Datasets:
File size: 23,967 Bytes
c468fad 98d1093 c468fad 8167f0d c468fad 8167f0d c468fad 8167f0d 1121d7d c468fad 8167f0d c468fad 8167f0d 98d1093 8167f0d 98d1093 8167f0d 98d1093 8167f0d 98d1093 c468fad | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 | {
"@context": {
"@language": "en",
"@vocab": "https://schema.org/",
"arrayShape": "cr:arrayShape",
"citeAs": "cr:citeAs",
"column": "cr:column",
"conformsTo": "dct:conformsTo",
"containedIn": "cr:containedIn",
"cr": "http://mlcommons.org/croissant/",
"data": {
"@id": "cr:data",
"@type": "@json"
},
"dataBiases": "cr:dataBiases",
"dataCollection": "cr:dataCollection",
"dataType": {
"@id": "cr:dataType",
"@type": "@vocab"
},
"dct": "http://purl.org/dc/terms/",
"extract": "cr:extract",
"field": "cr:field",
"fileProperty": "cr:fileProperty",
"fileObject": "cr:fileObject",
"fileSet": "cr:fileSet",
"format": "cr:format",
"includes": "cr:includes",
"isArray": "cr:isArray",
"isLiveDataset": "cr:isLiveDataset",
"jsonPath": "cr:jsonPath",
"key": "cr:key",
"md5": "cr:md5",
"parentField": "cr:parentField",
"path": "cr:path",
"personalSensitiveInformation": "cr:personalSensitiveInformation",
"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",
"rai": "http://mlcommons.org/croissant/RAI/",
"prov": "http://www.w3.org/ns/prov#"
},
"@type": "sc:Dataset",
"distribution": [
{
"@type": "cr:FileObject",
"@id": "repo",
"name": "repo",
"description": "The Hugging Face git repository.",
"contentUrl": "https://huggingface.co/datasets/ProcessBench-2026/RoboProcessBench/tree/refs%2Fconvert%2Fparquet",
"encodingFormat": "git+https",
"sha256": "https://github.com/mlcommons/croissant/issues/80"
},
{
"@type": "cr:FileSet",
"@id": "parquet-files-for-config-default",
"containedIn": {
"@id": "repo"
},
"encodingFormat": "application/x-parquet",
"includes": "default/*/*.parquet"
}
],
"recordSet": [
{
"@type": "cr:RecordSet",
"dataType": "cr:Split",
"key": {
"@id": "default_splits/split_name"
},
"@id": "default_splits",
"name": "default_splits",
"description": "Splits for the default config.",
"field": [
{
"@type": "cr:Field",
"@id": "default_splits/split_name",
"dataType": "sc:Text"
}
],
"data": [
{
"default_splits/split_name": "train"
},
{
"default_splits/split_name": "eval"
}
]
},
{
"@type": "cr:RecordSet",
"@id": "default",
"description": "ProcessBench-2026/RoboProcessBench - 'default' subset\n\nAdditional information:\n- 2 splits: train, eval",
"field": [
{
"@type": "cr:Field",
"@id": "default/split",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"fileProperty": "fullpath"
},
"transform": {
"regex": "default/(?:partial-)?(train|eval)/.+parquet$"
}
},
"references": {
"field": {
"@id": "default_splits/split_name"
}
}
},
{
"@type": "cr:Field",
"@id": "default/item_id",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "item_id"
}
}
},
{
"@type": "cr:Field",
"@id": "default/split",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "split"
}
}
},
{
"@type": "cr:Field",
"@id": "default/source",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "source"
}
}
},
{
"@type": "cr:Field",
"@id": "default/source_slug",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "source_slug"
}
}
},
{
"@type": "cr:Field",
"@id": "default/source_task_id",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "source_task_id"
}
}
},
{
"@type": "cr:Field",
"@id": "default/source_unit_type",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "source_unit_type"
}
}
},
{
"@type": "cr:Field",
"@id": "default/source_unit_id",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "source_unit_id"
}
}
},
{
"@type": "cr:Field",
"@id": "default/task_id",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "task_id"
}
}
},
{
"@type": "cr:Field",
"@id": "default/task_name",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "task_name"
}
}
},
{
"@type": "cr:Field",
"@id": "default/task_type_legacy",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "task_type_legacy"
}
}
},
{
"@type": "cr:Field",
"@id": "default/input_type",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "input_type"
}
}
},
{
"@type": "cr:Field",
"@id": "default/question",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "question"
}
}
},
{
"@type": "cr:Field",
"@id": "default/choice_A",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "choice_A"
}
}
},
{
"@type": "cr:Field",
"@id": "default/choice_B",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "choice_B"
}
}
},
{
"@type": "cr:Field",
"@id": "default/choice_C",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "choice_C"
}
}
},
{
"@type": "cr:Field",
"@id": "default/choice_D",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "choice_D"
}
}
},
{
"@type": "cr:Field",
"@id": "default/choice_E",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "choice_E"
}
}
},
{
"@type": "cr:Field",
"@id": "default/choice_F",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "choice_F"
}
}
},
{
"@type": "cr:Field",
"@id": "default/answer",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "answer"
}
}
},
{
"@type": "cr:Field",
"@id": "default/answer_text",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "answer_text"
}
}
