Abstract
Many industrial processes remain being executed manually due to the necessary dexterity to perform them and their complexity to be replicated and automated. In this paper, we present TRAINMAN-MAGOS, a finger-tracking based solution which uses a sensorized glove to gather hand movements, and translates them into a tangible set of instructions and trajectories to feed diverse combinations of robot and gripper models. TRAINMAN-MAGOS decreases the development time of robotic trajectories and gripping strategies in extreme dexterity implementations. The solution has been validated on automotive glass door assembly operations, introducing a new generation of technologies to impact in manufacturing productivity.
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