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  • DDPG_Multilateral_TMRB22

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Deep Reinforcement Learning-Based Control Framework for Multilateral Telesurgery

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

Deep Reinforcement Learning-Based Control Framework for Multilateral Telesurgery. / Bacha, Sarah; Bai, Weibang; Wang, Ziwei et al.
In: IEEE Transactions on Medical Robotics and Bionics, Vol. 4, No. 2, 31.05.2022, p. 352-355.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Bacha, S, Bai, W, Wang, Z, Xiao, B & Yeatman, E 2022, 'Deep Reinforcement Learning-Based Control Framework for Multilateral Telesurgery', IEEE Transactions on Medical Robotics and Bionics, vol. 4, no. 2, pp. 352-355. https://doi.org/10.1109/TMRB.2022.3170786

APA

Bacha, S., Bai, W., Wang, Z., Xiao, B., & Yeatman, E. (2022). Deep Reinforcement Learning-Based Control Framework for Multilateral Telesurgery. IEEE Transactions on Medical Robotics and Bionics, 4(2), 352-355. https://doi.org/10.1109/TMRB.2022.3170786

Vancouver

Bacha S, Bai W, Wang Z, Xiao B, Yeatman E. Deep Reinforcement Learning-Based Control Framework for Multilateral Telesurgery. IEEE Transactions on Medical Robotics and Bionics. 2022 May 31;4(2):352-355. Epub 2022 Apr 27. doi: 10.1109/TMRB.2022.3170786

Author

Bacha, Sarah ; Bai, Weibang ; Wang, Ziwei et al. / Deep Reinforcement Learning-Based Control Framework for Multilateral Telesurgery. In: IEEE Transactions on Medical Robotics and Bionics. 2022 ; Vol. 4, No. 2. pp. 352-355.

Bibtex

@article{f2dcd713013041928a55f69ee27cb55d,
title = "Deep Reinforcement Learning-Based Control Framework for Multilateral Telesurgery",
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{\textquoteright}s intention and interactive force. Kalman filter (KF) is employed to blend multiple surgeons{\textquoteright} 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.",
author = "Sarah Bacha and Weibang Bai and Ziwei Wang and Bo Xiao and Eric Yeatman",
note = "{\textcopyright}2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2022",
month = may,
day = "31",
doi = "10.1109/TMRB.2022.3170786",
language = "English",
volume = "4",
pages = "352--355",
journal = "IEEE Transactions on Medical Robotics and Bionics",
issn = "2576-3202",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "2",

}

RIS

TY - JOUR

T1 - Deep Reinforcement Learning-Based Control Framework for Multilateral Telesurgery

AU - Bacha, Sarah

AU - Bai, Weibang

AU - Wang, Ziwei

AU - Xiao, Bo

AU - Yeatman, Eric

N1 - ©2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2022/5/31

Y1 - 2022/5/31

N2 - 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.

AB - 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.

U2 - 10.1109/TMRB.2022.3170786

DO - 10.1109/TMRB.2022.3170786

M3 - Journal article

VL - 4

SP - 352

EP - 355

JO - IEEE Transactions on Medical Robotics and Bionics

JF - IEEE Transactions on Medical Robotics and Bionics

SN - 2576-3202

IS - 2

ER -