Add README.md

README.md

@@ -1,129 +1,129 @@
----
-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)
-
-![preview](preview.gif)
-
-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}
-}
-```
+---
+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)
+
+![preview](preview.gif)
+
+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 Annex-B H.264 without B-frames
+- 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, PNG encoding
+of the depth stacks, and re-encoding of the camera streams to Annex-B H.264
+without B-frames.
+
+## 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}
+}
+```