DMP and GMR based teaching by demonstration for a KUKA LBR robot

Alexander Hewitt, Chenguang Yang, Yong Li, Rongxin Cui

科研成果: 书/报告/会议事项章节会议稿件同行评审

13 引用 (Scopus)

摘要

This paper investigates the problem of Teaching by Demonstration (TbD) on a KUKA lightweight robot (LBR). Motions are recorded by a human operator, and then the data is used to model a nonlinear system, i.e., the dynamic motor primitive (DMP). In order to learn from multiple demonstrations, Gaussian Mixture Models (GMM) are employed rather than using conventional Gaussian process for the evaluation of the non-linear term of the DMP. Then the Gaussian mixture regression (GMR) algorithm is applied to generate a synthesized trajectory with smaller position errors in 3D space. The proposed approach is tested and demonstrated by performing two tasks with KUKA iiwa robot.

源语言英语
主期刊名ICAC 2017 - 2017 23rd IEEE International Conference on Automation and Computing
主期刊副标题Addressing Global Challenges through Automation and Computing
编辑Jie Zhang
出版商Institute of Electrical and Electronics Engineers Inc.
ISBN(电子版)9780701702618
DOI
出版状态已出版 - 23 10月 2017
活动23rd IEEE International Conference on Automation and Computing, ICAC 2017 - Huddersfield, 英国
期限: 7 9月 20178 9月 2017

出版系列

姓名ICAC 2017 - 2017 23rd IEEE International Conference on Automation and Computing: Addressing Global Challenges through Automation and Computing

会议

会议23rd IEEE International Conference on Automation and Computing, ICAC 2017
国家/地区英国
Huddersfield
时期7/09/178/09/17

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