We study the problem of detecting object grasp from an RGB-D image in cluttered scenes. In particular, we focus on grasping household objects by a two-finger robotic arm. Recent advances in this literature have made remarkable progress, thanks to the collection of large-scale object grasp datasets. However, due to the large shape variations of the real-world objects, existing approaches show weak capabilities on handling novel objects that have never been trained with. In this paper, to alleviate this problem, we propose a novel Domain Adaptation Grasp Network (DAGNet) to detect grasping poses for novel objects. The core of our method is a network training scheme that could efficiently transfer the grasp knowledge from known objects to novel ones. To demonstrate the effectiveness, we test the performance on both real and virtual robotic arm grasping scenarios. Experiments show that, compared with existing methods, the proposed DAGNet achieves better performance on grasping novel objects.


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    Title :

    Domain Adaptation Grasp Network for Novel Object Grasp Detection


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wu, Meiping (editor) / Niu, Yifeng (editor) / Gu, Mancang (editor) / Cheng, Jin (editor) / Cai, Xiangting (author) / Xu, Xin (author) / Ren, Shuai (author) / Shi, Yifei (author)

    Conference:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Publication date :

    2022-03-18


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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