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.

通过提出交互式机器人行动规划方法，利用大型语言模型（LLM）进行分析并向人类提问以获取缺失信息，以减少生成精确机器人指令的设计成本，并通过烹饪任务的具体示例证明了方法的有效性，同时揭示了对LLM的机器人行动规划的挑战，如提问与问题相关性不高以及未经询问而假设关键信息，为将LLM应用于机器人学方面的未来研究提供了有价值的见解。

基于大型语言模型的不确定性分析和主动问询的交互式机器人动作规划