Versatile In-Hand Manipulation of Objects with Different Sizes and Shapes Using Neural Networks

研究成果: Conference contribution

抄録

Changing the grasping posture of objects within a robot hand is hard to achieve, especially if the objects are of various shape and size. In this paper we use a neural network to learn such manipulation with variously sized and shaped objects. The TWENDY-ONE hand possesses various properties that are effective 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 soft skin. The object size information is extracted from the initial grasping posture. The training data includes tactile and the object information. After training the neural network, the robot is able to manipulate objects of not only trained but also untrained size and shape. The results show the importance of size and tactile information. Importantly, the features extracted by a stacked autoencoder (trained with a larger dataset) could reduce the number of required training samples for supervised learning of in-hand manipulation.

本文言語English
ホスト出版物のタイトル2018 IEEE-RAS 18th International Conference on Humanoid Robots, Humanoids 2018
出版社IEEE Computer Society
ページ768-775
ページ数8
ISBN(電子版)9781538672839
DOI
出版ステータスPublished - 2019 1 23
イベント18th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2018 - Beijing, China
継続期間: 2018 11 62018 11 9

出版物シリーズ

名前IEEE-RAS International Conference on Humanoid Robots
2018-November
ISSN(印刷版)2164-0572
ISSN(電子版)2164-0580

Conference

Conference18th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2018
国/地域China
CityBeijing
Period18/11/618/11/9

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ビジョンおよびパターン認識
  • ハードウェアとアーキテクチャ
  • 人間とコンピュータの相互作用
  • 電子工学および電気工学

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