The LearningGripper looks like an abstract version of the human hand. Its actions also have a lot in common with its natural role model. The four-fingered robot gripper uses machine learning to independently learn to rotate a ball in a raised position in any given orientation.
The four fingers are driven by a total of 12 pneumatic bellows actuators with low pressure between 2.5 and 3.5 bar. Each of them has three degrees of freedom and the basic functions of the index finger. In the initial state alone, the entire hand thus has a choice of 3¹² total actions to reposition the ball. Due to the intelligent coordination of the fingers and the flexible bellows structure, the kinematics are free-moving and pliable. It can safely grip, lift and rotate even the most sensitive objects – just like its natural role model.
At the LearningGripper trade fair exhibit, a gripper demonstrates how it takes less than an hour for it to learn a mechanical motion strategy – from its first attempt to the reliable execution of the required task. A second gripper shows the learned procedure in the desired target scenario. It lifts the ball and turns it so that the embossed lettering on the end is visible in the centre on the top.