ロボットダイナミクス学習のための形態対称性同変ヘテロジニアスグラフニューラルネットワーク
Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning
ロボットの運動学的構造と形態的対称性を単一のグラフネットワークに統合し、構造的事前知識を制約として組み込むことで汎化性とサンプル効率を高めたMS-HGNNを提案し、四足ロボットの実機・シミュレーションデータで有効性を検証した。
著者: Fengze Xie, Sizhe Wei, Yue Song, Yisong Yue, Lu Gan
分類: cs.RO, cs.LG
原文アブストラクト
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/.
