Functional advantages of an adaptive Theory of Mind for robotics: a review of current architectures

TL;DR

This review examines current robotic architectures with adaptive Theory of Mind (ToM), highlighting their implementation methods and functional advantages, and emphasizes the need for further development of ToM features for improved human-robot interaction.

Contribution

It provides a comprehensive review of existing architectures and analyzes how often and in what ways ToM features are integrated into robots.

Findings

ToM features are variably implemented across architectures.

ToM for false-belief understanding is common, but proactive ToM is less so.

Advances in adaptive ToM are needed for better human-robot interaction.

Abstract

Great advancements have been achieved in the field of robotics, however, main challenges remain, including building robots with an adaptive Theory of Mind (ToM). In the present paper, seven current robotic architectures for human-robot interactions were described as well as four main functional advantages of equipping robots with an adaptive ToM. The aim of the present paper was to determine in which way and how often ToM features are integrated in the architectures analyzed, and if they provide robots with the associated functional advantages. Our assessment shows that different methods are used to implement ToM features in robotic architectures. Furthermore, while a ToM for false-belief understanding and tracking is often built in social robotic architectures, a ToM for proactivity, active perception and learning is less common. Nonetheless, progresses towards better adaptive ToM features in robots are warranted to provide them with full access to the advantages of having a ToM resembling that of humans.