DexBench
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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
job_name: string
env_id: int64
episode_in_env: int64
seed: int64
success: bool
episode_length: int64
language_instruction: null
timestamp: string
variations: struct<pour_jug.pour_pose: list<item: double>, pour_goblet.pour_pose: list<item: double>, dexbench_r (... 46 chars omitted)
child 0, pour_jug.pour_pose: list<item: double>
child 0, item: double
child 1, pour_goblet.pour_pose: list<item: double>
child 0, item: double
child 2, dexbench_rby1_wuji2_joint.base_pose: list<item: double>
child 0, item: double
progress: struct<overall_score: double, all_complete: bool, criteria_by_name: struct<success: struct<score: do (... 157 chars omitted)
child 0, overall_score: double
child 1, all_complete: bool
child 2, criteria_by_name: struct<success: struct<score: double, is_complete: bool, completed_sequences: int64, total_sequences (... 62 chars omitted)
child 0, success: struct<score: double, is_complete: bool, completed_sequences: int64, total_sequences: int64, active_ (... 45 chars omitted)
child 0, score: double
child 1, is_complete: bool
child 2, completed_sequences: int64
child 3, total_sequences: int64
child 4, active_predicates: struct<default_sequence: string>
child 0, default_sequence: string
child 3, events: list<item: null>
child 0, item: null
hand_contact_material: struct<static_friction: double, dynamic_friction: double, friction_combine_mode: string, enabled: bo (.
...
child 4, parent_subtask_idx: int64
child 5, sequences: struct<default_sequence: list<item: struct<func: string, params: struct<jug_key: string, water: bool (... 4 chars omitted)
child 0, default_sequence: list<item: struct<func: string, params: struct<jug_key: string, water: bool>>>
child 0, item: struct<func: string, params: struct<jug_key: string, water: bool>>
child 0, func: string
child 1, params: struct<jug_key: string, water: bool>
child 0, jug_key: string
child 1, water: bool
child 1, subtasks_are_sequential: bool
child 2, desired_subtask_success_state: list<item: bool>
child 0, item: bool
child 8, policy: null
settle: struct<settled: bool, unsettled: list<item: null>, terminated_by: list<item: string>, lin_vel_thresh (... 39 chars omitted)
child 0, settled: bool
child 1, unsettled: list<item: null>
child 0, item: null
child 2, terminated_by: list<item: string>
child 0, item: string
child 3, lin_vel_threshold: double
child 4, ang_vel_threshold: double
task_object: struct<axis: string, key: string, asset: string, split: string, fit: struct<>, asset_version: string (... 1 chars omitted)
child 0, axis: string
child 1, key: string
child 2, asset: string
child 3, split: string
child 4, fit: struct<>
child 5, asset_version: string
scene_id: string
to
{'scene_id': Value('string'), 'environment_cfg': {'plate_variant': Value('string'), 'plate_diameter_m': Value('null')}, 'task_object': {'axis': Value('string'), 'key': Value('string')}, 'variations': {'dexbench_rby1_wuji2_joint.base_pose': List(Value('float64')), 'table_plate_00.source_stack_pose': List(Value('float64')), 'clearing_plate_00.dest_stack_pose': List(Value('float64'))}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1835, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 621, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
job_name: string
env_id: int64
episode_in_env: int64
seed: int64
success: bool
episode_length: int64
language_instruction: null
timestamp: string
variations: struct<pour_jug.pour_pose: list<item: double>, pour_goblet.pour_pose: list<item: double>, dexbench_r (... 46 chars omitted)
child 0, pour_jug.pour_pose: list<item: double>
child 0, item: double
child 1, pour_goblet.pour_pose: list<item: double>
child 0, item: double
child 2, dexbench_rby1_wuji2_joint.base_pose: list<item: double>
child 0, item: double
progress: struct<overall_score: double, all_complete: bool, criteria_by_name: struct<success: struct<score: do (... 157 chars omitted)
child 0, overall_score: double
child 1, all_complete: bool
child 2, criteria_by_name: struct<success: struct<score: double, is_complete: bool, completed_sequences: int64, total_sequences (... 62 chars omitted)
child 0, success: struct<score: double, is_complete: bool, completed_sequences: int64, total_sequences: int64, active_ (... 45 chars omitted)
child 0, score: double
child 1, is_complete: bool
child 2, completed_sequences: int64
child 3, total_sequences: int64
child 4, active_predicates: struct<default_sequence: string>
child 0, default_sequence: string
child 3, events: list<item: null>
child 0, item: null
hand_contact_material: struct<static_friction: double, dynamic_friction: double, friction_combine_mode: string, enabled: bo (.
