基于人类参考的全身人形机器人运动
Whole-body Humanoid Robot Locomotion with Human Reference
AI总结：
本文提出了一种基于对抗运动先验的模仿学习框架，应用于全尺寸人形机器人Adam，使其在复杂运动任务中达到与人类相当的性能，首次实现了人类运动数据在全尺寸人形机器人上的模仿学习。
摘要：
近年来，得益于强化学习（RL）的应用，人形机器人在执行挑战性任务的能力上取得了显著进展，然而，人形机器人固有的复杂性，包括设计复杂奖励函数和训练整个复杂系统的困难，仍然构成了一个显著的挑战。为了克服这些挑战，经过多次迭代和深入研究，我们精心开发了一款全尺寸人形机器人“Adam”，其创新的结构设计极大地提高了模仿学习过程的效率和有效性。此外，我们开发了一种基于对抗运动先验的新型模仿学习框架，该框架不仅适用于Adam，也适用于一般的人形机器人。使用该框架，Adam在运动任务中能够展现出前所未有的人类相似特征。我们的实验结果表明，所提出的框架使Adam能够在复杂运动任务中达到与人类相当的性能，标志着首次将人类运动数据用于全尺寸人形机器人的模仿学习。
英文摘要：
Recently, humanoid robots have made significant advances in their ability to perform challenging tasks due to the deployment of Reinforcement Learning (RL), however, the inherent complexity of humanoid robots, including the difficulty of designing complicated reward functions and training entire sophisticated systems, still poses a notable challenge. To conquer these challenges, after many iterations and in-depth investigations, we have meticulously developed a full-size humanoid robot, "Adam", whose innovative structural design greatly improves the efficiency and effectiveness of the imitation learning process. In addition, we have developed a novel imitation learning framework based on an adversarial motion prior, which applies not only to Adam but also to humanoid robots in general. Using the framework, Adam can exhibit unprecedented human-like characteristics in locomotion tasks. Our experimental results demonstrate that the proposed framework enables Adam to achieve human-comparable performance in complex locomotion tasks, marking the first time that human locomotion data has been used for imitation learning in a full-size humanoid robot.
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。
