用大模型自动识别自适应机器人的不确定性来源，提升系统可靠性。
Identifying Uncertainty in Self-Adaptive Robotics with Large Language Models
- 用大模型分析机器人软件全生命周期中的不确定性
- 在4个工业级案例中，63%-88%的模型回答获工程师认可
- 适合机器人开发与运维人员快速诊断风险
未来自适应机器人需在高度动态环境中运行并有效管理不确定性。然而，由于自适应机器人本身结构复杂，且对影响不确定性的因素缺乏全面认知，识别其来源与影响并制定应对策略极具挑战。目前从业者多依赖直觉和过往经验。本文评估大语言模型（LLMs）在机器人软件工程全生命周期中系统化、自动化识别不确定性方面的潜力。我们对比了10个能力各异的先进大模型，在4个工业规模的机器人案例中进行测试，并收集了从业者对模型输出结果的看法。结果显示，从业者对63%-88%的模型回答表示认同，并强烈认可其实际应用价值。
原文摘要 · Abstract (English)
Future self-adaptive robots are expected to operate in highly dynamic environments while effectively managing uncertainties. However, identifying the sources and impacts of uncertainties in such robotic systems and defining appropriate mitigation strategies is challenging due to the inherent complexity of self-adaptive robots and the lack of comprehensive knowledge about the various factors influencing uncertainty. Hence, practitioners often rely on intuition and past experiences from similar systems to address uncertainties. In this article, we evaluate the potential of large language models (LLMs) in enabling a systematic and automated approach to identify uncertainties in self-adaptive robotics throughout the software engineering lifecycle. For this evaluation, we analyzed 10 advanced LLMs with varying capabilities across four industrial-sized robotics case studies, gathering the practitioners' perspectives on the LLM-generated responses related to uncertainties. Results showed that practitioners agreed with 63-88% of the LLM responses and expressed strong interest in the practicality of LLMs for this purpose.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。