Visual-guided robot arm using self-supervised deep convolutional neural networks

Van Thanh Nguyen, Chao Lin, Chih Hung G. Li, Shu Mei Guo, Jenn Jier James Lien

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Perception-based learning approaches to robotic grasping have shown significant promise. This is further reinforced by using supervised deep learning in robotic arm. However, to properly train deep networks and prevent overfitting, massive datasets of labelled samples must be available. Creating such datasets by human labelling is an exhaustive task since most objects can be grasped at multiple points and in several orientations. Accordingly, this work employs a self-supervised learning technique in which the training dataset is labelled by the robot itself. Above all, we propose a cascaded network that reduces the time of the grasping task by eliminating ungraspable samples from the inference process. In addition to grasping task which performs pose estimation, we enlarge the network to perform an auxiliary task, object classification in which data labelling can be done easily by human. Notably, our network is capable of estimating 18 grasping poses and classifying 4 objects simultaneously. The experimental results show that the proposed network achieves an accuracy of 94.8% in estimating the grasping pose and 100% in classifying the object category, in 0.65 seconds.

Original languageEnglish
Title of host publication2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
PublisherIEEE Computer Society
Pages1415-1420
Number of pages6
ISBN (Electronic)9781728103556
DOIs
Publication statusPublished - 2019 Aug
Event15th IEEE International Conference on Automation Science and Engineering, CASE 2019 - Vancouver, Canada
Duration: 2019 Aug 222019 Aug 26

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2019-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference15th IEEE International Conference on Automation Science and Engineering, CASE 2019
CountryCanada
CityVancouver
Period19-08-2219-08-26

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Visual-guided robot arm using self-supervised deep convolutional neural networks'. Together they form a unique fingerprint.

  • Cite this

    Nguyen, V. T., Lin, C., Li, C. H. G., Guo, S. M., & Lien, J. J. J. (2019). Visual-guided robot arm using self-supervised deep convolutional neural networks. In 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019 (pp. 1415-1420). [8842899] (IEEE International Conference on Automation Science and Engineering; Vol. 2019-August). IEEE Computer Society. https://doi.org/10.1109/COASE.2019.8842899