--- 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) ![preview](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} } ```