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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type
fixed_size_list<item: float>[12]
to
List(Value('float32'), length=7)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 611, in wrapped
for item in generator(*args, **kwargs):
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/lance/lance.py", line 231, in _generate_tables
yield Key(frag_idx, batch_idx), self._cast_table(table)
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/lance/lance.py", line 188, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2303, in cast_table_to_schema
cast_array_to_feature(
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1852, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2149, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
fixed_size_list<item: float>[12]
to
List(Value('float32'), length=7)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1361, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 940, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
episode_idx int32 | step_idx int32 | pixels image | sideview image | robot0_eye_in_hand image | action list | state list | reward list | terminated list | truncated list | success list |
|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | [
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0 | 1 | [
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0 | 2 | [
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0 | 3 | [
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0 | 4 | [
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0 | 5 | [
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0 | 6 | [
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0 | 7 | [
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0 | 8 | [
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0 | 9 | [
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... | [
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0 | 10 | [
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0 | 11 | [
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0 | 12 | [
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0 | 13 | [
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0 | 14 | [
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0 | 16 | [
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0 | 19 | [
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0 | 20 | [
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End of preview.
robosuite + LeRobot demonstrations for world-model training (Lance, 3-view)
Preview bucket for the world-forge data pipeline. It holds 235 manipulation
datasets converted to the Lance format used
by the stable-worldmodel platform, across five sources:
- 5 robomimic proficient-human tasks
- 26 MimicGen task/level variants
- 130 LIBERO tasks (the LIBERO-130 split: 90 + 10 + object + spatial + goal)
- 65 RoboCasa atomic kitchen tasks
- 9 DexMimicGen bimanual dexterous tasks
Total ≈ 14.11M frames. Every dataset is a single-version
<name>.lance/ directory. Each per-task suite is nested under its own subdir — robomimic/, mimicgen/,
libero/, robocasa/, dexmimicgen/, plus the mirrored ogbench/. Single
multi-task corpora (droid, robotwin) sit at the top level.
These are source demonstrations, not policy rollouts. robomimic, MimicGen, LIBERO and DexMimicGen were replayed in robosuite from recorded MuJoCo states and re-rendered to 224×224 from three camera views. RoboCasa ships as pre-rendered LeRobot videos (v0.2), so its three views are decoded directly from those videos (no replay) and resized to 224×224 for consistency.
Cameras (3 views)
Every step carries three JPEG-encoded 224×224 RGB views, following the DROID two-exterior-plus-wrist layout:
| column | robomimic / MimicGen / LIBERO | RoboCasa | role |
|---|---|---|---|
pixels |
agentview |
robot0_agentview_left |
primary exterior |
sideview |
sideview |
robot0_agentview_right |
second exterior |
robot0_eye_in_hand |
wrist | robot0_eye_in_hand |
in-hand / eye-in-hand |
The PickPlace-family envs (mimicgen_pick_place_d0, robomimic_can) do not define
a sideview camera natively, so the standard robosuite world-level sideview is
injected at render time. All replay-rendered datasets therefore share the same
three views; RoboCasa's three come from its native left/right/wrist LeRobot videos.
Schema
All datasets share the framework columns below, named to match the
stable-worldmodel / ogbench convention:
| column | type | meaning |
|---|---|---|
episode_idx |
int32 | episode index |
step_idx |
int32 | step within episode |
pixels / sideview / robot0_eye_in_hand |
binary (JPEG) | the three 224×224 RGB views |
action |
float[] | controller action (robosuite 7-dim single-arm / 14-dim bimanual; RoboCasa 12-dim) |
state |
float[] | per-step state vector |
reward |
float[1] | task reward (sparse) |
terminated / truncated / success |
float[1] | episode-end / success flags |
Replay-rendered sources (robomimic, MimicGen, LIBERO) additionally carry
qpos / qvel (MuJoCo generalized position / velocity), render_time, id,
and each source's own named low-dim observation keys, kept verbatim — e.g.
robosuite's robot0_eef_pos, object, …; LIBERO's ee_pos, ee_ori,
gripper_states, joint_states. There state is the flattened MuJoCo state used
to drive replay.
RoboCasa differs: it is derived from LeRobot recordings, not a MuJoCo replay,
so it has no qpos/qvel/sim-state. Its state is the LeRobot 16-dim
observation.state proprioception and action is the 12-dim LeRobot action. This
per-source variation follows the same convention as other datasets in the bucket
(e.g. DROID keeps its own obs_*/act_* names).
