  In essence, successful grasp boils down to correct responses to multiple
contact events between fingertips and objects. In most scenarios, tactile
sensing is adequate to distinguish contact events. Due to the nature of high
dimensionality of tactile information, classifying spatiotemporal tactile
signals using conventional model-based methods is difficult. In this work, we
propose to predict and classify tactile signal using deep learning methods,
seeking to enhance the adaptability of the robotic grasp system to external
event changes that may lead to grasping failure. We develop a deep learning
framework and collect 6650 tactile image sequences with a vision-based tactile
sensor, and the neural network is integrated into a contact-event-based robotic
grasping system. In grasping experiments, we achieved 52% increase in terms of
object lifting success rate with contact detection, significantly higher
robustness under unexpected loads with slip prediction compared with open-loop
grasps, demonstrating that integration of the proposed framework into robotic
grasping system substantially improves picking success rate and capability to
withstand external disturbances.
