| 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} | |
| } | |
| ``` |