Self-learning for a humanoid robotic ping-pong player

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.