When manipulating textiles and thin objects, one challenging task is to push (tuck) of textiles into small openings. Further, for a number of manufacturing tasks it poses an obstacle to automation. However, current robot grippers are almost exclusively designed for grasping objects, or imitate the human hand with very fine mechanisms that break easily, so that they cannot withstand the loads required by the pushing task. Further, as textiles are very thin, even grippers with pressure pads cannot easily confirm if a textile was grasped successfully or if the gripper is empty. In this research, we present a gripper design that can detect the successful grasping of thin objects via active perception, can sustain significant pushing loads in order to perform tucking tasks and can perform dexterous grasping and in-hand manipulation. The gripper is open-source and can be 3D printed. We demonstrate the gripper's performance experimentally, its precision when controlling its grasp force, and the maximum grasping force.
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