论文标题
学习一个用于腿部运动的质心运动计划者
Learning a Centroidal Motion Planner for Legged Locomotion
论文作者
论文摘要
全身优化器已成功地自动计算复杂的动态运动行为。但是，它们通常仅限于离线计划，因为它们在计算上太昂贵了，无法以高频进行重新启动。然后，通常使用更简单的型号用于在线重新启动。在本文中，我们提出了一种实时生成全身运动的方法，以实现运动任务。我们的方法包括学习一个质心神经网络，该网络预测机器人的当前状态和所需的接触计划，可以预测所需的质心运动。使用现有的全身运动优化器对网络进行训练。我们的方法使很少有训练样品动态运动可以学习，这些动态动态运动可以在高频的完整全身控制框架中使用，这通常是典型的全身优化器无法实现的。我们演示了我们在真正的四倍机器人上生成一系列步行和跳跃运动的方法。
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.
