  Data-driven surrogate models of dynamical systems based on the extended
dynamic mode decomposition are nowadays well-established and widespread in
applications. Further, for non-holonomic systems exhibiting a multiplicative
coupling between states and controls, the usage of bi-linear surrogate models
has proven beneficial. However, an in-depth analysis of the approximation
quality and its dependence on different hyperparameters based on both
simulation and experimental data is still missing. We investigate a
differential-drive mobile robot to close this gap and provide first guidelines
on the systematic design of data-efficient surrogate models.
