| { |
| "@context": { |
| "@language": "en", |
| "@vocab": "https://schema.org/", |
| "citeAs": "cr:citeAs", |
| "column": "cr:column", |
| "conformsTo": "dct:conformsTo", |
| "cr": "http://mlcommons.org/croissant/", |
| "rai": "http://mlcommons.org/croissant/RAI/", |
| "data": { |
| "@id": "cr:data", |
| "@type": "@json" |
| }, |
| "dataType": { |
| "@id": "cr:dataType", |
| "@type": "@vocab" |
| }, |
| "equivalentProperty": { |
| "@id": "cr:equivalentProperty", |
| "@type": "@vocab" |
| }, |
| "samplingRate": "cr:samplingRate", |
| "dct": "http://purl.org/dc/terms/", |
| "examples": { |
| "@id": "cr:examples", |
| "@type": "@json" |
| }, |
| "extract": "cr:extract", |
| "field": "cr:field", |
| "fileProperty": "cr:fileProperty", |
| "fileObject": "cr:fileObject", |
| "fileSet": "cr:fileSet", |
| "format": "cr:format", |
| "includes": "cr:includes", |
| "isLiveDataset": "cr:isLiveDataset", |
| "jsonPath": "cr:jsonPath", |
| "key": "cr:key", |
| "md5": "cr:md5", |
| "parentField": "cr:parentField", |
| "path": "cr:path", |
| "recordSet": "cr:recordSet", |
| "references": "cr:references", |
| "regex": "cr:regex", |
| "repeated": "cr:repeated", |
| "replace": "cr:replace", |
| "sc": "https://schema.org/", |
| "separator": "cr:separator", |
| "source": "cr:source", |
| "subField": "cr:subField", |
| "transform": "cr:transform", |
| "prov": "http://www.w3.org/ns/prov#" |
| }, |
| "@type": "sc:Dataset", |
| "name": "EmbodiedRestore", |
| "description": "First-frame robotic observations rendered in Robosuite/MuJoCo and corrupted by 25 distortions from the TID2013/KADID-10k taxonomy. Each episode provides paired low-quality (LQ) and ground-truth (GT) images from base and wrist cameras, evaluated under three policies (pi0.5, pi0, openvla); per-rollout success and step counts are exposed via results.csv.", |
| "conformsTo": "http://mlcommons.org/croissant/1.0", |
| "url": "https://huggingface.co/datasets/qruisjtu/EmbodiedRestore", |
| "license": "https://creativecommons.org/licenses/by/4.0/", |
| "version": "0.2.0", |
| "datePublished": "2026-05-05", |
| "creator": [ |
| { |
| "@type": "sc:Person", |
| "name": "Rui Qing, Xinpeng Liu, Chunyi Li, Jianbo Zhang, Xiaogang Xu, Xingrui Yu, Ivor Tsang, Guangtao Zhai" |
| } |
| ], |
| "citeAs": "TODO: arXiv / NeurIPS bibtex", |
| "keywords": [ |
| "image restoration", |
| "image quality assessment", |
| "robot learning", |
| "robustness", |
| "Robosuite", |
| "MuJoCo", |
| "TID2013", |
| "KADID-10k", |
| "pi0.5", |
| "pi0", |
| "openvla" |
| ], |
| "rai:dataCollection": "Synthetic data: scenes were rendered in Robosuite (MIT) on top of MuJoCo (Apache-2.0). For every rollout we save the first frame from two cameras (base, wrist), then apply each of 25 distortions (TID2013/KADID-10k taxonomy) to produce LQ images while keeping the original render as GT. Three policies (pi0.5, pi0, openvla) were each evaluated independently, producing parallel image folders and per-distortion result.json files.", |
| "rai:dataCollectionType": "Synthetic", |
| "rai:dataCollectionTimeframe": [ |
| "2026-01-01", |
| "2026-04-30" |
| ], |
| "rai:dataAnnotationProtocol": "No human annotation. Image labels (policy, distort_id, eps, camera, role) are derived deterministically from filenames; rollout outcomes (success / steps / scene materials / target object) come directly from the simulator and are stored verbatim in each result.json (then flattened into results.csv).", |
| "rai:dataAnnotationPlatform": "N/A (programmatic)", |
| "rai:dataAnnotationAnalysis": "N/A", |
| "rai:dataPreprocessingProtocol": [ |
| "Render first-frame RGB from Robosuite scenes at the simulator's native resolution.", |
| "Apply each of 25 distortions with implementations matching TID2013/KADID-10k references.", |
| "Save as PNG; pair LQ/GT per (policy, episode, camera).", |
| "Flatten per-policy per-distortion result.json into results.csv for tabular consumption." |
| ], |
