# Autonomous Tissue Manipulation via Surgical Robot Using Learning Based Model Predictive Control

arXiv: https://arxiv.org/abs/1902.01459 · HTML: https://mlsanity.com/paper/1902.01459/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2019.

## Built on by (most cited first, 13 of 13)

- [Large AI Models in Health Informatics: Applications, Challenges, and the Future](https://mlsanity.com/paper/2303.11568/) (2023)
- [Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects](https://mlsanity.com/paper/2312.10008/) (2023)
- [Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks](https://mlsanity.com/paper/2407.12998/) (2024)
- [Sim-To-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery](https://mlsanity.com/paper/2406.06092/) (2024)
- [SRT-H: A Hierarchical Framework for Autonomous Surgery via Language Conditioned Imitation Learning](https://mlsanity.com/paper/2505.10251/) (2025)
- [VisuoSpatial Foresight for Physical Sequential Fabric Manipulation](https://mlsanity.com/paper/2102.09754/) (2021)
- [Learning from Demonstrations for Autonomous Soft-tissue Retraction](https://mlsanity.com/paper/2110.00336/) (2021)
- [From Screens to Scenes: A Survey of Embodied AI in Healthcare](https://mlsanity.com/paper/2501.07468/) (2025)
- [Event-Triggered Model Predictive Control with Deep Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2208.10302/) (2022)
- [Surgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots](https://mlsanity.com/paper/2310.04676/) (2023)
- [Autonomous Soft Tissue Retraction Using Demonstration-Guided Reinforcement Learning](https://mlsanity.com/paper/2309.00837/) (2023)
- [Learning needle insertion from sample task executions](https://mlsanity.com/paper/2103.07938/) (2021)
- [SurgIRL: Towards Life-Long Learning for Surgical Automation by Incremental Reinforcement Learning](https://mlsanity.com/paper/2409.15651/) (2024)

## Abstract (arXiv)

Tissue manipulation is a frequently used fundamental subtask of any surgical procedures, and in some cases it may require the involvement of a surgeon's assistant. The complex dynamics of soft tissue as an unstructured environment is one of the main challenges in any attempt to automate the manipulation of it via a surgical robotic system. Two AI learning based model predictive control algorithms using vision strategies are proposed and studied: (1) reinforcement learning and (2) learning from demonstration. Comparison of the performance of these AI algorithms in a simulation setting indicated that the learning from demonstration algorithm can boost the learning policy by initializing the predicted dynamics with given demonstrations. Furthermore, the learning from demonstration algorithm is implemented on a Raven IV surgical robotic system and successfully demonstrated feasibility of the proposed algorithm using an experimental approach. This study is part of a profound vision in which the role of a surgeon will be redefined as a pure decision maker whereas the vast majority of the manipulation will be conducted autonomously by a surgical robotic system. A supplementary video can be found at: http://bionics.seas.ucla.edu/research/surgeryproject17.html