EndoVLA-OOV: Language-Conditioned Out-of-View Target Acquisition in Robotic Endoscopy
Overview
EndoVLA-OOV is a vision-language-action policy for robotic endoscopy that translates anatomical descriptions into camera motions. It achieves 93.3% success in cross-embodiment transfer with 1,050 episodes and 262k observation-action pairs. Success rates improve with more training data, from 40.0% to 90.0%.
Generated from attributed reports · Updated 34 minutes ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · episodes: 1050 other · Basis not reported
“ioned 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 pair”
Exact source · revision 1Source owner not reported
Reported quantity · observation-action pairs: 262000 other · Basis not reported
“ioned 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 pair”
Exact source · revision 1Source owner not reported
Evidence dependency changes (2)
- 2026-10-30T22:19:43.960Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T22:19:28.964Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalEndoVLA-OOV: Language-Conditioned Out-of-View Target Acquisition in Robotic Endoscopy
This research presents EndoVLA-OOV, a vision-language-action policy for robotic endoscopy that translates anatomical descriptions into camera motions. The policy uses a pretrained backbone adapted to continuous distal bending commands and combines images to guide search. An open-source dataset with 1,050 episodes and 262k observation-action pairs supports policy learning. Physical evaluations show 93.3% success in cross-embodiment transfer, with success rates improving from 40.0% to 90.0% with more training data.
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