Robotic grasp detection using a novel two-stage approach

TL;DR

This paper introduces a two-stage robotic grasp detection method combining particle swarm optimization and CNNs, achieving high accuracy and real-time performance, with the ability to predict multiple grasps per object.

Contribution

It presents a novel two-stage approach that overcomes dataset requirements of end-to-end methods, improving accuracy and enabling multiple grasp predictions.

Findings

Achieved 92.8% accuracy on Cornell Grasp Dataset

Operates at real-time speeds

Can predict multiple grasps per object

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.