NaViRrator: 人間可読地図からの学習された視覚経路によるロボットナビゲーション
NaViRrator: Robot Navigation from Human-Readable Maps through a Learned Visual Route
人間可読地図上の出発点と目的地を、事前学習済みの視覚と言語によるナビゲーション（VLN）ポリシーへの指示に変換するフレームワークを提案。地図画像座標で明示的な経路骨格を生成し、視覚言語モデルでナビゲーション指示に変換する。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Ayun Lee, Jiseon Kim, Giseop Kim
分類: cs.RO
原文アブストラクト
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.