  Visual navigation by mobile robots is classically tackled through SLAM plus
optimal planning, and more recently through end-to-end training of policies
implemented as deep networks. While the former are often limited to waypoint
planning, but have proven their efficiency even on real physical environments,
the latter solutions are most frequently employed in simulation, but have been
shown to be able learn more complex visual reasoning, involving complex
semantical regularities. Navigation by real robots in physical environments is
still an open problem. End-to-end training approaches have been thoroughly
tested in simulation only, with experiments involving real robots being
restricted to rare performance evaluations in simplified laboratory conditions.
In this work we present an in-depth study of the performance and reasoning
capacities of real physical agents, trained in simulation and deployed to two
different physical environments. Beyond benchmarking, we provide insights into
the generalization capabilities of different agents training in different
conditions. We visualize sensor usage and the importance of the different types
of signals. We show, that for the PointGoal task, an agent pre-trained on wide
variety of tasks and fine-tuned on a simulated version of the target
environment can reach competitive performance without modelling any sim2real
transfer, i.e. by deploying the trained agent directly from simulation to a
real physical robot.
