NaViRrator: Robot Navigation from Human-Readable Maps through a Learned Visual Route

2026-09-18

AI Summary

为解决人类可读地图与机器人自我中心观察之间的连接问题，提出NaViRrator框架，通过学习视觉路线将地图上的起点和终点转换为导航指令。

Abstract

Human-readable maps provide an intuitive interface for specifying robot destinations, but connecting their schematic geometry to egocentric observations remains challenging. We present NaViRRator, a framework that translates user-specified start and goal locations on such maps into navigation instructions for a pretrained vision-and-language navigation (VLN) policy. Its core method, RouteScribe, separates route inference from verbalization by first generating an explicit route scaffold in map-image coordinates, which a pretrained vision-language model (VLM) converts into a navigation instruction. We construct the scaffold with start--goal line conditional flow matching (SGL-CFM), which deforms a straight start--goal waypoint sequence into a map-conditioned route. During execution, the VLN policy receives only the instruction and egocentric observations, while the map and scaffold remain upstream, allowing executor replacement without retraining the map-to-language modules. Real-world experiments show higher success rates and success weighted by path length (SPL) than direct map-to-instruction generation, A*-based scaffolding, and Gaussian-source conditional flow matching. Qualitative results further show clearer salient turns and better preservation of the intended maneuver sequence, supporting route-grounded language as a modular interface between human-readable maps and pretrained navigation policies.

Problem

• human-readable maps
• robot navigation
• vision-and-language navigation
• route inference
• navigation instructions

Innovation

• NaViRrator
• RouteScribe
• Vision-Language Navigation (VLN)
• Start-Goal Line Conditional Flow Matching (SGL-CFM)
• Map-to-Language