  In this work, the problem of human-robot collaborative object transfer to
unknown target poses is addressed. The desired pattern of the end-effector pose
trajectory to a known target pose is encoded using DMPs (Dynamic Movement
Primitives). During transportation of the object to new unknown targets, a
DMP-based reference model and an EKF (Extended Kalman Filter) for estimating
the target pose and time duration of the human's intended motion is proposed. A
stability analysis of the overall scheme is provided. Experiments using a Kuka
LWR4+ robot equipped with an ATI sensor at its end-effector validate its
efficacy with respect to the required human effort and compare it with an
admittance control scheme.
