gate-institute/GATE-VLAP-datasets

GATE-VLAP Datasets

Grounded Action Trajectory Embeddings with Vision-Language Action Planning

This repository contains preprocessed datasets from the LIBERO benchmark suite in WebDataset TAR format, specifically designed for training vision-language-action models with semantic action segmentation.

Data Format: WebDataset TAR

We provide datasets in WebDataset TAR format for optimal performance:

✅ Fast loading - Efficient streaming during training
✅ Easy downloading - Single file per subtask
✅ HuggingFace optimized - Quick browsing and file listing
✅ Inspectable - Extract locally to view individual frames

Extracting TAR Files

# Download a subtask
wget https://huggingface.co/datasets/gate-institute/GATE-VLAP-datasets/resolve/main/libero_10/pick_up_the_black_bowl.tar

# Extract all files
tar -xf pick_up_the_black_bowl.tar

# View structure
ls
# Output: demo_0/  demo_1/  demo_2/  ...

# View demo contents
ls demo_0/
# Output: demo_0_timestep_0000.png  demo_0_timestep_0000.json
#         demo_0_timestep_0001.png  demo_0_timestep_0001.json
#         ...

Loading Raw Data (After Extraction)

from pathlib import Path
import json
from PIL import Image
import numpy as np

def load_demo(demo_dir):
    """Load a single demonstration from extracted TAR."""
    frames = []
    demo_path = Path(demo_dir)
    
    for json_file in sorted(demo_path.glob("*.json")):
        # Load metadata
        with open(json_file) as f:
            data = json.load(f)
        
        # Load image
        png_file = json_file.with_suffix(".png")
        data["image"] = np.array(Image.open(png_file))
        
        frames.append(data)
    
    return frames

# After extracting pick_up_the_black_bowl.tar
demo = load_demo("demo_0")
print(f"Demo length: {len(demo)} frames")
print(f"Action shape: {demo[0]['action']}")

Loading with WebDataset (Direct Streaming)

import webdataset as wds
from PIL import Image
import json

# Stream data directly from HuggingFace (no download needed!)
url = "https://huggingface.co/datasets/gate-institute/GATE-VLAP-datasets/resolve/main/libero_10/pick_up_the_black_bowl.tar"

dataset = wds.WebDataset(url).decode("rgb")

for sample in dataset:
    # sample["png"] = PIL Image (128x128 RGB)
    # sample["json"] = bytes (JSON metadata)
    metadata = json.loads(sample["json"])
    image = sample["png"]
    
    print(f"Action: {metadata['action']}")
    print(f"Image shape: {np.array(image).shape}")
    break

Training with Multiple Subtasks

import webdataset as wds
import torch
from torch.utils.data import DataLoader

# Load multiple subtasks at once
base_url = "https://huggingface.co/datasets/gate-institute/GATE-VLAP-datasets/resolve/main/libero_10/"
subtasks = ["pick_up_the_black_bowl", "close_the_drawer", "open_the_top_drawer"]
urls = [f"{base_url}{task}.tar" for task in subtasks]

dataset = (
    wds.WebDataset(urls)
    .decode("rgb")
    .to_tuple("png", "json")
    .map(preprocess_fn)  # Your preprocessing function
)

dataloader = DataLoader(dataset, batch_size=32, num_workers=4)

for images, actions in dataloader:
    # Train your model
    pass

Datasets Included

LIBERO-10 (Long-Horizon Tasks)

• Task Type: 10 complex, long-horizon manipulation tasks
• Segmentation Method: Semantic Action Chunking using Gemini Vision API
• Demos: 1,354 demonstrations across 29 subtasks
• Frames: 103,650 total frames
• TAR Files: 29 files (one per subtask)

Example Tasks:

• pick_up_the_black_bowl.tar → Pick and place subtasks
• close_the_drawer.tar → Approach, grasp, close subtasks
• put_the_bowl_in_the_drawer.tar → Multi-step pick, open, place, close sequence

LIBERO-Object (Object Manipulation)

• Task Type: 10 object-centric manipulation tasks
• Segmentation Method: Semantic Action Chunking using Gemini Vision API
• Demos: 875 demonstrations across 20 subtasks
• Frames: 66,334 total frames
• TAR Files: 20 files (one per subtask)

Example Tasks:

• pick_up_the_alphabet_soup.tar → Approach, grasp, lift
• place_the_alphabet_soup_on_the_basket.tar → Move, position, place, release

