Incremental acquisition of task knowledge applying heuristic relevance estimation

Learning tasks from human demonstration is a core feature for household service robots. To increase the utility of future robot servants, the robot should go beyond simply imitating the user´s behavior but try to build flexible, extensible and general task knowledge. This knowledge should at the same time encode the constraints of a task while leaving as much flexibility for optimized reproduction at execution time. 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 manipulation task demonstrations is presented. A heuristic measure for relevance assessment of task features is introduced. This relevance measure relies both on general background knowledge as well as task-specific knowledge gathered from the user demonstrations and incrementally improves with more task demonstrations becoming available. The utility of this relevance heuristic is evaluated within the problem of recognizing equal operations performed in different demonstrations of the same task in different contexts