Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal

Understand

Model-free reinforcement learning has recently been shown to be effective at learning navigation policies from complex image input.

• However, these algorithms tend to require large amounts of interaction with the environment, which can be prohibitively costly to obtain on robots in the real world.
• We present an approach for efficiently learning goal-directed navigation policies on a mobile robot, from only a single coverage traversal of recorded data.
• The navigation agent learns an effective policy over a diverse action space in a large heterogeneous environment consisting of more than 2km of travel, through buildings and outdoor regions that collectively exhibit large variations in visual appearance, self-similarity, and connectivity.