Modular Anthropomorphic Hand Design via Multi-Parameter Finger Benchmarking and Selection

2026-06-10

AI Summary

This work addresses the challenge in anthropomorphic dexterous hand design where morphological, actuation, and sensing parameters are highly coupled and lack systematic multi-objective optimization methods. To tackle this, the authors propose a modular-finger-based multi-parameter benchmarking framework. Through modular mechanical design, the framework enables multidimensional quantitative evaluation of key components—including joints, skeletal structure, skin compliance, and sensor placement—and establishes quantitative relationships between mechanism-level characteristics and task-level performance. Optimized finger modules are then integrated into a teleoperated full hand for task-level validation. Experimental results demonstrate that the resulting high-performance dexterous hand significantly outperforms baseline designs in tasks such as multi-object grasping and bulb screwing, confirming that finger-level co-optimization effectively enhances overall hand dexterity.

Abstract

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.

Problem

• modular anthropomorphic hand
• finger design
• dexterity
• multi-parameter benchmarking
• robotic hand optimization

Innovation

• modular anthropomorphic hand
• multi-parameter benchmarking
• finger-level optimization
• dexterous robotic hand
• task-relevant metrics