AI Technique
Definition
A method for organizing and using large, changing, or incomplete knowledge so an AI system can solve problems efficiently.
Classical AI, NLP & Linguistics terms and explanations from the Agentic AI Glossary.
Definition
A method for organizing and using large, changing, or incomplete knowledge so an AI system can solve problems efficiently.
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A simulated or software-created setting where an agent acts without directly operating in the physical world.
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Learning by listening to spoken information, audio examples, or sound-based instruction.
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The ability to use body movement or physical control to solve problems or perform skilled tasks.
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The task of checking whether a statement or conclusion matches everyday knowledge about how the world works.
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Reasoning that starts from a general rule and applies it to a specific case to reach a logical conclusion.
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Learning from remembered sequences of events or experiences that occurred over time.
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AI tasks that require specialized domain knowledge, such as diagnosis, financial analysis, or engineering fault finding.
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AI tasks based on formal rules or symbols, such as mathematics, logic, games, and theorem proving.
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AI used to reason about possible moves, strategies, and outcomes in games such as chess, poker, or tic-tac-toe.
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The natural ability of people to learn, reason, communicate, perceive, remember, and adapt to new situations.
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Reasoning that uses specific observations to form a broader general conclusion that may still be uncertain.
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The ability to reason, learn, solve problems, use language, remember information, and adapt to new situations.
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A robot that can sense its surroundings, process information, learn from experience, and adapt its actions.
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A computer, robot, or software system that uses AI methods to perceive, reason, learn, or act toward a goal.
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The ability to understand other people's emotions, beliefs, intentions, and social signals.
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The ability to understand one's own feelings, goals, motivations, and internal mental state.
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The process of gaining knowledge or skill from study, practice, instruction, examples, or experience.
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The ability to understand and use spoken or written language effectively.
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The ability to reason with numbers, relationships, abstractions, and logical structures.
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The ability of a machine or software system to perform tasks that normally require human-like reasoning, learning, or perception.
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Learning physical actions or movement patterns through repeated practice and correction.
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Everyday tasks such as perception, language use, common sense, and movement that are natural for humans but hard for machines.
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The ability to recognize, create, and reason about sound patterns such as pitch, rhythm, and melody.
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Learning by watching another person, agent, or system perform a behavior and imitating it.
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Learning to recognize, distinguish, or classify stimuli based on previous sensory experience.
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The process of moving from a current state to a desired goal by choosing actions that overcome obstacles.
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Learning based on relationships among objects or properties rather than isolated features alone.
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The ability to understand, transform, and reason about visual or spatial relationships.
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Learning from maps, images, layouts, movement paths, or other visual-spatial signals.
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Learning to perform a particular behavior when a specific trigger or stimulus appears.
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A way to group AI work into categories such as mundane tasks, formal tasks, and expert tasks.
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The use of logic and search to prove that a mathematical or formal statement follows from known rules or assumptions.
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An AI system that interprets visual input such as images, scenes, maps, faces, or objects.
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A precise numerical input value before it is converted into fuzzy membership levels.
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A definite numerical result produced after fuzzy reasoning is converted back into a precise value.
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The process of converting fuzzy output values into a single crisp output value.
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The fuzzy-logic component that transforms fuzzy inference results into a usable crisp value.
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A number between 0 and 1 that shows how strongly an element belongs to a fuzzy set.
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The process of converting crisp inputs into fuzzy sets using membership functions.
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The fuzzy-logic component that maps precise input values into fuzzy linguistic categories.
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Reasoning over fuzzy inputs and rules to produce fuzzy conclusions.
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Input represented with degrees of possibility rather than one exact true-or-false value.
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A reasoning method that handles partial truth and uncertainty instead of only true or false values.
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A system that uses fuzzy sets, rules, and inference to produce useful outputs from uncertain inputs.
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A fuzzy conclusion produced by an inference engine before defuzzification.
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A collection of IF-THEN rules used by a fuzzy system to make decisions.
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A set where elements can partially belong with membership values between 0 and 1.
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A smooth bell-shaped membership function used to represent gradual fuzzy categories.
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A rule that connects a condition with an action or conclusion, often used in fuzzy and expert systems.
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A word or phrase such as cold, warm, or hot that represents a fuzzy category.
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A variable whose values are words or phrases instead of only numbers.
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A function that assigns each input value a degree of membership in a fuzzy set.
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A membership function that assigns membership at a single point or exact value.
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A membership function shaped like a trapezoid, often used for ranges with a flat middle.
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A simple membership function shaped like a triangle, commonly used in fuzzy systems.
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The complete range of values over which a fuzzy variable and its membership functions are defined.
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A grammar where each rewrite rule has a single nonterminal symbol on the left side.
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The way connected sentences or utterances influence the meaning of each other.
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The NLP step that interprets a sentence using the meaning of surrounding sentences.
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Uncertainty caused when a word can have multiple meanings or grammatical roles.
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The NLP step that breaks text into meaningful units such as paragraphs, sentences, and words.
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The collection of words and phrases known in a language or NLP system.
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The smallest meaningful unit in a language, such as a root, prefix, or suffix.
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The study of how words are built from smaller meaningful units.
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The AI field that helps computers understand, analyze, generate, or act on human language.
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A group of words centered on a noun that can act as a subject, object, or complement.
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A tree structure that shows how a sentence is built from grammar rules.
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The process of analyzing sentence structure according to grammar rules.
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The study of how speech sounds are organized in a language.
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The NLP step that interprets what a speaker likely means using situation and world knowledge.
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The study of how context affects the meaning and use of language.
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Uncertainty about what a pronoun or reference points to in a sentence or conversation.
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A grammar rule that expands one symbol into a sequence of other symbols.
