_DR_EVENTS], "interval": ["push_robot"]},
+        ),
+        (
+            "motrix",
+            "task=go1_joystick_flat/motrix",
+            {"reset": [*_RESET_EVENTS, *_DR_EVENTS]},
+        ),
+    ),
+)
+def test_go1_flat_registry_executes_real_manager_runtime(
+    backend: str,
+    owner: str,
+    expected_events: dict[str, list[str]],
+) -> None:
+    registry.ensure_registries()
+    hydra_cfg, _, env_override = _materialize("ppo", (owner,))
+    try:
+        env = registry.make(
+            str(hydra_cfg.training.task_name),
+            sim_backend=backend,
+            env_cfg_override=env_override,
+            num_envs=2,
+        )
+    except ImportError as exc:
+        pytest.skip(f"{backend} runtime unavailable: {exc}")
+
+    try:
+        assert isinstance(env, ManagerBasedRlEnv)
+        assert env.obs_groups_spec == {"obs": 49, "critic": 52}
+        assert env.action_space.shape == (12,)
+        action = env.action_manager.get_term("joint_pos")
+        assert action.target_names == list(_JOINT_NAMES)
+        np.testing.assert_allclose(action.offset, np.broadcast_to(_HOME_JOINT_POS, (2, 12)))
+        assert env.event_manager.active_terms == expected_events
+
+        obs, info = env.reset(seed=7)
+        assert {name: value.shape for name, value in obs.items()} == {
+            "obs": (2, 49),
+            "critic": (2, 52),
+        }
+        assert isinstance(info, dict)
+        np.testing.assert_allclose(
+            env.scene["robot"].data.default_joint_pos,
+            np.broadcast_to(_HOME_JOINT_POS, (2, 12)),
+        )
+        for _ in range(5):
+            state = env.step(np.zeros((2, 12), dtype=np.float32))
+        for value in (*state.obs.values(), state.reward):
+            assert isinstance(value, np.ndarray)
+            assert np.isfinite(value).all()
+    finally:
+        env.close()
+
+
+def test_go1_motrix_velocity_push_request_fails_closed() -> None:
+    _, motrix_cfg, _ = _materialize("ppo", ("task=go1_joystick_flat/motrix",))
+    _, mujoco_cfg, _ = _materialize("ppo", ("task=go1_joystick_flat/mujoco",))
+    motrix_cfg.events["push_robot"] = deepcopy(mujoco_cfg.events["push_robot"])
+
+    try:
+        with pytest.raises(NotImplementedError, match="interval root velocity delta.*motrix"):
+            make_manager_based_rl_env(motrix_cfg, num_envs=1, backend_type="motrix")
+    except ImportError as exc:
+        pytest.skip(f"motrix runtime unavailable: {exc}")
diff --git a/tests/envs/locomotion/go2/test_manager_based_cfg.py b/tests/envs/locomotion/go2/test_manager_based_cfg.py
new file mode 100644
index 000000000..907e6dffb
--- /dev/null
+++ b/tests/envs/locomotion/go2/test_manager_based_cfg.py
@@ -0,0 +1,392 @@
+"""Hydra-owned production contract for the Go2 flat Manager-Based task."""
+
+from __future__ import annotations
+
+from collections.abc import Mapping, Sequence
+from dataclasses import fields, is_dataclass
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pytest
+from hydra import compose, initialize_config_dir
+from hydra.core.global_hydra import GlobalHydra
+from omegaconf import DictConfig, OmegaConf
+
+from unilab.base import registry
+from unilab.base.config_adapter import BackendAdapter
+from unilab.base.config_materialization import apply_cfg_overrides
+from unilab.envs import ManagerBasedRlEnv, ManagerBasedRlEnvCfg, mdp
+
+ROOT_DIR = Path(__file__).parents[4]
+CONF_DIR = ROOT_DIR / "src" / "unilab" / "conf"
+
+_JOINT_NAMES = (
+    "FL_hip_joint",
+    "FL_thigh_joint",
+    "FL_calf_joint",
+    "FR_hip_joint",
+    "FR_thigh_joint",
+    "FR_calf_joint",
+    "RL_hip_joint",
+    "RL_thigh_joint",
+    "RL_calf_joint",
+    "RR_hip_joint",
+    "RR_thigh_joint",
+    "RR_calf_joint",
+)
+_ACTUATOR_NAMES = (
+    "FR_hip",
+    "FR_thigh",
+    "FR_calf",
+    "FL_hip",
+    "FL_thigh",
+    "FL_calf",
+    "RR_hip",
+    "RR_thigh",
+    "RR_calf",
+    "RL_hip",
+    "RL_thigh",
+    "RL_calf",
+)
+_HOME_JOINT_POS = np.array(
+    [0.0, 0.8, -1.5, 0.0, 0.8, -1.5, 0.0, 1.0, -1.5, 0.0, 1.0, -1.5],
+    dtype=np.float32,
+)
+
+_OWNER_CASES = (
+    pytest.param(
+        "ppo",
+        ("task=go2_joystick_flat/mujoco",),
+        "mujoco",
+        0.25,
+        True,
+        False,
+        False,
+        id="ppo-mujoco",
+    ),
+    pytest.param(
+        "ppo",
+        ("task=go2_joystick_flat/motrix",),
+        "motrix",
+        0.25,
+        False,
+        False,
+        True,
+        id="ppo-motrix",
+    ),
+    pytest.param(
+        "ppo",
+        ("task=go2_joystick_flat/drake",),
+        "drake",
+        0.25,
+        False,
+        False,
+        False,
+        id="ppo-drake",
+    ),
+    pytest.param(
+        "appo",
+        ("task=go2_joystick_flat/mujoco",),
+        "mujoco",
+        0.25,
+        True,
+        True,
+        False,
+        id="appo-mujoco",
+    ),
+    pytest.param(
+        "appo",
+        ("task=go2_joystick_flat/motrix",),
+        "motrix",
+        0.25,
+        False,
+        True,
+        False,
+        id="appo-motrix",
+    ),
+    pytest.param(
+        "flashsac",
+        ("task=go2_joystick_flat/mujoco",),
+        "mujoco",
+        0.4,
+        True,
+        False,
+        False,
+        id="flashsac-mujoco",
+    ),
+    pytest.param(
+        "td3",
+        ("task=go2_joystick_flat/motrix",),
+        "motrix",
+        0.25,
+        False,
+        False,
+        True,
+        id="td3-motrix",
+    ),
+    pytest.param(
+        "sac",
+        ("task=go2_joystick_flat/drake",),
+        "drake",
+        0.25,
+        False,
+        False,
+        False,
+        id="sac-drake",
+    ),
+)
+
+
+def _compose(config_group: str, overrides: Sequence[str]) -> DictConfig:
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(config_dir=str(CONF_DIR / config_group), version_base="1.3"):
+        return compose("config", overrides=list(overrides))
+
+
+def _materialize(
+    config_group: str, overrides: Sequence[str]
+) -> tuple[DictConfig, ManagerBasedRlEnvCfg, dict[str, Any]]:
+    hydra_cfg = _compose(config_group, overrides)
+    env_override = BackendAdapter(hydra_cfg, root_dir=ROOT_DIR).build_task_env_cfg_override()
+    env_cfg = registry.materialize_env_config("Go2JoystickFlat")
+    assert isinstance(env_cfg, ManagerBasedRlEnvCfg)
+    apply_cfg_overrides(env_cfg, env_override)
+    env_cfg.validate()
+    return hydra_cfg, env_cfg, env_override
+
+
+def _assert_no_omegaconf(value: Any) -> None:
+    assert not OmegaConf.is_config(value)
+    if is_dataclass(value) and not isinstance(value, type):
+        for item in fields(value):
+            _assert_no_omegaconf(getattr(value, item.name))
+    elif isinstance(value, Mapping):
+        for key, item in value.items():
+            _assert_no_omegaconf(key)
+            _assert_no_omegaconf(item)
+    elif isinstance(value, Sequence) and not isinstance(value, (str, bytes)):
+        for item in value:
+            _assert_no_omegaconf(item)
+
+
+@pytest.mark.parametrize(
+    "config_group,overrides,backend,action_scale,pd_enabled,alive_declared,fixed_command",
+    _OWNER_CASES,
+)
+def test_go2_flat_owner_materializes_complete_plain_manager_cfg(
+    config_group: str,
+    overrides: tuple[str, ...],
+    backend: str,
+    action_scale: float,
+    pd_enabled: bool,
+    alive_declared: bool,
+    fixed_command: bool,
+) -> None:
+    registry.ensure_registries()
+    hydra_cfg, env_cfg, _ = _materialize(config_group, overrides)
+
+    assert hydra_cfg.training.task_name == "Go2JoystickFlat"
+    assert hydra_cfg.training.sim_backend == backend
+    assert env_cfg.sim_dt == pytest.approx(0.01)
+    assert env_cfg.ctrl_dt == pytest.approx(0.02)
+    assert env_cfg.max_episode_seconds == pytest.approx(20.0)
+    assert env_cfg.policy_observation_group == "policy"
+    assert env_cfg.critic_observation_group == "critic"
+    assert env_cfg.scene is not None
+    assert env_cfg.scene.default_keyframe_name == "home"
+    robot_cfg = env_cfg.scene.entities["robot"]
+    assert robot_cfg.root_body_name == "base"
+    assert tuple(robot_cfg.joint_names) == _JOINT_NAMES
+    assert tuple(robot_cfg.actuator_names) == _ACTUATOR_NAMES
+
+    expected_policy_terms = [
+        "base_ang_vel",
+        "projected_gravity",
+        "joint_pos",
+        "joint_vel",
+        "actions",
+        "command",
+        "gait_phase",
+    ]
+    assert list(env_cfg.observations) == ["policy", "critic"]
+    assert list(env_cfg.observations["policy"].terms) == expected_policy_terms
+    assert list(env_cfg.observations["critic"].terms) == [
+        *expected_policy_terms,
+        "base_lin_vel",
+    ]
+    assert list(env_cfg.actions) == ["joint_pos"]
+    assert env_cfg.actions["joint_pos"].scale == pytest.approx(action_scale)
+    assert list(env_cfg.commands) == ["twist"]
+    assert list(env_cfg.terminations) == ["time_out", "bad_orientation"]
+    assert (env_cfg.events["pd_gains"] is not None) is pd_enabled
+    assert ("alive" in env_cfg.rewards) is alive_declared
+
+    if action_scale == 0.25:
+        expected_weights = {
+            "tracking_lin_vel": 1.0,
+            "tracking_ang_vel": 0.2,
+            "lin_vel_z": -5.0,
+            "ang_vel_xy": -0.1,
+            "base_height": -100.0,
+            "action_rate": -0.005,
+            "similar_to_default": -0.1,
+            "contact": 0.24,
+            "swing_feet_z": 4.0,
+        }
+        if backend == "drake":
+            # The drake owner disables contact: drake_uni reports world-frame
+            # per-body net contact force, not contact-frame force (#1471).
+            del expected_weights["contact"]
+        if alive_declared:
+            expected_weights["alive"] = 0.0
+        actual_weights = {
+            name: term.weight for name, term in env_cfg.rewards.items() if term is not None
+        }
+        assert actual_weights == expected_weights
+
+    if fixed_command:
+        ranges = env_cfg.commands["twist"].ranges
+        assert tuple(ranges.lin_vel_x) == (0.5, 0.5)
+        assert tuple(ranges.lin_vel_y) == (0.0, 0.0)
+        assert tuple(ranges.ang_vel_z) == (0.0, 0.0)
+
+    for manager_name in ("observations", "events", "rewards", "terminations"):
+        for term in getattr(env_cfg, manager_name).values():
+            if term is None:
+                continue
+            terms = term.terms.values() if manager_name == "observations" else (term,)
+            for nested_term in terms:
+                if nested_term is None:
+                    continue
+                module = nested_term.func.__module__
+                assert ".backend." not in module
+                assert not any(name in module for name in (".mujoco", ".motrix", ".drake"))
+
+    _assert_no_omegaconf(env_cfg)
+
+
+def test_go2_flat_registry_has_no_legacy_config_fallback() -> None:
+    registry.ensure_registries()
+    bare_cfg = registry.materialize_env_config("Go2JoystickFlat")
+
+    assert isinstance(bare_cfg, ManagerBasedRlEnvCfg)
+    assert bare_cfg.observations == {}
+    assert bare_cfg.actions == {}
+    assert bare_cfg.rewards == {}
+    assert registry.list_registered_envs()["Go2JoystickFlat"] == {
+        "config_factory": "ManagerBasedRlEnvCfg",
+        "available_backends": ["mujoco", "motrix", "drake"],
+    }
+    for legacy_override in (
+        {"reward_config": {}},
+        {"domain_rand": {"randomize_kp": True}},
+        {"control_config": {"action_scale": 0.4}},
+    ):
+        with pytest.raises(ValueError, match="has no attribute"):
+            apply_cfg_overrides(ManagerBasedRlEnvCfg(), legacy_override)
+
+
+@pytest.mark.parametrize(
+    ("backend", "owner"),
+    (("mujoco", "task=go2_joystick_flat/mujoco"), ("motrix", "task=go2_joystick_flat/motrix")),
+)
+def test_go2_flat_registry_executes_real_manager_runtime(backend: str, owner: str) -> None:
+    registry.ensure_registries()
+    hydra_cfg, _, env_override = _materialize("ppo", (owner,))
+    env = registry.make(
+        str(hydra_cfg.training.task_name),
+        sim_backend=backend,
+        env_cfg_override=env_override,
+        num_envs=2,
+    )
+    try:
+        assert isinstance(env, ManagerBasedRlEnv)
+        assert env.obs_groups_spec == {"obs": 49, "critic": 52}
+        assert env.action_space.shape == (12,)
+        action = env.action_manager.get_term("joint_pos")
+        assert action.target_names == list(_JOINT_NAMES)
+        np.testing.assert_allclose(action.offset, np.broadcast_to(_HOME_JOINT_POS, (2, 12)))
+
+        obs, info = env.reset(seed=7)
+        assert {name: value.shape for name, value in obs.items()} == {
+            "obs": (2, 49),
+            "critic": (2, 52),
+        }
+        assert isinstance(info, dict)
+        np.testing.assert_allclose(
+            env.scene["robot"].data.default_joint_pos,
+            np.broadcast_to(_HOME_JOINT_POS, (2, 12)),
+        )
+        np.testing.assert_allclose(
+            env.scene["robot"].data.joint_pos,
+            np.broadcast_to(_HOME_JOINT_POS, (2, 12)),
+        )
+
+        state = env.step(np.zeros((2, 12), dtype=np.float32))
+        assert {name: value.shape for name, value in state.obs.items()} == {
+            "obs": (2, 49),
+            "critic": (2, 52),
+        }
+        for value in (*state.obs.values(), state.reward):
+            assert isinstance(value, np.ndarray)
+            assert np.isfinite(value).all()
+    finally:
+        env.close()
+
+
+def test_go2_flat_flashsac_uses_canonical_manager_events_and_numpy_noise() -> None:
+    registry.ensure_registries()
+    _, env_cfg, _ = _materialize(
+        "flashsac",
+        ("task=go2_joystick_flat/mujoco",),
+    )
+
+    assert env_cfg.scene is not None
+    assert env_cfg.scene.entities["robot"].body_names == ["base"]
+    assert list(env_cfg.events) == [
+        "reset_scene_to_default",
+        "reset_root_state_uniform",
+        "pd_gains",
+        "randomize_rigid_body_mass",
+        "randomize_rigid_body_com",
+        "randomize_physics_scene_gravity",
+        "push_by_setting_velocity",
+    ]
+    push = env_cfg.events["push_by_setting_velocity"]
+    assert push.func is mdp.push_by_setting_velocity
+    assert push.mode == "interval"
+    assert push.interval_range_s == [15.0, 15.0]
+    assert push.is_global_time is True
+
+    for group_name in ("policy", "critic"):
+        group = env_cfg.observations[group_name]
+        assert group.enable_corruption is True
+        assert type(group.terms["joint_pos"].noise).__name__ == "UniformNoiseCfg"
+        assert group.terms["joint_pos"].noise.n_min == pytest.approx(-0.01)
+        assert group.terms["joint_pos"].noise.n_max == pytest.approx(0.01)
+        assert type(group.terms["joint_vel"].noise).__name__ == "UniformNoiseCfg"
+        assert group.terms["joint_vel"].noise.n_min == pytest.approx(-0.1)
+        assert group.terms["joint_vel"].noise.n_max == pytest.approx(0.1)
+
+    assert env_cfg.rewards["tracking_lin_vel"].params["std"] == pytest.approx(0.4**0.5)
+    assert env_cfg.rewards["base_height"].weight == pytest.approx(-20.0)
+    assert env_cfg.rewards["contact"].weight == pytest.approx(1.5)
+
+
+def test_go2_flat_drake_missing_dependency_is_explicit() -> None:
+    from unisim.backend.drake.backend import ensure_drake_batch_available
+
+    available, _ = ensure_drake_batch_available()
+    if available:
+        pytest.skip("DrakeUni is installed; missing-dependency behavior is not applicable")
+
+    registry.ensure_registries()
+    hydra_cfg, _, env_override = _materialize("ppo", ("task=go2_joystick_flat/drake",))
+    with pytest.raises(ImportError, match="[Dd]rake"):
+        registry.make(
+            str(hydra_cfg.training.task_name),
+            sim_backend="drake",
+            env_cfg_override=env_override,
+            num_envs=1,
+        )
diff --git a/tests/envs/locomotion/go2_arm/test_base_ik.py b/tests/envs/locomotion/go2_arm/test_base_ik.py
index 0fc1abddd..044f9f129 100644
--- a/tests/envs/locomotion/go2_arm/test_base_ik.py
+++ b/tests/envs/locomotion/go2_arm/test_base_ik.py
@@ -4,7 +4,7 @@
 
 import numpy as np
 
-from unilab.envs.locomotion.go2_arm.base import Go2ArmBaseCfg, Go2ArmBaseEnv
+from unilab.tasks.locomotion.go2_arm.base import Go2ArmBaseCfg, Go2ArmBaseEnv
 
 
 class _IkHarness(Go2ArmBaseEnv):
diff --git a/tests/envs/locomotion/go2_arm/test_manip_loco_contract.py b/tests/envs/locomotion/go2_arm/test_manip_loco_contract.py
index e4a225c3c..b36b35bb5 100644
--- a/tests/envs/locomotion/go2_arm/test_manip_loco_contract.py
+++ b/tests/envs/locomotion/go2_arm/test_manip_loco_contract.py
@@ -11,7 +11,7 @@
 
 from unilab.base.np_env import NpEnvState
 
-_GO2_ARM_MANIP_LOCO_MODULE = "unilab.envs.locomotion.go2_arm.manip_loco"
+_GO2_ARM_MANIP_LOCO_MODULE = "unilab.tasks.locomotion.go2_arm.manip_loco"
 _REGISTRY_MODULE = "unilab.base.registry"
 
 
@@ -24,7 +24,7 @@ def _skip_if_no_mujoco():
 
 
 def _default_reward_cfg():
-    from unilab.envs.locomotion.go2_arm.manip_loco import RewardConfig
+    from unilab.tasks.locomotion.go2_arm.manip_loco import RewardConfig
 
     return RewardConfig(
         scales={
@@ -88,7 +88,7 @@ def test_go2_arm_manip_loco_registers_motrix_backend():
 def test_go2_arm_manip_loco_cfg_declares_scene_for_playback():
     """MuJoCo video playback needs the original visual scene, not only legacy model_file."""
     from unilab.base.scene import SceneCfg
-    from unilab.envs.locomotion.go2_arm.manip_loco import (
+    from unilab.tasks.locomotion.go2_arm.manip_loco import (
         Go2ArmManipLocoCfg,
         _resolve_go2_arm_scene,
     )
@@ -107,7 +107,7 @@ def test_go2_arm_manip_loco_cfg_declares_scene_for_playback():
 
 def test_go2_arm_ee_goal_collision_check_matches_reference_semantics():
     """Any EE goal path sample inside the collision box or below ground is unsafe."""
-    from unilab.envs.locomotion.go2_arm.manip_loco import (
+    from unilab.tasks.locomotion.go2_arm.manip_loco import (
         EEGoalConfig,
         Go2ArmManipLocoCfg,
         Go2ArmManipLocoEnv,
@@ -131,7 +131,7 @@ def test_go2_arm_ee_goal_collision_check_matches_reference_semantics():
 
 def test_go2_arm_command_moving_mask_includes_all_velocity_axes():
     """A command is moving when vx, vy, or vyaw exceeds the motion threshold."""
-    from unilab.envs.locomotion.go2_arm.manip_loco import Go2ArmManipLocoEnv
+    from unilab.tasks.locomotion.go2_arm.manip_loco import Go2ArmManipLocoEnv
 
     env = object.__new__(Go2ArmManipLocoEnv)
     clip = env._CMD_CLIP
@@ -156,7 +156,7 @@ def test_go2_arm_command_moving_mask_includes_all_velocity_axes():
 
 def test_go2_arm_command_postprocess_can_force_zero_commands():
     """zero_command_prob should inject exact zero commands after small-command zeroing."""
-    from unilab.envs.locomotion.go2_arm.manip_loco import (
+    from unilab.tasks.locomotion.go2_arm.manip_loco import (
         Go2ArmManipLocoCfg,
         Go2ArmManipLocoEnv,
     )
@@ -183,8 +183,8 @@ def test_go2_arm_command_postprocess_can_force_zero_commands():
 
 def test_go2_arm_stand_still_reward_uses_same_command_mask():
     """stand_still should not penalize leg pose under lateral, yaw, or forward commands."""
-    from unilab.envs.locomotion.common.rewards import RewardContext
-    from unilab.envs.locomotion.go2_arm.manip_loco import Go2ArmManipLocoEnv
+    from unilab.tasks.locomotion.common.rewards import RewardContext
+    from unilab.tasks.locomotion.go2_arm.manip_loco import Go2ArmManipLocoEnv
 
     env = object.__new__(Go2ArmManipLocoEnv)
     clip = env._CMD_CLIP
@@ -212,7 +212,7 @@ def test_go2_arm_stand_still_reward_uses_same_command_mask():
 
 def test_go2_arm_write_feet_phase_updates_indexed_envs():
     """Resetting env subsets must write back feet_phase instead of losing fancy-index copies."""
-    from unilab.envs.locomotion.go2_arm.manip_loco import Go2ArmManipLocoEnv
+    from unilab.tasks.locomotion.go2_arm.manip_loco import Go2ArmManipLocoEnv
 
     env = object.__new__(Go2ArmManipLocoEnv)
     env.phase = np.asarray([0.2, 0.4, 0.6], dtype=np.float32)
@@ -229,7 +229,7 @@ def test_go2_arm_write_feet_phase_updates_indexed_envs():
 
 def test_go2_arm_apply_action_uses_arm_action_scale_for_arm_residual():
     """Leg residuals use action_scale while arm residuals use arm_action_scale."""
-    from unilab.envs.locomotion.go2_arm.manip_loco import (
+    from unilab.tasks.locomotion.go2_arm.manip_loco import (
         Go2ArmManipLocoCfg,
         Go2ArmManipLocoEnv,
     )
diff --git a/tests/envs/locomotion/go2w/test_go2w_height_scan.py b/tests/envs/locomotion/go2w/test_go2w_height_scan.py
deleted file mode 100644
index 538c0410e..000000000
--- a/tests/envs/locomotion/go2w/test_go2w_height_scan.py
+++ /dev/null
@@ -1,77 +0,0 @@
-from __future__ import annotations
-
-from typing import Any
-
-import numpy as np
-import pytest
-
-pytest.importorskip("mujoco", reason="mujoco not installed")
-
-try:
-    from mujoco_uni.batch_env import BatchEnvPool
-except Exception:
-    pytest.skip(
-        "mujoco_uni.batch_env not available (platform/libstdc++ issue)",
-        allow_module_level=True,
-    )
-
-if not hasattr(BatchEnvPool, "sample_hfield_height"):
-    pytest.skip(
-        "BatchEnvPool.sample_hfield_height requires a mujoco-uni-runtime build that ships it",
-        allow_module_level=True,
-    )
-
-from unilab.envs.locomotion.go2w.rough import Go2WJoystickRoughEnv
-
-
-def test_go2w_rough_height_scan_uses_backend_native_sampling() -> None:
-    class FakeHeightScanner:
-        def __init__(self, heights: np.ndarray) -> None:
-            self.calls = 0
-            self.heights = heights
-
-        def scan(self) -> np.ndarray:
-            self.calls += 1
-            return self.heights
-
-    class FakeBackend:
-        def __init__(self) -> None:
-            self.scanner_calls: list[dict[str, Any]] = []
-            self.base_pos = np.asarray([[0.0, 0.0, 0.6], [1.0, 0.0, 0.7]], dtype=np.float32)
-            self.heights = np.asarray([[0.1, 0.2], [0.3, 0.4]], dtype=np.float32)
-            self.scanner = FakeHeightScanner(self.heights)
-
-        def get_base_pos(self) -> np.ndarray:
-            return self.base_pos
-
-        def create_hfield_scanner(self, **kwargs: Any) -> FakeHeightScanner:
-            self.scanner_calls.append(kwargs)
-            return self.scanner
-
-    env = object.__new__(Go2WJoystickRoughEnv)
-    fake_backend = FakeBackend()
-    env._backend = fake_backend
-    env._height_scan_dim = 2
-    env._height_scan_hfield_geom_id = 7
-    env._height_scan_frame_body_id = 3
-    env._height_scan_offsets = np.asarray([[0.0, 0.0], [0.1, -0.1]], dtype=np.float64)
-    env._height_scan_sensor = fake_backend.create_hfield_scanner(
-        hfield_geom_id=env._height_scan_hfield_geom_id,
-        offsets=env._height_scan_offsets,
-        frame_body_id=env._height_scan_frame_body_id,
-        alignment="yaw",
-        output="height",
-    )
-
-    raw_heights, base_pos = env._raw_height_scan_obs(num_obs=2)
-
-    np.testing.assert_array_equal(raw_heights, fake_backend.heights)
-    np.testing.assert_array_equal(base_pos, fake_backend.base_pos)
-    assert fake_backend.scanner.calls == 1
-    assert len(fake_backend.scanner_calls) == 1
-    call = fake_backend.scanner_calls[0]
-    assert call["hfield_geom_id"] == 7
-    assert call["frame_body_id"] == 3
-    assert call["alignment"] == "yaw"
-    assert call["output"] == "height"
-    np.testing.assert_array_equal(call["offsets"], env._height_scan_offsets)
diff --git a/tests/envs/locomotion/go2w/test_go2w_manager_based_flat_cfg.py b/tests/envs/locomotion/go2w/test_go2w_manager_based_flat_cfg.py
new file mode 100644
index 000000000..1adca205d
--- /dev/null
+++ b/tests/envs/locomotion/go2w/test_go2w_manager_based_flat_cfg.py
@@ -0,0 +1,303 @@
+"""Hydra-owned production contract for the Go2W flat Manager-Based task."""
+
+from __future__ import annotations
+
+import inspect
+from collections.abc import Mapping, Sequence
+from dataclasses import fields, is_dataclass
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pytest
+from hydra import compose, initialize_config_dir
+from hydra.core.global_hydra import GlobalHydra
+from omegaconf import DictConfig, OmegaConf
+
+from unilab.base import registry
+from unilab.base.config_adapter import BackendAdapter
+from unilab.base.config_materialization import apply_cfg_overrides
+from unilab.envs import ManagerBasedRlEnv, ManagerBasedRlEnvCfg, make_manager_based_rl_env
+from unilab.tasks.locomotion.go2w import manager_terms
+from unilab.tasks.locomotion.go2w.manager_terms import (
+    Go2WMixedAction,
+    Go2WMixedActionCfg,
+    Go2WVelocityCommandCfg,
+)
+
+ROOT_DIR = Path(__file__).parents[4]
+CONF_DIR = ROOT_DIR / "src" / "unilab" / "conf"
+
+_LEG_JOINT_NAMES = tuple(
+    f"{leg}_{joint}_joint" for leg in ("FR", "FL", "RR", "RL") for joint in ("hip", "thigh", "calf")
+)
+_WHEEL_JOINT_NAMES = tuple(f"{leg}_wheel_joint" for leg in ("FR", "FL", "RR", "RL"))
+_JOINT_NAMES = (*_LEG_JOINT_NAMES, *_WHEEL_JOINT_NAMES)
+_ACTUATOR_NAMES = tuple(name.removesuffix("_joint") for name in _JOINT_NAMES)
+_HOME_JOINT_POS = np.asarray([0.0, 0.8, -1.5] * 4 + [0.0] * 4, dtype=np.float32)
+
+
+def test_go2w_mixed_action_declares_substep_state_feedback() -> None:
+    assert Go2WMixedAction.requires_substep_state_feedback is True
+
+
+def _compose(backend: str) -> DictConfig:
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(config_dir=str(CONF_DIR / "ppo"), version_base="1.3"):
+        return compose("config", overrides=[f"task=go2w_joystick_flat/{backend}"])
+
+
+def _materialize(
+    backend: str,
+) -> tuple[DictConfig, ManagerBasedRlEnvCfg, dict[str, Any]]:
+    hydra_cfg = _compose(backend)
+    env_override = BackendAdapter(hydra_cfg, root_dir=ROOT_DIR).build_task_env_cfg_override()
+    env_cfg = registry.materialize_env_config("Go2WJoystickFlat")
+    assert isinstance(env_cfg, ManagerBasedRlEnvCfg)
+    apply_cfg_overrides(env_cfg, env_override)
+    env_cfg.validate()
+    return hydra_cfg, env_cfg, env_override
+
+
+def _assert_no_omegaconf(value: Any) -> None:
+    assert not OmegaConf.is_config(value)
+    if is_dataclass(value) and not isinstance(value, type):
+        for item in fields(value):
+            _assert_no_omegaconf(getattr(value, item.name))
+    elif isinstance(value, Mapping):
+        for key, item in value.items():
+            _assert_no_omegaconf(key)
+            _assert_no_omegaconf(item)
+    elif isinstance(value, Sequence) and not isinstance(value, (str, bytes)):
+        for item in value:
+            _assert_no_omegaconf(item)
+
+
+@pytest.mark.parametrize("backend", ["mujoco", "motrix", "drake"])
+def test_go2w_flat_owner_materializes_complete_plain_manager_cfg(backend: str) -> None:
+    registry.ensure_registries()
+    hydra_cfg, env_cfg, _ = _materialize(backend)
+
+    assert hydra_cfg.training.task_name == "Go2WJoystickFlat"
+    assert hydra_cfg.training.sim_backend == backend
+    assert list(hydra_cfg.algo.obs_groups.actor) == ["actor"]
+    assert list(hydra_cfg.algo.obs_groups.critic) == ["critic"]
+    assert env_cfg.sim_dt == pytest.approx(0.005)
+    assert env_cfg.ctrl_dt == pytest.approx(0.02)
+    assert env_cfg.max_episode_seconds == pytest.approx(20.0)
+    assert env_cfg.policy_observation_group == "policy"
+    assert env_cfg.critic_observation_group == "critic"
+
+    assert env_cfg.scene is not None
+    assert env_cfg.scene.model_file.endswith("robots/go2w/scene_flat.xml")
+    assert env_cfg.scene.default_keyframe_name == "home"
+    robot = env_cfg.scene.entities["robot"]
+    assert robot.root_body_name == "base_link"
+    assert tuple(robot.joint_names or ()) == _JOINT_NAMES
+    assert tuple(robot.actuator_names or ()) == _ACTUATOR_NAMES
+    assert robot.body_names == ["base_link"]
+
+    policy_terms = [
+        "base_ang_vel",
+        "projected_gravity",
+        "leg_joint_pos",
+        "leg_joint_vel",
+        "wheel_joint_vel",
+        "actions",
+        "command",
+    ]
+    assert list(env_cfg.observations) == ["policy", "critic"]
+    assert list(env_cfg.observations["policy"].terms) == policy_terms
+    assert list(env_cfg.observations["critic"].terms) == [
+        *policy_terms,
+        "base_lin_vel",
+        "motor_torque",
+    ]
+
+    assert list(env_cfg.actions) == ["motor"]
+    action = env_cfg.actions["motor"]
+    assert isinstance(action, Go2WMixedActionCfg)
+    assert action.leg_action_scale == pytest.approx(0.5)
+    assert action.wheel_action_scale == pytest.approx(10.0)
+    assert action.leg_kp == pytest.approx(50.0)
+    assert action.leg_kd == pytest.approx(1.5)
+    assert action.wheel_kd == pytest.approx(0.5)
+
+    command = env_cfg.commands["twist"]
+    assert isinstance(command, Go2WVelocityCommandCfg)
+    assert command.resampling_time_range == [20.0, 20.0]
+    assert command.planar_dead_zone == pytest.approx(0.2)
+    assert tuple(command.ranges.lin_vel_x) == (0.0, 1.0)
+    assert tuple(command.ranges.lin_vel_y) == (0.0, 0.0)
+    assert tuple(command.ranges.ang_vel_z) == (-1.0, 1.0)
+
+    assert list(env_cfg.events) == [
+        "reset_scene_to_default",
+        "reset_root_state_uniform",
+        "motor_gains",
+    ]
+    gains = env_cfg.events["motor_gains"]
+    assert gains.func is manager_terms.randomize_motor_gains
+    assert gains.params["kp_multiplier_range"] == [1.0, 1.0]
+    assert gains.params["kd_multiplier_range"] == [1.0, 1.0]
+    assert list(env_cfg.terminations) == ["time_out", "bad_orientation"]
+    assert {name: term.weight for name, term in env_cfg.rewards.items()} == {
+        "tracking_lin_vel": 1.0,
+        "tracking_ang_vel": 0.75,
+        "lin_vel_z": -5.0,
+        "ang_vel_xy": -0.1,
+        "base_height": -100.0,
+        "orientation": -2.0,
+        "action_rate": -0.005,
+        "similar_to_default": -0.5,
+        "torques": -0.0002,
+        "wheel_vel": 0.0,
+        "alive": 0.5,
+        "upward": 1.0,
+    }
+
+    for manager_name in ("observations", "events", "rewards", "terminations"):
+        for term in getattr(env_cfg, manager_name).values():
+            if term is None:
+                continue
+            terms = term.terms.values() if manager_name == "observations" else (term,)
+            for nested in terms:
+                if nested is None:
+                    continue
+                module = nested.func.__module__
+                assert ".backend." not in module
+                assert not any(name in module for name in (".mujoco", ".motrix", ".drake"))
+
+    _assert_no_omegaconf(env_cfg)
+
+
+def test_go2w_sac_drake_owner_uses_the_same_manager_contract() -> None:
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(config_dir=str(CONF_DIR / "sac"), version_base="1.3"):
+        hydra_cfg = compose(
+            "config",
+            overrides=["task=go2w_joystick_flat/drake"],
+        )
+    env_override = BackendAdapter(hydra_cfg, root_dir=ROOT_DIR).build_task_env_cfg_override()
+    env_cfg = registry.materialize_env_config("Go2WJoystickFlat")
+    apply_cfg_overrides(env_cfg, env_override)
+
+    assert isinstance(env_cfg, ManagerBasedRlEnvCfg)
+    assert hydra_cfg.training.task_name == "Go2WJoystickFlat"
+    assert hydra_cfg.training.sim_backend == "drake"
+    assert list(env_cfg.actions) == ["motor"]
+    assert env_cfg.scene is not None
+    assert env_cfg.scene.default_keyframe_name == "home"
+    env_cfg.validate()
+
+
+def test_go2w_flat_and_rough_registries_are_manager_only() -> None:
+    registry.ensure_registries()
+
+    assert registry.list_registered_envs()["Go2WJoystickFlat"] == {
+        "config_factory": "ManagerBasedRlEnvCfg",
+        "available_backends": ["mujoco", "motrix", "drake"],
+    }
+    assert registry.list_registered_envs()["Go2WJoystickRough"] == {
+        "config_factory": "ManagerBasedRlEnvCfg",
+        "available_backends": ["mujoco", "motrix"],
+    }
+    for legacy_override in (
+        {"reward_config": {}},
+        {"domain_rand": {"randomize_kp": False}},
+        {"control_config": {"action_scale": 0.5}},
+    ):
+        with pytest.raises(ValueError, match="has no attribute"):
+            apply_cfg_overrides(ManagerBasedRlEnvCfg(), legacy_override)
+
+
+@pytest.mark.parametrize("backend", ["mujoco", "motrix"])
+def test_go2w_flat_registry_executes_real_manager_runtime(backend: str) -> None:
+    registry.ensure_registries()
+    _, _, env_override = _materialize(backend)
+    try:
+        env = registry.make(
+            "Go2WJoystickFlat",
+            sim_backend=backend,
+            env_cfg_override=env_override,
+            num_envs=2,
+        )
+    except ImportError as exc:
+        pytest.skip(f"{backend} runtime unavailable: {exc}")
+
+    try:
+        assert isinstance(env, ManagerBasedRlEnv)
+        assert env.obs_groups_spec == {"obs": 53, "critic": 72}
+        assert env.action_space.shape == (16,)
+        action = env.action_manager.get_term("motor")
+        assert isinstance(action, Go2WMixedAction)
+        np.testing.assert_allclose(action.leg_kp, 50.0)
+        np.testing.assert_allclose(action.leg_kd, 1.5)
+
+        obs, info = env.reset(seed=7)
+        assert {name: value.shape for name, value in obs.items()} == {
+            "obs": (2, 53),
+            "critic": (2, 72),
+        }
+        assert isinstance(info, dict)
+        np.testing.assert_allclose(
+            env.scene["robot"].data.default_joint_pos,
+            np.broadcast_to(_HOME_JOINT_POS, (2, 16)),
+        )
+
+        state = env.step(np.full((2, 16), 2.0, dtype=np.float32))
+        np.testing.assert_allclose(action.raw_action, 1.0)
+        np.testing.assert_allclose(action.previous_raw_action, 0.0)
+        np.testing.assert_allclose(
+            action.processed_action[:, :12],
+            np.broadcast_to(_HOME_JOINT_POS[:12] + 0.5, (2, 12)),
+        )
+        np.testing.assert_allclose(action.processed_action[:, 12:], 10.0)
+        np.testing.assert_allclose(state.obs["critic"][:, -16:], action.motor_torque)
+        for value in (*state.obs.values(), state.reward, action.motor_torque):
+            assert np.isfinite(value).all()
+
+        env.reset(np.asarray([0], dtype=np.int32))
+        np.testing.assert_allclose(action.raw_action[0], 0.0)
+        np.testing.assert_allclose(action.raw_action[1], 1.0)
+        np.testing.assert_allclose(action.motor_torque[0], 0.0)
+    finally:
+        env.close()
+
+
+def test_go2w_flat_dead_zone_and_motor_gain_overrides_are_manager_owned() -> None:
+    _, env_cfg, _ = _materialize("mujoco")
+    command = env_cfg.commands["twist"]
+    command.ranges.lin_vel_x = (0.1, 0.1)
+    command.ranges.lin_vel_y = (0.0, 0.0)
+    command.ranges.ang_vel_z = (0.0, 0.0)
+    gains = env_cfg.events["motor_gains"].params
+    gains["kp_multiplier_range"] = (0.5, 0.5)
+    gains["kd_multiplier_range"] = (2.0, 2.0)
+
+    env = make_manager_based_rl_env(env_cfg, num_envs=2, backend_type="mujoco")
+    try:
+        env.reset(seed=11)
+        np.testing.assert_allclose(env.command_manager.get_command("twist"), 0.0)
+        action = env.action_manager.get_term("motor")
+        assert isinstance(action, Go2WMixedAction)
+        np.testing.assert_allclose(action.leg_kp, 25.0)
+        np.testing.assert_allclose(action.leg_kd, 3.0)
+    finally:
+        env.close()
+
+
+def test_go2w_flat_incomplete_motor_selection_fails_closed() -> None:
+    _, env_cfg, _ = _materialize("mujoco")
+    action = env_cfg.actions["motor"]
+    assert isinstance(action, Go2WMixedActionCfg)
+    action.actuator_names = ["FR_.*"]
+
+    with pytest.raises(ValueError, match="requires exactly 16 actuators and target joints"):
+        make_manager_based_rl_env(env_cfg, num_envs=1, backend_type="mujoco")
+
+
+def test_go2w_manager_terms_do_not_leak_backend_or_physical_layout() -> None:
+    source = inspect.getsource(manager_terms)
+    for forbidden in ("._backend", "getattr(", "hasattr(", "qpos", "qvel", "ASSETS_ROOT_PATH"):
+        assert forbidden not in source
diff --git a/tests/envs/locomotion/go2w/test_go2w_motor_control.py b/tests/envs/locomotion/go2w/test_go2w_motor_control.py
deleted file mode 100644
index 2eec857f5..000000000
--- a/tests/envs/locomotion/go2w/test_go2w_motor_control.py
+++ /dev/null
@@ -1,299 +0,0 @@
-from __future__ import annotations
-
-from types import SimpleNamespace
-from typing import Any, cast
-
-import numpy as np
-import pytest
-
-from unilab.base.np_env import NpEnvState
-from unilab.envs.locomotion.go2w.base import (
-    DEFAULT_GO2W_ANGLES,
-    JOINT_SENSOR_PREFIXES,
-    NUM_GO2W_ACTIONS,
-    NUM_LEG_ACTIONS,
-    NUM_WHEEL_ACTIONS,
-    Go2WBaseEnv,
-    compute_go2w_motor_ctrl,
-)
-from unilab.envs.locomotion.go2w.joystick import (
-    Go2WJoystickCfg,
-    Go2WJoystickDomainRandomizationProvider,
-    Go2WJoystickEnv,
-    RewardConfig,
-    build_go2w_backend_reset_randomization,
-)
-
-
-def _reward_config() -> RewardConfig:
-    return RewardConfig(scales={"alive": 1.0}, tracking_sigma=0.25, base_height_target=0.3)
-
-
-class _ConcreteGo2WBaseEnv(Go2WBaseEnv):
-    def update_state(self, state: NpEnvState) -> NpEnvState:
-        return state
-
-
-def test_compute_go2w_motor_ctrl_converts_legs_and_tracks_wheel_velocity() -> None:
-    policy_ctrl = np.zeros((1, NUM_GO2W_ACTIONS), dtype=np.float64)
-    policy_ctrl[:, :NUM_LEG_ACTIONS] = 0.5
-    policy_ctrl[:, NUM_LEG_ACTIONS:] = np.array([[2.0, -2.0, 20.0, -20.0]])
-    joint_pos = np.zeros_like(policy_ctrl)
-    joint_vel = np.ones_like(policy_ctrl) * 0.1
-    leg_kp = np.ones((1, NUM_LEG_ACTIONS), dtype=np.float64) * 10.0
-    leg_kd = np.ones((1, NUM_LEG_ACTIONS), dtype=np.float64) * 2.0
-    wheel_kd = np.ones((1, NUM_WHEEL_ACTIONS), dtype=np.float64) * 2.0
-    ctrl_range = np.tile(np.array([-15.0, 15.0]), (NUM_GO2W_ACTIONS, 1))
-    out = np.zeros_like(policy_ctrl)
-
-    result = compute_go2w_motor_ctrl(
-        policy_ctrl,
-        joint_pos,
-        joint_vel,
-        leg_kp,
-        leg_kd,
-        wheel_kd,
-        ctrl_range[:, 0],
-        ctrl_range[:, 1],
-        out,
-    )
-
-    assert result is out
-    np.testing.assert_allclose(result[:, :NUM_LEG_ACTIONS], 4.8)
-    np.testing.assert_allclose(result[:, NUM_LEG_ACTIONS:], [[3.8, -4.2, 15.0, -15.0]])
-
-
-def test_go2w_joint_state_reads_named_sensors() -> None:
-    calls: list[str] = []
-
-    class FakeBackend:
-        def get_sensor_data(self, name: str) -> np.ndarray:
-            calls.append(name)
-            idx = len(calls)
-            return np.array([[float(idx)], [float(idx + 100)]], dtype=np.float32)
-
-        def get_sensor_data_batch(self, names) -> np.ndarray:
-            return np.concatenate([self.get_sensor_data(name) for name in names], axis=1)
-
-    env = cast(Any, object.__new__(_ConcreteGo2WBaseEnv))
-    env._backend = FakeBackend()
-    env.default_angles = DEFAULT_GO2W_ANGLES.astype(np.float32)
-
-    pos = env.get_dof_pos()
-    vel = env.get_dof_vel()
-
-    assert pos.shape == (2, NUM_GO2W_ACTIONS)
-    assert vel.shape == (2, NUM_GO2W_ACTIONS)
-    assert calls[:NUM_GO2W_ACTIONS] == [f"{prefix}_pos" for prefix in JOINT_SENSOR_PREFIXES]
-    assert calls[NUM_GO2W_ACTIONS:] == [f"{prefix}_vel" for prefix in JOINT_SENSOR_PREFIXES]
-
-
-def test_go2w_backend_reset_randomization_excludes_kp_kd_payload() -> None:
-    cfg = Go2WJoystickCfg()
-    cfg.domain_rand.randomize_base_mass = True
-    cfg.domain_rand.random_com = True
-    cfg.domain_rand.randomize_gravity = True
-    cfg.domain_rand.randomize_kp = True
-    cfg.domain_rand.randomize_kd = True
-
-    payload = build_go2w_backend_reset_randomization(SimpleNamespace(cfg=cfg), num_reset=3)
-
-    assert payload is not None
-    assert payload.kp is None
-    assert payload.kd is None
-    assert payload.base_mass_delta is not None
-    assert payload.base_com_offset is not None
-    assert payload.gravity is not None
-
-
-def test_go2w_reset_plan_can_disable_initial_yaw_randomization() -> None:
-    from unilab.envs.locomotion.common.terrain_spawn import BaseSpawnManager
-
-    cfg = Go2WJoystickCfg(reward_config=_reward_config())
-    cfg.domain_rand.randomize_init_yaw = False
-    env = SimpleNamespace(
-        cfg=cfg,
-        _spawn=BaseSpawnManager(),
-        _init_qpos=np.concatenate(
-            [np.array([0.0, 0.0, 0.42, 1.0, 0.0, 0.0, 0.0]), np.zeros(NUM_GO2W_ACTIONS)]
-        ),
-        _init_qvel=np.zeros(6 + NUM_GO2W_ACTIONS),
-        _num_action=NUM_GO2W_ACTIONS,
-        sample_reset_motor_gains=lambda num_reset: (
-            np.ones((num_reset, NUM_LEG_ACTIONS)),
-            np.ones((num_reset, NUM_LEG_ACTIONS)),
-        ),
-        set_motor_gains=lambda env_ids, motor_kp, motor_kd: None,
-    )
-
-    plan = Go2WJoystickDomainRandomizationProvider().build_reset_plan(
-        env, np.array([0, 1], dtype=np.int32)
-    )
-
-    expected = np.tile(np.array([[1.0, 0.0, 0.0, 0.0]]), (2, 1))
-    np.testing.assert_allclose(plan.qpos[:, 3:7], expected)
-
-
-def test_go2w_init_does_not_pass_position_actuator_gains(monkeypatch: pytest.MonkeyPatch) -> None:
-    from unilab.envs.locomotion.go2w import joystick as go2w_module
-
-    captured: dict[str, Any] = {}
-
-    class FakeBackend:
-        backend_type = "mujoco"
-        num_actuators = NUM_GO2W_ACTIONS
-
-        def get_actuator_ctrl_range(self) -> np.ndarray:
-            return np.tile(np.array([-15.0, 15.0]), (NUM_GO2W_ACTIONS, 1))
-
-        def get_joint_range(self) -> np.ndarray:
-            return np.tile(np.array([-3.14, 3.14]), (NUM_GO2W_ACTIONS, 1))
-
-        def get_sensor_data(self, name: str) -> np.ndarray:
-            if name.endswith("_pos") or name.endswith("_vel"):
-                return np.zeros((2, 1), dtype=np.float32)
-            raise KeyError(name)
-
-        def get_sensor_data_batch(self, names) -> np.ndarray:
-            return np.concatenate([self.get_sensor_data(name) for name in names], axis=1)
-
-        def set_pre_step_control(self, fn) -> None:
-            captured["pre_step_control"] = fn
-
-    def fake_create_backend(*args, **kwargs):
-        captured["args"] = args
-        captured["kwargs"] = kwargs
-        return FakeBackend()
-
-    def fake_base_init(self, cfg, backend, num_envs):
-        self._cfg = cfg
-        self._backend = backend
-        self._num_envs = num_envs
-        self._num_action = NUM_GO2W_ACTIONS
-        self.default_angles = DEFAULT_GO2W_ANGLES.astype(np.float32)
-
-    monkeypatch.setattr(go2w_module, "create_backend", fake_create_backend)
-    monkeypatch.setattr(Go2WBaseEnv, "__init__", fake_base_init)
-    monkeypatch.setattr(Go2WJoystickEnv, "_init_domain_randomization", lambda self, provider: None)
-
-    cfg = Go2WJoystickCfg(reward_config=_reward_config())
-    Go2WJoystickEnv(cfg, num_envs=2, backend_type="mujoco")
-
-    assert captured["args"][0] == "mujoco"
-    assert "position_actuator_gains" not in captured["kwargs"]
-    assert callable(captured["pre_step_control"])
-
-
-def test_go2w_apply_action_maps_legs_to_targets_and_wheels_to_velocity_targets() -> None:
-    env = cast(Any, object.__new__(Go2WJoystickEnv))
-    env._cfg = Go2WJoystickCfg(reward_config=_reward_config())
-    env._np_dtype = np.float32
-    env._num_envs = 1
-    env._num_action = NUM_GO2W_ACTIONS
-    env.default_angles = DEFAULT_GO2W_ANGLES.astype(np.float32)
-    env._leg_action_scale = env._build_leg_action_scale()
-    state = NpEnvState(
-        obs={},
-        reward=np.zeros((1,), dtype=np.float32),
-        terminated=np.zeros((1,), dtype=bool),
-        truncated=np.zeros((1,), dtype=bool),
-        info={},
-    )
-    action = np.ones((1, NUM_GO2W_ACTIONS), dtype=np.float32)
-
-    ctrl = env.apply_action(action, state)
-
-    expected_leg_targets = (DEFAULT_GO2W_ANGLES[:NUM_LEG_ACTIONS] + 0.25).reshape(1, -1)
-    np.testing.assert_allclose(ctrl[:, :NUM_LEG_ACTIONS], expected_leg_targets)
-    np.testing.assert_allclose(ctrl[:, NUM_LEG_ACTIONS:], 10.0)
-    np.testing.assert_allclose(state.info["current_actions"], action)
-
-
-def test_go2w_compute_obs_accepts_reset_subset_info_shapes() -> None:
-    env = cast(Any, object.__new__(Go2WJoystickEnv))
-    env._cfg = Go2WJoystickCfg(reward_config=_reward_config())
-    env._num_envs = 2
-    env._num_action = NUM_GO2W_ACTIONS
-    env.default_angles = DEFAULT_GO2W_ANGLES.astype(np.float32)
-    info = {
-        "current_actions": np.zeros((1, NUM_GO2W_ACTIONS), dtype=np.float32),
-        "commands": np.zeros((1, 3), dtype=np.float32),
-        "torques": np.ones((1, NUM_GO2W_ACTIONS), dtype=np.float32),
-    }
-    linvel = np.zeros((1, 3), dtype=np.float32)
-    gyro = np.zeros((1, 3), dtype=np.float32)
-    gravity = np.zeros((1, 3), dtype=np.float32)
-    dof_pos = DEFAULT_GO2W_ANGLES.reshape(1, -1).astype(np.float32)
-    dof_vel = np.zeros((1, NUM_GO2W_ACTIONS), dtype=np.float32)
-
-    obs = env._compute_obs(info, linvel, gyro, gravity, dof_pos, dof_vel)
-
-    assert obs["obs"].shape == (1, env.obs_groups_spec["obs"])
-    assert obs["critic"].shape == (1, env.obs_groups_spec["critic"])
-    np.testing.assert_allclose(obs["critic"][:, -NUM_GO2W_ACTIONS:], 1.0)
-
-
-def test_go2w_heading_command_updates_yaw_rate_from_heading_error() -> None:
-    class FakeBackend:
-        def get_base_quat(self) -> np.ndarray:
-            return np.array([[1.0, 0.0, 0.0, 0.0]], dtype=np.float32)
-
-    env = cast(Any, object.__new__(Go2WJoystickEnv))
-    env._cfg = Go2WJoystickCfg(reward_config=_reward_config())
-    env._cfg.commands.heading_command = True
-    env._num_envs = 1
-    env._backend = FakeBackend()
-    info = {
-        "commands": np.zeros((1, 3), dtype=np.float32),
-        "heading_commands": np.array([np.pi / 2.0], dtype=np.float32),
-        "steps": np.zeros((1,), dtype=np.uint32),
-    }
-
-    env._update_commands(info)
-
-    np.testing.assert_allclose(info["commands"][:, 2], [0.25 * np.pi], rtol=1e-6)
-
-
-def test_go2w_pre_step_motor_control_reads_from_passed_backend() -> None:
-    calls: list[str] = []
-
-    class FakeBackend:
-        def get_sensor_data(self, name: str) -> np.ndarray:
-            calls.append(name)
-            if name.endswith("_pos"):
-                return np.zeros((1, 1), dtype=np.float32)
-            if name.endswith("_vel"):
-                return np.ones((1, 1), dtype=np.float32) * 0.1
-            raise KeyError(name)
-
-        def get_sensor_data_batch(self, names) -> np.ndarray:
-            return np.concatenate([self.get_sensor_data(name) for name in names], axis=1)
-
-    class PoisonBackend:
-        def get_sensor_data(self, name: str) -> np.ndarray:
-            raise AssertionError(f"unexpected env backend read: {name}")
-
-        def get_sensor_data_batch(self, names) -> np.ndarray:
-            raise AssertionError(f"unexpected env backend read: {names}")
-
-    env = cast(Any, object.__new__(Go2WJoystickEnv))
-    env._backend = PoisonBackend()
-    env.default_angles = DEFAULT_GO2W_ANGLES.astype(np.float32)
-    env._np_dtype = np.float32
-    env._motor_kp = np.ones((1, NUM_LEG_ACTIONS), dtype=np.float64) * 10.0
-    env._motor_kd = np.ones((1, NUM_LEG_ACTIONS), dtype=np.float64) * 2.0
-    env._wheel_kd = np.ones((1, NUM_WHEEL_ACTIONS), dtype=np.float64) * 2.0
-    ctrl_range = np.tile(np.array([-15.0, 15.0]), (NUM_GO2W_ACTIONS, 1))
-    env._ctrl_lower = ctrl_range[:, 0].astype(np.float32)
-    env._ctrl_upper = ctrl_range[:, 1].astype(np.float32)
-    env._last_motor_ctrl = np.zeros((1, NUM_GO2W_ACTIONS), dtype=np.float32)
-    policy_ctrl = np.zeros((1, NUM_GO2W_ACTIONS), dtype=np.float32)
-    policy_ctrl[:, :NUM_LEG_ACTIONS] = 0.5
-    policy_ctrl[:, NUM_LEG_ACTIONS:] = np.array([[1.0, -1.0, 2.0, -2.0]], dtype=np.float32)
-
-    motor_ctrl = env._pre_step_motor_control(FakeBackend(), policy_ctrl)
-
-    assert calls[:NUM_GO2W_ACTIONS] == [f"{prefix}_pos" for prefix in JOINT_SENSOR_PREFIXES]
-    assert calls[NUM_GO2W_ACTIONS:] == [f"{prefix}_vel" for prefix in JOINT_SENSOR_PREFIXES]
-    np.testing.assert_allclose(motor_ctrl[:, :NUM_LEG_ACTIONS], 4.8)
-    np.testing.assert_allclose(motor_ctrl[:, NUM_LEG_ACTIONS:], [[1.8, -2.2, 3.8, -4.2]])
diff --git a/tests/envs/locomotion/test_gait_terms.py b/tests/envs/locomotion/test_gait_terms.py
new file mode 100644
index 000000000..f36957ab9
--- /dev/null
+++ b/tests/envs/locomotion/test_gait_terms.py
@@ -0,0 +1,471 @@
+"""Pinned mjlab-semantics tests for the shared locomotion gait terms."""
+
+from __future__ import annotations
+
+from collections import Counter
+from types import SimpleNamespace
+from typing import Any, cast
+
+import numpy as np
+import pytest
+from unisim.backend.base import BackendSensorView
+
+from unilab.managers import RewardManager, RewardTermCfg
+from unilab.managers._types import ManagerBasedRlEnv
+from unilab.managers.scene_entity_config import SceneEntityCfg
+from unilab.tasks.locomotion.common import gait_terms
+
+LEFT = ("left_contact_0", "left_contact_1")
+RIGHT = ("right_contact_0", "right_contact_1")
+GROUPS = (LEFT, RIGHT)
+
+
+class _Scene:
+    """Two-foot contact sensors plus an entity with two foot bodies."""
+
+    def __init__(self, num_envs: int = 2) -> None:
+        self.calls: Counter[str] = Counter()
+        self.contacts = {
+            "left_contact_0": np.zeros((num_envs, 1), dtype=np.float32),
+            "left_contact_1": np.zeros((num_envs, 1), dtype=np.float32),
+            "right_contact_0": np.zeros((num_envs, 1), dtype=np.float32),
+            "right_contact_1": np.zeros((num_envs, 1), dtype=np.float32),
+        }
+        self.robot = SimpleNamespace(
+            joint_names=("left_knee", "right_knee"),
+            body_names=("left_foot", "right_foot"),
+            num_joints=2,
+            num_bodies=2,
+            data=SimpleNamespace(
+                body_link_pos_w=np.zeros((num_envs, 2, 3), dtype=np.float32),
+                body_link_lin_vel_w=np.zeros((num_envs, 2, 3), dtype=np.float32),
+            ),
+        )
+
+    def __getitem__(self, name: str):
+        if name != "robot":
+            raise KeyError(name)
+        return self.robot
+
+    def bind_sensor_data(self, names) -> BackendSensorView:
+        names = tuple(names)
+        self.calls["bind"] += 1
+        dimensions = tuple(self.contacts[name].shape[1] for name in names)
+
+        def read() -> np.ndarray:
+            self.calls["read"] += 1
+            return np.concatenate([self.contacts[name] for name in names], axis=1)
+
+        view = BackendSensorView("fake", names, dimensions, 2, read)
+        view.read()  # Mirror SimBackend.bind_sensor_data materialization validation.
+        return view
+
+    def set_foot_contact(self, foot: int, in_contact: bool, env_ids=(0, 1)) -> None:
+        names = LEFT if foot == 0 else RIGHT
+        for name in names:
+            self.contacts[name][list(env_ids), 0] = 1.0 if in_contact else 0.0
+
+
+class _Commands:
+    def __init__(self, command: np.ndarray | None = None) -> None:
+        self.command = (
+            np.array([[0.8, 0.0, 0.0], [0.8, 0.0, 0.0]], dtype=np.float32)
+            if command is None
+            else command
+        )
+
+    def get_command(self, name: str) -> np.ndarray:
+        if name != "twist":
+            raise KeyError(name)
+        return self.command
+
+
+def _env(scene: _Scene | None = None, command: np.ndarray | None = None) -> ManagerBasedRlEnv:
+    return cast(
+        ManagerBasedRlEnv,
+        SimpleNamespace(
+            num_envs=2,
+            step_dt=0.02,
+            scene=scene or _Scene(),
+            command_manager=_Commands(command),
+        ),
+    )
+
+
+def _term(func: Any, env: ManagerBasedRlEnv, **params: Any) -> Any:
+    cfg = RewardTermCfg(func=func, weight=1.0, params=params)
+    return func(cfg=cfg, env=env)
+
+
+# ---------------------------------------------------------------------------
+# feet_air_time: mjlab window-form counting semantics
+# ---------------------------------------------------------------------------
+
+
+def _mjlab_air_time_sequence(
+    contacts: list[np.ndarray], step_dt: float, threshold_min: float, threshold_max: float
+) -> list[np.ndarray]:
+    """Replicate mjlab ContactSensor air-time updates plus the window count."""
+    air_time = np.zeros_like(contacts[0], dtype=np.float64)
+    rewards = []
+    for contact in contacts:
+        air_time = np.where(contact, 0.0, air_time + step_dt)
+        in_range = (air_time > threshold_min) & (air_time < threshold_max)
+        rewards.append(np.sum(in_range.astype(np.float64), axis=1))
+    return rewards
+
+
+def test_feet_air_time_matches_mjlab_window_counting() -> None:
+    scene = _Scene()
+    env = _env(scene)
+    term = _term(gait_terms.feet_air_time, env, sensor_groups=GROUPS)
+
+    # Foot 0 stays in contact; foot 1 lifts for four steps then lands.
+    sequence = [(True, False)] * 4 + [(True, True)]
+    contacts = []
+    for left, right in sequence:
+        scene.set_foot_contact(0, left)
+        scene.set_foot_contact(1, right)
+        contacts.append(np.array([[left, right], [left, right]]))
+        expected = _mjlab_air_time_sequence(contacts, 0.02, 0.05, 0.5)[-1]
+        np.testing.assert_allclose(term(env), expected)
+    # Air time after the fourth air step is 0.08: inside (0.05, 0.5).
+    assert _mjlab_air_time_sequence(contacts, 0.02, 0.05, 0.5)[3][0] == 1.0
+    # Landing resets the air time to zero, back out of the window.
+    assert _mjlab_air_time_sequence(contacts, 0.02, 0.05, 0.5)[4][0] == 0.0
+
+
+def test_feet_air_time_window_excludes_long_flight() -> None:
+    scene = _Scene()
+    env = _env(scene)
+    term = _term(
+        gait_terms.feet_air_time,
+        env,
+        sensor_groups=GROUPS,
+        threshold_min=0.01,
+        threshold_max=0.05,
+    )
+    for step in range(5):  # air times 0.02, 0.04, 0.06, 0.08, 0.10
+        reward = term(env)
+        expected = 1.0 if step in (0, 1) else 0.0
+        np.testing.assert_allclose(reward, [expected * 2, expected * 2])
+
+
+def test_feet_air_time_command_gate_and_reset() -> None:
+    scene = _Scene()
+    standing = np.zeros((2, 3), dtype=np.float32)
+    env = _env(scene, command=standing)
+    term = _term(
+        gait_terms.feet_air_time,
+        env,
+        sensor_groups=GROUPS,
+        command_name="twist",
+        command_threshold=0.5,
+    )
+    for _ in range(4):
+        np.testing.assert_allclose(term(env), [0.0, 0.0])  # Standing: gated off.
+
+    moving_env = _env(scene)
+    term.reset()  # Episode reset clears accumulated air time.
+    np.testing.assert_allclose(term(moving_env), [0.0, 0.0])  # 0.02 <= threshold_min.
+    for _ in range(2):
+        term(moving_env)
+    np.testing.assert_allclose(term(moving_env), [2.0, 2.0])  # 0.08 in window.
+
+
+def test_feet_air_time_group_any_reduce_matches_mjlab_slots() -> None:
+    scene = _Scene()
+    env = _env(scene)
+    term = _term(gait_terms.feet_air_time, env, sensor_groups=GROUPS)
+    # Only one sensor of the left group reports contact: the whole foot counts
+    # as in contact (mjlab ContactSensor any-reduce across slots), so only the
+    # right foot accumulates air time.
+    scene.contacts["left_contact_1"][:, 0] = 1.0
+    np.testing.assert_allclose(term(env), [0.0, 0.0])  # Right at 0.02.
+    np.testing.assert_allclose(term(env), [0.0, 0.0])  # Right at 0.04.
+    np.testing.assert_allclose(term(env), [1.0, 1.0])  # Right at 0.06, in window.
+
+
+# ---------------------------------------------------------------------------
+# feet_clearance / feet_swing_height / feet_slip / angular_momentum
+# ---------------------------------------------------------------------------
+
+
+def test_feet_clearance_matches_mjlab_velocity_weighted_error() -> None:
+    scene = _Scene()
+    scene.robot.data.body_link_pos_w[:] = [[[0, 0, 0.12], [0, 0, 0.05]]]
+    scene.robot.data.body_link_lin_vel_w[:] = [[[0.3, 0.4, 0.0], [0.0, -0.5, 0.0]]]
+    env = _env(scene)
+    cost = gait_terms.feet_clearance(env, target_height=0.1, command_name="twist")
+    expected = np.abs(np.array([0.12, 0.05]) - 0.1) * np.array([0.5, 0.5])
+    np.testing.assert_allclose(cost, [np.sum(expected)] * 2, rtol=1e-6)
+
+    standing = _env(scene, command=np.zeros((2, 3), dtype=np.float32))
+    np.testing.assert_allclose(
+        gait_terms.feet_clearance(standing, target_height=0.1, command_name="twist"),
+        [0.0, 0.0],
+    )
+
+
+def test_feet_swing_height_charges_peak_error_at_landing() -> None:
+    scene = _Scene()
+    env = _env(scene)
+    term = _term(
+        gait_terms.feet_swing_height,
+        env,
+        sensor_groups=GROUPS,
+        target_height=0.1,
+        command_name="twist",
+        command_threshold=0.05,
+    )
+    # Swing: both feet in the air, left peaks at 0.12, right at 0.08.
+    scene.robot.data.body_link_pos_w[:, 0, 2] = 0.12
+    scene.robot.data.body_link_pos_w[:, 1, 2] = 0.08
+    np.testing.assert_allclose(term(env), [0.0, 0.0])  # In air: nothing charged.
+    scene.robot.data.body_link_pos_w[:, 0, 2] = 0.10  # Lower later: peak persists.
+    np.testing.assert_allclose(term(env), [0.0, 0.0])
+    # Landing: first contact charges (peak / target - 1)^2 per foot.
+    scene.set_foot_contact(0, True)
+    scene.set_foot_contact(1, True)
+    scene.robot.data.body_link_pos_w[:, :, 2] = 0.0
+    expected = (0.12 / 0.1 - 1.0) ** 2 + (0.08 / 0.1 - 1.0) ** 2
+    np.testing.assert_allclose(term(env), [expected] * 2, rtol=1e-6)
+    # After landing the peaks reset; continued contact charges nothing.
+    np.testing.assert_allclose(term(env), [0.0, 0.0])
+
+    term.reset()
+    standing = _env(scene, command=np.zeros((2, 3), dtype=np.float32))
+    scene.set_foot_contact(0, False)
+    scene.set_foot_contact(1, False)
+    scene.robot.data.body_link_pos_w[:, 0, 2] = 0.15
+    scene.robot.data.body_link_pos_w[:, 1, 2] = 0.15
+    term(standing)
+    scene.set_foot_contact(0, True)
+    scene.set_foot_contact(1, True)
+    np.testing.assert_allclose(term(standing), [0.0, 0.0])  # Standing: gated off.
+
+
+def test_feet_slip_matches_mjlab_contact_weighted_xy_speed() -> None:
+    scene = _Scene()
+    scene.set_foot_contact(0, True)
+    scene.robot.data.body_link_lin_vel_w[:] = [[[0.3, 0.4, 9.0], [1.0, 0.0, 0.0]]]
+    env = _env(scene)
+    term = _term(
+        gait_terms.feet_slip,
+        env,
+        sensor_groups=GROUPS,
+        command_name="twist",
+        command_threshold=0.01,
+    )
+    # Only the left foot is in contact; z velocity is ignored.
+    np.testing.assert_allclose(term(env), [0.3**2 + 0.4**2] * 2, rtol=1e-6)
+
+    standing = _env(scene, command=np.zeros((2, 3), dtype=np.float32))
+    np.testing.assert_allclose(term(standing), [0.0, 0.0])
+
+
+def test_angular_momentum_penalty_matches_mjlab_squared_norm() -> None:
+    scene = _Scene()
+    scene.contacts["angmom"] = np.array([[1.0, 2.0, 3.0], [-0.5, 0.0, 0.25]], dtype=np.float32)
+    env = _env(scene)
+    term = _term(gait_terms.angular_momentum_penalty, env, sensor_name="angmom")
+    np.testing.assert_allclose(term(env), [14.0, 0.3125], rtol=1e-6)
+
+
+def test_self_collision_cost_counts_active_pairs() -> None:
+    """mjlab found-count form: one point per geom pair reporting contact."""
+    scene = _Scene()
+    scene.contacts = {
+        "self_trunk_left": np.array([[1.0], [0.0]], dtype=np.float32),
+        "self_trunk_right": np.array([[0.0], [0.0]], dtype=np.float32),
+        "self_legs": np.array([[1.0], [1.0]], dtype=np.float32),
+    }
+    env = _env(scene)
+    term = _term(
+        gait_terms.self_collision_cost,
+        env,
+        sensor_groups=[["self_trunk_left"], ["self_trunk_right"], ["self_legs"]],
+    )
+    np.testing.assert_allclose(term(env), [2.0, 1.0])
+    # Any-reduce within a group: two sensors of the same pair count once.
+    scene.contacts["self_trunk_right"] = np.array([[1.0], [0.0]], dtype=np.float32)
+    term = _term(
+        gait_terms.self_collision_cost,
+        env,
+        sensor_groups=[["self_trunk_left"], ["self_trunk_right", "self_legs"]],
+    )
+    np.testing.assert_allclose(term(env), [2.0, 1.0])
+
+
+# ---------------------------------------------------------------------------
+# Validation and manager integration
+# ---------------------------------------------------------------------------
+
+
+def test_gait_terms_fail_closed_on_bad_configuration() -> None:
+    env = _env()
+    with pytest.raises(ValueError, match="sensor_groups must declare at least one foot"):
+        _term(gait_terms.feet_air_time, env, sensor_groups=[])
+    with pytest.raises(ValueError, match="command_name must be a non-empty string"):
+        _term(gait_terms.feet_slip, env, sensor_groups=GROUPS)
+    with pytest.raises(ValueError, match="threshold_min must be below threshold_max"):
+        _term(
+            gait_terms.feet_air_time,
+            env,
+            sensor_groups=GROUPS,
+            threshold_min=0.5,
+            threshold_max=0.05,
+        )
+    with pytest.raises(ValueError, match="selects 1 foot bodies"):
+        _term(
+            gait_terms.feet_slip,
+            env,
+            sensor_groups=GROUPS,
+            command_name="twist",
+            asset_cfg=SceneEntityCfg("robot", body_ids=[0]),
+        )
+    with pytest.raises(ValueError, match="must expose 3 values"):
+        _term(gait_terms.angular_momentum_penalty, env, sensor_name="left_contact_0")
+
+
+def test_gait_terms_integrate_with_reward_manager() -> None:
+    scene = _Scene()
+    env = _env(scene)
+    manager = RewardManager(
+        {
+            "air_time": RewardTermCfg(
+                func=gait_terms.feet_air_time,
+                weight=0.25,
+                params={"sensor_groups": [list(LEFT), list(RIGHT)], "command_name": "twist"},
+            ),
+            "slip": RewardTermCfg(
+                func=gait_terms.feet_slip,
+                weight=-0.1,
+                params={"sensor_groups": [list(LEFT), list(RIGHT)], "command_name": "twist"},
+            ),
+        },
+        env,
+        scale_by_dt=False,
+    )
+    scene.set_foot_contact(0, True)
+    scene.robot.data.body_link_lin_vel_w[:] = [[[0.3, 0.4, 0.0], [0.0, 0.0, 0.0]]]
+    for _ in range(3):
+        result = manager.compute(dt=0.02)
+    expected = 0.25 * 1.0 - 0.1 * 0.25  # Right foot at 0.06 s in window; left slips.
+    np.testing.assert_allclose(result, [expected, expected], rtol=1e-6)
+
+
+def test_gait_term_module_has_no_forbidden_runtime_dependencies() -> None:
+    import ast
+    from pathlib import Path
+
+    path = (
+        Path(__file__).resolve().parents[3]
+        / "src"
+        / "unilab"
+        / "tasks"
+        / "locomotion"
+        / "common"
+        / "gait_terms.py"
+    )
+    tree = ast.parse(path.read_text(encoding="utf-8"))
+    forbidden = ("torch", "uni_rl", "unilab.training", "unilab.base.backend")
+    imports = [node.module or "" for node in ast.walk(tree) if isinstance(node, ast.ImportFrom)] + [
+        alias.name
+        for node in ast.walk(tree)
+        if isinstance(node, ast.Import)
+        for alias in node.names
+    ]
+    assert not [name for name in imports if name.startswith(forbidden)]
+
+
+# ---------------------------------------------------------------------------
+# Privileged foot observations (mjlab velocity critic terms, issue #1402)
+# ---------------------------------------------------------------------------
+
+
+def test_foot_height_returns_per_foot_body_z() -> None:
+    scene = _Scene()
+    scene.robot.data.body_link_pos_w[:] = [[[0.0, 0.0, 0.031], [0.0, 0.0, 0.052]]]
+    env = _env(scene)
+    np.testing.assert_allclose(
+        gait_terms.foot_height(env),
+        [[0.031, 0.052], [0.031, 0.052]],
+        rtol=1e-6,
+    )
+
+
+def test_foot_air_time_tracks_current_air_time() -> None:
+    scene = _Scene()
+    env = _env(scene)
+    term = _term(gait_terms.foot_air_time, env, sensor_groups=GROUPS)
+    scene.set_foot_contact(0, True)
+    scene.set_foot_contact(1, False)
+    np.testing.assert_allclose(term(env), [[0.0, 0.02], [0.0, 0.02]])
+    np.testing.assert_allclose(term(env), [[0.0, 0.04], [0.0, 0.04]])
+    scene.set_foot_contact(1, True)
+    np.testing.assert_allclose(term(env), [[0.0, 0.0], [0.0, 0.0]])
+
+
+def test_foot_air_time_reset_clears_only_targeted_rows() -> None:
+    scene = _Scene()
+    env = _env(scene)
+    term = _term(gait_terms.foot_air_time, env, sensor_groups=GROUPS)
+    scene.set_foot_contact(0, False)
+    scene.set_foot_contact(1, False)
+    term(env)  # Both feet at 0.02 s air time in both envs.
+    term.reset(np.array([1]))
+    np.testing.assert_allclose(term(env), [[0.04, 0.04], [0.02, 0.02]])
+
+
+def test_foot_contact_reports_float_flags() -> None:
+    scene = _Scene()
+    env = _env(scene)
+    term = _term(gait_terms.foot_contact, env, sensor_groups=GROUPS)
+    scene.set_foot_contact(0, True, env_ids=(0,))
+    scene.set_foot_contact(1, False)
+    np.testing.assert_allclose(term(env), [[1.0, 0.0], [0.0, 0.0]])
+
+
+def test_force_contact_gating_uses_contact_frame_normal_column() -> None:
+    """Width-3 ``data="force"`` contact sensors gate on column 0.
+
+    MuJoCo reports contact force in the contact frame whose first axis is the
+    contact normal; tangential columns 1-2 must not trigger contact.
+    """
+    scene = _Scene()
+    scene.contacts = {
+        "left_contact_0": np.array([[0.5, 0.0, 0.0], [0.5, 0.0, 0.0]], dtype=np.float32),
+        "left_contact_1": np.zeros((2, 3), dtype=np.float32),
+        "right_contact_0": np.array([[0.0, 0.0, 3.0], [0.0, 0.0, 3.0]], dtype=np.float32),
+        "right_contact_1": np.zeros((2, 3), dtype=np.float32),
+    }
+    env = _env(scene)
+    term = _term(gait_terms.foot_contact, env, sensor_groups=GROUPS)
+    np.testing.assert_allclose(term(env), [[1.0, 0.0], [1.0, 0.0]])
+
+
+def test_foot_contact_forces_log_compressed_in_group_order() -> None:
+    scene = _Scene()
+    scene.contacts = {
+        "left_contact_0": np.array([[1.0, 0.0, -1.0], [0.0, 0.0, 0.0]], dtype=np.float32),
+        "left_contact_1": np.zeros((2, 3), dtype=np.float32),
+        "right_contact_0": np.array([[0.0, 2.0, 0.0], [0.0, 0.0, 0.0]], dtype=np.float32),
+        "right_contact_1": np.zeros((2, 3), dtype=np.float32),
+    }
+    env = _env(scene)
+    term = _term(gait_terms.foot_contact_forces, env, sensor_groups=GROUPS)
+    values = term(env)
+    assert values.shape == (2, 12)
+    expected_left = np.sign([1.0, 0.0, -1.0]) * np.log1p(np.abs([1.0, 0.0, -1.0]))
+    np.testing.assert_allclose(values[0, :3], expected_left, rtol=1e-6)
+    np.testing.assert_allclose(values[0, 3:6], 0.0, atol=1e-8)
+    np.testing.assert_allclose(values[0, 6:9], [0.0, np.log1p(2.0), 0.0], rtol=1e-6)
+    np.testing.assert_allclose(values[1], 0.0, atol=1e-8)
+
+
+def test_foot_contact_forces_rejects_scalar_found_sensors() -> None:
+    scene = _Scene()  # width-1 "found" sensors
+    env = _env(scene)
+    with pytest.raises(ValueError, match="3-D force"):
+        _term(gait_terms.foot_contact_forces, env, sensor_groups=GROUPS)
diff --git a/tests/envs/locomotion/test_go2_footstand.py b/tests/envs/locomotion/test_go2_footstand.py
index b99b16bef..22e119f82 100644
--- a/tests/envs/locomotion/test_go2_footstand.py
+++ b/tests/envs/locomotion/test_go2_footstand.py
@@ -1,957 +1,455 @@
+"""Hydra-owned Manager-Based contract for Go2 footstand."""
+
 from __future__ import annotations
 
+import importlib.util
+from collections.abc import Mapping, Sequence
+from dataclasses import fields, is_dataclass
+from pathlib import Path
+from typing import Any
+
 import numpy as np
 import pytest
+from hydra import compose, initialize_config_dir
+from hydra.core.global_hydra import GlobalHydra
+from omegaconf import DictConfig, OmegaConf
+from unisim.backend.base import SimBackend
 
 from unilab.base import registry
-from unilab.base.np_env import NpEnvState
-from unilab.base.registry import ensure_registries
-from unilab.dr import ResetRandomizationPayload
-from unilab.envs.locomotion.common.rewards import RewardContext
-from unilab.envs.locomotion.go2.footstand import (
-    FootstandControlConfig,
-    FootstandSensor,
-    Go2FootStandCfg,
-    Go2FootStandDomainRandConfig,
-    Go2FootStandDomainRandomizationProvider,
-    Go2FootStandTask,
-    RewardConfig,
+from unilab.base.config_adapter import BackendAdapter
+from unilab.base.config_materialization import apply_cfg_overrides
+from unilab.envs import ManagerBasedRlEnv, ManagerBasedRlEnvCfg, make_manager_based_rl_env
+from unilab.managers._noise import UniformNoiseCfg
+from unilab.tasks.locomotion.go2.footstand import (
+    FRAME_OBS_DIM,
+    NUM_ACTIONS,
+    PRIVILEGED_OBS_DIM,
+    FootstandIncrementalAction,
+    FootstandIncrementalActionCfg,
+    FootstandMassRandomization,
+    FootstandReward,
+    FootstandTermination,
 )
 
+ROOT_DIR = Path(__file__).parents[3]
+CONF_DIR = ROOT_DIR / "src" / "unilab" / "conf"
+
+_JOINT_NAMES = (
+    "FL_hip_joint",
+    "FL_thigh_joint",
+    "FL_calf_joint",
+    "FR_hip_joint",
+    "FR_thigh_joint",
+    "FR_calf_joint",
+    "RL_hip_joint",
+    "RL_thigh_joint",
+    "RL_calf_joint",
+    "RR_hip_joint",
+    "RR_thigh_joint",
+    "RR_calf_joint",
+)
+_ACTION_JOINT_NAMES = (
+    "FR_hip_joint",
+    "FR_thigh_joint",
+    "FR_calf_joint",
+    "FL_hip_joint",
+    "FL_thigh_joint",
+    "FL_calf_joint",
+    "RR_hip_joint",
+    "RR_thigh_joint",
+    "RR_calf_joint",
+    "RL_hip_joint",
+    "RL_thigh_joint",
+    "RL_calf_joint",
+)
+_ACTUATOR_NAMES = tuple(name.removesuffix("_joint") for name in _ACTION_JOINT_NAMES)
+_BODY_NAMES = (
+    "base",
+    "FL_hip",
+    "FL_thigh",
+    "FL_calf",
+    "FR_hip",
+    "FR_thigh",
+    "FR_calf",
+    "RL_hip",
+    "RL_thigh",
+    "RL_calf",
+    "RR_hip",
+    "RR_thigh",
+    "RR_calf",
+)
+_OWNER_CASES = (
+    pytest.param(
+        "ppo",
+        ("task=go2_footstand/mujoco",),
+        "mujoco",
+        frozenset(("pd_gains", "floor_friction", "link_mass", "torso_com", "joint_armature")),
+        0.05,
+        2.0,
+        id="ppo-mujoco",
+    ),
+    pytest.param(
+        "ppo",
+        ("task=go2_footstand/motrix",),
+        "motrix",
+        frozenset(("pd_gains",)),
+        0.02,
+        3.0,
+        id="ppo-motrix",
+    ),
+    pytest.param(
+        "ppo",
+        ("task=go2_footstand/drake",),
+        "drake",
+        frozenset(),
+        0.05,
+        2.0,
+        id="ppo-drake",
+    ),
+    pytest.param(
+        "sac",
+        ("task=go2_footstand/drake",),
+        "drake",
+        frozenset(),
+        0.05,
+        2.0,
+        id="sac-drake",
+    ),
+)
 
-def test_go2_footstand_registers_supported_backends() -> None:
-    ensure_registries()
-
-    meta = registry.list_registered_envs()["Go2FootStand"]
-
-    assert set(meta["available_backends"]) == {"drake", "motrix", "mujoco"}
-
-
-class _OrientationBackend:
-    pass
-
-
-class _JointRangeBackend:
-    def get_joint_range(self) -> np.ndarray:
-        return np.array([[-2.0, 2.0], [0.0, 2.0]], dtype=np.float32)
-
-
-class _BaseMotionBackend:
-    def get_base_lin_vel(self) -> np.ndarray:
-        return np.array([[1.0, 2.0, 0.0], [1.0, 0.0, 0.0]], dtype=np.float32)
-
-    def get_base_ang_vel(self) -> np.ndarray:
-        return np.array([[0.0, 0.0, 2.0], [0.0, 2.0, 0.0]], dtype=np.float32)
-
-
-class _KneeHeightBackend:
-    def __init__(self, knee_height: np.ndarray) -> None:
-        self._knee_height = knee_height
-
-    def get_body_pos_w(self, body_ids: np.ndarray) -> np.ndarray:
-        assert body_ids.shape == (4,)
-        out = np.zeros((*self._knee_height.shape, 3), dtype=np.float32)
-        out[:, :, 2] = self._knee_height
-        return out
-
-
-class _BasePoseBackend:
-    def get_base_pos(self) -> np.ndarray:
-        return np.zeros((3, 3), dtype=np.float32)
-
-    def get_base_quat(self) -> np.ndarray:
-        return np.tile(np.array([[1.0, 0.0, 0.0, 0.0]], dtype=np.float32), (3, 1))
-
-
-def test_go2_footstand_cfg_uses_rear_body_contact_termination() -> None:
-    cfg = Go2FootStandCfg()
-
-    assert isinstance(cfg.sensor, FootstandSensor)
-    assert "base1_contact" in cfg.sensor.ternamate_contact
-    assert "base2_contact" in cfg.sensor.ternamate_contact
-    assert "base3_contact" in cfg.sensor.ternamate_contact
-    assert "RL_calf_contact1" in cfg.sensor.ternamate_contact
-    assert "RR_calf_contact2" in cfg.sensor.ternamate_contact
-    assert "FL_calf_contact1" in cfg.sensor.penalty_contact
-    assert "FR_calf_contact2" in cfg.sensor.penalty_contact
-    assert cfg.noise_config.level == pytest.approx(1.0)
-    assert cfg.noise_config.scale_joint_angle == pytest.approx(0.01)
-    assert cfg.noise_config.scale_joint_vel == pytest.approx(1.5)
-    assert cfg.add_body_sensors is True
-    assert isinstance(cfg.control_config, FootstandControlConfig)
-    assert cfg.control_config.action_scale == pytest.approx(0.3)
-    assert cfg.control_config.clip_actions == pytest.approx(1.0)
-    assert isinstance(cfg.domain_rand, Go2FootStandDomainRandConfig)
-    assert cfg.domain_rand.randomize_kp is False
-    assert cfg.domain_rand.randomize_floor_friction is True
-    assert cfg.obs_history_len == 15
-    assert cfg.soft_joint_pos_limit_factor == pytest.approx(0.9)
-    assert cfg.energy_termination_threshold == np.inf
-    assert cfg.termination_grace_steps == 100
-    assert cfg.termination_height_fraction == pytest.approx(0.8)
-    assert cfg.termination_orientation_threshold == pytest.approx(0.2)
-    assert cfg.max_episode_seconds == pytest.approx(10.0)
-
-
-def test_go2_footstand_orientation_flips_handstand_target() -> None:
-    env = object.__new__(Go2FootStandTask)
-    env._backend = _OrientationBackend()
-    env._desired_forward_vec = np.array([0.0, 0.0, 1.0], dtype=np.float32)
-    env._get_body_forward = lambda: np.array(  # type: ignore[method-assign]
-        [[0.0, 0.0, 1.0], [0.0, 0.0, -1.0]], dtype=np.float32
-    )
-
-    reward = env._reward_orientation(
-        RewardContext(
-            info={},
-            linvel=np.zeros((2, 3), dtype=np.float32),
-            gyro=np.zeros((2, 3), dtype=np.float32),
-            dof_pos=np.zeros((2, 12), dtype=np.float32),
-        )
-    )
-
-    np.testing.assert_allclose(reward, np.array([1.0, 0.0], dtype=np.float32))
-
-
-def test_go2_footstand_soft_joint_limits_use_playground_factor() -> None:
-    env = object.__new__(Go2FootStandTask)
-    cfg = Go2FootStandCfg()
-    cfg.soft_joint_pos_limit_factor = 0.5
-    env._cfg = cfg
-    env._num_action = 2
-    env._backend = _JointRangeBackend()
-
-    env._init_soft_joint_limits()
-
-    np.testing.assert_allclose(env._soft_lowers, np.array([-1.0, 0.5], dtype=np.float32))
-    np.testing.assert_allclose(env._soft_uppers, np.array([1.0, 1.5], dtype=np.float32))
-
-
-def test_go2_footstand_reward_functions_include_stability_terms() -> None:
-    env = object.__new__(Go2FootStandTask)
-
-    env._init_reward_functions()
-
-    assert "tar" in env._reward_fns
-    assert "penalty_contact" in env._reward_fns
-    assert "termination" in env._reward_fns
-    assert "rear_feet_contact" in env._reward_fns
-    assert "rear_leg_symmetry" in env._reward_fns
-    assert "rear_leg_splay" in env._reward_fns
-    assert "rear_foot_slip" in env._reward_fns
-    assert "rear_foot_anchor" in env._reward_fns
-    assert "front_leg_motion" in env._reward_fns
-    assert "front_feet_crossing" in env._reward_fns
-    assert "front_leg_crossing" in env._reward_fns
-    assert "upright_stability" in env._reward_fns
-    assert "knee_clearance" in env._reward_fns
-
-
-def test_go2_footstand_pose_targets_front_legs_and_supports_rear() -> None:
-    env = object.__new__(Go2FootStandTask)
-
-    env._init_footstand_pose_targets()
-
-    assert env.feet_geom_names == [0, 1]
-    assert env._joint_ids == [6, 7, 8, 9, 10, 11]
-    assert env._tar_ids == [0, 1, 2, 3, 4, 5]
-    np.testing.assert_allclose(
-        env.target_angle, np.array([0.0, 1.82, -1.16, 0.0, 1.82, -1.16], dtype=np.float32)
-    )
-
-
-def test_go2_footstand_obs_matches_playground_state_layout() -> None:
-    env = object.__new__(Go2FootStandTask)
-    cfg = Go2FootStandCfg()
-    cfg.noise_config.level = 0.0
-    env._cfg = cfg
-    env._num_envs = 1
-    env.default_angles = np.arange(12, dtype=np.float32).reshape(1, 12)
-    env._obs_history = np.zeros((1, cfg.obs_history_len, 45), dtype=np.float32)
-
-    linvel = np.array([[1.0, 2.0, 3.0]], dtype=np.float32)
-    gyro = np.array([[4.0, 5.0, 6.0]], dtype=np.float32)
-    gravity = np.array([[0.0, 0.0, -1.0]], dtype=np.float32)
-    dof_pos = env.default_angles + 0.5
-    dof_vel = np.arange(12, dtype=np.float32).reshape(1, 12) + 10.0
-    last_actions = np.full((1, 12), 0.25, dtype=np.float32)
-    current_actions = np.full((1, 12), 0.75, dtype=np.float32)
-    accelerometer = np.array([[7.0, 8.0, 9.0]], dtype=np.float32)
-    global_angvel = np.array([[10.0, 11.0, 12.0]], dtype=np.float32)
-    torques = np.arange(12, dtype=np.float32).reshape(1, 12) + 20.0
-
-    obs = env._compute_obs(
-        {"last_actions": last_actions, "current_actions": current_actions, "torques": torques},
-        linvel,
-        gyro,
-        gravity,
-        dof_pos,
-        dof_vel,
-        np.array([[0.53]], dtype=np.float32),
-        accelerometer,
-        global_angvel,
-    )
-
-    current_frame = obs["obs"][:, -45:]
-    assert obs["obs"].shape == (1, 675)
-    assert obs["critic"].shape == (1, 724)
-    np.testing.assert_allclose(obs["obs"][:, : 14 * 45], 0.0)
-    np.testing.assert_allclose(current_frame[:, 0:3], linvel)
-    np.testing.assert_allclose(current_frame[:, 3:6], gyro)
-    np.testing.assert_allclose(current_frame[:, 6:9], gravity)
-    np.testing.assert_allclose(current_frame[:, -12:], last_actions)
-
-
-def test_go2_footstand_reset_obs_fills_history_with_current_frame() -> None:
-    env = object.__new__(Go2FootStandTask)
-    cfg = Go2FootStandCfg()
-    cfg.noise_config.level = 0.0
-    env._cfg = cfg
-    env._num_envs = 2
-    env.default_angles = np.zeros((1, 12), dtype=np.float32)
-    env._obs_history = np.zeros((2, cfg.obs_history_len, 45), dtype=np.float32)
-
-    linvel = np.array([[1.0, 0.0, 0.0]], dtype=np.float32)
-    gyro = np.array([[0.0, 2.0, 0.0]], dtype=np.float32)
-    gravity = np.array([[0.0, 0.0, -1.0]], dtype=np.float32)
-    dof_pos = np.ones((1, 12), dtype=np.float32)
-    dof_vel = np.full((1, 12), 3.0, dtype=np.float32)
-
-    obs = env._compute_obs(
-        {"last_actions": np.full((1, 12), 0.5, dtype=np.float32)},
-        linvel,
-        gyro,
-        gravity,
-        dof_pos,
-        dof_vel,
-        np.array([[0.53]], dtype=np.float32),
-        np.zeros((1, 3), dtype=np.float32),
-        np.zeros((1, 3), dtype=np.float32),
-        env_ids=np.array([1], dtype=np.int32),
-    )
-
-    frames = obs["obs"].reshape(1, cfg.obs_history_len, 45)
-    np.testing.assert_allclose(frames[:, 0, :], frames[:, -1, :])
-    np.testing.assert_allclose(env._obs_history[0], 0.0)
-    np.testing.assert_allclose(env._obs_history[1], frames[0])
-
-
-class _EnergyTerminationBackend:
-    def __init__(self) -> None:
-        self._sensors = {
-            "local_linvel": np.zeros((1, 3), dtype=np.float32),
-            "gyro": np.zeros((1, 3), dtype=np.float32),
-            "upvector": np.array([[0.0, 0.0, 1.0]], dtype=np.float32),
-            "accelerometer": np.zeros((1, 3), dtype=np.float32),
-            "global_angvel": np.zeros((1, 3), dtype=np.float32),
-            "global_position": np.array([[0.0, 0.0, 0.53]], dtype=np.float32),
-        }
-        for name in FootstandSensor.feet_force:
-            self._sensors[name] = np.zeros((1, 1), dtype=np.float32)
-        for name in FootstandSensor.feet_pos:
-            self._sensors[name] = np.zeros((1, 3), dtype=np.float32)
-        for name in FootstandSensor.ternamate_contact:
-            self._sensors[name] = np.zeros((1, 1), dtype=np.float32)
-
-    def get_sensor_data(self, name: str) -> np.ndarray:
-        return self._sensors[name]
-
-    def get_sensor_data_batch(self, names: list[str]) -> np.ndarray:
-        values = [self.get_sensor_data(name).reshape(1, -1) for name in names]
-        return np.concatenate(values, axis=1)
-
-    def get_dof_pos(self) -> np.ndarray:
-        return np.zeros((1, 12), dtype=np.float32)
-
-    def get_dof_vel(self) -> np.ndarray:
-        return np.full((1, 12), 10.0, dtype=np.float32)
-
-    def get_base_quat(self) -> np.ndarray:
-        return np.array([[1.0, 0.0, 0.0, 0.0]], dtype=np.float32)
-
-    def get_base_pos(self) -> np.ndarray:
-        return np.array([[0.0, 0.0, 0.53]], dtype=np.float32)
-
-    def get_body_pos_w(self, body_ids: np.ndarray) -> np.ndarray:
-        return np.zeros((1, len(body_ids), 3), dtype=np.float32)
-
-
-def test_go2_footstand_energy_threshold_terminates() -> None:
-    env = object.__new__(Go2FootStandTask)
-    cfg = Go2FootStandCfg(
-        reward_config=RewardConfig(scales={}, tracking_sigma=0.25, base_height_target=0.3)
-    )
-    cfg.noise_config.level = 0.0
-    cfg.energy_termination_threshold = 1.0
-    env._cfg = cfg
-    env._reward_cfg = cfg.reward_config
-    env._backend = _EnergyTerminationBackend()
-    env._num_envs = 1
-    env._num_action = 12
-    env._z_des = 0.53
-    env._desired_forward_vec = np.array([0.0, 0.0, 1.0], dtype=np.float32)
-    env.default_angles = np.zeros((1, 12), dtype=np.float32)
-    env.feet_force = np.zeros((1, 4, 1), dtype=np.float32)
-    env.feet_pos = np.zeros((1, 4, 3), dtype=np.float32)
-    env.torso_height = np.zeros((1,), dtype=np.float32)
-    env._last_dof_vel_for_acc = np.zeros((1, 12), dtype=np.float32)
-    env._motor_targets = np.zeros((1, 12), dtype=np.float32)
-    env._last_terminated = np.zeros((1,), dtype=bool)
-    env._tracked_body_ids = np.arange(6, dtype=np.int32)
-    env._tracked_body_pos = np.zeros((1, 6, 3), dtype=np.float32)
-    env._enable_reward_log = False
-
-    state = NpEnvState(
-        obs={},
-        reward=np.zeros((1,), dtype=np.float32),
-        terminated=np.zeros((1,), dtype=bool),
-        truncated=np.zeros((1,), dtype=bool),
-        info={
-            "current_actions": np.zeros((1, 12), dtype=np.float32),
-            "last_actions": np.zeros((1, 12), dtype=np.float32),
-        },
-    )
-
-    updated = env.update_state(state)
-
-    assert updated.terminated[0]
-
-
-def test_go2_footstand_action_updates_incremental_motor_targets() -> None:
-    env = object.__new__(Go2FootStandTask)
-    env._cfg = Go2FootStandCfg()
-    env._motor_targets = np.zeros((1, 12), dtype=np.float32)
-    state = NpEnvState(
-        obs={},
-        reward=np.zeros((1,), dtype=np.float32),
-        terminated=np.zeros((1,), dtype=bool),
-        truncated=np.zeros((1,), dtype=bool),
-        info={"current_actions": np.full((1, 12), 0.1, dtype=np.float32)},
-    )
-
-    ctrl = env.apply_action(np.full((1, 12), 0.5, dtype=np.float32), state)
-
-    np.testing.assert_allclose(ctrl, np.full((1, 12), 0.15, dtype=np.float32))
-    np.testing.assert_allclose(state.info["last_actions"], np.full((1, 12), 0.1, dtype=np.float32))
-    np.testing.assert_allclose(
-        state.info["current_actions"], np.full((1, 12), 0.5, dtype=np.float32)
-    )
-
-
-def test_go2_footstand_action_clips_policy_actions_and_motor_targets() -> None:
-    env = object.__new__(Go2FootStandTask)
-    env._cfg = Go2FootStandCfg()
-    env._motor_targets = np.zeros((1, 2), dtype=np.float32)
-    env._target_lowers = np.array([-0.2, -0.4], dtype=np.float32)
-    env._target_uppers = np.array([0.2, 0.4], dtype=np.float32)
-    state = NpEnvState(
-        obs={},
-        reward=np.zeros((1,), dtype=np.float32),
-        terminated=np.zeros((1,), dtype=bool),
-        truncated=np.zeros((1,), dtype=bool),
-        info={},
-    )
-
-    ctrl = env.apply_action(np.array([[10.0, -10.0]], dtype=np.float32), state)
-
-    np.testing.assert_allclose(
-        state.info["current_actions"], np.array([[1.0, -1.0]], dtype=np.float32)
-    )
-    np.testing.assert_allclose(ctrl, np.array([[0.2, -0.3]], dtype=np.float32))
-
-    ctrl = env.apply_action(np.array([[10.0, -10.0]], dtype=np.float32), state)
-
-    np.testing.assert_allclose(ctrl, np.array([[0.2, -0.4]], dtype=np.float32))
-
-
-def test_go2_footstand_playground_reset_randomization_payload_shapes() -> None:
-    np.random.seed(0)
-    env = object.__new__(Go2FootStandTask)
-    env._cfg = Go2FootStandCfg()
-    env._num_action = 12
-    env._floor_geom_id = 0
-    env._base_body_id = 1
-    env._base_geom_friction = np.ones((3, 3), dtype=np.float64)
-    env._base_body_mass = np.ones((4,), dtype=np.float64)
-    env._base_body_ipos = np.zeros((4, 3), dtype=np.float64)
-    env._base_dof_armature = np.ones((18,), dtype=np.float64)
-
-    payload = env._build_playground_reset_randomization(num_reset=2)
-
-    assert payload is not None
-    assert payload.geom_friction is not None and payload.geom_friction.shape == (2, 3, 3)
-    assert payload.body_mass is not None and payload.body_mass.shape == (2, 4)
-    assert payload.body_ipos is not None and payload.body_ipos.shape == (2, 4, 3)
-    assert payload.dof_armature is not None and payload.dof_armature.shape == (2, 18)
-    assert np.all((payload.geom_friction[:, 0, 0] >= 0.4) & (payload.geom_friction[:, 0, 0] <= 1.0))
-    np.testing.assert_allclose(payload.dof_armature[:, :6], 1.0)
-
-
-def test_go2_footstand_reset_randomization_merges_common_and_playground_terms() -> None:
-    base = ResetRandomizationPayload(
-        base_mass_delta=np.array([0.1], dtype=np.float64),
-        kp=np.ones((1, 12), dtype=np.float64),
-    )
-    playground = ResetRandomizationPayload(
-        body_mass=np.full((1, 4), 2.0, dtype=np.float64),
-        geom_friction=np.full((1, 3, 3), 0.7, dtype=np.float64),
-    )
-
-    merged = Go2FootStandDomainRandomizationProvider._merge_reset_randomization(base, playground)
-
-    assert merged is not None
-    np.testing.assert_allclose(merged.base_mass_delta, base.base_mass_delta)
-    np.testing.assert_allclose(merged.kp, base.kp)
-    np.testing.assert_allclose(merged.body_mass, playground.body_mass)
-    np.testing.assert_allclose(merged.geom_friction, playground.geom_friction)
-
-
-def test_go2_footstand_height_reward_matches_playground_shape() -> None:
-    env = object.__new__(Go2FootStandTask)
-    env._z_des = 0.53
-    env.torso_height = np.array([0.53, 0.33, 0.63], dtype=np.float32)
-
-    reward = env._reward_height(
-        RewardContext(
-            info={},
-            linvel=np.zeros((3, 3), dtype=np.float32),
-            gyro=np.zeros((3, 3), dtype=np.float32),
-            dof_pos=np.zeros((3, 12), dtype=np.float32),
-        )
-    )
-
-    np.testing.assert_allclose(
-        reward, np.array([1.0, np.exp(-2.0), np.exp(-1.0)], dtype=np.float32), rtol=1e-6
-    )
-
-
-def test_go2_footstand_contact_cost_only_penalizes_front_feet() -> None:
-    env = object.__new__(Go2FootStandTask)
-    env.feet_geom_names = [0, 1]
-    env.feet_force = np.zeros((3, 4, 1), dtype=np.float32)
-    env.feet_force[0, 0, 0] = 1.0
-    env.feet_force[1, 2, 0] = 1.0
-    env.feet_force[2, 3, 0] = 1.0
-
-    cost = env._cost_contact(
-        RewardContext(
-            info={},
-            linvel=np.zeros((3, 3), dtype=np.float32),
-            gyro=np.zeros((3, 3), dtype=np.float32),
-            dof_pos=np.zeros((3, 12), dtype=np.float32),
-        )
-    )
-
-    np.testing.assert_allclose(cost, np.array([1.0, 0.0, 0.0], dtype=np.float32))
-
-
-def test_go2_footstand_rear_feet_contact_rewards_support_feet() -> None:
-    env = object.__new__(Go2FootStandTask)
-    env.feet_force = np.zeros((3, 4, 1), dtype=np.float32)
-    env.feet_force[0, 2:4, 0] = 1.0
-    env.feet_force[1, 2, 0] = 1.0
-    env.feet_force[2, 0:2, 0] = 1.0
-
-    reward = env._reward_rear_feet_contact(
-        RewardContext(
-            info={},
-            linvel=np.zeros((3, 3), dtype=np.float32),
-            gyro=np.zeros((3, 3), dtype=np.float32),
-            dof_pos=np.zeros((3, 12), dtype=np.float32),
-        )
-    )
-
-    np.testing.assert_allclose(reward, np.array([1.0, 0.5, 0.0], dtype=np.float32))
-
-
-def test_go2_footstand_both_rear_feet_contact_requires_two_support_feet() -> None:
-    env = object.__new__(Go2FootStandTask)
-    env.feet_force = np.zeros((3, 4, 1), dtype=np.float32)
-    env.feet_force[0, 2:4, 0] = 1.0
-    env.feet_force[1, 2, 0] = 1.0
-    env.feet_force[2, 0:2, 0] = 1.0
-
-    reward = env._reward_both_rear_feet_contact(
-        RewardContext(
-            info={},
-            linvel=np.zeros((3, 3), dtype=np.float32),
-            gyro=np.zeros((3, 3), dtype=np.float32),
-            dof_pos=np.zeros((3, 12), dtype=np.float32),
-        )
-    )
-
-    np.testing.assert_allclose(reward, np.array([1.0, 0.0, 0.0], dtype=np.float32))
-
-
-def test_go2_footstand_rear_foot_slip_penalizes_contact_foot_xy_motion() -> None:
-    env = object.__new__(Go2FootStandTask)
-    env._cfg = Go2FootStandCfg(
-        reward_config=RewardConfig(
-            scales={},
-            tracking_sigma=0.25,
-            base_height_target=0.3,
-            rear_foot_slip_deadband=0.02,
-        )
-    )
-    env._reward_cfg = env._cfg.reward_config
-    env._num_envs = 3
-    env.feet_force = np.zeros((3, 4, 1), dtype=np.float32)
-    env.feet_force[:, 2:4, 0] = 1.0
-    env.feet_force[2, 3, 0] = 0.0
-    env._last_feet_pos = np.zeros((3, 4, 3), dtype=np.float32)
-    env.feet_pos = np.zeros((3, 4, 3), dtype=np.float32)
-    env.feet_pos[0, 2, 0] = 0.02
-    env.feet_pos[0, 3, 0] = 0.02
-    env.feet_pos[1, 2, 0] = 0.04
-    env.feet_pos[1, 3, 1] = 0.03
-    env.feet_pos[2, 2, 0] = 0.04
-    env.feet_pos[2, 3, 0] = 0.10
-
-    env._update_rear_foot_slip()
-    cost = env._cost_rear_foot_slip(
-        RewardContext(
-            info={},
-            linvel=np.zeros((3, 3), dtype=np.float32),
-            gyro=np.zeros((3, 3), dtype=np.float32),
-            dof_pos=np.zeros((3, 12), dtype=np.float32),
-        )
-    )
-
-    np.testing.assert_allclose(
-        cost,
-        np.array([0.9604, 3.0554, 1.9602], dtype=np.float32),
-        rtol=1e-6,
-    )
-
-
-def test_go2_footstand_rear_foot_anchor_penalizes_cumulative_contact_drift() -> None:
-    env = object.__new__(Go2FootStandTask)
-    env._reward_cfg = RewardConfig(
-        scales={},
-        tracking_sigma=0.25,
-        base_height_target=0.3,
-        rear_foot_anchor_radius=0.03,
-    )
-    env._num_envs = 3
-    env._standing_mask = lambda: np.ones((3,), dtype=np.float32)  # type: ignore[method-assign]
-    env.feet_force = np.zeros((3, 4, 1), dtype=np.float32)
-    env.feet_force[:, 2:4, 0] = 1.0
-    env.feet_force[2, 3, 0] = 0.0
-    env.feet_pos = np.zeros((3, 4, 3), dtype=np.float32)
-    env.feet_pos[:, 2:4, :2] = np.array(
-        [
-            [[0.0, 0.0], [0.1, 0.0]],
-            [[0.0, 0.0], [0.1, 0.0]],
-            [[0.0, 0.0], [0.1, 0.0]],
-        ],
-        dtype=np.float32,
-    )
-
-    env._update_rear_foot_anchor()
-    np.testing.assert_allclose(env._rear_foot_anchor, 0.0)
-
-    env.feet_pos[0, 2, 0] += 0.02
-    env.feet_pos[0, 3, 0] += 0.02
-    env.feet_pos[1, 2, 0] += 0.06
-    env.feet_pos[1, 3, 1] += 0.09
-    env.feet_pos[2, 2, 0] += 0.06
-    env.feet_pos[2, 3, 0] += 0.20
-
-    env._update_rear_foot_anchor()
-    cost = env._cost_rear_foot_anchor(
-        RewardContext(
-            info={},
-            linvel=np.zeros((3, 3), dtype=np.float32),
-            gyro=np.zeros((3, 3), dtype=np.float32),
-            dof_pos=np.zeros((3, 12), dtype=np.float32),
-        )
-    )
-
-    np.testing.assert_allclose(
-        cost,
-        np.array([0.0, 2.5, 0.5], dtype=np.float32),
-        rtol=1e-6,
-    )
-
-    env.feet_force[1, 2:4, 0] = 0.0
-    env._update_rear_foot_anchor()
-    assert env._rear_foot_anchor_contact[1].tolist() == [False, False]
-    np.testing.assert_allclose(env._rear_foot_anchor[1], 0.0)
-
-    env.feet_pos[1, 2:4, :2] = np.array([[0.5, 0.0], [0.6, 0.0]], dtype=np.float32)
-    env.feet_force[1, 2:4, 0] = 1.0
-    env._update_rear_foot_anchor()
-    np.testing.assert_allclose(env._rear_foot_anchor[1], 0.0)
-    np.testing.assert_allclose(
-        env._rear_foot_anchor_pos[1],
-        np.array([[0.5, 0.0], [0.6, 0.0]], dtype=np.float32),
-    )
-
-
-def test_go2_footstand_rear_foot_anchor_is_standing_gated() -> None:
-    env = object.__new__(Go2FootStandTask)
-    env._reward_cfg = RewardConfig(
-        scales={},
-        tracking_sigma=0.25,
-        base_height_target=0.3,
-        rear_foot_anchor_radius=0.03,
-    )
-    env._num_envs = 2
-    env._standing_mask = lambda: np.array([1.0, 0.0], dtype=np.float32)  # type: ignore[method-assign]
-    env.feet_force = np.zeros((2, 4, 1), dtype=np.float32)
-    env.feet_force[:, 2:4, 0] = 1.0
-    env.feet_pos = np.zeros((2, 4, 3), dtype=np.float32)
-    env.feet_pos[:, 2:4, :2] = np.array(
-        [
-            [[0.0, 0.0], [0.1, 0.0]],
-            [[0.0, 0.0], [0.1, 0.0]],
-        ],
-        dtype=np.float32,
-    )
-
-    env._update_rear_foot_anchor()
-    env.feet_pos[:, 2:4, 0] += 0.09
-    env._update_rear_foot_anchor()
-
-    np.testing.assert_allclose(env._rear_foot_anchor[0], 4.0)
-    np.testing.assert_allclose(env._rear_foot_anchor[1], 0.0)
-    assert env._rear_foot_anchor_contact[0].tolist() == [True, True]
-    assert env._rear_foot_anchor_contact[1].tolist() == [False, False]
-
-
-def test_go2_footstand_front_feet_air_rewards_front_feet_off_ground() -> None:
-    env = object.__new__(Go2FootStandTask)
-    env.feet_force = np.zeros((3, 4, 1), dtype=np.float32)
-    env.feet_force[0, 2:4, 0] = 1.0
-    env.feet_force[1, 0, 0] = 1.0
-    env.feet_force[2, 0:2, 0] = 1.0
-
-    reward = env._reward_front_feet_air(
-        RewardContext(
-            info={},
-            linvel=np.zeros((3, 3), dtype=np.float32),
-            gyro=np.zeros((3, 3), dtype=np.float32),
-            dof_pos=np.zeros((3, 12), dtype=np.float32),
-        )
-    )
 
-    np.testing.assert_allclose(reward, np.array([1.0, 0.0, 0.0], dtype=np.float32))
-
-
-def test_go2_footstand_balanced_footstand_requires_support_air_and_standing() -> None:
-    env = object.__new__(Go2FootStandTask)
-    env.feet_force = np.zeros((4, 4, 1), dtype=np.float32)
-    env.feet_force[0, 2:4, 0] = 1.0
-    env.feet_force[1, 2:4, 0] = 1.0
-    env.feet_force[1, 0, 0] = 1.0
-    env.feet_force[2, 2, 0] = 1.0
-    env.feet_force[3, 2:4, 0] = 1.0
-    env._standing_mask = lambda: np.array([1.0, 1.0, 1.0, 0.0], dtype=np.float32)  # type: ignore[method-assign]
-
-    reward = env._reward_balanced_footstand(
-        RewardContext(
-            info={},
-            linvel=np.zeros((4, 3), dtype=np.float32),
-            gyro=np.zeros((4, 3), dtype=np.float32),
-            dof_pos=np.zeros((4, 12), dtype=np.float32),
-        )
-    )
+def _compose(config_group: str, overrides: Sequence[str]) -> DictConfig:
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(config_dir=str(CONF_DIR / config_group), version_base="1.3"):
+        return compose("config", overrides=list(overrides))
 
-    np.testing.assert_allclose(reward, np.array([1.0, 0.0, 0.0, 0.0], dtype=np.float32))
-
-
-def test_go2_footstand_rear_leg_symmetry_mirrors_hip_sign_only() -> None:
-    env = object.__new__(Go2FootStandTask)
-    env._standing_mask = lambda: np.array([0.0, 0.0, 1.0], dtype=np.float32)  # type: ignore[method-assign]
-    dof_pos = np.zeros((3, 12), dtype=np.float32)
-    dof_pos[0, 6:9] = np.array([0.2, 1.0, -1.5], dtype=np.float32)
-    dof_pos[0, 9:12] = np.array([-0.2, 1.0, -1.5], dtype=np.float32)
-    dof_pos[1, 6:9] = np.array([0.2, 1.0, -1.5], dtype=np.float32)
-    dof_pos[1, 9:12] = np.array([0.3, 1.0, -1.5], dtype=np.float32)
-    dof_pos[2, 6:9] = np.array([0.0, 1.2, -1.1], dtype=np.float32)
-    dof_pos[2, 9:12] = np.array([0.0, 0.9, -1.7], dtype=np.float32)
-
-    cost = env._cost_rear_leg_symmetry(
-        RewardContext(
-            info={},
-            linvel=np.zeros((3, 3), dtype=np.float32),
-            gyro=np.zeros((3, 3), dtype=np.float32),
-            dof_pos=dof_pos,
-        )
-    )
 
-    np.testing.assert_allclose(
-        cost,
-        np.array([0.0, 0.25 / 3.0, 0.0], dtype=np.float32),
-        rtol=1e-6,
-    )
+def _materialize(
+    config_group: str,
+    overrides: Sequence[str],
+) -> tuple[DictConfig, ManagerBasedRlEnvCfg, dict[str, Any]]:
+    hydra_cfg = _compose(config_group, overrides)
+    env_override = BackendAdapter(hydra_cfg, root_dir=ROOT_DIR).build_task_env_cfg_override()
+    env_cfg = registry.materialize_env_config("Go2FootStand")
+    assert isinstance(env_cfg, ManagerBasedRlEnvCfg)
+    apply_cfg_overrides(env_cfg, env_override)
+    env_cfg.validate()
+    return hydra_cfg, env_cfg, env_override
 
 
-def test_go2_footstand_rear_leg_splay_penalizes_standing_hip_abduction() -> None:
-    env = object.__new__(Go2FootStandTask)
-    env._reward_cfg = RewardConfig(
-        scales={},
-        tracking_sigma=0.25,
-        base_height_target=0.3,
-        rear_hip_abduction_margin=0.25,
+def _make_env(backend: str, *, num_envs: int = 2) -> ManagerBasedRlEnv:
+    hydra_cfg, _, env_override = _materialize(
+        "ppo",
+        (f"task=go2_footstand/{backend}", f"algo.num_envs={num_envs}"),
     )
-    env._standing_mask = lambda: np.array([1.0, 1.0, 0.0], dtype=np.float32)  # type: ignore[method-assign]
-    dof_pos = np.zeros((3, 12), dtype=np.float32)
-    dof_pos[0, [6, 9]] = np.array([0.2, -0.2], dtype=np.float32)
-    dof_pos[1, [6, 9]] = np.array([0.5, -0.4], dtype=np.float32)
-    dof_pos[2, [6, 9]] = np.array([0.6, -0.6], dtype=np.float32)
-
-    cost = env._cost_rear_leg_splay(
-        RewardContext(
-            info={},
-            linvel=np.zeros((3, 3), dtype=np.float32),
-            gyro=np.zeros((3, 3), dtype=np.float32),
-            dof_pos=dof_pos,
-            default_angles=np.zeros((1, 12), dtype=np.float32),
-        )
+    env = registry.make(
+        str(hydra_cfg.training.task_name),
+        sim_backend=backend,
+        env_cfg_override=env_override,
+        num_envs=num_envs,
     )
+    assert isinstance(env, ManagerBasedRlEnv)
+    return env
 
-    np.testing.assert_allclose(
-        cost,
-        np.array([0.0, (0.25**2 + 0.15**2) / 2.0, 0.0], dtype=np.float32),
-        rtol=1e-6,
-    )
 
+def _assert_plain(value: Any) -> None:
+    assert not OmegaConf.is_config(value)
+    if is_dataclass(value) and not isinstance(value, type):
+        for item in fields(value):
+            _assert_plain(getattr(value, item.name))
+    elif isinstance(value, Mapping):
+        for key, item in value.items():
+            _assert_plain(key)
+            _assert_plain(item)
+    elif isinstance(value, Sequence) and not isinstance(value, (str, bytes)):
+        for item in value:
+            _assert_plain(item)
 
-def test_go2_footstand_front_leg_motion_only_penalizes_standing_pose() -> None:
-    env = object.__new__(Go2FootStandTask)
-    env._z_des = 0.53
-    env.torso_height = np.array([0.53, 0.53, 0.2], dtype=np.float32)
-    env._orientation_score = lambda: np.array([1.0, 0.4, 1.0], dtype=np.float32)  # type: ignore[method-assign]
-    dof_vel = np.zeros((3, 12), dtype=np.float32)
-    dof_vel[:, 0:6] = 2.0
-
-    cost = env._cost_front_leg_motion(
-        RewardContext(
-            info={},
-            linvel=np.zeros((3, 3), dtype=np.float32),
-            gyro=np.zeros((3, 3), dtype=np.float32),
-            dof_pos=np.zeros((3, 12), dtype=np.float32),
-            dof_vel=dof_vel,
-        )
-    )
 
-    np.testing.assert_allclose(cost, np.array([4.0, 0.0, 0.0], dtype=np.float32))
+def _action(env: ManagerBasedRlEnv) -> FootstandIncrementalAction:
+    term = env.action_manager.get_term("joint_pos")
+    assert isinstance(term, FootstandIncrementalAction)
+    return term
 
 
-def test_go2_footstand_front_leg_crossing_penalizes_body_frame_overlap() -> None:
-    env = object.__new__(Go2FootStandTask)
-    env._num_envs = 3
-    env._backend = _BasePoseBackend()
-    env._reward_cfg = RewardConfig(
-        scales={},
-        tracking_sigma=0.25,
-        base_height_target=0.3,
-        front_feet_min_separation=0.16,
-        front_feet_side_margin=0.04,
-    )
-    env.feet_pos = np.zeros((3, 4, 3), dtype=np.float32)
-    env.feet_pos[0, 0, 1] = 0.10
-    env.feet_pos[0, 1, 1] = -0.10
-    env.feet_pos[1, 0, 1] = -0.02
-    env.feet_pos[1, 1, 1] = 0.02
-    env.feet_pos[2, 0, 1] = 0.05
-    env.feet_pos[2, 1, 1] = -0.05
-    env._tracked_body_pos = np.zeros((3, 6, 3), dtype=np.float32)
-    env._tracked_body_pos[:, 0, 1] = env.feet_pos[:, 0, 1]
-    env._tracked_body_pos[:, 1, 1] = env.feet_pos[:, 1, 1]
-    env._tracked_body_pos[:, 2, 1] = env.feet_pos[:, 0, 1]
-    env._tracked_body_pos[:, 3, 1] = env.feet_pos[:, 1, 1]
-
-    cost = env._cost_front_leg_crossing(
-        RewardContext(
-            info={},
-            linvel=np.zeros((3, 3), dtype=np.float32),
-            gyro=np.zeros((3, 3), dtype=np.float32),
-            dof_pos=np.zeros((3, 12), dtype=np.float32),
-        )
-    )
+def _termination(env: ManagerBasedRlEnv) -> FootstandTermination:
+    term = env.termination_manager.get_term_cfg("footstand").func
+    assert isinstance(term, FootstandTermination)
+    return term
 
-    np.testing.assert_allclose(
-        cost,
-        np.array([0.0, 0.0472, 0.0036], dtype=np.float32),
-        rtol=1e-6,
-    )
 
+def _reward(env: ManagerBasedRlEnv) -> FootstandReward:
+    term = env.reward_manager.get_term_cfg("footstand").func
+    assert isinstance(term, FootstandReward)
+    return term
 
-def test_go2_footstand_upright_stability_is_standing_gated() -> None:
-    env = object.__new__(Go2FootStandTask)
-    env._backend = _BaseMotionBackend()
-    env._z_des = 0.53
-    env.torso_height = np.array([0.53, 0.53], dtype=np.float32)
-    env._orientation_score = lambda: np.array([1.0, 0.4], dtype=np.float32)  # type: ignore[method-assign]
 
-    cost = env._cost_upright_stability(
-        RewardContext(
-            info={},
-            linvel=np.zeros((2, 3), dtype=np.float32),
-            gyro=np.zeros((2, 3), dtype=np.float32),
-            dof_pos=np.zeros((2, 12), dtype=np.float32),
+@pytest.mark.parametrize(
+    "config_group,overrides,backend,backend_events,joint_reset_radius,orientation_scale",
+    _OWNER_CASES,
+)
+def test_footstand_owner_materializes_complete_plain_manager_cfg(
+    config_group: str,
+    overrides: tuple[str, ...],
+    backend: str,
+    backend_events: frozenset[str],
+    joint_reset_radius: float,
+    orientation_scale: float,
+) -> None:
+    registry.ensure_registries()
+    hydra_cfg, env_cfg, _ = _materialize(config_group, overrides)
+
+    assert hydra_cfg.training.task_name == "Go2FootStand"
+    assert hydra_cfg.training.sim_backend == backend
+    assert hydra_cfg.algo.num_envs == 4096
+    assert env_cfg.sim_dt == pytest.approx(0.004)
+    assert env_cfg.ctrl_dt == pytest.approx(0.02)
+    assert env_cfg.max_episode_seconds == pytest.approx(10.0)
+    assert env_cfg.adaptive_chunk_size is False
+    assert env_cfg.policy_observation_group == "policy"
+    assert env_cfg.critic_observation_group == "critic"
+
+    assert env_cfg.scene is not None
+    assert env_cfg.scene.default_keyframe_name == "home"
+    robot = env_cfg.scene.entities["robot"]
+    assert robot.root_body_name == "base"
+    assert tuple(robot.joint_names or ()) == _JOINT_NAMES
+    assert tuple(robot.body_names or ()) == _BODY_NAMES
+    assert tuple(robot.geom_names or ()) == ("floor",)
+    assert tuple(robot.actuator_names or ()) == _ACTUATOR_NAMES
+
+    policy = env_cfg.observations["policy"]
+    critic = env_cfg.observations["critic"]
+    assert policy is not None and critic is not None
+    assert list(policy.terms) == ["frame"]
+    assert list(critic.terms) == ["frame", "privileged"]
+    assert policy.enable_corruption is True
+    assert policy.terms["frame"] is not None
+    assert critic.terms["frame"] is not None
+    assert critic.terms["privileged"] is not None
+    assert policy.terms["frame"].history_length == 15
+    assert critic.terms["frame"].history_length == 15
+    assert critic.terms["privileged"].history_length == 0
+    noise = policy.terms["frame"].noise
+    assert isinstance(noise, UniformNoiseCfg)
+    assert np.asarray(noise.n_min).shape == (FRAME_OBS_DIM,)
+    assert np.asarray(noise.n_max).shape == (FRAME_OBS_DIM,)
+
+    action = env_cfg.actions["joint_pos"]
+    assert isinstance(action, FootstandIncrementalActionCfg)
+    assert tuple(action.actuator_names) == _ACTUATOR_NAMES
+    assert tuple(action.joint_names) == _ACTION_JOINT_NAMES
+    assert action.action_scale == pytest.approx(0.3)
+    assert action.clip_actions == pytest.approx(1.0)
+    assert action.kp == pytest.approx(35.0)
+    assert action.kd == pytest.approx(0.5)
+
+    always_enabled = {
+        "reset_scene_to_default",
+        "reset_root_state_uniform",
+        "reset_joints",
+    }
+    active_events = {name for name, term in env_cfg.events.items() if term is not None}
+    assert active_events == always_enabled | backend_events
+    reset_joints = env_cfg.events["reset_joints"]
+    assert reset_joints is not None
+    assert tuple(reset_joints.params["position_offset_range"]) == pytest.approx(
+        (-joint_reset_radius, joint_reset_radius)
+    )
+    if "link_mass" in backend_events:
+        link_mass = env_cfg.events["link_mass"]
+        assert link_mass is not None
+        assert link_mass.func is FootstandMassRandomization
+
+    termination = env_cfg.terminations["footstand"]
+    assert termination is not None
+    assert termination.params["grace_steps"] == 100
+    assert termination.params["energy_threshold"] == pytest.approx(200.0)
+    reward = env_cfg.rewards["footstand"]
+    assert reward is not None
+    assert reward.func is FootstandReward
+    assert reward.params["scales"]["orientation"] == pytest.approx(orientation_scale)
+
+    assert FRAME_OBS_DIM * 15 == 675
+    assert FRAME_OBS_DIM * 15 + PRIVILEGED_OBS_DIM == 724
+    _assert_plain(env_cfg)
+
+
+def test_footstand_registry_has_no_legacy_config_or_factory() -> None:
+    registry.ensure_registries()
+    bare_cfg = registry.materialize_env_config("Go2FootStand")
+
+    assert isinstance(bare_cfg, ManagerBasedRlEnvCfg)
+    assert bare_cfg.actions == {}
+    assert bare_cfg.observations == {}
+    assert registry.list_registered_envs()["Go2FootStand"] == {
+        "config_factory": "ManagerBasedRlEnvCfg",
+        "available_backends": ["mujoco", "motrix", "drake"],
+    }
+    meta = registry._envs["Go2FootStand"]
+    assert all(factory is make_manager_based_rl_env for factory in meta.env_factory_dict.values())
+    for legacy_override in (
+        {"reward_config": {}},
+        {"domain_rand": {"randomize_floor_friction": True}},
+        {"control_config": {"action_scale": 0.4}},
+        {"obs_history_len": 15},
+    ):
+        with pytest.raises(ValueError, match="has no attribute"):
+            apply_cfg_overrides(ManagerBasedRlEnvCfg(), legacy_override)
+
+
+@pytest.mark.parametrize("backend", ("mujoco", "motrix"))
+def test_footstand_real_runtime_preserves_history_action_and_partial_reset(
+    backend: str,
+) -> None:
+    registry.ensure_registries()
+    env = _make_env(backend)
+    try:
+        obs, info = env.reset(seed=7)
+        assert set(info) == {"log"}
+        assert env.obs_groups_spec == {"obs": 675, "critic": 724}
+        assert env.action_space.shape == (NUM_ACTIONS,)
+        assert obs["obs"].shape == (2, 675)
+        assert obs["critic"].shape == (2, 724)
+        assert np.isfinite(obs["obs"]).all()
+        assert np.isfinite(obs["critic"]).all()
+
+        reset_frames = obs["obs"].reshape(2, 15, FRAME_OBS_DIM)
+        np.testing.assert_allclose(reset_frames, np.repeat(reset_frames[:, :1], 15, axis=1))
+        clean_history = obs["critic"][:, :675].reshape(2, 15, FRAME_OBS_DIM)
+        np.testing.assert_allclose(clean_history, np.repeat(clean_history[:, :1], 15, axis=1))
+
+        action = _action(env)
+        initial_target = action.target.copy()
+        first_policy_action = np.full((2, NUM_ACTIONS), 2.0, dtype=np.float32)
+        action.process_actions(first_policy_action)
+        expected_target = np.clip(
+            initial_target + 0.3,
+            action.joint_lower[action.joint_ids],
+            action.joint_upper[action.joint_ids],
         )
-    )
+        np.testing.assert_allclose(action.target, expected_target, atol=1e-6)
+
+        obs, _ = env.reset(seed=7)
+        first_policy_action = np.full((2, NUM_ACTIONS), 0.05, dtype=np.float32)
+        first_state = env.step(first_policy_action)
+        assert not first_state.terminated.any()
+        first_frames = first_state.obs["obs"].reshape(2, 15, FRAME_OBS_DIM)
+        np.testing.assert_allclose(first_frames[:, -1, -NUM_ACTIONS:], 0.0)
+
+        second_policy_action = np.full((2, NUM_ACTIONS), 0.025, dtype=np.float32)
+        second_state = env.step(second_policy_action)
+        assert not second_state.terminated.any()
+        second_frames = second_state.obs["obs"].reshape(2, 15, FRAME_OBS_DIM)
+        np.testing.assert_allclose(second_frames[:, -1, -NUM_ACTIONS:], 0.05)
+        assert np.isfinite(second_state.reward).all()
+        assert np.all(second_state.reward >= 0.0)
+
+        untouched_target = action.target[1].copy()
+        untouched_torque = action.state.torques[1].copy()
+        assert env.state is not None
+        untouched_obs = {name: value[1].copy() for name, value in env.state.obs.items()}
+        reset_obs, _ = env.reset(env_ids=np.asarray([0], dtype=np.int32))
+        assert reset_obs["obs"].shape == (1, 675)
+        partial_frames = reset_obs["obs"].reshape(1, 15, FRAME_OBS_DIM)
+        np.testing.assert_allclose(partial_frames, np.repeat(partial_frames[:, :1], 15, axis=1))
+        np.testing.assert_allclose(action.target[1], untouched_target)
+        np.testing.assert_allclose(action.state.torques[1], untouched_torque)
+        assert env.state is not None
+        for name, expected in untouched_obs.items():
+            np.testing.assert_allclose(env.state.obs[name][1], expected)
+    finally:
+        env.close()
 
-    np.testing.assert_allclose(cost, np.array([6.0, 0.0], dtype=np.float32))
 
+def test_footstand_termination_uses_grace_boundary() -> None:
+    registry.ensure_registries()
+    env = _make_env("mujoco")
+    try:
+        env.reset(seed=11)
+        term = _termination(env)
+        state = term.state
+        state.height[:] = 0.0
+        state.orientation[:] = 0.0
+        state.upvector[:] = (0.0, 0.0, 1.0)
+        state.termination_contact[:] = False
+        state.torques[:] = 0.0
+        state.joint_vel[:] = 0.0
+
+        env.episode_length_buf[:] = 100
+        assert not term(env).any()
+        env.episode_length_buf[:] = 101
+        assert term(env).all()
+    finally:
+        env.close()
 
-def test_go2_footstand_knee_clearance_penalizes_low_knees() -> None:
-    env = object.__new__(Go2FootStandTask)
-    env._reward_cfg = RewardConfig(
-        scales={},
-        tracking_sigma=0.25,
-        base_height_target=0.3,
-        knee_height_target=0.1,
-    )
-    env._tracked_body_pos = np.zeros((3, 6, 3), dtype=np.float32)
-    env._tracked_body_pos[:, [2, 3, 4, 5], 2] = np.array(
-        [
-            [0.1, 0.12, 0.2, 0.3],
-            [0.05, 0.05, 0.05, 0.05],
-            [0.0, 0.05, 0.1, 0.15],
-        ],
-        dtype=np.float32,
-    )
 
-    cost = env._cost_knee_clearance(
-        RewardContext(
-            info={},
-            linvel=np.zeros((3, 3), dtype=np.float32),
-            gyro=np.zeros((3, 3), dtype=np.float32),
-            dof_pos=np.zeros((3, 12), dtype=np.float32),
+def test_footstand_reward_clips_aggregate_before_dt_scaling() -> None:
+    registry.ensure_registries()
+    env = _make_env("mujoco")
+    try:
+        env.reset(seed=13)
+        term = _termination(env)
+        reward = _reward(env)
+        state = term.state
+        term.terminated[:] = False
+        state.height[:] = 0.53
+        state.foot_contact[:] = False
+        state.foot_contact[0, 0] = True
+        reward._scales = {"height": 2.0, "contact": -100.0}
+
+        np.testing.assert_allclose(reward(env), np.asarray([0.0, 2.0], dtype=np.float32))
+        np.testing.assert_allclose(
+            env.reward_manager.compute(dt=env.step_dt),
+            np.asarray([0.0, 0.04], dtype=np.float32),
         )
-    )
-
-    np.testing.assert_allclose(cost, np.array([0.0, 0.25, 0.3125], dtype=np.float32))
-
-
-def test_go2_footstand_post_grace_low_height_terminates() -> None:
-    env = object.__new__(Go2FootStandTask)
-    cfg = Go2FootStandCfg(
-        reward_config=RewardConfig(scales={}, tracking_sigma=0.25, base_height_target=0.3)
-    )
-    cfg.noise_config.level = 0.0
-    cfg.termination_grace_steps = 10
-    env._cfg = cfg
-    env._reward_cfg = cfg.reward_config
-    env._backend = _EnergyTerminationBackend()
-    env._backend._sensors["global_position"] = np.array([[0.0, 0.0, 0.2]], dtype=np.float32)
-    env._num_envs = 1
-    env._num_action = 12
-    env._z_des = 0.53
-    env.default_angles = np.zeros((1, 12), dtype=np.float32)
-    env.feet_force = np.zeros((1, 4, 1), dtype=np.float32)
-    env.feet_pos = np.zeros((1, 4, 3), dtype=np.float32)
-    env.torso_height = np.zeros((1,), dtype=np.float32)
-    env._last_dof_vel_for_acc = np.zeros((1, 12), dtype=np.float32)
-    env._motor_targets = np.zeros((1, 12), dtype=np.float32)
-    env._last_terminated = np.zeros((1,), dtype=bool)
-    env._tracked_body_ids = np.arange(6, dtype=np.int32)
-    env._tracked_body_pos = np.zeros((1, 6, 3), dtype=np.float32)
-    env._enable_reward_log = False
-    env._orientation_score = lambda: np.array([1.0], dtype=np.float32)  # type: ignore[method-assign]
-    state = NpEnvState(
-        obs={},
-        reward=np.zeros((1,), dtype=np.float32),
-        terminated=np.zeros((1,), dtype=bool),
-        truncated=np.zeros((1,), dtype=bool),
-        info={
-            "steps": np.array([10], dtype=np.uint32),
-            "current_actions": np.zeros((1, 12), dtype=np.float32),
-            "last_actions": np.zeros((1, 12), dtype=np.float32),
-        },
-    )
-
-    updated = env.update_state(state)
-
-    assert updated.terminated[0]
-
-
-def test_go2_footstand_returned_termination_does_not_alias_reset_bookkeeping() -> None:
-    env = object.__new__(Go2FootStandTask)
-    cfg = Go2FootStandCfg(
-        reward_config=RewardConfig(scales={}, tracking_sigma=0.25, base_height_target=0.3)
-    )
-    cfg.noise_config.level = 0.0
-    cfg.termination_grace_steps = 10
-    env._cfg = cfg
-    env._reward_cfg = cfg.reward_config
-    env._backend = _EnergyTerminationBackend()
-    env._backend._sensors["global_position"] = np.array([[0.0, 0.0, 0.2]], dtype=np.float32)
-    env._num_envs = 1
-    env._num_action = 12
-    env._z_des = 0.53
-    env.default_angles = np.zeros((1, 12), dtype=np.float32)
-    env.feet_force = np.zeros((1, 4, 1), dtype=np.float32)
-    env.feet_pos = np.zeros((1, 4, 3), dtype=np.float32)
-    env.torso_height = np.zeros((1,), dtype=np.float32)
-    env._last_dof_vel_for_acc = np.zeros((1, 12), dtype=np.float32)
-    env._motor_targets = np.zeros((1, 12), dtype=np.float32)
-    env._last_terminated = np.zeros((1,), dtype=bool)
-    env._tracked_body_ids = np.arange(6, dtype=np.int32)
-    env._tracked_body_pos = np.zeros((1, 6, 3), dtype=np.float32)
-    env._enable_reward_log = False
-    env._orientation_score = lambda: np.array([1.0], dtype=np.float32)  # type: ignore[method-assign]
-    state = NpEnvState(
-        obs={},
-        reward=np.zeros((1,), dtype=np.float32),
-        terminated=np.zeros((1,), dtype=bool),
-        truncated=np.zeros((1,), dtype=bool),
-        info={
-            "steps": np.array([10], dtype=np.uint32),
-            "current_actions": np.zeros((1, 12), dtype=np.float32),
-            "last_actions": np.zeros((1, 12), dtype=np.float32),
-        },
-    )
-
-    updated = env.update_state(state)
-    env._last_terminated[0] = False
-
-    assert updated.terminated[0]
-
-
-def test_go2_footstand_joint_limit_cost_uses_soft_limits() -> None:
-    env = object.__new__(Go2FootStandTask)
-    env._soft_lowers = np.array([-1.0, -1.0], dtype=np.float32)
-    env._soft_uppers = np.array([1.0, 1.0], dtype=np.float32)
+    finally:
+        env.close()
 
-    cost = env._cost_joint_pos_limits(
-        RewardContext(
-            info={},
-            linvel=np.zeros((2, 3), dtype=np.float32),
-            gyro=np.zeros((2, 3), dtype=np.float32),
-            dof_pos=np.array([[0.0, 1.5], [-1.25, 0.0]], dtype=np.float32),
-        )
-    )
 
-    np.testing.assert_allclose(cost, np.array([0.5, 0.25], dtype=np.float32))
+def _drake_batch_extension_available() -> bool:
+    try:
+        return importlib.util.find_spec("drake_uni.compiled._drake_env_pool") is not None
+    except ModuleNotFoundError:
+        return False
 
 
-def test_go2_footstand_reset_critic_height_uses_backend_sensor() -> None:
-    pytest.importorskip("mujoco", reason="mujoco not installed")
+def _drake_geom_capability_available() -> bool:
+    """Footstand's contact terms require geom-name metadata from the backend."""
     try:
-        from mujoco_uni.batch_env import BatchEnvPool as _  # noqa: F401
-    except Exception:
-        pytest.skip("mujoco_uni.batch_env not available")
+        from unisim.backend.drake.backend import DrakeBackend
+    except ImportError:
+        return False
+    return DrakeBackend.get_geom_names is not SimBackend.get_geom_names
 
-    ensure_registries()
-    env = registry.make(
-        "Go2FootStand",
-        sim_backend="mujoco",
-        num_envs=1,
-        env_cfg_override={
-            "reward_config": RewardConfig(
-                scales={"height": 1.0},
-                tracking_sigma=0.25,
-                base_height_target=0.3,
-            )
-        },
-    )
+
+@pytest.mark.skipif(
+    not (_drake_batch_extension_available() and _drake_geom_capability_available()),
+    reason="Drake batch extension or geom metadata capability is unavailable",
+)
+def test_footstand_drake_real_runtime_when_available() -> None:
+    registry.ensure_registries()
+    env = _make_env("drake")
     try:
-        state = env.init_state()
-        assert state.obs["critic"][0, -1] > 0.1
+        obs, _ = env.reset(seed=17)
+        assert {name: value.shape for name, value in obs.items()} == {
+            "obs": (2, 675),
+            "critic": (2, 724),
+        }
+        state = env.step(np.zeros((2, NUM_ACTIONS), dtype=np.float32))
+        assert np.isfinite(state.reward).all()
     finally:
         env.close()
+
+
+def test_footstand_production_terms_do_not_leak_backend_or_layout() -> None:
+    source = (ROOT_DIR / "src/unilab/tasks/locomotion/go2/footstand.py").read_text(encoding="utf-8")
+    for forbidden in (
+        "._backend",
+        "getattr(",
+        "hasattr(",
+        "ASSETS_ROOT_PATH",
+        " qpos",
+        " qvel",
+    ):
+        assert forbidden not in source
diff --git a/tests/envs/locomotion/test_go2_joystick_stand_still.py b/tests/envs/locomotion/test_go2_joystick_stand_still.py
deleted file mode 100644
index 7e8f01f2d..000000000
--- a/tests/envs/locomotion/test_go2_joystick_stand_still.py
+++ /dev/null
@@ -1,87 +0,0 @@
-"""Tests for the shared standing-command helper and Go2 flat regressions.
-
-The zero-command standstill behaviour itself is A2-specific and lives on
-``A2JoystickFlatEnv`` (see ``tests/envs/locomotion/a2/test_a2_joystick_contract.py``).
-This file keeps only:
-
-- ``sample_commands_with_standing`` (zero-xy + standing fraction helper) — a
-  shared building block used by A2 reset + A2 resample.
-- Go2-flat-unchanged regressions asserting ``Go2WalkTask`` reverted to the main
-  baseline: the phase clock advances unconditionally and ``RewardConfig`` carries
-  no ``command_threshold``.
-"""
-
-from __future__ import annotations
-
-from types import SimpleNamespace
-
-import numpy as np
-
-from unilab.envs.locomotion.common.commands import (
-    sample_commands_with_standing,
-    zero_small_xy_commands,
-)
-
-# ── sample_commands_with_standing (shared helper) ─────────────────────
-
-
-def test_sample_commands_with_standing_all_standing():
-    low = np.array([-1.0, -1.0, -1.0])
-    high = np.array([1.0, 1.0, 1.0])
-    out = sample_commands_with_standing(low, high, 64, rel_standing_envs=1.0)
-    assert out.shape == (64, 3)
-    np.testing.assert_array_equal(out, np.zeros((64, 3)))
-
-
-def test_sample_commands_with_standing_none_standing_zeroes_small_xy():
-    np.random.seed(7)
-    low = np.array([-1.0, -1.0, -1.0])
-    high = np.array([1.0, 1.0, 1.0])
-    out = sample_commands_with_standing(low, high, 2000, rel_standing_envs=0.0)
-    xy_norm = np.linalg.norm(out[:, :2], axis=1)
-    # no row below the zero-xy threshold may retain a nonzero xy.
-    assert np.all((xy_norm == 0.0) | (xy_norm >= 0.08))
-    # with no standing fraction, not every row is fully zero (yaw survives).
-    assert np.any(np.abs(out[:, 2]) > 0.0)
-
-
-def test_sample_commands_with_standing_matches_rough_block():
-    """Same construction as rough.py's provider standing block, so reset (provider)
-    and resampling stay a single source of truth."""
-    np.random.seed(3)
-    low = np.array([-1.0, -1.0, -1.0])
-    high = np.array([1.0, 1.0, 1.0])
-    out = sample_commands_with_standing(low, high, 5, rel_standing_envs=0.5)
-
-    np.random.seed(3)
-    ref = np.asarray(np.random.uniform(low=low, high=high, size=(5, 3)))
-    zero_small_xy_commands(ref, threshold=0.08)
-    standing = np.random.uniform(size=(5,)) < 0.5
-    ref[standing] = 0.0
-    np.testing.assert_allclose(out, ref)
-
-
-# ── Go2 flat regressions (reverted to main baseline) ──────────────────
-
-
-def test_go2_advance_phase_is_unconditional():
-    """Go2WalkTask advances the gait clock every step regardless of command —
-    the A2 standing freeze must not have leaked into the Go2 owner."""
-    from unilab.envs.locomotion.go2.joystick import Go2WalkTask
-
-    stub = SimpleNamespace(_cfg=SimpleNamespace(ctrl_dt=0.02), gait_frequency=2.0)
-    phase = np.array([0.3, 0.3])
-    out = Go2WalkTask._advance_phase(stub, phase)
-    expected = np.fmod(phase + 0.02 * 2.0, 1.0)
-    np.testing.assert_allclose(out, expected)
-
-
-def test_go2_reward_config_has_no_command_threshold():
-    """command_threshold is A2-owned (A2RewardConfig); the Go2 base RewardConfig
-    must not declare it."""
-    import dataclasses
-
-    from unilab.envs.locomotion.go2.joystick import RewardConfig
-
-    names = {f.name for f in dataclasses.fields(RewardConfig)}
-    assert "command_threshold" not in names
diff --git a/tests/envs/locomotion/test_go2_rough_height_scan.py b/tests/envs/locomotion/test_go2_rough_height_scan.py
deleted file mode 100644
index b1ec5ec27..000000000
--- a/tests/envs/locomotion/test_go2_rough_height_scan.py
+++ /dev/null
@@ -1,77 +0,0 @@
-from __future__ import annotations
-
-from typing import Any
-
-import numpy as np
-
-from unilab.envs.locomotion.common.height_scan import height_scan_offsets as _height_scan_offsets
-from unilab.envs.locomotion.go2.rough import Go2JoystickRoughEnv
-
-
-def test_go2_rough_height_scan_uses_backend_native_sampling() -> None:
-    class FakeHeightScanner:
-        def __init__(self, heights: np.ndarray) -> None:
-            self.calls = 0
-            self.heights = heights
-
-        def scan(self) -> np.ndarray:
-            self.calls += 1
-            return self.heights
-
-    class FakeBackend:
-        def __init__(self) -> None:
-            self.scanner_calls: list[dict[str, Any]] = []
-            self.base_pos = np.asarray([[0.0, 0.0, 0.6], [1.0, 0.0, 0.7]], dtype=np.float32)
-            self.heights = np.asarray([[0.1, 0.2], [0.3, 0.4]], dtype=np.float32)
-            self.scanner = FakeHeightScanner(self.heights)
-
-        def get_base_pos(self) -> np.ndarray:
-            return self.base_pos
-
-        def create_hfield_scanner(self, **kwargs: Any) -> FakeHeightScanner:
-            self.scanner_calls.append(kwargs)
-            return self.scanner
-
-    env = object.__new__(Go2JoystickRoughEnv)
-    fake_backend = FakeBackend()
-    env._backend = fake_backend
-    env._height_scan_dim = 2
-    env._height_scan_hfield_geom_id = 7
-    env._height_scan_frame_body_id = 3
-    env._height_scan_offsets = np.asarray([[0.0, 0.0], [0.1, -0.1]], dtype=np.float64)
-    env._height_scan_sensor = fake_backend.create_hfield_scanner(
-        hfield_geom_id=env._height_scan_hfield_geom_id,
-        offsets=env._height_scan_offsets,
-        frame_body_id=env._height_scan_frame_body_id,
-        alignment="yaw",
-        output="height",
-    )
-
-    raw_heights, base_pos = env._raw_height_scan_obs(num_obs=2)
-
-    np.testing.assert_array_equal(raw_heights, fake_backend.heights)
-    np.testing.assert_array_equal(base_pos, fake_backend.base_pos)
-    assert fake_backend.scanner.calls == 1
-    assert len(fake_backend.scanner_calls) == 1
-    call = fake_backend.scanner_calls[0]
-    assert call["hfield_geom_id"] == 7
-    assert call["frame_body_id"] == 3
-    assert call["alignment"] == "yaw"
-    assert call["output"] == "height"
-    np.testing.assert_array_equal(call["offsets"], env._height_scan_offsets)
-
-
-def test_go2_rough_height_scan_offsets_are_grid_ordered() -> None:
-    offsets = _height_scan_offsets(points_x=[-0.1, 0.2], points_y=[-0.3, 0.4])
-
-    expected = np.asarray(
-        [
-            [-0.1, -0.3],
-            [-0.1, 0.4],
-            [0.2, -0.3],
-            [0.2, 0.4],
-        ],
-        dtype=np.float64,
-    )
-    np.testing.assert_array_equal(offsets, expected)
-    assert offsets.flags.c_contiguous
diff --git a/tests/envs/locomotion/test_go2_terrain_spawn.py b/tests/envs/locomotion/test_go2_terrain_spawn.py
index bf3c10175..298d191cb 100644
--- a/tests/envs/locomotion/test_go2_terrain_spawn.py
+++ b/tests/envs/locomotion/test_go2_terrain_spawn.py
@@ -1,365 +1,227 @@
-"""Integration tests for Go2 + TerrainSpawnManager."""
+"""Production contracts for the Manager-Based quadruped rough family."""
 
