Add dataset card and metadata

README.md

@@ -0,0 +1,60 @@
+---
+task_categories:
+- robotics
+---
+
+# From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning (DICE-RL)
+
+This repository contains the datasets used in the paper [From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning](https://huggingface.co/papers/2603.10263).
+
+[**Project Website**](https://zhanyisun.github.io/dice.rl.2026/) | [**GitHub Repository**](https://github.com/zhanyisun/dice-rl)
+
+## Dataset Description
+
+Distribution Contractive Reinforcement Learning (DICE-RL) is a framework that uses reinforcement learning (RL) to refine pretrained generative robot policies. This repository hosts the data used for pretraining Behavior Cloning (BC) policies and finetuning them with DICE-RL across various Robomimic environments.
+
+The data covers both:
+- **Low-dimensional (state-based)** observations.
+- **Image-based (pixel-based)** observations.
+
+### Data Splits
+- `ph_pretrain`: Datasets used for pretraining the BC policies for broad behavioral coverage.
+- `ph_finetune`: Datasets used for DICE-RL finetuning. These trajectories are truncated to have exactly one success at the end to ensure consistent value learning.
+
+## Dataset Structure
+
+The datasets are provided in `numpy` format. Once downloaded, they follow this structure:
+
+```
+data_dir/
+└── robomimic
+    ├── {env_name}-low-dim
+    │   ├── ph_pretrain
+    │   └── ph_finetune
+    └── {env_name}-img
+        ├── ph_pretrain
+        └── ph_finetune
+```
+
+Each folder contains:
+- `train.npy`: The trajectory data.
+- `normalization.npz`: Statistics used for data normalization.
+
+## Sample Usage
+
+To download the datasets as intended by the authors, you can use the script provided in the [official repository](https://github.com/zhanyisun/dice-rl):
+
+```console
+bash script/download_hf.sh
+```
+
+## Citation
+
+```bibtex
+@article{sun2026prior,
+  title={From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning},
+  author={Sun, Zhanyi and Song, Shuran},
+  journal={arXiv preprint arXiv:2603.10263},
+  year={2026}
+}
+```