Safe Navigation for Robotic Digestive Endoscopy via Human Intervention-based Reinforcement Learning
This addresses safety challenges in automated robotic digestive endoscopy for clinical practice, though it appears incremental by enhancing existing methods with human intervention.
The paper tackled safe navigation for robotic digestive endoscopy by proposing a human intervention-based reinforcement learning framework, which achieved a mean ATE of 8.02 mm and a Security Score of 0.862, comparable to human experts in simulations.
With the increasing application of automated robotic digestive endoscopy (RDE), ensuring safe and efficient navigation in the unstructured and narrow digestive tract has become a critical challenge. Existing automated reinforcement learning navigation algorithms often result in potentially risky collisions due to the absence of essential human intervention, which significantly limits the safety and effectiveness of RDE in actual clinical practice. To address this limitation, we proposed a Human Intervention (HI)-based Proximal Policy Optimization (PPO) framework, dubbed HI-PPO, which incorporates expert knowledge to enhance RDE's safety. Specifically, HI-PPO combines Enhanced Exploration Mechanism (EEM), Reward-Penalty Adjustment (RPA), and Behavior Cloning Similarity (BCS) to address PPO's exploration inefficiencies for safe navigation in complex gastrointestinal environments. Comparative experiments were conducted on a simulation platform, and the results showed that HI-PPO achieved a mean ATE (Average Trajectory Error) of \(8.02\ \text{mm}\) and a Security Score of \(0.862\), demonstrating performance comparable to human experts. The code will be publicly available once this paper is published.