VRでの動的デモンストレーションから学ぶ奥行き視覚に基づくパーソナライズドロボットナビゲーション
Learning Depth Vision-Based Personalized Robot Navigation From Dynamic Demonstrations in Virtual Reality
VRインタフェースでユーザーの動きを伴うナビゲーション軌跡を収集し、変分オートエンコーダと運動予測器を組み合わせた知覚パイプラインで奥行き画像から個人の好みを反映したナビゲーション制御器を学習する。
著者: Jorge de Heuvel, Nathan Corral, Benedikt Kreis, Jacobus Conradi, Anne Driemel, Maren Bennewitz
分類: cs.RO
原文アブストラクト
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.
