A Systematic Practice of Judging the Success of a Robotic Grasp Using Convolutional Neural Network

Authors

  • Hengshuang Liu Central China Normal University
  • Pengcheng Ai Central China Normal University
  • Junling Chen Central China Normal University

DOI:

https://doi.org/10.1609/aaai.v31i1.11066

Keywords:

Robotic grasping, Convolutional neural network, Generalization capability

Abstract

In this abstract, we present a novel method using the deep convolutional neural network combined with traditional mechanical control techniques to solve the problem of determining whether a robotic grasp is successful or not. To finish the task, we construct a data acquisition platform capable of robot arm grasping and photo capturing, and collect a diversity of pictures by adjusting the shape and posture of the objects and controlling the robot arm to move randomly. For the purpose of validating the generalization capability, we adopt a stochastic sampling method based on cross validation to test our model. The experiment shows that, with an increasing number of shapes of objects involved in training, the network can identify new samples in a more accurate and steadier way. The accuracy rises from 89.2% when we use only one category of shape for training to above 99.7% when we use 17 categories for training.

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Published

2017-02-12

How to Cite

Liu, H., Ai, P., & Chen, J. (2017). A Systematic Practice of Judging the Success of a Robotic Grasp Using Convolutional Neural Network. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.11066