# Colonoscopy Navigation using End-to-End Deep Visuomotor Control: A User Study

arXiv: https://arxiv.org/abs/2206.15086 · HTML: https://mlsanity.com/paper/2206.15086/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 30 Jun 2022.

## Builds on (most cited first, 15 of 15)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [DeepMind Control Suite](https://mlsanity.com/paper/1801.00690/) (2018)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned](https://mlsanity.com/paper/2102.02915/) (2021)
- [SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning](https://mlsanity.com/paper/2108.13035/) (2021)
- [Open-Sourced Reinforcement Learning Environments for Surgical Robotics](https://mlsanity.com/paper/1903.02090/) (2019)
- [VR-Caps: A Virtual Environment for Capsule Endoscopy](https://mlsanity.com/paper/2008.12949/) (2020)
- [Learning from Demonstrations for Autonomous Soft-tissue Retraction](https://mlsanity.com/paper/2110.00336/) (2021)
- [Cooperative Assistance in Robotic Surgery through Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2110.04857/) (2021)
- [Exploring Convolutional Networks for End-to-End Visual Servoing](https://mlsanity.com/paper/1706.03220/) (2017)
- [ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning](https://mlsanity.com/paper/2202.02465/) (2022)
- [Depth estimation of endoscopy using sim-to-real transfer](https://mlsanity.com/paper/2112.13595/) (2021)
- [Learning Friction Model for Tethered Capsule Robot](https://mlsanity.com/paper/2108.07151/) (2021)

## Built on by (most cited first, 6 of 6)

- [Surgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots](https://mlsanity.com/paper/2310.04676/) (2023)
- [PR-ENDO: Physically Based Relightable Gaussian Splatting for Endoscopy](https://mlsanity.com/paper/2411.12510/) (2024)
- [BronchoCopilot: Towards Autonomous Robotic Bronchoscopy via Multimodal Reinforcement Learning](https://mlsanity.com/paper/2403.01483/) (2024)
- [Safe Navigation for Robotic Digestive Endoscopy via Human Intervention-based Reinforcement Learning](https://mlsanity.com/paper/2409.15688/) (2024)
- [V2-SfMLearner: Learning Monocular Depth and Ego-motion for Multimodal Wireless Capsule Endoscopy](https://mlsanity.com/paper/2412.17595/) (2024)
- [Curriculum-Based Reinforcement Learning for Autonomous UAV Navigation in Unknown Curved Tubular Conduit](https://mlsanity.com/paper/2512.10934/) (2025)

## Abstract (arXiv)

Flexible endoscopes for colonoscopy present several limitations due to their inherent complexity, resulting in patient discomfort and lack of intuitiveness for clinicians. Robotic devices together with autonomous control represent a viable solution to reduce the workload of endoscopists and the training time while improving the overall procedure outcome. Prior works on autonomous endoscope control use heuristic policies that limit their generalisation to the unstructured and highly deformable colon environment and require frequent human intervention. This work proposes an image-based control of the endoscope using Deep Reinforcement Learning, called Deep Visuomotor Control (DVC), to exhibit adaptive behaviour in convoluted sections of the colon tract. DVC learns a mapping between the endoscopic images and the control signal of the endoscope. A first user study of 20 expert gastrointestinal endoscopists was carried out to compare their navigation performance with DVC policies using a realistic virtual simulator. The results indicate that DVC shows equivalent performance on several assessment parameters, being more safer. Moreover, a second user study with 20 novice participants was performed to demonstrate easier human supervision compared to a state-of-the-art heuristic control policy. Seamless supervision of colonoscopy procedures would enable interventionists to focus on the medical decision rather than on the control problem of the endoscope.