Deep Reinforcement Learning-Based Control Framework for Multilateral Telesurgery

Bacha, Sarah and Bai, Weibang and Wang, Ziwei and Xiao, Bo and Yeatman, Eric (2022) Deep Reinforcement Learning-Based Control Framework for Multilateral Telesurgery. IEEE Transactions on Medical Robotics and Bionics, 4 (2). pp. 352-355. ISSN 2576-3202

Text (DDPG_Multilateral_TMRB22)
DDPG_Multilateral_TMRB22.pdf - Accepted Version
Available under License Creative Commons Attribution.
Download (1MB)

Abstract

The upper boundary of time delay is often required in traditional telesurgery control design, which would result in infeasibility of telesurgery across regions. To overcome this issue, this paper introduces a new control framework based on deep deterministic policy gradient (DDPG) reinforcement learning (RL) algorithm. The developed framework effectively overcomes the phase difference and data loss caused by time delays, which facilitates the restoration of surgeon’s intention and interactive force. Kalman filter (KF) is employed to blend multiple surgeons’ commands and predict the final local commands, respectively. The control framework ensures synchronization tracking performance and transparency. Prior knowledge of time delay is therefore not required. Simulation and experiment results have demonstrated the merits of the proposed framework.