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Humanoid Robotics Deployment

We don’t sell humanoid robots.
We deploy the system your factory floor runs on.

AIdeology partners with industrial teams to bring humanoid robotics into real production cells — designed and trained inside NVIDIA Omniverse, deployed and operated on NVIDIA AI Enterprise.

The result is not a tradeshow demo, but a production humanoid skill running inside your line — safe, observable, and built to grow into a global fleet.

Step 01

We start on the floor — understanding the physical operation, not the robot

Before talking about humanoids, NVIDIA stacks, or simulators, we walk the line, time the cycle, study the safety perimeter, and map the real physical workflow that humans run today.

Walk the cell, the line, and the operator workflow on-site
Measure cycle time, ergonomic load, and exception rates
Identify which physical tasks would create the most value if automated first
Plant Stuttgart · Building 4 · Floor Planscale 1:200 · 38m × 22m · drawn during AIdeology floor walk
Walk · in progress
38.0 m22.0 m─── MAIN AISLE ───INBOUND DOCKPACKING / OUTBOUND[out of pilot scope]N
STG-A1
Bin-picking
320h/mo
✓ WAVE 1
STG-A2
Bimanual kitting
240h/mo
✓ WAVE 1
STG-B1
Material handover
180h/mo
STG-QC
Visual QC
120h/mo
STG-CNC
Machine tending
90h/mo
Wave 1 cell Reviewed cellAudit walk path
5 cells mapped · 950 operator-h/mo · 1 plant · 1 building
STG-A1 & STG-A2 nominated for Wave 1 humanoid pilot
STG-A1 & STG-A2 selected — taking the floor walk into Omniverse
Step 02

Then we test feasibility inside Omniverse before touching real hardware

Each candidate task is rebuilt as an Omniverse digital twin and stress-tested in Isaac Sim. We compare physics, sensing, manipulation, and integration risk so we only build what is realistic to deploy.

Reconstruct the cell as a USD digital twin in NVIDIA Omniverse
Validate physics, sensors, and humanoid kinematics in Isaac Sim
Score each task by sim readiness, integration cost, and expected impact
Omniverse Kit · Cell Twin Builderproject: aideology_stuttgart_b4.usd · physics: PhysX 5 · render: RTX
GPU · 4× L40S
PhysX · 240 Hz
USD Stage17 prims
/World
Cell_STG-A1
Floor
Conveyor_01
PartsBin_A
Workbench_01
QC_Stationauth
Robots
Humanoid_01
Skeleton (32 joints)
Physics
PhysicsScene · 240 Hz
Sensors
RGBD_chest_cam
Wrist_cam_L
Wrist_cam_R
IMU · base_link
Isaac Sim · live
iso · 30°|60 fps|RTX
Domain-randomised runs5,240
policy reward · Isaac Lab96.8%
QC station · authoringxyz
Selected prim

/World/Robots/Humanoid_01

Sim metrics
success96.8%
cycle18.4s
twin readiness
90%
expected return
92%
Risk & effort
10–14 wksLow risk
NVIDIA stack
Omniverse · USDIsaac SimIsaac ROSWMS · MES

Clean cell geometry, well-defined parts — ready to train in Isaac Lab.

frame: 02:14:57:181.42 ms / frame9 prims · 32 jointspolicy: humanoid_pickplace_v07

Step 03

We train one humanoid policy end-to-end inside the digital twin

The first build is not a flashy demo. It is a single, well-scoped humanoid skill — trained at scale in Isaac Lab on synthetic data, evaluated against safety and quality KPIs, and ready to leave the simulator.

Define one focused humanoid skill around a measurable production outcome
Generate synthetic data and train with imitation + reinforcement learning in Isaac Lab
Validate the policy in the digital twin against real production KPIs and safety limits
Humanoid datasheet · GR00T-FT
8 subsystems
1.85 m1.20 m0.90 m0.00 m
Bin-picking · STG-A1 · policy v07

Foundation policy

GR00T as the brain

A humanoid foundation model (NVIDIA GR00T family) provides general manipulation and locomotion priors. We fine-tune it on the customer's cell so the robot starts with a strong baseline rather than learning from scratch.

GR00T fine-tuningPer-cell skill headsSim + real co-training

Click any capability to explore

Step 04

We move sim-to-real and integrate the humanoid into the enterprise stack

Once the policy is solid in simulation, we deploy it onto real humanoid hardware running on Jetson Thor and Isaac ROS, and wire the robot into MES, ERP, and WMS through NIM microservices on NVIDIA AI Enterprise.

