Robot Learning

Unsupervised Learning

Unsupervised learning is the extraction of structure from data without human-provided labels, encompassing clustering, dimensionality reduction, density estimation, and generative modeling. In modern practice it is often realized as self-supervised learning, where supervision is manufactured from the data itself, as in contrastive methods, masked reconstruction, and next-token prediction. Robotics applications include learning visual representations from unlabeled video, skill discovery, and world models trained on raw interaction streams.

Why it matters for physical AI

Physical interaction data is expensive to label at scale, so extracting representations, skills, and dynamics from unlabeled robot and human video is essential to feeding data-hungry foundation models economically.