# DeepMind's Gemini Robotics 2.5 hits 96% on Mercedes-Benz factory work and ships an open robot safety test

> The new model ties bows and handles pipettes, and DeepMind is releasing ASIMOV-Jailbreak, 1,500 prompts that try to talk robots into unsafe moves.

- URL: https://devisdead.ai/news/deepmind-s-gemini-robotics-2-5-hits-96-on-mercedes-benz-factory-work-and-ships-a
- Published: 2026-11-10T16:16:55Z
- Author: DevIsDead Newsroom
- Section: AI
- Tags: google deepmind, robotics, gemini, safety
- Originally reported by Ars Technica (https://arstechnica.com/google/2026/11/deepmind-reveals-gemini-robotics-2-5-and-open-safety-benchmark-asimov-jailbreak/).

Google DeepMind has announced Gemini Robotics 2.5, the newest version of its AI for physical robots, Ryan Whitwam reports for [Ars Technica](https://arstechnica.com/google/2026/11/deepmind-reveals-gemini-robotics-2-5-and-open-safety-benchmark-asimov-jailbreak/). In the factory trials, humanoid robots running it did real work at Mercedes-Benz plants, and DeepMind is releasing an open benchmark for testing whether robots can be tricked into unsafe actions.

## Two layers

DeepMind's robot stack has two main models. Gemini Embodied Reasoning, a vision-language model, reads your instruction and uses live video to plan the job. Gemini Robotics, the vision-language-action model that actually moves the motors, is the part that moved to 2.5.

## What got better

DeepMind tested the model on more than 200 tasks that need fine control, like serving food with tongs or using a lab pipette. Apptronik's Apollo 2 humanoid, running 2.5, can now manage a bow, knotting the ribbon with five-fingered hands. Ribbon is a big deal here, because soft, floppy objects are among the hardest things for robots to handle.

In the lab, success rates rose 30 to 100% over the previous version, and it runs 20 to 30% faster. Outside the lab, Apollo 2 robots kitted car parts at several European Mercedes-Benz factories with a 96% success rate.

That's strong for a robot, but a person doing the same repetitive job gets it right almost every time. DeepMind director Kanishka Rao called closing the last 4% a "steep road." Robotics head Carolina Parada framed it as a trade-off: these are meant to be general robots, and pushing generality can mean not reaching 99.9% on any single job.

## Argon in the loop

Robots lack the huge piles of training data chatbots got from the web. The team used Gemini 4 Argon to build a simulation engine that spun thousands of practice-run variations out of real camera footage, then mixed that synthetic data with ordinary demonstrations. Argon also helped analyze failures.

Speed is still a limit. Gemini Robotics 2.5 runs on cloud servers, not on the robot, so there's latency, and the demo video was sped up 2x.

## Safety

The model reasons about hazards as it works and stops or waits when something is in the way. DeepMind says that raised the safe-completion rate by 33% when there were obstacles and by 43% when people were in the path.

There's no standard way to safety-test physical AI, so DeepMind is publishing ASIMOV-Jailbreak on Hugging Face: 1,500 prompts designed to coax a robot into something unsafe. With Argon in the loop the system scored 98.8%. With Gemini Robotics ER 2 alone it scored 73.5%.

Gemini Robotics 2.5 isn't generally available. Google's robotics partners have it, among them Boston Dynamics, Apptronik and Agile Robots.

**[Scoreboard](/scoreboard) update:** Draw. Robots hit 96% on real factory work, but humans still get it right nearly every time.

Stay undead.