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Multi-phase sumo maneuver learning

Published online by Cambridge University Press:  05 January 2004

Jiming Liu  and
Shiwu Zhang
Jiming Liu
Affiliation:
Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong (P.R. of China). E-mail: [email protected]
Shiwu Zhang
Affiliation:
Department of Precision Machinery & Precision Instrumentation, University of Science and Technology of China (P.R. of China).
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Abstract

In this paper, we demonstrate a multi-phase genetic programming (MPGP) approach to an autonomous robot learning task, where a sumo wrestling robot is required to execute specialized pushing maneuvers in response to different opponents' postures. The sumo robot used has a very simple, minimalist hardware configuration. This example differs from the earlier studies in evolutionary robotics in that the former is carried out on-line during the performance of a robot, whereas the latter is concerned with the evolution of a controller in a simulated environment based on extended genetic algorithms. As illustrated in several sumo maneuver learning experiments, strategic maneuvers with respect to some possible changes in the shape and size of an opponent can readily emerge from the on-line MPGP learning sessions.

Keywords

Multi-phase genetic programming (MPGP)Autonomous robotsSumo tasksManeuver learningEvolutionary robotics
Type
Research Article
Information
Robotica , Volume 22 , Issue 1 , January 2004 , pp. 61 - 75
Copyright
© 2004 Cambridge University Press

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