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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 know-
ledge 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 op-
timise their own performance. This is why Festo is already closely
involved with machine learning capabilities.