发表机构
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences（波兰科学院理论与应用信息学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出风险感知语义锚定框架，通过多维风险估计建模不确定性，使LLM机器人规划器能决定执行、澄清或拒绝指令，并在TRUST-NAV基准上显著提升模糊检测与冲突拒绝能力。
AI 中文摘要
大型语言模型（LLMs）越来越多地被用作机器人导航中的高层规划器，但当指令模糊、环境不支持或语义不一致时，其输出可能变得不可靠。本文提出了一种用于可信LLM机器人规划的风险感知语义锚定框架。与主要优化规划生成的现有基于LLM的规划器不同，我们将语义锚定可靠性表述为一个多维风险估计问题。所提出的架构在规划发生之前，通过模糊性、幻觉和语义冲突风险显式建模锚定不确定性，使系统能够决定是执行指令、请求澄清还是拒绝指令。为评估该方法，我们引入了TRUST-NAV，一个包含标准导航任务和风险诱导指令场景的基准。实验结果表明，虽然传统LLM规划器在有效导航任务上表现强劲，但所提出的框架显著提高了模糊性检测和语义冲突拒绝能力。这些发现表明，可信机器人规划不仅应通过任务完成度来评估，还应通过识别何时不应执行的能力来评估。
英文摘要
Large language models (LLMs) are increasingly used as high-level planners in robot navigation, but their outputs may become unreliable when instructions are ambiguous, unsupported by the environment, or semantically inconsistent. This paper presents a Risk-Aware Semantic Grounding framework for trustworthy LLM-based robot planning. Unlike existing LLM-based planners that primarily optimize plan generation, we formulate semantic grounding reliability as a multi-dimensional risk estimation problem. The proposed architecture explicitly models grounding uncertainty through ambiguity, hallucination and semantic-conflict risks before planning occurs, enabling the system to decide whether to execute the instruction, request clarification, or reject it. To evaluate the approach, we introduce TRUST-NAV, a benchmark containing both standard navigation tasks and risk-inducing instruction scenarios. Experimental results show that while conventional LLM planners achieve strong performance on valid navigation tasks, the proposed framework substantially improves ambiguity detection and semantic conflict rejection. These findings suggest that trustworthy robot planning should be evaluated not only by task completion, but also by the ability to recognize when execution should not occur.
