Dataset Viewer
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
split: struct<akita_black_bowl_1: int64, none: int64>
child 0, akita_black_bowl_1: int64
child 1, none: int64
kept: struct<1|akita_black_bowl_1: int64>
child 0, 1|akita_black_bowl_1: int64
episodes: int64
prompt: string
policy: string
record_c: bool
argv: list<item: string>
child 0, item: string
act_prompt_map: string
act_prompts: struct<0: string, 1: string, 2: string, 3: string, 4: string, 5: string, 6: string, 7: string, 8: st (... 16 chars omitted)
child 0, 0: string
child 1, 1: string
child 2, 2: string
child 3, 3: string
child 4, 4: string
child 5, 5: string
child 6, 6: string
child 7, 7: string
child 8, 8: string
child 9, 9: string
c_prompt: string
commanded_bowl_map: string
commanded: struct<0: string, 1: string, 2: string, 3: string, 4: string, 5: string, 6: string, 7: string, 8: st (... 16 chars omitted)
child 0, 0: string
child 1, 1: string
child 2, 2: string
child 3, 3: string
child 4, 4: string
child 5, 5: string
child 6, 6: string
child 7, 7: string
child 8, 8: string
child 9, 9: string
init_min: int64
init_max: int64
rejected_not_followed: struct<1|akita_black_bowl_1: int64>
child 0, 1|akita_black_bowl_1: int64
language_follow: struct<1|akita_black_bowl_1: struct<followed: int64, not_followed: int64, follow_rate: double>>
child 0, 1|akita_black_bowl_1: struct<followed: int64, not_followed: int64, follow_rate: double>
child 0, followed: int64
child 1, not_followed: int64
child 2, follow_rate:
...
child 9, episodes_run_bowl_1: int64
child 10, episodes_run_bowl_2: int64
child 11, gate_ok: bool
child 4, 7: struct<kept_bowl_1: int64, kept_bowl_2: int64, scenes_bowl_1: int64, scenes_bowl_2: int64, scenes_vi (... 197 chars omitted)
child 0, kept_bowl_1: int64
child 1, kept_bowl_2: int64
child 2, scenes_bowl_1: int64
child 3, scenes_bowl_2: int64
child 4, scenes_visited_by_both_arms: int64
child 5, scenes_paired: int64
child 6, pairing_rate: double
child 7, follow_rate_bowl_1: double
child 8, follow_rate_bowl_2: double
child 9, episodes_run_bowl_1: int64
child 10, episodes_run_bowl_2: int64
child 11, gate_ok: bool
child 5, 9: struct<kept_bowl_1: int64, kept_bowl_2: int64, scenes_bowl_1: int64, scenes_bowl_2: int64, scenes_vi (... 197 chars omitted)
child 0, kept_bowl_1: int64
child 1, kept_bowl_2: int64
child 2, scenes_bowl_1: int64
child 3, scenes_bowl_2: int64
child 4, scenes_visited_by_both_arms: int64
child 5, scenes_paired: int64
child 6, pairing_rate: double
child 7, follow_rate_bowl_1: double
child 8, follow_rate_bowl_2: double
child 9, episodes_run_bowl_1: int64
child 10, episodes_run_bowl_2: int64
child 11, gate_ok: bool
gate_ok: bool
gate_tasks: list<item: int64>
child 0, item: int64
demos: string
total_c_records: int64
train_init_min: int64
total_kept: int64
tasks: list<item: int64>
child 0, item: int64
to
