Interactively Robot Action Planning with Uncertainty Analysis and Active Questioning by Large Language Model
This work addresses the issue of high design costs in generating precise robot instructions for robotics researchers, though it is incremental as it builds on existing LLM-based planning methods.
The paper tackles the problem of ambiguous or incomplete natural language instructions in robot action planning by proposing an interactive method where the LLM asks humans questions to gather missing information, thereby reducing the design cost for precise instructions, and demonstrates its effectiveness in cooking tasks while identifying challenges like asking unimportant questions or making assumptions.
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.