用接触瞬间的振动信号，实时估算物体软硬，提升假手抓取安全性。
At First Contact: Stiffness Estimation Using Vibrational Information for Prosthetic Grasp Modulation
- 通过指尖振动信号，无需力/位移测量即可估计刚度。
- 15毫秒内识别刚度，分类准确率达98.6%，回归误差低至2.39 Shore A。
- 推理速度<1.5毫秒，适合实时调节假手抓握动作。
刚度估算是机器人和假肢手精细操作的关键，但传统方法依赖力与位移测量，难以实现实时感知。本研究提出一种压电传感框架，在捏握首次接触时进行刚度估计，克服了传统力控方法的局限。受人类皮肤启发，开发了一种融合振动与力数据的多模态触觉传感器，并集成于假肢手指尖。支持向量机与卷积神经网络等机器学习模型表明，首次接触后关键15毫秒内的振动信号能可靠编码刚度信息，在真实物体上实现最高98.6%分类准确率和2.39 Shore A的回归误差。推理时间低于1.5毫秒，远快于平均抓握闭合时间（16.65毫秒），可在物体完全被握紧前完成刚度估计，从而实现早期抓握调节。利用抓握动力学中单指先行接触带来的瞬态不对称性，该方法提升了假肢操作的安全性与自然性，并具备广泛机器人应用前景。
原文摘要 · Abstract (English)
Stiffness estimation is crucial for delicate object manipulation in robotic and prosthetic hands but remains challenging due to dependence on force and displacement measurement and real-time sensory integration. This study presents a piezoelectric sensing framework for stiffness estimation at first contact during pinch grasps, addressing the limitations of traditional force-based methods. Inspired by human skin, a multimodal tactile sensor that captures vibrational and force data is developed and integrated into a prosthetic hand's fingertip. Machine learning models, including support vector machines and convolutional neural networks, demonstrate that vibrational signals within the critical 15 ms after first contact reliably encode stiffness, achieving classification accuracies up to 98.6% and regression errors as low as 2.39 Shore A on real-world objects of varying stiffness. Inference times of less than 1.5 ms are significantly faster than the average grasp closure time (16.65 ms in our dataset), enabling real-time stiffness estimation before the object is fully grasped. By leveraging the transient asymmetry in grasp dynamics, where one finger contacts the object before the others, this method enables early grasp modulation, enhancing safety and intuitiveness in prosthetic hands while offering broad applications in robotics.
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