The application of the Large Language Model (LLM) to robot action planning has been actively studied. The instructions given to the LLM by natural language may include ambiguity and lack of information depending on the task context. It is possible to adjust the output of LLM by making the instruction input more detailed; however, the design cost is high. In this paper, we propose the interactive robot action planning method that allows the LLM to analyze and gather missing information by asking questions to humans. The method can minimize the design cost of generating precise robot instructions. We demonstrated the effectiveness of our method through concrete examples in cooking tasks. However, our experiments also revealed challenges in robot action planning with LLM, such as asking unimportant questions and assuming crucial information without asking. Shedding light on these issues provides valuable insights for future research on utilizing LLM for robotics.

翻译：大语言模型在机器人动作规划中的应用已受到广泛研究。根据任务上下文，通过自然语言向大语言模型提供的指令可能包含歧义或信息缺失。虽然通过增加指令输入的细节可调整大语言模型的输出，但其设计成本较高。本文提出一种交互式机器人动作规划方法，允许大语言模型通过向人类提问来分析和获取缺失信息。该方法能最小化生成精确机器人指令的设计成本。我们通过烹饪任务中的具体案例验证了该方法的有效性。然而，实验也揭示了基于大语言模型的机器人动作规划面临的挑战，例如提出无关紧要的问题或未经提问即假设关键信息。阐明这些问题为未来利用大语言模型进行机器人技术的研究提供了宝贵见解。