学习四足机器人的高动态行为
Learning Highly Dynamic Behaviors for Quadrupedal Robots
AI总结：
本文提出一种基于动物运动数据的学习方法，使四足机器人能够复现冲刺、跳跃等高动态行为，并可通过带标记的棍子进行人机交互以激活行走、奔跑、坐下和跳跃等多种行为。
摘要：
学习机器人的高动态行为一直是一个长期挑战。传统方法已展示出鲁棒的运动能力，但所表现出的行为缺乏多样性和敏捷性。它们采用近似模型，这导致性能上的妥协。数据驱动方法已被证明能够复现动物的敏捷行为，但通常无法学习高动态行为。在本文中，我们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动态行为。学习到的控制器被部署在一台四足机器人上，结果表明该控制器能够复现包括冲刺、跳跃和急转弯在内的高动态行为。通过使用一根附有标记的棍子进行人类交互，可以激活各种行为。根据棍子的运动模式，机器人表现出行走、奔跑、坐下和跳跃，非常类似于人类与宠物互动的方式。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
