  We investigate the task of object goal navigation in unknown environments
where the target is specified by a semantic label (e.g. find a couch). Such a
navigation task is especially challenging as it requires understanding of
semantic context in diverse settings. Most of the prior work tackles this
problem under the assumption of a discrete action policy whereas we present an
approach with continuous control which brings it closer to real world
applications. We propose a deep neural network architecture and loss function
to predict dense cost maps that implicitly contain semantic context and guide
the robot towards the semantic goal. We also present a novel way of fusing
mid-level visual representations in our architecture to provide additional
semantic cues for cost map prediction. The estimated cost maps are then used by
a sampling-based model predictive controller (MPC) for generating continuous
robot actions. The preliminary experiments suggest that the cost maps generated
by our network are suitable for the MPC and can guide the agent to the semantic
goal more efficiently than a baseline approach. The results also indicate the
importance of mid-level representations for navigation by improving the success
rate by 7 percentage points.
