Reducing Trials by Thinning-Out in Skill Discovery

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


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.


Score Function Candidate Point Initial Motion Quadruped Robot Legged Robot 
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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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