Abstract
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.
Abstract (translated)
我们建议将模型预测控制与深度学习相结合，以便与机器人进行精确的人体运动跟踪。我们设计MPC以允许在学习和保守预测之间切换。我们还使用DyBM模型探索了在线学习。我们将此方法应用于UR-5机器人的人手写动作跟踪。结果表明该框架显着提高了跟踪性能。
URL
https://arxiv.org/abs/1808.02200
