| Learning parametric dynamic movement primitives from multiple demonstrations. | |
| | |
MedLine Citation:
|
PMID: 21388784 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
|
Learning from demonstration has shown to be a suitable approach for learning control policies (CPs). However, most previous studies learn CPs from a single demonstration, which results in limited scalability and insufficient generalization toward a wide range of applications in real environments. This paper proposes a novel approach to learn highly scalable CPs of basis movement skills from multiple demonstrations. In contrast to conventional studies with a single demonstration, i.e., dynamic movement primitives (DMPs), our approach efficiently encodes multiple demonstrations by shaping a parametric-attractor landscape in a set of differential equations. Assuming a certain similarity among multiple demonstrations, our approach learns the parametric-attractor landscape by extracting a small number of common factors in multiple demonstrations. The learned CPs allow the synthesis of novel movements with novel motion styles by specifying the linear coefficients of the bases as parameter vectors without losing useful properties of the DMPs, such as stability and robustness against perturbations. For both discrete and rhythmic movement skills, we present a unified learning procedure for learning a parametric-attractor landscape from multiple demonstrations. The feasibility and highly extended scalability of DMPs are demonstrated on an actual dual-arm robot. |
| | |
Authors:
|
Takamitsu Matsubara; Sang-Ho Hyon; Jun Morimoto |
Related Documents
:
|
7653464 - Design and analysis of community trials: lessons from the minnesota heart health program. 21273164 - Using vouchers to increase access to maternal healthcare in bangladesh. 21522074 - Personal best time and training volume, not anthropometry, is related to race performan... 20064454 - Weizmann young pi forum: the power of peer support. 2030874 - Resident scheduling: night float programs. 1492164 - Evaluating the effects of a family education intervention. |
Publication Detail:
|
Type: JOURNAL ARTICLE Date: 2011-2-16 |
Journal Detail:
|
Title: Neural networks : the official journal of the International Neural Network Society Volume: - ISSN: 1879-2782 ISO Abbreviation: - Publication Date: 2011 Feb |
Date Detail:
|
Created Date: 2011-3-10 Completed Date: - Revised Date: - |
Medline Journal Info:
|
Nlm Unique ID: 8805018 Medline TA: Neural Netw Country: - |
Other Details:
|
Languages: ENG Pagination: - Citation Subset: - |
Copyright Information:
|
Copyright © 2011 Elsevier Ltd. All rights reserved. |
Affiliation:
|
Graduate School of Information Science, NAIST, 8916-5, Takayama-cho, Ikoma, Nara, 630-0101, Japan; Department of Brain Robot Interface, ATR-CNS, 2-2-2, Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan. |
Export Citation:
|
APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
|
|
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
Previous Document: What influences participation in leisure activities of children and youth with physical disabilities...
Next Document: Parotid duct repair using an epidural catheter.