Document Detail


Learning emergent behaviours for a hierarchical Bayesian framework for active robotic perception.
MedLine Citation:
PMID:  22806665     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
In this research work, we contribute with a behaviour learning process for a hierarchical Bayesian framework for multimodal active perception, devised to be emergent, scalable and adaptive. This framework is composed by models built upon a common spatial configuration for encoding perception and action that is naturally fitting for the integration of readings from multiple sensors, using a Bayesian approach devised in previous work. The proposed learning process is shown to reproduce goal-dependent human-like active perception behaviours by learning model parameters (referred to as "attentional sets") for different free-viewing and active search tasks. Learning was performed by presenting several 3D audiovisual virtual scenarios using a head-mounted display, while logging the spatial distribution of fixations of the subject (in 2D, on left and right images, and in 3D space), data which are consequently used as the training set for the framework. As a consequence, the hierarchical Bayesian framework adequately implements high-level behaviour resulting from low-level interaction of simpler building blocks by using the attentional sets learned for each task, and is able to change these attentional sets "on the fly," allowing the implementation of goal-dependent behaviours (i.e., top-down influences).
Authors:
João Filipe Ferreira; Christiana Tsiourti; Jorge Dias
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-7-18
Journal Detail:
Title:  Cognitive processing     Volume:  -     ISSN:  1612-4790     ISO Abbreviation:  -     Publication Date:  2012 Jul 
Date Detail:
Created Date:  2012-7-18     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101177984     Medline TA:  Cogn Process     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
ISR, University of Coimbra, Coimbra, Portugal, jfilipe@isr.uc.pt.
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