Put-in-Box Task Generated from Multiple Discrete Tasks by aHumanoid Robot Using Deep Learning

Kei Kase, Kanata Suzuki, Pin Chu Yang, Hiroki Mori, Tetsuya Ogata

研究成果: Conference contribution

11 被引用数 (Scopus)

抄録

For robots to have a wide range of applications, they must be able to execute numerous tasks. However, recent studies into robot manipulation using deep neural networks (DNN) have primarily focused on single tasks. Therefore, we investigate a robot manipulation model that uses DNNs and can execute long sequential dynamic tasks by performing multiple short sequential tasks at appropriate times. To generate compound tasks, we propose a model comprising two DNNs: a convolutional autoencoder that extracts image features and a multiple timescale recurrent neural network (MTRNN) to generate motion. The internal state of the MTRNN is constrained to have similar values at the initial and final motion steps; thus, motions can be differentiated based on the initial image input. As an example compound task, we demonstrate that the robot can generate a 'Put-In-Box' task that is divided into three subtasks: open the box, grasp the object and put it into the box, and close the box. The subtasks were trained as discrete tasks, and the connections between each subtask were not trained. With the proposed model, the robot could perform the Put-In-Box task by switching among subtasks and could skip or repeat subtasks depending on the situation.

本文言語English
ホスト出版物のタイトル2018 IEEE International Conference on Robotics and Automation, ICRA 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ6447-6452
ページ数6
ISBN(電子版)9781538630815
DOI
出版ステータスPublished - 2018 9 10
イベント2018 IEEE International Conference on Robotics and Automation, ICRA 2018 - Brisbane, Australia
継続期間: 2018 5 212018 5 25

出版物シリーズ

名前Proceedings - IEEE International Conference on Robotics and Automation
ISSN(印刷版)1050-4729

Conference

Conference2018 IEEE International Conference on Robotics and Automation, ICRA 2018
国/地域Australia
CityBrisbane
Period18/5/2118/5/25

ASJC Scopus subject areas

  • ソフトウェア
  • 制御およびシステム工学
  • 人工知能
  • 電子工学および電気工学

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