Code Repository of IROS 22' paper Model-free Neural Lyapunov Control for Safe Robot Navigation

ArXiv | Demos

├── README.md ├── setup.py └── shrl ├── config.py # config file, including data path, default devices, ect. ├── envs # simulation environments ├── evaluation # evaluation utils ├── exps # experiment scripts ├── learn # low-level controller and neural Lyapunov function learning algorithms ├── monitor # high-level monitor ├── plan # high-level planner, RRT & RRT* └── tests # test cases 

1. Install necessary dependencies.

pip install -e . 

2. Configure MuJoCo-py by following official README.
3. (Optional) Download pretrained models (~35 MB)

bash download.sh 

Two quick start examples:

1. Co-learning low-level controller and neural Lyapunov function
   python exps/train/no_obstacle/lyapunov_td3/[robot-name].py

2. Pre-compute monitor and evaluate
   python exps/hierachical/rrt_lyapunov/[robot-name].py

One can start tracing code from exps folder.

@inproceedings{Xiong2022ModelfreeNL, title={Model-free Neural Lyapunov Control for Safe Robot Navigation}, author={Zikang Xiong and Joe Eappen and Ahmed H. Qureshi and Suresh Jagannathan}, booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year={2022}, }