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README.md

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+---
+license: mit
+tags:
+  - robotics
+  - reinforcement-learning
+  - imitation-learning
+  - robomimic
+  - mujoco
+  - d4rl
+language:
+  - en
+task_categories:
+  - robotics
+size_categories:
+  - 1M<n<10M
+---
+
+# DMPO Demonstration Datasets
+
+Pre-processed demonstration datasets for **DMPO: Dispersive MeanFlow Policy Optimization**.
+
+[![Paper](https://img.shields.io/badge/arXiv-2601.20701-B31B1B)](http://arxiv.org/abs/2601.20701)
+[![Code](https://img.shields.io/badge/GitHub-dmpo--release-blue)](https://github.com/Guowei-Zou/dmpo-release)
+[![Project Page](https://img.shields.io/badge/Project-Page-4285F4)](https://guowei-zou.github.io/dmpo-page/)
+
+## Overview
+
+This repository contains pre-processed demonstration data for pre-training DMPO policies. Each dataset includes trajectory data and normalization statistics.
+
+## Dataset Structure
+
+```
+gym/
+├── hopper-medium-v2/
+├── walker2d-medium-v2/
+├── ant-medium-expert-v2/
+├── Humanoid-medium-v3/
+├── kitchen-complete-v0/
+├── kitchen-mixed-v0/
+└── kitchen-partial-v0/
+
+robomimic/
+├── lift-img/
+├── can-img/
+├── square-img/
+└── transport-img/
+```
+
+Each task folder contains:
+- `train.npz` - Training trajectories
+- `normalization.npz` - Observation and action normalization statistics
+
+## Usage
+
+Use the `hf://` prefix in config files to auto-download:
+
+```yaml
+train_dataset_path: hf://gym/hopper-medium-v2/train.npz
+normalization_path: hf://gym/hopper-medium-v2/normalization.npz
+```
+
+## Data Sources
+
+- **Gym tasks**: Derived from [D4RL](https://github.com/Farama-Foundation/D4RL) datasets
+- **Robomimic tasks**: Derived from [Robomimic](https://github.com/ARISE-Initiative/robomimic) proficient-human demonstrations
+
+## Citation
+
+```bibtex
+@misc{zou2026stepenoughdispersivemeanflow,
+      title={One Step Is Enough: Dispersive MeanFlow Policy Optimization},
+      author={Guowei Zou and Haitao Wang and Hejun Wu and Yukun Qian and Yuhang Wang and Weibing Li},
+      year={2026},
+      eprint={2601.20701},
+      archivePrefix={arXiv},
+      primaryClass={cs.RO},
+      url={https://arxiv.org/abs/2601.20701},
+}
+```
+
+## License
+
+MIT License