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The learning algorithms replace complex programming …

… and allow for quick commissioning of the system.


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.