Incremental learning of full body motion primitives and their sequencing through human motion observation

Authors:
Dana Kulić;Christian Ott;Dongheui Lee;Junichi Ishikawa;Yoshihiko Nakamura
Affiliations:
Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada;Institute of Robotics and Mechatronics, DLR - German Aerospace Center, Wessling, Germany;Department of Electrical Engineering and Information Technology, Technical University of Munich, Munich, Germany;Department of Mechano-Informatics, University of Tokyo, Bunkyo-ku, Tokyo, Japan;Department of Mechano-Informatics, University of Tokyo, Bunkyo-ku, Tokyo, Japan
Venue:
International Journal of Robotics Research
Year:
2012

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Abstract

In this paper we describe 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. Next, motion segments are 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 temporal 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 during on-line observation and on the IRT humanoid robot.