Stochastic model of imitating a new observed motion based on the acquired motion primitives

Dongheui Lee, Yoshihiko Nakamura

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

24 Scopus citations

Abstract

Generally, imitation of a motion means generation of a close motion to the observation. Moreover, it means that conversion into its own motion, which is adoptable to its body structure, by integrating with its prior knowledge. From this perspective, a new imitation scheme is proposed. The scheme is based on Hidden Markov Models by employing Viterbi algorithm. The proposed scheme enables to imitate a new observed motion without learning the motion by applying its prior knowledge. Online motion primitive acquisition method is considered. Evaluation factors, such as inheritance coordinate and matching error, are introduced to evaluate imitation performance. The feasibility of the proposed scheme is demonstrated by simulation on a 20 degrees of freedom humanoid robot configuration with the evaluation factors.

Original languageEnglish
Title of host publication2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006
Pages4994-5000
Number of pages7
DOIs
StatePublished - 2006
Externally publishedYes
Event2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006 - Beijing, China
Duration: 9 Oct 200615 Oct 2006

Publication series

NameIEEE International Conference on Intelligent Robots and Systems

Conference

Conference2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006
Country/TerritoryChina
CityBeijing
Period9/10/0615/10/06

Keywords

  • Hidden Markov model
  • Mimesis
  • Motion primitive
  • Viterbi algorithm
  • Viterbi training

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