Uncertainty-aware Risk Assessment of Robotic Systems via Importance Sampling

In this paper, we introduce a probabilistic approach to risk assessment of robot systems by focusing on the impact of uncertainties. While various approaches to identifying systematic hazards (e.g., bugs, design flaws, etc.) can be found in current literature, little attention has been devoted to evaluating risks in robot systems in a probabilistic manner. Existing methods rely on discrete notions for dangerous events and assume that the consequences of these can be described by simple logical operations. In this work, we consider measurement uncertainties as one main contributor to the evolvement of risks. Specifically, we study the impact of temporal and spatial uncertainties on the occurrence probability of dangerous failures, thereby deriving an approach for an uncertainty-aware risk assessment. Secondly, we introduce a method to improve the statistical significance of our results: While the rare occurrence of hazardous events makes it challenging to draw conclusions with reliable accuracy, we show that importance sampling -- a technique that successively generates samples in regions with sparse probability densities -- allows for overcoming this issue. We demonstrate the validity of our novel uncertainty-aware risk assessment method in three simulation scenarios from the domain of human-robot collaboration. Finally, we show how the results can be used to evaluate arbitrary safety limits of robot systems.