---
task_categories:
- reinforcement-learning
---

This repository contains the datasets used in the paper [DADP: Domain Adaptive Diffusion Policy](https://huggingface.co/papers/2602.04037).

[Project Page](https://outsider86.github.io/DomainAdaptiveDiffusionPolicy/) | [GitHub Repository](https://github.com/QinghangLiu/DADP_official)

### Dataset Description

DADP (Domain Adaptive Diffusion Policy) is a framework for learning domain-adaptive policies that can generalize to unseen transition dynamics. The datasets provided include trajectories for various locomotion and manipulation benchmarks:
- **Locomotion:** Ant, Walker2d, HalfCheetah, Hopper.
- **Manipulation:** Adroit (Door, Relocate).

The manipulation datasets are sourced from the [ODRL (Off-Dynamics RL)](https://github.com/OffDynamicsRL/off-dynamics-rl) project and are noted to be smaller in size and near-random in quality compared to the locomotion environments.

### Usage

As per the official repository, these datasets are intended to be used with the [Minari](https://github.com/Farama-Foundation/Minari) framework. Once you have downloaded the datasets, extract and move them into your local Minari datasets directory (typically `~/.minari/datasets/`).

The expected directory structure is:
```text
~/.minari/
└── datasets/
    ├── RandomAnt/
    ├── RandomWalker2d/
    ├── Adroit/
    └── ...
```

For detailed instructions on training and evaluation, please refer to the [official GitHub README](https://github.com/QinghangLiu/DADP_official).

### Citation
```bibtex
@article{liu2024dadp,
  title={DADP: Domain Adaptive Diffusion Policy},
  author={Liu, Qinghang and others},
  journal={arXiv preprint arXiv:2602.04037},
  year={2024}
}
```