2 Self-learning: The LearningGripper’s four pneumatic fingers … … position the ball until the correct side is at the top. 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 12 pneumatic bellows actuators with low-level pressurisation. Thanks to the process of machine learning, it is able to teach itself to carry out complex actions such as, for example, gripping and positioning an object. Smart and intuitive – the LearningGripper principle In concrete terms, the gripper assigns itself the task of turning a ball so that a particular point of the ball points upwards. Based on the trial-and-error principle, the intelligent system thus acquires the motion sequences required to achieve this. The more time it spends learning, the more reliably it completes its task. Reduced programming effort With its LearningGripper, Festo demonstrates how, in the future, systems will be able to execute complex tasks independently without time-consuming programming. When the conventional procedure is used, the developer has to assign a separate action to each possible status of the fingers and the ball. Only the elementary actions and possible positions of the LearningGripper’s fingers, as well as the function for feedback from the environment, are defined in advance. The gripper is only told what to do, but not how to do it. The complex motion strategy required for this is developed independently by the gripper’s learning algorithms – without any further programming. Knowledge transfer to other grippers By transferring the strategy from one gripper to another, the second gripper is provided with the first gripper’s previous knowledge which it can use to develop its own strategy more efficiently. The more similar the hardware is for the two grippers, the more productive the transfer is. The more previous knowledge is available, the more quickly the system becomes fully functional. Potential for the factory of the future With this principle, self-learning systems like the LearningGripper could be built into future production lines and autonomously optimise their own performance. This is why Festo is already closely involved with machine learning capabilities.
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