The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
calls_completed: bool
calls: list<item: struct<index: int64, skill: string, arguments: struct<target: string, destination: string (... 393 chars omitted)
child 0, item: struct<index: int64, skill: string, arguments: struct<target: string, destination: string, receptacl (... 381 chars omitted)
child 0, index: int64
child 1, skill: string
child 2, arguments: struct<target: string, destination: string, receptacle: string, region: string>
child 0, target: string
child 1, destination: string
child 2, receptacle: string
child 3, region: string
child 3, success: bool
child 4, status: string
child 5, start_time: double
child 6, preconditions: struct<known_target: bool, hand_state: bool, empty_hand: bool, correct_fixture: bool, object_held: b (... 4 chars omitted)
child 0, known_target: bool
child 1, hand_state: bool
child 2, empty_hand: bool
child 3, correct_fixture: bool
child 4, object_held: bool
child 7, postconditions: struct<verified: bool>
child 0, verified: bool
child 8, end_time: double
child 9, wall_seconds: double
child 10, motion_timing: struct<simulated_seconds: double, target_seconds: list<item: double>, status: string>
child 0, simulated_seconds: double
child 1, target_seconds: list<item: double>
child 0, item: double
child 2, status: string
success: bool
goal_evidence: struct<bowls_in_cabinet: bool, cups_in_drawer: bool, cabinet_closed: bool, drawer_closed: bool, empt (... 13 chars omitted)
child 0, bowls_in_cabinet: bool
child 1, cups_in_drawer: bool
child 2, cabinet_closed: bool
child 3, drawer_closed: bool
child 4, empty_hand: bool
setup_evidence: struct<objects_on_requested_supports: bool, empty_hand: bool, initial_robot_clear: bool, parameters_ (... 14 chars omitted)
child 0, objects_on_requested_supports: bool
child 1, empty_hand: bool
child 2, initial_robot_clear: bool
child 3, parameters_applied: bool
video: string
video_speedup: double
scene_spec_sha256: string
selected: struct<bowl_01: string, bowl_02: string, cup_01: string, cup_02: string>
child 0, bowl_01: string
child 1, bowl_02: string
child 2, cup_01: string
child 3, cup_02: string
unavailable: list<item: null>
child 0, item: null
to
{'unavailable': List(Value('null')), 'selected': {'bowl_01': Value('string'), 'bowl_02': Value('string'), 'cup_01': Value('string'), 'cup_02': Value('string')}}
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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 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
calls_completed: bool
calls: list<item: struct<index: int64, skill: string, arguments: struct<target: string, destination: string (... 393 chars omitted)
child 0, item: struct<index: int64, skill: string, arguments: struct<target: string, destination: string, receptacl (... 381 chars omitted)
child 0, index: int64
child 1, skill: string
child 2, arguments: struct<target: string, destination: string, receptacle: string, region: string>
child 0, target: string
child 1, destination: string
child 2, receptacle: string
child 3, region: string
child 3, success: bool
child 4, status: string
child 5, start_time: double
child 6, preconditions: struct<known_target: bool, hand_state: bool, empty_hand: bool, correct_fixture: bool, object_held: b (... 4 chars omitted)
child 0, known_target: bool
child 1, hand_state: bool
child 2, empty_hand: bool
child 3, correct_fixture: bool
child 4, object_held: bool
child 7, postconditions: struct<verified: bool>
child 0, verified: bool
child 8, end_time: double
child 9, wall_seconds: double
child 10, motion_timing: struct<simulated_seconds: double, target_seconds: list<item: double>, status: string>
child 0, simulated_seconds: double
child 1, target_seconds: list<item: double>
child 0, item: double
child 2, status: string
success: bool
goal_evidence: struct<bowls_in_cabinet: bool, cups_in_drawer: bool, cabinet_closed: bool, drawer_closed: bool, empt (... 13 chars omitted)
child 0, bowls_in_cabinet: bool
child 1, cups_in_drawer: bool
child 2, cabinet_closed: bool
child 3, drawer_closed: bool
child 4, empty_hand: bool
setup_evidence: struct<objects_on_requested_supports: bool, empty_hand: bool, initial_robot_clear: bool, parameters_ (... 14 chars omitted)
child 0, objects_on_requested_supports: bool
child 1, empty_hand: bool
child 2, initial_robot_clear: bool
child 3, parameters_applied: bool
video: string
video_speedup: double
scene_spec_sha256: string
selected: struct<bowl_01: string, bowl_02: string, cup_01: string, cup_02: string>
child 0, bowl_01: string
child 1, bowl_02: string
child 2, cup_01: string
child 3, cup_02: string
unavailable: list<item: null>
child 0, item: null
to
{'unavailable': List(Value('null')), 'selected': {'bowl_01': Value('string'), 'bowl_02': Value('string'), 'cup_01': Value('string'), 'cup_02': Value('string')}}
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.
