The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 101, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
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/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.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.
VoLoAgent V9: atomic LIBERO train-4K + LIBERO-PRO test
This release contains 4,000 validated training episodes and 221,491 text + vision + action observations. Every policy input has a fixed atomic instruction and RGB guidance: green identifies the current object/handle/knob/switch, while yellow identifies the destination region.
The five parameterized atoms are GRASP, MOVE, PLACE, ARTICULATE
(open/close/on/off) and PUSH. Action targets are canonical 20x7 chunks.
action_valid is mandatory in the training loss; padded slots must not be
executed or trained.
Splits
train: exactly 4,000 unique validated trajectories; all prior local train/validation/test episodes were intentionally reassigned to train.test/libero_pro: LIBERO-PRO BDDL/init evaluation configurations.test/libero_pro_atomic: 80 task specifications across eight task/position cells, decomposed into atomic text and green/yellow marker bindings.
The test records contain no action labels or evaluation rollouts. They are evaluation configurations, not supervised samples. Because standard LIBERO demonstrations may share base task semantics with LIBERO-PRO, this evaluates perturbation/variant generalization rather than fully unseen-task generalization.
Use
sha256sum -c checksums.sha256
for shard in train-assets-*.tar; do tar -xf "$shard"; done
from atomic_v9_dataset import AtomicV9Dataset
dataset = AtomicV9Dataset('.', split='train')
sample = dataset[0]
# sample: image, wrist_image, state, prompt, actions, action_valid
Source HDF5/BDDL paths under upstream/ are provenance identifiers. All assets
needed by the included training loader are inside the train archives. Rejected
jobs, queue state, logs, credentials and machine-local paths are excluded.
LIBERO-PRO dataset artifacts are CC-BY-4.0; its codebase is MIT. LIBERO code is
MIT (see LICENSE_LIBERO). Upstream demonstration/object-asset terms remain
applicable. Please cite both LIBERO and LIBERO-PRO when using this release.
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