  In recent years, industrial robots have been installed in various industries
to handle advanced manufacturing and high precision tasks. However, further
integration of industrial robots is hampered by their limited flexibility,
adaptability and decision making skills compared to human operators. Assembly
tasks are especially challenging for robots since they are contact-rich and
sensitive to even small uncertainties. While reinforcement learning (RL) offers
a promising framework to learn contact-rich control policies from scratch, its
applicability to high-dimensional continuous state-action spaces remains rather
limited due to high brittleness and sample complexity. To address those issues,
we propose different pruning methods that facilitate convergence and
generalization. In particular, we divide the task into free and contact-rich
sub-tasks, perform the control in Cartesian rather than joint space, and
parameterize the control policy. Those pruning methods are naturally
implemented within the framework of dynamic movement primitives (DMP). To
handle contact-rich tasks, we extend the DMP framework by introducing a
coupling term that acts like the human wrist and provides active compliance
under contact with the environment. We demonstrate that the proposed method can
learn insertion skills that are invariant to space, size, shape, and closely
related scenarios, while handling large uncertainties. Finally we demonstrate
that the learned policy can be easily transferred from simulations to real
world and achieve similar performance on UR5e robot.
