The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
repository_id: string
error: string
episode_verdicts: null
report: struct<id: string, generated_at: string, calibra_version: string, status: string>
child 0, id: string
child 1, generated_at: string
child 2, calibra_version: string
child 3, status: string
results: struct<overall: struct<score: double, grade: string, confidence: double, certification: string, crit (... 4814 chars omitted)
child 0, overall: struct<score: double, grade: string, confidence: double, certification: string, critical_failures: l (... 18 chars omitted)
child 0, score: double
child 1, grade: string
child 2, confidence: double
child 3, certification: string
child 4, critical_failures: list<item: string>
child 0, item: string
child 1, dimensions: struct<temporal_integrity: struct<score: double, weight: double, metrics: struct<timestamp_jitter_cv (... 4231 chars omitted)
child 0, temporal_integrity: struct<score: double, weight: double, metrics: struct<timestamp_jitter_cv: struct<value: double, uni (... 458 chars omitted)
child 0, score: double
child 1, weight: double
child 2, metrics: struct<timestamp_jitter_cv: struct<value: double, unit: string, score: double, ci_lower: double, ci_ (... 410 chars omitted)
child 0, timestamp_jitter_cv: struct<value: double, unit: string, score: double, ci_lower: double, ci_upper: double, ci_level: dou (... 44 chars omitted)
child 0, value: double
...
d 0, behavior_cloning: struct<status: string, reason: null>
child 0, status: string
child 1, reason: null
child 1, act: struct<status: string, reason: null>
child 0, status: string
child 1, reason: null
child 2, diffusion_policy: struct<status: string, reason: null>
child 0, status: string
child 1, reason: null
child 3, gr00t: struct<status: string, reason: null>
child 0, status: string
child 1, reason: null
episode_hashes: struct<>
dataset: struct<provider: string, repository_id: string, revision: string, dataset_format: string, license: n (... 102 chars omitted)
child 0, provider: string
child 1, repository_id: string
child 2, revision: string
child 3, dataset_format: string
child 4, license: null
child 5, homepage: null
child 6, episodes_total: int64
child 7, episodes_audited: int64
child 8, frames_total: int64
child 9, robot: null
schema_version: string
audit: struct<profile: null, configuration_hash: string, scoring_rubric: string, sampling: struct<mode: str (... 90 chars omitted)
child 0, profile: null
child 1, configuration_hash: string
child 2, scoring_rubric: string
child 3, sampling: struct<mode: string, seed: null, fraction: double>
child 0, mode: string
child 1, seed: null
child 2, fraction: double
child 4, environment: struct<python: string, platform: string>
child 0, python: string
child 1, platform: string
to
{'schema_version': Value('string'), 'report': {'id': Value('string'), 'generated_at': Value('string'), 'calibra_version': Value('string'), 'status': Value('string')}, 'dataset': {'provider': Value('string'), 'repository_id': Value('string'), 'revision': Value('string'), 'dataset_format': Value('string'), 'license': Value('null'), 'homepage': Value('null'), 'episodes_total': Value('int64'), 'episodes_audited': Value('int64'), 'frames_total': Value('int64'), 'robot': Value('null')}, 'audit': {'profile': Value('null'), 'configuration_hash': Value('string'), 'scoring_rubric': Value('string'), 'sampling': {'mode': Value('string'), 'seed': Value('null'), 'fraction': Value('float64')}, 'environment': {'python': Value('string'), 'platform': Value('string')}}, 'results': {'overall': {'score': Value('float64'), 'grade': Value('string'), 'confidence': Value('float64'), 'certification': Value('string'), 'critical_failures': List(Value('string'))}, 'dimensions': {'temporal_integrity': {'score': Value('float64'), 'weight': Value('float64'), 'metrics': {'timestamp_jitter_cv': {'value': Value('float64'), 'unit': Value('string'), 'score': Value('float64'), 'ci_lower': Value('float64'), 'ci_upper': Value('float64'), 'ci_level': Value('float64'), 'ci_method': Value('string'), 'methodology': Value('string')}, 'timestamp_dropout_rate': {'value': Value('float64'), 'unit': Value('string'), 'score': Value('float64'), 'ci_lower': Value('float64'), 'ci_upper': Value('float64'), 'ci_level': Value('floa
...
