CyboRacket: A Perception-to-Action Framework for Humanoid Racket Sports
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915865020071936 |
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| author | Ren, Peng Qi, Chuan Ge, Haoyang Su, Qiyuan He, Xuguo Huang, Cong Chi, Pei Zhao, Jiang Chen, Kai |
| author_facet | Ren, Peng Qi, Chuan Ge, Haoyang Su, Qiyuan He, Xuguo Huang, Cong Chi, Pei Zhao, Jiang Chen, Kai |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_14605 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CyboRacket: A Perception-to-Action Framework for Humanoid Racket Sports Ren, Peng Qi, Chuan Ge, Haoyang Su, Qiyuan He, Xuguo Huang, Cong Chi, Pei Zhao, Jiang Chen, Kai Robotics 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. |
| title | CyboRacket: A Perception-to-Action Framework for Humanoid Racket Sports |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.14605 |