Cognitive Modeling Based on Perceiving-Acting Cycle in Robotic Avatar System for Disabled Patients

In this study, we aim to develop a system of remote-controlled avatar robot for elderly and disabled patients. Most of teleoperation systems have interfaces to visually present the state of the robots including feedback information and receive the control commands manually sent from the operator. However, in elderly and patients with disabilities, they might have difficulty in the manual control of the robot. This paper therefore presents a multimodal interface for remotely controlling a robotic avatar. We furthermore propose a cognitive platform for remotely controlling a robot based on a concept of perceiving-acting cycle. The platform consists of a perceptual system for incremental environment modeling and an action system for extracting patterns of operator behavior. In each system, a self-organized neural network based on unsupervised learning is used. Moreover, we use a spiking neural network for spatial-temporal modeling of interaction between an operator and environment.