  We study the challenging problem of releasing a robot in a previously unseen
environment, and having it follow unconstrained natural language navigation
instructions. Recent work on the task of Vision-and-Language Navigation (VLN)
has achieved significant progress in simulation. To assess the implications of
this work for robotics, we transfer a VLN agent trained in simulation to a
physical robot. To bridge the gap between the high-level discrete action space
learned by the VLN agent, and the robot's low-level continuous action space, we
propose a subgoal model to identify nearby waypoints, and use domain
randomization to mitigate visual domain differences. For accurate sim and real
comparisons in parallel environments, we annotate a 325m2 office space with
1.3km of navigation instructions, and create a digitized replica in simulation.
We find that sim-to-real transfer to an environment not seen in training is
successful if an occupancy map and navigation graph can be collected and
annotated in advance (success rate of 46.8% vs. 55.9% in sim), but much more
challenging in the hardest setting with no prior mapping at all (success rate
of 22.5%).
