Improve dataset card: add metadata, paper link, and usage instructions

README.md

@@ -1,13 +1,42 @@
+---
+license: mit
+task_categories:
+- robotics
+---
+
 # MetaFine Dataset
 
-This dataset is a companion release of the [MetaFine project](https://metafine.github.io/), a framework for fine-grained manipulation evaluation.
+This dataset is a companion release for the paper [Beyond Binary Success: A Diagnostic Meta-Evaluation Framework for Fine-Grained Manipulation](https://huggingface.co/papers/2605.19986).
+
+- **Project Page:** [https://metafine.github.io/](https://metafine.github.io/)
+- **GitHub Repository:** [https://github.com/Hiangx-robotics/MetaFine](https://github.com/Hiangx-robotics/MetaFine)
+
+MetaFine is a diagnostic meta-evaluation framework for fine-grained robotic manipulation. Instead of a single binary success rate, it disentangles manipulation competency along three fundamental axes: **understanding**, **perception**, and **behavior**.
+
+## Dataset Structure
+
+The repository contains two main parts:
+
+- **Fine-grained annotated simulation assets**: 40+ URDF-based simulation objects (including a PartNet-Mobility subset and custom URDFs) with fine-grained part annotations and related interaction metadata (`capabilities.json`).
+- **Fine-grained task data**: Task specifications (YAML) used for training and testing fine-grained manipulation policies across various environments.
+
+## Usage
+
+You can download the assets and configurations using the Hugging Face CLI:
 
-It contains two main parts:
+```bash
+huggingface-cli download hiangx/MetaFine --repo-type dataset
+```
 
-- **Fine-grained annotated simulation assets**: URDF-based simulation objects with fine-grained part annotations and related interaction metadata.
-- **Fine-grained task data**: task specifications used for training and testing fine-grained manipulation policies.
+Place the unpacked `assets/` and `configs/` directories next to your local MetaFine repository root to use them with the platform.
 
-These data are intended to support research on fine-grained manipulation, including task construction, policy training, and evaluation.
+## Citation
 
-- Project page: https://metafine.github.io/
-- Platform code: https://github.com/Hiangx-robotics/MetaFine
+```bibtex
+@article{xu2026metafine,
+  title   = {Beyond Binary Success: A Diagnostic Meta-Evaluation Framework for Fine-Grained Manipulation},
+  author  = {Xu, He-Yang and Zhang, Pengyuan and Ge, Zongyuan and Hao, Xiaoshuai and Belongie, Serge and Geng, Xin and Peng, Yuxin and Wei, Xiu-Shen},
+  journal = {arXiv preprint arXiv:2605.19986},
+  year    = {2026}
+}
+```