Data-Driven Model for Cyclic Tasks of Robotic Systems: Study of the Repeatability Conditions

Ensuring repeatability in robotic manipulators is critical for industrial applications, particularly in cyclic tasks where precision and consistency are required. This study aims to establish the conditions that are necessary for repeatability in data-driven task-space control, compare repeatability across redundant robotic manipulators, and reduce the computational costs associated with data-based control methods compared to traditional model-based approaches. To achieve this, a large number of cyclic actions are simulated, mimicking real-world industrial routines. The methodology evaluates the estimated Jacobian matrix’s manipulability and its role in facilitating effective task execution within the operational space of the robot. The results demonstrate that the proposed approach achieves consistent repeatability at the full pose level of the end-effector, integrating both position and orientation. In particular, the findings indicate that the proposed controller minimizes variability and ensures reliable motion execution, even in the presence of system redundancies, as observed in the eight-DoF KUKA YouBot manipulator. These insights contribute to advancing data-driven control strategies for redundant robotic systems, enhancing their applicability in industrial settings.