  This work develops a novel trajectory planner for human-robot handovers. The
handover requirements can naturally be handled by a path-following-based model
predictive controller, where the path progress serves as a progress measure of
the handover. Moreover, the deviations from the path are used to follow human
motion by adapting the path deviation bounds with a handover location
prediction. A Gaussian process regression model, which is trained on known
handover trajectories, is employed for this prediction. Experiments with a
collaborative 7-DoF robotic manipulator show the effectiveness and versatility
of the proposed approach.
