From Lab to Factory: What Physical AI Systems Actually Need

The demo was impressive. The robot picked objects with precision, handled variations smoothly, and recovered from disturbances. The research team achieved strong benchmark results. Everyone agreed: this system is ready for the real world.

Six months later, the pilot is struggling. The robot fails on objects that look slightly different from the training data. The lighting changes throughout the day cause perception errors. Integration with the warehouse management system took three months longer than expected. The maintenance team can't diagnose failures.

This is the lab-to-factory gap: the difference between systems that work in controlled research environments and systems that operate in production facilities. Closing this gap requires understanding what production environments actually demand — requirements that research environments don't impose.

Why do physical AI systems fail in production?

AI systems often fail in production because they are optimized for controlled lab conditions and are not tested under real-world variations, such as lighting, object variability, and operational complexity.

What Makes Production Different

Research labs and production facilities differ across nearly every dimension that matters for Physical AI:

Environmental Conditions

• Lab: Controlled lighting (consistent intensity, color temperature), Clean backgrounds (solid colors, minimal clutter), Stable temperature and humidity, Isolated from other activities.

• Production: Variable lighting (skylights, shift changes, seasonal variation), Cluttered backgrounds (other equipment, materials, people), Temperature swings (HVAC cycles, door openings, seasonal), Continuous activity from other operations.

A policy trained in lab conditions has never seen the variations it will encounter in production.

Object and Task Variability

• Lab: Standardized object sets, Controlled object presentation, Consistent task parameters, Known object properties.

• Production: Variable products (different SKUs, packaging changes, supplier variations), Random presentation (how items arrive, not how they're arranged), Changing task requirements (new products, process changes), Unknown properties (damaged items, mislabeled products).

Production throws constant variation at systems trained on standardized inputs.

Operational Context

• Lab: Isolated experiments, Researcher supervision, Flexible timing, Failure is data.

• Production: Part of larger workflows, Operator oversight (not researcher), Strict timing requirements, Failure is costly.

The operational context changes everything about how systems must behave.

How can I ensure my AI system is production-ready?

Test your AI system under varied environmental conditions, ensure it integrates with other systems, and ensure it meets safety and operational standards.

The Seven Requirements for Production Physical AI

Based on what production environments actually demand, here's what Physical AI systems must provide:

Robustness to Environmental Variation

Production systems must perform consistently despite environmental changes:

Lighting robustness:

• Handle intensity variations (2x-10x range)

• Adapt to color temperature changes

• Perform under flickering or inconsistent lighting

Background robustness:

• Ignore irrelevant visual clutter

• Distinguish objects from similar backgrounds

• Handle dynamic backgrounds (movement, other equipment)

Sensor robustness:

• Maintain calibration over time

• Handle sensor degradation gracefully

• Operate through a temporary sensor issue

How to evaluate: Test the system under deliberate environmental variation, not just optimal conditions. Vary lighting, add clutter, and introduce movement.

Handling of Object and Task Variation

Production systems encounter variation that research systems never see:

Object variation:

• Products change (new SKUs, packaging updates)

• Conditions vary (damaged, wet, dusty)

• Presentations differ (orientations, groupings)

Task variation:

• Requirements change (new products, process updates)

• Priorities shift (rush orders, exceptions)

• Edge cases appear (unusual requests)

How to evaluate: Introduce novel objects and task variations. How quickly does the system adapt? How does it handle complete novelty? 

Real-Time Performance on Deployment Hardware

Research can run inference on GPU clusters. Production runs on what fits in the facility:

Latency requirements:

• Control loops at 20-100Hz for manipulation

• Response within milliseconds for safety

• Consistent timing, not average timing

Hardware constraints:

• Edge compute within size/power/cost limits

• Reliable operation in industrial conditions

• Maintainable by facility technicians

How to evaluate: Measure latency on actual deployment hardware, not research infrastructure. Test under load and overextended operation

Integration with Enterprise Systems

Production robots don't operate in isolation:

Upstream integration:

• Receive tasks from WMS/MES/ERP

• Accept priority changes and exceptions

• Handle scheduling and sequencing

Peer integration:

• Coordinate with other robots

• Synchronize with conveyors, machinery

• Share floor space safely

Downstream integration:

• Report completion status

• Update inventory systems

• Log events for analytics

How to evaluate: Map all required integrations before deployment. Verify APIs, data formats, and timing requirements.

Safety for Human-Adjacent Operation

Production environments have people:

Regulatory compliance:

• ISO 10218 (industrial robot safety)

• ISO/TS 15066 (collaborative robots)

• Industry-specific requirements

Operational safety:

• Speed and force limits near humans

• Emergency stop integration

• Clear safety zones and procedures

Safety verification:

• Documented safety case

• Tested failure modes

• Audit trail for incidents

How to evaluate: Understand the regulatory requirements for your environment. Verify the system meets them with documentation.

The Production Readiness Checklist

Before deploying Physical AI, verify readiness across all dimensions. For each requirement, you should check if it has been verified and evaluated.

Environment

Performance

Integration

Operations

Improvement

Why Most Pilots Fail

Most Physical AI pilots don't reach production. The common failure modes map directly to these requirements:

• "It worked in the demo" → Environmental variation not tested.

• "Integration took forever." → Enterprise systems are underestimated.

• "It's too slow" → Hardware constraints not considered.

• "We can't diagnose failures" → Maintainability not designed.

• "It's not improving" → No continuous learning mechanism.

• "We can't certify it" → Safety requirements not addressed.

Each of these is predictable and avoidable — but only if you evaluate against production requirements, not research benchmarks.

Building for Production from Day One

The path from lab to factory isn't a phase after research. It must be designed from the beginning:

Architecture Decisions

• Edge-first design: Build for deployment hardware constraints, not research clusters.

• Hybrid architectures: Combine learned and programmed components for bounded failure modes.

• Modular integration: Design clean interfaces to enterprise systems.

Development Practices

• Production-distribution training: Train on data matching deployment conditions.

• Continuous testing: Evaluate on environmental variations, not just benchmarks.

• Failure analysis: Systematically understand and address failure modes.

Operational Readiness

• Documentation first: Maintenance procedures before deployment.

• Training programs: Operator readiness before go-live.

• Support structures: Escalation paths for issues.

Summary

Production environments differ from labs in environmental conditions, object/task variation, operational context, and constraints.

Seven requirements define production readiness:

• Robustness to environmental variation

• Handling of object and task variation

• Real-time performance on deployment hardware

• Integration with enterprise systems

• Safety for human-adjacent operation

• Maintainability by non-researchers

• Continuous improvement from deployment data

Most pilots fail because they're evaluated against research metrics, not production requirements. Production readiness must be designed from day one — in architecture, development practices, and operational preparation.

What is the importance of system integration in Physical AI?

Integration is crucial because physical AI systems must work within larger enterprise environments, coordinating with WMS, ERP, and other machinery, ensuring smooth workflows and data flow.