Search is not available for this dataset
image imagewidth (px) 256 256 | wrist_image imagewidth (px) 256 256 | state list | actions list | timestamp float32 0 49.9 | frame_index int64 0 499 | episode_index int64 0 150 | index int64 0 58.9k | task_index int64 0 6 |
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End of preview. Expand in Data Studio
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Check out the documentation for more information.
LIBERO 40 Suboptimal Flow-Policy Rollouts
This LeRobot dataset contains rollouts generated by suite-specific flow-matching behavior-cloning policies on all 40 tasks in LIBERO-Spatial, LIBERO-Object, LIBERO-Goal, and LIBERO-10.
Dataset summary
| Suite | Episodes | Successes | Failures | Success rate |
|---|---|---|---|---|
| LIBERO-Goal | 250 | 144 | 106 | 57.6% |
| LIBERO-Spatial | 250 | 161 | 89 | 64.4% |
| LIBERO-Object | 250 | 117 | 133 | 46.8% |
| LIBERO-10 | 250 | 145 | 105 | 58.0% |
| Total | 1,000 | 567 | 433 | 56.7% |
- Tasks: 40
- Frames: 317,187
- Episodes per task: 25
- FPS: 10
- Robot type: Panda
Task and episode order
Task indices exactly match physical-intelligence/libero:
| Task indices | Suite | Episode indices |
|---|---|---|
| 0–9 | LIBERO-10 | 0–249 |
| 10–19 | LIBERO-Goal | 250–499 |
| 20–29 | LIBERO-Object | 500–749 |
| 30–39 | LIBERO-Spatial | 750–999 |
Each task contains 25 consecutive episodes ordered as demo_0 through
demo_24.
Features
| Feature | Shape | Type | Description |
|---|---|---|---|
image |
256×256×3 | image | External camera |
wrist_image |
256×256×3 | image | Wrist camera |
state |
8 | float32 | 6-D end-effector pose plus 2-D gripper state |
actions |
7 | float32 | Native LIBERO delta action |
task |
scalar | string | Natural-language instruction |
The two camera images use the same convention as OpenPI's LIBERO inference
client: raw simulator frames are rotated 180 degrees with [::-1, ::-1] and
then resized to 256×256.
Actions are stored in native LIBERO delta-action space.
The rollouts were executed with MuJoCo 3.3.6 and robosuite 1.4.1. LIBERO environment source, assets, BDDL task files, and initial states were verified to match the OpenPI LIBERO checkout byte-for-byte.
Success and provenance metadata
episode_manifest.jsonl contains one record per episode:
{
"episode_index": 0,
"task_index": 0,
"suite": "libero_10",
"task_file": "LIVING_ROOM_SCENE5_put_the_white_mug_on_the_left_plate_and_put_the_yellow_and_white_mug_on_the_right_plate_demo.hdf5",
"raw_demo": "demo_0",
"task": "put the white mug on the left plate and put the yellow and white mug on the right plate",
"success": true,
"length": 255
}
This manifest should be used to construct expert/generated-success/failure sampling groups.
OpenPI normalization
openpi_assets/norm_stats.json is a reproducibility copy of the statistics
computed by OpenPI's scripts/compute_norm_stats.py for this dataset. It
contains mean, standard deviation, 1st percentile, and 99th percentile for
state and actions.
For π0.5, OpenPI applies quantile normalization in memory:
2 * (x - q01) / (q99 - q01 + 1e-6) - 1
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