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.
Highlights
S OCIAL interaction is considered an essential cognitive behavior
This study investigates mechanisms underlying synchronized imitation as a representative social cognitive act, by formulating the problem using the free energy principle (FEP) [1], [2]
We examine conflicting situations in which each robot prefers to generate different movement patterns, depending on its learned experience. Do they synchronize to generate the same movement pattern with one robot following the other or leading by adapting the intention? Or do they desynchronize by generating different movement patterns, ignoring their counterparts by following their own action intentions? The current study hypothesizes that these dyadic interaction outcomes depend on the relative strength of the intention between the robots as a result of regulating FEP complexity
Summary
S OCIAL interaction is considered an essential cognitive behavior. In both empirical studies and synthetic modeling, researchers have investigated underlying cognitive, psychological, and neuronal mechanisms accounting for various aspects of social cognitive behaviors. In simulation experiments of dyadic robot imitative interaction, we examine how a leader and follower can be determined in conflicting situations by investigating the underlying network dynamics. Analogous to PC and AIF, Ito and Tani showed that imitative interaction can be performed using an RNN model by minimizing the prediction error instead of free energy in order to update deterministic latent variables [13] This deterministic model does not account for the belief of action intention because the precision of prediction cannot be estimated. The current study hypothesizes that these dyadic interaction outcomes depend on the relative strength of the intention between the robots as a result of regulating FEP complexity Do they synchronize to generate the same movement pattern with one robot following the other or leading by adapting the intention? Or do they desynchronize by generating different movement patterns, ignoring their counterparts by following their own action intentions? The current study hypothesizes that these dyadic interaction outcomes depend on the relative strength of the intention between the robots as a result of regulating FEP complexity
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