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Gemini Robotics brings AI into the physical world

Research Published 12 March 2025 Authors Carolina Parada Introducing Gemini Robotics, our Gemini 2.0-based model designed for roboticsAt Google DeepMind, we’ve been making progress in how our Gemini models solve complex problems through multimodal reasoning across text, images, audio and video. So far however, those abilities have been largely confined to the digital realm. In order for AI to be useful and helpful to people in the physical realm, they have to demonstrate “embodied” reasoning — the humanlike ability to comprehend and react to the world around us— as well as safely take action to get things done.Today, we are introducing two new AI models, based on Gemini 2.0, which lay the foundation for a new generation of helpful robots.The first is Gemini Robotics, an advanced vision-language-action (VLA) model that was built on Gemini 2.0 with the addition of physical actions as a new output modality for the purpose of directly controlling robots. The second is Gemini Robotics-ER, a Gemini model with advanced spatial understanding, enabling roboticists to run their own programs using Gemini’s embodied reasoning (ER) abilities.Both of these models enable a variety of robots to perform a wider range of real-world tasks than ever before. As part of our efforts, we’re partnering with Apptronik to build the next generation of humanoid robots with Gemini 2.0. We’re also working with a selected number of trusted testers to guide the future of Gemini Robotics-ER.We look forward to exploring our models’ capabilities and continuing to develop them on the path to real-world applications. A summary of our efforts Gemini Robotics: Our most advanced vision-language-action modelTo be useful and helpful to people, AI models for robotics need three principal qualities: they have to be general, meaning they’re able to adapt to different situations; they have to be interactive, meaning they can understand and respond quickly to instructions or changes in their environment; and they have to be dexterous, meaning they can do the kinds of things people generally can do with their hands and fingers, like carefully manipulate objects.While our previous work demonstrated progress in these areas, Gemini Robotics represents a substantial step in performance on all three axes, getting us closer to truly general purpose robots.GeneralityGemini Robotics leverages Gemini’s world understanding to generalize to novel situations and solve a wide variety of tasks out of the box, including tasks it has never seen before in training. Gemini Robotics is also adept at dealing with new objects, diverse instructions, and new environments. In our tech report, we show that on average, Gemini Robotics more than doubles performance on a comprehensive generalization benchmark compared to other state-of-the-art vision-language-action models. A demonstration of Gemini Robotics’s world understanding. InteractivityTo operate in our dynamic, physical world, robots must be able to seamlessly interact with people and their surrounding environment, and adapt to changes on the fly.Because it’s built on a foundation of Gemini 2.0, Gemini Robotics is intuitively interactive. It taps into Gemini’s advanced language understanding capabilities and can understand and respond to commands phrased in everyday, conversational language and in different languages.It can understand and respond to a much broader set of natural language instructions than our previous models, adapting its behavior to your input. It also continuously monitors its surroundings, detects changes to its environment or instructions, and adjusts its actions accordingly. This kind of control, or “steerability,” can better help people collaborate with robot assistants in a range of settings, from home to the workplace. If an object slips from its grasp, or someone moves an item around, Gemini Robotics quickly replans and carries on — a crucial ability for robots in the real world, where surprises are the norm. DexterityThe third key pillar for building a helpful robot is acting with dexterity. Many everyday tasks that humans perform effortlessly require surprisingly fine motor skills and are still too difficult for robots. By contrast, Gemini Robotics can tackle extremely complex, multi-step tasks that require precise manipulation such as origami folding or packing a snack into a Ziploc bag. Gemini Robotics displays advanced levels of dexterity Multiple embodimentsFinally, because robots come in all shapes and sizes, Gemini Robotics was also designed to easily adapt to different robot types. We trained the model primarily on data from the bi-arm robotic platform, ALOHA 2, but we also demonstrated that it could control a bi-arm platform, based on the Franka arms used in many academic labs. Gemini Robotics can even be specialized for more complex embodiments, such as the humanoid Apollo robot developed by Apptronik, with the goal of completing real world tasks. Gemini Robotics works on different kinds of robots Enhancing Gemini’s world understandingAlongside Gemini Robotics, we’re introducing an advanced vision-language model called Gemini Robotics-ER (short for ‘“embodied reasoning”). This model enhances Gemini’s understanding of the world in ways necessary for robotics, focusing especially on spatial reasoning, and allows roboticists to connect it with their existing low level controllers.Gemini Robotics-ER improves Gemini 2.0’s existing abilities like pointing and 3D detection by a large margin. Combining spatial reasoning and Gemini’s coding abilities, Gemini Robotics-ER can instantiate entirely new capabilities on the fly. For example, when shown a coffee mug, the model can intuit an appropriate two-finger grasp for picking it up by the handle and a safe trajectory for approaching it.Gemini Robotics-ER can perform all the steps necessary to control a robot right out of the box, including perception, state estimation, spatial understanding, planning and code generation. In such an end-to-end setting the model achieves a 2x-3x success rate compared to Gemini 2.0. And where code generation is not sufficient, Gemini Robotics-ER can even tap into the power of in-context learning, following the patterns of a handful of human demonstrations to provide a solution. Gemini Robotics-ER excels at embodied reasoning capabilities including detecting objects and pointing at object parts, finding corresponding points and detecting objects in 3D. Responsibly advancing AI and roboticsAs we explore the continuing potential of AI and robotics, we’re taking a layered, holistic approach to addressing safety in our research, from low-level motor control to high-level semantic understanding.The physical safety of robots and the people around them is a longstanding, foundational concern in the science of robotics. That’s why roboticists have classic safety measures such as avoiding collisions, limiting the magnitude of contact forces, and ensuring the dynamic stability of mobile robots. Gemini Robotics-ER can be interfaced with these ‘low-level’ safety-critical controllers, specific to each particular embodiment. Building on Gemini’s core safety features, we enable Gemini Robotics-ER models to understand whether or not a potential action is safe to perform in a given context, and to generate appropriate responses.To advance robotics safety research across academia and industry, we are also releasing a new dataset to evaluate and improve semantic safety in embodied AI and robotics. In previous work, we showed how a Robot Constitution inspired by Isaac Asimov’s Three Laws of Robotics could help prompt an LLM to select safer tasks for robots. We have since developed a framework to automatically generate data-driven constitutions – rules expressed directly in natural language – to steer a robot’s behavior. This framework would allow people to create, modify and apply constitutions to develop robots that are safer and more aligned with human values. Finally, the new ASIMOV dataset will help researchers to rigorously measure the safety implications of robotic actions in real-world scenarios.To further assess the societal implications of our work, we collaborate with experts in our Responsible Development and Innovation team and as well as our Responsibility and Safety Council, an internal review group committed to ensure we develop AI applications responsibly. We also consult with external specialists on particular challenges and opportunities presented by embodied AI in robotics applications.In addition to our partnership with Apptronik, our Gemini Robotics-ER model is also available to trusted testers including Agile Robots, Agility Robots, Boston Dynamics, and Enchanted Tools. We look forward to exploring our models’ capabilities and continuing to develop AI for the next generation of more helpful robots. AcknowledgementsThis work was developed by the Gemini Robotics team. For a full list of authors and acknowledgements please view our technical report.

