Gripping and positioning through independent learning
The LearningGripper from Festo looks like an abstract form of the human hand. The four fingers of the gripper are driven by twelve pneumatic bellows actuators with low-level pressurisation. Thanks to the machine learning process, the gripper is able to teach itself to carry out complex actions such as the targeted gripping and positioning of an object.
Reduced programming effort thanks to machine learning
The LearningGripper trade fair display demonstrates a gripper which takes less than an hour to learn a mechanical motion strategy – from its first attempt to the reliable execution of the required task. And the gripper is only told what to do, but not how to do it. The complex strategy required for this is developed independently by the gripper’s learning algorithms – without any further programming.
A second gripper demonstrates a process it had previously learned within the desired target scenario: it lifts the ball and turns it so that the embossed lettering can finally be seen at the top.