HybridMimic: ハイブリッドRL-セントロイダル制御による人型ロボットの動作模倣
HybridMimic: Hybrid RL-Centroidal Control for Humanoid Motion Mimicking
学習ポリシーがセントロイダルモデルベースのコントローラを動的に変調し、連続的な接触状態と目標重心速度を予測することで、ドメインシフト下でも物理的に実現可能なフィードフォワードトルクを生成する人型ロボットの動作模倣フレームワークを提案した。実機実験でベース位置追従誤差を13%削減した。
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著者: Ludwig Chee-Ying Tay, I-Chia Chang, Yan Gu
分類: cs.RO
原文アブストラクト
Motion mimicking, i.e., encouraging the control policy to mimic human motion, facilitates the learning of complex tasks via reinforcement learning (RL) for humanoid robots. Although standard RL frameworks demonstrate impressive locomotion agility, they often bypass explicit reasoning about robot dynamics during deployment, which is a design choice that can lead to physically infeasible commands when the robot encounters out-of-distribution environments. By integrating model-based principles, hybrid approaches can improve performance; however, existing methods typically rely on predefined contact timing, limiting their versatility. This paper introduces HybridMimic, a framework in which a learned policy dynamically modulates a centroidal-model-based controller by predicting continuous contact states and desired centroidal velocities. This architecture exploits the physical grounding of centroidal dynamics to generate feedforward torques that remain feasible even under domain shift. Using physics-informed rewards, the policy is trained to efficiently utilize the centroidal controller's optimization by outputting precise control targets and reference torques. Through hardware experiments on the Booster T1 humanoid, HybridMimic reduces the average base position tracking error by 13\% compared to a state-of-the-art RL baseline, demonstrating the robustness of dynamics-aware deployment.
