Robotic grasp detection using a novel two-stage approach

• URL: http://arxiv.org/abs/2011.14123v1
• Date: Sat, 28 Nov 2020 12:26:35 GMT
• Title: Robotic grasp detection using a novel two-stage approach
• Authors: Zhe Chu, Mengkai Hu, Xiangyu Chen
• Abstract summary: Deep learning has been successfully applied to robotic grasp detection. We propose a two-stage approach using particle estimator (PSO) candidate swarm and CNN to detect the most likely grasp. Our approach achieved an accuracy of 92.8% on the Cornell Grasp dataset.
• Score: 5.595910672022999
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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