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., Springer, volume 28, number 1, pages 15-20, Mar/2014.
Zusammenfassung (Abstract)
:
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.
Stichworte
:
Multi-Task Learning · Skill Learning · Movement Primitives · Transfer Learning · Reinforcement Learning
Files:
131009_Towards_Learning_of_Generic_Skills_for_Robotic_Manipulation_KI_Metzen.pdf
Links:
https://link.springer.com/article/10.1007/s13218-013-0280-1