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Learning from demonstration for semi-autonomous teleoperation

Abstract:

Teleoperation in domains such as deep-sea or space often requires the completion of a set of recurrent tasks. We present a framework that uses a probabilistic approach to learn from demonstration models of manipulation tasks. We show how such a framework can be used in an underwater ROV teleoperation context to assist the operator. The learned representation can be used to resolve inconsistencies between the operator’s and the robot’s space in a structured manner, and as a fall-back system to...

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Publication status:
Published
Peer review status:
Peer reviewed

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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:
Engineering and Physical Sciences Research Council
Funding agency for:
Havoutis, I
Grant:
EP/M019918/1,EP/R026084/1
EP/R026173/1
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Name:
European Commission
Grant:
DexROV project through the EC Horizon 2020 programme (Grant #635491
Publisher:
Springer US
Journal:
Autonomous Robots More from this journal
Volume:
43
Issue:
3
Pages:
713–726
Publication date:
2018-04-25
Acceptance date:
2018-04-02
DOI:
EISSN:
1573-7527
ISSN:
0929-5593
Keywords:
Pubs id:
pubs:844531
UUID:
uuid:29f41451-5783-4926-b42e-cf19ebcdc6b1
Local pid:
pubs:844531
Source identifiers:
844531
Deposit date:
2018-04-27

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