| --- |
| 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} |
| } |
| ``` |
|
|