Datasets:

Mashood
/

Language_Tactile

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

Language-Guided Representation Learning
for Robust Cross-Sensor Tactile Perception

Mashood M. Mohsan · Muhayy Ud Din · Binzhao Xu · Ahmad Abubakar · Irfan Hussain
Khalifa University Center for Autonomous Robotic Systems (KUCARS)
Khalifa University, UAE

Khalifa University · TouchRIPE · KUCARS · AERIS Lab

Project website · Video · Quick start · Dataset · Citation

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.

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:

dataset/
├── images/
│   ├── hct/
│   └── ssvtp/
├── splits/
└── additional/

Citation

@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 and SSVTP. Code builds on MDistiller, Transformers and the distillation example.

Language-Guided Representation Learningfor Robust Cross-Sensor Tactile Perception