面向自主机器人鲁棒验证的贝叶斯学习
Bayesian Learning for the Robust Verification of Autonomous Robots
AI总结：
针对自主机器人在动态关键任务中需持续验证任务能力的问题，提出贝叶斯学习框架，通过学习事件发生率范围构建区间马尔可夫模型分析系统属性，实现参数不确定下的鲁棒验证。
摘要：
用于基础设施巡检、太空探索及其他关键任务的自主机器人需在高度动态的环境中运行，因此必须持续验证自身安全、高效完成相关任务的能力。本文提出一种贝叶斯学习框架，可实现自主机器人的运行时验证。该框架利用先验知识与被验证机器人的观测数据，学习常规事件和异常（如灾难性故障）事件发生率的预期范围；随后对基于这些范围定义的区间连续时间马尔可夫模型进行分析，得到任务时长、成功概率等系统属性的预期变化区间。我们将该框架应用于一项水下基础设施巡检与维修的自主机器人任务。文中的形式化证明与实验表明，该框架的输出结果能反映诸多真实世界系统固有的不确定性，可在参数不确定条件下实现系统量化属性的鲁棒验证。
英文摘要：
Autonomous robots used in infrastructure inspection, space exploration and other critical missions operate in highly dynamic environments. As such, they must continually verify their ability to complete the tasks associated with these missions safely and effectively. Here we present a Bayesian learning framework that enables this runtime verification of autonomous robots. The framework uses prior knowledge and observations of the verified robot to learn expected ranges for the occurrence rates of regular and singular (e.g., catastrophic failure) events. Interval continuous-time Markov models defined using these ranges are then analysed to obtain expected intervals of variation for system properties such as mission duration and success probability. We apply the framework to an autonomous robotic mission for underwater infrastructure inspection and repair. The formal proofs and experiments presented in the paper show that our framework produces results that reflect the uncertainty intrinsic to many real-world systems, enabling the robust verification of their quantitative properties under parametric uncertainty.
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。
