Neural network based model for visual-motor integration learning of robot's drawing behavior: Association of a drawing motion from a drawn image

Kazuma Sasaki, Hadi Tjandra, Kuniaki Noda, Kuniyuki Takahashi, Tetsuya Ogata

    研究成果: Conference contribution

    11 被引用数 (Scopus)

    抄録

    In this study, we propose a neural network based model for learning a robot's drawing sequences in an unsupervised manner. We focus on the ability to learn visual-motor relationships, which can work as a reusable memory in association of drawing motion from a picture image. Assuming that a humanoid robot can draw a shape on a pen tablet, the proposed model learns drawing sequences, which comprises drawing motion and drawn picture image frames. To learn raw pixel data without any given specific features, we utilized a deep neural network for compressing large dimensional picture images and a continuous time recurrent neural network for integration of motion and picture images. To confirm the ability of the proposed model, we performed an experiment for learning 15 sequences comprising three types of shapes. The model successfully learns all the sequences and can associate a drawing motion from a not trained picture image and a trained picture with similar success. We also show that the proposed model self-organizes its behavior according to types shapes.

    本文言語English
    ホスト出版物のタイトルIEEE International Conference on Intelligent Robots and Systems
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ2736-2741
    ページ数6
    2015-December
    ISBN(印刷版)9781479999941
    DOI
    出版ステータスPublished - 2015 12 11
    イベントIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015 - Hamburg, Germany
    継続期間: 2015 9 282015 10 2

    Other

    OtherIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015
    国/地域Germany
    CityHamburg
    Period15/9/2815/10/2

    ASJC Scopus subject areas

    • 制御およびシステム工学
    • ソフトウェア
    • コンピュータ ビジョンおよびパターン認識
    • コンピュータ サイエンスの応用

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