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Home/Hardware & Robotics/Humanoid Robots
Humanoid Robot Safety Standards

Humanoid Robot Safety Standards

Humanoid Robots

An explainer on humanoid robot safety standards, why shared-space human-robot collaboration needs different approaches, and how this shapes deployment timelines.

As humanoid robots move from lab demos toward real deployment, safety standards, how they’re actually being developed and what they’ll require, matter as much as the underlying capability itself.

Traditional industrial robots typically operate in caged, restricted areas separated from workers. Humanoid robots working alongside people in shared spaces, the deployment model covered in our look at manufacturing use cases, need genuinely different safety approaches: real-time collision avoidance, force limiting on contact, reliable emergency stop behavior.

Existing industrial robot safety standards are being extended and adapted specifically for human-robot collaboration in shared spaces, an active area of development rather than a settled framework. Part of why staged, narrow deployment plans, rather than immediate broad rollout, are the actual pattern serious programs follow.

Our explainer on sim-to-real training is directly relevant: a robot’s ability to predict and respond safely to unexpected physical situations is exactly the capability hardest to fully validate before real-world deployment.

Safety standards for humanoid robots working alongside people are still actively being developed, a real, legitimate part of why deployment timelines stay conservative and staged. See Boston Dynamics’ own Atlas page for more detail.

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Modular Data Centers, Explained

Modular Data Centers, Explained

Data Centers

An explainer on modular data center construction, why speed of deployment matters for AI infrastructure, and the real tradeoffs versus custom builds.

Modular data centers, built from prefabricated, standardized units rather than constructed entirely on-site, have become a real strategy for deploying AI capacity faster than traditional construction allows.

Rather than building an entire facility from the ground up, modular data centers use standardized units, power, cooling, compute modules manufactured off-site and assembled on location, closer to modular housing than traditional commercial construction.

Given how much capital is being committed to AI infrastructure, deploying new capacity in months rather than years is a genuine competitive advantage. Modular construction can meaningfully compress timelines compared to fully custom builds.

Prefabricated modules are less customizable to a site’s exact conditions than a bespoke build, and they still depend on the same power and cooling constraints covered in our explainer on data center energy use. Speed of construction doesn’t remove the power bottleneck, it just gets a facility ready to use that power faster.

A real advantage given how fast AI infrastructure demand is growing, but it doesn’t eliminate the underlying constraints that determine where new capacity can actually go. See the International Energy Agency’s own data.