  Does progress in simulation translate to progress on robots? If one method
outperforms another in simulation, how likely is that trend to hold in reality
on a robot? We examine this question for embodied PointGoal navigation,
developing engineering tools and a research paradigm for evaluating a simulator
by its sim2real predictivity. First, we develop Habitat-PyRobot Bridge (HaPy),
a library for seamless execution of identical code on simulated agents and
robots, transferring simulation-trained agents to a LoCoBot platform with a
one-line code change. Second, we investigate the sim2real predictivity of
Habitat-Sim for PointGoal navigation. We 3D-scan a physical lab space to create
a virtualized replica, and run parallel tests of 9 different models in reality
and simulation. We present a new metric called Sim-vs-Real Correlation
Coefficient (SRCC) to quantify predictivity. We find that SRCC for Habitat as
used for the CVPR19 challenge is low (0.18 for the success metric), suggesting
that performance differences in this simulator-based challenge do not persist
after physical deployment. This gap is largely due to AI agents learning to
exploit simulator imperfections, abusing collision dynamics to 'slide' along
walls, leading to shortcuts through otherwise non-navigable space. Naturally,
such exploits do not work in the real world. Our experiments show that it is
possible to tune simulation parameters to improve sim2real predictivity (e.g.
improving $SRCC_{Succ}$ from 0.18 to 0.844), increasing confidence that
in-simulation comparisons will translate to deployed systems in reality.
