  The advances in deep reinforcement learning recently revived interest in
data-driven learning based approaches to navigation. In this paper we propose
to learn viewpoint invariant and target invariant visual servoing for local
mobile robot navigation; given an initial view and the goal view or an image of
a target, we train deep convolutional network controller to reach the desired
goal. We present a new architecture for this task which rests on the ability of
establishing correspondences between the initial and goal view and novel reward
structure motivated by the traditional feedback control error. The advantage of
the proposed model is that it does not require calibration and depth
information and achieves robust visual servoing in a variety of environments
and targets without any parameter fine tuning. We present comprehensive
evaluation of the approach and comparison with other deep learning
architectures as well as classical visual servoing methods in visually
realistic simulation environment. The presented model overcomes the brittleness
of classical visual servoing based methods and achieves significantly higher
generalization capability compared to the previous learning approaches.
