分析人机回退中关键目标在灵巧操作中的应用
Analyzing Key Objectives in Human-to-Robot Retargeting for Dexterous Manipulation
AI总结：
本文通过实验分析回退目标在灵巧操作中的应用，提出综合目标公式并评估各因素的重要性。
摘要：
从人类手部到机器人手部的运动学回退对于在操控远程操作和模仿学习中将人类的灵巧性转移到机器人中是至关重要的。然而，由于人类和机器人手部在机械上的差异，完全在机器人手上重现人类动作是不可能的。现有的回退工作纳入了各种优化目标，专注于手部配置的不同方面。然而，缺乏实验比较研究使这些目标的意义和有效性不明确。本工作旨在通过广泛的现实比较实验分析这些回退目标在灵巧操作中的应用。具体来说，我们提出了一种综合的回退目标公式，整合了近期方法中出现的直观关键因素。通过在运动学姿态回退和现实远程操控任务上的实验消融研究，评估每个因素的重要性。实验结果和结论为设计更准确和有效的回退算法提供了有价值的见解，以应对现实中的灵巧操作。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