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The NLP step that checks meaning and maps sentence structure to concepts in a domain.
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The study of word and sentence meaning.
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The NLG step that selects words, phrasing, tone, and sentence organization.
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Speech recognition that is trained or tuned for a particular speaker's voice.
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Speech recognition designed to work across many speakers without speaker-specific training.
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Spoken language used as input to or output from an AI or NLP system.
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The process of identifying what words were spoken from an audio signal.
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Uncertainty caused when a sentence can be parsed into more than one structure.
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The NLP step that checks grammar and the structural relationships among words.
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The rules for arranging words into valid phrases and sentences.
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A grammar symbol that appears in the final sentence and is not expanded further.
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The NLG step that decides what content should be included in the generated text.
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The NLG step that turns a sentence plan into actual sentence structure and wording.
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A parser that starts with a high-level grammar symbol and rewrites it until it matches the input sentence.
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A system component that converts text from a source language into a target language.
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A group of words centered on a verb that describes an action, state, or event.
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The process of identifying who is speaking from characteristics of a voice.
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General knowledge about people, objects, events, and situations used to interpret language.
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Language represented as characters or words rather than speech audio.
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An inference strategy that starts with a possible conclusion and works backward to find supporting facts.
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A shared memory area used by cooperating expert systems or components to exchange information.
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Knowledge that states facts, relationships, or truths about a domain.
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A person with deep practical knowledge in the problem area an expert system is designed to solve.
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Specialized knowledge about a particular field, task, or problem area.
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A computer application that solves complex domain problems using expert-level knowledge and inference rules.
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The hardware, programming tools, languages, and databases used to build an expert system.
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An expert-system feature that explains how a recommendation or conclusion was reached.
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Domain information that is widely accepted as true by experts or scholars.
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An inference strategy that starts from known facts and applies rules forward to reach conclusions.
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Practical rule-of-thumb knowledge based on experience, judgment, evaluation, or experimentation.
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Experience-based rules or shortcuts that guide problem solving when exact methods are costly or unavailable.
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A knowledgeable person whose expertise is captured or consulted when building an expert system.
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The expert-system component that applies rules to facts and knowledge to derive conclusions.
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Java Expert System Shell, a Java-based shell used to build rule-based expert systems.
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The process of gathering domain knowledge from experts, documents, cases, or observations for an expert system.
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An expert system that uses a knowledge base and inference engine to solve domain-specific problems.
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A specialist who collects, organizes, and encodes expert knowledge for an AI or expert system.
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The discipline of acquiring, structuring, and representing expert knowledge for AI systems.
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The method used to organize knowledge so an AI system can reason over it.
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A symbolic programming language historically used for building AI and expert-system applications.
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Knowledge about how to perform steps, methods, or procedures to complete a task.
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A logic programming language historically used for rule-based reasoning and AI applications.
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An early working version of a system used for testing, feedback, and refinement.
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A development approach that quickly builds working models so users and engineers can refine requirements.
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A knowledge representation pattern that links conditions to conclusions or actions, often written as IF-THEN-ELSE.
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An expert system that reasons by repeatedly applying IF-THEN rules to facts.
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A situation where multiple expert-system rules apply at the same time and the inference engine must choose among them.
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An expert-system framework that provides inference, interface, and explanation features without domain knowledge.
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The part of an expert system that lets users ask questions, provide inputs, and receive explanations.
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An expert-system shell developed in India for encoding knowledge as IF-THEN rules.
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A continuous signal, such as speech waveform or image data, used as raw input in robotic systems.
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A robot that can operate independently without direct external control.
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An omnidirectional spherical wheel that can move in multiple directions.
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The use of measurable human traits such as face, fingerprint, or voice for identification.
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A vision sensor that captures images or video so a robot can perceive visual information.
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A wheel that rotates around both its axle and an offset steering joint for easier turning.
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The AI field that extracts, analyzes, and understands useful information from images or video.
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A robotic actuator that converts electrical energy into rotational movement.
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A computer-vision task that determines an object's position relative to a camera or scene.
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A computer-vision task that locates human faces in images or video.
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A repeated pattern of leg movements used by a walking robot to move.
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A computer-vision task that interprets human body or hand movements.
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Hardware such as a camera that captures visual input for a computer-vision system.
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Robot movement using one or more legs, often useful on uneven terrain.
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The physical form, structure, and mechanical design of a robot.
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A wire actuator that contracts when electric current passes through it.
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A computer-vision task that identifies objects in images or video.
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Optical character recognition, the process of converting scanned or image-based text into editable text.
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A wheel with rollers that allows sideways or multidirectional movement.
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A motor that uses piezoelectric materials to create precise motion.
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A soft actuator that contracts when air pressure changes.
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The battery, solar, hydraulic, pneumatic, or electrical source that powers a robot.
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Collecting information about an object or area from a distance using sensors or imaging devices.
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An artificial agent that acts in the physical world through sensors, computation, and effectors.
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The mechanism that enables a robot to move through its environment.
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The software logic that determines what a robot does, when it acts, and how it performs actions.
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The AI and engineering field focused on designing, building, and controlling robots.
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Robot movement using tracks or skid steering, often seen in tank-like vehicles.
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A wheel that rotates around its axle and supports straightforward rolling motion.
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An omni-wheel design with angled rollers that supports multidirectional movement.
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A sensor that detects touch, pressure, or contact forces.
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Robot movement using continuous tracks for stability and traction.
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A motor that uses ultrasonic vibration to produce motion, often for precise positioning.
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A sensor that captures visual data, allowing a robot or AI system to perceive its surroundings.
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Robot movement using wheels, usually more power-efficient and easier to implement than legged motion.
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