Robot Learning

Contrastive Learning

Contrastive learning is a self-supervised representation learning approach that trains encoders to pull embeddings of positive pairs, different views or modalities of the same underlying content, together while pushing apart negatives, typically using the InfoNCE loss. Landmark methods include SimCLR and MoCo for images and CLIP for image-text alignment. In robotics, contrastive objectives learn visual representations from unlabeled robot video, align observations with goals, and pretrain encoders for policies.

Why it matters for physical AI

Contrastive pretraining converts abundant unlabeled robot and human video into useful visual representations, cutting the demonstration count needed to train manipulation policies and improving robustness to visual variation.