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.

翻译：刚度估计对于机器人手和假肢手的精细物体操作至关重要，但由于依赖于力和位移测量以及实时传感集成，该任务仍具挑战性。本研究提出了一种压电传感框架，用于在捏取抓握首次接触时进行刚度估计，以解决传统基于力的方法的局限性。受人类皮肤启发，我们开发了一种能够捕获振动和力数据的多模态触觉传感器，并将其集成到假肢手指尖。机器学习模型（包括支持向量机和卷积神经网络）表明，首次接触后关键15毫秒内的振动信号能够可靠地编码刚度信息，在不同刚度的真实物体上实现了高达98.6%的分类准确率和低至2.39 Shore A的回归误差。小于1.5毫秒的推理时间显著快于平均抓取闭合时间（本数据集中为16.65毫秒），使得在物体被完全抓握前即可实现实时刚度估计。通过利用抓取动力学中的瞬态不对称性（即一个手指先于其他手指接触物体），该方法能够实现早期抓取调制，从而增强假肢手的安全性和直观性，同时在机器人领域具有广泛的应用前景。