SimRepairBench

Repairing simulation-ready OpenUSD objects

CASTAIGNE25 · 25 modelling-error repair tasks

Chris von Csefalvay, Pranav Doma, Achyuthan Unni Krishnan and Tamas Foldi

HCLTech

SimRepairBench is a benchmark for repairing OpenUSD models used in robotic simulation. Its CASTAIGNE25 sample contains 25 tasks drawn from workshop equipment, tools, medical furniture and household objects. Each task starts with a complete object whose representation has been deliberately corrupted. The model must diagnose the error from the supplied USD package and observation, then repair it while keeping the healthy parts and their behaviour.

A joint may turn about the wrong axis, mesh faces may connect to the wrong vertices, or a surface may have lost its material assignment. The correctly modelled object can still be dented, worn or partly open. Those features belong to the object and should survive the repair.

Browse the cases · Harbor tasks · Scoring · Dataset format · Model and attempt records · SimReadyBench

Objects and repair tasks

The cases use the identifiers R01–R25. Release 0.4.13 contains all 25, with one controlled modelling error in each task. The case definitions describe what needs repairing, and the release index links each case to its Harbor task. A task revision fixes the input package, repair requirements and verifier so that models can be compared on the same problem.

Objects	Modelling errors
Articulated equipment and guided mechanisms	Joint ownership, local frames, motion axes and contact geometry
Conveyor, ladder and folding knife	Incorrect face-to-vertex connectivity
Pallet jack and patient chair	Incorrect effective surface normals
Dishwasher, drill, car door and drum	Shader resolution and surface assignment

The viewer shows the correctly modelled reference beside the broken input. You can browse both as complete USD packages or download their archives. The images use matched cameras and lighting. Some show the result of a recorded physical test, such as a drop or a pull at a handle. The image_state field identifies these observations, while the USD file retains the object's starting pose. The native 3D gallery opens the broken inputs and uses square thumbnails from their observations.

During a repair, the agent receives the broken package, its observation and the repair brief. The reference and grading code belong in the separate verifier environment. Keep the public reference out of the agent's environment when running a model comparison.

Use the data

from datasets import load_dataset

cases = load_dataset(
    "hcltech-robotics/simrepairbench", "cases", split="castaigne25"
)
case = cases[0]
print(case["case_id"], case["title"], case["faults"])

Each row includes the reference and broken images, a list of faults and the repair brief. It also links to both USD entrypoints, the complete archives and the versioned Harbor task. The verification_scope list describes the checks used for that case.

from huggingface_hub import hf_hub_download

archive = hf_hub_download(
    "hcltech-robotics/simrepairbench",
    case["package_path"],
    repo_type="dataset",
)

Extract the whole archive so that textures and other resources keep their relative paths. Open asset.usdc as the broken entrypoint and use visual-observation.png as the initial observation. Read the file named by repair_brief for what the submitted package must restore and preserve. To run the task through Harbor, use the published collection, which includes the agent environment and a separate verifier.

Evaluation

The objective repair reward ranges from 0 to 1. The verifier checks the submitted package and whether healthy content has survived, then measures how much of the error has been corrected. Partial credit follows the repair: surface tasks measure recovered area, while alignment tasks measure the reduction in frame or geometric error. Some checks, such as resolving a shader program, have a categorical outcome. Full reward requires all applicable validity and preservation checks to pass. Repair scoring gives the rules for each kind of error and explains the separate visual and physical assessments.

When comparing models, keep task_revision fixed and record the model settings under model_config_id. Each attempt has its own attempt_id and episode_id, linking the broken input to the recorded actions, submitted USD and grading reports. Objective, visual and physical scores remain separate, with null values for assessments that were not run or could not be measured. The episode format and schema describe how to retain this record of a repair.

CASTAIGNE25 is a public sample, so model comparisons should report any prior exposure to its objects or repair traces. Evaluation on unseen objects requires separate asset lineages, with near-duplicate geometry excluded from training.

Attribution and licence

The objects come from NVIDIA's SimReady asset collection, pinned to revision 4763fbfaa4f8e97658e68b57d01b4c86659e08b5. They retain their Creative Commons Attribution 4.0 licence and package notices. When redistributing them, retain NVIDIA's attribution and notices, and mark packaging changes, injected modelling errors and repairs as modifications. Inclusion in SimRepairBench does not imply NVIDIA endorsement.

Documentation and dataset records use CC BY 4.0, while SimRepairBench software is licensed under MIT. Reused SimReadyBench evaluator code retains its original MIT notice.

Citation

@misc{simrepairbench2026,
  title = {SimRepairBench: CASTAIGNE25 OpenUSD Asset Repair Tasks},
  author = {von Csefalvay, Chris and Doma, Pranav and
            Unni Krishnan, Achyuthan and Foldi, Tamas},
  organization = {HCLTech},
  year = {2026},
  url = {https://huggingface.co/datasets/hcltech-robotics/simrepairbench}
}