Imitation learning for aerobatic maneuvering in fixed-wing aircraft.

This study focuses on the task of developing automated models for complex aerobatic aircraft maneuvers. The approach employed here utilizes Behavioral Cloning, a technique in which human pilots supply a series of sample maneuvers. These maneuvers serve as training data for a Machine Learning algorithm, enabling the system to generate control models for each maneuver. The optimal instances for each maneuver were chosen based on a set of objective evaluation criteria. By utilizing these selected sets of examples, resilient models were developed, capable of reproducing the maneuvers performed by the human pilots who supplied the examples. In certain instances, these models even exhibited superior performance compared to the pilots themselves, a phenomenon referred to as the "clean-up effect". We also explore the application of transfer learning to adapt the developed controllers to various airplane models, revealing compelling evidence that transfer learning is effective for refining them for targeted aircraft. A comprehensive set of intricate maneuvers was executed through a meta-controller capable of orchestrating the fundamental maneuvers acquired through imitation. This undertaking yielded promising outcomes, demonstrating the proficiency of several Machine Learning models in successfully executing highly intricate aircraft maneuvers. This paper is an extended version of the previously ICCS 2023 published conference paper [1]. [ABSTRACT FROM AUTHOR]