A Neurorobotics Approach to Investigating the Emergence of Communication in Robots

TL;DR

This paper presents a neurorobotics approach using stochastic neural dynamics and prediction error minimization to enable communication and imitation in robots, leading to emergent communicative patterns.

Contribution

It introduces a novel neurorobotics framework combining stochastic neural dynamics and PEM for robot communication and demonstrates emergent communication behaviors.

Findings

Robots can imitate actions using the proposed neural features.

Emergent communication patterns inferred intentions behind sensory observations.

Preliminary results show potential for autonomous communication in robots.

Abstract

This paper introduces our approach to building a robot with communication capability based on the two key features: stochastic neural dynamics and prediction error minimization (PEM). A preliminary experiment with humanoid robots showed that the robot was able to imitate other's action by means of those key features. In addition, we found that some sorts of communicative patterns emerged between two robots in which the robots inferred the intention of another agent behind the sensory observation.