Abstract: This course introduces a motion-planning framework for robotic systems that combines optimization-based control with differential flatness. Guidance generates optimal trajectories by parameterizing the flat output as a curve, while control ensures accurate tracking of the planned motion. Differential flatness enables exact closed-loop linearization but introduces complex constraints. These constraints can be efficiently learned using neural networks as unions of polytopes, allowing them to be enforced through Mixed-Integer Programming (MIP).