Online segmentation and clustering from continuous observation of whole body motions

Dana Kulić, Wataru Takano, Yoshihiko Nakamura

Research output: Contribution to journalArticleResearchpeer-review

86 Citations (Scopus)

Abstract

This paper describes a novel approach for incremental learning of human motion pattern primitives through online observation of human motion. The observed time series data stream is first stochastically segmented into potential motion primitive segments, based on the assumption that data belonging to the same motion primitive will have the same underlying distribution. The motion segments are then abstracted into a stochastic model representation and automatically clustered and organized. As new motion patterns are observed, they are incrementally grouped together into a tree structure, based on their relative distance in the model space. The tree leaves, which represent the most specialized learned motion primitives, are then passed back to the segmentation algorithm so that as the number of known motion primitives increases, the accuracy of the segmentation can also be improved. The combined algorithm is tested on a sequence of continuous human motion data that are obtained through motion capture, and demonstrates the performance of the proposed approach.

Original languageEnglish
Pages (from-to)1158-1166
Number of pages9
JournalIEEE Transactions on Robotics
Volume25
Issue number5
DOIs
Publication statusPublished - 31 Jul 2009
Externally publishedYes

Keywords

  • Humanoid robots
  • Incremental learning
  • Learning from observation
  • Motion segmentation and clustering

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