# Human-Touch-Inspired Material Recognition for Robotic Tactile Sensing
## 1 Introduction  
As robots are required to cooperate with humans in unstructured environments, developing intelligent tactile sensing for robots is urgently needed [1; 2]. For instance, surgical robots are required to ablate the diseased tissue, while avoiding harm to the human body [3; 4]. A prosthetic device with tactile sensing feedback improves the success rate of the grasp task [5]. Additionally, tactile sensing is particularly important, since many tasks require robots to recognize unintentional collisions or to make intentional physical contact with objects or humans [6]. Material recognition is an important capability for robotic tactile sensing, since the ability enables robots not only the sense of touch but also compliance, roughness, and texture, which improves environmental awareness when interacting with objects or humans. Specifically, the roughness and texture sensed by material can help robots adjust the control strategies during grasp, manipulation, and other tasks, while the awareness of compliance can keep humans or objects away from potentially destructive effects [7]. Hence, incorporating material recognition in robotic tactile sensing is important. In order to realize material recognition for robots, dynamic tactile sensing, which humans employ to explore and sense materials, is a good reference. By perceiving high spatial and temporal frequencies during motion, dynamic tactile sensing is able to sense fine surface features and monitor contact conditions, further recognizing materials [8; 9; 10]. In recent years, integrated tactile sensors that are large-scale and high-density have been well developed to realize dynamic tactile sensing for robots [10; 11]. Compared with force or torque sensorsthat record a single value on a fingertip or joint, these tactile sensors can be integrated on robots over a large area, meanwhile monitoring pressure distribution during interaction, which enables robots to localize objects, detect slippage, and recognize objects' sizes and surface properties [12; 13; 14; 15]. Since tactile sensors can provide abundant contact data, in one study kinds of dynamic tactile data that are relevant to vibration, pressure distribution, and thermal properties were obtained during pressing, sliding, scratching, and grasping, then the data were further analyzed to identify the material with its compliance, texture, and other properties [15; 16; 17; 18]. However, it is still challenging for robots to recognize interactive objects quickly and accurately, which is significant for robotic tactile sensing. Mimicking the manner of dynamic touch and tactile perception of the human hand, this paper presents a novel and effective material recognition method using a high-density tactile sensor. This method is composed of two steps. This first is a short-duration slide action conducted by the robot to obtain the tactile data of the material. The second step is the processing of tactile data with machine learning, which includes feature extraction and classifier training. In this method, a one-second human-touch-inspired slide action was employed as the exploratory procedure (EP), which assures the rapidness of the method. For robots, implementing the slide action is easier than grasping and other actions; for safety, the slide action is less harmful than grasp or press actions. Secondly, three material-related time sequences were calculated to abstract the tactile data. Then, 11 bioinspired features were extracted from the sequences based on the statistics and Wavelet transformation to mimic human tactile perception. Since the features extracted by statistic methods characterize the consistency and complexity of the sequence, the information imitates the signals of Ruffini Corpuscle and Merkel Cells of human touch. In addition, the features extracted by Wavelet transformation provide the information of vibration at different frequencies, which imitates the signals of the Pacinian Corpuscle and Meissner's Corpuscle. The finial feature vector was generated by principal component analysis (PCA).