The inherent probabilistic nature of Large Language Models (LLMs) introduces an element of unpredictability, raising concerns about potential discrepancies in their output. This paper introduces an innovative approach aims to generate correct and optimal robotic task plans for diverse real-world demands and scenarios. LLMs have been used to generate task plans, but they are unreliable and may contain wrong, questionable, or high-cost steps. The proposed approach uses LLM to generate a number of task plans as trees and amalgamates them into a graph by removing questionable paths. Then an optimal task tree can be retrieved to circumvent questionable and high-cost nodes, thereby improving planning accuracy and execution efficiency. The approach is further improved by incorporating a large knowledge network. Leveraging GPT-4 further, the high-level task plan is converted into a low-level Planning Domain Definition Language (PDDL) plan executable by a robot. Evaluation results highlight the superior accuracy and efficiency of our approach compared to previous methodologies in the field of task planning.

翻译：大语言模型（LLMs）固有的概率特性引入了不可预测性，引发了对其输出潜在偏差的担忧。本文提出了一种创新方法，旨在针对多样化的现实需求和场景生成正确且最优的机器人任务规划。LLMs已被用于生成任务规划，但它们不可靠，且可能包含错误、有疑问或高成本的步骤。所提出的方法利用LLM生成多个任务规划作为树，并通过移除有疑问的路径将其合并为一个图。随后可以检索出最优任务树，以规避有疑问和高成本的节点，从而提高规划准确性和执行效率。该方法通过融入大型知识网络进一步得到改进。借助GPT-4，高级任务规划被转换为机器人可执行的底层规划领域定义语言（PDDL）规划。评估结果表明，与先前任务规划领域的方法相比，我们的方法在准确性和效率上具有显著优势。