Motion scaling is essential to empower users to conduct precise manipulation during teleoperation for robot-assisted microsurgery (RAMS). A constant, small motion scaling ratio can enhance the precision of teleoperation but hinder the operator from quickly reaching distant targets. The concept of self-adaptive motion scaling has been proposed in previous work. However, previous frameworks required extensive manual tuning of core parameters, which significantly depends on prior knowledge and may potentially lead to non-optimal solutions. This paper presents a hybrid offline reinforcement learning and damping control approach to regulate the motion scaling ratio for different operations during offline training. This method can take user-specific characteristics into consideration and help them achieve better teleoperation performance. Comparisons are made with and without using the adaptive motion-scaling algorithm. Detailed user studies indicate that a suitable motion-scaling ratio can be obtained and adjusted online. The overall performance of the operators in terms of time cost for task completion is significantly improved, while the variance of average speed and the total distance for robot operation is reduced.

This paper presents a novel digital twin (DT)-driven framework for immersive teleoperation in the domain of robot-assisted microsurgery (RAMS). The proposed method leverages the power of DT with mixed reality (MR) technology to create an interactive, immersive teleoperation environment for surgeons to conduct RAMS with higher precision, improved safety, and higher efficiency. More specifically, the MR device can provide operators with the 3D visualization of a digital microsurgical robot mimicking the motions of the physical one as well as the 2D real-time microscopic images during microsurgical operation. We evaluated the proposed framework through user studies based on a Trajectory Following task and conducted comparisons between scenarios with and without using the proposed framework for RAMS. The NASA-TLX questionnaire, along with additional evaluation metrics such as total trajectory, time cost, mean velocity, and a predefined collision metric, were used to analyze the user studies. Results indicated that the proposed DT-driven immersive teleoperation framework could enhance the precision, safety, and efficiency of teleoperation, and provide a satisfactory user experience to operators during microsurgical operation.