Audio-Visual Object Classification for Human-Robot Collaboration

Xompero, A; Pang, YL; Patten, T; Prabhakar, A; Calli, B; Cavallaro, A

Audio-Visual Object Classification for Human-Robot Collaboration

Xompero, A Pang, YL Patten, T

Prabhakar, A Calli, B Cavallaro, A

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, 2022-May, pp. 9137-9141
Issue Date:: 2022-04-27

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Field	Value	Language
dc.contributor.author	Xompero, A
dc.contributor.author	Pang, YL
dc.contributor.author	Patten, T https://orcid.org/0000-0003-1139-9451
dc.contributor.author	Prabhakar, A
dc.contributor.author	Calli, B
dc.contributor.author	Cavallaro, A
dc.date	2022-05-23
dc.date.accessioned	2023-06-27T22:54:27Z
dc.date.available	2023-06-27T22:54:27Z
dc.date.issued	2022-04-27
dc.identifier.citation	ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, 2022-May, pp. 9137-9141
dc.identifier.isbn	978-1-6654-0540-9
dc.identifier.issn	1520-6149
dc.identifier.issn	2379-190X
dc.identifier.uri	http://hdl.handle.net/10453/170909
dc.description.abstract	Human robot collaboration requires the contactless estimation of the physical properties of containers manipulated by a person for example while pouring content in a cup or moving a food box Acoustic and visual signals can be used to estimate the physical properties of such objects which may vary substantially in shape material and size and also be occluded by the hands of the person To facilitate comparisons and stimulate progress in solving this problem we present the CORSMAL challenge and a dataset to assess the performance of the algorithms through a set of well defined performance scores The tasks of the challenge are the estimation of the mass capacity and dimensions of the object container and the classification of the type and amount of its content A novel feature of the challenge is our real to simulation framework for visualising and assessing the impact of estimation errors in human to robot handovers
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
dc.relation.ispartof	IEEE International Conference on Acoustics, Speech and Signal Processing
dc.relation.isbasedon	10.1109/icassp43922.2022.9746336
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Audio-Visual Object Classification for Human-Robot Collaboration
dc.type	Conference Proceeding
utslib.citation.volume	2022-May
utslib.location.activity	Singapore, Singapore
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
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-06-27T22:54:20Z
pubs.finish-date	2022-05-27
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2022-05-23
pubs.volume	2022-May
dc.location	Piscataway, USA

Abstract:

Human robot collaboration requires the contactless estimation of the physical properties of containers manipulated by a person for example while pouring content in a cup or moving a food box Acoustic and visual signals can be used to estimate the physical properties of such objects which may vary substantially in shape material and size and also be occluded by the hands of the person To facilitate comparisons and stimulate progress in solving this problem we present the CORSMAL challenge and a dataset to assess the performance of the algorithms through a set of well defined performance scores The tasks of the challenge are the estimation of the mass capacity and dimensions of the object container and the classification of the type and amount of its content A novel feature of the challenge is our real to simulation framework for visualising and assessing the impact of estimation errors in human to robot handovers

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http://hdl.handle.net/10453/170909