## 1 Introduction  
With the recent developments in computer vision and range sensing, robots can detect objects reliably. However, grasping remains to be a question even with the correct location and pose of the object. The main reason is that predicting the grasping stability of the object in autonomous robotic manipulation tasks is still an important and difficult research topic. Grasping stability is defined as the capacity of the grasping to resist external forces and disturbances. A stable grasping can be viewed as a static equilibrium state of the object that is maintained when the grippers are closed. In the case of stable grasping, the grasped objects will keep the static equilibrium state when the manipulator moves. If the grippers do not grasp the object stably during the operation of the manipulator, it can lead to a state in the desired action sequence from which the system cannot recover easily. In comparison, humans can easily rely on remarkable tactile sensing capabilities and perceive the stability in grasping [1]. We can quickly identify textures and fine features using our fingertips, and while holding objects we subconsciously control grip forces and prevent slippage. When it comes to grasping with tools, we act as if they are extensions of our limbs, which requires not only dexterous manipulation but a sophisticated and implicit understanding of how the sensations at our fingertips related to the interactions between the tool and the environment. As for manipulator, it is similar and tactile sensors are applied in grasping. There are kinds of tactile sensors based on electric [2], optic [3], acoustic [4], or other effects [5,6]. Developments in tactile sensors also push the method of grasping stability prediction with these advanced sensors. In some cases, traditional tactile features or descriptors are used as the input. Ref [7] builds tactile features and they are fed to a support vector machine. [8] presents a modified SIFT descriptor, which deems the input as an image. In other cases, raw input is directly fed into the machine learning models or deep learning models. [9] uses support vector machines and random forests to predict the sensor. Similarly, [10] uses a multi-layer perceptron to classify grasping stability. As deep learning models have achieved large success in classification tasks in other domains such as vision and audio, [11] explores the possibilities of convolutional neural networks for the task of stability prediction. In addition, [12,13] use the graph convolutional network to predict the grasp stability with tactile sensors, which also receives a good performance. Most of the current methods to study this problem focus on how to identify the stability of the entire grasping process, while our goal is to predict the grasping process stable after the gripper has caught the object and before lifting it. Some works pay attention to this prediction such as [14] and [15]. While methods mentioned before are all designed to the specific sensing array and models cannot perform well on other sensors. We cannot transfer the existing prediction model and we have to train a new model for the new sensor from scratch. In this paper, we propose a unified tactile representation for grasping stability prediction and provide an end-to-end inference system for multiple sensors. The main contributions of this work can be divided into two parts. Firstly, a unified tactile representation is proposed for the utilization of transfer learning on different tactile sensors. Secondly, a novel network structure based on the tactile representation is proposed to provide an end-to-end approach to inference the grasping stability prediction for multiple sensors and we call it MM-CNN. The rest of the paper is organized as follows. In Section 2, we introduce the details of our dataset building. We propose a unified tactile representation and an end-to-end convolutional neural network for multiple tactile inputs with weight sharing mechanism in Section 3.