Deep Learning with Predictive Control for Human Motion Tracking
This work addresses motion tracking for robotics applications, but it appears incremental as it combines existing methods like MPC and deep learning without introducing a fundamentally new approach.
The paper tackled the problem of accurate human motion tracking with a robot by combining model predictive control with deep learning, resulting in significantly improved tracking performance as applied to human handwriting motion tracking with a UR-5 robot.
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.