In a society in which people and robots cooperate together in the same environment, robot technology (RT) services will be required to consider user intent and situation. This paper focuses on RT ontology providing RT services appropriate to the situation inKukanchi— “interactive human-space design and intelligence.” A user’s direct requests are defined as a “main task” and incidental tasks required to achieve the main task as “tsuidetasks.” This paper proposes and introduces RT ontology and details validation experiments for certain “tsuidetasks.”

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In this research, we propose the service management system design of robot technology (RT) Service for Interactive Human-Space Design and Intelligence to make robot provide appropriate services according to situations. For this, we propose to define that "Service" as an order that the end user requests and "Task" as elements that compose "Service". We focus on the development of the system that manages services for various robots to work by cooperating. And we systematize RAC (Robot Action Command) for service generation by the system. In this paper, we describe the outline of RT-Service management system and experiments.

This paper presents a novel approach for RT Ontology development, including ontology learning and evolution mechanism. In service robotics systems, understanding the relationship between everyday objects and user intention is the key feature to provide suitable services according to context. RT Ontology has shown to be an efficient technique to represent this relationship. In the proposed method, text corpus grabbed from search engines and lightweight natural language processing techniques were used for term extraction and enabling RT Ontology automatic creation. On the other hand, ontology evolution mechanism is introduced. With these learning and evolution capabilities, the presented RT Ontology model may adapt dynamically to the changes of environment and human activities. This will help to improve the robustness of current RT service generation systems, while reduce much of required labor work for ontology development. Experiments were conducted to show the effectiveness of proposed method.