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---
language:
- en
license: mit
size_categories:
- 10K<n<100K
task_categories:
- robotics
pretty_name: FEATS - Finite Element Analysis for Tactile Sensing
tags:
- tactile-sensing
- gelsight
- force-distribution
arxiv: 2411.03315
---
# FEATS: Finite Element Analysis for Tactile Sensing
[**Project Page**](https://feats-ai.github.io) | [**Paper**](https://huggingface.co/papers/2411.03315) | [**Code**](https://github.com/feats-ai/feats)
FEATS (Finite Element Analysis for Tactile Sensing) is a framework and dataset designed to estimate contact force distributions directly from raw images of a GelSight Mini optical tactile sensor. By using a U-net architecture trained on labels generated through Finite Element Analysis (FEA), the model can predict normal and shear force distributions from gel deformations.
## Dataset Description
The dataset consists of GelSight Mini images paired with shear and normal force distribution labels inferred from FEA simulations.
- **Data format**: The dataset is stored as `.npy` files.
- **Requirements**: It is recommended to use `numpy` version 2.X or higher to load the dataset.
- **Contents**: The data includes training, validation, and test sets featuring raw sensor images and corresponding force maps.
For detailed instructions on data preparation, training, and evaluation, please refer to the [official GitHub repository](https://github.com/feats-ai/feats).
## Citation
If you use this dataset or the associated code in your research, please cite:
```bibtex
@misc{helmut_dziarski2025feats,
title={Learning Force Distribution Estimation for the GelSight Mini Optical Tactile Sensor Based on Finite Element Analysis},
author={Erik Helmut and Luca Dziarski and Niklas Funk and Boris Belousov and Jan Peters},
year={2025},
eprint={2411.03315},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2411.03315},
}
``` |