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    VGQ-CNN: Moving Beyond Fixed Cameras and Top-Grasps for Grasp Quality Prediction


    Konrad, Anna and McDonald, John and Villing, Rudi (2022) VGQ-CNN: Moving Beyond Fixed Cameras and Top-Grasps for Grasp Quality Prediction. In: International Joint Conference on Neural Networks (IJCNN) 2022, July 18-23, 2022, Padua (Italy). (Unpublished)

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    Abstract

    We present the Versatile Grasp Quality Convo- lutional Neural Network (VGQ-CNN), a grasp quality pre- diction network for 6-DOF grasps. VGQ-CNN can be used when evaluating grasps for objects seen from a wide range of camera poses or mobile robots without the need to retrain the network. By defining the grasp orientation explicitly as an input to the network, VGQ-CNN can evaluate 6-DOF grasp poses, moving beyond the 4-DOF grasps used in most image- based grasp evaluation methods like GQ-CNN. To train VGQ- CNN, we generate the new Versatile Grasp dataset (VG-dset) containing 6-DOF grasps observed from a wide range of camera poses. VGQ-CNN achieves a balanced accuracy of 82.1% on our test-split while generalising to a variety of camera poses. Meanwhile, it achieves competitive performance for overhead cameras and top-grasps with a balanced accuracy of 74.2% compared to GQ-CNN’s 76.6%. We also propose a modified network architecture, Fast-VGQ-CNN, that speeds up inference using a shared encoder architecture and can make 128 grasp quality predictions in 12ms on a CPU. Code and data are available at https://aucoroboticsmu.github.io/vgq-cnn/

    Item Type: Conference or Workshop Item (Paper)
    Keywords: VGQ-CNN; Fixed Cameras; Top-Grasps; Grasp Quality Prediction;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 16564
    Depositing User: John McDonald
    Date Deposited: 22 Sep 2022 11:19
    Refereed: No
    URI:

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