| Field |
Value |
Language |
|
dc.contributor.author |
Lai, Y
https://orcid.org/0000-0002-9381-5836
|
|
|
dc.contributor.author |
Paul, G
https://orcid.org/0000-0002-3478-0020
|
|
|
dc.contributor.author |
Cui, Y |
|
|
dc.contributor.author |
Matsubara, T |
|
|
dc.date.accessioned |
2022-01-17T22:26:06Z |
|
|
dc.date.available |
2022-01-17T22:26:06Z |
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dc.identifier.citation |
Autonomous Robots |
|
|
dc.identifier.issn |
0929-5593 |
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|
dc.identifier.issn |
1573-7527 |
|
|
dc.identifier.uri |
http://hdl.handle.net/10453/153230
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|
|
dc.description.abstract |
<jats:title>Abstract</jats:title><jats:p>As robotic systems transition from traditional setups to collaborative work spaces, the prevalence of physical Human Robot Interaction has risen in both industrial and domestic environments. A popular representation for robot behavior is movement primitives which learn, imitate, and generalize from expert demonstrations. While there are existing works in context-aware movement primitives, they are usually limited to contact-free human robot interactions. This paper presents physical Human Robot Interaction Primitives (pHRIP), which utilize only the interaction forces between the human user and robot to estimate user intent and generate the appropriate robot response during physical human robot interactions. The efficacy of pHRIP is evaluated through multiple experiments based on target-directed reaching and obstacle avoidance tasks using a real seven degree of freedom robot arm. The results are validated against Interaction Primitives which use observations of robotic trajectories, with discussions of future pHRI applications utilizing pHRIP.</jats:p> |
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dc.language |
en |
|
|
dc.publisher |
Springer Science and Business Media LLC |
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|
dc.relation |
New South Wales Government |
|
|
dc.relation |
University of Technology Sydney |
|
|
dc.relation |
Innovative Manufacturing Cooperative Research Centre |
|
|
dc.relation.ispartof |
Autonomous Robots |
|
|
dc.relation.isbasedon |
10.1007/s10514-021-10030-9 |
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|
dc.rights |
info:eu-repo/semantics/embargoedAccess |
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dc.rights |
This is a post-peer-review, pre-copyedit version of an article published in [Autonomous Robots Published: 15 January 2022]. The final authenticated version is available online at: [ https://link.springer.com/article/10.1007%2Fs10514-021-10030-9]” |
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|
dc.subject |
0801 Artificial Intelligence and Image Processing, 0913 Mechanical Engineering, 1702 Cognitive Sciences |
|
|
dc.subject.classification |
Industrial Engineering & Automation |
|
|
dc.title |
User intent estimation during robot learning using physical human robot interaction primitives |
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|
dc.type |
Journal Article |
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|
utslib.for |
0801 Artificial Intelligence and Image Processing |
|
|
utslib.for |
0913 Mechanical Engineering |
|
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utslib.for |
1702 Cognitive Sciences |
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pubs.organisational-group |
/University of Technology Sydney |
|
|
pubs.organisational-group |
/University of Technology Sydney/Faculty of Engineering and Information Technology |
|
|
pubs.organisational-group |
/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems |
|
|
pubs.organisational-group |
/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering |
|
|
utslib.copyright.status |
open_access |
* |
|
utslib.copyright.embargo |
2023-01-15T00:00:00+1000Z |
|
|
dc.date.updated |
2022-01-17T22:26:01Z |
|
|
pubs.publication-status |
Published online |
|
