A free place to start learning robotics. Try short interactive concepts, read practical handouts, and follow a roadmap from your first circuit to a robot that maps and navigates on its own.
Maintained by Prof. Xian (Shawn) Li, Department of Electrical and Computer Engineering, Northeastern University.
Almost every robot, from a vacuum to a Mars rover, runs the same loop many times per second. It senses, it thinks, and it acts. Each concept on this site is one piece of that loop.
Measure the world and the robot itself, with wheel encoders, IMUs, cameras, and LiDAR.
Work out where the robot is and what to do next, even when every sensor is noisy.
Turn a decision into motion, through wheels, motors, and joints.
Acting changes the world, so the robot senses again, and the loop repeats.
Short interactive pages that show one robotics idea at a time. Move the sliders and watch what changes.
Sort everyday machines into robot and not robot, and see what sensing, thinking, and acting each add.
Run a robot loop one tick at a time and see how the loop rate decides whether the robot stops before a wall.
Build simple mechanisms, count links and joints, and see how many independent ways each one can move.
Wire two light sensors to two wheels in four ways and watch fear, aggression, love, and curiosity appear from simple wiring.
Tune a two-sensor and a five-sensor line follower on a curved track and compare on-off control with proportional control.
Watch Bug 1 and Bug 2 reach a goal around unknown obstacles using only a touch sensor and the direction to the goal.
Stack avoid, seek, and wander behaviors and see which layer takes control at each moment.
Drag two vectors and watch the projection, angle, and cross product update, then use them to tell if a target is ahead or to the left.
Move and rotate a robot frame inside a world frame, and see how a point seen by the robot maps to world coordinates.
Rotate a 3D frame about x, y, and z and see that changing the order of the same rotations gives a different result.
Turn the yaw, pitch, and roll rings of a gimbal until two of them line up and one degree of freedom disappears.
Pick a rotation axis and an angle and watch a vector sweep around the axis, then see the rotation matrix from Rodrigues' formula.
Blend between two orientations with slerp and with naive Euler-angle blending, and compare the paths the nose takes.
Chain world, base, arm, and camera frames and follow a point seen by the camera through every transform to the world.
Pick any end pose for a rigid body and see that the motion is a single rotation about one fixed point, the planar version of a screw axis.
Drive a robot with heading noise and see why its uncertainty is a banana, not an ellipse, and how the Lie group exponential map captures it.
Drag noisy points, fit a line or curve, and see the squared residuals the fit makes as small as possible.
Fit a curve with Gauss-Newton and Levenberg-Marquardt from the same start, and watch where Gauss-Newton overshoots and LM stays safe.
Drop a ball on a cost surface, change the step size until it diverges, and see how momentum helps in a narrow valley.
Stretch and rotate a 2D Gaussian and see the 1, 2, and 3 sigma ellipses and how many samples fall inside each.
A robot checks whether a door is open with a noisy sensor. See how each reading updates its belief, drawn as areas you can see.
Estimate the free area of a cluttered room by throwing random points, and watch the error shrink as more points land.
Simulate the same pendulum with three integrators and see Euler gain energy while RK4 stays accurate.
Film a spinning wheel at different frame rates and see it appear to slow down, stop, or turn backward.
Build a sensor signal from sine waves and noise, see its spectrum, and remove the noise with a low-pass cutoff.
Set the left and right wheel speeds and watch how a two-wheeled robot like the TurtleBot 4 drives, turns, and spins in place.
Drive a robot around a square and compare where it really is with where its wheel encoders say it is. Small errors grow with every lap.
Turn the joints of a planar two-link arm to see forward kinematics, or click a target to have the arm solve inverse kinematics and reach it.
Tune the P, I, and D gains and watch a robot drive to a target. See overshoot, oscillation, and steady-state error appear and disappear.
A robot moves along a line and gets noisy position readings. Watch the Kalman filter blend its motion model with each reading to track the robot.
Hundreds of guesses about where a robot might be, each scored against range readings to landmarks. Watch the cloud collapse onto the true pose.
Draw walls on a grid and watch A*, Dijkstra, and greedy search find a path. See how a good heuristic cuts down the search.
Longer guides you can read on screen or print. Every link inside is clickable.
A six-month self-study roadmap with curated resources, practice projects, and milestones. It assumes no prior electronics background and about two to three hours of work per day.
Adapted from work by Ronin (@deronin_) on X. The original ideas and structure are the original author's work.
Documentation, tools, courses, and help for the TurtleBot 4 on ROS 2 Jazzy and Ubuntu 24.04. Covers setup and networking, driving, sensors, simulation, mapping and navigation, and common fixes.
Original handout, written for this collection.
Ready to go further? The roadmap handout breaks robotics into six monthly steps, each with a goal and a signature build. Keep every build in its own GitHub repository, with a photo, your measured results, and a note on what broke and how you fixed it.
Read a schematic, build a working circuit, and find faults.
Build a robot that moves, senses, and corrects itself.
Design a part, make it, and have it fit.
Simulate a robot that maps and navigates on its own.
Understand the math under the libraries.
Pick a direction, finish a portfolio piece, and apply.