This repository implements a Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning (MS-HGNN) for robotic dynamics learning, that integrates robotic kinematic structures and morphological symmetries into a single graph network.
For information on our method, see our project page and paper.
To get started, setup a Conda Python environment with Python=3.11:
conda create -n ms-hgnn python=3.11
conda activate ms-hgnn
Then, install the library (and dependencies) with the following command:
pip install .
Note, if you have any issues with setup, refer to environment_files/README.md so you can install the exact libraries we used.
The necessary URDF files are part of git submodules in this repository, so run the following commands to download them:
git submodule init
git submodule update
To replicate the experiments referenced in our paper or access our trained model weights, see paper/README.md.
We would like to thank Daniel Chase Butterfield for the awesome work on the original implementation of the Morphology-Informed-HGNN. And thank Lingjun Zhao for the helpful discussions on the implementation of the code.
If you find our repository or our work useful, please cite the relevant publication:
@misc{xie2024morphologicalsymmetryequivariantheterogeneousgraphneural,
title={Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning},
author={Fengze Xie and Sizhe Wei and Yue Song and Yisong Yue and Lu Gan},
year={2024},
eprint={2412.01297},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2412.01297},
}
For any issues with this repository, feel free to open an issue on GitHub. For other inquiries, please contact Fengze Xie ([email protected]), Sizhe Wei ([email protected]), or the Lunar Lab (https://sites.gatech.edu/lunarlab/).