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Supervisory teleoperation with online learning and optimal control

Abstract:

We present a general approach for online learning and optimal control of manipulation tasks in a supervisory teleoperation context, targeted to underwater remotely operated vehicles (ROVs). We use an online Bayesian nonparametric learning algorithm to build models of manipulation motions as task-parametrized hidden semi-Markov models (TP-HSMM) that capture the spatiotemporal characteristics of demonstrated motions in a probabilistic representation. Motions are then executed autonomously usin...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/ICRA.2017.7989183

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-4371-4623
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
IEEE International Conference on Robotics and Automation (ICRA 2017). Journal website
Host title:
IEEE International Conference on Robotics and Automation (ICRA 2017)
Publication date:
2017-07-01
Acceptance date:
2017-01-15
DOI:
ISSN:
1050-4729
Source identifiers:
726064
ISBN:
9781509046331
Pubs id:
pubs:726064
UUID:
uuid:efcbaa5a-03a4-4691-9a73-e5d491df0bbf
Local pid:
pubs:726064
Deposit date:
2018-02-02

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