基于人类干预强化学习的机器人消化内镜安全导航
Safe Navigation for Robotic Digestive Endoscopy via Human Intervention-based Reinforcement Learning
AI总结：
针对机器人消化内镜自动导航缺乏人类干预易致碰撞的问题，提出基于人类干预的近端策略优化框架HI-PPO，结合EEM、RPA和BCS机制提升复杂环境下的安全导航效率，实验证明其轨迹误差和安全得分达到人类专家水平。
摘要：
随着自动化机器人消化内镜（RDE）应用的日益增加，在非结构化且狭窄的消化道中确保安全高效的导航已成为一项关键挑战。现有的自动化强化学习导航算法由于缺乏必要的人类干预，常导致潜在的危险碰撞，这极大限制了RDE在实际临床实践中的安全性和有效性。为解决这一局限性，我们提出了一种基于人类干预（HI）的近端策略优化（PPO）框架，称为HI-PPO，该框架结合了专家知识以增强RDE的安全性。具体而言，HI-PPO结合了增强探索机制（EEM）、奖励惩罚调整（RPA）和行为克隆相似性（BCS），以解决PPO在复杂胃肠道环境中安全导航的探索效率低下问题。在仿真平台上进行了对比实验，结果表明，HI-PPO的平均ATE（平均轨迹误差）为8.02 mm，安全得分为0.862，展现出与人类专家相当的性能。本文发表后代码将公开。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
