Learning object, grasping and manipulation activities using hierarchical HMMs

Patel, M; Miro, JV; Kragic, D; Ek, CH; Dissanayake, G

Learning object, grasping and manipulation activities using hierarchical HMMs

Patel, M Miro, JV

Kragic, D Ek, CH Dissanayake, G

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Publication Type:: Journal Article
Citation:: Autonomous Robots, 2014, 37 (3), pp. 317 - 331
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Patel, M	en_US
dc.contributor.author	Miro, JV https://orcid.org/0000-0002-0083-7797	en_US
dc.contributor.author	Kragic, D	en_US
dc.contributor.author	Ek, CH	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Autonomous Robots, 2014, 37 (3), pp. 317 - 331	en_US
dc.identifier.issn	0929-5593	en_US
dc.identifier.uri	http://hdl.handle.net/10453/34405
dc.description.abstract	This article presents a probabilistic algorithm for representing and learning complex manipulation activities performed by humans in everyday life. The work builds on the multi-level Hierarchical Hidden Markov Model (HHMM) framework which allows decomposition of longer-term complex manipulation activities into layers of abstraction whereby the building blocks can be represented by simpler action modules called action primitives. This way, human task knowledge can be synthesised in a compact, effective representation suitable, for instance, to be subsequently transferred to a robot for imitation. The main contribution is the use of a robust framework capable of dealing with the uncertainty or incomplete data inherent to these activities, and the ability to represent behaviours at multiple levels of abstraction for enhanced task generalisation. Activity data from 3D video sequencing of human manipulation of different objects handled in everyday life is used for evaluation. A comparison with a mixed generative-discriminative hybrid model HHMM/SVM (support vector machine) is also presented to add rigour in highlighting the benefit of the proposed approach against comparable state of the art techniques. © 2014 Springer Science+Business Media New York.	en_US
dc.relation.ispartof	Autonomous Robots	en_US
dc.relation.isbasedon	10.1007/s10514-014-9392-1	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Learning object, grasping and manipulation activities using hierarchical HMMs	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	37	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	1702 Cognitive Sciences	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.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	37	en_US

Abstract:

This article presents a probabilistic algorithm for representing and learning complex manipulation activities performed by humans in everyday life. The work builds on the multi-level Hierarchical Hidden Markov Model (HHMM) framework which allows decomposition of longer-term complex manipulation activities into layers of abstraction whereby the building blocks can be represented by simpler action modules called action primitives. This way, human task knowledge can be synthesised in a compact, effective representation suitable, for instance, to be subsequently transferred to a robot for imitation. The main contribution is the use of a robust framework capable of dealing with the uncertainty or incomplete data inherent to these activities, and the ability to represent behaviours at multiple levels of abstraction for enhanced task generalisation. Activity data from 3D video sequencing of human manipulation of different objects handled in everyday life is used for evaluation. A comparison with a mixed generative-discriminative hybrid model HHMM/SVM (support vector machine) is also presented to add rigour in highlighting the benefit of the proposed approach against comparable state of the art techniques. © 2014 Springer Science+Business Media New York.

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http://hdl.handle.net/10453/34405