LLM-Flax automates neuro-symbolic robotic task planning with three LLM stages for rule generation, failure recovery, and zero-shot scoring, outperforming manual baselines on MazeNamo grids.
PDDL2.2: The language for the classical part of the 4th international planning competition
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LLM-Flax : Generalizable Robotic Task Planning via Neuro-Symbolic Approaches with Large Language Models
LLM-Flax automates neuro-symbolic robotic task planning with three LLM stages for rule generation, failure recovery, and zero-shot scoring, outperforming manual baselines on MazeNamo grids.