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The MIM Lab develops robotic and mechatronics surgical systems for a variety of procedures.
Twitter/X: @MIMLab_Robotics
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The Mechatronics in Medicine Laboratory develops robotic and mechatronics surgical systems for a variety of procedures including neuro, cardiovascular, orthopaedic surgeries, and colonoscopies. Examples include bio-inspired catheters that can navigate along complex paths within the brain (such as EDEN2020), soft robots to explore endoluminal anatomies (such as the colon), and virtual reality solutions to support surgeons during knee replacement surgeries.
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Mr Ayhan Akta_
Research Postgraduate
Dr Daniel Bautista Salinas
Research Associate
Dr Kaiwen Chen
Research Associate
Mr Zejian Cui
Research Assistant
Mr Connor Daly
Research Postgraduate
Emeritus Professor Brian L Davies FREng
Emeritus Professor
Mr Zhaoyang Jacopo Hu
Research Postgraduate
Dr Hisham M Iqbal
Research Associate
Mr Alex Ranne
Research Postgraduate
Professor Ferdinando M Rodriguez y Baena
Co-Director of Hamlyn Centre, Professor of Medical Robotics
Dr Jialei Shi
Research Associate
Mr Spyridon Souipas
Casual - Other work
Ms Emilia Zari
Research Postgraduate
@article{Hu:2023:10.1109/LRA.2023.3285478,
author = {Hu, ZJ and Wang, Z and Huang, Y and Sena, A and Rodriguez, y Baena F and Burdet, E},
doi = {10.1109/LRA.2023.3285478},
journal = {IEEE Robotics and Automation Letters},
pages = {4553--4560},
title = {Towards human-robot collaborative surgery: trajectory and strategy learning in bimanual peg transfer},
url = {http://dx.doi.org/10.1109/LRA.2023.3285478},
volume = {8},
year = {2023}
}
TY - JOUR
AB - While the traditional control of surgical robots relies on fully manual teleoperations, human-robot collaborative systems promise to address issues such as workspace constrains and laborious tasks. In particular, shared control can reduce the surgeon's workload and improve the overall surgical performance by supporting the surgeon effort during movements while keeping them in charge of complex control phases. In this letter, we propose a segmentation of the bimanual peg transfer task that alternates manual and autonomous control correspondingly. The authority allocation in this shared control framework considers both the limitation of learning-based methods and the higher dexterity of humans during physical interaction. The motion and strategies are transferred from an expert human to a da Vinci Research Kit (dVRK) using an epsilon-greedy on a maximum entropy inverse reinforcement learning algorithm. The model generated enables to train an intelligent agent that can skillfully collaborate with the human operator during the surgical task. The proposed shared control framework is verified both on a virtual platform and then on a real dVRK to assess its usability and robustness. The results show that, compared to traditional teleoperation, our method can achieve faster and more consistent peg transfers. An analysis of the participants' effort also reveals a significantly lower perception of the workload.
AU - Hu,ZJ
AU - Wang,Z
AU - Huang,Y
AU - Sena,A
AU - Rodriguez,y Baena F
AU - Burdet,E
DO - 10.1109/LRA.2023.3285478
EP - 4560
PY - 2023///
SN - 2377-3766
SP - 4553
TI - Towards human-robot collaborative surgery: trajectory and strategy learning in bimanual peg transfer
T2 - IEEE Robotics and Automation Letters
UR - http://dx.doi.org/10.1109/LRA.2023.3285478
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:001017231400007&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR - https://ieeexplore.ieee.org/document/10149474
UR - http://hdl.handle.net/10044/1/112478
VL - 8
ER -
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