SOURCE-LINKED INTELLIGENCE

ARCGym: Benchmarking Deep Reinforcement Learning in Autonomous Robotic Colonoscopy

arXiv · AI, language, vision and robotics · article · Sep 19, 2026 · UTC

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 c

Read original source ↗ Open in workspace

recordType

paper

region

Global