# A New Tensioning Method using Deep Reinforcement Learning for Surgical Pattern Cutting

arXiv: https://arxiv.org/abs/1901.03327 · HTML: https://mlsanity.com/paper/1901.03327/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2019.

## Built on by (most cited first, 9 of 9)

- [Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical Robot](https://mlsanity.com/paper/2302.09772/) (2023)
- [CoPESD: A Multi-Level Surgical Motion Dataset for Training Large Vision-Language Models to Co-Pilot Endoscopic Submucosal Dissection](https://mlsanity.com/paper/2410.07540/) (2024)
- [Value-Informed Skill Chaining for Policy Learning of Long-Horizon Tasks with Surgical Robot](https://mlsanity.com/paper/2307.16503/) (2023)
- [Integrating Artificial Intelligence and Augmented Reality in Robotic Surgery: An Initial dVRK Study Using a Surgical Education Scenario](https://mlsanity.com/paper/2201.00383/) (2022)
- [Surgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots](https://mlsanity.com/paper/2310.04676/) (2023)
- [A Survey for Deep Reinforcement Learning Based Network Intrusion Detection](https://mlsanity.com/paper/2410.07612/) (2024)
- [Learning Autonomous Surgical Irrigation and Suction with the da Vinci Research Kit Using Reinforcement Learning](https://mlsanity.com/paper/2411.14622/) (2024)
- [RL-Based Guidance in Outpatient Hysteroscopy Training: A Feasibility Study](https://mlsanity.com/paper/2211.14541/) (2022)
- [Task Allocation for Autonomous Machines using Computational Intelligence and Deep Reinforcement Learning](https://mlsanity.com/paper/2508.20688/) (2025)

## Abstract (arXiv)

Surgeons normally need surgical scissors and tissue grippers to cut through a deformable surgical tissue. The cutting accuracy depends on the skills to manipulate these two tools. Such skills are part of basic surgical skills training as in the Fundamentals of Laparoscopic Surgery. The gripper is used to pinch a point on the surgical sheet and pull the tissue to a certain direction to maintain the tension while the scissors cut through a trajectory. As the surgical materials are deformable, it requires a comprehensive tensioning policy to yield appropriate tensioning direction at each step of the cutting process. Automating a tensioning policy for a given cutting trajectory will support not only the human surgeons but also the surgical robots to improve the cutting accuracy and reliability. This paper presents a multiple pinch point approach to modelling an autonomous tensioning planner based on a deep reinforcement learning algorithm. Experiments on a simulator show that the proposed method is superior to existing methods in terms of both performance and robustness.