A physics-accurate virtual replica of your robot, cell or line. Design, train and validate in the twin, generate synthetic data from it, and deploy only what already works.
A digital twin is a physics-accurate virtual replica of a robot, work cell or production line. It lets you design, test and train in software first — generating synthetic training data, validating motion, timing and safety, and de-risking a deployment before any hardware is bought or moved. The payoff is simple: by the time a robot is installed, its job has already been practiced millions of times. Haink builds the twin and the pipeline from it to the real machine, and supplies the GPU workstations that run it.
A virtual replica of the workspace, robot, sensors and the objects it handles — accurate enough to develop and test against before any metal moves.
Large volumes of perfectly-labeled training data generated from the twin with domain randomization — feeding model training.
Motion, reach, throughput, timing and safety bounds tested in the twin, so problems surface in software, not on the floor.
The validated model and configuration carried to the real machine via edge inference and integration.
Built on the standard physical-AI simulation toolchain — the same one robot makers train on.
| Layer | Tooling | Role |
|---|---|---|
| Scene & world | NVIDIA Omniverse (OpenUSD) | Author and assemble the twin; collaborate on one scene |
| Robot simulation | Isaac Sim / Isaac Lab | Simulate the robot, sensors and reinforcement learning |
| Physics | PhysX / MuJoCo | Accurate contact, friction and dynamics |
| Synthetic data | Domain randomization / replicator | Labeled data generation at scale |
Replicate the cell, robot, sensors and task in Omniverse / Isaac Sim with realistic physics.
Produce synthetic data with domain randomization and train the policy — see robotics training.
Prove layout, motion, timing and safety against the twin before touching hardware.
Close the sim-to-real gap and hand the validated model to the edge and the machine.
Design and commission a line virtually, so the build starts from a layout already proven in software.
Wring out reach, collisions and cycle time in the twin, compressing costly on-site commissioning.
Test layout and flow changes against the twin before committing floor space and capital.
Verify bounded autonomy and fail-safe behaviour in simulation before people are near the machine.
In fast-building markets, the twin is built before the floor is. Engineers in the Middle East increasingly perfect every movement in a high-fidelity virtual replica — so by the time a robotic arm is installed in Abu Dhabi or a fulfilment centre in the Gulf, its task has been practiced millions of times in software. It is a natural fit for Vision 2030 industrial programmes and the new manufacturing and logistics capacity being built across the region — and for Haink’s footprint in Dubai, Hong Kong and Singapore.
One partner for the simulation rig and the deployment hardware.
| Stage | Typical hardware |
|---|---|
| Twin development | RTX 6000 Ada-class workstation (from ~$12K) |
| Scale simulation / synthetic data | Multi-GPU systems / GPU cluster |
| Deployment target | Jetson AGX Thor / AGX Orin on the robot |
Illustrative target profile for a single robot-cell twin — representative figures, not a specific client result.
| Metric | Target |
|---|---|
| Synthetic episodes generated | Millions, with domain randomization |
| Scenarios validated pre-build | Layout, reach, cycle time, collisions, safety bounds |
| On-site commissioning | Compressed from weeks toward days |
| Hardware bought before validation | None — proven in the twin first |
We agree the scope and success criteria up front, and validate against the twin before any hardware commitment.
A digital twin is a physics-accurate virtual replica of a robot, work cell or production line. It lets teams design, test, train and validate in software first — generating synthetic data and de-risking a deployment before any hardware is bought or moved.
The twin generates large volumes of labeled synthetic data with domain randomization, used to train perception, control and vision-language-action models. The validated model is then transferred to the real robot (sim-to-real) — see sim-to-real training.
Commonly NVIDIA Omniverse (scene authoring on OpenUSD), Isaac Sim and Isaac Lab for robot simulation and reinforcement learning, physics engines such as PhysX or MuJoCo, and synthetic-data tools for labeled data generation.
By proving layout, motion, timing and safety in simulation before hardware is bought or installed, a digital twin catches problems early, cuts on-site commissioning time, and lets a robot’s task be rehearsed millions of times virtually before it runs for real.
GPU workstations or clusters — typically RTX 6000 Ada-class workstations for development and larger multi-GPU systems for scale. Haink configures and supplies these alongside the deployment hardware.
Tell us the cell or line — we’ll scope a digital twin, the data it generates, and the path to the real machine.