| "rai:dataUseCases": "Primary: benchmarking image restoration / IQA algorithms on robot-observation distributions. Secondary: studying how restoration quality translates to downstream policy success rate across three policies (pi0.5, pi0, openvla); see results.csv for the rollout-level table and the per-distortion result.json files for the raw success_rate plus env_metadata.", |
| "rai:dataLimitations": "[\"Only the first frame of each rollout is kept; no temporal information.\", \"Single simulator (Robosuite/MuJoCo); domain gap to real cameras is not characterized.\", \"Three policies (pi0.5, pi0, openvla) but a single task suite; rankings may not generalize.\", \"Distortion strength is fixed per category; severity sweeps are not provided.\"]", |
| "rai:dataBiases": "[\"Scene/object distribution inherited from the underlying Robosuite tasks.\", \"Class imbalance across distortion families (noise has more categories than blur).\", \"Camera intrinsics and lighting reflect simulator defaults, not real hardware.\"]", |
| "rai:dataSocialImpact": "Low direct social impact: synthetic robotic frames with no humans. Indirect benefit through more robust perception for assistive robotics.", |
| "rai:personalSensitiveInformation": "None. All images are synthetic; no humans, faces, voices, or PII.", |
| "rai:dataReleaseMaintenancePlan": "Hosted at the URL above. The authors will fix metadata issues reported via GitHub issues for at least 2 years post-publication. Versioned releases via tags.", |
| "distribution": [ |
| { |
| "@type": "cr:FileObject", |
| "@id": "manifest.csv", |
| "name": "manifest.csv", |
| "description": "Per-image manifest with policy, distortion id, episode, camera, role.", |
| "contentUrl": "manifest.csv", |
| "encodingFormat": "text/csv", |
| "sha256": "4598fd6319b97d39b9b39f3c01bd5e1ba5672b84a34084148b7d8f97ea55686a" |
| }, |
| { |
| "@type": "cr:FileObject", |
| "@id": "distort_taxonomy.csv", |
| "name": "distort_taxonomy.csv", |
| "description": "Mapping of distort_id to name, family, reference benchmark.", |
| "contentUrl": "distort_taxonomy.csv", |
| "encodingFormat": "text/csv", |
| "sha256": "ad04e20a07b9f6d272a86aa48db9d00bdfe72e7336b58c1f9c8c22063ec613e9" |
| }, |
| { |
| "@type": "cr:FileObject", |
| "@id": "results.csv", |
| "name": "results.csv", |
| "description": "Rollout-level outcomes flattened from every <policy>_DistortOnly/distort_<id>/result.json. Columns: policy, distort_id, distort_name, eps, wall_material, table_material, object, success, steps, success_rate. Joins with manifest.csv on (policy, distort_id, eps).", |
| "contentUrl": "results.csv", |
| "encodingFormat": "text/csv", |
| "sha256": "b58733153c123ea902a2aa82aa9c95e4c6fc6b2a9e15f85247d721fec9b2ae5c" |
| }, |
| { |
| "@type": "cr:FileSet", |
| "@id": "policy_results", |
| "name": "policy_results", |
| "description": "Raw per-(policy, distortion) result.json files containing the distortion-level success_rate plus 100-element env_metadata array.", |
| "encodingFormat": "application/json", |
| "includes": "*_DistortOnly/distort_*/result.json" |
| }, |
| { |
| "@type": "cr:FileSet", |
| "@id": "images", |
| "name": "images", |
| "description": "All PNG frames across 3 policies x 25 distortions x 100 episodes x 4 views.", |
| "encodingFormat": "image/png", |
| "includes": "*_DistortOnly/distort_*/firstframe_distort_*_eps*_*.png" |
| } |
| ], |
| "recordSet": [ |
| { |
| "@type": "cr:RecordSet", |
| "@id": "frames", |
| "name": "frames", |
| "description": "One record per PNG frame.", |
| "key": { |
| "@id": "frames/path" |
| }, |
| "field": [ |
| { |
| "@type": "cr:Field", |
| "@id": "frames/path", |
| "name": "path", |
| "dataType": "sc:Text", |
| "source": { |
| "fileObject": { |
| "@id": "manifest.csv" |
| }, |
| "extract": { |
| "column": "path" |
| } |
| } |
| }, |
| { |
| "@type": "cr:Field", |
| "@id": "frames/policy", |
| "name": "policy", |
| "description": "pi0.5 | pi0 | openvla", |
| "dataType": "sc:Text", |
| "source": { |
