Hoch, SebastianSebastianHochLange, SaschaSaschaLangeKeuper, JanisJanisKeuper2023-05-232023-05-232021https://publica.fraunhofer.de/handle/publica/44208510.14428/esann/2021.ES2021-24Engineering, construction and operation of complex machines involves a wide range of complicated, simultaneous tasks, which potentially could be automated. In this work, we focus on perception tasks in such systems, investigating deep learning approaches for multi-task transfer learning with limited training data. We show an approach that takes advantage of a technical systems’ focus on selected objects and their properties. We create focused representations and simultaneously solve joint objectives in a system through multi-task learning with convolutional autoencoders. The focused representations are used as a starting point for the data-saving solution of the additional tasks. The efficiency of this approach is demonstrated using images and tasks of an autonomous circular crane with a grapple.enDDC::500 Naturwissenschaften und Mathematikconference paper