A Model-free Approach to Stroke Learning for Robotic Table Tennis

Abstract

We introduce a model-free approach to predict the future state of the ball and learn the appropriate stroke accordingly for robotic table tennis. Based on the gated recurrent unit (GRU) and the encoder-decoder (ED), a GRU-ED approach is developed for predicting the future state (position, velocity and acceleration) of the ball when observing a partial trajectory. By taking as input the predicted state at hitting time, we learn an appropriate stroke movement with a model-free reinforcement learning (RL) approach. The experimental results show that the proposed approach outperforms others in trajectory prediction. Acceleration and spin of the ball provide an equivalent effect in learning an accurate stroke motion. An additional experiment conducted with a real table tennis robot shows that the robot can accurately hit the ball and return the ball to the desired target with a pretrained RL model.