让机器人像人一样灵活完成复杂动作，实时自适应控制。
AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control
- 结合强化学习与轨迹优化，实现动态自适应控制。
- 在29自由度机器人上验证，工作空间扩展且更稳定。
- 适合需要高灵巧性的真实机器人任务部署。
类人机器人通过全身高灵巧运动实现大范围操作能力，例如从地面拾取物体。然而，由于其高自由度（29个）和非线性动力学，实机实现仍具挑战。本文提出自适应运动优化（AMO）框架，融合仿真到现实的强化学习（RL）与轨迹优化，实现实时、自适应的全身控制。为缓解运动模仿强化学习中的分布偏移问题，构建混合AMO数据集，并训练网络以鲁棒地应对可能的分布外（O.O.D.）指令。在仿真和29自由度的Unitree G1人形机器人上验证，AMO相比强基线展现出更高的稳定性与更大的操作工作空间。最终实验表明，AMO性能一致，支持通过模仿学习实现自主任务执行，凸显系统的多功能性与鲁棒性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。