Learning optimal striking points for a ping-pong playing robot

In this paper, an approach for learning optimal striking points is proposed. Based on a ball-flight model and a rebound model, a set of reachable striking points within the robot´s workspace can be obtained. However, while these striking points are geometrically reachable, their success probability differs substantially due to the robot´s nonlinear dynamics, the distance to the ball, the need to reach sufficient velocity as well as the right angle at interception and non-uniform sensitivity to errors. Thus, it is crucial for a ping-pong robotic system to select striking points well. As a successful ball interception is the result of various factors that cannot be modeled straightforwardly, we suggest determining optimal striking points based on a reward function that measures how well the ping-pong ball´s trajectory and the racket´s movement coincidence. In this approach, we propose to learn a stochastic policy over the reward given the prospective striking point in order to facilitate exploration of a wide range of prospective striking points. The resulting learning method takes both the amount of experience data and its confidence into account to reach optimal solutions reliably. Evaluation with a real robotic system demonstrates the applicability of the proposed method.