Solves the hand-eye problem: given a series of gripper-to-base poses read from the robot and the matching target-to-camera poses seen by the camera, it recovers the rigid transform between camera and gripper that stays constant as the arm moves. This is what lets a robot act on what its camera sees. The poses must be genuinely synchronized — a pairing that is off by one sample produces a confident and entirely wrong transform, and nothing in the data reveals it.

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