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WMRA skill learning through segmentation of demonstration

  • Yao Yufeng
  • , Chi Mingshan
  • , Liu Yaxin*
  • , Du Qilong
  • , Wang Zhaomin
  • *Corresponding author for this work
  • Harbin Institute of Technology
  • Harbin University of Science and Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In order to let the wheelchair mounted robotic arm (WMRA) be more intelligent and adaptable, aiming to offer simple and convenient assistance for the elders and disabilities to cope with the complex tasks in the daily life, we present a learning method based on robot learning from demonstration to solve the problem. This method adopts the Beta Process Autoregressive Hidden Markov Model to segment the demonstrations of related task, acquire the contained skills and recognize the repeated skills. After that, it uses the Dynamic Movement Primitives to adjust the related skill according to the given goal position, so as to replay the demonstrated task in a new environment. This learning framework was validated on a six-degree-of-freedom JACO robotic arm, performing the task of drinking water from the bottle through a straw.

Original languageEnglish
Title of host publication2017 2nd International Conference on Advanced Robotics and Mechatronics, ICARM 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages540-545
Number of pages6
ISBN (Electronic)9781538632604
DOIs
StatePublished - 2 Jul 2017
Event2nd International Conference on Advanced Robotics and Mechatronics, ICARM 2017 - Hefei and Tai'an, China
Duration: 27 Aug 201731 Aug 2017

Publication series

Name2017 2nd International Conference on Advanced Robotics and Mechatronics, ICARM 2017
Volume2018-January

Conference

Conference2nd International Conference on Advanced Robotics and Mechatronics, ICARM 2017
Country/TerritoryChina
CityHefei and Tai'an
Period27/08/1731/08/17

Keywords

  • Learning from Demonstration
  • Skill Learning
  • WMRA

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