Learning a Centroidal Motion Planner for Legged Locomotion

2021

Conference Paper

mg

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.

@conference{viereck2021learning,
  title = {Learning a Centroidal Motion Planner for Legged Locomotion},
  author = {Viereck, Julian and Righetti, Ludovic},
  booktitle = {2021 IEEE International Conference on Robotics and Automation (ICRA)},
  pages = {4905--4911 },
  publisher = {IEEE},
  month = jun,
  year = {2021},
  doi = {10.1109/ICRA48506.2021.9562022},
  month_numeric = {6}
}