Datasets
robomimic — proficient-human (ph, low_dim source), re-rendered
| dataset | frames |
|---|---|
robomimic_can |
23,207 |
robomimic_lift |
9,666 |
robomimic_square |
30,154 |
robomimic_tool_hang |
95,962 |
robomimic_transport |
93,752 |
MimicGen — reset-distribution levels
| dataset | frames | dataset | frames | |
|---|---|---|---|---|
mimicgen_coffee_d0 |
223,130 | mimicgen_square_d0 |
153,477 | |
mimicgen_coffee_d1 |
224,403 | mimicgen_square_d1 |
152,400 | |
mimicgen_coffee_d2 |
224,204 | mimicgen_square_d2 |
153,112 | |
mimicgen_coffee_preparation_d0 |
689,273 | mimicgen_stack_d0 |
107,590 | |
mimicgen_coffee_preparation_d1 |
687,674 | mimicgen_stack_d1 |
108,233 | |
mimicgen_hammer_cleanup_d0 |
285,359 | mimicgen_stack_three_d0 |
254,810 | |
mimicgen_hammer_cleanup_d1 |
286,847 | mimicgen_stack_three_d1 |
255,096 | |
mimicgen_kitchen_d0 |
616,751 | mimicgen_threading_d0 |
224,508 | |
mimicgen_kitchen_d1 |
619,273 | mimicgen_threading_d1 |
223,115 | |
mimicgen_mug_cleanup_d0 |
338,136 | mimicgen_threading_d2 |
227,084 | |
mimicgen_mug_cleanup_d1 |
338,034 | mimicgen_three_piece_assembly_d0 |
336,695 | |
mimicgen_nut_assembly_d0 |
358,907 | mimicgen_three_piece_assembly_d1 |
334,869 | |
mimicgen_pick_place_d0 |
677,340 | mimicgen_three_piece_assembly_d2 |
335,949 |
The D0/D1/D2 suffix is the environment reset-distribution level, not a
data-size level. D0 initializes objects over a region resembling the source
demonstrations; D1 and D2 broaden the initial-pose distribution. See the
MimicGen dataset docs.
LIBERO — libero/ (130 tasks, 1,007,618 frames)
The LIBERO-130 lifelong-learning suite, replayed from its recorded states:
| suite | tasks | frames |
|---|---|---|
libero_10 |
10 | 138,090 |
libero_90 |
90 | 669,043 |
libero_goal |
10 | 63,728 |
libero_object |
10 | 74,507 |
libero_spatial |
10 | 62,250 |
Each task is libero/<suite>_<scene>_<language-goal>.lance.
RoboCasa — robocasa/ (65 atomic tasks, 1,495,313 frames)
RoboCasa v0.2 atomic kitchen tasks (PandaOmron mobile manipulator, procedurally generated kitchens), decoded from the released LeRobot videos:
| dataset | frames |
|---|---|
robocasa_AdjustToasterOvenTemperature |
21,328 |
robocasa_AdjustWaterTemperature |
20,953 |
robocasa_CheesyBread |
31,141 |
robocasa_CloseBlenderLid |
36,933 |
robocasa_CloseCabinet |
27,754 |
robocasa_CloseDishwasher |
15,781 |
robocasa_CloseDrawer |
15,670 |
robocasa_CloseElectricKettleLid |
7,530 |
robocasa_CloseFridge |
26,888 |
robocasa_CloseFridgeDrawer |
14,946 |
robocasa_CloseMicrowave |
20,075 |
robocasa_CloseOven |
18,230 |
robocasa_CloseStandMixerHead |
11,593 |
robocasa_CloseToasterOvenDoor |
19,815 |
robocasa_CoffeeServeMug |
16,921 |
robocasa_CoffeeSetupMug |
23,636 |
robocasa_LowerHeat |
31,174 |
robocasa_MakeIcedCoffee |
29,048 |
robocasa_NavigateKitchen |
79,550 |
robocasa_OpenBlenderLid |
20,124 |
robocasa_OpenCabinet |
37,492 |
robocasa_OpenDishwasher |
18,086 |
robocasa_OpenDrawer |
20,488 |
robocasa_OpenElectricKettleLid |
10,928 |
robocasa_OpenFridge |
33,138 |
robocasa_OpenFridgeDrawer |
18,517 |
robocasa_OpenMicrowave |
26,017 |
robocasa_OpenOven |
15,555 |
robocasa_OpenStandMixerHead |
13,411 |
robocasa_OpenToasterOvenDoor |
15,469 |
robocasa_PackDessert |
27,994 |
robocasa_PickPlaceCabinetToCounter |
20,201 |
robocasa_PickPlaceCounterToBlender |
38,892 |
robocasa_PickPlaceCounterToCabinet |
24,225 |
robocasa_PickPlaceCounterToDrawer |
28,225 |
