Welcome to the Machine Learning for Robotics workshop! This self-paced program will guide you through 10 exciting levels where you will learn to apply machine learning techniques to robotics. Complete all levels and a mini-project to earn your certificate.
- Foundational concepts of robotics and machine learning (ML).
- Hands-on experience with tools like Python, OpenCV, TensorFlow, and ROS.
- Practical applications like computer vision, reinforcement learning, and SLAM.
- Building an end-to-end robot simulation integrating ML techniques.
- The workshop is divided into 10 levels, each hosted on its own GitHub folder.
- Each level contains:
- A
README.md
with objectives, resources, and tasks. - Code templates and datasets (if applicable).
- A
- Participants must complete the tasks and submit their solutions on GitHub.
To earn your certificate:
- Complete all 10 levels.
- Submit a mini-project that integrates the skills learned.
- Pass all submissions reviewed by mentors.
- All discussions, announcements, and Q&A will take place on our Discord server.
- Make sure to:
- Check the
#announcements
channel for updates. - Use level-specific channels (e.g.,
#level-1
,#level-2
) for queries.
- Check the
- Fork this repository to your GitHub account.
- Clone the repository to your local machine:
git clone https://github.com/<your-username>/ml-robotics-workshop.git
- Navigate to the folder for your current level:
cd ml-robotics-workshop/level-1
- Read the objectives and tasks in the
README.md
file for the level. - Follow the provided resources to complete the tasks.
- Submit your solutions as instructed in the level folder.
- Push your changes to your forked repository:
git add . git commit -m "Completed Level X" git push origin main
- Share the link to your repository in the
#submissions
channel on Discord. - Mentors will review your work and provide feedback.
Level | Topic | Key Skills |
---|---|---|
Level 1 | Introduction to Robotics and ML | Basics of ML and robotics |
Level 2 | Python Basics and Simulation Setup | Python scripting, robot simulation setup |
Level 3 | Linear Algebra and Probability | Mathematical foundations for robotics |
Level 4 | Computer Vision Basics | Object detection and tracking |
Level 5 | Supervised Learning for Robotics | Regression models, performance evaluation |
Level 6 | Reinforcement Learning Basics | Q-learning, policy optimization |
Level 7 | Deep Learning Applications | CNNs, object classification |
Level 8 | Path Planning and SLAM | Navigation, mapping, A* algorithm |
Level 9 | Integrating NLP | Voice-controlled robotics |
Level 10 | Mini-Project | Full integration of concepts learned |
- Objective: Build a robot simulation that integrates navigation, object recognition, and voice commands.
- Requirements:
- Use a robot simulation platform like Gazebo or Webots.
- Apply at least 3 concepts learned during the workshop.
- Document your project with a
README.md
(overview, setup instructions, and demonstration).
- Submission:
- Upload your project to GitHub.
- Share the repository link in the
#mini-project-submissions
channel on Discord.
- Programming:
- Python: Official Docs, W3Schools
- NumPy: Documentation
- Computer Vision:
- OpenCV: Documentation
- Tutorials: OpenCV Python Tutorials
- Machine Learning:
- TensorFlow: Documentation
- PyTorch: Getting Started
- Robotics:
- Join the discussion in the
#discussion
or level-specific channels on Discord. - Use the
#help
channel for technical questions. - Tag mentors or moderators for urgent assistance.
Dive into the exciting world of machine learning and robotics. We’re thrilled to have you on this journey. Good luck, and don’t hesitate to reach out for help!