@ARTICLE{Howard2009b,
  author = {Matthew Howard and Stefan Klanke and Michael Gienger and Christian
	Goerick and Sethu Vijayakumar},
  title = {A Novel Method for Learning Policies from Variable Constraint Data},
  journal = {Autonomous Robots},
  year = {2009},
  volume = {27},
  pages = {105-121},
  abstract = {Many everyday human skills can be framed in terms of performing some
	task subject to constraints imposed by the environment. Constraints
	are usually unobservable and frequently change between contexts.
	In this paper, we present a novel approach for learning (unconstrained)
	control policies from movement data, where observations come from
	movements under different constraints. As a key ingredient, we introduce
	a small but highly effective modification to the standard risk functional,
	allowing us to make a meaningful comparison between the estimated
	policy and constrained observations. We demonstrate our approach
	on systems of varying complexity, including kinematic data from the
	ASIMO humanoid robot with 27 degrees of freedom, and present results
	for learning from human demonstration.},
  doi = {10.1007/s10514-009-9129-8},
  keywords = {Direct policy learning, Constrained motion, Imitation, Nullspace control},
  url = {http://www.springerlink.com/content/r5u85525p6171g17/}
}