Sim-to-real transfer onto Jetson Thor with Isaac ROS perception and control
Expose perception, planning, and skills as NIM microservices on NVIDIA AI Enterprise
Connect telemetry and control to MES, ERP, and WMS — under enterprise governance
AIdeology Humanoid Stack · STG-A1
cloud → sim → policy → services → edge · sim ↔ real bridge
policy v07 · live
Sim · digital twin
Isaac Sim
240 Hz
DR runs5,240
success96.8%
← train policy in sim
NVIDIA AI Enterprise · Stack5 layers
L5
NVIDIA AI EnterpriseCloud
Fleet control plane · governance · OTA
L4
Omniverse · Isaac Sim/LabSim
Digital twin + RL training in sim
L3
GR00T fine-tuned for STG-A1Policy
Humanoid foundation policy
L2
NIM microservicesServices
Perception, planning, safety as APIs
L1
Jetson Thor on Humanoid_01Edge
On-robot inference @ 60 Hz
Real · cell STG-A1
Humanoid_01
60 Hz
cycles142 / hr
success98.6%
← run on real cell
Sensors stream into the stack
RGB-D cameras
30 Hz · multi-view
LiDAR / depth
Cell occupancy + humans
Force / torque
Contact + grasp
MES order
Task, part, sequence
Actions back to the cell
Pick & place part
Bin → fixture
Update MES
Operation complete
Reserve next kit
WMS pick request
Notify supervisor
Exception or approval
NVIDIA components in this stack
Omniverse · USDIsaac SimIsaac Lab · RLGR00T foundationNIM microservicesJetson Thor edgeNVIDIA AI Enterprise
Every humanoid skill in the cell follows this same blueprint
Step 05

We design the experience for operators, supervisors, and safety officers

A humanoid only creates value when the people around it trust it. We shape the operator and supervisor interfaces — teleop, exception handling, dashboards, audit — so the floor can run the robots, not the other way around.

Operator UX for handovers, pauses, retries, and shared autonomy
Supervisor dashboard with live tasks, KPIs, exceptions, and approvals
Safety officer console with full audit trail and policy controls
Mission Control · STG · Building 4
shift A · 07:42:18 CET · supervisor: SM · 5 humanoids on the floor
REC
Cycles / hr
142
Success rate
98.6%
Avg cycle
18.4s
Mean energy
342 W
Fleet rack4/5 live
H-01RUNFOCUS
Bin-picking · A1
84%
72%
H-02RUN
Kitting · A2
67%
46%
H-03RUN
QC inspection
92%
88%
H-04IDLE
Material handover
41%
30%
H-05RUN
Machine tending · CNC
76%
60%
part_7821-B · 0.71
H-01 · chest cam
STG-A1|30 fps|Bin-picking
cycle 73 of est. 142grasp confidence 71%
θ132°
θ258°
θ314°
θ471°
θ522°
θ68°
Wrist force / torqueF = 4.2 N · safe
Fx
1.2 N
Fy
4.2 N
Fz
0.8 N
Live event logstreaming
[07:42:18]H-01 completed 142 picks in the last hour (98.6% success)
[07:28:18]H-02 finished kit BOM-2039 and reported to MES
[07:14:18]H-03 flagged 3 parts for rework on batch #883
Approval queue2 pending
H-01 · Bin-pickingManipulation

Approve non-standard grasp on part 7821-B

Confidence 71% on a deformed tote item. Approve attempt or fall back to operator?

H-04 · Material handoverSafety

Authorise route through walkway 3B

Crowd density at safe limit. Switch to alternate path adds 28s to cycle.

Reports2 ready
H-03 · Inspection ready

QC report ready · batch #884

172 of 180 parts passed. 8 flagged for rework with annotated images and reason codes for line lead review…

H-02 · Kitting ready

Skill update v0.34 ready to promote

New kitting policy improved success rate from 94.1% to 96.8% across 5,400 sim runs. Promote to Cell A2?

NVIDIA AI Enterprise · control plane @ 12 ms RTTpolicy v07 · GR00T-FT4 humanoids · 1 idle (H-04)all interlocks armed
Step 06

Finally, we scale across cells, sites, and the full humanoid fleet

Once one cell is in production, we replicate the deployment across lines and plants, manage the fleet centrally on NVIDIA AI Enterprise, and run continuous learning loops so every robot improves the next one.

Roll proven skills out to additional cells, lines, and plants
Centralised fleet operations, OTA policy updates, and lifecycle support
Continuous learning loop — every deployment teaches the next one
Humanoid Fleet · Operating model
From one pilot cell to a global humanoid fleet
Humanoids live1
NVIDIA AI EnterpriseFleet Control PlaneGR00T policies · OTA updates · telemetry
STG · DEStuttgart
Pilot
×6
Pilot site98.4%
Bin-picking · A1
Kitting · A2
QC inspection
CNC tending
BCN · ESBarcelona
×4
Wave 2 rollout96.1%
Bin-picking · B1
Kitting · B2
Material handover
MTY · MXMonterrey
×3
Wave 3 rollout94.8%
Bin-picking · C1
QC inspection
Machine tending
HUB · NLHub EU
×5
Logistics fleet97.2%
Pallet move
Order pick
Loading dock
From one pilot cell to a global humanoid operating model

End Result

Not another tradeshow demo.
A humanoid working inside your line.

From the floor walk to a global humanoid fleet — robots that earn their cycle inside your production, not videos that stay on a stage.

Production, not prototypes

Humanoid skills deployed into real cells with real operators — measurable on cycle time, success rate, and safety from day one.

Built to scale on NVIDIA AI Enterprise

From one cell to a global humanoid fleet — managed centrally, with OTA policy updates and continuous learning across sites.

Safe, controllable, auditable

Safety interlocks, force and speed limits, supervisor approvals, and full sensor replay — designed in from the first sim run.

Ready to bring humanoids into your first cell?

We start with a deployment session, walk one cell on-site, and quickly move it into Omniverse — with a production humanoid skill as the goal, not a tradeshow demo.