  Many robot control scenarios involve assessing system robustness against a
task specification. If either the controller or environment are composed of
"black-box" components with unknown dynamics, we cannot rely on formal
verification to assess our system. Assessing robustness via exhaustive testing
is also often infeasible if the space of environments is large compared to
experiment cost.
  Given limited budget, we provide a method to choose experiment inputs which
give greatest insight into system performance against a given specification
across the domain. By combining smooth robustness metrics for signal temporal
logic with techniques from adaptive experiment design, our method chooses the
most informative experimental inputs by incrementally constructing a surrogate
model of the specification robustness. This model then chooses the next
experiment to be in an area where there is either high prediction error or
uncertainty.
  Our experiments show how this adaptive experimental design technique results
in sample-efficient descriptions of system robustness. Further, we show how to
use the model built via the experiment design process to assess the behaviour
of a data-driven control system under domain shift.
