  A complex visual navigation task puts an agent in different situations which
call for a diverse range of visual perception abilities. For example, to "go to
the nearest chair", the agent might need to identify a chair in a living room
using semantics, follow along a hallway using vanishing point cues, and avoid
obstacles using depth. Therefore, utilizing the appropriate visual perception
abilities based on a situational understanding of the visual environment can
empower these navigation models in unseen visual environments. We propose to
train an agent to fuse a large set of visual representations that correspond to
diverse visual perception abilities. To fully utilize each representation, we
develop an action-level representation fusion scheme, which predicts an action
candidate from each representation and adaptively consolidate these action
candidates into the final action. Furthermore, we employ a data-driven
inter-task affinity regularization to reduce redundancies and improve
generalization. Our approach leads to a significantly improved performance in
novel environments over ImageNet-pretrained baseline and other fusion methods.
