GMT：人形機器人全身控制之通用運動追蹤技術

摘要

在現實世界中追蹤全身運動的能力，是打造通用型人形機器人的有效途徑。然而，由於運動的時間與運動學多樣性、策略的能力，以及上下半身協調的難度，實現這一目標頗具挑戰性。為解決這些問題，我們提出了GMT，這是一個通用且可擴展的運動追蹤框架，它訓練單一統一策略，使人形機器人能在現實世界中追蹤多樣化的運動。GMT建立在兩個核心組件之上：自適應採樣策略和運動專家混合（MoE）架構。自適應採樣在訓練過程中自動平衡簡單與困難的運動，而MoE則確保了運動流形不同區域的更好專業化。通過在模擬與現實世界中的廣泛實驗，我們展示了GMT的有效性，利用統一通用策略在廣泛的運動範圍內達到了頂尖性能。視頻及更多資訊請訪問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：人形機器人全身控制之通用運動追蹤技術

GMT: General Motion Tracking for Humanoid Whole-Body Control

摘要

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