  When searching for an object humans navigate through a scene using semantic
information and spatial relationships. We look for an object using our
knowledge of its attributes and relationships with other objects to infer the
probable location. In this paper, we propose to tackle the visual navigation
problem using rich semantic representations of the observed scene and
object-oriented targets to train an agent. We show that both allows the agent
to generalize to new targets and unseen scene in a short amount of training
time.
