@inproceedings {pub2716,
	title = {Learning to Sequence Movement Primitives from Demonstrations},
	author = {Simon Manschitz AND Jens Kober AND Michael Gienger AND Jan Peters},
	year = {2014},
	month = {September},
	abstract = {We present an approach for learning sequential robot skills through kinesthetic teaching. The demonstrations are represented by a sequence graph. Finding the transitions between consecutive basic movements is treated as classification problem where both Support Vector Machines and Hidden Markov Models are evaluated as classifiers. We show how the observed primitive order of all demonstrations can help to improve the movement reproduction by restricting the classification outcome to the currently executed primitive and its possible successors in the graph. The approach is validated with an experiment in which a seven-DoF Barrett WAM robot learns to unscrew a light bulb.},
	publisher = {IEEE},
	booktitle = {International Conference on Intelligent Robots and Systems (IROS)}
}