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Google DeepMind Details Gemini Robotics ER 2 System

Google DeepMind introduced Gemini Robotics ER 2, an AI model designed to act as a high-level brain for physical robots.

WHAT YOU NEED TO KNOW
  • Google DeepMind released Gemini Robotics ER 2 on July 30, 2026, across the Gemini API, Google AI Studio, and Gemini Enterprise Agent Platform.
  • The model achieved 57.4% accuracy in task progress classification and 91.3% accuracy in critical moment-finding evaluations.
  • Demonstrations included multi-robot workflows with Apptronik's Apollo 2 and Franka F3 Duo, alongside API orchestration on Boston Dynamics' Spot.
  • Safety tests confirmed the system halts humanoid robot operations when a human approaches and resumes once the space is clear.

Google DeepMind launched Gemini Robotics ER 2 on July 30, 2026, introducing a model designed to serve as a high-level reasoning engine for physical robots. The model handles video understanding, real-time spatial reasoning, task orchestration, and multi-robot collaboration. Developers can access the system through the Gemini API, Google AI Studio, and in private preview on the Gemini Enterprise Agent Platform.

The system processes continuous video, text, and audio streams to plan multi-step tasks while handing off lower-level motor execution to vision-language-action models or robotics control interfaces. It can also call external tools natively, including Google Search and user-defined functions. Integration with the Gemini Live API provides a bidirectional streaming endpoint that allows robots to determine upcoming actions without pausing during physical execution.

In performance evaluations across real vision-language-action control, simulated control, and human tele-operation, Google DeepMind reported that Gemini Robotics ER 2 consistently outperformed the previous ER 1.6 model. To track task progress, the system assigns video feed frames across five level bands between 0% and 100%. The model achieved 57.4 percent accuracy on progress classification tasks.

For moment-finding tasks that identify the exact video frame where a critical event occurs, the model reached 91.3 percent accuracy with a 0.96-second mean absolute distance. Google DeepMind demonstrated this orchestration capability using Boston Dynamics' Spot robot, which fetched a popcorn snack based on natural language commands by executing navigation and manipulator movement APIs.

Gemini Robotics ER 2 also supports multi-robot coordination across different hardware models, including shared workflows between Apptronik's Apollo 2 and Franka F3 Duo machines. For spatial intelligence, the model evaluates raw video feeds to identify mid-execution errors such as spills or slips and reads 10 instrument types, including liquid thermometers, rulers, linear scales, and digital displays. Safety evaluations showed that the model automatically halts a humanoid robot when a human enters its immediate vicinity and resumes operation only after the area clears.

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