TacEva: a performance evaluation framework for vision-based tactile sensors
Author(s)
Type
Journal Article
Abstract
Vision-Based Tactile Sensors (VBTSs) are widely used in robotic tasks because of the high spatial resolution and relatively low manufacturing costs they offer. However, variations in their sensing mechanisms, structural dimension, and other parameters lead to significant performance disparities between existing VBTSs. This makes it challenging to optimize them for specific tasks, as both the initial choice and subsequent fine-tuning are hindered by the lack of standardized metrics. To address this issue, TacEva is introduced as a comprehensive evaluation framework for the quantitative analysis of VBTS performance. This framework defines a set of performance metrics that capture key characteristics in typical application scenarios. For each metric, a structured experimental pipeline is designed to ensure consistent and repeatable quantification. TacEva has been applied to multiple VBTSs with distinct sensing mechanisms, and the results demonstrate its ability to provide a thorough evaluation of each design and quantitative indicators for each performance dimension. This enables researchers to pre-select the most appropriate VBTS on a task by application basis, while also offering performance-guided insights into the optimization of VBTS design. A list of existing VBTS evaluation methods and additional evaluations can be found on our website: https://stevenoh2003.github.io/TacEva/.
Date Issued
2026-04-01
Date Acceptance
2025-12-11
Citation
Advanced Intelligent Systems, 2026, 8 (4), pp.1-19
ISSN
2640-4567
Publisher
Wiley
Start Page
1
End Page
19
Journal / Book Title
Advanced Intelligent Systems
Volume
8
Issue
4
Copyright Statement
© 2026 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
10.1002/aisy.202501179
Publication Status
Published
Article Number
202501179
Date Publish Online
2026-04-22