Robot teaching by teleoperation based on visual interaction and neural network learning

Yang Xu, Chenguang Yang, Junpei Zhong, Hongbin Ma, Lijun Zhao, Min Wang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Traditional methods of Robot teaching require human demonstrators to program with a teaching pendant, which is a complex and time-consuming exercise. In this paper, we propose a novel method based on teleoperation which allows a demonstrator to train robot in an intuitive way. More specifically, at the beginning the demonstrator controls a robot by visual interaction. And then a learning algorithm based on radial basis function (RBF) network is used to transfer the demonstrator's motions to the robot. To verify the effectiveness of this developed methods, several simulation experiments have been carried out which based on Microsoft Kinect Sensor and the Virtual Robot Experimentation Platform (V-REP). The experimental results show that this method has achieved satisfactory performance. With the help of this method, the robot can not only complete the task autonomously after teaching, but also can learn the details of demonstrator's behavior.

Original languageEnglish
Title of host publicationProceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1068-1073
Number of pages6
ISBN (Electronic)9781509065738
DOIs
Publication statusPublished - 21 Mar 2018
Externally publishedYes
Event9th International Conference on Modelling, Identification and Control, ICMIC 2017 - Kunming, China
Duration: 10 Jul 201712 Jul 2017

Publication series

NameProceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017
Volume2018-March

Conference

Conference9th International Conference on Modelling, Identification and Control, ICMIC 2017
Country/TerritoryChina
CityKunming
Period10/07/1712/07/17

Keywords

  • radial basis function (RBF) network
  • Robot Teaching
  • Teleoperation

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Mechanics of Materials
  • Control and Optimization
  • Energy Engineering and Power Technology
  • Biomedical Engineering
  • Computational Mechanics
  • Modelling and Simulation

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