A Learning-Free Characterization Framework for the Resilience and Sensitivity of Polyurethane Vision-Based Tactile Sensors

Abstract:Vision-based tactile sensors (VBTSs) are a promising technology for robots, providing them with dense signals that can be translated into a multi-faceted understanding of contact. However, existing VBTS tactile surfaces make use of silicone gels, which provide high sensitivity but easily deteri- orate from loading and surface wear. Furthermore, existing literature lacks rigorous durability and sensitivity evaluations targeted for intrinsic sensor performance. We propose that polyurethane rubber, a typically harder material used for high-load applications like shoe soles, rubber wheels, and industrial gaskets, may provide improved physical gel resilience, potentially at the cost of sensitivity. In addition, we propose a methodological framework to evaluate and compare tactile sensor hardware across designs and material compositions. Our resilience tests assess sensor durability across normal loading, shear loading, and abrasion. For sensitivity, we introduce learning-free assessments of force and spatial sensitivity to isolate intrinsic sensor capabilities from the confounding effects of downstream dataset and network architecture choices. We also perform a system-level validation using a bottle cap loosening and tightening task to show the translation of our controlled test results with a real-world example. Our results show that polyurethane substantially improves resilience. While it sacrifices sensitivity at low forces, the effective force range is largely increased, revealing the utility of polyurethane VBTSs over silicone versions in more rugged, high-load applications.

Robotics (cs.RO)

arXiv:2511.07797 [cs.RO]

(or arXiv:2511.07797v3 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2511.07797 Focus to learn more arXiv-issued DOI via DataCite

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