  Deep reinforcement learning models are notoriously data hungry, yet
real-world data is expensive and time consuming to obtain. The solution that
many have turned to is to use simulation for training before deploying the
robot in a real environment. Simulation offers the ability to train large
numbers of robots in parallel, and offers an abundance of data. However, no
simulation is perfect, and robots trained solely in simulation fail to
generalize to the real-world, resulting in a "sim-vs-real gap". How can we
overcome the trade-off between the abundance of less accurate, artificial data
from simulators and the scarcity of reliable, real-world data? In this paper,
we propose Bi-directional Domain Adaptation (BDA), a novel approach to bridge
the sim-vs-real gap in both directions -- real2sim to bridge the visual domain
gap, and sim2real to bridge the dynamics domain gap. We demonstrate the
benefits of BDA on the task of PointGoal Navigation. BDA with only 5k
real-world (state, action, next-state) samples matches the performance of a
policy fine-tuned with ~600k samples, resulting in a speed-up of ~120x.
