@inproceedings {pub2932,
	title = {Probabilistic Progress Prediction and Sequencing of Concurrent Movement Primitives},
	author = {Simon Manschitz AND Jens Kober AND Michael Gienger AND Jan Peters},
	year = {2015},
	abstract = {Concurrent execution of separate movement primitives is a crucial ability 
of humans needed by anthropomorphic robots. Concurrency is, for 
example, required in order to execute both a foot step forward and 
a grasping or striking movement with the arms.
We present an approach for predicting the progress of a movement primitive
in a probabilistic manner. 
The progress is learned using kernel logistic regression and interpreted as (de-)activation probability.
By conditioning the probabilities on a feature set which is influenced by all movement primitives,
it is possible to activate movement primitives concurrently and to synchronize them implicitely.
The concurrency leads to a simpler representation of movement primitives,
making them reusable in more situations.
The approach is evaluated with an experiment in simulation where a two-armed robot has to
put different boxes in a container.},
	publisher = {IEEE},
	booktitle = {International Conference on Intelligent Robots and Systems (IROS)}
}