This is an updated repository for customize Quadruped Robot Training (JAMAL). We have tested the code in Ubuntu 22 and Python 3.8

This repository contains learning-based locomotion control research from OpenRobotLab, currently including Hybrid Internal Model & H-Infinity Locomotion Control.

• [2024-04] Code of HIMLoco is released.
• [2024-04] We release the paper of H-Infinity Locomotion Control. Please check the 👉 webpage 👈 and view our demos! 🎇
• [2024-01] HIMLoco is accepted by ICLR 2024.
• [2023-12] We release the paper of HIMLoco. Please check the 👉 webpage 👈 and view our demos! 🎇

• Release the training code of HIMLoco, please see rsl_rl/rsl_rl/algorithms/him_ppo.py.
• Release deployment guidance of HIMLoco.
• Release the training code of H-Infinity Locomotion Control.
• Release deployment guidance of H-Infinity Locomotion Control.

We test our codes under the following environment:

• Ubuntu 22
• NVIDIA Driver: 570.195.03
• CUDA 12.0
• Python 3.8.20
• PyTorch 2.3.1+cu121
• Isaac Gym: Preview 4

1. Create an environment and install PyTorch:

• conda create -n himloco python=3.8.20
• conda activate himloco

• conda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=12.1 -c pytorch -c nvidia

2. Install Isaac Gym:

• Download and install Isaac Gym Preview 4 from https://developer.nvidia.com/isaac-gym
• cd isaacgym/python && pip install -e .

3. Clone this repository.

• cd
• git clone https://github.com/hassan-mehmood1/HIMLoco.git
• cd HIMLoco

4. Install HIMLoco.

• cd ~/HIMLoco/rsl_rl && pip install -e .
• cd ~/HIMLoco/legged_gym && pip install -e .
• pip install "numpy<1.24" pydelatin wandb tqdm opencv-python ipdb pyfqmr flask

Note: Please use legged_gym and rsl_rl provided in this repo, we have modefications on these repos.

1. Train a policy:

• cd ~/HIMLoco/legged_gym/legged_gym/scripts
• python train.py

2. Play and export the latest policy:

• cd ~/HIMLoco/legged_gym/legged_gym/scripts
• python play.py

If you find our work helpful, please cite:

@inproceedings{long2023him,
  title={Hybrid Internal Model: Learning Agile Legged Locomotion with Simulated Robot Response},
  author={Long, Junfeng and Wang, ZiRui and Li, Quanyi and Cao, Liu and Gao, Jiawei and Pang, Jiangmiao},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2024}
}

@misc{long2024hinf,
  title={Learning H-Infinity Locomotion Control}, 
  author={Junfeng Long and Wenye Yu and Quanyi Li and Zirui Wang and Dahua Lin and Jiangmiao Pang},
  year={2024},
  eprint={2404.14405},
  archivePrefix={arXiv},
}

This work is under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

• legged_gym: Our codebase is built upon legged_gym.