Human-robot shared control for surgical robot based on context-aware sim-to-real adaptation

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Title: Human-robot shared control for surgical robot based on context-aware sim-to-real adaptation
Authors: Zhang, D
Wu, Z
Chen, J
Zhu, R
Munawar, A
Xiao, B
Guan, Y
Su, H
Hong, W
Guo, Y
Fischer, GS
Lo, B
Yang, G-Z
Item Type: Conference Paper
Abstract: 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 sub tasks for the construction of the shared control mechanism. 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 (DMP) 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.
Issue Date: 12-Jul-2022
Date of Acceptance: 1-Jul-2022
URI: http://hdl.handle.net/10044/1/99373
DOI: 10.1109/icra46639.2022.9812379
Publisher: IEEE
Start Page: 7701
End Page: 7707
Journal / Book Title: 2022 International Conference on Robotics and Automation (ICRA)
Copyright Statement: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Imperial College Healthcare NHS Trust- BRC Funding
Imperial College Healthcare NHS Trust- BRC Funding
Engineering & Physical Science Research Council (EPSRC)
Multi-Scale Medical Robotics Center Limited
Funder's Grant Number: RDB04 79560
RD207
EP/L014149/1
MEME_P84520
Conference Name: 2022 IEEE International Conference on Robotics and Automation (ICRA)
Keywords: cs.RO
cs.RO
Publication Status: Published
Start Date: 2022-05-23
Finish Date: 2022-05-27
Conference Place: Philadelphia, PA, USA
Online Publication Date: 2022-07-12
Appears in Collections:Department of Surgery and Cancer
Electrical and Electronic Engineering
Faculty of Medicine
Institute of Global Health Innovation