Motion generation by reference-point-dependent trajectory HMMs

Komei Sugiura, Naoto Iwahashi, Hideki Kashioka

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

6 Citations (Scopus)

Abstract

This paper presents an imitation learning method for object manipulation such as rotating an object or placing one object on another. In the proposed method, motions are learned using reference-point-dependent probabilistic models. Trajectory hidden Markov models (HMMs) are used as the probabilistic models so that smooth trajectories can be generated from the HMMs. The method was evaluated in physical experiments in terms of motion generation. In the experiments, a robot learned motions from observation, and it generated motions under different object placement. Experimental results showed that appropriate motions were generated even when the object placement was changed.

Original languageEnglish
Title of host publicationIROS'11 - 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
Subtitle of host publicationCelebrating 50 Years of Robotics
Pages350-356
Number of pages7
DOIs
Publication statusPublished - 2011 Dec 29
Externally publishedYes
Event2011 IEEE/RSJ International Conference on Intelligent Robots and Systems: Celebrating 50 Years of Robotics, IROS'11 - San Francisco, CA, United States
Duration: 2011 Sep 252011 Sep 30

Publication series

NameIEEE International Conference on Intelligent Robots and Systems

Other

Other2011 IEEE/RSJ International Conference on Intelligent Robots and Systems: Celebrating 50 Years of Robotics, IROS'11
CountryUnited States
CitySan Francisco, CA
Period11/9/2511/9/30

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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  • Cite this

    Sugiura, K., Iwahashi, N., & Kashioka, H. (2011). Motion generation by reference-point-dependent trajectory HMMs. In IROS'11 - 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems: Celebrating 50 Years of Robotics (pp. 350-356). [6048398] (IEEE International Conference on Intelligent Robots and Systems). https://doi.org/10.1109/IROS.2011.6048398