Foundation Models

Embedding

Embedding is a learned dense vector representation that places discrete or high-dimensional inputs, words, images, states, or skills, into a continuous space where geometric proximity reflects semantic similarity. Embeddings are produced by encoder networks and underpin retrieval, clustering, and multimodal alignment, as in CLIP's joint image-text space. In robotics, observation and language embeddings serve as the interface between perception, instructions, and policy networks.

Why it matters for physical AI

Shared embedding spaces are what let a language instruction retrieve the right visual features and condition a manipulation policy, forming the connective tissue of vision-language-action systems.