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Many robot control scenarios involve assessing system robustness against atask specification. If either the controller or environment are composed of"black-box" components with unknown dynamics, we cannot rely on formalverification to assess our system. Assessing robustness via exhaustive testingis also often infeasible if the space of environments is large compared toexperiment cost.  Given limited budget, we provide a method to choose experiment inputs whichgive greatest insight into system performance against a given specificationacross the domain. By combining smooth robustness metrics for signal temporallogic with techniques from adaptive experiment design, our method chooses themost informative experimental inputs by incrementally constructing a surrogatemodel of the specification robustness. This model then chooses the nextexperiment to be in an area where there is either high prediction error oruncertainty.  Our experiments show how this adaptive experimental design technique resultsin sample-efficient descriptions of system robustness. Further, we show how touse the model built via the experiment design process to assess the behaviourof a data-driven control system under domain shift.

Automated Testing with Temporal Logic Specifications for Robotic Controllers using Adaptive Experiment Design