robotics

An honest map of RL in 2026 — the domains where it actually reaches production (LLM post-training, robotics policies, ad bidding, RLHF, reasoning models) and the places where it still cannot reliably cross the lab-to-deployment gap.

Open-loop control is a guess; closed-loop control is a conversation. Learn why feedback transforms an unreliable robot into a reliable one, how the read-compute-actuate cycle works, what microcontrollers and single-board computers each do best, and why loop timing is just as critical as the algorithm running inside it.

A robot is only as good as what it can measure. Explore the full sensor toolkit — encoders, IMUs, ultrasonic rangefinders, LiDAR, cameras, and force/torque sensors — and learn the four metrics that determine whether a sensor is fit for purpose: resolution, range, noise, and sampling rate.

Strip any robot to its bones and you find three things: sensors that gather data, a controller that thinks, and actuators that move. Learn how these pieces fit together through the Sense-Plan-Act paradigm, why reactive control sometimes beats planning, and what makes a Roomba and a welding arm both qualify as robots.

Every robot motion — spinning a wheel, bending a joint, squeezing a gripper — starts with an actuator. Learn the key differences between DC, stepper, and servo motors; how gears trade speed for torque; how PWM lets a microcontroller dial motor power; and where hydraulics and pneumatics step in when electricity isn't enough.

When grids fail in high dimensions, random sampling saves you. Understand why PRM builds reusable roadmaps, how RRT grows a tree toward the goal, what probabilistic completeness really means, and how RRT* achieves asymptotic optimality.

Discretise C-space into a grid, then search it intelligently. Understand Dijkstra's optimality guarantee, how A* accelerates it with admissible heuristics ($f=g+h$), why consistency matters, and where greedy search goes wrong.

Understand why every motion planner secretly works in configuration space: how robots become points, obstacles inflate, and why high-dimensional C-spaces make naive search intractable.

Trace the full perceive-plan-act loop on a mobile robot: AMCL localization feeds a global costmap, a global planner (A*/NavFn) sets the course, and DWA or TEB local planners execute it — with recovery behaviors when things go wrong.

The chicken-and-egg problem of robot autonomy: to localise you need a map; to build a map you need a pose. Covers front-end matching, back-end pose-graph optimisation, loop closure, and visual vs LiDAR SLAM systems.

How LiDAR fires pulses and measures time-of-flight to build a 3D point cloud; data structures, voxel downsampling, ICP registration, ground segmentation, and an honest comparison with cameras for robot perception.

Fuse noisy sensors over time with provably optimal estimates. Covers the state and noise model, predict and update equations with the Kalman gain, why it's optimal for linear-Gaussian systems, and the Extended KF for nonlinear robots.

From photons to 3D geometry: the pinhole model, intrinsic matrix K, lens distortion, feature matching, stereo depth, and where CNNs help (and fail) in robot perception pipelines.

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.

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.

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.

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.