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DexiTac: Soft Dexterous Tactile Gripping

Chenghua Lu, Kailuan Tang, Max Yang, Tianqi Yue, Haoran Li, Nathan F. Lepora

Year
2024
Citations
19

Abstract

Grasping objects—whether they are flat, round, or narrow and whether they have regular or irregular shapes—introduces difficulties in determining the ideal grasping posture, even for the most state-of-the-art grippers. In this article, we presented a reconfigurable pneumatic gripper with fingers that could be set in various configurations, such as hooking, supporting, closuring, and pinching. Each finger incorporates a dexterous joint, a rotating joint, and a customized plug-and-play visuotactile sensor, the DigiTac-v1.5, to control manipulation in real time. We propose a tactile kernel density manipulation strategy for simple and versatile control, including detecting grasp stability, responding to disturbances, and guiding dexterous manipulations. We develop a double closed-loop control system that separately focuses on secure grasping and task management, demonstrated with tasks that highlight the previous capabilities. The gripper is relatively easy to fabricate and customize, offering a promising and extensible way to combine soft dexterity and tactile sensing for diverse applications in robotic manipulation.

Keywords

Soft roboticsComputer scienceHuman–computer interactionComputer visionArtificial intelligenceRobot

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