论文标题
使用用户研究中的虚拟现实演示来学习个性化的人类意识机器人导航
Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Study
论文作者
论文摘要
为了达到最舒适，最有意识的机器人导航，需要考虑主观的用户偏好。本文提出了一个新颖的增强学习框架，以训练个性化的导航控制器以及直观的虚拟现实演示界面。进行的用户研究提供了证据，表明我们的个性化方法可以用更舒适的人类机器人体验优于经典方法。我们仅使用非专家用户的一些演示轨迹来实现这些结果，他们主要欣赏直观的演示设置。正如我们在实验中显示的那样，学习的控制器很好地概括了演示数据中未涵盖的状态，同时仍反映了导航期间用户的偏好。最后，我们将导航控制器转移而不损失性能的机器人。
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.
