今天，我们推出了 Gemini Robotics 2.5，这是迄今为止我们最强大的视觉-语言-动作（VLA）模型。Gemini Robotics 2.5 既增加了机器人可学习的技能广度，也提高了其获取这些技能的速度。这增强了机器人的灵巧性、可靠性和安全性，帮助机器人走出实验室，进入更多非结构化、真实的世界环境，在那里它们可以协助人们完成更广泛的任务。
借助 Gemini Robotics 2.5，我们扩大了模型在任务广度、性能和复杂性方面的能力。我们在 200 项不同的任务中对模型进行了评估——例如用筷子夹菜、给礼品盒系蝴蝶结以及进行液体移液。这些任务代表了迈向人类水平灵巧性的一步，结合了多项技能，包括高级工具使用、可变形的复杂操作以及以细腻手法处理精细物体。
我们的评估显示，任务精度、鲁棒性和速度有显著提高。与之前的基线相比，Gemini Robotics 2.5 根据任务类型的不同，成功率提高了 30% 到 100%，同时完成任务的速度提高了 20% 到 30%。
衡量一个机器人模型的最终标准是它是否能在活跃的工作场所中可靠地执行任务。为了测试 Gemini Robotics 2.5，我们与 Apptronik 和梅赛德斯-奔驰合作，在匈牙利凯奇凯梅特和德国辛德尔芬根的汽车生产线上使用 Apollo 2 人形机器人。
机器人难以适应新的不可预测的环境，而我们的 Gemini Robotics 模型赋予 Apollo 思考、多步推理并根据变化采取行动的能力。在这个概念验证中，Apollo 2 自主执行汽车套件装配：这是一项重复性高、体力消耗大的任务，即将散装容器中的单个零件按生产线每辆车所需的精确顺序分拣到整理好的托盘中。卸载这些单调的准备工作有助于保持装配线的运转，同时腾出技术人员去专注于复杂的组装任务。
Gemini Robotics 2.5 使用时推理引导来实时避开物理危险。就像安全分类器为 Gemini 的响应添加保护层一样，这种引导允许机器人安全地导航复杂环境。在我们的评估中，这项能力使围绕障碍物的安全任务完成率提高了 33%，围绕附近人员的安全任务完成率提高了 43%。
Argon 可以从现实世界的相机场景生成数千次多样的模拟练习运行，并构建逼真的数字孪生体。结合真实的训练演示，这使得机器人能够更快地泛化到新场景和新对象。Argon 还可以帮助研究人员从视频片段中假设任务失败的原因，并在仿真中和直接在真实机器人上测试最佳改进措施。
在像 Gemini 4 Argon 这样的前沿智能模型的加速下，Gemini Robotics 2.5 标志着通向解决物理 AGI 道路上的一个重要里程碑。通过在灵巧性、现实世界可靠性和严格安全性方面取得进步，我们正在构建使机器人能够与人并肩工作并解决现实世界挑战所需的物理智能。
这项工作由 Gemini Robotics 团队开发。对于他们在这一工作中的领导和支持，我们要感谢：Huizhong Chen、Radu Soricut、Jean-Baptiste Alayrac、Melvin Johnson、Edward Grefenstette、Claire Cui、Jon Shlens、David Thacker、Zoubin Ghahramani、Clement Farabet、Koray Kavukcuoglu 和 Demis Hassabis。我们要认可谷歌和 Google DeepMind 内部许多为此做出贡献的团队，包括研究、法律、营销、传播、责任与安全委员会、负责任的发展与创新、政策、战略与运营，以及我们的业务和企业开发团队。最后，我们要感谢我们的合作伙伴：Agile、Apptronik 和梅赛德斯-奔驰团队对本发布的支持。
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November 10, 2026 Models
Carolina Parada
We’re enabling robots to learn faster for the real world - from everyday skills to the factory floor
Today, we are introducing Gemini Robotics 2.5, our most capable vision-language-action (VLA) model to date. Gemini Robotics 2.5 increases both the breadth of skills a robot can learn and the speed at which it can acquire them. This enhances dexterity, reliability, and safety to help robots step out of the lab and into more unstructured, real-world environments, where they can assist people with a greater range of tasks.
Earlier this year, we brought whole-body intelligence to robots of many types, including humanoids. Now, in large part thanks to Gemini 4 Argon's ability to accelerate robotics research, we have further pushed the frontier of physical intelligence. We tested Gemini Robotics 2.5’s capabilities in task breadth, performance, and complexity across over 200 distinct tasks, though this approach could scale to many more.
Gemini Robotics 2.5 marks a leap forward in three key areas:
- Dexterity: Our robots can perform significantly more tasks with greater performance and dexterity — from using everyday tools to handling delicate, flexible objects.
- Reliability: During a real-world proof-of-concept on Mercedes-Benz production lines, our models helped Apptronik’s Apollo 2 humanoid pick and pack automotive parts with a 96% success rate. This could enable robots to take on monotonous work, to enable technicians to focus on complex build tasks.
- Safety: As robots move into everyday environments, we have improved physical safety steerability and introduced a new benchmark to test against adversarial jailbreaks specific to robotics.
Gemini Robotics 2.5 is rolling out to our robotics partners.
