Spatio-temporal attention model for tactile texture recognition

Cao, Guanqun; Zhou, Yi; Bollegala, Danushka; Luo, Shan

Abstract

Recently, tactile sensing has attracted great interest in robotics, especially for facilitating exploration of unstructured environments and effective manipulation. A detailed understanding of the surface textures via tactile sensing is essential for many of these tasks. Previous works on texture recognition using camera based tactile sensors have been limited to treating all regions in one tactile image or all samples in one tactile sequence equally, which includes much irrelevant or redundant information. In this paper, we propose a novel Spatio-Temporal Attention Model (STAM) for tactile texture recognition, which is the very first of its kind to our best knowledge. The proposed STAM pays attention to both spatial focus of each single tactile texture and the temporal correlation of a tactile sequence. In the experiments to discriminate 100 different fabric textures, the spatially and temporally selective attention has resulted in a significant improvement of the recognition accuracy, by up to 18.8%, compared to the non-attention based models. Specifically, after introducing noisy data that is collected before the contact happens, our proposed STAM can learn the salient features efficiently and the accuracy can increase by 15.23% on average compared with the CNN based baseline approach. The improved tactile texture perception can be applied to facilitate robot tasks like grasping and manipulation.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• Start Date: Oct 24, 2020
• End Date: Jan 24, 2021
• Online Publication Date: Feb 10, 2021
• Publication Date: Feb 10, 2021
• Deposit Date: Jul 10, 2025
• Publicly Available Date: Jul 14, 2025
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Book Title: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• ISBN: 9781728162133
• DOI: https://doi.org/10.1109/iros45743.2020.9341333
• Public URL: https://uwe-repository.worktribe.com/output/14687557