ATHLETE：通过轨迹引导运动匹配学习反应式人形机器人网球
ATHLETE: Learning Reactive Humanoid Tennis via Trajectory-Guided Motion Matching
AI总结：
ATHLETE是从通用运动数据和网球视频中学习反应式人形机器人网球的框架，通过TGMM构建参考，经师生学习与PPO微调，在真实机器人上实现90.00%正手、86.67%反手命中率及多回合对打。
摘要：
人形机器人网球需要在球的动态快速变化下实现全身的反应式协调，但收集涵盖不同接近状态、接触位置和时机条件的任务特定动作捕捉演示成本高昂。受人类通过将可复用运动经验与任务特定演示相结合来获取新技能的启发，我们提出了ATHLETE，这一框架可从通用运动数据和普通网球视频中学习反应式人形机器人网球。ATHLETE结合LAFAN1运动数据与视频衍生的击球动作，并使用轨迹引导运动匹配（TGMM）构建多样化的任务条件全身参考。这些参考通过基于物理的师生学习（含在线DAgger蒸馏）转化为反应式策略，随后通过PPO针对网球任务进行微调。最终策略直接响应机器人和球的观测，部署时无需参考运动输入。在真实人形机器人上，ATHLETE的正手击球命中率达90.00%，反手击球命中率达86.67%，并能与人类玩家实现多回合对打。这些结果表明，ATHLETE可从可复用运动数据和视频演示中学习动态人形机器人网球技能。结果视频和代码可在该https URL获取。
英文摘要：
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 this https URL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
