Exploring the Synergistic Effects of Teleoperation Scaling Ratio and Learning From Demonstration

Teleoperation and Learning from Demonstration (LfD) are complementary paradigms for robotic control, yet their synergistic potential remains underexplored. This work proposes a unified framework that dynamically adjusts teleoperation precision using task-specific priors from LfD while optimizing demonstration learning through teleoperation scaling. By analyzing human operator signals (e.g., muscle activity), our method autonomously scales robot movements to balance the precision requirements and execution efficiency during teleoperation. Conversely, teleoperation data informs the adaptive arrangement of motion primitives in LfD, improving trajectory accuracy in critical task phases. Experiments show that the proposed method can improve the efficiency of teleoperation tasks and the accuracy of task learning. This bidirectional synergy offers a practical pathway to enhance both human-robot interaction and skill learning, particularly in applications demanding variable precision, such as assembly and surgical robotics. Note to Practitioners—Robot teleoperation technology is widely used in medical, aviation and industrial fields, and LfD technology is a method for rapid programming and deployment of robot autonomous tasks. The work of this paper explores the common points of robot teleoperation technology and LfD technology, as well as the demand for task accuracy. Through the fusion and mutual promotion of the two methods, the autonomous zooming teleoperation method can be applied to teleoperation tasks such as surgery, and the time problem caused by the zooming adjustment can be reduced. At the same time, the LfD method, which considers scaling, is formed and can be used in learning and programming for different task precision requirements.