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---
tags:
- lerobot
- robotics
- libero
- libero-plus
pretty_name: LIBERO Plus libero_plus_object (detailed LeRobot v3.0)
---
# libero_plus_object: detailed LeRobot v3.0
This dataset was converted from the LIBERO Plus LeRobot v2.1 `libero_plus_object` partition.
The original 8D state and 7D action vectors are preserved exactly as
`raw_state.ref_state` and `raw_action.ref_action`. Canonical low-dimensional fields follow
`failure_rollout_data/dataset.md`; `debug.gripper_eef_*` contains the ground-truth next-step
relative EEF motion for inspection.
## Required camera transform for canonical training
The source `observation.images.front` and `observation.images.wrist` videos are preserved
unchanged. **For canonical training, horizontally flip both camera views at load time.** The
videos in this repository are deliberately not rewritten or re-encoded.
The source-pipeline root cause is a composition of two image transforms: robosuite returns a
vertically inverted render, and the original dataset writer then rotates it by 180 degrees (flips
both image axes). The vertical flips cancel, leaving the stored image horizontally mirrored. This
is also tracked in [LeRobot issue #3830](https://github.com/huggingface/lerobot/issues/3830).
For an array whose layout ends in `(height, width, channels)`:
```python
image = np.flip(image, axis=-2)
```
For a tensor whose width is the last dimension, such as `(..., channels, height, width)`:
```python
image = torch.flip(image, dims=(-1,))
```
Apply the image transform only to the camera pixels. Do not flip or negate `raw_state.*`,
`raw_target.*`, `state.*`, `target.*`, or `debug.*`; those fields remain proper right-handed
coordinate representations.
## Conversion notes
- No frames were filtered. The historical LIBERO no-op predicate is audit-only; see
`meta/noop_audit.json` and `meta/noop_audit_episodes.jsonl`.
- Rotation and reconstruction checks are recorded in `meta/conversion_validation.json`.
- Alignment and controller-scale assumptions are recorded in `meta/conversion_config.json`.
- `meta/stats.json` includes `q01` and `q99` for every numeric and video feature. Numeric
quantiles use every frame; video quantiles use a deterministic uniform sample of stored frames.