Progress-Guided Preference Generation for Aligning Generative Control Policies
Paper • 2610.25692 • 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 1539, in _prepare_split_single
for key, record in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 621, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 131, 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 1419, 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 976, 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 794, 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 1379, 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 956, 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 1401, 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 1579, 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.
hdf5 unknown | __key__ string | __url__ string |
|---|---|---|
"iUhERg0KGgoAAAAAAAgIAAQAEAAAAAAAAAAAAAAAAAD//////////wBovQYAAAAA//////////8AAAAAAAAAAGAAAAAAAAAAAQA(...TRUNCATED) | logs/collect_data/can_lowdim/demo_mh/low_dim_abs | hf://datasets/tungluu2203/pgpg-data@7c00159a74470dd4013b708701b487396ccb5000/data/can.tar.gz |
"iUhERg0KGgoAAAAAAAgIAAQAEAAAAAAAAAAAAAAAAAD//////////1IQlAsAAAAA//////////8AAAAAAAAAAGAAAAAAAAAAAQA(...TRUNCATED) | "logs/collect_data/can_lowdim/datacollect_diffusion_transformer_lowdim_can_lowdim/collect_can_lowdim(...TRUNCATED) | hf://datasets/tungluu2203/pgpg-data@7c00159a74470dd4013b708701b487396ccb5000/data/can.tar.gz |
"iUhERg0KGgoAAAAAAAgIAAQAEAAAAAAAAAAAAAAAAAD//////////6jE4AIAAAAA//////////8AAAAAAAAAAGAAAAAAAAAAAQA(...TRUNCATED) | logs/collect_data/lift_lowdim/demo_mh/low_dim_abs | hf://datasets/tungluu2203/pgpg-data@7c00159a74470dd4013b708701b487396ccb5000/data/lift.tar.gz |
"iUhERg0KGgoAAAAAAAgIAAQAEAAAAAAAAAAAAAAAAAD//////////zV+LAgAAAAA//////////8AAAAAAAAAAGAAAAAAAAAAAQA(...TRUNCATED) | "logs/collect_data/lift_lowdim/datacollect_diffusion_transformer_lowdim_lift_lowdim/collect_lift_low(...TRUNCATED) | hf://datasets/tungluu2203/pgpg-data@7c00159a74470dd4013b708701b487396ccb5000/data/lift.tar.gz |
"iUhERg0KGgoAAAAAAAgIAAQAEAAAAAAAAAAAAAAAAAD//////////wA3bQcAAAAA//////////8AAAAAAAAAAGAAAAAAAAAAAQA(...TRUNCATED) | logs/collect_data/square_lowdim/demo_mh/low_dim_abs | hf://datasets/tungluu2203/pgpg-data@7c00159a74470dd4013b708701b487396ccb5000/data/square.tar.gz |
"iUhERg0KGgoAAAAAAAgIAAQAEAAAAAAAAAAAAAAAAAD//////////wh3awoAAAAA//////////8AAAAAAAAAAGAAAAAAAAAAAQA(...TRUNCATED) | "logs/collect_data/square_lowdim/datacollect_diffusion_transformer_lowdim_square_lowdim/collect_squa(...TRUNCATED) | hf://datasets/tungluu2203/pgpg-data@7c00159a74470dd4013b708701b487396ccb5000/data/square.tar.gz |
"iUhERg0KGgoAAAAAAAgIAAQAEAAAAAAAAAAAAAAAAAD//////////1hREQwAAAAA//////////8AAAAAAAAAAGAAAAAAAAAAAQA(...TRUNCATED) | logs/collect_data/tool_hang_lowdim/demo_ph/low_dim_abs | hf://datasets/tungluu2203/pgpg-data@7c00159a74470dd4013b708701b487396ccb5000/data/tool_hang.tar.gz |
"iUhERg0KGgoAAAAAAAgIAAQAEAAAAAAAAAAAAAAAAAD///////////H4MhoAAAAA//////////8AAAAAAAAAAGAAAAAAAAAAAQA(...TRUNCATED) | "logs/collect_data/tool_hang_lowdim/datacollect_diffusion_transformer_lowdim_tool_hang_lowdim/collec(...TRUNCATED) | hf://datasets/tungluu2203/pgpg-data@7c00159a74470dd4013b708701b487396ccb5000/data/tool_hang.tar.gz |
"iUhERg0KGgoAAAAAAAgIAAQAEAAAAAAAAAAAAAAAAAD//////////6DMECYAAAAA//////////8AAAAAAAAAAGAAAAAAAAAAAQA(...TRUNCATED) | logs/collect_data/transport_lowdim/demo_mh/low_dim_abs | hf://datasets/tungluu2203/pgpg-data@7c00159a74470dd4013b708701b487396ccb5000/data/transport.tar.gz |
"iUhERg0KGgoAAAAAAAgIAAQAEAAAAAAAAAAAAAAAAAD//////////xG/+SgAAAAA//////////8AAAAAAAAAAGAAAAAAAAAAAQA(...TRUNCATED) | "logs/collect_data/transport_lowdim/datacollect_diffusion_transformer_lowdim_transport_lowdim/collec(...TRUNCATED) | hf://datasets/tungluu2203/pgpg-data@7c00159a74470dd4013b708701b487396ccb5000/data/transport.tar.gz |
End of preview.
