Unsupervised Imitation In Evolved Robots

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Imitation learning has been studied from a large range of disciplines, including adaptive robotics. In adaptive robotics the focus is often on how robots can learn tasks by imitating experts. In order to build robots able to imitate a number of problems must be solved, including: How does the robot know when and what to imitate? How does the robot link the recognition of observed actions to the execution of the same actions? This thesis presents an approach using unsupervised imitation where artificial evolution is used to find solutions to the problems. The approach is tested in a number of experiments where robots are being evolved to solve a number of navigation tasks of varying difficulty. Two sets of experiments are made for each task. In the first set the robots are trained without any demonstrator present. The second set is identical to the first one except for the presence of a demonstrator. The demonstrator is present in the beginning of the training and thereafter removed. The robots are not being programmed to imitate the demonstrator but are only instructed to solve the navigation tasks. By comparing the performance of the robots of the two sets the impact of the demonstrator is investigated. The results show that the robots evolved with a demonstrator need less training time than the robots evolved without any demonstrator except when the task is easy to solve in which case the demonstrator seems to have no effect on the performance...