Offline Learning
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Offline Learning
In machine learning, systems which employ offline learning do not change their approximation of the target function when the initial training phase has been completed. These systems are also typically examples of eager learning. While in online learning, only the set of possible elements is known, in offline learning, the identity of the elements as well as the order in which they are presented is known to the learner. Applications for robotics control The ability of robots to learn is equal to create a table (information) which is filled with values. One option for doing so is programming by demonstration. Here, the table is filled with values by a human teacher. The demonstration is provided either as direct numerical control policy which is equal to a trajectory, or as an indirect objective function which is given in advance. Offline learning is working in batch mode. In step 1 the task is demonstrated and stored in the table, and in step 2 the task is reproduced by the robot ...
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Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ...
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Eager Learning
In artificial intelligence, eager learning is a learning method in which the system tries to construct a general, input-independent target function during training of the system, as opposed to lazy learning, where generalization beyond the training data is delayed until a query is made to the system. The main advantage gained in employing an eager learning method, such as an artificial neural network, is that the target function will be approximated globally during training, thus requiring much less space than using a lazy learning system. Eager learning systems also deal much better with noise in the training data. Eager learning is an example of offline learning In machine learning, systems which employ offline learning do not change their approximation of the target function when the initial training phase has been completed. These systems are also typically examples of eager learning. While in online ..., in which post-training queries to the system have no effect on the syst ...
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Table (information)
A table is an arrangement of information or data, typically in rows and columns, or possibly in a more complex structure. Tables are widely used in communication, research, and data analysis. Tables appear in print media, handwritten notes, computer software, architectural ornamentation, traffic signs, and many other places. The precise conventions and terminology for describing tables vary depending on the context. Further, tables differ significantly in variety, structure, flexibility, notation, representation and use. Information or data conveyed in table form is said to be in tabular format (adjective). In books and technical articles, tables are typically presented apart from the main text in numbered and captioned floating blocks. Basic description A table consists of an ordered arrangement of rows and columns. This is a simplified description of the most basic kind of table. Certain considerations follow from this simplified description: * the term row has several c ...
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Programming By Demonstration
In computer science, programming by demonstration (PbD) is an end-user development technique for teaching a computer or a robot new behaviors by demonstrating the task to transfer directly instead of programming it through machine commands. The terms ''programming by example'' (PbE) and ''programming by demonstration'' (PbD) appeared in software development research as early as the mid 1980s to define a way to define a sequence of operations without having to learn a programming language. The usual distinction in literature between these terms is that in PbE the user gives a prototypical product of the computer execution, such as a row in the desired results of a query; while in PbD the user performs a sequence of actions that the computer must repeat, generalizing it to be used in different data sets. These two terms were first undifferentiated, but PbE then tended to be mostly adopted by software development researchers while PbD tended to be adopted by robotics researchers. Tod ...
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Direct Numerical Control
Direct numerical control (DNC), also known as distributed numerical control (also DNC), is a common manufacturing term for networking CNC machine tools. On some CNC machine controllers, the available memory is too small to contain the machining program (for example machining complex surfaces), so in this case the program is stored in a separate computer and sent ''directly'' to the machine, one block at a time. If the computer is connected to a number of machines it can ''distribute'' programs to different machines as required. Usually, the manufacturer of the control provides suitable DNC software. However, if this provision is not possible, some software companies provide DNC applications that fulfill the purpose. DNC networking or DNC communication is always required when CAM programs are to run on some CNC machine control. Wireless DNC is also used in place of hard-wired versions. Controls of this type are very widely used in industries with significant sheet metal fabricatio ...
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Objective Function
In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a loss function. An objective function is either a loss function or its opposite (in specific domains, variously called a reward function, a profit function, a utility function, a fitness function, etc.), in which case it is to be maximized. The loss function could include terms from several levels of the hierarchy. In statistics, typically a loss function is used for parameter estimation, and the event in question is some function of the difference between estimated and true values for an instance of data. The concept, as old as Laplace, was reintroduced in statistics by Abraham Wald in the middle of the 20th century. In the context of economics, for example, this ...
