Conference item icon

Conference item

Learning task-space synergies using Riemannian geometry,

Abstract:

In the context of robotic control, synergies can form elementary units of behavior. By specifying taskdependent coordination behaviors at a low control level, one can achieve task-specific disturbance rejection. In this work we present an approach to learn the parameters of such lowlevel controllers by demonstration. We identify a synergy by extracting covariance information from demonstration data. The extracted synergy is used to derive a time-invariant state feedback controller through opt...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed

Actions


Access Document


Files:
Publisher copy:
10.1109/IROS.2017.8202140

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-4371-4623
More from this funder
Name:
Horizon 2020
Grant:
635491
More from this funder
Name:
Seventh Framework Programme
Grant:
608022
More from this funder
Name:
Swiss National Science Foundation
Publisher:
Institute of Electrical and Electronics Engineers
Host title:
International Conference on Intelligent Robots and Systems (IROS 2017)
Journal:
International Conference on Intelligent Robots and Systems More from this journal
Publication date:
2017-12-01
Acceptance date:
2017-06-29
DOI:
Pubs id:
pubs:822411
UUID:
uuid:057d7b55-66fd-49a6-a931-8986929f1df7
Local pid:
pubs:822411
Source identifiers:
822411
Deposit date:
2018-02-02

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP