The human hand plays a vital role in daily life and industrial applications, yet replicating its multifunctional capabilities-including motion, sensing, and coordinated manipulation-with robotic systems remains a formidable challenge. Developing a dexterous robotic hand requires balancing human-like agility with engineering constraints such as complexity, size-to-weight ratio, durability, and force-sensing performance. This letter presents Dex-Hand 021, a high-performance, cable-driven five-finger robotic hand with 12 active and 7 passive degrees of freedom (DoFs), achieving 19 DoFs dexterity in a lightweight 1 kg design. We propose a proprioceptive force-sensing-based admittance control method to enhance manipulation. Experimental results demonstrate its superior performance: a single-finger load capacity exceeding 10 N, fingertip repeatability under 0.001 m, and force estimation errors below 0.2 N. Compared to PID control, joint torques in multi-object grasping are reduced by 31.19%, significantly improves force-sensing capability while preventing overload during collisions. The hand excels in both power and precision grasps, successfully executing 33 GRASP taxonomy motions and complex manipulation tasks. This work advances the design of lightweight, industrial-grade dexterous hands and enhances proprioceptive control, contributing to robotic manipulation and intelligent manufacturing.

翻译：人手在日常生活中和工业应用中发挥着至关重要的作用，然而，利用机器人系统复现其多功能能力——包括运动、感知和协调操作——仍然是一项艰巨的挑战。开发灵巧的机器人手需要在类人敏捷性与工程约束（如复杂性、尺寸重量比、耐用性和力传感性能）之间取得平衡。本文介绍了DexHand 021，这是一种高性能、缆绳驱动的五指机器人手，具有12个主动自由度和7个被动自由度，在轻量化的1千克设计中实现了19个自由度的灵巧性。我们提出了一种基于本体感知力传感的导纳控制方法以增强操作能力。实验结果表明其具有卓越性能：单指负载能力超过10 N，指尖重复定位精度低于0.001 m，力估计误差小于0.2 N。与PID控制相比，在多物体抓取中的关节扭矩降低了31.19%，显著提升了力传感能力，同时防止了碰撞过程中的过载。该手在强力抓取和精密抓取方面均表现出色，成功执行了33种GRASP分类动作和复杂的操作任务。这项工作推进了轻量化、工业级灵巧手的设计，并增强了本体感知控制，为机器人操作和智能制造做出了贡献。