Date Published: May 8, 2019
Publisher: Public Library of Science
Author(s): Amy LaViers, Denis Horvath.
Animal movement encodes information that is meaningfully interpreted by natural counterparts. This is a behavior that roboticists are trying to replicate in artificial systems but that is not well understood even in natural systems. This paper presents a count on the cardinality of a discretized posture space—an aspect of expressivity—of articulated platforms. The paper uses an information-theoretic measure, Shannon entropy, to create observations analogous to Moore’s Law, providing a measure that complements traditional measures of the capacity of robots. This analysis, applied to a variety of natural and artificial systems, shows 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. The quantitative measure proposed in this paper provides an additional lens through which to compare natural and artificial systems.
Moving bodies seem to express information: animals, including humans, communicate through non-verbal, visual cues. For example, a nervous twitch of the eye, slight straightening of the spine, or subtle quickening of breath of a poker player may involuntarily occur because they have just drawn a fortuitous card, reflecting a change of their internal estimate of their likelihood to win the hand. Another player may, having watched this opponent over the course of several hands, recognize this “tell” and adjust their own strategy accordingly. Or, perhaps the first player is bluffing, and the action of the eye, spine, and breath are created to throw off their opponents. In either case, information is being transmitted and received. Inside the context of a poker game, these changes in external presentation have meaning.
To begin, consider some established examples of information sources:
The measure and procedure presented in the previous section can categorize and compare artificial systems. The results of applying this measure to a variety of robotic platforms and natural systems is presented here. Limitations and assumptions in the analysis are presented as well as discussion of trends in the data.
The paper has introduced a measure for postural expressivity, clarifying prior points of view on function versus expression in movement by relating these two ideas in the same measure. Finally, the paper uses this proposed measure to compare extant artificial systems to natural systems, suggesting that, through the lens of complexity, many modern robots, including humanoids, are comparable to a microscopic worm. This provides a quantitative model for ways in which robots still have to improve to recreate the behavior of natural counterparts (without negating the fact that the force and velocity profiles of these machines can certainly exceed that of C. Elegans and many natural systems). Future work will extend the discretized configuration space presented here to a discretized state space, according to actuator force and torque limits, in order to account for information transmitted through variable velocity.