  Recent work in visual end-to-end learning for robotics has shown the promise
of imitation learning across a variety of tasks. Such approaches are expensive
both because they require large amounts of real world training demonstrations
and because identifying the best model to deploy in the real world requires
time-consuming real-world evaluations. These challenges can be mitigated by
simulation: by supplementing real world data with simulated demonstrations and
using simulated evaluations to identify high performing policies. However, this
introduces the well-known "reality gap" problem, where simulator inaccuracies
decorrelate performance in simulation from that of reality. In this paper, we
build on top of prior work in GAN-based domain adaptation and introduce the
notion of a Task Consistency Loss (TCL), a self-supervised loss that encourages
sim and real alignment both at the feature and action-prediction levels. We
demonstrate the effectiveness of our approach by teaching a mobile manipulator
to autonomously approach a door, turn the handle to open the door, and enter
the room. The policy performs control from RGB and depth images and generalizes
to doors not encountered in training data. We achieve 72% success across
sixteen seen and unseen scenes using only ~16.2 hours of teleoperated
demonstrations in sim and real. To the best of our knowledge, this is the first
work to tackle latched door opening from a purely end-to-end learning approach,
where the task of navigation and manipulation are jointly modeled by a single
neural network.