PGPG data
Data and pretrained reference policies for Progress-Guided Preference Generation for Aligning Generative Control Policies (CoRL 2026).
Code: github.com/tunglm2203/pgpg · Paper: arXiv:2610.25692
Contents
Five robomimic low-dim tasks: lift, can, square, transport, tool_hang.
| folder | per task | what it is |
|---|---|---|
data/<task>.tar.gz |
HDF5 | expert demonstrations + 500 policy rollouts |
videos/<task>.tar |
MP4 | the rendered episodes for both of the above |
pretrain/<task>.tar |
2 × .ckpt |
reference policies π_ref: diffusion and rectified-flow |
videos/ is required, not optional. Visual progress scoring encodes the demonstration
videos with R3M on every run, so training fails without them.
Usage
The code ships a script that downloads, checksums and extracts in one step:
bash bash_script/others/download_data.sh # all five tasks
bash bash_script/others/download_data.sh square lift # only these
Manually:
huggingface-cli download tungluu2203/pgpg-data --repo-type dataset \
--local-dir /tmp/pgpg --include "data/square.tar.gz" "videos/square.tar" "pretrain/square.tar"
# every archive stores repo-root-relative paths, so extraction is always the same
for a in /tmp/pgpg/*/square.tar*; do tar -xf "$a" -C /path/to/pgpg; done
This yields logs/collect_data/<task>_lowdim/... and logs/pretrain/<task>_lowdim/....
How the demonstrations were produced
They are the robomimic multi-human (mh) datasets — proficient-human
(ph) for tool_hang — replayed in simulation. actions, states, obs and dones are
bit-identical to the originals, and all mask splits are preserved. The replay adds:
success: per-step binary flag, equal to the original sparse robomimic reward.rewards: overwritten with dense shaped rewards onlift,canandsquare.transportandtool_hangstay sparse, because robosuite implements no shaped reward for those two environments.videos/: rendered during the same replay.
Rollouts are 500 episodes per task, collected by rolling out the pretrained policies in pretrain/.
They are not part of the robomimic release. You can regenerate them with bash_script/data_collect/
in the code repository, but sampling is stochastic, so the episodes will not be the same ones.
To regenerate the demonstrations instead of downloading them, see bash_script/data_collect/ in the
code repository.
License and attribution
MIT, following robomimic. Derived from the robomimic datasets (Mandlekar et al., CoRL 2021); please cite robomimic alongside this work if you use these files.
@inproceedings{
luu2026progress,
title={Progress-Guided Preference Generation for Aligning Generative Control Policies},
author={Tung M. Luu and Tri Ton and Duc M. Nguyen and Xuan Thanh Nguyen and Nguyen Gia Binh and Quang Tan Nguyen and Vu Duong and Daniel Sonntag and Duy M. H. Nguyen and Vien Anh Ngo and Chang D. Yoo},
booktitle={Conference on Robot Learning},
year={2026}
}
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