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Datasets:
ajaysri
/
barx-raw-hdf5

Languages:
English
Size:
10K<n<100K
Tags:
robotics
imitation-learning
hdf5
robocasa
mimicgen
barx
License:
Dataset card Files
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Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 49, in _split_generators
                  import h5py
              ModuleNotFoundError: No module named 'h5py'
              
              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/split_names.py", line 71, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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.

BARX Raw HDF5 Demonstrations

This repository contains the normalized raw simulator demonstrations released with BARX: Cross-Embodiment Transfer via Behavior-Aligned Representations. It is intended for users who want to inspect the source trajectories, convert them into another training format, reproduce the BARX RLDS conversion, or prepare source demonstrations for new data-generation work.

Dataset summary

  • 23,400 demonstrations in 240 HDF5 files
  • approximately 284.03 GiB
  • 22,800 MimicGen-generated demonstrations and 600 human-teleoperated simulator demonstrations
  • six embodiments: IIWA, Jaco, Kinova3, Panda, Panda-OG, and UR5e
  • four tasks with 5,850 demonstrations each: pick-and-place counter-to-sink, pick-and-place sink-to-counter, turn-on-sink-faucet, and flip-mug-upright
  • paper subsets: XP-900, XP-3K, SP-900, and target-50

The human subset records simulated robot teleoperation; it contains no camera recordings or personal information from the human operators.

Repository layout 

human/                 # 12 target/task files, 50 demonstrations each
mg/                    # 228 generated files, 100 demonstrations each
manifest.csv           # paths, sizes, SHA-256 hashes, metadata, and paper sets

Each file stores demonstrations below data/demo_*. Principal fields include:

  • actions: [T, 12] canonical simulator actions;
  • states: simulator states for replay and data generation;
  • obs/agentview_rgb: [T, 180, 320, 3] RGB observations;
  • obs/ee_states and obs/gripper_states: policy state inputs;
  • aux_info/eef_normalized_image_pts and aux_info/bboxes_2d/*: BARX representation annotations; and
  • ep_meta: JSON episode metadata including the language instruction.

The canonical action layout is [arm(6), gripper(1), base(3), torso(1), mode(1)]. Stored simulator asset paths use the portable <ROBOCASA>/ token rather than installation-specific paths.

Download and verification

Download the full repository with the Hugging Face CLI:

hf download ajaysri/barx-raw-hdf5 --repo-type dataset --local-dir barx-raw-hdf5

The BARX code release provides a manifest-driven verifier and RLDS converter. See the BARX project page for the release code, installation instructions, and pinned dataset revision.

Intended use

The dataset is intended for robotics and imitation-learning research, cross-embodiment representation learning, format conversion, simulator replay, and preparation of new data-generation pipelines. Users should preserve the included attribution and cite BARX and RoboCasa in resulting work.

Limitations

  • These are simulator demonstrations and do not directly characterize real-world robot performance.
  • The task and embodiment distribution is intentionally narrow and should not be treated as a general-purpose robotics corpus.
  • The raw demonstrations preserve the information needed to prepare MimicGen source datasets, but the separately pinned BARX MimicGen generator and task configurations are required for end-to-end regeneration.
  • HDF5 is provided for exact source preservation and flexible conversion; the processed BARX RLDS repositories are more convenient for policy training.

License and attribution

The BARX dataset is released under the Creative Commons Attribution 4.0 International license. Rendered simulator observations incorporate RoboCasa environments and assets; see RoboCasa for its attribution and upstream terms.

Citation 

@inproceedings{sridhar2026barx,
  title     = {Cross-Embodiment Transfer via Behavior-Aligned Representations},
  author    = {Sridhar, Ajay and Gao, Jensen and Yang, Jonathan and Mercat, Jean and Belkhale, Suneel and Sadigh, Dorsa},
  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
  year      = {2026}
}

Please also cite RoboCasa and MimicGen when using the corresponding simulator assets or generated subset.
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