Language for learning complex human-object interactions

Patel, M; Ek, CH; Kyriazis, N; Argyros, A; Miro, JV; Kragic, D

Language for learning complex human-object interactions

Patel, M Ek, CH Kyriazis, N Argyros, A Miro, JV

Kragic, D

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Robotics and Automation, 2013, pp. 4997 - 5002
Issue Date:: 2013-11-14

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Field	Value	Language
dc.contributor.author	Patel, M	en_US
dc.contributor.author	Ek, CH	en_US
dc.contributor.author	Kyriazis, N	en_US
dc.contributor.author	Argyros, A	en_US
dc.contributor.author	Miro, JV https://orcid.org/0000-0002-0083-7797	en_US
dc.contributor.author	Kragic, D	en_US
dc.date.issued	2013-11-14	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Robotics and Automation, 2013, pp. 4997 - 5002	en_US
dc.identifier.isbn	9781467356411	en_US
dc.identifier.issn	1050-4729	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28372
dc.description.abstract	In this paper we use a Hierarchical Hidden Markov Model (HHMM) to represent and learn complex activities/task performed by humans/robots in everyday life. Action primitives are used as a grammar to represent complex human behaviour and learn the interactions and behaviour of human/robots with different objects. The main contribution is the use of a probabilistic model capable of representing behaviours at multiple levels of abstraction to support the proposed hypothesis. The hierarchical nature of the model allows decomposition of the complex task into simple action primitives. The framework is evaluated with data collected for tasks of everyday importance performed by a human user. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Robotics and Automation	en_US
dc.relation.isbasedon	10.1109/ICRA.2013.6631291	en_US
dc.title	Language for learning complex human-object interactions	en_US
dc.type	Conference Proceeding
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
dc.location.activity	Karlsruhe, Germany	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/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

In this paper we use a Hierarchical Hidden Markov Model (HHMM) to represent and learn complex activities/task performed by humans/robots in everyday life. Action primitives are used as a grammar to represent complex human behaviour and learn the interactions and behaviour of human/robots with different objects. The main contribution is the use of a probabilistic model capable of representing behaviours at multiple levels of abstraction to support the proposed hypothesis. The hierarchical nature of the model allows decomposition of the complex task into simple action primitives. The framework is evaluated with data collected for tasks of everyday importance performed by a human user. © 2013 IEEE.

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http://hdl.handle.net/10453/28372