浮遊ベース深層ラグランジュネットワーク
Floating-Base Deep Lagrangian Networks
浮遊ベース系の慣性行列の物理的制約を満たすパラメータ化を導入し、ラグランジュ力学に基づくニューラルネットワークで逆動力学誤差を最小化する手法を提案。四足歩行ロボットとヒューマノイドのデータセットで評価し、シミュレーションと実機の両方で性能と解釈性が向上。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Lucas Schulze, Juliano Decico Negri, Victor Barasuol, Vivian Suzano Medeiros, Marcelo Becker, Jan Peters, Oleg Arenz
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Grey-box methods for system identification combine deep learning with physics-informed constraints, capturing complex dependencies while improving out-of-distribution generalization. Despite the growing importance of floating-base systems such as humanoids and quadrupeds, current grey-box models ignore their specific physical constraints. For instance, the inertia matrix is not only positive definite but also exhibits branch-induced sparsity and input independence. Moreover, the 6x6 composite spatial inertia of the floating base inherits properties of single-rigid-body inertia matrices. As we show, this includes the triangle inequality on the eigenvalues of the composite rotational inertia. To address the lack of physical consistency in deep learning models of floating-base systems, we introduce a parameterization of inertia matrices that satisfies all these constraints. Inspired by Deep Lagrangian Networks (DeLaN), we train neural networks to predict physically plausible inertia matrices that minimize inverse dynamics error under Lagrangian mechanics. For evaluation, we collected and released a dataset on multiple quadrupeds and humanoids. In these experiments, our Floating-Base Deep Lagrangian Networks (FeLaN) achieve better overall performance on both simulated and real robots, while providing greater physical interpretability.
