Task planning for robotic cooking involves generating 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 sequential 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 improve execution efficiency. Our evaluation results show its superior performance compared to previous works in task planning accuracy and efficiency.

翻译：机器人烹饪的任务规划涉及生成一系列动作，使机器人能够成功完成餐食制备。本文提出了一种新颖的任务树生成流水线，可实现对烹饪任务的正确规划与高效执行。该方法首先利用大语言模型检索食谱指令，随后通过微调后的GPT-3将其转化为任务树，捕捉子任务间的顺序与并行依赖关系。该流水线通过任务树检索机制，缓解了大语言模型输出的不确定性与不可靠特征。我们将多个大语言模型生成的任务树输出合并为一张图，并执行任务树检索，以避免可疑节点和高代价节点，从而提升规划正确性与执行效率。评估结果表明，与先前工作相比，本方法在任务规划的准确性与效率方面具有更优性能。