This talk presents a hierarchical control framework that enables motion planning for fast and efficient 3D dynamic walkers in a similar manner to what is possible for bipeds using Center of Pressure (CoP) equilibrium constraints. Given any low-level controller that produces a set of “asymptotically stable gait primitives,” a dynamic walker can be controlled as a discrete-time switched system that sequentially composes gait primitives from step to step. We derive switching rules by which the robot can follow a walking path that is a sequence of these gaits, so dynamically stable planning reduces to a simple tree search. Passivity-based controlled reduction is used to construct an example set of gait primitives for a 3D compass-gait biped, where each primitive corresponds to walking along a nominal arc of constant curvature for a fixed number of steps. We conclude with ongoing efforts at translating these ideas into systematic methods for designing and prescribing control strategies for personalized robot-assisted locomotor therapy.