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Short, free tutorials you can watch or read in any order. For a complete guided curriculum, browse the courses .
34 free tutorials · No purchase required
What Is a Robot?
A plain-language introduction to what a robot actually is: a machine that senses the world, decides what to do and acts on it. And how that differs from an ordinary machine.
What Is Robotics?
Robotics is the field that designs, builds and programs robots. See how mechanical engineering, electronics and computing overlap. And what appears only where they meet.
A Short History of Robotics
From the word “robot” in a 1921 play to today's warehouse fleets and walking machines. The milestones that shaped modern robotics and why each one mattered.
The Anatomy of a Robot
Every robot, however different, is built from the same subsystems: sensors, controller, actuators, transmission, power and frame. Learn what each does and how they connect.
The Sense–Think–Act Loop
The single loop that runs inside every robot: sense the world, decide what to do, act, then sense again. Learn why it must repeat, how fast and what breaks when it doesn't.
Degrees of Freedom
Degrees of freedom count the independent ways a robot can move. Learn the six DOF of a rigid body, how joints supply them and why six is the magic number for an arm.
Robot Configurations and Workspaces
The five classic arm layouts (Cartesian, cylindrical, spherical, SCARA and articulated) plus parallel robots. And the reachable workspace each geometry carves out of space.
Reading a Robot's Specifications
What payload, reach, repeatability, accuracy, resolution and cycle time actually mean on a robot datasheet. Plus why repeatability and accuracy are not the same number.
Robots vs. Ordinary Automation
Not every automated machine is a robot. Learn the line between fixed automation that repeats one motion and a robot that senses, adapts and can be reprogrammed.
Robotics, Ethics and Society
Robots already work in factories, hospitals, farms and homes. Explore the real questions they raise about safety, accountability, work and fairness, beyond Asimov's three laws.
The Families of Robots
Robots come in families defined by the job they do. Learn the main ways of classifying them: by mobility, by application, by autonomy. Plus why shape follows task.
Sensors and Transducers
How a physical thing, whether a temperature, a force or an angle, becomes a number a robot can use, and what the words sensor and transducer actually mean.
Actuators: How a Robot Moves
The three actuator families (electric, hydraulic and pneumatic) and the handful of properties that decide which one a robot should use.
Coordinate Frames and Transforms
Every robot carries a dozen coordinate frames. Learn what a frame is, how to name them without getting confused and the one rule that makes chaining transforms safe.
Control Systems: Open and Closed Loop
The difference between commanding a machine and controlling one. What feedback buys, how to read a step response, and why every real loop is fighting delay.
What Is Human–Robot Interaction?
The field that studies robots and people together: its three levels of interaction, the roles a human can play and why it borrows as much from psychology as it does from engineering.
What Is ROS?
ROS is not an operating system. Find out what it really is: a middleware, a build system and a huge ecosystem, plus the problem it exists to solve.
Installing ROS 2 and the CLI
Three ways to get a working ROS 2 onto your machine, what sourcing the setup script really does, and the command-line tools you will use every day.
Project: A Line-Following Robot
The classic first robot, built properly: an IR sensor array, a PD controller, calibration, and the failure cases that separate a demo from a robot that works.
What Is Machine Learning?
A plain-language introduction to machine learning: what it is, why it exists and how it differs from the step-by-step programming you may already know.
AI vs. Machine Learning vs. Deep Learning
How artificial intelligence, machine learning and deep learning relate: three nested rings rather than three separate fields, each one a specific way of doing the one outside it.
Types of Machine Learning: Supervised, Unsupervised and Reinforcement
Machine learning splits into three families by what the learner is told: the answer every time, nothing at all, or a score after acting. Supervised, unsupervised and reinforcement, mapped.
How Machines Learn From Data: Features, Labels and Models
Every supervised model is built from three pieces: features (the inputs), labels (the answers) and a model that maps one to the other. This lesson defines the vocabulary you will use everywhere else.
The Machine Learning Workflow: From Data to Deployment
Real machine learning is a circuit: frame the problem, get data, clean and split it, train, evaluate, deploy, then go round again. This lesson walks the whole pipeline end to end.
Training, Validation and Test Sets
Splitting data into training, validation and test sets is how you measure a model honestly. This lesson explains each set's job and why the test set may be touched only once.
Overfitting and Underfitting
Overfitting memorizes noise. Underfitting misses the pattern. This lesson explains the bias and variance trade-off, and the practical signs and fixes for each failure.
Evaluating Model Performance
The right metric depends on what a mistake costs. This lesson covers accuracy, precision, recall, F1 and the confusion matrix for classification, plus MAE, RMSE and R² for regression.
Setting Up Your Python Environment
A practical, step-by-step setup: installing Python, using pip, isolating projects with virtual environments and getting NumPy, Pandas, Matplotlib and scikit-learn installed.
Understanding Your Dataset: Rows, Columns and Data Types
Before cleaning or modeling anything, you have to know what you are holding. This lesson covers the first look: shape, types, ranges, gaps and the questions each one answers.
Supervised Learning
Supervised learning is the branch of machine learning where the model learns from examples whose correct answer is already known, using features to predict a label.
Unsupervised Learning
Unsupervised learning is machine learning without an answer key: the model receives data with no predefined labels and tries to discover structure on its own.
Neural Networks
Neural networks build predictions from simple neurons stacked in layers. This lesson covers a single neuron, forward propagation and how backpropagation trains one.
Reinforcement Learning
Reinforcement learning trains an agent through trial and reward rather than labels. This lesson covers the agent-environment loop, exploration and Q-learning.
Choosing the Right Algorithm for Your Problem
With dozens of algorithms available, how do you choose? This lesson gives a practical decision process based on your task, data size and need for interpretability.