Dataset Viewer
Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
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 match

Need 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
Downloads last month
224

Space using omert27/calibra-robot-dataset-quality-benchmark 1