We present Human to Humanoid (H2O), a reinforcement learning (RL) based framework that enables real-time whole-body teleoperation of a full-sized humanoid robot with only an RGB camera. To create a large-scale retargeted motion dataset of human movements for humanoid robots, we propose a scalable "sim-to-data" process to filter and pick feasible motions using a privileged motion imitator. Afterwards, we train a robust real-time humanoid motion imitator in simulation using these refined motions and transfer it to the real humanoid robot in a zero-shot manner. We successfully achieve teleoperation of dynamic whole-body motions in real-world scenarios, including walking, back jumping, kicking, turning, waving, pushing, boxing, etc. To the best of our knowledge, this is the first demonstration to achieve learning-based real-time whole-body humanoid teleoperation.

翻译：我们提出了人类到人形机器人（H2O）框架，这是一种基于强化学习（RL）的方法，仅通过RGB摄像头即可实现对全尺寸人形机器人的实时全身遥操作。为了创建适用于人形机器人的大规模人类动作重定位数据集，我们提出了一种可扩展的“模拟到数据”流程，利用特权动作模仿器筛选并选取可行动作。随后，我们使用这些精炼动作在仿真中训练鲁棒的实时人形机器人动作模仿器，并以零样本方式将其迁移至真实人形机器人。我们成功实现了真实场景下动态全身动作的遥操作，包括行走、后跳、踢腿、转身、挥手、推搡、拳击等。据我们所知，这是首个实现基于学习的实时全身人形机器人遥操作的演示。