Gemini Robotics 2.5 pushes robots toward factories, but reliability still lags

A meaningful step into physical work

Google DeepMind said in its announcement that Gemini Robotics 2.5 was evaluated on more than 200 tasks, including activities such as pipetting, folding garments and tying bows. Compared with its predecessor, the company reported success-rate improvements ranging from 30% to 100%, alongside speed gains of 20% to 30%.

Those figures matter because robotics companies have historically struggled to build systems that can generalize beyond tightly controlled demonstrations. A robot that can perform one assembly task repeatedly may be useful, but a commercially valuable system must handle variation in objects, positioning, lighting and instructions without extensive reprogramming.

Google’s strongest industrial result came from a proof of concept involving an Apptronik Apollo 2 humanoid robot at Mercedes-Benz. DeepMind said the system achieved a 96% success rate while handling automotive parts in a kitting workflow. For the AI industry, that is evidence that large multimodal models are becoming more capable of controlling physical machines in environments with economic value.

For manufacturers, however, the number has a different meaning. A 96% success rate can still produce too many failures when a robot performs thousands of operations, particularly if a mistake damages a component, stops a production line or creates a safety risk.

The final percentage points are the market

Ars Technica reported that Ryan Whitwam viewed the Mercedes result as short of human-level reliability. The report also quoted DeepMind’s Carolina Parada, who said that generalization may come at the expense of reaching 99.9% reliability in every application.

That tradeoff is central to the commercial strategy behind general-purpose robotics. Broader capability can reduce engineering and deployment costs because customers may not need to build a separate control system for every task. But if that flexibility introduces even occasional failures, buyers may limit the robot to low-risk workflows or retain human oversight.

This creates a difficult business calculation for Google and its robotics partners. A general model can offer a wider addressable market than a specialized industrial system, but the value of that market depends on whether customers can trust the system enough to use it at scale. The competitive advantage will not come from demonstrations alone. It will come from reducing supervision, downtime and integration costs while maintaining predictable performance.

Latency adds another constraint. Ars Technica noted that Gemini Robotics 2.5 still runs on cloud infrastructure, which can make response time a concern. In factory operations, connectivity and inference delays can undermine the benefits of faster physical execution. Cloud dependence may also raise questions about data security, operational resilience and the cost of running models continuously.

Safety benchmarks are useful, not decisive

DeepMind also introduced ASIMOV-Jailbreak, an open benchmark designed to test whether robotic systems reject unsafe instructions and resist adversarial prompts. The Asimov Agentic Safety Evaluation dataset includes the dataset files and evaluation scripts for the physical-safety and jailbreak testing suite.

The benchmark gives developers a shared way to measure whether a model follows instructions that conflict with physical constraints or safety policies. That is strategically important as robotics systems become more autonomous and are exposed to natural-language commands from workers, software agents and potentially untrusted users.

Still, New Runtime said its coverage that benchmark scores alone cannot establish that a robot is safe in deployment. The publication argued that testing must be combined with runtime controls, hardware safeguards and validation in real-world conditions.

The supplied reception of Gemini Robotics 2.5 is critical, and no opposing view was found in the provided coverage. That criticism does not erase the progress. It identifies the business test Google must now pass: turning better generalization into factory-grade consistency.

Gemini Robotics 2.5 may give Google a stronger position in the race to supply the intelligence layer for commercial robots. But until reliability approaches the standards of industrial automation, the model is more likely to expand pilots than replace established production systems.