{'min_kept': Value('int64'), 'train_init_min': Value('int64'), 'demos': Value('string'), 'tasks': List(Value('int64')), 'gate_tasks': List(Value('int64')), 'tasks_below_min_kept': List(Value('null')), 'gate_ok': Value('bool'), 'total_kept': Value('int64'), 'total_c_records': Value('int64'), 'per_task': {'1': {'kept_bowl_1': Value('int64'), 'kept_bowl_2': Value('int64'), 'scenes_bowl_1': Value('int64'), 'scenes_bowl_2': Value('int64'), 'scenes_visited_by_both_arms': Value('int64'), 'scenes_paired': Value('int64'), 'pairing_rate': Value('float64'), 'follow_rate_bowl_1': Value('float64'), 'follow_rate_bowl_2': Value('float64'), 'episodes_run_bowl_1': Value('int64'), 'episodes_run_bowl_2': Value('int64'), 'gate_ok': Value('bool')}, '3': {'kept_bowl_1': Value('int64'), 'kept_bowl_2': Value('int64'), 'scenes_bowl_1': Value('int64'), 'scenes_bowl_2': Value('int64'), 'scenes_visited_by_both_arms': Value('int64'), 'scenes_paired': Value('int64'), 'pairing_rate': Value('float64'), 'follow_rate_bowl_1': Value('float64'), 'follow_rate_bowl_2': Value('float64'), 'episodes_run_bowl_1': Value('int64'), 'episodes_run_bowl_2': Value('int64'), 'gate_ok': Value('bool')}, '5': {'kept_bowl_1': Value('int64'), 'kept_bowl_2': Value('int64'), 'scenes_bowl_1': Value('int64'), 'scenes_bowl_2': Value('int64'), 'scenes_visited_by_both_arms': Value('int64'), 'scenes_paired': Value('int64'), 'pairing_rate': Value('float64'), 'follow_rate_bowl_1': Value('float64'), 'follow_rate_bowl_2': Value('float64'), 'episodes_run_bowl_1': Value('int64'), 'episodes_run_bowl_2': Value('int64'), 'gate_ok': Value('bool')}, '6': {'kept_bowl_1': Value('int64'), 'kept_bowl_2': Value('int64'), 'scenes_bowl_1': Value('int64'), 'scenes_bowl_2': Value('int64'), 'scenes_visited_by_both_arms': Value('int64'), 'scenes_paired': Value('int64'), 'pairing_rate': Value('float64'), 'follow_rate_bowl_1': Value('float64'), 'follow_rate_bowl_2': Value('float64'), 'episodes_run_bowl_1': Value('int64'), 'episodes_run_bowl_2': Value('int64'), 'gate_ok': Value('bool')}, '7': {'kept_bowl_1': Value('int64'), 'kept_bowl_2': Value('int64'), 'scenes_bowl_1': Value('int64'), 'scenes_bowl_2': Value('int64'), 'scenes_visited_by_both_arms': Value('int64'), 'scenes_paired': Value('int64'), 'pairing_rate': Value('float64'), 'follow_rate_bowl_1': Value('float64'), 'follow_rate_bowl_2': Value('float64'), 'episodes_run_bowl_1': Value('int64'), 'episodes_run_bowl_2': Value('int64'), 'gate_ok': Value('bool')}, '9': {'kept_bowl_1': Value('int64'), 'kept_bowl_2': Value('int64'), 'scenes_bowl_1': Value('int64'), 'scenes_bowl_2': Value('int64'), 'scenes_visited_by_both_arms': Value('int64'), 'scenes_paired': Value('int64'), 'pairing_rate': Value('float64'), 'follow_rate_bowl_1': Value('float64'), 'follow_rate_bowl_2': Value('float64'), 'episodes_run_bowl_1': Value('int64'), 'episodes_run_bowl_2': Value('int64'), 'gate_ok': Value('bool')}}}
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
split: struct<akita_black_bowl_1: int64, none: int64>
child 0, akita_black_bowl_1: int64
child 1, none: int64
kept: struct<1|akita_black_bowl_1: int64>
child 0, 1|akita_black_bowl_1: int64
episodes: int64
prompt: string
policy: string
record_c: bool
argv: list<item: string>
child 0, item: string
act_prompt_map: string
act_prompts: struct<0: string, 1: string, 2: string, 3: string, 4: string, 5: string, 6: string, 7: string, 8: st (... 16 chars omitted)
child 0, 0: string
child 1, 1: string
child 2, 2: string
