Deep Imitation Learning of Nonlinear Model Predictive Control Laws for a Safe Physical Human-Robot Interaction
Nurbayeva A. Shintemirov A. Rubagotti M.
1 July 2023IEEE Computer Society
IEEE Transactions on Industrial Informatics
2023#19Issue 78384 - 8395 pp.
This article proposes motion planning algorithms for industrial manipulators in the presence of human operators based on deep neural networks (DNNs), aimed at imitating the behavior of a nonlinear model predictive control (NMPC) scheme. The proposed DNN solutions retain the safety features of NMPC in terms of speed and separation monitoring, defined according to the guidelines in the ISO/TS 15066 standard. At the same time, they improve the robot performance in terms of task completion time, and of a posteriori evaluation of the NMPC cost function on experimental data. The reasons for this improvement are the reduced computational delay of running a DNN compared to solving the nonlinear programs associated to NMPC, and the ability to implicitly learn how to predict the human operators motion from the training set.
Industrial robotics , model predictive control (MPC) , neural networks , physical human robot interaction
Text of the article Перейти на текст статьи
Nazarbayev University, Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Astana, 010000, Kazakhstan
Nazarbayev University
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026