ViaCatalyst/mobile-aloha-cabinet-episode0-lerobot-v3

79.5 MB Mobile ALOHA HDF5 → 3-camera LeRobot v3


Before → after: a native Mobile ALOHA episode with nested robot signals and three padded-JPEG camera arrays becomes a validated, multimodal LeRobot v3.0 dataset. [Convert ALOHA HDF5 free →](https://byod.viacatalyst.com/login?utm_source=huggingface&utm_medium=organic&utm_campaign=robotics_conversion_gallery&utm_content=mobile_aloha_card)

Community conversion produced by ViaCatalyst BYOD. This repository is not an official upstream release and is not affiliated with the Mobile ALOHA authors or the source-mirror maintainer.

This is a provenance-complete conversion of one pinned Mobile ALOHA Cabinet episode. It demonstrates the awkward parts that generic HDF5 exporters miss: a root-level episode layout, nested joint telemetry, separately logged arm and mobile-base actions, and camera frames stored as padded JPEG byte arrays.

At a glance

What this conversion proves

• —Native ALOHA episode files do not need to be manually reshaped into robomimic-style data/demo_* groups.
• —Padded JPEG arrays are decoded frame by frame, checked for complete alignment, and emitted as three standard LeRobot video features.
• —Arm and mobile-base commands remain distinguishable in the 16-D action names while being presented as one training-ready action vector.
• —Every published claim is backed by a pinned input revision, SHA-256, feature mapping, machine-readable validation report, and official-reader test.

Features and source mapping

Original-action preservation

The converter copies the source arm and base command rows directly, with a float32 cast only, and concatenates them in the documented order. It does not replay a policy, regenerate commands, interpolate action values, or infer base motion from images. Camera frames are decoded from source JPEG payloads and re-encoded as H.264, so pixels are training-aligned but not byte-identical to the HDF5 payload.

Validation evidence

The complete evidence is in `validation-report.json`. All eight critical automated checks passed:

• —Dataset metadata counts
• —Parquet schema and frame count
• —Episode boundaries and 50 Hz timestamp regularity
• —Feature dimensions and finite values across 1,500 frames
• —LeRobot v3 relational metadata
• —Three-stream video decoding and frame alignment
• —License and provenance completeness
• —Official LeRobot reader smoke test (1 episode / 1,500 frames loaded)

The readiness score is 84/100 — Review recommended. The report discloses two medium-confidence robust-outlier signals: 78.00% of frames for observation.state and 53.13% for action. Because each frame is flagged when any one of 42 state or 16 action dimensions exceeds six median absolute deviations, these rates are not proof of corruption; they are a prompt to inspect robot-specific units, low-variance channels, gripper transitions, and base-motion events before training.

Source, revision, and integrity

• —Source mirror: `sumo43/mobile-aloha`
• —Pinned source revision: `855619e6f1a0aa3effca74fdcdfd651bc80bab9a`
• —Source file: public_aloha_mobile_dataset/aloha_mobile_cabinet/episode_0.hdf5
• —Source SHA-256: d82fedbf16a054c126db6e5f7c7dbc4732398ce6d1ffcd242adfbe1d979cfd0e
• —Converter adapter: aloha adapter 1.1.0
• —Adapter implementation SHA-256: 98ca4808d193a7afb5fe63bf33ca2731f560079addbba5070f07ef3634175111

Audit artifacts: `provenance.json`, `bundle-manifest.json`, `bundle-manifest.external.json`, and `UPSTREAM_LICENSE.md`.

License and attribution

The pinned source-mirror card declares `MIT` and links the Mobile ALOHA paper. This conversion retains that declared identifier, source-mirror identity, and paper citation. Conversion does not transfer ownership, create affiliation, or replace the source terms.

Intended use

• —Validating native ALOHA/Mobile ALOHA HDF5 ingestion and LeRobot v3 readers
• —Testing multimodal loaders with synchronized high, left-wrist, and right-wrist cameras
• —Auditing arm-plus-base action mappings before imitation-learning experiments
• —Reproducing conversion and validation from a pinned source artifact

Limitations

• —This is a community conversion of one mirrored episode, not the complete Mobile ALOHA Cabinet dataset or an official upstream release.
• —Users should independently review the mirror's terse license declaration before high-stakes commercial redistribution.
• —The 42-D state includes qpos, qvel, and effort; policies may require a narrower state selection.
• —JPEG decoding followed by H.264 encoding is not pixel-byte-preserving.
• —Automated validation does not measure task success, demonstration quality, or downstream policy performance.

Load with LeRobot

from lerobot.datasets.lerobot_dataset import LeRobotDataset

dataset = LeRobotDataset("ViaCatalyst/mobile-aloha-cabinet-episode0-lerobot-v3")
print(dataset.meta.total_episodes, dataset.meta.total_frames)

Conversion tooling

Converted and validated with the ViaCatalyst BYOD Processing Platform, a free web workflow for converting robotics datasets to LeRobot format. For high-volume ALOHA data, contact ViaCatalyst support through the platform.

Citation

Please cite the original Mobile ALOHA work:

@inproceedings{fu2024mobile,
  title={Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation},
  author={Fu, Zipeng and Zhao, Tony Z. and Finn, Chelsea},
  booktitle={Conference on Robot Learning},
  year={2024}
}