<p>A data-driven predictive motion planner for mobile robots, referred to as LPM-Net, has been proposed in this paper. Conventional predictive motion planners are computationally expensive, often resulting in insufficient throughput on mobile robot hardware. LPM-Net is an imitation learning-assisted local predictive non-holonomic motion planner that is capable of learning from conventional motion planners regarded as paradigm models and replicating their behavior while satisfying the same kinodynamic constraints. In addition, LPM-Net is compatible with GPU and TPU hardware, allowing for faster and more efficient processing. LPM-Net uses convolutional and recurrent long short-term memory deep neural networks to predict steering commands. This has improved computational efficiency which allows autonomous vehicles to be equipped with more cost-effective computers. In the present study, LPM-Net was tuned to mimic the behavior of a model predictive controller paradigm model. Measurements in this study demonstrate that the proposed mimic planner, LPM-Net, consumes approximately half the processing power of the conventional predictive planner, albeit with a slight increase in hesitation when reaching goals.</p>