发表机构
University of California, Berkeley; Amazon FAR; Carnegie Mellon University（加州大学伯克利分校; 亚马逊FAR; 卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对人形全身运动中的末端执行器跟踪难题，提出结合几何导纳控制与残差强化学习的ResGAC控制器，在真实G1机器人上实现更精确的SE(3)跟踪，插销任务成功率90%。
AI 中文摘要
由于浮动基座振荡、重力、动态耦合以及运动引起的扰动，在人形全身运动过程中实现精确的末端执行器跟踪具有挑战性。我们提出ResGAC，一种用于精确末端执行器位姿跟踪的全身人形控制器，它结合了几何导纳控制（GAC）与残差强化学习。GAC提供结构化的SE(3)任务空间反馈，并生成标称手臂关节位置目标，而残差强化学习补偿未建模的动力学，并在共享的关节位置动作空间中协调运动与平衡。左不变的几何公式允许相同的GAC律在不同操作参考系中使用。这使得可以使用地面附着的航向参考系，该参考系保持平面运动，同时从操作参考中去除骨盆的横滚、俯仰和升沉，从而减少运动过程中参考引起的末端执行器运动。ResGAC在真实的Unitree G1人形机器人上进行了验证。在四个站立末端执行器跟踪基准中，ResGAC持续优于代表性基线（包括SONIC），实现了更低的平移和旋转误差。真实世界实验进一步证明，使用所提出的地面附着航向参考系，可以减少骨盆运动向期望末端执行器位姿的传播。ResGAC在站立插销孔任务中实现了90%的成功率，而SONIC为50%，并在下肢运动过程中实现了准确的基于世界坐标系的SE(3)末端执行器位姿跟踪。实验视频包含在补充材料中，也可在项目网站上获取：此https URL。
英文摘要
Precise end-effector tracking during humanoid whole-body motion is challenging due to floating-base oscillations, gravity, dynamic coupling, and locomotion-induced disturbances. We propose ResGAC, a whole-body humanoid controller for precise end-effector pose tracking that combines geometric admittance control (GAC) with residual reinforcement learning. GAC provides structured $\SE$ task-space feedback and generates nominal arm joint-position targets, while residual RL compensates for unmodeled dynamics and coordinates locomotion and balance in the shared joint-position action space. The left-invariant geometric formulation allows the same GAC law to be used across manipulation reference frames. This enables the use of a ground-attached heading frame that preserves planar locomotion while removing pelvis roll, pitch, and heave from the manipulation reference, thereby reducing reference-induced end-effector motion during locomotion. ResGAC is validated on a real Unitree G1 humanoid. Across four standing end-effector tracking benchmarks, ResGAC consistently outperforms representative baselines, including SONIC, achieving lower translational and rotational errors. Real-world experiments further demonstrate reduced propagation of pelvis motion to the desired end-effector pose using the proposed ground-attached heading frame. ResGAC achieves $90\%$ success in a standing peg-in-hole task compared with $50\%$ for SONIC, and accurate world-frame $\SE$ end-effector pose tracking during lower-body motion. Experimental videos are included in the supplementary material and are also available on the project website: https://resgac.github.io/ResGAC-website/.
