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Grasping has been studied from various perspectives including planning, control, and learning. In this paper, we take a learning approach to predict successful grasps for a universal jamming gripper. A jamming gripper is comprised of a flexible membrane filled with granular material, and it can quickly harden or soften to grip objects of varying shape by modulating the air pressure within the membrane. Although this gripper is easy to control, it is difficult to develop a physical model of its gripping mechanism because it undergoes significant deformation during use. Thus, many grasping approaches based on physical models (such as based on form- and force-closure) would be challenging to apply to a jamming gripper. Here we instead use a supervised learning algorithm and design both visual and shape features for capturing the property of good grasps. We show that given a RGB image and a point cloud of the target object, our algorithm can predict successful grasps for the jamming gripper without requiring a physical model. It can therefore be applied to both a parallel plate gripper and a jamming gripper without modification. We demonstrate that our learning algorithm enables both grippers to pick up a wide variety of objects, and through robotic experiments we are able to define the type of objects each gripper is best suited for handling.

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