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dc.contributor.author
Egli, Pascal
dc.contributor.author
Hutter, Marco
dc.date.accessioned
2021-03-22T13:53:50Z
dc.date.available
2021-03-22T13:52:33Z
dc.date.available
2021-03-22T13:53:50Z
dc.date.issued
2020
dc.identifier.isbn
978-1-7281-6212-6
en_US
dc.identifier.isbn
978-1-7281-6213-3
en_US
dc.identifier.other
10.1109/IROS45743.2020.9341598
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/475773
dc.identifier.doi
10.3929/ethz-b-000431607
dc.description.abstract
In this article we present a data-driven approach for automated arm control of a hydraulic excavator. Except for the link lengths of the excavator, our method does not require machine-specific knowledge nor gain tuning. Using data collected during operation of the excavator, we train a general purpose model to effectively represent the highly non-linear dynamics of the hydraulic actuation and joint linkage. Together with the link lengths a simulation is set up to train a neural network control policy for end-effector position tracking using reinforcement learning (RL). The control policy directly outputs the actuator commands that can be applied to the machine without unfounded filtering or modification. The proposed method is implemented and tested on a 12t hydraulic excavator, controlling its 4 main arm joints to track desired positions of the shovel in free-space. The results demonstrate the feasibility of directly applying control policies trained in simulation to the physical excavator for accurate and stable position tracking.
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.subject
Hydraulic Excavator
en_US
dc.subject
Reinforcement Learning
en_US
dc.title
Towards RL-Based Hydraulic Excavator Automation
en_US
dc.type
Conference Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2021-02-10
ethz.book.title
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
en_US
ethz.pages.start
2692
en_US
ethz.pages.end
2697
en_US
ethz.size
6 p. accepted version
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.event
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020) (virtual)
en_US
ethz.event.location
Las Vegas, NV, USA
en_US
ethz.event.date
October 24, 2020 - January 24, 2021
en_US
ethz.notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.
en_US
ethz.identifier.scopus
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::09570 - Hutter, Marco / Hutter, Marco
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02100 - Dep. Architektur / Dep. of Architecture::02284 - NFS Digitale Fabrikation / NCCR Digital Fabrication
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::09570 - Hutter, Marco / Hutter, Marco
en_US
ethz.tag
RSL
en_US
ethz.tag
dfab
en_US
ethz.relation.isPartOf
10.3929/ethz-b-000487440
ethz.date.deposited
2020-08-17T06:32:52Z
ethz.source
SCOPUS
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-03-22T13:52:44Z
ethz.rosetta.lastUpdated
2022-03-29T05:55:34Z
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/475366
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/431607
ethz.COinS
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