Abstract
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 novel continuous-time model predictive controller (MPC) for agile full-body control of a quadrupedal robot equipped with an arm.
This formulation enables the flexibility to place time-critical constraints, such as those required to strike a ball, anywhere along the trajectory. We further develop a hybrid model-based and learned spin estimator that can accurately predict ball spin from its observed trajectory and an aiming planner that dictates how the ball must be struck. Notably, a continuous set of stroke strategies emerge automatically from different ball return objectives after combining the aiming planner and whole-body MPC. We demonstrate the system on hardware with a Spot quadruped, evaluate the accuracy of each system component, and exhibit coordination through the ability to aim and return balls with varying spin types. As a further demonstration, the system is able to rally with human players.
