# Manipulating Soft Tissues by Deep Reinforcement Learning for Autonomous Robotic Surgery

arXiv: https://arxiv.org/abs/1902.05183 · HTML: https://mlsanity.com/paper/1902.05183/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2019.

## Built on by (most cited first, 12 of 12)

- [Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical Robot](https://mlsanity.com/paper/2302.09772/) (2023)
- [Learning from Demonstrations for Autonomous Soft-tissue Retraction](https://mlsanity.com/paper/2110.00336/) (2021)
- [Value-Informed Skill Chaining for Policy Learning of Long-Horizon Tasks with Surgical Robot](https://mlsanity.com/paper/2307.16503/) (2023)
- [Surgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots](https://mlsanity.com/paper/2310.04676/) (2023)
- [Truly Deterministic Policy Optimization](https://mlsanity.com/paper/2205.15379/) (2022)
- [Roadmap to Autonomous Surgery -- A Framework to Surgical Autonomy](https://mlsanity.com/paper/2206.10516/) (2022)
- [Teach Me How to Learn: A Perspective Review towards User-centered Neuro-symbolic Learning for Robotic Surgical Systems](https://mlsanity.com/paper/2307.03853/) (2023)
- [Toward a Surgeon-in-the-Loop Ophthalmic Robotic Apprentice using Reinforcement and Imitation Learning](https://mlsanity.com/paper/2311.17693/) (2023)
- [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)
- [SurgIRL: Towards Life-Long Learning for Surgical Automation by Incremental Reinforcement Learning](https://mlsanity.com/paper/2409.15651/) (2024)
- [Task Allocation for Autonomous Machines using Computational Intelligence and Deep Reinforcement Learning](https://mlsanity.com/paper/2508.20688/) (2025)

## Abstract (arXiv)

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.