Physical AI

Physical AI workflows need more than visual dashboards. Robots, equipment agents, and process models need structured scenes, physical constraints, operating rules, and repeatable feedback loops. FactVerse AI Agent connects FactVerse Designer, SimReady assets, simulation workflows, and operational data so industrial scenarios can be prepared and tested before real-site changes.

Operating context

Context	FactVerse source	Why it matters
Scene structure	FactVerse Designer layouts, digital twin objects, asset hierarchy, and spatial relationships	Gives simulation and training workflows a usable world model
Simulation-ready assets	Designer-created SimReady assets, metadata, constraints, and version records	Keeps geometry, semantics, and physical assumptions tied together
Physics workflow	Omniverse, Isaac, PhysX, Newton-oriented simulation paths, and other simulation services	Tests layout, motion, interaction, and process assumptions at iteration speed
Operating feedback	DFS data, Inspector execution records, and field validation notes	Links simulation assumptions with real operating evidence

Workflow

1. Build a digital twin scene with assets, layout, operating zones, and relevant process context.
2. Prepare SimReady assets with geometry, metadata, constraints, and version history.
3. Run simulation or planning experiments for layout, motion, collision, sequence, process, or training context.
4. Compare results with real operating data and field validation notes.
5. Package findings for human review, robot training context, equipment training, or process planning.
6. Keep scenarios, assumptions, and validation results versioned for the next iteration.

Typical outputs

• Scene packages for robot training, equipment behavior review, or process planning.
• Layout and process comparisons with assumptions and validation notes.
• Simulation context that can run repeatedly for fast iteration before on-site trials.
• Training data references for robots, equipment agents, and operator training workflows.
• Feedback records that connect field results with later scene and model improvement.

Governance

Physical AI workflows should preserve the link between scene version, asset version, simulation assumptions, and field validation. Fast simulation can improve iteration speed, while real-site deployment still requires engineering review, safety checks, and customer approval.