#### NOTE
This is a **Hugging Face dataset**. For large datasets, ensure `huggingface_hub>=1.1.3` to avoid rate limits. Learn more in the <a href="https://docs.voxel51.com/integrations/huggingface.html#loading-datasets-from-the-hub" target="_blank">Hugging Face integration docs</a>.

<a href="https://huggingface.co/datasets/Voxel51/RoboMIND" target="_blank">![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-yellow)</a>

# RoboMIND → FiftyOne (Native Multimodal MCAP)

![preview](https://huggingface.co/datasets/Voxel51/RoboMIND/resolve/main/preview.gif)

A 32-episode subset of
[x-humanoid-robomind/RoboMIND](https://huggingface.co/datasets/x-humanoid-robomind/RoboMIND),
eight episodes from each of four robot embodiments (Franka, AgileX, Tien Kung,
UR), converted to native multimodal MCAP episodes. Each episode carries
per-camera RGB and depth streams, joint telemetry for every recorded arm with
timeline plot channels, and the language instruction.

## Installation

```bash
pip install fiftyone
```

## Usage

```python
import fiftyone as fo
import fiftyone.utils.huggingface as fouh

dataset = fouh.load_from_hub(
    "Voxel51/RoboMIND",
    name="RoboMIND",
    persistent=True,
)
fo.launch_app(dataset)
```

## What you get

- 32 `.mcap` episodes across `franka`, `agilex`, `tienkung`, and `ur`
- Per-embodiment telemetry channels discovered from the source layout
  (for example `/puppet-joint-position`, `/master-joint-velocity-left`),
  each with a timeline plot channel
- Per-episode fields: `embodiment`, `task`, `episode_id`, `num_frames`,
  `duration`

## License & attribution

The source dataset is released under
[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0). Changes from the
source: episode subsetting and conversion from HDF5 to MCAP. The source files
carry no per-frame timestamps; frames are timed at the 10 Hz cadence used by
RoboMIND’s own visualization tooling.

## Citation

```bibtex
@article{wu2024robomind,
  title={RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation},
  author={Wu, Kun and Hou, Chengkai and Liu, Jiaming and Che, Zhengping and others},
  journal={arXiv preprint arXiv:2412.13877},
  year={2024}
}
```