Toward Permissive Teaching through Interactive Architecture

We propose a new approach for teaching a humanoid-robot a task online without pre-set data provided in advance. In our approach, human acts as a collaborator and also as a teacher. The proposed approach enables the humanoid-robot to learn a task through multi-component interactive architecture. The components are designed with the respect to human methodology for learning a task through empirical interactions. For efficient performance, the components are isolated within one single API. Our approach can be divided into five main roles: perception, representation, state/knowledge-updating , decision making and expression. Important components in our approach such as, decision making, process tracking, observation and knowledge data are described for teaching the robot a task. A conducted empirical experiment for the proposed approach is to be done by teaching a Fujitsu's humanoid-robot 'Hoap-3' an X-O game strategy and its results are to be done and explained.