Robots learn before they move. We train perception, control and vision-language-action models — mostly in simulation, topped up with real data — and supply the GPU and edge hardware to do it.
Robotics training is how a model learns to perceive and act before it ever touches the real machine. Because robot data does not exist in bulk on the internet, it has to be created — mostly in simulation, with targeted real-world demonstrations. Haink trains perception, control and vision-language-action (VLA) models and supplies the GPU compute to train them and the edge hardware to run them, so one partner owns the path from data to deployment.
Detection, segmentation, pose and depth estimation tuned to your objects, lighting and camera setup.
Closed-loop policies for manipulation and navigation — learned behaviour that adapts where a fixed program cannot.
Instruction-following policies that map what the robot sees plus a command to motion — see what VLA models are.
Adapting a robot foundation model (such as NVIDIA Isaac GR00T or Cosmos) to your task and environment, instead of training from scratch.
Train cheaply and safely in simulation, then transfer to the real machine. This is where most of the model value is built.
Construct or import a scene and the task in a simulator — NVIDIA Isaac Sim, Isaac Lab or MuJoCo — with realistic physics and sensors.
Millions of labeled episodes with domain randomization, plus targeted teleoperation for the hardest parts of the task.
Train the policy on GPU compute and validate in simulation before any real hardware is involved.
Close the sim-to-real gap with domain randomization and limited real-world fine-tuning, then hand off to integration and edge inference.
In 2026, teams at CMU and Stanford reported policies trained on 40% synthetic data matching 100%-real policies on held-out tasks — strong evidence the sim-heavy approach works. Background: sim-to-real training.
The data layer is where most of the cost sits — and where it has fallen fastest.
| Teleoperation | Synthetic / simulation | |
|---|---|---|
| Quality | Highest — real physics | High, with a sim-to-real gap to close |
| Cost | ~$118/hour (2026, down from ~$340 in 2024) | Near-zero marginal cost per episode |
| Scale | Slow, human-bound | Millions of episodes in parallel |
| We use it for | The hardest, highest-value parts of a task | Bulk coverage and rare or unsafe cases |
More detail: teleoperation and synthetic data →
The same single-contract model as the rest of Haink — we build the pipeline and supply the compute it runs on.
| Stage | Typical hardware |
|---|---|
| Development / simulation | RTX 6000 Ada-class workstation (from ~$12K) |
| Scale training | Multi-GPU systems / GPU cluster |
| Deployment target | Jetson AGX Thor / AGX Orin on the robot |
Browse robotics & physical-AI hardware → · for large GPU clusters see training infrastructure →
Illustrative target profile for a single-task manipulation policy — representative figures, not a specific client result.
| Metric | Target |
|---|---|
| Synthetic share of training data | ~80–95% |
| Teleoperation top-up | Tens of hours, on the hardest sub-tasks |
| Simulated episodes | Millions, with domain randomization |
| Data & training budget | ~$50K–$150K (pilot) |
| Deliverable | Edge-ready policy, validated and handed to integration |
We agree the success metric and budget up front, and prove transfer on the target hardware.
Robotics model training teaches a model to perceive and act before it touches the real machine. It uses simulation, synthetic data and teleoperation demonstrations to train perception, control and vision-language-action (VLA) models, which are then transferred to the robot (sim-to-real).
By collecting demonstrations from simulation, synthetic data and teleoperation, fine-tuning a robot foundation model (such as GR00T) to the task and environment, validating in simulation, and transferring to the edge with limited real-world fine-tuning.
High-quality teleoperation data fell from about $340/hour in 2024 to roughly $118/hour in 2026, and simulation plus synthetic data lowers it further. A typical enterprise pilot budgets around $50K–$150K for the data and training stage — see deployment cost.
Both, blended. Most of the data is synthetic from simulation (Isaac Sim, Isaac Lab, MuJoCo) for scale and safety, topped up with targeted teleoperation data and limited real-world fine-tuning. In 2026 studies, 40% synthetic data matched 100% real on held-out tasks.
GPU workstations or clusters for simulation and training — RTX 6000 Ada-class workstations for development (from around $12K) and larger GPU systems for scale — with the trained model deployed to edge platforms such as Jetson AGX Thor or AGX Orin.
Tell us the task and the success metric — we’ll propose a data and training plan and supply the compute to run it.