Compensation for Undefined Behaviors during Robot Task Execution by Switching Controllers Depending on Embedded Dynamics in RNN

Kanata Suzuki, Hiroki Mori, Tetsuya Ogata

研究成果: Article査読

1 被引用数 (Scopus)

抄録

Robotic applications require both correct task performance and compensation for undefined behaviors. Although deep learning is a promising approach to perform complex tasks, the response to undefined behaviors that are not reflected in the training dataset remains challenging. In a human-robot collaborative task, the robot may adopt an unexpected posture due to collisions and other unexpected events. Therefore, robots should be able to recover from disturbances for completing the execution of the intended task. We propose a compensation method for undefined behaviors by switching between two controllers. Specifically, the proposed method switches between learning-based and model-based controllers depending on the internal representation of a recurrent neural network that learns task dynamics. We applied the proposed method to a pick-And-place task and evaluated the compensation for undefined behaviors. Experimental results from simulations and on a real robot demonstrate the effectiveness and high performance of the proposed method.

本文言語English
論文番号9368970
ページ(範囲)3475-3482
ページ数8
ジャーナルIEEE Robotics and Automation Letters
6
2
DOI
出版ステータスPublished - 2021 4

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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