...
child 4, parent_subtask_idx: int64
child 5, sequences: struct<default_sequence: list<item: struct<func: string, params: struct<jug_key: string, water: bool (... 4 chars omitted)
child 0, default_sequence: list<item: struct<func: string, params: struct<jug_key: string, water: bool>>>
child 0, item: struct<func: string, params: struct<jug_key: string, water: bool>>
child 0, func: string
child 1, params: struct<jug_key: string, water: bool>
child 0, jug_key: string
child 1, water: bool
child 1, subtasks_are_sequential: bool
child 2, desired_subtask_success_state: list<item: bool>
child 0, item: bool
child 8, policy: null
settle: struct<settled: bool, unsettled: list<item: null>, terminated_by: list<item: string>, lin_vel_thresh (... 39 chars omitted)
child 0, settled: bool
child 1, unsettled: list<item: null>
child 0, item: null
child 2, terminated_by: list<item: string>
child 0, item: string
child 3, lin_vel_threshold: double
child 4, ang_vel_threshold: double
task_object: struct<axis: string, key: string, asset: string, split: string, fit: struct<>, asset_version: string (... 1 chars omitted)
child 0, axis: string
child 1, key: string
child 2, asset: string
child 3, split: string
child 4, fit: struct<>
child 5, asset_version: string
scene_id: string
to
{'scene_id': Value('string'), 'environment_cfg': {'plate_variant': Value('string'), 'plate_diameter_m': Value('null')}, 'task_object': {'axis': Value('string'), 'key': Value('string')}, 'variations': {'dexbench_rby1_wuji2_joint.base_pose': List(Value('float64')), 'table_plate_00.source_stack_pose': List(Value('float64')), 'clearing_plate_00.dest_stack_pose': List(Value('float64'))}}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1379, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 956, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
scene_id string | environment_cfg dict | task_object dict | variations dict |
|---|---|---|---|
h1-Train-000 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
-0.013566077131339598,
-0.008689039535133865,
0.04655532229634846
],
"table_plate_00.source_stack_pose": [
-0.002212652929048795,
-0.0208823114499569,
0
],
"clearing_plate_00.dest_stack_pose": [
-0.018922888542487068,
-0.00798210377505... |
h1-Train-003 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
0.003643172122479965,
-0.02288256980382943,
-0.029851583612282087
],
"table_plate_00.source_stack_pose": [
0.01583617360640331,
0.01652049293353326,
0
],
"clearing_plate_00.dest_stack_pose": [
-0.0014106171219648333,
-0.005629392075346... |
h1-Train-006 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
-0.0038423989376341504,
0.02717631647064337,
-0.008694752170237637
],
"table_plate_00.source_stack_pose": [
-0.004223154411381992,
0.00588057430762944,
0
],
"clearing_plate_00.dest_stack_pose": [
0.005741086800668434,
0.025854393031704... |
h1-Train-009 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
0.02192027930918077,
-0.01835924832977649,
0.07582956637892815
],
"table_plate_00.source_stack_pose": [
-0.024209663981788837,
-0.005683904590733942,
0
],
"clearing_plate_00.dest_stack_pose": [
0.013844949380814646,
-0.0228282363238167... |
h1-Train-012 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
-0.010993094211876836,
-0.01130167131569978,
-0.05269091042143132
],
"table_plate_00.source_stack_pose": [
-0.008327244123244884,
-0.011323682021542585,
0
],
"clearing_plate_00.dest_stack_pose": [
-0.007205308515289735,
0.0196039370803... |
h1-Train-015 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
-0.01982071071459367,
-0.0060038776596601715,
-0.009907390099350993
],
"table_plate_00.source_stack_pose": [
-0.015111748788197361,
-0.011889391218158872,
0
],
"clearing_plate_00.dest_stack_pose": [
0.01502059439829232,
0.0242269785659... |
