Designing anthropomorphic dexterous robotic hands remains challenging as the design space straddles morphology, actuation, and sensing properties, and performance metrics span both task-dependent and task-agnostic. Existing optimization methods are often unstructured or consider only a single performance metric, limiting systematic comparison and targeted refinement. While the design considerations of the entire hand are significant, the individual finger properties play a key role in dexterity. By developing a robotic hand platform where fingers can be modularly integrated into a full teleoperated hand, we propose that optimizing the fingers can significantly improve overall hand performance. This approach enables rapid screening of different finger-level prototypes through a number of quantitative benchmarks before their integration into the hand for task-level validation. Candidate finger designs (incorporating variations in joint, bone, skin, and sensor placement) are assessed using both mechanism-oriented and task-relevant metrics, which establish a quantitative link between component design and full hand embodiment. The framework is validated through the development of an anthropomorphic robotic hand with optimized fingers, demonstrating how these fingers enable performance improvements across tasks, including multi-object grasping and light bulb screwing.

翻译：设计类人灵巧机械手仍具挑战性，因为其设计空间涵盖形态、驱动和传感特性，而性能指标既包含任务相关也包含任务无关因素。现有优化方法往往缺乏结构化或仅考虑单一性能指标，限制了系统比较与定向改进。虽整体手部设计考量至关重要，但单指特性对灵巧性起关键作用。通过开发可模块化集成至完整遥操作手的机械手平台，我们提出优化手指可显著提升整体手部性能。该方法通过多项定量基准测试快速筛选不同手指级原型，再将其集成至手部进行任务级验证。候选手指设计（包含关节、骨骼、皮肤和传感器布局的变体）采用面向机制和任务相关的指标进行评估，从而在组件设计与完整手部具身化之间建立定量联系。该框架通过开发具有优化手指的类人机械手得到验证，展示了这些手指如何在多物体抓取和灯泡拧转等任务中提升性能表现。