  Dynamic movement primitives (DMPs) allow complex position trajectories to be
efficiently demonstrated to a robot. In contact-rich tasks, where position
trajectories alone may not be safe or robust over variation in contact
geometry, DMPs have been extended to include force trajectories. However,
different task phases or degrees of freedom may require the tracking of either
position or force -- e.g., once contact is made, it may be more important to
track the force demonstration trajectory in the contact direction. The robot
impedance balances between following a position or force reference trajectory,
where a high stiffness tracks position and a low stiffness tracks force. This
paper proposes using DMPs to learn position and force trajectories from
demonstrations, then adapting the impedance parameters online with a
higher-level control policy trained by reinforcement learning. This allows
one-shot demonstration of the task with DMPs, and improved robustness and
performance from the impedance adaptation. The approach is validated on
peg-in-hole and adhesive strip application tasks.
