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The Robotics Course - Exam 1 - Classical Robotics
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Test your knowledge of Classical Robotics
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Welcome to the 🤗 Robotics Course
This free course will take you on a journey, from classical robotics to modern learning-based approaches, in understanding, implementing, and applying machine learning techniques to real robotic systems.
This course is based on the Robot Learning Tutorial, which is a comprehensive guide to robot learning for researchers and practitioners. Here, we are attempting to distill the tutorial into a more accessible format for the community.
This first unit will help you onboard. You’ll see the course syllabus and learning objectives, understand the structure and prerequisites, meet the team behind the course, learn about LeRobot and the surrounding Huggnig Face ecosystem, and explore the community resources that support your journey.
This course bridges theory and practice in Robotics! It's designed for students interested in understanding how machine learning is transforming robotics. Whether you're new to robotics or looking to understand learning-based approaches, this course will guide you step by step.
What to expect from this course?
Across the course you will study classical robotics foundations and modern learning‑based approaches, learn to use LeRobot, work with real robotics datasets, and implement state‑of‑the‑art algorithms. The emphasis is on practical skills you can apply to real robotic systems.
At the end of this course, you'll understand:
- how robots learn from data
- why learning-based approaches are transforming robotics
- how to implement these techniques using modern tools like LeRobot
What's the syllabus?
Here is the general syllabus for the robotics course. Each unit builds on the previous ones to give you a comprehensive understanding of Robotics.
| # | Topic | Description | Released |
|---|---|---|---|
| 0 | Course Introduction | Welcome, prerequisites, and course overview | ✅ |
| 1 | Introduction to Robotics | Why Robotics matters and LeRobot ecosystem | ✅ |
| 2 | Classical Robotics | Traditional approaches and their limitations | ✅ |
| 5 | Reinforcement Learning | How robots learn through trial and error | Coming Soon |
| 6 | Imitation Learning | Learning from demonstrations and behavioral cloning | Coming Soon |
| 7 | Foundation Models | Large-scale models for general robotics | Coming Soon |
Acknowledgments
We would like to extend our gratitude to the following projects and communities:
- LeRobot - The open-source robotics library that powers this course
- PyTorch - The deep learning framework used throughout
- The broader robotics research community for creating and sharing the datasets and algorithms that make Robotics possible
I found a bug, or I want to improve the course
Contributions are welcome 🤗
- If you found a bug or error, please open an issue and describe the problem.
- If you want to improve the course, you can contribute to the robotics community through LeRobot development.
- If you want to add content or suggest improvements, engage with the robotics community and share your ideas.