Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Study
This work addresses the need for personalized and comfortable robot navigation for users, though it is incremental as it builds on existing methods with a focus on user-specific adaptation.
The paper tackled the problem of human-aware robot navigation by developing a reinforcement learning framework that incorporates user preferences through virtual reality demonstrations, resulting in significantly more comfortable human-robot experiences compared to classical approaches.
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.