緑は進め、赤は止まれ：VLAナビゲーションポリシーのためのセマンティックセグメンテーションによる視覚的グラウンディング
Green for Go, Red for No: Visual Grounding via Semantic Segmentation for VLA Navigation Policies
VLAナビゲーションモデルの性能を、SegFormerによるセマンティックセグメンテーションで走行可能領域を緑、非走行可能領域を赤に色付けする視覚的グラウンディング手法を提案し、誤差を最大44%削減することを示した。
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著者: Adrian Szvoren, Dimitrios Kanoulas, Nilufer Tuptuk
分類: cs.CV, cs.RO
原文アブストラクト
Vision-language-action (VLA) models enable robot navigation from natural language and visual goals, but remain susceptible to perceptual distractions and ambiguous scene interpretations. This paper presents the first empirical evaluation of visual grounding for VLA navigation policies. We propose a real-time segmentation-based grounding method that highlights traversable areas in green and non-traversable areas in red using SegFormer. Two variants are evaluated: observation-only segmentation and joint observation-goal augmentation. Using OmniVLA on the Grand Tour dataset, we show that visual grounding reduces the mean waypoint error by 27-44% at the farthest waypoint, depending on the instruction length. The benefits are greater for long instructions than for short instructions, and grounding provides little improvement for image goals. Normalized error analysis indicates that grounding primarily acts as a trajectory length regularizer, reducing the predicted path length by 30% without improving per-unit-distance reasoning. Our results indicate that visual grounding offers a simple, computationally inexpensive method to improve VLA navigation without model retraining, although it cannot compensate for missing training signals in out-of-distribution instructions.
