This article presents a high-sensitivity magnetic tactile sensor comprising a dipole magnetic film and a z-axis Hall sensor. The tactile sensor primarily focuses on pressure response along the z-axis, minimizing interference from magnetic fields in other axes and thereby simplifying signal processing complexity. To enhance the overall performance of the tactile sensor, a qualitative analysis and structural optimization are conducted on both the magnetic film and the Hall sensor. The magnetic film is optimized by comparing different thicknesses and magnetic powder mass fractions, and the Hall sensor undergoes structural optimization through a comparative analysis of different length-to-width (L/W) ratios. By embedding the Hall sensor into the printed circuit board (PCB), we can ensure a snug fit with the ultrathin flat magnetic film, resulting in an advantageous packaging effect. Employing these approaches, we substantially improve sensitivity, measuring at 5.92 Gs/N, surpassing the sensitivity level (about 0.17 Gs/N)reported in previous works by one  order of magnitude. Finally, the device performs comprehensive characterizations, revealing excellent properties, including low hysteresis (6.82%), the rapid response time (<2 ms), remarkable stability (0.09%), and high repeatability (0.48%). This research will actively promote the development of tactile sensors, which offer substantial applications in robotics, health monitoring, and electronic skin devices. 

The advancements in tactile sensor technology have found wide-ranging applications in robotic fields, resulting in remarkable achievements in object manipulation and overall human-machine interactions. However, the widespread availability of high-resolution tactile skins remains limited, due to the challenges of incorporating large-sized, robust sensing units and increased wiring complexity. One approach to achieve high-resolution and robust tactile skins is to integrate a limited number of sensor units (taxels) into a flexible surface material and leverage signal processing techniques to achieve super-resolution sensing. Here, we present a magnetic skin consisting of multi-direction magnetized flexible films and a contactless Hall sensor array. The key features of the proposed sensor include the specific magnetization arrangement, K-Nearest Neighbors (KNN) clustering algorithm and convolutional neural network (CNN) model for signal processing. Using only an array of 4*4 taxels, our magnetic skin is capable of achieving super-resolution perception over an area of 48400 mm2, with an average localization error of 1.2 mm. By employing neural network algorithms to decouple the multidimensional signals, the skin can achieve multi-point and multi-scale perception. We also demonstrate the promising potentials of the proposed sensor in intelligent control, by simultaneously controlling two vehicles with trajectory mapping on the magnetic skin. 

Human skin can sense subtle changes of both normal and shear forces (i.e., self-decoupled) and perceive stimuli with finer resolution than the average spacing between mechanoreceptors (i.e., super-resolved). By contrast, existing tactile sensors for robotic applications are inferior, lacking accurate force decoupling and proper spatial resolution at the same time. Here, we present a soft tactile sensor with self-decoupling and super-resolution abilities by designing a sinusoidally magnetized flexible film (with the thickness ~0.5 millimeters), whose deformation can be detected by a Hall sensor according to the change of magnetic flux densities under external forces. The sensor can accurately measure the normal force and the shear force (demonstrated in one dimension) with a single unit and achieve a 60-fold super-resolved accuracy enhanced by deep learning. By mounting our sensor at the fingertip of a robotic gripper, we show that robots can accomplish challenging tasks such as stably grasping fragile objects under external disturbance and threading a needle via teleoperation. This research provides new insight into tactile sensor design and could be beneficial to various applications in robotics field, such as adaptive grasping, dexterous manipulation, and human-robot interaction. 

Robotic fingers with multidirectional tactile perception are of great importance for the robotic exploration of complex unknown space, especially in environments in which visualization is not possible. Unfortunately, most existing tactile sensors for robotic fingers cannot detect multidirectional forces, which greatly limits their potential for further development in navigating complex environments. Here, we demonstrate a soft magnetoelectric finger (SMF) that can achieve self-generated-signal and multidirectional tactile sensing. The SMF is composed of two parts: a ‘finger’ covered with a skin-like flexible sheath containing five liquid metal (LM) coils and a ‘phalangeal bone’ containing a magnet. Due to the changes in magnetic flux through the LM coils caused by external forces, diverse induced voltages are generated and collected in real-time, which can be explained by Maxwell’s numerical simulation. By the analysis of the signals generated by the five LM coils, the SMF can detect forces in varied directions and distinguish 6 different common objects with varied Young’s moduli with an accuracy of 97.46%. These capabilities make the SMF suitable for complex unknown space exploration tasks, as proved by the black box exploration.