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.
翻译:最近,深层次的学习被成功地应用于机器人掌握的探测。根据进化神经网络(CNNs),有很多端到端的检测方法。但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,在实际使用上很难实现。因此,我们提出了使用粒子群优化(PSO)候选估测器和CNN的两阶段方法,以探测最可能的捕捉。我们的方法在Cornell Grasp数据集上达到了92.8%的精确度,该数据集跳入了现有方法的前列,能够以实时速度运行。在对方法稍作改变后,我们可以预测每个物体的多重捕捉力,从而可以以多种方式捕捉物体。