Factories are becoming increasingly connected and automated. Yet production does not always follow a predefined plan. A component may arrive outside its expected position, or a new product variant may require a different grip. In such cases, predefined movements may not provide the required flexibility.

Physical AI combines AI-based perception and decision-making with action in the physical world. Models and simulations that account for physical conditions can help a system evaluate possible actions before applying them.

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For example, a robot could practice grasping a new product variant in simulation, then use sensor feedback to adjust to positional or geometric variation on the production line. The robot’s behavior, however, must remain within a validated operational envelope, with appropriate controls and independent safety functions.

This link between learned behavior and physical action extends beyond stationary robot cells to autonomous mobile robots, manipulators, and industrial inspection drones. Humanoid robots may also be relevant, where human reach and mobility matter, although their suitability must be assessed for each use case.

Physical AI could support both product and production-system lifecycles: Potential applications range from prototype testing and disassembly to virtual commissioning and adaptation to process changes.

Preparing such systems requires more than an individual AI model: Engineers need task-relevant simulations, product and factory models, operational data, and evidence from physical tests. The industrial metaverse can provide a shared context for connecting these resources and comparing virtual expectations with performance in production.

The industrial metaverse as a shared digital context

Factories may have detailed digital representations of products, machines, and processes, but changes in one system may not be reflected in another. Differences in formats, meaning, versions, and access rights can limit their usefulness.

In this context, the industrial metaverse can be understood as an interoperable environment that connects relevant models, data, tools, and stakeholders across engineering and operations.

It does not create interoperability automatically: Practical implementation depends on shared semantics, persistent identifiers, standardized interfaces, version and configuration management, data governance, cybersecurity, and defined access rights.

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Information should move between authorized tools while preserving their meaning, origin, version, and quality. A digital thread can link requirements, design decisions, production configurations, operating data, and validation results. To remain trustworthy, it should preserve data provenance, including the information source, the applicable product or factory configuration, measurement conditions, and transformations applied to the data.

Consider a change to an assembly cell layout. Robotics engineers may need to check reachability and collisions, production planners its effect on cycle time, and maintenance personnel access to equipment.

Each needs an appropriate view and level of detail, with clear information on the data’s source, quality, configuration, access conditions, and not unrestricted access to every dataset.

With interoperable models and agreed rules for sharing data, collaboration can also extend to suppliers, integrators, and service partners, provided that intellectual property, contractual responsibilities, cybersecurity, and control over secondary data use are addressed.

Training physical AI with simulation, synthetic data and digital twins

Training physical AI systems through unrestricted trial and error on a live production line can disrupt operations and incur costs through downtime, engineering effort, consumables, rejected products, or equipment damage. It may also introduce unacceptable safety risks.

Reliable performance also requires exposure to variation in component positions, tolerances, lighting, and sensor behavior. Simulation and synthetic data complement real-world experience through controlled, repeatable experiments: Connected digital twins can generate labeled images for perception training, evaluate robot paths, and simulate robot-environment interactions for task learning.

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The required level of fidelity depends on the intended task. For example, a perception application may require realistic representations of illumination, materials, camera properties, and sensor noise. A manipulation task may depend more strongly on friction, compliance, contact behavior, deformation, and manufacturing tolerances.

Motion planning requires accurate geometry, kinematics, and collision constraints, while cycle-time analysis may also need to represent process logic, communication delays, and equipment dynamics. The goal is therefore fit-for-purpose fidelity, justified by its effect on the relevant engineering decision or performance metric.

Differences between simulated and real environments create the sim-to-real gap. They may result from incomplete physical models, sensor characteristics, wear, latency, or conditions absent from training. Engineers can reduce this gap through calibration with real data, domain randomization or adaptation, and training with both synthetic and real data.

Software-in-the-loop and hardware-in-the-loop testing can further support the transition to physical equipment. None of these methods guarantee real-world robustness. The system must still be evaluated on physical hardware under representative operating conditions, including relevant disturbances, corner cases, and known failure modes.

Example: Electric vehicle battery assembly

One example of how simulation, connected models, and physical AI could be combined is electric vehicle battery assembly. Tasks involving battery modules and connectors may require robots to account for dimensional tolerances, monitor contact forces, and verify completed assembly steps.

Engineers could use connected models of the components, equipment, and assembly cell to vary component positions, geometries, lighting, and contact parameters. Synthetic labeled images could support visual perception, while simulated interactions could help evaluate grasping, alignment, and insertion strategies.

These tasks may require a combination of learned and conventional methods. Visual perception can estimate component position and orientation, motion planning can generate collision-free paths, and force or impedance control can support compliant insertion. Learned policies may help address variation that fixed rules cannot easily capture, but their actions should remain within defined process and safety limits.

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Before changes reach physical production, teams can evaluate robot reachability, trajectories, assembly sequences, potential collisions, and selected process variations in simulation. This may reduce commissioning effort and support quality assurance. Nevertheless, virtual evaluation cannot replace physical validation.

From demonstration to industrial deployment

Despite rapid technical progress, scaling physical AI in industry remains challenging. Industrial systems must combine perception and adaptation with process repeatability, real-time performance, and safety constraints.

Deployment should begin with a clearly defined use case: a task with measurable value and requirements for quality, cycle time, safety, and cost. The intended operational domain should specify the product variants, process conditions, and disturbances the system is expected to handle.

Defining this domain is only a first step: a successful demonstration does not establish readiness for production. Verification should establish whether the system meets its technical specifications; validation should determine whether it performs the intended task under representative operating conditions.

Commissioning and acceptance testing should confirm its suitability for the specific production environment. Safety-related functions may need to remain separate from AI-based functions, with independent, validated components enforcing safety-critical limits.

For adaptive systems, validation cannot be treated as a one-time activity. Changes to models, training data, software, equipment, or process configurations may alter system behavior. Organizations therefore need version management, traceability, controlled updates, and the ability to restore a previously validated state.

Operational monitoring should track performance degradation, unexpected behavior, and fallback events, with clear criteria for intervention and revalidation. Cybersecurity must also be addressed through access control, authentication, integrity protection, secure updates, and incident response.

Building a trusted foundation for physical AI

Connected models do not eliminate uncertainty, but they can make assumptions and validation evidence more visible across organizational and technical boundaries. The industrial metaverse can provide a shared context for this integration, provided that interoperability, governance, and real-world validation are treated as engineering requirements instead of assumptions.

Fraunhofer IPK applies this principle by linking product, production, and operational information across relevant engineering and factory processes. For physical AI, such connections can support the comparison of intended system behavior with the products, equipment, and process conditions the system will encounter.

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Results from physical operation can then inform model calibration, risk assessment, and subsequent engineering decisions.

The practical path to adoption is therefore incremental. Organizations should begin with a defined task, establish measurable acceptance criteria, and evaluate the system under representative operating conditions. They should expand its use only when the evidence supports the required levels of performance, safety, maintainability, and economic value.

Physical AI’s value depends not on AI models alone, but on the disciplined integration of simulation, engineering data, physical equipment, safety mechanisms, and operational feedback.