This paper presents a new learning framework that leverages the knowledge from imitation learning, deep reinforcement learning, and control theories to achieve human-style locomotion that is natural, dynamic, and robust for humanoids. We proposed novel approaches to introduce human bias, i.e. motion capture data and a special Multi-Expert network structure. We used the Multi-Expert network structure to smoothly blend behavioral features, and used the augmented reward design for the task and imitation rewards. Our reward design is composable, tunable, and explainable by using fundamental concepts from conventional humanoid control. We rigorously validated and benchmarked the learning framework which consistently produced robust locomotion behaviors in various test scenarios. Further, we demonstrated the capability of learning robust and versatile policies in the presence of disturbances, such as terrain irregularities and external pushes.

中文翻译：

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利用人类知识学习仿人机器人的自然运动行为

本文提出了一种新的学习框架，该框架利用模仿学习、深度强化学习和控制理论的知识来实现​​类人机器人自然、动态和稳健的人类式运动。我们提出了引入人类偏见的新方法，即运动捕捉数据和特殊的多专家网络结构。我们使用 Multi-Expert 网络结构平滑混合行为特征，并使用增强奖励设计用于任务和模仿奖励。通过使用来自传统人形控制的基本概念，我们的奖励设计是可组合、可调整和可解释的。我们对学习框架进行了严格的验证和基准测试，该框架在各种测试场景中始终如一地产生了稳健的运动行为。更多，