  Recent work has shown that complex manipulation skills, such as pushing or
pouring, can be learned through state-of-the-art learning based techniques,
such as Reinforcement Learning (RL). However, these methods often have high
sample-complexity, are susceptible to domain changes, and produce unsafe
motions that a robot should not perform. On the other hand, purely geometric
model-based planning can produce complex behaviors that satisfy all the
geometric constraints of the robot but might not be dynamically feasible for a
given environment. In this work, we leverage a geometric model-based planner to
build a mixture of path-policies on which a task-specific meta-policy can be
learned to complete the task. In our results, we demonstrate that a successful
meta-policy can be learned to push a door, while requiring little data and
being robust to model uncertainty of the environment. We tested our method on a
7-DOF Franka-Emika Robot pushing a cabinet door in simulation.
