  This paper focuses on visual semantic navigation, the task of producing
actions for an active agent to navigate to a specified target object category
in an unknown environment. To complete this task, the algorithm should
simultaneously locate and navigate to an instance of the category. In
comparison to the traditional point goal navigation, this task requires the
agent to have a stronger contextual prior to indoor environments. We introduce
SSCNav, an algorithm that explicitly models scene priors using a
confidence-aware semantic scene completion module to complete the scene and
guide the agent's navigation planning. Given a partial observation of the
environment, SSCNav first infers a complete scene representation with semantic
labels for the unobserved scene together with a confidence map associated with
its own prediction. Then, a policy network infers the action from the scene
completion result and confidence map. Our experiments demonstrate that the
proposed scene completion module improves the efficiency of the downstream
navigation policies. Video, code, and data: https://sscnav.cs.columbia.edu/
