  Whole-body optimizers have been successful at automatically computing complex
dynamic locomotion behaviors. However they are often limited to offline
planning as they are computationally too expensive to replan with a high
frequency. Simpler models are then typically used for online replanning. In
this paper we present a method to generate whole body movements in real-time
for locomotion tasks. Our approach consists in learning a centroidal neural
network that predicts the desired centroidal motion given the current state of
the robot and a desired contact plan. The network is trained using an existing
whole body motion optimizer. Our approach enables to learn with few training
samples dynamic motions that can be used in a complete whole-body control
framework at high frequency, which is usually not attainable with typical
full-body optimizers. We demonstrate our method to generate a rich set of
walking and jumping motions on a real quadruped robot.
