A Cognitive Approach to Enhancing Human-Robot Interaction for Service Robots

As robots become more intelligent and their application fields continue to grow, the decisions and interaction of robots that share work domains with humans become increasingly important. Traditional robots have received only simple commands, and humans' roles have been limited to supervisor. However, for successive task performance, robots' decision making should be approached via collaboration between the human and robot. Interaction also should be regarded as an issue closely associated with joint work plans rather than a simple function. Interaction between the human and robot, moreover, should be systemized in order to decrease the workload of the human and maximize user satisfaction. Accordingly, we developed several cognitive models: a task model, truth-maintenance model, interaction model, and intention rule-base. These models can manage and initiate the interactions based on their tasks and modify robot's activities by using the result of the interaction. We demonstrated the adaptability and usability of the developed models by applying them to the home-service robot, T-Rot.