# What Matters for Active Texture Recognition With Vision-Based Tactile Sensors

arXiv: https://arxiv.org/abs/2403.13701 · HTML: https://mlsanity.com/paper/2403.13701/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Builds on (most cited first, 6 of 6)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation](https://mlsanity.com/paper/2005.14679/) (2020)
- [Active Clothing Material Perception using Tactile Sensing and Deep Learning](https://mlsanity.com/paper/1711.00574/) (2017)
- [Spatio-temporal Attention Model for Tactile Texture Recognition](https://mlsanity.com/paper/2008.04442/) (2020)

## Built on by (most cited first, 7 of 7)

- [FeelAnyForce: Estimating Contact Force Feedback from Tactile Sensation for Vision-Based Tactile Sensors](https://mlsanity.com/paper/2410.02048/) (2024)
- [AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models](https://mlsanity.com/paper/2605.07308/) (2026)
- [Tactile MNIST: Benchmarking Active Tactile Perception](https://mlsanity.com/paper/2506.06361/) (2025)
- [Apple: Toward General Active Perception via Reinforcement Learning](https://mlsanity.com/paper/2505.06182/) (2025)
- [Learning Tactile Insertion in the Real World](https://mlsanity.com/paper/2405.00383/) (2024)
- [Investigating Active Sampling for Hardness Classification with Vision-Based Tactile Sensors](https://mlsanity.com/paper/2505.13231/) (2025)
- [SemanticFeels: Semantic Labeling during In-Hand Manipulation](https://mlsanity.com/paper/2602.14099/) (2026)

## Abstract (arXiv)

This paper explores active sensing strategies that employ vision-based tactile sensors for robotic perception and classification of fabric textures. We formalize the active sampling problem in the context of tactile fabric recognition and provide an implementation of information-theoretic exploration strategies based on minimizing predictive entropy and variance of probabilistic models. Through ablation studies and human experiments, we investigate which components are crucial for quick and reliable texture recognition. Along with the active sampling strategies, we evaluate neural network architectures, representations of uncertainty, influence of data augmentation, and dataset variability. By evaluating our method on a previously published Active Clothing Perception Dataset and on a real robotic system, we establish that the choice of the active exploration strategy has only a minor influence on the recognition accuracy, whereas data augmentation and dropout rate play a significantly larger role. In a comparison study, while humans achieve 66.9% recognition accuracy, our best approach reaches 90.0% in under 5 touches, highlighting that vision-based tactile sensors are highly effective for fabric texture recognition.