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

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
發行者IEEE Computer Society
頁面1415-1420
頁數6
ISBN(電子)9781728103556
DOIs
出版狀態Published - 2019 八月
事件15th IEEE International Conference on Automation Science and Engineering, CASE 2019 - Vancouver, Canada
持續時間: 2019 八月 222019 八月 26

出版系列

名字IEEE International Conference on Automation Science and Engineering
2019-August
ISSN(列印)2161-8070
ISSN(電子)2161-8089

Conference

Conference15th IEEE International Conference on Automation Science and Engineering, CASE 2019
國家/地區Canada
城市Vancouver
期間19-08-2219-08-26

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 電氣與電子工程

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