Tactile sensing is crucial in robotics and wearable devices for safe perception and interaction with the environment. Optical tactile sensors have emerged as promising solutions, as they are immune to electromagnetic interference and have high spatial resolution. However, existing optical approaches, particularly vision-based tactile sensors, rely on complex optical assemblies that involve lenses and cameras, resulting in bulky, rigid, and alignment-sensitive designs. In this study, we present a thin, compact, and soft optical tactile sensor featuring an alignment-free configuration. The soft optical sensor operates by capturing deformation-induced changes in speckle patterns generated within a soft silicone material, thereby enabling precise force measurements and texture recognition via machine learning. The experimental results show a root-mean-square error of 40 mN in the force measurement and a classification accuracy of 93.33% over nine classes of textured surfaces, including Mahjong tiles. The proposed speckle-based approach provides a compact, easily fabricated, and mechanically compliant platform that bridges optical sensing with flexible shape-adaptive architectures, thereby demonstrating its potential as a novel tactile-sensing paradigm for soft robotics and wearable haptic interfaces.

翻译：触觉传感在机器人与可穿戴设备中对于实现安全的环境感知与交互至关重要。光学触觉传感器因其抗电磁干扰能力强和空间分辨率高的特点，已成为一种前景广阔的解决方案。然而，现有的光学方法，特别是基于视觉的触觉传感器，依赖于包含透镜和相机的复杂光学组件，导致其设计通常笨重、刚性且对装配对准敏感。在本研究中，我们提出了一种薄型、紧凑且柔性的光学触觉传感器，其采用免对准结构。该柔性光学传感器通过捕捉软质硅胶材料内部产生的、由形变引起的散斑图案变化来工作，从而能够实现精确的力测量并通过机器学习进行纹理识别。实验结果表明，在力测量中其均方根误差为40 mN，在对包括麻将牌在内的九类纹理表面的分类中，准确率达到93.33%。所提出的基于散斑的方法提供了一个紧凑、易于制造且机械顺应性好的平台，它将光学传感与柔性形状自适应架构相结合，从而展示了其作为软体机器人和可穿戴触觉接口的新型触觉传感范式的潜力。