From data extraction to data-driven dynamic modeling for cobots: A method using multi-objective optimization

Controlling collaborative robots (cobots) is a new and challenging paradigm within the field of robot motion control and safe human–robot interaction (HRI). The safety measures needed for a reliable interaction between the robot and its environment hinder the use of classical position control methods, pushing researchers to explore alternative motor control techniques, with a strong focus on those rooted in machine learning (ML). While reinforcement learning has emerged as the predominant approach for creating intelligent controllers for cobots, supervised learning represents a promising alternative in developing data-driven model-based ML controllers in a faster and safer way. In this work, we study several aspects of the methodology needed to create a dataset for learning the dynamics of a robot. To this aim, we fine-tune several PD controllers across different benchmark trajectories using multi-objective evolutionary algorithms (MOEAs) that take into account controller accuracy, and compliance in terms of low torques in the framework of safe HRI. We delve into various aspects of the data extraction methodology including the selection and calibration of the MOEAs. We also demonstrate the need to tune controllers individually for each trajectory and how the speed of a trajectory influences both the tuning process and the resulting dynamics of the robot. Finally, we create a novel dataset and validate its use by feeding all the extracted dynamic data into an inverse dynamic robot model and integrating it into a feedforward control loop. Our approach significantly outperforms individual standard PD controllers previously tuned, thus illustrating the effectiveness of the proposed methodology. • A methodology for dataset creation of a data-driven dynamic model. • Comparison among different MOEAs for tuning PD controllers. • Improving PD torque control by using a data-driven feedforward controller. • Comparison between the use of fine-tuned data and generic data for dynamic model training.