Most prior methods for learning navigation policies require access to
simulation environments, as they need online policy interaction and rely on
ground-truth maps for rewards. However, building simulators is expensive
(requires manual effort for each and every scene) and creates challenges in
transferring learned policies to robotic platforms in the real-world, due to
the sim-to-real domain gap. In this paper, we pose a simple question: Do we
really need active interaction, ground-truth maps or even
reinforcement-learning (RL) in order to solve the image-goal navigation task?
We propose a self-supervised approach to learn to navigate from only passive
videos of roaming. Our approach, No RL, No Simulator (NRNS), is simple and
scalable, yet highly effective. NRNS outperforms RL-based formulations by a
significant margin. We present NRNS as a strong baseline for any future
image-based navigation tasks that use RL or Simulation.

本研究提出了一种基于自监督学习的方法，通过对漫游的被动视频学习导航，而无需交互、地图信息或强化学习，旨在解决基于图像目标的导航任务，该方法可以作为使用强化学习或模拟的任何未来基于图像的导航任务的强大基准。