arXiv 2003.056242020-03-12cs.RO

基于多任务学习的抓取位置检测和Few-Shot分类框架

A Multi-task Learning Framework for Grasping-Position Detection and Few-Shot Classification

Fujitsu Laboratories LTD.（富士通实验室有限公司）

Yasuto Yokota, Kanata Suzuki, Yuzi Kanazawa, Tomoyoshi Takebayashi

备注：: 7 pages

摘要：

采摘机器人的深度学习模型需要大量的标记图像是一个很大的问题。由于产品或零件的目标形状在工厂中经常发生变化，重新训练模型的运营成本变得非常昂贵。重要的是减少训练拣选机器人模型所需的标记图像的量。在这项研究中，我们提出了一个多任务的学习框架，Few-Shot分类使用的特征向量，从一个中间层的模型，检测抓取位置。在制造业中，拾取机器人常常需要完成形状分类和抓取位置检测的多任务。先前的多任务学习研究包括从为另一任务学习的深度神经网络（DNN）学习一个任务的特征向量的方法。然而，用于检测抓握位置的DNN在从用于形状分类的层中提取特征向量方面存在两个问题：（1）由于抓握位置检测DNN的每一层由输入图像中的所有对象激活，因此需要细化每个抓握位置的特征。(2)需要选择一个层来提取适合于形状分类的特征。为了解决这些问题，我们提出了一种方法来细化每个抓取位置的特征，并从DNN的最佳层中选择特征。然后，我们评估了形状分类的准确性，使用这些功能从抓的位置。我们的研究结果证实，我们提出的框架可以分类对象的形状，即使当输入图像包括多个对象和可用于训练的图像的数量是小的。

英文摘要：

It is a big problem that a model of deep learning for a picking robot needs many labeled images. Operating costs of retraining a model becomes very expensive because the object shape of a product or a part often is changed in a factory. It is important to reduce the amount of labeled images required to train a model for a picking robot. In this study, we propose a multi-task learning framework for few-shot classification using feature vectors from an intermediate layer of a model that detects grasping positions. In the field of manufacturing, multitask for shape classification and grasping-position detection is often required for picking robots. Prior multi-task learning studies include methods to learn one task with feature vectors from a deep neural network (DNN) learned for another task. However, the DNN that was used to detect grasping positions has two problems with respect to extracting feature vectors from a layer for shape classification: (1) Because each layer of the grasping position detection DNN is activated by all objects in the input image, it is necessary to refine the features for each grasping position. (2) It is necessary to select a layer to extract the features suitable for shape classification. To tackle these issues, we propose a method to refine the features for each grasping position and to select features from the optimal layer of the DNN. We then evaluated the shape classification accuracy using these features from the grasping positions. Our results confirm that our proposed framework can classify object shapes even when the input image includes multiple objects and the number of images available for training is small.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

摘要：

英文摘要：

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