PSALM-V autonomously learns PDDL action preconditions and effects by executing LLM-proposed plans, predicting error messages, and refining a tree-structured belief, raising ALFRED plan success from 37% to 74% and inducing BlocksWorld domains at F1 100%.
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PSALM-V: Automating Symbolic Planning in Interactive Visual Environments with Large Language Models
PSALM-V autonomously learns PDDL action preconditions and effects by executing LLM-proposed plans, predicting error messages, and refining a tree-structured belief, raising ALFRED plan success from 37% to 74% and inducing BlocksWorld domains at F1 100%.