解析的力学正則化による物理情報を活用した方策最適化
Physics-Informed Policy Optimization via Analytic Dynamics Regularization
ロボット制御の強化学習に、シミュレータから得られる微分可能なラグランジュ残差を正則化項として導入し、物理的に一貫した方策学習を効率化する手法PIPERを提案した。
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著者: Namai Chandra, Liu Mohan, Zhihao Gu, Lin Wang
分類: cs.RO, cs.LG
原文アブストラクト
Reinforcement learning (RL) has achieved strong performance in robotic control; however, state-of-the-art policy learning methods, such as actor-critic methods, still suffer from high sample complexity and often produce physically inconsistent actions. This limitation stems from neural policies implicitly rediscovering complex physics from data alone, despite accurate dynamics models being readily available in simulators. In this paper, we introduce a novel physics-informed RL framework, called PIPER, that seamlessly integrates physical constraints directly into neural policy optimization with analytical soft physics constraints. At the core of our method is the integration of a differentiable Lagrangian residual as a regularization term within the actor's objective. This residual, extracted from a robot's simulator description, subtly biases policy updates towards dynamically consistent solutions. Crucially, this physics integration is realized through an additional loss term during policy optimization, requiring no alterations to existing simulators or core RL algorithms. Extensive experiments demonstrate that our method significantly improves learning efficiency, stability, and control accuracy, establishing a new paradigm for efficient and physically consistent robotic control.
