AI RESEARCH
BEACON: Language-Conditioned Navigation Affordance Prediction under Occlusion
arXiv CS.AI
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ArXi:2603.09961v1 Announce Type: cross Language-conditioned local navigation requires a robot to infer a nearby traversable target location from its current observation and an open-vocabulary, relational instruction. Existing vision-language spatial grounding methods usually rely on vision-language models (VLMs) to reason in image space, producing 2D predictions tied to visible pixels. As a result, they struggle to infer target locations in occluded regions, typically caused by furniture or moving humans.