TouchSafeBench evaluates VLMs on collision grounding, finding best Macro-F1 below 50% and that explicit depth does not yield reliable robot-body contact inference.
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VLM-GLoc is a hierarchical semantic Monte Carlo Localization system that uses VLMs for discriminative observations and inverse text-to-map proposals, reporting 70% and 74% success in a grocery store and lab respectively.
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Probing Collision Grounding in Vision-Language Models for Safe Human-Robot Collaboration
TouchSafeBench evaluates VLMs on collision grounding, finding best Macro-F1 below 50% and that explicit depth does not yield reliable robot-body contact inference.