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arXiv · 2011.02818

Learning a Centroidal Motion Planner for Legged Locomotion

Julian Viereck·Ludovic Righetti
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Abstract

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.

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Julian Viereck, Ludovic Righetti. 2020-11-05. Learning a Centroidal Motion Planner for Legged Locomotion. https://arxiv.org/abs/2011.02818

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