Researchers introduced GAVEL, a framework for verifying and repairing long-horizon LLM task plans that often fail to respect embodiment constraints, recover from planning errors, or reason under partial observability. GAVEL is described as a graph world model for verified and efficient long-horizon LLM task planning.
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
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