README.md

metadata

license: mit
task_categories:
  - robotics
  - reinforcement-learning
tags:
  - drone
  - simulation
  - liftoff
  - imitation-learning
  - robot-learning
size_categories:
  - n<1K
  - 1K<n<10K
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: chunk-*/data/*.parquet

liftoff_drone_dataset

Dataset Description

This dataset contains telemetry and control data from the Liftoff drone simulator, recorded for robot learning and imitation learning tasks.

Dataset Summary

• Robot Type: liftoff_drone_simulator
• Total Episodes: 1
• Total Frames: 1357
• Recording FPS: 30.0 Hz
• Video FPS: 30.0 Hz
• LeRobot Version: v3.0
• Chunk Size: 100 episodes per chunk

Data Structure

The dataset is organized into chunks for efficient storage and retrieval:

dataset/
├── chunk-000/          # Episodes 0-99
│   ├── data/          # Parquet files with telemetry
│   ├── videos/        # MP4 videos (if available)
│   └── meta/          # Episode metadata
├── chunk-001/          # Episodes 100-199
│   └── ...
└── meta/              # Global metadata
    ├── info.json      # Dataset information
    └── stats.json     # Statistics

Features

Observation State (16 dimensions)

The observation state contains drone telemetry:

Index	Feature	Description
0-2	pos_x, pos_y, pos_z	Position in 3D space (meters)
3-6	ori_x, ori_y, ori_z, ori_w	Orientation quaternion
7-9	vel_x, vel_y, vel_z	Linear velocity (m/s)
10-12	ang_vel_x, ang_vel_y, ang_vel_z	Angular velocity (rad/s)
13-15	acc_x, acc_y, acc_z	Linear acceleration (m/s²)

Action (4 dimensions)

RC controller inputs:

Index	Feature	Description
0	throttle	Throttle input
1	yaw	Yaw control
2	pitch	Pitch control
3	roll	Roll control

Video Data

Each episode includes synchronized video at 30.0 FPS.

• Format: MP4
• Codec: mp4v
• Location: chunk-XXX/videos/episode_XXXXXX_liftoff.mp4

Statistics

Observation State Statistics

Feature	Mean	Std	Min	Max
pos_x	1108.4283	45.6066	1062.3221	1266.5591
pos_y	-1072.1070	45.1135	-1180.4570	-1014.4241
pos_z	2.3304	1.5687	0.0495	8.0291
ori_x	-0.1632	0.3821	-0.8127	0.9584
ori_y	0.1264	0.3482	-0.6710	0.7001
ori_z	0.2719	0.5303	-0.8301	0.8648
ori_w	-0.3539	0.4580	-0.8651	0.7685
vel_x	3.1551	16.4758	-31.6221	35.7065
vel_y	-0.4859	20.3263	-35.7660	33.6974
vel_z	0.1774	2.3431	-8.8202	11.1492
ang_vel_x	0.2769	1.6505	-8.2796	13.7966
ang_vel_y	0.1181	1.6863	-35.1017	35.8174
ang_vel_z	-0.1881	1.4257	-7.3040	34.0098
acc_x	0.1231	34.5961	-669.9283	359.1005
acc_y	-0.3573	24.0967	-140.9671	253.7777
acc_z	0.1464	14.7532	-63.3092	382.4714

Action Statistics

Feature	Mean	Std	Min	Max
throttle	0.4076	0.6514	-0.9951	0.9843
yaw	-0.0242	0.0779	-0.4100	0.2675
pitch	-0.0541	0.1754	-0.9043	0.5213
roll	0.0623	0.3170	-0.9440	0.8151

Usage

Loading the Dataset

from datasets import load_dataset

# Load from Hugging Face Hub
dataset = load_dataset("YOUR_USERNAME/YOUR_DATASET_NAME")

# Or load locally
dataset = load_dataset("parquet", data_files="chunk-*/data/*.parquet")

Using with LeRobot

from lerobot.common.datasets.lerobot_dataset import LeRobotDataset

# Load dataset
dataset = LeRobotDataset("YOUR_USERNAME/YOUR_DATASET_NAME")

print(f"Total episodes: {dataset.num_episodes}")
print(f"Total frames: {len(dataset)}")

# Iterate through data
for item in dataset:
    observation = item['observation.state']  # Shape: [16]
    action = item['action']  # Shape: [4]
    # ... use for training

Accessing Videos

Videos are stored as MP4 files in chunk directories:

import pandas as pd
from pathlib import Path

# Load episode data
df = pd.read_parquet("chunk-000/data/episode_000000.parquet")

# Video path is stored in metadata
# Load from: chunk-000/videos/episode_000000_liftoff.mp4

Dataset Creation

Recording Setup

• Simulator: Liftoff Drone Simulator
• Bridge: ROS2 Jazzy
• Recorder: Custom LeRobot-compatible recorder
• Control: RC controller or gamepad

Data Collection Process

1. Start Liftoff simulator
2. Launch ROS2 bridge (liftoff_bridge_ros2.py)
3. Start recorder with gamepad control
4. Fly manually and record episodes
5. Data automatically organized into chunks

Quality Control

• Episodes with crashes or poor control are discarded using emergency stop
• Each episode includes full telemetry and synchronized video
• Statistics computed across all episodes for normalization

Licensing

This dataset is released under the MIT License.

Citation

If you use this dataset in your research, please cite:

@misc{liftoff_drone_dataset,
  title={Liftoff Drone Simulator Dataset},
  author={Your Name},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/YOUR_USERNAME/YOUR_DATASET_NAME}}
}

Contact

For questions or issues, please open an issue on the dataset repository.

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Generated with LeRobot Liftoff Bridge