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We present IndoorSim-to-OutdoorReal (I2O), an end-to-end learned visualnavigation approach, trained solely in simulated short-range indoorenvironments, and demonstrates zero-shot sim-to-real transfer to the outdoorsfor long-range navigation on the Spot robot. Our method uses zero real-worldexperience (indoor or outdoor), and requires the simulator to model nopredominantly-outdoor phenomenon (sloped grounds, sidewalks, etc). The key toI2O transfer is in providing the robot with additional context of theenvironment (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 donot need to be accurate or complete -- real-world obstacles (e.g., trees,bushes, pedestrians, etc.) are not drawn on the map, and openings are notaligned with where they are in the real-world. Crucially, these inaccuratecontext-maps provide a hint to the robot about a route to take to the goal. Wefind that our method that leverages Context-Maps is able to successfullynavigate hundreds of meters in novel environments, avoiding novel obstacles onits 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 ofnoise to the map in simulation. We find that the Context-Map policy issurprisingly robust to noise in the provided context-map. In the presence ofsignificantly inaccurate maps (corrupted with 50% noise, or entirely blankmaps), the policy gracefully regresses to the behavior of a policy with nocontext. Videos are available at https://www.joannetruong.com/projects/i2o.html

IndoorSim-to-OutdoorReal: Learning to Navigate Outdoors without any Outdoor Experience