Robust in-hand manipulation of variously sized and shaped objects

研究成果: Conference contribution

14 被引用数 (Scopus)

抄録

Moving objects within the hand is challenging, especially if the objects are of various shape and size. In this paper we use machine learning to learn in-hand manipulation of such various sized and shaped objects. The TWENDY-ONE hand is used, which has various properties that makes it well suited for in-hand manipulation: a high number of actuated joints, passive degrees of freedom and soft skin, six-axis force/torque (F/T) sensors in each fingertip, and distributed tactile sensors in the skin. A dataglove is used to gather training samples for teaching the required behavior. The object size information is extracted from the initial grasping posture. After training a neural network, the robot is able to manipulate objects of untrained sizes and shape. The results show the importance of size and tactile information. Compared to interpolation control, the adaptability for the initial posture gap could be greatly extended. Final results show that with deep learning the number of required training sets can be drastically reduced.

本文言語English
ホスト出版物のタイトルIEEE International Conference on Intelligent Robots and Systems
出版社Institute of Electrical and Electronics Engineers Inc.
ページ257-263
ページ数7
2015-December
ISBN(印刷版)9781479999941
DOI
出版ステータスPublished - 2015 12 11
イベントIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015 - Hamburg, Germany
継続期間: 2015 9 282015 10 2

Other

OtherIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015
国/地域Germany
CityHamburg
Period15/9/2815/10/2

ASJC Scopus subject areas

  • 制御およびシステム工学
  • ソフトウェア
  • 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

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