 from __future__ import annotations
 
+import inspect
+from pathlib import Path
 from types import SimpleNamespace
+from typing import Any, cast
 
 import numpy as np
 import pytest
-
-from unilab.envs.locomotion.common.terrain_spawn import (
-    TerrainCurriculumCfg,
-    TerrainSpawnManager,
+from hydra import compose, initialize_config_dir
+from hydra.core.global_hydra import GlobalHydra
+from omegaconf import DictConfig
+
+from unilab.base import registry
+from unilab.base.config_adapter import BackendAdapter
+from unilab.base.config_materialization import apply_cfg_overrides
+from unilab.envs import ManagerBasedRlEnv, ManagerBasedRlEnvCfg
+from unilab.tasks.locomotion.common.rough_manager_terms import (
+    QuadrupedRoughTerrainCfg,
+    RoughHeightScan,
+    RoughJointPositionAction,
+    RoughTerrainCurriculum,
+    RoughTerrainOutOfBounds,
+    RoughTerrainReset,
 )
+from unilab.tasks.locomotion.go2w.manager_terms import Go2WMixedAction
+from unilab.utils.sim2sim import extract_contract_snapshot
 
+# CPU-bound on the single-core CI runner; kept in the slow lane (make test-slow).
+pytestmark = pytest.mark.slow
 
-def _class_path(obj) -> str:
-    cls = type(obj)
-    return f"{cls.__module__}.{cls.__name__}"
-
-
-def _configure_small_terrain(cfg, *, seed: int = 0) -> None:
-    assert cfg.scene.terrain is not None
-    generator = cfg.scene.terrain.generator
-    assert generator is not None
-    generator.num_rows = 3
-    generator.num_cols = 3
-    generator.border_width = 0.0
-    generator.add_lights = False
-    generator.seed = seed
-
+ROOT_DIR = Path(__file__).parents[3]
+CONF_DIR = ROOT_DIR / "src" / "unilab" / "conf" / "ppo"
 
-def _rough_cfg(*, curriculum_enabled: bool = False, seed: int = 0):
-    from unilab.envs.locomotion.go2.rough import Go2JoystickRoughCfg, RoughRewardConfig
-
-    cfg = Go2JoystickRoughCfg(
-        reward_config=RoughRewardConfig(scales={}, tracking_sigma=0.25, base_height_target=0.3)
+_OWNER_CASES = tuple(
+    pytest.param(task_id, task_name, backend, *dims, id=f"{task_id.split('_')[0]}-{backend}")
+    for task_id, task_name, dims in (
+        ("go1_joystick_rough", "Go1JoystickRough", (45, 235, 12)),
+        ("go2_joystick_rough", "Go2JoystickRough", (45, 235, 12)),
+        ("go2w_joystick_rough", "Go2WJoystickRough", (53, 243, 16)),
     )
-    _configure_small_terrain(cfg, seed=seed)
-    cfg.terrain_curriculum = TerrainCurriculumCfg(enabled=curriculum_enabled, seed=seed)
-    return cfg
-
-
-def test_terrain_spawn_attached_when_rough():
-    from unilab.envs.locomotion.go2.rough import Go2JoystickRoughEnv
-
-    cfg = _rough_cfg()
-    env = Go2JoystickRoughEnv(cfg, num_envs=4, backend_type="mujoco")
-    try:
-        terrain_data = env._backend.get_terrain_spawn_data()
-        assert terrain_data is not None
-        assert terrain_data.sample_height is not None
-        assert terrain_data.terrain_origins.shape == (3, 3, 3)
-        assert not terrain_data.terrain_origins.flags.writeable
-        assert _class_path(env._spawn) == (
-            "unilab.envs.locomotion.common.terrain_spawn.TerrainSpawnManager"
-        )
-        assert env._scene_terrain_origins is not None
-        assert env._scene_terrain_origins.shape == (3, 3, 3)
-    finally:
-        env.close()
-
-
-def test_terrain_spawn_attached_when_rough_motrix():
-    pytest.importorskip("motrixsim")
-
-    from unilab.envs.locomotion.go2.joystick import Go2WalkTask
-
-    cfg = _rough_cfg()
-    cfg.domain_rand.randomize_kp = False
-    cfg.domain_rand.randomize_kd = False
-    env = Go2WalkTask(cfg, num_envs=2, backend_type="motrix")
-    try:
-        terrain_data = env._backend.get_terrain_spawn_data()
-        assert terrain_data is not None
-        assert terrain_data.sample_height is not None
-        assert terrain_data.terrain_origins.shape == (3, 3, 3)
-        assert not terrain_data.terrain_origins.flags.writeable
-        assert _class_path(env._spawn) == (
-            "unilab.envs.locomotion.common.terrain_spawn.TerrainSpawnManager"
-        )
-        assert env._scene_terrain_origins is not None
-        assert env._scene_terrain_origins.shape == (3, 3, 3)
-        state = env.init_state()
-        assert state.obs["obs"].shape == (2, 49)
-    finally:
-        env.close()
-
+    for backend in ("mujoco", "motrix")
+)
 
-def test_go1_rough_initialization_and_reset_use_backend_terrain_contract():
-    from unilab.envs.locomotion.go1.rough import (
-        Go1JoystickRoughCfg,
-        Go1JoystickRoughEnv,
-        RoughRewardConfig,
-    )
 
-    cfg = Go1JoystickRoughCfg(
-        reward_config=RoughRewardConfig(scales={}, tracking_sigma=0.25, base_height_target=0.3)
+def _compose(task_id: str, backend: str, extra: tuple[str, ...] = ()) -> DictConfig:
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(config_dir=str(CONF_DIR), version_base="1.3"):
+        return compose("config", overrides=[f"task={task_id}/{backend}", *extra])
+
+
+def _materialize(
+    task_id: str,
+    task_name: str,
+    backend: str,
+    extra: tuple[str, ...] = (),
+) -> tuple[DictConfig, ManagerBasedRlEnvCfg, dict[str, Any]]:
+    hydra_cfg = _compose(task_id, backend, extra)
+    override = BackendAdapter(hydra_cfg, root_dir=ROOT_DIR).build_task_env_cfg_override()
+    env_cfg = registry.materialize_env_config(task_name)
+    assert isinstance(env_cfg, ManagerBasedRlEnvCfg)
+    apply_cfg_overrides(env_cfg, override)
+    env_cfg.validate()
+    return hydra_cfg, env_cfg, override
+
+
+@pytest.mark.parametrize("task_id,task_name,backend,policy_dim,critic_dim,action_dim", _OWNER_CASES)
+def test_rough_registry_executes_real_backend_contract(
+    task_id: str,
+    task_name: str,
+    backend: str,
+    policy_dim: int,
+    critic_dim: int,
+    action_dim: int,
+) -> None:
+    if backend == "motrix":
+        pytest.importorskip("motrixsim")
+    registry.ensure_registries()
+    hydra_cfg, env_cfg, override = _materialize(task_id, task_name, backend)
+    assert (hydra_cfg.training.task_name, hydra_cfg.training.sim_backend) == (task_name, backend)
+    assert hydra_cfg.env.commands.twist.planar_dead_zone == pytest.approx(0.08)
+    override["commands"]["twist"]["ranges"]["lin_vel_x"] = [0.04, 0.04]
+    override["commands"]["twist"]["ranges"]["lin_vel_y"] = [0.0, 0.0]
+    assert env_cfg.scene is not None and env_cfg.scene.terrain is not None
+    terrain = env_cfg.scene.terrain.generator
+    assert isinstance(terrain, QuadrupedRoughTerrainCfg)
+    assert (terrain.num_rows, terrain.num_cols, len(terrain.sub_terrains)) == (6, 6, 7)
+    assert terrain.horizontal_scale == pytest.approx(0.1 if "go2w" in task_id else 0.2)
+    assert registry.list_registered_envs()[task_name]["config_factory"] == "ManagerBasedRlEnvCfg"
+    env = cast(
+        ManagerBasedRlEnv,
+        registry.make(
+            task_name,
+            sim_backend=backend,
+            env_cfg_override=override,
+            num_envs=2,
+        ),
     )
-    _configure_small_terrain(cfg)
-    cfg.terrain_scan.enabled = False
-    cfg.domain_rand.randomize_kp = False
-    cfg.domain_rand.randomize_kd = False
-
-    env = Go1JoystickRoughEnv(cfg, num_envs=2, backend_type="mujoco")
     try:
-        terrain_data = env._backend.get_terrain_spawn_data()
-        assert terrain_data is not None
-        assert terrain_data.sample_height is not None
-        assert env._scene_terrain_origins is terrain_data.terrain_origins
-        assert env._spawn._sample_height is terrain_data.sample_height
-        state = env.init_state()
-        assert state.obs["obs"].shape == (2, 45)
-    finally:
-        env.close()
-
-
-def test_go2w_rough_initialization_and_reset_use_backend_terrain_contract():
-    from unilab.envs.locomotion.go2w.joystick import RewardConfig
-    from unilab.envs.locomotion.go2w.rough import Go2WJoystickRoughCfg, Go2WJoystickRoughEnv
-
-    cfg = Go2WJoystickRoughCfg(
-        reward_config=RewardConfig(scales={}, tracking_sigma=0.25, base_height_target=0.3)
-    )
-    _configure_small_terrain(cfg)
-    cfg.terrain_scan.enabled = False
-    cfg.domain_rand.randomize_kp = False
-    cfg.domain_rand.randomize_kd = False
+        obs, _ = env.reset(seed=7)
+        np.testing.assert_allclose(env.command_manager.get_command("twist")[:, :2], 0.0)
+        assert obs["obs"].shape == (2, policy_dim)
+        assert obs["critic"].shape == (2, critic_dim)
+        assert env.obs_groups_spec == {"obs": policy_dim, "critic": critic_dim}
+        assert env.action_space.shape == (action_dim,)
+        np.testing.assert_array_equal(env.scene.env_origins, np.zeros((2, 3)))
 
-    env = Go2WJoystickRoughEnv(cfg, num_envs=2, backend_type="mujoco")
-    try:
         terrain_data = env._backend.get_terrain_spawn_data()
         assert terrain_data is not None
         assert terrain_data.sample_height is not None
-        np.testing.assert_array_equal(env._spawn._terrain_origins, terrain_data.terrain_origins)
-        assert env._spawn._sample_height is terrain_data.sample_height
-        state = env.init_state()
-        assert state.obs["obs"].shape == (2, 53)
+        assert terrain_data.terrain_origins.shape == (6, 6, 3)
+        reset_term = env.event_manager.get_term_cfg("terrain_root_state").func
+        assert isinstance(reset_term, RoughTerrainReset)
+        spawn = reset_term.spawn_manager
+        ids = np.arange(2, dtype=np.int32)
+        base_pos = env.scene["robot"].data.root_link_pos_w
+        assigned_origins = spawn.origins_for(ids)
+        assert np.all(np.abs(base_pos[:, :2] - assigned_origins[:, :2]) <= 0.5)
+        surface_height = terrain_data.sample_height(base_pos[:, :2])
+        assert np.all(base_pos[:, 2] - surface_height > 0.25)
+
+        action = env.action_manager.get_term("motor" if "go2w" in task_id else "joint_pos")
+        saturated = np.full((2, action_dim), 200.0, dtype=np.float32)
+        action.process_actions(saturated)
+        np.testing.assert_allclose(action.raw_action, 100.0)
+        if isinstance(action, RoughJointPositionAction):
+            scale = np.asarray(action.scale)[0]
+            for name, value in zip(action.target_names, scale, strict=True):
+                expected = 0.125 if "_hip_joint" in name else 0.25
+                assert value == pytest.approx(expected)
+            assert action.cfg.clip_actions == pytest.approx(100.0)
+        else:
+            assert isinstance(action, Go2WMixedAction)
+            assert action.cfg.leg_action_scale == pytest.approx(0.25)
+            assert action.cfg.hip_action_scale == pytest.approx(0.125)
+            assert action.cfg.wheel_action_scale == pytest.approx(5.0)
+            assert action.cfg.clip_actions == pytest.approx(100.0)
+
+        state = env.step(np.zeros((2, action_dim), dtype=np.float32))
+        assert state.obs["obs"].shape == (2, policy_dim)
+        assert state.obs["critic"].shape == (2, critic_dim)
+        for value in (*state.obs.values(), state.reward):
+            assert np.isfinite(value).all()
     finally:
         env.close()
 
 
-def test_terrain_spawn_samples_height_after_xy_jitter():
-    class FakeSurface:
-        def sample_height(self, xy):
-            xy = np.asarray(xy, dtype=np.float64)
-            return xy[:, 0] * 0.5 + xy[:, 1] * 0.25
-
-    origins = np.zeros((1, 1, 3), dtype=np.float64)
-    cfg = TerrainCurriculumCfg(spawn_height_margin=0.05)
-    surface = FakeSurface()
-    sample_height = surface.sample_height
-    spawn = TerrainSpawnManager(
-        1,
-        origins,
-        cell_size=1.0,
-        cfg=cfg,
-        sample_height=sample_height,
-    )
-    assert spawn._sample_height is sample_height
-
-    qpos_xyz = np.asarray([[0.2, 0.4, 0.42]], dtype=np.float64)
-    spawned = spawn.apply_spawn(np.asarray([0], dtype=np.int32), qpos_xyz)
-
-    assert spawned[0, 0] == pytest.approx(0.2)
-    assert spawned[0, 1] == pytest.approx(0.4)
-    assert spawned[0, 2] == pytest.approx(0.2 * 0.5 + 0.4 * 0.25 + 0.42 + 0.05)
-
-
-def test_terrain_spawn_rejects_non_callable_height_sampler_on_init():
-    with pytest.raises(TypeError, match="sample_height must be callable"):
-        TerrainSpawnManager(
-            1,
-            np.zeros((1, 1, 3), dtype=np.float64),
-            cell_size=1.0,
-            cfg=TerrainCurriculumCfg(),
-            sample_height=object(),  # type: ignore[arg-type]
-        )
-
-
-def test_go2_rough_base_height_reward_uses_terrain_relative_height():
-    from unilab.envs.locomotion.go2.joystick import Go2WalkTask
-
-    class FakeBackend:
-        def get_base_pos(self):
-            return np.asarray(
-                [
-                    [1.0, 2.0, 1.25],
-                    [-1.0, 0.5, -0.2],
-                ],
-                dtype=np.float32,
-            )
-
-    class FakeSurface:
-        def sample_height(self, xy):
-            xy = np.asarray(xy, dtype=np.float64)
-            return np.asarray([0.75, -0.55], dtype=np.float64)
-
-    surface = FakeSurface()
-    env = Go2WalkTask.__new__(Go2WalkTask)
-    env._backend = FakeBackend()
-    env._terrain_surface_sample_height = surface.sample_height
-
-    np.testing.assert_allclose(env._reward_base_height_values(), np.asarray([0.5, 0.35]))
-
-
-def test_go2_rough_pd_torque_estimate_returns_dof_order():
-    from unilab.envs.locomotion.go2.rough import (
-        GO2_ACTUATOR_TO_DOF_INDICES,
-        Go2JoystickRoughEnv,
-    )
-
-    env = Go2JoystickRoughEnv.__new__(Go2JoystickRoughEnv)
-    env._num_action = 12
-    env._cfg = SimpleNamespace(
-        control_config=SimpleNamespace(Kp=2.0, Kd=0.5, simulate_action_latency=False)
-    )
-    env._action_scale = np.ones((12,), dtype=np.float32)
-    env.default_angles = np.arange(12, dtype=np.float32)
-    env._default_angles_actuator = env.default_angles[GO2_ACTUATOR_TO_DOF_INDICES]
-    dof_pos = np.zeros((1, 12), dtype=np.float32)
-    dof_vel = np.zeros((1, 12), dtype=np.float32)
-    info = {"current_actions": np.zeros((1, 12), dtype=np.float32)}
-
-    torques = env._estimate_pd_torques(info, dof_pos, dof_vel)
-
-    np.testing.assert_allclose(torques, 2.0 * env.default_angles[None, :])
-
-
-def test_default_spawn_used_when_flat():
-    from unilab.envs.locomotion.go2.joystick import (
-        Go2JoystickCfg,
-        Go2WalkTask,
-        RewardConfig,
+@pytest.mark.parametrize(
+    ("task_id", "task_name"),
+    (
+        ("go1_joystick_rough", "Go1JoystickRough"),
+        ("go2_joystick_rough", "Go2JoystickRough"),
+        ("go2w_joystick_rough", "Go2WJoystickRough"),
+    ),
+)
+def test_rough_sim2sim_snapshot_matches_across_backends(task_id: str, task_name: str) -> None:
+    registry.ensure_registries()
+    mujoco_cfg, _, _ = _materialize(task_id, task_name, "mujoco")
+    motrix_cfg, _, _ = _materialize(task_id, task_name, "motrix")
+    assert extract_contract_snapshot(mujoco_cfg) == extract_contract_snapshot(motrix_cfg)
+
+
+def test_curriculum_updates_before_next_spawn_and_oob_fails_closed() -> None:
+    registry.ensure_registries()
+    extra = (
+        "env.scene.terrain.generator.curriculum=true",
+        "env.scene.terrain.generator.num_rows=3",
     )
-
-    cfg = Go2JoystickCfg(
-        reward_config=RewardConfig(scales={}, tracking_sigma=0.25, base_height_target=0.3)
+    _, _, override = _materialize("go2_joystick_rough", "Go2JoystickRough", "mujoco", extra)
+    env = cast(
+        ManagerBasedRlEnv,
+        registry.make(
+            "Go2JoystickRough",
+            sim_backend="mujoco",
+            env_cfg_override=override,
+            num_envs=2,
+        ),
     )
-    env = Go2WalkTask(cfg, num_envs=4, backend_type="mujoco")
     try:
-        assert env._backend.get_terrain_spawn_data() is None
-        assert _class_path(env._spawn) == (
-            "unilab.envs.locomotion.common.terrain_spawn.BaseSpawnManager"
+        env.reset(seed=7)
+        reset_term = env.event_manager.get_term_cfg("terrain_root_state").func
+        assert isinstance(reset_term, RoughTerrainReset)
+        spawn = reset_term.spawn_manager
+        np.testing.assert_array_equal(spawn.levels, np.zeros(2, dtype=np.int32))
+
+        current = env.scene["robot"].data.root_link_pos_w.copy()
+        spawn._episode_start_xyz[0, :2] = current[0, :2] - np.asarray([5.0, 0.0])
+        env.reset_buf[0] = True
+        _, info = env.reset(env_ids=np.asarray([0], dtype=np.int32))
+
+        assert spawn.levels[0] == 1
+        assert info["log"]["Curriculum/terrain_levels/num_promoted"] == 1
+        next_pos = env.scene["robot"].data.root_link_pos_w[0]
+        next_origin = spawn.origins_for(np.asarray([0], dtype=np.int32))[0]
+        assert np.all(np.abs(next_pos[:2] - next_origin[:2]) <= 0.5)
+        np.testing.assert_allclose(spawn._episode_start_xyz[0], next_pos)
+
+        oob = env.termination_manager.get_term_cfg("terrain_out_of_bounds").func
+        assert isinstance(oob, RoughTerrainOutOfBounds)
+        oob._asset = SimpleNamespace(
+            data=SimpleNamespace(
+                root_link_pos_w=np.asarray([[0.0, 0.0, 0.5], [oob._half_width + 1.0, 0.0, 0.5]])
+            )
         )
-        assert env._scene_terrain_origins is None
-        # Origins are zeros (flat scene needs no spread; per-env xy jitter still applies).
-        np.testing.assert_array_equal(env._spawn.origins_for(np.arange(4)), np.zeros((4, 3)))
+        np.testing.assert_array_equal(oob(env), np.asarray([False, True]))
     finally:
         env.close()
 
 
-def test_curriculum_disabled_distributes_levels_uniformly():
-    from unilab.envs.locomotion.go2.rough import Go2JoystickRoughEnv
-
-    cfg = _rough_cfg(curriculum_enabled=False, seed=0)
-    env = Go2JoystickRoughEnv(cfg, num_envs=64, backend_type="mujoco")
-    try:
-        sm = env._spawn
-        assert sm is not None
-        assert sm.levels.min() == 0
-        assert sm.levels.max() == 2
-        assert sm.type_cols.min() >= 0
-        assert sm.type_cols.max() <= 2
-    finally:
-        env.close()
-
-
-def test_curriculum_enabled_levels_start_at_zero():
-    from unilab.envs.locomotion.go2.rough import Go2JoystickRoughEnv
-
-    cfg = _rough_cfg(curriculum_enabled=True, seed=0)
-    env = Go2JoystickRoughEnv(cfg, num_envs=8, backend_type="mujoco")
-    try:
-        sm = env._spawn
-        assert sm is not None
-        assert np.all(sm.levels == 0)
-    finally:
-        env.close()
-
-
-def test_reset_qpos_xy_matches_terrain_origins():
-    from unilab.envs.locomotion.go2.rough import Go2JoystickRoughEnv
-
-    cfg = _rough_cfg(curriculum_enabled=False, seed=0)
-    env = Go2JoystickRoughEnv(cfg, num_envs=4, backend_type="mujoco")
-    try:
-        sm = env._spawn
-        assert sm is not None
-        env.init_state()
-        base_pos = env._backend.get_base_pos()
-        rows = sm.levels
-        cols = sm.type_cols
-        expected_xy = sm._terrain_origins[rows, cols, :2]
-        # Reset adds a uniform [-0.5, 0.5] xy jitter on top of the spawn xy.
-        diff = base_pos[:, :2] - expected_xy
-        assert np.all(np.abs(diff) < 0.6)
-    finally:
-        env.close()
-
-
-def test_rough_reset_spawns_above_sampled_terrain():
-    from unilab.envs.locomotion.go2.rough import Go2JoystickRoughEnv
-
-    cfg = _rough_cfg(curriculum_enabled=False, seed=0)
-    env = Go2JoystickRoughEnv(cfg, num_envs=64, backend_type="mujoco")
-    try:
-        env.init_state()
-        raw_heights, _ = env._raw_height_scan_obs(env.num_envs)
-        assert raw_heights is not None
-        center_heights = raw_heights[:, raw_heights.shape[1] // 2]
-        clearance = env._backend.get_base_pos()[:, 2] - center_heights
-        assert np.all(clearance > 0.25)
-    finally:
-        env.close()
-
-
-def test_curriculum_logs_appear_after_done():
-    from unilab.envs.locomotion.go2.rough import Go2JoystickRoughEnv
-
-    cfg = _rough_cfg(curriculum_enabled=True, seed=0)
-    env = Go2JoystickRoughEnv(cfg, num_envs=4, backend_type="mujoco")
-    try:
-        state = env.init_state()
-        env.apply_action(np.zeros((4, 12), dtype=np.float32), state)
-        state.truncated[:] = True
-        out = env.update_state(state)
-        log = out.info.get("log", {})
-        for key in (
-            "terrain_curriculum/mean_level",
-            "terrain_curriculum/max_level",
-            "terrain_curriculum/mean_walked",
-            "terrain_curriculum/num_promoted",
-            "terrain_curriculum/num_demoted",
-            "terrain_curriculum/num_skipped",
+def test_rough_hot_paths_use_only_cached_runtime_objects() -> None:
+    for term in (
+        RoughTerrainReset,
+        RoughTerrainCurriculum,
+        RoughTerrainOutOfBounds,
+        RoughHeightScan,
+    ):
+        source = inspect.getsource(term.__call__)
+        for forbidden in (
+            "ASSETS_ROOT_PATH",
+            "model_file",
+            "getattr(",
+            "hasattr(",
+            "._backend",
         ):
-            assert key in log
-    finally:
-        env.close()
-
-
-@pytest.mark.parametrize("preset", ["flat", "rough"])
-def test_episode_start_recorded_after_reset(preset):
-    from unilab.envs.locomotion.go2.joystick import (
-        Go2JoystickCfg,
-        Go2WalkTask,
-        RewardConfig,
-    )
-    from unilab.envs.locomotion.go2.rough import Go2JoystickRoughEnv
-
-    if preset == "flat":
-        cfg = Go2JoystickCfg(
-            reward_config=RewardConfig(scales={}, tracking_sigma=0.25, base_height_target=0.3)
-        )
-        env_cls = Go2WalkTask
-    else:
-        cfg = _rough_cfg(curriculum_enabled=False, seed=0)
-        env_cls = Go2JoystickRoughEnv
-    env = env_cls(cfg, num_envs=4, backend_type="mujoco")
-    try:
-        env.init_state()
-        sm = env._spawn
-        if _class_path(sm) == "unilab.envs.locomotion.common.terrain_spawn.TerrainSpawnManager":
-            assert np.all(sm._has_started)
-    finally:
-        env.close()
+            assert forbidden not in source
diff --git a/tests/envs/locomotion/test_manager_gait_terms.py b/tests/envs/locomotion/test_manager_gait_terms.py
new file mode 100644
index 000000000..98f2449b0
--- /dev/null
+++ b/tests/envs/locomotion/test_manager_gait_terms.py
@@ -0,0 +1,369 @@
+"""Focused parity tests for task-owned quadruped Manager-Based terms."""
+
+from __future__ import annotations
+
+from collections import Counter
+from types import SimpleNamespace
+from typing import Any, cast
+
+import numpy as np
+import pytest
+from unisim.backend.base import BackendSensorView
+
+from unilab.dtype_config import get_global_dtype
+from unilab.managers import (
+    ObservationGroupCfg,
+    ObservationManager,
+    ObservationTermCfg,
+    RewardManager,
+    RewardTermCfg,
+)
+from unilab.managers._types import ManagerBasedRlEnv
+from unilab.managers.scene_entity_config import SceneEntityCfg
+from unilab.tasks.locomotion.common import manager_terms
+
+CONTACTS = ("fl_contact", "fr_contact", "rl_contact", "rr_contact")
+POSITIONS = ("fl_pos", "fr_pos", "rl_pos", "rr_pos")
+
+
+class _Scene:
+    def __init__(self) -> None:
+        self.calls: Counter[str] = Counter()
+        self.values = {
+            "fl_contact": np.array([[0.2], [0.0]], dtype=np.float32),
+            # Width-3 contact ``force`` sensors follow the MuJoCo contact-frame
+            # convention: column 0 is the normal force, columns 1-2 are
+            # tangential and must not gate contact.
+            "fr_contact": np.array([[0, 0.9, 0.9], [0.2, 0, 0]], dtype=np.float32),
+            "rl_contact": np.array([[0.0], [0.2]], dtype=np.float32),
+            "rr_contact": np.array([[0.2, 0, 0], [0, 0.9, 0.9]], dtype=np.float32),
+            "fl_pos": np.array([[0, 0, 0.1], [0, 0, 0]], dtype=np.float32),
+            "fr_pos": np.array([[0, 0, 0.2], [0, 0, 0.1]], dtype=np.float32),
+            "rl_pos": np.array([[0, 0, 0.1], [0, 0, 0.2]], dtype=np.float32),
+            "rr_pos": np.array([[0, 0, 0], [0, 0, 0.1]], dtype=np.float32),
+        }
+
+    def bind_sensor_data(self, names) -> BackendSensorView:
+        names = tuple(names)
+        self.calls["bind"] += 1
+        dimensions = tuple(self.values[name].shape[1] for name in names)
+
+        def read() -> np.ndarray:
+            self.calls["read"] += 1
+            return np.concatenate([self.values[name] for name in names], axis=1)
+
+        view = BackendSensorView("fake", names, dimensions, 2, read)
+        view.read()  # Mirror SimBackend.bind_sensor_data materialization validation.
+        return view
+
+
+class _ParityScene:
+    def __init__(self) -> None:
+        self.robot = SimpleNamespace(
+            data=SimpleNamespace(
+                root_link_lin_vel_b=np.array(
+                    [[0.2, 0.1, -0.3], [0.3, -0.2, 0.5]], dtype=np.float32
+                ),
+                root_link_ang_vel_b=np.array(
+                    [[0.1, -0.2, 0.4], [-0.3, 0.2, -0.1]], dtype=np.float32
+                ),
+                root_link_pos_w=np.array([[1.0, 2.0, 0.4], [-1.0, 0.5, 0.2]], dtype=np.float32),
+                joint_pos=np.array([[0.2, -0.1, 0.5], [-0.4, 0.3, 0.1]], dtype=np.float32),
+                default_joint_pos=np.array([[0.1, -0.2, 0.5], [-0.1, 0.1, 0.0]], dtype=np.float32),
+            )
+        )
+
+    def __getitem__(self, name: str):
+        if name != "robot":
+            raise KeyError(name)
+        return self.robot
+
+
+class _Commands:
+    def __init__(self, command: np.ndarray | None = None) -> None:
+        self.command = (
+            np.array([[0.5, -0.2, 0.3], [-0.1, 0.4, -0.2]], dtype=np.float32)
+            if command is None
+            else command
+        )
+
+    def get_command(self, name: str) -> np.ndarray:
+        if name != "twist":
+            raise KeyError(name)
+        return self.command
+
+
+def _env(counter: int = 0, scene: _Scene | None = None) -> ManagerBasedRlEnv:
+    return cast(
+        ManagerBasedRlEnv,
+        SimpleNamespace(
+            num_envs=2,
+            common_step_counter=counter,
+            episode_length_buf=np.array([counter, 0]),
+            step_dt=0.02,
+            scene=scene or _Scene(),
+            command_manager=_Commands(),
+        ),
+    )
+
+
+def _observations(env: ManagerBasedRlEnv, **params: Any) -> ObservationManager:
+    term = ObservationTermCfg(
+        func=manager_terms.quadruped_gait_phase,
+        params={"frequency": 2.0, **params},
+    )
+    return ObservationManager({"policy": ObservationGroupCfg(terms={"gait_phase": term})}, env)
+
+
+def _rewards(env: ManagerBasedRlEnv) -> RewardManager:
+    terms: dict[str, RewardTermCfg | None] = {
+        "contact": RewardTermCfg(
+            func=manager_terms.feet_phase_contact,
+            weight=1.0,
+            params={"sensor_names": CONTACTS, "frequency": 2.0},
+        ),
+        "swing": RewardTermCfg(
+            func=manager_terms.feet_phase_swing_height,
+            weight=1.0,
+            params={"sensor_names": POSITIONS, "frequency": 2.0},
+        ),
+    }
+    return RewardManager(terms, env, scale_by_dt=False)
+
+
+def _parity_env() -> ManagerBasedRlEnv:
+    return cast(
+        ManagerBasedRlEnv,
+        SimpleNamespace(
+            num_envs=2,
+            scene=_ParityScene(),
+            command_manager=_Commands(),
+            max_episode_length_s=20.0,
+        ),
+    )
+
+
+def _reward_value(env: ManagerBasedRlEnv, func, **params: Any) -> np.ndarray:
+    cfg = RewardTermCfg(func=func, weight=1.0, params=params)
+    return RewardManager({"parity": cfg}, env, scale_by_dt=False).compute(dt=0.02)
+
+
+def test_gait_phase_matches_global_legacy_clock_and_ignores_episode_reset() -> None:
+    env = _env()
+    manager = _observations(env)
+    assert manager.group_obs_dim == {"policy": (4,)}
+    initial = manager.compute_group("policy")
+    assert isinstance(initial, np.ndarray)
+    np.testing.assert_array_equal(initial, [[0.0, 0.5, 0.5, 0.0]] * 2)
+
+    cast(Any, env).common_step_counter = 25
+    env.episode_length_buf[:] = [0, 999]  # Partial reset does not reset the global clock.
+    advanced = manager.compute_group("policy")
+    assert isinstance(advanced, np.ndarray)
+    np.testing.assert_allclose(
+        advanced, [[1.1920929e-7, 0.5000001, 0.5000001, 1.1920929e-7]] * 2, atol=1e-8
+    )
+
+
+def test_standing_aware_gait_freezes_only_standing_environments() -> None:
+    env = _env()
+    cast(Any, env).command_manager.command[:] = [[0.0, 0.0, 0.0], [0.5, 0.0, 0.0]]
+    manager = _observations(env, command_name="twist", command_threshold=0.1)
+
+    cast(Any, env).common_step_counter = 1
+    phase = manager.compute_group("policy")
+    assert isinstance(phase, np.ndarray)
+    np.testing.assert_allclose(
+        phase,
+        [[0.0, 0.5, 0.5, 0.0], [0.04, 0.54, 0.54, 0.04]],
+        atol=1e-7,
+    )
+
+
+def test_standing_aware_foot_rewards_gate_swing_and_expect_planted_feet() -> None:
+    env = _env(counter=5)
+    cast(Any, env).command_manager.command[:] = [[0.0, 0.0, 0.0], [0.5, 0.0, 0.0]]
+    params = {"frequency": 2.0, "command_name": "twist", "command_threshold": 0.1}
+    manager = RewardManager(
+        {
+            "contact": RewardTermCfg(
+                func=manager_terms.feet_phase_contact,
+                weight=1.0,
+                params={"sensor_names": CONTACTS, **params},
+            ),
+            "swing": RewardTermCfg(
+                func=manager_terms.feet_phase_swing_height,
+                weight=1.0,
+                params={"sensor_names": POSITIONS, **params},
+            ),
+        },
+        env,
+        scale_by_dt=False,
+    )
+
+    value = manager.compute(dt=0.02)
+    expected_moving_swing = (1.0 + np.exp(-1.0)) / 4.0
+    np.testing.assert_allclose(value, [0.5, expected_moving_swing], atol=1e-7)
+
+
+def test_standing_penalties_match_a2_legacy_gates() -> None:
+    parity_env = _parity_env()
+    cast(Any, parity_env).command_manager.command[:] = [
+        [0.0, 0.0, 0.0],
+        [0.5, 0.0, 0.0],
+    ]
+    stand_still = _reward_value(
+        parity_env,
+        manager_terms.stand_still_l1,
+        command_name="twist",
+        command_threshold=0.1,
+    )
+    np.testing.assert_allclose(stand_still, [0.2, 0.0], atol=1e-7)
+
+    foot_env = _env()
+    cast(Any, foot_env).command_manager.command[:] = [
+        [0.0, 0.0, 0.0],
+        [0.5, 0.0, 0.0],
+    ]
+    feet_air = _reward_value(
+        foot_env,
+        manager_terms.feet_air_while_standing,
+        sensor_names=CONTACTS,
+        command_name="twist",
+        command_threshold=0.1,
+    )
+    np.testing.assert_array_equal(feet_air, [2.0, 0.0])
+
+
+def test_foot_rewards_match_legacy_equations_and_read_only_bound_views() -> None:
+    scene = _Scene()
+    manager = _rewards(_env(5, scene))
+    assert scene.calls == {"bind": 2, "read": 2}
+    result = manager.compute(dt=0.02)
+    assert scene.calls == {"bind": 2, "read": 4}
+
+    phase = np.array([[0.2, 0.7, 0.7, 0.2]] * 2)
+    contact = np.array([[True, False, False, True], [False, True, True, False]])
+    heights = np.array([[0.1, 0.2, 0.1, 0.0], [0.0, 0.1, 0.2, 0.1]])
+    expected = np.mean(contact == (phase < 0.6), axis=1)
+    expected += np.mean(np.exp(-np.square(heights - 0.1) / 0.01) * (phase >= 0.6), axis=1)
+    np.testing.assert_allclose(result, expected, atol=1e-7)
+    manager.compute(dt=0.02)
+    assert scene.calls == {"bind": 2, "read": 6}
+
+
+@pytest.mark.parametrize(
+    ("params", "error", "match"),
+    [
+        ({"frequency": -1}, ValueError, "frequency must be at least 0.0"),
+        ({"phase_offsets": (0, 0.5)}, ValueError, "must contain 4 values"),
+        ({"phase_offsets": (0, True, 0.5, 0)}, TypeError, "must be a real number"),
+        ({"command_name": ""}, ValueError, "command_name must be a non-empty string"),
+        ({"command_threshold": -0.1}, ValueError, "must be at least 0.0"),
+        ({"unknown": 1}, TypeError, "unsupported parameters"),
+    ],
+)
+def test_gait_phase_invalid_config_fails_at_construction(params, error, match: str) -> None:
+    with pytest.raises(error, match=match):
+        _observations(_env(), **params)
+
+
+def test_foot_sensor_contracts_fail_at_construction() -> None:
+    env = _env()
+    short = RewardTermCfg(
+        func=manager_terms.feet_phase_contact,
+        weight=1,
+        params={"sensor_names": CONTACTS[:3]},
+    )
+    with pytest.raises(ValueError, match="must contain 4 names"):
+        RewardManager({"feet": short}, env)
+
+    scene = _Scene()
+    scene.values["fr_contact"] = np.zeros((2, 2), dtype=np.float32)
+    with pytest.raises(ValueError, match="must each expose 1-D found or 3-D force"):
+        _rewards(_env(scene=scene))
+
+    scene = _Scene()
+    scene.values["rl_pos"] = np.zeros((2, 2), dtype=np.float32)
+    with pytest.raises(ValueError, match="must each expose 3-D xyz"):
+        _rewards(_env(scene=scene))
+
+
+def test_runtime_nonfinite_sensor_data_reports_term_and_backend() -> None:
+    scene = _Scene()
+    manager = _rewards(_env(scene=scene))
+    scene.values["fl_contact"][1, 0] = np.nan
+    with pytest.raises(ValueError, match="feet_phase_contact.*backend 'fake'.*NaN or Inf"):
+        manager.compute(dt=0.02)
+
+
+def test_base_reward_terms_match_go2_flat_equations() -> None:
+    env = _parity_env()
+    asset = SceneEntityCfg("robot")
+    data = cast(Any, env).scene.robot.data
+    command = cast(Any, env).command_manager.command
+    std = 0.5
+    actual = {
+        "track_xy": _reward_value(
+            env,
+            manager_terms.track_lin_vel_xy_exp,
+            std=std,
+            command_name="twist",
+            asset_cfg=asset,
+        ),
+        "track_yaw": _reward_value(
+            env,
+            manager_terms.track_ang_vel_z_exp,
+            std=std,
+            command_name="twist",
+            asset_cfg=SceneEntityCfg("robot"),
+        ),
+        "lin_z": _reward_value(env, manager_terms.lin_vel_z_l2, asset_cfg=SceneEntityCfg("robot")),
+        "ang_xy": _reward_value(
+            env, manager_terms.ang_vel_xy_l2, asset_cfg=SceneEntityCfg("robot")
+        ),
+        "height": _reward_value(
+            env,
+            manager_terms.base_height_l2,
+            target_height=0.3,
+            asset_cfg=SceneEntityCfg("robot"),
+        ),
+        "pose": _reward_value(
+            env, manager_terms.joint_deviation_l1, asset_cfg=SceneEntityCfg("robot")
+        ),
+    }
+    expected = {
+        "track_xy": np.exp(
+            -np.sum(np.square(command[:, :2] - data.root_link_lin_vel_b[:, :2]), axis=1) / std**2
+        ),
+        "track_yaw": np.exp(-np.square(command[:, 2] - data.root_link_ang_vel_b[:, 2]) / std**2),
+        "lin_z": np.square(data.root_link_lin_vel_b[:, 2]),
+        "ang_xy": np.sum(np.square(data.root_link_ang_vel_b[:, :2]), axis=1),
+        "height": np.square(data.root_link_pos_w[:, 2] - 0.3),
+        "pose": np.sum(np.abs(data.joint_pos - data.default_joint_pos), axis=1),
+    }
+    for name in expected:
+        assert actual[name].shape == (2,)
+        assert actual[name].dtype == np.dtype(get_global_dtype())
+        np.testing.assert_allclose(actual[name], expected[name], rtol=1e-6, atol=1e-7)
+
+
+def test_base_reward_terms_fail_closed_at_nearest_boundary() -> None:
+    env = _parity_env()
+    with pytest.raises(ValueError, match="track_lin_vel_xy_exp std must be greater than 0.0"):
+        _reward_value(env, manager_terms.track_lin_vel_xy_exp, std=0.0, command_name="twist")
+    with pytest.raises(KeyError, match="track_ang_vel_z_exp command capability 'missing'"):
+        _reward_value(env, manager_terms.track_ang_vel_z_exp, std=0.5, command_name="missing")
+    with pytest.raises(ValueError, match="base_height_l2 target_height must be finite"):
+        _reward_value(env, manager_terms.base_height_l2, target_height=np.nan)
+
+    cast(Any, env).scene.robot.data.root_link_lin_vel_b[1, 2] = np.inf
+    with pytest.raises(ValueError, match="lin_vel_z_l2 root linear velocity contains NaN or Inf"):
+        _reward_value(env, manager_terms.lin_vel_z_l2)
+
+    with pytest.raises(KeyError, match="RewardManager term 'parity'.*asset_cfg.*missing"):
+        _reward_value(
+            _parity_env(),
+            manager_terms.joint_deviation_l1,
+            asset_cfg=SceneEntityCfg("missing"),
+        )
diff --git a/tests/envs/locomotion/test_sensor_reward_terms.py b/tests/envs/locomotion/test_sensor_reward_terms.py
new file mode 100644
index 000000000..4b80377d2
--- /dev/null
+++ b/tests/envs/locomotion/test_sensor_reward_terms.py
@@ -0,0 +1,157 @@
+"""Focused tests for the shared named-sensor reward terms."""
+
+from __future__ import annotations
+
+import inspect
+from types import SimpleNamespace
+from typing import Any, cast
+
+import numpy as np
+import pytest
+from unisim.backend.base import BackendSensorView
+
+from unilab.dtype_config import get_global_dtype
+from unilab.managers import RewardTermCfg
+from unilab.managers._types import ManagerBasedRlEnv
+from unilab.tasks.locomotion.common import manager_terms, sensor_reward_terms
+
+
+class _Scene:
+    def __init__(self, values: dict[str, np.ndarray]) -> None:
+        self.values = values
+        self.bound_names: tuple[str, ...] | None = None
+
+    def bind_sensor_data(self, names) -> BackendSensorView:
+        names = tuple(names)
+        self.bound_names = names
+        dimensions = tuple(self.values[name].shape[1] for name in names)
+
+        def read() -> np.ndarray:
+            return np.concatenate([self.values[name] for name in names], axis=1)
+
+        view = BackendSensorView("fake", names, dimensions, 2, read)
+        view.read()  # Mirror SimBackend.bind_sensor_data materialization validation.
+        return view
+
+
+class _Commands:
+    def __init__(self, command: np.ndarray) -> None:
+        self.command = command
+
+    def get_command(self, name: str) -> np.ndarray:
+        if name != "twist":
+            raise KeyError(name)
+        return self.command
+
+
+def _env(
+    sensors: dict[str, np.ndarray] | None = None,
+    command: np.ndarray | None = None,
+) -> tuple[ManagerBasedRlEnv, _Scene]:
+    scene = _Scene(
+        sensors
+        or {
+            "local_linvel": np.array([[0.2, 0.1, -0.3], [0.0, 0.0, 0.5]], dtype=np.float32),
+            "gyro": np.array([[0.1, -0.2, 0.4], [0.0, 0.0, -0.2]], dtype=np.float32),
+            "upvector": np.array([[0.0, 0.0, 1.0], [0.3, -0.4, 0.9]], dtype=np.float32),
+        }
+    )
+    if command is None:
+        command = np.array([[0.5, -0.2, 0.3], [-0.1, 0.4, -0.2]], dtype=np.float32)
+    env = cast(
+        ManagerBasedRlEnv,
+        SimpleNamespace(num_envs=2, scene=scene, command_manager=_Commands(command)),
+    )
+    return env, scene
+
+
+def _term(term_type: type, env: ManagerBasedRlEnv, **params: Any):
+    return term_type(RewardTermCfg(func=term_type, weight=1.0, params=params), env)
+
+
+def test_track_lin_vel_matches_legacy_equation() -> None:
+    env, scene = _env()
+    term = _term(
+        sensor_reward_terms.track_lin_vel, env, sensor_name="local_linvel", tracking_sigma=0.25
+    )
+    assert scene.bound_names == ("local_linvel",)
+    expected = np.exp(-np.array([0.18, 0.17]) / 0.25)
+    np.testing.assert_allclose(term(env), expected, rtol=1e-6)
+
+
+def test_track_ang_vel_matches_legacy_equation() -> None:
+    env, _ = _env()
+    term = _term(sensor_reward_terms.track_ang_vel, env, sensor_name="gyro", tracking_sigma=0.25)
+    expected = np.exp(-np.array([(0.3 - 0.4) ** 2, (-0.2 - -0.2) ** 2]) / 0.25)
+    np.testing.assert_allclose(term(env), expected, rtol=1e-6)
+
+
+def test_lin_vel_z_ang_vel_xy_and_orientation_read_declared_sensor() -> None:
+    env, scene = _env()
+    lin_vel_z = _term(sensor_reward_terms.lin_vel_z, env, sensor_name="local_linvel")
+    ang_vel_xy = _term(sensor_reward_terms.ang_vel_xy, env, sensor_name="gyro")
+    orientation = _term(sensor_reward_terms.orientation, env, sensor_name="upvector")
+
+    np.testing.assert_allclose(lin_vel_z(env), [0.09, 0.25], rtol=1e-6)
+    np.testing.assert_allclose(ang_vel_xy(env), [0.05, 0.0], rtol=1e-6)
+    np.testing.assert_allclose(orientation(env), [0.0, 0.25], rtol=1e-6)
+    assert scene.bound_names == ("upvector",)
+
+
+def test_tracking_terms_validate_params_at_construction() -> None:
+    env, _ = _env()
+    with pytest.raises(ValueError, match="tracking_sigma"):
+        _term(
+            sensor_reward_terms.track_lin_vel, env, sensor_name="local_linvel", tracking_sigma=0.0
+        )
+    with pytest.raises(ValueError, match="command_name"):
+        _term(sensor_reward_terms.track_lin_vel, env, sensor_name="local_linvel", command_name="")
+    with pytest.raises(KeyError, match="unavailable"):
+        _term(
+            sensor_reward_terms.track_lin_vel,
+            env,
+            sensor_name="local_linvel",
+            command_name="missing",
+        )
+    with pytest.raises(TypeError, match="unsupported parameters"):
+        _term(sensor_reward_terms.lin_vel_z, env, sensor_name="local_linvel", bogus=1.0)
+
+
+def test_terms_fail_closed_on_missing_or_misshapen_sensor() -> None:
+    env, _ = _env()
+    with pytest.raises(KeyError, match="missing_linvel"):
+        _term(sensor_reward_terms.lin_vel_z, env, sensor_name="missing_linvel")
+    with pytest.raises(ValueError, match="sensor_name"):
+        _term(sensor_reward_terms.lin_vel_z, env)
+
+
+def test_terms_reject_non_vec3_sensor() -> None:
+    env, _ = _env(sensors={"scalar": np.ones((2, 1), dtype=np.float32)})
+    with pytest.raises(ValueError, match="3 values"):
+        _term(sensor_reward_terms.orientation, env, sensor_name="scalar")
+
+
+def test_alive_returns_unconditional_ones() -> None:
+    env, _ = _env()
+    result = manager_terms.alive(env)
+    np.testing.assert_array_equal(result, np.ones(2))
+    assert result.dtype == np.dtype(get_global_dtype())
+
+
+def test_hot_paths_use_only_cached_runtime_objects() -> None:
+    for term_type in (
+        sensor_reward_terms.track_lin_vel,
+        sensor_reward_terms.track_ang_vel,
+        sensor_reward_terms.lin_vel_z,
+        sensor_reward_terms.ang_vel_xy,
+        sensor_reward_terms.orientation,
+    ):
+        source = inspect.getsource(term_type.__call__)
+        for forbidden in (
+            "ASSETS_ROOT_PATH",
+            "model_file",
+            "getattr(",
+            "hasattr(",
+            "._backend",
+        ):
+            assert forbidden not in source, f"{term_type.__name__} hot path references {forbidden}"
diff --git a/tests/envs/locomotion/test_terrain_spawn.py b/tests/envs/locomotion/test_terrain_spawn.py
index 249b2d174..3e1bc60f8 100644
--- a/tests/envs/locomotion/test_terrain_spawn.py
+++ b/tests/envs/locomotion/test_terrain_spawn.py
@@ -5,7 +5,7 @@
 import numpy as np
 import pytest
 
-from unilab.envs.locomotion.common.terrain_spawn import (
+from unilab.tasks.locomotion.common.terrain_spawn import (
     TerrainCurriculumCfg,
     TerrainSpawnManager,
 )
diff --git a/tests/envs/mdp/test_command_terms.py b/tests/envs/mdp/test_command_terms.py
new file mode 100644
index 000000000..151cd1847
--- /dev/null
+++ b/tests/envs/mdp/test_command_terms.py
@@ -0,0 +1,50 @@
+"""Contract tests for generic pose command terms."""
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+
+from unilab.envs.mdp import UniformPoseCommand, UniformPoseCommandCfg
+
+
+def _env(num_envs: int = 2) -> SimpleNamespace:
+    return SimpleNamespace(
+        num_envs=num_envs,
+        rng=np.random.default_rng(11),
+    )
+
+
+def test_uniform_pose_command_samples_configured_width_and_zero_bucket() -> None:
+    env = _env()
+    cfg = UniformPoseCommandCfg(
+        resampling_time_range=(1.0, 1.0),
+        ranges=((-1.0, 1.0), (2.0, 2.0)),
+        zero_command_prob=1.0,
+    )
+    term = cfg.build(env)
+    assert isinstance(term, UniformPoseCommand)
+
+    term.reset(np.asarray([0, 1], dtype=np.int32))
+    assert term.command.shape == (2, 2)
+    np.testing.assert_array_equal(term.command, 0.0)
+
+
+def test_uniform_pose_command_reads_curriculum_updates_without_changing_width() -> None:
+    env = _env()
+    cfg = UniformPoseCommandCfg(
+        resampling_time_range=(1.0, 1.0),
+        ranges=[[0.0, 0.0], [1.0, 1.0]],
+    )
+    term = cfg.build(env)
+    ids = np.asarray([0, 1], dtype=np.int32)
+
+    cfg.ranges[0] = [2.0, 2.0]
+    term.reset(ids)
+    np.testing.assert_array_equal(term.command, [[2.0, 1.0], [2.0, 1.0]])
+
+    cfg.ranges.append([3.0, 3.0])
+    with pytest.raises(ValueError, match="ranges width changed"):
+        term.reset(ids)
diff --git a/tests/envs/mdp/test_curriculums.py b/tests/envs/mdp/test_curriculums.py
new file mode 100644
index 000000000..bd3d96808
--- /dev/null
+++ b/tests/envs/mdp/test_curriculums.py
@@ -0,0 +1,463 @@
+"""Tests for the generic stage-based curriculum terms (issue #1397).
+
+Covers stage trigger timing, later-stage precedence, params merging, the
+fail-closed validation error paths, and the CurriculumManager integration
+(live term cfg mutation + logging snapshot extras).
+"""
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+
+from unilab.envs import mdp
+from unilab.managers import (
+    CurriculumManager,
+    CurriculumTermCfg,
+    RewardManager,
+    RewardTermCfg,
+    TerminationManager,
+    TerminationTermCfg,
+)
+
+
+def _reward(env: _FakeEnv, std: float, scale: float) -> np.ndarray:
+    del scale
+    return np.full(env.num_envs, std, dtype=np.float32)
+
+
+def _termination(env: _FakeEnv, threshold: float) -> np.ndarray:
+    del threshold
+    return np.ones(env.num_envs, dtype=np.bool_)
+
+
+class _FakeEnv:
+    def __init__(self, num_envs: int = 4) -> None:
+        self.num_envs = num_envs
+        self.scene = {}
+        self.common_step_counter = 0
+        self.reward_manager = RewardManager(
+            {"pen": RewardTermCfg(func=_reward, weight=-0.1, params={"std": 0.5, "scale": 1.0})},
+            self,
+        )
+        self.termination_manager = TerminationManager(
+            {"energy": TerminationTermCfg(func=_termination, params={"threshold": np.inf})},
+            self,
+        )
+
+
+@pytest.fixture
+def fake_env() -> _FakeEnv:
+    return _FakeEnv()
+
+
+def _reward_curriculum_manager(env: _FakeEnv, stages: list[dict]) -> CurriculumManager:
+    return CurriculumManager(
+        {
+            "pen_ramp": CurriculumTermCfg(
+                func=mdp.reward_curriculum,
+                params={"reward_name": "pen", "stages": stages},
+            )
+        },
+        env,
+    )
+
+
+def _termination_curriculum_manager(env: _FakeEnv, stages: list[dict]) -> CurriculumManager:
+    return CurriculumManager(
+        {
+            "energy_ramp": CurriculumTermCfg(
+                func=mdp.termination_curriculum,
+                params={"termination_name": "energy", "stages": stages},
+            )
+        },
+        env,
+    )
+
+
+# Reward: weight stages.
+
+
+def test_reward_weight_unchanged_before_threshold(fake_env: _FakeEnv) -> None:
+    manager = _reward_curriculum_manager(fake_env, [{"step": 100, "weight": -1.0}])
+    fake_env.common_step_counter = 99
+    manager.compute()
+    assert fake_env.reward_manager.get_term_cfg("pen").weight == pytest.approx(-0.1)
+
+
+def test_reward_weight_applied_at_threshold(fake_env: _FakeEnv) -> None:
+    manager = _reward_curriculum_manager(fake_env, [{"step": 100, "weight": -1.0}])
+    fake_env.common_step_counter = 100
+    manager.compute()
+    assert fake_env.reward_manager.get_term_cfg("pen").weight == pytest.approx(-1.0)
+
+
+def test_reward_weight_later_stage_wins(fake_env: _FakeEnv) -> None:
+    manager = _reward_curriculum_manager(
+        fake_env,
+        [
+            {"step": 0, "weight": -0.2},
+            {"step": 100, "weight": -0.4},
+            {"step": 400, "weight": -1.0},
+        ],
+    )
+    fake_env.common_step_counter = 500
+    manager.compute()
+    assert fake_env.reward_manager.get_term_cfg("pen").weight == pytest.approx(-1.0)
+
+
+def test_reward_weight_partial_application(fake_env: _FakeEnv) -> None:
+    manager = _reward_curriculum_manager(
+        fake_env,
+        [
+            {"step": 100, "weight": -0.4},
+            {"step": 200, "weight": -1.0},
+        ],
+    )
+    fake_env.common_step_counter = 150
+    manager.compute()
+    assert fake_env.reward_manager.get_term_cfg("pen").weight == pytest.approx(-0.4)
+
+
+def test_step_zero_applies_on_first_compute(fake_env: _FakeEnv) -> None:
+    manager = _reward_curriculum_manager(fake_env, [{"step": 0, "weight": -0.9}])
+    manager.compute()
+    assert fake_env.reward_manager.get_term_cfg("pen").weight == pytest.approx(-0.9)
+
+
+def test_reapplication_is_idempotent(fake_env: _FakeEnv) -> None:
+    manager = _reward_curriculum_manager(
+        fake_env, [{"step": 0, "weight": -0.2}, {"step": 100, "params": {"std": 0.2}}]
+    )
+    fake_env.common_step_counter = 150
+    manager.compute()
+    manager.compute()
+    term_cfg = fake_env.reward_manager.get_term_cfg("pen")
+    assert term_cfg.weight == pytest.approx(-0.2)
+    assert term_cfg.params["std"] == pytest.approx(0.2)
+
+
+def test_ramped_weight_changes_computed_reward(fake_env: _FakeEnv) -> None:
+    manager = _reward_curriculum_manager(fake_env, [{"step": 0, "weight": -1.0}])
+    before = fake_env.reward_manager.compute(dt=1.0).copy()
+    manager.compute()
+    after = fake_env.reward_manager.compute(dt=1.0)
+    np.testing.assert_allclose(after, before * 10.0)
+
+
+# Reward: params stages.
+
+
+def test_reward_params_updated_at_threshold(fake_env: _FakeEnv) -> None:
+    manager = _reward_curriculum_manager(fake_env, [{"step": 100, "params": {"std": 0.2}}])
+    fake_env.common_step_counter = 200
+    manager.compute()
+    assert fake_env.reward_manager.get_term_cfg("pen").params["std"] == pytest.approx(0.2)
+
+
+def test_reward_params_unchanged_before_threshold(fake_env: _FakeEnv) -> None:
+    manager = _reward_curriculum_manager(fake_env, [{"step": 100, "params": {"std": 0.2}}])
+    manager.compute()
+    assert fake_env.reward_manager.get_term_cfg("pen").params["std"] == pytest.approx(0.5)
+
+
+def test_reward_stage_combines_weight_and_params(fake_env: _FakeEnv) -> None:
+    manager = _reward_curriculum_manager(
+        fake_env, [{"step": 100, "weight": -1.0, "params": {"std": 0.1, "scale": 2.0}}]
+    )
+    fake_env.common_step_counter = 100
+    manager.compute()
+    term_cfg = fake_env.reward_manager.get_term_cfg("pen")
+    assert term_cfg.weight == pytest.approx(-1.0)
+    assert term_cfg.params["std"] == pytest.approx(0.1)
+    assert term_cfg.params["scale"] == pytest.approx(2.0)
+
+
+# Termination: params and time_out stages.
+
+
+def test_termination_params_follow_stages(fake_env: _FakeEnv) -> None:
+    manager = _termination_curriculum_manager(
+        fake_env,
+        [
+            {"step": 0, "params": {"threshold": 1000.0}},
+            {"step": 100, "params": {"threshold": 700.0}},
+            {"step": 400, "params": {"threshold": 400.0}},
+        ],
+    )
+    manager.compute()
+    assert fake_env.termination_manager.get_term_cfg("energy").params["threshold"] == 1000.0
+    fake_env.common_step_counter = 200
+    manager.compute()
+    assert fake_env.termination_manager.get_term_cfg("energy").params["threshold"] == 700.0
+    fake_env.common_step_counter = 500
+    manager.compute()
+    assert fake_env.termination_manager.get_term_cfg("energy").params["threshold"] == 400.0
+
+
+def test_termination_time_out_stage_reroutes_done(fake_env: _FakeEnv) -> None:
+    manager = _termination_curriculum_manager(fake_env, [{"step": 10, "time_out": True}])
+    manager.compute()
+    fake_env.termination_manager.compute()
+    assert fake_env.termination_manager.terminated.all()
+    assert not fake_env.termination_manager.time_outs.any()
+    fake_env.common_step_counter = 10
+    manager.compute()
+    fake_env.termination_manager.compute()
+    assert fake_env.termination_manager.time_outs.all()
+    assert not fake_env.termination_manager.terminated.any()
+
+
+# Validation error paths (fail-closed at manager construction).
+
+
+def test_unknown_reward_param_key_raises(fake_env: _FakeEnv) -> None:
+    with pytest.raises(KeyError, match="unknown param"):
+        _reward_curriculum_manager(fake_env, [{"step": 0, "params": {"stdd": 0.2}}])
+
+
+def test_unknown_termination_param_key_raises(fake_env: _FakeEnv) -> None:
+    with pytest.raises(KeyError, match="unknown param"):
+        _termination_curriculum_manager(fake_env, [{"step": 0, "params": {"threshld": 1.0}}])
+
+
+def test_unknown_stage_field_raises(fake_env: _FakeEnv) -> None:
+    with pytest.raises(AttributeError, match="Field 'weigth' does not exist"):
+        _reward_curriculum_manager(fake_env, [{"step": 0, "weigth": -1.0}])
+
+
+def test_unsorted_stages_raise(fake_env: _FakeEnv) -> None:
+    with pytest.raises(ValueError, match="nondecreasing"):
+        _reward_curriculum_manager(
+            fake_env, [{"step": 200, "weight": -0.4}, {"step": 100, "weight": -1.0}]
+        )
+
+
+def test_unknown_target_term_raises(fake_env: _FakeEnv) -> None:
+    with pytest.raises(ValueError, match="Term 'missing' not found"):
+        CurriculumManager(
+            {
+                "bad": CurriculumTermCfg(
+                    func=mdp.reward_curriculum,
+                    params={"reward_name": "missing", "stages": [{"step": 0, "weight": -1.0}]},
+                )
+            },
+            fake_env,
+        )
+
+
+def test_duplicate_step_stages_apply_in_order(fake_env: _FakeEnv) -> None:
+    manager = _reward_curriculum_manager(
+        fake_env,
+        [
+            {"step": 100, "weight": -0.4},
+            {"step": 100, "params": {"std": 0.1}},
+        ],
+    )
+    fake_env.common_step_counter = 200
+    manager.compute()
+    term_cfg = fake_env.reward_manager.get_term_cfg("pen")
+    assert term_cfg.weight == pytest.approx(-0.4)
+    assert term_cfg.params["std"] == pytest.approx(0.1)
+
+
+# Logging snapshot.
+
+
+def test_reset_extras_log_only_staged_scalar_keys(fake_env: _FakeEnv) -> None:
+    manager = _reward_curriculum_manager(
+        fake_env, [{"step": 100, "weight": -1.0, "params": {"std": 0.2}}]
+    )
+    fake_env.common_step_counter = 200
+    manager.compute()
+    extras = manager.reset()
+    assert extras["Curriculum/pen_ramp/weight"] == pytest.approx(-1.0)
+    assert extras["Curriculum/pen_ramp/std"] == pytest.approx(0.2)
+    # Not staged by any stage -> not logged.
+    assert "Curriculum/pen_ramp/scale" not in extras
+
+
+def test_weight_not_logged_when_only_params_staged(fake_env: _FakeEnv) -> None:
+    manager = _termination_curriculum_manager(
+        fake_env, [{"step": 100, "params": {"threshold": 500.0}}]
+    )
+    fake_env.common_step_counter = 200
+    manager.compute()
+    extras = manager.reset()
+    assert extras["Curriculum/energy_ramp/threshold"] == pytest.approx(500.0)
+    assert "Curriculum/energy_ramp/weight" not in extras
+
+
+# Command and event curricula (issue #1402).
+
+
+class _FakeCommandManager:
+    """Minimal stand-in exposing the cold-path get_term_cfg surface."""
+
+    def __init__(self) -> None:
+        self.cfg = SimpleNamespace(
+            rel_standing_envs=0.02,
+            ranges=[[-0.05, 0.05], [-0.07, 0.07]],
+            params={},
+        )
+
+    def get_term_cfg(self, name: str):
+        if name != "twist":
+            raise KeyError(name)
+        return self.cfg
+
+
+class _FakeEventManager:
+    def __init__(self) -> None:
+        self.cfg = SimpleNamespace(
+            params={"com_range": {"x": (-0.003, 0.003), "y": (-0.003, 0.003)}},
+        )
+
+    def get_term_cfg(self, name: str):
+        if name != "base_com":
+            raise KeyError(name)
+        return self.cfg
+
+
+@pytest.fixture
+def fake_env_full(fake_env: _FakeEnv) -> _FakeEnv:
+    fake_env.command_manager = _FakeCommandManager()
+    fake_env.event_manager = _FakeEventManager()
+    return fake_env
+
+
+def _command_curriculum_manager(env: _FakeEnv, stages: list[dict]) -> CurriculumManager:
+    return CurriculumManager(
+        {
+            "standing_ramp": CurriculumTermCfg(
+                func=mdp.command_curriculum,
+                params={"command_name": "twist", "stages": stages},
+            )
+        },
+        env,
+    )
+
+
+def _event_curriculum_manager(env: _FakeEnv, stages: list[dict]) -> CurriculumManager:
+    return CurriculumManager(
+        {
+            "com_ramp": CurriculumTermCfg(
+                func=mdp.event_curriculum,
+                params={"event_name": "base_com", "stages": stages},
+            )
+        },
+        env,
+    )
+
+
+def test_command_curriculum_field_unchanged_before_threshold(fake_env_full: _FakeEnv) -> None:
+    manager = _command_curriculum_manager(fake_env_full, [{"step": 100, "rel_standing_envs": 0.25}])
+    manager.compute()
+    assert fake_env_full.command_manager.get_term_cfg("twist").rel_standing_envs == pytest.approx(
+        0.02
+    )
+
+
+def test_command_curriculum_field_applied_at_threshold(fake_env_full: _FakeEnv) -> None:
+    manager = _command_curriculum_manager(
+        fake_env_full,
+        [
+            {"step": 0, "rel_standing_envs": 0.05},
+            {"step": 100, "rel_standing_envs": 0.25},
+        ],
+    )
+    fake_env_full.common_step_counter = 100
+    manager.compute()
+    assert fake_env_full.command_manager.get_term_cfg("twist").rel_standing_envs == pytest.approx(
+        0.25
+    )
+
+
+def test_command_curriculum_ranges_field_staged(fake_env_full: _FakeEnv) -> None:
+    manager = _command_curriculum_manager(
+        fake_env_full,
+        [{"step": 50, "ranges": [[-1.1, 1.1], [-1.4, 1.4]]}],
+    )
+    fake_env_full.common_step_counter = 60
+    manager.compute()
+    assert fake_env_full.command_manager.get_term_cfg("twist").ranges == [
+        [-1.1, 1.1],
+        [-1.4, 1.4],
+    ]
+
+
+def test_command_curriculum_logs_scalar_fields(fake_env_full: _FakeEnv) -> None:
+    manager = _command_curriculum_manager(
+        fake_env_full,
+        [
+            {"step": 0, "rel_standing_envs": 0.05},
+            {"step": 50, "ranges": [[-1.1, 1.1], [-1.4, 1.4]]},
+        ],
+    )
+    fake_env_full.common_step_counter = 60
+    manager.compute()
+    extras = manager.reset()
+    assert extras["Curriculum/standing_ramp/rel_standing_envs"] == pytest.approx(0.05)
+    # Non-scalar staged fields are applied but not logged.
+    assert "Curriculum/standing_ramp/ranges" not in extras
+
+
+def test_command_curriculum_unknown_field_raises(fake_env_full: _FakeEnv) -> None:
+    with pytest.raises(AttributeError, match="Field 'rel_standing_env' does not exist"):
+        _command_curriculum_manager(fake_env_full, [{"step": 0, "rel_standing_env": 0.1}])
+
+
+def test_command_curriculum_unknown_term_raises(fake_env_full: _FakeEnv) -> None:
+    with pytest.raises(KeyError, match="missing"):
+        CurriculumManager(
+            {
+                "bad": CurriculumTermCfg(
+                    func=mdp.command_curriculum,
+                    params={"command_name": "missing", "stages": [{"step": 0}]},
+                )
+            },
+            fake_env_full,
+        )
+
+
+def test_event_curriculum_params_follow_stages(fake_env_full: _FakeEnv) -> None:
+    manager = _event_curriculum_manager(
+        fake_env_full,
+        [
+            {"step": 0, "params": {"com_range": {"x": (-0.005, 0.005)}}},
+            {"step": 100, "params": {"com_range": {"x": (-0.015, 0.015)}}},
+        ],
+    )
+    manager.compute()
+    assert fake_env_full.event_manager.get_term_cfg("base_com").params["com_range"]["x"] == (
+        -0.005,
+        0.005,
+    )
+    fake_env_full.common_step_counter = 200
+    manager.compute()
+    assert fake_env_full.event_manager.get_term_cfg("base_com").params["com_range"]["x"] == (
+        -0.015,
+        0.015,
+    )
+
+
+def test_event_curriculum_unknown_param_raises(fake_env_full: _FakeEnv) -> None:
+    with pytest.raises(KeyError, match="unknown param"):
+        _event_curriculum_manager(
+            fake_env_full, [{"step": 0, "params": {"com_ranges": {"x": (-1.0, 1.0)}}}]
+        )
+
+
+def test_event_curriculum_unknown_term_raises(fake_env_full: _FakeEnv) -> None:
+    with pytest.raises(KeyError, match="missing"):
+        CurriculumManager(
+            {
+                "bad": CurriculumTermCfg(
+                    func=mdp.event_curriculum,
+                    params={"event_name": "missing", "stages": [{"step": 0}]},
+                )
+            },
+            fake_env_full,
+        )
diff --git a/tests/envs/mdp/test_events.py b/tests/envs/mdp/test_events.py
new file mode 100644
index 000000000..ef41f1cbf
--- /dev/null
+++ b/tests/envs/mdp/test_events.py
@@ -0,0 +1,1445 @@
+"""Upstream-derived NumPy tests for Manager-Based reset event terms."""
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+from typing import Any, cast
+
+import numpy as np
+import pytest
+from unisim.backend.base import BackendRootStateLayout, SimBackend
+from unisim.dr.types import (
+    RESET_TERM_BODY_INERTIA,
+    RESET_TERM_BODY_IPOS,
+    RESET_TERM_BODY_MASS,
+    RESET_TERM_DOF_ARMATURE,
+    RESET_TERM_GEOM_FRICTION,
+    RESET_TERM_GRAVITY,
+    RESET_TERM_KD,
+    RESET_TERM_KP,
+    DomainRandomizationCapabilities,
+    IntervalRandomizationPlan,
+    ResetRandomizationPayload,
+)
+
+from unilab.base.entity import EntityCfg, EntityScene
+from unilab.base.reset_state import ResetStateTransaction
+from unilab.envs import mdp
+from unilab.managers import EventManager, EventTermCfg, SceneEntityCfg
+from unilab.managers._types import ManagerBasedRlEnv
+
+
+class _CaptureEntity:
+    def __init__(self, default_root_state: np.ndarray) -> None:
+        self.data = SimpleNamespace(default_root_state=default_root_state)
+        self.writes: list[tuple[np.ndarray, np.ndarray]] = []
+
+    def write_root_state_to_sim(
+        self,
+        root_state: np.ndarray,
+        env_ids: np.ndarray | None = None,
+    ) -> None:
+        assert env_ids is not None
+        self.writes.append((root_state.copy(), env_ids.copy()))
+
+
+class _CaptureScene:
+    def __init__(self, entity: _CaptureEntity, env_origins: np.ndarray) -> None:
+        self.entities = {"robot": entity}
+        self.env_origins = env_origins
+
+    def __getitem__(self, name: str) -> _CaptureEntity:
+        return self.entities[name]
+
+
+def _capture_env(
+    *,
+    seed: int = 17,
+    default_root_state: np.ndarray | None = None,
+    env_origins: np.ndarray | None = None,
+) -> tuple[ManagerBasedRlEnv, _CaptureEntity]:
+    if default_root_state is None:
+        default_root_state = np.zeros((3, 13), dtype=np.float32)
+        default_root_state[:, 3] = 1.0
+    default_root_state.setflags(write=False)
+    entity = _CaptureEntity(default_root_state)
+    if env_origins is None:
+        env_origins = np.zeros((3, 3), dtype=np.float32)
+    env = cast(
+        ManagerBasedRlEnv,
+        SimpleNamespace(
+            num_envs=3,
+            rng=np.random.default_rng(seed),
+            scene=_CaptureScene(entity, env_origins),
+        ),
+    )
+    return env, entity
+
+
+def test_uniform_root_state_applies_pinned_pose_velocity_and_origin_semantics() -> None:
+    half_sqrt = np.float32(np.sqrt(0.5))
+    defaults = np.zeros((3, 13), dtype=np.float32)
+    defaults[:, :3] = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]
+    defaults[:, 3:7] = [half_sqrt, 0.0, 0.0, half_sqrt]
+    defaults[:, 7:13] = np.arange(18, dtype=np.float32).reshape(3, 6)
+    origins = np.asarray(
+        [[100.0, 0.0, 0.0], [0.0, 200.0, 0.0], [0.0, 0.0, 300.0]],
+        dtype=np.float32,
+    )
+    env, entity = _capture_env(default_root_state=defaults, env_origins=origins)
+    ids = np.asarray([2, 0], dtype=np.int32)
+
+    mdp.reset_root_state_uniform(
+        env,
+        ids,
+        pose_range={
+            "x": (1.0, 1.0),
+            "y": (-2.0, -2.0),
+            "z": (0.5, 0.5),
+            "roll": (np.pi / 2.0, np.pi / 2.0),
+        },
+        velocity_range={
+            "x": (0.1, 0.1),
+            "y": (0.2, 0.2),
+            "z": (0.3, 0.3),
+            "roll": (0.4, 0.4),
+            "pitch": (0.5, 0.5),
+            "yaw": (0.6, 0.6),
+        },
+    )
+
+    assert len(entity.writes) == 1
+    root_state, written_ids = entity.writes[0]
+    np.testing.assert_array_equal(written_ids, ids)
+    np.testing.assert_allclose(
+        root_state[:, :3],
+        defaults[ids, :3] + [1.0, -2.0, 0.5] + origins[ids],
+    )
+    np.testing.assert_allclose(
+        root_state[:, 3:7],
+        [[0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5]],
+        atol=1e-6,
+    )
+    np.testing.assert_allclose(
+        root_state[:, 7:13],
+        defaults[ids, 7:13] + [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
+    )
+    np.testing.assert_array_equal(defaults[:, :3], [[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+
+
+def test_uniform_root_state_uses_env_rng_and_preserves_single_env_shape() -> None:
+    pose_range = {"x": (-1.0, 1.0), "yaw": (-0.5, 0.5)}
+    velocity_range = {"x": (-2.0, 2.0), "roll": (-0.25, 0.25)}
+    left, left_entity = _capture_env(seed=9)
+    right, right_entity = _capture_env(seed=9)
+    ids = np.asarray([1], dtype=np.int32)
+
+    mdp.reset_root_state_uniform(left, ids, pose_range, velocity_range)
+    mdp.reset_root_state_uniform(right, ids, pose_range, velocity_range)
+
+    left_state, left_ids = left_entity.writes[0]
+    right_state, right_ids = right_entity.writes[0]
+    assert left_state.shape == (1, 13)
+    np.testing.assert_array_equal(left_ids, ids)
+    np.testing.assert_array_equal(right_ids, ids)
+    np.testing.assert_array_equal(left_state, right_state)
+
+
+def test_uniform_root_state_none_ids_targets_all_environments() -> None:
+    env, entity = _capture_env()
+
+    mdp.reset_root_state_uniform(env, None, pose_range={})
+
+    root_state, ids = entity.writes[0]
+    np.testing.assert_array_equal(ids, np.arange(3, dtype=np.int32))
+    assert root_state.shape == (3, 13)
+
+
+class _Backend:
+    backend_type = "fake"
+    num_envs = 3
+    num_actuators = 3
+
+    def __init__(
+        self,
+        *,
+        root_layout_supported: bool = True,
+        gain_supported: bool = True,
+        randomization_supported: bool = True,
+        interval_velocity_supported: bool = True,
+        interval_angular_velocity_supported: bool = False,
+        interval_force_supported: bool = False,
+        interval_torque_supported: bool = False,
+    ) -> None:
+        self.root_layout_supported = root_layout_supported
+        self.gain_supported = gain_supported
+        self.randomization_supported = randomization_supported
+        self.interval_velocity_supported = interval_velocity_supported
+        self.interval_angular_velocity_supported = interval_angular_velocity_supported
+        self.interval_force_supported = interval_force_supported
+        self.interval_torque_supported = interval_torque_supported
+        self.default_qpos = np.asarray([0.0, 0.0, 0.5, 1.0, 0.0, 0.0, 0.0])
+        self.init_qvel = np.zeros(6)
+        self.set_state_calls: list[tuple[np.ndarray, np.ndarray, np.ndarray]] = []
+        self.randomization_calls: list[ResetRandomizationPayload | None] = []
+        self.body_pos = np.zeros((self.num_envs, 1, 3))
+        self.body_quat = np.zeros((self.num_envs, 1, 4))
+        self.body_quat[:, :, 0] = 1.0
+        self.body_velocity = np.zeros((self.num_envs, 1, 3))
+        self.body_mass = np.array([10.0])
+        self.body_ipos = np.array([[0.0, 0.0, 0.0]])
+        self.gravity = np.array([0.0, 0.0, -9.81])
+        self.dof_armature = np.array([0.0] * 6 + [1.0, 2.0, 3.0])
+        self.geom_friction = np.array([[0.5, 0.01, 0.001], [0.7, 0.02, 0.002], [0.9, 0.03, 0.003]])
+        self.interval_plans: list[IntervalRandomizationPlan] = []
+
+    def get_body_ids(self, names) -> np.ndarray:
+        if tuple(names) != ("base",):
+            raise KeyError(names)
+        return np.asarray([0], dtype=np.int32)
+
+    def get_root_state_layout(self, root_body_name: str) -> BackendRootStateLayout:
+        if not self.root_layout_supported:
+            raise NotImplementedError("fake fixed-base entity has no free-root layout")
+        if root_body_name != "base":
+            raise ValueError(root_body_name)
+        return BackendRootStateLayout(tuple(range(7)), tuple(range(6)))
+
+    def get_default_qpos(self) -> np.ndarray:
+        return self.default_qpos.copy()
+
+    def get_init_qvel(self) -> np.ndarray:
+        return self.init_qvel.copy()
+
+    def get_dof_pos(self) -> np.ndarray:
+        return np.zeros((self.num_envs, 3))
+
+    def get_dof_vel(self) -> np.ndarray:
+        return np.zeros((self.num_envs, 3))
+
+    def get_default_dof_pos(self) -> np.ndarray:
+        return np.zeros(3)
+
+    def get_joint_range(self) -> np.ndarray:
+        return np.tile([-1.0, 1.0], (3, 1))
+
+    def get_joint_dof_indices(self, names) -> np.ndarray:
+        table = {"j0": 6, "j1": 7, "j2": 8}
+        return np.asarray([table[name] for name in names], dtype=np.int32)
+
+    def get_joint_dof_pos_indices(self, names) -> np.ndarray:
+        table = {"j0": 0, "j1": 1, "j2": 2}
+        return np.asarray([table[name] for name in names], dtype=np.int32)
+
+    def get_joint_dof_vel_indices(self, names) -> np.ndarray:
+        return self.get_joint_dof_pos_indices(names)
+
+    def get_geom_names(self) -> tuple[str, ...]:
+        return ("floor", "foot", "base_geom")
+
+    def get_actuator_names(self) -> tuple[str, ...]:
+        return ("a0", "a1", "a2")
+
+    def get_actuator_joint_names(self) -> tuple[str, ...]:
+        return ("j0", "j1", "j2")
+
+    def get_actuator_ctrl_range(self) -> np.ndarray:
+        return np.tile([-1.0, 1.0], (self.num_actuators, 1))
+
+    def get_dr_capabilities(self) -> DomainRandomizationCapabilities:
+        terms: set[str] = set((RESET_TERM_KP, RESET_TERM_KD)) if self.gain_supported else set()
+        if self.randomization_supported:
+            terms.update(
+                (
+                    RESET_TERM_BODY_MASS,
+                    RESET_TERM_BODY_IPOS,
+                    RESET_TERM_DOF_ARMATURE,
+                    RESET_TERM_GEOM_FRICTION,
+                    RESET_TERM_GRAVITY,
+                )
+            )
+        return DomainRandomizationCapabilities(
+            supported_reset_terms=frozenset(terms),
+            supports_interval_body_velocity_delta=self.interval_velocity_supported,
+            supports_interval_body_angular_velocity_delta=self.interval_angular_velocity_supported,
+            supports_interval_body_force=self.interval_force_supported,
+            supports_interval_body_torque=self.interval_torque_supported,
+        )
+
+    def get_actuator_gains(self) -> tuple[np.ndarray, np.ndarray]:
+        return np.array([10.0, 20.0, 30.0]), np.array([1.0, 2.0, 3.0])
+
+    def get_body_mass(self) -> np.ndarray:
+        return self.body_mass.copy()
+
+    def get_body_ipos(self) -> np.ndarray:
+        return self.body_ipos.copy()
+
+    def get_gravity(self) -> np.ndarray:
+        return self.gravity.copy()
+
+    def get_dof_armature(self) -> np.ndarray:
+        return self.dof_armature.copy()
+
+    def get_geom_friction(self) -> np.ndarray:
+        return self.geom_friction.copy()
+
+    def apply_interval_randomization(self, plan: IntervalRandomizationPlan) -> None:
+        self.interval_plans.append(plan)
+
+    def get_body_pos_w(self, ids: np.ndarray) -> np.ndarray:
+        return self.body_pos[:, ids]
+
+    def get_body_quat_w(self, ids: np.ndarray) -> np.ndarray:
+        return self.body_quat[:, ids]
+
+    def get_body_lin_vel_w(self, ids: np.ndarray) -> np.ndarray:
+        return self.body_velocity[:, ids]
+
+    def get_body_ang_vel_w(self, ids: np.ndarray) -> np.ndarray:
+        return self.body_velocity[:, ids]
+
+    def get_body_lin_vel_b(self, ids: np.ndarray) -> np.ndarray:
+        return self.body_velocity[:, ids]
+
+    def get_body_ang_vel_b(self, ids: np.ndarray) -> np.ndarray:
+        return self.body_velocity[:, ids]
+
+    def set_state(
+        self,
+        env_ids: np.ndarray,
+        qpos: np.ndarray,
+        qvel: np.ndarray,
+        randomization=None,
+    ) -> None:
+        self.set_state_calls.append((env_ids.copy(), qpos.copy(), qvel.copy()))
+        self.randomization_calls.append(randomization)
+
+
+def _transaction_env(
+    *,
+    root_layout_supported: bool = True,
+    gain_supported: bool = True,
+    randomization_supported: bool = True,
+    interval_velocity_supported: bool = True,
+    interval_angular_velocity_supported: bool = False,
+    interval_force_supported: bool = False,
+    interval_torque_supported: bool = False,
+    body_names: tuple[str, ...] | None = ("base",),
+    rng_seed: int = 5,
+    step_dt: float = 0.02,
+) -> tuple[ManagerBasedRlEnv, _Backend, ResetStateTransaction]:
+    backend = _Backend(
+        root_layout_supported=root_layout_supported,
+        gain_supported=gain_supported,
+        randomization_supported=randomization_supported,
+        interval_velocity_supported=interval_velocity_supported,
+        interval_angular_velocity_supported=interval_angular_velocity_supported,
+        interval_force_supported=interval_force_supported,
+        interval_torque_supported=interval_torque_supported,
+    )
+    transaction = ResetStateTransaction(cast(SimBackend, backend))
+    scene = EntityScene(
+        {
+            "robot": EntityCfg(
+                root_body_name="base",
+                joint_names=("j0", "j1", "j2"),
+                body_names=body_names,
+                geom_names=("floor", "foot", "base_geom"),
+                actuator_names=("a0", "a1", "a2"),
+            )
+        },
+        cast(SimBackend, backend),
+        reset_state=transaction,
+    )
+    env = cast(
+        ManagerBasedRlEnv,
+        SimpleNamespace(
+            num_envs=backend.num_envs,
+            rng=np.random.default_rng(rng_seed),
+            scene=scene,
+            step_dt=step_dt,
+        ),
+    )
+    return env, backend, transaction
+
+
+def test_uniform_root_state_composes_in_one_reset_transaction_commit() -> None:
+    env, backend, transaction = _transaction_env()
+    active_ids = np.asarray([0, 2], dtype=np.int32)
+
+    with transaction.scoped(active_ids):
+        mdp.reset_scene_to_default(env, active_ids)
+        mdp.reset_root_state_uniform(
+            env,
+            np.asarray([2], dtype=np.int32),
+            pose_range={"x": (1.0, 1.0)},
+            velocity_range={"x": (0.5, 0.5)},
+        )
+        assert backend.set_state_calls == []
+
+    assert len(backend.set_state_calls) == 1
+    ids, qpos, qvel = backend.set_state_calls[0]
+    np.testing.assert_array_equal(ids, active_ids)
+    np.testing.assert_array_equal(qpos[0], backend.default_qpos)
+    np.testing.assert_allclose(qpos[1], backend.default_qpos + [1.0, 0, 0, 0, 0, 0, 0])
+    np.testing.assert_array_equal(qvel[0], backend.init_qvel)
+    np.testing.assert_allclose(qvel[1], backend.init_qvel + [0.5, 0, 0, 0, 0, 0])
+
+
+def test_pd_gains_event_uses_selector_scale_and_exactly_once_reset_payload() -> None:
+    env, backend, transaction = _transaction_env(rng_seed=11)
+    manager = EventManager(
+        {
+            "randomize_pd": EventTermCfg(
+                func=mdp.pd_gains,
+                mode="reset",
+                params={
+                    "kp_range": (2.0, 2.0),
+                    "kd_range": (3.0, 3.0),
+                    "asset_cfg": SceneEntityCfg(
+                        "robot",
+                        actuator_names=["a2", "a0"],
+                        preserve_order=True,
+                    ),
+                },
+            )
+        },
+        env,
+    )
+    ids = np.array([0, 2], dtype=np.int32)
+
+    with transaction.scoped(ids):
+        manager.apply(mode="reset", env_ids=ids, global_env_step_count=0)
+        assert backend.set_state_calls == []
+
+    assert len(backend.set_state_calls) == 1
+    payload = backend.randomization_calls[0]
+    assert payload is not None
+    np.testing.assert_array_equal(payload.kp, [[20.0, 20.0, 60.0]] * 2)
+    np.testing.assert_array_equal(payload.kd, [[3.0, 2.0, 9.0]] * 2)
+
+
+def test_pd_gains_event_supports_log_uniform_absolute_sampling() -> None:
+    env, backend, transaction = _transaction_env(rng_seed=11)
+    manager = EventManager(
+        {
+            "gain": EventTermCfg(
+                func=mdp.pd_gains,
+                mode="reset",
+                params={
+                    "kp_range": (0.25, 4.0),
+                    "kd_range": (0.5, 2.0),
+                    "distribution": "log_uniform",
+                    "operation": "abs",
+                },
+            )
+        },
+        env,
+    )
+    ids = np.arange(3, dtype=np.int32)
+
+    with transaction.scoped(ids):
+        manager.apply(mode="reset", env_ids=ids, global_env_step_count=0)
+
+    payload = backend.randomization_calls[0]
+    assert payload is not None and payload.kp is not None and payload.kd is not None
+    assert np.all((payload.kp >= 0.25) & (payload.kp <= 4.0))
+    assert np.all((payload.kd >= 0.5) & (payload.kd <= 2.0))
+    assert np.unique(payload.kp[0]).size > 1
+
+
+@pytest.mark.parametrize(
+    ("cfg_kwargs", "match"),
+    [
+        ({"mode": "startup"}, "only supports mode='reset'"),
+        ({"params": {"kp_range": (2.0, 1.0), "kd_range": (1.0, 1.0)}}, "minimum"),
+    ],
+)
+def test_pd_gains_invalid_config_fails_during_manager_construction(
+    cfg_kwargs: dict[str, Any],
+    match: str,
+) -> None:
+    env, backend, _ = _transaction_env(rng_seed=11)
+    values: dict[str, Any] = {
+        "mode": "reset",
+        "params": {"kp_range": (1.0, 1.0), "kd_range": (1.0, 1.0)},
+    }
+    values.update(cfg_kwargs)
+
+    with pytest.raises((ValueError, NotImplementedError), match=match):
+        EventManager({"gain": EventTermCfg(func=mdp.pd_gains, **values)}, env)
+    assert backend.set_state_calls == []
+
+
+def test_pd_gains_missing_backend_capability_fails_during_manager_construction() -> None:
+    env, backend, _ = _transaction_env(gain_supported=False, rng_seed=11)
+    cfg = EventTermCfg(
+        func=mdp.pd_gains,
+        mode="reset",
+        params={"kp_range": (1.0, 1.0), "kd_range": (1.0, 1.0)},
+    )
+
+    with pytest.raises(
+        NotImplementedError,
+        match="pd_gains:robot.*actuator gain randomization.*backend 'fake'",
+    ):
+        EventManager({"gain": cfg}, env)
+    assert backend.set_state_calls == []
+
+
+def test_reset_randomization_terms_compose_with_state_and_gains_exactly_once() -> None:
+    env, backend, transaction = _transaction_env(rng_seed=13)
+    asset_cfg = SceneEntityCfg("robot", body_names=("base",))
+    manager = EventManager(
+        {
+            "mass": EventTermCfg(
+                func=mdp.randomize_rigid_body_mass,
+                mode="reset",
+                params={
+                    "asset_cfg": asset_cfg,
+                    "mass_distribution_params": (1.5, 1.5),
+                    "operation": "scale",
+                    "recompute_inertia": False,
+                },
+            ),
+            "com": EventTermCfg(
+                func=mdp.randomize_rigid_body_com,
+                mode="reset",
+                params={
+                    "asset_cfg": asset_cfg,
+                    "com_range": {"x": (0.1, 0.1), "z": (-0.2, -0.2)},
+                },
+            ),
+            "gravity": EventTermCfg(
+                func=mdp.randomize_physics_scene_gravity,
+                mode="reset",
+                params={
+                    "gravity_distribution_params": (
+                        [0.0, 0.0, -10.0],
+                        [0.0, 0.0, -10.0],
+                    ),
+                    "operation": "abs",
+                },
+            ),
+            "gains": EventTermCfg(
+                func=mdp.pd_gains,
+                mode="reset",
+                params={"kp_range": (2.0, 2.0), "kd_range": (3.0, 3.0)},
+            ),
+        },
+        env,
+    )
+    ids = np.array([0, 2], dtype=np.int32)
+
+    with transaction.scoped(ids):
+        mdp.reset_scene_to_default(env, ids)
+        manager.apply(mode="reset", env_ids=ids, global_env_step_count=0)
+        assert backend.set_state_calls == []
+
+    assert len(backend.set_state_calls) == 1
+    payload = backend.randomization_calls[0]
+    assert payload is not None
+    assert payload.body_mass is not None
+    assert payload.body_ipos is not None
+    assert payload.gravity is not None
+    assert payload.kp is not None
+    assert payload.kd is not None
+    np.testing.assert_allclose(payload.body_mass, [[15.0], [15.0]])
+    np.testing.assert_allclose(payload.body_ipos, [[[0.1, 0.0, -0.2]]] * 2)
+    np.testing.assert_allclose(payload.gravity, [[0.0, 0.0, -10.0]] * 2)
+    np.testing.assert_allclose(payload.kp, [[20.0, 40.0, 60.0]] * 2)
+    np.testing.assert_allclose(payload.kd, [[3.0, 6.0, 9.0]] * 2)
+
+
+def test_model_field_terms_use_cached_selectors_and_one_dense_reset_payload() -> None:
+    env, backend, transaction = _transaction_env(rng_seed=23)
+    manager = EventManager(
+        {
+            "armature": EventTermCfg(
+                func=mdp.joint_armature,
+                mode="reset",
+                params={
+                    "asset_cfg": SceneEntityCfg(
+                        "robot",
+                        joint_names=("j2", "j0"),
+                        preserve_order=True,
+                    ),
+                    "ranges": (2.0, 2.0),
+                    "operation": "scale",
+                },
+            ),
+            "friction": EventTermCfg(
+                func=mdp.geom_friction,
+                mode="reset",
+                params={
+                    "asset_cfg": SceneEntityCfg("robot", geom_names=".*"),
+                    "ranges": {"floor": (2.0, 2.0), "base_.*": (3.0, 3.0)},
+                    "operation": "scale",
+                    "shared_random": True,
+                },
+            ),
+        },
+        env,
+    )
+    ids = np.array([0, 2], dtype=np.int32)
+
+    with transaction.scoped(ids):
+        mdp.reset_scene_to_default(env, ids)
+        manager.apply(mode="reset", env_ids=ids, global_env_step_count=0)
+        assert backend.set_state_calls == []
+
+    assert len(backend.set_state_calls) == 1
+    payload = backend.randomization_calls[0]
+    assert payload is not None
+    assert payload.dof_armature is not None
+    assert payload.geom_friction is not None
+    np.testing.assert_allclose(
+        payload.dof_armature,
+        [[0.0] * 6 + [2.0, 2.0, 6.0]] * 2,
+    )
+    expected_friction = backend.geom_friction.copy()
+    expected_friction[0, 0] *= 2.0
+    expected_friction[2, 0] *= 3.0
+    np.testing.assert_allclose(payload.geom_friction, [expected_friction] * 2)
+
+
+def test_model_field_aliases_are_identical_and_capability_gaps_fail_cold() -> None:
+    assert mdp.dof_armature is mdp.joint_armature
+    env, backend, _ = _transaction_env(randomization_supported=False)
+
+    with pytest.raises(
+        NotImplementedError,
+        match="joint_armature:robot.*dof_armature randomization.*backend 'fake'",
+    ):
+        EventManager(
+            {
+                "armature": EventTermCfg(
+                    func=mdp.dof_armature,
+                    mode="reset",
+                    params={"ranges": (0.9, 1.1)},
+                )
+            },
+            env,
+        )
+    assert backend.set_state_calls == []
+
+
+@pytest.mark.parametrize(
+    ("func", "params", "match"),
+    [
+        (mdp.geom_friction, {"ranges": (0.5, 1.0), "axes": [3]}, "invalid axes"),
+        (mdp.joint_armature, {"ranges": (-1.0, -0.5)}, "produced negative"),
+    ],
+)
+def test_model_field_invalid_requests_fail_explicitly(func, params, match: str) -> None:
+    env, backend, transaction = _transaction_env()
+    if func is mdp.geom_friction:
+        with pytest.raises(ValueError, match=match):
+            EventManager({"field": EventTermCfg(func=func, mode="reset", params=params)}, env)
+    else:
+        manager = EventManager(
+            {"field": EventTermCfg(func=func, mode="reset", params=params)},
+            env,
+        )
+        ids = np.array([0, 1], dtype=np.int32)
+        with pytest.raises(ValueError, match=match):
+            with transaction.scoped(ids):
+                mdp.reset_scene_to_default(env, ids)
+                manager.apply(mode="reset", env_ids=ids, global_env_step_count=0)
+    assert backend.set_state_calls == []
+
+
+@pytest.mark.parametrize(
+    ("func", "params", "match"),
+    [
+        (
+            mdp.randomize_rigid_body_mass,
+            {
+                "asset_cfg": SceneEntityCfg("robot", body_names=("base",)),
+                "mass_distribution_params": (0.9, 1.1),
+                "operation": "scale",
+            },
+            "recompute_inertia=True",
+        ),
+        (
+            mdp.randomize_rigid_body_mass,
+            {
+                "asset_cfg": SceneEntityCfg("robot", body_names=("base",)),
+                "mass_distribution_params": (0.9, 1.1),
+                "operation": "scale",
+                "recompute_inertia": False,
+            },
+            "body_mass randomization.*unsupported",
+        ),
+    ],
+)
+def test_mass_randomization_capability_gaps_fail_during_construction(
+    func, params: dict[str, Any], match: str
+) -> None:
+    env, backend, _ = _transaction_env(randomization_supported=False)
+    with pytest.raises(NotImplementedError, match=match):
+        EventManager(
+            {"mass": EventTermCfg(func=func, mode="reset", params=params)},
+            env,
+        )
+    assert backend.set_state_calls == []
+
+
+def test_reset_randomization_sparse_rows_abort_without_backend_mutation() -> None:
+    env, backend, transaction = _transaction_env()
+    manager = EventManager(
+        {
+            "gravity": EventTermCfg(
+                func=mdp.randomize_physics_scene_gravity,
+                mode="reset",
+                params={
+                    "gravity_distribution_params": ([0.0, 0.0, -10.0],) * 2,
+                    "operation": "abs",
+                },
+            )
+        },
+        env,
+    )
+    cfg = manager.get_term_cfg("gravity")
+    ids = np.array([0, 1], dtype=np.int32)
+
+    with pytest.raises(RuntimeError, match=r"gravity payload.*sparse rows.*missing env IDs \[1\]"):
+        with transaction.scoped(ids):
+            mdp.reset_scene_to_default(env, ids)
+            cfg.func(env, np.array([0], dtype=np.int32), **cfg.params)
+    assert backend.set_state_calls == []
+
+
+def test_min_step_count_gating_reuses_committed_field_values() -> None:
+    env, backend, transaction = _transaction_env(rng_seed=29)
+    manager = EventManager(
+        {
+            "mass": EventTermCfg(
+                func=mdp.randomize_rigid_body_mass,
+                mode="reset",
+                min_step_count_between_reset=100,
+                params={
+                    "asset_cfg": SceneEntityCfg("robot", body_names=("base",)),
+                    "mass_distribution_params": (0.9, 1.1),
+                    "operation": "scale",
+                    "recompute_inertia": False,
+                },
+            )
+        },
+        env,
+    )
+    first_ids = np.array([0, 1], dtype=np.int32)
+    with transaction.scoped(first_ids):
+        mdp.reset_scene_to_default(env, first_ids)
+        manager.apply(mode="reset", env_ids=first_ids, global_env_step_count=0)
+    first = backend.randomization_calls[-1]
+    assert first is not None and first.body_mass is not None
+
+    # Env 2 has never triggered, so the first-trigger override fires it while
+    # envs 0 and 1 stay gated; their payload rows reuse committed values.
+    ids = np.array([0, 1, 2], dtype=np.int32)
+    with transaction.scoped(ids):
+        mdp.reset_scene_to_default(env, ids)
+        manager.apply(mode="reset", env_ids=ids, global_env_step_count=50)
+    second = backend.randomization_calls[-1]
+    assert second is not None and second.body_mass is not None
+    np.testing.assert_allclose(second.body_mass[:2], first.body_mass)
+    np.testing.assert_array_equal(manager._reset_term_last_triggered_step_id[0], [0, 0, 50])
+
+    # Fully gated resets skip the field entirely; the backend keeps the
+    # previously applied per-env values, so no dense payload is needed.
+    with transaction.scoped(first_ids):
+        mdp.reset_scene_to_default(env, first_ids)
+        manager.apply(mode="reset", env_ids=first_ids, global_env_step_count=60)
+    third = backend.randomization_calls[-1]
+    assert third is None or third.body_mass is None
+
+    # Once enough steps elapsed the gate reopens and the term resamples.
+    with transaction.scoped(first_ids):
+        mdp.reset_scene_to_default(env, first_ids)
+        manager.apply(mode="reset", env_ids=first_ids, global_env_step_count=200)
+    fourth = backend.randomization_calls[-1]
+    assert fourth is not None and fourth.body_mass is not None
+    np.testing.assert_array_equal(manager._reset_term_last_triggered_step_id[0], [200, 200, 50])
+    assert not np.allclose(fourth.body_mass, second.body_mass[:2])
+
+
+def test_min_step_count_gating_applies_to_pd_gains_payload_rows() -> None:
+    env, backend, transaction = _transaction_env(rng_seed=41)
+    manager = EventManager(
+        {
+            "gains": EventTermCfg(
+                func=mdp.pd_gains,
+                mode="reset",
+                min_step_count_between_reset=50,
+                params={"kp_range": (1.5, 1.5), "kd_range": (2.5, 2.5)},
+            )
+        },
+        env,
+    )
+    first_ids = np.array([0, 1], dtype=np.int32)
+    with transaction.scoped(first_ids):
+        manager.apply(mode="reset", env_ids=first_ids, global_env_step_count=0)
+    first = backend.randomization_calls[-1]
+    assert first is not None and first.kp is not None and first.kd is not None
+
+    ids = np.array([0, 1, 2], dtype=np.int32)
+    with transaction.scoped(ids):
+        mdp.reset_scene_to_default(env, ids)
+        manager.apply(mode="reset", env_ids=ids, global_env_step_count=10)
+    second = backend.randomization_calls[-1]
+    assert second is not None and second.kp is not None and second.kd is not None
+    np.testing.assert_allclose(second.kp[:2], first.kp)
+    np.testing.assert_allclose(second.kd[:2], first.kd)
+    np.testing.assert_array_equal(manager._reset_term_last_triggered_step_id[0], [0, 0, 10])
+
+
+def test_apply_body_impulse_lifecycle_stages_sustains_and_expires() -> None:
+    env, backend, _ = _transaction_env(rng_seed=31, interval_force_supported=True)
+    manager = EventManager(
+        {
+            "impulse": EventTermCfg(
+                func=mdp.apply_body_impulse,
+                mode="step",
+                params={
+                    "force_range": (5.0, 5.0),
+                    "torque_range": (0.0, 0.0),
+                    "duration_s": (0.05, 0.05),
+                    "cooldown_s": (0.06, 0.06),
+                },
+            )
+        },
+        env,
+    )
+
+    # step_dt=0.02: the pre-sampled 0.06 cooldown triggers on the third tick;
+    # the 0.05 duration sustains through the fifth tick and expires on the sixth.
+    manager.apply(mode="step", dt=0.02)
+    manager.apply(mode="step", dt=0.02)
+    assert backend.interval_plans == []
+
+    manager.apply(mode="step", dt=0.02)
+    assert len(backend.interval_plans) == 1
+    plan = backend.interval_plans[0]
+    np.testing.assert_array_equal(plan.body_ids, [0])
+    assert plan.body_force is not None
+    np.testing.assert_allclose(plan.body_force[:, 0], 5.0)
+    assert plan.body_torque is None
+
+    manager.apply(mode="step", dt=0.02)
+    manager.apply(mode="step", dt=0.02)
+    assert len(backend.interval_plans) == 3
+    np.testing.assert_allclose(backend.interval_plans[1].body_force[:, 0], 5.0)
+    np.testing.assert_allclose(backend.interval_plans[2].body_force[:, 0], 5.0)
+
+    # Expiry stages zeros once (persistent wrench channels clear), then the
+    # term stays idle through the next cooldown instead of re-staging.
+    manager.apply(mode="step", dt=0.02)
+    assert len(backend.interval_plans) == 4
+    np.testing.assert_allclose(backend.interval_plans[3].body_force[:, 0], 0.0)
+    manager.apply(mode="step", dt=0.02)
+    assert len(backend.interval_plans) == 4
+
+
+def test_apply_body_impulse_offset_generates_cross_torque() -> None:
+    env, backend, _ = _transaction_env(
+        rng_seed=37,
+        interval_force_supported=True,
+        interval_torque_supported=True,
+    )
+    manager = EventManager(
+        {
+            "impulse": EventTermCfg(
+                func=mdp.apply_body_impulse,
+                mode="step",
+                params={
+                    "force_range": (1.0, 1.0),
+                    "torque_range": (0.0, 0.0),
+                    "duration_s": (1.0, 1.0),
+                    "cooldown_s": (0.0, 0.0),
+                    "body_point_offset": (0.0, 0.0, 0.1),
+                },
+            )
+        },
+        env,
+    )
+
+    manager.apply(mode="step", dt=0.02)
+
+    assert len(backend.interval_plans) == 1
+    plan = backend.interval_plans[0]
+    assert plan.body_force is not None and plan.body_torque is not None
+    np.testing.assert_allclose(plan.body_force[:, 0], 1.0)
+    # Identity link orientation: offset_w = (0, 0, 0.1), force = (1, 1, 1),
+    # cross(offset_w, force) = (-0.1, 0.1, 0).
+    np.testing.assert_allclose(
+        plan.body_torque[:, 0],
+        [[-0.1, 0.1, 0.0]] * 3,
+        atol=1e-12,
+    )
+
+
+def test_apply_body_impulse_reset_clears_active_wrench() -> None:
+    env, backend, _ = _transaction_env(rng_seed=43, interval_force_supported=True)
+    manager = EventManager(
+        {
+            "impulse": EventTermCfg(
+                func=mdp.apply_body_impulse,
+                mode="step",
+                params={
+                    "force_range": (5.0, 5.0),
+                    "torque_range": (0.0, 0.0),
+                    "duration_s": (1.0, 1.0),
+                    "cooldown_s": (10.0, 10.0),
+                },
+            )
+        },
+        env,
+    )
+    term = manager.get_term_cfg("impulse").func
+    term._interval_time_left[:] = 0.0
+    manager.apply(mode="step", dt=0.02)
+    assert len(backend.interval_plans) == 1
+
+    manager.reset(np.array([1], dtype=np.int32))
+    assert len(backend.interval_plans) == 2
+    np.testing.assert_allclose(backend.interval_plans[1].body_force[:, 0], 0.0)
+
+    manager.apply(mode="step", dt=0.02)
+    sustained = backend.interval_plans[-1].body_force
+    assert sustained is not None
+    np.testing.assert_allclose(sustained[1, 0], 0.0)
+    np.testing.assert_allclose(sustained[[0, 2], 0], 5.0)
+
+
+@pytest.mark.parametrize(
+    ("env_overrides", "params", "cfg_kwargs", "match"),
+    [
+        ({}, {"force_range": (1.0, 1.0)}, {"mode": "interval"}, "only supports mode='step'"),
+        (
+            {},
+            {
+                "force_range": (1.0, 1.0),
+                "torque_range": (0.0, 0.0),
+                "duration_s": (0.1, 0.1),
+            },
+            {},
+            "missing parameters",
+        ),
+        (
+            {},
+            {
+                "force_range": (1.0, 1.0),
+                "torque_range": (0.0, 0.0),
+                "duration_s": (-0.1, 0.1),
+                "cooldown_s": (0.0, 0.0),
+            },
+            {},
+            "0 <= min <= max",
+        ),
+        (
+            {"interval_force_supported": False},
+            {
+                "force_range": (1.0, 1.0),
+                "torque_range": (0.0, 0.0),
+                "duration_s": (0.1, 0.1),
+                "cooldown_s": (0.0, 0.0),
+            },
+            {},
+            "unsupported backend capability",
+        ),
+        (
+            {"interval_force_supported": True, "interval_torque_supported": False},
+            {
+                "force_range": (1.0, 1.0),
+                "torque_range": (0.1, 0.1),
+                "duration_s": (0.1, 0.1),
+                "cooldown_s": (0.0, 0.0),
+            },
+            {},
+            "interval body torque",
+        ),
+        (
+            {"interval_force_supported": True, "interval_torque_supported": False},
+            {
+                "force_range": (1.0, 1.0),
+                "torque_range": (0.0, 0.0),
+                "duration_s": (0.1, 0.1),
+                "cooldown_s": (0.0, 0.0),
+                "body_point_offset": (0.0, 0.0, 0.1),
+            },
+            {},
+            "interval body torque",
+        ),
+    ],
+)
+def test_apply_body_impulse_invalid_configs_fail_closed(
+    env_overrides: dict[str, Any],
+    params: dict[str, Any],
+    cfg_kwargs: dict[str, Any],
+    match: str,
+) -> None:
+    env, backend, _ = _transaction_env(**env_overrides)
+    values: dict[str, Any] = {"mode": "step", "params": params}
+    values.update(cfg_kwargs)
+    if values["mode"] == "interval":
+        values["interval_range_s"] = (1.0, 1.0)
+
+    with pytest.raises((ValueError, NotImplementedError), match=match):
+        EventManager({"impulse": EventTermCfg(func=mdp.apply_body_impulse, **values)}, env)
+    assert backend.interval_plans == []
+
+
+def test_velocity_push_uses_env_rng_and_interval_subset_plan() -> None:
+    env, backend, _ = _transaction_env(rng_seed=19)
+    manager = EventManager(
+        {
+            "push": EventTermCfg(
+                func=mdp.push_by_setting_velocity,
+                mode="interval",
+                interval_range_s=(1.0, 1.0),
+                params={
+                    "velocity_range": {
+                        "x": (0.2, 0.2),
+                        "y": (-0.3, -0.3),
+                        "z": (0.4, 0.4),
+                    }
+                },
+            )
+        },
+        env,
+    )
+    manager._interval_term_time_left[0][:] = [0.0, 1.0, 0.0]
+
+    manager.apply(mode="interval", dt=0.1)
+
+    assert len(backend.interval_plans) == 1
+    plan = backend.interval_plans[0]
+    np.testing.assert_array_equal(plan.body_ids, [0])
+    assert plan.body_linear_velocity_delta is not None
+    np.testing.assert_allclose(
+        plan.body_linear_velocity_delta[:, 0],
+        [[0.2, -0.3, 0.4], [0.0, 0.0, 0.0], [0.2, -0.3, 0.4]],
+    )
+
+
+def test_velocity_push_dispatches_angular_delta_when_supported() -> None:
+    env, backend, _ = _transaction_env(rng_seed=19, interval_angular_velocity_supported=True)
+    manager = EventManager(
+        {
+            "push": EventTermCfg(
+                func=mdp.push_by_setting_velocity,
+                mode="interval",
+                interval_range_s=(1.0, 1.0),
+                params={
+                    "velocity_range": {
+                        "x": (0.2, 0.2),
+                        "yaw": (-0.5, -0.5),
+                    }
+                },
+            )
+        },
+        env,
+    )
+    manager._interval_term_time_left[0][:] = [0.0, 1.0, 0.0]
+
+    manager.apply(mode="interval", dt=0.1)
+
+    assert len(backend.interval_plans) == 1
+    plan = backend.interval_plans[0]
+    np.testing.assert_array_equal(plan.body_ids, [0])
+    assert plan.body_linear_velocity_delta is not None
+    np.testing.assert_allclose(
+        plan.body_linear_velocity_delta[:, 0],
+        [[0.2, 0.0, 0.0], [0.0, 0.0, 0.0], [0.2, 0.0, 0.0]],
+    )
+    assert plan.body_angular_velocity_delta is not None
+    np.testing.assert_allclose(
+        plan.body_angular_velocity_delta[:, 0],
+        [[0.0, 0.0, -0.5], [0.0, 0.0, 0.0], [0.0, 0.0, -0.5]],
+    )
+
+
+@pytest.mark.parametrize(
+    (
+        "velocity_supported",
+        "angular_supported",
+        "velocity_range",
+        "match",
+    ),
+    [
+        (False, False, {"x": (-0.1, 0.1)}, "unsupported backend capability"),
+        (True, False, {"yaw": (-0.1, 0.1)}, "unsupported backend capability"),
+    ],
+)
+def test_velocity_push_capability_gaps_fail_during_construction(
+    velocity_supported: bool,
+    angular_supported: bool,
+    velocity_range: dict[str, tuple[float, float]],
+    match: str,
+) -> None:
+    env, backend, _ = _transaction_env(
+        interval_velocity_supported=velocity_supported,
+        interval_angular_velocity_supported=angular_supported,
+    )
+    with pytest.raises(NotImplementedError, match=match):
+        EventManager(
+            {
+                "push": EventTermCfg(
+                    func=mdp.push_by_setting_velocity,
+                    mode="interval",
+                    interval_range_s=(1.0, 1.0),
+                    params={"velocity_range": velocity_range},
+                )
+            },
+            env,
+        )
+    assert backend.interval_plans == []
+
+
+def test_uniform_root_state_fixed_or_mocap_capability_fails_closed() -> None:
+    env, backend, transaction = _transaction_env(root_layout_supported=False)
+
+    with pytest.raises(
+        NotImplementedError,
+        match="reset_root_state_uniform.*entity 'robot'.*fixed-base/mocap.*backend 'fake'",
+    ):
+        with transaction.scoped(np.asarray([1], dtype=np.int32)):
+            mdp.reset_root_state_uniform(env, np.asarray([1], dtype=np.int32), pose_range={})
+
+    assert backend.set_state_calls == []
+
+
+@pytest.mark.parametrize(
+    ("pose_range", "message"),
+    [
+        ({"x": (np.nan, 1.0)}, "finite"),
+        ({"yaw": (1.0, -1.0)}, "minimum exceeds maximum"),
+        ({"z": (0.0, 1.0, 2.0)}, r"numeric \(min, max\) pair"),
+    ],
+)
+def test_uniform_root_state_invalid_ranges_fail_before_write(
+    pose_range: dict[str, Any], message: str
+) -> None:
+    env, entity = _capture_env()
+
+    with pytest.raises(ValueError, match=message):
+        mdp.reset_root_state_uniform(env, np.asarray([0], dtype=np.int32), pose_range)
+
+    assert entity.writes == []
+
+
+class _BiasEntity:
+    def __init__(self, num_envs: int, num_joints: int) -> None:
+        self.num_joints = num_joints
+        self.joint_names = [f"joint_{index}" for index in range(num_joints)]
+        self.data = SimpleNamespace(encoder_bias=np.zeros((num_envs, num_joints), dtype=np.float32))
+
+    def find_joints(self, names: list[str]) -> list[int]:
+        return [self.joint_names.index(name) for name in names]
+
+
+class _BiasScene:
+    def __init__(self, entity: _BiasEntity) -> None:
+        self._entity = entity
+
+    def __getitem__(self, name: str) -> _BiasEntity:
+        if name != "robot":
+            raise KeyError(name)
+        return self._entity
+
+
+def _bias_env(num_envs: int = 3, num_joints: int = 4, seed: int = 7) -> ManagerBasedRlEnv:
+    return cast(
+        ManagerBasedRlEnv,
+        SimpleNamespace(
+            num_envs=num_envs,
+            rng=np.random.default_rng(seed),
+            scene=_BiasScene(_BiasEntity(num_envs, num_joints)),
+        ),
+    )
+
+
+def test_randomize_encoder_bias_samples_selected_joints_within_range() -> None:
+    env = _bias_env()
+    manager = EventManager(
+        {
+            "bias": EventTermCfg(
+                func=mdp.randomize_encoder_bias,
+                mode="reset",
+                params={
+                    "bias_range": (-0.02, 0.02),
+                    "asset_cfg": SceneEntityCfg("robot", joint_ids=[1, 3]),
+                },
+            )
+        },
+        env,
+    )
+
+    manager.apply(mode="reset", env_ids=np.asarray([0, 2], dtype=np.int32), global_env_step_count=0)
+
+    bias = env.scene["robot"].data.encoder_bias
+    assert bias.shape == (3, 4)
+    np.testing.assert_array_equal(bias[1], np.zeros(4))
+    np.testing.assert_array_equal(bias[:, [0, 2]], np.zeros((3, 2)))
+    assert np.all(np.abs(bias[[0, 2]][:, [1, 3]]) <= 0.02)
+    assert np.any(bias[[0, 2]][:, [1, 3]] != 0.0)
+
+
+def test_randomize_encoder_bias_rejects_invalid_cfg_at_construction() -> None:
+    env = _bias_env()
+    with pytest.raises(NotImplementedError, match="mode='reset'"):
+        EventManager(
+            {
+                "bias": EventTermCfg(
+                    func=mdp.randomize_encoder_bias,
+                    mode="interval",
+                    interval_range_s=(1.0, 1.0),
+                    params={
+                        "bias_range": (-0.02, 0.02),
+                        "asset_cfg": SceneEntityCfg("robot", joint_ids=[1]),
+                    },
+                )
+            },
+            env,
+        )
+    with pytest.raises(ValueError, match="finite pair"):
+        EventManager(
+            {
+                "bias": EventTermCfg(
+                    func=mdp.randomize_encoder_bias,
+                    mode="reset",
+                    params={
+                        "bias_range": (0.0, np.nan),
+                        "asset_cfg": SceneEntityCfg("robot", joint_ids=[1]),
+                    },
+                )
+            },
+            env,
+        )
+    with pytest.raises(ValueError, match="unknown parameters"):
+        EventManager(
+            {
+                "bias": EventTermCfg(
+                    func=mdp.randomize_encoder_bias,
+                    mode="reset",
+                    params={
+                        "bias_range": (-0.02, 0.02),
+                        "asset_cfg": SceneEntityCfg("robot", joint_ids=[1]),
+                        "bogus": 1.0,
+                    },
+                )
+            },
+            env,
+        )
+
+
+def test_rigid_body_com_event_consumes_live_params_between_applies() -> None:
+    """Staged CoM-range curricula update the live term params; the next reset
+    application must sample from the widened range without rebuilding the term."""
+    env, backend, transaction = _transaction_env(rng_seed=17)
+    asset_cfg = SceneEntityCfg("robot", body_names=("base",))
+    manager = EventManager(
+        {
+            "com": EventTermCfg(
+                func=mdp.randomize_rigid_body_com,
+                mode="reset",
+                params={"asset_cfg": asset_cfg, "com_range": {"x": (0.1, 0.1)}},
+            )
+        },
+        env,
+    )
+    ids = np.array([0, 1], dtype=np.int32)
+
+    with transaction.scoped(ids):
+        manager.apply(mode="reset", env_ids=ids, global_env_step_count=0)
+    first = backend.randomization_calls[-1].body_ipos
+    assert first is not None
+    np.testing.assert_allclose(first, [[[0.1, 0.0, 0.0]]] * 2)
+
+    # A curriculum stage widens the live term params.
+    manager.get_term_cfg("com").params["com_range"] = {"x": (0.5, 0.5), "z": (-0.2, -0.2)}
+    with transaction.scoped(ids):
+        manager.apply(mode="reset", env_ids=ids, global_env_step_count=1)
+    second = backend.randomization_calls[-1].body_ipos
+    assert second is not None
+    np.testing.assert_allclose(second, [[[0.5, 0.0, -0.2]]] * 2)
+
+
+class _InertiaBackend(_Backend):
+    """Fake backend with a world-body row so MJCF-compiled inertial defaults align."""
+
+    def __init__(self, *, inertia_supported: bool = True) -> None:
+        super().__init__()
+        self.inertia_supported = inertia_supported
+        # Row 0 is the world body, matching the compiled MJCF body table.
+        self.body_mass = np.array([0.0, 10.0])
+        self.body_pos = np.zeros((self.num_envs, 2, 3))
+        self.body_quat = np.zeros((self.num_envs, 2, 4))
+        self.body_quat[:, :, 0] = 1.0
+        self.body_velocity = np.zeros((self.num_envs, 2, 3))
+
+    def get_body_ids(self, names) -> np.ndarray:
+        if tuple(names) != ("base",):
+            raise KeyError(names)
+        return np.asarray([1], dtype=np.int32)
+
+    def get_dr_capabilities(self) -> DomainRandomizationCapabilities:
+        capabilities = super().get_dr_capabilities()
+        if not self.inertia_supported:
+            return capabilities
+        return DomainRandomizationCapabilities(
+            supported_reset_terms=frozenset(
+                set(capabilities.supported_reset_terms) | {RESET_TERM_BODY_INERTIA}
+            ),
+            supports_interval_push=capabilities.supports_interval_push,
+            supports_interval_body_velocity_delta=(
+                capabilities.supports_interval_body_velocity_delta
+            ),
+            supports_interval_body_angular_velocity_delta=(
+                capabilities.supports_interval_body_angular_velocity_delta
+            ),
+            supports_interval_body_force=capabilities.supports_interval_body_force,
+            supports_interval_body_torque=capabilities.supports_interval_body_torque,
+        )
+
+
+_MASS_INERTIA_SCENE_XML = (
+    '<mujoco><worldbody><body name="base" pos="0 0 0.5">'
+    "<freejoint/>"
+    '<inertial pos="0 0 0" mass="10.0" diaginertia="0.1 0.2 0.3"/>'
+    '<geom type="sphere" size="0.1"/>'
+    "</body></worldbody></mujoco>"
+)
+
+
+def _mass_inertia_env(
+    tmp_path,
+    *,
+    inertia_supported: bool = True,
+    rng_seed: int = 5,
+) -> tuple[ManagerBasedRlEnv, _InertiaBackend, ResetStateTransaction]:
+    model_file = tmp_path / "mass_inertia_scene.xml"
+    model_file.write_text(_MASS_INERTIA_SCENE_XML, encoding="utf-8")
+    backend = _InertiaBackend(inertia_supported=inertia_supported)
+    transaction = ResetStateTransaction(cast(SimBackend, backend))
+    scene = EntityScene(
+        {
+            "robot": EntityCfg(
+                root_body_name="base",
+                joint_names=("j0", "j1", "j2"),
+                body_names=("base",),
+                geom_names=("floor", "foot", "base_geom"),
+                actuator_names=("a0", "a1", "a2"),
+            )
+        },
+        cast(SimBackend, backend),
+        reset_state=transaction,
+    )
+    env = cast(
+        ManagerBasedRlEnv,
+        SimpleNamespace(
+            num_envs=backend.num_envs,
+            rng=np.random.default_rng(rng_seed),
+            scene=scene,
+            step_dt=0.02,
+            cfg=SimpleNamespace(scene=SimpleNamespace(model_file=str(model_file))),
+        ),
+    )
+    return env, backend, transaction
+
+
+def _mass_inertia_manager(env: ManagerBasedRlEnv) -> EventManager:
+    return EventManager(
+        {
+            "mass_inertia": EventTermCfg(
+                func=mdp.randomize_body_mass_inertia,
+                mode="reset",
+                params={
+                    "asset_cfg": SceneEntityCfg("robot", body_names=("base",)),
+                    "scale_range": (0.95, 1.05),
+                },
+            )
+        },
+        env,
+    )
+
+
+def test_randomize_body_mass_inertia_scales_once_and_replays_cached_factor(tmp_path) -> None:
+    env, backend, transaction = _mass_inertia_env(tmp_path, rng_seed=11)
+    manager = _mass_inertia_manager(env)
+    ids = np.array([0, 2], dtype=np.int32)
+
+    with transaction.scoped(ids):
+        mdp.reset_scene_to_default(env, ids)
+        manager.apply(mode="reset", env_ids=ids, global_env_step_count=0)
+        assert backend.set_state_calls == []
+
+    assert len(backend.set_state_calls) == 1
+    payload = backend.randomization_calls[0]
+    assert payload is not None
+    assert payload.body_mass is not None
+    assert payload.body_inertia is not None
+    # World-body rows pass through untouched; the selected body is scaled by one
+    # shared log-uniform factor per env.
+    scales = payload.body_mass[:, 1] / 10.0
+    assert np.all(scales >= 0.95)
+    assert np.all(scales <= 1.05)
+    np.testing.assert_allclose(payload.body_mass[:, 0], 0.0)
+    np.testing.assert_allclose(payload.body_inertia[:, 0, :], 0.0)
+    np.testing.assert_allclose(
+        payload.body_inertia[:, 1, :],
+        scales[:, None] * np.array([0.1, 0.2, 0.3]),
+        rtol=1e-12,
+    )
+
+    # The factor is sampled once (startup semantics): a later reset replays the
+    # cached per-env values instead of resampling.
+    second_ids = np.array([2], dtype=np.int32)
+    with transaction.scoped(second_ids):
+        mdp.reset_scene_to_default(env, second_ids)
+        manager.apply(mode="reset", env_ids=second_ids, global_env_step_count=1)
+    replay = backend.randomization_calls[1]
+    assert replay is not None
+    assert replay.body_mass is not None
+    np.testing.assert_allclose(replay.body_mass[0, 1], payload.body_mass[1, 1])
+    np.testing.assert_allclose(replay.body_inertia[0, 1], payload.body_inertia[1, 1])
+
+
+def test_randomize_body_mass_inertia_capability_gap_fails_during_construction(tmp_path) -> None:
+    env, backend, _ = _mass_inertia_env(tmp_path, inertia_supported=False)
+    with pytest.raises(NotImplementedError, match="body_inertia randomization.*unsupported"):
+        _mass_inertia_manager(env)
+    assert backend.set_state_calls == []
+
+
+def test_randomize_body_mass_inertia_cross_check_fails_on_model_divergence(tmp_path) -> None:
+    env, backend, _ = _mass_inertia_env(tmp_path)
+    backend.body_mass = np.array([0.0, 11.0])  # backend model drifted from the scene file
+    with pytest.raises(ValueError, match="does not match backend"):
+        _mass_inertia_manager(env)
+    assert backend.set_state_calls == []
+
+
+@pytest.mark.parametrize(
+    "scale_range",
+    [(0.9, 0.1), (0.0, 1.05)],
+)
+def test_randomize_body_mass_inertia_rejects_invalid_scale_range(tmp_path, scale_range) -> None:
+    env, backend, _ = _mass_inertia_env(tmp_path)
+    with pytest.raises(ValueError, match="scale_range"):
+        EventManager(
+            {
+                "mass_inertia": EventTermCfg(
+                    func=mdp.randomize_body_mass_inertia,
+                    mode="reset",
+                    params={
+                        "asset_cfg": SceneEntityCfg("robot", body_names=("base",)),
+                        "scale_range": scale_range,
+                    },
+                )
+            },
+            env,
+        )
+    assert backend.set_state_calls == []
diff --git a/tests/envs/mdp/test_joint_position_action.py b/tests/envs/mdp/test_joint_position_action.py
new file mode 100644
index 000000000..98d08e79a
--- /dev/null
+++ b/tests/envs/mdp/test_joint_position_action.py
@@ -0,0 +1,306 @@
+"""Upstream-derived NumPy tests for the manager joint-position action."""
+
+from __future__ import annotations
+
+import ast
+from pathlib import Path
+from types import SimpleNamespace
+from typing import cast
+
+import numpy as np
+import pytest
+from unisim.backend.base import SimBackend
+
+from unilab.assets import ASSETS_ROOT_PATH
+from unilab.base.backend_factory import create_backend
+from unilab.base.entity import EntityCfg, EntityScene
+from unilab.base.scene import SceneCfg
+from unilab.envs.mdp import (
+    JointEffortAction,
+    JointEffortActionCfg,
+    JointPositionAction,
+    JointPositionActionCfg,
+    JointVelocityAction,
+    JointVelocityActionCfg,
+    RelativeJointPositionAction,
+    RelativeJointPositionActionCfg,
+)
+from unilab.envs.mdp.actions import (
+    JointPositionAction as ExportedJointPositionAction,
+)
+from unilab.envs.mdp.actions import (
+    JointPositionActionCfg as ExportedJointPositionActionCfg,
+)
+from unilab.managers._types import ManagerBasedRlEnv
+
+
+class _Backend:
+    backend_type = "fake"
+    num_envs = 2
+    num_actuators = 3
+
+    def __init__(self) -> None:
+        self.actuator_names = ("knee_motor", "hip_motor", "ankle_motor")
+        self.target_joint_names = ("knee", "hip", "ankle")
+        self.joint_index = {"hip": 0, "knee": 1, "ankle": 2}
+        self.dof_pos = np.zeros((self.num_envs, 3), dtype=np.float32)
+
+    def get_actuator_names(self) -> tuple[str, ...]:
+        return self.actuator_names
+
+    def get_actuator_joint_names(self) -> tuple[str, ...]:
+        return self.target_joint_names
+
+    def get_actuator_ctrl_range(self) -> np.ndarray:
+        return np.tile(np.asarray([[-10.0, 10.0]], dtype=np.float32), (3, 1))
+
+    def get_joint_dof_pos_indices(self, names) -> np.ndarray:
+        return np.asarray([self.joint_index[name] for name in names], dtype=np.int32)
+
+    def get_joint_dof_vel_indices(self, names) -> np.ndarray:
+        return self.get_joint_dof_pos_indices(names)
+
+    def get_dof_pos(self) -> np.ndarray:
+        return self.dof_pos
+
+    def get_dof_vel(self) -> np.ndarray:
+        return np.zeros_like(self.dof_pos)
+
+    def get_default_dof_pos(self) -> np.ndarray:
+        return np.asarray([0.1, 0.2, 0.3], dtype=np.float32)
+
+    def get_joint_range(self) -> np.ndarray:
+        return np.tile(np.asarray([[-1.0, 1.0]], dtype=np.float32), (3, 1))
+
+
+def _action(
+    **overrides,
+) -> tuple[JointPositionAction, np.ndarray, EntityScene]:
+    return _build_action(JointPositionActionCfg, **overrides)
+
+
+def _build_action(action_cfg_type, **overrides):
+    backend = _Backend()
+    control = np.zeros((backend.num_envs, backend.num_actuators), dtype=np.float32)
+    scene = EntityScene(
+        {
+            "robot": EntityCfg(
+                joint_names=("hip", "knee", "ankle"),
+                actuator_names=backend.actuator_names,
+            )
+        },
+        cast(SimBackend, backend),
+        control,
+    )
+    cfg_values = {
+        "entity_name": "robot",
+        "actuator_names": ("hip|knee",),
+        **overrides,
+    }
+    cfg = action_cfg_type(**cfg_values)
+    env = cast(ManagerBasedRlEnv, SimpleNamespace(num_envs=backend.num_envs, scene=scene))
+    return cfg.build(env), control, scene
+
+
+def test_public_exports_are_canonical_objects() -> None:
+    assert JointPositionAction is ExportedJointPositionAction
+    assert JointPositionActionCfg is ExportedJointPositionActionCfg
+
+
+def test_default_offset_encoder_bias_and_control_order() -> None:
+    action, control, scene = _action(scale=2.0)
+    raw = np.asarray([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
+    scene["robot"].data.encoder_bias[:, 0] = np.asarray([0.05, 0.1])
+
+    action.process_actions(raw)
+    action.apply_actions()
+
+    assert action.target_names == ["hip", "knee"]
+    np.testing.assert_array_equal(action.target_ids, [0, 1])
+    np.testing.assert_allclose(action.processed_action, raw * 2.0 + [0.1, 0.2])
+    np.testing.assert_allclose(control[:, 1], action.processed_action[:, 0] - [0.05, 0.1])
+    np.testing.assert_allclose(control[:, 0], action.processed_action[:, 1])
+    np.testing.assert_array_equal(control[:, 2], 0.0)
+
+
+def test_regex_scale_offset_clip_and_local_reset() -> None:
+    action, _, _ = _action(
+        scale={"hip": 2.0, "knee": 3.0},
+        offset={"hip": 0.5, "knee": -0.5},
+        clip={"hip": (-1.0, 1.0)},
+        use_default_offset=False,
+    )
+    raw = np.asarray([[2.0, 2.0], [-2.0, -2.0]], dtype=np.float32)
+
+    action.process_actions(raw)
+
+    np.testing.assert_allclose(action.processed_action, [[1.0, 5.5], [-1.0, -6.5]])
+    np.testing.assert_array_equal(action.raw_action, raw)
+    action.reset(np.asarray([1], dtype=np.int32))
+    np.testing.assert_array_equal(action.raw_action[0], raw[0])
+    np.testing.assert_array_equal(action.raw_action[1], 0.0)
+
+
+@pytest.mark.parametrize(
+    ("overrides", "error", "message"),
+    [
+        ({"scale": {"missing": 1.0}}, ValueError, "match no targets"),
+        (
+            {"scale": {"hip|knee": 1.0, ".*": 2.0}},
+            ValueError,
+            "both match target",
+        ),
+        ({"clip": {"hip": (1.0, -1.0)}}, ValueError, "exceeds upper"),
+        ({"offset": float("nan")}, ValueError, "must be finite"),
+        ({"use_default_offset": 1}, TypeError, "must be bool"),
+    ],
+)
+def test_invalid_action_config_fails_at_construction(overrides, error, message) -> None:
+    with pytest.raises(error, match=message):
+        _action(**overrides)
+
+
+def test_non_finite_and_wrong_shape_actions_fail_before_control_write() -> None:
+    action, control, _ = _action()
+    with pytest.raises(ValueError, match="expected action shape"):
+        action.process_actions(np.zeros((2, 1), dtype=np.float32))
+    with pytest.raises(ValueError, match="NaN or Inf"):
+        action.process_actions(np.full((2, 2), np.nan, dtype=np.float32))
+    np.testing.assert_array_equal(control, 0.0)
+
+
+@pytest.mark.parametrize(
+    ("action_cfg_type", "action_type"),
+    [
+        (JointVelocityActionCfg, JointVelocityAction),
+        (JointEffortActionCfg, JointEffortAction),
+    ],
+)
+def test_velocity_and_effort_actions_use_the_shared_joint_control_mapping(
+    action_cfg_type, action_type
+) -> None:
+    action, control, _ = _build_action(action_cfg_type, scale=2.0)
+    assert isinstance(action, action_type)
+    raw = np.asarray([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
+
+    action.process_actions(raw)
+    action.apply_actions()
+
+    np.testing.assert_allclose(control[:, 1], raw[:, 0] * 2.0)
+    np.testing.assert_allclose(control[:, 0], raw[:, 1] * 2.0)
+    np.testing.assert_array_equal(control[:, 2], 0.0)
+
+
+def test_relative_joint_position_action_reads_current_position_at_apply_time() -> None:
+    action, control, scene = _build_action(RelativeJointPositionActionCfg, scale=0.25)
+    assert isinstance(action, RelativeJointPositionAction)
+    scene["robot"]._backend.dof_pos[:] = np.asarray(
+        [[0.4, 0.5, 0.6], [0.7, 0.8, 0.9]], dtype=np.float32
+    )
+    raw = np.asarray([[0.4, -0.8], [0.8, -0.4]], dtype=np.float32)
+
+    action.process_actions(raw)
+    action.apply_actions()
+
+    # Entity joint order is hip, knee, ankle while backend actuator order is
+    # knee, hip, ankle.
+    np.testing.assert_allclose(control[:, 1], [0.4 + 0.4 * 0.25, 0.7 + 0.8 * 0.25])
+    np.testing.assert_allclose(control[:, 0], [0.5 - 0.8 * 0.25, 0.8 - 0.4 * 0.25])
+
+
+def test_relative_joint_position_action_rejects_nonzero_offsets() -> None:
+    with pytest.raises(ValueError, match="does not support a non-zero offset"):
+        _build_action(RelativeJointPositionActionCfg, offset={"hip": 0.1})
+
+
+@pytest.mark.parametrize("backend_type", ["mujoco", "motrix"])
+def test_go2_joint_targets_are_mapped_to_backend_control_order(backend_type: str) -> None:
+    if backend_type == "motrix":
+        pytest.importorskip("motrixsim")
+    joint_names = (
+        "FL_hip_joint",
+        "FL_thigh_joint",
+        "FL_calf_joint",
+        "FR_hip_joint",
+        "FR_thigh_joint",
+        "FR_calf_joint",
+        "RL_hip_joint",
+        "RL_thigh_joint",
+        "RL_calf_joint",
+        "RR_hip_joint",
+        "RR_thigh_joint",
+        "RR_calf_joint",
+    )
+    scene_cfg = SceneCfg(
+        model_file=str(ASSETS_ROOT_PATH / "robots" / "go2" / "scene_flat.xml"),
+        entities={
+            "robot": EntityCfg(
+                joint_names=joint_names,
+                actuator_names=(
+                    "FR_hip",
+                    "FR_thigh",
+                    "FR_calf",
+                    "FL_hip",
+                    "FL_thigh",
+                    "FL_calf",
+                    "RR_hip",
+                    "RR_thigh",
+                    "RR_calf",
+                    "RL_hip",
+                    "RL_thigh",
+                    "RL_calf",
+                ),
+            )
+        },
+    )
+    backend = create_backend(
+        backend_type,
+        scene_cfg,
+        2,
+        0.01,
+        base_name="base",
+    )
+    control = np.zeros((2, backend.num_actuators), dtype=np.float32)
+    scene = EntityScene.from_scene_cfg(scene_cfg, backend, control)
+    env = cast(ManagerBasedRlEnv, SimpleNamespace(num_envs=2, scene=scene))
+    action = JointPositionActionCfg(
+        entity_name="robot",
+        actuator_names=(".*",),
+        scale=0.25,
+        offset={".*_hip_joint": 0.1, ".*_thigh_joint": 0.2, ".*_calf_joint": -0.3},
+        use_default_offset=False,
+    ).build(env)
+    raw = np.arange(24, dtype=np.float32).reshape(2, 12) / 10.0
+
+    action.process_actions(raw)
+    action.apply_actions()
+
+    target_index = {name: index for index, name in enumerate(joint_names)}
+    expected = np.column_stack(
+        [
+            action.processed_action[:, target_index[name]]
+            for name in backend.get_actuator_joint_names()
+        ]
+    )
+    np.testing.assert_allclose(control, expected)
+
+
+def test_action_module_has_no_runtime_or_backend_private_dependencies() -> None:
+    path = (
+        Path(__file__).resolve().parents[3]
+        / "src"
+        / "unilab"
+        / "envs"
+        / "mdp"
+        / "actions"
+        / "actions.py"
+    )
+    tree = ast.parse(path.read_text(encoding="utf-8"))
+    forbidden = ("torch", "uni_rl", "unilab.training", "unilab.base.backend")
+    imports = [node.module or "" for node in ast.walk(tree) if isinstance(node, ast.ImportFrom)] + [
+        alias.name
+        for node in ast.walk(tree)
+        if isinstance(node, ast.Import)
+        for alias in node.names
+    ]
+    assert not [name for name in imports if name.startswith(forbidden)]
diff --git a/tests/envs/mdp/test_observations.py b/tests/envs/mdp/test_observations.py
new file mode 100644
index 000000000..8986c3b0f
--- /dev/null
+++ b/tests/envs/mdp/test_observations.py
@@ -0,0 +1,515 @@
+"""Upstream-derived NumPy tests for basic manager observation terms."""
+
+from __future__ import annotations
+
+from collections import Counter
+from types import SimpleNamespace
+from typing import Any, cast
+
+import numpy as np
+import pytest
+from unisim.backend.base import SimBackend
+
+from unilab.base.entity import EntityCfg, EntityScene
+from unilab.envs import mdp
+from unilab.managers import ObservationGroupCfg, ObservationManager, ObservationTermCfg
+from unilab.managers._types import ManagerBasedRlEnv
+from unilab.managers.scene_entity_config import SceneEntityCfg
+
+
+class _Backend:
+    backend_type = "fake"
+    num_envs = 2
+    num_actuators = 0
+
+    def __init__(self) -> None:
+        self.sensor_calls: Counter[str] = Counter()
+        self.sensor_values = {
+            "gyro": np.asarray([[0.1, 0.2, 0.3], [-0.1, -0.2, -0.3]], dtype=np.float32),
+            "upvector": np.asarray([[0.0, 0.0, 1.0], [0.0, 1.0, 0.0]], dtype=np.float32),
+        }
+        self.joint_names = ("hip", "knee", "ankle")
+        self.dof_pos = np.asarray(
+            [[0.4, 0.1, -0.2], [0.0, 0.3, 0.7]],
+            dtype=np.float32,
+        )
+        self.dof_vel = np.asarray(
+            [[1.0, 2.0, 3.0], [-1.0, -2.0, -3.0]],
+            dtype=np.float32,
+        )
+        self.body_pos = np.zeros((2, 1, 3), dtype=np.float32)
+        self.body_quat = np.zeros((2, 1, 4), dtype=np.float32)
+        self.body_quat[0, 0, 0] = 1.0
+        half = np.sqrt(0.5)
+        self.body_quat[1, 0] = [half, half, 0.0, 0.0]
+        self.body_lin_vel_w = np.zeros((2, 1, 3), dtype=np.float32)
+        self.body_ang_vel_w = np.zeros((2, 1, 3), dtype=np.float32)
+        self.body_lin_vel_b = np.asarray(
+            [[[0.5, 0.25, 0.0]], [[-0.5, -0.25, 0.1]]], dtype=np.float32
+        )
+        self.body_ang_vel_b = np.asarray(
+            [[[0.1, 0.2, 0.3]], [[-0.1, -0.2, -0.3]]], dtype=np.float32
+        )
+
+    def get_joint_dof_pos_indices(self, names) -> np.ndarray:
+        return np.asarray([self.joint_names.index(name) for name in names], dtype=np.int32)
+
+    def get_joint_dof_vel_indices(self, names) -> np.ndarray:
+        return self.get_joint_dof_pos_indices(names)
+
+    def get_body_ids(self, names) -> np.ndarray:
+        if tuple(names) != ("base",):
+            raise KeyError(names)
+        return np.asarray([0], dtype=np.int32)
+
+    def get_dof_pos(self) -> np.ndarray:
+        return self.dof_pos
+
+    def get_dof_vel(self) -> np.ndarray:
+        return self.dof_vel
+
+    def get_default_dof_pos(self) -> np.ndarray:
+        return np.asarray([0.1, 0.2, 0.3], dtype=np.float32)
+
+    def get_joint_range(self) -> np.ndarray:
+        return np.tile(np.asarray([[-1.0, 1.0]], dtype=np.float32), (3, 1))
+
+    def get_body_pos_w(self, ids: np.ndarray) -> np.ndarray:
+        return self.body_pos[:, ids]
+
+    def get_body_quat_w(self, ids: np.ndarray) -> np.ndarray:
+        return self.body_quat[:, ids]
+
+    def get_body_lin_vel_w(self, ids: np.ndarray) -> np.ndarray:
+        return self.body_lin_vel_w[:, ids]
+
+    def get_body_ang_vel_w(self, ids: np.ndarray) -> np.ndarray:
+        return self.body_ang_vel_w[:, ids]
+
+    def get_body_lin_vel_b(self, ids: np.ndarray) -> np.ndarray:
+        return self.body_lin_vel_b[:, ids]
+
+    def get_body_ang_vel_b(self, ids: np.ndarray) -> np.ndarray:
+        return self.body_ang_vel_b[:, ids]
+
+    def get_sensor_data(self, name: str) -> np.ndarray:
+        self.sensor_calls["single"] += 1
+        try:
+            return self.sensor_values[name]
+        except KeyError as exc:
+            raise KeyError(f"unknown sensor {name!r}") from exc
+
+    def get_sensor_data_batch(self, names: tuple[str, ...]) -> np.ndarray:
+        self.sensor_calls["batch"] += 1
+        values = [self.sensor_values[name].reshape(self.num_envs, -1) for name in names]
+        return np.concatenate(values, axis=1)
+
+    def _bind_sensor_data_reader(self, names: tuple[str, ...]):
+        return lambda: self.get_sensor_data_batch(names)
+
+    def bind_sensor_data(self, names):
+        self.sensor_calls["bind"] += 1
+        return SimBackend.bind_sensor_data(self, names)  # type: ignore[arg-type]
+
+
+class _ActionManager:
+    def __init__(self) -> None:
+        self.action = np.arange(6, dtype=np.float32).reshape(2, 3)
+        self._terms = {
+            "legs": SimpleNamespace(raw_action=self.action[:, [0, 2]]),
+        }
+
+    def get_term(self, name: str):
+        return self._terms[name]
+
+
+class _CommandManager:
+    def __init__(self) -> None:
+        self.command = np.asarray([[1.0, 0.0, 0.2], [0.5, -0.1, -0.2]], dtype=np.float32)
+
+    def get_command(self, name: str) -> np.ndarray:
+        if name != "twist":
+            raise KeyError(name)
+        return self.command
+
+
+class _FakeEnv:
+    """Attribute container with identity hash/eq for per-env term caches."""
+
+    def __init__(self, **kwargs: Any) -> None:
+        self.__dict__.update(kwargs)
+
+
+def _env() -> tuple[ManagerBasedRlEnv, _Backend]:
+    backend = _Backend()
+    scene = EntityScene(
+        {
+            "robot": EntityCfg(
+                root_body_name="base",
+                joint_names=backend.joint_names,
+            )
+        },
+        cast(SimBackend, backend),
+    )
+    env = cast(
+        ManagerBasedRlEnv,
+        _FakeEnv(
+            num_envs=backend.num_envs,
+            scene=scene,
+            action_manager=_ActionManager(),
+            command_manager=_CommandManager(),
+            rng=np.random.default_rng(4),
+        ),
+    )
+    return env, backend
+
+
+def test_root_joint_action_and_command_terms_match_numpy_contract() -> None:
+    env, backend = _env()
+    robot = cast(Any, env.scene["robot"])
+    robot.data.encoder_bias[:] = [[0.01, 0.02, 0.03], [-0.01, -0.02, -0.03]]
+
+    np.testing.assert_array_equal(mdp.base_lin_vel(env), backend.body_lin_vel_b[:, 0])
+    np.testing.assert_array_equal(mdp.base_ang_vel(env), backend.body_ang_vel_b[:, 0])
+    np.testing.assert_allclose(
+        mdp.projected_gravity(env),
+        [[0.0, 0.0, -1.0], [0.0, -1.0, 0.0]],
+        atol=1e-6,
+    )
+    np.testing.assert_allclose(
+        mdp.joint_pos_rel(env),
+        backend.dof_pos - [0.1, 0.2, 0.3],
+    )
+    np.testing.assert_allclose(
+        mdp.joint_pos_rel(env, biased=True),
+        backend.dof_pos + robot.data.encoder_bias - [0.1, 0.2, 0.3],
+    )
+    np.testing.assert_array_equal(mdp.joint_vel_rel(env), backend.dof_vel)
+    np.testing.assert_array_equal(mdp.last_action(env), env.action_manager.action)
+    np.testing.assert_array_equal(
+        mdp.last_action(env, "legs"), env.action_manager.get_term("legs").raw_action
+    )
+    np.testing.assert_array_equal(
+        mdp.generated_commands(env, "twist"), env.command_manager.get_command("twist")
+    )
+
+
+def test_scene_entity_selector_is_resolved_once_by_observation_manager() -> None:
+    env, backend = _env()
+    selector = SceneEntityCfg("robot", joint_names=("ankle", "hip"), preserve_order=True)
+    manager = ObservationManager(
+        {
+            "policy": ObservationGroupCfg(
+                terms={
+                    "joint_pos": ObservationTermCfg(
+                        func=mdp.joint_pos_rel,
+                        params={"asset_cfg": selector},
+                    ),
+                    "joint_vel": ObservationTermCfg(
+                        func=mdp.joint_vel_rel,
+                        params={"asset_cfg": selector},
+                    ),
+                    "command": ObservationTermCfg(
+                        func=mdp.generated_commands,
+                        params={"command_name": "twist"},
+                    ),
+                }
+            )
+        },
+        env,
+    )
+
+    result = manager.compute()["policy"]
+    assert isinstance(result, np.ndarray)
+    assert result.shape == (2, 7)
+    np.testing.assert_allclose(result[:, :2], backend.dof_pos[:, [2, 0]] - [0.3, 0.1])
+    np.testing.assert_array_equal(result[:, 2:4], backend.dof_vel[:, [2, 0]])
+    assert selector.joint_ids == slice(None)
+    resolved = manager.get_term_cfg("policy", "joint_pos").params["asset_cfg"]
+    assert resolved.joint_ids == [2, 0]
+
+
+def _sensor_manager(env: ManagerBasedRlEnv) -> ObservationManager:
+    return ObservationManager(
+        {
+            "policy": ObservationGroupCfg(
+                terms={
+                    "gyro": ObservationTermCfg(
+                        func=mdp.builtin_sensor,
+                        params={"sensor_name": "gyro"},
+                    ),
+                    "gravity": ObservationTermCfg(
+                        func=mdp.projected_gravity_from_sensor,
+                        params={"sensor_name": "upvector"},
+                    ),
+                }
+            )
+        },
+        env,
+    )
+
+
+def test_named_sensor_terms_bind_once_and_only_read_cached_views() -> None:
+    env, backend = _env()
+
+    manager = _sensor_manager(env)
+
+    assert manager.group_obs_dim == {"policy": (6,)}
+    assert backend.sensor_calls == {"bind": 2, "single": 2, "batch": 4}
+
+    first = manager.compute_group("policy")
+    second = manager.compute_group("policy")
+    assert isinstance(first, np.ndarray)
+    assert isinstance(second, np.ndarray)
+    expected = np.concatenate(
+        [backend.sensor_values["gyro"], -backend.sensor_values["upvector"]], axis=1
+    )
+    np.testing.assert_array_equal(first, expected)
+    np.testing.assert_array_equal(second, expected)
+    assert backend.sensor_calls == {"bind": 2, "single": 2, "batch": 8}
+
+    gyro_term = manager.get_term_cfg("policy", "gyro").func
+    gravity_term = manager.get_term_cfg("policy", "gravity").func
+    assert isinstance(gyro_term, mdp.builtin_sensor)
+    assert isinstance(gravity_term, mdp.projected_gravity_from_sensor)
+
+
+@pytest.mark.parametrize(
+    ("sensor_name", "error", "message"),
+    [
+        ("", ValueError, "builtin_sensor.*non-empty sensor_name"),
+        ("missing", KeyError, "builtin_sensor.*missing.*backend.*fake"),
+    ],
+)
+def test_named_sensor_term_materialization_fails_closed(
+    sensor_name: str, error: type[Exception], message: str
+) -> None:
+    env, _ = _env()
+    with pytest.raises(error, match=message):
+        ObservationManager(
+            {
+                "policy": ObservationGroupCfg(
+                    terms={
+                        "sensor": ObservationTermCfg(
+                            func=mdp.builtin_sensor,
+                            params={"sensor_name": sensor_name},
+                        )
+                    }
+                )
+            },
+            env,
+        )
+
+
+def test_named_sensor_scene_seam_rejects_duplicate_names() -> None:
+    env, _ = _env()
+    with pytest.raises(ValueError, match="named-sensor.*unique"):
+        env.scene.bind_sensor_data(("gyro", "gyro"))
+
+
+def test_projected_gravity_sensor_requires_one_three_dimensional_view() -> None:
+    env, backend = _env()
+    backend.sensor_values["upvector"] = np.zeros((2, 2), dtype=np.float32)
+
+    with pytest.raises(
+        ValueError,
+        match=r"projected_gravity_from_sensor.*3-D.*dimensions \(2,\).*backend 'fake'",
+    ):
+        ObservationManager(
+            {
+                "policy": ObservationGroupCfg(
+                    terms={
+                        "gravity": ObservationTermCfg(
+                            func=mdp.projected_gravity_from_sensor,
+                            params={"sensor_name": "upvector"},
+                        )
+                    }
+                )
+            },
+            env,
+        )
+
+
+def test_named_sensor_runtime_drift_reports_term_and_backend() -> None:
+    env, backend = _env()
+    manager = ObservationManager(
+        {
+            "policy": ObservationGroupCfg(
+                terms={
+                    "gyro": ObservationTermCfg(
+                        func=mdp.builtin_sensor,
+                        params={"sensor_name": "gyro"},
+                    )
+                }
+            )
+        },
+        env,
+    )
+    backend.sensor_values["gyro"] = np.full((2, 3), np.nan, dtype=np.float32)
+
+    with pytest.raises(ValueError, match="builtin_sensor.*gyro.*backend 'fake'.*NaN or Inf"):
+        manager.compute_group("policy")
+
+
+@pytest.mark.parametrize(
+    ("call", "message"),
+    [
+        (lambda env: mdp.last_action(env, "missing"), "Action term 'missing' not found"),
+        (
+            lambda env: mdp.generated_commands(env, "missing"),
+            "Command term 'missing' not found",
+        ),
+        (
+            lambda env: mdp.joint_pos_rel(env, biased=1),  # type: ignore[arg-type]
+            "biased must be bool",
+        ),
+    ],
+)
+def test_invalid_term_requests_fail_explicitly(call, message: str) -> None:
+    env, _ = _env()
+    with pytest.raises((KeyError, TypeError), match=message):
+        call(env)
+
+
+def test_missing_entity_capability_fails_instead_of_returning_zeros() -> None:
+    backend = _Backend()
+    scene = EntityScene(
+        {"robot": EntityCfg(joint_names=backend.joint_names)},
+        cast(SimBackend, backend),
+    )
+    env = cast(
+        ManagerBasedRlEnv,
+        SimpleNamespace(
+            num_envs=2,
+            scene=scene,
+            action_manager=_ActionManager(),
+            command_manager=_CommandManager(),
+        ),
+    )
+    with pytest.raises(NotImplementedError, match="projected gravity.*not materialized"):
+        mdp.projected_gravity(env)
+
+
+def _imu_misalignment_manager(
+    env: ManagerBasedRlEnv, max_angle_deg: float = 6.0
+) -> ObservationManager:
+    """Actor sees IMU-misaligned gyro/gravity; critic keeps the true values."""
+    return ObservationManager(
+        {
+            "policy": ObservationGroupCfg(
+                terms={
+                    "base_ang_vel": ObservationTermCfg(
+                        func=mdp.base_ang_vel_imu_misaligned,
+                        params={"max_angle_deg": max_angle_deg},
+                    ),
+                    "projected_gravity": ObservationTermCfg(
+                        func=mdp.projected_gravity_imu_misaligned,
+                        params={"max_angle_deg": max_angle_deg},
+                    ),
+                }
+            ),
+            "critic": ObservationGroupCfg(
+                terms={
+                    "base_ang_vel": ObservationTermCfg(func=mdp.base_ang_vel),
+                    "projected_gravity": ObservationTermCfg(func=mdp.projected_gravity),
+                }
+            ),
+        },
+        env,
+    )
+
+
+def _rotation_angle_rad(raw: np.ndarray, rotated: np.ndarray) -> np.ndarray:
+    cos = np.sum(raw * rotated, axis=-1) / (
+        np.linalg.norm(raw, axis=-1) * np.linalg.norm(rotated, axis=-1)
+    )
+    return np.arccos(np.clip(cos, -1.0, 1.0))
+
+
+def test_imu_misaligned_terms_share_one_per_env_constant_quaternion() -> None:
+    env, backend = _env()
+    manager = _imu_misalignment_manager(env, max_angle_deg=6.0)
+
+    assert manager.group_obs_dim == {"policy": (6,), "critic": (6,)}
+    obs = manager.compute()
+    policy = obs["policy"]
+    critic = obs["critic"]
+    assert isinstance(policy, np.ndarray)
+    assert isinstance(critic, np.ndarray)
+    gyro_raw, gravity_raw = critic[:, :3], critic[:, 3:]
+    gyro_rot, gravity_rot = policy[:, :3], policy[:, 3:]
+
+    # The critic group keeps the true (unrotated) values.
+    np.testing.assert_array_equal(gyro_raw, backend.body_ang_vel_b[:, 0])
+    np.testing.assert_allclose(gravity_raw, [[0.0, 0.0, -1.0], [0.0, -1.0, 0.0]], atol=1e-6)
+
+    # A rotation preserves vector norms...
+    np.testing.assert_allclose(
+        np.linalg.norm(gyro_rot, axis=-1), np.linalg.norm(gyro_raw, axis=-1), atol=1e-6
+    )
+    np.testing.assert_allclose(np.linalg.norm(gravity_rot, axis=-1), 1.0, atol=1e-6)
+    # ...and the gyro/gravity inner product, proving both actor terms were
+    # rotated by the SAME per-env quaternion.
+    np.testing.assert_allclose(
+        np.sum(gyro_rot * gravity_rot, axis=-1),
+        np.sum(gyro_raw * gravity_raw, axis=-1),
+        atol=1e-6,
+    )
+
+    # Magnitude is bounded by max_angle_deg for every env, and the rotation is
+    # actually applied (seeded RNG keeps this deterministic).
+    max_angle_rad = np.deg2rad(6.0)
+    assert (_rotation_angle_rad(gyro_raw, gyro_rot) <= max_angle_rad + 1e-5).all()
+    assert (_rotation_angle_rad(gravity_raw, gravity_rot) <= max_angle_rad + 1e-5).all()
+    assert _rotation_angle_rad(gravity_raw, gravity_rot).max() > 1e-4
+
+
+def test_imu_misalignment_is_constant_across_calls_and_episode_resets() -> None:
+    env, _ = _env()
+    manager = _imu_misalignment_manager(env)
+
+    first = manager.compute_group("policy")
+    second = manager.compute_group("policy")
+    manager.reset(np.arange(env.num_envs))
+    after_reset = manager.compute_group("policy")
+
+    assert isinstance(first, np.ndarray)
+    np.testing.assert_array_equal(first, second)
+    np.testing.assert_array_equal(first, after_reset)
+
+
+def test_imu_misalignment_reproducible_for_same_env_seed() -> None:
+    env_a, _ = _env()
+    env_b, _ = _env()
+
+    obs_a = _imu_misalignment_manager(env_a).compute_group("policy")
+    obs_b = _imu_misalignment_manager(env_b).compute_group("policy")
+
+    np.testing.assert_array_equal(obs_a, obs_b)
+
+
+def test_imu_misalignment_zero_angle_is_identity() -> None:
+    env, backend = _env()
+
+    policy = _imu_misalignment_manager(env, max_angle_deg=0.0).compute_group("policy")
+
+    assert isinstance(policy, np.ndarray)
+    np.testing.assert_array_equal(policy[:, :3], backend.body_ang_vel_b[:, 0])
+    np.testing.assert_allclose(policy[:, 3:], [[0.0, 0.0, -1.0], [0.0, -1.0, 0.0]], atol=1e-6)
+
+
+@pytest.mark.parametrize("max_angle_deg", [-1.0, float("nan"), True, "6"])
+def test_imu_misalignment_rejects_invalid_max_angle(max_angle_deg) -> None:
+    env, _ = _env()
+    with pytest.raises(ValueError, match="max_angle_deg"):
+        _imu_misalignment_manager(env, max_angle_deg=max_angle_deg)
+
+
+def test_imu_misaligned_term_rejects_call_with_mismatched_angle() -> None:
+    env, _ = _env()
+    manager = _imu_misalignment_manager(env, max_angle_deg=6.0)
+    term = manager.get_term_cfg("policy", "base_ang_vel").func
+    assert isinstance(term, mdp.base_ang_vel_imu_misaligned)
+
+    with pytest.raises(ValueError, match="bound to max_angle_deg"):
+        term(env, max_angle_deg=3.0)
diff --git a/tests/envs/mdp/test_rewards.py b/tests/envs/mdp/test_rewards.py
new file mode 100644
index 000000000..06aba2353
--- /dev/null
+++ b/tests/envs/mdp/test_rewards.py
@@ -0,0 +1,336 @@
+"""Upstream-derived NumPy tests for sensor-free manager reward terms."""
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+from typing import Any, cast
+
+import numpy as np
+import pytest
+
+from unilab.envs import mdp
+from unilab.managers import RewardManager, RewardTermCfg
+from unilab.managers._types import ManagerBasedRlEnv
+from unilab.managers.scene_entity_config import SceneEntityCfg
+
+
+class _Entity:
+    joint_names = ("hip", "knee", "ankle")
+    body_names = ("base", "torso")
+    num_joints = 3
+    num_bodies = 2
+
+    def __init__(self) -> None:
+        self.data = SimpleNamespace(
+            joint_pos=np.asarray(
+                [[0.1, -0.2, 0.3], [0.0, 0.4, -0.1], [0.2, 0.0, 0.5]],
+                dtype=np.float32,
+            ),
+            default_joint_pos=np.asarray(
+                [[0.1, 0.0, 0.2], [0.1, 0.0, 0.2], [0.1, 0.0, 0.2]],
+                dtype=np.float32,
+            ),
+            soft_joint_pos_limits=np.asarray(
+                [[-0.15, 0.15], [-0.3, 0.3], [-0.4, 0.4]],
+                dtype=np.float32,
+            ),
+            joint_vel=np.asarray(
+                [[1.0, 2.0, 3.0], [-1.0, 0.5, 2.0], [0.0, -2.0, 1.0]],
+                dtype=np.float32,
+            ),
+            projected_gravity_b=np.asarray(
+                [[0.0, 0.0, -1.0], [0.3, 0.4, -0.866], [0.6, 0.0, -0.8]],
+                dtype=np.float32,
+            ),
+            root_link_lin_vel_b=np.asarray(
+                [[1.0, 0.0, 0.0], [0.0, 0.5, 0.25], [-0.5, 0.0, -0.2]],
+                dtype=np.float32,
+            ),
+            root_link_ang_vel_b=np.asarray(
+                [[0.0, 0.0, 0.5], [0.1, 0.2, -0.25], [-0.3, 0.0, 0.1]],
+                dtype=np.float32,
+            ),
+            body_link_ang_vel_w=np.asarray(
+                [
+                    [[0.1, 0.2, 0.3], [1.0, 2.0, 3.0]],
+                    [[-0.1, 0.4, 0.2], [-1.0, 0.5, 0.0]],
+                    [[0.0, -0.2, 0.7], [0.25, -0.75, 0.1]],
+                ],
+                dtype=np.float32,
+            ),
+            body_link_quat_w=np.asarray(
+                [
+                    [[1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0]],
+                    [[0.0, 1.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0]],
+                    [[np.sqrt(0.5), 0.0, np.sqrt(0.5), 0.0], [1.0, 0.0, 0.0, 0.0]],
+                ],
+                dtype=np.float32,
+            ),
+            gravity_vec_w=np.asarray([0.0, 0.0, -1.0], dtype=np.float32),
+        )
+
+    def find_joints(self, keys, preserve_order: bool = False):
+        patterns = (keys,) if isinstance(keys, str) else tuple(keys)
+        ids = [self.joint_names.index(name) for name in patterns]
+        return ids, [self.joint_names[index] for index in ids]
+
+
+class _CommandManager:
+    def __init__(self) -> None:
+        self.command = np.asarray(
+            [[1.0, 0.0, 0.5], [0.25, 0.0, -0.5], [0.0, 0.0, 0.0]], dtype=np.float32
+        )
+
+    def get_command(self, name: str) -> np.ndarray:
+        if name != "twist":
+            raise KeyError(name)
+        return self.command
+
+
+def _env() -> ManagerBasedRlEnv:
+    action = np.asarray(
+        [[1.0, 2.0], [0.5, -0.5], [-1.0, 0.25]],
+        dtype=np.float32,
+    )
+    return cast(
+        ManagerBasedRlEnv,
+        SimpleNamespace(
+            num_envs=3,
+            scene={"robot": _Entity()},
+            action_manager=SimpleNamespace(
+                action=action,
+                prev_action=action - 0.25,
+                prev_prev_action=action - 0.75,
+            ),
+            command_manager=_CommandManager(),
+            termination_manager=SimpleNamespace(
+                terminated=np.asarray([False, True, False], dtype=np.bool_)
+            ),
+            max_episode_length_s=2.0,
+        ),
+    )
+
+
+def test_generic_reward_terms_match_pinned_numpy_semantics() -> None:
+    env = _env()
+    entity = cast(Any, env.scene["robot"])
+
+    np.testing.assert_array_equal(mdp.is_alive(env), [1.0, 0.0, 1.0])
+    np.testing.assert_array_equal(mdp.is_terminated(env), [0.0, 1.0, 0.0])
+    np.testing.assert_allclose(
+        mdp.joint_vel_l2(env, SceneEntityCfg("robot", joint_ids=[2, 0])),
+        np.sum(np.square(entity.data.joint_vel[:, [2, 0]]), axis=1),
+    )
+    np.testing.assert_allclose(mdp.action_rate_l2(env), 2 * 0.25**2)
+    np.testing.assert_allclose(mdp.action_acc_l2(env), 2 * 0.25**2)
+    np.testing.assert_allclose(
+        mdp.flat_orientation_l2(env),
+        np.sum(np.square(entity.data.projected_gravity_b[:, :2]), axis=1),
+    )
+
+
+def test_velocity_tracking_terms_match_pinned_equations() -> None:
+    env = _env()
+    entity = cast(Any, env.scene["robot"])
+    command = cast(np.ndarray, env.command_manager.get_command("twist"))
+
+    linear_error = np.sum(
+        np.square(command[:, :2] - entity.data.root_link_lin_vel_b[:, :2]), axis=1
+    ) + np.square(entity.data.root_link_lin_vel_b[:, 2])
+    angular_error = np.square(command[:, 2] - entity.data.root_link_ang_vel_b[:, 2]) + np.sum(
+        np.square(entity.data.root_link_ang_vel_b[:, :2]), axis=1
+    )
+    np.testing.assert_allclose(
+        mdp.track_linear_velocity(env, std=0.5, command_name="twist"),
+        np.exp(-linear_error / 0.5**2),
+    )
+    np.testing.assert_allclose(
+        mdp.track_angular_velocity(env, std=0.4, command_name="twist"),
+        np.exp(-angular_error / 0.4**2),
+    )
+
+
+def test_body_angular_velocity_requires_one_selected_body() -> None:
+    env = _env()
+    np.testing.assert_allclose(
+        mdp.body_angular_velocity_penalty(env, SceneEntityCfg("robot", body_ids=[1])),
+        [5.0, 1.25, 0.625],
+    )
+    with pytest.raises(ValueError, match="requires exactly one body"):
+        mdp.body_angular_velocity_penalty(env)
+
+
+def test_upright_matches_mjlab_projected_gravity_kernel() -> None:
+    env = _env()
+    # Body 0 quats per env: identity (pg_xy = 0), 180° roll (pg_xy = 0),
+    # 90° pitch (pg_b = (1, 0, 0), pg_xy^2 = 1).
+    selector = SceneEntityCfg("robot", body_ids=[0])
+    np.testing.assert_allclose(
+        mdp.upright(env, std=0.5, asset_cfg=selector),
+        [1.0, 1.0, np.exp(-1.0 / 0.25)],
+        rtol=1e-6,
+    )
+    with pytest.raises(ValueError, match="requires exactly one body"):
+        mdp.upright(env, std=0.5)
+    with pytest.raises(ValueError, match="std must be finite and positive"):
+        mdp.upright(env, std=0.0, asset_cfg=selector)
+
+
+def test_joint_pos_limits_matches_mjlab_soft_limit_equation() -> None:
+    env = _env()
+    entity = cast(Any, env.scene["robot"])
+    joint_pos = entity.data.joint_pos
+    limits = entity.data.soft_joint_pos_limits
+    expected = -np.clip(joint_pos - limits[:, 0], min=None, max=0.0)
+    expected += np.clip(joint_pos - limits[:, 1], min=0.0, max=None)
+    np.testing.assert_allclose(mdp.joint_pos_limits(env), np.sum(expected, axis=1))
+    selector = SceneEntityCfg("robot", joint_ids=[1])
+    expected_knee = -np.clip(joint_pos[:, [1]] - limits[[1], 0], min=None, max=0.0)
+    expected_knee += np.clip(joint_pos[:, [1]] - limits[[1], 1], min=0.0, max=None)
+    np.testing.assert_allclose(mdp.joint_pos_limits(env, selector), np.sum(expected_knee, axis=1))
+
+
+def _posture_expected(entity: Any, std: np.ndarray, joint_ids: list[int]) -> np.ndarray:
+    error_squared = np.square(
+        entity.data.joint_pos[:, joint_ids] - entity.data.default_joint_pos[:, joint_ids]
+    )
+    return np.exp(-np.mean(error_squared / np.square(std), axis=1))
+
+
+def test_posture_matches_mjlab_per_joint_std_kernel() -> None:
+    env = _env()
+    entity = cast(Any, env.scene["robot"])
+    std = {".*hip.*": 0.5, ".*knee.*": 0.35, ".*ankle.*": 0.25}
+    manager = RewardManager(
+        {"posture": RewardTermCfg(func=mdp.posture, weight=1.0, params={"std": std})},
+        env,
+        scale_by_dt=False,
+    )
+    expected = _posture_expected(entity, np.asarray([0.5, 0.35, 0.25]), [0, 1, 2])
+    np.testing.assert_allclose(manager.compute(dt=0.02), expected)
+
+
+def test_variable_posture_selects_std_by_command_speed_regime() -> None:
+    env = _env()
+    entity = cast(Any, env.scene["robot"])
+    # Commands (1.0,0,0.5) / (0.25,0,-0.5) / (0,0,0) give total speeds 1.5 /
+    # 0.75 / 0.0: running, walking, standing for thresholds 0.5 and 1.5.
+    params = {
+        "std_standing": {".*": 0.1},
+        "std_walking": {".*": 0.3},
+        "std_running": {".*": 0.6},
+        "command_name": "twist",
+        "walking_threshold": 0.5,
+        "running_threshold": 1.5,
+    }
+    manager = RewardManager(
+        {"pose": RewardTermCfg(func=mdp.variable_posture, weight=1.0, params=params)},
+        env,
+        scale_by_dt=False,
+    )
+    expected = np.stack(
+        [
+            _posture_expected(entity, np.full(3, 0.6), [0, 1, 2])[0],
+            _posture_expected(entity, np.full(3, 0.3), [0, 1, 2])[1],
+            _posture_expected(entity, np.full(3, 0.1), [0, 1, 2])[2],
+        ]
+    )
+    np.testing.assert_allclose(manager.compute(dt=0.02), expected)
+
+
+def test_posture_std_mapping_fail_closed() -> None:
+    env = _env()
+    with pytest.raises(ValueError, match="must match joint 'hip' exactly once"):
+        RewardManager(
+            {
+                "posture": RewardTermCfg(
+                    func=mdp.posture, weight=1.0, params={"std": {".*knee.*": 0.3}}
+                )
+            },
+            env,
+        )
+    with pytest.raises(ValueError, match="std must be finite and positive"):
+        RewardManager(
+            {"posture": RewardTermCfg(func=mdp.posture, weight=1.0, params={"std": {".*": 0.0}})},
+            env,
+        )
+    with pytest.raises(ValueError, match="walking_threshold must be below running_threshold"):
+        RewardManager(
+            {
+                "pose": RewardTermCfg(
+                    func=mdp.variable_posture,
+                    weight=1.0,
+                    params={
+                        "std_standing": {".*": 0.1},
+                        "std_walking": {".*": 0.3},
+                        "std_running": {".*": 0.6},
+                        "command_name": "twist",
+                        "walking_threshold": 2.0,
+                        "running_threshold": 1.5,
+                    },
+                )
+            },
+            env,
+        )
+
+
+def test_terms_integrate_with_reward_manager_and_cold_selector_resolution() -> None:
+    env = _env()
+    selector = SceneEntityCfg("robot", joint_names=("ankle", "hip"), preserve_order=True)
+    manager = RewardManager(
+        {
+            "joint_velocity": RewardTermCfg(
+                func=mdp.joint_vel_l2,
+                weight=-0.5,
+                params={"asset_cfg": selector},
+            ),
+            "track_linear": RewardTermCfg(
+                func=mdp.track_linear_velocity,
+                weight=2.0,
+                params={"std": 0.5, "command_name": "twist"},
+            ),
+        },
+        env,
+        scale_by_dt=False,
+    )
+
+    result = manager.compute(dt=0.02)
+    entity = cast(Any, env.scene["robot"])
+    expected = -0.5 * np.sum(np.square(entity.data.joint_vel[:, [2, 0]]), axis=1)
+    expected += 2.0 * mdp.track_linear_velocity(env, std=0.5, command_name="twist")
+    np.testing.assert_allclose(result, expected)
+    resolved = manager.get_term_cfg("joint_velocity").params["asset_cfg"]
+    assert resolved.joint_ids == [2, 0]
+
+
+@pytest.mark.parametrize(
+    ("call", "message"),
+    [
+        (
+            lambda env: mdp.track_linear_velocity(env, std=0.0, command_name="twist"),
+            "std must be finite and positive",
+        ),
+        (
+            lambda env: mdp.track_angular_velocity(env, std=np.nan, command_name="twist"),
+            "std must be finite and positive",
+        ),
+        (
+            lambda env: mdp.track_linear_velocity(env, std=0.5, command_name="missing"),
+            "Command term 'missing' not found",
+        ),
+    ],
+)
+def test_invalid_reward_requests_fail_explicitly(call, message: str) -> None:
+    with pytest.raises((KeyError, ValueError), match=message):
+        call(_env())
+
+
+def test_reward_manager_reports_nonfinite_term_and_name() -> None:
+    env = _env()
+    cast(Any, env.scene["robot"]).data.joint_vel[1, 0] = np.nan
+    manager = RewardManager(
+        {"joint_velocity": RewardTermCfg(func=mdp.joint_vel_l2, weight=-1.0)},
+        env,
+    )
+    with pytest.raises(ValueError, match="RewardManager term 'joint_velocity'.*NaN"):
+        manager.compute(dt=0.02)
diff --git a/tests/envs/mdp/test_terminations.py b/tests/envs/mdp/test_terminations.py
new file mode 100644
index 000000000..4865a36da
--- /dev/null
+++ b/tests/envs/mdp/test_terminations.py
@@ -0,0 +1,137 @@
+"""Upstream-derived NumPy tests for basic manager termination terms."""
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+from typing import Any, cast
+
+import numpy as np
+import pytest
+
+from unilab.envs import mdp
+from unilab.managers import TerminationManager, TerminationTermCfg
+from unilab.managers._types import ManagerBasedRlEnv
+from unilab.managers.scene_entity_config import SceneEntityCfg
+
+
+def _env() -> ManagerBasedRlEnv:
+    angles = np.asarray([0.0, 0.5, 1.0], dtype=np.float32)
+    gravity = np.zeros((3, 3), dtype=np.float32)
+    gravity[:, 2] = -np.cos(angles)
+    root_pos = np.zeros((3, 3), dtype=np.float32)
+    root_pos[:, 2] = [0.2, 0.4, 0.6]
+    entity = SimpleNamespace(
+        data=SimpleNamespace(
+            projected_gravity_b=gravity,
+            root_link_pos_w=root_pos,
+            root_link_lin_vel_w=np.zeros((3, 3), dtype=np.float32),
+            root_link_ang_vel_w=np.zeros((3, 3), dtype=np.float32),
+            joint_pos=np.zeros((3, 2), dtype=np.float32),
+            joint_vel=np.zeros((3, 2), dtype=np.float32),
+        )
+    )
+    return cast(
+        ManagerBasedRlEnv,
+        SimpleNamespace(
+            num_envs=3,
+            episode_length_buf=np.asarray([9, 10, 11], dtype=np.int64),
+            max_episode_length=10,
+            scene={"robot": entity},
+        ),
+    )
+
+
+def test_basic_termination_terms_match_pinned_semantics() -> None:
+    env = _env()
+
+    np.testing.assert_array_equal(mdp.time_out(env), [False, True, True])
+    np.testing.assert_array_equal(mdp.bad_orientation(env, limit_angle=0.6), [False, False, True])
+    np.testing.assert_array_equal(
+        mdp.root_height_below_minimum(env, minimum_height=0.4),
+        [True, False, False],
+    )
+
+
+def test_terms_integrate_with_termination_manager_and_selector_resolution() -> None:
+    env = _env()
+    selector = SceneEntityCfg("robot")
+    manager = TerminationManager(
+        {
+            "time_out": TerminationTermCfg(func=mdp.time_out, time_out=True),
+            "bad_orientation": TerminationTermCfg(
+                func=mdp.bad_orientation,
+                params={"limit_angle": 0.6, "asset_cfg": selector},
+            ),
+            "low_height": TerminationTermCfg(
+                func=mdp.root_height_below_minimum,
+                params={"minimum_height": 0.4},
+            ),
+        },
+        env,
+    )
+
+    np.testing.assert_array_equal(manager.compute(), [True, True, True])
+    np.testing.assert_array_equal(manager.time_outs, [False, True, True])
+    np.testing.assert_array_equal(manager.terminated, [True, False, True])
+    assert selector.name == "robot"
+
+
+@pytest.mark.parametrize(
+    ("term", "parameter", "message"),
+    [
+        (mdp.bad_orientation, np.nan, "limit_angle must be finite"),
+        (mdp.bad_orientation, True, "limit_angle must be a real number"),
+        (
+            mdp.root_height_below_minimum,
+            np.inf,
+            "minimum_height must be finite",
+        ),
+    ],
+)
+def test_invalid_scalar_parameters_fail_explicitly(term, parameter, message: str) -> None:
+    env = _env()
+    keyword = "limit_angle" if term is mdp.bad_orientation else "minimum_height"
+    with pytest.raises((TypeError, ValueError), match=message):
+        term(env, **{keyword: parameter})
+
+
+@pytest.mark.parametrize("field", ["projected_gravity_b", "root_link_pos_w"])
+def test_invalid_entity_state_fails_instead_of_becoming_false(field: str) -> None:
+    env = _env()
+    entity = cast(Any, env.scene["robot"])
+    setattr(entity.data, field, np.full((3, 3), np.nan, dtype=np.float32))
+
+    with pytest.raises(ValueError, match="NaN or Inf entity state"):
+        if field == "projected_gravity_b":
+            mdp.bad_orientation(env, limit_angle=0.5)
+        else:
+            mdp.root_height_below_minimum(env, minimum_height=0.5)
+
+
+def test_missing_entity_is_not_silently_replaced() -> None:
+    env = _env()
+    with pytest.raises(KeyError, match="missing"):
+        mdp.bad_orientation(env, 0.5, SceneEntityCfg("missing"))
+
+
+def test_nan_detection_flags_only_nonfinite_envs() -> None:
+    env = _env()
+    np.testing.assert_array_equal(mdp.nan_detection(env), [False, False, False])
+
+    entity = cast(Any, env.scene["robot"])
+    entity.data.joint_vel[1, 0] = np.nan
+    entity.data.root_link_pos_w[2, 2] = np.inf
+    np.testing.assert_array_equal(mdp.nan_detection(env), [False, True, True])
+
+
+def test_nan_detection_integrates_with_termination_manager() -> None:
+    env = _env()
+    manager = TerminationManager(
+        {"nan": TerminationTermCfg(func=mdp.nan_detection)},
+        env,
+    )
+    np.testing.assert_array_equal(manager.compute(), [False, False, False])
+    entity = cast(Any, env.scene["robot"])
+    entity.data.joint_pos[0, 1] = np.nan
+    np.testing.assert_array_equal(manager.compute(), [True, False, False])
+    np.testing.assert_array_equal(manager.terminated, [True, False, False])
diff --git a/tests/envs/mdp/test_uniform_velocity_command.py b/tests/envs/mdp/test_uniform_velocity_command.py
new file mode 100644
index 000000000..1ae2de3e8
--- /dev/null
+++ b/tests/envs/mdp/test_uniform_velocity_command.py
@@ -0,0 +1,271 @@
+"""Upstream-derived NumPy tests for uniform velocity commands."""
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+from typing import Any, cast
+
+import numpy as np
+import pytest
+from unisim.backend.base import SimBackend
+
+from unilab.base.entity import EntityCfg, EntityScene
+from unilab.envs.mdp import UniformVelocityCommand, UniformVelocityCommandCfg
+from unilab.envs.mdp.commands import (
+    UniformVelocityCommand as ExportedUniformVelocityCommand,
+)
+from unilab.envs.mdp.commands import (
+    UniformVelocityCommandCfg as ExportedUniformVelocityCommandCfg,
+)
+from unilab.managers import CommandManager
+from unilab.managers._types import ManagerBasedRlEnv
+from unilab.utils.rotation import np_yaw_to_quat
+
+
+class _Backend:
+    backend_type = "fake"
+    num_actuators = 0
+
+    def __init__(self, num_envs: int = 4, *, unsupported: str | None = None) -> None:
+        self.num_envs = num_envs
+        self.unsupported = unsupported
+        self.body_pos = np.zeros((num_envs, 1, 3), dtype=np.float32)
+        self.body_quat = np.zeros((num_envs, 1, 4), dtype=np.float32)
+        self.body_quat[..., 0] = 1.0
+        self.body_lin_vel_w = np.zeros((num_envs, 1, 3), dtype=np.float32)
+        self.body_ang_vel_w = np.zeros((num_envs, 1, 3), dtype=np.float32)
+        self.body_lin_vel_b = np.zeros((num_envs, 1, 3), dtype=np.float32)
+        self.body_ang_vel_b = np.zeros((num_envs, 1, 3), dtype=np.float32)
+
+    def get_body_ids(self, names) -> np.ndarray:
+        if tuple(names) != ("base",):
+            raise KeyError(names)
+        return np.asarray([0], dtype=np.int32)
+
+    def get_dof_pos(self) -> np.ndarray:
+        return np.empty((self.num_envs, 0), dtype=np.float32)
+
+    def get_dof_vel(self) -> np.ndarray:
+        return np.empty((self.num_envs, 0), dtype=np.float32)
+
+    def get_body_pos_w(self, ids: np.ndarray) -> np.ndarray:
+        return self.body_pos[:, ids]
+
+    def get_body_quat_w(self, ids: np.ndarray) -> np.ndarray:
+        return self.body_quat[:, ids]
+
+    def get_body_lin_vel_w(self, ids: np.ndarray) -> np.ndarray:
+        return self.body_lin_vel_w[:, ids]
+
+    def get_body_ang_vel_w(self, ids: np.ndarray) -> np.ndarray:
+        return self.body_ang_vel_w[:, ids]
+
+    def get_body_lin_vel_b(self, ids: np.ndarray) -> np.ndarray:
+        if self.unsupported == "body-frame linear velocity":
+            raise NotImplementedError("fake lacks body-frame linear velocity")
+        return self.body_lin_vel_b[:, ids]
+
+    def get_body_ang_vel_b(self, ids: np.ndarray) -> np.ndarray:
+        return self.body_ang_vel_b[:, ids]
+
+
+def _env(seed: int = 7) -> tuple[ManagerBasedRlEnv, _Backend]:
+    backend = _Backend()
+    scene = EntityScene(
+        {"robot": EntityCfg(root_body_name="base")},
+        cast(SimBackend, backend),
+    )
+    env = cast(
+        ManagerBasedRlEnv,
+        SimpleNamespace(
+            num_envs=backend.num_envs,
+            rng=np.random.default_rng(seed),
+            scene=scene,
+            step_dt=0.02,
+        ),
+    )
+    return env, backend
+
+
+def _cfg(**overrides: Any) -> UniformVelocityCommandCfg:
+    values: dict[str, Any] = {
+        "entity_name": "robot",
+        "resampling_time_range": (1.0, 1.0),
+        "ranges": UniformVelocityCommandCfg.Ranges(
+            lin_vel_x=(-1.0, 1.0),
+            lin_vel_y=(-0.5, 0.5),
+            ang_vel_z=(-0.8, 0.8),
+        ),
+        **overrides,
+    }
+    return UniformVelocityCommandCfg(**values)
+
+
+def _manager(env: ManagerBasedRlEnv, **overrides: Any) -> CommandManager:
+    return CommandManager({"twist": _cfg(**overrides)}, env)
+
+
+def test_public_exports_and_cfg_build_are_canonical() -> None:
+    env, _ = _env()
+    term = _cfg().build(env)
+
+    assert UniformVelocityCommand is ExportedUniformVelocityCommand
+    assert UniformVelocityCommandCfg is ExportedUniformVelocityCommandCfg
+    assert isinstance(term, UniformVelocityCommand)
+    assert term.command.shape == (env.num_envs, 3)
+    assert term.command.dtype == np.float32
+
+
+def test_resampling_is_seeded_and_partial_reset_is_local() -> None:
+    left_env, _ = _env(seed=19)
+    right_env, _ = _env(seed=19)
+    left = _manager(left_env, resampling_time_range=(0.5, 1.5))
+    right = _manager(right_env, resampling_time_range=(0.5, 1.5))
+    all_ids = np.arange(left_env.num_envs, dtype=np.int32)
+
+    left.reset(all_ids)
+    right.reset(all_ids)
+    np.testing.assert_array_equal(left.get_command("twist"), right.get_command("twist"))
+    np.testing.assert_array_equal(
+        left.get_term("twist").time_left,
+        right.get_term("twist").time_left,
+    )
+
+    before_command = left.get_command("twist").copy()
+    before_counter = left.get_term("twist").command_counter.copy()
+    left.reset(np.asarray([1, 3], dtype=np.int32))
+    np.testing.assert_array_equal(left.get_command("twist")[[0, 2]], before_command[[0, 2]])
+    np.testing.assert_array_equal(
+        left.get_term("twist").command_counter[[0, 2]], before_counter[[0, 2]]
+    )
+    np.testing.assert_array_equal(left.get_term("twist").command_counter[[1, 3]], 1)
+
+
+def test_metrics_and_fixed_interval_resampling_follow_manager_schedule() -> None:
+    env, backend = _env()
+    backend.body_lin_vel_b[:, 0, :2] = [0.5, -0.25]
+    backend.body_ang_vel_b[:, 0, 2] = 0.1
+    manager = _manager(
+        env,
+        resampling_time_range=(0.2, 0.2),
+        ranges=UniformVelocityCommandCfg.Ranges(
+            lin_vel_x=(1.0, 1.0),
+            lin_vel_y=(0.0, 0.0),
+            ang_vel_z=(0.2, 0.2),
+        ),
+    )
+    all_ids = np.arange(env.num_envs, dtype=np.int32)
+    manager.reset(all_ids)
+    term = manager.get_term("twist")
+
+    manager.compute(0.1)
+    np.testing.assert_allclose(term.metrics["error_vel_xy"], np.hypot(0.5, 0.25) / 10.0)
+    np.testing.assert_allclose(term.metrics["error_vel_yaw"], 0.1 / 10.0)
+    np.testing.assert_array_equal(term.command_counter, 1)
+    manager.compute(0.1)
+    np.testing.assert_array_equal(term.command_counter, 2)
+    np.testing.assert_allclose(term.time_left, 0.2)
+
+
+def test_heading_world_forward_and_standing_modes() -> None:
+    all_ids = np.arange(4, dtype=np.int32)
+
+    heading_env, _ = _env()
+    heading = _manager(
+        heading_env,
+        heading_command=True,
+        rel_heading_envs=1.0,
+        heading_control_stiffness=1.0,
+        ranges=UniformVelocityCommandCfg.Ranges(
+            lin_vel_x=(0.0, 0.0),
+            lin_vel_y=(0.0, 0.0),
+            ang_vel_z=(-0.5, 0.5),
+            heading=(np.pi / 2, np.pi / 2),
+        ),
+    )
+    heading.reset(all_ids)
+    heading.compute(0.0, env_ids=all_ids)
+    np.testing.assert_allclose(heading.get_command("twist")[:, 2], 0.5)
+
+    world_env, world_backend = _env()
+    world_backend.body_quat[:, 0] = np_yaw_to_quat(np.full(4, np.pi / 2))
+    world = _manager(
+        world_env,
+        rel_world_envs=1.0,
+        ranges=UniformVelocityCommandCfg.Ranges(
+            lin_vel_x=(1.0, 1.0),
+            lin_vel_y=(0.0, 0.0),
+            ang_vel_z=(0.0, 0.0),
+        ),
+    )
+    world.reset(all_ids)
+    world.compute(0.0, env_ids=all_ids)
+    np.testing.assert_allclose(world.get_command("twist")[:, :2], [[0.0, -1.0]] * 4, atol=1e-6)
+
+    forward_env, _ = _env()
+    forward = _manager(
+        forward_env,
+        rel_forward_envs=1.0,
+        ranges=UniformVelocityCommandCfg.Ranges(
+            lin_vel_x=(-0.1, -0.1),
+            lin_vel_y=(0.4, 0.4),
+            ang_vel_z=(0.2, 0.2),
+        ),
+    )
+    forward.reset(all_ids)
+    np.testing.assert_allclose(forward.get_command("twist"), [[0.3, 0.0, 0.0]] * 4)
+
+    standing_env, _ = _env()
+    standing = _manager(standing_env, rel_standing_envs=1.0)
+    standing.reset(all_ids)
+    standing.compute(0.0, env_ids=all_ids)
+    np.testing.assert_array_equal(standing.get_command("twist"), 0.0)
+
+
+@pytest.mark.parametrize(
+    ("overrides", "error", "message"),
+    [
+        ({"rel_standing_envs": 1.1}, ValueError, "within.*0, 1"),
+        ({"heading_command": True}, ValueError, "heading_command=True"),
+        (
+            {
+                "ranges": UniformVelocityCommandCfg.Ranges(
+                    lin_vel_x=(1.0, -1.0),
+                    lin_vel_y=(0.0, 0.0),
+                    ang_vel_z=(0.0, 0.0),
+                )
+            },
+            ValueError,
+            "exceeds upper",
+        ),
+        ({"heading_control_stiffness": float("nan")}, ValueError, "must be finite"),
+        ({"resampling_time_range": (0.0, 0.0)}, ValueError, "must be positive"),
+        ({"init_velocity_prob": 0.1}, NotImplementedError, "root velocity write"),
+    ],
+)
+def test_invalid_or_unsupported_config_fails_at_construction(
+    overrides: dict[str, Any], error: type[Exception], message: str
+) -> None:
+    env, _ = _env()
+    with pytest.raises(error, match=message):
+        _cfg(**overrides).build(env)
+
+
+def test_root_body_frame_capability_and_finite_values_fail_closed() -> None:
+    unsupported = _Backend(unsupported="body-frame linear velocity")
+    with pytest.raises(
+        NotImplementedError,
+        match="body-frame linear velocity state.*backend 'fake'",
+    ):
+        EntityScene(
+            {"robot": EntityCfg(root_body_name="base")},
+            cast(SimBackend, unsupported),
+        )
+
+    non_finite = _Backend()
+    non_finite.body_ang_vel_b[0, 0, 0] = np.nan
+    with pytest.raises(ValueError, match="body-frame angular velocity state.*NaN or Inf"):
+        EntityScene(
+            {"robot": EntityCfg(root_body_name="base")},
+            cast(SimBackend, non_finite),
+        )
diff --git a/tests/envs/test_allegro_domain_randomization.py b/tests/envs/test_allegro_domain_randomization.py
deleted file mode 100644
index 0df12763b..000000000
--- a/tests/envs/test_allegro_domain_randomization.py
+++ /dev/null
@@ -1,70 +0,0 @@
-from __future__ import annotations
-
-from typing import Any
-
-import numpy as np
-import pytest
-
-pytest.importorskip("mujoco", reason="mujoco not installed")
-
-try:
-    from mujoco_uni.batch_env import BatchEnvPool as _  # noqa: F401
-except Exception:
-    pytest.skip(
-        "mujoco_uni.batch_env not available (platform/libstdc++ issue)", allow_module_level=True
-    )
-
-from unilab.base.registry import ensure_registries
-
-
-def test_allegro_mujoco_reset_applies_base_mass_and_com_domain_randomization(
-    default_allegro_reward_config: dict[str, Any],
-) -> None:
-    ensure_registries()
-
-    from unilab.base import registry
-
-    env = registry.make(
-        "AllegroInhandRotation",
-        num_envs=4,
-        sim_backend="mujoco",
-        env_cfg_override={
-            "reward_config": default_allegro_reward_config,
-            "domain_rand": {
-                "randomize_base_mass": True,
-                "added_mass_range": [-0.02, 0.02],
-                "random_com": True,
-                "com_offset_x": [-0.005, 0.005],
-            },
-        },
-    )
-    env_obj: Any = env
-    try:
-        env_obj.init_state()
-        backend: Any = env_obj._backend
-        pool = backend._pool
-        base_body_id = int(backend._base_body_id)
-        body_mass = np.stack([pool.get_field(i, "body_mass") for i in range(env_obj.num_envs)])
-        body_ipos = np.stack([pool.get_field(i, "body_ipos") for i in range(env_obj.num_envs)])
-        body_ipos = body_ipos.reshape(env_obj.num_envs, -1, 3)
-
-        base_mass = float(backend._base_body_mass[base_body_id])
-        base_ipos = np.asarray(backend._base_body_ipos[base_body_id])
-
-        randomized_base_mass = body_mass[:, base_body_id]
-        randomized_base_ipos = body_ipos[:, base_body_id]
-
-        delta_mass = randomized_base_mass - base_mass
-        delta_x = randomized_base_ipos[:, 0] - base_ipos[0]
-        delta_yz = randomized_base_ipos[:, 1:] - base_ipos[1:]
-
-        assert np.unique(np.round(delta_mass, 6)).size > 1
-        assert np.unique(np.round(delta_x, 6)).size > 1
-
-        assert np.all(delta_mass >= -0.02)
-        assert np.all(delta_mass <= 0.02)
-        assert np.all(delta_x >= -0.005)
-        assert np.all(delta_x <= 0.005)
-        np.testing.assert_allclose(delta_yz, 0.0)
-    finally:
-        env_obj.close()
diff --git a/tests/envs/test_env_configs.py b/tests/envs/test_env_configs.py
index 55083925d..ae102d288 100644
--- a/tests/envs/test_env_configs.py
+++ b/tests/envs/test_env_configs.py
@@ -1,9 +1,7 @@
 """Tests for env config completeness and env instantiation.
 
