Teaching an anthropomorphic robot from human example offers the opportunity to impart humanlike qualities on its movement. In this work we present a reinforcement learning based method for teaching a real world bipedal robot to perform movements directly from human motion capture data. Our method seamlessly transitions from training in a simulation environment to executing on a physical robot without requiring any real world training iterations or offline steps. To overcome the disparity in joint configurations between the robot and the motion capture actor, our method incorporates motion re-targeting into the training process. Domain randomization techniques are used to compensate for the differences between the simulated and physical systems. We demonstrate our method on an internally developed humanoid robot with movements ranging from a dynamic walk cycle to complex balancing and waving. Our controller preserves the style imparted by the motion capture data and exhibits graceful failure modes resulting in safe operation for the robot. This work was performed for research purposes only.

翻译：从人类实例中教授人类形态机器人为传授人类运动品质提供了机会。在这项工作中,我们提出了一个强化学习方法,用于教授真实世界的双脚机器人,直接从人类运动捕获数据中进行运动。我们的方法从模拟环境中的培训无缝地向执行物理机器人的过渡,而不需要任何真实世界的培训迭代或离线步骤。为了克服机器人和运动捕捉者之间在联合配置方面的差异,我们的方法将运动重新定位纳入培训过程。域随机化技术用于弥补模拟系统和物理系统之间的差异。我们演示了我们内部开发的人类机器人的方法,其运动范围从动态行走周期到复杂的平衡和挥舞。我们的控制器保存了运动所传授的风格,并展示了使机器人安全运行的优异失败模式。这项工作仅用于研究目的。