Informational Embodiment: Computational role of information structure in codes and robots
Alexandre Pitti, Kohei Nakajima, Yasuo Kuniyoshi

TL;DR
This paper explores how the physical structure of robots influences information processing, proposing an information-theoretic framework called Informational Embodiment that enhances understanding of embodied intelligence and efficient coding.
Contribution
It introduces the concept of viewing robot bodies as communication channels and applies entropy maximization and Shannon's theorem to optimize design and coding strategies.
Findings
Robot body morphology affects information perception and processing.
Efficient codes reaching Shannon limits improve robustness and parsimony.
The framework links information theory with bio-inspired robotics and motor control.
Abstract
The body morphology plays an important role in the way information is perceived and processed by an agent. We address an information theory (IT) account on how the precision of sensors, the accuracy of motors, their placement, the body geometry, shape the information structure in robots and computational codes. As an original idea, we envision the robot's body as a physical communication channel through which information is conveyed, in and out, despite intrinsic noise and material limitations. Following this, entropy, a measure of information and uncertainty, can be used to maximize the efficiency of robot design and of algorithmic codes per se. This is known as the principle of Entropy Maximization (PEM) introduced in biology by Barlow in 1969. The Shannon's source coding theorem provides then a framework to compare different types of bodies in terms of sensorimotor information. In…
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Taxonomy
TopicsComputability, Logic, AI Algorithms · Evolutionary Algorithms and Applications
