README.md

---
license: cc-by-4.0
task_categories:
- robotics
tags:
- LeRobot
configs:
- config_name: default
  data_files: data/*/*.parquet
---


# Gello Dataset - LeRobot v2.1 Format (10Hz)

This dataset contains robotic manipulation demonstrations converted to the LeRobot v2.1 data format, matching the structure of the berkeley_autolab_ur5 dataset. This version captures data at 10Hz for efficient storage and processing.

## Dataset Overview

- **Task**: Pick up the hard drive and place it in the grey box.
- **Total Episodes**: 256
- **Total Frames**: 42,145
- **Total Videos**: 512 (2 video types per episode)
- **Format**: LeRobot v2.1
- **FPS**: 10
- **Video Resolution**: 640x480

## Directory Structure

```
gello_lerobot_v21_zed_10hz/
├── data/
│   └── chunk-000/
│       ├── episode_000000.parquet
│       ├── episode_000001.parquet
│       └── ... (256 episodes total)
├── meta/
│   ├── episodes_stats.jsonl     # Per-episode statistics
│   ├── episodes.jsonl           # Episode metadata
│   ├── info.json               # Dataset metadata
│   └── tasks.jsonl             # Task descriptions
└── videos/
    └── chunk-000/
        ├── observation.images.hand_image/
        │   ├── episode_000000.mp4
        │   └── ... (256 videos)
        └── observation.images.image/
            ├── episode_000000.mp4
            └── ... (256 videos)
```

## Data Fields

### Observation Fields
- **`observation.state`**: Robot joint positions and gripper state (7D float32)
- **`observation.images.hand_image`**: Wrist camera view (640x480x3 video)
- **`observation.images.image`**: Base camera view (640x480x3 video)

### Action Fields
- **`action`**: Robot actions (7D float32)

### Metadata Fields
- **`timestamp`**: Time step in seconds (float32)
- **`episode_index`**: Episode identifier (int64)
- **`frame_index`**: Frame index within episode (int64)
- **`next.reward`**: Reward signal (float32)
- **`next.done`**: Episode termination flag (bool)
- **`index`**: Global frame index (int64)
- **`task_index`**: Task identifier (int64)

## Usage with LeRobot

This dataset is compatible with LeRobot v2.1 tools and can be loaded using:

```python
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset

# Load the dataset
dataset = LeRobotDataset("/path/to/gello_lerobot_v21_zed_10hz")

# Access episodes
for episode in dataset:
    print(f"Episode {episode['episode_index']}: {episode['total_frames']} frames")
```

## Video Format

- **Codec**: AV1
- **Resolution**: 640x480 pixels
- **Channels**: 3 (RGB)
- **Pixel Format**: YUV420p
- **Frame Rate**: 10 FPS

## Task Description

All episodes in this dataset demonstrate the same task:
> "Pick up the hard drive and place it in the grey box."

## Dataset Statistics

- **Average Episode Length**: ~165 frames
- **Average Episode Duration**: ~16.5 seconds

## File Formats

### Parquet Files
Each episode is stored as a Parquet file containing all the data fields with proper data types.

### JSONL Files
- **`episodes_stats.jsonl`**: Detailed statistics for each episode
- **`episodes.jsonl`**: Episode metadata including task assignments
- **`tasks.jsonl`**: Task definitions

### JSON Files
- **`info.json`**: Dataset metadata including feature specifications, splits, and paths

## Conversion Notes

This dataset was converted from raw Gello pickle files using the `gello_to_lerobot.py` script. The conversion process:

1. Loads raw demonstration data from pickle files
2. Processes images (resize to 640x480, convert to videos)
3. Creates LeRobot v2.1 directory structure
4. Saves episodes as individual Parquet files
5. Generates metadata files in the required format
6. **Captures data at 10Hz for efficient storage and processing**

## Compatibility

This dataset follows the same structure as the berkeley_autolab_ur5 dataset and is fully compatible with:
- LeRobot v2.1 data loading utilities
- HuggingFace datasets library
- Standard machine learning frameworks (PyTorch, TensorFlow)

## License

This dataset is provided for research and educational purposes. Please refer to the original Gello dataset license for usage terms.