Self-learning for a humanoid robotic ping-pong player

C. H. Lai, T. I.James Tsay

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1183-1208
Number of pages26
JournalAdvanced Robotics
Issue number9-10
Publication statusPublished - 2011

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Science Applications


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