An End-to-End Grasping Stability Prediction Network for Multiple Sensors

Abstract

As we all know, the output of the tactile sensing array on the gripper can be used to predict grasping stability. Some methods utilize traditional tactile features to make the decision and some advanced methods use machine learning or deep learning ways to build a prediction model. While these methods are all limited to the specific sensing array and have two common disadvantages. On the one hand, these models cannot perform well on different sensors. On the other hand, they do not have the ability of inferencing on multiple sensors in an end-to-end manner. Thus, we aim to find the internal relationships among different sensors and inference the grasping stability of multiple sensors in an end-to-end way. In this paper, we propose the MM-CNN (mask multi-head convolutional neural network), which can be utilized to predict the grasping stability on the output of multiple sensors with the weight sharing mechanism. We train this model and evaluate it on our own collected datasets. This model achieves 99.49% and 94.25% prediction accuracy on two different sensing arrays, separately. In addition, we show that our proposed structure is also available for other CNN backbones and can be easily integrated.