docs/data_format.md

Data format and usage notes

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Directory structure

Each task/variation instance contains 30 stored demonstrations and five review videos:

data/<suite>/task_<id>/<case>/
  episodes.hdf5
  metadata.json
  source_index.json
  review_videos/
    success_000.mp4
    ...
    success_004.mp4
viewer/
  episodes/<suite>/*.parquet
  review_videos/*.parquet
manifests/
scripts/inspect_episode.py

The case directory identifies a base task (BASE) or a perturbation variant. Coverage varies by task.

Suite directory	Included base task IDs
libero_spatial	1, 2, 3, 4, 5, 6, 7, 9
libero_object	0, 2, 3, 4, 5, 6, 7, 9
libero_goal	0, 1, 2, 4, 5, 7, 8, 9
libero_10	0, 1, 2, 3, 4, 6, 7, 9

Paths in original source_index.json files are relative to data/; a suffix after :: identifies an HDF5 group. Video references are relative to the instance directory. Referenced simulator and BDDL assets are not bundled with this dataset.

HDF5 schema

Each HDF5 file contains groups data/demo_0 through data/demo_29. In the table below, T is the number of steps in a demonstration.

Field under a demo group	Shape	Description
actions	(T, 7)	float32 relative control commands
obs/agentview_image	(T, 128, 128, 3)	uint8 RGB scene images
obs/robot0_eye_in_hand_image	(T, 128, 128, 3)	uint8 RGB wrist images
obs/robot0_eef_pos	(T, 3)	End-effector position
obs/robot0_eef_quat_xyzw	(T, 4)	End-effector quaternion, xyzw order
obs/robot0_gripper_qpos	(T, 2)	Gripper joint positions
obs/robot0_joint_pos	(T, 7)	Robot joint positions
obs/robot0_joint_vel	(T, 7)	Robot joint velocities
rewards	(T,)	Rewards
dones	(T,)	Episode termination flags
server_step_seconds	(T,)	Server timing, where present

HDF5 attributes include environment and generation metadata. Demo attributes contain success and source metadata, including the language instruction. Consult schema_example.json and the original attributes for controller, control frequency, and gripper semantics before integrating a training loader.

The HDF5 observations and actions retain their original values. Training code should select observation/action fields explicitly and apply any normalization required by the policy.

To inspect a downloaded instance:

pip install h5py
python LIBERO-Pro2.0/scripts/inspect_episode.py \
  LIBERO-Pro2.0/data/libero_spatial/task_1/BASE/episodes.hdf5

Episode index

The episodes Viewer configuration contains one row per stored demonstration. Each row includes the initial scene and wrist images, instruction, suite, task, variation, step count, success label, and HDF5 file/group. The four suite configurations are filtered views of these same records.

You can stream the index without downloading the full HDF5 data:

from datasets import load_dataset

# Install the datasets package first: pip install datasets
episodes = load_dataset(
    "zhouxueyang/LIBERO-Pro2.0-Train", "episodes", split="train", streaming=True
)
row = next(iter(episodes))
print(row["instruction"], row["num_steps"])
print(row["hdf5_path"], row["hdf5_group"])
row["agentview_image"].save("example.png")

Camera previews are lossless PNG encodings of the first HDF5 frame. Full temporal observations and actions remain in HDF5.

The review_videos configuration contains five successful example clips per instance. It identifies each clip's instance, but does not provide a verified clip-to-demo mapping. These clips are examples, not evaluation results or videos for every stored demonstration.

Trajectory provenance

The demonstrations were generated by a privileged-state oracle, rather than human teleoperation. The supplied metadata states that oracle privileged state is not recorded in the observations.

Of the 1,349 instances, 1,243 have 30 distinct source trajectories each. The remaining 106 replicate one successful source trajectory into 30 stored demonstrations. The source audit therefore reports 37,396 unique source trajectories among 40,470 stored demonstrations.

The Viewer index and episodes.jsonl expose keep_without_declared_fanout. Selecting true retains all ordinary records and one representative from each declared replicated instance:

episodes = episodes.filter(lambda row: row["keep_without_declared_fanout"])

This retains 37,396 records and removes declared copies only; it is not a new global content-based deduplication. Original source fingerprints are preserved when available. See fanout_instances.json for the affected instances and source_final_audit.json for the source audit.

This is a training-only dataset. When constructing your own evaluation split, keep related and replicated trajectories together to avoid leakage.

Statistics and integrity

statistics.json contains the dataset counts. release_validation.json describes structural checks; these do not independently replay the simulator or establish task success.

For a complete local download, verify payload checksums with:

cd LIBERO-Pro2.0
sha256sum -c manifests/SHA256SUMS

Subset downloads will omit files listed in the full checksum manifest. Viewer tables have separate hashes in viewer_files.jsonl.