h1-Train-018 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
-0.022436841803761425,
0.012712362074608618,
0.02390744402162005
],
"table_plate_00.source_stack_pose": [
-0.015777330132204986,
-0.02335694676059202,
0
],
"clearing_plate_00.dest_stack_pose": [
-0.007429212916789399,
-0.01476673958612... |
h1-Train-021 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
-0.009455370927778957,
0.0023708237789810864,
0.0358335547762603
],
"table_plate_00.source_stack_pose": [
-0.0015590060583828423,
0.024434389845481838,
0
],
"clearing_plate_00.dest_stack_pose": [
0.002914953829892239,
-0.00790154376719... |
h1-Train-024 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
-0.02610679714314685,
-0.0012857288501293917,
0.07071186488225578
],
"table_plate_00.source_stack_pose": [
0.002454979331360713,
-0.018313893195319775,
0
],
"clearing_plate_00.dest_stack_pose": [
-0.011230306402496337,
-0.0276164566574... |
h1-Train-027 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
0.016659551134027395,
-0.022735001466963736,
0.0015628933650246434
],
"table_plate_00.source_stack_pose": [
-0.014235054006063216,
0.020918114016556043,
0
],
"clearing_plate_00.dest_stack_pose": [
0.0005639725347801192,
0.0122326402327... |
h1-Train-030 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
0.004690636570964982,
-0.001802427272719126,
0.06905302045936787
],
"table_plate_00.source_stack_pose": [
0.01045018273980741,
0.016903806146575936,
0
],
"clearing_plate_00.dest_stack_pose": [
-0.01953774793524429,
-0.01538624885506797... |
h1-Train-033 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
-0.013440869042019288,
-0.008563163372755317,
0.07714270494624109
],
"table_plate_00.source_stack_pose": [
0.004017855931989912,
-0.0014704354247896876,
0
],
"clearing_plate_00.dest_stack_pose": [
-0.0075227203362200745,
-0.01786520076... |
h1-Train-036 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
0.005953940619075536,
0.010052900041057938,
-0.08168402056646995
],
"table_plate_00.source_stack_pose": [
-0.004242553155144582,
-0.01754608803079112,
0
],
"clearing_plate_00.dest_stack_pose": [
0.015703785612885757,
0.0024960074430671... |
h1-Train-039 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
-0.020723498132695535,
0.01389696494839655,
-0.05071092583870739
],
"table_plate_00.source_stack_pose": [
-0.010258678018562369,
0.01082816608646399,
0
],
"clearing_plate_00.dest_stack_pose": [
0.00040085592024462143,
-0.02791877760305... |
h1-Train-042 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
-0.011561868319207693,
0.008746262479670993,
-0.015036521398395578
],
"table_plate_00.source_stack_pose": [
0.008476329205475898,
0.002135718264622926,
0
],
"clearing_plate_00.dest_stack_pose": [
-0.01987089372307929,
0.014471917285295... |
h1-Train-045 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
0.0018829826141571315,
-0.010411604913564559,
-0.04547245150072802
],
"table_plate_00.source_stack_pose": [
-0.006610785768326324,
0.011240057076837394,
0
],
"clearing_plate_00.dest_stack_pose": [
0.013081147441297871,
0.00618255352023... |
h1-Train-048 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
-0.018576243232447335,
-0.02193752872356067,
0.04077058804437177
],
"table_plate_00.source_stack_pose": [
0.01863083084165706,
-0.010046520826747037,
0
],
"clearing_plate_00.dest_stack_pose": [
0.0069049096783917975,
0.0246029701187503... |
h1-Train-051 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
0.013881960126512135,
0.01336030408153959,
0.0841458544043093
],
"table_plate_00.source_stack_pose": [
-0.02514373925194281,
0.013659716055110162,
0
],
"clearing_plate_00.dest_stack_pose": [
-0.021538736862715884,
-0.002920180640067525... |
h1-Train-054 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
0.015383239961498847,
