Skip to main content

Research Repository

Advanced Search

Multi-purpose tactile perception based on deep learning in a new tendon-driven optical tactile sensor

Zhao, Zhou; Lu, Zhenyu


Zhou Zhao

Zhenyu Lu


In this paper, we create a new tendon-connected multi-functional optical tactile sensor, MechTac, for object perception in the field of view (TacTip) and location of touching points in the blind area of vision (TacSide). In a multi-point touch task, the information of the TacSide and the TacTip are overlapped to commonly affect the distribution of papillae pins on the TacTip. Since the effects of TacSide are much less obvious to those affected on the TacTip, a perceiving out-of-view neural network (O2VNet) is created to separate the mixed information with unequal affection. To reduce the dependence of the O2VNet on the grayscale information of the image, we create one new binarized convolutional (BConv) layer in front of the backbone of the O2VNet. The O2VNet can not only achieve real-time temporal sequence prediction (34 ms per image), but also attain the average classification accuracy of 99.06%. The experimental results show that the O2VNet can hold a high classification accuracy even facing the image contrast changes.

Presentation Conference Type Conference Paper (Published)
Conference Name 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Start Date Oct 23, 2022
End Date Oct 27, 2022
Acceptance Date Jun 30, 2022
Online Publication Date Dec 26, 2022
Publication Date Dec 26, 2022
Deposit Date Jan 14, 2023
Publicly Available Date Dec 27, 2024
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Volume 2022-October
Pages 2099-2104
Series ISSN 2153-0866
Book Title 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
ISBN 9781665479288
Keywords Optical design, Neural networks, Tactile sensors, Optical computing, Optical imaging, Real-time systems, Pins
Public URL
Publisher URL
Related Public URLs