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An approach for imitation learning on Riemannian manifolds

Abstract:

In imitation learning, multivariate Gaussians are widely used to encode robot behaviors. Such approaches do not provide the ability to properly represent end-effector orientation, as the distance metric in the space of orientations is not Euclidean. In this work we present an extension of common imitation learning techniques to Riemannian manifolds. This generalization enables the encoding of joint distributions that include the robot pose. We show that Gaussian conditioning, Gaussian product...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/LRA.2017.2657001

Authors


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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
Journal:
IEEE Robotics and Automation Letters More from this journal
Volume:
2
Issue:
3
Pages:
1240-1247
Publication date:
2017-01-01
Acceptance date:
2017-01-23
DOI:
ISSN:
2377-3774
Keywords:
Pubs id:
pubs:822412
UUID:
uuid:d580f7ea-476d-4a0c-a39a-2a000a91717d
Local pid:
pubs:822412
Source identifiers:
822412
Deposit date:
2018-02-02

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