  This paper tackles the task of singulating and grasping paper-like deformable
objects. We refer to such tasks as paper-flipping. In contrast to manipulating
deformable objects that lack compression strength (such as shirts and ropes),
minor variations in the physical properties of the paper-like deformable
objects significantly impact the results, making manipulation highly
challenging. Here, we present Flipbot, a novel solution for flipping paper-like
deformable objects. Flipbot allows the robot to capture object physical
properties by integrating exteroceptive and proprioceptive perceptions that are
indispensable for manipulating deformable objects. Furthermore, by
incorporating a proposed coarse-to-fine exploration process, the system is
capable of learning the optimal control parameters for effective paper-flipping
through proprioceptive and exteroceptive inputs. We deploy our method on a
real-world robot with a soft gripper and learn in a self-supervised manner. The
resulting policy demonstrates the effectiveness of Flipbot on paper-flipping
tasks with various settings beyond the reach of prior studies, including but
not limited to flipping pages throughout a book and emptying paper sheets in a
box.
