Morphology-specific convolutional neural networks for tactile object recognition with a multi-fingered hand

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

Distributed tactile sensors on multi-fingered hands can provide high-dimensional information for grasping objects, but it is not clear how to optimally process such abundant tactile information. The current paper explores the possibility of using a morphology-specific convolutional neural network (MS-CNN). uSkin tactile sensors are mounted on an Allegro Hand, which provides 720 force measurements (15 patches of uSkin modules with 16 triaxial force sensors each) in addition to 16 joint angle measurements. Consecutive layers in the CNN get input from parts of one finger segment, one finger, and the whole hand. Since the sensors give 3D (x, y, z) vector tactile information, inputs with 3 channels (x, y and z) are used in the first layer, based on the idea of such inputs for RGB images from cameras. Overall, the layers are combined, resulting in the building of a tactile map based on the relative position of the tactile sensors on the hand. Seven different combination variations were evaluated, and an over-95% object recognition rate with 20 objects was achieved, even though only one random time instance from a repeated squeezing motion of an object in an unknown pose within the hand was used as input.

本文言語English
ホスト出版物のタイトル2019 International Conference on Robotics and Automation, ICRA 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ57-63
ページ数7
ISBN(電子版)9781538660263
DOI
出版ステータスPublished - 2019 5
イベント2019 International Conference on Robotics and Automation, ICRA 2019 - Montreal, Canada
継続期間: 2019 5 202019 5 24

出版物シリーズ

名前Proceedings - IEEE International Conference on Robotics and Automation
2019-May
ISSN(印刷版)1050-4729

Conference

Conference2019 International Conference on Robotics and Automation, ICRA 2019
CountryCanada
CityMontreal
Period19/5/2019/5/24

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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