  Robotic manipulation of cloth has applications ranging from fabrics
manufacturing to handling blankets and laundry. Cloth manipulation is
challenging for robots largely due to their high degrees of freedom, complex
dynamics, and severe self-occlusions when in folded or crumpled configurations.
Prior work on robotic manipulation of cloth relies primarily on vision sensors
alone, which may pose challenges for fine-grained manipulation tasks such as
grasping a desired number of cloth layers from a stack of cloth. In this paper,
we propose to use tactile sensing for cloth manipulation; we attach a tactile
sensor (ReSkin) to one of the two fingertips of a Franka robot and train a
classifier to determine whether the robot is grasping a specific number of
cloth layers. During test-time experiments, the robot uses this classifier as
part of its policy to grasp one or two cloth layers using tactile feedback to
determine suitable grasping points. Experimental results over 180 physical
trials suggest that the proposed method outperforms baselines that do not use
tactile feedback and has better generalization to unseen cloth compared to
methods that use image classifiers. Code, data, and videos are available at
https://sites.google.com/view/reskin-cloth.
