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Gemini Robotics 2.5
Gemini Robotics 2.5: Google’s most capable robot model
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Gemini Robotics 2.5: Google’s most capable robot model
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Gemini Robotics 2.5 is Google DeepMind’s most capable vision-language-action model, turning what a robot sees and is told into hand and arm movements. Per DeepMind, it has a 30% to 100% higher success rate than the previous baseline, finishes tasks 20% to 30% faster, and reached 96% success in a Mercedes-Benz parts-kitting pilot with Apptronik’s Apollo 2 humanoid. Works across robot types. Rolling out to robotics partners.
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Gemini Robotics 2.5 is Google DeepMind’s most capable vision-language-action (VLA) model to date. In plain terms, it turns what a robot sees and what it’s told into hand and arm movements, with the aim of teaching robots more skills, faster, in less structured real-world settings.
What’s new, per DeepMind: a 30% to 100% higher success rate than previous baselines, depending on the task, and tasks completed 20% to 30% faster. It was evaluated across more than 200 tasks, including serving food with tongs, tying a bow on a gift box and pipetting liquid. It works with tool use, deformable objects like cloth and ribbon, and delicate items, and it runs across robot types, including Apptronik’s Apollo 2 humanoid and the Franka Duo two-arm platform.
Some results from DeepMind’s charts, comparing Gemini Robotics 2 with 2.5:
Packing pastries with tongs: 5% to 100%
Closing a toolbox lid: 0% to 93%
Untying ribbon bows: 0% to 70%
Hanging a cardigan: 0% to 62%
Folding pants: 0% to 40%
The real-world test: Apptronik’s Apollo 2 humanoids did automotive kitting (sorting parts from bulk bins into ordered trays) on active Mercedes-Benz production lines in Hungary and Germany, reaching a 96% success rate. DeepMind calls it “promising progress” toward industrial use, so it’s a pilot, not a finished deployment.
On safety, DeepMind added reasoning guidance at run time to steer robots around hazards, and reports 33% more safe task completion around obstacles and 43% around nearby people. It also released ASIMOV-Jailbreak, an open-source benchmark of prompts that disguise unsafe requests.
Good fit if you work on robotics or embodied AI and want to see where general-purpose robot models are heading, especially for dexterous tasks and real production lines.
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