---
license: cc-by-4.0
language:
- en
pretty_name: BARX Raw HDF5 Demonstrations
size_categories:
- 10K<n<100K
tags:
- robotics
- imitation-learning
- hdf5
- robocasa
- mimicgen
- barx
---

# 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

```text
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:

```bash
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](https://ajaysridhar.com/barx/) 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](https://creativecommons.org/licenses/by/4.0/).
Rendered simulator observations incorporate RoboCasa environments and assets;
see [RoboCasa](https://robocasa.ai/) for its attribution and upstream terms.

## Citation

```bibtex
@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.