From Cooking Recipes to Robot Task Trees � Improving Planning Correctness and Task Efficiency by Leveraging LLMs with a Knowledge Network

Task planning for robotic cooking involves gen- erating a sequence of actions for a robot to prepare a meal successfully. This paper introduces a novel task tree generation pipeline producing correct planning and efficient execution for cooking tasks. Our method first uses a large language model (LLM) to retrieve recipe instructions and then utilizes a fine- tuned GPT-3 to convert them into a task tree, capturing sequen- tial and parallel dependencies among subtasks. The pipeline then mitigates the uncertainty and unreliable features of LLM outputs using task tree retrieval. We combine multiple LLM task tree outputs into a graph and perform a task tree retrieval to avoid questionable nodes and high-cost nodes to improve planning correctness and execution efficiency. Our evaluation results show its superior performance in task planning accuracy and efficiency compared to previous works.