AMO: 超柔軟ヒューマノイド全身制御のための適応運動最適化
AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control
シミュレーションから実機への強化学習と軌道最適化を統合し、29自由度のヒューマノイドでリアルタイム全身制御を実現した研究。
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著者: Jialong Li, Xuxin Cheng, Tianshu Huang, Shiqi Yang, Ri-Zhao Qiu, Xiaolong Wang
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Humanoid robots derive much of their dexterity from hyper-dexterous whole-body movements, enabling tasks that require a large operational workspace: such as picking objects off the ground. However, achieving these capabilities on real humanoids remains challenging due to their high degrees of freedom (DoF) and nonlinear dynamics. We propose Adaptive Motion Optimization (AMO), a framework that integrates sim-to-real reinforcement learning (RL) with trajectory optimization for real-time, adaptive whole-body control. To mitigate distribution bias in motion imitation RL, we construct a hybrid AMO dataset and train a network capable of robust, on-demand adaptation to potentially O.O.D. commands. We validate AMO in simulation and on a 29-DoF Unitree G1 humanoid robot, demonstrating superior stability and an expanded workspace compared to strong baselines. Finally, we show that AMO's consistent performance supports autonomous task execution via imitation learning, underscoring the system's versatility and robustness.
