PingpongDuo：具备安全团队协作的敏捷人形乒乓球双打
PingpongDuo: Agile Humanoid Table Tennis Doubles with Safe Team Coordination
AI总结：
研究人形乒乓球双打需解决交替击球、碰撞风险等问题，提出PingpongDuo分层框架，仿真与现实实验验证其提升回球成功率、实现快速定位与连续击球的性能。
摘要：
与单打不同，人形机器人乒乓球双打要求队友严格交替击球，同时共享有限的工作空间。快速重新定位可能会降低击球精度，而队友动作不协调则会带来碰撞风险。我们提出了PingpongDuo，这是一个用于敏捷人形双打且具备安全团队协作的分层框架。在团队层面，规划器共同选择定位目标，并利用学习到的风险估计来过滤有风险的提议，优先保留当前击球者的目标。在个体层面，独立的步法和击球技能将敏捷运动与精确的球拍控制相结合。这些技能由人类运动参考引导，包括从人类双打比赛中收集的步法，并通过彼此产生的状态进行训练以支持快速过渡。球拍臂残差在运动后使用模拟器的回球反馈进一步优化击球，该模拟器已通过真实球飞行和接触测量进行校准。在仿真中，PingpongDuo比最先进的基线将回球成功率提高了13.2%，同时在 clearance 期间实现了约2厘米的定位误差。在现实世界实验中，一个机器人在2.4秒内完成了2.15米的 clearance-and-recovery 循环，峰值速度为2.23米/秒，而两个机器人严格交替完成了超过10次连续击球。
英文摘要：
Unlike singles, humanoid table tennis doubles requires teammates to strike in strict alternation while sharing a confined workspace. Rapid repositioning can compromise striking accuracy, while uncoordinated teammate movements introduce collision risks. We present PingpongDuo, a hierarchical framework for agile humanoid doubles with safe team coordination. At the team level, a planner jointly selects positioning targets and uses learned risk estimates to filter risky proposals, preferentially preserving the current striker's target. At the individual level, separate footwork and striking skills combine agile movement with precise racket control. The skills are guided by human motion references, including footwork collected from human doubles play, and trained from states produced by one another to support rapid transitions. A racket-arm residual further refines strikes after movement using return feedback from a simulator calibrated with real ball-flight and contact measurements. In simulation, PingpongDuo improves the ball-return success rate by 13.2% over the state-of-the-art baseline, while achieving approximately 2 cm positioning error during clearance. In real-world experiments, a robot completes a 2.15 m clearance-and-recovery cycle in 2.4 s with a peak speed of 2.23 m/s, while two robots sustain more than 10 consecutive strikes in strict alternation.
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。
