Discovery of other individuals by projecting a self-model through imitation

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

研究成果: Conference contribution

7 被引用数 (Scopus)

抄録

This paper proposes a novel model which enables a humanoid robot infant to discover other individual (e.g. human parent). In this work, the authors define "other individual" as an actor which can be predicted by a self-model. For modeling the developmental process of discovering ability, the following three approaches are employed. (i) Projection of a self-model for predicting other individual's actions. (ii) Mediation by a physical object between self and other individual. (iii) Introduction of infant imitation by parent. For creating the self-model of a robot, we apply Recurrent Neural Network with Parametric Bias (RNNPB) model which can learn the robot's body dynamics. For the other-model of a human, conventional hierarchical neural networks are attached to the RNNPB model as "conversion modules". Our target task is a moving an object. For evaluation of our model, human discovery experiments by the robot projecting its self-model were conducted. The results demonstrated that our method enabled the robot to predict the human's motions, and to estimate the human's position fairly accurately, which proved its adequacy.

本文言語English
ホスト出版物のタイトルProceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
ページ1009-1014
ページ数6
DOI
出版ステータスPublished - 2007 12 1
外部発表はい
イベント2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007 - San Diego, CA, United States
継続期間: 2007 10 292007 11 2

出版物シリーズ

名前IEEE International Conference on Intelligent Robots and Systems

Conference

Conference2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
CountryUnited States
CitySan Diego, CA
Period07/10/2907/11/2

ASJC Scopus subject areas

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

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