Deep learning for detecting robotic grasps
Deep learning for detecting robotic grasps
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DOI：
10.1177/0278364914549607
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发表时间：
2015-04-01
影响因子：
9.2
通讯作者：
Saxena, Ashutosh
中科院分区：
文献类型：
--
作者：
Lenz, Ian;Lee, Honglak;Saxena, Ashutosh
我们考虑的问题，检测机器人掌握在一个RGB-D视图的场景包含对象。在这项工作中，我们应用深度学习方法来解决这个问题，避免了耗时的手工设计功能。这带来了两个主要挑战。首先，我们需要评估大量的候选抓取。为了使检测快速和鲁棒，我们提出了一个具有两个深度网络的两步级联系统，其中第一个深度网络的顶部检测由第二个深度网络重新评估。第一个网络具有更少的特征，运行速度更快，并且可以有效地修剪掉不太可能的候选抓取。第二个，有更多的功能，速度较慢，但只能运行在顶部的几个检测。其次，我们需要有效地处理多模态输入，为此，我们提出了一种基于多模态组正则化对权重应用结构化正则化的方法。我们表明，我们的方法提高了RGBD机器人抓取数据集的性能，并可用于在两个不同的机器人平台上成功执行抓取。
We consider the problem of detecting robotic grasps in an RGB-D view of a scene containing objects. In this work, we apply a deep learning approach to solve this problem, which avoids time-consuming hand-design of features. This presents two main challenges. First, we need to evaluate a huge number of candidate grasps. In order to make detection fast and robust, we present a two-step cascaded system with two deep networks, where the top detections from the first are re-evaluated by the second. The first network has fewer features, is faster to run, and can effectively prune out unlikely candidate grasps. The second, with more features, is slower but has to run only on the top few detections. Second, we need to handle multimodal inputs effectively, for which we present a method that applies structured regularization on the weights based on multimodal group regularization. We show that our method improves performance on an RGBD robotic grasping dataset, and can be used to successfully execute grasps on two different robotic platforms.