  Modelling of contact-rich tasks is challenging and cannot be entirely solved
using classical control approaches due to the difficulty of constructing an
analytic description of the contact dynamics. Additionally, in a manipulation
task like food-cutting, purely learning-based methods such as Reinforcement
Learning, require either a vast amount of data that is expensive to collect on
a real robot, or a highly realistic simulation environment, which is currently
not available. This paper presents a data-driven control approach that employs
a recurrent neural network to model the dynamics for a Model Predictive
Controller. We build upon earlier work limited to torque-controlled robots and
redefine it for velocity controlled ones. We incorporate force/torque sensor
measurements, reformulate and further extend the control problem formulation.
We evaluate the performance on objects used for training, as well as on unknown
objects, by means of the cutting rates achieved and demonstrate that the method
can efficiently treat different cases with only one dynamic model. Finally we
investigate the behavior of the system during force-critical instances of
cutting and illustrate its adaptive behavior in difficult cases.
