Towards Learning of Generic Skills for Robotic Manipulation

Jan Hendrik Metzen, Alexander Fabisch, Lisa Gutzeit, José de Gea Fernández, Elsa Andrea Kirchner

In: KI - Künstliche Intelligenz, German Journal on Artificial Intelligence - Organ des Fachbereiches "Künstliche Intelligenz" der Gesellschaft für Informatik e.V. (KI) 28 1 Seiten 15-20 Springer Berlin Heidelberg 3/2014.


Learning versatile, reusable skills is one of the key prerequisites for autonomous robots. Imitation and reinforcement learning are among the most prominent approaches for learning basic robotic skills. However, the learned skills are often very specific and cannot be reused in different but related tasks. In the project BesMan, we develop hierarchical and transfer learning methods which allow a robot to learn a repertoire of versatile skills that can be reused in different situations. The development of new methods is closely integrated with the analysis of complex human behavior.


Weitere Links

131009_Towards_Learning_of_Generic_Skills_for_Robotic_Manipulation_KI_Metzen.pdf (pdf, 600 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence