通过主动远端伸展提升抓握精度，实现细长物体稳定操控
ARISTO Hand: Sensing-Driven Distal Hyperextension for Fine-Grained Manipulation
- 采用肌腱驱动+主动远端伸展结构，突破传统弯曲极限
- 1-20mm厚物体拔出力提升2.76倍，保持原有抓取能力
- 融合刚性传感器与柔性触觉阵列，适合高精度力感知场景
操控细长物体需要精确的接触几何与可靠的力感知，但多数仿人机器人手缺乏相应机械与传感能力。本文提出ARISTO Hand，一种肌腱驱动的机器人手，集成主动远端伸展与混合指尖传感架构——包含刚性指甲安装式力/力矩传感器和软电容触觉阵列。主动伸展使指尖在超越常规屈曲极限下仍可精准接触，对1-20mm厚度物体的拔出力提升2.76倍，同时维持原始抓取能力。刚性传感器在边缘接触时提供可靠力测量，克服了本体感觉力估计在运动学奇异点附近敏感度下降的问题。通过定量力特性分析及多阶段SD卡插拔任务验证了该架构的有效性。视频与补充材料见：https://aristohand.github.io
原文摘要 · Abstract (English)
Manipulating thin objects requires precise contact geometry and reliable force perception, yet many anthropomorphic robotic hands lack the mechanical and sensing capabilities needed for such interactions. We present the ARISTO Hand, a tendon-driven robotic hand that integrates active distal hyperextension with a hybrid fingertip-sensing architecture that combines a rigid, nail-mounted force-torque sensor and a soft capacitive tactile array. Active hyperextension enables controlled fingertip engagement beyond the kinematic limits of standard flexion, increasing pull-out force by 2.76x for object thicknesses of 1-20 mm while preserving the nominal grasp capability. The rigid nail-mounted sensor provides reliable force measurements during edge contacts, where the sensitivity of proprioceptive force estimation degrades as the contact geometry approaches kinematic singularities. We validate the proposed architecture through quantitative force characterization and a multi-stage SD card extraction and insertion task. Video and supplementary materials are available at: https://aristohand.github.io
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