Rule-based logic is a traditional approach in robotics where system behavior is defined through explicit if–then rules. These rules map specific conditions to predefined actions, making the system predictable and easy to interpret. Rule-based logic is commonly used in structured environments where all relevant scenarios can be anticipated in advance. In robotics, it often appears alongside finite-state machines and control theory to handle task sequencing and decision-making. However, rule-based systems struggle with scalability and adaptability, as new situations require manual updates to the rule set. This limitation has driven interest in learning-based approaches, which can infer behavior from data rather than relying on exhaustive human-defined logic.