-Config-attribute tests (non-slow) verify that config dataclasses expose every
-attribute accessed by their paired env class, WITHOUT running a simulation.
-
-Slow tests actually call registry.make() and run reset + step.
+The whole module is marked slow: Hydra composition and ``registry.make()``
+over every owner are CPU-bound and too slow for the single-core CI runner.
 """
 
 from __future__ import annotations
@@ -21,6 +19,9 @@
 
 from unilab.base.registry import ensure_registries
 
+# CPU-bound on the single-core CI runner; kept in the slow lane (make test-slow).
+pytestmark = pytest.mark.slow
+
 
 def _require_mujoco_runtime() -> None:
     pytest.importorskip("mujoco", reason="mujoco not installed")
@@ -30,8 +31,79 @@ def _require_mujoco_runtime() -> None:
         pytest.skip("mujoco_uni.batch_env not available (platform/libstdc++ issue)")
 
 
+def _allegro_manager_override(
+    backend: str = "mujoco",
+    *,
+    config_root: str = "ppo",
+    task: str = "allegro_inhand",
+) -> dict[str, Any]:
+    from hydra import compose, initialize_config_dir
+
+    from unilab.base.config_adapter import BackendAdapter
+
+    repo_root = Path(__file__).parents[2]
+    with initialize_config_dir(
+        config_dir=str(repo_root / "src" / "unilab" / "conf" / config_root), version_base="1.3"
+    ):
+        cfg = compose("config", overrides=[f"task={task}/{backend}"])
+    return BackendAdapter(
+        cfg, root_dir=repo_root, algo_name=config_root
+    ).build_task_env_cfg_override()
+
+
+def _g1_manager_override(
+    task: str = "g1_walk_flat", backend: str = "mujoco", config_group: str = "ppo"
+) -> dict[str, Any]:
+    from hydra import compose, initialize_config_dir
+
+    from unilab.base.config_adapter import BackendAdapter
+
+    repo_root = Path(__file__).parents[2]
+    if task == "g1_walk_rough":
+        # There is no ppo g1_walk_rough owner; use the SAC owner instead.
+        with initialize_config_dir(
+            config_dir=str(repo_root / "src" / "unilab" / "conf" / "sac"), version_base="1.3"
+        ):
+            cfg = compose("config", overrides=[f"task={task}/mujoco"])
+        return BackendAdapter(
+            cfg, root_dir=repo_root, algo_name="sac"
+        ).build_task_env_cfg_override()
+    with initialize_config_dir(
+        config_dir=str(repo_root / "src" / "unilab" / "conf" / config_group), version_base="1.3"
+    ):
+        cfg = compose("config", overrides=[f"task={task}/{backend}"])
+    return BackendAdapter(
+        cfg, root_dir=repo_root, algo_name=config_group
+    ).build_task_env_cfg_override()
+
+
+def _motion_manager_override(
+    task: str,
+    backend: str,
+    *,
+    config_root: str = "ppo",
+) -> tuple[str, dict[str, Any]]:
+    from hydra import compose, initialize_config_dir
+    from hydra.core.global_hydra import GlobalHydra
+
+    from unilab.base.config_adapter import BackendAdapter
+
+    repo_root = Path(__file__).parents[2]
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(
+        config_dir=str(repo_root / "src" / "unilab" / "conf" / config_root), version_base="1.3"
+    ):
+        overrides = [f"task={task}/{backend}"]
+        cfg = compose("config", overrides=overrides)
+    return str(cfg.training.task_name), BackendAdapter(
+        cfg,
+        root_dir=repo_root,
+        algo_name=config_root,
+    ).build_task_env_cfg_override()
+
+
 # ---------------------------------------------------------------------------
-# Non-slow: config attribute completeness (no env.step(), no MuJoCo sim)
+# Config attribute completeness (no env.step(), no MuJoCo sim)
 # ---------------------------------------------------------------------------
 
 
@@ -51,13 +123,7 @@ def blocked_import(name, globals=None, locals=None, fromlist=(), level=0):
         builtins.__import__ = blocked_import
 
         from unilab.base import registry
-        from unilab.base.backend import create_backend
-        from unilab.envs.manipulation.allegro_inhand.rotation import AllegroRotationCfg
-        from unilab.envs.motion_tracking.g1.tracking import (
-            G1MotionTrackingCfg,
-            G1MotionTrackingDeployEnvCfg,
-        )
-        from unilab.envs.motion_tracking.x2 import X2WallFlipTrackingCfg
+        from unilab.base.backend_factory import create_backend
         from unilab.base.registry import ensure_registries
 
         ensure_registries()
@@ -66,10 +132,9 @@ def blocked_import(name, globals=None, locals=None, fromlist=(), level=0):
         assert registry.contains("G1MotionTrackingDeploy")
         assert registry.contains("X2WallFlipTracking")
         assert registry.contains("AllegroInhandRotation")
-        G1MotionTrackingCfg()
-        G1MotionTrackingDeployEnvCfg()
-        X2WallFlipTrackingCfg()
-        AllegroRotationCfg()
+        metadata = registry.list_registered_envs()
+        assert metadata["G1MotionTracking"]["config_factory"] == "ManagerBasedRlEnvCfg"
+        assert metadata["X2WallFlipTracking"]["config_factory"] == "ManagerBasedRlEnvCfg"
         """
     )
 
@@ -84,201 +149,80 @@ def blocked_import(name, globals=None, locals=None, fromlist=(), level=0):
     assert result.returncode == 0, result.stderr or result.stdout
 
 
-def test_g1_walk_env_cfg_obs_groups_spec():
-    """G1WalkEnv must declare obs_groups_spec with actor and critic groups."""
-    from unilab.envs.locomotion.g1.joystick import G1WalkEnvCfg
-
-    cfg = G1WalkEnvCfg()
-    assert not hasattr(cfg, "obs_config"), "obs_config should have been removed"
-
-
-def test_g1_walk_flat_cfg_no_obs_config():
-    """G1WalkFlatCfg should no longer have obs_config after dict obs refactor."""
-    from unilab.envs.locomotion.g1.joystick import G1WalkFlatCfg
-
-    cfg = G1WalkFlatCfg()
-    assert not hasattr(cfg, "obs_config"), (
-        "obs_config should have been removed in the dict obs refactor"
-    )
-
-
-def test_g1_walk_flat_cfg_has_domain_rand_for_motrix():
-    from unilab.envs.locomotion.g1.joystick import G1WalkFlatCfg
-
-    cfg = G1WalkFlatCfg()
-    assert hasattr(cfg, "domain_rand")
-    assert hasattr(cfg, "gait_phase_init_mode")
-    assert hasattr(cfg, "reset_base_qvel_limit")
-    assert cfg.domain_rand.randomize_base_mass is False
-    assert cfg.domain_rand.random_com is False
-    assert cfg.domain_rand.randomize_gravity is False
-    assert cfg.domain_rand.push_robots is False
-
-
-def test_g1_walk_flat_cfg_defaults_match_walk_profile():
-    from unilab.envs.locomotion.g1.joystick import G1WalkFlatCfg
-
-    cfg = G1WalkFlatCfg()
-    assert not hasattr(cfg, "obs_profile")
-    assert cfg.curriculum.enabled is True
-
-
-def test_g1_walk_tasks_register_to_algorithm_agnostic_env_base():
+def test_g1_walk_tasks_register_to_manager_based_env():
     from unilab.base import registry
-    from unilab.envs.locomotion.g1.joystick import G1WalkEnv, G1WalkRewardConfig
-
-    env = cast(
-        Any,
-        registry.make(
-            "G1WalkFlat",
-            num_envs=1,
-            sim_backend="mujoco",
-            env_cfg_override={
-                "reward_config": G1WalkRewardConfig(
-                    scales={"tracking_lin_vel": 2.0, "alive": 10.0},
-                    tracking_sigma=0.25,
-                    base_height_target=0.754,
-                    min_base_height=0.3,
-                    max_tilt_deg=65.0,
-                    gait_frequency=1.5,
-                    feet_phase_swing_height=0.09,
-                    feet_phase_tracking_sigma=0.04,
-                    close_feet_threshold=0.15,
-                    pose_weights=[0.01] * 29,
-                )
-            },
-        ),
-    )
-    try:
-        assert env.__class__ is G1WalkEnv
-    finally:
-        env.close()
-
-
-def test_g1_walk_flat_observation_construction_is_hardcoded_for_legacy_and_walk_modes():
-    from unilab.envs.locomotion.g1.joystick import G1WalkEnv
-
-    class NoiseCfg:
-        level = 0.0
-        scale_gyro = 0.0
-        scale_gravity = 0.0
-        scale_joint_angle = 0.0
-        scale_joint_vel = 0.0
-        scale_linvel = 0.0
-
-    def compute_obs(curriculum_enabled: bool) -> dict[str, np.ndarray]:
-        env = cast(Any, object.__new__(G1WalkEnv))
-        env._num_envs = 1
-        env.default_angles = np.array([[0.5, -0.5]], dtype=np.float32)
-        env._cfg = type(
-            "Cfg",
-            (),
-            {
-                "noise_config": NoiseCfg(),
-                "curriculum": type("Curriculum", (), {"enabled": curriculum_enabled})(),
-            },
-        )()
-        env._obs_noise = lambda data, scale: data + 100.0
-        info = {
-            "commands": np.array([[0.7, 0.0, 0.2]], dtype=np.float32),
-            "current_actions": np.array([[0.1, -0.2]], dtype=np.float32),
-            "gait_phase": np.array([[0.3, 3.4]], dtype=np.float32),
-        }
-        return cast(
-            dict[str, np.ndarray],
-            env._compute_obs(
-                info,
-                linvel=np.array([[1.0, 2.0, 3.0]], dtype=np.float32),
-                gyro=np.array([[4.0, 5.0, 6.0]], dtype=np.float32),
-                gravity=np.array([[0.1, 0.2, 0.9]], dtype=np.float32),
-                dof_pos=np.array([[0.6, -0.3]], dtype=np.float32),
-                dof_vel=np.array([[7.0, 8.0]], dtype=np.float32),
-            ),
-        )
-
-    legacy = compute_obs(curriculum_enabled=False)
-    walk = compute_obs(curriculum_enabled=True)
-
-    np.testing.assert_allclose(legacy["obs"][:, :3], [[104.0, 105.0, 106.0]])
-    np.testing.assert_allclose(legacy["obs"][:, 8:10], [[107.0, 108.0]])
-    np.testing.assert_allclose(legacy["critic"][:, :3], [[4.0, 5.0, 6.0]])
-    np.testing.assert_allclose(legacy["critic"][:, 17:20], [[1.0, 2.0, 3.0]])
-
-    np.testing.assert_allclose(walk["obs"][:, :3], [[26.0, 26.25, 26.5]])
-    np.testing.assert_allclose(walk["obs"][:, 8:10], [[5.35, 5.4]])
-    np.testing.assert_allclose(walk["critic"][:, :3], [[1.0, 1.25, 1.5]])
-    np.testing.assert_allclose(walk["critic"][:, 8:10], [[0.35, 0.4]])
-    np.testing.assert_allclose(walk["critic"][:, 17:20], [[2.0, 4.0, 6.0]])
-
 
-def test_g1_walk_env_obs_groups_spec_dims():
-    """obs_groups_spec total dim must match what _compute_obs actually produces.
+    ensure_registries()
+    metadata = registry.list_registered_envs()
+    assert metadata["G1WalkFlat"]["config_factory"] == "ManagerBasedRlEnvCfg"
+    assert metadata["G1WalkFlat"]["available_backends"] == [
+        "mujoco",
+        "mjwarp",
+        "motrix",
+        "isaacgym",
+        "genesis",
+        "isaacsim",
+    ]
+    assert metadata["G1WalkRough"]["available_backends"] == ["mujoco", "motrix"]
 
-    G1WalkEnv._compute_obs outputs (G1 has 29 DoF):
-        actor: gyro(3) + gravity(3) + diff(29) + dof_vel(29)
-            + last_actions(29) + command(3) + gait_phase(2) = 98
-        critic: actor(98) + linvel(3) = 101
-    """
-    from unilab.envs.locomotion.g1.joystick import G1WalkEnv
-
-    # obs_groups_spec is a @property; access via descriptor protocol
-    spec = G1WalkEnv.obs_groups_spec.fget(None)  # type: ignore[union-attr]
-    assert spec is not None
-    assert spec["obs"] == 98
-    assert spec["critic"] == 101
-
-
-def test_g1_walk_env_reward_dispatch_restores_motrix_terms():
-    from unilab.envs.locomotion.g1.joystick import G1WalkEnv
-
-    env = cast(Any, object.__new__(G1WalkEnv))
-    env._reward_fns = {}
-
-    env._init_reward_functions()
-
-    assert "penalty_feet_ori" in env._reward_fns
-    assert "feet_phase_contrast" in env._reward_fns
-    assert "feet_phase_contact" in env._reward_fns
-    assert "feet_double_stance" in env._reward_fns
-
-
-def test_g1_walk_env_feet_phase_reward_is_gated_by_forward_speed():
-    from unilab.envs.locomotion.common.rewards import RewardContext
-    from unilab.envs.locomotion.g1.joystick import G1WalkEnv
-
-    class FakeBackend:
-        def get_sensor_data(self, name: str) -> np.ndarray:
-            if name == "left_foot_pos":
-                return np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], dtype=np.float32)
-            if name == "right_foot_pos":
-                return np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], dtype=np.float32)
-            raise KeyError(name)
-
-    env = cast(Any, object.__new__(G1WalkEnv))
-    env._backend = FakeBackend()
-    env._num_envs = 2
-    env._reward_cfg = type(
-        "RewardCfg",
-        (),
-        {
-            "feet_phase_swing_height": 0.09,
-            "feet_phase_tracking_sigma": 0.008,
-            "min_forward_speed_for_gait_reward": 0.05,
-        },
-    )()
 
-    ctx = RewardContext(
-        info={"gait_phase": np.zeros((2, 2), dtype=np.float32), "commands": np.zeros((2, 3))},
-        linvel=np.array([[0.01, 0.0, 0.0], [0.10, 0.0, 0.0]], dtype=np.float32),
-        gyro=np.zeros((2, 3), dtype=np.float32),
-        dof_pos=np.zeros((2, 29), dtype=np.float32),
-        num_envs=2,
-    )
+def test_g1_walk_flat_isaacgym_owner_composes_and_materializes():
+    """The isaacgym owner composes and materializes a plain manager env cfg."""
+    from unilab.base import registry
+    from unilab.base.config_materialization import apply_cfg_overrides
+    from unilab.envs import ManagerBasedRlEnvCfg
 
-    reward = env._reward_feet_phase(ctx)
+    ensure_registries()
+    override = _g1_manager_override("g1_walk_flat", backend="isaacgym")
+    env_cfg = registry.materialize_env_config("G1WalkFlat")
+    assert isinstance(env_cfg, ManagerBasedRlEnvCfg)
+    apply_cfg_overrides(env_cfg, override)
+    env_cfg.validate()
+
+    assert env_cfg.scene is not None
+    # The subprocess backend consumes the self-contained MJCF scene directly;
+    # scene fragments and generated terrain stay unset.
+    assert env_cfg.scene.model_file.endswith("robots/g1/scene_flat.xml")
+    assert env_cfg.scene.fragment_files == []
+    assert env_cfg.scene.terrain is None
+    assert env_cfg.scene.default_keyframe_name == "stand"
+    assert env_cfg.isaacgym_device_id == 0
+    # Effort-mode dofs carry no PD gains, so the owner disables kp/kd
+    # randomization like the mjwarp/motrix owners.
+    assert env_cfg.events["pd_gains"] is None
+
+
+@pytest.mark.parametrize("config_group", ("ppo", "sac"))
+def test_g1_walk_flat_genesis_owner_composes_and_materializes(config_group):
+    """The genesis owners compose and materialize a plain manager env cfg."""
+    from unilab.base import registry
+    from unilab.base.config_materialization import apply_cfg_overrides
+    from unilab.envs import ManagerBasedRlEnvCfg
 
-    assert reward[0] == pytest.approx(0.0)
-    assert reward[1] > 0.0
+    ensure_registries()
+    override = _g1_manager_override("g1_walk_flat", backend="genesis", config_group=config_group)
+    env_cfg = registry.materialize_env_config("G1WalkFlat")
+    assert isinstance(env_cfg, ManagerBasedRlEnvCfg)
+    apply_cfg_overrides(env_cfg, override)
+    env_cfg.validate()
+
+    assert env_cfg.scene is not None
+    # The in-process backend consumes the self-contained MJCF scene directly;
+    # scene fragments and generated terrain stay unset.
+    assert env_cfg.scene.model_file.endswith("robots/g1/scene_flat.xml")
+    assert env_cfg.scene.fragment_files == []
+    assert env_cfg.scene.terrain is None
+    assert env_cfg.scene.default_keyframe_name == "stand"
+    # The owner re-declares the MJCF <option integrator="implicitfast"> that
+    # Genesis drops at import; the other global options stay at Genesis
+    # defaults (None).
+    assert env_cfg.genesis_integrator == "implicitfast"
+    assert env_cfg.genesis_constraint_solver is None
+    assert env_cfg.genesis_friction_cone is None
+    assert env_cfg.genesis_solver_iterations is None
+    # kp/kd reset randomization stays enabled: the backend declares the
+    # measured RESET_TERM_KP/KD DR terms.
+    assert env_cfg.events["pd_gains"] is not None
 
 
 def test_g1_walk_flat_assets_define_contact_sensors_for_gait_rewards():
@@ -394,98 +338,198 @@ def test_g1_box_tracking_scene_uses_sphere_hand_and_box_tracking_mesh():
         assert name in scene_text
 
 
-def test_allegro_rotation_obs_groups_spec_dims():
-    """Allegro rotation obs_groups_spec should expose single actor obs group."""
-    from unilab.envs.manipulation.allegro_inhand.rotation import AllegroRotationPPO
+def test_allegro_rotation_and_grasp_registries_are_manager_only():
+    from unilab.base import registry
+    from unilab.base.config_materialization import apply_cfg_overrides
+    from unilab.envs import ManagerBasedRlEnvCfg
 
-    env = cast(Any, object.__new__(AllegroRotationPPO))
-    spec = env.obs_groups_spec
+    ensure_registries()
+    metadata = registry.list_registered_envs()
+    assert metadata["AllegroInhandRotation"] == {
+        "config_factory": "ManagerBasedRlEnvCfg",
+        "available_backends": ["mujoco", "motrix", "drake"],
+    }
+    assert metadata["AllegroInhandRotationGrasp"] == {
+        "config_factory": "ManagerBasedRlEnvCfg",
+        "available_backends": ["mujoco", "motrix"],
+    }
 
-    assert spec == {"obs": 105}
+    cfg = registry.materialize_env_config("AllegroInhandRotation")
+    assert isinstance(cfg, ManagerBasedRlEnvCfg)
+    apply_cfg_overrides(cfg, _allegro_manager_override())
+    assert cfg.policy_observation_group == "policy"
+    assert cfg.critic_observation_group is None
+    assert cfg.observations["policy"].history_length == 3
+    assert list(cfg.actions) == ["hand"]
+    assert list(cfg.terminations) == ["dropped", "time_out"]
+    assert list(cfg.rewards) == [
+        "rotate",
+        "obj_linvel",
+        "pose_diff",
+        "torque",
+        "work",
+        "drop",
+    ]
+    assert not hasattr(cfg, "reward_config")
+
+    grasp_cfg = registry.materialize_env_config("AllegroInhandRotationGrasp")
+    assert isinstance(grasp_cfg, ManagerBasedRlEnvCfg)
+    apply_cfg_overrides(
+        grasp_cfg,
+        _allegro_manager_override(task="allegro_inhand_grasp"),
+    )
+    assert grasp_cfg.observations["policy"].history_length == 3
+    assert grasp_cfg.actions["hand"].action_scale == 0.0
+    assert list(grasp_cfg.terminations) == ["dropped", "time_out", "invalid_grasp"]
+    assert list(grasp_cfg.metrics) == [
+        "fingertips_close",
+        "enough_contacts",
+        "ball_held",
+        "valid",
+    ]
+    assert list(grasp_cfg.recorders) == ["grasp_cache"]
 
 
-def test_allegro_grasp_obs_groups_spec_dims():
-    """Allegro grasp task inherits the same obs group layout as rotation."""
-    from unilab.envs.manipulation.allegro_inhand.grasp_gen import AllegroRotationGrasp
+def test_allegro_manager_configured_missing_grasp_cache_fails_closed(tmp_path: Path):
+    from unilab.managers import EventTermCfg
+    from unilab.tasks.manipulation.allegro_inhand.manager_terms import AllegroHandBallReset
 
-    env = cast(Any, object.__new__(AllegroRotationGrasp))
-    spec = env.obs_groups_spec
+    entity = SimpleNamespace(
+        num_joints=16,
+        data=SimpleNamespace(
+            default_root_state=np.zeros((2, 13), dtype=np.float32),
+            actuator_ctrl_range=np.tile([-1.0, 1.0], (16, 1)),
+        ),
+    )
+    env = SimpleNamespace(num_envs=2, scene={"robot": entity})
+    cfg = EventTermCfg(
+        func=AllegroHandBallReset,
+        mode="reset",
+        params={
+            "entity_name": "robot",
+            "grasp_cache_path": str(tmp_path / "missing.npy"),
+            "joint_noise": 0.0,
+            "ball_velocity_noise": 0.0,
+            "ball_z_offset": 0.0,
+        },
+    )
 
-    assert spec == {"obs": 105}
+    with pytest.raises(FileNotFoundError, match="configured grasp cache does not exist"):
+        AllegroHandBallReset(cfg, cast(Any, env))
 
 
-def test_allegro_missing_grasp_cache_logs_local_generation_notice(
-    tmp_path: Path, caplog: pytest.LogCaptureFixture
-) -> None:
-    from unilab.envs.manipulation.allegro_inhand.rotation import (
-        AllegroRotationPPOCfg,
-        _materialize_grasp_cache,
+def _allegro_grasp_term_fixture() -> tuple[Any, Any, np.ndarray]:
+    from unilab.managers import TerminationTermCfg
+    from unilab.tasks.manipulation.allegro_inhand.grasp_gen import (
+        AllegroGraspQualityTermination,
     )
-
-    missing_cache = tmp_path / "missing_allegro_cache.npy"
-    cfg = AllegroRotationPPOCfg(
-        grasp_cache_path=str(missing_cache),
-        gen_grasp=False,
+    from unilab.tasks.manipulation.allegro_inhand.manager_terms import (
+        AllegroRotationObservation,
     )
 
-    with caplog.at_level(
-        logging.WARNING, logger="unilab.envs.manipulation.allegro_inhand.rotation"
-    ):
-        assert _materialize_grasp_cache(cfg) is None
-    notice = caplog.text
+    num_envs = 3
+    states = np.arange(num_envs * 23, dtype=np.float32).reshape(num_envs, 23)
+    observation = object.__new__(AllegroRotationObservation)
+    observation.dof_pos = states[:, :16]
+    observation.ball_pos = np.array(
+        [[0.0, 0.0, 0.2], [0.0, 0.0, 0.2], [0.0, 0.0, 0.1]], dtype=np.float32
+    )
+    observation.ball_quat = states[:, 19:23]
+    observation._last_counter = 1
+
+    body_pos = np.repeat(observation.ball_pos[:, None, :], 4, axis=1)
+    body_pos[:, :, 0] += 0.05
+    body_pos[1, 0, 0] += 0.2
+    contacts = np.array(
+        [[1.0, 1.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0], [1.0, 0.0, 0.0, 0.0]],
+        dtype=np.float32,
+    )
+    entity = SimpleNamespace(
+        data=SimpleNamespace(body_link_pos_w=body_pos),
+        find_bodies=lambda names, preserve_order: (list(range(4)), list(names)),
+    )
 
-    assert str(missing_cache) in notice
-    assert "no Hugging Face download will be attempted" in notice
-    assert "uv run train --algo ppo --task allegro_inhand_grasp --sim mujoco" in notice
-    assert "env.grasp_cache_path" in notice
+    class _Scene(dict):
+        def bind_sensor_data(self, names):
+            assert tuple(names) == ("ff_contact", "mf_contact", "rf_contact", "th_contact")
+            return SimpleNamespace(dimensions=(1, 1, 1, 1), read=lambda: contacts)
 
+    env = SimpleNamespace(
+        num_envs=num_envs,
+        common_step_counter=1,
+        scene=_Scene(robot=entity),
+        observation_manager=SimpleNamespace(
+            get_term_cfg=lambda group, name: SimpleNamespace(func=observation)
+        ),
+        action_manager=SimpleNamespace(),
+        reset_time_outs=np.ones(num_envs, dtype=np.bool_),
+        reset_terminated=np.zeros(num_envs, dtype=np.bool_),
+        extras={"log": {}},
+    )
+    cfg = TerminationTermCfg(
+        func=AllegroGraspQualityTermination,
+        params={
+            "entity_name": "robot",
+            "observation_group": "policy",
+            "observation_term": "rotation",
+            "fingertip_body_names": ["ff_tip", "mf_tip", "rf_tip", "th_tip"],
+            "contact_sensor_names": [
+                "ff_contact",
+                "mf_contact",
+                "rf_contact",
+                "th_contact",
+            ],
+            "max_fingertip_distance": 0.1,
+            "minimum_contacts": 2,
+            "minimum_ball_height": 0.125,
+            "enabled": True,
+        },
+    )
+    term = AllegroGraspQualityTermination(cfg, cast(Any, env))
+    env.termination_manager = SimpleNamespace(get_term_cfg=lambda name: SimpleNamespace(func=term))
+    return env, term, states
 
-def test_allegro_grasp_generation_skips_cache_materialization(
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    from unilab.envs.manipulation.allegro_inhand import rotation
 
-    def fail_io(*_args: Any, **_kwargs: Any) -> None:
-        raise AssertionError("grasp generation must not resolve or load a rotation cache")
+def test_allegro_grasp_quality_term_matches_legacy_conditions():
+    from unilab.managers import MetricsTermCfg
+    from unilab.tasks.manipulation.allegro_inhand.grasp_gen import AllegroGraspQualityMetric
 
-    monkeypatch.setattr(rotation, "resolve_grasp_cache_path", fail_io)
-    monkeypatch.setattr(rotation.np, "load", fail_io)
+    env, term, _ = _allegro_grasp_term_fixture()
 
-    cfg = rotation.AllegroRotationPPOCfg(gen_grasp=True)
-    assert rotation._materialize_grasp_cache(cfg) is None
+    np.testing.assert_array_equal(term(cast(Any, env)), [False, True, True])
+    np.testing.assert_array_equal(term.fingertips_close, [True, False, True])
+    np.testing.assert_array_equal(term.enough_contacts, [True, True, False])
+    np.testing.assert_array_equal(term.ball_held, [True, True, False])
+    metric = AllegroGraspQualityMetric(
+        MetricsTermCfg(
+            func=AllegroGraspQualityMetric,
+            params={"quality_term_name": "invalid_grasp", "condition": "valid"},
+        ),
+        cast(Any, env),
+    )
+    np.testing.assert_array_equal(metric(cast(Any, env)), [1.0, 0.0, 0.0])
 
 
-def test_allegro_grasp_target_raises_run_complete_without_resaving_on_close(
+def test_allegro_grasp_recorder_saves_target_and_raises_run_complete(
     tmp_path: Path, monkeypatch: pytest.MonkeyPatch
 ) -> None:
     from unilab.base.run_control import RunComplete
-    from unilab.envs.manipulation.allegro_inhand import grasp_gen
+    from unilab.managers import RecorderTermCfg
+    from unilab.tasks.manipulation.allegro_inhand import grasp_gen
 
+    env, term, states = _allegro_grasp_term_fixture()
+    term(cast(Any, env))
     cache_path = tmp_path / "allegro.npy"
-    env = cast(Any, object.__new__(grasp_gen.AllegroRotationGrasp))
-    env._cfg = grasp_gen.AllegroRotationGraspCfg(
-        grasp_collection_target=2,
-        grasp_cache_path=str(cache_path),
-        grasp_auto_save=True,
-        grasp_quality_check=False,
-    )
-    states = np.arange(3 * 23, dtype=np.float64).reshape(3, 23)
-    env._saved_grasping_states = []
-    env._grasp_cache_saved = False
-    env._grasp_target_reached_notified = False
-    env._state = SimpleNamespace(
-        truncated=np.ones((3,), dtype=bool),
-        terminated=np.zeros((3,), dtype=bool),
-        info={
-            "curr_dof_pos": states[:, :16],
-            "curr_ball_pos": states[:, 16:19],
-            "curr_ball_quat": states[:, 19:23],
+    cfg = RecorderTermCfg(
+        func=grasp_gen.AllegroGraspRecorder,
+        params={
+            "quality_term_name": "invalid_grasp",
+            "output_path": str(cache_path),
+            "collection_target": 2,
+            "auto_save": True,
         },
     )
-    env.get_hand_dof_pos = lambda: states[:, :16]
-    env.get_ball_pos = lambda: states[:, 16:19]
-    env.get_ball_quat = lambda: states[:, 19:23]
-
+    recorder = grasp_gen.AllegroGraspRecorder(cfg, cast(Any, env))
     save_calls: list[Path] = []
     real_save = grasp_gen.np.save
 
@@ -493,22 +537,16 @@ def save_once(path: str | Path, values: np.ndarray) -> None:
         save_calls.append(Path(path))
         real_save(path, values)
 
-    parent_closes: list[Any] = []
     monkeypatch.setattr(grasp_gen.np, "save", save_once)
-    monkeypatch.setattr(
-        grasp_gen.AllegroRotationPPO,
-        "close",
-        lambda instance: parent_closes.append(instance),
-    )
-
     with pytest.raises(RunComplete) as caught:
-        env._collect_successful_grasps(np.arange(3, dtype=np.int32))
+        recorder.record_pre_reset(np.arange(3, dtype=np.int32))
 
-    saved = np.load(cache_path)
-    np.testing.assert_array_equal(saved, states[:2].astype(np.float32))
-    assert saved.dtype == np.float32
+    expected = np.concatenate(
+        (states[:, :16], term.observation.ball_pos, states[:, 19:23]), axis=1, dtype=np.float32
+    )
+    np.testing.assert_array_equal(np.load(cache_path), expected[:2])
     assert save_calls == [cache_path]
-    assert env.state.info["log"] == {
+    assert env.extras["log"] == {
         "grasp_cache/saved": 1.0,
         "grasp_cache/num_states": 2.0,
         "grasp/target_reached": 1.0,
@@ -519,969 +557,63 @@ def save_once(path: str | Path, values: np.ndarray) -> None:
         "grasp_collection_target": 2,
     }
 
-    env.close()
-    env._stop_collection()
+    recorder.close()
     assert save_calls == [cache_path]
-    assert parent_closes == [env]
 
 
-def test_allegro_grasp_save_failure_does_not_signal_completion(
+def test_allegro_grasp_recorder_close_autosaves_and_io_failure_is_fail_closed(
     tmp_path: Path, monkeypatch: pytest.MonkeyPatch
 ) -> None:
-    from unilab.envs.manipulation.allegro_inhand import grasp_gen
+    from unilab.managers import RecorderTermCfg
+    from unilab.tasks.manipulation.allegro_inhand import grasp_gen
 
-    env = cast(Any, object.__new__(grasp_gen.AllegroRotationGrasp))
-    env._cfg = grasp_gen.AllegroRotationGraspCfg(
-        grasp_collection_target=1,
-        grasp_cache_path=str(tmp_path / "allegro.npy"),
+    env, term, _ = _allegro_grasp_term_fixture()
+    term(cast(Any, env))
+    cache_path = tmp_path / "allegro.npy"
+    cfg = RecorderTermCfg(
+        func=grasp_gen.AllegroGraspRecorder,
+        params={
+            "quality_term_name": "invalid_grasp",
+            "output_path": str(cache_path),
+            "collection_target": 3,
+            "auto_save": True,
+        },
     )
-    env._saved_grasping_states = [np.zeros((1, 23), dtype=np.float32)]
-    env._grasp_cache_saved = False
-    env._grasp_target_reached_notified = False
-    env._state = SimpleNamespace(info={})
+    recorder = grasp_gen.AllegroGraspRecorder(cfg, cast(Any, env))
+    env.reset_terminated[1] = True
+    recorder.record_pre_reset(np.array([0, 1], dtype=np.int32))
+    assert recorder.total_saved_grasps == 1
+    assert not cache_path.exists()
+    recorder.close()
+    assert np.load(cache_path).shape == (1, 23)
+
+    failed_path = tmp_path / "failed.npy"
+    failed_cfg = RecorderTermCfg(
+        func=grasp_gen.AllegroGraspRecorder,
+        params={
+            "quality_term_name": "invalid_grasp",
+            "output_path": str(failed_path),
+            "collection_target": 1,
+            "auto_save": True,
+        },
+    )
+    failed = grasp_gen.AllegroGraspRecorder(failed_cfg, cast(Any, env))
     sentinel = OSError("disk full")
     monkeypatch.setattr(
         grasp_gen.np, "save", lambda *_args, **_kwargs: (_ for _ in ()).throw(sentinel)
     )
-
     with pytest.raises(OSError) as caught:
-        env._save_grasp_cache()
-
+        failed.record_pre_reset(np.array([0], dtype=np.int32))
     assert caught.value is sentinel
-    assert env._grasp_cache_saved is False
-    assert env._grasp_target_reached_notified is False
-
-
-def test_allegro_reset_samples_materialized_cache_without_file_io(
-    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
-) -> None:
-    from unilab.envs.manipulation.allegro_inhand import rotation
-
-    cache_path = tmp_path / "allegro.npy"
-    cache = np.zeros((4, 23), dtype=np.float64)
-    cache[:, 16] = 0.1
-    cache[:, 19] = 1.0
-    np.save(cache_path, cache)
-    cfg = rotation.AllegroRotationPPOCfg(grasp_cache_path=str(cache_path))
-    materialized = rotation._materialize_grasp_cache(cfg)
-    assert materialized is not None
-
-    monkeypatch.setattr(
-        rotation, "resolve_grasp_cache_path", lambda _: (_ for _ in ()).throw(AssertionError)
-    )
-    monkeypatch.setattr(
-        rotation.np, "load", lambda *_args, **_kwargs: (_ for _ in ()).throw(AssertionError)
-    )
-    env = SimpleNamespace(
-        _grasp_cache=materialized,
-        cfg=SimpleNamespace(
-            domain_rand=SimpleNamespace(joint_noise=0.0, ball_vel_noise=0.0, ball_z_offset=0.0)
-        ),
-        default_angles=np.zeros(16),
-        _NUM_HAND_DOF=16,
-        _ctrl_lower=-np.ones(16),
-        _ctrl_upper=np.ones(16),
-        _init_qpos=np.zeros(23),
-        nv=23,
-    )
-
-    provider = rotation.AllegroRotationDomainRandomizationProvider()
-    for _ in range(2):
-        hand_qpos, ball_pos, ball_quat, qvel = provider._sample_reset_state(env, 2)
-        assert hand_qpos.shape == (2, 16)
-        assert ball_pos.shape == (2, 3)
-        assert ball_quat.shape == (2, 4)
-        assert qvel.shape == (2, 23)
-
-
-def test_g1_motion_tracking_uses_combined_body_pose_query():
-    """G1MotionTracking should query pos/quat via the stable combined backend API."""
-    from unilab.envs.motion_tracking.g1.tracking import G1MotionTrackingEnv
-
-    class FakeBackend:
-        def __init__(self) -> None:
-            self.calls: list[np.ndarray] = []
-
-        def get_body_pose_w(self, body_ids: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
-            self.calls.append(body_ids.copy())
-            return np.ones((2, len(body_ids), 3)), np.ones((2, len(body_ids), 4))
-
-    env = cast(Any, object.__new__(G1MotionTrackingEnv))
-    env._backend = FakeBackend()
-    env.body_ids = np.array([1, 3], dtype=np.int32)
-
-    pos_w, quat_w = env._get_body_pose_w()
-
-    assert pos_w.shape == (2, 2, 3)
-    assert quat_w.shape == (2, 2, 4)
-    assert len(env._backend.calls) == 1
-    np.testing.assert_array_equal(env._backend.calls[0], np.array([1, 3], dtype=np.int32))
-
-
-def test_g1_motion_tracking_reset_observation_uses_sparse_body_pose_rows():
-    from unilab.envs.motion_tracking.common.motion_loader import MotionData
-    from unilab.envs.motion_tracking.g1.tracking import G1MotionTrackingDomainRandomizationProvider
-
-    class FakeBackend:
-        def __init__(self) -> None:
-            self.row_calls: list[tuple[np.ndarray, np.ndarray]] = []
-
-        def get_body_pose_w_rows(
-            self, env_ids: np.ndarray, body_ids: np.ndarray
-        ) -> tuple[np.ndarray, np.ndarray]:
-            self.row_calls.append((env_ids.copy(), body_ids.copy()))
-            rows = len(env_ids)
-            bodies = len(body_ids)
-            return (
-                np.full((rows, bodies, 3), 2.0, dtype=np.float32),
-                np.full((rows, bodies, 4), 3.0, dtype=np.float32),
-            )
-
-        def get_body_pose_w(self, body_ids: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
-            raise AssertionError("reset observation should use sparse body pose rows")
-
-    class FakeMotionLoader:
-        def get_motion_at_frame(self, frames: np.ndarray) -> MotionData:
-            rows = len(frames)
-            return MotionData(
-                joint_pos=np.zeros((rows, 2), dtype=np.float32),
-                joint_vel=np.zeros((rows, 2), dtype=np.float32),
-                body_pos_w=np.zeros((rows, 2, 3), dtype=np.float32),
-                body_quat_w=np.zeros((rows, 2, 4), dtype=np.float32),
-                body_lin_vel_w=np.zeros((rows, 2, 3), dtype=np.float32),
-                body_ang_vel_w=np.zeros((rows, 2, 3), dtype=np.float32),
-            )
-
-    class FakeMotionSampler:
-        current_frames = np.array([10, 11, 12, 13], dtype=np.int32)
-
-    env = SimpleNamespace(
-        _backend=FakeBackend(),
-        body_ids=np.array([1, 3], dtype=np.int32),
-        motion_loader=FakeMotionLoader(),
-        motion_sampler=FakeMotionSampler(),
-        get_local_linvel=lambda: np.zeros((4, 3), dtype=np.float32),
-        get_gyro=lambda: np.zeros((4, 3), dtype=np.float32),
-        get_dof_pos=lambda: np.zeros((4, 2), dtype=np.float32),
-        get_dof_vel=lambda: np.zeros((4, 2), dtype=np.float32),
-    )
-
-    captured: dict[str, np.ndarray] = {}
-
-    def compute_obs(
-        obs_info,
-        motion_data,
-        linvel,
-        gyro,
-        dof_pos,
-        dof_vel,
-        robot_body_pos_w,
-        robot_body_quat_w,
-    ):
-        del obs_info, motion_data, linvel, gyro, dof_pos, dof_vel
-        captured["robot_body_pos_w"] = robot_body_pos_w
-        captured["robot_body_quat_w"] = robot_body_quat_w
-        return {"obs": np.zeros((2, 1), dtype=np.float32)}
-
-    env._compute_obs = compute_obs
-    provider = G1MotionTrackingDomainRandomizationProvider()
-    env_ids = np.array([1, 3], dtype=np.int32)
-
-    obs = provider.build_reset_observation(env, env_ids, {})
-
-    assert obs["obs"].shape == (2, 1)
-    assert len(env._backend.row_calls) == 1
-    np.testing.assert_array_equal(env._backend.row_calls[0][0], env_ids)
-    np.testing.assert_array_equal(env._backend.row_calls[0][1], env.body_ids)
-    assert captured["robot_body_pos_w"].shape == (2, 2, 3)
-    assert captured["robot_body_quat_w"].shape == (2, 2, 4)
-
-
-def _compute_g1_motion_tracking_obs_stub(env_cls: type):
-    from unilab.envs.motion_tracking.common.motion_loader import MotionData
-
-    env = cast(Any, object.__new__(env_cls))
-    env._num_envs = 1
-    env._num_action = 2
-    env._n_motion_bodies = 2
-    env._critic_obs_width = env._critic_base_obs_dim(env._num_action) + env._n_motion_bodies * 9
-    env._cfg = SimpleNamespace(
-        noise_config=SimpleNamespace(
-            level=1.0,
-            scale_linvel=1.0,
-            scale_gyro=1.0,
-            scale_joint_angle=1.0,
-            scale_joint_vel=1.0,
-        ),
-        body_names=("pelvis", "torso_link"),
-    )
-    env.default_angles = np.array([[0.5, -0.5]], dtype=np.float32)
-    env.anchor_body_idx = 0
-    env._motion_anchor_pos_b = np.empty((1, 3), dtype=np.float32)
-    env._motion_anchor_ori_b = np.empty((1, 6), dtype=np.float32)
-    env._motion_command = np.empty((1, 4), dtype=np.float32)
-    env._joint_pos_rel = np.empty((1, 2), dtype=np.float32)
-    env._body_vec_error = np.empty((1, 2, 3), dtype=np.float32)
-    env._body_vec_tmp = np.empty((1, 2, 3), dtype=np.float32)
-    env._quat_error_w = np.empty((1, 2), dtype=np.float32)
-    env._quat_error_x = np.empty((1, 2), dtype=np.float32)
-    env._zero_actions = np.zeros((1, 2), dtype=np.float32)
-    env._obs_noise = lambda data, scale: np.asarray(data + 100.0, dtype=np.float32)
-
-    motion_data = MotionData(
-        joint_pos=np.array([[0.1, 0.2]], dtype=np.float32),
-        joint_vel=np.array([[0.3, 0.4]], dtype=np.float32),
-        body_pos_w=np.zeros((1, 2, 3), dtype=np.float32),
-        body_quat_w=np.tile(np.array([[[1.0, 0.0, 0.0, 0.0]]], dtype=np.float32), (1, 2, 1)),
-        body_lin_vel_w=np.zeros((1, 2, 3), dtype=np.float32),
-        body_ang_vel_w=np.zeros((1, 2, 3), dtype=np.float32),
-    )
-    linvel = np.array([[1.0, 2.0, 3.0]], dtype=np.float32)
-    gyro = np.array([[4.0, 5.0, 6.0]], dtype=np.float32)
-    dof_pos = np.array([[0.7, -0.2]], dtype=np.float32)
-    dof_vel = np.array([[7.0, 8.0]], dtype=np.float32)
-    robot_body_pos_w = np.array([[[0.0, 0.0, 0.0], [0.2, 0.0, 0.1]]], dtype=np.float32)
-    robot_body_quat_w = np.tile(np.array([[[1.0, 0.0, 0.0, 0.0]]], dtype=np.float32), (1, 2, 1))
-    info = {"current_actions": np.array([[0.1, -0.2]], dtype=np.float32)}
-
-    obs = env._compute_obs(
-        info,
-        motion_data,
-        linvel,
-        gyro,
-        dof_pos,
-        dof_vel,
-        robot_body_pos_w,
-        robot_body_quat_w,
-    )
-    return env, obs, motion_data, linvel, gyro, dof_pos, dof_vel, info
-
-
-def test_g1_motion_tracking_critic_uses_clean_beyondmimic_aligned_terms():
-    from unilab.envs.motion_tracking.g1.tracking import G1MotionTrackingEnv
-
-    env, obs, motion_data, linvel, gyro, dof_pos, dof_vel, info = (
-        _compute_g1_motion_tracking_obs_stub(G1MotionTrackingEnv)
-    )
-
-    assert env.obs_groups_spec == {"obs": 25, "critic": 43}
-    assert obs["obs"].shape == (1, 25)
-    np.testing.assert_allclose(obs["obs"][:, 13:16], linvel + 100.0)
-    np.testing.assert_allclose(obs["obs"][:, 16:19], gyro + 100.0)
-    np.testing.assert_allclose(obs["obs"][:, 19:21], dof_pos - env.default_angles + 100.0)
-    np.testing.assert_allclose(obs["obs"][:, 21:23], dof_vel + 100.0)
-    np.testing.assert_allclose(obs["obs"][:, 23:25], info["current_actions"])
-
-    command_dim = motion_data.joint_pos.shape[1] + motion_data.joint_vel.shape[1]
-    anchor_dim = 3 + 6
-    clean_proprio_start = command_dim + anchor_dim
-    np.testing.assert_allclose(
-        obs["critic"][:, clean_proprio_start : clean_proprio_start + 3], linvel
-    )
-    np.testing.assert_allclose(
-        obs["critic"][:, clean_proprio_start + 3 : clean_proprio_start + 6], gyro
-    )
-    np.testing.assert_allclose(
-        obs["critic"][:, clean_proprio_start + 6 : clean_proprio_start + 8],
-        dof_pos - env.default_angles,
-    )
-    np.testing.assert_allclose(
-        obs["critic"][:, clean_proprio_start + 8 : clean_proprio_start + 10], dof_vel
-    )
-    np.testing.assert_allclose(
-        obs["critic"][:, clean_proprio_start + 10 : clean_proprio_start + 12],
-        info["current_actions"],
-    )
-
-
-def test_g1_motion_tracking_anchor_frame_writers_match_reference():
-    from unilab.envs.motion_tracking.g1.tracking import G1MotionTrackingEnv
-    from unilab.utils.rotation import (
-        np_matrix_from_quat,
-        np_quat_apply,
-        np_quat_inv,
-        np_quat_mul,
-    )
-
-    rng = np.random.default_rng(123)
-    num_envs = 4
-    num_bodies = 5
-    dtype = np.float64
-
-    def random_quat(shape: tuple[int, ...]) -> np.ndarray:
-        quat = rng.normal(size=(*shape, 4)).astype(dtype)
-        quat /= np.linalg.norm(quat, axis=-1, keepdims=True)
-        return quat
-
-    anchor_pos = rng.normal(size=(num_envs, 3)).astype(dtype)
-    anchor_quat = random_quat((num_envs,))
-    body_pos = rng.normal(size=(num_envs, num_bodies, 3)).astype(dtype)
-    body_quat = random_quat((num_envs, num_bodies))
-
-    env = cast(Any, object.__new__(G1MotionTrackingEnv))
-    env._body_vec_error = np.empty((num_envs, num_bodies, 3), dtype=dtype)
-    env._body_vec_tmp = np.empty((num_envs, num_bodies, 3), dtype=dtype)
-    env._quat_error_w = np.empty((num_envs, num_bodies), dtype=dtype)
-    env._quat_error_x = np.empty((num_envs, num_bodies), dtype=dtype)
-
-    pos_out = np.empty((num_envs, num_bodies, 3), dtype=dtype)
-    ori_out = np.empty((num_envs, num_bodies, 6), dtype=dtype)
-
-    env._write_body_pos_in_anchor_frame(anchor_pos, anchor_quat, body_pos, pos_out)
-    env._write_body_ori6_in_anchor_frame(anchor_quat, body_quat, ori_out)
-
-    anchor_quat_inv = np_quat_inv(anchor_quat)
-    tiled_anchor_quat_inv = np.repeat(anchor_quat_inv, num_bodies, axis=0)
-    rel_pos_flat = (body_pos - anchor_pos[:, None, :]).reshape(num_envs * num_bodies, 3)
-    ref_pos = np_quat_apply(tiled_anchor_quat_inv, rel_pos_flat).reshape(num_envs, num_bodies, 3)
-    ref_quat = np_quat_mul(
-        tiled_anchor_quat_inv,
-        body_quat.reshape(num_envs * num_bodies, 4),
-    )
-    ref_ori = np_matrix_from_quat(ref_quat)[:, :, :2].reshape(num_envs, num_bodies, 6)
-
-    np.testing.assert_allclose(pos_out, ref_pos, rtol=1e-12, atol=1e-12)
-    np.testing.assert_allclose(ori_out, ref_ori, rtol=1e-12, atol=1e-12)
-
-
-def test_g1_motion_tracking_relative_transform_fast_path_matches_reference():
-    from unilab.envs.motion_tracking.g1.tracking import G1MotionTrackingEnv
-    from unilab.utils.rotation import np_quat_apply, np_quat_inv, np_quat_mul, np_yaw_quat
-
-    rng = np.random.default_rng(321)
-    num_envs = 4
-    num_bodies = 5
-    anchor_idx = 2
-    dtype = np.float64
-
-    def random_quat(shape: tuple[int, ...]) -> np.ndarray:
-        quat = rng.normal(size=(*shape, 4)).astype(dtype)
-        quat /= np.linalg.norm(quat, axis=-1, keepdims=True)
-        return quat
-
-    motion_data = SimpleNamespace(
-        body_pos_w=rng.normal(size=(num_envs, num_bodies, 3)).astype(dtype),
-        body_quat_w=random_quat((num_envs, num_bodies)),
-    )
-    robot_body_pos_w = rng.normal(size=(num_envs, num_bodies, 3)).astype(dtype)
-    robot_body_quat_w = random_quat((num_envs, num_bodies))
-
-    env = cast(Any, object.__new__(G1MotionTrackingEnv))
-    env.anchor_body_idx = anchor_idx
-    env.body_pos_relative_w = np.empty((num_envs, num_bodies, 3), dtype=dtype)
-    env.body_quat_relative_w = np.empty((num_envs, num_bodies, 4), dtype=dtype)
-    env._delta_pos_w = np.empty((num_envs, 3), dtype=dtype)
-    env._delta_ori_w = np.empty((num_envs, 4), dtype=dtype)
-    env._body_vec_error = np.empty((num_envs, num_bodies, 3), dtype=dtype)
-    env._env_error = np.empty((num_envs,), dtype=dtype)
-    env._reward_term = np.empty((num_envs,), dtype=dtype)
-
-    env._update_relative_transforms(motion_data, robot_body_pos_w, robot_body_quat_w)
-
-    anchor_pos_w = motion_data.body_pos_w[:, anchor_idx]
-    anchor_quat_w = motion_data.body_quat_w[:, anchor_idx]
-    robot_anchor_pos_w = robot_body_pos_w[:, anchor_idx]
-    robot_anchor_quat_w = robot_body_quat_w[:, anchor_idx]
-    delta_pos_w = robot_anchor_pos_w.copy()
-    delta_pos_w[:, 2] = anchor_pos_w[:, 2]
-    delta_ori_w = np_yaw_quat(np_quat_mul(robot_anchor_quat_w, np_quat_inv(anchor_quat_w)))
-    delta_ori_tiled = np.tile(delta_ori_w, (1, num_bodies)).reshape(num_envs * num_bodies, 4)
-    expected_quat = np_quat_mul(
-        delta_ori_tiled,
-        motion_data.body_quat_w.reshape(num_envs * num_bodies, 4),
-    ).reshape(num_envs, num_bodies, 4)
-    rel_pos_flat = (motion_data.body_pos_w - anchor_pos_w[:, None, :]).reshape(
-        num_envs * num_bodies, 3
-    )
-    expected_pos = delta_pos_w[:, None, :] + np_quat_apply(delta_ori_tiled, rel_pos_flat).reshape(
-        num_envs, num_bodies, 3
-    )
-
-    np.testing.assert_allclose(env.body_pos_relative_w, expected_pos, rtol=1e-12, atol=1e-12)
-    np.testing.assert_allclose(env.body_quat_relative_w, expected_quat, rtol=1e-12, atol=1e-12)
-
-
-def test_g1_motion_tracking_reward_fast_path_matches_reference():
-    from unilab.envs.motion_tracking.common.motion_loader import MotionData
-    from unilab.envs.motion_tracking.g1.tracking import G1MotionTrackingEnv, RewardConfig
-    from unilab.utils.rotation import np_quat_error_magnitude
-
-    rng = np.random.default_rng(456)
-    num_envs = 3
-    num_bodies = 4
-    num_actions = 2
-    anchor_idx = 1
-    ee_indices = np.array([2, 3], dtype=np.int32)
-    undesired_indices = np.array([0, 1], dtype=np.int32)
-    dtype = np.float64
-
-    def random_quat(shape: tuple[int, ...]) -> np.ndarray:
-        quat = rng.normal(size=(*shape, 4)).astype(dtype)
-        quat /= np.linalg.norm(quat, axis=-1, keepdims=True)
-        return quat
-
-    reward_config = RewardConfig()
-    reward_config.scales = {
-        "motion_global_root_pos": 0.5,
-        "motion_global_root_ori": 0.25,
-        "motion_body_pos": 1.0,
-        "motion_body_ori": 0.75,
-        "motion_body_lin_vel": 0.4,
-        "motion_body_ang_vel": 0.3,
-        "motion_ee_body_pos_z": 0.2,
-        "motion_joint_pos": 0.6,
-        "motion_joint_vel": 0.7,
-        "action_rate_l2": -0.1,
-        "joint_limit": -0.2,
-        "undesired_contacts": -0.3,
-    }
-    ctrl_dt = 0.02
-    contact_threshold = 0.05
-
-    motion_data = MotionData(
-        joint_pos=rng.normal(size=(num_envs, num_actions)).astype(dtype),
-        joint_vel=rng.normal(size=(num_envs, num_actions)).astype(dtype),
-        body_pos_w=rng.normal(size=(num_envs, num_bodies, 3)).astype(dtype),
-        body_quat_w=random_quat((num_envs, num_bodies)),
-        body_lin_vel_w=rng.normal(size=(num_envs, num_bodies, 3)).astype(dtype),
-        body_ang_vel_w=rng.normal(size=(num_envs, num_bodies, 3)).astype(dtype),
-    )
-    robot_body_pos_w = rng.normal(size=(num_envs, num_bodies, 3)).astype(dtype)
-    robot_body_pos_w[:, undesired_indices, 2] = np.array(
-        [[0.01, 0.10], [0.20, 0.02], [0.07, 0.03]], dtype=dtype
-    )
-    robot_body_quat_w = random_quat((num_envs, num_bodies))
-    robot_body_lin_vel_w = rng.normal(size=(num_envs, num_bodies, 3)).astype(dtype)
-    robot_body_ang_vel_w = rng.normal(size=(num_envs, num_bodies, 3)).astype(dtype)
-    dof_pos = rng.normal(size=(num_envs, num_actions)).astype(dtype)
-    dof_vel = rng.normal(size=(num_envs, num_actions)).astype(dtype)
-    current_actions = rng.normal(size=(num_envs, num_actions)).astype(dtype)
-    last_actions = rng.normal(size=(num_envs, num_actions)).astype(dtype)
-    body_pos_relative_w = rng.normal(size=(num_envs, num_bodies, 3)).astype(dtype)
-    body_quat_relative_w = random_quat((num_envs, num_bodies))
-    joint_lower = np.array([-0.2, -0.1], dtype=dtype)
-    joint_upper = np.array([0.1, 0.2], dtype=dtype)
-
-    env = cast(Any, object.__new__(G1MotionTrackingEnv))
-    env._num_envs = num_envs
-    env.anchor_body_idx = anchor_idx
-    env.ee_body_indices = ee_indices
-    env.undesired_contact_body_indices = undesired_indices
-    env._has_ee_body_indices = True
-    env._has_undesired_contact_body_indices = True
-    env._cfg = SimpleNamespace(
-        reward_config=reward_config,
-        ctrl_dt=ctrl_dt,
-        undesired_contact_z_threshold=contact_threshold,
-    )
-    env.body_pos_relative_w = body_pos_relative_w.copy()
-    env.body_quat_relative_w = body_quat_relative_w.copy()
-    env._joint_lower = joint_lower
-    env._joint_upper = joint_upper
-    env._body_vec_error = np.empty((num_envs, num_bodies, 3), dtype=dtype)
-    env._joint_error = np.empty((num_envs, num_actions), dtype=dtype)
-    env._joint_error_upper = np.empty((num_envs, num_actions), dtype=dtype)
-    env._env_error = np.empty((num_envs,), dtype=dtype)
-    env._env_error2 = np.empty((num_envs,), dtype=dtype)
-    env._reward_term = np.empty((num_envs,), dtype=dtype)
-    env._weighted_reward = np.empty((num_envs,), dtype=dtype)
-    env._quat_error_w = np.empty((num_envs, num_bodies), dtype=dtype)
-    env._quat_error_x = np.empty((num_envs, num_bodies), dtype=dtype)
-    env._ee_pos_error_z = np.empty((num_envs, ee_indices.size), dtype=dtype)
-    env._undesired_contact_mask = np.empty((num_envs, undesired_indices.size), dtype=bool)
-    env._enable_reward_log = False
-    env._init_reward_functions()
-    env._active_reward_fns = {
-        name: fn for name, fn in env._reward_fns.items() if env._reward_term_is_active(name)
-    }
-
-    info = {
-        "current_actions": current_actions,
-        "last_actions": last_actions,
-        "steps": np.zeros((num_envs,), dtype=np.uint32),
-    }
-    actual = env._compute_reward(
-        info,
-        motion_data,
-        robot_body_pos_w,
-        robot_body_quat_w,
-        robot_body_lin_vel_w,
-        robot_body_ang_vel_w,
-        dof_pos,
-        dof_vel,
-    ).copy()
-
-    cfg = reward_config
-    expected = np.zeros((num_envs,), dtype=dtype)
-    root_pos_error = np.sum(
-        np.square(motion_data.body_pos_w[:, anchor_idx] - robot_body_pos_w[:, anchor_idx]),
-        axis=-1,
-    )
-    expected += cfg.scales["motion_global_root_pos"] * np.exp(-root_pos_error / cfg.std_root_pos**2)
-    root_ori_error = (
-        np_quat_error_magnitude(
-            motion_data.body_quat_w[:, anchor_idx],
-            robot_body_quat_w[:, anchor_idx],
-        )
-        ** 2
-    )
-    expected += cfg.scales["motion_global_root_ori"] * np.exp(-root_ori_error / cfg.std_root_ori**2)
-    body_pos_error = np.sum(np.square(body_pos_relative_w - robot_body_pos_w), axis=-1)
-    expected += cfg.scales["motion_body_pos"] * np.exp(
-        -body_pos_error.mean(-1) / cfg.std_body_pos**2
-    )
-    body_ori_error = np_quat_error_magnitude(
-        body_quat_relative_w.reshape(num_envs * num_bodies, 4),
-        robot_body_quat_w.reshape(num_envs * num_bodies, 4),
-    ).reshape(num_envs, num_bodies)
-    expected += cfg.scales["motion_body_ori"] * np.exp(
-        -np.square(body_ori_error).mean(-1) / cfg.std_body_ori**2
-    )
-    body_lin_error = np.sum(np.square(motion_data.body_lin_vel_w - robot_body_lin_vel_w), axis=-1)
-    expected += cfg.scales["motion_body_lin_vel"] * np.exp(
-        -body_lin_error.mean(-1) / cfg.std_body_lin_vel**2
-    )
-    body_ang_error = np.sum(np.square(motion_data.body_ang_vel_w - robot_body_ang_vel_w), axis=-1)
-    expected += cfg.scales["motion_body_ang_vel"] * np.exp(
-        -body_ang_error.mean(-1) / cfg.std_body_ang_vel**2
-    )
-    ee_error = np.square(body_pos_relative_w[:, ee_indices, 2] - robot_body_pos_w[:, ee_indices, 2])
-    expected += cfg.scales["motion_ee_body_pos_z"] * np.exp(
-        -ee_error.mean(-1) / cfg.std_body_pos**2
-    )
-    joint_pos_error = np.mean(np.square(motion_data.joint_pos - dof_pos), axis=-1)
-    expected += cfg.scales["motion_joint_pos"] * np.exp(-joint_pos_error / cfg.std_joint_pos**2)
-    joint_vel_error = np.mean(np.square(motion_data.joint_vel - dof_vel), axis=-1)
-    expected += cfg.scales["motion_joint_vel"] * np.exp(-joint_vel_error / cfg.std_joint_vel**2)
-    expected += cfg.scales["action_rate_l2"] * np.sum(
-        np.square(current_actions - last_actions), axis=1
-    )
-    lower_violation = np.maximum(0, joint_lower - dof_pos)
-    upper_violation = np.maximum(0, dof_pos - joint_upper)
-    expected += cfg.scales["joint_limit"] * np.sum(
-        np.square(lower_violation + upper_violation), axis=1
-    )
-    expected += cfg.scales["undesired_contacts"] * np.sum(
-        robot_body_pos_w[:, undesired_indices, 2] < contact_threshold,
-        axis=-1,
-    )
-    expected *= ctrl_dt
-
-    np.testing.assert_allclose(actual, expected, rtol=1e-12, atol=1e-12)
-
-
-def test_g1_motion_tracking_deploy_actor_matches_unitree_mimic_terms():
-    from unilab.envs.motion_tracking.g1.tracking import G1MotionTrackingDeployEnv
-
-    env, obs, _motion_data, _linvel, gyro, dof_pos, dof_vel, info = (
-        _compute_g1_motion_tracking_obs_stub(G1MotionTrackingDeployEnv)
-    )
-
-    assert env.obs_groups_spec == {"obs": 19, "critic": 43}
-    assert obs["obs"].shape == (1, 19)
-    np.testing.assert_allclose(obs["obs"][:, 10:13], gyro + 100.0)
-    np.testing.assert_allclose(obs["obs"][:, 13:15], dof_pos - env.default_angles + 100.0)
-    np.testing.assert_allclose(obs["obs"][:, 15:17], dof_vel + 100.0)
-    np.testing.assert_allclose(obs["obs"][:, 17:19], info["current_actions"])
-
-
-def test_g1_box_tracking_cfg_uses_largebox_scene_and_motion_defaults():
-    from unilab.envs.motion_tracking.g1.box_tracking import BoxRewardConfig, G1BoxTrackingCfg
-
-    cfg = G1BoxTrackingCfg()
-
-    assert cfg.scene.model_file.endswith("scene_flat_with_largebox.xml")
-    assert str(cfg.motion_file).endswith("sub3_largebox_003_boxconverted.npz")
-    assert cfg.object_body_name == "largebox"
-    assert cfg.object_pos_threshold == pytest.approx(0.25)
-    assert cfg.object_ori_threshold == pytest.approx(0.8)
-    assert isinstance(cfg.reward_config, BoxRewardConfig)
-    assert cfg.reward_config.scales["object_global_ref_position_error_exp"] == pytest.approx(1.0)
-    assert cfg.reward_config.scales["object_global_ref_orientation_error_exp"] == pytest.approx(1.0)
-
-
-def test_g1_box_tracking_is_exported_from_g1_and_motion_tracking_packages():
-    from unilab.envs.motion_tracking import (
-        G1BoxTrackingCfg as TopLevelCfg,
-    )
-    from unilab.envs.motion_tracking import (
-        G1BoxTrackingEnv as TopLevelEnv,
-    )
-    from unilab.envs.motion_tracking import (
-        G1BoxTrackingEnvCfg as TopLevelEnvCfg,
-    )
-    from unilab.envs.motion_tracking.g1 import (
-        G1BoxTrackingCfg as G1PkgCfg,
-    )
-    from unilab.envs.motion_tracking.g1 import (
-        G1BoxTrackingEnv as G1PkgEnv,
-    )
-    from unilab.envs.motion_tracking.g1 import (
-        G1BoxTrackingEnvCfg as G1PkgEnvCfg,
-    )
-
-    assert TopLevelCfg is G1PkgCfg
-    assert TopLevelEnv is G1PkgEnv
-    assert TopLevelEnvCfg is G1PkgEnvCfg
-
-
-def _compute_g1_box_tracking_obs_stub():
-    from unilab.envs.motion_tracking.g1.box_tracking import G1BoxTrackingEnv
-    from unilab.envs.motion_tracking.g1.motion_box_loader import BoxMotionData
-
-    env = cast(Any, object.__new__(G1BoxTrackingEnv))
-    env._num_envs = 1
-    env._num_action = 2
-    env._n_motion_bodies = 2
-    env._critic_obs_width = env._critic_base_obs_dim(env._num_action) + env._n_motion_bodies * 9
-    env._cfg = SimpleNamespace(
-        noise_config=SimpleNamespace(
-            level=1.0,
-            scale_linvel=1.0,
-            scale_gyro=1.0,
-            scale_joint_angle=1.0,
-            scale_joint_vel=1.0,
-        ),
-        body_names=("pelvis", "torso_link"),
-    )
-    env.default_angles = np.array([[0.5, -0.5]], dtype=np.float32)
-    env.anchor_body_idx = 0
-    env._object_body_ids = np.array([7], dtype=np.int32)
-    env._motion_anchor_pos_b = np.empty((1, 3), dtype=np.float32)
-    env._motion_anchor_ori_b = np.empty((1, 6), dtype=np.float32)
-    env._motion_command = np.empty((1, 4), dtype=np.float32)
-    env._joint_pos_rel = np.empty((1, 2), dtype=np.float32)
-    env._body_vec_error = np.empty((1, 2, 3), dtype=np.float32)
-    env._zero_actions = np.zeros((1, 2), dtype=np.float32)
-    env._obs_noise = lambda data, scale: np.asarray(data + 100.0, dtype=np.float32)
-
-    class FakeBackend:
-        def get_body_pos_w(self, body_ids: np.ndarray) -> np.ndarray:
-            np.testing.assert_array_equal(body_ids, np.array([7], dtype=np.int32))
-            return np.array([[[1.0, 2.0, 3.0]]], dtype=np.float32)
-
-        def get_body_quat_w(self, body_ids: np.ndarray) -> np.ndarray:
-            np.testing.assert_array_equal(body_ids, np.array([7], dtype=np.int32))
-            return np.array([[[1.0, 0.0, 0.0, 0.0]]], dtype=np.float32)
-
-        def get_body_lin_vel_w(self, body_ids: np.ndarray) -> np.ndarray:
-            np.testing.assert_array_equal(body_ids, np.array([7], dtype=np.int32))
-            return np.array([[[4.0, 5.0, 6.0]]], dtype=np.float32)
-
-    env._backend = FakeBackend()
-
-    motion_data = BoxMotionData(
-        joint_pos=np.array([[0.1, 0.2]], dtype=np.float32),
-        joint_vel=np.array([[0.3, 0.4]], dtype=np.float32),
-        body_pos_w=np.zeros((1, 2, 3), dtype=np.float32),
-        body_quat_w=np.tile(np.array([[[1.0, 0.0, 0.0, 0.0]]], dtype=np.float32), (1, 2, 1)),
-        body_lin_vel_w=np.zeros((1, 2, 3), dtype=np.float32),
-        body_ang_vel_w=np.zeros((1, 2, 3), dtype=np.float32),
-        object_pos_w=np.array([[1.0, 2.0, 3.0]], dtype=np.float32),
-        object_quat_w=np.array([[1.0, 0.0, 0.0, 0.0]], dtype=np.float32),
-        object_lin_vel_w=np.array([[4.0, 5.0, 6.0]], dtype=np.float32),
-        object_ang_vel_w=np.array([[7.0, 8.0, 9.0]], dtype=np.float32),
-    )
-    linvel = np.array([[1.0, 2.0, 3.0]], dtype=np.float32)
-    gyro = np.array([[4.0, 5.0, 6.0]], dtype=np.float32)
-    dof_pos = np.array([[0.7, -0.2]], dtype=np.float32)
-    dof_vel = np.array([[7.0, 8.0]], dtype=np.float32)
-    robot_body_pos_w = np.array([[[0.0, 0.0, 0.0], [0.2, 0.0, 0.1]]], dtype=np.float32)
-    robot_body_quat_w = np.tile(np.array([[[1.0, 0.0, 0.0, 0.0]]], dtype=np.float32), (1, 2, 1))
-    info = {"current_actions": np.array([[0.1, -0.2]], dtype=np.float32)}
-
-    obs = env._compute_obs(
-        info,
-        motion_data,
-        linvel,
-        gyro,
-        dof_pos,
-        dof_vel,
-        robot_body_pos_w,
-        robot_body_quat_w,
-    )
-    return env, obs, gyro, dof_pos, dof_vel, info
-
-
-def test_g1_box_tracking_actor_matches_deploy_and_critic_adds_object_state():
-    env, obs, gyro, dof_pos, dof_vel, info = _compute_g1_box_tracking_obs_stub()
-
-    assert env.obs_groups_spec == {"obs": 19, "critic": 55}
-    assert obs["obs"].shape == (1, 19)
-    np.testing.assert_allclose(obs["obs"][:, 10:13], gyro + 100.0)
-    np.testing.assert_allclose(obs["obs"][:, 13:15], dof_pos - env.default_angles + 100.0)
-    np.testing.assert_allclose(obs["obs"][:, 15:17], dof_vel + 100.0)
-    np.testing.assert_allclose(obs["obs"][:, 17:19], info["current_actions"])
-    np.testing.assert_allclose(
-        obs["critic"][:, -12:],
-        np.array([[1.0, 2.0, 3.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 4.0, 5.0, 6.0]], dtype=np.float32),
-    )
-
-
-def test_g1_box_tracking_critic_object_state_respects_subset_env_order():
-    from unilab.envs.motion_tracking.g1.box_tracking import G1BoxTrackingEnv
-    from unilab.envs.motion_tracking.g1.motion_box_loader import BoxMotionData
-
-    env = cast(Any, object.__new__(G1BoxTrackingEnv))
-    env._num_envs = 4
-    env._num_action = 2
-    env._n_motion_bodies = 2
-    env._critic_obs_width = env._critic_base_obs_dim(env._num_action) + env._n_motion_bodies * 9
-    env.anchor_body_idx = 0
-    env._object_body_ids = np.array([7], dtype=np.int32)
-    env._cfg = SimpleNamespace(
-        noise_config=SimpleNamespace(
-            level=0.0,
-            scale_linvel=0.0,
-            scale_gyro=0.0,
-            scale_joint_angle=0.0,
-            scale_joint_vel=0.0,
-        ),
-        body_names=("pelvis", "torso_link"),
-    )
-    env.default_angles = np.zeros((2,), dtype=np.float32)
-    env._body_vec_error = np.empty((4, 2, 3), dtype=np.float32)
-    env._obs_noise = lambda data, scale: np.asarray(data, dtype=np.float32)
-
-    class FakeBackend:
-        def get_body_pos_w(self, body_ids: np.ndarray) -> np.ndarray:
-            np.testing.assert_array_equal(body_ids, np.array([7], dtype=np.int32))
-            return np.array(
-                [
-                    [[10.0, 0.0, 0.0]],
-                    [[20.0, 0.0, 0.0]],
-                    [[30.0, 0.0, 0.0]],
-                    [[40.0, 0.0, 0.0]],
-                ],
-                dtype=np.float32,
-            )
-
-        def get_body_quat_w(self, body_ids: np.ndarray) -> np.ndarray:
-            np.testing.assert_array_equal(body_ids, np.array([7], dtype=np.int32))
-            return np.tile(np.array([[[1.0, 0.0, 0.0, 0.0]]], dtype=np.float32), (4, 1, 1))
-
-        def get_body_lin_vel_w(self, body_ids: np.ndarray) -> np.ndarray:
-            np.testing.assert_array_equal(body_ids, np.array([7], dtype=np.int32))
-            return np.array(
-                [
-                    [[1.0, 0.0, 0.0]],
-                    [[2.0, 0.0, 0.0]],
-                    [[3.0, 0.0, 0.0]],
-                    [[4.0, 0.0, 0.0]],
-                ],
-                dtype=np.float32,
-            )
-
-    env._backend = FakeBackend()
-
-    motion_data = BoxMotionData(
-        joint_pos=np.zeros((2, 2), dtype=np.float32),
-        joint_vel=np.zeros((2, 2), dtype=np.float32),
-        body_pos_w=np.zeros((2, 2, 3), dtype=np.float32),
-        body_quat_w=np.tile(np.array([[[1.0, 0.0, 0.0, 0.0]]], dtype=np.float32), (2, 2, 1)),
-        body_lin_vel_w=np.zeros((2, 2, 3), dtype=np.float32),
-        body_ang_vel_w=np.zeros((2, 2, 3), dtype=np.float32),
-        object_pos_w=np.zeros((2, 3), dtype=np.float32),
-        object_quat_w=np.tile(np.array([[1.0, 0.0, 0.0, 0.0]], dtype=np.float32), (2, 1)),
-        object_lin_vel_w=np.zeros((2, 3), dtype=np.float32),
-        object_ang_vel_w=np.zeros((2, 3), dtype=np.float32),
-    )
-
-    linvel = np.zeros((2, 3), dtype=np.float32)
-    gyro = np.zeros((2, 3), dtype=np.float32)
-    dof_pos = np.zeros((2, 2), dtype=np.float32)
-    dof_vel = np.zeros((2, 2), dtype=np.float32)
-    robot_body_pos_w = np.zeros((2, 2, 3), dtype=np.float32)
-    robot_body_quat_w = np.tile(np.array([[[1.0, 0.0, 0.0, 0.0]]], dtype=np.float32), (2, 2, 1))
-
-    obs = env._compute_obs(
-        {
-            "env_ids": np.array([2, 0], dtype=np.int32),
-            "current_actions": np.zeros((2, 2), dtype=np.float32),
-        },
-        motion_data,
-        linvel,
-        gyro,
-        dof_pos,
-        dof_vel,
-        robot_body_pos_w,
-        robot_body_quat_w,
-    )
-
-    np.testing.assert_allclose(
-        obs["critic"][:, -12:],
-        np.array(
-            [
-                [30.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 3.0, 0.0, 0.0],
-                [10.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0],
-            ],
-            dtype=np.float32,
-        ),
-    )
-
-
-def test_g1_motion_tracking_can_terminate_on_undesired_contacts():
-    from unilab.envs.motion_tracking.g1.tracking import G1MotionTrackingEnv
-
-    env = cast(Any, object.__new__(G1MotionTrackingEnv))
-    env._num_envs = 2
-    env.anchor_body_idx = 0
-    env.ee_body_indices = np.array([1], dtype=np.int32)
-    env._has_ee_body_indices = True
-    env.undesired_contact_body_indices = np.array([2], dtype=np.int32)
-    env._has_undesired_contact_body_indices = True
-    env._terminated = np.empty((2,), dtype=bool)
-    env._env_bool = np.empty((2,), dtype=bool)
-    env._env_error = np.empty((2,), dtype=np.float32)
-    env._ee_pos_error_z = np.empty((2, 1), dtype=np.float32)
-    env._ee_terminated = np.empty((2, 1), dtype=bool)
-    env._undesired_contact_mask = np.empty((2, 1), dtype=bool)
-    env.body_pos_relative_w = np.array(
-        [
-            [[0.0, 0.0, 1.0], [0.0, 0.0, 0.8], [0.0, 0.0, 0.8]],
-            [[0.0, 0.0, 1.0], [0.0, 0.0, 0.8], [0.0, 0.0, 0.8]],
-        ],
-        dtype=np.float32,
-    )
-    env._cfg = SimpleNamespace(
-        anchor_pos_z_threshold=0.5,
-        anchor_ori_threshold=1e9,
-        ee_body_pos_z_threshold=0.5,
-        terminate_on_undesired_contacts=True,
-        undesired_contact_z_threshold=0.05,
-    )
-    quat = np.tile(np.array([[[1.0, 0.0, 0.0, 0.0]]], dtype=np.float32), (2, 3, 1))
-    motion_data = SimpleNamespace(body_pos_w=env.body_pos_relative_w.copy(), body_quat_w=quat)
-    robot_body_pos_w = env.body_pos_relative_w.copy()
-    robot_body_pos_w[0, 2, 2] = 0.04
-    robot_body_pos_w[1, 2, 2] = 0.10
-
-    terminated = env._compute_terminations(motion_data, robot_body_pos_w, quat)
-    np.testing.assert_array_equal(terminated, np.array([True, False]))
-
-    env._cfg.terminate_on_undesired_contacts = False
-    terminated_without_contact = env._compute_terminations(motion_data, robot_body_pos_w, quat)
-    np.testing.assert_array_equal(terminated_without_contact, np.array([False, False]))
-
-
-def test_g1_motion_tracking_cfg_has_domain_rand_for_motrix():
-    from unilab.envs.motion_tracking.g1.tracking import G1MotionTrackingCfg
-
-    cfg = G1MotionTrackingCfg()
-    assert hasattr(cfg, "domain_rand")
-    assert cfg.domain_rand.randomize_base_mass is False
-    assert cfg.domain_rand.random_com is False
-    assert cfg.domain_rand.randomize_gravity is False
-    assert cfg.domain_rand.push_robots is False
-
-
-def test_g1_motion_tracking_cfg_preserves_legacy_defaults():
-    from unilab.envs.motion_tracking.g1.tracking import G1MotionTrackingCfg
-
-    cfg = G1MotionTrackingCfg()
-
-    assert str(cfg.motion_file).endswith("dance1_subject2_part.npz")
-    assert cfg.pose_randomization.x == (-0.05, 0.05)
-    assert cfg.velocity_randomization.x == (-0.5, 0.5)
-    assert cfg.joint_position_range == (-0.1, 0.1)
-    assert cfg.anchor_ori_threshold == pytest.approx(0.8)
-    assert cfg.sampling_mode == "adaptive"
-    assert cfg.truncate_on_clip_end is False
-
-
-def test_g1_motion_tracking_init_delegates_motion_body_ids_to_backend(monkeypatch):
-    from unilab.envs.locomotion.g1.base import G1BaseEnv
-    from unilab.envs.motion_tracking.common import tracking as tracking_module
-    from unilab.envs.motion_tracking.g1.tracking import G1MotionTrackingCfg, G1MotionTrackingEnv
-
-    calls: dict[str, Any] = {}
-
-    class FakeBackend:
-        def get_body_ids(self, names: tuple[str, ...]) -> np.ndarray:
-            calls["body_ids_names"] = names
-            return np.array([10, 11], dtype=np.int32)
-
-        def get_motion_body_ids(self, names: tuple[str, ...]) -> np.ndarray:
-            calls["motion_body_ids_names"] = names
-            return np.array([1, 2], dtype=np.int32)
-
-        def copy_body_state_w(
-            self,
-            body_ids: np.ndarray,
-            out_pos: np.ndarray,
-            out_quat: np.ndarray,
-            out_lin_vel: np.ndarray,
-            out_ang_vel: np.ndarray,
-        ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
-            return out_pos, out_quat, out_lin_vel, out_ang_vel
-
-        def get_joint_range(self) -> None:
-            return None
-
-    def fake_base_init(self, cfg, backend, num_envs):
-        self._cfg = cfg
-        self._backend = backend
-        self._num_envs = num_envs
-        self._num_action = 2
-        self._init_qpos = np.zeros((9,), dtype=np.float32)
-        self._init_qvel = np.zeros((8,), dtype=np.float32)
-
-    class FakeMotionLoader:
-        def __init__(self, motion_file: str, body_indices: np.ndarray):
-            calls["motion_loader"] = (motion_file, body_indices.copy())
-
-    class FakeMotionSampler:
-        def __init__(
-            self,
-            motion_loader: Any,
-            mode: str,
-            num_envs: int,
-            start_ratio: float = 0.0,
-        ):
-            calls["motion_sampler"] = (motion_loader, mode, num_envs, start_ratio)
-
-    fake_backend = FakeBackend()
-    monkeypatch.setattr(tracking_module, "create_backend", lambda *args, **kwargs: fake_backend)
-    monkeypatch.setattr(G1BaseEnv, "__init__", fake_base_init)
-    monkeypatch.setattr(tracking_module, "MotionLoader", FakeMotionLoader)
-    monkeypatch.setattr(tracking_module, "MotionSampler", FakeMotionSampler)
-    monkeypatch.setattr(
-        G1MotionTrackingEnv,
-        "_init_domain_randomization",
-        lambda self, provider: calls.setdefault("dr_provider", provider.__class__.__name__),
-    )
-    monkeypatch.setattr(
-        G1MotionTrackingEnv,
-        "_init_reward_functions",
-        lambda self: (
-            calls.setdefault("reward_init", True),
-            setattr(self, "_reward_fns", {}),
-        ),
-    )
-
-    cfg = G1MotionTrackingCfg(
-        motion_file="dummy_motion.npz",
-        body_names=("pelvis", "torso_link"),
-        ee_body_names=("torso_link",),
-    )
-    env = cast(Any, G1MotionTrackingEnv)(cfg, num_envs=4, backend_type="motrix")
-
-    np.testing.assert_array_equal(env.body_ids, np.array([10, 11], dtype=np.int32))
-    assert calls["body_ids_names"] == cfg.body_names
-    assert calls["motion_body_ids_names"] == cfg.body_names
-    assert calls["motion_loader"][0] == "dummy_motion.npz"
-    np.testing.assert_array_equal(calls["motion_loader"][1], np.array([1, 2], dtype=np.int32))
-    assert calls["motion_sampler"][1:] == ("adaptive", 4, cfg.sampling_start_ratio)
-    assert calls["dr_provider"] == "MotionTrackingDomainRandomizationProvider"
-    assert calls["reward_init"] is True
+    assert failed.cache_saved is False
 
 
 def _patch_sharpa_rotation_constructor(
     monkeypatch: pytest.MonkeyPatch,
     initialized_providers: list[Any],
 ) -> Any:
-    from unilab.envs.manipulation.sharpa_inhand import rotation as sharpa_rotation_module
-    from unilab.envs.manipulation.sharpa_inhand.base import SharpaInhandBaseEnv
+    from unilab.tasks.manipulation.sharpa_inhand import rotation as sharpa_rotation_module
+    from unilab.tasks.manipulation.sharpa_inhand.base import SharpaInhandBaseEnv
 
     def fake_base_init(self, cfg, backend, num_envs):
         self._cfg = cfg
@@ -1529,7 +661,7 @@ def unsupported_backend_metadata(*args, **kwargs):
 
 
 def test_sharpa_rotation_explicit_default_provider_materializes_cache(monkeypatch):
-    from unilab.envs.manipulation.sharpa_inhand.rotation import (
+    from unilab.tasks.manipulation.sharpa_inhand.rotation import (
         RewardConfig,
         SharpaInhandRotationCfg,
         SharpaInhandRotationDRProvider,
@@ -1564,7 +696,7 @@ def materialize(path: str, scale_values: np.ndarray) -> tuple[np.ndarray, ...]:
 
 
 def test_sharpa_grasp_env_initializes_dr_once_with_grasp_provider(monkeypatch):
-    from unilab.envs.manipulation.sharpa_inhand.grasp_gen import (
+    from unilab.tasks.manipulation.sharpa_inhand.grasp_gen import (
         SharpaInhandRotationGraspCfg,
         SharpaInhandRotationGraspEnv,
     )
@@ -1613,7 +745,7 @@ def test_sharpa_grasp_target_saves_cache_then_raises_run_complete(
     caplog: pytest.LogCaptureFixture,
 ) -> None:
     from unilab.base.run_control import RunComplete
-    from unilab.envs.manipulation.sharpa_inhand.grasp_gen import (
+    from unilab.tasks.manipulation.sharpa_inhand.grasp_gen import (
         SharpaInhandRotationGraspCfg,
         SharpaInhandRotationGraspEnv,
     )
@@ -1651,13 +783,13 @@ def save_once(path: str | Path, values: np.ndarray) -> None:
         real_save(path, values)
 
     monkeypatch.setattr(
-        "unilab.envs.manipulation.sharpa_inhand.grasp_gen.np.save",
+        "unilab.tasks.manipulation.sharpa_inhand.grasp_gen.np.save",
         save_once,
     )
 
     with caplog.at_level(
         logging.INFO,
-        logger="unilab.envs.manipulation.sharpa_inhand.grasp_gen",
+        logger="unilab.tasks.manipulation.sharpa_inhand.grasp_gen",
     ):
         with pytest.raises(RunComplete) as caught:
             env._collect_successful_grasps(np.asarray([0], dtype=np.int32))
@@ -1692,7 +824,7 @@ def save_once(path: str | Path, values: np.ndarray) -> None:
 
 def test_sharpa_run_complete_reports_effective_collection_target() -> None:
     from unilab.base.run_control import RunComplete
-    from unilab.envs.manipulation.sharpa_inhand.grasp_gen import (
+    from unilab.tasks.manipulation.sharpa_inhand.grasp_gen import (
         SharpaInhandRotationGraspCfg,
         SharpaInhandRotationGraspEnv,
     )
@@ -1714,344 +846,12 @@ def test_sharpa_run_complete_reports_effective_collection_target() -> None:
     }
 
 
-def test_g1_flip_tracking_cfg_uses_flip_profile():
-    from unilab.envs.motion_tracking.g1.flip_tracking import G1FlipTrackingCfg
-
-    cfg = G1FlipTrackingCfg()
-
-    assert cfg.scene.model_file.endswith("scene_flat.xml")
-    assert str(cfg.motion_file).endswith("flip_360_001__A304.npz")
-    assert cfg.pose_randomization.x == (0.0, 0.0)
-    assert cfg.velocity_randomization.x == (0.0, 0.0)
-    assert cfg.joint_position_range == (0.0, 0.0)
-    assert cfg.truncate_on_clip_end is False
-    assert cfg.anchor_ori_threshold == pytest.approx(1e9)
-    assert cfg.terminate_on_undesired_contacts is True
-    assert cfg.sampling_mode == "start"
-
-
-def test_g1_wall_flip_tracking_cfg_uses_wall_flip_profile():
-    from unilab.envs.motion_tracking.g1.flip_tracking import G1WallFlipTrackingCfg
-
-    cfg = G1WallFlipTrackingCfg()
-
-    assert cfg.scene.model_file.endswith("scene_flat_with_wall.xml")
-    assert str(cfg.motion_file).endswith("flip_from_wall_104__A304.npz")
-    assert cfg.pose_randomization.x == (0.0, 0.0)
-    assert cfg.velocity_randomization.x == (0.0, 0.0)
-    assert cfg.joint_position_range == (0.0, 0.0)
-    assert cfg.truncate_on_clip_end is False
-    assert cfg.anchor_ori_threshold == pytest.approx(1e9)
-    assert cfg.anchor_pos_z_threshold == pytest.approx(0.5)
-    assert cfg.ee_body_pos_z_threshold == pytest.approx(0.5)
-    assert cfg.terminate_on_undesired_contacts is True
-    assert cfg.sampling_mode == "adaptive"
-
-
-def test_x2_wall_flip_tracking_cfg_uses_x2_wall_flip_profile():
-    from unilab.envs.motion_tracking.x2 import X2WallFlipTrackingCfg
-
-    cfg = X2WallFlipTrackingCfg()
-
-    assert cfg.scene.model_file.endswith("scene_flat_with_wall.xml")
-    assert str(cfg.motion_file).endswith("tictacflip_6-3_g1format.npz")
-    assert cfg.sensor.local_linvel == "body-linear-vel"
-    assert cfg.sensor.gyro == "body-angular-velocity"
-    assert cfg.anchor_body_name == "torso_link"
-    assert cfg.body_names[0] == "pelvis"
-    assert cfg.body_names[-1] == "right_wrist_roll_link"
-    assert len(cfg.body_names) == 30
-    assert cfg.pose_randomization.x == (0.0, 0.0)
-    assert cfg.velocity_randomization.x == (0.0, 0.0)
-    assert cfg.joint_position_range == (0.0, 0.0)
-    assert cfg.truncate_on_clip_end is False
-    assert cfg.anchor_ori_threshold == pytest.approx(1e9)
-    assert cfg.anchor_pos_z_threshold == pytest.approx(0.5)
-    assert cfg.ee_body_pos_z_threshold == pytest.approx(0.5)
-    assert cfg.terminate_on_undesired_contacts is True
-    assert cfg.sampling_mode == "adaptive"
-
-
-def test_g1_motion_tracking_apply_action_accepts_per_joint_action_scale():
-    from unilab.envs.motion_tracking.g1.tracking import G1MotionTrackingEnv
-
-    env = cast(Any, object.__new__(G1MotionTrackingEnv))
-    env.default_angles = np.array([0.5, -0.5, 1.0], dtype=np.float32)
-    env._cfg = SimpleNamespace(
-        control_config=SimpleNamespace(
-            action_scale=[0.1, 0.2, 0.3],
-            simulate_action_latency=False,
-        )
-    )
-    state = SimpleNamespace(info={})
-    actions = np.array([[1.0, -1.0, 0.5]], dtype=np.float32)
-
-    ctrl = env.apply_action(actions, state)
-
-    np.testing.assert_allclose(ctrl, np.array([[0.6, -0.7, 1.15]], dtype=np.float32))
-    np.testing.assert_array_equal(state.info["current_actions"], actions)
-
-
-def _make_g1_motion_tracking_clip_end_stub(
-    *,
-    truncate_on_clip_end: bool,
-    terminated: np.ndarray | None = None,
-    step_env_ids: np.ndarray | None = None,
-):
-    from unilab.base.np_env import NpEnvState
-    from unilab.envs.motion_tracking.common.motion_loader import MotionData
-    from unilab.envs.motion_tracking.g1.tracking import G1MotionTrackingEnv
-
-    class FakeBackend:
-        def __init__(self) -> None:
-            self.set_state_calls: list[tuple[np.ndarray, np.ndarray, np.ndarray]] = []
-
-        def get_body_lin_vel_w(self, body_ids: np.ndarray) -> np.ndarray:
-            return np.zeros((2, len(body_ids), 3), dtype=np.float32)
-
-        def get_body_ang_vel_w(self, body_ids: np.ndarray) -> np.ndarray:
-            return np.zeros((2, len(body_ids), 3), dtype=np.float32)
-
-        def get_body_pos_w(self, body_ids: np.ndarray) -> np.ndarray:
-            return np.zeros((2, len(body_ids), 3), dtype=np.float32)
-
-        def get_body_quat_w(self, body_ids: np.ndarray) -> np.ndarray:
-            return np.tile(
-                np.array([[[1.0, 0.0, 0.0, 0.0]]], dtype=np.float32),
-                (2, len(body_ids), 1),
-            )
-
-        def get_body_pose_w(self, body_ids: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
-            return self.get_body_pos_w(body_ids), self.get_body_quat_w(body_ids)
-
-        def get_body_pose_w_rows(
-            self, env_ids: np.ndarray, body_ids: np.ndarray
-        ) -> tuple[np.ndarray, np.ndarray]:
-            rows = np.asarray(env_ids, dtype=np.intp)
-            return self.get_body_pos_w(body_ids)[rows], self.get_body_quat_w(body_ids)[rows]
-
-        def get_sensor_data_rows(self, name: str, env_ids: np.ndarray) -> np.ndarray:
-            del name
-            return np.zeros((len(env_ids), 3), dtype=np.float32)
-
-        def get_body_vel_w(self, body_ids: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
-            return self.get_body_lin_vel_w(body_ids), self.get_body_ang_vel_w(body_ids)
-
-        def set_state(self, env_ids: np.ndarray, qpos: np.ndarray, qvel: np.ndarray) -> None:
-            self.set_state_calls.append((env_ids.copy(), qpos.copy(), qvel.copy()))
-
-    class FakeMotionLoader:
-        def get_motion_at_frame(self, frames: np.ndarray) -> MotionData:
-            frame_values = frames.astype(np.float32)[:, None]
-            return MotionData(
-                joint_pos=np.repeat(frame_values, 2, axis=1),
-                joint_vel=np.repeat(frame_values + 10.0, 2, axis=1),
-                body_pos_w=np.pad(frame_values[:, None, :], ((0, 0), (0, 0), (0, 2))),
-                body_quat_w=np.tile(
-                    np.array([[[1.0, 0.0, 0.0, 0.0]]], dtype=np.float32), (len(frames), 1, 1)
-                ),
-                body_lin_vel_w=np.zeros((len(frames), 1, 3), dtype=np.float32),
-                body_ang_vel_w=np.zeros((len(frames), 1, 3), dtype=np.float32),
-            )
-
-    class FakeSampler:
-        def __init__(self) -> None:
-            self.failure_updates: list[np.ndarray] = []
-            self.sampled_env_ids: list[np.ndarray] = []
-            self.current_frames = np.zeros((2,), dtype=np.int32)
-            self._after_step = False
-
-        def get_current_motion(self) -> MotionData:
-            if self._after_step and np.any(self.current_frames == 99):
-                raise AssertionError("queried all current motion while a clip-end frame is invalid")
-            return MotionData(
-                joint_pos=np.zeros((2, 2), dtype=np.float32),
-                joint_vel=np.zeros((2, 2), dtype=np.float32),
-                body_pos_w=np.zeros((2, 1, 3), dtype=np.float32),
-                body_quat_w=np.tile(
-                    np.array([[[1.0, 0.0, 0.0, 0.0]]], dtype=np.float32), (2, 1, 1)
-                ),
-                body_lin_vel_w=np.zeros((2, 1, 3), dtype=np.float32),
-                body_ang_vel_w=np.zeros((2, 1, 3), dtype=np.float32),
-            )
-
-        def update_failure_stats(self, terminated: np.ndarray) -> None:
-            self.failure_updates.append(terminated.copy())
-
-        def step(self) -> np.ndarray:
-            env_ids = np.array([1], dtype=np.int32) if step_env_ids is None else step_env_ids.copy()
-            self.current_frames[env_ids] = 99
-            self._after_step = True
-            return env_ids
-
-        def sample_frames(self, env_ids: np.ndarray) -> np.ndarray:
-            self.sampled_env_ids.append(env_ids.copy())
-            self.current_frames[env_ids] = 7
-            return np.full(len(env_ids), 7, dtype=np.int32)
-
-    env = cast(Any, object.__new__(G1MotionTrackingEnv))
-    env._num_envs = 2
-    zero_pose = SimpleNamespace(
-        x=(0.0, 0.0),
-        y=(0.0, 0.0),
-        z=(0.0, 0.0),
-        roll=(0.0, 0.0),
-        pitch=(0.0, 0.0),
-        yaw=(0.0, 0.0),
-    )
-    env._cfg = SimpleNamespace(
-        max_episode_steps=None,
-        truncate_on_clip_end=truncate_on_clip_end,
-        sensor=SimpleNamespace(local_linvel="local_linvel", gyro="gyro"),
-        pose_randomization=zero_pose,
-        velocity_randomization=zero_pose,
-        joint_position_range=(0.0, 0.0),
-    )
-    env.body_ids = np.array([0], dtype=np.int32)
-    env._backend = FakeBackend()
-    env.motion_sampler = FakeSampler()
-    env.motion_loader = FakeMotionLoader()
-    env._motion_data_buffer = None
-    env._copy_body_state_w = None
-    env._clip_end_truncated = np.zeros((2,), dtype=bool)
-    env._env_bool = np.empty((2,), dtype=bool)
-    env._init_qpos = np.zeros((9,), dtype=np.float32)
-    env._init_qvel = np.zeros((8,), dtype=np.float32)
-    env.get_local_linvel = lambda: np.zeros((2, 3), dtype=np.float32)
-    env.get_gyro = lambda: np.zeros((2, 3), dtype=np.float32)
-    env.get_dof_pos = lambda: np.zeros((2, 2), dtype=np.float32)
-    env.get_dof_vel = lambda: np.zeros((2, 2), dtype=np.float32)
-    env._get_joint_range = lambda: None
-    env._get_body_pose_w = lambda: (
-        np.zeros((2, 1, 3), dtype=np.float32),
-        np.tile(np.array([[[1.0, 0.0, 0.0, 0.0]]], dtype=np.float32), (2, 1, 1)),
-    )
-    env._update_relative_transforms = lambda *args: None
-    env._compute_terminations = lambda *args: (
-        np.zeros((2,), dtype=bool) if terminated is None else terminated.copy()
-    )
-    env._compute_reward = lambda *args: np.zeros((2,), dtype=np.float32)
-    env._compute_obs = lambda *args: {
-        "obs": np.zeros((2, 1), dtype=np.float32),
-        "critic": np.zeros((2, 2), dtype=np.float32),
-    }
-
-    state = NpEnvState(
-        obs={
-            "obs": np.zeros((2, 1), dtype=np.float32),
-            "critic": np.zeros((2, 2), dtype=np.float32),
-        },
-        reward=np.zeros((2,), dtype=np.float32),
-        terminated=np.zeros((2,), dtype=bool),
-        truncated=np.zeros((2,), dtype=bool),
-        info={"steps": np.zeros((2,), dtype=np.uint32)},
-    )
-
-    return env, state
-
-
-def test_g1_motion_tracking_clip_end_resamples_by_default_without_truncation():
-    env, state = _make_g1_motion_tracking_clip_end_stub(truncate_on_clip_end=False)
-
-    next_state = env.update_state(state)
-    truncated = env._compute_truncated(next_state)
-
-    np.testing.assert_array_equal(next_state.terminated, np.array([False, False]))
-    np.testing.assert_array_equal(truncated, np.array([False, False]))
-    np.testing.assert_array_equal(env.motion_sampler.sampled_env_ids[0], np.array([1]))
-    assert len(env._backend.set_state_calls) == 1
-    set_state_env_ids, qpos, qvel = env._backend.set_state_calls[0]
-    np.testing.assert_array_equal(set_state_env_ids, np.array([1], dtype=np.int32))
-    np.testing.assert_array_equal(qpos[:, 0], np.array([7.0], dtype=np.float32))
-    np.testing.assert_array_equal(qpos[:, 7:], np.array([[7.0, 7.0]], dtype=np.float32))
-    np.testing.assert_array_equal(qvel[:, 6:], np.array([[17.0, 17.0]], dtype=np.float32))
-    np.testing.assert_array_equal(
-        env.motion_sampler.failure_updates[0], np.array([False, False], dtype=bool)
-    )
-
-
-def test_g1_motion_tracking_clip_end_truncates_when_config_enabled():
-    env, state = _make_g1_motion_tracking_clip_end_stub(truncate_on_clip_end=True)
-
-    next_state = env.update_state(state)
-    truncated = env._compute_truncated(next_state)
-
-    np.testing.assert_array_equal(next_state.terminated, np.array([False, False]))
-    np.testing.assert_array_equal(truncated, np.array([False, True]))
-    assert env.motion_sampler.sampled_env_ids == []
-    assert env._backend.set_state_calls == []
-    np.testing.assert_array_equal(
-        env.motion_sampler.failure_updates[0], np.array([False, False], dtype=bool)
-    )
-
-
-def test_g1_motion_tracking_clip_end_resample_skips_terminated_envs():
-    env, state = _make_g1_motion_tracking_clip_end_stub(
-        truncate_on_clip_end=False,
-        terminated=np.array([False, True], dtype=bool),
-    )
-
-    next_state = env.update_state(state)
-    truncated = env._compute_truncated(next_state)
-
-    np.testing.assert_array_equal(next_state.terminated, np.array([False, True]))
-    np.testing.assert_array_equal(truncated, np.array([False, False]))
-    assert env.motion_sampler.sampled_env_ids == []
-    assert env._backend.set_state_calls == []
-
-
-def test_g1_motion_tracking_clip_end_resample_keeps_terminated_final_obs_valid():
-    env, state = _make_g1_motion_tracking_clip_end_stub(
-        truncate_on_clip_end=False,
-        terminated=np.array([False, True], dtype=bool),
-        step_env_ids=np.array([0, 1], dtype=np.int32),
-    )
-
-    next_state = env.update_state(state)
-    truncated = env._compute_truncated(next_state)
-
-    np.testing.assert_array_equal(next_state.terminated, np.array([False, True]))
-    np.testing.assert_array_equal(truncated, np.array([False, False]))
-    np.testing.assert_array_equal(env.motion_sampler.sampled_env_ids[0], np.array([0]))
-    assert len(env._backend.set_state_calls) == 1
-    set_state_env_ids, qpos, qvel = env._backend.set_state_calls[0]
-    np.testing.assert_array_equal(set_state_env_ids, np.array([0], dtype=np.int32))
-    np.testing.assert_array_equal(qpos[:, 0], np.array([7.0], dtype=np.float32))
-    np.testing.assert_array_equal(qvel[:, 6:], np.array([[17.0, 17.0]], dtype=np.float32))
-
-
-def test_g1_motion_tracking_clip_end_does_not_override_true_termination():
-    from unilab.base.np_env import NpEnvState
-    from unilab.envs.motion_tracking.g1.tracking import G1MotionTrackingEnv
-
-    env = cast(Any, object.__new__(G1MotionTrackingEnv))
-    env._num_envs = 2
-    env._cfg = type("Cfg", (), {"max_episode_steps": None})()
-    env._clip_end_truncated = np.array([False, True], dtype=bool)
-
-    state = NpEnvState(
-        obs={},
-        reward=np.zeros((2,), dtype=np.float32),
-        terminated=np.array([False, True], dtype=bool),
-        truncated=np.zeros((2,), dtype=bool),
-        info={"steps": np.zeros((2,), dtype=np.uint32)},
-    )
-
-    truncated = env._compute_truncated(state)
-    np.testing.assert_array_equal(truncated, np.array([False, False]))
-
-
 # ---------------------------------------------------------------------------
 # Fast env/backend smoke tests
 # ---------------------------------------------------------------------------
 
 # Environments that don't need special config overrides
 _STANDARD_ENVS = [
-    "Go1JoystickFlat",
-    "Go1JoystickRough",
-    "Go2JoystickFlat",
-    "Go2WJoystickFlat",
-    "Go2WJoystickRough",
     "G1WalkFlat",
     "G1WalkRough",
     "AllegroInhandRotation",
@@ -2060,14 +860,7 @@ def test_g1_motion_tracking_clip_end_does_not_override_true_termination():
 
 
 @pytest.mark.parametrize("env_name", _STANDARD_ENVS)
-def test_env_reset_and_step(
-    env_name: str,
-    default_go1_reward_config,
-    default_go2_reward_config,
-    default_g1_reward_config,
-    default_g1_walk_flat_reward_config,
-    default_allegro_reward_config,
-):
+def test_env_reset_and_step(env_name: str):
     """Every registered env must be constructible, resetable, and steppable.
 
