〒 560-85311-3, Machikaneyama-cho,Toyonaka, Osaka, Japan
This research presents a Non-Invasive Brain- Machine Interface (BMI) that allows persons, who have suffered from motor paralysis conditions, to control appliances in a hospital room by using Electroencephalogram signals (EEG).
We present a software interface that allows a user to control different types of robotic systems by using a Brain-Machine Interface. Unlike common device-specific BMI systems, our software architecture maps simple EEG-based commands to diverse functionalities depending on the robotic platform, so the user does not have learn to generate new EEG commands for different robots.
We present our method for learning object categories from the internet using cues obtained through human-robot interaction. Such cues include an object model acquired by observation and the name of the object.
We present our semi-supervised technique for building object category classifiers using real image data from the Internet. Our technique not only reduces the overhead of manual training by humans, but also achieves robust classifiers that can be evaluated in real time.
We propose an automatic object modeling framework composed of a network of cameras distributed in different places of the house. Our system can automatically construct multi-appearance object models by simply observing when humans move objects from one place to another as part of their daily activities.