Expanding task breadth, reliability, and dexterity
Because real-world environments are unstructured and unpredictable, robots must be able to handle everyday objects with both fine precision and high reliability.
With Gemini Robotics 2.5, we expanded the model capabilities in task breadth, performance, and complexity. We evaluated the model across 200 distinct tasks — such as serving food with tongs, tying a bow on a gift box, and pipetting a liquid. These tasks represent a step toward human-level dexterity by combining a number of skills, including advanced tool use, complex manipulation of deformables, and handling delicate objects with finesse.
Our evaluations show significant increases in task precision, robustness, and speed. Compared to previous baselines, Gemini Robotics 2.5 achieves a 30% to 100% higher success rate depending on the type of task, while completing tasks 20% to 30% faster.
Like our previous Gemini Robotics models, it natively works with many different robot types, including Apptronik’s Apollo 2 humanoid robot (featuring the five-finger, 22 degree-of-freedom SharpaWave hand) and the Franka Duo platform with two-fingered parallel grippers.
Testing real-world reliability with Apptronik’s Apollo and Mercedes-Benz
The ultimate measure of a robotics model is whether it can perform dependably in an active workplace. To put Gemini Robotics 2.5 to the test, we partnered with Apptronik and Mercedes-Benz to use Apollo 2 humanoid robots on active automotive production lines in Kecskemét, Hungary, and Sindelfingen, Germany.
Robots struggle to adapt to new unpredictable environments, our Gemini Robotics models give Apollo the ability to think, reason over multiple steps, and take action adapting to changes. In this proof-of-concept, Apollo 2 autonomously performs automotive kitting: the repetitive, physically taxing task of sorting individual parts from bulk bins into organized trays in the exact order needed for each vehicle on the line. Offloading this monotonous prep work helps keep the assembly line moving, while freeing up technicians to focus on complex build tasks.
Using our embodied reasoning model (Gemini Robotics ER) to keep track of the workflow, and the Gemini Robotics 2.5 actions model to control its hands and arms, the robot reaches into supply bins, picks up metal automotive brackets, and places them into sequencing containers, all while correcting for any gaps. Achieving a 96% success rate, this pilot shows promising progress toward widespread real-world industrial applications.
Apollo 2 using Gemini Robotics 2.5 to autonomously perform automotive kitting at a Mercedes-Benz factory.
Advancing steerability and agentic safety for robots
As robots operate in messy, everyday spaces, physical safety is paramount. To help ensure this, we have significantly advanced Gemini Robotics 2.5’s safety features.
Gemini Robotics 2.5 uses inference-time reasoning guidance to steer around physical hazards in real-time. Much like safety classifiers add a protective layer around Gemini’s responses, this guidance allows the robot to navigate complex environments securely. In our evaluations, this capability boosted safe task completion around obstacles by 33% and around nearby people by 43%.
We are also advancing safety for long-horizon, agentic tasks. Gemini 4 Argon currently leads our agentic robotics evaluations across unsafe task refusal, resolving uncertainty, and working in proximity to humans. To test jailbreak robustness, we are releasing ASIMOV-Jailbreak, a new open-source benchmark containing hundreds of prompts that disguise unsafe requests across dozens of scenes. Argon achieves a 99.5% jailbreak resistance — a significant improvement over the 82% rate of our previous baseline, Gemini Robotics ER 2.
Safe human-aware robot actions enabled by GR-2.5 steerability
Accelerating robotics with Gemini 4 Argon
To teach robots new skills, researchers need massive amounts of physical data and highly efficient modeling algorithms. Gemini 4 Argon’s robotics research capabilities helped solve these bottlenecks by acting as a simulation engine, which greatly accelerated our development of Gemini Robotics 2.5.
Examples of Gemini 4 Argon’s robotics research capabilities accelerating Gemini Robotics 2.5
Argon can generate thousands of varied, simulated practice runs from a real-world camera scene and build a realistic digital twin. Combined with real training demonstrations, this enables the robot to more quickly generalize to new scenes and objects. Argon can also help researchers hypothesize why task failures happen from video footage and test the best improvements both in simulation and directly on real robots.
Accelerated by frontier intelligence models like Gemini 4 Argon, Gemini Robotics 2.5 marks an important milestone on the path toward solving physical AGI. By advancing in dexterity, real-world reliability, and rigorous safety, we are building the physical intelligence necessary for robots to work alongside people and solve real-world challenges
Acknowledgements
This work was developed by the Gemini Robotics team. For their leadership and support of this effort, we’d like to thank: Huizhong Chen, Radu Soricut, Jean-Baptiste Alayrac, Melvin Johnson, Edward Grefenstette, Claire Cui, Jon Shlens, David Thacker, Zoubin Ghahramani, Clement Farabet, Koray Kavukcuoglu, and Demis Hassabis. We’d like to recognize the many teams across Google and Google DeepMind that have contributed to this effort including Research, Legal, Marketing, Communications, Responsibility and Safety Council, Responsible Development and Innovation, Policy, Strategy and Operations, and our Business and Corporate Development teams. Finally, we’d like to thank our partners: Agile, Apptronik, and Mercedes-Benz teams for their support on this release.
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