Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation
This enables real-time humanoid robot control for applications like remote operations or human-robot interaction, representing a novel demonstration in learning-based teleoperation.
The paper tackles the problem of real-time whole-body teleoperation of a humanoid robot using only an RGB camera, achieving dynamic motions like walking and kicking in real-world scenarios with zero-shot transfer from simulation.
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.