[2605.08269] Anatomical Landmark-Guided Deep Reinforcement Learning for Autonomous Gastric Navigation
[Submitted on 8 May 2026]
Title:Anatomical Landmark-Guided Deep Reinforcement Learning for Autonomous Gastric Navigation
View a PDF of the paper titled Anatomical Landmark-Guided Deep Reinforcement Learning for Autonomous Gastric Navigation, by Haoxuan Wu and 5 other authors
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Abstract:Wireless capsule endoscopy (WCE) enables painless visualization of the gastrointestinal tract, but its diagnostic potential is limited by incomplete mucosal coverage and poor transferability of existing navigation methods across patient anatomies. We propose a transferable, anatomical landmarkguided deep reinforcement learning (AL-DRL) framework for autonomous gastric navigation. Leveraging a lightweight edgecontour-depth fusion module, our policy operates on stable, lowdimensional landmark coordinates rather than high-dimensional video streams, effectively bridging the sim-to-real gap. In simulations across eight patient-derived models, the method achieves over 97% coverage within 50 seconds, significantly outperforming vanilla PPO, SAC, and DQN agents. A two-stage sim-to-real pipeline with an adaptive dynamic programming controller actively mitigates physical disturbances. Ex-vivo experiments demonstrate a mean coverage of 87% and a 53% reduction in procedure time compared with expert manual control.
Subjects:
Robotics (cs.RO); Systems and Control (eess.SY)
Cite as: arXiv:2605.08269 [cs.RO]
(or arXiv:2605.08269v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.08269
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sishen Yuan [view email] [v1] Fri, 8 May 2026 03:11:21 UTC (12,087 KB)
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