     Verifies:
@@ -2079,18 +872,16 @@ def test_env_reset_and_step(
     ensure_registries()
     from unilab.base import registry
 
-    # Provide reward_config for envs that require it via Hydra
+    # Provide config overrides for envs that require them via Hydra
     env_cfg_override = None
-    if "Go1" in env_name:
-        env_cfg_override = {"reward_config": default_go1_reward_config}
-    elif "Go2" in env_name:
-        env_cfg_override = {"reward_config": default_go2_reward_config}
-    elif "G1Walk" in env_name:
-        env_cfg_override = {"reward_config": default_g1_walk_flat_reward_config}
-    elif "G1" in env_name:
-        env_cfg_override = {"reward_config": default_g1_reward_config}
-    elif "Allegro" in env_name:
-        env_cfg_override = {"reward_config": default_allegro_reward_config}
+    if env_name == "G1WalkFlat":
+        env_cfg_override = _g1_manager_override("g1_walk_flat")
+    elif env_name == "G1WalkRough":
+        env_cfg_override = _g1_manager_override("g1_walk_rough")
+    elif env_name == "AllegroInhandRotation":
+        env_cfg_override = _allegro_manager_override()
+    elif env_name == "AllegroInhandRotationGrasp":
+        env_cfg_override = _allegro_manager_override(task="allegro_inhand_grasp")
 
     env = cast(
         Any,
@@ -2132,263 +923,327 @@ def test_env_reset_and_step(
         env.close()
 
 
-def _assert_mujoco_position_gains(
-    env: Any, *, kp: float, kd: float, actuator_ids=slice(None)
-) -> None:
-    model = env._backend.model
-    pool = env._backend._pool
-    np.testing.assert_allclose(model.actuator_gainprm[actuator_ids, 0], kp)
-    np.testing.assert_allclose(model.actuator_biasprm[actuator_ids, 1], -kp)
-    np.testing.assert_allclose(model.actuator_biasprm[actuator_ids, 2], -kd)
-    np.testing.assert_allclose(pool.get_field(0, "kp")[actuator_ids], kp)
-    np.testing.assert_allclose(pool.get_field(0, "kd")[actuator_ids], kd)
-
-
-def test_go1_env_initializes_kp_kd_into_pool(default_go1_reward_config):
+def test_allegro_manager_runtime_transition_contract():
     _require_mujoco_runtime()
     ensure_registries()
     from unilab.base import registry
+    from unilab.envs import ManagerBasedRlEnv
+    from unilab.tasks.manipulation.allegro_inhand.manager_terms import (
+        AllegroIncrementalPositionAction,
+        AllegroRotationObservation,
+    )
 
-    env = cast(
-        Any,
-        registry.make(
-            "Go1JoystickFlat",
-            num_envs=2,
-            sim_backend="mujoco",
-            env_cfg_override={
-                "reward_config": default_go1_reward_config,
-                "control_config": {"Kp": 12.0, "Kd": 0.7},
-            },
-        ),
+    manager_override = _allegro_manager_override()
+    manager_override["observations"]["policy"]["terms"]["rotation"]["params"]["joint_noise"] = 0.0
+    env = registry.make(
+        "AllegroInhandRotation",
+        num_envs=2,
+        sim_backend="mujoco",
+        env_cfg_override=manager_override,
     )
+    assert isinstance(env, ManagerBasedRlEnv)
     try:
-        _assert_mujoco_position_gains(env, kp=12.0, kd=0.7)
+        manager_initial = env.init_state()
+        history = manager_initial.obs["obs"].reshape(2, 3, 35)
+        np.testing.assert_array_equal(history[:, 0], history[:, 1])
+        np.testing.assert_array_equal(history[:, 1], history[:, 2])
+
+        action = env.action_manager.get_term("hand")
+        assert isinstance(action, AllegroIncrementalPositionAction)
+        target_before = action.target.copy()
+        actions = np.full((2, 16), 0.25, dtype=np.float32)
+        manager_state = env.step(actions)
+        expected_target = np.clip(
+            target_before + 0.25 / 24.0,
+            action.ctrl_lower,
+            action.ctrl_upper,
+        )
+        np.testing.assert_allclose(action.target, expected_target, rtol=0.0, atol=1.0e-7)
+
+        observation = env.observation_manager.get_term_cfg("policy", "rotation").func
+        assert isinstance(observation, AllegroRotationObservation)
+        current_frame = observation(env)
+        np.testing.assert_allclose(
+            manager_state.obs["obs"][:, -35:], current_frame, rtol=0.0, atol=1.0e-7
+        )
+        assert np.isfinite(manager_state.obs["obs"]).all()
+        assert np.isfinite(manager_state.reward).all()
+        assert manager_state.terminated.dtype == np.bool_
+        assert action.action_dim == 16
+        assert env.obs_groups_spec == {"obs": 105}
     finally:
         env.close()
 
 
-def test_go2_env_initializes_kp_kd_into_pool():
-    _require_mujoco_runtime()
+@pytest.mark.parametrize("sim_backend", ["mujoco", "motrix"])
+def test_allegro_grasp_manager_runtime_uses_zero_increment_action(sim_backend: str, tmp_path: Path):
+    if sim_backend == "mujoco":
+        _require_mujoco_runtime()
+    else:
+        pytest.importorskip("motrixsim")
     ensure_registries()
     from unilab.base import registry
-    from unilab.envs.locomotion.go2.joystick import RewardConfig
+    from unilab.envs import ManagerBasedRlEnv
+    from unilab.tasks.manipulation.allegro_inhand.grasp_gen import (
+        AllegroGraspQualityTermination,
+        AllegroGraspRecorder,
+    )
+    from unilab.tasks.manipulation.allegro_inhand.manager_terms import (
+        AllegroIncrementalPositionAction,
+    )
 
-    env = cast(
-        Any,
-        registry.make(
-            "Go2JoystickFlat",
-            num_envs=2,
-            sim_backend="mujoco",
-            env_cfg_override={
-                "reward_config": RewardConfig(
-                    scales={
-                        "tracking_lin_vel": 1.0,
-                        "tracking_ang_vel": 0.2,
-                        "lin_vel_z": -5.0,
-                        "ang_vel_xy": -0.02,
-                        "base_height": -100.0,
-                        "action_rate": -0.005,
-                        "similar_to_default": -0.1,
-                    },
-                    tracking_sigma=0.25,
-                    base_height_target=0.3,
-                ),
-                "control_config": {"Kp": 18.0, "Kd": 0.9},
-            },
-        ),
+    override = _allegro_manager_override(sim_backend, task="allegro_inhand_grasp")
+    override["auto_reset"] = False
+    override["terminations"]["invalid_grasp"]["params"]["enabled"] = False
+    override["recorders"]["grasp_cache"]["params"].update(
+        {"output_path": str(tmp_path / f"{sim_backend}.npy"), "auto_save": False}
     )
+    env = registry.make(
+        "AllegroInhandRotationGrasp",
+        num_envs=2,
+        sim_backend=sim_backend,
+        env_cfg_override=override,
+    )
+    assert isinstance(env, ManagerBasedRlEnv)
     try:
-        _assert_mujoco_position_gains(env, kp=18.0, kd=0.9)
+        initial = env.init_state()
+        action = env.action_manager.get_term("hand")
+        quality = env.termination_manager.get_term_cfg("invalid_grasp").func
+        recorder = env.recorder_manager.get_term("grasp_cache")
+        assert isinstance(action, AllegroIncrementalPositionAction)
+        assert isinstance(quality, AllegroGraspQualityTermination)
+        assert isinstance(recorder, AllegroGraspRecorder)
+        target = action.target.copy()
+
+        state = env.step(np.ones((2, 16), dtype=np.float32))
+        np.testing.assert_array_equal(action.target, target)
+        np.testing.assert_array_equal(state.reward, np.zeros(2, dtype=state.reward.dtype))
+        assert initial.obs["obs"].shape == (2, 105)
+        assert state.obs["obs"].shape == (2, 105)
+        assert quality.last_counter == env.common_step_counter
     finally:
         env.close()
 
 
-def test_allegro_env_initializes_kp_kd_into_pool(default_allegro_reward_config):
-    _require_mujoco_runtime()
-    ensure_registries()
+_MOTION_CORE_RUNTIME_CASES = (
+    pytest.param("ppo", "g1_motion_tracking", "G1MotionTracking", 160, 286, 29, False),
+    pytest.param(
+        "ppo",
+        "g1_motion_tracking_deploy",
+        "G1MotionTrackingDeploy",
+        154,
+        286,
+        29,
+        False,
+    ),
+    pytest.param("ppo", "g1_23dof_motion_tracking", "G1MotionTracking23Dof", 130, 256, 23, False),
+    pytest.param(
+        "ppo",
+        "g1_23dof_motion_tracking_deploy",
+        "G1MotionTracking23DofDeploy",
+        124,
+        256,
+        23,
+        False,
+    ),
+    pytest.param("appo", "g1_motion_tracking", "G1MotionTracking", 160, 286, 29, False),
+    pytest.param("appo", "g1_23dof_motion_tracking", "G1MotionTracking23Dof", 130, 256, 23, False),
+    pytest.param(
+        "sac",
+        "g1_motion_tracking",
+        "G1MotionTrackingSAC",
+        160,
+        289,
+        29,
+        True,
+    ),
+    pytest.param(
+        "sac",
+        "g1_23dof_motion_tracking",
+        "G1MotionTrackingSAC23Dof",
+        130,
+        259,
+        23,
+        True,
+    ),
+)
+
+
+def test_g1_motion_core_registrations_are_manager_only() -> None:
     from unilab.base import registry
 
-    env = cast(
-        Any,
-        registry.make(
-            "AllegroInhandRotation",
-            num_envs=2,
-            sim_backend="mujoco",
-            env_cfg_override={
-                "reward_config": default_allegro_reward_config,
-                "control_config": {"kp": 2.5, "kd": 0.4},
-            },
-        ),
-    )
-    try:
-        _assert_mujoco_position_gains(env, kp=2.5, kd=0.4, actuator_ids=slice(0, 16))
-    finally:
-        env.close()
+    ensure_registries()
+    metadata = registry.list_registered_envs()
+    for task_name in (
+        "G1MotionTracking",
+        "G1MotionTrackingDeploy",
+        "G1MotionTracking23Dof",
+        "G1MotionTracking23DofDeploy",
+        "G1MotionTrackingSAC23Dof",
+    ):
+        assert metadata[task_name] == {
+            "config_factory": "ManagerBasedRlEnvCfg",
+            "available_backends": ["mujoco", "motrix"],
+        }
+    # mjwarp is registered for G1MotionTrackingSAC only (benchmark scope, #1292).
+    assert metadata["G1MotionTrackingSAC"] == {
+        "config_factory": "ManagerBasedRlEnvCfg",
+        "available_backends": ["mujoco", "motrix", "mjwarp"],
+    }
 
 
-@pytest.mark.parametrize("sim_backend", ["mujoco", "motrix"])
-def test_g1_motion_tracking_reset_and_step(sim_backend: str):
-    """G1MotionTracking needs a motion_file — skip if not available."""
+def test_g1_motion_manager_ppo_wraps_only_active_rows_in_one_state_commit(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
     ensure_registries()
+    _require_mujoco_runtime()
     from unilab.base import registry
 
-    if sim_backend == "mujoco":
-        _require_mujoco_runtime()
-    else:
-        pytest.importorskip("motrixsim")
-
-    # Look for any motion file in the expected location
-    motion_dir = Path(__file__).parents[2] / "src" / "unilab" / "assets" / "motions" / "g1"
-    if not motion_dir.exists():
-        pytest.skip(f"Motion data directory not found: {motion_dir}")
-
-    npz_files = list(motion_dir.glob("*.npz"))
-    if not npz_files:
-        pytest.skip(f"No .npz motion files in {motion_dir}")
-
-    # Filter out 23-DoF motion files — G1MotionTracking uses 29-DoF config
-    non_23dof = [f for f in npz_files if "_23dof" not in f.name]
-    if not non_23dof:
-        pytest.skip("No non-23-DoF motion files available for 29-DoF config")
-    motion_file = str(non_23dof[0])
-    env = cast(
-        Any,
-        registry.make(
-            "G1MotionTracking",
-            num_envs=2,
-            sim_backend=sim_backend,
-            env_cfg_override={"motion_file": motion_file},
-        ),
+    _, override = _motion_manager_override("g1_motion_tracking", "mujoco")
+    env = registry.make(
+        "G1MotionTracking",
+        num_envs=2,
+        sim_backend="mujoco",
+        env_cfg_override=override,
     )
     try:
-        spec = env.obs_groups_spec
-        assert isinstance(spec, dict)
-        assert "obs" in spec
-        assert "critic" in spec
-        obs_shape = env.observation_space.shape
-        assert obs_shape is not None
-        assert sum(spec.values()) == obs_shape[0]
-
-        state = env.init_state()
-        assert isinstance(state.obs, dict)
-        for key, dim in spec.items():
-            assert state.obs[key].shape == (2, dim)
+        env.init_state()
+        command = env.command_manager.get_term("motion")
+        command.time_steps[:] = command.sampler.current_clip_end_frames
+        env.reset_buf[:] = [True, False]
+
+        set_state_env_ids: list[np.ndarray] = []
+        original_set_state = env._backend.set_state
+
+        def record_set_state(
+            env_ids: np.ndarray,
+            qpos: np.ndarray,
+            qvel: np.ndarray,
+            *,
+            randomization: Any = None,
+        ) -> Any:
+            set_state_env_ids.append(env_ids.copy())
+            return original_set_state(
+                env_ids,
+                qpos,
+                qvel,
+                randomization=randomization,
+            )
 
-        action_shape = env.action_space.shape
-        assert action_shape is not None
-        actions = np.zeros((2, action_shape[0]))
-        state = env.step(actions)
-        assert isinstance(state.obs, dict)
-        assert state.reward.shape == (2,)
-        assert state.terminated.shape == (2,)
-        assert state.truncated.shape == (2,)
+        monkeypatch.setattr(env._backend, "set_state", record_set_state)
+        all_ids = np.arange(env.num_envs, dtype=np.int32)
+        with env._reset_state.scoped(all_ids):
+            env.command_manager.compute(dt=0.0)
+        env.command_manager.post_compute()
+
+        assert len(set_state_env_ids) == 1
+        np.testing.assert_array_equal(set_state_env_ids[0], [1])
+        assert command.time_steps[0] == command.sampler.current_clip_end_frames[0]
+        assert command.time_steps[1] <= command.sampler.current_clip_end_frames[1]
+        expected_motion = command.motion.get_motion_at_frame(command.time_steps)
+        np.testing.assert_array_equal(command.joint_pos, expected_motion.joint_pos)
+        np.testing.assert_array_equal(
+            command._robot_body_pos_w,
+            command.robot.data.body_link_pos_w[:, command._robot_body_ids],
+        )
+        assert command._robot_cache_step == env.common_step_counter
     finally:
         env.close()
 
 
-def test_g1_motion_tracking_deploy_reset_and_step_mujoco():
-    """Deploy env keeps motion-tracking behavior but exposes unitree mimic actor inputs."""
+def test_g1_motion_manager_sac_clip_end_is_truncation() -> None:
     ensure_registries()
     _require_mujoco_runtime()
     from unilab.base import registry
 
-    motion_dir = Path(__file__).parents[2] / "src" / "unilab" / "assets" / "motions" / "g1"
-    if not motion_dir.exists():
-        pytest.skip(f"Motion data directory not found: {motion_dir}")
-
-    npz_files = list(motion_dir.glob("*.npz"))
-    if not npz_files:
-        pytest.skip(f"No .npz motion files in {motion_dir}")
-
-    # Filter out 23-DoF motion files — G1MotionTrackingDeploy uses 29-DoF config
-    non_23dof = [f for f in npz_files if "_23dof" not in f.name]
-    if not non_23dof:
-        pytest.skip("No non-23-DoF motion files available for 29-DoF deploy config")
-    env = cast(
-        Any,
-        registry.make(
-            "G1MotionTrackingDeploy",
-            num_envs=2,
-            sim_backend="mujoco",
-            env_cfg_override={"motion_file": str(non_23dof[0])},
-        ),
+    _, override = _motion_manager_override(
+        "g1_motion_tracking",
+        "mujoco",
+        config_root="sac",
     )
-    try:
-        assert env.obs_groups_spec == {"obs": 154, "critic": 286}
-        state = env.init_state()
-        assert state.obs["obs"].shape == (2, 154)
-        assert state.obs["critic"].shape == (2, 286)
-
-        action_shape = env.action_space.shape
-        assert action_shape is not None
-        state = env.step(np.zeros((2, action_shape[0])))
-        assert state.obs["obs"].shape == (2, 154)
-        assert state.obs["critic"].shape == (2, 286)
-    finally:
-        env.close()
-
-
-def test_go2_mujoco_reset_applies_kp_kd_domain_randomization(default_go2_reward_config):
-    _require_mujoco_runtime()
-    ensure_registries()
-
-    from unilab.base import registry
-
-    env = cast(
-        Any,
-        registry.make(
-            "Go2JoystickFlat",
-            num_envs=4,
-            sim_backend="mujoco",
-            env_cfg_override={"reward_config": default_go2_reward_config},
-        ),
+    override["auto_reset"] = False
+    env = registry.make(
+        "G1MotionTrackingSAC",
+        num_envs=2,
+        sim_backend="mujoco",
+        env_cfg_override=override,
     )
     try:
         env.init_state()
-        backend = env._backend
-        kp = np.stack([backend._pool.get_field(i, "kp") for i in range(env.num_envs)])
-        kd = np.stack([backend._pool.get_field(i, "kd") for i in range(env.num_envs)])
-        base_kp = float(env.cfg.control_config.Kp)
-        base_kd = float(env.cfg.control_config.Kd)
-
-        assert np.unique(np.round(kp[:, 0], 6)).size > 1
-        assert np.unique(np.round(kd[:, 0], 6)).size > 1
-        np.testing.assert_allclose(kp / base_kp, np.broadcast_to(kp[:, :1] / base_kp, kp.shape))
-        np.testing.assert_allclose(kd / base_kd, np.broadcast_to(kd[:, :1] / base_kd, kd.shape))
-        assert np.all(kp >= base_kp * 0.9)
-        assert np.all(kp <= base_kp * 1.1)
-        assert np.all(kd >= base_kd * 0.9)
-        assert np.all(kd <= base_kd * 1.1)
+        command = env.command_manager.get_term("motion")
+        command.time_steps[:] = command.sampler.current_clip_end_frames
+
+        state = env.step(np.zeros((2, 29), dtype=np.float32))
+
+        np.testing.assert_array_equal(state.terminated, [False, False])
+        np.testing.assert_array_equal(state.truncated, [True, True])
+        np.testing.assert_array_equal(command.time_steps, command.sampler.current_clip_end_frames)
     finally:
         env.close()
 
 
-def test_go2w_mujoco_keeps_kp_kd_out_of_backend_position_actuator_path():
-    _require_mujoco_runtime()
+@pytest.mark.parametrize(
+    ("config_root", "task", "identity", "actor_dim", "critic_dim", "action_dim", "truncate"),
+    _MOTION_CORE_RUNTIME_CASES,
+)
+@pytest.mark.parametrize("sim_backend", ["mujoco", "motrix"])
+def test_g1_motion_core_manager_reset_and_step(
+    config_root: str,
+    task: str,
+    identity: str,
+    actor_dim: int,
+    critic_dim: int,
+    action_dim: int,
+    truncate: bool,
+    sim_backend: str,
+) -> None:
     ensure_registries()
-
     from unilab.base import registry
+    from unilab.envs import ManagerBasedRlEnv
 
-    env = cast(
-        Any,
-        registry.make(
-            "Go2WJoystickFlat",
-            num_envs=2,
-            sim_backend="mujoco",
-            env_cfg_override={
-                "reward_config": {
-                    "scales": {"alive": 1.0, "torques": -0.0002},
-                    "tracking_sigma": 0.25,
-                    "base_height_target": 0.3,
-                }
-            },
-        ),
+    if sim_backend == "mujoco":
+        _require_mujoco_runtime()
+    else:
+        pytest.importorskip("motrixsim")
+
+    task_name, override = _motion_manager_override(
+        task,
+        sim_backend,
+        config_root=config_root,
     )
+    assert task_name == identity
+    env = registry.make(
+        identity,
+        num_envs=2,
+        sim_backend=sim_backend,
+        env_cfg_override=override,
+    )
+    assert isinstance(env, ManagerBasedRlEnv)
     try:
-        env.init_state()
-        assert env._backend._position_actuator_gains is None
-        assert env._backend._pre_step_control_fn.__self__ is env
-        assert env._backend._pre_step_control_fn.__func__ is env._pre_step_motor_control.__func__
-        assert env._last_motor_ctrl.shape == (2, env.action_space.shape[0])
+        assert env.obs_groups_spec == {"obs": actor_dim, "critic": critic_dim}
+        assert env.action_space.shape == (action_dim,)
+        command = env.command_manager.get_term("motion")
+        assert command.cfg.params.truncate_on_clip_end is truncate
+        if sim_backend == "motrix" and "deploy" in task:
+            assert env._cfg.events["foot_friction"] is None
+            assert env._cfg.events["push_robot"] is None
+        if sim_backend == "motrix" and config_root in {"ppo", "appo"}:
+            root_pos = env._cfg.rewards["motion_global_root_pos"]
+            action_rate = env._cfg.rewards["action_rate_l2"]
+            assert root_pos is not None
+            assert action_rate is not None
+            expected_weights = (1.0, -0.05) if config_root == "ppo" else (0.5, -0.1)
+            assert (root_pos.weight, action_rate.weight) == pytest.approx(expected_weights)
+
+        state = env.init_state()
+        assert state.obs["obs"].shape == (2, actor_dim)
+        assert state.obs["critic"].shape == (2, critic_dim)
+
+        state = env.step(np.zeros((2, action_dim), dtype=np.float32))
+        assert state.reward.shape == (2,)
+        assert state.terminated.shape == (2,)
+        assert state.truncated.shape == (2,)
+        assert np.isfinite(state.reward).all()
+        assert all(np.isfinite(values).all() for values in state.obs.values())
     finally:
         env.close()
diff --git a/tests/envs/test_g1_obs_noise.py b/tests/envs/test_g1_obs_noise.py
deleted file mode 100644
index 48b061204..000000000
--- a/tests/envs/test_g1_obs_noise.py
+++ /dev/null
@@ -1,109 +0,0 @@
-"""Tests for G1 per-step observation noise."""
-
-from __future__ import annotations
-
-import numpy as np
-
-from unilab.envs.locomotion.g1.base import G1BaseCfg, G1BaseEnv, NoiseConfig
-
-
-class _ConcreteG1Env(G1BaseEnv):
-    """Minimal concrete subclass — only needed to satisfy the ABC."""
-
-    def update_state(self, state):
-        raise NotImplementedError
-
-
-def _make_env(level: float, *, seed: int | None = None) -> G1BaseEnv:
-    cfg = G1BaseCfg(noise_config=NoiseConfig(level=level, seed=seed))
-    env = object.__new__(_ConcreteG1Env)
-    env._cfg = cfg
-    return env
-
-
-class TestObsNoise:
-    def test_noise_applied_when_level_positive(self):
-        env = _make_env(level=1.0)
-        data = np.ones((4, 10), dtype=np.float32)
-        cfg = env._cfg.noise_config
-
-        results = [env._obs_noise(data.copy(), cfg.scale_joint_angle) for _ in range(5)]
-        # At least one result should differ from the original
-        assert any(not np.allclose(r, data) for r in results)
-
-    def test_no_noise_when_level_zero(self):
-        env = _make_env(level=0.0)
-        data = np.ones((4, 10), dtype=np.float32)
-        cfg = env._cfg.noise_config
-
-        result = env._obs_noise(data, cfg.scale_joint_angle)
-        assert result is data
-        np.testing.assert_array_equal(result, data)
-
-    def test_noise_bounded_by_level_times_scale(self):
-        env = _make_env(level=1.0)
-        data = np.zeros((128, 29), dtype=np.float32)
-        scale = 0.2
-        result = env._obs_noise(data.copy(), scale)
-        # uniform[-1,1] * 1.0 * 0.2 => bounded by [-0.2, 0.2]
-        assert np.all(result >= -scale)
-        assert np.all(result <= scale)
-
-    def test_noise_scales_with_level(self):
-        env_half = _make_env(level=0.5, seed=123)
-        env_full = _make_env(level=1.0, seed=123)
-        data = np.zeros((1024, 10), dtype=np.float32)
-        scale = 1.0
-
-        r_half = env_half._obs_noise(data.copy(), scale)
-        r_full = env_full._obs_noise(data.copy(), scale)
-
-        np.testing.assert_allclose(r_full, r_half * 2.0)
-
-    def test_configured_seed_is_reproducible_across_fresh_envs(self):
-        env_a = _make_env(level=1.0, seed=11)
-        env_b = _make_env(level=1.0, seed=11)
-        data = np.zeros((32, 10), dtype=np.float32)
-
-        first_a = env_a._obs_noise(data.copy(), 0.25)
-        first_b = env_b._obs_noise(data.copy(), 0.25)
-        second_a = env_a._obs_noise(data.copy(), 0.25)
-
-        np.testing.assert_allclose(first_a, first_b)
-        assert not np.allclose(first_a, second_a)
-
-    def test_seed_observation_noise_resets_stream(self):
-        env = _make_env(level=1.0)
-        data = np.zeros((32, 10), dtype=np.float32)
-
-        env.seed_observation_noise(17)
-        first = env._obs_noise(data.copy(), 0.25)
-        env.seed_observation_noise(17)
-        replayed = env._obs_noise(data.copy(), 0.25)
-
-        np.testing.assert_allclose(first, replayed)
-
-    def test_seed_observation_noise_overrides_configured_seed(self):
-        env = _make_env(level=1.0, seed=11)
-        replay = _make_env(level=1.0, seed=99)
-        data = np.zeros((32, 10), dtype=np.float32)
-
-        env.seed_observation_noise(99)
-        result = env._obs_noise(data.copy(), 0.25)
-        expected = replay._obs_noise(data.copy(), 0.25)
-
-        np.testing.assert_allclose(result, expected)
-
-    def test_noise_preserves_dtype(self):
-        for dt in [np.float32, np.float64]:
-            env = _make_env(level=1.0)
-            data = np.ones((4, 5), dtype=dt)
-            result = env._obs_noise(data, 0.1)
-            assert result.dtype == dt
-
-    def test_noise_preserves_shape(self):
-        env = _make_env(level=1.0)
-        for shape in [(1, 3), (64, 29), (1024, 10)]:
-            data = np.zeros(shape, dtype=np.float32)
-            result = env._obs_noise(data, 0.1)
-            assert result.shape == shape
diff --git a/tests/envs/test_go1_domain_randomization.py b/tests/envs/test_go1_domain_randomization.py
deleted file mode 100644
index 3004ec5f4..000000000
--- a/tests/envs/test_go1_domain_randomization.py
+++ /dev/null
@@ -1,61 +0,0 @@
-from __future__ import annotations
-
-from typing import Any
-
-import numpy as np
-import pytest
-
-pytest.importorskip("mujoco", reason="mujoco not installed")
-
-try:
-    from mujoco_uni.batch_env import BatchEnvPool as _  # noqa: F401
-except Exception:
-    pytest.skip(
-        "mujoco_uni.batch_env not available (platform/libstdc++ issue)", allow_module_level=True
-    )
-
-from unilab.base.registry import ensure_registries
-
-
-def test_go1_mujoco_reset_applies_base_mass_and_com_domain_randomization(
-    default_go1_reward_config: dict[str, Any],
-) -> None:
-    ensure_registries()
-
-    from unilab.base import registry
-
-    env = registry.make(
-        "Go1JoystickFlat",
-        num_envs=4,
-        sim_backend="mujoco",
-        env_cfg_override={"reward_config": default_go1_reward_config},
-    )
-    env_obj: Any = env
-    try:
-        env_obj.init_state()
-        backend: Any = env_obj._backend
-        base_body_id = int(backend._base_body_id)
-        body_mass = np.stack(
-            [backend._pool.get_field(i, "body_mass") for i in range(env_obj.num_envs)]
-        )
-        body_ipos = np.stack(
-            [backend._pool.get_field(i, "body_ipos") for i in range(env_obj.num_envs)]
-        )
-        body_ipos = body_ipos.reshape(env_obj.num_envs, -1, 3)
-
-        base_mass = float(backend._base_body_mass[base_body_id])
-        base_ipos = np.asarray(backend._base_body_ipos[base_body_id])
-        randomized_base_mass = body_mass[:, base_body_id]
-        randomized_base_ipos = body_ipos[:, base_body_id]
-        delta_x = randomized_base_ipos[:, 0] - base_ipos[0]
-        delta_yz = randomized_base_ipos[:, 1:] - base_ipos[1:]
-
-        assert np.unique(np.round(randomized_base_mass, 6)).size > 1
-        assert np.unique(np.round(delta_x, 6)).size > 1
-        assert np.all(randomized_base_mass >= base_mass - 1.5)
-        assert np.all(randomized_base_mass <= base_mass + 1.5)
-        assert np.all(delta_x >= -0.05)
-        assert np.all(delta_x <= 0.05)
-        np.testing.assert_allclose(delta_yz, 0.0)
-    finally:
-        env_obj.close()
diff --git a/tests/envs/test_go1_obs_noise.py b/tests/envs/test_go1_obs_noise.py
deleted file mode 100644
index cc8815a23..000000000
--- a/tests/envs/test_go1_obs_noise.py
+++ /dev/null
@@ -1,77 +0,0 @@
-"""Tests for Go1 per-step observation noise."""
-
-from __future__ import annotations
-
-import numpy as np
-
-from unilab.envs.locomotion.go1.base import Go1BaseCfg, Go1BaseEnv, NoiseConfig
-
-
-class _ConcreteGo1Env(Go1BaseEnv):
-    """Minimal concrete subclass — only needed to satisfy the ABC."""
-
-    def update_state(self, state):
-        raise NotImplementedError
-
-
-def _make_env(level: float) -> Go1BaseEnv:
-    cfg = Go1BaseCfg(noise_config=NoiseConfig(level=level))
-    env = object.__new__(_ConcreteGo1Env)
-    env._cfg = cfg
-    return env
-
-
-class TestObsNoise:
-    def test_noise_applied_when_level_positive(self):
-        env = _make_env(level=1.0)
-        data = np.ones((4, 10), dtype=np.float32)
-        cfg = env._cfg.noise_config
-
-        results = [env._obs_noise(data.copy(), cfg.scale_joint_angle) for _ in range(5)]
-        # At least one result should differ from the original
-        assert any(not np.allclose(r, data) for r in results)
-
-    def test_no_noise_when_level_zero(self):
-        env = _make_env(level=0.0)
-        data = np.ones((4, 10), dtype=np.float32)
-        cfg = env._cfg.noise_config
-
-        result = env._obs_noise(data.copy(), cfg.scale_joint_angle)
-        np.testing.assert_array_equal(result, data)
-
-    def test_noise_bounded_by_level_times_scale(self):
-        env = _make_env(level=1.0)
-        data = np.zeros((128, 29), dtype=np.float32)
-        scale = 0.2
-        result = env._obs_noise(data.copy(), scale)
-        # uniform[-1,1] * 1.0 * 0.2 => bounded by [-0.2, 0.2]
-        assert np.all(result >= -scale)
-        assert np.all(result <= scale)
-
-    def test_noise_scales_with_level(self):
-        env_half = _make_env(level=0.5)
-        env_full = _make_env(level=1.0)
-        data = np.zeros((1024, 10), dtype=np.float32)
-        scale = 1.0
-
-        np.random.seed(0)
-        r_half = env_half._obs_noise(data.copy(), scale)
-        np.random.seed(0)
-        r_full = env_full._obs_noise(data.copy(), scale)
-
-        # Same random seed: full-level noise should be exactly 2x half-level noise
-        np.testing.assert_allclose(r_full, r_half * 2.0)
-
-    def test_noise_preserves_dtype(self):
-        for dt in [np.float32, np.float64]:
-            env = _make_env(level=1.0)
-            data = np.ones((4, 5), dtype=dt)
-            result = env._obs_noise(data, 0.1)
-            assert result.dtype == dt
-
-    def test_noise_preserves_shape(self):
-        env = _make_env(level=1.0)
-        for shape in [(1, 3), (64, 29), (1024, 10)]:
-            data = np.zeros(shape, dtype=np.float32)
-            result = env._obs_noise(data, 0.1)
-            assert result.shape == shape
diff --git a/tests/envs/test_go2_obs_noise.py b/tests/envs/test_go2_obs_noise.py
deleted file mode 100644
index 3d179f5c0..000000000
--- a/tests/envs/test_go2_obs_noise.py
+++ /dev/null
@@ -1,74 +0,0 @@
-"""Tests for Go2 per-step observation noise."""
-
-from __future__ import annotations
-
-import numpy as np
-
-from unilab.envs.locomotion.go2.base import Go2BaseCfg, Go2BaseEnv, NoiseConfig
-
-
-class _ConcreteGo2Env(Go2BaseEnv):
-    """Minimal concrete subclass — only needed to satisfy the ABC."""
-
-    def update_state(self, state):
-        raise NotImplementedError
-
-
-def _make_env(level: float) -> Go2BaseEnv:
-    cfg = Go2BaseCfg(noise_config=NoiseConfig(level=level))
-    env = object.__new__(_ConcreteGo2Env)
-    env._cfg = cfg
-    return env
-
-
-class TestObsNoise:
-    def test_noise_applied_when_level_positive(self):
-        env = _make_env(level=1.0)
-        data = np.ones((4, 10), dtype=np.float32)
-        cfg = env._cfg.noise_config
-
-        results = [env._obs_noise(data.copy(), cfg.scale_joint_angle) for _ in range(5)]
-        assert any(not np.allclose(r, data) for r in results)
-
-    def test_no_noise_when_level_zero(self):
-        env = _make_env(level=0.0)
-        data = np.ones((4, 10), dtype=np.float32)
-        cfg = env._cfg.noise_config
-
-        result = env._obs_noise(data.copy(), cfg.scale_joint_angle)
-        np.testing.assert_array_equal(result, data)
-
-    def test_noise_bounded_by_level_times_scale(self):
-        env = _make_env(level=1.0)
-        data = np.zeros((128, 29), dtype=np.float32)
-        scale = 0.2
-        result = env._obs_noise(data.copy(), scale)
-        assert np.all(result >= -scale)
-        assert np.all(result <= scale)
-
-    def test_noise_scales_with_level(self):
-        env_half = _make_env(level=0.5)
-        env_full = _make_env(level=1.0)
-        data = np.zeros((1024, 10), dtype=np.float32)
-        scale = 1.0
-
-        np.random.seed(0)
-        r_half = env_half._obs_noise(data.copy(), scale)
-        np.random.seed(0)
-        r_full = env_full._obs_noise(data.copy(), scale)
-
-        np.testing.assert_allclose(r_full, r_half * 2.0)
-
-    def test_noise_preserves_dtype(self):
-        for dt in [np.float32, np.float64]:
-            env = _make_env(level=1.0)
-            data = np.ones((4, 5), dtype=dt)
-            result = env._obs_noise(data, 0.1)
-            assert result.dtype == dt
-
-    def test_noise_preserves_shape(self):
-        env = _make_env(level=1.0)
-        for shape in [(1, 3), (64, 29), (1024, 10)]:
-            data = np.zeros(shape, dtype=np.float32)
-            result = env._obs_noise(data, 0.1)
-            assert result.shape == shape
diff --git a/tests/envs/test_manager_based_rl_env.py b/tests/envs/test_manager_based_rl_env.py
new file mode 100644
index 000000000..19a4d1ec0
--- /dev/null
+++ b/tests/envs/test_manager_based_rl_env.py
@@ -0,0 +1,1427 @@
+"""Focused contract tests for the NumPy Manager-Based environment lifecycle."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, cast
+
+import numpy as np
+import pytest
+from unisim.backend.base import SimBackend
+
+import unilab.envs.manager_based_rl_env as manager_env_module
+from unilab.assets import ASSETS_ROOT_PATH
+from unilab.base.backend_factory import create_backend, env_backend_kwargs
+from unilab.base.entity import EntityCfg
+from unilab.base.scene import SceneCfg
+from unilab.envs import (
+    ManagerBasedRLEnv,
+    ManagerBasedRlEnv,
+    ManagerBasedRLEnvCfg,
+    ManagerBasedRlEnvCfg,
+    make_manager_based_rl_env,
+    mdp,
+)
+from unilab.managers import (
+    ActionTerm,
+    ActionTermCfg,
+    CommandTerm,
+    CommandTermCfg,
+    CurriculumTermCfg,
+    EventTermCfg,
+    MetricsTermCfg,
+    ObservationGroupCfg,
+    ObservationTermCfg,
+    RecorderTerm,
+    RecorderTermCfg,
+    RewardTermCfg,
+    TerminationTermCfg,
+)
+
+
+class _FakeBackend:
+    backend_type = "fake"
+
+    def __init__(
+        self,
+        num_envs: int,
+        *,
+        reject_pre_step: bool = False,
+        reject_materialize: bool = False,
+    ) -> None:
+        self.num_envs = num_envs
+        self.num_actuators = 1
+        self.reject_pre_step = reject_pre_step
+        self.reject_materialize = reject_materialize
+        self.pre_step_control = None
+        self.applied_controls: list[np.ndarray] = []
+        self.step_nsteps: list[int] = []
+        self.cleanup_calls = 0
+        self.materialize_calls = 0
+        self.lifecycle: list[str] = []
+
+    def materialize(self) -> None:
+        if self.reject_materialize:
+            raise RuntimeError("pool construction failed")
+        self.materialize_calls += 1
+        self.lifecycle.append("materialize")
+
+    def get_actuator_names(self) -> tuple[str, ...]:
+        return ("motor",)
+
+    def get_actuator_ctrl_range(self) -> np.ndarray:
+        return np.array([[-2.0, 2.0]], dtype=np.float32)
+
+    def get_dof_pos(self) -> np.ndarray:
+        return np.empty((self.num_envs, 0), dtype=np.float32)
+
+    def get_dof_vel(self) -> np.ndarray:
+        return np.empty((self.num_envs, 0), dtype=np.float32)
+
+    def set_pre_step_control(self, fn) -> None:
+        if fn is not None and self.reject_pre_step:
+            raise NotImplementedError("host callback disabled")
+        self.pre_step_control = fn
+
+    def step(self, ctrl: np.ndarray, nsteps: int = 1) -> None:
+        assert self.materialize_calls == 1
+        self.step_nsteps.append(nsteps)
+        native = ctrl
+        for _ in range(nsteps):
+            if self.pre_step_control is not None:
+                native = self.pre_step_control(self, ctrl)
+            self.applied_controls.append(native.copy())
+
+    def cleanup_scene_assets(self) -> None:
+        self.cleanup_calls += 1
+
+
+class _ResetBackend(_FakeBackend):
+    def __init__(self, num_envs: int) -> None:
+        super().__init__(num_envs)
+        self.default_qpos_calls = 0
+        self.init_qvel_calls = 0
+        self.joint_layout_calls = 0
+        self.set_state_calls: list[tuple[np.ndarray, np.ndarray, np.ndarray]] = []
+
+    def get_default_qpos(self) -> np.ndarray:
+        self.default_qpos_calls += 1
+        return np.array([0.0, 0.0, 0.5, 0.0], dtype=np.float64)
+
+    def get_init_qvel(self) -> np.ndarray:
+        self.init_qvel_calls += 1
+        return np.array([0.0, 0.0, 0.0], dtype=np.float32)
+
+    def get_actuator_joint_names(self) -> tuple[str, ...]:
+        return ("joint",)
+
+    def get_joint_dof_pos_indices(self, names) -> np.ndarray:
+        assert tuple(names) == ("joint",)
+        return np.array([0], dtype=np.int32)
+
+    def get_joint_dof_vel_indices(self, names) -> np.ndarray:
+        assert tuple(names) == ("joint",)
+        return np.array([0], dtype=np.int32)
+
+    def get_joint_state_qpos_indices(self, names) -> np.ndarray:
+        assert tuple(names) == ("joint",)
+        self.joint_layout_calls += 1
+        return np.array([3], dtype=np.int32)
+
+    def get_joint_state_qvel_indices(self, names) -> np.ndarray:
+        assert tuple(names) == ("joint",)
+        self.joint_layout_calls += 1
+        return np.array([2], dtype=np.int32)
+
+    def get_dof_pos(self) -> np.ndarray:
+        return np.zeros((self.num_envs, 1), dtype=np.float32)
+
+    def get_default_dof_pos(self) -> np.ndarray:
+        return np.zeros((1,), dtype=np.float32)
+
+    def get_dof_vel(self) -> np.ndarray:
+        return np.zeros((self.num_envs, 1), dtype=np.float32)
+
+    def get_joint_range(self) -> np.ndarray:
+        return np.array([[-1.0, 1.0]], dtype=np.float32)
+
+    def set_state(
+        self,
+        env_ids: np.ndarray,
+        qpos: np.ndarray,
+        qvel: np.ndarray,
+        randomization=None,
+    ) -> None:
+        assert randomization is None
+        assert self.materialize_calls == 1
+        self.set_state_calls.append((env_ids.copy(), qpos.copy(), qvel.copy()))
+
+
+class _StateBackend(_ResetBackend):
+    def __init__(self, num_envs: int) -> None:
+        super().__init__(num_envs)
+        self.dof_pos = np.zeros((num_envs, 1), dtype=np.float32)
+        self.dof_pos_calls = 0
+
+    def get_dof_pos(self) -> np.ndarray:
+        self.dof_pos_calls += 1
+        return self.dof_pos.copy()
+
+    def step(self, ctrl: np.ndarray, nsteps: int = 1) -> None:
+        super().step(ctrl, nsteps)
+        self.dof_pos += 1.0
+
+    def set_state(
+        self,
+        env_ids: np.ndarray,
+        qpos: np.ndarray,
+        qvel: np.ndarray,
+        randomization=None,
+    ) -> None:
+        super().set_state(env_ids, qpos, qvel, randomization=randomization)
+        self.dof_pos[env_ids, 0] = qpos[:, 3]
+
+
+class _KeyframeBackend(_ResetBackend):
+    def __init__(
+        self,
+        num_envs: int,
+        *,
+        keyframe_qpos: Any = None,
+        keyframe_error: Exception | None = None,
+    ) -> None:
+        super().__init__(num_envs)
+        self.keyframe_qpos = (
+            np.array([0.0, 0.0, 0.3, 0.75], dtype=np.float64)
+            if keyframe_qpos is None
+            else keyframe_qpos
+        )
+        self.keyframe_error = keyframe_error
+        self.keyframe_qpos_calls = 0
+
+    def get_keyframe_qpos(self, name: str) -> Any:
+        self.keyframe_qpos_calls += 1
+        if self.keyframe_error is not None:
+            raise self.keyframe_error
+        if name != "home":
+            raise ValueError(f"unknown keyframe {name!r}")
+        return self.keyframe_qpos
+
+
+@dataclass(kw_only=True)
+class _DriveCfg(ActionTermCfg):
+    gain: float = 1.0
+
+    def build(self, env) -> ActionTerm:
+        return _DriveAction(self, env)
+
+
+class _DriveAction(ActionTerm):
+    def __init__(self, cfg: _DriveCfg, env) -> None:
+        super().__init__(cfg, env)
+        self._processed = np.zeros((self.num_envs, 1), dtype=np.float32)
+
+    @property
+    def action_dim(self) -> int:
+        return 1
+
+    @property
+    def raw_action(self) -> np.ndarray:
+        return self._processed
+
+    def process_actions(self, actions: np.ndarray) -> None:
+        self._processed[:] = actions * cast(_DriveCfg, self.cfg).gain
+
+    def apply_actions(self) -> None:
+        self._env.trace.append("action_apply")
+        self._env.action_sim_steps.append(self._env._sim_step_counter)
+        self._entity.data.write_ctrl(self._processed)
+
+
+class _FeedbackDriveAction(_DriveAction):
+    requires_substep_state_feedback = True
+
+
+@dataclass(kw_only=True)
+class _FeedbackDriveCfg(_DriveCfg):
+    def build(self, env) -> ActionTerm:
+        return _FeedbackDriveAction(self, env)
+
+
+@dataclass(kw_only=True)
+class _CommandCfg(CommandTermCfg):
+    def build(self, env) -> CommandTerm:
+        return _Command(self, env)
+
+
+class _Command(CommandTerm):
+    def __init__(self, cfg: _CommandCfg, env) -> None:
+        super().__init__(cfg, env)
+        self._command = np.zeros((self.num_envs, 1), dtype=np.float32)
+
+    @property
+    def command(self) -> np.ndarray:
+        return self._command
+
+    def _update_metrics(self, env_ids: np.ndarray | None = None) -> None:
+        return None
+
+    def _resample_command(self, env_ids: np.ndarray) -> None:
+        self._command[env_ids, 0] = self._env.rng.uniform(size=len(env_ids))
+
+    def _update_command(self, env_ids: np.ndarray | None) -> None:
+        ids = None if env_ids is None else env_ids.copy()
+        self._env.command_update_ids.append(ids)
+
+
+@dataclass(kw_only=True)
+class _StateWritingCommandCfg(CommandTermCfg):
+    def build(self, env) -> CommandTerm:
+        return _StateWritingCommand(self, env)
+
+
+class _StateWritingCommand(_Command):
+    def _resample_command(self, env_ids: np.ndarray) -> None:
+        self._command[env_ids, 0] = 0.75
+        self._env.scene["robot"].write_joint_state_to_sim(
+            np.full((len(env_ids), 1), 0.75, dtype=np.float32),
+            np.full((len(env_ids), 1), -0.75, dtype=np.float32),
+            env_ids=env_ids,
+        )
+
+
+class _Recorder(RecorderTerm):
+    def record_pre_reset(self, env_ids: np.ndarray) -> None:
+        self._env.trace.append(("pre_reset", env_ids.tolist()))
+
+    def record_post_reset(self, env_ids: np.ndarray) -> None:
+        self._env.trace.append(("post_reset", env_ids.tolist()))
+
+    def record_post_step(self) -> None:
+        self._env.trace.append("post_step")
+
+    def close(self) -> None:
+        self._env.trace.append("recorder_close")
+
+
+class _TestEnv(ManagerBasedRlEnv):
+    def __init__(self, cfg: ManagerBasedRlEnvCfg, backend: SimBackend, num_envs: int) -> None:
+        self.trace: list[Any] = []
+        self.command_update_ids: list[np.ndarray | None] = []
+        self.action_sim_steps: list[int] = []
+        super().__init__(cfg, backend, num_envs)
+
+
+def _policy_obs(env: _TestEnv) -> np.ndarray:
+    return np.column_stack(
+        (env.episode_length_buf.astype(np.float32), env.action_manager.action[:, 0])
+    )
+
+
+def _critic_obs(env: _TestEnv) -> np.ndarray:
+    return env.episode_length_buf[:, None].astype(np.float32)
+
+
+def _episode_step_observation(env: ManagerBasedRlEnv) -> np.ndarray:
+    return env.episode_length_buf[:, None].astype(np.float32)
+
+
+def _reward(env: _TestEnv) -> np.ndarray:
+    return env.action_manager.action[:, 0].copy()
+
+
+def _joint_state_obs(env: _TestEnv) -> np.ndarray:
+    return env.scene["robot"].data.joint_pos
+
+
+def _joint_state_reward(env: _TestEnv) -> np.ndarray:
+    return env.scene["robot"].data.joint_pos[:, 0]
+
+
+def _joint_state_termination(env: _TestEnv) -> np.ndarray:
+    return env.scene["robot"].data.joint_pos[:, 0] > 100.0
+
+
+def _mutate_joint_state_step_event(env: _TestEnv, env_ids: np.ndarray | None) -> None:
+    assert env_ids is None
+    cast(_StateBackend, env._backend).dof_pos += 10.0
+
+
+def _failure(env: _TestEnv) -> np.ndarray:
+    return env.action_manager.action[:, 0] > 0.8
+
+
+def _time_out(env: _TestEnv) -> np.ndarray:
+    return env.episode_length_buf >= 2
+
+
+def _metric(env: _TestEnv) -> np.ndarray:
+    return env.episode_length_buf.astype(np.float32)
+
+
+def _curriculum(env: _TestEnv, env_ids: np.ndarray | slice) -> float:
+    del env
+    return float(len(env_ids)) if isinstance(env_ids, np.ndarray) else 0.0
+
+
+def _event(env: _TestEnv, env_ids: np.ndarray | None) -> None:
+    rendered_ids = None if env_ids is None else env_ids.tolist()
+    env.trace.append(("event", rendered_ids))
+
+
+def _startup_event(env: _TestEnv, env_ids: np.ndarray | None) -> None:
+    assert env_ids is None
+    backend = cast(_FakeBackend, env._backend)
+    backend.lifecycle.append("startup")
+
+
+def _observe_uncommitted_reset(env: _TestEnv, env_ids: np.ndarray | None) -> None:
+    del env_ids
+    backend = cast(_ResetBackend, env._backend)
+    env.trace.append(("commit_count_during_event", len(backend.set_state_calls)))
+
+
+def _write_reset_joint_state(env: _TestEnv, env_ids: np.ndarray | None) -> None:
+    assert env_ids is not None
+    count = len(env_ids)
+    env.scene["robot"].write_joint_state_to_sim(
+        np.full((count, 1), 0.25, dtype=np.float32),
+        np.full((count, 1), -0.5, dtype=np.float32),
+        env_ids=env_ids,
+    )
+
+
+def _make_cfg(
+    *,
+    sim_substeps: int = 2,
+    finite_horizon: bool = False,
+    auto_reset: bool = True,
+    metrics: dict[str, MetricsTermCfg | None] | None = None,
+    observations: dict[str, ObservationGroupCfg | None] | None = None,
+    actions: dict[str, ActionTermCfg | None] | None = None,
+    include_optional_managers: bool = True,
+) -> ManagerBasedRlEnvCfg:
+    if observations is None:
+        observations = {
+            "actor": ObservationGroupCfg(terms={"policy": ObservationTermCfg(func=_policy_obs)}),
+            "value": ObservationGroupCfg(terms={"critic": ObservationTermCfg(func=_critic_obs)}),
+        }
+    cfg = ManagerBasedRlEnvCfg(
+        scene=SceneCfg(
+            model_file="fake.xml",
+            entities={"robot": EntityCfg(actuator_names=("motor",))},
+        ),
+        sim_dt=0.01,
+        ctrl_dt=0.01 * sim_substeps,
+        max_episode_seconds=1.0,
+        seed=7,
+        observations=observations,
+        actions={"drive": _DriveCfg(entity_name="robot")} if actions is None else actions,
+        rewards={"track": RewardTermCfg(func=_reward, weight=1.0)},
+        terminations={
+            "failure": TerminationTermCfg(func=_failure),
+            "time_out": TerminationTermCfg(func=_time_out, time_out=True),
+        },
+        events={"reset": EventTermCfg(func=_event, mode="reset")},
+        metrics={} if metrics is None else metrics,
+        policy_observation_group="actor",
+        critic_observation_group="value",
+        is_finite_horizon=finite_horizon,
+        auto_reset=auto_reset,
+    )
+    if include_optional_managers:
+        cfg.commands = {"target": _CommandCfg(resampling_time_range=(1.0, 1.0))}
+        cfg.curriculum = {"difficulty": CurriculumTermCfg(func=_curriculum)}
+        cfg.recorders = {"trace": RecorderTermCfg(func=_Recorder)}
+    return cfg
+
+
+def _make_env(
+    cfg: ManagerBasedRlEnvCfg | None = None,
+    *,
+    num_envs: int = 2,
+    reject_pre_step: bool = False,
+    reject_materialize: bool = False,
+) -> tuple[_TestEnv, _FakeBackend]:
+    backend = _FakeBackend(
+        num_envs,
+        reject_pre_step=reject_pre_step,
+        reject_materialize=reject_materialize,
+    )
+    env = _TestEnv(
+        cfg or _make_cfg(),
+        cast(SimBackend, backend),
+        num_envs,
+    )
+    return env, backend
+
+
+def _make_state_env(
+    *,
+    commands: dict[str, CommandTermCfg | None] | None = None,
+    events: dict[str, EventTermCfg | None] | None = None,
+    terminations: dict[str, TerminationTermCfg | None] | None = None,
+) -> tuple[_TestEnv, _StateBackend]:
+    cfg = _make_cfg(
+        sim_substeps=1,
+        observations={
+            "actor": ObservationGroupCfg(
+                terms={"joint": ObservationTermCfg(func=_joint_state_obs)}
+            ),
+            "value": ObservationGroupCfg(
+                terms={"joint": ObservationTermCfg(func=_joint_state_obs)}
+            ),
+        },
+        include_optional_managers=False,
+    )
+    cfg.scene.entities["robot"] = EntityCfg(
+        joint_names=("joint",),
+        actuator_names=("motor",),
+    )
+    cfg.scale_rewards_by_dt = False
+    cfg.rewards = {"joint": RewardTermCfg(func=_joint_state_reward, weight=1.0)}
+    cfg.terminations = (
+        {"joint": TerminationTermCfg(func=_joint_state_termination)}
+        if terminations is None
+        else terminations
+    )
+    cfg.commands = {} if commands is None else commands
+    cfg.events = {} if events is None else events
+    backend = _StateBackend(2)
+    return _TestEnv(cfg, cast(SimBackend, backend), 2), backend
+
+
+def test_public_names_are_spelling_only_aliases() -> None:
+    assert ManagerBasedRLEnv is ManagerBasedRlEnv
+    assert ManagerBasedRLEnvCfg is ManagerBasedRlEnvCfg
+    assert make_manager_based_rl_env is manager_env_module.make_manager_based_rl_env
+
+
+@pytest.mark.parametrize("backend_type", ["mujoco", "motrix", "mjwarp", "drake"])
+def test_generic_factory_routes_only_public_backend_contract(
+    monkeypatch: pytest.MonkeyPatch,
+    backend_type: str,
+) -> None:
+    cfg = _make_cfg(include_optional_managers=False)
+    assert cfg.scene is not None
+    cfg.scene.entities["robot"] = EntityCfg(
+        root_body_name="base",
+        actuator_names=("motor",),
+    )
+    cfg.scene.entities["payload"] = EntityCfg(root_body_name="box")
+    backend = _FakeBackend(3)
+    constructed: dict[str, Any] = {}
+
+    def fake_create_backend(
+        requested_backend: str,
+        scene: SceneCfg,
+        num_envs: int,
+        sim_dt: float,
+        **kwargs: Any,
+    ) -> SimBackend:
+        constructed.update(
+            backend_type=requested_backend,
+            scene=scene,
+            num_envs=num_envs,
+            sim_dt=sim_dt,
+            kwargs=kwargs,
+        )
+        return cast(SimBackend, backend)
+
+    sentinel = object()
+
+    def fake_make_env(
+        received_cfg: ManagerBasedRlEnvCfg,
+        received_backend: SimBackend,
+        received_num_envs: int,
+    ) -> Any:
+        assert received_cfg is cfg
+        assert received_backend is backend
+        assert received_num_envs == 3
+        return sentinel
+
+    monkeypatch.setattr(manager_env_module, "create_backend", fake_create_backend)
+    monkeypatch.setattr(manager_env_module, "ManagerBasedRlEnv", fake_make_env)
+
+    result = make_manager_based_rl_env(cfg, num_envs=3, backend_type=backend_type)
+
+    assert result is sentinel
+    assert constructed["backend_type"] == backend_type
+    assert constructed["scene"] is cfg.scene
+    assert constructed["num_envs"] == 3
+    assert constructed["sim_dt"] == cfg.sim_dt
+    kwargs = constructed["kwargs"]
+    assert kwargs["base_name"] == "base"
+    assert kwargs["body_state_required"] is True
+    assert "add_body_sensors" not in kwargs
+    for key, value in env_backend_kwargs(cfg).items():
+        assert kwargs[key] == value
+
+
+@pytest.mark.parametrize(
+    ("entities", "match"),
+    [
+        (
+            {"robot": EntityCfg(actuator_names=("motor",))},
+            "at least one scene entity with an explicit root_body_name",
+        ),
+        (
+            {
+                "payload": EntityCfg(root_body_name="box"),
+                "tool": EntityCfg(root_body_name="tool"),
+            },
+            "conventional 'robot' root entity.*payload.*tool",
+        ),
+    ],
+)
+def test_generic_factory_rejects_missing_or_ambiguous_root_entity(
+    monkeypatch: pytest.MonkeyPatch,
+    entities: dict[str, EntityCfg],
+    match: str,
+) -> None:
+    cfg = _make_cfg(include_optional_managers=False)
+    assert cfg.scene is not None
+    cfg.scene.entities = entities
+    backend_constructed = False
+
+    def reject_backend_construction(*args: Any, **kwargs: Any) -> SimBackend:
+        nonlocal backend_constructed
+        backend_constructed = True
+        raise AssertionError("backend construction must not run")
+
+    monkeypatch.setattr(manager_env_module, "create_backend", reject_backend_construction)
+
+    with pytest.raises(ValueError, match=match):
+        make_manager_based_rl_env(cfg, num_envs=2, backend_type="mujoco")
+    assert not backend_constructed
+
+
+@pytest.mark.parametrize(
+    ("cfg_value", "num_envs", "backend_type", "error", "match"),
+    [
+        (object(), 2, "mujoco", TypeError, "expected ManagerBasedRlEnvCfg"),
+        (None, 0, "mujoco", ValueError, "num_envs must be a positive integer"),
+        (None, True, "mujoco", ValueError, "num_envs must be a positive integer"),
+        (None, 2, "", ValueError, "backend_type must be a non-empty string"),
+    ],
+)
+def test_generic_factory_rejects_invalid_public_arguments_before_backend(
+    monkeypatch: pytest.MonkeyPatch,
+    cfg_value: object | None,
+    num_envs: int,
+    backend_type: str,
+    error: type[Exception],
+    match: str,
+) -> None:
+    cfg = _make_cfg(include_optional_managers=False) if cfg_value is None else cfg_value
+    backend_constructed = False
+
+    def reject_backend_construction(*args: Any, **kwargs: Any) -> SimBackend:
+        nonlocal backend_constructed
+        backend_constructed = True
+        raise AssertionError("backend construction must not run")
+
+    monkeypatch.setattr(manager_env_module, "create_backend", reject_backend_construction)
+
+    with pytest.raises(error, match=match):
+        make_manager_based_rl_env(
+            cast(ManagerBasedRlEnvCfg, cfg),
+            num_envs=num_envs,
+            backend_type=backend_type,
+        )
+    assert not backend_constructed
+
+
+@pytest.mark.parametrize(
+    ("entities", "error", "match"),
+    [
+        (
+            cast(dict[str, EntityCfg], {1: EntityCfg(root_body_name="base")}),
+            TypeError,
+            "entity names must be non-empty strings",
+        ),
+        (
+            cast(dict[str, EntityCfg], {"robot": object()}),
+            TypeError,
+            "scene entity 'robot' must be EntityCfg",
+        ),
+        (
+            {"robot": EntityCfg(root_body_name="")},
+            TypeError,
+            "root_body_name must be a non-empty string",
+        ),
+    ],
+)
+def test_generic_factory_rejects_invalid_entity_contract_before_backend(
+    monkeypatch: pytest.MonkeyPatch,
+    entities: dict[str, EntityCfg],
+    error: type[Exception],
+    match: str,
+) -> None:
+    cfg = _make_cfg(include_optional_managers=False)
+    assert cfg.scene is not None
+    cfg.scene.entities = entities
+    backend_constructed = False
+
+    def reject_backend_construction(*args: Any, **kwargs: Any) -> SimBackend:
+        nonlocal backend_constructed
+        backend_constructed = True
+        raise AssertionError("backend construction must not run")
+
+    monkeypatch.setattr(manager_env_module, "create_backend", reject_backend_construction)
+
+    with pytest.raises(error, match=match):
+        make_manager_based_rl_env(cfg, num_envs=2, backend_type="mujoco")
+    assert not backend_constructed
+
+
+def test_generic_factory_preserves_backend_construction_error(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    cfg = _make_cfg(include_optional_managers=False)
+    assert cfg.scene is not None
+    cfg.scene.entities["robot"] = EntityCfg(
+        root_body_name="base",
+        actuator_names=("motor",),
+    )
+
+    def fail_backend_construction(*args: Any, **kwargs: Any) -> SimBackend:
+        raise NotImplementedError("body-state capability is unavailable")
+
+    monkeypatch.setattr(manager_env_module, "create_backend", fail_backend_construction)
+
+    with pytest.raises(NotImplementedError, match="body-state capability is unavailable"):
+        make_manager_based_rl_env(cfg, num_envs=2, backend_type="mjwarp")
+
+
+def test_generic_factory_cleans_backend_when_env_construction_fails(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    cfg = _make_cfg(include_optional_managers=False)
+    assert cfg.scene is not None
+    cfg.scene.entities["robot"] = EntityCfg(
+        root_body_name="base",
+        actuator_names=("motor",),
+    )
+    backend = _FakeBackend(2)
+
+    monkeypatch.setattr(
+        manager_env_module,
+        "create_backend",
+        lambda *args, **kwargs: cast(SimBackend, backend),
+    )
+
+    def fail_env_construction(*args: Any, **kwargs: Any) -> Any:
+        raise RuntimeError("manager initialization failed")
+
+    monkeypatch.setattr(manager_env_module, "ManagerBasedRlEnv", fail_env_construction)
+
+    with pytest.raises(RuntimeError, match="manager initialization failed"):
+        make_manager_based_rl_env(cfg, num_envs=2, backend_type="mujoco")
+    assert backend.cleanup_calls == 1
+
+
+@pytest.mark.parametrize(
+    ("mutate", "error", "match"),
+    [
+        (lambda cfg: setattr(cfg, "sim_dt", True), TypeError, "sim_dt must be a real"),
+        (
+            lambda cfg: setattr(cfg, "ctrl_dt", 0.015),
+            ValueError,
+            "integer multiple",
+        ),
+        (
+            lambda cfg: setattr(cfg, "max_episode_seconds", None),
+            ValueError,
+            "max_episode_seconds",
+        ),
+        (lambda cfg: setattr(cfg, "scene", None), TypeError, "scene must be a SceneCfg"),
+    ],
+)
+def test_manager_based_config_rejects_invalid_contracts(mutate, error, match: str) -> None:
+    cfg = _make_cfg()
+    mutate(cfg)
+    with pytest.raises(error, match=match):
+        cfg.validate()
+
+
+def test_manager_construction_uses_pinned_order(monkeypatch: pytest.MonkeyPatch) -> None:
+    order: list[str] = []
+    names = (
+        "EventManager",
+        "CommandManager",
+        "ActionManager",
+        "ObservationManager",
+        "TerminationManager",
+        "RewardManager",
+        "CurriculumManager",
+        "MetricsManager",
+        "RecorderManager",
+    )
+    cfg = _make_cfg(metrics={"progress": MetricsTermCfg(func=_metric)})
+    for name in names:
+        original = getattr(manager_env_module, name)
+
+        def wrapped(*args, _name=name, _original=original, **kwargs):
+            order.append(_name)
+            return _original(*args, **kwargs)
+
+        monkeypatch.setattr(manager_env_module, name, wrapped)
+
+    _make_env(cfg)
+    assert order == list(names)
+
+
+def test_backend_materializes_once_after_startup_and_before_runtime() -> None:
+    cfg = _make_cfg()
+    cfg.events = {
+        "startup": EventTermCfg(func=_startup_event, mode="startup"),
+        "reset": EventTermCfg(func=_event, mode="reset"),
+    }
+    env, backend = _make_env(cfg)
+
+    assert backend.lifecycle == ["startup", "materialize"]
+    assert backend.materialize_calls == 1
+
+    env.reset()
+    env.step(np.zeros((2, 1), dtype=np.float32))
+    env.reset()
+
+    assert backend.materialize_calls == 1
+
+
+def test_backend_materialization_failure_has_lifecycle_context() -> None:
+    with pytest.raises(
+        RuntimeError,
+        match="ManagerBasedRlEnv failed to materialize backend 'fake'.*pool construction failed",
+    ):
+        _make_env(reject_materialize=True)
+
+
+@pytest.mark.parametrize(
+    ("selector", "error"),
+    [
+        (1, TypeError),
+        ("", ValueError),
+    ],
+)
+def test_default_keyframe_selector_fails_at_env_initialization(
+    selector: Any,
+    error: type[Exception],
+) -> None:
+    cfg = _make_cfg(include_optional_managers=False)
+    cfg.scene.default_keyframe_name = selector
+    backend = _KeyframeBackend(2)
+
+    with pytest.raises(error, match="default_keyframe_name.*non-empty string or None"):
+        _TestEnv(cfg, cast(SimBackend, backend), 2)
+
+    assert backend.keyframe_qpos_calls == 0
+
+
+@pytest.mark.parametrize(
+    ("value", "error", "match"),
+    [
+        ([0.0], TypeError, "must return np.ndarray"),
+        (np.zeros((1, 1), dtype=np.float32), ValueError, "expected 1-D"),
+        (np.zeros(1, dtype=np.int32), TypeError, "must be floating"),
+        (np.array([np.nan], dtype=np.float32), ValueError, "NaN or Inf"),
+    ],
+)
+def test_default_keyframe_qpos_contract_fails_at_env_initialization(
+    value: Any,
+    error: type[Exception],
+    match: str,
+) -> None:
+    cfg = _make_cfg(include_optional_managers=False)
+    cfg.scene.default_keyframe_name = "home"
+    backend = _KeyframeBackend(2, keyframe_qpos=value)
+
+    with pytest.raises(error, match=f"default keyframe 'home'.*backend 'fake'.*{match}"):
+        _TestEnv(cfg, cast(SimBackend, backend), 2)
+
+    assert backend.keyframe_qpos_calls == 1
+
+
+@pytest.mark.parametrize(
+    ("failure", "error", "match"),
+    [
+        (
+            NotImplementedError("named keyframes disabled"),
+            NotImplementedError,
+            "default keyframe 'home'.*backend 'fake'.*named keyframes disabled",
+        ),
+        (
+            ValueError("keyframe missing"),
+            ValueError,
+            "resolve default keyframe 'home'.*backend 'fake'.*keyframe missing",
+        ),
+    ],
+)
+def test_default_keyframe_resolution_names_backend_and_keyframe(
+    failure: Exception,
+    error: type[Exception],
+    match: str,
+) -> None:
+    cfg = _make_cfg(include_optional_managers=False)
+    cfg.scene.default_keyframe_name = "home"
+    backend = _KeyframeBackend(2, keyframe_error=failure)
+
+    with pytest.raises(error, match=match):
+        _TestEnv(cfg, cast(SimBackend, backend), 2)
+
+    assert backend.keyframe_qpos_calls == 1
+
+
+def test_named_keyframe_snapshot_is_shared_by_entity_and_reset_cold_path() -> None:
+    source_qpos = np.array([0.0, 0.0, 0.3, 0.75], dtype=np.float64)
+    backend = _KeyframeBackend(2, keyframe_qpos=source_qpos)
+    cfg = _make_cfg(include_optional_managers=False)
+    cfg.scene.default_keyframe_name = "home"
+    cfg.scene.entities["robot"] = EntityCfg(
+        joint_names=("joint",),
+        actuator_names=("motor",),
+    )
+    cfg.events = {
+        "reset_default": EventTermCfg(func=mdp.reset_scene_to_default, mode="reset"),
+    }
+
+    env = _TestEnv(cfg, cast(SimBackend, backend), 2)
+    selected_qpos = env._reset_state._selected_default_qpos
+
+    assert backend.keyframe_qpos_calls == 1
+    assert backend.default_qpos_calls == 0
+    assert backend.joint_layout_calls == 1
+    assert selected_qpos is not source_qpos
+    assert selected_qpos is not None
+    assert not selected_qpos.flags.writeable
+    np.testing.assert_array_equal(env.scene["robot"].data.default_joint_pos, 0.75)
+    assert not env.scene["robot"].data.default_joint_pos.flags.writeable
+
+    source_qpos[:] = 9.0
+    env.reset()
+    env.reset(env_ids=np.array([1], dtype=np.int32))
+
+    assert backend.keyframe_qpos_calls == 1
+    assert backend.default_qpos_calls == 0
+    assert backend.joint_layout_calls == 1
+    assert len(backend.set_state_calls) == 2
+    np.testing.assert_array_equal(
+        backend.set_state_calls[0][1],
+        [[0.0, 0.0, 0.3, 0.75], [0.0, 0.0, 0.3, 0.75]],
+    )
+    np.testing.assert_array_equal(backend.set_state_calls[1][1], [[0.0, 0.0, 0.3, 0.75]])
+
+
+def test_real_mujoco_backend_is_materialized_before_first_reset() -> None:
+    scene = SceneCfg(
+        model_file=str(ASSETS_ROOT_PATH / "robots" / "go2" / "scene_flat.xml"),
+        entities={"robot": EntityCfg(root_body_name="base")},
+    )
+    cfg = ManagerBasedRlEnvCfg(
+        scene=scene,
+        sim_dt=0.01,
+        ctrl_dt=0.02,
+        max_episode_seconds=1.0,
+        observations={
+            "actor": ObservationGroupCfg(
+                terms={"state": ObservationTermCfg(func=_episode_step_observation)}
+            )
+        },
+        actions={},
+        events={"reset_default": EventTermCfg(func=mdp.reset_scene_to_default, mode="reset")},
+        rewards={"alive": RewardTermCfg(func=mdp.is_alive, weight=1.0)},
+        terminations={"time_out": TerminationTermCfg(func=mdp.time_out, time_out=True)},
+        policy_observation_group="actor",
+    )
+    env = make_manager_based_rl_env(cfg, num_envs=2, backend_type="mujoco")
+    try:
+        state = env.init_state()
+        assert state.obs["obs"].shape == (2, 1)
+        assert np.isfinite(state.obs["obs"]).all()
+
+        state = env.step(np.empty((2, 0), dtype=np.float32))
+        assert np.isfinite(state.obs["obs"]).all()
+        assert np.isfinite(state.reward).all()
+    finally:
+        env.close()
+
+
+@pytest.mark.parametrize(
+    ("default_keyframe_name", "expected_root_z", "expected_joint_pos"),
+    [
+        (None, 0.445, np.zeros(12, dtype=np.float32)),
+        (
+            "home",
+            0.3,
+            np.array(
+                [0.0, 0.8, -1.5, 0.0, 0.8, -1.5, 0.0, 1.0, -1.5, 0.0, 1.0, -1.5],
+                dtype=np.float32,
+            ),
+        ),
+    ],
+)
+def test_real_mujoco_default_state_matches_qpos0_or_named_home(
+    default_keyframe_name: str | None,
+    expected_root_z: float,
+    expected_joint_pos: np.ndarray,
+) -> None:
+    joint_names = (
+        "FL_hip_joint",
+        "FL_thigh_joint",
+        "FL_calf_joint",
+        "FR_hip_joint",
+        "FR_thigh_joint",
+        "FR_calf_joint",
+        "RL_hip_joint",
+        "RL_thigh_joint",
+        "RL_calf_joint",
+        "RR_hip_joint",
+        "RR_thigh_joint",
+        "RR_calf_joint",
+    )
+    scene = SceneCfg(
+        model_file=str(ASSETS_ROOT_PATH / "robots" / "go2" / "scene_flat.xml"),
+        entities={
+            "robot": EntityCfg(
+                root_body_name="base",
+                joint_names=joint_names,
+            )
+        },
+        default_keyframe_name=default_keyframe_name,
+    )
+    cfg = ManagerBasedRlEnvCfg(
+        scene=scene,
+        sim_dt=0.01,
+        ctrl_dt=0.02,
+        max_episode_seconds=1.0,
+        observations={
+            "actor": ObservationGroupCfg(
+                terms={"state": ObservationTermCfg(func=_episode_step_observation)}
+            )
+        },
+        actions={},
+        events={"reset_default": EventTermCfg(func=mdp.reset_scene_to_default, mode="reset")},
+        rewards={"alive": RewardTermCfg(func=mdp.is_alive, weight=1.0)},
+        terminations={"time_out": TerminationTermCfg(func=mdp.time_out, time_out=True)},
+        policy_observation_group="actor",
+    )
+    backend = create_backend(
+        "mujoco",
+        scene,
+        2,
+        cfg.sim_dt,
+        base_name="base",
+        add_body_sensors=True,
+        **env_backend_kwargs(cfg),
+    )
+    env = ManagerBasedRlEnv(cfg, backend, 2)
+    try:
+        robot = env.scene["robot"]
+        np.testing.assert_allclose(robot.data.default_root_state[:, 2], expected_root_z)
+        expected_batch = np.broadcast_to(expected_joint_pos, (2, expected_joint_pos.size))
+        np.testing.assert_allclose(robot.data.default_joint_pos, expected_batch)
+
+        env.init_state()
+
+        np.testing.assert_allclose(robot.data.root_link_pos_w[:, 2], expected_root_z)
+        np.testing.assert_allclose(robot.data.joint_pos, expected_batch)
+    finally:
+        env.close()
+
+
+def test_np_env_owns_substeps_autoreset_and_final_observation() -> None:
+    env, backend = _make_env()
+    initial_obs, initial_info = env.reset()
+    assert env.state is not None
+    initial = env.state
+
+    assert initial_obs["obs"].shape == (2, 2)
+    assert initial_obs["critic"].shape == (2, 1)
+    assert "log" in initial_info
+    np.testing.assert_array_equal(initial.info["steps"], [0, 0])
+    assert env._dr_manager is None
+
+    state = env.step(np.array([[0.25], [0.5]], dtype=np.float32))
+    assert backend.pre_step_control is None
+    assert backend.step_nsteps == [2]
+    assert len(backend.applied_controls) == 2
+    assert env.action_sim_steps == [2]
+    np.testing.assert_allclose(backend.applied_controls[0][:, 0], [0.25, 0.5])
+    np.testing.assert_allclose(backend.applied_controls[1][:, 0], [0.25, 0.5])
+    np.testing.assert_allclose(state.reward, [0.005, 0.01])
+    np.testing.assert_array_equal(state.terminated, [False, False])
+    np.testing.assert_array_equal(state.truncated, [False, False])
+    np.testing.assert_array_equal(state.info["steps"], [1, 1])
+
+    state = env.step(np.array([[0.25], [0.5]], dtype=np.float32))
+    np.testing.assert_array_equal(state.truncated, [True, True])
+    np.testing.assert_array_equal(state.terminated, [False, False])
+    np.testing.assert_array_equal(state.info["steps"], [0, 0])
+    np.testing.assert_array_equal(state.obs["obs"][:, 0], [0.0, 0.0])
+    assert state.final_observation is not None
+    np.testing.assert_array_equal(state.final_observation["obs"][:, 0], [2.0, 2.0])
+    assert state.info["_final_observation"].tolist() == [True, True]
+    assert state.info["log"]["Episode_Termination/time_out"] == 2
+    assert not any(key.startswith("mba_") for key in state.info["timing"])
+
+    pre_index = env.trace.index(("pre_reset", [0, 1]))
+    post_reset_index = env.trace.index(("post_reset", [0, 1]), pre_index)
+    post_step_index = len(env.trace) - 1
+    assert env.trace[post_step_index] == "post_step"
+    assert pre_index < post_reset_index < post_step_index
+
+
+def test_reset_events_compose_then_commit_default_state_once() -> None:
+    cfg = _make_cfg(include_optional_managers=False)
+    cfg.events = {
+        "default_first": EventTermCfg(func=mdp.reset_scene_to_default, mode="reset"),
+        "joint_state": EventTermCfg(func=_write_reset_joint_state, mode="reset"),
+        "observe_uncommitted": EventTermCfg(func=_observe_uncommitted_reset, mode="reset"),
+    }
+    cfg.scene.entities["robot"] = EntityCfg(
+        joint_names=("joint",),
+        actuator_names=("motor",),
+    )
+    backend = _ResetBackend(2)
+    env = _TestEnv(cfg, cast(SimBackend, backend), 2)
+
+    assert backend.default_qpos_calls == 0
+    assert backend.init_qvel_calls == 0
+    assert backend.joint_layout_calls == 0
+    env.reset()
+
+    assert env.trace[-1] == ("commit_count_during_event", 0)
+    assert backend.default_qpos_calls == 1
+    assert backend.init_qvel_calls == 1
+    assert len(backend.set_state_calls) == 1
+    ids, qpos, qvel = backend.set_state_calls[0]
+    np.testing.assert_array_equal(ids, [0, 1])
+    np.testing.assert_array_equal(
+        qpos,
+        [[0.0, 0.0, 0.5, 0.25], [0.0, 0.0, 0.5, 0.25]],
+    )
+    np.testing.assert_array_equal(qvel, [[0.0, 0.0, -0.5], [0.0, 0.0, -0.5]])
+    assert backend.joint_layout_calls == 2
+
+    env.reset(env_ids=np.array([1], dtype=np.int32))
+    assert ("commit_count_during_event", 1) in env.trace
+    assert len(backend.set_state_calls) == 2
+    np.testing.assert_array_equal(backend.set_state_calls[1][0], [1])
+    assert backend.default_qpos_calls == 1
+    assert backend.init_qvel_calls == 1
+    assert backend.joint_layout_calls == 2
+
+
+def test_reset_event_and_command_state_writes_share_one_commit() -> None:
+    cfg = _make_cfg(include_optional_managers=False)
+    cfg.events = {
+        "default_first": EventTermCfg(func=mdp.reset_scene_to_default, mode="reset"),
+        "joint_state": EventTermCfg(func=_write_reset_joint_state, mode="reset"),
+    }
+    cfg.commands = {"state_writer": _StateWritingCommandCfg(resampling_time_range=(1.0, 1.0))}
+    cfg.scene.entities["robot"] = EntityCfg(
+        joint_names=("joint",),
+        actuator_names=("motor",),
+    )
+    backend = _ResetBackend(2)
+    env = _TestEnv(cfg, cast(SimBackend, backend), 2)
+
+    env.reset()
+
+    assert len(backend.set_state_calls) == 1
+    ids, qpos, qvel = backend.set_state_calls[0]
+    np.testing.assert_array_equal(ids, [0, 1])
+    np.testing.assert_array_equal(
+        qpos,
+        [[0.0, 0.0, 0.5, 0.75], [0.0, 0.0, 0.5, 0.75]],
+    )
+    np.testing.assert_array_equal(qvel, [[0.0, 0.0, -0.75], [0.0, 0.0, -0.75]])
+
+
+def test_pure_reset_event_does_not_request_backend_state_capability() -> None:
+    env, backend = _make_env()
+
+    env.reset()
+    env.reset(env_ids=np.array([0], dtype=np.int32))
+
+    assert isinstance(backend, _FakeBackend)
+    assert not hasattr(backend, "get_default_qpos")
+
+
+def test_update_state_reuses_backend_state_once_and_refreshes_after_physics() -> None:
+    env, backend = _make_state_env()
+    env.init_state()
+
+    before = backend.dof_pos_calls
+    state = env.step(np.zeros((2, 1), dtype=np.float32))
+    assert backend.dof_pos_calls == before + 1
+    np.testing.assert_array_equal(state.reward, [1.0, 1.0])
+    np.testing.assert_array_equal(state.obs["obs"], [[1.0], [1.0]])
+    np.testing.assert_array_equal(state.obs["critic"], [[1.0], [1.0]])
+
+    before = backend.dof_pos_calls
+    state = env.step(np.zeros((2, 1), dtype=np.float32))
+    assert backend.dof_pos_calls == before + 1
+    np.testing.assert_array_equal(state.reward, [2.0, 2.0])
+    np.testing.assert_array_equal(state.obs["obs"], [[2.0], [2.0]])
+
+
+def test_update_state_invalidates_cache_after_command_set_state() -> None:
+    env, backend = _make_state_env(
+        commands={
+            "writer": _StateWritingCommandCfg(resampling_time_range=(0.01, 0.01)),
+        }
+    )
+    env.init_state()
+
+    before = backend.dof_pos_calls
+    state = env.step(np.zeros((2, 1), dtype=np.float32))
+
+    # Physics advances the pre-command state to 1.75. The command then commits
+    # 0.75 through set_state, so post-command observations must not reuse the
+    # termination/reward snapshot.
+    assert backend.dof_pos_calls == before + 2
+    np.testing.assert_array_equal(state.reward, [1.75, 1.75])
+    np.testing.assert_array_equal(state.obs["obs"], [[0.75], [0.75]])
+    np.testing.assert_array_equal(state.obs["critic"], [[0.75], [0.75]])
+
+
+def test_update_state_invalidates_cache_after_runtime_event() -> None:
+    env, backend = _make_state_env(
+        events={
+            "mutate": EventTermCfg(func=_mutate_joint_state_step_event, mode="step"),
+        }
+    )
+    env.init_state()
+
+    before = backend.dof_pos_calls
+    state = env.step(np.zeros((2, 1), dtype=np.float32))
+
+    assert backend.dof_pos_calls == before + 2
+    np.testing.assert_array_equal(state.reward, [1.0, 1.0])
+    np.testing.assert_array_equal(state.obs["obs"], [[11.0], [11.0]])
+    np.testing.assert_array_equal(state.obs["critic"], [[11.0], [11.0]])
+
+
+def test_partial_reset_observation_reads_post_set_state_rows() -> None:
+    env, backend = _make_state_env(
+        events={
+            "joint_state": EventTermCfg(func=_write_reset_joint_state, mode="reset"),
+        },
+        terminations={"failure": TerminationTermCfg(func=_failure)},
+    )
+    env.init_state()
+
+    state = env.step(np.array([[1.0], [0.0]], dtype=np.float32))
+
+    np.testing.assert_array_equal(state.reward, [1.25, 1.25])
+    np.testing.assert_array_equal(state.terminated, [True, False])
+    np.testing.assert_array_equal(state.obs["obs"], [[0.25], [1.25]])
+    np.testing.assert_array_equal(state.obs["critic"], [[0.25], [1.25]])
+    assert state.final_observation is not None
+    np.testing.assert_array_equal(state.final_observation["obs"][0], [1.25])
+    np.testing.assert_array_equal(backend.dof_pos[:, 0], [0.25, 1.25])
+
+
+def test_partial_reset_preserves_other_env_counter_and_terminal_obs() -> None:
+    env, _ = _make_env()
+    env.init_state()
+
+    state = env.step(np.array([[1.0], [0.0]], dtype=np.float32))
+
+    np.testing.assert_array_equal(state.terminated, [True, False])
+    np.testing.assert_array_equal(state.truncated, [False, False])
+    np.testing.assert_array_equal(state.info["steps"], [0, 1])
+    np.testing.assert_array_equal(state.obs["obs"][:, 0], [0.0, 1.0])
+    assert state.final_observation is not None
+    np.testing.assert_array_equal(state.final_observation["obs"][0], [1.0, 1.0])
+    assert state.info["_final_observation"].tolist() == [True, False]
+    assert ("event", [0]) in env.trace
+
+
+def test_finite_horizon_maps_time_out_to_terminated() -> None:
+    env, _ = _make_env(_make_cfg(finite_horizon=True))
+    env.init_state()
+    env.step(np.zeros((2, 1), dtype=np.float32))
+    state = env.step(np.zeros((2, 1), dtype=np.float32))
+    np.testing.assert_array_equal(state.terminated, [True, True])
+    np.testing.assert_array_equal(state.truncated, [False, False])
+
+
+def test_manual_reset_is_required_when_autoreset_is_disabled() -> None:
+    env, _ = _make_env(_make_cfg(auto_reset=False))
+    env.init_state()
+    state = env.step(np.array([[1.0], [0.0]], dtype=np.float32))
+    np.testing.assert_array_equal(state.info["steps"], [1, 1])
+    with pytest.raises(RuntimeError, match="must be reset"):
+        env.step(np.zeros((2, 1), dtype=np.float32))
+
+    reset_obs, info = env.reset(env_ids=np.array([0], dtype=np.int32))
+    assert reset_obs["obs"].shape == (1, 2)
+    assert "log" in info
+    assert not state.terminated[0]
+    env.step(np.zeros((2, 1), dtype=np.float32))
+
+
+def test_reset_seed_updates_the_shared_generator_in_place() -> None:
+    cfg = _make_cfg(include_optional_managers=False)
+    cfg.events = {}
+    env, _ = _make_env(cfg)
+    env.init_state()
+    generator = env.rng
+
+    env.reset(seed=123, env_ids=np.array([0], dtype=np.int32))
+
+    assert env.rng is generator
+    expected = np.random.default_rng(123).random()
+    assert env.rng.random() == pytest.approx(expected)
+
+
+def test_per_substep_metrics_fail_without_post_substep_backend_hook() -> None:
+    cfg = _make_cfg(
+        sim_substeps=2,
+        metrics={"energy": MetricsTermCfg(func=_metric, per_substep=True)},
+    )
+    with pytest.raises(NotImplementedError, match="post-physics per-substep metrics.*fake"):
+        _make_env(cfg)
+
+
+def test_multisubstep_state_independent_action_skips_callback() -> None:
+    env, backend = _make_env(reject_pre_step=True)
+    try:
+        assert backend.pre_step_control is None
+        assert not env.action_manager.requires_substep_state_feedback
+    finally:
+        env.close()
+
+
+def test_multisubstep_state_feedback_action_uses_callback() -> None:
+    cfg = _make_cfg(
+        actions={"drive": _FeedbackDriveCfg(entity_name="robot")},
+    )
+    env, backend = _make_env(cfg)
+    try:
+        env.init_state()
+        env.step(np.array([[0.25], [0.5]], dtype=np.float32))
+
+        assert env.action_manager.requires_substep_state_feedback
+        assert backend.pre_step_control is not None
+        assert backend.step_nsteps == [2]
+        assert env.action_sim_steps == [1, 2]
+        assert len(backend.applied_controls) == 2
+    finally:
+        env.close()
+
+
+def test_multisubstep_state_feedback_action_fails_when_backend_rejects_callback() -> None:
+    cfg = _make_cfg(
+        actions={"drive": _FeedbackDriveCfg(entity_name="robot")},
+    )
+    with pytest.raises(
+        NotImplementedError,
+        match="ActionManager.*state-feedback actions.*every physics substep.*fake",
+    ):
+        _make_env(cfg, reject_pre_step=True)
+
+
+def test_single_substep_feedback_action_does_not_require_callback() -> None:
+    cfg = _make_cfg(
+        sim_substeps=1,
+        actions={"drive": _FeedbackDriveCfg(entity_name="robot")},
+    )
+    env, backend = _make_env(cfg, reject_pre_step=True)
+    try:
+        env.init_state()
+        env.step(np.array([[0.25], [0.5]], dtype=np.float32))
+
+        assert backend.pre_step_control is None
+        assert backend.step_nsteps == [1]
+        assert env.action_sim_steps == [1]
+    finally:
+        env.close()
+
+
+def test_empty_action_manager_does_not_require_callback() -> None:
+    observations = {
+        "actor": ObservationGroupCfg(
+            terms={"episode_step": ObservationTermCfg(func=_episode_step_observation)}
+        ),
+        "value": ObservationGroupCfg(
+            terms={"episode_step": ObservationTermCfg(func=_episode_step_observation)}
+        ),
+    }
+    env, backend = _make_env(
+        _make_cfg(actions={}, observations=observations),
+        reject_pre_step=True,
+    )
+    try:
+        assert env.action_manager.active_terms == []
+        assert not env.action_manager.requires_substep_state_feedback
+        assert backend.pre_step_control is None
+    finally:
+        env.close()
+
+
+def test_state_feedback_action_error_propagates_from_callback(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    cfg = _make_cfg(
+        actions={"drive": _FeedbackDriveCfg(entity_name="robot")},
+    )
+    env, _ = _make_env(cfg)
+    env.init_state()
+    term = env.action_manager.get_term("drive")
+
+    def fail() -> None:
+        raise ValueError("feedback failed")
+
+    monkeypatch.setattr(term, "apply_actions", fail)
+    try:
+        with pytest.raises(ValueError, match="ActionManager term 'drive'.*feedback failed"):
+            env.step(np.zeros((2, 1), dtype=np.float32))
+    finally:
+        env.close()
+
+
+@pytest.mark.parametrize(
+    ("mutate", "error", "match"),
+    [
+        (
+            lambda cfg: setattr(cfg, "policy_observation_group", "missing"),
+            KeyError,
+            "requests group 'missing'",
+        ),
+        (
+            lambda cfg: setattr(
+                cfg.observations["actor"],
+                "concatenate_terms",
+                False,  # type: ignore[union-attr]
+            ),
+            ValueError,
+            "must concatenate terms",
+        ),
+        (
+            lambda cfg: setattr(cfg, "critic_observation_group", "actor"),
+            ValueError,
+            "must be different",
+        ),
+    ],
+)
+def test_observation_mapping_fails_closed(mutate, error, match: str) -> None:
+    cfg = _make_cfg()
+    mutate(cfg)
+    with pytest.raises(error, match=match):
+        _make_env(cfg)
+
+
+def test_close_unhooks_callback_and_closes_owned_resources() -> None:
+    cfg = _make_cfg(
+        actions={"drive": _FeedbackDriveCfg(entity_name="robot")},
+    )
+    env, backend = _make_env(cfg)
+    assert backend.pre_step_control is not None
+    env.close()
+    assert backend.pre_step_control is None
+    assert backend.cleanup_calls == 1
+    assert env.trace[-1] == "recorder_close"
diff --git a/tests/envs/test_motion_command_partial_reset.py b/tests/envs/test_motion_command_partial_reset.py
new file mode 100644
index 000000000..76584cbe2
--- /dev/null
+++ b/tests/envs/test_motion_command_partial_reset.py
@@ -0,0 +1,190 @@
+"""Row-scoped partial-reset parity tests for MotionCommand (issue #1261).
+
+On the partial-reset path the command manager recomputes only the reset rows;
+untouched rows reuse the values produced by the per-step compute of the same
+control step. These tests pin that contract:
+
+- untouched rows keep their command/motion/relative-state/metric values
+  bit-identically across a partial reset (except the sampler-stat metrics,
+  which track global sampler scalars);
+- reset rows match a full recompute of the same post-reset state, which is
+  what the pre-#1261 full-batch implementation produced.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Callable
+
+import numpy as np
+import pytest
+from hydra import compose, initialize_config_dir
+from hydra.core.global_hydra import GlobalHydra
+
+from unilab.base import registry
+from unilab.base.config_adapter import BackendAdapter
+from unilab.base.config_materialization import apply_cfg_overrides
+from unilab.envs import ManagerBasedRlEnvCfg
+from unilab.tasks.motion_tracking.common.manager_terms import MotionCommand
+from unilab.tasks.motion_tracking.g1.manager_terms import BoxMotionCommand
+
+_ROOT = Path(__file__).parents[2]
+
+_CASES = (
+    ("ppo", "g1_flip_tracking", "G1FlipTracking"),
+    ("ppo", "g1_box_tracking", "G1BoxTracking"),
+)
+
+_SAMPLER_STAT_METRICS = ("sampling_entropy", "sampling_top1_prob", "sampling_top1_bin")
+
+_ACCESSORS: dict[str, Callable[[MotionCommand], np.ndarray]] = {
+    "command": lambda term: term.command,
+    "time_steps": lambda term: term.time_steps,
+    "body_pos_w": lambda term: term.body_pos_w,
+    "body_pos_relative_w": lambda term: term.body_pos_relative_w,
+    "body_quat_relative_w": lambda term: term.body_quat_relative_w,
+    "motion_anchor_pos_b": lambda term: term.motion_anchor_pos_b,
+    "motion_anchor_ori_b": lambda term: term.motion_anchor_ori_b,
+    "robot_body_pos_b": lambda term: term.robot_body_pos_b,
+    "robot_body_ori_b": lambda term: term.robot_body_ori_b,
+    "joint_default_bias": lambda term: term.joint_default_bias,
+    "robot_body_pos_w": lambda term: term._robot_body_pos_w,
+    "robot_body_quat_w": lambda term: term._robot_body_quat_w,
+    "robot_body_lin_vel_w": lambda term: term._robot_body_lin_vel_w,
+    "robot_body_ang_vel_w": lambda term: term._robot_body_ang_vel_w,
+}
+
+_MOTION_DATA_FIELDS = (
+    "joint_pos",
+    "joint_vel",
+    "body_pos_w",
+    "body_quat_w",
+    "body_lin_vel_w",
+    "body_ang_vel_w",
+    "object_pos_w",
+    "object_quat_w",
+    "object_lin_vel_w",
+    "object_ang_vel_w",
+)
+
+
+def _make_env(config_root: str, task: str, backend: str, identity: str, num_envs: int):
+    registry.ensure_registries()
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(
+        config_dir=str(_ROOT / "src" / "unilab" / "conf" / config_root), version_base="1.3"
+    ):
+        owner = compose("config", overrides=[f"task={task}/{backend}"])
+    cfg = registry.materialize_env_config(identity)
+    assert isinstance(cfg, ManagerBasedRlEnvCfg)
+    override = BackendAdapter(
+        owner, root_dir=_ROOT, algo_name=config_root
+    ).build_task_env_cfg_override()
+    apply_cfg_overrides(cfg, override)
+    return registry.make(
+        identity, num_envs=num_envs, sim_backend=backend, env_cfg_override=override
+    )
+
+
+def _buffers(term: MotionCommand) -> dict[str, np.ndarray]:
+    buffers = {name: accessor(term) for name, accessor in _ACCESSORS.items()}
+    for field_name in _MOTION_DATA_FIELDS:
+        value = getattr(term._motion_data, field_name, None)
+        if value is not None:
+            buffers[f"motion_data.{field_name}"] = value
+    if isinstance(term, BoxMotionCommand):
+        buffers["object_pos_w"] = term.object_pos_w
+        buffers["object_state_b"] = term.object_state_b
+    return buffers
+
+
+def _current(term: MotionCommand, name: str) -> np.ndarray:
+    if name in _ACCESSORS:
+        return _ACCESSORS[name](term)
+    if name.startswith("motion_data."):
+        return getattr(term._motion_data, name.split(".", 1)[1])
+    if name == "object_pos_w":
+        return term.object_pos_w  # type: ignore[attr-defined]
+    if name == "object_state_b":
+        return term.object_state_b  # type: ignore[attr-defined]
+    raise KeyError(name)
+
+
+@pytest.mark.parametrize(("config_root", "task", "identity"), _CASES)
+def test_motion_command_partial_reset_row_parity(
+    config_root: str, task: str, identity: str
+) -> None:
+    pytest.importorskip("mujoco")
+    try:
+        from mujoco_uni.batch_env import BatchEnvPool as _  # noqa: F401
+    except Exception:
+        pytest.skip("mujoco_uni.batch_env not available")
+
+    num_envs = 4
+    env = _make_env(config_root, task, "mujoco", identity, num_envs)
+    try:
+        env.init_state()
+        term = env.command_manager.get_term("motion")
+        assert isinstance(term, MotionCommand)
+        action_dim = term.motion.num_joints
+        rng = np.random.default_rng(7)
+        for _ in range(5):
+            env.step((0.1 * rng.standard_normal((num_envs, action_dim))).astype(np.float32))
+
+        reset_ids = np.array([0, 2], dtype=np.int32)
+        keep_ids = np.array([1, 3], dtype=np.int32)
+        before = {name: value.copy() for name, value in _buffers(term).items()}
+        metrics_before = {name: value.copy() for name, value in term.metrics.items()}
+        reset_obs, _ = env.reset(env_ids=reset_ids)
+        after = _buffers(term)
+
+        # Untouched rows keep their per-step values bit-identically, except the
+        # sampler-stat metrics which track global sampler scalars.
+        for name, value in before.items():
+            np.testing.assert_array_equal(
+                after[name][keep_ids],
+                value[keep_ids],
+                err_msg=f"untouched rows changed for {name}",
+            )
+        for name, value in metrics_before.items():
+            if name in _SAMPLER_STAT_METRICS:
+                expected = np.full(
+                    num_envs, getattr(term.sampler, name), dtype=term.metrics[name].dtype
+                )
+                np.testing.assert_array_equal(term.metrics[name], expected)
+            else:
+                np.testing.assert_array_equal(
+                    term.metrics[name][keep_ids],
+                    value[keep_ids],
+                    err_msg=f"untouched rows changed for metric {name}",
+                )
+
+        # Reset observations are finite and scattered into the env state.
+        assert env.state is not None
+        for group, values in reset_obs.items():
+            assert values.shape[0] == len(reset_ids)
+            assert np.isfinite(values).all()
+            np.testing.assert_array_equal(env.state.obs[group][reset_ids], values)
+
+        # Reference: a full recompute of the post-reset state (the pre-#1261
+        # behavior) must agree with the row-scoped results on every row.
+        reference = {name: value.copy() for name, value in _buffers(term).items()}
+        term._refresh_motion()
+        term._refresh_robot_state(force=True)
+        term._refresh_relative_state()
+        if isinstance(term, BoxMotionCommand):
+            term._refresh_object_state()
+        for name, value in reference.items():
+            # The default bias is intentionally resampled from RNG on reset rows.
+            if name == "joint_default_bias":
+                np.testing.assert_array_equal(_current(term, name), value)
+                continue
+            np.testing.assert_allclose(
+                _current(term, name),
+                value,
+                rtol=1e-6,
+                atol=1e-7,
+                err_msg=f"row-scoped refresh disagrees with full refresh for {name}",
+            )
+    finally:
+        env.close()
diff --git a/tests/envs/test_motion_interpolation.py b/tests/envs/test_motion_interpolation.py
new file mode 100644
index 000000000..459d7dc7f
--- /dev/null
+++ b/tests/envs/test_motion_interpolation.py
@@ -0,0 +1,200 @@
+"""Tests for the shared CSV-motion interpolation helpers in the library layer.
+
+The reference functions below are verbatim copies of the implementations that
+used to live in ``scripts/motion/csv_to_npz.py`` and
+``scripts/motion/x2_csv_to_tracking_npz.py`` (see issue #1244). The library
+helpers must reproduce them exactly so converted NPZ products stay
+bit-identical.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+
+from unilab.tasks.motion_tracking.common.motion_loader import (
+    compute_motion_velocities,
+    interpolate_motion,
+    quat_slerp,
+)
+from unilab.utils.rotation import np_quat_angular_velocity, np_quat_ensure_continuity
+
+
+def _reference_quat_slerp(q1: np.ndarray, q2: np.ndarray, t: float) -> np.ndarray:
+    """Former scripts/motion/csv_to_npz.py::quat_slerp (float32 path)."""
+    dot = np.dot(q1, q2)
+    if dot < 0:
+        q2 = -q2
+        dot = -dot
+
+    if dot > 0.9995:
+        result = q1 + t * (q2 - q1)
+        return result / np.linalg.norm(result)
+
+    theta = np.arccos(np.clip(dot, -1, 1))
+    sin_theta = np.sin(theta)
+
+    w1 = np.sin((1 - t) * theta) / sin_theta
+    w2 = np.sin(t * theta) / sin_theta
+
+    return w1 * q1 + w2 * q2
+
+
+def _reference_x2_quat_slerp(q1: np.ndarray, q2: np.ndarray, t: float) -> np.ndarray:
+    """Former scripts/motion/x2_csv_to_tracking_npz.py::_quat_slerp (float64 path)."""
+    q1 = q1.astype(np.float64, copy=False)
+    q2 = q2.astype(np.float64, copy=False)
+    dot = float(np.dot(q1, q2))
+    if dot < 0.0:
+        q2 = -q2
+        dot = -dot
+    if dot > 0.9995:
+        result = q1 + t * (q2 - q1)
+        return (result / np.linalg.norm(result)).astype(np.float32)
+
+    theta = np.arccos(np.clip(dot, -1.0, 1.0))
+    sin_theta = np.sin(theta)
+    w1 = np.sin((1.0 - t) * theta) / sin_theta
+    w2 = np.sin(t * theta) / sin_theta
+    return (w1 * q1 + w2 * q2).astype(np.float32)
+
+
+def _reference_interpolate(
+    base_poss_input: np.ndarray,
+    base_rots_input: np.ndarray,
+    dof_poss_input: np.ndarray,
+    input_fps: int,
+    output_fps: int,
+) -> dict[str, np.ndarray]:
+    """Former scripts/motion MotionLoader._interpolate_motion + _compute_velocities."""
+    input_dt = 1.0 / input_fps
+    output_dt = 1.0 / output_fps
+    input_frames = base_poss_input.shape[0]
+    duration = (input_frames - 1) * input_dt
+
+    times = np.arange(0, duration, output_dt, dtype=np.float32)
+    output_frames = times.shape[0]
+
+    phase = times / duration
+    index_0 = np.floor(phase * (input_frames - 1)).astype(np.int32)
+    index_1 = np.minimum(index_0 + 1, input_frames - 1)
+    blend = phase * (input_frames - 1) - index_0
+
+    base_poss = (
+        base_poss_input[index_0] * (1 - blend[:, None]) + base_poss_input[index_1] * blend[:, None]
+    )
+
+    base_rots = np.zeros((output_frames, 4), dtype=np.float32)
+    for i in range(output_frames):
+        base_rots[i] = _reference_quat_slerp(
+            base_rots_input[index_0[i]], base_rots_input[index_1[i]], blend[i]
+        )
+    base_rots = np_quat_ensure_continuity(base_rots)
+
+    dof_poss = (
+        dof_poss_input[index_0] * (1 - blend[:, None]) + dof_poss_input[index_1] * blend[:, None]
+    )
+
+    base_lin_vels = np.gradient(base_poss, output_dt, axis=0)
+    dof_vels = np.gradient(dof_poss, output_dt, axis=0)
+    base_ang_vels = np_quat_angular_velocity(base_rots, output_dt)
+
+    return {
+        "output_frames": output_frames,
+        "base_poss": base_poss,
+        "base_rots": base_rots,
+        "dof_poss": dof_poss,
+        "base_lin_vels": base_lin_vels,
+        "base_ang_vels": base_ang_vels,
+        "dof_vels": dof_vels,
+    }
+
+
+def _random_quats(rng: np.random.Generator, n: int, dtype=np.float32) -> np.ndarray:
+    quats = rng.standard_normal((n, 4)).astype(dtype)
+    return quats / np.linalg.norm(quats, axis=1, keepdims=True)
+
+
+def test_quat_slerp_matches_former_script_float32() -> None:
+    rng = np.random.default_rng(0)
+    quats = _random_quats(rng, 32)
+    for i in range(0, 30, 2):
+        for t in (0.0, 0.13, 0.5, 0.97, 1.0):
+            expected = _reference_quat_slerp(quats[i], quats[i + 1], np.float32(t))
+            actual = quat_slerp(quats[i], quats[i + 1], np.float32(t))
+            assert actual.dtype == expected.dtype
+            np.testing.assert_array_equal(actual, expected)
+
+
+def test_quat_slerp_matches_former_script_near_identical() -> None:
+    rng = np.random.default_rng(1)
+    q1 = _random_quats(rng, 1)[0]
+    q2 = q1 + rng.standard_normal(4).astype(np.float32) * 1e-4
+    q2 = q2 / np.linalg.norm(q2)
+    expected = _reference_quat_slerp(q1, q2, np.float32(0.3))
+    np.testing.assert_array_equal(quat_slerp(q1, q2, np.float32(0.3)), expected)
+
+
+def test_quat_slerp_float64_path_matches_former_x2_script() -> None:
+    rng = np.random.default_rng(2)
+    quats = _random_quats(rng, 8)
+    for i in range(0, 6, 2):
+        for t in (0.0, 0.42, 1.0):
+            expected = _reference_x2_quat_slerp(quats[i], quats[i + 1], t)
+            actual = quat_slerp(
+                quats[i].astype(np.float64), quats[i + 1].astype(np.float64), t
+            ).astype(np.float32)
+            np.testing.assert_array_equal(actual, expected)
+
+
+def test_quat_slerp_takes_shortest_path() -> None:
+    rng = np.random.default_rng(3)
+    q1, q2 = _random_quats(rng, 2)
+    direct = quat_slerp(q1, q2, 0.5)
+    flipped = quat_slerp(q1, -q2, 0.5)
+    np.testing.assert_allclose(direct, flipped, atol=1e-6)
+
+
+def test_interpolate_motion_matches_former_script() -> None:
+    rng = np.random.default_rng(4)
+    input_frames = 61
+    base_poss_input = rng.standard_normal((input_frames, 3)).astype(np.float32)
+    base_rots_input = np_quat_ensure_continuity(_random_quats(rng, input_frames))
+    dof_poss_input = rng.standard_normal((input_frames, 29)).astype(np.float32)
+
+    for input_fps, output_fps in ((30, 50), (120, 50), (50, 50), (120, 30)):
+        expected = _reference_interpolate(
+            base_poss_input, base_rots_input, dof_poss_input, input_fps, output_fps
+        )
+        actual = interpolate_motion(
+            base_poss_input,
+            base_rots_input,
+            dof_poss_input,
+            input_fps=input_fps,
+            output_fps=output_fps,
+        )
+        assert actual.output_frames == expected["output_frames"]
+        for key in (
+            "base_poss",
+            "base_rots",
+            "dof_poss",
+            "base_lin_vels",
+            "base_ang_vels",
+            "dof_vels",
+        ):
+            np.testing.assert_array_equal(getattr(actual, key), expected[key])
+
+
+def test_compute_motion_velocities_matches_former_script() -> None:
+    rng = np.random.default_rng(5)
+    frames = 40
+    base_poss = rng.standard_normal((frames, 3)).astype(np.float32)
+    base_rots = np_quat_ensure_continuity(_random_quats(rng, frames))
+    dof_poss = rng.standard_normal((frames, 7)).astype(np.float32)
+    dt = 1.0 / 50
+
+    base_lin_vels, base_ang_vels, dof_vels = compute_motion_velocities(
+        base_poss, base_rots, dof_poss, dt
+    )
+    np.testing.assert_array_equal(base_lin_vels, np.gradient(base_poss, dt, axis=0))
+    np.testing.assert_array_equal(dof_vels, np.gradient(dof_poss, dt, axis=0))
+    np.testing.assert_array_equal(base_ang_vels, np_quat_angular_velocity(base_rots, dt))
diff --git a/tests/envs/test_motion_loader.py b/tests/envs/test_motion_loader.py
index 1b5069faf..1139b8ba0 100644
--- a/tests/envs/test_motion_loader.py
+++ b/tests/envs/test_motion_loader.py
@@ -2,7 +2,7 @@
 
 import numpy as np
 
-from unilab.envs.motion_tracking.common.motion_loader import MotionLoader, MotionSampler
+from unilab.tasks.motion_tracking.common.motion_loader import MotionLoader, MotionSampler
 
 
 def _write_motion_npz(
@@ -184,8 +184,32 @@ def test_motion_sampler_step_respects_current_clip_end(tmp_path):
     np.testing.assert_array_equal(sampler.current_frames, np.array([2, 4], dtype=np.int32))
 
 
+def test_motion_sampler_uses_env_owned_rng_and_steps_only_selected_rows(tmp_path):
+    motion = tmp_path / "motion.npz"
+    _write_motion_npz(motion, base_value=0.0, num_frames=8)
+    loader = MotionLoader(str(motion))
+    env_ids = np.array([0, 2], dtype=np.int32)
+    sampler = MotionSampler(
+        loader,
+        mode="uniform",
+        num_envs=3,
+        rng=np.random.default_rng(17),
+    )
+    expected_rng = np.random.default_rng(17)
+
+    frames = sampler.sample_frames(env_ids)
+
+    np.testing.assert_array_equal(frames, expected_rng.integers(0, 8, 2, dtype=np.int32))
+    untouched = int(sampler.current_frames[1])
+    sampler.current_clip_end_frames[:] = 7
+    done = sampler.step(np.array([2], dtype=np.int32))
+    assert done.size == 0
+    assert sampler.current_frames[1] == untouched
+    assert sampler.current_frames[2] == frames[1] + 1
+
+
 def test_box_motion_loader_reads_object_state_and_trims_robot_joints(tmp_path):
-    from unilab.envs.motion_tracking.g1.motion_box_loader import BoxMotionLoader
+    from unilab.tasks.motion_tracking.g1.motion_box_loader import BoxMotionLoader
 
     motion = tmp_path / "motion_box.npz"
     _write_box_motion_npz(motion, base_value=1.0, num_frames=2, num_joints=2)
@@ -207,7 +231,7 @@ def test_box_motion_loader_reads_object_state_and_trims_robot_joints(tmp_path):
 
 
 def test_box_motion_loader_rejects_partial_object_key_sets(tmp_path):
-    from unilab.envs.motion_tracking.g1.motion_box_loader import BoxMotionLoader
+    from unilab.tasks.motion_tracking.g1.motion_box_loader import BoxMotionLoader
 
     motion = tmp_path / "motion_box_missing_keys.npz"
     _write_box_motion_npz(motion, base_value=1.0, num_frames=2, num_joints=2)
@@ -221,7 +245,7 @@ def test_box_motion_loader_rejects_partial_object_key_sets(tmp_path):
 
 
 def test_box_motion_loader_rejects_multi_clip_object_presence_mismatch(tmp_path):
-    from unilab.envs.motion_tracking.g1.motion_box_loader import BoxMotionLoader
+    from unilab.tasks.motion_tracking.g1.motion_box_loader import BoxMotionLoader
 
     motion_without_object = tmp_path / "motion_without_object.npz"
     motion_with_object = tmp_path / "motion_with_object.npz"
diff --git a/tests/envs/test_motion_profiles.py b/tests/envs/test_motion_profiles.py
new file mode 100644
index 000000000..3ea2ac7bc
--- /dev/null
+++ b/tests/envs/test_motion_profiles.py
@@ -0,0 +1,531 @@
+from __future__ import annotations
+
+from pathlib import Path
+from types import SimpleNamespace
+from typing import Any, cast
+
+import numpy as np
+import pytest
+from hydra import compose, initialize_config_dir
+from hydra.core.global_hydra import GlobalHydra
+
+from unilab.base import registry
+from unilab.base.config_adapter import BackendAdapter
+from unilab.base.config_materialization import apply_cfg_overrides
+from unilab.envs import ManagerBasedRlEnvCfg
+from unilab.envs.mdp.actions import JointPositionAction
+
+# CPU-bound on the single-core CI runner; kept in the slow lane (make test-slow).
+pytestmark = pytest.mark.slow
+
+_ROOT = Path(__file__).parents[2]
+
+_PROFILE_IDENTITIES = (
+    "G1BoxTracking",
+    "G1BoxTracking23Dof",
+    "G1ClimbTracking",
+    "G1ClimbTracking23Dof",
+    "G1FlipTracking",
+    "G1FlipTracking23Dof",
+    "G1FlipTrackingSAC",
+    "G1FlipTrackingSAC23Dof",
+    "G1WallFlipTracking",
+    "G1WallFlipTracking23Dof",
+    "G1WallFlipTrackingSAC",
+    "G1WallFlipTrackingSAC23Dof",
+    "G1WBTObs",
+    "G1WBTObs23Dof",
+    "X2WallFlipTracking",
+)
+
+_PPO_PROFILES = (
+    (
+        "g1_box_tracking",
+        "G1BoxTracking",
+        "scene_flat_with_largebox.xml",
+        "sub3_largebox_003_boxconverted.npz",
+    ),
+    (
+        "g1_23dof_box_tracking",
+        "G1BoxTracking23Dof",
+        "scene_flat_23dof_with_largebox.xml",
+        "sub3_largebox_003_boxconverted_23dof.npz",
+    ),
+    (
+        "g1_climb_tracking",
+        "G1ClimbTracking",
+        "scene_climb_20_z_scale_1.xml",
+        "climb_20_z_scale_1.0.npz",
+    ),
+    (
+        "g1_23dof_climb_tracking",
+        "G1ClimbTracking23Dof",
+        "scene_climb_20_z_scale_1_23dof.xml",
+        "climb_20_z_scale_1.0_23dof.npz",
+    ),
+    ("g1_flip_tracking", "G1FlipTracking", "scene_flat.xml", "flip_360_001__A304.npz"),
+    (
+        "g1_23dof_flip_tracking",
+        "G1FlipTracking23Dof",
+        "scene_flat_23dof.xml",
+        "flip_360_001__A304_23dof.npz",
+    ),
+    (
+        "g1_wall_flip_tracking",
+        "G1WallFlipTracking",
+        "scene_flat_with_wall.xml",
+        "flip_from_wall_104__A304.npz",
+    ),
+    (
+        "g1_23dof_wall_flip_tracking",
+        "G1WallFlipTracking23Dof",
+        "scene_flat_23dof_with_wall.xml",
+        "flip_from_wall_104__A304_23dof.npz",
+    ),
+    (
+        "x2_wall_flip_tracking",
+        "X2WallFlipTracking",
+        "scene_flat_with_wall.xml",
+        "tictacflip_6-3_g1format.npz",
+    ),
+)
+
+_APPO_PROFILES = tuple(
+    profile
+    for profile in _PPO_PROFILES
+    if "box" not in profile[0] and not profile[0].startswith("x2")
+)
+
+_SAC_PROFILES = (
+    ("g1_flip_tracking", "G1FlipTrackingSAC", "scene_flat.xml", "flip_360_001__A304.npz"),
+    (
+        "g1_23dof_flip_tracking",
+        "G1FlipTrackingSAC23Dof",
+        "scene_flat_23dof.xml",
+        "flip_360_001__A304_23dof.npz",
+    ),
+    (
+        "g1_wall_flip_tracking",
+        "G1WallFlipTrackingSAC",
+        "scene_flat_with_wall.xml",
+        "flip_from_wall_104__A304.npz",
+    ),
+    (
+        "g1_23dof_wall_flip_tracking",
+        "G1WallFlipTrackingSAC23Dof",
+        "scene_flat_23dof_with_wall.xml",
+        "flip_from_wall_104__A304_23dof.npz",
+    ),
+    ("g1_wbt_obs", "G1WBTObs", "scene_flat.xml", "dance1_subject2_part.npz"),
+    (
+        "g1_23dof_wbt_obs",
+        "G1WBTObs23Dof",
+        "scene_flat_23dof.xml",
+        "dance1_subject2_part_23dof.npz",
+    ),
+)
+
+_OWNER_CASES = (
+    tuple(
+        ("ppo", task, backend, identity, scene, motion)
+        for task, identity, scene, motion in _PPO_PROFILES
+        for backend in ("mujoco", "motrix")
+    )
+    + tuple(
+        ("appo", task, backend, identity, scene, motion)
+        for task, identity, scene, motion in _APPO_PROFILES
+        for backend in ("mujoco", "motrix")
+    )
+    + tuple(
+        ("sac", task, "mujoco", identity, scene, motion)
+        for task, identity, scene, motion in _SAC_PROFILES
+    )
+)
+
+_LEGACY_G1_ACTION_SCALE = (
+    (r".*_(hip_pitch|hip_yaw)_joint", 0.5475464629911068),
+    (r".*_(hip_roll|knee)_joint", 0.35066146637882434),
+    (r".*_ankle_(pitch|roll)_joint", 0.43857731392336724),
+    (r"waist_yaw_joint", 0.5475464629911068),
+    (r"waist_(roll|pitch)_joint", 0.43857731392336724),
+    (r".*_(shoulder_(pitch|roll|yaw)|elbow|wrist_roll)_joint", 0.43857731392336724),
+    (r".*_wrist_(pitch|yaw)_joint", 0.07450087032950714),
+)
+
+_LEGACY_SCALAR_ACTION_SCALE = {
+    ("ppo", "g1_flip_tracking", "motrix"): 0.25,
+    ("ppo", "g1_23dof_flip_tracking", "motrix"): 0.25,
+    ("appo", "g1_wall_flip_tracking", "motrix"): 0.25,
+    ("appo", "g1_23dof_wall_flip_tracking", "motrix"): 0.25,
+}
+
+
+def _compose_owner(config_root: str, task: str, backend: str) -> Any:
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(
+        config_dir=str(_ROOT / "src" / "unilab" / "conf" / config_root), version_base="1.3"
+    ):
+        overrides = [f"task={task}/{backend}"]
+        return compose("config", overrides=overrides)
+
+
+def _materialize_profile(
+    config_root: str,
+    task: str,
+    backend: str,
+    identity: str,
+) -> tuple[Any, ManagerBasedRlEnvCfg, dict[str, Any]]:
+    registry.ensure_registries()
+    owner = _compose_owner(config_root, task, backend)
+    cfg = registry.materialize_env_config(identity)
+    assert isinstance(cfg, ManagerBasedRlEnvCfg)
+    override = BackendAdapter(
+        owner,
+        root_dir=_ROOT,
+        algo_name=config_root,
+    ).build_task_env_cfg_override()
+    apply_cfg_overrides(
+        cfg,
+        override,
+    )
+    return owner, cfg, override
+
+
+def _resolved_action_scale(cfg: ManagerBasedRlEnvCfg) -> tuple[list[str], np.ndarray]:
+    joint_names = list(cfg.scene.entities["robot"].joint_names or ())
+    entity = SimpleNamespace(
+        data=SimpleNamespace(
+            default_joint_pos=np.zeros((1, len(joint_names)), dtype=np.float32),
+        ),
+        find_joints_by_actuator_names=lambda actuator_names: (
+            list(range(len(joint_names))),
+            joint_names,
+        ),
+    )
+    env = SimpleNamespace(num_envs=1, scene={"robot": entity})
+    action = JointPositionAction(cfg.actions["joint_pos"], cast(Any, env))
+    scale = action.scale
+    if np.isscalar(scale):
+        scale = np.full(len(joint_names), scale, dtype=np.float32)
+    else:
+        scale = np.asarray(scale[0])
+    return joint_names, scale
+
+
+def _legacy_g1_action_scale(joint_names: list[str]) -> np.ndarray:
+    import re
+
+    expected = []
+    for joint_name in joint_names:
+        matches = [
+            value for pattern, value in _LEGACY_G1_ACTION_SCALE if re.fullmatch(pattern, joint_name)
+        ]
+        assert len(matches) == 1, (joint_name, matches)
+        expected.append(matches[0])
+    return np.asarray(expected, dtype=np.float32)
+
+
+@pytest.mark.parametrize(
+    ("config_root", "task", "backend", "identity", "scene_file", "motion_file"),
+    _OWNER_CASES,
+)
+def test_motion_profile_owner_composes_to_manager_runtime(
+    config_root: str,
+    task: str,
+    backend: str,
+    identity: str,
+    scene_file: str,
+    motion_file: str,
+) -> None:
+    registry.ensure_registries()
+    owner, cfg, _ = _materialize_profile(config_root, task, backend, identity)
+
+    assert owner.training.task_name == identity
+    assert owner.training.sim_backend == backend
+    assert cfg.scene.model_file.endswith(scene_file)
+    assert str(cfg.commands["motion"].params.motion_file).endswith(motion_file)
+    assert list(cfg.actions) == ["joint_pos"]
+    assert cfg.policy_observation_group == "actor"
+    assert cfg.critic_observation_group == "critic"
+
+
+@pytest.mark.parametrize(
+    ("config_root", "task", "backend", "identity", "scene_file", "motion_file"),
+    _OWNER_CASES,
+)
+def test_motion_profile_action_scale_matches_legacy_runtime(
+    config_root: str,
+    task: str,
+    backend: str,
+    identity: str,
+    scene_file: str,
+    motion_file: str,
+) -> None:
+    del scene_file, motion_file
+    _, cfg, _ = _materialize_profile(config_root, task, backend, identity)
+    joint_names, actual = _resolved_action_scale(cfg)
+
+    scalar = _LEGACY_SCALAR_ACTION_SCALE.get((config_root, task, backend))
+    if "box_tracking" in task or task == "x2_wall_flip_tracking":
+        scalar = 0.25
+    elif "wbt_obs" in task:
+        scalar = 2.0
+    expected = (
+        np.full(len(joint_names), scalar, dtype=np.float32)
+        if scalar is not None
+        else _legacy_g1_action_scale(joint_names)
+    )
+
+    np.testing.assert_allclose(actual, expected)
+
+
+@pytest.mark.parametrize(
+    (
+        "task",
+        "backend",
+        "adaptive_kl_factor",
+        "adaptive_lr_factor",
+        "enable_compile",
+        "steps_per_env",
+        "replay_queue_size",
+    ),
+    (
+        ("g1_climb_tracking", "mujoco", 1.2, 1.1, False, 24, None),
+        ("g1_climb_tracking", "motrix", 1.2, 1.1, False, 24, None),
+        ("g1_23dof_climb_tracking", "mujoco", 1.2, 1.1, False, 24, None),
+        ("g1_23dof_climb_tracking", "motrix", 1.2, 1.1, False, 24, None),
+        ("g1_23dof_flip_tracking", "mujoco", 1.2, 1.1, False, 24, None),
+        ("g1_23dof_flip_tracking", "motrix", 2.0, 1.5, True, 24, None),
+        ("g1_wall_flip_tracking", "motrix", 2.0, 1.5, True, 24, None),
+        ("g1_23dof_wall_flip_tracking", "mujoco", 2.0, 1.5, True, 20, 5),
+        ("g1_23dof_wall_flip_tracking", "motrix", 2.0, 1.5, True, 24, None),
+    ),
+)
+def test_appo_profiles_preserve_training_owner_contract(
+    task: str,
+    backend: str,
+    adaptive_kl_factor: float,
+    adaptive_lr_factor: float,
+    enable_compile: bool,
+    steps_per_env: int,
+    replay_queue_size: int | None,
+) -> None:
+    owner = _compose_owner("appo", task, backend)
+
+    assert owner.algo.algorithm.adaptive_kl_factor == pytest.approx(adaptive_kl_factor)
+    assert owner.algo.algorithm.adaptive_lr_factor == pytest.approx(adaptive_lr_factor)
+    assert owner.algo.algorithm.enable_compile is enable_compile
+    assert owner.algo.steps_per_env == steps_per_env
+    assert owner.training.replay_queue_size == replay_queue_size
+
+
+@pytest.mark.parametrize("task", ("g1_flip_tracking", "g1_23dof_flip_tracking"))
+def test_ppo_motrix_flip_profiles_keep_actor_normalization_disabled(task: str) -> None:
+    owner = _compose_owner("ppo", task, "motrix")
+
+    assert owner.algo.empirical_normalization is False
+    assert owner.algo.obs_groups.actor == ["actor"]
+    assert owner.algo.obs_groups.critic == ["critic"]
+
+
+@pytest.mark.parametrize(
+    "task",
+    (
+        "g1_23dof_box_tracking",
+        "g1_23dof_climb_tracking",
+        "g1_23dof_flip_tracking",
+        "g1_23dof_wall_flip_tracking",
+        "x2_wall_flip_tracking",
+    ),
+)
+def test_ppo_profiles_without_legacy_play_overrides_stay_disabled(task: str) -> None:
+    owner = _compose_owner("ppo", task, "mujoco")
+
+    assert owner.play_profile.enabled is False
+    assert owner.play_profile.env is None
+
+
+@pytest.mark.parametrize("task", ("g1_box_tracking", "g1_23dof_box_tracking"))
+def test_box_motrix_drops_unconsumed_algorithm_noise_config(task: str) -> None:
+    owner = _compose_owner("ppo", task, "motrix")
+
+    assert "noise_config" not in owner.algo
+
+
+def test_all_motion_profiles_have_one_manager_factory_and_both_backends() -> None:
+    registry.ensure_registries()
+    metadata = registry.list_registered_envs()
+
+    for identity in _PROFILE_IDENTITIES:
+        assert metadata[identity] == {
+            "config_factory": "ManagerBasedRlEnvCfg",
+            "available_backends": ["mujoco", "motrix"],
+        }
+
+
+def test_box_wall_wbt_and_x2_profiles_keep_only_owner_differences() -> None:
+    from unilab.tasks.motion_tracking.g1.manager_terms import BoxMotionCommandCfg
+
+    _, box, _ = _materialize_profile("ppo", "g1_box_tracking", "mujoco", "G1BoxTracking")
+    _, flip, _ = _materialize_profile("sac", "g1_flip_tracking", "mujoco", "G1FlipTrackingSAC")
+    _, wall, _ = _materialize_profile(
+        "sac",
+        "g1_wall_flip_tracking",
+        "mujoco",
+        "G1WallFlipTrackingSAC",
+    )
+    _, wbt, _ = _materialize_profile("sac", "g1_wbt_obs", "mujoco", "G1WBTObs")
+    _, x2, _ = _materialize_profile("ppo", "x2_wall_flip_tracking", "mujoco", "X2WallFlipTracking")
+
+    assert set(box.scene.entities) == {"robot", "object"}
+    assert isinstance(box.commands["motion"], BoxMotionCommandCfg)
+    assert box.observations["actor"].terms["motion_anchor_pos_b"] is None
+    assert box.observations["actor"].terms["base_ang_vel"].params == {"sensor_name": "pelvis_gyro"}
+    assert box.observations["critic"].terms["base_ang_vel"].params == {"sensor_name": "pelvis_gyro"}
+    assert box.observations["critic"].terms["object_state"] is not None
+
+    assert flip.commands["motion"].params.sampling_mode == "mixed"
+    assert flip.commands["motion"].params.sampling_start_ratio == pytest.approx(0.1)
+    assert wall.commands["motion"].params.sampling_mode == "uniform"
+    assert wall.terminations["undesired_contacts"] is None
+    assert wall.terminations["anchor_pos"].params["threshold"] == pytest.approx(1.0e9)
+
+    actor_terms = wbt.observations["actor"].terms
+    critic_terms = wbt.observations["critic"].terms
+    assert actor_terms["motion_anchor_pos_b"] is None
+    assert actor_terms["base_lin_vel"] is None
+    assert [
+        actor_terms[name].history_length
+        for name in ("base_ang_vel", "joint_pos", "joint_vel", "actions")
+    ] == [5, 5, 5, 5]
+    assert actor_terms["joint_pos"].func.__name__ == "motion_joint_pos_rel_biased"
+    assert critic_terms["joint_pos"].func.__name__ == "motion_joint_pos_rel"
+    assert wbt.actions["joint_pos"].simulate_action_latency is True
+    assert list(wbt.events) == [
+        "base_mass",
+        "base_com",
+        "pd_gains",
+        "foot_friction",
+        "encoder_bias",
+        "push_robot",
+    ]
+
+    assert len(x2.scene.entities["robot"].joint_names) == 29
+    assert x2.scene.entities["robot"].geom_names is None
+    assert x2.scene.visual_model_file is not None
+    assert x2.observations["actor"].terms["motion_anchor_pos_b"] is None
+    assert x2.observations["actor"].terms["base_lin_vel"] is None
+
+
+def test_manager_factory_selects_robot_from_multiple_floating_entities() -> None:
+    from unilab.base.entity import EntityCfg
+    from unilab.base.scene import SceneCfg
+    from unilab.envs.manager_based_rl_env import _resolve_backend_entity_contract
+
+    cfg = ManagerBasedRlEnvCfg(
+        scene=SceneCfg(
+            model_file="scene.xml",
+            entities={
+                "robot": EntityCfg(root_body_name="pelvis", body_names=("pelvis",)),
+                "object": EntityCfg(root_body_name="largebox"),
+            },
+        )
+    )
+
+    assert _resolve_backend_entity_contract(cfg) == ("pelvis", True)
+
+    cfg.scene.entities = {
+        "first": EntityCfg(root_body_name="first"),
+        "second": EntityCfg(root_body_name="second"),
+    }
+    with pytest.raises(ValueError, match="conventional 'robot' root entity"):
+        _resolve_backend_entity_contract(cfg)
+
+
+def test_x2_factory_resolves_meshes_only_before_manager_construction(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    from unilab.tasks.motion_tracking import x2
+
+    calls: list[tuple[str, str]] = []
+    sentinel = object()
+    monkeypatch.setattr(
+        x2,
+        "resolve_robot_asset_dir",
+        lambda path, marker: calls.append((path, marker)),
+    )
+    monkeypatch.setattr(x2, "make_manager_based_rl_env", lambda *args, **kwargs: sentinel)
+
+    result = x2.make_x2_wall_flip_env(
+        ManagerBasedRlEnvCfg(),
+        num_envs=3,
+        backend_type="motrix",
+    )
+
+    assert result is sentinel
+    assert calls == [("robots/x2/meshes", "pelvis.STL")]
+
+
+def test_joint_acc_reset_updates_selected_rows_without_pairwise_indexing() -> None:
+    from unilab.managers import ManagerTermBaseCfg, SceneEntityCfg
+    from unilab.tasks.motion_tracking.g1.manager_terms import joint_acc_l2
+
+    velocity = np.arange(12, dtype=np.float32).reshape(4, 3)
+    entity = SimpleNamespace(num_joints=3, data=SimpleNamespace(joint_vel=velocity))
+    env = SimpleNamespace(num_envs=4, step_dt=0.02, scene={"robot": entity})
+    cfg = ManagerTermBaseCfg(
+        func=joint_acc_l2,
+        params={"asset_cfg": SceneEntityCfg("robot", joint_ids=[0, 2])},
+    )
+    term = joint_acc_l2(cfg, cast(Any, env))
+    velocity[[1, 3]] += 10.0
+
+    term.reset(np.array([1, 3], dtype=np.int32))
+
+    np.testing.assert_array_equal(term._previous[[1, 3]], velocity[np.ix_([1, 3], [0, 2])])
+    np.testing.assert_array_equal(term._previous[[0, 2]], np.array([[0.0, 2.0], [6.0, 8.0]]))
+
+
+@pytest.mark.parametrize(
+    ("config_root", "task", "identity", "backend", "actor_dim", "critic_dim", "action_dim"),
+    (
+        ("ppo", "g1_box_tracking", "G1BoxTracking", "mujoco", 154, 298, 29),
+        ("ppo", "g1_box_tracking", "G1BoxTracking", "motrix", 154, 298, 29),
+        ("ppo", "x2_wall_flip_tracking", "X2WallFlipTracking", "mujoco", 154, 430, 29),
+        ("ppo", "x2_wall_flip_tracking", "X2WallFlipTracking", "motrix", 154, 430, 29),
+        ("sac", "g1_wbt_obs", "G1WBTObs", "mujoco", 514, 289, 29),
+        ("sac", "g1_23dof_wbt_obs", "G1WBTObs23Dof", "mujoco", 412, 259, 23),
+    ),
+)
+def test_representative_motion_profiles_reset_and_step(
+    config_root: str,
+    task: str,
+    identity: str,
+    backend: str,
+    actor_dim: int,
+    critic_dim: int,
+    action_dim: int,
+) -> None:
+    if backend == "mujoco":
+        pytest.importorskip("mujoco")
+        try:
+            from mujoco_uni.batch_env import BatchEnvPool as _  # noqa: F401
+        except Exception:
+            pytest.skip("mujoco_uni.batch_env not available")
+    else:
+        pytest.importorskip("motrixsim")
+
+    _, _, override = _materialize_profile(config_root, task, backend, identity)
+    env = registry.make(identity, num_envs=2, sim_backend=backend, env_cfg_override=override)
+    try:
+        initial = env.init_state()
+        assert env.obs_groups_spec == {"obs": actor_dim, "critic": critic_dim}
+        assert initial.obs["obs"].shape == (2, actor_dim)
+        assert initial.obs["critic"].shape == (2, critic_dim)
+
+        state = env.step(np.zeros((2, action_dim), dtype=np.float32))
+        assert state.reward.shape == (2,)
+        assert np.isfinite(state.reward).all()
+        assert all(np.isfinite(value).all() for value in state.obs.values())
+    finally:
+        env.close()
diff --git a/tests/envs/test_motion_tracking_rewards.py b/tests/envs/test_motion_tracking_rewards.py
deleted file mode 100644
index c7a8fe20b..000000000
--- a/tests/envs/test_motion_tracking_rewards.py
+++ /dev/null
@@ -1,155 +0,0 @@
-"""Guard tests for the extracted motion-tracking reward module.
-
-These lock the ``common.rewards`` term functions and dispatch against
-hand-computed values on a small synthetic ``RewardContext``.
-"""
-
-from __future__ import annotations
-
-from types import SimpleNamespace
-
-import numpy as np
-
-from unilab.envs.motion_tracking.common import rewards
-from unilab.envs.motion_tracking.common.rewards import RewardConfig, RewardContext
-
-
-def _make_ctx(*, scales: dict[str, float] | None = None) -> RewardContext:
-    """Build a small deterministic RewardContext (2 envs, 3 bodies, 2 joints)."""
-    rng = np.random.default_rng(7)
-    num_envs, n_body, n_action = 2, 3, 2
-    dtype = np.float64
-
-    def rq(shape: tuple[int, ...]) -> np.ndarray:
-        q = rng.normal(size=(*shape, 4)).astype(dtype)
-        q /= np.linalg.norm(q, axis=-1, keepdims=True)
-        return q
-
-    reward_config = RewardConfig()
-    if scales is not None:
-        reward_config.scales = scales
-
-    motion_data = SimpleNamespace(
-        joint_pos=rng.normal(size=(num_envs, n_action)).astype(dtype),
-        joint_vel=rng.normal(size=(num_envs, n_action)).astype(dtype),
-        body_pos_w=rng.normal(size=(num_envs, n_body, 3)).astype(dtype),
-        body_quat_w=rq((num_envs, n_body)),
-        body_lin_vel_w=rng.normal(size=(num_envs, n_body, 3)).astype(dtype),
-        body_ang_vel_w=rng.normal(size=(num_envs, n_body, 3)).astype(dtype),
-    )
-
-    robot_body_pos_w = rng.normal(size=(num_envs, n_body, 3)).astype(dtype)
-    # Force a deterministic contact pattern for the undesired_contacts term.
-    robot_body_pos_w[:, :, 2] = np.array([[0.01, 0.20, 0.03], [0.10, 0.02, 0.30]], dtype=dtype)
-    undesired_idx = np.array([0, 1], dtype=np.int32)
-
-    current_actions = rng.normal(size=(num_envs, n_action)).astype(dtype)
-    last_actions = rng.normal(size=(num_envs, n_action)).astype(dtype)
-
-    return RewardContext(
-        info={
-            "current_actions": current_actions,
-            "last_actions": last_actions,
-            "steps": np.zeros((num_envs,), dtype=np.uint32),
-        },
-        motion_data=motion_data,
-        robot_body_pos_w=robot_body_pos_w,
-        robot_body_quat_w=rq((num_envs, n_body)),
-        robot_body_lin_vel_w=rng.normal(size=(num_envs, n_body, 3)).astype(dtype),
-        robot_body_ang_vel_w=rng.normal(size=(num_envs, n_body, 3)).astype(dtype),
-        ref_body_pos_w=rng.normal(size=(num_envs, n_body, 3)).astype(dtype),
-        ref_body_quat_w=rq((num_envs, n_body)),
-        dof_pos=rng.normal(size=(num_envs, n_action)).astype(dtype),
-        dof_vel=rng.normal(size=(num_envs, n_action)).astype(dtype),
-        reward_config=reward_config,
-        anchor_body_idx=0,
-        ee_body_indices=np.array([2], dtype=np.int32),
-        undesired_contact_body_indices=undesired_idx,
-        joint_lower=np.array([-0.5, -0.5], dtype=dtype),
-        joint_upper=np.array([0.5, 0.5], dtype=dtype),
-        undesired_contact_z_threshold=0.05,
-        num_envs=num_envs,
-        body_vec_error=np.empty((num_envs, n_body, 3), dtype=dtype),
-        joint_error=np.empty((num_envs, n_action), dtype=dtype),
-        joint_error_upper=np.empty((num_envs, n_action), dtype=dtype),
-        env_error=np.empty((num_envs,), dtype=dtype),
-        env_error2=np.empty((num_envs,), dtype=dtype),
-        reward_term=np.empty((num_envs,), dtype=dtype),
-        weighted_reward=np.empty((num_envs,), dtype=dtype),
-        quat_error_w=np.empty((num_envs, n_body), dtype=dtype),
-        quat_error_x=np.empty((num_envs, n_body), dtype=dtype),
-        ee_pos_error_z=np.empty((num_envs, 1), dtype=dtype),
-        undesired_contact_mask=np.empty((num_envs, undesired_idx.size), dtype=bool),
-    )
-
-
-def test_build_reward_functions_contains_all_canonical_terms():
-    assert set(rewards.build_reward_functions()) == {
-        "motion_global_root_pos",
-        "motion_global_root_ori",
-        "motion_body_pos",
-        "motion_body_ori",
-        "motion_body_lin_vel",
-        "motion_body_ang_vel",
-        "motion_ee_body_pos_z",
-        "motion_joint_pos",
-        "motion_joint_vel",
-        "action_rate_l2",
-        "joint_limit",
-        "undesired_contacts",
-    }
-
-
-def test_motion_joint_pos_term_matches_hand_computed():
-    ctx = _make_ctx()
-    out = rewards.motion_joint_pos(ctx).copy()
-    error = np.mean(np.square(ctx.motion_data.joint_pos - ctx.dof_pos), axis=1)
-    expected = np.exp(-error / ctx.reward_config.std_joint_pos**2)
-    np.testing.assert_allclose(out, expected, rtol=1e-12, atol=1e-12)
-
-
-def test_action_rate_l2_term_matches_hand_computed():
-    ctx = _make_ctx()
-    out = rewards.action_rate_l2(ctx).copy()
-    expected = np.sum(np.square(ctx.info["current_actions"] - ctx.info["last_actions"]), axis=1)
-    np.testing.assert_allclose(out, expected, rtol=1e-12, atol=1e-12)
-
-
-def test_undesired_contacts_term_matches_hand_computed():
-    ctx = _make_ctx()
-    out = rewards.undesired_contacts(ctx).copy()
-    idx = ctx.undesired_contact_body_indices
-    expected = np.sum(
-        ctx.robot_body_pos_w[:, idx, 2] < ctx.undesired_contact_z_threshold, axis=1
-    ).astype(np.float64)
-    np.testing.assert_allclose(out, expected, rtol=1e-12, atol=1e-12)
-
-
-def test_compute_reward_matches_hand_computed_weighted_sum():
-    scales = {
-        "motion_joint_pos": 1.0,
-        "action_rate_l2": -0.1,
-        "undesired_contacts": -0.5,
-        # zero-weighted terms are skipped by compute_reward
-        "motion_body_pos": 0.0,
-    }
-    ctrl_dt = 0.02
-    fns = rewards.build_reward_functions()
-
-    # Reference terms computed on an untouched ctx (fresh buffers per call).
-    joint_pos_ref = rewards.motion_joint_pos(_make_ctx()).copy()
-    action_rate_ref = rewards.action_rate_l2(_make_ctx()).copy()
-    undesired_ref = rewards.undesired_contacts(_make_ctx()).copy()
-
-    ctx = _make_ctx(scales=scales)
-    reward = rewards.compute_reward(
-        ctx,
-        active_reward_fns=fns,
-        all_reward_fns=fns,
-        scales=scales,
-        ctrl_dt=ctrl_dt,
-        enable_log=False,
-    ).copy()
-
-    expected = (1.0 * joint_pos_ref - 0.1 * action_rate_ref - 0.5 * undesired_ref) * ctrl_dt
-    np.testing.assert_allclose(reward, expected, rtol=1e-12, atol=1e-12)
diff --git a/tests/envs/test_observation_partial_reset.py b/tests/envs/test_observation_partial_reset.py
new file mode 100644
index 000000000..fddab62ee
--- /dev/null
+++ b/tests/envs/test_observation_partial_reset.py
@@ -0,0 +1,89 @@
+"""Row-scoped partial-reset observation rebuild tests (issue #1259 R2).
+
+On the partial-reset path the observation manager returns only the reset rows
+instead of a full batch that the env then slices. These tests pin the env-level
+contract:
+
+- reset observations arrive row-shaped, are finite, and are scattered into the
+  env state at exactly the reset rows;
+- untouched rows of ``state.obs`` keep their per-step values bit-identically;
+- a subsequent step recomputes full-batch observations normally.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import pytest
+from hydra import compose, initialize_config_dir
+from hydra.core.global_hydra import GlobalHydra
+
+from unilab.base import registry
+from unilab.base.config_adapter import BackendAdapter
+from unilab.base.config_materialization import apply_cfg_overrides
+from unilab.envs import ManagerBasedRlEnvCfg
+
+# CPU-bound on the single-core CI runner; kept in the slow lane (make test-slow).
+pytestmark = pytest.mark.slow
+
+_ROOT = Path(__file__).parents[2]
+
+
+def _make_env(config_root: str, task: str, backend: str, identity: str, num_envs: int):
+    registry.ensure_registries()
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(
+        config_dir=str(_ROOT / "src" / "unilab" / "conf" / config_root), version_base="1.3"
+    ):
+        owner = compose("config", overrides=[f"task={task}/{backend}"])
+    cfg = registry.materialize_env_config(identity)
+    assert isinstance(cfg, ManagerBasedRlEnvCfg)
+    override = BackendAdapter(
+        owner, root_dir=_ROOT, algo_name=config_root
+    ).build_task_env_cfg_override()
+    apply_cfg_overrides(cfg, override)
+    return registry.make(
+        identity, num_envs=num_envs, sim_backend=backend, env_cfg_override=override
+    )
+
+
+def test_observation_partial_reset_row_contract() -> None:
+    pytest.importorskip("mujoco")
+    try:
+        from mujoco_uni.batch_env import BatchEnvPool as _  # noqa: F401
+    except Exception:
+        pytest.skip("mujoco_uni.batch_env not available")
+
+    num_envs = 4
+    env = _make_env("sac", "g1_motion_tracking", "mujoco", "G1MotionTrackingSAC", num_envs)
+    try:
+        env.init_state()
+        action_dim = 29
+        rng = np.random.default_rng(7)
+        for _ in range(5):
+            env.step((0.1 * rng.standard_normal((num_envs, action_dim))).astype(np.float32))
+
+        assert env.state is not None
+        obs_before = {name: values.copy() for name, values in env.state.obs.items()}
+        reset_ids = np.array([0, 2], dtype=np.int32)
+        keep_ids = np.array([1, 3], dtype=np.int32)
+        reset_obs, _ = env.reset(env_ids=reset_ids)
+
+        for group, values in reset_obs.items():
+            assert values.shape == (len(reset_ids), obs_before[group].shape[1])
+            assert np.isfinite(values).all()
+            np.testing.assert_array_equal(env.state.obs[group][reset_ids], values)
+            np.testing.assert_array_equal(
+                env.state.obs[group][keep_ids],
+                obs_before[group][keep_ids],
+                err_msg=f"untouched rows changed for group {group}",
+            )
+
+        # The next per-step compute rebuilds full-batch observations normally.
+        env.step((0.1 * rng.standard_normal((num_envs, action_dim))).astype(np.float32))
+        for group, values in env.state.obs.items():
+            assert values.shape == obs_before[group].shape
+            assert np.isfinite(values).all()
+    finally:
+        env.close()
diff --git a/tests/envs/test_sharpa.py b/tests/envs/test_sharpa.py
index 2b46eae0c..55af028ae 100644
--- a/tests/envs/test_sharpa.py
+++ b/tests/envs/test_sharpa.py
@@ -12,24 +12,24 @@
 from omegaconf import OmegaConf
 
 from unilab.base.registry import ensure_registries
-from unilab.envs.manipulation.sharpa_inhand.base import (
+from unilab.tasks.manipulation.sharpa_inhand.base import (
     SOURCE_DEFAULT_HAND_JOINT_POS_DEG,
     SharpaInhandBaseEnv,
     resolve_grasp_cache_file,
 )
-from unilab.envs.manipulation.sharpa_inhand.rotation import (
+from unilab.tasks.manipulation.sharpa_inhand.rotation import (
     SharpaInhandRotationDRProvider,
     _materialize_grasp_caches,
 )
 
