R2BOT Lab — Interactive Robotics Simulations.
9 hands-on simulations. No installation. No signup. Just learn.
3D Robot Explorer
Rotate, zoom, and move the joints of 4 real robot types — TurtleBot3, UR5 arm, quadruped, drone. All rendered live with Three.js.
Robot Code Playground — Write code, see it move.
A full Monaco editor wired to a 2D robot. Write commands in Python-like syntax, watch the robot drive, share your program with a link.
📚 Master this in W-06: Capstone Navigator →Write Python-style robot commands. Supported: robot.forward(), robot.turn_right(), robot.wait(), robot.set_speed(l, r), sensor.distance(), and print().
Sense → Think → Act
Every robot, from a £20 line follower to a Mars rover, runs this loop thousands of times a second.
📚 Master this in S-01: What is a Robot? →PID controller — tune it yourself
Drag the sliders to see how Proportional, Integral, and Derivative gains shape how a robot reaches its target.
📚 Master this in W-03: PID Control in Practice →Drag the sliders to retune the controller. The cyan line is the robot trying to reach the dashed target. What is PID?
Types of robots
Six families of robot you'll meet in the real world. Tap each card for the key tech behind it.
📚 Master this in S-02: Types of Robots →Inverse Kinematics — Drag to move
Drag the cyan gripper anywhere. Watch the math figure out which joint angles get the arm there.
📚 Master this in F-02: Robot Manipulation →Inverse Kinematics converts a target position (x, y) into joint angles. Every robot arm uses this to move its gripper to the right place.
Read: Inverse Kinematics →A* Path Planning — Draw your own maze
Draw walls, set a start and goal, and watch A* search for the shortest path step by step.
📚 Master this in F-01: ROS2 Navigation Stack →A* is the most common pathfinding algorithm in robotics. ROS2's Nav2 uses a variant of A* for global path planning.
Read: Path Planning →Sensor Fusion — GPS + IMU = accuracy
GPS is noisy. IMU drifts. Combining them produces a smooth, accurate estimate — the secret behind every drone and self-driving car.
📚 Master this in W-02: Sensor Integration →GPS is accurate but noisy. IMU is smooth but drifts. Sensor fusion (Kalman Filter) combines both — getting smooth, accurate positioning. Every autonomous vehicle and drone does this 100+ times per second.
Read: Sensor Fusion →SLAM — Watch the map build itself
A robot enters an unknown room with no map. It uses LiDAR rays to scan, then builds the map as it explores.
📚 Master this in F-01: ROS2 Navigation Stack →SLAM — Simultaneous Localization and Mapping. The robot explores an unknown environment, building a map while tracking its own position within that map. Used in warehouse robots, drones, and self-driving cars.
Read: SLAM →Motor Control — PWM explained visually
See the PWM square wave on the left, the motor spinning on the right. Drag the duty cycle slider and watch them lock together.
📚 Master this in S-04: How Robots Move →PWM (Pulse Width Modulation) controls motor speed by switching power on and off thousands of times per second. A 50% duty cycle delivers 50% average voltage = half speed. This is how every Arduino, Raspberry Pi, and microcontroller controls motors.