  Tracking control for soft robots is challenging due to uncertainties in the
system model and environment. Using high feedback gains to overcome this issue
results in an increasing stiffness that clearly destroys the inherent safety
property of soft robots. However, accurate models for feed-forward control are
often difficult to obtain. In this article, we employ Gaussian Process
regression to obtain a data-driven model that is used for the feed-forward
compensation of unknown dynamics. The model fidelity is used to adapt the
feed-forward and feedback part allowing low feedback gains in regions of high
model confidence.
