Bridging Learned Visual Perception and Symbolic Belief-Space Planning
In partially observable settings, agents must act without full knowledge of the world state and rely on uncertain state-estimation pipelines. Obtaining grounded and verifiable symbolic plans under such uncertainty remains a key challenge. Recent work has integrated Vision-Language Models (VLMs) to bridge perception and symbolic reasoning, following two main paradigms. The first, VLM-as-planner, maps images directly to action sequences, and the second, VLM-as-grounder, grounds observations into symbolic predicates used as the initial state by off-the-shelf planners. Both approaches ignore uncertainty in the planning process, compromising robustness. We introduce a third paradigm, VLM-as-pr...
| Authors | Guy Azran, Michael Navat, Sarah Keren |
|---|---|
| Primary category | cs.AI |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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