---
license: cc-by-4.0
pretty_name: "1.62 GB MimicGen HDF5 → multimodal LeRobot v3"
task_categories:
- robotics
tags:
- LeRobot
- robotics
- imitation-learning
- offline-rl
- multimodal
- mimicgen
- community-conversion
- v3.0
---

# 1.62 GB MimicGen HDF5 → multimodal LeRobot v3

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

> **Before → after:** HDF5 demonstrations with two RGB streams become a validated, multimodal LeRobot v3.0 subset. **[Convert HDF5 free →](https://byod.viacatalyst.com/login?utm_source=huggingface&utm_medium=organic&utm_campaign=robotics_conversion_gallery&utm_content=mimicgen_card)**

> Community conversion produced by ViaCatalyst BYOD. This repository is not an official upstream release and is not affiliated with the MimicGen authors.

This is a compact, provenance-complete conversion of the first 10 episodes from the pinned MimicGen Square D0 core HDF5 file. It provides a reproducible **multimodal** LeRobot v3 reference dataset and conversion-quality example.

## At a glance

| Property | Value |
|---|---:|
| LeRobot format | `v3.0` |
| Robot | Panda |
| Task | Assemble the square nut on its matching peg |
| Episodes | 10 |
| Frames | 1,444 |
| Duration | 72.20 seconds |
| FPS | 20 |
| Observation modalities | State + 2 RGB camera streams |
| RGB streams | `agentview_image`, `robot0_eye_in_hand_image` |
| Image resolution | 84 × 84 × 3, H.264 |
| Action / state dimension | 7 / 59 |
| Official LeRobot reader | Passed with LeRobot `0.6.0` |

## Features and source mapping

| LeRobot feature | dtype | shape | Source mapping |
|---|---|---:|---|
| `action` | `float32` | `[7]` | Direct per-frame copy from each demonstration's `actions` dataset, with a float32 cast only |
| `observation.state` | `float32` | `[59]` | Deterministic concatenation of numeric `obs/*` vectors; names are documented in `meta/info.json` |
| `observation.images.agentview_image` | video | `[84, 84, 3]` | Aligned RGB frames from `obs/agentview_image`, encoded H.264 at 20 FPS |
| `observation.images.robot0_eye_in_hand_image` | video | `[84, 84, 3]` | Aligned RGB frames from `obs/robot0_eye_in_hand_image`, encoded H.264 at 20 FPS |
| `episode_index` / `frame_index` | `int64` | `[1]` | Preserved HDF5 episode boundaries and zero-based frame positions |
| `timestamp` | `float32` | `[1]` | `frame_index / 20` seconds |
| `task_index` | `int64` | `[1]` | Maps to the Square task in `meta/tasks.parquet` |

### Original-action preservation

The conversion reads the original `actions` array for every selected episode and writes each row directly to `action`. It does **not** replay a learned policy, regenerate actions, interpolate action values, or relabel demonstrations. The two camera streams are retained only when aligned to their episode frames; output timestamps are normalized to the declared 20 FPS.

## Validation evidence

The complete machine-readable 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 timestamp regularity
- Feature dimensions and finite values
- LeRobot v3 relational metadata
- Video-frame alignment
- License and provenance completeness
- Official LeRobot reader smoke test

The readiness score is **84/100 — Review recommended**. Two medium-confidence statistical signals are disclosed: `observation.state` has an 84.76% robust-outlier frame rate and `action` has a 4.85% rate. These are robust distribution signals, not proof of corruption; review the feature-level evidence before training.

## Source, revision, and integrity

- Source dataset: [`amandlek/mimicgen_datasets`](https://huggingface.co/datasets/amandlek/mimicgen_datasets)
- Pinned source revision: [`33016f8a62c02334f929f2913af8fdd2a8a129e1`](https://huggingface.co/datasets/amandlek/mimicgen_datasets/tree/33016f8a62c02334f929f2913af8fdd2a8a129e1)
- Source file: `core/square_d0.hdf5`
- Source file size: `1,621,351,476` bytes
- Source SHA-256: `41fc24bce0f88343099c0b1b5bf6eee08cbc35851e71276d4509a01c9b75481c`
- Converter adapter: `mimicgen` adapter `1.0.0`
- Converter code revision: `4230bc4b56abe85116b202398b1a625a81c1c55c`

Additional audit files: [`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 upstream MimicGen project states that its datasets are released under [`CC-BY 4.0`](https://huggingface.co/datasets/amandlek/mimicgen_datasets/blob/33016f8a62c02334f929f2913af8fdd2a8a129e1/README.md). This conversion retains that identifier, attribution, and citation. Conversion does not transfer ownership, create affiliation, or replace upstream terms.

## Intended use

- Testing multimodal LeRobot v3 readers, video handling, and data pipelines
- Small offline imitation-learning or educational experiments
- Reproducible HDF5-to-LeRobot conversion evaluation
- Comparing schema, provenance, and validation tooling across state and camera streams

## Limitations

- This is a community conversion, not an official upstream release.
- It contains only the first 10 episodes from the pinned Square D0 core file, not the complete MimicGen corpus or full Square D0 split.
- It retains only adapter-recognized state, action, and RGB streams; inspect `meta/info.json` before training.
- Statistical quality signals require domain-aware review before training.
- Automated validation does not measure policy performance, simulation transfer, or task success.

## Load with LeRobot

```python
from lerobot.datasets.lerobot_dataset import LeRobotDataset

dataset = LeRobotDataset("ViaCatalyst/mimicgen-square-d0-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 workflow for converting robotics datasets to LeRobot format. For high-volume datasets, contact ViaCatalyst support through the platform.

## Citation

Please cite the original MimicGen work:

```bibtex
@inproceedings{mandlekar2023mimicgen,
  title={MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations},
  author={Mandlekar, Ajay and Nasiriany, Soroush and Wen, Bowen and Akinola, Iretiayo and Narang, Yashraj and Fan, Linxi and Zhu, Yuke and Fox, Dieter},
  booktitle={7th Annual Conference on Robot Learning},
  year={2023}
}
```