@inproceedings{meierIROS2014,
  title = {Efficient Bayesian Local Model Learning for Control},
  booktitle = {Proceedings of the IEEE International Conference on Intelligent Robots and Systems},
  abstract = {Model-based control is essential for compliant controland force control in many modern complex robots, like humanoidor disaster robots. Due to many unknown and hard tomodel nonlinearities, analytical models of such robots are oftenonly very rough approximations. However, modern optimizationcontrollers frequently depend on reasonably accurate models,and degrade greatly in robustness and performance if modelerrors are too large. For a long time, machine learning hasbeen expected to provide automatic empirical model synthesis,yet so far, research has only generated feasibility studies butno learning algorithms that run reliably on complex robots.In this paper, we combine two promising worlds of regressiontechniques to generate a more powerful regression learningsystem. On the one hand, locally weighted regression techniquesare computationally efficient, but hard to tune due to avariety of data dependent meta-parameters. On the other hand,Bayesian regression has rather automatic and robust methods toset learning parameters, but becomes quickly computationallyinfeasible for big and high-dimensional data sets. By reducingthe complexity of Bayesian regression in the spirit of local modellearning through variational approximations, we arrive at anovel algorithm that is computationally efficient and easy toinitialize for robust learning. Evaluations on several datasetsdemonstrate very good learning performance and the potentialfor a general regression learning tool for robotics.},
  pages = {2244 - 2249},
  year = {2014},
  note = {clmc},
  slug = {meieriros2014},
  author = {Meier, F. and Hennig, P. and Schaal, S.},
  crossref = {p10593},
  url = {http://www-clmc.usc.edu/publications/M/meier-IROS2014.pdf}
}