ロバストで汎化可能なヒューマノイド動作追従
Robust and Generalized Humanoid Motion Tracking
因果的時系列エンコーダとマルチヘッドクロスアテンションで動力学に応じて参照動作を集約し、転倒回復カリキュラムを組み合わせることで、少ないデータで汎化・ロバストなヒューマノイド全身制御を実現した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Yubiao Ma, Han Yu, Jiayin Xie, Changtai Lv, Qiang Luo, Chi Zhang, Yunpeng Yin, Boyang Xing, Xuemei Ren, Dongdong Zheng
分類: cs.RO
原文アブストラクト
Learning a general humanoid whole-body controller is challenging because practical reference motions can exhibit noise and inconsistencies after being transferred to the robot domain, and local defects may be amplified by closed-loop execution, causing drift or failure in highly dynamic and contact-rich behaviors. We propose a dynamics-conditioned command aggregation framework that uses a causal temporal encoder to summarize recent proprioception and a multi-head cross-attention command encoder to selectively aggregate a context window based on the current dynamics. We further integrate a fall recovery curriculum with random unstable initialization and an annealed upward assistance force to improve robustness and disturbance rejection. The resulting policy requires only about 3.5 hours of motion data and supports single-stage end-to-end training without distillation. The proposed method is evaluated under diverse reference inputs and challenging motion regimes, demonstrating zero-shot transfer to unseen motions as well as robust sim-to-real transfer on a physical humanoid robot.
