Abstract

In this paper, we first propose a novel interaction model, CEA. It can explain the way how some existing systems reduce the work-load of their user. We next extend the CEA and build ECEA model. The ECEA enables robots to achieve more complicated tasks. On this extension, we employ ACS which can describe segmented human acts and clarifies the relationship between user's actions and robot's actions in a task. The ACS utilizes the CEA's strong point which enables a user to send a command to a robot by his/her natural action for the task. The instance of the ECEA led by using the ACS is a temporal extension which has the user keep a final state of a previous his/her action. We apply the temporal extension of the ECEA for a sweeping task. The high-level task, a cooperative task between the user and the robot can be realized. The robot with simple reactive behavior can sweep the region of under an object when the user picks up the object. In addition, we measure user's cognitive loads on the ECEA and a traditional method, DCM in the sweeping task, and compare between them. The results show that the ECEA has a lower cognitive load than the DCM significantly.