STRIDE: フローマッチングによる構造化ラグランジアンと確率的残差ダイナミクス
STRIDE: Structured Lagrangian and Stochastic Residual Dynamics via Flow Matching
剛体力学をラグランジアンニューラルネットで、不確実な接触・摩擦などの残差力を条件付きフローマッチングで分離して学習するダイナミクスモデルを提案し、四足歩行やヒューマノイドで長期予測誤差を削減した。
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著者: Prakrut Kotecha, Ganga Nair B, Shishir Kolathaya
分類: cs.RO, cs.LG
原文アブストラクト
Robotic systems operating in unstructured environments must operate under significant uncertainty arising from intermittent contacts, frictional variability, and unmodeled compliance. While recent model-free approaches have demonstrated impressive performance, many deployment settings still require predictive models that support planning, constraint handling, and online adaptation. Analytical rigid-body models provide strong physical structure but often fail to capture complex interaction effects, whereas purely data-driven models may violate physical consistency, exhibit data bias, and accumulate long-horizon drift. In this work, we propose STRIDE, a dynamics learning framework that explicitly separates conservative rigid-body mechanics from uncertain, effectively stochastic non-conservative interaction effects. The structured component is modeled using a Lagrangian Neural Network (LNN) to preserve energy-consistent inertial dynamics, while residual interaction forces are represented using Conditional Flow Matching (CFM) to capture multi-modal interaction phenomena. The two components are trained jointly end-to-end, enabling the model to retain physical structure while representing complex stochastic behavior. We evaluate STRIDE on systems of increasing complexity, including a pendulum, the Unitree Go1 quadruped, and the Unitree G1 humanoid. Results show 20% reduction in long-horizon prediction error and 30% reduction in contact force prediction error compared to deterministic residual baselines, supporting more reliable model-based control in uncertain robotic environments.
