Contrastive Learning

A self-supervised representation learning approach that trains an encoder to produce similar embeddings for semantically related inputs (positive pairs) and dissimilar embeddings for unrelated inputs (negative pairs). In robotics, contrastive learning is used to learn state representations from unlabeled video, train reward models, and pre-train visual encoders before policy fine-tuning.