Large language models (LLMs) exhibit advanced reasoning skills, enabling
robots to comprehend natural language instructions and strategically plan
high-level actions through proper grounding. However, LLM hallucination may
result in robots confidently executing plans that are misaligned with user
goals or, in extreme cases, unsafe. Additionally, inherent ambiguity in natural
language instructions can induce task uncertainty, particularly in situations
where multiple valid options exist. To address this issue, LLMs must identify
such uncertainty and proactively seek clarification. This paper explores the
concept of introspective planning as a systematic method for guiding LLMs in
forming uncertainty--aware plans for robotic task execution without the need
for fine-tuning. We investigate uncertainty quantification in task-level robot
planning and demonstrate that introspection significantly improves both success
rates and safety compared to state-of-the-art LLM-based planning approaches.
Furthermore, we assess the effectiveness of introspective planning in
conjunction with conformal prediction, revealing that this combination yields
tighter confidence bounds, thereby maintaining statistical success guarantees
with fewer superfluous user clarification queries.

通过引入内省计划的概念，该论文探讨了使用无需微调的系统化方法来引导大型语言模型进行感知不确定性的规划，以此改进机器人任务执行的成功率和安全性。并且研究了结合符合预测方法的内省计划的有效性，结果显示此组合可以提供更紧密的置信区间，在减少用户澄清查询的同时保持统计成功保证。