-0.021834625160525357,
0.01294636788779748
],
"table_plate_00.source_stack_pose": [
0.023645255971577347,
-0.009265387749729204,
0
],
"clearing_plate_00.dest_stack_pose": [
0.014675392172462456,
-0.000692469716211... |
h1-Train-057 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
0.022226666609933183,
-0.004832095260450924,
-0.07882094830309588
],
"table_plate_00.source_stack_pose": [
-0.010441492783634224,
-0.012273188446394474,
0
],
"clearing_plate_00.dest_stack_pose": [
-0.0010375428880305929,
0.025951723999... |
h1-Train-060 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
-0.006628244712935042,
-0.007028465031581566,
-0.012858192441192759
],
"table_plate_00.source_stack_pose": [
-0.01869616899398344,
-0.017545756298175695,
0
],
"clearing_plate_00.dest_stack_pose": [
0.02197165515257191,
-0.0194092481241... |
h1-Train-063 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
-0.004929841873391945,
0.025456062695361912,
-0.02752179705593718
],
"table_plate_00.source_stack_pose": [
-0.005241530229402751,
-0.011586623965536152,
0
],
"clearing_plate_00.dest_stack_pose": [
-0.015258845612159655,
-0.017116247020... |
h1-Train-066 | {
"plate_variant": "harmynta_deep_plate_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "harmynta_deep_plate_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
0.014194609818408503,
-0.012635291970931042,
0.07989375710852675
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-0.024797403294218246,
0.0005473207771921808,
-0.06427430373341433
],
"table_plate_00.source_stack_pose": [
-0.007874898458924175,
-0.023313033392522862,
0
],
"clearing_plate_00.dest_stack_pose": [
0.003315845725913467,
-0.021623728029... |
h1-Train-047 | {
"plate_variant": "ikea_365_bowl_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "ikea_365_bowl_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
-0.001254321632355482,
-0.010626009193047323,
0.07103881532619626
],
"table_plate_00.source_stack_pose": [
0.01643633515996274,
0.02054571862418615,
0
],
"clearing_plate_00.dest_stack_pose": [
0.02211194285479552,
0.018776347721289555,... |
h1-Train-050 | {
"plate_variant": "ikea_365_bowl_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "ikea_365_bowl_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
-0.00756273289931155,
-0.007876478078575158,
-0.02080943951692924
],
"table_plate_00.source_stack_pose": [
0.00837237489347055,
0.011767093936695483,
0
],
"clearing_plate_00.dest_stack_pose": [
-0.018845589880291853,
0.0108999852302921... |
h1-Train-053 | {
"plate_variant": "ikea_365_bowl_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "ikea_365_bowl_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
-0.015159011254086942,
0.016563246947672687,
0.07143038593222165
],
"table_plate_00.source_stack_pose": [
-0.008524310534064163,
0.017057912777526454,
0
],
"clearing_plate_00.dest_stack_pose": [
-0.014344787030478463,
-0.02454233809388... |
h1-Train-056 | {
"plate_variant": "ikea_365_bowl_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "ikea_365_bowl_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
0.0186083233974921,
0.02127813640946486,
-0.07639032340576371
],
"table_plate_00.source_stack_pose": [
-0.0020341144536181175,
-0.00032965958372638455,
0
],
"clearing_plate_00.dest_stack_pose": [
0.012888572090488007,
-0.00184687865635... |
h1-Train-059 | {
"plate_variant": "ikea_365_bowl_22",
"plate_diameter_m": null
} | {
"axis": "plate_variant",
"key": "ikea_365_bowl_22"
} | {
"dexbench_rby1_wuji2_joint.base_pose": [
-0.016762605106904478,
-0.0027331997248663978,
-0.002183265817882239
],
"table_plate_00.source_stack_pose": [
0.0006660935340243156,
-0.01736892358552114,
0
],
"clearing_plate_00.dest_stack_pose": [
-0.020545954729839326,
0.01120205733... |
End of preview.
