Learning Representative Trajectories of Dynamical Systems via Domain-Adaptive Imitation
This work addresses the challenge of learning representative trajectories across domains, with applications in tasks like traffic anomaly detection, though it appears incremental as it builds on existing imitation learning and generative adversarial methods.
The paper tackles the problem of domain-adaptive trajectory imitation for dynamical systems, proposing DATI, a deep reinforcement learning agent that outperforms baseline methods in synthetic experiments and generalizes to detect abnormal motion in real-world maritime traffic.
Domain-adaptive trajectory imitation is a skill that some predators learn for survival, by mapping dynamic information from one domain (their speed and steering direction) to a different domain (current position of the moving prey). An intelligent agent with this skill could be exploited for a diversity of tasks, including the recognition of abnormal motion in traffic once it has learned to imitate representative trajectories. Towards this direction, we propose DATI, a deep reinforcement learning agent designed for domain-adaptive trajectory imitation using a cycle-consistent generative adversarial method. Our experiments on a variety of synthetic families of reference trajectories show that DATI outperforms baseline methods for imitation learning and optimal control in this setting, keeping the same per-task hyperparameters. Its generalization to a real-world scenario is shown through the discovery of abnormal motion patterns in maritime traffic, opening the door for the use of deep reinforcement learning methods for spatially-unconstrained trajectory data mining.