Robotics
GAVEL: Graph World Models for Verified and Efficient Long-Horizon LLM Task Planning
Bridging abstract reasoning with physical reality through graph-based verification for autonomous robots
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 verifyin...
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ArXiv CS.RO
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ArXiv CS.RO
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GAVEL bridges the gap between abstract LLM reasoning and the physical constraints of robotics by integrating graph world models for reliable long-horizon task execution. This advancement allows autonomous agents to self-correct and operate effectively in environments where visibility is limited.
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