Revisiting Embodied Chain-of-Thought for Generalizable Robot Manipulation
Paper • 2606.03784 • Published
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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.
json dict | __key__ string | __url__ string |
|---|---|---|
{"task_name":"episode_0000000","episode_json":"json/train_0_100/chunk-0000/episode_0000000.json","da(...TRUNCATED) | cot/train_0_100/chunk-0000/episode_0000000_ecot | hf://datasets/ERVLA/AgiBot@f18b8b7778c9764be29cdb5afc774d0615e7ec27/archives/cot-00000.tar.zst |
{"task_name":"episode_0000001","episode_json":"json/train_0_100/chunk-0000/episode_0000001.json","da(...TRUNCATED) | cot/train_0_100/chunk-0000/episode_0000001_ecot | hf://datasets/ERVLA/AgiBot@f18b8b7778c9764be29cdb5afc774d0615e7ec27/archives/cot-00000.tar.zst |
{"task_name":"episode_0000002","episode_json":"json/train_0_100/chunk-0000/episode_0000002.json","da(...TRUNCATED) | cot/train_0_100/chunk-0000/episode_0000002_ecot | hf://datasets/ERVLA/AgiBot@f18b8b7778c9764be29cdb5afc774d0615e7ec27/archives/cot-00000.tar.zst |
{"task_name":"episode_0000003","episode_json":"json/train_0_100/chunk-0000/episode_0000003.json","da(...TRUNCATED) | cot/train_0_100/chunk-0000/episode_0000003_ecot | hf://datasets/ERVLA/AgiBot@f18b8b7778c9764be29cdb5afc774d0615e7ec27/archives/cot-00000.tar.zst |
{"task_name":"episode_0000004","episode_json":"json/train_0_100/chunk-0000/episode_0000004.json","da(...TRUNCATED) | cot/train_0_100/chunk-0000/episode_0000004_ecot | hf://datasets/ERVLA/AgiBot@f18b8b7778c9764be29cdb5afc774d0615e7ec27/archives/cot-00000.tar.zst |
{"task_name":"episode_0000005","episode_json":"json/train_0_100/chunk-0000/episode_0000005.json","da(...TRUNCATED) | cot/train_0_100/chunk-0000/episode_0000005_ecot | hf://datasets/ERVLA/AgiBot@f18b8b7778c9764be29cdb5afc774d0615e7ec27/archives/cot-00000.tar.zst |
{"task_name":"episode_0000006","episode_json":"json/train_0_100/chunk-0000/episode_0000006.json","da(...TRUNCATED) | cot/train_0_100/chunk-0000/episode_0000006_ecot | hf://datasets/ERVLA/AgiBot@f18b8b7778c9764be29cdb5afc774d0615e7ec27/archives/cot-00000.tar.zst |
{"task_name":"episode_0000007","episode_json":"json/train_0_100/chunk-0000/episode_0000007.json","da(...TRUNCATED) | cot/train_0_100/chunk-0000/episode_0000007_ecot | hf://datasets/ERVLA/AgiBot@f18b8b7778c9764be29cdb5afc774d0615e7ec27/archives/cot-00000.tar.zst |
{"task_name":"episode_0000008","episode_json":"json/train_0_100/chunk-0000/episode_0000008.json","da(...TRUNCATED) | cot/train_0_100/chunk-0000/episode_0000008_ecot | hf://datasets/ERVLA/AgiBot@f18b8b7778c9764be29cdb5afc774d0615e7ec27/archives/cot-00000.tar.zst |
{"task_name":"episode_0000009","episode_json":"json/train_0_100/chunk-0000/episode_0000009.json","da(...TRUNCATED) | cot/train_0_100/chunk-0000/episode_0000009_ecot | hf://datasets/ERVLA/AgiBot@f18b8b7778c9764be29cdb5afc774d0615e7ec27/archives/cot-00000.tar.zst |
End of preview.
ERVLA AgiBot
AgiBot demonstrations with embodied chain-of-thought annotations for ERVLA. The demonstrations originate from AgiBot World.
| Content | Files |
|---|---|
| Episode JSON | 776,158 |
| CoT JSON | 776,158 |
| Videos | 2,327,916 |
The dataset is distributed as independent tar.zst archives in archives/.
All archives extract into the same dataset root:
json/
cot/
videos/
Paths in JSON and CoT are relative to this root. Archives contain the video
contents, with no symlinks or machine-specific ownership metadata.
Download every archive to restore the full dataset; individual archives may
contain only one content type. archives.json lists available archives and
their checksums. Its complete field becomes true after all archives have
been uploaded.
hf download ERVLA/AgiBot --repo-type dataset --local-dir agibot
cd agibot
sha256sum -c SHA256SUMS
for archive in archives/*.tar.zst; do
tar --zstd -xf "$archive"
done
Extraction requires GNU tar and Zstandard (zstd).
End-effector projections were refreshed where camera calibration was available; 2,145 CoT files retain their original projections.
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