child 3, 3: string
child 4, 4: string
child 5, 5: string
child 6, 6: string
child 7, 7: string
child 8, 8: string
child 9, 9: string
c_prompt: string
commanded_bowl_map: string
commanded: struct<0: string, 1: string, 2: string, 3: string, 4: string, 5: string, 6: string, 7: string, 8: st (... 16 chars omitted)
child 0, 0: string
child 1, 1: string
child 2, 2: string
child 3, 3: string
child 4, 4: string
child 5, 5: string
child 6, 6: string
child 7, 7: string
child 8, 8: string
child 9, 9: string
init_min: int64
init_max: int64
rejected_not_followed: struct<1|akita_black_bowl_1: int64>
child 0, 1|akita_black_bowl_1: int64
language_follow: struct<1|akita_black_bowl_1: struct<followed: int64, not_followed: int64, follow_rate: double>>
child 0, 1|akita_black_bowl_1: struct<followed: int64, not_followed: int64, follow_rate: double>
child 0, followed: int64
child 1, not_followed: int64
child 2, follow_rate:
...
child 9, episodes_run_bowl_1: int64
child 10, episodes_run_bowl_2: int64
child 11, gate_ok: bool
child 4, 7: struct<kept_bowl_1: int64, kept_bowl_2: int64, scenes_bowl_1: int64, scenes_bowl_2: int64, scenes_vi (... 197 chars omitted)
child 0, kept_bowl_1: int64
child 1, kept_bowl_2: int64
child 2, scenes_bowl_1: int64
child 3, scenes_bowl_2: int64
child 4, scenes_visited_by_both_arms: int64
child 5, scenes_paired: int64
child 6, pairing_rate: double
child 7, follow_rate_bowl_1: double
child 8, follow_rate_bowl_2: double
child 9, episodes_run_bowl_1: int64
child 10, episodes_run_bowl_2: int64
child 11, gate_ok: bool
child 5, 9: struct<kept_bowl_1: int64, kept_bowl_2: int64, scenes_bowl_1: int64, scenes_bowl_2: int64, scenes_vi (... 197 chars omitted)
child 0, kept_bowl_1: int64
child 1, kept_bowl_2: int64
child 2, scenes_bowl_1: int64
child 3, scenes_bowl_2: int64
child 4, scenes_visited_by_both_arms: int64
child 5, scenes_paired: int64
child 6, pairing_rate: double
child 7, follow_rate_bowl_1: double
child 8, follow_rate_bowl_2: double
child 9, episodes_run_bowl_1: int64
child 10, episodes_run_bowl_2: int64
child 11, gate_ok: bool
gate_ok: bool
gate_tasks: list<item: int64>
child 0, item: int64
demos: string
total_c_records: int64
train_init_min: int64
total_kept: int64
tasks: list<item: int64>
child 0, item: int64
to
{'min_kept': Value('int64'), 'train_init_min': Value('int64'), 'demos': Value('string'), 'tasks': List(Value('int64')), 'gate_tasks': List(Value('int64')), 'tasks_below_min_kept': List(Value('null')), 'gate_ok': Value('bool'), 'total_kept': Value('int64'), 'total_c_records': Value('int64'), 'per_task': {'1': {'kept_bowl_1': Value('int64'), 'kept_bowl_2': Value('int64'), 'scenes_bowl_1': Value('int64'), 'scenes_bowl_2': Value('int64'), 'scenes_visited_by_both_arms': Value('int64'), 'scenes_paired': Value('int64'), 'pairing_rate': Value('float64'), 'follow_rate_bowl_1': Value('float64'), 'follow_rate_bowl_2': Value('float64'), 'episodes_run_bowl_1': Value('int64'), 'episodes_run_bowl_2': Value('int64'), 'gate_ok': Value('bool')}, '3': {'kept_bowl_1': Value('int64'), 'kept_bowl_2': Value('int64'), 'scenes_bowl_1': Value('int64'), 'scenes_bowl_2': Value('int64'), 'scenes_visited_by_both_arms': Value('int64'), 