Abstract Learning tasks from human demonstration is a core feature for household service robots. Task knowledge should at the same time encode the constraints of a task while leaving as much flexibility for optimized reproduction at execution time as possible. This raises the question, which features of a task are the constraining or relevant ones both for execution of and reasoning over the task knowledge. In this paper, a system to record and interpret demonstrations of household tasks is presented. A measure for the assessment of information content of task features is introduced. This measure for the relevance of certain features relies both on general background knowledge as well as task-specific knowledge gathered from the user demonstrations. The results of the incremental growth of the task knowledge when more task demonstrations become available is demonstrated within the task of laying a table.


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    Title :

    Incremental Learning of Task Sequences with Information-Theoretic Metrics


    Contributors:


    Publication date :

    2006-01-01


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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