arXiv AI
AirAnchor: Bridging Local and Global Spatial Information for Zero-Shot Aerial Vision-and-Language Navigation
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arXiv AI
Jul 24
VoLN: Vision-Only Long-Horizon Navigation---Paradigm, Benchmark, and Method
arXiv:2607. 21400v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) enables embodied agents to follow natural-language instructions.
arXiv AI
Aug 11
From Semantic Grounding to Decision Optimization: A Unified Framework for Long-Horizon UAV Vision-Language Navigation
arXiv:2608. 09564v1 Announce Type: cross Abstract: UAV vision-language navigation (UAV-VLN) focuses on enabling an aerial agent to follow natural-language instructions in open 3D environments from egocentric visual observations.
Hugging Face Trending Papers
Jul 13
Parse, Search, and Confirmation: Training-Free Aerial Vision-and-Dialog Navigation with Chain-of-Thought Reasoning and Structured Spatial Memory
In this paper, we tackle the Aerial Vision-and-Dialog Navigation (AVDN) task in the training-free setting for resource-efficient high-altitude UAV navigation. Naively applying MLLMs leads to unreliable navigation due to weak directional grounding and the lack of explicit spatial memory.
arXiv AI
Jul 22
Fly0: Persistent Metric Anchoring for Zero-Shot Aerial Vision-Language Navigation
arXiv:2602. 15875v2 Announce Type: replace-cross Abstract: Current Visual-Language Navigation (VLN) methodologies face a trade-off between semantic understanding and control precision.
arXiv AI
Jun 9
SpaceVLN: A Zero-Shot Vision-and-Language Navigation Agent with Online Spatial Cognitive Memory and Reasoning
arXiv:2606. 08992v1 Announce Type: cross Abstract: Vision-and-Language Navigation in continuous environments requires agents to understand the spatial structure of previously unseen environments in order to follow language instructions.
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.