1毫秒内完成人手到机械手的精准映射，适合实时遥操作
Geometric Retargeting: A Principled, Ultrafast Neural Hand Retargeting Algorithm
- 基于几何目标函数，无须人工标注直接训练
- 每秒处理1000帧，精度与速度均达当前最佳
- 适用于需要快速动作校正的机器人控制场景
我们提出几何重定向（GeoRT），一种超高速、原理严谨的神经手部重定向算法，用于遥操作，作为近期Dexterity Gen（DexGen）系统的一部分。GeoRT以1KHz的速度将人类手指关键点转换为机械手关键点，在显著减少超参数的前提下实现业界领先的精度与速度。该高速能力支持灵活后处理，例如利用基础控制器进行动作修正（如DexGen）。GeoRT采用无监督方式训练，无需手动标注手部配对数据。其核心在于新型几何目标函数，有效捕捉重定向本质：保持运动保真度、确保配置空间（C-space）覆盖、维持高平坦性带来的均匀响应、捏合对应关系，并防止自碰撞。该方法无需测试时优化，提供更可扩展、更实用的实时手部重定向解决方案。
原文摘要 · Abstract (English)
We introduce Geometric Retargeting (GeoRT), an ultrafast, and principled neural hand retargeting algorithm for teleoperation, developed as part of our recent Dexterity Gen (DexGen) system. GeoRT converts human finger keypoints to robot hand keypoints at 1KHz, achieving state-of-the-art speed and accuracy with significantly fewer hyperparameters. This high-speed capability enables flexible postprocessing, such as leveraging a foundational controller for action correction like DexGen. GeoRT is trained in an unsupervised manner, eliminating the need for manual annotation of hand pairs. The core of GeoRT lies in novel geometric objective functions that capture the essence of retargeting: preserving motion fidelity, ensuring configuration space (C-space) coverage, maintaining uniform response through high flatness, pinch correspondence and preventing self-collisions. This approach is free from intensive test-time optimization, offering a more scalable and practical solution for real-time hand retargeting.
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