Add dataset card and documentation for DICE-RL

README.md

@@ -0,0 +1,54 @@
+---
+task_categories:
+- robotics
+tags:
+- reinforcement-learning
+- robomimic
+---
+
+# From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning (DICE-RL)
+
+[**Project Website**](https://zhanyisun.github.io/dice.rl.2026/) | [**Paper**](https://huggingface.co/papers/2603.10263) | [**GitHub**](https://github.com/zhanyisun/dice-rl)
+
+This repository contains the datasets used in **DICE-RL**, a framework that uses reinforcement learning as a "distribution contraction" operator to refine pretrained generative robot policies. The data includes both pretraining data (for Behavior Cloning) and finetuning data (for DICE-RL) across various Robomimic environments.
+
+## Dataset Structure
+
+The datasets are provided in `numpy` format, and each folder typically contains `train.npy` and `normalization.npz`. The data is organized following this structure:
+
+```
+data_dir/
+└── robomimic
+    ├── {env_name}-low-dim
+    │   ├── ph_pretrain
+    │   └── ph_finetune
+    └── {env_name}-img
+        ├── ph_pretrain
+        └── ph_finetune
+```
+
+- **ph_pretrain**: Contains the datasets used for pretraining the BC policies.
+- **ph_finetune**: Contains the datasets used for finetuning the DICE-RL policies. These are similar to the pretraining sets but with trajectories truncated to ensure value learning consistency between offline and online data (truncated to have exactly one success at the end).
+- **low-dim**: State-based observations.
+- **img**: High-dimensional pixel (image) observations.
+
+## Usage
+
+You can download the datasets using the scripts provided in the [GitHub repository](https://github.com/zhanyisun/dice-rl):
+
+```console
+bash script/download_hf.sh
+```
+
+For more details on generating your own data or processing raw Robomimic datasets, please refer to the project's [dataset processing guide](https://github.com/zhanyisun/dice-rl/blob/main/script/dataset/README.md).
+
+## 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}
+}
+```