Informational embodiment: Computational role of information structure in codes and robots

Area of Science:

• Robotics
• Information Theory
• Computational Neuroscience

Background:

• Agent body morphology significantly influences information processing.
• Existing models often overlook the body's role as a physical communication channel.
• Information theory (IT) provides tools to analyze sensorimotor information transfer.

Purpose of the Study:

• To propose an 'Informational Embodiment' framework.
• To apply information theory and entropy maximization to robot design and algorithms.
• To explore how body morphology shapes information structure in intelligent systems.

Main Methods:

• Utilizing information theory (IT) principles, including entropy maximization (PEM).
• Applying Shannon's source coding theorem for sensorimotor information comparison.
• Introducing efficient codes that approach Shannon limits for error correction and robustness.

Main Results:

• The robot's body can be conceptualized as a physical communication channel.
• Entropy maximization (PEM) can optimize robot design and algorithmic efficiency.
• Efficient codes leveraging quantization and randomness enhance perception and control.

Conclusions:

• Informational Embodiment offers a novel perspective on intelligence and embodiment.
• The framework has implications for bio-inspired robotics and understanding natural systems.
• IT provides a powerful lens for analyzing and designing intelligent embodied agents.