Human task reproduction with Gaussian mixture models

Tomohiro Nakano, Koyo Yu, Kouhei Ohnishi

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル2015 IEEE International Conference on Industrial Technology, ICIT 2015
出版社Institute of Electrical and Electronics Engineers Inc.
ページ283-288
ページ数6
June
ISBN(電子版)9781479978007
DOI
出版ステータスPublished - 2015 6月 16
イベント2015 IEEE International Conference on Industrial Technology, ICIT 2015 - Seville, Spain
継続期間: 2015 3月 172015 3月 19

出版物シリーズ

名前Proceedings of the IEEE International Conference on Industrial Technology
番号June
2015-June

Other

Other2015 IEEE International Conference on Industrial Technology, ICIT 2015
国/地域Spain
CitySeville
Period15/3/1715/3/19

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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