2022/04/23 by Dandan Zhang, Zhang, Dandan, Zicong Wu +23 · 1 citation
Engineering · Medicine · #FOS: Computer and information sciences #Robotics (cs.RO) #Soft Robotics and Applications #Surgical Simulation and Training #Teleoperation and Haptic Systems
paper · pdf · doi:10.48550/arxiv.2204.11116
openalex publication_date 2022/04/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Human-robot shared control, which integrates the advantages of both humans and robots, is an effective approach to facilitate efficient surgical operation. Learning from demonstration (LfD) techniques can be used to automate some of the surgical subtasks for the construction of the shared control framework. However, a sufficient amount of data is required for the robot to learn the manoeuvres. Using a surgical simulator to collect data is a less resource-demanding approach. With sim-to-real adaptation, the manoeuvres learned from a simulator can be transferred to a physical robot. To this end, we propose a sim-to-real adaptation method to construct a human-robot shared control framework for robotic surgery. In this paper, a desired trajectory is generated from a simulator using LfD method, while dynamic motion primitives (DMPs) based method is used to transfer the desired trajectory from the simulator to the physical robotic platform. Moreover, a role adaptation mechanism is developed such that the robot can adjust its role according to the surgical operation contexts predicted by a neural network model. The effectiveness of the proposed framework is validated on the da Vinci Research Kit (dVRK). Results of the user studies indicated that with the adaptive human-robot shared control framework, the path length of the remote controller, the total clutching number and the task completion time can be reduced significantly. The proposed method outperformed the traditional manual control via teleoperation.