Motion switching with sensory and instruction signals by designing dynamical systems using deep neural network

Kanata Suzuki*, Hiroki Mori, Tetsuya Ogata

*この研究の対応する著者

研究成果: Article査読

12 被引用数 (Scopus)

抄録

To ensure that a robot is able to accomplish an extensive range of tasks, it is necessary to achieve a flexible combination of multiple behaviors. This is because the design of task motions suited to each situation would become increasingly difficult as the number of situations and the types of tasks performed by them increase. To handle the switching and combination of multiple behaviors, we propose a method to design dynamical systems based on point attractors that accept (i) 'instruction signals' for instruction-driven switching. We incorporate the (ii) 'instruction phase' to form a point attractor and divide the target task into multiple subtasks. By forming an instruction phase that consists of point attractors, the model embeds a subtask in the form of trajectory dynamics that can be manipulated using sensory and instruction signals. Our model comprises two deep neural networks: A convolutional autoencoder and a multiple time-scale recurrent neural network. In this study, we apply the proposed method to manipulate soft materials. To evaluate our model, we design a cloth-folding task that consists of four subtasks and three patterns of instruction signals, which indicate the direction of motion. The results depict that the robot can perform the required task by combining subtasks based on sensory and instruction signals. And, our model determined the relations among these signals using its internal dynamics.

本文言語English
論文番号8405582
ページ(範囲)3481-3488
ページ数8
ジャーナルIEEE Robotics and Automation Letters
3
4
DOI
出版ステータスPublished - 2018 10

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 生体医工学
  • 人間とコンピュータの相互作用
  • 機械工学
  • 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
  • 制御と最適化
  • 人工知能

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