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.
@article{arxiv.2510.08754,
title = {Whole Body Model Predictive Control for Spin-Aware Quadrupedal Table Tennis},
author = {David Nguyen and Zulfiqar Zaidi and Kevin Karol and Jessica Hodgins and Zhaoming Xie},
journal= {arXiv preprint arXiv:2510.08754},
year = {2025}
}