Representation Learning

Learning useful feature representations from raw data (images, point clouds, proprioception) that capture task-relevant structure. Good representations compress high-dimensional inputs into informative, compact vectors that make downstream policy learning easier. Self-supervised methods (contrastive learning, masked autoencoders), pre-trained vision models, and world models all serve as representation learning approaches for robotics.