Whole Body Model Predictive Control for Spin-Aware Quadrupedal Table Tennis
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911201612529664 |
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| author | Nguyen, David Zaidi, Zulfiqar Karol, Kevin Hodgins, Jessica Xie, Zhaoming |
| author_facet | Nguyen, David Zaidi, Zulfiqar Karol, Kevin Hodgins, Jessica Xie, Zhaoming |
| contents | Developing table tennis robots that mirror human speed, accuracy, and ability to predict and respond to the full range of ball spins remains a significant challenge for legged robots. To demonstrate these capabilities we present a system to play dynamic table tennis for quadrupedal robots that integrates high speed perception, trajectory prediction, and agile control. Our system uses external cameras for high-speed ball localization, physical models with learned residuals to infer spin and predict trajectories, and a novel model predictive control (MPC) formulation for agile full-body control. Notably, a continuous set of stroke strategies emerge automatically from different ball return objectives using this control paradigm. We demonstrate our system in the real world on a Spot quadruped, evaluate accuracy of each system component, and exhibit coordination through the system's ability to aim and return balls with varying spin types. As a further demonstration, the system is able to rally with human players. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08754 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Whole Body Model Predictive Control for Spin-Aware Quadrupedal Table Tennis Nguyen, David Zaidi, Zulfiqar Karol, Kevin Hodgins, Jessica Xie, Zhaoming Robotics Systems and Control Developing table tennis robots that mirror human speed, accuracy, and ability to predict and respond to the full range of ball spins remains a significant challenge for legged robots. To demonstrate these capabilities we present a system to play dynamic table tennis for quadrupedal robots that integrates high speed perception, trajectory prediction, and agile control. Our system uses external cameras for high-speed ball localization, physical models with learned residuals to infer spin and predict trajectories, and a novel model predictive control (MPC) formulation for agile full-body control. Notably, a continuous set of stroke strategies emerge automatically from different ball return objectives using this control paradigm. We demonstrate our system in the real world on a Spot quadruped, evaluate accuracy of each system component, and exhibit coordination through the system's ability to aim and return balls with varying spin types. As a further demonstration, the system is able to rally with human players. |
| title | Whole Body Model Predictive Control for Spin-Aware Quadrupedal Table Tennis |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2510.08754 |