The world
robots learn in.
Mirrorworld is the decentralized robotics simulation layer. Synthetic worlds where humanoid fleets are trained, validated, and stress-tested 1000x cheaper than real collection, before any of it touches reality.
Built on the open standards under Isaac. Launching on Virtuals (Base).
Robots are starving
for data.
- 01Only about 500,000 hours of quality physical-interaction data exists globally, against the tens of millions needed. A reported ~99% gap.
- 02A single hour of high-fidelity manipulation data costs $1,000 to $10,000.
- 03Simulation collapses that curve. Isaac generated ~9 months of human demos in 11 compute hours, and sim plus real beat real-only. The world-model moment just landed (Cosmos 3).
"The engine arrived. The decentralized layer that refines it into robot training data does not exist yet."
We build the world
they train in.
Synthetic environments
Physics-aware worlds where fleets train from imagination, not captured reality.
Sim-to-real validation
The winning pipeline (sim pretrain, then real fine-tune) that needs 3-5x less real data.
Fleet-scale stress-testing
Heterogeneous fleets run thousands of parallel failure modes before deployment.
Domain randomization + physics fidelity
The hard layers (friction, slip, contact, reward shaping) that close the sim-to-real gap.
Crypto built the data-collection layer for robots. Nobody built the simulation layer.
NVIDIA Isaac is centralized, but the standards beneath it (Newton, OpenUSD) are open. Mirrorworld builds on those as the decentralized refinery: the cheap pre-training and validation data the whole stack runs on, one tier beneath the hardware plays.
The collection projects capture data. We multiply it 1000x in sim before it touches a real robot.
Compute in.
Robot training data out.
Mirrorworld is a refinery. $MIRROR coordinates the two sides of it: the compute that runs the worlds, and the teams that need the data those worlds produce.
Run a world
Compute providers stake $MIRROR and run sims to generate synthetic training data. Validated output earns, junk is slashed.
Pull the data
Robotics teams pay $MIRROR for refined training data and validated sim-to-real runs.
Raise the fidelity
Contributors who improve domain randomization, physics fidelity, and sim-to-real validation earn for fidelity gains the network adopts.
Govern the standards
Holders steer how worlds, fidelity, and validation are defined.
$MIRROR is a utility token for the Mirrorworld network. Nothing here is financial advice.
On the horizon.
- NowBOOT
Brand and foundation public, first community, the simulation thesis published.
- NextLAUNCH
$MIRROR on Virtuals (Base), docs live.
- MVPFIRST WORLDS
Synthetic-environment generation live, first fleets training in sim, domain randomization and physics fidelity, first trajectory datasets.
- ValidationSIM TO REAL
The validation pipeline live, 3-5x less real data, first sim-to-real handoffs with partners.
- DePINREFINERY
Decentralized compute providers running worlds at scale, the staked data economy live.
- HorizonTHE STANDARD
An open decentralized refinery on Newton and OpenUSD, neutral infrastructure the robotics stack runs on.
Forged in sim.
Proven in reality.
SIM + REAL // ALWAYS