# Google's robotic models could shift both AI and robots > Source: > Published: 2026-07-31 00:06:19+00:00 The hardware behind humanoid robots has been available for years, but getting those machines to learn and interact with the world in a truly human-like way has remained the central challenge. Google says it's one step closer. On Thursday, Google launched [Gemini Robotics 2](https://deepmind.google/blog/gemini-robotics-2-brings-whole-body-intelligence-to-robots/), its most advanced vision-language action (VLA) model yet, which converts the robot's visual and language inputs into motor control for the full humanoid. This means the robot can reason and take action with every part of its body. Google also released two other models: Gemini Robotics ER 2 and On-Device 2, which together bring robots a step closer to acting like humans. - Gemini ER 2's function is to interpret a human's command and determine the steps to complete the task, with an understanding of the physical world. It is also responsible for allowing robots to work as a team. - Meanwhile, On-Device 2, Google's more efficient VLA model, is optimized to run locally on robotic devices, allowing it to adapt to a new robotic body in just hours. Ultimately, the models should enable robots to take better [real-world action](https://www.thedeepview.com/articles/the-race-to-give-ai-a-body) with whole-body control and dexterity, and even interact with others, allowing them to perform a wider range of tasks, rather than the one fixed, repetitive task that humanoid robots have typically been limited to. This points to a broader industry trend toward generalist models that allow robots to handle a wide range of tasks without significant training. For instance, [UMA](https://www.thedeepview.com/articles/why-ex-tesla-scientist-bet-on-robotics-in-europe), a physical AI startup founded last year by a former staff scientist at Tesla, unveiled a Real-Time Learning architecture in which robots can learn through demonstration rather than manual programming, enabling them to better mimic human interactions in the physical world. Meanwhile, [robotics firm Generalist](https://www.thedeepview.com/articles/robotics-startup-is-on-a-quest-for-physical-agi) is on a mission to build a universal AI brain that allows robots to learn and perform more tasks, and AI video firm Runway has started an [open physical AI initiative](https://www.thedeepview.com/articles/can-open-source-robotics-win-the-physical-agi-race) dedicated to the generalization in robotics. All of this is happening against the backdrop of the FCC's decision this week to ban [foreign-made advanced robotic devices](https://www.theverge.com/policy/972312/us-robot-ban-sweep-up-chinese-vacuums) that weigh over 4.4 pounds, which includes humanoid robots, robot vacuums and even robot lawn mowers. China dominates the robotics sector, especially in humanoid robots, so the ban could lead to a setback for the US robotics sector. ## Our Deeper *View* While AI is being rapidly adopted in software by working professionals and enterprises, robotics looms as a long-term opportunity to drive even bigger changes in work and life. And let's not forget that AI and robotics have a symbiotic relationship. AI models are often used to create synthetic data that mimics real-world situations where collecting actual data is difficult, and that data is used to train robots to handle a wider range of tasks, creating a feedback loop that generates more data to improve the models. Additionally, AI can help robots reason in real time, decide which steps to take next, and tap into more of their physical capabilities, enabling them to be much more useful, especially in situations they may not have been initially trained for. It's through this symbiotic relationship that AI and robotics could reach mass adoption at a much quicker pace.