A geometric trajectory controller combines dual-quaternion representations with Gaussian-process learning to compensate for unknown disturbances in robotic motion. The framework treats orientation and position together and analyses the tracking error under bounded learning uncertainty. Simulation examples examine coupled rotational and translational disturbances. The results concern a velocity-level controller and do not establish safety for every real-world operating condition. This is a preprint.