Google's Gemini Robotics 2.5 scores 96% in factory trial after tackling 200 tasks
Google’s Gemini Robotics 2.5 scores 96% in factory trial after tackling 200 tasks
Google DeepMind introduced Gemini Robotics 2.5, a new vision-language-action model designed to accelerate robot learning of physical tasks and improve reliability in real-world settings. In a factory proof-of-concept involving Apptronik's Apollo 2 humanoid robots on Mercedes-Benz production lines in Hungary and Germany, the model achieved a 96% success rate across more than 200 distinct tasks, including sorting automotive parts, handling delicate objects, and using tools. The trial demonstrated capabilities such as navigating physical hazards with improved safety metrics and resisting unsafe instructions via an open-source benchmark called ASIMOV-Jailbreak. Google stated the model is currently rolling out to its robotics partners, noting that the factory result remains a proof-of-concept rather than evidence of widespread commercial deployment.
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Control
- Reported autonomousOpen source S2
Reported numbers
Reported success rate
96%
Unique elapsed hours
View original evidence
Google DeepMind has introduced Gemini Robotics 2.5, a new model designed to help robots learn physical tasks faster, handle delicate objects, and operate more reliably in real-world environments. The model achieved a 96 percent success rate in a factory proof-of-concept involving Apptronik’s Apollo 2 humanoid robots on Mercedes-Benz production lines. The robots autonomously sorted automotive parts from bulk bins into
Open source S2tasks evaluated
200
View original evidence
which translates visual information and instructions into physical robot movements. It is currently rolling out to Google’s robotics partners. Model tackles 200 tasks Google evaluated Gemini Robotics 2.5 across more than 200 distinct tasks, including serving food with tongs, tying bows on gift boxes, and transferring liquids with pipettes. These activities test a range of capabilities, from handling delicate objects
Open source S4
- paper: not_reported
- code: not_reported
- weights: not_reported
- dataset: not_reported
What remains unknown
Not established in the collected evidence: Environment, Data origin.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
Google DeepMind has introduced Gemini Robotics 2.5, a new model designed to help robots learn physical tasks faster, handle delicate objects, and operate more reliably in real-world environments. The model achieved a 96 percent success rate in a factory proof-of-concept involving Apptronik’s Apollo 2 humanoid robots on Mercedes-Benz production lines. The robots autonomously sorted automotive parts from bulk bins into
Open source S2
which translates visual information and instructions into physical robot movements. It is currently rolling out to Google’s robotics partners. Model tackles 200 tasks Google evaluated Gemini Robotics 2.5 across more than 200 distinct tasks, including serving food with tongs, tying bows on gift boxes, and transferring liquids with pipettes. These activities test a range of capabilities, from handling delicate objects
Open source S4
navigate physical hazards. Its inference-time reasoning guidance helps robots avoid obstacles and operate around people. In company evaluations, the capability increased safe task completion by 33 percent around obstacles and 43 percent near people. Google also introduced ASIMOV-Jailbreak, an open-source benchmark designed to test whether robotic systems can resist disguised unsafe instructions. It contains hundreds
Open source S7
of prompts spanning dozens of scenes. Google reported that Gemini 4 Argon achieved 99.5 percent resistance to these jailbreak attempts, compared with 82 percent for the previous Gemini Robotics ER 2 baseline. The company also credited Gemini 4 Argon with accelerating the development of its new robotics model. The system can generate thousands of simulated practice runs from real-world camera footage and create digit
Open source S8
ory result remains a proof-of-concept, rather than evidence of widespread commercial deployment. Gemini Robotics 2.5 is currently being rolled out to Google’s robotics partners.
Open source S10
Source：Robotics — Web news discovery · msn.com