# Autonomous Soft Tissue Retraction Using Demonstration-Guided Reinforcement Learning

arXiv: https://arxiv.org/abs/2309.00837 · HTML: https://mlsanity.com/paper/2309.00837/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 2 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Overcoming Exploration in Reinforcement Learning with Demonstrations](https://mlsanity.com/paper/1709.10089/) (2017)
- [SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning](https://mlsanity.com/paper/2108.13035/) (2021)
- [Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical Robot](https://mlsanity.com/paper/2302.09772/) (2023)
- [Autonomous Tissue Manipulation via Surgical Robot Using Learning Based Model Predictive Control](https://mlsanity.com/paper/1902.01459/) (2019)
- [Roadmap to Autonomous Surgery -- A Framework to Surgical Autonomy](https://mlsanity.com/paper/2206.10516/) (2022)

## Built on by (most cited first, 3 of 3)

- [EndoWAM: A Grounded World-Action Model for Generalizable Endoscopic Navigation](https://mlsanity.com/paper/2608.01221/) (2026)
- [Deformable State Estimation for Autonomous Surgical Tissue Retraction Under Partial Observability](https://mlsanity.com/paper/2607.13475/) (2026)
- [Trajectory Divergence Horizon Decision for Reliable Dual-Arm Surgical Subtask Manipulation](https://mlsanity.com/paper/2608.09125/) (2026)

## Abstract (arXiv)

In the context of surgery, robots can provide substantial assistance by performing small, repetitive tasks such as suturing, needle exchange, and tissue retraction, thereby enabling surgeons to concentrate on more complex aspects of the procedure. However, existing surgical task learning mainly pertains to rigid body interactions, whereas the advancement towards more sophisticated surgical robots necessitates the manipulation of soft bodies. Previous work focused on tissue phantoms for soft tissue task learning, which can be expensive and can be an entry barrier to research. Simulation environments present a safe and efficient way to learn surgical tasks before their application to actual tissue. In this study, we create a Robot Operating System (ROS)-compatible physics simulation environment with support for both rigid and soft body interactions within surgical tasks. Furthermore, we investigate the soft tissue interactions facilitated by the patient-side manipulator of the DaVinci surgical robot. Leveraging the pybullet physics engine, we simulate kinematics and establish anchor points to guide the robotic arm when manipulating soft tissue. Using demonstration-guided reinforcement learning (RL) algorithms, we investigate their performance in comparison to traditional reinforcement learning algorithms. Our in silico trials demonstrate a proof-of-concept for autonomous surgical soft tissue retraction. The results corroborate the feasibility of learning soft body manipulation through the application of reinforcement learning agents. This work lays the foundation for future research into the development and refinement of surgical robots capable of managing both rigid and soft tissue interactions. Code is available at https://github.com/amritpal-001/tissue_retract.