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.

翻译：提出一种面向信号时态逻辑（STL）控制需求的模仿学习新方法。具体而言，我们聚焦于训练神经网络以模仿复杂控制器的任务。该学习过程通过基于反例的数据高效聚合与覆盖度度量进行引导。此外，我们引入一种通过STL需求参数化与参数估计来评估学习控制器性能的方法。通过飞行机器人案例研究验证了所提方法的有效性。