  For the best human-robot interaction experience, the robot's navigation
policy should take into account personal preferences of the user. In this
paper, we present a learning framework complemented by a perception pipeline to
train a depth vision-based, personalized navigation controller from user
demonstrations. Our virtual reality interface enables the demonstration of
robot navigation trajectories under motion of the user for dynamic interaction
scenarios. The novel perception pipeline enrolls a variational autoencoder in
combination with a motion predictor. It compresses the perceived depth images
to a latent state representation to enable efficient reasoning of the learning
agent about the robot's dynamic environment. In a detailed analysis and
ablation study, we evaluate different configurations of the perception
pipeline. To further quantify the navigation controller's quality of
personalization, we develop and apply a novel metric to measure preference
reflection based on the Fr\'echet Distance. We discuss the robot's navigation
performance in various virtual scenes and demonstrate the first personalized
robot navigation controller that solely relies on depth images. A supplemental
video highlighting our approach is available online.
