VRデモンストレーションで学ぶ個人適応型ヒューマンアウェアロボットナビゲーション
Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Study
VRインタフェースで非専門家が示した少数のデモ軌道から強化学習により個人の好みを反映したナビゲーション制御器を学習し、実機にも転移できることを示した論文。
著者: Jorge de Heuvel, Nathan Corral, Lilli Bruckschen, Maren Bennewitz
分類: cs.RO, cs.AI
原文アブストラクト
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.
