Environment-adaptive interaction primitives for human-robot motor skill learning

Cui, Y; Poon, J; Matsubara, T; Miro, JV; Sugimoto, K; Yamazaki, K

Environment-adaptive interaction primitives for human-robot motor skill learning

Cui, Y Poon, J Matsubara, T Miro, JV

Sugimoto, K Yamazaki, K

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Publication Type:: Conference Proceeding
Citation:: IEEE-RAS International Conference on Humanoid Robots, 2016, pp. 711 - 717
Issue Date:: 2016-12-30

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Field	Value	Language
dc.contributor.author	Cui, Y	en_US
dc.contributor.author	Poon, J	en_US
dc.contributor.author	Matsubara, T	en_US
dc.contributor.author	Miro, JV https://orcid.org/0000-0002-0083-7797	en_US
dc.contributor.author	Sugimoto, K	en_US
dc.contributor.author	Yamazaki, K	en_US
dc.date.issued	2016-12-30	en_US
dc.identifier.citation	IEEE-RAS International Conference on Humanoid Robots, 2016, pp. 711 - 717	en_US
dc.identifier.isbn	9781509047185	en_US
dc.identifier.issn	2164-0572	en_US
dc.identifier.uri	http://hdl.handle.net/10453/66472
dc.description.abstract	© 2016 IEEE. In complex environments where robots are expected to co-operate with human partners, it is vital for the robot to consider properties of their collaborative activity in addition to the behavior of its partner. In this paper, we propose to learn such complex interactive skills by observing the demonstrations of a human-robot team with additional external attributes. We propose Environment-adaptive Interaction Primitives (EalPs) as an extension of Interaction Primitives. In cooperation tasks between human and robot with different environmental conditions, EalPs not only improve the predicted motor skills of robot within a brief observed human motion, but also obtain the generalization ability to adapt to new environmental conditions by learning the relationships between each condition and the corresponding motor skills from training samples. Our method is validated in the collaborative task of covering objects by plastic bag with a humanoid Baxter robot. To achieve the task successfully, the robot needs to coordinate itself to its partner while also considering information about the object to be covered.	en_US
dc.relation.ispartof	IEEE-RAS International Conference on Humanoid Robots	en_US
dc.relation.isbasedon	10.1109/HUMANOIDS.2016.7803352	en_US
dc.title	Environment-adaptive interaction primitives for human-robot motor skill learning	en_US
dc.type	Conference Proceeding
utslib.for	091303 Autonomous Vehicles	en_US
pubs.embargo.period	Not known	en_US
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/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. In complex environments where robots are expected to co-operate with human partners, it is vital for the robot to consider properties of their collaborative activity in addition to the behavior of its partner. In this paper, we propose to learn such complex interactive skills by observing the demonstrations of a human-robot team with additional external attributes. We propose Environment-adaptive Interaction Primitives (EalPs) as an extension of Interaction Primitives. In cooperation tasks between human and robot with different environmental conditions, EalPs not only improve the predicted motor skills of robot within a brief observed human motion, but also obtain the generalization ability to adapt to new environmental conditions by learning the relationships between each condition and the corresponding motor skills from training samples. Our method is validated in the collaborative task of covering objects by plastic bag with a humanoid Baxter robot. To achieve the task successfully, the robot needs to coordinate itself to its partner while also considering information about the object to be covered.

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http://hdl.handle.net/10453/66472