Built alongside
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Meet illusio
Text-to-physics environments for robots.
Interactive Demo
Describe a rare failure mode in plain English. illusio uses Claude to build the 3D scene, run the physics, and stream synthetic training data.
- Reasoning Backend
- Claude Opus 5 & Sonnet 5
- Scene Export
- OpenUSD · MJCF · RLDS
- Sensor Output
- RGB-D · 128-Beam LiDAR · 3D BBox
Built for physical AI applications
Warehouse AMRs
Spill recovery & blind aisle collisions.
Autonomous Vehicles
Night rain & occluded pedestrian hazards.
Dexterous Grasping
600Hz deformable cables, cloth & glass.
Aerial Drones
GPS-denied wire & wind-field navigation.
How Claude runs illusio
Instead of hallucinating video pixels, Anthropic's Claude writes structured 3D physics code executed by our deterministic engine.
Plain English to 3D coordinates
Claude Opus 5 parses crash reports or test prompts into exact object positions, speeds, and surface friction.
Physics & collision verification
Claude Sonnet 5 calls illusio's OpenUSD tool schemas to spawn robots, vehicles, and obstacles with valid collision bounds.
Automatic failure mutation
When a robot fails a test, Claude inspects the replay in illusio and generates harder variations until the policy passes.
Generate training data in 10 lines of code
Robotics engineers call the illusio Python SDK to stream thousands of labelled training episodes directly into PyTorch.
import illusio
client = illusio.Client(model="claude-opus-5")
dataset = client.simulate(
prompt="Wet warehouse aisle, forklift drops pallet at t=1.4s",
robot="unitree_g1.urdf",
sensors=["rgbd", "lidar_128", "bbox_3d"],
episodes=250
)
for batch in dataset.to_torch(batch_size=16):
policy.train_step(batch)