Expressivity in Natural and Artificial Systems
This work addresses the gap in understanding motion complexity for roboticists aiming to replicate animal behavior, highlighting a key limitation in current robotics.
The paper tackles the problem of quantifying the capacity of articulated platforms to express information through motion, finding that robotic systems have stagnant mechanical state capacity compared to natural systems, which show increasing internal and external complexity.
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.