---
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},
}
```