Incremental learning of full body motion primitives for humanoid robots

Dana Kulić, Dongheui Lee, Christian Ott, Yoshihiko Nakamura

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

31 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2008 8th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2008
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages326-332
Number of pages7
ISBN (Print)9781424428229
DOIs
Publication statusPublished - 1 Dec 2008
Externally publishedYes
EventIEEE-RAS International Conference on Humanoid Robots 2008 - Daejeon, Korea, Republic of (South)
Duration: 1 Dec 20083 Dec 2008
Conference number: 8th

Publication series

Name2008 8th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2008

Conference

ConferenceIEEE-RAS International Conference on Humanoid Robots 2008
Abbreviated titleHumanoids 2008
CountryKorea, Republic of (South)
CityDaejeon
Period1/12/083/12/08

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