Dataset Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the 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 supported

Need 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 parts
  • SHA256SUMS.txt — per-part SHA256 for all parts
  • manifest.csv — file,size_bytes,sha256, same data with sizes
  • .pack_tmp/<group>.{manifest,sha} — per-group subsets of the above, written during packaging

Resources

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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