  This paper introduces the task of {\em Planar Robot Casting (PRC)}: where one
planar motion of a robot arm holding one end of a cable causes the other end to
slide across the plane toward a desired target. PRC allows the cable to reach
points beyond the robot workspace and has applications for cable management in
homes, warehouses, and factories. To efficiently learn a PRC policy for a given
cable, we propose Real2Sim2Real, a self-supervised framework that automatically
collects physical trajectory examples to tune parameters of a dynamics
simulator using Differential Evolution, generates many simulated examples, and
then learns a policy using a weighted combination of simulated and physical
data. We evaluate Real2Sim2Real with three simulators, Isaac Gym-segmented,
Isaac Gym-hybrid, and PyBullet, two function approximators, Gaussian Processes
and Neural Networks (NNs), and three cables with differing stiffness, torsion,
and friction. Results with 240 physical trials suggest that the PRC policies
can attain median error distance (as % of cable length) ranging from 8% to 14%,
outperforming baselines and policies trained on only real or only simulated
examples. Code, data, and videos are available at
https://tinyurl.com/robotcast.
