人形机器人羽毛球：从有限人体运动数据学习动态球拍技能
Humanoid Badminton: Learning Dynamic Racket Skills from Limited Human Motion Data
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中文总结 AI 辅助
针对人形机器人羽毛球中数据稀缺与动作不自然的问题，提出三阶段分层强化学习框架，通过动作增强、技能规划和对抗正则化，首次实现真实机器人多技能人机对打。
中文摘要 AI 辅助
高速球拍运动为人形机器人提供了一个极具挑战性的测试平台，要求具备时间关键决策、精确击球以及动态全身协调能力。在羽毛球运动中，快速变化的羽毛球轨迹要求及时做出接触决策，而成功回球则需要在短暂的接触窗口内、跨越广阔的三维击球工作空间，精确控制球拍的姿态和速度。人体运动数据为此类运动技能提供了宝贵的先验知识，但可用的羽毛球参考数据既有限又不完美。直接跟踪无法为多样的羽毛球条件提供足够的可执行变化，而纯粹的任务驱动优化可能产生不自然的动作。为应对这些挑战，我们提出了一种用于动态人形机器人羽毛球的三阶段分层强化学习框架。首先，任务随机化的动作增强将稀疏的标注击球事件扩展为可执行的、目标条件的击球变体，形成连续的潜在技能空间。其次，一个高层规划器根据观察到的羽毛球状态，输出连续的潜在技能编码，以在线组合这些技能。第三，一个上下文条件的对抗性正则化器在保持回球性能的同时，鼓励更自然的规划层技能使用。在真实人形机器人上部署时，我们的系统实现了与人类玩家的持续多技能对打，包括正手、反手以及高度动态的跳跃回球。这是首个在真实世界中展示包含高度动态跳跃回球的多技能人机对打的类人机器人球拍运动系统。
英文摘要
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.
