Tetsuya Ogata

We present a connectionist model that combines motions and language based on the behavioral experiences of a real robot. Two models of recurrent neural network with parametric bias (RNNPB) were trained using motion sequences and linguistic sequences. These sequences were combined using their respective parameters so that the robot could handle many-to-many relationships between motion sequences and linguistic sequences. Motion sequences were articulated into some primitives corresponding to give

Discusses the communication between autonomous robots and humans through the development of a robot (WAMOEBA-2) which has an emotion model. The model refers to the internal secretion system of humans and it has four kinds of the hormone parameters to use to adjust various internal conditions such as motor output, cooling fan output and sensor gain. We surveyed 126 visitors at '97 International Robot Exhibition held in Tokyo, Japan (Oct. 1997) in order to evaluate psychological impressions of the

This paper proposes an efficient technique for human motion recognition based on motion history images and an eigenspace technique.In recent years, human motion recognition has become one of the most popular research fields.It is expected to be applied in a security system, man-machine communication, and so on.In the proposed technique, we use two feature images and the eigenspace technique to realize highspeed recognition.An experiment was performed on recognizing six human motions and the resu

Recently, applying computational models developed in cognitive science to psychiatric disorders has been recognized as an essential approach for understanding cognitive mechanisms underlying psychiatric symptoms. Autism spectrum disorder is a neurodevelopmental disorder that is hypothesized to affect information processes in the brain involving the estimation of sensory precision (uncertainty), but the mechanism by which observed symptoms are generated from such abnormalities has not been thorou

To work cooperatively with humans by using language, robots must not only acquire a mapping between language and their behavior but also autonomously utilize the mapping in appropriate contexts of interactive tasks online. To this end, we propose a novel learning method linking language to robot behavior by means of a recurrent neural network. In this method, the network learns from correct examples of the imposed task that are given not as explicitly separated sets of language and behavior but