Robot Learning

Contrastive Predictive Coding (CPC)

Contrastive Predictive Coding is a self-supervised learning method from van den Oord et al. (2018) that learns representations by predicting future latent states in a sequence and distinguishing true futures from negatives via the InfoNCE loss, which maximizes a lower bound on mutual information. Applied to audio, images, text, and RL, CPC-style temporal contrast underlies representation learning from sequential robot experience and video.

Why it matters for physical AI

Temporal predictive objectives extract dynamics-aware features from raw robot experience without labels, providing pretrained encoders that accelerate downstream policy and world-model learning.