  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.
