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 language | English |
|---|---|
| Title of host publication | SII 2016 - 2016 IEEE/SICE International Symposium on System Integration |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 453-458 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509033294 |
| DOIs | |
| Publication status | Published - Feb 6 2017 |
| Event | 2016 IEEE/SICE International Symposium on System Integration, SII 2016 - Sapporo, Japan Duration: Dec 13 2016 → Dec 15 2016 |
Conference
| Conference | 2016 IEEE/SICE International Symposium on System Integration, SII 2016 |
|---|---|
| Country | Japan |
| City | Sapporo |
| Period | 12/13/16 → 12/15/16 |
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ASJC Scopus subject areas
- Biomedical Engineering
- Control and Systems Engineering
- Mechanical Engineering
- Artificial Intelligence
- Hardware and Architecture
- Control and Optimization
Cite this
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 proceeding › Conference contribution
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TY - GEN
T1 - Identifying the relevant frames of reference in programming by demonstration using task-parameterized Gaussian mixture regression
AU - Alizadeh, Tohid
AU - Malekzadeh, Milad
PY - 2017/2/6
Y1 - 2017/2/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85015392032&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015392032&partnerID=8YFLogxK
U2 - 10.1109/SII.2016.7844040
DO - 10.1109/SII.2016.7844040
M3 - Conference contribution
AN - SCOPUS:85015392032
SP - 453
EP - 458
BT - SII 2016 - 2016 IEEE/SICE International Symposium on System Integration
PB - Institute of Electrical and Electronics Engineers Inc.
ER -