内省式规划：使机器人的不确定性与任务固有模糊性对齐
Introspective Planning: Aligning Robots' Uncertainty with Inherent Task Ambiguity
AI总结：
针对LLM幻觉导致机器人计划与用户目标或安全要求不符，以及自然语言指令固有模糊性引入的不确定性问题，提出内省式规划方法，通过构建含内省推理示例的知识库对齐LLM不确定性与任务模糊性，结合保形预测提升合规性和安全性，减少用户澄清请求。
摘要：
大型语言模型（LLMs）展现出先进的推理能力，通过适当的基础接地，能让机器人理解自然语言指令并战略性规划高层级动作。然而，LLM的幻觉问题可能导致机器人自信地执行与用户目标不符的计划，甚至在关键场景中不安全。此外，自然语言指令中的固有模糊性会给LLM的推理和规划过程引入不确定性。我们提出内省式规划，一种系统方法，将LLM的不确定性与任务固有模糊性对齐。该方法构建知识库，包含内省推理示例，作为人类选择的安全合规计划的事后合理化解释，并在部署时检索。在三个任务（含新引入的安全移动操作基准）上的评估表明，内省式规划相比最先进的基于LLM的规划方法，大幅提升了合规性和安全性。此外，我们实证表明，内省式规划结合保形预测能实现更紧的置信边界，在保持统计成功保证的同时，最小化不必要的用户澄清请求。网页和代码可在https://introplan.github.io获取。
英文摘要：
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 even unsafe in critical scenarios. Additionally, inherent ambiguity in natural language instructions can introduce uncertainty into the LLM's reasoning and planning processes.We propose introspective planning, a systematic approach that align LLM's uncertainty with the inherent ambiguity of the task. Our approach constructs a knowledge base containing introspective reasoning examples as post-hoc rationalizations of human-selected safe and compliant plans, which are retrieved during deployment. Evaluations on three tasks, including a newly introduced safe mobile manipulation benchmark, demonstrate that introspection substantially improves both compliance and safety over state-of-the-art LLM-based planning methods. Furthermore, we empirically show that introspective planning, in combination with conformal prediction, achieves tighter confidence bounds, maintaining statistical success guarantees while minimizing unnecessary user clarification requests. The webpage and code are accessible at https://introplan.github.io.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
