  Robust dynamic interactions are required to move robots in daily environments
alongside humans. Optimisation and learning methods have been used to mimic and
reproduce human movements. However, they are often not robust and their
generalisation is limited. This work proposed a hierarchical control
architecture for robot manipulators and provided capabilities of reproducing
human-like motions during unknown interaction dynamics. Our results show that
the reproduced end-effector trajectories can preserve the main characteristics
of the initial human motion recorded via a motion capture system, and are
robust against external perturbations. The data indicate that some detailed
movements are hard to reproduce due to the physical limits of the hardware that
cannot reach the same velocity recorded in human movements. Nevertheless, these
technical problems can be addressed by using better hardware and our proposed
algorithms can still be applied to produce imitated motions.
