HMM-based dance step estimation for dance partner robot -MS DanceR-

Takahiro Takeda, Kazuhiro Kosuge, Yasuhisa Hirata

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

22 Citations (Scopus)

Abstract

We have proposed a dance partner robot, which has been developed as a platform for realizing the effective human-robot coordination with physical interactions. In this paper, especially, we improve an estimation system for dance steps, which estimates a next dance step intended by a human. For estimating the dance step, time series data of force/moment applied by a human to the robot are utilized. The time series data of force/moment measured during dancing by a human and the robot include the uncertainty such as time-lag and variations for each repeated trial, because a human can not always apply the same force/moment to the robot exactly. In order to treat the time series data including such uncertainty, Hidden Markov Models are utilized for designing the dance step estimation system. With the proposed system, the robot estimates a next dance step based on human intention successfully.

Original languageEnglish
Title of host publication2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
PublisherIEEE Computer Society
Pages3245-3250
Number of pages6
ISBN (Print)0780389123, 9780780389120
DOIs
Publication statusPublished - 2005

Publication series

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

Keywords

  • Ballroom dances
  • Dance step estimation
  • Hidden Markov Models
  • Human intention
  • Mobile robot

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