README.md

---
license: cc-by-4.0
task_categories:
- robotics
language:
- en
tags:
- robot-learning
- manipulation
- safety
- lerobot
- biochemical-lab
size_categories:
- 1K<n<10K
---

# LabSafe-1700

**LabSafe-1700** is the first VLA (Vision-Language-Action) dataset targeting **safe robotic manipulation in biochemical laboratory scenarios**. It contains 1,700 real-robot teleoperation trajectories collected on a SO-100 robotic arm, covering 17 task configurations including obstacle-free grasping and active obstacle avoidance tasks.

This dataset is introduced in the paper:

> **NeuroMPC-VLA: Embedding MPC-Structured Safety Mechanisms into Vision-Language-Action Models for Safe Manipulation in Biochemical Laboratories**  


---

## 📦 Dataset Overview

| Property | Value |
|---|---|
| Total trajectories | 1,700 (17 tasks × ~100 episodes each) |
| Task configurations | 17 (10 simple + 7 obstacle avoidance) |
| Robot platform | SO-100 6-DOF robotic arm |
| Camera views | 3× RGB cameras (OBS_IMAGE_1/2/3), 480×640, 30 fps |
| Annotation | Task labels per episode |
| Scene | Biochemical laboratory tabletop manipulation |
| Format | LeRobot v3.0 native (parquet + AV1 video) |

### Task Configurations

| Type | Tasks | Description |
|---|---|---|
| Simple (obstacle-free) | T01–T10 | Grasping and placing lab instruments (test tubes, beakers, funnels, etc.) without obstacles |
| Obstacle avoidance | T11–T17 | Same manipulation goals with one or two obstacles placed randomly on the path |

---

## 🗂️ Repository Structure

LeRobot records data in the following structure:

```
LabSafe-1700/
├── data/
│   └── chunk-000/
│       ├── file-000.parquet    # frame-level data (actions, states, timestamps)
│       └── ...
├── videos/
│   ├── observation.images.OBS_IMAGE_1/
│   │   └── chunk-000/
│   │       ├── file-000.mp4   # AV1, 480×640, 30fps
│   │       └── ...
│   ├── observation.images.OBS_IMAGE_2/
│   └── observation.images.OBS_IMAGE_3/
├── meta/
│   ├── info.json               # dataset-level metadata (LeRobot v3.0)
│   ├── episodes.jsonl          # per-episode metadata (task index, length)
│   ├── tasks.jsonl             # task descriptions
│   └── stats.json              # feature statistics
├── LICENSE
└── README.md
```

---

## 🚀 Quick Start

### Install LeRobot

```bash
pip install lerobot
```

### Load the dataset

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

dataset = LeRobotDataset("Merlyn-L/LabSafe-1700")

# Iterate over episodes
for i in range(dataset.num_episodes):
    episode = dataset.get_episode(i)
    frames_cam1 = episode["observation.images.OBS_IMAGE_1"]  # (T, 480, 640, 3)
    frames_cam2 = episode["observation.images.OBS_IMAGE_2"]  # (T, 480, 640, 3)
    frames_cam3 = episode["observation.images.OBS_IMAGE_3"]  # (T, 480, 640, 3)
    actions = episode["action"]                    # (T, 6) joint positions
    print(f"Episode {i}: {len(actions)} steps")
```

---

## 📊 Dataset Statistics

| Split | Episodes | Avg. steps | Description |
|---|---|---|---|
| Simple tasks (T01–T10) | ~1,000 | ~120 | Obstacle-free |
| Obstacle avoidance (T11–T17) | ~700 | ~180 | With obstacles |
| **Total** | **1,700** | — | — |

---

## 🤖 Hardware Setup

- **Robot**: SO-100 follower arm (so100_follower)
- **Teleoperation**: Leader-follower SO-100 arm via [LeRobot](https://github.com/huggingface/lerobot)
- **Cameras**: 3× RGB cameras (OBS_IMAGE_1/2/3), 480×640 @ 30 fps, AV1 codec
- **Lab instruments**: Test tubes, beakers, funnels, micro-well plates, centrifuge tubes

---

## 📄 Citation

If you use LabSafe-1700 in your research, please cite:

```bibtex
@misc{neurompc_vla_2025,
  title  = {NeuroMPC-VLA: Embedding MPC-Structured Safety Mechanisms into
            Vision-Language-Action Models for Safe Manipulation in
            Biochemical Laboratories},
  author = {NEU-ECAL},
  year   = {2025}
}
```

---

## 📜 License

This dataset is released under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](LICENSE) license.  
You are free to share and adapt the data for any purpose, provided appropriate credit is given.

---

## 🙏 Acknowledgements

Data collection was conducted using the [LeRobot](https://github.com/huggingface/lerobot) teleoperation framework on a SO-100 robotic arm. We thank the Hugging Face Robotics team for the open-source tools that made this dataset possible.