Reasoning of abstract motion of a target object through task order with natural language — pre-knowledge of object-handling-task programming for a service robot

In this paper, an attempt is made to reason an abstract motion of a target object when the object is manipulated through a task order. The reasoning algorithm is used as a function in the system that navigates layman's robot programming. The task order consists of two words: Task and Target (e.g., 'switch on' and 'light'). There are three kinds of motions: Linear, Circular and Point-To-Point motion. The system chooses a suitable motion. In the reasoning, it is important to be able to reason from various input words using a limited knowledge base. Therefore, a knowledge base is proposed that consists of a thesaurus and minimum knowledge. The knowledge defines only words that directly stand for the motions (e.g., 'turn' means Circular motion). The knowledge is propagated through hypernyms and hyponyms in the thesaurus. A motion is reasoned using the propagated knowledge in Task and Target. Moreover, learning, which results from on-site updating of the knowledge from the user, achieves reinforcement/customization of the knowledge base. The system successfully reasoned motions from various task orders. Moreover, for the robot programming of a door-opening task, a robot with the reasoning system reasoned a motion of the door and realized the task.