Learning from demonstration with partially observable task parameters using dynamic movement primitives and Gaussian process regression

Tohid Alizadeh, Milad Malekzadeh, Soheila Barzegari

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

7 Citations (Scopus)

Abstract

The problem of learning from demonstration in the case of partially observable external task parameters is addressed in this paper. Such a situation could be present in the daily life scenarios, where information regarding some task parameters are missing or partially available. In the first step, one dynamic movement primitives (DMP) model is learned for each demonstration trajectory. The parameters of the learned DMP model are recorded together with the corresponding external task parameters (query points), to create a database. Then Gaussian process regression (GPR) is used to create a model for the external task parameters and the DMP parameters. During reproduction, DMP parameters are retrieved by providing the new external task parameters and are used to regenerate the trajectory. It is shown how the learning approach could be adapted to deal with the partially observable external task parameters and regenerate the proper trajectory. The proposed methodology is applied to learn a via-point passing experiment with a lightweight robot manipulator (KUKA robot) to illustrate the efficacy of the proposed approach.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages889-894
Number of pages6
Volume2016-September
ISBN (Electronic)9781509020652
DOIs
Publication statusPublished - Sep 26 2016
Event2016 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2016 - Banff, Canada
Duration: Jul 12 2016Jul 15 2016

Conference

Conference2016 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2016
CountryCanada
CityBanff
Period7/12/167/15/16

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications
  • Software

Fingerprint Dive into the research topics of 'Learning from demonstration with partially observable task parameters using dynamic movement primitives and Gaussian process regression'. Together they form a unique fingerprint.

  • Cite this

    Alizadeh, T., Malekzadeh, M., & Barzegari, S. (2016). Learning from demonstration with partially observable task parameters using dynamic movement primitives and Gaussian process regression. In 2016 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2016 (Vol. 2016-September, pp. 889-894). [7576881] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AIM.2016.7576881