Hugging Face Trending Papers

Uncertainty-Aware World Model for Aerial Image-Goal Navigation

Read the original on Hugging Face Trending Papers →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Hugging Face Trending Papers.

Hugging Face Trending Papers
Sep 8

Dual-Layer Semantic-Spatial Belief Mapping for Aerial Object Goal Navigation

The paper introduces AeroBelief, a dual‑layer semantic‑spatial belief mapping framework for aerial object goal navigation. It separates broad contextual plausibility (intuition layer) from target‑specific evidence (evidence layer) and fuses them into persistent spatial belief hotspots, while also employing object‑conditioned visual reasoning and temporally stable regional guidance. Experiments on the UAV‑ON benchmark show AeroBelief outperforms prior methods in success rate, object success rate, and SPL.

arXiv AI
Sep 10

Dual-Layer Semantic-Spatial Belief Mapping for Aerial Object Goal Navigation

The paper introduces AeroBelief, a dual‑layer semantic‑spatial belief mapping framework for aerial object goal navigation. It separates broad contextual plausibility (intuition layer) from target‑specific evidence (evidence layer) and fuses them into persistent spatial belief hotspots. The method also employs object‑conditioned visual reasoning and egocentric regional guidance, achieving state‑of‑the‑art success rates on the UAV‑ON benchmark.

By Jianqiang Xiao, Xiang Deng, Yuexuan Sun, Yanjin Wu, Wenbiao Yan, Liqiang Nie
arXiv AI
Jun 15

Schr\"odinger's Navigator: Imagining an Ensemble of Futures for Zero-Shot Object Navigation

arXiv:2512. 21201v3 Announce Type: replace-cross Abstract: Zero-shot object navigation (ZSON) requires robots to find target objects in unseen environments without task-specific fine-tuning or pre-built maps, a key capability for general-purpose service robots.

By Yu He, Da Huang, Zhenyang Liu, Zixiao Gu, Qiang Sun, Guangnan Ye, Yanwei Fu, Yu-Gang Jiang