| "fileObject": { |
| "@id": "manifest.csv" |
| }, |
| "extract": { |
| "column": "policy" |
| } |
| } |
| }, |
| { |
| "@type": "cr:Field", |
| "@id": "frames/distort_id", |
| "name": "distort_id", |
| "dataType": "sc:Integer", |
| "source": { |
| "fileObject": { |
| "@id": "manifest.csv" |
| }, |
| "extract": { |
| "column": "distort_id" |
| } |
| } |
| }, |
| { |
| "@type": "cr:Field", |
| "@id": "frames/distort_name", |
| "name": "distort_name", |
| "dataType": "sc:Text", |
| "source": { |
| "fileObject": { |
| "@id": "manifest.csv" |
| }, |
| "extract": { |
| "column": "distort_name" |
| } |
| } |
| }, |
| { |
| "@type": "cr:Field", |
| "@id": "frames/eps", |
| "name": "eps", |
| "dataType": "sc:Integer", |
| "source": { |
| "fileObject": { |
| "@id": "manifest.csv" |
| }, |
| "extract": { |
| "column": "eps" |
| } |
| } |
| }, |
| { |
| "@type": "cr:Field", |
| "@id": "frames/camera", |
| "name": "camera", |
| "description": "base | wrist", |
| "dataType": "sc:Text", |
| "source": { |
| "fileObject": { |
| "@id": "manifest.csv" |
| }, |
| "extract": { |
| "column": "camera" |
| } |
| } |
| }, |
| { |
| "@type": "cr:Field", |
| "@id": "frames/role", |
| "name": "role", |
| "description": "lq (distorted) | gt (clean)", |
| "dataType": "sc:Text", |
| "source": { |
| "fileObject": { |
| "@id": "manifest.csv" |
| }, |
| "extract": { |
| "column": "role" |
| } |
| } |
| }, |
| { |
| "@type": "cr:Field", |
| "@id": "frames/image", |
| "name": "image", |
| "dataType": "sc:ImageObject", |
| "source": { |
| "fileSet": { |
| "@id": "images" |
| }, |
| "extract": { |
| "fileProperty": "content" |
| } |
| }, |
| "references": { |
| "fileObject": { |
| "@id": "manifest.csv" |
| }, |
| "extract": { |
| "column": "path" |
| } |
| } |
| } |
| ] |
| }, |
| { |
| "@type": "cr:RecordSet", |
| "@id": "rollouts", |
| "name": "rollouts", |
| "description": "One record per simulated rollout (3 policies x 25 distortions x 100 episodes = 7500 rows). Joins with the frames RecordSet on (policy, distort_id, eps).", |
| "field": [ |
| { |
| "@type": "cr:Field", |
| "@id": "rollouts/policy", |
| "name": "policy", |
| "description": "pi0.5 | pi0 | openvla", |
| "dataType": "sc:Text", |
| "source": { |
| "fileObject": { |
| "@id": "results.csv" |
| }, |
| "extract": { |
| "column": "policy" |
| } |
| } |
| }, |
| { |
| "@type": "cr:Field", |
| "@id": "rollouts/distort_id", |
| "name": "distort_id", |
| "dataType": "sc:Integer", |
| "source": { |
| "fileObject": { |
| "@id": "results.csv" |
| }, |
| "extract": { |
| "column": "distort_id" |
| } |
| } |
| }, |
| { |
| "@type": "cr:Field", |
| "@id": "rollouts/distort_name", |
| "name": "distort_name", |
| "dataType": "sc:Text", |
| "source": { |
| "fileObject": { |
| "@id": "results.csv" |
| }, |
| "extract": { |
| "column": "distort_name" |
| } |
| } |
| }, |
| { |
| "@type": "cr:Field", |
| "@id": "rollouts/eps", |
| "name": "eps", |
| "description": "1..100, episode index in filename convention; env_metadata[i-1] in result.json corresponds to eps i", |
| "dataType": "sc:Integer", |
| "source": { |
| "fileObject": { |
| "@id": "results.csv" |
| }, |
| "extract": { |
| "column": "eps" |
| } |
| } |
| }, |
| { |
| "@type": "cr:Field", |
| "@id": "rollouts/wall_material", |
| "name": "wall_material", |
| "dataType": "sc:Text", |
| "source": { |
| "fileObject": { |
| "@id": "results.csv" |
| }, |
| "extract": { |
| "column": "wall_material" |
| } |
| } |
| }, |
| { |
| "@type": "cr:Field", |
| "@id": "rollouts/table_material", |
| "name": "table_material", |
| "dataType": "sc:Text", |
| "source": { |
| "fileObject": { |
| "@id": "results.csv" |
| }, |
| "extract": { |
| "column": "table_material" |
| } |
| } |
| }, |
| { |
| "@type": "cr:Field", |
| "@id": "rollouts/object", |
| "name": "object", |
| "dataType": "sc:Text", |
| "source": { |
| "fileObject": { |
| "@id": "results.csv" |
| }, |
| "extract": { |
| "column": "object" |
| } |
| } |
| }, |
| { |
