# HySenSe: A Hyper-Sensitive and High-Fidelity Vision-Based Tactile Sensor

arXiv: https://arxiv.org/abs/2211.04571 · HTML: https://mlsanity.com/paper/2211.04571/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2022.

## Built on by (most cited first, 4 of 4)

- [Towards Reliable Colorectal Cancer Polyps Classification via Vision Based Tactile Sensing and Confidence-Calibrated Neural Networks](https://mlsanity.com/paper/2304.13192/) (2023)
- [Pit-Pattern Classification of Colorectal Cancer Polyps Using a Hyper Sensitive Vision-Based Tactile Sensor and Dilated Residual Networks](https://mlsanity.com/paper/2211.06814/) (2022)
- [Towards Design and Development of an ArUco Markers-Based Quantitative Surface Tactile Sensor](https://mlsanity.com/paper/2310.08398/) (2023)
- [Robot-Enabled Machine Learning-Based Diagnosis of Gastric Cancer Polyps Using Partial Surface Tactile Imaging](https://mlsanity.com/paper/2408.01554/) (2024)

## Abstract (arXiv)

In this paper, to address the sensitivity and durability trade-off of Vision-based Tactile Sensor (VTSs), we introduce a hyper-sensitive and high-fidelity VTS called HySenSe. We demonstrate that by solely changing one step during the fabrication of the gel layer of the GelSight sensor (as the most well-known VTS), we can substantially improve its sensitivity and durability. Our experimental results clearly demonstrate the outperformance of the HySenSe compared with a similar GelSight sensor in detecting textural details of various objects under identical experimental conditions and low interaction forces (<= 1.5 N).