软关节嵌入应变片，让机械手像人一样自适应抓握。
A Human-Inspired Thumb-Index Robotic Hand with Strain Gauges Embedded in Soft Joints
- 用柔性应变片嵌入硅胶软关节，实现自适应抓握与内外感知。
- 抓20种物体时分类准确率高，可识别大小、形状和材质硬度。
- 无需触觉传感器或摄像头，适合轻量级安全抓取场景。
人类手部抓握适应主要依赖于物理结构与生物反馈的协同作用。受此生物力学原理启发，本文开发了名为安全拇指-食指机械手（STIR Hand）的最小化、轻量化、低成本双指原型，采用非对称拇指-食指构型。通过将欠驱动、绳索驱动的机械结构与嵌入硅胶封装软关节的柔性应变片相结合，系统实现了被动抓握适应，并建立了内部本体感觉与外部感知能力。在包含20种不同物体的抓握数据集上进行了无监督分析，完成了物体分类任务并开展消融实验以验证软关节传感器的贡献。物体分类任务中成功区分了物体的尺寸、形状及材料刚度，准确率较高。相较于传统工业夹爪和机器人手，STIR Hand表明传感柔性关节显著提升了整体灵敏度并确保了安全抓握，且无需额外指尖触觉元件或外部视觉系统。最后，与同类设备在相同物体上的抓握表现对比，验证了STIR Hand的有效性。
原文摘要 · Abstract (English)
Human hand grasp adaptation depends mainly on the synergy between physical structure and biological feedback. Inspired by this biomechanical principle, the Safe Thumb-Index Robotic (STIR) Hand was developed as a minimal, lightweight, and low-cost two-digit prototype featuring an asymmetric thumb-index configuration. By pairing an underactuated, tendon-driven mechanical design with flexible strain gauges embedded into silicone-encapsulated soft joints, the system achieves passive grasp adaptation while establishing both internal proprioception and external perception. Unsupervised analysis was carried out on a dataset of the STIR hand grasping 20 different objects, along with an object classification task and an ablation study to highlight the contribution of the soft joint sensors. The object classification task discriminated object size, shape, and material stiffness with a high classification accuracy. In contrast to traditional industrial grippers and robotic hands, the STIR Hand demonstrates that sensorized compliant joints significantly improve overall sensitivity and ensure safe grasping, while remaining independent of additional fingertip tactile elements or external vision systems. Finally, a comparison to similar devices grasping identical objects validates the utility of the STIR Hand.
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