The application of robotic manipulation of deformable materials, such as cloth, spans various sectors including fabric manufacturing and domestic laundry management. Historically, most methodologies have employed vision-based sensors as the proprioceptive input to robot policies. However, this study aims to explore an alternate route by leveraging tactile sensing for the manipulation of deformable objects.The research commences with a comprehensive review of the array of tactile sensors applicable to robotics, offering a streamlined framework for sensor selection. Subsequently, it delves into the integrating the tactile sensor data into robot policies. This encompasses challenges such as managing sensor noise, gathering annotated tactile data, and applying machine learning techniques to further parse the tactile sensor readings. The research culminates in an exhibition of a custom-made gripper equipped with tactile sensors, demonstrated in a task of grasping multiple layers of thin cloth. Remarkably, the performance  supersedes traditional vision-based methodologies. Moreover, the tactile sensors facilitate the generalization to out-of-distribution cloth, a task in which vision-based methods falter.The study concludes by articulating the advantages and limitations of employing tactile sensors and proposes potential future work aimed at further incorporation of tactile sensing.