Robotics lessons & courses
32 lessons · 8 learning paths · free, quiz-checked, no signup required
From feedback control and PID to state-space models, kinematics, and trajectory tracking. The control-theory core that makes physical systems do what you intend.
Learning paths
How Robots Walk: Central Pattern Generators
Walking is rhythmic, fast, unstable, and made discontinuous by every footfall, which is why trajectory planning fits it badly. Biology solves it with spinal circuits that generate rhythm without the brain. This path builds that architecture: coupled phase oscillators whose limit cycles absorb disturbances, gaits as nothing more than sets of phase relationships, the salamander robot where one scalar drive switches swimming to walking, and how sensory entrainment and reinforcement learning compose with it.
Predicting Human Motion: Socially-Aware AI
Any machine sharing space with people must answer one question continuously: where will they be in five seconds? This path builds human trajectory forecasting from the problem statement to deployment: why the answer is a distribution rather than a path, the social pooling layer that let networks learn collision avoidance nobody programmed, why the field's standard metric rewards predicting a walk into a wall over a plausible wrong turn, and the four gaps that separate a benchmark number from a robot that is safe around people.
Vision-Language-Action: How Robots Learn to Act
The frontier where robotics meets large multimodal models. This cursus builds the modern robot-learning stack from the ground up. First, why robots learn skills from demonstrations instead of hand-written controllers, and the compounding-error problem that makes it hard. Then the two ideas that fixed it: action chunking and diffusion policies, plus the pooled multi-robot datasets behind generalist control. Finally, vision-language-action models, RT-2, OpenVLA, and pi0, that put a web-pretrained brain behind the robot, with an honest look at what they can and cannot yet do.
Robotics Foundations
By the end of this path you will be able to describe a robot's three-pillar anatomy, select appropriate sensors for a given task, choose and size the right motor type, write a basic closed-loop controller on a microcontroller, and explain why loop rate and latency matter in real systems.
Robot Perception and State Estimation
By the end of this cursus you will be able to model a pinhole camera, calibrate it with OpenCV, recover stereo depth, downsample and register LiDAR point clouds with ICP, implement a Kalman filter predict-update cycle from scratch, and explain how ORB-SLAM3 and Cartographer solve the full SLAM problem including loop closure and pose-graph optimisation.
Robot Kinematics
Build a complete kinematic toolbox for serial robot arms. You will attach coordinate frames to every link using homogeneous transforms, compute end-effector pose from joint angles with DH-parameter-based forward kinematics, invert that map analytically and numerically to find joint angles from a desired pose, and differentiate FK to get the Jacobian for velocity control and singularity analysis.
Robot Control Systems
Build a complete understanding of robot control from first principles. By the end of this path you will be able to design and implement feedback loops, tune PID controllers for real actuators, model multi-joint systems in state space, apply pole placement and LQR, and generate smooth trajectories that a robot arm or mobile robot can track with minimal error.
Motion Planning and Navigation
Go from first principles to a working ROS navigation stack. You will model any robot as a point in configuration space, implement and compare Dijkstra, A*, PRM, RRT, and RRT*, and wire together a global planner, local planner, and AMCL localizer for a real mobile robot.
All Robotics lessons
The Jacobian and Velocity Kinematics
Connect joint velocities to end-effector velocity through the Jacobian matrix. Learn how to build J, spot singularities where det J = 0, invert J with the pseudoinverse for velocity control, and measure manipulability — with NumPy code for the 2-link arm.
Inverse Kinematics: From Pose to Joint Angles
Flip the FK problem: given a desired end-effector pose, find the joint angles that achieve it. Master analytical closed-form IK for the 2-link arm, the elbow-up/elbow-down duality, atan2 arithmetic, and the basics of numerical IK via the Jacobian.
Forward Kinematics and DH Parameters
Translate a list of joint angles into an end-effector pose. Learn the four Denavit-Hartenberg parameters, build per-link transforms, multiply them into T_0^n, and implement FK for a 2-link planar arm in NumPy.
Coordinate Frames and Homogeneous Transforms
Master how roboticists describe rigid-body pose. Build rotation matrices from scratch, pack them into 4x4 homogeneous transforms, and compose multiple frames with NumPy to track every link in a robot arm.
Trajectory Generation and Tracking
Learn how to generate smooth robot trajectories — trapezoidal velocity profiles, cubic and quintic polynomials — and how to combine feedforward and feedback to track them with minimal error on real arms and mobile robots.
State-Space Models and Pole Placement
Move beyond single-input PID to the state-space framework: the state vector, matrix dynamics, controllability, pole placement via state feedback, and LQR — the tool that scales to full robot arms and drones.
PID Controllers
Master the proportional-integral-derivative controller: what each term fixes, the PID equation, integral windup, a discrete Python implementation, and a Ziegler-Nichols tuning guide for real loops.
Feedback Control Fundamentals
Understand why feedback beats open-loop, how the classic closed-loop architecture works, and what the key performance metrics — rise time, overshoot, settling time, steady-state error — actually mean for a real system.