Stroke Motion Learning for a Humanoid Robotic Ping-Pong Player Using a Novel Motion Capture System

Abstract

Problem statement: Human ping-pong players determine the stroke trajectory according to their experience before the ball enters their court. However, to enable a humanoid robot to select the appropriate stroke motion based on skills learned from 3D motion, important patterns must be generated to simplify the complex 3D motion. Approach: This study developed an effective strategy for teaching ping-pong skills to a humanoid robot. An optical/inertial motion-capture system that retrieves the stroke motion was constructed, along with the retrieved stroke motion trajectories analyzed to obtain the desired stroke patterns of the robot. Results: A motion capture system was implemented mainly to orient the robot on the stroke motion trajectory. This system was applied directly to a ping-pong game between a human player and a pitching machine to enable the robot to learn backhand strokes through human demonstration. The ball was continuously struck to the opponent so that it hit the anticipated region on the opposite side of the court while the pitching machine served the ball. The data were then classified using the proposed stopping detector and then processed by Principal Components Analysis (PCA) to generate the stroke patterns after collecting 50 datasets for stroke trajectories. Conclusion: The right arm of the humanoid robot was successfully instructed to perform the actual ping-pong stroke using the generated trajectory.