Example Concept: Machine vision systems in robotics often employ convolutional neural networks (CNNs) to process image data. These networks automatically learn hierarchical feature representations from raw pixel data, where lower layers detect simple features like edges and corners, while higher layers capture more complex patterns and object-specific features. The final layer classifies the object based on the learned features, allowing the robot to make decisions or perform tasks based on the identified objects.