  We present a novel method for learning hybrid force/position control from
demonstration. We learn a dynamic constraint frame aligned to the direction of
desired force using Cartesian Dynamic Movement Primitives. In contrast to
approaches that utilize a fixed constraint frame, our approach easily
accommodates tasks with rapidly changing task constraints over time. We
activate only one degree of freedom for force control at any given time,
ensuring motion is always possible orthogonal to the direction of desired
force. Since we utilize demonstrated forces to learn the constraint frame, we
are able to compensate for forces not detected by methods that learn only from
demonstrated kinematic motion, such as frictional forces between the
end-effector and contact surface. We additionally propose novel extensions to
the Dynamic Movement Primitive framework that encourage robust transition from
free-space motion to in-contact motion in spite of environment uncertainty. We
incorporate force feedback and a dynamically shifting goal to reduce forces
applied to the environment and retain stable contact while enabling force
control. Our methods exhibit low impact forces on contact and low steady-state
tracking error.
