Learning highly dynamic behaviors for robots has been a longstanding challenge. Traditional approaches have demonstrated robust locomotion, but the exhibited behaviors lack diversity and agility. They employ approximate models, which lead to compromises in performance. Data-driven approaches have been shown to reproduce agile behaviors of animals, but typically have not been able to learn highly dynamic behaviors. In this paper, we propose a learning-based approach to enable robots to learn highly dynamic behaviors from animal motion data. The learned controller is deployed on a quadrupedal robot and the results show that the controller is able to reproduce highly dynamic behaviors including sprinting, jumping and sharp turning. Various behaviors can be activated through human interaction using a stick with markers attached to it. Based on the motion pattern of the stick, the robot exhibits walking, running, sitting and jumping, much like the way humans interact with a pet.

翻译：学习机器人的高度动态行为一直是一个长期挑战。传统方法已展示出稳健的运动能力，但所表现的行为缺乏多样性和敏捷性。这些方法采用近似模型，导致性能上有所妥协。数据驱动方法已被证明能复现动物的敏捷行为，但通常未能学会高度动态的行为。本文提出一种基于学习的方法，使机器人能够从动物运动数据中学习高度动态的行为。该学习控制器部署于四足机器人上，结果显示，控制器能够复现包括冲刺、跳跃和急转弯在内的高度动态行为。通过使用带有标记的棍棒进行人机交互，可激活多种行为。根据棍棒的运动模式，机器人表现出行走、奔跑、坐下和跳跃等行为，其方式类似于人类与宠物互动。