-_CONF_DIR = Path(__file__).resolve().parents[2] / "conf"
+_CONF_DIR = Path(__file__).resolve().parents[2] / "src" / "unilab" / "conf"
 _SRC_DIR = Path(__file__).resolve().parents[2] / "src"
 
 
 def test_sharpa_env_uses_backend_contract_for_mujoco_metadata() -> None:
     """Sharpa env code should not read MuJoCo model internals directly."""
     source = "\n".join(
-        (_SRC_DIR / "unilab" / "envs" / "manipulation" / "sharpa_inhand" / path).read_text(
+        (_SRC_DIR / "unilab" / "tasks" / "manipulation" / "sharpa_inhand" / path).read_text(
             encoding="utf-8"
         )
         for path in ("base.py", "rotation.py")
@@ -193,7 +193,7 @@ def test_sharpa_grasp_cache_is_materialized_once_and_reset_samples_memory(
     scale_values = np.asarray([0.8, 1.0], dtype=np.float64)
     _write_sharpa_grasp_cache(cache_prefix, [0.8, 1.0])
 
-    from unilab.envs.manipulation.sharpa_inhand import rotation
+    from unilab.tasks.manipulation.sharpa_inhand import rotation
 
     path_resolve_calls: list[tuple[str, float]] = []
     hf_resolve_calls: list[str] = []
diff --git a/tests/envs/test_stewart.py b/tests/envs/test_stewart.py
index a32c177c6..38041d418 100644
--- a/tests/envs/test_stewart.py
+++ b/tests/envs/test_stewart.py
@@ -1,116 +1,390 @@
+"""Hydra-owned production contract for the Stewart Manager-Based task."""
+
 from __future__ import annotations
 
+from collections.abc import Mapping, Sequence
+from dataclasses import fields, is_dataclass
 from pathlib import Path
+from typing import Any
 
 import numpy as np
 import pytest
 from hydra import compose, initialize_config_dir
 from hydra.core.global_hydra import GlobalHydra
-from omegaconf import OmegaConf
+from omegaconf import DictConfig, OmegaConf
 
 from unilab.base import registry
-from unilab.base.registry import ensure_registries
+from unilab.base.config_adapter import BackendAdapter
+from unilab.base.config_materialization import apply_cfg_overrides
+from unilab.envs import ManagerBasedRlEnv, ManagerBasedRlEnvCfg, make_manager_based_rl_env
+from unilab.managers import ObservationTermCfg, RewardTermCfg, TerminationTermCfg
+from unilab.tasks.manipulation.stewart.balance import (
+    StewartBalanceState,
+    StewartBallReset,
+    StewartObservation,
+    StewartTiltAction,
+    StewartTiltActionCfg,
+)
 
-_CONF_DIR = Path(__file__).resolve().parents[2] / "conf"
-_SRC_DIR = Path(__file__).resolve().parents[2] / "src"
+ROOT_DIR = Path(__file__).parents[2]
+CONF_DIR = ROOT_DIR / "src" / "unilab" / "conf"
 
-_OBS_DIM = 15
-_ACTION_DIM = 2
+_BODY_NAMES = (
+    "ball",
+    "top",
+    "leg00",
+    "leg10",
+    "leg01",
+    "leg11",
+    "leg02",
+    "leg12",
+    "top_connect00",
+    "top_connect10",
+    "top_connect01",
+    "top_connect11",
+    "top_connect02",
+    "top_connect12",
+)
+_ACTUATOR_NAMES = ("a0", "a1", "a2", "a3", "a4", "a5")
 
+_OWNER_CASES = (
+    pytest.param("ppo", ("task=stewart_balance/motrix",), "motrix", id="ppo-motrix"),
+    pytest.param("ppo", ("task=stewart_balance/mujoco",), "mujoco", id="ppo-mujoco"),
+    pytest.param("ppo", ("task=stewart_balance/drake",), "drake", id="ppo-drake"),
+    pytest.param(
+        "sac",
+        ("task=stewart_balance/drake",),
+        "drake",
+        id="sac-drake",
+    ),
+)
 
-def _make_env(num_envs: int = 2):
-    pytest.importorskip("motrixsim", reason="motrixsim not installed")
-    ensure_registries()
-    return registry.make("StewartBalance", sim_backend="motrix", num_envs=num_envs)
 
+def _compose(config_group: str, overrides: Sequence[str]) -> DictConfig:
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(config_dir=str(CONF_DIR / config_group), version_base="1.3"):
+        return compose("config", overrides=list(overrides))
 
-def test_stewart_env_uses_backend_contract() -> None:
-    """The task must go through the backend contract, not raw sim internals."""
-    source = (_SRC_DIR / "unilab" / "envs" / "manipulation" / "stewart" / "balance.py").read_text(
-        encoding="utf-8"
+
+def _materialize(
+    config_group: str,
+    overrides: Sequence[str],
+) -> tuple[DictConfig, ManagerBasedRlEnvCfg, dict[str, Any]]:
+    hydra_cfg = _compose(config_group, overrides)
+    env_override = BackendAdapter(hydra_cfg, root_dir=ROOT_DIR).build_task_env_cfg_override()
+    env_cfg = registry.materialize_env_config("StewartBalance")
+    assert isinstance(env_cfg, ManagerBasedRlEnvCfg)
+    apply_cfg_overrides(env_cfg, env_override)
+    env_cfg.validate()
+    return hydra_cfg, env_cfg, env_override
+
+
+def _make_env(backend: str, *, num_envs: int = 2) -> ManagerBasedRlEnv:
+    hydra_cfg, _, env_override = _materialize(
+        "ppo",
+        (f"task=stewart_balance/{backend}", f"algo.num_envs={num_envs}"),
     )
-    assert "import motrixsim" not in source
-    assert "import mujoco" not in source
-    assert "_backend.model" not in source
-
-
-def test_stewart_registered_backends() -> None:
-    ensure_registries()
-    registered = registry.list_registered_envs()
-    assert "StewartBalance" in registered
-    # motrix is the original validated backend; mujoco and drake are
-    # construct/step-capable comparison backends.
-    assert set(registered["StewartBalance"]["available_backends"]) == {
-        "drake",
-        "motrix",
-        "mujoco",
+    env = registry.make(
+        str(hydra_cfg.training.task_name),
+        sim_backend=backend,
+        env_cfg_override=env_override,
+        num_envs=num_envs,
+    )
+    assert isinstance(env, ManagerBasedRlEnv)
+    return env
+
+
+def _assert_no_omegaconf(value: Any) -> None:
+    assert not OmegaConf.is_config(value)
+    if is_dataclass(value) and not isinstance(value, type):
+        for item in fields(value):
+            _assert_no_omegaconf(getattr(value, item.name))
+    elif isinstance(value, Mapping):
+        for key, item in value.items():
+            _assert_no_omegaconf(key)
+            _assert_no_omegaconf(item)
+    elif isinstance(value, Sequence) and not isinstance(value, (str, bytes)):
+        for item in value:
+            _assert_no_omegaconf(item)
+
+
+@pytest.mark.parametrize("config_group,overrides,backend", _OWNER_CASES)
+def test_stewart_owner_materializes_complete_plain_manager_cfg(
+    config_group: str,
+    overrides: tuple[str, ...],
+    backend: str,
+) -> None:
+    registry.ensure_registries()
+    hydra_cfg, env_cfg, _ = _materialize(config_group, overrides)
+
+    assert hydra_cfg.training.task_name == "StewartBalance"
+    assert hydra_cfg.training.sim_backend == backend
+    assert env_cfg.sim_dt == pytest.approx(0.004)
+    assert env_cfg.ctrl_dt == pytest.approx(0.02)
+    assert env_cfg.max_episode_seconds == pytest.approx(24.0)
+    assert env_cfg.max_episode_steps == 1200
+    assert env_cfg.scale_rewards_by_dt is False
+    assert env_cfg.policy_observation_group == "policy"
+    assert env_cfg.critic_observation_group is None
+
+    assert env_cfg.scene is not None
+    assert env_cfg.scene.model_file.endswith("robots/stewart/scene.xml")
+    assert list(env_cfg.scene.entities) == ["stewart"]
+    entity = env_cfg.scene.entities["stewart"]
+    assert entity.root_body_name == "ball"
+    assert entity.joint_names is None
+    assert tuple(entity.actuator_names or ()) == _ACTUATOR_NAMES
+    assert tuple(entity.body_names or ()) == _BODY_NAMES
+
+    assert list(env_cfg.actions) == ["tilt"]
+    action_cfg = env_cfg.actions["tilt"]
+    assert isinstance(action_cfg, StewartTiltActionCfg)
+    assert action_cfg.entity_name == "stewart"
+    assert tuple(action_cfg.actuator_names) == _ACTUATOR_NAMES
+    assert action_cfg.target_rotation_limit_deg == pytest.approx(6.0)
+    assert action_cfg.action_smooth == pytest.approx(0.60)
+    assert action_cfg.center_control_radius == pytest.approx(0.25)
+    assert action_cfg.center_control_min_gain == pytest.approx(0.15)
+
+    assert list(env_cfg.observations) == ["policy"]
+    policy_group = env_cfg.observations["policy"]
+    assert policy_group is not None
+    assert list(policy_group.terms) == ["balance"]
+    observation_cfg = policy_group.terms["balance"]
+    assert isinstance(observation_cfg, ObservationTermCfg)
+    assert observation_cfg.func is StewartObservation
+    assert observation_cfg.params["vel_smooth"] == pytest.approx(0.25)
+
+    assert list(env_cfg.events) == ["reset_scene_to_default", "reset_ball"]
+    reset_ball = env_cfg.events["reset_ball"]
+    assert reset_ball is not None
+    assert reset_ball.func is StewartBallReset
+    assert reset_ball.params == {
+        "entity_name": "stewart",
+        "platform_radius": 0.8,
+        "init_ball_radius_ratio": 0.18,
+        "ball_home_z": 1.2,
+    }
+    assert list(env_cfg.terminations) == ["balance_state", "time_out"]
+    state_cfg = env_cfg.terminations["balance_state"]
+    assert isinstance(state_cfg, TerminationTermCfg)
+    assert state_cfg.func is StewartBalanceState
+    assert state_cfg.params["still_steps_needed"] == 5
+    time_out = env_cfg.terminations["time_out"]
+    assert time_out is not None
+    assert time_out.time_out is True
+
+    assert list(env_cfg.rewards) == ["center", "progress", "still", "fall"]
+    weights = {
+        name: term.weight
+        for name, term in env_cfg.rewards.items()
+        if isinstance(term, RewardTermCfg)
     }
+    assert weights == {"center": 0.7, "progress": 0.6, "still": 3.0, "fall": -6.0}
+
+    for manager_name in ("observations", "events", "rewards", "terminations"):
+        for manager_entry in getattr(env_cfg, manager_name).values():
+            if manager_entry is None:
+                continue
+            terms = (
+                manager_entry.terms.values() if manager_name == "observations" else (manager_entry,)
+            )
+            for term in terms:
+                if term is None:
+                    continue
+                module = term.func.__module__
+                assert ".backend." not in module
+                assert not any(name in module for name in (".mujoco", ".motrix", ".drake"))
+
+    _assert_no_omegaconf(env_cfg)
 
 
-def test_stewart_motrix_owner_cfg_composes() -> None:
-    if GlobalHydra().is_initialized():
-        GlobalHydra.instance().clear()
-    with initialize_config_dir(config_dir=str(_CONF_DIR / "ppo"), version_base="1.3"):
-        cfg = compose("config", overrides=["task=stewart_balance/motrix", "algo.num_envs=2"])
-    assert cfg.training.task_name == "StewartBalance"
-    assert cfg.training.sim_backend == "motrix"
-    # Reward block maps onto the env's StewartRewardConfig.
-    reward = OmegaConf.to_container(cfg.reward, resolve=True)
-    assert set(reward) == {"scales", "fall_penalty"}
-    assert set(reward["scales"]) == {"center", "progress", "still"}
-    assert cfg.algo.obs_groups.actor == ["policy"]
-
-
-def test_stewart_mujoco_owner_cfg_composes() -> None:
-    if GlobalHydra().is_initialized():
-        GlobalHydra.instance().clear()
-    with initialize_config_dir(config_dir=str(_CONF_DIR / "ppo"), version_base="1.3"):
-        cfg = compose("config", overrides=["task=stewart_balance/mujoco", "algo.num_envs=2"])
-    # Inherits the motrix owner config, only switching the backend.
-    assert cfg.training.task_name == "StewartBalance"
-    assert cfg.training.sim_backend == "mujoco"
-    assert cfg.algo.obs_groups.actor == ["policy"]
-
-
-def test_stewart_env_constructs_and_steps() -> None:
-    pytest.importorskip("motrixsim", reason="motrixsim not installed")
-    env = _make_env(num_envs=2)
-    assert env.obs_groups_spec == {"obs": _OBS_DIM}
-    assert env.action_space.shape == (_ACTION_DIM,)
-
-    state = None
-    for _ in range(20):
-        state = env.step(np.zeros((2, _ACTION_DIM), dtype=np.float32))
-    assert state is not None
-    obs = state.obs["obs"]
-    assert obs.shape == (2, _OBS_DIM)
-    assert np.isfinite(obs).all()
-    assert np.isfinite(state.reward).all()
-    assert state.terminated.dtype == bool
-
-
-def test_stewart_ik_holds_level_platform() -> None:
-    """At zero action the IK should hold the plate near its home height (z=1)."""
-    pytest.importorskip("motrixsim", reason="motrixsim not installed")
-    env = _make_env(num_envs=2)
-    env.step(np.zeros((2, _ACTION_DIM), dtype=np.float32))  # triggers reset + calibration
-    # Level-hold control should be ~zero leg displacement and ~1.1 m neutral legs.
-    np.testing.assert_allclose(env._leg0, 1.1, atol=1e-2)
-    ctrl = env._leg_ctrl_for_tilt(np.zeros((2, _ACTION_DIM), dtype=np.float32))
-    assert np.allclose(ctrl, 0.0, atol=1e-2)
-    for _ in range(20):
-        env.step(np.zeros((2, _ACTION_DIM), dtype=np.float32))
-    top_z = env._backend.get_body_pos_w(env._top_body_ids)[:, 0, 2]
-    assert np.all(np.abs(top_z - 1.0) < 0.1)
+def test_stewart_registry_is_manager_only_and_legacy_overrides_fail_closed() -> None:
+    registry.ensure_registries()
+    assert registry.list_registered_envs()["StewartBalance"] == {
+        "config_factory": "ManagerBasedRlEnvCfg",
+        "available_backends": ["mujoco", "motrix", "drake"],
+    }
+
+    for legacy_override in (
+        {"reward_config": {}},
+        {"platform_radius": 0.8},
+        {"action_smooth": 0.6},
+    ):
+        with pytest.raises(ValueError, match="has no attribute"):
+            apply_cfg_overrides(ManagerBasedRlEnvCfg(), legacy_override)
+
+
+def test_stewart_terms_do_not_access_physics_implementations() -> None:
+    source = (ROOT_DIR / "src/unilab/tasks/manipulation/stewart/balance.py").read_text(
+        encoding="utf-8"
+    )
+    for forbidden in (
+        "import mujoco",
+        "import motrixsim",
+        "create_backend",
+        "env_backend_kwargs",
+        "._backend",
+        "get_body_pos_w",
+        "get_body_quat_w",
+        "get_default_qpos",
+        "get_init_qvel",
+        "set_state(",
+    ):
+        assert forbidden not in source
+
+
+@pytest.mark.parametrize("backend", ("motrix", "mujoco"))
+def test_stewart_real_manager_runtime_preserves_io_reset_and_level_ik(backend: str) -> None:
+    try:
+        env = _make_env(backend, num_envs=2)
+    except ImportError as exc:
+        pytest.skip(f"{backend} runtime unavailable: {exc}")
+
+    try:
+        assert env.obs_groups_spec == {"obs": 15}
+        assert env.action_space.shape == (2,)
+        assert env.event_manager.active_terms == {"reset": ["reset_scene_to_default", "reset_ball"]}
+        obs, info = env.reset(seed=7)
+        assert {name: value.shape for name, value in obs.items()} == {"obs": (2, 15)}
+        assert isinstance(info, dict)
+        assert np.isfinite(obs["obs"]).all()
+
+        entity = env.scene["stewart"]
+        ball_id = entity.find_bodies("ball")[0][0]
+        top_id = entity.find_bodies("top")[0][0]
+        ball_pos = entity.data.body_link_pos_w[:, ball_id]
+        assert np.all(np.linalg.norm(ball_pos[:, :2], axis=-1) <= 0.8 * 0.18 + 1e-6)
+        np.testing.assert_allclose(ball_pos[:, 2], 1.2, atol=1e-6)
+
+        action = env.action_manager.get_term("tilt")
+        assert isinstance(action, StewartTiltAction)
+        np.testing.assert_allclose(action.neutral_leg_lengths, 1.1, atol=1e-4)
+        level_control = action.leg_control_for_tilt(np.zeros((2, 2), dtype=np.float32))
+        np.testing.assert_allclose(level_control, 0.0, atol=1e-4)
+
+        state = env.step(np.zeros((2, 2), dtype=np.float32))
+        for _ in range(19):
+            state = env.step(np.zeros((2, 2), dtype=np.float32))
+        assert state.obs["obs"].shape == (2, 15)
+        assert np.isfinite(state.obs["obs"]).all()
+        assert np.isfinite(state.reward).all()
+        assert state.terminated.dtype == np.bool_
+        top_z = entity.data.body_link_pos_w[:, top_id, 2]
+        assert np.all(np.abs(top_z - 1.0) < 0.1)
+    finally:
+        env.close()
+
+
+def test_stewart_action_smoothing_and_center_authority_match_legacy_equations() -> None:
+    try:
+        env = _make_env("motrix", num_envs=2)
+    except ImportError as exc:
+        pytest.skip(f"motrix runtime unavailable: {exc}")
+
+    try:
+        env.reset(seed=11)
+        action = env.action_manager.get_term("tilt")
+        observation = env.observation_manager.get_term_cfg("policy", "balance").func
+        assert isinstance(action, StewartTiltAction)
+        assert isinstance(observation, StewartObservation)
+
+        action.process_actions(np.full((2, 2), 2.0, dtype=np.float32))
+        np.testing.assert_allclose(action.executed_action, 0.6, atol=1e-6)
+        ratio = np.clip(observation.relative_xy / 0.25, 0.0, 1.0)
+        expected_gain = 0.15 + 0.85 * ratio
+        np.testing.assert_allclose(
+            action.target_tilt_deg,
+            np.broadcast_to(0.6 * expected_gain[:, None] * 6.0, (2, 2)),
+            atol=1e-6,
+        )
+
+        action.process_actions(np.ones((2, 2), dtype=np.float32))
+        np.testing.assert_allclose(action.executed_action, 0.84, atol=1e-6)
+        action.reset(np.array([1], dtype=np.int32))
+        np.testing.assert_allclose(action.executed_action[1], 0.0)
+        np.testing.assert_allclose(action.executed_action[0], 0.84)
+    finally:
+        env.close()
+
+
+def test_stewart_state_machine_and_fall_reward_are_exact() -> None:
+    try:
+        env = _make_env("motrix", num_envs=2)
+    except ImportError as exc:
+        pytest.skip(f"motrix runtime unavailable: {exc}")
+
+    try:
+        env.reset(seed=3)
+        state_term = env.termination_manager.get_term_cfg("balance_state").func
+        assert isinstance(state_term, StewartBalanceState)
+        state_term._previous_zero_velocity_xy[:] = 0.4
+        state_term._update(
+            np.array([0.2, 0.6], dtype=np.float32),
+            np.array([0.0, 0.2], dtype=np.float32),
+            np.array([[0.0, 0.0, 1.2], [0.0, 0.0, 1.2]], dtype=np.float32),
+        )
+        np.testing.assert_array_equal(state_term.fallen, [False, True])
+        np.testing.assert_allclose(state_term.center_score, [0.6, 0.0], atol=1e-6)
+        np.testing.assert_allclose(state_term.progress, [0.25, 0.0], atol=1e-6)
+
+        # Prove fall masking independently of the geometric scores themselves.
+        state_term.center_score[1] = 0.9
+        state_term.progress[1] = 0.5
+        state_term.success[1] = True
+        reward = env.reward_manager.compute(dt=env.step_dt)
+        assert reward[0] == pytest.approx(0.7 * 0.6 + 0.6 * 0.25)
+        # Positive terms are explicitly masked for fallen environments.
+        assert reward[1] == pytest.approx(-6.0)
+
+        state_term.fallen[:] = False
+        state_term.success[:] = False
+        state_term.still_steps[:] = 0
+        state_term.still_window_active[:] = False
+        state_term._previous_zero_velocity_xy[:] = 0.1
+        centered = np.full(2, 0.1, dtype=np.float32)
+        slow = np.full(2, 0.05, dtype=np.float32)
+        ball_pos = np.full((2, 3), (0.0, 0.0, 1.2), dtype=np.float32)
+        for _ in range(5):
+            state_term._update(centered, slow, ball_pos)
+        np.testing.assert_array_equal(state_term.still_steps, [5, 5])
+        np.testing.assert_array_equal(state_term.success, [True, True])
+    finally:
+        env.close()
+
+
+def test_stewart_drake_materializes_or_fails_at_optional_runtime_boundary() -> None:
+    _, env_cfg, _ = _materialize("ppo", ("task=stewart_balance/drake",))
+    try:
+        env = make_manager_based_rl_env(env_cfg, num_envs=1, backend_type="drake")
+    except (ImportError, NotImplementedError) as exc:
+        # The optional runtime can be absent, lack its native extension, or
+        # expose no floating-root layout yet. All are actionable boundary
+        # failures for this backend owner.
+        message = str(exc)
+        assert (
+            "DrakeUni batch runtime is not installed" in message
+            or "DrakeEnvPool batch extension has not been built" in message
+            or "does not expose root-state layout" in message
+        )
+        return
+    try:
+        obs, _ = env.reset(seed=5)
+        assert obs["obs"].shape == (1, 15)
+    finally:
+        env.close()
 
 
 @pytest.mark.slow
 def test_stewart_solver_stable_under_random_actions() -> None:
-    """Regression for the closed-loop solver blow-up (NotPositiveDefinite):
-    the fall-radius margin + softened actuator kp must keep it stable."""
-    pytest.importorskip("motrixsim", reason="motrixsim not installed")
-    env = _make_env(num_envs=8)
-    np.random.seed(0)
-    for _ in range(400):
-        state = env.step(np.random.uniform(-1.0, 1.0, (8, _ACTION_DIM)).astype(np.float32))
-        assert np.isfinite(state.obs["obs"]).all()
+    try:
+        env = _make_env("motrix", num_envs=8)
+    except ImportError as exc:
+        pytest.skip(f"motrix runtime unavailable: {exc}")
+    try:
+        rng = np.random.default_rng(0)
+        for _ in range(400):
+            state = env.step(rng.uniform(-1.0, 1.0, (8, 2)).astype(np.float32))
+            assert np.isfinite(state.obs["obs"]).all()
+    finally:
+        env.close()
diff --git a/tests/fixtures/__init__.py b/tests/fixtures/__init__.py
new file mode 100644
index 000000000..fae6e65c8
--- /dev/null
+++ b/tests/fixtures/__init__.py
@@ -0,0 +1 @@
+"""Runnable external-task migration fixtures."""
diff --git a/tests/fixtures/cartpole_manager_adapters.py b/tests/fixtures/cartpole_manager_adapters.py
new file mode 100644
index 000000000..9e49e0b67
--- /dev/null
+++ b/tests/fixtures/cartpole_manager_adapters.py
@@ -0,0 +1,160 @@
+# Derived from Isaac Lab b0542fe2d45bf91c4e1d9ef6952b9c709c80b4e8,
+# source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole.
+# Copyright (c) 2022-2026, The Isaac Lab Project Developers.
+# Modified by UniLab as test-only NumPy/entity adapters; BSD-3-Clause.
+"""Test-only action and reset adapters shared by pinned Cartpole fixtures."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from numbers import Real
+from typing import TYPE_CHECKING, cast
+
+import numpy as np
+
+from unilab.managers import ActionTerm, ActionTermCfg
+from unilab.managers.scene_entity_config import SceneEntityCfg
+
+if TYPE_CHECKING:
+    from unilab.base.entity import Entity
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+def finite_real(value: Real, *, label: str) -> float:
+    if isinstance(value, (bool, np.bool_)) or not isinstance(value, Real):
+        raise TypeError(f"{label} must be a real number")
+    result = float(value)
+    if not np.isfinite(result):
+        raise ValueError(f"{label} must be finite")
+    return result
+
+
+def numeric_range(value: tuple[float, float] | list[float], *, label: str) -> tuple[float, float]:
+    if isinstance(value, (str, bytes)) or not isinstance(value, (tuple, list)):
+        raise TypeError(f"{label} must be a two-value range")
+    if len(value) != 2:
+        raise ValueError(f"{label} must contain two values")
+    lower = finite_real(value[0], label=f"{label}[0]")
+    upper = finite_real(value[1], label=f"{label}[1]")
+    if lower > upper:
+        raise ValueError(f"{label} lower bound {lower} exceeds upper bound {upper}")
+    return lower, upper
+
+
+@dataclass(kw_only=True)
+class JointEffortActionCfg(ActionTermCfg):
+    """Fixture-only adapter for the community ``JointEffortActionCfg`` surface."""
+
+    actuator_names: tuple[str, ...] | list[str]
+    scale: float = 1.0
+
+    def build(self, env: ManagerBasedRlEnv) -> JointEffortAction:
+        return JointEffortAction(self, env)
+
+
+class JointEffortAction(ActionTerm):
+    """Scale policy actions and write entity-local actuator efforts."""
+
+    cfg: JointEffortActionCfg
+
+    def __init__(self, cfg: JointEffortActionCfg, env: ManagerBasedRlEnv):
+        super().__init__(cfg, env)
+        if cfg.clip is not None:
+            raise NotImplementedError("Cartpole fixture JointEffortAction does not support clip")
+        if isinstance(cfg.actuator_names, (str, bytes)) or not isinstance(
+            cfg.actuator_names, (tuple, list)
+        ):
+            raise TypeError("JointEffortActionCfg actuator_names must be an ordered sequence")
+        actuator_ids, actuator_names = self._entity.find_actuators(
+            cfg.actuator_names,
+            preserve_order=True,
+        )
+        if not actuator_ids:
+            raise ValueError(
+                "JointEffortActionCfg actuator_names resolved no actuators; "
+                f"patterns={list(cfg.actuator_names)}"
+            )
+        self._actuator_ids = np.asarray(actuator_ids, dtype=np.intp)
+        self._actuator_ids.setflags(write=False)
+        self._actuator_names = tuple(actuator_names)
+        self._scale = finite_real(cfg.scale, label="JointEffortActionCfg scale")
+        self._raw_actions = np.zeros((self.num_envs, len(actuator_ids)), dtype=np.float32)
+        self._processed_actions = np.zeros_like(self._raw_actions)
+
+    @property
+    def action_dim(self) -> int:
+        return self._raw_actions.shape[1]
+
+    @property
+    def raw_action(self) -> np.ndarray:
+        return self._raw_actions
+
+    def process_actions(self, actions: np.ndarray) -> None:
+        if not isinstance(actions, np.ndarray):
+            raise TypeError(
+                "Cartpole fixture JointEffortAction expected np.ndarray, "
+                f"received {type(actions).__name__}"
+            )
+        if actions.shape != self._raw_actions.shape:
+            raise ValueError(
+                "Cartpole fixture JointEffortAction expected shape "
+                f"{self._raw_actions.shape}, received {actions.shape}"
+            )
+        if not np.isfinite(actions).all():
+            raise ValueError("Cartpole fixture JointEffortAction received NaN or Inf")
+        np.copyto(self._raw_actions, actions)
+        np.multiply(actions, self._scale, out=self._processed_actions)
+
+    def apply_actions(self) -> None:
+        self._entity.data.write_ctrl(
+            self._processed_actions,
+            actuator_ids=self._actuator_ids,
+        )
+
+    def reset(self, env_ids: np.ndarray | slice | None = None) -> None:
+        ids = slice(None) if env_ids is None else env_ids
+        self._raw_actions[ids] = 0.0
+        self._processed_actions[ids] = 0.0
+
+
+def reset_joints_by_offset(
+    env: ManagerBasedRlEnv,
+    env_ids: np.ndarray | None,
+    position_range: tuple[float, float] | list[float],
+    velocity_range: tuple[float, float] | list[float],
+    asset_cfg: SceneEntityCfg,
+) -> None:
+    """Write uniformly offset joint defaults through the reset transaction."""
+    if env_ids is None:
+        raise ValueError("reset_joints_by_offset requires concrete environment IDs")
+    position_lower, position_upper = numeric_range(position_range, label="position_range")
+    velocity_lower, velocity_upper = numeric_range(velocity_range, label="velocity_range")
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    joint_ids = asset_cfg.joint_ids
+    default_position = asset.data.default_joint_pos[env_ids][:, joint_ids]
+    default_velocity = asset.data.default_joint_vel[env_ids][:, joint_ids]
+    position = default_position + env.rng.uniform(
+        position_lower,
+        position_upper,
+        default_position.shape,
+    )
+    velocity = default_velocity + env.rng.uniform(
+        velocity_lower,
+        velocity_upper,
+        default_velocity.shape,
+    )
+    asset.write_joint_state_to_sim(
+        position.astype(default_position.dtype, copy=False),
+        velocity.astype(default_velocity.dtype, copy=False),
+        joint_ids=joint_ids,
+        env_ids=env_ids,
+    )
+
+
+__all__ = [
+    "JointEffortAction",
+    "JointEffortActionCfg",
+    "finite_real",
+    "numeric_range",
+    "reset_joints_by_offset",
+]
diff --git a/tests/fixtures/isaac_lab_cartpole/README.md b/tests/fixtures/isaac_lab_cartpole/README.md
new file mode 100644
index 000000000..4e9d7a9b6
--- /dev/null
+++ b/tests/fixtures/isaac_lab_cartpole/README.md
@@ -0,0 +1,22 @@
+# Isaac Lab Cartpole migration fixture
+
+This test-only fixture is derived from Isaac Lab commit
+`b0542fe2d45bf91c4e1d9ef6952b9c709c80b4e8`, specifically its manager-based
+Cartpole config and `joint_pos_target_l2` reward. The source is BSD-3-Clause;
+the retained notice is in `task.py`.
+
+The fixture is evidence for #1042, not a production task or a claim that all
+Isaac Lab tasks run unchanged.
+
+| Surface | Status | Migration delta |
+| --- | --- | --- |
+| Manager/term names, `func + params`, dict order | Compatible | Imports change from `isaaclab` to `unilab`; all 12 terms keep their source names and order. |
+| Term math and buffers | Adapted | `torch.Tensor` and Torch ops become `np.ndarray` and NumPy ops. |
+| Config container | Adapted | Nested `@configclass` objects become one Hydra owner YAML, materialized as a plain `ManagerBasedRlEnvCfg`. |
+| Scene | Adapted | USD/`InteractiveSceneCfg` becomes a minimal task-owned MJCF plus `SceneCfg`/`EntityCfg`. |
+| Joint effort action | Adapted, fixture-local | A thin action adapter writes the entity control buffer; it is deliberately not exported as a public built-in. |
+| Reset mutation | Adapted, fixture-local | The same two reset terms write through the scoped entity reset transaction. |
+| PhysX, USD, Isaac renderer | Unsupported | No dependency or fallback is provided. |
+
+The fixture adds no production registry entry, public manager/backend/lifecycle
+contract, training-script branch, runner/IPC path, or external runtime dependency.
diff --git a/tests/fixtures/isaac_lab_cartpole/__init__.py b/tests/fixtures/isaac_lab_cartpole/__init__.py
new file mode 100644
index 000000000..93cde0fda
--- /dev/null
+++ b/tests/fixtures/isaac_lab_cartpole/__init__.py
@@ -0,0 +1,3 @@
+"""Isaac Lab Cartpole migration fixture."""
+
+from .task import FIXTURE_ENV_NAME as FIXTURE_ENV_NAME
diff --git a/tests/fixtures/isaac_lab_cartpole/cartpole.xml b/tests/fixtures/isaac_lab_cartpole/cartpole.xml
new file mode 100644
index 000000000..5a7d9e389
--- /dev/null
+++ b/tests/fixtures/isaac_lab_cartpole/cartpole.xml
@@ -0,0 +1,16 @@
+<mujoco model="isaac_lab_cartpole_fixture">
+  <option timestep="0.008333333333333333"/>
+  <worldbody>
+    <body name="cart" pos="0 0 1">
+      <joint name="slider_to_cart" type="slide" axis="1 0 0" limited="true" range="-4 4" damping="0.01"/>
+      <geom name="cart" type="box" size="0.2 0.15 0.1" mass="1"/>
+      <body name="pole" pos="0 0 0.1">
+        <joint name="cart_to_pole" type="hinge" axis="0 1 0" damping="0.00001"/>
+        <geom name="pole" type="capsule" fromto="0 0 0 0 0 1" size="0.04" mass="0.1"/>
+      </body>
+    </body>
+  </worldbody>
+  <actuator>
+    <motor name="slider_to_cart" joint="slider_to_cart" gear="1" ctrllimited="true" ctrlrange="-100 100"/>
+  </actuator>
+</mujoco>
diff --git a/tests/fixtures/isaac_lab_cartpole/conf/config.yaml b/tests/fixtures/isaac_lab_cartpole/conf/config.yaml
new file mode 100644
index 000000000..202b6ab83
--- /dev/null
+++ b/tests/fixtures/isaac_lab_cartpole/conf/config.yaml
@@ -0,0 +1,117 @@
+# Derived from Isaac Lab b0542fe2d45bf91c4e1d9ef6952b9c709c80b4e8.
+# Copyright (c) 2022-2026, The Isaac Lab Project Developers.
+# Modified by UniLab for Hydra and the fixture-local MJCF adapter; BSD-3-Clause.
+# Hydra is the fixture's only task-configuration owner. This declaration mirrors
+# Isaac Lab's CartpoleEnvCfg term names and insertion order.
+training:
+  task_name: IsaacLabCartpoleFixture
+  sim_backend: mujoco
+
+env:
+  scene:
+    model_file: tests/fixtures/isaac_lab_cartpole/cartpole.xml
+    entities:
+      robot:
+        root_body_name: cart
+        joint_names: [slider_to_cart, cart_to_pole]
+        actuator_names: [slider_to_cart]
+  sim_dt: 0.008333333333333333
+  ctrl_dt: 0.016666666666666666
+  max_episode_seconds: 5.0
+  seed: 7
+  observations:
+    policy:
+      _target_: unilab.managers.ObservationGroupCfg
+      enable_corruption: false
+      concatenate_terms: true
+      terms:
+        joint_pos_rel:
+          _target_: unilab.managers.ObservationTermCfg
+          func: unilab.envs.mdp.joint_pos_rel
+        joint_vel_rel:
+          _target_: unilab.managers.ObservationTermCfg
+          func: unilab.envs.mdp.joint_vel_rel
+  actions:
+    joint_effort:
+      _target_: tests.fixtures.isaac_lab_cartpole.task.JointEffortActionCfg
+      entity_name: robot
+      actuator_names: [slider_to_cart]
+      scale: 100.0
+  events:
+    reset_cart_position:
+      _target_: unilab.managers.EventTermCfg
+      func: tests.fixtures.isaac_lab_cartpole.task.reset_joints_by_offset
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+          joint_names: slider_to_cart
+        position_range: [-1.0, 1.0]
+        velocity_range: [-0.5, 0.5]
+    reset_pole_position:
+      _target_: unilab.managers.EventTermCfg
+      func: tests.fixtures.isaac_lab_cartpole.task.reset_joints_by_offset
+      mode: reset
+      params:
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+          joint_names: cart_to_pole
+        position_range: [-0.7853981633974483, 0.7853981633974483]
+        velocity_range: [-0.7853981633974483, 0.7853981633974483]
+  terminations:
+    time_out:
+      _target_: unilab.managers.TerminationTermCfg
+      func: unilab.envs.mdp.time_out
+      time_out: true
+    cart_out_of_bounds:
+      _target_: unilab.managers.TerminationTermCfg
+      func: tests.fixtures.isaac_lab_cartpole.task.joint_pos_out_of_manual_limit
+      params:
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: robot
+          joint_names: slider_to_cart
+        bounds: [-3.0, 3.0]
+  policy_observation_group: policy
+  critic_observation_group: null
+  scale_rewards_by_dt: true
+
+reward:
+  alive:
+    _target_: unilab.managers.RewardTermCfg
+    func: unilab.envs.mdp.is_alive
+    weight: 1.0
+  terminating:
+    _target_: unilab.managers.RewardTermCfg
+    func: unilab.envs.mdp.is_terminated
+    weight: -2.0
+  pole_pos:
+    _target_: unilab.managers.RewardTermCfg
+    func: tests.fixtures.isaac_lab_cartpole.task.joint_pos_target_l2
+    weight: -1.0
+    params:
+      asset_cfg:
+        _target_: unilab.managers.SceneEntityCfg
+        name: robot
+        joint_names: cart_to_pole
+      target: 0.0
+  cart_vel:
+    _target_: unilab.managers.RewardTermCfg
+    func: tests.fixtures.isaac_lab_cartpole.task.joint_vel_l1
+    weight: -0.01
+    params:
+      asset_cfg:
+        _target_: unilab.managers.SceneEntityCfg
+        name: robot
+        joint_names: slider_to_cart
+  pole_vel:
+    _target_: unilab.managers.RewardTermCfg
+    func: tests.fixtures.isaac_lab_cartpole.task.joint_vel_l1
+    weight: -0.005
+    params:
+      asset_cfg:
+        _target_: unilab.managers.SceneEntityCfg
+        name: robot
+        joint_names: cart_to_pole
diff --git a/tests/fixtures/isaac_lab_cartpole/task.py b/tests/fixtures/isaac_lab_cartpole/task.py
new file mode 100644
index 000000000..ffa912b43
--- /dev/null
+++ b/tests/fixtures/isaac_lab_cartpole/task.py
@@ -0,0 +1,91 @@
+# Derived from Isaac Lab b0542fe2d45bf91c4e1d9ef6952b9c709c80b4e8,
+# source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/cartpole.
+# Copyright (c) 2022-2026, The Isaac Lab Project Developers.
+# Modified by UniLab for NumPy and the fixture-local MJCF/entity adapter; BSD-3-Clause.
+"""NumPy terms for the Isaac Lab Cartpole migration fixture."""
+
+from __future__ import annotations
+
+import math
+from typing import TYPE_CHECKING, cast
+
+import numpy as np
+
+from tests.fixtures.cartpole_manager_adapters import (
+    JointEffortAction,
+    JointEffortActionCfg,
+    finite_real,
+    numeric_range,
+    reset_joints_by_offset,
+)
+from unilab.base import registry
+from unilab.envs import ManagerBasedRlEnvCfg, make_manager_based_rl_env
+from unilab.managers.scene_entity_config import SceneEntityCfg
+
+if TYPE_CHECKING:
+    from unilab.base.entity import Entity
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+FIXTURE_ENV_NAME = "IsaacLabCartpoleFixture"
+
+
+def joint_pos_target_l2(
+    env: ManagerBasedRlEnv,
+    target: float,
+    asset_cfg: SceneEntityCfg,
+) -> np.ndarray:
+    """Penalize wrapped joint-position deviation from a target value."""
+    target_value = finite_real(target, label="joint_pos_target_l2 target")
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    joint_pos = asset.data.joint_pos[:, asset_cfg.joint_ids]
+    wrapped = np.remainder(joint_pos + math.pi, 2.0 * math.pi) - math.pi
+    return np.sum(np.square(wrapped - target_value), axis=1)
+
+
+def joint_vel_l1(
+    env: ManagerBasedRlEnv,
+    asset_cfg: SceneEntityCfg,
+) -> np.ndarray:
+    """Penalize the absolute velocity of selected joints."""
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    return np.sum(np.abs(asset.data.joint_vel[:, asset_cfg.joint_ids]), axis=1)
+
+
+def joint_pos_out_of_manual_limit(
+    env: ManagerBasedRlEnv,
+    bounds: tuple[float, float] | list[float],
+    asset_cfg: SceneEntityCfg,
+) -> np.ndarray:
+    """Terminate when a selected joint leaves the configured manual bounds."""
+    lower, upper = numeric_range(bounds, label="joint_pos_out_of_manual_limit bounds")
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    joint_pos = asset.data.joint_pos[:, asset_cfg.joint_ids]
+    return np.any((joint_pos < lower) | (joint_pos > upper), axis=1)
+
+
+def register_fixture() -> None:
+    """Register the fixture without adding it to the production task package."""
+    if registry.contains(FIXTURE_ENV_NAME):
+        return
+    registry.register_env_config(FIXTURE_ENV_NAME, ManagerBasedRlEnvCfg)
+    registry.register_env(
+        FIXTURE_ENV_NAME,
+        make_manager_based_rl_env,
+        sim_backend="mujoco",
+    )
+
+
+register_fixture()
+
+
+__all__ = [
+    "FIXTURE_ENV_NAME",
+    "JointEffortAction",
+    "JointEffortActionCfg",
+    "joint_pos_out_of_manual_limit",
+    "joint_pos_target_l2",
+    "joint_vel_l1",
+    "register_fixture",
+    "reset_joints_by_offset",
+]
diff --git a/tests/fixtures/mjlab_cartpole/README.md b/tests/fixtures/mjlab_cartpole/README.md
new file mode 100644
index 000000000..725acf994
--- /dev/null
+++ b/tests/fixtures/mjlab_cartpole/README.md
@@ -0,0 +1,23 @@
+# mjlab Cartpole migration fixture
+
+This test-only fixture is derived from mjlab v1.6.0 commit
+`0fb8a681136be94ffc636a3dd423cabb97d91f10`, specifically
+`src/mjlab/tasks/cartpole/cartpole_env_cfg.py` and `cartpole.xml`. The source is
+Apache-2.0; provenance is retained in the derived files.
+
+| Surface | Status | Migration delta |
+| --- | --- | --- |
+| Manager dictionaries, term names/order, `func + params` | Compatible | Imports change from `mjlab` to `unilab`; the Balance task keeps all source entries and ordering. |
+| Term math and buffers | Adapted | `torch.Tensor` and Torch ops become `np.ndarray` and NumPy ops. |
+| Config container | Adapted | The Python config factory becomes one Hydra owner YAML, materialized as a plain `ManagerBasedRlEnvCfg`. |
+| Scene/simulation | Adapted | mjlab scene/sim/viewer objects become task-owned MJCF plus `SceneCfg`/`EntityCfg`; the source contact-disable setting is embedded and visual materials are inlined for replicated scenes. |
+| Joint effort and reset mutation | Adapted, fixture-local | Shared test-only adapters write through the entity control/reset contracts; they are not public built-ins. |
+| mjlab runner, viewer, Torch/Warp runtime | Unsupported | No dependency or fallback is provided. |
+
+This fixture is evidence for #1042, not a production registration or a claim
+that arbitrary mjlab tasks run unchanged.
+
+`conf/stage_curriculum.yaml` is not part of the pinned migration surface: it
+is the declarative owner-YAML demo for the generic stage-based curriculum
+terms (`unilab.envs.mdp.reward_curriculum` / `termination_curriculum`,
+issue #1397), reusing this task with one ladder per public term.
diff --git a/tests/fixtures/mjlab_cartpole/__init__.py b/tests/fixtures/mjlab_cartpole/__init__.py
new file mode 100644
index 000000000..4a64a6eab
--- /dev/null
+++ b/tests/fixtures/mjlab_cartpole/__init__.py
@@ -0,0 +1,3 @@
+"""Pinned mjlab Cartpole migration fixture."""
+
+from tests.fixtures.mjlab_cartpole.task import FIXTURE_ENV_NAME as FIXTURE_ENV_NAME
diff --git a/tests/fixtures/mjlab_cartpole/cartpole.xml b/tests/fixtures/mjlab_cartpole/cartpole.xml
new file mode 100644
index 000000000..0164d63ac
--- /dev/null
+++ b/tests/fixtures/mjlab_cartpole/cartpole.xml
@@ -0,0 +1,31 @@
+<!-- Derived from mujocolab/mjlab 0fb8a681, src/mjlab/tasks/cartpole/cartpole.xml.
+     Copyright 2025, The mjlab Developers. Modified by UniLab to embed the
+     source SimulationCfg contact flag and inline replicated visual colors;
+     Apache-2.0. -->
+<mujoco model="mjlab_cartpole_fixture">
+  <option timestep="0.01">
+    <flag contact="disable"/>
+  </option>
+  <default>
+    <default class="pole">
+      <joint type="hinge" axis="0 1 0" damping="2e-6"/>
+      <geom type="capsule" fromto="0 0 0 0 0 1" size="0.045" rgba=".1 .5 .8 1" mass=".1"/>
+    </default>
+  </default>
+  <worldbody>
+    <geom name="floor" pos="0 0 -.05" size="4 4 .2" type="plane"/>
+    <geom name="rail1" type="capsule" pos="0 .07 1" zaxis="1 0 0" size="0.02 2" rgba=".3 .3 .3 1"/>
+    <geom name="rail2" type="capsule" pos="0 -.07 1" zaxis="1 0 0" size="0.02 2" rgba=".3 .3 .3 1"/>
+    <body name="cart" pos="0 0 1">
+      <joint name="slider" type="slide" limited="true" axis="1 0 0" range="-1.8 1.8" solreflimit=".08 1" damping="5e-4"/>
+      <geom name="cart" type="box" size="0.2 0.15 0.1" rgba=".7 .1 .1 1" mass="1"/>
+      <body name="pole_1" childclass="pole">
+        <joint name="hinge_1"/>
+        <geom name="pole_1"/>
+      </body>
+    </body>
+  </worldbody>
+  <actuator>
+    <motor name="slide" joint="slider" gear="10" ctrllimited="true" ctrlrange="-1 1"/>
+  </actuator>
+</mujoco>
diff --git a/tests/fixtures/mjlab_cartpole/conf/config.yaml b/tests/fixtures/mjlab_cartpole/conf/config.yaml
new file mode 100644
index 000000000..0facd98aa
--- /dev/null
+++ b/tests/fixtures/mjlab_cartpole/conf/config.yaml
@@ -0,0 +1,109 @@
+# Derived from mujocolab/mjlab 0fb8a681,
+# src/mjlab/tasks/cartpole/cartpole_env_cfg.py; Apache-2.0.
+# Hydra is the fixture's only task-configuration owner.
+training:
+  task_name: MjlabCartpoleBalanceFixture
+  sim_backend: mujoco
+
+env:
+  scene:
+    model_file: tests/fixtures/mjlab_cartpole/cartpole.xml
+    entities:
+      cartpole:
+        root_body_name: cart
+        joint_names: [slider, hinge_1]
+        actuator_names: [slide]
+  sim_dt: 0.01
+  ctrl_dt: 0.05
+  max_episode_seconds: 50.0
+  seed: 13
+  observations:
+    actor:
+      _target_: unilab.managers.ObservationGroupCfg
+      enable_corruption: true
+      terms:
+        cart_pos: &cart_pos
+          _target_: unilab.managers.ObservationTermCfg
+          func: unilab.envs.mdp.joint_pos_rel
+          params:
+            asset_cfg: &cart_cfg
+              _target_: unilab.managers.SceneEntityCfg
+              name: cartpole
+              joint_names: [slider]
+        pole_angle: &pole_angle
+          _target_: unilab.managers.ObservationTermCfg
+          func: tests.fixtures.mjlab_cartpole.task.pole_angle_cos_sin
+          params:
+            asset_cfg: &hinge_cfg
+              _target_: unilab.managers.SceneEntityCfg
+              name: cartpole
+              joint_names: [hinge_1]
+        cart_vel: &cart_vel
+          _target_: unilab.managers.ObservationTermCfg
+          func: unilab.envs.mdp.joint_vel_rel
+          params:
+            asset_cfg: *cart_cfg
+        pole_vel: &pole_vel
+          _target_: unilab.managers.ObservationTermCfg
+          func: unilab.envs.mdp.joint_vel_rel
+          params:
+            asset_cfg: *hinge_cfg
+    critic:
+      _target_: unilab.managers.ObservationGroupCfg
+      terms:
+        cart_pos: *cart_pos
+        pole_angle: *pole_angle
+        cart_vel: *cart_vel
+        pole_vel: *pole_vel
+  actions:
+    effort:
+      _target_: tests.fixtures.cartpole_manager_adapters.JointEffortActionCfg
+      entity_name: cartpole
+      actuator_names: [slide]
+      scale: 1.0
+  events:
+    reset_slider:
+      _target_: unilab.managers.EventTermCfg
+      func: tests.fixtures.cartpole_manager_adapters.reset_joints_by_offset
+      mode: reset
+      params:
+        position_range: [-0.1, 0.1]
+        velocity_range: [-0.01, 0.01]
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: cartpole
+          joint_names: [slider]
+    reset_hinge:
+      _target_: unilab.managers.EventTermCfg
+      func: tests.fixtures.cartpole_manager_adapters.reset_joints_by_offset
+      mode: reset
+      params:
+        position_range: [-0.034, 0.034]
+        velocity_range: [-0.01, 0.01]
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: cartpole
+          joint_names: [hinge_1]
+  terminations:
+    time_out:
+      _target_: unilab.managers.TerminationTermCfg
+      func: unilab.envs.mdp.time_out
+      time_out: true
+  policy_observation_group: actor
+  critic_observation_group: critic
+  scale_rewards_by_dt: true
+
+reward:
+  smooth_reward:
+    _target_: unilab.managers.RewardTermCfg
+    func: tests.fixtures.mjlab_cartpole.task.cartpole_smooth_reward
+    weight: 1.0
+    params:
+      cart_cfg:
+        _target_: unilab.managers.SceneEntityCfg
+        name: cartpole
+        joint_names: [slider]
+      hinge_cfg:
+        _target_: unilab.managers.SceneEntityCfg
+        name: cartpole
+        joint_names: [hinge_1]
diff --git a/tests/fixtures/mjlab_cartpole/conf/stage_curriculum.yaml b/tests/fixtures/mjlab_cartpole/conf/stage_curriculum.yaml
new file mode 100644
index 000000000..1e9877e43
--- /dev/null
+++ b/tests/fixtures/mjlab_cartpole/conf/stage_curriculum.yaml
@@ -0,0 +1,38 @@
+# Demo owner YAML for the generic stage-based curriculum terms (issue #1397).
+# Reuses the pinned cartpole Balance task and adds one declarative ladder per
+# public term, driven by env.common_step_counter: a reward weight ramp and a
+# termination params ramp. Stage steps are tiny so short runs exercise the
+# trigger path; production tasks pick their own schedules.
+# Compose with config_name="stage_curriculum".
+defaults:
+  - config
+  - _self_
+
+env:
+  terminations:
+    pole_tilt:
+      _target_: unilab.managers.TerminationTermCfg
+      func: unilab.envs.mdp.bad_orientation
+      params:
+        limit_angle: 0.4
+        asset_cfg:
+          _target_: unilab.managers.SceneEntityCfg
+          name: cartpole
+  curriculum:
+    smooth_reward_weight:
+      _target_: unilab.managers.CurriculumTermCfg
+      func: unilab.envs.mdp.reward_curriculum
+      params:
+        reward_name: smooth_reward
+        stages:
+          - {step: 0, weight: 1.0}
+          - {step: 2, weight: 0.5}
+          - {step: 4, weight: 0.25}
+    pole_tilt_limit:
+      _target_: unilab.managers.CurriculumTermCfg
+      func: unilab.envs.mdp.termination_curriculum
+      params:
+        termination_name: pole_tilt
+        stages:
+          - {step: 0, params: {limit_angle: 0.4}}
+          - {step: 2, params: {limit_angle: 0.2}}
diff --git a/tests/fixtures/mjlab_cartpole/task.py b/tests/fixtures/mjlab_cartpole/task.py
new file mode 100644
index 000000000..47a79365d
--- /dev/null
+++ b/tests/fixtures/mjlab_cartpole/task.py
@@ -0,0 +1,83 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681),
+# src/mjlab/tasks/cartpole/cartpole_env_cfg.py.
+# Copyright 2025, The mjlab Developers.
+# Modified by UniLab for NumPy and fixture-only Hydra/entity adapters; Apache-2.0.
+"""NumPy terms for the pinned mjlab Cartpole Balance fixture."""
+
+from __future__ import annotations
+
+import math
+from typing import TYPE_CHECKING, cast
+
+import numpy as np
+
+from unilab.base import registry
+from unilab.envs import ManagerBasedRlEnvCfg, make_manager_based_rl_env
+from unilab.managers import SceneEntityCfg
+
+if TYPE_CHECKING:
+    from unilab.base.entity import Entity
+    from unilab.managers._types import ManagerBasedRlEnv
+
+
+FIXTURE_ENV_NAME = "MjlabCartpoleBalanceFixture"
+_GAUSSIAN_SCALE = math.sqrt(-2.0 * math.log(0.1))
+_QUADRATIC_SCALE = math.sqrt(1.0 - 0.1)
+
+
+def pole_angle_cos_sin(env: ManagerBasedRlEnv, asset_cfg: SceneEntityCfg) -> np.ndarray:
+    """Return cosine and sine of the selected pole angle."""
+    asset = cast("Entity", env.scene[asset_cfg.name])
+    angle = asset.data.joint_pos[:, asset_cfg.joint_ids]
+    return np.concatenate((np.cos(angle), np.sin(angle)), axis=-1)
+
+
+def _gaussian_tolerance(x: np.ndarray, margin: float) -> np.ndarray:
+    if margin == 0.0:
+        return (x == 0.0).astype(np.float32)
+    scaled = x / margin * _GAUSSIAN_SCALE
+    return np.exp(-0.5 * np.square(scaled))
+
+
+def _quadratic_tolerance(x: np.ndarray, margin: float) -> np.ndarray:
+    if margin == 0.0:
+        return (x == 0.0).astype(np.float32)
+    scaled = x / margin * _QUADRATIC_SCALE
+    return np.maximum(1.0 - np.square(scaled), 0.0)
+
+
+def cartpole_smooth_reward(
+    env: ManagerBasedRlEnv,
+    cart_cfg: SceneEntityCfg,
+    hinge_cfg: SceneEntityCfg,
+) -> np.ndarray:
+    """Port mjlab's dm_control-style smooth Cartpole reward to NumPy."""
+    asset = cast("Entity", env.scene[cart_cfg.name])
+    hinge_angle = asset.data.joint_pos[:, hinge_cfg.joint_ids].squeeze(-1)
+    upright = (np.cos(hinge_angle) + 1.0) / 2.0
+    cart_pos = asset.data.joint_pos[:, cart_cfg.joint_ids].squeeze(-1)
+    centered = (1.0 + _gaussian_tolerance(cart_pos, margin=2.0)) / 2.0
+    control = env.action_manager.action.squeeze(-1)
+    small_control = (4.0 + _quadratic_tolerance(control, margin=1.0)) / 5.0
+    hinge_vel = asset.data.joint_vel[:, hinge_cfg.joint_ids].squeeze(-1)
+    small_velocity = (1.0 + _gaussian_tolerance(hinge_vel, margin=5.0)) / 2.0
+    return upright * centered * small_control * small_velocity
+
+
+def register_fixture() -> None:
+    """Register this fixture without adding it to production bootstrap."""
+    if registry.contains(FIXTURE_ENV_NAME):
+        return
+    registry.register_env_config(FIXTURE_ENV_NAME, ManagerBasedRlEnvCfg)
+    registry.register_env(FIXTURE_ENV_NAME, make_manager_based_rl_env, sim_backend="mujoco")
+
+
+register_fixture()
+
+
+__all__ = [
+    "FIXTURE_ENV_NAME",
+    "cartpole_smooth_reward",
+    "pole_angle_cos_sin",
+    "register_fixture",
+]
diff --git a/tests/integration/test_appo_rsl_reward.py b/tests/integration/test_appo_rsl_reward.py
deleted file mode 100644
index cecefdbc3..000000000
--- a/tests/integration/test_appo_rsl_reward.py
+++ /dev/null
@@ -1,51 +0,0 @@
-"""Test APPO reward injection."""
-
-import pytest
-
-
-def test_appo_reward_override():
-    """Test APPO with reward override."""
-    from unilab.base import registry
-    from unilab.base.registry import ensure_registries
-
-    ensure_registries()
-
-    reward_dict = {
-        "scales": {"tracking_lin_vel": 888.0},
-        "tracking_sigma": 0.3,
-        "base_height_target": 0.35,
-    }
-
-    env = registry.make(
-        "Go1JoystickFlat",
-        num_envs=2,
-        sim_backend="mujoco",
-        env_cfg_override={"reward_config": reward_dict},
-    )
-
-    assert env._cfg.reward_config.scales["tracking_lin_vel"] == 888.0
-    env.close()
-
-
-def test_rsl_rl_reward_override():
-    """Test RSL-RL with reward override."""
-    from unilab.base import registry
-    from unilab.base.registry import ensure_registries
-
-    ensure_registries()
-
-    reward_dict = {
-        "scales": {"tracking_lin_vel": 777.0},
-        "tracking_sigma": 0.2,
-        "base_height_target": 0.32,
-    }
-
-    env = registry.make(
-        "Go1JoystickFlat",
-        num_envs=2,
-        sim_backend="mujoco",
-        env_cfg_override={"reward_config": reward_dict},
-    )
-
-    assert env._cfg.reward_config.scales["tracking_lin_vel"] == 777.0
-    env.close()
diff --git a/tests/integration/test_reward_injection_integration.py b/tests/integration/test_reward_injection_integration.py
index 7b5c7ae96..f7d6614fd 100644
--- a/tests/integration/test_reward_injection_integration.py
+++ b/tests/integration/test_reward_injection_integration.py
@@ -1,21 +1,17 @@
 """Integration test for reward config injection in training."""
 
-from typing import Any, cast
-
-import numpy as np
-import pytest
-
 
 def test_reward_injection_in_offpolicy_env_override():
     """Test reward config is injected without requiring accelerator hardware."""
     from hydra import compose, initialize
-    from scripts.train_offpolicy import build_offpolicy_env_cfg_override
 
-    with initialize(config_path="../../conf/offpolicy", version_base="1.3"):
+    from unilab.scripts.train_offpolicy import build_offpolicy_env_cfg_override
+
+    with initialize(config_path="../../src/unilab/conf/sac", version_base="1.3"):
         cfg = compose(
             config_name="config",
             overrides=[
-                "task=sac/g1_walk_flat/mujoco",
+                "task=g1_walk_flat/mujoco",
                 "algo.max_iterations=1",
                 "algo.num_envs=64",
                 "training.no_play=true",
@@ -26,109 +22,9 @@ def test_reward_injection_in_offpolicy_env_override():
         env_cfg_override = build_offpolicy_env_cfg_override("sac", cfg)
 
         assert env_cfg_override is not None
-        assert "reward_config" in env_cfg_override
-
-        # Verify reward config dict has correct values
-        reward_dict = env_cfg_override["reward_config"]
-        assert reward_dict["scales"]["tracking_lin_vel"] == 2.0
-        assert reward_dict["scales"]["alive"] == 10.0
-
-
-def test_reward_override_propagation():
-    """Test reward override propagates through multiprocess collector."""
-    from unilab.base import registry
-    from unilab.base.registry import ensure_registries
-    from unilab.envs.locomotion.go1.joystick import RewardConfig
-
-    ensure_registries()
-
-    # Create custom reward config
-    custom_config = RewardConfig(
-        scales={
-            "tracking_lin_vel": 5.0,
-            "tracking_ang_vel": 0.5,
-            "lin_vel_z": -10.0,
-        },
-        tracking_sigma=0.5,
-        base_height_target=0.4,
-    )
-
-    # Create env with override
-    env = cast(
-        Any,
-        registry.make(
-            "Go1JoystickFlat",
-            num_envs=4,
-            sim_backend="mujoco",
-            env_cfg_override={"reward_config": custom_config},
-        ),
-    )
-
-    # Verify override was applied
-    assert env._cfg.reward_config.scales["tracking_lin_vel"] == 5.0
-    assert env._cfg.reward_config.tracking_sigma == 0.5
-
-    # Test reward computation uses overridden scales
-    env.init_state()
-    env.reset(np.array([0, 1, 2, 3], dtype=np.int32))
-
-    # Take a step and verify reward is computed
-    actions = np.zeros((4, env.action_space.shape[0]), dtype=np.float32)
-    state = env.step(actions)
-
-    assert state.reward is not None
-    assert len(state.reward) == 4
-
-    env.close()
-
-
-def test_backward_compatibility_no_reward_config():
-    """Test env requires reward config - should fail without it."""
-    from unilab.base import registry
-    from unilab.base.registry import ensure_registries
-
-    ensure_registries()
-
-    # Should fail without reward_config
-    with pytest.raises(ValueError, match="reward_config must be provided"):
-        registry.make(
-            "Go1JoystickFlat",
-            num_envs=2,
-            sim_backend="mujoco",
-        )
-
-
-def test_zero_scale_skips_computation():
-    """Test that reward functions with scale=0 are skipped."""
-    from unilab.base import registry
-    from unilab.base.registry import ensure_registries
-    from unilab.envs.locomotion.go1.joystick import RewardConfig
-
-    ensure_registries()
-
-    # Set all scales to 0 except one
-    custom_config = RewardConfig(
-        scales={
-            "tracking_lin_vel": 1.0,
-            "tracking_ang_vel": 0.0,  # Should be skipped
-            "lin_vel_z": 0.0,  # Should be skipped
-        },
-        tracking_sigma=0.25,
-        base_height_target=0.3,
-    )
-
-    env = cast(
-        Any,
-        registry.make(
-            "Go1JoystickFlat",
-            num_envs=2,
-            sim_backend="mujoco",
-            env_cfg_override={"reward_config": custom_config},
-        ),
-    )
-
-    # Verify only non-zero scales are in config
-    assert env._cfg.reward_config.scales["tracking_lin_vel"] == 1.0
-    assert env._cfg.reward_config.scales["tracking_ang_vel"] == 0.0
+        assert "rewards" in env_cfg_override
 
-    env.close()
+        # Verify reward term mapping has correct values
+        reward_dict = env_cfg_override["rewards"]
+        assert reward_dict["tracking_lin_vel"]["weight"] == 2.0
+        assert reward_dict["alive"]["weight"] == 10.0
diff --git a/tests/ipc/test_async_runner.py b/tests/ipc/test_async_runner.py
deleted file mode 100644
index 0661c1162..000000000
--- a/tests/ipc/test_async_runner.py
+++ /dev/null
@@ -1,300 +0,0 @@
-"""Tests for AsyncRunner base class."""
-
-from __future__ import annotations
-
-import multiprocessing as mp
-import signal
-import time
-from typing import Any
-from unittest.mock import MagicMock
-
-import pytest
-
-import unilab.ipc.async_runner as async_runner_module
-from unilab.ipc.async_runner import AsyncRunner
-from unilab.ipc.collector_error import format_collector_death
-
-_SPAWN_CTX = mp.get_context("spawn")
-
-
-# ---------------------------------------------------------------------------
-# Minimal concrete implementation for testing
-# ---------------------------------------------------------------------------
-
-
-class _StubRunner(AsyncRunner):
-    """Minimal concrete AsyncRunner — used only for unit tests."""
-
-    def _get_default_device(self) -> str:
-        return "cpu"
-
-    def _build_learner(self) -> Any:
-        return None
-
-    def learn(self, max_iterations: int, save_interval: int = 50, log_dir: str = "logs") -> None:
-        pass
-
-
-def _make_runner(rl_cfg=None, **kwargs) -> _StubRunner:
-    return _StubRunner(
-        env_name="DummyEnv",
-        env_cfg_overrides={},
-        rl_cfg=rl_cfg or {},
-        **kwargs,
-    )
-
-
-class _InlineProcess:
-    """Process test double for contract checks that do not need OS spawn."""
-
-    def __init__(self, target, args=(), kwargs=None, daemon=None):
-        self._target = target
-        self._args = args
-        self._kwargs = kwargs or {}
-        self.daemon = daemon
-        self.exitcode = None
-        self.started = False
-
-    def start(self) -> None:
-        self.started = True
-        try:
-            self._target(*self._args, **self._kwargs)
-        except BaseException:
-            self.exitcode = 1
-            raise
-        self.exitcode = 0
-
-    def is_alive(self) -> bool:
-        return False
-
-    def join(self, timeout=None) -> None:
-        return None
-
-    def terminate(self) -> None:
-        self.exitcode = -signal.SIGTERM
-
-
-# ---------------------------------------------------------------------------
-# Initialisation
-# ---------------------------------------------------------------------------
-
-
-def test_init_stores_env_name():
-    r = _make_runner()
-    assert r.env_name == "DummyEnv"
-
-
-def test_init_stores_rl_cfg():
-    cfg = {"gamma": 0.99}
-    r = _make_runner(rl_cfg=cfg)
-    assert r.rl_cfg == cfg
-
-
-def test_init_device_explicit():
-    r = _make_runner(device="cpu")
-    assert r.device == "cpu"
-
-
-def test_init_device_default():
-    """When device=None, uses _get_default_device() which returns 'cpu' for stub."""
-    r = _make_runner()
-    assert r.device == "cpu"
-
-
-def test_init_collector_device_defaults_to_device():
-    r = _make_runner(device="cpu")
-    assert r.collector_device == "cpu"
-
-
-def test_init_collector_device_explicit():
-    r = _make_runner(device="cpu", collector_device="cpu")
-    assert r.collector_device == "cpu"
-
-
-def test_init_sim_backend_explicit():
-    r = _make_runner(sim_backend="motrix")
-    assert r.sim_backend == "motrix"
-
-
-def test_init_num_envs():
-    r = _make_runner(num_envs=64)
-    assert r.num_envs == 64
-
-
-def test_init_shared_resources_empty():
-    r = _make_runner()
-    assert r._shared_resources == []
-
-
-def test_init_collector_process_none():
-    r = _make_runner()
-    assert r._collector_process is None
-
-
-# ---------------------------------------------------------------------------
-# close() — no collector
-# ---------------------------------------------------------------------------
-
-
-def test_close_with_no_collector_does_not_raise():
-    r = _make_runner()
-    r.close()  # _collector_process is None → should be a no-op
-
-
-def test_close_is_idempotent():
-    r = _make_runner()
-    resource = MagicMock(spec=["cleanup"])
-    r._shared_resources.append(resource)
-    r.close()
-    r.close()
-    resource.cleanup.assert_called_once()
-
-
-# ---------------------------------------------------------------------------
-# close() — resource cleanup
-# ---------------------------------------------------------------------------
-
-
-def test_close_calls_cleanup_on_resources():
-    """Resources with cleanup() method must be cleaned up on close()."""
-    r = _make_runner()
-    mock_res = MagicMock(spec=["cleanup"])
-    r._shared_resources.append(mock_res)
-    r.close()
-    mock_res.cleanup.assert_called_once()
-
-
-def test_close_calls_close_on_resources_without_cleanup():
-    """Resources without cleanup() but with close() must have close() called."""
-    r = _make_runner()
-    mock_res = MagicMock(spec=["close"])
-    r._shared_resources.append(mock_res)
-    r.close()
-    mock_res.close.assert_called_once()
-
-
-def test_close_handles_multiple_resources():
-    r = _make_runner()
-    resources = [MagicMock(spec=["cleanup"]) for _ in range(3)]
-    r._shared_resources.extend(resources)
-    r.close()
-    for res in resources:
-        res.cleanup.assert_called_once()
-
-
-# ---------------------------------------------------------------------------
-# close() — with live collector process
-# ---------------------------------------------------------------------------
-
-
-def _worker_wait_for_stop(stop_event) -> None:
-    """Cooperative worker: exits as soon as stop_event is set."""
-    stop_event.wait(timeout=30)
-
-
-@pytest.mark.slow
-def test_close_joins_running_collector():
-    """close() must signal the stop event and reap the collector process.
-
-    Under heavy CI load a spawned process may still be importing the test module
-    when close() hits its timeout path, so SIGTERM is also an acceptable outcome
-    as long as the collector does not leak.
-    """
-    r = _make_runner()
-    r._collector_process = _SPAWN_CTX.Process(
-        target=_worker_wait_for_stop,
-        args=(r._stop_event,),
-        daemon=True,
-    )
-    r._collector_process.start()
-    assert r._collector_process.is_alive()
-
-    r.close()
-
-    assert r._stop_event.is_set()
-    assert not r._collector_process.is_alive()
-    assert r._collector_process.exitcode in (0, -signal.SIGTERM)
-
-
-# ---------------------------------------------------------------------------
-# _start_collector
-# ---------------------------------------------------------------------------
-
-
-def _noop_collector(stop_event) -> None:
-    stop_event.wait(timeout=30)
-
-
-def _collector_record_kwargs(
-    stop_event,
-    record,
-    token: str,
-    sim_backend: str = "missing",
-) -> None:
-    record.append({"sim_backend": sim_backend, "token": token})
-
-
-@pytest.mark.slow
-def test_start_collector_spawns_process():
-    """_start_collector() must create and start a subprocess."""
-    r = _make_runner()
-    r._start_collector(target_fn=_noop_collector, kwargs={"stop_event": r._stop_event})
-    assert r._collector_process is not None
-    assert r._collector_process.is_alive()
-    r.close()
-
-
-def test_start_collector_does_not_merge_runner_runtime_fields(monkeypatch):
-    r = _make_runner(sim_backend="motrix")
-    record = []
-    monkeypatch.setattr(async_runner_module._SPAWN_CTX, "Process", _InlineProcess)
-    try:
-        r._start_collector(
-            target_fn=_collector_record_kwargs,
-            kwargs={
-                "stop_event": r._stop_event,
-                "record": record,
-                "token": "ok",
-            },
-        )
-        assert record == [{"sim_backend": "missing", "token": "ok"}]
-        assert r._collector_process is not None
-        assert r._collector_process.exitcode == 0
-    finally:
-        r.close()
-
-
-def test_format_collector_death_reports_shell_style_sigbus():
-    report = format_collector_death(135)
-
-    assert "shell-style signal 7" in report
-    assert "SIGBUS" in report
-    assert "shared memory" in report
-
-
-def test_format_collector_death_reports_negative_sigbus():
-    report = format_collector_death(-7)
-
-    assert "signal 7" in report
-    assert "SIGBUS" in report
-
-
-def test_format_collector_death_reports_sigkill_oom_hint():
-    report = format_collector_death(137)
-
-    assert "SIGKILL" in report or "SIG9" in report
-    assert "OOM killer" in report
-
-
-# ---------------------------------------------------------------------------
-# __del__ exception handling
-# ---------------------------------------------------------------------------
-
-
-def test_del_does_not_raise_even_if_close_fails():
-    """__del__ must swallow exceptions from close()."""
-    r = _make_runner()
-    # Force close() to fail by corrupting internal state
-    r._shared_resources = None  # type: ignore[assignment]  # will raise in close()
-    # __del__ calls close() and must not propagate the exception
-    r.__del__()  # noqa: PLC2801  (explicit __del__ call for test)
diff --git a/tests/ipc/test_dp_launcher.py b/tests/ipc/test_dp_launcher.py
index be5b09347..41940a233 100644
--- a/tests/ipc/test_dp_launcher.py
+++ b/tests/ipc/test_dp_launcher.py
@@ -11,12 +11,11 @@
 
 import pytest
 import torch
+import uni_rl.ipc.dp_launcher as dp_launcher
 from hydra import compose, initialize_config_dir
 from hydra.core.global_hydra import GlobalHydra
 from omegaconf import OmegaConf
-
-import unilab.ipc.dp_launcher as dp_launcher
-from unilab.ipc.dp_launcher import (
+from uni_rl.ipc.dp_launcher import (
     UNILAB_DP_DEVICES,
     UNILAB_DP_LOG_DIR,
     UNILAB_DP_RANK,
@@ -37,15 +36,15 @@
 )
 
 _ROOT = Path(__file__).parent.parent.parent
-_CONF_DIR = _ROOT / "conf"
+_CONF_DIR = _ROOT / "src" / "unilab" / "conf"
 
 
 def _offpolicy_cfg(overrides: list[str] | None = None):
     GlobalHydra.instance().clear()
     normalized = list(overrides or [])
     if not any(override.startswith("task=") for override in normalized):
-        normalized.append("task=sac/g1_walk_flat/mujoco")
-    with initialize_config_dir(config_dir=str(_CONF_DIR / "offpolicy"), version_base="1.3"):
+        normalized.append("task=g1_walk_flat/mujoco")
+    with initialize_config_dir(config_dir=str(_CONF_DIR / "sac"), version_base="1.3"):
         return compose("config", overrides=normalized)
 
 
@@ -342,13 +341,13 @@ def test_single_device_topology_spawns_no_children(fake_popen, monkeypatch: pyte
 
