  Visual navigation is essential for many applications in robotics, from
manipulation, through mobile robotics to automated driving. Deep reinforcement
learning (DRL) provides an elegant map-free approach integrating image
processing, localization, and planning in one module, which can be trained and
therefore optimized for a given environment. However, to date, DRL-based visual
navigation was validated exclusively in simulation, where the simulator
provides information that is not available in the real world, e.g., the robot's
position or image segmentation masks. This precludes the use of the learned
policy on a real robot. Therefore, we propose a novel approach that enables a
direct deployment of the trained policy on real robots. We have designed visual
auxiliary tasks, a tailored reward scheme, and a new powerful simulator to
facilitate domain randomization. The policy is fine-tuned on images collected
from real-world environments. We have evaluated the method on a mobile robot in
a real office environment. The training took ~30 hours on a single GPU. In 30
navigation experiments, the robot reached a 0.3-meter neighborhood of the goal
in more than 86.7% of cases. This result makes the proposed method directly
applicable to tasks like mobile manipulation.
