Language-Conditioned Behavioral Cloning for Autonomous Endoscope Guidance

The scan-then-revisit workflow of upper gastrointestinal endoscopy requires bringing a previously observed suspicious region back into view for closer inspection. Acquiring such targets requires translating anatomical and appearance descriptions into camera motions from partial observations of a confined, deformable environment. We present EndoVLA-OOV, a language-conditioned vision-language-action policy for out-of-view target acquisition in robotic endoscopy. The policy adapts a pretrained backbone to continuous two-degree-of-freedom distal bending commands and combines initial, intermediate, and current images to guide search. To support policy learning, we provide an open-source VLA dataset containing 1,050 episodes across six tasks and approximately 262k observation-action pairs. Physical evaluation spans wet-lens and low-light conditions, two unseen stomach phantoms, and cross-embodiment transfer to an unseen ex-vivo environment. In the cross-embodiment evaluation, the full-data policy achieves 93.3% target acquisition success (28/30). In a controlled training-data comparison, expanding the corpus from 312 to 1,050 episodes increases acquisition success from 40.0% to 90.0% under the same model, training, and test configuration. Instruction and observation interventions further support the use of language and temporal context in the evaluated acquisition tasks. These results support language-guided endoscopic revisitation across the evaluated environments and robot embodiments and demonstrate the benefit of expanded task-specific demonstrations.

Autonomous laparoscopic camera control must maintain a stable and safe surgical view under rapid tool-tissue interactions while remaining interpretable to surgeons. We present a strategy-grounded framework that couples high-level vision-language inference with low-level closed-loop control. Offline, raw surgical videos are parsed into camera-relevant temporal events (e.g., interaction, working-distance deviation, and view-quality degradation) and structured as attributed event graphs. Mining these graphs yields a compact set of reusable camera-handling strategy primitives, which provide structured supervision for learning. Online, a fine-tuned Vision-Language Model (VLM) processes the live laparoscopic view to predict the dominant strategy and discrete image-based motion commands, executed by an IBVS-RCM controller under strict safety constraints; optional speech input enables intuitive human-in-the-loop conditioning. On a surgeon-annotated dataset, event parsing achieves reliable temporal localization (F1-score 0.86), and the mined strategies show strong semantic alignment with expert interpretation (cluster purity 0.81). Extensive ex vivo experiments on silicone phantoms and porcine tissues demonstrate that the proposed system outperforms junior surgeons in standardized camera-handling evaluations, reducing field-of-view centering error by 35.26% and image shaking by 62.33%, while preserving smooth motion and stable working-distance regulation.

Flexible endoscopes exploit structural compliance to traverse narrow lumens, pipes, and cavities, where navigation involves distributed environmental contact. This compliance makes tip motion depend on the evolving support configuration. We present external bird's-eye-view contact-aided navigation (EBV-CAN), a vision-language policy that combines one initial global static bird's-eye image with evolving endoscopic observations and motor state. Three supervised semantic queries locate the tip, next waypoint, and instructed target on the cached global image; their residual representations predict bending and insertion commands. We introduce the Vision-Language Contact-Aided (VLCA) dataset, linking 517 demonstrations and 75k action samples to 958k spatial entries and 93k remaining-route records. On 50 held-out episodes, tip-coordinate and waypoint localization reach 97.5% and 94.8%. Swapping the target instruction under identical observations changes target attention and motor output across 17 dual-target episodes. Across 1,920 physical trials of eight policies trained under a shared protocol, EBV-CAN achieves 183/240 successes (76.25%) under automatic blue-area scoring. Gated recurrent unit behavior cloning (GRU-BC) achieves the lowest matched-task offline mean absolute error (MAE), 0.056, and 1/240 physical successes, establishing distinct rankings for action imitation and task completion. Successful multi-guide executions and representative failures connect spatial progress, steering transitions, and visibility to physical navigation.