Hands on Computer Vision for Robotics OpenCV, PyTorch, YOLOPublished 9/2026
Created by Saroj Debnath
MP4 |
Video: h264, 1280x720 |
Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner |
Genre: eLearning |
Language: English |
Duration: 110 Lectures ( 31h 39m ) |
Size: 14.5 GB
⚡ Master OpenCV for real-world image processing: color spaces, edge detection, contours, blob detection, and camera calibration.
⚡ Build and train deep learning models with PyTorch - CNNs, YOLO, ResNet, and SAM - for object detection and segmentation.
⚡ Integrate computer vision with robot arms using eye-to-hand/eye-in-hand setups, visual servoing, and ArUco markers.
⚡ Deploy complete vision pipelines with FastAPI and simulate robot vision projects in PyBullet with Franka Panda.
⚡ Simulate Robots using Pybullets
⚡ Hands on Robotic Vision with Real Industrial Robot
Requirements❗ Basic Python programming (variables, loops, functions)
❗ No prior robotics or computer vision experience needed - everything is taught from scratch
❗ A computer capable of running Python 3 and Anaconda
❗ Familiarity with running scripts from the command line
❗ Windows is preferred
DescriptionComputer 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.
Who this course is for⭐ Robotics engineers and students who want to add computer vision skills to their toolkit
⭐ Python developers curious about applying machine learning to real-world visual tasks
⭐ Hobbyists and makers who want to build vision-guided robot projects at home
⭐ Professionals in manufacturing or automation looking to implement visual inspection systems
⭐ Anyone who has completed a basic Python course and wants a practical, project-driven path into robot vision
Homepagehttps://www.udemy.com/course/hands-on-computer-vision-for-robotics-opencv-pytorch-yoloRecommend Download Link Hight Speed | Please Say Thanks Keep Topic Live
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