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Physical AI systems development
Custom perception, control, and learning systems — simulation, synthetic data, imitation and reinforcement learning, and sim-to-real transfer built around your robot and task.
- Stack
- ROS 2 · Isaac
- Delivery
- Your repo, your models
What this covers
The engineering behind a capable robot: building the simulated environment, generating labelled synthetic data, training behaviours, measuring them honestly, and getting them onto real hardware without a quality cliff.
The loop
- Simulate — environments and scenarios in Isaac Sim, Isaac Lab, Gazebo, or your own simulator.
- Generate — synthetic datasets with RGB, depth, segmentation, and 6D pose; domain randomisation tuned to your site.
- Learn — imitation learning from teleoperation, reinforcement learning, or hybrid policies.
- Evaluate — task success, cycle time, collision and recovery rates, measured on held-out scenarios.
- Deploy — ROS 2 nodes, edge inference, and robot adapters, with sim-to-real validation.
- Improve — failure capture from the field feeding the next iteration.
What you get
A working system, the pipeline that produced it, and evaluation numbers you can trust.
Common questions
Do we own the output?
Yes. Code, models, datasets, and documentation are delivered to you. We can also maintain them under a separate agreement.
Can you work with our existing stack?
Yes. We adapt to your simulator, middleware, and hardware. ROS 2 and NVIDIA Isaac are our defaults, not a requirement.