| --- |
| license: apache-2.0 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/** |
| annotations_creators: [] |
| language: en |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - robotics |
| pretty_name: RoboLab-EgoX (FiftyOne multimodal MCAP) |
| tags: |
| - fiftyone |
| - multimodal |
| - mcap |
| - robotics |
| - manipulation |
| - egocentric |
| - depth |
| - benchmark |
| --- |
| |
| # RoboLab-EgoX → FiftyOne (Native Multimodal MCAP) |
|
|
|  |
|
|
| The complete |
| [DAVIAN-Robotics/RoboLab-EgoX](https://huggingface.co/datasets/DAVIAN-Robotics/RoboLab-EgoX) |
| corpus, policy rollouts recorded on NVIDIA's |
| [RoboLab](https://github.com/NVLabs/RoboLab) manipulation benchmark, |
| converted to native multimodal MCAP episodes. |
|
|
| Each take carries three synchronized camera views with a matching 16-bit |
| depth stream, per-camera intrinsics, whole-arm telemetry, end-effector pose, |
| and the task instruction. Takes keep the benchmark's own success label, so |
| failed rollouts sit alongside successful ones. |
|
|
| ## Installation |
|
|
| ```bash |
| pip install fiftyone |
| ``` |
|
|
| ## Usage |
|
|
| ```python |
| import fiftyone as fo |
| import fiftyone.utils.huggingface as fouh |
| |
| dataset = fouh.load_from_hub( |
| "Voxel51/RoboLab-EgoX", |
| name="RoboLab-EgoX", |
| persistent=True, |
| ) |
| fo.launch_app(dataset) |
| ``` |
|
|
| Successful rollouts only: |
|
|
| ```python |
| view = dataset.match({"success": True}) |
| ``` |
|
|
| ## What you get |
|
|
| - 4,000 `.mcap` episodes of 81 frames each at 15 fps, 6.0 hours total |
| - All **28 RoboLab tasks** across all **99 background scenes** |
| - **632 successful and 3,368 failed** rollouts, labelled, with a median |
| of 126 failures per task |
| - Cameras as `foxglove.CompressedVideo`: `/ego-camera` from the wrist, |
| `/exo-left-camera` and `/exo-right-camera` from the exterior views, all |
| 640x360 H.264 passed through without re-encoding |
| - Depth as 16-bit PNG on `/ego-depth`, `/exo-left-depth` and |
| `/exo-right-depth`, at 320x180 |
| - `/ego-calibration`, `/exo-left-calibration`, `/exo-right-calibration` as |
| `foxglove.CameraCalibration` |
| - `/joint-positions` (13 joints) and `/actions` (8), each with a timeline plot |
| - `/end-effector-pose` as `foxglove.PoseInFrame` |
| - `/instruction` carrying the task's language instruction |
| - Per-episode fields: `take_name`, `task`, `background`, `instruction`, |
| `success`, `source_episode`, `num_frames`, `duration` |
|
|
| ## Notes on the conversion |
|
|
| Depth marks no-return pixels with the uint16 ceiling, `65535`, rather than |
| zero. That is over 60% of a typical exterior frame and about 12% of a wrist |
| frame, so any viewer that maps the full 16-bit range will render the real |
| depth as flat black. Mask `65535` before scaling. The values are carried |
| through unchanged. |
|
|
| Depth is half the camera resolution, 320x180 against 640x360. The |
| published `CameraCalibration` describes the 640x360 colour frame, so |
| projecting a depth frame to 3D means halving `fx`, `fy`, `cx` and `cy` |
| first. |
|
|
| In 153 of the 4,000 takes the source mp4s carry a trailing empty packet and |
| hold 80 real frames rather than 81, so all three camera streams are one |
| frame shorter than depth and telemetry. `num_frames` follows the |
| robot-state clock and reports 81. |
|
|
| Eight of the 28 tasks have no successful rollout anywhere in the source, so |
| those contribute failures only. |
|
|
| ## License & attribution |
|
|
| The RoboLab benchmark, its tasks and its scene assets are released by NVIDIA |
| under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0); see |
| [NVLabs/RoboLab](https://github.com/NVLabs/RoboLab). The rollouts converted |
| here were recorded and published by |
| [DAVIAN-Robotics](https://huggingface.co/DAVIAN-Robotics). This subset is |
| distributed under Apache 2.0 and ships the license text as `LICENSE`. |
|
|
| Changes from the source: conversion from mp4 and HDF5 to MCAP, and PNG |
| encoding of the depth stacks. Camera streams are the source |
| H.264 packets passed through unchanged. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{yang2026robolab, |
| title={RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies}, |
| author={Yang, Xuning and Dagli, Rishit and Zook, Alex and Hadfield, Hugo and Goyal, Ankit and Birchfield, Stan and Ramos, Fabio and Tremblay, Jonathan}, |
| journal={arXiv preprint arXiv:2604.09860}, |
| year={2026} |
| } |
| ``` |
|
|
