Sandström, Viktor (Swedish Defense Research Agency), Oskarsson, Daniel (Swedish Defence Research Agency), Luotsinen, Linus (Swedish Defence Research Agency (FOI))

Fighter Pilot Behavior Cloning

Scheduled for presentation during the Regular Session "Manned/Unmanned Aviation" (ThA5), Thursday, June 23, 2022, 10:30−10:50, Elafiti

2022 International Conference on Unmanned Aircraft Systems (ICUAS), June 21-24, 2022, Dubrovnik, Croatia

This information is tentative and subject to change. Compiled on April 26, 2024

Abstract

In this paper, a feed-forward neural network is trained on a small dataset of human fighter pilot data, recorded from maneuvering a fixed-wing fighter aircraft in a flight simulator. The goal is to model the pilot behavior, using a technique called behavior cloning. By carefully preprocessing the training data, it is shown that this simple and intuitive approach results in a model that can successfully fly the aircraft at high velocity on flight tracks that demand sharp turns, and even perform maneuvers not explicitly represented in the data. Furthermore, it is demonstrated that a pretrained neural network will adapt to a significant change in flight dynamics with less training, compared to a previously untrained model. This transfer learning scenario is important since fine-tuning pretrained models could simplify the development of a wide fleet of AI aircraft.