  For the most comfortable, human-aware robot navigation, subjective user
preferences need to be taken into account. This paper presents a novel
reinforcement learning framework to train a personalized navigation controller
along with an intuitive virtual reality demonstration interface. The conducted
user study provides evidence that our personalized approach significantly
outperforms classical approaches with more comfortable human-robot experiences.
We achieve these results using only a few demonstration trajectories from
non-expert users, who predominantly appreciate the intuitive demonstration
setup. As we show in the experiments, the learned controller generalizes well
to states not covered in the demonstration data, while still reflecting user
preferences during navigation. Finally, we transfer the navigation controller
without loss in performance to a real robot.
