FailBench is a MuJoCo-based simulation framework designed to study how robots interact with their environments during sudden unexpected failures — such as hardware shutdown, sensor degradation, or actuator malfunctions. The goal is to systematically understand failure-induced behaviors and use this knowledge to develop safer motion plans or robust learned policies, depending on the control approach. By simulating and analyzing failure scenarios, FailBench helps inform decision-making strategies that proactively account for potential failure modes, ultimately improving robot safety and resilience during real-world missions.
We implemented a custom planner that combines:
- Rapidly-Exploring Random Tree (RRT) for path planning
- Inverse Kinematics (IK) via Mink
- MuJoCo-based collision checking
This pipeline enables robust motion planning in cluttered environments with physical constraints.
We provide a wrapper around Mink to interface with our MuJoCo simulation.
📄 Read the IK wrapper documentation
Clone this repository and create a Conda virtual environment using the provided environment.yml file:
conda env create -f environment.yml- MuJoCo 3.3.4
- Python 3.10
All other required packages will be installed automatically when setting up the environment using environment.yml.
This project is inspired by and builds upon the excellent work of existing simulation platforms such as:
We extend their design philosophies and modular stacks to focus specifically on simulating and understanding robotic failures.
If you use this project or find it helpful, please consider citing the foundational work we build upon:
@inproceedings{[FAILBENCH2025],
title={TBA},
author={Duc M. Nguyen, Saad Ghani, Andrew Marshall, Allison Andreyev, Gregory J. Stein and Xuesu Xiao},
booktitle={TBA},
year={2025}
}This project is licensed under the MIT License.