Improving human-robot interaction based on joint attention

The current study proposes a novel cognitive architecture for a computational model of the limbic system, inspired by human brain activity, which improves interactions between a humanoid robot and preschool children using joint attention during turn-taking gameplay. Using human-robot interaction (HR...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2017-07, Vol.47 (1), p.62-82
Main Authors: Dalarli, Evren, Dalarli, Sare Funda, Günel, Gülay Öke, Köse, Hatice
Format: Article
Language:English
Subjects:
Adaptation
Architecture
Artificial Intelligence
Attention deficit hyperactivity disorder
Brain
Children
Cognition & reasoning
Computer Science
Computer simulation
Control systems
Ganglia
Hippocampus
Humanoid
Hypothalamus
Machines
Manufacturing
Mathematical models
Mechanical Engineering
Preschool children
Processes
Reinforcement
Robots
Unsupervised learning
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Summary:The current study proposes a novel cognitive architecture for a computational model of the limbic system, inspired by human brain activity, which improves interactions between a humanoid robot and preschool children using joint attention during turn-taking gameplay. Using human-robot interaction (HRI), this framework may be useful for ameliorating problems related to attracting and maintaining attention levels of children suffering from attention deficit hyperactivity disorder (ADHD). In the proposed framework, computational models including the amygdala, hypothalamus, hippocampus, and basal ganglia are used to simulate a range of cognitive processes such as emotional responses, episodic memory formation, and selection of appropriate behavioral responses. In the currently proposed model limbic system, we applied reinforcement and unsupervised learning-based adaptation processes to a dynamic neural field model, resulting in a system that was capable of observing and controlling the physical and cognitive processes of a humanoid robot. Several interaction scenarios were tested to evaluate the performance of the model. Finally, we compared the results of our methodology with a neural mass model.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-016-0876-x