Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning
Abstract
We propose MS-HGNN, a Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for robotic dynamics learning, which integrates robotic kinematic structures and morphological symmetries into a unified graph network. By embedding these structural priors as inductive biases, MS-HGNN ensures high generalizability, sample and model efficiency. This architecture is versatile and broadly applicable to various multi-body dynamic systems and dynamics learning tasks. We prove the morphological-symmetry-equivariant property of MS-HGNN and demonstrate its effectiveness across multiple quadruped robot dynamics learning problems using real-world and simulated data. Our code is available at https://github.com/lunarlab-gatech/MorphSym-HGNN/.
Cite
Text
Xie et al. "Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning." Proceedings of the 7th Annual Learning for Dynamics \& Control Conference, 2025.Markdown
[Xie et al. "Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning." Proceedings of the 7th Annual Learning for Dynamics \& Control Conference, 2025.](https://mlanthology.org/l4dc/2025/xie2025l4dc-morphologicalsymmetryequivariant/)BibTeX
@inproceedings{xie2025l4dc-morphologicalsymmetryequivariant,
title = {{Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning}},
author = {Xie, Fengze and Wei, Sizhe and Song, Yue and Yue, Yisong and Gan, Lu},
booktitle = {Proceedings of the 7th Annual Learning for Dynamics \& Control Conference},
year = {2025},
pages = {1392-1405},
volume = {283},
url = {https://mlanthology.org/l4dc/2025/xie2025l4dc-morphologicalsymmetryequivariant/}
}