  We present IndoorSim-to-OutdoorReal (I2O), an end-to-end learned visual
navigation approach, trained solely in simulated short-range indoor
environments, and demonstrates zero-shot sim-to-real transfer to the outdoors
for long-range navigation on the Spot robot. Our method uses zero real-world
experience (indoor or outdoor), and requires the simulator to model no
predominantly-outdoor phenomenon (sloped grounds, sidewalks, etc). The key to
I2O transfer is in providing the robot with additional context of the
environment (i.e., a satellite map, a rough sketch of a map by a human, etc.)
to guide the robot's navigation in the real-world. The provided context-maps do
not need to be accurate or complete -- real-world obstacles (e.g., trees,
bushes, pedestrians, etc.) are not drawn on the map, and openings are not
aligned with where they are in the real-world. Crucially, these inaccurate
context-maps provide a hint to the robot about a route to take to the goal. We
find that our method that leverages Context-Maps is able to successfully
navigate hundreds of meters in novel environments, avoiding novel obstacles on
its path, to a distant goal without a single collision or human intervention.
In comparison, policies without the additional context fail completely. Lastly,
we test the robustness of the Context-Map policy by adding varying degrees of
noise to the map in simulation. We find that the Context-Map policy is
surprisingly robust to noise in the provided context-map. In the presence of
significantly inaccurate maps (corrupted with 50% noise, or entirely blank
maps), the policy gracefully regresses to the behavior of a policy with no
context. Videos are available at https://www.joannetruong.com/projects/i2o.html
