ATHLETE: 軌道誘導型モーションマッチングによる反応型ヒューマノイドテニスの学習
ATHLETE: Learning Reactive Humanoid Tennis via Trajectory-Guided Motion Matching
一般的な歩行データと普通のテニス動画を組み合わせ、軌道誘導型モーションマッチングと物理ベースの教師-生徒学習で、実機ヒューマノイドがラリーできる反応型テニス技能を獲得した研究。
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著者: Zhihao Tian, Zifan Wang, Enzheng Zhu, Xun Yang, Jialong Qin, Mengbo Wang, Junwei Liang
分類: cs.RO
原文アブストラクト
Humanoid tennis requires reactive whole-body coordination under rapidly changing ball dynamics, yet collecting task-specific motion-capture demonstrations that cover diverse approach states, contact locations, and timing conditions is costly. Inspired by how humans acquire new skills by combining reusable motor experience with task-specific demonstrations, we present ATHLETE, a framework for learning reactive humanoid tennis from generic locomotion data and ordinary tennis videos. ATHLETE combines LAFAN1 locomotion with video-derived striking motions and uses Trajectory-Guided Motion Matching (TGMM) to construct diverse task-conditioned whole-body references. These references are transformed into a reactive policy through physics-based teacher--student learning with online DAgger distillation, followed by PPO fine-tuning for the tennis task. The resulting policy responds directly to robot and ball observations without requiring reference-motion inputs at deployment. On a real humanoid robot, ATHLETE achieves forehand and backhand hit rates of $90.00\%$ and $86.67\%$, respectively, and enables multi-shot rallies with human players. These results demonstrate that ATHLETE can learn dynamic humanoid tennis skills from reusable locomotion data and video demonstrations. Result videos and code can be found at https://anonymous.4open.science/w/ATHLETE2027/.