BodyGen: Advancing Towards Efficient Embodiment Co-Design

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This repository contains the PyTorch implementation of "BodyGen: Advancing Towards Efficient Embodiment Co-Design." (ICLR 2025, Spotlight)

🛠️ Setup

Let's start with python 3.9. It's recommend to create a conda env:

Create a new conda environment

conda create -n BodyGen python=3.9
conda activate BodyGen

Install for MuJoCo Simulator and mujoco-py (Important)

Install mujoco-py following the instruction here.

Set the following environment variable to avoid problems with multiprocess trajectory sampling:

export OMP_NUM_THREADS=1

(Optional) For MacOS user, you can follow the README_FOR_MAC.md to install MuJoCo on M1/M2/M3 Mac, which is helpful for embodied agents visualization.

Install

pip install -r requirements.txt

Note you may have to follow https://pytorch.org/ setup instructions for installation on your own machine.

👀 Visualization

Please refer to this webpage for more visualization results.

Pretrained Models

We also provide pretrained models in BodyGen/pretrained_models for visualization.

• You can download pretrained models from Google Drive

• Once the pretrained_models.zip file is downloaded, unzip it under the folder BodyGen of this repo:

  unzip pretrained_models.zip
  

After you unzip the file, an example directory hierarchy is:

assets/
design_opt/
...
pretrained_models/
  |-- cheetah/
  |-- crawler/
  |-- ...
  |-- walker-regular/
...
scripts/

Interactive Visualization

If you have a GUI display, you can run the following command to visualize the pretrained model:

python design_opt/eval.py --train_dir <path_of_model_folder>

For example, you can use pretrained_models to visualize the co-design embodied agent on cheetah generated by BodyGen:

python design_opt/eval.py --train_dir /Path/to/BodyGen/pretrained_models/cheetah

Press S to slow the agent, and F to speed up.

💻 Training

cd BodyGen
chmod 777 scripts/Run_BodyGen.sh
./scripts/Run_BodyGen.sh

Use the scripts scripts/Run_BodyGen.sh, which contain preconfigured arguments and hyperparameters for all the experiments in the paper. Experiments use Hydra to manage configuration.

Visualizing results requires wandb: configure project name with the project key in BodyGen/design_opt/conf/config.yaml.

As an example of how to run, this runs BodyGen on the crawler environment:

EXPT="crawler"
OMP_NUM_THREADS=1 python -m design_opt.train -m cfg=$EXPT group=$EXPT

Replace crawler with {crawler, terraincrosser, cheetah, swimmer, glider-regular, glider-medium, glider-hard, walker-regular, walker-medium, walker-hard} to train other environments.

• OMP_NUM_THREADS=1 is essential to prevent CPU ops from hanging.

• The environment is selected with the cfg= flag, each of which corresponds to a YAML file in BodyGen/design_opt/cfg. See that folder for the list of available experiments.

• Other hyperparameters are explained in BodyGen/design_opt/conf/config.yaml and our paper.

💗 Acknowledgement

• Transform2Act
• NGE
• One-Policy-to-Control-Them-All
• SARD

🏷️ License

Please see the license for further details.

📝 Citation

If you find our work useful in your research, please consider citing:

@inproceedings{
lu2025bodygen,
title={BodyGen: Advancing Towards Efficient Embodiment Co-Design},
author={Haofei Lu and Zhe Wu and Junliang Xing and Jianshu Li and Ruoyu Li and Zhe Li and Yuanchun Shi},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=cTR17xl89h}
}