Learning Movement Primitive Attractor Goals and Sequential Skills from Kinesthetic Demonstrations

Simon Manschitz, Jens Kober, Michael Gienger, Jan Peters

Abstract: We present an approach for learning sequential robot skills through kinesthetic teaching. In our work, finding the transitions between consecutive movement primitives is treated as multiclass classification problem. We show how the goal parameters of linear attractor movement primitives can be learned from manually segmented and labelled demonstrations and how the observed movement primitive order can help to improve the movement reproduction. The improvement is achieved by restricting the classification result to the currently activated movement primitive and its possible successors in a graph representation of the sequence, which is also learned from the demonstrations. The approach is validated with three experiments using a Barrett WAM robot.