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Electronic skins are essential for real-time health monitoring and tactile perception in robots. Although the use of soft elastomers and microstructures have improved the sensitivity and pressure-sensing range of tactile sensors, the intrinsic viscoelasticity of soft polymeric materials remains a long-standing challenge resulting in cyclic hysteresis. This causes sensor data variations between contact events that negatively impact the accuracy and reliability. Here, we introduce the Tactile Resistive Annularly Cracked E-Skin (TRACE) sensor to address the inherent trade-off between sensitivity and hysteresis in tactile sensors when using soft materials. We discovered that piezoresistive sensors made using an array of three-dimensional (3D) metallic annular cracks on polymeric microstructures possess high sensitivities (> 107 Ω ⋅ kPa-1), low hysteresis (2.99 ± 1.37%) over a wide pressure range (0-20 kPa), and fast response (400 Hz). We demonstrate that TRACE sensors can accurately detect and measure the pulse wave velocity (PWV) when skin mounted. Moreover, we show that these tactile sensors when arrayed enabled fast reliable one-touch surface texture classification with neuromorphic encoding and deep learning algorithms. [ABSTRACT FROM AUTHOR]

Compliance sensation is a unique feature of the human skin that electronic devices could not mimic via compact and thin formfactor devices. Due to the complex nature of the sensing mechanism, up to now, only high-precision or bulky handheld devices have been used to measure compliance of materials. This also prevents the development of electronic skin that is fully capable of mimicking human skin. Here, we developed a thin sensor that consists of a strain sensor coupled to a pressure sensor and is capable of identifying compliance of touched materials. The sensor can be easily integrated into robotic systems due to its small form factor. Results showed that the sensor is capable of classifying compliance of materials with high sensitivity allowing materials with various compliance to be identified. We integrated the sensor to a robotic finger to demonstrate the capability of the sensor for robotics. Further, the arrayed sensor configuration allows a compliance mapping which can enable humanlike sensations to robotic systems when grasping objects composed of multiple materials of varying compliance. These highly tunable sensors enable robotic systems to handle more advanced and complicated tasks such as classifying touched materials. [ABSTRACT FROM AUTHOR]