Deep Reinforcement Learning-Based Control Framework for Multilateral Telesurgery

Sarah Chams Bacha, Weibang Bai, Ziwei Wang*, Bo Xiao, Eric M. Yeatman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)352-355
Number of pages4
JournalIEEE Transactions on Medical Robotics and Bionics
Volume4
Issue number2
DOIs
Publication statusPublished - 1 May 2022
Externally publishedYes

Keywords

  • Deep deterministic policy gradient
  • Reinforcement learning
  • Telesurgery
  • Time delay

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