LATIDIA · Robótica

GAVEL: Graph World Models for Verified and Efficient Long-Horizon LLM Task Planning

arXiv: 2609.19315v1Tipo de anuncio: nuevo Resumen: Los modelos de lenguaje grandes (LLM) proporcionan una interfaz flexible para la planificación de robots a largo plazo, pero los planes generados a menudo no respetan las limitaciones de la realización, se recuperan de pl

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arXiv:2609.19315v1 Announce Type: new Abstract: Large language models (LLMs) provide a flexible interface for long-horizon robot planning, but generated plans often fail to respect embodiment constraints, recover from planning errors, or reason effectively under partial observability. We present GAVEL, a framework for verifying and repairing long-horizon LLM planning built around an explicit graph world model. The graph represents relevant object-relations, action pre-conditions and effects, and probabilistic beliefs over unobserved object locations. This model can predict the consequences of LLM-generated actions before execution, detect violations, and repair those whose corrections follow directly from the world model. This method also reserves LLM replanning solely for errors requiring semantic reasoning. For multi-task instructions,