Sense real work
3D geometry · force · grip · timing
Field-trained Physical AI
Stop teaching factories to fit robots.Teach robots to fit factories.
Your Factory Floor Is Smarter Than Any Lab. We Just Listen to It.
Most industrial robots are trained in sterile labs, then fail within 48 hours of hitting your production line. Engel OS learns exclusively inside your actual facility, capturing the invisible physics of human dexterity: grip pressure, torque shifts, and 3D spatial timing. Real deployment for real results.
ENGEL OS / FIELD-TO-FLEETFacility trained
- 01Capture the real task
3D motion · force · grip · timing
- 02Learn the invisible physics
Your operators · products · conditions
- 03Deploy the motor policy
Compatible robots · one repeatable skill
NOT LAB SIMULATIONYOUR FLOOR → YOUR POLICY
01Full-spectrum telemetry
Motion, force, pressure, and timing in context.
02Hardware-agnostic
Policies for humanoid, dual-arm, mobile, and cobot platforms.
03First production pilot within 21 days
Three-week target from field capture to a scoped production pilot.
GRIP / 04.218
Human skill, made legible
Expert hands know what no lab can simulate.
An experienced parcel operator knows exactly when to ease pressure on a polybag and when to clamp down on a heavy carton. Without a way to capture it, that knowledge can be lost to turnover, injury, or retirement.
- 01 / TACIT
Expert technique lives in motion, pressure, and timing.
- 02 / CAPTURE
Observe the real task with operator participation and defined safety boundaries.
- 03 / SCALE
Train a robotics policy and scale the technique across a fleet running three shifts, 365 days a year.
The future of production is not replacing human skill. It is digitizing human dexterity so physical work can scale.
The core difference
Lab-grown robots vs. field-trained robots.
Reality-grounded. Simulation-accelerated.
A lab can simplify the world. A production floor refuses to. Training data should preserve the conditions that make the task difficult in the first place.
ConditionLab trainingEngel OS / field trained
LightingPerfect and controlledDust, glare, shadows, and flicker
ObjectsPristine, repeated geometryDamaged, wet, misshapen, and non-rigid
Force dataSynthesized or estimatedObserved torque and grip-pressure signals
EnvironmentStatic and quietVibration, temperature shifts, and noise
Learning loopStatic lab datasetContinuous learning from production data
Training sourceComputer simulationsOperators performing the real task on the real floor
We do not simulate reality. We capture it.
Pilot outcome targets
Designed around measurable outcomes.
Typical pilot deployments target 20% to 35% labor cost savings on repetitive workflows. Each target is set against the facility's own baseline and validated in production.
015% → 2.2%
Target damage rate
0263% REDUCTION
Target overtime
0328% → 18%
Target staff turnover
0415 MONTHS
Projected ROI
Pilot target model
Example baseline: 5% damage on soft-packaged goods · 28% annual staff turnover · recurring overtime on repetitive workflows
Targets are defined before deployment, then measured against production data.
Track quality, labor, uptime, and ROI against the starting baseline.
- 01 2 weeks on-site recording
- 02 5 days policy training
- 03 10 robotic workstations
- 04 First pilot / within 21 days
Three-step deployment
From your floor to autonomous production.
Capture the task in its real environment, train on your own multi-sensor data, then deploy the policy to compatible robotic hardware.
- 01
Capture on your floor
Map constraints and record expert motion, force, and timing around a defined workflow.
- Timing: 2 weeks on site
- Your input: workflow access + operators
- Output: field dataset + task map
- Disruption: designed around daily production
- 02
Train on your data
Structure multi-sensor data and train motor policies around your products, machines, and edge cases.
- Timing: 5-day training target
- Your input: edge-case + acceptance review
- Output: custom neural motor policy
- Disruption: off-floor training
- 03
Deploy and improve
Calibrate on compatible hardware, stage the rollout, and use field observations to guide iteration.
- Timing: First production pilot within 21 days (3 weeks)
- Your input: acceptance criteria
- Output: staged deployment plan
- Disruption: controlled commissioning
Technical architecture
The technology behind Physical AI.