Cross-Sim Data: Fine-Tuning and Evaluation
Successful simulated household composite demonstrations for task-specific fine-tuning and evaluation. This snapshot contains 147 episodes across five tasks: set table, clear and store, prepare and serve meal, unpack and sort groceries, and warm two pots.
Fine-tuning and evaluation stages
The fine-tuning stage adapts a pretrained VLA using the fine-tuning set.
The evaluation stage measures the resulting policy on the two evaluation
sets. The machine-readable train key and existing paths remain unchanged for
archive/tool compatibility; they denote the fine-tuning set, not pretraining.
- Fine-tuning set (
train): two-room scenes with limited variations; target at least 60 successful minutes per task, with required procedure and door-opening coverage. - Two-room evaluation set (
test_two_rooms): two-room scenes with the configured evaluation variations; target at least 15 successful minutes per task. - Multi-room evaluation set (
test_multi_room): multiple-room scenes with the configured evaluation variations; target at least 15 successful minutes per task.
These are minimum duration targets, not exact caps. Whole episodes and concurrent
workers produce overshoot. Durations are finalized 1× videos, including explicit
task waits. Inspect episode configurations and asset_overlap.json for actual
variation and asset overlap; this card does not assert every variation is disjoint.
| Task | Fine-tuning minutes | Two-room evaluation minutes | Multi-room evaluation minutes |
|---|---|---|---|
| set_table | 90.96 | 24.65 | 29.66 |
| clear_and_store | 74.33 | 70.60 | 36.84 |
| prepare_and_serve_meal | 84.82 | 29.02 | 28.07 |
| unpack_and_sort_groceries | 71.88 | 17.48 | 17.07 |
| warm_two_pots | 74.25 | 25.90 | 22.86 |
Files and use
episodes.jsonl indexes each episode archive, SHA-256, task, split, duration,
and browser-accessible preview video and layout. previews/ also exposes prompts,
configuration, skill calls, success reports, and asset selections.
Each archives/<split>/<task>/<episode>.tar.gz contains the complete accepted
episode folder: video, scene/layout metadata, prompt, ordered skill calls,
simulation trace, action recording, initial state and compiled models, along with
the saved diagnostic metadata. Rejected speculative-trial directories and failed
episodes are excluded. Source episodes are not modified or re-rendered.
Download with huggingface_hub.snapshot_download("plnguyen2908/cross-sim-data", repo_type="dataset").
Extract chosen archives into the downloaded dataset root, for example:
tar -xzf archives/train/set_table/EPISODE.tar.gz -C DATASET_ROOT
Archive members already include their original split/batch/episode paths.
After extraction, relative paths in split manifests and per-task manifests under
tasks/<task>/<split>/ resolve within the dataset. Layout indexes and object
inventories are included. Preview copies duplicate a small subset for browsing.
The raw archive action recordings are simulator commands. Standard LeRobot v3
fine-tuning/test exports are also available under lerobot/ (see below). Some source XML/configuration fields retain original
machine-specific asset paths. Re-execution requires the matching simulator and
assets; compiled MuJoCo models are version-dependent. Consult per-episode files
for actual observation modalities and recording details.
LeRobot v3 release
All 147 episodes are also available as 15 standalone LeRobot v3 datasets (five tasks × three splits), containing 2,094,957 synchronized frames at 50 Hz. Use separate navigation/wrist RGB views and named 11D robot states/actions. This RGB release preserves layout/configuration metadata; the raw archives retain models and states for depth reconstruction. Raw archives remain unchanged.
Select lerobot/<task>/fine_tuning, test_two_rooms, or test_multi_room as the
local dataset root after downloading. The repository root is not one combined
LeRobot dataset. Decoder tolerance is 1ms to accommodate float32 timestamp
rounding in long videos, without changing the 20ms frame interval.
Only fine_tuning is for model fitting. Source demonstrations succeeded, but 50 Hz sample-and-hold physical replay has not been requalified; this distinction is retained in each dataset's provenance metadata. Read-back validation checks format, endpoint image decoding, counts and state/action dimensions, not learned policy success.
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