Vision-based tactile sensing enhanced by microstructures and lightweight convolutional neural network
File(s)
Published version
Author(s)
Shi, Mayue
Zhang, Yongqi
Guo, Xiaotong
Yeatman, Eric M
Type
Journal Article
Abstract
Tactile sensing can provide a critical function in advanced interactive systems by emulating the human sense of touch to detect stimuli. Vision-based tactile sensors are promising for providing multimodal capabilities and high robustness, yet existing technologies still have limitations in sensitivity, spatial resolution and the high computational demands of deep learning-based image processing. This paper presents a comprehensive approach combining a novel microstructure-based sensor design and efficient image processing, demonstrating that carefully engineered microstructures can significantly enhance performance while reducing computational load. Without traditional tracking markers, our sensor incorporates a surface with micromachined trenches, as an example of microstructures which can modulate light transmission and amplify the visual response to applied force. The amplified image features can be extracted by an ultra-lightweight convolutional neural network to accurately infer contact location, displacement, and applied force with high precision. Through theoretical analysis, we demonstrate that the micro trenches significantly amplify the visual effects of surface deformation. Using only a commercial webcam, the sensor system effectively detected forces below 5 mN and achieved a millimetre-level single-point spatial resolution. Using a model with only one convolutional layer, a mean absolute error below 0.05 mm was achieved. The compliant sensor body and optical readout design make the system inherently compatible with soft robotic integration and immune to electrical crosstalk or electromagnetic interference that often affects electronic tactile arrays. These characteristics highlight its potential for reliable operation in complex human–machine environments.
Date Issued
2026-06-15
Date Acceptance
2026-04-20
Citation
Microsystems & Nanoengineering, 2026, 12
ISSN
2055-7434
Publisher
Nature Publishing Group
Journal / Book Title
Microsystems & Nanoengineering
Volume
12
Copyright Statement
© The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/42289404
PII: 10.1038/s41378-026-01355-5
Publication Status
Published
Coverage Spatial
England
Article Number
231
Date Publish Online
2026-06-15