Body model transition by tool grasping during motor babbling using deep learning and RNN

Kuniyuki Takahashi, Hadi Tjandra, Tetsuya Ogata, Shigeki Sugano

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

    Abstract

    We propose a method of tool use considering the transition process of a body model from not grasping to grasping a tool using a single model. In our previous research, we proposed a tool-body assimilation model in which a robot autonomously learns tool functions using a deep neural network (DNN) and recurrent neural network (RNN) through experiences of motor babbling. However, the robot started its motion already holding the tools. In real-life situations, the robot would make decisions regarding grasping (handling) or not grasping (manipulating) a tool. To achieve this, the robot performs motor babbling without the tool pre-attached to the hand with the same motion twice, in which the robot handles the tool or manipulates without graping it. To evaluate the model, we have the robot generate motions with showing the initial and target states. As a result, the robot could generate the correct motions with grasping decisions.

    Original languageEnglish
    Title of host publicationArtificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings
    PublisherSpringer Verlag
    Pages166-174
    Number of pages9
    Volume9886 LNCS
    ISBN (Print)9783319447773
    DOIs
    Publication statusPublished - 2016
    Event25th International Conference on Artificial Neural Networks, ICANN 2016 - Barcelona, Spain
    Duration: 2016 Sep 62016 Sep 9

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9886 LNCS
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other25th International Conference on Artificial Neural Networks, ICANN 2016
    CountrySpain
    CityBarcelona
    Period16/9/616/9/9

    Keywords

    • Deep neural network
    • Grasping
    • Recurrent neural network

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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  • Cite this

    Takahashi, K., Tjandra, H., Ogata, T., & Sugano, S. (2016). Body model transition by tool grasping during motor babbling using deep learning and RNN. In Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings (Vol. 9886 LNCS, pp. 166-174). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9886 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-44778-0_20