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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Robotics and Automation, ICRA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2627-2634
Number of pages8
ISBN (Electronic)9781728190778
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Robotics and Automation, ICRA 2021 - Xi'an, China
Duration: 2021 May 302021 Jun 5

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2021-May
ISSN (Print)1050-4729

Conference

Conference2021 IEEE International Conference on Robotics and Automation, ICRA 2021
Country/TerritoryChina
CityXi'an
Period21/5/3021/6/5

ASJC Scopus subject areas

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

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