GAE: General Action Expert for Real-Time Humanoid Teleoperation
This study addresses the limited diversity of whole-body behaviors and human-robot synchronization latency in humanoid teleoperation by proposing a unified learning framework. Methodologically, we construct a large-scale standardized motion dataset and adopt a two-stage training paradigm that decouples a privileged generator from a deployment executor to synthesize and execute feasible trajectories. To enhance generalization, multi-source data augmentation, curriculum-based domain randomization, and reinforcement learning-based imitation policies are integrated. Furthermore, an adaptive delay prediction and compensation mechanism is designed to ensure real-time control. Experimental results demonstrate that the proposed system enables humanoid platforms, such as the Unitree G1, to fluidly mirror diverse, agile, and expressive human behaviors with minimal latency in real-world settings.