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, developing a physical model of its gripping mechanism is difficult 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 properties of good grasps. We show that given target object data from an RGBD sensor, 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, including objects from outside the training set. Through robotic experiments we are then able to define the type of objects each gripper is best suited for handling.

We describe a simple passive universal gripper, consisting of a mass of granular material encased in an elastic membrane. Using a combination of positive and negative pressure, the gripper can rapidly grip and release a wide range of objects that are typically challenging for universal grippers, such as flat objects, soft objects, or objects with complex geometries. The gripper passively conforms to the shape of a target object, then vacuum-hardens to grip it rigidly, later utilizing positive pressure to reverse this transition-releasing the object and returning to a deformable state. We describe the mechanical design and implementation of this gripper and quantify its performance in real-world testing situations. By using both positive and negative pressure, we demonstrate performance increases of up to 85% in reliability, 25% in error tolerance, and the added capability to shoot objects by fast ejection. In addition, multiple objects are gripped and placed at once while maintaining their relative distance and orientation. We conclude by comparing the performance of the proposed gripper with others in the field.