@article{Ting_NN_2010,
  title = {A Bayesian approach to nonlinear parameter identification for rigid-body dynamics},
  booktitle = {Neural Networks},
  abstract = {For complex robots such as humanoids, model-based control is highly beneficial for accurate tracking
  while keeping negative feedback gains low for compliance. However, in such multi degree-of-freedom
  lightweight systems, conventional identification of rigid body dynamics models using CAD data and
  actuator models is inaccurate due to unknown nonlinear robot dynamic effects. An alternative method
  is data-driven parameter estimation, but significant noise in measured and inferred variables affects it
  adversely. Moreover, standard estimation procedures may give physically inconsistent results due to
  unmodeled nonlinearities or insufficiently rich data. This paper addresses these problems, proposing
  a Bayesian system identification technique for linear or piecewise linear systems. Inspired by Factor
  Analysis regression, we develop a computationally efficient variational Bayesian regression algorithm
  that is robust to ill-conditioned data, automatically detects relevant features, and identifies input and
  output noise. We evaluate our approach on rigid body parameter estimation for various robotic systems,
  achieving an error of up to three times lower than other state-of-the-art machine learning methods.},
  year = {2010},
  note = {clmc},
  slug = {ting_nn_2010},
  author = {Ting, J. and DSouza, A. and Schaal, S.},
  crossref = {p10253},
  url = {http://www-clmc.usc.edu/publications/T/ting-NN2010.pdf}
}