Deep Learning with Predictive Control for Human Motion Tracking
Don Joven Agravante, Giovanni De Magistris, Asim Munawar, Phongtharin Vinayavekhin, Ryuki Tachibana · arXiv (Cornell University) · 2018
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.