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arXiv · 2412.01297

Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning

Fengze Xie·Sizhe Wei·Yue Song·Yisong Yue·Lu Gan
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Abstract

We present a morphological-symmetry-equivariant heterogeneous graph neural network, namely MS-HGNN, for robotic dynamics learning, that integrates robotic kinematic structures and morphological symmetries into a single graph network. These structural priors are embedded into the learning architecture as constraints, ensuring high generalizability, sample and model efficiency. The proposed MS-HGNN is a versatile and general architecture that is applicable to various multi-body dynamic systems and a wide range of dynamics learning problems. We formally prove the morphological-symmetry-equivariant property of our MS-HGNN and validate its effectiveness across multiple quadruped robot learning problems using both real-world and simulated data. Our code is made publicly available at https://github.com/lunarlab-gatech/MorphSym-HGNN/.

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Fengze Xie, Sizhe Wei, Yue Song, Yisong Yue, Lu Gan. 2024-12-02. Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning. https://arxiv.org/abs/2412.01297

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