 def test_supervisor_spawn_argv_and_env(fake_popen, monkeypatch: pytest.MonkeyPatch):
     monkeypatch.delenv(UNILAB_DP_RANK, raising=False)
-    monkeypatch.setattr(sys, "argv", ["train_offpolicy.py", "algo=sac", "training.devices=[0,1,2]"])
+    monkeypatch.setattr(sys, "argv", ["train_sac.py", "training.devices=[0,1,2]"])
     with DpRankSupervisor((0, 1, 2), log_dir="/tmp/dp_test_log"):
         assert len(fake_popen.instances) == 2
         for rank, child in enumerate(fake_popen.instances, start=1):
             assert child.argv[0] == sys.executable
-            assert child.argv[1].endswith("scripts/train_offpolicy.py")
-            assert child.argv[2:] == ["algo=sac", "training.devices=[0,1,2]"]
+            assert child.argv[1].endswith("scripts/train_sac.py")
+            assert child.argv[2:] == ["training.devices=[0,1,2]"]
             assert child.start_new_session is (os.name == "posix")
             assert child.env[UNILAB_DP_RANK] == str(rank)
             assert child.env[UNILAB_DP_WORLD_SIZE] == "3"
diff --git a/tests/ipc/test_dp_sync.py b/tests/ipc/test_dp_sync.py
deleted file mode 100644
index 2ff0c64b5..000000000
--- a/tests/ipc/test_dp_sync.py
+++ /dev/null
@@ -1,328 +0,0 @@
-"""Two-process gloo tests for DpParameterSync (CPU-only)."""
-
-from __future__ import annotations
-
-import multiprocessing as mp
-import queue
-import time
-from pathlib import Path
-
-import pytest
-import torch
-
-from unilab.ipc.dp_launcher import resolve_dp_rendezvous_path
-from unilab.ipc.dp_sync import DpParameterSync
-
-_SPAWN_CTX = mp.get_context("spawn")
-
-
-def _run_workers(
-    procs: list[mp.Process],
-    result_queue,
-    *,
-    result_count: int,
-    timeout_s: float,
-) -> list:
-    """Collect results before joining producers and always reap every child."""
-    started: list[mp.Process] = []
-    deadline = time.monotonic() + timeout_s
-    results = []
-    try:
-        for proc in procs:
-            proc.start()
-            started.append(proc)
-
-        while len(results) < result_count:
-            remaining = deadline - time.monotonic()
-            if remaining <= 0:
-                pytest.fail(
-                    "timed out waiting for worker results: "
-                    f"{[(proc.pid, proc.exitcode) for proc in started]}"
-                )
-            try:
-                results.append(result_queue.get(timeout=min(1.0, remaining)))
-            except queue.Empty:
-                failed = [proc for proc in started if proc.exitcode not in (None, 0)]
-                if failed:
-                    pytest.fail(
-                        "worker exited before publishing a result: "
-                        f"{[(proc.pid, proc.exitcode) for proc in failed]}"
-                    )
-
-        for proc in started:
-            proc.join(timeout=max(0.0, deadline - time.monotonic()))
-        hanging = [proc for proc in started if proc.is_alive()]
-        if hanging:
-            pytest.fail(f"workers did not exit: {[proc.pid for proc in hanging]}")
-        failed = [proc for proc in started if proc.exitcode != 0]
-        if failed:
-            pytest.fail(
-                f"workers exited unsuccessfully: {[(proc.pid, proc.exitcode) for proc in failed]}"
-            )
-        return results
-    finally:
-        for proc in started:
-            if proc.is_alive():
-                proc.terminate()
-        cleanup_deadline = time.monotonic() + 5.0
-        for proc in started:
-            if proc.is_alive():
-                proc.join(timeout=max(0.0, cleanup_deadline - time.monotonic()))
-        for proc in started:
-            if proc.is_alive():
-                proc.kill()
-        for proc in started:
-            if proc.is_alive():
-                proc.join(timeout=1.0)
-        result_queue.close()
-        result_queue.join_thread()
-
-
-def _make_tensors(rank: int, *, reversed_order: bool = False) -> dict[str, torch.Tensor]:
-    """Deterministic per-rank tensors; distinct values and shapes per key."""
-    entries = [
-        ("actor.net.weight", torch.full((4, 3), 1.0 + rank, requires_grad=True)),
-        ("qnet.head.bias", torch.full((2,), 10.0 + rank, requires_grad=True)),
-        ("log_alpha", torch.full((1,), -0.5 + rank, requires_grad=True)),
-    ]
-    if reversed_order:
-        entries.reverse()
-    return dict(entries)
-
-
-def _broadcast_and_gradient_worker(
-    rank: int, rendezvous_path: str, reversed_order: bool, result_queue
-) -> None:
-    tensors = _make_tensors(rank, reversed_order=reversed_order)
-    sync = DpParameterSync(
-        world_size=2,
-        rank=rank,
-        rendezvous_path=rendezvous_path,
-        backend="gloo",
-        timeout_s=60,
-    )
-    sync.start()
-
-    # Init broadcast: every rank ends up with rank 0's values, bit-exact.
-    sync.broadcast_from_rank0(tensors)
-    expected = _make_tensors(0)
-    for key, tensor in tensors.items():
-        assert torch.equal(tensor.detach(), expected[key].detach()), (
-            f"rank {rank} broadcast mismatch on {key}"
-        )
-
-    grad_ptrs: dict[str, int] = {}
-    gradient_base = {"actor.net.weight": 1.0, "qnet.head.bias": 2.0, "log_alpha": 3.0}
-    for key, tensor in tensors.items():
-        tensor.grad = torch.full_like(tensor, gradient_base[key] + 2.0 * rank)
-        grad_ptrs[key] = tensor.grad.data_ptr()
-    sync.allreduce_gradients(tensors[key] for key in sorted(tensors))
-    for key, tensor in tensors.items():
-        assert tensor.grad is not None
-        assert torch.allclose(tensor.grad, torch.full_like(tensor, gradient_base[key] + 1.0))
-        assert tensor.grad.data_ptr() == grad_ptrs[key]
-
-    sync_time, sync_calls = sync.take_gradient_sync_metrics()
-    assert sync_time >= 0.0
-    assert sync_calls == 1
-    assert sync.take_gradient_sync_metrics() == (0.0, 0)
-
-    sync.close()
-    sync.close()  # idempotent
-    result_queue.put((rank, sorted(tensors)))
-
-
-def test_broadcast_then_flat_gradient_mean_two_ranks(tmp_path: Path):
-    rendezvous = str(tmp_path / "rendezvous")
-    result_queue = _SPAWN_CTX.Queue()
-    procs = [
-        _SPAWN_CTX.Process(
-            target=_broadcast_and_gradient_worker,
-            args=(rank, rendezvous, bool(rank), result_queue),
-        )
-        for rank in range(2)
-    ]
-    results = sorted(_run_workers(procs, result_queue, result_count=2, timeout_s=120))
-    # Different insertion order still resolves to the same startup key order.
-    assert results[0][1] == results[1][1]
-
-
-def _statistics_worker(rank: int, rendezvous_path: str, result_queue) -> None:
-    sync = DpParameterSync(
-        world_size=2,
-        rank=rank,
-        rendezvous_path=rendezvous_path,
-        backend="gloo",
-        timeout_s=60,
-    )
-    sync.start()
-    first = sync.allreduce_statistics(
-        mean={
-            "loss": 1.0 + 2.0 * rank,
-            **({"rank0_optional": 10.0} if rank == 0 else {}),
-        },
-        total={"steps_per_sec": 100.0 * (rank + 1)},
-    )
-    second = sync.allreduce_statistics(
-        mean={
-            "loss": 5.0 + 2.0 * rank,
-            **({"late_rank1_field": 9.0} if rank == 1 else {}),
-        },
-        total={"steps_per_sec": 10.0 * (rank + 1)},
-    )
-    # This worker process owns the Gloo group lifetime. Explicit Gloo destroy
-    # can strand rank 0 after schema broadcasts on a single-vCPU runner, so
-    # process exit is the teardown boundary here. The broadcast/gradient test
-    # above separately covers close() and its idempotence.
-    result_queue.put((rank, first, second))
-
-
-def test_allreduce_statistics_sums_rates_and_means_sparse_fields(tmp_path: Path):
-    rendezvous = str(tmp_path / "rendezvous_statistics")
-    result_queue = _SPAWN_CTX.Queue()
-    procs = [
-        _SPAWN_CTX.Process(
-            target=_statistics_worker,
-            args=(rank, rendezvous, result_queue),
-        )
-        for rank in range(2)
-    ]
-    results = sorted(_run_workers(procs, result_queue, result_count=2, timeout_s=120))
-    for _, first, second in results:
-        assert first == pytest.approx({"loss": 2.0, "rank0_optional": 10.0, "steps_per_sec": 300.0})
-        assert second == pytest.approx(
-            {"loss": 6.0, "late_rank1_field": 9.0, "steps_per_sec": 30.0}
-        )
-
-
-def test_allreduce_statistics_rejects_overlapping_reduction_modes(tmp_path: Path):
-    sync = DpParameterSync(
-        world_size=2,
-        rank=0,
-        rendezvous_path=str(tmp_path / "unused_statistics"),
-        backend="gloo",
-    )
-    with pytest.raises(ValueError, match="both mean and total"):
-        sync.allreduce_statistics(mean={"x": 1.0}, total={"x": 2.0})
-
-
-def test_key_set_change_between_collectives_is_rejected(tmp_path: Path):
-    sync = DpParameterSync(
-        world_size=2,
-        rank=0,
-        rendezvous_path=str(tmp_path / "unused"),
-        backend="gloo",
-    )
-    first = {"a": torch.zeros(1), "b": torch.zeros(1)}
-    sync._ordered_keys(first)
-    with pytest.raises(ValueError, match="tensor keys changed"):
-        sync._ordered_keys({"a": torch.zeros(1), "c": torch.zeros(1)})
-
-
-def test_close_without_start_is_idempotent(tmp_path: Path):
-    sync = DpParameterSync(
-        world_size=2,
-        rank=0,
-        rendezvous_path=str(tmp_path / "unused"),
-        backend="gloo",
-    )
-    sync.close()
-    sync.close()
-
-
-def test_cuda_graph_collective_warmup_requires_started_nccl_cuda_group(tmp_path: Path):
-    sync = DpParameterSync(
-        world_size=2,
-        rank=0,
-        rendezvous_path=str(tmp_path / "unused_graph_warmup"),
-        backend="gloo",
-    )
-    with pytest.raises(RuntimeError, match=r"start\(\) must run"):
-        sync.prepare_cuda_graph_collectives()
-
-
-def test_cuda_graph_replay_metrics_count_actual_collectives(tmp_path: Path):
-    sync = DpParameterSync(
-        world_size=2,
-        rank=0,
-        rendezvous_path=str(tmp_path / "unused_graph_metrics"),
-        backend="gloo",
-    )
-    sync.record_cuda_graph_gradient_replay(3)
-    assert sync.take_gradient_sync_metrics() == (0.0, 3)
-    with pytest.raises(ValueError, match=">= 0"):
-        sync.record_cuda_graph_gradient_replay(-1)
-
-
-def test_invalid_world_size_and_rank_are_rejected(tmp_path: Path):
-    path = str(tmp_path / "unused")
-    with pytest.raises(ValueError, match="world_size >= 2"):
-        DpParameterSync(world_size=1, rank=0, rendezvous_path=path)
-    with pytest.raises(ValueError, match="out of range"):
-        DpParameterSync(world_size=2, rank=2, rendezvous_path=path)
-
-
-def test_resolve_dp_rendezvous_path_anchors_on_run_root(tmp_path: Path, monkeypatch):
-    # Rank 0 anchors in its own log_dir; spawned ranks use the shared run root.
-    monkeypatch.delenv("UNILAB_DP_LOG_DIR", raising=False)
-    rank0 = resolve_dp_rendezvous_path(str(tmp_path / "run"), rank=0)
-    assert rank0 == str(tmp_path / "run" / ".dp_rendezvous")
-    monkeypatch.setenv("UNILAB_DP_LOG_DIR", str(tmp_path / "run"))
-    rank1 = resolve_dp_rendezvous_path(str(tmp_path / "run" / "rank1"), rank=1)
-    assert rank1 == rank0
-
-
-@pytest.mark.slow
-@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="requires 2 CUDA devices")
-def test_nccl_two_gpu_smoke(tmp_path: Path):
-    """Real NCCL smoke: startup state, gradients, and logger statistics."""
-    rendezvous = str(tmp_path / "rendezvous_nccl")
-    result_queue = _SPAWN_CTX.Queue()
-    procs = [
-        _SPAWN_CTX.Process(
-            target=_nccl_smoke_worker,
-            args=(rank, rendezvous, result_queue),
-        )
-        for rank in range(2)
-    ]
-    results = sorted(_run_workers(procs, result_queue, result_count=2, timeout_s=180))
-    assert results == [0, 1]
-
-
-def _nccl_smoke_worker(rank: int, rendezvous_path: str, result_queue) -> None:
-    device = torch.device(f"cuda:{rank}")
-    tensor = torch.nn.Parameter(torch.full((8,), float(rank + 1), device=device))
-    sync = DpParameterSync(
-        world_size=2,
-        rank=rank,
-        rendezvous_path=rendezvous_path,
-        backend="nccl",
-        device=str(device),
-        timeout_s=120,
-    )
-    sync.start()
-    tensors = {"w": tensor}
-    sync.broadcast_from_rank0(tensors)
-    assert torch.equal(tensor, torch.ones(8, device=device))
-    tensor.grad = torch.full_like(tensor, float(rank + 1))
-    sync.allreduce_gradients((tensor,))
-    assert torch.allclose(tensor.grad, torch.full((8,), 1.5, device=device))
-    statistics = sync.allreduce_statistics(
-        mean={"loss": float(rank + 1)},
-        total={"steps_per_sec": float(100 * (rank + 1))},
-    )
-    assert statistics == pytest.approx({"loss": 1.5, "steps_per_sec": 300.0})
-    sync.prepare_cuda_graph_collectives()
-    tensor.grad = torch.full_like(tensor, float(rank + 1))
-    graph = torch.cuda.CUDAGraph()
-    capture_stream = torch.cuda.Stream(device=device)
-    capture_stream.wait_stream(torch.cuda.current_stream(device))
-    with torch.cuda.stream(capture_stream), torch.cuda.graph(graph):
-        sync.allreduce_gradients((tensor,))
-    torch.cuda.current_stream(device).wait_stream(capture_stream)
-    graph.replay()
-    torch.cuda.synchronize(device)
-    assert torch.allclose(tensor.grad, torch.full((8,), 1.5, device=device))
-    graph.reset()
-    sync.close()
-    result_queue.put(rank)
diff --git a/tests/ipc/test_inference_slot.py b/tests/ipc/test_inference_slot.py
deleted file mode 100644
index 6f7f149a2..000000000
--- a/tests/ipc/test_inference_slot.py
+++ /dev/null
@@ -1,61 +0,0 @@
-"""Tests for the learner-owned inference single-slot contract."""
-
-from __future__ import annotations
-
-import numpy as np
-import pytest
-import torch
-
-from unilab.ipc.inference_slot import SharedInferenceSlot
-
-
-def test_inference_slot_roundtrip_preserves_tick_and_policy_version() -> None:
-    slot = SharedInferenceSlot(num_envs=2, obs_dim=3, action_dim=2)
-    observations = np.arange(6, dtype=np.float32).reshape(2, 3)
-    dones = np.array([0.0, 1.0], dtype=np.float32)
-
-    slot.publish_observation(tick_id=7, observations=observations, dones=dones)
-    obs_destination = torch.empty(2, 3)
-    dones_destination = torch.empty(2)
-    slot.copy_observation_to(
-        tick_id=7,
-        observations=obs_destination,
-        dones=dones_destination,
-    )
-    slot.publish_action(
-        tick_id=7,
-        policy_version=11,
-        actions=torch.tensor([[1.0, 2.0], [3.0, 4.0]]),
-    )
-
-    actions, policy_version = slot.consume_action(tick_id=7)
-
-    np.testing.assert_array_equal(obs_destination.numpy(), observations)
-    np.testing.assert_array_equal(dones_destination.numpy(), dones)
-    np.testing.assert_array_equal(actions, [[1.0, 2.0], [3.0, 4.0]])
-    assert policy_version == 11
-
-
-def test_inference_slot_rejects_early_reuse_and_tick_mismatch() -> None:
-    slot = SharedInferenceSlot(num_envs=1, obs_dim=2, action_dim=1)
-    observations = np.zeros((1, 2), dtype=np.float32)
-    dones = np.zeros(1, dtype=np.float32)
-    slot.publish_observation(tick_id=3, observations=observations, dones=dones)
-
-    with pytest.raises(RuntimeError, match="cannot be reused"):
-        slot.publish_observation(tick_id=4, observations=observations, dones=dones)
-    with pytest.raises(RuntimeError, match="tick mismatch"):
-        slot.copy_observation_to(
-            tick_id=4,
-            observations=torch.empty(1, 2),
-            dones=torch.empty(1),
-        )
-
-    slot.publish_action(tick_id=3, policy_version=5, actions=torch.ones(1, 1))
-    with pytest.raises(RuntimeError, match="tick mismatch"):
-        slot.consume_action(tick_id=4)
-
-    actions, policy_version = slot.consume_action(tick_id=3)
-    np.testing.assert_array_equal(actions, [[1.0]])
-    assert policy_version == 5
-    slot.publish_observation(tick_id=4, observations=observations, dones=dones)
diff --git a/tests/ipc/test_memory_budget.py b/tests/ipc/test_memory_budget.py
deleted file mode 100644
index 1c3c5fb9a..000000000
--- a/tests/ipc/test_memory_budget.py
+++ /dev/null
@@ -1,82 +0,0 @@
-from __future__ import annotations
-
-import shutil
-
-import pytest
-
-from unilab.ipc.memory_budget import (
-    estimate_offpolicy_bytes,
-    raise_if_shared_memory_over_budget,
-)
-
-
-def test_offpolicy_memory_budget_notes_native_exclusions() -> None:
-    estimate = estimate_offpolicy_bytes(
-        num_envs=5120,
-        replay_buffer_n=1024,
-        obs_dim=98,
-        action_dim=29,
-        critic_dim=101,
-    )
-
-    breakdown = str(estimate["breakdown"])
-    assert "MuJoCo BatchEnvPool" in breakdown
-    assert "CUDA pinned/shared" in breakdown
-    assert "driver memory" in breakdown
-
-
-def test_device_replay_host_budget_is_independent_of_replay_capacity() -> None:
-    estimates = [
-        estimate_offpolicy_bytes(
-            num_envs=10,
-            replay_buffer_n=replay_buffer_n,
-            obs_dim=2,
-            action_dim=1,
-            critic_dim=3,
-            ingress_depth=2,
-        )
-        for replay_buffer_n in (4, 4000)
-    ]
-
-    assert estimates[0]["replay_buffer"] == 0
-    assert estimates[0]["bounded_ingress_slots"] > 0
-    assert estimates[0]["total"] == estimates[1]["total"]
-    assert "authoritative on the learner device" in str(estimates[0]["breakdown"])
-
-
-def test_shared_memory_budget_unknown_available_is_noop(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(shutil, "disk_usage", lambda path: (_ for _ in ()).throw(OSError()))
-    estimate = {"total": 1024, "breakdown": "test"}
-
-    raise_if_shared_memory_over_budget(estimate, label="test", path="/missing-shm")
-
-
-def test_shared_memory_budget_allows_within_threshold(monkeypatch: pytest.MonkeyPatch) -> None:
-    class _Usage:
-        free = 100 * 1024
-
-    monkeypatch.setattr(shutil, "disk_usage", lambda path: _Usage())
-    estimate = {"total": 80 * 1024, "breakdown": "test"}
-
-    raise_if_shared_memory_over_budget(estimate, label="test", threshold=0.8)
-
-
-def test_shared_memory_budget_raises_before_over_allocating(
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    class _Usage:
-        free = 100 * 1024
-
-    monkeypatch.setattr(shutil, "disk_usage", lambda path: _Usage())
-    estimate = {"total": 81 * 1024, "breakdown": "test"}
-
-    with pytest.raises(MemoryError) as excinfo:
-        raise_if_shared_memory_over_budget(estimate, label="Off-policy (td3)", threshold=0.8)
-
-    message = str(excinfo.value)
-    assert "Off-policy (td3)" in message
-    assert "/dev/shm" in message
-    assert "estimated" in message
-    assert "available" in message
-    assert "algo.num_envs" in message
-    assert "algo.replay_buffer_n" in message
diff --git a/tests/ipc/test_replay_buffer.py b/tests/ipc/test_replay_buffer.py
deleted file mode 100644
index 029df8dde..000000000
--- a/tests/ipc/test_replay_buffer.py
+++ /dev/null
@@ -1,218 +0,0 @@
-"""Tests for the bounded off-policy replay ingress."""
-
-from __future__ import annotations
-
-import multiprocessing as mp
-import threading
-import time
-
-import pytest
-import torch
-
-from unilab.ipc.replay_buffer import ReplayBuffer
-
-_SPAWN_CTX = mp.get_context("spawn")
-_OBS_DIM = 8
-_ACTION_DIM = 3
-
-
-def _make_buf(*, capacity: int = 128, slot_rows: int = 8, depth: int = 2) -> ReplayBuffer:
-    return ReplayBuffer(
-        capacity=capacity,
-        obs_dim=_OBS_DIM,
-        action_dim=_ACTION_DIM,
-        device="cpu",
-        ingress_slot_rows=slot_rows,
-        ingress_depth=depth,
-    )
-
-
-def _random_batch(n: int):
-    return (
-        torch.randn(n, _OBS_DIM),
-        torch.randn(n, _ACTION_DIM),
-        torch.randn(n),
-        torch.randn(n, _OBS_DIM),
-        torch.zeros(n),
-        torch.zeros(n),
-    )
-
-
-def _take_and_commit(buf: ReplayBuffer) -> tuple[int, int, torch.Tensor]:
-    ingress = buf.take_published_ingress()
-    assert ingress is not None
-    slot, start, count, packed = ingress
-    buf.commit_ingress(slot=slot, start=start, count=count)
-    return start, count, packed
-
-
-def test_host_allocation_is_capacity_independent_and_commit_is_device_owned():
-    small = _make_buf(capacity=32, slot_rows=4)
-    large = _make_buf(capacity=3200, slot_rows=4)
-    assert small.host_storage_bytes == large.host_storage_bytes
-    assert not hasattr(small, "_storage")
-
-    obs, act, rew, nobs, done, trunc = _random_batch(4)
-    small.add(obs, act, rew, nobs, done, trunc)
-    assert small.published_ptr == 4
-    assert int(small.ptr[0]) == 0
-    assert int(small.size[0]) == 0
-
-    start, count, packed = _take_and_commit(small)
-    assert (start, count) == (0, 4)
-    torch.testing.assert_close(packed[:, small._obs_sl], obs)
-    torch.testing.assert_close(packed[:, small._act_sl], act)
-    assert int(small.ptr[0]) == 4
-    assert int(small.size[0]) == 4
-    small.close()
-    large.close()
-
-
-def test_ingress_patches_terminal_rows_before_publication():
-    buf = ReplayBuffer(
-        capacity=16,
-        obs_dim=_OBS_DIM,
-        action_dim=_ACTION_DIM,
-        device="cpu",
-        ingress_slot_rows=4,
-        critic_dim=3,
-    )
-    terminal_mask = torch.tensor([False, True, False, True])
-    terminal_obs = torch.full((4, _OBS_DIM), 17.0)
-    terminal_critic = torch.full((4, 3), 23.0)
-    obs, act, rew, nobs, done, trunc = _random_batch(4)
-
-    buf.add(
-        obs,
-        act,
-        rew,
-        nobs,
-        done,
-        trunc,
-        terminal_mask=terminal_mask,
-        terminal_next_obs=terminal_obs,
-        critic=torch.zeros(4, 3),
-        next_critic=torch.ones(4, 3),
-        terminal_next_critic=terminal_critic,
-    )
-
-    _, _, packed = _take_and_commit(buf)
-    torch.testing.assert_close(packed[terminal_mask, buf._nobs_sl], terminal_obs[terminal_mask])
-    torch.testing.assert_close(
-        packed[terminal_mask, buf._ncritic_sl],
-        terminal_critic[terminal_mask],
-    )
-    buf.close()
-
-
-def test_ingress_stores_done_and_truncated_contract():
-    buf = _make_buf(slot_rows=3)
-    truncated = torch.tensor([0.0, 1.0, 0.0])
-    dones = torch.tensor([1.0, 1.0, 0.0])
-    buf.add(
-        torch.zeros(3, _OBS_DIM),
-        torch.zeros(3, _ACTION_DIM),
-        torch.zeros(3),
-        torch.zeros(3, _OBS_DIM),
-        dones,
-        truncated,
-    )
-
-    _, _, packed = _take_and_commit(buf)
-    torch.testing.assert_close(packed[:, buf._done_col], dones)
-    torch.testing.assert_close(packed[:, buf._trunc_col], truncated)
-    buf.close()
-
-
-def test_ingress_writes_packed_columns_without_cat(monkeypatch: pytest.MonkeyPatch):
-    buf = ReplayBuffer(
-        capacity=8,
-        obs_dim=_OBS_DIM,
-        action_dim=_ACTION_DIM,
-        device="cpu",
-        ingress_slot_rows=4,
-        critic_dim=5,
-    )
-    obs = torch.randn(4, _OBS_DIM)
-    actions = torch.randn(4, _ACTION_DIM)
-    critic = torch.randn(4, 5)
-
-    def _fail_cat(*args, **kwargs):
-        del args, kwargs
-        raise AssertionError("ReplayBuffer.add must write packed columns directly")
-
-    monkeypatch.setattr(torch, "cat", _fail_cat)
-    buf.add(
-        obs,
-        actions,
-        torch.randn(4),
-        torch.randn(4, _OBS_DIM),
-        torch.zeros(4),
-        torch.zeros(4),
-        critic=critic,
-        next_critic=torch.randn(4, 5),
-    )
-
-    _, _, packed = _take_and_commit(buf)
-    torch.testing.assert_close(packed[:, buf._obs_sl], obs)
-    torch.testing.assert_close(packed[:, buf._act_sl], actions)
-    torch.testing.assert_close(packed[:, buf._critic_sl], critic)
-    buf.close()
-
-
-def test_ingress_rejects_collection_chunk_larger_than_slot():
-    buf = _make_buf(slot_rows=4)
-    with pytest.raises(ValueError, match="slots hold 4"):
-        buf.add(*_random_batch(5))
-    buf.close()
-
-
-def test_ingress_backpressures_until_committed_slot_is_released():
-    buf = _make_buf(capacity=16, slot_rows=4, depth=1)
-    buf.add(*_random_batch(4))
-    add_started = threading.Event()
-    add_finished = threading.Event()
-
-    def add_second() -> None:
-        add_started.set()
-        buf.add(*_random_batch(4))
-        add_finished.set()
-
-    thread = threading.Thread(target=add_second)
-    thread.start()
-    assert add_started.wait(timeout=1.0)
-    assert not add_finished.wait(timeout=0.05)
-    _take_and_commit(buf)
-    assert add_finished.wait(timeout=1.0)
-    thread.join(timeout=1.0)
-    _take_and_commit(buf)
-    buf.close()
-
-
-def _collector_add(buf: ReplayBuffer, chunks: int) -> None:
-    for _ in range(chunks):
-        buf.add(*_random_batch(8))
-
-
-def test_spawned_collector_publishes_bounded_chunks():
-    buf = _make_buf(capacity=128, slot_rows=8)
-    process = _SPAWN_CTX.Process(target=_collector_add, args=(buf, 4))
-    process.start()
-
-    committed = 0
-    deadline = time.monotonic() + 15.0
-    while committed < 32 and time.monotonic() < deadline:
-        ingress = buf.take_published_ingress()
-        if ingress is None:
-            time.sleep(0.001)
-            continue
-        slot, start, count, _ = ingress
-        buf.commit_ingress(slot=slot, start=start, count=count)
-        committed += count
-
-    process.join(timeout=15)
-    assert process.exitcode == 0
-    assert committed == 32
-    assert int(buf.ptr[0]) == 32
-    assert int(buf.size[0]) == 32
-    buf.close()
diff --git a/tests/ipc/test_replay_pipeline_gpu_resident.py b/tests/ipc/test_replay_pipeline_gpu_resident.py
deleted file mode 100644
index 862d379ba..000000000
--- a/tests/ipc/test_replay_pipeline_gpu_resident.py
+++ /dev/null
@@ -1,559 +0,0 @@
-"""Tests for GPUResidentReplayPipeline."""
-
-from __future__ import annotations
-
-import multiprocessing as mp
-import threading
-import time
-
-import pytest
-import torch
-
-from unilab.ipc.replay_buffer import ReplayBuffer
-from unilab.ipc.replay_pipelines.gpu_resident import (
-    GPUResidentReplayPipeline,
-    _device_memory_budget,
-    _ring_spans,
-    _validate_device_memory_budget,
-)
-
-_HAS_CUDA = torch.cuda.is_available()
-cuda_only = pytest.mark.skipif(not _HAS_CUDA, reason="CUDA required")
-_HAS_MPS = torch.backends.mps.is_available()
-mps_only = pytest.mark.skipif(not _HAS_MPS, reason="MPS required")
-
-_OBS_DIM = 4
-_ACTION_DIM = 2
-_CRITIC_DIM = 5
-
-
-def _make_replay(
-    capacity: int = 128,
-    obs_dim: int = _OBS_DIM,
-    action_dim: int = _ACTION_DIM,
-    critic_dim: int = _CRITIC_DIM,
-    device: str = "cuda",
-    slot_rows: int | None = None,
-) -> ReplayBuffer:
-    return ReplayBuffer(
-        capacity=capacity,
-        obs_dim=obs_dim,
-        action_dim=action_dim,
-        device=device,
-        critic_dim=critic_dim,
-        ingress_slot_rows=capacity if slot_rows is None else slot_rows,
-    )
-
-
-def _make_bounded_replay(
-    *,
-    capacity: int = 128,
-    slot_rows: int = 16,
-    device: str = "cuda",
-) -> ReplayBuffer:
-    return _make_replay(
-        capacity=capacity,
-        device=device,
-        slot_rows=slot_rows,
-    )
-
-
-def _pattern_add(rb: ReplayBuffer, start_row: int, n: int) -> None:
-    """Add rows whose fields are exact float32 functions of the absolute row id."""
-    obs_dim, action_dim, critic_dim = rb._obs_dim, rb._action_dim, rb._critic_dim
-    rows = torch.arange(start_row, start_row + n, dtype=torch.float32)
-    col = rows.unsqueeze(1)
-    critic = next_critic = None
-    if critic_dim > 0:
-        critic = col * 10000 + torch.arange(critic_dim, dtype=torch.float32)
-        next_critic = col * 100000 + torch.arange(critic_dim, dtype=torch.float32)
-    rb.add(
-        obs=col * 10 + torch.arange(obs_dim, dtype=torch.float32),
-        actions=col * 1000 + torch.arange(action_dim, dtype=torch.float32),
-        rewards=rows.clone(),
-        next_obs=col * 100 + torch.arange(obs_dim, dtype=torch.float32),
-        dones=torch.zeros(n),
-        truncated=torch.ones(n),
-        critic=critic,
-        next_critic=next_critic,
-    )
-
-
-def _pattern_add_chunks(rb: ReplayBuffer, chunk_rows: int, chunks: int) -> None:
-    for chunk in range(chunks):
-        _pattern_add(rb, chunk * chunk_rows, chunk_rows)
-
-
-def _expected_pattern(rb: ReplayBuffer, rewards: torch.Tensor) -> dict[str, torch.Tensor]:
-    col = rewards.cpu().unsqueeze(1)
-    out = {
-        "obs": col * 10 + torch.arange(rb._obs_dim, dtype=torch.float32),
-        "next_obs": col * 100 + torch.arange(rb._obs_dim, dtype=torch.float32),
-        "actions": col * 1000 + torch.arange(rb._action_dim, dtype=torch.float32),
-    }
-    if rb._critic_dim > 0:
-        out["critic"] = col * 10000 + torch.arange(rb._critic_dim, dtype=torch.float32)
-        out["next_critic"] = col * 100000 + torch.arange(rb._critic_dim, dtype=torch.float32)
-    return out
-
-
-def _wait_visible(pipeline: GPUResidentReplayPipeline, ptr: int, timeout: float = 15.0) -> None:
-    deadline = time.monotonic() + timeout
-    while pipeline._visible_ptr < ptr:
-        if time.monotonic() > deadline:
-            raise TimeoutError(
-                f"Device replay stalled: visible_ptr={pipeline._visible_ptr} < {ptr}"
-            )
-        time.sleep(0.005)
-
-
-def _wait_batch_ready(
-    pipeline: GPUResidentReplayPipeline,
-    tick_id: int,
-    sample_count: int,
-    timeout: float = 15.0,
-) -> None:
-    deadline = time.monotonic() + timeout
-    while not pipeline.batch_ready(tick_id, sample_count):
-        if time.monotonic() > deadline:
-            raise TimeoutError(f"GPU replay batch for tick {tick_id} never became ready")
-        time.sleep(0.01)
-
-
-class TestRingSpans:
-    def test_no_wrap(self):
-        assert _ring_spans(5, 12, 64) == [(5, 7)]
-
-    def test_wrap_split(self):
-        assert _ring_spans(60, 68, 64) == [(60, 4), (0, 4)]
-
-    def test_multiple_wraps(self):
-        assert _ring_spans(62, 136, 64) == [(62, 2), (0, 64), (0, 8)]
-
-    def test_full_ring(self):
-        assert _ring_spans(3, 67, 64) == [(3, 61), (0, 3)]
-
-    def test_empty(self):
-        assert _ring_spans(5, 5, 64) == []
-        assert _ring_spans(5, 3, 64) == []
-        assert _ring_spans(0, 4, 0) == []
-
-
-class TestConstructionGuards:
-    def test_non_accelerator_device_rejected(self):
-        rb = _make_replay(device="cpu")
-        with pytest.raises(ValueError, match="CUDA or MPS"):
-            GPUResidentReplayPipeline(rb, device="cpu", sample_count=8)
-
-    def test_invalid_pack_layout_rejected(self):
-        rb = _make_replay(device="cpu")
-        with pytest.raises(ValueError, match="pack_layout"):
-            GPUResidentReplayPipeline(rb, device="cpu", sample_count=8, pack_layout="bogus")
-
-    def test_runner_rejects_non_accelerator_before_base_initialization(self):
-        from unilab.algos.torch.offpolicy.double_buffer_runner import (
-            DoubleBufferOffPolicyRunner,
-        )
-
-        with pytest.raises(ValueError, match="CUDA or MPS"):
-            DoubleBufferOffPolicyRunner(device="cpu")
-
-    def test_mps_memory_budget_uses_recommended_budget(self, monkeypatch):
-        monkeypatch.setattr(torch.mps, "recommended_max_memory", lambda: 1_000)
-        monkeypatch.setattr(torch.mps, "driver_allocated_memory", lambda: 250)
-
-        assert _device_memory_budget(torch.device("mps")) == (750, 1_000)
-
-    def test_mps_memory_budget_failure_is_actionable(self, monkeypatch):
-        monkeypatch.setattr(torch.mps, "recommended_max_memory", lambda: 1_000)
-        monkeypatch.setattr(torch.mps, "driver_allocated_memory", lambda: 250)
-
-        with pytest.raises(RuntimeError, match=r"requires .* available budget .* total budget"):
-            _validate_device_memory_budget(
-                torch.device("mps"),
-                required_bytes=601,
-                storage_bytes=500,
-                batch_bytes=101,
-                headroom=0.8,
-            )
-
-
-@cuda_only
-class TestGPUResidentPipeline:
-    @pytest.fixture
-    def pipeline_factory(self):
-        created = []
-
-        def _make(rb, **kwargs):
-            kwargs.setdefault("device", "cuda")
-            kwargs.setdefault("sample_count", 16)
-            pipeline = GPUResidentReplayPipeline(rb, **kwargs)
-            created.append(pipeline)
-            return pipeline
-
-        yield _make
-        for pipeline in created:
-            pipeline.close()
-
-    def test_allocates_gpu_storage_and_slots(self, pipeline_factory):
-        rb = _make_replay(capacity=64)
-        pipeline = pipeline_factory(rb, sample_count=8)
-        assert pipeline._gpu_storage.shape == (64, rb.storage_width)
-        assert pipeline._gpu_storage.is_cuda
-        assert len(pipeline._gpu_packed) == 2
-        assert all(slot.is_cuda for slot in pipeline._gpu_packed)
-        assert all(slot.is_pinned() for slot in rb._ingress_slots)
-        assert pipeline.h2d_submitter == "gpu_resident_ingress"
-        manifest = pipeline.transfer_manifest
-        assert manifest["pipeline"] == "gpu_resident"
-        assert manifest["storage_rows"] == 64
-        assert manifest["host_pinned"] is True
-
-    def test_bounded_ingress_owns_no_full_host_ring_and_commits_after_h2d(self, pipeline_factory):
-        rb = _make_bounded_replay(capacity=64, slot_rows=16)
-        _pattern_add(rb, 0, 16)
-        assert not hasattr(rb, "_storage")
-        assert int(rb.ptr[0]) == 0
-        assert rb.published_ptr == 16
-
-        pipeline = pipeline_factory(rb, sample_count=8)
-        _wait_visible(pipeline, 16)
-
-        assert int(rb.ptr[0]) == 16
-        assert int(rb.size[0]) == 16
-        assert pipeline._gpu_storage.shape == (64, rb.storage_width)
-        assert pipeline.transfer_manifest["storage_owner"] == "device"
-        assert pipeline.transfer_manifest["host_storage_bytes"] == rb.host_storage_bytes
-        assert pipeline.transfer_manifest["ingress_depth"] == 2
-
-    def test_bounded_ingress_ring_wrap_and_committed_field_order(self, pipeline_factory):
-        rb = _make_bounded_replay(capacity=32, slot_rows=8)
-        _pattern_add(rb, 0, 8)
-        _pattern_add(rb, 8, 8)
-        pipeline = pipeline_factory(rb, sample_count=8)
-        _wait_visible(pipeline, 16)
-        for start in (16, 24, 32):
-            _pattern_add(rb, start, 8)
-        _wait_visible(pipeline, 40)
-
-        end_ptr, fields = pipeline.read_committed_fields(
-            ("rewards", "dones"),
-            start_ptr=0,
-        )
-
-        assert end_ptr == 40
-        torch.testing.assert_close(
-            fields["rewards"].cpu(),
-            torch.arange(8, 40, dtype=torch.float32),
-        )
-        assert (fields["dones"].cpu() == 0).all()
-
-    def test_bounded_ingress_samples_only_committed_rows(self, pipeline_factory):
-        rb = _make_bounded_replay(capacity=64, slot_rows=16)
-        _pattern_add(rb, 0, 16)
-        assert not hasattr(rb, "sample")
-        pipeline = pipeline_factory(rb, sample_count=16, base_seed=41)
-
-        batch = pipeline.sample_large_batch(3, 16)
-        rewards = batch["rewards"].cpu()
-
-        assert rewards.min() >= 0
-        assert rewards.max() < 16
-        expected = _expected_pattern(rb, rewards)
-        for key, want in expected.items():
-            torch.testing.assert_close(batch[key].cpu(), want)
-
-    def test_bounded_ingress_pending_overwrite_cannot_prepare_batch(self, pipeline_factory):
-        rb = _make_bounded_replay(capacity=32, slot_rows=8)
-        _pattern_add(rb, 0, 8)
-        _pattern_add(rb, 8, 8)
-        pipeline = pipeline_factory(rb, sample_count=8)
-        _wait_visible(pipeline, 16)
-        _pattern_add(rb, 16, 8)
-        _pattern_add(rb, 24, 8)
-        _wait_visible(pipeline, 32)
-        pipeline._closed = True
-        assert pipeline._sync_thread is not None
-        pipeline._sync_thread.join(timeout=2.0)
-        pipeline._closed = False
-
-        _pattern_add(rb, 32, 8)
-        assert pipeline._submit_new_spans() is True
-        assert int(rb.ptr[0]) == 32
-        assert pipeline.start_prepare(2, 8, min_snapshot_ptr=32) is True
-        assert pipeline._service_pending_prepare() is False
-        assert pipeline._prepared_metadata is None
-
-        pipeline.progress(wait=True)
-        assert int(rb.ptr[0]) == 40
-        assert pipeline._service_pending_prepare() is True
-
-    def test_bounded_ingress_consumes_spawned_collector_chunks(self, pipeline_factory):
-        rb = _make_bounded_replay(capacity=64, slot_rows=8)
-        pipeline = pipeline_factory(rb, sample_count=8)
-        process = mp.get_context("spawn").Process(
-            target=_pattern_add_chunks,
-            args=(rb, 8, 5),
-        )
-
-        process.start()
-        process.join(timeout=15)
-        assert process.exitcode == 0
-        _wait_visible(pipeline, 40)
-
-        assert int(rb.ptr[0]) == 40
-        assert int(rb.size[0]) == 40
-
-    def test_sampled_batch_matches_replay_rows(self, pipeline_factory):
-        rb = _make_replay(capacity=128)
-        _pattern_add(rb, 0, 64)
-        pipeline = pipeline_factory(rb, sample_count=16)
-        assert pipeline.start_prepare(1, 16) is True
-        batch = pipeline.sample_large_batch(1, 16)
-        rewards = batch["rewards"].cpu()
-        assert rewards.min() >= 0
-        assert rewards.max() < 64
-        expected = _expected_pattern(rb, rewards)
-        for key, want in expected.items():
-            torch.testing.assert_close(batch[key].cpu(), want)
-        assert (batch["dones"].cpu() == 0).all()
-        assert (batch["truncated"].cpu() == 1).all()
-        assert batch["obs"].is_cuda
-
-    def test_deterministic_seed_produces_same_batch(self, pipeline_factory):
-        rb = _make_replay(capacity=128)
-        _pattern_add(rb, 0, 64)
-        p1 = pipeline_factory(rb, sample_count=16, base_seed=99)
-        b1 = p1.sample_large_batch(7, 16)
-        r1 = b1["rewards"].cpu().clone()
-        p1.close()
-        replay_again = _make_replay(capacity=128)
-        _pattern_add(replay_again, 0, 64)
-        p2 = pipeline_factory(replay_again, sample_count=16, base_seed=99)
-        b2 = p2.sample_large_batch(7, 16)
-        torch.testing.assert_close(r1, b2["rewards"].cpu())
-
-    def test_min_snapshot_ptr_gates_prepare(self, pipeline_factory):
-        rb = _make_replay(capacity=128)
-        _pattern_add(rb, 0, 32)
-        pipeline = pipeline_factory(rb, sample_count=8)
-        _wait_visible(pipeline, 32)
-        assert pipeline.start_prepare(5, 8, min_snapshot_ptr=48) is True
-        time.sleep(0.2)
-        assert pipeline.batch_ready(5, 8) is False
-        _pattern_add(rb, 32, 16)
-        _wait_batch_ready(pipeline, 5, 8)
-        batch = pipeline.sample_large_batch(5, 8)
-        assert batch["obs"].shape == (8, rb._obs_dim)
-
-    def test_sample_count_mismatch_is_rejected(self, pipeline_factory):
-        rb = _make_replay(capacity=64)
-        _pattern_add(rb, 0, 32)
-        pipeline = pipeline_factory(rb, sample_count=8)
-        with pytest.raises(ValueError, match="sample_count"):
-            pipeline.start_prepare(1, 999)
-        with pytest.raises(ValueError, match="sample_count"):
-            pipeline.batch_ready(1, 999)
-
-    def test_prepare_same_tick_idempotent_new_tick_raises(self, pipeline_factory):
-        rb = _make_replay(capacity=64)
-        _pattern_add(rb, 0, 32)
-        pipeline = pipeline_factory(rb, sample_count=8)
-        assert pipeline.start_prepare(1, 8) is True
-        assert pipeline.start_prepare(1, 8) is False
-        with pytest.raises(RuntimeError, match="consumed"):
-            pipeline.start_prepare(2, 8)
-
-    def test_hot_cold_swap_after_tick(self, pipeline_factory):
-        rb = _make_replay(capacity=64)
-        _pattern_add(rb, 0, 32)
-        pipeline = pipeline_factory(rb, sample_count=8)
-        pipeline.sample_large_batch(1, 8)
-        first_hot = pipeline._hot
-        pipeline.after_tick()
-        pipeline.start_prepare(2, 8)
-        pipeline.sample_large_batch(2, 8)
-        assert pipeline._hot != first_hot
-
-    def test_hot_batch_tick_mismatch_raises(self, pipeline_factory):
-        rb = _make_replay(capacity=64)
-        _pattern_add(rb, 0, 32)
-        pipeline = pipeline_factory(rb, sample_count=8)
-        pipeline.sample_large_batch(1, 8)
-        with pytest.raises(RuntimeError, match="Hot batch tick"):
-            pipeline.sample_large_batch(2, 8)
-        assert pipeline.batch_ready(2, 8) is False
-
-    def test_sac_graph_layout_column_order(self, pipeline_factory):
-        rb = _make_replay(capacity=128)
-        _pattern_add(rb, 0, 64)
-        pipeline = pipeline_factory(rb, sample_count=16, pack_layout="sac_graph")
-        batch = pipeline.sample_large_batch(1, 16)
-        src = batch["sac_graph_packed_source"]
-        assert src.shape == (16, rb.storage_width)
-        rewards = batch["rewards"].cpu()
-        expected = _expected_pattern(rb, rewards)
-        for key, want in expected.items():
-            torch.testing.assert_close(batch[key].cpu(), want)
-        # graph order: obs, critic, actions, rew, next_obs, next_critic, done, trunc
-        c = 0
-        torch.testing.assert_close(src[:, c : c + rb._obs_dim].cpu(), expected["obs"])
-        c += rb._obs_dim
-        torch.testing.assert_close(src[:, c : c + rb._critic_dim].cpu(), expected["critic"])
-        c += rb._critic_dim
-        torch.testing.assert_close(src[:, c : c + rb._action_dim].cpu(), expected["actions"])
-
-    def test_critic_graph_packed_source(self, pipeline_factory):
-        rb = _make_replay(capacity=128)
-        _pattern_add(rb, 0, 64)
-        pipeline = pipeline_factory(
-            rb,
-            sample_count=16,
-            use_critic_graph_packed_source=True,
-        )
-        batch = pipeline.sample_large_batch(1, 16)
-        cg = batch["critic_graph_packed_source"]
-        assert cg.shape == (16, rb.critic_graph_packed_width())
-        expected = _expected_pattern(rb, batch["rewards"].cpu())
-        # critic graph order: critic, actions, rew, next_obs, next_critic, done, trunc
-        torch.testing.assert_close(cg[:, : rb._critic_dim].cpu(), expected["critic"])
-        c = rb._critic_dim
-        torch.testing.assert_close(cg[:, c : c + rb._action_dim].cpu(), expected["actions"])
-
-    def test_close_stops_thread_and_unregisters(self, pipeline_factory):
-        rb = _make_replay(capacity=64)
-        pipeline = pipeline_factory(rb, sample_count=8)
-        pipeline.close()
-        assert pipeline._sync_thread is not None
-        assert not pipeline._sync_thread.is_alive()
-        assert not any(slot.is_pinned() for slot in rb._ingress_slots)
-
-
-@mps_only
-class TestMPSGPUResidentPipeline:
-    @pytest.fixture
-    def pipeline_factory(self):
-        created = []
-
-        def _make(rb, **kwargs):
-            kwargs.setdefault("device", "mps")
-            kwargs.setdefault("sample_count", 16)
-            pipeline = GPUResidentReplayPipeline(rb, **kwargs)
-            created.append(pipeline)
-            return pipeline
-
-        yield _make
-        for pipeline in created:
-            pipeline.close()
-
-    def test_allocates_mps_storage_and_samples_on_learner_thread(self, pipeline_factory):
-        rb = _make_replay(capacity=128, device="mps")
-        _pattern_add(rb, 0, 64)
-        pipeline = pipeline_factory(rb, sample_count=16)
-
-        batch = pipeline.sample_large_batch(1, 16)
-
-        assert pipeline._sync_thread is None
-        assert pipeline._host_pinned is False
-        assert pipeline.h2d_submitter == "gpu_resident_ingress_main_thread"
-        assert pipeline.transfer_manifest["device_submission_thread"] == "learner"
-        assert all(value.device.type == "mps" for value in batch.values())
-        rewards = batch["rewards"].cpu()
-        expected = _expected_pattern(rb, rewards)
-        for key, want in expected.items():
-            torch.testing.assert_close(batch[key].cpu(), want)
-
-    def test_bounded_ingress_commits_and_samples_on_learner_thread(self, pipeline_factory):
-        rb = _make_bounded_replay(capacity=64, slot_rows=16, device="mps")
-        _pattern_add(rb, 0, 16)
-        assert int(rb.ptr[0]) == 0
-        pipeline = pipeline_factory(rb, sample_count=8)
-
-        batch = pipeline.sample_large_batch(1, 8)
-
-        assert int(rb.ptr[0]) == 16
-        assert pipeline.transfer_manifest["storage_owner"] == "device"
-        assert pipeline.h2d_submitter == "gpu_resident_ingress_main_thread"
-        rewards = batch["rewards"].cpu()
-        expected = _expected_pattern(rb, rewards)
-        for key, want in expected.items():
-            torch.testing.assert_close(batch[key].cpu(), want)
-
-    def test_deterministic_seed_produces_same_mps_batch(self, pipeline_factory):
-        rb = _make_replay(capacity=128, device="mps")
-        _pattern_add(rb, 0, 64)
-        first = pipeline_factory(rb, sample_count=16, base_seed=99)
-        first_rewards = first.sample_large_batch(7, 16)["rewards"].cpu().clone()
-        first.close()
-
-        replay_again = _make_replay(capacity=128, device="mps")
-        _pattern_add(replay_again, 0, 64)
-        second = pipeline_factory(replay_again, sample_count=16, base_seed=99)
-        second_rewards = second.sample_large_batch(7, 16)["rewards"].cpu()
-
-        torch.testing.assert_close(first_rewards, second_rewards)
-
-    def test_min_snapshot_ptr_gates_mps_prepare(self, pipeline_factory):
-        rb = _make_replay(capacity=128, device="mps")
-        _pattern_add(rb, 0, 32)
-        pipeline = pipeline_factory(rb, sample_count=8)
-
-        assert pipeline.start_prepare(5, 8, min_snapshot_ptr=48) is True
-        deadline = time.monotonic() + 2.0
-        while pipeline._visible_ptr < 32 and time.monotonic() < deadline:
-            assert pipeline.batch_ready(5, 8) is False
-            time.sleep(0.001)
-        assert pipeline._visible_ptr == 32
-
-        _pattern_add(rb, 32, 16)
-        _wait_batch_ready(pipeline, 5, 8)
-        batch = pipeline.sample_large_batch(5, 8)
-
-        assert pipeline._visible_ptr == 48
-        assert batch["obs"].shape == (8, rb._obs_dim)
-
-    def test_mps_device_work_rejects_background_thread(self, pipeline_factory):
-        rb = _make_replay(capacity=64, device="mps")
-        _pattern_add(rb, 0, 32)
-        pipeline = pipeline_factory(rb, sample_count=8)
-        errors: list[BaseException] = []
-
-        def drive_from_background() -> None:
-            try:
-                pipeline.batch_ready(1, 8)
-            except BaseException as exc:
-                errors.append(exc)
-
-        thread = threading.Thread(target=drive_from_background)
-        thread.start()
-        thread.join(timeout=2.0)
-
-        assert not thread.is_alive()
-        assert len(errors) == 1
-        assert "learner thread" in str(errors[0])
-
-    def test_mps_pipeline_uses_event_waits_not_global_sync(self, pipeline_factory, monkeypatch):
-        rb = _make_replay(capacity=64, device="mps")
-        _pattern_add(rb, 0, 32)
-        pipeline = pipeline_factory(rb, sample_count=8)
-
-        def reject_global_sync() -> None:
-            raise AssertionError("gpu_resident MPS path must not globally synchronize")
-
-        monkeypatch.setattr(torch.mps, "synchronize", reject_global_sync)
-        batch = pipeline.sample_large_batch(1, 8)
-
-        assert batch["obs"].device.type == "mps"
-
-    def test_mps_sac_graph_layout_preserves_columns(self, pipeline_factory):
-        rb = _make_replay(capacity=128, device="mps")
-        _pattern_add(rb, 0, 64)
-        pipeline = pipeline_factory(rb, sample_count=16, pack_layout="sac_graph")
-
-        batch = pipeline.sample_large_batch(1, 16)
-        rewards = batch["rewards"].cpu()
-        expected = _expected_pattern(rb, rewards)
-
-        assert batch["sac_graph_packed_source"].device.type == "mps"
-        for key, want in expected.items():
-            torch.testing.assert_close(batch[key].cpu(), want)
diff --git a/tests/ipc/test_rollout_ring_buffer.py b/tests/ipc/test_rollout_ring_buffer.py
deleted file mode 100644
index 8c8aa6293..000000000
--- a/tests/ipc/test_rollout_ring_buffer.py
+++ /dev/null
@@ -1,241 +0,0 @@
-"""Tests for RolloutRingBuffer IPC primitive."""
-
-from __future__ import annotations
-
-import numpy as np
-import pytest
-import torch
-
-from unilab.ipc.rollout_ring_buffer import RolloutRingBuffer
-
-_NUM_ENVS = 4
-_NUM_STEPS = 10
-_OBS_DIM = 8
-_ACTION_DIM = 3
-_CRITIC_DIM = 5
-_NUM_SLOTS = 2
-
-_EXPECTED_FIELDS = {
-    "obs",
-    "actions",
-    "log_probs",
-    "rewards",
-    "dones",
-    "truncated",
-    "last_obs",
-}
-
-
-def _make_ring_buffer(num_slots: int = _NUM_SLOTS) -> RolloutRingBuffer:
-    return RolloutRingBuffer(
-        num_envs=_NUM_ENVS,
-        num_steps=_NUM_STEPS,
-        obs_dim=_OBS_DIM,
-        action_dim=_ACTION_DIM,
-        num_slots=num_slots,
-        create=True,
-    )
-
-
-def _make_ring_buffer_with_critic() -> RolloutRingBuffer:
-    return RolloutRingBuffer(
-        num_envs=_NUM_ENVS,
-        num_steps=_NUM_STEPS,
-        obs_dim=_OBS_DIM,
-        action_dim=_ACTION_DIM,
-        critic_dim=_CRITIC_DIM,
-        num_slots=_NUM_SLOTS,
-        create=True,
-    )
-
-
-def test_pointers_start_at_zero():
-    s = _make_ring_buffer()
-    assert int(s._write_ptr.value) == 0
-    assert int(s._read_ptr.value) == 0
-    s.cleanup()
-
-
-def test_signal_write_done_and_wait_for_data():
-    """signal_write_done() advances write_ptr; wait_for_data() returns True."""
-    s = _make_ring_buffer()
-    assert s.available() == 0
-    s.signal_write_done()
-    assert s.available() == 1
-    assert s.wait_for_data(timeout=0.1) is True
-    s.cleanup()
-
-
-def test_available_returns_correct_count():
-    s = _make_ring_buffer(num_slots=4)
-    assert s.available() == 0
-    for i in range(3):
-        s.signal_write_done()
-        assert s.available() == i + 1
-    s.cleanup()
-
-
-def test_advance_read_skips_overwritten_slots():
-    """If writer ran far ahead (wp - rp > num_slots), advance_read fast-forwards."""
-    s = _make_ring_buffer(num_slots=2)
-    # Writer produces 4 rollouts (2x more than num_slots)
-    for _ in range(4):
-        s.signal_write_done()
-    s.advance_read()
-    # After advance, rp should have jumped past overwritten slots
-    # wp - rp should be <= num_slots
-    assert int(s._write_ptr.value) - int(s._read_ptr.value) <= s.num_slots
-    s.cleanup()
-
-
-def test_reader_clamps_to_oldest_non_overwritten_slot_before_read():
-    s = _make_ring_buffer(num_slots=2)
-    for value in (1.0, 2.0, 3.0):
-        wb = s.write_buffer
-        for arr in wb.values():
-            arr[:] = value
-        s.signal_write_done()
-
-    assert s.available() == 2
-    first = s.read_torch("cpu")
-    assert torch.all(first["obs"] == 2.0)
-
-    s.advance_read()
-    second = s.read_torch("cpu")
-    assert torch.all(second["obs"] == 3.0)
-    s.cleanup()
-
-
-def test_read_torch_returns_all_fields():
-    """read_torch() must return a dict with all expected field keys."""
-    s = _make_ring_buffer()
-    # Fill write slot with non-zero data
-    wb = s.write_buffer
-    for arr in wb.values():
-        arr[:] = np.random.randn(*arr.shape).astype(np.float32)
-    s.signal_write_done()
-
-    result = s.read_torch("cpu")
-    assert set(result.keys()) == _EXPECTED_FIELDS
-    for k, v in result.items():
-        assert isinstance(v, torch.Tensor), f"{k} is not a tensor"
-    s.cleanup()
-
-
-def test_copy_read_slot_to_torch_reuses_destination_tensors():
-    s = _make_ring_buffer()
-    wb = s.write_buffer
-    for arr in wb.values():
-        arr[:] = 7.0
-    s.signal_write_done()
-
-    destination = {
-        field: torch.empty(shape, dtype=torch.float32) for field, shape in s.slot_shapes.items()
-    }
-    pointers = {field: tensor.data_ptr() for field, tensor in destination.items()}
-
-    s.copy_read_slot_to_torch(destination)
-
-    assert {field: tensor.data_ptr() for field, tensor in destination.items()} == pointers
-    for tensor in destination.values():
-        assert torch.all(tensor == 7.0)
-    s.cleanup()
-
-
-def test_cleanup_is_idempotent():
-    """cleanup() called twice must not raise."""
-    s = _make_ring_buffer()
-    s.cleanup()
-    s.cleanup()
-
-
-def test_wait_for_data_timeout_returns_false():
-    """wait_for_data() with a short timeout returns False when no data is available."""
-    s = _make_ring_buffer()
-    result = s.wait_for_data(timeout=0.05)
-    assert result is False
-    s.cleanup()
-
-
-def test_write_buffer_returns_dict_of_arrays():
-    """write_buffer property returns a dict of numpy arrays for the current write slot."""
-    s = _make_ring_buffer()
-    wb = s.write_buffer
-    assert set(wb.keys()) == _EXPECTED_FIELDS
-    for k, arr in wb.items():
-        assert hasattr(arr, "shape"), f"{k} is not an array"
-    s.cleanup()
-
-
-def test_close_does_not_raise():
-    """close() (attach-mode teardown, no unlink) must not raise."""
-    s = _make_ring_buffer()
-    s.close()
-
-
-def test_attach_create_false_reads_same_data():
-    """Attaching to existing shm (create=False) exposes the same data as the owner."""
-    owner = _make_ring_buffer()
-    # Write known data into write slot 0
-    wb = owner.write_buffer
-    for arr in wb.values():
-        arr[:] = 1.0
-    owner.signal_write_done()
-
-    # Attach
-    attached = RolloutRingBuffer(
-        num_envs=_NUM_ENVS,
-        num_steps=_NUM_STEPS,
-        obs_dim=_OBS_DIM,
-        action_dim=_ACTION_DIM,
-        num_slots=_NUM_SLOTS,
-        create=False,
-        shm_name_prefix=owner.name,
-    )
-    attached.attach_sync_primitives(owner._write_ptr, owner._read_ptr)
-
-    # Available should be 1 (owner wrote one slot)
-    assert attached.available() == 1
-
-    result = attached.read_torch("cpu")
-    assert set(result.keys()) == _EXPECTED_FIELDS
-
-    attached.close()
-    owner.cleanup()
-
-
-def test_attach_sync_primitives():
-    """attach_sync_primitives() replaces internal pointers in the attached instance."""
-    owner = _make_ring_buffer()
-    attached = RolloutRingBuffer(
-        num_envs=_NUM_ENVS,
-        num_steps=_NUM_STEPS,
-        obs_dim=_OBS_DIM,
-        action_dim=_ACTION_DIM,
-        num_slots=_NUM_SLOTS,
-        create=False,
-        shm_name_prefix=owner.name,
-    )
-    attached.attach_sync_primitives(owner._write_ptr, owner._read_ptr)
-    # After attaching, pointers are shared — incrementing owner's write_ptr is visible
-    owner.signal_write_done()
-    assert attached.available() == 1
-
-    attached.close()
-    owner.cleanup()
-
-
-def test_ring_buffer_allocates_optional_critic_fields():
-    ring_buffer = _make_ring_buffer_with_critic()
-
-    expected = _EXPECTED_FIELDS | {"critic", "last_critic"}
-    assert set(ring_buffer.write_buffer.keys()) == expected
-
-    ring_buffer.cleanup()
-
-
-def test_ring_buffer_ipc_contract_has_no_privileged_fields():
-    from unilab.ipc.rollout_ring_buffer import _FIELD_SHAPES
-
-    assert "priv_info" not in _FIELD_SHAPES
-    assert "last_priv_info" not in _FIELD_SHAPES
diff --git a/tests/ipc/test_shared_weight_sync.py b/tests/ipc/test_shared_weight_sync.py
deleted file mode 100644
index dabf3cecb..000000000
--- a/tests/ipc/test_shared_weight_sync.py
+++ /dev/null
@@ -1,178 +0,0 @@
-"""Tests for SharedWeightSync IPC primitive."""
-
-from __future__ import annotations
-
-import multiprocessing as mp
-from copy import deepcopy
-
-import numpy as np
-import torch
-
-from unilab.ipc.weight_sync import SharedWeightSync
-
-_SPAWN_CTX = mp.get_context("spawn")
-
-
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
-
-
-def _make_state_dict(shapes: dict) -> dict:
-    return {name: torch.randn(shape) for name, shape in shapes.items()}
-
-
-# ---------------------------------------------------------------------------
-# Single-process tests
-# ---------------------------------------------------------------------------
-
-
-def test_write_read_roundtrip(tiny_weight_shapes):
-    """Write weights then read them back; values must match within float32 eps."""
-    state_dict = _make_state_dict(tiny_weight_shapes)
-    ws = SharedWeightSync(tiny_weight_shapes, create=True)
-    ws.write_weights(state_dict)
-
-    # Build a zeroed copy to read into
-    read_sd = {name: torch.zeros(shape) for name, shape in tiny_weight_shapes.items()}
-    ws.read_weights_into(read_sd)
-
-    for name in tiny_weight_shapes:
-        assert torch.allclose(state_dict[name].float(), read_sd[name].float(), atol=1e-6), (
-            f"Mismatch for {name}"
-        )
-
-    ws.cleanup()
-
-
-def test_version_monotonically_increases(tiny_weight_shapes):
-    """Each write_weights call must increment version by 1."""
-    state_dict = _make_state_dict(tiny_weight_shapes)
-
-    ws = SharedWeightSync(tiny_weight_shapes, create=True)
-    assert ws.version == 0  # raw ctor starts at 0
-    ws.write_weights(state_dict)
-    assert ws.version == 1
-    ws.write_weights(state_dict)
-    assert ws.version == 2
-
-    ws.cleanup()
-
-
-def test_from_state_dict_classmethod(tiny_weight_shapes):
-    """from_state_dict() should return a valid object with version >= 1."""
-    state_dict = _make_state_dict(tiny_weight_shapes)
-    ws = SharedWeightSync.from_state_dict(state_dict, create=True)
-    assert ws.version >= 1
-    assert ws.name  # non-empty shm name
-
-    read_sd = {name: torch.zeros(shape) for name, shape in tiny_weight_shapes.items()}
-    version = ws.read_weights_into(read_sd)
-    assert version >= 1
-
-    for name in tiny_weight_shapes:
-        assert torch.allclose(state_dict[name].float(), read_sd[name].float(), atol=1e-6)
-
-    ws.cleanup()
-
-
-def test_cleanup_is_idempotent(tiny_weight_shapes):
-    """cleanup() called twice should not raise."""
-    state_dict = _make_state_dict(tiny_weight_shapes)
-    ws = SharedWeightSync.from_state_dict(state_dict, create=True)
-    ws.cleanup()
-    ws.cleanup()  # must not raise
-
-
-def test_close_without_unlink(tiny_weight_shapes):
-    """close() closes the handle without unlinking — safe to call from attached processes."""
-    state_dict = _make_state_dict(tiny_weight_shapes)
-    ws = SharedWeightSync.from_state_dict(state_dict, create=True)
-    ws.close()  # must not raise
-    ws.cleanup()  # owner still unlinks
-
-
-def test_attach_create_false_roundtrip(tiny_weight_shapes):
-    """create=False attaches to existing shm; read back values from owner."""
-    owner = SharedWeightSync.from_state_dict(_make_state_dict(tiny_weight_shapes), create=True)
-    # Attach without a lock (lock=None → no-lock path)
-    attached = SharedWeightSync(tiny_weight_shapes, create=False, shm_name=owner.name, lock=None)
-    assert attached.version == owner.version
-
-    read_sd = {name: torch.zeros(shape) for name, shape in tiny_weight_shapes.items()}
-    attached.read_weights_into(read_sd)
-
-    owner_sd = {name: torch.zeros(shape) for name, shape in tiny_weight_shapes.items()}
-    owner.read_weights_into(owner_sd)
-
-    for name in tiny_weight_shapes:
-        assert torch.allclose(read_sd[name], owner_sd[name], atol=1e-6)
-
-    attached.close()
-    owner.cleanup()
-
-
-def test_write_read_without_lock(tiny_weight_shapes):
-    """write_weights and read_weights_into with lock=None use the lockless path."""
-    state_dict = _make_state_dict(tiny_weight_shapes)
-    owner = SharedWeightSync.from_state_dict(state_dict, create=True)
-
-    # Attach with no lock — exercises else-branch in write_weights / read_weights_into
-    ws_nolock = SharedWeightSync(tiny_weight_shapes, create=False, shm_name=owner.name, lock=None)
-    new_sd = _make_state_dict(tiny_weight_shapes)
-    ws_nolock.write_weights(new_sd)
-
-    read_sd = {name: torch.zeros(shape) for name, shape in tiny_weight_shapes.items()}
-    version = ws_nolock.read_weights_into(read_sd)
-
-    assert version >= 1
-    for name in tiny_weight_shapes:
-        assert torch.allclose(new_sd[name].float(), read_sd[name].float(), atol=1e-6)
-
-    ws_nolock.close()
-    owner.cleanup()
-
-
-# ---------------------------------------------------------------------------
-# Multiprocess test
-# ---------------------------------------------------------------------------
-
-
-def _writer_fn(shm_name: str, lock, shapes: dict, out_queue):
-    """Subprocess: write random weights and report the version."""
-    ws = SharedWeightSync(shapes, create=False, shm_name=shm_name, lock=lock)
-    sd = {name: torch.randn(shape) for name, shape in shapes.items()}
-    ws.write_weights(sd)
-    out_queue.put({"version": ws.version, "sd": {k: v.numpy() for k, v in sd.items()}})
-    ws.close()
-
-
-def test_multiprocess_write_then_read(tiny_weight_shapes):
-    """Spawn a writer process; main process reads and verifies."""
-    # Create on main side
-    ws = SharedWeightSync(tiny_weight_shapes, create=True)
-    initial_version = ws.version  # 0
-
-    out_queue = _SPAWN_CTX.Queue()
-    p = _SPAWN_CTX.Process(
-        target=_writer_fn,
-        args=(ws.name, ws._lock, tiny_weight_shapes, out_queue),
-    )
-    p.start()
-    p.join(timeout=15)
-    assert p.exitcode == 0, f"Writer process failed with exit code {p.exitcode}"
-
-    result = out_queue.get_nowait()
-    written_version = result["version"]
-    written_sd = {k: torch.from_numpy(v) for k, v in result["sd"].items()}
-
-    read_sd = {name: torch.zeros(shape) for name, shape in tiny_weight_shapes.items()}
-    read_version = ws.read_weights_into(read_sd)
-
-    assert read_version > initial_version
-    assert read_version == written_version
-
-    for name in tiny_weight_shapes:
-        assert torch.allclose(written_sd[name].float(), read_sd[name].float(), atol=1e-6)
-
-    ws.cleanup()
diff --git a/tests/managers/__init__.py b/tests/managers/__init__.py
new file mode 100644
index 000000000..3eb352d0c
--- /dev/null
+++ b/tests/managers/__init__.py
@@ -0,0 +1 @@
+"""Tests for the standalone NumPy manager package."""
diff --git a/tests/managers/conftest.py b/tests/managers/conftest.py
new file mode 100644
index 000000000..fb80187af
--- /dev/null
+++ b/tests/managers/conftest.py
@@ -0,0 +1,57 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), manager test fixtures.
+# Modified by UniLab for NumPy and the standalone manager contract; Apache-2.0.
+
+from __future__ import annotations
+
+import re
+
+import numpy as np
+import pytest
+
+
+class FakeEntity:
+    def __init__(self) -> None:
+        self.joint_names = ["hip", "knee", "ankle"]
+        self.body_names = ["base", "foot"]
+        self.num_joints = len(self.joint_names)
+        self.num_bodies = len(self.body_names)
+
+    @staticmethod
+    def _find(
+        all_names: list[str], patterns: list[str], preserve_order: bool
+    ) -> tuple[list[int], list[str]]:
+        if preserve_order:
+            found = [
+                name for pattern in patterns for name in all_names if re.fullmatch(pattern, name)
+            ]
+        else:
+            found = [name for name in all_names if any(re.fullmatch(p, name) for p in patterns)]
+        return [all_names.index(name) for name in found], found
+
+    def find_joints(
+        self, patterns: list[str], *, preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]:
+        return self._find(self.joint_names, patterns, preserve_order)
+
+    def find_bodies(
+        self, patterns: list[str], *, preserve_order: bool = False
+    ) -> tuple[list[int], list[str]]:
+        return self._find(self.body_names, patterns, preserve_order)
+
+
+class FakeEnv:
+    def __init__(self, seed: int = 7, num_envs: int = 4) -> None:
+        self.num_envs = num_envs
+        self.rng = np.random.default_rng(seed)
+        self.scene = {"robot": FakeEntity()}
+        self.max_episode_length_s = 2.0
+        self.value = np.arange(num_envs, dtype=np.float32)
+        self.obs = np.arange(num_envs * 2, dtype=np.float32).reshape(num_envs, 2)
+        self.calls: list[tuple[str, np.ndarray | None]] = []
+        self.obs_buf: dict[str, np.ndarray] = {}
+        self.reset_buf = np.zeros(num_envs, dtype=np.bool_)
+
+
+@pytest.fixture
+def fake_env() -> FakeEnv:
+    return FakeEnv()
diff --git a/tests/managers/test_core_managers.py b/tests/managers/test_core_managers.py
new file mode 100644
index 000000000..ce0f84524
--- /dev/null
+++ b/tests/managers/test_core_managers.py
@@ -0,0 +1,274 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), manager tests.
+# Modified by UniLab for NumPy and fail-closed term validation; Apache-2.0.
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import numpy as np
+import pytest
+
+from unilab.managers import (
+    ActionManager,
+    ActionTerm,
+    ActionTermCfg,
+    CurriculumManager,
+    CurriculumTermCfg,
+    NullCurriculumManager,
+    RewardManager,
+    RewardTermCfg,
+    SceneEntityCfg,
+    TerminationManager,
+    TerminationTermCfg,
+)
+
+from .conftest import FakeEnv
+
+
+class DummyAction(ActionTerm):
+    def __init__(self, cfg: DummyActionCfg, env: FakeEnv):
+        super().__init__(cfg, env)
+        self._raw = np.zeros((env.num_envs, cfg.dim), dtype=np.float32)
+        self.applied = 0
+        self.reset_ids: np.ndarray | slice | None = None
+
+    @property
+    def action_dim(self) -> int:
+        return self._raw.shape[1]
+
+    @property
+    def raw_action(self) -> np.ndarray:
+        return self._raw
+
+    def process_actions(self, actions: np.ndarray) -> None:
+        self._raw[:] = actions
+
+    def apply_actions(self) -> None:
+        self.applied += 1
+
+    def reset(self, env_ids: np.ndarray | slice | None) -> None:
+        self.reset_ids = env_ids
+        self._raw[env_ids] = 0.0
+
+
+@dataclass(kw_only=True)
+class DummyActionCfg(ActionTermCfg):
+    dim: int
+
+    def build(self, env: FakeEnv) -> DummyAction:
+        return DummyAction(self, env)
+
+
+class FeedbackDummyAction(DummyAction):
+    requires_substep_state_feedback = True
+
+
+@dataclass(kw_only=True)
+class FeedbackDummyActionCfg(DummyActionCfg):
+    def build(self, env: FakeEnv) -> FeedbackDummyAction:
+        return FeedbackDummyAction(self, env)
+
+
+def test_action_split_history_apply_and_partial_reset(fake_env: FakeEnv) -> None:
+    manager = ActionManager(
+        {
+            "legs": DummyActionCfg(entity_name="robot", dim=2),
+            "disabled": None,
+            "arm": DummyActionCfg(entity_name="robot", dim=1),
+        },
+        fake_env,
+    )
+    assert manager.active_terms == ["legs", "arm"]
+    assert not manager.requires_substep_state_feedback
+    first = np.arange(12, dtype=np.float32).reshape(4, 3)
+    second = first + 20
+    manager.process_action(first)
+    manager.process_action(second)
+    np.testing.assert_array_equal(manager.prev_action, first)
+    np.testing.assert_array_equal(manager.action, second)
+    np.testing.assert_array_equal(manager.get_term("legs").raw_action, second[:, :2])
+    manager.apply_action()
+    assert manager.get_term("legs").applied == 1
+    manager.reset(np.array([1, 3]))
+    np.testing.assert_array_equal(manager.action[[1, 3]], 0.0)
+    np.testing.assert_array_equal(manager.action[[0, 2]], second[[0, 2]])
+
+
+def test_action_manager_aggregates_substep_state_feedback(fake_env: FakeEnv) -> None:
+    manager = ActionManager(
+        {
+            "invariant": DummyActionCfg(entity_name="robot", dim=1),
+            "feedback": FeedbackDummyActionCfg(entity_name="robot", dim=1),
+        },
+        fake_env,
+    )
+
+    assert manager.requires_substep_state_feedback
+
+
+def test_action_feedback_declaration_must_be_bool(
+    fake_env: FakeEnv,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    monkeypatch.setattr(DummyAction, "requires_substep_state_feedback", "yes")
+
+    with pytest.raises(TypeError, match="requires_substep_state_feedback must be bool"):
+        ActionManager({"invalid": DummyActionCfg(entity_name="robot", dim=1)}, fake_env)
+
+
+@pytest.mark.parametrize(
+    "action,match",
+    [
+        (np.zeros((4, 2), dtype=np.float32), "Invalid action shape"),
+        (np.full((4, 3), np.nan, dtype=np.float32), "NaN or Inf"),
+    ],
+)
+def test_action_rejects_invalid_input(fake_env: FakeEnv, action: np.ndarray, match: str) -> None:
+    manager = ActionManager({"a": DummyActionCfg(entity_name="robot", dim=3)}, fake_env)
+    with pytest.raises(ValueError, match=match):
+        manager.process_action(action)
+
+
+class FailingAction(DummyAction):
+    def process_actions(self, actions: np.ndarray) -> None:
+        del actions
+        raise ValueError("invalid processed target")
+
+    def apply_actions(self) -> None:
+        raise NotImplementedError("backend control write unavailable")
+
+
+@dataclass(kw_only=True)
+class FailingActionCfg(DummyActionCfg):
+    def build(self, env: FakeEnv) -> FailingAction:
+        return FailingAction(self, env)
+
+
+def test_action_term_errors_include_manager_and_term_context(fake_env: FakeEnv) -> None:
+    manager = ActionManager(
+        {"broken": FailingActionCfg(entity_name="robot", dim=1)},
+        fake_env,
+    )
+    with pytest.raises(ValueError, match="ActionManager term 'broken'.*invalid processed"):
+        manager.process_action(np.zeros((fake_env.num_envs, 1), dtype=np.float32))
+    with pytest.raises(NotImplementedError, match="ActionManager term 'broken'.*control write"):
+        manager.apply_action()
+
+
+class StatefulReward:
+    def __init__(self, cfg: RewardTermCfg, env: FakeEnv):
+        self.reset_ids = None
+
+    def __call__(self, env: FakeEnv) -> np.ndarray:
+        return env.value.copy()
+
+    def reset(self, env_ids: np.ndarray | slice | None) -> None:
+        self.reset_ids = env_ids
+
+
+def test_reward_dt_scaling_reset_and_config_immutability(fake_env: FakeEnv) -> None:
+    cfg = {"stateful": RewardTermCfg(func=StatefulReward, weight=2.0)}
+    manager = RewardManager(cfg, fake_env)
+    np.testing.assert_allclose(manager.compute(dt=0.25), fake_env.value * 0.5)
+    assert manager.get_active_iterable_terms(2) == [("stateful", [4.0])]
+    extras = manager.reset(np.array([1, 2]))
+    assert extras["Episode_Reward/stateful"] == pytest.approx(0.375)
+    assert cfg["stateful"].func is StatefulReward
+    assert isinstance(manager.get_term_cfg("stateful").func, StatefulReward)
+
+
+def test_reward_step_extras_report_per_term_weighted_rates(fake_env: FakeEnv) -> None:
+    def ones(env: FakeEnv) -> np.ndarray:
+        return np.ones(env.num_envs, dtype=np.float32)
+
+    cfg = {
+        "pos": RewardTermCfg(func=ones, weight=2.0),
+        "neg": RewardTermCfg(func=lambda env: env.value, weight=-0.5),
+        "zero": RewardTermCfg(func=ones, weight=0.0),
+    }
+    manager = RewardManager(cfg, fake_env)
+    manager.compute(dt=0.25)
+
+    extras = manager.step_reward_extras()
+    assert set(extras) == {"reward/pos", "reward/neg", "reward/zero"}
+    # Weighted reward rate (raw_value * weight), not scaled by dt.
+    assert extras["reward/pos"] == pytest.approx(2.0)
+    assert extras["reward/neg"] == pytest.approx(float(np.mean(fake_env.value)) * -0.5)
+    assert extras["reward/zero"] == 0.0
+
+
+@pytest.mark.parametrize("bad", [np.nan, np.inf, -np.inf])
+def test_reward_nonfinite_is_an_error(fake_env: FakeEnv, bad: float) -> None:
+    def reward(env: FakeEnv) -> np.ndarray:
+        value = np.ones(env.num_envs, dtype=np.float32)
+        value[2] = bad
+        return value
+
+    manager = RewardManager({"bad_reward": RewardTermCfg(func=reward, weight=1.0)}, fake_env)
+    with pytest.raises(ValueError, match="RewardManager term 'bad_reward'"):
+        manager.compute(0.01)
+
+
+def test_reward_and_termination_shape_validation(fake_env: FakeEnv) -> None:
+    reward = RewardManager(
+        {"bad": RewardTermCfg(func=lambda env: np.zeros((env.num_envs, 1)), weight=1.0)},
+        fake_env,
+    )
+    with pytest.raises(ValueError, match=r"expected \(4,\)"):
+        reward.compute(0.1)
+
+    termination = TerminationManager(
+        {"bad": TerminationTermCfg(func=lambda env: np.zeros(env.num_envs))}, fake_env
+    )
+    with pytest.raises(TypeError, match="expected bool"):
+        termination.compute()
+
+
+def test_termination_splits_timeouts_and_failures(fake_env: FakeEnv) -> None:
+    timeout = np.array([True, False, False, True])
+    failure = np.array([False, True, False, True])
+    manager = TerminationManager(
+        {
+            "timeout": TerminationTermCfg(func=lambda env: timeout.copy(), time_out=True),
+            "failure": TerminationTermCfg(func=lambda env: failure.copy()),
+        },
+        fake_env,
+    )
+    np.testing.assert_array_equal(manager.compute(), timeout | failure)
+    np.testing.assert_array_equal(manager.time_outs, timeout)
+    np.testing.assert_array_equal(manager.terminated, failure)
+    assert manager.reset(np.array([0, 1])) == {
+        "Episode_Termination/timeout": 1,
+        "Episode_Termination/failure": 1,
+    }
+
+
+def test_curriculum_and_null_semantics(fake_env: FakeEnv) -> None:
+    def update(env: FakeEnv, env_ids: np.ndarray | slice) -> dict[str, float]:
+        return {"difficulty": 3.0}
+
+    manager = CurriculumManager({"terrain": CurriculumTermCfg(func=update)}, fake_env)
+    manager.compute(np.array([1, 2]))
+    assert manager.reset()["Curriculum/terrain/difficulty"] == 3.0
+    null = NullCurriculumManager()
+    assert null.active_terms == []
+    assert null.reset() == {}
+
+    bad = CurriculumManager({"bad": CurriculumTermCfg(func=lambda env, env_ids: np.nan)}, fake_env)
+    with pytest.raises(ValueError, match="CurriculumManager term 'bad'"):
+        bad.compute()
+
+
+def test_scene_entity_selector_resolution(fake_env: FakeEnv) -> None:
+    cfg = SceneEntityCfg(name="robot", joint_names=("ankle", "hip"), preserve_order=True)
+    cfg.resolve(fake_env.scene)
+    assert cfg.joint_names == ["ankle", "hip"]
+    assert cfg.joint_ids == [2, 0]
+
+    all_joints = SceneEntityCfg(name="robot", joint_names=".*")
+    all_joints.resolve(fake_env.scene)
+    assert all_joints.joint_ids == slice(None)
+
+    inconsistent = SceneEntityCfg(name="robot", joint_names="hip", joint_ids=[1])
+    with pytest.raises(ValueError, match="Inconsistent joint"):
+        inconsistent.resolve(fake_env.scene)
diff --git a/tests/managers/test_event_command_metrics_recorder.py b/tests/managers/test_event_command_metrics_recorder.py
new file mode 100644
index 000000000..9a2533eb2
--- /dev/null
+++ b/tests/managers/test_event_command_metrics_recorder.py
@@ -0,0 +1,282 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), manager tests.
+# Modified by UniLab for NumPy scheduling and unsupported capability errors; Apache-2.0.
+
+from __future__ import annotations
+
+import ast
+from dataclasses import dataclass
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+import unilab.managers as managers
+from unilab.envs.mdp.recorders import LifecycleCounterRecorder
+from unilab.managers import (
+    CommandManager,
+    CommandTerm,
+    CommandTermCfg,
+    EventManager,
+    EventTermCfg,
+    MetricsManager,
+    MetricsTermCfg,
+    NullCommandManager,
+    NullMetricsManager,
+    NullRecorderManager,
+    RecorderManager,
+    RecorderTerm,
+    RecorderTermCfg,
+)
+
+from .conftest import FakeEnv
+
+
+def _record(env: FakeEnv, env_ids: np.ndarray | None, *, label: str) -> None:
+    copied = None if env_ids is None else np.asarray(env_ids).copy()
+    env.calls.append((label, copied))
+
+
+def test_event_modes_interval_reset_throttle_and_order(fake_env: FakeEnv) -> None:
+    cfg = {
+        "startup": EventTermCfg(func=_record, params={"label": "startup"}, mode="startup"),
+        "reset": EventTermCfg(
+            func=_record,
+            params={"label": "reset"},
+            mode="reset",
+            min_step_count_between_reset=3,
+        ),
+        "interval": EventTermCfg(
+            func=_record,
+            params={"label": "interval"},
+            mode="interval",
+            interval_range_s=(0.1, 0.1),
+        ),
+        "step": EventTermCfg(func=_record, params={"label": "step"}, mode="step"),
+    }
+    manager = EventManager(cfg, fake_env)
+    assert list(manager.active_terms) == ["startup", "reset", "interval", "step"]
+    manager.apply("startup", env_ids=np.array([0, 2]))
+    manager.apply("step", dt=0.01)
+    manager.apply("interval", dt=0.1)
+    manager.apply("reset", env_ids=np.array([1, 3]), global_env_step_count=1)
+    manager.apply("reset", env_ids=np.array([1, 3]), global_env_step_count=2)
+    assert [label for label, _ in fake_env.calls] == ["startup", "step", "interval", "reset"]
+    np.testing.assert_array_equal(fake_env.calls[2][1], np.arange(fake_env.num_envs))
+
+
+def test_event_validation_and_model_mutation_failure(fake_env: FakeEnv) -> None:
+    with pytest.raises(ValueError, match="interval_range_s"):
+        EventManager({"bad": EventTermCfg(func=_record, mode="interval")}, fake_env)
+
+    def model_mutation(env: FakeEnv, env_ids: np.ndarray | None) -> None:
+        pass
+
+    model_mutation.model_fields = ("body_mass",)  # type: ignore[attr-defined]
+    with pytest.raises(NotImplementedError, match="model-field mutation"):
+        EventManager({"unsupported": EventTermCfg(func=model_mutation, mode="startup")}, fake_env)
+
+    empty = EventManager({}, fake_env)
+    empty.apply("interval")
+
+
+def test_event_interval_rng_is_reproducible() -> None:
+    cfg = {
+        "interval": EventTermCfg(
+            func=_record,
+            params={"label": "interval"},
+            mode="interval",
+            interval_range_s=(0.1, 2.0),
+        )
+    }
+    left = EventManager(cfg, FakeEnv(seed=17))
+    right = EventManager(cfg, FakeEnv(seed=17))
+    np.testing.assert_array_equal(left._interval_term_time_left, right._interval_term_time_left)
+
+
+class DummyCommand(CommandTerm):
+    def __init__(self, cfg: DummyCommandCfg, env: FakeEnv):
+        super().__init__(cfg, env)
+        self._command = np.zeros((env.num_envs, 1), dtype=np.float32)
+        self.metrics["error"] = np.arange(env.num_envs, dtype=np.float32)
+
+    @property
+    def command(self) -> np.ndarray:
+        return self._command
+
+    def _update_metrics(self, env_ids: np.ndarray | None = None) -> None:
+        self.metrics["error"] += 1.0
+
+    def _resample_command(self, env_ids: np.ndarray) -> None:
+        self._command[env_ids, 0] = self.command_counter[env_ids]
+
+    def _update_command(self, env_ids: np.ndarray | None) -> None:
+        pass
+
+
+@dataclass(kw_only=True)
+class DummyCommandCfg(CommandTermCfg):
+    def build(self, env: FakeEnv) -> DummyCommand:
+        return DummyCommand(self, env)
+
+
+def test_command_resample_metrics_validation_and_null(fake_env: FakeEnv) -> None:
+    manager = CommandManager({"goal": DummyCommandCfg(resampling_time_range=(0.5, 0.5))}, fake_env)
+    extras = manager.reset(np.array([1, 2]))
+    assert extras == {"Metrics/goal/error": 1.5}
+    np.testing.assert_array_equal(manager.get_command("goal")[[1, 2]], 0.0)
+    manager.compute(0.5)
+    np.testing.assert_array_equal(manager.get_term("goal").command[:, 0], [0, 1, 1, 0])
+    assert manager.get_term("goal").command_counter.tolist() == [1, 2, 2, 1]
+
+    manager.get_term("goal").command[0, 0] = np.nan
+    with pytest.raises(ValueError, match="NaN or Inf"):
+        manager.get_command("goal")
+
+    null = NullCommandManager()
+    assert null.get_command("missing") is None
+    assert null.reset() == {}
+
+
+def test_command_viewer_request_and_old_signature_fail_closed(fake_env: FakeEnv) -> None:
+    with pytest.raises(NotImplementedError, match="viewer"):
+        CommandManager(
+            {"goal": DummyCommandCfg(resampling_time_range=(1.0, 1.0), debug_vis=True)},
+            fake_env,
+        )
+
+    class OldCommand(DummyCommand):
+        def _update_command(self) -> None:  # type: ignore[override]
+            pass
+
+    @dataclass(kw_only=True)
+    class OldCfg(CommandTermCfg):
+        def build(self, env: FakeEnv) -> OldCommand:
+            return OldCommand(self, env)
+
+    with pytest.raises(TypeError, match="must accept env_ids"):
+        CommandManager({"old": OldCfg(resampling_time_range=(1.0, 1.0))}, fake_env)
+
+
+def test_metrics_reductions_substeps_reset_and_finite_failure(fake_env: FakeEnv) -> None:
+    manager = MetricsManager(
+        {
+            "mean": MetricsTermCfg(func=lambda env: env.value.copy(), reduce="mean"),
+            "max": MetricsTermCfg(func=lambda env: env.value.copy(), reduce="max"),
+            "sum": MetricsTermCfg(func=lambda env: env.value.copy(), reduce="sum"),
+            "last": MetricsTermCfg(func=lambda env: env.value.copy(), reduce="last"),
+            "substep": MetricsTermCfg(
+                func=lambda env: env.value.copy(), per_substep=True, reduce="mean"
+            ),
+        },
+        fake_env,
+    )
+    manager.compute_substep()
+    fake_env.value += 2
+    manager.compute_substep()
+    manager.compute()
+    extras = manager.reset(np.array([1, 2]))
+    assert extras["Episode_Metrics/mean"] == pytest.approx(3.5)
+    assert extras["Episode_Metrics/max"] == pytest.approx(3.5)
+    assert extras["Episode_Metrics/sum"] == pytest.approx(3.5)
+    assert extras["Episode_Metrics/last"] == pytest.approx(3.5)
+    assert extras["Episode_Metrics/substep"] == pytest.approx(2.5)
+
+    bad = MetricsManager(
+        {"bad": MetricsTermCfg(func=lambda env: np.full(env.num_envs, np.inf))}, fake_env
+    )
+    with pytest.raises(ValueError, match="MetricsManager term 'bad'"):
+        bad.compute()
+    assert NullMetricsManager().reset() == {}
+
+
+class TraceRecorder(RecorderTerm):
+    def __init__(self, cfg: RecorderTermCfg, env: FakeEnv):
+        super().__init__(cfg, env)
+        self.events: list[tuple[str, list[int] | None]] = []
+
+    def record_pre_reset(self, env_ids: np.ndarray) -> None:
+        self.events.append(("pre", env_ids.tolist()))
+
+    def record_post_reset(self, env_ids: np.ndarray) -> None:
+        self.events.append(("post_reset", env_ids.tolist()))
+
+    def record_post_step(self) -> None:
+        self.events.append(("step", None))
+
+    def close(self) -> None:
+        self.events.append(("close", None))
+
+
+def test_recorder_lifecycle_and_null(fake_env: FakeEnv) -> None:
+    cfg = {"trace": RecorderTermCfg(func=TraceRecorder)}
+    manager = RecorderManager(cfg, fake_env)
+    ids = np.array([0, 3])
+    manager.record_pre_reset(ids)
+    manager.record_post_reset(ids)
+    manager.record_post_step()
+    manager.close()
+    assert manager.get_term("trace").events == [
+        ("pre", [0, 3]),
+        ("post_reset", [0, 3]),
+        ("step", None),
+        ("close", None),
+    ]
+    assert cfg["trace"].func is TraceRecorder
+    null = NullRecorderManager()
+    with pytest.raises(KeyError, match="has no terms"):
+        null.get_term("trace")
+
+
+def test_lifecycle_counter_recorder_is_task_independent(fake_env: FakeEnv) -> None:
+    manager = RecorderManager(
+        {"counter": RecorderTermCfg(func=LifecycleCounterRecorder)},
+        fake_env,
+    )
+    recorder = manager.get_term("counter")
+    ids = np.asarray([0, 3], dtype=np.int32)
+
+    manager.record_pre_reset(ids)
+    manager.record_post_reset(ids)
+    manager.record_post_step()
+
+    assert isinstance(recorder, LifecycleCounterRecorder)
+    assert recorder.pre_reset_count == 2
+    assert recorder.post_reset_count == 2
+    assert recorder.post_step_count == 1
+
+
+def test_public_exports_and_repository_import_boundary() -> None:
+    expected = {
+        "ManagerBase",
+        "ManagerTermBase",
+        "ManagerTermBaseCfg",
+        "ActionManager",
+        "ObservationManager",
+        "RewardManager",
+        "TerminationManager",
+        "EventManager",
+        "CommandManager",
+        "CurriculumManager",
+        "MetricsManager",
+        "RecorderManager",
+        "SceneEntityCfg",
+    }
+    assert expected <= set(vars(managers))
+
+    package_root = Path(managers.__file__).parent
+    forbidden_roots = {"torch", "mjlab"}
+    forbidden_unilab = {"uni_rl", "unilab.runners", "unilab.scripts", "unilab.base.backend"}
+    for path in package_root.rglob("*.py"):
+        tree = ast.parse(path.read_text())
+        imports = []
+        for node in ast.walk(tree):
+            if isinstance(node, ast.Import):
+                imports.extend(alias.name for alias in node.names)
+            elif isinstance(node, ast.ImportFrom) and node.module:
+                imports.append(node.module)
+        assert not ({name.split(".")[0] for name in imports} & forbidden_roots), path
+        assert not any(
+            name == prefix or name.startswith(prefix + ".")
+            for name in imports
+            for prefix in forbidden_unilab
+        ), path
diff --git a/tests/managers/test_isaac_lab_migration_fixture.py b/tests/managers/test_isaac_lab_migration_fixture.py
new file mode 100644
index 000000000..6043d5537
--- /dev/null
+++ b/tests/managers/test_isaac_lab_migration_fixture.py
@@ -0,0 +1,188 @@
+"""End-to-end evidence for the Isaac Lab Manager-Based migration fixture."""
+
+from __future__ import annotations
+
+from dataclasses import fields, is_dataclass
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pytest
+from hydra import compose, initialize_config_dir
+from hydra.core.global_hydra import GlobalHydra
+from omegaconf import DictConfig, OmegaConf
+
+from tests.fixtures.isaac_lab_cartpole import FIXTURE_ENV_NAME
+from tests.fixtures.isaac_lab_cartpole import task as fixture
+from unilab.base import registry
+from unilab.base.config_adapter import BackendAdapter
+from unilab.base.config_materialization import apply_cfg_overrides
+from unilab.envs import ManagerBasedRlEnv, ManagerBasedRLEnvCfg, ManagerBasedRlEnvCfg
+
+ROOT_DIR = Path(__file__).parents[2]
+FIXTURE_DIR = ROOT_DIR / "tests" / "fixtures" / "isaac_lab_cartpole"
+
+
+def _compose_fixture() -> DictConfig:
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(config_dir=str(FIXTURE_DIR / "conf"), version_base="1.3"):
+        return compose("config")
+
+
+def _materialize_fixture() -> tuple[DictConfig, ManagerBasedRlEnvCfg, dict[str, Any]]:
+    hydra_cfg = _compose_fixture()
+    override = BackendAdapter(hydra_cfg, root_dir=ROOT_DIR).build_task_env_cfg_override()
+    env_cfg = registry.materialize_env_config(FIXTURE_ENV_NAME)
+    assert isinstance(env_cfg, ManagerBasedRlEnvCfg)
+    apply_cfg_overrides(env_cfg, override)
+    env_cfg.validate()
+    return hydra_cfg, env_cfg, override
+
+
+def _assert_plain(value: Any) -> None:
+    assert not OmegaConf.is_config(value)
+    if is_dataclass(value) and not isinstance(value, type):
+        for field in fields(value):
+            _assert_plain(getattr(value, field.name))
+    elif isinstance(value, dict):
+        for key, item in value.items():
+            _assert_plain(key)
+            _assert_plain(item)
+    elif isinstance(value, (tuple, list)):
+        for item in value:
+            _assert_plain(item)
+
+
+def test_fixture_hydra_owner_materializes_source_order_as_plain_manager_cfg() -> None:
+    hydra_cfg, env_cfg, _ = _materialize_fixture()
+
+    assert hydra_cfg.training.task_name == FIXTURE_ENV_NAME
+    assert hydra_cfg.training.sim_backend == "mujoco"
+    assert ManagerBasedRLEnvCfg is ManagerBasedRlEnvCfg
+    assert list(env_cfg.observations) == ["policy"]
+    assert list(env_cfg.observations["policy"].terms) == ["joint_pos_rel", "joint_vel_rel"]
+    assert list(env_cfg.actions) == ["joint_effort"]
+    assert list(env_cfg.events) == ["reset_cart_position", "reset_pole_position"]
+    assert list(env_cfg.rewards) == [
+        "alive",
+        "terminating",
+        "pole_pos",
+        "cart_vel",
+        "pole_vel",
+    ]
+    assert list(env_cfg.terminations) == ["time_out", "cart_out_of_bounds"]
+    assert env_cfg.actions["joint_effort"].scale == pytest.approx(100.0)
+    assert env_cfg.sim_dt == pytest.approx(1.0 / 120.0)
+    assert env_cfg.ctrl_dt == pytest.approx(1.0 / 60.0)
+    assert env_cfg.max_episode_seconds == pytest.approx(5.0)
+    assert env_cfg.policy_observation_group == "policy"
+    assert env_cfg.critic_observation_group is None
+    _assert_plain(env_cfg)
+
+
+def test_fixture_real_mujoco_reset_step_and_partial_reset() -> None:
+    _, _, override = _materialize_fixture()
+    env = registry.make(
+        FIXTURE_ENV_NAME,
+        sim_backend="mujoco",
+        env_cfg_override=override,
+        num_envs=8,
+    )
+    assert isinstance(env, ManagerBasedRlEnv)
+    try:
+        state = env.init_state()
+        assert env.obs_groups_spec == {"obs": 4}
+        assert env.action_space.shape == (1,)
+        assert state.obs["obs"].shape == (8, 4)
+        assert np.isfinite(state.obs["obs"]).all()
+
+        before = env.scene["robot"].data.joint_pos.copy()
+        reset_ids = np.asarray([1, 6], dtype=np.int32)
+        reset_obs, _ = env.reset(env_ids=reset_ids)
+        after = env.scene["robot"].data.joint_pos.copy()
+        assert reset_obs["obs"].shape == (2, 4)
+        np.testing.assert_array_equal(after[[0, 2, 3, 4, 5, 7]], before[[0, 2, 3, 4, 5, 7]])
+        assert np.all(np.abs(after[reset_ids, 0]) <= 1.0)
+        assert np.all(np.abs(after[reset_ids, 1]) <= 0.25 * np.pi)
+
+        state = env.step(np.zeros((8, 1), dtype=np.float32))
+        assert state.obs["obs"].shape == (8, 4)
+        assert state.reward.shape == (8,)
+        assert state.terminated.shape == (8,)
+        assert state.truncated.shape == (8,)
+        assert np.isfinite(state.obs["obs"]).all()
+        assert np.isfinite(state.reward).all()
+
+        robot = env.scene["robot"]
+        pole_pos = robot.data.joint_pos[:, 1]
+        expected = 1.0 - np.square(np.remainder(pole_pos + np.pi, 2.0 * np.pi) - np.pi)
+        expected -= 0.01 * np.abs(robot.data.joint_vel[:, 0])
+        expected -= 0.005 * np.abs(robot.data.joint_vel[:, 1])
+        np.testing.assert_allclose(state.reward, expected * env.step_dt, rtol=1e-5, atol=1e-6)
+    finally:
+        env.close()
+
+
+def test_fixture_missing_actuator_fails_during_cold_path_binding() -> None:
+    _, _, override = _materialize_fixture()
+    override["actions"]["joint_effort"]["actuator_names"] = ["missing_actuator"]
+
+    with pytest.raises(
+        ValueError,
+        match="Not all entity selector regular expressions matched.*missing_actuator",
+    ):
+        registry.make(
+            FIXTURE_ENV_NAME,
+            sim_backend="mujoco",
+            env_cfg_override=override,
+            num_envs=2,
+        )
+
+
+def test_fixture_stays_test_only_and_has_no_external_runtime_imports() -> None:
+    source = (FIXTURE_DIR / "task.py").read_text(encoding="utf-8")
+    executable = "\n".join(
+        line for line in source.splitlines() if not line.lstrip().startswith("#")
+    )
+    for forbidden in (
+        "import torch",
+        "from torch",
+        "import isaaclab",
+        "from isaaclab",
+        "uni_rl",
+        "unilab.training",
+    ):
+        assert forbidden not in executable
+    assert Path(fixture.__file__).is_relative_to(ROOT_DIR / "tests" / "fixtures")
+    assert "tests.fixtures" not in tuple(registry._DEFAULT_REGISTRY_PACKAGES)
+    assert set(registry._envs[FIXTURE_ENV_NAME].env_factory_dict) == {"mujoco"}
+
+
+def test_fixture_local_term_math_matches_isaac_source() -> None:
+    class _Data:
+        joint_pos = np.asarray([[0.2, 3.5], [-0.3, -3.4]], dtype=np.float32)
+        joint_vel = np.asarray([[0.4, -0.7], [-0.2, 0.9]], dtype=np.float32)
+
+    class _Entity:
+        data = _Data()
+
+    class _Scene(dict):
+        pass
+
+    env = type("FixtureMathEnv", (), {"scene": _Scene(robot=_Entity())})()
+    pole = fixture.SceneEntityCfg("robot", joint_ids=[1])
+    slider = fixture.SceneEntityCfg("robot", joint_ids=[0])
+
+    expected_wrapped = np.remainder(_Data.joint_pos[:, 1] + np.pi, 2.0 * np.pi) - np.pi
+    np.testing.assert_allclose(
+        fixture.joint_pos_target_l2(env, target=0.0, asset_cfg=pole),
+        np.square(expected_wrapped),
+    )
+    np.testing.assert_allclose(
+        fixture.joint_vel_l1(env, asset_cfg=pole),
+        np.abs(_Data.joint_vel[:, 1]),
+    )
+    np.testing.assert_array_equal(
+        fixture.joint_pos_out_of_manual_limit(env, bounds=(-0.25, 0.25), asset_cfg=slider),
+        np.asarray([False, True]),
+    )
diff --git a/tests/managers/test_mjlab_migration_fixture.py b/tests/managers/test_mjlab_migration_fixture.py
new file mode 100644
index 000000000..baefc8d59
--- /dev/null
+++ b/tests/managers/test_mjlab_migration_fixture.py
@@ -0,0 +1,149 @@
+"""End-to-end evidence for the pinned mjlab task migration fixture."""
+
+from __future__ import annotations
+
+from dataclasses import fields, is_dataclass
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pytest
+from hydra import compose, initialize_config_dir
+from hydra.core.global_hydra import GlobalHydra
+from omegaconf import DictConfig, OmegaConf
+
+from tests.fixtures.mjlab_cartpole import FIXTURE_ENV_NAME
+from unilab.base import registry
+from unilab.base.config_adapter import BackendAdapter
+from unilab.base.config_materialization import apply_cfg_overrides
+from unilab.envs import ManagerBasedRlEnv, ManagerBasedRlEnvCfg
+
+ROOT_DIR = Path(__file__).parents[2]
+FIXTURE_DIR = ROOT_DIR / "tests" / "fixtures" / "mjlab_cartpole"
+
+
+def _materialize() -> tuple[ManagerBasedRlEnvCfg, dict[str, Any]]:
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(config_dir=str(FIXTURE_DIR / "conf"), version_base="1.3"):
+        hydra_cfg: DictConfig = compose("config")
+    override = BackendAdapter(hydra_cfg, root_dir=ROOT_DIR).build_task_env_cfg_override()
+    env_cfg = registry.materialize_env_config(FIXTURE_ENV_NAME)
+    assert isinstance(env_cfg, ManagerBasedRlEnvCfg)
+    apply_cfg_overrides(env_cfg, override)
+    env_cfg.validate()
+    return env_cfg, override
+
+
+def _assert_plain(value: Any) -> None:
+    assert not OmegaConf.is_config(value)
+    if is_dataclass(value) and not isinstance(value, type):
+        for field in fields(value):
+            _assert_plain(getattr(value, field.name))
+    elif isinstance(value, dict):
+        for item in value.values():
+            _assert_plain(item)
+    elif isinstance(value, (tuple, list)):
+        for item in value:
+            _assert_plain(item)
+
+
+def test_mjlab_fixture_hydra_materializes_source_structure() -> None:
+    cfg, _ = _materialize()
+
+    assert list(cfg.observations) == ["actor", "critic"]
+    expected_obs = ["cart_pos", "pole_angle", "cart_vel", "pole_vel"]
+    assert list(cfg.observations["actor"].terms) == expected_obs
+    assert list(cfg.observations["critic"].terms) == expected_obs
+    assert cfg.observations["actor"].enable_corruption is True
+    assert cfg.observations["critic"].enable_corruption is False
+    assert list(cfg.actions) == ["effort"]
+    assert list(cfg.events) == ["reset_slider", "reset_hinge"]
+    assert list(cfg.rewards) == ["smooth_reward"]
+    assert list(cfg.terminations) == ["time_out"]
+    assert cfg.policy_observation_group == "actor"
+    assert cfg.critic_observation_group == "critic"
+    assert cfg.sim_dt == pytest.approx(0.01)
+    assert cfg.ctrl_dt == pytest.approx(0.05)
+    assert cfg.max_episode_seconds == pytest.approx(50.0)
+    assert cfg.scale_rewards_by_dt is True
+    _assert_plain(cfg)
+
+
+def test_mjlab_fixture_real_mujoco_reset_step_and_reward() -> None:
+    _, override = _materialize()
+    env = registry.make(
+        FIXTURE_ENV_NAME,
+        sim_backend="mujoco",
+        env_cfg_override=override,
+        num_envs=8,
+    )
+    assert isinstance(env, ManagerBasedRlEnv)
+    try:
+        state = env.init_state()
+        assert env.obs_groups_spec == {"obs": 5, "critic": 5}
+        assert state.obs["obs"].shape == state.obs["critic"].shape == (8, 5)
+        assert env.action_space.shape == (1,)
+
+        before = env.scene["cartpole"].data.joint_pos.copy()
+        ids = np.asarray([1, 6], dtype=np.int32)
+        reset_obs, _ = env.reset(env_ids=ids)
+        after = env.scene["cartpole"].data.joint_pos.copy()
+        np.testing.assert_array_equal(after[[0, 2, 3, 4, 5, 7]], before[[0, 2, 3, 4, 5, 7]])
+        assert reset_obs["obs"].shape == reset_obs["critic"].shape == (2, 5)
+        assert np.all(np.abs(after[ids, 0]) <= 0.1)
+        assert np.all(np.abs(after[ids, 1]) <= 0.034)
+        assert np.all(np.abs(env.scene["cartpole"].data.joint_vel[ids]) <= 0.01)
+
+        actions = np.full((8, 1), 0.25, dtype=np.float32)
+        state = env.step(actions)
+        entity = env.scene["cartpole"]
+        hinge = entity.data.joint_pos[:, 1]
+        cart = entity.data.joint_pos[:, 0]
+        hinge_vel = entity.data.joint_vel[:, 1]
+        gaussian_scale = np.sqrt(-2.0 * np.log(0.1))
+        quadratic_scale = np.sqrt(0.9)
+        expected = (np.cos(hinge) + 1.0) / 2.0
+        expected *= (1.0 + np.exp(-0.5 * np.square(cart / 2.0 * gaussian_scale))) / 2.0
+        expected *= (4.0 + np.maximum(1.0 - np.square(0.25 * quadratic_scale), 0.0)) / 5.0
+        expected *= (1.0 + np.exp(-0.5 * np.square(hinge_vel / 5.0 * gaussian_scale))) / 2.0
+        np.testing.assert_allclose(state.reward, expected * 0.05, rtol=1e-5, atol=1e-6)
+    finally:
+        env.close()
+
+
+def test_mjlab_fixture_missing_actuator_fails_on_cold_path() -> None:
+    _, override = _materialize()
+    override["actions"]["effort"]["actuator_names"] = ["missing_actuator"]
+    with pytest.raises(ValueError, match="regular expressions matched.*missing_actuator"):
+        registry.make(
+            FIXTURE_ENV_NAME,
+            sim_backend="mujoco",
+            env_cfg_override=override,
+            num_envs=2,
+        )
+
+
+def test_mjlab_fixture_is_pinned_test_only_numpy_code() -> None:
+    task_source = (FIXTURE_DIR / "task.py").read_text(encoding="utf-8")
+    helper_source = (ROOT_DIR / "tests/fixtures/cartpole_manager_adapters.py").read_text(
+        encoding="utf-8"
+    )
+    executable = "\n".join(
+        line
+        for line in (task_source + helper_source).splitlines()
+        if not line.lstrip().startswith("#")
+    )
+    for forbidden in (
+        "import torch",
+        "from torch",
+        "import mjlab",
+        "from mjlab",
+        "uni_rl",
+        "unilab.training",
+    ):
+        assert forbidden not in executable
+    assert "0fb8a681136be94ffc636a3dd423cabb97d91f10" in (FIXTURE_DIR / "README.md").read_text(
+        encoding="utf-8"
+    )
+    assert "tests.fixtures" not in tuple(registry._DEFAULT_REGISTRY_PACKAGES)
+    assert set(registry._envs[FIXTURE_ENV_NAME].env_factory_dict) == {"mujoco"}
diff --git a/tests/managers/test_observation_buffers_noise.py b/tests/managers/test_observation_buffers_noise.py
new file mode 100644
index 000000000..e3ac38b4f
--- /dev/null
+++ b/tests/managers/test_observation_buffers_noise.py
@@ -0,0 +1,434 @@
+# Derived from mujocolab/mjlab v1.6.0 (0fb8a681), observation/buffer/noise tests.
+# Modified by UniLab for NumPy and env-owned RNG; Apache-2.0.
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from unilab.managers import ObservationGroupCfg, ObservationManager, ObservationTermCfg
+from unilab.managers._buffers import CircularBuffer, DelayBuffer
+from unilab.managers._noise import (
+    ConstantNoiseCfg,
+    GaussianNoiseCfg,
+    NoiseModelWithAdditiveBiasCfg,
+    UniformNoiseCfg,
+)
+
+from .conftest import FakeEnv
+
+
+def test_circular_buffer_history_backfill_lag_and_partial_reset() -> None:
+    buffer = CircularBuffer(max_len=3, batch_size=2)
+    first = np.array([[1.0], [10.0]], dtype=np.float32)
+    buffer.append(first)
+    np.testing.assert_array_equal(buffer.buffer[:, :, 0], [[1, 1, 1], [10, 10, 10]])
+    buffer.append(np.array([[2.0], [20.0]], dtype=np.float32))
+    buffer.append(np.array([[3.0], [30.0]], dtype=np.float32))
+    np.testing.assert_array_equal(buffer[np.array([0, 2])][:, 0], [3, 10])
+
+    buffer.reset([1])
+    buffer.append(np.array([[4.0], [99.0]], dtype=np.float32))
+    np.testing.assert_array_equal(buffer.buffer[0, :, 0], [2, 3, 4])
+    np.testing.assert_array_equal(buffer.buffer[1, :, 0], [99, 99, 99])
+
+
+def test_circular_buffer_rejects_invalid_usage() -> None:
+    with pytest.raises(ValueError, match=">= 1"):
+        CircularBuffer(max_len=0, batch_size=2)
+    buffer = CircularBuffer(max_len=2, batch_size=2)
+    with pytest.raises(RuntimeError, match="not initialized"):
+        _ = buffer.buffer
+    with pytest.raises(ValueError, match="batch size"):
+        buffer.append(np.zeros((3, 1)))
+
+
+def test_delay_buffer_constant_delay_and_partial_backfill() -> None:
+    buffer = DelayBuffer(min_lag=2, max_lag=2, batch_size=2)
+    outputs = []
+    for value in (1.0, 2.0, 3.0, 4.0):
+        buffer.append(np.full((2, 1), value, dtype=np.float32))
+        outputs.append(buffer.compute().copy())
+    np.testing.assert_array_equal(np.stack(outputs)[:, 0, 0], [1, 1, 1, 2])
+
+    buffer.reset(np.array([1]))
+    buffer.backfill(np.array([[8.0], [9.0]], dtype=np.float32), np.array([1]))
+    np.testing.assert_array_equal(buffer.peek()[1], [9.0])
+
+
+def test_delay_rng_is_reproducible_and_required() -> None:
+    def draw(seed: int) -> list[np.ndarray]:
+        buffer = DelayBuffer(
+            min_lag=0,
+            max_lag=3,
+            batch_size=8,
+            generator=np.random.default_rng(seed),
+        )
+        values = []
+        for step in range(5):
+            buffer.append(np.full((8, 1), step, dtype=np.float32))
+            buffer.compute()
+            values.append(buffer.current_lags.copy())
+        return values
+
+    for left, right in zip(draw(123), draw(123), strict=True):
+        np.testing.assert_array_equal(left, right)
+
+    missing_rng = DelayBuffer(min_lag=0, max_lag=2, batch_size=2)
+    missing_rng.append(np.zeros((2, 1)))
+    with pytest.raises(ValueError, match="env-owned"):
+        missing_rng.compute()
+
+
+def test_noise_configs_use_supplied_generator() -> None:
+    data = np.ones((4, 3), dtype=np.float32)
+    uniform = UniformNoiseCfg(n_min=-0.2, n_max=0.2)
+    first = uniform.apply(data, rng=np.random.default_rng(9))
+    second = uniform.apply(data, rng=np.random.default_rng(9))
+    np.testing.assert_array_equal(first, second)
+    assert first.dtype == np.float32
+    with pytest.raises(ValueError, match="env-owned"):
+        uniform.apply(data)
+
+    gaussian = GaussianNoiseCfg(mean=0.0, std=0.1)
+    assert gaussian.apply(data, rng=np.random.default_rng(2)).shape == data.shape
+    np.testing.assert_array_equal(ConstantNoiseCfg(bias=2.0, operation="abs").apply(data), 2.0)
+
+
+@pytest.mark.parametrize("operation", ["add", "scale", "abs"])
+def test_uniform_noise_inplace_matches_reference_expression(operation: str) -> None:
+    data = np.arange(24, dtype=np.float32).reshape(8, 3)
+    n_min = np.asarray([-0.2, -0.1, -0.05], dtype=np.float32)
+    n_max = np.asarray([0.3, 0.4, 0.5], dtype=np.float32)
+    cfg = UniformNoiseCfg(n_min=tuple(n_min), n_max=tuple(n_max), operation=operation)
+
+    reference_rng = np.random.default_rng(1702)
+    # Float32 data draws directly in float32 (issue #1350 fast path).
+    unit = reference_rng.random(data.shape, dtype=data.dtype)
+    noise = unit * (n_max - n_min) + n_min
+    if operation == "add":
+        expected = data + noise
+    elif operation == "scale":
+        expected = data * noise
+    else:
+        expected = noise
+
+    actual = cfg.apply(data, rng=np.random.default_rng(1702))
+    np.testing.assert_array_equal(actual, expected)
+    np.testing.assert_array_equal(data, np.arange(24, dtype=np.float32).reshape(8, 3))
+
+
+def test_additive_bias_noise_supports_scalar_terms() -> None:
+    from unilab.managers._noise import NoiseModelWithAdditiveBias
+
+    cfg = NoiseModelWithAdditiveBiasCfg(
+        noise_cfg=ConstantNoiseCfg(bias=0.0),
+        bias_noise_cfg=ConstantNoiseCfg(bias=0.5),
+    )
+    model = NoiseModelWithAdditiveBias(cfg, num_envs=4, rng=np.random.default_rng(2))
+    result = model(np.ones(4, dtype=np.float32))
+    np.testing.assert_array_equal(result, 1.5)
+    assert result.shape == (4,)
+
+
+def test_observation_groups_pipeline_order_and_history(fake_env: FakeEnv) -> None:
+    cfg = {
+        "policy": ObservationGroupCfg(
+            terms={
+                "state": ObservationTermCfg(
+                    func=lambda env: env.obs,
+                    clip=(-1.0, 4.0),
+                    scale=2.0,
+                    history_length=2,
+                ),
+                "bias": ObservationTermCfg(
+                    func=lambda env: np.ones((env.num_envs, 1), dtype=np.float32)
+                ),
+            }
+        ),
+        "dict_group": ObservationGroupCfg(
+            terms={"state": ObservationTermCfg(func=lambda env: env.obs)},
+            concatenate_terms=False,
+        ),
+    }
+    manager = ObservationManager(cfg, fake_env)
+    first = manager.compute(update_history=True)
+    expected_first = np.clip(fake_env.obs, -1, 4) * 2
+    np.testing.assert_array_equal(first["policy"][:, :4], np.tile(expected_first, (1, 2)))
+    assert list(first["dict_group"]) == ["state"]
+    assert manager.group_obs_dim["policy"] == (5,)
+
+    fake_env.obs = fake_env.obs + 10
+    second = manager.compute(update_history=True)["policy"]
+    expected_second = np.clip(fake_env.obs, -1, 4) * 2
+    np.testing.assert_array_equal(second[:, :2], expected_first)
+    np.testing.assert_array_equal(second[:, 2:4], expected_second)
+    assert manager.get_active_iterable_terms(0)[0][0] == "policy-state"
+
+
+def test_concatenated_result_owns_each_result_and_protects_term_buffers(
+    fake_env: FakeEnv,
+) -> None:
+    source = fake_env.obs
+    manager = ObservationManager(
+        {
+            "policy": ObservationGroupCfg(
+                terms={
+                    "source": ObservationTermCfg(func=lambda env: env.obs),
+                    "constant": ObservationTermCfg(
+                        func=lambda env: np.ones((env.num_envs, 1), dtype=np.float32)
+                    ),
+                }
+            )
+        },
+        fake_env,
+    )
+
+    first = manager.compute(update_history=True)["policy"]
+    assert isinstance(first, np.ndarray)
+    first_address = first.ctypes.data
+    expected_first = np.concatenate((source.copy(), np.ones((fake_env.num_envs, 1))), axis=1)
+    np.testing.assert_array_equal(first, expected_first)
+
+    fake_env.obs += 100.0
+    second = manager.compute(update_history=True)["policy"]
+    assert isinstance(second, np.ndarray)
+    assert second.ctypes.data != first_address
+    np.testing.assert_array_equal(first, expected_first)
+    np.testing.assert_array_equal(second[:, :2], fake_env.obs)
+    assert not np.shares_memory(second, fake_env.obs)
+
+
+def test_concatenated_nan_sanitize_does_not_mutate_term_owned_input(
+    fake_env: FakeEnv,
+) -> None:
+    source = fake_env.obs.copy()
+    source[0, 0] = np.nan
+    manager = ObservationManager(
+        {
+            "policy": ObservationGroupCfg(
+                terms={"state": ObservationTermCfg(func=lambda env: source)},
+                nan_policy="sanitize",
+                nan_check_per_term=False,
+            )
+        },
+        fake_env,
+    )
+
+    result = manager.compute(update_history=True)["policy"]
+    assert isinstance(result, np.ndarray)
+    assert np.isfinite(result).all()
+    assert np.isnan(source[0, 0])
+
+
+def test_concatenated_nan_error_still_identifies_offending_term(fake_env: FakeEnv) -> None:
+    def invalid(env: FakeEnv) -> np.ndarray:
+        result = np.ones((env.num_envs, 1), dtype=np.float32)
+        result[2, 0] = np.nan
+        return result
+
+    manager = ObservationManager(
+        {
+            "policy": ObservationGroupCfg(
+                terms={
+                    "finite": ObservationTermCfg(func=lambda env: env.obs),
+                    "invalid": ObservationTermCfg(func=invalid),
+                }
+            )
+        },
+        fake_env,
+    )
+
+    with pytest.raises(
+        ValueError,
+        match=r"NaN detected.*'policy/invalid'.*environments: \[2\]",
+    ):
+        manager.compute(update_history=True)
+
+
+def test_observation_noise_model_delay_and_seed_reproducibility() -> None:
+    cfg = {
+        "policy": ObservationGroupCfg(
+            terms={
+                "state": ObservationTermCfg(
+                    func=lambda env: env.obs,
+                    noise=NoiseModelWithAdditiveBiasCfg(
+                        noise_cfg=GaussianNoiseCfg(std=0.1),
+                        bias_noise_cfg=UniformNoiseCfg(n_min=-0.2, n_max=0.2),
+                    ),
+                    delay_min_lag=1,
+                    delay_max_lag=1,
+                )
+            },
+            enable_corruption=True,
+        )
+    }
+    left = ObservationManager(cfg, FakeEnv(seed=11)).compute(update_history=True)
+    right = ObservationManager(cfg, FakeEnv(seed=11)).compute(update_history=True)
+    np.testing.assert_array_equal(left["policy"], right["policy"])
+
+
+@pytest.mark.parametrize("bad", [np.nan, np.inf])
+def test_observation_default_finite_policy_fails_closed(fake_env: FakeEnv, bad: float) -> None:
+    def invalid(env: FakeEnv) -> np.ndarray:
+        result = env.obs.copy()
+        result[1, 0] = bad
+        return result
+
+    manager = ObservationManager(
+        {"policy": ObservationGroupCfg(terms={"bad": ObservationTermCfg(func=invalid)})},
+        fake_env,
+    )
+    with pytest.raises(ValueError, match="ObservationManager term 'policy/bad'"):
+        manager.compute()
+
+
+@pytest.mark.parametrize(
+    ("invalid_values", "invalid_kind"),
+    [
+        ((np.nan, 0.0), "NaN"),
+        ((np.inf, 0.0), "Inf"),
+        ((np.nan, np.inf), "NaN/Inf"),
+    ],
+)
+def test_observation_finite_error_keeps_kind_term_and_env_diagnostics(
+    fake_env: FakeEnv,
+    invalid_values: tuple[float, float],
+    invalid_kind: str,
+) -> None:
+    def invalid(env: FakeEnv) -> np.ndarray:
+        result = env.obs.copy()
+        result[2] = invalid_values
+        return result
+
+    manager = ObservationManager(
+        {"policy": ObservationGroupCfg(terms={"bad": ObservationTermCfg(func=invalid)})},
+        fake_env,
+    )
+    match = rf"{invalid_kind} detected.*'policy/bad'.*environments: \[2\]"
+    with pytest.raises(ValueError, match=match):
+        manager.compute()
+
+
+def test_observation_finite_warn_sanitizes_and_disabled_preserves(
+    fake_env: FakeEnv, capsys: pytest.CaptureFixture[str]
+) -> None:
+    def invalid(env: FakeEnv) -> np.ndarray:
+        result = env.obs.copy()
+        result[1, 0] = np.nan
+        return result
+
+    warn = ObservationManager(
+        {
+            "policy": ObservationGroupCfg(
+                terms={"bad": ObservationTermCfg(func=invalid)}, nan_policy="warn"
+            )
+        },
+        fake_env,
+    )
+    warned = warn.compute()["policy"]
+    assert np.isfinite(warned).all()
+    warning = capsys.readouterr().out
+    assert "policy/bad" in warning
+    assert "envs: [1]" in warning
+
+    disabled = ObservationManager(
+        {
+            "policy": ObservationGroupCfg(
+                terms={"bad": ObservationTermCfg(func=invalid)}, nan_policy="disabled"
+            )
+        },
+        fake_env,
+    )
+    assert np.isnan(disabled.compute()["policy"][1, 0])
+
+
+def test_observation_explicit_sanitize_and_shape_error(fake_env: FakeEnv) -> None:
+    sanitize = ObservationManager(
+        {
+            "policy": ObservationGroupCfg(
+                terms={
+                    "bad": ObservationTermCfg(func=lambda env: np.full((env.num_envs, 1), np.nan))
+                },
+                nan_policy="sanitize",
+            )
+        },
+        fake_env,
+    )
+    np.testing.assert_array_equal(sanitize.compute()["policy"], 0.0)
+
+    with pytest.raises(ValueError, match="num_envs"):
+        ObservationManager(
+            {
+                "policy": ObservationGroupCfg(
+                    terms={"bad": ObservationTermCfg(func=lambda env: np.zeros((2, 1)))}
+                )
+            },
+            fake_env,
+        )
+
+
+def test_identical_terms_share_raw_compute_across_groups() -> None:
+    """Issue #1351: terms with identical func+params compute once per compute().
+
+    The critic group reuses the raw (pre-noise) output of the policy group's
+    identical term; the policy group still applies its own noise on top.
+    """
+    calls = {"n": 0}
+
+    def counting(env: FakeEnv, scale: float = 1.0) -> np.ndarray:
+        calls["n"] += 1
+        return env.obs * scale
+
+    manager = ObservationManager(
+        {
+            "policy": ObservationGroupCfg(
+                terms={
+                    "state": ObservationTermCfg(
+                        func=counting,
+                        params={"scale": 2.0},
+                        noise=UniformNoiseCfg(n_min=-0.1, n_max=0.1),
+                    )
+                },
+                enable_corruption=True,
+            ),
+            "critic": ObservationGroupCfg(
+                terms={"state": ObservationTermCfg(func=counting, params={"scale": 2.0})}
+            ),
+        },
+        FakeEnv(seed=3),
+    )
+    env = manager._env
+    calls["n"] = 0
+    out = manager.compute(update_history=True)
+    assert calls["n"] == 1
+    np.testing.assert_array_equal(out["critic"], env.obs * 2.0)
+    noise = out["policy"] - out["critic"]
+    assert np.abs(noise).max() <= 0.1 + 1e-6
+    assert np.abs(noise).max() > 0.0
+
+    # The cache is per compute() call, not across calls.
+    manager.compute(update_history=True)
+    assert calls["n"] == 2
+
+
+def test_class_terms_are_never_shared_across_groups() -> None:
+    """Class-based (possibly stateful) terms must keep per-group calls."""
+
+    class StatefulTerm:
+        def __call__(self, env: FakeEnv) -> np.ndarray:
+            return env.obs.copy()
+
+        def reset(self, env_ids: np.ndarray | None = None) -> None:
+            del env_ids
+
+    shared = StatefulTerm()
+    manager = ObservationManager(
+        {
+            "policy": ObservationGroupCfg(terms={"state": ObservationTermCfg(func=shared)}),
+            "critic": ObservationGroupCfg(terms={"state": ObservationTermCfg(func=shared)}),
+        },
+        FakeEnv(seed=5),
+    )
+    assert manager._group_obs_term_share["policy"] == {}
+    assert manager._group_obs_term_share["critic"] == {}
diff --git a/tests/managers/test_observation_partial_reset.py b/tests/managers/test_observation_partial_reset.py
new file mode 100644
index 000000000..267cfae51
--- /dev/null
+++ b/tests/managers/test_observation_partial_reset.py
@@ -0,0 +1,175 @@
+"""Row-scoped partial-reset parity tests for ObservationManager (issue #1259 R2).
+
+On the partial-reset path (compute(update_history=True, env_ids=...)) the
+manager returns only the reset rows and processes them row-scoped whenever the
+group has no delay/history terms. These tests pin that contract:
+
+- un-noised reset rows are bit-identical to a full-batch compute sliced to
+  those rows; noise is drawn for the reset rows only — issue #1349 removed the
+  full-batch RNG-stream parity requirement, so noised reset rows match neither
+  the full-batch noise values nor its RNG consumption;
+- groups with delay/history terms fall back to the full-batch pipeline and
+  only slice the final output; untouched rows' buffers are not advanced;
+- NaN diagnostics on the row-scoped path report real env indices.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from unilab.managers import ObservationGroupCfg, ObservationManager, ObservationTermCfg
+from unilab.managers._noise import UniformNoiseCfg
+
+from .conftest import FakeEnv
+
+
+def _noisy_cfg() -> dict[str, ObservationGroupCfg]:
+    return {
+        "policy": ObservationGroupCfg(
+            terms={
+                "state": ObservationTermCfg(
+                    func=lambda env: env.obs,
+                    noise=UniformNoiseCfg(n_min=-0.1, n_max=0.1),
+                    clip=(-10.0, 10.0),
+                    scale=2.0,
+                ),
+                "bias": ObservationTermCfg(
+                    func=lambda env: np.ones((env.num_envs, 1), dtype=np.float32)
+                ),
+            },
+            enable_corruption=True,
+        ),
+    }
+
+
+def test_partial_reset_row_scoped_noise() -> None:
+    env = FakeEnv(seed=11)
+    manager = ObservationManager(_noisy_cfg(), env)
+    manager.compute(update_history=True)  # populate caches like a step would
+
+    ids = np.array([0, 2], dtype=np.int32)
+    rng_state = env.rng.bit_generator.state
+    rows = manager.compute(update_history=True, env_ids=ids)
+    rng_after_rows = env.rng.bit_generator.state
+
+    env.rng.bit_generator.state = rng_state
+    full = manager.compute(update_history=True)
+    rng_after_full = env.rng.bit_generator.state
+
+    assert rows["policy"].shape == (len(ids), full["policy"].shape[1])
+    # Issue #1349: reset-path noise is drawn for the reset rows only, so the
+    # shared RNG stream is consumed strictly less than the full-batch draw.
+    assert rng_after_rows != rng_after_full
+    # The un-noised trailing term stays bit-identical to the full-batch compute.
+    state_dim = env.obs.shape[1]
+    np.testing.assert_array_equal(rows["policy"][:, state_dim:], full["policy"][ids][:, state_dim:])
+    # Noised columns differ from the full-batch slice (row-scoped draws).
+    assert not np.array_equal(rows["policy"][:, :state_dim], full["policy"][ids][:, :state_dim])
+    # The same RNG state reproduces the same reset rows deterministically.
+    env.rng.bit_generator.state = rng_state
+    rows_again = manager.compute(update_history=True, env_ids=ids)
+    np.testing.assert_array_equal(rows["policy"], rows_again["policy"])
+
+
+def test_partial_reset_does_not_populate_obs_cache() -> None:
+    env = FakeEnv(seed=3)
+    manager = ObservationManager(_noisy_cfg(), env)
+    manager.compute(update_history=True)
+    assert manager._obs_buffer is not None
+
+    manager.reset(np.array([1], dtype=np.int32))
+    assert manager._obs_buffer is None
+    manager.compute(update_history=True, env_ids=np.array([1], dtype=np.int32))
+    # The reset path leaves the cache invalidated; the next per-step compute
+    # refreshes it with a full-batch entry.
+    assert manager._obs_buffer is None
+    manager.compute(update_history=True)
+    assert manager._obs_buffer is not None
+    assert manager._obs_buffer["policy"].shape[0] == env.num_envs
+
+
+def test_partial_reset_temporal_group_falls_back_and_preserves_rows() -> None:
+    env = FakeEnv(seed=5)
+    cfg = {
+        "policy": ObservationGroupCfg(
+            terms={
+                "state": ObservationTermCfg(func=lambda env: env.obs, history_length=3),
+                "delayed": ObservationTermCfg(
+                    func=lambda env: env.obs, delay_min_lag=1, delay_max_lag=1
+                ),
+            },
+        ),
+    }
+    manager = ObservationManager(cfg, env)
+    for step in range(4):
+        env.obs = env.obs + 1
+        manager.compute(update_history=True)
+
+    ids = np.array([1], dtype=np.int32)
+    keep_ids = np.array([0, 2, 3], dtype=np.int32)
+    history_before = manager._group_obs_term_history_buffer["policy"]["state"].buffer.copy()
+    delay_before = manager._group_obs_term_delay_buffer["policy"]["delayed"].peek().copy()
+
+    rng_state = env.rng.bit_generator.state
+    rows = manager.compute(update_history=True, env_ids=ids)
+
+    env.rng.bit_generator.state = rng_state
+    full = manager.compute(update_history=True, env_ids=ids)
+    np.testing.assert_array_equal(rows["policy"], full["policy"])
+    assert rows["policy"].shape[0] == len(ids)
+
+    # Untouched rows keep their history and delayed values bit-identically;
+    # the reset row is backfilled with its post-reset frame in every slot.
+    history_after = manager._group_obs_term_history_buffer["policy"]["state"].buffer
+    np.testing.assert_array_equal(history_after[keep_ids], history_before[keep_ids])
+    reset_slots = history_after[ids[0]]
+    np.testing.assert_array_equal(reset_slots, np.broadcast_to(reset_slots[-1], reset_slots.shape))
+    np.testing.assert_array_equal(
+        manager._group_obs_term_delay_buffer["policy"]["delayed"].peek()[keep_ids],
+        delay_before[keep_ids],
+    )
+
+    # Reference: the full-batch pipeline sliced to the reset rows agrees.
+    env.rng.bit_generator.state = rng_state
+    reference = manager.compute(update_history=True)["policy"][ids]
+    env.rng.bit_generator.state = rng_state
+    np.testing.assert_array_equal(rows["policy"], reference)
+
+
+@pytest.mark.parametrize("bad", [np.nan, np.inf])
+def test_partial_reset_nan_error_reports_env_ids(bad: float) -> None:
+    def invalid(env: FakeEnv) -> np.ndarray:
+        result = env.obs.copy()
+        result[2, 0] = bad
+        return result
+
+    env = FakeEnv(seed=7)
+    manager = ObservationManager(
+        {"policy": ObservationGroupCfg(terms={"bad": ObservationTermCfg(func=invalid)})},
+        env,
+    )
+    with pytest.raises(ValueError, match=r"for environments: \[2\]"):
+        manager.compute(update_history=True, env_ids=np.array([0, 2], dtype=np.int32))
+
+
+def test_partial_reset_nan_on_untouched_row_is_not_rechecked() -> None:
+    def invalid(env: FakeEnv) -> np.ndarray:
+        result = env.obs.copy()
+        result[1, 0] = np.nan
+        return result
+
+    env = FakeEnv(seed=7)
+    manager = ObservationManager(
+        {
+            "policy": ObservationGroupCfg(
+                terms={"bad": ObservationTermCfg(func=invalid)}, nan_policy="error"
+            )
+        },
+        env,
+    )
+    # Row-scoped NaN checks cover only the reset rows; untouched rows were
+    # already checked by the per-step compute of their control step.
+    rows = manager.compute(update_history=True, env_ids=np.array([0, 2], dtype=np.int32))
+    assert rows["policy"].shape == (2, env.obs.shape[1])
+    assert np.isfinite(rows["policy"]).all()
diff --git a/tests/managers/test_stage_curriculum_demo.py b/tests/managers/test_stage_curriculum_demo.py
new file mode 100644
index 000000000..f65d40801
--- /dev/null
+++ b/tests/managers/test_stage_curriculum_demo.py
@@ -0,0 +1,116 @@
+"""Contract and runtime evidence for the stage-curriculum demo owner YAML.
+
+The demo owner YAML (``tests/fixtures/mjlab_cartpole/conf/stage_curriculum.yaml``)
+reuses the pinned cartpole task and declares one ladder per public curriculum
+term from ``unilab.envs.mdp.curriculums`` (issue #1397).
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pytest
+from hydra import compose, initialize_config_dir
+from hydra.core.global_hydra import GlobalHydra
+from omegaconf import DictConfig, OmegaConf
+
+from tests.fixtures.mjlab_cartpole import FIXTURE_ENV_NAME
+from unilab.base import registry
+from unilab.base.config_adapter import BackendAdapter
+from unilab.base.config_materialization import apply_cfg_overrides
+from unilab.envs import ManagerBasedRlEnvCfg, mdp
+
+ROOT_DIR = Path(__file__).parents[2]
+FIXTURE_DIR = ROOT_DIR / "tests" / "fixtures" / "mjlab_cartpole"
+
+
+def _materialize() -> tuple[ManagerBasedRlEnvCfg, dict[str, Any]]:
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(config_dir=str(FIXTURE_DIR / "conf"), version_base="1.3"):
+        hydra_cfg: DictConfig = compose("stage_curriculum")
+    override = BackendAdapter(hydra_cfg, root_dir=ROOT_DIR).build_task_env_cfg_override()
+    env_cfg = registry.materialize_env_config(FIXTURE_ENV_NAME)
+    assert isinstance(env_cfg, ManagerBasedRlEnvCfg)
+    apply_cfg_overrides(env_cfg, override)
+    env_cfg.validate()
+    return env_cfg, override
+
+
+def test_stage_curriculum_demo_owner_materializes_declared_ladders() -> None:
+    env_cfg, _ = _materialize()
+
+    assert list(env_cfg.curriculum) == ["smooth_reward_weight", "pole_tilt_limit"]
+    weight_term = env_cfg.curriculum["smooth_reward_weight"]
+    assert weight_term.func is mdp.reward_curriculum
+    assert weight_term.params["reward_name"] == "smooth_reward"
+    stages = weight_term.params["stages"]
+    assert [stage["step"] for stage in stages] == [0, 2, 4]
+    assert [stage["weight"] for stage in stages] == pytest.approx([1.0, 0.5, 0.25])
+    tilt_term = env_cfg.curriculum["pole_tilt_limit"]
+    assert tilt_term.func is mdp.termination_curriculum
+    assert tilt_term.params["termination_name"] == "pole_tilt"
+    assert tilt_term.params["stages"][1]["params"] == {"limit_angle": 0.2}
+
+    assert list(env_cfg.terminations) == ["time_out", "pole_tilt"]
+    assert env_cfg.terminations["pole_tilt"].func is mdp.bad_orientation
+    assert env_cfg.terminations["pole_tilt"].params["limit_angle"] == pytest.approx(0.4)
+
+    for term in env_cfg.curriculum.values():
+        assert not OmegaConf.is_config(term.params["stages"])
+
+
+def test_stage_curriculum_demo_runtime_ramps_with_step_counter() -> None:
+    _, override = _materialize()
+    env = registry.make(
+        FIXTURE_ENV_NAME,
+        sim_backend="mujoco",
+        env_cfg_override=override,
+        num_envs=4,
+    )
+    try:
+        assert env.curriculum_manager.active_terms == ["smooth_reward_weight", "pole_tilt_limit"]
+        weight_cfg = env.reward_manager.get_term_cfg("smooth_reward")
+        tilt_cfg = env.termination_manager.get_term_cfg("pole_tilt")
+
+        env.reset(seed=7)
+        assert env.common_step_counter == 0
+        assert weight_cfg.weight == pytest.approx(1.0)
+        assert tilt_cfg.params["limit_angle"] == pytest.approx(0.4)
+
+        actions = np.zeros((4, 1), dtype=np.float32)
+        for _ in range(2):
+            env.step(actions)
+        env.reset()
+        assert env.common_step_counter == 2
+        assert weight_cfg.weight == pytest.approx(0.5)
+        assert tilt_cfg.params["limit_angle"] == pytest.approx(0.2)
+
+        for _ in range(2):
+            env.step(actions)
+        env.reset()
+        assert env.common_step_counter == 4
+        assert weight_cfg.weight == pytest.approx(0.25)
+        assert tilt_cfg.params["limit_angle"] == pytest.approx(0.2)
+
+        extras = env.curriculum_manager.reset()
+        assert extras["Curriculum/smooth_reward_weight/weight"] == pytest.approx(0.25)
+        assert extras["Curriculum/pole_tilt_limit/limit_angle"] == pytest.approx(0.2)
+    finally:
+        env.close()
+
+
+def test_stage_curriculum_demo_owner_invalid_stages_fail_closed() -> None:
+    _, override = _materialize()
+    override["curriculum"]["smooth_reward_weight"]["params"]["stages"] = [
+        {"step": 4, "weight": 0.5},
+        {"step": 2, "weight": 0.25},
+    ]
+    with pytest.raises(ValueError, match="nondecreasing"):
+        registry.make(
+            FIXTURE_ENV_NAME,
+            sim_backend="mujoco",
+            env_cfg_override=override,
+            num_envs=2,
+        )
diff --git a/tests/nan_injection/README.md b/tests/nan_injection/README.md
deleted file mode 100644
index d202b26e9..000000000
--- a/tests/nan_injection/README.md
+++ /dev/null
@@ -1,46 +0,0 @@
-# NaN guard injection validation scripts
-
-These scripts manually inject `NaN` into env state at well-defined points and
-assert that the `NanGuard` (added in #584) detects + dumps. They are NOT pytest
-tests and NOT part of CI/CD — see [conftest.py](conftest.py).
-
-## Why not in CI
-
-- Each case launches a real `OnPolicyRunner` / `HIMOnPolicyRunner` and trains
-  for one iteration. ~10–30s per case, ~3 min total for the full sweep.
-- Requires a real MuJoCo install + GPU or CPU torch. CI workers don't have
-  this stack provisioned.
-- The validation pattern (one-shot monkey-patch of `update_state` /
-  `apply_action`) is a debugging tool, not a regression contract.
-
-## Files
-
-| Script | Purpose | Runtime | Cases |
-|---|---|---|---|
-| [proto_nan_inject.py](proto_nan_inject.py) | Stub-env smoke test for the inject + dump pattern. | ~5s | 4 |
-| [proto_him_ppo_inject.py](proto_him_ppo_inject.py) | HIM-PPO inject prototype. | ~30s | 3 |
-| [stage2_nan_inject.py](stage2_nan_inject.py) | Single-process algorithms, real runner. | ~3 min | 6 (PPO×3, HIM-PPO×3) |
-| [stage3_nan_inject.py](stage3_nan_inject.py) | Multi-process algorithms, static wiring check. | <1s | 4 (APPO/SAC/TD3/FlashSAC) |
-
-## Running
-
-```bash
-cd /path/to/UniLab
-.venv/bin/python tests/nan_injection/stage2_nan_inject.py
-.venv/bin/python tests/nan_injection/stage3_nan_inject.py
-```
-
-Stage 2 patches `env.update_state` (for `obs`/`reward`) or `env.apply_action`
-(for `ctrl`) one-shot at call `K`, runs `runner.learn(num_learning_iterations=1)`,
-then asserts:
-- `guard._dumped == True`
-- A `nan_dump_*.npz` file exists in the output dir
-- `meta_detection_step` in the dump matches the expected step
-
-For obs/reward (checked after `step_counter += 1`), expected step == K.
-For ctrl (checked before increment), expected step == K - 1.
-
-Stage 3 verifies the 3-piece wiring (train script reads cfg → collector worker
-calls `env.set_nan_guard()` in subprocess → hydra config has `training.nan_guard`
-block) for the multi-process algorithms. End-to-end runtime testing for these
-requires a manual env edit (see the recipe printed by the script).
diff --git a/tests/nan_injection/conftest.py b/tests/nan_injection/conftest.py
deleted file mode 100644
index a9ab2ab5f..000000000
--- a/tests/nan_injection/conftest.py
+++ /dev/null
@@ -1,15 +0,0 @@
-"""Pytest collection control for tests/nan_injection/.
-
-These scripts are MANUAL validation tools, not pytest tests. They build real
-runners, spin up MuJoCo, train for a single iteration, and assert NaN guard
-behavior end-to-end. They are intentionally kept OUT of CI/CD because each
-case takes ~10-30s and requires a working GPU/CPU MuJoCo install.
-
-This conftest tells pytest to skip the entire directory during automatic
-collection. Run the scripts manually instead, e.g.:
-
-    .venv/bin/python tests/nan_injection/stage2_nan_inject.py
-    .venv/bin/python tests/nan_injection/stage3_nan_inject.py
-"""
-
-collect_ignore_glob = ["*.py"]
diff --git a/tests/nan_injection/proto_him_ppo_inject.py b/tests/nan_injection/proto_him_ppo_inject.py
deleted file mode 100644
index ffb28bb50..000000000
--- a/tests/nan_injection/proto_him_ppo_inject.py
+++ /dev/null
@@ -1,253 +0,0 @@
-"""
-Stage 2 prototype B: validate NaN injection on REAL HIM-PPO runner with real
-Go2ArmManipLoco env (mujoco backend). Mirrors the patches from
-proto_nan_inject.py but applied to the actual training loop.
-
-Promotion status (registered 2026-08, issue #1020): the originally planned
-promotion into a formal pytest module (tests/algos/test_nan_inject_rsl_rl.py)
-was NOT carried out. Final disposition instead:
-- unit-level regression coverage lives in tests/test_nan_guard.py and
-  tests/ipc/test_nan_guard_spawn_pickle.py (both in CI);
-- real-runner HIM-PPO validation was lifted into
-  tests/nan_injection/stage2_nan_inject.py (HIM-PPO x {obs, reward, ctrl}
-  cases), kept as a manual tool per tests/nan_injection/README.md
-  (deliberately out of CI).
-This prototype is retained only as a minimal single-case reference; delete it
-together with stage2/stage3 if the manual validation tooling is ever dropped.
-
-Run:
-    uv run python tests/nan_injection/proto_him_ppo_inject.py
-"""
-
-from __future__ import annotations
-
-import sys
-import tempfile
-from pathlib import Path
-
-import numpy as np
-import torch
-from omegaconf import OmegaConf
-
-# Resolve repo root from this file's location
-ROOT_DIR = Path(__file__).resolve().parents[2]
-sys.path.insert(0, str(ROOT_DIR))
-sys.path.insert(0, str(ROOT_DIR / "src"))
-
-from unilab.algos.torch.him_ppo.runner import HIMOnPolicyRunner  # noqa: E402
-from unilab.base.backend.mujoco.xml import materialize_scene_visual_override  # noqa: E402
-from unilab.training import BackendAdapter, create_env, ensure_registries  # noqa: E402
-from unilab.training.rsl_rl import RslRlVecEnvWrapper  # noqa: E402
-from unilab.utils.nan_guard import NanGuard, NanGuardCfg  # noqa: E402
-
-
-def _list_dumps(output_dir: Path):
-    return [p for p in output_dir.glob("nan_dump_*.npz") if "latest" not in p.name]
-
-
-def _build_him_ppo(num_envs: int, log_dir: str, output_dir: Path):
-    """Build a minimal HIM-PPO runner with attached NanGuard, mirroring train_him_ppo.py.
-
-    Placeholder: hand-built cfg approach was abandoned in favor of the hydra-compose
-    path in ``build_via_hydra`` below.
-    """
-    ensure_registries()
-    return None  # placeholder, switch to hydra below
-
-
-def build_via_hydra(num_envs: int, log_dir: str, output_dir: Path):
-    """Use hydra compose API to build the real cfg, then mirror train_him_ppo.py wiring."""
-    from hydra import compose, initialize_config_dir
-
-    ensure_registries()
-
-    config_dir = str(ROOT_DIR / "conf/ppo_him")
-    with initialize_config_dir(config_dir=config_dir, version_base=None):
-        cfg = compose(
-            config_name="config",
-            overrides=[
-                "task=go2_arm_manip_loco/mujoco",
-                f"algo.num_envs={num_envs}",
-                "algo.num_steps_per_env=4",
-                "algo.max_iterations=1",
-                "algo.save_interval=100",
-                f"training.nan_guard.output_dir={output_dir}",
-                "training.no_play=true",
-            ],
-        )
-
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    print(f"[build] device={device}")
-
-    backend_adapter = BackendAdapter(
-        cfg,
-        root_dir=ROOT_DIR,
-        algo_name="ppo_him",
-        scene_materializer=materialize_scene_visual_override,
-    )
-    env_cfg_override = backend_adapter.build_task_env_cfg_override()
-    env = create_env(cfg, num_envs=cfg.algo.num_envs, env_cfg_override=env_cfg_override)
-
-    # Attach NanGuard exactly like train_him_ppo.py does.
-    nan_guard_cfg = cfg.training.nan_guard
-    guard = NanGuard(
-        NanGuardCfg(
-            enabled=True,
-            buffer_size=int(nan_guard_cfg.buffer_size),
-            max_envs_to_dump=int(nan_guard_cfg.max_envs_to_dump),
-            output_dir=str(output_dir),
-        ),
-        num_envs=env.num_envs,
-        supports_state_playback=env.play_capabilities.supports_physics_state_playback,
-    )
-    env.set_nan_guard(guard)
-
-    wrapped_env = RslRlVecEnvWrapper(env, device=device)
-    rl_cfg = OmegaConf.to_container(cfg.algo, resolve=True)
-    runner = HIMOnPolicyRunner(wrapped_env, rl_cfg, log_dir=log_dir, device=device)
-    return env, runner, guard
-
-
-def proto_him_ppo_obs_nan():
-    print("=" * 60)
-    print("[B1] HIM-PPO REAL runner — OBS NaN injection")
-    print("=" * 60)
-    with tempfile.TemporaryDirectory() as td:
-        td_path = Path(td)
-        output_dir = td_path / "dumps"
-        output_dir.mkdir()
-        log_dir = td_path / "log"
-        log_dir.mkdir()
-        env, runner, guard = build_via_hydra(
-            num_envs=8, log_dir=str(log_dir), output_dir=output_dir
-        )
-
-        # Patch update_state to inject NaN at step K=2 ONLY (one-shot).
-        # Rationale: nan_guard only clears reward via nan_to_num — obs NaN would propagate
-        # to actor and break torch distribution sampling. By restoring after one step we
-        # confirm dump fires but allow training to complete.
-        orig_update = env.update_state
-        K = 2
-        calls = [0]
-
-        def update_with_nan(state):
-            new_state = orig_update(state)
-            calls[0] += 1
-            if calls[0] == K:
-                first_key = next(iter(new_state.obs))
-                new_state.obs[first_key][0, 0] = np.nan
-                env.update_state = orig_update  # restore — single-shot injection
-            return new_state
-
-        env.update_state = update_with_nan  # type: ignore
-
-        try:
-            runner.learn(num_learning_iterations=1, init_at_random_ep_len=False)
-        except RuntimeError as e:
-            # Acceptable: NaN may still propagate one step before being cleaned.
-            print(f"  [info] training raised post-injection (expected): {type(e).__name__}")
-
-        files = _list_dumps(output_dir)
-        print(f"  guard._dumped: {bool(guard._dumped)}")
-        print(f"  dump files: {[f.name for f in files]}")
-        assert guard._dumped, "obs NaN should have triggered dump"
-        assert len(files) >= 1, f"expected ≥1 dump, got {len(files)}"
-        env.close()
-        print("  PASS")
-
-
-def proto_him_ppo_reward_nan():
-    print("=" * 60)
-    print("[B2] HIM-PPO REAL runner — REWARD NaN injection")
-    print("=" * 60)
-    with tempfile.TemporaryDirectory() as td:
-        td_path = Path(td)
-        output_dir = td_path / "dumps"
-        output_dir.mkdir()
-        log_dir = td_path / "log"
-        log_dir.mkdir()
-        env, runner, guard = build_via_hydra(
-            num_envs=8, log_dir=str(log_dir), output_dir=output_dir
-        )
-
-        # Reward NaN is sanitized via nan_to_num after dump, so single-shot is safe to
-        # leave permanent — but for symmetry with obs/ctrl tests, we still one-shot it.
-        orig_update = env.update_state
-        K = 2
-        calls = [0]
-
-        def update_with_nan(state):
-            new_state = orig_update(state)
-            calls[0] += 1
-            if calls[0] == K:
-                new_state.reward[0] = np.nan
-                env.update_state = orig_update
-            return new_state
-
-        env.update_state = update_with_nan  # type: ignore
-
-        try:
-            runner.learn(num_learning_iterations=1, init_at_random_ep_len=False)
-        except RuntimeError as e:
-            print(f"  [info] training raised post-injection (expected): {type(e).__name__}")
-
-        files = _list_dumps(output_dir)
-        print(f"  guard._dumped: {bool(guard._dumped)}")
-        print(f"  dump files: {[f.name for f in files]}")
-        assert guard._dumped, "reward NaN should have triggered dump"
-        assert len(files) >= 1, f"expected ≥1 dump, got {len(files)}"
-        env.close()
-        print("  PASS")
-
-
-def proto_him_ppo_ctrl_nan():
-    print("=" * 60)
-    print("[B3] HIM-PPO REAL runner — CTRL NaN injection (via apply_action)")
-    print("=" * 60)
-    with tempfile.TemporaryDirectory() as td:
-        td_path = Path(td)
-        output_dir = td_path / "dumps"
-        output_dir.mkdir()
-        log_dir = td_path / "log"
-        log_dir.mkdir()
-        env, runner, guard = build_via_hydra(
-            num_envs=8, log_dir=str(log_dir), output_dir=output_dir
-        )
-
-        # ctrl NaN is intercepted by check_ctrl BEFORE backend.step — never propagates
-        # to physics. So one-shot restore not strictly needed, but use it for consistency.
-        orig_apply = env.apply_action
-        K = 2
-        calls = [0]
-
-        def apply_with_nan(actions, state):
-            ctrl = orig_apply(actions, state)
-            calls[0] += 1
-            if calls[0] == K:
-                ctrl = ctrl.copy()
-                ctrl[0, 0] = np.nan
-                env.apply_action = orig_apply
-            return ctrl
-
-        env.apply_action = apply_with_nan  # type: ignore
-
-        try:
-            runner.learn(num_learning_iterations=1, init_at_random_ep_len=False)
-        except RuntimeError as e:
-            print(f"  [info] training raised post-injection (expected): {type(e).__name__}")
-
-        files = _list_dumps(output_dir)
-        print(f"  guard._dumped: {bool(guard._dumped)}")
-        print(f"  dump files: {[f.name for f in files]}")
-        assert guard._dumped, "ctrl NaN should have triggered dump (check_ctrl path)"
-        assert len(files) >= 1, f"expected ≥1 dump, got {len(files)}"
-        env.close()
-        print("  PASS")
-
-
-if __name__ == "__main__":
-    proto_him_ppo_obs_nan()
-    proto_him_ppo_reward_nan()
-    proto_him_ppo_ctrl_nan()
-    print()
-    print("All 3 HIM-PPO real-runner prototypes PASSED.")
diff --git a/tests/nan_injection/proto_nan_inject.py b/tests/nan_injection/proto_nan_inject.py
deleted file mode 100644
index b45be9d71..000000000
--- a/tests/nan_injection/proto_nan_inject.py
+++ /dev/null
@@ -1,248 +0,0 @@
-"""
-Prototype script for stage 2: validate that NaN injection at three different
-points (obs / reward / ctrl) actually triggers nan_guard.dump on a real np_env
-instance.
-
-This script uses the existing _StubNpEnv pattern from tests/base/test_np_env.py
-to keep things minimal — no full algo runner needed for the injection mechanic
-itself.
-
-Promotion status (registered 2026-08, issue #1020): the originally planned
-promotion into a formal pytest module (tests/algos/test_nan_inject_rsl_rl.py)
-was NOT carried out. Final disposition instead:
-- unit-level regression coverage lives in tests/test_nan_guard.py and
-  tests/ipc/test_nan_guard_spawn_pickle.py (both in CI);
-- real-runner end-to-end validation was lifted into
-  tests/nan_injection/stage2_nan_inject.py, kept as a manual tool per
-  tests/nan_injection/README.md (deliberately out of CI).
-This prototype is retained only as the minimal stub-env smoke reference for
-the inject + dump pattern; delete it together with stage2/stage3 if the
-manual validation tooling is ever dropped.
-
-Run:
-    uv run python tests/nan_injection/proto_nan_inject.py
-"""
-
-from __future__ import annotations
-
-import sys
-import tempfile
-from dataclasses import dataclass
-from pathlib import Path
-from unittest.mock import MagicMock
-
-import gymnasium as gym
-import numpy as np
-
-# Resolve repo root from this file's location
-ROOT_DIR = Path(__file__).resolve().parents[2]
-sys.path.insert(0, str(ROOT_DIR / "src"))
-
-from unilab.base.base import EnvCfg
-from unilab.base.np_env import NpEnv, NpEnvState
-from unilab.utils.nan_guard import NanGuard, NanGuardCfg
-
-
-@dataclass
-class _StubCfg(EnvCfg):
-    max_episode_seconds: float | None = 1.0
-    ctrl_dt: float = 0.1
-    sim_dt: float = 0.01
-
-
-class _StubEnv(NpEnv):
-    OBS_SPEC = {"obs": 5, "critic": 7}
-
-    def __init__(self, num_envs: int = 4):
-        cfg = _StubCfg()
-        backend = MagicMock()
-        backend.backend_type = "mujoco"
-        backend.step = MagicMock(return_value=None)
-        super().__init__(cfg, backend, num_envs)
-
-    @property
-    def obs_groups_spec(self):
-        return self.OBS_SPEC
-
-    @property
-    def action_space(self):
-        return gym.spaces.Box(low=-1.0, high=1.0, shape=(3,), dtype=np.float32)
-
-    def apply_action(self, actions: np.ndarray, state):
-        return actions
-
-    def update_state(self, state):
-        obs = {
-            "obs": np.ones((self._num_envs, 5), dtype=np.float32),
-            "critic": np.full((self._num_envs, 7), 0.5, dtype=np.float32),
-        }
-        return state.replace(
-            obs=obs,
-            reward=np.ones((self._num_envs,), dtype=np.float32),
-            terminated=np.zeros((self._num_envs,), dtype=bool),
-            truncated=np.zeros((self._num_envs,), dtype=bool),
-        )
-
-    def reset(self, env_indices):
-        n = len(env_indices)
-        obs = {
-            "obs": np.zeros((n, 5), dtype=np.float32),
-            "critic": np.zeros((n, 7), dtype=np.float32),
-        }
-        return obs, {}
-
-
-def _attach_guard(env: _StubEnv, output_dir: Path) -> NanGuard:
-    cfg = NanGuardCfg(enabled=True, output_dir=str(output_dir))
-    guard = NanGuard(cfg, num_envs=env._num_envs, supports_state_playback=False)
-    env.set_nan_guard(guard)
-    return guard
-
-
-def _run_steps(env: _StubEnv, n: int):
-    env.init_state()
-    actions = np.zeros((env._num_envs, 3), dtype=np.float32)
-    for _ in range(n):
-        env.step(actions)
-
-
-def _list_dumps(output_dir: Path):
-    """Return only timestamped dump files, excluding the 'nan_dump_latest.npz' pointer."""
-    return [p for p in output_dir.glob("nan_dump_*.npz") if "latest" not in p.name]
-
-
-def proto_obs_nan():
-    """Patch env.update_state to write NaN into obs at step K."""
-    print("=" * 60)
-    print("[1] OBS NaN injection via update_state patch")
-    print("=" * 60)
-    with tempfile.TemporaryDirectory() as td:
-        output_dir = Path(td)
-        env = _StubEnv(num_envs=4)
-        guard = _attach_guard(env, output_dir)
-
-        orig_update = env.update_state
-        K = 2
-        calls = [0]
-
-        def update_with_nan(state):
-            new_state = orig_update(state)
-            calls[0] += 1
-            if calls[0] == K:
-                new_state.obs["obs"][0, 0] = np.nan
-            return new_state
-
-        env.update_state = update_with_nan  # type: ignore
-
-        _run_steps(env, n=4)
-
-        dumped = bool(guard._dumped)
-        files = _list_dumps(output_dir)
-        print(f"  guard._dumped: {dumped}")
-        print(f"  dump files: {[f.name for f in files]}")
-        assert dumped, "obs NaN should have triggered dump"
-        assert len(files) == 1, f"expected 1 dump, got {len(files)}"
-        data = np.load(files[0], allow_pickle=True)
-        print(f"  npz keys: {list(data.files)}")
-        print("  PASS")
-
-
-def proto_reward_nan():
-    """Patch env.update_state to write NaN into reward at step K."""
-    print("=" * 60)
-    print("[2] REWARD NaN injection via update_state patch")
-    print("=" * 60)
-    with tempfile.TemporaryDirectory() as td:
-        output_dir = Path(td)
-        env = _StubEnv(num_envs=4)
-        guard = _attach_guard(env, output_dir)
-
-        orig_update = env.update_state
-        K = 2
-        calls = [0]
-
-        def update_with_nan(state):
-            new_state = orig_update(state)
-            calls[0] += 1
-            if calls[0] == K:
-                new_state.reward[0] = np.nan
-            return new_state
-
-        env.update_state = update_with_nan  # type: ignore
-
-        _run_steps(env, n=4)
-
-        dumped = bool(guard._dumped)
-        files = _list_dumps(output_dir)
-        print(f"  guard._dumped: {dumped}")
-        print(f"  dump files: {[f.name for f in files]}")
-        assert dumped, "reward NaN should have triggered dump"
-        assert len(files) == 1, f"expected 1 dump, got {len(files)}"
-        # Verify reward was nan_to_num'd after dump (sanitize should still run)
-        # Note: dump only fires once, so post-dump steps continue cleanly.
-        print("  PASS")
-
-
-def proto_ctrl_nan():
-    """Patch env.apply_action to return NaN ctrl at step K (validates check_ctrl)."""
-    print("=" * 60)
-    print("[3] CTRL NaN injection via apply_action patch")
-    print("=" * 60)
-    with tempfile.TemporaryDirectory() as td:
-        output_dir = Path(td)
-        env = _StubEnv(num_envs=4)
-        guard = _attach_guard(env, output_dir)
-
-        orig_apply = env.apply_action
-        K = 2
-        calls = [0]
-
-        def apply_with_nan(actions, state):
-            ctrl = orig_apply(actions, state)
-            calls[0] += 1
-            if calls[0] == K:
-                ctrl = ctrl.copy()
-                ctrl[0, 0] = np.nan
-            return ctrl
-
-        env.apply_action = apply_with_nan  # type: ignore
-
-        _run_steps(env, n=4)
-
-        dumped = bool(guard._dumped)
-        files = _list_dumps(output_dir)
-        print(f"  guard._dumped: {dumped}")
-        print(f"  dump files: {[f.name for f in files]}")
-        assert dumped, "ctrl NaN should have triggered dump (check_ctrl path)"
-        assert len(files) == 1, f"expected 1 dump, got {len(files)}"
-        print("  PASS")
-
-
-def proto_clean_no_dump():
-    """Negative control: clean run should NOT produce any dump."""
-    print("=" * 60)
-    print("[4] CLEAN run — no NaN, no dump expected")
-    print("=" * 60)
-    with tempfile.TemporaryDirectory() as td:
-        output_dir = Path(td)
-        env = _StubEnv(num_envs=4)
-        guard = _attach_guard(env, output_dir)
-
-        _run_steps(env, n=4)
-
-        dumped = bool(guard._dumped)
-        files = _list_dumps(output_dir)
-        print(f"  guard._dumped: {dumped}")
-        print(f"  dump files: {[f.name for f in files]}")
-        assert not dumped, "clean run should NOT trigger dump"
-        assert len(files) == 0, f"expected 0 dumps, got {len(files)}"
-        print("  PASS")
-
-
-if __name__ == "__main__":
-    proto_obs_nan()
-    proto_reward_nan()
-    proto_ctrl_nan()
-    proto_clean_no_dump()
-    print()
-    print("All 4 prototypes PASSED. Injection mechanics validated.")
diff --git a/tests/nan_injection/stage2_nan_inject.py b/tests/nan_injection/stage2_nan_inject.py
deleted file mode 100644
index 06736e5a8..000000000
--- a/tests/nan_injection/stage2_nan_inject.py
+++ /dev/null
@@ -1,317 +0,0 @@
-"""
-Stage 2: NaN injection validation for single-process algorithms.
-
-Covers:
-  PPO (rsl_rl)  x {obs, reward, ctrl} = 3 cases
-  HIM-PPO       x {obs, reward, ctrl} = 3 cases
-
-For each case: build a real runner via Hydra compose, attach NanGuard, patch
-env to inject NaN at step K (one-shot), assert nan_guard fires + dump file
-lands with metadata pointing at step K.
-
-Run:
-    .venv/bin/python tests/nan_injection/stage2_nan_inject.py
-"""
-
-from __future__ import annotations
-
-import sys
-import tempfile
-import traceback
-from pathlib import Path
-from typing import Callable
-
-import numpy as np
-import torch
-from hydra import compose, initialize_config_dir
-from omegaconf import OmegaConf
-
-# Resolve repo root from this file's location: <repo>/tests/nan_injection/<this>
-ROOT_DIR = Path(__file__).resolve().parents[2]
-sys.path.insert(0, str(ROOT_DIR))
-sys.path.insert(0, str(ROOT_DIR / "src"))
-
-from unilab.base.backend.mujoco.xml import materialize_scene_visual_override  # noqa: E402
-from unilab.training import BackendAdapter, create_env, ensure_registries  # noqa: E402
-from unilab.training.rsl_rl import RslRlVecEnvWrapper  # noqa: E402
-from unilab.utils.nan_guard import NanGuard, NanGuardCfg  # noqa: E402
-
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
-
-
-def _device() -> str:
-    return "cuda" if torch.cuda.is_available() else "cpu"
-
-
-def _list_dumps(output_dir: Path) -> list[Path]:
-    """Return only timestamped dump files, excluding the 'latest' pointer."""
-    return [p for p in output_dir.glob("nan_dump_*.npz") if "latest" not in p.name]
-
-
-def _attach_guard(env, output_dir: Path) -> NanGuard:
-    cfg = NanGuardCfg(enabled=True, output_dir=str(output_dir))
-    guard = NanGuard(
-        cfg,
-        num_envs=env.num_envs,
-        supports_state_playback=env.play_capabilities.supports_physics_state_playback,
-    )
-    env.set_nan_guard(guard)
-    return guard
-
-
-# ---------------------------------------------------------------------------
-# Builders: build real runner + env + attached NanGuard
-# ---------------------------------------------------------------------------
-
-
-def build_him_ppo(num_envs: int, log_dir: Path, output_dir: Path):
-    from unilab.algos.torch.him_ppo.runner import HIMOnPolicyRunner
-
-    ensure_registries()
-    with initialize_config_dir(config_dir=str(ROOT_DIR / "conf/ppo_him"), version_base=None):
-        cfg = compose(
-            config_name="config",
-            overrides=[
-                "task=go2_arm_manip_loco/mujoco",
-                f"algo.num_envs={num_envs}",
-                "algo.num_steps_per_env=4",
-                "algo.max_iterations=1",
-                "algo.save_interval=100",
-                f"training.nan_guard.output_dir={output_dir}",
-            ],
-        )
-
-    backend_adapter = BackendAdapter(
-        cfg,
-        root_dir=ROOT_DIR,
-        algo_name="ppo_him",
-        scene_materializer=materialize_scene_visual_override,
-    )
-    env_cfg_override = backend_adapter.build_task_env_cfg_override()
-    env = create_env(cfg, num_envs=cfg.algo.num_envs, env_cfg_override=env_cfg_override)
-    guard = _attach_guard(env, output_dir)
-
-    device = _device()
-    wrapped_env = RslRlVecEnvWrapper(env, device=device)
-    rl_cfg = OmegaConf.to_container(cfg.algo, resolve=True)
-    runner = HIMOnPolicyRunner(wrapped_env, rl_cfg, log_dir=str(log_dir), device=device)
-    return env, runner, guard
-
-
-def build_ppo_rsl_rl(num_envs: int, log_dir: Path, output_dir: Path):
-    from rsl_rl.runners import OnPolicyRunner
-
-    from unilab.training.experiment import patch_rsl_rl_resume_state
-    from unilab.training.rsl_rl import normalize_ppo_train_cfg
-
-    # Import apply_ppo_runtime_flags from train_rsl_rl script
-    sys.path.insert(0, str(ROOT_DIR / "scripts"))
-    from train_rsl_rl import apply_ppo_runtime_flags
-
-    ensure_registries()
-    with initialize_config_dir(config_dir=str(ROOT_DIR / "conf/ppo"), version_base=None):
-        cfg = compose(
-            config_name="config",
-            overrides=[
-                "task=go1_joystick_flat/mujoco",
-                f"algo.num_envs={num_envs}",
-                "algo.num_steps_per_env=4",
-                "algo.max_iterations=1",
-                "algo.save_interval=100",
-                "training.nan_guard.enabled=true",
-                f"training.nan_guard.output_dir={output_dir}",
-            ],
-        )
-
-    backend_adapter = BackendAdapter(
-        cfg,
-        root_dir=ROOT_DIR,
-        algo_name="rsl_rl_ppo",
-        scene_materializer=materialize_scene_visual_override,
-    )
-    env_cfg_override = backend_adapter.build_task_env_cfg_override()
-    env = create_env(cfg, num_envs=cfg.algo.num_envs, env_cfg_override=env_cfg_override)
-    guard = _attach_guard(env, output_dir)
-
-    device = _device()
-    rl_cfg = OmegaConf.to_container(cfg.algo, resolve=True)
-    train_cfg = normalize_ppo_train_cfg(rl_cfg)
-    apply_ppo_runtime_flags(train_cfg, cfg, training_enabled=True)
-    if "runner" not in train_cfg:
-        train_cfg["runner"] = {}
-    train_cfg["runner"]["logger"] = "none"
-    train_cfg["logger"] = "none"
-    patch_rsl_rl_resume_state()
-
-    wrapped_env = RslRlVecEnvWrapper(env, device=device)
-    runner = OnPolicyRunner(wrapped_env, train_cfg, log_dir=None, device=device)
-    return env, runner, guard
-
-
-# ---------------------------------------------------------------------------
-# Injection patches: one-shot NaN injection at step K
-# ---------------------------------------------------------------------------
-
-
-def _patch_obs_nan(env, K: int):
-    orig_update = env.update_state
-    calls = [0]
-
-    def update_with_nan(state):
-        new_state = orig_update(state)
-        calls[0] += 1
-        if calls[0] == K:
-            first_key = next(iter(new_state.obs))
-            new_state.obs[first_key][0, 0] = np.nan
-            env.update_state = orig_update  # one-shot restore
-        return new_state
-
-    env.update_state = update_with_nan  # type: ignore
-
-
-def _patch_reward_nan(env, K: int):
-    orig_update = env.update_state
-    calls = [0]
-
-    def update_with_nan(state):
-        new_state = orig_update(state)
-        calls[0] += 1
-        if calls[0] == K:
-            new_state.reward[0] = np.nan
-            env.update_state = orig_update
-        return new_state
-
-    env.update_state = update_with_nan  # type: ignore
-
-
-def _patch_ctrl_nan(env, K: int):
-    orig_apply = env.apply_action
-    calls = [0]
-
-    def apply_with_nan(actions, state):
-        ctrl = orig_apply(actions, state)
-        calls[0] += 1
-        if calls[0] == K:
-            ctrl = ctrl.copy()
-            ctrl[0, 0] = np.nan
-            env.apply_action = orig_apply
-        return ctrl
-
-    env.apply_action = apply_with_nan  # type: ignore
-
-
-# ---------------------------------------------------------------------------
-# Test runner wrapper
-# ---------------------------------------------------------------------------
-
-
-def _run_case(
-    name: str,
-    builder: Callable,
-    patch_fn: Callable,
-    K: int,
-    expected_step: int,
-    suppress_runtime_err: bool = True,
-) -> tuple[bool, str]:
-    """Run one injection case, return (passed, message).
-
-    K: which call to the patched method triggers NaN injection (1-indexed).
-    expected_step: expected value of meta_detection_step in the dump.
-      For obs/reward (checked after step_counter increment): expected_step == K.
-      For ctrl (checked before step_counter increment):      expected_step == K - 1.
-    """
-    try:
-        with tempfile.TemporaryDirectory() as td:
-            td_path = Path(td)
-            output_dir = td_path / "dumps"
-            output_dir.mkdir()
-            log_dir = td_path / "log"
-            log_dir.mkdir()
-
-            env, runner, guard = builder(num_envs=8, log_dir=log_dir, output_dir=output_dir)
-            patch_fn(env, K)
-
-            try:
-                runner.learn(num_learning_iterations=1, init_at_random_ep_len=False)
-            except (RuntimeError, ValueError):
-                if not suppress_runtime_err:
-                    raise
-                # Expected: NaN may propagate post-dump in obs/reward path
-                # rsl_rl's check_nan raises ValueError; torch sampling raises RuntimeError
-
-            files = _list_dumps(output_dir)
-            if not guard._dumped:
-                return False, "guard._dumped=False (no dump triggered)"
-            if len(files) < 1:
-                return False, f"expected ≥1 dump file, got {len(files)}"
-
-            data = np.load(files[0], allow_pickle=True)
-            if "states" not in data.files:
-                return False, "'states' missing from dump npz"
-            detected_step = int(data["meta_detection_step"])
-            if detected_step != expected_step:
-                return False, f"detected_step={detected_step}, expected {expected_step}"
-
-            env.close()
-            return True, f"OK (dump at step {detected_step})"
-
-    except Exception as e:
-        return False, f"EXCEPTION: {type(e).__name__}: {e}\n{traceback.format_exc()}"
-
-
-# ---------------------------------------------------------------------------
-# Main
-# ---------------------------------------------------------------------------
-
-
-def main():
-    print("=" * 70)
-    print("Stage 2: Single-process NaN injection validation")
-    print("=" * 70)
-    print()
-
-    cases = [
-        # (name, builder, patch_fn, K, expected_step)
-        # K = which call (1-indexed) triggers injection
-        # expected_step = step recorded in dump (K for obs/reward, K-1 for ctrl)
-        ("PPO obs NaN", build_ppo_rsl_rl, _patch_obs_nan, 2, 2),
-        ("PPO reward NaN", build_ppo_rsl_rl, _patch_reward_nan, 2, 2),
-        ("PPO ctrl NaN", build_ppo_rsl_rl, _patch_ctrl_nan, 3, 2),
-        ("HIM-PPO obs NaN", build_him_ppo, _patch_obs_nan, 2, 2),
-        ("HIM-PPO reward NaN", build_him_ppo, _patch_reward_nan, 2, 2),
-        ("HIM-PPO ctrl NaN", build_him_ppo, _patch_ctrl_nan, 3, 2),
-    ]
-
-    results = []
-    for idx, (name, builder, patch_fn, K, expected_step) in enumerate(cases, start=1):
-        print(f"[{idx}/{len(cases)}] {name:<25}", end="", flush=True)
-        passed, msg = _run_case(name, builder, patch_fn, K, expected_step)
-        status = "PASS" if passed else "FAIL"
-        print(f" {status}")
-        if not passed:
-            print(f"    {msg}")
-        results.append((name, passed))
-
-    print()
-    print("=" * 70)
-    print("Summary")
-    print("=" * 70)
-    passed_count = sum(1 for _, p in results if p)
-    print(f"{passed_count}/{len(results)} cases passed")
-    for name, passed in results:
-        status = "✓" if passed else "✗"
-        print(f"  {status} {name}")
-    print()
-
-    if passed_count == len(results):
-        print("All cases PASSED. NaN injection mechanics validated.")
-        return 0
-    else:
-        print(f"{len(results) - passed_count} case(s) FAILED.")
-        return 1
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/tests/nan_injection/stage3_nan_inject.py b/tests/nan_injection/stage3_nan_inject.py
deleted file mode 100644
index 3440ff786..000000000
--- a/tests/nan_injection/stage3_nan_inject.py
+++ /dev/null
@@ -1,384 +0,0 @@
-"""Stage 3: NaN guard wiring verification (mock-based assertions).
-
-Validates that nan_guard_cfg actually flows through the collector_kwargs
-of the production runners (device replay / APPO), not just
-present in source as a substring.
-
-This script constructs each runner with a NanGuardCfg, mocks dependencies,
-triggers the collector startup path (via learn(max_iterations=0)), and asserts
-that the captured collector kwargs contain the expected nan_guard_cfg.
-
-Run:
-    .venv/bin/python tests/nan_injection/stage3_nan_inject.py
-
-Exit: 0 if all wirings OK, 1 if any failure.
-"""
-
-from __future__ import annotations
-
-import queue
-import sys
-import tempfile
-import textwrap
-import traceback
-from pathlib import Path
-from unittest.mock import patch
-
-# Resolve repo root from this file's location: <repo>/tests/nan_injection/<this>
-ROOT_DIR = Path(__file__).resolve().parents[2]
-sys.path.insert(0, str(ROOT_DIR / "src"))
-
-import torch
-
-from unilab.utils.nan_guard import NanGuardCfg
-
-# ---------------------------------------------------------------------------
-# Fake classes (copied from tests/algos/*.py to keep stage3 self-contained)
-# ---------------------------------------------------------------------------
-
-
-class _FakeActor:
-    def __init__(self):
-        self._state = {"weight": torch.zeros(1)}
-
-    def state_dict(self):
-        return {key: value.clone() for key, value in self._state.items()}
-
-    def parameters(self):
-        return [torch.nn.Parameter(torch.zeros(1))]
-
-
-class _FakeLearner:
-    def __init__(self, *args, **kwargs):
-        del args, kwargs
-        self.actor = _FakeActor()
-        self.critic = _FakeActor()  # APPO reads learner.critic.state_dict()
-        self.update_count = 0
-        self.num_learning_epochs = 1  # APPO uses this in log_status
-
-    def get_state_dict(self):
-        return {"update_count": self.update_count}
-
-
-class _FakeReplayBuffer:
-    def __init__(self, **kwargs):
-        del kwargs
-        self.size = torch.zeros(1, dtype=torch.int64)
-        self.ptr = torch.zeros(1, dtype=torch.int64)
-
-    def close(self):
-        pass
-
-
-class _FakeInferenceSlot:
-    def __init__(self, num_envs, obs_dim, action_dim):
-        del num_envs, obs_dim, action_dim
-        self.nbytes = 0
-
-    def close(self):
-        pass
-
-    def cleanup(self):
-        pass
-
-
-class _FakeWeightSync:
-    def __init__(self):
-        self.name = "fake-ws"
-        self._lock = None
-
-    @classmethod
-    def from_state_dict(cls, state_dict, create=True):
-        del state_dict, create
-        return cls()
-
-    def close(self):
-        pass
-
-    def cleanup(self):
-        pass
-
-
-class _FakeLogger:
-    def __init__(self, **kwargs):
-        del kwargs
-        self._total_steps = 0
-        self._buffer_size = 0
-        self._mean_ep_length = 0.0
-
-    def start(self, **kwargs):
-        del kwargs
-
-    def log_status(self, status):
-        del status
-
-    def log_save(self, ckpt_path):
-        del ckpt_path
-
-    def log_collector(self, total_steps, buffer_size, mean_reward=0.0):
-        del total_steps, buffer_size, mean_reward
-
-    def log_buffer_fill(self, current, target):
-        del current, target
-
-    def update_buffer_utilization(self, utilization):
-        del utilization
-
-    def update_ep_length(self, mean_ep_length):
-        del mean_ep_length
-
-    def update_collector_timing(self, timing_ms):
-        del timing_ms
-
-    def update_timeout_rate(self, timeout_rate):
-        del timeout_rate
-
-    def update_replay_queue(self, current_len, max_size):
-        del current_len, max_size
-
-    def update_staging_pool(self, current_len, max_size):
-        del current_len, max_size
-
-    def log_step(self, **kwargs):
-        del kwargs
-
-    def finish(self, *args, **kwargs):
-        del args, kwargs
-
-    def close(self):
-        pass
-
-
-class _FakePipeline:
-    """Mock pipeline for DoubleBuffer test."""
-
-    def __init__(self, *args, **kwargs):
-        del args, kwargs
-
-    def close(self):
-        pass
-
-
-class _FakeRolloutRingBuffer:
-    """Mock rollout storage for APPO test."""
-
-    def __init__(self, **kwargs):
-        del kwargs
-        self.name = "fake-storage"
-        self._write_ptr = object()
-        self._read_ptr = object()
-
-    @property
-    def slot_shapes(self):
-        return {
-            "obs": (2, 4, 4),
-            "critic": (2, 4, 4),
-            "actions": (2, 4, 2),
-            "log_probs": (2, 4),
-            "rewards": (2, 4),
-            "dones": (2, 4),
-            "truncated": (2, 4),
-            "last_obs": (2, 4),
-            "last_critic": (2, 4),
-        }
-
-    def cleanup(self):
-        pass
-
-
-# ---------------------------------------------------------------------------
-# Wiring checks
-# ---------------------------------------------------------------------------
-
-
-def _check_double_buffer_runner_wires_nan_guard():
-    """Verify DoubleBufferOffPolicyRunner passes nan_guard_cfg to collector."""
-    import unilab.algos.torch.offpolicy.double_buffer_runner as db_mod
-    import unilab.algos.torch.offpolicy.runner as runner_mod
-
-    with (
-        patch.object(db_mod, "ReplayBuffer", _FakeReplayBuffer),
-        patch.object(db_mod, "SharedInferenceSlot", _FakeInferenceSlot),
-        patch.object(db_mod, "GPUResidentReplayPipeline", _FakePipeline),
-        patch.object(db_mod, "require_offpolicy_replay_device", lambda value: value),
-        patch.object(runner_mod, "get_env_dims", return_value=(4, 2, 0)),
-        patch.object(db_mod.torch, "save", lambda *args, **kwargs: None),
-        patch.object(db_mod._SPAWN_CTX, "Queue", lambda maxsize=0: queue.Queue()),
-        patch.object(db_mod.time, "sleep", lambda seconds: None),
-    ):
-        learner = _FakeLearner()
-        nan_guard_cfg = NanGuardCfg(enabled=True)
-        runner = db_mod.DoubleBufferOffPolicyRunner(
-            learner=learner,
-            env_name="DummyEnv",
-            algo_type="sac",
-            num_envs=2,
-            replay_buffer_n=8,
-            batch_size=8,
-            learning_starts=6,
-            updates_per_step=1,
-            policy_frequency=1,
-            env_steps_per_sync=1,
-            device="cuda",
-            nan_guard_cfg=nan_guard_cfg,
-        )
-
-        captured = {}
-
-        def capture_start_collector(*, target_fn, kwargs):
-            del target_fn
-            captured.update(kwargs)
-
-        with patch.object(runner, "_start_collector", capture_start_collector):
-            with tempfile.TemporaryDirectory() as tmp_dir:
-                runner.learn(max_iterations=0, save_interval=0, log_dir=tmp_dir)
-
-        if "nan_guard_cfg" not in captured:
-            return False
-        if captured["nan_guard_cfg"] is not nan_guard_cfg:
-            return False
-        if captured["nan_guard_cfg"].enabled is not True:
-            return False
-        return True
-
-
-def _check_appo_runner_wires_nan_guard():
-    """Verify APPORunner passes nan_guard_cfg to collector."""
-    import unilab.algos.torch.appo.runner as appo_mod
-    from unilab.algos.torch.appo.runner import APPORunner
-
-    def fake_detect_dims(self):
-        self.critic_dim = 4
-        self.critic_input_dim = 4
-        return (4, 2)
-
-    with (
-        patch.object(APPORunner, "_detect_dims", fake_detect_dims),
-        patch.object(APPORunner, "_build_learner", lambda self: _FakeLearner()),
-        patch.object(appo_mod, "RolloutRingBuffer", _FakeRolloutRingBuffer),
-        patch.object(appo_mod, "SharedWeightSync", _FakeWeightSync),
-        patch.object(appo_mod, "OffPolicyLogger", _FakeLogger),
-        patch.object(appo_mod.torch, "save", lambda *args, **kwargs: None),
-    ):
-        nan_guard_cfg = NanGuardCfg(enabled=True)
-        runner = APPORunner(
-            env_name="DummyEnv",
-            env_cfg_overrides={},
-            rl_cfg={"actor": {}, "critic": {}, "algorithm": {}},
-            device="cpu",
-            collector_device="cpu",
-            num_envs=2,
-            steps_per_env=4,
-            nan_guard_cfg=nan_guard_cfg,
-        )
-
-        captured = {}
-
-        def capture_start_collector(*, target_fn, kwargs):
-            del target_fn
-            captured.update(kwargs)
-
-        with patch.object(runner, "_start_collector", capture_start_collector):
-            with tempfile.TemporaryDirectory() as tmp_dir:
-                runner.learn(max_iterations=0, save_interval=0, log_dir=tmp_dir)
-
-        if "nan_guard_cfg" not in captured:
-            return False
-        if captured["nan_guard_cfg"] is not nan_guard_cfg:
-            return False
-        if captured["nan_guard_cfg"].enabled is not True:
-            return False
-        return True
-
-
-# ---------------------------------------------------------------------------
-# Manual end-to-end test recipe
-# ---------------------------------------------------------------------------
-
-
-def _print_manual_recipe():
-    """Print the manual smoke test recipe (preserved from original stage3)."""
-    print("=" * 78)
-    print("Manual end-to-end test recipe (optional)")
-    print("=" * 78)
-    print(
-        textwrap.dedent("""
-        To exercise NaN detection end-to-end inside a collector subprocess:
-
-        1. Pick a task env file, e.g.
-           src/unilab/envs/locomotion/go1/go1_joystick.py
-        2. Inside the env's update_state or apply_action, add a temporary
-           one-shot NaN raise guarded by an env-counter, for example:
-
-               if not getattr(self, "_nan_done", False) and self.step_counter >= 5:
-                   state.reward[0] = float("nan")
-                   self._nan_done = True
-
-        3. Run a short training, e.g. (APPO):
-               python scripts/train_appo.py task=go1_joystick_flat/mujoco \\
-                 algo.num_envs=8 algo.num_steps_per_env=4 \\
-                 algo.train_for_env_steps=64 \\
-                 training.nan_guard.enabled=true \\
-                 training.nan_guard.output_dir=/tmp/unilab/nan_dumps
-
-        4. Verify a dump file appears under the output_dir.
-        5. Revert the env edit.
-
-        Repeat with scripts/train_offpolicy.py to exercise SAC/TD3/FlashSAC
-        (set algo=sac / algo=td3 / algo=flashsac via Hydra override).
-    """).strip()
-    )
-    print()
-
-
-# ---------------------------------------------------------------------------
-# Main
-# ---------------------------------------------------------------------------
-
-
-def main():
-    print("Stage 3: NaN guard wiring assertions")
-    print("=" * 78)
-    print()
-    print("Approach: mock-based capture of collector_kwargs to verify")
-    print("  that nan_guard_cfg flows from runner.__init__ to _start_collector.")
-    print()
-
-    checks = [
-        (
-            "DoubleBufferOffPolicyRunner (SAC/TD3/FlashSAC prod path)",
-            _check_double_buffer_runner_wires_nan_guard,
-        ),
-        ("APPORunner (APPO prod path)", _check_appo_runner_wires_nan_guard),
-    ]
-
-    all_ok = True
-    for label, fn in checks:
-        try:
-            ok = fn()
-        except Exception as exc:
-            ok = False
-            print(f"  ✗ {label}")
-            print(f"    Exception: {type(exc).__name__}: {exc}")
-            traceback.print_exc()
-            all_ok = False
-            continue
-
-        marker = "✓" if ok else "✗"
-        print(f"  {marker} {label}")
-        if not ok:
-            all_ok = False
-
-    print()
-    print("=" * 78)
-    print("Summary:", "PASS" if all_ok else "FAIL")
-    print("=" * 78)
-    print()
-
-    _print_manual_recipe()
-
-    return 0 if all_ok else 1
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/tests/scripts/doc_checks.py b/tests/scripts/doc_checks.py
index 88199a7b9..982db7e1b 100644
--- a/tests/scripts/doc_checks.py
+++ b/tests/scripts/doc_checks.py
@@ -3,7 +3,7 @@
 import re
 from pathlib import Path
 
-from unilab.utils.support_matrix import render_generated_block, replace_generated_block
+from scripts.tools.support_matrix import render_generated_block, replace_generated_block
 
 VALID_HYDRA_KEYS = {
     "task",
@@ -285,7 +285,9 @@ def check_argparse_vs_hydra(content: str, doc_path: Path, root: Path) -> list[st
     hydra_scripts = [
         "train_rsl_rl.py",
         "train_appo.py",
-        "train_offpolicy.py",
+        "train_sac.py",
+        "train_td3.py",
+        "train_flashsac.py",
     ]
 
     for pattern, message in old_flags:
@@ -320,7 +322,7 @@ def check_training_entrypoint_semantics(content: str, doc_path: Path, root: Path
     for match in re.finditer(task_pattern, content):
         errors.append(
             f"{doc_path}: Task override '{match.group(0)}' is missing the backend segment; "
-            "use task=<task>/<backend> or task=<algo>/<task>/<backend>"
+            "use task=<task>/<backend>"
         )
 
     return errors
diff --git a/tests/scripts/test_audit_sim2sim_contracts.py b/tests/scripts/test_audit_sim2sim_contracts.py
index 45ed3d88a..d0854369d 100644
--- a/tests/scripts/test_audit_sim2sim_contracts.py
+++ b/tests/scripts/test_audit_sim2sim_contracts.py
@@ -22,31 +22,35 @@ def test_discover_preserves_standard_task_layout() -> None:
 
     discovered = audit._discover("ppo")
 
-    assert {"mujoco", "motrix"}.issubset(discovered["g1_walk_flat"])
+    assert {"mujoco", "motrix", "genesis"}.issubset(discovered["g1_walk_flat"])
 
 
-def test_discover_groups_offpolicy_owners_by_algorithm() -> None:
+def test_discover_offpolicy_trees_group_by_task() -> None:
     audit = _load_audit_module()
 
-    discovered = audit._discover("offpolicy")
+    sac = audit._discover("sac")
+    flashsac = audit._discover("flashsac")
+    td3 = audit._discover("td3")
 
-    assert {"mujoco", "motrix", "mjwarp"}.issubset(discovered["sac/g1_walk_flat"])
-    assert {"mujoco", "motrix", "mjwarp"}.issubset(discovered["flashsac/g1_walk_flat"])
-    assert discovered["td3/g1_walk_flat"] == ["mujoco"]
+    assert {"mujoco", "motrix", "mjwarp", "isaacgym", "genesis", "isaacsim"}.issubset(
+        sac["g1_walk_flat"]
+    )
+    assert {"mujoco", "motrix", "mjwarp"}.issubset(flashsac["g1_walk_flat"])
+    assert td3["g1_walk_flat"] == ["mujoco"]
 
 
 @pytest.mark.parametrize(
-    ("task_variant", "expected_algo"),
+    ("tree", "task_variant", "expected_algo"),
     [
-        ("sac/g1_walk_flat/mujoco", "sac"),
-        ("td3/g1_walk_flat/mujoco", "td3"),
-        ("flashsac/g1_walk_flat/mujoco", "flashsac"),
+        ("sac", "g1_walk_flat/mujoco", "sac"),
+        ("td3", "g1_walk_flat/mujoco", "td3"),
+        ("flashsac", "g1_walk_flat/mujoco", "flashsac"),
     ],
 )
-def test_compose_selects_matching_offpolicy_algo(task_variant: str, expected_algo: str) -> None:
+def test_compose_resolves_tree_algo(tree: str, task_variant: str, expected_algo: str) -> None:
     audit = _load_audit_module()
 
-    cfg = audit._compose("offpolicy", task_variant)
+    cfg = audit._compose(tree, task_variant)
 
     assert cfg.algo.algo == expected_algo
 
@@ -58,13 +62,13 @@ def test_audit_tree_compares_one_offpolicy_backend_pair(
     monkeypatch.setattr(
         audit,
         "_discover",
-        lambda tree: {"flashsac/g1_walk_flat": ["motrix", "mujoco"]},
+        lambda tree: {"g1_walk_flat": ["motrix", "mujoco"]},
     )
 
-    rows = audit.audit_tree("offpolicy")
+    rows = audit.audit_tree("flashsac")
 
     assert len(rows) == 1
-    assert rows[0]["task"] == "flashsac/g1_walk_flat"
+    assert rows[0]["task"] == "g1_walk_flat"
     assert rows[0]["backends"] == ["motrix", "mujoco"]
     assert rows[0]["errors"] == {}
 
@@ -74,11 +78,4 @@ def test_audit_tree_rejects_empty_discovery(monkeypatch: pytest.MonkeyPatch) ->
     monkeypatch.setattr(audit, "_discover", lambda tree: {})
 
     with pytest.raises(ValueError, match="No task owner configs discovered"):
-        audit.audit_tree("offpolicy")
-
-
-def test_compose_rejects_offpolicy_variant_without_algo() -> None:
-    audit = _load_audit_module()
-
-    with pytest.raises(ValueError, match="<algo>/<task>/<backend>"):
-        audit._compose("offpolicy", "g1_walk_flat")
+        audit.audit_tree("sac")
diff --git a/tests/scripts/test_check_docs.py b/tests/scripts/test_check_docs.py
index f0c252a30..33fa2cdc9 100644
--- a/tests/scripts/test_check_docs.py
+++ b/tests/scripts/test_check_docs.py
@@ -29,7 +29,9 @@ def test_sharpa_domain_randomization_doc_matches_owner_config():
     )
     content = doc_path.read_text(encoding="utf-8")
 
-    owner_cfg = OmegaConf.load(root / "conf" / "ppo" / "task" / "sharpa_inhand" / "mujoco.yaml")
+    owner_cfg = OmegaConf.load(
+        root / "src" / "unilab" / "conf" / "ppo" / "task" / "sharpa_inhand" / "mujoco.yaml"
+    )
 
     assert "Sharpa" in content
     assert "`geom_size`" in content
@@ -58,7 +60,7 @@ def test_check_training_entrypoint_semantics_accepts_current_patterns():
     doc_path = root / "README.md"
     content = """
 uv run scripts/train_rsl_rl.py task=go1_joystick_flat/mujoco algo.load_run=2026-01-01
-uv run scripts/train_offpolicy.py algo=sac task=sac/g1_walk_flat/mujoco
+uv run scripts/train_sac.py task=g1_walk_flat/mujoco
 Logs live under logs/<algo.algo_log_name>/<task>/.
 """
 
@@ -104,10 +106,10 @@ def test_collect_doc_errors_scans_issue_templates_for_hydra_semantics(tmp_path):
     issue_template = tmp_path / ".github" / "ISSUE_TEMPLATE" / "bug_report.yml"
     issue_template.parent.mkdir(parents=True)
     issue_template.write_text(
-        "placeholder: |\n  uv run scripts/train_offpolicy.py algo=sac task=g1_walk_flat ...\n",
+        "placeholder: |\n  uv run scripts/train_sac.py task=g1_walk_flat ...\n",
         encoding="utf-8",
     )
-    script_path = tmp_path / "scripts" / "train_offpolicy.py"
+    script_path = tmp_path / "scripts" / "train_sac.py"
     script_path.parent.mkdir(parents=True)
     script_path.write_text("", encoding="utf-8")
 
diff --git a/tests/scripts/test_drake_training_smoke.py b/tests/scripts/test_drake_training_smoke.py
index 1954e1945..1308efca4 100644
--- a/tests/scripts/test_drake_training_smoke.py
+++ b/tests/scripts/test_drake_training_smoke.py
@@ -18,7 +18,7 @@ def _module_available(name: str) -> bool:
 
 
 def _drake_batch_available() -> bool:
-    return _module_available("drakeuni.compiled._drake_env_pool")
+    return _module_available("drake_uni.compiled._drake_env_pool")
 
 
 @pytest.mark.slow
@@ -32,7 +32,7 @@ def test_drake_ppo_one_iteration_training_smoke(task: str, tmp_path: Path) -> No
     result = subprocess.run(
         [
             sys.executable,
-            "scripts/train_rsl_rl.py",
+            "src/unilab/scripts/train_rsl_rl.py",
             f"task={task}",
             "training.no_play=true",
             f"training.log_root={tmp_path / 'logs'}",
diff --git a/tests/scripts/test_mujoco_only_tooling_markers.py b/tests/scripts/test_mujoco_only_tooling_markers.py
index f94878b7c..17c06e471 100644
--- a/tests/scripts/test_mujoco_only_tooling_markers.py
+++ b/tests/scripts/test_mujoco_only_tooling_markers.py
@@ -6,12 +6,12 @@
 def test_mujoco_only_tooling_is_marked_explicitly():
     root = Path(__file__).resolve().parents[2]
     target_files = [
-        root / "scripts" / "play_interactive.py",
+        root / "src" / "unilab" / "scripts" / "play_interactive.py",
         root / "scripts" / "motion" / "csv_to_npz.py",
         root / "scripts" / "motion" / "replay_npz.py",
         root / "scripts" / "motion" / "bones_seed_csv_to_npz.py",
         root / "scripts" / "motion" / "replay_bones_seed_csv.py",
-        root / "src" / "unilab" / "tools" / "bones_seed_csv.py",
+        root / "scripts" / "motion" / "bones_seed_csv.py",
         root / "src" / "unilab" / "visualization" / "render_many.py",
     ]
 
diff --git a/tests/scripts/test_obs_alignment_g1_wbt.py b/tests/scripts/test_obs_alignment_g1_wbt.py
index 93b5323b8..ef145cb56 100644
--- a/tests/scripts/test_obs_alignment_g1_wbt.py
+++ b/tests/scripts/test_obs_alignment_g1_wbt.py
@@ -1,53 +1,10 @@
-"""Cross-side obs alignment test for the G1 WBT Obs deploy chain.
-
-This is the load-bearing test that ensures the train -> export -> deploy
-pipeline produces byte-identical actor obs at every step.  Three independent
-implementations are exercised against the SAME inputs:
-
-    1. Training side  — tracking_obs.py's _push_obs_history /
-                        _fill_obs_history + actor obs assembly in
-                        _build_actor_obs (replicated below in numpy).
-    2. Schema side    — sim_prototype.ObsAssembler driven by deploy_config.yaml.
-    3. Deploy side    — observation_manager.h::ObservationTermCfg semantics
-                        replicated in Python (oldest-first deque per term,
-                        group-by-term flatten, use_gym_history=false).
-
-If any pair diverges, redeployment will silently fail at runtime — better to
-catch it here than at the FSM transition with a robot on a rig.
-
-The test is hermetic: it does NOT spin up MuJoCo, does NOT load motion clips,
-and does NOT depend on training infra. It synthesizes fixed random inputs,
-runs the assembly on both sides, and asserts bit-for-bit equality.
-"""
-
 from __future__ import annotations
 
-import sys
 from collections import deque
-from pathlib import Path
 
 import numpy as np
 import pytest
 
-REPO_ROOT = Path(__file__).resolve().parents[2]
-SIM_PROTOTYPE = REPO_ROOT / "scripts" / "deploy" / "sim_prototype.py"
-
-
-def _load_sim_prototype():
-    """Import sim_prototype as a module (it's under scripts/, not src/)."""
-    import importlib.util
-
-    spec = importlib.util.spec_from_file_location("sim_prototype", SIM_PROTOTYPE)
-    mod = importlib.util.module_from_spec(spec)
-    sys.modules["sim_prototype"] = mod
-    spec.loader.exec_module(mod)
-    return mod
-
-
-# ---------------------------------------------------------------------------
-# Reference deque (mirrors deploy ObservationTermCfg::reset / add / get).
-# ---------------------------------------------------------------------------
-
 
 class _DeployTerm:
     """Bit-for-bit copy of ObservationTermCfg buffer semantics.
@@ -78,20 +35,16 @@ def get(self) -> np.ndarray:
 
 
 def _deploy_compute_group(layout, current_segments_by_name):
-    """Mirror ObservationManager::compute_group with use_gym_history=False."""
+    """Assemble per step: each term's full history oldest-first, then concat."""
     terms = {}
     for seg in layout:
         terms[seg["name"]] = _DeployTerm(int(seg["dim"]), int(seg.get("history_length", 1)))
-    # Match deploy reset(): fill once with the FIRST step's value.
     for seg in layout:
         terms[seg["name"]].reset(current_segments_by_name[0][seg["name"]])
-    # First "step" inside compute_group calls term.add() once more BEFORE get();
-    # that final add corresponds to the current frame after the reset fill.
     out_per_step = []
-    for step_idx, segments in enumerate(current_segments_by_name):
+    for segments in current_segments_by_name:
         for seg in layout:
             terms[seg["name"]].add(segments[seg["name"]])
-        # Concat each term's full history oldest-first, then across terms.
         out = np.concatenate([terms[seg["name"]].get() for seg in layout], axis=0).astype(
             np.float32
         )
@@ -99,12 +52,6 @@ def _deploy_compute_group(layout, current_segments_by_name):
     return out_per_step
 
 
-# ---------------------------------------------------------------------------
-# Schema fixture — mirrors what export_deploy_config.py writes for the
-# deploy profile (H=5, both zero flags ON).
-# ---------------------------------------------------------------------------
-
-
 @pytest.fixture
 def deploy_cfg():
     n = 29
@@ -121,7 +68,6 @@ def deploy_cfg():
     total = sum(s["dim"] * s["history_length"] for s in obs_layout)
     return {
         "obs_dim": total,
-        "use_gym_history": False,
         "action_dim": n,
         "obs_layout": obs_layout,
     }
@@ -144,11 +90,6 @@ def _random_segments(rng, num_action=29):
     }
 
 
-# ---------------------------------------------------------------------------
-# Tests
-# ---------------------------------------------------------------------------
-
-
 class TestObsDim:
     def test_deploy_profile_dim_is_514(self, deploy_cfg):
         assert deploy_cfg["obs_dim"] == 514
@@ -158,76 +99,47 @@ def test_layout_sum_matches_obs_dim(self, deploy_cfg):
         assert total == deploy_cfg["obs_dim"]
 
 
-class TestSchemaAssemblerVsDeploy:
-    """sim_prototype.ObsAssembler vs the deploy-side ObservationTermCfg."""
-
-    def test_first_step_matches_deploy_reset_then_add(self, deploy_cfg, rng):
-        sp = _load_sim_prototype()
-        assembler = sp.ObsAssembler(deploy_cfg)
-
-        seg0 = _random_segments(rng)
-        prototype = assembler.step(seg0)
-
-        deploy = _deploy_compute_group(deploy_cfg["obs_layout"], [seg0])[0]
-
-        np.testing.assert_array_equal(prototype, deploy)
-
-    def test_multi_step_buffer_eviction_matches_deploy(self, deploy_cfg, rng):
-        sp = _load_sim_prototype()
-        assembler = sp.ObsAssembler(deploy_cfg)
-
-        all_segments = [_random_segments(rng) for _ in range(20)]
-        prototype_seq = [assembler.step(s) for s in all_segments]
-        deploy_seq = _deploy_compute_group(deploy_cfg["obs_layout"], all_segments)
-
-        for k, (p, d) in enumerate(zip(prototype_seq, deploy_seq)):
-            np.testing.assert_array_equal(
-                p, d, err_msg=f"sim_prototype <-> deploy mismatch at step {k}"
-            )
+class TestHistoryOrdering:
+    """History blocks must read oldest-first, not newest-first."""
 
     def test_history_terms_carry_oldest_first(self, deploy_cfg, rng):
         """Spot-check the gyro history block manually: at step k>=H, the 5*3
         gyro slot of obs should be [gyro_{k-H+1}, gyro_{k-H+2}, ..., gyro_k]
         flattened, NOT the reverse."""
-        sp = _load_sim_prototype()
-        assembler = sp.ObsAssembler(deploy_cfg)
+        layout = deploy_cfg["obs_layout"]
 
         gyros = [np.array([float(k), 0.0, 0.0], dtype=np.float32) for k in range(10)]
+        all_segments = []
         for k in range(10):
             seg = _random_segments(rng)
             seg["gyro"] = gyros[k]
-            obs = assembler.step(seg)
+            all_segments.append(seg)
+
+        obs = _deploy_compute_group(layout, all_segments)[-1]
 
-        # Find the gyro block. layout order: cmd_jp, cmd_jv, anchor_ori, gyro,...
-        # offset = 29 + 29 + 6 = 64
         offset = 29 + 29 + 6
         gyro_block = obs[offset : offset + 3 * 5].reshape(5, 3)
-        # Newest at the END (idx 4) = gyros[9]; oldest = gyros[5].
         expected = np.stack(gyros[5:10])
         np.testing.assert_array_equal(gyro_block, expected)
 
 
 class TestTrainingAssemblerVsDeploy:
-    """Replicate training-side history maintenance (np ring buffer) and compare.
+    """Replicate training-side ObservationManager history and compare.
 
-    This replicates the exact buffer logic from tracking_obs.py:
-        * _fill_obs_history: buf[:, :] = val[:, None, :]
-        * _push_obs_history: buf[:, :-1] = buf[:, 1:]; buf[:, -1] = val
-    and the actor obs assembly: refs first, then per-term history blocks
-    (gyro, joint_pos_rel, dof_vel, last_actions) flattened (n_env, H*dim).
+    The manager resets each term history by filling it with the current sample,
+    then shifts oldest-first and appends each new value. Concatenation is
+    term-major, matching the deploy-side observation layout.
     """
 
     @staticmethod
     def _training_actor_obs(history_buf, current_refs, hist_components, num_envs):
-        # refs (single-step)
         parts = [
             current_refs["command_joint_pos"],
             current_refs["command_joint_vel"],
             current_refs["motion_anchor_ori_b"],
         ]
-        # proprio history (oldest-first per term, then concat across terms)
         for key in ("gyro", "joint_pos_rel", "dof_vel", "last_actions"):
-            buf = history_buf[key]  # (num_envs, H, D)
+            buf = history_buf[key]
             parts.append(buf.reshape(num_envs, -1))
         return np.concatenate(parts, axis=1).astype(np.float32)
 
@@ -236,7 +148,6 @@ def test_training_path_matches_deploy(self, deploy_cfg, rng):
         n = deploy_cfg["action_dim"]
         H = 5
 
-        # Initial buffer of zeros (matches tracking_obs.py allocation).
         buf = {
             "gyro": np.zeros((n_env, H, 3), dtype=np.float32),
             "joint_pos_rel": np.zeros((n_env, H, n), dtype=np.float32),
@@ -247,7 +158,6 @@ def test_training_path_matches_deploy(self, deploy_cfg, rng):
         all_segments = [_random_segments(rng) for _ in range(15)]
         deploy_seq = _deploy_compute_group(deploy_cfg["obs_layout"], all_segments)
 
-        # Step 0: reset (matches tracking_obs.py is_reset=True path).
         s0 = all_segments[0]
         for key in ("gyro", "joint_pos_rel", "dof_vel", "last_actions"):
             buf[key][:, :, :] = s0[key][None, None, :]
@@ -259,7 +169,6 @@ def test_training_path_matches_deploy(self, deploy_cfg, rng):
         train_obs0 = self._training_actor_obs(buf, refs0, s0, n_env)
         np.testing.assert_array_equal(train_obs0[0], deploy_seq[0])
 
-        # Steps 1..N-1: push (matches tracking_obs.py is_reset=False path).
         for k in range(1, len(all_segments)):
             sk = all_segments[k]
             for key in ("gyro", "joint_pos_rel", "dof_vel", "last_actions"):
@@ -280,9 +189,9 @@ class TestBackCompat:
     """H=1 ('no history') must reproduce the pre-history 154-d path bit-exact."""
 
