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Physical AI

YOUR ROBOT CAN NOW WALK. WE MAKE IT INTELLIGENT.

For a decade, AI lived in your data warehouse. Now it's leaving the screen. Physical AI gives machines what rule-based automation never had: the ability to see, reason and act in a messy, unpredictable world.

Rimbus the Robot 5

Why now?

2024 was humanoid hype. 2025 brought commercial validation.

DHL is ordering 1,000+ robots; Unitree is selling 5,000+ humanoids in a single year. The question isn't whether AI robotics will reach your plant. It's whether you'll spot the right use case before your competitor does.

This is the worst AI robotics will ever be. The foundations you build now decide whether you’re leading in 2028, or only starting to learn then.
Jérémy Keusters 6
Jérémy KeustersDomain owner Supply Chain & Manufacturing
THE STAKES

WHY MOST ROBOTICS PILOTS STALL.

Robots have mastered structured, repetitive work for decades. Welding. Palletising. Pick-and-place. The moment the task varies, they stop. That's where 80% of your remaining manual work lives, and where AI robotics finally gets interesting. But it's not a plug-and-play upgrade.

Four things trip up nearly everyone.
Cost savings_hoto by Алекс Арцибашев on Unsplash_L0
The 99.9% problem

AI robots aren't yet as reliable as traditional automation. Before you deploy, you need to know exactly what a failure costs.

robot_fall
Data is expensive

Learning-based robots learn from demonstrations. Collecting high-quality teleoperation data takes time, hardware and know-how.

memory 2
Multi-step tasks are harder

Today's models handle bounded tasks well and long-horizon workflows poorly. Choose a task with too many dependent steps, and a promising demo may never complete the full workflow reliably.

Engineer woman working in advance machine factory. Indian female engineering staff work checking
Safety is still being written

The guardrails for AI in a physical environment aren't fully understood yet. Cut corners here and you don't get a second chance.

HOW ML6 MAKES THE DIFFERENCE

AI JUST GOT
A BODY. PUT
IT TO WORK.

We're not integrators pushing hardware, and we're not a research lab chasing demos. We're AI engineers who've spent 13 years putting vision, reasoning, and control models into production—from detecting defects to triggering machine actions on live production lines. We help you find where AI robotics can create real value, develop the perception and control the task needs, and connect it to the automation you already run. You stay in the driver's seat; we make the machine see, think and act.

How it works

Three pillars. One pipeline.

  • Perceive

    Understand the task as it unfolds. Cameras, force sensors and machine signals help locate objects, detect changes and estimate the state of the workspace.

  • Reason

    Decide what should happen next. Depending on the task, that may mean established planning and control methods or a policy learned from demonstrations or simulation.

  • Interact

    Turn the decision into a physical action. Connect it to the robot, gripper, or machine controls, then observe the result so the system can adjust or stop.

From Opportunity to Operations.

Not every task wants a learning robot. Structured and repetitive? Rule-based robotics wins on speed and precision. Variable but bounded? Training-based. Unpredictable and instruction-driven? Context-based. We map your processes against task complexity, variability and volume, and tell you straight which ones are ready.

Secure & Compliant,
by Design

When AI can move physical assets and make operational decisions at scale, governance is non-negotiable. ML6’s Physical AI is built on strict, transparent control frameworks that mirror — and often exceed — enterprise and regulatory standards.

Every agent operates within the exact permissions of its assigned role, ensuring no overreach into sensitive systems or data.

Arne Vandendorpe
Arne VandendorpeSenior ML Engineer

Turn vision into execution at machine speed — let’s talk. 

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Arne Vandendorpe
Arne VandendorpeSenior ML Engineer

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