论文标题
使用新型的两阶段方法的机器人抓取检测
Robotic grasp detection using a novel two-stage approach
论文作者
论文摘要
最近，深度学习已成功地应用于机器人的掌握检测。基于卷积神经网络（CNN），已经采用了许多端到端检测方法。但是，端到端的方法对用于培训神经网络模型的数据集有严格的要求，并且在实际使用中很难实现。因此，我们提出了使用粒子群优化器（PSO）候选估计量和CNN进行两阶段方法来检测最可能的掌握。我们的方法在Cornell Grasp数据集上达到了92.8％的准确性，该数据集进入了现有方法的前排，并能够以实时的速度运行。经过一小段的方法，我们可以在此期间预测每个对象的多个grasps，以便可以通过多种方式抓住对象。
Recently, deep learning has been successfully applied to robotic grasp detection. Based on convolutional neural networks (CNNs), there have been lots of end-to-end detection approaches. But end-to-end approaches have strict requirements for the dataset used for training the neural network models and it's hard to achieve in practical use. Therefore, we proposed a two-stage approach using particle swarm optimizer (PSO) candidate estimator and CNN to detect the most likely grasp. Our approach achieved an accuracy of 92.8% on the Cornell Grasp Dataset, which leaped into the front ranks of the existing approaches and is able to run at real-time speeds. After a small change of the approach, we can predict multiple grasps per object in the meantime so that an object can be grasped in a variety of ways.
