  Motivated by the recursive Newton-Euler formulation, we propose a novel
cascaded Gaussian process learning framework for the inverse dynamics of robot
manipulators. This approach leads to a significant dimensionality reduction
which in turn results in better learning and data efficiency. We explore two
formulations for the cascading: the inward and outward, both along the
manipulator chain topology. The learned modeling is tested in conjunction with
the classical inverse dynamics model (semi-parametric) and on its own
(non-parametric) in the context of feed-forward control of the arm.
Experimental results are obtained with Jaco 2 six-DOF and SARCOS seven-DOF
manipulators for randomly defined sinusoidal motions of the joints in order to
evaluate the performance of cascading against the standard GP learning. In
addition, experiments are conducted using Jaco 2 on a task emulating a pouring
maneuver. Results indicate a consistent improvement in learning speed with the
inward cascaded GP model and an overall improvement in data efficiency and
generalization.
