论文标题
视觉导航的无监督域改编
Unsupervised Domain Adaptation for Visual Navigation
论文作者
论文摘要
视觉导航方法的进步导致了能够从原始RGB图像中学习有意义表示的智能体现导航代理，并执行涉及结构和语义推理的各种任务。但是，大多数基于学习的导航政策在模拟环境中接受了培训和测试。为了使这些政策实际上有用，需要将它们转移到现实世界中。在本文中，我们提出了一种无监督的域适应方法，以进行视觉导航。我们的方法将目标域中的图像转换为源域中，以使翻译与导航策略所学的表示一致。所提出的方法在模拟中的两个不同导航任务上优于几个基线。我们进一步表明，我们的方法可用于将模拟中学到的导航政策转移到现实世界中。
Advances in visual navigation methods have led to intelligent embodied navigation agents capable of learning meaningful representations from raw RGB images and perform a wide variety of tasks involving structural and semantic reasoning. However, most learning-based navigation policies are trained and tested in simulation environments. In order for these policies to be practically useful, they need to be transferred to the real-world. In this paper, we propose an unsupervised domain adaptation method for visual navigation. Our method translates the images in the target domain to the source domain such that the translation is consistent with the representations learned by the navigation policy. The proposed method outperforms several baselines across two different navigation tasks in simulation. We further show that our method can be used to transfer the navigation policies learned in simulation to the real world.
