The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
algorithm: string
method: string
quantiles: list<item: double>
child 0, item: double
dataset_files: struct<data/chunk-000/file-000.parquet: string>
child 0, data/chunk-000/file-000.parquet: string
sample_scope: string
features: struct<observation.state: struct<samples: int64, dimensions: int64, zero_quantile_range_dimensions: (... 1445 chars omitted)
child 0, observation.state: struct<samples: int64, dimensions: int64, zero_quantile_range_dimensions: list<item: null>, outside_ (... 55 chars omitted)
child 0, samples: int64
child 1, dimensions: int64
child 2, zero_quantile_range_dimensions: list<item: null>
child 0, item: null
child 3, outside_q01_q99_fraction: list<item: double>
child 0, item: double
child 4, clipping: string
child 1, action: struct<samples: int64, dimensions: int64, zero_quantile_range_dimensions: list<item: null>, outside_ (... 55 chars omitted)
child 0, samples: int64
child 1, dimensions: int64
child 2, zero_quantile_range_dimensions: list<item: null>
child 0, item: null
child 3, outside_q01_q99_fraction: list<item: double>
child 0, item: double
child 4, clipping: string
child 2, retime.source_seed: struct<samples: int64, dimensions: int64, zero_quantile_range_dimensions: list<item: null>, outside_ (... 55 chars omitted)
child 0, samples: int64
child 1, dimensions: int64
child 2, zero_quantile_range_dimensions: list<item: null>
...
ng: string
child 8, task_index: struct<samples: int64, dimensions: int64, zero_quantile_range_dimensions: list<item: int64>, outside (... 56 chars omitted)
child 0, samples: int64
child 1, dimensions: int64
child 2, zero_quantile_range_dimensions: list<item: int64>
child 0, item: int64
child 3, outside_q01_q99_fraction: list<item: double>
child 0, item: double
child 4, clipping: string
stats_sha256: string
candidate_overlap: list<item: null>
child 0, item: null
total_planner_attempts: int64
training_seeds: list<item: int64>
child 0, item: int64
attempted: int64
initial_failed_seeds: list<item: int64>
child 0, item: int64
accepted: int64
retry_history: list<item: struct<status: string, seed: int64, slot: int64, attempt_index: int64, planner_seed: int6 (... 57 chars omitted)
child 0, item: struct<status: string, seed: int64, slot: int64, attempt_index: int64, planner_seed: int64, error: s (... 45 chars omitted)
child 0, status: string
child 1, seed: int64
child 2, slot: int64
child 3, attempt_index: int64
child 4, planner_seed: int64
child 5, error: string
child 6, type: string
child 7, raw_hdf5_sha256: string
retry_policy: string
test_suite: string
test_overlap: list<item: null>
child 0, item: null
failures: list<item: null>
child 0, item: null
replacement_seeds: bool
excluded_candidate_seeds: list<item: int64>
child 0, item: int64
initial_attempts_per_scene: int64
to
{'attempted': Value('int64'), 'accepted': Value('int64'), 'failures': List(Value('null')), 'replacement_seeds': Value('bool'), 'initial_attempts_per_scene': Value('int64'), 'initial_failed_seeds': List(Value('int64')), 'retry_history': List({'status': Value('string'), 'seed': Value('int64'), 'slot': Value('int64'), 'attempt_index': Value('int64'), 'planner_seed': Value('int64'), 'error': Value('string'), 'type': Value('string'), 'raw_hdf5_sha256': Value('string')}), 'total_planner_attempts': Value('int64'), 'retry_policy': Value('string'), 'training_seeds': List(Value('int64')), 'excluded_candidate_seeds': List(Value('int64')), 'test_suite': Value('string'), 'test_overlap': List(Value('null')), 'candidate_overlap': List(Value('null'))}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
algorithm: string
method: string
quantiles: list<item: double>
child 0, item: double
dataset_files: struct<data/chunk-000/file-000.parquet: string>
child 0, data/chunk-000/file-000.parquet: string
sample_scope: string
features: struct<observation.state: struct<samples: int64, dimensions: int64, zero_quantile_range_dimensions: (... 1445 chars omitted)
child 0, observation.state: struct<samples: int64, dimensions: int64, zero_quantile_range_dimensions: list<item: null>, outside_ (... 55 chars omitted)
child 0, samples: int64
child 1, dimensions: int64
child 2, zero_quantile_range_dimensions: list<item: null>
child 0, item: null
child 3, outside_q01_q99_fraction: list<item: double>
child 0, item: double
child 4, clipping: string
child 1, action: struct<samples: int64, dimensions: int64, zero_quantile_range_dimensions: list<item: null>, outside_ (... 55 chars omitted)
child 0, samples: int64
child 1, dimensions: int64
child 2, zero_quantile_range_dimensions: list<item: null>
child 0, item: null
child 3, outside_q01_q99_fraction: list<item: double>
child 0, item: double
child 4, clipping: string
child 2, retime.source_seed: struct<samples: int64, dimensions: int64, zero_quantile_range_dimensions: list<item: null>, outside_ (... 55 chars omitted)
child 0, samples: int64
child 1, dimensions: int64
child 2, zero_quantile_range_dimensions: list<item: null>
...
