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The physical world doesn't pause while a model makes sense of it. Pallets move, trailers arrive, aisles fill and clear. People and machines make decisions that change what happens next.

For AI to act usefully in that environment, it needs an understanding of space that holds together over time. What is there, where it is, what has changed, and how activity connects.

Ask a warehouse manager how efficient their fleet is, and most will point to a dashboard: units picked per hour, orders shipped per shift, maybe a utilization percentage pulled from the WMS. Ask how much of a truck's shift is spent actually moving product, versus driving empty or sitting idle, and the answer usually gets vaguer fast.

That gap is not a reporting problem. It is a visibility problem. Forklifts and tow tractors run constantly, but almost nothing captures what they are doing between the start and end of a task — only that the task happened.

This week's Autonomy Exchange gathering in Malmö included a session on a question that does not usually get its own slot at a robotics conference: what does the back office of a physical AI company look like once agents are doing a share of the daily work. It is worth writing down plainly, because it says something about the same bet the product makes on the warehouse floor — give a system continuous, structured visibility into what is actually happening, and the work around it changes shape.

Most companies selling autonomous mobile robots and forklift automation into warehouses are organized, on the back end, the way enterprise software companies have been organized for two decades: people doing account research, people drafting outreach, people scanning for competitor and customer signals on a weekly or monthly cadence. There is rarely anything structurally unusual to report, because the operating model looks the same everywhere.

What we do can feel like magic. Cameras on mobile robots and operated vehicles capture the spaces they move through. Staer turns those images into a live picture of what is there and where it is, continuously indexing and positioning what the cameras see. It makes the activity on a warehouse floor visible and measurable in ways that were not possible before.

That takes a lot of work, and explaining it clearly is a challenge of its own. As the product has taken shape, we have more to show and share. We need a brand that helps make that work understandable, so customers can see what it means for them.

Physical AI is having a moment. Humanoids that walk, models that fold laundry, vehicles that drive without a person behind the wheel — the demos are striking, and the funding has followed them. But the setting where physical AI is quietest and closest to daily, measurable use isn't the one getting the attention. It's the warehouse.

For most of the last decade, "AI" meant software that lived on a screen: a model reading text, ranking search results, classifying images drawn from a training set. Physical AI is a different problem. The model has to understand one specific three-dimensional space, not a distribution of examples — a particular floor, a particular aisle, a particular rack that moved four inches since Tuesday.

We're thrilled to announce that Staer has been selected for the Google DeepMind Robotics Accelerator — a three-month program supporting early-stage European robotics startups working at the frontier of AI. We're one of 15 companies in the cohort, chosen from across Europe.

The 15 warehouse environments included in Staer Warehouses v0.1, from open staging areas to dense high-bay racking.

In the spring of 2025, a logistics director at a large Nordic retailer did something that would have seemed odd to anyone watching. He pulled out his phone, opened a stopwatch, and followed one of his warehouse workers around for an entire shift.

The worker's name doesn't matter. What matters is what the logistics director counted: fourteen times that day, the worker left what he was doing to go look for something. A pallet that had arrived but hadn't been racked. A shipment that the system said was in aisle 12 but wasn't. A product that a customer needed and nobody could locate without walking the floor.

Staer joins an distinguished list of Nav2 Sponsors committing to the long-term development and maintenance of the industry standard framework. Staer is building the physical AI platform for robust, efficient, and safe fleet coordination for autonomous mobile robots (AMRs). The partnership with Open Navigation seeks to unlock the potential for high-fidelity navigation across diverse environments including homes, warehouses, agricultural fields, and more.

Preview of 3D semantic map integration in Nav2

When Amazon announced their one-millionth robot deployment this summer, it seemed like a watershed moment. But Amazon is the exception, not the rule. Most logistics operations don't look anything like Amazon's hyper-structured environments.

At Staer, we build intelligence for mobile robot fleets operating in the reality most enterprises face: messy operations, ad hoc processes, and under-instrumented facilities. Recently, I joined Søren Halskov Nissen from Yaak and Sam Baker from Planet A for a panel at The Drop, where we unpacked hard truths about mobile robotics. Several persistent myths aren't just wrong—they're actively holding our industry back.