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...

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Bibliographic Details
Published in:Advanced robotics 2011-01, Vol.25 (9-10), p.1183-1208
Main Authors: Lai, C. H., Tsay, T. I. James
Format: Article
Language:English
Subjects:
ADAPTIVE RESONANCE THEORY
Human
Humanoid
HUMANOID ROBOT
Learning
Networks
Players
REDUNDANT ANTHROPOMORPHIC ROBOT ARM
REINFORCEMENT LEARNING
Robotics
Robots
SELF-ORGANIZING MAP
Strikes
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Summary: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.
ISSN:0169-1864
1568-5535
DOI:10.1163/016918611X574678