Machine learning for fast quadrupedal locomotion

  • Authors:
  • Nate Kohl;Peter Stone

  • Affiliations:
  • Department of Computer Sciences, The University of Texas at Austin, Austin, Texas;Department of Computer Sciences, The University of Texas at Austin, Austin, Texas

  • Venue:
  • AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
  • Year:
  • 2004

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Abstract

For a robot, the ability to get from one place to another is one of the most basic skills. However, locomotion on legged robots is a challenging multidimensional control problem. This paper presents a machine learning approach to legged locomotion, with all training done on the physical robots. The main contributions are a specification of our fully automated learning environment and a detailed empirical comparison of four different machine learning algorithms for learning quadrupedal locomotion. The resulting learned walk is considerably faster than all previously reported hand-coded walks for the same robot platform.