Robotics is an interdisciplinary research area at the interface of computer science and engineering. Robotics involves design, construction, operation, and use of robots. The goal of robotics is to design intelligent machines that can help and assist humans in their day-to-day lives and keep everyone safe. Robotics draws on the achievement of information engineering, computer engineering, mechanical engineering, electronic engineering and others.

Robotics develops machines that can substitute for humans and replicate human actions. Robots can be used in many situations and for many purposes, but today many are used in dangerous environments (including inspection of radioactive materials, bomb detection and deactivation), manufacturing processes, or where humans cannot survive (e.g. in space, underwater, in high heat, and clean up and containment of hazardous materials and radiation). Robots can take on any form but some are made to resemble humans in appearance. This is said to help in the acceptance of a robot in certain replicative behaviors usually performed by people. Such robots attempt to replicate walking, lifting, speech, cognition, or any other human activity. Many of today's robots are inspired by nature, contributing to the field of bio-inspired robotics.

The concept of creating machines that can operate autonomously dates back to classical times, but research into the functionality and potential uses of robots did not grow substantially until the 20th century. Throughout history, it has been frequently assumed by various scholars, inventors, engineers, and technicians that robots will one day be able to mimic human behavior and manage tasks in a human-like fashion. Today, robotics is a rapidly growing field, as technological advances continue; researching, designing, and building new robots serve various practical purposes, whether domestically, commercially, or militarily. Many robots are built to do jobs that are hazardous to people, such as defusing bombs, finding survivors in unstable ruins, and exploring mines and shipwrecks. Robotics is also used in STEM (science, technology, engineering, and mathematics) as a teaching aid.

Robotics is a branch of engineering that involves the conception, design, manufacture, and operation of robots. This field overlaps with computer engineering, computer science (especially artificial intelligence), electronics, mechatronics, mechanical, nanotechnology and bioengineering.

 

Date	Significance	Robot name	Inventor
Third century B.C. and earlier	One of the earliest descriptions of automata appears in the Lie Zi text, on a much earlier encounter between King Mu of Zhou (1023–957 BC) and a mechanical engineer known as Yan Shi, an 'artificer'. The latter allegedly presented the king with a life-size, human-shaped figure of his mechanical handiwork.		Yan Shi (Chinese: 偃师)
First century A.D. and earlier	Descriptions of more than 100 machines and automata, including a fire engine, a wind organ, a coin-operated machine, and a steam-powered engine, in Pneumatica and Automata by Heron of Alexandria		Ctesibius, Philo of Byzantium, Heron of Alexandria, and others
c. 420 B.C	A wooden, steam propelled bird, which was able to fly	Flying pigeon	Archytas of Tarentum
1206	Created early humanoid automata, programmable automaton band	Robot band, hand-washing automaton, automated moving peacocks	Al-Jazari
1495	Designs for a humanoid robot	Mechanical Knight	Leonardo da Vinci
1738	Mechanical duck that was able to eat, flap its wings, and excrete	Digesting Duck	Jacques de Vaucanson
1898	Nikola Tesla demonstrates first radio-controlled vessel.	Teleautomaton	Nikola Tesla
1921	First fictional automatons called "robots" appear in the play R.U.R.	Rossum's Universal Robots	Karel Čapek
1930s	Humanoid robot exhibited at the 1939 and 1940 World's Fairs	Elektro	Westinghouse Electric Corporation
1946	First general-purpose digital computer	Whirlwind	Multiple people
1948	Simple robots exhibiting biological behaviors	Elsie and Elmer	William Grey Walter
1956	First commercial robot, from the Unimation company founded by George Devol and Joseph Engelberger, based on Devol's patents	Unimate	George Devol
1961	First installed industrial robot.	Unimate	George Devol
1967 to 1972	First full-scale humanoid intelligent robot, and first android. Its limb control system allowed it to walk with the lower limbs, and to grip and transport objects with hands, using tactile sensors. Its vision system allowed it to measure distances and directions to objects using external receptors, artificial eyes and ears. And its conversation system allowed it to communicate with a person in Japanese, with an artificial mouth.	WABOT-1	Waseda University
1973	First industrial robot with six electromechanically driven axes	Famulus	KUKA Robot Group
1974	The world's first microcomputer controlled electric industrial robot, IRB 6 from ASEA, was delivered to a small mechanical engineering company in southern Sweden. The design of this robot had been patented already 1972.	IRB 6	ABB Robot Group
1975	Programmable universal manipulation arm, a Unimation product	PUMA	Victor Scheinman
1978	First object-level robot programming language, allowing robots to handle variations in object position, shape, and sensor noise.	Freddy I and II, RAPT robot programming language	Patricia Ambler and Robin Popplestone
1983	First multitasking, parallel programming language used for a robot control. It was the Event Driven Language (EDL) on the IBM/Series/1 process computer, with implementation of both inter process communication (WAIT/POST) and mutual exclusion (ENQ/DEQ) mechanisms for robot control.	ADRIEL I	Stevo Bozinovski and Mihail Sestakov

