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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 1405, 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 982, 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.
0 unknown | __key__ string | __url__ string |
|---|---|---|
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19... | 20260811/ep_teacher_waffles_1786471849/arm_q.zarr/data/2 | hf://datasets/armteam/hapticwam-rollouts@14a2438d6565c1cb8cec451551fdd1bf78bcfa3a/deploy_20260811.tar.zst |
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12... | 20260811/ep_teacher_waffles_1786471849/arm_q.zarr/data/4 | hf://datasets/armteam/hapticwam-rollouts@14a2438d6565c1cb8cec451551fdd1bf78bcfa3a/deploy_20260811.tar.zst |
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1... | 20260811/ep_teacher_waffles_1786471849/arm_q.zarr/data/8 | hf://datasets/armteam/hapticwam-rollouts@14a2438d6565c1cb8cec451551fdd1bf78bcfa3a/deploy_20260811.tar.zst |
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28,... | 20260811/ep_teacher_waffles_1786471849/arm_q.zarr/data/5 | hf://datasets/armteam/hapticwam-rollouts@14a2438d6565c1cb8cec451551fdd1bf78bcfa3a/deploy_20260811.tar.zst |
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12... | 20260811/ep_teacher_waffles_1786471849/arm_q.zarr/data/6 | hf://datasets/armteam/hapticwam-rollouts@14a2438d6565c1cb8cec451551fdd1bf78bcfa3a/deploy_20260811.tar.zst |
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224... | 20260811/ep_teacher_waffles_1786471849/arm_ft.zarr/data/2 | hf://datasets/armteam/hapticwam-rollouts@14a2438d6565c1cb8cec451551fdd1bf78bcfa3a/deploy_20260811.tar.zst |
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60,... | 20260811/ep_teacher_waffles_1786471849/arm_tcp_pose.zarr/data/1 | hf://datasets/armteam/hapticwam-rollouts@14a2438d6565c1cb8cec451551fdd1bf78bcfa3a/deploy_20260811.tar.zst |
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47... | 20260811/ep_teacher_waffles_1786471849/arm_tcp_pose.zarr/data/4 | hf://datasets/armteam/hapticwam-rollouts@14a2438d6565c1cb8cec451551fdd1bf78bcfa3a/deploy_20260811.tar.zst |
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null | 20260811/ep_teacher_waffles_1786472069/tactile_right_infer_img.zarr/data/0 | hf://datasets/armteam/hapticwam-rollouts@14a2438d6565c1cb8cec451551fdd1bf78bcfa3a/deploy_20260811.tar.zst |
End of preview.
HapticWAM policy rollouts
Closed-loop policy rollouts recorded on the rig: episodes produced by a policy, not by a
human teleoperator. They back the DAgger rounds and the deployment sessions. Human
teleoperation lives in
armteam/hapticwam-teleop-dataset;
nothing in this repo is teleoperation.
Contents
| File | Size | What it is |
|---|---|---|
rollouts_0908_0909.tar.zst |
4.29 GB | policy rollouts from 2026-09-08 and 09-09. The 123 *_rollout episodes in the DAgger round-3 manifest come from here. |
rollouts_0901_0904.tar.zst |
3.72 GB | policy rollouts from 2026-09-01 and 09-04. |
deploy_20260811.tar.zst |
1.95 GB | rig deploy day, packed on the rig box |
deploy_20260813.tar.zst |
0.08 GB | rig deploy day |
deploy_20260814.tar.zst |
1.02 GB | rig deploy day |
deploy_20260818.tar.zst |
0.52 GB | rig deploy day |
deploy_20260820.tar.zst |
1.39 GB | rig deploy day |
manifests_r3.tar |
1.2 MB | the DAgger round-3 manifest, see below |
zarr_rollouts/deploy_20260813.tar |
0.08 GB | 1 episode, 283 files |
zarr_rollouts/deploy_20260908.tar |
1.11 GB | 18 episodes, 4,637 files |
zarr_rollouts/deploy_20260909.tar |
3.19 GB | 61 episodes, 13,820 files |
rig_sessions/rig_20260818.tar.gz |
0.52 GB | rig session archive, 2026-08-18 |
rig_sessions/rig_20260820.tar.gz |
1.38 GB | rig session archive, 2026-08-20 |
index.jsonl |
— | every file here with size, LFS sha256 and the path it came from |
The two forms, and why both exist
The same kind of data appears twice, in two containers.
deploy_<day>.tar.zstare the archives the rig box packed and uploaded at the end of each deploy day, one per day, zstd-compressed.zarr_rollouts/deploy_<day>.tarare repacks of a loose per-chunk zarr tree that was uploaded separately, file by file, and ran to 18,741 objects. One uncompressed tar per deploy session, members rooted atdeploy_<YYYYMMDD>/. They were repacked because a repo of that many loose chunk files is slow to enumerate and awkward to clone; no bytes were changed.
deploy_20260813 exists in both forms. Its zstd archive and its zarr repack cover the same
session.
tar -xf zarr_rollouts/deploy_20260909.tar # -> deploy_20260909/ep_student_*/...
tar -I zstd -xf rollouts_0908_0909.tar.zst
rig_sessions/
Whole-session archives written on the rig box at the end of a run day, as opposed to the
per-episode deploy_<day> packs above. They are kept for provenance: they hold the session as
the rig recorded it, including the operator notes and configuration alongside the episodes.
A third archive, rig_20260820_final.tar.gz, existed upstream and is not here: it was
byte-identical to rig_20260820.tar.gz (same size, same sha256), so only one copy was kept.
Episode format
Same layout as the teleop corpus: one zarr group per stream (actions, actions_abs,
arm_{q,qd,ft,tcp_pose,tcp_speed}, camera_scene_color, gripper,
tactile_{left,right}_{area,fields_ds,infer_img,keyframes,wrench}) with data + ts arrays,
plus meta.json. The difference from teleop is in the metadata: these episodes carry a policy
name and a dagger_round >= 0 instead of policy: teleop / dagger_round: -1. Episode
directories are named ep_teacher_* or ep_student_* after the policy that drove them.
manifests_r3.tar — the round-3 manifest
all_r3.jsonl has 1,238 rows: the 1,115 teleop episodes plus 123 policy rollout
episodes under *_rollout directories (waffles_rollout 80, egg_rollout 24,
Carton_rollout 18, whiteboard_rollout 1), from sessions 20260901, 20260904 and the
deploy_* days. all_r3_windows.jsonl has 1,214 rows, the same set minus the episodes that
yielded no training window; dropped_zero_window.txt lists those.
The teleop rows in this manifest point at episodes in
armteam/hapticwam-teleop-dataset; only the *_rollout rows resolve inside this repo. The
manifest of record for teleoperation is manifests_v6.tar in that repo, not this one.
Licence
Data: CC-BY-4.0.
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