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.

翻译：开发能够媲美人类速度、精度，并能预测和应对各种乒乓球旋转的四足机器人乒乓球系统，对腿式机器人而言仍是一项重大挑战。为展示这些能力，我们提出了一种用于四足机器人进行动态乒乓球对打的系统，该系统集成了高速感知、轨迹预测和敏捷控制。我们的系统使用外部摄像头进行高速球体定位，采用带学习残差的物理模型来推断旋转并预测轨迹，并提出了一种新颖的模型预测控制（MPC）框架以实现敏捷的全身控制。值得注意的是，在此控制范式下，不同的回球目标会自动生成一系列连续的击球策略。我们在真实世界的Spot四足机器人上演示了该系统，评估了各系统组件的精度，并通过系统瞄准并回击不同类型旋转球的能力展现了其协调性。作为进一步的演示，该系统能够与人类玩家进行连续对打。