## 1 Introduction  
The artificial tactile system [1] is a revolutionary technology developed to replicate human tactile perception capabilities [2], enabling robots and other intelligent devices to achieve refined environmental recognition and interaction [3]. This technology integrates advanced sensors and algorithms [4], allowing the system to sense and interpret physical stimuli--such as contact force, pressure, and temperature--thereby enabling precise object recognition and manipulation [5]. Human tactile perception generally involves three main stages: sensing, transmission, and processing [6]. In the sensing stage, tactile receptors in the skin detect various types of physical stimuli; in the transmission stage, signals are transmitted through the nervous system to the brain; and finally, in the processing stage, the brain interprets these signals, enabling the recognition of object properties. The primary objective of artificial tactile systems is to emulate this process, allowing robots,prosthetics, and virtual reality systems to interact seamlessly with the physical world [7,8]. Tactile feedback is essential in this technology, enabling robots to perceive and respond to environmental information in real time and thus perform more complex tasks [9, 10, 11, 12, 13, 14]. For instance, in surgical applications, robots equipped with tactile feedback can enhance both surgical precision and safety [15]. Similarly, in manufacturing, artificial tactile systems support automated production lines by ensuring that each step meets quality standards through real-time product monitoring. Tactile sensors can detect surface defects and make necessary adjustments to maintain the final product's quality [16]. Current research is focused on enhancing the sensitivity [17,18], resolution [19,20], stretchability [21,22], and multi-modal integration [23,24] of artificial tactile systems. Researchers are developing more sensitive sensor materials capable of detecting multiple tactile cues, such as pressure, temperature, and vibration [25]. These advanced sensors more accurately simulate the sensing abilities of human skin, thereby improving system performance [26]. To meet the demands for flexibility and stretchability, significant research is devoted to creating flexible electronic materials [27], allowing sensors to conform to irregular surfaces and maintain functionality during movement. For instance, flexible sensors can be applied to robotic fingers, retaining their sensing capabilities while grasping objects of diverse shapes [28]. Furthermore, multi-modal integration has emerged as a crucial research direction [29]. By effectively combining various types of tactile information, researchers aim to replicate the human tactile experience more closely. This approach requires both advanced sensor design and robust data processing to extract meaningful information from large datasets. With advancements in Artificial Intelligence (AI), artificial tactile systems are increasingly integrated with machine learning and deep learning algorithms, allowing these systems to adapt to new tasks, enhance responsiveness to environmental changes, and support more complex decision-making and predictive capabilities [30, 31, 32, 33]. By learning and extracting features from extensive tactile data, artificial tactile systems can more accurately identify object attributes, including material, shape, and surface texture. Convolutional neural networks (CNNs), for instance, are highly effective in processing complex tactile data and accurately recognizing various types of tactile information [16,34, 35, 36]. In the future, artificial tactile systems are expected to play a critical role in a wide range of applications [37,38]. In human-robot collaboration, as robotic technology advances, artificial tactile systems will enable robots to better understand and adapt to human operational methods, facilitating smoother cooperation [39,40].