  Learning control policies for visual servoing in novel environments is an
important problem. However, standard model-free policy learning methods are
slow. This paper explores planner cloning: using behavior cloning to learn
policies that mimic the behavior of a full-state motion planner in simulation.
We propose Penalized Q Cloning (PQC), a new behavior cloning algorithm. We show
that it outperforms several baselines and ablations on some challenging
problems involving visual servoing in novel environments while avoiding
obstacles. Finally, we demonstrate that these policies can be transferred
effectively onto a real robotic platform, achieving approximately an 87%
success rate both in simulation and on a real robot.