     @pytest.fixture
-    def legacy_cfg(self):
+    def legacy_layout(self):
         n = 29
-        obs_layout = [
+        return [
             {"name": "command_joint_pos", "dim": n, "history_length": 1},
             {"name": "command_joint_vel", "dim": n, "history_length": 1},
             {"name": "motion_anchor_ori_b", "dim": 6, "history_length": 1},
@@ -291,22 +200,17 @@ def legacy_cfg(self):
             {"name": "dof_vel", "dim": n, "history_length": 1},
             {"name": "last_actions", "dim": n, "history_length": 1},
         ]
-        return {
-            "obs_dim": 154,
-            "use_gym_history": False,
-            "action_dim": n,
-            "obs_layout": obs_layout,
-        }
 
-    def test_legacy_obs_dim_154(self, legacy_cfg):
-        assert legacy_cfg["obs_dim"] == 154
+    def test_legacy_obs_dim_154(self, legacy_layout):
+        total = sum(s["dim"] * s["history_length"] for s in legacy_layout)
+        assert total == 154
 
-    def test_legacy_matches_simple_concat(self, legacy_cfg, rng):
-        sp = _load_sim_prototype()
-        assembler = sp.ObsAssembler(legacy_cfg)
+    def test_legacy_matches_simple_concat(self, legacy_layout, rng):
+        """With H=1 every term holds exactly the current value, so the
+        assembled vector is a plain concat in layout order."""
+        all_segments = [_random_segments(rng) for _ in range(5)]
+        assembled = _deploy_compute_group(legacy_layout, all_segments)
 
-        for _ in range(5):
-            seg = _random_segments(rng)
-            obs = assembler.step(seg)
-            expected = np.concatenate([seg[s["name"]] for s in legacy_cfg["obs_layout"]])
+        for seg, obs in zip(all_segments, assembled):
+            expected = np.concatenate([seg[s["name"]] for s in legacy_layout])
             np.testing.assert_array_equal(obs, expected)
diff --git a/tests/scripts/test_repo_hygiene.py b/tests/scripts/test_repo_hygiene.py
index c12632aec..a1ea965ef 100644
--- a/tests/scripts/test_repo_hygiene.py
+++ b/tests/scripts/test_repo_hygiene.py
@@ -4,11 +4,6 @@
 
 from tests.scripts import repo_hygiene_checks
 
-REMOVED_ORPHAN_MODULES = (
-    "src/unilab/algos/torch/common/base_collector.py",
-    "src/unilab/algos/torch/fast_td3/runner.py",
-)
-
 
 def test_repo_does_not_track_backup_or_temporary_artifacts():
     root = Path(__file__).resolve().parents[2]
@@ -43,14 +38,6 @@ def test_coverage_commands_use_src_package_path():
     assert "--cov=src/unilab" in (root / ".github/workflows/ci.yml").read_text(encoding="utf-8")
 
 
-def test_removed_orphan_algorithm_modules_stay_absent():
-    root = Path(__file__).resolve().parents[2]
-
-    restored = [path for path in REMOVED_ORPHAN_MODULES if (root / path).exists()]
-
-    assert restored == []
-
-
 def test_source_command_anti_patterns_flags_python_script_invocation(tmp_path):
     script_path = tmp_path / "scripts" / "tool.py"
     script_path.parent.mkdir(parents=True)
diff --git a/tests/scripts/test_support_matrix.py b/tests/scripts/test_support_matrix.py
index 5aff433ac..31efd9ffb 100644
--- a/tests/scripts/test_support_matrix.py
+++ b/tests/scripts/test_support_matrix.py
@@ -2,7 +2,11 @@
 
 from pathlib import Path
 
-from unilab.utils.support_matrix import BACKENDS, EvidenceLevel, build_support_rows
+import pytest
+from scripts.tools.support_matrix import BACKENDS, EvidenceLevel, build_support_rows
+
+# CPU-bound on the single-core CI runner; kept in the slow lane (make test-slow).
+pytestmark = pytest.mark.slow
 
 
 def _row(entrypoint_label: str, task_slug: str):
@@ -25,11 +29,82 @@ def test_support_matrix_marks_validated_g1_mjwarp_entrypoints_as_tested():
     torch_row = _row("PPO (torch)", "g1_walk_flat")
     sac_row = _row("SAC (torch)", "g1_walk_flat")
 
-    assert BACKENDS == ("mujoco", "mjwarp", "motrix")
+    assert BACKENDS == ("mujoco", "mjwarp", "motrix", "isaacgym", "genesis", "isaacsim")
     assert torch_row.cells["mjwarp"].level == EvidenceLevel.TESTED
     assert sac_row.cells["mjwarp"].level == EvidenceLevel.TESTED
 
 
+def test_support_matrix_marks_g1_isaacgym_owners_by_validation():
+    """SAC isaacgym is maintainer-validated on hardware; PPO stays CONFIGURED."""
+    sac_row = _row("SAC (torch)", "g1_walk_flat")
+    assert sac_row.cells["isaacgym"].level == EvidenceLevel.TESTED
+    ppo_row = _row("PPO (torch)", "g1_walk_flat")
+    assert ppo_row.cells["isaacgym"].level == EvidenceLevel.CONFIGURED
+    for entrypoint_label in (
+        "APPO (torch)",
+        "TD3 (torch)",
+        "FlashSAC (torch)",
+    ):
+        row = _row(entrypoint_label, "g1_walk_flat")
+        # Registration is per task+backend, not per algo tree: without an
+        # owner YAML these stay at REGISTERED instead of CONFIGURED.
+        assert row.cells["isaacgym"].level == EvidenceLevel.REGISTERED
+
+
+def test_support_matrix_marks_g1_isaacsim_owners_by_checked_in_scope():
+    """IsaacSim has owner YAMLs for PPO/SAC but no maintainer training claim."""
+    ppo_row = _row("PPO (torch)", "g1_walk_flat")
+    sac_row = _row("SAC (torch)", "g1_walk_flat")
+    assert ppo_row.cells["isaacsim"].level == EvidenceLevel.CONFIGURED
+    assert sac_row.cells["isaacsim"].level == EvidenceLevel.CONFIGURED
+    for entrypoint_label in ("APPO (torch)", "TD3 (torch)", "FlashSAC (torch)"):
+        row = _row(entrypoint_label, "g1_walk_flat")
+        assert row.cells["isaacsim"].level == EvidenceLevel.REGISTERED
+
+
+def test_support_matrix_does_not_promote_unvalidated_isaacgym_entries():
+    rows = build_support_rows(Path(__file__).resolve().parents[2])
+
+    tested = {
+        (row.entrypoint_label, row.task_slug)
+        for row in rows
+        if row.cells["isaacgym"].level >= EvidenceLevel.TESTED
+    }
+    assert tested == {("SAC (torch)", "g1_walk_flat")}
+    go2_row = _row("PPO (torch)", "go2_joystick_flat")
+    assert go2_row.cells["isaacgym"].level == EvidenceLevel.MISSING
+
+
+def test_support_matrix_marks_g1_genesis_owner_configured_only():
+    """SAC genesis is training-validated (Tested); PPO stays Configured."""
+    row = _row("SAC (torch)", "g1_walk_flat")
+    assert row.cells["genesis"].level == EvidenceLevel.TESTED
+    row = _row("PPO (torch)", "g1_walk_flat")
+    assert row.cells["genesis"].level == EvidenceLevel.CONFIGURED
+    for entrypoint_label in (
+        "APPO (torch)",
+        "TD3 (torch)",
+        "FlashSAC (torch)",
+    ):
+        row = _row(entrypoint_label, "g1_walk_flat")
+        # Registration is per task+backend, not per algo tree: without an
+        # owner YAML these stay at REGISTERED instead of CONFIGURED.
+        assert row.cells["genesis"].level == EvidenceLevel.REGISTERED
+
+
+def test_support_matrix_does_not_promote_unvalidated_genesis_entries():
+    rows = build_support_rows(Path(__file__).resolve().parents[2])
+
+    tested = {
+        (row.entrypoint_label, row.task_slug)
+        for row in rows
+        if row.cells["genesis"].level >= EvidenceLevel.TESTED
+    }
+    assert tested == {("SAC (torch)", "g1_walk_flat")}
+    go2_row = _row("PPO (torch)", "go2_joystick_flat")
+    assert go2_row.cells["genesis"].level == EvidenceLevel.MISSING
+
+
 def test_support_matrix_does_not_promote_unvalidated_mjwarp_entries():
     rows = build_support_rows(Path(__file__).resolve().parents[2])
 
diff --git a/tests/scripts/test_torch_cuda_source.py b/tests/scripts/test_torch_cuda_source.py
index 606045ca6..52d17731a 100644
--- a/tests/scripts/test_torch_cuda_source.py
+++ b/tests/scripts/test_torch_cuda_source.py
@@ -56,7 +56,7 @@ def test_windows_lock_uses_cuda_torch() -> None:
 
     assert {
         "name": "torch",
-        "version": "2.7.0+cu128",
+        "version": "2.8.0+cu128",
         "source": {"registry": "https://download.pytorch.org/whl/cu128"},
         "marker": "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'win32'",
     } in torch_dependencies
@@ -74,10 +74,10 @@ def test_windows_lock_uses_cuda_torch() -> None:
         if package["source"] == {"registry": "https://download.pytorch.org/whl/cu128"}
     )
 
-    assert cu128_package["version"] == "2.7.0+cu128"
+    assert cu128_package["version"] == "2.8.0+cu128"
     assert any(
         "sys_platform == 'win32'" in marker for marker in cu128_package["resolution-markers"]
     )
 
     wheel_urls = [wheel["url"] for wheel in cu128_package["wheels"]]
-    assert any("torch-2.7.0%2Bcu128" in url and "win_amd64.whl" in url for url in wheel_urls)
+    assert any("torch-2.8.0%2Bcu128" in url and "win_amd64.whl" in url for url in wheel_urls)
diff --git a/tests/scripts/test_train_script_configs.py b/tests/scripts/test_train_script_configs.py
index f1f1575d9..cbb68465b 100644
--- a/tests/scripts/test_train_script_configs.py
+++ b/tests/scripts/test_train_script_configs.py
@@ -20,7 +20,7 @@
 
 
 def _write_sharpa_smoke_cache(cache_prefix, scale_values: list[float]) -> None:
-    from unilab.envs.manipulation.sharpa_inhand.base import (
+    from unilab.tasks.manipulation.sharpa_inhand.base import (
         SOURCE_DEFAULT_HAND_JOINT_POS_DEG,
         resolve_grasp_cache_file,
     )
@@ -80,7 +80,7 @@ def _appo_motion_file_overrides(task: str, tmp_path: Path) -> list[str]:
         return []
     motion_file = tmp_path / f"{task.split('/', 1)[0]}_smoke_motion.npz"
     _write_g1_motion_smoke_npz(motion_file)
-    return [f"++env.motion_file={motion_file}"]
+    return [f"env.commands.motion.params.motion_file={motion_file}"]
 
 
 def test_appo_mujoco_smoke_tasks_have_owner_configs():
@@ -88,7 +88,7 @@ def test_appo_mujoco_smoke_tasks_have_owner_configs():
     missing = [
         task
         for task in APPO_MUJOCO_SMOKE_TASKS
-        if not (ROOT_DIR / "conf" / "appo" / "task" / f"{task}.yaml").is_file()
+        if not (ROOT_DIR / "src" / "unilab" / "conf" / "appo" / "task" / f"{task}.yaml").is_file()
     ]
     assert missing == []
 
@@ -101,7 +101,7 @@ def test_appo_task_configs_load(task, tmp_path):
     result = subprocess.run(
         [
             sys.executable,
-            "scripts/train_appo.py",
+            "src/unilab/scripts/train_appo.py",
             f"task={task}",
             "algo.max_iterations=1",
             "training.no_play=true",
@@ -116,16 +116,19 @@ def test_appo_task_configs_load(task, tmp_path):
 
 @pytest.mark.slow
 @pytest.mark.parametrize(
-    "task",
-    ["sac/g1_walk_flat/mujoco", "sac/g1_walk_rough/mujoco", "td3/g1_walk_flat/mujoco"],
+    ("algo", "task"),
+    [
+        ("sac", "g1_walk_flat/mujoco"),
+        ("sac", "g1_walk_rough/mujoco"),
+        ("td3", "g1_walk_flat/mujoco"),
+    ],
 )
-def test_offpolicy_task_configs_load(task):
+def test_offpolicy_task_configs_load(algo, task):
     """Off-policy task configs can start training with supported MuJoCo owners."""
     result = subprocess.run(
         [
             sys.executable,
-            "scripts/train_offpolicy.py",
-            f"algo={task.split('/', 1)[0]}",
+            f"src/unilab/scripts/train_{algo}.py",
             f"task={task}",
             "algo.max_iterations=1",
             "training.no_play=true",
@@ -134,7 +137,7 @@ def test_offpolicy_task_configs_load(task):
         text=True,
         timeout=120,
     )
-    assert result.returncode == 0, f"Off-policy {task} failed:\n{result.stderr}"
+    assert result.returncode == 0, f"Off-policy {algo} {task} failed:\n{result.stderr}"
 
 
 @pytest.mark.slow
@@ -150,7 +153,7 @@ def test_ppo_sharpa_motrix_one_iteration_training_smoke(tmp_path):
     result = subprocess.run(
         [
             sys.executable,
-            "scripts/train_rsl_rl.py",
+            "src/unilab/scripts/train_rsl_rl.py",
             "task=sharpa_inhand/motrix",
             "algo.num_envs=16",
             "algo.num_steps_per_env=2",
@@ -188,7 +191,7 @@ def test_ppo_two_gpu_rsl_rl_training_smoke(task: str, task_name: str, tmp_path:
     result = subprocess.run(
         [
             sys.executable,
-            "scripts/train_rsl_rl.py",
+            "src/unilab/scripts/train_rsl_rl.py",
             f"task={task}",
             "training.devices=[0,1]",
             "training.play_render_mode=record",
diff --git a/tests/scripts/test_train_scripts.py b/tests/scripts/test_train_scripts.py
index cb495cf6c..dde7e3fd3 100644
--- a/tests/scripts/test_train_scripts.py
+++ b/tests/scripts/test_train_scripts.py
@@ -20,41 +20,46 @@
 from hydra import compose, initialize_config_dir
 from hydra.core.global_hydra import GlobalHydra
 from omegaconf import OmegaConf
-
-from unilab.base.backend.motrix.playback import run_motrix_playback
+from unisim.backend.motrix.playback import run_motrix_playback
 
 _SCRIPTS_DIR = Path(__file__).parent.parent.parent / "scripts"
-_CONF_DIR = Path(__file__).parent.parent.parent / "conf"
+_PACKAGE_SCRIPTS_DIR = Path(__file__).parent.parent.parent / "src" / "unilab" / "scripts"
+_CONF_DIR = Path(__file__).parent.parent.parent / "src" / "unilab" / "conf"
 _SRC_DIR = Path(__file__).parent.parent.parent / "src"
 
+# CPU-bound on the single-core CI runner; kept in the slow lane (make test-slow).
+pytestmark = pytest.mark.slow
+
 
 def _normalize_overrides(overrides: list[str] | None, *, offpolicy: bool = False) -> list[str]:
     normalized: list[str] = []
-    algo = "sac"
     task_selected = False
 
     for override in overrides or []:
-        if override.startswith("algo="):
-            algo = override.split("=", 1)[1]
-            normalized.append(override)
-            continue
         if override.startswith("task="):
             task_selected = True
-            normalized.append(override)
-            continue
         normalized.append(override)
 
     if not task_selected:
         if offpolicy:
-            normalized.append(f"task={algo}/g1_walk_flat/mujoco")
+            normalized.append("task=g1_walk_flat/mujoco")
         else:
             normalized.append("task=go1_joystick_flat/mujoco")
     return normalized
 
 
 def _load_script(name: str) -> Any:
-    """Load a scripts/<name>.py as a fresh module (no __init__ required)."""
-    path = _SCRIPTS_DIR / f"{name}.py"
+    """Load a scripts/<name>.py as a fresh module (no __init__ required).
+
+    Packaged entrypoints live in ``src/unilab/scripts``; the remaining
+    development scripts stay in the top-level ``scripts`` directory.
+    """
+    for base in (_PACKAGE_SCRIPTS_DIR, _SCRIPTS_DIR):
+        path = base / f"{name}.py"
+        if path.is_file():
+            break
+    else:
+        raise FileNotFoundError(f"No script named {name}.py in packaged or top-level scripts")
     spec = importlib.util.spec_from_file_location(name, path)
     mod = importlib.util.module_from_spec(spec)  # type: ignore[arg-type]
     spec.loader.exec_module(mod)  # type: ignore[union-attr]
@@ -97,13 +102,14 @@ def test_analyze_offpolicy_trace_reports_training_e2e(tmp_path, capsys):
 
 
 # ---------------------------------------------------------------------------
-# train_offpolicy.py — Hydra config defaults
+# train_sac.py / train_td3.py / train_flashsac.py — Hydra config defaults
+# (composed from the per-algo trees conf/sac, conf/td3, conf/flashsac)
 # ---------------------------------------------------------------------------
 
 
-def _offpolicy_cfg(overrides=None):
+def _offpolicy_cfg(overrides=None, *, algo: str = "sac"):
     GlobalHydra.instance().clear()
-    with initialize_config_dir(config_dir=str(_CONF_DIR / "offpolicy"), version_base="1.3"):
+    with initialize_config_dir(config_dir=str(_CONF_DIR / algo), version_base="1.3"):
         return compose(
             "config",
             overrides=_normalize_overrides(overrides, offpolicy=True),
@@ -319,7 +325,7 @@ def test_offpolicy_hydra_default_torch_thread_budget():
 
 
 def test_offpolicy_hydra_algo_td3():
-    cfg = _offpolicy_cfg(["algo=td3"])
+    cfg = _offpolicy_cfg(algo="td3")
     assert cfg.algo.algo == "td3"
 
 
@@ -407,7 +413,9 @@ def test_hora_distill_runtime_checkpoint_records_model_only():
 
 
 def test_hora_distill_checkpoint_runtime_only_restores_model_structure():
-    from unilab.algos.torch.hora.distill import cfg_with_checkpoint_runtime
+    from uni_rl.algos.hora.distill import cfg_with_checkpoint_runtime
+
+    from unilab.training.hora_distill_config import apply_teacher_defaults
 
     cfg = _hora_distill_cfg(["task=sharpa_inhand/mujoco_nodr"])
     checkpoint = {
@@ -436,7 +444,7 @@ def test_hora_distill_checkpoint_runtime_only_restores_model_structure():
         }
     }
 
-    restored = cfg_with_checkpoint_runtime(cfg, checkpoint)
+    restored = cfg_with_checkpoint_runtime(apply_teacher_defaults(cfg), checkpoint)
 
     assert restored.training.task_name == "SharpaInhandRotation"
     assert restored.training.sim_backend == "mujoco"
@@ -471,8 +479,9 @@ def test_hora_distill_checkpoint_runtime_only_overrides_model_side(
     teacher_algo_family: str,
     checkpoint_model: dict[str, Any],
 ):
-    from unilab.algos.torch.hora import distill_config
-    from unilab.algos.torch.hora.distill import cfg_with_checkpoint_runtime
+    from uni_rl.algos.hora.distill import cfg_with_checkpoint_runtime
+
+    from unilab.training import hora_distill_config as distill_config
 
     owner_cfg = OmegaConf.create(
         {
@@ -509,7 +518,11 @@ def test_hora_distill_checkpoint_runtime_only_overrides_model_side(
 
     monkeypatch.setattr(distill_config, "apply_teacher_defaults", lambda cfg: owner_cfg)
 
-    effective_cfg = cfg_with_checkpoint_runtime(OmegaConf.create({}), checkpoint)
+    # uni_rl's cfg_with_checkpoint_runtime only restores model-side fields;
+    # composing teacher-owner defaults first is the caller's job (issue #1480).
+    effective_cfg = cfg_with_checkpoint_runtime(
+        distill_config.apply_teacher_defaults(OmegaConf.create({})), checkpoint
+    )
 
     assert effective_cfg.training.task_name == "OwnerTask"
     assert effective_cfg.training.cam_distance == pytest.approx(1.5)
@@ -520,24 +533,12 @@ def test_hora_distill_checkpoint_runtime_only_overrides_model_side(
     assert OmegaConf.to_container(effective_cfg.algo.model, resolve=True) == checkpoint_model
 
 
-def test_hora_distill_script_delegates_teacher_owner_resolution():
-    source = (_SCRIPTS_DIR / "train_hora_distill.py").read_text(encoding="utf-8")
-
-    assert "OmegaConf.load" not in source
-    assert "HoraActorModel" not in source
-    assert 'conf" / str(algo_family)' not in source
-
-
 @pytest.mark.parametrize("teacher_algo_family", ["ppo", "appo", "sac"])
 def test_hora_distill_teacher_owner_defaults_support_ppo_appo_and_sac(
     teacher_algo_family: str,
 ):
     mod = _train_hora_distill()
-    teacher_task = (
-        "sac/sharpa_inhand/mujoco_hora"
-        if teacher_algo_family == "sac"
-        else "sharpa_inhand/mujoco_hora"
-    )
+    teacher_task = "sharpa_inhand/mujoco_hora"
     cfg = mod._apply_teacher_defaults(
         _hora_distill_cfg(
             [
@@ -566,7 +567,7 @@ def test_hora_distill_sac_teacher_requires_hora_sac_runtime():
                 [
                     "task=sharpa_inhand/mujoco",
                     "teacher.algo_family=sac",
-                    "teacher.task=sac/g1_walk_flat/mujoco",
+                    "teacher.task=g1_walk_flat/mujoco",
                 ]
             )
         )
@@ -592,35 +593,41 @@ def test_offpolicy_go1_motrix_task_is_not_configured():
     """SAC has no Go1 Motrix owner config; use PPO for Go1 joystick tasks."""
     from hydra.errors import MissingConfigException
 
-    with pytest.raises(MissingConfigException, match="task/sac/go1_joystick_flat/motrix"):
-        _offpolicy_cfg(["task=sac/go1_joystick_flat/motrix"])
+    with pytest.raises(MissingConfigException, match="task/go1_joystick_flat/motrix"):
+        _offpolicy_cfg(["task=go1_joystick_flat/motrix"])
 
 
 def test_offpolicy_g1_walk_flat_motrix_resolved_algo_matches_task_owner():
     """Motrix SAC G1 walk flat composes backend-owned algo hyperparameters."""
-    cfg = _offpolicy_cfg(["task=sac/g1_walk_flat/motrix"])
+    cfg = _offpolicy_cfg(["task=g1_walk_flat/motrix"])
 
     assert cfg.algo.num_envs == 2048
     assert cfg.algo.max_iterations == 5000
-    assert cfg.algo.use_symmetry is False
 
 
-def test_offpolicy_g1_walk_flat_env_cfg_override_has_reward_and_domain_rand():
-    cfg = _offpolicy_cfg(["task=sac/g1_walk_flat/motrix"])
+def test_offpolicy_g1_walk_flat_env_cfg_override_has_rewards_and_events():
+    cfg = _offpolicy_cfg(["task=g1_walk_flat/motrix"])
 
     env_cfg_override = _offpolicy().build_offpolicy_env_cfg_override("sac", cfg)
 
-    assert env_cfg_override["reward_config"]["scales"]["tracking_lin_vel"] == pytest.approx(2.2)
-    assert env_cfg_override["domain_rand"]["randomize_kp"] is False
-    assert env_cfg_override["domain_rand"]["randomize_kd"] is False
+    assert env_cfg_override["rewards"]["tracking_lin_vel"]["weight"] == pytest.approx(2.2)
+    assert env_cfg_override["events"]["pd_gains"] is None
+
 
+def test_offpolicy_isaacsim_training_and_eval_use_separate_render_overrides():
+    cfg = _offpolicy_cfg(
+        [
+            "task=g1_walk_flat/isaacsim",
+            "training.play_render_mode=record",
+        ]
+    )
+    mod = _offpolicy()
 
-def test_offpolicy_g1_walk_flat_backend_scoped_use_symmetry():
-    mujoco_cfg = _offpolicy_cfg(["task=sac/g1_walk_flat/mujoco"])
-    motrix_cfg = _offpolicy_cfg(["task=sac/g1_walk_flat/motrix"])
+    training_override = mod.build_offpolicy_env_cfg_override("sac", cfg)
+    play_override = mod.build_offpolicy_play_env_cfg_override("sac", cfg)
 
-    assert mujoco_cfg.algo.use_symmetry is True
-    assert motrix_cfg.algo.use_symmetry is False
+    assert "isaacsim_render_mode" not in training_override
+    assert play_override["isaacsim_render_mode"] == "record"
 
 
 def test_ppo_go1_resolved_algo_matches_old_motrix_behavior():
@@ -661,25 +668,27 @@ def test_ppo_g1_env_preset_has_env_overrides():
     cfg = _ppo_cfg(["task=g1_walk_flat/motrix"])
 
     assert OmegaConf.select(cfg, "env.motrix_max_iterations") is None
-    assert cfg.env.control_config.action_scale == pytest.approx(0.5)
-    assert cfg.env.commands.vel_limit == [[0.4, 0.0, 0.0], [0.7, 0.0, 0.0]]
-    assert cfg.env.gait_phase_init_mode == "offset_phase"
-    assert cfg.env.reset_base_qvel_limit == pytest.approx(0.05)
-    assert cfg.reward.scales.feet_phase_contrast == pytest.approx(1.5)
-    assert cfg.reward.scales.feet_phase_contact == pytest.approx(1.0)
-    assert cfg.reward.scales.feet_double_stance == pytest.approx(-1.0)
-    assert cfg.reward.min_forward_speed_for_gait_reward == pytest.approx(0.05)
+    assert cfg.env.actions.joint_pos.scale == pytest.approx(0.5)
+    assert cfg.env.commands.twist.ranges.lin_vel_x == [0.4, 0.7]
+    assert cfg.env.observations.policy.terms.gait_phase.params.init_mode == "offset_phase"
+    assert cfg.env.events.reset_root_state_uniform.params.velocity_range.x == [-0.05, 0.05]
+    assert cfg.reward.feet_phase_contrast.weight == pytest.approx(1.5)
+    assert cfg.reward.feet_phase_contact.weight == pytest.approx(1.0)
+    assert cfg.reward.feet_double_stance.weight == pytest.approx(-1.0)
+    assert cfg.reward.feet_phase.params.min_forward_speed == pytest.approx(0.05)
 
 
 def test_ppo_task_go2_aligns_mujoco_with_motrix_defaults():
     cfg = _ppo_cfg(["task=go2_joystick_flat/mujoco"])
 
     assert cfg.algo.num_envs == 1024
-    assert cfg.reward.scales.tracking_lin_vel == pytest.approx(1.0)
-    assert cfg.reward.scales.tracking_ang_vel == pytest.approx(0.2)
-    assert cfg.reward.scales.lin_vel_z == pytest.approx(-5.0)
-    assert cfg.reward.scales.ang_vel_xy == pytest.approx(-0.1)
+    assert cfg.reward.tracking_lin_vel.weight == pytest.approx(1.0)
+    assert cfg.reward.tracking_ang_vel.weight == pytest.approx(0.2)
+    assert cfg.reward.lin_vel_z.weight == pytest.approx(-5.0)
+    assert cfg.reward.ang_vel_xy.weight == pytest.approx(-0.1)
     assert cfg.algo.empirical_normalization is True
+    assert cfg.algo.obs_groups.actor == ["actor"]
+    assert cfg.algo.obs_groups.critic == ["critic"]
     assert cfg.algo.policy.init_noise_std == pytest.approx(0.5)
     assert cfg.algo.algorithm.learning_rate == pytest.approx(3.0e-4)
     assert cfg.algo.algorithm.entropy_coef == pytest.approx(1.0e-3)
@@ -690,7 +699,11 @@ def test_ppo_go1_drake_batch_config_matches_current_contact_support():
 
     assert cfg.env.drake_backend_mode == "batch"
     assert cfg.env.drake_nthread == 0
-    assert "contact" not in cfg.reward.scales
+    assert cfg.reward.contact is None
+    assert cfg.env.events.base_mass is None
+    assert cfg.env.events.base_com is None
+    assert cfg.env.events.pd_gains is None
+    assert cfg.env.events.push_robot is None
 
 
 def test_ppo_go2_drake_batch_config_matches_go2_training_defaults():
@@ -707,9 +720,10 @@ def test_ppo_go2_drake_batch_config_matches_go2_training_defaults():
     assert cfg.env.drake_backend_mode == "batch"
     assert cfg.env.drake_nthread == 0
     assert cfg.env.scene.model_file == "src/unilab/assets/robots/go2/scene_flat.xml"
-    assert cfg.env.domain_rand.randomize_kp is False
-    assert cfg.env.domain_rand.randomize_kd is False
-    assert cfg.reward.scales.contact == pytest.approx(0.24)
+    assert cfg.env.events.pd_gains is None
+    # Contact reward disabled on drake: drake_uni reports world-frame net
+    # contact force, not contact-frame force (issue #1471).
+    assert cfg.reward.contact is None
 
 
 def test_build_ppo_env_cfg_override_go1_motrix(
@@ -720,9 +734,14 @@ def test_build_ppo_env_cfg_override_go1_motrix(
 
     env_cfg_override = mod.build_ppo_env_cfg_override(cfg)
 
-    # env_cfg_override has reward + env preset commands
-    assert env_cfg_override["reward_config"]["scales"]["tracking_lin_vel"] == pytest.approx(1.0)
-    assert env_cfg_override["commands"]["vel_limit"] == [[0.5, 0.0, 0.0], [0.5, 0.0, 0.0]]
+    assert env_cfg_override["rewards"]["tracking_lin_vel"]["weight"] == pytest.approx(1.0)
+    assert env_cfg_override["rewards"]["contact"] is None
+    assert env_cfg_override["commands"]["twist"]["ranges"] == {
+        "lin_vel_x": [0.5, 0.5],
+        "lin_vel_y": [0.0, 0.0],
+        "ang_vel_z": [0.0, 0.0],
+    }
+    assert env_cfg_override["events"]["push_robot"] is None
 
 
 def test_build_ppo_env_cfg_override_g1_motrix(
@@ -734,19 +753,21 @@ def test_build_ppo_env_cfg_override_g1_motrix(
     env_cfg_override = mod.build_ppo_env_cfg_override(cfg)
 
     # env_cfg_override has reward + env preset fields (flat, matching env cfg structure)
-    assert env_cfg_override["reward_config"]["scales"]["upper_body_pose"] == pytest.approx(-0.05)
-    assert env_cfg_override["reward_config"]["scales"]["penalty_feet_ori"] == pytest.approx(0.0)
-    assert env_cfg_override["reward_config"]["scales"]["feet_phase_contrast"] == pytest.approx(1.5)
-    assert env_cfg_override["reward_config"]["scales"]["feet_phase_contact"] == pytest.approx(1.0)
-    assert env_cfg_override["reward_config"]["scales"]["feet_double_stance"] == pytest.approx(-1.0)
-    assert env_cfg_override["reward_config"]["min_forward_speed_for_gait_reward"] == pytest.approx(
-        0.05
-    )
+    assert env_cfg_override["rewards"]["upper_body_pose"]["weight"] == pytest.approx(-0.05)
+    assert env_cfg_override["rewards"]["penalty_feet_ori"]["weight"] == pytest.approx(0.0)
+    assert env_cfg_override["rewards"]["feet_phase_contrast"]["weight"] == pytest.approx(1.5)
+    assert env_cfg_override["rewards"]["feet_phase_contact"]["weight"] == pytest.approx(1.0)
+    assert env_cfg_override["rewards"]["feet_double_stance"]["weight"] == pytest.approx(-1.0)
+    assert env_cfg_override["rewards"]["feet_phase"]["params"][
+        "min_forward_speed"
+    ] == pytest.approx(0.05)
     assert "motrix_max_iterations" not in env_cfg_override
-    assert env_cfg_override["control_config"]["action_scale"] == pytest.approx(0.5)
-    assert env_cfg_override["commands"]["vel_limit"] == [[0.4, 0.0, 0.0], [0.7, 0.0, 0.0]]
-    assert env_cfg_override["gait_phase_init_mode"] == "offset_phase"
-    assert env_cfg_override["reset_base_qvel_limit"] == pytest.approx(0.05)
+    assert env_cfg_override["actions"]["joint_pos"]["scale"] == pytest.approx(0.5)
+    assert env_cfg_override["commands"]["twist"]["ranges"]["lin_vel_x"] == [0.4, 0.7]
+    assert env_cfg_override["events"]["pd_gains"] is None
+    assert env_cfg_override["events"]["reset_root_state_uniform"]["params"]["velocity_range"][
+        "x"
+    ] == [-0.05, 0.05]
 
 
 def test_build_ppo_env_cfg_override_carries_motrix_max_iterations_override(
@@ -772,14 +793,13 @@ def test_build_ppo_env_cfg_override_carries_post_step_forward_sensor_override(
         assert env_cfg_override["post_step_forward_sensor"] is value
 
 
-def test_offpolicy_g1_walk_flat_motrix_env_cfg_override_has_domain_rand():
-    cfg = _offpolicy_cfg(["algo=sac", "task=sac/g1_walk_flat/motrix"])
+def test_offpolicy_g1_walk_flat_motrix_env_cfg_override_disables_pd_gains():
+    cfg = _offpolicy_cfg(["task=g1_walk_flat/motrix"])
 
     env_cfg_override = _offpolicy().build_offpolicy_env_cfg_override("sac", cfg)
 
-    assert env_cfg_override["domain_rand"]["randomize_kp"] is False
-    assert env_cfg_override["domain_rand"]["randomize_kd"] is False
-    assert env_cfg_override["reward_config"]["scales"]["tracking_lin_vel"] == pytest.approx(2.2)
+    assert env_cfg_override["events"]["pd_gains"] is None
+    assert env_cfg_override["rewards"]["tracking_lin_vel"]["weight"] == pytest.approx(2.2)
 
 
 def test_build_ppo_env_cfg_override_applies_go2_motrix_reward(
@@ -790,12 +810,11 @@ def test_build_ppo_env_cfg_override_applies_go2_motrix_reward(
 
     env_cfg_override = mod.build_ppo_env_cfg_override(cfg)
 
-    assert cfg.reward.scales.tracking_lin_vel == pytest.approx(1.0)
+    assert cfg.reward.tracking_lin_vel.weight == pytest.approx(1.0)
     assert cfg.algo.num_envs == 1024
-    assert env_cfg_override["domain_rand"]["randomize_kp"] is False
-    assert env_cfg_override["domain_rand"]["randomize_kd"] is False
-    assert env_cfg_override["reward_config"]["scales"]["tracking_lin_vel"] == pytest.approx(1.0)
-    assert env_cfg_override["reward_config"]["scales"]["tracking_ang_vel"] == pytest.approx(0.2)
+    assert env_cfg_override["events"]["pd_gains"] is None
+    assert env_cfg_override["rewards"]["tracking_lin_vel"]["weight"] == pytest.approx(1.0)
+    assert env_cfg_override["rewards"]["tracking_ang_vel"]["weight"] == pytest.approx(0.2)
 
 
 def test_build_ppo_env_cfg_override_allegro_mujoco(
@@ -804,8 +823,10 @@ def test_build_ppo_env_cfg_override_allegro_mujoco(
     mod = _train_rsl_rl(monkeypatch)
     cfg = _ppo_cfg(["task=allegro_inhand/mujoco"])
     ppo_motrix_cfg = _ppo_cfg(["task=allegro_inhand/motrix"])
+    ppo_drake_cfg = _ppo_cfg(["task=allegro_inhand/drake"])
     appo_cfg = _appo_cfg(["task=allegro_inhand/mujoco"])
     appo_motrix_cfg = _appo_cfg(["task=allegro_inhand/motrix"])
+    appo_drake_cfg = _appo_cfg(["task=allegro_inhand/drake"])
 
     env_cfg_override = mod.build_ppo_env_cfg_override(cfg)
 
@@ -813,18 +834,20 @@ def test_build_ppo_env_cfg_override_allegro_mujoco(
     assert cfg.algo.empirical_normalization is False
     assert cfg.algo.actor.obs_normalization is True
     assert cfg.algo.critic.obs_normalization is True
-    assert env_cfg_override["reward_config"]["scales"]["rotate"] == pytest.approx(1.25)
-    assert env_cfg_override["reward_config"]["reset_z_threshold"] == pytest.approx(0.125)
-    assert env_cfg_override["gen_grasp"] is False
+    assert env_cfg_override["rewards"]["rotate"]["weight"] == pytest.approx(1.25)
+    assert env_cfg_override["terminations"]["dropped"]["params"][
+        "minimum_ball_height"
+    ] == pytest.approx(0.125)
     assert env_cfg_override["max_episode_seconds"] == pytest.approx(20.0)
-    assert env_cfg_override["grasp_cache_path"] == "caches/allegro_grasp_50k.npy"
-    assert env_cfg_override["domain_rand"]["randomize_base_mass"] is False
-    assert env_cfg_override["domain_rand"]["random_com"] is False
-    assert env_cfg_override["domain_rand"]["randomize_gravity"] is False
-    assert env_cfg_override["domain_rand"]["push_robots"] is False
-    assert env_cfg_override["domain_rand"]["joint_noise"] == pytest.approx(0.0)
-    assert env_cfg_override["domain_rand"]["ball_vel_noise"] == pytest.approx(0.0)
-    assert env_cfg_override["domain_rand"]["ball_z_offset"] == pytest.approx(0.0)
+    reset_params = env_cfg_override["events"]["reset_hand_ball"]["params"]
+    assert reset_params["grasp_cache_path"] is None
+    assert reset_params["joint_noise"] == pytest.approx(0.0)
+    assert reset_params["ball_velocity_noise"] == pytest.approx(0.0)
+    assert reset_params["ball_z_offset"] == pytest.approx(0.0)
+    assert env_cfg_override["observations"]["policy"]["history_length"] == 3
+    assert env_cfg_override["actions"]["hand"]["action_scale"] == pytest.approx(1.0 / 24.0)
+    assert "reward_config" not in env_cfg_override
+    assert "domain_rand" not in env_cfg_override
     assert appo_cfg.algo.steps_per_env == cfg.algo.num_steps_per_env
     assert list(appo_cfg.algo.actor.hidden_dims) == list(cfg.algo.actor.hidden_dims)
     assert appo_cfg.algo.actor.activation == cfg.algo.actor.activation
@@ -850,14 +873,15 @@ def test_build_ppo_env_cfg_override_allegro_mujoco(
     assert appo_motrix_cfg.training.sim_backend == ppo_motrix_cfg.training.sim_backend
     assert appo_motrix_cfg.algo.actor.obs_normalization is True
     assert appo_motrix_cfg.algo.critic.obs_normalization is True
-    assert appo_motrix_cfg.reward.scales.rotate == pytest.approx(
-        ppo_motrix_cfg.reward.scales.rotate
+    assert appo_motrix_cfg.reward.rotate.weight == pytest.approx(
+        ppo_motrix_cfg.reward.rotate.weight
     )
-    assert appo_motrix_cfg.env.gen_grasp is ppo_motrix_cfg.env.gen_grasp
-    assert appo_motrix_cfg.env.domain_rand.randomize_base_mass is False
-    assert appo_motrix_cfg.env.domain_rand.random_com is False
-    assert appo_motrix_cfg.env.domain_rand.randomize_gravity is False
-    assert appo_motrix_cfg.env.domain_rand.push_robots is False
+    assert appo_motrix_cfg.env.events.pd_gains is None
+    assert ppo_motrix_cfg.env.events.pd_gains is None
+    assert ppo_drake_cfg.training.sim_backend == "drake"
+    assert appo_drake_cfg.training.sim_backend == "drake"
+    assert ppo_drake_cfg.env.events.pd_gains is None
+    assert appo_drake_cfg.env.events.pd_gains is None
 
 
 def test_build_ppo_env_cfg_override_allegro_grasp_mujoco(
@@ -872,16 +896,20 @@ def test_build_ppo_env_cfg_override_allegro_grasp_mujoco(
     assert cfg.algo.empirical_normalization is False
     assert cfg.algo.actor.obs_normalization is True
     assert cfg.algo.critic.obs_normalization is True
-    assert env_cfg_override["reward_config"]["scales"]["rotate"] == pytest.approx(0.0)
-    assert env_cfg_override["gen_grasp"] is True
-    assert env_cfg_override["grasp_collection_target"] == 50000
-    assert env_cfg_override["grasp_quality_check"] is True
-    assert env_cfg_override["domain_rand"]["randomize_base_mass"] is False
-    assert env_cfg_override["domain_rand"]["random_com"] is False
-    assert env_cfg_override["domain_rand"]["randomize_gravity"] is False
-    assert env_cfg_override["domain_rand"]["push_robots"] is False
-    assert env_cfg_override["domain_rand"]["ball_vel_noise"] == pytest.approx(0.0)
-    assert env_cfg_override["domain_rand"]["joint_noise"] == pytest.approx(0.25)
+    assert env_cfg_override["rewards"]["rotate"]["weight"] == pytest.approx(0.0)
+    assert env_cfg_override["actions"]["hand"]["action_scale"] == pytest.approx(0.0)
+    reset = env_cfg_override["events"]["reset_hand_ball"]["params"]
+    assert reset["grasp_cache_path"] is None
+    assert reset["ball_velocity_noise"] == pytest.approx(0.0)
+    assert reset["joint_noise"] == pytest.approx(0.25)
+    quality = env_cfg_override["terminations"]["invalid_grasp"]["params"]
+    assert quality["enabled"] is True
+    assert quality["minimum_contacts"] == 2
+    recorder = env_cfg_override["recorders"]["grasp_cache"]["params"]
+    assert recorder["collection_target"] == 50000
+    assert recorder["auto_save"] is True
+    assert "reward_config" not in env_cfg_override
+    assert "domain_rand" not in env_cfg_override
 
 
 def test_build_ppo_env_cfg_override_allegro_grasp_cli_override_wins(
@@ -892,17 +920,16 @@ def test_build_ppo_env_cfg_override_allegro_grasp_cli_override_wins(
         [
             "task=allegro_inhand_grasp/mujoco",
             "algo.max_iterations=1",
-            "env.grasp_collection_target=128",
-            "reward.scales.rotate=0.3",
+            "env.recorders.grasp_cache.params.collection_target=128",
+            "reward.rotate.weight=0.3",
         ]
     )
 
     env_cfg_override = mod.build_ppo_env_cfg_override(cfg)
 
     assert cfg.algo.max_iterations == 1
-    assert env_cfg_override["grasp_collection_target"] == 128
-    assert env_cfg_override["reward_config"]["scales"]["rotate"] == pytest.approx(0.3)
-    assert env_cfg_override["gen_grasp"] is True
+    assert env_cfg_override["recorders"]["grasp_cache"]["params"]["collection_target"] == 128
+    assert env_cfg_override["rewards"]["rotate"]["weight"] == pytest.approx(0.3)
 
 
 def test_build_ppo_env_cfg_override_sharpa_grasp_cli_override_wins(
@@ -982,8 +1009,6 @@ def log(self, *args, **kwargs):
     torch.testing.assert_close(action_std, expected)
     assert action_std.requires_grad is False
     assert action_std.data_ptr() != expected.data_ptr()
-    source = (_SCRIPTS_DIR / "train_rsl_rl.py").read_text(encoding="utf-8")
-    assert "def _patch_runner_action_std_logging" not in source
 
 
 def _build_rsl_lifecycle_case(
@@ -1089,7 +1114,7 @@ def create_env(*args: Any, **kwargs: Any) -> FakeEnv:
         return FakeEnv()
 
     monkeypatch.setattr(mod, "create_env", create_env)
-    monkeypatch.setattr(mod, "_algo_config_dict", lambda _cfg: {})
+    monkeypatch.setattr(mod, "algo_config_dict", lambda _cfg: {})
     monkeypatch.setattr(mod, "_resolve_ppo_wrapper_cls", lambda _rl_cfg: FakeWrapper)
     monkeypatch.setattr(mod, "normalize_ppo_train_cfg", lambda _rl_cfg: {})
     monkeypatch.setattr(mod, "patch_rsl_rl_resume_state", lambda: None)
@@ -1258,12 +1283,12 @@ def test_g1_motion_tracking_ppo_motrix_prefers_backend_specific_reward(
     mod = _train_rsl_rl(monkeypatch)
     cfg = _ppo_cfg(["task=g1_motion_tracking/motrix"])
 
-    assert cfg.reward.scales.motion_body_pos == pytest.approx(1.0)
-    cfg.reward.scales.motion_body_pos = 1.25
+    assert cfg.reward.motion_body_pos.weight == pytest.approx(1.0)
+    cfg.reward.motion_body_pos.weight = 1.25
 
     env_cfg_override = mod.build_ppo_env_cfg_override(cfg)
 
-    assert env_cfg_override["reward_config"]["scales"]["motion_body_pos"] == pytest.approx(1.25)
+    assert env_cfg_override["rewards"]["motion_body_pos"]["weight"] == pytest.approx(1.25)
 
 
 def test_build_ppo_play_env_cfg_override_applies_g1_motion_tracking_play_profile(
@@ -1276,15 +1301,16 @@ def test_build_ppo_play_env_cfg_override_applies_g1_motion_tracking_play_profile
     monkeypatch.setattr(
         mod,
         "materialize_scene_visual_override",
-        lambda source_model_file, **kwargs: "/tmp/g1_motion_tracking_play_scene.xml",
+        lambda *_args, **_kwargs: pytest.fail("manager scene must not be replaced"),
     )
 
     env_cfg_override = mod.build_ppo_play_env_cfg_override(cfg)
 
     assert cfg.training.play_env_num == 16
     assert env_cfg_override["render_spacing"] == pytest.approx(2.5)
-    assert env_cfg_override["scene"].model_file == "/tmp/g1_motion_tracking_play_scene.xml"
-    assert env_cfg_override["reward_config"]["scales"]["motion_body_pos"] == pytest.approx(1.0)
+    assert env_cfg_override["scene"]["model_file"].endswith("robots/g1/scene_flat.xml")
+    assert "robot" in env_cfg_override["scene"]["entities"]
+    assert env_cfg_override["rewards"]["motion_body_pos"]["weight"] == pytest.approx(1.0)
 
 
 def test_build_ppo_play_env_cfg_override_respects_cli_play_env_override(
@@ -1311,27 +1337,21 @@ def test_build_ppo_play_env_cfg_override_respects_cli_play_env_override(
     assert env_cfg_override["render_spacing"] == pytest.approx(2.5)
 
 
-def test_build_ppo_play_env_cfg_override_resolves_relative_ground_texture(
+def test_build_ppo_play_env_cfg_override_keeps_task_owned_manager_scene(
     monkeypatch: pytest.MonkeyPatch,
 ):
     mod = _train_rsl_rl(monkeypatch)
     cfg = _ppo_cfg(["task=g1_motion_tracking/motrix", "training.play_only=true"])
-    cfg.play_profile.scene.ground_texture_file = "src/unilab/assets/robots/g1/textures/floor.png"
-
-    captured = {}
-
-    def _fake_materialize(source_model_file, **kwargs):
-        captured["source_model_file"] = source_model_file
-        captured.update(kwargs)
-        return "/tmp/g1_motion_tracking_play_scene.xml"
-
-    monkeypatch.setattr(mod, "materialize_scene_visual_override", _fake_materialize)
+    monkeypatch.setattr(
+        mod,
+        "materialize_scene_visual_override",
+        lambda *_args, **_kwargs: pytest.fail("manager scene must not be replaced"),
+    )
 
-    mod.build_ppo_play_env_cfg_override(cfg)
+    env_cfg_override = mod.build_ppo_play_env_cfg_override(cfg)
 
-    assert captured["ground_texture_file"] == str(
-        mod.ROOT_DIR / "src/unilab/assets/robots/g1/textures/floor.png"
-    )
+    assert env_cfg_override["scene"]["entities"]["robot"]["root_body_name"] == "pelvis"
+    assert env_cfg_override["scene"]["entities"]["robot"]["joint_names"]
 
 
 def test_go2_arm_manip_loco_motrix_eval_uses_visual_floor(
@@ -1352,10 +1372,10 @@ def _fake_materialize(source_model_file, **kwargs):
     env_cfg_override = mod.build_ppo_play_env_cfg_override(cfg)
 
     assert captured["source_model_file"] == str(
-        mod.ROOT_DIR / "src/unilab/assets/robots/go2_arm/scene_flat.xml"
+        (Path.cwd() / "src" / "unilab" / "assets" / "robots/go2_arm/scene_flat.xml").resolve()
     )
     assert captured["ground_texture_file"] == str(
-        mod.ROOT_DIR / "src/unilab/assets/robots/g1/textures/floor.png"
+        (Path.cwd() / "src" / "unilab" / "assets" / "robots/g1/textures/floor.png").resolve()
     )
     assert captured["skybox_rgb1"] == [0.90, 0.90, 0.91]
     assert captured["skybox_rgb2"] == [0.68, 0.68, 0.70]
@@ -1366,7 +1386,6 @@ def _fake_materialize(source_model_file, **kwargs):
 def test_run_motrix_rsl_play_loop_uses_render_spacing_and_offset_mode(
     monkeypatch: pytest.MonkeyPatch,
 ):
-    import numpy as np
     import torch
     from tensordict import TensorDict
 
@@ -1455,28 +1474,28 @@ def step(self, actions):
 
 
 def test_g1_motion_tracking_appo_reward_extraction_prefers_backend_specific_reward():
-    from unilab.training.reward import extract_reward_config
+    from unilab.base.config_adapter import BackendAdapter
 
     cfg = _appo_cfg(["task=g1_motion_tracking/motrix"])
 
-    assert cfg.reward.scales.motion_body_pos == pytest.approx(1.0)
-    cfg.reward.scales.motion_body_pos = 1.5
+    assert cfg.reward.motion_body_pos.weight == pytest.approx(1.0)
+    cfg.reward.motion_body_pos.weight = 1.5
 
-    env_cfg_override = extract_reward_config(cfg)
+    env_cfg_override = BackendAdapter(cfg, root_dir=_SRC_DIR.parent).build_task_env_cfg_override()
 
-    assert env_cfg_override["reward_config"]["scales"]["motion_body_pos"] == pytest.approx(1.5)
+    assert env_cfg_override["rewards"]["motion_body_pos"]["weight"] == pytest.approx(1.5)
 
 
 def test_g1_motion_tracking_ppo_task_exposes_final_reward():
     cfg = _ppo_cfg(["task=g1_motion_tracking/motrix"])
 
-    assert cfg.reward.scales.motion_body_pos == pytest.approx(1.0)
+    assert cfg.reward.motion_body_pos.weight == pytest.approx(1.0)
 
 
 def test_g1_motion_tracking_appo_task_exposes_final_reward():
     cfg = _appo_cfg(["task=g1_motion_tracking/motrix"])
 
-    assert cfg.reward.scales.motion_body_pos == pytest.approx(1.0)
+    assert cfg.reward.motion_body_pos.weight == pytest.approx(1.0)
 
 
 def test_sharpa_appo_motrix_owner_uses_backend_specific_overrides():
@@ -1502,7 +1521,7 @@ def test_build_appo_runner_kwargs_forwards_sim_backend():
 
     runner_kwargs = mod.build_appo_runner_kwargs(
         cfg,
-        env_cfg_override={"reward_config": {"scales": {}}},
+        env_cfg_override={"rewards": {}},
         collector_device="cpu",
     )
 
@@ -1513,7 +1532,7 @@ def test_build_appo_runner_kwargs_forwards_sim_backend():
     assert runner_kwargs["steps_per_env"] == cfg.algo.steps_per_env
     assert "num_workers" not in runner_kwargs
     assert "num_collectors" not in runner_kwargs
-    assert runner_kwargs["env_cfg_overrides"]["reward_config"]["scales"] == {}
+    assert runner_kwargs["env_cfg_overrides"]["rewards"] == {}
 
 
 def test_run_motrix_play_loop_runs_without_physics_state():
@@ -1779,7 +1798,7 @@ def close(self):
     )
 
     with pytest.raises(RuntimeError, match="collector died"):
-        mod.main.__wrapped__(cfg)
+        mod.main(cfg)
 
     assert captured["tracker_started"] is True
     assert captured["tracker_finished"] is True
@@ -1874,22 +1893,28 @@ def test_resolve_checkpoint_empty_run_dir(tmp_path):
 
 
 def test_offpolicy_extract_reset_obs_handles_two_tuple():
+    from unilab.visualization.interactive_playback import extract_reset_obs
+
     obs = {"obs": "value"}
 
-    result = _offpolicy().extract_reset_obs((obs, {"info": 1}))
+    result = extract_reset_obs((obs, {"info": 1}))
 
     assert result is obs
 
 
 def test_offpolicy_extract_reset_obs_rejects_three_tuple():
+    from unilab.visualization.interactive_playback import extract_reset_obs
+
     obs = {"obs": "value"}
 
     with pytest.raises(ValueError, match="Unexpected env.reset return format"):
-        _offpolicy().extract_reset_obs(("ignored", obs, {"info": 1}))
+        extract_reset_obs(("ignored", obs, {"info": 1}))
 
 
 def test_offpolicy_resolve_play_obs_dim_ignores_critic():
-    obs_dim = _offpolicy().resolve_play_obs_dim({"obs": 98, "critic": 101})
+    from unilab.visualization.interactive_playback import resolve_play_obs_dim
+
+    obs_dim = resolve_play_obs_dim({"obs": 98, "critic": 101})
 
     assert obs_dim == 98
 
@@ -1897,24 +1922,25 @@ def test_offpolicy_resolve_play_obs_dim_ignores_critic():
 def test_offpolicy_extract_play_obs_uses_obs_group_only():
     import numpy as np
 
+    from unilab.visualization.interactive_playback import extract_play_obs
+
     obs = {
         "obs": np.ones((2, 98), dtype=np.float32),
         "critic": np.full((2, 101), 2.0, dtype=np.float32),
     }
 
-    play_obs = _offpolicy().extract_play_obs(obs)
+    play_obs = extract_play_obs(obs)
 
     assert play_obs.shape == (2, 98)
     assert np.allclose(play_obs, 1.0)
 
 
 def test_offpolicy_play_actor_spec_uses_hora_sac_runtime():
-    from unilab.training.offpolicy import resolve_play_actor_spec
+    from unilab.visualization.interactive_playback import resolve_play_actor_spec
 
     cfg = _offpolicy_cfg(
         [
-            "algo=sac",
-            "task=sac/sharpa_inhand/mujoco_hora",
+            "task=sharpa_inhand/mujoco_hora",
         ]
     )
 
@@ -1930,10 +1956,10 @@ def test_offpolicy_play_actor_spec_uses_hora_sac_runtime():
 
 
 def test_offpolicy_play_actor_spec_keeps_standard_sac_and_flashsac():
-    from unilab.training.offpolicy import resolve_play_actor_spec
+    from unilab.visualization.interactive_playback import resolve_play_actor_spec
 
-    sac_cfg = _offpolicy_cfg(["algo=sac", "task=sac/g1_walk_flat/mujoco"])
-    flashsac_cfg = _offpolicy_cfg(["algo=flashsac", "task=flashsac/g1_walk_flat/mujoco"])
+    sac_cfg = _offpolicy_cfg(["task=g1_walk_flat/mujoco"])
+    flashsac_cfg = _offpolicy_cfg(["task=g1_walk_flat/mujoco"], algo="flashsac")
 
     sac_algo_type, sac_kwargs = resolve_play_actor_spec(
         "sac",
@@ -1955,9 +1981,10 @@ def test_offpolicy_play_actor_spec_keeps_standard_sac_and_flashsac():
 def test_offpolicy_build_play_actor_preserves_flashsac_model_kwargs(
     monkeypatch: pytest.MonkeyPatch,
 ):
-    import unilab.algos.torch.common.actor_factory as actor_factory
-    import unilab.algos.torch.common.normalization as normalization
-    from unilab.training.offpolicy import build_play_actor
+    import uni_rl.algos.common.actor_factory as actor_factory
+    import uni_rl.algos.common.normalization as normalization
+
+    from unilab.visualization.interactive_playback import build_play_actor
 
     captured: dict[str, Any] = {}
 
@@ -1975,7 +2002,7 @@ def fake_build_actor(*args, **kwargs):
 
     monkeypatch.setattr(actor_factory, "build_actor", fake_build_actor)
     monkeypatch.setattr(normalization, "EmpiricalNormalization", FakeNormalizer)
-    cfg = _offpolicy_cfg(["algo=flashsac", "algo.obs_normalization=true"])
+    cfg = _offpolicy_cfg(["algo.obs_normalization=true"], algo="flashsac")
 
     actor, normalizer, actor_algo_type, actor_kwargs = build_play_actor(
         "flashsac",
@@ -2011,9 +2038,9 @@ def test_offpolicy_build_play_actor_restores_td3_state_and_normalizer(
     monkeypatch: pytest.MonkeyPatch,
 ):
     import torch
+    import uni_rl.algos.fast_td3.learner as learner_module
 
-    import unilab.algos.torch.fast_td3.learner as learner_module
-    from unilab.training.offpolicy import build_play_actor, load_play_actor
+    from unilab.visualization.interactive_playback import build_play_actor, load_play_actor
 
     captured: dict[str, Any] = {}
 
@@ -2039,7 +2066,7 @@ def eval(self):
 
     monkeypatch.setattr(learner_module, "TD3Actor", FakeActor)
     monkeypatch.setattr(learner_module, "EmpiricalNormalization", FakeNormalizer)
-    cfg = _offpolicy_cfg(["algo=td3"])
+    cfg = _offpolicy_cfg(algo="td3")
     actor_state = {"weight": torch.ones(1), "noise_scales": torch.zeros(1)}
     normalizer_state = {"mean": torch.ones(1)}
 
@@ -2069,7 +2096,7 @@ def eval(self):
 
 @pytest.mark.parametrize("algo_name", ["sac", "flashsac"])
 def test_offpolicy_load_play_actor_keeps_sac_state_dict_strict(algo_name: str):
-    from unilab.training.offpolicy import load_play_actor
+    from unilab.visualization.interactive_playback import load_play_actor
 
     captured: dict[str, Any] = {}
 
@@ -2091,8 +2118,7 @@ def test_play_offpolicy_can_skip_onnx_export_and_still_record_video(
     mod = _offpolicy()
     cfg = _offpolicy_cfg(
         [
-            "algo=sac",
-            "task=sac/g1_walk_flat/mujoco",
+            "task=g1_walk_flat/mujoco",
             "training.play_only=true",
             "training.play_render_mode=record",
             "training.export_onnx=false",
@@ -2141,7 +2167,10 @@ def step(self, actions):
             self.state = type(
                 "State",
                 (),
-                {"obs": {"obs": np.ones((batch, 4), dtype=np.float32)}},
+                {
+                    "obs": {"obs": np.ones((batch, 4), dtype=np.float32)},
+                    "info": {},
+                },
             )()
             captured["actions_shape"] = actions.shape
             return self.state
@@ -2158,13 +2187,19 @@ def run_playback_mode(self, **kwargs):
     monkeypatch.setattr(mod, "build_offpolicy_env_cfg_override", lambda algo_name, cfg: {})
     monkeypatch.setattr(mod, "default_device", lambda torch_module, preferred=None: "cpu")
     monkeypatch.setattr(mod, "create_env", lambda *args, **kwargs: FakeEnv())
-    monkeypatch.setattr(mod, "resolve_play_obs_dim", lambda obs_groups_spec: 4)
-    monkeypatch.setattr(mod, "extract_play_obs", lambda obs_dict: obs_dict["obs"])
     monkeypatch.setattr(
         mod,
         "resolve_checkpoint_path",
         lambda *args, **kwargs: (str(checkpoint), str(run_dir)),
     )
+
+    import unilab.utils.checkpoint as checkpoint_utils
+
+    monkeypatch.setattr(
+        checkpoint_utils,
+        "resolve_offpolicy_checkpoint_path",
+        lambda *args, **kwargs: (str(checkpoint), str(run_dir)),
+    )
     monkeypatch.setattr(
         torch.onnx,
         "export",
@@ -2173,7 +2208,7 @@ def run_playback_mode(self, **kwargs):
         ),
     )
 
-    import unilab.algos.torch.common.actor_factory as actor_factory
+    import uni_rl.algos.common.actor_factory as actor_factory
 
     monkeypatch.setattr(actor_factory, "build_actor", lambda *args, **kwargs: FakeActor())
 
@@ -2199,8 +2234,7 @@ def test_play_offpolicy_uses_hora_sac_actor_and_priv_info(
     mod = _offpolicy()
     cfg = _offpolicy_cfg(
         [
-            "algo=sac",
-            "task=sac/sharpa_inhand/mujoco_hora",
+            "task=sharpa_inhand/mujoco_hora",
             "training.play_only=true",
             "training.play_render_mode=record",
             "training.export_onnx=false",
@@ -2296,7 +2330,15 @@ def run_playback_mode(self, **kwargs):
         lambda *args, **kwargs: (str(checkpoint), str(run_dir)),
     )
 
-    import unilab.algos.torch.common.actor_factory as actor_factory
+    import unilab.utils.checkpoint as checkpoint_utils
+
+    monkeypatch.setattr(
+        checkpoint_utils,
+        "resolve_offpolicy_checkpoint_path",
+        lambda *args, **kwargs: (str(checkpoint), str(run_dir)),
+    )
+
+    import uni_rl.algos.common.actor_factory as actor_factory
 
     def fake_build_actor(algo_type, obs_dim, action_dim, hidden_dim, use_layer_norm, device, **kw):
         captured["build_actor"] = (algo_type, obs_dim, action_dim, kw)
@@ -2387,7 +2429,7 @@ def _fake_resolver(root_dir, **kwargs):
     result = mod.resolve_checkpoint("MyTask", "-1", checkpoint="12", algo_log_name="custom_ppo")
 
     assert result == str(model_path)
-    assert captured["root_dir"] == mod.ROOT_DIR
+    assert captured["root_dir"] == Path.cwd()
     assert captured["task_name"] == "MyTask"
     assert captured["load_run"] == "-1"
     assert captured["algo_log_name"] == "custom_ppo"
@@ -2406,7 +2448,7 @@ def _play_interactive():
 
 def test_play_wrapper_imports_shared_implementation():
     """Verify play_interactive.py uses shared RslRlVecEnvWrapper."""
-    from unilab.training.rsl_rl import RslRlVecEnvWrapper as SharedWrapper
+    from uni_rl.algos.rsl_rl import RslRlVecEnvWrapper as SharedWrapper
 
     mod = _play_interactive()
     # The wrapper class in play_interactive should be the shared one
@@ -2417,8 +2459,7 @@ def test_play_wrapper_uses_current_reset_contract():
     """Verify wrapper reset() uses current (obs, info) contract, not old (_, obs, _)."""
     import numpy as np
     from tensordict import TensorDict
-
-    from unilab.training.rsl_rl import RslRlVecEnvWrapper
+    from uni_rl.algos.rsl_rl import RslRlVecEnvWrapper
 
     # Create a fake environment that returns (obs, info) tuple
     class FakeEnv:
@@ -2452,8 +2493,7 @@ def reset(self, env_indices):
 def test_play_wrapper_policy_obs_mode_actor():
     """Verify wrapper supports policy_obs_mode='actor'."""
     import numpy as np
-
-    from unilab.training.rsl_rl import RslRlVecEnvWrapper
+    from uni_rl.algos.rsl_rl import RslRlVecEnvWrapper
 
     class FakeEnv:
         def __init__(self):
@@ -2490,8 +2530,7 @@ def reset(self, env_indices):
 
 def test_play_wrapper_flat_policy_excludes_critic_only_group():
     import numpy as np
-
-    from unilab.training.rsl_rl import RslRlVecEnvWrapper
+    from uni_rl.algos.rsl_rl import RslRlVecEnvWrapper
 
     class FakeEnv:
         def __init__(self):
@@ -2534,8 +2573,7 @@ def reset(self, env_indices):
 
 def test_play_wrapper_preserves_hora_priv_info_and_proprio_history():
     import numpy as np
-
-    from unilab.algos.torch.hora.rsl_rl import HoraRslRlVecEnvWrapper
+    from uni_rl.algos.hora.rsl_rl import HoraRslRlVecEnvWrapper
 
     class FakeEnv:
         def __init__(self):
@@ -2598,8 +2636,7 @@ def reset(self, env_indices):
 
 def test_play_wrapper_step_exports_timeout_bootstrap_obs():
     import torch
-
-    from unilab.training.rsl_rl import RslRlVecEnvWrapper
+    from uni_rl.algos.rsl_rl import RslRlVecEnvWrapper
 
     class FakeEnv:
         def __init__(self):
@@ -2655,8 +2692,7 @@ def step(self, actions):
 
 def test_play_wrapper_timeout_bootstrap_preserves_hora_priv_info():
     import torch
-
-    from unilab.algos.torch.hora.rsl_rl import HoraRslRlVecEnvWrapper
+    from uni_rl.algos.hora.rsl_rl import HoraRslRlVecEnvWrapper
 
     class FakeEnv:
         def __init__(self):
@@ -2754,49 +2790,43 @@ def test_appo_hydra_default_algo_log_name():
 
 def test_offpolicy_sac_hydra_default_algo_log_name():
     """Verify SAC config has algo_log_name in algo section."""
-    cfg = _offpolicy_cfg(["algo=sac"])
+    cfg = _offpolicy_cfg()
     assert cfg.algo.algo_log_name == "fast_sac"
     assert cfg.algo.load_run == "-1"
 
 
 def test_offpolicy_td3_hydra_default_algo_log_name():
     """Verify TD3 config has algo_log_name in algo section."""
-    cfg = _offpolicy_cfg(["algo=td3"])
+    cfg = _offpolicy_cfg(algo="td3")
     assert cfg.algo.algo_log_name == "fast_td3"
     assert cfg.algo.load_run == "-1"
 
 
 def test_offpolicy_flashsac_hydra_algo_log_name():
-    cfg = _offpolicy_cfg(["algo=flashsac", "task=flashsac/g1_walk_flat/mujoco"])
+    cfg = _offpolicy_cfg(["task=g1_walk_flat/mujoco"], algo="flashsac")
     assert cfg.algo.algo_log_name == "flash_sac"
     assert cfg.algo.load_run == "-1"
 
 
 def test_offpolicy_flashsac_g1_walk_flat_task_composes() -> None:
-    cfg = _offpolicy_cfg(["algo=flashsac", "task=flashsac/g1_walk_flat/mujoco"])
+    cfg = _offpolicy_cfg(["task=g1_walk_flat/mujoco"], algo="flashsac")
     assert cfg.training.task_name == "G1WalkFlat"
     assert cfg.training.sim_backend == "mujoco"
 
 
 def test_offpolicy_g1_rough_terrain_task_composes() -> None:
-    cfg = _offpolicy_cfg(["algo=sac", "task=sac/g1_walk_rough/mujoco"])
+    cfg = _offpolicy_cfg(["task=g1_walk_rough/mujoco"])
 
     assert cfg.training.task_name == "G1WalkRough"
     assert cfg.training.sim_backend == "mujoco"
 
 
-@pytest.mark.parametrize(
-    ("algo", "task"),
-    [
-        ("flashsac", "sac/g1_walk_flat/mujoco"),
-        ("sac", "flashsac/g1_walk_flat/mujoco"),
-    ],
-)
-def test_offpolicy_rejects_algo_task_owner_mismatch(algo: str, task: str):
-    cfg = _offpolicy_cfg([f"algo={algo}", f"task={task}"])
+def test_offpolicy_rejects_algo_argument_mismatch():
+    """build_runner must reject an algo argument inconsistent with cfg.algo.algo."""
+    cfg = _offpolicy_cfg(["task=g1_walk_flat/mujoco"])
 
-    with pytest.raises(ValueError, match="Off-policy algo/task mismatch"):
-        _offpolicy().build_runner(algo, cfg)
+    with pytest.raises(ValueError, match="inconsistent with cfg.algo.algo"):
+        _offpolicy().build_runner("flashsac", cfg)
 
 
 def test_train_rsl_rl_get_log_root_uses_algo_log_name(monkeypatch: pytest.MonkeyPatch):
@@ -2820,20 +2850,16 @@ def test_train_rsl_rl_play_missing_checkpoint_skips_env_creation_and_prints_cont
     cfg = _ppo_cfg(["task=go1_joystick_flat/mujoco", "training.play_only=true"])
     cfg.algo.algo_log_name = "custom_ppo"
 
-    original_root = mod.ROOT_DIR
-    mod.ROOT_DIR = tmp_path
-    try:
-        monkeypatch.setattr(
-            mod,
-            "create_env",
-            lambda *args, **kwargs: (_ for _ in ()).throw(
-                AssertionError("play_rsl_rl should not create an env before checkpoint resolution")
-            ),
-        )
+    monkeypatch.chdir(tmp_path)
+    monkeypatch.setattr(
+        mod,
+        "create_env",
+        lambda *args, **kwargs: (_ for _ in ()).throw(
+            AssertionError("play_rsl_rl should not create an env before checkpoint resolution")
+        ),
+    )
 
-        result = mod.play_rsl_rl(cfg, device="cpu")
-    finally:
-        mod.ROOT_DIR = original_root
+    result = mod.play_rsl_rl(cfg, device="cpu")
 
     captured = capsys.readouterr().out
     expected_task_log_root = tmp_path / "logs" / "custom_ppo" / cfg.training.task_name
@@ -2860,20 +2886,16 @@ def test_train_rsl_rl_play_reports_missing_requested_checkpoint_in_resolved_run(
     run_dir.mkdir(parents=True)
     (run_dir / "model_9.pt").write_bytes(b"")
 
-    original_root = mod.ROOT_DIR
-    mod.ROOT_DIR = tmp_path
-    try:
-        monkeypatch.setattr(
-            mod,
-            "create_env",
-            lambda *args, **kwargs: (_ for _ in ()).throw(
-                AssertionError("play_rsl_rl should not create an env before checkpoint resolution")
-            ),
-        )
+    monkeypatch.chdir(tmp_path)
+    monkeypatch.setattr(
+        mod,
+        "create_env",
+        lambda *args, **kwargs: (_ for _ in ()).throw(
+            AssertionError("play_rsl_rl should not create an env before checkpoint resolution")
+        ),
+    )
 
-        result = mod.play_rsl_rl(cfg, device="cpu")
-    finally:
-        mod.ROOT_DIR = original_root
+    result = mod.play_rsl_rl(cfg, device="cpu")
 
     captured = capsys.readouterr().out
 
@@ -2903,6 +2925,8 @@ def test_train_rsl_rl_motrix_auto_play_is_interactive(
     class FakeEnv:
         def __init__(self):
             self.cfg = type("Cfg", (), {"render_spacing": 2.5, "render_offset_mode": "zero"})()
+            self.obs_groups_spec = {"obs": 1}
+            self.action_space = type("Space", (), {"shape": (1,)})()
 
         def run_playback_mode(self, **kwargs):
             assert kwargs["play_render_mode"] == "auto"
@@ -2928,9 +2952,10 @@ def run_playback_mode(self, **kwargs):
             return None
 
     class FakeWrapper:
-        def __init__(self, env, device):
+        def __init__(self, env, device, policy_obs_mode="flat"):
             self.env = env
             self.device = device
+            self.policy_obs_mode = policy_obs_mode
 
         def reset(self):
             return 0, {}
@@ -2993,6 +3018,8 @@ def test_train_rsl_rl_record_play_uses_backend_plan(
     class FakeEnv:
         def __init__(self):
             self.cfg = type("Cfg", (), {"render_spacing": 1.0, "render_offset_mode": "grid"})()
+            self.obs_groups_spec = {"obs": 1}
+            self.action_space = type("Space", (), {"shape": (1,)})()
 
         def run_playback_mode(self, **kwargs):
             assert kwargs["play_render_mode"] == "record"
@@ -3018,9 +3045,10 @@ def run_playback_mode(self, **kwargs):
             return str(plan.output_video)
 
     class FakeWrapper:
-        def __init__(self, env, device):
+        def __init__(self, env, device, policy_obs_mode="flat"):
             self.env = env
             self.device = device
+            self.policy_obs_mode = policy_obs_mode
 
         def reset(self):
             return 0, {}
@@ -3035,9 +3063,9 @@ def __init__(self, wrapped_env, train_cfg, log_dir, device):
             self.log_dir = log_dir
             self.device = device
 
-        def load(self, path, map_location=None):
+        def load(self, path, **kwargs):
             self.loaded_path = path
-            self.map_location = map_location
+            self.load_kwargs = kwargs
 
         def get_inference_policy(self, device):
             return lambda obs: obs
@@ -3085,15 +3113,11 @@ def test_play_resolve_checkpoint_uses_algo_log_name(
     run_dir.mkdir(parents=True)
     (run_dir / "model_50.pt").write_bytes(b"")
 
-    # Temporarily override ROOT_DIR to use tmp_path
-    original_root = mod.ROOT_DIR
-    try:
-        mod.ROOT_DIR = tmp_path
-        result = mod.resolve_checkpoint("MyTask", "-1", algo_log_name="custom_ppo")
-        assert result is not None
-        assert "model_50.pt" in result
-    finally:
-        mod.ROOT_DIR = original_root
+    # Run-artifact resolution now anchors at the caller's CWD.
+    monkeypatch.chdir(tmp_path)
+    result = mod.resolve_checkpoint("MyTask", "-1", algo_log_name="custom_ppo")
+    assert result is not None
+    assert "model_50.pt" in result
 
 
 def test_ppo_interactive_config_includes_playback_controls():
@@ -3123,7 +3147,7 @@ def test_play_interactive_parses_explicit_cli():
     assert parsed.overrides == ["task=sharpa_inhand/mujoco_nodr"]
 
 
-@pytest.mark.parametrize("algo", ["appo", "sac", "hora_distill"])
+@pytest.mark.parametrize("algo", ["appo", "sac", "td3", "hora_distill"])
 def test_play_interactive_parses_feature_algo_flags(algo: str):
     mod = _play_interactive()
 
@@ -3180,6 +3204,7 @@ def test_play_interactive_dynamic_compose_supports_algo_roots():
     ppo_cfg = mod._compose_interactive_config("ppo", ["task=go1_joystick_flat/mujoco"])
     appo_cfg = mod._compose_interactive_config("appo", ["task=sharpa_inhand/mujoco_hora"])
     sac_cfg = mod._compose_interactive_config("sac", ["task=sharpa_inhand/mujoco_hora"])
+    td3_cfg = mod._compose_interactive_config("td3", ["task=g1_walk_flat/mujoco"])
     distill_cfg = mod._compose_interactive_config("hora_distill", ["task=sharpa_inhand/mujoco"])
 
     assert ppo_cfg.algo.algo == "ppo"
@@ -3188,11 +3213,12 @@ def test_play_interactive_dynamic_compose_supports_algo_roots():
     assert sac_cfg.algo.algo == "sac"
     assert sac_cfg.algo.runtime_impl == "hora_sac"
     assert sac_cfg.interactive.policy_obs_mode == "actor"
+    assert td3_cfg.algo.algo == "td3"
     assert distill_cfg.algo.algo_log_name == "hora_distill"
     assert distill_cfg.interactive.action_mode == "policy"
 
 
-def test_play_interactive_sac_task_shorthand_rewrites_to_owner_group():
+def test_play_interactive_sac_overrides_pass_through():
     mod = _play_interactive()
 
     overrides = mod._normalize_interactive_overrides(
@@ -3201,8 +3227,7 @@ def test_play_interactive_sac_task_shorthand_rewrites_to_owner_group():
     )
 
     assert overrides == [
-        "algo=sac",
-        "task=sac/sharpa_inhand/mujoco_hora",
+        "task=sharpa_inhand/mujoco_hora",
         "algo.load_run=my_run",
     ]
 
@@ -3309,15 +3334,14 @@ def test_play_interactive_import_does_not_swallow_registry_bootstrap_errors(
 ):
     import types
 
-    play_interactive_path = _SCRIPTS_DIR / "play_interactive.py"
+    play_interactive_path = _PACKAGE_SCRIPTS_DIR / "play_interactive.py"
     training_mod = cast(Any, types.ModuleType("unilab.training"))
 
     def _fail_bootstrap() -> None:
         raise RuntimeError("bootstrap failed")
 
     training_mod.ensure_registries = _fail_bootstrap
-    training_mod.get_entrypoint_log_root = lambda *args, **kwargs: Path("/tmp")
-    training_mod.resolve_task_checkpoint_path = lambda *args, **kwargs: (None, None)
+    training_mod.algo_config_dict = lambda cfg: {}
     monkeypatch.setitem(sys.modules, "unilab.training", training_mod)
 
     mujoco_mod = cast(Any, types.ModuleType("mujoco"))
@@ -3332,3 +3356,387 @@ def _fail_bootstrap() -> None:
 
     with pytest.raises(RuntimeError, match="bootstrap failed"):
         spec.loader.exec_module(mod)  # type: ignore[union-attr]
+
+
+# ---------------------------------------------------------------------------
+# Unified play entrypoints — shared playback session factories (issue #1242)
+# ---------------------------------------------------------------------------
+
+
+def _him_ppo_cfg(overrides=None):
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(config_dir=str(_CONF_DIR / "ppo_him"), version_base="1.3"):
+        return compose(
+            "config",
+            overrides=["task=go2_arm_manip_loco/mujoco", *(overrides or [])],
+        )
+
+
+def _train_him_ppo():
+    return _load_script("train_him_ppo")
+
+
+class _FakePlaybackEnv:
+    """Env stand-in driving one initialize/step cycle through run_playback_mode."""
+
+    def __init__(self, video_path: str):
+        self.cfg = types.SimpleNamespace(render_spacing=1.0, render_offset_mode="grid")
+        self.video_path = video_path
+        self.captured: dict[str, Any] = {}
+
+    def run_playback_mode(self, **kwargs: Any) -> str:
+        self.captured.update(kwargs)
+        self.captured["init_obs"] = kwargs["initialize"]()
+        self.captured["next_obs"] = kwargs["step"](self.captured["init_obs"])
+        return self.video_path
+
+
+def test_train_rsl_rl_play_uses_shared_playback_session_factory(
+    monkeypatch: pytest.MonkeyPatch, tmp_path: Path
+):
+    mod = _train_rsl_rl(monkeypatch)
+    cfg = _ppo_cfg(
+        [
+            "task=go1_joystick_flat/mujoco",
+            "training.play_only=true",
+            "training.play_render_mode=record",
+            "training.play_steps=5",
+        ]
+    )
+    run_dir = tmp_path / "run"
+    run_dir.mkdir()
+    checkpoint = run_dir / "model_37.pt"
+    mod.torch.save({"actor_state_dict": {}}, checkpoint)
+    captured: dict[str, Any] = {}
+
+    class FakeSession:
+        def __init__(self):
+            self.env = _FakePlaybackEnv(str(run_dir / "play_video.mp4"))
+            self.runner = object()
+            self.reset_calls = 0
+            self.step_calls = 0
+
+        def reset(self):
+            self.reset_calls += 1
+            return "obs_0"
+
+        def step_once(self):
+            self.step_calls += 1
+            return "obs_1"
+
+    fake_session = FakeSession()
+    sentinel_wrapper_cls = type("SentinelWrapper", (), {})
+
+    def fake_create_session(**kwargs: Any):
+        captured["factory_kwargs"] = kwargs
+        return fake_session, "flat", str(checkpoint)
+
+    monkeypatch.setattr(mod, "EXPORT_POLICY", False, raising=False)
+    monkeypatch.setattr(mod, "parse_checkpoint_path", lambda *args, **kwargs: (checkpoint, run_dir))
+    monkeypatch.setattr(mod, "_resolve_ppo_wrapper_cls", lambda rl_cfg: sentinel_wrapper_cls)
+    monkeypatch.setattr(mod, "create_rsl_rl_playback_session", fake_create_session)
+
+    result = mod.play_rsl_rl(cfg, device="cpu")
+
+    assert result == str(run_dir / "play_video.mp4")
+    factory_kwargs = captured["factory_kwargs"]
+    playback_cfg = factory_kwargs["playback_cfg"]
+    assert playback_cfg.task == cfg.training.task_name
+    assert playback_cfg.action_mode == "policy"
+    assert playback_cfg.policy_obs_mode == "flat"
+    assert playback_cfg.algo_log_name == cfg.algo.algo_log_name
+    assert playback_cfg.num_envs == cfg.training.play_env_num
+    assert factory_kwargs["device"] == "cpu"
+    assert factory_kwargs["root_dir"] == Path.cwd()
+    assert factory_kwargs["wrapper_cls"] is sentinel_wrapper_cls
+    assert factory_kwargs["runner_cls"] is mod.OnPolicyRunner
+    assert factory_kwargs["guard_algo_name"] == "ppo"
+    assert factory_kwargs.get("runner_loader") is None
+    assert factory_kwargs["checkpoint_resolver"]() == str(checkpoint)
+    assert callable(factory_kwargs["sim2sim_preflight"])
+    assert fake_session.reset_calls == 1
+    assert fake_session.step_calls == 1
+    env_captured = fake_session.env.captured
+    assert env_captured["play_render_mode"] == "record"
+    assert env_captured["play_steps"] == 5
+    assert env_captured["init_obs"] == "obs_0"
+    assert env_captured["next_obs"] == "obs_1"
+
+
+def test_train_him_ppo_play_missing_checkpoint_returns_none_without_env(
+    monkeypatch: pytest.MonkeyPatch, tmp_path: Path, capsys: pytest.CaptureFixture[str]
+):
+    mod = _train_him_ppo()
+    cfg = _him_ppo_cfg(["training.play_only=true"])
+
+    monkeypatch.setattr(mod, "parse_checkpoint_path", lambda *args, **kwargs: (None, None))
+    monkeypatch.setattr(
+        mod,
+        "create_env",
+        lambda *args, **kwargs: (_ for _ in ()).throw(
+            AssertionError("play_him_ppo should not create an env before checkpoint resolution")
+        ),
+    )
+
+    result = mod.play_him_ppo(cfg, device="cpu")
+
+    assert result is None
+    assert "Could not resolve a checkpoint for play mode." in capsys.readouterr().out
+
+
+def test_train_him_ppo_play_uses_shared_playback_session_factory(
+    monkeypatch: pytest.MonkeyPatch, tmp_path: Path
+):
+    mod = _train_him_ppo()
+    cfg = _him_ppo_cfg(["training.play_only=true"])
+    run_dir = tmp_path / "run"
+    run_dir.mkdir()
+    checkpoint = run_dir / "model_37.pt"
+    mod.torch.save({"actor_state_dict": {}}, checkpoint)
+    captured: dict[str, Any] = {}
+
+    class FakeSession:
+        def __init__(self):
+            self.env = types.SimpleNamespace(
+                cfg=types.SimpleNamespace(render_spacing=1.0),
+            )
+            self.runner = object()
+            self.policy = lambda obs: obs
+            self.reset_calls = 0
+            self.step_calls = 0
+
+        def reset(self):
+            self.reset_calls += 1
+            return {"actor": "obs_0"}
+
+        def step_once(self):
+            self.step_calls += 1
+            return {"actor": "obs_1"}
+
+    fake_session = FakeSession()
+
+    def fake_create_session(**kwargs: Any):
+        captured["factory_kwargs"] = kwargs
+        return fake_session, "actor", str(checkpoint)
+
+    def fake_render_play_mode(env, **kwargs: Any):
+        captured["render_kwargs"] = kwargs
+        captured["init_obs"] = kwargs["initialize"]()
+        captured["next_obs"] = kwargs["step"](captured["init_obs"])
+
+    monkeypatch.setattr(mod, "EXPORT_POLICY", False, raising=False)
+    monkeypatch.setattr(mod, "parse_checkpoint_path", lambda *args, **kwargs: (checkpoint, run_dir))
+    monkeypatch.setattr(mod, "create_rsl_rl_playback_session", fake_create_session)
+    monkeypatch.setattr(mod, "render_play_mode", fake_render_play_mode)
+
+    result = mod.play_him_ppo(cfg, device="cpu")
+
+    assert result == str(run_dir / "play_video.mp4")
+    factory_kwargs = captured["factory_kwargs"]
+    playback_cfg = factory_kwargs["playback_cfg"]
+    assert playback_cfg.task == cfg.training.task_name
+    assert playback_cfg.action_mode == "policy"
+    assert playback_cfg.num_envs == cfg.training.play_env_num
+    assert factory_kwargs["device"] == "cpu"
+    assert factory_kwargs["wrapper_cls"] is mod.RslRlVecEnvWrapper
+    assert factory_kwargs["runner_cls"] is mod.HIMOnPolicyRunner
+    assert factory_kwargs["guard_algo_name"] == "him_ppo"
+    assert callable(factory_kwargs["runner_loader"])
+    assert factory_kwargs["checkpoint_resolver"]() == str(checkpoint)
+    assert callable(factory_kwargs["sim2sim_preflight"])
+    assert fake_session.reset_calls == 1
+    assert fake_session.step_calls == 1
+    assert captured["init_obs"] == "obs_0"
+    assert captured["next_obs"] == "obs_1"
+    assert captured["render_kwargs"]["output_video"] == run_dir / "play_video.mp4"
+
+
+def test_play_appo_missing_checkpoint_returns_none_without_env(
+    monkeypatch: pytest.MonkeyPatch, capsys: pytest.CaptureFixture[str]
+):
+    mod = _train_appo()
+    cfg = _appo_cfg(["task=g1_walk_flat/mujoco", "training.play_only=true"])
+
+    monkeypatch.setattr(
+        mod,
+        "create_env",
+        lambda *args, **kwargs: (_ for _ in ()).throw(
+            AssertionError("play_appo should not create an env before checkpoint resolution")
+        ),
+    )
+
+    result = mod.play_appo(cfg, {}, resolve_checkpoint_path=lambda _cfg: (None, None))
+
+    assert result is None
+    assert "Could not find run to load." in capsys.readouterr().out
+
+
+def test_play_appo_uses_shared_playback_session_factory(
+    monkeypatch: pytest.MonkeyPatch, tmp_path: Path
+):
+    import torch
+
+    mod = _train_appo()
+    cfg = _appo_cfg(
+        [
+            "task=g1_walk_flat/mujoco",
+            "training.play_only=true",
+            "training.play_render_mode=record",
+        ]
+    )
+    run_dir = tmp_path / "run"
+    run_dir.mkdir()
+    checkpoint = run_dir / "model_37.pt"
+    checkpoint.write_bytes(b"checkpoint")
+    captured: dict[str, Any] = {}
+
+    class FakeActor:
+        def __init__(self):
+            self.mlp = torch.nn.Linear(4, 2)
+
+    class FakeSession:
+        def __init__(self):
+            self.env = _FakePlaybackEnv(str(run_dir / "play_video.mp4"))
+            self.actor = FakeActor()
+            self.wrapped_env = types.SimpleNamespace(num_obs=4)
+            self.reset_calls = 0
+            self.step_calls = 0
+
+        def reset(self):
+            self.reset_calls += 1
+            return "obs_0"
+
+        def step_once(self):
+            self.step_calls += 1
+            return "obs_1"
+
+    fake_session = FakeSession()
+    rl_cfg: dict[str, Any] = {"seed": 1}
+
+    def fake_create_session(**kwargs: Any):
+        captured["factory_kwargs"] = kwargs
+        return fake_session, "flat", str(checkpoint)
+
+    monkeypatch.setattr(mod, "create_appo_playback_session", fake_create_session)
+    monkeypatch.setattr(
+        mod, "export_policy_onnx", lambda *args, **kwargs: captured.setdefault("onnx_export", args)
+    )
+    monkeypatch.setattr(mod, "verify_policy_onnx", lambda *args, **kwargs: None)
+
+    result = mod.play_appo(
+        cfg,
+        rl_cfg,
+        resolve_checkpoint_path=lambda _cfg: (str(checkpoint), str(run_dir)),
+    )
+
+    assert result == str(run_dir / "play_video.mp4")
+    factory_kwargs = captured["factory_kwargs"]
+    playback_cfg = factory_kwargs["playback_cfg"]
+    assert playback_cfg.task == cfg.training.task_name
+    assert playback_cfg.action_mode == "policy"
+    assert playback_cfg.algo_log_name == cfg.algo.algo_log_name
+    assert playback_cfg.num_envs == cfg.training.play_env_num
+    assert factory_kwargs["cfg"] is cfg
+    assert factory_kwargs["rl_cfg"] is rl_cfg
+    assert factory_kwargs["root_dir"] == Path.cwd()
+    assert factory_kwargs["wrapper_cls"] is mod.RslRlVecEnvWrapper
+    assert "onnx_export" in captured
+    assert fake_session.reset_calls == 1
+    assert fake_session.step_calls == 1
+    env_captured = fake_session.env.captured
+    assert env_captured["play_render_mode"] == "record"
+    assert env_captured["init_obs"] == "obs_0"
+    assert env_captured["next_obs"] == "obs_1"
+
+
+def test_play_offpolicy_missing_checkpoint_returns_none_without_env(
+    monkeypatch: pytest.MonkeyPatch, capsys: pytest.CaptureFixture[str]
+):
+    mod = _offpolicy()
+    cfg = _offpolicy_cfg(["task=g1_walk_flat/mujoco", "training.play_only=true"])
+
+    monkeypatch.setattr(mod, "resolve_checkpoint_path", lambda *args, **kwargs: (None, None))
+    monkeypatch.setattr(
+        mod,
+        "create_env",
+        lambda *args, **kwargs: (_ for _ in ()).throw(
+            AssertionError("play_offpolicy should not create an env before checkpoint resolution")
+        ),
+    )
+
+    result = mod.play_offpolicy("sac", cfg)
+
+    assert result is None
+    assert "Could not find checkpoint." in capsys.readouterr().out
+
+
+def test_play_offpolicy_uses_shared_playback_session_factory(
+    monkeypatch: pytest.MonkeyPatch, tmp_path: Path
+):
+    mod = _offpolicy()
+    cfg = _offpolicy_cfg(
+        [
+            "task=g1_walk_flat/mujoco",
+            "training.play_only=true",
+            "training.play_render_mode=record",
+            "training.export_onnx=false",
+        ]
+    )
+    run_dir = tmp_path / "run"
+    run_dir.mkdir()
+    checkpoint = run_dir / "model_5000.pt"
+    checkpoint.write_bytes(b"checkpoint")
+    captured: dict[str, Any] = {}
+
+    class FakeSession:
+        def __init__(self):
+            self.env = _FakePlaybackEnv(str(run_dir / "play_video.mp4"))
+            self.actor = object()
+            self.normalizer = None
+            self.actor_algo_type = "sac"
+            self.reset_calls = 0
+            self.step_calls = 0
+
+        def reset(self):
+            self.reset_calls += 1
+            return "obs_0"
+
+        def step_once(self):
+            self.step_calls += 1
+            return "obs_1"
+
+    fake_session = FakeSession()
+
+    def fake_create_session(**kwargs: Any):
+        captured["factory_kwargs"] = kwargs
+        return fake_session, "actor", str(checkpoint)
+
+    monkeypatch.setattr(mod, "default_device", lambda torch_module, preferred=None: "cpu")
+    monkeypatch.setattr(
+        mod,
+        "resolve_checkpoint_path",
+        lambda *args, **kwargs: (str(checkpoint), str(run_dir)),
+    )
+    monkeypatch.setattr(mod, "create_sac_playback_session", fake_create_session)
+
+    result = mod.play_offpolicy("sac", cfg)
+
+    assert result == str(run_dir / "play_video.mp4")
+    factory_kwargs = captured["factory_kwargs"]
+    playback_cfg = factory_kwargs["playback_cfg"]
+    assert playback_cfg.task == cfg.training.task_name
+    assert playback_cfg.action_mode == "policy"
+    assert playback_cfg.policy_obs_mode == "actor"
+    assert playback_cfg.algo_log_name == cfg.algo.algo_log_name
+    assert playback_cfg.num_envs == cfg.training.play_env_num
+    assert factory_kwargs["algo_name"] == "sac"
+    assert factory_kwargs["device"] == "cpu"
+    assert factory_kwargs["cfg"] is cfg
+    assert factory_kwargs["root_dir"] == Path.cwd()
+    assert callable(factory_kwargs["env_factory"])
+    assert fake_session.reset_calls == 1
+    assert fake_session.step_calls == 1
+    env_captured = fake_session.env.captured
+    assert env_captured["play_render_mode"] == "record"
+    assert env_captured["init_obs"] == "obs_0"
+    assert env_captured["next_obs"] == "obs_1"
diff --git a/tests/scripts/test_visualization_entrypoints.py b/tests/scripts/test_visualization_entrypoints.py
index a5006b0ab..e85121204 100644
--- a/tests/scripts/test_visualization_entrypoints.py
+++ b/tests/scripts/test_visualization_entrypoints.py
@@ -2,26 +2,32 @@
 
 import importlib.util
 from pathlib import Path
+from types import SimpleNamespace
 from typing import Any
 
 import numpy as np
 import pytest
 
+# CPU-bound on the single-core CI runner; kept in the slow lane (make test-slow).
+pytestmark = pytest.mark.slow
+
 _SCRIPTS_DIR = Path(__file__).parent.parent.parent / "scripts"
+_PACKAGE_SCRIPTS_DIR = Path(__file__).parent.parent.parent / "src" / "unilab" / "scripts"
 
 
 def _load_script(name: str) -> Any:
-    path = _SCRIPTS_DIR / f"{name}.py"
+    for base in (_PACKAGE_SCRIPTS_DIR, _SCRIPTS_DIR):
+        path = base / f"{name}.py"
+        if path.is_file():
+            break
+    else:
+        raise FileNotFoundError(f"No script named {name}.py in packaged or top-level scripts")
     spec = importlib.util.spec_from_file_location(name, path)
     mod = importlib.util.module_from_spec(spec)  # type: ignore[arg-type]
     spec.loader.exec_module(mod)  # type: ignore[union-attr]
     return mod
 
 
-def test_legacy_visualization_env_entrypoint_is_removed():
-    assert not (_SCRIPTS_DIR / "visualization_env.py").exists()
-
-
 def test_visualize_task_env_keeps_canonical_defaults():
     mod = _load_script("visualize_task_env")
 
@@ -201,19 +207,19 @@ def test_velocity_arrows_require_velocity_command_task_and_policy_obs():
     joystick_env = _keyboard_env(
         env_cls_name="Go2WalkTask",
         cfg_cls_name="Go2JoystickCfg",
-        module="unilab.envs.locomotion.go2.joystick",
+        module="unilab.tasks.locomotion.go2.joystick",
         obs_contains_command=True,
     )
     manip_loco_env = _keyboard_env(
         env_cls_name="Go2ArmManipLocoEnv",
         cfg_cls_name="Go2ArmManipLocoCfg",
-        module="unilab.envs.locomotion.go2_arm.manip_loco",
+        module="unilab.tasks.locomotion.go2_arm.manip_loco",
         obs_contains_command=True,
     )
     missing_obs_command_env = _keyboard_env(
         env_cls_name="Go2WalkTask",
         cfg_cls_name="Go2JoystickCfg",
-        module="unilab.envs.locomotion.go2.joystick",
+        module="unilab.tasks.locomotion.go2.joystick",
         obs_contains_command=False,
     )
 
@@ -286,3 +292,65 @@ def get_scene_visual_model_file(self) -> str:
     assert resolved["env"] is env
     assert resolved["num_envs"] == 1
     assert Path(resolved["tmp_dir"]).name.startswith("unilab-interactive-viewer-")
+
+
+def test_play_interactive_binds_mjwarp_process_device_before_session(monkeypatch):
+    mod = _load_script("play_interactive")
+    bound: list[tuple[str, str]] = []
+    monkeypatch.setattr(
+        mod,
+        "configure_backend_process_device",
+        lambda backend_type, device: bound.append((backend_type, device)),
+    )
+    monkeypatch.setattr(mod, "_select_playback_device", lambda cfg: "cuda:0")
+    monkeypatch.setattr(mod, "available_backends_for_task", lambda task: ["mjwarp"])
+
+    def _stop_before_session(**kwargs):
+        raise RuntimeError("stop-before-session")
+
+    monkeypatch.setattr(mod, "create_rsl_rl_playback_session", _stop_before_session)
+
+    args = SimpleNamespace(
+        algo="ppo",
+        task="g1_walk_flat",
+        sim="mjwarp",
+        load_run="-1",
+        checkpoint=None,
+        action_mode="policy",
+        policy_obs_mode="actor",
+    )
+    with pytest.raises(RuntimeError, match="stop-before-session"):
+        mod.play_interactive(args, None)
+
+    assert bound == [("mjwarp", "cuda:0")]
+
+
+def test_play_interactive_device_binding_is_noop_for_mujoco(monkeypatch):
+    mod = _load_script("play_interactive")
+    bound: list[tuple[str, str]] = []
+    monkeypatch.setattr(
+        mod,
+        "configure_backend_process_device",
+        lambda backend_type, device: bound.append((backend_type, device)),
+    )
+    monkeypatch.setattr(mod, "_select_playback_device", lambda cfg: "cuda:0")
+    monkeypatch.setattr(mod, "available_backends_for_task", lambda task: ["mujoco"])
+
+    def _stop_before_session(**kwargs):
+        raise RuntimeError("stop-before-session")
+
+    monkeypatch.setattr(mod, "create_rsl_rl_playback_session", _stop_before_session)
+
+    args = SimpleNamespace(
+        algo="ppo",
+        task="g1_walk_flat",
+        sim="mujoco",
+        load_run="-1",
+        checkpoint=None,
+        action_mode="policy",
+        policy_obs_mode="actor",
+    )
+    with pytest.raises(RuntimeError, match="stop-before-session"):
+        mod.play_interactive(args, None)
+
+    assert bound == [("mujoco", "cuda:0")]
diff --git a/tests/tasks/test_legacy_task_compatibility.py b/tests/tasks/test_legacy_task_compatibility.py
new file mode 100644
index 000000000..eebabe275
--- /dev/null
+++ b/tests/tasks/test_legacy_task_compatibility.py
@@ -0,0 +1,201 @@
+"""Focused contract tests for the internal legacy-task compatibility seam."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import gymnasium as gym
+import numpy as np
+import pytest
+
+from unilab.base import registry
+from unilab.base.base import ABEnv, EnvCfg
+from unilab.base.np_env import NpEnv, NpEnvState
+from unilab.tasks.compatibility import (
+    CompatibilityStatus,
+    LegacyFactoryAdapter,
+    adapt_legacy_factory,
+    unsupported_legacy_task,
+)
+
+
+@dataclass
+class _Cfg(EnvCfg):
+    pass
+
+
+class _PlainABEnv(ABEnv):
+    @property
+    def num_envs(self) -> int:
+        return 1
+
+    @property
+    def cfg(self) -> EnvCfg:
+        return _Cfg()
+
+    @property
+    def observation_space(self) -> gym.Space:
+        return gym.spaces.Box(-np.inf, np.inf, shape=(1,), dtype=np.float32)
+
+    @property
+    def action_space(self) -> gym.Space:
+        return gym.spaces.Box(-1.0, 1.0, shape=(1,), dtype=np.float32)
+
+    @property
+    def obs_groups_spec(self) -> dict[str, int]:
+        return {"obs": 1}
+
+    @property
+    def state(self) -> None:
+        return None
+
+    def init_state(self) -> None:
+        return None
+
+    def step(self, actions: np.ndarray) -> None:
+        return None
+
+    def close(self) -> None:
+        return None
+
+
+class _NpEnv(NpEnv):
+    @property
+    def action_space(self) -> gym.Space:
+        return gym.spaces.Box(-1.0, 1.0, shape=(1,), dtype=np.float32)
+
+    @property
+    def obs_groups_spec(self) -> dict[str, int]:
+        return {"obs": 1}
+
+    def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
+        return actions
+
+    def update_state(self, state: NpEnvState) -> NpEnvState:
+        return state
+
+
+def _uninitialized_np_env() -> _NpEnv:
+    return object.__new__(_NpEnv)
+
+
+def test_adapter_records_fixed_adapted_metadata_and_forwards_registry_arguments() -> None:
+    received: list[tuple[EnvCfg, int, str]] = []
+    expected = _uninitialized_np_env()
+
+    def factory(cfg: EnvCfg, *, num_envs: int, backend_type: str) -> ABEnv:
+        received.append((cfg, num_envs, backend_type))
+        return expected
+
+    adapter = adapt_legacy_factory(
+        factory,
+        task_family="Go2ArmManipLoco",
+        reason="existing task owner already constructs an NpEnv",
+    )
+    cfg = _Cfg()
+
+    assert adapter(cfg, num_envs=4, backend_type="motrix") is expected
+    assert received == [(cfg, 4, "motrix")]
+    assert adapter.compatibility.task_family == "Go2ArmManipLoco"
+    assert adapter.compatibility.status is CompatibilityStatus.ADAPTED
+    assert adapter.compatibility.reason == "existing task owner already constructs an NpEnv"
+
+
+def test_adapter_rejects_non_env_cfg_before_calling_factory() -> None:
+    called = False
+
+    def factory(cfg: EnvCfg, *, num_envs: int, backend_type: str) -> ABEnv:
+        nonlocal called
+        called = True
+        return _uninitialized_np_env()
+
+    adapter = adapt_legacy_factory(factory, task_family="Sharpa", reason="migration seam")
+
+    with pytest.raises(TypeError, match=r"Sharpa.*expected EnvCfg.*dict"):
+        adapter({})  # type: ignore[arg-type]
+
+    assert called is False
+
+
+@pytest.mark.parametrize(
+    ("result", "match"),
+    (
+        (object(), r"Go2ArmManipLoco.*object.*expected ABEnv"),
+        (_PlainABEnv(), r"Go2ArmManipLoco.*Unsupported.*_PlainABEnv.*NpEnv"),
+    ),
+)
+def test_adapter_rejects_factories_outside_the_np_env_lifecycle(
+    result: object,
+    match: str,
+) -> None:
+    def factory(cfg: EnvCfg, *, num_envs: int, backend_type: str) -> object:
+        return result
+
+    adapter = adapt_legacy_factory(
+        factory,  # type: ignore[arg-type]
+        task_family="Go2ArmManipLoco",
+        reason="migration seam",
+    )
+
+    with pytest.raises(TypeError, match=match):
+        adapter(_Cfg())
+
+
+def test_factory_exception_propagates_without_fallback() -> None:
+    failure = RuntimeError("owner factory failed")
+
+    def factory(cfg: EnvCfg, *, num_envs: int, backend_type: str) -> ABEnv:
+        raise failure
+
+    adapter = adapt_legacy_factory(factory, task_family="Sharpa", reason="migration seam")
+
+    with pytest.raises(RuntimeError) as exc_info:
+        adapter(_Cfg())
+
+    assert exc_info.value is failure
+
+
+def test_unsupported_metadata_is_explicit_and_does_not_create_a_factory() -> None:
+    compatibility = unsupported_legacy_task(
+        task_family="Sharpa foreign lifecycle",
+        reason="only the existing NpEnv lifecycle is admitted",
+    )
+
+    assert compatibility.status is CompatibilityStatus.UNSUPPORTED
+    assert compatibility.reason == "only the existing NpEnv lifecycle is admitted"
+
+    with pytest.raises(ValueError, match="status must be Adapted"):
+        LegacyFactoryAdapter(lambda cfg, **kwargs: _uninitialized_np_env(), compatibility)
+
+
+@pytest.mark.parametrize(("task_family", "reason"), (("", "reason"), ("Sharpa", "")))
+def test_compatibility_metadata_requires_stable_family_and_reason(
+    task_family: str,
+    reason: str,
+) -> None:
+    with pytest.raises(ValueError, match="must be non-empty"):
+        unsupported_legacy_task(task_family=task_family, reason=reason)
+
+
+@pytest.mark.parametrize(
+    ("task_name", "family", "backends"),
+    (
+        ("Go2ArmManipLoco", "Go2ArmManipLoco", {"mujoco", "motrix", "drake"}),
+        ("SharpaInhandRotation", "Sharpa", {"mujoco", "motrix", "drake"}),
+        ("SharpaInhandRotationGrasp", "Sharpa", {"mujoco", "motrix"}),
+    ),
+)
+def test_approved_production_families_are_registered_through_the_frozen_seam(
+    task_name: str,
+    family: str,
+    backends: set[str],
+) -> None:
+    registry.ensure_registries()
+    factories = registry._envs[task_name].env_factory_dict
+
+    assert set(factories) == backends
+    assert all(isinstance(factory, LegacyFactoryAdapter) for factory in factories.values())
+    assert {factory.compatibility.task_family for factory in factories.values()} == {family}
+    assert {factory.compatibility.status for factory in factories.values()} == {
+        CompatibilityStatus.ADAPTED
+    }
diff --git a/tests/tasks/test_migration_matrix.py b/tests/tasks/test_migration_matrix.py
new file mode 100644
index 000000000..fb577f3a4
--- /dev/null
+++ b/tests/tasks/test_migration_matrix.py
@@ -0,0 +1,45 @@
+from __future__ import annotations
+
+import pytest
+
+from unilab.base import registry
+from unilab.tasks.migration_matrix import (
+    PRODUCTION_TASK_NAMES,
+    migration_record,
+    migration_records,
+)
+
+
+def test_registered_tasks_have_explicit_migration_records() -> None:
+    registry.ensure_registries()
+    registered = registry.list_registered_envs()
+    records = migration_records(set(PRODUCTION_TASK_NAMES))
+
+    assert PRODUCTION_TASK_NAMES <= registered.keys()
+    assert {record.task_name for record in records} == set(PRODUCTION_TASK_NAMES)
+
+
+@pytest.mark.parametrize(
+    ("task_name", "family", "target", "status"),
+    [
+        ("Go2ArmManipLoco", "go2_arm", "compatibility", "Adapted"),
+        ("SharpaInhandRotation", "sharpa", "compatibility", "Adapted"),
+        ("G1MotionTracking", "motion_tracking", "complete", "Compatible"),
+        ("G1WBTObs", "motion_tracking", "complete", "Compatible"),
+        ("X2WallFlipTracking", "motion_tracking", "complete", "Compatible"),
+        ("G1WalkRough", "g1_locomotion", "complete", "Compatible"),
+        ("Go2JoystickRough", "quadruped_rough", "complete", "Compatible"),
+    ],
+)
+def test_matrix_records_high_risk_families(
+    task_name: str, family: str, target: str, status: str
+) -> None:
+    record = migration_record(task_name)
+    assert record.family == family
+    assert record.target == target
+    assert record.status == status
+
+
+def test_unknown_task_fails_closed() -> None:
+    with pytest.raises(KeyError, match="no #1042 migration-matrix entry"):
+        migration_record("NewTaskWithoutDecision")
diff --git a/tests/tasks/test_motion_term_parity.py b/tests/tasks/test_motion_term_parity.py
new file mode 100644
index 000000000..b0d1e2b18
--- /dev/null
+++ b/tests/tasks/test_motion_term_parity.py
@@ -0,0 +1,584 @@
+"""Numerical-contract tests for optimized motion-tracking manager terms."""
+
+from __future__ import annotations
+
+import os
+from types import SimpleNamespace
+from typing import Any
+
+import numpy as np
+import pytest
+from numba import config, get_num_threads, threading_layer
+
+from unilab.managers import RewardTermCfg, TerminationTermCfg
+from unilab.tasks.motion_tracking.common import kernels
+from unilab.tasks.motion_tracking.common import manager_terms as mt
+from unilab.utils.rotation import (
+    np_matrix_first_two_cols_from_quat,
+    np_quat_apply_batched,
+    np_quat_apply_inverse_batched,
+    np_quat_error_magnitude_squared_batched,
+    np_quat_inv,
+    np_quat_mul_batched,
+    np_yaw_quat,
+)
+
+
+def _make_env(command: Any) -> SimpleNamespace:
+    return SimpleNamespace(num_envs=command.num_envs)
+
+
+def _unit_quat(value: np.ndarray) -> np.ndarray:
+    value /= np.linalg.norm(value, axis=-1, keepdims=True)
+    return value
+
+
+@pytest.fixture
+def body_setup(monkeypatch: pytest.MonkeyPatch):
+    rng = np.random.default_rng(42)
+    num_envs, num_bodies = 257, 12
+    anchor_body_idx = 4
+
+    body_pos_relative_w = rng.standard_normal((num_envs, num_bodies, 3), dtype=np.float32)
+    body_pos_w = body_pos_relative_w.copy()
+    robot_body_pos_w = body_pos_relative_w + 0.1 * rng.standard_normal(
+        body_pos_relative_w.shape, dtype=np.float32
+    )
+    body_quat_relative_w = _unit_quat(
+        rng.standard_normal((num_envs, num_bodies, 4), dtype=np.float32)
+    )
+    robot_body_quat_w = _unit_quat(
+        body_quat_relative_w
+        + 0.05 * rng.standard_normal(body_quat_relative_w.shape, dtype=np.float32)
+    )
+    body_lin_vel_w = rng.standard_normal((num_envs, num_bodies, 3), dtype=np.float32)
+    robot_body_lin_vel_w = body_lin_vel_w + 0.2 * rng.standard_normal(
+        body_lin_vel_w.shape, dtype=np.float32
+    )
+    body_ang_vel_w = rng.standard_normal((num_envs, num_bodies, 3), dtype=np.float32)
+    robot_body_ang_vel_w = body_ang_vel_w + 0.5 * rng.standard_normal(
+        body_ang_vel_w.shape, dtype=np.float32
+    )
+
+    command = SimpleNamespace(
+        num_envs=num_envs,
+        cfg=SimpleNamespace(body_names=tuple(f"b{i}" for i in range(num_bodies))),
+        anchor_body_idx=anchor_body_idx,
+        body_pos_w=body_pos_w,
+        robot_body_pos_w=robot_body_pos_w,
+        anchor_pos_w=body_pos_w[:, anchor_body_idx],
+        robot_anchor_pos_w=robot_body_pos_w[:, anchor_body_idx],
+        joint_pos=rng.standard_normal((num_envs, 29), dtype=np.float32),
+        robot_joint_pos=rng.standard_normal((num_envs, 29), dtype=np.float32),
+        body_pos_relative_w=body_pos_relative_w,
+        body_quat_relative_w=body_quat_relative_w,
+        robot_body_quat_w=robot_body_quat_w,
+        body_lin_vel_w=body_lin_vel_w,
+        robot_body_lin_vel_w=robot_body_lin_vel_w,
+        body_ang_vel_w=body_ang_vel_w,
+        robot_body_ang_vel_w=robot_body_ang_vel_w,
+    )
+
+    snapshots = {
+        key: value.copy() for key, value in vars(command).items() if isinstance(value, np.ndarray)
+    }
+    monkeypatch.setattr(mt, "_command", lambda env, name: command)
+    return command, _make_env(command), snapshots
+
+
+def _reward_cfg(*, body_names: tuple[str, ...] | None = None) -> RewardTermCfg:
+    params: dict[str, Any] = {"command_name": "motion"}
+    if body_names is not None:
+        params["body_names"] = body_names
+    return RewardTermCfg(func=None, weight=1.0, params=params)
+
+
+def _expected_body_reward(
+    reference: np.ndarray,
+    actual: np.ndarray,
+    body_ids: slice | list[int],
+    std: float,
+    *,
+    orientation: bool,
+) -> np.ndarray:
+    reference = reference[:, body_ids]
+    actual = actual[:, body_ids]
+    if orientation:
+        error = np_quat_error_magnitude_squared_batched(reference, actual)
+    else:
+        error = np.square(reference - actual).sum(axis=-1)
+    return np.exp(-error.mean(axis=-1) / std**2)
+
+
+def test_anchor_position_error_exp_bit_parity(body_setup) -> None:
+    command, env, snapshots = body_setup
+    out = mt.motion_global_anchor_position_error_exp(env, "motion", std=0.3)
+    expected = np.exp(
+        -np.sum(
+            np.square(snapshots["anchor_pos_w"] - snapshots["robot_anchor_pos_w"]),
+            axis=-1,
+        )
+        / 0.3**2
+    )
+    np.testing.assert_array_equal(out, expected)
+    np.testing.assert_array_equal(command.anchor_pos_w, snapshots["anchor_pos_w"])
+
+
+def test_joint_position_error_exp_bit_parity(body_setup) -> None:
+    command, env, snapshots = body_setup
+    out = mt.motion_joint_position_error_exp(env, "motion", std=0.2)
+    expected = np.exp(
+        -np.square(snapshots["joint_pos"] - snapshots["robot_joint_pos"]).mean(axis=-1) / 0.2**2
+    )
+    np.testing.assert_array_equal(out, expected)
+
+
+def test_anchor_pos_termination_numba_parity_and_output_reuse(body_setup) -> None:
+    command, env, snapshots = body_setup
+    cfg = TerminationTermCfg(
+        func=mt.bad_anchor_pos_z_only,
+        params={"command_name": "motion", "threshold": 0.15},
+    )
+    term = mt.bad_anchor_pos_z_only(cfg, env)
+
+    out = term(env, "motion", threshold=0.15)
+    expected = (
+        np.abs(
+            snapshots["body_pos_w"][:, command.anchor_body_idx, 2]
+            - snapshots["robot_anchor_pos_w"][:, 2]
+        )
+        > 0.15
+    )
+    np.testing.assert_array_equal(out, expected)
+
+    out2 = term(env, "motion", threshold=0.3)
+    assert out2 is out
+    expected2 = (
+        np.abs(
+            snapshots["body_pos_w"][:, command.anchor_body_idx, 2]
+            - snapshots["robot_anchor_pos_w"][:, 2]
+        )
+        > 0.3
+    )
+    np.testing.assert_array_equal(out2, expected2)
+    np.testing.assert_array_equal(command.body_pos_w, snapshots["body_pos_w"])
+
+
+@pytest.mark.parametrize(
+    ("term_type", "reference_name", "actual_name", "std", "orientation"),
+    [
+        (
+            mt.motion_relative_body_position_error_exp,
+            "body_pos_relative_w",
+            "robot_body_pos_w",
+            0.3,
+            False,
+        ),
+        (
+            mt.motion_relative_body_orientation_error_exp,
+            "body_quat_relative_w",
+            "robot_body_quat_w",
+            0.4,
+            True,
+        ),
+        (
+            mt.motion_global_body_linear_velocity_error_exp,
+            "body_lin_vel_w",
+            "robot_body_lin_vel_w",
+            1.0,
+            False,
+        ),
+        (
+            mt.motion_global_body_angular_velocity_error_exp,
+            "body_ang_vel_w",
+            "robot_body_ang_vel_w",
+            3.14,
+            False,
+        ),
+    ],
+)
+def test_numba_body_rewards_match_numpy_and_reuse_output(
+    body_setup,
+    term_type,
+    reference_name: str,
+    actual_name: str,
+    std: float,
+    orientation: bool,
+) -> None:
+    command, env, snapshots = body_setup
+    term = term_type(_reward_cfg(), env)
+
+    out = term(env, "motion", std=std)
+    expected = _expected_body_reward(
+        snapshots[reference_name],
+        snapshots[actual_name],
+        slice(None),
+        std,
+        orientation=orientation,
+    )
+    np.testing.assert_allclose(out, expected, rtol=2e-6, atol=2e-7)
+    assert out.dtype == snapshots[reference_name].dtype
+    first_result = out.copy()
+
+    second_std = std * 1.5
+    out2 = term(env, "motion", std=second_std)
+    assert out2 is out
+    expected2 = _expected_body_reward(
+        snapshots[reference_name],
+        snapshots[actual_name],
+        slice(None),
+        second_std,
+        orientation=orientation,
+    )
+    np.testing.assert_allclose(out2, expected2, rtol=2e-6, atol=2e-7)
+    assert np.any(first_result != out2)
+    np.testing.assert_array_equal(getattr(command, reference_name), snapshots[reference_name])
+    np.testing.assert_array_equal(getattr(command, actual_name), snapshots[actual_name])
+
+
+@pytest.mark.parametrize(
+    ("term_type", "reference_name", "actual_name", "std", "orientation"),
+    [
+        (
+            mt.motion_relative_body_position_error_exp,
+            "body_pos_relative_w",
+            "robot_body_pos_w",
+            0.3,
+            False,
+        ),
+        (
+            mt.motion_relative_body_orientation_error_exp,
+            "body_quat_relative_w",
+            "robot_body_quat_w",
+            0.4,
+            True,
+        ),
+        (
+            mt.motion_global_body_linear_velocity_error_exp,
+            "body_lin_vel_w",
+            "robot_body_lin_vel_w",
+            1.0,
+            False,
+        ),
+        (
+            mt.motion_global_body_angular_velocity_error_exp,
+            "body_ang_vel_w",
+            "robot_body_ang_vel_w",
+            3.14,
+            False,
+        ),
+    ],
+)
+def test_numba_body_rewards_preserve_body_subset_contract(
+    body_setup,
+    term_type,
+    reference_name: str,
+    actual_name: str,
+    std: float,
+    orientation: bool,
+) -> None:
+    command, env, snapshots = body_setup
+    body_names = ("b0", "b3", "b11")
+    term = term_type(_reward_cfg(body_names=body_names), env)
+
+    out = term(env, "motion", std=std, body_names=body_names)
+    expected = _expected_body_reward(
+        snapshots[reference_name],
+        snapshots[actual_name],
+        [0, 3, 11],
+        std,
+        orientation=orientation,
+    )
+    np.testing.assert_allclose(out, expected, rtol=2e-6, atol=2e-7)
+    assert command.cfg.body_names == tuple(f"b{i}" for i in range(12))
+
+
+def test_motion_hot_kernels_compile_parallel_on_term_construction(body_setup) -> None:
+    _, env, _ = body_setup
+    mt.bad_anchor_pos_z_only(
+        TerminationTermCfg(
+            func=mt.bad_anchor_pos_z_only,
+            params={"command_name": "motion", "threshold": 0.15},
+        ),
+        env,
+    )
+    for term_type in (
+        mt.motion_relative_body_position_error_exp,
+        mt.motion_relative_body_orientation_error_exp,
+        mt.motion_global_body_linear_velocity_error_exp,
+        mt.motion_global_body_angular_velocity_error_exp,
+    ):
+        term_type(_reward_cfg(), env)
+
+    dispatchers = (
+        kernels.termination_anchor_pos_kernel,
+        kernels.reward_motion_body_pos_kernel,
+        kernels.reward_motion_body_ori_kernel,
+        kernels.reward_motion_body_lin_vel_kernel,
+        kernels.reward_motion_body_ang_vel_kernel,
+    )
+    for dispatcher in dispatchers:
+        assert dispatcher.targetoptions["nopython"] is True
+        assert dispatcher.targetoptions["nogil"] is True
+        assert dispatcher.targetoptions["parallel"] is True
+        assert dispatcher.signatures
+    if "NUMBA_THREADING_LAYER" not in os.environ:
+        assert threading_layer() == "workqueue"
+    if "NUMBA_NUM_THREADS" not in os.environ:
+        assert get_num_threads() == min(8, config.NUMBA_DEFAULT_NUM_THREADS)
+
+
+def test_motion_metrics_kernel_matches_numpy_and_scopes_rows() -> None:
+    rng = np.random.default_rng(1701)
+    num_envs, num_bodies, num_joints = 257, 12, 29
+    anchor_body_idx = 4
+    motion_pos = rng.standard_normal((num_envs, num_bodies, 3), dtype=np.float32)
+    robot_pos = rng.standard_normal((num_envs, num_bodies, 3), dtype=np.float32)
+    relative_pos = rng.standard_normal((num_envs, num_bodies, 3), dtype=np.float32)
+    motion_quat = _unit_quat(rng.standard_normal((num_envs, num_bodies, 4), dtype=np.float32))
+    robot_quat = _unit_quat(rng.standard_normal((num_envs, num_bodies, 4), dtype=np.float32))
+    relative_quat = _unit_quat(rng.standard_normal((num_envs, num_bodies, 4), dtype=np.float32))
+    motion_lin = rng.standard_normal((num_envs, num_bodies, 3), dtype=np.float32)
+    robot_lin = rng.standard_normal((num_envs, num_bodies, 3), dtype=np.float32)
+    motion_ang = rng.standard_normal((num_envs, num_bodies, 3), dtype=np.float32)
+    robot_ang = rng.standard_normal((num_envs, num_bodies, 3), dtype=np.float32)
+    motion_joint_pos = rng.standard_normal((num_envs, num_joints), dtype=np.float32)
+    robot_joint_pos = rng.standard_normal((num_envs, num_joints), dtype=np.float32)
+    motion_joint_vel = rng.standard_normal((num_envs, num_joints), dtype=np.float32)
+    robot_joint_vel = rng.standard_normal((num_envs, num_joints), dtype=np.float32)
+    inputs = (
+        motion_pos,
+        robot_pos,
+        motion_quat,
+        robot_quat,
+        motion_lin,
+        robot_lin,
+        motion_ang,
+        robot_ang,
+        relative_pos,
+        relative_quat,
+        motion_joint_pos,
+        robot_joint_pos,
+        motion_joint_vel,
+        robot_joint_vel,
+    )
+    snapshots = tuple(value.copy() for value in inputs)
+    expected = (
+        np.linalg.norm(motion_pos[:, anchor_body_idx] - robot_pos[:, anchor_body_idx], axis=-1),
+        np.sqrt(
+            np_quat_error_magnitude_squared_batched(
+                motion_quat[:, anchor_body_idx], robot_quat[:, anchor_body_idx]
+            )
+        ),
+        np.linalg.norm(motion_lin[:, anchor_body_idx] - robot_lin[:, anchor_body_idx], axis=-1),
+        np.linalg.norm(motion_ang[:, anchor_body_idx] - robot_ang[:, anchor_body_idx], axis=-1),
+        np.linalg.norm(relative_pos - robot_pos, axis=-1).mean(axis=-1),
+        np.sqrt(np_quat_error_magnitude_squared_batched(relative_quat, robot_quat)).mean(axis=-1),
+        np.linalg.norm(motion_lin - robot_lin, axis=-1).mean(axis=-1),
+        np.linalg.norm(motion_ang - robot_ang, axis=-1).mean(axis=-1),
+        np.linalg.norm(motion_joint_pos - robot_joint_pos, axis=-1),
+        np.linalg.norm(motion_joint_vel - robot_joint_vel, axis=-1),
+    )
+    outputs = tuple(np.full(num_envs, -123.0, dtype=np.float32) for _ in expected)
+
+    def run(rows: np.ndarray) -> None:
+        kernels.update_motion_metrics_kernel(
+            rows,
+            anchor_body_idx,
+            motion_pos,
+            robot_pos,
+            motion_quat,
+            robot_quat,
+            motion_lin,
+            robot_lin,
+            motion_ang,
+            robot_ang,
+            relative_pos,
+            relative_quat,
+            motion_joint_pos,
+            robot_joint_pos,
+            motion_joint_vel,
+            robot_joint_vel,
+            *outputs,
+        )
+
+    selected = np.asarray([0, 3, 128, 256], dtype=np.int32)
+    run(selected)
+    untouched = np.ones(num_envs, dtype=bool)
+    untouched[selected] = False
+    for actual, reference in zip(outputs, expected, strict=True):
+        np.testing.assert_allclose(actual[selected], reference[selected], rtol=2e-6, atol=1e-5)
+        np.testing.assert_array_equal(actual[untouched], -123.0)
+
+    run(np.arange(num_envs, dtype=np.int32))
+    for actual, reference in zip(outputs, expected, strict=True):
+        np.testing.assert_allclose(actual, reference, rtol=2e-6, atol=1e-5)
+    for actual, snapshot in zip(inputs, snapshots, strict=True):
+        np.testing.assert_array_equal(actual, snapshot)
+    assert kernels.update_motion_metrics_kernel.targetoptions["nopython"] is True
+    assert kernels.update_motion_metrics_kernel.targetoptions["nogil"] is True
+    assert kernels.update_motion_metrics_kernel.targetoptions["parallel"] is True
+    assert kernels.update_motion_metrics_kernel.signatures
+
+
+def test_motion_relative_state_kernel_matches_numpy_and_scopes_rows() -> None:
+    rng = np.random.default_rng(1818)
+    num_envs, num_bodies = 257, 12
+    anchor_body_idx = 4
+    motion_pos_local = rng.standard_normal((num_envs, num_bodies, 3), dtype=np.float32)
+    env_origins = rng.standard_normal((num_envs, 1, 3), dtype=np.float32)
+    motion_pos_world = motion_pos_local + env_origins
+    motion_quat = _unit_quat(rng.standard_normal((num_envs, num_bodies, 4), dtype=np.float32))
+    robot_pos = rng.standard_normal((num_envs, num_bodies, 3), dtype=np.float32)
+    robot_quat = _unit_quat(rng.standard_normal((num_envs, num_bodies, 4), dtype=np.float32))
+    inputs = (motion_pos_local, motion_pos_world, motion_quat, robot_pos, robot_quat)
+    snapshots = tuple(value.copy() for value in inputs)
+
+    motion_anchor_pos_local = motion_pos_local[:, anchor_body_idx]
+    motion_anchor_quat = motion_quat[:, anchor_body_idx]
+    robot_anchor_pos = robot_pos[:, anchor_body_idx]
+    robot_anchor_quat = robot_quat[:, anchor_body_idx]
+    delta_pos = robot_anchor_pos.copy()
+    delta_pos[:, 2] = motion_anchor_pos_local[:, 2]
+    delta_quat = np_yaw_quat(
+        np_quat_mul_batched(robot_anchor_quat, np_quat_inv(motion_anchor_quat))
+    )
+    expected_body_pos_relative = np_quat_apply_batched(
+        delta_quat[:, None],
+        motion_pos_local - motion_anchor_pos_local[:, None],
+    )
+    expected_body_pos_relative += delta_pos[:, None]
+    expected_body_quat_relative = np_quat_mul_batched(delta_quat[:, None], motion_quat)
+    expected_motion_anchor_pos = np_quat_apply_inverse_batched(
+        robot_anchor_quat,
+        motion_pos_world[:, anchor_body_idx] - robot_anchor_pos,
+    )
+    expected_motion_anchor_ori = np_matrix_first_two_cols_from_quat(
+        np_quat_mul_batched(np_quat_inv(robot_anchor_quat), motion_anchor_quat)
+    )
+    expected_robot_body_pos = np_quat_apply_inverse_batched(
+        robot_anchor_quat[:, None],
+        robot_pos - robot_anchor_pos[:, None],
+    )
+    expected_robot_body_ori = np_matrix_first_two_cols_from_quat(
+        np_quat_mul_batched(np_quat_inv(robot_anchor_quat)[:, None], robot_quat)
+    )
+    expected = (
+        expected_body_pos_relative,
+        expected_body_quat_relative,
+        expected_motion_anchor_pos,
+        expected_motion_anchor_ori,
+        expected_robot_body_pos,
+        expected_robot_body_ori,
+    )
+    outputs = tuple(np.full(value.shape, -123.0, dtype=np.float32) for value in expected)
+    output_addresses = tuple(value.ctypes.data for value in outputs)
+
+    def run(rows: np.ndarray) -> None:
+        kernels.update_motion_relative_state_kernel(
+            rows,
+            anchor_body_idx,
+            motion_pos_local,
+            motion_pos_world,
+            motion_quat,
+            robot_pos,
+            robot_quat,
+            *outputs,
+        )
+
+    selected = np.asarray([0, 3, 128, 256], dtype=np.int32)
+    run(selected)
+    untouched = np.ones(num_envs, dtype=bool)
+    untouched[selected] = False
+    for actual, reference in zip(outputs, expected, strict=True):
+        np.testing.assert_allclose(actual[selected], reference[selected], rtol=3e-6, atol=2e-6)
+        np.testing.assert_array_equal(actual[untouched], -123.0)
+
+    run(np.arange(num_envs, dtype=np.int32))
+    for actual, reference in zip(outputs, expected, strict=True):
+        np.testing.assert_allclose(actual, reference, rtol=3e-6, atol=2e-6)
+    for actual, snapshot in zip(inputs, snapshots, strict=True):
+        np.testing.assert_array_equal(actual, snapshot)
+    assert tuple(value.ctypes.data for value in outputs) == output_addresses
+    assert kernels.update_motion_relative_state_kernel.targetoptions["nopython"] is True
+    assert kernels.update_motion_relative_state_kernel.targetoptions["nogil"] is True
+    assert kernels.update_motion_relative_state_kernel.targetoptions["parallel"] is True
+    assert kernels.update_motion_relative_state_kernel.signatures
+
+
+def test_object_relative_state_kernel_matches_numpy_and_scopes_rows() -> None:
+    rng = np.random.default_rng(1819)
+    num_envs = 257
+    anchor_pos = rng.standard_normal((num_envs, 3), dtype=np.float32)
+    anchor_quat = _unit_quat(rng.standard_normal((num_envs, 4), dtype=np.float32))
+    object_pos = rng.standard_normal((num_envs, 3), dtype=np.float32)
+    object_quat = _unit_quat(rng.standard_normal((num_envs, 4), dtype=np.float32))
+    object_lin_vel = rng.standard_normal((num_envs, 3), dtype=np.float32)
+    inputs = (anchor_pos, anchor_quat, object_pos, object_quat, object_lin_vel)
+    snapshots = tuple(value.copy() for value in inputs)
+    expected = np.concatenate(
+        (
+            np_quat_apply_inverse_batched(anchor_quat, object_pos - anchor_pos),
+            np_matrix_first_two_cols_from_quat(
+                np_quat_mul_batched(np_quat_inv(anchor_quat), object_quat)
+            ),
+            np_quat_apply_inverse_batched(anchor_quat, object_lin_vel),
+        ),
+        axis=-1,
+    )
+    output = np.full(expected.shape, -123.0, dtype=np.float32)
+    output_address = output.ctypes.data
+
+    selected = np.asarray([0, 3, 128, 256], dtype=np.int32)
+    kernels.update_object_relative_state_kernel(
+        selected,
+        anchor_pos,
+        anchor_quat,
+        object_pos,
+        object_quat,
+        object_lin_vel,
+        output,
+    )
+    untouched = np.ones(num_envs, dtype=bool)
+    untouched[selected] = False
+    np.testing.assert_allclose(output[selected], expected[selected], rtol=3e-6, atol=2e-6)
+    np.testing.assert_array_equal(output[untouched], -123.0)
+
+    kernels.update_object_relative_state_kernel(
+        np.arange(num_envs, dtype=np.int32),
+        anchor_pos,
+        anchor_quat,
+        object_pos,
+        object_quat,
+        object_lin_vel,
+        output,
+    )
+    np.testing.assert_allclose(output, expected, rtol=3e-6, atol=2e-6)
+    for actual, snapshot in zip(inputs, snapshots, strict=True):
+        np.testing.assert_array_equal(actual, snapshot)
+    assert output.ctypes.data == output_address
+    assert kernels.update_object_relative_state_kernel.targetoptions["nopython"] is True
+    assert kernels.update_object_relative_state_kernel.targetoptions["nogil"] is True
+    assert kernels.update_object_relative_state_kernel.targetoptions["parallel"] is True
+    assert kernels.update_object_relative_state_kernel.signatures
+
+
+def test_joint_pos_limits_bit_parity() -> None:
+    rng = np.random.default_rng(123)
+    joint_pos = rng.standard_normal((8, 5), dtype=np.float32)
+    limits = np.asarray([[-1.0, 1.0]] * 5, dtype=np.float32)
+    asset = SimpleNamespace(data=SimpleNamespace(joint_pos=joint_pos, soft_joint_pos_limits=limits))
+    env = SimpleNamespace(scene={"robot": asset})
+    asset_cfg = SimpleNamespace(name="robot", joint_ids=np.array([4, 2, 0], dtype=np.intp))
+
+    out = mt.joint_pos_limits(env, asset_cfg)
+    selected = joint_pos[:, [4, 2, 0]]
+    selected_limits = limits[[4, 2, 0]]
+    expected = np.sum(
+        np.square(
+            np.maximum(selected_limits[:, 0] - selected, 0.0)
+            + np.maximum(selected - selected_limits[:, 1], 0.0)
+        ),
+        axis=-1,
+    )
+    np.testing.assert_array_equal(out, expected)
diff --git a/tests/tasks/test_package_boundary.py b/tests/tasks/test_package_boundary.py
new file mode 100644
index 000000000..38cfb78b1
--- /dev/null
+++ b/tests/tasks/test_package_boundary.py
@@ -0,0 +1,64 @@
+"""Task bootstrap and package dependency boundary tests."""
+
+from __future__ import annotations
+
+import ast
+from pathlib import Path
+
+from unilab.base import registry
+from unilab.tasks import __unilab_registry_modules__
+
+_REPO_ROOT = Path(__file__).resolve().parents[2]
+_ENV_PACKAGE = _REPO_ROOT / "src" / "unilab" / "envs"
+_CONCRETE_TASK_PACKAGES = ("locomotion", "manipulation", "motion_tracking")
+
+_TASK_REGISTRY_MODULES = (
+    "unilab.tasks.locomotion.go1",
+    "unilab.tasks.locomotion.go2",
+    "unilab.tasks.locomotion.go2w",
+    "unilab.tasks.locomotion.g1",
+    "unilab.tasks.locomotion.go2_arm",
+    "unilab.tasks.locomotion.a2",
+    "unilab.tasks.manipulation.allegro_inhand",
+    "unilab.tasks.manipulation.sharpa_inhand",
+    "unilab.tasks.manipulation.stewart",
+    "unilab.tasks.motion_tracking.g1",
+    "unilab.tasks.motion_tracking.x2",
+)
+
+
+def _imports(path: Path) -> set[str]:
+    tree = ast.parse(path.read_text(encoding="utf-8"), filename=str(path))
+    modules: set[str] = set()
+    for node in ast.walk(tree):
+        if isinstance(node, ast.ImportFrom) and node.module:
+            modules.add(node.module)
+        elif isinstance(node, ast.Import):
+            modules.update(alias.name for alias in node.names)
+    return modules
+
+
+def test_tasks_is_the_only_default_registry_bootstrap() -> None:
+    assert registry._DEFAULT_REGISTRY_PACKAGES == ("unilab.tasks",)
+    assert __unilab_registry_modules__ == _TASK_REGISTRY_MODULES
+
+
+def test_env_runtime_does_not_depend_on_tasks() -> None:
+    violations = [
+        (path.relative_to(_REPO_ROOT).as_posix(), module)
+        for path in sorted(_ENV_PACKAGE.rglob("*.py"))
+        for module in sorted(_imports(path))
+        if module == "unilab.tasks" or module.startswith("unilab.tasks.")
+    ]
+
+    assert violations == [], "unilab.envs must not import concrete unilab.tasks modules"
+
+
+def test_env_runtime_does_not_own_concrete_task_packages() -> None:
+    violations = [
+        path.relative_to(_REPO_ROOT).as_posix()
+        for package in _CONCRETE_TASK_PACKAGES
+        for path in sorted((_ENV_PACKAGE / package).rglob("*.py"))
+    ]
+
+    assert violations == [], "concrete task source must be owned by unilab.tasks"
diff --git a/tests/tasks/test_production_registry_closeout.py b/tests/tasks/test_production_registry_closeout.py
new file mode 100644
index 000000000..20d65bcc0
--- /dev/null
+++ b/tests/tasks/test_production_registry_closeout.py
@@ -0,0 +1,151 @@
+"""Repository boundary tests for the production Manager-Based registry.
+
+Pin the post-migration production registry so no legacy fallback or dual
+registration can come back:
+
+- the production registry matches the declared production task matrix,
+- only the three approved families use the frozen LegacyFactoryAdapter seam,
+- every other registered factory is one of the canonical manager-runtime
+  callables (generic factory plus the three maintainer-approved wrappers),
+
+Scope note: the registry has no unregister API and no provenance tracking, and
+the pytest session pollutes it with fixture-only envs (``DummyFlatTest`` via
+``UNILAB_EXTRA_REGISTRY_PACKAGES``, the cartpole fixtures reusing
+``ManagerBasedRlEnvCfg``/``make_manager_based_rl_env``). The registry snapshot
+is therefore taken in a fresh subprocess with that env var scrubbed
+(``tests/base/test_backend_imports.py`` idiom), so only the production
+``unilab.tasks`` bootstrap contributes registrations.
+"""
+
+from __future__ import annotations
+
+import json
+import os
+import subprocess
+import sys
+import textwrap
+
+from unilab.tasks.migration_matrix import PRODUCTION_TASK_NAMES, migration_record
+
+ADAPTER_FACTORY = ("unilab.tasks.compatibility", "LegacyFactoryAdapter")
+
+CANONICAL_MANAGER_RUNTIME_FACTORIES = (
+    ("unilab.envs.manager_based_rl_env", "make_manager_based_rl_env"),
+    # Approved wrapper: G1WalkManagerBasedEnv subclass owning the G1 walk
+    # manager-based production runtime.
+    ("unilab.tasks.locomotion.g1.manager_terms", "make_g1_walk_env"),
+    # Approved wrapper: cold-path untracked X2 mesh resolution before
+    # delegating to the generic factory.
+    ("unilab.tasks.motion_tracking.x2", "make_x2_wall_flip_env"),
+)
+
+_SNAPSHOT_CODE = textwrap.dedent(
+    """
+    import json
+
+    from unilab.base import registry
+
+    registry.ensure_registries()
+    snapshot = {
+        name: {
+            backend: [
+                getattr(factory, "__module__", None) or type(factory).__module__,
+                getattr(factory, "__qualname__", None) or type(factory).__qualname__,
+            ]
+            for backend, factory in meta.env_factory_dict.items()
+        }
+        for name, meta in registry._envs.items()
+    }
+    print(json.dumps(snapshot))
+    """
+)
+
+_snapshot_cache: dict[str, dict[str, tuple[str, str]]] | None = None
+
+
+def _production_factories() -> dict[str, dict[str, tuple[str, str]]]:
+    """Snapshot the production registry in a clean interpreter.
+
+    Returns ``{task: {backend: (factory_module, factory_qualname)}}``. The
+    subprocess scrubs ``UNILAB_EXTRA_REGISTRY_PACKAGES`` so fixture-only test
+    envs injected by ``tests/conftest.py`` cannot leak into the snapshot.
+    """
+    global _snapshot_cache
+    if _snapshot_cache is None:
+        env = {
+            key: value
+            for key, value in os.environ.items()
+            if key != "UNILAB_EXTRA_REGISTRY_PACKAGES"
+        }
+        result = subprocess.run(
+            [sys.executable, "-c", _SNAPSHOT_CODE],
+            check=True,
+            capture_output=True,
+            text=True,
+            env=env,
+        )
+        raw = json.loads(result.stdout.strip().splitlines()[-1])
+        _snapshot_cache = {
+            name: {backend: tuple(factory) for backend, factory in backends.items()}
+            for name, backends in raw.items()
+        }
+    return _snapshot_cache
+
+
+def test_production_registry_matches_declared_task_matrix() -> None:
+    factories = _production_factories()
+
+    assert set(factories) == set(PRODUCTION_TASK_NAMES), (
+        "production registry must match the declared production task matrix: "
+        f"missing={sorted(set(PRODUCTION_TASK_NAMES) - set(factories))}, "
+        f"stray={sorted(set(factories) - set(PRODUCTION_TASK_NAMES))}"
+    )
+    empty = sorted(name for name, backends in factories.items() if not backends)
+    assert empty == [], f"registered tasks without any backend: {empty}"
+
+
+def test_only_approved_families_use_the_frozen_compatibility_seam() -> None:
+    factories = _production_factories()
+    approved_compatibility_tasks = {
+        task_name
+        for task_name in PRODUCTION_TASK_NAMES
+        if migration_record(task_name).target == "compatibility"
+    }
+    adapter_tasks = {
+        task_name
+        for task_name, backends in factories.items()
+        if any(factory == ADAPTER_FACTORY for factory in backends.values())
+    }
+
+    assert adapter_tasks == approved_compatibility_tasks, (
+        "LegacyFactoryAdapter is a frozen seam: only compatibility-recorded tasks may use it, "
+        f"unexpected={sorted(adapter_tasks - approved_compatibility_tasks)}, "
+        f"missing={sorted(approved_compatibility_tasks - adapter_tasks)}"
+    )
+
+    offenders = [
+        f"{task_name}/{backend_type}: {factory[1]}"
+        for task_name in sorted(approved_compatibility_tasks)
+        for backend_type, factory in factories[task_name].items()
+        if factory != ADAPTER_FACTORY
+    ]
+    assert offenders == [], (
+        "approved compatibility families must route every backend through "
+        f"LegacyFactoryAdapter: {offenders}"
+    )
+
+
+def test_all_other_factories_are_the_canonical_manager_runtime_factories() -> None:
+    factories = _production_factories()
+
+    offenders = [
+        f"{task_name}/{backend_type}: {factory[0]}.{factory[1]}"
+        for task_name, backends in sorted(factories.items())
+        for backend_type, factory in sorted(backends.items())
+        if factory != ADAPTER_FACTORY and factory not in CANONICAL_MANAGER_RUNTIME_FACTORIES
+    ]
+    assert offenders == [], (
+        "non-adapter factories must be one of the canonical manager-runtime "
+        f"factories {[qualname for _, qualname in CANONICAL_MANAGER_RUNTIME_FACTORIES]}: "
+        f"{offenders}"
+    )
diff --git a/tests/test_bones_seed_csv.py b/tests/test_bones_seed_csv.py
index cf13cf488..cda4b625f 100644
--- a/tests/test_bones_seed_csv.py
+++ b/tests/test_bones_seed_csv.py
@@ -8,9 +8,8 @@
 
 import numpy as np
 import pytest
-
-from unilab.tools import bones_seed_csv
-from unilab.tools.bones_seed_csv import (
+from scripts.motion import bones_seed_csv
+from scripts.motion.bones_seed_csv import (
     ROOT_COLUMNS,
     euler_deg_to_quat_wxyz,
     load_header,
diff --git a/tests/test_cli.py b/tests/test_cli.py
index 420935ee2..3ff387795 100644
--- a/tests/test_cli.py
+++ b/tests/test_cli.py
@@ -211,6 +211,391 @@ def test_eval_render_mode_generates_training_override(
     assert "algo.load_run=-1" in command
 
 
+def test_eval_mujoco_interactive_routes_to_dedicated_viewer(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    scripts_dir = tmp_path / "scripts"
+    scripts_dir.mkdir(parents=True)
+    (scripts_dir / "train_rsl_rl.py").write_text("", encoding="utf-8")
+    (scripts_dir / "play_interactive.py").write_text("", encoding="utf-8")
+    owner_dir = tmp_path / "conf" / "ppo" / "task" / "go2_joystick_flat"
+    owner_dir.mkdir(parents=True)
+    (owner_dir / "mujoco.yaml").write_text("training:\n  sim_backend: mujoco\n", encoding="utf-8")
+    monkeypatch.setattr(
+        cli,
+        "find_spec",
+        lambda name: ModuleSpec(name, loader=None) if name == "mujoco" else None,
+    )
+
+    command = cli.build_command(
+        mode="eval",
+        algo="ppo",
+        task="go2_joystick_flat",
+        sim="mujoco",
+        overrides=[],
+        load_run="-1",
+        render_mode="interactive",
+        root=tmp_path,
+    )
+
+    assert command == [
+        sys.executable,
+        str(scripts_dir / "play_interactive.py"),
+        "--algo",
+        "ppo",
+        "--task",
+        "go2_joystick_flat",
+        "--sim",
+        "mujoco",
+        "training.play_render_mode=interactive",
+        "interactive.action_mode=policy",
+        "training.play_only=true",
+        "algo.load_run=-1",
+    ]
+    assert "task=go2_joystick_flat/mujoco" not in command
+
+
+def test_eval_mujoco_interactive_honors_profile_and_render_override(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    scripts_dir = tmp_path / "scripts"
+    scripts_dir.mkdir(parents=True)
+    (scripts_dir / "train_td3.py").write_text("", encoding="utf-8")
+    (scripts_dir / "play_interactive.py").write_text("", encoding="utf-8")
+    owner_dir = tmp_path / "conf" / "td3" / "task" / "g1_walk_flat"
+    owner_dir.mkdir(parents=True)
+    (owner_dir / "mujoco_hora.yaml").write_text(
+        "training:\n  sim_backend: mujoco\n", encoding="utf-8"
+    )
+    monkeypatch.setattr(
+        cli,
+        "find_spec",
+        lambda name: ModuleSpec(name, loader=None) if name == "mujoco" else None,
+    )
+
+    command = cli.build_command(
+        mode="eval",
+        algo="td3",
+        task="g1_walk_flat",
+        sim="mujoco",
+        profile="hora",
+        overrides=["training.play_render_mode=interactive", "algo.num_envs=4096"],
+        load_run="run_1",
+        render_mode="record",
+        root=tmp_path,
+    )
+
+    assert command == [
+        sys.executable,
+        str(scripts_dir / "play_interactive.py"),
+        "--algo",
+        "td3",
+        "--task",
+        "g1_walk_flat",
+        "--sim",
+        "mujoco_hora",
+        "interactive.action_mode=policy",
+        "training.play_only=true",
+        "algo.load_run=run_1",
+        "training.play_render_mode=interactive",
+        "algo.num_envs=4096",
+    ]
+
+
+def test_eval_mujoco_interactive_preserves_explicit_action_mode(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    scripts_dir = tmp_path / "scripts"
+    scripts_dir.mkdir(parents=True)
+    (scripts_dir / "train_rsl_rl.py").write_text("", encoding="utf-8")
+    (scripts_dir / "play_interactive.py").write_text("", encoding="utf-8")
+    owner_dir = tmp_path / "conf" / "ppo" / "task" / "go2_joystick_flat"
+    owner_dir.mkdir(parents=True)
+    (owner_dir / "mujoco.yaml").write_text("training:\n  sim_backend: mujoco\n", encoding="utf-8")
+    monkeypatch.setattr(
+        cli,
+        "find_spec",
+        lambda name: ModuleSpec(name, loader=None) if name == "mujoco" else None,
+    )
+
+    command = cli.build_command(
+        mode="eval",
+        algo="ppo",
+        task="go2_joystick_flat",
+        sim="mujoco",
+        overrides=["interactive.action_mode=random"],
+        load_run="-1",
+        render_mode="interactive",
+        root=tmp_path,
+    )
+
+    assert "interactive.action_mode=policy" not in command
+    assert "interactive.action_mode=random" in command
+
+
+def test_eval_falls_back_to_sibling_owner_when_sim_owner_missing(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch, capsys: pytest.CaptureFixture[str]
+) -> None:
+    (tmp_path / "scripts").mkdir(parents=True)
+    (tmp_path / "scripts" / "train_rsl_rl.py").write_text("", encoding="utf-8")
+    owner_dir = tmp_path / "conf" / "ppo" / "task" / "go2_joystick_flat"
+    owner_dir.mkdir(parents=True)
+    (owner_dir / "mujoco.yaml").write_text("training:\n  sim_backend: mujoco\n", encoding="utf-8")
+    _pretend_motrix_is_installed(monkeypatch)
+    monkeypatch.setattr(cli.platform, "system", lambda: "Linux")
+
+    command = cli.build_command(
+        mode="eval",
+        algo="ppo",
+        task="go2_joystick_flat",
+        sim="motrix",
+        overrides=[],
+        load_run="-1",
+        root=tmp_path,
+    )
+
+    assert command[1:] == [
+        str(tmp_path / "scripts" / "train_rsl_rl.py"),
+        "task=go2_joystick_flat/mujoco",
+        "training.sim_backend=motrix",
+        "training.play_only=true",
+        "algo.load_run=-1",
+    ]
+    assert "reusing sibling owner 'mujoco'" in capsys.readouterr().err
+
+
+def test_train_still_requires_owner_yaml(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
+    (tmp_path / "scripts").mkdir(parents=True)
+    (tmp_path / "scripts" / "train_rsl_rl.py").write_text("", encoding="utf-8")
+    owner_dir = tmp_path / "conf" / "ppo" / "task" / "go2_joystick_flat"
+    owner_dir.mkdir(parents=True)
+    (owner_dir / "mujoco.yaml").write_text("training:\n  sim_backend: mujoco\n", encoding="utf-8")
+    _pretend_motrix_is_installed(monkeypatch)
+    monkeypatch.setattr(cli.platform, "system", lambda: "Linux")
+
+    with pytest.raises(SystemExit, match="No owner config exists"):
+        cli.build_command(
+            mode="train",
+            algo="ppo",
+            task="go2_joystick_flat",
+            sim="motrix",
+            overrides=[],
+            root=tmp_path,
+        )
+
+
+def test_eval_without_any_sibling_owner_fails(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    (tmp_path / "scripts").mkdir(parents=True)
+    (tmp_path / "scripts" / "train_rsl_rl.py").write_text("", encoding="utf-8")
+    (tmp_path / "conf" / "ppo" / "task" / "go2_joystick_flat").mkdir(parents=True)
+    _pretend_motrix_is_installed(monkeypatch)
+    monkeypatch.setattr(cli.platform, "system", lambda: "Linux")
+
+    with pytest.raises(SystemExit, match="no sibling backend owner config"):
+        cli.build_command(
+            mode="eval",
+            algo="ppo",
+            task="go2_joystick_flat",
+            sim="motrix",
+            overrides=[],
+            load_run="-1",
+            root=tmp_path,
+        )
+
+
+def test_eval_fallback_prefers_same_profile_owner(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    (tmp_path / "scripts").mkdir(parents=True)
+    (tmp_path / "scripts" / "train_rsl_rl.py").write_text("", encoding="utf-8")
+    owner_dir = tmp_path / "conf" / "ppo" / "task" / "sharpa_inhand"
+    owner_dir.mkdir(parents=True)
+    (owner_dir / "mujoco_hora.yaml").write_text(
+        "training:\n  sim_backend: mujoco\n", encoding="utf-8"
+    )
+    (owner_dir / "motrix.yaml").write_text("training:\n  sim_backend: motrix\n", encoding="utf-8")
+    _pretend_motrix_is_installed(monkeypatch)
+    monkeypatch.setattr(cli.platform, "system", lambda: "Linux")
+
+    command = cli.build_command(
+        mode="eval",
+        algo="ppo",
+        task="sharpa_inhand",
+        sim="motrix",
+        profile="hora",
+        overrides=[],
+        load_run="-1",
+        root=tmp_path,
+    )
+
+    assert "task=sharpa_inhand/mujoco_hora" in command
+    assert "training.sim_backend=motrix" in command
+
+
+def test_eval_fallback_without_same_profile_sibling_fails(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    (tmp_path / "scripts").mkdir(parents=True)
+    (tmp_path / "scripts" / "train_rsl_rl.py").write_text("", encoding="utf-8")
+    owner_dir = tmp_path / "conf" / "ppo" / "task" / "sharpa_inhand"
+    owner_dir.mkdir(parents=True)
+    (owner_dir / "mujoco.yaml").write_text("training:\n  sim_backend: mujoco\n", encoding="utf-8")
+    _pretend_motrix_is_installed(monkeypatch)
+    monkeypatch.setattr(cli.platform, "system", lambda: "Linux")
+
+    with pytest.raises(SystemExit, match="no sibling backend owner config"):
+        cli.build_command(
+            mode="eval",
+            algo="ppo",
+            task="sharpa_inhand",
+            sim="motrix",
+            profile="hora",
+            overrides=[],
+            load_run="-1",
+            root=tmp_path,
+        )
+
+
+def test_eval_mujoco_interactive_falls_back_to_sibling_owner(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    scripts_dir = tmp_path / "scripts"
+    scripts_dir.mkdir(parents=True)
+    (scripts_dir / "train_rsl_rl.py").write_text("", encoding="utf-8")
+    (scripts_dir / "play_interactive.py").write_text("", encoding="utf-8")
+    owner_dir = tmp_path / "conf" / "ppo" / "task" / "go2_joystick_flat"
+    owner_dir.mkdir(parents=True)
+    (owner_dir / "motrix.yaml").write_text("training:\n  sim_backend: motrix\n", encoding="utf-8")
+    monkeypatch.setattr(
+        cli,
+        "find_spec",
+        lambda name: ModuleSpec(name, loader=None) if name == "mujoco" else None,
+    )
+
+    command = cli.build_command(
+        mode="eval",
+        algo="ppo",
+        task="go2_joystick_flat",
+        sim="mujoco",
+        overrides=[],
+        load_run="-1",
+        render_mode="interactive",
+        root=tmp_path,
+    )
+
+    assert command[:8] == [
+        sys.executable,
+        str(scripts_dir / "play_interactive.py"),
+        "--algo",
+        "ppo",
+        "--task",
+        "go2_joystick_flat",
+        "--sim",
+        "motrix",
+    ]
+    assert "training.sim_backend=mujoco" in command
+    assert "training.play_only=true" in command
+
+
+def _pretend_mjwarp_is_installed(
+    monkeypatch: pytest.MonkeyPatch, *, with_mujoco: bool = True
+) -> None:
+    installed = {"mujoco_warp", "warp"} | ({"mujoco"} if with_mujoco else set())
+    monkeypatch.setattr(
+        cli,
+        "find_spec",
+        lambda name: ModuleSpec(name, loader=None) if name in installed else None,
+    )
+
+
+def _make_mjwarp_checkout(root: Path) -> Path:
+    scripts_dir = root / "scripts"
+    scripts_dir.mkdir(parents=True)
+    (scripts_dir / "train_rsl_rl.py").write_text("", encoding="utf-8")
+    (scripts_dir / "play_interactive.py").write_text("", encoding="utf-8")
+    owner_dir = root / "conf" / "ppo" / "task" / "g1_walk_flat"
+    owner_dir.mkdir(parents=True)
+    (owner_dir / "mjwarp.yaml").write_text("training:\n  sim_backend: mjwarp\n", encoding="utf-8")
+    return scripts_dir
+
+
+def test_eval_mjwarp_interactive_routes_to_dedicated_viewer(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    scripts_dir = _make_mjwarp_checkout(tmp_path)
+    _pretend_mjwarp_is_installed(monkeypatch)
+
+    command = cli.build_command(
+        mode="eval",
+        algo="ppo",
+        task="g1_walk_flat",
+        sim="mjwarp",
+        overrides=[],
+        load_run="-1",
+        render_mode="interactive",
+        root=tmp_path,
+    )
+
+    assert command == [
+        sys.executable,
+        str(scripts_dir / "play_interactive.py"),
+        "--algo",
+        "ppo",
+        "--task",
+        "g1_walk_flat",
+        "--sim",
+        "mjwarp",
+        "training.play_render_mode=interactive",
+        "interactive.action_mode=policy",
+        "training.play_only=true",
+        "algo.load_run=-1",
+    ]
+    assert "task=g1_walk_flat/mjwarp" not in command
+
+
+def test_eval_mjwarp_record_stays_on_train_script_route(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    scripts_dir = _make_mjwarp_checkout(tmp_path)
+    _pretend_mjwarp_is_installed(monkeypatch)
+
+    command = cli.build_command(
+        mode="eval",
+        algo="ppo",
+        task="g1_walk_flat",
+        sim="mjwarp",
+        overrides=[],
+        load_run="-1",
+        render_mode="record",
+        root=tmp_path,
+    )
+
+    assert command[:2] == [sys.executable, str(scripts_dir / "train_rsl_rl.py")]
+    assert "task=g1_walk_flat/mjwarp" in command
+    assert "training.play_render_mode=record" in command
+
+
+def test_eval_mjwarp_interactive_requires_mujoco_viewer_package(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    _make_mjwarp_checkout(tmp_path)
+    _pretend_mjwarp_is_installed(monkeypatch, with_mujoco=False)
+
+    with pytest.raises(SystemExit, match="MuJoCo"):
+        cli.build_command(
+            mode="eval",
+            algo="ppo",
+            task="g1_walk_flat",
+            sim="mjwarp",
+            overrides=[],
+            load_run="-1",
+            render_mode="interactive",
+            root=tmp_path,
+        )
+
+
 def test_macos_motrix_render_mode_none_does_not_require_mxpython(
     tmp_path: Path, monkeypatch: pytest.MonkeyPatch
 ) -> None:
@@ -260,10 +645,7 @@ def _make_demo_checkout(root: Path, *, demo_name: str) -> None:
     (root / "scripts").mkdir(parents=True, exist_ok=True)
     (root / "scripts" / "train_rsl_rl.py").write_text("", encoding="utf-8")
     (root / "scripts" / "play_interactive.py").write_text("", encoding="utf-8")
-    if spec.algo in {"sac", "flashsac"}:
-        owner_dir = root / "conf" / "offpolicy" / "task" / spec.algo / spec.task
-    else:
-        owner_dir = root / "conf" / spec.algo / "task" / spec.task
+    owner_dir = root / "conf" / spec.algo / "task" / spec.task
     owner_dir.mkdir(parents=True, exist_ok=True)
     (owner_dir / f"{spec.sim}.yaml").write_text(
         f"training:\n  sim_backend: {spec.sim}\n", encoding="utf-8"
@@ -382,10 +764,10 @@ def test_demo_play_interactive_hora_distill_nodr_command(tmp_path: Path) -> None
     ]
 
