GMT：面向人形机器人全身控制的通用运动追踪

摘要

在现实世界中追踪全身运动的能力，是构建通用人形机器人的有效途径。然而，实现这一目标颇具挑战，原因在于运动的时空与运动学多样性、策略的适应能力，以及上下肢协调的复杂性。为解决这些问题，我们提出了GMT（通用运动追踪框架），该框架通过训练单一统一策略，使人形机器人能够在现实世界中追踪多样化的运动。GMT的核心由两大组件构成：自适应采样策略与运动专家混合（MoE）架构。自适应采样在训练过程中自动平衡简单与复杂运动，而MoE则确保了对运动流形不同区域更精细的专门化处理。通过大量仿真与真实世界的实验，我们验证了GMT的有效性，其采用统一通用策略，在广泛运动范围内达到了业界领先的性能。更多视频及详细信息，请访问https://gmt-humanoid.github.io。

English

The ability to track general whole-body motions in the real world is a useful way to build general-purpose humanoid robots. However, achieving this can be challenging due to the temporal and kinematic diversity of the motions, the policy's capability, and the difficulty of coordination of the upper and lower bodies. To address these issues, we propose GMT, a general and scalable motion-tracking framework that trains a single unified policy to enable humanoid robots to track diverse motions in the real world. GMT is built upon two core components: an Adaptive Sampling strategy and a Motion Mixture-of-Experts (MoE) architecture. The Adaptive Sampling automatically balances easy and difficult motions during training. The MoE ensures better specialization of different regions of the motion manifold. We show through extensive experiments in both simulation and the real world the effectiveness of GMT, achieving state-of-the-art performance across a broad spectrum of motions using a unified general policy. Videos and additional information can be found at https://gmt-humanoid.github.io.

GMT：面向人形机器人全身控制的通用运动追踪

GMT: General Motion Tracking for Humanoid Whole-Body Control

摘要

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