@inproceedings {pub2571,
	title = {Receding Horizon Optimization of Robot Motions generated by Hierarchical Movement Primitives},
	author = {Manuel M{\"u}hlig AND Akinobu Hayashi AND Soshi Iba AND Michael Gienger AND Takahide Yoshiike},
	year = {2014},
	month = {September},
	abstract = {This paper introduces an approach to couple a
motion generation framework based on hierarchical movement
primitives (MPs) with optimal control in form of receding
horizon optimization. In order to not loose the benefits of fast
reactions on the MP-level, the optimization can be overridden
in risky situations to generate quick, though non-optimal
solutions. By this, the system fulfills four desirable properties.
It continuously adapts the robot{\textquoteright}s motion without noticeable
delay (1) by optimizing for collision and joint limit avoidance
based on a future time horizon instead of the current state
only (2). Further, it accounts for the full robot motion that
may result from multiple actived MPs at the same time (3) and
despite a possibly slow optimization still provides the robustness
and quick reaction capabilities of MPs (4). The framework is
validated in an experiment in which a humanoid robot performs
a movement, optimized wrt. collisions and joint limit avoidance,
but still can react within 50 ms after detection of a potential
risk.},
	publisher = {IEEE},
	booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems}
}