EndoVLA-OOV: Language-Conditioned Out-of-View Target Acquisition in Robotic Endoscopy
We present EndoVLA-OOV, a language-conditioned vision-language-action policy for out-of-view target acquisition in robotic endoscopy.
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Key points
- 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.
- To support policy learning, we provide an open-source VLA dataset containing 1,050 episodes across six tasks and approximately 262k observation-action pairs.
- These results support language-guided endoscopic revisitation across the evaluated environments and robot embodiments and demonstrate the benefit of expanded task-specific demonstrations.
Sources (1)
- [1]EndoVLA-OOV: Language-Conditioned Out-of-View Target Acquisition in Robotic EndoscopyarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 03:28 PM
We present EndoVLA-OOV, a language-conditioned vision-language-action policy for out-of-view target acquisition in robotic endoscopy.
The scan-then-revisit workflow of upper gastrointestinal endoscopy requires bringing a previously observed suspicious region back into view for closer inspection.
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