'scenes_paired': Value('int64'), 'pairing_rate': Value('float64'), 'follow_rate_bowl_1': Value('float64'), 'follow_rate_bowl_2': Value('float64'), 'episodes_run_bowl_1': Value('int64'), 'episodes_run_bowl_2': Value('int64'), 'gate_ok': Value('bool')}, '5': {'kept_bowl_1': Value('int64'), 'kept_bowl_2': Value('int64'), 'scenes_bowl_1': Value('int64'), 'scenes_bowl_2': Value('int64'), 'scenes_visited_by_both_arms': Value('int64'), 'scenes_paired': Value('int64'), 'pairing_rate': Value('float64'), 'follow_rate_bowl_1': Value('float64'), 'follow_rate_bowl_2': Value('float64'), 'episodes_run_bowl_1': Value('int64'), 'episodes_run_bowl_2': Value('int64'), 'gate_ok': Value('bool')}, '6': {'kept_bowl_1': Value('int64'), 'kept_bowl_2': Value('int64'), 'scenes_bowl_1': Value('int64'), 'scenes_bowl_2': Value('int64'), 'scenes_visited_by_both_arms': Value('int64'), 'scenes_paired': Value('int64'), 'pairing_rate': Value('float64'), 'follow_rate_bowl_1': Value('float64'), 'follow_rate_bowl_2': Value('float64'), 'episodes_run_bowl_1': Value('int64'), 'episodes_run_bowl_2': Value('int64'), 'gate_ok': Value('bool')}, '7': {'kept_bowl_1': Value('int64'), 'kept_bowl_2': Value('int64'), 'scenes_bowl_1': Value('int64'), 'scenes_bowl_2': Value('int64'), 'scenes_visited_by_both_arms': Value('int64'), 'scenes_paired': Value('int64'), 'pairing_rate': Value('float64'), 'follow_rate_bowl_1': Value('float64'), 'follow_rate_bowl_2': Value('float64'), 'episodes_run_bowl_1': Value('int64'), 'episodes_run_bowl_2': Value('int64'), 'gate_ok': Value('bool')}, '9': {'kept_bowl_1': Value('int64'), 'kept_bowl_2': Value('int64'), 'scenes_bowl_1': Value('int64'), 'scenes_bowl_2': Value('int64'), 'scenes_visited_by_both_arms': Value('int64'), 'scenes_paired': Value('int64'), 'pairing_rate': Value('float64'), 'follow_rate_bowl_1': Value('float64'), 'follow_rate_bowl_2': Value('float64'), 'episodes_run_bowl_1': Value('int64'), 'episodes_run_bowl_2': Value('int64'), 'gate_ok': Value('bool')}}}
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.
Simulated manipulation — demonstration sets and evaluation inputs
Curated demonstration sets and cached conditioning features from simulated robot manipulation experiments, plus the small inputs the evaluation scripts read.
These are our own recordings — a pretrained policy rolled through simulated scenes by our own collector scripts. They are not copies of any benchmark's distributed demonstration files, and no benchmark assets are included.
Contents
Tabletop pick-and-place, grasp-yaw, sweep and push families in a WidowX simulator; a
bowl-selection benchmark at several data mixtures; occlusion-augmented sets; and cached
conditioning features (cond_*.npz, sketch_dataset.npz).
Some directories are earlier or superseded variants, retained deliberately so that no experiment has to be regenerated. Sizes range from a few hundred KB to ~9 GB per entry.
Related
Raw per-scene simulation dumps, before curation, are in a separate archival repository.
Verifying a download
A manifest pinning every file by content hash accompanies the code.
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