Papers

• An existing, ecologically-successful genus of collectively intelligent artificial creatures
• Exploiting Local Perceptual Models for Topological Map-Building
• Towards Autonomous Topological Place Detection Using the Extended Voronoi Graph
• Adapting Proposal Distributions for Accurate, Efficient Mobile Robot Localization
• Factoring the Mapping Problem: Mobile Robot Map-building in the Hybrid Spatial Semantic Hierarchy
• Creating and Utilizing Symbolic Representations of Spatial Knowledge using Mobile Robots
• Ethical Considerations in Artificial Intelligence Courses
• The Constructivist Learning Architecture: A Model of Cognitive Development for Robust Autonomous Robots
• Post-Piagetian Constructivism for Grounded Knowledge Acquisition
• VisAGGE: Visible Angle Grid for Glass Environments
• High Performance Control for Graceful Motion of an Intelligent Wheelchair
• A Framework for Planning Comfortable and Customizable Motion of an Assistive Mobile Robot
• A Framework for Characterization and Planning of Safe, Comfortable, and Customizable Motion of Assistive Mobile Robots
• Socially-Aware Navigation Using Topological Maps and Social Norm Learning
• Efficient Search for Correct and Useful Topological Maps
• Topological Mapping and Navigation in Real-World Environments
• Learning and Acting in Peripersonal Space: Moving, Reaching, and Grasping
• Learning to Reach by Building a Representation of Peri-Personal Space
• Learning to Grasp by Extending the Peri-Personal Space Graph
• Bootstrap Learning for Place Recognition
• A Robust, Qualitative Method for Robot Spatial Learning
• A Robot Exploration and Mapping Strategy Based on a Semantic Hierarchy of Spatial Representations
• Navigation and Mapping in Large Scale Space
• Consciousness: Drinking from the Firehose of Experience
• Learning from Uninterpreted Experience in the SSH
• Human-like Morality and Ethics for Robots
• The Spatial Semantic Hierarchy
• Drinking from the firehose of experience
• How Can We Trust a Robot?
• An Existing, Ecologically-successful Genus Of Collectively Intelligent Artificial Creatures
• Modeling Spatial Knowledge
• Bootstrap learning of foundational representations
• THE SKELETON IN THE COGNITIVE MAP: A Computational and Empirical Exploration
• Toward Bootstrap Learning for Place Recognition
• Sneaking Up On the Hard Problem of Consciousness
• Qualitative Modeling and Heterogeneous Control of Global System Behavior
• Local Metrical and Global Topological Maps in the Hybrid Spatial Semantic Hierarchy
• An Intellectual History of the Spatial Semantic Hierarchy
• Perspectives on Ethics of AI: Computer Science
• Representing Knowledge of Large-Scale Space
• Why and How Should Robots Behave Ethically?
• The Skeleton in the Cognitive Map: A Computational Hypothesis
• Bootstrap Learning of Foundational Representations
• Toward Morality and Ethics for Robots
• Recognizing Human Actions by Attributes
• Walk the Talk: Connecting Language, Knowledge, Action in Route Instructions
• Following Natural Language Route Instructions
• Weakly Supervised Learning of Mid-Level Features with Beta-Bernoulli Process Restricted Boltzmann Machines
• A Bayesian generative model for learning semantic hierarchies
• Structured Recurrent Temporal Restricted Boltzmann Machines
• Learning Hierarchical Linguistic Descriptions of Visual Datasets
• Autonomous Development of a Grounded Object Ontology by a Learning Robot
• Using the Topological Skeleton for Scalable Global Metrical Map-Building
• Bootstrap Learning for Object Discovery
• Robot Developmental Learning of an Object Ontology Grounded in Sensorimotor Experience
• The initial development of object knowledge by a learning robot
