Green for Go, Red for No: Visual Grounding via Semantic Segmentation for VLA Navigation Policies
For robot navigation researchers, it provides a simple, computationally inexpensive method to improve VLA navigation without retraining, but benefits are limited to certain instruction types and cannot compensate for out-of-distribution instructions.
This paper evaluates visual grounding for VLA navigation policies, proposing a real-time segmentation-based method that reduces mean waypoint error by 27-44% at the farthest waypoint on the Grand Tour dataset using OmniVLA, primarily by regularizing trajectory length.
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.