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.

翻译：现有类人乒乓球系统受限于对外部感知的依赖，以及无法实现敏捷的全身协调以完成精确任务执行。这些局限源于两个核心挑战：在快速机器人运动下实现低延迟且鲁棒的板载自我中心感知，以及获取足够多样化的任务对齐击球动作以学习精确而自然的全身行为。在本工作中，我们提出 SMASH——一个面向敏捷类人乒乓球的模块化系统，它统一了可扩展的全身技能学习与板载自我中心感知，在部署时无需外部相机。我们的工作在前代类人乒乓球系统基础上取得三项关键进展：第一，我们通过紧密协调的全身控制而非依赖解耦的上下半身行为，实现了敏捷且精确的球体交互。这使得系统能够展现多样化的击球动作，包括爆发性全身扣杀和低姿击球。第二，通过生成模型增强并多样化击球动作，我们的框架受益于可扩展的运动先验，并在广阔工作空间内产生自然、鲁棒的击球行为。第三，据我们所知，我们首次展示了仅依赖板载感知即可实现连续击球的类人乒乓球系统，尽管面临低延迟感知、自我运动导致的不稳定性及有限视野的挑战。大量真实世界实验验证了高速条件下稳定精确的球体对打，证实了面向动态类人交互任务的可扩展、感知驱动型全身技能学习。