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Markov decision processes with unknown state feature values for safe exploration using Gaussian processes

Abstract:

When exploring an unknown environment, a mobile robot must decide where to observe next. It must do this whilst minimising the risk of failure, by only exploring areas that it expects to be safe. In this context, safety refers to the robot remaining in regions where critical environment features (e.g. terrain steepness, radiation levels) are within ranges the robot is able to tolerate. More specifically, we consider a setting where a robot explores an environment modelled with a Markov decisi...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/IROS45743.2020.9341589

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0001-9052-6919
Publisher:
Institute of Electrical and Electronics Engineers
Host title:
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Pages:
7344-7350
Publication date:
2021-02-10
Acceptance date:
2020-09-29
Event title:
IROS 2020: International Conference on Intelligent Robots and Systems
Event location:
Virtual event
Event website:
https://www.iros2020.org/
Event start date:
2020-10-25
Event end date:
2020-11-25
DOI:
EISSN:
2153-0866
EISBN:
9781728162126
ISBN:
9781728162133
Language:
English
Keywords:
Pubs id:
1138277
Local pid:
pubs:1138277
Deposit date:
2020-10-19

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