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Auflistung nach Autor:in "Fabisch, Alexander"

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  • Zeitschriftenartikel
    Accounting for Task-Difficulty in Active Multi-Task Robot Control Learning
    (KI - Künstliche Intelligenz: Vol. 29, No. 4, 2015) Fabisch, Alexander; Metzen, Jan Hendrik; Krell, Mario Michael; Kirchner, Frank
    Contextual policy search is a reinforcement learning approach for multi-task learning in the context of robot control learning. It can be used to learn versatilely applicable skills that generalize over a range of tasks specified by a context vector. In this work, we combine contextual policy search with ideas from active learning for selecting the task in which the next trial will be performed. Moreover, we use active training set selection for reducing detrimental effects of exploration in the sampling policy. A core challenge in this approach is that the distribution of the obtained rewards may not be directly comparable between different tasks. We propose the novel approach PUBSVE for estimating a reward baseline and investigate empirically on benchmark problems and simulated robotic tasks to which extent this method can remedy the issue of non-comparable reward.
  • Zeitschriftenartikel
    Towards Learning of Generic Skills for Robotic Manipulation
    (KI - Künstliche Intelligenz: Vol. 28, No. 1, 2014) Metzen, Jan Hendrik; Fabisch, Alexander; Senger, Lisa; Gea Fernández, José; Kirchner, Elsa Andrea
    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 'Behaviors for Mobile Manipulation', 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.