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AI-controlled robotic systems pose a risk to human workers and theenvironment. Classical risk assessment methods cannot adequately describe suchblack box systems. Therefore, new methods for a dynamic risk assessment of suchAI-controlled systems are required. In this paper, we introduce the concept ofa new dynamic risk assessment approach for AI-controlled robotic systems. Theapproach pipelines five blocks: (i) a Data Logging that logs the data of thegiven simulation, (ii) a Skill Detection that automatically detects theexecuted skills with a deep learning technique, (iii) a Behavioral Analysisthat creates the behavioral profile of the robotic systems, (iv) a Risk ModelGeneration that automatically transforms the behavioral profile and risk datacontaining the failure probabilities of robotic hardware components intoadvanced hybrid risk models, and (v) Risk Model Solvers for the numericalevaluation of the generated hybrid risk models.  Keywords: Dynamic Risk Assessment, Hybrid Risk Models, M2M Transformation,ROS, AI-Controlled Robotic Systems, Deep Learning, Reinforcement Learning

Concept: Dynamic Risk Assessment for AI-Controlled Robotic Systems