# 🤖 Robot Data Collection Guide

A practical guide for collecting clean, diverse, and meaningful datasets to train and evaluate robot models.

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## 📌 Why This Matters

High-quality data is the foundation for:

- Behavior cloning
- Multi-robot coordination
- Sim-to-real transfer
- Sensor fusion and modeling

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## 📂 What's Inside

- ✅ Types of data to collect (images, joint states, LiDAR, IMU)
- ✅ How to record multi-modal data (ROS 2 bag files)
- ✅ Labeling methods (manual, semi-auto)
- ✅ Data cleaning and preprocessing
- ✅ Storage & formats: `.db3`, `.csv`, `JSON`

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## 🛠 Tools You Can Use

| Tool            | Purpose                         |
|-----------------|----------------------------------|
| ROS 2           | Real-time data collection        |
| `rosbag2_py`    | Python access to .db3 files      |
| DB Browser      | Inspect SQLite `.db3` bags       |
| OpenCV / PIL    | Preprocess visual data           |
| Pandas          | Work with CSVs                   |
| Hugging Face    | Store models/datasets online     |

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## 📦 Example Commands

```bash
# Record robot states + camera stream
ros2 bag record /joint_states /camera/image_raw /tf