"We are investigating how the most versatile training data possible from multiple locations can be used to develop more robust and efficient solutions using artificial intelligence algorithms than with data from just one robot," says Jonathan Auberle from the Institute for Material Handling and Logistics (IFL) at Karlsruhe Institute of Technology (KIT).
As part of this, autonomous robots process items by gripping and moving them at multiple picking stations. The robots are trained using totally different items at the various stations. At the end, they should also be able to grip items from other stations that they have not yet learned about. "By using distributed learning, also called federated learning, we are able to strike the right balance between the amount of data and the security of data in an industrial environment," says Jonathan Auberle.