DocumentCode :
2028893
Title :
Building specific contexts for on-line learning of dynamical tasks through non-verbal interaction
Author :
de Rengerve, Antoine ; Hanoune, Souheil ; Andry, Paul ; Quoy, Mathias ; Gaussier, Philippe
Author_Institution :
ETIS, Univ. of Cergy-Pontoise, Cergy-Pontoise, France
fYear :
2013
fDate :
18-22 Aug. 2013
Firstpage :
1
Lastpage :
6
Abstract :
Trajectories can be encoded as attraction basin resulting from recruited associations between visually based localization and orientations to follow (low level behaviors). Navigation to different places according to some other multimodal information needs a particular learning. We propose a minimal model explaining such a behavior adaptation from non-verbal interaction with a teacher. Specific contexts can be recruited to prevent the behaviors to activate in cases the interaction showed they were inadequate. Still, the model is compatible with the recruitment of new low level behaviors. The tests done in simulation show the capabilities of the architecture, the limitations regarding the generalization and the learning speed. We also discuss the possible evolutions towards more bio-inspired models.
Keywords :
generalisation (artificial intelligence); human-robot interaction; learning (artificial intelligence); learning systems; navigation; neurocontrollers; trajectory control; behavior adaptation; bioinspired model; dynamical task; generalization; learning speed; multimodal information; navigation; neural network based architecture; nonverbal interaction; online learning; specific context building; trajectory encoding; visual based localization; visual based orientation; Adaptation models; Context; Navigation; Neurons; Robot sensing systems; Trajectory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Development and Learning and Epigenetic Robotics (ICDL), 2013 IEEE Third Joint International Conference on
Conference_Location :
Osaka
Type :
conf
DOI :
10.1109/DevLrn.2013.6652564
Filename :
6652564
Link To Document :
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