Such convolutional neural networks are unable by their internal data representation struggle to maintain spatial hierarchies between simple and complex objects. Whereas capsule networks, which encode their data as vectors, can encode the probability of feature detection as the magnitude of the vector and the state of the detected feature in the direction of the vector. So a detected feature that moves around will have its associated vector maintain the same magnitude throughout the movement but alter their vector's orientation. Via dynamic routing, a capsule network sends lower-level capsule outputs to higher-level capsules with similar outputs--where the dot product measures similarity of vector outputs. The task of autonomous navigation is one of reinforcement learning.