---
task_categories:
- image-classification
- image-text-to-image
- image-to-text
- text-classification
- text-to-image
language:
- en
tags:
- tactile
- tactile-sensing
- touch
- vision
- language
- multimodal
- touch-vision-language
- robotics
- material-recognition
- material-classification
- cross-sensor
- cross-modal
- representation-learning
- knowledge-distillation
- vision-transformer
- digit-sensor
- hct
- ssvtp
- VBTS
pretty_name: Language-Guided Tactile Perception Dataset
size_categories:
- 10K<n<100K
---
<h1 align="center">Language-Guided Representation Learning<br>for Robust Cross-Sensor Tactile Perception</h1>

<p align="center">
  <img src="https://img.shields.io/badge/IROS_2026-Accepted-164E63" alt="Accepted at IROS 2026">
  <a href="https://github.com/Mashood3624/Language_Tactile/blob/main/environment.yml"><img src="https://img.shields.io/badge/Python-3.10-3776AB?logo=python&amp;logoColor=white" alt="Python 3.10"></a>
  <a href="https://github.com/Mashood3624/Language_Tactile/blob/main/environment.yml"><img src="https://img.shields.io/badge/PyTorch-2.0.1-EE4C2C?logo=pytorch&amp;logoColor=white" alt="PyTorch 2.0.1"></a>
</p>

<p align="center">
  <a href="https://www.linkedin.com/in/mashood3624/">Mashood M. Mohsan</a> · Muhayy Ud Din · Binzhao Xu · Ahmad Abubakar · Irfan Hussain<br>
  Khalifa University Center for Autonomous Robotic Systems (KUCARS)<br>
  Khalifa University, UAE
</p>

<p align="center">
  <a href="https://www.ku.ac.ae/">Khalifa University</a> ·
  <a href="https://touchripe.com/">TouchRIPE</a> ·
  <a href="https://www.ku.ac.ae/kucars">KUCARS</a> ·
  <a href="https://www.linkedin.com/company/ihlab/">AERIS Lab</a>
</p>

<p align="center">
  <a href="https://mashood3624.github.io/Language_Tactile/">Project website</a> ·
  <a href="https://youtu.be/QaMzg2h5LKA">Video</a> ·
  <a href="#quick-start">Quick start</a> ·
  <a href="#dataset">Dataset</a> ·
  <a href="#citation">Citation</a>
</p>

Language descriptions guide a tactile image encoder to learn material representations across sensors. Training has two stages: distill a frozen BART language teacher into a ViT tactile student, then freeze the tactile encoder and train a material classifier. Inference uses tactile images alone.

![Language–tactile distillation and downstream classification](assets/architecture.png)

## Dataset

**Language-Guided Tactile Perception** is a multimodal tactile dataset combining data from **TVL/HCT** and **SSVTP**. Each experiment sample contains paired tactile and visual observations together with a natural-language description and a material label.

The dataset is intended for research in tactile perception, multimodal representation learning, cross-sensor generalization, material recognition, and language-guided tactile learning.

### Dataset Contents

**Modalities:** Tactile images · RGB images · Tactile–vision pairs · Natural-language descriptions  
**Annotations:** Material labels · 32 material categories  
**Splits:** Train / evaluation · Distillation · Few-shot classification

### Dataset composition

The four experiment CSVs contain **39,717 tactile–vision pairs** used in the experiments.

| Source | Distillation | Few-shot | Total |
|---|---:|---:|---:|
| TVL / HCT | 27,223 | 8,683 | 35,906 |
| SSVTP | 2,475 | 1,336 | 3,811 |
| **Combined** | **29,698** | **10,019** | **39,717** |

The experiment splits are further divided as follows:

| Task | Train | Evaluation | Total |
|---|---:|---:|---:|
| Language-guided distillation | 21,035 | 8,663 | 29,698 |
| Material classification | 7,147 | 2,872 | 10,019 |
| **Combined** | **28,182** | **11,535** | **39,717** |

### Data organization

After extracting `dataset.zip`, the dataset is organized as:

```text
dataset/
├── images/
│   ├── hct/
│   └── ssvtp/
├── splits/
└── additional/
```
## Citation

```bibtex
@misc{mohsan2026languageguidedrepresentationlearningrobust,
      title={Language-Guided Representation Learning for Robust Cross-Sensor Material Recognition}, 
      author={Mashood M. Mohsan and Muhayy Ud Din and Binzhao Xu and Ahmad Abubakar and Irfan Hussain},
      year={2026},
      eprint={2609.14783},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2609.14783}, 
}
```

## Acknowledgements

Data sources: [TVL/HCT](https://tactile-vlm.github.io/) and [SSVTP](https://sites.google.com/berkeley.edu/ssvtp). Code builds on [MDistiller](https://github.com/megvii-research/mdistiller), [Transformers](https://github.com/huggingface/transformers) and the [distillation example](https://github.com/philschmid/knowledge-distillation-transformers-pytorch-sagemaker).