suite stringclasses 4
values | task_name stringlengths 17 88 | demo_path stringlengths 79 153 | resolution int64 256 256 | state_semantics listlengths 3 3 | cameras dict | episodes listlengths 50 50 |
|---|---|---|---|---|---|---|
libero_10 | KITCHEN_SCENE3_turn_on_the_stove_and_put_the_moka_pot_on_it | /root/gpufree-data/code/libero/datasets/libero_10/KITCHEN_SCENE3_turn_on_the_stove_and_put_the_moka_pot_on_it_demo.hdf5 | 256 | [
"eef_pos_x_y_z",
"eef_axis_angle_x_y_z",
"gripper_qpos_2"
] | {
"agentview": {
"rgb_key": "agentview_rgb",
"depth_key": "agentview_depth",
"intrinsic_static": [
[
309.01934814453125,
0,
128
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[
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],
[
0,
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],
"extrinsic_s... | [
{
"demo_name": "demo_0",
"episode_dir": "episode_000",
"num_steps": 272,
"npz_file": "data.npz",
"shapes": {
"state": [
272,
8
],
"actions": [
272,
7
],
"agentview_depth": [
272,
256,
256
],
"wrist_... |
libero_10 | KITCHEN_SCENE4_put_the_black_bowl_in_the_bottom_drawer_of_the_cabinet_and_close_it | /root/gpufree-data/code/libero/datasets/libero_10/KITCHEN_SCENE4_put_the_black_bowl_in_the_bottom_drawer_of_the_cabinet_and_close_it_demo.hdf5 | 256 | [
"eef_pos_x_y_z",
"eef_axis_angle_x_y_z",
"gripper_qpos_2"
] | {
"agentview": {
"rgb_key": "agentview_rgb",
"depth_key": "agentview_depth",
"intrinsic_static": [
[
309.01934814453125,
0,
128
],
[
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],
[
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]
],
"extrinsic_s... | [
{
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"episode_dir": "episode_000",
"num_steps": 261,
"npz_file": "data.npz",
"shapes": {
"state": [
261,
8
],
"actions": [
261,
7
],
"agentview_depth": [
261,
256,
256
],
"wrist_... |
libero_goal | open_the_middle_drawer_of_the_cabinet | /root/gpufree-data/code/libero/datasets/libero_goal/open_the_middle_drawer_of_the_cabinet_demo.hdf5 | 256 | [
"eef_pos_x_y_z",
"eef_axis_angle_x_y_z",
"gripper_qpos_2"
] | {
"agentview": {
"rgb_key": "agentview_rgb",
"depth_key": "agentview_depth",
"intrinsic_static": [
[
309.01934814453125,
0,
128
],
[
0,
309.01934814453125,
128
],
[
0,
0,
1
]
],
"extrinsic_s... | [
{
"demo_name": "demo_0",
"episode_dir": "episode_000",
"num_steps": 138,
"npz_file": "data.npz",
"shapes": {
"state": [
138,
8
],
"actions": [
138,
7
],
"agentview_depth": [
138,
256,
256
],
"wrist_... |
libero_goal | open_the_top_drawer_and_put_the_bowl_inside | /root/gpufree-data/code/libero/datasets/libero_goal/open_the_top_drawer_and_put_the_bowl_inside_demo.hdf5 | 256 | [
"eef_pos_x_y_z",
"eef_axis_angle_x_y_z",
"gripper_qpos_2"
] | {
"agentview": {
"rgb_key": "agentview_rgb",
"depth_key": "agentview_depth",
"intrinsic_static": [
[
309.01934814453125,
0,
128
],
[
0,
309.01934814453125,
128
],
[
0,
0,
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]
],
"extrinsic_s... | [
{
"demo_name": "demo_0",
"episode_dir": "episode_000",
"num_steps": 170,
"npz_file": "data.npz",
"shapes": {
"state": [
170,
8
],
"actions": [
170,
7
],
"agentview_depth": [
170,
256,
256
],
"wrist_... |
libero_goal | push_the_plate_to_the_front_of_the_stove | /root/gpufree-data/code/libero/datasets/libero_goal/push_the_plate_to_the_front_of_the_stove_demo.hdf5 | 256 | [
"eef_pos_x_y_z",
"eef_axis_angle_x_y_z",
"gripper_qpos_2"