DexBench GA · eval conditions
The frozen start states every DexBench GA benchmark cell replays, as pinned by DexBench-Arena main. Each row is one episode's initial scene: object choices, poses and the robot's start, sampled once and frozen so every model is scored on the same episodes.
Contents
eval/db_conditions/ holds the row files (JSONL, one start state per line) and, as <set>_frozen.json, copies of DexBench-Arena's benchmark/frozen/ pin files, which list every row file with its SHA-256 and row count.
| Task | Headline cells (120 rows each) | Sensitivity rows |
|---|---|---|
| DB-R1 | Train, GA-O | 200 |
| DB-R2 | Train, GA-O, GA-S, GA-OS | 200 |
| DB-R3 | Train, GA-O, GA-S, GA-OS | 200 |
| DB-L1 | Train, GA-O, GA-S, GA-OS | 200 |
| DB-L2 | Train, GA-O, GA-S, GA-OS | 200 |
| DB-L3 | Train, GA-S | 200 |
| DB-L4 | Train, GA-S, GA-P, GA-SP | 200 |
| DB-M1 | Train, GA-S | 200 |
| DB-M2 | Train, GA-S | 200 |
| DB-M3 | Train, GA-S | 200 |
| DB-M4 | Train, GA-S | 200 |
| DB-M5 | Train, GA-O, GA-S, GA-OS | 200 |
| DB-H1 | Train, GA-O, GA-S, GA-OS | 200 |
| DB-H2 | Train, GA-O, GA-S, GA-OS | 200 |
| DB-H3 | Train, GA-OS | 200 |
46 headline cells (5,520 rows) and 3,000 sensitivity rows, in 963 row files.
Beside the rows, eval/db_conditions/ holds the start-state sets some cells read at every reset, which the rows bind by digest:
| Set | Files | Read by | Bound in each row as |
|---|---|---|---|
db_r1_layouts/eval/ |
240 | DB-R1's cells and sweep (r1_layouts): the 30 settled evaluation layouts of each of the 8 fruit kinds (db_r1_layouts/batch_1/, 320 more, are the collection layouts, which no cell reads) |
task_object.fit.layouts and layouts_sha256, the kind's layout count and digest |
db_m5_piles/ |
617 | DB-M5's cells and sweep (parts_bin_clutter_dir): the settled parts-bin piles of its 15 parts; its own README.md describes them |
parts_bin_clutter_set_sha256 |
db_h3_starts/ |
166 | DB-H3, from DexBench-Arena PR #574 on: the saved in-hand starts of eval/db_h3_inputs/, byte for byte, as a declared start-state set (dexbench_h3_starts_v1) |
task_object.fit.runtime_bundle_sha256 and each row's state_sha256 |
eval/db_h3_inputs/ holds DB-H3's runtime inputs: the saved start states and runtime bundles its rows are drawn from (172 files; its own README.md describes them). DB-H3's utensils are not included; they are asset-pool packages of DexBench-Arena's assets/, which each bundle names with the SHA-256 of every package file.
2,510 files, 212 MB.
Use
DexBench-Arena fetches and checks them for you. From a DexBench-Arena checkout:
./scripts/download_datasets.sh --eval-conditions --repo-id RLWRLD/DexBench-eval
That downloads eval/db_conditions/ and eval/db_h3_inputs/, checks every pinned file's SHA-256 and row count (and, in a checkout with the fix for DexBench-Arena issue #576, the start-state sets' digests) against the checkout's benchmark/frozen/ pins and that DB-H3's inputs reproduce all 240 of its frozen rows before installing anything, and places them in outputs/db_conditions/ and outputs/db_h3_inputs/, where the runner and the scorer read them. The scorer refuses a result that ran on any other rows. Without --repo-id, the same command reads the rows from the revision of RLWRLD/DexBench_datasets in the checkout's reproducibility/datasets.lock.json.
