Safely Imitating Predictive Control Policies for Real-Time Human-Aware Manipulator Motion Planning: A Dataset Aggregation Approach
Nurbayeva A. Rubagotti M.
2025Institute of Electrical and Electronics Engineers Inc.
IEEE Access
2025#133204 - 3214 pp.
This paper proposes a dataset-aggregation approach for imitating a nonlinear model predictive control law via deep neural networks, to safely allow a robot manipulator to share its workspace with a human operator. As the robot approaches the human, its speed is gradually reduced using the speed and separation monitoringframework. Specific time-varying upper bounds are explicitly imposed on the control input generated by the deep neural network through a safety filterbased on real-time numerical optimization. The proposed method is experimentally tested on a UR5 manipulator, comparing the performance of different neural network structures and types of training. As a result, it is shown that the dataset-aggregation approach provides better performance with respect to a naiveapproach to training, and that the presence of the safety filter is indeed needed to avoid the violation of the speed-and-separation-monitoring constraints.
Industrial robotics , model predictive control , neural networks , physical human-robot interaction
Text of the article Перейти на текст статьи
Nazarbayev University, School of Engineering and Digital Sciences, Department of Robotics, 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