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In machine learning, learning a task is expensive (many training samples are needed) and it is therefore of general interest to be able to reuse knowledge across tasks. This is the case in aerial robotics applications, where an autonomous aerial robot cannot interact with the environment hazard free. Prototype generation is a well known technique commonly used in supervised learning to help reduce the number of samples needed to learn a task. However, little is known about how such techniquesdoi:10.1177/1059712316664570 fatcat:bldq4w5qtbhudledq34uy4tyay