对比分析人手到机器人手动作迁移的关键目标，提升操作灵巧性。
Analyzing Key Objectives in Human-to-Robot Retargeting for Dexterous Manipulation
- 整合多种手部姿态优化目标，构建全面的迁移框架
- 通过实测实验验证各目标有效性，明确关键影响因素
- 为真实场景下的灵巧操作迁移提供设计参考
从人类手部到机器人手部的运动学迁移是实现操纵遥操作与模仿学习中灵巧性传递的关键。然而，由于人类与机器人手部机械结构差异，完全复现人类动作在机器人手上不可行。现有研究采用多种优化目标，关注手部构型的不同方面，但缺乏系统的实验对比，使得这些目标的重要性与效果不清晰。本文通过大量真实世界对比实验，分析不同迁移目标在灵巧操作中的作用。提出一种综合性的迁移目标公式，融合近期方法中直观重要的因素。通过在运动学姿态迁移和真实世界遥操作任务中的消融实验，评估每个因素的显著性。实验结果与结论为设计更准确、高效的迁移算法提供了重要启示。
原文摘要 · Abstract (English)
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.
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