  This work focuses on object goal visual navigation, aiming at finding the
location of an object from a given class, where in each step the agent is
provided with an egocentric RGB image of the scene. We propose to learn the
agent's policy using a reinforcement learning algorithm. Our key contribution
is a novel attention probability model for visual navigation tasks. This
attention encodes semantic information about observed objects, as well as
spatial information about their place. This combination of the "what" and the
"where" allows the agent to navigate toward the sought-after object
effectively. The attention model is shown to improve the agent's policy and to
achieve state-of-the-art results on commonly-used datasets.
