Experience based imitation using RNNPB

Ryunosuke Yokoya, Tetsuya Ogata, Jun Tani, Kazunori Komatani, Hiroshi G. Okuno

研究成果: Conference contribution

11 被引用数 (Scopus)

抄録

Robot imitation is a useful and promising alternative to robot programming. Robot imitation involves two crucial issues. The first is how a robot can imitate a human whose physical structure and properties differ greatly from its own. The second is how the robot can generate various motions from finite programmable patterns (generalization). This paper describes a novel approach to robot imitation based on its own physical experiences. Let us consider a target task of moving an object on a table. For imitation, we focused on an active sensing process in which the robot acquires the relation between the object's motion and its own arm motion. For generalization, we applied a recurrent neural network with parametric bias (RNNPB) model to enable recognition/generation of imitation motions. The robot associates the arm motion which reproduces the observed object's motion presented by a human operator. Experimental results demonstrated that our method enabled the robot to imitate not only motion it has experienced but also unknown motion, which proved its capability for generalization.

本文言語English
ホスト出版物のタイトル2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006
ページ3669-3674
ページ数6
DOI
出版ステータスPublished - 2006 12 1
外部発表はい
イベント2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006 - Beijing, China
継続期間: 2006 10 92006 10 15

出版物シリーズ

名前IEEE International Conference on Intelligent Robots and Systems

Conference

Conference2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006
CountryChina
CityBeijing
Period06/10/906/10/15

ASJC Scopus subject areas

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

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