  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.
