Physical AI · Enterprise deployment
Physical AI: Autonomous Intelligence Embedded in the Real World
How to take humanoids, quadrupeds, autonomous vehicles and drones from demo to operation: decision criteria, real platforms compared and every figure linked to its source.
Real-world deployment indicators
Published figures, with date and evidence level. Not projections.
- professional service robots sold worldwide in 2025 (+24%)
- ≈250,000
- IFR, World Robotics 2026 · supplier-sample data
- AView source: Global Sales of Professional Service Robots Surge 24% (World Robotics 2026 – Service Robots), International Federation of Robotics (IFR)
- paid driverless robotaxi rides per week
- 500,000
- Waymo, 10 US cities · March 2026
- CView source: Waymo's skyrocketing ridership in one chart, TechCrunch
- commercial autonomous drone deliveries
- >2M
- Zipline, cumulative · January 2026
- BView source: Zipline surpasses 2 million deliveries, raises more than $600M to power next phase of growth and expands operations to Houston and Phoenix, Zipline
- autonomous sidewalk-robot deliveries
- >10M
- Starship, Europe and US · April 2026
- CView source: Starship Passes 10 Million Deliveries as Autonomous Delivery Moves Toward Mainstream Adoption, Robotics & Automation News
How to use this site
Five steps to decide a deployment
Start from the operational problem and finish with a business case you can defend with data.
- 1Define the problemFit criteria and the operational design domain (ODD).
- 2Assess readinessInteractive checklist with a downloadable score.
- 3Compare platformsReal robots with specs and their conditions.
- 4Review the evidenceDocumented deployments and what was learned.
- 5Calculate the returnTCO and payback with your own assumptions.
Platform forms
Four ways to bring AI into the physical world
We classify non-industrial robotics by form factor, because form determines where a robot can operate, what payload it carries and which business model it fits.
Humanoids
Bipedal systems with dexterous manipulation, built to use tools and infrastructure designed for humans.
4 platforms in the dataset
Quadrupeds
Articulated legged platforms with high traction and stability on uneven terrain, stairs and rock.
5 platforms in the dataset
Ground vehicles
Wheeled or tracked systems for cargo transport, logistics and long-range inspection.
4 platforms in the dataset
Drones & aerial robots
Rotary or fixed wing for monitoring, vertical inspection, express logistics and swarms.
5 platforms in the dataset
Value proposition
Every technical capability, translated into a business decision
Technical capabilities only matter when they change a decision: what to deploy, where, at what risk and with what return.
- Localization & SLAMVisual, LiDAR, hybridWhere it can operateDefining the ODD
- Sensor fusionThermal, radar, RGB-D, IMUWhich data you getSensor suite and cost
- Fleet coordinationCentral, distributed, swarmHow it scalesContinuity under failures
- Teleoperation & autonomyInterventions per hourHow many staff it needsTCO and operations
- Functional safetyStandards and certificationWhether you can deploy itCompliance and risk
Perception
How a robot sees its surroundings
The same industrial gallery through four modalities: RGB image, LiDAR point cloud, SLAM mesh with its occupancy grid, and thermal vision. Each sensor sees a different reality; sensor fusion combines them into a single estimate of the environment. This is a didactic visualization, not the output of a real sensor.
Taxonomy
Four axes to classify any robot
A common language to assess technology maturity, operational safety and return on investment, without collapsing everything into one score.
01
Autonomy level
Level 0 → Level 5
From direct teleoperation to full distributed autonomy.
02
Networks & comms
Standalone → Mesh swarm
From an isolated robot to decentralized peer decisions.
03
Perception
LiDAR → VIO → Thermal
Sensor modalities, sensor fusion and GPS-denied localization.
04
Control & navigation
Manual FPV → Learned policy
From first-person piloting to learned policies.
Resilience
Keeping operations running when communications or positioning fail
Environments with interference and no GNSS have accelerated techniques that are now reaching industry. The key question for the enterprise is which tasks can continue safely, which degrade and when the mission must stop.
- 01Coordination over mesh networksInspection of large sites with patchy coverage
- 02GNSS-denied visual navigationUnderground mining, tunnels and indoor sites
- 03Low-latency teleoperationRemote intervention in emergencies
Site map
Tools to decide
Guide
From operational problem to fleet: readiness, vendors, pilot, KPIs and regulation.
Platforms
Parallel coordinates, comparison matrix and side-by-side comparator of real platforms.
Cases
Documented deployments with sources, published metrics and lessons.
ROI
Automation matrix by sector and 36-month payback with visible assumptions.
Configurator
Design the sensor suite and estimate autonomy, power, cost and resilience.
Taxonomy
The reference framework: autonomy levels, coordination, perception and control.
Swarms
Fleet continuity under communication and positioning failures.
Methodology & sources
Evidence levels, specification conditions and the full bibliography.
Which part of your operation can a robot do today?
Start with the guide, check your readiness and validate the return with your own numbers.