Perceptual Distortions and Autonomous Representation Learning in a Minimal Robotic System

Autonomous agents, particularly in the field of robotics, rely on sensory information to perceive and navigate their environment. However, these sensory inputs are often imperfect, leading to distortions in the agent's internal representation of the world. This paper investigates the nature of these perceptual distortions and how they influence autonomous representation learning using a minimal robotic system. We utilize a simulated two-wheeled robot equipped with distance sensors and a compass, operating within a simple square environment. Through analysis of the robot's sensor data during random exploration, we demonstrate how a distorted perceptual space emerges. Despite these distortions, we identify emergent structures within the perceptual space that correlate with the physical environment, revealing how the robot autonomously learns a structured representation for navigation without explicit spatial information. This work contributes to the understanding of embodied cognition, minimal agency, and the role of perception in self-generated navigation strategies in artificial life.