The dataset viewer is not available for this dataset.
Error code: ConfigNamesError
Exception: BadZipFile
Message: zipfiles that span multiple disks are not supported
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
config_names = get_dataset_config_names(
path=dataset,
token=hf_token,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
path,
...<4 lines>...
**download_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1215, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1190, in dataset_module_factory
).get_module()
~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 655, in get_module
module_name, default_builder_kwargs = infer_module_for_data_files(
~~~~~~~~~~~~~~~~~~~~~~~~~~~^
data_files=data_files,
^^^^^^^^^^^^^^^^^^^^^^
path=self.name,
^^^^^^^^^^^^^^^
download_config=self.download_config,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 309, in infer_module_for_data_files
split: infer_module_for_data_files_list(data_files_list, download_config=download_config)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 253, in infer_module_for_data_files_list
return infer_module_for_data_files_list_in_archives(data_files_list, download_config=download_config)
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 280, in infer_module_for_data_files_list_in_archives
f.split("::")[0] for f in xglob(extracted, recursive=True, download_config=download_config)
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1057, in xglob
fs, *_ = url_to_fs(urlpath, **storage_options)
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/fsspec/core.py", line 408, in url_to_fs
fs = filesystem(protocol, **inkwargs)
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 303, in filesystem
return cls(**storage_options)
File "/usr/local/lib/python3.14/site-packages/fsspec/spec.py", line 81, in __call__
obj = super().__call__(*args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/fsspec/implementations/zip.py", line 62, in __init__
self.zip = zipfile.ZipFile(
~~~~~~~~~~~~~~~^
self.fo,
^^^^^^^^
...<3 lines>...
compresslevel=compresslevel,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/zipfile/__init__.py", line 1472, in __init__
self._RealGetContents()
~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/zipfile/__init__.py", line 1535, in _RealGetContents
endrec = _EndRecData(fp)
File "/usr/local/lib/python3.14/zipfile/__init__.py", line 375, in _EndRecData
return _EndRecData64(fpin, filesize - sizeEndCentDir, endrec)
File "/usr/local/lib/python3.14/zipfile/__init__.py", line 303, in _EndRecData64
raise BadZipFile("zipfiles that span multiple disks are not supported")
zipfile.BadZipFile: zipfiles that span multiple disks are not supportedNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
PRIMO Video Media
The video half of the PRIMO R1 release (paper): source clips plus the pre-extracted anchor frames, packaged as multipart ZIP archives, one group per data source. The JSON annotations that reference these files live in separate repos — primo-bench-json, primo-sft-json, primo-rl-json.
Read this before you download anything
The full release is 6.58 TB across 1,231 archive parts, and behavior-1k alone is 5,981 GB of it — 91%. Nobody needs all of it. Every group is independently downloadable and independently extractable, and one group is enough to run a complete benchmark split or train on a subset mixture.
| Group | Parts | Size |
|---|---|---|
behavior-1k |
1115 | 5,981 GB |
agibot |
66 | 349 GB |
seed-bench-r1 |
28 | 148 GB |
real-humanoid |
6 | 27.6 GB |
perceptiontest |
5 | 24.9 GB |
nextqa |
4 | 17.9 GB |
robotwin |
3 | 12.5 GB |
robovqa |
2 | 9.8 GB |
star |
1 | 5.1 GB |
sharerobot |
1 | 0.97 GB |
Start with robotwin (12.5 GB). It backs both benchmark robotwin splits and both robotwin training subsets, so it is the cheapest way to get an end-to-end run working before committing disk to anything larger.
Which group does my split need?
Benchmark (primo-bench-json)
| Split | Group |
|---|---|
primo-bench-{id,ood}-robotwin |
robotwin |
primo-bench-{id,ood}-agibot |
agibot |
primo-bench-{id,ood}-behavior-1k |
behavior-1k |
primo-bench-ood-real-humanoid |
real-humanoid |
Training (primo-sft-json · primo-rl-json)
| Subset | Group |
|---|---|
robotwin-clean, robotwin-randomized |
robotwin (shared) |
agibot |
agibot |
behavior-1k |
behavior-1k |
robovqa |
robovqa |
sharerobot |
sharerobot |
seed-bench-r1 (SFT only) |
seed-bench-r1 |
nextqa (SFT only) |
nextqa |
perceptiontest (SFT only) |
perceptiontest |
star (SFT only) |
star |
Download
One glob per group. --include "robotwin.z*" matches every part including the final .zip:
hf download LeonOverload/primo-video-media --repo-type dataset \
--include "robotwin.z*" --local-dir /tmp/primo-video-zips
Several groups at once:
hf download LeonOverload/primo-video-media --repo-type dataset \
--include "robotwin.z*" --include "sharerobot.z*" --include "robovqa.z*" \
--local-dir /tmp/primo-video-zips
Or from Python:
from huggingface_hub import snapshot_download
for group in ["robotwin", "sharerobot"]:
snapshot_download(
"LeonOverload/primo-video-media",
repo_type="dataset",
allow_patterns=[f"{group}.z*"],
local_dir="/tmp/primo-video-zips",
)
Add --dry-run to any hf download to see the file list and total size before committing. Use --include / --exclude on their own — passing filenames positionally makes the client ignore both.
