Google DeepMind Unveils Gemini Robotics: AI-Powered Robots for the Physical World
Created on March 13|Last edited on March 13
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Google DeepMind has announced Gemini Robotics, a new AI model designed to bring multimodal intelligence to the physical world. Built on the foundation of Gemini 2.0, this advanced system enables robots to process and act on real-world information, moving beyond digital problem-solving to physical task execution. DeepMind is also launching Gemini Robotics-ER, an enhanced model focused on spatial reasoning, which allows roboticists to integrate AI-driven control with existing robotic systems.
Bridging AI with Physical Actions
While AI has made significant strides in text, image, and video understanding, applying this intelligence to real-world robotics remains a challenge. Gemini Robotics addresses this by introducing vision-language-action (VLA) capabilities, allowing robots to interpret visual and linguistic inputs and translate them into physical actions. Unlike previous AI models, which primarily assist with perception and planning, Gemini Robotics directly controls robotic movement, enabling dexterous and responsive task execution.
Key Capabilities of Gemini Robotics
To be effective in real-world applications, AI-driven robots must be general, interactive, and dexterous. Gemini Robotics demonstrates all three of these qualities, outperforming previous vision-language-action models in adaptability, responsiveness, and fine motor skills.
Gemini Robotics can generalize to new environments and tasks without extensive retraining. The model exhibits strong adaptability, handling novel objects, different instructions, and unfamiliar surroundings with ease. DeepMind’s technical report highlights that Gemini Robotics more than doubles performance on generalization benchmarks compared to other state-of-the-art models.
Interactivity is another crucial feature. Built on Gemini 2.0, the model understands and responds to natural language commands in real time. This allows it to follow instructions fluidly, adjust to environmental changes, and maintain continuous interaction with users. For example, if an object is moved mid-task, Gemini Robotics recalibrates and continues its work without needing explicit reprogramming.
Dexterity remains one of the most difficult challenges in robotics. Many everyday human actions—such as folding paper or handling fragile objects—require precise manipulation. Gemini Robotics advances in this area by successfully executing complex, multi-step physical tasks, making robots more useful in real-world settings.
Versatility Across Robotic Platforms
A major strength of Gemini Robotics is its ability to control different types of robots. Initially trained on the ALOHA 2 bi-arm robotic platform, it has demonstrated compatibility with other robotic arms, such as the Franka system used in academic research. DeepMind is also working with Apptronik to integrate Gemini Robotics into humanoid robots, allowing them to perform everyday tasks in human-centered environments.
Advancing Spatial Understanding with Gemini Robotics-ER
Alongside Gemini Robotics, DeepMind has introduced Gemini Robotics-ER (Embodied Reasoning), a specialized model designed to enhance spatial understanding. This model builds on Gemini 2.0’s capabilities, improving tasks such as object detection, grasping, and 3D spatial reasoning.
Gemini Robotics-ER enables robots to autonomously determine how to interact with objects in their environment. For example, when presented with a coffee mug, it can analyze the best way to grasp the handle and plan a safe trajectory for picking it up. The model achieves a two to three times higher success rate compared to Gemini 2.0 in tasks requiring spatial awareness.
By integrating perception, state estimation, and motion planning into a single AI system, Gemini Robotics-ER enables robots to perform complex tasks with minimal human intervention. The model also supports in-context learning, allowing it to improve performance based on human demonstrations.
Ensuring Safety in AI-Driven Robotics
As AI-powered robots become more capable, ensuring their safety in real-world applications is a priority. DeepMind is implementing a multi-layered safety approach, incorporating both low-level motor control safeguards and high-level semantic understanding.
Gemini Robotics-ER can interface with existing robotic safety mechanisms, including collision avoidance and force-limiting systems. Additionally, DeepMind is introducing a new dataset, ASIMOV, to evaluate and refine AI-driven safety protocols. Inspired by Isaac Asimov’s Three Laws of Robotics, the ASIMOV dataset allows researchers to develop rule-based frameworks for guiding robot behavior in a safe and ethical manner.
DeepMind is also engaging with experts from its Responsible Development and Innovation team, alongside external specialists, to assess the broader societal impacts of AI in robotics. Trusted testers, including Agile Robots, Agility Robotics, Boston Dynamics, and Enchanted Tools, are collaborating on real-world testing of Gemini Robotics-ER.
The Future of AI-Powered Robotics
With Gemini Robotics and Gemini Robotics-ER, DeepMind is pushing the boundaries of AI-driven robotics, making robots more adaptive, interactive, and capable of performing practical tasks. By bridging the gap between AI reasoning and physical execution, these models mark a significant step toward the development of general-purpose robots that can assist humans in a variety of environments.
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