Flexible tactile sensors are increasingly used in real-world applications such as robotic grippers, prosthetic hands, wearable gloves, and assistive devices, where they need to conform to curved and irregular surfaces. However, most existing tactile sensors are calibrated only on flat substrates, and their accuracy and consistency degrade once mounted on curved geometries. This limitation restricts their reliability in practical use. To address this challenge, we develop a calibration model for a widely used resistive tactile sensor design that enables accurate force estimation on one-dimensional curved surfaces. We then train a neural network (a multilayer perceptron) to predict local curvature from baseline sensor outputs recorded under no applied load, achieving an R2 score of 0.91. The proposed approach is validated on five daily objects with varying curvatures under forces from 2 N to 8 N. Results show that the curvature-aware calibration maintains consistent force accuracy across all surfaces, while flat-surface calibration underestimates force as curvature increases. Our results demonstrate that curvature-aware modeling improves the accuracy, consistency, and reliability of flexible tactile sensors, enabling dependable performance across real-world applications.

翻译：柔性触觉传感器在机器人夹爪、假肢手、可穿戴手套及辅助设备等实际应用中日益普及，这些场景要求传感器能够贴合曲面与不规则表面。然而，现有触觉传感器大多仅在平面基底上进行标定，一旦安装于曲面几何结构上，其精度与一致性便会下降，这一局限影响了实际应用的可靠性。为应对该挑战，我们针对一种广泛应用的电阻式触觉传感器设计，开发了一种能够在一维曲面上实现精确力估计的标定模型。随后，我们训练了一个多层感知机神经网络，通过无负载状态下记录的传感器基线输出预测局部曲率，其R2分数达到0.91。该方法在五种不同曲率的日常物体上，于2N至8N的施力范围内进行了验证。结果表明：曲率感知标定在所有表面上均保持了稳定的力测量精度，而基于平面的标定方法会随曲率增大而低估受力。我们的研究证实，曲率感知建模能提升柔性触觉传感器的精度、一致性与可靠性，从而在实际应用中实现稳定可靠的性能。