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For a safe, natural and effective human-robot social interaction, it is essential to develop a system that allows a robot to demonstrate the perceivable responsive behaviors to complex human behaviors. We introduce the Multimodal Deep Attention Recurrent Q-Network using which the robot exhibits human-like social interaction skills after 14 days of interacting with people in an uncontrolled real world...
We present a minimalist two-hidden-layer neural architecture for emotional state estimation using electroencephalogram (EEG) data. Our model introduces a new meta-parameter, referred to as reinforced gradient coefficient, to overcome the peculiar vanishing gradient behaviour, thereby enabling the network to further explore the gradient landscape. This allows our model to further reduce its deviation...
This paper deals with a multi-agent path planning problem for tracking humans in order to obtain detail information such like human behavior and characteristics. To achieve this, paths of agents are planned based on similarity between the predicted positions of humans and agents' field of views, and the path length in the path planning is determined according to the consistency between the current...
This study explores the nature of the multi-robot control problem for social robots. It begins by modeling the overall structure of a human-robot team for social interactions, and implements it for specific application to dialog-based interactions. Operator activity during control of a social robot is studied. Customer satisfaction is proposed as an important metric for evaluating the performance...
In recent years, security camera systems have been installed in various public facilities. More intelligent processes are needed to track people in image sequences for security camera systems. In this paper, we propose a face tracking and recognition method based on a Bayesian framework. We assume that an observed space is three-dimensional, and we estimate the 3D position of a person. We use facial...
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