# Learning from Demonstrations for Autonomous Soft-tissue Retraction

arXiv: https://arxiv.org/abs/2110.00336 · HTML: https://mlsanity.com/paper/2110.00336/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in Safe offline RL, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Placed in

- Offline RL and imitation learning from data / [Safe offline RL](https://mlsanity.com/offlinerl/safe-offline-rl/)

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks](https://mlsanity.com/paper/1903.08792/) (2019)
- [How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned](https://mlsanity.com/paper/2102.02915/) (2021)
- [SuPer: A Surgical Perception Framework for Endoscopic Tissue Manipulation with Surgical Robotics](https://mlsanity.com/paper/1909.05405/) (2019)
- [Open-Sourced Reinforcement Learning Environments for Surgical Robotics](https://mlsanity.com/paper/1903.02090/) (2019)
- [Autonomous Tissue Manipulation via Surgical Robot Using Learning Based Model Predictive Control](https://mlsanity.com/paper/1902.01459/) (2019)
- [Manipulating Soft Tissues by Deep Reinforcement Learning for Autonomous Robotic Surgery](https://mlsanity.com/paper/1902.05183/) (2019)
- [Medical Technologies and Challenges of Robot Assisted Minimally Invasive Intervention and Diagnostics](https://mlsanity.com/paper/1807.03731/) (2018)

## Built on by (most cited first, 18 of 18)

- [Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects](https://mlsanity.com/paper/2312.10008/) (2023)
- [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)
- [Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical Robot](https://mlsanity.com/paper/2302.09772/) (2023)
- [Human-in-the-loop Embodied Intelligence with Interactive Simulation Environment for Surgical Robot Learning](https://mlsanity.com/paper/2301.00452/) (2023)
- [Value-Informed Skill Chaining for Policy Learning of Long-Horizon Tasks with Surgical Robot](https://mlsanity.com/paper/2307.16503/) (2023)
- [Colonoscopy Navigation using End-to-End Deep Visuomotor Control: A User Study](https://mlsanity.com/paper/2206.15086/) (2022)
- [D-Cubed: Latent Diffusion Trajectory Optimisation for Dexterous Deformable Manipulation](https://mlsanity.com/paper/2403.12861/) (2024)
- [Robust Path Planning via Learning from Demonstrations for Robotic Catheters in Deformable Environments](https://mlsanity.com/paper/2402.00537/) (2024)
- [Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery](https://mlsanity.com/paper/2404.07185/) (2024)
- [FF-SRL: High Performance GPU-Based Surgical Simulation For Robot Learning](https://mlsanity.com/paper/2503.18616/) (2025)
- [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)
- [SurgIRL: Towards Life-Long Learning for Surgical Automation by Incremental Reinforcement Learning](https://mlsanity.com/paper/2409.15651/) (2024)
- [DINO-CVA: A Multimodal Goal-Conditioned Vision-to-Action Model for Autonomous Catheter Navigation](https://mlsanity.com/paper/2510.17038/) (2025)
- [SurgVLA-Bench: Towards Evaluating Vision-Language-Action Models for Laparoscopic Surgical Robotics](https://mlsanity.com/paper/2606.29247/) (2026)
- [Deformable State Estimation for Autonomous Surgical Tissue Retraction Under Partial Observability](https://mlsanity.com/paper/2607.13475/) (2026)
- [Occupancy Network-Guided Autonomous Robotic Partial Nephrectomy](https://mlsanity.com/paper/2609.16186/) (2026)

## Abstract (arXiv)

The current research focus in Robot-Assisted Minimally Invasive Surgery (RAMIS) is directed towards increasing the level of robot autonomy, to place surgeons in a supervisory position. Although Learning from Demonstrations (LfD) approaches are among the preferred ways for an autonomous surgical system to learn expert gestures, they require a high number of demonstrations and show poor generalization to the variable conditions of the surgical environment. In this work, we propose an LfD methodology based on Generative Adversarial Imitation Learning (GAIL) that is built on a Deep Reinforcement Learning (DRL) setting. GAIL combines generative adversarial networks to learn the distribution of expert trajectories with a DRL setting to ensure generalisation of trajectories providing human-like behaviour. We consider automation of tissue retraction, a common RAMIS task that involves soft tissues manipulation to expose a region of interest. In our proposed methodology, a small set of expert trajectories can be acquired through the da Vinci Research Kit (dVRK) and used to train the proposed LfD method inside a simulated environment. Results indicate that our methodology can accomplish the tissue retraction task with human-like behaviour while being more sample-efficient than the baseline DRL method. Towards the end, we show that the learnt policies can be successfully transferred to the real robotic platform and deployed for soft tissue retraction on a synthetic phantom.