Mimesis from partial observations

Dongheui Lee, Yoshihiko Nakamura

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

19 Scopus citations

Abstract

In this paper, a new mimesis scheme is proposed. This scheme enables for a humanoid to imitate human's motion even though the humanoid cannot see human's whole-body motion and the humanoid has not seen the exactly same motion so far. Mimesis framework is based on continuous Hidden Markov Model. Viterbi algorithm is applied in order to generate more various motion patterns than the number of existing Hidden Markov Models. In order to imitate other's motion in a smooth way, a smoothing technique in generation problem is realized. The feasibility of this method is demonstrated by simulation on a 20 degrees of freedom humanoid robot configuration.

Original languageEnglish
Title of host publication2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
PublisherIEEE Computer Society
Pages3758-3763
Number of pages6
ISBN (Print)0780389123, 9780780389120
DOIs
StatePublished - 2005
Externally publishedYes

Publication series

Name2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS

Keywords

  • Hidden Markov Models
  • Imitation
  • Learning

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