Flapping-wing drones serve as an excellent benchmark for the growing trend of increased complexity in aerospace systems, with their chaotic aerodynamics making them extremely hard to model and control. To address this, we used behavior cloning with real flight data to enable autonomous flight without the use of a physics model. Through combining a high-level motion controller with a neural policy that maps onboard sensor data directly to wing commands, we achieved robust repeatable flight on a platform where conventional modeling is often too costly or inaccurate.