外观
论文arXiv 2609.213161 个月前
NaViRrator：通过学习得到的视觉路线从人类可读地图进行机器人导航
原文链接：NaViRrator: Robot Navigation from Human-Readable Maps through a Learned Visual Route（arXiv 2609.21316 · 发布日期：2026-09-18）
NaViRrator: Robot Navigation from Human-Readable Maps through a Learned Visual Route
NaViRRator将人类可读地图上的起终点转为VLN指令，借学习路线脚手架连接地图与自视角观测，并在真实世界实验验证。
核心要点
- 人类可读地图便于指定目的地，但其示意几何与机器人自视角观测难以衔接。
- RouteScribe先在地图图像坐标生成路线脚手架，再由预训练VLM转成导航指令。
- SGL-CFM将起终点直线路点序列变形为地图条件路线，执行时VLN仅用指令和自视角观测。
- 真实世界实验中，其成功率和SPL高于直接地图到指令生成、A*脚手架和高斯源CFM。
验证范围与局限：验证范围为真实世界实验，摘要未说明具体机器人数量或任务规模。
英文摘要 (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.
作者：이아윤, Jiseon Kim, Giseop Kim
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