RoboMME-Plus
Collection
3 items • Updated
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: TypeError
Message: Couldn't cast array of type
struct<task: string, seed: int64, difficulty: string, oracle_policy_step: int64, continuous_motion: bool, motion_workspace: struct<bounds_xy_m: list<item: list<item: double>>, central_bounds_xy_m: list<item: list<item: double>>, board_x_m: double>, suite: string, episode: int64, control_freq: int64, oracle_wall_seconds: double, continuous_motion_version: int64>
to
{'task': Value('string'), 'seed': Value('int64'), 'difficulty': Value('string'), 'oracle_policy_step': Value('int64'), 'suite': Value('string'), 'episode': Value('int64'), 'oracle_wall_seconds': Value('float64'), 'control_freq': Value('int64')}
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 2315, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2121, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2161, 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
struct<task: string, seed: int64, difficulty: string, oracle_policy_step: int64, continuous_motion: bool, motion_workspace: struct<bounds_xy_m: list<item: list<item: double>>, central_bounds_xy_m: list<item: list<item: double>>, board_x_m: double>, suite: string, episode: int64, control_freq: int64, oracle_wall_seconds: double, continuous_motion_version: int64>
to
{'task': Value('string'), 'seed': Value('int64'), 'difficulty': Value('string'), 'oracle_policy_step': Value('int64'), 'suite': Value('string'), 'episode': Value('int64'), 'oracle_wall_seconds': Value('float64'), 'control_freq': Value('int64')}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.
RoboMME-OOD training data (HDF5)
50 successful training episodes · 16 tasks · 56,462 frames · 20 Hz. Three examples (easy, medium, hard) per task, except MoveCube with one example for each of five methods. Train seeds are disjoint from the 800 test episodes.
Source and seeds
Matching RoboMME-OOD code: d8a7e67852031d0f6e32e683873966c969601773.
Training seeds are in the RoboMME repo at
src/robomme_ood/env_metadata/train/record_dataset_<Task>_metadata.json.
Record k corresponds to H5 episode_<k>. This dataset also includes
train_seeds.csv, train_plan.json and metadata/train/.
Files and conventions
h5/record_dataset_<Task>.h5 contains episode_<k>/setup and timestep_<t>/{obs,action,info}.
Observations precede actions. Video-conditioned episodes include demonstration
frames followed by execution; is_video_demo distinguishes them. Angles are radians
and positions metres. VideoPlace examples finish with an open-gripper upward
retreat of at least 5 cm. LeRobot H264 videos are lossy; H5 stores the original RGB.
See the field reference.
Validation
All 50 execution trajectories pass public-evaluator physics replay with zero
joint trajectory difference. All 117 format checks and both dataset-loader checks
passed. manifest.json lists file sizes and SHA-256 checksums;
validation.json summarizes the checks and source revision.
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