Robotics & Embodied AI

Robots, embodiment, control and self-driving vehicles. 70 entries live here, and 72 from other domains touch it.

Essential

autonomous vehicleA vehicle, usually a car, truck or shuttle, that can drive itself, sensing the road, deciding what to do and steering, braking and accelerating with no human driving.
droneAn aircraft that carries no one aboard to fly it: a person steers it from the ground, or its own software does. Small camera-carrying quadcopters are the most familiar kind.
humanoid robotA robot built roughly in human shape, with a head, torso, two arms and usually two legs, so that it can work in spaces and with tools designed for people.
levels of driving automationA six-step scale, from Level 0 to Level 5, describing how much of the driving a car's automation does and how much the human must still watch the road and be ready to take over.
physical AIAI built to work in the physical world rather than on a screen: the intelligence inside robots, self-driving cars, drones and other machines that must sense real surroundings and move through them.
robotA machine that senses its surroundings, decides what to do and physically acts, moving itself or handling objects, with some independence from a human operator. Factory arms, robot vacuums and self-driving cars are all robots.
roboticsThe science and engineering of building robots and making them work: designing their bodies, sensors and motors, and writing the software that lets them see, plan and move safely.

All other entries

action chunkingHaving a robot's AI plan a short burst of upcoming movements, say the next second, all at once instead of one tiny motion at a time, which makes motion smoother and mistakes rarer.
actuatorThe 'muscle' of a machine: a motor or similar device that turns electrical, hydraulic or air power into movement, such as turning a robot's joint or closing its gripper.
advanced driver-assistance systemCar features that help the driver but do not replace them, such as automatic emergency braking, lane-keeping help, adaptive cruise control and blind-spot warnings.
collaborative robotA robot designed to work safely right next to people without a safety cage, for example by moving gently and stopping at once if it bumps into someone.
configuration spaceAn imaginary space in which a robot's whole body position is a single point, which turns 'move this many-jointed robot without hitting things' into 'find a path for a dot'.
control theoryThe mathematics of making machines behave as intended by constantly measuring what they are doing and correcting them, as a thermostat, cruise control or self-balancing robot does.
cross-embodiment learningTraining one robot AI on data from many differently shaped robots, such as arms, robot dogs, drones and humanoids, so that skills learned on one body help another.
DAggerA way of teaching a robot by imitation in which an expert keeps labeling what it should have done in the situations it actually gets into, so it learns to recover from its own mistakes.
degrees of freedomThe number of independent ways something can move. A robot arm with six motorized joints has six; a free-floating object can slide three ways and turn three ways.
dexterous manipulationSkilled handling of objects with robot fingers, such as spinning a pen, turning a key or rotating a cube in one hand: the fine finger work people do without thinking.
diffusion policyA way of training robots in which the same kind of AI that generates images instead generates the robot's next movements, letting it copy demonstrations even when there are several good ways to do a task.
digital twinA detailed virtual copy of a real machine, factory, robot or city, kept in sync with live sensor data and used to monitor it, test changes safely and predict problems.
domain randomizationTraining a robot's AI in a simulation whose colors, lighting, weights, friction and other details are shuffled at random on every run, so that the real world holds few surprises it has not already practiced for.
embodied AIAI that has a body, usually a robot and sometimes a simulated character, and learns or acts by sensing and moving in a world rather than only processing text or images.
end effectorThe tool at the end of a robot arm that actually touches the world, such as a gripper, suction cup, welding torch, paint sprayer or robotic hand.
forward kinematicsThe math that works out where a robot's hand or tool ends up, given the angles of all its joints, by chaining together the movement each joint adds.
Gemini RoboticsA family of AI models from Google DeepMind, built on its Gemini models, that let robots understand what they see and what people ask of them, and turn that understanding into physical actions.
generalist robot policyA single robot-control program trained to do many different tasks in many places, often on several kinds of robots: the opposite of a specialist trained for one job.
graspingHow a robot picks up and holds an object securely with a gripper or hand: deciding where to put its fingers or suction cup and how hard to squeeze.
human–robot interactionThe study of how people and robots communicate, cooperate and share spaces, and of how to design robots that people find safe, understandable, trustworthy and pleasant to live or work with.
impedance controlA way of controlling a robot so that it behaves like a spring and shock absorber when it touches things, giving way gently instead of pushing rigidly.
industrial robotA programmable robot arm used in factories for jobs such as welding, painting, assembling and moving parts, traditionally working inside a safety cage.
inverse kinematicsWorking backward from where you want a robot's hand to be to the joint angles that will put it there, like working out how to bend your arm to touch your nose.
