  One of the key benefits of model predictive control is the capability of
controlling a system proactively in the sense of taking the future system
evolution into account. However, often external disturbances or references are
not a priori known, which renders the predictive controllers shortsighted or
uninformed. Adaptive prediction models can be used to overcome this issue and
provide predictions of these signals to the controller. In this work we propose
to learn references via Gaussian processes for model predictive controllers. To
illustrate the approach, we consider robot assisted surgery, where a robotic
manipulator needs to follow a learned reference position based on optical
tracking measurements.
