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Concept: Dynamic Risk Assessment for AI-Controlled Robotic Systems

Philipp Grimmeisen, Friedrich Sautter, Andrey Morozov

TL;DR

The paper addresses the challenge of risk assessment for AI-controlled robotic systems with black-box policies by proposing a dynamic risk assessment pipeline that continuously estimates risk during operation. It introduces a five-block workflow—Data Logging, Skill Detection, Behavioral Analysis, Risk Model Generation, and Risk Model Solver—that converts runtime robot behavior into hybrid risk models combining fault trees with Markov-chain-based representations, solved via the OpenPRA framework. The approach provides a concrete pathway for generating traceable risk assessments from simulation data, mapping hardware components to risk data, and evaluating dynamic risk under various execution conditions. The authors also outline future work to extend the framework with large language models for automated failure-mode discovery and reinforcement learning-driven analysis, along with fault-injection methods to quantify software risk, improving safety in human-robot collaboration.

Abstract

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

Concept: Dynamic Risk Assessment for AI-Controlled Robotic Systems

TL;DR

The paper addresses the challenge of risk assessment for AI-controlled robotic systems with black-box policies by proposing a dynamic risk assessment pipeline that continuously estimates risk during operation. It introduces a five-block workflow—Data Logging, Skill Detection, Behavioral Analysis, Risk Model Generation, and Risk Model Solver—that converts runtime robot behavior into hybrid risk models combining fault trees with Markov-chain-based representations, solved via the OpenPRA framework. The approach provides a concrete pathway for generating traceable risk assessments from simulation data, mapping hardware components to risk data, and evaluating dynamic risk under various execution conditions. The authors also outline future work to extend the framework with large language models for automated failure-mode discovery and reinforcement learning-driven analysis, along with fault-injection methods to quantify software risk, improving safety in human-robot collaboration.

Abstract

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
Paper Structure (8 sections, 2 figures)

This paper contains 8 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Concept
  3. Data Logging
  4. Skill Detection
  5. Behavioral Analysis
  6. Risk Model Generation
  7. Risk Model Solver
  8. Conclusion

Figures (2)

  • Figure 1: Robotic manipulator which has been trained to grasp a test tube at position A and place it to position B.
  • Figure 2: The proposed approach to the dynamic risk assessment of AI-controlled robotic systems.