📁 Dataset Structure

gate-institute/GATE-VLAP-datasets/
├── libero_10/                          # Long-horizon tasks (29 TAR files)
│   ├── close_the_drawer.tar
│   ├── pick_up_the_black_bowl.tar
│   ├── open_the_top_drawer.tar
│   └── ... (26 more)
│
├── libero_object/                      # Object manipulation (20 TAR files)
│   ├── pick_up_the_alphabet_soup.tar
│   ├── place_the_alphabet_soup_on_the_basket.tar
│   └── ... (18 more)
│
└── metadata/                           # Dataset statistics & segmentation
    ├── libero_10_complete_stats.json
    ├── libero_10_all_segments.json
    ├── libero_object_complete_stats.json
    └── libero_object_all_segments.json

Inside Each TAR File

After extracting pick_up_the_black_bowl.tar:

pick_up_the_black_bowl/
├── demo_0/
│   ├── demo_0_timestep_0000.png        # RGB observation (128×128)
│   ├── demo_0_timestep_0000.json       # Action + metadata
│   ├── demo_0_timestep_0001.png
│   ├── demo_0_timestep_0001.json
│   └── ...
├── demo_1/
│   └── ...
└── ... (all demos for this subtask)

Data Format

JSON Metadata (per timestep)

Each .json file contains:

{
  "action": [0.1, -0.2, 0.0, 0.0, 0.0, 0.0, 1.0],  // 7-DOF action
  "robot_state": [...],                             // Joint state
  "demo_id": "demo_0",
  "timestep": 42,
  "subtask": "pick_up_the_black_bowl",
  "parent_task": "LIBERO_10",
  "is_stop_signal": false                           // Segment boundary
}

Action Space

• Dimensions: 7-DOF
  • [0:3]: End-effector position delta (x, y, z)
  • [3:6]: End-effector orientation delta (roll, pitch, yaw)
  • [6]: Gripper action (0.0 = close, 1.0 = open)
• Range: Normalized to [-1, 1]
• Control: Delta actions (relative to current pose)

Image Format

• Resolution: 128×128 pixels
• Channels: RGB (3 channels)
• Format: PNG (lossless compression)
• Camera: Front-facing agentview camera

Metadata Files Explained

1. libero_10_complete_stats.json

Purpose: Overview statistics for the entire LIBERO-10 dataset

Use Cases:

• Understand dataset composition
• Plan training splits
• Check demo/frame distribution across tasks

2. libero_10_all_segments.json

Purpose: Detailed segmentation metadata for each demonstration

Contains semantic action chunks with:

• Segment boundaries (start/end frames)
• Action descriptions
• Segment types (reach, grasp, move, place, etc.)
• Gemini Vision API segmentation method

Use Cases:

• Train with semantic action chunks
• Implement hierarchical policies
• Analyze action primitives
• Filter by segment type

3. libero_object_complete_stats.json

Purpose: Statistics for LIBERO-Object dataset

4. libero_object_all_segments.json

Purpose: Segmentation for LIBERO-Object demonstrations with semantic action chunking

Citation

If you use this dataset, please cite:

@article{gateVLAP@SAC2026,
  title={Atomic Action Slicing: Planner-Aligned Options for Generalist VLA Agents},
  author={Stefan Tabakov, Asen Popov, Dimitar Dimitrov, Ensiye Kiyamousavi and Boris Kraychev},
  journal={arXiv preprint arXiv:XXXX.XXXXX},
  conference={The 41st ACM/SIGAPP Symposium On Applied Computing (SAC2026), track on Intelligent Robotics and Multi-Agent Systems (IRMAS)},
  year={2025}
}

@inproceedings{liu2023libero,
  title={LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning},
  author={Liu, Bo and Zhu, Yifeng and Gao, Chongkai and Feng, Yihao and Liu, Qiang and Zhu, Yuke and Stone, Peter},
  booktitle={NeurIPS Datasets and Benchmarks Track},
  year={2023}
}

Related Resources

• Model Checkpoints: gate-institute/GATE-VLAP
• Original LIBERO: https://github.com/Lifelong-Robot-Learning/LIBERO
• Paper: Coming soon

Acknowledgments

• LIBERO Benchmark: Original dataset by Liu et al. (2023)
• Segmentation: Gemini Vision API for semantic action chunking
• Institution: GATE Institute, Sofia, Bulgaria

Contact

For questions or issues, please contact the GATE Institute.

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Dataset Version: 1.0
Last Updated: December 2025
Maintainer: GATE Institute