  Dynamic manipulation of free-end cables has applications for cable management
in homes, warehouses and manufacturing plants. We present a supervised learning
approach for dynamic manipulation of free-end cables, focusing on the problem
of getting the cable endpoint to a designated target position, which may lie
outside the reachable workspace of the robot end effector. We present a
simulator, tune it to closely match experiments with physical cables, and then
collect training data for learning dynamic cable manipulation. We evaluate with
3 cables and a physical UR5 robot. Results over 32x5 trials on 3 cables suggest
that a physical UR5 robot can attain a median error distance ranging from 22%
to 35% of the cable length among cables, outperforming an analytic baseline by
21% and a Gaussian Process baseline by 7% with lower interquartile range (IQR).
