  Visual navigation is a task of training an embodied agent by intelligently
navigating to a target object (e.g., television) using only visual
observations. A key challenge for current deep reinforcement learning models
lies in the requirements for a large amount of training data. It is exceedingly
expensive to construct sufficient 3D synthetic environments annotated with the
target object information. In this paper, we focus on visual navigation in the
low-resource setting, where we have only a few training environments annotated
with object information. We propose a novel unsupervised reinforcement learning
approach to learn transferable meta-skills (e.g., bypass obstacles, go
straight) from unannotated environments without any supervisory signals. The
agent can then fast adapt to visual navigation through learning a high-level
master policy to combine these meta-skills, when the
visual-navigation-specified reward is provided. Evaluation in the AI2-THOR
environments shows that our method significantly outperforms the baseline by
53.34% relatively on SPL, and further qualitative analysis demonstrates that
our method learns transferable motor primitives for visual navigation.
