事前学習と高速適応による汎用人型全身制御
General Humanoid Whole-Body Control via Pretraining and Fast Adaptation
人型ロボットの全身制御を、事前学習したポリシーに軽量な残差ポリシーを高速適応させることで、未知の動作にも安定して追従できるフレームワークFASTを提案した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Zepeng Wang, Jiangxing Wang, Shiqing Yao, Yu Zhang, Ziluo Ding, Ming Yang, Yuxuan Wang, Haobin Jiang, Chao Ma, Xiaochuan Shi, Zongqing Lu
分類: cs.RO
原文アブストラクト
Learning a general whole-body controller for humanoid robots remains challenging due to the diversity of motion distributions, the difficulty of fast adaptation, and the need for robust balance in high-dynamic scenarios. Existing approaches often require task-specific training or suffer from performance degradation when adapting to new motions. In this paper, we present FAST, a general humanoid whole-body control framework that enables Fast Adaptation and Stable Motion Tracking. FAST introduces Parseval-Guided Residual Policy Adaptation, which learns a lightweight delta action policy under orthogonality and KL constraints, enabling efficient adaptation to out-of-distribution motions while mitigating catastrophic forgetting. To further improve physical robustness, we propose Center-of-Mass-Aware Control, which incorporates CoM-related observations and objectives to enhance balance when tracking challenging reference motions. Extensive experiments in simulation and real-world deployment demonstrate that FAST consistently outperforms state-of-the-art baselines in robustness, adaptation efficiency, and generalization.
