Human manipulation skills represent a pinnacle of their voluntary motor functions, requiring the coordination of many degrees of freedom and processing of high-dimensional sensor input to achieve remarkable dexterity. Thus, we set out to answer whether the human hand, with its associated biomechanical properties, sensors, and control mechanisms, is an ideal that we should strive for in robotics. Do robots need anthropomorphic hands? We start by extracting characteristics of the human hand in terms of biomechanics and perception to compare them with currently commercially available robotic hands. From this comparison, we derive our research questions that connect manipulation system complexity to skill repertoire size and dexterity. We attempt to answer these with a systematic literature review, in which we analyze the manipulation capabilities demonstrated in 125 papers from 2019-2025. Although complex five-fingered hands are often considered the ultimate goal for robotic manipulators, they are not necessary for all tasks. We find that in-hand manipulation does not benefit from anthropomorphic hand design as simpler mechanisms are sufficient, but mechanism complexity correlates with the breadth of manipulation tasks a hand can perform. Sensor integration and intelligent manipulation strategies remain underexplored, which may be because of a misalignment with hand design: instead of replicating the number of fingers and degrees of freedom, focusing on robustness and softness would allow more intelligent control and learning to exploit environmental contacts and integrate more sensors. Finally, we argue for standardized evaluation criteria to enable systematic comparison of hand designs and manipulation systems.

翻译：人类操作技能是其自主运动功能的巅峰，需要协调众多自由度并处理高维传感器输入以实现非凡的灵巧性。因此，我们试图回答：人类手及其相关的生物力学特性、传感器和控制机制，是否是我们应追求的理想机器人形态？机器人是否需要拟人化手？我们首先从生物力学和感知角度提取人类手的特征，并将其与当前商用机器人手进行比较。通过这一比较，我们提出研究问题：操作系统的复杂度如何与技能库规模及灵巧性相关联。我们通过系统文献综述尝试回答这些问题，分析了2019-2025年间125篇论文中展示的操作能力。尽管复杂的五指手常被视为机器人操作器的终极目标，但并非所有任务都必需。我们发现，手内操作并未从拟人化手设计中受益，因为更简单的机制已足够，但机制复杂度与手能执行的操作任务广度相关。传感器集成和智能操作策略仍未被充分探索，这可能源于手设计的错位：与其复制手指数量和自由度，不如专注于鲁棒性和柔软性，以支持更智能的控制和学习，从而利用环境接触并集成更多传感器。最后，我们呼吁建立标准化评估准则，以系统性地比较手设计和操作系统的性能。