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arXiv AI By Guy Azran, Michael Navat, Sarah Keren

Bridging Learned Visual Perception and Symbolic Belief-Space Planning

Read the original on arXiv AI →

The paper introduces a new paradigm called VLM-as-probabilistic-grounder, which models the uncertainty of Vision‑Language Model (VLM) predicate groundings as a probability distribution over symbolic states. This probabilistic grounding allows belief‑space planning, producing more robust plans in partially observable settings. Experiments in simulated household robot environments demonstrate that this approach improves robustness and task success compared to deterministic grounding methods.

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