FIGURE 1 Build an interactive world, align it to the frozen policy, then improve the policy. Select an image to enlarge it, or a stage to jump to it.
Approach
A simulator must reproduce a policy’s behavior before it can improve it.
Geometric and physical validity
Claude drives twelve checkpointed reconstruction stages through an MCP server: Depth Anything V3, SAM3, Hunyuan3D 2.1, FoundationPose and GPT-6 Astra, with five review gates and checks for completeness, registration, scale, penetration and settling.
Interaction validity
From a plain-language description, the agent fits hinges, socket cavities or threads to the reconstructed mesh, keeps its appearance, and tests each mechanism with a physics probe.
The frozen π0.5 policy is the discriminator: compare its actions at matched real and simulated states and correct the simulator until it reproduces real behavior. Then learn a small residual in simulation.
Load the reconstructed bench, pickup object and bin.
Interactive reconstruction of the pickup bench, object and bin; robot omitted.
Tabletop reconstruction
Real / input photograph
Simulation / camera tour
Input photograph beside the reconstructed tabletop.
Camera tour; no manipulation shown.
Full tabletop scene · 78 MB · loads on request.
Desk scene from one photograph
Real / input photograph
Simulation / settled scene
Five objects · 15.6 MB · loads on request.
One phone photograph, five simulated rigid bodies that settle in 142 steps.
Preview: the name plate was removed because the physics stability check found no predicted support for it, so it was not stable in simulation. Masses are VLM estimates.
Robot interactions
Real recordings beside their reconstructed simulations.
Camera pickup
Real / wrist camera, pick and place
Simulation / learned residual, wrist + overview
Real pick and place beside the learned residual grasping in simulation.
Different trials. Simulation at 2× from a saved grasp state.
Box folding
Real / demonstration 0133
Simulation / scripted oracle
Real demonstration beside the scripted folding sequence in simulation.
Scripted oracle, not a learned policy.
Plug insertion
Real / teleoperated insertion, base + wrist
Simulation / learned residual
Real teleoperated insertion beside a learned-residual rollout in simulation.
Teleoperated demonstration, not a policy rollout. Simulation at 3×.
Jar opening
Real / bimanual opening, wrist cameras
Simulation / bimanual scripted oracle
One arm holds the jar while the other turns the lid.
Scripted oracle in simulation; different trials.
Policy-guided alignment
The first render passed every check, yet the frozen policy started acting in only 6/12 simulated observations. Using the policy as the judge, appearance corrections brought that to 12/12.
Accepted changes, in order · head camera as the policy saw it · home-state descent unless noted
ReferenceReal head camera12/12ViewpointInitial fit, pitch ≈50°grasp +2.6 cmViewpointRefit to 36.7°, raw colour6/12Appearance+ colour affine on head12/12Appearance+ hard shadows off10/10
Tried and rejected
AppearanceDark room4/6ViewpointCropped to real table0/12EmbodimentBoard under the robot0/3 episodesEmbodimentRobot mounting plate4/10
Search space · plug insertion, π0.5 checkpoint 14999
Axis
Searched
Kept
Appearance
Colour gain, gamma, blur and per-channel affine on each camera; shadows; room lighting; table crop
Per-channel colour affine on the head camera only; hard shadows off
Viewpoint
Head-camera extrinsics, fit by least squares to landmarks in a real frame
Pitch 36.7°, yaw −1.6°, 0.62 m above the table. The first fit (≈50°) put every grasp 2.5–4 cm toward the robot
Embodiment
Robot base pose (9 variants), wrist-camera housing, gripper force
Base z +3 cm, y −2.5 cm; CAD wrist housing; ±20 N grip
State and prompt
Gripper reading fed to the policy; instruction string
Real finger readout; the training prompt
Insertion still failed after alignment, by as little as 7 mm. The residual closes that gap.
Residual adaptation
Two tasks, two π0.5 checkpoints. The base stays frozen; a residual trained in simulation corrects it.