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R to @GoogleDeepMind: FInd out more → https://goo.gle/3Tvi61l

Industry & News Robotics Foundation Models

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Representative image for R to @GoogleDeepMind: FInd out more → https://goo.gle/3Tvi61l

Merged summary

TL;DR - Google DeepMind announced Gemini Robotics 2, a robotics model that extends Gemini's capabilities to "whole-body" robot control rather than just arm/hand manipulation. It matters because it signals a push from foundation-model-driven grasping toward full-body, multi-robot embodied autonomy.

  • Positioned as "whole body intelligence" — coordinated control spanning locomotion and manipulation ("from feet to fingertips"), not isolated end-effector control.
  • Emphasizes fine dexterity alongside coarse body motion, implying a single policy stack handling multiple control granularities.
  • Highlights robot "teamwork," suggesting multi-robot coordination on shared tasks.
  • Content is a promotional announcement post only: no benchmarks, hardware platforms, training details, or evaluation results are provided, so capability claims are unverified here.

Sources (1)

R to @GoogleDeepMind: FInd out more → https://goo.gle/3Tvi61l

@GoogleDeepMind 2026-08-07
Public signals N/A
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-08 14:16:15.180785 UTC

TL;DR - Google DeepMind announced Gemini Robotics 2, a robotics model that extends Gemini's capabilities to "whole-body" robot control rather than just arm/hand manipulation. It matters because it signals a push from foundation-model-driven grasping toward full-body, multi-robot embodied autonomy.

  • Positioned as "whole body intelligence" — coordinated control spanning locomotion and manipulation ("from feet to fingertips"), not isolated end-effector control.
  • Emphasizes fine dexterity alongside coarse body motion, implying a single policy stack handling multiple control granularities.
  • Highlights robot "teamwork," suggesting multi-robot coordination on shared tasks.
  • Content is a promotional announcement post only: no benchmarks, hardware platforms, training details, or evaluation results are provided, so capability claims are unverified here.
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