Can a language model actually control a robot?

1. It already runs, on cheap hardware

Paige Bailey, AI Developer Relations Lead at Google DeepMind, states the current position plainly: “Gemini already runs on robots.”

The demo she describes is deliberately unimpressive as hardware. Stanford’s open-source “Pupper” robot dog can be built from 3D-printed components and runs on a Raspberry Pi. What is doing the work is the model: “you can say like, hey, pupper, shake my hand. Hey, Pupper, follow me… Like, all of these things are are possible today just with the Gemini APIs by themselves running and invoking all of the different robotic components on device on a Raspberry Pi.”

This is what physical AI means concretely: a general-purpose model perceiving and issuing commands to hardware, rather than task-specific control software written per behaviour.

2. Nothing bespoke sits between the model and the actuators

The architectural claim inside that sentence is the interesting one. The instruction arrives in natural language, the model decides which robotic components to invoke, and it does so through the same general APIs a web application would call. There is no separate robotics stack translating intent into motion.

That is why the hardware being commodity matters rather than being a footnote. Bailey expects the pattern to spread on that basis: “We’ll start to see many more kinds of robots built with commodity hardware,” citing Raspberry Pi and cheaper alternatives that ship faster. She frames this as a forecast, not a fact.

3. Who is testing it

DeepMind runs a robotics trusted-tester program for the Gemini models and for open robotics models it publishes on Hugging Face. Bailey names the participants as “Enchanted Tools, Boston Dynamics, the Figure Team,” describing them as “testing out or using Gemini models in order to operate robotics.”

Worth reading that at the right strength: these are testers of a program, not shipped products built on it.

4. The application she is most interested in is not a robot dog

Bailey’s own enthusiasm points at laboratories rather than consumer hardware. She cites Periodic Labs as “using AI to to control robotics, but also to to have those robots do really interesting material science work. So they can design experiments, run the experiments, test out, you know, the likelihood of different component parts being successful for the creation of semiconductors.”

Her argument for it is about error rates rather than speed: the work is repetitive, and repetitive work is where humans make mistakes. Her conclusion is hedged: “I think the more that AI is incorporated into the scientific process, both for designing experiments, but also executing on them, I think the better off we’re all gonna be.”

Why it matters

Robotics used to be gated on control software written for one body and one task. When the controller is a multimodal model that takes instructions in language, the expensive part moves from the robot to the model, and the robot is allowed to get cheap. A 3D-printed dog taking spoken commands (Bailey puts the self-build cost at roughly $2,000) is not a product, but it is a working demonstration that the gate moved.