脚式移動のための重心運動プランナの学習
Learning a Centroidal Motion Planner for Legged Locomotion
全身最適化器の出力を教師として重心運動を予測するニューラルネットを学習し、実機四足ロボットで高頻度な歩行・跳躍動作生成を実現した。
著者: Julian Viereck, Ludovic Righetti
分類: cs.RO
原文アブストラクト
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.
