ARCGym: Benchmarking Deep Reinforcement Learning in Autonomous Robotic Colonoscopy

2026-09-19

AI Summary

为解决柔性结肠镜导航问题，ARCGym提供了一个基于图像的强化学习环境和基准，支持多种机器人类型，并引入新的奖励机制以改善遮挡下的学习效果。

Abstract

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.

Problem

• Autonomous Robotic Colonoscopy
• Flexible Endoscopes
• Reinforcement Learning
• Colon Anatomies
• Navigation

Innovation

• Autonomous Robotic Colonoscopy
• Flexible Endoscopes
• Reinforcement Learning Environment
• Lumen-Visibility Score
• Open-Source Benchmark