  Navigation is one of the most heavily studied problems in robotics, and is
conventionally approached as a geometric mapping and planning problem. However,
real-world navigation presents a complex set of physical challenges that defies
simple geometric abstractions. Machine learning offers a promising way to go
beyond geometry and conventional planning, allowing for navigational systems
that make decisions based on actual prior experience. Such systems can reason
about traversability in ways that go beyond geometry, accounting for the
physical outcomes of their actions and exploiting patterns in real-world
environments. They can also improve as more data is collected, potentially
providing a powerful network effect. In this article, we present a general
toolkit for experiential learning of robotic navigation skills that unifies
several recent approaches, describe the underlying design principles, summarize
experimental results from several of our recent papers, and discuss open
problems and directions for future work.
