Cognitive Architecture for Mutual Modelling
This work tackles the problem of improving robot adaptability in educational human-robot interactions, though it appears incremental as it builds on existing mutual modeling concepts.
The paper addresses the challenge of enabling robots to model whether humans have understood their mental states in collaborative educational tasks, proposing a cognitive architecture for second-order mutual modeling in human-robot interaction.
In social robotics, robots needs to be able to be understood by humans. Especially in collaborative tasks where they have to share mutual knowledge. For instance, in an educative scenario, learners share their knowledge and they must adapt their behaviour in order to make sure they are understood by others. Learners display behaviours in order to show their understanding and teachers adapt in order to make sure that the learners' knowledge is the required one. This ability requires a model of their own mental states perceived by others: \textit{"has the human understood that I(robot) need this object for the task or should I explain it once again ?"} In this paper, we discuss the importance of a cognitive architecture enabling second-order Mutual Modelling for Human-Robot Interaction in educative contexts.