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It first selects the instruction-relevant direction from a 360{\\deg} panorama, and then predicts a pixel-level target point from the downview RGB observation in that direction.","title":"P2DNav: Panorama-to-Downview Reasoning for Zero-shot Vision-and-Language Navigation","url":"https://arxiv.org/abs/2605.19634","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.19634v1 Announce Type: cross \nAbstract: Vision-and-language navigation (VLN) requires an embodied agent to ground natural-language instructions into executable navigation actions in unseen environments. Existing zero-shot methods typically rely on additional waypoint prediction modules, which often entangle high-level directional reasoning with fine-grained local grounding, leading to error-prone and unstable decisions. In this paper, we propose P2DNav, a hierarchical framework for zero-shot vision-and-language navigation. 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