LAW
Collection
LAW reproduction resources: four final models, three training initializations, and two complete training datasets. • 9 items • Updated
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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1531, in _prepare_split_single
for key, record in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 127, in _generate_examples
for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
for filename, f in tar_iterator:
^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
for x in self.generator(*self.args):
~~~~~~~~~~~~~~^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1400, in _iter_from_urlpath
with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 977, in xopen
file_obj = fs.open(paths[0], mode)
File "<string>", line 3, in open
File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
return self._mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
return self._execute_mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
result = effect(*args, **kwargs)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 786, in wrapped
tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
~~~^^^^^^^^
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
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 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1393, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1571, 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.
__key__ string | __url__ string | json dict |
|---|---|---|
accepted_seedbank | "hf://datasets/KAITANG2003/LAW-RoboTwin-WAM-Training@98cc782ed3a427be72f91b038d0ee7d31f9fa428/shards(...TRUNCATED) | {"version":1,"format":"fastwam_robotwin_accepted_seedbank","created_at":"2026-08-05T01:34:34.896871+(...TRUNCATED) |
adjust_bottle-aloha-agilex_clean_50/anno_semantic/episode_000000 | "hf://datasets/KAITANG2003/LAW-RoboTwin-WAM-Training@98cc782ed3a427be72f91b038d0ee7d31f9fa428/shards(...TRUNCATED) | {
"version": null,
"format": null,
"created_at": null,
"source_run": null,
"config": null,
"records": null
} |
adjust_bottle-aloha-agilex_clean_50/anno_semantic/episode_000001 | "hf://datasets/KAITANG2003/LAW-RoboTwin-WAM-Training@98cc782ed3a427be72f91b038d0ee7d31f9fa428/shards(...TRUNCATED) | {
"version": null,
"format": null,
"created_at": null,
"source_run": null,
"config": null,
"records": null
} |
adjust_bottle-aloha-agilex_clean_50/anno_semantic/episode_000002 | "hf://datasets/KAITANG2003/LAW-RoboTwin-WAM-Training@98cc782ed3a427be72f91b038d0ee7d31f9fa428/shards(...TRUNCATED) | {
"version": null,
"format": null,
"created_at": null,
"source_run": null,
"config": null,
"records": null
} |
adjust_bottle-aloha-agilex_clean_50/anno_semantic/episode_000003 | "hf://datasets/KAITANG2003/LAW-RoboTwin-WAM-Training@98cc782ed3a427be72f91b038d0ee7d31f9fa428/shards(...TRUNCATED) | {
"version": null,
"format": null,
"created_at": null,
"source_run": null,
"config": null,
"records": null
} |
adjust_bottle-aloha-agilex_clean_50/anno_semantic/episode_000004 | "hf://datasets/KAITANG2003/LAW-RoboTwin-WAM-Training@98cc782ed3a427be72f91b038d0ee7d31f9fa428/shards(...TRUNCATED) | {
"version": null,
"format": null,
"created_at": null,
"source_run": null,
"config": null,
"records": null
} |
adjust_bottle-aloha-agilex_clean_50/anno_semantic/episode_000005 | "hf://datasets/KAITANG2003/LAW-RoboTwin-WAM-Training@98cc782ed3a427be72f91b038d0ee7d31f9fa428/shards(...TRUNCATED) | {
"version": null,
"format": null,
"created_at": null,
"source_run": null,
"config": null,
"records": null
} |
adjust_bottle-aloha-agilex_clean_50/anno_semantic/episode_000006 | "hf://datasets/KAITANG2003/LAW-RoboTwin-WAM-Training@98cc782ed3a427be72f91b038d0ee7d31f9fa428/shards(...TRUNCATED) | {
"version": null,
"format": null,
"created_at": null,
"source_run": null,
"config": null,
"records": null
} |
adjust_bottle-aloha-agilex_clean_50/anno_semantic/episode_000007 | "hf://datasets/KAITANG2003/LAW-RoboTwin-WAM-Training@98cc782ed3a427be72f91b038d0ee7d31f9fa428/shards(...TRUNCATED) | {
"version": null,
"format": null,
"created_at": null,
"source_run": null,
"config": null,
"records": null
} |
adjust_bottle-aloha-agilex_clean_50/anno_semantic/episode_000008 | "hf://datasets/KAITANG2003/LAW-RoboTwin-WAM-Training@98cc782ed3a427be72f91b038d0ee7d31f9fa428/shards(...TRUNCATED) | {
"version": null,
"format": null,
"created_at": null,
"source_run": null,
"config": null,
"records": null
} |
End of preview.
LAW-RoboTwin-WAM-Training
Expert demonstrations for training both LAW + FastWAM and LAW + OpenWAM. Includes state/action trajectories, camera videos, annotations and text/memory inputs. 165 verified shards.
Follow the LAW README for setup, preprocessing and training. Download from the LAW repository:
"$PYTHON_BIN" scripts/download.py datasets --dataset wam
The downloader verifies shard sizes/SHA-256 and restores the training directory layout. Video latent caches are excluded; generate them before WAM training as described in the README.
LAW-authored annotations: CC-BY-4.0. Original media and third-party materials retain their terms; see license scope.
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