DVIDIA · ROBOT SIMULATION LAB

See the motion. Inspect the evidence.

A native six-joint arm, an articulated gripper and a free rigid object. Recorded task attempts make the simulator's behavior visible, and the complete receipts make its limits checkable.

This is recorded simulation from an authored controller with privileged simulator state. The browser does not run physics or connect to hardware. Learned skills, shoe manipulation and physical transfer remain future stages.

Frozen confirmation cohort: 18 planned attempts across 6 synthetic scenes. Counts are descriptive, with no population or physical reliability claim.
nominal / placement: 4/4 completed, 4 numerically valid
nominal / replay_open_jaw: 0/4 completed, 4 numerically valid
nominal / hold: 0/4 completed, 4 numerically valid
low_friction / placement: 2/2 completed, 2 numerically valid
low_friction / replay_open_jaw: 0/2 completed, 2 numerically valid
low_friction / hold: 0/2 completed, 2 numerically valid

A live PiPER shoe workstation.

Drag the gripper target, open and close the jaws, and push a free rigid shoe. MuJoCo physics runs locally in your browser. Explore contacts and download a session receipt.

Authored control and synthetic shoe properties; no learned policy or hardware qualification.

Enter the live workstation →

The shoe asset library is runnable.

Choose one of three robot profiles, inspect six original rigid shoe proxies, download a reusable scene and run CPU simulation locally. Synthetic properties are labelled as assumptions; gravity/support benchmarks retain every attempt and matched control.

Open the asset library →

Native grasp, carry and release.

Use orbit, zoom, recorded motion selection and the timeline inside the viewer. Replay is an excerpt; the full benchmark includes every matched control and invalid attempt. The native meshes, binary poses, complete receipts and license notices are served locally by this Space.

Hardware profiles and the next experiments.

A native mechanics benchmark establishes model structure and recorded motion. A native task benchmark adds measured contact outcomes. Neither establishes hardware reliability or a learned motor policy. Upcoming profiles have no verified implementation claim.

Run it on your own machine.

The native task is a local CPU workload. Fetch the pinned dependencies and assets once, then simulate offline. WebGL2 supports viewing; no hosted inference or paid GPU server is requested by this static Space. Minimum RAM and arbitrary-dataset speed guarantees remain unmeasured.