  We study the problem of learning to perform multi-stage robotic manipulation
tasks, with applications to cable routing, where the robot must route a cable
through a series of clips. This setting presents challenges representative of
complex multi-stage robotic manipulation scenarios: handling deformable
objects, closing the loop on visual perception, and handling extended behaviors
consisting of multiple steps that must be executed successfully to complete the
entire task. In such settings, learning individual primitives for each stage
that succeed with a high enough rate to perform a complete temporally extended
task is impractical: if each stage must be completed successfully and has a
non-negligible probability of failure, the likelihood of successful completion
of the entire task becomes negligible. Therefore, successful controllers for
such multi-stage tasks must be able to recover from failure and compensate for
imperfections in low-level controllers by smartly choosing which controllers to
trigger at any given time, retrying, or taking corrective action as needed. To
this end, we describe an imitation learning system that uses vision-based
policies trained from demonstrations at both the lower (motor control) and the
upper (sequencing) level, present a system for instantiating this method to
learn the cable routing task, and perform evaluations showing great performance
in generalizing to very challenging clip placement variations. Supplementary
videos, datasets, and code can be found at
https://sites.google.com/view/cablerouting.
