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Google DeepMind
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Gemini Robotics 2 Brings Whole Body Intelligence to Robots

Google DeepMind has introduced Gemini Robotics 2, a new AI model that enables robots to perform complex tasks using whole-body intelligence. The model integrates vision, language, and physical control to allow robots to understand and interact with their environment more naturally.

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Gemini Robotics 2: From Fingertips to Full Body, the Next Leap in Robotic Intelligence

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Google DeepMind unveils the Gemini Robotics 2 model family, achieving full-body control of humanoid robots, dexterous five-finger manipulation, and multi-robot collaboration for the first time, though fine multi-finger manipulation remains a challenge.

  • Gemini Robotics 2 is a vision-language-action (VLA) model that controls humanoid robots from toes to fingertips for full-body motion.
  • Gemini Robotics ER 2 is an embodied reasoning model supporting multi-step task planning, progress understanding, and multi-robot collaboration.
  • Gemini Robotics On-Device 2 runs locally on-device, adapting to new robot morphologies in just hours with fewer than 200 examples.
  • On five-fingered hands, the model can perform fine manipulations like tying knots and sealing zipper bags, but success rates for multi-finger dexterous tasks remain low.
  • All models are available to early partners, with the ER 2 model accessible on Google AI Studio and the Gemini Enterprise Agent Platform.
Open section navigationFull-Body Intelligence: From Desktop to Real-World Space

Full-Body Intelligence: From Desktop to Real-World Space

Previous models only controlled the upper body of humanoid robots for desktop tasks; Gemini Robotics 2 extends control to the entire body for the first time. On the Apptronik Apollo 2 humanoid robot, the model can follow the command "put the watering can into the green box on the bottom shelf," autonomously performing coordinated actions like walking, grasping, and placing. However, DeepMind acknowledges that the robot's movement speed still needs improvement.

This model is DeepMind's most advanced vision-language-action (VLA) model, converting visual and language inputs directly into motor control signals, capable of controlling full humanoid robots and dual-arm robots. The same model weights can adapt to three different robot morphologies: Apollo 2 with SharpaWave hand or Inspire hand, and Franka Duo with Robotiq gripper.

Dexterous Manipulation: Breakthroughs in Fine Control with Five-Fingered Hands and Grippers

Gemini Robotics 2 achieves new levels of dexterity on various end-effectors. On the Apollo 2 equipped with a 22-degree-of-freedom five-fingered SharpaWave hand, the model can perform fine actions like tying knots and sealing zipper bags; on the Franka Duo platform with standard two-finger parallel grippers, it can also execute complex tasks such as tight packing.

However, multi-finger dexterous manipulation remains a challenge. Official charts show low success rates for five-fingered tasks, while full-body control and gripper tasks achieve medium to high success rates. DeepMind states it will continue to improve precision and speed to approach human-level dexterity.

Reasoning and Collaboration: Multi-Step Tasks and Multi-Robot Coordination

Gemini Robotics ER 2 serves as the robot's "advanced brain," responsible for understanding user instructions, observing the environment, planning steps, and coordinating execution with the VLA model. It can execute complex task sequences lasting several minutes and involving hundreds of decisions, and can self-correct and generalize to new scenarios.

This model also introduces multi-robot collaboration for the first time, allowing different types of robots to communicate and cooperate to complete complex workflows that a single robot cannot accomplish. The ER 2 model is now available on Google AI Studio and in private preview on the Gemini Enterprise Agent Platform.

Localization and Rapid Adaptation: Capabilities of the On-Device Model

Gemini Robotics On-Device 2 is a VLA model optimized for local operation without network connectivity. It natively supports multiple morphologies, inheriting the "motion transfer" technology from Gemini Robotics 1.5, and can adapt to entirely new dual-arm robot morphologies—including platforms with vastly different shapes, sensors, and degrees of freedom (such as Dexmate, SO101, Trossen)—in just hours with fewer than 200 examples.

Safety and Responsibility: Multi-Layered Safeguards

DeepMind emphasizes that safety is the cornerstone of robotics research. As physical capabilities increase, they adopt a multi-layered approach combining traditional physical safety measures with robust AI safety frameworks to ensure end-to-end safety and alignment. Specific safety mechanisms are not detailed in this article.

Credibility boundary

All information in this article comes from the official Google DeepMind blog, a first-party release with high factual credibility. However, all performance data (e.g., success rates, adaptation times) are self-reported by DeepMind and have not been independently verified by third parties.

Insight takeaway

Gemini Robotics 2 makes significant progress in full-body control, dexterous manipulation, and multi-robot collaboration, but fine multi-finger manipulation remains a bottleneck. The models are now open to partners, marking an important step for embodied intelligence moving from the lab to practical applications.

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Google DeepMind

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