Robot self/other distinction: active inference meets neural networks learning in a mirror

TL;DR

This paper presents a novel algorithm combining active inference and neural networks enabling a robot to recognize itself in a mirror and distinguish its actions from others, advancing self-recognition in artificial agents.

Contribution

It introduces a new method for robot self-recognition based on active inference and neural network learning, capable of distinguishing itself from other entities in a mirror.

Findings

Robot successfully recognizes itself in a mirror from various perspectives.

Algorithm reliably distinguishes between robot, other robots, and humans.

Self-recognition performance is robust across different initial conditions.

Abstract

Self/other distinction and self-recognition are important skills for interacting with the world, as it allows humans to differentiate own actions from others and be self-aware. However, only a selected group of animals, mainly high order mammals such as humans, has passed the mirror test, a behavioural experiment proposed to assess self-recognition abilities. In this paper, we describe self-recognition as a process that is built on top of body perception unconscious mechanisms. We present an algorithm that enables a robot to perform non-appearance self-recognition on a mirror and distinguish its simple actions from other entities, by answering the following question: am I generating these sensations? The algorithm combines active inference, a theoretical model of perception and action in the brain, with neural network learning. The robot learns the relation between its actions and its body with the effect produced in the visual field and its body sensors. The prediction error generated between the models and the real observations during the interaction is used to infer the body configuration through free energy minimization and to accumulate evidence for recognizing its body. Experimental results on a humanoid robot show the reliability of the algorithm for different initial conditions, such as mirror recognition in any perspective, robot-robot distinction and human-robot differentiation.