  We have observed significant progress in visual navigation for embodied
agents. A common assumption in studying visual navigation is that the
environments are static; this is a limiting assumption. Intelligent navigation
may involve interacting with the environment beyond just moving
forward/backward and turning left/right. Sometimes, the best way to navigate is
to push something out of the way. In this paper, we study the problem of
interactive navigation where agents learn to change the environment to navigate
more efficiently to their goals. To this end, we introduce the Neural
Interaction Engine (NIE) to explicitly predict the change in the environment
caused by the agent's actions. By modeling the changes while planning, we find
that agents exhibit significant improvements in their navigational
capabilities. More specifically, we consider two downstream tasks in the
physics-enabled, visually rich, AI2-THOR environment: (1) reaching a target
while the path to the target is blocked (2) moving an object to a target
location by pushing it. For both tasks, agents equipped with an NIE
significantly outperform agents without the understanding of the effect of the
actions indicating the benefits of our approach.
