Leading or Following? Dyadic Robot Imitative Interaction Using the Active Inference Framework

TL;DR

This paper explores how dyadic robot interactions, modeled through active inference and variational recurrent neural networks, dynamically shift between leading and following roles based on belief regulation, enhancing synchronization.

Contribution

It introduces a novel simulation framework using active inference to analyze how robots dynamically assume leader or follower roles based on belief strength regulation.

Findings

Leader robots exert stronger action intentions with looser regulation.

Follower robots adapt by reducing action intention with tighter regulation.

High synchronization occurs when roles are established by belief strength differences.

Abstract

This study investigated how social interaction among robotic agents changes dynamically depending on the individual belief of action intention. In a set of simulation studies, we examine dyadic imitative interactions of robots using a variational recurrent neural network model. The model is based on the free energy principle such that a pair of interacting robots find themselves in a loop, attempting to predict and infer each other's actions using active inference. We examined how regulating the complexity term to minimize free energy determines the dynamic characteristics of networks and interactions. When one robot trained with tighter regulation and another trained with looser regulation interact, the latter tends to lead the interaction by exerting stronger action intention, while the former tends to follow by adapting to its observations. The study confirms that the dyadic imitative interaction becomes successful by achieving a high synchronization rate when a leader and a follower are determined by developing action intentions with strong belief and weak belief, respectively.