  Visual navigation models based on deep learning can learn effective policies
when trained on large amounts of visual observations through reinforcement
learning. Unfortunately, collecting the required experience in the real world
requires the deployment of a robotic platform, which is expensive and
time-consuming. To deal with this limitation, several simulation platforms have
been proposed in order to train visual navigation policies on virtual
environments efficiently. Despite the advantages they offer, simulators present
a limited realism in terms of appearance and physical dynamics, leading to
navigation policies that do not generalize in the real world.
  In this paper, we propose a tool based on the Habitat simulator which
exploits real world images of the environment, together with sensor and
actuator noise models, to produce more realistic navigation episodes. We
perform a range of experiments to assess the ability of such policies to
generalize using virtual and real-world images, as well as observations
transformed with unsupervised domain adaptation approaches. We also assess the
impact of sensor and actuation noise on the navigation performance and
investigate whether it allows to learn more robust navigation policies. We show
that our tool can effectively help to train and evaluate navigation policies on
real-world observations without running navigation pisodes in the real world.
