Reasoning of motion through task order for teaching by non-professional user

This paper proposes a method that reasons intent of motion through task order such as "switch on the light." Intent of motion means kinds of path of a contact point between a target and an end effector when a robot recreates ordered task. The intent is used for teaching. After a robot fixes a framework of task using the intent, it asks questions to a user to specify concrete values in the framework, the user replies them. This interaction creates motions of the robot. The kinds of path are defined as linear, circular and point to point. Moreover, impossible task is also considered. Four sets of P_f(w): Probability that a word represents a path, and C_p(w}: Certainty of P_p(w) are added to each word in an electric thesaurus to enable the reasoning. After values are input in part of the sets by 8 production rules, they are propagated to the other sets through hierarchical relationship of words in the thesaurus. When the user enters two words, namely task and target, argmax_p{P_p(w)*C_p(w)} becomes answer. Moreover, update and re-propagation of values in the sets using answers from the user make the reasoning more precise.