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This paper tackles the task of singulating and grasping paper-like deformableobjects. We refer to such tasks as paper-flipping. In contrast to manipulatingdeformable objects that lack compression strength (such as shirts and ropes),minor variations in the physical properties of the paper-like deformableobjects significantly impact the results, making manipulation highlychallenging. Here, we present Flipbot, a novel solution for flipping paper-likedeformable objects. Flipbot allows the robot to capture object physicalproperties by integrating exteroceptive and proprioceptive perceptions that areindispensable for manipulating deformable objects. Furthermore, byincorporating a proposed coarse-to-fine exploration process, the system iscapable of learning the optimal control parameters for effective paper-flippingthrough proprioceptive and exteroceptive inputs. We deploy our method on areal-world robot with a soft gripper and learn in a self-supervised manner. Theresulting policy demonstrates the effectiveness of Flipbot on paper-flippingtasks with various settings beyond the reach of prior studies, including butnot limited to flipping pages throughout a book and emptying paper sheets in abox.

Flipbot: Learning Continuous Paper Flipping via Coarse-to-Fine Exteroceptive-Proprioceptive Exploration