Physical AI brings perception, decision and movement together: robots that see what is in front of them and can deal with it, even if the part is not lying exactly as it did yesterday. For the Mittelstand that means: automation where classic robots were too rigid and too expensive so far: bin picking, order picking, palletising changing goods.
Not every movement needs AI. But wherever parts, packaging or paths vary, fixed programs fail, and people step in.
Parts lie jumbled, in changing positions and sizes. The robot recognises what it grips, and how, without anyone sorting beforehand.
Mobile robots that find their own way and work with people in the same aisle. The order comes from the ERP, the goods arrive at the packing line.
Changing box sizes, mixed pallets, seasonal goods: the robot plans the pattern itself and adapts it when the next layer looks different.
A classic robot moves through points someone programmed. A physical AI system closes the loop: cameras and sensors capture the situation, a model decides the next grip, the arm carries it out, and what happens flows back into the model.
That is why it gets better every day instead of worse with every new part. And that is why the cell stays flexible when your range changes.
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At the end you haveThe use case with parts, cycle, space and an honest verdict on whether physical AI holds here.
Which hand movements repeat, how do parts and packaging vary, where is there space? We look at the process on site, and we also say when a classic gripper is the better choice.
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At the end there runsA cell that grips your parts in test operation, with numbers on hit rate and cycle from real runs.
Robot, camera, gripper and model, set up as a test cell with your real parts. We train with recordings from your operation and measure before anyone talks about production.
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At the end there worksThe cell in shift operation, connected to ERP and control system, with safety, maintenance and relearning settled.
Connection to ERP and control system, safety concept with your operation, training of staff. And we stay until the cell runs in shift operation, not just until acceptance.
Protection zones, speeds and emergency stop are not add-ons. We plan them with your occupational safety specialist from the start, and document them the way auditors expect.
What the robot learns in your plant is your operational knowledge. Recordings, models and training data stay with you, not with a manufacturer who uses them to train the next generation for everyone.
We look at the process on site, and say honestly whether physical AI holds here.
A classic robot moves through programmed points and needs parts in exactly the same place. A physical AI system sees what is in front of it, decides the grip itself and learns from every run. That is why it copes with changing parts, positions and packaging.
Where variance forces manual work today, bin picking, mixed pallets, order picking, often yes. We check that on your process and build a test cell before you invest in a plant. And we say so if a simple gripper is the better choice.
Not necessarily. Existing arms and cobots can often be extended with camera, gripper and model. We work manufacturer-independent and choose the hardware to fit your process, not the other way round.
Safety is part of the planning, not of the acceptance: protection zones, speeds, emergency stop and the applicable standards are defined and documented with your occupational safety specialist. Before the cell runs in operation, it is approved.
Checking the use case takes a few weeks. Until a test cell grips your parts reliably, a few months pass depending on the variance of the parts. In the first conversation we give you an honest estimate for your case.