Physical AI Research
Understanding How Physical AI Fails.
Fail Theory is a physical AI research organization focused on understanding failure modes in intelligent physical systems to build more reliable AI for manufacturing, retail, warehousing, airports, robotics, and industrial automation.
What We Do
We help companies collect the data physical AI actually needs.
Most physical AI models fail not because of the model, but because of the data underneath it. We work with manufacturing, retail, warehousing, airports, robotics, and industrial automation teams to capture real operational data — sensor, visual, and telemetry — engineered from the start to cover the edge cases and failure conditions that matter.
On-site data capture
We come to your line, floor, or fleet — data collection scoped to the deployment you’re actually shipping.
Failure-aware annotation
Labeling built around our own failure taxonomy, so datasets capture the rare, high-value edge cases that generic annotation pipelines miss.
Deployment-ready delivery
Structured, versioned datasets delivered in the formats your training and evaluation pipelines already use.
Industries
Six industries, one failure taxonomy.
Manufacturing
Defect detection, predictive maintenance, and process control under real production-line conditions.
Retail
Shelf perception, inventory estimation, and checkout automation across cluttered, high-variance store environments.
Warehousing
Fleet navigation, pick-and-pack automation, and inventory tracking across high-throughput logistics operations.
Airports
Baggage handling, ground operations, and passenger-flow systems operating under strict safety and uptime constraints.
Robotics
Manipulation, navigation, and multi-robot coordination failures under uncertainty and partial observability.
Industrial Automation
Process control, safety systems, and human-machine collaboration in automated industrial environments.
Research
Eight domains where physical AI meets real-world failure.
Physical AI
How perception, planning, and control degrade when models leave the simulator and meet friction, latency, and noise.
Manufacturing Intelligence
Defect detection, predictive maintenance, and process control models under real production-line conditions.
Retail Intelligence
Shelf perception, inventory estimation, and checkout automation across cluttered, high-variance environments.
Robotics
Manipulation, navigation, and multi-robot coordination failures under uncertainty and partial observability.
Vision Systems
Robustness of industrial computer vision to occlusion, distribution shift, and adversarial physical conditions.
Agentic Automation
Failure propagation in multi-step autonomous agents operating industrial and operational workflows.
Failure Analysis
Root-cause methodology for classifying and reproducing AI failures across physical deployments.
Reliability Engineering
Turning failure taxonomies into monitoring, guardrails, and design practices that prevent recurrence.
Failure Atlas
Open catalog of AI failure modes.
Compounding Failure in Multi-Step Industrial Agents
We trace how small per-step error rates in agentic pipelines compound into system-level failure, and propose a checkpoint-based scoring method for isolating the origin step.
Insights
Field notes, not case studies.
What Six Months of Warehouse Robot Failures Taught Us
Most navigation failures we logged weren't localization errors — they were disagreements between the robot's map and a warehouse that never stops changing.
The Defect Detector That Was Too Good on Camera Day
A vision model that hit 99.2% accuracy in validation was quietly missing a whole class of defects — because the validation lighting rig didn't match the line.
The Agent That Never Said It Was Stuck
An inventory-reconciliation agent kept retrying the same failed action for eleven hours because nothing in its loop distinguished 'still working' from 'stuck.'
About
“Every intelligent system fails. Understanding those failures is the first step toward building systems that earn trust.”