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Still, its diagnostic potential is limited by incomplete mucosal coverage and poor transferability of existing navigation methods across patient anatomies. We propose a transferable, anatomical landmark\u2010guided deep reinforcement learning framework for robust autonomous gastric navigation. Leveraging a lightweight edge\u2010contour\u2010depth fusion module, our policy operates on stable, low\u2010dimensional landmark coordinates rather than high\u2010dimensional video streams. This design effectively bridges the sim\u2010to\u2010real visual gap and ensures robustness across diverse anatomies, enabling low\u2010cost deployment by reducing computational overhead. In simulations across eight patient\u2010derived models, the method achieves &gt;97% coverage within 50 s, significantly outperforming vanilla Proximal Policy Optimization, Soft Actor\u2010Critic, and Deep Q\u2010Network agents by enhancing coverage and minimizing variance. To ensure deployment reliability, a two\u2010stage sim\u2010to\u2010real pipeline supported by an adaptive dynamic programming controller actively mitigates physical disturbances, including actuator latency and peristalsis. Ex vivo experiments across five independent scans demonstrate high coverage stability, achieving a mean coverage of 87% and a 53% reduction in procedure time compared with expert manual control. This study establishes a scalable paradigm for autonomous, high\u2011coverage endoscopic navigation, advancing the clinical deployment of intelligent WCE systems for GI diagnostics.<\/jats:p>","DOI":"10.1002\/advs.202600008","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T19:32:21Z","timestamp":1781292741000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Transferable Deep Reinforcement Learning With Edge\u2010Contour\u2010Depth Fusion for Autonomous Wireless Capsule Endoscopy Navigation"],"prefix":"10.1002","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4555-1020","authenticated-orcid":false,"given":"Haoxuan","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering The Chinese University of Hong Kong  Hong Kong 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