Kalman filterA method that keeps a running best guess of something you cannot measure exactly, such as a vehicle's position, by blending predictions from a motion model with noisy sensor readings.
legged robotA robot that walks on legs instead of rolling on wheels, such as a four-legged 'robot dog' or a two-legged humanoid, so it can climb stairs and cross rough ground.
lidarA sensor that measures distances by firing laser pulses and timing their reflections, building a 3D picture of its surroundings out of millions of points; widely used on robots and self-driving cars.
locomotionHow a robot moves itself from place to place: rolling on wheels, walking on legs, crawling, swimming or flying.
manipulationA robot's use of arms, hands or grippers to change the world around it: picking things up, moving, pushing, opening, assembling or using tools.
mobile robotA robot that travels around by itself — on wheels, tracks, legs, propellers or thrusters — such as a robot vacuum, warehouse cart, delivery robot or planetary rover.
model predictive controlA control method that repeatedly plans the best moves a few seconds ahead using a model of the machine, carries out only the first move, then plans again with fresh measurements.
Moravec's paradoxThe observation that what is hard for people, like chess or algebra, is often easy for computers, while what toddlers do effortlessly, such as seeing, walking and picking things up, is very hard for machines.
motion planningWorking out how a robot should move from where it is to where it needs to be, without hitting anything and within the limits of what its body can do.
MuJoCoA free physics simulator, now maintained by Google DeepMind, that AI researchers use to train and test simulated robots and bodies, especially with trial-and-error learning.
occupancy gridA robot's map of the world drawn as a grid of small squares or cubes, each marked with the chance that something solid is there, so the robot knows where it can safely go.
odometryEstimating how far and in which direction a robot or vehicle has moved by adding up its own motion, such as wheel turns or how camera images shift, like counting your steps.
Open X-EmbodimentA large shared collection of robot training data, pooled by dozens of research labs in 2023 and covering many kinds of robots, so that one AI model could learn from all of them.
operational design domainThe specific conditions an automated vehicle or robot is designed to work in, such as particular roads, speeds, weather and times of day, outside of which it must not operate on its own.
optimal controlFinding the best way to steer a system over time, such as the most fuel-efficient path for a rocket or the smoothest motion for a robot arm, by minimizing a cost while obeying the laws of motion.
particle filterA way to track something uncertain, such as a robot's location, by keeping thousands of random guesses, scoring each against what the sensors see, and copying the best ones.
path planningFinding a route for a robot or vehicle from a start to a goal that avoids obstacles, usually the shortest or safest one, much as a navigation app finds a route on a map.
physics simulatorSoftware that imitates how real objects move, collide, bend and fall, so robots and their AI can be designed, trained and tested on a computer before touching the real world.
π0A general-purpose AI model for controlling robots, released in 2024 by the start-up Physical Intelligence, that sees through cameras, follows typed or spoken instructions and moves robot arms deftly enough to fold laundry.
PID controllerThe most common automatic controller in industry. It corrects a machine using three ingredients: how far off it is now, how long it has been off, and how fast the error is changing.
probabilistic roadmapA planning method that scatters many random safe robot poses through a space and links nearby ones into a road network, so routes can later be looked up quickly, as on a map.
proprioceptionA robot's sense of its own body: where its joints are, how fast they are moving and how hard its motors are pushing, measured by internal sensors rather than cameras.
rapidly exploring random treeA planning method that grows a branching tree of random moves outward from the robot's starting position, spreading quickly into open space until a branch reaches the goal.
robot foundation modelOne large AI model trained on huge amounts of robot and internet data so that it can control many robots on many tasks, and be adapted quickly to new ones.
robot learningTeaching robots skills from data and experience, such as watching demonstrations, practicing by trial and error or training in simulation, instead of programming every movement by hand.
Robot Operating SystemFree, widely used software that serves as the plumbing of many robots, letting separate programs for sensors, planning and motors pass messages to each other, with a large library of ready-made tools.
robotaxiA taxi with no human driver: you summon it with a phone app, and a self-driving car picks you up and drives you to your destination.
