Combining Measurement Uncertainties with the Probabilistic Robustness for Safety Evaluation of Robot Systems

In this paper, we present a method to engage measurement uncertainties with the probabilistic robustness to one system uncertainty measure. Providing a metric indicating the potential occurrence of dangerous situations is highly essential for safety-critical robot applications. Due to the difficulty of finding a quantifiable, unambiguous representation however, such a metric has not been derived to date. In case of sensory devices, measurement uncertainties are usually provided by manufacturer specifications. Apart from that, several contributions demonstrate that the accuracy of neural networks is verifiable via the robustness. However, state-of-the-art literature is mainly concerned with theoretical investigations such that scarce attention has been devoted to the transfer of the robustness to real-world applications. To fill this gap, we show how the probabilistic robustness can be made useful for evaluating quantitative safety limits. Our key idea is to exploit the analogy between measurement uncertainties and the probabilistic robustness: While measurement uncertainties reflect possible shifts due to technical limitations, the robustness refers to the tolerated amount of distortions in the input data for an unaltered output. Inspired by this analogy, we combine both measures to quantify the system uncertainty online. We validate our method in different settings under real-world conditions. Our findings exemplify that incorporating the novel uncertainty metric effectively prevents the rate of dangerous situations in Human-Robot Collaboration.