},
{
"@type": "cr:Field",
"@id": "default/num_choices",
"dataType": "cr:Int64",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "num_choices"
}
}
},
{
"@type": "cr:Field",
"@id": "default/num_frames",
"dataType": "cr:Int64",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "num_frames"
}
}
},
{
"@type": "cr:Field",
"@id": "default/frame_indices_json",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "frame_indices_json"
}
}
},
{
"@type": "cr:Field",
"@id": "default/display_labels_json",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "display_labels_json"
}
}
},
{
"@type": "cr:Field",
"@id": "default/camera",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "camera"
}
}
},
{
"@type": "cr:Field",
"@id": "default/arm_type",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "arm_type"
}
}
},
{
"@type": "cr:Field",
"@id": "default/visual_ref",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "visual_ref"
}
}
},
{
"@type": "cr:Field",
"@id": "default/source_episode_ref",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "source_episode_ref"
}
}
},
{
"@type": "cr:Field",
"@id": "default/reconstruction_key_json",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "reconstruction_key_json"
}
}
},
{
"@type": "cr:Field",
"@id": "default/task_meta_in_source",
"dataType": "sc:Boolean",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "task_meta_in_source"
}
}
},
{
"@type": "cr:Field",
"@id": "default/task_meta_public",
"dataType": "sc:Boolean",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "task_meta_public"
}
}
},
{
"@type": "cr:Field",
"@id": "default/split_version",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "split_version"
}
}
},
{
"@type": "cr:Field",
"@id": "default/split_group_id",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "split_group_id"
}
}
},
{
"@type": "cr:Field",
"@id": "default/builder_version",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "builder_version"
}
}
},
{
"@type": "cr:Field",
"@id": "default/prompt_version",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "prompt_version"
}
}
},
{
"@type": "cr:Field",
"@id": "default/sft_target",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "parquet-files-for-config-default"
},
"extract": {
"column": "sft_target"
}
}
}
]
}
],
"conformsTo": "http://mlcommons.org/croissant/1.1",
"name": "RoboProcessBench",
"description": "\n\t\n\t\t\n\t\tRoboProcessBench\n\t\n\n\n\t\n\t\t\n\t\tDataset Summary\n\t\n\nRoboProcessBench is a process-aware benchmark for vision-language robotic manipulation understanding. It evaluates whether VLMs can infer how a manipulation execution unfolds, including phase, contact, motion, bimanual coordination, primitive-local progress, temporal order, outcome, and primitive-level transitions.\nThis release contains 57,892 QA rows: 48,841 SFT rows and 9,051 evaluation rows across 12 task families and 260 manipulation tasks.… See the full description on the dataset page: https://huggingface.co/datasets/ProcessBench-2026/RoboProcessBench.",
"alternateName": [
"ProcessBench-2026/RoboProcessBench",
"RoboProcessBench"
],
"creator": {
"@type": "Organization",
"name": "ProcessBench-2026",
"url": "https://huggingface.co/ProcessBench-2026"
},
"keywords": [
"visual-question-answering",
"English",
"other",
"10K - 100K",
"parquet",
"Image",
"Tabular",
"Text",
"Datasets",
"pandas",
"Polars",
"Croissant",
"🇺🇸 Region: US",
"robotics",
"embodied-ai",
"benchmark",
"vision-language-models",
"process-understanding",
"manipulation"
],
"license": "https://choosealicense.com/licenses/other/",
"url": "https://huggingface.co/datasets/ProcessBench-2026/RoboProcessBench",
"rai:dataLimitations": "RoboProcessBench evaluates VLM-side process understanding in robotic manipulation, not closed-loop robot control or robot safety. It is derived from GM-100, RH20T, REASSEMBLE, and AIST-Bimanual, so source coverage, viewpoints, embodiments, sensing modalities, and task-family support are heterogeneous. Not every source supports every task family, and primitive-aware tasks depend on available primitive annotations. The benchmark uses multiple-choice QA and should be interpreted with task-level scores, random/majority baselines, human audit, and bootstrap CIs. It is not recommended for robot safety certification, deployment readiness claims, generic VLM ranking, or causal claims about downstream VLA policy improvement without additional validation.",
"rai:dataBiases": "RoboProcessBench inherits biases from its four upstream robotic datasets, including robot embodiment, camera viewpoint, tabletop scene layout, object category, task-family coverage, sensing availability, annotation granularity, and success/failure distribution. Some process cues are available only for sources with suitable native signals or annotations, so task-family coverage is uneven. Labels are generated by deterministic builders from source-native signals, and label quality depends on upstream signal resolution, annotation quality, and calibration choices. Models may learn source-specific visual or embodiment priors if results are not interpreted by task and source.",
