# Contact-Aided Navigation of Flexible Robotic Endoscope Using Deep Reinforcement Learning in Dynamic Stomach

arXiv: https://arxiv.org/abs/2509.00319 · HTML: https://mlsanity.com/paper/2509.00319/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 30 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned](https://mlsanity.com/paper/2102.02915/) (2021)
- [Elastica: A compliant mechanics environment for soft robotic control](https://mlsanity.com/paper/2009.08422/) (2020)
- [Reinforcement Learning Controllers for Soft Robots using Learned Environments](https://mlsanity.com/paper/2410.18519/) (2024)
- [Robust Navigation of a Soft Growing Robot by Exploiting Contact with the Environment](https://mlsanity.com/paper/1908.08645/) (2019)
- [Domain Randomization for Robust, Affordable and Effective Closed-loop Control of Soft Robots](https://mlsanity.com/paper/2303.04136/) (2023)
- [Towards Contact-Aided Motion Planning for Tendon-Driven Continuum Robots](https://mlsanity.com/paper/2402.14175/) (2024)

## Abstract (arXiv)

Navigating a flexible robotic endoscope (FRE) through the gastrointestinal tract is critical for surgical diagnosis and treatment. However, navigation in the dynamic stomach is particularly challenging because the FRE must learn to effectively use contact with the deformable stomach walls to reach target locations. To address this, we introduce a deep reinforcement learning (DRL) based Contact-Aided Navigation (CAN) strategy for FREs, leveraging contact force feedback to enhance motion stability and navigation precision. The training environment is established using a physics-based finite element method (FEM) simulation of a deformable stomach. Trained with the Proximal Policy Optimization (PPO) algorithm, our approach achieves high navigation success rates (within 3 mm error between the FRE's end-effector and target) and significantly outperforms baseline policies. In both static and dynamic stomach environments, the CAN agent achieved a 100% success rate with 1.6 mm average error, and it maintained an 85% success rate in challenging unseen scenarios with stronger external disturbances. These results validate that the DRL-based CAN strategy substantially enhances FRE navigation performance over prior methods.