ヒューマノイドバドミントン：限られた人間動作データからの動的ラケットスキル学習
Humanoid Badminton: Learning Dynamic Racket Skills from Limited Human Motion Data
三段階の階層型強化学習により、限られた人間のバドミントン動作データからヒューマノイドがフォアハンド・バックハンド・ジャンプ返球などの動的ラケットスキルを獲得し、実機で人間とのラリーを実現した。
著者: Jingzhi Cui, Zhexiong Wang, Bangjie Xu, Pengyu Zhao, Youyuan Li, Zhi Su, Peng Ren, Mengdi Xu, Chao Yu, Yi Wu, Luyang Wang, Zhongyu Li
分類: cs.RO
原文アブストラクト
High-speed racket sports provide a demanding testbed for humanoid robots, requiring time-critical decisions, precise striking, and dynamic whole-body coordination. In badminton, fast-changing shuttle trajectories require timely contact decisions, while successful returns demand precise racket pose and velocity within a brief contact window and across a broad three-dimensional striking workspace. Human motion data provide valuable priors for such athletic skills, but usable badminton references are limited and imperfect. Direct tracking provides insufficient executable variation for diverse shuttle conditions, while purely task-driven optimization may produce unnatural motion. To address these challenges, we present a three-stage hierarchical reinforcement learning framework for dynamic humanoid badminton. First, task-randomized motion augmentation expands sparse annotated hitting events into executable target-conditioned stroke variations, forming a continuous latent skill space. Second, a high-level planner outputs continuous latent skill codes to compose these skills online according to the observed shuttle state. Third, a context-conditioned adversarial regularizer encourages more natural planner-level skill usage while preserving return performance. When deployed on a real humanoid robot, our system achieves sustained multi-skill rallies with human players, including forehand, backhand, and highly dynamic jump returns. This is the first real-world humanoid racket-sport system to demonstrate multi-skill human--robot rallies including highly dynamic jump returns.