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.

翻译：为获得最佳人机交互体验，机器人的导航策略应考虑用户的个人偏好。本文提出一种结合感知管道的学习框架，通过用户示范训练基于深度视觉的个性化导航控制器。我们的虚拟现实接口能够在用户运动状态下演示机器人导航轨迹，从而实现动态交互场景的示范。该新型感知管道结合变分自编码器与运动预测器，将感知到的深度图像压缩为潜在状态表征，使学习代理能够高效推理机器人的动态环境。通过详细分析与消融研究，我们评估了感知管道的不同配置。为量化导航控制器的个性化质量，我们基于弗雷歇距离提出并应用了一种衡量偏好反映的新指标。我们在多种虚拟场景中讨论了机器人的导航性能，并展示了首个完全依赖深度图像的个性化机器人导航控制器。本文方法的补充视频已在线发布。