This paper presents a method of generating natural-looking motion primitives for humanoid robots. An optimization-based approach is used to generate these primitives, but the objective function is tailored to each one and complexity is reduced by identifying relevant degrees of freedom. Several examples are shown in simulation: for an arm movement to reach an object, it is better to minimize the acceleration of key parts of the robot over its entire trajectory; for a single step on flat ground, it is better to minimize the torque and instantaneous angular momentum at every posture. The primitives are precomputed off-line, but might be used by on-line planner either to provide a fixed set of maneuvers or to bias a probabilistic, sample-based search for motions. © 2006 IEEE.