Incremental learning and memory consolidation of whole body human motion primitives

Dana Kulić, Yoshihiko Nakamura

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)


The ability to learn during continuous and on-line observation would be advantageous for humanoid robots, as it would enable them to learn during co-location and interaction in the human environment. However, when motions are being learned and clustered on-line, there is a trade-off between classification accuracy and the number of training examples, resulting in potential misclassifications both at the motion and hierarchy formation level. This article presents an approach enabling fast on-line incremental learning, combined with an incremental memory consolidation process correcting initial misclassifications and errors in organization, to improve the stability and accuracy of the learned motions, analogous to the memory consolidation process following motor learning observed in humans. Following initial organization, motions are randomly selected for reclassification, at both low and high levels of the hierarchy. If a better reclassification is found, the knowledge structure is reorganized to comply. The approach is validated during incremental acquisition of a motion database containing a variety of full body motions.

Original languageEnglish
Pages (from-to)484-507
Number of pages24
JournalAdaptive Behavior
Issue number6
Publication statusPublished - 1 Dec 2009
Externally publishedYes


  • Humanoid robots
  • Incremental learning
  • Motion primitives
  • Whole body motions

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