BiliVLA: Scene-Aware Vision-Language-Action Model with Reinforcement Learning for Autonomous Biliary Endoscopic Navigation

Jinsong Lin; Chi Kit Ng; Zhiyong Xiong; Zikang Pan; Yihan Hu; Tabassum Tamima; Ziyi Hao; Eddie Cheung; Jiewen Lai; Huxin Gao; Hongliang Ren

arXiv · 2606.23531

BiliVLA: Scene-Aware Vision-Language-Action Model with Reinforcement Learning for Autonomous Biliary Endoscopic Navigation

Jinsong Lin·Chi Kit Ng·Zhiyong Xiong·Zikang Pan·Yihan Hu·Tabassum Tamima·Ziyi Hao·Eddie Cheung·Jiewen Lai·Huxin Gao·Hongliang Ren

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Abstract

Endoscopic retrograde cholangiopancreatography (ERCP) demands precise endoscopic navigation and stable biliary cannulation within a narrow monocular field characterized by specular reflections, partial occlusions, and frequent tissue contact. Although recent robotic systems and vision-based assistance techniques improve operator ergonomics and provide perceptual cues, their performance degrades under pronounced anatomical variability and safety-critical visual artifacts, which hinders reliable autonomy in cannulation-grade procedures. Here, we present BiliVLA, a scene-aware Vision-Language-Action (VLA) framework that formulates biliary endoscopic navigation as an instruction-conditioned visuomotor learning problem. Given an endoscopic observation and a stage-specific language instruction, BiliVLA jointly predicts the target category, a grounded bounding box, and a discrete three-degree-of-freedom (3-DoF) motor command for a continuum endoscope. The proposed framework incorporates scene-aware supervision to improve semantic target consistency and safety-aware recovery supervision to induce conservative retreat behaviors under luminal wall contact. A key component of BiliVLA is a two-stage training paradigm that combines grounding-enhanced supervised fine-tuning (SFT) with Group Relative Policy Optimization (GRPO), thereby improving action reliability and decision consistency during closed-loop navigation. Across three ERCP subtasks, BiliVLA achieves the best overall performance in physical phantom experiments, with a total mIoU of 0.9625, an overall action precision of 91.96\%, and an overall success rate (SR) of 84.85\%. These results indicate that integrating semantic grounding, scene-aware learning, and reward-guided optimization strengthens perception--action alignment and enables more robust autonomous biliary endoscopic navigation.

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Jinsong Lin, Chi Kit Ng, Zhiyong Xiong, Zikang Pan, Yihan Hu, Tabassum Tamima, Ziyi Hao, Eddie Cheung, Jiewen Lai, Huxin Gao, Hongliang Ren. 2026-06-22. BiliVLA: Scene-Aware Vision-Language-Action Model with Reinforcement Learning for Autonomous Biliary Endoscopic Navigation. https://arxiv.org/abs/2606.23531

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