Research

Published
12 March 2025
Authors

Carolina Parada

Hands from the Robot’s POV. A pair of robotic hands move tiles into the word ‘world’ under the text ‘Gemini for the Physical’.

Introducing Gemini Robotics, our Gemini 2.0-based model designed for robotics

At Google DeepMind, we’ve been making progress in how our Gemini models solve complex problems through multimodal reasoning across text, images, audio and video. So far however, those abilities have been largely confined to the digital realm. In order for AI to be useful and helpful to people in the physical realm, they have to demonstrate “embodied” reasoning — the humanlike ability to comprehend and react to the world around us— as well as safely take action to get things done.

Today, we are introducing two new AI models, based on Gemini 2.0, which lay the foundation for a new generation of helpful robots.

The first is Gemini Robotics, an advanced vision-language-action (VLA) model that was built on Gemini 2.0 with the addition of physical actions as a new output modality for the purpose of directly controlling robots. The second is Gemini Robotics-ER, a Gemini model with advanced spatial understanding, enabling roboticists to run their own programs using Gemini’s embodied reasoning (ER) abilities.

Both of these models enable a variety of robots to perform a wider range of real-world tasks than ever before. As part of our efforts, we’re partnering with Apptronik to build the next generation of humanoid robots with Gemini 2.0. We’re also working with a selected number of trusted testers to guide the future of Gemini Robotics-ER.

We look forward to exploring our models’ capabilities and continuing to develop them on the path to real-world applications.

A summary of our efforts

Gemini Robotics: Our most advanced vision-language-action model

To be useful and helpful to people, AI models for robotics need three principal qualities: they have to be general, meaning they’re able to adapt to different situations; they have to be interactive, meaning they can understand and respond quickly to instructions or changes in their environment; and they have to be dexterous, meaning they can do the kinds of things people generally can do with their hands and fingers, like carefully manipulate objects.

