Cognitive Architecture for Mutual Modelling

TL;DR

This paper discusses the development of a cognitive architecture that enables robots to model and understand human mental states, facilitating more effective mutual understanding in educational human-robot interactions.

Contribution

It introduces a cognitive architecture capable of second-order mutual modeling, enhancing robots' ability to interpret human mental states in collaborative tasks.

Findings

Proposes a cognitive architecture for mutual modeling

Highlights importance in educative human-robot interaction

Facilitates adaptive robot behavior based on human understanding

Abstract

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.