Higher coordination with less control - A result of information maximization in the sensorimotor loop

TL;DR

This paper introduces a novel learning method based on maximizing predictive information in sensorimotor loops, leading to higher coordination with less control, especially in longer robot chains with simpler controllers.

Contribution

It proposes a new information-theoretic learning rule for embodied AI that improves coordination without complex assumptions or models.

Findings

Maximizing predictive information per wheel enhances robot coordination.

Longer robot chains with simpler controllers outperform shorter, more complex ones.

Information-geometric analysis explains the effectiveness of the approach.

Abstract

This work presents a novel learning method in the context of embodied artificial intelligence and self-organization, which has as few assumptions and restrictions as possible about the world and the underlying model. The learning rule is derived from the principle of maximizing the predictive information in the sensorimotor loop. It is evaluated on robot chains of varying length with individually controlled, non-communicating segments. The comparison of the results shows that maximizing the predictive information per wheel leads to a higher coordinated behavior of the physically connected robots compared to a maximization per robot. Another focus of this paper is the analysis of the effect of the robot chain length on the overall behavior of the robots. It will be shown that longer chains with less capable controllers outperform those of shorter length and more complex controllers. The reason is found and discussed in the information-geometric interpretation of the learning process.