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Statistically Informed Multimodal (Domain Adaptation by Transfer) Learning Framework : A Domain Adaptation Use-Case for Industrial Human–Robot Communication Mukherjee, Debasmita; Najjaran, Homayoun
Abstract
Cohesive human–robot collaboration can be achieved through seamless communication between human and robot partners. We posit that the design aspects of human–robot communication (HRCom) can take inspiration from human communication to create more intuitive systems. A key component of HRCom systems is perception models developed using machine learning. Being data-driven, these models suffer from the dearth of comprehensive, labelled datasets while models trained on standard, publicly available datasets do not generalize well to application-specific scenarios. Complex interactions and real-world variability lead to shifts in data that require domain adaptation by the models. Existing domain adaptation techniques do not account for incommensurable modes of communication between humans and robot perception systems. Taking into account these challenges, a novel framework is presented that leverages existing domain adaptation techniques off-the-shelf and uses statistical measures to start and stop the training of models when they encounter domain-shifted data. Statistically informed multimodal (domain adaptation by transfer) learning (SIMLea) takes inspiration from human communication to use human feedback to auto-label for iterative domain adaptation. The framework can handle incommensurable multimodal inputs, is mode and model agnostic, and allows statistically informed extension of datasets, leading to more intuitive and naturalistic HRCom systems.
Item Metadata
Title |
Statistically Informed Multimodal (Domain Adaptation by Transfer) Learning Framework : A Domain Adaptation Use-Case for Industrial Human–Robot Communication
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Creator | |
Publisher |
Multidisciplinary Digital Publishing Institute
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Date Issued |
2025-03-31
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Description |
Cohesive human–robot collaboration can be achieved through seamless communication
between human and robot partners. We posit that the design aspects of human–robot
communication (HRCom) can take inspiration from human communication to create more
intuitive systems. A key component of HRCom systems is perception models developed
using machine learning. Being data-driven, these models suffer from the dearth of comprehensive,
labelled datasets while models trained on standard, publicly available datasets do
not generalize well to application-specific scenarios. Complex interactions and real-world
variability lead to shifts in data that require domain adaptation by the models. Existing
domain adaptation techniques do not account for incommensurable modes of communication
between humans and robot perception systems. Taking into account these challenges,
a novel framework is presented that leverages existing domain adaptation techniques
off-the-shelf and uses statistical measures to start and stop the training of models when
they encounter domain-shifted data. Statistically informed multimodal (domain adaptation
by transfer) learning (SIMLea) takes inspiration from human communication to use
human feedback to auto-label for iterative domain adaptation. The framework can handle
incommensurable multimodal inputs, is mode and model agnostic, and allows statistically
informed extension of datasets, leading to more intuitive and naturalistic HRCom systems.
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Subject | |
Genre | |
Type | |
Language |
eng
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Date Available |
2025-05-09
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Provider |
Vancouver : University of British Columbia Library
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Rights |
CC BY 4.0
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DOI |
10.14288/1.0448835
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URI | |
Affiliation | |
Citation |
Electronics 14 (7): 1419 (2025)
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Publisher DOI |
10.3390/electronics14071419
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Peer Review Status |
Reviewed
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Scholarly Level |
Faculty; Researcher
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
CC BY 4.0