AI RESEARCH
HaltNav: Reactive Visual Halting over Lightweight Topological Priors for Robust Vision-Language Navigation
arXiv CS.CV
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ArXi:2603.12696v1 Announce Type: cross Vision-and-Language Navigation (VLN) is shifting from rigid, step-by-step instruction following toward open-vocabulary, goal-oriented autonomy. Achieving this transition without exhaustive routing prompts requires agents to leverage structural priors. While prior work often assumes computationally heavy 2D/3D metric maps, we instead exploit a lightweight, text-based osmAG (OpenStreetMap Area Graph), a floorplan-level topological representation that is easy to obtain and maintain.