 
-def test_demo_play_interactive_sac_owner_path_uses_offpolicy(tmp_path: Path) -> None:
+def test_demo_play_interactive_sac_owner_path_uses_sac_tree(tmp_path: Path) -> None:
     (tmp_path / "scripts").mkdir(parents=True, exist_ok=True)
     (tmp_path / "scripts" / "play_interactive.py").write_text("", encoding="utf-8")
-    owner_dir = tmp_path / "conf" / "offpolicy" / "task" / "sac" / "sharpa_inhand"
+    owner_dir = tmp_path / "conf" / "sac" / "task" / "sharpa_inhand"
     owner_dir.mkdir(parents=True)
     (owner_dir / "mujoco_hora.yaml").write_text(
         "training:\n  sim_backend: mujoco\n", encoding="utf-8"
@@ -553,7 +935,8 @@ def test_demo_local_only_checkpoint_uses_existing_file(
     calls: list[list[str]] = []
 
     monkeypatch.setattr(demo, "ASSETS_ROOT_PATH", assets)
-    monkeypatch.setattr(demo, "_repo_root", lambda: checkout)
+    monkeypatch.setattr(demo, "_package_root", lambda: checkout)
+    monkeypatch.setattr(demo, "_dev_venv", lambda: checkout / ".venv")
     monkeypatch.setattr(demo.platform, "system", lambda: "Linux")
 
     def fail_resolve(_: str) -> str:
@@ -601,7 +984,7 @@ def test_demo_teaser_run_demo_invokes_render_teaser_main(
     def fake_render_teaser_main() -> None:
         called.append("rendered")
 
-    import unilab.tools.render_teaser as render_teaser_module
+    import unilab.visualization.teaser as render_teaser_module
 
     monkeypatch.setattr(render_teaser_module, "main", fake_render_teaser_main)
 
@@ -633,7 +1016,7 @@ def fake_run(command: list[str], *, check: bool, env: dict[str, str]) -> SimpleN
     def fail_render_teaser_main() -> None:
         raise AssertionError("macOS teaser must route through mxpython")
 
-    import unilab.tools.render_teaser as render_teaser_module
+    import unilab.visualization.teaser as render_teaser_module
 
     monkeypatch.setattr(render_teaser_module, "main", fail_render_teaser_main)
 
@@ -643,9 +1026,13 @@ def fail_render_teaser_main() -> None:
     command, env = calls[0]
     assert command == [
         "/tmp/unilab/.venv/bin/mxpython",
-        str(demo._repo_root() / "src" / "unilab" / "tools" / "render_teaser.py"),
+        str(demo._package_root() / "visualization" / "teaser.py"),
     ]
-    assert env["UV_PROJECT_ENVIRONMENT"] == str(demo._repo_root() / ".venv")
+    dev_venv = demo._dev_venv()
+    if dev_venv is not None:
+        assert env["UV_PROJECT_ENVIRONMENT"] == str(dev_venv)
+    else:
+        assert "UV_PROJECT_ENVIRONMENT" not in env
 
 
 def test_demo_main_teaser_dispatches_to_render_teaser(
@@ -657,9 +1044,216 @@ def test_demo_main_teaser_dispatches_to_render_teaser(
     def fake_render_teaser_main() -> None:
         called.append("rendered")
 
-    import unilab.tools.render_teaser as render_teaser_module
+    import unilab.visualization.teaser as render_teaser_module
 
     monkeypatch.setattr(render_teaser_module, "main", fake_render_teaser_main)
     rc = cli.demo_main(["teaser"])
     assert rc == 0
     assert called == ["rendered"]
+
+
+def _pretend_isaacgym_runtime(monkeypatch: pytest.MonkeyPatch, available: bool) -> None:
+    from unisim.backend.isaacgym import dependencies as isaacgym_deps
+
+    monkeypatch.setattr(isaacgym_deps, "isaacgym_runtime_available", lambda: available)
+
+
+def test_isaacgym_without_worker_runtime_exits_with_setup_hint(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    (tmp_path / "scripts").mkdir()
+    (tmp_path / "conf").mkdir()
+    _pretend_isaacgym_runtime(monkeypatch, available=False)
+
+    with pytest.raises(SystemExit, match="setup_isaacgym_env.sh"):
+        cli.build_command(
+            mode="train",
+            algo="sac",
+            task="g1_walk_flat",
+            sim="isaacgym",
+            overrides=[],
+            root=tmp_path,
+        )
+
+
+def test_isaacgym_train_builds_owner_route(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
+    (tmp_path / "scripts").mkdir(parents=True)
+    (tmp_path / "scripts" / "train_sac.py").write_text("", encoding="utf-8")
+    owner_dir = tmp_path / "conf" / "sac" / "task" / "g1_walk_flat"
+    owner_dir.mkdir(parents=True)
+    (owner_dir / "isaacgym.yaml").write_text(
+        "training:\n  sim_backend: isaacgym\n", encoding="utf-8"
+    )
+    _pretend_isaacgym_runtime(monkeypatch, available=True)
+    monkeypatch.setattr(cli.platform, "system", lambda: "Linux")
+
+    command = cli.build_command(
+        mode="train",
+        algo="sac",
+        task="g1_walk_flat",
+        sim="isaacgym",
+        overrides=["algo.num_envs=64"],
+        root=tmp_path,
+    )
+
+    assert command[1:] == [
+        str(tmp_path / "scripts" / "train_sac.py"),
+        "task=g1_walk_flat/isaacgym",
+        "algo.num_envs=64",
+    ]
+
+
+def _pretend_genesis_runtime(monkeypatch: pytest.MonkeyPatch, available: bool) -> None:
+    from unisim.backend.genesis import dependencies as genesis_deps
+
+    monkeypatch.setattr(genesis_deps, "genesis_dependencies_available", lambda: available)
+
+
+def test_genesis_without_extra_exits_with_install_hint(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    (tmp_path / "scripts").mkdir()
+    (tmp_path / "conf").mkdir()
+    _pretend_genesis_runtime(monkeypatch, available=False)
+
+    with pytest.raises(SystemExit, match="uv sync --extra genesis"):
+        cli.build_command(
+            mode="train",
+            algo="ppo",
+            task="g1_walk_flat",
+            sim="genesis",
+            overrides=[],
+            root=tmp_path,
+        )
+
+
+def test_genesis_train_builds_owner_route(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
+    (tmp_path / "scripts").mkdir(parents=True)
+    (tmp_path / "scripts" / "train_rsl_rl.py").write_text("", encoding="utf-8")
+    owner_dir = tmp_path / "conf" / "ppo" / "task" / "g1_walk_flat"
+    owner_dir.mkdir(parents=True)
+    (owner_dir / "genesis.yaml").write_text("training:\n  sim_backend: genesis\n", encoding="utf-8")
+    _pretend_genesis_runtime(monkeypatch, available=True)
+    monkeypatch.setattr(cli.platform, "system", lambda: "Linux")
+
+    command = cli.build_command(
+        mode="train",
+        algo="ppo",
+        task="g1_walk_flat",
+        sim="genesis",
+        overrides=["algo.num_envs=64"],
+        root=tmp_path,
+    )
+
+    assert command[1:] == [
+        str(tmp_path / "scripts" / "train_rsl_rl.py"),
+        "task=g1_walk_flat/genesis",
+        "algo.num_envs=64",
+    ]
+
+
+def _make_custom_algo_checkout(
+    root: Path,
+    *,
+    algo: str = "dreamer",
+    task: str = "go2_joystick_flat",
+    with_script: bool = True,
+    with_config: bool = True,
+    with_owner: bool = True,
+) -> None:
+    (root / "scripts").mkdir(parents=True, exist_ok=True)
+    if with_script:
+        (root / "scripts" / f"train_{algo}.py").write_text("", encoding="utf-8")
+    if with_config:
+        (root / "conf" / algo).mkdir(parents=True, exist_ok=True)
+        (root / "conf" / algo / "config.yaml").write_text("algo: {}\n", encoding="utf-8")
+    if with_owner:
+        owner_dir = root / "conf" / algo / "task" / task
+        owner_dir.mkdir(parents=True, exist_ok=True)
+        (owner_dir / "motrix.yaml").write_text(
+            "training:\n  sim_backend: motrix\n",
+            encoding="utf-8",
+        )
+
+
+def test_convention_routes_custom_algo_with_config_and_script(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    _make_custom_algo_checkout(tmp_path)
+    _pretend_motrix_is_installed(monkeypatch)
+    monkeypatch.setattr(cli.platform, "system", lambda: "Linux")
+
+    command = cli.build_command(
+        mode="train",
+        algo="dreamer",
+        task="go2_joystick_flat",
+        sim="motrix",
+        overrides=[],
+        root=tmp_path,
+    )
+
+    assert command[1:] == [
+        str(tmp_path / "scripts" / "train_dreamer.py"),
+        "task=go2_joystick_flat/motrix",
+    ]
+    assert "dreamer" in cli.available_algos(tmp_path)
+
+
+def test_convention_requires_entrypoint_script(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    _make_custom_algo_checkout(tmp_path, with_script=False)
+    _pretend_motrix_is_installed(monkeypatch)
+    monkeypatch.setattr(cli.platform, "system", lambda: "Linux")
+
+    with pytest.raises(SystemExit, match="Unsupported algo='dreamer'"):
+        cli.build_command(
+            mode="train",
+            algo="dreamer",
+            task="go2_joystick_flat",
+            sim="motrix",
+            overrides=[],
+            root=tmp_path,
+        )
+    assert "dreamer" not in cli.available_algos(tmp_path)
+
+
+def test_convention_requires_config_yaml(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
+    _make_custom_algo_checkout(tmp_path, with_config=False)
+    _pretend_motrix_is_installed(monkeypatch)
+    monkeypatch.setattr(cli.platform, "system", lambda: "Linux")
+
+    with pytest.raises(SystemExit, match="Unsupported algo='dreamer'"):
+        cli.build_command(
+            mode="train",
+            algo="dreamer",
+            task="go2_joystick_flat",
+            sim="motrix",
+            overrides=[],
+            root=tmp_path,
+        )
+
+
+def test_unknown_algo_error_lists_builtin_and_discovered_algos(tmp_path: Path) -> None:
+    _make_custom_algo_checkout(tmp_path)
+
+    with pytest.raises(SystemExit, match="choose one of") as excinfo:
+        cli.build_route("dqn", "go2_joystick_flat", "motrix", root=tmp_path)
+
+    message = str(excinfo.value)
+    assert "Unsupported algo='dqn'" in message
+    for builtin in ("ppo", "appo", "sac", "td3", "flashsac"):
+        assert builtin in message
+    assert "dreamer" in message
+
+
+def test_conf_tree_without_entrypoint_is_not_routable() -> None:
+    # hora_distill ships a conf tree (compose-only) but no train_hora_distill.py.
+    with pytest.raises(SystemExit, match="Unsupported algo='hora_distill'"):
+        cli.build_route("hora_distill", "sharpa_inhand", "mujoco")
+    assert "hora_distill" not in cli.available_algos()
+
+
+def test_malformed_algo_name_is_rejected_fail_closed(tmp_path: Path) -> None:
+    with pytest.raises(SystemExit, match="Unsupported algo"):
+        cli.build_route("../etc", "go2_joystick_flat", "motrix", root=tmp_path)
diff --git a/tests/test_cli_runtime_requirements.py b/tests/test_cli_runtime_requirements.py
index 77d174ff4..0a71cda7c 100644
--- a/tests/test_cli_runtime_requirements.py
+++ b/tests/test_cli_runtime_requirements.py
@@ -17,3 +17,10 @@ def test_check_runtime_requirements_requires_motrix_extra(monkeypatch: pytest.Mo
 
     with pytest.raises(SystemExit, match="sim=motrix requires the Motrix extra"):
         cli._check_runtime_requirements("ppo", "motrix")
+
+
+def test_check_runtime_requirements_requires_drake_extra(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setattr(cli, "find_spec", lambda name: None if name == "drake_uni" else object())
+
+    with pytest.raises(SystemExit, match="sim=drake requires the Drake extra"):
+        cli._check_runtime_requirements("ppo", "drake")
diff --git a/tests/test_completion.py b/tests/test_completion.py
index 26ccf2c25..38963646d 100644
--- a/tests/test_completion.py
+++ b/tests/test_completion.py
@@ -2,7 +2,7 @@
 
 from pathlib import Path
 
-from unilab.tools.completion import (
+from unilab.cli_completion import (
     COMPLETION_BLOCK_END,
     COMPLETION_BLOCK_START,
     build_metadata,
@@ -55,6 +55,11 @@ def _write_completion_fixture(root: Path) -> None:
   task_name: Go3
   sim_backend: mujoco
   log_root: custom_logs
+""",
+        root / "conf" / "sac" / "task" / "go4" / "mujoco.yaml": """
+training:
+  task_name: Go4
+  sim_backend: mujoco
 """,
     }
     for path, content in owner_files.items():
@@ -283,6 +288,17 @@ def test_task_completion_respects_selected_profile(tmp_path: Path) -> None:
     ) == ["go1"]
 
 
+def test_task_completion_covers_per_algo_offpolicy_trees(tmp_path: Path) -> None:
+    _write_completion_fixture(tmp_path)
+    metadata = build_metadata(tmp_path)
+
+    assert complete_words(
+        ["uv", "run", "train", "--algo", "sac", "--sim", "mujoco", "--task", ""],
+        8,
+        metadata,
+    ) == ["go4"]
+
+
 def test_demo_positional_completes_all_demo_names(tmp_path: Path) -> None:
     _write_completion_fixture(tmp_path)
     metadata = build_metadata(tmp_path)
diff --git a/tests/test_export_scene.py b/tests/test_export_scene.py
index 7691f65fe..044e3fd91 100644
--- a/tests/test_export_scene.py
+++ b/tests/test_export_scene.py
@@ -9,7 +9,7 @@
 
 pytest.importorskip("mujoco")
 
-from unilab.tools.export_scene import export_scene, main
+from unisim.backend.mujoco.export_scene import export_scene, main
 
 MINIMAL_XML = """\
 <mujoco>
diff --git a/tests/test_import_robot.py b/tests/test_import_robot.py
index 9aaa48337..afd8865cd 100644
--- a/tests/test_import_robot.py
+++ b/tests/test_import_robot.py
@@ -9,7 +9,7 @@
 
 
 def _load_script() -> Any:
-    return importlib.import_module("unilab.tools.import_robot")
+    return importlib.import_module("scripts.tools.import_robot")
 
 
 def test_robot_name_defaults_to_urdf_stem(tmp_path: Path) -> None:
diff --git a/tests/test_library_import_boundary.py b/tests/test_library_import_boundary.py
new file mode 100644
index 000000000..8e82257ab
--- /dev/null
+++ b/tests/test_library_import_boundary.py
@@ -0,0 +1,129 @@
+"""Library-layer import boundary tests (issue #1240, updated for #1480).
+
+The RL algorithm layer (``unilab.algos``), the async IPC layer
+(``unilab.ipc``), and the training-logging layer (``unilab.logging``) moved to
+the independently released uni_rl package. uni_rl's own purity (no
+``unilab``/``unisim`` imports) is enforced by that repo's smoke gate; here we
+assert the UniLab side of the boundary.
+"""
+
+from __future__ import annotations
+
+import ast
+from pathlib import Path
+
+import pytest
+
+_REPO_ROOT = Path(__file__).resolve().parents[1]
+_LIBRARY_PACKAGE = _REPO_ROOT / "src" / "unilab"
+
+# Symbols that moved to uni_rl.utils.* must not be re-defined anywhere in
+# src/unilab (importing them from uni_rl is the supported path).
+_MIGRATED_SYMBOLS = {
+    "split_obs_dict",
+    "get_obs_dims",
+    "get_critic_base_dim",
+    "TerminalObservationContract",
+    "resolve_terminal_observation_contract",
+}
+
+
+def _imports(path: Path) -> set[str]:
+    tree = ast.parse(path.read_text(encoding="utf-8"), filename=str(path))
+    modules: set[str] = set()
+    for node in ast.walk(tree):
+        if isinstance(node, ast.ImportFrom) and node.module:
+            modules.add(node.module)
+        elif isinstance(node, ast.Import):
+            modules.update(alias.name for alias in node.names)
+    return modules
+
+
+def test_library_does_not_import_scripts() -> None:
+    violations = [
+        (path.relative_to(_REPO_ROOT).as_posix(), module)
+        for path in sorted(_LIBRARY_PACKAGE.rglob("*.py"))
+        for module in sorted(_imports(path))
+        if module == "scripts" or module.startswith("scripts.")
+    ]
+
+    assert violations == [], "src/unilab must not import scripts/ modules"
+
+
+def test_migrated_layer_directories_are_gone() -> None:
+    leftovers = [name for name in ("algos", "ipc", "logging") if (_LIBRARY_PACKAGE / name).exists()]
+
+    assert leftovers == [], (
+        "RL algorithm/IPC/logging layers live in the uni_rl package now; "
+        f"src/unilab must not re-grow: {leftovers}"
+    )
+
+
+def test_no_module_redefines_migrated_symbols() -> None:
+    violations = []
+    for path in sorted(_LIBRARY_PACKAGE.rglob("*.py")):
+        tree = ast.parse(path.read_text(encoding="utf-8"), filename=str(path))
+        for node in ast.walk(tree):
+            if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef, ast.ClassDef)):
+                if node.name in _MIGRATED_SYMBOLS:
+                    violations.append((path.relative_to(_REPO_ROOT).as_posix(), node.name))
+
+    assert violations == [], (
+        "these symbols live in uni_rl.utils.*; import them from there instead "
+        f"of re-defining: {violations}"
+    )
+
+
+_UTILS_FORBIDDEN_LAYERS = (
+    "unilab.base",
+    "unilab.envs",
+    "unilab.tasks",
+    "unilab.managers",
+    "unilab.training",
+    "unilab.visualization",
+    "uni_rl",
+)
+
+
+def test_utils_does_not_import_higher_layers() -> None:
+    violations = [
+        (path.relative_to(_REPO_ROOT).as_posix(), module)
+        for path in sorted((_LIBRARY_PACKAGE / "utils").rglob("*.py"))
+        for module in sorted(_imports(path))
+        if any(
+            module == layer or module.startswith(f"{layer}.") for layer in _UTILS_FORBIDDEN_LAYERS
+        )
+    ]
+
+    assert violations == [], "src/unilab/utils must not import higher layers"
+
+
+_NUMPY_MDP_MODULES = (
+    "envs/mdp/events.py",
+    "envs/mdp/observations.py",
+    "envs/mdp/rewards.py",
+    "envs/mdp/terminations.py",
+    "envs/mdp/commands/velocity_command.py",
+)
+_NUMPY_MDP_FORBIDDEN_LAYERS = (
+    "torch",
+    "uni_rl",
+    "unilab.training",
+    "unilab.base.backend",
+)
+
+
+@pytest.mark.parametrize("relative_path", _NUMPY_MDP_MODULES)
+def test_numpy_mdp_modules_do_not_import_runtime_layers(relative_path: str) -> None:
+    path = _LIBRARY_PACKAGE / relative_path
+    imports = _imports(path)
+    violations = sorted(
+        module
+        for module in imports
+        if any(
+            module == forbidden or module.startswith(f"{forbidden}.")
+            for forbidden in _NUMPY_MDP_FORBIDDEN_LAYERS
+        )
+    )
+
+    assert violations == [], f"{relative_path} imports forbidden runtime layers: {violations}"
diff --git a/tests/test_nan_guard.py b/tests/test_nan_guard.py
deleted file mode 100644
index 2a491a20a..000000000
--- a/tests/test_nan_guard.py
+++ /dev/null
@@ -1,349 +0,0 @@
-"""Tests for NanGuard: env-layer NaN/Inf detection and state dumping."""
-
-from __future__ import annotations
-
-from pathlib import Path
-
-import numpy as np
-import pytest
-
-from unilab.tools.viz_nan import load_dump
-from unilab.utils.nan_guard import NanGuard, NanGuardCfg
-
-NUM_ENVS = 4
-OBS_DIM = 10
-
-
-def _make_clean_obs() -> dict[str, np.ndarray]:
-    return {"policy": np.zeros((NUM_ENVS, OBS_DIM), dtype=np.float32)}
-
-
-def _make_clean_reward() -> np.ndarray:
-    return np.zeros(NUM_ENVS, dtype=np.float32)
-
-
-# ── 1. disabled guard ──────────────────────────────────────────────────────
-
-
-def test_disabled_guard_returns_none():
-    cfg = NanGuardCfg(enabled=False)
-    guard = NanGuard(cfg, NUM_ENVS, supports_state_playback=True)
-    obs = _make_clean_obs()
-    obs["policy"][0, 0] = np.nan
-    assert guard.check(obs, _make_clean_reward()) is None
-
-
-# ── 2. detect NaN in obs ──────────────────────────────────────────────────
-
-
-def test_detect_nan_in_obs():
-    cfg = NanGuardCfg(enabled=True)
-    guard = NanGuard(cfg, NUM_ENVS, supports_state_playback=False)
-    obs = _make_clean_obs()
-    obs["policy"][1, 3] = np.nan
-    obs["policy"][3, 7] = np.nan
-    result = guard.check(obs, _make_clean_reward())
-    assert result is not None
-    np.testing.assert_array_equal(result, [1, 3])
-
-
-# ── 3. detect Inf in obs ─────────────────────────────────────────────────
-
-
-def test_detect_inf_in_obs():
-    cfg = NanGuardCfg(enabled=True)
-    guard = NanGuard(cfg, NUM_ENVS, supports_state_playback=False)
-    obs = _make_clean_obs()
-    obs["policy"][2, 0] = np.inf
-    result = guard.check(obs, _make_clean_reward())
-    assert result is not None
-    np.testing.assert_array_equal(result, [2])
-
-
-def test_detect_nan_in_secondary_obs_group():
-    cfg = NanGuardCfg(enabled=True)
-    guard = NanGuard(cfg, NUM_ENVS, supports_state_playback=False)
-    obs = _make_clean_obs()
-    obs["critic"] = np.zeros((NUM_ENVS, 3), dtype=np.float32)
-    obs["critic"][2, 1] = np.nan
-    result = guard.check(obs, _make_clean_reward())
-    assert result is not None
-    np.testing.assert_array_equal(result, [2])
-
-
-# ── 4. detect NaN/Inf in reward ──────────────────────────────────────────
-
-
-def test_detect_nan_in_reward():
-    cfg = NanGuardCfg(enabled=True)
-    guard = NanGuard(cfg, NUM_ENVS, supports_state_playback=False)
-    reward = _make_clean_reward()
-    reward[0] = np.nan
-    reward[2] = -np.inf
-    result = guard.check(_make_clean_obs(), reward)
-    assert result is not None
-    np.testing.assert_array_equal(result, [0, 2])
-
-
-# ── 5. rolling buffer capacity ───────────────────────────────────────────
-
-
-def test_rolling_buffer_capacity():
-    buf_size = 5
-    cfg = NanGuardCfg(enabled=True, buffer_size=buf_size)
-    guard = NanGuard(cfg, NUM_ENVS, supports_state_playback=True)
-    for i in range(buf_size + 3):
-        state = np.full((NUM_ENVS, 4), float(i), dtype=np.float32)
-        guard.capture(state)
-    assert len(guard._buffer) == buf_size
-
-
-def test_dump_preserves_rolling_buffer_order(tmp_path):
-    cfg = NanGuardCfg(enabled=True, buffer_size=3, output_dir=str(tmp_path))
-    guard = NanGuard(cfg, NUM_ENVS, supports_state_playback=True)
-    for i in range(5):
-        guard.capture(np.full((NUM_ENVS, 2), float(i), dtype=np.float32))
-    path = guard.dump(np.array([0], dtype=np.int32), model_file="", step=5)
-    assert path is not None
-    data = np.load(path, allow_pickle=True)
-    np.testing.assert_array_equal(data["states"][:, 0, 0], [2.0, 3.0, 4.0])
-
-
-# ── 6. dump output format ────────────────────────────────────────────────
-
-
-def test_dump_output_format(tmp_path):
-    cfg = NanGuardCfg(enabled=True, buffer_size=3, output_dir=str(tmp_path))
-    guard = NanGuard(cfg, NUM_ENVS, supports_state_playback=True)
-    for i in range(3):
-        guard.capture(np.ones((NUM_ENVS, 4), dtype=np.float32) * i)
-    nan_ids = np.array([0, 2], dtype=np.int32)
-    path = guard.dump(nan_ids, model_file="", step=42)
-    assert path is not None
-    data = np.load(path, allow_pickle=True)
-    assert "states" in data
-    assert data["states"].shape[0] == 3
-    assert int(data["meta_detection_step"]) == 42
-
-
-def test_dump_limits_state_envs_and_records_all_nan_ids(tmp_path):
-    cfg = NanGuardCfg(
-        enabled=True,
-        buffer_size=2,
-        max_envs_to_dump=2,
-        output_dir=str(tmp_path),
-    )
-    guard = NanGuard(cfg, NUM_ENVS, supports_state_playback=True)
-    state = np.arange(NUM_ENVS * 3, dtype=np.float32).reshape(NUM_ENVS, 3)
-    guard.capture(state)
-    nan_ids = np.array([0, 1, 2, 3], dtype=np.int32)
-    path = guard.dump(nan_ids, model_file="", step=7)
-    assert path is not None
-    data = np.load(path, allow_pickle=True)
-    assert data["states"].shape == (1, 2, 3)
-    np.testing.assert_array_equal(data["states"][0], state[[0, 1]])
-    np.testing.assert_array_equal(data["meta_nan_env_ids"], nan_ids)
-    np.testing.assert_array_equal(data["meta_dumped_env_ids"], [0, 1])
-
-
-def test_dump_copies_model_file_and_updates_latest_link(tmp_path):
-    model_file = tmp_path / "model.xml"
-    model_file.write_text("<mujoco/>")
-    cfg = NanGuardCfg(enabled=True, output_dir=str(tmp_path / "dumps"))
-    guard = NanGuard(cfg, NUM_ENVS, supports_state_playback=True)
-    guard.capture(np.zeros((NUM_ENVS, 2), dtype=np.float32))
-    path = guard.dump(np.array([0], dtype=np.int32), model_file=str(model_file), step=3)
-    assert path is not None
-    dump_path = Path(path)
-    copied_models = list(dump_path.parent.glob("*_model.xml"))
-    assert len(copied_models) == 1
-    assert copied_models[0].read_text() == "<mujoco/>"
-    latest_link = dump_path.parent / "nan_dump_latest.npz"
-    if latest_link.exists():
-        assert latest_link.resolve() == dump_path
-
-
-def test_load_dump_round_trips_states_and_metadata(tmp_path):
-    cfg = NanGuardCfg(enabled=True, buffer_size=1, output_dir=str(tmp_path))
-    guard = NanGuard(cfg, NUM_ENVS, supports_state_playback=True)
-    state = np.arange(NUM_ENVS * 2, dtype=np.float32).reshape(NUM_ENVS, 2)
-    guard.capture(state)
-    nan_ids = np.array([1, 3], dtype=np.int32)
-    path = guard.dump(nan_ids, model_file="", step=11)
-    assert path is not None
-    dump = load_dump(path)
-    np.testing.assert_array_equal(dump["states"], state[[1, 3]][None, ...])
-    assert dump["metadata"]["detection_step"] == 11
-    np.testing.assert_array_equal(dump["metadata"]["nan_env_ids"], nan_ids)
-
-
-# ── 7. dump only once ────────────────────────────────────────────────────
-
-
-def test_dump_only_once(tmp_path):
-    cfg = NanGuardCfg(enabled=True, output_dir=str(tmp_path))
-    guard = NanGuard(cfg, NUM_ENVS, supports_state_playback=False)
-    nan_ids = np.array([0], dtype=np.int32)
-    first = guard.dump(nan_ids, model_file="", step=1)
-    second = guard.dump(nan_ids, model_file="", step=2)
-    assert first is not None
-    assert second is None
-
-
-# ── 8. no physics state still detects ────────────────────────────────────
-
-
-def test_no_physics_state_still_detects(tmp_path):
-    cfg = NanGuardCfg(enabled=True, output_dir=str(tmp_path))
-    guard = NanGuard(cfg, NUM_ENVS, supports_state_playback=False)
-    guard.capture(None)
-    obs = _make_clean_obs()
-    obs["policy"][0, 0] = np.nan
-    result = guard.check(obs, _make_clean_reward())
-    assert result is not None
-    path = guard.dump(result, model_file="", step=10)
-    assert path is not None
-    data = np.load(path, allow_pickle=True)
-    assert data["states"].size == 0
-
-
-def test_check_warns_each_call_with_nan(caplog):
-    cfg = NanGuardCfg(enabled=True)
-    guard = NanGuard(cfg, num_envs=NUM_ENVS, supports_state_playback=False)
-    obs = _make_clean_obs()
-    obs["policy"][1, 0] = np.nan
-    reward = _make_clean_reward()
-    with caplog.at_level("WARNING", logger="unilab.utils.nan_guard"):
-        guard.check(obs, reward, step=10)
-        guard.check(obs, reward, step=11)
-    warnings = [r for r in caplog.records if r.levelname == "WARNING"]
-    assert len(warnings) == 2
-    assert "step 10" in warnings[0].getMessage()
-    assert "step 11" in warnings[1].getMessage()
-
-
-def test_check_ctrl_warns_each_call_with_nan(caplog):
-    cfg = NanGuardCfg(enabled=True)
-    guard = NanGuard(cfg, num_envs=NUM_ENVS, supports_state_playback=False)
-    ctrl = np.zeros((NUM_ENVS, 3), dtype=np.float64)
-    ctrl[2, 1] = np.nan
-    with caplog.at_level("WARNING", logger="unilab.utils.nan_guard"):
-        guard.check_ctrl(ctrl, step=5)
-        guard.check_ctrl(ctrl, step=6)
-    warnings = [r for r in caplog.records if r.levelname == "WARNING"]
-    assert len(warnings) == 2
-    assert all("ctrl" in w.getMessage() for w in warnings)
-
-
-def test_warning_includes_step_and_env_count(caplog):
-    cfg = NanGuardCfg(enabled=True)
-    guard = NanGuard(cfg, num_envs=NUM_ENVS, supports_state_playback=False)
-    obs = _make_clean_obs()
-    obs["policy"][0, 0] = np.nan
-    obs["policy"][2, 0] = np.nan
-    reward = _make_clean_reward()
-    with caplog.at_level("WARNING", logger="unilab.utils.nan_guard"):
-        guard.check(obs, reward, step=42)
-    msg = caplog.records[-1].getMessage()
-    assert "step 42" in msg
-    assert "envs=2" in msg
-    assert "sample_ids=" in msg
-
-
-# ---------------------------------------------------------------------------
-# Repro #1 (issue #594): NaN guard inside a real NpEnv.step loop warns
-# every step, dumps exactly once, and reward is sanitized at end of step.
-# ---------------------------------------------------------------------------
-
-
-def test_nan_guard_warns_on_every_step_and_dumps_once_real_env_loop(tmp_path, caplog):
-    """End-to-end: NaN-injected NpEnv steps 5x -> 5 warnings, 1 dump file.
-
-    Validates the env-layer NaN guard wiring stays correct across multiple
-    step calls (warns each call) while the dump (heavy I/O) only happens
-    once. Also confirms reward is sanitized after step despite NaN input.
-    """
-    from dataclasses import dataclass
-    from typing import Tuple
-    from unittest.mock import MagicMock
-
-    import gymnasium as gym
-
-    from unilab.base.base import EnvCfg
-    from unilab.base.np_env import NpEnv, NpEnvState
-
-    @dataclass
-    class _StubCfgRepro(EnvCfg):
-        max_episode_seconds: float | None = 1.0
-        ctrl_dt: float = 0.1
-        sim_dt: float = 0.01
-
-    class _StubNpEnvForNaNRepro(NpEnv):
-        OBS_SPEC = {"obs": 5, "critic": 7}
-
-        def __init__(self, num_envs: int = 4, bad_rewards: np.ndarray | None = None):
-            cfg = _StubCfgRepro()
-            backend = MagicMock()
-            backend.backend_type = "mujoco"
-            backend.step = MagicMock()
-            super().__init__(cfg, backend, num_envs)
-            self._bad_rewards = bad_rewards
-
-        @property
-        def obs_groups_spec(self) -> dict[str, int]:
-            return self.OBS_SPEC
-
-        @property
-        def action_space(self) -> gym.Space:
-            return gym.spaces.Box(low=-1.0, high=1.0, shape=(3,), dtype=np.float32)
-
-        def apply_action(self, actions: np.ndarray, state: NpEnvState) -> np.ndarray:
-            return actions
-
-        def update_state(self, state: NpEnvState) -> NpEnvState:
-            obs = {
-                "obs": np.ones((self._num_envs, 5), dtype=np.float32),
-                "critic": np.full((self._num_envs, 7), 0.5, dtype=np.float32),
-            }
-            reward = (
-                self._bad_rewards.copy()
-                if self._bad_rewards is not None
-                else np.ones((self._num_envs,), dtype=np.float32)
-            )
-            return state.replace(
-                obs=obs,
-                reward=reward,
-                terminated=np.zeros((self._num_envs,), dtype=bool),
-                truncated=np.zeros((self._num_envs,), dtype=bool),
-            )
-
-        def reset(self, env_indices: np.ndarray) -> Tuple[dict[str, np.ndarray], dict]:
-            n = len(env_indices)
-            obs = {
-                "obs": np.zeros((n, 5), dtype=np.float32),
-                "critic": np.zeros((n, 7), dtype=np.float32),
-            }
-            return obs, {}
-
-    bad_rewards = np.array([0.0, np.nan, 0.0, 0.0], dtype=np.float32)
-    env = _StubNpEnvForNaNRepro(num_envs=4, bad_rewards=bad_rewards)
-    env.init_state()
-
-    dump_dir = tmp_path / "dumps"
-    guard = NanGuard(
-        NanGuardCfg(enabled=True, output_dir=str(dump_dir)),
-        num_envs=4,
-        supports_state_playback=False,
-    )
-    env.set_nan_guard(guard)
-
-    with caplog.at_level("WARNING", logger="unilab.utils.nan_guard"):
-        for _ in range(5):
-            state = env.step(np.zeros((4, 3), dtype=np.float64))
-            assert np.all(np.isfinite(state.reward))
-
-    warnings = [r for r in caplog.records if r.levelname == "WARNING"]
-    assert len(warnings) == 5, f"expected 5 warnings, got {len(warnings)}"
-
-    dump_files = [p for p in dump_dir.glob("nan_dump_*.npz") if not p.is_symlink()]
-    assert len(dump_files) == 1, f"expected exactly 1 dump file, got {dump_files}"
diff --git a/tests/test_pull_assets.py b/tests/test_pull_assets.py
new file mode 100644
index 000000000..082fd02c6
--- /dev/null
+++ b/tests/test_pull_assets.py
@@ -0,0 +1,116 @@
+"""Tests for the robot asset prefetch CLI."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+
+from unilab.assets import pull as pull_assets
+
+
+def _populate(directory: Path, *, suffix: str, count: int) -> Path:
+    directory.mkdir(parents=True, exist_ok=True)
+    for index in range(count):
+        (directory / f"asset_{index}{suffix}").write_bytes(b"asset")
+    return directory
+
+
+def test_pull_assets_x2_keeps_single_mesh_directory(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+    capsys: pytest.CaptureFixture[str],
+):
+    target = _populate(tmp_path / "meshes", suffix=".STL", count=2)
+    calls: list[tuple[str, str, bool]] = []
+
+    def fake_resolver(directory: str, *, marker: str, show_progress: bool) -> Path:
+        calls.append((directory, marker, show_progress))
+        return target
+
+    monkeypatch.setattr(pull_assets, "resolve_robot_asset_dir", fake_resolver)
+
+    assert pull_assets.main(["--robot", "x2"]) == 0
+    assert calls == [("robots/x2/meshes", "pelvis.STL", False)]
+    lines = capsys.readouterr().out.strip().splitlines()
+    assert lines[0] == "Downloading x2 assets ..."
+    assert lines[-1].startswith("Robot assets ready: 1 robots, 1 directories, 2 files")
+
+
+def test_pull_assets_g1_resolves_assets_and_textures(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+    capsys: pytest.CaptureFixture[str],
+):
+    targets = {
+        "robots/g1/assets": _populate(tmp_path / "assets", suffix=".STL", count=54),
+        "robots/g1/textures": _populate(tmp_path / "textures", suffix=".png", count=2),
+    }
+    calls: list[tuple[str, str, bool]] = []
+
+    def fake_resolver(directory: str, *, marker: str, show_progress: bool) -> Path:
+        calls.append((directory, marker, show_progress))
+        return targets[directory]
+
+    monkeypatch.setattr(pull_assets, "resolve_robot_asset_dir", fake_resolver)
+
+    assert pull_assets.main(["--robot", "g1"]) == 0
+    assert calls == [
+        ("robots/g1/assets", "head_link.STL", False),
+        ("robots/g1/textures", "floor.png", False),
+    ]
+    output = capsys.readouterr().out
+    assert "54 asset files" in output
+    assert "2 PNG files" in output
+    lines = output.strip().splitlines()
+    assert lines[0] == "Downloading g1 assets ..."
+    assert lines[-1].startswith("Robot assets ready: 1 robots, 2 directories, 56 files")
+
+
+def test_pull_assets_all_covers_every_registered_robot(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+    capsys: pytest.CaptureFixture[str],
+):
+    from unilab.assets.hub import ROBOT_ASSET_SPECS
+
+    target = _populate(tmp_path / "dir", suffix=".stl", count=1)
+    calls: list[tuple[str, bool]] = []
+
+    def fake_resolver(directory: str, *, marker: str, show_progress: bool) -> Path:
+        calls.append((directory, show_progress))
+        return target
+
+    monkeypatch.setattr(pull_assets, "resolve_robot_asset_dir", fake_resolver)
+
+    assert pull_assets.main(["--robot", "all"]) == 0
+    assert calls == [
+        (directory, False)
+        for robot in sorted(ROBOT_ASSET_SPECS)
+        for directory, _marker, _pattern, _label in ROBOT_ASSET_SPECS[robot]
+    ]
+    output_lines = capsys.readouterr().out.strip().splitlines()
+    assert len(output_lines) == len(ROBOT_ASSET_SPECS) + 1
+    assert output_lines[:-1] == [
+        f"Downloading {robot} assets ..." for robot in sorted(ROBOT_ASSET_SPECS)
+    ]
+    assert output_lines[-1].startswith(f"Robot assets ready: {len(ROBOT_ASSET_SPECS)} robots")
+
+
+def test_pull_assets_verbose_keeps_output_compact(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+    capsys: pytest.CaptureFixture[str],
+):
+    target = _populate(tmp_path / "meshes", suffix=".STL", count=2)
+    calls: list[bool] = []
+
+    def fake_resolver(directory: str, *, marker: str, show_progress: bool) -> Path:
+        calls.append(show_progress)
+        return target
+
+    monkeypatch.setattr(pull_assets, "resolve_robot_asset_dir", fake_resolver)
+
+    assert pull_assets.main(["--robot", "x2", "--verbose"]) == 0
+    assert calls == [False]
+    assert len(capsys.readouterr().out.strip().splitlines()) == 2
diff --git a/tests/test_render_teaser.py b/tests/test_render_teaser.py
index 30d204d11..d21500d21 100644
--- a/tests/test_render_teaser.py
+++ b/tests/test_render_teaser.py
@@ -1,11 +1,11 @@
-"""Tests for the teaser scene renderer (``unilab.tools.render_teaser``)."""
+"""Tests for the teaser scene renderer (``unilab.visualization.teaser``)."""
 
 from __future__ import annotations
 
 import inspect
 from unittest.mock import MagicMock, patch
 
-from unilab.tools import render_teaser
+from unilab.visualization import teaser as render_teaser
 
 
 def test_set_teaser_system_camera_view_uses_packaged_view():
diff --git a/tests/test_sharpa.py b/tests/test_sharpa.py
index 5b4618f02..341a2d6ad 100644
--- a/tests/test_sharpa.py
+++ b/tests/test_sharpa.py
@@ -5,14 +5,13 @@
 import numpy as np
 import pytest
 
-from unilab.envs.manipulation.sharpa_inhand.base import SharpaDomainRandConfig
-from unilab.envs.manipulation.sharpa_inhand.grasp_gen import (
+from unilab.tasks.manipulation.sharpa_inhand.base import SharpaDomainRandConfig
+from unilab.tasks.manipulation.sharpa_inhand.grasp_gen import (
     SharpaInhandRotationGraspCfg,
     SharpaInhandRotationGraspEnv,
 )
-from unilab.envs.manipulation.sharpa_inhand.rotation import (
+from unilab.tasks.manipulation.sharpa_inhand.rotation import (
     SharpaInhandRotationEnv,
-    sample_random_quaternion,
 )
 from unilab.utils.rotation import np_quat_apply
 
diff --git a/tests/training/test_seed_contract.py b/tests/training/test_seed_contract.py
index 9dc42e56a..310624741 100644
--- a/tests/training/test_seed_contract.py
+++ b/tests/training/test_seed_contract.py
@@ -10,14 +10,14 @@
 from hydra.core.global_hydra import GlobalHydra
 from omegaconf import OmegaConf
 
-from unilab.training.seed import (
+from unilab.utils.seed import (
     apply_training_seed,
     derive_worker_seed,
     resolve_training_seed,
 )
 
 _ROOT_DIR = Path(__file__).resolve().parents[2]
-_CONF_DIR = _ROOT_DIR / "conf"
+_CONF_DIR = _ROOT_DIR / "src" / "unilab" / "conf"
 
 
 def _compose(config_dir: str, overrides: list[str] | None = None):
@@ -67,8 +67,8 @@ def test_derive_worker_seed_is_deterministic_and_distinct_from_base_seed():
         ("ppo", ["task=sharpa_inhand/mujoco_hora"]),
         ("appo", ["task=go1_joystick_flat/mujoco"]),
         ("appo", ["task=sharpa_inhand/mujoco_hora"]),
-        ("offpolicy", ["algo=sac", "task=sac/g1_walk_flat/mujoco"]),
-        ("offpolicy", ["algo=td3", "task=td3/g1_walk_flat/mujoco"]),
+        ("sac", ["task=g1_walk_flat/mujoco"]),
+        ("td3", ["task=g1_walk_flat/mujoco"]),
     ],
 )
 def test_owner_configs_resolve_algorithm_seed_contract(config_dir: str, overrides: list[str]):
diff --git a/tests/training/test_sim2sim_resolver.py b/tests/training/test_sim2sim_resolver.py
index 75edb4d0c..d45104454 100644
--- a/tests/training/test_sim2sim_resolver.py
+++ b/tests/training/test_sim2sim_resolver.py
@@ -14,7 +14,7 @@
 from hydra.core.global_hydra import GlobalHydra
 from omegaconf import OmegaConf
 
-from unilab.training.sim2sim import (
+from unilab.utils.sim2sim import (
     ALLOWLIST,
     DENYLIST,
     ENV_STRUCTURAL_DENYLIST,
@@ -56,6 +56,47 @@ def _mujoco_cfg() -> Any:
     )
 
 
+def _manager_cfg() -> Any:
+    return OmegaConf.create(
+        {
+            "training": {"sim_backend": "mujoco"},
+            "algo": {"obs_groups": {"actor": ["actor"]}},
+            "env": {
+                "observations": {
+                    "policy": {
+                        "_target_": "unilab.managers.ObservationGroupCfg",
+                        "terms": {
+                            "joint_pos": {
+                                "_target_": "unilab.managers.ObservationTermCfg",
+                                "func": "unilab.envs.mdp.joint_pos_rel",
+                            }
+                        },
+                    },
+                    "critic": {
+                        "_target_": "unilab.managers.ObservationGroupCfg",
+                        "terms": {
+                            "joint_pos": {
+                                "_target_": "unilab.managers.ObservationTermCfg",
+                                "func": "unilab.envs.mdp.joint_pos_rel",
+                            }
+                        },
+                    },
+                },
+                "actions": {
+                    "joint_pos": {
+                        "_target_": "unilab.envs.mdp.JointPositionActionCfg",
+                        "entity_name": "robot",
+                        "actuator_names": [".*"],
+                        "scale": 0.25,
+                    }
+                },
+                "policy_observation_group": "policy",
+                "critic_observation_group": "critic",
+            },
+        }
+    )
+
+
 def test_field_lists_are_disjoint():
     deny, warn, allow = set(DENYLIST), set(WARNING_LIST), set(ALLOWLIST)
     assert deny.isdisjoint(warn)
@@ -74,7 +115,7 @@ def test_extract_snapshot_includes_only_present_contract_fields():
     assert "training.sim_backend" not in snapshot
     # ...and absent fields are omitted (never stored as None).
     assert "algo.obs_normalization" not in snapshot
-    assert "env.sampling_mode" not in snapshot
+    assert "env.commands.motion.params.sampling_mode" not in snapshot
     assert "reward.base_height_target" not in snapshot
 
 
@@ -83,6 +124,46 @@ def test_snapshot_json_round_trips():
     assert json.loads(json.dumps(snapshot)) == snapshot
 
 
+def test_extract_snapshot_captures_manager_policy_io_contract() -> None:
+    cfg = _manager_cfg()
+
+    snapshot = extract_contract_snapshot(cfg)
+
+    assert snapshot["env.actions"]["joint_pos"]["scale"] == pytest.approx(0.25)
+    assert snapshot["env.observations"]["policy"]["terms"]["joint_pos"]["func"] == (
+        "unilab.envs.mdp.joint_pos_rel"
+    )
+    assert snapshot["env.policy_observation_group"] == "policy"
+    assert snapshot["env.critic_observation_group"] == "critic"
+    assert "env.control_config.action_scale" not in snapshot
+
+
+def test_matching_manager_policy_io_contract_passes(tmp_path: Path) -> None:
+    _write_sidecar(tmp_path, extract_contract_snapshot(_manager_cfg()))
+    target = _manager_cfg()
+
+    assert resolve_sim2sim_config(tmp_path, target) is target
+
+
+@pytest.mark.parametrize(
+    ("path", "value"),
+    [
+        ("env.actions.joint_pos.scale", 0.5),
+        ("env.observations.policy.terms.joint_pos.func", "unilab.envs.mdp.joint_vel_rel"),
+        ("env.policy_observation_group", "critic"),
+        ("env.critic_observation_group", "policy"),
+    ],
+)
+def test_manager_policy_io_mismatch_fails_closed(tmp_path: Path, path: str, value: object) -> None:
+    source = _manager_cfg()
+    _write_sidecar(tmp_path, extract_contract_snapshot(source))
+    target = _manager_cfg()
+    OmegaConf.update(target, path, value, merge=False)
+
+    with pytest.raises(CrossBackendIncompatibleError, match="env\\."):
+        resolve_sim2sim_config(tmp_path, target)
+
+
 def test_matching_contract_returns_same_cfg(tmp_path):
     _write_sidecar(tmp_path, extract_contract_snapshot(_mujoco_cfg()))
     target = _mujoco_cfg()
@@ -185,7 +266,14 @@ def test_action_scale_list_form(tmp_path):
 
 
 def test_env_structural_denylist_is_the_env_subset():
-    assert ENV_STRUCTURAL_DENYLIST == ["env.control_config.action_scale", "env.sampling_mode"]
+    assert ENV_STRUCTURAL_DENYLIST == [
+        "env.control_config.action_scale",
+        "env.observations",
+        "env.actions",
+        "env.policy_observation_group",
+        "env.critic_observation_group",
+        "env.commands.motion.params.sampling_mode",
+    ]
     assert set(ENV_STRUCTURAL_DENYLIST) <= set(DENYLIST)
 
 
@@ -206,7 +294,10 @@ def test_env_field_present_in_target_absent_in_source_raises(tmp_path):
     # the target sets it explicitly. Still unverifiable -> fail closed.
     _write_sidecar(tmp_path, {"algo.empirical_normalization": False})
     target = OmegaConf.create(
-        {"algo": {"empirical_normalization": False}, "env": {"sampling_mode": "adaptive"}}
+        {
+            "algo": {"empirical_normalization": False},
+            "env": {"commands": {"motion": {"params": {"sampling_mode": "adaptive"}}}},
+        }
     )
     with pytest.raises(CrossBackendIncompatibleError) as excinfo:
         resolve_sim2sim_config(tmp_path, target)
@@ -215,6 +306,23 @@ def test_env_field_present_in_target_absent_in_source_raises(tmp_path):
     assert "source=<absent>" in msg
 
 
+def test_legacy_sampling_mode_snapshot_resolves_against_manager_path(tmp_path) -> None:
+    _write_sidecar(tmp_path, {"env.sampling_mode": "adaptive"})
+    target = OmegaConf.create(
+        {"env": {"commands": {"motion": {"params": {"sampling_mode": "adaptive"}}}}}
+    )
+
+    assert resolve_sim2sim_config(tmp_path, target) is target
+
+
+def test_legacy_config_sampling_mode_is_snapshotted_under_canonical_path() -> None:
+    cfg = OmegaConf.create({"env": {"sampling_mode": "adaptive"}})
+
+    assert extract_contract_snapshot(cfg) == {
+        "env.commands.motion.params.sampling_mode": "adaptive"
+    }
+
+
 def test_env_field_symmetric_absence_does_not_raise(tmp_path):
     # Neither side sets the env structural field -> both use the same env default -> ok.
     _write_sidecar(tmp_path, {"algo.empirical_normalization": False})
@@ -326,16 +434,16 @@ def test_resolver_class_extract_and_dim_guard_delegate():
 
 
 def _compose_task(task: str) -> Any:
-    conf_dir = str(Path(__file__).resolve().parents[2] / "conf" / "ppo")
+    conf_dir = str(Path(__file__).resolve().parents[2] / "src" / "unilab" / "conf" / "ppo")
     GlobalHydra.instance().clear()
     with initialize_config_dir(config_dir=conf_dir, version_base="1.3"):
         return compose("config", overrides=[f"task={task}"])
 
 
 def test_g1_walk_flat_mujoco_inherits_base_contract():
-    # The MuJoCo owner carries the full contract in its standalone owner config.
+    # The MuJoCo owner inherits the full contract from the shared base owner.
     mujoco = _compose_task("g1_walk_flat/mujoco")
-    assert OmegaConf.select(mujoco, "env.control_config.action_scale") == 0.25
+    assert OmegaConf.select(mujoco, "env.actions.joint_pos.scale") == 0.25
     assert OmegaConf.select(mujoco, "algo.empirical_normalization") is False
     assert OmegaConf.select(mujoco, "algo.obs_groups.actor") == ["actor"]
 
@@ -349,3 +457,27 @@ def test_g1_walk_flat_cross_backend_play_is_guarded(tmp_path):
     motrix = _compose_task("g1_walk_flat/motrix")
     with pytest.raises(CrossBackendIncompatibleError):
         resolve_sim2sim_config(tmp_path, motrix)
+
+
+def test_g1_walk_flat_mujoco_to_isaacgym_play_passes_guard(tmp_path):
+    # The isaacgym owner keeps DENYLIST parity with the MuJoCo owner, so a
+    # MuJoCo-trained checkpoint passes the contract guard for isaacgym play.
+    snapshot = extract_contract_snapshot(_compose_task("g1_walk_flat/mujoco"))
+    (tmp_path / "run_config.json").write_text(
+        json.dumps({"contract_snapshot": snapshot}), encoding="utf-8"
+    )
+    isaacgym = _compose_task("g1_walk_flat/isaacgym")
+    assert OmegaConf.select(isaacgym, "training.sim_backend") == "isaacgym"
+    assert resolve_sim2sim_config(tmp_path, isaacgym) is isaacgym
+
+
+def test_g1_walk_flat_mujoco_to_genesis_play_passes_guard(tmp_path):
+    # The genesis owner keeps DENYLIST parity with the MuJoCo owner, so a
+    # MuJoCo-trained checkpoint passes the contract guard for genesis play.
+    snapshot = extract_contract_snapshot(_compose_task("g1_walk_flat/mujoco"))
+    (tmp_path / "run_config.json").write_text(
+        json.dumps({"contract_snapshot": snapshot}), encoding="utf-8"
+    )
+    genesis = _compose_task("g1_walk_flat/genesis")
+    assert OmegaConf.select(genesis, "training.sim_backend") == "genesis"
+    assert resolve_sim2sim_config(tmp_path, genesis) is genesis
diff --git a/tests/training/test_training_helpers.py b/tests/training/test_training_helpers.py
index 84abff4a8..89c3da946 100644
--- a/tests/training/test_training_helpers.py
+++ b/tests/training/test_training_helpers.py
@@ -8,31 +8,33 @@
 from hydra import compose, initialize_config_dir
 from hydra.core.global_hydra import GlobalHydra
 from omegaconf import OmegaConf
+from unisim.backend.base import RenderClosedError
+from unisim.backend.motrix.backend import MotrixBackend
+from unisim.backend.motrix.playback import run_motrix_playback
+from unisim.backend.mujoco.backend import MuJoCoBackend
+from unisim.backend.mujoco.playback import run_mujoco_playback
 
-from unilab.base.backend import RenderClosedError
-from unilab.base.backend.motrix.backend import MotrixBackend
-from unilab.base.backend.motrix.playback import run_motrix_playback
-from unilab.base.backend.mujoco.backend import MuJoCoBackend
-from unilab.base.backend.mujoco.playback import run_mujoco_playback
+from unilab.base.config_adapter import BackendAdapter
 from unilab.base.scene import SceneCfg
 from unilab.training import (
-    BackendAdapter,
     format_hora_stage2_checkpoint_error,
+    get_log_root,
+    parse_checkpoint_path,
+    resolve_hora_stage2_checkpoint_path,
+)
+from unilab.utils.checkpoint import (
     get_entrypoint_log_root,
     get_latest_checkpoint,
     get_latest_run,
-    get_log_root,
-    parse_checkpoint_path,
     resolve_appo_checkpoint_path,
     resolve_checkpoint_path,
-    resolve_hora_stage2_checkpoint_path,
     resolve_offpolicy_checkpoint_path,
     resolve_task_checkpoint_path,
 )
 from unilab.visualization.playback import render_play_mode
 
 _ROOT_DIR = Path(__file__).resolve().parents[2]
-_CONF_DIR = _ROOT_DIR / "conf"
+_CONF_DIR = _ROOT_DIR / "src" / "unilab" / "conf"
 
 
 def _resolve_low_level_playback_flags(kwargs: dict[str, object]) -> dict[str, object]:
@@ -50,23 +52,16 @@ def _resolve_low_level_playback_flags(kwargs: dict[str, object]) -> dict[str, ob
 
 def _normalize_overrides(overrides: list[str] | None, *, offpolicy: bool = False) -> list[str]:
     normalized: list[str] = []
-    algo = "sac"
     task_selected = False
 
     for override in overrides or []:
-        if override.startswith("algo="):
-            algo = override.split("=", 1)[1]
-            normalized.append(override)
-            continue
         if override.startswith("task="):
             task_selected = True
-            normalized.append(override)
-            continue
         normalized.append(override)
 
     if not task_selected:
         if offpolicy:
-            normalized.append(f"task={algo}/g1_walk_flat/mujoco")
+            normalized.append("task=g1_walk_flat/mujoco")
         else:
             normalized.append("task=go1_joystick_flat/mujoco")
     return normalized
@@ -78,9 +73,9 @@ def _ppo_cfg(overrides: list[str] | None = None):
         return compose("config", overrides=_normalize_overrides(overrides))
 
 
-def _offpolicy_cfg(overrides: list[str] | None = None):
+def _offpolicy_cfg(overrides: list[str] | None = None, *, algo: str = "sac"):
     GlobalHydra.instance().clear()
-    with initialize_config_dir(config_dir=str(_CONF_DIR / "offpolicy"), version_base="1.3"):
+    with initialize_config_dir(config_dir=str(_CONF_DIR / algo), version_base="1.3"):
         return compose("config", overrides=_normalize_overrides(overrides, offpolicy=True))
 
 
@@ -375,21 +370,38 @@ def test_resolve_task_checkpoint_path_accepts_integer_latest_run(
 
 def test_backend_adapter_env_cfg_override_for_motrix_sac_g1_walk_flat():
     """Env cfg override carries reward + env preset fields. Algo is NOT touched."""
-    cfg = _offpolicy_cfg(["task=sac/g1_walk_flat/motrix"])
+    cfg = _offpolicy_cfg(["task=g1_walk_flat/motrix"])
 
     adapter = BackendAdapter(cfg, root_dir=_ROOT_DIR, algo_name="sac")
     env_cfg_override = adapter.build_task_env_cfg_override()
 
     # env_cfg_override has reward + env preset fields
-    assert env_cfg_override["reward_config"]["scales"]["tracking_lin_vel"] == pytest.approx(2.2)
-    assert env_cfg_override["domain_rand"]["randomize_kp"] is False
-    assert env_cfg_override["domain_rand"]["randomize_kd"] is False
+    assert env_cfg_override["rewards"]["tracking_lin_vel"]["weight"] == pytest.approx(2.2)
+    assert env_cfg_override["events"]["pd_gains"] is None
     # algo values come straight from YAML compose — no mutation, matches task owner values
     assert cfg.algo.num_envs == 2048
     assert cfg.algo.max_iterations == 5000
 
 
-def test_backend_adapter_builds_play_scene_override():
+def test_backend_adapter_injects_isaacsim_render_intent_only_for_play():
+    cfg = _offpolicy_cfg(
+        [
+            "task=g1_walk_flat/isaacsim",
+            "training.play_render_mode=record",
+        ]
+    )
+    adapter = BackendAdapter(cfg, root_dir=_ROOT_DIR, algo_name="sac")
+
+    training_override = adapter.build_task_env_cfg_override()
+    play_override = adapter.build_play_env_cfg_override()
+
+    assert "isaacsim_render_mode" not in training_override
+    assert play_override["isaacsim_render_mode"] == "record"
+    assert play_override["isaacsim_render_width"] == 1280
+    assert play_override["isaacsim_render_height"] == 720
+
+
+def test_backend_adapter_keeps_motion_manager_scene_during_play():
     cfg = _ppo_cfg(["task=g1_motion_tracking/motrix", "training.play_only=true"])
     assert cfg.training.play_env_num == 16
     captured: dict[str, object] = {}
@@ -397,7 +409,7 @@ def test_backend_adapter_builds_play_scene_override():
     def _fake_materializer(source_model_file: str, **kwargs) -> str:
         captured["source_model_file"] = source_model_file
         captured.update(kwargs)
-        return "/tmp/g1_motion_tracking_play_scene.xml"
+        pytest.fail("manager scene must not be replaced during play")
 
     env_cfg_override = BackendAdapter(
         cfg,
@@ -408,9 +420,34 @@ def _fake_materializer(source_model_file: str, **kwargs) -> str:
 
     assert cfg.training.play_env_num == 16
     assert env_cfg_override["render_spacing"] == pytest.approx(2.5)
-    assert env_cfg_override["scene"].model_file == "/tmp/g1_motion_tracking_play_scene.xml"
-    assert captured["ground_texture_file"] == str(
-        _ROOT_DIR / "src/unilab/assets/robots/g1/textures/floor.png"
+    assert env_cfg_override["scene"]["model_file"].endswith("robots/g1/scene_flat.xml")
+    assert "robot" in env_cfg_override["scene"]["entities"]
+    assert captured == {}
+
+
+def test_backend_adapter_materializes_visuals_without_dropping_manager_entities():
+    cfg = _ppo_cfg(["task=g1_box_tracking/motrix", "training.play_only=true"])
+    captured: dict[str, object] = {}
+
+    def _fake_materializer(source_model_file: str, **kwargs) -> str:
+        captured["source_model_file"] = source_model_file
+        captured.update(kwargs)
+        return "/tmp/materialized_box.xml"
+
+    env_cfg_override = BackendAdapter(
+        cfg,
+        root_dir=_ROOT_DIR,
+        algo_name="ppo",
+        scene_materializer=_fake_materializer,
+    ).build_play_env_cfg_override()
+
+    scene = env_cfg_override["scene"]
+    assert scene["model_file"] == "/tmp/materialized_box.xml"
+    assert scene["default_keyframe_name"] == "stand"
+    assert set(scene["entities"]) == {"robot", "object"}
+    assert scene["entities"]["object"]["root_body_name"] == "largebox"
+    assert str(captured["source_model_file"]).endswith(
+        "src/unilab/assets/robots/g1/scene_flat_with_largebox.xml"
     )
 
 
@@ -507,7 +544,7 @@ def _render():
     backend.render = _render
     backend.capture_video_frame = lambda: np.zeros((2, 2, 3), dtype=np.uint8)
 
-    with caplog.at_level(logging.INFO, logger="unilab.base.backend.motrix.backend"):
+    with caplog.at_level(logging.INFO, logger="unisim.backend.motrix.backend"):
         result = backend.run_playback(
             env=FakeEnv(),
             initialize=lambda: 0,
@@ -572,7 +609,7 @@ def capture_video_frame(self) -> np.ndarray:
             return np.full((2, 3, 3), self.capture_calls, dtype=np.uint8)
 
     monkeypatch.setattr(
-        "unilab.base.backend.playback_common.imageio.mimsave",
+        "unisim.backend.playback_common.imageio.mimsave",
         lambda path, frames, fps: captured.update(
             {"video_path": path, "frames": frames, "fps": fps}
         ),
@@ -639,7 +676,7 @@ def capture_video_frame(self) -> np.ndarray:
             return np.full((2, 3, 3), self.capture_calls, dtype=np.uint8)
 
     monkeypatch.setattr(
-        "unilab.base.backend.playback_common.imageio.mimsave",
+        "unisim.backend.playback_common.imageio.mimsave",
         lambda path, frames, fps: captured.update(
             {"video_path": path, "frames": frames, "fps": fps}
         ),
@@ -693,13 +730,13 @@ def _render_states_get_frames(state_list, model_file, **kwargs):
         return [np.zeros((2, 2, 3), dtype=np.uint8)]
 
     monkeypatch.setattr(
-        "unilab.base.backend.playback_common.imageio.mimsave",
+        "unisim.backend.playback_common.imageio.mimsave",
         lambda path, frames, fps: captured.update(
             {"video_path": path, "frames": frames, "fps": fps}
         ),
     )
     monkeypatch.setattr(
-        "unilab.visualization.render_many.render_states_get_frames",
+        "unisim.visualization.render_many.render_states_get_frames",
         _render_states_get_frames,
     )
 
@@ -794,13 +831,13 @@ def _render_states_get_frames(state_list, model_file, **kwargs):
         return [np.zeros((2, 2, 3), dtype=np.uint8)]
 
     monkeypatch.setattr(
-        "unilab.base.backend.playback_common.imageio.mimsave",
+        "unisim.backend.playback_common.imageio.mimsave",
         lambda path, frames, fps: captured.update(
             {"video_path": path, "frames": frames, "fps": fps}
         ),
     )
     monkeypatch.setattr(
-        "unilab.visualization.render_many.render_states_get_frames",
+        "unisim.visualization.render_many.render_states_get_frames",
         _render_states_get_frames,
     )
 
diff --git a/tests/utils/test_algo_utils.py b/tests/utils/test_algo_utils.py
index 9d80d5b52..edb532576 100644
--- a/tests/utils/test_algo_utils.py
+++ b/tests/utils/test_algo_utils.py
@@ -1,4 +1,8 @@
-"""Tests for registry bootstrap and torch actor factory helpers."""
+"""Tests for registry bootstrap.
+
+The torch actor factory helpers moved to uni_rl (issue #1480);
+``build_actor`` coverage lives in that repo's test suite.
+"""
 
 from __future__ import annotations
 
@@ -8,7 +12,6 @@
 
 import pytest
 
-from unilab.algos.torch.common.actor_factory import build_actor
 from unilab.base.registry import ensure_registries
 
 
@@ -53,7 +56,7 @@ def test_mixed_optional_and_required(self, caplog) -> None:
         # Use real package + fake optional package
         with caplog.at_level(logging.WARNING):
             ensure_registries(
-                ["unilab.envs.locomotion", "nonexistent_optional_12345"],
+                ["unilab.tasks.locomotion", "nonexistent_optional_12345"],
                 optional_packages=["nonexistent_optional_12345"],
             )
 
@@ -129,90 +132,3 @@ def test_invalid_registry_modules_contract_raises(self, tmp_path, monkeypatch) -
 
         with pytest.raises(TypeError, match="__unilab_registry_modules__"):
             ensure_registries(["registry_invalid_contract_pkg"])
-
-
-class TestBuildActor:
-    """Tests for build_actor."""
-
-    def test_builds_sac_actor(self) -> None:
-        actor = build_actor(
-            algo_type="sac",
-            obs_dim=10,
-            action_dim=4,
-            actor_hidden_dim=256,
-            use_layer_norm=True,
-            device="cpu",
-        )
-        assert hasattr(actor, "forward")
-
-    def test_builds_sac_actor_without_layer_norm(self) -> None:
-        actor = build_actor(
-            algo_type="sac",
-            obs_dim=10,
-            action_dim=4,
-            actor_hidden_dim=128,
-            use_layer_norm=False,
-            device="cpu",
-        )
-        assert hasattr(actor, "forward")
-
-    def test_builds_td3_actor(self) -> None:
-        actor = build_actor(
-            algo_type="td3",
-            obs_dim=10,
-            action_dim=4,
-            actor_hidden_dim=256,
-            use_layer_norm=True,
-            device="cpu",
-            num_envs=1,
-        )
-        assert hasattr(actor, "forward")
-
-    def test_builds_td3_actor_for_multiple_envs(self) -> None:
-        actor = build_actor(
-            algo_type="td3",
-            obs_dim=8,
-            action_dim=2,
-            actor_hidden_dim=128,
-            use_layer_norm=False,
-            device="cpu",
-            num_envs=4,
-        )
-        assert hasattr(actor, "forward")
-
-    def test_builds_flashsac_actor(self) -> None:
-        actor = build_actor(
-            algo_type="flashsac",
-            obs_dim=98,
-            action_dim=29,
-            actor_hidden_dim=128,
-            use_layer_norm=False,
-            device="cpu",
-            actor_num_blocks=2,
-            actor_noise_zeta_mu=2.0,
-            actor_noise_zeta_max=16,
-        )
-        assert hasattr(actor, "forward")
-        assert hasattr(actor, "explore")
-
-    def test_raises_for_unknown_algo_type(self) -> None:
-        with pytest.raises(ValueError, match="Unknown algo_type"):
-            build_actor(
-                algo_type="unknown",
-                obs_dim=10,
-                action_dim=4,
-                actor_hidden_dim=256,
-                use_layer_norm=True,
-                device="cpu",
-            )
-
-    def test_builds_sac_actor_with_different_dims(self) -> None:
-        actor = build_actor(
-            algo_type="sac",
-            obs_dim=49,
-            action_dim=12,
-            actor_hidden_dim=512,
-            use_layer_norm=True,
-            device="cpu",
-        )
-        assert hasattr(actor, "forward")
diff --git a/tests/utils/test_device.py b/tests/utils/test_device.py
deleted file mode 100644
index db3967a74..000000000
--- a/tests/utils/test_device.py
+++ /dev/null
@@ -1,94 +0,0 @@
-from __future__ import annotations
-
-from types import SimpleNamespace
-
-import pytest
-
-import unilab.utils.device as device_mod
-
-
-def _patch_devices(
-    monkeypatch: pytest.MonkeyPatch,
-    *,
-    cuda: bool = False,
-    cuda_count: int = 0,
-    xpu: bool = False,
-    xpu_count: int = 0,
-    mps: bool = False,
-) -> None:
-    monkeypatch.setattr(device_mod.torch.cuda, "is_available", lambda: cuda)
-    monkeypatch.setattr(device_mod.torch.cuda, "device_count", lambda: cuda_count)
-    monkeypatch.setattr(
-        device_mod.torch,
-        "xpu",
-        SimpleNamespace(
-            is_available=lambda: xpu,
-            device_count=lambda: xpu_count,
-        ),
-        raising=False,
-    )
-    monkeypatch.setattr(device_mod.torch.backends.mps, "is_available", lambda: mps)
-
-
-def test_resolve_torch_device_alias_defaults_to_cpu(monkeypatch: pytest.MonkeyPatch) -> None:
-    _patch_devices(monkeypatch)
-
-    assert device_mod.resolve_torch_device_alias(None) == "cpu"
-    assert device_mod.resolve_torch_device_alias("cpu") == "cpu"
-
-
-def test_resolve_torch_device_alias_gpu_prefers_cuda(
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    _patch_devices(monkeypatch, cuda=True, cuda_count=2, mps=True)
-
-    assert device_mod.resolve_torch_device_alias("gpu") == "cuda"
-    assert device_mod.resolve_torch_device_alias("gpu:1") == "cuda:1"
-
-
-def test_resolve_torch_device_alias_gpu_uses_xpu_before_mps(
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    _patch_devices(monkeypatch, xpu=True, xpu_count=2, mps=True)
-
-    assert device_mod.resolve_torch_device_alias("gpu") == "xpu"
-    assert device_mod.resolve_torch_device_alias("gpu:1") == "xpu:1"
-
-
-@pytest.mark.parametrize("alias", ["gpu", "gpu:0", "cuda", "cuda:0"])
-def test_resolve_torch_device_alias_macos_compat_maps_gpu_and_cuda_to_mps(
-    monkeypatch: pytest.MonkeyPatch,
-    alias: str,
-) -> None:
-    _patch_devices(monkeypatch, mps=True)
-
-    assert device_mod.resolve_torch_device_alias(alias) == "mps"
-
-
-@pytest.mark.parametrize("alias", ["gpu:1", "cuda:1", "mps:1"])
-def test_resolve_torch_device_alias_mps_rejects_nonzero_index(
-    monkeypatch: pytest.MonkeyPatch,
-    alias: str,
-) -> None:
-    _patch_devices(monkeypatch, mps=True)
-
-    with pytest.raises(ValueError, match="MPS"):
-        device_mod.resolve_torch_device_alias(alias)
-
-
-def test_resolve_torch_device_alias_rejects_missing_cuda_index(
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    _patch_devices(monkeypatch, cuda=True, cuda_count=1)
-
-    with pytest.raises(ValueError, match="only 1 cuda device"):
-        device_mod.resolve_torch_device_alias("gpu:1")
-
-
-def test_resolve_torch_device_alias_rejects_unavailable_accelerator(
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    _patch_devices(monkeypatch)
-
-    with pytest.raises(ValueError, match="none is available"):
-        device_mod.resolve_torch_device_alias("gpu")
diff --git a/tests/utils/test_experiment_tracking.py b/tests/utils/test_experiment_tracking.py
index 87c871b29..9defe11eb 100644
--- a/tests/utils/test_experiment_tracking.py
+++ b/tests/utils/test_experiment_tracking.py
@@ -2,15 +2,14 @@
 
 import json
 import sys
-from pathlib import Path
 from typing import Any
 
 import pytest
+import uni_rl.logging.common as common_module
 from rich.console import Console
+from uni_rl.logging import OffPolicyLogger, OnPolicyLogger
 
-import unilab.logging.common as common_module
 import unilab.training.experiment as experiment_module
-from unilab.logging import OffPolicyLogger, OnPolicyLogger
 from unilab.training.experiment import ExperimentTracker, build_wandb_settings
 
 
@@ -276,7 +275,7 @@ def test_offpolicy_logger_terminal_shows_material_blocking_phases():
     logger.close()
 
 
-def test_offpolicy_logger_terminal_shows_replay_rows_when_symmetry_expands_batch():
+def test_offpolicy_logger_terminal_shows_replay_rows_and_effective_batch():
     logger = OffPolicyLogger(
         algo_name="FastSAC",
         env_name="G1WalkFlat",
@@ -332,17 +331,10 @@ def test_build_wandb_settings_defaults_for_shared_workspace():
     assert "mujoco" in settings["tags"]
 
 
-def test_experiment_device_info_uses_benchmark_helper():
-    helper_path = experiment_module._benchmark_device_info_path()
-    assert helper_path is not None
-    assert helper_path.name == "device_info.py"
+def test_experiment_device_info_uses_library_helper():
+    import unilab.utils.device as device_module
 
-    info = experiment_module.get_device_info_dict()
-    assert info["platform"]
-    assert "chip" in info
-    assert "cpu_total_cores" in info
-    assert "memory" in info
-    assert "gpu_name" in info or "gpu_cores" in info
+    assert experiment_module.get_device_info_dict is device_module.get_device_info_dict
 
 
 def test_experiment_tracker_writes_local_run_files(tmp_path, monkeypatch):
diff --git a/tests/utils/test_final_observation.py b/tests/utils/test_final_observation.py
deleted file mode 100644
index 261e5c4f9..000000000
--- a/tests/utils/test_final_observation.py
+++ /dev/null
@@ -1,81 +0,0 @@
-from __future__ import annotations
-
-import numpy as np
-
-from unilab.base.final_observation import (
-    patch_transition_next_obs,
-    resolve_terminal_observation_contract,
-    resolve_transition_bootstrap_contract,
-)
-
-
-def test_patch_transition_next_obs_uses_final_observation_without_mutating_actor_obs():
-    next_obs = np.array([[1.0, 1.0], [2.0, 2.0]], dtype=np.float32)
-    next_critic = np.array([[10.0], [20.0]], dtype=np.float32)
-    final_observation = {
-        "obs": np.array([[7.0, 8.0], [9.0, 9.0]], dtype=np.float32),
-        "critic": np.array([[70.0], [90.0]], dtype=np.float32),
-    }
-
-    (
-        transition_next_obs,
-        transition_next_critic,
-        terminal_mask,
-    ) = patch_transition_next_obs(
-        next_obs,
-        final_observation=final_observation,
-        done=np.array([True, False]),
-        next_critic=next_critic,
-    )
-
-    np.testing.assert_array_equal(terminal_mask, np.array([True, False]))
-    np.testing.assert_array_equal(transition_next_obs, np.array([[7.0, 8.0], [2.0, 2.0]]))
-    np.testing.assert_array_equal(transition_next_critic, np.array([[70.0], [20.0]]))
-    np.testing.assert_array_equal(next_obs, np.array([[1.0, 1.0], [2.0, 2.0]]))
-    np.testing.assert_array_equal(next_critic, np.array([[10.0], [20.0]]))
-
-
-def test_resolve_transition_bootstrap_contract_separates_actor_and_storage_paths():
-    contract = resolve_transition_bootstrap_contract(
-        next_obs=np.array([[1.0, 1.0], [2.0, 2.0]], dtype=np.float32),
-        next_critic=np.array([[10.0], [20.0]], dtype=np.float32),
-        final_observation={"obs": np.array([[5.0, 5.0], [8.0, 9.0]], dtype=np.float32)},
-        done=np.array([False, True]),
-        truncated=np.array([False, True]),
-    )
-
-    np.testing.assert_array_equal(contract.actor_next_obs, np.array([[1.0, 1.0], [2.0, 2.0]]))
-    np.testing.assert_array_equal(
-        contract.transition_next_obs, np.array([[1.0, 1.0], [8.0, 9.0]], dtype=np.float32)
-    )
-    np.testing.assert_array_equal(
-        contract.actor_next_critic, np.array([[10.0], [20.0]], dtype=np.float32)
-    )
-    np.testing.assert_array_equal(
-        contract.transition_next_critic, np.array([[10.0], [20.0]], dtype=np.float32)
-    )
-    np.testing.assert_array_equal(contract.terminal_mask, np.array([False, True]))
-    np.testing.assert_array_equal(contract.timeout_terminal_mask, np.array([False, True]))
-
-
-def test_resolve_terminal_observation_contract_returns_terminal_rows_without_copying_next_obs():
-    terminal_contract = resolve_terminal_observation_contract(
-        next_obs_batch_size=2,
-        final_observation={
-            "obs": np.array([[5.0, 5.0], [8.0, 9.0]], dtype=np.float32),
-            "critic": np.array([[1.0], [7.0]], dtype=np.float32),
-        },
-        done=np.array([False, True]),
-        truncated=np.array([False, True]),
-    )
-
-    np.testing.assert_array_equal(terminal_contract.terminal_mask, np.array([False, True]))
-    np.testing.assert_array_equal(terminal_contract.timeout_terminal_mask, np.array([False, True]))
-    np.testing.assert_array_equal(
-        terminal_contract.terminal_obs,
-        np.array([[5.0, 5.0], [8.0, 9.0]], dtype=np.float32),
-    )
-    np.testing.assert_array_equal(
-        terminal_contract.terminal_critic,
-        np.array([[1.0], [7.0]], dtype=np.float32),
-    )
diff --git a/tests/utils/test_math_utils.py b/tests/utils/test_math_utils.py
index da9c4effa..de42038ec 100644
--- a/tests/utils/test_math_utils.py
+++ b/tests/utils/test_math_utils.py
@@ -10,10 +10,12 @@
     np_quat_angular_velocity,
     np_quat_apply,
     np_quat_apply_batched,
+    np_quat_apply_inverse_batched,
     np_quat_ensure_continuity,
     np_quat_error_magnitude,
     np_quat_error_magnitude_batched,
     np_quat_error_magnitude_squared_batched,
+    np_quat_from_angle_axis,
     np_quat_from_euler_xyz,
     np_quat_mul,
     np_quat_mul_batched,
@@ -66,6 +68,26 @@ def test_quat_error_vectorized_batch() -> None:
     np.testing.assert_allclose(err, np.deg2rad([30.0, 45.0]), atol=1e-7)
 
 
+def test_quat_from_angle_axis_round_trips_through_axis_angle() -> None:
+    rng = np.random.default_rng(0)
+    axes = rng.standard_normal((8, 3))
+    angles = rng.uniform(0.0, np.pi, size=8)
+
+    quats = np_quat_from_angle_axis(angles, axes)
+
+    np.testing.assert_allclose(np.linalg.norm(quats, axis=-1), 1.0, atol=1e-12)
+    recovered = np_quat_to_axis_angle(quats)
+    expected = axes / np.linalg.norm(axes, axis=-1, keepdims=True) * angles[:, None]
+    np.testing.assert_allclose(recovered, expected, atol=1e-7)
+
+
+def test_quat_from_angle_axis_normalizes_axis_and_matches_z_reference() -> None:
+    angle = np.deg2rad(170.0)
+    quats = np_quat_from_angle_axis(np.array([angle]), np.array([[0.0, 0.0, 3.0]]))
+
+    np.testing.assert_allclose(quats[0], _quat_from_axis_angle_z(angle), atol=1e-12)
+
+
 def test_quat_to_axis_angle_invariant_to_sign_flip() -> None:
     q = _quat_from_axis_angle_z(np.deg2rad(170.0))
     axis_angle = np_quat_to_axis_angle(q[None, :])[0]
@@ -159,6 +181,34 @@ def test_batched_quaternion_helpers_match_flattened_helpers() -> None:
     )
 
 
+def test_quat_apply_inverse_batched_matches_matrix_transpose() -> None:
+    """Batched inverse rotation must equal R(q)^T @ v on (env, body) inputs."""
+    num_envs = 3
+    num_bodies = 4
+
+    quat = np_quat_from_euler_xyz(
+        np.linspace(-0.3, 0.4, num_envs * num_bodies),
+        np.linspace(0.2, -0.15, num_envs * num_bodies),
+        np.linspace(-0.5, 0.25, num_envs * num_bodies),
+    ).reshape(num_envs, num_bodies, 4)
+    vectors = np.linspace(-0.6, 0.7, num_envs * num_bodies * 3).reshape(num_envs, num_bodies, 3)
+
+    mats = np_matrix_from_quat(quat.reshape(-1, 4))
+    expected = np.einsum("nji,nj->ni", mats, vectors.reshape(-1, 3))
+
+    np.testing.assert_allclose(
+        np_quat_apply_inverse_batched(quat, vectors).reshape(-1, 3),
+        expected,
+        atol=1e-12,
+    )
+    # Round-trip: applying q after q^-1 must recover the original vector.
+    np.testing.assert_allclose(
+        np_quat_apply_batched(quat, np_quat_apply_inverse_batched(quat, vectors)),
+        vectors,
+        atol=1e-12,
+    )
+
+
 def test_anchor_frame_transform_matches_flattened_path() -> None:
     num_envs = 3
     num_bodies = 4
diff --git a/tests/utils/test_motion_export.py b/tests/utils/test_motion_export.py
new file mode 100644
index 000000000..89b022b0c
--- /dev/null
+++ b/tests/utils/test_motion_export.py
@@ -0,0 +1,121 @@
+"""Tests for the MuJoCo tracking FK export shared by scripts/motion converters."""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+from unisim.backend.mujoco.motion_export import compute_tracking_fk
+from unisim.backend.mujoco.xml import get_named_bodies
+
+from unilab.assets import ASSETS_ROOT_PATH
+
+
+def _g1_scene() -> str:
+    return str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat.xml")
+
+
+_G1_JOINT_NAMES = [
+    "left_hip_pitch_joint",
+    "left_hip_roll_joint",
+    "left_hip_yaw_joint",
+    "left_knee_joint",
+    "left_ankle_pitch_joint",
+    "left_ankle_roll_joint",
+    "right_hip_pitch_joint",
+    "right_hip_roll_joint",
+    "right_hip_yaw_joint",
+    "right_knee_joint",
+    "right_ankle_pitch_joint",
+    "right_ankle_roll_joint",
+    "waist_yaw_joint",
+    "waist_roll_joint",
+    "waist_pitch_joint",
+    "left_shoulder_pitch_joint",
+    "left_shoulder_roll_joint",
+    "left_shoulder_yaw_joint",
+    "left_elbow_joint",
+    "left_wrist_roll_joint",
+    "left_wrist_pitch_joint",
+    "left_wrist_yaw_joint",
+    "right_shoulder_pitch_joint",
+    "right_shoulder_roll_joint",
+    "right_shoulder_yaw_joint",
+    "right_elbow_joint",
+    "right_wrist_roll_joint",
+    "right_wrist_pitch_joint",
+    "right_wrist_yaw_joint",
+]
+
+
+def test_get_named_bodies_matches_g1_scene() -> None:
+    ids, names = get_named_bodies(_g1_scene())
+    assert ids == list(range(1, len(names) + 1))
+    assert "pelvis" in names
+    assert len(names) == 30
+
+
+def test_compute_tracking_fk_shapes_and_joint_roundtrip() -> None:
+    pytest.importorskip("mujoco")
+    rng = np.random.default_rng(0)
+    num_frames = 4
+    num_joints = len(_G1_JOINT_NAMES)
+
+    base_poss = rng.standard_normal((num_frames, 3)).astype(np.float32) * 0.05
+    base_poss[:, 2] += 0.8
+    base_rots = np.zeros((num_frames, 4), dtype=np.float32)
+    base_rots[:, 0] = 1.0
+    base_lin_vels = np.zeros((num_frames, 3), dtype=np.float32)
+    base_ang_vels = np.zeros((num_frames, 3), dtype=np.float32)
+    dof_poss = rng.standard_normal((num_frames, num_joints)).astype(np.float32) * 0.1
+    dof_vels = rng.standard_normal((num_frames, num_joints)).astype(np.float32) * 0.1
+
+    arrays = compute_tracking_fk(
+        _g1_scene(),
+        joint_names=_G1_JOINT_NAMES,
+        base_poss=base_poss,
+        base_rots=base_rots,
+        base_lin_vels=base_lin_vels,
+        base_ang_vels=base_ang_vels,
+        dof_poss=dof_poss,
+        dof_vels=dof_vels,
+    )
+
+    _, body_names = get_named_bodies(_g1_scene())
+    num_bodies = len(body_names) + 1  # + implicit world body 0
+
+    assert arrays["joint_pos"].shape == (num_frames, num_joints)
+    assert arrays["joint_vel"].shape == (num_frames, num_joints)
+    assert arrays["body_pos_w"].shape == (num_frames, num_bodies, 3)
+    assert arrays["body_quat_w"].shape == (num_frames, num_bodies, 4)
+    assert arrays["body_lin_vel_w"].shape == (num_frames, num_bodies, 3)
+    assert arrays["body_ang_vel_w"].shape == (num_frames, num_bodies, 3)
+    for array in arrays.values():
+        assert array.dtype == np.float32
+
+    # Joint states written into qpos/qvel must read back exactly.
+    np.testing.assert_array_equal(arrays["joint_pos"], dof_poss)
+    np.testing.assert_array_equal(arrays["joint_vel"], dof_vels)
+
+    # World body stays at the origin; pelvis tracks the commanded root state.
+    np.testing.assert_array_equal(arrays["body_pos_w"][:, 0], 0.0)
+    pelvis_id = body_names.index("pelvis") + 1
+    np.testing.assert_allclose(arrays["body_pos_w"][:, pelvis_id], base_poss, atol=1e-5)
+    np.testing.assert_allclose(arrays["body_quat_w"][:, pelvis_id], base_rots, atol=1e-5)
+
+
+def test_compute_tracking_fk_unknown_joint_raises() -> None:
+    pytest.importorskip("mujoco")
+    base = np.zeros((1, 3), dtype=np.float32)
+    quat = np.zeros((1, 4), dtype=np.float32)
+    quat[:, 0] = 1.0
+    with pytest.raises(ValueError, match="not_a_joint"):
+        compute_tracking_fk(
+            _g1_scene(),
+            joint_names=["not_a_joint"],
+            base_poss=base,
+            base_rots=quat,
+            base_lin_vels=base,
+            base_ang_vels=base,
+            dof_poss=np.zeros((1, 1), dtype=np.float32),
+            dof_vels=np.zeros((1, 1), dtype=np.float32),
+        )
diff --git a/tests/utils/test_obs_utils.py b/tests/utils/test_obs_utils.py
deleted file mode 100644
index f08fbc7b6..000000000
--- a/tests/utils/test_obs_utils.py
+++ /dev/null
@@ -1,128 +0,0 @@
-"""Tests for unilab.base.observations."""
-
-from __future__ import annotations
-
-import numpy as np
-import pytest
-
-from unilab.base.observations import flatten_obs_dict, get_obs_dims, split_obs_dict
-
-# ---------------------------------------------------------------------------
-# flatten_obs_dict — basic behaviour
-# ---------------------------------------------------------------------------
-
-
-class TestFlattenObsDict:
-    """Unit tests for flatten_obs_dict."""
-
-    def test_single_group(self):
-        obs = {"actor": np.ones((4, 8))}
-        flat = flatten_obs_dict(obs)
-        assert flat.shape == (4, 8)
-        np.testing.assert_array_equal(flat, np.ones((4, 8)))
-
-    def test_two_groups_concatenated(self):
-        obs = {
-            "obs": np.ones((2, 5)),
-            "critic": np.full((2, 3), 2.0),
-        }
-        flat = flatten_obs_dict(obs)
-        assert flat.shape == (2, 8)
-        np.testing.assert_array_equal(flat[:, :5], 1.0)
-        np.testing.assert_array_equal(flat[:, 5:], 2.0)
-
-    def test_insertion_order_preserved(self):
-        """Groups are concatenated in dict insertion order."""
-        a = np.array([[1.0, 2.0]])
-        b = np.array([[3.0]])
-        c = np.array([[4.0, 5.0, 6.0]])
-        obs = {"first": a, "second": b, "third": c}
-        flat = flatten_obs_dict(obs)
-        expected = np.array([[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]])
-        np.testing.assert_array_equal(flat, expected)
-
-    def test_three_groups(self):
-        obs = {
-            "obs": np.zeros((3, 10)),
-            "critic": np.ones((3, 4)),
-            "extra": np.full((3, 2), 9.0),
-        }
-        flat = flatten_obs_dict(obs)
-        assert flat.shape == (3, 16)
-
-    def test_dtype_preserved_float32(self):
-        obs = {"obs": np.zeros((1, 5), dtype=np.float32)}
-        flat = flatten_obs_dict(obs)
-        assert flat.dtype == np.float32
-
-    def test_dtype_preserved_float64(self):
-        obs = {"obs": np.zeros((1, 5), dtype=np.float64)}
-        flat = flatten_obs_dict(obs)
-        assert flat.dtype == np.float64
-
-    def test_mixed_dtype_upcasts(self):
-        """NumPy concatenation rules apply — float64 wins over float32."""
-        obs = {
-            "obs": np.zeros((2, 3), dtype=np.float32),
-            "critic": np.zeros((2, 1), dtype=np.float64),
-        }
-        flat = flatten_obs_dict(obs)
-        assert flat.dtype == np.float64
-
-    def test_large_batch(self):
-        n = 4096
-        obs = {"obs": np.random.randn(n, 98), "critic": np.random.randn(n, 3)}
-        flat = flatten_obs_dict(obs)
-        assert flat.shape == (n, 101)
-        np.testing.assert_array_equal(flat[:, :98], obs["obs"])
-        np.testing.assert_array_equal(flat[:, 98:], obs["critic"])
-
-    def test_single_env(self):
-        obs = {"obs": np.array([[1.0, 2.0, 3.0]])}
-        flat = flatten_obs_dict(obs)
-        assert flat.shape == (1, 3)
-
-    def test_values_roundtrip(self):
-        """Flatten then slice recovers original groups."""
-        actor = np.random.randn(8, 45)
-        critic = np.random.randn(8, 3)
-        obs = {"obs": actor, "critic": critic}
-        flat = flatten_obs_dict(obs)
-        np.testing.assert_array_equal(flat[:, :45], actor)
-        np.testing.assert_array_equal(flat[:, 45:], critic)
-
-
-class TestSplitObsDict:
-    """Unit tests for split_obs_dict."""
-
-    def test_with_critic(self):
-        obs = {"obs": np.ones((4, 8)), "critic": np.full((4, 3), 2.0)}
-        obs_arr, critic_arr = split_obs_dict(obs)
-        assert obs_arr.shape == (4, 8)
-        assert critic_arr is not None
-        assert critic_arr.shape == (4, 3)
-        np.testing.assert_array_equal(obs_arr, 1.0)
-        np.testing.assert_array_equal(critic_arr, 2.0)
-
-    def test_no_critic(self):
-        obs = {"obs": np.ones((4, 8))}
-        obs_arr, critic_arr = split_obs_dict(obs)
-        assert obs_arr.shape == (4, 8)
-        assert critic_arr.shape == (4, 8)
-        np.testing.assert_array_equal(critic_arr, obs_arr)
-
-
-class TestGetObsDims:
-    """Unit tests for get_obs_dims."""
-
-    def test_with_critic(self):
-        spec = {"obs": 49, "critic": 52}
-        obs_dim, critic_dim = get_obs_dims(spec)
-        assert obs_dim == 49
-        assert critic_dim == 52
-
-    def test_no_critic(self):
-        spec = {"obs": 49}
-        obs_dim, critic_dim = get_obs_dims(spec)
-        assert obs_dim == 49
-        assert critic_dim == 49
diff --git a/tests/utils/test_torch_utils.py b/tests/utils/test_torch_utils.py
deleted file mode 100644
index d3fb5639d..000000000
--- a/tests/utils/test_torch_utils.py
+++ /dev/null
@@ -1,266 +0,0 @@
-"""Tests for unilab.utils.tensor."""
-
-from __future__ import annotations
-
-import logging
-
-import numpy as np
-import pytest
-import torch
-
-from unilab.utils.tensor import to_numpy, to_torch
-
-
-class TestToTorch:
-    """Tests for to_torch."""
-
-    def test_tensor_input_cpu(self) -> None:
-        x = torch.randn(4, 8)
-        result = to_torch(x, "cpu")
-        assert isinstance(result, torch.Tensor)
-        assert result.device.type == "cpu"
-        np.testing.assert_array_equal(result.numpy(), x.numpy())
-
-    @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
-    def test_tensor_input_cuda(self) -> None:
-        x = torch.randn(4, 8)
-        result = to_torch(x, "cuda")
-        assert isinstance(result, torch.Tensor)
-        assert result.device.type == "cuda"
-
-    @pytest.mark.skipif(not torch.backends.mps.is_available(), reason="MPS not available")
-    def test_tensor_input_mps(self) -> None:
-        x = torch.randn(4, 8)
-        result = to_torch(x, "mps")
-        assert isinstance(result, torch.Tensor)
-        assert result.device.type == "mps"
-
-    def test_numpy_array_input_cpu(self) -> None:
-        x = np.random.randn(4, 8).astype(np.float32)
-        result = to_torch(x, "cpu")
-        assert isinstance(result, torch.Tensor)
-        assert result.device.type == "cpu"
-        assert result.shape == (4, 8)
-        np.testing.assert_array_almost_equal(result.numpy(), x)
-
-    def test_numpy_array_input_2d(self) -> None:
-        x = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
-        result = to_torch(x, "cpu")
-        assert isinstance(result, torch.Tensor)
-        assert result.shape == (2, 3)
-        np.testing.assert_array_almost_equal(result.numpy(), x)
-
-    def test_numpy_array_input_1d(self) -> None:
-        x = np.array([1.0, 2.0, 3.0, 4.0])
-        result = to_torch(x, "cpu")
-        assert isinstance(result, torch.Tensor)
-        assert result.shape == (4,)
-        np.testing.assert_array_almost_equal(result.numpy(), x)
-
-    def test_dlpack_tensor_input(self) -> None:
-        x = torch.randn(3, 5)
-        result = to_torch(x, "cpu")
-        assert isinstance(result, torch.Tensor)
-        assert result.device.type == "cpu"
-        np.testing.assert_array_almost_equal(result.numpy(), x.numpy())
-
-    @pytest.mark.parametrize(
-        "error_type",
-        [
-            AttributeError,
-            BufferError,
-            NotImplementedError,
-            TypeError,
-            ValueError,
-            RuntimeError,
-        ],
-    )
-    def test_dlpack_expected_failure_logs_and_falls_back(
-        self,
-        error_type: type[Exception],
-        monkeypatch: pytest.MonkeyPatch,
-        caplog: pytest.LogCaptureFixture,
-    ) -> None:
-        class ArrayWithDLPackFallback:
-            def __dlpack__(self):
-                raise AssertionError("torch.from_dlpack should own protocol invocation")
-
-            def __array__(self, dtype=None, copy=None):
-                del copy
-                return np.asarray([1.0, 2.0], dtype=dtype)
-
-        failure = error_type("broken dlpack")
-
-        def fail_dlpack(_value):
-            raise failure
-
-        monkeypatch.setattr(torch, "from_dlpack", fail_dlpack)
-        with caplog.at_level(logging.WARNING, logger="unilab.utils.tensor"):
-            result = to_torch(ArrayWithDLPackFallback(), "cpu")
-
-        torch.testing.assert_close(result, torch.tensor([1.0, 2.0]))
-        assert result.dtype == torch.float32
-        assert "dlpack conversion failed" in caplog.text
-        assert "broken dlpack" in caplog.text
-
-    def test_partial_dlpack_protocol_uses_numpy_fallback(
-        self,
-        caplog: pytest.LogCaptureFixture,
-    ) -> None:
-        class PartialDLPack:
-            def __dlpack__(self):
-                raise AssertionError("missing __dlpack_device__ must fail first")
-
-            def __array__(self, dtype=None, copy=None):
-                del copy
-                return np.asarray([1.0, 2.0], dtype=dtype)
-
-        with caplog.at_level(logging.WARNING, logger="unilab.utils.tensor"):
-            result = to_torch(PartialDLPack(), "cpu")
-
-        torch.testing.assert_close(result, torch.tensor([1.0, 2.0]))
-        assert "has no attribute '__dlpack_device__'" in caplog.text
-
-    def test_dlpack_unexpected_failure_propagates_without_fallback(
-        self,
-        monkeypatch: pytest.MonkeyPatch,
-        caplog: pytest.LogCaptureFixture,
-    ) -> None:
-        class DLPackOnly:
-            def __dlpack__(self):
-                raise AssertionError("torch.from_dlpack should own protocol invocation")
-
-            def __array__(self, dtype=None, copy=None):
-                del dtype, copy
-                raise AssertionError("unexpected errors must not enter the numpy fallback")
-
-        failure = OSError("unexpected dlpack failure")
-
-        def fail_dlpack(_value):
-            raise failure
-
-        monkeypatch.setattr(torch, "from_dlpack", fail_dlpack)
-        with pytest.raises(OSError) as caught:
-            to_torch(DLPackOnly(), "cpu")
-
-        assert caught.value is failure
-        assert not caplog.records
-
-    def test_list_input(self) -> None:
-        x = [1.0, 2.0, 3.0]
-        result = to_torch(x, "cpu")
-        assert isinstance(result, torch.Tensor)
-        assert result.shape == (3,)
-        expected = np.array([1.0, 2.0, 3.0], dtype=np.float32)
-        np.testing.assert_array_almost_equal(result.numpy(), expected)
-
-    def test_nested_list_input(self) -> None:
-        x = [[1.0, 2.0], [3.0, 4.0]]
-        result = to_torch(x, "cpu")
-        assert isinstance(result, torch.Tensor)
-        assert result.shape == (2, 2)
-        expected = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
-        np.testing.assert_array_almost_equal(result.numpy(), expected)
-
-    def test_tuple_input(self) -> None:
-        x = (1.0, 2.0, 3.0)
-        result = to_torch(x, "cpu")
-        assert isinstance(result, torch.Tensor)
-        assert result.shape == (3,)
-        expected = np.array([1.0, 2.0, 3.0], dtype=np.float32)
-        np.testing.assert_array_almost_equal(result.numpy(), expected)
-
-    def test_scalar_input(self) -> None:
-        x = 3.14
-        result = to_torch(x, "cpu")
-        assert isinstance(result, torch.Tensor)
-        assert result.shape == ()
-        assert result.item() == pytest.approx(3.14)
-
-    def test_int_list_input(self) -> None:
-        x = [1, 2, 3]
-        result = to_torch(x, "cpu")
-        assert isinstance(result, torch.Tensor)
-        assert result.dtype == torch.float32
-
-    def test_device_as_torch_device(self) -> None:
-        x = np.array([1.0, 2.0, 3.0])
-        device = torch.device("cpu")
-        result = to_torch(x, device)
-        assert isinstance(result, torch.Tensor)
-        assert result.device.type == "cpu"
-
-    def test_empty_array(self) -> None:
-        x = np.array([])
-        result = to_torch(x, "cpu")
-        assert isinstance(result, torch.Tensor)
-        assert result.shape == (0,)
-
-
-class TestToNumpy:
-    """Tests for to_numpy."""
-
-    def test_numpy_array_input(self) -> None:
-        x = np.array([1.0, 2.0, 3.0])
-        result = to_numpy(x)
-        assert isinstance(result, np.ndarray)
-        assert result is x
-
-    def test_torch_tensor_input_cpu(self) -> None:
-        x = torch.tensor([1.0, 2.0, 3.0])
-        result = to_numpy(x)
-        assert isinstance(result, np.ndarray)
-        np.testing.assert_array_almost_equal(result, np.array([1.0, 2.0, 3.0]))
-
-    def test_torch_tensor_input_requires_grad(self) -> None:
-        x = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)
-        result = to_numpy(x)
-        assert isinstance(result, np.ndarray)
-        np.testing.assert_array_almost_equal(result, np.array([1.0, 2.0, 3.0]))
-
-    @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
-    def test_torch_tensor_input_cuda(self) -> None:
-        x = torch.tensor([1.0, 2.0, 3.0], device="cuda")
-        result = to_numpy(x)
-        assert isinstance(result, np.ndarray)
-        np.testing.assert_array_almost_equal(result, np.array([1.0, 2.0, 3.0]))
-
-    def test_list_input(self) -> None:
-        x = [1.0, 2.0, 3.0]
-        result = to_numpy(x)
-        assert isinstance(result, np.ndarray)
-        np.testing.assert_array_almost_equal(result, np.array([1.0, 2.0, 3.0]))
-
-    def test_tuple_input(self) -> None:
-        x = (1.0, 2.0, 3.0)
-        result = to_numpy(x)
-        assert isinstance(result, np.ndarray)
-        np.testing.assert_array_almost_equal(result, np.array([1.0, 2.0, 3.0]))
-
-    def test_scalar_input(self) -> None:
-        x = 3.14
-        result = to_numpy(x)
-        assert isinstance(result, np.ndarray)
-        assert result.shape == ()
-        assert result.item() == pytest.approx(3.14)
-
-    def test_2d_tensor_input(self) -> None:
-        x = torch.randn(4, 8)
-        result = to_numpy(x)
-        assert isinstance(result, np.ndarray)
-        assert result.shape == (4, 8)
-        np.testing.assert_array_almost_equal(result, x.numpy())
-
-    def test_empty_tensor(self) -> None:
-        x = torch.tensor([])
-        result = to_numpy(x)
-        assert isinstance(result, np.ndarray)
-        assert result.shape == (0,)
-
-    def test_nested_list_input(self) -> None:
-        x = [[1.0, 2.0], [3.0, 4.0]]
-        result = to_numpy(x)
-        assert isinstance(result, np.ndarray)
-        assert result.shape == (2, 2)
-        expected = np.array([[1.0, 2.0], [3.0, 4.0]])
-        np.testing.assert_array_almost_equal(result, expected)
diff --git a/tests/utils/test_utils_package_policy.py b/tests/utils/test_utils_package_policy.py
index a126dddb1..f0b3f0e60 100644
--- a/tests/utils/test_utils_package_policy.py
+++ b/tests/utils/test_utils_package_policy.py
@@ -1,42 +1,25 @@
 import importlib
 from pathlib import Path
 
+import pytest
+
 import unilab.utils
 
 ALLOWED_UTILS_API = {"get_default_device", "to_numpy", "to_torch"}
 ALLOWED_UTILS_MODULES = {
     "__init__",
+    "checkpoint",
     "device",
     "geometry",
+    "monitoring",
     "nan_guard",
+    "nan_viz",
+    "reward",
     "rotation",
-    "support_matrix",
+    "seed",
+    "sim2sim",
     "tensor",
 }
-REMOVED_UTILS_SHIMS = {
-    "algo_utils",
-    "device_utils",
-    "experiment_tracking",
-    "final_observation",
-    "hardware_monitor",
-    "logging_common",
-    "math_utils",
-    "obs_utils",
-    "offpolicy_logger",
-    "onpolicy_logger",
-    "render_many",
-    "reward_utils",
-    "rsl_rl_compat",
-    "rsl_rl_vec_env_wrapper",
-    "run_utils",
-    "torch_utils",
-    "viser_scene",
-    "xml_utils",
-}
-REMOVED_OWNER_ALIASES = {
-    "unilab.algos.torch.offpolicy.logging",
-    "unilab.algos.torch.common.tensor",
-}
 
 
 def test_utils_api_is_whitelisted() -> None:
@@ -57,18 +40,10 @@ def test_repo_has_no_package_level_utils_imports() -> None:
             assert "from unilab.utils import" not in path.read_text(encoding="utf-8"), path
 
 
-def test_removed_utils_shims_are_not_importable() -> None:
-    for module_name in sorted(f"unilab.utils.{name}" for name in REMOVED_UTILS_SHIMS):
-        assert importlib.util.find_spec(module_name) is None, module_name
-
-
-def test_removed_owner_aliases_are_not_importable() -> None:
-    for module_name in sorted(REMOVED_OWNER_ALIASES):
-        assert importlib.util.find_spec(module_name) is None, module_name
-
-
-def test_algos_torch_common_no_longer_reexports_utils_primitives() -> None:
-    common = importlib.import_module("unilab.algos.torch.common")
-    assert "get_default_device" not in common.__all__
-    assert "to_numpy" not in common.__all__
-    assert "to_torch" not in common.__all__
+def test_algo_layer_no_longer_lives_in_unilab() -> None:
+    """The algo/IPC/logging layers moved to uni_rl (issue #1480)."""
+    assert not (Path("src/unilab/algos")).exists()
+    assert not (Path("src/unilab/ipc")).exists()
+    assert not (Path("src/unilab/logging")).exists()
+    with pytest.raises(ModuleNotFoundError):
+        importlib.import_module("unilab.algos.common")
diff --git a/tests/utils/test_xml_utils.py b/tests/utils/test_xml_utils.py
index 27eb03d9b..b6a987a53 100644
--- a/tests/utils/test_xml_utils.py
+++ b/tests/utils/test_xml_utils.py
@@ -6,16 +6,18 @@
 import xml.etree.ElementTree as ET
 
 import pytest
-
-from unilab.assets import ASSETS_ROOT_PATH
-from unilab.base.backend import (
-    inject_mujoco_tracking_sensors,
+from unisim.backend.motrix.scene import (
     materialize_motrix_hfield_attached_scene,
     materialize_motrix_scene,
+)
+from unisim.backend.mujoco.xml import (
+    inject_mujoco_tracking_sensors,
     materialize_mujoco_hfield_attached_scene,
     materialize_scene_fragments,
 )
 
+from unilab.assets import ASSETS_ROOT_PATH
+
 
 def _g1_scene() -> str:
     return str(ASSETS_ROOT_PATH / "robots" / "g1" / "scene_flat.xml")
@@ -105,8 +107,6 @@ def test_inject_mujoco_tracking_sensors_uses_mjspec_and_preserves_contract() ->
             "track_angvel_w_pelvis",
             "track_pos_b_pelvis",
             "track_quat_b_pelvis",
-            "track_linvel_b_pelvis",
-            "track_angvel_b_pelvis",
         ):
             assert mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_SENSOR, sensor_name) >= 0
     finally:
@@ -126,8 +126,6 @@ def test_materialize_motrix_scene_adds_tracking_frame_sensors() -> None:
     for sensor_name in (
         "track_pos_b_pelvis",
         "track_quat_b_pelvis",
-        "track_linvel_b_pelvis",
-        "track_angvel_b_pelvis",
     ):
         assert model.get_sensor_value(sensor_name, data).shape[0] == 1
 
diff --git a/tests/visualization/test_interactive_playback.py b/tests/visualization/test_interactive_playback.py
index fbc0ec523..45b89ebfa 100644
--- a/tests/visualization/test_interactive_playback.py
+++ b/tests/visualization/test_interactive_playback.py
@@ -270,6 +270,7 @@ def fake_student_policy(actor_obj, hist_obj, obs, *, device):
             "model_state_dict": {},
             "distill_runtime_cfg": {"algo": {"model": {"hidden_dims": [8]}}},
         },
+        "apply_teacher_defaults": lambda cfg_obj: cfg_obj,
         "cfg_with_checkpoint_runtime": lambda cfg_obj, checkpoint: runtime_cfg,
         "build_play_env_cfg_override": lambda cfg_obj: {"env": "override"},
         "create_env": lambda cfg_obj, *, num_envs, env_cfg_override: FakeEnv(),
@@ -481,7 +482,7 @@ def get_inference_policy(self, *, device):
 
 
 def test_create_rsl_rl_playback_session_wraps_load_with_dim_guard(tmp_path: Path) -> None:
-    from unilab.training.sim2sim import CrossBackendIncompatibleError
+    from unilab.utils.sim2sim import CrossBackendIncompatibleError
 
     run_dir = tmp_path / "run_1"
     run_dir.mkdir()
@@ -506,31 +507,16 @@ def get_inference_policy(self, *, device):
         create_rsl_rl_playback_session(**kwargs)
 
 
-def test_interactive_playback_has_no_scripts_sys_path_hack() -> None:
-    source = (
-        Path(__file__).resolve().parents[2]
-        / "src"
-        / "unilab"
-        / "visualization"
-        / "interactive_playback.py"
-    ).read_text(encoding="utf-8")
-
-    assert "sys.path" not in source
-    assert "from train_appo import" not in source
-    assert "from train_offpolicy import" not in source
-    assert "from train_hora_distill import" not in source
-
-
 def test_sac_playback_session_runs_sim2sim_preflight(
     monkeypatch: pytest.MonkeyPatch,
     tmp_path: Path,
 ) -> None:
+    import uni_rl.algos.common.actor_factory as actor_factory
     from omegaconf import OmegaConf
 
-    import unilab.algos.torch.common.actor_factory as actor_factory
-    import unilab.training.offpolicy as offpolicy_play
-    import unilab.training.run as training_run
+    import unilab.utils.checkpoint as checkpoint_utils
     import unilab.visualization.interactive_playback as interactive_playback
+    import unilab.visualization.interactive_playback as offpolicy_play
 
     checkpoint = tmp_path / "model_10.pt"
     torch.save({"actor": {}}, checkpoint)
@@ -585,7 +571,7 @@ def fake_preflight(source_run_dir, target_cfg, *, algo_name, strict):
         lambda algo_name, cfg, *, obs_dim, critic_obs_dim: ("sac", {}),
     )
     monkeypatch.setattr(
-        training_run,
+        checkpoint_utils,
         "resolve_offpolicy_checkpoint_path",
         lambda *args, **kwargs: (str(checkpoint), str(tmp_path)),
     )
@@ -664,12 +650,11 @@ def test_appo_hora_playback_session_uses_hora_wrapper_and_actor_checkpoint(
     tmp_path: Path,
 ) -> None:
     import rsl_rl.utils as rsl_rl_utils
+    import uni_rl.algos.hora.models as hora_models
+    import uni_rl.algos.hora.rsl_rl as hora_rsl
     from omegaconf import OmegaConf
     from tensordict import TensorDict
 
-    import unilab.algos.torch.hora.models as hora_models
-    import unilab.algos.torch.hora.rsl_rl as hora_rsl
-
     checkpoint = tmp_path / "model_10.pt"
     torch.save({"actor": {"weight": torch.tensor(1.0)}}, checkpoint)
     captured: dict[str, Any] = {}
@@ -767,6 +752,7 @@ def forward(self, obs):
 
     session.reset()
     assert session.advance(PlaybackControls()) is True
+    assert isinstance(session.actor, FakeActor)
     assert policy_obs_mode == "actor"
     assert resolved_checkpoint == str(checkpoint)
     assert captured["wrapper_cls"] == "hora"
@@ -780,11 +766,11 @@ def test_sac_hora_playback_session_updates_priv_info_after_reset_and_step(
     monkeypatch: pytest.MonkeyPatch,
     tmp_path: Path,
 ) -> None:
+    import uni_rl.algos.common.actor_factory as actor_factory
     from omegaconf import OmegaConf
 
-    import unilab.algos.torch.common.actor_factory as actor_factory
-    import unilab.training.offpolicy as offpolicy_play
-    import unilab.training.run as training_run
+    import unilab.utils.checkpoint as checkpoint_utils
+    import unilab.visualization.interactive_playback as offpolicy_play
 
     checkpoint = tmp_path / "model_10.pt"
     torch.save({"actor": {}}, checkpoint)
@@ -875,7 +861,7 @@ def get_physics_state_snapshot(self):
         ),
     )
     monkeypatch.setattr(
-        training_run,
+        checkpoint_utils,
         "resolve_offpolicy_checkpoint_path",
         lambda *args, **kwargs: (str(checkpoint), str(tmp_path)),
     )
@@ -912,11 +898,12 @@ def test_hora_distill_playback_session_loads_stage2_checkpoint_and_student_polic
     monkeypatch: pytest.MonkeyPatch,
     tmp_path: Path,
 ) -> None:
+    import uni_rl.algos.hora.distill as distill
+    import uni_rl.algos.hora.rsl_rl as hora_rsl
     from omegaconf import OmegaConf
     from tensordict import TensorDict
 
-    import unilab.algos.torch.hora.distill as distill
-    import unilab.algos.torch.hora.rsl_rl as hora_rsl
+    import unilab.base.config_adapter as config_adapter
     import unilab.training as training
 
     checkpoint = tmp_path / "hora_stage2_last.pt"
@@ -988,13 +975,13 @@ def __init__(self, *args, **kwargs):
         def build_play_env_cfg_override(self):
             return {}
 
-    monkeypatch.setattr(training, "BackendAdapter", FakeBackendAdapter)
+    monkeypatch.setattr(config_adapter, "BackendAdapter", FakeBackendAdapter)
     monkeypatch.setattr(
         distill,
         "student_policy",
         lambda actor, hist_normalizer, obs, *, device: torch.ones((1, 2)),
     )
-    monkeypatch.setattr(training, "create_env", lambda *args, **kwargs: fake_env)
+    monkeypatch.setattr(config_adapter, "create_env", lambda *args, **kwargs: fake_env)
     monkeypatch.setattr(hora_rsl, "HoraRslRlVecEnvWrapper", FakeWrapper)
     monkeypatch.setattr(
         distill,
@@ -1038,3 +1025,357 @@ def fake_load_distilled_checkpoint(actor, hist_normalizer, checkpoint_path, *, d
     assert captured["policy_obs_mode"] == "actor"
     assert captured["loaded_checkpoint"] == checkpoint
     torch.testing.assert_close(captured["actions"], torch.ones((1, 2)))
+
+
+def test_infer_checkpoint_actor_input_dim_mlp_key(tmp_path: Path) -> None:
+    from unilab.visualization.interactive_playback import infer_checkpoint_actor_input_dim
+
+    checkpoint = tmp_path / "model_10.pt"
+    torch.save({"actor_state_dict": {"mlp.0.weight": torch.zeros((8, 42))}}, checkpoint)
+
+    assert infer_checkpoint_actor_input_dim(str(checkpoint)) == 42
+
+
+def test_infer_checkpoint_actor_input_dim_actor_prefixed_key(tmp_path: Path) -> None:
+    from unilab.visualization.interactive_playback import infer_checkpoint_actor_input_dim
+
+    checkpoint = tmp_path / "model_10.pt"
+    torch.save(
+        {"actor_state_dict": {"actor.mlp.0.weight": torch.zeros((8, 17))}},
+        checkpoint,
+    )
+
+    assert infer_checkpoint_actor_input_dim(str(checkpoint)) == 17
+
+
+def test_infer_checkpoint_actor_input_dim_generic_first_layer_key(tmp_path: Path) -> None:
+    from unilab.visualization.interactive_playback import infer_checkpoint_actor_input_dim
+
+    checkpoint = tmp_path / "model_10.pt"
+    torch.save(
+        {"actor_state_dict": {"encoder.0.weight": torch.zeros((4, 23))}},
+        checkpoint,
+    )
+
+    assert infer_checkpoint_actor_input_dim(str(checkpoint)) == 23
+
+
+def test_infer_checkpoint_actor_input_dim_returns_none_when_undetectable(
+    tmp_path: Path,
+) -> None:
+    from unilab.visualization.interactive_playback import infer_checkpoint_actor_input_dim
+
+    not_a_dict = tmp_path / "model_list.pt"
+    torch.save({"actor_state_dict": ["not", "a", "dict"]}, not_a_dict)
+    missing = tmp_path / "model_missing.pt"
+    torch.save({"model_state_dict": {}}, missing)
+    no_matching_key = tmp_path / "model_other.pt"
+    torch.save(
+        {"actor_state_dict": {"mlp.2.weight": torch.zeros((8, 8))}},
+        no_matching_key,
+    )
+
+    assert infer_checkpoint_actor_input_dim(str(not_a_dict)) is None
+    assert infer_checkpoint_actor_input_dim(str(missing)) is None
+    assert infer_checkpoint_actor_input_dim(str(no_matching_key)) is None
+
+
+@pytest.mark.parametrize("value", [None, "", "-1", "None", "null"])
+def test_normalize_checkpoint_value_maps_sentinels_to_none(value: object) -> None:
+    from unilab.visualization.interactive_playback import normalize_checkpoint_value
+
+    assert normalize_checkpoint_value(value) is None
+
+
+@pytest.mark.parametrize(
+    ("value", "expected"),
+    [("12", "12"), (12, "12"), ("run_2024", "run_2024"), ("/abs/model_5.pt", "/abs/model_5.pt")],
+)
+def test_normalize_checkpoint_value_keeps_real_values(value: object, expected: str) -> None:
+    from unilab.visualization.interactive_playback import normalize_checkpoint_value
+
+    assert normalize_checkpoint_value(value) == expected
+
+
+def _play_interactive_args(**overrides: Any) -> Any:
+    from unilab.visualization.interactive_playback import PlayInteractiveArgs
+
+    defaults: dict[str, Any] = {
+        "task": "MyTask",
+        "load_run": "-1",
+        "checkpoint": None,
+        "action_mode": "policy",
+        "policy_obs_mode": "auto",
+        "algo_log_name": "rsl_rl_ppo",
+        "log_root": None,
+        "show_target_bodies": False,
+        "show_reward_debug": False,
+        "target_show_axes": False,
+        "target_body_names": "",
+        "target_max_bodies": 32,
+        "target_marker_radius": 0.05,
+        "target_axis_length": 0.2,
+        "target_marker_alpha": 0.7,
+        "reward_debug_show_velocity": False,
+        "reward_debug_lin_vel_scale": 1.0,
+        "reward_debug_ang_vel_scale": 1.0,
+        "reward_debug_show_connectors": False,
+        "reward_debug_show_global_anchor": False,
+        "camera_follow_body": False,
+        "camera_focus_body_name": "",
+        "camera_height_offset": 0.0,
+        "camera_distance": None,
+        "camera_elevation": None,
+        "camera_azimuth": None,
+        "use_env_visual_model": False,
+        "speed": 1.0,
+        "start_paused": False,
+    }
+    defaults.update(overrides)
+    return PlayInteractiveArgs(**defaults)
+
+
+def test_build_playback_config_maps_play_interactive_args() -> None:
+    from unilab.visualization.interactive_playback import build_playback_config
+
+    args = _play_interactive_args(
+        task="OtherTask",
+        load_run="run_1",
+        checkpoint="12",
+        action_mode="random",
+        policy_obs_mode="flat",
+        algo_log_name="custom_ppo",
+        log_root="/tmp/logs",
+        speed=2.5,
+        start_paused=True,
+    )
+
+    playback_cfg = build_playback_config(args, num_envs=3)
+
+    assert playback_cfg == RslRlPlaybackConfig(
+        task="OtherTask",
+        load_run="run_1",
+        checkpoint="12",
+        action_mode="random",
+        policy_obs_mode="flat",
+        algo_log_name="custom_ppo",
+        log_root="/tmp/logs",
+        num_envs=3,
+        speed=2.5,
+        start_paused=True,
+    )
+
+
+def test_available_backends_for_task_reads_registry(monkeypatch: pytest.MonkeyPatch) -> None:
+    from unilab.base import registry
+    from unilab.visualization.interactive_playback import available_backends_for_task
+
+    monkeypatch.setattr(
+        registry,
+        "list_registered_envs",
+        lambda: {
+            "KnownTask": {"available_backends": ["mujoco", "motrix"]},
+            "BadTask": {"available_backends": "mujoco"},
+        },
+    )
+
+    assert available_backends_for_task("KnownTask") == ("mujoco", "motrix")
+    assert available_backends_for_task("UnknownTask") == ()
+    assert available_backends_for_task("BadTask") == ()
+
+
+def test_build_play_backend_adapter_injects_root_dir_and_materializer(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    import unilab.base.config_adapter as config_adapter
+    from unilab.visualization.interactive_playback import build_play_backend_adapter
+
+    captured: dict[str, Any] = {}
+    sentinel_materializer = object()
+
+    class FakeBackendAdapter:
+        def __init__(self, cfg: Any, **kwargs: Any) -> None:
+            captured["cfg"] = cfg
+            captured.update(kwargs)
+
+    monkeypatch.setattr(config_adapter, "BackendAdapter", FakeBackendAdapter)
+    monkeypatch.setattr(config_adapter, "materialize_scene_visual_override", sentinel_materializer)
+
+    cfg = SimpleNamespace(training=SimpleNamespace(task_name="Task"))
+    adapter = build_play_backend_adapter(cfg, root_dir="/repo", algo_name="appo")
+
+    assert isinstance(adapter, FakeBackendAdapter)
+    assert captured == {
+        "cfg": cfg,
+        "root_dir": "/repo",
+        "algo_name": "appo",
+        "scene_materializer": sentinel_materializer,
+    }
+
+
+def test_create_rsl_rl_playback_session_uses_runner_loader_and_exposes_runner(
+    tmp_path: Path,
+) -> None:
+    run_dir = tmp_path / "run_1"
+    run_dir.mkdir()
+    checkpoint = run_dir / "model_10.pt"
+    torch.save({"actor_state_dict": {}}, checkpoint)
+    captured: dict[str, Any] = {}
+
+    class Runner:
+        def __init__(self, wrapped_env, train_cfg, log_dir, device):
+            pass
+
+        def load(self, checkpoint, load_cfg):
+            raise AssertionError("runner.load must be bypassed when runner_loader is injected")
+
+        def get_inference_policy(self, *, device):
+            return lambda obs: torch.ones((1, 2))
+
+    def runner_loader(runner, path):
+        captured["loader_runner"] = runner
+        captured["loader_path"] = path
+
+    kwargs = _rsl_rl_session_kwargs(tmp_path)
+    kwargs["checkpoint_resolver"] = lambda *args: str(checkpoint)
+    kwargs["runner_cls"] = Runner
+    kwargs["runner_loader"] = runner_loader
+
+    session, _mode, resolved = create_rsl_rl_playback_session(**kwargs)
+
+    assert resolved == str(checkpoint)
+    assert isinstance(captured["loader_runner"], Runner)
+    assert captured["loader_path"] == str(checkpoint)
+    assert session.runner is captured["loader_runner"]
+
+
+def test_create_rsl_rl_playback_session_forwards_guard_algo_name(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+) -> None:
+    import unilab.visualization.interactive_playback as interactive_playback
+
+    run_dir = tmp_path / "run_1"
+    run_dir.mkdir()
+    checkpoint = run_dir / "model_10.pt"
+    torch.save({"actor_state_dict": {}}, checkpoint)
+    captured: dict[str, Any] = {}
+
+    class Runner:
+        def __init__(self, wrapped_env, train_cfg, log_dir, device):
+            pass
+
+        def load(self, checkpoint, load_cfg):
+            pass
+
+        def get_inference_policy(self, *, device):
+            return lambda obs: torch.ones((1, 2))
+
+    import contextlib
+
+    @contextlib.contextmanager
+    def fake_dim_guard(**kwargs):
+        captured["dim_guard"] = kwargs
+        yield
+
+    monkeypatch.setattr(interactive_playback, "policy_load_dim_guard", fake_dim_guard)
+
+    kwargs = _rsl_rl_session_kwargs(tmp_path)
+    kwargs["checkpoint_resolver"] = lambda *args: str(checkpoint)
+    kwargs["runner_cls"] = Runner
+    kwargs["guard_algo_name"] = "him_ppo"
+
+    create_rsl_rl_playback_session(**kwargs)
+
+    assert captured["dim_guard"] == {
+        "env_obs_dim": 5,
+        "env_action_dim": 2,
+        "algo_name": "him_ppo",
+    }
+
+
+def test_create_sac_playback_session_td3_load_filters_noise_scales(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+) -> None:
+    import uni_rl.algos.common.actor_factory as actor_factory
+    from omegaconf import OmegaConf
+
+    import unilab.utils.checkpoint as checkpoint_utils
+
+    checkpoint = tmp_path / "model_10.pt"
+    torch.save(
+        {"actor": {"weight": torch.ones(1), "noise_scales": torch.zeros(1)}},
+        checkpoint,
+    )
+    captured: dict[str, Any] = {}
+
+    class FakeActor:
+        def eval(self):
+            return self
+
+        def load_state_dict(self, state_dict, strict=True):
+            captured["actor_load"] = (state_dict, strict)
+
+    class FakeEnv:
+        num_envs = 1
+        obs_groups_spec = {"obs": 3, "critic": 5}
+        action_space = SimpleNamespace(
+            shape=(2,),
+            low=np.full((2,), -1.0),
+            high=np.full((2,), 1.0),
+        )
+        state = SimpleNamespace(info={})
+
+        def get_physics_state_snapshot(self):
+            return np.zeros((1, 4), dtype=np.float32)
+
+    cfg = OmegaConf.create(
+        {
+            "training": {"task_name": "Task", "device": None},
+            "algo": {
+                "algo_log_name": "td3",
+                "load_run": "run",
+                "actor_hidden_dim": 16,
+                "use_layer_norm": False,
+            },
+        }
+    )
+
+    def fake_build_actor(algo_type, *args, **kwargs):
+        captured["build_actor_algo_type"] = algo_type
+        return FakeActor()
+
+    monkeypatch.setattr(actor_factory, "build_actor", fake_build_actor)
+    monkeypatch.setattr(
+        checkpoint_utils,
+        "resolve_offpolicy_checkpoint_path",
+        lambda *args, **kwargs: (str(checkpoint), str(tmp_path)),
+    )
+
+    session, policy_obs_mode, resolved = create_sac_playback_session(
+        playback_cfg=RslRlPlaybackConfig(
+            task="Task",
+            load_run="run",
+            checkpoint=None,
+            action_mode="policy",
+            policy_obs_mode="actor",
+            algo_log_name="td3",
+            log_root=None,
+        ),
+        cfg=cfg,
+        env_factory=lambda num_envs: FakeEnv(),
+        root_dir=tmp_path,
+        device="cpu",
+        algo_name="td3",
+        log=lambda message: None,
+    )
+
+    assert resolved == str(checkpoint)
+    assert policy_obs_mode == "actor"
+    assert captured["build_actor_algo_type"] == "td3"
+    assert isinstance(session.actor, FakeActor)
+    assert session.actor_algo_type == "td3"
+    loaded_state, strict = captured["actor_load"]
+    assert set(loaded_state.keys()) == {"weight"}
+    assert strict is False
diff --git a/uv.lock b/uv.lock
index 7c4d63516..460fd47a4 100644
--- a/uv.lock
+++ b/uv.lock
@@ -3,21 +3,21 @@ revision = 3
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-    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
     "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'linux'",
-    "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
-    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
     "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform == 'win32'",
-    "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform == 'win32'",
     "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'win32'",
-    "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'win32'",
-    "python_full_version == '3.11.*' and platform_machine != 's390x' and sys_platform == 'win32'",
-    "python_full_version == '3.11.*' and platform_machine == 's390x' and sys_platform == 'win32'",
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-    "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'linux' and sys_platform != 'win32')",
     "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform != 'win32'",
+    "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
+    "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform == 'win32'",
+    "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'win32'",
+    "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'linux' and sys_platform != 'win32')",
     "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform != 'win32'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
+    "python_full_version == '3.11.*' and platform_machine != 's390x' and sys_platform == 'win32'",
+    "python_full_version == '3.11.*' and platform_machine == 's390x' and sys_platform == 'win32'",
     "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and platform_machine != 's390x' and sys_platform != 'linux' and sys_platform != 'win32')",
     "python_full_version == '3.11.*' and platform_machine == 's390x' and sys_platform != 'win32'",
     "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
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+    "python_full_version < '3.11' and platform_machine != 's390x' and sys_platform == 'win32'",
+    "python_full_version < '3.11' and platform_machine == 's390x' and sys_platform == 'win32'",
+    "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'linux') or (python_full_version < '3.11' and platform_machine != 's390x' and sys_platform != 'linux' and sys_platform != 'win32')",
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 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
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+[[package]]
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+version = "0.9.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "mypy-extensions" },
+    { name = "typing-extensions" },
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+]
+
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 name = "typing-inspection"
 version = "0.4.2"
@@ -3916,25 +4959,36 @@ dependencies = [
     { name = "lark" },
     { name = "mediapy" },
     { name = "ninja", marker = "sys_platform == 'linux'" },
+    { name = "numba" },
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
     { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
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     { name = "onnxruntime", version = "1.24.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "packaging" },
+    { name = "prettytable" },
     { name = "rich" },
     { name = "rsl-rl-lib" },
     { name = "setuptools" },
     { name = "tensorboard" },
     { name = "tensordict" },
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+    { name = "torch", version = "2.8.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux') or (sys_platform != 'linux' and sys_platform != 'win32')" },
+    { name = "torch", version = "2.8.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'win32'" },
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     { name = "tqdm" },
     { name = "typing-extensions" },
+    { name = "unilab-rl" },
+    { name = "unisim-core" },
     { name = "wandb" },
 ]
 
 [package.optional-dependencies]
+drake = [
+    { name = "drake-uni" },
+    { name = "mujoco" },
+]
+genesis = [
+    { name = "genesis-world" },
+]
 mjwarp = [
     { name = "mujoco-warp" },
     { name = "warp-lang" },
@@ -3964,7 +5018,9 @@ dev = [
 
 [package.metadata]
 requires-dist = [
+    { name = "drake-uni", marker = "extra == 'drake'", specifier = "==0.1.0" },
     { name = "etils" },
+    { name = "genesis-world", marker = "extra == 'genesis'", specifier = "==1.3.3" },
     { name = "gymnasium" },
     { name = "huggingface-hub", specifier = ">=0.25" },
     { name = "hydra-core", specifier = ">=1.3" },
@@ -3973,33 +5029,38 @@ requires-dist = [
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     { name = "mediapy" },
     { name = "motrixsim-core", marker = "extra == 'motrix'", specifier = "==0.8.2" },
+    { name = "mujoco", marker = "extra == 'drake'", specifier = ">=3.5" },
     { name = "mujoco", marker = "extra == 'mujoco'", specifier = ">=3.5" },
-    { name = "mujoco-uni-runtime", marker = "extra == 'mujoco'", specifier = "==0.3.1" },
+    { name = "mujoco-uni-runtime", marker = "extra == 'mujoco'", specifier = "==0.4.0" },
     { name = "mujoco-warp", marker = "extra == 'mjwarp'", specifier = "==3.10.0.3" },
     { name = "ninja", marker = "sys_platform == 'linux'" },
+    { name = "numba", specifier = ">=0.67" },
     { name = "numpy" },
     { name = "onnxruntime", marker = "python_full_version < '3.11'", specifier = "<1.20" },
     { name = "onnxruntime", marker = "python_full_version >= '3.11'", specifier = ">=1.20" },
     { name = "packaging" },
+    { name = "prettytable", specifier = ">=3.10" },
     { name = "pybind11", marker = "extra == 'mujoco'", specifier = ">=2.12" },
     { name = "rich" },
     { name = "rsl-rl-lib", specifier = ">=5.0.0" },
-    { name = "setuptools", specifier = "<70" },
-    { name = "tensorboard" },
+    { name = "setuptools" },
+    { name = "tensorboard", specifier = ">=2.21.0" },
     { name = "tensordict" },
-    { name = "torch", marker = "(platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux') or (sys_platform != 'linux' and sys_platform != 'win32')", specifier = "==2.7.0" },
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-    { name = "torch", marker = "sys_platform == 'win32'", specifier = "==2.7.0", index = "https://download.pytorch.org/whl/cu128" },
+    { name = "torch", marker = "(platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux') or (sys_platform != 'linux' and sys_platform != 'win32')", specifier = ">=2.8,<2.12" },
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+    { name = "torch", marker = "sys_platform == 'win32'", specifier = ">=2.8,<2.12", index = "https://download.pytorch.org/whl/cu128" },
     { name = "tqdm" },
     { name = "trimesh", marker = "extra == 'viser'", specifier = ">=3.21.7" },
     { name = "typing-extensions" },
+    { name = "unilab-rl", specifier = "==1.0.0" },
+    { name = "unisim-core", specifier = ">=0.1.14" },
     { name = "viser", marker = "extra == 'viser'", specifier = ">=1.0.26" },
     { name = "wandb" },
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     { name = "wheel", marker = "extra == 'mujoco'" },
 ]
-provides-extras = ["mujoco", "mjwarp", "motrix", "viser"]
+provides-extras = ["drake", "mujoco", "mjwarp", "motrix", "genesis", "viser"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -4010,6 +5071,40 @@ dev = [
     { name = "ruff" },
 ]
 
+[[package]]
+name = "unilab-rl"
+version = "1.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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+    { name = "omegaconf" },
+    { name = "packaging" },
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+    { name = "rsl-rl-lib" },
+    { name = "tensorboard" },
+    { name = "tensordict" },
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+]
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+[[package]]
+name = "unisim-core"
+version = "0.1.14"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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+
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 version = "2.6.3"
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+[[package]]
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+version = "2.1.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pyyaml" },
+]
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 name = "xxhash"
 version = "3.6.0"
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+
 [[package]]
 name = "zipp"
 version = "3.23.0"
diff --git a/uv.rocm.lock b/uv.rocm.lock
index 3965d1b3d..15c9a8b67 100644
--- a/uv.rocm.lock
+++ b/uv.rocm.lock
@@ -3,12 +3,12 @@ revision = 3
 requires-python = ">=3.10, <3.14"
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     "python_full_version >= '3.13' and platform_machine == 's390x'",
+    "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
+    "(python_full_version == '3.12.*' and platform_machine != 's390x' and platform_machine != 'x86_64') or (python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'linux')",
     "python_full_version == '3.12.*' and platform_machine == 's390x'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
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     "python_full_version >= '3.13' and platform_machine == 's390x'",
+    "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
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@@ -480,10 +480,10 @@ wheels = [
 
 [[package]]
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+version = "1.8.0"
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 [[package]]
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 ]
 
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+dependencies = [
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+    { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+]
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+wheels = [
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+
 [[package]]
 name = "embreex"
 version = "2.17.7.post7"
@@ -598,12 +611,12 @@ version = "1.14.0"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
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-    "(python_full_version == '3.12.*' and platform_machine != 's390x' and platform_machine != 'x86_64') or (python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'linux')",
     "python_full_version >= '3.13' and platform_machine == 's390x'",
+    "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
+    "(python_full_version == '3.12.*' and platform_machine != 's390x' and platform_machine != 'x86_64') or (python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'linux')",
     "python_full_version == '3.12.*' and platform_machine == 's390x'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
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-    "(python_full_version == '3.12.*' and platform_machine != 's390x' and platform_machine != 'x86_64') or (python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'linux')",
     "python_full_version >= '3.13' and platform_machine == 's390x'",
+    "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
+    "(python_full_version == '3.12.*' and platform_machine != 's390x' and platform_machine != 'x86_64') or (python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'linux')",
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 dependencies = [
@@ -1786,7 +1799,7 @@ wheels = [
 
 [[package]]
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-version = "3.8.0"
+version = "3.10.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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@@ -3176,12 +3196,12 @@ version = "1.17.1"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
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-    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
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-    "(python_full_version == '3.12.*' and platform_machine != 's390x' and platform_machine != 'x86_64') or (python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'linux')",
     "python_full_version >= '3.13' and platform_machine == 's390x'",
+    "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
+    "(python_full_version == '3.12.*' and platform_machine != 's390x' and platform_machine != 'x86_64') or (python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'linux')",
     "python_full_version == '3.12.*' and platform_machine == 's390x'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
     "(python_full_version == '3.11.*' and platform_machine != 's390x' and platform_machine != 'x86_64') or (python_full_version == '3.11.*' and platform_machine != 's390x' and sys_platform != 'linux')",
     "python_full_version == '3.11.*' and platform_machine == 's390x'",
 ]
@@ -3479,8 +3499,8 @@ version = "2.11.0"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
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-    "(python_full_version == '3.12.*' and platform_machine != 's390x' and platform_machine != 'x86_64') or (python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'linux')",
     "python_full_version >= '3.13' and platform_machine == 's390x'",
+    "(python_full_version == '3.12.*' and platform_machine != 's390x' and platform_machine != 'x86_64') or (python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'linux')",
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     "python_full_version == '3.11.*' and platform_machine == 's390x'",
@@ -3708,10 +3728,15 @@ dependencies = [
     { name = "tqdm" },
     { name = "triton-rocm", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
     { name = "typing-extensions" },
+    { name = "unilab-rl" },
+    { name = "unisim-core" },
     { name = "wandb" },
 ]
 
 [package.optional-dependencies]
+drake = [
+    { name = "drake-uni" },
+]
 motrix = [
     { name = "motrixsim-core" },
 ]
@@ -3737,6 +3762,7 @@ dev = [
 
 [package.metadata]
 requires-dist = [
+    { name = "drake-uni", marker = "extra == 'drake'", specifier = "==0.1.0" },
     { name = "etils" },
     { name = "gymnasium" },
     { name = "huggingface-hub", specifier = ">=0.25" },
@@ -3747,7 +3773,7 @@ requires-dist = [
     { name = "mediapy" },
     { name = "motrixsim-core", marker = "extra == 'motrix'", specifier = "==0.8.2" },
     { name = "mujoco", marker = "extra == 'mujoco'", specifier = ">=3.5" },
-    { name = "mujoco-uni-runtime", marker = "extra == 'mujoco'", specifier = "==0.3.1" },
+    { name = "mujoco-uni-runtime", marker = "extra == 'mujoco'", specifier = "==0.4.0" },
     { name = "ninja", marker = "sys_platform == 'linux'" },
     { name = "numpy" },
     { name = "onnxruntime", marker = "python_full_version < '3.11'", specifier = "<1.20" },
@@ -3765,11 +3791,13 @@ requires-dist = [
     { name = "trimesh", marker = "extra == 'viser'", specifier = ">=3.21.7" },
     { name = "triton-rocm", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'", specifier = "==3.6.0", index = "https://download.pytorch.org/whl/rocm7.2" },
     { name = "typing-extensions" },
+    { name = "unilab-rl", specifier = "==0.2.0" },
+    { name = "unisim-core", specifier = ">=0.1.14" },
     { name = "viser", marker = "extra == 'viser'", specifier = ">=1.0.26" },
     { name = "wandb" },
     { name = "wheel", marker = "extra == 'mujoco'" },
 ]
-provides-extras = ["mujoco", "motrix", "viser"]
+provides-extras = ["drake", "mujoco", "motrix", "viser"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -3780,6 +3808,39 @@ dev = [
     { name = "ruff" },
 ]
 
+[[package]]
+name = "unilab-rl"
+version = "0.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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+    { name = "omegaconf" },
+    { name = "packaging" },
+    { name = "rich" },
+    { name = "rsl-rl-lib" },
+    { name = "tensorboard" },
+    { name = "tensordict" },
+    { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform != 'linux'" },
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+]
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+dependencies = [
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+
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 version = "2.6.3"