How Capgemini and Intel are turning AI insight into reliable, real-world action

Physical AI is moving intelligence beyond the screen and into the world around us. By enabling machines to perceive, understand, decide, and act, it is creating new possibilities across manufacturing, logistics, inspection, mobility, and human-machine collaboration.

But when AI operates in the physical world, the rules change. A digital application may be able to tolerate a delay, a temporary loss of connectivity, or a request processed elsewhere. A robot often cannot. For physical AI, where intelligence runs can matter as much as the intelligence itself.

To make that challenge tangible, Capgemini and Intel have created a demonstration built around a deceptively simple task: teaching a robot to play the Tower of Hanoi. It shows how AI, conventional software, and edge computing can work together as one complete system, locally, and without an internet connection.

The demonstration will be introduced for the first time at Intel’s booth AI Infra Summit 2026 in Santa Clara, September 15-17, 2026.

A simple game reveals a complex system

Dexterity, the demo, showcases a familiar puzzle, The Tower of Hanoi. A set of pieces must be moved between three pegs, one at a time, without placing a larger piece on a smaller one.

For a person, the rules are straightforward. For a robot, the task brings together multiple capabilities. The system must see the board, locate each piece, understand the current state, choose the next valid move, and control the robotic arm. After every move, it must observe the board again and continue.

See  >  Understand  >  Decide  >  Act  >  Observe

This continuous loop sits at the heart of physical AI. The Tower of Hanoi makes it visible in a controlled setting where the rules, actions, and outcomes are clear. The value is not simply seeing whether the robot completes the puzzle. It is understanding how the system works as a whole.

The right intelligence for each task

No single AI model runs the entire demonstration. Instead, the architecture brings together the technologies best suited to each part of the task.

• Computer vision identifies the location of the game pieces.
• Conventional software applies the rules and selects the next valid move.
• A vision language model adds contextual understanding of the physical scene.
• A vision language action model translates the selected move into actions for the robotic arm.

This division of responsibility is deliberate. Deterministic software is the right choice for some problems. AI is better suited to others. Physical AI becomes powerful when these capabilities work together, rather than when one model is expected to solve everything.

The result is a system that turns a visual observation into a physical action, checks the outcome, and continues.

Why the edge matters

Sending every camera image to the cloud, waiting for a response, and then instructing a robot to move would introduce latency and create a dependency on network connectivity. The demonstration avoids that dependency by keeping the process at the edge.

The Intel® Core™ Ultra 3 platform provides different compute resources for different workloads:

• CPU manages the application and conventional software.
• Integrated GPU runs the AI model responsible for robotic movement.
• NPU supports the model that interprets the scene.

Matching each workload to the most appropriate resource supports fast, consistent movement while allowing scene understanding to operate separately. The robot can perceive, reason, and act without relying on a remote service.

From a capable model to a working robot

A model that performs well in development is not yet a complete physical AI system. It must also deliver the responsiveness, coordination, and reliability the real-world application requires.

The vision language action model used in the demonstration was developed and trained in a high-performance AI environment, then deployed to the Intel Core Ultra 3 platform without retraining. Intel® OpenVINO™ provides the software foundation for moving the model from development into an optimized edge deployment.

Testing across the available compute resources showed that the integrated GPU delivered the responsiveness needed for robotic movement, while the NPU was well suited to the separate scene-understanding workload. The final architecture reflects the needs of the application, with each form of intelligence running where it makes the most sense.

Making physical AI easier to understand

When an AI system controls a physical machine, visibility matters. If the machine behaves unexpectedly, teams need to understand why.

The demonstration includes a live view of the camera feeds and AI outputs as the robot operates. Observers can see what the system sees and how that information contributes to each action.

This also makes failures easier to investigate. An unexpected move might begin with a misread scene, an incorrect state, an unsuitable action, or a physical constraint. Observability connects those steps, helping teams understand system behavior and improve performance.

A small demonstration with real-world relevance

The Tower of Hanoi is intentionally simple. The goal is not to prove that a robot can play a game. It is to show what it takes to build a complete physical AI system.

The same pattern can support more complex industrial environments:

• A manufacturing system that identifies an object, determines the required operation, and directs a robot to perform it.
• An inspection system that interprets a camera image and initiates the right physical response.
• A logistics system that continuously understands its surroundings and adapts its actions.

In each case, perception, AI, software, hardware, and physical action must work together. Keeping intelligence close to the machines it controls can reduce latency, support continuity when connectivity is limited, and keep data processing close to the point of activity.

Turning physical AI into practical impact

The move from digital AI to physical AI is not simply about putting more capable models into robots. It calls for a different approach to system design.

AI models must work alongside conventional software. Different forms of AI must work together. Compute resources must be matched to the workloads they support. And the complete system must operate reliably in the physical world.

The Tower of Hanoi demonstration brings those requirements to life. A camera observes. Software interprets. AI adds understanding and physical skills. The robot acts. Then the system checks the result and begins again, all locally at the edge.

Through their collaboration, Capgemini and Intel are showing how AI can move from generating information to understanding and interacting with the physical world. That is how physical AI becomes practical, observable, and ready to create value in real operations.