Manipulating Soft Tissues by Deep Reinforcement Learning for Autonomous Robotic Surgery

Abstract

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-ofthe-art method in soft pattern cutting task with respect to both accuracy and reliability.

Deep learning robotic guidance for autonomous vascular access

“…It is possible, however, that such policies should be context-specific. Learning-based strategies, for instance based on recurrent networks or reinforcement paradigms 46 , have shown promise in producing optimal policies for robotic surgical tasks 47 . Subsequent efforts will investigate whether learned trajectories result in better performance outcomes compared to traditional methods of robotic control.…”

A Review on Deep Learning in Minimally Invasive Surgery

“…They implemented this approach using a dVRK in both simulated and physical scenarios. In later works, this method is improved with a multiple pinch point Deep RL algorithm that exhibits better results [93]- [94].…”

Safe Reinforcement Learning using Formal Verification for Tissue Retraction in Autonomous Robotic-Assisted Surgery

“…DRL for soft-tissue manipulation: Previous works used a DRL based approach to learn a tensioning policy for surgical soft tissue multiple pinch point cutting task [15], [4]. Shin [3].…”

“…Recent efforts in surgical subtask automation have shown a surge in interest in employing data-driven approaches such as Deep Reinforcement Learning (DRL) [3], [4]. DRL has provided breakthrough success in other robotic domains, namely manipulation [5], navigation and locomotion tasks [6].…”