| title | Uncertainty-Aware World Model for Aerial Image-Goal Navigation |
|---|---|
| authors | Deyi Zhu, Haoyu Fan, Yinan Zhu, Weichen Zhang, Shilin Ma, Xinlei Chen, Yansong Tang |
| date | 2026-08-06 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.05597 |
| affiliation | Tsinghua University |
| upvotes | 10 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| primer | World models |
| abstract | Aerial image-goal navigation requires an unmanned aerial vehicle (UAV) to reach a target location specified by a goal image. Existing world-model-based methods rank candidate trajectories using predicted futures, but typically rely on only one or a few point predictions, which is inadequate for large-scale outdoor environments with substantial future-state uncertainty. To address this limitation, we propose the Uncertainty-Aware Navigation World Model (UA-NWM), an efficient latent world model for aerial image-goal navigation, which formulates trajectory scoring as conditional out-of-distribution detection. UA-NWM represents plausible futures with an uncertainty subspace and decomposes the prediction--goal discrepancy into uncertainty-explainable and unexplainable components. Only the unexplainable residual is used for scoring, enabling robust selection without multiple future samples. Extensive experiments demonstrate that UA-NWM consistently outperforms existing navigation world models while maintaining low inference latency. Real-world UAV experiments further validate its practical applicability. Project page: https://duryi.github.io/UA-NWM-Project-Page |
cite
@misc{zhu2026uncertainty,
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},
url={https://arxiv.org/abs/2608.05597}
}