Foundation Models for Robotics: What's Actually Different About Them

A newer approach to robot software, borrowing ideas from large language models.

A robotics foundation model is trained on a very large and diverse dataset — often combining internet-scale image and text data with robot demonstration data across many different robots and tasks — with the goal of producing a single model that generalizes across tasks, rather than one narrow model trained per task.

Why this is a departure from older approaches

Traditional robot control software was typically hand-engineered or trained narrowly for one specific task and one specific robot. Foundation models aim for the same kind of broad transferability that made large language models useful across many different text tasks without task-specific retraining.

The honest state of things

Early results are genuinely promising for certain manipulation tasks, but reliable, general-purpose robot foundation models that work robustly across arbitrary new tasks and environments are still more of an active research frontier than a solved, shipping reality — worth reading skeptically past the more optimistic marketing framing.