ARCGym：自主机器人结肠镜检查中的深度强化学习基准
ARCGym: Benchmarking Deep Reinforcement Learning in Autonomous Robotic Colonoscopy
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中文总结 AI 辅助
ARCGym是用于自主机器人结肠镜检查的强化学习环境与基准，支持多种柔性内窥镜，实验表明自主导航仍具挑战性，近端插入致动是开放问题。
中文摘要 AI 辅助
用于基于学习的自主结肠镜导航的仿真主要集中于全驱动胶囊机器人，未能捕捉长而柔韧的临床结肠镜的接触丰富导航。我们提出了自主机器人结肠镜检查健身房（ARCGym），一个开源的强化学习环境和基准，用于在临床衍生的可变形结肠解剖结构中进行基于图像的导航。ARCGym支持多种类型的结肠镜机器人，涵盖胶囊机器人和柔性内窥镜，本工作聚焦于柔性内窥镜，包括磁驱动尖端致动和临床使用的近端平移致动。本工作包括五个CT重建的结肠，代表典型临床场景，一组临床有意义的导航子任务，以及统一的成功指标。我们引入了一种结合基于深度的管腔对齐和管腔可见性评分的奖励，以改善遮挡下的学习。跨任务、机器人和解剖结构的实验表明，自主导航对于磁驱动和近端插入柔性机器人仍然具有挑战性，近端插入致动仍是一个开放问题。
英文摘要
Simulations for learning-based autonomous colonoscopic navigation focus mainly on fully actuated capsule robots, failing to capture the contact-rich navigation of long and flexible clinical colonoscopes. We present the Autonomous Robotic Colonoscopy Gym (ARCGym), an open-source reinforcement learning environment and benchmark for image-based navigation in clinically derived deformable colon anatomies. ARCGym supports multiple types of colonoscope robots, spanning capsule robots and flexible endoscopes, with this work focusing on flexible endoscopes including magnetic-driven tip actuation and clinically used proximally translational actuation. This work includes five CT-reconstructed colons representing typical clinical scenarios, a set of clinically meaningful navigation subtasks, and unified success metrics. We introduce a reward combining depth-based lumen alignment with a lumen-visibility score to improve learning under occlusions. Experiments across tasks, robots, and anatomies show that autonomous navigation remains challenging for both magnetic-driven and proximal-insertion flexible robots, with proximal-insertion actuation remaining an open problem.
发表机构
- University of Copenhagen（哥本哈根大学）
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