Robot Learning

Representation Learning

Representation learning is the acquisition of feature embeddings from raw sensor data that expose task-relevant structure, typically without manual labels, through objectives such as contrastive learning, masked reconstruction, temporal prediction, or language alignment. In robotics it spans visual encoders pretrained on human video, state representations learned from interaction, and latent dynamics models, all aiming to make downstream policy learning easier and more sample-efficient.

Why it matters for physical AI

Good representations are the leverage point of data-limited robot learning: a policy trained on a few hundred demonstrations succeeds or fails largely on what its pretrained encoder already understands.