门控残差身体-手部协调用于全身人形遥操作

中文摘要 AI 辅助

全身人形遥操作通常将运动跟踪策略与独立的灵巧手重定向器相结合。然而，独立生成的命令不能显式地保持身体-手的几何关系，导致在双手交互过程中手腕姿态和指尖位置的相对不匹配。我们提出了一种门控残差协调框架，该框架保持两个模块冻结，并对其输出施加有界修正。一个运动条件动作门控在关节组之间分配修正权限，而参考几何相关的奖励门控在训练期间强调相关的交互目标。为了在Agile One上建立标称身体控制器，我们引入了多姿态形态校准，该校准联合估计三轴尺度和效应器局部偏移，并配合分阶段运动数据集策划，以训练基于SONIC的跟踪器。残差策略使用人体运动参考、初始命令和机器人本体感觉，无需显式物体或接触观测。在仿真中，与在保留的GRAB运动上直接组合相比，它将手腕和指尖几何误差减少了39.2%-56.3%，同时在AMASS上保持了全身跟踪，无残差协调的成功率为89.03%，有残差协调的成功率为89.29%。消融研究表征了奖励门控、自适应修正权限以及分离的身体和手部修正头的贡献。

英文摘要

Whole-body humanoid teleoperation commonly combines a motion-tracking policy with a separate dexterous-hand retargeter. However, independently generated commands do not explicitly preserve body-hand geometric relations, leading to mismatches in relative wrist poses and fingertip positions during bimanual interaction. We present a gated residual coordination framework that keeps both modules frozen and applies bounded corrections to their outputs. A motion-conditioned action gate allocates correction authority across joint groups, while reference-geometry-dependent reward gates emphasize relevant interaction objectives during training. To establish the nominal body controller on Agile One, we introduce multi-pose morphology calibration that jointly estimates triaxial scales and effector-local offsets, together with staged motion dataset curation for training a SONIC-based tracker. The residual policy uses human motion references, initial commands, and robot proprioception without explicit object or contact observations. In simulation, it reduces wrist and fingertip geometry errors by 39.2-56.3% over direct composition on held-out GRAB motions, while preserving whole-body tracking on AMASS, with success rates of 89.03% without residual coordination and 89.29% with it. Ablations characterize the contributions of reward gating, adaptive correction authority, and separate body and hand correction heads.

发表机构

• Technical University of Munich（慕尼黑工业大学）
• Agile Robots SE

机构由 AI 辅助整理，请以论文原文为准。