Model Predictive Control and Imitation Learning Algorithms for Robot Motion Planning in Physical Human-Robot Interaction
This seminar presents a framework for safe and efficient human-robot workspace sharing by using Deep Neural Networks (DNN) and safety filters to rapidly imitate computationally heavy Nonlinear Model Predictive Control method (NMPC), with successful experimental validation on a UR5 manipulator.
Overview
This seminar explores the development and experimental validation of safe motion planning algorithms for human-robot workspace sharing. These algorithms are based on the use of nonlinear model predictive control (NMPC), a model-based method for motion planning relying on numerical optimization. The main idea is the approximation of NMPC laws using deep neural networks (DNNs), often referred to as “imitation learning”. This is motivated by the fact that the execution of NMPC laws might require a considerable amount of time, which restricts the performance of the closed-loop system. Calculating the output of a DNN for a given input is instead a much faster process. Therefore, replacing the optimization solver of NMPC with a DNN can reduce computation times, thus improving performance. It is crucial, though, to suitably train the DNN to imitate the NMPC law in order to improve performance and at the same time guarantee safety.
The final result obtained in this area consists of using the so-called dataset-aggregation approach for DNN training, together with properly designed safety filters, which ensure that the safety constraints imposed in the NMPC problem also hold for the robot motion generated by the DNN. All the proposed motion planning strategies were tested experimentally on a UR5 collaborative manipulator.
Presenters
Brief Biography
Aigerim Nurbayeva is a Postdoctoral Fellow at the Robotics, Intelligent Systems, and Control (RISC) Lab at KAUST, where she works under the supervision of Professor Shinkyu Park. She received her Ph.D. in Robotics from Nazarbayev University in 2024, focusing her doctoral research on Model Predictive Control (MPC) and Imitation Learning for human-robot interaction.
She has authored five peer-reviewed publications in prestigious journals, including IEEE Transactions on Industrial Informatics and Control Engineering Practice.
