Active Learning of Signal Temporal Logic Specifications

In this paper, we propose a method to infer temporal logic behaviour models of an a priori unknown system. We use the formalism of Signal Temporal Logic (STL), which can express various robot motion planning and control specifications, including spatial preferences. In our setting, data is collected through a series of queries the learning algorithm poses to the system under test. This active learning approach incrementally builds a hypothesis solution which, over time, converges to the actual behaviour of the system. Active learning presents several benefits compared to supervised learning: in the case of costly prior labelling of data, and if the system to test is accessible, the learning algorithm can interact with the system to refine its guess of the specification of the system. Inspired by mobile robot navigation tasks, we present experimental case studies to ensure the relevance of our method.