A learning method for returning ball in robotic table tennis

Abstract

A learning method of the point for a robot to hit a coming ball in table tennis is proposed in this paper. The learning is performed based on the artificial neural network. In order to learn the effects of the rotational velocity and the air resistance, the inputs and outputs are defined as the variations of the measured data and the hitting point from those produced by a simple model, which consists of the equations of motion without the air resistance and the Newton's rebound model without friction. The learning and verification are performed using the simulation and experimental data, where the simulation is executed with the aerodynamics model and the table rebound model, and the ball trajectories in the experiment are measured when two humans play table tennis.