  Kinodynamic Motion Planning (KMP) is to find a robot motion subject to
concurrent kinematics and dynamics constraints. To date, quite a few methods
solve KMP problems and those that exist struggle to find near-optimal solutions
and exhibit high computational complexity as the planning space dimensionality
increases. To address these challenges, we present a scalable, imitation
learning-based, Model-Predictive Motion Planning Networks framework that
quickly finds near-optimal path solutions with worst-case theoretical
guarantees under kinodynamic constraints for practical underactuated systems.
Our framework introduces two algorithms built on a neural generator,
discriminator, and a parallelizable Model Predictive Controller (MPC). The
generator outputs various informed states towards the given target, and the
discriminator selects the best possible subset from them for the extension. The
MPC locally connects the selected informed states while satisfying the given
constraints leading to feasible, near-optimal solutions. We evaluate our
algorithms on a range of cluttered, kinodynamically constrained, and
underactuated planning problems with results indicating significant
improvements in computation times, path qualities, and success rates over
existing methods.
