Authors: Hang Liu, Sangli Teng, Ben Liu, Wei Zhang, Maani Ghaffari
Website: https://umich-curly.github.io/DHAL/
Paper: https://arxiv.org/pdf/2503.01842
Contact: hangliu@umich.edu
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Create environment and install torch
conda create -n dhal python=3.8 pip3 install torch torchvision torchaudio
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Install Isaac Gym preview 4 release https://developer.nvidia.com/isaac-gym
unzip files to a folder, then install with pip:
cd isaacgym/python && pip install -e . -
Clone our repo and install
git clone git@github.com:UMich-CURLY/DHAL.git cd DHAL/legged_gym pip install -e . cd ../rsl_rl pip install -e .
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go to
legged_gym/legged_gym/scriptspython train.py --exptid=dhal
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go to
legged_gym/legged_gympython play.py --exptid=dhal
- --exptid: string, can be
WHATEVERand the weights would be saved in corresponding directory - --checkpoint: the specific checkpoint you want to load. If not specified load the latest one.
- --resume: resume training from previous checkpoint, need to use with
--exptid. - --wandb: with wandb logging.
- --debug: debug mode, less agents.
Our code are based on these previous outstanding repo:
- https://github 610C .com/leggedrobotics/rsl_rl
- https://github.com/leggedrobotics/legged_gym
- https://github.com/chengxuxin/extreme-parkour
- https://github.com/MarkFzp/Deep-Whole-Body-Control
If our work does help you, please consider citing us and the following works:
@misc{liu2025discretetimehybridautomatalearning,
title={Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets Skateboarding},
author={Hang Liu and Sangli Teng and Ben Liu and Wei Zhang and Maani Ghaffari},
year={2025},
eprint={2503.01842},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2503.01842},
}We used codes in Legged Gym and RSL RL, based on the paper:
- Rudin, Nikita, et al. "Learning to walk in minutes using massively parallel deep reinforcement learning." CoRL 2022.