RT-2A 2023 Google DeepMind model that controlled robot arms with a chatbot-style vision-and-language model, letting robots follow instructions about objects and ideas they had never practiced with.
sensor fusionCombining readings from several different sensors, such as cameras, radar, lidar, GPS and motion sensors, to get a more accurate and reliable picture than any one sensor gives alone.
sim-to-real transferTraining a robot's AI in a computer simulation, where practice is fast, cheap and safe, and then making it work on the real robot despite the differences between simulation and reality.
simultaneous localization and mappingHow a robot, drone or headset builds a map of an unfamiliar place while at the same time working out where it is on that map, using its cameras or laser scanners.
subsumption architectureA way to build robot brains from stacked, reflex-like behaviors — a bottom layer that keeps clear of obstacles, others that roam or explore — where higher layers can take over from lower ones, with no central map or plan.
swarm roboticsCoordinating large groups of simple robots that follow local rules and talk only to their neighbors, so useful group behavior emerges without a central controller, as in ant colonies or bird flocks.
tactile sensingGiving robots a sense of touch, with sensors in fingertips or skin that feel pressure, texture, vibration and slipping, so they can handle objects gently and securely.
teleoperationControlling a robot or machine from a distance, with a person steering it in real time using a joystick, a matching 'puppet' arm, a VR headset or a screen.
trajectory optimizationCalculating the best way for a robot to move over time, choosing exact positions, speeds and forces moment by moment so the motion is smooth, fast or energy-efficient and avoids obstacles.
uncanny valleyThe eerie feeling people get from robots, dolls or computer-made faces that look almost, but not quite, human: liking grows with human likeness, then suddenly plunges.
vision-language-action modelAn AI model that looks through a robot's cameras, reads an instruction such as 'put the cup in the sink', and turns both straight into the robot's movements.
world foundation modelA large AI model trained on vast amounts of video that can imagine how a scene will unfold, used to create realistic practice worlds for robots and self-driving cars.
zero moment pointThe spot on the ground beneath a walking robot where the floor's push balances out; keeping it inside the area under the robot's feet stops the robot from tipping over.

Also relevant

3D reconstructionBuilding a 3D model of an object or place, its shape and often its colors, from photos, video or depth-sensor scans.
A* searchA method for finding the shortest route on a map or network that weighs both how far you have already come and a smart guess of how far is left, so it tries the most promising paths first.
Pieter AbbeelA Belgian-American roboticist at Berkeley whose research taught robots to learn from demonstrations and from trial and error, and who co-founded the robotics company Covariant.
action spaceThe full menu of moves an AI agent is allowed to make — a handful of buttons in a video game, or any combination of motor forces for a robot arm.
adversarial patchA printed sticker or picture designed so that, when it appears in a camera's view, an AI vision system misreads the scene — seeing a toaster, say, or missing a person.
Isaac AsimovA Russian-born American science-fiction writer and biochemist who invented the Three Laws of Robotics and is credited with the first use of the word 'robotics'.
automated planningThe branch of AI that works out a sequence of actions to reach a goal, such as the steps a robot must take to assemble a part, from descriptions of what each action needs and what it changes.
behavioral cloningThe simplest way to copy an expert: record what the expert did in each situation and train an AI to predict the same action whenever it sees a similar situation.
blocks worldA simplified pretend world of toy blocks on a table that early AI researchers used to test programs for planning, seeing and understanding language, because it was simple enough to describe completely.
Rodney BrooksAn Australian-American roboticist who argued that intelligence grows out of acting in the world rather than abstract reasoning, and who co-founded the company behind the Roomba vacuum.
Karel ČapekA Czech writer whose 1920 play about artificial workers who rise up against their makers gave the world the word 'robot'.
clankerA slang insult for robots and AI systems, borrowed from Star Wars, that went viral in 2025 as a way of expressing annoyance at, or rejection of, AI.
CoRLA yearly academic conference, started in 2017, for research where robotics meets machine learning.
cyberneticsThe study of how animals, machines and organizations steer themselves using feedback: sensing the result of an action and correcting course. It was both a parent and an early rival of AI.
