強化学習への古典的バランス制御原理の埋め込みによる人型ロボットの回復動作
Embedding Classical Balance Control Principles in Reinforcement Learning for Humanoid Recovery
強化学習にキャプチャポイントや重心状態などの古典的バランス指標を報酬設計に組み込み、単一ポリシーで外乱回復から立ち上がりまでを実現する手法を提案した。
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著者: Nehar Poddar, Stephen McCrory, Luigi Penco, Geoffrey Clark, Hakki Erhan Svil, Robert Griffin
分類: cs.RO
原文アブストラクト
Humanoid robots remain vulnerable to falls and unrecoverable failure states, limiting their practical utility in unstructured environments. While reinforcement learning has demonstrated stand-up behaviors, existing approaches treat recovery as a pure task-reward problem without an explicit representation of the balance state. We present a unified RL policy that addresses this limitation by embedding classical balance metrics: capture point, center-of-mass state, and centroidal momentum, as privileged critic inputs and shaping rewards directly around these quantities during training, while the actor relies solely on proprioception for zero-shot hardware transfer. Without reference trajectories or scripted contacts, a single policy spans the full recovery spectrum: ankle and hip strategies for small disturbances, corrective stepping under large pushes, and compliant falling with multi-contact stand-up using the hands, elbows, and knees. Trained on the Unitree H1-2 in Isaac Lab, the policy achieves a 93.4% recovery rate across randomized initial poses and unscripted fall configurations. An ablation study shows that removing the balance-informed structure causes stand-up learning to fail entirely, confirming that these metrics provide a meaningful learning signal rather than incidental structure. Sim-to-sim transfer to MuJoCo and preliminary hardware experiments further demonstrate cross-environment generalization. These results show that embedding interpretable balance structure into the learning framework substantially reduces time spent in failure states and broadens the envelope of autonomous recovery.