e('float64'), 'ci_method': Value('string'), 'methodology': Value('string')}, 'dynamics_predictability_r2': {'value': Value('float64'), 'unit': Value('string'), 'score': Value('float64'), 'ci_lower': Value('null'), 'ci_upper': Value('null'), 'ci_level': Value('float64'), 'ci_method': Value('string'), 'methodology': Value('string')}, 'causal_action_effect_mi': {'value': Value('float64'), 'unit': Value('string'), 'score': Value('float64'), 'ci_lower': Value('null'), 'ci_upper': Value('null'), 'ci_level': Value('float64'), 'ci_method': Value('string'), 'methodology': Value('string')}, 'outlier_transition_fraction': {'value': Value('float64'), 'unit': Value('string'), 'score': Value('float64'), 'ci_lower': Value('null'), 'ci_upper': Value('null'), 'ci_level': Value('float64'), 'ci_method': Value('string'), 'methodology': Value('string')}}}}, 'findings': List({'severity': Value('string'), 'code': Value('string'), 'metric': Value('string'), 'message': Value('string'), 'implication': Value('string'), 'affected_fraction': Value('float64'), 'observed_value': Value('float64'), 'observed_unit': Value('string'), 'threshold': Value('float64')}), 'recommendations': {'behavior_cloning': {'status': Value('string'), 'reason': Value('null')}, 'act': {'status': Value('string'), 'reason': Value('null')}, 'diffusion_policy': {'status': Value('string'), 'reason': Value('null')}, 'gr00t': {'status': Value('string'), 'reason': Value('null')}}}, 'episode_verdicts': Value('null'), 'episode_hashes': {}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
repository_id: string
error: string
episode_verdicts: null
report: struct<id: string, generated_at: string, calibra_version: string, status: string>
child 0, id: string
child 1, generated_at: string
child 2, calibra_version: string
child 3, status: string
results: struct<overall: struct<score: double, grade: string, confidence: double, certification: string, crit (... 4814 chars omitted)
child 0, overall: struct<score: double, grade: string, confidence: double, certification: string, critical_failures: l (... 18 chars omitted)
child 0, score: double
child 1, grade: string
child 2, confidence: double
child 3, certification: string
child 4, critical_failures: list<item: string>
child 0, item: string
child 1, dimensions: struct<temporal_integrity: struct<score: double, weight: double, metrics: struct<timestamp_jitter_cv (... 4231 chars omitted)
child 0, temporal_integrity: struct<score: double, weight: double, metrics: struct<timestamp_jitter_cv: struct<value: double, uni (... 458 chars omitted)
child 0, score: double
child 1, weight: double
child 2, metrics: struct<timestamp_jitter_cv: struct<value: double, unit: string, score: double, ci_lower: double, ci_ (... 410 chars omitted)
child 0, timestamp_jitter_cv: struct<value: double, unit: string, score: double, ci_lower: double, ci_upper: double, ci_level: dou (... 44 chars omitted)
child 0, value: double
...
d 0, behavior_cloning: struct<status: string, reason: null>
child 0, status: string
child 1, reason: null
child 1, act: struct<status: string, reason: null>
child 0, status: string
child 1, reason: null
child 2, diffusion_policy: struct<status: string, reason: null>
child 0, status: string
child 1, reason: null
child 3, gr00t: struct<status: string, reason: null>
child 0, status: string
child 1, reason: null
episode_hashes: struct<>
dataset: struct<provider: string, repository_id: string, revision: string, dataset_format: string, license: n (... 102 chars omitted)
child 0, provider: string
child 1, repository_id: string
child 2, revision: string
child 3, dataset_format: string
child 4, license: null
child 5, homepage: null
child 6, episodes_total: int64
child 7, episodes_audited: int64
child 8, frames_total: int64
child 9, robot: null
schema_version: string
audit: struct<profile: null, configuration_hash: string, scoring_rubric: string, sampling: struct<mode: str (... 90 chars omitted)
child 0, profile: null
child 1, configuration_hash: string
child 2, scoring_rubric: string
child 3, sampling: struct<mode: string, seed: null, fraction: double>
child 0, mode: string
child 1, seed: null
child 2, fraction: double
child 4, environment: struct<python: string, platform: string>
child 0, python: string
child 1, platform: string
to
{'schema_version': Value('string'), 'report': {'id': Value('string'), 'generated_at': Value('string'), 'calibra_version': Value('string'), 'status': Value('string')}, 'dataset': {'provider': Value('string'), 'repository_id': Value('string'), 'revision': Value('string'), 'dataset_format': Value('string'), 'license': Value('null'), 'homepage': Value('null'), 'episodes_total': Value('int64'), 'episodes_audited': Value('int64'), 'frames_total': Value('int64'), 'robot': Value('null')}, 'audit': {'profile': Value('null'), 'configuration_hash': Value('string'), 'scoring_rubric': Value('string'), 'sampling': {'mode': Value('string'), 'seed': Value('null'), 'fraction': Value('float64')}, 'environment': {'python': Value('string'), 'platform': Value('string')}}, 'results': {'overall': {'score': Value('float64'), 'grade': Value('string'), 'confidence': Value('float64'), 'certification': Value('string'), 'critical_failures': List(Value('string'))}, 'dimensions': {'temporal_integrity': {'score': Value('float64'), 'weight': Value('float64'), 'metrics': {'timestamp_jitter_cv': {'value': Value('float64'), 'unit': Value('string'), 'score': Value('float64'), 'ci_lower': Value('float64'), 'ci_upper': Value('float64'), 'ci_level': Value('float64'), 'ci_method': Value('string'), 'methodology': Value('string')}, 'timestamp_dropout_rate': {'value': Value('float64'), 'unit': Value('string'), 'score': Value('float64'), 'ci_lower': Value('float64'), 'ci_upper': Value('float64'), 'ci_level': Value('floa
...