DARPA Grand ChallengeA series of US government-sponsored races for driverless vehicles held in 2004, 2005 and 2007. No robot finished the first race; within three years, cars were navigating mock city traffic.
deep deterministic policy gradientA method for teaching AI agents smooth, precise movements, such as controlling a robot arm, by learning a scoring network for actions and steering a decision network toward higher scores.
depth estimationWorking out how far away each point in a scene is from the camera, producing a 'depth map' that turns a flat picture into a sense of near and far.
Dijkstra's algorithmA classic method for finding the shortest routes from one starting point to every other point on a map or network, by always settling the nearest not-yet-settled point next.
edge AIDoing AI's computing on or near the devices that collect the data, such as cameras, cars, factory machines, phones or local servers, instead of sending everything off to distant data centers.
embodied cognitionThe idea that thinking is not something the brain does on its own: the body, its senses and movements, and its surroundings play a real part in how we think and understand.
end-to-end learningTraining one AI system to go straight from raw input, such as camera images, to the final output, such as steering commands, instead of chaining hand-designed steps.
Chelsea FinnAn American computer scientist at Stanford known for 'learning to learn' methods that let AI adapt to new tasks from a few examples, and a co-founder of the robotics company Physical Intelligence.
Frankenstein complexIsaac Asimov's name for the fear that intelligent machines will turn on the people who made them, after Mary Shelley's novel about a scientist destroyed by his own creation.
fuzzy logicA kind of logic that allows degrees of truth, so a statement like 'the room is warm' can be 70 percent true, which helps machines handle vague everyday categories.
GatoA 2022 DeepMind AI that used one set of learned settings for hundreds of jobs, from chatting and describing pictures to playing video games and moving a real robot arm — an early step toward general-purpose AI agents.
goal-conditioned reinforcement learningTraining one AI agent to reach many different goals — whichever target it is given — instead of a single fixed task, so a robot can, say, place an object anywhere asked.
groundingConnecting an AI's words to something real — trusted documents, a live search, images or the physical world — so that its answers rest on evidence rather than only on what it absorbed in training.
hierarchical reinforcement learningReinforcement learning organized in layers, where a high-level part chooses goals or skills — 'go to the kitchen' — and lower-level parts handle the detailed actions to achieve them.
hierarchical task networkA planning method that breaks a big task, such as 'travel to Paris', into smaller tasks, such as 'book a flight' and 'pack', using stored recipes, until only simple actions remain.
ICRAThe largest yearly robotics conference, run by the engineering society IEEE, covering everything from robot arms and drones to self-driving cars.
imitation learningTeaching an AI by showing it examples of an expert doing a task, so it learns to copy the behavior instead of discovering it through trial and error.
international humanitarian lawThe rules of international law that limit how wars may be fought, protecting civilians, the wounded and prisoners. They apply to every weapon, including ones that use AI.
inverse reinforcement learningWorking backward from behavior to goals: watching an expert act and inferring what they must be trying to achieve, so an AI can pursue the same aims.
joint-embedding predictive architectureA way of training AI on images or video in which the model predicts an abstract summary of a hidden part, rather than every pixel, so it learns what matters and ignores unpredictable detail.
keypoint detectionFinding specific, recognizable points in an image, such as the corners of a building, a person's elbows and knees, or the tip of a nose.
lethal autonomous weapons systemA weapon that, after a person sets it going, picks out and attacks targets — including people — on its own, with no human deciding each strike. Critics call them killer robots.
levels of autonomyLadders that grade how independently an AI system acts — from a tool a person fully controls, through suggesting and asking for approval, up to acting on its own while people merely watch.
Sergey LevineA Berkeley computer scientist, one of the most prolific researchers on how robots can learn skills from data and experience, and a co-founder of the robotics company Physical Intelligence.
liquid neural networkA small, brain-inspired network for time-varying data whose neurons change how quickly they react depending on their input, letting a few neurons handle tasks such as steering a car.
model-based reinforcement learningLearning in which an AI agent builds, or is given, an inner simulation of how its world responds, and uses it to plan or practice in imagination before acting.
Hans MoravecAn Austrian-born roboticist who observed that skills humans find effortless, like seeing and moving, are the hardest for machines, and who forecast when computers would match the brain.
Elon MuskA South African-born American entrepreneur who runs Tesla and SpaceX, co-founded OpenAI, founded the AI company xAI, and has long warned that AI could endanger humanity.
negative side effectsHarmful changes an AI causes while pursuing its goal that nobody told it to avoid — like a cleaning robot knocking over a vase on its way to the mess.
