Human task reproduction with Gaussian mixture models

Tomohiro Nakano, Koyo Yu, Kouhei Ohnishi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper proposes a new motion-copying system which uses statistical approaches for recording and reproducing of human tasks. In conventional motion-copying systems, haptic data of human motions is recorded directly to the database at every sampling. As a result, the amount of haptic data for the database is large in general. In addition to that, it is hard to segment and reorganize the recorded human motions. Therefore, the motion-copying system proposed in this paper uses Gaussian mixture model (GMM) to model human motions for the recording. The modeled GMM are recorded in the database instead of raw haptic data. Therefore, the recorded data size is reduced compared with conventional methods. Furthermore, the automatic segmentation and reorganization of recorded human motions are possible. Proposed method uses Gaussian mixture regression (GMR) to retrieve haptic information from GMM for the reproducing. The validity of the proposed method was confirmed through 1DOF motion-copying experiment.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Industrial Technology, ICIT 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781479978007
Publication statusPublished - 2015 Jun 16
Event2015 IEEE International Conference on Industrial Technology, ICIT 2015 - Seville, Spain
Duration: 2015 Mar 172015 Mar 19

Publication series

NameProceedings of the IEEE International Conference on Industrial Technology


Other2015 IEEE International Conference on Industrial Technology, ICIT 2015


  • Gaussian mixture model
  • Gaussian mixture regression
  • Haptics
  • Lossy compression
  • Motion-copying system
  • Skill acquisition

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering


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