How Robots Read the World

This text originates from a volume of **Studies in Computational Intelligence** focused on **machine learning and robot perception**. The book compiles research on how autonomous systems identify their surroundings, primarily advocating for **topological navigation** over traditional geometric methods. It details a specific **deformable model system** that uses evolutionary algorithms to detect and learn landmarks in both indoor and outdoor settings. Several chapters explore advanced vision techniques, including **foveated vision sensors**, reinforcement learning for robot skills, and **3D modelling** of real-world objects. The collection aims to enhance robot autonomy by improving how machines interpret **visual landmarks** and human motion in unconstrained environments. Ultimately, the work bridges the gap between raw sensorial data and **high-level decision-making** for future robotic applications.