推論より構造：小型視覚言語モデルによる統合ゼロショットナビゲーション
Structure over Reasoning: Unified Zero-Shot Navigation with a Small Vision-Language Model
小型VLMの方向判断・停止判断を行動ではなく証拠として扱い、鳥瞰マップ上の意味場とスケルトングラフで経路を構築するゼロショット視覚言語ナビゲーション手法を提案し、四足歩行ロボットで実機検証した。
著者: Zeqi Li, Xunyi Zhao, Zerui Li, Sihao Lin, Qi Wu, ShiJie Li
分類: cs.RO
原文アブストラクト
Vision-language navigation is moving into photorealistic, physically simulated scenes, where the leading zero-shot agents put a vision-language model in the control loop to choose every move and stop. Robots favour small models, whose abilities are uneven: Qwen3-VL-4B can judge whether a single view looks like the goal, but its route reasoning is at chance, its distance estimates nearly constant, its stop votes unreliable. Our agent therefore asks it two perceptual questions per step, which direction looks like the goal and whether the view justifies stopping, plus one gated frontier query on route-bearing instructions, and treats the answers as evidence, not actions: a route-blind semantic field over a bird's-eye map accumulates the directional scores through a tempered update, a skeleton graph supplies frontiers and geodesic distances, and a two-stage terminator turns stop votes into termini through a trail-consistency gate and a budget-forced return. With every baseline re-run in one installation at one budget, the agent exceeds all four zero-shot baselines of the VLNVerse subset in mean success on both granularities, though the paired interval against the strongest coarse-grained baseline includes zero. Termination is the dominant lever in ablation, thirteen attempts to give the model more of the navigation establish no reliable gain, and the agent runs on a Unitree Go2 quadruped with off-board inference. Code, per-episode logs and a robot video will be released at https://anonymous.4open.science/r/structure-over-reasoning-PAGE.