Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Study

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.

  • Published in:
    IEEE International Conference on Robot and Human Interactive Communication
  • Type:
    Inproceedings
  • Authors:
    Heuvel, Jorge de; Corral, Nathan; Bruckschen, Lilli; Bennewitz, Maren
  • Year:
    2022

Citation information

Heuvel, Jorge de; Corral, Nathan; Bruckschen, Lilli; Bennewitz, Maren: Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Study, IEEE International Conference on Robot and Human Interactive Communication, 2022, https://ieeexplore.ieee.org/document/9900554/, Heuvel.etal.2022a,

Associated Lamarr Researchers

lamarr institute person Bennewitz Maren - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Prof. Dr. Maren Bennewitz

Principal Investigator Embodied AI to the profile