  We present a novel method for imitation learning for control requirements
expressed using Signal Temporal Logic (STL). More concretely we focus on the
problem of training a neural network to imitate a complex controller. The
learning process is guided by efficient data aggregation based on
counter-examples and a coverage measure. Moreover, we introduce a method to
evaluate the performance of the learned controller via parameterization and
parameter estimation of the STL requirements. We demonstrate our approach with
a flying robot case study.
