Robotic grasp detection using a novel two-stage approach
This work provides an incremental improvement in robotic grasp detection accuracy and speed for robotic manipulation tasks.
This paper addresses the challenge of robotic grasp detection, proposing a two-stage method that combines a particle swarm optimizer (PSO) for candidate estimation with a convolutional neural network (CNN). The approach achieved 92.8% accuracy on the Cornell Grasp Dataset and operates in real-time.
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.