Experience based imitation using RNNPB

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

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

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationIEEE International Conference on Intelligent Robots and Systems
Pages3669-3674
Number of pages6
DOIs
Publication statusPublished - 2006
Externally publishedYes
Event2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006 - Beijing
Duration: 2006 Oct 92006 Oct 15

Other

Other2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006
CityBeijing
Period06/10/906/10/15

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ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Yokoya, R., Ogata, T., Tani, J., Komatani, K., & Okuno, H. G. (2006). Experience based imitation using RNNPB. In IEEE International Conference on Intelligent Robots and Systems (pp. 3669-3674). [4058974] https://doi.org/10.1109/IROS.2006.281724