  We propose to combine model predictive control with deep learning for the
task of accurate human motion tracking with a robot. We design the MPC to allow
switching between the learned and a conservative prediction. We also explored
online learning with a DyBM model. We applied this method to human handwriting
motion tracking with a UR-5 robot. The results show that the framework
significantly improves tracking performance.
