一个统一框架让人形机器人能通用追踪各种复杂动作。
GMT: General Motion Tracking for Humanoid Whole-Body Control
- 用自适应采样和专家混合架构，统一训练追踪多种动作
- 在仿真与真实世界均达当前最优，无需为不同动作重训
- 适合研究通用人形机器人控制与运动规划的团队
在真实世界中实现对多样化全身动作的通用追踪，是构建通用型人形机器人的关键能力。然而，由于动作的时间与运动学多样性、策略能力限制以及上下肢协调困难，这一目标极具挑战。为此，本文提出GMT——一种通用且可扩展的动作追踪框架，通过单一统一策略使机器人在真实环境中追踪多种动作。该框架基于两个核心组件：自适应采样策略，可自动平衡训练中难易动作的比例；运动专家混合（Motion Mixture-of-Experts, MoE）架构，提升运动流形不同区域的专属性。通过大量仿真与真实世界实验验证，GMT在广泛动作类型上均取得领先性能，仅用一个通用策略即达成最优表现。视频与更多信息详见 https://gmt-humanoid.github.io。
原文摘要 · Abstract (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.
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