ng: string
child 8, task_index: struct<samples: int64, dimensions: int64, zero_quantile_range_dimensions: list<item: int64>, outside (... 56 chars omitted)
child 0, samples: int64
child 1, dimensions: int64
child 2, zero_quantile_range_dimensions: list<item: int64>
child 0, item: int64
child 3, outside_q01_q99_fraction: list<item: double>
child 0, item: double
child 4, clipping: string
stats_sha256: string
candidate_overlap: list<item: null>
child 0, item: null
total_planner_attempts: int64
training_seeds: list<item: int64>
child 0, item: int64
attempted: int64
initial_failed_seeds: list<item: int64>
child 0, item: int64
accepted: int64
retry_history: list<item: struct<status: string, seed: int64, slot: int64, attempt_index: int64, planner_seed: int6 (... 57 chars omitted)
child 0, item: struct<status: string, seed: int64, slot: int64, attempt_index: int64, planner_seed: int64, error: s (... 45 chars omitted)
child 0, status: string
child 1, seed: int64
child 2, slot: int64
child 3, attempt_index: int64
child 4, planner_seed: int64
child 5, error: string
child 6, type: string
child 7, raw_hdf5_sha256: string
retry_policy: string
test_suite: string
test_overlap: list<item: null>
child 0, item: null
failures: list<item: null>
child 0, item: null
replacement_seeds: bool
excluded_candidate_seeds: list<item: int64>
child 0, item: int64
initial_attempts_per_scene: int64
to
{'attempted': Value('int64'), 'accepted': Value('int64'), 'failures': List(Value('null')), 'replacement_seeds': Value('bool'), 'initial_attempts_per_scene': Value('int64'), 'initial_failed_seeds': List(Value('int64')), 'retry_history': List({'status': Value('string'), 'seed': Value('int64'), 'slot': Value('int64'), 'attempt_index': Value('int64'), 'planner_seed': Value('int64'), 'error': Value('string'), 'type': Value('string'), 'raw_hdf5_sha256': Value('string')}), 'total_planner_attempts': Value('int64'), 'retry_policy': Value('string'), 'training_seeds': List(Value('int64')), 'excluded_candidate_seeds': List(Value('int64')), 'test_suite': Value('string'), 'test_overlap': List(Value('null')), 'candidate_overlap': List(Value('null'))}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Scan Object — Original native expert, fixed training scenes
50 accepted trajectories, 11238 state/action samples,
from the existing50 training scenes. The initial attempt accepted47/50; user-authorized planner-only retries recovered the remaining3 without replacing any scene.
This is a newly simulated Original dataset using official RoboTwin
scan_object.play_once and unchanged native motion controls. It is not a renamed
Concurrent, timed, CTR or Mixed dataset. IdleMask is disabled; no idle-mask
features are included.
Coverage and failures
Attempted:50. Accepted:50. Failed:0.
Failed scene seeds: None. No replacement seeds were used. Planner-only retry attempts: 5; total planning attempts including the initial50: 55. Every retry holds the scene seed, object identity, initial snapshot and native expert geometry fixed; only planner random seeds vary, stopping at first success.
See meta/collection_summary.json for exact ordered training seeds and failures.
The50-scene training cohort is disjoint from E257 Scan Native100 and its complete
300-candidate pool (400001–400300), as selected by the user.
All50 fixed training scenes are represented by one successful trajectory each. Initial failures and all retry outcomes remain recorded.
Capture and schema
Aloha AgileX,250Hz physics,25FPS, synchronized320×240 head and dual wrist RGB.
Both wrists use centered_fovy90,90° vertical FOV and the calibrated centered
optical-axis mount, RT32 rendering. Actual per-frame intrinsics and mount
transforms were validated. Original scene actor configurations, poses and robot
qpos match the existing parent snapshot with absolute tolerance1e-7 before
planning. Native grasp/lift/object-position/scan stages are unchanged.
observation.state records measured joint angles and measured normalized gripper
positions. action is the control target at the next sample: state[t] → target[t+1].
The final observation supplies a target/validation frame and is not an extra
training row. Three video streams decode completely and were checked against
per-episode frame timestamps. LeRobot v3 numeric rows match raw HDF5 exactly
at float32 precision. Source seed and slot are stored for each row.
Numeric quantiles in meta/stats.json use exact global per-feature samples with
NumPy linear interpolation, not averages of episode quantiles. See
meta/global_quantile_receipt.json for sample identities, constant dimensions
and out-of-range fractions. These are dataset metadata, not trained-model
normalization assets. No training or policy evaluation was performed.
Provenance
Experiment E351; R001 recorded the first16 attempts, and R002 continued only the remaining34 unattempted scenes using two processes. Initial failures were75/76/151. R003 then retried only those three under explicit user authorization, preserving all47 existing successful trajectories. All raw data and Run receipts are preserved on the collection host; the published tree contains the qualified LeRobot dataset.
Official upstream RoboTwin: c3ddfa8b97d5519efa828b075999bd0006778e5e. Native task source SHA256: 860dfe2cc86d180096089bc87502c7904f7ce2efb64fb32620c1720ca0178c2b. Initial capture source commit: ded2cfb6ccf2152e17dbc393df3dacd7f6d351e4. Retry capture source commit:9d792fa15436d001790a70f44fab757aeb6f2fcd. Actual left/right IK and graph planner seed binding was checked for retries. Input snapshot metadata SHA256: a18f9069087c62d4db657f6a80afdf5d8a85ac24088b9db80af04c3e994ebe47. The revision marker and SHA256SUMS identify the delivered files.
Lifecycle: reported → audit pending. Automated qualification passed; this does not claim user-approved archival/confirmation or learned-policy success.
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