抄録

Recently, tactile sensing has attracted great interest for robotic manipulation. Predicting if a grasp will be stable or not, i.e. if the grasped object will drop out of the gripper while being lifted, can aid robust robotic grasping. Previous methods paid equal attention to all regions of the tactile data matrix or all time-steps in the tactile sequence, which may include irrelevant or redundant information. In this paper, we propose to equip Convolutional Neural Networks with spatial-channel and temporal attention mechanisms (SCT attention CNN) to predict future grasp stability. To the best of our knowledge, this is the first time to use attention mechanisms for predicting grasp stability only relying on tactile information. We implement our experiments with 52 daily objects. Moreover, we compare different spatio-temporal models and attention mechanisms as an empirical study. We found a significant accuracy improvement of up to 5% when using SCT attention. We believe that attention mechanisms can also improve the performance of other tactile learning tasks in the future, such as slip detection and hardness perception.

本文言語English
ホスト出版物のタイトル2021 IEEE International Conference on Robotics and Automation, ICRA 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ2627-2634
ページ数8
ISBN(電子版)9781728190778
DOI
出版ステータスPublished - 2021
イベント2021 IEEE International Conference on Robotics and Automation, ICRA 2021 - Xi'an, China
継続期間: 2021 5月 302021 6月 5

出版物シリーズ

名前Proceedings - IEEE International Conference on Robotics and Automation
2021-May
ISSN(印刷版)1050-4729

Conference

Conference2021 IEEE International Conference on Robotics and Automation, ICRA 2021
国/地域China
CityXi'an
Period21/5/3021/6/5

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

「SCT-CNN: A Spatio-Channel-Temporal Attention CNN for Grasp Stability Prediction」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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