## 1 Introduction  
Haptic interpretation is an important component of perception, providing real-time feedback on changes in the external environment, and has been widely used in practical applications, such as smart machines, wearable devices, and human-computer interaction [1; 2]. The essence of haptic recognition is the recharacterization and feature extraction of pressure information to obtain multiple properties of the contact object, which is the basis for subsequent actions such as grasping, manipulating, and moving. The generic method is to consider the haptic information as a two-dimensional image. Therefore, visual data-based approaches are introduced for understanding haptic information. SIFT, SURF, chain code and other descriptors combined with clustering are applied for haptic recognition [3; 4]. [PERSON] et al. applied the BoW technique using SIFT for feature extraction and k-nearest neighbors (KNN) for evaluation to achieve object recognition by a tactile glove [5]. These local feature methods require manual feature design for specific task and are labor-intensive. Recent research has proved that convolutional neural networks (CNNs) are suitable for two-dimensional information and can automatically learn features and recognize objects [6; 7]. Therefore, the haptic information can be ported to the CNN for automatic recognition. [PERSON] et al. proposed classical transfer CNN models combined with transfer learning to identify large items [8]. [PERSON] et al. designed a CNN encoder structure to reduce the dimensionality of the optical-based tactile sensor image output [9]. [PERSON] et al. trained an end-to-end CNN for tactile recognition using residual orthogonal tiling and pyramid convolution ensemble [10]. Although the existing CNN methods can achieve better recognition performance than traditional artificial features, there are still the following challenges. Firstly, the size and pixels'amount of haptic image depend on the density and area of the pressure sensor, and thus they are at least two orders of magnitude lower than visual RGB images [11; 12]. Current mainstream CNNs are constructed for high-resolution visual images. The nonlinear fitting capability is enhanced by deepening the network [13]. However, the low-resolution haptic image limits the configured network depth, so the features extracted by shallow CNN are insufficient. Secondly, the inevitable nonideal effects of the sensing element itself reduce the accuracy of the original information mapping. Due to the flexible requirements and complex manufacturing process of the sensing elements, the pseudo-outputs caused by elastic coupling and restricted response range are inevitable, resulting in blurred pressure images [14]. In addition, the fineness of the haptic image relies on the sensitivity and response range of the sensor, which is much lower than that of the visual image. Different objects show similar shapes, so more distinguishable features are needed. The traditional shallow CNNs are not adequate in feature extraction capability and feature utilization efficiency. To address the low-resolution and blur problem in haptic perception, a multiorder attentional spatial interactive convolutional neural network (MoAS-CNN) is proposed and a pressure information acquisition platform is built (flowchart of the overall framework is shown in Figure 1). The former improves the nonlinear fitting capability of the network by deepening the network and adding channel responses to enhance the representation of first-order high-level features; introducing spatial interactive second-order features to enhance the representation of edges and fusing multiorder features to enhance the efficiency of feature utilization. The latter validates the proposed method by constructing haptic letter shapes with complex edges. The remainder of the paper is organized as follows. Section 2 discusses the nonideal characteristics about haptic images and the inadequacy of shallow CNN. In Section 3, the MoAS-CNN is proposed and each part is described.