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

Kuniyuki Takahashi*, Hadi Tjandra, Tetsuya Ogata, Shigeki Sugano

*この研究の対応する著者

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルArtificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings
編集者Alessandro E.P. Villa, Paolo Masulli, Antonio Javier Pons Rivero
出版社Springer Verlag
ページ166-174
ページ数9
ISBN(印刷版)9783319447773
DOI
出版ステータスPublished - 2016
イベント25th International Conference on Artificial Neural Networks, ICANN 2016 - Barcelona, Spain
継続期間: 2016 9 62016 9 9

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
9886 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other25th International Conference on Artificial Neural Networks, ICANN 2016
国/地域Spain
CityBarcelona
Period16/9/616/9/9

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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