  Learning high-level navigation behaviors has important implications: it
enables robots to build compact visual memory for repeating demonstrations and
to build sparse topological maps for planning in novel environments. Existing
approaches only learn discrete, short-horizon behaviors. These standalone
behaviors usually assume a discrete action space with simple robot dynamics,
thus they cannot capture the intricacy and complexity of real-world
trajectories. To this end, we propose Composable Behavior Embedding (CBE), a
continuous behavior representation for long-horizon visual navigation. CBE is
learned in an end-to-end fashion; it effectively captures path geometry and is
robust to unseen obstacles. We show that CBE can be used to performing
memory-efficient path following and topological mapping, saving more than an
order of magnitude of memory than behavior-less approaches.
