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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
.npzdemonstrations - 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
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"])
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