WoCoCo: Learning Whole-Body Humanoid Control with Sequential Contacts

Chong Zhang*, Wenli Xiao*, Tairan He, Guanya Shi

CoRL 2024 (oral)

[Website] [Arxiv] [Video]

License

Important

This codebase is under CC BY-NC 4.0 license, with inherited license in Legged Gym and RSL RL from ETH Zurich, Nikita Rudin and NVIDIA CORPORATION & AFFILIATES. You may not use the material for commercial purposes, e.g., to make demos to advertise your commercial products.

Quick Start

Install

Note: Before running our code, it's highly recommended to first play with RSL's Legged Gym version to get a basic understanding of the Isaac-LeggedGym-RslRL framework.

Similar to basic legged-gym based codebase, the install pipeline is:

1. Create environment and install torch

   conda create -n omnih2o python=3.8 
   pip3 install torch torchvision torchaudio 
   

2. 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 .

   check it is correctly installed by playing:

   cd examples && python 1080_balls_of_solitude.py

3. Clone our repo

   git clone git@github.com:LeCAR-Lab/wococo.git
   

4. install packages

   pip install -r requirements.txt
   

Training

• go to legged_gym/legged_gym

  python3 scripts/train.py --task=h1:jumpjack
  

Play

• go to legged_gym/legged_gym

  python scripts/play.py --task=h1:jumpjack --num_envs=3
  

Note

Since our framework is quite intuitive and we de-engineered a lot of things, (i.e., we do not use certain engineering tricks that can be applied to specific tasks such as terrain curriculum) to showcase the framework's capability, we provide a clap-and-dance example here for the reward and MDP implementations, and encourage anyone to engineer the specific environments for better performance on their own applications.

The engineering is encouraged for terrain design, curiosity observation space design (which can be further reduced based on specific tasks), and sim-to-real pipeline (where tricks like teacher-student and system identification may make training and deployment more stable).

Cite

@inproceedings{
    zhang2024wococo,
    title={WoCoCo: Learning Whole-Body Humanoid Control with Sequential Contacts},
    author={Chong Zhang and Wenli Xiao and Tairan He and Guanya Shi},
    booktitle={8th Annual Conference on Robot Learning},
    year={2024},
    url={https://openreview.net/forum?id=Czs2xH9114}
}