| --- |
| license: cc-by-4.0 |
| task_categories: |
| - robotics |
| tags: |
| - LeRobot |
| - robotics |
| - manipulation |
| - synthetic-augmentation |
| - reactor-x2 |
| configs: |
| - config_name: default |
| data_files: data/*/*.parquet |
| --- |
| |
| # reactor_x2_100 |
|
|
| 79 episodes, 6,818 frames of `so101_follower` arm data, each episode built by |
| adding a different distractor-fruit combo to one real recorded pick-and-place |
| episode with [Reactor](https://reactor.ai) XMAX X2 video editing. Standard |
| [LeRobot](https://github.com/huggingface/lerobot) v2.1 layout |
| (`meta/`, `data/`, `videos/` at the repo root), so it loads directly with: |
|
|
| ```python |
| from lerobot.common.datasets.lerobot_dataset import LeRobotDataset |
| ds = LeRobotDataset("kabilanKB/reactor_x2_100") |
| ``` |
|
|
| ## Task |
|
|
| `"Grab orange and place into plate"` — every episode keeps the same recorded |
| grasp; only the scene around the target orange changes. |
|
|
| ## Source data |
|
|
| - Base episode: `LightwheelAI/leisaac-pick-orange`, `episodes/orange_ep0` |
| - Robot: SO-101 follower arm, 6-DoF action / state, single front camera |
| (480x640, 30 fps) |
|
|
| ## How it was built |
|
|
| `build_fruit_addition_dataset.py` re-renders the source episode 95 times |
| (one per scene in `fruit_scenes95.json`), each time asking Reactor X2 to add a |
| different distractor fruit under a different lighting style, while the |
| original orange, plate, and arm motion stay put. Every output frame is gated |
| on the **recorded orange surviving per-frame** (not just a per-episode |
| average) — a scene only keeps the frames where the target is still |
| identifiable, and is truncated at the first sustained run of frames where it |
| isn't. This is the "add distractor objects" result from this project's |
| `PROJECT.md`: it works, conditionally, because the original action labels are |
| only valid where the target the policy was reaching for is still visibly the |
| same object. |
|
|
| Of 95 scenes attempted, **79 survived** with enough usable frames to keep; |
| 16 were dropped for producing too few frames after the survival gate (see |
| `meta/dropped.json` — mostly heavy occlusion cases like `kiwi_lamp` and |
| `pear_plum_midday`, which kept 0 frames). |
|
|
| ## Scene variation |
|
|
| - **9 distractor fruits**: apple, banana, grape, kiwi, lemon, peach, pear, |
| plum, strawberry (single fruits and multi-fruit combinations) |
| - **5 lighting styles**: gold (warm late-afternoon), overcast, midday, |
| lamp, morning |
|
|
| ## Dataset stats |
|
|
| | | | |
| |---|---| |
| | Episodes | 79 (of 95 scenes attempted) | |
| | Frames | 6,818 | |
| | FPS | 30 | |
| | Action / state dims | 6 | |
| | Camera | `observation.images.front`, 480x640 | |
| | Mean per-frame coverage | 0.873 | |
| | Mean target ΔE (median / p90) | 20.7 / 24.8 | |
| | Episodes truncated by the survival gate | 10 / 79 | |
| | Frames kept vs. rendered | 6,818 / 7,108 (96%) | |
|
|
| Per-episode provenance (scene id, prompt, coverage, ΔE, truncation, source |
| frame range) is recorded in `meta/augmentations.jsonl`. |
|
|
| ## Caveats |
|
|
| - This augments an existing action-labeled episode; X2 is pixels-only and |
| contributes no actions, proprioception, or camera pose of its own. |
| - Whether training on this augmented data actually improves a policy is |
| **unproven** — no A/B run against the un-augmented source has been done yet. |
| - Coverage and ΔE are automated proxies for "does the label still match the |
| pixels," not a guarantee of visual quality. |
|
|