2D video shows how a task looks. Physical deployment also needs the surface resistance, torque shifts, contact, timing, and sub-millimeter 3D dynamics required to build foundation motor models on real physics.
CLOSED-LOOP PHYSICAL INTELLIGENCEField learning active
Build the motor policy
Training · adaptation · evaluation
Run on hardware
Robot behavior · calibration · outcomes
Observe, evaluate, improve.
Exceptions · drift · force · timing · outcomes
↩ Update capture↩ Update policy
01Data capture
3D point-cloud mapping at 100 Hz is aligned with multi-axis torque and force sensing, micro-tactile pressure distribution, and millimeter-precise joint kinematics.
02Policy training
Expert demonstrations seed imitation learning and spatial reinforcement learning. Domain randomization and continuous field calibration target real-world robustness.
03Calibration
Commissioning tests the policy against representative materials, environmental variation, and safety constraints before wider rollout.
04Deployment
Policies deploy across humanoid or bipedal robots, dual-arm manipulators, autonomous mobile units, and collaborative robots.
Humanoid
Bipedal task environments
Dual-arm
Coordinated manipulation
Mobile manipulator
Work across stations
Cobot
Guarded collaborative cells
Why Guangzhou
Built close to the systems Physical AI must understand.
Physical AI requires constant access to real workflows, industrial hardware, and high-volume logistics hubs. Engel OS is headquartered in Guangzhou, built where Physical AI meets the physical economy.
GUANGZHOU / GREATER BAY AREAINDUSTRY WITHIN REACH
Large-scale industrial enterprises in Guangdong
Factory robots active in China / 54% of global total
Annual industrial output
Access to the densest supply-chain corridor in the world
We build Physical AI next door to major automotive plants, electronics foundries, and logistics hubs. Models can be tested on real production workflows before deployment.
Industry applications
Built for the real world. Scoped to your workflow.
Start with the variable that breaks generic automation, then define the platform and acceptance criteria around it.
Representative workflow
Irregular parcels under real throughput pressure
- Workflow
- Mixed-parcel picking and adaptive depalletizing
- Difficult variable
- Soft goods, polybags, damaged cartons, changing grip
- Platform fit
- Dual-arm or mobile manipulator
- Evaluation target
- Desired outcome: consistent handling with measurable damage controls
- Applications
- Depalletizing mixed parcels with adaptive grip
- High-speed picking of soft goods, polybags, and irregular items
- Target: reduce damaged goods by 60%
Physical Intelligence Data Infrastructure
Every deployment makes the platform more intelligent.
Engelos does more than complete a one-time robotics integration.
Every programme contributes to a structured system of physical intelligence:
PLATFORM / COMPOUNDING ASSETSProgramme by programme
- 01Task and skill representations
- 02Multimodal field datasets
- 03Embodiment mappings
- 04Failure-mode libraries
- 05Policy and dataset versions
- 06Deployment telemetry
- 07Evaluation benchmarks
- 08Continuous-learning workflows
DEPLOYMENT → STRUCTURED INTELLIGENCE → TRANSFER
Over time, this becomes the infrastructure through which human physical expertise can be translated across machines, sites and industries.
Team and trust
Engineered by leaders in AI and robotics.
Our team combines AI research experience from Apple, ByteDance, and DeepSeek with robotics systems experience from Unitree and other hardware firms.
Multimodal AI
Align visual, spatial, force, and temporal signals around a physical task.
Spatial learning
Model the relationship between the operator, object, workstation, and environment.
Motor control
Translate learned behavior into platform-aware motion and calibration requirements.
Field robotics
Commission against real materials, safety boundaries, and operating constraints.
Start with the real floor
Find the workflow where field-trained intelligence matters.
Reduce repetitive manual operations while preserving your workforce's expertise.
- admin@engelos.com
- Location
- Guangzhou, Guangdong, China
- Audit
- Free on-site workflow assessment