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Which Terrain Is Better? Preference Learning with VLM Prototypes for Off-Road Traversability Ranking

arXiv · AI, language, vision and robotics · article · Sep 20, 2026 · UTC

In vision-based off-road navigation, a robot needs to know not only which obstacles to avoid but also which terrain is better. The first is handled by freespace detection or semantic segmentation. The second is usually answered with a traversability score, but no universal ground truth exists for such a score, so perception falls back on a predefined value per semantic class or a freespace confidence. These scores say what a region is, not which region a robot should prefer. We therefore formulate this preference as visual traversability ranking, an ordering of visible terrain that can be supe

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