Raise it in simulation. Ship the memory. It keeps learning.
Putting AI on a robot usually means putting a heavy model on the robot: a GPU, a lot of power, and answers that take seconds. NeuraFrame Studio™ Embodied does the opposite. You train with the big model in simulation, then deploy only NeuraFrame™ to the machine. The robot carries the learned memory, not the model.
A model on the machine is the expensive way to be smart. It needs a GPU, it draws power the robot would rather spend on moving, it answers in seconds when the robot needs milliseconds, and it can behave unpredictably the first time it meets something new. Every robot in a fleet then carries that same cost.
NeuraFrame™ is a memory and a light engine, not a model. So you can teach it where teaching is cheap, then move what it learned to where it needs to run.
Run a virtual copy of the machine on a server, where the heavy model, the compute, and a supervisor are all available. Mistakes are free. NeuraFrame™ learns the right action for each situation, and needs the model less and less as it goes.
The learned experience is just a file. Export it and copy it onto the real robot. That is the whole transfer, no retraining. The robot boots warm, already knowing what the simulation taught it.
On the device it keeps improving from its own sensors and from human corrections, using the same correction engine it learned with in simulation. A new situation is taught once and remembered from then on.
This is the part that changes the economics. The heavy model stays in simulation, where power and compute are cheap. Only NeuraFrame™ goes on the machine.
The robot runs the learned memory and a light engine. It does not need a GPU or a large model on board, so it runs on modest, lower-cost, lower-power hardware.
A learned situation is a memory lookup, not a model call. The machine acts on what it knows in milliseconds instead of waiting seconds for a model to think.
Train once with the big model, then deploy the distilled experience to every device. A fleet of robots does not each need to carry, power, and pay for a model.
A machine that acts on a guess is dangerous. NeuraFrame™ only acts on what it has verified. Under the memory sit hard rules, the safety laws, and when it is unsure or a rule would be broken, it does not guess. It escalates: it stops and asks, and a human or a sensor outcome teaches it, so next time it knows.
The rules and escalation are tools, not a guarantee. Embodied is a new capability, and you are responsible for your own model, your integration, your testing, independent hardware safeguards such as emergency stops, and not bypassing the rule layer. See the EULA.
Embodied is honest about its boundary. It does not replace your robot's brain; it gives that brain a memory and a conscience.
| Your stack | NeuraFrame Embodied |
|---|---|
| Perception: the vision model, the sensors | Verified memory of which action goes with which situation |
| Control: the motors, the planner, the base policy | The correction and learning loop, in simulation and on the device |
| Turning a sensor reading into a situation | Recall, safety gating, and the portable memory pack |
Because the learned memory is a file, it is easy to move. Licensing is enforced at the device, so moving the file does not move the right to run. A machine without its own license serves nothing from memory and escalates, which means a copied memory pack is inert until that device is licensed. Copying the memory onto a fleet is a per-device matter, one seat per machine, the same as any NeuraFrame™ install.
When people push back, these are the honest replies.
Not for what it has learned. Learned situations are served from memory with no model on board. Something genuinely new is escalated rather than run on a model the robot is not carrying.
Distillation bakes a smaller model. This ships a memory that keeps learning on the device from corrections and outcomes, with provenance and safety gating, and it escalates instead of guessing.
Adding a sensor augments what the machine can sense; it does not erase what it learned, the way a new sense enriches memory. Only the decisions the sensor actually changes are re-examined. Removing a sometimes-used sensor changes nothing it already knows.
It is memory-first and escalates when unsure, with hard safety rules underneath. But the rules are a tool, not a guarantee. You own your model, your testing, and independent safeguards.
Raise it in simulation, ship the memory, and it keeps learning within your safety rules. Per-device licensing with a 7-day free trial. Read the technical guide for how to wire it into your own stack.