  In this work we propose an approach to learn a robust policy for solving the
pivoting task. Recently, several model-free continuous control algorithms were
shown to learn successful policies without prior knowledge of the dynamics of
the task. However, obtaining successful policies required thousands to millions
of training episodes, limiting the applicability of these approaches to real
hardware. We developed a training procedure that allows us to use a simple
custom simulator to learn policies robust to the mismatch of simulation vs
robot. In our experiments, we demonstrate that the policy learned in the
simulator is able to pivot the object to the desired target angle on the real
robot. We also show generalization to an object with different inertia, shape,
mass and friction properties than those used during training. This result is a
step towards making model-free reinforcement learning available for solving
robotics tasks via pre-training in simulators that offer only an imprecise
match to the real-world dynamics.

Robotics;Machine Learning