---
license: apache-2.0
task_categories:
  - robotics
tags:
  - robotics
  - manipulation
  - libero
  - libero-pro
  - imitation-learning
  - vla
size_categories:
  - 1K<n<10K
---

# Robotics Dataset

Synthetic demonstration dataset for LIBERO-Pro robot manipulation, generated with the OpenRobot massive dataset pipeline.

## Contents

- **4,800 episodes** across **16 LIBERO-Pro suites** (300 episodes each)
- **~77 GB** of compressed `.npz` demonstrations
- **30 episodes per task** × **10 tasks** per suite

### Suites

| Suite | Episodes |
|-------|----------|
| libero_spatial | 300 |
| libero_spatial_lan | 300 |
| libero_spatial_object | 300 |
| libero_spatial_swap | 300 |
| libero_object | 300 |
| libero_object_lan | 300 |
| libero_object_object | 300 |
| libero_object_swap | 300 |
| libero_goal | 300 |
| libero_goal_lan | 300 |
| libero_goal_object | 300 |
| libero_goal_swap | 300 |
| libero_10 | 300 |
| libero_10_lan | 300 |
| libero_10_object | 300 |
| libero_10_swap | 300 |

## Episode format (`.npz`)

Each file is a compressed NumPy archive with:

| Key | Shape | Description |
|-----|-------|-------------|
| `image` | `(T, 224, 224, 3)` | Third-person RGB observations |
| `wrist_image` | `(T, 224, 224, 3)` | Wrist camera RGB observations |
| `state` | `(T, 8)` | Proprioceptive state |
| `actions` | `(T, 7)` | Robot actions (6-DOF + gripper) |
| `task` | scalar str | Natural-language task instruction |
| `suite` | scalar str | LIBERO-Pro suite name |

Files are named `ep_t{task_id:02d}_{trial:04d}.npz` inside per-suite subdirectories.

## Generation

Produced by `generate_massive_dataset.py` using privileged MuJoCo oracle trajectories, swap/standard layouts, linguistic paraphrases, and initial-state randomization.

## Usage

```python
import numpy as np

ep = np.load("libero_spatial/ep_t00_0010.npz", allow_pickle=True)
images = ep["image"]       # (T, 224, 224, 3)
actions = ep["actions"]    # (T, 7)
instruction = str(ep["task"])
```