  Data-driven approaches for modelling contact-rich tasks address many of the
difficulties that analytical models bear. For real-world scenarios, the
hardware capabilities constrain the available measurements and consequently,
every step of the problem's formulation. In this work, we propose a formulation
that encapsulates knowledge from a baseline controller for the contact-rich
task of food-cutting. Based on this formulation, we employ deep networks to
model the dynamics within a model predictive controller. We design a training
process based on curriculum training with learning rate decay for multi-step
predictions, which are essential for receding horizon control. Experimental
results demonstrate that even with a simple architecture, our model achieves
consistently good predictive performance on known and unknown object classes
and exhibits a good understanding of the long term dynamics.