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Marcia O'Malley
Marcia Kilchenman O'Malley is an American mechanical engineer, the Thomas Michael Panos Family Professor in Mechanical Engineering and associate dean for research and innovation for the George R. Brown School of Engineering at Rice University. Her research concerns "systems for enhancing the human sensorimotor control system", including work on exoskeletons, neuroprosthetics, haptic technology, and brain–computer interfaces. Education and career O'Malley is a 1996 graduate of Purdue University, where she majored in mechanical engineering. She went to Vanderbilt University for graduate study, earning a master's degree in 1999 and completing her Ph.D. in 2001. She has been on the Rice University faculty since 2001, and holds courtesy appointments in electrical and computer engineering and computer science at Rice, and in physical medicine and rehabilitation at the Baylor College of Medicine and University of Texas Health Science Center at Houston. Before being named the Thomas ...
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Batch Mode
Computerized batch processing is a method of running software programs called jobs in batches automatically. While users are required to submit the jobs, no other interaction by the user is required to process the batch. Batches may automatically be run at scheduled times as well as being run contingent on the availability of computer resources. History The term "batch processing" originates in the traditional classification of methods of production as job production (one-off production), batch production (production of a "batch" of multiple items at once, one stage at a time), and flow production (mass production, all stages in process at once). Early history Early computers were capable of running only one program at a time. Each user had sole control of the machine for a scheduled period of time. They would arrive at the computer with program and data, often on punched paper cards and magnetic or paper tape, and would load their program, run and debug it, and carry off their ou ...
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Maze Solving Algorithm
A maze-solving algorithm is an automated method for solving a maze. The random mouse, wall follower, Pledge, and Trémaux's algorithms are designed to be used inside the maze by a traveler with no prior knowledge of the maze, whereas the dead-end filling and shortest path algorithms are designed to be used by a person or computer program that can see the whole maze at once. Mazes containing no loops are known as "simply connected", or "perfect" mazes, and are equivalent to a ''tree'' in graph theory. Maze-solving algorithms are closely related to graph theory. Intuitively, if one pulled and stretched out the paths in the maze in the proper way, the result could be made to resemble a tree. Random mouse algorithm This is a trivial method that can be implemented by a very unintelligent robot or perhaps a mouse. It is simply to proceed following the current passage until a junction is reached, and then to make a random decision about the next direction to follow. Although such a ...
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Teleoperation
Teleoperation (or remote operation) indicates operation of a system or machine at a distance. It is similar in meaning to the phrase "remote control" but is usually encountered in research, academia and technology. It is most commonly associated with robotics and mobile robots but can be applied to a whole range of circumstances in which a device or machine is operated by a person from a distance. The term ''teleoperation'' is in use in research and technical communities as a standard term for referring to operation at a distance. This is as opposed to ''telepresence'' which is a less standard term and might refer to a whole range of existence or interaction that include a remote connotation. History The 19th century saw many inventors working on remotely operated weapons (torpedoes) including prototypes built by John Louis Lay (1872), John Ericsson (1873), Victor von Scheliha (1873), and the first practical wire guided torpedo, the Brennan torpedo, patented by Louis Brenna ...
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Pointing Device
A pointing device is a human interface device that allows a user to input spatial (i.e., continuous and multi-dimensional) data to a computer. CAD systems and graphical user interfaces (GUI) allow the user to control and provide data to the computer using physical gestures by moving a hand-held mouse or similar device across the surface of the physical desktop and activating switches on the mouse. Movements of the pointing device are echoed on the screen by movements of the pointer (or cursor) and other visual changes. Common gestures are point and click and drag and drop. While the most common pointing device by far is the mouse, many more devices have been developed. However, the term ''mouse'' is commonly used as a metaphor for devices that move a computer cursor. Fitts's law can be used to predict the speed with which users can use a pointing device. Classification To classify several pointing devices, a certain number of features can be considered. For example, ...
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Online Machine Learning
In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms. It is also used in situations where it is necessary for the algorithm to dynamically adapt to new patterns in the data, or when the data itself is generated as a function of time, e.g., stock price prediction. Online learning algorithms may be prone to catastrophic interference, a problem that can be addressed by incremental learning approaches. Introduction In the setting of supervised learning, a function of f : X \to Y is to be learned, where X is thought of as a ...
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