Supporting mixed initiative human-robot interaction: A script-based cognitive architecture approach

As complex indoor-robot systems are developed and deployed into the real-world, the demand for human-robot interaction is increasing. Mixed-initiative human-robot interaction is a good method to coordinate actions of a human and a robot in a complementary fashion. In order to support such interactions, we employ scripts that are rich, flexible, and extensible for a robot's interactions in a variety of situations. Scripts are amenable for expressing knowledge in an applicable form, especially describing a sequence of actions in organizing tasks. In this paper, we propose a script-based cognitive architecture for collaboration, which is based on three-level cognitive models. It incorporates Dynamic Bayesian Network (DBN) to automatically govern action sequences in the scripts and detect user's intention or goal. Starting from an understanding of user initiatives, our intelligent task manager suggests the most relevant initiatives for an efficient collaboration. DBN has been evaluated in real indoor task scenarios for its efficacy in interaction reduction, error minimization, and task satisfaction.