Exploring the Synergistic Effects of Teleoperation Scaling Ratio and Learning From Demonstration
Donghao Shi, Sihan Jin, Chenguang Yang, Zhenyu Lu, Qinchuan Li · IEEE Transactions on Automation Science and Engineering · 2025
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.