Kinematic retargeting from human hands to robot hands is essential for transferring dexterity from humans to robots in manipulation teleoperation and imitation learning. However, due to mechanical differences between human and robot hands, completely reproducing human motions on robot hands is impossible. Existing works on retargeting incorporate various optimization objectives, focusing on different aspects of hand configuration. However, the lack of experimental comparative studies leaves the significance and effectiveness of these objectives unclear. This work aims to analyze these retargeting objectives for dexterous manipulation through extensive real-world comparative experiments. Specifically, we propose a comprehensive retargeting objective formulation that integrates intuitively crucial factors appearing in recent approaches. The significance of each factor is evaluated through experimental ablation studies on the full objective in kinematic posture retargeting and real-world teleoperated manipulation tasks. Experimental results and conclusions provide valuable insights for designing more accurate and effective retargeting algorithms for real-world dexterous manipulation.

翻译：在遥操作和模仿学习中，从人手到机器人手的运动学重定向对于将人类的灵巧性迁移至机器人至关重要。然而，由于人手与机器人手之间存在机械结构差异，在机器人手上完全复现人体运动是不可能的。现有重定向研究纳入了多种优化目标，关注手部构型的不同方面。但由于缺乏实验性比较研究，这些目标的重要性和有效性尚不明确。本研究旨在通过大量真实世界比较实验，分析这些面向灵巧操作的重定向目标。具体而言，我们提出了一种全面的重定向目标公式，整合了近期方法中出现的直观关键因素。通过在全目标运动学姿态重定向和真实世界遥操作任务中进行实验消融研究，评估了每个因素的重要性。实验结果与结论为设计更准确、有效的真实世界灵巧操作重定向算法提供了宝贵见解。