Imitating the learning process of a human playing ping-pong is extremely complex. This work proposes a suitable learning strategy. First, an inverse kinematics solution is presented to obtain the smooth joint angles of a redundant anthropomorphic robot arm in order to imitate the paddle motion of a human ping-pong player. As humans instinctively determine which posture is suitable for striking a ball, this work proposes two novel processes: (i) estimating ball states and predicting trajectory using a fuzzy adaptive resonance theory network, and (ii) self-learning the behavior for each strike using a self-organizing map-based reinforcement learning network that imitates human learning behavior. Experimental results demonstrate that the proposed algorithms work effectively when applied to an actual humanoid robot playing ping-pong.
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.
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