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license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/**
annotations_creators: []
language: en
size_categories:
- n<1K
task_categories:
- robotics
pretty_name: RoboMIND (FiftyOne multimodal MCAP subset)
tags:
- fiftyone
- multimodal
- mcap
- robotics
- manipulation
---
# RoboMIND → FiftyOne (Native Multimodal MCAP)
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}
}
```
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