  Humans are remarkable in their ability to navigate without metric
information. We can read abstract 2D maps, such as floor-plans or hand-drawn
sketches, and use them to navigate in unseen rich 3D environments, without
requiring prior traversals to map out these scenes in detail. We posit that
this is enabled by the ability to represent the environment abstractly as
interconnected navigational behaviours, e.g., "follow the corridor" or "turn
right", while avoiding detailed, accurate spatial information at the metric
level. We introduce the Scene Action Map (SAM), a behavioural topological
graph, and propose a learnable map-reading method, which parses a variety of 2D
maps into SAMs. Map-reading extracts salient information about navigational
behaviours from the overlooked wealth of pre-existing, abstract and inaccurate
maps, ranging from floor-plans to sketches. We evaluate the performance of SAMs
for navigation, by building and deploying a behavioural navigation stack on a
quadrupedal robot. Videos and more information is available at:
https://scene-action-maps.github.io.
