Modular and Multimodal Generative Adversarial Imitation Learning for Modeling Flight Trajectories

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We aim to imitate the execution of modular tasks by exploiting unsegmented trajectories that demonstrate the execution of these tasks. This is challenging since the execution of tasks follows different modes (i.e., patterns of behavior), which may exist in various mixtures within subtasks, and the identification of trajectories’ modules (i.e., subtrajectories executing subtasks) may not be easy. This paper addresses the modularity of trajectories in conjunction with multimodality toward imitating the execution of aircraft trajectories. It proposes an imitation learning framework for the aircraft trajectory prediction problem, which segments demonstrated aircraft trajectories into subtrajectories corresponding to flight phases. This facilitates disentangling modes and learning a mixture of policies per flight phase. While trajectories are segmented using domain-specific rules, a mixture of policies per flight phase is learned by a generative multimodal imitation learning method. This modular approach enables accurate prediction of both modes and subtrajectories, which finally results in predicting the evolution of the aircraft state across the whole trajectory in a compositional way. Experiments using a real-world dataset of long flights show the potential of the proposed framework to disentangle multimodal trajectories in real-world settings and predict trajectories with high accuracy, in comparison to methods that do not exploit subtrajectories.¶