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Reducing Trials by Thinning-Out in Skill Discovery

  • Hayato Kobayashi
  • Kohei Hatano
  • Akira Ishino
  • Ayumi Shinohara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4755)

Abstract

In this paper, we propose a new concept, thinning-out, for reducing the number of trials in skill discovery. Thinning-out means to skip over such trials that are unlikely to improve discovering results, in the same way as “pruning” in a search tree. We show that our thinning-out technique significantly reduces the number of trials. In addition, we apply thinning-out to the discovery of good physical motions by legged robots in a simulation environment. By using thinning-out, our virtual robots can discover sophisticated motions that is much different from the initial motion in a reasonable amount of trials.

Keywords

Score Function Candidate Point Initial Motion Quadruped Robot Legged Robot 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Hayato Kobayashi
    • 1
  • Kohei Hatano
    • 2
  • Akira Ishino
    • 1
  • Ayumi Shinohara
    • 1
  1. 1.Graduate School of Information Science, Tohoku UniversityJapan
  2. 2.Department of Informatics, Kyushu UniversityJapan

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