  Object Goal Navigation requires a robot to find and navigate to an instance
of a target object class in a previously unseen environment. Our framework
incrementally builds a semantic map of the environment over time, and then
repeatedly selects a long-term goal ('where to go') based on the semantic map
to locate the target object instance. Long-term goal selection is formulated as
a vision-based deep reinforcement learning problem. Specifically, an Encoder
Network is trained to extract high-level features from a semantic map and
select a long-term goal. In addition, we incorporate data augmentation and
Q-function regularization to make the long-term goal selection more effective.
We report experimental results using the photo-realistic Gibson benchmark
dataset in the AI Habitat 3D simulation environment to demonstrate substantial
performance improvement on standard measures in comparison with a state of the
art data-driven baseline.
