Magnetically levitated planar motor is a new-generation motion device in modern precision industry while its advanced motion controller design is still of main concern. In this paper, a learning adaptive robust control (LARC) motion controller is proposed for a developed magnetically levitated planar motor to achieve good tracking performance. The planar motor consists of a Halbach permanent magnetic array as the stator, and a levitated platen containing four groups of three-phase windings as the mover. Based on the Lorentz force law, the mover placed in the magnetic field is subject to vertical force for levitation and horizonal force for planar motion through dynamics decoupling and current allocation. A LARC control scheme including iterative learning term and adaptive model compensation term in a parallel structure, is then proposed for the magnetically levitated planar motor to meet the challenge of high-performance tracking even under parametric uncertainty. Comparative experiments are carried out on the planar motor to track point-to-point and planar circular motions, respectively. The results consistently validate that the proposed LARC control strategy achieves good tracking performance even with payload. The proposed scheme actually provide a practically effective technique and a guidance for motion control of magnetically levitated planar motors in industrial applications.