Incremental Learning of Full Body Motion Primitives for Humanoid Robots

  • Kulic D
  • Lee D
  • Ott C
 et al. 
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Abstract

This paper describes an approach for on-line, incremental learning of full body motion primitives from observation of human motion. The continuous observation sequence is first partitioned into motion segments, using stochastic segmentation. Motion segments are next incrementally clustered and organized into a hierarchical tree structure representing the known motion primitives. Motion primitives are encoded using hidden Markov models, so that the same model can be used for both motion recognition and motion generation. At the same time, the relationship between motion primitives is learned via the construction of a motion primitive graph. The motion primitive graph can then be used to construct motions, consisting of sequences of motion primitives. The approach is implemented and tested on the IRT humanoid robot.

Author-supplied keywords

  • continuous observation sequence
  • full body motion primitive graph incremental learn
  • hidden Markov model
  • hierarchical tree structure representation
  • humanoid robot
  • inverse kinematic
  • motion generation
  • motion recognition
  • motion segmentation
  • stochastic segmentation

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Authors

  • Dana Kulic

  • Dongheui Lee

  • Christian Ott

  • Yoshihiko Nakamura

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