Perception

Camera-to-Robot Calibration

Camera-to-robot calibration is the estimation of the transform between a camera frame and a robot's kinematic frames, enabling perception outputs to be expressed in coordinates the robot can act on. Classical approaches solve the hand-eye equation AX = XB from paired robot and marker motions, while newer learning-based methods such as DREAM regress robot joint keypoints directly from RGB images, enabling markerless, self-supervised calibration. Both eye-in-hand and eye-to-hand configurations are common.

Why it matters for physical AI

Every vision-guided action passes through this transform, so its accuracy bounds manipulation precision; markerless approaches also make large-scale, heterogeneous data collection practical by removing manual calibration from the loop.