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Recent advances in metric, semantic, and topological mapping have equippedautonomous robots with semantic concept grounding capabilities to interpretnatural language tasks. This work aims to leverage these new capabilities withan efficient task planning algorithm for hierarchical metric-semantic models.We consider a scene graph representation of the environment and utilize a largelanguage model (LLM) to convert a natural language task into a linear temporallogic (LTL) automaton. Our main contribution is to enable optimal hierarchicalLTL planning with LLM guidance over scene graphs. To achieve efficiency, weconstruct a hierarchical planning domain that captures the attributes andconnectivity of the scene graph and the task automaton, and provide semanticguidance via an LLM heuristic function. To guarantee optimality, we design anLTL heuristic function that is provably consistent and supplements thepotentially inadmissible LLM guidance in multi-heuristic planning. Wedemonstrate efficient planning of complex natural language tasks in scenegraphs of virtualized real environments.

Optimal Scene Graph Planning with Large Language Model Guidance