| DeepMind Creates A Gemini Brain For Robotics |
| Written by Sue Gee | ||| | Sunday, 02 August 2026 | ||| | Google has released Gemini Robotics 2, a family of AI models that take us ever closer towards intelligent robots. Gemini Robotics ER2 acts as the brain for one or more robots, enabling robots to plan and to cooperate. This video reveals how Gemini Robotics 2 provides the intelligence layer powering the next generation of truly adaptable robots and is a major advance that unlocks intelligent whole-body control, advanced dexterity, and multi-robot collaboration. While foundation models for text and code have advanced rapidly over the last few years, applying AI to physical hardware has been a longer journey for Google DeepMind. The company has been working on solving robotics for nearly a decade, progressing from early reinforcement learning experiments in simulation to its Robotic Transformer (RT-1 and RT-2) research. Google launched the first Gemini Robotics family built on top of Gemini 2.0 in March 2025. This marked the transition from single-purpose policies to multimodal foundation models capable of turning visual camera streams directly into robotic action. Without robots of it own, Google formed partnership with third-party robot manufacturers including, as I Programmer reported in January 2026, Boston Dynamics. By incorporating Gemini's multimodal intelligence, Atlas is now much better able to perform complex, adaptive workplace tasks. Recently we have seen robotics adopt reinforcement learning which proved highly successful for mastering physical dynamics through trial-and-error simulation — teaching quadrupeds to sprint, humanoids to recover their balance after being pushed, and arms to handle complex joint torques. However, pure RL hits a wall when faced with high-level cognitive tasks. Designing a mathematical reward function to train an RL policy to do a task such as "find the spare part in the back cupboard, ask a coworker for help if it's heavy, and bring it here" is virtually impossible. Gemini Robotics 2 signals a shift away from relying solely on end-to-end RL for physical AI. Instead, Google DeepMind has adopted a decoupled, two-tier architecture driven by vision-language foundation models (VLMs): Where reinforcement learning teaches a robot The new release splits physical AI into three distinct components, balancing high-level reasoning, cloud execution, and local edge processing:
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