SMASH：掌握可扩展的全身技能以实现人形乒乓球的敏捷性
SMASH: Mastering Scalable Whole-Body Skills for Humanoid Ping-Pong with Egocentric Vision
AI总结：
本文提出SMASH系统，通过整合可扩展的全身技能学习与 onboard 装置视觉感知，实现无需外部摄像头的敏捷人形乒乓球任务，提升了全身协调与击球动作的多样性和鲁棒性。
摘要：
现有的人形乒乓球系统受限于对外部传感的依赖和无法实现敏捷的全身协调。本文提出SMASH，一种模块化系统，整合了可扩展的全身技能学习与 onboard 装置视觉感知，无需外部摄像头即可部署。本文在三个方面推进了人形乒乓球系统：首先，通过紧密协调的全身控制实现敏捷且精准的球互动，而非依赖分离的上半身和下半身行为；其次，通过生成模型增强和多样化击球动作，产生自然且鲁棒的击球行为；最后，展示了首个仅依靠 onboard 感知实现连续击球的人形乒乓球系统，克服了低延迟感知、自我运动引起的不稳定性以及有限视野的挑战。大量实际实验验证了在高速条件下稳定且精准的球交换，证明了可扩展的感知驱动全身技能学习在动态人形交互任务中的有效性。
英文摘要：
Existing humanoid table tennis systems remain limited by their reliance on external sensing and their inability to achieve agile whole-body coordination for precise task execution. These limitations stem from two core challenges: achieving low-latency and robust onboard egocentric perception under fast robot motion, and obtaining sufficiently diverse task-aligned strike motions for learning precise yet natural whole-body behaviors. In this work, we present \methodname, a modular system for agile humanoid table tennis that unifies scalable whole-body skill learning with onboard egocentric perception, eliminating the need for external cameras during deployment. Our work advances prior humanoid table-tennis systems in three key aspects. First, we achieve agile and precise ball interaction with tightly coordinated whole-body control, rather than relying on decoupled upper- and lower-body behaviors. This enables the system to exhibit diverse strike motions, including explosive whole-body smashes and low crouching shots. Second, by augmenting and diversifying strike motions with a generative model, our framework benefits from scalable motion priors and produces natural, robust striking behaviors across a wide workspace. Third, to the best of our knowledge, we demonstrate the first humanoid table-tennis system capable of consecutive strikes using onboard sensing alone, despite the challenges of low-latency perception, ego-motion-induced instability, and limited field of view. Extensive real-world experiments demonstrate stable and precise ball exchanges under high-speed conditions, validating scalable, perception-driven whole-body skill learning for dynamic humanoid interaction tasks.
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