# Google’s new AI can now walk a humanoid across the room and tidy up, feet to fingertips > Source: > Published: 2026-07-30 15:55:36+00:00 Google DeepMind wants one AI brain to run every robot, and it just taught that brain to use its whole body. It has released [Gemini Robotics 2](https://deepmind.google/blog/gemini-robotics-2-brings-whole-body-intelligence-to-robots/), a family of models that can control a humanoid from its feet to its fingertips. The system can also coordinate several machines at once and adapt to a new robot body in a few hours. It is the clearest sign yet of what the industry calls “physical AI,” and Wired framed it as a real step toward [“physical AGI.”](https://www.wired.com/story/google-gemini-can-control-humanoid-robots/) The pitch is simple. The same kind of model that writes your emails is now learning to walk across a cluttered room and tidy it up. ## From arms to the whole body The headline change is control. DeepMind’s earlier robot models mostly moved an upper body to do tabletop tasks. Gemini Robotics 2 drives the entire machine. It can make a humanoid walk, crouch, stretch and balance while manipulating objects in tight spaces built for humans. In a demo, Apptronik’s Apollo 2 robot heard a single instruction: put the watering can in the green bin on the bottom shelf. It walked to a table, picked up the can, stepped over to the shelves, bent down and placed it. That sounds trivial. Coordinating legs, torso, arms and hands from one prompt is not. Dexterity has improved too. The model can drive Apollo’s five-fingered, 22-joint hand to tie knots and seal a ziplock bag, and it can run simpler two-fingered grippers on other platforms. “Our goal is to bring AI into the physical world and then build the intelligence layer that can be used by every robot,” said Carolina Parada, DeepMind’s head of robotics. ## Three models, one system The release is really three models. Gemini Robotics 2 is the vision-language-action model that turns what a robot sees and hears into motor commands. It handles the physical doing. Gemini Robotics ER 2 is the reasoning layer, a high-level brain that plans multi-step jobs. In its [developer briefing](https://blog.google/innovation-and-ai/models-and-research/google-deepmind/gemini-robotics-er-2/), Google showed it tracking its own progress on a live video feed. It can call tools like Google Search, and even steer a Boston Dynamics Spot robot to fetch a snack. It also lets different robots work as a team, a wheeled machine and a humanoid splitting a task. It is available now to developers through the Gemini API and Google AI Studio. The third, On-Device 2, runs locally with no internet connection. It can be fitted to an entirely new robot body with fewer than 200 examples and a few hours of training. That last point matters, because moving a learned skill from one machine to another has long been one of robotics’ hardest problems. ## Impressive, and still slow DeepMind was unusually frank about the limits. Its own numbers show the gap. Bloomberg reported the system could [unscrew a light bulb 92 per cent of the time](https://www.bloomberg.com/news/articles/2026-07-30/google-unveils-gemini-ai-for-robots-struggling-with-dexterity), but fiddlier jobs lagged badly. The Chosun Daily put the trash-bag tie at 44 per cent and the ziplock seal at 40 per cent. The robots are also slow. They pause to think through moves a person makes without a second thought. Kanishka Rao, a DeepMind robotics director, said true dexterity remains a distant goal, and that robots still learn far less efficiently than humans, who adjust after one or two mistakes. This is a pattern across the field. Rival efforts from [robotics foundation-model startups](https://thenextweb.com/news/genesis-ai-500m-raise-robotics-foundation-model), and dexterity work on [rival humanoids](https://thenextweb.com/news/1x-neo-robot-tendon-driven-hands), keep hitting the same wall. The demos dazzle, but the machines are [still far from ready for the home](https://thenextweb.com/news/china-humanoid-robot-boom-commercialisation-reality-check). ## Safety moves up the stack As robots gain the ability to move around people, DeepMind put more weight on safety. Gemini Robotics ER 2 is its safest model yet, it says, better at spotting when a person is close and halting until the area is clear. It resumes only when the space is empty again. The company also released a benchmark, ASIMOV-Agentic, that tests whether the reasoning model will refuse an unsafe command from the action model, and whether it flags when a task is impossible or asks a human for help. Naming a robot-safety benchmark after Isaac Asimov is on the nose. The underlying worry, machines acting on flawed instructions, is not. ## A hardware problem Google cannot solve There is an awkward backdrop. Google makes the software, not the robots, and the hardware supply is getting political. As Axios [noted](https://www.axios.com/2026/07/30/google-robotics-software-update), the US just moved to [ban future sales of Chinese-made robots](https://thenextweb.com/news/fcc-covered-list-foreign-robots-inverters-china) on security grounds. Many of the bodies this software might run on are built in China. Google is working with Western partners including Apptronik, [Boston Dynamics](https://thenextweb.com/news/boston-dynamics-spot-delivery-robot-porch-gap) and Agile Robots, and more than 100 trusted testers. Its rivals are circling the same prize. OpenAI and Nvidia are both building robot models, and everyone is chasing the same idea: one model, any body. DeepMind is careful to call this a milestone, not the finish line. Gemini Robotics 2 makes robots more general and more useful than before. It also makes plain how far a machine still is from the easy competence of a human tidying a room. ## Get the TNW newsletter Get the most important tech news in your inbox each week.