# Constrained Reinforcement Learning and Formal Verification for Safe Colonoscopy Navigation

arXiv: https://arxiv.org/abs/2303.03207 · HTML: https://mlsanity.com/paper/2303.03207/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2023.

## Built on by (most cited first, 9 of 9)

- [Formally Explaining Neural Networks within Reactive Systems](https://mlsanity.com/paper/2308.00143/) (2023)
- [Enumerating Safe Regions in Deep Neural Networks with Provable Probabilistic Guarantees](https://mlsanity.com/paper/2308.09842/) (2023)
- [Analyzing Adversarial Inputs in Deep Reinforcement Learning](https://mlsanity.com/paper/2402.05284/) (2024)
- [Advancing Neural Network Verification through Hierarchical Safety Abstract Interpretation](https://mlsanity.com/paper/2505.05235/) (2025)
- [Inductive Learning of Robot Task Knowledge from Raw Data and Online Expert Feedback](https://mlsanity.com/paper/2501.07507/) (2025)
- [Probabilistically Tightened Linear Relaxation-based Perturbation Analysis for Neural Network Verification](https://mlsanity.com/paper/2507.05405/) (2025)
- [Scaling #DNN-Verification Tools with Efficient Bound Propagation and Parallel Computing](https://mlsanity.com/paper/2312.05890/) (2023)
- [Safe Navigation for Robotic Digestive Endoscopy via Human Intervention-based Reinforcement Learning](https://mlsanity.com/paper/2409.15688/) (2024)
- [Curriculum-Based Reinforcement Learning for Autonomous UAV Navigation in Unknown Curved Tubular Conduit](https://mlsanity.com/paper/2512.10934/) (2025)

## Abstract (arXiv)

The field of robotic Flexible Endoscopes (FEs) has progressed significantly, offering a promising solution to reduce patient discomfort. However, the limited autonomy of most robotic FEs results in non-intuitive and challenging manoeuvres, constraining their application in clinical settings. While previous studies have employed lumen tracking for autonomous navigation, they fail to adapt to the presence of obstructions and sharp turns when the endoscope faces the colon wall. In this work, we propose a Deep Reinforcement Learning (DRL)-based navigation strategy that eliminates the need for lumen tracking. However, the use of DRL methods poses safety risks as they do not account for potential hazards associated with the actions taken. To ensure safety, we exploit a Constrained Reinforcement Learning (CRL) method to restrict the policy in a predefined safety regime. Moreover, we present a model selection strategy that utilises Formal Verification (FV) to choose a policy that is entirely safe before deployment. We validate our approach in a virtual colonoscopy environment and report that out of the 300 trained policies, we could identify three policies that are entirely safe. Our work demonstrates that CRL, combined with model selection through FV, can improve the robustness and safety of robotic behaviour in surgical applications.