e('float64'), 'ci_method': Value('string'), 'methodology': Value('string')}, 'dynamics_predictability_r2': {'value': Value('float64'), 'unit': Value('string'), 'score': Value('float64'), 'ci_lower': Value('null'), 'ci_upper': Value('null'), 'ci_level': Value('float64'), 'ci_method': Value('string'), 'methodology': Value('string')}, 'causal_action_effect_mi': {'value': Value('float64'), 'unit': Value('string'), 'score': Value('float64'), 'ci_lower': Value('null'), 'ci_upper': Value('null'), 'ci_level': Value('float64'), 'ci_method': Value('string'), 'methodology': Value('string')}, 'outlier_transition_fraction': {'value': Value('float64'), 'unit': Value('string'), 'score': Value('float64'), 'ci_lower': Value('null'), 'ci_upper': Value('null'), 'ci_level': Value('float64'), 'ci_method': Value('string'), 'methodology': Value('string')}}}}, 'findings': List({'severity': Value('string'), 'code': Value('string'), 'metric': Value('string'), 'message': Value('string'), 'implication': Value('string'), 'affected_fraction': Value('float64'), 'observed_value': Value('float64'), 'observed_unit': Value('string'), 'threshold': Value('float64')}), 'recommendations': {'behavior_cloning': {'status': Value('string'), 'reason': Value('null')}, 'act': {'status': Value('string'), 'reason': Value('null')}, 'diffusion_policy': {'status': Value('string'), 'reason': Value('null')}, 'gr00t': {'status': Value('string'), 'reason': Value('null')}}}, 'episode_verdicts': Value('null'), 'episode_hashes': {}}
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.
Calibra Robot Dataset Quality Benchmark
Automated quality audits of 30 public LeRobot datasets, produced by Calibra.
Mean health score: 85.8 / 100 across 30 datasets (206–800 episodes each).
Key finding: every audited dataset has at least one critical quality flag, confirming that silent data issues are the norm, not the exception, in public robot demonstration corpora.
Calibra Community Dataset Quality Benchmark
Generated 2026-07-27 19:07 UTC by Calibra
Audited 30 public LeRobot datasets. 1 datasets failed to load.
