  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.
