4 Smart: The learning algorithms replace complex programming … … and allow for quick commissioning of the system. Simple: knowledge transfer from one gripper to another Machine learning Machine learning methods are comparable to those of human beings. Their practical implementation within this subdivision of artificial intelligence is the work of algorithms which develop performance or motion strategies on the basis of feedback received on its behaviour. Just like people, machines also need feedback on their actions – positive as well as negative – in order to classify them and continue learning. Trial and error – learning through reinforcement The LearningGripper makes use of the method of reinforcement learning. The gripper optimises its capabilities exclusively on the basis of feedback that it receives concerning its previous actions. The system is not provided with specific actions which it has to imitate, as would be the case with supervised learning. The learning system alternates its actions with the key objective of maximising feedback over the long term. Consequently, this increases the probability that a successful action will be executed and that a less successful action will therefore not be repeated again. The reward principle At first, the LearningGripper attempts to randomly rotate the ball so that its label is on the top. It receives feedback from a position sensor inside the ball indicating how far the label is from the palm of the gripper’s hand – the greater the distance, the more positive the feedback. In time, the learning algorithms develop a motion strategy on the basis of this feedback. The gripper learns which action needs to be executed for each given status. It knows how to modify its motion so that it receives as much positive feedback as possible, and finally executes its task reliably. The LearningGripper display for trade fairs 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. A second gripper demonstrates a process it had learned previously within the desired target scenario: it lifts the ball and positions it so that the embossed lettering can finally be seen at the top.
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