Leaderboard
| # | Dataset | Score | Grade | Certification | Episodes | Frames | Critical |
|---|---|---|---|---|---|---|---|
| 1 | aloha_mobile_elevator | 91.9 | A | ✗ Not Certified | 20 | 45,000 | 1 |
| 2 | aloha_mobile_wipe_wine | 91.9 | A | ✗ Not Certified | 50 | 65,000 | 1 |
| 3 | aloha_mobile_shrimp | 91.3 | A | ✗ Not Certified | 18 | 67,500 | 1 |
| 4 | aloha_static_coffee | 90.3 | A | ✗ Not Certified | 50 | 55,000 | 1 |
| 5 | aloha_mobile_chair | 89.7 | B | ✗ Not Certified | 55 | 110,000 | 1 |
| 6 | aloha_static_candy | 89.3 | B | ✗ Not Certified | 50 | 35,000 | 1 |
| 7 | aloha_mobile_wash_pan | 89.3 | B | ✗ Not Certified | 50 | 55,000 | 1 |
| 8 | aloha_static_screw_driver | 88.9 | B | ✗ Not Certified | 50 | 20,000 | 1 |
| 9 | aloha_static_towel | 88.7 | B | ✗ Not Certified | 50 | 25,000 | 1 |
| 10 | aloha_mobile_cabinet | 88.7 | B | ✗ Not Certified | 85 | 127,500 | 1 |
| 11 | aloha_static_ziploc_slide | 88.4 | B | ✗ Not Certified | 56 | 16,800 | 2 |
| 12 | aloha_static_pingpong_test | 88.3 | B | ✗ Not Certified | 10 | 6,000 | 1 |
| 13 | aloha_static_battery | 87.9 | B | ✗ Not Certified | 49 | 29,400 | 2 |
| 14 | aloha_sim_insertion_human | 87.3 | B | ✗ Not Certified | 50 | 25,000 | 2 |
| 15 | aloha_static_fork_pick_up | 87.3 | B | ✗ Not Certified | 100 | 60,000 | 2 |
| 16 | aloha_static_tape | 86.6 | B | ✗ Not Certified | 50 | 35,000 | 2 |
| 17 | aloha_static_thread_velcro | 86.6 | B | ✗ Not Certified | 49 | 34,300 | 2 |
| 18 | aloha_static_cups_open | 86.2 | B | ✗ Not Certified | 50 | 20,000 | 2 |
| 19 | aloha_sim_transfer_cube_human | 86.0 | B | ✗ Not Certified | 50 | 20,000 | 1 |
| 20 | xarm_push_medium_replay | 85.3 | B | ✗ Not Certified | 800 | 20,000 | 2 |
| 21 | aloha_sim_insertion_scripted | 84.7 | B | ✗ Not Certified | 50 | 20,000 | 2 |
| 22 | xarm_lift_medium | 83.9 | B | ✗ Not Certified | 800 | 20,000 | 3 |
| 23 | aloha_sim_transfer_cube_scripted | 83.4 | B | ✗ Not Certified | 50 | 20,000 | 2 |
| 24 | xarm_lift_medium_replay | 82.6 | B | ✗ Not Certified | 800 | 20,000 | 3 |
| 25 | xarm_push_medium | 81.2 | B | ✗ Not Certified | 800 | 20,000 | 1 |
| 26 | unitreeh1_two_robot_greeting | 80.8 | B | ✗ Not Certified | 30 | 3,750 | 2 |
| 27 | unitreeh1_rearrange_objects | 79.1 | C | ✗ Not Certified | 30 | 7,150 | 3 |
| 28 | pusht | 76.7 | C | ✗ Not Certified | 206 | 25,650 | 4 |
| 29 | unitreeh1_warehouse | 76.1 | C | ✗ Not Certified | 24 | 11,275 | 3 |
| 30 | unitreeh1_fold_clothes | 75.0 | C | ✗ Not Certified | 38 | 19,000 | 3 |
Failed to Load
| Dataset | Error |
|---|---|
| xarm_lift_medium_unlabeled | Dataset 'lerobot/xarm_lift_medium_unlabeled' doesn't exist on the Hub or cannot |
Score Interpretation
| Score | Grade | Certification |
|---|---|---|
| 90–100 | A | ✓ Certified |
| 75–89 | B | ✓ Certified |
| 60–74 | C | ~ Provisional |
| 40–59 | D | ✗ Not Certified |
| 0–39 | F | ✗ Not Certified |
Reproduce
pip install 'calibra-robotics[lerobot]'
python scripts/audit_lerobot_community.py
Files in this repository
| File | Description |
|---|---|
manifest.json |
Machine-readable summary of all 30 audits |
community_stats.json |
Aggregate statistics for percentile comparison in the Space |
leaderboard.md |
This leaderboard table |
lerobot/<dataset>/latest.json |
Full CalibraReport JSON per dataset |
Reproduce
pip install 'calibra-robotics[lerobot]'
git clone https://github.com/omertt27/Calibra
python scripts/audit_lerobot_community.py
About
Calibra is an open-source dataset quality tooling library for robotics imitation learning. It checks timing integrity, control smoothness, trajectory diversity, and more — then produces an actionable 0–100 health score with per-episode verdicts.
pip install 'calibra-robotics[lerobot]'
calibra audit hf://lerobot/pusht
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