仿生手通过解剖结构实现灵巧操作,用物理架构减少控制复杂度。
MCR-Bionic Hand: Anatomical Structural Priors for Dexterous Manipulation

- 基于人体解剖结构设计肌骨系统,实现低维输入生成默认抓握姿态。
- 实验表明手腕姿势可预设多关节形态,伸肌腱膜联动指间关节运动。
- 适合研究机器人灵巧手、生物力学建模及人机交互系统的开发者。
灵巧机器人手通常被建模为高维主动控制系统,受自由度、驱动和算法支配。然而,人类手部灵巧性部分编码于骨骼、韧带、肌腱、腱膜及内在肌肉的物理结构中。本文将这种贡献归纳为两种关联的结构智能:结构先验生成,即通过腕到指的十节肌腱连接、指浅屈肌/深屈肌路径分布以及背侧伸肌帽结构,将低维姿态输入转化为默认抓握配置与近端指间关节(PIP)到远端指间关节(DIP)的协调;肌肉介导调节,即外在肌、蚓状肌和骨间肌调控掌指关节(MCP)姿态、远端稳定性、指尖受力方向及抓握后的接触状态。基于此框架,开发了MCR-Bionic Hand,该手为1:1肌骨仿生手,集成双排八骨腕部、交叉腕肌腱、解剖学屈肌走行、掌侧板与侧副韧带约束、背侧伸肌帽及内在肌路径。功能演示与几何力学模型显示,腕部姿态诱导多关节预成型,伸肌帽将PIP姿态映射为耦合的DIP响应,内在肌路径则在抓握形成后调节远端稳定性和指尖作用方向。在包含硬币旋转、笔转移、背面翻转硬币和立方体操控等丰富接触任务中,MCR-Bionic手成功实现低维状态生成与精细抓握后调节的联动。结果表明,解剖仿生的价值不在于视觉相似,而在于识别执行部分控制功能的人体结构。
原文摘要 · Abstract (English)
Dexterous robotic hands are usually formulated as high dimensional active control systems governed by degrees of freedom, actuation, and algorithms. Human hand dexterity, however, is partly encoded in the physical architecture of bones, ligaments, tendons, aponeuroses, and intrinsic muscles. This work describes that contribution as two linked forms of structural intelligence: structural prior generation, in which wrist to finger tenodesis, FDS/FDP routing, and the dorsal extensor hood transform low dimensional posture inputs into default grasp configurations and PIP to DIP coordination; and muscle mediated modulation, in which extrinsic muscles, lumbricals, and interossei regulate MCP posture, distal stability, fingertip force paths, and contact states around that default state. Based on this framework, MCR-Bionic Hand is developed as a 1:1 musculoskeletal biomimetic hand integrating a two row eight bone wrist, cross wrist tendons, anatomical flexor routing, volar plate and collateral ligament constraints, the dorsal extensor hood, and intrinsic muscle pathways within one body. Functional demonstrations and geometric mechanical models show that wrist posture induces multi joint pre shaping, the extensor hood maps PIP posture to a coupled DIP response, and intrinsic plus pathways modulate distal stability and fingertip action direction after grasp formation. Contact rich tasks, including coin rotation, pen transfer, dorsal coin flipping, and cube manipulation, show that MCR-Bionic links low dimensional state generation with fine post contact modulation. These results suggest that anatomical biomimetics is valuable not for visual similarity, but for identifying human hand structures that perform part of control.
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