Identifying the relevant frames of reference in programming by demonstration using task-parameterized Gaussian mixture regression

Tohid Alizadeh, Milad Malekzadeh

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

2 Citations (Scopus)

Abstract

Automatic identification of the relevant frames of references (or external task parameters) in programming by demonstration using the task-parameterized Gaussian mixture regression (TP-GMM) is addressed in this paper. While performing a given task, there may be several external task parameters, some of which are relevant to the specific task, while some others are not relevant. Identifying the irrelevant task parameters could help to automatize the selection of task parameters, construct a more compact model of the task and achieve better performances in the reproduction phase. At first, all the potential candidate frames of references will be taken into account, then, after identifying the irrelevant ones, they will be removed from the model, and only the relevant frames will be used for the reproduction. The performance of the approach is testified through an experiment, where the reproduction with only the relevant frames of references provides much better results compared to the case of including all candidate frames of references.

Original languageEnglish
Title of host publicationSII 2016 - 2016 IEEE/SICE International Symposium on System Integration
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages453-458
Number of pages6
ISBN (Electronic)9781509033294
DOIs
Publication statusPublished - Feb 6 2017
Event2016 IEEE/SICE International Symposium on System Integration, SII 2016 - Sapporo, Japan
Duration: Dec 13 2016Dec 15 2016

Conference

Conference2016 IEEE/SICE International Symposium on System Integration, SII 2016
CountryJapan
CitySapporo
Period12/13/1612/15/16

Fingerprint

Gaussian Mixture
Demonstrations
Programming
Regression
Experiments
Model
Experiment

ASJC Scopus subject areas

  • Biomedical Engineering
  • Control and Systems Engineering
  • Mechanical Engineering
  • Artificial Intelligence
  • Hardware and Architecture
  • Control and Optimization

Cite this

Alizadeh, T., & Malekzadeh, M. (2017). Identifying the relevant frames of reference in programming by demonstration using task-parameterized Gaussian mixture regression. In SII 2016 - 2016 IEEE/SICE International Symposium on System Integration (pp. 453-458). [7844040] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SII.2016.7844040

Identifying the relevant frames of reference in programming by demonstration using task-parameterized Gaussian mixture regression. / Alizadeh, Tohid; Malekzadeh, Milad.

SII 2016 - 2016 IEEE/SICE International Symposium on System Integration. Institute of Electrical and Electronics Engineers Inc., 2017. p. 453-458 7844040.

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

Alizadeh, T & Malekzadeh, M 2017, Identifying the relevant frames of reference in programming by demonstration using task-parameterized Gaussian mixture regression. in SII 2016 - 2016 IEEE/SICE International Symposium on System Integration., 7844040, Institute of Electrical and Electronics Engineers Inc., pp. 453-458, 2016 IEEE/SICE International Symposium on System Integration, SII 2016, Sapporo, Japan, 12/13/16. https://doi.org/10.1109/SII.2016.7844040
Alizadeh T, Malekzadeh M. Identifying the relevant frames of reference in programming by demonstration using task-parameterized Gaussian mixture regression. In SII 2016 - 2016 IEEE/SICE International Symposium on System Integration. Institute of Electrical and Electronics Engineers Inc. 2017. p. 453-458. 7844040 https://doi.org/10.1109/SII.2016.7844040
Alizadeh, Tohid ; Malekzadeh, Milad. / Identifying the relevant frames of reference in programming by demonstration using task-parameterized Gaussian mixture regression. SII 2016 - 2016 IEEE/SICE International Symposium on System Integration. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 453-458
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