Learning robot behaviour and skills based on human demonstration and advice:: The machine learning paradigm

Service robots require easy programming methods allowing the unexperienced human user to easily integrate motion and perception skills or complex problem solving strategies. To achieve this goal, robots should learn from operators how and what to do considering hard- and software constraints. Various approaches modelling the man-machine skill transfer have been proposed. Systems following the Programming by Demonstration (PbD) paradigm that were developed within the last decade are getting closer to this goal. However, most of these systems lack the possibility for the user to supervise and influence the process of program generation after the initial demonstration was performed. In this paper a principle learning methodology is discussed, which allows to transfer human skills and to supervise the learning process including subsymbolic and symbolic task knowledge. Here, several existing approaches will be discussed and compared to each other. Moreover, a system approach is presented, integrating the overall process of skill transfer from a human to a robotic manipulation system. One major goal is to modify information gained by the demonstration in that way that different target systems are supported. The resulting PbD-system yields towards a hybrid learning approach in robotics to support natural programming based on human demonstrations and user advice.