用强化学习打造血管机器人手术仿真平台，提升操作自动化水平。
Sim4EndoR: A Reinforcement Learning Centered Simulation Platform for Task Automation of Endovascular Robotics
- 基于强化学习构建血管介入机器人仿真环境，支持自主任务训练。
- 通过血管几何特征设计奖励函数，引导智能体学习符合解剖约束的操作策略。
- 实现高效仿真到现实的迁移，适合医疗机器人研发与临床应用研究者。
机器人辅助经皮冠状动脉介入治疗（PCI）在提升心血管手术精度与安全性方面具有巨大潜力。然而，现有系统仍严重依赖人工操作，导致结果变异且存在人为失误风险。为此，本文首次提出Sim4EndoR——一个以强化学习为核心的仿真平台，旨在增强PCI任务级自动化。该平台提供全面、无风险的环境，用于开发、评估和优化潜在自主系统，显著提升数据收集效率，并减少对昂贵硬件试验的依赖。其核心创新在于奖励函数设计：结合血管解剖约束，利用血管几何特征引导学习过程。通过将先进物理仿真与神经网络驱动的策略学习无缝集成，Sim4EndoR实现高效的仿真到现实迁移，为临床实践中更安全、更一致的机器人干预铺平道路，最终改善患者预后。
原文摘要 · Abstract (English)
Robotic-assisted percutaneous coronary intervention (PCI) holds considerable promise for elevating precision and safety in cardiovascular procedures. Nevertheless, current systems heavily depend on human operators, resulting in variability and the potential for human error. To tackle these challenges, Sim4EndoR, an innovative reinforcement learning (RL) based simulation environment, is first introduced to bolster task-level autonomy in PCI. This platform offers a comprehensive and risk-free environment for the development, evaluation, and refinement of potential autonomous systems, enhancing data collection efficiency and minimizing the need for costly hardware trials. A notable aspect of the groundbreaking Sim4EndoR is its reward function, which takes into account the anatomical constraints of the vascular environment, utilizing the geometric characteristics of vessels to steer the learning process. By seamlessly integrating advanced physical simulations with neural network-driven policy learning, Sim4EndoR fosters efficient sim-to-real translation, paving the way for safer, more consistent robotic interventions in clinical practice, ultimately improving patient outcomes.
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