  Approaches for teaching learning agents via human demonstrations have been
widely studied and successfully applied to multiple domains. However, the
majority of imitation learning work utilizes only behavioral information from
the demonstrator, i.e. which actions were taken, and ignores other useful
information. In particular, eye gaze information can give valuable insight
towards where the demonstrator is allocating visual attention, and holds the
potential to improve agent performance and generalization. In this work, we
propose Gaze Regularized Imitation Learning (GRIL), a novel context-aware,
imitation learning architecture that learns concurrently from both human
demonstrations and eye gaze to solve tasks where visual attention provides
important context. We apply GRIL to a visual navigation task, in which an
unmanned quadrotor is trained to search for and navigate to a target vehicle in
a photorealistic simulated environment. We show that GRIL outperforms several
state-of-the-art gaze-based imitation learning algorithms, simultaneously
learns to predict human visual attention, and generalizes to scenarios not
present in the training data. Supplemental videos and code can be found at
https://sites.google.com/view/gaze-regularized-il/.