] | {
"agentview": {
"rgb_key": "agentview_rgb",
"depth_key": "agentview_depth",
"intrinsic_static": [
[
309.01934814453125,
0,
128
],
[
0,
309.01934814453125,
128
],
[
0,
0,
1
]
],
"extrinsic_s... | [
{
"demo_name": "demo_0",
"episode_dir": "episode_000",
"num_steps": 155,
"npz_file": "data.npz",
"shapes": {
"state": [
155,
8
],
"actions": [
155,
7
],
"agentview_depth": [
155,
256,
256
],
"wrist_... |
libero_goal | put_the_bowl_on_the_plate | /root/gpufree-data/code/libero/datasets/libero_goal/put_the_bowl_on_the_plate_demo.hdf5 | 256 | [
"eef_pos_x_y_z",
"eef_axis_angle_x_y_z",
"gripper_qpos_2"
] | {
"agentview": {
"rgb_key": "agentview_rgb",
"depth_key": "agentview_depth",
"intrinsic_static": [
[
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128
],
[
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],
[
0,
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]
],
"extrinsic_s... | [
{
"demo_name": "demo_0",
"episode_dir": "episode_000",
"num_steps": 90,
"npz_file": "data.npz",
"shapes": {
"state": [
90,
8
],
"actions": [
90,
7
],
"agentview_depth": [
90,
256,
256
],
"wrist_dept... |
libero_goal | put_the_bowl_on_the_stove | /root/gpufree-data/code/libero/datasets/libero_goal/put_the_bowl_on_the_stove_demo.hdf5 | 256 | [
"eef_pos_x_y_z",
"eef_axis_angle_x_y_z",
"gripper_qpos_2"
] | {
"agentview": {
"rgb_key": "agentview_rgb",
"depth_key": "agentview_depth",
"intrinsic_static": [
[
309.01934814453125,
0,
128
],
[
0,
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128
],
[
0,
0,
1
]
],
"extrinsic_s... | [
{
"demo_name": "demo_0",
"episode_dir": "episode_000",
"num_steps": 94,
"npz_file": "data.npz",
"shapes": {
"state": [
94,
8
],
"actions": [
94,
7
],
"agentview_depth": [
94,
256,
256
],
"wrist_dept... |
libero_goal | put_the_bowl_on_top_of_the_cabinet | /root/gpufree-data/code/libero/datasets/libero_goal/put_the_bowl_on_top_of_the_cabinet_demo.hdf5 | 256 | [
"eef_pos_x_y_z",
"eef_axis_angle_x_y_z",
"gripper_qpos_2"
] | {
"agentview": {
"rgb_key": "agentview_rgb",
"depth_key": "agentview_depth",
"intrinsic_static": [
[
309.01934814453125,
0,
128
],
[
0,
309.01934814453125,
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],
[
0,
0,
1
]
],
"extrinsic_s... | [
{
"demo_name": "demo_0",
"episode_dir": "episode_000",
"num_steps": 91,
"npz_file": "data.npz",
"shapes": {
"state": [
91,
8
],
"actions": [
91,
7
],
"agentview_depth": [
91,
256,
256
],
"wrist_dept... |
libero_goal | put_the_cream_cheese_in_the_bowl | /root/gpufree-data/code/libero/datasets/libero_goal/put_the_cream_cheese_in_the_bowl_demo.hdf5 | 256 | [
"eef_pos_x_y_z",
"eef_axis_angle_x_y_z",
"gripper_qpos_2"
] | {
"agentview": {
"rgb_key": "agentview_rgb",
"depth_key": "agentview_depth",
"intrinsic_static": [
[
309.01934814453125,
0,
128
],
[
0,
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],
[
0,
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]
],
"extrinsic_s... | [
{
"demo_name": "demo_0",
"episode_dir": "episode_000",
"num_steps": 92,
"npz_file": "data.npz",
"shapes": {
"state": [
92,
8
],
"actions": [
92,
7
],
"agentview_depth": [
92,
256,
256
],
"wrist_dept... |
libero_goal | put_the_wine_bottle_on_the_rack | /root/gpufree-data/code/libero/datasets/libero_goal/put_the_wine_bottle_on_the_rack_demo.hdf5 | 256 | [
"eef_pos_x_y_z",
"eef_axis_angle_x_y_z",
"gripper_qpos_2"
] | {
"agentview": {
"rgb_key": "agentview_rgb",
