Unsupervised and Incremental Acquisition of and Reasoning on Holistic Task Knowledge forHousehold Robot Companions

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 requires higher level reasoning methods that allow the robot to consider its task knowledge in a holistic way. In order to cope with the vast datasets of task knowledge databases, these should be structured in a way that reflects the different classes of tasks as well as the specific characteristics of each task. As the complete set of tasks requested by the user can not be built into the robot, this structure should be learned from the database itself. In this paper, a system to record and interpret manipulation task demonstrations is presented. Unsupervised clustering methods on task knowledge are discussed that at the same time find the task class boundaries and the associated characteristics of each task class. These are used to group task demonstrations in the highly anisotropic feature space and to recognize similar demonstrations belonging to the same class of tasks. In each of the found task classes reasoning methods recover the sequential reordering possibilities, representing them in task precedence graphs. This equips the robot with the ability to update and improve its task knowledge without being put in a dedicated learning mode

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