论文标题
使用人类知识学习人形机器人的自然运动行为
Learning natural locomotion behaviors for humanoid robots using human knowledge
论文作者
论文摘要
本文提出了一个新的学习框架，该框架利用模仿学习，深入的强化学习和控制理论来实现人类风格的自然，动态和强大的人类的运动。我们提出了新的方法来引入人类偏见，即运动捕获数据和特殊的多型网络结构。我们使用多型专家网络结构来平滑地融合了行为特征，并将增强奖励设计用于任务和模仿奖励。我们的奖励设计是可以通过使用常规类人体控制的基本概念来组合，可调且可以解释的。我们对学习框架进行了严格的验证和基准测试，该框架在各种测试方案中始终如一地产生了强大的运动行为。此外，我们证明了在存在干扰（例如地形不规则和外部推动）的情况下学习强大和多功能政策的能力。
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.
