Foundation Models

Training Data Mixture

A training data mixture is the weighted composition of data sources used to train a model, which for robot foundation models may span teleoperated demonstrations across multiple embodiments, simulation rollouts, human video, and web-scale vision-language corpora. Mixture ratios materially affect capability: cross-embodiment efforts like Open X-Embodiment and RT-X showed careful blending across robot datasets improves transfer, while VLA models co-train on web data to preserve semantic generalization.

Why it matters for physical AI

What a robot foundation model can do is largely determined by what it was fed, and mixture design, balancing embodiments, tasks, and modalities, has become as consequential as architecture choices for final policy performance.