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dc.contributor.author
Holzherr, Luc
dc.contributor.author
Förster, Julian
dc.contributor.author
Breyer, Michel
dc.contributor.author
Nieto, Juan
dc.contributor.author
Siegwart, Roland
dc.contributor.author
Chung, Jen Jen
dc.date.accessioned
2021-11-18T09:02:03Z
dc.date.available
2021-11-18T08:36:27Z
dc.date.available
2021-11-18T09:02:03Z
dc.date.issued
2021
dc.identifier.isbn
978-1-7281-9077-8
en_US
dc.identifier.isbn
978-1-7281-9078-5
en_US
dc.identifier.other
10.1109/icra48506.2021.9561047
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/515767
dc.identifier.doi
10.3929/ethz-b-000515767
dc.description.abstract
We present a novel hierarchical POMDP framework to solve an object search and delivery task where the agent is given a prior belief about the possible item locations. Solving POMDPs is computationally demanding and, as such, applications have typically been limited to small environments. The proposed hierarchical POMDP framework performs reasoning on multiple spatial scales in order to reduce computation time. The problem is first solved in the top layer of the hierarchy with a coarsely discretized state space. Its solution is refined in the lower layers with increasing resolution. Three different methods for propagating information down the spatial hierarchy are discussed and validated in simulation. We show that a two-layer multi-scale POMDP decreases computation time by an order of magnitude allowing for real-time applications while maintaining high solution quality. For large problems that require three layers to reach the desired resolution, computation time speedups by two orders of magnitude are achieved.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.title
Efficient Multi-scale POMDPs for Robotic Object Search and Delivery
en_US
dc.type
Conference Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2021-10-18
ethz.book.title
2021 IEEE International Conference on Robotics and Automation (ICRA)
en_US
ethz.pages.start
6585
en_US
ethz.pages.end
6591
en_US
ethz.size
7 p. accepted version
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.event
IEEE International Conference on Robotics and Automation (ICRA 2021)
en_US
ethz.event.location
Xi'an, China
en_US
ethz.event.date
May 30 – June 5, 2021
en_US
ethz.notes
Conference lecture held on June 1, 2021
en_US
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02620 - Inst. f. Robotik u. Intelligente Systeme / Inst. Robotics and Intelligent Systems::03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02620 - Inst. f. Robotik u. Intelligente Systeme / Inst. Robotics and Intelligent Systems::03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
en_US
ethz.date.deposited
2021-11-18T08:36:34Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-11-18T09:02:19Z
ethz.rosetta.lastUpdated
2021-11-18T09:02:19Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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