用大模型辅助机器人设计阶段的不确定性分析，提升安全性。
Human-in-the-Loop Uncertainty Analysis in Self-Adaptive Robots Using LLMs
- 引入人类参与的大模型方法，系统化识别机器人不确定性的来源与影响。
- 16名工业从业者参与测试，普遍认为工具易用且有效。
- 适合机器人研发团队在设计阶段进行风险预判与优化。
自适应机器人在动态、不可预测的环境中运行，未解决的不确定性可能导致安全违规和操作失败。然而，由于现实环境的复杂性、机器人行为的动态性以及技术的快速迭代，系统地识别和分析不确定性（包括其来源、影响及缓解策略）仍是重大挑战。为此，我们提出 RoboULM——一种基于大语言模型（LLMs）的人类在环方法与工具，支持从业者在设计阶段系统探索不确定性。此外，我们构建了一个不确定性分类体系，详细列举了自适应机器人中的各类不确定性。我们在四个工业应用场景中对 RoboULM 进行评估，共纳入16名实践者。结果表明，参与者普遍认为 RoboULM 既实用又易于理解，尤其赞赏其结构化提示与迭代优化支持。这些发现证明 RoboULM 在复杂机器人系统中具备系统性不确定性分析的可行性。
原文摘要 · Abstract (English)
Self-adaptive robots operate in dynamic, unpredictable environments where unaddressed uncertainties can lead to safety violations and operational failures. However, systematically identifying and analyzing these uncertainties, including their sources, impacts, and mitigation strategies, remains a significant challenge given the inherent complexity of real-world environments, dynamic robotic behavior, and the rapid evolution of robotic technologies. To address this, we introduce RoboULM, a human-in-the-loop methodology and tool that supports practitioners in systematically exploring uncertainties at the design stage using large language models (LLMs). Moreover, we present an uncertainty taxonomy that provides a detailed catalog of uncertainties in self-adaptive robots. We evaluated RoboULM with 16 practitioners from four industrial use cases. The results show that RoboULM was perceived as both useful and easy to understand, with the participants particularly valuing structured prompting and iterative refinement support. These findings demonstrate the potential of RoboULM as a viable solution for systematic uncertainty analysis in complex robots.
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