• Learning Distinctions and Rules in a Continuous World through Active Exploration
• Towards the Application of Reinforcement Learning to Undirected Developmental Learning
• A Comparison of Strategies for Developmental Action Acquisition in QLAP
• Learning to Predict the Effects of Actions: Synergy between Rules and Landmarks
• Autonomously Learning an Action Hierarchy Using a Learned Qualitative State Representation
• Autonomous Qualitative Learning of Distinctions and Actions in a Developing Agent
• Continuous-Domain Reinforcement Learning Using a Learned Qualitative State Representation
• Autonomous Learning of High-Level States and Actions in Continuous Environments
• Building Local Safety Maps for a Wheelchair Robot using Vision and Lasers
• Detecting Obstacles and Drop-offs using Stereo and Motion Cues for Safe Local Motion
• A Stereo Vision Based Mapping Algorithm for Detecting Inclines, Drop-offs, and Obstacles for Safe Local Navigation
• Building Safety Maps using Vision for Safe Local Mobile Robot Navigation
• A Smooth Control Law for Graceful Motion of Differential Wheeled Mobile Robots in 2D Environment
• Autonomous Person Pacing and Following with Model Predictive Equilibrium Point Control
• Discrete-time Dynamic Modeling and Calibration of Differential-Drive Mobile Robots with Friction
• Robot Navigation with Model Predictive Equilibrium Point Control
• Feedback Motion Planning via Non-holonomic RRT* for Mobile Robots
• Graceful Navigation for Mobile Robots in Dynamic and Uncertain Environments
• Map Learning with Uninterpreted Sensors and Effectors
• Developing navigation behavior through self-organizing distinctive state abstraction
• Self-Organizing Distinctive State Abstraction Using Options
• Reinforcement Learning In High-Diameter, Continuous Environments
• Toward Learning the Causal Layer of the Spatial Semantic Hierarchy using SOMs
• Qualitative Heterogeneous Control of Higher Order Systems
• Trajectory Generation for Dynamic Bipedal Walking through Qualitative Model Based Manifold Learning
• Task Encoding, Motion Planning and Intelligent Control using Qualitative Models
• Controller Synthesis using Qualitative Models and Constraints
• Towards a general theory of topological maps (1-60)
• A Logical Account of Causal and Topological Maps (1-7)
• Towards a general theory of topological maps (1-56)
• A Logical Account of Causal and Topological Maps (1-264)
• Loop-Closing and Planarity in Topological Map-Building
• Topological Mapping of Ambiguous Space: Combining Qualitative Biases and Metrical Information
• Learning the Sensorimotor Structure of the Foveated Retina
• Sensor Map Discovery for Developing Robots
• From Pixels to Policies: A Bootstrapping Agent
• Learning Geometry from Sensorimotor Experience
• Focusing Attention on Visual Features that Matter
• Semantic Visual Understanding of Indoor Environments: from Structures to Opportunities for Action
• Real-time Indoor Scene Understanding using Bayesian Filtering with Motion Cues
• Dynamic visual understanding of the local environment for an indoor navigating robot
• Handling Perceptual Clutter for Robot Vision with Partial Model-Based Interpretations
• On-line, incremental visual scene understanding for an indoor navigating robot
• A Tale of Two Architectures: A Dual-Citizenship Integration of Natural Language and the Cognitive Map
• 3D Pose Estimation for Planes
• Ray-based Color Image Segmentation
• Motion Segmentation by Learning Homography Matrices from Motor Signals
• Object Detection Using Principal Contour Fragments
• Moving Object Segmentation Using Motor Signals
• Towards the Object Semantic Hierarchy
• Construction of the Object Semantic Hierarchy
• Steps Towards the Object Semantic Hierarchy