While our previous work demonstrated progress in these areas, Gemini Robotics represents a substantial step in performance on all three axes, getting us closer to truly general purpose robots.

Generality

Gemini Robotics leverages Gemini’s world understanding to generalize to novel situations and solve a wide variety of tasks out of the box, including tasks it has never seen before in training. Gemini Robotics is also adept at dealing with new objects, diverse instructions, and new environments. In our tech report, we show that on average, Gemini Robotics more than doubles performance on a comprehensive generalization benchmark compared to other state-of-the-art vision-language-action models.

A demonstration of Gemini Robotics’s world understanding.

Interactivity

To operate in our dynamic, physical world, robots must be able to seamlessly interact with people and their surrounding environment, and adapt to changes on the fly.

Because it’s built on a foundation of Gemini 2.0, Gemini Robotics is intuitively interactive. It taps into Gemini’s advanced language understanding capabilities and can understand and respond to commands phrased in everyday, conversational language and in different languages.

It can understand and respond to a much broader set of natural language instructions than our previous models, adapting its behavior to your input. It also continuously monitors its surroundings, detects changes to its environment or instructions, and adjusts its actions accordingly. This kind of control, or “steerability,” can better help people collaborate with robot assistants in a range of settings, from home to the workplace.

If an object slips from its grasp, or someone moves an item around, Gemini Robotics quickly replans and carries on — a crucial ability for robots in the real world, where surprises are the norm.

Dexterity

The third key pillar for building a helpful robot is acting with dexterity. Many everyday tasks that humans perform effortlessly require surprisingly fine motor skills and are still too difficult for robots. By contrast, Gemini Robotics can tackle extremely complex, multi-step tasks that require precise manipulation such as origami folding or packing a snack into a Ziploc bag.

Gemini Robotics displays advanced levels of dexterity

Multiple embodiments

Finally, because robots come in all shapes and sizes, Gemini Robotics was also designed to easily adapt to different robot types. We trained the model primarily on data from the bi-arm robotic platform, ALOHA 2, but we also demonstrated that it could control a bi-arm platform, based on the Franka arms used in many academic labs. Gemini Robotics can even be specialized for more complex embodiments, such as the humanoid Apollo robot developed by Apptronik, with the goal of completing real world tasks.

Gemini Robotics works on different kinds of robots

Enhancing Gemini’s world understanding

Alongside Gemini Robotics, we’re introducing an advanced vision-language model called Gemini Robotics-ER (short for ‘“embodied reasoning”). This model enhances Gemini’s understanding of the world in ways necessary for robotics, focusing especially on spatial reasoning, and allows roboticists to connect it with their existing low level controllers.

Gemini Robotics-ER improves Gemini 2.0’s existing abilities like pointing and 3D detection by a large margin. Combining spatial reasoning and Gemini’s coding abilities, Gemini Robotics-ER can instantiate entirely new capabilities on the fly. For example, when shown a coffee mug, the model can intuit an appropriate two-finger grasp for picking it up by the handle and a safe trajectory for approaching it.

Gemini Robotics-ER can perform all the steps necessary to control a robot right out of the box, including perception, state estimation, spatial understanding, planning and code generation. In such an end-to-end setting the model achieves a 2x-3x success rate compared to Gemini 2.0. And where code generation is not sufficient, Gemini Robotics-ER can even tap into the power of in-context learning, following the patterns of a handful of human demonstrations to provide a solution.

Gemini Robotics-ER excels at embodied reasoning capabilities including detecting objects and pointing at object parts, finding corresponding points and detecting objects in 3D.

Responsibly advancing AI and robotics

As we explore the continuing potential of AI and robotics, we’re taking a layered, holistic approach to addressing safety in our research, from low-level motor control to high-level semantic understanding.

The physical safety of robots and the people around them is a longstanding, foundational concern in the science of robotics. That’s why roboticists have classic safety measures such as avoiding collisions, limiting the magnitude of contact forces, and ensuring the dynamic stability of mobile robots. Gemini Robotics-ER can be interfaced with these ‘low-level’ safety-critical controllers, specific to each particular embodiment. Building on Gemini’s core safety features, we enable Gemini Robotics-ER models to understand whether or not a potential action is safe to perform in a given context, and to generate appropriate responses.

