| --- |
| license: apache-2.0 |
| pipeline_tag: robotics |
| tags: |
| - world-model |
| - aerial-navigation |
| - image-goal-navigation |
| - uncertainty-estimation |
| - dino |
| - raev2 |
| --- |
| |
| # UA-NWM Checkpoints |
|
|
| This repository provides the released checkpoints for **UA-NWM: Uncertainty-Aware World Model for |
| Aerial Image-Goal Navigation**. |
|
|
| Project page: [https://duryi.github.io/UA-NWM-Project-Page/](https://duryi.github.io/UA-NWM-Project-Page/) |
| Code: [https://github.com/DurYi/UA-NWM](https://github.com/DurYi/UA-NWM) |
| Paper: [https://arxiv.org/abs/2608.05597](https://arxiv.org/abs/2608.05597) |
|
|
| ## Files |
|
|
| ```text |
| ua-nwm/ |
| └── ua-nwm_best.pt |
| |
| raev2-dinov3b-k1-airgoal10k-ft/ |
| └── step_0010000.pt |
| ``` |
|
|
| - `ua-nwm/ua-nwm_best.pt`: the released UA-NWM checkpoint. It contains the shared deterministic backbone and the HEP module. Both inference strategies in the codebase use this same checkpoint: |
| `hep` for uncertainty-aware scoring and `dinov3_cosine` for the deterministic baseline. |
| - `raev2-dinov3b-k1-airgoal10k-ft/step_0010000.pt`: the fine-tuned RAEv2 RGB decoder used only for |
| DINO latent visualization. |
|
|
| The DINOv3 ViT-B/16 encoder is not redistributed here. Please download the official Meta checkpoint |
| from [facebook/dinov3-vitb16-pretrain-lvd1689m](https://huggingface.co/facebook/dinov3-vitb16-pretrain-lvd1689m). |
|
|
| ## Download |
|
|
| From the root of the UA-NWM code repository: |
|
|
| ```bash |
| hf download DurYi/UA-NWM-Checkpoints \ |
| ua-nwm/ua-nwm_best.pt \ |
| raev2-dinov3b-k1-airgoal10k-ft/step_0010000.pt \ |
| --local-dir pretrained |
| ``` |
|
|
| After downloading, the expected local layout is: |
|
|
| ```text |
| pretrained/ |
| ├── ua-nwm/ |
| │ └── ua-nwm_best.pt |
| └── raev2-dinov3b-k1-airgoal10k-ft/ |
| └── step_0010000.pt |
| ``` |
|
|
| You still need to place the DINOv3 encoder at: |
|
|
| ```text |
| pretrained/dinov3-vitb16/ |
| ``` |
|
|
| ## Usage |
|
|
| See the main repository for environment setup, dataset preparation, evaluation, visualization, and |
| training instructions. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{zhu2026uanwm, |
| title={Uncertainty-Aware World Model for Aerial Image-Goal Navigation}, |
| author={Deyi Zhu and Haoyu Fan and Yinan Zhu and Weichen Zhang and Shilin Ma and Xinlei Chen and Yansong Tang}, |
| year={2026}, |
| eprint={2608.05597}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2608.05597}, |
| } |
| ``` |
|
|
