Learn Physical AI — ABXK.AI

How Machines Move: Feedback Control Explained with a Simulation

Sun, 18 Oct 2026 00:00:00 +0000

Every physical AI system ends in a motor. The AI decides “move the gripper 30 centimeters to the left” or “hold this lane at the current speed”, but something still has to make the motors do exactly that, against friction, heavy loads, wind and bumps in the road. That job belongs to feedback control, a field much older than modern AI. It is the layer that sits underneath every robot, drone and self-driving car.

How Robots Build a Map: Lidar Scans and Occupancy Grids

Sun, 18 Oct 2026 00:00:00 +0000

A robot that enters a new building knows nothing about it. Before it can plan a route to the loading dock or the kitchen, it has to find out where the walls, shelves and doors are. It builds this knowledge from what its sensors see, scan by scan, into a map. The most common kind of map for this is the occupancy grid: the floor is divided into small squares, and for each square the robot estimates whether it is free, occupied or still unknown.

What Is Physical AI? Machines That Sense, Decide and Act

Sun, 18 Oct 2026 00:00:00 +0000

Most AI you use lives on a screen: you type a question, and a language model writes an answer. Physical AI leaves the screen. It is AI that senses the real world through cameras, microphones or electrodes and acts in it through wheels, motors or a computer cursor. A robotaxi that drives without a driver, a robot arm that picks parts it has never seen and an implant that turns brain signals into words are all physical AI.

How a Robot Finds Its Route: Dijkstra and A* on a Real Street Map

Sun, 18 Oct 2026 00:00:00 +0000

Once a machine has a map, it can plan. A navigation app, a delivery robot and a robotaxi all solve the same basic task thousands of times a day: find the shortest route from here to there through a network of streets or corridors. The task sounds simple, but a city has tens of thousands of intersections, and checking every possible route would take forever. The methods that solve it quickly are some of the oldest and most important algorithms in computing.

How a Robot Knows Where It Is: Odometry, GPS and Sensor Fusion

Sun, 18 Oct 2026 00:00:00 +0000

Before a robot can plan a path, it has to answer a simpler question: where am I? This task is called localization, and it is harder than it sounds. A delivery robot on a sidewalk, a vacuum robot in a living room and a self-driving car in a city all need to know their position, often to within a few centimeters, many times per second. No single sensor can deliver that on its own.

How Self-Driving Cars See the Road: Cameras, Radar and Lidar

Sun, 18 Oct 2026 00:00:00 +0000

A human driver uses two eyes, two ears and years of experience. A self-driving car replaces them with a ring of sensors and software that turns millions of measurements per second into three decisions: where to steer, when to accelerate and when to brake. In several US cities this already works without anyone behind the wheel, and every crash of these cars is reported to the federal road safety authority.

How Robots Learn to Grasp and Walk

Sun, 18 Oct 2026 00:00:00 +0000

For decades, robots were fast and precise but blind to change. A welding robot on an assembly line repeats the same path millions of times; move the car body by two centimeters and it welds into the air. Robots that work among people, in warehouses, kitchens or hospitals, need something different: they have to cope with objects they have never seen, in places that change every day. That is where AI comes in.

Staying on the Path: How a Self-Driving Car Steers

Sun, 18 Oct 2026 00:00:00 +0000

A planned route is only a line on a map. To drive it, a car has to turn the steering wheel at the right moment and by the right amount, many times per second, while it moves at speed. This task is called path tracking. One of the simplest and most widely used methods for it in robots and research vehicles has a vivid name: pure pursuit, the car chases a point on its path that always stays a fixed distance ahead.

Where Self-Driving Cars Drive: The Operational Design Domain

Sun, 18 Oct 2026 00:00:00 +0000

No self-driving car today can drive everywhere. Each one is designed for a specific set of conditions: certain cities, certain types of roads, certain speeds, certain weather. Engineers call this set the operational design domain, or ODD. It is one of the most important ideas for understanding automated driving, because it explains why a robotaxi can drive without anyone at the wheel in one city and not at all in the next.

How Brain-Computer Interfaces Turn Brain Signals into Commands

Sun, 18 Oct 2026 00:00:00 +0000

A person with a severe spinal cord injury cannot move their hands, yet the part of the brain that plans hand movements still works. When they try to move, nerve cells fire in patterns that a computer can learn to read. A brain-computer interface (BCI) does exactly that: it records brain activity and turns it into commands, so the person can move a cursor, type or speak through a synthetic voice.

Reading Brain Signals: What an EEG Can and Cannot Decode

Sun, 18 Oct 2026 00:00:00 +0000

Brain-computer interfaces come in two very different forms. Some use electrodes implanted in the brain, which requires surgery but picks up detailed signals. Most use an EEG, electrodes in a cap on the scalp: no surgery, cheap and safe, but much less precise. Of the 280 brain-computer interface studies in the US clinical trial registry, we classified 129 as non-invasive and 59 as implanted. So what can a computer actually read from an EEG?

Trained in Simulation: Why Robots Need Messy Practice Worlds

Sun, 18 Oct 2026 00:00:00 +0000

Robots that learn by trial and error need a lot of trials, often millions. In the real world, that would take years, wear out the hardware and break things along the way. That is why most robot learning today happens in simulation, where a computer can run thousands of practice attempts per minute. The problem: no simulation matches reality exactly. Motors are a bit weaker, sensors a bit noisier, signals a bit later. A strategy that works perfectly in the simulator can fail on the real robot. This is called the sim-to-real gap.

Inside a Brain Decoder: Electrodes, Training Data and Other People

Sun, 18 Oct 2026 00:00:00 +0000

In Level 1 of this learning path, we tested whether a computer can tell from an EEG which hand a person imagines moving. The answer was sobering: the decoder was right 58.9% of the time for the median person, where guessing gives 50%, and only 31 of 106 people reached the 70% that BCI research often names as the minimum for useful control. So what would make such a decoder better?

Inside AI Chips: Compute, Memory and the Memory Wall

Sun, 18 Oct 2026 00:00:00 +0000

Every robot brain, every self-driving car and every chatbot runs on chips. When a new AI chip is announced, one number gets the headlines: how many calculations it can do per second. But that number alone says surprisingly little about how fast a chip runs AI in practice. The real limit is often somewhere else: how fast the chip can get data from its memory.

This is part 5 of Level 1 in our learning path Physical AI. Using data from Epoch AI, a research institute that tracks AI hardware, we compare the top AI chips of each year since 2017. You will learn the three numbers that describe an AI chip, why compute has grown more than twice as fast as memory speed and what this memory wall means for the speed of AI answers.

What Powers AI: Chips, Data Centers, Electricity and Water

Sun, 18 Oct 2026 00:00:00 +0000

When you ask a language model a question, the answer does not come from your phone. Your question travels to a data center, where thousands of specialized chips run the model and send the answer back within seconds. Every self-driving car, every robot foundation model and every speech decoder for a brain implant was also trained in such a building. AI is software, but it rests on a very physical foundation: chips, buildings, power lines and cooling water.