| "@type": "cr:Field", |
| "@id": "rollouts/success", |
| "name": "success", |
| "description": "literal 'success' or 'failure' as emitted by the simulator", |
| "dataType": "sc:Text", |
| "source": { |
| "fileObject": { |
| "@id": "results.csv" |
| }, |
| "extract": { |
| "column": "success" |
| } |
| } |
| }, |
| { |
| "@type": "cr:Field", |
| "@id": "rollouts/steps", |
| "name": "steps", |
| "description": "rollout length in env steps; capped at 250 on failure", |
| "dataType": "sc:Integer", |
| "source": { |
| "fileObject": { |
| "@id": "results.csv" |
| }, |
| "extract": { |
| "column": "steps" |
| } |
| } |
| }, |
| { |
| "@type": "cr:Field", |
| "@id": "rollouts/success_rate", |
| "name": "success_rate", |
| "description": "Distortion-level success rate for (policy, distort_id); repeated across all 100 rows of the same group.", |
| "dataType": "sc:Float", |
| "source": { |
| "fileObject": { |
| "@id": "results.csv" |
| }, |
| "extract": { |
| "column": "success_rate" |
| } |
| } |
| } |
| ] |
| } |
| ], |
| "rai:hasSyntheticData": true, |
| "prov:wasDerivedFrom": [ |
| { |
| "@id": "https://drive.google.com/drive/folders/1uU1vnxE5rk-ok8MNgQ1sgFkZB5UWx3nf", |
| "prov:label": "EmbodiedComp", |
| "sc:license": "CC-BY-4.0" |
| } |
| ], |
| "prov:wasGeneratedBy": [ |
| { |
| "@type": "prov:Activity", |
| "prov:type": { |
| "@id": "https://www.wikidata.org/wiki/Q4929239" |
| }, |
| "prov:label": "Scene rendering", |
| "sc:description": "Robotic manipulation scenes were rendered in https://github.com/ARISE-Initiative/robosuite (MIT licence) on top of the\n https://github.com/google-deepmind/mujoco physics engine (Apache-2.0). Each scene varies the wall material, table\n material, and target object drawn from Robosuite's built-in asset library. For every rollout we capture the first\n frame from two simulated cameras: a base (third-person) camera and a wrist (egocentric) camera, at the simulator's\n native resolution.", |
| "prov:wasAttributedTo": [ |
| { |
| "@type": "prov:SoftwareAgent", |
| "@id": "robosuite", |
| "prov:label": "robosuite", |
| "sc:description": "automated rendering pipeline executed by the authors; no human-in-the-loop." |
| } |
| ] |
| }, |
| { |
| "@type": "prov:Activity", |
| "prov:type": { |
| "@id": "https://www.wikidata.org/wiki/Q5227332" |
| }, |
| "prov:label": "Distortion synthesis", |
| "sc:description": "For every captured first frame we apply 25 image distortions drawn from the TID2013 and KADID-10k taxonomies, covering\n ten families: noise, blur, denoising, compression, pattern, structural, intensity_color, color, optical, and\n sampling. Each distortion is implemented as a deterministic image-processing function with a fixed strength per\n category (no severity sweep). The original render is retained as the ground-truth (GT) image; the distorted output is\n the low-quality (LQ) image, paired by (policy, episode, camera). Agent: the authors' Python distortion library,\n executed offline; no learned model is involved in producing the LQ images." |
| }, |
| { |
| "@type": "prov:Activity", |
| "prov:type": { |
| "@id": "https://www.wikidata.org/wiki/Q5227332" |
| }, |
| "prov:label": "Metadata flattening", |
| "sc:description": "A single Python script (build_metadata.py) walks the three policy directories and emits: manifest.csv (one row per\n PNG, 30,000 rows), results.csv (one row per rollout, 7,500 rows, joining manifest.csv on (policy, distort_id, eps)),\n distort_taxonomy.csv, and the Croissant 1.0 + RAI metadata document croissant.json. Labels in manifest.csv (policy,\n distort_id, distort_name, eps, camera, role) are derived deterministically from filenames and directory layout — there\n is no human or model-based annotation step.", |
| "prov:wasAttributedTo": [ |
| { |
| "@type": "prov:SoftwareAgent", |
| "@id": "build_metadata.py", |
| "prov:label": "build_metadata.py", |
| "sc:description": "the authors' code, executed locally" |
| } |
| ] |
| } |
| ] |
| } |
