Robot policies can achieve view robustness without camera calibration by learning to predict both action in camera space and camera-to-robot geometry, making deployment more practical when camera positions vary.
This paper introduces CamVLA, a robot vision-language-action model that learns to figure out camera positioning automatically instead of requiring explicit calibration. By predicting both camera-relative actions and the geometric relationship between camera and robot, the model works with any camera setup without needing depth data or prior calibration.