The status quo vs Cadenza
| Typical stack today | Cadenza Labs | |
|---|---|---|
| Simulation | A general physics engine you wire up yourself | MuJoCo core with robots, scenes, and an action library included |
| Data collection | Custom logging and labeling per project | Every tick logged and scored automatically |
| Training | Separate framework, glue code to reshape data | Rollouts → VLA data → LoRA in a couple of commands |
| Sim-to-real | A rewrite from the sim loop to the robot loop | The same control loop runs in sim and on hardware |
| Iteration | Re-plumb the pipeline on every change | One stack — a change flows straight through |
Where the advantage comes from
One coherent stack
Every layer shares the same physics core, action library, and policy
interface — no glue code, no format conversions between stages.
The loop you test is the loop you ship
Simulation and hardware run the identical control loop, so sim-to-real
transfer isn’t a separate, fragile rewrite.
Data that labels itself
Every mission is logged tick-by-tick and scored automatically, so training
data arrives clean and labeled — not as a separate annotation project.
Fine-tuning without the boilerplate
LoRA-based fine-tuning keeps specialization cheap, fast, and composable —
swap task-specific adapters on a shared base model.
Why it matters: iteration speed
The point of a unified stack is velocity. Because every layer shares the same core, a change to a scene, a reward, or a policy flows through simulation, data, and fine-tuning without re-plumbing anything. Faster loops mean more rollouts, more data, and better policies — which is the whole game in physical AI.Start building
Build with the SDK
The foundation — simulate robots, build scenes, and run policies in Python.
Operate with the CLI
The workflow on top — gather data, fine-tune, and deploy.