ADAPT: 解析的擾乱認識型ポリシートレーニングによる人型ロコモーション
ADAPT: Analytical Disturbance-Aware Policy Training for Humanoid Locomotion
外力による擾乱を物理モデルに基づく観測器で推定し、その情報をポリシーに直接入力することで、人型ロボットの外乱に対する頑健性と速度追従精度を向上させるフレームワークを提案した。
詳しい要約
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著者: Bofan Lyu, Jindou Jia, Kuangji Zuo, Yanshuo Lu, Shijia Han, Gen Li, Boyu Ma, Jingliang Li, Geng Li, Jianfei Yang
分類: cs.RO
原文アブストラクト
Humanoids deployed in human-centered environments must handle force-interactive tasks, where external contacts introduce unexpected disturbances that disrupt locomotion accuracy and stability. Existing learning-based approaches rely on broad domain randomization, task-specific force objectives, or learning-based force estimators from motion history, each of which compromises accuracy, task transferability, or out-of-distribution (OOD) robustness. We present Analytical Disturbance-Aware Policy Training (ADAPT), a framework that equips humanoid policies with a physically grounded disturbance observer. The core of ADAPT is an analytical whole-body disturbance observer that estimates residual force/torque online with the accessible robot dynamics, without requiring force/torque sensors. Fed directly into the policy, the estimated disturbances give the humanoid an explicit, physics-derived sense of external force/torque that can generalize across diverse unseen scenes. Experiments on a Unitree G1 humanoid show that ADAPT achieves accurate disturbance prediction and stronger robustness than a proprioception-only baseline under torso perturbations, standing pushes, and asymmetric hand payloads, with improved velocity tracking even on OOD disturbances. Moreover, ADAPT enables penalizing inferred disturbances at lower-body joints to encourage lighter locomotion.