Verify
Per-part SHA256 is published in two equivalent forms: SHA256SUMS.txt (checksum + filename) and manifest.csv (file,size_bytes,sha256).
cd /tmp/primo-video-zips
# Only the parts you actually downloaded
grep -E '^\S+ robotwin\.' /path/to/SHA256SUMS.txt | sha256sum -c -
# macOS
grep -E '^\S+ robotwin\.' /path/to/SHA256SUMS.txt | shasum -a 256 -c -
Running sha256sum -c SHA256SUMS.txt unfiltered reports every part you did not download as missing, which is expected rather than an error.
Reassemble and extract
Each group is a zip split into ~5.37 GB parts named <group>.z01, <group>.z02, …, with the last part being <group>.zip — not the first. All parts must sit in the same directory.
7-Zip reads multipart archives directly, so this is the path to prefer:
cd /tmp/primo-video-zips
7z x robotwin.zip -o"$VIDEO_DATA_ROOT/primo-video/"
Without 7-Zip, concatenate first with zip -FF, then extract:
zip -FF robotwin.zip --out robotwin_fixed.zip
unzip robotwin_fixed.zip -d "$VIDEO_DATA_ROOT/primo-video/"
The zip -FF route needs roughly double the disk of the group while it runs, since robotwin_fixed.zip is a full second copy.
Where to extract, and why
Extract into $VIDEO_DATA_ROOT/primo-video/. Archive entries are rooted at <group>/, and the JSON paths resolve against exactly this prefix — DatasetLoader joins $VIDEO_DATA_ROOT/primo-video/ with the relative path field, and init_frame_path / current_frame_path already start with ./primo-video/. Extracting one level up or down is the usual cause of "video not found" at load time.
The resulting tree, alongside annotations:
$VIDEO_DATA_ROOT/
├── primo-bench/robotwin/{id,ood}.json
├── primo-sft/robotwin-clean/train_cot.json
├── primo-rl/robotwin-clean/train.json
└── primo-video/
└── robotwin/
├── videos/...
└── frames/...
Frames are already here
Each archive ships the pre-extracted anchor frames next to the clips — for example sharerobot/videos/entity_25460.mp4 alongside sharerobot/frames/videos/entity_25460_init.jpg and ..._current.jpg. Training reads those JPEG paths straight from the JSON, so there is no preprocessing step for these archives. src/preprocess_video_frames.py in the code repo exists for datasets you add yourself.
Evaluation is different: eval_interleave.py ignores the frame fields and re-derives the first and last frame with OpenCV at run time. Either way, nothing to run ahead of time.
Repo contents
<group>.z01 … <group>.zip— the multipart archives, 10 groups, 1,231 partsSHA256SUMS.txt— per-part SHA256 for all partsmanifest.csv—file,size_bytes,sha256, same data with sizes.pack_tmp/<group>.{manifest,sha}— per-group subsets of the above, written during packaging
Resources
| Code | 10-OASIS-01/PRIMO-R1 |
| Collection | PRIMO R1 |
| Paper | arXiv 2603.15600 · project page |
| Models | PRIMO-R1-7B · PRIMO-COT-SFT-7B |
| Benchmark | primo-bench-json |
| Training data | primo-sft-json · primo-rl-json |
Citations
If you find our work helpful for your research, please consider citing our work.
@misc{liu2026passiveobserveractivecritic,
title={From Passive Observer to Active Critic: Reinforcement Learning Elicits Process Reasoning for Robotic Manipulation},
author={Yibin Liu and Yaxing Lyu and Daqi Gao and Zhixuan Liang and Weiliang Tang and Shilong Mu and Xiaokang Yang and Yao Mu},
year={2026},
eprint={2603.15600},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2603.15600},
}
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