| license: mit | |
| task_categories: | |
| - robotics | |
| # MetaFine Dataset | |
| 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: | |
| ```bash | |
| huggingface-cli download hiangx/MetaFine --repo-type dataset | |
| ``` | |
| Place the unpacked `assets/` and `configs/` directories next to your local MetaFine repository root to use them with the platform. | |
| ## Citation | |
| ```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} | |
| } | |
| ``` |