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arXiv · 2603.14605

CyboRacket: A Perception-to-Action Framework for Humanoid Racket Sports

Peng Ren·Chuan Qi·Haoyang Ge·Qiyuan Su·Xuguo He·Cong Huang·Pei Chi·Jiang Zhao·Kai Chen
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Abstract

Dynamic ball-interaction tasks remain challenging for robots because they require tight perception-action coupling under limited reaction time. This challenge is especially pronounced in humanoid racket sports, where successful interception depends on accurate visual tracking, trajectory prediction, coordinated stepping, and stable whole-body striking. Existing robotic racket-sport systems often rely on external motion capture for state estimation or on task-specific low-level controllers that must be retrained across tasks and platforms. We present CyboRacket, a hierarchical perception-to-action framework for humanoid racket sports that integrates onboard visual perception, physics-based trajectory prediction, and large-scale pre-trained whole-body control. The framework uses onboard cameras to track the incoming object, predicts its future trajectory, and converts the estimated interception state into target end-effector and base-motion commands for whole-body execution by SONIC on the Unitree G1 humanoid robot. We evaluate the proposed framework in a vision-based humanoid tennis-hitting task. Experimental results demonstrate real-time visual tracking, trajectory prediction, and successful striking using purely onboard sensing.

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Peng Ren, Chuan Qi, Haoyang Ge, Qiyuan Su, Xuguo He, Cong Huang, Pei Chi, Jiang Zhao, Kai Chen. 2026-03-15. CyboRacket: A Perception-to-Action Framework for Humanoid Racket Sports. https://arxiv.org/abs/2603.14605

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