AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control

Figure 1: AMO enables hyper-dexterous whole-body movements for humanoid robots. Abstract --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. 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. Humans can expand their workspace of hands using whole-body movements. The joint configurations of humanoid robots closely mimic humans' functionality and degree of freedom while facing challenges of achieving similar movements with Metrics AMO (Ours) HOVER [30] Opt2Skill [41] Ref. Type Hybrid MoCap Traj. T orso means if the robot is able to adjust its' torso's orientation and height to expand the workspace.