Initial experiments on reinforcement learning control of cooperative manipulations

Mikhail Mikhailovich Svinin, F. Kojima, Y. Katada, K. Ueda

Research output: Contribution to journalArticle

3 Citations (Scopus)


The paper deals with instance-based reinforcement learning control of autonomous robots. A classifier system, defined in the continuous state and action spaces, is outlined. Based on the sensory state space analysis, we define a learning strategy and fix structure of the action rules. The classifier system features a nonconservative bucket brigade algorithm and a fast reproduction mechanism. The system developed is then applied to learning cooperative behavior by two robots coupled via a common object, with each robot controlled by its own classifier. Feasibility of this scheme is tested under experiment with two Lynxmotion robots, and a motion pattern of cooperative behavior (lifting up an object) is evolved using the two interacting classifier systems.

Original languageEnglish
Pages (from-to)416-422
Number of pages7
JournalIEEE International Conference on Intelligent Robots and Systems
Publication statusPublished - Jan 1 2000


All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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