抄録

Detecting the onset/ongoing of slip, i.e. if a grasped object is slipping or will slip from the gripper while being lifted, is crucial. Conventionally, it is regarded as a tactile sensing related problem. However, recently multi-modal robotic learning has become popular and is expected to boost the performance. In this paper we propose a novel CNN-TCN model to fuse tactile and visual information for detecting the onset/ongoing of slip. In our experiments, two uSkin tactile sensors and one Realsense435i camera are used. Data is collected by randomly grasping and lifting 35 daily objects 1050 times in total. Furthermore, we compare our CNN-TCN model with the widely used CNN-LSTM model. As a result, our proposed model achieves a 88.75% detection accuracy and outperforms the CNN-LSTM model combined with different pretrained vision networks.

本文言語English
ホスト出版物のタイトル2022 IEEE International Conference on Robotics and Automation, ICRA 2022
出版社Institute of Electrical and Electronics Engineers Inc.
ページ3537-3543
ページ数7
ISBN(電子版)9781728196817
DOI
出版ステータスPublished - 2022
イベント39th IEEE International Conference on Robotics and Automation, ICRA 2022 - Philadelphia, United States
継続期間: 2022 5月 232022 5月 27

出版物シリーズ

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

Conference

Conference39th IEEE International Conference on Robotics and Automation, ICRA 2022
国/地域United States
CityPhiladelphia
Period22/5/2322/5/27

ASJC Scopus subject areas

  • ソフトウェア
  • 制御およびシステム工学
  • 人工知能
  • 電子工学および電気工学

フィンガープリント

「Detection of Slip from Vision and Touch」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

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