Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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. ,  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. c 2006 Elsevier B.V. All rights reserved.
['Ulrich Nehmzow', 'Roberto Iglesias', 'Theocharis Kyriacou', 'S.A. Billings']
I am writing a research paper on a idea Robot learning through task identification
[{"from": "human", "value": "I am writing a research paper on a idea Robot learning through task identification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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\u2010environment 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\u2019s interaction with its environment, and uses system identification techniques to identify the system robot\u2010task\u2010environment. ,  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 \u2010 sensory perception in both cases \u2010 to output \u2014 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. c 2006 Elsevier B.V. All rights reserved."}]