Robot Learning

Self-Supervised Learning

Self-supervised learning is a paradigm in which models learn representations from unlabeled data by solving pretext tasks whose labels are derived from the data itself, such as contrastive instance discrimination, masked reconstruction, or temporal prediction. Vision encoders like DINOv2 and robotics-oriented representations like R3M (trained on the Ego4D human video corpus) are self-supervised. Robots can also self-supervise directly, for example labeling grasp outcomes from their own trial data.

Why it matters for physical AI

Unlabeled video and autonomous robot experience vastly outnumber curated demonstrations, so self-supervision is the main mechanism for turning cheap data into pretrained perception and dynamics representations.