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Gemini Robotics 2: Google's AI Learns to Move the Whole Body

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With Gemini Robotics 2, Google DeepMind brings whole-body control, fine dexterity and teamwork to robots. Three models — one runs entirely on-device. Here's why it matters even if you live in Claude land.

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Robots that fit into our world — we’ve been talking about it for decades. On July 30, Google DeepMind took a real step in that direction: Gemini Robotics 2, the intelligence layer for its next generation of robots. And this time it’s no longer just gripper arms on a tabletop.

What’s new

Until now, DeepMind’s models mostly controlled a robot’s upper body for table-height tasks. Gemini Robotics 2 can now control the entire body — walking, crouching, stretching, balancing. In the demo, an Apollo 2 humanoid tidies a cluttered room: it walks to a table, picks up a watering can, steps over to the shelves, and places it precisely where it belongs.

On top of that comes real dexterity. The model drives a five-fingered hand with 22 degrees of freedom, pulling off things like tying knots or sealing a ziplock bag. To be fair, success rates on the trickiest multi-finger tasks are still all over the place — screwing in a lightbulb sits at 36 percent. But the direction is right.

The three models

DeepMind isn’t shipping one model but three: Gemini Robotics 2, a vision-language-action model for the actual movement; Gemini Robotics ER 2, the reasoning brain that plans multi-minute tasks and coordinates several robots; and Gemini Robotics On-Device 2, which runs entirely locally — no network, no latency. That last one adapts to brand-new robot bodies in a few hours, often with fewer than 200 examples.

There’s also multi-robot collaboration: different robots talk to each other and solve tasks together that a single one couldn’t handle alone.

Why it matters

For those of us in the Claude corner, this is a Google story on the surface. But the trend behind it touches everyone: the big labs are pushing their language models out of the chat window and into the physical world. Anthropic is experimenting with Project Fetch on a robot dog, OpenAI is building a robotics team. The race for “AI that touches things” has started.

My take

What reassures me about this release: DeepMind makes safety a core theme. There’s a new benchmark called ASIMOV-Agentic that measures whether the robot refuses unsafe commands and, when uncertain, asks a human instead. That’s exactly what you want once these machines stand next to us. The videos still look a little slow and clumsy — but I remember how clumsy the first chatbots sounded. That moves fast.


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