Hands on Robotic Vision: OpenCV, PyTorch, YOLO, Simulation

About this course

Computer vision is one of the most in-demand skills in modern robotics — and this course gives you everything you need to master it, from the very first pixel to a fully vision-guided robot.

You will start with the fundamentals of image processing using OpenCV, learning how to work with color spaces, detect edges and contours, recognize ArUco markers, and calibrate cameras. These are the building blocks every robotics engineer needs.

From there, you will move into deep learning for vision. You will build convolutional neural networks from scratch in PyTorch, apply transfer learning with ResNet, detect objects in real time with YOLO, and perform instance segmentation. Every concept is tied to a practical robotics use case.

The course then bridges theory and hardware. You will learn how cameras are modeled mathematically, how to set up eye-to-hand and eye-in-hand configurations, and how to implement Image-Based Visual Servoing — the technique that lets a robot move based on what it sees.

Finally, you will build complete projects in PyBullet simulation using a Franka Panda robot arm, including color-based grasping, vision-guided pick and place, ArUco-based navigation. The course closes with real hardware projects on a Universal Robot arm.

By the end, you will have a portfolio of robot vision projects and the confidence to apply computer vision to real-world robotics challenges.

Requirements: Basic Python. No prior robotics or CV experience needed.

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