NavOne: One-Step Global Planning for Vision-Language Navigation on Top-Down Maps

• URL: http://arxiv.org/abs/2605.06317v2
• Date: Fri, 08 May 2026 03:00:23 GMT
• Title: NavOne: One-Step Global Planning for Vision-Language Navigation on Top-Down Maps
• Abstract summary: We propose Top-Down VLN, reformulating navigation as a one-step global path planning problem on pre-built top-down maps.<n>We introduce NavOne, a unified framework that directly predicts dense path probabilities over multi-modal maps in a single end-to-end forward pass.<n>NavOne achieves state-of-the-art performance among map-based VLN methods, with a planning-stage speedup of 8x over existing map-based baselines and 80x over egocentric methods.
• Score: 28.730617570877317
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing Vision-Language Navigation (VLN) methods typically adopt an egocentric, step-by-step paradigm, which struggles with error accumulation and limits efficiency. While recent approaches attempt to leverage pre-built environment maps, they often rely on incrementally updating memory graphs or scoring discrete path proposals, which restricts continuous spatial reasoning and creates discrete bottlenecks. We propose Top-Down VLN (TD-VLN), reformulating navigation as a one-step global path planning problem on pre-built top-down maps, supported by our newly constructed R2R-TopDown dataset. To solve this, we introduce NavOne, a unified framework that directly predicts dense path probabilities over multi-modal maps in a single end-to-end forward pass. NavOne features a Top-Down Map Fuser for joint multi-modal map representation, and extends Attention Residuals for spatial-aware depth mixing. Extensive experiments on R2R-TopDown show that NavOne achieves state-of-the-art performance among map-based VLN methods, with a planning-stage speedup of 8x over existing map-based baselines and 80x over egocentric methods, enabling highly efficient global navigation.

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