Describe a robot task once. WanoRobotics compiles it to Isaac Lab, MuJoCo, Gazebo and Webots, records LeRobot-ready datasets, and deploys trained policies to real ROS 2 robots. Local-first.
Robots and scenes from the formats you already have.
Datasets and policies in the formats training stacks expect.
Trained policies talking to real hardware.
The problem
Every simulator has its own scene format. Every dataset format needs manual conversion. Sim-to-real is a separate engineering project. A single manipulation task means gluing together Isaac Sim, Python training code, dataset tooling and ROS 2 — by hand, every time.
URDF, MJCF, USD, SDF — each one re-authored by hand.
Every training stack expects its own layout and stats.
The ROS 2 package is written twice: once in sim, once for real.
Isaac Sim + Python + dataset tooling + ROS 2, re-wired per task.
Our answer
The Wano Framework describes robots, scenes, sensors and tasks in one declarative blueprint — then compiles it to Isaac Lab, MuJoCo, Gazebo, Webots, two in-browser physics lanes, and a ready-to-run ROS 2 package. Change the blueprint, not four codebases.
task: pick_and_place
robot: so_arm100 # 1 of 150
sensors: [cam_top, cam_wrist]
targets: [isaac_lab, mujoco, gazebo, webots, ros2]The product
Every screen below uses the Wano Studio light interface, populated with one coherent robotics workflow.
Scene
Drop a table, props, lights and cameras into the scene, place them with the gizmo, and set the task zones. Everything you place is written into the blueprint — the single declarative source the studio compiles from.
Robot
Arms, mobile bases, quadrupeds, humanoids, hands and drones ship with the studio, each with its actuators, sensors and meshes already resolved. Import your own from a URDF or MJCF file when the catalog doesn't have it.
Task
Choose the behavior, robot and environment while the live recap stays in view. Wano checks the composition as you go, then seeds the complete mission — task zones, success condition and training branch included.
Demonstration
Drive the robot from one cockpit while the 3D workcell, controls, telemetry and latest takes stay visible together. Every accepted motion becomes a synchronized episode ready for review.
Curation
One timeline drives the 3D replay, task phases and synchronized signals. Review the motion, inspect the active steps and accept or reject the take with the evidence still on screen.
Training
Launch a training run from the curated data, then follow convergence and compare every run. Progress, checkpoints and GPU state stay beside the metrics, so the run is readable without leaving the studio.
A few of the 150
Why it's different
Your models, your GPUs, your data. Everything runs on your machine — nothing leaves it. The architecture is already split for a SaaS tier, but local is the product, not the demo.
39 MCP tools expose the whole studio to AI agents. A Guard Engine validates every agent action before it touches your scene, your dataset or your robot.
Isaac Lab, MuJoCo, Gazebo, Webots, plus MuJoCo WASM and Rapier in the browser — from one declarative source. Cross-validate physics instead of trusting one engine.
196 test files. Real torch networks driving simulated robots. Real ROS 2 Humble driven by generated code. Every claim on this page is a passing test, not a render.
Scaling
Wano ships local-first: your machine, your GPUs, your data. When a run outgrows the workstation — a hundred parallel environments, a full fine-tune — the studio connects to managed GPUs from the same window. Same blueprint, same datasets, nothing to re-learn.
Author, simulate and record locally. That never stops working, with or without an account.
Point the run at managed GPUs. No cluster to provision, no YAML, no DevOps on your side.
Datasets and checkpoints come back where you left them, ready to deploy to the robot.
Opt-in by design: nothing leaves your machine unless you send it.
Roadmap 2026
Two quarters already in your hands; the third is landing now.
Web studio · 3D scene editor · Isaac Sim streaming
Wano Framework: blueprint → multi-sim · MCP server + Guard Engine · 150-robot catalog
Multi-sim Docker E2E · multi-robot instancing · domain randomization · LeRobot / GR00T / HDF5 / RLDS datasets · episode viewer · GPU fine-tuning
Early access · VR teleoperation (Quest 3, CloudXR) · SaaS tier
FAQ
No. MuJoCo, Gazebo and Webots are first-class targets, and two physics engines run directly in the browser (MuJoCo WASM, Rapier). Isaac Lab is one of six targets — the one you'll want for GPU-parallel RL.
No. Scenes, datasets, model weights and training runs stay local. A SaaS tier is planned for teams that want managed GPUs — it will be opt-in, not the default.
150 models ship in the catalog — arms (SO-ARM100, ALOHA 2), quadrupeds (Spot), humanoids and more. Anything with a URDF or MJCF can be imported.
Yes. Generated Python, ROS 2 packages and datasets are plain files on your disk, in standard formats (LeRobot, HDF5, RLDS). No lock-in by construction: if you stop using Wano, the code still runs.
Q4 2026, in small cohorts. ROS 2 developers, Isaac Lab users and LeRobot teams get priority. Register below — one email when your seat opens, nothing else.
Small cohorts. Priority to ROS 2, Isaac Lab and LeRobot teams.
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