Copied unchanged from RLWRLD/DexBench_datasets at revision 862b71d4 (862b71d45114d857a4d25c2595b9c8098db7996e, the datasets lock on DexBench-Arena main); every file was checked against main's pins before upload.
Changes
2026-11-10: the start-state sets DB-R1, DB-M5 and DB-H3 read (DexBench-Arena issue #576, defect 5)
Revision 7c299134 had the rows but not the sets DB-R1's and DB-M5's cells read beside them (outputs/db_conditions/db_r1_layouts/eval, outputs/db_conditions/db_m5_piles), so no DB-R1 or DB-M5 build could start from a checkout set up from this repository. Revision c6844526 added them (db_r1_layouts/ with its batch_1/ collection layouts, 560 files, and db_m5_piles/, 617), and this revision adds DB-H3's declared start set and this record. No file of c6844526 changed except this README.
A set's digest is the SHA-256 of one <path relative to the set> <file SHA-256> line per file, sorted by path (DexBench-Arena benchmark.frozen.tree_digest). The fix for DexBench-Arena issue #576 pins the first two in benchmark/frozen/inputs.json, and download_datasets.sh --eval-conditions then refuses a download whose sets differ.
| Path | Old SHA-256 | New SHA-256 |
|---|---|---|
eval/db_conditions/db_r1_layouts/eval/ (240 files; added in c6844526) |
(absent at 7c299134) |
16de44a138182d83dd685640be1d6de6accf91cf5f338568b187a76bd767f082 |
eval/db_conditions/db_m5_piles/ (617 files; added in c6844526) |
(absent at 7c299134) |
e161628a33faa8f2c34101d855e283915f13ccaf6fd672faa756ba9316798a46 |
eval/db_conditions/db_h3_starts/ (166 files) |
(absent) | fb63a621049ba01c7b1055842d2dd4a80aad8d866f5aaee45d3ed397a329073c |
eval/db_conditions/db_h3_starts/manifest.json |
(absent) | c8b8a891c8027ecd455724b321a59a9d9adaa6afc37be00b6181ad81370d0977 |
README.md |
56fff6076ecf576cb05b718e595fdfa34dcce9bb597b73a18e5feb9bb3f94008 |
this file |
db_r1_layouts/anddb_m5_piles/are the files ofRLWRLD/DexBench_datasetsat862b71d4(eval/db_conditions/), byte for byte, and the same bytes as the evaluation tree of the GA15 runs. Checked on 2026-11-10: every DB-R1 row file of this repository (Train, GA-O and the sweep, 440 rows) names its kind's layout count and digest as computed from these files, and every layout it names is here; every DB-M5 row file (480 cell rows and the sweep's 200) names its part's pile-set hash as computed from these files (the first 24 seeds by rank,M5_PILE_SEED_QUANTILES);db_m5_piles/SHA256SUMSholds.db_h3_starts/is the conversion ofeval/db_h3_inputs/by DexBench-Arena PR #574'sscripts/tools/migrate_db_h3_starts.py(--source RLWRLD/DexBench_datasets@862b71d45114d857a4d25c2595b9c8098db7996e). Run again on 2026-11-10 against this repository'seval/db_h3_inputs/and row files, it reproduced the uploaded set byte for byte: it checks that the package reproduces every frozen DB-H3 row file and restores every row's start from both and compares them. The bundles and states keep the bytes they have ineval/db_h3_inputs/, which stays as it is. DexBench-Arena reads the set only once PR #574 is merged; until then it is installed and unused.- Not here: the other tasks' collection dumps, which no evaluation cell reads. DB-R3's cells read nothing beside their rows.
License
Creative Commons Attribution 4.0 International (CC BY 4.0); see LICENSE.md.
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