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.

本研究提出了几何重新定位（GeoRT）算法，这是一种超快速且原则性的神经手部重新定位方法，旨在解决远程操作中的手部重定向问题。GeoRT通过新颖的几何目标函数，以无监督方式将人类手指关键点转换为机器人手关键点，实现1KHz的处理速度和极高的精确度。此方法的显著发现是，它在无需手动标注和测试时优化的情况下，提供了更具可扩展性的实时手部重定向解决方案。

几何重新定位：一种原则性超快速神经手部重新定位算法