Nils NilssonAn American computer scientist who co-created the A* search algorithm and the STRIPS planner for Shakey, the first robot to plan its own moves by reasoning, and wrote standard AI textbooks.
object detectionFinding every object of interest in a picture or video frame, saying what each one is and drawing a box around it, for example all the cars, cyclists and pedestrians in a street scene.
object trackingFollowing objects from frame to frame in a video and keeping track of which is which, so a system knows that the car it sees now is the same car it saw a second ago.
offline reinforcement learningLearning a strategy entirely from a fixed collection of recorded experience — logs, past robot runs, medical records — without trying anything new in the real world while learning.
optical flowHow each part of a video picture seems to shift from one frame to the next, recorded as a tiny arrow per pixel giving the direction and distance moved.
partially observable Markov decision processA version of the standard decision-making model in which the decision-maker cannot see the whole situation — like a poker player who cannot see other hands — and must act on clues and memory.
perceptionHow an AI system or robot turns raw sensor signals, such as camera pixels, microphone sound or laser scans, into an understanding of what is around it: objects, people, words and distances.
Physical IntelligenceA San Francisco company, founded in 2024, that builds general-purpose AI 'brains' meant to control many different kinds of robots doing many different tasks.
point cloudA 3D picture made of many dots floating in space, each marking a spot on a surface, as captured by a laser scanner or worked out from photos.
pose estimationWorking out the position and orientation of something from images: where a person's joints are and how their body is posed, or exactly how an object or camera is placed and turned in space.
quality diversityA family of evolution-inspired search methods that aim to collect many different good solutions — for example, many distinct ways for a robot to walk — rather than a single best one.
safe reinforcement learningReinforcement learning designed to avoid dangerous or unacceptable behavior — both in what the finished agent does and in the risky experiments it might try while still learning.
semantic segmentationColoring every pixel of an image by what it shows, such as road, sky, person or car, without telling one person or car apart from another.
Shakey the robotA wheeled robot built at a California research institute between 1966 and 1972 that could look around, make a plan and carry it out: the first mobile robot to work out its own steps toward a goal.
soft actor–criticA method for training AI agents, popular in robotics, that rewards the agent both for succeeding and for keeping its behavior varied, which helps it explore and learn reliably from reused experience.
spatial intelligenceThe ability to understand, imagine and act in three-dimensional space and the physical world — effortless for people, and treated by many AI researchers as the next big goal after language.
SRI InternationalA nonprofit research institute in California, once part of Stanford University, where the Shakey robot was built and the technology behind Apple's Siri assistant began.
STRIPSAn early AI planning program, and the simple action language it introduced, in which each action lists what must be true beforehand, what becomes true and what stops being true.
structure from motionRecovering both the 3D shape of a scene and the spot from which each photo was taken, using nothing but a collection of overlapping photographs.
swarm intelligenceThe teamwork that appears when crowds of simple creatures or machines, such as ants, bees or small robots, each respond only to what is nearby, with nobody in charge, yet together solve problems.
TeslaThe American electric-car company led by Elon Musk, whose driver-assistance software, robotaxis and humanoid robot make it a major, and controversial, developer of AI.
Three Laws of RoboticsThree rules that science-fiction writer Isaac Asimov built into his fictional robots — do not harm people, obey orders, protect yourself — in stories that mostly show how such simple rules go wrong.
Sebastian ThrunA German-American roboticist who led the Stanford team that won the 2005 desert race for self-driving cars, started Google's self-driving car project, and founded the online-learning company Udacity.
TinyMLMachine learning shrunk to run on tiny, cheap, battery-powered chips inside sensors, wearables or appliances, using so little power that a coin-sized battery can last for months or years.
trajectoryThe record of what happened as an AI agent acted: the sequence of situations it was in, the actions it took and the rewards it received.
trolley problemA thought experiment asking whether you should divert a runaway tram so that it kills one person instead of five, often used to ask how self-driving cars should choose in an unavoidable crash.
vision-language modelAn AI model that understands images and text together — it can describe a photo, answer questions about a chart or find the picture that matches a sentence.
WaymoA company owned by Google's parent, Alphabet, that runs fully driverless robotaxi services in several American cities.
world modelAn AI's internal simulation of how its world works, which lets it predict what will happen next — especially in response to its own actions — and so plan, imagine or practice without acting for real.