Training on the Job — Collecting Experience with Hierarchical Hybrid Automata
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We propose a novel approach to experience collection for autonomous service robots performing complex activities. This approach enables robots to collect data for multiple learning problems at a time, abstract it and transform it into information specific to the learning tasks and thereby speeding up the learning process. The approach is based on the concept of hierarchical hybrid automata, which are used as expressive representational mechanisms that allow for the specification of these experience-related capabilities independent of the program itself.
KeywordsLearning Task Reactive Plan Manipulation Task Experience Acquisition Meaningful Learning Experience
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