Expressivity in Natural and Artificial Systems

TL;DR

This paper introduces a measure called expressivity to quantify the information capacity of motion in natural and artificial systems, revealing that current robots lag behind animals in behavioral complexity.

Contribution

It proposes a new measure for motion expressivity, analyzes its limits in existing robotic systems, and compares the trends in complexity between natural and artificial systems.

Findings

Robots have limited expressivity compared to animals.

Artificial systems increased internal complexity but not external mechanical complexity.

Natural systems show increasing complexity in both internal and external aspects.

Abstract

Roboticists are trying to replicate animal behavior in artificial systems. Yet, quantitative bounds on capacity of a moving platform (natural or artificial) to express information in the environment are not known. This paper presents a measure for the capacity of motion complexity -- the expressivity -- of articulated platforms (both natural and artificial) and shows that this measure is stagnant and unexpectedly limited in extant robotic systems. This analysis indicates trends in increasing capacity in both internal and external complexity for natural systems while artificial, robotic systems have increased significantly in the capacity of computational (internal) states but remained more or less constant in mechanical (external) state capacity. This work presents a way to analyze trends in animal behavior and shows that robots are not capable of the same multi-faceted behavior in rich, dynamic environments as natural systems.