"rai:personalSensitiveInformation": "None of the listed sensitive categories are intentionally included.",
"rai:dataUseCases": "RoboProcessBench measures process-aware VLM understanding in robotic manipulation: whether a model can infer how an execution is unfolding from visual observations. It covers phase, contact, motion, coordination, primitive-local progress, temporal order, outcome, and primitive-level transitions. Validated use cases include diagnostic VLM evaluation, task-family-level failure analysis, and process-aware VLM post-training, supported by deterministic GT builders, strict split isolation, human audit, random/majority baselines, and bootstrap CIs. Not validated: closed-loop robot policy evaluation, robot safety certification, deployment readiness, generic VLM ranking, fairness auditing of human populations, or causal claims about downstream VLA improvement.",
"rai:dataSocialImpact": "RoboProcessBench can improve transparency around VLM failure modes in robotic manipulation by evaluating process-level cues beyond final success. It may support research on visual critics, progress monitors, failure detectors, and process-aware VLM evaluators. The main risk is over-interpreting benchmark accuracy as evidence of safe or reliable robot behavior. Mitigations include intended-use and out-of-scope statements, task-level reporting, random/majority baselines, human audit summaries, bootstrap CIs, and reconstruction notes. Full upstream raw videos are not redistributed, and downstream robot deployment requires additional validation.",
"rai:hasSyntheticData": false,
"prov:wasDerivedFrom": [
{
"@id": "https://huggingface.co/datasets/rhos-ai/gm100-cobotmagic-lerobot",
"prov:label": "GM-100",
"sc:license": "MIT"
},
{
"@id": "https://rh20t.github.io/#download",
"prov:label": "RH20T",
"sc:license": "CC BY-SA 4.0 & CC BY-NC 4.0"
},
{
"@id": "https://researchdata.tuwien.ac.at/records/0ewrv-8cb44",
"prov:label": "REASSEMBLE",
"sc:license": "CC BY 4.0"
},
{
"@id": "https://aistairc.github.io/aist_bimanip_site/dataset.html",
"prov:label": "AIST-Bimanual",
"sc:license": "CC BY 4.0"
}
],
"prov:wasGeneratedBy": [
{
"@type": "prov:Activity",
"prov:type": {
"@id": "https://www.wikidata.org/wiki/Q4929239"
},
"prov:label": "Source data collection",
"sc:description": "RoboProcessBench is derived from existing robotic manipulation datasets: GM-100, RH20T, REASSEMBLE, and AIST-Bimanual. The original robotic trajectories, videos, sensor signals, task metadata, primitive annotations, and success/failure records were collected by the upstream dataset creators. ProcessBench does not recollect raw robot trajectories and does not redistribute full upstream raw videos or full frame dumps."
},
{
"@type": "prov:Activity",
"prov:type": {
"@id": "https://www.wikidata.org/wiki/Q5227332"
},
"prov:label": "Source-aware preprocessing",
"sc:description": "We standardize source metadata into a unified RoboProcessBench schema with item IDs, source identifiers, task-family IDs, questions, answer choices, GT answers, visual references, reconstruction keys, split identifiers, builder versions, and prompt versions. Source-specific fields are preserved as reconstruction metadata when needed. Samples with unstable source references, missing visual inputs, or ambiguous local decision units are filtered."
},
{
"@type": "prov:Activity",
"prov:type": {
"@id": "https://www.wikidata.org/wiki/Q109719325"
},
"prov:label": "Ground-truth construction",
"sc:description": "Ground-truth labels are generated by deterministic, source-aware builders from dataset-native signals and annotations, including timestamps, force/torque, gripper state, TCP or motion statistics, stage/segment annotations, primitive chains, and success/failure records. The released task families cover phase, contact, motion direction, bimanual coordination, primitive-local progress, motion state, outcome prediction, temporal ordering, temporal priority, current primitive, next primitive, and primitive-chain restoration. Source-native signals used for GT construction are not exposed to evaluated models."
},
{
"@type": "prov:Activity",
"prov:type": {
"@id": "https://www.wikidata.org/wiki/Q5227332"
},
"prov:label": "Split generation",
"sc:description": "We generate SFT and evaluation splits with strict episode / recording / scene isolation to avoid leakage across training and evaluation. The released split contains 48,841 SFT items and 9,051 evaluation items across 12 task families and 260 manipulation tasks. Split metadata and source-level statistics are included in the release."
},
{
"@type": "prov:Activity",
"prov:type": {
"@id": "https://www.wikidata.org/wiki/Q3306762"
},
"prov:label": "Human reliability audit",
"sc:description": "Human reliability audit is used only for benchmark validation and quality control, not for dense frame-wise relabeling. Two annotators answer sampled benchmark items using the same rendered visual input, question, and choices shown to models. The audit checks visual answerability and supports interpretation of task difficulty, including temporal reasoning tasks. No personal or behavioral data about annotators is collected."
}
]
} |