WorldMAP bootstraps reliable trajectory prediction in vision-language navigation by converting world-model-generated futures into structured supervision, cutting ADE by 18% and FDE by 42.1% on Target-Bench while making small VLMs competitive with large ones.
Bridging the gap be- tween learning in discrete and continuous environments for vision-and- language navigation
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P2DNav proposes a three-part hierarchical framework (panorama-to-downview reasoning, sliding-window dialogue memory, and reflective reorientation) that reports large success-rate gains on the R2R-CE zero-shot VLN benchmark.
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WorldMAP: Bootstrapping Vision-Language Navigation Trajectory Prediction with Generative World Models
WorldMAP bootstraps reliable trajectory prediction in vision-language navigation by converting world-model-generated futures into structured supervision, cutting ADE by 18% and FDE by 42.1% on Target-Bench while making small VLMs competitive with large ones.
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P2DNav: Panorama-to-Downview Reasoning for Zero-shot Vision-and-Language Navigation
P2DNav proposes a three-part hierarchical framework (panorama-to-downview reasoning, sliding-window dialogue memory, and reflective reorientation) that reports large success-rate gains on the R2R-CE zero-shot VLN benchmark.