通过模块化手指评估优化，提升仿人机械手整体灵巧性。
Modular Anthropomorphic Hand Design via Multi-Parameter Finger Benchmarking and Selection
- 构建可模块化替换的手指平台，量化评估关节、骨骼等设计参数。
- 优化后手指在多物体抓取和拧灯泡任务中性能显著提升。
- 适合机器人手部设计、灵巧操作研究者参考。
仿人灵巧机械手的设计面临形态、驱动与感知属性的复杂权衡，且性能指标涵盖任务相关与无关两类。现有优化方法常缺乏系统性或仅关注单一指标，难以实现全面比较与精准改进。尽管整手设计重要，但单个手指特性对灵巧性影响关键。本文提出一种可模块化集成手指的遥操作机械手平台，通过一系列定量基准测试，在整合前快速筛选不同手指原型。候选设计（包含关节、骨骼、皮肤及传感器布局变化）采用机制导向与任务相关指标进行评估，建立部件设计与整手功能之间的量化关联。该框架通过开发一款具优化手指的仿人机械手得以验证，证明其在多物体抓取与拧灯泡等任务中均实现性能提升。
原文摘要 · Abstract (English)
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.
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