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Spatial-Semantic Uncertainty in VLM-Based Target Search: Balancing Exploration and Identification

arXiv · AI, language, vision and robotics · article · Sep 17, 2026 · UTC

Robots searching for a target from a natural-language description must determine not only where to search, but also which observed candidate is the desired target. These decisions reflect two distinct sources of uncertainty - spatial uncertainty over candidate locations and semantic uncertainty over target identity - that are often conflated in VLM-based search systems. We introduce a spatial-semantic uncertainty formulation that maintains separate beliefs over each component and integrates probabilistic VLM evidence into a global target-identity posterior, including probability mass for undis

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