  In the context of autonomous navigation of terrestrial robots, the creation
of realistic models for agent dynamics and sensing is a widespread habit in the
robotics literature and in commercial applications, where they are used for
model based control and/or for localization and mapping. The more recent
Embodied AI literature, on the other hand, focuses on modular or end-to-end
agents trained in simulators like Habitat or AI-Thor, where the emphasis is put
on photo-realistic rendering and scene diversity, but high-fidelity robot
motion is assigned a less privileged role. The resulting sim2real gap
significantly impacts transfer of the trained models to real robotic platforms.
In this work we explore end-to-end training of agents in simulation in settings
which minimize the sim2real gap both, in sensing and in actuation. Our agent
directly predicts (discretized) velocity commands, which are maintained through
closed-loop control in the real robot. The behavior of the real robot
(including the underlying low-level controller) is identified and simulated in
a modified Habitat simulator. Noise models for odometry and localization
further contribute in lowering the sim2real gap. We evaluate on real navigation
scenarios, explore different localization and point goal calculation methods
and report significant gains in performance and robustness compared to prior
work.
