Towards life-long learning in household robots: The Piagetian approach

Learning is a core feature of future household robot systems. Nonetheless, present-day learning approaches fail to take into account that learning is never a finished process but an everyday task for biological systems. Additionally, humans always learn a various number of different tasks at the same time. This paper proposes an approach to these two problems by applying the concept of Piagetian learning to the problem of robot task learning. It proposes a method for the autonomous recognition of different task classes in the robots experiences and gives one possibility, how this task knowledge can be exploited for incremental learning of sequential reordering features of a task. This framework is evaluated using three different tasks from the household domain.