robocasa_PickPlaceCounterToMicrowave |
42,012 |
robocasa_PickPlaceCounterToOven |
32,014 |
robocasa_PickPlaceCounterToSink |
22,410 |
robocasa_PickPlaceCounterToStandMixer |
25,467 |
robocasa_PickPlaceCounterToStove |
24,039 |
robocasa_PickPlaceCounterToToasterOven |
24,313 |
robocasa_PickPlaceDrawerToCounter |
31,819 |
robocasa_PickPlaceFridgeDrawerToShelf |
26,396 |
robocasa_PickPlaceFridgeShelfToDrawer |
27,047 |
robocasa_PickPlaceMicrowaveToCounter |
38,729 |
robocasa_PickPlaceSinkToCounter |
26,397 |
robocasa_PickPlaceStoveToCounter |
23,003 |
robocasa_PickPlaceToasterOvenToCounter |
19,323 |
robocasa_PickPlaceToasterToCounter |
26,907 |
robocasa_PreheatOven |
21,102 |
robocasa_SlideDishwasherRack |
19,052 |
robocasa_SlideOvenRack |
23,958 |
robocasa_SlideToasterOvenRack |
11,496 |
robocasa_StartCoffeeMachine |
13,722 |
robocasa_TurnOffMicrowave |
15,233 |
robocasa_TurnOffSinkFaucet |
12,309 |
robocasa_TurnOffStove |
32,741 |
robocasa_TurnOnBlender |
11,698 |
robocasa_TurnOnElectricKettle |
12,460 |
robocasa_TurnOnMicrowave |
14,010 |
robocasa_TurnOnSinkFaucet |
23,795 |
robocasa_TurnOnStove |
17,910 |
robocasa_TurnOnToaster |
10,042 |
robocasa_TurnOnToasterOven |
17,051 |
robocasa_TurnSinkSpout |
11,130 |
DexMimicGen — dexmimicgen/ (9 bimanual tasks, 2,915,177 frames)
DexMimicGen bimanual dexterous tasks (NVIDIA,
ICRA 2025) across three robot configs — bimanual Panda, Panda + dexterous hands
(PandaDexRH/LH), and the GR1 humanoid — replayed from recorded states. Native
cameras are agentview + two wrist views, so sideview is the injected world camera.
action is 14-dim (bimanual Panda) or 24-dim (dexterous / GR1); proprio obs keys
span robot0/robot1 (and GR1 left/right).
| dataset | frames |
|---|---|
dexmimicgen_two_arm_box_cleanup |
234,398 |
dexmimicgen_two_arm_can_sort_random |
322,073 |
dexmimicgen_two_arm_coffee |
326,707 |
dexmimicgen_two_arm_drawer_cleanup |
298,235 |
dexmimicgen_two_arm_lift_tray |
516,848 |
dexmimicgen_two_arm_pouring |
338,519 |
dexmimicgen_two_arm_threading |
218,858 |
dexmimicgen_two_arm_three_piece_assembly |
239,827 |
dexmimicgen_two_arm_transport |
419,712 |
Loading
from huggingface_hub import snapshot_download
import lance
local = snapshot_download(
"mh-hf/exp-bucket", repo_type="dataset",
allow_patterns="robomimic/robomimic_lift.lance/*", # or "libero/*"
)
ds = lance.dataset(f"{local}/robomimic/robomimic_lift.lance")
print(ds.schema.names) # note: pixels, sideview, robot0_eye_in_hand
print(ds.to_table(limit=2).to_pandas())
Provenance and licensing
Rendering and Lance conversion were done with the stable-worldmodel platform
(three 224×224 cameras). The underlying demonstrations come from:
- robomimic — Mandlekar et al., What Matters in Learning from Offline Human Demonstrations for Robot Manipulation, CoRL 2021.
- MimicGen — Mandlekar et al., MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations, CoRL 2023.
- LIBERO — Liu et al., LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning, NeurIPS 2023.
- RoboCasa — Nasiriany et al., RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots, RSS 2024 (v0.2 LeRobot release).
- DexMimicGen — Jiang et al., DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning, ICRA 2025.
Upstream sources are MIT-licensed; this bucket redistributes derived renders for research use. It is a throwaway preview account for collaboration review.
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