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Manipulating soft tissues by deep reinforcement learning for autonomous robotic surgery

conference contribution
posted on 2019-01-01, 00:00 authored by Ngoc Duy Nguyen, Thanh Thi NguyenThanh Thi Nguyen, Saeid NahavandiSaeid Nahavandi, Asim BhattiAsim Bhatti, Glenn GuestGlenn Guest
© 2019 IEEE. In robotic surgery, pattern cutting through a deformable material is a challenging research field. The cutting procedure requires a robot to concurrently manipulate a scissor and a gripper to cut through a predefined contour trajectory on the deformable sheet. The gripper ensures the cutting accuracy by nailing a point on the sheet and continuously tensioning the pinch point to different directions while the scissor is in action. The goal is to find a pinch point and a corresponding tensioning policy to minimize damage to the material and increase cutting accuracy measured by the symmetric difference between the predefined contour and the cut contour. Previous study considers finding one fixed pinch point during the course of cutting, which is inaccurate and unsafe when the contour trajectory is complex. In this paper, we examine the soft tissue cutting task by using multiple pinch points, which imitates human operations while cutting. This approach, however, does not require the use of a multi-gripper robot. We use a deep reinforcement learning algorithm to find an optimal tensioning policy of a pinch point. Simulation results show that the multi-point approach outperforms the state-of the-art method in soft pattern cutting task with respect to both accuracy and reliability.



IEEE International Systems Conference (2019 : Orlando, Florida)


1 - 7




Orlando, Florida

Place of publication

Piscataway, N.J.

Start date


End date






Publication classification

E1 Full written paper - refereed

Title of proceedings

SysCon 2019 : Proceedings of the 2019 IEEE International Systems Conference