Waseda University · 공학
Tetsuya Ogata 교수의 연구실은 인간과 로봇 간의 상호작용을 극대화하기 위한 지능형 로봇 기술을 핵심으로 연구합니다. 언어와 운동의 통합적 학습, 감정 모델링, 인간 운동 인식 기술을 바탕으로 로봇이 인간의 의도를 이해하고 상황에 맞게 행동할 수 있도록 하는 인공지능 기반의 로봇 제어 시스템을 개발하고 있습니다. 특히, 신경망 기반의 예측 오차 최소화 메커니즘과 정밀도 가중 평균 오차 기반의 인지 모델링을 통해 자폐 스펙트럼 장애와 같은 정서적·인지적 장애의 메커니즘을 해석하는 데에도 기여하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
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
