The task of grasp pattern recognition aims to derive the applicable grasp types of an object according to the visual information. Current state-of-the-art methods ignore category information of objects which is crucial for grasp pattern recognition. This paper presents a novel dual-branch convolutional neural network (DcnnGrasp) to achieve joint learning of object category classification and grasp pattern recognition. DcnnGrasp takes object category classification as an auxiliary task to improve the effectiveness of grasp pattern recognition. Meanwhile, a new loss function called joint cross-entropy with an adaptive regularizer is derived through maximizing a posterior, which significantly improves the model performance. Besides, based on the new loss function, a training strategy is proposed to maximize the collaborative learning of the two tasks. The experiment was performed on five household objects datasets including the RGB-D Object dataset, Hit-GPRec dataset, Amsterdam library of object images (ALOI), Columbia University Image Library (COIL-100), and MeganePro dataset 1. The experimental results demonstrated that the proposed method can achieve competitive performance on grasp pattern recognition with several state-of-the-art methods. Specifically, our method even outperformed the second-best one by nearly 15% in terms of global accuracy for the case of testing a novel object on the RGB-D Object dataset.
翻译:抓取模式识别的任务旨在根据视觉信息得出一个对象的适用抓取类型。当前最先进的方法忽略了对于抓取模式识别至关重要的物体类别信息。本文件展示了一个新的双部门进化神经网络(DcnnGrasp),以共同学习对象类别分类和抓取模式识别。DcnnGrasp将对象类别分类作为一项辅助任务,以提高抓取模式识别的有效性。与此同时,通过最大限度地增加一个后台,从而大大改进模型性能,从而产生一个新的损失函数,称为与适应性定律器的交叉渗透性联合体。此外,根据新的损失函数,还提议了一项培训战略,以最大限度地合作学习这两项任务。实验针对五个家用对象数据集,包括 RGB-D 对象数据集、Hit-GPRec数据集、阿姆斯特丹对象图像图书馆(ALOI)、哥伦比亚大学图像图书馆(COIL-100)和MeanePro数据集。1. 实验结果表明,拟议的方法可以在掌握模式识别模式识别方面实现竞争性业绩,以若干州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-