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• W2151409225 abstract "The operation of an autonomous mobile robot in a semi-structured environment is a complex, usually non-linear and partly unpredictable process. Lacking a theory of robot–environment interaction that allows the design of robot control code based on theoretical analysis, roboticists still have to resort to trial-and-error methods in mobile robotics. The RobotMODIC project aims to develop a theoretical understanding of a robot’s interaction with its environment, and uses system identification techniques to identify the system robot–task–environment. In this paper, we present two practical examples of the RobotMODIC process: mobile robot self-localisation and mobile robot training to achieve door traversal. In both examples, a transparent mathematical function is obtained that maps inputs–sensory perception in both cases–to output — location and steering velocity respectively. Analysis of the obtained models reveals further information about the way in which a task is achieved, the relevance of individual sensors, possible ways of obtaining more parsimonious models, etc." @default.
• W2151409225 created "2016-06-24" @default.
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• W2151409225 date "2006-09-01" @default.
• W2151409225 modified "2023-09-26" @default.
• W2151409225 title "Robot learning through task identification" @default.
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• W2151409225 doi "https://doi.org/10.1016/j.robot.2006.04.015" @default.
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