AI-controlled robotic systems pose a risk to human workers and the environment. Classical risk assessment methods cannot adequately describe such black box systems. Therefore, new methods for a dynamic risk assessment of such AI-controlled systems are required. In this paper, we introduce the concept of a new dynamic risk assessment approach for AI-controlled robotic systems. The approach pipelines five blocks: (i) a Data Logging that logs the data of the given simulation, (ii) a Skill Detection that automatically detects the executed skills with a deep learning technique, (iii) a Behavioral Analysis that creates the behavioral profile of the robotic systems, (iv) a Risk Model Generation that automatically transforms the behavioral profile and risk data containing the failure probabilities of robotic hardware components into advanced hybrid risk models, and (v) Risk Model Solvers for the numerical evaluation of the generated hybrid risk models. Keywords: Dynamic Risk Assessment, Hybrid Risk Models, M2M Transformation, ROS, AI-Controlled Robotic Systems, Deep Learning, Reinforcement Learning

翻译：AI控制的机器人系统对人类工人和环境构成风险。传统的风险评估方法无法充分描述此类黑箱系统。因此，需要针对此类AI控制系统开发新的动态风险评估方法。本文提出了一种用于AI控制机器人系统的动态风险评估新方法概念。该方法包含五个模块的流水线：(i) 数据记录模块，记录给定仿真的数据；(ii) 技能检测模块，利用深度学习技术自动检测执行中的技能；(iii) 行为分析模块，创建机器人系统的行为特征；(iv) 风险模型生成模块，自动将行为特征与包含机器人硬件组件失效概率的风险数据转化为高级混合风险模型；以及(v) 风险模型求解器，用于对生成的混合风险模型进行数值评估。关键词：动态风险评估，混合风险模型，M2M转换，ROS，AI控制机器人系统，深度学习，强化学习