Add paper and code links, update task category and license

Hi! I'm Niels, part of the community science team at Hugging Face. I've opened this PR to improve the documentation of the FEATS dataset.

Key changes:
- Added the `robotics` task category to the metadata.
- Updated the license to `mit` as specified in the official GitHub repository.
- Added links to the paper, project page, and code repository.
- Included a description of the dataset and the official BibTeX citation.

README.md

@@ -1,18 +1,47 @@
 ---
-license: apache-2.0
-task_categories:
-- image-feature-extraction
 language:
 - en
+license: mit
+size_categories:
+- 10K<n<100K
+task_categories:
+- robotics
+pretty_name: FEATS - Finite Element Analysis for Tactile Sensing
 tags:
 - tactile-sensing
-- robotics
 - gelsight
 - force-distribution
-pretty_name: >-
-  Learning Force Distribution Estimation for the GelSight Mini Optical Tactile Sensor Based on Finite Element Analysis
-size_categories:
-- 10K<n<100K
 arxiv: 2411.03315
-website: https://feats-ai.github.io
----
+---
+
+# 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}, 
+}
+```