Real-time whole-body teleoperation is a critical method for humanoid robots to perform complex tasks in unstructured environments. However, developing a unified controller that robustly supports diverse human motions remains a significant challenge. Existing methods typically distill multiple expert policies into a single general policy, which often inevitably leads to performance degradation, particularly on highly dynamic motions. This paper presents TeleGate, a unified whole-body teleoperation framework for humanoid robots that achieves high-precision tracking across various motions while avoiding the performance loss inherent in knowledge distillation. Our key idea is to preserve the full capability of domain-specific expert policies by training a lightweight gating network, which dynamically activates experts in real-time based on proprioceptive states and reference trajectories. Furthermore, to compensate for the absence of future reference trajectories in real-time teleoperation, we introduce a VAE-based motion prior module that extracts implicit future motion intent from historical observations, enabling anticipatory control for motions requiring prediction such as jumping and standing up. We conducted empirical evaluations in simulation and also deployed our technique on the Unitree G1 humanoid robot. Using only 2.5 hours of motion capture data for training, our TeleGate achieves high-precision real-time teleoperation across diverse dynamic motions (e.g., running, fall recovery, and jumping), significantly outperforming the baseline methods in both tracking accuracy and success rate.

翻译：实时全身遥操作是人形机器人在非结构化环境中执行复杂任务的关键方法。然而，开发一个能够鲁棒支持多样化人体运动的统一控制器仍然是一个重大挑战。现有方法通常将多个专家策略提炼为一个通用策略，这往往不可避免地导致性能下降，特别是在高动态运动上。本文提出了TeleGate，一个用于人形机器人的统一全身遥操作框架，该框架实现了对各种运动的高精度跟踪，同时避免了知识蒸馏固有的性能损失。我们的核心思想是通过训练一个轻量级的门控网络来保留领域特定专家策略的全部能力，该网络根据本体感知状态和参考轨迹实时动态激活专家。此外，为了弥补实时遥操作中未来参考轨迹的缺失，我们引入了一个基于VAE的运动先验模块，该模块从历史观测中提取隐式的未来运动意图，从而实现对跳跃和站起等需要预测的运动进行前瞻性控制。我们在仿真中进行了实证评估，并将我们的技术部署在Unitree G1人形机器人上。仅使用2.5小时的运动捕捉数据进行训练，我们的TeleGate就在多种动态运动（例如，跑步、跌倒恢复和跳跃）上实现了高精度的实时遥操作，在跟踪精度和成功率方面均显著优于基线方法。