Incremental Learning of Task Sequences with Information-Theoretic Metrics

摘要

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.rnIn 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 fdr 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.