"depth_key": "agentview_depth",
"intrinsic_static": [
[
309.01934814453125,
0,
128
],
[
0,
309.01934814453125,
128
],
[
0,
0,
1
]
],
"extrinsic_s... | [
{
"demo_name": "demo_0",
"episode_dir": "episode_000",
"num_steps": 347,
"npz_file": "data.npz",
"shapes": {
"state": [
347,
8
],
"actions": [
347,
7
],
"agentview_depth": [
347,
256,
256
],
"wrist_... |
End of preview. Expand in Data Studio
LIBERO-3D
LIBERO-3D is a processed LIBERO demonstration dataset packaged for 3D-aware robot learning. It keeps the original LIBERO task suites while converting each trajectory into per-episode .npz files with RGB, depth, camera parameters, segmentation, states, and actions.
This repository is intended as a storage and distribution repo for dataset files. The Hugging Face dataset viewer is not expected to render these files directly because the payload is stored as binary .npz episodes rather than tabular data.
Contents
The repository is organized into four suites:
libero_goal/
libero_object/
libero_spatial/
libero_10/
README.md
Each suite contains 10 tasks. Each task contains 50 episodes. In total this repo contains 40 tasks and 2000 episodes.
Each task directory follows this structure:
<suite>/<task_name>/
metadata.json
_SUCCESS
episode_000/data.npz
episode_001/data.npz
...
episode_049/data.npz
Data Format
Each data.npz episode stores trajectory-aligned arrays. The exact keys can be checked from the accompanying metadata.json. The processed files include at least the following modalities:
state: robot state, shape(T, 8)actions: control actions, shape(T, 7)agentview_rgb: static camera RGB frames, shape(T, 256, 256, 3)agentview_depth: static camera depth frames, shape(T, 256, 256)wrist_rgb: wrist camera RGB frames, shape(T, 256, 256, 3)wrist_depth: wrist camera depth frames, shape(T, 256, 256)wrist_intrinsic: wrist camera intrinsics, shape(T, 3, 3)wrist_extrinsic: wrist camera extrinsics, shape(T, 4, 4)wrist_segmentation: wrist segmentation maps, shape(T, 256, 256, 2)
The task-level metadata.json also records:
- suite name and task name
- original source demo path
- image resolution
- state semantics
- static and dynamic camera calibration fields
- per-episode step counts and tensor shapes
Suite Summary
libero_goal: 10 goal-conditioned manipulation taskslibero_object: 10 object-centric pick-and-place taskslibero_spatial: 10 spatial-relation manipulation taskslibero_10: 10 long-horizon tasks from the LIBERO-10 suite
Source
This dataset is derived from the LIBERO benchmark datasets and reorganized into processed per-episode .npz files for downstream 3D learning pipelines.
- Official LIBERO repository: https://github.com/Lifelong-Robot-Learning/LIBERO
- Official Hugging Face dataset release for the original demo files: https://huggingface.co/datasets/yifengzhu-hf/LIBERO-datasets
Usage Notes
- This repo is for file hosting and dataset download, not for in-browser preview.
- Keep the directory names stable if downstream code expects suite names such as
libero_goalorlibero_10. - Do not upload macOS metadata files such as
._*or.DS_Store. - Large-file upload tooling such as
huggingface_huborhf upload-large-folderis recommended.
License
This processed release follows the upstream LIBERO dataset license labeling used in the official Hugging Face dataset repository: apache-2.0.
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