---
license: mit
pretty_name: "79.5 MB Mobile ALOHA HDF5 → 3-camera LeRobot v3"
task_categories:
- robotics
tags:
- LeRobot
- robotics
- imitation-learning
- multimodal
- hdf5
- aloha
- mobile-aloha
- community-conversion
- v3.0
---

# 79.5 MB Mobile ALOHA HDF5 → 3-camera LeRobot v3

[![Converted and validated with ViaCatalyst](https://img.shields.io/badge/Converted%20%26%20validated-ViaCatalyst-0F766E)](https://byod.viacatalyst.com)

> **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

| Property | Value |
|---|---:|
| Input format / size | Native HDF5 / 79,478,352 bytes |
| LeRobot format | `v3.0` |
| Robot | Mobile ALOHA |
| Task | Open the top cabinet, store the pot inside it, then close the cabinet |
| Episodes / frames | 1 / 1,500 |
| Duration / FPS | 30.0 seconds / 50 |
| Observation modalities | 42-D state + 3 RGB camera streams |
| Action | 14-D arm action + 2-D base action |
| Image resolution | 480 × 640 × 3, H.264 |
| Official LeRobot reader | Passed with LeRobot `0.6.0` |

## 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

| LeRobot feature | dtype | shape | Source mapping |
|---|---|---:|---|
| `action` | `float32` | `[16]` | Direct concatenation of root `action` `[14]` followed by `base_action` `[2]` |
| `observation.state` | `float32` | `[42]` | `observations/qpos`, `observations/qvel`, and `observations/effort`, 14 values each |
| `observation.images.cam_high` | video | `[480, 640, 3]` | JPEG frames decoded from `observations/images/cam_high` |
| `observation.images.cam_left_wrist` | video | `[480, 640, 3]` | JPEG frames decoded from `observations/images/cam_left_wrist` |
| `observation.images.cam_right_wrist` | video | `[480, 640, 3]` | JPEG frames decoded from `observations/images/cam_right_wrist` |
| `episode_index` / `frame_index` | `int64` | `[1]` | One source HDF5 file becomes one episode; frame order is preserved |
| `timestamp` | `float32` | `[1]` | `frame_index / 50` seconds |
| `task_index` | `int64` | `[1]` | Maps to the Cabinet instruction in `meta/tasks.parquet` |

### 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`](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`](https://huggingface.co/datasets/sumo43/mobile-aloha)
- Pinned source revision: [`855619e6f1a0aa3effca74fdcdfd651bc80bab9a`](https://huggingface.co/datasets/sumo43/mobile-aloha/tree/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`](provenance.json), [`bundle-manifest.json`](bundle-manifest.json), [`bundle-manifest.external.json`](bundle-manifest.external.json), and [`UPSTREAM_LICENSE.md`](UPSTREAM_LICENSE.md).

## License and attribution

The pinned source-mirror card declares [`MIT`](https://huggingface.co/datasets/sumo43/mobile-aloha/blob/855619e6f1a0aa3effca74fdcdfd651bc80bab9a/README.md) 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

```python
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](https://byod.viacatalyst.com), 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:

```bibtex
@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}
}
```