  How to effectively and reliably guarantee the correct functioning of
safety-critical cyber-physical systems in uncertain conditions is a challenging
problem. This paper presents a data-driven algorithm to derive approximate
abstractions for piecewise affine systems with unknown dynamics. It advocates a
significant shift from the current paradigm of abstraction, which starts from a
model with known dynamics. Given a black-box system with unknown dynamics and a
linear temporal logic specification, the proposed algorithm is able to obtain
an abstraction of the system with an arbitrarily small error and a bounded
probability. The algorithm consists of three components, system identification,
system abstraction, and active sampling. The effectiveness of the algorithm is
demonstrated by a case study with a soft robot.
