  Accurate models of robots' dynamics are critical for control, stability,
motion optimization, and interaction. Semi-Parametric approaches to dynamics
learning combine physics-based Parametric models with unstructured
Non-Parametric regression with the hope to achieve both accuracy and
generalizablity. In this paper we highlight the non-stationary problem created
when attempting to adapt both Parametric and Non-Parametric components
simultaneously. We present a consistency transform designed to compensate for
this non-stationary effect, such that the contributions of both models can
adapt simultaneously without adversely affecting the performance of the
platform. Thus we are able to apply the Semi-Parametric learning approach for
continuous iterative online adaptation, without relying on batch or offline
updates. We validate the transform via a perfect virtual model as well as by
applying the overall system on a Kuka LWR IV manipulator. We demonstrate
improved tracking performance during online learning and show a clear
transference of contribution between the two components with a learning bias
towards the Parametric component.
