# Spatio-temporal Attention Model for Tactile Texture Recognition

arXiv: https://arxiv.org/abs/2008.04442 · HTML: https://mlsanity.com/paper/2008.04442/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2020.

## Built on by (most cited first, 11 of 11)

- [Binding Touch to Everything: Learning Unified Multimodal Tactile Representations](https://mlsanity.com/paper/2401.18084/) (2024)
- [ConViTac: Aligning Visual-Tactile Fusion with Contrastive Representations](https://mlsanity.com/paper/2506.20757/) (2025)
- [What Matters for Active Texture Recognition With Vision-Based Tactile Sensors](https://mlsanity.com/paper/2403.13701/) (2024)
- [Vision-Guided Active Tactile Perception for Crack Detection and Reconstruction](https://mlsanity.com/paper/2105.06325/) (2021)
- [Jointly Modeling Spatio-Temporal Features of Tactile Signals for Action Classification](https://mlsanity.com/paper/2404.15279/) (2024)
- [ViTacGen: Robotic Pushing with Vision-to-Touch Generation](https://mlsanity.com/paper/2510.14117/) (2025)
- [Multimodal perception for dexterous manipulation](https://mlsanity.com/paper/2112.14298/) (2021)
- [Attention for Robot Touch: Tactile Saliency Prediction for Robust Sim-to-Real Tactile Control](https://mlsanity.com/paper/2307.14510/) (2023)
- [Towards Generalization of Tactile Image Generation: Reference-Free Evaluation in a Leakage-Free Setting](https://mlsanity.com/paper/2503.06860/) (2025)
- [Open-Set Object Recognition Using Mechanical Properties During Interaction](https://mlsanity.com/paper/2311.01540/) (2023)
- [SemanticFeels: Semantic Labeling during In-Hand Manipulation](https://mlsanity.com/paper/2602.14099/) (2026)

## Abstract (arXiv)

Recently, tactile sensing has attracted great interest in robotics, especially for facilitating exploration of unstructured environments and effective manipulation. A detailed understanding of the surface textures via tactile sensing is essential for many of these tasks. Previous works on texture recognition using camera based tactile sensors have been limited to treating all regions in one tactile image or all samples in one tactile sequence equally, which includes much irrelevant or redundant information. In this paper, we propose a novel Spatio-Temporal Attention Model (STAM) for tactile texture recognition, which is the very first of its kind to our best knowledge. The proposed STAM pays attention to both spatial focus of each single tactile texture and the temporal correlation of a tactile sequence. In the experiments to discriminate 100 different fabric textures, the spatially and temporally selective attention has resulted in a significant improvement of the recognition accuracy, by up to 18.8%, compared to the non-attention based models. Specifically, after introducing noisy data that is collected before the contact happens, our proposed STAM can learn the salient features efficiently and the accuracy can increase by 15.23% on average compared with the CNN based baseline approach. The improved tactile texture perception can be applied to facilitate robot tasks like grasping and manipulation.