スイッチ：ヒューマノイドロボットの俊敏なスキル切り替えの実現
Switch: Learning Agile Skills Switching for Humanoid Robots
ヒューマノイドロボットが複数の移動スキルをシームレスに切り替えられるようにする階層型マルチスキルシステムを提案した論文。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Yuen-Fui Lau, Qihan Zhao, Yinhuai Wang, Runyi Yu, Hok Wai Tsui, Qifeng Chen, Ping Tan
分類: cs.RO
原文アブストラクト
Recent advancements in whole-body control through deep reinforcement learning have enabled humanoid robots to achieve remarkable progress in real-world chal lenging locomotion skills. However, existing approaches often struggle with flexible transitions between distinct skills, cre ating safety concerns and practical limitations. To address this challenge, we introduce a hierarchical multi-skill system, Switch, enabling seamless skill transitions at any moment. Our approach comprises three key components: (1) a Skill Graph (SG) that establishes potential cross-skill transitions based on kinematic similarity within multi-skill motion data, (2) a whole-body tracking policy trained on this skill graph through deep reinforcement learning, and (3) an online skill scheduler to drive the tracking policy for robust skill execution and smooth transitions. For skill switching or significant tracking deviations, the scheduler performs online graph search to find the optimal feasible path, which ensures efficient, stable, and real-time execution of diverse locomotion skills. Comprehensive experiments demonstrate that Switch empowers humanoid to execute agile skill transitions with high success rates while maintaining strong motion imitation performance.
