  If we want to train robots in simulation before deploying them in reality, it
seems natural and almost self-evident to presume that reducing the sim2real gap
involves creating simulators of increasing fidelity (since reality is what it
is). We challenge this assumption and present a contrary hypothesis -- sim2real
transfer of robots may be improved with lower (not higher) fidelity simulation.
We conduct a systematic large-scale evaluation of this hypothesis on the
problem of visual navigation -- in the real world, and on 2 different
simulators (Habitat and iGibson) using 3 different robots (A1, AlienGo, Spot).
Our results show that, contrary to expectation, adding fidelity does not help
with learning; performance is poor due to slow simulation speed (preventing
large-scale learning) and overfitting to inaccuracies in simulation physics.
Instead, building simple models of the robot motion using real-world data can
improve learning and generalization.
