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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
Version:
Accepted manuscript

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Publisher copy:
10.1109/LRA.2017.2657001

Authors


Zeestraten, MJA More by this author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
ORCID:
0000-0002-4371-4623
Silverio, J More by this author
Calinon, S More by this author
Caldwell, DG More by this author
Swiss National Science Foundation More from this funder
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
IEEE Robotics and Automation Letters Journal website
Volume:
2
Issue:
3
Pages:
1240-1247
Publication date:
2017-01-05
Acceptance date:
2017-01-23
DOI:
ISSN:
2377-3774
Pubs id:
pubs:822412
URN:
uri:d580f7ea-476d-4a0c-a39a-2a000a91717d
UUID:
uuid:d580f7ea-476d-4a0c-a39a-2a000a91717d
Local pid:
pubs:822412

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