To advance robotics safety research across academia and industry, we are also releasing a new dataset to evaluate and improve semantic safety in embodied AI and robotics. In previous work, we showed how a Robot Constitution inspired by Isaac Asimov’s Three Laws of Robotics could help prompt an LLM to select safer tasks for robots. We have since developed a framework to automatically generate data-driven constitutions – rules expressed directly in natural language – to steer a robot’s behavior. This framework would allow people to create, modify and apply constitutions to develop robots that are safer and more aligned with human values. Finally, the new ASIMOV dataset will help researchers to rigorously measure the safety implications of robotic actions in real-world scenarios.

To further assess the societal implications of our work, we collaborate with experts in our Responsible Development and Innovation team and as well as our Responsibility and Safety Council, an internal review group committed to ensure we develop AI applications responsibly. We also consult with external specialists on particular challenges and opportunities presented by embodied AI in robotics applications.

In addition to our partnership with Apptronik, our Gemini Robotics-ER model is also available to trusted testers including Agile Robots, Agility Robots, Boston Dynamics, and Enchanted Tools. We look forward to exploring our models’ capabilities and continuing to develop AI for the next generation of more helpful robots.

Acknowledgements

This work was developed by the Gemini Robotics team. For a full list of authors and acknowledgements please view our technical report.

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New Reports Show How AI, Power, and Investment Trends Are Reshaping the Data Center Landscape

Today we provide a comprehensive roundup of the latest industry analyst reports from CBRE, PwC, and Synergy Research, offering a data-driven perspective on the state of the North American data center market.  To wit, CBRE’s latest findings highlight record-breaking growth in supply, soaring colocation pricing, and mounting power constraints shaping site selection. For its part, PwC’s analysis underscores the sector’s broader economic impact, quantifying its trillion-dollar contribution to GDP, rapid job growth, and surging tax revenues.  Meanwhile, the latest industry analysis from Synergy Research details the acceleration of cloud spending, AI’s role in fueling infrastructure demand, and an unprecedented surge in data center mergers and acquisitions.  Together, these reports paint a picture of an industry at an inflection point—balancing explosive expansion with evolving challenges in power availability, cost pressures, and infrastructure investment. Let’s examine them. CBRE: Surging Demand Fuels Record Data Center Expansion CBRE says the North American data center sector is scaling at an unprecedented pace, driven by unrelenting demand from artificial intelligence (AI), hyperscale, and cloud service providers. The latest North America Data Center Trends H2 2024 report from CBRE reveals that total supply across primary markets surged by 34% year-over-year to 6,922.6 megawatts (MW), outpacing the 26% growth recorded in 2023. This accelerating expansion has triggered record-breaking construction activity and intensified competition for available capacity. Market Momentum: Scaling Amid Power Constraints According to CBRE, data center construction activity reached historic levels, with 6,350 MW under development at the close of 2024—more than doubling the 3,077.8 MW recorded a year prior. Yet, the report finds the surge in development is being met with significant hurdles, including power constraints and supply chain challenges affecting critical electrical infrastructure. As a result, the vacancy rate across primary markets has plummeted to an all-time low of 1.9%, with only a handful of sites

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Minnesota PUC Says No to Amazon’s Bid to Fast-Track 250 Diesel Generators for Data Center

Amazon is facing scrutiny and significant pushbacks over its plan to install 250 diesel backup generators for a proposed data center in Becker, Minnesota. Much of the concern had been due to the fact that the hyperscaler was seeking an exemption from the state’s standard permitting process, a move that has sparked opposition from environmental groups and state officials. Aggregate Power that Matches Nuclear Power Generation Amazon’s proposed fleet of diesel generators would have a maximum power output almost equivalent to the 647 MW that is produced by Xcel Energy’s nuclear plant in Monticello, one of the two existing nuclear generation stations in the state. Meanwhile, as reported by Datacenter Dynamics, according to a real estate filing published with the Minnesota Department of Revenue, the land parcel assigned for the Amazon data center in Becker was previously part of Minneapolis-based utility Xcel’s coal-powered Sherco Site. Amazon argues that the diesel generators in question are essential to ensuring reliable and secure access to critical data and applications for its customers, including hospitals and first responders. However, opponents worry about the environmental impact and the precedent it may set for future large-scale data center developments in the state. The Law and Its Exception Under Minnesota state law, any power plant capable of generating 50 megawatts or more that connects to the grid via transmission lines must obtain a Certificate of Need from the Public Utilities Commission (PUC). This certification ensures that the infrastructure is necessary and that no cheaper, cleaner alternatives exist. Amazon, however, contends that its generators do not fall under this requirement because they are not connected to the larger electric grid; power generated would be strictly used by the data center suffering an outage from its primary power source. That power would be generated locally, and not transmitted over

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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