Foundation Models

Foundation Model

A foundation model is a large neural network pretrained on broad data at scale such that it can be adapted, via fine-tuning or prompting, to a wide range of downstream tasks. The term was coined by Stanford researchers in 2021 in the wake of models like GPT-3 and CLIP. In robotics, the concept extends to robot foundation models such as RT-2, pi0, and GR00T N1, trained on large multi-embodiment datasets like Open X-Embodiment to produce generalist visuomotor policies.

Why it matters for physical AI

The bet behind modern physical AI is that broad pretraining transfers to embodied control the way it did to language and vision, replacing task-specific engineering with generalist models adapted per deployment.