Settings

Now showing1 - 2 of 2
  • Some of the metrics are blocked by your 
    consent settings
    Item type:Publication, Access status: without files ,
    Neurocognitive Shared Visuomotor Network for End-to-end Learning of Object Identification, Localization and Grasping on a Humanoid
    (2019-08)
    Kerzel, Matthias
    ;
    Eppe, Manfred
    ;
    Heinrich, Stefan
    ;
    Abawi, Fares
    ;
    Wermter, Stefan
    We present a unified visuomotor neural architecture for the robotic task of identifying, localizing, and grasping a goal object in a cluttered scene. The RetinaNet-based neural architecture enables end-to-end training of visuomotor abilities in a biological-inspired developmental approach. We demonstrate a successful development and evaluation of the method on a humanoid robot platform. The proposed architecture outperforms previous work on single object grasping as well as a modular architecture for object picking. An analysis of grasp errors suggests similarities to infant grasp learning: While the end-to-end architecture successfully learns grasp configurations, sometimes object confusions occur: when multiple objects are presented, salient objects are picked instead of the intended object.
    Publicationtype: Conference Paper
      16
  • Some of the metrics are blocked by your 
    consent settings
    Item type:Publication, Access status: without files ,
    An analysis of subtask-dependency in robot command interpretation with dilated CNNs
    (2018-04)
    Eppe, Manfred
    ;
    Alpay, Tayfun
    ;
    Abawi, Fares
    ;
    Wermter, Stefan
    In this paper, we tackle sequence-to-tree transduction for language processing with neural networks implementing several subtasks, namely tokenization, semantic annotation, and tree generation. Our research question is how the individual subtasks influence the overall end-to-end learning performance in case of a convolutional network with dilated perceptive fields. We investigate a benchmark problem for robot command interpretation and conclude that dilation has a strong positive effect for performing character-level transduction and for generating parsing trees.
    Publicationtype: Conference Paper
      18