  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
